diff --git a/.github/workflows/gh-pages.yml b/.github/workflows/gh-pages.yml deleted file mode 100644 index 3cac8e2a6..000000000 --- a/.github/workflows/gh-pages.yml +++ /dev/null @@ -1,60 +0,0 @@ -# Simple workflow for deploying site to GitHub Pages -name: Build and Deploy Demo Website - -on: - # Runs on pushes targeting the default branch - push: - branches: ["main"] - - # Allows you to run this workflow manually from the Actions tab - workflow_dispatch: - - # Also trigger after the publish workflow has completed - workflow_run: - workflows: ["Publish Package to npmjs"] - types: - - completed - -# Sets permissions of the GITHUB_TOKEN to allow deployment to GitHub Pages -permissions: - contents: read - pages: write - id-token: write - -# Allow only one concurrent deployment, skipping runs queued between the run in-progress and latest queued. -# However, do NOT cancel in-progress runs as we want to allow these production deployments to complete. -concurrency: - group: "pages" - cancel-in-progress: false - -# Set base path -env: - BASE_PATH: "/transformers.js/" - -jobs: - # Single deploy job since we're just deploying - deploy: - environment: - name: github-pages - url: ${{ steps.deployment.outputs.page_url }} - runs-on: ubuntu-latest - defaults: - run: - working-directory: ./examples/demo-site/ - steps: - - name: Checkout - uses: actions/checkout@v4 - - name: Setup Pages - uses: actions/configure-pages@v5 - - name: Build website - run: | - npm install - npm run build - - name: Upload artifact - uses: actions/upload-pages-artifact@v3 - with: - # Upload built files - path: './examples/demo-site/dist/' - - name: Deploy to GitHub Pages - id: deployment - uses: actions/deploy-pages@v4 diff --git a/.prettierignore b/.prettierignore index bd1927ab2..e66246bae 100644 --- a/.prettierignore +++ b/.prettierignore @@ -2,7 +2,6 @@ .github dist docs -examples scripts types *.md diff --git a/.prettierrc b/.prettierrc index 57d5ce89a..a9e0acb4f 100644 --- a/.prettierrc +++ b/.prettierrc @@ -1,10 +1,19 @@ { - "overrides": [ - { - "files": ["tests/**/*.js"], - "options": { - "printWidth": 10000000 - } - } - ] + "overrides": [ + { + "files": ["src/**/*.{js,mjs,cjs}"], + "options": { + "singleQuote": true, + "tabWidth": 4, + "printWidth": 120 + } + }, + { + "files": ["tests/**/*.{js,mjs,cjs}"], + "options": { + "tabWidth": 2, + "printWidth": 10000000 + } + } + ] } diff --git a/README.md b/README.md index 9b9c4778d..8ba8aaa0d 100644 --- a/README.md +++ b/README.md @@ -47,7 +47,7 @@ npm i @huggingface/transformers Alternatively, you can use it in vanilla JS, without any bundler, by using a CDN or static hosting. For example, using [ES Modules](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Modules), you can import the library with: ```html ``` @@ -126,36 +126,15 @@ const pipe = await pipeline('sentiment-analysis', 'Xenova/distilbert-base-uncase }); ``` +Ready to dive in? Explore our wide variety of demo applications and templates [here](https://github.com/huggingface/transformers.js-examples). You can also launch your own project instantly using the official Transformers.js [template](https://huggingface.co/new-space?template=static-templates%2Ftransformers.js) on Hugging Face! -## Examples - -Want to jump straight in? Get started with one of our sample applications/templates, which can be found [here](https://github.com/huggingface/transformers.js-examples). - -| Name | Description | Links | -|-------------------|----------------------------------|-------------------------------| -| Whisper Web | Speech recognition w/ Whisper | [code](https://github.com/xenova/whisper-web), [demo](https://huggingface.co/spaces/Xenova/whisper-web) | -| Doodle Dash | Real-time sketch-recognition game | [blog](https://huggingface.co/blog/ml-web-games), [code](https://github.com/xenova/doodle-dash), [demo](https://huggingface.co/spaces/Xenova/doodle-dash) | -| Code Playground | In-browser code completion website | [code](https://github.com/huggingface/transformers.js/tree/main/examples/code-completion/), [demo](https://huggingface.co/spaces/Xenova/ai-code-playground) | -| Semantic Image Search (client-side) | Search for images with text | [code](https://github.com/huggingface/transformers.js/tree/main/examples/semantic-image-search-client/), [demo](https://huggingface.co/spaces/Xenova/semantic-image-search-client) | -| Semantic Image Search (server-side) | Search for images with text (Supabase) | [code](https://github.com/huggingface/transformers.js/tree/main/examples/semantic-image-search/), [demo](https://huggingface.co/spaces/Xenova/semantic-image-search) | -| Vanilla JavaScript | In-browser object detection | [video](https://scrimba.com/scrim/cKm9bDAg), [code](https://github.com/huggingface/transformers.js/tree/main/examples/vanilla-js/), [demo](https://huggingface.co/spaces/Scrimba/vanilla-js-object-detector) | -| React | Multilingual translation website | [code](https://github.com/huggingface/transformers.js/tree/main/examples/react-translator/), [demo](https://huggingface.co/spaces/Xenova/react-translator) | -| Text to speech (client-side) | In-browser speech synthesis | [code](https://github.com/huggingface/transformers.js/tree/main/examples/text-to-speech-client/), [demo](https://huggingface.co/spaces/Xenova/text-to-speech-client) | -| Browser extension | Text classification extension | [code](https://github.com/huggingface/transformers.js/tree/main/examples/extension/) | -| Electron | Text classification application | [code](https://github.com/huggingface/transformers.js/tree/main/examples/electron/) | -| Next.js (client-side) | Sentiment analysis (in-browser inference) | [code](https://github.com/huggingface/transformers.js/tree/main/examples/next-client/), [demo](https://huggingface.co/spaces/Xenova/next-example-app) | -| Next.js (server-side) | Sentiment analysis (Node.js inference) | [code](https://github.com/huggingface/transformers.js/tree/main/examples/next-server/), [demo](https://huggingface.co/spaces/Xenova/next-server-example-app) | -| Node.js | Sentiment analysis API | [code](https://github.com/huggingface/transformers.js/tree/main/examples/node/) | -| Demo site | A collection of demos | [code](https://github.com/huggingface/transformers.js/tree/main/examples/demo-site/), [demo](https://huggingface.github.io/transformers.js/) | - -Check out the Transformers.js [template](https://huggingface.co/new-space?template=static-templates%2Ftransformers.js) on Hugging Face to get started in one click! ## Custom usage -By default, Transformers.js uses [hosted pretrained models](https://huggingface.co/models?library=transformers.js) and [precompiled WASM binaries](https://cdn.jsdelivr.net/npm/@huggingface/transformers@3.7.0/dist/), which should work out-of-the-box. You can customize this as follows: +By default, Transformers.js uses [hosted pretrained models](https://huggingface.co/models?library=transformers.js) and [precompiled WASM binaries](https://cdn.jsdelivr.net/npm/@huggingface/transformers@4.0.0-alpha.0/dist/), which should work out-of-the-box. You can customize this as follows: ### Settings diff --git a/docs/scripts/build_readme.py b/docs/scripts/build_readme.py index 20379855e..254e628cd 100644 --- a/docs/scripts/build_readme.py +++ b/docs/scripts/build_readme.py @@ -30,10 +30,6 @@ {quick_tour} -## Examples - -{examples} - ## Custom usage {custom_usage} @@ -57,10 +53,9 @@ intro='./docs/snippets/0_introduction.snippet', quick_tour='./docs/snippets/1_quick-tour.snippet', installation='./docs/snippets/2_installation.snippet', - examples='./docs/snippets/3_examples.snippet', - custom_usage='./docs/snippets/4_custom-usage.snippet', - tasks='./docs/snippets/5_supported-tasks.snippet', - models='./docs/snippets/6_supported-models.snippet', + custom_usage='./docs/snippets/3_custom-usage.snippet', + tasks='./docs/snippets/4_supported-tasks.snippet', + models='./docs/snippets/5_supported-models.snippet', ) DOCS_BASE_URL = 'https://huggingface.co/docs/transformers.js' diff --git a/docs/snippets/1_quick-tour.snippet b/docs/snippets/1_quick-tour.snippet index 27fdd2214..fbfc2921e 100644 --- a/docs/snippets/1_quick-tour.snippet +++ b/docs/snippets/1_quick-tour.snippet @@ -69,3 +69,6 @@ const pipe = await pipeline('sentiment-analysis', 'Xenova/distilbert-base-uncase dtype: 'q4', }); ``` + +Ready to dive in? Explore our wide variety of demo applications and templates [here](https://github.com/huggingface/transformers.js-examples). You can also launch your own project instantly using the official Transformers.js [template](https://huggingface.co/new-space?template=static-templates%2Ftransformers.js) on Hugging Face! + diff --git a/docs/snippets/2_installation.snippet b/docs/snippets/2_installation.snippet index d417b7bfd..25e477764 100644 --- a/docs/snippets/2_installation.snippet +++ b/docs/snippets/2_installation.snippet @@ -7,6 +7,6 @@ npm i @huggingface/transformers Alternatively, you can use it in vanilla JS, without any bundler, by using a CDN or static hosting. For example, using [ES Modules](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Modules), you can import the library with: ```html ``` diff --git a/docs/snippets/4_custom-usage.snippet b/docs/snippets/3_custom-usage.snippet similarity index 94% rename from docs/snippets/4_custom-usage.snippet rename to docs/snippets/3_custom-usage.snippet index 5743b3a38..bd4d2fb0e 100644 --- a/docs/snippets/4_custom-usage.snippet +++ b/docs/snippets/3_custom-usage.snippet @@ -1,6 +1,6 @@ -By default, Transformers.js uses [hosted pretrained models](https://huggingface.co/models?library=transformers.js) and [precompiled WASM binaries](https://cdn.jsdelivr.net/npm/@huggingface/transformers@3.7.0/dist/), which should work out-of-the-box. You can customize this as follows: +By default, Transformers.js uses [hosted pretrained models](https://huggingface.co/models?library=transformers.js) and [precompiled WASM binaries](https://cdn.jsdelivr.net/npm/@huggingface/transformers@4.0.0-alpha.0/dist/), which should work out-of-the-box. You can customize this as follows: ### Settings diff --git a/docs/snippets/3_examples.snippet b/docs/snippets/3_examples.snippet deleted file mode 100644 index d829426b7..000000000 --- a/docs/snippets/3_examples.snippet +++ /dev/null @@ -1,20 +0,0 @@ -Want to jump straight in? Get started with one of our sample applications/templates, which can be found [here](https://github.com/huggingface/transformers.js-examples). - -| Name | Description | Links | -|-------------------|----------------------------------|-------------------------------| -| Whisper Web | Speech recognition w/ Whisper | [code](https://github.com/xenova/whisper-web), [demo](https://huggingface.co/spaces/Xenova/whisper-web) | -| Doodle Dash | Real-time sketch-recognition game | [blog](https://huggingface.co/blog/ml-web-games), [code](https://github.com/xenova/doodle-dash), [demo](https://huggingface.co/spaces/Xenova/doodle-dash) | -| Code Playground | In-browser code completion website | [code](https://github.com/huggingface/transformers.js/tree/main/examples/code-completion/), [demo](https://huggingface.co/spaces/Xenova/ai-code-playground) | -| Semantic Image Search (client-side) | Search for images with text | [code](https://github.com/huggingface/transformers.js/tree/main/examples/semantic-image-search-client/), [demo](https://huggingface.co/spaces/Xenova/semantic-image-search-client) | -| Semantic Image Search (server-side) | Search for images with text (Supabase) | [code](https://github.com/huggingface/transformers.js/tree/main/examples/semantic-image-search/), [demo](https://huggingface.co/spaces/Xenova/semantic-image-search) | -| Vanilla JavaScript | In-browser object detection | [video](https://scrimba.com/scrim/cKm9bDAg), [code](https://github.com/huggingface/transformers.js/tree/main/examples/vanilla-js/), [demo](https://huggingface.co/spaces/Scrimba/vanilla-js-object-detector) | -| React | Multilingual translation website | [code](https://github.com/huggingface/transformers.js/tree/main/examples/react-translator/), [demo](https://huggingface.co/spaces/Xenova/react-translator) | -| Text to speech (client-side) | In-browser speech synthesis | [code](https://github.com/huggingface/transformers.js/tree/main/examples/text-to-speech-client/), [demo](https://huggingface.co/spaces/Xenova/text-to-speech-client) | -| Browser extension | Text classification extension | [code](https://github.com/huggingface/transformers.js/tree/main/examples/extension/) | -| Electron | Text classification application | [code](https://github.com/huggingface/transformers.js/tree/main/examples/electron/) | -| Next.js (client-side) | Sentiment analysis (in-browser inference) | [code](https://github.com/huggingface/transformers.js/tree/main/examples/next-client/), [demo](https://huggingface.co/spaces/Xenova/next-example-app) | -| Next.js (server-side) | Sentiment analysis (Node.js inference) | [code](https://github.com/huggingface/transformers.js/tree/main/examples/next-server/), [demo](https://huggingface.co/spaces/Xenova/next-server-example-app) | -| Node.js | Sentiment analysis API | [code](https://github.com/huggingface/transformers.js/tree/main/examples/node/) | -| Demo site | A collection of demos | [code](https://github.com/huggingface/transformers.js/tree/main/examples/demo-site/), [demo](https://huggingface.github.io/transformers.js/) | - -Check out the Transformers.js [template](https://huggingface.co/new-space?template=static-templates%2Ftransformers.js) on Hugging Face to get started in one click! diff --git a/docs/snippets/5_supported-tasks.snippet b/docs/snippets/4_supported-tasks.snippet similarity index 100% rename from docs/snippets/5_supported-tasks.snippet rename to docs/snippets/4_supported-tasks.snippet diff --git a/docs/snippets/6_supported-models.snippet b/docs/snippets/5_supported-models.snippet similarity index 100% rename from docs/snippets/6_supported-models.snippet rename to docs/snippets/5_supported-models.snippet diff --git a/docs/source/custom_usage.md b/docs/source/custom_usage.md index 4b7d961da..3c5fa2df8 100644 --- a/docs/source/custom_usage.md +++ b/docs/source/custom_usage.md @@ -2,6 +2,6 @@ { - "path": "../snippets/4_custom-usage.snippet" + "path": "../snippets/3_custom-usage.snippet" } \ No newline at end of file diff --git a/docs/source/index.md b/docs/source/index.md index 6551e303f..46a848e58 100644 --- a/docs/source/index.md +++ b/docs/source/index.md @@ -42,13 +42,13 @@ You can refine your search by selecting the task you're interested in (e.g., [te { - "path": "../snippets/5_supported-tasks.snippet" + "path": "../snippets/4_supported-tasks.snippet" } { - "path": "../snippets/6_supported-models.snippet" + "path": "../snippets/5_supported-models.snippet" } diff --git a/docs/source/pipelines.md b/docs/source/pipelines.md index 2edc61b83..56b83d516 100644 --- a/docs/source/pipelines.md +++ b/docs/source/pipelines.md @@ -221,6 +221,6 @@ If you would like more control over the inference process, you can use the [`Aut { - "path": "../snippets/5_supported-tasks.snippet" + "path": "../snippets/4_supported-tasks.snippet" } diff --git a/examples/adaptive-retrieval/.eslintrc.cjs b/examples/adaptive-retrieval/.eslintrc.cjs deleted file mode 100644 index 4dcb43901..000000000 --- a/examples/adaptive-retrieval/.eslintrc.cjs +++ /dev/null @@ -1,20 +0,0 @@ -module.exports = { - root: true, - env: { browser: true, es2020: true }, - extends: [ - 'eslint:recommended', - 'plugin:react/recommended', - 'plugin:react/jsx-runtime', - 'plugin:react-hooks/recommended', - ], - ignorePatterns: ['dist', '.eslintrc.cjs'], - parserOptions: { ecmaVersion: 'latest', sourceType: 'module' }, - settings: { react: { version: '18.2' } }, - plugins: ['react-refresh'], - rules: { - 'react-refresh/only-export-components': [ - 'warn', - { allowConstantExport: true }, - ], - }, -} diff --git a/examples/adaptive-retrieval/.gitignore b/examples/adaptive-retrieval/.gitignore deleted file mode 100644 index a547bf36d..000000000 --- a/examples/adaptive-retrieval/.gitignore +++ /dev/null @@ -1,24 +0,0 @@ -# Logs -logs -*.log -npm-debug.log* -yarn-debug.log* -yarn-error.log* -pnpm-debug.log* -lerna-debug.log* - -node_modules -dist -dist-ssr -*.local - -# Editor directories and files -.vscode/* -!.vscode/extensions.json -.idea -.DS_Store -*.suo -*.ntvs* -*.njsproj -*.sln -*.sw? diff --git a/examples/adaptive-retrieval/index.html b/examples/adaptive-retrieval/index.html deleted file mode 100644 index 4dbcf54ff..000000000 --- a/examples/adaptive-retrieval/index.html +++ /dev/null @@ -1,13 +0,0 @@ - - - - - - - Adaptive Retrieval w/ Transformers.js - - -
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"react-dom": "^18.2.0" - }, - "devDependencies": { - "@types/react": "^18.2.43", - "@types/react-dom": "^18.2.17", - "@vitejs/plugin-react": "^4.2.1", - "autoprefixer": "^10.4.16", - "eslint": "^8.55.0", - "eslint-plugin-react": "^7.33.2", - "eslint-plugin-react-hooks": "^4.6.0", - "eslint-plugin-react-refresh": "^0.4.5", - "postcss": "^8.4.33", - "tailwindcss": "^3.4.1", - "vite": "^5.1.7" - } -} diff --git a/examples/adaptive-retrieval/postcss.config.js b/examples/adaptive-retrieval/postcss.config.js deleted file mode 100644 index 2e7af2b7f..000000000 --- a/examples/adaptive-retrieval/postcss.config.js +++ /dev/null @@ -1,6 +0,0 @@ -export default { - plugins: { - tailwindcss: {}, - autoprefixer: {}, - }, -} diff --git a/examples/adaptive-retrieval/src/App.css b/examples/adaptive-retrieval/src/App.css deleted file mode 100644 index c3951888c..000000000 --- a/examples/adaptive-retrieval/src/App.css +++ /dev/null @@ -1,5 +0,0 @@ -#root { - height: 100vh; - width: 100vw; - padding: 1rem; -} diff --git a/examples/adaptive-retrieval/src/App.jsx b/examples/adaptive-retrieval/src/App.jsx deleted file mode 100644 index 9fc130e0d..000000000 --- a/examples/adaptive-retrieval/src/App.jsx +++ /dev/null @@ -1,173 +0,0 @@ -import { useState, useRef, useEffect, useCallback } from 'react' -import './App.css' - -const PLACEHOLDER_TEXTS = [ - "A panda is a large black-and-white bear native to China.", - "The typical life span of a panda is 20 years in the wild.", - "A panda's diet consists almost entirely of bamboo.", - "Ailuropoda melanoleuca is a bear species endemic to China.", - "I love pandas so much!", - "Bamboo is a fast-growing, woody grass.", - "My favorite movie is Kung Fu Panda.", - "I love the color blue.", - "Once upon a time, in a land far, far away...", - "Hello world.", - "This is an example sentence.", -].sort(() => Math.random() - 0.5); - -function normalize(embedding) { - const magnitude = Math.sqrt(embedding.reduce((sum, val) => sum + val * val, 0)); - return embedding.map((val) => val / magnitude); -} - -function dot(a, b) { - return a.reduce((acc, val, i) => acc + val * b[i], 0); -} - -function App() { - const [status, setStatus] = useState('idle'); - - const [source, setSource] = useState('What is a panda?'); - const [text, setText] = useState(PLACEHOLDER_TEXTS.join('\n')); - - const [dimensions, setDimensions] = useState(768); - - const [embeddings, setEmbeddings] = useState([]); - const [results, setResults] = useState([]); - - // Create a reference to the worker object. - const worker = useRef(null); - - // We use the `useEffect` hook to setup the worker as soon as the `App` component is mounted. - useEffect(() => { - if (!worker.current) { - // Create the worker if it does not yet exist. - worker.current = new Worker(new URL('./worker.js', import.meta.url), { - type: 'module' - }); - } - - // Create a callback function for messages from the worker thread. - const onMessageReceived = (e) => { - const status = e.data.status; - if (status === 'initiate') { - setStatus('loading'); - } else if (status === 'ready') { - setStatus('ready'); - } else if (status === 'complete') { - const embeddings = e.data.embeddings; - setDimensions(embeddings[0].length); - setEmbeddings(embeddings); - setStatus('idle'); - } - }; - - // Attach the callback function as an event listener. - worker.current.addEventListener('message', onMessageReceived); - - // Define a cleanup function for when the component is unmounted. - return () => worker.current.removeEventListener('message', onMessageReceived); - }, []); - - const run = useCallback(() => { - setStatus('processing'); - worker.current.postMessage({ - source, - text, - }); - }, [source, text]) - - useEffect(() => { - if (embeddings.length === 0) return; - const slicedEmbeddings = embeddings.map(x => normalize(x.slice(0, dimensions))); - - const sourceEmbedding = slicedEmbeddings[0]; - const sentenceEmbeddings = slicedEmbeddings.slice(1); - - // Compute the cosine similarity between the source sentence and the other sentences. - // NOTE: Since vectors are normalized, we use the dot product. - const similarities = sentenceEmbeddings.map((embedding) => dot(sourceEmbedding, embedding)); - - setResults(text.trim().split('\n').map((sentence, i) => ({ - sentence, - similarity: similarities[i] - })).sort((a, b) => b.similarity - a.similarity)); - }, [text, embeddings, dimensions]) - - const busy = status !== 'idle'; - - return ( -
-

Adaptive Retrieval w/ Matryoshka Embeddings

-

Powered by Nomic Embed v1.5 and 🤗 Transformers.js

-
-
- - - - - - -
-
- {embeddings.length > 0 && (<> - - { - setDimensions(e.target.value); - }} - className="w-[98%] h-[10px]" - /> -

{dimensions}

-
- -
- {results.map((result, i) => ( -
- {result.similarity.toFixed(3)} - {result.sentence} -
- ))} -
-
- ) - } -
-
-
- ) -} - -export default App diff --git a/examples/adaptive-retrieval/src/index.css b/examples/adaptive-retrieval/src/index.css deleted file mode 100644 index b5c61c956..000000000 --- a/examples/adaptive-retrieval/src/index.css +++ /dev/null @@ -1,3 +0,0 @@ -@tailwind base; -@tailwind components; -@tailwind utilities; diff --git a/examples/adaptive-retrieval/src/main.jsx b/examples/adaptive-retrieval/src/main.jsx deleted file mode 100644 index 54b39dd1d..000000000 --- a/examples/adaptive-retrieval/src/main.jsx +++ /dev/null @@ -1,10 +0,0 @@ -import React from 'react' -import ReactDOM from 'react-dom/client' -import App from './App.jsx' -import './index.css' - -ReactDOM.createRoot(document.getElementById('root')).render( - - - , -) diff --git a/examples/adaptive-retrieval/src/worker.js b/examples/adaptive-retrieval/src/worker.js deleted file mode 100644 index 23c9e7a39..000000000 --- a/examples/adaptive-retrieval/src/worker.js +++ /dev/null @@ -1,47 +0,0 @@ -import { env, pipeline } from '@xenova/transformers'; - -// Skip local model check since we are downloading the model from the Hugging Face Hub. -env.allowLocalModels = false; - -class MyFeatureExtractionPipeline { - static task = 'feature-extraction'; - static model = 'nomic-ai/nomic-embed-text-v1.5'; - static instance = null; - - static async getInstance(progress_callback = null) { - if (this.instance === null) { - this.instance = pipeline(this.task, this.model, { - quantized: true, - progress_callback, - }); - } - - return this.instance; - } -} - -// https://huggingface.co/nomic-ai/nomic-embed-text-v1.5#usage -const SEARCH_PREFIX = 'search_query: '; -const DOCUMENT_PREFIX = 'search_document: '; - -// Listen for messages from the main thread -self.addEventListener('message', async (event) => { - // Retrieve the pipeline. When called for the first time, - // this will load the pipeline and save it for future use. - const extractor = await MyFeatureExtractionPipeline.getInstance(x => { - // We also add a progress callback to the pipeline so that we can - // track model loading. - self.postMessage(x); - }); - - const { source, text } = event.data; - - const split = [ - SEARCH_PREFIX + source, - ...text.trim().split('\n').map(x => DOCUMENT_PREFIX + x), - ]; - const embeddings = await extractor(split, { pooling: 'mean', normalize: true }); - - // Send the output back to the main thread - self.postMessage({ status: 'complete', embeddings: embeddings.tolist() }); -}); diff --git a/examples/adaptive-retrieval/tailwind.config.js b/examples/adaptive-retrieval/tailwind.config.js deleted file mode 100644 index d37737fc0..000000000 --- a/examples/adaptive-retrieval/tailwind.config.js +++ /dev/null @@ -1,12 +0,0 @@ -/** @type {import('tailwindcss').Config} */ -export default { - content: [ - "./index.html", - "./src/**/*.{js,ts,jsx,tsx}", - ], - theme: { - extend: {}, - }, - plugins: [], -} - diff --git a/examples/adaptive-retrieval/vite.config.js b/examples/adaptive-retrieval/vite.config.js deleted file mode 100644 index 5a33944a9..000000000 --- a/examples/adaptive-retrieval/vite.config.js +++ /dev/null @@ -1,7 +0,0 @@ -import { defineConfig } from 'vite' -import react from '@vitejs/plugin-react' - -// https://vitejs.dev/config/ -export default defineConfig({ - plugins: [react()], -}) diff --git a/examples/code-completion/.eslintrc.cjs b/examples/code-completion/.eslintrc.cjs deleted file mode 100644 index c12e55cec..000000000 --- a/examples/code-completion/.eslintrc.cjs +++ /dev/null @@ -1,16 +0,0 @@ -module.exports = { - env: { browser: true, es2020: true, 'node': true }, - extends: [ - 'eslint:recommended', - 'plugin:react/recommended', - 'plugin:react/jsx-runtime', - 'plugin:react-hooks/recommended', - ], - parserOptions: { ecmaVersion: 'latest', sourceType: 'module' }, - settings: { react: { version: '18.2' } }, - plugins: ['react-refresh'], - rules: { - 'react-refresh/only-export-components': 'warn', - 'react/prop-types': 'off', - }, -} diff --git a/examples/code-completion/.gitignore b/examples/code-completion/.gitignore deleted file mode 100644 index a547bf36d..000000000 --- a/examples/code-completion/.gitignore +++ /dev/null @@ -1,24 +0,0 @@ -# Logs -logs -*.log -npm-debug.log* -yarn-debug.log* -yarn-error.log* -pnpm-debug.log* -lerna-debug.log* - -node_modules -dist -dist-ssr -*.local - -# Editor directories and files -.vscode/* -!.vscode/extensions.json -.idea -.DS_Store -*.suo -*.ntvs* -*.njsproj -*.sln -*.sw? diff --git a/examples/code-completion/index.html b/examples/code-completion/index.html deleted file mode 100644 index b23c61fb3..000000000 --- a/examples/code-completion/index.html +++ /dev/null @@ -1,12 +0,0 @@ - - - - - - Transformers.js - Code completion playground - - -
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"name": "code-completion", - "private": true, - "version": "0.0.0", - "type": "module", - "scripts": { - "dev": "vite", - "build": "vite build", - "lint": "eslint src --ext js,jsx --report-unused-disable-directives --max-warnings 0", - "preview": "vite preview" - }, - "dependencies": { - "@xenova/transformers": "^2.4.4", - "@monaco-editor/react": "^4.5.1", - "react": "^18.2.0", - "react-dom": "^18.2.0" - }, - "devDependencies": { - "@types/react": "^18.2.15", - "@types/react-dom": "^18.2.7", - "@vitejs/plugin-react": "^4.0.3", - "autoprefixer": "^10.4.14", - "eslint": "^8.45.0", - "eslint-plugin-react": "^7.32.2", - "eslint-plugin-react-hooks": "^4.6.0", - "eslint-plugin-react-refresh": "^0.4.3", - "postcss": "^8.4.31", - "tailwindcss": "^3.3.3", - "vite": "^4.5.3" - }, - "overrides": { - "protobufjs": "^7.2.4" - } -} diff --git a/examples/code-completion/postcss.config.js b/examples/code-completion/postcss.config.js deleted file mode 100644 index 2e7af2b7f..000000000 --- a/examples/code-completion/postcss.config.js +++ /dev/null @@ -1,6 +0,0 @@ -export default { - plugins: { - tailwindcss: {}, - autoprefixer: {}, - }, -} diff --git a/examples/code-completion/src/App.css b/examples/code-completion/src/App.css deleted file mode 100644 index 749e884f3..000000000 --- a/examples/code-completion/src/App.css +++ /dev/null @@ -1,34 +0,0 @@ -.sidebar { - background-color: #181818; - color: #CCCCCC; -} - -body{ - background-color: #1F1F1F; - color: white; -} - -.progress-container { - position: relative; - font-size: 16px; - color: white; - /* background-color: #e9ecef; */ - border-radius: 8px; - text-align: left; - overflow: hidden; -} - -.progress-bar { - padding: 2px 4px; - z-index: 0; - top: 0; - width: 1%; - height: 100%; - overflow: hidden; - background-color: #007bff; - white-space: nowrap; -} - -.progress-text { - z-index: 2; -} diff --git a/examples/code-completion/src/App.jsx b/examples/code-completion/src/App.jsx deleted file mode 100644 index 7fc84f538..000000000 --- a/examples/code-completion/src/App.jsx +++ /dev/null @@ -1,251 +0,0 @@ -import { useState, useRef, useEffect } from "react"; - -import Editor from "@monaco-editor/react"; -import Progress from './components/Progress'; - -import './App.css' - - -const MODELS = [ - 'Xenova/tiny_starcoder_py', - 'Xenova/codegen-350M-mono', - // 'Xenova/starcoderbase-1b', -] -function App() { - // Editor setup - const monaco = useRef(null); - const [monacoReady, setMonacoReady] = useState(false); - const [language, setLanguage] = useState('python'); // Only allow python for now - - // Model loading - const [ready, setReady] = useState(null); - const [disabled, setDisabled] = useState(false); - const [progressItems, setProgressItems] = useState([]); - - // Inputs and outputs - const [model, setModel] = useState(MODELS[0]); - const [maxNewTokens, setMaxNewTokens] = useState(45); - const [code, setCode] = useState('\ndef fib(n):\n """Calculates the nth Fibonacci number"""\n'); - - // Generation parameters - const [temperature, setTemperature] = useState(0.5); - const [topK, setTopK] = useState(5); - const [doSample, setDoSample] = useState(false); - - // Create a reference to the worker object. - const worker = useRef(null); - - // We use the `useEffect` hook to setup the worker as soon as the `App` component is mounted. - useEffect(() => { - if (!worker.current) { - // Create the worker if it does not yet exist. - worker.current = new Worker(new URL('./worker.js', import.meta.url), { - type: 'module' - }); - } - - // Create a callback function for messages from the worker thread. - const onMessageReceived = (e) => { - switch (e.data.status) { - case 'initiate': - // Model file start load: add a new progress item to the list. - setReady(false); - setProgressItems(prev => [...prev, e.data]); - break; - - case 'progress': - // Model file progress: update one of the progress items. - setProgressItems( - prev => prev.map(item => { - if (item.file === e.data.file) { - return { ...item, ...e.data } - } - return item; - }) - ); - break; - - case 'done': - // Model file loaded: remove the progress item from the list. - setProgressItems( - prev => prev.filter(item => item.file !== e.data.file) - ); - break; - - case 'ready': - // Pipeline ready: the worker is ready to accept messages. - setReady(true); - break; - - case 'update': - // Generation update: update the output text. - setCode(e.data.output); - break; - - case 'complete': - // Generation complete: re-enable the "Generate" button - setDisabled(false); - break; - } - }; - - // Attach the callback function as an event listener. - worker.current.addEventListener('message', onMessageReceived); - - // Define a cleanup function for when the component is unmounted. - return () => worker.current.removeEventListener('message', onMessageReceived); - }); - - - useEffect(() => { - const m = monaco.current; - if (!m) return; - - let actionRegistration = m.addAction({ - id: "generate", - label: "Generate", - contextMenuGroupId: "0_custom", - run: () => { - const val = m.getValue(); - if (!val) return; - - worker.current.postMessage({ - model, - text: val, - max_new_tokens: maxNewTokens, - temperature, - top_k: topK, - do_sample: doSample - }); - } - }); - - // Define a cleanup function for when the component is unmounted. - return () => actionRegistration.dispose(); - }, [monacoReady, model, maxNewTokens, temperature, topK, doSample]); - - const showLoading = ready === false || progressItems.length > 0 - - return ( -
-
- - {ready === false && ( - - )} - {progressItems.map(data => ( -
- -
- ))} -
-
- { - monaco.current = m; - setMonacoReady(true); - }} - options={{ - fontSize: 24 - }} - /> -
-
-

In-browser code completion

-
- Made with 🤗 Transformers.js -
- - - - -
- - -
- { - const newValue = parseInt(event.target.value); - setMaxNewTokens(newValue); - }} - /> - -
- - -
- { - const newValue = Number(event.target.value); - setTemperature(newValue); - }} - /> -
- { - setDoSample(event.target.checked); - }} - className="w-4 h-4 text-blue-600 rounded focus:ring-blue-600 ring-offset-gray-800 focus:ring-2 bg-gray-700 border-gray-600" - /> - -
- -
- - -
- { - const newValue = parseInt(event.target.value); - setTopK(newValue); - }} - /> - -
- -
- - - - Source code -
-
-
- ); -} - -export default App; diff --git a/examples/code-completion/src/assets/react.svg b/examples/code-completion/src/assets/react.svg deleted file mode 100644 index 6c87de9bb..000000000 --- a/examples/code-completion/src/assets/react.svg +++ /dev/null @@ -1 +0,0 @@ - \ No newline at end of file diff --git a/examples/code-completion/src/components/Progress.jsx b/examples/code-completion/src/components/Progress.jsx deleted file mode 100644 index 28491df43..000000000 --- a/examples/code-completion/src/components/Progress.jsx +++ /dev/null @@ -1,20 +0,0 @@ -function formatBytes(bytes, decimals = 0) { - const sizes = ["Bytes", "KB", "MB", "GB", "TB"]; - if (bytes === 0) return "0 Bytes"; - const i = parseInt(Math.floor(Math.log(bytes) / Math.log(1000)), 10); - const rounded = (bytes / Math.pow(1000, i)).toFixed(decimals); - return rounded + " " + sizes[i]; -} - -export default function Progress({ data }) { - const progress = data.progress ?? 0; - const text = data.file; - - const a = formatBytes(data.loaded); - const b = formatBytes(data.total); - return ( -
-
{text} ({`${a} / ${b}`})
-
- ); -} \ No newline at end of file diff --git a/examples/code-completion/src/index.css b/examples/code-completion/src/index.css deleted file mode 100644 index b90bf2853..000000000 --- a/examples/code-completion/src/index.css +++ /dev/null @@ -1,72 +0,0 @@ -@tailwind base; -@tailwind components; -@tailwind utilities; - -:root { - font-family: Inter, system-ui, Avenir, Helvetica, Arial, sans-serif; - line-height: 1.5; - font-weight: 400; - - color-scheme: light dark; - color: rgba(255, 255, 255, 0.87); - background-color: #242424; - - font-synthesis: none; - text-rendering: optimizeLegibility; - -webkit-font-smoothing: antialiased; - -moz-osx-font-smoothing: grayscale; - -webkit-text-size-adjust: 100%; -} - - -a { - font-weight: 500; - color: #646cff; - text-decoration: inherit; -} -a:hover { - color: #535bf2; -} - -body { - margin: 0; - display: flex; - place-items: center; -} - -h1 { - font-size: 3.2em; - line-height: 1.1; -} - -button { - border-radius: 8px; - border: 1px solid transparent; - padding: 0.6em 1.2em; - font-size: 1em; - font-weight: 500; - font-family: inherit; - background-color: #1a1a1a; - cursor: pointer; - transition: border-color 0.25s; -} -button:hover { - border-color: #646cff; -} -button:focus, -button:focus-visible { - outline: 4px auto -webkit-focus-ring-color; -} - -@media (prefers-color-scheme: light) { - :root { - color: #213547; - background-color: #ffffff; - } - a:hover { - color: #747bff; - } - button { - background-color: #f9f9f9; - } -} diff --git a/examples/code-completion/src/main.jsx b/examples/code-completion/src/main.jsx deleted file mode 100644 index 54b39dd1d..000000000 --- a/examples/code-completion/src/main.jsx +++ /dev/null @@ -1,10 +0,0 @@ -import React from 'react' -import ReactDOM from 'react-dom/client' -import App from './App.jsx' -import './index.css' - -ReactDOM.createRoot(document.getElementById('root')).render( - - - , -) diff --git a/examples/code-completion/src/worker.js b/examples/code-completion/src/worker.js deleted file mode 100644 index 31ceed610..000000000 --- a/examples/code-completion/src/worker.js +++ /dev/null @@ -1,73 +0,0 @@ - -import { pipeline, env } from '@xenova/transformers'; - -env.allowLocalModels = false; - -/** - * This class uses the Singleton pattern to ensure that only one instance of the pipeline is loaded. - */ -class CodeCompletionPipeline { - static task = 'text-generation'; - static model = null; - static instance = null; - - static async getInstance(progress_callback = null) { - if (this.instance === null) { - this.instance = pipeline(this.task, this.model, { progress_callback }); - } - - return this.instance; - } -} - -// Listen for messages from the main thread -self.addEventListener('message', async (event) => { - const { - model, text, max_new_tokens, - - // Generation parameters - temperature, - top_k, - do_sample, - } = event.data; - - if (CodeCompletionPipeline.model !== model) { - // Invalidate model if different - CodeCompletionPipeline.model = model; - - if (CodeCompletionPipeline.instance !== null) { - (await CodeCompletionPipeline.getInstance()).dispose(); - CodeCompletionPipeline.instance = null; - } - } - - // Retrieve the code-completion pipeline. When called for the first time, - // this will load the pipeline and save it for future use. - let generator = await CodeCompletionPipeline.getInstance(x => { - // We also add a progress callback to the pipeline so that we can - // track model loading. - self.postMessage(x); - }); - - // Actually perform the code-completion - let output = await generator(text, { - max_new_tokens, - temperature, - top_k, - do_sample, - - // Allows for partial output - callback_function: x => { - self.postMessage({ - status: 'update', - output: generator.tokenizer.decode(x[0].output_token_ids, { skip_special_tokens: true }) - }); - } - }); - - // Send the output back to the main thread - self.postMessage({ - status: 'complete', - output: output, - }); -}); diff --git a/examples/code-completion/tailwind.config.js b/examples/code-completion/tailwind.config.js deleted file mode 100644 index d37737fc0..000000000 --- a/examples/code-completion/tailwind.config.js +++ /dev/null @@ -1,12 +0,0 @@ -/** @type {import('tailwindcss').Config} */ -export default { - content: [ - "./index.html", - "./src/**/*.{js,ts,jsx,tsx}", - ], - theme: { - extend: {}, - }, - plugins: [], -} - diff --git a/examples/code-completion/vite.config.js b/examples/code-completion/vite.config.js deleted file mode 100644 index 5a33944a9..000000000 --- a/examples/code-completion/vite.config.js +++ /dev/null @@ -1,7 +0,0 @@ -import { defineConfig } from 'vite' -import react from '@vitejs/plugin-react' - -// https://vitejs.dev/config/ -export default defineConfig({ - plugins: [react()], -}) diff --git a/examples/cross-encoder/.eslintrc.cjs b/examples/cross-encoder/.eslintrc.cjs deleted file mode 100644 index 4dcb43901..000000000 --- a/examples/cross-encoder/.eslintrc.cjs +++ /dev/null @@ -1,20 +0,0 @@ -module.exports = { - root: true, - env: { browser: true, es2020: true }, - extends: [ - 'eslint:recommended', - 'plugin:react/recommended', - 'plugin:react/jsx-runtime', - 'plugin:react-hooks/recommended', - ], - ignorePatterns: ['dist', '.eslintrc.cjs'], - parserOptions: { ecmaVersion: 'latest', sourceType: 'module' }, - settings: { react: { version: '18.2' } }, - plugins: ['react-refresh'], - rules: { - 'react-refresh/only-export-components': [ - 'warn', - { allowConstantExport: true }, - ], - }, -} diff --git a/examples/cross-encoder/.gitignore b/examples/cross-encoder/.gitignore deleted file mode 100644 index a547bf36d..000000000 --- a/examples/cross-encoder/.gitignore +++ /dev/null @@ -1,24 +0,0 @@ -# Logs -logs -*.log -npm-debug.log* -yarn-debug.log* -yarn-error.log* -pnpm-debug.log* -lerna-debug.log* - -node_modules -dist -dist-ssr -*.local - -# Editor directories and files -.vscode/* -!.vscode/extensions.json -.idea -.DS_Store -*.suo -*.ntvs* -*.njsproj -*.sln -*.sw? diff --git a/examples/cross-encoder/index.html b/examples/cross-encoder/index.html deleted file mode 100644 index f3497ecad..000000000 --- a/examples/cross-encoder/index.html +++ /dev/null @@ -1,13 +0,0 @@ - - - - - - - Reranking w/ Transformers.js - - -
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}, - "devDependencies": { - "@types/react": "^18.2.43", - "@types/react-dom": "^18.2.17", - "@vitejs/plugin-react": "^4.2.1", - "autoprefixer": "^10.4.16", - "eslint": "^8.55.0", - "eslint-plugin-react": "^7.33.2", - "eslint-plugin-react-hooks": "^4.6.0", - "eslint-plugin-react-refresh": "^0.4.5", - "postcss": "^8.4.33", - "tailwindcss": "^3.4.1", - "vite": "^5.1.7" - } -} diff --git a/examples/cross-encoder/postcss.config.js b/examples/cross-encoder/postcss.config.js deleted file mode 100644 index 2e7af2b7f..000000000 --- a/examples/cross-encoder/postcss.config.js +++ /dev/null @@ -1,6 +0,0 @@ -export default { - plugins: { - tailwindcss: {}, - autoprefixer: {}, - }, -} diff --git a/examples/cross-encoder/src/App.css b/examples/cross-encoder/src/App.css deleted file mode 100644 index c3951888c..000000000 --- a/examples/cross-encoder/src/App.css +++ /dev/null @@ -1,5 +0,0 @@ -#root { - height: 100vh; - width: 100vw; - padding: 1rem; -} diff --git a/examples/cross-encoder/src/App.jsx b/examples/cross-encoder/src/App.jsx deleted file mode 100644 index 5de65a7bd..000000000 --- a/examples/cross-encoder/src/App.jsx +++ /dev/null @@ -1,124 +0,0 @@ -import { useState, useRef, useEffect, useCallback } from 'react' -import './App.css' - -const PLACEHOLDER_TEXTS = [ - "'To Kill a Mockingbird' is a novel by Harper Lee published in 1960. It was immediately successful, winning the Pulitzer Prize, and has become a classic of modern American literature.", - "The novel 'Moby-Dick' was written by Herman Melville and first published in 1851. It is considered a masterpiece of American literature and deals with complex themes of obsession, revenge, and the conflict between good and evil.", - "Harper Lee, an American novelist widely known for her novel 'To Kill a Mockingbird', was born in 1926 in Monroeville, Alabama. She received the Pulitzer Prize for Fiction in 1961.", - "Jane Austen was an English novelist known primarily for her six major novels, which interpret, critique and comment upon the British landed gentry at the end of the 18th century.", - "The 'Harry Potter' series, which consists of seven fantasy novels written by British author J.K. Rowling, is among the most popular and critically acclaimed books of the modern era.", - "'The Great Gatsby', a novel written by American author F. Scott Fitzgerald, was published in 1925. The story is set in the Jazz Age and follows the life of millionaire Jay Gatsby and his pursuit of Daisy Buchanan." -].sort(() => Math.random() - 0.5); - -function App() { - const [status, setStatus] = useState('idle'); - - const [query, setQuery] = useState(`Who wrote 'To Kill a Mockingbird'?`); - const [documents, setDocuments] = useState(PLACEHOLDER_TEXTS.join('\n')); - - const [results, setResults] = useState([]); - - // Create a reference to the worker object. - const worker = useRef(null); - - // We use the `useEffect` hook to setup the worker as soon as the `App` component is mounted. - useEffect(() => { - if (!worker.current) { - // Create the worker if it does not yet exist. - worker.current = new Worker(new URL('./worker.js', import.meta.url), { - type: 'module' - }); - } - - // Create a callback function for messages from the worker thread. - const onMessageReceived = (e) => { - const status = e.data.status; - if (e.data.file?.endsWith('.onnx')) { - if (status === 'initiate') { - setStatus('loading'); - } else if (status === 'done') { - setStatus('ready'); - } - } else if (status === 'complete') { - setResults(e.data.output); - setStatus('idle'); - } - }; - - // Attach the callback function as an event listener. - worker.current.addEventListener('message', onMessageReceived); - - // Define a cleanup function for when the component is unmounted. - return () => worker.current.removeEventListener('message', onMessageReceived); - }, []); - - const run = useCallback(() => { - setStatus('processing'); - worker.current.postMessage({ - query, - documents, - }); - }, [query, documents]) - - const busy = status !== 'idle'; - - return ( -
-

Reranking w/ The Crispy mixedbread Rerank Models

-

Powered by mxbai-rerank-xsmall-v1 and 🤗 Transformers.js

-
-
- - - - - - -
-
- {results.length > 0 && (<> -
- -
- {results.map((result, i) => ( -
- {result.score.toFixed(3)} - {result.text} -
- ))} -
-
- ) - } -
-
-
- ) -} - -export default App diff --git a/examples/cross-encoder/src/index.css b/examples/cross-encoder/src/index.css deleted file mode 100644 index b5c61c956..000000000 --- a/examples/cross-encoder/src/index.css +++ /dev/null @@ -1,3 +0,0 @@ -@tailwind base; -@tailwind components; -@tailwind utilities; diff --git a/examples/cross-encoder/src/main.jsx b/examples/cross-encoder/src/main.jsx deleted file mode 100644 index 54b39dd1d..000000000 --- a/examples/cross-encoder/src/main.jsx +++ /dev/null @@ -1,10 +0,0 @@ -import React from 'react' -import ReactDOM from 'react-dom/client' -import App from './App.jsx' -import './index.css' - -ReactDOM.createRoot(document.getElementById('root')).render( - - - , -) diff --git a/examples/cross-encoder/src/worker.js b/examples/cross-encoder/src/worker.js deleted file mode 100644 index 0507baf6a..000000000 --- a/examples/cross-encoder/src/worker.js +++ /dev/null @@ -1,62 +0,0 @@ -import { env, AutoTokenizer, AutoModelForSequenceClassification } from '@xenova/transformers'; - -// Skip local model check since we are downloading the model from the Hugging Face Hub. -env.allowLocalModels = false; - -class CrossEncoderSingleton { - static model_id = 'mixedbread-ai/mxbai-rerank-xsmall-v1'; - static model = null; - static tokenizer = null; - - static async getInstance(progress_callback) { - if (!this.tokenizer) { - this.tokenizer = AutoTokenizer.from_pretrained(this.model_id); - } - - if (!this.model) { - this.model = AutoModelForSequenceClassification.from_pretrained(this.model_id, { - quantized: true, - progress_callback, - }); - } - - return Promise.all([this.tokenizer, this.model]); - } -} - -// Listen for messages from the main thread -self.addEventListener('message', async (event) => { - // Retrieve the pipeline. When called for the first time, - // this will load the pipeline and save it for future use. - const [tokenizer, model] = await CrossEncoderSingleton.getInstance(x => { - // We also add a progress callback to the pipeline so that we can - // track model loading. - self.postMessage(x); - }); - - const { query, documents } = event.data; - - const docs = documents.trim().split('\n'); - - const inputs = tokenizer( - new Array(docs.length).fill(query), - { - text_pair: docs, - padding: true, - truncation: true, - } - ) - const { logits } = await model(inputs); - const output = logits - .sigmoid() - .tolist() - .map(([score], i) => ({ - corpus_id: i, - score, - text: docs[i], - })) - .sort((a, b) => b.score - a.score); - - // Send the output back to the main thread - self.postMessage({ status: 'complete', output }); -}); diff --git a/examples/cross-encoder/tailwind.config.js b/examples/cross-encoder/tailwind.config.js deleted file mode 100644 index d37737fc0..000000000 --- a/examples/cross-encoder/tailwind.config.js +++ /dev/null @@ -1,12 +0,0 @@ -/** @type {import('tailwindcss').Config} */ -export default { - content: [ - "./index.html", - "./src/**/*.{js,ts,jsx,tsx}", - ], - theme: { - extend: {}, - }, - plugins: [], -} - diff --git a/examples/cross-encoder/vite.config.js b/examples/cross-encoder/vite.config.js deleted file mode 100644 index 5a33944a9..000000000 --- a/examples/cross-encoder/vite.config.js +++ /dev/null @@ -1,7 +0,0 @@ -import { defineConfig } from 'vite' -import react from '@vitejs/plugin-react' - -// https://vitejs.dev/config/ -export default defineConfig({ - plugins: [react()], -}) diff --git a/examples/demo-site/.gitignore b/examples/demo-site/.gitignore deleted file mode 100644 index a547bf36d..000000000 --- a/examples/demo-site/.gitignore +++ /dev/null @@ -1,24 +0,0 @@ -# Logs -logs -*.log -npm-debug.log* -yarn-debug.log* -yarn-error.log* -pnpm-debug.log* -lerna-debug.log* - -node_modules -dist -dist-ssr -*.local - -# Editor directories and files -.vscode/* -!.vscode/extensions.json -.idea -.DS_Store -*.suo -*.ntvs* -*.njsproj -*.sln -*.sw? diff --git a/examples/demo-site/package.json b/examples/demo-site/package.json deleted file mode 100644 index 026244ea0..000000000 --- a/examples/demo-site/package.json +++ /dev/null @@ -1,19 +0,0 @@ -{ - "name": "demo-site", - "private": true, - "version": "0.0.0", - "type": "module", - "scripts": { - "dev": "vite", - "build": "vite build", - "preview": "vite preview" - }, - "devDependencies": { - "vite": "^4.3.2" - }, - "dependencies": { - "@xenova/transformers": "^2.0.0-alpha.3", - "chart.js": "^4.3.0", - "prismjs": "^1.29.0" - } -} diff --git a/examples/demo-site/public/audio/jfk.wav b/examples/demo-site/public/audio/jfk.wav deleted file mode 100644 index bd627bfe3..000000000 Binary files a/examples/demo-site/public/audio/jfk.wav and /dev/null differ diff --git a/examples/demo-site/public/audio/ted.wav b/examples/demo-site/public/audio/ted.wav deleted file mode 100644 index 662c5c2c6..000000000 Binary files a/examples/demo-site/public/audio/ted.wav and /dev/null differ diff --git a/examples/demo-site/public/audio/ted_60.wav b/examples/demo-site/public/audio/ted_60.wav deleted file mode 100644 index 1d3a28fb2..000000000 Binary files a/examples/demo-site/public/audio/ted_60.wav and /dev/null differ diff --git a/examples/demo-site/public/css/bootstrap-icons.css b/examples/demo-site/public/css/bootstrap-icons.css deleted file mode 100644 index d7f6b0fd1..000000000 --- a/examples/demo-site/public/css/bootstrap-icons.css +++ /dev/null @@ -1,1555 +0,0 @@ -@font-face { - font-family: "bootstrap-icons"; 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} -.bi-cart-plus::before { content: "\f23f"; } -.bi-cart-x-fill::before { content: "\f240"; } -.bi-cart-x::before { content: "\f241"; } -.bi-cart::before { content: "\f242"; } -.bi-cart2::before { content: "\f243"; } -.bi-cart3::before { content: "\f244"; } -.bi-cart4::before { content: "\f245"; } -.bi-cash-stack::before { content: "\f246"; } -.bi-cash::before { content: "\f247"; } -.bi-cast::before { content: "\f248"; } -.bi-chat-dots-fill::before { content: "\f249"; } -.bi-chat-dots::before { content: "\f24a"; } -.bi-chat-fill::before { content: "\f24b"; } -.bi-chat-left-dots-fill::before { content: "\f24c"; } -.bi-chat-left-dots::before { content: "\f24d"; } -.bi-chat-left-fill::before { content: "\f24e"; } -.bi-chat-left-quote-fill::before { content: "\f24f"; } -.bi-chat-left-quote::before { content: "\f250"; } -.bi-chat-left-text-fill::before { content: "\f251"; } -.bi-chat-left-text::before { content: "\f252"; } -.bi-chat-left::before { content: "\f253"; } -.bi-chat-quote-fill::before { content: "\f254"; } -.bi-chat-quote::before { content: "\f255"; } -.bi-chat-right-dots-fill::before { content: "\f256"; } -.bi-chat-right-dots::before { content: "\f257"; } -.bi-chat-right-fill::before { content: "\f258"; } -.bi-chat-right-quote-fill::before { content: "\f259"; } -.bi-chat-right-quote::before { content: "\f25a"; } -.bi-chat-right-text-fill::before { content: "\f25b"; } -.bi-chat-right-text::before { content: "\f25c"; } -.bi-chat-right::before { content: "\f25d"; } -.bi-chat-square-dots-fill::before { content: "\f25e"; } -.bi-chat-square-dots::before { content: "\f25f"; } -.bi-chat-square-fill::before { content: "\f260"; } -.bi-chat-square-quote-fill::before { content: "\f261"; } -.bi-chat-square-quote::before { content: "\f262"; } -.bi-chat-square-text-fill::before { content: "\f263"; } -.bi-chat-square-text::before { content: "\f264"; } -.bi-chat-square::before { content: "\f265"; } -.bi-chat-text-fill::before { content: "\f266"; } -.bi-chat-text::before { content: "\f267"; } -.bi-chat::before { content: "\f268"; } -.bi-check-all::before { content: "\f269"; } -.bi-check-circle-fill::before { content: "\f26a"; } -.bi-check-circle::before { content: "\f26b"; } -.bi-check-square-fill::before { content: "\f26c"; } -.bi-check-square::before { content: "\f26d"; } -.bi-check::before { content: "\f26e"; } -.bi-check2-all::before { content: "\f26f"; } -.bi-check2-circle::before { content: "\f270"; } -.bi-check2-square::before { content: "\f271"; } -.bi-check2::before { content: "\f272"; } -.bi-chevron-bar-contract::before { content: "\f273"; } -.bi-chevron-bar-down::before { content: "\f274"; } -.bi-chevron-bar-expand::before { content: "\f275"; } -.bi-chevron-bar-left::before { content: "\f276"; } -.bi-chevron-bar-right::before { content: "\f277"; } -.bi-chevron-bar-up::before { content: "\f278"; } -.bi-chevron-compact-down::before { content: "\f279"; } -.bi-chevron-compact-left::before { content: "\f27a"; } -.bi-chevron-compact-right::before { content: "\f27b"; } -.bi-chevron-compact-up::before { content: "\f27c"; } -.bi-chevron-contract::before { content: "\f27d"; } -.bi-chevron-double-down::before { content: "\f27e"; } -.bi-chevron-double-left::before { content: "\f27f"; } -.bi-chevron-double-right::before { content: "\f280"; } -.bi-chevron-double-up::before { content: "\f281"; } -.bi-chevron-down::before { content: "\f282"; } -.bi-chevron-expand::before { content: "\f283"; } -.bi-chevron-left::before { content: "\f284"; } -.bi-chevron-right::before { content: "\f285"; } -.bi-chevron-up::before { content: "\f286"; } -.bi-circle-fill::before { content: "\f287"; } -.bi-circle-half::before { content: "\f288"; } -.bi-circle-square::before { content: "\f289"; } -.bi-circle::before { content: "\f28a"; } -.bi-clipboard-check::before { content: "\f28b"; } -.bi-clipboard-data::before { content: "\f28c"; } -.bi-clipboard-minus::before { content: "\f28d"; } -.bi-clipboard-plus::before { content: "\f28e"; } -.bi-clipboard-x::before { content: "\f28f"; } -.bi-clipboard::before { content: "\f290"; } -.bi-clock-fill::before { content: "\f291"; } -.bi-clock-history::before { content: "\f292"; } -.bi-clock::before { content: "\f293"; } -.bi-cloud-arrow-down-fill::before { content: "\f294"; } -.bi-cloud-arrow-down::before { content: "\f295"; } -.bi-cloud-arrow-up-fill::before { content: "\f296"; } -.bi-cloud-arrow-up::before { content: "\f297"; } -.bi-cloud-check-fill::before { content: "\f298"; } -.bi-cloud-check::before { content: "\f299"; } -.bi-cloud-download-fill::before { content: "\f29a"; } -.bi-cloud-download::before { content: "\f29b"; } -.bi-cloud-drizzle-fill::before { content: "\f29c"; } -.bi-cloud-drizzle::before { content: "\f29d"; } -.bi-cloud-fill::before { content: "\f29e"; } -.bi-cloud-fog-fill::before { content: "\f29f"; } -.bi-cloud-fog::before { content: "\f2a0"; } -.bi-cloud-fog2-fill::before { content: "\f2a1"; } -.bi-cloud-fog2::before { content: "\f2a2"; } -.bi-cloud-hail-fill::before { content: "\f2a3"; } -.bi-cloud-hail::before { content: "\f2a4"; } -.bi-cloud-haze-1::before { content: "\f2a5"; } -.bi-cloud-haze-fill::before { content: "\f2a6"; } -.bi-cloud-haze::before { content: "\f2a7"; } -.bi-cloud-haze2-fill::before { content: "\f2a8"; } -.bi-cloud-lightning-fill::before { content: "\f2a9"; } -.bi-cloud-lightning-rain-fill::before { content: "\f2aa"; } -.bi-cloud-lightning-rain::before { content: "\f2ab"; } -.bi-cloud-lightning::before { content: "\f2ac"; } -.bi-cloud-minus-fill::before { content: "\f2ad"; } -.bi-cloud-minus::before { content: "\f2ae"; } -.bi-cloud-moon-fill::before { content: "\f2af"; } -.bi-cloud-moon::before { content: "\f2b0"; } -.bi-cloud-plus-fill::before { content: "\f2b1"; } -.bi-cloud-plus::before { content: "\f2b2"; } -.bi-cloud-rain-fill::before { content: "\f2b3"; } -.bi-cloud-rain-heavy-fill::before { content: "\f2b4"; } -.bi-cloud-rain-heavy::before { content: "\f2b5"; } -.bi-cloud-rain::before { content: "\f2b6"; } -.bi-cloud-slash-fill::before { content: "\f2b7"; } -.bi-cloud-slash::before { content: "\f2b8"; } -.bi-cloud-sleet-fill::before { content: "\f2b9"; } -.bi-cloud-sleet::before { content: "\f2ba"; } -.bi-cloud-snow-fill::before { content: "\f2bb"; } -.bi-cloud-snow::before { content: "\f2bc"; } -.bi-cloud-sun-fill::before { content: "\f2bd"; } -.bi-cloud-sun::before { content: "\f2be"; } -.bi-cloud-upload-fill::before { content: "\f2bf"; } -.bi-cloud-upload::before { content: "\f2c0"; } -.bi-cloud::before { content: "\f2c1"; } -.bi-clouds-fill::before { content: "\f2c2"; } -.bi-clouds::before { content: "\f2c3"; } -.bi-cloudy-fill::before { content: "\f2c4"; } -.bi-cloudy::before { content: "\f2c5"; } -.bi-code-slash::before { content: "\f2c6"; } -.bi-code-square::before { content: "\f2c7"; } -.bi-code::before { content: "\f2c8"; } -.bi-collection-fill::before { content: "\f2c9"; } -.bi-collection-play-fill::before { content: "\f2ca"; } -.bi-collection-play::before { content: "\f2cb"; } -.bi-collection::before { content: "\f2cc"; } -.bi-columns-gap::before { content: "\f2cd"; } -.bi-columns::before { content: "\f2ce"; } -.bi-command::before { content: "\f2cf"; } -.bi-compass-fill::before { content: "\f2d0"; } -.bi-compass::before { content: "\f2d1"; } -.bi-cone-striped::before { content: "\f2d2"; } -.bi-cone::before { content: "\f2d3"; } -.bi-controller::before { content: "\f2d4"; } -.bi-cpu-fill::before { content: "\f2d5"; } -.bi-cpu::before { content: "\f2d6"; } -.bi-credit-card-2-back-fill::before { content: "\f2d7"; } -.bi-credit-card-2-back::before { content: "\f2d8"; } -.bi-credit-card-2-front-fill::before { content: "\f2d9"; } -.bi-credit-card-2-front::before { content: "\f2da"; } -.bi-credit-card-fill::before { content: "\f2db"; } -.bi-credit-card::before { content: "\f2dc"; } -.bi-crop::before { content: "\f2dd"; } -.bi-cup-fill::before { content: "\f2de"; } -.bi-cup-straw::before { content: "\f2df"; } -.bi-cup::before { content: "\f2e0"; } -.bi-cursor-fill::before { content: "\f2e1"; } -.bi-cursor-text::before { content: "\f2e2"; } -.bi-cursor::before { content: "\f2e3"; } -.bi-dash-circle-dotted::before { content: "\f2e4"; } -.bi-dash-circle-fill::before { content: "\f2e5"; } -.bi-dash-circle::before { content: "\f2e6"; } -.bi-dash-square-dotted::before { content: "\f2e7"; } -.bi-dash-square-fill::before { content: "\f2e8"; } -.bi-dash-square::before { content: "\f2e9"; } -.bi-dash::before { content: "\f2ea"; } -.bi-diagram-2-fill::before { content: "\f2eb"; } -.bi-diagram-2::before { content: "\f2ec"; } -.bi-diagram-3-fill::before { content: "\f2ed"; } -.bi-diagram-3::before { content: "\f2ee"; } -.bi-diamond-fill::before { content: "\f2ef"; } -.bi-diamond-half::before { content: "\f2f0"; } -.bi-diamond::before { content: "\f2f1"; } -.bi-dice-1-fill::before { content: "\f2f2"; } -.bi-dice-1::before { content: "\f2f3"; } -.bi-dice-2-fill::before { content: "\f2f4"; } -.bi-dice-2::before { content: "\f2f5"; } -.bi-dice-3-fill::before { content: "\f2f6"; } -.bi-dice-3::before { content: "\f2f7"; } -.bi-dice-4-fill::before { content: "\f2f8"; } -.bi-dice-4::before { content: "\f2f9"; } -.bi-dice-5-fill::before { content: "\f2fa"; } -.bi-dice-5::before { content: "\f2fb"; } -.bi-dice-6-fill::before { content: "\f2fc"; } -.bi-dice-6::before { content: "\f2fd"; } -.bi-disc-fill::before { content: "\f2fe"; } -.bi-disc::before { content: "\f2ff"; } -.bi-discord::before { content: "\f300"; } -.bi-display-fill::before { content: "\f301"; } -.bi-display::before { content: "\f302"; } -.bi-distribute-horizontal::before { content: "\f303"; } -.bi-distribute-vertical::before { content: "\f304"; } -.bi-door-closed-fill::before { content: "\f305"; } -.bi-door-closed::before { content: "\f306"; } -.bi-door-open-fill::before { content: "\f307"; } -.bi-door-open::before { content: "\f308"; } -.bi-dot::before { content: "\f309"; } -.bi-download::before { content: "\f30a"; } -.bi-droplet-fill::before { content: "\f30b"; } -.bi-droplet-half::before { content: "\f30c"; } -.bi-droplet::before { content: "\f30d"; } -.bi-earbuds::before { content: "\f30e"; } -.bi-easel-fill::before { content: "\f30f"; } -.bi-easel::before { content: "\f310"; } -.bi-egg-fill::before { content: "\f311"; } -.bi-egg-fried::before { content: "\f312"; } -.bi-egg::before { content: "\f313"; } -.bi-eject-fill::before { content: "\f314"; } -.bi-eject::before { content: "\f315"; } -.bi-emoji-angry-fill::before { content: "\f316"; } -.bi-emoji-angry::before { content: "\f317"; } -.bi-emoji-dizzy-fill::before { content: "\f318"; } -.bi-emoji-dizzy::before { content: "\f319"; } -.bi-emoji-expressionless-fill::before { content: "\f31a"; } -.bi-emoji-expressionless::before { content: "\f31b"; } -.bi-emoji-frown-fill::before { content: "\f31c"; } -.bi-emoji-frown::before { content: "\f31d"; } -.bi-emoji-heart-eyes-fill::before { content: "\f31e"; } -.bi-emoji-heart-eyes::before { content: "\f31f"; } -.bi-emoji-laughing-fill::before { content: "\f320"; } -.bi-emoji-laughing::before { content: "\f321"; } -.bi-emoji-neutral-fill::before { content: "\f322"; } -.bi-emoji-neutral::before { content: "\f323"; } -.bi-emoji-smile-fill::before { content: "\f324"; } -.bi-emoji-smile-upside-down-fill::before { content: "\f325"; } -.bi-emoji-smile-upside-down::before { content: "\f326"; } -.bi-emoji-smile::before { content: "\f327"; } -.bi-emoji-sunglasses-fill::before { content: "\f328"; } -.bi-emoji-sunglasses::before { content: "\f329"; } -.bi-emoji-wink-fill::before { content: "\f32a"; } -.bi-emoji-wink::before { content: "\f32b"; } -.bi-envelope-fill::before { content: "\f32c"; } -.bi-envelope-open-fill::before { content: "\f32d"; } -.bi-envelope-open::before { content: "\f32e"; } -.bi-envelope::before { content: "\f32f"; } -.bi-eraser-fill::before { content: "\f330"; } -.bi-eraser::before { content: "\f331"; } -.bi-exclamation-circle-fill::before { content: "\f332"; } -.bi-exclamation-circle::before { content: "\f333"; } -.bi-exclamation-diamond-fill::before { content: "\f334"; } -.bi-exclamation-diamond::before { content: "\f335"; } -.bi-exclamation-octagon-fill::before { content: "\f336"; } -.bi-exclamation-octagon::before { content: "\f337"; } -.bi-exclamation-square-fill::before { content: "\f338"; } -.bi-exclamation-square::before { content: "\f339"; } -.bi-exclamation-triangle-fill::before { content: "\f33a"; } -.bi-exclamation-triangle::before { content: "\f33b"; } -.bi-exclamation::before { content: "\f33c"; } -.bi-exclude::before { content: "\f33d"; } -.bi-eye-fill::before { content: "\f33e"; } -.bi-eye-slash-fill::before { content: "\f33f"; } -.bi-eye-slash::before { content: "\f340"; } -.bi-eye::before { content: "\f341"; } -.bi-eyedropper::before { content: "\f342"; } -.bi-eyeglasses::before { content: "\f343"; } -.bi-facebook::before { content: "\f344"; } -.bi-file-arrow-down-fill::before { content: "\f345"; } -.bi-file-arrow-down::before { content: "\f346"; } -.bi-file-arrow-up-fill::before { content: "\f347"; } -.bi-file-arrow-up::before { content: "\f348"; } -.bi-file-bar-graph-fill::before { content: "\f349"; } -.bi-file-bar-graph::before { content: "\f34a"; } -.bi-file-binary-fill::before { content: "\f34b"; } -.bi-file-binary::before { content: "\f34c"; } -.bi-file-break-fill::before { content: "\f34d"; } -.bi-file-break::before { content: "\f34e"; } -.bi-file-check-fill::before { content: "\f34f"; } -.bi-file-check::before { content: "\f350"; } -.bi-file-code-fill::before { content: "\f351"; } -.bi-file-code::before { content: "\f352"; } -.bi-file-diff-fill::before { content: "\f353"; } -.bi-file-diff::before { content: "\f354"; } -.bi-file-earmark-arrow-down-fill::before { content: "\f355"; } -.bi-file-earmark-arrow-down::before { content: "\f356"; } -.bi-file-earmark-arrow-up-fill::before { content: "\f357"; } -.bi-file-earmark-arrow-up::before { content: "\f358"; } -.bi-file-earmark-bar-graph-fill::before { content: "\f359"; } -.bi-file-earmark-bar-graph::before { content: "\f35a"; } -.bi-file-earmark-binary-fill::before { content: "\f35b"; } -.bi-file-earmark-binary::before { content: "\f35c"; } -.bi-file-earmark-break-fill::before { content: "\f35d"; } -.bi-file-earmark-break::before { content: "\f35e"; } -.bi-file-earmark-check-fill::before { content: "\f35f"; } -.bi-file-earmark-check::before { content: "\f360"; } -.bi-file-earmark-code-fill::before { content: "\f361"; } -.bi-file-earmark-code::before { content: "\f362"; } -.bi-file-earmark-diff-fill::before { content: "\f363"; } -.bi-file-earmark-diff::before { content: "\f364"; } -.bi-file-earmark-easel-fill::before { content: "\f365"; } -.bi-file-earmark-easel::before { content: "\f366"; } -.bi-file-earmark-excel-fill::before { content: "\f367"; } -.bi-file-earmark-excel::before { content: "\f368"; } -.bi-file-earmark-fill::before { content: "\f369"; } -.bi-file-earmark-font-fill::before { content: "\f36a"; } -.bi-file-earmark-font::before { content: "\f36b"; } -.bi-file-earmark-image-fill::before { content: "\f36c"; } -.bi-file-earmark-image::before { content: "\f36d"; } -.bi-file-earmark-lock-fill::before { content: "\f36e"; } -.bi-file-earmark-lock::before { content: "\f36f"; } -.bi-file-earmark-lock2-fill::before { content: "\f370"; } -.bi-file-earmark-lock2::before { content: "\f371"; } -.bi-file-earmark-medical-fill::before { content: "\f372"; } -.bi-file-earmark-medical::before { content: "\f373"; } -.bi-file-earmark-minus-fill::before { content: "\f374"; } -.bi-file-earmark-minus::before { content: "\f375"; } -.bi-file-earmark-music-fill::before { content: "\f376"; } -.bi-file-earmark-music::before { content: "\f377"; } -.bi-file-earmark-person-fill::before { content: "\f378"; } -.bi-file-earmark-person::before { content: "\f379"; } -.bi-file-earmark-play-fill::before { content: "\f37a"; } -.bi-file-earmark-play::before { content: "\f37b"; } -.bi-file-earmark-plus-fill::before { content: "\f37c"; } -.bi-file-earmark-plus::before { content: "\f37d"; } -.bi-file-earmark-post-fill::before { content: "\f37e"; } -.bi-file-earmark-post::before { content: "\f37f"; } -.bi-file-earmark-ppt-fill::before { content: "\f380"; } -.bi-file-earmark-ppt::before { content: "\f381"; } -.bi-file-earmark-richtext-fill::before { content: "\f382"; } -.bi-file-earmark-richtext::before { content: "\f383"; } -.bi-file-earmark-ruled-fill::before { content: "\f384"; } -.bi-file-earmark-ruled::before { content: "\f385"; } -.bi-file-earmark-slides-fill::before { content: "\f386"; } -.bi-file-earmark-slides::before { content: "\f387"; } -.bi-file-earmark-spreadsheet-fill::before { content: "\f388"; } -.bi-file-earmark-spreadsheet::before { content: "\f389"; } -.bi-file-earmark-text-fill::before { content: "\f38a"; } -.bi-file-earmark-text::before { content: "\f38b"; } -.bi-file-earmark-word-fill::before { content: "\f38c"; } -.bi-file-earmark-word::before { content: "\f38d"; } -.bi-file-earmark-x-fill::before { content: "\f38e"; } -.bi-file-earmark-x::before { content: "\f38f"; } -.bi-file-earmark-zip-fill::before { content: "\f390"; } -.bi-file-earmark-zip::before { content: "\f391"; } -.bi-file-earmark::before { content: "\f392"; } -.bi-file-easel-fill::before { content: "\f393"; } -.bi-file-easel::before { content: "\f394"; } -.bi-file-excel-fill::before { content: "\f395"; } -.bi-file-excel::before { content: "\f396"; } -.bi-file-fill::before { content: "\f397"; } -.bi-file-font-fill::before { content: "\f398"; } -.bi-file-font::before { content: "\f399"; } -.bi-file-image-fill::before { content: "\f39a"; } -.bi-file-image::before { content: "\f39b"; } -.bi-file-lock-fill::before { content: "\f39c"; } -.bi-file-lock::before { content: "\f39d"; } -.bi-file-lock2-fill::before { content: "\f39e"; } -.bi-file-lock2::before { content: "\f39f"; } -.bi-file-medical-fill::before { content: "\f3a0"; } -.bi-file-medical::before { content: "\f3a1"; } -.bi-file-minus-fill::before { content: "\f3a2"; } -.bi-file-minus::before { content: "\f3a3"; } -.bi-file-music-fill::before { content: "\f3a4"; } -.bi-file-music::before { content: "\f3a5"; } -.bi-file-person-fill::before { content: "\f3a6"; } -.bi-file-person::before { content: "\f3a7"; } -.bi-file-play-fill::before { content: "\f3a8"; } -.bi-file-play::before { content: "\f3a9"; } -.bi-file-plus-fill::before { content: "\f3aa"; } -.bi-file-plus::before { content: "\f3ab"; } -.bi-file-post-fill::before { content: "\f3ac"; } -.bi-file-post::before { content: "\f3ad"; } -.bi-file-ppt-fill::before { content: "\f3ae"; } -.bi-file-ppt::before { content: "\f3af"; } -.bi-file-richtext-fill::before { content: "\f3b0"; } -.bi-file-richtext::before { content: "\f3b1"; } -.bi-file-ruled-fill::before { content: "\f3b2"; } -.bi-file-ruled::before { content: "\f3b3"; } -.bi-file-slides-fill::before { content: "\f3b4"; } -.bi-file-slides::before { content: "\f3b5"; } -.bi-file-spreadsheet-fill::before { content: "\f3b6"; } -.bi-file-spreadsheet::before { content: "\f3b7"; } -.bi-file-text-fill::before { content: "\f3b8"; } -.bi-file-text::before { content: "\f3b9"; } -.bi-file-word-fill::before { content: "\f3ba"; } -.bi-file-word::before { content: "\f3bb"; } -.bi-file-x-fill::before { content: "\f3bc"; } -.bi-file-x::before { content: "\f3bd"; } -.bi-file-zip-fill::before { content: "\f3be"; } -.bi-file-zip::before { content: "\f3bf"; } -.bi-file::before { content: "\f3c0"; } -.bi-files-alt::before { content: "\f3c1"; } -.bi-files::before { content: "\f3c2"; } -.bi-film::before { content: "\f3c3"; } -.bi-filter-circle-fill::before { content: "\f3c4"; } -.bi-filter-circle::before { content: "\f3c5"; } -.bi-filter-left::before { content: "\f3c6"; } -.bi-filter-right::before { content: "\f3c7"; } -.bi-filter-square-fill::before { content: "\f3c8"; } -.bi-filter-square::before { content: "\f3c9"; } -.bi-filter::before { content: "\f3ca"; } -.bi-flag-fill::before { content: "\f3cb"; } -.bi-flag::before { content: "\f3cc"; } -.bi-flower1::before { content: "\f3cd"; } -.bi-flower2::before { content: "\f3ce"; } -.bi-flower3::before { content: "\f3cf"; } -.bi-folder-check::before { content: "\f3d0"; } -.bi-folder-fill::before { content: "\f3d1"; } -.bi-folder-minus::before { content: "\f3d2"; } -.bi-folder-plus::before { content: "\f3d3"; } -.bi-folder-symlink-fill::before { content: "\f3d4"; } -.bi-folder-symlink::before { content: "\f3d5"; } -.bi-folder-x::before { content: "\f3d6"; } -.bi-folder::before { content: "\f3d7"; } -.bi-folder2-open::before { content: "\f3d8"; } -.bi-folder2::before { content: "\f3d9"; } -.bi-fonts::before { content: "\f3da"; } -.bi-forward-fill::before { content: "\f3db"; } -.bi-forward::before { content: "\f3dc"; } -.bi-front::before { content: "\f3dd"; } -.bi-fullscreen-exit::before { content: "\f3de"; } -.bi-fullscreen::before { content: "\f3df"; } -.bi-funnel-fill::before { content: "\f3e0"; } -.bi-funnel::before { content: "\f3e1"; } -.bi-gear-fill::before { content: "\f3e2"; } -.bi-gear-wide-connected::before { content: "\f3e3"; } -.bi-gear-wide::before { content: "\f3e4"; } -.bi-gear::before { content: "\f3e5"; } -.bi-gem::before { content: "\f3e6"; } -.bi-geo-alt-fill::before { content: "\f3e7"; } -.bi-geo-alt::before { content: "\f3e8"; } -.bi-geo-fill::before { content: "\f3e9"; } -.bi-geo::before { content: "\f3ea"; } -.bi-gift-fill::before { content: "\f3eb"; } -.bi-gift::before { content: "\f3ec"; } -.bi-github::before { content: "\f3ed"; } -.bi-globe::before { content: "\f3ee"; } -.bi-globe2::before { content: "\f3ef"; } -.bi-google::before { content: "\f3f0"; } -.bi-graph-down::before { content: "\f3f1"; } -.bi-graph-up::before { content: "\f3f2"; } -.bi-grid-1x2-fill::before { content: "\f3f3"; } -.bi-grid-1x2::before { content: "\f3f4"; } -.bi-grid-3x2-gap-fill::before { content: "\f3f5"; } -.bi-grid-3x2-gap::before { content: "\f3f6"; } -.bi-grid-3x2::before { content: "\f3f7"; } -.bi-grid-3x3-gap-fill::before { content: "\f3f8"; } -.bi-grid-3x3-gap::before { content: "\f3f9"; } -.bi-grid-3x3::before { content: "\f3fa"; } -.bi-grid-fill::before { content: "\f3fb"; } -.bi-grid::before { content: "\f3fc"; } -.bi-grip-horizontal::before { content: "\f3fd"; } -.bi-grip-vertical::before { content: "\f3fe"; } -.bi-hammer::before { content: "\f3ff"; } -.bi-hand-index-fill::before { content: "\f400"; } -.bi-hand-index-thumb-fill::before { content: "\f401"; } -.bi-hand-index-thumb::before { content: "\f402"; } -.bi-hand-index::before { content: "\f403"; } -.bi-hand-thumbs-down-fill::before { content: "\f404"; } -.bi-hand-thumbs-down::before { content: "\f405"; } -.bi-hand-thumbs-up-fill::before { content: "\f406"; } -.bi-hand-thumbs-up::before { content: "\f407"; } -.bi-handbag-fill::before { content: "\f408"; } -.bi-handbag::before { content: "\f409"; } -.bi-hash::before { content: "\f40a"; } -.bi-hdd-fill::before { content: "\f40b"; } -.bi-hdd-network-fill::before { content: "\f40c"; } -.bi-hdd-network::before { content: "\f40d"; } -.bi-hdd-rack-fill::before { content: "\f40e"; } -.bi-hdd-rack::before { content: "\f40f"; } -.bi-hdd-stack-fill::before { content: "\f410"; } -.bi-hdd-stack::before { content: "\f411"; } -.bi-hdd::before { content: "\f412"; } -.bi-headphones::before { content: "\f413"; } -.bi-headset::before { content: "\f414"; } -.bi-heart-fill::before { content: "\f415"; } -.bi-heart-half::before { content: "\f416"; } -.bi-heart::before { content: "\f417"; } -.bi-heptagon-fill::before { content: "\f418"; } -.bi-heptagon-half::before { content: "\f419"; } -.bi-heptagon::before { content: "\f41a"; } -.bi-hexagon-fill::before { content: "\f41b"; } -.bi-hexagon-half::before { content: "\f41c"; } -.bi-hexagon::before { content: "\f41d"; } -.bi-hourglass-bottom::before { content: "\f41e"; } -.bi-hourglass-split::before { content: "\f41f"; } -.bi-hourglass-top::before { content: "\f420"; } -.bi-hourglass::before { content: "\f421"; } -.bi-house-door-fill::before { content: "\f422"; } -.bi-house-door::before { content: "\f423"; } -.bi-house-fill::before { content: "\f424"; } -.bi-house::before { content: "\f425"; } -.bi-hr::before { content: "\f426"; } -.bi-hurricane::before { content: "\f427"; } -.bi-image-alt::before { content: "\f428"; } -.bi-image-fill::before { content: "\f429"; } -.bi-image::before { content: "\f42a"; } -.bi-images::before { content: "\f42b"; } -.bi-inbox-fill::before { content: "\f42c"; } -.bi-inbox::before { content: "\f42d"; } -.bi-inboxes-fill::before { content: "\f42e"; } -.bi-inboxes::before { content: "\f42f"; } -.bi-info-circle-fill::before { content: "\f430"; } -.bi-info-circle::before { content: "\f431"; } -.bi-info-square-fill::before { content: "\f432"; } -.bi-info-square::before { content: "\f433"; } -.bi-info::before { content: "\f434"; } -.bi-input-cursor-text::before { content: "\f435"; } -.bi-input-cursor::before { content: "\f436"; } -.bi-instagram::before { content: "\f437"; } -.bi-intersect::before { content: "\f438"; } -.bi-journal-album::before { content: "\f439"; } -.bi-journal-arrow-down::before { content: "\f43a"; } -.bi-journal-arrow-up::before { content: "\f43b"; } -.bi-journal-bookmark-fill::before { content: "\f43c"; } -.bi-journal-bookmark::before { content: "\f43d"; } -.bi-journal-check::before { content: "\f43e"; } -.bi-journal-code::before { content: "\f43f"; } -.bi-journal-medical::before { content: "\f440"; } -.bi-journal-minus::before { content: "\f441"; } -.bi-journal-plus::before { content: "\f442"; } -.bi-journal-richtext::before { content: "\f443"; } -.bi-journal-text::before { content: "\f444"; } -.bi-journal-x::before { content: "\f445"; } -.bi-journal::before { content: "\f446"; } -.bi-journals::before { content: "\f447"; } -.bi-joystick::before { content: "\f448"; } -.bi-justify-left::before { content: "\f449"; } -.bi-justify-right::before { content: "\f44a"; } -.bi-justify::before { content: "\f44b"; } -.bi-kanban-fill::before { content: "\f44c"; } -.bi-kanban::before { content: "\f44d"; } -.bi-key-fill::before { content: "\f44e"; } -.bi-key::before { content: "\f44f"; } -.bi-keyboard-fill::before { content: "\f450"; } -.bi-keyboard::before { content: "\f451"; } -.bi-ladder::before { content: "\f452"; } -.bi-lamp-fill::before { content: "\f453"; } -.bi-lamp::before { content: "\f454"; } -.bi-laptop-fill::before { content: "\f455"; } -.bi-laptop::before { content: "\f456"; } -.bi-layer-backward::before { content: "\f457"; } -.bi-layer-forward::before { content: "\f458"; } -.bi-layers-fill::before { content: "\f459"; } -.bi-layers-half::before { content: "\f45a"; } -.bi-layers::before { content: "\f45b"; } -.bi-layout-sidebar-inset-reverse::before { content: "\f45c"; } -.bi-layout-sidebar-inset::before { content: "\f45d"; } -.bi-layout-sidebar-reverse::before { content: "\f45e"; } -.bi-layout-sidebar::before { content: "\f45f"; } -.bi-layout-split::before { content: "\f460"; } -.bi-layout-text-sidebar-reverse::before { content: "\f461"; } -.bi-layout-text-sidebar::before { content: "\f462"; } -.bi-layout-text-window-reverse::before { content: "\f463"; } -.bi-layout-text-window::before { content: "\f464"; } -.bi-layout-three-columns::before { content: "\f465"; } -.bi-layout-wtf::before { content: "\f466"; } -.bi-life-preserver::before { content: "\f467"; } -.bi-lightbulb-fill::before { content: "\f468"; } -.bi-lightbulb-off-fill::before { content: "\f469"; } -.bi-lightbulb-off::before { content: "\f46a"; } -.bi-lightbulb::before { content: "\f46b"; } -.bi-lightning-charge-fill::before { content: "\f46c"; } -.bi-lightning-charge::before { content: "\f46d"; } -.bi-lightning-fill::before { content: "\f46e"; } -.bi-lightning::before { content: "\f46f"; } -.bi-link-45deg::before { content: "\f470"; } -.bi-link::before { content: "\f471"; } -.bi-linkedin::before { content: "\f472"; } -.bi-list-check::before { content: "\f473"; } -.bi-list-nested::before { content: "\f474"; } -.bi-list-ol::before { content: "\f475"; } -.bi-list-stars::before { content: "\f476"; } -.bi-list-task::before { content: "\f477"; } -.bi-list-ul::before { content: "\f478"; } -.bi-list::before { content: "\f479"; } -.bi-lock-fill::before { content: "\f47a"; } -.bi-lock::before { content: "\f47b"; } -.bi-mailbox::before { content: "\f47c"; } -.bi-mailbox2::before { content: "\f47d"; } -.bi-map-fill::before { content: "\f47e"; } -.bi-map::before { content: "\f47f"; } -.bi-markdown-fill::before { content: "\f480"; } -.bi-markdown::before { content: "\f481"; } -.bi-mask::before { content: "\f482"; } -.bi-megaphone-fill::before { content: "\f483"; } -.bi-megaphone::before { content: "\f484"; } -.bi-menu-app-fill::before { content: "\f485"; } -.bi-menu-app::before { content: "\f486"; } -.bi-menu-button-fill::before { content: "\f487"; } -.bi-menu-button-wide-fill::before { content: "\f488"; } -.bi-menu-button-wide::before { content: "\f489"; } -.bi-menu-button::before { content: "\f48a"; } -.bi-menu-down::before { content: "\f48b"; } -.bi-menu-up::before { content: "\f48c"; } -.bi-mic-fill::before { content: "\f48d"; } -.bi-mic-mute-fill::before { content: "\f48e"; } -.bi-mic-mute::before { content: "\f48f"; } -.bi-mic::before { content: "\f490"; } -.bi-minecart-loaded::before { content: "\f491"; } -.bi-minecart::before { content: "\f492"; } -.bi-moisture::before { content: "\f493"; } -.bi-moon-fill::before { content: "\f494"; } -.bi-moon-stars-fill::before { content: "\f495"; } -.bi-moon-stars::before { content: "\f496"; } -.bi-moon::before { content: "\f497"; } -.bi-mouse-fill::before { content: "\f498"; } -.bi-mouse::before { content: "\f499"; } -.bi-mouse2-fill::before { content: "\f49a"; } -.bi-mouse2::before { content: "\f49b"; } -.bi-mouse3-fill::before { content: "\f49c"; } -.bi-mouse3::before { content: "\f49d"; } -.bi-music-note-beamed::before { content: "\f49e"; } -.bi-music-note-list::before { content: "\f49f"; } -.bi-music-note::before { content: "\f4a0"; } -.bi-music-player-fill::before { content: "\f4a1"; } -.bi-music-player::before { content: "\f4a2"; } -.bi-newspaper::before { content: "\f4a3"; } -.bi-node-minus-fill::before { content: "\f4a4"; } -.bi-node-minus::before { content: "\f4a5"; } -.bi-node-plus-fill::before { content: "\f4a6"; } -.bi-node-plus::before { content: "\f4a7"; } -.bi-nut-fill::before { content: "\f4a8"; } -.bi-nut::before { content: "\f4a9"; } -.bi-octagon-fill::before { content: "\f4aa"; } -.bi-octagon-half::before { content: "\f4ab"; } -.bi-octagon::before { content: "\f4ac"; } -.bi-option::before { content: "\f4ad"; } -.bi-outlet::before { content: "\f4ae"; } -.bi-paint-bucket::before { content: "\f4af"; } -.bi-palette-fill::before { content: "\f4b0"; } -.bi-palette::before { content: "\f4b1"; } -.bi-palette2::before { content: "\f4b2"; } -.bi-paperclip::before { content: "\f4b3"; } -.bi-paragraph::before { content: "\f4b4"; } -.bi-patch-check-fill::before { content: "\f4b5"; } -.bi-patch-check::before { content: "\f4b6"; } -.bi-patch-exclamation-fill::before { content: "\f4b7"; } -.bi-patch-exclamation::before { content: "\f4b8"; } -.bi-patch-minus-fill::before { content: "\f4b9"; } -.bi-patch-minus::before { content: "\f4ba"; } -.bi-patch-plus-fill::before { content: "\f4bb"; } -.bi-patch-plus::before { content: "\f4bc"; } -.bi-patch-question-fill::before { content: "\f4bd"; } -.bi-patch-question::before { content: "\f4be"; } -.bi-pause-btn-fill::before { content: "\f4bf"; } -.bi-pause-btn::before { content: "\f4c0"; } -.bi-pause-circle-fill::before { content: "\f4c1"; } -.bi-pause-circle::before { content: "\f4c2"; } -.bi-pause-fill::before { content: "\f4c3"; } -.bi-pause::before { content: "\f4c4"; } -.bi-peace-fill::before { content: "\f4c5"; } -.bi-peace::before { content: "\f4c6"; } -.bi-pen-fill::before { content: "\f4c7"; } -.bi-pen::before { content: "\f4c8"; } -.bi-pencil-fill::before { content: "\f4c9"; } -.bi-pencil-square::before { content: "\f4ca"; } -.bi-pencil::before { content: "\f4cb"; } -.bi-pentagon-fill::before { content: "\f4cc"; } -.bi-pentagon-half::before { content: "\f4cd"; } -.bi-pentagon::before { content: "\f4ce"; } -.bi-people-fill::before { content: "\f4cf"; } -.bi-people::before { content: "\f4d0"; } -.bi-percent::before { content: "\f4d1"; } -.bi-person-badge-fill::before { content: "\f4d2"; } -.bi-person-badge::before { content: "\f4d3"; } -.bi-person-bounding-box::before { content: "\f4d4"; } -.bi-person-check-fill::before { content: "\f4d5"; } -.bi-person-check::before { content: "\f4d6"; } -.bi-person-circle::before { content: "\f4d7"; } -.bi-person-dash-fill::before { content: "\f4d8"; } -.bi-person-dash::before { content: "\f4d9"; } -.bi-person-fill::before { content: "\f4da"; } -.bi-person-lines-fill::before { content: "\f4db"; } -.bi-person-plus-fill::before { content: "\f4dc"; } -.bi-person-plus::before { content: "\f4dd"; } -.bi-person-square::before { content: "\f4de"; } -.bi-person-x-fill::before { content: "\f4df"; } -.bi-person-x::before { content: "\f4e0"; } -.bi-person::before { content: "\f4e1"; } -.bi-phone-fill::before { content: "\f4e2"; } -.bi-phone-landscape-fill::before { content: "\f4e3"; } -.bi-phone-landscape::before { content: "\f4e4"; } -.bi-phone-vibrate-fill::before { content: "\f4e5"; } -.bi-phone-vibrate::before { content: "\f4e6"; } -.bi-phone::before { content: "\f4e7"; } -.bi-pie-chart-fill::before { content: "\f4e8"; } -.bi-pie-chart::before { content: "\f4e9"; } -.bi-pin-angle-fill::before { content: "\f4ea"; } -.bi-pin-angle::before { content: "\f4eb"; } -.bi-pin-fill::before { content: "\f4ec"; } -.bi-pin::before { content: "\f4ed"; } -.bi-pip-fill::before { content: "\f4ee"; } -.bi-pip::before { content: "\f4ef"; } -.bi-play-btn-fill::before { content: "\f4f0"; } -.bi-play-btn::before { content: "\f4f1"; } -.bi-play-circle-fill::before { content: "\f4f2"; } -.bi-play-circle::before { content: "\f4f3"; } -.bi-play-fill::before { content: "\f4f4"; } -.bi-play::before { content: "\f4f5"; } -.bi-plug-fill::before { content: "\f4f6"; } -.bi-plug::before { content: "\f4f7"; } -.bi-plus-circle-dotted::before { content: "\f4f8"; } -.bi-plus-circle-fill::before { content: "\f4f9"; } -.bi-plus-circle::before { content: "\f4fa"; } -.bi-plus-square-dotted::before { content: "\f4fb"; } -.bi-plus-square-fill::before { content: "\f4fc"; } -.bi-plus-square::before { content: "\f4fd"; } -.bi-plus::before { content: "\f4fe"; } -.bi-power::before { content: "\f4ff"; } -.bi-printer-fill::before { content: "\f500"; } -.bi-printer::before { content: "\f501"; } -.bi-puzzle-fill::before { content: "\f502"; } -.bi-puzzle::before { content: "\f503"; } -.bi-question-circle-fill::before { content: "\f504"; } -.bi-question-circle::before { content: "\f505"; } -.bi-question-diamond-fill::before { content: "\f506"; } -.bi-question-diamond::before { content: "\f507"; } -.bi-question-octagon-fill::before { content: "\f508"; } -.bi-question-octagon::before { content: "\f509"; } -.bi-question-square-fill::before { content: "\f50a"; } -.bi-question-square::before { content: "\f50b"; } -.bi-question::before { content: "\f50c"; } -.bi-rainbow::before { content: "\f50d"; } -.bi-receipt-cutoff::before { content: "\f50e"; } -.bi-receipt::before { content: "\f50f"; } -.bi-reception-0::before { content: "\f510"; } -.bi-reception-1::before { content: "\f511"; } -.bi-reception-2::before { content: "\f512"; } -.bi-reception-3::before { content: "\f513"; } -.bi-reception-4::before { content: "\f514"; } -.bi-record-btn-fill::before { content: "\f515"; } -.bi-record-btn::before { content: "\f516"; } -.bi-record-circle-fill::before { content: "\f517"; } -.bi-record-circle::before { content: "\f518"; } -.bi-record-fill::before { content: "\f519"; } -.bi-record::before { content: "\f51a"; } -.bi-record2-fill::before { content: "\f51b"; } -.bi-record2::before { content: "\f51c"; } -.bi-reply-all-fill::before { content: "\f51d"; } -.bi-reply-all::before { content: "\f51e"; } -.bi-reply-fill::before { content: "\f51f"; } -.bi-reply::before { content: "\f520"; } -.bi-rss-fill::before { content: "\f521"; } -.bi-rss::before { content: "\f522"; } -.bi-rulers::before { content: "\f523"; } -.bi-save-fill::before { content: "\f524"; } -.bi-save::before { content: "\f525"; } -.bi-save2-fill::before { content: "\f526"; } -.bi-save2::before { content: "\f527"; } -.bi-scissors::before { content: "\f528"; } -.bi-screwdriver::before { content: "\f529"; } -.bi-search::before { content: "\f52a"; } -.bi-segmented-nav::before { content: "\f52b"; } -.bi-server::before { content: "\f52c"; } -.bi-share-fill::before { content: "\f52d"; } -.bi-share::before { content: "\f52e"; } -.bi-shield-check::before { content: "\f52f"; } -.bi-shield-exclamation::before { content: "\f530"; } -.bi-shield-fill-check::before { content: "\f531"; } -.bi-shield-fill-exclamation::before { content: "\f532"; } -.bi-shield-fill-minus::before { content: "\f533"; } -.bi-shield-fill-plus::before { content: "\f534"; } -.bi-shield-fill-x::before { content: "\f535"; } -.bi-shield-fill::before { content: "\f536"; } -.bi-shield-lock-fill::before { content: "\f537"; } -.bi-shield-lock::before { content: "\f538"; } -.bi-shield-minus::before { content: "\f539"; } -.bi-shield-plus::before { content: "\f53a"; } -.bi-shield-shaded::before { content: "\f53b"; } -.bi-shield-slash-fill::before { content: "\f53c"; } -.bi-shield-slash::before { content: "\f53d"; } -.bi-shield-x::before { content: "\f53e"; } -.bi-shield::before { content: "\f53f"; } -.bi-shift-fill::before { content: "\f540"; } -.bi-shift::before { content: "\f541"; } -.bi-shop-window::before { content: "\f542"; } -.bi-shop::before { content: "\f543"; } -.bi-shuffle::before { content: "\f544"; } -.bi-signpost-2-fill::before { content: "\f545"; } -.bi-signpost-2::before { content: "\f546"; } -.bi-signpost-fill::before { content: "\f547"; } -.bi-signpost-split-fill::before { content: "\f548"; } -.bi-signpost-split::before { content: "\f549"; } -.bi-signpost::before { content: "\f54a"; } -.bi-sim-fill::before { content: "\f54b"; } -.bi-sim::before { content: "\f54c"; } -.bi-skip-backward-btn-fill::before { content: "\f54d"; } -.bi-skip-backward-btn::before { content: "\f54e"; } -.bi-skip-backward-circle-fill::before { content: "\f54f"; } -.bi-skip-backward-circle::before { content: "\f550"; } -.bi-skip-backward-fill::before { content: "\f551"; } -.bi-skip-backward::before { content: "\f552"; } -.bi-skip-end-btn-fill::before { content: "\f553"; } -.bi-skip-end-btn::before { content: "\f554"; } -.bi-skip-end-circle-fill::before { content: "\f555"; } -.bi-skip-end-circle::before { content: "\f556"; } -.bi-skip-end-fill::before { content: "\f557"; } -.bi-skip-end::before { content: "\f558"; } -.bi-skip-forward-btn-fill::before { content: "\f559"; } -.bi-skip-forward-btn::before { content: "\f55a"; } -.bi-skip-forward-circle-fill::before { content: "\f55b"; } -.bi-skip-forward-circle::before { content: "\f55c"; } -.bi-skip-forward-fill::before { content: "\f55d"; } -.bi-skip-forward::before { content: "\f55e"; } -.bi-skip-start-btn-fill::before { content: "\f55f"; } -.bi-skip-start-btn::before { content: "\f560"; } -.bi-skip-start-circle-fill::before { content: "\f561"; } -.bi-skip-start-circle::before { content: "\f562"; } -.bi-skip-start-fill::before { content: "\f563"; } -.bi-skip-start::before { content: "\f564"; } -.bi-slack::before { content: "\f565"; } -.bi-slash-circle-fill::before { content: "\f566"; } -.bi-slash-circle::before { content: "\f567"; } -.bi-slash-square-fill::before { content: "\f568"; } -.bi-slash-square::before { content: "\f569"; } -.bi-slash::before { content: "\f56a"; } -.bi-sliders::before { content: "\f56b"; } -.bi-smartwatch::before { content: "\f56c"; } -.bi-snow::before { content: "\f56d"; } -.bi-snow2::before { content: "\f56e"; } -.bi-snow3::before { content: "\f56f"; } -.bi-sort-alpha-down-alt::before { content: "\f570"; } -.bi-sort-alpha-down::before { content: "\f571"; } -.bi-sort-alpha-up-alt::before { content: "\f572"; } -.bi-sort-alpha-up::before { content: "\f573"; } -.bi-sort-down-alt::before { content: "\f574"; } -.bi-sort-down::before { content: "\f575"; } -.bi-sort-numeric-down-alt::before { content: "\f576"; } -.bi-sort-numeric-down::before { content: "\f577"; } -.bi-sort-numeric-up-alt::before { content: "\f578"; } -.bi-sort-numeric-up::before { content: "\f579"; } -.bi-sort-up-alt::before { content: "\f57a"; } -.bi-sort-up::before { content: "\f57b"; } -.bi-soundwave::before { content: "\f57c"; } -.bi-speaker-fill::before { content: "\f57d"; } -.bi-speaker::before { content: "\f57e"; } -.bi-speedometer::before { content: "\f57f"; } -.bi-speedometer2::before { content: "\f580"; } -.bi-spellcheck::before { content: "\f581"; } -.bi-square-fill::before { content: "\f582"; } -.bi-square-half::before { content: "\f583"; } -.bi-square::before { content: "\f584"; } -.bi-stack::before { content: "\f585"; } -.bi-star-fill::before { content: "\f586"; } -.bi-star-half::before { content: "\f587"; } -.bi-star::before { content: "\f588"; } -.bi-stars::before { content: "\f589"; } -.bi-stickies-fill::before { content: "\f58a"; } -.bi-stickies::before { content: "\f58b"; } -.bi-sticky-fill::before { content: "\f58c"; } -.bi-sticky::before { content: "\f58d"; } -.bi-stop-btn-fill::before { content: "\f58e"; } -.bi-stop-btn::before { content: "\f58f"; } -.bi-stop-circle-fill::before { content: "\f590"; } -.bi-stop-circle::before { content: "\f591"; } -.bi-stop-fill::before { content: "\f592"; } -.bi-stop::before { content: "\f593"; } -.bi-stoplights-fill::before { content: "\f594"; } -.bi-stoplights::before { content: "\f595"; } -.bi-stopwatch-fill::before { content: "\f596"; } -.bi-stopwatch::before { content: "\f597"; } -.bi-subtract::before { content: "\f598"; } -.bi-suit-club-fill::before { content: "\f599"; } -.bi-suit-club::before { content: "\f59a"; } -.bi-suit-diamond-fill::before { content: "\f59b"; } -.bi-suit-diamond::before { content: "\f59c"; } -.bi-suit-heart-fill::before { content: "\f59d"; } -.bi-suit-heart::before { content: "\f59e"; } -.bi-suit-spade-fill::before { content: "\f59f"; } -.bi-suit-spade::before { content: "\f5a0"; } -.bi-sun-fill::before { content: "\f5a1"; } -.bi-sun::before { content: "\f5a2"; } -.bi-sunglasses::before { content: "\f5a3"; } -.bi-sunrise-fill::before { content: "\f5a4"; } -.bi-sunrise::before { content: "\f5a5"; } -.bi-sunset-fill::before { content: "\f5a6"; } -.bi-sunset::before { content: "\f5a7"; } -.bi-symmetry-horizontal::before { content: "\f5a8"; } -.bi-symmetry-vertical::before { content: "\f5a9"; } -.bi-table::before { content: "\f5aa"; } -.bi-tablet-fill::before { content: "\f5ab"; } -.bi-tablet-landscape-fill::before { content: "\f5ac"; } -.bi-tablet-landscape::before { content: "\f5ad"; } -.bi-tablet::before { content: "\f5ae"; } -.bi-tag-fill::before { content: "\f5af"; } -.bi-tag::before { content: "\f5b0"; } -.bi-tags-fill::before { content: "\f5b1"; } -.bi-tags::before { content: "\f5b2"; } -.bi-telegram::before { content: "\f5b3"; } -.bi-telephone-fill::before { content: "\f5b4"; } -.bi-telephone-forward-fill::before { content: "\f5b5"; } -.bi-telephone-forward::before { content: "\f5b6"; } -.bi-telephone-inbound-fill::before { content: "\f5b7"; } -.bi-telephone-inbound::before { content: "\f5b8"; } -.bi-telephone-minus-fill::before { content: "\f5b9"; } -.bi-telephone-minus::before { content: "\f5ba"; } -.bi-telephone-outbound-fill::before { content: "\f5bb"; } -.bi-telephone-outbound::before { content: "\f5bc"; } -.bi-telephone-plus-fill::before { content: "\f5bd"; } -.bi-telephone-plus::before { content: "\f5be"; } -.bi-telephone-x-fill::before { content: "\f5bf"; } -.bi-telephone-x::before { content: "\f5c0"; } -.bi-telephone::before { content: "\f5c1"; } -.bi-terminal-fill::before { content: "\f5c2"; } -.bi-terminal::before { content: "\f5c3"; } -.bi-text-center::before { content: "\f5c4"; } -.bi-text-indent-left::before { content: "\f5c5"; } -.bi-text-indent-right::before { content: "\f5c6"; } -.bi-text-left::before { content: "\f5c7"; } -.bi-text-paragraph::before { content: "\f5c8"; } -.bi-text-right::before { content: "\f5c9"; } -.bi-textarea-resize::before { content: "\f5ca"; } -.bi-textarea-t::before { content: "\f5cb"; } -.bi-textarea::before { content: "\f5cc"; } -.bi-thermometer-half::before { content: "\f5cd"; } -.bi-thermometer-high::before { content: "\f5ce"; } -.bi-thermometer-low::before { content: "\f5cf"; } -.bi-thermometer-snow::before { content: "\f5d0"; } -.bi-thermometer-sun::before { content: "\f5d1"; } -.bi-thermometer::before { content: "\f5d2"; } -.bi-three-dots-vertical::before { content: "\f5d3"; } -.bi-three-dots::before { content: "\f5d4"; } -.bi-toggle-off::before { content: "\f5d5"; } -.bi-toggle-on::before { content: "\f5d6"; } -.bi-toggle2-off::before { content: "\f5d7"; } -.bi-toggle2-on::before { content: "\f5d8"; } -.bi-toggles::before { content: "\f5d9"; } -.bi-toggles2::before { content: "\f5da"; } -.bi-tools::before { content: "\f5db"; } -.bi-tornado::before { content: "\f5dc"; } -.bi-trash-fill::before { content: "\f5dd"; } -.bi-trash::before { content: "\f5de"; } -.bi-trash2-fill::before { content: "\f5df"; } -.bi-trash2::before { content: "\f5e0"; } -.bi-tree-fill::before { content: "\f5e1"; } -.bi-tree::before { content: "\f5e2"; } -.bi-triangle-fill::before { content: "\f5e3"; } -.bi-triangle-half::before { content: "\f5e4"; } -.bi-triangle::before { content: "\f5e5"; } -.bi-trophy-fill::before { content: "\f5e6"; } -.bi-trophy::before { content: "\f5e7"; } -.bi-tropical-storm::before { content: "\f5e8"; } -.bi-truck-flatbed::before { content: "\f5e9"; } -.bi-truck::before { content: "\f5ea"; } -.bi-tsunami::before { content: "\f5eb"; } -.bi-tv-fill::before { content: "\f5ec"; } -.bi-tv::before { content: "\f5ed"; } -.bi-twitch::before { content: "\f5ee"; } -.bi-twitter::before { content: "\f5ef"; } -.bi-type-bold::before { content: "\f5f0"; } -.bi-type-h1::before { content: "\f5f1"; } -.bi-type-h2::before { content: "\f5f2"; } -.bi-type-h3::before { content: "\f5f3"; } -.bi-type-italic::before { content: "\f5f4"; } -.bi-type-strikethrough::before { content: "\f5f5"; } -.bi-type-underline::before { content: "\f5f6"; } -.bi-type::before { content: "\f5f7"; } -.bi-ui-checks-grid::before { content: "\f5f8"; } -.bi-ui-checks::before { content: "\f5f9"; } -.bi-ui-radios-grid::before { content: "\f5fa"; } -.bi-ui-radios::before { content: "\f5fb"; } -.bi-umbrella-fill::before { content: "\f5fc"; } -.bi-umbrella::before { content: "\f5fd"; } -.bi-union::before { content: "\f5fe"; } -.bi-unlock-fill::before { content: "\f5ff"; } -.bi-unlock::before { content: "\f600"; } -.bi-upc-scan::before { content: "\f601"; } -.bi-upc::before { content: "\f602"; } -.bi-upload::before { content: "\f603"; } -.bi-vector-pen::before { content: "\f604"; } -.bi-view-list::before { content: "\f605"; } -.bi-view-stacked::before { content: "\f606"; } -.bi-vinyl-fill::before { content: "\f607"; } -.bi-vinyl::before { content: "\f608"; } -.bi-voicemail::before { content: "\f609"; } -.bi-volume-down-fill::before { content: "\f60a"; } -.bi-volume-down::before { content: "\f60b"; } -.bi-volume-mute-fill::before { content: "\f60c"; } -.bi-volume-mute::before { content: "\f60d"; } -.bi-volume-off-fill::before { content: "\f60e"; } -.bi-volume-off::before { content: "\f60f"; } -.bi-volume-up-fill::before { content: "\f610"; } -.bi-volume-up::before { content: "\f611"; } -.bi-vr::before { content: "\f612"; } -.bi-wallet-fill::before { content: "\f613"; } -.bi-wallet::before { content: "\f614"; } -.bi-wallet2::before { content: "\f615"; } -.bi-watch::before { content: "\f616"; } -.bi-water::before { content: "\f617"; } -.bi-whatsapp::before { content: "\f618"; } -.bi-wifi-1::before { content: "\f619"; } -.bi-wifi-2::before { content: "\f61a"; } -.bi-wifi-off::before { content: "\f61b"; } -.bi-wifi::before { content: "\f61c"; } -.bi-wind::before { content: "\f61d"; } -.bi-window-dock::before { content: "\f61e"; } -.bi-window-sidebar::before { content: "\f61f"; } -.bi-window::before { content: "\f620"; } -.bi-wrench::before { content: "\f621"; } -.bi-x-circle-fill::before { content: "\f622"; } -.bi-x-circle::before { content: "\f623"; } -.bi-x-diamond-fill::before { content: "\f624"; } -.bi-x-diamond::before { content: "\f625"; } -.bi-x-octagon-fill::before { content: "\f626"; } -.bi-x-octagon::before { content: "\f627"; } -.bi-x-square-fill::before { content: "\f628"; } -.bi-x-square::before { content: "\f629"; } -.bi-x::before { content: "\f62a"; } -.bi-youtube::before { content: "\f62b"; } -.bi-zoom-in::before { content: "\f62c"; } -.bi-zoom-out::before { content: "\f62d"; } -.bi-bank::before { content: "\f62e"; } -.bi-bank2::before { content: "\f62f"; } -.bi-bell-slash-fill::before { content: "\f630"; } -.bi-bell-slash::before { content: "\f631"; } -.bi-cash-coin::before { content: "\f632"; } -.bi-check-lg::before { content: "\f633"; } -.bi-coin::before { content: "\f634"; } -.bi-currency-bitcoin::before { content: "\f635"; } -.bi-currency-dollar::before { content: "\f636"; } -.bi-currency-euro::before { content: "\f637"; } -.bi-currency-exchange::before { content: "\f638"; } -.bi-currency-pound::before { content: "\f639"; } -.bi-currency-yen::before { content: "\f63a"; } -.bi-dash-lg::before { content: "\f63b"; } -.bi-exclamation-lg::before { content: "\f63c"; } -.bi-file-earmark-pdf-fill::before { content: "\f63d"; } -.bi-file-earmark-pdf::before { content: "\f63e"; } -.bi-file-pdf-fill::before { content: "\f63f"; } -.bi-file-pdf::before { content: "\f640"; } -.bi-gender-ambiguous::before { content: "\f641"; } -.bi-gender-female::before { content: "\f642"; } -.bi-gender-male::before { content: "\f643"; } -.bi-gender-trans::before { content: "\f644"; } -.bi-headset-vr::before { content: "\f645"; } -.bi-info-lg::before { content: "\f646"; } -.bi-mastodon::before { content: "\f647"; } -.bi-messenger::before { content: "\f648"; } -.bi-piggy-bank-fill::before { content: "\f649"; } -.bi-piggy-bank::before { content: "\f64a"; } -.bi-pin-map-fill::before { content: "\f64b"; } -.bi-pin-map::before { content: "\f64c"; } -.bi-plus-lg::before { content: "\f64d"; } -.bi-question-lg::before { content: "\f64e"; } -.bi-recycle::before { content: "\f64f"; } -.bi-reddit::before { content: "\f650"; } -.bi-safe-fill::before { content: "\f651"; } -.bi-safe2-fill::before { content: "\f652"; } -.bi-safe2::before { content: "\f653"; } -.bi-sd-card-fill::before { content: "\f654"; } -.bi-sd-card::before { content: "\f655"; } -.bi-skype::before { content: "\f656"; } -.bi-slash-lg::before { content: "\f657"; } -.bi-translate::before { content: "\f658"; } -.bi-x-lg::before { content: "\f659"; } -.bi-safe::before { content: "\f65a"; } -.bi-apple::before { content: "\f65b"; } -.bi-microsoft::before { content: "\f65d"; } -.bi-windows::before { content: "\f65e"; } -.bi-behance::before { content: "\f65c"; } -.bi-dribbble::before { content: "\f65f"; } -.bi-line::before { content: "\f660"; } -.bi-medium::before { content: "\f661"; } -.bi-paypal::before { content: "\f662"; } -.bi-pinterest::before { content: "\f663"; } -.bi-signal::before { content: "\f664"; } -.bi-snapchat::before { content: "\f665"; } -.bi-spotify::before { content: "\f666"; } -.bi-stack-overflow::before { content: "\f667"; } -.bi-strava::before { content: "\f668"; } -.bi-wordpress::before { content: "\f669"; } -.bi-vimeo::before { content: "\f66a"; } -.bi-activity::before { content: "\f66b"; } -.bi-easel2-fill::before { content: "\f66c"; } -.bi-easel2::before { content: "\f66d"; } -.bi-easel3-fill::before { content: "\f66e"; } -.bi-easel3::before { content: "\f66f"; } -.bi-fan::before { content: "\f670"; } -.bi-fingerprint::before { content: "\f671"; } -.bi-graph-down-arrow::before { content: "\f672"; } -.bi-graph-up-arrow::before { content: "\f673"; } -.bi-hypnotize::before { content: "\f674"; } -.bi-magic::before { content: "\f675"; } -.bi-person-rolodex::before { content: "\f676"; } -.bi-person-video::before { content: "\f677"; } -.bi-person-video2::before { content: "\f678"; } -.bi-person-video3::before { content: "\f679"; } -.bi-person-workspace::before { content: "\f67a"; } -.bi-radioactive::before { content: "\f67b"; } -.bi-webcam-fill::before { content: "\f67c"; } -.bi-webcam::before { content: "\f67d"; } -.bi-yin-yang::before { content: "\f67e"; } -.bi-bandaid-fill::before { content: "\f680"; } -.bi-bandaid::before { content: "\f681"; } -.bi-bluetooth::before { content: "\f682"; } -.bi-body-text::before { content: "\f683"; } -.bi-boombox::before { content: "\f684"; } -.bi-boxes::before { content: "\f685"; } -.bi-dpad-fill::before { content: "\f686"; } -.bi-dpad::before { content: "\f687"; } -.bi-ear-fill::before { content: "\f688"; } -.bi-ear::before { content: "\f689"; } -.bi-envelope-check-1::before { content: "\f68a"; } -.bi-envelope-check-fill::before { content: "\f68b"; } -.bi-envelope-check::before { content: "\f68c"; } -.bi-envelope-dash-1::before { content: "\f68d"; } -.bi-envelope-dash-fill::before { content: "\f68e"; } -.bi-envelope-dash::before { content: "\f68f"; } -.bi-envelope-exclamation-1::before { content: "\f690"; } -.bi-envelope-exclamation-fill::before { content: "\f691"; } -.bi-envelope-exclamation::before { content: "\f692"; } -.bi-envelope-plus-fill::before { content: "\f693"; } -.bi-envelope-plus::before { content: "\f694"; } -.bi-envelope-slash-1::before { content: "\f695"; } -.bi-envelope-slash-fill::before { content: "\f696"; } -.bi-envelope-slash::before { content: "\f697"; } -.bi-envelope-x-1::before { content: "\f698"; } -.bi-envelope-x-fill::before { content: "\f699"; } -.bi-envelope-x::before { content: "\f69a"; } -.bi-explicit-fill::before { content: "\f69b"; } -.bi-explicit::before { content: "\f69c"; } -.bi-git::before { content: "\f69d"; } -.bi-infinity::before { content: "\f69e"; } -.bi-list-columns-reverse::before { content: "\f69f"; } -.bi-list-columns::before { content: "\f6a0"; } -.bi-meta::before { content: "\f6a1"; } -.bi-mortorboard-fill::before { content: "\f6a2"; } -.bi-mortorboard::before { content: "\f6a3"; } -.bi-nintendo-switch::before { content: "\f6a4"; } -.bi-pc-display-horizontal::before { content: "\f6a5"; } -.bi-pc-display::before { content: "\f6a6"; } -.bi-pc-horizontal::before { content: "\f6a7"; } -.bi-pc::before { content: "\f6a8"; } -.bi-playstation::before { content: "\f6a9"; } -.bi-plus-slash-minus::before { content: "\f6aa"; } -.bi-projector-fill::before { content: "\f6ab"; } -.bi-projector::before { content: "\f6ac"; } -.bi-qr-code-scan::before { content: "\f6ad"; } -.bi-qr-code::before { content: "\f6ae"; } -.bi-quora::before { content: "\f6af"; } -.bi-quote::before { content: "\f6b0"; } -.bi-robot::before { content: "\f6b1"; } -.bi-send-check-fill::before { content: "\f6b2"; } -.bi-send-check::before { content: "\f6b3"; } -.bi-send-dash-fill::before { content: "\f6b4"; } -.bi-send-dash::before { content: "\f6b5"; } -.bi-send-exclamation-1::before { content: "\f6b6"; } -.bi-send-exclamation-fill::before { content: "\f6b7"; } -.bi-send-exclamation::before { content: "\f6b8"; } -.bi-send-fill::before { content: "\f6b9"; } -.bi-send-plus-fill::before { content: "\f6ba"; } -.bi-send-plus::before { content: "\f6bb"; } -.bi-send-slash-fill::before { content: "\f6bc"; } -.bi-send-slash::before { content: "\f6bd"; } -.bi-send-x-fill::before { content: "\f6be"; } -.bi-send-x::before { content: "\f6bf"; } -.bi-send::before { content: "\f6c0"; } -.bi-steam::before { content: "\f6c1"; } -.bi-terminal-dash-1::before { content: "\f6c2"; } -.bi-terminal-dash::before { content: "\f6c3"; } -.bi-terminal-plus::before { content: "\f6c4"; } -.bi-terminal-split::before { content: "\f6c5"; } -.bi-ticket-detailed-fill::before { content: "\f6c6"; } -.bi-ticket-detailed::before { content: "\f6c7"; } -.bi-ticket-fill::before { content: "\f6c8"; } -.bi-ticket-perferated-fill::before { content: "\f6c9"; } -.bi-ticket-perferated::before { content: "\f6ca"; } -.bi-ticket::before { content: "\f6cb"; } -.bi-tiktok::before { content: "\f6cc"; } -.bi-window-dash::before { content: "\f6cd"; } -.bi-window-desktop::before { content: "\f6ce"; } -.bi-window-fullscreen::before { content: "\f6cf"; } -.bi-window-plus::before { content: "\f6d0"; } -.bi-window-split::before { content: "\f6d1"; } -.bi-window-stack::before { content: "\f6d2"; } -.bi-window-x::before { content: "\f6d3"; } -.bi-xbox::before { content: "\f6d4"; } -.bi-ethernet::before { content: "\f6d5"; } -.bi-hdmi-fill::before { content: "\f6d6"; } -.bi-hdmi::before { content: "\f6d7"; } -.bi-usb-c-fill::before { content: "\f6d8"; } -.bi-usb-c::before { content: "\f6d9"; } -.bi-usb-fill::before { content: "\f6da"; } -.bi-usb-plug-fill::before { content: "\f6db"; } -.bi-usb-plug::before { content: "\f6dc"; } -.bi-usb-symbol::before { content: "\f6dd"; } -.bi-usb::before { content: "\f6de"; } -.bi-boombox-fill::before { content: "\f6df"; } -.bi-displayport-1::before { content: "\f6e0"; } -.bi-displayport::before { content: "\f6e1"; } -.bi-gpu-card::before { content: "\f6e2"; } -.bi-memory::before { content: "\f6e3"; } -.bi-modem-fill::before { content: "\f6e4"; } -.bi-modem::before { content: "\f6e5"; } -.bi-motherboard-fill::before { content: "\f6e6"; } -.bi-motherboard::before { content: "\f6e7"; } -.bi-optical-audio-fill::before { content: "\f6e8"; } -.bi-optical-audio::before { content: "\f6e9"; } -.bi-pci-card::before { content: "\f6ea"; 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- Run 🤗 Transformers in your browser! -

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Quick tour

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Installation
- To install via NPM, run: - 
npm i @xenova/transformers
- - Alternatively, you can use it in vanilla JS, without any bundler, by using a CDN - or static hosting. For example, using - ES Modules, - you can import the library with: - - 
<script type="module">
-    import { pipeline } from 'https://cdn.jsdelivr.net/npm/@xenova/transformers';
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Basic example
- It's super easy to translate from existing code! -
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from transformers import pipeline
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-# Allocate a pipeline for sentiment-analysis
-pipe = pipeline('sentiment-analysis')
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-out = pipe('I love transformers!')
-# [{'label': 'POSITIVE', 'score': 0.999806941}]
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Python (original)

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import { pipeline } from '@xenova/transformers';
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-// Allocate a pipeline for sentiment-analysis
-let pipe = await pipeline('sentiment-analysis');
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-let out = await pipe('I love transformers!');
-// [{'label': 'POSITIVE', 'score': 0.999817686}]
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JavaScript (ours)

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- In the same way as the Python library, you can use a different model by providing its - name as the second argument to the pipeline function. For example: - 
// Use a different model for sentiment-analysis
-let pipe = await pipeline('sentiment-analysis', 'nlptown/bert-base-multilingual-uncased-sentiment');
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- For the full list of available tasks and architectures, check out the - documentation. -
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- - - \ No newline at end of file diff --git a/examples/demo-site/src/main.js b/examples/demo-site/src/main.js deleted file mode 100644 index aa8ae3e7c..000000000 --- a/examples/demo-site/src/main.js +++ /dev/null @@ -1,761 +0,0 @@ - - -import Chart from 'chart.js/auto'; -import Prism from 'prismjs'; - -// Import code and styles for supported languages -import 'prismjs/components/prism-javascript'; -import 'prismjs/components/prism-python'; -import 'prismjs/components/prism-markdown'; -import 'prismjs/components/prism-clike'; -import 'prismjs/themes/prism.css' - -import './theme.css'; -import './style.css'; - -// Initialise worker -const worker = new Worker(new URL('./worker.js', import.meta.url), { - type: 'module', -}); - - -// Define elements -const TASK_SELECTOR = document.getElementById('task'); - -let searchParams = new URLSearchParams(location.search); -let defaultDemo = searchParams.get('demo'); -if (defaultDemo) { - TASK_SELECTOR.value = defaultDemo; -} - -// translation inputs -const LANGUAGE_FROM = document.getElementById('language-from'); -const LANGUAGE_TO = document.getElementById('language-to'); -const INPUT_TEXTBOX = document.getElementById('input-textbox'); -const OUTPUT_TEXTBOX = document.getElementById('output-textbox'); - -// text generation inputs -const TEXT_GENERATION_TEXTBOX = document.getElementById('text-generation-textbox'); - -const TASKS = document.getElementsByClassName('task-settings') -const PROGRESS = document.getElementById('progress'); -const PROGRESS_BARS = document.getElementById('progress-bars'); - -const GENERATE_BUTTON = document.getElementById('generate'); - -const MLM_INPUT_TEXTBOX = document.getElementById('mlm-input-textbox'); -const MLM_OUTPUT_TEXTBOX = document.getElementById('mlm-output-textbox'); - -const SC_INPUT_TEXTBOX = document.getElementById('sc-input-textbox'); -const SC_OUTPUT_CANVAS = document.getElementById('sc-canvas'); - -const TC_INPUT_TEXTBOX = document.getElementById('tc-input-textbox'); -const TC_OUTPUT = document.getElementById('tc-output'); - -const QA_CONTEXT_TEXTBOX = document.getElementById('qa-context-textbox'); -const QA_QUESTION_TEXTBOX = document.getElementById('qa-question-textbox'); -const QA_ANSWER_TEXTBOX = document.getElementById('qa-answer-textbox'); - -const SUMMARIZATION_INPUT_TEXTBOX = document.getElementById('summarization-input-textbox'); -const SUMMARIZATION_OUTPUT_TEXTBOX = document.getElementById('summarization-output-textbox'); - -const SPEECH2TEXT_SELECT = document.getElementById('audio-select'); -const SPEECH2TEXT_INPUT = document.getElementById('audio-file'); -const SPEECH2TEXT_AUDIO = document.getElementById('audio-player'); -const SPEECH2TEXT_OUTPUT_TEXTBOX = document.getElementById('speech2text-output-textbox'); - - -const TEXT2IMAGE_SELECT = document.getElementById('image-select'); -const TEXT2IMAGE_INPUT = document.getElementById('image-file'); -const TEXT2IMAGE_IMG = document.getElementById('image-viewer'); -const TEXT2IMAGE_OUTPUT_TEXTBOX = document.getElementById('image2text-output-textbox'); - -const IMAGE_CLASSIFICATION_SELECT = document.getElementById('ic-select'); -const IMAGE_CLASSIFICATION_INPUT = document.getElementById('ic-file'); -const IMAGE_CLASSIFICATION_IMG = document.getElementById('ic-viewer'); -const IMAGE_CLASSIFICATION_OUTPUT_CANVAS = document.getElementById('ic-canvas'); - -const CODE_COMPLETION_CONTAINER = document.getElementById('code-completion-container'); - - -const ZSIC_SELECT = document.getElementById('zsic-select'); -const ZSIC_INPUT = document.getElementById('zsic-file'); -const ZSIC_CLASSES = document.getElementById('zsic-classes'); -const ZSIC_IMG = document.getElementById('zsic-viewer'); -const ZSIC_OUTPUT_CANVAS = document.getElementById('zsic-canvas'); - - -const OD_SELECT = document.getElementById('od-select'); -const OD_INPUT = document.getElementById('od-file'); -const OD_IMG = document.getElementById('od-viewer'); -const OD_OUTPUT_OVERLAY = document.getElementById('od-overlay'); -const OD_OUTPUT_CANVAS = document.getElementById('od-canvas'); - - -const ZSC_INPUT_TEXTBOX = document.getElementById('zsc-input-textbox'); -const ZSC_CLASSES = document.getElementById('zsc-classes'); -const ZSC_OUTPUT_CANVAS = document.getElementById('zsc-canvas'); - - -const DEFAULT_GREEDY_PARAMS = { - max_new_tokens: 50, - num_beams: 1, - temperature: 1, - top_k: 0, - do_sample: false -} - -const TASK_DEFAULT_PARAMS = { - 'translation': DEFAULT_GREEDY_PARAMS, - 'text-generation': { - max_new_tokens: 100, - num_beams: 1, - temperature: 1, - top_k: 20, - do_sample: true - }, - 'code-completion': DEFAULT_GREEDY_PARAMS, - 'masked-language-modelling': { - topk: 5 // number of samples - }, - 'sequence-classification': {}, - 'token-classification': {}, - 'zero-shot-classification': { - multi_label: false - }, - 'question-answering': {}, - 'summarization': { - max_new_tokens: 50, - num_beams: 2, - temperature: 1, - top_k: 0, - do_sample: false - }, - 'automatic-speech-recognition': DEFAULT_GREEDY_PARAMS, - 'image-to-text': DEFAULT_GREEDY_PARAMS, - 'image-classification': {}, - 'zero-shot-image-classification': {}, - 'object-detection': {}, -}; - -[ - [SPEECH2TEXT_SELECT, SPEECH2TEXT_INPUT, SPEECH2TEXT_AUDIO], - [TEXT2IMAGE_SELECT, TEXT2IMAGE_INPUT, TEXT2IMAGE_IMG], - [IMAGE_CLASSIFICATION_SELECT, IMAGE_CLASSIFICATION_INPUT, IMAGE_CLASSIFICATION_IMG], - [ZSIC_SELECT, ZSIC_INPUT, ZSIC_IMG], - [OD_SELECT, OD_INPUT, OD_IMG], -].forEach(x => { - let [select, input, media] = x; - - select.addEventListener('input', (e) => { - if (select.options[select.selectedIndex].hasAttribute('show-custom')) { - input.style.display = 'block'; - } else { - input.style.display = 'none'; - - media.src = select.value - } - }) - - input.addEventListener("change", () => { - const file = input.files[0]; - const url = URL.createObjectURL(file); - media.src = url; - }); -}); - -const NER_TAGS = { - // tag: [textColour, backgroundColour, tagColour] - 'ORG': ['#115E59', '#CCFBF1', '#14B8A6'], - 'PER': ['#9D174D', '#FCE7F3', '#EC4899'], - 'LOC': ['#86198F', '#FAE8FF', '#D946EF'], -} - - -// Predefined list of unique colours -const COLOURS = [ - '255, 99, 132', - '54, 162, 235', - '255, 206, 86', - '75, 192, 192', - '153, 102, 255', - '255, 159, 64', -] - - - -OD_SELECT.addEventListener('change', () => { - // Clear overlay and chart data on change - OD_OUTPUT_OVERLAY.innerHTML = ''; - - const chart = CHARTS[OD_OUTPUT_CANVAS.id]; - chart.data = structuredClone(DEFAULT_DATA); - chart.update(); -}); - -OD_OUTPUT_OVERLAY.addEventListener('mousemove', (e) => { - let rects = OD_OUTPUT_OVERLAY.querySelectorAll('rect') - - let colours = []; - let borderColours = []; - - - rects.forEach((rect, i) => { - let colour = COLOURS[i % COLOURS.length]; - - // Display if hovering over background (tagName === 'svg') - let toDisplay = e.target.tagName !== 'rect'; - if (!toDisplay) { - // Perform additional check - let bb = rect.getBoundingClientRect() - - // Check if box intersects with current mouse positition - toDisplay = e.clientX >= bb.left && e.clientX <= bb.right && e.clientY >= bb.top && e.clientY <= bb.bottom - } - - if (toDisplay) { - // Set back to original - rect.style.fillOpacity = 0.1; - rect.style.opacity = 1; - colours.push(`rgba(${colour}, 0.5)`); - borderColours.push(`rgba(${colour}, 1)`); - } else { - // Hovering over a rect, so set all other rects to 0 opacity - rect.style.fillOpacity = 0; - rect.style.opacity = 0; - colours.push(`rgba(${colour}, 0.05)`); - borderColours.push(`rgba(${colour}, 0.5)`); - } - - }) - - const chart = CHARTS['od-canvas']; - chart.data.datasets[0].backgroundColor = colours; - chart.data.datasets[0].borderColor = borderColours; - chart.update(); -}) - -function updateParams(task) { - let params = TASK_DEFAULT_PARAMS[task] - if (!params) return; - - for (let [key, value] of Object.entries(params)) { - let element = document.querySelector(`.generation-option[param-name="${key}"]`) - if (!element) continue; - element.value = value; - } -} - -// Parameters -const GENERATION_OPTIONS = document.getElementsByClassName('generation-option'); - - -const CHART_OPTIONS = { - responsive: true, - maintainAspectRatio: false, - indexAxis: 'y', - scales: { - y: { - beginAtZero: true, - }, - x: { - min: 0, - max: 1, - } - }, - plugins: { - legend: { - display: false - }, - }, - layout: { - padding: { - bottom: -5, - } - }, -}; - - -// Initialise all code blocks -const CODE_BLOCKS = {}; -[...document.querySelectorAll('.code-container')].forEach(element => { - - // Guide to add editable code block: - // https://codepen.io/WebCoder49/pen/dyNyraq - // https://css-tricks.com/creating-an-editable-textarea-that-supports-syntax-highlighted-code/ - - const CODE_HIGHLIGHT = element.querySelector('pre'); - const CODE_HIGHLIGHT_CONTENT = element.querySelector('code'); - const CODE_COMPLETION_TEXTBOX = element.querySelector('textarea'); - - let sync_scroll = () => { - /* Scroll result to scroll coords of event - sync with textarea */ - CODE_HIGHLIGHT.scrollTop = CODE_COMPLETION_TEXTBOX.scrollTop; - CODE_HIGHLIGHT.scrollLeft = CODE_COMPLETION_TEXTBOX.scrollLeft; - } - let update = (text) => { - // Handle final newlines (see article) - if (text[text.length - 1] == "\n") { - text += " "; - } - // Update code - CODE_HIGHLIGHT_CONTENT.innerHTML = escapeHtml(text); - - // Syntax Highlight - Prism.highlightElement(CODE_HIGHLIGHT_CONTENT); - } - - // Update code function - let updateCode = (text) => { - update(text); - sync_scroll(); - }; - - CODE_BLOCKS[element.id] = { - update: (text) => { - CODE_COMPLETION_TEXTBOX.value = text; - updateCode(text); - - // When updating, set scroll to bottom - // https://stackoverflow.com/a/9170709 - CODE_COMPLETION_TEXTBOX.scrollTop = CODE_COMPLETION_TEXTBOX.scrollHeight; - }, - text: () => CODE_COMPLETION_TEXTBOX.value - }; - - CODE_COMPLETION_TEXTBOX.oninput = () => updateCode(CODE_COMPLETION_TEXTBOX.value); - - CODE_COMPLETION_TEXTBOX.onscroll = sync_scroll; - CODE_COMPLETION_TEXTBOX.onkeydown = (event) => { - let code = CODE_COMPLETION_TEXTBOX.value; - if (event.key == "Tab") { - /* Tab key pressed */ - event.preventDefault(); // stop normal - let before_tab = code.slice(0, CODE_COMPLETION_TEXTBOX.selectionStart); // text before tab - let after_tab = code.slice(CODE_COMPLETION_TEXTBOX.selectionEnd, CODE_COMPLETION_TEXTBOX.value.length); // text after tab - let cursor_pos = CODE_COMPLETION_TEXTBOX.selectionStart + 1; // where cursor moves after tab - moving forward by 1 char to after tab - CODE_COMPLETION_TEXTBOX.value = before_tab + "\t" + after_tab; // add tab char - // move cursor - CODE_COMPLETION_TEXTBOX.selectionStart = cursor_pos; - CODE_COMPLETION_TEXTBOX.selectionEnd = cursor_pos; - update(CODE_COMPLETION_TEXTBOX.value); // Update text to include indent - } - }; - -}); - -const DEFAULT_DATA = { - labels: ['label', 'label', 'label', 'label', 'label'], - datasets: [{ - borderWidth: 1 - }] -} - -const CHARTS = { - 'sc-canvas': new Chart(SC_OUTPUT_CANVAS, { - type: 'bar', - data: { - labels: ['5 stars', '4 stars', '3 stars', '2 stars', '1 star'], - datasets: [{ - borderWidth: 1 - }] - }, - options: CHART_OPTIONS, - - }), - 'ic-canvas': new Chart(IMAGE_CLASSIFICATION_OUTPUT_CANVAS, { - type: 'bar', - data: structuredClone(DEFAULT_DATA), - options: CHART_OPTIONS - }), - - 'zsic-canvas': new Chart(ZSIC_OUTPUT_CANVAS, { - type: 'bar', - data: { - labels: ['football', 'airport', 'animals'], - datasets: [{ - borderWidth: 1 - }] - }, - options: CHART_OPTIONS - }), - - 'od-canvas': new Chart(OD_OUTPUT_CANVAS, { - type: 'bar', - data: structuredClone(DEFAULT_DATA), - options: CHART_OPTIONS - }), - - 'zsc-canvas': new Chart(ZSC_OUTPUT_CANVAS, { - type: 'bar', - data: { - labels: ['urgent', 'not urgent', 'phone', 'tablet', 'microwave'], - datasets: [{ - borderWidth: 1 - }] - }, - options: CHART_OPTIONS - }), -}; - - -[ - [ZSIC_CLASSES, ZSIC_OUTPUT_CANVAS], - [ZSC_CLASSES, ZSC_OUTPUT_CANVAS], -].forEach(x => { - let [input, chart] = x; - - input.addEventListener('input', () => { - // Update labels of graph - let chartToUpdate = CHARTS[chart.id]; - - chartToUpdate.data.labels = getZSClasses(input); - chartToUpdate.data.datasets[0].data = new Array(chartToUpdate.data.labels.length).fill(0); - chartToUpdate.update(); - }) -}); - - -function getZSClasses(elem) { - // Get zero-shot classes from input element - return elem.value.split(/\s*,+\s*/g).filter(x => x); -} - -function updateVisibility() { - - // Set default parameters for task - updateParams(TASK_SELECTOR.value); - - for (let element of TASKS) { - if (element.getAttribute('task').split(',').includes(TASK_SELECTOR.value)) { - element.style.display = 'block'; - } else { - element.style.display = 'none'; - } - } -} -updateVisibility(); - -// Add event listeners -TASK_SELECTOR.addEventListener('input', updateVisibility); - -function parseValue(value, type) { - switch (type) { - case 'number': - return Number(value); - case 'bool': - return value === 'true' - default: - return value - } -} - -function isVisible(e) { - // https://stackoverflow.com/a/38873788 - return !!(e.offsetWidth || e.offsetHeight || e.getClientRects().length); -} - -GENERATE_BUTTON.addEventListener('click', async (e) => { - // Set and pass generation settings to web worker - let data = { - task: TASK_SELECTOR.value, - generation: Object.fromEntries([...GENERATION_OPTIONS] - .filter(isVisible) // Only use parameters that are visible on screen - .map(x => { - let value = parseValue(x.value, x.getAttribute('datatype')); - return [x.getAttribute('param-name'), value] - })) - }; - switch (TASK_SELECTOR.value) { - case 'translation': - data.languageFrom = LANGUAGE_FROM.value - data.languageTo = LANGUAGE_TO.value - data.text = INPUT_TEXTBOX.value - data.elementIdToUpdate = OUTPUT_TEXTBOX.id - break; - - case 'text-generation': - data.text = TEXT_GENERATION_TEXTBOX.value - data.elementIdToUpdate = TEXT_GENERATION_TEXTBOX.id - break; - - case 'code-completion': - data.text = CODE_BLOCKS[CODE_COMPLETION_CONTAINER.id].text(); - data.elementIdToUpdate = CODE_COMPLETION_CONTAINER.id - data.targetType = 'code' - break; - - case 'masked-language-modelling': - data.text = MLM_INPUT_TEXTBOX.value - data.elementIdToUpdate = MLM_OUTPUT_TEXTBOX.id - break; - - case 'sequence-classification': - data.text = SC_INPUT_TEXTBOX.value - data.elementIdToUpdate = SC_OUTPUT_CANVAS.id - data.targetType = 'chart' - break; - - case 'token-classification': - data.text = TC_INPUT_TEXTBOX.value - data.elementIdToUpdate = TC_OUTPUT.id - data.targetType = 'tokens' - break; - - case 'zero-shot-classification': - data.text = ZSC_INPUT_TEXTBOX.value - data.classes = getZSClasses(ZSC_CLASSES); - data.elementIdToUpdate = ZSC_OUTPUT_CANVAS.id - data.targetType = 'chart' - data.updateLabels = true - break; - - case 'question-answering': - data.context = QA_CONTEXT_TEXTBOX.value - data.question = QA_QUESTION_TEXTBOX.value - data.elementIdToUpdate = QA_ANSWER_TEXTBOX.id - break; - - case 'summarization': - data.text = SUMMARIZATION_INPUT_TEXTBOX.value - data.elementIdToUpdate = SUMMARIZATION_OUTPUT_TEXTBOX.id - break; - - case 'automatic-speech-recognition': - const sampling_rate = 16000; - const audioCTX = new AudioContext({ sampleRate: sampling_rate }) - - const response = await (await fetch(SPEECH2TEXT_AUDIO.currentSrc)).arrayBuffer() - const decoded = await audioCTX.decodeAudioData(response) - - data.audio = decoded.getChannelData(0); - data.elementIdToUpdate = SPEECH2TEXT_OUTPUT_TEXTBOX.id - break; - - case 'image-to-text': - data.image = getImageDataFromImage(TEXT2IMAGE_IMG) - data.elementIdToUpdate = TEXT2IMAGE_OUTPUT_TEXTBOX.id - break; - - case 'image-classification': - data.image = getImageDataFromImage(IMAGE_CLASSIFICATION_IMG) - data.elementIdToUpdate = IMAGE_CLASSIFICATION_OUTPUT_CANVAS.id - data.targetType = 'chart' - data.updateLabels = true - break; - - - case 'zero-shot-image-classification': - data.image = getImageDataFromImage(ZSIC_IMG) - data.classes = getZSClasses(ZSIC_CLASSES); - data.elementIdToUpdate = ZSIC_OUTPUT_CANVAS.id - data.targetType = 'chart' - data.updateLabels = true - break; - - case 'object-detection': - data.image = getImageDataFromImage(OD_IMG) - data.targetType = 'overlay' - data.chartId = OD_OUTPUT_CANVAS.id - data.elementIdToUpdate = OD_OUTPUT_OVERLAY.id - break; - - default: - return; - } - - worker.postMessage(data); -}); - -// Handle result returned by the web worker - -worker.addEventListener('message', (event) => { - const message = event.data; - - switch (message.type) { - case 'download': // for session creation - - if (message.data.status === 'initiate') { - PROGRESS.style.display = 'block'; - - // create progress bar - PROGRESS_BARS.appendChild(htmlToElement(` -
-
-
- `)); - - } else { - let bar = PROGRESS_BARS.querySelector(`.progress[model="${message.data.name}"][file="${message.data.file}"]> .progress-bar`) - - switch (message.data.status) { - case 'progress': - // update existing bar - bar.style.width = message.data.progress.toFixed(2) + '%'; - bar.textContent = `${message.data.file} (${formatBytes(message.data.loaded)} / ${formatBytes(message.data.total)})`; - break; - - case 'done': - // Remove the progress bar - bar.parentElement.remove(); - break; - - case 'ready': - // Pipeline is ready - hide container - PROGRESS.style.display = 'none'; - PROGRESS_BARS.innerHTML = ''; - break; - } - } - - break; - case 'update': // for generation - let target = message.target; - let elem = document.getElementById(target); - - switch (message.targetType) { - case 'code': - CODE_BLOCKS[target].update(message.data); - break; - default: // is textbox - elem.value = message.data - break; - } - - break; - - case 'complete': - switch (message.targetType) { - case 'chart': - const chartToUpdate = CHARTS[message.target]; - - let chartData = chartToUpdate.data.datasets[0].data; - - if (message.updateLabels) { - for (let i = 0; i < message.data.length; ++i) { - let item = message.data[i]; - chartData[i] = item.score; - chartToUpdate.data.labels[i] = item.label; - } - } else { - // set data, ensuring labels align correctly - for (let item of message.data) { - chartData[ - chartToUpdate.data.labels.indexOf(item.label) - ] = item.score - } - } - - chartToUpdate.update(); // update the chart - break; - - case 'tokens': - let target = document.getElementById(message.target); - target.innerHTML = ''; - - let tokens = message.data; - - for (let token of tokens) { - let elem; - if (token.type === 'O') { - elem = document.createTextNode(token.text); - } else { - let [textColour, backgroundColour, tagColour] = NER_TAGS[token.type]; - elem = htmlToElement(`${token.text}${token.type}`); - } - target.appendChild(elem); - - } - break; - - case 'overlay': - let parent = document.getElementById(message.target); - - // Clear previous output, just in case - parent.innerHTML = ''; - - let viewbox = parent.viewBox.baseVal; - - let colours = []; - let borderColours = []; - - let items = message.data; - for (let i = 0; i < items.length; ++i) { - const box = items[i].box; - - let svgns = "http://www.w3.org/2000/svg"; - let rect = document.createElementNS(svgns, 'rect'); - - rect.setAttribute('x', viewbox.width * box.xmin); - rect.setAttribute('y', viewbox.height * box.ymin); - rect.setAttribute('width', viewbox.width * (box.xmax - box.xmin)); - rect.setAttribute('height', viewbox.height * (box.ymax - box.ymin)); - - const colour = COLOURS[i % COLOURS.length]; - rect.style.stroke = rect.style.fill = `rgba(${colour}, 1)`; - - colours.push(`rgba(${colour}, 0.5)`); - borderColours.push(`rgba(${colour}, 1)`); - parent.appendChild(rect); - } - - // Update chart label and data - const chart = CHARTS[message.chartId]; - chart.data.labels = items.map(x => x.label); - chart.data.datasets[0] = { - data: items.map(x => x.score), - backgroundColor: colours, - borderColor: borderColours - }; - chart.update() - break; - default: // is text - document.getElementById(message.target).value = message.data - break; - } - break; - default: - break; - } -}); - -// Utility functions - -function escapeHtml(unsafe) { - return unsafe.replaceAll('&', '&').replaceAll('<', '<').replaceAll('>', '>').replaceAll('"', '"').replaceAll("'", '''); -} - - -function htmlToElement(html) { - // https://stackoverflow.com/a/35385518 - let template = document.createElement('template'); - html = html.trim(); // Never return a text node of whitespace as the result - template.innerHTML = html; - return template.content.firstChild; -} - -function formatBytes(bytes, decimals = 0) { - const sizes = ["Bytes", "KB", "MB", "GB", "TB"]; - if (bytes === 0) return "0 Bytes"; - const i = parseInt(Math.floor(Math.log(bytes) / Math.log(1000)), 10); - const rounded = (bytes / Math.pow(1000, i)).toFixed(decimals); - return rounded + " " + sizes[i]; -} - -function getImageDataFromImage(original) { - - // Helper function to get image data from image element - const canvas = document.createElement('canvas'); - canvas.width = original.naturalWidth; - canvas.height = original.naturalHeight; - - const ctx = canvas.getContext('2d'); - // TODO play around with ctx options? - // ctx.patternQuality = 'bilinear'; - // ctx.quality = 'bilinear'; - // ctx.antialias = 'default'; - // ctx.imageSmoothingQuality = 'high'; - - ctx.drawImage(original, 0, 0, canvas.width, canvas.height); - return canvas.toDataURL(); -} diff --git a/examples/demo-site/src/style.css b/examples/demo-site/src/style.css deleted file mode 100644 index 28bf7e6e7..000000000 --- a/examples/demo-site/src/style.css +++ /dev/null @@ -1,149 +0,0 @@ -.progress-bar { - align-items: start; - width: 0%; - padding: 2px 8px; - min-height: 22px; -} - -.progress { - height: auto; -} - -.form-control:checked[type=checkbox] { - background-image: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 20 20'%3e%3cpath fill='none' stroke='%23fff' stroke-linecap='round' stroke-linejoin='round' stroke-width='3' d='M6 10l3 3l6-6'/%3e%3c/svg%3e"); -} - - -.form-control[type=checkbox]:indeterminate { - background-color: #0d6efd; - border-color: #0d6efd; - background-image: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 20 20'%3e%3cpath fill='none' stroke='%23fff' stroke-linecap='round' stroke-linejoin='round' stroke-width='3' d='M6 10h8'/%3e%3c/svg%3e"); -} - -.form-control[type=checkbox] { - border-radius: 0.25em; -} - -.form-control:checked { - background-color: #0d6efd; - border-color: #0d6efd; -} - -.code-container { - height: 300px; - position: relative; -} - -.code-container>textarea, -.code-container>pre { - /* Both elements need the same text and space styling so they are directly on top of each other */ - margin: 0; - padding: 16px; - border: 0; - width: 100%; - height: 100%; -} - -.code-container>textarea, -.code-container>pre, -.code-container>pre * { - /* Also add text styles to highlighing tokens */ - font-size: 15pt; - font-family: monospace; - line-height: 20pt; - tab-size: 4; -} - - -.code-container>textarea, -.code-container>pre { - /* In the same place */ - position: absolute; - top: 0; - left: 0; -} - - -/* Move the textarea in front of the result */ - -.code-container>textarea { - z-index: 1; -} - -.code-container>pre { - z-index: 0; - white-space: pre-wrap; - pointer-events: none; -} - - -/* Make textarea almost completely transparent */ - -.code-container>textarea { - color: transparent; - background: transparent; - caret-color: black; -} - -/* Can be scrolled */ -.code-container>textarea, -.code-container>pre { - overflow: auto; - white-space: nowrap; - /* Allows textarea to scroll horizontally */ -} - -/* No resize on textarea */ -.code-container>textarea { - resize: none; -} - - -code#highlighting-content { - border-radius: 2px; - /* background-color: #eee; */ - color: #111; -} - -#od-overlay>rect { - fill-opacity: 0.1; - opacity: 1; - transition: all 0.2s ease-in-out; - stroke-width: 2px; - stroke: white; -} - -#tc-output { - border: 1px solid #ced4da; - min-height: 134px; - max-height: 134px; - border-radius: 0.25rem; - color: #4B5563; - line-height: 1.75; - margin-top: 0.5rem; - overflow-y: auto; -} - -.ner-container, -.ner-tag { - border-radius: 0.25rem; - font-weight: 600; - -} - -.ner-container { - padding-left: 0.25rem; - padding-right: 0.25rem; -} - -.ner-tag { - font-size: 0.75rem; - padding-left: 0.125rem; - padding-right: 0.125rem; - margin-left: 0.125rem; -} - -/* Override default code highlighting for operators */ -.token.operator { - background: none; -} diff --git a/examples/demo-site/src/theme.css b/examples/demo-site/src/theme.css deleted file mode 100644 index 2ddf8e833..000000000 --- a/examples/demo-site/src/theme.css +++ /dev/null @@ -1,12247 +0,0 @@ -@charset "UTF-8"; - -/*! -* Start Bootstrap - Business Frontpage v5.0.7 (https://startbootstrap.com/template/business-frontpage) -* Copyright 2013-2021 Start Bootstrap -* Licensed under MIT (https://github.com/StartBootstrap/startbootstrap-business-frontpage/blob/master/LICENSE) -*/ -/*! - * Bootstrap v5.1.3 (https://getbootstrap.com/) - * Copyright 2011-2021 The Bootstrap Authors - * Copyright 2011-2021 Twitter, Inc. - * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE) - */ -:root { - --bs-blue: #0d6efd; - --bs-indigo: #6610f2; - --bs-purple: #6f42c1; - --bs-pink: #d63384; - --bs-red: #dc3545; - --bs-orange: #fd7e14; - --bs-yellow: #ffc107; - --bs-green: #198754; - --bs-teal: #20c997; - --bs-cyan: #0dcaf0; - --bs-white: #fff; - --bs-gray: #6c757d; - --bs-gray-dark: #343a40; - --bs-gray-100: #f8f9fa; - --bs-gray-200: #e9ecef; - --bs-gray-300: #dee2e6; - --bs-gray-400: #ced4da; - --bs-gray-500: #adb5bd; - --bs-gray-600: #6c757d; - --bs-gray-700: #495057; - --bs-gray-800: #343a40; - --bs-gray-900: #212529; - --bs-primary: #0d6efd; - --bs-secondary: #6c757d; - --bs-success: #198754; - --bs-info: #0dcaf0; - --bs-warning: #ffc107; - --bs-danger: #dc3545; - --bs-light: #f8f9fa; - --bs-dark: #212529; - --bs-primary-rgb: 13, 110, 253; - --bs-secondary-rgb: 108, 117, 125; - --bs-success-rgb: 25, 135, 84; - --bs-info-rgb: 13, 202, 240; - --bs-warning-rgb: 255, 193, 7; - --bs-danger-rgb: 220, 53, 69; - --bs-light-rgb: 248, 249, 250; - --bs-dark-rgb: 33, 37, 41; - --bs-white-rgb: 255, 255, 255; - --bs-black-rgb: 0, 0, 0; - --bs-body-color-rgb: 33, 37, 41; - --bs-body-bg-rgb: 255, 255, 255; - --bs-font-sans-serif: system-ui, -apple-system, "Segoe UI", Roboto, "Helvetica Neue", Arial, "Noto Sans", "Liberation Sans", sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol", "Noto Color Emoji"; - --bs-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace; - --bs-gradient: linear-gradient(180deg, rgba(255, 255, 255, 0.15), rgba(255, 255, 255, 0)); - --bs-body-font-family: var(--bs-font-sans-serif); - --bs-body-font-size: 1rem; - --bs-body-font-weight: 400; - --bs-body-line-height: 1.5; - --bs-body-color: #212529; - --bs-body-bg: #fff; -} - -*, -*::before, -*::after { - box-sizing: border-box; -} - -@media (prefers-reduced-motion: no-preference) { - :root { - scroll-behavior: smooth; - } -} - -body { - margin: 0; - font-family: var(--bs-body-font-family); - font-size: var(--bs-body-font-size); - font-weight: var(--bs-body-font-weight); - line-height: var(--bs-body-line-height); - color: var(--bs-body-color); - text-align: var(--bs-body-text-align); - background-color: var(--bs-body-bg); - -webkit-text-size-adjust: 100%; - -webkit-tap-highlight-color: rgba(0, 0, 0, 0); -} - -hr { - margin: 1rem 0; - color: inherit; - background-color: currentColor; - border: 0; - opacity: 0.25; -} - -hr:not([size]) { - height: 1px; -} - -h6, -.h6, -h5, -.h5, -h4, -.h4, -h3, -.h3, -h2, -.h2, -h1, -.h1 { - margin-top: 0; - margin-bottom: 0.5rem; - font-weight: 500; - line-height: 1.2; -} - -h1, -.h1 { - font-size: calc(1.375rem + 1.5vw); -} - -@media (min-width: 1200px) { - - h1, - .h1 { - font-size: 2.5rem; - } -} - -h2, -.h2 { - font-size: calc(1.325rem + 0.9vw); -} - -@media (min-width: 1200px) { - - h2, - .h2 { - font-size: 2rem; - } -} - -h3, -.h3 { - font-size: calc(1.3rem + 0.6vw); -} - -@media (min-width: 1200px) { - - h3, - .h3 { - font-size: 1.75rem; - } -} - -h4, -.h4 { - font-size: calc(1.275rem + 0.3vw); -} - -@media (min-width: 1200px) { - - h4, - .h4 { - font-size: 1.5rem; - } -} - -h5, -.h5 { - font-size: 1.25rem; -} - -h6, -.h6 { - font-size: 1rem; -} - -p { - margin-top: 0; - margin-bottom: 1rem; -} - -abbr[title], -abbr[data-bs-original-title] { - -webkit-text-decoration: underline dotted; - text-decoration: underline dotted; - cursor: help; - -webkit-text-decoration-skip-ink: none; - text-decoration-skip-ink: none; -} - -address { - margin-bottom: 1rem; - font-style: normal; - line-height: inherit; -} - -ol, -ul { - padding-left: 2rem; -} - -ol, -ul, -dl { - margin-top: 0; - margin-bottom: 1rem; -} - -ol ol, -ul ul, -ol ul, -ul ol { - margin-bottom: 0; -} - -dt { - font-weight: 700; -} - -dd { - margin-bottom: 0.5rem; - margin-left: 0; -} - -blockquote { - margin: 0 0 1rem; -} - -b, -strong { - font-weight: bolder; -} - -small, -.small { - font-size: 0.875em; -} - -mark, -.mark { - padding: 0.2em; - background-color: #fcf8e3; -} - -sub, -sup { - position: relative; - font-size: 0.75em; - line-height: 0; - vertical-align: baseline; -} - -sub { - bottom: -0.25em; -} - -sup { - top: -0.5em; -} - -a { - color: #0d6efd; - text-decoration: underline; -} - -a:hover { - color: #0a58ca; -} - -a:not([href]):not([class]), -a:not([href]):not([class]):hover { - color: inherit; - text-decoration: none; -} - -pre, -code, -kbd, -samp { - font-family: var(--bs-font-monospace); - font-size: 1em; - direction: ltr - /* rtl:ignore */ - ; - unicode-bidi: bidi-override; -} - -pre { - display: block; - margin-top: 0; - margin-bottom: 1rem; - overflow: auto; - font-size: 0.875em; -} - -pre code { - font-size: inherit; - color: inherit; - word-break: normal; -} - -code { - font-size: 0.875em; - color: #d63384; - word-wrap: break-word; -} - -a>code { - color: inherit; -} - -kbd { - padding: 0.2rem 0.4rem; - font-size: 0.875em; - color: #fff; - background-color: #212529; - border-radius: 0.2rem; -} - -kbd kbd { - padding: 0; - font-size: 1em; - font-weight: 700; -} - -figure { - margin: 0 0 1rem; -} - -img, -svg { - vertical-align: middle; -} - -table { - caption-side: bottom; - border-collapse: collapse; -} - -caption { - padding-top: 0.5rem; - padding-bottom: 0.5rem; - color: #6c757d; - text-align: left; -} - -th { - text-align: inherit; - text-align: -webkit-match-parent; -} - -thead, -tbody, -tfoot, -tr, -td, -th { - border-color: inherit; - border-style: solid; - border-width: 0; -} - -label { - display: inline-block; -} - -button { - border-radius: 0; -} - -button:focus:not(:focus-visible) { - outline: 0; -} - -input, -button, -select, -optgroup, -textarea { - margin: 0; - font-family: inherit; - font-size: inherit; - line-height: inherit; -} - -button, -select { - text-transform: none; -} - -[role=button] { - cursor: pointer; -} - -select { - word-wrap: normal; -} - -select:disabled { - opacity: 1; -} - -[list]::-webkit-calendar-picker-indicator { - display: none; -} - -button, -[type=button], -[type=reset], -[type=submit] { - -webkit-appearance: button; -} - -button:not(:disabled), -[type=button]:not(:disabled), -[type=reset]:not(:disabled), -[type=submit]:not(:disabled) { - cursor: pointer; -} - -::-moz-focus-inner { - padding: 0; - border-style: none; -} - -textarea { - resize: vertical; -} - -fieldset { - min-width: 0; - padding: 0; - margin: 0; - border: 0; -} - -legend { - float: left; - width: 100%; - padding: 0; - margin-bottom: 0.5rem; - font-size: calc(1.275rem + 0.3vw); - line-height: inherit; -} - -@media (min-width: 1200px) { - legend { - font-size: 1.5rem; - } -} - -legend+* { - clear: left; -} - -::-webkit-datetime-edit-fields-wrapper, -::-webkit-datetime-edit-text, -::-webkit-datetime-edit-minute, -::-webkit-datetime-edit-hour-field, -::-webkit-datetime-edit-day-field, -::-webkit-datetime-edit-month-field, -::-webkit-datetime-edit-year-field { - padding: 0; -} - -::-webkit-inner-spin-button { - height: auto; -} - -[type=search] { - outline-offset: -2px; - -webkit-appearance: textfield; -} - -/* rtl:raw: -[type="tel"], -[type="url"], -[type="email"], -[type="number"] { - direction: ltr; -} -*/ -::-webkit-search-decoration { - -webkit-appearance: none; -} - -::-webkit-color-swatch-wrapper { - padding: 0; -} - -::-webkit-file-upload-button { - font: inherit; -} - -::file-selector-button { - font: inherit; -} - -::-webkit-file-upload-button { - font: inherit; - -webkit-appearance: button; -} - -output { - display: inline-block; -} - -iframe { - border: 0; -} - -summary { - display: list-item; - cursor: pointer; -} - -progress { - vertical-align: baseline; -} - -[hidden] { - display: none !important; -} - -.lead { - font-size: 1.25rem; - font-weight: 300; -} - -.display-1 { - font-size: calc(1.625rem + 4.5vw); - font-weight: 300; - line-height: 1.2; -} - -@media (min-width: 1200px) { - .display-1 { - font-size: 5rem; - } -} - -.display-2 { - font-size: calc(1.575rem + 3.9vw); - font-weight: 300; - line-height: 1.2; -} - -@media (min-width: 1200px) { - .display-2 { - font-size: 4.5rem; - } -} - -.display-3 { - font-size: calc(1.525rem + 3.3vw); - font-weight: 300; - line-height: 1.2; -} - -@media (min-width: 1200px) { - .display-3 { - font-size: 4rem; - } -} - -.display-4 { - font-size: calc(1.475rem + 2.7vw); - font-weight: 300; - line-height: 1.2; -} - -@media (min-width: 1200px) { - .display-4 { - font-size: 3.5rem; - } -} - -.display-5 { - font-size: calc(1.425rem + 2.1vw); - font-weight: 300; - line-height: 1.2; -} - -@media (min-width: 1200px) { - .display-5 { - font-size: 3rem; - } -} - -.display-6 { - font-size: calc(1.375rem + 1.5vw); - font-weight: 300; - line-height: 1.2; -} - -@media (min-width: 1200px) { - .display-6 { - font-size: 2.5rem; - } -} - -.list-unstyled { - padding-left: 0; - list-style: none; -} - -.list-inline { - padding-left: 0; - list-style: none; -} - -.list-inline-item { - display: inline-block; -} - -.list-inline-item:not(:last-child) { - margin-right: 0.5rem; -} - -.initialism { - font-size: 0.875em; - text-transform: uppercase; -} - -.blockquote { - margin-bottom: 1rem; - font-size: 1.25rem; -} - -.blockquote> :last-child { - margin-bottom: 0; -} - -.blockquote-footer { - margin-top: -1rem; - margin-bottom: 1rem; - font-size: 0.875em; - color: #6c757d; -} - -.blockquote-footer::before { - content: "— "; -} - -.img-fluid { - max-width: 100%; - height: auto; -} - -.img-thumbnail { - padding: 0.25rem; - background-color: #fff; - border: 1px solid #dee2e6; - border-radius: 0.25rem; - max-width: 100%; - height: auto; -} - -.figure { - display: inline-block; -} - -.figure-img { - margin-bottom: 0.5rem; - line-height: 1; -} - -.figure-caption { - font-size: 0.875em; - color: #6c757d; -} - -.container, -.container-fluid, -.container-xxl, -.container-xl, -.container-lg, -.container-md, -.container-sm { - width: 100%; - padding-right: var(--bs-gutter-x, 0.75rem); - padding-left: var(--bs-gutter-x, 0.75rem); - margin-right: auto; - margin-left: auto; -} - -@media (min-width: 576px) { - - .container-sm, - .container { - max-width: 540px; - } -} - -@media (min-width: 768px) { - - .container-md, - .container-sm, - .container { - max-width: 720px; - } -} - -@media (min-width: 992px) { - - .container-lg, - .container-md, - .container-sm, - .container { - max-width: 960px; - } -} - -@media (min-width: 1200px) { - - .container-xl, - .container-lg, - .container-md, - .container-sm, - .container { - max-width: 1140px; - } -} - -@media (min-width: 1400px) { - - .container-xxl, - .container-xl, - .container-lg, - .container-md, - .container-sm, - .container { - max-width: 1320px; - } -} - -.row { - --bs-gutter-x: 1.5rem; - --bs-gutter-y: 0; - display: flex; - flex-wrap: wrap; - margin-top: calc(-1 * var(--bs-gutter-y)); - margin-right: calc(-0.5 * var(--bs-gutter-x)); - margin-left: calc(-0.5 * var(--bs-gutter-x)); -} - -.row>* { - flex-shrink: 0; - width: 100%; - max-width: 100%; - padding-right: calc(var(--bs-gutter-x) * 0.5); - padding-left: calc(var(--bs-gutter-x) * 0.5); - margin-top: var(--bs-gutter-y); -} - -.col { - flex: 1 0 0%; -} - -.row-cols-auto>* { - flex: 0 0 auto; - width: auto; -} - -.row-cols-1>* { - flex: 0 0 auto; - width: 100%; -} - -.row-cols-2>* { - flex: 0 0 auto; - width: 50%; -} - -.row-cols-3>* { - flex: 0 0 auto; - width: 33.3333333333%; -} - -.row-cols-4>* { - flex: 0 0 auto; - width: 25%; -} - -.row-cols-5>* { - flex: 0 0 auto; - width: 20%; -} - -.row-cols-6>* { - flex: 0 0 auto; - width: 16.6666666667%; -} - -.col-auto { - flex: 0 0 auto; - width: auto; -} - -.col-1 { - flex: 0 0 auto; - width: 8.33333333%; -} - -.col-2 { - flex: 0 0 auto; - width: 16.66666667%; -} - -.col-3 { - flex: 0 0 auto; - width: 25%; -} - -.col-4 { - flex: 0 0 auto; - width: 33.33333333%; -} - -.col-5 { - flex: 0 0 auto; - width: 41.66666667%; -} - -.col-6 { - flex: 0 0 auto; - width: 50%; -} - -.col-7 { - flex: 0 0 auto; - width: 58.33333333%; -} - -.col-8 { - flex: 0 0 auto; - width: 66.66666667%; -} - -.col-9 { - flex: 0 0 auto; - width: 75%; -} - -.col-10 { - flex: 0 0 auto; - width: 83.33333333%; -} - -.col-11 { - flex: 0 0 auto; - width: 91.66666667%; -} - -.col-12 { - flex: 0 0 auto; - width: 100%; -} - -.offset-1 { - margin-left: 8.33333333%; -} - -.offset-2 { - margin-left: 16.66666667%; -} - -.offset-3 { - margin-left: 25%; -} - -.offset-4 { - margin-left: 33.33333333%; -} - -.offset-5 { - margin-left: 41.66666667%; -} - -.offset-6 { - margin-left: 50%; -} - -.offset-7 { - margin-left: 58.33333333%; -} - -.offset-8 { - margin-left: 66.66666667%; -} - -.offset-9 { - margin-left: 75%; -} - -.offset-10 { - margin-left: 83.33333333%; -} - -.offset-11 { - margin-left: 91.66666667%; -} - -.g-0, -.gx-0 { - --bs-gutter-x: 0; -} - -.g-0, -.gy-0 { - --bs-gutter-y: 0; -} - -.g-1, -.gx-1 { - --bs-gutter-x: 0.25rem; -} - -.g-1, -.gy-1 { - --bs-gutter-y: 0.25rem; -} - -.g-2, -.gx-2 { - --bs-gutter-x: 0.5rem; -} - -.g-2, -.gy-2 { - --bs-gutter-y: 0.5rem; -} - -.g-3, -.gx-3 { - --bs-gutter-x: 1rem; -} - -.g-3, -.gy-3 { - --bs-gutter-y: 1rem; -} - -.g-4, -.gx-4 { - --bs-gutter-x: 1.5rem; -} - -.g-4, -.gy-4 { - --bs-gutter-y: 1.5rem; -} - -.g-5, -.gx-5 { - --bs-gutter-x: 3rem; -} - -.g-5, -.gy-5 { - --bs-gutter-y: 3rem; -} - -@media (min-width: 576px) { - .col-sm { - flex: 1 0 0%; - } - - .row-cols-sm-auto>* { - flex: 0 0 auto; - width: auto; - } - - .row-cols-sm-1>* { - flex: 0 0 auto; - width: 100%; - } - - .row-cols-sm-2>* { - flex: 0 0 auto; - width: 50%; - } - - .row-cols-sm-3>* { - flex: 0 0 auto; - width: 33.3333333333%; - } - - .row-cols-sm-4>* { - flex: 0 0 auto; - width: 25%; - } - - .row-cols-sm-5>* { - flex: 0 0 auto; - width: 20%; - } - - .row-cols-sm-6>* { - flex: 0 0 auto; - width: 16.6666666667%; - } - - .col-sm-auto { - flex: 0 0 auto; - width: auto; - } - - .col-sm-1 { - flex: 0 0 auto; - width: 8.33333333%; - } - - .col-sm-2 { - flex: 0 0 auto; - width: 16.66666667%; - } - - .col-sm-3 { - flex: 0 0 auto; - width: 25%; - } - - .col-sm-4 { - flex: 0 0 auto; - width: 33.33333333%; - } - - .col-sm-5 { - flex: 0 0 auto; - width: 41.66666667%; - } - - .col-sm-6 { - flex: 0 0 auto; - width: 50%; - } - - .col-sm-7 { - flex: 0 0 auto; - width: 58.33333333%; - } - - .col-sm-8 { - flex: 0 0 auto; - width: 66.66666667%; - } - - .col-sm-9 { - flex: 0 0 auto; - width: 75%; - } - - .col-sm-10 { - flex: 0 0 auto; - width: 83.33333333%; - } - - .col-sm-11 { - flex: 0 0 auto; - width: 91.66666667%; - } - - .col-sm-12 { - flex: 0 0 auto; - width: 100%; - } - - .offset-sm-0 { - margin-left: 0; - } - - .offset-sm-1 { - margin-left: 8.33333333%; - } - - .offset-sm-2 { - margin-left: 16.66666667%; - } - - .offset-sm-3 { - margin-left: 25%; - } - - .offset-sm-4 { - margin-left: 33.33333333%; - } - - .offset-sm-5 { - margin-left: 41.66666667%; - } - - .offset-sm-6 { - margin-left: 50%; - } - - .offset-sm-7 { - margin-left: 58.33333333%; - } - - .offset-sm-8 { - margin-left: 66.66666667%; - } - - .offset-sm-9 { - margin-left: 75%; - } - - .offset-sm-10 { - margin-left: 83.33333333%; - } - - .offset-sm-11 { - margin-left: 91.66666667%; - } - - .g-sm-0, - .gx-sm-0 { - --bs-gutter-x: 0; - } - - .g-sm-0, - .gy-sm-0 { - --bs-gutter-y: 0; - } - - .g-sm-1, - .gx-sm-1 { - --bs-gutter-x: 0.25rem; - } - - .g-sm-1, - .gy-sm-1 { - --bs-gutter-y: 0.25rem; - } - - .g-sm-2, - .gx-sm-2 { - --bs-gutter-x: 0.5rem; - } - - .g-sm-2, - .gy-sm-2 { - --bs-gutter-y: 0.5rem; - } - - .g-sm-3, - .gx-sm-3 { - --bs-gutter-x: 1rem; - } - - .g-sm-3, - .gy-sm-3 { - --bs-gutter-y: 1rem; - } - - .g-sm-4, - .gx-sm-4 { - --bs-gutter-x: 1.5rem; - } - - .g-sm-4, - .gy-sm-4 { - --bs-gutter-y: 1.5rem; - } - - .g-sm-5, - .gx-sm-5 { - --bs-gutter-x: 3rem; - } - - .g-sm-5, - .gy-sm-5 { - --bs-gutter-y: 3rem; - } -} - -@media (min-width: 768px) { - .col-md { - flex: 1 0 0%; - } - - .row-cols-md-auto>* { - flex: 0 0 auto; - width: auto; - } - - .row-cols-md-1>* { - flex: 0 0 auto; - width: 100%; - } - - .row-cols-md-2>* { - flex: 0 0 auto; - width: 50%; - } - - .row-cols-md-3>* { - flex: 0 0 auto; - width: 33.3333333333%; - } - - .row-cols-md-4>* { - flex: 0 0 auto; - width: 25%; - } - - .row-cols-md-5>* { - flex: 0 0 auto; - width: 20%; - } - - .row-cols-md-6>* { - flex: 0 0 auto; - width: 16.6666666667%; - } - - .col-md-auto { - flex: 0 0 auto; - width: auto; - } - - .col-md-1 { - flex: 0 0 auto; - width: 8.33333333%; - } - - .col-md-2 { - flex: 0 0 auto; - width: 16.66666667%; - } - - .col-md-3 { - flex: 0 0 auto; - width: 25%; - } - - .col-md-4 { - flex: 0 0 auto; - width: 33.33333333%; - } - - .col-md-5 { - flex: 0 0 auto; - width: 41.66666667%; - } - - .col-md-6 { - flex: 0 0 auto; - width: 50%; - } - - .col-md-7 { - flex: 0 0 auto; - width: 58.33333333%; - } - - .col-md-8 { - flex: 0 0 auto; - width: 66.66666667%; - } - - .col-md-9 { - flex: 0 0 auto; - width: 75%; - } - - .col-md-10 { - flex: 0 0 auto; - width: 83.33333333%; - } - - .col-md-11 { - flex: 0 0 auto; - width: 91.66666667%; - } - - .col-md-12 { - flex: 0 0 auto; - width: 100%; - } - - .offset-md-0 { - margin-left: 0; - } - - .offset-md-1 { - margin-left: 8.33333333%; - } - - .offset-md-2 { - margin-left: 16.66666667%; - } - - .offset-md-3 { - margin-left: 25%; - } - - .offset-md-4 { - margin-left: 33.33333333%; - } - - .offset-md-5 { - margin-left: 41.66666667%; - } - - .offset-md-6 { - margin-left: 50%; - } - - .offset-md-7 { - margin-left: 58.33333333%; - } - - .offset-md-8 { - margin-left: 66.66666667%; - } - - .offset-md-9 { - margin-left: 75%; - } - - .offset-md-10 { - margin-left: 83.33333333%; - } - - .offset-md-11 { - margin-left: 91.66666667%; - } - - .g-md-0, - .gx-md-0 { - --bs-gutter-x: 0; - } - - .g-md-0, - .gy-md-0 { - --bs-gutter-y: 0; - } - - .g-md-1, - .gx-md-1 { - --bs-gutter-x: 0.25rem; - } - - .g-md-1, - .gy-md-1 { - --bs-gutter-y: 0.25rem; - } - - .g-md-2, - .gx-md-2 { - --bs-gutter-x: 0.5rem; - } - - .g-md-2, - .gy-md-2 { - --bs-gutter-y: 0.5rem; - } - - .g-md-3, - .gx-md-3 { - --bs-gutter-x: 1rem; - } - - .g-md-3, - .gy-md-3 { - --bs-gutter-y: 1rem; - } - - .g-md-4, - .gx-md-4 { - --bs-gutter-x: 1.5rem; - } - - .g-md-4, - .gy-md-4 { - --bs-gutter-y: 1.5rem; - } - - .g-md-5, - .gx-md-5 { - --bs-gutter-x: 3rem; - } - - .g-md-5, - .gy-md-5 { - --bs-gutter-y: 3rem; - } -} - -@media (min-width: 992px) { - .col-lg { - flex: 1 0 0%; - } - - .row-cols-lg-auto>* { - flex: 0 0 auto; - width: auto; - } - - .row-cols-lg-1>* { - flex: 0 0 auto; - width: 100%; - } - - .row-cols-lg-2>* { - flex: 0 0 auto; - width: 50%; - } - - .row-cols-lg-3>* { - flex: 0 0 auto; - width: 33.3333333333%; - } - - .row-cols-lg-4>* { - flex: 0 0 auto; - width: 25%; - } - - .row-cols-lg-5>* { - flex: 0 0 auto; - width: 20%; - } - - .row-cols-lg-6>* { - flex: 0 0 auto; - width: 16.6666666667%; - } - - .col-lg-auto { - flex: 0 0 auto; - width: auto; - } - - .col-lg-1 { - flex: 0 0 auto; - width: 8.33333333%; - } - - .col-lg-2 { - flex: 0 0 auto; - width: 16.66666667%; - } - - .col-lg-3 { - flex: 0 0 auto; - width: 25%; - } - - .col-lg-4 { - flex: 0 0 auto; - width: 33.33333333%; - } - - .col-lg-5 { - flex: 0 0 auto; - width: 41.66666667%; - } - - .col-lg-6 { - flex: 0 0 auto; - width: 50%; - } - - .col-lg-7 { - flex: 0 0 auto; - width: 58.33333333%; - } - - .col-lg-8 { - flex: 0 0 auto; - width: 66.66666667%; - } - - .col-lg-9 { - flex: 0 0 auto; - width: 75%; - } - - .col-lg-10 { - flex: 0 0 auto; - width: 83.33333333%; - } - - .col-lg-11 { - flex: 0 0 auto; - width: 91.66666667%; - } - - .col-lg-12 { - flex: 0 0 auto; - width: 100%; - } - - .offset-lg-0 { - margin-left: 0; - } - - .offset-lg-1 { - margin-left: 8.33333333%; - } - - .offset-lg-2 { - margin-left: 16.66666667%; - } - - .offset-lg-3 { - margin-left: 25%; - } - - .offset-lg-4 { - margin-left: 33.33333333%; - } - - .offset-lg-5 { - margin-left: 41.66666667%; - } - - .offset-lg-6 { - margin-left: 50%; - } - - .offset-lg-7 { - margin-left: 58.33333333%; - } - - .offset-lg-8 { - margin-left: 66.66666667%; - } - - .offset-lg-9 { - margin-left: 75%; - } - - .offset-lg-10 { - margin-left: 83.33333333%; - } - - .offset-lg-11 { - margin-left: 91.66666667%; - } - - .g-lg-0, - .gx-lg-0 { - --bs-gutter-x: 0; - } - - .g-lg-0, - .gy-lg-0 { - --bs-gutter-y: 0; - } - - .g-lg-1, - .gx-lg-1 { - --bs-gutter-x: 0.25rem; - } - - .g-lg-1, - .gy-lg-1 { - --bs-gutter-y: 0.25rem; - } - - .g-lg-2, - .gx-lg-2 { - --bs-gutter-x: 0.5rem; - } - - .g-lg-2, - .gy-lg-2 { - --bs-gutter-y: 0.5rem; - } - - .g-lg-3, - .gx-lg-3 { - --bs-gutter-x: 1rem; - } - - .g-lg-3, - .gy-lg-3 { - --bs-gutter-y: 1rem; - } - - .g-lg-4, - .gx-lg-4 { - --bs-gutter-x: 1.5rem; - } - - .g-lg-4, - .gy-lg-4 { - --bs-gutter-y: 1.5rem; - } - - .g-lg-5, - .gx-lg-5 { - --bs-gutter-x: 3rem; - } - - .g-lg-5, - .gy-lg-5 { - --bs-gutter-y: 3rem; - } -} - -@media (min-width: 1200px) { - .col-xl { - flex: 1 0 0%; - } - - .row-cols-xl-auto>* { - flex: 0 0 auto; - width: auto; - } - - .row-cols-xl-1>* { - flex: 0 0 auto; - width: 100%; - } - - .row-cols-xl-2>* { - flex: 0 0 auto; - width: 50%; - } - - .row-cols-xl-3>* { - flex: 0 0 auto; - width: 33.3333333333%; - } - - .row-cols-xl-4>* { - flex: 0 0 auto; - width: 25%; - } - - .row-cols-xl-5>* { - flex: 0 0 auto; - width: 20%; - } - - .row-cols-xl-6>* { - flex: 0 0 auto; - width: 16.6666666667%; - } - - .col-xl-auto { - flex: 0 0 auto; - width: auto; - } - - .col-xl-1 { - flex: 0 0 auto; - width: 8.33333333%; - } - - .col-xl-2 { - flex: 0 0 auto; - width: 16.66666667%; - } - - .col-xl-3 { - flex: 0 0 auto; - width: 25%; - } - - .col-xl-4 { - flex: 0 0 auto; - width: 33.33333333%; - } - - .col-xl-5 { - flex: 0 0 auto; - width: 41.66666667%; - } - - .col-xl-6 { - flex: 0 0 auto; - width: 50%; - } - - .col-xl-7 { - flex: 0 0 auto; - width: 58.33333333%; - } - - .col-xl-8 { - flex: 0 0 auto; - width: 66.66666667%; - } - - .col-xl-9 { - flex: 0 0 auto; - width: 75%; - } - - .col-xl-10 { - flex: 0 0 auto; - width: 83.33333333%; - } - - .col-xl-11 { - flex: 0 0 auto; - width: 91.66666667%; - } - - .col-xl-12 { - flex: 0 0 auto; - width: 100%; - } - - .offset-xl-0 { - margin-left: 0; - } - - .offset-xl-1 { - margin-left: 8.33333333%; - } - - .offset-xl-2 { - margin-left: 16.66666667%; - } - - .offset-xl-3 { - margin-left: 25%; - } - - .offset-xl-4 { - margin-left: 33.33333333%; - } - - .offset-xl-5 { - margin-left: 41.66666667%; - } - - .offset-xl-6 { - margin-left: 50%; - } - - .offset-xl-7 { - margin-left: 58.33333333%; - } - - .offset-xl-8 { - margin-left: 66.66666667%; - } - - .offset-xl-9 { - margin-left: 75%; - } - - .offset-xl-10 { - margin-left: 83.33333333%; - } - - .offset-xl-11 { - margin-left: 91.66666667%; - } - - .g-xl-0, - .gx-xl-0 { - --bs-gutter-x: 0; - } - - .g-xl-0, - .gy-xl-0 { - --bs-gutter-y: 0; - } - - .g-xl-1, - .gx-xl-1 { - --bs-gutter-x: 0.25rem; - } - - .g-xl-1, - .gy-xl-1 { - --bs-gutter-y: 0.25rem; - } - - .g-xl-2, - .gx-xl-2 { - --bs-gutter-x: 0.5rem; - } - - .g-xl-2, - .gy-xl-2 { - --bs-gutter-y: 0.5rem; - } - - .g-xl-3, - .gx-xl-3 { - --bs-gutter-x: 1rem; - } - - .g-xl-3, - .gy-xl-3 { - --bs-gutter-y: 1rem; - } - - .g-xl-4, - .gx-xl-4 { - --bs-gutter-x: 1.5rem; - } - - .g-xl-4, - .gy-xl-4 { - --bs-gutter-y: 1.5rem; - } - - .g-xl-5, - .gx-xl-5 { - --bs-gutter-x: 3rem; - } - - .g-xl-5, - .gy-xl-5 { - --bs-gutter-y: 3rem; - } -} - -@media (min-width: 1400px) { - .col-xxl { - flex: 1 0 0%; - } - - .row-cols-xxl-auto>* { - flex: 0 0 auto; - width: auto; - } - - .row-cols-xxl-1>* { - flex: 0 0 auto; - width: 100%; - } - - .row-cols-xxl-2>* { - flex: 0 0 auto; - width: 50%; - } - - .row-cols-xxl-3>* { - flex: 0 0 auto; - width: 33.3333333333%; - } - - .row-cols-xxl-4>* { - flex: 0 0 auto; - width: 25%; - } - - .row-cols-xxl-5>* { - flex: 0 0 auto; - width: 20%; - } - - .row-cols-xxl-6>* { - flex: 0 0 auto; - width: 16.6666666667%; - } - - .col-xxl-auto { - flex: 0 0 auto; - width: auto; - } - - .col-xxl-1 { - flex: 0 0 auto; - width: 8.33333333%; - } - - .col-xxl-2 { - flex: 0 0 auto; - width: 16.66666667%; - } - - .col-xxl-3 { - flex: 0 0 auto; - width: 25%; - } - - .col-xxl-4 { - flex: 0 0 auto; - width: 33.33333333%; - } - - .col-xxl-5 { - flex: 0 0 auto; - width: 41.66666667%; - } - - .col-xxl-6 { - flex: 0 0 auto; - width: 50%; - } - - .col-xxl-7 { - flex: 0 0 auto; - width: 58.33333333%; - } - - .col-xxl-8 { - flex: 0 0 auto; - width: 66.66666667%; - } - - .col-xxl-9 { - flex: 0 0 auto; - width: 75%; - } - - .col-xxl-10 { - flex: 0 0 auto; - width: 83.33333333%; - } - - .col-xxl-11 { - flex: 0 0 auto; - width: 91.66666667%; - } - - .col-xxl-12 { - flex: 0 0 auto; - width: 100%; - } - - .offset-xxl-0 { - margin-left: 0; - } - - .offset-xxl-1 { - margin-left: 8.33333333%; - } - - .offset-xxl-2 { - margin-left: 16.66666667%; - } - - .offset-xxl-3 { - margin-left: 25%; - } - - .offset-xxl-4 { - margin-left: 33.33333333%; - } - - .offset-xxl-5 { - margin-left: 41.66666667%; - } - - .offset-xxl-6 { - margin-left: 50%; - } - - .offset-xxl-7 { - margin-left: 58.33333333%; - } - - .offset-xxl-8 { - margin-left: 66.66666667%; - } - - .offset-xxl-9 { - margin-left: 75%; - } - - .offset-xxl-10 { - margin-left: 83.33333333%; - } - - .offset-xxl-11 { - margin-left: 91.66666667%; - } - - .g-xxl-0, - .gx-xxl-0 { - --bs-gutter-x: 0; - } - - .g-xxl-0, - .gy-xxl-0 { - --bs-gutter-y: 0; - } - - .g-xxl-1, - .gx-xxl-1 { - --bs-gutter-x: 0.25rem; - } - - .g-xxl-1, - .gy-xxl-1 { - --bs-gutter-y: 0.25rem; - } - - .g-xxl-2, - .gx-xxl-2 { - --bs-gutter-x: 0.5rem; - } - - .g-xxl-2, - .gy-xxl-2 { - --bs-gutter-y: 0.5rem; - } - - .g-xxl-3, - .gx-xxl-3 { - --bs-gutter-x: 1rem; - } - - .g-xxl-3, - .gy-xxl-3 { - --bs-gutter-y: 1rem; - } - - .g-xxl-4, - .gx-xxl-4 { - --bs-gutter-x: 1.5rem; - } - - .g-xxl-4, - .gy-xxl-4 { - --bs-gutter-y: 1.5rem; - } - - .g-xxl-5, - .gx-xxl-5 { - --bs-gutter-x: 3rem; - } - - .g-xxl-5, - .gy-xxl-5 { - --bs-gutter-y: 3rem; - } -} - -.table { - --bs-table-bg: transparent; - --bs-table-accent-bg: transparent; - --bs-table-striped-color: #212529; - --bs-table-striped-bg: rgba(0, 0, 0, 0.05); - --bs-table-active-color: #212529; - --bs-table-active-bg: rgba(0, 0, 0, 0.1); - --bs-table-hover-color: #212529; - --bs-table-hover-bg: rgba(0, 0, 0, 0.075); - width: 100%; - margin-bottom: 1rem; - color: #212529; - vertical-align: top; - border-color: #dee2e6; -} - -.table> :not(caption)>*>* { - padding: 0.5rem 0.5rem; - background-color: var(--bs-table-bg); - border-bottom-width: 1px; - box-shadow: inset 0 0 0 9999px var(--bs-table-accent-bg); -} - -.table>tbody { - vertical-align: inherit; -} - -.table>thead { - vertical-align: bottom; -} - -.table> :not(:first-child) { - border-top: 2px solid currentColor; -} - -.caption-top { - caption-side: top; -} - -.table-sm> :not(caption)>*>* { - padding: 0.25rem 0.25rem; -} - -.table-bordered> :not(caption)>* { - border-width: 1px 0; -} - -.table-bordered> :not(caption)>*>* { - border-width: 0 1px; -} - -.table-borderless> :not(caption)>*>* { - border-bottom-width: 0; -} - -.table-borderless> :not(:first-child) { - border-top-width: 0; -} - -.table-striped>tbody>tr:nth-of-type(odd)>* { - --bs-table-accent-bg: var(--bs-table-striped-bg); - color: var(--bs-table-striped-color); -} - -.table-active { - --bs-table-accent-bg: var(--bs-table-active-bg); - color: var(--bs-table-active-color); -} - -.table-hover>tbody>tr:hover>* { - --bs-table-accent-bg: var(--bs-table-hover-bg); - color: var(--bs-table-hover-color); -} - -.table-primary { - --bs-table-bg: #cfe2ff; - --bs-table-striped-bg: #c5d7f2; - --bs-table-striped-color: #000; - --bs-table-active-bg: #bacbe6; - --bs-table-active-color: #000; - --bs-table-hover-bg: #bfd1ec; - --bs-table-hover-color: #000; - color: #000; - border-color: #bacbe6; -} - -.table-secondary { - --bs-table-bg: #e2e3e5; - --bs-table-striped-bg: #d7d8da; - --bs-table-striped-color: #000; - --bs-table-active-bg: #cbccce; - --bs-table-active-color: #000; - --bs-table-hover-bg: #d1d2d4; - --bs-table-hover-color: #000; - color: #000; - border-color: #cbccce; -} - -.table-success { - --bs-table-bg: #d1e7dd; - --bs-table-striped-bg: #c7dbd2; - --bs-table-striped-color: #000; - --bs-table-active-bg: #bcd0c7; - --bs-table-active-color: #000; - --bs-table-hover-bg: #c1d6cc; - --bs-table-hover-color: #000; - color: #000; - border-color: #bcd0c7; -} - -.table-info { - --bs-table-bg: #cff4fc; - --bs-table-striped-bg: #c5e8ef; - --bs-table-striped-color: #000; - --bs-table-active-bg: #badce3; - --bs-table-active-color: #000; - --bs-table-hover-bg: #bfe2e9; - --bs-table-hover-color: #000; - color: #000; - border-color: #badce3; -} - -.table-warning { - --bs-table-bg: #fff3cd; - --bs-table-striped-bg: #f2e7c3; - --bs-table-striped-color: #000; - --bs-table-active-bg: #e6dbb9; - --bs-table-active-color: #000; - --bs-table-hover-bg: #ece1be; - --bs-table-hover-color: #000; - color: #000; - border-color: #e6dbb9; -} - -.table-danger { - --bs-table-bg: #f8d7da; - --bs-table-striped-bg: #eccccf; - --bs-table-striped-color: #000; - --bs-table-active-bg: #dfc2c4; - --bs-table-active-color: #000; - --bs-table-hover-bg: #e5c7ca; - --bs-table-hover-color: #000; - color: #000; - border-color: #dfc2c4; -} - -.table-light { - --bs-table-bg: #f8f9fa; - --bs-table-striped-bg: #ecedee; - --bs-table-striped-color: #000; - --bs-table-active-bg: #dfe0e1; - --bs-table-active-color: #000; - --bs-table-hover-bg: #e5e6e7; - --bs-table-hover-color: #000; - color: #000; - border-color: #dfe0e1; -} - -.table-dark { - --bs-table-bg: #212529; - --bs-table-striped-bg: #2c3034; - --bs-table-striped-color: #fff; - --bs-table-active-bg: #373b3e; - --bs-table-active-color: #fff; - --bs-table-hover-bg: #323539; - --bs-table-hover-color: #fff; - color: #fff; - border-color: #373b3e; -} - -.table-responsive { - overflow-x: auto; - -webkit-overflow-scrolling: touch; -} - -@media (max-width: 575.98px) { - .table-responsive-sm { - overflow-x: auto; - -webkit-overflow-scrolling: touch; - } -} - -@media (max-width: 767.98px) { - .table-responsive-md { - overflow-x: auto; - -webkit-overflow-scrolling: touch; - } -} - -@media (max-width: 991.98px) { - .table-responsive-lg { - overflow-x: auto; - -webkit-overflow-scrolling: touch; - } -} - -@media (max-width: 1199.98px) { - .table-responsive-xl { - overflow-x: auto; - -webkit-overflow-scrolling: touch; - } -} - -@media (max-width: 1399.98px) { - .table-responsive-xxl { - overflow-x: auto; - -webkit-overflow-scrolling: touch; - } -} - -.form-label { - margin-bottom: 0.5rem; -} - -.col-form-label { - padding-top: calc(0.375rem + 1px); - padding-bottom: calc(0.375rem + 1px); - margin-bottom: 0; - font-size: inherit; - line-height: 1.5; -} - -.col-form-label-lg { - padding-top: calc(0.5rem + 1px); - padding-bottom: calc(0.5rem + 1px); - font-size: 1.25rem; -} - -.col-form-label-sm { - padding-top: calc(0.25rem + 1px); - padding-bottom: calc(0.25rem + 1px); - font-size: 0.875rem; -} - -.form-text { - margin-top: 0.25rem; - font-size: 0.875em; - color: #6c757d; -} - -.form-control { - display: block; - width: 100%; - padding: 0.375rem 0.75rem; - font-size: 1rem; - font-weight: 400; - line-height: 1.5; - color: #212529; - background-color: #fff; - background-clip: padding-box; - border: 1px solid #ced4da; - -webkit-appearance: none; - -moz-appearance: none; - appearance: none; - border-radius: 0.25rem; - transition: border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out; -} - -@media (prefers-reduced-motion: reduce) { - .form-control { - transition: none; - } -} - -.form-control[type=file] { - overflow: hidden; -} - -.form-control[type=file]:not(:disabled):not([readonly]) { - cursor: pointer; -} - -.form-control:focus { - color: #212529; - background-color: #fff; - border-color: #86b7fe; - outline: 0; - box-shadow: 0 0 0 0.25rem rgba(13, 110, 253, 0.25); -} - -.form-control::-webkit-date-and-time-value { - height: 1.5em; -} - -.form-control::-moz-placeholder { - color: #6c757d; - opacity: 1; -} - -.form-control:-ms-input-placeholder { - color: #6c757d; - opacity: 1; -} - -.form-control::placeholder { - color: #6c757d; - opacity: 1; -} - -.form-control:disabled, -.form-control[readonly] { - background-color: #e9ecef; - opacity: 1; -} - -.form-control::-webkit-file-upload-button { - padding: 0.375rem 0.75rem; - margin: -0.375rem -0.75rem; - -webkit-margin-end: 0.75rem; - margin-inline-end: 0.75rem; - color: #212529; - background-color: #e9ecef; - pointer-events: none; - border-color: inherit; - border-style: solid; - border-width: 0; - border-inline-end-width: 1px; - border-radius: 0; - -webkit-transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out; - transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out; -} - -.form-control::file-selector-button { - padding: 0.375rem 0.75rem; - margin: -0.375rem -0.75rem; - -webkit-margin-end: 0.75rem; - margin-inline-end: 0.75rem; - color: #212529; - background-color: #e9ecef; - pointer-events: none; - border-color: inherit; - border-style: solid; - border-width: 0; - border-inline-end-width: 1px; - border-radius: 0; - transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out; -} - -@media (prefers-reduced-motion: reduce) { - .form-control::-webkit-file-upload-button { - -webkit-transition: none; - transition: none; - } - - .form-control::file-selector-button { - transition: none; - } -} - -.form-control:hover:not(:disabled):not([readonly])::-webkit-file-upload-button { - background-color: #dde0e3; -} - -.form-control:hover:not(:disabled):not([readonly])::file-selector-button { - background-color: #dde0e3; -} - -.form-control::-webkit-file-upload-button { - padding: 0.375rem 0.75rem; - margin: -0.375rem -0.75rem; - -webkit-margin-end: 0.75rem; - margin-inline-end: 0.75rem; - color: #212529; - background-color: #e9ecef; - pointer-events: none; - border-color: inherit; - border-style: solid; - border-width: 0; - border-inline-end-width: 1px; - border-radius: 0; - -webkit-transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out; - transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out; -} - -@media (prefers-reduced-motion: reduce) { - .form-control::-webkit-file-upload-button { - -webkit-transition: none; - transition: none; - } -} - -.form-control:hover:not(:disabled):not([readonly])::-webkit-file-upload-button { - background-color: #dde0e3; -} - -.form-control-plaintext { - display: block; - width: 100%; - padding: 0.375rem 0; - margin-bottom: 0; - line-height: 1.5; - color: #212529; - background-color: transparent; - border: solid transparent; - border-width: 1px 0; -} - -.form-control-plaintext.form-control-sm, -.form-control-plaintext.form-control-lg { - padding-right: 0; - padding-left: 0; -} - -.form-control-sm { - min-height: calc(1.5em + 0.5rem + 2px); - padding: 0.25rem 0.5rem; - font-size: 0.875rem; - border-radius: 0.2rem; -} - -.form-control-sm::-webkit-file-upload-button { - padding: 0.25rem 0.5rem; - margin: -0.25rem -0.5rem; - -webkit-margin-end: 0.5rem; - margin-inline-end: 0.5rem; -} - -.form-control-sm::file-selector-button { - padding: 0.25rem 0.5rem; - margin: -0.25rem -0.5rem; - -webkit-margin-end: 0.5rem; - margin-inline-end: 0.5rem; -} - -.form-control-sm::-webkit-file-upload-button { - padding: 0.25rem 0.5rem; - margin: -0.25rem -0.5rem; - -webkit-margin-end: 0.5rem; - margin-inline-end: 0.5rem; -} - -.form-control-lg { - min-height: calc(1.5em + 1rem + 2px); - padding: 0.5rem 1rem; - font-size: 1.25rem; - border-radius: 0.3rem; -} - -.form-control-lg::-webkit-file-upload-button { - padding: 0.5rem 1rem; - margin: -0.5rem -1rem; - -webkit-margin-end: 1rem; - margin-inline-end: 1rem; -} - -.form-control-lg::file-selector-button { - padding: 0.5rem 1rem; - margin: -0.5rem -1rem; - -webkit-margin-end: 1rem; - margin-inline-end: 1rem; -} - -.form-control-lg::-webkit-file-upload-button { - padding: 0.5rem 1rem; - margin: -0.5rem -1rem; - -webkit-margin-end: 1rem; - margin-inline-end: 1rem; -} - -textarea.form-control { - min-height: calc(1.5em + 0.75rem + 2px); -} - -textarea.form-control-sm { - min-height: calc(1.5em + 0.5rem + 2px); -} - -textarea.form-control-lg { - min-height: calc(1.5em + 1rem + 2px); -} - -.form-control-color { - width: 3rem; - height: auto; - padding: 0.375rem; -} - -.form-control-color:not(:disabled):not([readonly]) { - cursor: pointer; -} - -.form-control-color::-moz-color-swatch { - height: 1.5em; - border-radius: 0.25rem; -} - -.form-control-color::-webkit-color-swatch { - height: 1.5em; - border-radius: 0.25rem; -} - -.form-select { - display: block; - width: 100%; - padding: 0.375rem 2.25rem 0.375rem 0.75rem; - -moz-padding-start: calc(0.75rem - 3px); - font-size: 1rem; - font-weight: 400; - line-height: 1.5; - color: #212529; - background-color: #fff; - background-image: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16'%3e%3cpath fill='none' stroke='%23343a40' stroke-linecap='round' stroke-linejoin='round' stroke-width='2' d='M2 5l6 6 6-6'/%3e%3c/svg%3e"); - background-repeat: no-repeat; - background-position: right 0.75rem center; - background-size: 16px 12px; - border: 1px solid #ced4da; - border-radius: 0.25rem; - transition: border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out; - -webkit-appearance: none; - -moz-appearance: none; - appearance: none; -} - -@media (prefers-reduced-motion: reduce) { - .form-select { - transition: none; - } -} - -.form-select:focus { - border-color: #86b7fe; - outline: 0; - box-shadow: 0 0 0 0.25rem rgba(13, 110, 253, 0.25); -} - -.form-select[multiple], -.form-select[size]:not([size="1"]) { - padding-right: 0.75rem; - background-image: none; -} - -.form-select:disabled { - background-color: #e9ecef; -} - -.form-select:-moz-focusring { - color: transparent; - text-shadow: 0 0 0 #212529; -} - -.form-select-sm { - padding-top: 0.25rem; - padding-bottom: 0.25rem; - padding-left: 0.5rem; - font-size: 0.875rem; - border-radius: 0.2rem; -} - -.form-select-lg { - padding-top: 0.5rem; - padding-bottom: 0.5rem; - padding-left: 1rem; - font-size: 1.25rem; - border-radius: 0.3rem; -} - -.form-check { - display: block; - min-height: 1.5rem; - padding-left: 1.5em; - margin-bottom: 0.125rem; -} - -.form-check .form-check-input { - float: left; - margin-left: -1.5em; -} - -.form-check-input { - width: 1em; - height: 1em; - margin-top: 0.25em; - vertical-align: top; - background-color: #fff; - background-repeat: no-repeat; - background-position: center; - background-size: contain; - border: 1px solid rgba(0, 0, 0, 0.25); - -webkit-appearance: none; - -moz-appearance: none; - appearance: none; - -webkit-print-color-adjust: exact; - color-adjust: exact; -} - -.form-check-input[type=checkbox] { - border-radius: 0.25em; -} - -.form-check-input[type=radio] { - border-radius: 50%; -} - -.form-check-input:active { - filter: brightness(90%); -} - -.form-check-input:focus { - border-color: #86b7fe; - outline: 0; - box-shadow: 0 0 0 0.25rem rgba(13, 110, 253, 0.25); -} - -.form-check-input:checked { - background-color: #0d6efd; - border-color: #0d6efd; -} - -.form-check-input:checked[type=checkbox] { - background-image: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 20 20'%3e%3cpath fill='none' stroke='%23fff' stroke-linecap='round' stroke-linejoin='round' stroke-width='3' d='M6 10l3 3l6-6'/%3e%3c/svg%3e"); -} - -.form-check-input:checked[type=radio] { - background-image: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'%3e%3ccircle r='2' fill='%23fff'/%3e%3c/svg%3e"); -} - -.form-check-input[type=checkbox]:indeterminate { - background-color: #0d6efd; - border-color: #0d6efd; - background-image: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 20 20'%3e%3cpath fill='none' stroke='%23fff' stroke-linecap='round' stroke-linejoin='round' stroke-width='3' d='M6 10h8'/%3e%3c/svg%3e"); -} - -.form-check-input:disabled { - pointer-events: none; - filter: none; - opacity: 0.5; -} - -.form-check-input[disabled]~.form-check-label, -.form-check-input:disabled~.form-check-label { - opacity: 0.5; -} - -.form-switch { - padding-left: 2.5em; -} - -.form-switch .form-check-input { - width: 2em; - margin-left: -2.5em; - background-image: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'%3e%3ccircle r='3' fill='rgba%280, 0, 0, 0.25%29'/%3e%3c/svg%3e"); - background-position: left center; - border-radius: 2em; - transition: background-position 0.15s ease-in-out; -} - -@media (prefers-reduced-motion: reduce) { - .form-switch .form-check-input { - transition: none; - } -} - -.form-switch .form-check-input:focus { - background-image: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'%3e%3ccircle r='3' fill='%2386b7fe'/%3e%3c/svg%3e"); -} - -.form-switch .form-check-input:checked { - background-position: right center; - background-image: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'%3e%3ccircle r='3' fill='%23fff'/%3e%3c/svg%3e"); -} - -.form-check-inline { - display: inline-block; - margin-right: 1rem; -} - -.btn-check { - position: absolute; - clip: rect(0, 0, 0, 0); - pointer-events: none; -} - -.btn-check[disabled]+.btn, -.btn-check:disabled+.btn { - pointer-events: none; - filter: none; - opacity: 0.65; -} - -.form-range { - width: 100%; - height: 1.5rem; - padding: 0; - background-color: transparent; - -webkit-appearance: none; - -moz-appearance: none; - appearance: none; -} - -.form-range:focus { - outline: 0; -} - -.form-range:focus::-webkit-slider-thumb { - box-shadow: 0 0 0 1px #fff, 0 0 0 0.25rem rgba(13, 110, 253, 0.25); -} - -.form-range:focus::-moz-range-thumb { - box-shadow: 0 0 0 1px #fff, 0 0 0 0.25rem rgba(13, 110, 253, 0.25); -} - -.form-range::-moz-focus-outer { - border: 0; -} - -.form-range::-webkit-slider-thumb { - width: 1rem; - height: 1rem; - margin-top: -0.25rem; - background-color: #0d6efd; - border: 0; - border-radius: 1rem; - -webkit-transition: background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out; - transition: background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out; - -webkit-appearance: none; - appearance: none; -} - -@media (prefers-reduced-motion: reduce) { - .form-range::-webkit-slider-thumb { - -webkit-transition: none; - transition: none; - } -} - -.form-range::-webkit-slider-thumb:active { - background-color: #b6d4fe; -} - -.form-range::-webkit-slider-runnable-track { - width: 100%; - height: 0.5rem; - color: transparent; - cursor: pointer; - background-color: #dee2e6; - border-color: transparent; - border-radius: 1rem; -} - -.form-range::-moz-range-thumb { - width: 1rem; - height: 1rem; - background-color: #0d6efd; - border: 0; - border-radius: 1rem; - -moz-transition: background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out; - transition: background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out; - -moz-appearance: none; - appearance: none; -} - -@media (prefers-reduced-motion: reduce) { - .form-range::-moz-range-thumb { - -moz-transition: none; - transition: none; - } -} - -.form-range::-moz-range-thumb:active { - background-color: #b6d4fe; -} - -.form-range::-moz-range-track { - width: 100%; - height: 0.5rem; - color: transparent; - cursor: pointer; - background-color: #dee2e6; - border-color: transparent; - border-radius: 1rem; -} - -.form-range:disabled { - pointer-events: none; -} - -.form-range:disabled::-webkit-slider-thumb { - background-color: #adb5bd; -} - -.form-range:disabled::-moz-range-thumb { - background-color: #adb5bd; -} - -.form-floating { - position: relative; -} - -.form-floating>.form-control, -.form-floating>.form-select { - height: calc(3.5rem + 2px); - line-height: 1.25; -} - -.form-floating>label { - position: absolute; - top: 0; - left: 0; - height: 100%; - padding: 1rem 0.75rem; - pointer-events: none; - border: 1px solid transparent; - transform-origin: 0 0; - transition: opacity 0.1s ease-in-out, transform 0.1s ease-in-out; -} - -@media (prefers-reduced-motion: reduce) { - .form-floating>label { - transition: none; - } -} - -.form-floating>.form-control { - padding: 1rem 0.75rem; -} - -.form-floating>.form-control::-moz-placeholder { - color: transparent; -} - -.form-floating>.form-control:-ms-input-placeholder { - color: transparent; -} - -.form-floating>.form-control::placeholder { - color: transparent; -} - -.form-floating>.form-control:not(:-moz-placeholder-shown) { - padding-top: 1.625rem; - padding-bottom: 0.625rem; -} - -.form-floating>.form-control:not(:-ms-input-placeholder) { - padding-top: 1.625rem; - padding-bottom: 0.625rem; -} - -.form-floating>.form-control:focus, -.form-floating>.form-control:not(:placeholder-shown) { - padding-top: 1.625rem; - padding-bottom: 0.625rem; -} - -.form-floating>.form-control:-webkit-autofill { - padding-top: 1.625rem; - padding-bottom: 0.625rem; -} - -.form-floating>.form-select { - padding-top: 1.625rem; - padding-bottom: 0.625rem; -} - -.form-floating>.form-control:not(:-moz-placeholder-shown)~label { - opacity: 0.65; - transform: scale(0.85) translateY(-0.5rem) translateX(0.15rem); -} - -.form-floating>.form-control:not(:-ms-input-placeholder)~label { - opacity: 0.65; - transform: scale(0.85) translateY(-0.5rem) translateX(0.15rem); -} - -.form-floating>.form-control:focus~label, -.form-floating>.form-control:not(:placeholder-shown)~label, -.form-floating>.form-select~label { - opacity: 0.65; - transform: scale(0.85) translateY(-0.5rem) translateX(0.15rem); -} - -.form-floating>.form-control:-webkit-autofill~label { - opacity: 0.65; - transform: scale(0.85) translateY(-0.5rem) translateX(0.15rem); -} - -.input-group { - position: relative; - display: flex; - flex-wrap: wrap; - align-items: stretch; - width: 100%; -} - -.input-group>.form-control, -.input-group>.form-select { - position: relative; - flex: 1 1 auto; - width: 1%; - min-width: 0; -} - -.input-group>.form-control:focus, -.input-group>.form-select:focus { - z-index: 3; -} - -.input-group .btn { - position: relative; - z-index: 2; -} - -.input-group .btn:focus { - z-index: 3; -} - -.input-group-text { - display: flex; - align-items: center; - padding: 0.375rem 0.75rem; - font-size: 1rem; - font-weight: 400; - line-height: 1.5; - color: #212529; - text-align: center; - white-space: nowrap; - background-color: #e9ecef; - border: 1px solid #ced4da; - border-radius: 0.25rem; -} - -.input-group-lg>.form-control, -.input-group-lg>.form-select, -.input-group-lg>.input-group-text, -.input-group-lg>.btn { - padding: 0.5rem 1rem; - font-size: 1.25rem; - border-radius: 0.3rem; -} - -.input-group-sm>.form-control, -.input-group-sm>.form-select, -.input-group-sm>.input-group-text, -.input-group-sm>.btn { - padding: 0.25rem 0.5rem; - font-size: 0.875rem; - border-radius: 0.2rem; -} - -.input-group-lg>.form-select, -.input-group-sm>.form-select { - padding-right: 3rem; -} - -.input-group:not(.has-validation)> :not(:last-child):not(.dropdown-toggle):not(.dropdown-menu), -.input-group:not(.has-validation)>.dropdown-toggle:nth-last-child(n+3) { - border-top-right-radius: 0; - border-bottom-right-radius: 0; -} - -.input-group.has-validation> :nth-last-child(n+3):not(.dropdown-toggle):not(.dropdown-menu), -.input-group.has-validation>.dropdown-toggle:nth-last-child(n+4) { - border-top-right-radius: 0; - border-bottom-right-radius: 0; -} - -.input-group> :not(:first-child):not(.dropdown-menu):not(.valid-tooltip):not(.valid-feedback):not(.invalid-tooltip):not(.invalid-feedback) { - margin-left: -1px; - border-top-left-radius: 0; - border-bottom-left-radius: 0; -} - -.valid-feedback { - display: none; - width: 100%; - margin-top: 0.25rem; - font-size: 0.875em; - color: #198754; -} - -.valid-tooltip { - position: absolute; - top: 100%; - z-index: 5; - display: none; - max-width: 100%; - padding: 0.25rem 0.5rem; - margin-top: 0.1rem; - font-size: 0.875rem; - color: #fff; - background-color: rgba(25, 135, 84, 0.9); - border-radius: 0.25rem; -} - -.was-validated :valid~.valid-feedback, -.was-validated :valid~.valid-tooltip, -.is-valid~.valid-feedback, -.is-valid~.valid-tooltip { - display: block; -} - -.was-validated .form-control:valid, -.form-control.is-valid { - border-color: #198754; - padding-right: calc(1.5em + 0.75rem); - background-image: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 8 8'%3e%3cpath fill='%23198754' d='M2.3 6.73L.6 4.53c-.4-1.04.46-1.4 1.1-.8l1.1 1.4 3.4-3.8c.6-.63 1.6-.27 1.2.7l-4 4.6c-.43.5-.8.4-1.1.1z'/%3e%3c/svg%3e"); - background-repeat: no-repeat; - background-position: right calc(0.375em + 0.1875rem) center; - background-size: calc(0.75em + 0.375rem) calc(0.75em + 0.375rem); -} - -.was-validated .form-control:valid:focus, -.form-control.is-valid:focus { - border-color: #198754; - box-shadow: 0 0 0 0.25rem rgba(25, 135, 84, 0.25); -} - -.was-validated textarea.form-control:valid, -textarea.form-control.is-valid { - padding-right: calc(1.5em + 0.75rem); - background-position: top calc(0.375em + 0.1875rem) right calc(0.375em + 0.1875rem); -} - -.was-validated .form-select:valid, -.form-select.is-valid { - border-color: #198754; -} - -.was-validated .form-select:valid:not([multiple]):not([size]), -.was-validated .form-select:valid:not([multiple])[size="1"], -.form-select.is-valid:not([multiple]):not([size]), -.form-select.is-valid:not([multiple])[size="1"] { - padding-right: 4.125rem; - background-image: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16'%3e%3cpath fill='none' stroke='%23343a40' stroke-linecap='round' stroke-linejoin='round' stroke-width='2' d='M2 5l6 6 6-6'/%3e%3c/svg%3e"), url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 8 8'%3e%3cpath fill='%23198754' d='M2.3 6.73L.6 4.53c-.4-1.04.46-1.4 1.1-.8l1.1 1.4 3.4-3.8c.6-.63 1.6-.27 1.2.7l-4 4.6c-.43.5-.8.4-1.1.1z'/%3e%3c/svg%3e"); - background-position: right 0.75rem center, center right 2.25rem; - background-size: 16px 12px, calc(0.75em + 0.375rem) calc(0.75em + 0.375rem); -} - -.was-validated .form-select:valid:focus, -.form-select.is-valid:focus { - border-color: #198754; - box-shadow: 0 0 0 0.25rem rgba(25, 135, 84, 0.25); -} - -.was-validated .form-check-input:valid, -.form-check-input.is-valid { - border-color: #198754; -} - -.was-validated .form-check-input:valid:checked, -.form-check-input.is-valid:checked { - background-color: #198754; -} - -.was-validated .form-check-input:valid:focus, -.form-check-input.is-valid:focus { - box-shadow: 0 0 0 0.25rem rgba(25, 135, 84, 0.25); -} - -.was-validated .form-check-input:valid~.form-check-label, -.form-check-input.is-valid~.form-check-label { - color: #198754; -} - -.form-check-inline .form-check-input~.valid-feedback { - margin-left: 0.5em; -} - -.was-validated .input-group .form-control:valid, -.input-group .form-control.is-valid, -.was-validated .input-group .form-select:valid, -.input-group .form-select.is-valid { - z-index: 1; -} - -.was-validated .input-group .form-control:valid:focus, -.input-group .form-control.is-valid:focus, -.was-validated .input-group .form-select:valid:focus, -.input-group .form-select.is-valid:focus { - z-index: 3; -} - -.invalid-feedback { - display: none; - width: 100%; - margin-top: 0.25rem; - font-size: 0.875em; - color: #dc3545; -} - -.invalid-tooltip { - position: absolute; - top: 100%; - z-index: 5; - display: none; - max-width: 100%; - padding: 0.25rem 0.5rem; - margin-top: 0.1rem; - font-size: 0.875rem; - color: #fff; - background-color: rgba(220, 53, 69, 0.9); - border-radius: 0.25rem; -} - -.was-validated :invalid~.invalid-feedback, -.was-validated :invalid~.invalid-tooltip, -.is-invalid~.invalid-feedback, -.is-invalid~.invalid-tooltip { - display: block; -} - -.was-validated .form-control:invalid, -.form-control.is-invalid { - border-color: #dc3545; - padding-right: calc(1.5em + 0.75rem); - background-image: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 12 12' width='12' height='12' fill='none' stroke='%23dc3545'%3e%3ccircle cx='6' cy='6' r='4.5'/%3e%3cpath stroke-linejoin='round' d='M5.8 3.6h.4L6 6.5z'/%3e%3ccircle cx='6' cy='8.2' r='.6' fill='%23dc3545' stroke='none'/%3e%3c/svg%3e"); - background-repeat: no-repeat; - background-position: right calc(0.375em + 0.1875rem) center; - background-size: calc(0.75em + 0.375rem) calc(0.75em + 0.375rem); -} - -.was-validated .form-control:invalid:focus, -.form-control.is-invalid:focus { - border-color: #dc3545; - box-shadow: 0 0 0 0.25rem rgba(220, 53, 69, 0.25); -} - -.was-validated textarea.form-control:invalid, -textarea.form-control.is-invalid { - padding-right: calc(1.5em + 0.75rem); - background-position: top calc(0.375em + 0.1875rem) right calc(0.375em + 0.1875rem); -} - -.was-validated .form-select:invalid, -.form-select.is-invalid { - border-color: #dc3545; -} - -.was-validated .form-select:invalid:not([multiple]):not([size]), -.was-validated .form-select:invalid:not([multiple])[size="1"], -.form-select.is-invalid:not([multiple]):not([size]), -.form-select.is-invalid:not([multiple])[size="1"] { - padding-right: 4.125rem; - background-image: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16'%3e%3cpath fill='none' stroke='%23343a40' stroke-linecap='round' stroke-linejoin='round' stroke-width='2' d='M2 5l6 6 6-6'/%3e%3c/svg%3e"), url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 12 12' width='12' height='12' fill='none' stroke='%23dc3545'%3e%3ccircle cx='6' cy='6' r='4.5'/%3e%3cpath stroke-linejoin='round' d='M5.8 3.6h.4L6 6.5z'/%3e%3ccircle cx='6' cy='8.2' r='.6' fill='%23dc3545' stroke='none'/%3e%3c/svg%3e"); - background-position: right 0.75rem center, center right 2.25rem; - background-size: 16px 12px, calc(0.75em + 0.375rem) calc(0.75em + 0.375rem); -} - -.was-validated .form-select:invalid:focus, -.form-select.is-invalid:focus { - border-color: #dc3545; - box-shadow: 0 0 0 0.25rem rgba(220, 53, 69, 0.25); -} - -.was-validated .form-check-input:invalid, -.form-check-input.is-invalid { - border-color: #dc3545; -} - -.was-validated .form-check-input:invalid:checked, -.form-check-input.is-invalid:checked { - background-color: #dc3545; -} - -.was-validated .form-check-input:invalid:focus, -.form-check-input.is-invalid:focus { - box-shadow: 0 0 0 0.25rem rgba(220, 53, 69, 0.25); -} - -.was-validated .form-check-input:invalid~.form-check-label, -.form-check-input.is-invalid~.form-check-label { - color: #dc3545; -} - -.form-check-inline .form-check-input~.invalid-feedback { - margin-left: 0.5em; -} - -.was-validated .input-group .form-control:invalid, -.input-group .form-control.is-invalid, -.was-validated .input-group .form-select:invalid, -.input-group .form-select.is-invalid { - z-index: 2; -} - -.was-validated .input-group .form-control:invalid:focus, -.input-group .form-control.is-invalid:focus, -.was-validated .input-group .form-select:invalid:focus, -.input-group .form-select.is-invalid:focus { - z-index: 3; -} - -.btn { - display: inline-block; - font-weight: 400; - line-height: 1.5; - color: #212529; - text-align: center; - text-decoration: none; - vertical-align: middle; - cursor: pointer; - -webkit-user-select: none; - -moz-user-select: none; - -ms-user-select: none; - user-select: none; - background-color: transparent; - border: 1px solid transparent; - padding: 0.375rem 0.75rem; - font-size: 1rem; - border-radius: 0.25rem; - transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out; -} - -@media (prefers-reduced-motion: reduce) { - .btn { - transition: none; - } -} - -.btn:hover { - color: #212529; -} - -.btn-check:focus+.btn, -.btn:focus { - outline: 0; - box-shadow: 0 0 0 0.25rem rgba(13, 110, 253, 0.25); -} - -.btn:disabled, -.btn.disabled, -fieldset:disabled .btn { - pointer-events: none; - opacity: 0.65; -} - -.btn-primary { - color: #fff; - background-color: #0d6efd; - border-color: #0d6efd; -} - -.btn-primary:hover { - color: #fff; - background-color: #0b5ed7; - border-color: #0a58ca; -} - -.btn-check:focus+.btn-primary, -.btn-primary:focus { - color: #fff; - background-color: #0b5ed7; - border-color: #0a58ca; - box-shadow: 0 0 0 0.25rem rgba(49, 132, 253, 0.5); -} - -.btn-check:checked+.btn-primary, -.btn-check:active+.btn-primary, -.btn-primary:active, -.btn-primary.active, -.show>.btn-primary.dropdown-toggle { - color: #fff; - background-color: #0a58ca; - border-color: #0a53be; -} - -.btn-check:checked+.btn-primary:focus, -.btn-check:active+.btn-primary:focus, -.btn-primary:active:focus, -.btn-primary.active:focus, -.show>.btn-primary.dropdown-toggle:focus { - box-shadow: 0 0 0 0.25rem rgba(49, 132, 253, 0.5); -} - -.btn-primary:disabled, -.btn-primary.disabled { - color: #fff; - background-color: #0d6efd; - border-color: #0d6efd; -} - -.btn-secondary { - color: #fff; - background-color: #6c757d; - border-color: #6c757d; -} - -.btn-secondary:hover { - color: #fff; - background-color: #5c636a; - border-color: #565e64; -} - -.btn-check:focus+.btn-secondary, -.btn-secondary:focus { - color: #fff; - background-color: #5c636a; - border-color: #565e64; - box-shadow: 0 0 0 0.25rem rgba(130, 138, 145, 0.5); -} - -.btn-check:checked+.btn-secondary, -.btn-check:active+.btn-secondary, -.btn-secondary:active, -.btn-secondary.active, -.show>.btn-secondary.dropdown-toggle { - color: #fff; - background-color: #565e64; - border-color: #51585e; -} - -.btn-check:checked+.btn-secondary:focus, -.btn-check:active+.btn-secondary:focus, -.btn-secondary:active:focus, -.btn-secondary.active:focus, -.show>.btn-secondary.dropdown-toggle:focus { - box-shadow: 0 0 0 0.25rem rgba(130, 138, 145, 0.5); -} - -.btn-secondary:disabled, -.btn-secondary.disabled { - color: #fff; - background-color: #6c757d; - border-color: #6c757d; -} - -.btn-success { - color: #fff; - background-color: #198754; - border-color: #198754; -} - -.btn-success:hover { - color: #fff; - background-color: #157347; - border-color: #146c43; -} - -.btn-check:focus+.btn-success, -.btn-success:focus { - color: #fff; - background-color: #157347; - border-color: #146c43; - box-shadow: 0 0 0 0.25rem rgba(60, 153, 110, 0.5); -} - -.btn-check:checked+.btn-success, -.btn-check:active+.btn-success, -.btn-success:active, -.btn-success.active, -.show>.btn-success.dropdown-toggle { - color: #fff; - background-color: #146c43; - border-color: #13653f; -} - -.btn-check:checked+.btn-success:focus, -.btn-check:active+.btn-success:focus, -.btn-success:active:focus, -.btn-success.active:focus, -.show>.btn-success.dropdown-toggle:focus { - box-shadow: 0 0 0 0.25rem rgba(60, 153, 110, 0.5); -} - -.btn-success:disabled, -.btn-success.disabled { - color: #fff; - background-color: #198754; - border-color: #198754; -} - -.btn-info { - color: #000; - background-color: #0dcaf0; - border-color: #0dcaf0; -} - -.btn-info:hover { - color: #000; - background-color: #31d2f2; - border-color: #25cff2; -} - -.btn-check:focus+.btn-info, -.btn-info:focus { - color: #000; - background-color: #31d2f2; - border-color: #25cff2; - box-shadow: 0 0 0 0.25rem rgba(11, 172, 204, 0.5); -} - -.btn-check:checked+.btn-info, -.btn-check:active+.btn-info, -.btn-info:active, -.btn-info.active, -.show>.btn-info.dropdown-toggle { - color: #000; - background-color: #3dd5f3; - border-color: #25cff2; -} - -.btn-check:checked+.btn-info:focus, -.btn-check:active+.btn-info:focus, -.btn-info:active:focus, -.btn-info.active:focus, -.show>.btn-info.dropdown-toggle:focus { - box-shadow: 0 0 0 0.25rem rgba(11, 172, 204, 0.5); -} - -.btn-info:disabled, -.btn-info.disabled { - color: #000; - background-color: #0dcaf0; - border-color: #0dcaf0; -} - -.btn-warning { - color: #000; - background-color: #ffc107; - border-color: #ffc107; -} - -.btn-warning:hover { - color: #000; - background-color: #ffca2c; - border-color: #ffc720; -} - -.btn-check:focus+.btn-warning, -.btn-warning:focus { - color: #000; - background-color: #ffca2c; - border-color: #ffc720; - box-shadow: 0 0 0 0.25rem rgba(217, 164, 6, 0.5); -} - -.btn-check:checked+.btn-warning, -.btn-check:active+.btn-warning, -.btn-warning:active, -.btn-warning.active, -.show>.btn-warning.dropdown-toggle { - color: #000; - background-color: #ffcd39; - border-color: #ffc720; -} - -.btn-check:checked+.btn-warning:focus, -.btn-check:active+.btn-warning:focus, -.btn-warning:active:focus, -.btn-warning.active:focus, -.show>.btn-warning.dropdown-toggle:focus { - box-shadow: 0 0 0 0.25rem rgba(217, 164, 6, 0.5); -} - -.btn-warning:disabled, -.btn-warning.disabled { - color: #000; - background-color: #ffc107; - border-color: #ffc107; -} - -.btn-danger { - color: #fff; - background-color: #dc3545; - border-color: #dc3545; -} - -.btn-danger:hover { - color: #fff; - background-color: #bb2d3b; - border-color: #b02a37; -} - -.btn-check:focus+.btn-danger, -.btn-danger:focus { - color: #fff; - background-color: #bb2d3b; - border-color: #b02a37; - box-shadow: 0 0 0 0.25rem rgba(225, 83, 97, 0.5); -} - -.btn-check:checked+.btn-danger, -.btn-check:active+.btn-danger, -.btn-danger:active, -.btn-danger.active, -.show>.btn-danger.dropdown-toggle { - color: #fff; - background-color: #b02a37; - border-color: #a52834; -} - -.btn-check:checked+.btn-danger:focus, -.btn-check:active+.btn-danger:focus, -.btn-danger:active:focus, -.btn-danger.active:focus, -.show>.btn-danger.dropdown-toggle:focus { - box-shadow: 0 0 0 0.25rem rgba(225, 83, 97, 0.5); -} - -.btn-danger:disabled, -.btn-danger.disabled { - color: #fff; - background-color: #dc3545; - border-color: #dc3545; -} - -.btn-light { - color: #000; - background-color: #f8f9fa; - border-color: #f8f9fa; -} - -.btn-light:hover { - color: #000; - background-color: #f9fafb; - border-color: #f9fafb; -} - -.btn-check:focus+.btn-light, -.btn-light:focus { - color: #000; - background-color: #f9fafb; - border-color: #f9fafb; - box-shadow: 0 0 0 0.25rem rgba(211, 212, 213, 0.5); -} - -.btn-check:checked+.btn-light, -.btn-check:active+.btn-light, -.btn-light:active, -.btn-light.active, -.show>.btn-light.dropdown-toggle { - color: #000; - background-color: #f9fafb; - border-color: #f9fafb; -} - -.btn-check:checked+.btn-light:focus, -.btn-check:active+.btn-light:focus, -.btn-light:active:focus, -.btn-light.active:focus, -.show>.btn-light.dropdown-toggle:focus { - box-shadow: 0 0 0 0.25rem rgba(211, 212, 213, 0.5); -} - -.btn-light:disabled, -.btn-light.disabled { - color: #000; - background-color: #f8f9fa; - border-color: #f8f9fa; -} - -.btn-dark { - color: #fff; - background-color: #212529; - border-color: #212529; -} - -.btn-dark:hover { - color: #fff; - background-color: #1c1f23; - border-color: #1a1e21; -} - -.btn-check:focus+.btn-dark, -.btn-dark:focus { - color: #fff; - background-color: #1c1f23; - border-color: #1a1e21; - box-shadow: 0 0 0 0.25rem rgba(66, 70, 73, 0.5); -} - -.btn-check:checked+.btn-dark, -.btn-check:active+.btn-dark, -.btn-dark:active, -.btn-dark.active, -.show>.btn-dark.dropdown-toggle { - color: #fff; - background-color: #1a1e21; - border-color: #191c1f; -} - -.btn-check:checked+.btn-dark:focus, -.btn-check:active+.btn-dark:focus, -.btn-dark:active:focus, -.btn-dark.active:focus, -.show>.btn-dark.dropdown-toggle:focus { - box-shadow: 0 0 0 0.25rem rgba(66, 70, 73, 0.5); -} - -.btn-dark:disabled, -.btn-dark.disabled { - color: #fff; - background-color: #212529; - border-color: #212529; -} - -.btn-outline-primary { - color: #0d6efd; - border-color: #0d6efd; -} - -.btn-outline-primary:hover { - color: #fff; - background-color: #0d6efd; - border-color: #0d6efd; -} - -.btn-check:focus+.btn-outline-primary, -.btn-outline-primary:focus { - box-shadow: 0 0 0 0.25rem rgba(13, 110, 253, 0.5); -} - -.btn-check:checked+.btn-outline-primary, -.btn-check:active+.btn-outline-primary, -.btn-outline-primary:active, -.btn-outline-primary.active, -.btn-outline-primary.dropdown-toggle.show { - color: #fff; - background-color: #0d6efd; - border-color: #0d6efd; -} - -.btn-check:checked+.btn-outline-primary:focus, -.btn-check:active+.btn-outline-primary:focus, -.btn-outline-primary:active:focus, -.btn-outline-primary.active:focus, -.btn-outline-primary.dropdown-toggle.show:focus { - box-shadow: 0 0 0 0.25rem rgba(13, 110, 253, 0.5); -} - -.btn-outline-primary:disabled, -.btn-outline-primary.disabled { - color: #0d6efd; - background-color: transparent; -} - -.btn-outline-secondary { - color: #6c757d; - border-color: #6c757d; -} - -.btn-outline-secondary:hover { - color: #fff; - background-color: #6c757d; - border-color: #6c757d; -} - -.btn-check:focus+.btn-outline-secondary, -.btn-outline-secondary:focus { - box-shadow: 0 0 0 0.25rem rgba(108, 117, 125, 0.5); -} - -.btn-check:checked+.btn-outline-secondary, -.btn-check:active+.btn-outline-secondary, -.btn-outline-secondary:active, -.btn-outline-secondary.active, -.btn-outline-secondary.dropdown-toggle.show { - color: #fff; - background-color: #6c757d; - border-color: #6c757d; -} - -.btn-check:checked+.btn-outline-secondary:focus, -.btn-check:active+.btn-outline-secondary:focus, -.btn-outline-secondary:active:focus, -.btn-outline-secondary.active:focus, -.btn-outline-secondary.dropdown-toggle.show:focus { - box-shadow: 0 0 0 0.25rem rgba(108, 117, 125, 0.5); -} - -.btn-outline-secondary:disabled, -.btn-outline-secondary.disabled { - color: #6c757d; - background-color: transparent; -} - -.btn-outline-success { - color: #198754; - border-color: #198754; -} - -.btn-outline-success:hover { - color: #fff; - background-color: #198754; - border-color: #198754; -} - -.btn-check:focus+.btn-outline-success, -.btn-outline-success:focus { - box-shadow: 0 0 0 0.25rem rgba(25, 135, 84, 0.5); -} - -.btn-check:checked+.btn-outline-success, -.btn-check:active+.btn-outline-success, -.btn-outline-success:active, -.btn-outline-success.active, -.btn-outline-success.dropdown-toggle.show { - color: #fff; - background-color: #198754; - border-color: #198754; -} - -.btn-check:checked+.btn-outline-success:focus, -.btn-check:active+.btn-outline-success:focus, -.btn-outline-success:active:focus, -.btn-outline-success.active:focus, -.btn-outline-success.dropdown-toggle.show:focus { - box-shadow: 0 0 0 0.25rem rgba(25, 135, 84, 0.5); -} - -.btn-outline-success:disabled, -.btn-outline-success.disabled { - color: #198754; - background-color: transparent; -} - -.btn-outline-info { - color: #0dcaf0; - border-color: #0dcaf0; -} - -.btn-outline-info:hover { - color: #000; - background-color: #0dcaf0; - border-color: #0dcaf0; -} - -.btn-check:focus+.btn-outline-info, -.btn-outline-info:focus { - box-shadow: 0 0 0 0.25rem rgba(13, 202, 240, 0.5); -} - -.btn-check:checked+.btn-outline-info, -.btn-check:active+.btn-outline-info, -.btn-outline-info:active, -.btn-outline-info.active, -.btn-outline-info.dropdown-toggle.show { - color: #000; - background-color: #0dcaf0; - border-color: #0dcaf0; -} - -.btn-check:checked+.btn-outline-info:focus, -.btn-check:active+.btn-outline-info:focus, -.btn-outline-info:active:focus, -.btn-outline-info.active:focus, -.btn-outline-info.dropdown-toggle.show:focus { - box-shadow: 0 0 0 0.25rem rgba(13, 202, 240, 0.5); -} - -.btn-outline-info:disabled, -.btn-outline-info.disabled { - color: #0dcaf0; - background-color: transparent; -} - -.btn-outline-warning { - color: #ffc107; - border-color: #ffc107; -} - -.btn-outline-warning:hover { - color: #000; - background-color: #ffc107; - border-color: #ffc107; -} - -.btn-check:focus+.btn-outline-warning, -.btn-outline-warning:focus { - box-shadow: 0 0 0 0.25rem rgba(255, 193, 7, 0.5); -} - -.btn-check:checked+.btn-outline-warning, -.btn-check:active+.btn-outline-warning, -.btn-outline-warning:active, -.btn-outline-warning.active, -.btn-outline-warning.dropdown-toggle.show { - color: #000; - background-color: #ffc107; - border-color: #ffc107; -} - -.btn-check:checked+.btn-outline-warning:focus, -.btn-check:active+.btn-outline-warning:focus, -.btn-outline-warning:active:focus, -.btn-outline-warning.active:focus, -.btn-outline-warning.dropdown-toggle.show:focus { - box-shadow: 0 0 0 0.25rem rgba(255, 193, 7, 0.5); -} - -.btn-outline-warning:disabled, -.btn-outline-warning.disabled { - color: #ffc107; - background-color: transparent; -} - -.btn-outline-danger { - color: #dc3545; - border-color: #dc3545; -} - -.btn-outline-danger:hover { - color: #fff; - background-color: #dc3545; - border-color: #dc3545; -} - -.btn-check:focus+.btn-outline-danger, -.btn-outline-danger:focus { - box-shadow: 0 0 0 0.25rem rgba(220, 53, 69, 0.5); -} - -.btn-check:checked+.btn-outline-danger, -.btn-check:active+.btn-outline-danger, -.btn-outline-danger:active, -.btn-outline-danger.active, -.btn-outline-danger.dropdown-toggle.show { - color: #fff; - background-color: #dc3545; - border-color: #dc3545; -} - -.btn-check:checked+.btn-outline-danger:focus, -.btn-check:active+.btn-outline-danger:focus, -.btn-outline-danger:active:focus, -.btn-outline-danger.active:focus, -.btn-outline-danger.dropdown-toggle.show:focus { - box-shadow: 0 0 0 0.25rem rgba(220, 53, 69, 0.5); -} - -.btn-outline-danger:disabled, -.btn-outline-danger.disabled { - color: #dc3545; - background-color: transparent; -} - -.btn-outline-light { - color: #f8f9fa; - border-color: #f8f9fa; -} - -.btn-outline-light:hover { - color: #000; - background-color: #f8f9fa; - border-color: #f8f9fa; -} - -.btn-check:focus+.btn-outline-light, -.btn-outline-light:focus { - box-shadow: 0 0 0 0.25rem rgba(248, 249, 250, 0.5); -} - -.btn-check:checked+.btn-outline-light, -.btn-check:active+.btn-outline-light, -.btn-outline-light:active, -.btn-outline-light.active, -.btn-outline-light.dropdown-toggle.show { - color: #000; - background-color: #f8f9fa; - border-color: #f8f9fa; -} - -.btn-check:checked+.btn-outline-light:focus, -.btn-check:active+.btn-outline-light:focus, -.btn-outline-light:active:focus, -.btn-outline-light.active:focus, -.btn-outline-light.dropdown-toggle.show:focus { - box-shadow: 0 0 0 0.25rem rgba(248, 249, 250, 0.5); -} - -.btn-outline-light:disabled, -.btn-outline-light.disabled { - color: #f8f9fa; - background-color: transparent; -} - -.btn-outline-dark { - color: #212529; - border-color: #212529; -} - -.btn-outline-dark:hover { - color: #fff; - background-color: #212529; - border-color: #212529; -} - -.btn-check:focus+.btn-outline-dark, -.btn-outline-dark:focus { - box-shadow: 0 0 0 0.25rem rgba(33, 37, 41, 0.5); -} - -.btn-check:checked+.btn-outline-dark, -.btn-check:active+.btn-outline-dark, -.btn-outline-dark:active, -.btn-outline-dark.active, -.btn-outline-dark.dropdown-toggle.show { - color: #fff; - background-color: #212529; - border-color: #212529; -} - -.btn-check:checked+.btn-outline-dark:focus, -.btn-check:active+.btn-outline-dark:focus, -.btn-outline-dark:active:focus, -.btn-outline-dark.active:focus, -.btn-outline-dark.dropdown-toggle.show:focus { - box-shadow: 0 0 0 0.25rem rgba(33, 37, 41, 0.5); -} - -.btn-outline-dark:disabled, -.btn-outline-dark.disabled { - color: #212529; - background-color: transparent; -} - -.btn-link { - font-weight: 400; - color: #0d6efd; - text-decoration: underline; -} - -.btn-link:hover { - color: #0a58ca; -} - -.btn-link:disabled, -.btn-link.disabled { - color: #6c757d; -} - -.btn-lg, -.btn-group-lg>.btn { - padding: 0.5rem 1rem; - font-size: 1.25rem; - border-radius: 0.3rem; -} - -.btn-sm, -.btn-group-sm>.btn { - padding: 0.25rem 0.5rem; - font-size: 0.875rem; - border-radius: 0.2rem; -} - -.fade { - transition: opacity 0.15s linear; -} - -@media (prefers-reduced-motion: reduce) { - .fade { - transition: none; - } -} - -.fade:not(.show) { - opacity: 0; -} - -.collapse:not(.show) { - display: none; -} - -.collapsing { - height: 0; - overflow: hidden; - transition: height 0.35s ease; -} - -@media (prefers-reduced-motion: reduce) { - .collapsing { - transition: none; - } -} - -.collapsing.collapse-horizontal { - width: 0; - height: auto; - transition: width 0.35s ease; -} - -@media (prefers-reduced-motion: reduce) { - .collapsing.collapse-horizontal { - transition: none; - } -} - -.dropup, -.dropend, -.dropdown, -.dropstart { - position: relative; -} - -.dropdown-toggle { - white-space: nowrap; -} - -.dropdown-toggle::after { - display: inline-block; - margin-left: 0.255em; - vertical-align: 0.255em; - content: ""; - border-top: 0.3em solid; - border-right: 0.3em solid transparent; - border-bottom: 0; - border-left: 0.3em solid transparent; -} - -.dropdown-toggle:empty::after { - margin-left: 0; -} - -.dropdown-menu { - position: absolute; - z-index: 1000; - display: none; - min-width: 10rem; - padding: 0.5rem 0; - margin: 0; - font-size: 1rem; - color: #212529; - text-align: left; - list-style: none; - background-color: #fff; - background-clip: padding-box; - border: 1px solid rgba(0, 0, 0, 0.15); - border-radius: 0.25rem; -} - -.dropdown-menu[data-bs-popper] { - top: 100%; - left: 0; - margin-top: 0.125rem; -} - -.dropdown-menu-start { - --bs-position: start; -} - -.dropdown-menu-start[data-bs-popper] { - right: auto; - left: 0; -} - -.dropdown-menu-end { - --bs-position: end; -} - -.dropdown-menu-end[data-bs-popper] { - right: 0; - left: auto; -} - -@media (min-width: 576px) { - .dropdown-menu-sm-start { - --bs-position: start; - } - - .dropdown-menu-sm-start[data-bs-popper] { - right: auto; - left: 0; - } - - .dropdown-menu-sm-end { - --bs-position: end; - } - - .dropdown-menu-sm-end[data-bs-popper] { - right: 0; - left: auto; - } -} - -@media (min-width: 768px) { - .dropdown-menu-md-start { - --bs-position: start; - } - - .dropdown-menu-md-start[data-bs-popper] { - right: auto; - left: 0; - } - - .dropdown-menu-md-end { - --bs-position: end; - } - - .dropdown-menu-md-end[data-bs-popper] { - right: 0; - left: auto; - } -} - -@media (min-width: 992px) { - .dropdown-menu-lg-start { - --bs-position: start; - } - - .dropdown-menu-lg-start[data-bs-popper] { - right: auto; - left: 0; - } - - .dropdown-menu-lg-end { - --bs-position: end; - } - - .dropdown-menu-lg-end[data-bs-popper] { - right: 0; - left: auto; - } -} - -@media (min-width: 1200px) { - .dropdown-menu-xl-start { - --bs-position: start; - } - - .dropdown-menu-xl-start[data-bs-popper] { - right: auto; - left: 0; - } - - .dropdown-menu-xl-end { - --bs-position: end; - } - - .dropdown-menu-xl-end[data-bs-popper] { - right: 0; - left: auto; - } -} - -@media (min-width: 1400px) { - .dropdown-menu-xxl-start { - --bs-position: start; - } - - .dropdown-menu-xxl-start[data-bs-popper] { - right: auto; - left: 0; - } - - .dropdown-menu-xxl-end { - --bs-position: end; - } - - .dropdown-menu-xxl-end[data-bs-popper] { - right: 0; - left: auto; - } -} - -.dropup .dropdown-menu[data-bs-popper] { - top: auto; - bottom: 100%; - margin-top: 0; - margin-bottom: 0.125rem; -} - -.dropup .dropdown-toggle::after { - display: inline-block; - margin-left: 0.255em; - vertical-align: 0.255em; - content: ""; - border-top: 0; - border-right: 0.3em solid transparent; - border-bottom: 0.3em solid; - border-left: 0.3em solid transparent; -} - -.dropup .dropdown-toggle:empty::after { - margin-left: 0; -} - -.dropend .dropdown-menu[data-bs-popper] { - top: 0; - right: auto; - left: 100%; - margin-top: 0; - margin-left: 0.125rem; -} - -.dropend .dropdown-toggle::after { - display: inline-block; - margin-left: 0.255em; - vertical-align: 0.255em; - content: ""; - border-top: 0.3em solid transparent; - border-right: 0; - border-bottom: 0.3em solid transparent; - border-left: 0.3em solid; -} - -.dropend .dropdown-toggle:empty::after { - margin-left: 0; -} - -.dropend .dropdown-toggle::after { - vertical-align: 0; -} - -.dropstart .dropdown-menu[data-bs-popper] { - top: 0; - right: 100%; - left: auto; - margin-top: 0; - margin-right: 0.125rem; -} - -.dropstart .dropdown-toggle::after { - display: inline-block; - margin-left: 0.255em; - vertical-align: 0.255em; - content: ""; -} - -.dropstart .dropdown-toggle::after { - display: none; -} - -.dropstart .dropdown-toggle::before { - display: inline-block; - margin-right: 0.255em; - vertical-align: 0.255em; - content: ""; - border-top: 0.3em solid transparent; - border-right: 0.3em solid; - border-bottom: 0.3em solid transparent; -} - -.dropstart .dropdown-toggle:empty::after { - margin-left: 0; -} - -.dropstart .dropdown-toggle::before { - vertical-align: 0; -} - -.dropdown-divider { - height: 0; - margin: 0.5rem 0; - overflow: hidden; - border-top: 1px solid rgba(0, 0, 0, 0.15); -} - -.dropdown-item { - display: block; - width: 100%; - padding: 0.25rem 1rem; - clear: both; - font-weight: 400; - color: #212529; - text-align: inherit; - text-decoration: none; - white-space: nowrap; - background-color: transparent; - border: 0; -} - -.dropdown-item:hover, -.dropdown-item:focus { - color: #1e2125; - background-color: #e9ecef; -} - -.dropdown-item.active, -.dropdown-item:active { - color: #fff; - text-decoration: none; - background-color: #0d6efd; -} - -.dropdown-item.disabled, -.dropdown-item:disabled { - color: #adb5bd; - pointer-events: none; - background-color: transparent; -} - -.dropdown-menu.show { - display: block; -} - -.dropdown-header { - display: block; - padding: 0.5rem 1rem; - margin-bottom: 0; - font-size: 0.875rem; - color: #6c757d; - white-space: nowrap; -} - -.dropdown-item-text { - display: block; - padding: 0.25rem 1rem; - color: #212529; -} - -.dropdown-menu-dark { - color: #dee2e6; - background-color: #343a40; - border-color: rgba(0, 0, 0, 0.15); -} - -.dropdown-menu-dark .dropdown-item { - color: #dee2e6; -} - -.dropdown-menu-dark .dropdown-item:hover, -.dropdown-menu-dark .dropdown-item:focus { - color: #fff; - background-color: rgba(255, 255, 255, 0.15); -} - -.dropdown-menu-dark .dropdown-item.active, -.dropdown-menu-dark .dropdown-item:active { - color: #fff; - background-color: #0d6efd; -} - -.dropdown-menu-dark .dropdown-item.disabled, -.dropdown-menu-dark .dropdown-item:disabled { - color: #adb5bd; -} - -.dropdown-menu-dark .dropdown-divider { - border-color: rgba(0, 0, 0, 0.15); -} - -.dropdown-menu-dark .dropdown-item-text { - color: #dee2e6; -} - -.dropdown-menu-dark .dropdown-header { - color: #adb5bd; -} - -.btn-group, -.btn-group-vertical { - position: relative; - display: inline-flex; - vertical-align: middle; -} - -.btn-group>.btn, -.btn-group-vertical>.btn { - position: relative; - flex: 1 1 auto; -} - -.btn-group>.btn-check:checked+.btn, -.btn-group>.btn-check:focus+.btn, -.btn-group>.btn:hover, -.btn-group>.btn:focus, -.btn-group>.btn:active, -.btn-group>.btn.active, -.btn-group-vertical>.btn-check:checked+.btn, -.btn-group-vertical>.btn-check:focus+.btn, -.btn-group-vertical>.btn:hover, -.btn-group-vertical>.btn:focus, -.btn-group-vertical>.btn:active, -.btn-group-vertical>.btn.active { - z-index: 1; -} - -.btn-toolbar { - display: flex; - flex-wrap: wrap; - justify-content: flex-start; -} - -.btn-toolbar .input-group { - width: auto; -} - -.btn-group>.btn:not(:first-child), -.btn-group>.btn-group:not(:first-child) { - margin-left: -1px; -} - -.btn-group>.btn:not(:last-child):not(.dropdown-toggle), -.btn-group>.btn-group:not(:last-child)>.btn { - border-top-right-radius: 0; - border-bottom-right-radius: 0; -} - -.btn-group>.btn:nth-child(n+3), -.btn-group> :not(.btn-check)+.btn, -.btn-group>.btn-group:not(:first-child)>.btn { - border-top-left-radius: 0; - border-bottom-left-radius: 0; -} - -.dropdown-toggle-split { - padding-right: 0.5625rem; - padding-left: 0.5625rem; -} - -.dropdown-toggle-split::after, -.dropup .dropdown-toggle-split::after, -.dropend .dropdown-toggle-split::after { - margin-left: 0; -} - -.dropstart .dropdown-toggle-split::before { - margin-right: 0; -} - -.btn-sm+.dropdown-toggle-split, -.btn-group-sm>.btn+.dropdown-toggle-split { - padding-right: 0.375rem; - padding-left: 0.375rem; -} - -.btn-lg+.dropdown-toggle-split, -.btn-group-lg>.btn+.dropdown-toggle-split { - padding-right: 0.75rem; - padding-left: 0.75rem; -} - -.btn-group-vertical { - flex-direction: column; - align-items: flex-start; - justify-content: center; -} - -.btn-group-vertical>.btn, -.btn-group-vertical>.btn-group { - width: 100%; -} - -.btn-group-vertical>.btn:not(:first-child), -.btn-group-vertical>.btn-group:not(:first-child) { - margin-top: -1px; -} - -.btn-group-vertical>.btn:not(:last-child):not(.dropdown-toggle), -.btn-group-vertical>.btn-group:not(:last-child)>.btn { - border-bottom-right-radius: 0; - border-bottom-left-radius: 0; -} - -.btn-group-vertical>.btn~.btn, -.btn-group-vertical>.btn-group:not(:first-child)>.btn { - border-top-left-radius: 0; - border-top-right-radius: 0; -} - -.nav { - display: flex; - flex-wrap: wrap; - padding-left: 0; - margin-bottom: 0; - list-style: none; -} - -.nav-link { - display: block; - padding: 0.5rem 1rem; - color: #0d6efd; - text-decoration: none; - transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out; -} - -@media (prefers-reduced-motion: reduce) { - .nav-link { - transition: none; - } -} - -.nav-link:hover, -.nav-link:focus { - color: #0a58ca; -} - -.nav-link.disabled { - color: #6c757d; - pointer-events: none; - cursor: default; -} - -.nav-tabs { - border-bottom: 1px solid #dee2e6; -} - -.nav-tabs .nav-link { - margin-bottom: -1px; - background: none; - border: 1px solid transparent; - border-top-left-radius: 0.25rem; - border-top-right-radius: 0.25rem; -} - -.nav-tabs .nav-link:hover, -.nav-tabs .nav-link:focus { - border-color: #e9ecef #e9ecef #dee2e6; - isolation: isolate; -} - -.nav-tabs .nav-link.disabled { - color: #6c757d; - background-color: transparent; - border-color: transparent; -} - -.nav-tabs .nav-link.active, -.nav-tabs .nav-item.show .nav-link { - color: #495057; - background-color: #fff; - border-color: #dee2e6 #dee2e6 #fff; -} - -.nav-tabs .dropdown-menu { - margin-top: -1px; - border-top-left-radius: 0; - border-top-right-radius: 0; -} - -.nav-pills .nav-link { - background: none; - border: 0; - border-radius: 0.25rem; -} - -.nav-pills .nav-link.active, -.nav-pills .show>.nav-link { - color: #fff; - background-color: #0d6efd; -} - -.nav-fill>.nav-link, -.nav-fill .nav-item { - flex: 1 1 auto; - text-align: center; -} - -.nav-justified>.nav-link, -.nav-justified .nav-item { - flex-basis: 0; - flex-grow: 1; - text-align: center; -} - -.nav-fill .nav-item .nav-link, -.nav-justified .nav-item .nav-link { - width: 100%; -} - -.tab-content>.tab-pane { - display: none; -} - -.tab-content>.active { - display: block; -} - -.navbar { - position: relative; - display: flex; - flex-wrap: wrap; - align-items: center; - justify-content: space-between; - padding-top: 0.5rem; - padding-bottom: 0.5rem; -} - -.navbar>.container, -.navbar>.container-fluid, -.navbar>.container-sm, -.navbar>.container-md, -.navbar>.container-lg, -.navbar>.container-xl, -.navbar>.container-xxl { - display: flex; - flex-wrap: inherit; - align-items: center; - justify-content: space-between; -} - -.navbar-brand { - padding-top: 0.3125rem; - padding-bottom: 0.3125rem; - margin-right: 1rem; - font-size: 1.25rem; - text-decoration: none; - white-space: nowrap; -} - -.navbar-nav { - display: flex; - flex-direction: column; - padding-left: 0; - margin-bottom: 0; - list-style: none; -} - -.navbar-nav .nav-link { - padding-right: 0; - padding-left: 0; -} - -.navbar-nav .dropdown-menu { - position: static; -} - -.navbar-text { - padding-top: 0.5rem; - padding-bottom: 0.5rem; -} - -.navbar-collapse { - flex-basis: 100%; - flex-grow: 1; - align-items: center; -} - -.navbar-toggler { - padding: 0.25rem 0.75rem; - font-size: 1.25rem; - line-height: 1; - background-color: transparent; - border: 1px solid transparent; - border-radius: 0.25rem; - transition: box-shadow 0.15s ease-in-out; -} - -@media (prefers-reduced-motion: reduce) { - .navbar-toggler { - transition: none; - } -} - -.navbar-toggler:hover { - text-decoration: none; -} - -.navbar-toggler:focus { - text-decoration: none; - outline: 0; - box-shadow: 0 0 0 0.25rem; -} - -.navbar-toggler-icon { - display: inline-block; - width: 1.5em; - height: 1.5em; - vertical-align: middle; - background-repeat: no-repeat; - background-position: center; - background-size: 100%; -} - -.navbar-nav-scroll { - max-height: var(--bs-scroll-height, 75vh); - overflow-y: auto; -} - -@media (min-width: 576px) { - .navbar-expand-sm { - flex-wrap: nowrap; - justify-content: flex-start; - } - - .navbar-expand-sm .navbar-nav { - flex-direction: row; - } - - .navbar-expand-sm .navbar-nav .dropdown-menu { - position: absolute; - } - - .navbar-expand-sm .navbar-nav .nav-link { - padding-right: 0.5rem; - padding-left: 0.5rem; - } - - .navbar-expand-sm .navbar-nav-scroll { - overflow: visible; - } - - .navbar-expand-sm .navbar-collapse { - display: flex !important; - flex-basis: auto; - } - - .navbar-expand-sm .navbar-toggler { - display: none; - } - - .navbar-expand-sm .offcanvas-header { - display: none; - } - - .navbar-expand-sm .offcanvas { - position: inherit; - bottom: 0; - z-index: 1000; - flex-grow: 1; - visibility: visible !important; - background-color: transparent; - border-right: 0; - border-left: 0; - transition: none; - transform: none; - } - - .navbar-expand-sm .offcanvas-top, - .navbar-expand-sm .offcanvas-bottom { - height: auto; - border-top: 0; - border-bottom: 0; - } - - .navbar-expand-sm .offcanvas-body { - display: flex; - flex-grow: 0; - padding: 0; - overflow-y: visible; - } -} - -@media (min-width: 768px) { - .navbar-expand-md { - flex-wrap: nowrap; - justify-content: flex-start; - } - - .navbar-expand-md .navbar-nav { - flex-direction: row; - } - - .navbar-expand-md .navbar-nav .dropdown-menu { - position: absolute; - } - - .navbar-expand-md .navbar-nav .nav-link { - padding-right: 0.5rem; - padding-left: 0.5rem; - } - - .navbar-expand-md .navbar-nav-scroll { - overflow: visible; - } - - .navbar-expand-md .navbar-collapse { - display: flex !important; - flex-basis: auto; - } - - .navbar-expand-md .navbar-toggler { - display: none; - } - - .navbar-expand-md .offcanvas-header { - display: none; - } - - .navbar-expand-md .offcanvas { - position: inherit; - bottom: 0; - z-index: 1000; - flex-grow: 1; - visibility: visible !important; - background-color: transparent; - border-right: 0; - border-left: 0; - transition: none; - transform: none; - } - - .navbar-expand-md .offcanvas-top, - .navbar-expand-md .offcanvas-bottom { - height: auto; - border-top: 0; - border-bottom: 0; - } - - .navbar-expand-md .offcanvas-body { - display: flex; - flex-grow: 0; - padding: 0; - overflow-y: visible; - } -} - -@media (min-width: 992px) { - .navbar-expand-lg { - flex-wrap: nowrap; - justify-content: flex-start; - } - - .navbar-expand-lg .navbar-nav { - flex-direction: row; - } - - .navbar-expand-lg .navbar-nav .dropdown-menu { - position: absolute; - } - - .navbar-expand-lg .navbar-nav .nav-link { - padding-right: 0.5rem; - padding-left: 0.5rem; - } - - .navbar-expand-lg .navbar-nav-scroll { - overflow: visible; - } - - .navbar-expand-lg .navbar-collapse { - display: flex !important; - flex-basis: auto; - } - - .navbar-expand-lg .navbar-toggler { - display: none; - } - - .navbar-expand-lg .offcanvas-header { - display: none; - } - - .navbar-expand-lg .offcanvas { - position: inherit; - bottom: 0; - z-index: 1000; - flex-grow: 1; - visibility: visible !important; - background-color: transparent; - border-right: 0; - border-left: 0; - transition: none; - transform: none; - } - - .navbar-expand-lg .offcanvas-top, - .navbar-expand-lg .offcanvas-bottom { - height: auto; - border-top: 0; - border-bottom: 0; - } - - .navbar-expand-lg .offcanvas-body { - display: flex; - flex-grow: 0; - padding: 0; - overflow-y: visible; - } -} - -@media (min-width: 1200px) { - .navbar-expand-xl { - flex-wrap: nowrap; - justify-content: flex-start; - } - - .navbar-expand-xl .navbar-nav { - flex-direction: row; - } - - .navbar-expand-xl .navbar-nav .dropdown-menu { - position: absolute; - } - - .navbar-expand-xl .navbar-nav .nav-link { - padding-right: 0.5rem; - padding-left: 0.5rem; - } - - .navbar-expand-xl .navbar-nav-scroll { - overflow: visible; - } - - .navbar-expand-xl .navbar-collapse { - display: flex !important; - flex-basis: auto; - } - - .navbar-expand-xl .navbar-toggler { - display: none; - } - - .navbar-expand-xl .offcanvas-header { - display: none; - } - - .navbar-expand-xl .offcanvas { - position: inherit; - bottom: 0; - z-index: 1000; - flex-grow: 1; - visibility: visible !important; - background-color: transparent; - border-right: 0; - border-left: 0; - transition: none; - transform: none; - } - - .navbar-expand-xl .offcanvas-top, - .navbar-expand-xl .offcanvas-bottom { - height: auto; - border-top: 0; - border-bottom: 0; - } - - .navbar-expand-xl .offcanvas-body { - display: flex; - flex-grow: 0; - padding: 0; - overflow-y: visible; - } -} - -@media (min-width: 1400px) { - .navbar-expand-xxl { - flex-wrap: nowrap; - justify-content: flex-start; - } - - .navbar-expand-xxl .navbar-nav { - flex-direction: row; - } - - .navbar-expand-xxl .navbar-nav .dropdown-menu { - position: absolute; - } - - .navbar-expand-xxl .navbar-nav .nav-link { - padding-right: 0.5rem; - padding-left: 0.5rem; - } - - .navbar-expand-xxl .navbar-nav-scroll { - overflow: visible; - } - - .navbar-expand-xxl .navbar-collapse { - display: flex !important; - flex-basis: auto; - } - - .navbar-expand-xxl .navbar-toggler { - display: none; - } - - .navbar-expand-xxl .offcanvas-header { - display: none; - } - - .navbar-expand-xxl .offcanvas { - position: inherit; - bottom: 0; - z-index: 1000; - flex-grow: 1; - visibility: visible !important; - background-color: transparent; - border-right: 0; - border-left: 0; - transition: none; - transform: none; - } - - .navbar-expand-xxl .offcanvas-top, - .navbar-expand-xxl .offcanvas-bottom { - height: auto; - border-top: 0; - border-bottom: 0; - } - - .navbar-expand-xxl .offcanvas-body { - display: flex; - flex-grow: 0; - padding: 0; - overflow-y: visible; - } -} - -.navbar-expand { - flex-wrap: nowrap; - justify-content: flex-start; -} - -.navbar-expand .navbar-nav { - flex-direction: row; -} - -.navbar-expand .navbar-nav .dropdown-menu { - position: absolute; -} - -.navbar-expand .navbar-nav .nav-link { - padding-right: 0.5rem; - padding-left: 0.5rem; -} - -.navbar-expand .navbar-nav-scroll { - overflow: visible; -} - -.navbar-expand .navbar-collapse { - display: flex !important; - flex-basis: auto; -} - -.navbar-expand .navbar-toggler { - display: none; -} - -.navbar-expand .offcanvas-header { - display: none; -} - -.navbar-expand .offcanvas { - position: inherit; - bottom: 0; - z-index: 1000; - flex-grow: 1; - visibility: visible !important; - background-color: transparent; - border-right: 0; - border-left: 0; - transition: none; - transform: none; -} - -.navbar-expand .offcanvas-top, -.navbar-expand .offcanvas-bottom { - height: auto; - border-top: 0; - border-bottom: 0; -} - -.navbar-expand .offcanvas-body { - display: flex; - flex-grow: 0; - padding: 0; - overflow-y: visible; -} - -.navbar-light .navbar-brand { - color: rgba(0, 0, 0, 0.9); -} - -.navbar-light .navbar-brand:hover, -.navbar-light .navbar-brand:focus { - color: rgba(0, 0, 0, 0.9); -} - -.navbar-light .navbar-nav .nav-link { - color: rgba(0, 0, 0, 0.55); -} - -.navbar-light .navbar-nav .nav-link:hover, -.navbar-light .navbar-nav .nav-link:focus { - color: rgba(0, 0, 0, 0.7); -} - -.navbar-light .navbar-nav .nav-link.disabled { - color: rgba(0, 0, 0, 0.3); -} - -.navbar-light .navbar-nav .show>.nav-link, -.navbar-light .navbar-nav .nav-link.active { - color: rgba(0, 0, 0, 0.9); -} - -.navbar-light .navbar-toggler { - color: rgba(0, 0, 0, 0.55); - border-color: rgba(0, 0, 0, 0.1); -} - -.navbar-light .navbar-toggler-icon { - background-image: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='rgba%280, 0, 0, 0.55%29' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e"); -} - -.navbar-light .navbar-text { - color: rgba(0, 0, 0, 0.55); -} - -.navbar-light .navbar-text a, -.navbar-light .navbar-text a:hover, -.navbar-light .navbar-text a:focus { - color: rgba(0, 0, 0, 0.9); -} - -.navbar-dark .navbar-brand { - color: #fff; -} - -.navbar-dark .navbar-brand:hover, -.navbar-dark .navbar-brand:focus { - color: #fff; -} - -.navbar-dark .navbar-nav .nav-link { - color: rgba(255, 255, 255, 0.55); -} - -.navbar-dark .navbar-nav .nav-link:hover, -.navbar-dark .navbar-nav .nav-link:focus { - color: rgba(255, 255, 255, 0.75); -} - -.navbar-dark .navbar-nav .nav-link.disabled { - color: rgba(255, 255, 255, 0.25); -} - -.navbar-dark .navbar-nav .show>.nav-link, -.navbar-dark .navbar-nav .nav-link.active { - color: #fff; -} - -.navbar-dark .navbar-toggler { - color: rgba(255, 255, 255, 0.55); - border-color: rgba(255, 255, 255, 0.1); -} - -.navbar-dark .navbar-toggler-icon { - background-image: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='rgba%28255, 255, 255, 0.55%29' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e"); -} - -.navbar-dark .navbar-text { - color: rgba(255, 255, 255, 0.55); -} - -.navbar-dark .navbar-text a, -.navbar-dark .navbar-text a:hover, -.navbar-dark .navbar-text a:focus { - color: #fff; -} - -.card { - position: relative; - display: flex; - flex-direction: column; - min-width: 0; - word-wrap: break-word; - background-color: #fff; - background-clip: border-box; - border: 1px solid rgba(0, 0, 0, 0.125); - border-radius: 0.25rem; -} - -.card>hr { - margin-right: 0; - margin-left: 0; -} - -.card>.list-group { - border-top: inherit; - border-bottom: inherit; -} - -.card>.list-group:first-child { - border-top-width: 0; - border-top-left-radius: calc(0.25rem - 1px); - border-top-right-radius: calc(0.25rem - 1px); -} - -.card>.list-group:last-child { - border-bottom-width: 0; - border-bottom-right-radius: calc(0.25rem - 1px); - border-bottom-left-radius: calc(0.25rem - 1px); -} - -.card>.card-header+.list-group, -.card>.list-group+.card-footer { - border-top: 0; -} - -.card-body { - flex: 1 1 auto; - padding: 1rem 1rem; -} - -.card-title { - margin-bottom: 0.5rem; -} - -.card-subtitle { - margin-top: -0.25rem; - margin-bottom: 0; -} - -.card-text:last-child { - margin-bottom: 0; -} - -.card-link+.card-link { - margin-left: 1rem; -} - -.card-header { - padding: 0.5rem 1rem; - margin-bottom: 0; - background-color: rgba(0, 0, 0, 0.03); - border-bottom: 1px solid rgba(0, 0, 0, 0.125); -} - -.card-header:first-child { - border-radius: calc(0.25rem - 1px) calc(0.25rem - 1px) 0 0; -} - -.card-footer { - padding: 0.5rem 1rem; - background-color: rgba(0, 0, 0, 0.03); - border-top: 1px solid rgba(0, 0, 0, 0.125); -} - -.card-footer:last-child { - border-radius: 0 0 calc(0.25rem - 1px) calc(0.25rem - 1px); -} - -.card-header-tabs { - margin-right: -0.5rem; - margin-bottom: -0.5rem; - margin-left: -0.5rem; - border-bottom: 0; -} - -.card-header-pills { - margin-right: -0.5rem; - margin-left: -0.5rem; -} - -.card-img-overlay { - position: absolute; - top: 0; - right: 0; - bottom: 0; - left: 0; - padding: 1rem; - border-radius: calc(0.25rem - 1px); -} - -.card-img, -.card-img-top, -.card-img-bottom { - width: 100%; -} - -.card-img, -.card-img-top { - border-top-left-radius: calc(0.25rem - 1px); - border-top-right-radius: calc(0.25rem - 1px); -} - -.card-img, -.card-img-bottom { - border-bottom-right-radius: calc(0.25rem - 1px); - border-bottom-left-radius: calc(0.25rem - 1px); -} - -.card-group>.card { - margin-bottom: 0.75rem; -} - -@media (min-width: 576px) { - .card-group { - display: flex; - flex-flow: row wrap; - } - - .card-group>.card { - flex: 1 0 0%; - margin-bottom: 0; - } - - .card-group>.card+.card { - margin-left: 0; - border-left: 0; - } - - .card-group>.card:not(:last-child) { - border-top-right-radius: 0; - border-bottom-right-radius: 0; - } - - .card-group>.card:not(:last-child) .card-img-top, - .card-group>.card:not(:last-child) .card-header { - border-top-right-radius: 0; - } - - .card-group>.card:not(:last-child) .card-img-bottom, - .card-group>.card:not(:last-child) .card-footer { - border-bottom-right-radius: 0; - } - - .card-group>.card:not(:first-child) { - border-top-left-radius: 0; - border-bottom-left-radius: 0; - } - - .card-group>.card:not(:first-child) .card-img-top, - .card-group>.card:not(:first-child) .card-header { - border-top-left-radius: 0; - } - - .card-group>.card:not(:first-child) .card-img-bottom, - .card-group>.card:not(:first-child) .card-footer { - border-bottom-left-radius: 0; - } -} - -.accordion-button { - position: relative; - display: flex; - align-items: center; - width: 100%; - padding: 1rem 1.25rem; - font-size: 1rem; - color: #212529; - text-align: left; - background-color: #fff; - border: 0; - border-radius: 0; - overflow-anchor: none; - transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out, border-radius 0.15s ease; -} - -@media (prefers-reduced-motion: reduce) { - .accordion-button { - transition: none; - } -} - -.accordion-button:not(.collapsed) { - color: #0c63e4; - background-color: #e7f1ff; - box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.125); -} - -.accordion-button:not(.collapsed)::after { - background-image: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%230c63e4'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e"); - transform: rotate(-180deg); -} - -.accordion-button::after { - flex-shrink: 0; - width: 1.25rem; - height: 1.25rem; - margin-left: auto; - content: ""; - background-image: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23212529'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e"); - background-repeat: no-repeat; - background-size: 1.25rem; - transition: transform 0.2s ease-in-out; -} - -@media (prefers-reduced-motion: reduce) { - .accordion-button::after { - transition: none; - } -} - -.accordion-button:hover { - z-index: 2; -} - -.accordion-button:focus { - z-index: 3; - border-color: #86b7fe; - outline: 0; - box-shadow: 0 0 0 0.25rem rgba(13, 110, 253, 0.25); -} - -.accordion-header { - margin-bottom: 0; -} - -.accordion-item { - background-color: #fff; - border: 1px solid rgba(0, 0, 0, 0.125); -} - -.accordion-item:first-of-type { - border-top-left-radius: 0.25rem; - border-top-right-radius: 0.25rem; -} - -.accordion-item:first-of-type .accordion-button { - border-top-left-radius: calc(0.25rem - 1px); - border-top-right-radius: calc(0.25rem - 1px); -} - -.accordion-item:not(:first-of-type) { - border-top: 0; -} - -.accordion-item:last-of-type { - border-bottom-right-radius: 0.25rem; - border-bottom-left-radius: 0.25rem; -} - -.accordion-item:last-of-type .accordion-button.collapsed { - border-bottom-right-radius: calc(0.25rem - 1px); - border-bottom-left-radius: calc(0.25rem - 1px); -} - -.accordion-item:last-of-type .accordion-collapse { - border-bottom-right-radius: 0.25rem; - border-bottom-left-radius: 0.25rem; -} - -.accordion-body { - padding: 1rem 1.25rem; -} - -.accordion-flush .accordion-collapse { - border-width: 0; -} - -.accordion-flush .accordion-item { - border-right: 0; - border-left: 0; - border-radius: 0; -} - -.accordion-flush .accordion-item:first-child { - border-top: 0; -} - -.accordion-flush .accordion-item:last-child { - border-bottom: 0; -} - -.accordion-flush .accordion-item .accordion-button { - border-radius: 0; -} - -.breadcrumb { - display: flex; - flex-wrap: wrap; - padding: 0 0; - margin-bottom: 1rem; - list-style: none; -} - -.breadcrumb-item+.breadcrumb-item { - padding-left: 0.5rem; -} - -.breadcrumb-item+.breadcrumb-item::before { - float: left; - padding-right: 0.5rem; - color: #6c757d; - content: var(--bs-breadcrumb-divider, "/") - /* rtl: var(--bs-breadcrumb-divider, "/") */ - ; -} - -.breadcrumb-item.active { - color: #6c757d; -} - -.pagination { - display: flex; - padding-left: 0; - list-style: none; -} - -.page-link { - position: relative; - display: block; - color: #0d6efd; - text-decoration: none; - background-color: #fff; - border: 1px solid #dee2e6; - transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out; -} - -@media (prefers-reduced-motion: reduce) { - .page-link { - transition: none; - } -} - -.page-link:hover { - z-index: 2; - color: #0a58ca; - background-color: #e9ecef; - border-color: #dee2e6; -} - -.page-link:focus { - z-index: 3; - color: #0a58ca; - background-color: #e9ecef; - outline: 0; - box-shadow: 0 0 0 0.25rem rgba(13, 110, 253, 0.25); -} - -.page-item:not(:first-child) .page-link { - margin-left: -1px; -} - -.page-item.active .page-link { - z-index: 3; - color: #fff; - background-color: #0d6efd; - border-color: #0d6efd; -} - -.page-item.disabled .page-link { - color: #6c757d; - pointer-events: none; - background-color: #fff; - border-color: #dee2e6; -} - -.page-link { - padding: 0.375rem 0.75rem; -} - -.page-item:first-child .page-link { - border-top-left-radius: 0.25rem; - border-bottom-left-radius: 0.25rem; -} - -.page-item:last-child .page-link { - border-top-right-radius: 0.25rem; - border-bottom-right-radius: 0.25rem; -} - -.pagination-lg .page-link { - padding: 0.75rem 1.5rem; - font-size: 1.25rem; -} - -.pagination-lg .page-item:first-child .page-link { - border-top-left-radius: 0.3rem; - border-bottom-left-radius: 0.3rem; -} - -.pagination-lg .page-item:last-child .page-link { - border-top-right-radius: 0.3rem; - border-bottom-right-radius: 0.3rem; -} - -.pagination-sm .page-link { - padding: 0.25rem 0.5rem; - font-size: 0.875rem; -} - -.pagination-sm .page-item:first-child .page-link { - border-top-left-radius: 0.2rem; - border-bottom-left-radius: 0.2rem; -} - -.pagination-sm .page-item:last-child .page-link { - border-top-right-radius: 0.2rem; - border-bottom-right-radius: 0.2rem; -} - -.badge { - display: inline-block; - padding: 0.35em 0.65em; - font-size: 0.75em; - font-weight: 700; - line-height: 1; - color: #fff; - text-align: center; - white-space: nowrap; - vertical-align: baseline; - border-radius: 0.25rem; -} - -.badge:empty { - display: none; -} - -.btn .badge { - position: relative; - top: -1px; -} - -.alert { - position: relative; - padding: 1rem 1rem; - margin-bottom: 1rem; - border: 1px solid transparent; - border-radius: 0.25rem; -} - -.alert-heading { - color: inherit; -} - -.alert-link { - font-weight: 700; -} - -.alert-dismissible { - padding-right: 3rem; -} - -.alert-dismissible .btn-close { - position: absolute; - top: 0; - right: 0; - z-index: 2; - padding: 1.25rem 1rem; -} - -.alert-primary { - color: #084298; - background-color: #cfe2ff; - border-color: #b6d4fe; -} - -.alert-primary .alert-link { - color: #06357a; -} - -.alert-secondary { - color: #41464b; - background-color: #e2e3e5; - border-color: #d3d6d8; -} - -.alert-secondary .alert-link { - color: #34383c; -} - -.alert-success { - color: #0f5132; - background-color: #d1e7dd; - border-color: #badbcc; -} - -.alert-success .alert-link { - color: #0c4128; -} - -.alert-info { - color: #055160; - background-color: #cff4fc; - border-color: #b6effb; -} - -.alert-info .alert-link { - color: #04414d; -} - -.alert-warning { - color: #664d03; - background-color: #fff3cd; - border-color: #ffecb5; -} - -.alert-warning .alert-link { - color: #523e02; -} - -.alert-danger { - color: #842029; - background-color: #f8d7da; - border-color: #f5c2c7; -} - -.alert-danger .alert-link { - color: #6a1a21; -} - -.alert-light { - color: #636464; - background-color: #fefefe; - border-color: #fdfdfe; -} - -.alert-light .alert-link { - color: #4f5050; -} - -.alert-dark { - color: #141619; - background-color: #d3d3d4; - border-color: #bcbebf; -} - -.alert-dark .alert-link { - color: #101214; -} - -@-webkit-keyframes progress-bar-stripes { - 0% { - background-position-x: 1rem; - } -} - -@keyframes progress-bar-stripes { - 0% { - background-position-x: 1rem; - } -} - -.progress { - display: flex; - height: 1rem; - overflow: hidden; - font-size: 0.75rem; - background-color: #e9ecef; - border-radius: 0.25rem; -} - -.progress-bar { - display: flex; - flex-direction: column; - justify-content: center; - overflow: hidden; - color: #fff; - text-align: center; - white-space: nowrap; - background-color: #0d6efd; - transition: width 0.6s ease; -} - -@media (prefers-reduced-motion: reduce) { - .progress-bar { - transition: none; - } -} - -.progress-bar-striped { - background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); - background-size: 1rem 1rem; -} - -.progress-bar-animated { - -webkit-animation: 1s linear infinite progress-bar-stripes; - animation: 1s linear infinite progress-bar-stripes; -} - -@media (prefers-reduced-motion: reduce) { - .progress-bar-animated { - -webkit-animation: none; - animation: none; - } -} - -.list-group { - display: flex; - flex-direction: column; - padding-left: 0; - margin-bottom: 0; - border-radius: 0.25rem; -} - -.list-group-numbered { - list-style-type: none; - counter-reset: section; -} - -.list-group-numbered>li::before { - content: counters(section, ".") ". "; - counter-increment: section; -} - -.list-group-item-action { - width: 100%; - color: #495057; - text-align: inherit; -} - -.list-group-item-action:hover, -.list-group-item-action:focus { - z-index: 1; - color: #495057; - text-decoration: none; - background-color: #f8f9fa; -} - -.list-group-item-action:active { - color: #212529; - background-color: #e9ecef; -} - -.list-group-item { - position: relative; - display: block; - padding: 0.5rem 1rem; - color: #212529; - text-decoration: none; - background-color: #fff; - border: 1px solid rgba(0, 0, 0, 0.125); -} - -.list-group-item:first-child { - border-top-left-radius: inherit; - border-top-right-radius: inherit; -} - -.list-group-item:last-child { - border-bottom-right-radius: inherit; - border-bottom-left-radius: inherit; -} - -.list-group-item.disabled, -.list-group-item:disabled { - color: #6c757d; - pointer-events: none; - background-color: #fff; -} - -.list-group-item.active { - z-index: 2; - color: #fff; - background-color: #0d6efd; - border-color: #0d6efd; -} - -.list-group-item+.list-group-item { - border-top-width: 0; -} - -.list-group-item+.list-group-item.active { - margin-top: -1px; - border-top-width: 1px; -} - -.list-group-horizontal { - flex-direction: row; -} - -.list-group-horizontal>.list-group-item:first-child { - border-bottom-left-radius: 0.25rem; - border-top-right-radius: 0; -} - -.list-group-horizontal>.list-group-item:last-child { - border-top-right-radius: 0.25rem; - border-bottom-left-radius: 0; -} - -.list-group-horizontal>.list-group-item.active { - margin-top: 0; -} - -.list-group-horizontal>.list-group-item+.list-group-item { - border-top-width: 1px; - border-left-width: 0; -} - -.list-group-horizontal>.list-group-item+.list-group-item.active { - margin-left: -1px; - border-left-width: 1px; -} - -@media (min-width: 576px) { - .list-group-horizontal-sm { - flex-direction: row; - } - - .list-group-horizontal-sm>.list-group-item:first-child { - border-bottom-left-radius: 0.25rem; - border-top-right-radius: 0; - } - - .list-group-horizontal-sm>.list-group-item:last-child { - border-top-right-radius: 0.25rem; - border-bottom-left-radius: 0; - } - - .list-group-horizontal-sm>.list-group-item.active { - margin-top: 0; - } - - .list-group-horizontal-sm>.list-group-item+.list-group-item { - border-top-width: 1px; - border-left-width: 0; - } - - .list-group-horizontal-sm>.list-group-item+.list-group-item.active { - margin-left: -1px; - border-left-width: 1px; - } -} - -@media (min-width: 768px) { - .list-group-horizontal-md { - flex-direction: row; - } - - .list-group-horizontal-md>.list-group-item:first-child { - border-bottom-left-radius: 0.25rem; - border-top-right-radius: 0; - } - - .list-group-horizontal-md>.list-group-item:last-child { - border-top-right-radius: 0.25rem; - border-bottom-left-radius: 0; - } - - .list-group-horizontal-md>.list-group-item.active { - margin-top: 0; - } - - .list-group-horizontal-md>.list-group-item+.list-group-item { - border-top-width: 1px; - border-left-width: 0; - } - - .list-group-horizontal-md>.list-group-item+.list-group-item.active { - margin-left: -1px; - border-left-width: 1px; - } -} - -@media (min-width: 992px) { - .list-group-horizontal-lg { - flex-direction: row; - } - - .list-group-horizontal-lg>.list-group-item:first-child { - border-bottom-left-radius: 0.25rem; - border-top-right-radius: 0; - } - - .list-group-horizontal-lg>.list-group-item:last-child { - border-top-right-radius: 0.25rem; - border-bottom-left-radius: 0; - } - - .list-group-horizontal-lg>.list-group-item.active { - margin-top: 0; - } - - .list-group-horizontal-lg>.list-group-item+.list-group-item { - border-top-width: 1px; - border-left-width: 0; - } - - .list-group-horizontal-lg>.list-group-item+.list-group-item.active { - margin-left: -1px; - border-left-width: 1px; - } -} - -@media (min-width: 1200px) { - .list-group-horizontal-xl { - flex-direction: row; - } - - .list-group-horizontal-xl>.list-group-item:first-child { - border-bottom-left-radius: 0.25rem; - border-top-right-radius: 0; - } - - .list-group-horizontal-xl>.list-group-item:last-child { - border-top-right-radius: 0.25rem; - border-bottom-left-radius: 0; - } - - .list-group-horizontal-xl>.list-group-item.active { - margin-top: 0; - } - - .list-group-horizontal-xl>.list-group-item+.list-group-item { - border-top-width: 1px; - border-left-width: 0; - } - - .list-group-horizontal-xl>.list-group-item+.list-group-item.active { - margin-left: -1px; - border-left-width: 1px; - } -} - -@media (min-width: 1400px) { - .list-group-horizontal-xxl { - flex-direction: row; - } - - .list-group-horizontal-xxl>.list-group-item:first-child { - border-bottom-left-radius: 0.25rem; - border-top-right-radius: 0; - } - - .list-group-horizontal-xxl>.list-group-item:last-child { - border-top-right-radius: 0.25rem; - border-bottom-left-radius: 0; - } - - .list-group-horizontal-xxl>.list-group-item.active { - margin-top: 0; - } - - .list-group-horizontal-xxl>.list-group-item+.list-group-item { - border-top-width: 1px; - border-left-width: 0; - } - - .list-group-horizontal-xxl>.list-group-item+.list-group-item.active { - margin-left: -1px; - border-left-width: 1px; - } -} - -.list-group-flush { - border-radius: 0; -} - -.list-group-flush>.list-group-item { - border-width: 0 0 1px; -} - -.list-group-flush>.list-group-item:last-child { - border-bottom-width: 0; -} - -.list-group-item-primary { - color: #084298; - background-color: #cfe2ff; -} - -.list-group-item-primary.list-group-item-action:hover, -.list-group-item-primary.list-group-item-action:focus { - color: #084298; - background-color: #bacbe6; -} - -.list-group-item-primary.list-group-item-action.active { - color: #fff; - background-color: #084298; - border-color: #084298; -} - -.list-group-item-secondary { - color: #41464b; - background-color: #e2e3e5; -} - -.list-group-item-secondary.list-group-item-action:hover, -.list-group-item-secondary.list-group-item-action:focus { - color: #41464b; - background-color: #cbccce; -} - -.list-group-item-secondary.list-group-item-action.active { - color: #fff; - background-color: #41464b; - border-color: #41464b; -} - -.list-group-item-success { - color: #0f5132; - background-color: #d1e7dd; -} - -.list-group-item-success.list-group-item-action:hover, -.list-group-item-success.list-group-item-action:focus { - color: #0f5132; - background-color: #bcd0c7; -} - -.list-group-item-success.list-group-item-action.active { - color: #fff; - background-color: #0f5132; - border-color: #0f5132; -} - -.list-group-item-info { - color: #055160; - background-color: #cff4fc; -} - -.list-group-item-info.list-group-item-action:hover, -.list-group-item-info.list-group-item-action:focus { - color: #055160; - background-color: #badce3; -} - -.list-group-item-info.list-group-item-action.active { - color: #fff; - background-color: #055160; - border-color: #055160; -} - -.list-group-item-warning { - color: #664d03; - background-color: #fff3cd; -} - -.list-group-item-warning.list-group-item-action:hover, -.list-group-item-warning.list-group-item-action:focus { - color: #664d03; - background-color: #e6dbb9; -} - -.list-group-item-warning.list-group-item-action.active { - color: #fff; - background-color: #664d03; - border-color: #664d03; -} - -.list-group-item-danger { - color: #842029; - background-color: #f8d7da; -} - -.list-group-item-danger.list-group-item-action:hover, -.list-group-item-danger.list-group-item-action:focus { - color: #842029; - background-color: #dfc2c4; -} - -.list-group-item-danger.list-group-item-action.active { - color: #fff; - background-color: #842029; - border-color: #842029; -} - -.list-group-item-light { - color: #636464; - background-color: #fefefe; -} - -.list-group-item-light.list-group-item-action:hover, -.list-group-item-light.list-group-item-action:focus { - color: #636464; - background-color: #e5e5e5; -} - -.list-group-item-light.list-group-item-action.active { - color: #fff; - background-color: #636464; - border-color: #636464; -} - -.list-group-item-dark { - color: #141619; - background-color: #d3d3d4; -} - -.list-group-item-dark.list-group-item-action:hover, -.list-group-item-dark.list-group-item-action:focus { - color: #141619; - background-color: #bebebf; -} - -.list-group-item-dark.list-group-item-action.active { - color: #fff; - background-color: #141619; - border-color: #141619; -} - -.btn-close { - box-sizing: content-box; - width: 1em; - height: 1em; - padding: 0.25em 0.25em; - color: #000; - background: transparent url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23000'%3e%3cpath d='M.293.293a1 1 0 011.414 0L8 6.586 14.293.293a1 1 0 111.414 1.414L9.414 8l6.293 6.293a1 1 0 01-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 01-1.414-1.414L6.586 8 .293 1.707a1 1 0 010-1.414z'/%3e%3c/svg%3e") center/1em auto no-repeat; - border: 0; - border-radius: 0.25rem; - opacity: 0.5; -} - -.btn-close:hover { - color: #000; - text-decoration: none; - opacity: 0.75; -} - -.btn-close:focus { - outline: 0; - box-shadow: 0 0 0 0.25rem rgba(13, 110, 253, 0.25); - opacity: 1; -} - -.btn-close:disabled, -.btn-close.disabled { - pointer-events: none; - -webkit-user-select: none; - -moz-user-select: none; - -ms-user-select: none; - user-select: none; - opacity: 0.25; -} - -.btn-close-white { - filter: invert(1) grayscale(100%) brightness(200%); -} - -.toast { - width: 350px; - max-width: 100%; - font-size: 0.875rem; - pointer-events: auto; - background-color: rgba(255, 255, 255, 0.85); - background-clip: padding-box; - border: 1px solid rgba(0, 0, 0, 0.1); - box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15); - border-radius: 0.25rem; -} - -.toast.showing { - opacity: 0; -} - -.toast:not(.show) { - display: none; -} - -.toast-container { - width: -webkit-max-content; - width: -moz-max-content; - width: max-content; - max-width: 100%; - pointer-events: none; -} - -.toast-container> :not(:last-child) { - margin-bottom: 0.75rem; -} - -.toast-header { - display: flex; - align-items: center; - padding: 0.5rem 0.75rem; - color: #6c757d; - background-color: rgba(255, 255, 255, 0.85); - background-clip: padding-box; - border-bottom: 1px solid rgba(0, 0, 0, 0.05); - border-top-left-radius: calc(0.25rem - 1px); - border-top-right-radius: calc(0.25rem - 1px); -} - -.toast-header .btn-close { - margin-right: -0.375rem; - margin-left: 0.75rem; -} - -.toast-body { - padding: 0.75rem; - word-wrap: break-word; -} - -.modal { - position: fixed; - top: 0; - left: 0; - z-index: 1055; - display: none; - width: 100%; - height: 100%; - overflow-x: hidden; - overflow-y: auto; - outline: 0; -} - -.modal-dialog { - position: relative; - width: auto; - margin: 0.5rem; - pointer-events: none; -} - -.modal.fade .modal-dialog { - transition: transform 0.3s ease-out; - transform: translate(0, -50px); -} - -@media (prefers-reduced-motion: reduce) { - .modal.fade .modal-dialog { - transition: none; - } -} - -.modal.show .modal-dialog { - transform: none; -} - -.modal.modal-static .modal-dialog { - transform: scale(1.02); -} - -.modal-dialog-scrollable { - height: calc(100% - 1rem); -} - -.modal-dialog-scrollable .modal-content { - max-height: 100%; - overflow: hidden; -} - -.modal-dialog-scrollable .modal-body { - overflow-y: auto; -} - -.modal-dialog-centered { - display: flex; - align-items: center; - min-height: calc(100% - 1rem); -} - -.modal-content { - position: relative; - display: flex; - flex-direction: column; - width: 100%; - pointer-events: auto; - background-color: #fff; - background-clip: padding-box; - border: 1px solid rgba(0, 0, 0, 0.2); - border-radius: 0.3rem; - outline: 0; -} - -.modal-backdrop { - position: fixed; - top: 0; - left: 0; - z-index: 1050; - width: 100vw; - height: 100vh; - background-color: #000; -} - -.modal-backdrop.fade { - opacity: 0; -} - -.modal-backdrop.show { - opacity: 0.5; -} - -.modal-header { - display: flex; - flex-shrink: 0; - align-items: center; - justify-content: space-between; - padding: 1rem 1rem; - border-bottom: 1px solid #dee2e6; - border-top-left-radius: calc(0.3rem - 1px); - border-top-right-radius: calc(0.3rem - 1px); -} - -.modal-header .btn-close { - padding: 0.5rem 0.5rem; - margin: -0.5rem -0.5rem -0.5rem auto; -} - -.modal-title { - margin-bottom: 0; - line-height: 1.5; -} - -.modal-body { - position: relative; - flex: 1 1 auto; - padding: 1rem; -} - -.modal-footer { - display: flex; - flex-wrap: wrap; - flex-shrink: 0; - align-items: center; - justify-content: flex-end; - padding: 0.75rem; - border-top: 1px solid #dee2e6; - border-bottom-right-radius: calc(0.3rem - 1px); - border-bottom-left-radius: calc(0.3rem - 1px); -} - -.modal-footer>* { - margin: 0.25rem; -} - -@media (min-width: 576px) { - .modal-dialog { - max-width: 500px; - margin: 1.75rem auto; - } - - .modal-dialog-scrollable { - height: calc(100% - 3.5rem); - } - - .modal-dialog-centered { - min-height: calc(100% - 3.5rem); - } - - .modal-sm { - max-width: 300px; - } -} - -@media (min-width: 992px) { - - .modal-lg, - .modal-xl { - max-width: 800px; - } -} - -@media (min-width: 1200px) { - .modal-xl { - max-width: 1140px; - } -} - -.modal-fullscreen { - width: 100vw; - max-width: none; - height: 100%; - margin: 0; -} - -.modal-fullscreen .modal-content { - height: 100%; - border: 0; - border-radius: 0; -} - -.modal-fullscreen .modal-header { - border-radius: 0; -} - -.modal-fullscreen .modal-body { - overflow-y: auto; -} - -.modal-fullscreen .modal-footer { - border-radius: 0; -} - -@media (max-width: 575.98px) { - .modal-fullscreen-sm-down { - width: 100vw; - max-width: none; - height: 100%; - margin: 0; - } - - .modal-fullscreen-sm-down .modal-content { - height: 100%; - border: 0; - border-radius: 0; - } - - .modal-fullscreen-sm-down .modal-header { - border-radius: 0; - } - - .modal-fullscreen-sm-down .modal-body { - overflow-y: auto; - } - - .modal-fullscreen-sm-down .modal-footer { - border-radius: 0; - } -} - -@media (max-width: 767.98px) { - .modal-fullscreen-md-down { - width: 100vw; - max-width: none; - height: 100%; - margin: 0; - } - - .modal-fullscreen-md-down .modal-content { - height: 100%; - border: 0; - border-radius: 0; - } - - .modal-fullscreen-md-down .modal-header { - border-radius: 0; - } - - .modal-fullscreen-md-down .modal-body { - overflow-y: auto; - } - - .modal-fullscreen-md-down .modal-footer { - border-radius: 0; - } -} - -@media (max-width: 991.98px) { - .modal-fullscreen-lg-down { - width: 100vw; - max-width: none; - height: 100%; - margin: 0; - } - - .modal-fullscreen-lg-down .modal-content { - height: 100%; - border: 0; - border-radius: 0; - } - - .modal-fullscreen-lg-down .modal-header { - border-radius: 0; - } - - .modal-fullscreen-lg-down .modal-body { - overflow-y: auto; - } - - .modal-fullscreen-lg-down .modal-footer { - border-radius: 0; - } -} - -@media (max-width: 1199.98px) { - .modal-fullscreen-xl-down { - width: 100vw; - max-width: none; - height: 100%; - margin: 0; - } - - .modal-fullscreen-xl-down .modal-content { - height: 100%; - border: 0; - border-radius: 0; - } - - .modal-fullscreen-xl-down .modal-header { - border-radius: 0; - } - - .modal-fullscreen-xl-down .modal-body { - overflow-y: auto; - } - - .modal-fullscreen-xl-down .modal-footer { - border-radius: 0; - } -} - -@media (max-width: 1399.98px) { - .modal-fullscreen-xxl-down { - width: 100vw; - max-width: none; - height: 100%; - margin: 0; - } - - .modal-fullscreen-xxl-down .modal-content { - height: 100%; - border: 0; - border-radius: 0; - } - - .modal-fullscreen-xxl-down .modal-header { - border-radius: 0; - } - - .modal-fullscreen-xxl-down .modal-body { - overflow-y: auto; - } - - .modal-fullscreen-xxl-down .modal-footer { - border-radius: 0; - } -} - -.tooltip { - position: absolute; - z-index: 1080; - display: block; - margin: 0; - font-family: var(--bs-font-sans-serif); - font-style: normal; - font-weight: 400; - line-height: 1.5; - text-align: left; - text-align: start; - text-decoration: none; - text-shadow: none; - text-transform: none; - letter-spacing: normal; - word-break: normal; - word-spacing: normal; - white-space: normal; - line-break: auto; - font-size: 0.875rem; - word-wrap: break-word; - opacity: 0; -} - -.tooltip.show { - opacity: 0.9; -} - -.tooltip .tooltip-arrow { - position: absolute; - display: block; - width: 0.8rem; - height: 0.4rem; -} - -.tooltip .tooltip-arrow::before { - position: absolute; - content: ""; - border-color: transparent; - border-style: solid; -} - -.bs-tooltip-top, -.bs-tooltip-auto[data-popper-placement^=top] { - padding: 0.4rem 0; -} - -.bs-tooltip-top .tooltip-arrow, -.bs-tooltip-auto[data-popper-placement^=top] .tooltip-arrow { - bottom: 0; -} - -.bs-tooltip-top .tooltip-arrow::before, -.bs-tooltip-auto[data-popper-placement^=top] .tooltip-arrow::before { - top: -1px; - border-width: 0.4rem 0.4rem 0; - border-top-color: #000; -} - -.bs-tooltip-end, -.bs-tooltip-auto[data-popper-placement^=right] { - padding: 0 0.4rem; -} - -.bs-tooltip-end .tooltip-arrow, -.bs-tooltip-auto[data-popper-placement^=right] .tooltip-arrow { - left: 0; - width: 0.4rem; - height: 0.8rem; -} - -.bs-tooltip-end .tooltip-arrow::before, -.bs-tooltip-auto[data-popper-placement^=right] .tooltip-arrow::before { - right: -1px; - border-width: 0.4rem 0.4rem 0.4rem 0; - border-right-color: #000; -} - -.bs-tooltip-bottom, -.bs-tooltip-auto[data-popper-placement^=bottom] { - padding: 0.4rem 0; -} - -.bs-tooltip-bottom .tooltip-arrow, -.bs-tooltip-auto[data-popper-placement^=bottom] .tooltip-arrow { - top: 0; -} - -.bs-tooltip-bottom .tooltip-arrow::before, -.bs-tooltip-auto[data-popper-placement^=bottom] .tooltip-arrow::before { - bottom: -1px; - border-width: 0 0.4rem 0.4rem; - border-bottom-color: #000; -} - -.bs-tooltip-start, -.bs-tooltip-auto[data-popper-placement^=left] { - padding: 0 0.4rem; -} - -.bs-tooltip-start .tooltip-arrow, -.bs-tooltip-auto[data-popper-placement^=left] .tooltip-arrow { - right: 0; - width: 0.4rem; - height: 0.8rem; -} - -.bs-tooltip-start .tooltip-arrow::before, -.bs-tooltip-auto[data-popper-placement^=left] .tooltip-arrow::before { - left: -1px; - border-width: 0.4rem 0 0.4rem 0.4rem; - border-left-color: #000; -} - -.tooltip-inner { - max-width: 200px; - padding: 0.25rem 0.5rem; - color: #fff; - text-align: center; - background-color: #000; - border-radius: 0.25rem; -} - -.popover { - position: absolute; - top: 0; - left: 0 - /* rtl:ignore */ - ; - z-index: 1070; - display: block; - max-width: 276px; - font-family: var(--bs-font-sans-serif); - font-style: normal; - font-weight: 400; - line-height: 1.5; - text-align: left; - text-align: start; - text-decoration: none; - text-shadow: none; - text-transform: none; - letter-spacing: normal; - word-break: normal; - word-spacing: normal; - white-space: normal; - line-break: auto; - font-size: 0.875rem; - word-wrap: break-word; - background-color: #fff; - background-clip: padding-box; - border: 1px solid rgba(0, 0, 0, 0.2); - border-radius: 0.3rem; -} - -.popover .popover-arrow { - position: absolute; - display: block; - width: 1rem; - height: 0.5rem; -} - -.popover .popover-arrow::before, -.popover .popover-arrow::after { - position: absolute; - display: block; - content: ""; - border-color: transparent; - border-style: solid; -} - -.bs-popover-top>.popover-arrow, -.bs-popover-auto[data-popper-placement^=top]>.popover-arrow { - bottom: calc(-0.5rem - 1px); -} - -.bs-popover-top>.popover-arrow::before, -.bs-popover-auto[data-popper-placement^=top]>.popover-arrow::before { - bottom: 0; - border-width: 0.5rem 0.5rem 0; - border-top-color: rgba(0, 0, 0, 0.25); -} - -.bs-popover-top>.popover-arrow::after, -.bs-popover-auto[data-popper-placement^=top]>.popover-arrow::after { - bottom: 1px; - border-width: 0.5rem 0.5rem 0; - border-top-color: #fff; -} - -.bs-popover-end>.popover-arrow, -.bs-popover-auto[data-popper-placement^=right]>.popover-arrow { - left: calc(-0.5rem - 1px); - width: 0.5rem; - height: 1rem; -} - -.bs-popover-end>.popover-arrow::before, -.bs-popover-auto[data-popper-placement^=right]>.popover-arrow::before { - left: 0; - border-width: 0.5rem 0.5rem 0.5rem 0; - border-right-color: rgba(0, 0, 0, 0.25); -} - -.bs-popover-end>.popover-arrow::after, -.bs-popover-auto[data-popper-placement^=right]>.popover-arrow::after { - left: 1px; - border-width: 0.5rem 0.5rem 0.5rem 0; - border-right-color: #fff; -} - -.bs-popover-bottom>.popover-arrow, -.bs-popover-auto[data-popper-placement^=bottom]>.popover-arrow { - top: calc(-0.5rem - 1px); -} - -.bs-popover-bottom>.popover-arrow::before, -.bs-popover-auto[data-popper-placement^=bottom]>.popover-arrow::before { - top: 0; - border-width: 0 0.5rem 0.5rem 0.5rem; - border-bottom-color: rgba(0, 0, 0, 0.25); -} - -.bs-popover-bottom>.popover-arrow::after, -.bs-popover-auto[data-popper-placement^=bottom]>.popover-arrow::after { - top: 1px; - border-width: 0 0.5rem 0.5rem 0.5rem; - border-bottom-color: #fff; -} - -.bs-popover-bottom .popover-header::before, -.bs-popover-auto[data-popper-placement^=bottom] .popover-header::before { - position: absolute; - top: 0; - left: 50%; - display: block; - width: 1rem; - margin-left: -0.5rem; - content: ""; - border-bottom: 1px solid #f0f0f0; -} - -.bs-popover-start>.popover-arrow, -.bs-popover-auto[data-popper-placement^=left]>.popover-arrow { - right: calc(-0.5rem - 1px); - width: 0.5rem; - height: 1rem; -} - -.bs-popover-start>.popover-arrow::before, -.bs-popover-auto[data-popper-placement^=left]>.popover-arrow::before { - right: 0; - border-width: 0.5rem 0 0.5rem 0.5rem; - border-left-color: rgba(0, 0, 0, 0.25); -} - -.bs-popover-start>.popover-arrow::after, -.bs-popover-auto[data-popper-placement^=left]>.popover-arrow::after { - right: 1px; - border-width: 0.5rem 0 0.5rem 0.5rem; - border-left-color: #fff; -} - -.popover-header { - padding: 0.5rem 1rem; - margin-bottom: 0; - font-size: 1rem; - background-color: #f0f0f0; - border-bottom: 1px solid rgba(0, 0, 0, 0.2); - border-top-left-radius: calc(0.3rem - 1px); - border-top-right-radius: calc(0.3rem - 1px); -} - -.popover-header:empty { - display: none; -} - -.popover-body { - padding: 1rem 1rem; - color: #212529; -} - -.carousel { - position: relative; -} - -.carousel.pointer-event { - touch-action: pan-y; -} - -.carousel-inner { - position: relative; - width: 100%; - overflow: hidden; -} - -.carousel-inner::after { - display: block; - clear: both; - content: ""; -} - -.carousel-item { - position: relative; - display: none; - float: left; - width: 100%; - margin-right: -100%; - -webkit-backface-visibility: hidden; - backface-visibility: hidden; - transition: transform 0.6s ease-in-out; -} - -@media (prefers-reduced-motion: reduce) { - .carousel-item { - transition: none; - } -} - -.carousel-item.active, -.carousel-item-next, -.carousel-item-prev { - display: block; -} - -/* rtl:begin:ignore */ -.carousel-item-next:not(.carousel-item-start), -.active.carousel-item-end { - transform: translateX(100%); -} - -.carousel-item-prev:not(.carousel-item-end), -.active.carousel-item-start { - transform: translateX(-100%); -} - -/* rtl:end:ignore */ -.carousel-fade .carousel-item { - opacity: 0; - transition-property: opacity; - transform: none; -} - -.carousel-fade .carousel-item.active, -.carousel-fade .carousel-item-next.carousel-item-start, -.carousel-fade .carousel-item-prev.carousel-item-end { - z-index: 1; - opacity: 1; -} - -.carousel-fade .active.carousel-item-start, -.carousel-fade .active.carousel-item-end { - z-index: 0; - opacity: 0; - transition: opacity 0s 0.6s; -} - -@media (prefers-reduced-motion: reduce) { - - .carousel-fade .active.carousel-item-start, - .carousel-fade .active.carousel-item-end { - transition: none; - } -} - -.carousel-control-prev, -.carousel-control-next { - position: absolute; - top: 0; - bottom: 0; - z-index: 1; - display: flex; - align-items: center; - justify-content: center; - width: 15%; - padding: 0; - color: #fff; - text-align: center; - background: none; - border: 0; - opacity: 0.5; - transition: opacity 0.15s ease; -} - -@media (prefers-reduced-motion: reduce) { - - .carousel-control-prev, - .carousel-control-next { - transition: none; - } -} - -.carousel-control-prev:hover, -.carousel-control-prev:focus, -.carousel-control-next:hover, -.carousel-control-next:focus { - color: #fff; - text-decoration: none; - outline: 0; - opacity: 0.9; -} - -.carousel-control-prev { - left: 0; -} - -.carousel-control-next { - right: 0; -} - -.carousel-control-prev-icon, -.carousel-control-next-icon { - display: inline-block; - width: 2rem; - height: 2rem; - background-repeat: no-repeat; - background-position: 50%; - background-size: 100% 100%; -} - -/* rtl:options: { - "autoRename": true, - "stringMap":[ { - "name" : "prev-next", - "search" : "prev", - "replace" : "next" - } ] -} */ -.carousel-control-prev-icon { - background-image: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23fff'%3e%3cpath d='M11.354 1.646a.5.5 0 0 1 0 .708L5.707 8l5.647 5.646a.5.5 0 0 1-.708.708l-6-6a.5.5 0 0 1 0-.708l6-6a.5.5 0 0 1 .708 0z'/%3e%3c/svg%3e"); -} - -.carousel-control-next-icon { - background-image: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23fff'%3e%3cpath d='M4.646 1.646a.5.5 0 0 1 .708 0l6 6a.5.5 0 0 1 0 .708l-6 6a.5.5 0 0 1-.708-.708L10.293 8 4.646 2.354a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e"); -} - -.carousel-indicators { - position: absolute; - right: 0; - bottom: 0; - left: 0; - z-index: 2; - display: flex; - justify-content: center; - padding: 0; - margin-right: 15%; - margin-bottom: 1rem; - margin-left: 15%; - list-style: none; -} - -.carousel-indicators [data-bs-target] { - box-sizing: content-box; - flex: 0 1 auto; - width: 30px; - height: 3px; - padding: 0; - margin-right: 3px; - margin-left: 3px; - text-indent: -999px; - cursor: pointer; - background-color: #fff; - background-clip: padding-box; - border: 0; - border-top: 10px solid transparent; - border-bottom: 10px solid transparent; - opacity: 0.5; - transition: opacity 0.6s ease; -} - -@media (prefers-reduced-motion: reduce) { - .carousel-indicators [data-bs-target] { - transition: none; - } -} - -.carousel-indicators .active { - opacity: 1; -} - -.carousel-caption { - position: absolute; - right: 15%; - bottom: 1.25rem; - left: 15%; - padding-top: 1.25rem; - padding-bottom: 1.25rem; - color: #fff; - text-align: center; -} - -.carousel-dark .carousel-control-prev-icon, -.carousel-dark .carousel-control-next-icon { - filter: invert(1) grayscale(100); -} - -.carousel-dark .carousel-indicators [data-bs-target] { - background-color: #000; -} - -.carousel-dark .carousel-caption { - color: #000; -} - -@-webkit-keyframes spinner-border { - to { - transform: rotate(360deg) - /* rtl:ignore */ - ; - } -} - -@keyframes spinner-border { - to { - transform: rotate(360deg) - /* rtl:ignore */ - ; - } -} - -.spinner-border { - display: inline-block; - width: 2rem; - height: 2rem; - vertical-align: -0.125em; - border: 0.25em solid currentColor; - border-right-color: transparent; - border-radius: 50%; - -webkit-animation: 0.75s linear infinite spinner-border; - animation: 0.75s linear infinite spinner-border; -} - -.spinner-border-sm { - width: 1rem; - height: 1rem; - border-width: 0.2em; -} - -@-webkit-keyframes spinner-grow { - 0% { - transform: scale(0); - } - - 50% { - opacity: 1; - transform: none; - } -} - -@keyframes spinner-grow { - 0% { - transform: scale(0); - } - - 50% { - opacity: 1; - transform: none; - } -} - -.spinner-grow { - display: inline-block; - width: 2rem; - height: 2rem; - vertical-align: -0.125em; - background-color: currentColor; - border-radius: 50%; - opacity: 0; - -webkit-animation: 0.75s linear infinite spinner-grow; - animation: 0.75s linear infinite spinner-grow; -} - -.spinner-grow-sm { - width: 1rem; - height: 1rem; -} - -@media (prefers-reduced-motion: reduce) { - - .spinner-border, - .spinner-grow { - -webkit-animation-duration: 1.5s; - animation-duration: 1.5s; - } -} - -.offcanvas { - position: fixed; - bottom: 0; - z-index: 1045; - display: flex; - flex-direction: column; - max-width: 100%; - visibility: hidden; - background-color: #fff; - background-clip: padding-box; - outline: 0; - transition: transform 0.3s ease-in-out; -} - -@media (prefers-reduced-motion: reduce) { - .offcanvas { - transition: none; - } -} - -.offcanvas-backdrop { - position: fixed; - top: 0; - left: 0; - z-index: 1040; - width: 100vw; - height: 100vh; - background-color: #000; -} - -.offcanvas-backdrop.fade { - opacity: 0; -} - -.offcanvas-backdrop.show { - opacity: 0.5; -} - -.offcanvas-header { - display: flex; - align-items: center; - justify-content: space-between; - padding: 1rem 1rem; -} - -.offcanvas-header .btn-close { - padding: 0.5rem 0.5rem; - margin-top: -0.5rem; - margin-right: -0.5rem; - margin-bottom: -0.5rem; -} - -.offcanvas-title { - margin-bottom: 0; - line-height: 1.5; -} - -.offcanvas-body { - flex-grow: 1; - padding: 1rem 1rem; - overflow-y: auto; -} - -.offcanvas-start { - top: 0; - left: 0; - width: 400px; - border-right: 1px solid rgba(0, 0, 0, 0.2); - transform: translateX(-100%); -} - -.offcanvas-end { - top: 0; - right: 0; - width: 400px; - border-left: 1px solid rgba(0, 0, 0, 0.2); - transform: translateX(100%); -} - -.offcanvas-top { - top: 0; - right: 0; - left: 0; - height: 30vh; - max-height: 100%; - border-bottom: 1px solid rgba(0, 0, 0, 0.2); - transform: translateY(-100%); -} - -.offcanvas-bottom { - right: 0; - left: 0; - height: 30vh; - max-height: 100%; - border-top: 1px solid rgba(0, 0, 0, 0.2); - transform: translateY(100%); -} - -.offcanvas.show { - transform: none; -} - -.placeholder { - display: inline-block; - min-height: 1em; - vertical-align: middle; - cursor: wait; - background-color: currentColor; - opacity: 0.5; -} - -.placeholder.btn::before { - display: inline-block; - content: ""; -} - -.placeholder-xs { - min-height: 0.6em; -} - -.placeholder-sm { - min-height: 0.8em; -} - -.placeholder-lg { - min-height: 1.2em; -} - -.placeholder-glow .placeholder { - -webkit-animation: placeholder-glow 2s ease-in-out infinite; - animation: placeholder-glow 2s ease-in-out infinite; -} - -@-webkit-keyframes placeholder-glow { - 50% { - opacity: 0.2; - } -} - -@keyframes placeholder-glow { - 50% { - opacity: 0.2; - } -} - -.placeholder-wave { - -webkit-mask-image: linear-gradient(130deg, #000 55%, rgba(0, 0, 0, 0.8) 75%, #000 95%); - mask-image: linear-gradient(130deg, #000 55%, rgba(0, 0, 0, 0.8) 75%, #000 95%); - -webkit-mask-size: 200% 100%; - mask-size: 200% 100%; - -webkit-animation: placeholder-wave 2s linear infinite; - animation: placeholder-wave 2s linear infinite; -} - -@-webkit-keyframes placeholder-wave { - 100% { - -webkit-mask-position: -200% 0%; - mask-position: -200% 0%; - } -} - -@keyframes placeholder-wave { - 100% { - -webkit-mask-position: -200% 0%; - mask-position: -200% 0%; - } -} - -.clearfix::after { - display: block; - clear: both; - content: ""; -} - -.link-primary { - color: #0d6efd; -} - -.link-primary:hover, -.link-primary:focus { - color: #0a58ca; -} - -.link-secondary { - color: #6c757d; -} - -.link-secondary:hover, -.link-secondary:focus { - color: #565e64; -} - -.link-success { - color: #198754; -} - -.link-success:hover, -.link-success:focus { - color: #146c43; -} - -.link-info { - color: #0dcaf0; -} - -.link-info:hover, -.link-info:focus { - color: #3dd5f3; -} - -.link-warning { - color: #ffc107; -} - -.link-warning:hover, -.link-warning:focus { - color: #ffcd39; -} - -.link-danger { - color: #dc3545; -} - -.link-danger:hover, -.link-danger:focus { - color: #b02a37; -} - -.link-light { - color: #f8f9fa; -} - -.link-light:hover, -.link-light:focus { - color: #f9fafb; -} - -.link-dark { - color: #212529; -} - -.link-dark:hover, -.link-dark:focus { - color: #1a1e21; -} - -.ratio { - position: relative; - width: 100%; -} - -.ratio::before { - display: block; - padding-top: var(--bs-aspect-ratio); - content: ""; -} - -.ratio>* { - position: absolute; - top: 0; - left: 0; - width: 100%; - height: 100%; -} - -.ratio-1x1 { - --bs-aspect-ratio: 100%; -} - -.ratio-4x3 { - --bs-aspect-ratio: 75%; -} - -.ratio-16x9 { - --bs-aspect-ratio: 56.25%; -} - -.ratio-21x9 { - --bs-aspect-ratio: 42.8571428571%; -} - -.fixed-top { - position: fixed; - top: 0; - right: 0; - left: 0; - z-index: 1030; -} - -.fixed-bottom { - position: fixed; - right: 0; - bottom: 0; - left: 0; - z-index: 1030; -} - -.sticky-top { - position: -webkit-sticky; - position: sticky; - top: 0; - z-index: 1020; -} - -@media (min-width: 576px) { - .sticky-sm-top { - position: -webkit-sticky; - position: sticky; - top: 0; - z-index: 1020; - } -} - -@media (min-width: 768px) { - .sticky-md-top { - position: -webkit-sticky; - position: sticky; - top: 0; - z-index: 1020; - } -} - -@media (min-width: 992px) { - .sticky-lg-top { - position: -webkit-sticky; - position: sticky; - top: 0; - z-index: 1020; - } -} - -@media (min-width: 1200px) { - .sticky-xl-top { - position: -webkit-sticky; - position: sticky; - top: 0; - z-index: 1020; - } -} - -@media (min-width: 1400px) { - .sticky-xxl-top { - position: -webkit-sticky; - position: sticky; - top: 0; - z-index: 1020; - } -} - -.hstack { - display: flex; - flex-direction: row; - align-items: center; - align-self: stretch; -} - -.vstack { - display: flex; - flex: 1 1 auto; - flex-direction: column; - align-self: stretch; -} - -.visually-hidden, -.visually-hidden-focusable:not(:focus):not(:focus-within) { - position: absolute !important; - width: 1px !important; - height: 1px !important; - padding: 0 !important; - margin: -1px !important; - overflow: hidden !important; - clip: rect(0, 0, 0, 0) !important; - white-space: nowrap !important; - border: 0 !important; -} - -.stretched-link::after { - position: absolute; - top: 0; - right: 0; - bottom: 0; - left: 0; - z-index: 1; - content: ""; -} - -.text-truncate { - overflow: hidden; - text-overflow: ellipsis; - white-space: nowrap; -} - -.vr { - display: inline-block; - align-self: stretch; - width: 1px; - min-height: 1em; - background-color: currentColor; - opacity: 0.25; -} - -.align-baseline { - vertical-align: baseline !important; -} - -.align-top { - vertical-align: top !important; -} - -.align-middle { - vertical-align: middle !important; -} - -.align-bottom { - vertical-align: bottom !important; -} - -.align-text-bottom { - vertical-align: text-bottom !important; -} - -.align-text-top { - vertical-align: text-top !important; -} - -.float-start { - float: left !important; -} - -.float-end { - float: right !important; -} - -.float-none { - float: none !important; -} - -.opacity-0 { - opacity: 0 !important; -} - -.opacity-25 { - opacity: 0.25 !important; -} - -.opacity-50 { - opacity: 0.5 !important; -} - -.opacity-75 { - opacity: 0.75 !important; -} - -.opacity-100 { - opacity: 1 !important; -} - -.overflow-auto { - overflow: auto !important; -} - -.overflow-hidden { - overflow: hidden !important; -} - -.overflow-visible { - overflow: visible !important; -} - -.overflow-scroll { - overflow: scroll !important; -} - -.d-inline { - display: inline !important; -} - -.d-inline-block { - display: inline-block !important; -} - -.d-block { - display: block !important; -} - -.d-grid { - display: grid !important; -} - -.d-table { - display: table !important; -} - -.d-table-row { - display: table-row !important; -} - -.d-table-cell { - display: table-cell !important; -} - -.d-flex { - display: flex !important; -} - -.d-inline-flex { - display: inline-flex !important; -} - -.d-none { - display: none !important; -} - -.shadow { - box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15) !important; -} - -.shadow-sm { - box-shadow: 0 0.125rem 0.25rem rgba(0, 0, 0, 0.075) !important; -} - -.shadow-lg { - box-shadow: 0 1rem 3rem rgba(0, 0, 0, 0.175) !important; -} - -.shadow-none { - box-shadow: none !important; -} - -.position-static { - position: static !important; -} - -.position-relative { - position: relative !important; -} - -.position-absolute { - position: absolute !important; -} - -.position-fixed { - position: fixed !important; -} - -.position-sticky { - position: -webkit-sticky !important; - position: sticky !important; -} - -.top-0 { - top: 0 !important; -} - -.top-50 { - top: 50% !important; -} - -.top-100 { - top: 100% !important; -} - -.bottom-0 { - bottom: 0 !important; -} - -.bottom-50 { - bottom: 50% !important; -} - -.bottom-100 { - bottom: 100% !important; -} - -.start-0 { - left: 0 !important; -} - -.start-50 { - left: 50% !important; -} - -.start-100 { - left: 100% !important; -} - -.end-0 { - right: 0 !important; -} - -.end-50 { - right: 50% !important; -} - -.end-100 { - right: 100% !important; -} - -.translate-middle { - transform: translate(-50%, -50%) !important; -} - -.translate-middle-x { - transform: translateX(-50%) !important; -} - -.translate-middle-y { - transform: translateY(-50%) !important; -} - -.border { - border: 1px solid #dee2e6 !important; -} - -.border-0 { - border: 0 !important; -} - -.border-top { - border-top: 1px solid #dee2e6 !important; -} - -.border-top-0 { - border-top: 0 !important; -} - -.border-end { - border-right: 1px solid #dee2e6 !important; -} - -.border-end-0 { - border-right: 0 !important; -} - -.border-bottom { - border-bottom: 1px solid #dee2e6 !important; -} - -.border-bottom-0 { - border-bottom: 0 !important; -} - -.border-start { - border-left: 1px solid #dee2e6 !important; -} - -.border-start-0 { - border-left: 0 !important; -} - -.border-primary { - border-color: #0d6efd !important; -} - -.border-secondary { - border-color: #6c757d !important; -} - -.border-success { - border-color: #198754 !important; -} - -.border-info { - border-color: #0dcaf0 !important; -} - -.border-warning { - border-color: #ffc107 !important; -} - -.border-danger { - border-color: #dc3545 !important; -} - -.border-light { - border-color: #f8f9fa !important; -} - -.border-dark { - border-color: #212529 !important; -} - -.border-white { - border-color: #fff !important; -} - -.border-1 { - border-width: 1px !important; -} - -.border-2 { - border-width: 2px !important; -} - -.border-3 { - border-width: 3px !important; -} - -.border-4 { - border-width: 4px !important; -} - -.border-5 { - border-width: 5px !important; -} - -.w-25 { - width: 25% !important; -} - -.w-50 { - width: 50% !important; -} - -.w-75 { - width: 75% !important; -} - -.w-100 { - width: 100% !important; -} - -.w-auto { - width: auto !important; -} - -.mw-100 { - max-width: 100% !important; -} - -.vw-100 { - width: 100vw !important; -} - -.min-vw-100 { - min-width: 100vw !important; -} - -.h-25 { - height: 25% !important; -} - -.h-50 { - height: 50% !important; -} - -.h-75 { - height: 75% !important; -} - -.h-100 { - height: 100% !important; -} - -.h-auto { - height: auto !important; -} - -.mh-100 { - max-height: 100% !important; -} - -.vh-100 { - height: 100vh !important; -} - -.min-vh-100 { - min-height: 100vh !important; -} - -.flex-fill { - flex: 1 1 auto !important; -} - -.flex-row { - flex-direction: row !important; -} - -.flex-column { - flex-direction: column !important; -} - -.flex-row-reverse { - flex-direction: row-reverse !important; -} - -.flex-column-reverse { - flex-direction: column-reverse !important; -} - -.flex-grow-0 { - flex-grow: 0 !important; -} - -.flex-grow-1 { - flex-grow: 1 !important; -} - -.flex-shrink-0 { - flex-shrink: 0 !important; -} - -.flex-shrink-1 { - flex-shrink: 1 !important; -} - -.flex-wrap { - flex-wrap: wrap !important; -} - -.flex-nowrap { - flex-wrap: nowrap !important; -} - -.flex-wrap-reverse { - flex-wrap: wrap-reverse !important; -} - -.gap-0 { - gap: 0 !important; -} - -.gap-1 { - gap: 0.25rem !important; -} - -.gap-2 { - gap: 0.5rem !important; -} - -.gap-3 { - gap: 1rem !important; -} - -.gap-4 { - gap: 1.5rem !important; -} - -.gap-5 { - gap: 3rem !important; -} - -.justify-content-start { - justify-content: flex-start !important; -} - -.justify-content-end { - justify-content: flex-end !important; -} - -.justify-content-center { - justify-content: center !important; -} - -.justify-content-between { - justify-content: space-between !important; -} - -.justify-content-around { - justify-content: space-around !important; -} - -.justify-content-evenly { - justify-content: space-evenly !important; -} - -.align-items-start { - align-items: flex-start !important; -} - -.align-items-end { - align-items: flex-end !important; -} - -.align-items-center { - align-items: center !important; -} - -.align-items-baseline { - align-items: baseline !important; -} - -.align-items-stretch { - align-items: stretch !important; -} - -.align-content-start { - align-content: flex-start !important; -} - -.align-content-end { - align-content: flex-end !important; -} - -.align-content-center { - align-content: center !important; -} - -.align-content-between { - align-content: space-between !important; -} - -.align-content-around { - align-content: space-around !important; -} - -.align-content-stretch { - align-content: stretch !important; -} - -.align-self-auto { - align-self: auto !important; -} - -.align-self-start { - align-self: flex-start !important; -} - -.align-self-end { - align-self: flex-end !important; -} - -.align-self-center { - align-self: center !important; -} - -.align-self-baseline { - align-self: baseline !important; -} - -.align-self-stretch { - align-self: stretch !important; -} - -.order-first { - order: -1 !important; -} - -.order-0 { - order: 0 !important; -} - -.order-1 { - order: 1 !important; -} - -.order-2 { - order: 2 !important; -} - -.order-3 { - order: 3 !important; -} - -.order-4 { - order: 4 !important; -} - -.order-5 { - order: 5 !important; -} - -.order-last { - order: 6 !important; -} - -.m-0 { - margin: 0 !important; -} - -.m-1 { - margin: 0.25rem !important; -} - -.m-2 { - margin: 0.5rem !important; -} - -.m-3 { - margin: 1rem !important; -} - -.m-4 { - margin: 1.5rem !important; -} - -.m-5 { - margin: 3rem !important; -} - -.m-auto { - margin: auto !important; -} - -.mx-0 { - margin-right: 0 !important; - margin-left: 0 !important; -} - -.mx-1 { - margin-right: 0.25rem !important; - margin-left: 0.25rem !important; -} - -.mx-2 { - margin-right: 0.5rem !important; - margin-left: 0.5rem !important; -} - -.mx-3 { - margin-right: 1rem !important; - margin-left: 1rem !important; -} - -.mx-4 { - margin-right: 1.5rem !important; - margin-left: 1.5rem !important; -} - -.mx-5 { - margin-right: 3rem !important; - margin-left: 3rem !important; -} - -.mx-auto { - margin-right: auto !important; - margin-left: auto !important; -} - -.my-0 { - margin-top: 0 !important; - margin-bottom: 0 !important; -} - -.my-1 { - margin-top: 0.25rem !important; - margin-bottom: 0.25rem !important; -} - -.my-2 { - margin-top: 0.5rem !important; - margin-bottom: 0.5rem !important; -} - -.my-3 { - margin-top: 1rem !important; - margin-bottom: 1rem !important; -} - -.my-4 { - margin-top: 1.5rem !important; - margin-bottom: 1.5rem !important; -} - -.my-5 { - margin-top: 3rem !important; - margin-bottom: 3rem !important; -} - -.my-auto { - margin-top: auto !important; - margin-bottom: auto !important; -} - -.mt-0 { - margin-top: 0 !important; -} - -.mt-1 { - margin-top: 0.25rem !important; -} - -.mt-2 { - margin-top: 0.5rem !important; -} - -.mt-3 { - margin-top: 1rem !important; -} - -.mt-4 { - margin-top: 1.5rem !important; -} - -.mt-5 { - margin-top: 3rem !important; -} - -.mt-auto { - margin-top: auto !important; -} - -.me-0 { - margin-right: 0 !important; -} - -.me-1 { - margin-right: 0.25rem !important; -} - -.me-2 { - margin-right: 0.5rem !important; -} - -.me-3 { - margin-right: 1rem !important; -} - -.me-4 { - margin-right: 1.5rem !important; -} - -.me-5 { - margin-right: 3rem !important; -} - -.me-auto { - margin-right: auto !important; -} - -.mb-0 { - margin-bottom: 0 !important; -} - -.mb-1 { - margin-bottom: 0.25rem !important; -} - -.mb-2 { - margin-bottom: 0.5rem !important; -} - -.mb-3 { - margin-bottom: 1rem !important; -} - -.mb-4 { - margin-bottom: 1.5rem !important; -} - -.mb-5 { - margin-bottom: 3rem !important; -} - -.mb-auto { - margin-bottom: auto !important; -} - -.ms-0 { - margin-left: 0 !important; -} - -.ms-1 { - margin-left: 0.25rem !important; -} - -.ms-2 { - margin-left: 0.5rem !important; -} - -.ms-3 { - margin-left: 1rem !important; -} - -.ms-4 { - margin-left: 1.5rem !important; -} - -.ms-5 { - margin-left: 3rem !important; -} - -.ms-auto { - margin-left: auto !important; -} - -.p-0 { - padding: 0 !important; -} - -.p-1 { - padding: 0.25rem !important; -} - -.p-2 { - padding: 0.5rem !important; -} - -.p-3 { - padding: 1rem !important; -} - -.p-4 { - padding: 1.5rem !important; -} - -.p-5 { - padding: 3rem !important; -} - -.px-0 { - padding-right: 0 !important; - padding-left: 0 !important; -} - -.px-1 { - padding-right: 0.25rem !important; - padding-left: 0.25rem !important; -} - -.px-2 { - padding-right: 0.5rem !important; - padding-left: 0.5rem !important; -} - -.px-3 { - padding-right: 1rem !important; - padding-left: 1rem !important; -} - -.px-4 { - padding-right: 1.5rem !important; - padding-left: 1.5rem !important; -} - -.px-5 { - padding-right: 3rem !important; - padding-left: 3rem !important; -} - -.py-0 { - padding-top: 0 !important; - padding-bottom: 0 !important; -} - -.py-1 { - padding-top: 0.25rem !important; - padding-bottom: 0.25rem !important; -} - -.py-2 { - padding-top: 0.5rem !important; - padding-bottom: 0.5rem !important; -} - -.py-3 { - padding-top: 1rem !important; - padding-bottom: 1rem !important; -} - -.py-4 { - padding-top: 1.5rem !important; - padding-bottom: 1.5rem !important; -} - -.py-5 { - padding-top: 3rem !important; - padding-bottom: 3rem !important; -} - -.pt-0 { - padding-top: 0 !important; -} - -.pt-1 { - padding-top: 0.25rem !important; -} - -.pt-2 { - padding-top: 0.5rem !important; -} - -.pt-3 { - padding-top: 1rem !important; -} - -.pt-4 { - padding-top: 1.5rem !important; -} - -.pt-5 { - padding-top: 3rem !important; -} - -.pe-0 { - padding-right: 0 !important; -} - -.pe-1 { - padding-right: 0.25rem !important; -} - -.pe-2 { - padding-right: 0.5rem !important; -} - -.pe-3 { - padding-right: 1rem !important; -} - -.pe-4 { - padding-right: 1.5rem !important; -} - -.pe-5 { - padding-right: 3rem !important; -} - -.pb-0 { - padding-bottom: 0 !important; -} - -.pb-1 { - padding-bottom: 0.25rem !important; -} - -.pb-2 { - padding-bottom: 0.5rem !important; -} - -.pb-3 { - padding-bottom: 1rem !important; -} - -.pb-4 { - padding-bottom: 1.5rem !important; -} - -.pb-5 { - padding-bottom: 3rem !important; -} - -.ps-0 { - padding-left: 0 !important; -} - -.ps-1 { - padding-left: 0.25rem !important; -} - -.ps-2 { - padding-left: 0.5rem !important; -} - -.ps-3 { - padding-left: 1rem !important; -} - -.ps-4 { - padding-left: 1.5rem !important; -} - -.ps-5 { - padding-left: 3rem !important; -} - -.font-monospace { - font-family: var(--bs-font-monospace) !important; -} - -.fs-1 { - font-size: calc(1.375rem + 1.5vw) !important; -} - -.fs-2 { - font-size: calc(1.325rem + 0.9vw) !important; -} - -.fs-3 { - font-size: calc(1.3rem + 0.6vw) !important; -} - -.fs-4 { - font-size: calc(1.275rem + 0.3vw) !important; -} - -.fs-5 { - font-size: 1.25rem !important; -} - -.fs-6 { - font-size: 1rem !important; -} - -.fst-italic { - font-style: italic !important; -} - -.fst-normal { - font-style: normal !important; -} - -.fw-light { - font-weight: 300 !important; -} - -.fw-lighter { - font-weight: lighter !important; -} - -.fw-normal { - font-weight: 400 !important; -} - -.fw-bold { - font-weight: 700 !important; -} - -.fw-bolder { - font-weight: bolder !important; -} - -.lh-1 { - line-height: 1 !important; -} - -.lh-sm { - line-height: 1.25 !important; -} - -.lh-base { - line-height: 1.5 !important; -} - -.lh-lg { - line-height: 2 !important; -} - -.text-start { - text-align: left !important; -} - -.text-end { - text-align: right !important; -} - -.text-center { - text-align: center !important; -} - -.text-decoration-none { - text-decoration: none !important; -} - -.text-decoration-underline { - text-decoration: underline !important; -} - -.text-decoration-line-through { - text-decoration: line-through !important; -} - -.text-lowercase { - text-transform: lowercase !important; -} - -.text-uppercase { - text-transform: uppercase !important; -} - -.text-capitalize { - text-transform: capitalize !important; -} - -.text-wrap { - white-space: normal !important; -} - -.text-nowrap { - white-space: nowrap !important; -} - -/* rtl:begin:remove */ -.text-break { - word-wrap: break-word !important; - word-break: break-word !important; -} - -/* rtl:end:remove */ -.text-primary { - --bs-text-opacity: 1; - color: rgba(var(--bs-primary-rgb), var(--bs-text-opacity)) !important; -} - -.text-secondary { - --bs-text-opacity: 1; - color: rgba(var(--bs-secondary-rgb), var(--bs-text-opacity)) !important; -} - -.text-success { - --bs-text-opacity: 1; - color: rgba(var(--bs-success-rgb), var(--bs-text-opacity)) !important; -} - -.text-info { - --bs-text-opacity: 1; - color: rgba(var(--bs-info-rgb), var(--bs-text-opacity)) !important; -} - -.text-warning { - --bs-text-opacity: 1; - color: rgba(var(--bs-warning-rgb), var(--bs-text-opacity)) !important; -} - -.text-danger { - --bs-text-opacity: 1; - color: rgba(var(--bs-danger-rgb), var(--bs-text-opacity)) !important; -} - -.text-light { - --bs-text-opacity: 1; - color: rgba(var(--bs-light-rgb), var(--bs-text-opacity)) !important; -} - -.text-dark { - --bs-text-opacity: 1; - color: rgba(var(--bs-dark-rgb), var(--bs-text-opacity)) !important; -} - -.text-black { - --bs-text-opacity: 1; - color: rgba(var(--bs-black-rgb), var(--bs-text-opacity)) !important; -} - -.text-white { - --bs-text-opacity: 1; - color: rgba(var(--bs-white-rgb), var(--bs-text-opacity)) !important; -} - -.text-body { - --bs-text-opacity: 1; - color: rgba(var(--bs-body-color-rgb), var(--bs-text-opacity)) !important; -} - -.text-muted { - --bs-text-opacity: 1; - color: #6c757d !important; -} - -.text-black-50 { - --bs-text-opacity: 1; - color: rgba(0, 0, 0, 0.5) !important; -} - -.text-white-50 { - --bs-text-opacity: 1; - color: rgba(255, 255, 255, 0.5) !important; -} - -.text-reset { - --bs-text-opacity: 1; - color: inherit !important; -} - -.text-opacity-25 { - --bs-text-opacity: 0.25; -} - -.text-opacity-50 { - --bs-text-opacity: 0.5; -} - -.text-opacity-75 { - --bs-text-opacity: 0.75; -} - -.text-opacity-100 { - --bs-text-opacity: 1; -} - -.bg-primary { - --bs-bg-opacity: 1; - background-color: rgba(var(--bs-primary-rgb), var(--bs-bg-opacity)) !important; -} - -.bg-secondary { - --bs-bg-opacity: 1; - background-color: rgba(var(--bs-secondary-rgb), var(--bs-bg-opacity)) !important; -} - -.bg-success { - --bs-bg-opacity: 1; - background-color: rgba(var(--bs-success-rgb), var(--bs-bg-opacity)) !important; -} - -.bg-info { - --bs-bg-opacity: 1; - background-color: rgba(var(--bs-info-rgb), var(--bs-bg-opacity)) !important; -} - -.bg-warning { - --bs-bg-opacity: 1; - background-color: rgba(var(--bs-warning-rgb), var(--bs-bg-opacity)) !important; -} - -.bg-danger { - --bs-bg-opacity: 1; - background-color: rgba(var(--bs-danger-rgb), var(--bs-bg-opacity)) !important; -} - -.bg-light { - --bs-bg-opacity: 1; - background-color: rgba(var(--bs-light-rgb), var(--bs-bg-opacity)) !important; -} - -.bg-dark { - --bs-bg-opacity: 1; - background-color: rgba(var(--bs-dark-rgb), var(--bs-bg-opacity)) !important; -} - -.bg-black { - --bs-bg-opacity: 1; - background-color: rgba(var(--bs-black-rgb), var(--bs-bg-opacity)) !important; -} - -.bg-white { - --bs-bg-opacity: 1; - background-color: rgba(var(--bs-white-rgb), var(--bs-bg-opacity)) !important; -} - -.bg-body { - --bs-bg-opacity: 1; - background-color: rgba(var(--bs-body-bg-rgb), var(--bs-bg-opacity)) !important; -} - -.bg-transparent { - --bs-bg-opacity: 1; - background-color: transparent !important; -} - -.bg-opacity-10 { - --bs-bg-opacity: 0.1; -} - -.bg-opacity-25 { - --bs-bg-opacity: 0.25; -} - -.bg-opacity-50 { - --bs-bg-opacity: 0.5; -} - -.bg-opacity-75 { - --bs-bg-opacity: 0.75; -} - -.bg-opacity-100 { - --bs-bg-opacity: 1; -} - -.bg-gradient { - background-image: var(--bs-gradient) !important; -} - -.user-select-all { - -webkit-user-select: all !important; - -moz-user-select: all !important; - user-select: all !important; -} - -.user-select-auto { - -webkit-user-select: auto !important; - -moz-user-select: auto !important; - -ms-user-select: auto !important; - user-select: auto !important; -} - -.user-select-none { - -webkit-user-select: none !important; - -moz-user-select: none !important; - -ms-user-select: none !important; - user-select: none !important; -} - -.pe-none { - pointer-events: none !important; -} - -.pe-auto { - pointer-events: auto !important; -} - -.rounded { - border-radius: 0.25rem !important; -} - -.rounded-0 { - border-radius: 0 !important; -} - -.rounded-1 { - border-radius: 0.2rem !important; -} - -.rounded-2 { - border-radius: 0.25rem !important; -} - -.rounded-3 { - border-radius: 0.3rem !important; -} - -.rounded-circle { - border-radius: 50% !important; -} - -.rounded-pill { - border-radius: 50rem !important; -} - -.rounded-top { - border-top-left-radius: 0.25rem !important; - border-top-right-radius: 0.25rem !important; -} - -.rounded-end { - border-top-right-radius: 0.25rem !important; - border-bottom-right-radius: 0.25rem !important; -} - -.rounded-bottom { - border-bottom-right-radius: 0.25rem !important; - border-bottom-left-radius: 0.25rem !important; -} - -.rounded-start { - border-bottom-left-radius: 0.25rem !important; - border-top-left-radius: 0.25rem !important; -} - -.visible { - visibility: visible !important; -} - -.invisible { - visibility: hidden !important; -} - -@media (min-width: 576px) { - .float-sm-start { - float: left !important; - } - - .float-sm-end { - float: right !important; - } - - .float-sm-none { - float: none !important; - } - - .d-sm-inline { - display: inline !important; - } - - .d-sm-inline-block { - display: inline-block !important; - } - - .d-sm-block { - display: block !important; - } - - .d-sm-grid { - display: grid !important; - } - - .d-sm-table { - display: table !important; - } - - .d-sm-table-row { - display: table-row !important; - } - - .d-sm-table-cell { - display: table-cell !important; - } - - .d-sm-flex { - display: flex !important; - } - - .d-sm-inline-flex { - display: inline-flex !important; - } - - .d-sm-none { - display: none !important; - } - - .flex-sm-fill { - flex: 1 1 auto !important; - } - - .flex-sm-row { - flex-direction: row !important; - } - - .flex-sm-column { - flex-direction: column !important; - } - - .flex-sm-row-reverse { - flex-direction: row-reverse !important; - } - - .flex-sm-column-reverse { - flex-direction: column-reverse !important; - } - - .flex-sm-grow-0 { - flex-grow: 0 !important; - } - - .flex-sm-grow-1 { - flex-grow: 1 !important; - } - - .flex-sm-shrink-0 { - flex-shrink: 0 !important; - } - - .flex-sm-shrink-1 { - flex-shrink: 1 !important; - } - - .flex-sm-wrap { - flex-wrap: wrap !important; - } - - .flex-sm-nowrap { - flex-wrap: nowrap !important; - } - - .flex-sm-wrap-reverse { - flex-wrap: wrap-reverse !important; - } - - .gap-sm-0 { - gap: 0 !important; - } - - .gap-sm-1 { - gap: 0.25rem !important; - } - - .gap-sm-2 { - gap: 0.5rem !important; - } - - .gap-sm-3 { - gap: 1rem !important; - } - - .gap-sm-4 { - gap: 1.5rem !important; - } - - .gap-sm-5 { - gap: 3rem !important; - } - - .justify-content-sm-start { - justify-content: flex-start !important; - } - - .justify-content-sm-end { - justify-content: flex-end !important; - } - - .justify-content-sm-center { - justify-content: center !important; - } - - .justify-content-sm-between { - justify-content: space-between !important; - } - - .justify-content-sm-around { - justify-content: space-around !important; - } - - .justify-content-sm-evenly { - justify-content: space-evenly !important; - } - - .align-items-sm-start { - align-items: flex-start !important; - } - - .align-items-sm-end { - align-items: flex-end !important; - } - - .align-items-sm-center { - align-items: center !important; - } - - .align-items-sm-baseline { - align-items: baseline !important; - } - - .align-items-sm-stretch { - align-items: stretch !important; - } - - .align-content-sm-start { - align-content: flex-start !important; - } - - .align-content-sm-end { - align-content: flex-end !important; - } - - .align-content-sm-center { - align-content: center !important; - } - - .align-content-sm-between { - align-content: space-between !important; - } - - .align-content-sm-around { - align-content: space-around !important; - } - - .align-content-sm-stretch { - align-content: stretch !important; - } - - .align-self-sm-auto { - align-self: auto !important; - } - - .align-self-sm-start { - align-self: flex-start !important; - } - - .align-self-sm-end { - align-self: flex-end !important; - } - - .align-self-sm-center { - align-self: center !important; - } - - .align-self-sm-baseline { - align-self: baseline !important; - } - - .align-self-sm-stretch { - align-self: stretch !important; - } - - .order-sm-first { - order: -1 !important; - } - - .order-sm-0 { - order: 0 !important; - } - - .order-sm-1 { - order: 1 !important; - } - - .order-sm-2 { - order: 2 !important; - } - - .order-sm-3 { - order: 3 !important; - } - - .order-sm-4 { - order: 4 !important; - } - - .order-sm-5 { - order: 5 !important; - } - - .order-sm-last { - order: 6 !important; - } - - .m-sm-0 { - margin: 0 !important; - } - - .m-sm-1 { - margin: 0.25rem !important; - } - - .m-sm-2 { - margin: 0.5rem !important; - } - - .m-sm-3 { - margin: 1rem !important; - } - - .m-sm-4 { - margin: 1.5rem !important; - } - - .m-sm-5 { - margin: 3rem !important; - } - - .m-sm-auto { - margin: auto !important; - } - - .mx-sm-0 { - margin-right: 0 !important; - margin-left: 0 !important; - } - - .mx-sm-1 { - margin-right: 0.25rem !important; - margin-left: 0.25rem !important; - } - - .mx-sm-2 { - margin-right: 0.5rem !important; - margin-left: 0.5rem !important; - } - - .mx-sm-3 { - margin-right: 1rem !important; - margin-left: 1rem !important; - } - - .mx-sm-4 { - margin-right: 1.5rem !important; - margin-left: 1.5rem !important; - } - - .mx-sm-5 { - margin-right: 3rem !important; - margin-left: 3rem !important; - } - - .mx-sm-auto { - margin-right: auto !important; - margin-left: auto !important; - } - - .my-sm-0 { - margin-top: 0 !important; - margin-bottom: 0 !important; - } - - .my-sm-1 { - margin-top: 0.25rem !important; - margin-bottom: 0.25rem !important; - } - - .my-sm-2 { - margin-top: 0.5rem !important; - margin-bottom: 0.5rem !important; - } - - .my-sm-3 { - margin-top: 1rem !important; - margin-bottom: 1rem !important; - } - - .my-sm-4 { - margin-top: 1.5rem !important; - margin-bottom: 1.5rem !important; - } - - .my-sm-5 { - margin-top: 3rem !important; - margin-bottom: 3rem !important; - } - - .my-sm-auto { - margin-top: auto !important; - margin-bottom: auto !important; - } - - .mt-sm-0 { - margin-top: 0 !important; - } - - .mt-sm-1 { - margin-top: 0.25rem !important; - } - - .mt-sm-2 { - margin-top: 0.5rem !important; - } - - .mt-sm-3 { - margin-top: 1rem !important; - } - - .mt-sm-4 { - margin-top: 1.5rem !important; - } - - .mt-sm-5 { - margin-top: 3rem !important; - } - - .mt-sm-auto { - margin-top: auto !important; - } - - .me-sm-0 { - margin-right: 0 !important; - } - - .me-sm-1 { - margin-right: 0.25rem !important; - } - - .me-sm-2 { - margin-right: 0.5rem !important; - } - - .me-sm-3 { - margin-right: 1rem !important; - } - - .me-sm-4 { - margin-right: 1.5rem !important; - } - - .me-sm-5 { - margin-right: 3rem !important; - } - - .me-sm-auto { - margin-right: auto !important; - } - - .mb-sm-0 { - margin-bottom: 0 !important; - } - - .mb-sm-1 { - margin-bottom: 0.25rem !important; - } - - .mb-sm-2 { - margin-bottom: 0.5rem !important; - } - - .mb-sm-3 { - margin-bottom: 1rem !important; - } - - .mb-sm-4 { - margin-bottom: 1.5rem !important; - } - - .mb-sm-5 { - margin-bottom: 3rem !important; - } - - .mb-sm-auto { - margin-bottom: auto !important; - } - - .ms-sm-0 { - margin-left: 0 !important; - } - - .ms-sm-1 { - margin-left: 0.25rem !important; - } - - .ms-sm-2 { - margin-left: 0.5rem !important; - } - - .ms-sm-3 { - margin-left: 1rem !important; - } - - .ms-sm-4 { - margin-left: 1.5rem !important; - } - - .ms-sm-5 { - margin-left: 3rem !important; - } - - .ms-sm-auto { - margin-left: auto !important; - } - - .p-sm-0 { - padding: 0 !important; - } - - .p-sm-1 { - padding: 0.25rem !important; - } - - .p-sm-2 { - padding: 0.5rem !important; - } - - .p-sm-3 { - padding: 1rem !important; - } - - .p-sm-4 { - padding: 1.5rem !important; - } - - .p-sm-5 { - padding: 3rem !important; - } - - .px-sm-0 { - padding-right: 0 !important; - padding-left: 0 !important; - } - - .px-sm-1 { - padding-right: 0.25rem !important; - padding-left: 0.25rem !important; - } - - .px-sm-2 { - padding-right: 0.5rem !important; - padding-left: 0.5rem !important; - } - - .px-sm-3 { - padding-right: 1rem !important; - padding-left: 1rem !important; - } - - .px-sm-4 { - padding-right: 1.5rem !important; - padding-left: 1.5rem !important; - } - - .px-sm-5 { - padding-right: 3rem !important; - padding-left: 3rem !important; - } - - .py-sm-0 { - padding-top: 0 !important; - padding-bottom: 0 !important; - } - - .py-sm-1 { - padding-top: 0.25rem !important; - padding-bottom: 0.25rem !important; - } - - .py-sm-2 { - padding-top: 0.5rem !important; - padding-bottom: 0.5rem !important; - } - - .py-sm-3 { - padding-top: 1rem !important; - padding-bottom: 1rem !important; - } - - .py-sm-4 { - padding-top: 1.5rem !important; - padding-bottom: 1.5rem !important; - } - - .py-sm-5 { - padding-top: 3rem !important; - padding-bottom: 3rem !important; - } - - .pt-sm-0 { - padding-top: 0 !important; - } - - .pt-sm-1 { - padding-top: 0.25rem !important; - } - - .pt-sm-2 { - padding-top: 0.5rem !important; - } - - .pt-sm-3 { - padding-top: 1rem !important; - } - - .pt-sm-4 { - padding-top: 1.5rem !important; - } - - .pt-sm-5 { - padding-top: 3rem !important; - } - - .pe-sm-0 { - padding-right: 0 !important; - } - - .pe-sm-1 { - padding-right: 0.25rem !important; - } - - .pe-sm-2 { - padding-right: 0.5rem !important; - } - - .pe-sm-3 { - padding-right: 1rem !important; - } - - .pe-sm-4 { - padding-right: 1.5rem !important; - } - - .pe-sm-5 { - padding-right: 3rem !important; - } - - .pb-sm-0 { - padding-bottom: 0 !important; - } - - .pb-sm-1 { - padding-bottom: 0.25rem !important; - } - - .pb-sm-2 { - padding-bottom: 0.5rem !important; - } - - .pb-sm-3 { - padding-bottom: 1rem !important; - } - - .pb-sm-4 { - padding-bottom: 1.5rem !important; - } - - .pb-sm-5 { - padding-bottom: 3rem !important; - } - - .ps-sm-0 { - padding-left: 0 !important; - } - - .ps-sm-1 { - padding-left: 0.25rem !important; - } - - .ps-sm-2 { - padding-left: 0.5rem !important; - } - - .ps-sm-3 { - padding-left: 1rem !important; - } - - .ps-sm-4 { - padding-left: 1.5rem !important; - } - - .ps-sm-5 { - padding-left: 3rem !important; - } - - .text-sm-start { - text-align: left !important; - } - - .text-sm-end { - text-align: right !important; - } - - .text-sm-center { - text-align: center !important; - } -} - -@media (min-width: 768px) { - .float-md-start { - float: left !important; - } - - .float-md-end { - float: right !important; - } - - .float-md-none { - float: none !important; - } - - .d-md-inline { - display: inline !important; - } - - .d-md-inline-block { - display: inline-block !important; - } - - .d-md-block { - display: block !important; - } - - .d-md-grid { - display: grid !important; - } - - .d-md-table { - display: table !important; - } - - .d-md-table-row { - display: table-row !important; - } - - .d-md-table-cell { - display: table-cell !important; - } - - .d-md-flex { - display: flex !important; - } - - .d-md-inline-flex { - display: inline-flex !important; - } - - .d-md-none { - display: none !important; - } - - .flex-md-fill { - flex: 1 1 auto !important; - } - - .flex-md-row { - flex-direction: row !important; - } - - .flex-md-column { - flex-direction: column !important; - } - - .flex-md-row-reverse { - flex-direction: row-reverse !important; - } - - .flex-md-column-reverse { - flex-direction: column-reverse !important; - } - - .flex-md-grow-0 { - flex-grow: 0 !important; - } - - .flex-md-grow-1 { - flex-grow: 1 !important; - } - - .flex-md-shrink-0 { - flex-shrink: 0 !important; - } - - .flex-md-shrink-1 { - flex-shrink: 1 !important; - } - - .flex-md-wrap { - flex-wrap: wrap !important; - } - - .flex-md-nowrap { - flex-wrap: nowrap !important; - } - - .flex-md-wrap-reverse { - flex-wrap: wrap-reverse !important; - } - - .gap-md-0 { - gap: 0 !important; - } - - .gap-md-1 { - gap: 0.25rem !important; - } - - .gap-md-2 { - gap: 0.5rem !important; - } - - .gap-md-3 { - gap: 1rem !important; - } - - .gap-md-4 { - gap: 1.5rem !important; - } - - .gap-md-5 { - gap: 3rem !important; - } - - .justify-content-md-start { - justify-content: flex-start !important; - } - - .justify-content-md-end { - justify-content: flex-end !important; - } - - .justify-content-md-center { - justify-content: center !important; - } - - .justify-content-md-between { - justify-content: space-between !important; - } - - .justify-content-md-around { - justify-content: space-around !important; - } - - .justify-content-md-evenly { - justify-content: space-evenly !important; - } - - .align-items-md-start { - align-items: flex-start !important; - } - - .align-items-md-end { - align-items: flex-end !important; - } - - .align-items-md-center { - align-items: center !important; - } - - .align-items-md-baseline { - align-items: baseline !important; - } - - .align-items-md-stretch { - align-items: stretch !important; - } - - .align-content-md-start { - align-content: flex-start !important; - } - - .align-content-md-end { - align-content: flex-end !important; - } - - .align-content-md-center { - align-content: center !important; - } - - .align-content-md-between { - align-content: space-between !important; - } - - .align-content-md-around { - align-content: space-around !important; - } - - .align-content-md-stretch { - align-content: stretch !important; - } - - .align-self-md-auto { - align-self: auto !important; - } - - .align-self-md-start { - align-self: flex-start !important; - } - - .align-self-md-end { - align-self: flex-end !important; - } - - .align-self-md-center { - align-self: center !important; - } - - .align-self-md-baseline { - align-self: baseline !important; - } - - .align-self-md-stretch { - align-self: stretch !important; - } - - .order-md-first { - order: -1 !important; - } - - .order-md-0 { - order: 0 !important; - } - - .order-md-1 { - order: 1 !important; - } - - .order-md-2 { - order: 2 !important; - } - - .order-md-3 { - order: 3 !important; - } - - .order-md-4 { - order: 4 !important; - } - - .order-md-5 { - order: 5 !important; - } - - .order-md-last { - order: 6 !important; - } - - .m-md-0 { - margin: 0 !important; - } - - .m-md-1 { - margin: 0.25rem !important; - } - - .m-md-2 { - margin: 0.5rem !important; - } - - .m-md-3 { - margin: 1rem !important; - } - - .m-md-4 { - margin: 1.5rem !important; - } - - .m-md-5 { - margin: 3rem !important; - } - - .m-md-auto { - margin: auto !important; - } - - .mx-md-0 { - margin-right: 0 !important; - margin-left: 0 !important; - } - - .mx-md-1 { - margin-right: 0.25rem !important; - margin-left: 0.25rem !important; - } - - .mx-md-2 { - margin-right: 0.5rem !important; - margin-left: 0.5rem !important; - } - - .mx-md-3 { - margin-right: 1rem !important; - margin-left: 1rem !important; - } - - .mx-md-4 { - margin-right: 1.5rem !important; - margin-left: 1.5rem !important; - } - - .mx-md-5 { - margin-right: 3rem !important; - margin-left: 3rem !important; - } - - .mx-md-auto { - margin-right: auto !important; - margin-left: auto !important; - } - - .my-md-0 { - margin-top: 0 !important; - margin-bottom: 0 !important; - } - - .my-md-1 { - margin-top: 0.25rem !important; - margin-bottom: 0.25rem !important; - } - - .my-md-2 { - margin-top: 0.5rem !important; - margin-bottom: 0.5rem !important; - } - - .my-md-3 { - margin-top: 1rem !important; - margin-bottom: 1rem !important; - } - - .my-md-4 { - margin-top: 1.5rem !important; - margin-bottom: 1.5rem !important; - } - - .my-md-5 { - margin-top: 3rem !important; - margin-bottom: 3rem !important; - } - - .my-md-auto { - margin-top: auto !important; - margin-bottom: auto !important; - } - - .mt-md-0 { - margin-top: 0 !important; - } - - .mt-md-1 { - margin-top: 0.25rem !important; - } - - .mt-md-2 { - margin-top: 0.5rem !important; - } - - .mt-md-3 { - margin-top: 1rem !important; - } - - .mt-md-4 { - margin-top: 1.5rem !important; - } - - .mt-md-5 { - margin-top: 3rem !important; - } - - .mt-md-auto { - margin-top: auto !important; - } - - .me-md-0 { - margin-right: 0 !important; - } - - .me-md-1 { - margin-right: 0.25rem !important; - } - - .me-md-2 { - margin-right: 0.5rem !important; - } - - .me-md-3 { - margin-right: 1rem !important; - } - - .me-md-4 { - margin-right: 1.5rem !important; - } - - .me-md-5 { - margin-right: 3rem !important; - } - - .me-md-auto { - margin-right: auto !important; - } - - .mb-md-0 { - margin-bottom: 0 !important; - } - - .mb-md-1 { - margin-bottom: 0.25rem !important; - } - - .mb-md-2 { - margin-bottom: 0.5rem !important; - } - - .mb-md-3 { - margin-bottom: 1rem !important; - } - - .mb-md-4 { - margin-bottom: 1.5rem !important; - } - - .mb-md-5 { - margin-bottom: 3rem !important; - } - - .mb-md-auto { - margin-bottom: auto !important; - } - - .ms-md-0 { - margin-left: 0 !important; - } - - .ms-md-1 { - margin-left: 0.25rem !important; - } - - .ms-md-2 { - margin-left: 0.5rem !important; - } - - .ms-md-3 { - margin-left: 1rem !important; - } - - .ms-md-4 { - margin-left: 1.5rem !important; - } - - .ms-md-5 { - margin-left: 3rem !important; - } - - .ms-md-auto { - margin-left: auto !important; - } - - .p-md-0 { - padding: 0 !important; - } - - .p-md-1 { - padding: 0.25rem !important; - } - - .p-md-2 { - padding: 0.5rem !important; - } - - .p-md-3 { - padding: 1rem !important; - } - - .p-md-4 { - padding: 1.5rem !important; - } - - .p-md-5 { - padding: 3rem !important; - } - - .px-md-0 { - padding-right: 0 !important; - padding-left: 0 !important; - } - - .px-md-1 { - padding-right: 0.25rem !important; - padding-left: 0.25rem !important; - } - - .px-md-2 { - padding-right: 0.5rem !important; - padding-left: 0.5rem !important; - } - - .px-md-3 { - padding-right: 1rem !important; - padding-left: 1rem !important; - } - - .px-md-4 { - padding-right: 1.5rem !important; - padding-left: 1.5rem !important; - } - - .px-md-5 { - padding-right: 3rem !important; - padding-left: 3rem !important; - } - - .py-md-0 { - padding-top: 0 !important; - padding-bottom: 0 !important; - } - - .py-md-1 { - padding-top: 0.25rem !important; - padding-bottom: 0.25rem !important; - } - - .py-md-2 { - padding-top: 0.5rem !important; - padding-bottom: 0.5rem !important; - } - - .py-md-3 { - padding-top: 1rem !important; - padding-bottom: 1rem !important; - } - - .py-md-4 { - padding-top: 1.5rem !important; - padding-bottom: 1.5rem !important; - } - - .py-md-5 { - padding-top: 3rem !important; - padding-bottom: 3rem !important; - } - - .pt-md-0 { - padding-top: 0 !important; - } - - .pt-md-1 { - padding-top: 0.25rem !important; - } - - .pt-md-2 { - padding-top: 0.5rem !important; - } - - .pt-md-3 { - padding-top: 1rem !important; - } - - .pt-md-4 { - padding-top: 1.5rem !important; - } - - .pt-md-5 { - padding-top: 3rem !important; - } - - .pe-md-0 { - padding-right: 0 !important; - } - - .pe-md-1 { - padding-right: 0.25rem !important; - } - - .pe-md-2 { - padding-right: 0.5rem !important; - } - - .pe-md-3 { - padding-right: 1rem !important; - } - - .pe-md-4 { - padding-right: 1.5rem !important; - } - - .pe-md-5 { - padding-right: 3rem !important; - } - - .pb-md-0 { - padding-bottom: 0 !important; - } - - .pb-md-1 { - padding-bottom: 0.25rem !important; - } - - .pb-md-2 { - padding-bottom: 0.5rem !important; - } - - .pb-md-3 { - padding-bottom: 1rem !important; - } - - .pb-md-4 { - padding-bottom: 1.5rem !important; - } - - .pb-md-5 { - padding-bottom: 3rem !important; - } - - .ps-md-0 { - padding-left: 0 !important; - } - - .ps-md-1 { - padding-left: 0.25rem !important; - } - - .ps-md-2 { - padding-left: 0.5rem !important; - } - - .ps-md-3 { - padding-left: 1rem !important; - } - - .ps-md-4 { - padding-left: 1.5rem !important; - } - - .ps-md-5 { - padding-left: 3rem !important; - } - - .text-md-start { - text-align: left !important; - } - - .text-md-end { - text-align: right !important; - } - - .text-md-center { - text-align: center !important; - } -} - -@media (min-width: 992px) { - .float-lg-start { - float: left !important; - } - - .float-lg-end { - float: right !important; - } - - .float-lg-none { - float: none !important; - } - - .d-lg-inline { - display: inline !important; - } - - .d-lg-inline-block { - display: inline-block !important; - } - - .d-lg-block { - display: block !important; - } - - .d-lg-grid { - display: grid !important; - } - - .d-lg-table { - display: table !important; - } - - .d-lg-table-row { - display: table-row !important; - } - - .d-lg-table-cell { - display: table-cell !important; - } - - .d-lg-flex { - display: flex !important; - } - - .d-lg-inline-flex { - display: inline-flex !important; - } - - .d-lg-none { - display: none !important; - } - - .flex-lg-fill { - flex: 1 1 auto !important; - } - - .flex-lg-row { - flex-direction: row !important; - } - - .flex-lg-column { - flex-direction: column !important; - } - - .flex-lg-row-reverse { - flex-direction: row-reverse !important; - } - - .flex-lg-column-reverse { - flex-direction: column-reverse !important; - } - - .flex-lg-grow-0 { - flex-grow: 0 !important; - } - - .flex-lg-grow-1 { - flex-grow: 1 !important; - } - - .flex-lg-shrink-0 { - flex-shrink: 0 !important; - } - - .flex-lg-shrink-1 { - flex-shrink: 1 !important; - } - - .flex-lg-wrap { - flex-wrap: wrap !important; - } - - .flex-lg-nowrap { - flex-wrap: nowrap !important; - } - - .flex-lg-wrap-reverse { - flex-wrap: wrap-reverse !important; - } - - .gap-lg-0 { - gap: 0 !important; - } - - .gap-lg-1 { - gap: 0.25rem !important; - } - - .gap-lg-2 { - gap: 0.5rem !important; - } - - .gap-lg-3 { - gap: 1rem !important; - } - - .gap-lg-4 { - gap: 1.5rem !important; - } - - .gap-lg-5 { - gap: 3rem !important; - } - - .justify-content-lg-start { - justify-content: flex-start !important; - } - - .justify-content-lg-end { - justify-content: flex-end !important; - } - - .justify-content-lg-center { - justify-content: center !important; - } - - .justify-content-lg-between { - justify-content: space-between !important; - } - - .justify-content-lg-around { - justify-content: space-around !important; - } - - .justify-content-lg-evenly { - justify-content: space-evenly !important; - } - - .align-items-lg-start { - align-items: flex-start !important; - } - - .align-items-lg-end { - align-items: flex-end !important; - } - - .align-items-lg-center { - align-items: center !important; - } - - .align-items-lg-baseline { - align-items: baseline !important; - } - - .align-items-lg-stretch { - align-items: stretch !important; - } - - .align-content-lg-start { - align-content: flex-start !important; - } - - .align-content-lg-end { - align-content: flex-end !important; - } - - .align-content-lg-center { - align-content: center !important; - } - - .align-content-lg-between { - align-content: space-between !important; - } - - .align-content-lg-around { - align-content: space-around !important; - } - - .align-content-lg-stretch { - align-content: stretch !important; - } - - .align-self-lg-auto { - align-self: auto !important; - } - - .align-self-lg-start { - align-self: flex-start !important; - } - - .align-self-lg-end { - align-self: flex-end !important; - } - - .align-self-lg-center { - align-self: center !important; - } - - .align-self-lg-baseline { - align-self: baseline !important; - } - - .align-self-lg-stretch { - align-self: stretch !important; - } - - .order-lg-first { - order: -1 !important; - } - - .order-lg-0 { - order: 0 !important; - } - - .order-lg-1 { - order: 1 !important; - } - - .order-lg-2 { - order: 2 !important; - } - - .order-lg-3 { - order: 3 !important; - } - - .order-lg-4 { - order: 4 !important; - } - - .order-lg-5 { - order: 5 !important; - } - - .order-lg-last { - order: 6 !important; - } - - .m-lg-0 { - margin: 0 !important; - } - - .m-lg-1 { - margin: 0.25rem !important; - } - - .m-lg-2 { - margin: 0.5rem !important; - } - - .m-lg-3 { - margin: 1rem !important; - } - - .m-lg-4 { - margin: 1.5rem !important; - } - - .m-lg-5 { - margin: 3rem !important; - } - - .m-lg-auto { - margin: auto !important; - } - - .mx-lg-0 { - margin-right: 0 !important; - margin-left: 0 !important; - } - - .mx-lg-1 { - margin-right: 0.25rem !important; - margin-left: 0.25rem !important; - } - - .mx-lg-2 { - margin-right: 0.5rem !important; - margin-left: 0.5rem !important; - } - - .mx-lg-3 { - margin-right: 1rem !important; - margin-left: 1rem !important; - } - - .mx-lg-4 { - margin-right: 1.5rem !important; - margin-left: 1.5rem !important; - } - - .mx-lg-5 { - margin-right: 3rem !important; - margin-left: 3rem !important; - } - - .mx-lg-auto { - margin-right: auto !important; - margin-left: auto !important; - } - - .my-lg-0 { - margin-top: 0 !important; - margin-bottom: 0 !important; - } - - .my-lg-1 { - margin-top: 0.25rem !important; - margin-bottom: 0.25rem !important; - } - - .my-lg-2 { - margin-top: 0.5rem !important; - margin-bottom: 0.5rem !important; - } - - .my-lg-3 { - margin-top: 1rem !important; - margin-bottom: 1rem !important; - } - - .my-lg-4 { - margin-top: 1.5rem !important; - margin-bottom: 1.5rem !important; - } - - .my-lg-5 { - margin-top: 3rem !important; - margin-bottom: 3rem !important; - } - - .my-lg-auto { - margin-top: auto !important; - margin-bottom: auto !important; - } - - .mt-lg-0 { - margin-top: 0 !important; - } - - .mt-lg-1 { - margin-top: 0.25rem !important; - } - - .mt-lg-2 { - margin-top: 0.5rem !important; - } - - .mt-lg-3 { - margin-top: 1rem !important; - } - - .mt-lg-4 { - margin-top: 1.5rem !important; - } - - .mt-lg-5 { - margin-top: 3rem !important; - } - - .mt-lg-auto { - margin-top: auto !important; - } - - .me-lg-0 { - margin-right: 0 !important; - } - - .me-lg-1 { - margin-right: 0.25rem !important; - } - - .me-lg-2 { - margin-right: 0.5rem !important; - } - - .me-lg-3 { - margin-right: 1rem !important; - } - - .me-lg-4 { - margin-right: 1.5rem !important; - } - - .me-lg-5 { - margin-right: 3rem !important; - } - - .me-lg-auto { - margin-right: auto !important; - } - - .mb-lg-0 { - margin-bottom: 0 !important; - } - - .mb-lg-1 { - margin-bottom: 0.25rem !important; - } - - .mb-lg-2 { - margin-bottom: 0.5rem !important; - } - - .mb-lg-3 { - margin-bottom: 1rem !important; - } - - .mb-lg-4 { - margin-bottom: 1.5rem !important; - } - - .mb-lg-5 { - margin-bottom: 3rem !important; - } - - .mb-lg-auto { - margin-bottom: auto !important; - } - - .ms-lg-0 { - margin-left: 0 !important; - } - - .ms-lg-1 { - margin-left: 0.25rem !important; - } - - .ms-lg-2 { - margin-left: 0.5rem !important; - } - - .ms-lg-3 { - margin-left: 1rem !important; - } - - .ms-lg-4 { - margin-left: 1.5rem !important; - } - - .ms-lg-5 { - margin-left: 3rem !important; - } - - .ms-lg-auto { - margin-left: auto !important; - } - - .p-lg-0 { - padding: 0 !important; - } - - .p-lg-1 { - padding: 0.25rem !important; - } - - .p-lg-2 { - padding: 0.5rem !important; - } - - .p-lg-3 { - padding: 1rem !important; - } - - .p-lg-4 { - padding: 1.5rem !important; - } - - .p-lg-5 { - padding: 3rem !important; - } - - .px-lg-0 { - padding-right: 0 !important; - padding-left: 0 !important; - } - - .px-lg-1 { - padding-right: 0.25rem !important; - padding-left: 0.25rem !important; - } - - .px-lg-2 { - padding-right: 0.5rem !important; - padding-left: 0.5rem !important; - } - - .px-lg-3 { - padding-right: 1rem !important; - padding-left: 1rem !important; - } - - .px-lg-4 { - padding-right: 1.5rem !important; - padding-left: 1.5rem !important; - } - - .px-lg-5 { - padding-right: 3rem !important; - padding-left: 3rem !important; - } - - .py-lg-0 { - padding-top: 0 !important; - padding-bottom: 0 !important; - } - - .py-lg-1 { - padding-top: 0.25rem !important; - padding-bottom: 0.25rem !important; - } - - .py-lg-2 { - padding-top: 0.5rem !important; - padding-bottom: 0.5rem !important; - } - - .py-lg-3 { - padding-top: 1rem !important; - padding-bottom: 1rem !important; - } - - .py-lg-4 { - padding-top: 1.5rem !important; - padding-bottom: 1.5rem !important; - } - - .py-lg-5 { - padding-top: 3rem !important; - padding-bottom: 3rem !important; - } - - .pt-lg-0 { - padding-top: 0 !important; - } - - .pt-lg-1 { - padding-top: 0.25rem !important; - } - - .pt-lg-2 { - padding-top: 0.5rem !important; - } - - .pt-lg-3 { - padding-top: 1rem !important; - } - - .pt-lg-4 { - padding-top: 1.5rem !important; - } - - .pt-lg-5 { - padding-top: 3rem !important; - } - - .pe-lg-0 { - padding-right: 0 !important; - } - - .pe-lg-1 { - padding-right: 0.25rem !important; - } - - .pe-lg-2 { - padding-right: 0.5rem !important; - } - - .pe-lg-3 { - padding-right: 1rem !important; - } - - .pe-lg-4 { - padding-right: 1.5rem !important; - } - - .pe-lg-5 { - padding-right: 3rem !important; - } - - .pb-lg-0 { - padding-bottom: 0 !important; - } - - .pb-lg-1 { - padding-bottom: 0.25rem !important; - } - - .pb-lg-2 { - padding-bottom: 0.5rem !important; - } - - .pb-lg-3 { - padding-bottom: 1rem !important; - } - - .pb-lg-4 { - padding-bottom: 1.5rem !important; - } - - .pb-lg-5 { - padding-bottom: 3rem !important; - } - - .ps-lg-0 { - padding-left: 0 !important; - } - - .ps-lg-1 { - padding-left: 0.25rem !important; - } - - .ps-lg-2 { - padding-left: 0.5rem !important; - } - - .ps-lg-3 { - padding-left: 1rem !important; - } - - .ps-lg-4 { - padding-left: 1.5rem !important; - } - - .ps-lg-5 { - padding-left: 3rem !important; - } - - .text-lg-start { - text-align: left !important; - } - - .text-lg-end { - text-align: right !important; - } - - .text-lg-center { - text-align: center !important; - } -} - -@media (min-width: 1200px) { - .float-xl-start { - float: left !important; - } - - .float-xl-end { - float: right !important; - } - - .float-xl-none { - float: none !important; - } - - .d-xl-inline { - display: inline !important; - } - - .d-xl-inline-block { - display: inline-block !important; - } - - .d-xl-block { - display: block !important; - } - - .d-xl-grid { - display: grid !important; - } - - .d-xl-table { - display: table !important; - } - - .d-xl-table-row { - display: table-row !important; - } - - .d-xl-table-cell { - display: table-cell !important; - } - - .d-xl-flex { - display: flex !important; - } - - .d-xl-inline-flex { - display: inline-flex !important; - } - - .d-xl-none { - display: none !important; - } - - .flex-xl-fill { - flex: 1 1 auto !important; - } - - .flex-xl-row { - flex-direction: row !important; - } - - .flex-xl-column { - flex-direction: column !important; - } - - .flex-xl-row-reverse { - flex-direction: row-reverse !important; - } - - .flex-xl-column-reverse { - flex-direction: column-reverse !important; - } - - .flex-xl-grow-0 { - flex-grow: 0 !important; - } - - .flex-xl-grow-1 { - flex-grow: 1 !important; - } - - .flex-xl-shrink-0 { - flex-shrink: 0 !important; - } - - .flex-xl-shrink-1 { - flex-shrink: 1 !important; - } - - .flex-xl-wrap { - flex-wrap: wrap !important; - } - - .flex-xl-nowrap { - flex-wrap: nowrap !important; - } - - .flex-xl-wrap-reverse { - flex-wrap: wrap-reverse !important; - } - - .gap-xl-0 { - gap: 0 !important; - } - - .gap-xl-1 { - gap: 0.25rem !important; - } - - .gap-xl-2 { - gap: 0.5rem !important; - } - - .gap-xl-3 { - gap: 1rem !important; - } - - .gap-xl-4 { - gap: 1.5rem !important; - } - - .gap-xl-5 { - gap: 3rem !important; - } - - .justify-content-xl-start { - justify-content: flex-start !important; - } - - .justify-content-xl-end { - justify-content: flex-end !important; - } - - .justify-content-xl-center { - justify-content: center !important; - } - - .justify-content-xl-between { - justify-content: space-between !important; - } - - .justify-content-xl-around { - justify-content: space-around !important; - } - - .justify-content-xl-evenly { - justify-content: space-evenly !important; - } - - .align-items-xl-start { - align-items: flex-start !important; - } - - .align-items-xl-end { - align-items: flex-end !important; - } - - .align-items-xl-center { - align-items: center !important; - } - - .align-items-xl-baseline { - align-items: baseline !important; - } - - .align-items-xl-stretch { - align-items: stretch !important; - } - - .align-content-xl-start { - align-content: flex-start !important; - } - - .align-content-xl-end { - align-content: flex-end !important; - } - - .align-content-xl-center { - align-content: center !important; - } - - .align-content-xl-between { - align-content: space-between !important; - } - - .align-content-xl-around { - align-content: space-around !important; - } - - .align-content-xl-stretch { - align-content: stretch !important; - } - - .align-self-xl-auto { - align-self: auto !important; - } - - .align-self-xl-start { - align-self: flex-start !important; - } - - .align-self-xl-end { - align-self: flex-end !important; - } - - .align-self-xl-center { - align-self: center !important; - } - - .align-self-xl-baseline { - align-self: baseline !important; - } - - .align-self-xl-stretch { - align-self: stretch !important; - } - - .order-xl-first { - order: -1 !important; - } - - .order-xl-0 { - order: 0 !important; - } - - .order-xl-1 { - order: 1 !important; - } - - .order-xl-2 { - order: 2 !important; - } - - .order-xl-3 { - order: 3 !important; - } - - .order-xl-4 { - order: 4 !important; - } - - .order-xl-5 { - order: 5 !important; - } - - .order-xl-last { - order: 6 !important; - } - - .m-xl-0 { - margin: 0 !important; - } - - .m-xl-1 { - margin: 0.25rem !important; - } - - .m-xl-2 { - margin: 0.5rem !important; - } - - .m-xl-3 { - margin: 1rem !important; - } - - .m-xl-4 { - margin: 1.5rem !important; - } - - .m-xl-5 { - margin: 3rem !important; - } - - .m-xl-auto { - margin: auto !important; - } - - .mx-xl-0 { - margin-right: 0 !important; - margin-left: 0 !important; - } - - .mx-xl-1 { - margin-right: 0.25rem !important; - margin-left: 0.25rem !important; - } - - .mx-xl-2 { - margin-right: 0.5rem !important; - margin-left: 0.5rem !important; - } - - .mx-xl-3 { - margin-right: 1rem !important; - margin-left: 1rem !important; - } - - .mx-xl-4 { - margin-right: 1.5rem !important; - margin-left: 1.5rem !important; - } - - .mx-xl-5 { - margin-right: 3rem !important; - margin-left: 3rem !important; - } - - .mx-xl-auto { - margin-right: auto !important; - margin-left: auto !important; - } - - .my-xl-0 { - margin-top: 0 !important; - margin-bottom: 0 !important; - } - - .my-xl-1 { - margin-top: 0.25rem !important; - margin-bottom: 0.25rem !important; - } - - .my-xl-2 { - margin-top: 0.5rem !important; - margin-bottom: 0.5rem !important; - } - - .my-xl-3 { - margin-top: 1rem !important; - margin-bottom: 1rem !important; - } - - .my-xl-4 { - margin-top: 1.5rem !important; - margin-bottom: 1.5rem !important; - } - - .my-xl-5 { - margin-top: 3rem !important; - margin-bottom: 3rem !important; - } - - .my-xl-auto { - margin-top: auto !important; - margin-bottom: auto !important; - } - - .mt-xl-0 { - margin-top: 0 !important; - } - - .mt-xl-1 { - margin-top: 0.25rem !important; - } - - .mt-xl-2 { - margin-top: 0.5rem !important; - } - - .mt-xl-3 { - margin-top: 1rem !important; - } - - .mt-xl-4 { - margin-top: 1.5rem !important; - } - - .mt-xl-5 { - margin-top: 3rem !important; - } - - .mt-xl-auto { - margin-top: auto !important; - } - - .me-xl-0 { - margin-right: 0 !important; - } - - .me-xl-1 { - margin-right: 0.25rem !important; - } - - .me-xl-2 { - margin-right: 0.5rem !important; - } - - .me-xl-3 { - margin-right: 1rem !important; - } - - .me-xl-4 { - margin-right: 1.5rem !important; - } - - .me-xl-5 { - margin-right: 3rem !important; - } - - .me-xl-auto { - margin-right: auto !important; - } - - .mb-xl-0 { - margin-bottom: 0 !important; - } - - .mb-xl-1 { - margin-bottom: 0.25rem !important; - } - - .mb-xl-2 { - margin-bottom: 0.5rem !important; - } - - .mb-xl-3 { - margin-bottom: 1rem !important; - } - - .mb-xl-4 { - margin-bottom: 1.5rem !important; - } - - .mb-xl-5 { - margin-bottom: 3rem !important; - } - - .mb-xl-auto { - margin-bottom: auto !important; - } - - .ms-xl-0 { - margin-left: 0 !important; - } - - .ms-xl-1 { - margin-left: 0.25rem !important; - } - - .ms-xl-2 { - margin-left: 0.5rem !important; - } - - .ms-xl-3 { - margin-left: 1rem !important; - } - - .ms-xl-4 { - margin-left: 1.5rem !important; - } - - .ms-xl-5 { - margin-left: 3rem !important; - } - - .ms-xl-auto { - margin-left: auto !important; - } - - .p-xl-0 { - padding: 0 !important; - } - - .p-xl-1 { - padding: 0.25rem !important; - } - - .p-xl-2 { - padding: 0.5rem !important; - } - - .p-xl-3 { - padding: 1rem !important; - } - - .p-xl-4 { - padding: 1.5rem !important; - } - - .p-xl-5 { - padding: 3rem !important; - } - - .px-xl-0 { - padding-right: 0 !important; - padding-left: 0 !important; - } - - .px-xl-1 { - padding-right: 0.25rem !important; - padding-left: 0.25rem !important; - } - - .px-xl-2 { - padding-right: 0.5rem !important; - padding-left: 0.5rem !important; - } - - .px-xl-3 { - padding-right: 1rem !important; - padding-left: 1rem !important; - } - - .px-xl-4 { - padding-right: 1.5rem !important; - padding-left: 1.5rem !important; - } - - .px-xl-5 { - padding-right: 3rem !important; - padding-left: 3rem !important; - } - - .py-xl-0 { - padding-top: 0 !important; - padding-bottom: 0 !important; - } - - .py-xl-1 { - padding-top: 0.25rem !important; - padding-bottom: 0.25rem !important; - } - - .py-xl-2 { - padding-top: 0.5rem !important; - padding-bottom: 0.5rem !important; - } - - .py-xl-3 { - padding-top: 1rem !important; - padding-bottom: 1rem !important; - } - - .py-xl-4 { - padding-top: 1.5rem !important; - padding-bottom: 1.5rem !important; - } - - .py-xl-5 { - padding-top: 3rem !important; - padding-bottom: 3rem !important; - } - - .pt-xl-0 { - padding-top: 0 !important; - } - - .pt-xl-1 { - padding-top: 0.25rem !important; - } - - .pt-xl-2 { - padding-top: 0.5rem !important; - } - - .pt-xl-3 { - padding-top: 1rem !important; - } - - .pt-xl-4 { - padding-top: 1.5rem !important; - } - - .pt-xl-5 { - padding-top: 3rem !important; - } - - .pe-xl-0 { - padding-right: 0 !important; - } - - .pe-xl-1 { - padding-right: 0.25rem !important; - } - - .pe-xl-2 { - padding-right: 0.5rem !important; - } - - .pe-xl-3 { - padding-right: 1rem !important; - } - - .pe-xl-4 { - padding-right: 1.5rem !important; - } - - .pe-xl-5 { - padding-right: 3rem !important; - } - - .pb-xl-0 { - padding-bottom: 0 !important; - } - - .pb-xl-1 { - padding-bottom: 0.25rem !important; - } - - .pb-xl-2 { - padding-bottom: 0.5rem !important; - } - - .pb-xl-3 { - padding-bottom: 1rem !important; - } - - .pb-xl-4 { - padding-bottom: 1.5rem !important; - } - - .pb-xl-5 { - padding-bottom: 3rem !important; - } - - .ps-xl-0 { - padding-left: 0 !important; - } - - .ps-xl-1 { - padding-left: 0.25rem !important; - } - - .ps-xl-2 { - padding-left: 0.5rem !important; - } - - .ps-xl-3 { - padding-left: 1rem !important; - } - - .ps-xl-4 { - padding-left: 1.5rem !important; - } - - .ps-xl-5 { - padding-left: 3rem !important; - } - - .text-xl-start { - text-align: left !important; - } - - .text-xl-end { - text-align: right !important; - } - - .text-xl-center { - text-align: center !important; - } -} - -@media (min-width: 1400px) { - .float-xxl-start { - float: left !important; - } - - .float-xxl-end { - float: right !important; - } - - .float-xxl-none { - float: none !important; - } - - .d-xxl-inline { - display: inline !important; - } - - .d-xxl-inline-block { - display: inline-block !important; - } - - .d-xxl-block { - display: block !important; - } - - .d-xxl-grid { - display: grid !important; - } - - .d-xxl-table { - display: table !important; - } - - .d-xxl-table-row { - display: table-row !important; - } - - .d-xxl-table-cell { - display: table-cell !important; - } - - .d-xxl-flex { - display: flex !important; - } - - .d-xxl-inline-flex { - display: inline-flex !important; - } - - .d-xxl-none { - display: none !important; - } - - .flex-xxl-fill { - flex: 1 1 auto !important; - } - - .flex-xxl-row { - flex-direction: row !important; - } - - .flex-xxl-column { - flex-direction: column !important; - } - - .flex-xxl-row-reverse { - flex-direction: row-reverse !important; - } - - .flex-xxl-column-reverse { - flex-direction: column-reverse !important; - } - - .flex-xxl-grow-0 { - flex-grow: 0 !important; - } - - .flex-xxl-grow-1 { - flex-grow: 1 !important; - } - - .flex-xxl-shrink-0 { - flex-shrink: 0 !important; - } - - .flex-xxl-shrink-1 { - flex-shrink: 1 !important; - } - - .flex-xxl-wrap { - flex-wrap: wrap !important; - } - - .flex-xxl-nowrap { - flex-wrap: nowrap !important; - } - - .flex-xxl-wrap-reverse { - flex-wrap: wrap-reverse !important; - } - - .gap-xxl-0 { - gap: 0 !important; - } - - .gap-xxl-1 { - gap: 0.25rem !important; - } - - .gap-xxl-2 { - gap: 0.5rem !important; - } - - .gap-xxl-3 { - gap: 1rem !important; - } - - .gap-xxl-4 { - gap: 1.5rem !important; - } - - .gap-xxl-5 { - gap: 3rem !important; - } - - .justify-content-xxl-start { - justify-content: flex-start !important; - } - - .justify-content-xxl-end { - justify-content: flex-end !important; - } - - .justify-content-xxl-center { - justify-content: center !important; - } - - .justify-content-xxl-between { - justify-content: space-between !important; - } - - .justify-content-xxl-around { - justify-content: space-around !important; - } - - .justify-content-xxl-evenly { - justify-content: space-evenly !important; - } - - .align-items-xxl-start { - align-items: flex-start !important; - } - - .align-items-xxl-end { - align-items: flex-end !important; - } - - .align-items-xxl-center { - align-items: center !important; - } - - .align-items-xxl-baseline { - align-items: baseline !important; - } - - .align-items-xxl-stretch { - align-items: stretch !important; - } - - .align-content-xxl-start { - align-content: flex-start !important; - } - - .align-content-xxl-end { - align-content: flex-end !important; - } - - .align-content-xxl-center { - align-content: center !important; - } - - .align-content-xxl-between { - align-content: space-between !important; - } - - .align-content-xxl-around { - align-content: space-around !important; - } - - .align-content-xxl-stretch { - align-content: stretch !important; - } - - .align-self-xxl-auto { - align-self: auto !important; - } - - .align-self-xxl-start { - align-self: flex-start !important; - } - - .align-self-xxl-end { - align-self: flex-end !important; - } - - .align-self-xxl-center { - align-self: center !important; - } - - .align-self-xxl-baseline { - align-self: baseline !important; - } - - .align-self-xxl-stretch { - align-self: stretch !important; - } - - .order-xxl-first { - order: -1 !important; - } - - .order-xxl-0 { - order: 0 !important; - } - - .order-xxl-1 { - order: 1 !important; - } - - .order-xxl-2 { - order: 2 !important; - } - - .order-xxl-3 { - order: 3 !important; - } - - .order-xxl-4 { - order: 4 !important; - } - - .order-xxl-5 { - order: 5 !important; - } - - .order-xxl-last { - order: 6 !important; - } - - .m-xxl-0 { - margin: 0 !important; - } - - .m-xxl-1 { - margin: 0.25rem !important; - } - - .m-xxl-2 { - margin: 0.5rem !important; - } - - .m-xxl-3 { - margin: 1rem !important; - } - - .m-xxl-4 { - margin: 1.5rem !important; - } - - .m-xxl-5 { - margin: 3rem !important; - } - - .m-xxl-auto { - margin: auto !important; - } - - .mx-xxl-0 { - margin-right: 0 !important; - margin-left: 0 !important; - } - - .mx-xxl-1 { - margin-right: 0.25rem !important; - margin-left: 0.25rem !important; - } - - .mx-xxl-2 { - margin-right: 0.5rem !important; - margin-left: 0.5rem !important; - } - - .mx-xxl-3 { - margin-right: 1rem !important; - margin-left: 1rem !important; - } - - .mx-xxl-4 { - margin-right: 1.5rem !important; - margin-left: 1.5rem !important; - } - - .mx-xxl-5 { - margin-right: 3rem !important; - margin-left: 3rem !important; - } - - .mx-xxl-auto { - margin-right: auto !important; - margin-left: auto !important; - } - - .my-xxl-0 { - margin-top: 0 !important; - margin-bottom: 0 !important; - } - - .my-xxl-1 { - margin-top: 0.25rem !important; - margin-bottom: 0.25rem !important; - } - - .my-xxl-2 { - margin-top: 0.5rem !important; - margin-bottom: 0.5rem !important; - } - - .my-xxl-3 { - margin-top: 1rem !important; - margin-bottom: 1rem !important; - } - - .my-xxl-4 { - margin-top: 1.5rem !important; - margin-bottom: 1.5rem !important; - } - - .my-xxl-5 { - margin-top: 3rem !important; - margin-bottom: 3rem !important; - } - - .my-xxl-auto { - margin-top: auto !important; - margin-bottom: auto !important; - } - - .mt-xxl-0 { - margin-top: 0 !important; - } - - .mt-xxl-1 { - margin-top: 0.25rem !important; - } - - .mt-xxl-2 { - margin-top: 0.5rem !important; - } - - .mt-xxl-3 { - margin-top: 1rem !important; - } - - .mt-xxl-4 { - margin-top: 1.5rem !important; - } - - .mt-xxl-5 { - margin-top: 3rem !important; - } - - .mt-xxl-auto { - margin-top: auto !important; - } - - .me-xxl-0 { - margin-right: 0 !important; - } - - .me-xxl-1 { - margin-right: 0.25rem !important; - } - - .me-xxl-2 { - margin-right: 0.5rem !important; - } - - .me-xxl-3 { - margin-right: 1rem !important; - } - - .me-xxl-4 { - margin-right: 1.5rem !important; - } - - .me-xxl-5 { - margin-right: 3rem !important; - } - - .me-xxl-auto { - margin-right: auto !important; - } - - .mb-xxl-0 { - margin-bottom: 0 !important; - } - - .mb-xxl-1 { - margin-bottom: 0.25rem !important; - } - - .mb-xxl-2 { - margin-bottom: 0.5rem !important; - } - - .mb-xxl-3 { - margin-bottom: 1rem !important; - } - - .mb-xxl-4 { - margin-bottom: 1.5rem !important; - } - - .mb-xxl-5 { - margin-bottom: 3rem !important; - } - - .mb-xxl-auto { - margin-bottom: auto !important; - } - - .ms-xxl-0 { - margin-left: 0 !important; - } - - .ms-xxl-1 { - margin-left: 0.25rem !important; - } - - .ms-xxl-2 { - margin-left: 0.5rem !important; - } - - .ms-xxl-3 { - margin-left: 1rem !important; - } - - .ms-xxl-4 { - margin-left: 1.5rem !important; - } - - .ms-xxl-5 { - margin-left: 3rem !important; - } - - .ms-xxl-auto { - margin-left: auto !important; - } - - .p-xxl-0 { - padding: 0 !important; - } - - .p-xxl-1 { - padding: 0.25rem !important; - } - - .p-xxl-2 { - padding: 0.5rem !important; - } - - .p-xxl-3 { - padding: 1rem !important; - } - - .p-xxl-4 { - padding: 1.5rem !important; - } - - .p-xxl-5 { - padding: 3rem !important; - } - - .px-xxl-0 { - padding-right: 0 !important; - padding-left: 0 !important; - } - - .px-xxl-1 { - padding-right: 0.25rem !important; - padding-left: 0.25rem !important; - } - - .px-xxl-2 { - padding-right: 0.5rem !important; - padding-left: 0.5rem !important; - } - - .px-xxl-3 { - padding-right: 1rem !important; - padding-left: 1rem !important; - } - - .px-xxl-4 { - padding-right: 1.5rem !important; - padding-left: 1.5rem !important; - } - - .px-xxl-5 { - padding-right: 3rem !important; - padding-left: 3rem !important; - } - - .py-xxl-0 { - padding-top: 0 !important; - padding-bottom: 0 !important; - } - - .py-xxl-1 { - padding-top: 0.25rem !important; - padding-bottom: 0.25rem !important; - } - - .py-xxl-2 { - padding-top: 0.5rem !important; - padding-bottom: 0.5rem !important; - } - - .py-xxl-3 { - padding-top: 1rem !important; - padding-bottom: 1rem !important; - } - - .py-xxl-4 { - padding-top: 1.5rem !important; - padding-bottom: 1.5rem !important; - } - - .py-xxl-5 { - padding-top: 3rem !important; - padding-bottom: 3rem !important; - } - - .pt-xxl-0 { - padding-top: 0 !important; - } - - .pt-xxl-1 { - padding-top: 0.25rem !important; - } - - .pt-xxl-2 { - padding-top: 0.5rem !important; - } - - .pt-xxl-3 { - padding-top: 1rem !important; - } - - .pt-xxl-4 { - padding-top: 1.5rem !important; - } - - .pt-xxl-5 { - padding-top: 3rem !important; - } - - .pe-xxl-0 { - padding-right: 0 !important; - } - - .pe-xxl-1 { - padding-right: 0.25rem !important; - } - - .pe-xxl-2 { - padding-right: 0.5rem !important; - } - - .pe-xxl-3 { - padding-right: 1rem !important; - } - - .pe-xxl-4 { - padding-right: 1.5rem !important; - } - - .pe-xxl-5 { - padding-right: 3rem !important; - } - - .pb-xxl-0 { - padding-bottom: 0 !important; - } - - .pb-xxl-1 { - padding-bottom: 0.25rem !important; - } - - .pb-xxl-2 { - padding-bottom: 0.5rem !important; - } - - .pb-xxl-3 { - padding-bottom: 1rem !important; - } - - .pb-xxl-4 { - padding-bottom: 1.5rem !important; - } - - .pb-xxl-5 { - padding-bottom: 3rem !important; - } - - .ps-xxl-0 { - padding-left: 0 !important; - } - - .ps-xxl-1 { - padding-left: 0.25rem !important; - } - - .ps-xxl-2 { - padding-left: 0.5rem !important; - } - - .ps-xxl-3 { - padding-left: 1rem !important; - } - - .ps-xxl-4 { - padding-left: 1.5rem !important; - } - - .ps-xxl-5 { - padding-left: 3rem !important; - } - - .text-xxl-start { - text-align: left !important; - } - - .text-xxl-end { - text-align: right !important; - } - - .text-xxl-center { - text-align: center !important; - } -} - -@media (min-width: 1200px) { - .fs-1 { - font-size: 2.5rem !important; - } - - .fs-2 { - font-size: 2rem !important; - } - - .fs-3 { - font-size: 1.75rem !important; - } - - .fs-4 { - font-size: 1.5rem !important; - } -} - -@media print { - .d-print-inline { - display: inline !important; - } - - .d-print-inline-block { - display: inline-block !important; - } - - .d-print-block { - display: block !important; - } - - .d-print-grid { - display: grid !important; - } - - .d-print-table { - display: table !important; - } - - .d-print-table-row { - display: table-row !important; - } - - .d-print-table-cell { - display: table-cell !important; - } - - .d-print-flex { - display: flex !important; - } - - .d-print-inline-flex { - display: inline-flex !important; - } - - .d-print-none { - display: none !important; - } -} - -.feature { - display: inline-flex; - align-items: center; - justify-content: center; - height: 4rem; - width: 4rem; - font-size: 2rem; -} diff --git a/examples/demo-site/src/worker.js b/examples/demo-site/src/worker.js deleted file mode 100644 index a4dc29c15..000000000 --- a/examples/demo-site/src/worker.js +++ /dev/null @@ -1,537 +0,0 @@ - -///////////////////////////////////////////////////////////////// -// Worker.js file for doing all transformer-based computations // -// Needed to ensure the UI thread is not blocked when running // -///////////////////////////////////////////////////////////////// - -import { pipeline, env } from "@xenova/transformers"; -env.allowLocalModels = false; - -// Define task function mapping -const TASK_FUNCTION_MAPPING = { - 'translation': translate, - 'text-generation': text_generation, - 'code-completion': code_completion, - 'masked-language-modelling': masked_lm, - 'sequence-classification': sequence_classification, - 'token-classification': token_classification, - 'zero-shot-classification': zero_shot_classification, - 'question-answering': question_answering, - 'summarization': summarize, - 'automatic-speech-recognition': speech_to_text, - 'image-to-text': image_to_text, - 'image-classification': image_classification, - 'zero-shot-image-classification': zero_shot_image_classification, - 'object-detection': object_detection, -} - -// Listen for messages from UI -self.addEventListener('message', async (event) => { - const data = event.data; - let fn = TASK_FUNCTION_MAPPING[data.task]; - - if (!fn) return; - - let result = await fn(data); - self.postMessage({ - task: data.task, - type: 'result', - data: result - }); -}); - -// Define model factories -// Ensures only one model is created of each type -class PipelineFactory { - static task = null; - static model = null; - - // NOTE: instance stores a promise that resolves to the pipeline - static instance = null; - - constructor(tokenizer, model) { - this.tokenizer = tokenizer; - this.model = model; - } - - /** - * Get pipeline instance - * @param {*} progressCallback - * @returns {Promise} - */ - static getInstance(progressCallback = null) { - if (this.task === null || this.model === null) { - throw Error("Must set task and model") - } - if (this.instance === null) { - this.instance = pipeline(this.task, this.model, { - progress_callback: progressCallback - }); - } - - return this.instance; - } -} - -class TranslationPipelineFactory extends PipelineFactory { - static task = 'translation'; - static model = 'Xenova/t5-small'; -} - -class TextGenerationPipelineFactory extends PipelineFactory { - static task = 'text-generation'; - static model = 'Xenova/distilgpt2'; -} - -class CodeCompletionPipelineFactory extends PipelineFactory { - static task = 'text-generation'; - static model = 'Xenova/codegen-350M-mono'; -} - -class MaskedLMPipelineFactory extends PipelineFactory { - static task = 'fill-mask'; - static model = 'Xenova/bert-base-cased'; -} - -class SequenceClassificationPipelineFactory extends PipelineFactory { - static task = 'text-classification'; - static model = 'Xenova/bert-base-multilingual-uncased-sentiment'; -} - -class TokenClassificationPipelineFactory extends PipelineFactory { - static task = 'token-classification'; - static model = 'Xenova/bert-base-multilingual-cased-ner-hrl'; -} - -class ZeroShotClassificationPipelineFactory extends PipelineFactory { - static task = 'zero-shot-classification'; - static model = 'Xenova/distilbert-base-uncased-mnli'; -} - -class QuestionAnsweringPipelineFactory extends PipelineFactory { - static task = 'question-answering'; - static model = 'Xenova/distilbert-base-cased-distilled-squad'; -} - -class SummarizationPipelineFactory extends PipelineFactory { - static task = 'summarization'; - static model = 'Xenova/distilbart-cnn-6-6'; -} - -class AutomaticSpeechRecognitionPipelineFactory extends PipelineFactory { - static task = 'automatic-speech-recognition'; - static model = 'Xenova/whisper-tiny.en'; -} - -class ImageToTextPipelineFactory extends PipelineFactory { - static task = 'image-to-text'; - static model = 'Xenova/vit-gpt2-image-captioning'; -} - -class ImageClassificationPipelineFactory extends PipelineFactory { - static task = 'image-classification'; - static model = 'Xenova/vit-base-patch16-224'; -} - - -class ZeroShotImageClassificationPipelineFactory extends PipelineFactory { - static task = 'zero-shot-image-classification'; - static model = 'Xenova/clip-vit-base-patch16'; -} - -class ObjectDetectionPipelineFactory extends PipelineFactory { - static task = 'object-detection'; - static model = 'Xenova/detr-resnet-50'; -} - -async function translate(data) { - - let pipeline = await TranslationPipelineFactory.getInstance(data => { - self.postMessage({ - type: 'download', - task: 'translation', - data: data - }); - }) - - // Update task based on source and target languages - // Doing it this way prevents the same model from being loaded multiple times - pipeline.task = `translation_${data.languageFrom}_to_${data.languageTo}`; - - return await pipeline(data.text, { - ...data.generation, - callback_function: function (beams) { - const decodedText = pipeline.tokenizer.decode(beams[0].output_token_ids, { - skip_special_tokens: true, - }) - - self.postMessage({ - type: 'update', - target: data.elementIdToUpdate, - data: decodedText - }); - } - }) -} - -async function text_generation(data) { - - let pipeline = await TextGenerationPipelineFactory.getInstance(data => { - self.postMessage({ - type: 'download', - task: 'text-generation', - data: data - }); - }) - - let text = data.text.trim(); - - return await pipeline(text, { - ...data.generation, - callback_function: function (beams) { - const decodedText = pipeline.tokenizer.decode(beams[0].output_token_ids, { - skip_special_tokens: true, - }) - - self.postMessage({ - type: 'update', - target: data.elementIdToUpdate, - data: decodedText - }); - } - }) -} - -async function code_completion(data) { - - let pipeline = await CodeCompletionPipelineFactory.getInstance(data => { - self.postMessage({ - type: 'download', - task: 'code-completion', - data: data, - }); - }) - - let text = data.text; - - return await pipeline(text, { - ...data.generation, - callback_function: function (beams) { - const decodedText = pipeline.tokenizer.decode(beams[0].output_token_ids, { - skip_special_tokens: true, - }) - - self.postMessage({ - type: 'update', - target: data.elementIdToUpdate, - targetType: data.targetType, - data: decodedText - }); - } - }) -} - -async function masked_lm(data) { - - let pipeline = await MaskedLMPipelineFactory.getInstance(data => { - self.postMessage({ - type: 'download', - task: 'masked-language-modelling', - data: data - }); - }) - - let output = await pipeline(data.text, data.generation) - - self.postMessage({ - type: 'update', - target: data.elementIdToUpdate, - data: output.map(x => x.sequence).join('\n') - }); - - return output; -} - -async function sequence_classification(data) { - - let pipeline = await SequenceClassificationPipelineFactory.getInstance(data => { - self.postMessage({ - type: 'download', - task: 'sequence-classification', - data: data - }); - }); - - let outputs = await pipeline(data.text, { - topk: 5 // return all - }) - - self.postMessage({ - type: 'complete', - target: data.elementIdToUpdate, - targetType: data.targetType, - data: outputs - }); -} - - -async function token_classification(data) { - - let pipeline = await TokenClassificationPipelineFactory.getInstance(data => { - self.postMessage({ - type: 'download', - task: 'token-classification', - data: data - }); - }); - - let outputs = await pipeline(data.text, { - ignore_labels: [] // Return all labels - }); - - let chunks = []; - let currentChunk = { type: '', text: [] }; - - for (let i = 0; i < outputs.length; i++) { - let word = pipeline.tokenizer.model.tokens_to_ids.get(outputs[i].word); - let entity = outputs[i].entity; - - if (entity.startsWith('B-')) { // beginning of a new chunk - if (currentChunk.text.length > 0) { // push the current chunk if it exists - chunks.push(currentChunk); - currentChunk = { type: '', text: [] }; - } - currentChunk.type = entity.slice(2); // get the type of the chunk - currentChunk.text = [word]; - } else if (entity.startsWith('I-')) { // continuation of a chunk - currentChunk.text.push(word); - } else { // not part of a chunk (O tag) - if (currentChunk.text.length > 0) { // push the current chunk if it exists - - if (currentChunk.type === 'O') { - currentChunk.text.push(word); - } else { - chunks.push(currentChunk); - currentChunk = { type: 'O', text: [word] }; - } - } else { - currentChunk = { type: 'O', text: [word] }; - } - } - } - - // push the last chunk if it exists - if (currentChunk.text.length > 0) { - chunks.push(currentChunk); - } - - let postProcessedChunks = chunks.map( - x => ({ - type: x.type, - text: pipeline.tokenizer.decode(x.text) - }) - ) - - self.postMessage({ - type: 'complete', - target: data.elementIdToUpdate, - targetType: data.targetType, - data: postProcessedChunks, - }); -} - - -async function zero_shot_classification(data) { - - let pipeline = await ZeroShotClassificationPipelineFactory.getInstance(data => { - self.postMessage({ - type: 'download', - task: 'zero-shot-classification', - data: data - }); - }); - - let outputs = await pipeline(data.text, data.classes, data.generation); - let formattedOutputs = outputs.labels.map((x, i) => { - return { - label: x, - score: outputs.scores[i], - } - }); - - self.postMessage({ - type: 'complete', - target: data.elementIdToUpdate, - targetType: data.targetType, - data: formattedOutputs - }); -} -async function question_answering(data) { - - let pipeline = await QuestionAnsweringPipelineFactory.getInstance(data => { - self.postMessage({ - type: 'download', - task: 'question-answering', - data: data - }); - }) - - let answer = await pipeline(data.question, data.context) - self.postMessage({ - type: 'complete', - target: data.elementIdToUpdate, - data: answer.answer - }); - - return answer; -} - -async function summarize(data) { - let pipeline = await SummarizationPipelineFactory.getInstance(data => { - self.postMessage({ - type: 'download', - task: 'summarization', - data: data - }); - }) - - return await pipeline(data.text, { - ...data.generation, - callback_function: function (beams) { - const decodedText = pipeline.tokenizer.decode(beams[0].output_token_ids, { - skip_special_tokens: true, - }) - - self.postMessage({ - type: 'update', - target: data.elementIdToUpdate, - data: decodedText.trim() - }); - } - }) -} - -async function speech_to_text(data) { - let pipeline = await AutomaticSpeechRecognitionPipelineFactory.getInstance(data => { - self.postMessage({ - type: 'download', - task: 'automatic-speech-recognition', - data: data - }); - }) - - return await pipeline(data.audio, { - // Choose good defaults for the demo - chunk_length_s: 30, - stride_length_s: 5, - - ...data.generation, - callback_function: function (beams) { - const decodedText = pipeline.tokenizer.decode(beams[0].output_token_ids, { - skip_special_tokens: true, - }) - - self.postMessage({ - type: 'update', - target: data.elementIdToUpdate, - data: decodedText.trim() - }); - } - }) -} - -async function image_to_text(data) { - let pipeline = await ImageToTextPipelineFactory.getInstance(data => { - self.postMessage({ - type: 'download', - task: 'image-to-text', - data: data - }); - }) - - return await pipeline(data.image, { - ...data.generation, - callback_function: function (beams) { - const decodedText = pipeline.tokenizer.decode(beams[0].output_token_ids, { - skip_special_tokens: true, - }) - - self.postMessage({ - type: 'update', - target: data.elementIdToUpdate, - data: decodedText.trim() - }); - } - }) -} - -async function image_classification(data) { - let pipeline = await ImageClassificationPipelineFactory.getInstance(data => { - self.postMessage({ - type: 'download', - task: 'image-classification', - data: data - }); - }) - - let outputs = await pipeline(data.image, { - topk: 5 // return all - }) - - self.postMessage({ - type: 'complete', - target: data.elementIdToUpdate, - targetType: data.targetType, - updateLabels: data.updateLabels, - data: outputs - }); - -} - - -async function zero_shot_image_classification(data) { - let pipeline = await ZeroShotImageClassificationPipelineFactory.getInstance(data => { - self.postMessage({ - type: 'download', - task: 'image-classification', - data: data - }); - }) - - let outputs = await pipeline(data.image, data.classes) - - self.postMessage({ - type: 'complete', - target: data.elementIdToUpdate, - targetType: data.targetType, - updateLabels: data.updateLabels, - data: outputs - }); - -} - - -async function object_detection(data) { - - let pipeline = await ObjectDetectionPipelineFactory.getInstance(data => { - self.postMessage({ - type: 'download', - task: 'object-detection', - data: data - }); - }) - - let outputs = await pipeline(data.image, { - threshold: 0.9, - percentage: true - }) - - self.postMessage({ - type: 'complete', - target: data.elementIdToUpdate, - targetType: data.targetType, - chartId: data.chartId, - data: outputs - }); -} - diff --git a/examples/demo-site/vite.config.js b/examples/demo-site/vite.config.js deleted file mode 100644 index 5e444add6..000000000 --- a/examples/demo-site/vite.config.js +++ /dev/null @@ -1,14 +0,0 @@ -import path from 'path'; - -// Needed for deploying to GitHub pages -const BASE_PATH = process.env.BASE_PATH ?? ''; - -export default { - // config options - base: BASE_PATH, - root: path.join(__dirname, 'src'), - build: { - outDir: path.join(__dirname, 'dist') - }, - publicDir: path.join(__dirname, 'public'), -} \ No newline at end of file diff --git a/examples/depth-anything-client/.gitignore b/examples/depth-anything-client/.gitignore deleted file mode 100644 index a547bf36d..000000000 --- a/examples/depth-anything-client/.gitignore +++ /dev/null @@ -1,24 +0,0 @@ -# Logs -logs -*.log -npm-debug.log* -yarn-debug.log* -yarn-error.log* -pnpm-debug.log* -lerna-debug.log* - -node_modules -dist -dist-ssr -*.local - -# Editor directories and files -.vscode/* -!.vscode/extensions.json -.idea -.DS_Store -*.suo -*.ntvs* -*.njsproj -*.sln -*.sw? diff --git a/examples/depth-anything-client/index.html b/examples/depth-anything-client/index.html deleted file mode 100644 index 92c3fe079..000000000 --- a/examples/depth-anything-client/index.html +++ /dev/null @@ -1,29 +0,0 @@ - - - - - - - Transformers.js - Depth Anything - - - -

Depth Anything w/ 🤗 Transformers.js

-
- -
- - - - - - - \ No newline at end of file diff --git a/examples/depth-anything-client/main.js b/examples/depth-anything-client/main.js deleted file mode 100644 index 253468aca..000000000 --- a/examples/depth-anything-client/main.js +++ /dev/null @@ -1,150 +0,0 @@ -import './style.css'; - -import * as THREE from 'three'; -import { OrbitControls } from 'three/addons/controls/OrbitControls.js'; - -import { pipeline, env, RawImage } from '@xenova/transformers'; - -// Since we will download the model from the Hugging Face Hub, we can skip the local model check -env.allowLocalModels = false; - -// Proxy the WASM backend to prevent the UI from freezing -env.backends.onnx.wasm.proxy = true; - -// Constants -const EXAMPLE_URL = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/bread_small.png'; -const DEFAULT_SCALE = 0.75; - -// Reference the elements that we will need -const status = document.getElementById('status'); -const fileUpload = document.getElementById('upload'); -const imageContainer = document.getElementById('container'); -const example = document.getElementById('example'); - -// Create a new depth-estimation pipeline -status.textContent = 'Loading model...'; -const depth_estimator = await pipeline('depth-estimation', 'Xenova/depth-anything-small-hf'); -status.textContent = 'Ready'; - -example.addEventListener('click', (e) => { - e.preventDefault(); - predict(EXAMPLE_URL); -}); - -fileUpload.addEventListener('change', function (e) { - const file = e.target.files[0]; - if (!file) { - return; - } - - const reader = new FileReader(); - - // Set up a callback when the file is loaded - reader.onload = e2 => predict(e2.target.result); - - reader.readAsDataURL(file); -}); - -let onSliderChange; - -// Predict depth map for the given image -async function predict(url) { - imageContainer.innerHTML = ''; - const image = await RawImage.fromURL(url); - - // Set up scene and slider controls - const { canvas, setDisplacementMap } = setupScene(url, image.width, image.height); - - imageContainer.append(canvas); - - status.textContent = 'Analysing...'; - const { depth } = await depth_estimator(image); - - setDisplacementMap(depth.toCanvas()); - status.textContent = ''; - - // Add slider control - const slider = document.createElement('input'); - slider.type = 'range'; - slider.min = 0; - slider.max = 1; - slider.step = 0.01; - slider.addEventListener('input', (e) => { - onSliderChange(parseFloat(e.target.value)); - }); - slider.defaultValue = DEFAULT_SCALE; - imageContainer.append(slider); -} - -function setupScene(url, w, h) { - - // Create new scene - const canvas = document.createElement('canvas'); - const width = canvas.width = imageContainer.offsetWidth; - const height = canvas.height = imageContainer.offsetHeight; - - const scene = new THREE.Scene(); - - // Create camera and add it to the scene - const camera = new THREE.PerspectiveCamera(30, width / height, 0.01, 10); - camera.position.z = 2; - scene.add(camera); - - const renderer = new THREE.WebGLRenderer({ canvas, antialias: true }); - renderer.setSize(width, height); - renderer.setPixelRatio(window.devicePixelRatio); - - // Add ambient light - const light = new THREE.AmbientLight(0xffffff, 2); - scene.add(light); - - // Load depth texture - const image = new THREE.TextureLoader().load(url); - image.colorSpace = THREE.SRGBColorSpace; - const material = new THREE.MeshStandardMaterial({ - map: image, - side: THREE.DoubleSide, - }); - material.displacementScale = DEFAULT_SCALE; - - const setDisplacementMap = (canvas) => { - material.displacementMap = new THREE.CanvasTexture(canvas); - material.needsUpdate = true; - } - - const setDisplacementScale = (scale) => { - material.displacementScale = scale; - material.needsUpdate = true; - } - onSliderChange = setDisplacementScale; - - // Create plane and rescale it so that max(w, h) = 1 - const [pw, ph] = w > h ? [1, h / w] : [w / h, 1]; - const geometry = new THREE.PlaneGeometry(pw, ph, w, h); - const plane = new THREE.Mesh(geometry, material); - scene.add(plane); - - // Add orbit controls - const controls = new OrbitControls(camera, renderer.domElement); - controls.enableDamping = true; - - renderer.setAnimationLoop(() => { - renderer.render(scene, camera); - controls.update(); - }); - - window.addEventListener('resize', () => { - const width = imageContainer.offsetWidth; - const height = imageContainer.offsetHeight; - - camera.aspect = width / height; - camera.updateProjectionMatrix(); - - renderer.setSize(width, height); - }, false); - - return { - canvas: renderer.domElement, - setDisplacementMap, - }; -} diff --git a/examples/depth-anything-client/package-lock.json b/examples/depth-anything-client/package-lock.json deleted file mode 100644 index c24c4db67..000000000 --- a/examples/depth-anything-client/package-lock.json +++ /dev/null @@ -1,1472 +0,0 @@ -{ - "name": "depth-anything-client", - 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} - } -} diff --git a/examples/depth-anything-client/package.json b/examples/depth-anything-client/package.json deleted file mode 100644 index 0adf621a0..000000000 --- a/examples/depth-anything-client/package.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "name": "depth-anything-client", - "private": true, - "version": "0.0.0", - "type": "module", - "scripts": { - "dev": "vite", - "build": "vite build", - "preview": "vite preview" - }, - "devDependencies": { - "vite": "^5.0.13" - }, - "dependencies": { - "@xenova/transformers": "^2.14.1", - "three": "^0.160.1" - } -} diff --git a/examples/depth-anything-client/style.css b/examples/depth-anything-client/style.css deleted file mode 100644 index 1b0fb3f23..000000000 --- a/examples/depth-anything-client/style.css +++ /dev/null @@ -1,92 +0,0 @@ -* { - box-sizing: border-box; - padding: 0; - margin: 0; - font-family: sans-serif; -} - -html, -body { - height: 100%; -} - -body { - padding: 16px 32px; -} - -body, -#container, -#upload-button { - display: flex; - flex-direction: column; - justify-content: center; - align-items: center; -} - -h1 { - text-align: center; -} - -#container { - position: relative; - width: 640px; - height: 420px; - max-width: 100%; - max-height: 100%; - border: 2px dashed #D1D5DB; - border-radius: 0.75rem; - overflow: hidden; - margin-top: 1rem; - background-size: 100% 100%; - background-position: center; - background-repeat: no-repeat; -} - -#mask-output { - position: absolute; - width: 100%; - height: 100%; - pointer-events: none; -} - -#upload-button { - gap: 0.4rem; - font-size: 18px; - cursor: pointer; -} - -#upload { - display: none; -} - -svg { - pointer-events: none; -} - -#example { - font-size: 14px; - text-decoration: underline; - cursor: pointer; -} - -#example:hover { - color: #2563EB; -} - -canvas { - position: absolute; - width: 100%; - height: 100%; -} - -#status { - min-height: 16px; - margin: 8px 0; -} - -input[type="range"] { - position: absolute; - top: 10px; - right: 10px; - z-index: 1; -} \ No newline at end of file diff --git a/examples/depth-anything-client/vite.config.js b/examples/depth-anything-client/vite.config.js deleted file mode 100644 index 6c32f52df..000000000 --- a/examples/depth-anything-client/vite.config.js +++ /dev/null @@ -1,6 +0,0 @@ -import { defineConfig } from 'vite'; -export default defineConfig({ - build: { - target: 'esnext' - } -}); diff --git a/examples/electron/.gitignore b/examples/electron/.gitignore deleted file mode 100644 index 8296128de..000000000 --- a/examples/electron/.gitignore +++ /dev/null @@ -1,92 +0,0 @@ -# Logs -logs -*.log -npm-debug.log* -yarn-debug.log* -yarn-error.log* -lerna-debug.log* - -# Diagnostic reports (https://nodejs.org/api/report.html) -report.[0-9]*.[0-9]*.[0-9]*.[0-9]*.json - -# Runtime data -pids -*.pid -*.seed -*.pid.lock -.DS_Store - -# Directory for instrumented libs generated by jscoverage/JSCover -lib-cov - -# Coverage directory used by tools like istanbul -coverage -*.lcov - -# nyc test coverage -.nyc_output - -# node-waf configuration -.lock-wscript - -# Compiled binary addons (https://nodejs.org/api/addons.html) -build/Release - -# Dependency directories -node_modules/ -jspm_packages/ - -# TypeScript v1 declaration files -typings/ - -# TypeScript cache -*.tsbuildinfo - -# Optional npm cache directory -.npm - -# Optional eslint cache -.eslintcache - -# Optional REPL history -.node_repl_history - -# Output of 'npm pack' -*.tgz - -# Yarn Integrity file -.yarn-integrity - -# dotenv environment variables file -.env -.env.test - -# parcel-bundler cache (https://parceljs.org/) -.cache - -# next.js build output -.next - -# nuxt.js build output -.nuxt - -# vuepress build output -.vuepress/dist - -# Serverless directories -.serverless/ - -# FuseBox cache -.fusebox/ - -# DynamoDB Local files -.dynamodb/ - -# Webpack -.webpack/ - -# Vite -.vite/ - -# Electron-Forge -out/ diff --git a/examples/electron/README.md b/examples/electron/README.md deleted file mode 100644 index 898801a12..000000000 --- a/examples/electron/README.md +++ /dev/null @@ -1,34 +0,0 @@ - -# Transformers.js - Sample Electron application - -An example project to show how to run 🤗 Transformers in an [Electron](https://www.electronjs.org/) application. - -## Getting Started -1. Clone the repo and enter the project directory: - ```bash - git clone https://github.com/huggingface/transformers.js.git - cd transformers.js/examples/electron/ - ``` -1. Install the necessary dependencies: - ```bash - npm install - ``` - -1. Run the application: - ```bash - npm run start - ``` - - After a few seconds, a new window should pop up on your screen! - - -## Editing the template - - -All source code can be found in `./src/`: -- `index.js` - Serves as the entry point for the application's main process. When an Electron app is launched, this is the first file that gets executed, and it is responsible for setting up the main process of the application. You will need to restart the application after editing this file for your changes to take effect. -- `preload.js` - Used to preload scripts and modules in a renderer process before any other scripts run. In our case, we use the `contextBridge` API to expose the `run` function to the renderer, which runs the model in the background. You will need to restart the application after editing this file for your changes to take effect. -- `model.js` - Contains all the logic for loading the model and running predictions. You will need to restart the application after editing this file for your changes to take effect. - -- `client.js` - Handles interaction with the interface, as well as communication between the renderer thread (UI) and the worker thread (processing). To see changes made to this file made while editing, simply refresh the window (Ctrl + R or "View" → "Reload"). -- `index.html`, `index.css` - The user interface which is displayed to the user. To see changes made to this file made while editing, simply refresh the window (Ctrl + R or "View" → "Reload"). diff --git a/examples/electron/forge.config.js b/examples/electron/forge.config.js deleted file mode 100644 index 9a4060f97..000000000 --- a/examples/electron/forge.config.js +++ /dev/null @@ -1,22 +0,0 @@ -module.exports = { - packagerConfig: {}, - rebuildConfig: {}, - makers: [ - { - name: '@electron-forge/maker-squirrel', - config: {}, - }, - { - name: '@electron-forge/maker-zip', - platforms: ['darwin'], - }, - { - name: '@electron-forge/maker-deb', - config: {}, - }, - { - name: '@electron-forge/maker-rpm', - config: {}, - }, - ], -}; diff --git a/examples/electron/package-lock.json b/examples/electron/package-lock.json deleted file mode 100644 index 38170cc5e..000000000 --- a/examples/electron/package-lock.json +++ /dev/null @@ -1,5877 +0,0 @@ -{ - "name": "electron", - "version": "1.0.0", - "lockfileVersion": 3, - "requires": true, - "packages": { - "": { - "name": "electron", - "version": "1.0.0", - "license": "MIT", - "dependencies": { - "@xenova/transformers": "^2.6.2", - "electron-squirrel-startup": "^1.0.0" - }, - "devDependencies": { - "@electron-forge/cli": "^6.1.1", - "@electron-forge/maker-deb": "^6.1.1", - "@electron-forge/maker-rpm": "^6.1.1", - "@electron-forge/maker-squirrel": "^6.1.1", - "@electron-forge/maker-zip": "^6.1.1", - "electron": "^24.1.1" - } - }, - "node_modules/@electron-forge/cli": { - "version": "6.4.2", - "resolved": "https://registry.npmjs.org/@electron-forge/cli/-/cli-6.4.2.tgz", - "integrity": "sha512-bM6YVTV0uUEpIL1jkpARlSm4Li26XZn+avC/lyTdpPqnd65T/oXZNkrAD+2Jb0RlgplOaM21qWm7ybtvKDGDyA==", - "dev": true, - "funding": [ - { - "type": "individual", - "url": "https://github.com/sponsors/malept" - }, - { - "type": "tidelift", - "url": "https://tidelift.com/subscription/pkg/npm-.electron-forge-cli?utm_medium=referral&utm_source=npm_fund" - } - ], - "dependencies": { - "@electron-forge/core": "6.4.2", - "@electron-forge/shared-types": "6.4.2", - 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"lint": "echo \"No linting configured\"" - }, - "keywords": [], - "author": "Xenova", - "license": "MIT", - "dependencies": { - "@xenova/transformers": "^2.6.2", - "electron-squirrel-startup": "^1.0.0" - }, - "devDependencies": { - "@electron-forge/cli": "^6.1.1", - "@electron-forge/maker-deb": "^6.1.1", - "@electron-forge/maker-rpm": "^6.1.1", - "@electron-forge/maker-squirrel": "^6.1.1", - "@electron-forge/maker-zip": "^6.1.1", - "electron": "^24.1.1" - } -} diff --git a/examples/electron/src/client.js b/examples/electron/src/client.js deleted file mode 100644 index 466eb562b..000000000 --- a/examples/electron/src/client.js +++ /dev/null @@ -1,14 +0,0 @@ -// This script handles interaction with the user interface, as well as communication -// between the renderer thread (UI) and the worker thread (processing). - -const inputElement = document.getElementById('text'); -const outputElement = document.getElementById('output'); - -// 1. Send input data to the worker thread when it changes. -inputElement.addEventListener('input', async (event) => { - // 2. Await the result from the worker thread. - const result = await window.electronAPI.run(event.target.value); - - // 3. Update the UI. - outputElement.innerText = JSON.stringify(result, null, 2); -}); diff --git a/examples/electron/src/index.css b/examples/electron/src/index.css deleted file mode 100644 index 2938ca87e..000000000 --- a/examples/electron/src/index.css +++ /dev/null @@ -1,49 +0,0 @@ -/* Styles go here */ - -* { - padding: 0; - margin: 0; - box-sizing: border-box; - font-family: 'Roboto', sans-serif; -} - -h1 { - font-size: 54px; - text-align: center; - font-weight: 500; -} - -h2 { - font-size: 24px; - text-align: center; - font-weight: 400; - margin-bottom: 16px; -} - -.container { - width: 400px; -} - -html, -body { - height: 100%; -} - -body { - display: flex; - justify-content: center; - align-items: center; -} - -#text { - width: 100%; - padding: 8px; - font-size: 20px; - margin-bottom: 8px; -} - -#output { - font-size: 20px; - font-family: 'Roboto Mono', monospace; - height: 100px; -} \ No newline at end of file diff --git a/examples/electron/src/index.html b/examples/electron/src/index.html deleted file mode 100644 index 098ac1303..000000000 --- a/examples/electron/src/index.html +++ /dev/null @@ -1,24 +0,0 @@ - - - - - - Transformers.js | Sample Electron application - - - - - - - - - -
-

Transformers.js

-

Run 🤗 Transformers in Electron!

- - 

-    

-
-
-
\ No newline at end of file
diff --git a/examples/electron/src/index.js b/examples/electron/src/index.js
deleted file mode 100644
index 3100c333a..000000000
--- a/examples/electron/src/index.js
+++ /dev/null
@@ -1,72 +0,0 @@
-const { app, BrowserWindow, ipcMain } = require('electron');
-const path = require('path');
-const { session } = require('electron');
-const { run } = require('./model.js');
-
-
-// Handle creating/removing shortcuts on Windows when installing/uninstalling.
-if (require('electron-squirrel-startup')) {
-    app.quit();
-}
-
-const createWindow = () => {
-    // Create the browser window.
-    const mainWindow = new BrowserWindow({
-        width: 800,
-        height: 600,
-        webPreferences: {
-            preload: path.join(__dirname, 'preload.js'),
-        },
-    });
-
-    // and load the index.html of the app.
-    mainWindow.loadFile(path.join(__dirname, 'index.html'));
-
-    // Open the DevTools.
-    mainWindow.webContents.openDevTools();
-};
-
-
-// This method will be called when Electron has finished
-// initialization and is ready to create browser windows.
-// Some APIs can only be used after this event occurs.
-app.on('ready', () => {
-
-    // Add a handler for the `transformers:run` event. This enables 2-way communication
-    // between the renderer process (UI) and the main process (processing).
-    // https://www.electronjs.org/docs/latest/tutorial/ipc#pattern-2-renderer-to-main-two-way
-    ipcMain.handle('transformers:run', run)
-
-    createWindow();
-
-    // Define a custom Content Security Policy to only allow loading resources from the app's origin.
-    session.defaultSession.webRequest.onHeadersReceived((details, callback) => {
-        callback({
-            responseHeaders: {
-                ...details.responseHeaders,
-                'Content-Security-Policy': ["default-src 'self'"]
-            }
-        })
-    })
-});
-
-// Quit when all windows are closed, except on macOS. There, it's common
-// for applications and their menu bar to stay active until the user quits
-// explicitly with Cmd + Q.
-app.on('window-all-closed', () => {
-    if (process.platform !== 'darwin') {
-        app.quit();
-    }
-});
-
-app.on('activate', () => {
-    // On OS X it's common to re-create a window in the app when the
-    // dock icon is clicked and there are no other windows open.
-    if (BrowserWindow.getAllWindows().length === 0) {
-        createWindow();
-    }
-});
-
-
-// In this file you can include the rest of your app's specific main process
-// code. You can also put them in separate files and import them here.
diff --git a/examples/electron/src/model.js b/examples/electron/src/model.js
deleted file mode 100644
index b4ee66bf5..000000000
--- a/examples/electron/src/model.js
+++ /dev/null
@@ -1,32 +0,0 @@
-// This file (model.js) contains all the logic for loading the model and running predictions.
-
-class MyClassificationPipeline {
-    // NOTE: Replace this with your own task and model
-    static task = 'text-classification';
-    static model = 'Xenova/distilbert-base-uncased-finetuned-sst-2-english';
-    static instance = null;
-
-    static async getInstance(progress_callback = null) {
-        if (this.instance === null) {
-            // Dynamically import the Transformers.js library
-            let { pipeline, env } = await import('@xenova/transformers');
-
-            // NOTE: Uncomment this to change the cache directory
-            // env.cacheDir = './.cache';
-
-            this.instance = pipeline(this.task, this.model, { progress_callback });
-        }
-
-        return this.instance;
-    }
-}
-
-// The run function is used by the `transformers:run` event handler.
-async function run(event, text) {
-    const classifier = await MyClassificationPipeline.getInstance();
-    return await classifier(text);
-}
-
-module.exports = {
-    run
-}
diff --git a/examples/electron/src/preload.js b/examples/electron/src/preload.js
deleted file mode 100644
index cf824722a..000000000
--- a/examples/electron/src/preload.js
+++ /dev/null
@@ -1,10 +0,0 @@
-// See the Electron documentation for details on how to use preload scripts:
-// https://www.electronjs.org/docs/latest/tutorial/process-model#preload-scripts
-
-const { contextBridge, ipcRenderer } = require('electron');
-
-// Here, we use the `contextBridge` API to expose a custom API to the renderer process.
-// This API allows the renderer process to invoke the `transformers:run` event in the main process.
-contextBridge.exposeInMainWorld('electronAPI', {
-    run: (text) => ipcRenderer.invoke('transformers:run', text)
-});
diff --git a/examples/extension/.gitignore b/examples/extension/.gitignore
deleted file mode 100644
index c2658d7d1..000000000
--- a/examples/extension/.gitignore
+++ /dev/null
@@ -1 +0,0 @@
-node_modules/
diff --git a/examples/extension/README.md b/examples/extension/README.md
deleted file mode 100644
index f1073d8c5..000000000
--- a/examples/extension/README.md
+++ /dev/null
@@ -1,35 +0,0 @@
-
-# Transformers.js - Sample browser extension
-
-An example project to show how to run 🤗 Transformers in a browser extension. Although we only provide instructions for running in Chrome, it should be similar for other browsers.
-
-## Getting Started
-1. Clone the repo and enter the project directory:
-    ```bash
-    git clone https://github.com/huggingface/transformers.js.git
-    cd transformers.js/examples/extension/
-    ```
-1. Install the necessary dependencies:
-    ```bash
-    npm install 
-    ```
-
-1. Build the project:
-    ```bash
-    npm run build 
-    ```
-
-1. Add the extension to your browser. To do this, go to `chrome://extensions/`, enable developer mode (top right), and click "Load unpacked". Select the `build` directory from the dialog which appears and click "Select Folder".
-
-1. That's it! You should now be able to open the extension's popup and use the model in your browser!
-
-## Editing the template
-
-We recommend running `npm run dev` while editing the template as it will rebuild the project when changes are made. 
-
-All source code can be found in the `./src/` directory:
-- `background.js` ([service worker](https://developer.chrome.com/docs/extensions/mv3/service_workers/)) - handles all the requests from the UI, does processing in the background, then returns the result. You will need to reload the extension (by visiting `chrome://extensions/` and clicking the refresh button) after editing this file for changes to be visible in the extension.
-
-- `content.js` ([content script](https://developer.chrome.com/docs/extensions/mv3/content_scripts/)) - contains the code which is injected into every page the user visits. You can use the `sendMessage` api to make requests to the background script. Similarly, you will need to reload the extension after editing this file for changes to be visible in the extension.
-
-- `popup.html`, `popup.css`, `popup.js` ([toolbar action](https://developer.chrome.com/docs/extensions/reference/action/)) - contains the code for the popup which is visible to the user when they click the extension's icon from the extensions bar. For development, we recommend opening the `popup.html` file in its own tab by visiting `chrome-extension:///popup.html` (remember to replace `` with the extension's ID). You will need to refresh the page while you develop to see the changes you make.
diff --git a/examples/extension/build/.gitignore b/examples/extension/build/.gitignore
deleted file mode 100644
index ce5e83aca..000000000
--- a/examples/extension/build/.gitignore
+++ /dev/null
@@ -1,3 +0,0 @@
-# Running `npm run build` will build the project and output the files here.
-*
-!.gitignore
diff --git a/examples/extension/package.json b/examples/extension/package.json
deleted file mode 100644
index 8d33266a1..000000000
--- a/examples/extension/package.json
+++ /dev/null
@@ -1,20 +0,0 @@
-{
-  "name": "extension",
-  "version": "0.0.1",
-  "description": "Transformers.js | Sample browser extension",
-  "scripts": {
-    "build": "webpack",
-    "dev": "webpack --watch"
-  },
-  "type": "module",
-  "author": "Xenova",
-  "license": "MIT",
-  "devDependencies": {
-    "copy-webpack-plugin": "^11.0.0",
-    "html-webpack-plugin": "^5.5.1",
-    "webpack": "^5.79.0"
-  },
-  "dependencies": {
-    "@huggingface/transformers": "^3.4.0"
-  }
-}
diff --git a/examples/extension/public/icons/icon.png b/examples/extension/public/icons/icon.png
deleted file mode 100644
index e04507e93..000000000
Binary files a/examples/extension/public/icons/icon.png and /dev/null differ
diff --git a/examples/extension/public/manifest.json b/examples/extension/public/manifest.json
deleted file mode 100644
index ab860899f..000000000
--- a/examples/extension/public/manifest.json
+++ /dev/null
@@ -1,45 +0,0 @@
-{
-  "manifest_version": 3,
-  "name": "extension",
-  "description": "Transformers.js | Sample browser extension",
-  "version": "0.0.1",
-  "permissions": [
-    "activeTab",
-    "scripting",
-    "contextMenus",
-    "storage",
-    "unlimitedStorage"
-  ],
-  "background": {
-    "service_worker": "background.js",
-    "type": "module"
-  },
-  "content_scripts": [
-    {
-      "matches": [
-        ""
-      ],
-      "js": [
-        "content.js"
-      ]
-    }
-  ],
-  "minimum_chrome_version": "92",
-  "action": {
-    "default_icon": {
-      "16": "icons/icon.png",
-      "24": "icons/icon.png",
-      "32": "icons/icon.png"
-    },
-    "default_title": "Transformers.js",
-    "default_popup": "popup.html"
-  },
-  "content_security_policy": {
-    "extension_pages": "script-src 'self' 'wasm-unsafe-eval'"
-  },
-  "icons": {
-    "16": "icons/icon.png",
-    "48": "icons/icon.png",
-    "128": "icons/icon.png"
-  }
-}
\ No newline at end of file
diff --git a/examples/extension/src/background.js b/examples/extension/src/background.js
deleted file mode 100644
index ec85e1cf4..000000000
--- a/examples/extension/src/background.js
+++ /dev/null
@@ -1,86 +0,0 @@
-// background.js - Handles requests from the UI, runs the model, then sends back a response
-
-import { pipeline } from '@huggingface/transformers';
-
-class PipelineSingleton {
-    static task = 'text-classification';
-    static model = 'Xenova/distilbert-base-uncased-finetuned-sst-2-english';
-    static instance = null;
-
-    static async getInstance(progress_callback = null) {
-        this.instance ??= pipeline(this.task, this.model, { progress_callback });
-
-        return this.instance;
-    }
-}
-
-// Create generic classify function, which will be reused for the different types of events.
-const classify = async (text) => {
-    // Get the pipeline instance. This will load and build the model when run for the first time.
-    let model = await PipelineSingleton.getInstance((data) => {
-        // You can track the progress of the pipeline creation here.
-        // e.g., you can send `data` back to the UI to indicate a progress bar
-        // console.log('progress', data)
-    });
-
-    // Actually run the model on the input text
-    let result = await model(text);
-    return result;
-};
-
-////////////////////// 1. Context Menus //////////////////////
-//
-// Add a listener to create the initial context menu items,
-// context menu items only need to be created at runtime.onInstalled
-chrome.runtime.onInstalled.addListener(function () {
-    // Register a context menu item that will only show up for selection text.
-    chrome.contextMenus.create({
-        id: 'classify-selection',
-        title: 'Classify "%s"',
-        contexts: ['selection'],
-    });
-});
-
-// Perform inference when the user clicks a context menu
-chrome.contextMenus.onClicked.addListener(async (info, tab) => {
-    // Ignore context menu clicks that are not for classifications (or when there is no input)
-    if (info.menuItemId !== 'classify-selection' || !info.selectionText) return;
-
-    // Perform classification on the selected text
-    let result = await classify(info.selectionText);
-
-    // Do something with the result
-    chrome.scripting.executeScript({
-        target: { tabId: tab.id },    // Run in the tab that the user clicked in
-        args: [result],               // The arguments to pass to the function
-        function: (result) => {       // The function to run
-            // NOTE: This function is run in the context of the web page, meaning that `document` is available.
-            console.log('result', result)
-            console.log('document', document)
-        },
-    });
-});
-//////////////////////////////////////////////////////////////
-
-////////////////////// 2. Message Events /////////////////////
-// 
-// Listen for messages from the UI, process it, and send the result back.
-chrome.runtime.onMessage.addListener((message, sender, sendResponse) => {
-    console.log('sender', sender)
-    if (message.action !== 'classify') return; // Ignore messages that are not meant for classification.
-
-    // Run model prediction asynchronously
-    (async function () {
-        // Perform classification
-        let result = await classify(message.text);
-
-        // Send response back to UI
-        sendResponse(result);
-    })();
-
-    // return true to indicate we will send a response asynchronously
-    // see https://stackoverflow.com/a/46628145 for more information
-    return true;
-});
-//////////////////////////////////////////////////////////////
-
diff --git a/examples/extension/src/content.js b/examples/extension/src/content.js
deleted file mode 100644
index 2974bd27c..000000000
--- a/examples/extension/src/content.js
+++ /dev/null
@@ -1,11 +0,0 @@
-// content.js - the content scripts which is run in the context of web pages, and has access
-// to the DOM and other web APIs.
-
-// Example usage:
-// const message = {
-//     action: 'classify',
-//     text: 'text to classify',
-// }
-// chrome.runtime.sendMessage(message, (response) => {
-//     console.log('received user data', response)
-// });
diff --git a/examples/extension/src/popup.css b/examples/extension/src/popup.css
deleted file mode 100644
index 5a7a8e10e..000000000
--- a/examples/extension/src/popup.css
+++ /dev/null
@@ -1,50 +0,0 @@
-/* Styles go here */
-
-* {
-    padding: 0;
-    margin: 0;
-    box-sizing: border-box;
-    font-family: 'Roboto', sans-serif;
-}
-
-h1 {
-    font-size: 40px;
-    text-align: center;
-    font-weight: 500;
-}
-
-h2 {
-    font-size: 20px;
-    text-align: center;
-    font-weight: 400;
-    margin-bottom: 16px;
-}
-
-.container {
-    width: 360px;
-}
-
-html,
-body {
-    min-width: 400px;
-    min-height: 500px;
-}
-
-body {
-    display: flex;
-    justify-content: center;
-    align-items: center;
-}
-
-#text {
-    width: 100%;
-    padding: 8px;
-    font-size: 20px;
-    margin-bottom: 8px;
-}
-
-#output {
-    font-size: 20px;
-    font-family: 'Roboto Mono', monospace;
-    height: 100px;
-}
\ No newline at end of file
diff --git a/examples/extension/src/popup.html b/examples/extension/src/popup.html
deleted file mode 100644
index a4a817d2a..000000000
--- a/examples/extension/src/popup.html
+++ /dev/null
@@ -1,23 +0,0 @@
-
-
-
-
-    
-    
-    
-    Transformers.js | Sample Browser Extension
-
-    
-    
-
-
-
-    

-        
Transformers.js

-        
Run 🤗 Transformers in a Browser Extension!

-        
-        

-    

-
-
-
\ No newline at end of file
diff --git a/examples/extension/src/popup.js b/examples/extension/src/popup.js
deleted file mode 100644
index 720304c1a..000000000
--- a/examples/extension/src/popup.js
+++ /dev/null
@@ -1,21 +0,0 @@
-// popup.js - handles interaction with the extension's popup, sends requests to the
-// service worker (background.js), and updates the popup's UI (popup.html) on completion.
-
-const inputElement = document.getElementById('text');
-const outputElement = document.getElementById('output');
-
-// Listen for changes made to the textbox.
-inputElement.addEventListener('input', (event) => {
-
-    // Bundle the input data into a message.
-    const message = {
-        action: 'classify',
-        text: event.target.value,
-    }
-
-    // Send this message to the service worker.
-    chrome.runtime.sendMessage(message, (response) => {
-        // Handle results returned by the service worker (`background.js`) and update the popup's UI.
-        outputElement.innerText = JSON.stringify(response, null, 2);
-    });
-});
diff --git a/examples/extension/webpack.config.js b/examples/extension/webpack.config.js
deleted file mode 100644
index ae71c9b11..000000000
--- a/examples/extension/webpack.config.js
+++ /dev/null
@@ -1,45 +0,0 @@
-
-import path from 'path';
-import { fileURLToPath } from 'url';
-
-import HtmlWebpackPlugin from 'html-webpack-plugin';
-import CopyPlugin from 'copy-webpack-plugin';
-
-const __dirname = path.dirname(fileURLToPath(import.meta.url));
-
-const config = {
-    mode: 'development',
-    devtool: 'inline-source-map',
-    entry: {
-        background: {
-            import: './src/background.js',
-            chunkLoading: `import-scripts`,
-        },
-        popup: './src/popup.js',
-        content: './src/content.js',
-    },
-    output: {
-        path: path.resolve(__dirname, 'build'),
-        filename: '[name].js',
-    },
-    plugins: [
-        new HtmlWebpackPlugin({
-            template: './src/popup.html',
-            filename: 'popup.html',
-        }),
-        new CopyPlugin({
-            patterns: [
-                {
-                    from: "public",
-                    to: "." // Copies to build folder
-                },
-                {
-                    from: "src/popup.css",
-                    to: "popup.css"
-                }
-            ],
-        })
-    ],
-};
-
-export default config;
diff --git a/examples/florence2-webgpu/.eslintrc.cjs b/examples/florence2-webgpu/.eslintrc.cjs
deleted file mode 100644
index 3e212e1d4..000000000
--- a/examples/florence2-webgpu/.eslintrc.cjs
+++ /dev/null
@@ -1,21 +0,0 @@
-module.exports = {
-  root: true,
-  env: { browser: true, es2020: true },
-  extends: [
-    'eslint:recommended',
-    'plugin:react/recommended',
-    'plugin:react/jsx-runtime',
-    'plugin:react-hooks/recommended',
-  ],
-  ignorePatterns: ['dist', '.eslintrc.cjs'],
-  parserOptions: { ecmaVersion: 'latest', sourceType: 'module' },
-  settings: { react: { version: '18.2' } },
-  plugins: ['react-refresh'],
-  rules: {
-    'react/jsx-no-target-blank': 'off',
-    'react-refresh/only-export-components': [
-      'warn',
-      { allowConstantExport: true },
-    ],
-  },
-}
diff --git a/examples/florence2-webgpu/.gitignore b/examples/florence2-webgpu/.gitignore
deleted file mode 100644
index a547bf36d..000000000
--- a/examples/florence2-webgpu/.gitignore
+++ /dev/null
@@ -1,24 +0,0 @@
-# Logs
-logs
-*.log
-npm-debug.log*
-yarn-debug.log*
-yarn-error.log*
-pnpm-debug.log*
-lerna-debug.log*
-
-node_modules
-dist
-dist-ssr
-*.local
-
-# Editor directories and files
-.vscode/*
-!.vscode/extensions.json
-.idea
-.DS_Store
-*.suo
-*.ntvs*
-*.njsproj
-*.sln
-*.sw?
diff --git a/examples/florence2-webgpu/README.md b/examples/florence2-webgpu/README.md
deleted file mode 100644
index f768e33fc..000000000
--- a/examples/florence2-webgpu/README.md
+++ /dev/null
@@ -1,8 +0,0 @@
-# React + Vite
-
-This template provides a minimal setup to get React working in Vite with HMR and some ESLint rules.
-
-Currently, two official plugins are available:
-
-- [@vitejs/plugin-react](https://github.com/vitejs/vite-plugin-react/blob/main/packages/plugin-react/README.md) uses [Babel](https://babeljs.io/) for Fast Refresh
-- [@vitejs/plugin-react-swc](https://github.com/vitejs/vite-plugin-react-swc) uses [SWC](https://swc.rs/) for Fast Refresh
diff --git a/examples/florence2-webgpu/index.html b/examples/florence2-webgpu/index.html
deleted file mode 100644
index 77f8f0a0c..000000000
--- a/examples/florence2-webgpu/index.html
+++ /dev/null
@@ -1,12 +0,0 @@
-
-
-  
-    
-    
-    Florence2 WebGPU
-  
-  
-    

-    
-  
-
diff --git a/examples/florence2-webgpu/package.json b/examples/florence2-webgpu/package.json
deleted file mode 100644
index 490ad589f..000000000
--- a/examples/florence2-webgpu/package.json
+++ /dev/null
@@ -1,30 +0,0 @@
-{
-  "name": "florence2-webgpu",
-  "private": true,
-  "version": "0.0.0",
-  "type": "module",
-  "scripts": {
-    "dev": "vite",
-    "build": "vite build",
-    "lint": "eslint . --ext js,jsx --report-unused-disable-directives --max-warnings 0",
-    "preview": "vite preview"
-  },
-  "dependencies": {
-    "@xenova/transformers": "github:xenova/transformers.js#v3",
-    "react": "^18.3.1",
-    "react-dom": "^18.3.1"
-  },
-  "devDependencies": {
-    "@types/react": "^18.3.3",
-    "@types/react-dom": "^18.3.0",
-    "@vitejs/plugin-react": "^4.3.1",
-    "autoprefixer": "^10.4.19",
-    "eslint": "^8.57.0",
-    "eslint-plugin-react": "^7.34.2",
-    "eslint-plugin-react-hooks": "^4.6.2",
-    "eslint-plugin-react-refresh": "^0.4.7",
-    "postcss": "^8.4.38",
-    "tailwindcss": "^3.4.4",
-    "vite": "^5.3.1"
-  }
-}
diff --git a/examples/florence2-webgpu/postcss.config.js b/examples/florence2-webgpu/postcss.config.js
deleted file mode 100644
index 2e7af2b7f..000000000
--- a/examples/florence2-webgpu/postcss.config.js
+++ /dev/null
@@ -1,6 +0,0 @@
-export default {
-  plugins: {
-    tailwindcss: {},
-    autoprefixer: {},
-  },
-}
diff --git a/examples/florence2-webgpu/src/App.jsx b/examples/florence2-webgpu/src/App.jsx
deleted file mode 100644
index 36ac67e0f..000000000
--- a/examples/florence2-webgpu/src/App.jsx
+++ /dev/null
@@ -1,218 +0,0 @@
-import { useEffect, useState, useRef, useCallback } from 'react';
-
-import Progress from './components/Progress';
-import ImageInput from './components/ImageInput';
-
-const IS_WEBGPU_AVAILABLE = !!navigator.gpu;
-
-function App() {
-
-  // Create a reference to the worker object.
-  const worker = useRef(null);
-
-  // Model loading and progress
-  const [status, setStatus] = useState(null);
-  const [loadingMessage, setLoadingMessage] = useState('');
-  const [progressItems, setProgressItems] = useState([]);
-
-  const [task, setTask] = useState('');
-  const [text, setText] = useState('');
-  const [image, setImage] = useState(null);
-  const [result, setResult] = useState(null);
-  const [time, setTime] = useState(null);
-
-  // We use the `useEffect` hook to setup the worker as soon as the `App` component is mounted.
-  useEffect(() => {
-    if (!worker.current) {
-      // Create the worker if it does not yet exist.
-      worker.current = new Worker(new URL('./worker.js', import.meta.url), {
-        type: 'module'
-      });
-    }
-
-    // Create a callback function for messages from the worker thread.
-    const onMessageReceived = (e) => {
-      switch (e.data.status) {
-        case 'loading':
-          // Model file start load: add a new progress item to the list.
-          setStatus('loading');
-          setLoadingMessage(e.data.data);
-          break;
-
-        case 'initiate':
-          setProgressItems(prev => [...prev, e.data]);
-          break;
-
-        case 'progress':
-          // Model file progress: update one of the progress items.
-          setProgressItems(
-            prev => prev.map(item => {
-              if (item.file === e.data.file) {
-                return { ...item, ...e.data }
-              }
-              return item;
-            })
-          );
-          break;
-
-        case 'done':
-          // Model file loaded: remove the progress item from the list.
-          setProgressItems(
-            prev => prev.filter(item => item.file !== e.data.file)
-          );
-          break;
-
-        case 'ready':
-          // Pipeline ready: the worker is ready to accept messages.
-          setStatus('ready');
-          break;
-
-        case 'complete':
-          setResult(e.data.result);
-          setTime(e.data.time);
-          setStatus('ready');
-          break;
-      }
-    };
-
-    // Attach the callback function as an event listener.
-    worker.current.addEventListener('message', onMessageReceived);
-
-    // Define a cleanup function for when the component is unmounted.
-    return () => {
-      worker.current.removeEventListener('message', onMessageReceived);
-    };
-  }, []);
-
-  const handleClick = useCallback(() => {
-    if (status === null) {
-      setStatus('loading');
-      worker.current.postMessage({ type: 'load' });
-    } else {
-      setStatus('running');
-      worker.current.postMessage({
-        type: 'run', data: { text, url: image, task }
-      });
-    }
-  }, [status, task, image, text]);
-
-  return (
-    IS_WEBGPU_AVAILABLE
-      ? (

-
-        {status === 'loading' && (
-          

-            

-              
{loadingMessage}

-              {progressItems.map(({ file, progress, total }, i) => (
-                
-              ))}
-            

-          

-        )}
-        

-          

-            
Florence2 WebGPU

-            
Powerful vision foundation model running locally in your browser.

-          

-
-          

-
-            

-              You are about to download Florence-2-base-ft,
-              a 230 million parameter vision foundation model that uses a prompt-based approach to handle a wide range of vision and vision-language tasks like captioning, object detection, and segmentation.
-              Once loaded, the model (340 MB) will be cached and reused when you revisit the page.

-              

-              Everything runs locally in your browser using 🤗 Transformers.js and ONNX Runtime Web,
-              meaning no API calls are made to a server for inference. You can even disconnect from the internet after the model has loaded!
-            

-
-            

-              

-                

-                  Task
-                  
-                

-                

-                  Input Image
-                   {
-                    worker.current.postMessage({ type: 'reset' }); // Reset image cache
-                    setResult(null);
-                    setImage(result);
-                  }} />
-                

-              

-              

-                {
-                  task === ''
-                  && (

-                    Text input
-                     setText(e.target.value)}
-                    />
-                  
)
-                }
-
-                

-                  Output
-                  

-                    {result?.[task] && (<>
-                      {
-                        typeof result[task] === 'string'
-                          ? 
{result[task]}

-                          : 
-                            {JSON.stringify(result[task], null, 2)}
-                          

-                      }
-                      {
-                        time && 
Execution time: {time.toFixed(2)} ms

-                      }
-                    )
-                    }
-                  

-
-                

-              

-            

-
-            
-          

-        

-
-      
)
-      : (
WebGPU is not supported
by this browser :(
)
-  )
-}
-
-export default App
diff --git a/examples/florence2-webgpu/src/components/ImageInput.jsx b/examples/florence2-webgpu/src/components/ImageInput.jsx
deleted file mode 100644
index 9f24d9d5b..000000000
--- a/examples/florence2-webgpu/src/components/ImageInput.jsx
+++ /dev/null
@@ -1,68 +0,0 @@
-import { useState, useRef } from 'react';
-
-const EXAMPLE_URL = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/beetle.png';
-
-const ImageInput = ({ onImageChange, ...props }) => {
-    const [imagePreview, setImagePreview] = useState(null);
-    const fileInputRef = useRef(null);
-
-    const readFile = (file) => {
-        if (!file) return;
-        const reader = new FileReader();
-        reader.onloadend = () => {
-            setImagePreview(reader.result);
-            if (onImageChange) {
-                onImageChange(file, reader.result);
-            }
-        };
-        reader.readAsDataURL(file);
-    }
-
-    const handleImageChange = (event) => {
-        readFile(event.target.files[0]);
-    };
-
-    const handleDragOver = (event) => {
-        event.preventDefault();
-    };
-
-    const handleDrop = (event) => {
-        event.preventDefault();
-        readFile(event.dataTransfer.files[0]);
-    };
-
-    const handleClick = () => {
-        fileInputRef.current.click();
-    };
-
-    return (
-        

-            
-            {imagePreview ? (
-                Selected
-            ) : (
-                

-                    Drag & drop or click
to select an image
-                     {
-                        e.stopPropagation();
-                        setImagePreview(EXAMPLE_URL);
-                        onImageChange(null, EXAMPLE_URL);
-                    }}>(or try an example)
-                

-            )}
-        

-    );
-};
-
-export default ImageInput;
diff --git a/examples/florence2-webgpu/src/components/Progress.jsx b/examples/florence2-webgpu/src/components/Progress.jsx
deleted file mode 100644
index 9ce024cc8..000000000
--- a/examples/florence2-webgpu/src/components/Progress.jsx
+++ /dev/null
@@ -1,15 +0,0 @@
-function formatBytes(size) {
-    const i = size == 0 ? 0 : Math.floor(Math.log(size) / Math.log(1024));
-    return +((size / Math.pow(1024, i)).toFixed(2)) * 1 + ['B', 'kB', 'MB', 'GB', 'TB'][i];
-}
-
-export default function Progress({ text, percentage, total }) {
-    percentage ??= 0;
-    return (
-        

-            

-                {text} ({percentage.toFixed(2)}%{isNaN(total) ? '' : ` of ${formatBytes(total)}`})
-            

-        

-    );
-}
diff --git a/examples/florence2-webgpu/src/index.css b/examples/florence2-webgpu/src/index.css
deleted file mode 100644
index c4a1285e0..000000000
--- a/examples/florence2-webgpu/src/index.css
+++ /dev/null
@@ -1,21 +0,0 @@
-@tailwind base;
-@tailwind components;
-@tailwind utilities;
-
-@layer utilities {
-  .scrollbar-thin::-webkit-scrollbar {
-    @apply w-2;
-  }
-
-  .scrollbar-thin::-webkit-scrollbar-track {
-    @apply rounded-full bg-gray-100 dark:bg-gray-700;
-  }
-
-  .scrollbar-thin::-webkit-scrollbar-thumb {
-    @apply rounded-full bg-gray-300 dark:bg-gray-600;
-  }
-
-  .scrollbar-thin::-webkit-scrollbar-thumb:hover {
-    @apply bg-gray-500;
-  }
-}
diff --git a/examples/florence2-webgpu/src/main.jsx b/examples/florence2-webgpu/src/main.jsx
deleted file mode 100644
index 54b39dd1d..000000000
--- a/examples/florence2-webgpu/src/main.jsx
+++ /dev/null
@@ -1,10 +0,0 @@
-import React from 'react'
-import ReactDOM from 'react-dom/client'
-import App from './App.jsx'
-import './index.css'
-
-ReactDOM.createRoot(document.getElementById('root')).render(
-  
-    
-  ,
-)
diff --git a/examples/florence2-webgpu/src/worker.js b/examples/florence2-webgpu/src/worker.js
deleted file mode 100644
index 92c1732f4..000000000
--- a/examples/florence2-webgpu/src/worker.js
+++ /dev/null
@@ -1,140 +0,0 @@
-
-import {
-    Florence2ForConditionalGeneration,
-    AutoProcessor,
-    AutoTokenizer,
-    RawImage,
-    full,
-} from '@xenova/transformers';
-
-async function hasFp16() {
-    try {
-        const adapter = await navigator.gpu.requestAdapter();
-        return adapter.features.has('shader-f16');
-    } catch (e) {
-        return false;
-    }
-}
-
-/**
- * This class uses the Singleton pattern to ensure that only one instance of the model is loaded.
- */
-class Florence2Singleton {
-    static model_id = 'onnx-community/Florence-2-base-ft';
-
-    static async getInstance(progress_callback = null) {
-        this.processor ??= AutoProcessor.from_pretrained(this.model_id);
-        this.tokenizer ??= AutoTokenizer.from_pretrained(this.model_id);
-
-        this.supports_fp16 ??= await hasFp16();
-        this.model ??= Florence2ForConditionalGeneration.from_pretrained(this.model_id, {
-            dtype: {
-                embed_tokens: this.supports_fp16 ? 'fp16' : 'fp32',
-                vision_encoder: this.supports_fp16 ? 'fp16' : 'fp32',
-                encoder_model: 'q4', // or 'fp16' or 'fp32'
-                decoder_model_merged: 'q4', // or 'fp16' or 'fp32'
-            },
-            device: 'webgpu',
-            progress_callback,
-        });
-
-        return Promise.all([this.model, this.tokenizer, this.processor]);
-    }
-}
-
-
-async function load() {
-    self.postMessage({
-        status: 'loading',
-        data: 'Loading model...'
-    });
-
-    // Load the pipeline and save it for future use.
-    const [model, tokenizer, processor] = await Florence2Singleton.getInstance(x => {
-        // We also add a progress callback to the pipeline so that we can
-        // track model loading.
-        self.postMessage(x);
-    });
-
-    self.postMessage({
-        status: 'loading',
-        data: 'Compiling shaders and warming up model...'
-    });
-
-    // Dummy text and vision inputs
-    const text_inputs = tokenizer('a');
-    const pixel_values = full([1, 3, 768, 768], 0.0);
-
-    // Run model with dummy input to compile shaders
-    await model.generate({
-        ...text_inputs,
-        pixel_values,
-        max_new_tokens: 1,
-    });
-
-    self.postMessage({ status: 'ready' });
-}
-
-const TASKS_WITH_INPUTS = [
-    '',
-]
-
-let vision_inputs;
-let image_size;
-async function run({ text, url, task }) {
-    const [model, tokenizer, processor] = await Florence2Singleton.getInstance();
-
-    // Read and preprocess image
-    const start = performance.now();
-    if (!vision_inputs) {
-        // Cache vision inputs when possible
-        const image = await RawImage.fromURL(url);
-        image_size = image.size;
-        vision_inputs = await processor(image);
-    }
-
-    let user_input = task;
-    if (TASKS_WITH_INPUTS.includes(task) && text) {
-        user_input += text;
-    }
-    const prompts = processor.construct_prompts(user_input);
-    const text_inputs = tokenizer(prompts);
-
-    // Generate text
-    const generated_ids = await model.generate({
-        ...text_inputs,
-        ...vision_inputs,
-        max_new_tokens: 128,
-        num_beams: 1,
-        do_sample: false,
-    });
-
-    // Decode generated text
-    const generated_text = tokenizer.batch_decode(generated_ids, { skip_special_tokens: false })[0];
-
-    // Post-process the generated text
-    const result = processor.post_process_generation(generated_text, task, image_size);
-
-    const end = performance.now();
-
-    self.postMessage({ status: 'complete', result, time: end - start });
-}
-
-// Listen for messages from the main thread
-self.addEventListener('message', async (e) => {
-    const { type, data } = e.data;
-
-    switch (type) {
-        case 'load':
-            load();
-            break;
-
-        case 'run':
-            run(data);
-            break;
-
-        case 'reset':
-            vision_inputs = image_size = null;
-            break;
-    }
-});
diff --git a/examples/florence2-webgpu/tailwind.config.js b/examples/florence2-webgpu/tailwind.config.js
deleted file mode 100644
index d37737fc0..000000000
--- a/examples/florence2-webgpu/tailwind.config.js
+++ /dev/null
@@ -1,12 +0,0 @@
-/** @type {import('tailwindcss').Config} */
-export default {
-  content: [
-    "./index.html",
-    "./src/**/*.{js,ts,jsx,tsx}",
-  ],
-  theme: {
-    extend: {},
-  },
-  plugins: [],
-}
-
diff --git a/examples/florence2-webgpu/vite.config.js b/examples/florence2-webgpu/vite.config.js
deleted file mode 100644
index 5a33944a9..000000000
--- a/examples/florence2-webgpu/vite.config.js
+++ /dev/null
@@ -1,7 +0,0 @@
-import { defineConfig } from 'vite'
-import react from '@vitejs/plugin-react'
-
-// https://vitejs.dev/config/
-export default defineConfig({
-  plugins: [react()],
-})
diff --git a/examples/musicgen-web/.eslintrc.cjs b/examples/musicgen-web/.eslintrc.cjs
deleted file mode 100644
index 3e212e1d4..000000000
--- a/examples/musicgen-web/.eslintrc.cjs
+++ /dev/null
@@ -1,21 +0,0 @@
-module.exports = {
-  root: true,
-  env: { browser: true, es2020: true },
-  extends: [
-    'eslint:recommended',
-    'plugin:react/recommended',
-    'plugin:react/jsx-runtime',
-    'plugin:react-hooks/recommended',
-  ],
-  ignorePatterns: ['dist', '.eslintrc.cjs'],
-  parserOptions: { ecmaVersion: 'latest', sourceType: 'module' },
-  settings: { react: { version: '18.2' } },
-  plugins: ['react-refresh'],
-  rules: {
-    'react/jsx-no-target-blank': 'off',
-    'react-refresh/only-export-components': [
-      'warn',
-      { allowConstantExport: true },
-    ],
-  },
-}
diff --git a/examples/musicgen-web/.gitignore b/examples/musicgen-web/.gitignore
deleted file mode 100644
index a547bf36d..000000000
--- a/examples/musicgen-web/.gitignore
+++ /dev/null
@@ -1,24 +0,0 @@
-# Logs
-logs
-*.log
-npm-debug.log*
-yarn-debug.log*
-yarn-error.log*
-pnpm-debug.log*
-lerna-debug.log*
-
-node_modules
-dist
-dist-ssr
-*.local
-
-# Editor directories and files
-.vscode/*
-!.vscode/extensions.json
-.idea
-.DS_Store
-*.suo
-*.ntvs*
-*.njsproj
-*.sln
-*.sw?
diff --git a/examples/musicgen-web/README.md b/examples/musicgen-web/README.md
deleted file mode 100644
index f768e33fc..000000000
--- a/examples/musicgen-web/README.md
+++ /dev/null
@@ -1,8 +0,0 @@
-# React + Vite
-
-This template provides a minimal setup to get React working in Vite with HMR and some ESLint rules.
-
-Currently, two official plugins are available:
-
-- [@vitejs/plugin-react](https://github.com/vitejs/vite-plugin-react/blob/main/packages/plugin-react/README.md) uses [Babel](https://babeljs.io/) for Fast Refresh
-- [@vitejs/plugin-react-swc](https://github.com/vitejs/vite-plugin-react-swc) uses [SWC](https://swc.rs/) for Fast Refresh
diff --git a/examples/musicgen-web/index.html b/examples/musicgen-web/index.html
deleted file mode 100644
index cad1bcd1a..000000000
--- a/examples/musicgen-web/index.html
+++ /dev/null
@@ -1,12 +0,0 @@
-
-
-  
-    
-    
-    MusicGen Web | In-browser text-to-music w/ 🤗 Transformers.js!
-  
-  
-    

-    
-  
-
diff --git a/examples/musicgen-web/package.json b/examples/musicgen-web/package.json
deleted file mode 100644
index 0175494d7..000000000
--- a/examples/musicgen-web/package.json
+++ /dev/null
@@ -1,30 +0,0 @@
-{
-  "name": "musicgen-web",
-  "private": true,
-  "version": "0.0.0",
-  "type": "module",
-  "scripts": {
-    "dev": "vite",
-    "build": "vite build",
-    "lint": "eslint . --ext js,jsx --report-unused-disable-directives --max-warnings 0",
-    "preview": "vite preview"
-  },
-  "dependencies": {
-    "@xenova/transformers": "github:xenova/transformers.js#v3",
-    "react": "^18.2.0",
-    "react-dom": "^18.2.0"
-  },
-  "devDependencies": {
-    "@types/react": "^18.2.66",
-    "@types/react-dom": "^18.2.22",
-    "@vitejs/plugin-react": "^4.2.1",
-    "autoprefixer": "^10.4.19",
-    "eslint": "^8.57.0",
-    "eslint-plugin-react": "^7.34.1",
-    "eslint-plugin-react-hooks": "^4.6.0",
-    "eslint-plugin-react-refresh": "^0.4.6",
-    "postcss": "^8.4.38",
-    "tailwindcss": "^3.4.3",
-    "vite": "^5.2.0"
-  }
-}
diff --git a/examples/musicgen-web/postcss.config.js b/examples/musicgen-web/postcss.config.js
deleted file mode 100644
index 2e7af2b7f..000000000
--- a/examples/musicgen-web/postcss.config.js
+++ /dev/null
@@ -1,6 +0,0 @@
-export default {
-  plugins: {
-    tailwindcss: {},
-    autoprefixer: {},
-  },
-}
diff --git a/examples/musicgen-web/src/App.css b/examples/musicgen-web/src/App.css
deleted file mode 100644
index 91ab868f6..000000000
--- a/examples/musicgen-web/src/App.css
+++ /dev/null
@@ -1,9 +0,0 @@
-#root {
-  max-width: 960px;
-  height: 100vh;
-  margin: 0 auto;
-  text-align: center;
-  display: flex;
-  justify-content: center;
-  align-items: center;
-}
diff --git a/examples/musicgen-web/src/App.jsx b/examples/musicgen-web/src/App.jsx
deleted file mode 100644
index a64e8b655..000000000
--- a/examples/musicgen-web/src/App.jsx
+++ /dev/null
@@ -1,229 +0,0 @@
-import { useEffect, useState, useRef } from 'react';
-import { AutoTokenizer, MusicgenForConditionalGeneration, BaseStreamer } from '@xenova/transformers';
-import { encodeWAV, share } from './utils.js';
-
-import './App.css';
-
-const MODEL_ID = 'Xenova/musicgen-small';
-
-// Adapted from https://huggingface.co/spaces/facebook/MusicGen
-const EXAMPLES = [
-  '80s pop track with bassy drums and synth',
-  '90s rock song with loud guitars and heavy drums',
-  'a light and cheerly EDM track, with syncopated drums, aery pads, and strong emotions bpm: 130',
-  'A cheerful country song with acoustic guitars',
-  'lofi slow bpm electro chill with organic samples',
-];
-
-// Enable sharing if running on Hugging Face Spaces
-const SHARING_ENABLED = window.location.host.endsWith('.hf.space');
-
-// Streamer to update progress
-class CallbackStreamer extends BaseStreamer {
-  constructor(callback_fn) {
-    super();
-    this.callback_fn = callback_fn;
-  }
-
-  put(value) {
-    return this.callback_fn(value);
-  }
-
-  end() {
-    return this.callback_fn();
-  }
-}
-
-// Main App component
-const App = () => {
-  // Input/output state
-  const [textInput, setTextInput] = useState(EXAMPLES[0]);
-  const [progress, setProgress] = useState(0);
-  const [loadProgress, setLoadProgress] = useState({});
-  const [statusText, setStatusText] = useState('Loading model (656MB)...');
-  const [result, setResult] = useState(null);
-  const audioRef = useRef(null);
-
-  // Model and tokenizer references
-  const modelPromise = useRef(null);
-  const tokenizerPromise = useRef(null);
-
-  // Generation parameters
-  const [guidance_scale, setGuidanceScale] = useState(3);
-  const [temperature, setTemperature] = useState(1);
-  const [duration, setDuration] = useState(10);
-
-  // Load model and tokenizer on first render
-  useEffect(() => {
-    modelPromise.current ??= MusicgenForConditionalGeneration.from_pretrained(MODEL_ID, {
-      progress_callback: (data) => {
-        if (data.status !== 'progress') return;
-        setLoadProgress(prev => ({ ...prev, [data.file]: data }))
-      },
-      dtype: {
-        text_encoder: 'q8',
-        decoder_model_merged: 'q8',
-        encodec_decode: 'fp32',
-      },
-      device: 'wasm',
-    });
-
-    tokenizerPromise.current ??= AutoTokenizer.from_pretrained(MODEL_ID);
-  }, []);
-
-  // Update progress bar based on load progress
-  useEffect(() => {
-    const items = Object.values(loadProgress);
-    if (items.length !== 5) return; // 5 files to load
-    let loaded = 0;
-    let total = 0;
-    for (const data of Object.values(loadProgress)) {
-      loaded += data.loaded;
-      total += data.total;
-    }
-    const progress = loaded / total;
-    setProgress(progress);
-    setStatusText(progress === 1
-      ? 'Ready!'
-      : `Loading model (${(progress * 100).toFixed()}% of 656MB)...`
-    );
-  }, [loadProgress]);
-
-  // Function to handle generating music
-  const generateMusic = async () => {
-    // Reset audio player and result
-    audioRef.current.src = '';
-    setResult(null);
-
-    // Get model and tokenizer
-    const tokenizer = await tokenizerPromise.current;
-    const model = await modelPromise.current;
-
-    // Get number of tokens to match user-specified duration (more intuitive for user)
-    // 503 tokens -> 10 seconds generated => ~50 tokens per second
-    // https://huggingface.co/docs/transformers/model_doc/musicgen#generation
-    const max_length = Math.min(
-      Math.max(Math.floor(duration * 50), 1) + 4,
-      model.generation_config.max_length ?? 1500,
-    );
-
-    // Create a streamer to update progress
-    let num_tokens = 0;
-    const streamer = new CallbackStreamer((value) => {
-      const percent = value === undefined ? 1 : ++num_tokens / max_length;
-      setStatusText(`Generating (${(percent * 100).toFixed()}%)...`);
-      setProgress(percent);
-    });
-
-    // Tokenize input text
-    const inputs = tokenizer(textInput);
-
-    // Generate music
-    const audio_values = await model.generate({
-      // Inputs
-      ...inputs,
-
-      // Generation parameters
-      max_length,
-      guidance_scale,
-      temperature,
-
-      // Outputs
-      streamer,
-    });
-
-    setStatusText('Encoding audio...');
-
-    // Encode audio values to WAV
-    const sampling_rate = model.config.audio_encoder.sampling_rate;
-    const wav = encodeWAV(audio_values.data, sampling_rate);
-    const blob = new Blob([wav], { type: 'audio/wav' });
-    setResult(blob);
-
-    audioRef.current.src = URL.createObjectURL(blob);
-    setStatusText('Done!');
-  };
-
-  return (
-    

-      
MusicGen Web

-      
In-browser text-to-music w/ 🤗 Transformers.js!
-      

-
-      {/* Text input for user */}
-       setTextInput(e.target.value)}
-        className="border border-gray-300 p-2 mb-4 w-full rounded"
-      />
-
-      {/* Example buttons */}
-      

-        {EXAMPLES.map((example, i) => (
-          
-        ))}
-      

-
-      {/* Generation parameters */}
-      

-        {/* Duration */}
-        

-          
-           setDuration(e.target.value)} />
-          
{`${duration} second${duration > 1 ? 's' : ''}`}

-        

-
-        {/* Guidance Scale */}
-        

-          
-           setGuidanceScale(e.target.value)} />
-          
{guidance_scale}

-        

-
-        {/* Temperature */}
-        

-          
-           setTemperature(e.target.value)} />
-          
{temperature}

-        

-      

-
-      {/* Button to generate music */}
-      
-
-      {/* Progress bar */}
-      

-        

-          

-        

-        
{statusText}

-      

-
-      {/* Audio player */}
-      {

-        
}
-    

-  );
-};
-
-export default App;
diff --git a/examples/musicgen-web/src/index.css b/examples/musicgen-web/src/index.css
deleted file mode 100644
index bd6213e1d..000000000
--- a/examples/musicgen-web/src/index.css
+++ /dev/null
@@ -1,3 +0,0 @@
-@tailwind base;
-@tailwind components;
-@tailwind utilities;
\ No newline at end of file
diff --git a/examples/musicgen-web/src/main.jsx b/examples/musicgen-web/src/main.jsx
deleted file mode 100644
index 54b39dd1d..000000000
--- a/examples/musicgen-web/src/main.jsx
+++ /dev/null
@@ -1,10 +0,0 @@
-import React from 'react'
-import ReactDOM from 'react-dom/client'
-import App from './App.jsx'
-import './index.css'
-
-ReactDOM.createRoot(document.getElementById('root')).render(
-  
-    
-  ,
-)
diff --git a/examples/musicgen-web/src/utils.js b/examples/musicgen-web/src/utils.js
deleted file mode 100644
index 436c9daab..000000000
--- a/examples/musicgen-web/src/utils.js
+++ /dev/null
@@ -1,59 +0,0 @@
-
-// Adapted from https://www.npmjs.com/package/audiobuffer-to-wav
-export function encodeWAV(samples, sampleRate = 16000) {
-    let offset = 44;
-    const buffer = new ArrayBuffer(offset + samples.length * 4);
-    const view = new DataView(buffer);
-
-    /* RIFF identifier */
-    writeString(view, 0, 'RIFF')
-    /* RIFF chunk length */
-    view.setUint32(4, 36 + samples.length * 4, true)
-    /* RIFF type */
-    writeString(view, 8, 'WAVE')
-    /* format chunk identifier */
-    writeString(view, 12, 'fmt ')
-    /* format chunk length */
-    view.setUint32(16, 16, true)
-    /* sample format (raw) */
-    view.setUint16(20, 3, true)
-    /* channel count */
-    view.setUint16(22, 1, true)
-    /* sample rate */
-    view.setUint32(24, sampleRate, true)
-    /* byte rate (sample rate * block align) */
-    view.setUint32(28, sampleRate * 4, true)
-    /* block align (channel count * bytes per sample) */
-    view.setUint16(32, 4, true)
-    /* bits per sample */
-    view.setUint16(34, 32, true)
-    /* data chunk identifier */
-    writeString(view, 36, 'data')
-    /* data chunk length */
-    view.setUint32(40, samples.length * 4, true)
-
-    for (let i = 0; i < samples.length; ++i, offset += 4) {
-        view.setFloat32(offset, samples[i], true)
-    }
-
-    return buffer
-}
-function writeString(view, offset, string) {
-    for (let i = 0; i < string.length; ++i) {
-        view.setUint8(offset + i, string.charCodeAt(i))
-    }
-}
-
-export async function share(body, settings) {
-    const response = await fetch('https://huggingface.co/uploads', { method: 'POST', body });
-    if (!response.ok) throw new Error(`Failed to upload audio: ${response.statusText}`);
-    const url = await response.text();
-
-    const params = new URLSearchParams({
-        title: `🎵 ${settings.prompt}`,
-        description: `\n${JSON.stringify(settings, null, 2)}`,
-    });
-
-    const shareURL = `https://huggingface.co/spaces/Xenova/musicgen-web/discussions/new?${params.toString()}`;
-    window.open(shareURL, '_blank');
-}
\ No newline at end of file
diff --git a/examples/musicgen-web/tailwind.config.js b/examples/musicgen-web/tailwind.config.js
deleted file mode 100644
index d37737fc0..000000000
--- a/examples/musicgen-web/tailwind.config.js
+++ /dev/null
@@ -1,12 +0,0 @@
-/** @type {import('tailwindcss').Config} */
-export default {
-  content: [
-    "./index.html",
-    "./src/**/*.{js,ts,jsx,tsx}",
-  ],
-  theme: {
-    extend: {},
-  },
-  plugins: [],
-}
-
diff --git a/examples/musicgen-web/vite.config.js b/examples/musicgen-web/vite.config.js
deleted file mode 100644
index 5a33944a9..000000000
--- a/examples/musicgen-web/vite.config.js
+++ /dev/null
@@ -1,7 +0,0 @@
-import { defineConfig } from 'vite'
-import react from '@vitejs/plugin-react'
-
-// https://vitejs.dev/config/
-export default defineConfig({
-  plugins: [react()],
-})
diff --git a/examples/next-client/.eslintrc.json b/examples/next-client/.eslintrc.json
deleted file mode 100644
index bffb357a7..000000000
--- a/examples/next-client/.eslintrc.json
+++ /dev/null
@@ -1,3 +0,0 @@
-{
-  "extends": "next/core-web-vitals"
-}
diff --git a/examples/next-client/.gitignore b/examples/next-client/.gitignore
deleted file mode 100644
index 8f322f0d8..000000000
--- a/examples/next-client/.gitignore
+++ /dev/null
@@ -1,35 +0,0 @@
-# See https://help.github.com/articles/ignoring-files/ for more about ignoring files.
-
-# dependencies
-/node_modules
-/.pnp
-.pnp.js
-
-# testing
-/coverage
-
-# next.js
-/.next/
-/out/
-
-# production
-/build
-
-# misc
-.DS_Store
-*.pem
-
-# debug
-npm-debug.log*
-yarn-debug.log*
-yarn-error.log*
-
-# local env files
-.env*.local
-
-# vercel
-.vercel
-
-# typescript
-*.tsbuildinfo
-next-env.d.ts
diff --git a/examples/next-client/README.md b/examples/next-client/README.md
deleted file mode 100644
index e5f733efc..000000000
--- a/examples/next-client/README.md
+++ /dev/null
@@ -1,34 +0,0 @@
-This is a [Next.js](https://nextjs.org/) project bootstrapped with [`create-next-app`](https://github.com/vercel/next.js/tree/canary/packages/create-next-app).
-
-## Getting Started
-
-First, run the development server:
-
-```bash
-npm run dev
-# or
-yarn dev
-# or
-pnpm dev
-```
-
-Open [http://localhost:3000](http://localhost:3000) with your browser to see the result.
-
-You can start editing the page by modifying `app/page.js`. The page auto-updates as you edit the file.
-
-This project uses [`next/font`](https://nextjs.org/docs/basic-features/font-optimization) to automatically optimize and load Inter, a custom Google Font.
-
-## Learn More
-
-To learn more about Next.js, take a look at the following resources:
-
-- [Next.js Documentation](https://nextjs.org/docs) - learn about Next.js features and API.
-- [Learn Next.js](https://nextjs.org/learn) - an interactive Next.js tutorial.
-
-You can check out [the Next.js GitHub repository](https://github.com/vercel/next.js/) - your feedback and contributions are welcome!
-
-## Deploy on Vercel
-
-The easiest way to deploy your Next.js app is to use the [Vercel Platform](https://vercel.com/new?utm_medium=default-template&filter=next.js&utm_source=create-next-app&utm_campaign=create-next-app-readme) from the creators of Next.js.
-
-Check out our [Next.js deployment documentation](https://nextjs.org/docs/deployment) for more details.
diff --git a/examples/next-client/jsconfig.json b/examples/next-client/jsconfig.json
deleted file mode 100644
index b8d6842d7..000000000
--- a/examples/next-client/jsconfig.json
+++ /dev/null
@@ -1,7 +0,0 @@
-{
-  "compilerOptions": {
-    "paths": {
-      "@/*": ["./src/*"]
-    }
-  }
-}
diff --git a/examples/next-client/next.config.js b/examples/next-client/next.config.js
deleted file mode 100644
index 3d4df4ec4..000000000
--- a/examples/next-client/next.config.js
+++ /dev/null
@@ -1,20 +0,0 @@
-/** @type {import('next').NextConfig} */
-const nextConfig = {
-    // (Optional) Export as a static site
-    // See https://nextjs.org/docs/pages/building-your-application/deploying/static-exports#configuration
-    output: 'export', // Feel free to modify/remove this option
-
-    // Override the default webpack configuration
-    webpack: (config) => {
-        // Ignore node-specific modules when bundling for the browser
-        // See https://webpack.js.org/configuration/resolve/#resolvealias
-        config.resolve.alias = {
-            ...config.resolve.alias,
-            "sharp$": false,
-            "onnxruntime-node$": false,
-        }
-        return config;
-    },
-};
-
-module.exports = nextConfig;
diff --git a/examples/next-client/package-lock.json b/examples/next-client/package-lock.json
deleted file mode 100644
index d03dc91e7..000000000
--- a/examples/next-client/package-lock.json
+++ /dev/null
@@ -1,5251 +0,0 @@
-{
-  "name": "next",
-  "version": "0.1.0",
-  "lockfileVersion": 3,
-  "requires": true,
-  "packages": {
-    "": {
-      "name": "next",
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-        "next": "^14.2.3",
-        "postcss": "8.4.31",
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-        "react-dom": "18.2.0",
-        "tailwindcss": "3.3.3"
-      }
-    },
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diff --git a/examples/next-client/package.json b/examples/next-client/package.json
deleted file mode 100644
index 7bccaea67..000000000
--- a/examples/next-client/package.json
+++ /dev/null
@@ -1,25 +0,0 @@
-{
-  "name": "next",
-  "version": "0.1.0",
-  "private": true,
-  "scripts": {
-    "dev": "next dev",
-    "build": "next build",
-    "start": "next start",
-    "lint": "next lint"
-  },
-  "dependencies": {
-    "@huggingface/transformers": "^3.0.0-alpha.5",
-    "autoprefixer": "10.4.14",
-    "eslint": "8.45.0",
-    "eslint-config-next": "13.4.12",
-    "next": "^14.2.3",
-    "postcss": "8.4.31",
-    "react": "18.2.0",
-    "react-dom": "18.2.0",
-    "tailwindcss": "3.3.3"
-  },
-  "overrides": {
-    "protobufjs": "^7.2.4"
-  }
-}
diff --git a/examples/next-client/postcss.config.js b/examples/next-client/postcss.config.js
deleted file mode 100644
index 33ad091d2..000000000
--- a/examples/next-client/postcss.config.js
+++ /dev/null
@@ -1,6 +0,0 @@
-module.exports = {
-  plugins: {
-    tailwindcss: {},
-    autoprefixer: {},
-  },
-}
diff --git a/examples/next-client/public/next.svg b/examples/next-client/public/next.svg
deleted file mode 100644
index 5174b28c5..000000000
--- a/examples/next-client/public/next.svg
+++ /dev/null
@@ -1 +0,0 @@
-
\ No newline at end of file
diff --git a/examples/next-client/public/vercel.svg b/examples/next-client/public/vercel.svg
deleted file mode 100644
index d2f842227..000000000
--- a/examples/next-client/public/vercel.svg
+++ /dev/null
@@ -1 +0,0 @@
-
\ No newline at end of file
diff --git a/examples/next-client/src/app/favicon.ico b/examples/next-client/src/app/favicon.ico
deleted file mode 100644
index 718d6fea4..000000000
Binary files a/examples/next-client/src/app/favicon.ico and /dev/null differ
diff --git a/examples/next-client/src/app/globals.css b/examples/next-client/src/app/globals.css
deleted file mode 100644
index fd81e8858..000000000
--- a/examples/next-client/src/app/globals.css
+++ /dev/null
@@ -1,27 +0,0 @@
-@tailwind base;
-@tailwind components;
-@tailwind utilities;
-
-:root {
-  --foreground-rgb: 0, 0, 0;
-  --background-start-rgb: 214, 219, 220;
-  --background-end-rgb: 255, 255, 255;
-}
-
-@media (prefers-color-scheme: dark) {
-  :root {
-    --foreground-rgb: 255, 255, 255;
-    --background-start-rgb: 0, 0, 0;
-    --background-end-rgb: 0, 0, 0;
-  }
-}
-
-body {
-  color: rgb(var(--foreground-rgb));
-  background: linear-gradient(
-      to bottom,
-      transparent,
-      rgb(var(--background-end-rgb))
-    )
-    rgb(var(--background-start-rgb));
-}
diff --git a/examples/next-client/src/app/layout.js b/examples/next-client/src/app/layout.js
deleted file mode 100644
index c93f80617..000000000
--- a/examples/next-client/src/app/layout.js
+++ /dev/null
@@ -1,17 +0,0 @@
-import './globals.css'
-import { Inter } from 'next/font/google'
-
-const inter = Inter({ subsets: ['latin'] })
-
-export const metadata = {
-  title: 'Create Next App',
-  description: 'Generated by create next app',
-}
-
-export default function RootLayout({ children }) {
-  return (
-    
-      {children}
-    
-  )
-}
diff --git a/examples/next-client/src/app/page.js b/examples/next-client/src/app/page.js
deleted file mode 100644
index 006a20cca..000000000
--- a/examples/next-client/src/app/page.js
+++ /dev/null
@@ -1,71 +0,0 @@
-'use client'
-
-import { useState, useEffect, useRef, useCallback } from 'react'
-
-export default function Home() {
-
-  // Keep track of the classification result and the model loading status.
-  const [result, setResult] = useState(null);
-  const [ready, setReady] = useState(null);
-
-  // Create a reference to the worker object.
-  const worker = useRef(null);
-
-  // We use the `useEffect` hook to set up the worker as soon as the `App` component is mounted.
-  useEffect(() => {
-    if (!worker.current) {
-      // Create the worker if it does not yet exist.
-      worker.current = new Worker(new URL('./worker.js', import.meta.url), {
-        type: 'module'
-      });
-    }
-
-    // Create a callback function for messages from the worker thread.
-    const onMessageReceived = (e) => {
-      switch (e.data.status) {
-        case 'initiate':
-          setReady(false);
-          break;
-        case 'ready':
-          setReady(true);
-          break;
-        case 'complete':
-          setResult(e.data.output[0])
-          break;
-      }
-    };
-
-    // Attach the callback function as an event listener.
-    worker.current.addEventListener('message', onMessageReceived);
-
-    // Define a cleanup function for when the component is unmounted.
-    return () => worker.current.removeEventListener('message', onMessageReceived);
-  });
-
-  const classify = useCallback((text) => {
-    if (worker.current) {
-      worker.current.postMessage({ text });
-    }
-  }, []);
-  return (
-    

-      
Transformers.js

-      
Next.js template (client-side)

-       {
-          classify(e.target.value);
-        }}
-      />
-
-      {ready !== null && (
-        
-          {
-            (!ready || !result) ? 'Loading...' : JSON.stringify(result, null, 2)}
-        

-      )}
-    

-  )
-}
diff --git a/examples/next-client/src/app/worker.js b/examples/next-client/src/app/worker.js
deleted file mode 100644
index 4b9960009..000000000
--- a/examples/next-client/src/app/worker.js
+++ /dev/null
@@ -1,33 +0,0 @@
-import { pipeline } from "@huggingface/transformers";
-
-// Use the Singleton pattern to enable lazy construction of the pipeline.
-class PipelineSingleton {
-    static task = 'text-classification';
-    static model = 'Xenova/distilbert-base-uncased-finetuned-sst-2-english';
-    static instance = null;
-
-    static async getInstance(progress_callback = null) {
-        this.instance ??= pipeline(this.task, this.model, { progress_callback });
-        return this.instance;
-    }
-}
-
-// Listen for messages from the main thread
-self.addEventListener('message', async (event) => {
-    // Retrieve the classification pipeline. When called for the first time,
-    // this will load the pipeline and save it for future use.
-    const classifier = await PipelineSingleton.getInstance(x => {
-        // We also add a progress callback to the pipeline so that we can
-        // track model loading.
-        self.postMessage(x);
-    });
-
-    // Actually perform the classification
-    const output = await classifier(event.data.text);
-
-    // Send the output back to the main thread
-    self.postMessage({
-        status: 'complete',
-        output: output,
-    });
-});
diff --git a/examples/next-client/tailwind.config.js b/examples/next-client/tailwind.config.js
deleted file mode 100644
index d53b2eaa0..000000000
--- a/examples/next-client/tailwind.config.js
+++ /dev/null
@@ -1,18 +0,0 @@
-/** @type {import('tailwindcss').Config} */
-module.exports = {
-  content: [
-    './src/pages/**/*.{js,ts,jsx,tsx,mdx}',
-    './src/components/**/*.{js,ts,jsx,tsx,mdx}',
-    './src/app/**/*.{js,ts,jsx,tsx,mdx}',
-  ],
-  theme: {
-    extend: {
-      backgroundImage: {
-        'gradient-radial': 'radial-gradient(var(--tw-gradient-stops))',
-        'gradient-conic':
-          'conic-gradient(from 180deg at 50% 50%, var(--tw-gradient-stops))',
-      },
-    },
-  },
-  plugins: [],
-}
diff --git a/examples/next-server/.eslintrc.json b/examples/next-server/.eslintrc.json
deleted file mode 100644
index bffb357a7..000000000
--- a/examples/next-server/.eslintrc.json
+++ /dev/null
@@ -1,3 +0,0 @@
-{
-  "extends": "next/core-web-vitals"
-}
diff --git a/examples/next-server/.gitignore b/examples/next-server/.gitignore
deleted file mode 100644
index 8f322f0d8..000000000
--- a/examples/next-server/.gitignore
+++ /dev/null
@@ -1,35 +0,0 @@
-# See https://help.github.com/articles/ignoring-files/ for more about ignoring files.
-
-# dependencies
-/node_modules
-/.pnp
-.pnp.js
-
-# testing
-/coverage
-
-# next.js
-/.next/
-/out/
-
-# production
-/build
-
-# misc
-.DS_Store
-*.pem
-
-# debug
-npm-debug.log*
-yarn-debug.log*
-yarn-error.log*
-
-# local env files
-.env*.local
-
-# vercel
-.vercel
-
-# typescript
-*.tsbuildinfo
-next-env.d.ts
diff --git a/examples/next-server/Dockerfile b/examples/next-server/Dockerfile
deleted file mode 100644
index 3feaf9c7e..000000000
--- a/examples/next-server/Dockerfile
+++ /dev/null
@@ -1,69 +0,0 @@
-# syntax=docker/dockerfile:1.4
-
-# Adapted from https://github.com/vercel/next.js/blob/e60a1e747c3f521fc24dfd9ee2989e13afeb0a9b/examples/with-docker/Dockerfile
-# For more information, see https://nextjs.org/docs/pages/building-your-application/deploying#docker-image
-
-FROM node:18 AS base
-
-# Install dependencies only when needed
-FROM base AS deps
-WORKDIR /app
-
-# Install dependencies based on the preferred package manager
-COPY --link package.json yarn.lock* package-lock.json* pnpm-lock.yaml* ./
-RUN \
-  if [ -f yarn.lock ]; then yarn --frozen-lockfile; \
-  elif [ -f package-lock.json ]; then npm ci; \
-  elif [ -f pnpm-lock.yaml ]; then yarn global add pnpm && pnpm i --frozen-lockfile; \
-  else echo "Lockfile not found." && exit 1; \
-  fi
-
-
-# Rebuild the source code only when needed
-FROM base AS builder
-WORKDIR /app
-COPY --from=deps --link /app/node_modules ./node_modules
-COPY --link  . .
-
-# Next.js collects completely anonymous telemetry data about general usage.
-# Learn more here: https://nextjs.org/telemetry
-# Uncomment the following line in case you want to disable telemetry during the build.
-# ENV NEXT_TELEMETRY_DISABLED 1
-
-RUN npm run build
-
-# If using yarn comment out above and use below instead
-# RUN yarn build
-
-# Production image, copy all the files and run next
-FROM base AS runner
-WORKDIR /app
-
-ENV NODE_ENV production
-# Uncomment the following line in case you want to disable telemetry during runtime.
-# ENV NEXT_TELEMETRY_DISABLED 1
-
-RUN \
-  addgroup --system --gid 1001 nodejs; \
-  adduser --system --uid 1001 nextjs
-
-COPY --from=builder --link /app/public ./public
-
-# Automatically leverage output traces to reduce image size
-# https://nextjs.org/docs/advanced-features/output-file-tracing
-COPY --from=builder --link --chown=1001:1001 /app/.next/standalone ./
-COPY --from=builder --link --chown=1001:1001 /app/.next/static ./.next/static
-
-USER nextjs
-
-EXPOSE 3000
-
-ENV PORT 3000
-ENV HOSTNAME 0.0.0.0
-
-# Allow the running process to write model files to the cache folder.
-# NOTE: In practice, you would probably want to pre-download the model files to avoid having to download them on-the-fly.
-RUN mkdir -p /app/node_modules/@xenova/.cache/
-RUN chmod 777 -R /app/node_modules/@xenova/
-
-CMD ["node", "server.js"]
diff --git a/examples/next-server/README.md b/examples/next-server/README.md
deleted file mode 100644
index e5f733efc..000000000
--- a/examples/next-server/README.md
+++ /dev/null
@@ -1,34 +0,0 @@
-This is a [Next.js](https://nextjs.org/) project bootstrapped with [`create-next-app`](https://github.com/vercel/next.js/tree/canary/packages/create-next-app).
-
-## Getting Started
-
-First, run the development server:
-
-```bash
-npm run dev
-# or
-yarn dev
-# or
-pnpm dev
-```
-
-Open [http://localhost:3000](http://localhost:3000) with your browser to see the result.
-
-You can start editing the page by modifying `app/page.js`. The page auto-updates as you edit the file.
-
-This project uses [`next/font`](https://nextjs.org/docs/basic-features/font-optimization) to automatically optimize and load Inter, a custom Google Font.
-
-## Learn More
-
-To learn more about Next.js, take a look at the following resources:
-
-- [Next.js Documentation](https://nextjs.org/docs) - learn about Next.js features and API.
-- [Learn Next.js](https://nextjs.org/learn) - an interactive Next.js tutorial.
-
-You can check out [the Next.js GitHub repository](https://github.com/vercel/next.js/) - your feedback and contributions are welcome!
-
-## Deploy on Vercel
-
-The easiest way to deploy your Next.js app is to use the [Vercel Platform](https://vercel.com/new?utm_medium=default-template&filter=next.js&utm_source=create-next-app&utm_campaign=create-next-app-readme) from the creators of Next.js.
-
-Check out our [Next.js deployment documentation](https://nextjs.org/docs/deployment) for more details.
diff --git a/examples/next-server/jsconfig.json b/examples/next-server/jsconfig.json
deleted file mode 100644
index b8d6842d7..000000000
--- a/examples/next-server/jsconfig.json
+++ /dev/null
@@ -1,7 +0,0 @@
-{
-  "compilerOptions": {
-    "paths": {
-      "@/*": ["./src/*"]
-    }
-  }
-}
diff --git a/examples/next-server/next.config.js b/examples/next-server/next.config.js
deleted file mode 100644
index a1a4c56e3..000000000
--- a/examples/next-server/next.config.js
+++ /dev/null
@@ -1,13 +0,0 @@
-/** @type {import('next').NextConfig} */
-const nextConfig = {
-    // (Optional) Export as a standalone site
-    // See https://nextjs.org/docs/pages/api-reference/next-config-js/output#automatically-copying-traced-files
-    output: 'standalone', // Feel free to modify/remove this option
-    
-    // Indicate that these packages should not be bundled by webpack
-    experimental: {
-        serverComponentsExternalPackages: ['sharp', 'onnxruntime-node'],
-    },
-};
-
-module.exports = nextConfig;
diff --git a/examples/next-server/package-lock.json b/examples/next-server/package-lock.json
deleted file mode 100644
index 3648a4273..000000000
--- a/examples/next-server/package-lock.json
+++ /dev/null
@@ -1,4775 +0,0 @@
-{
-  "name": "next",
-  "version": "0.1.0",
-  "lockfileVersion": 3,
-  "requires": true,
-  "packages": {
-    "": {
-      "name": "next",
-      "version": "0.1.0",
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-        "@xenova/transformers": "^2.4.2",
-        "autoprefixer": "10.4.14",
-        "eslint": "8.45.0",
-        "eslint-config-next": "13.4.12",
-        "next": "^14.2.3",
-        "postcss": "^8.4.31",
-        "react": "18.2.0",
-        "react-dom": "18.2.0",
-        "tailwindcss": "3.3.3"
-      }
-    },
-    "node_modules/@aashutoshrathi/word-wrap": {
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-      "engines": {
-        "node": ">=0.10.0"
-      }
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-      "engines": {
-        "node": ">=10"
-      },
-      "funding": {
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-      }
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-    "node_modules/@babel/runtime": {
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-      "resolved": "https://registry.npmjs.org/@babel/runtime/-/runtime-7.22.6.tgz",
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diff --git a/examples/next-server/package.json b/examples/next-server/package.json
deleted file mode 100644
index d822cc7e3..000000000
--- a/examples/next-server/package.json
+++ /dev/null
@@ -1,25 +0,0 @@
-{
-  "name": "next",
-  "version": "0.1.0",
-  "private": true,
-  "scripts": {
-    "dev": "next dev",
-    "build": "next build",
-    "start": "next start",
-    "lint": "next lint"
-  },
-  "dependencies": {
-    "@xenova/transformers": "^2.4.2",
-    "autoprefixer": "10.4.14",
-    "eslint": "8.45.0",
-    "eslint-config-next": "13.4.12",
-    "next": "^14.2.3",
-    "postcss": "^8.4.31",
-    "react": "18.2.0",
-    "react-dom": "18.2.0",
-    "tailwindcss": "3.3.3"
-  },
-  "overrides": {
-    "protobufjs": "^7.2.4"
-  }
-}
diff --git a/examples/next-server/postcss.config.js b/examples/next-server/postcss.config.js
deleted file mode 100644
index 33ad091d2..000000000
--- a/examples/next-server/postcss.config.js
+++ /dev/null
@@ -1,6 +0,0 @@
-module.exports = {
-  plugins: {
-    tailwindcss: {},
-    autoprefixer: {},
-  },
-}
diff --git a/examples/next-server/public/next.svg b/examples/next-server/public/next.svg
deleted file mode 100644
index 5174b28c5..000000000
--- a/examples/next-server/public/next.svg
+++ /dev/null
@@ -1 +0,0 @@
-
\ No newline at end of file
diff --git a/examples/next-server/public/vercel.svg b/examples/next-server/public/vercel.svg
deleted file mode 100644
index d2f842227..000000000
--- a/examples/next-server/public/vercel.svg
+++ /dev/null
@@ -1 +0,0 @@
-
\ No newline at end of file
diff --git a/examples/next-server/src/app/classify/pipeline.js b/examples/next-server/src/app/classify/pipeline.js
deleted file mode 100644
index 44e73e7eb..000000000
--- a/examples/next-server/src/app/classify/pipeline.js
+++ /dev/null
@@ -1,30 +0,0 @@
-import { pipeline } from "@xenova/transformers";
-
-// Use the Singleton pattern to enable lazy construction of the pipeline.
-// NOTE: We wrap the class in a function to prevent code duplication (see below).
-const P = () => class PipelineSingleton {
-    static task = 'text-classification';
-    static model = 'Xenova/distilbert-base-uncased-finetuned-sst-2-english';
-    static instance = null;
-
-    static async getInstance(progress_callback = null) {
-        if (this.instance === null) {
-            this.instance = pipeline(this.task, this.model, { progress_callback });
-        }
-        return this.instance;
-    }
-}
-
-let PipelineSingleton;
-if (process.env.NODE_ENV !== 'production') {
-    // When running in development mode, attach the pipeline to the
-    // global object so that it's preserved between hot reloads.
-    // For more information, see https://vercel.com/guides/nextjs-prisma-postgres
-    if (!global.PipelineSingleton) {
-        global.PipelineSingleton = P();
-    }
-    PipelineSingleton = global.PipelineSingleton;
-} else {
-    PipelineSingleton = P();
-}
-export default PipelineSingleton;
diff --git a/examples/next-server/src/app/classify/route.js b/examples/next-server/src/app/classify/route.js
deleted file mode 100644
index 88348351a..000000000
--- a/examples/next-server/src/app/classify/route.js
+++ /dev/null
@@ -1,22 +0,0 @@
-// Create a custom request handler for the /classify route.
-// For more information, see https://nextjs.org/docs/app/building-your-application/routing/router-handlers
-
-import { NextResponse } from 'next/server'
-import PipelineSingleton from './pipeline.js';
-
-export async function GET(request) {
-    const text = request.nextUrl.searchParams.get('text');
-    if (!text) {
-        return NextResponse.json({
-            error: 'Missing text parameter',
-        }, { status: 400 });
-    }
-    // Get the classification pipeline. When called for the first time,
-    // this will load the pipeline and cache it for future use.
-    const classifier = await PipelineSingleton.getInstance();
-
-    // Actually perform the classification
-    const result = await classifier(text);
-
-    return NextResponse.json(result);
-}
diff --git a/examples/next-server/src/app/favicon.ico b/examples/next-server/src/app/favicon.ico
deleted file mode 100644
index 718d6fea4..000000000
Binary files a/examples/next-server/src/app/favicon.ico and /dev/null differ
diff --git a/examples/next-server/src/app/globals.css b/examples/next-server/src/app/globals.css
deleted file mode 100644
index fd81e8858..000000000
--- a/examples/next-server/src/app/globals.css
+++ /dev/null
@@ -1,27 +0,0 @@
-@tailwind base;
-@tailwind components;
-@tailwind utilities;
-
-:root {
-  --foreground-rgb: 0, 0, 0;
-  --background-start-rgb: 214, 219, 220;
-  --background-end-rgb: 255, 255, 255;
-}
-
-@media (prefers-color-scheme: dark) {
-  :root {
-    --foreground-rgb: 255, 255, 255;
-    --background-start-rgb: 0, 0, 0;
-    --background-end-rgb: 0, 0, 0;
-  }
-}
-
-body {
-  color: rgb(var(--foreground-rgb));
-  background: linear-gradient(
-      to bottom,
-      transparent,
-      rgb(var(--background-end-rgb))
-    )
-    rgb(var(--background-start-rgb));
-}
diff --git a/examples/next-server/src/app/layout.js b/examples/next-server/src/app/layout.js
deleted file mode 100644
index c93f80617..000000000
--- a/examples/next-server/src/app/layout.js
+++ /dev/null
@@ -1,17 +0,0 @@
-import './globals.css'
-import { Inter } from 'next/font/google'
-
-const inter = Inter({ subsets: ['latin'] })
-
-export const metadata = {
-  title: 'Create Next App',
-  description: 'Generated by create next app',
-}
-
-export default function RootLayout({ children }) {
-  return (
-    
-      {children}
-    
-  )
-}
diff --git a/examples/next-server/src/app/page.js b/examples/next-server/src/app/page.js
deleted file mode 100644
index 6cd6aec54..000000000
--- a/examples/next-server/src/app/page.js
+++ /dev/null
@@ -1,45 +0,0 @@
-'use client'
-
-import { useState } from 'react'
-
-export default function Home() {
-
-  // Keep track of the classification result and the model loading status.
-  const [result, setResult] = useState(null);
-  const [ready, setReady] = useState(null);
-
-  const classify = async (text) => {
-    if (!text) return;
-    if (ready === null) setReady(false);
-
-    // Make a request to the /classify route on the server.
-    const result = await fetch(`/classify?text=${encodeURIComponent(text)}`);
-
-    // If this is the first time we've made a request, set the ready flag.
-    if (!ready) setReady(true);
-
-    const json = await result.json();
-    setResult(json);
-  };
-  return (
-    

-      
Transformers.js

-      
Next.js template (server-side)

-       {
-          classify(e.target.value);
-        }}
-      />
-
-      {ready !== null && (
-        
-          {
-            (!ready || !result) ? 'Loading...' : JSON.stringify(result, null, 2)}
-        

-      )}
-    

-  )
-}
diff --git a/examples/next-server/tailwind.config.js b/examples/next-server/tailwind.config.js
deleted file mode 100644
index d53b2eaa0..000000000
--- a/examples/next-server/tailwind.config.js
+++ /dev/null
@@ -1,18 +0,0 @@
-/** @type {import('tailwindcss').Config} */
-module.exports = {
-  content: [
-    './src/pages/**/*.{js,ts,jsx,tsx,mdx}',
-    './src/components/**/*.{js,ts,jsx,tsx,mdx}',
-    './src/app/**/*.{js,ts,jsx,tsx,mdx}',
-  ],
-  theme: {
-    extend: {
-      backgroundImage: {
-        'gradient-radial': 'radial-gradient(var(--tw-gradient-stops))',
-        'gradient-conic':
-          'conic-gradient(from 180deg at 50% 50%, var(--tw-gradient-stops))',
-      },
-    },
-  },
-  plugins: [],
-}
diff --git a/examples/node-audio-processing/index.js b/examples/node-audio-processing/index.js
deleted file mode 100644
index bac47156b..000000000
--- a/examples/node-audio-processing/index.js
+++ /dev/null
@@ -1,36 +0,0 @@
-import { pipeline } from '@xenova/transformers';
-import wavefile from 'wavefile';
-
-// Load model
-let transcriber = await pipeline('automatic-speech-recognition', 'Xenova/whisper-tiny.en');
-
-// Load audio data
-let url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/jfk.wav';
-let buffer = Buffer.from(await fetch(url).then(x => x.arrayBuffer()))
-
-// Read .wav file and convert it to required format
-let wav = new wavefile.WaveFile(buffer);
-wav.toBitDepth('32f'); // Pipeline expects input as a Float32Array
-wav.toSampleRate(16000); // Whisper expects audio with a sampling rate of 16000
-let audioData = wav.getSamples();
-if (Array.isArray(audioData)) {
-    if (audioData.length > 1) {
-        const SCALING_FACTOR = Math.sqrt(2);
-
-        // Merge channels (into first channel to save memory)
-        for (let i = 0; i < audioData[0].length; ++i) {
-            audioData[0][i] = SCALING_FACTOR * (audioData[0][i] + audioData[1][i]) / 2;
-        }
-    }
-
-    // Select first channel
-    audioData = audioData[0];
-}
-
-// Run model
-let start = performance.now();
-let output = await transcriber(audioData);
-let end = performance.now();
-console.log(`Execution duration: ${(end - start) / 1000} seconds`);
-console.log(output);
-// { text: ' And so my fellow Americans ask not what your country can do for you, ask what you can do for your country.' }
diff --git a/examples/node-audio-processing/package.json b/examples/node-audio-processing/package.json
deleted file mode 100644
index 212b56b54..000000000
--- a/examples/node-audio-processing/package.json
+++ /dev/null
@@ -1,17 +0,0 @@
-{
-  "name": "audio-processing",
-  "version": "1.0.0",
-  "description": "",
-  "main": "index.js",
-  "type": "module",
-  "scripts": {
-    "test": "echo \"Error: no test specified\" && exit 1"
-  },
-  "keywords": [],
-  "author": "",
-  "license": "ISC",
-  "dependencies": {
-    "@xenova/transformers": "^2.2.0",
-    "wavefile": "^11.0.0"
-  }
-}
diff --git a/examples/node/commonjs/app.js b/examples/node/commonjs/app.js
deleted file mode 100644
index 6612b6d95..000000000
--- a/examples/node/commonjs/app.js
+++ /dev/null
@@ -1,63 +0,0 @@
-
-const http = require('http');
-const querystring = require('querystring');
-const url = require('url');
-
-
-class MyClassificationPipeline {
-  static task = 'text-classification';
-  static model = 'Xenova/distilbert-base-uncased-finetuned-sst-2-english';
-  static instance = null;
-
-  static async getInstance(progress_callback = null) {
-    if (this.instance === null) {
-      // Dynamically import the Transformers.js library
-      let { pipeline, env } = await import('@xenova/transformers');
-
-      // NOTE: Uncomment this to change the cache directory
-      // env.cacheDir = './.cache';
-
-      this.instance = pipeline(this.task, this.model, { progress_callback });
-    }
-
-    return this.instance;
-  }
-}
-
-// Comment out this line if you don't want to start loading the model as soon as the server starts.
-// If commented out, the model will be loaded when the first request is received (i.e,. lazily).
-MyClassificationPipeline.getInstance();
-
-// Define the HTTP server
-const server = http.createServer();
-const hostname = '127.0.0.1';
-const port = 3000;
-
-// Listen for requests made to the server
-server.on('request', async (req, res) => {
-  // Parse the request URL
-  const parsedUrl = url.parse(req.url);
-
-  // Extract the query parameters
-  const { text } = querystring.parse(parsedUrl.query);
-
-  // Set the response headers
-  res.setHeader('Content-Type', 'application/json');
-
-  let response;
-  if (parsedUrl.pathname === '/classify' && text) {
-    const classifier = await MyClassificationPipeline.getInstance();
-    response = await classifier(text);
-    res.statusCode = 200;
-  } else {
-    response = { 'error': 'Bad request' }
-    res.statusCode = 400;
-  }
-
-  // Send the JSON response
-  res.end(JSON.stringify(response));
-});
-
-server.listen(port, hostname, () => {
-  console.log(`Server running at http://${hostname}:${port}/`);
-});
diff --git a/examples/node/commonjs/package.json b/examples/node/commonjs/package.json
deleted file mode 100644
index 5e4d59b0f..000000000
--- a/examples/node/commonjs/package.json
+++ /dev/null
@@ -1,12 +0,0 @@
-{
-  "name": "commonjs",
-  "version": "1.0.0",
-  "description": "Server-side inference with Transformers.js (CommonJS)",
-  "main": "app.js",
-  "keywords": [],
-  "author": "Xenova",
-  "license": "ISC",
-  "dependencies": {
-    "@xenova/transformers": "^2.0.0"
-  }
-}
diff --git a/examples/node/esm/app.js b/examples/node/esm/app.js
deleted file mode 100644
index 08c873d83..000000000
--- a/examples/node/esm/app.js
+++ /dev/null
@@ -1,62 +0,0 @@
-
-import http from 'http';
-import querystring from 'querystring';
-import url from 'url';
-
-import { pipeline, env } from '@xenova/transformers';
-
-class MyClassificationPipeline {
-  static task = 'text-classification';
-  static model = 'Xenova/distilbert-base-uncased-finetuned-sst-2-english';
-  static instance = null;
-
-  static async getInstance(progress_callback = null) {
-    if (this.instance === null) {
-
-      // NOTE: Uncomment this to change the cache directory
-      // env.cacheDir = './.cache';
-
-      this.instance = pipeline(this.task, this.model, { progress_callback });
-    }
-
-    return this.instance;
-  }
-}
-
-// Comment out this line if you don't want to start loading the model as soon as the server starts.
-// If commented out, the model will be loaded when the first request is received (i.e,. lazily).
-MyClassificationPipeline.getInstance();
-
-// Define the HTTP server
-const server = http.createServer();
-const hostname = '127.0.0.1';
-const port = 3000;
-
-// Listen for requests made to the server
-server.on('request', async (req, res) => {
-  // Parse the request URL
-  const parsedUrl = url.parse(req.url);
-
-  // Extract the query parameters
-  const { text } = querystring.parse(parsedUrl.query);
-
-  // Set the response headers
-  res.setHeader('Content-Type', 'application/json');
-
-  let response;
-  if (parsedUrl.pathname === '/classify' && text) {
-    const classifier = await MyClassificationPipeline.getInstance();
-    response = await classifier(text);
-    res.statusCode = 200;
-  } else {
-    response = { 'error': 'Bad request' }
-    res.statusCode = 400;
-  }
-
-  // Send the JSON response
-  res.end(JSON.stringify(response));
-});
-
-server.listen(port, hostname, () => {
-  console.log(`Server running at http://${hostname}:${port}/`);
-});
diff --git a/examples/node/esm/package.json b/examples/node/esm/package.json
deleted file mode 100644
index 72ecb47ca..000000000
--- a/examples/node/esm/package.json
+++ /dev/null
@@ -1,13 +0,0 @@
-{
-  "name": "esm",
-  "version": "1.0.0",
-  "description": "Server-side inference with Transformers.js (ESM)",
-  "type": "module",
-  "main": "app.js",
-  "keywords": [],
-  "author": "Xenova",
-  "license": "ISC",
-  "dependencies": {
-    "@xenova/transformers": "^2.0.0"
-  }
-}
diff --git a/examples/react-translator/.eslintrc.cjs b/examples/react-translator/.eslintrc.cjs
deleted file mode 100644
index c12e55cec..000000000
--- a/examples/react-translator/.eslintrc.cjs
+++ /dev/null
@@ -1,16 +0,0 @@
-module.exports = {
-  env: { browser: true, es2020: true, 'node': true },
-  extends: [
-    'eslint:recommended',
-    'plugin:react/recommended',
-    'plugin:react/jsx-runtime',
-    'plugin:react-hooks/recommended',
-  ],
-  parserOptions: { ecmaVersion: 'latest', sourceType: 'module' },
-  settings: { react: { version: '18.2' } },
-  plugins: ['react-refresh'],
-  rules: {
-    'react-refresh/only-export-components': 'warn',
-    'react/prop-types': 'off',
-  },
-}
diff --git a/examples/react-translator/.gitignore b/examples/react-translator/.gitignore
deleted file mode 100644
index a547bf36d..000000000
--- a/examples/react-translator/.gitignore
+++ /dev/null
@@ -1,24 +0,0 @@
-# Logs
-logs
-*.log
-npm-debug.log*
-yarn-debug.log*
-yarn-error.log*
-pnpm-debug.log*
-lerna-debug.log*
-
-node_modules
-dist
-dist-ssr
-*.local
-
-# Editor directories and files
-.vscode/*
-!.vscode/extensions.json
-.idea
-.DS_Store
-*.suo
-*.ntvs*
-*.njsproj
-*.sln
-*.sw?
diff --git a/examples/react-translator/index.html b/examples/react-translator/index.html
deleted file mode 100644
index e5609ec99..000000000
--- a/examples/react-translator/index.html
+++ /dev/null
@@ -1,12 +0,0 @@
-
-
-  
-    
-    
-    Transformers.js - Sample react application
-  
-  
-    

-    
-  
-
diff --git a/examples/react-translator/package.json b/examples/react-translator/package.json
deleted file mode 100644
index 9d54f93d9..000000000
--- a/examples/react-translator/package.json
+++ /dev/null
@@ -1,27 +0,0 @@
-{
-  "name": "react-translator",
-  "private": true,
-  "version": "0.0.0",
-  "type": "module",
-  "scripts": {
-    "dev": "vite",
-    "build": "vite build",
-    "lint": "eslint src --ext js,jsx --report-unused-disable-directives --max-warnings 0",
-    "preview": "vite preview"
-  },
-  "dependencies": {
-    "@xenova/transformers": "^2.0.0",
-    "react": "^18.2.0",
-    "react-dom": "^18.2.0"
-  },
-  "devDependencies": {
-    "@types/react": "^18.0.28",
-    "@types/react-dom": "^18.0.11",
-    "@vitejs/plugin-react": "^4.0.0",
-    "eslint": "^8.38.0",
-    "eslint-plugin-react": "^7.32.2",
-    "eslint-plugin-react-hooks": "^4.6.0",
-    "eslint-plugin-react-refresh": "^0.3.4",
-    "vite": "^4.3.2"
-  }
-}
diff --git a/examples/react-translator/src/App.css b/examples/react-translator/src/App.css
deleted file mode 100644
index 46f43f91b..000000000
--- a/examples/react-translator/src/App.css
+++ /dev/null
@@ -1,69 +0,0 @@
-#root {
-  max-width: 1280px;
-  margin: 0 auto;
-  padding: 2rem;
-  text-align: center;
-}
-
-.language-container {
-  display: flex;
-  gap: 20px;
-}
-
-.textbox-container {
-  display: flex;
-  justify-content: center;
-  gap: 20px;
-  width: 800px;
-}
-
-.textbox-container>textarea, .language-selector {
-  width: 50%;
-}
-
-.language-selector>select {
-  width: 150px;
-}
-
-.progress-container {
-  position: relative;
-  font-size: 14px;
-  color: white;
-  background-color: #e9ecef;
-  border: solid 1px;
-  border-radius: 8px;
-  text-align: left;
-  overflow: hidden;
-}
-
-.progress-bar {
-  padding: 0 4px;
-  z-index: 0;
-  top: 0;
-  width: 1%;
-  height: 100%;
-  overflow: hidden;
-  background-color: #007bff;
-  white-space: nowrap;
-}
-
-.progress-text {
-  z-index: 2;
-}
-
-.selector-container {
-  display: flex;
-  gap: 20px;
-}
-
-.progress-bars-container {
-  padding: 8px;
-  height: 140px;
-}
-
-.container {
-  margin: 25px;
-  display: flex;
-  flex-direction: column;
-  gap: 10px;
-}
\ No newline at end of file
diff --git a/examples/react-translator/src/App.jsx b/examples/react-translator/src/App.jsx
deleted file mode 100644
index 6c2043619..000000000
--- a/examples/react-translator/src/App.jsx
+++ /dev/null
@@ -1,126 +0,0 @@
-import { useEffect, useRef, useState } from 'react'
-import LanguageSelector from './components/LanguageSelector';
-import Progress from './components/Progress';
-
-import './App.css'
-
-function App() {
-
-  // Model loading
-  const [ready, setReady] = useState(null);
-  const [disabled, setDisabled] = useState(false);
-  const [progressItems, setProgressItems] = useState([]);
-
-  // Inputs and outputs
-  const [input, setInput] = useState('I love walking my dog.');
-  const [sourceLanguage, setSourceLanguage] = useState('eng_Latn');
-  const [targetLanguage, setTargetLanguage] = useState('fra_Latn');
-  const [output, setOutput] = useState('');
-
-  // Create a reference to the worker object.
-  const worker = useRef(null);
-
-  // We use the `useEffect` hook to setup the worker as soon as the `App` component is mounted.
-  useEffect(() => {
-    if (!worker.current) {
-      // Create the worker if it does not yet exist.
-      worker.current = new Worker(new URL('./worker.js', import.meta.url), {
-        type: 'module'
-      });
-    }
-
-    // Create a callback function for messages from the worker thread.
-    const onMessageReceived = (e) => {
-      switch (e.data.status) {
-        case 'initiate':
-          // Model file start load: add a new progress item to the list.
-          setReady(false);
-          setProgressItems(prev => [...prev, e.data]);
-          break;
-
-        case 'progress':
-          // Model file progress: update one of the progress items.
-          setProgressItems(
-            prev => prev.map(item => {
-              if (item.file === e.data.file) {
-                return { ...item, progress: e.data.progress }
-              }
-              return item;
-            })
-          );
-          break;
-
-        case 'done':
-          // Model file loaded: remove the progress item from the list.
-          setProgressItems(
-            prev => prev.filter(item => item.file !== e.data.file)
-          );
-          break;
-
-        case 'ready':
-          // Pipeline ready: the worker is ready to accept messages.
-          setReady(true);
-          break;
-
-        case 'update':
-          // Generation update: update the output text.
-          setOutput(e.data.output);
-          break;
-
-        case 'complete':
-          // Generation complete: re-enable the "Translate" button
-          setDisabled(false);
-          break;
-      }
-    };
-
-    // Attach the callback function as an event listener.
-    worker.current.addEventListener('message', onMessageReceived);
-
-    // Define a cleanup function for when the component is unmounted.
-    return () => worker.current.removeEventListener('message', onMessageReceived);
-  });
-
-  const translate = () => {
-    setDisabled(true);
-    worker.current.postMessage({
-      text: input,
-      src_lang: sourceLanguage,
-      tgt_lang: targetLanguage,
-    });
-  }
-
-  return (
-    <>
-      
Transformers.js

-      
ML-powered multilingual translation in React!

-
-      

-        

-           setSourceLanguage(x.target.value)} />
-           setTargetLanguage(x.target.value)} />
-        

-
-        

-          
-          
-        

-      

-
-      
-
-      

-        {ready === false && (
-          
-        )}
-        {progressItems.map(data => (
-          

-            
-          

-        ))}
-      

-    
-  )
-}
-
-export default App
diff --git a/examples/react-translator/src/components/LanguageSelector.jsx b/examples/react-translator/src/components/LanguageSelector.jsx
deleted file mode 100644
index 1224367cc..000000000
--- a/examples/react-translator/src/components/LanguageSelector.jsx
+++ /dev/null
@@ -1,223 +0,0 @@
-
-// The full list of languages in FLORES-200 is available here:
-// https://github.com/facebookresearch/flores/blob/main/flores200/README.md#languages-in-flores-200
-
-const LANGUAGES = {
-  "Acehnese (Arabic script)": "ace_Arab",
-  "Acehnese (Latin script)": "ace_Latn",
-  "Afrikaans": "afr_Latn",
-  "Akan": "aka_Latn",
-  "Amharic": "amh_Ethi",
-  "Armenian": "hye_Armn",
-  "Assamese": "asm_Beng",
-  "Asturian": "ast_Latn",
-  "Awadhi": "awa_Deva",
-  "Ayacucho Quechua": "quy_Latn",
-  "Balinese": "ban_Latn",
-  "Bambara": "bam_Latn",
-  "Banjar (Arabic script)": "bjn_Arab",
-  "Banjar (Latin script)": "bjn_Latn",
-  "Bashkir": "bak_Cyrl",
-  "Basque": "eus_Latn",
-  "Belarusian": "bel_Cyrl",
-  "Bemba": "bem_Latn",
-  "Bengali": "ben_Beng",
-  "Bhojpuri": "bho_Deva",
-  "Bosnian": "bos_Latn",
-  "Buginese": "bug_Latn",
-  "Bulgarian": "bul_Cyrl",
-  "Burmese": "mya_Mymr",
-  "Catalan": "cat_Latn",
-  "Cebuano": "ceb_Latn",
-  "Central Atlas Tamazight": "tzm_Tfng",
-  "Central Aymara": "ayr_Latn",
-  "Central Kanuri (Arabic script)": "knc_Arab",
-  "Central Kanuri (Latin script)": "knc_Latn",
-  "Central Kurdish": "ckb_Arab",
-  "Chhattisgarhi": "hne_Deva",
-  "Chinese (Simplified)": "zho_Hans",
-  "Chinese (Traditional)": "zho_Hant",
-  "Chokwe": "cjk_Latn",
-  "Crimean Tatar": "crh_Latn",
-  "Croatian": "hrv_Latn",
-  "Czech": "ces_Latn",
-  "Danish": "dan_Latn",
-  "Dari": "prs_Arab",
-  "Dutch": "nld_Latn",
-  "Dyula": "dyu_Latn",
-  "Dzongkha": "dzo_Tibt",
-  "Eastern Panjabi": "pan_Guru",
-  "Eastern Yiddish": "ydd_Hebr",
-  "Egyptian Arabic": "arz_Arab",
-  "English": "eng_Latn",
-  "Esperanto": "epo_Latn",
-  "Estonian": "est_Latn",
-  "Ewe": "ewe_Latn",
-  "Faroese": "fao_Latn",
-  "Fijian": "fij_Latn",
-  "Finnish": "fin_Latn",
-  "Fon": "fon_Latn",
-  "French": "fra_Latn",
-  "Friulian": "fur_Latn",
-  "Galician": "glg_Latn",
-  "Ganda": "lug_Latn",
-  "Georgian": "kat_Geor",
-  "German": "deu_Latn",
-  "Greek": "ell_Grek",
-  "Guarani": "grn_Latn",
-  "Gujarati": "guj_Gujr",
-  "Haitian Creole": "hat_Latn",
-  "Halh Mongolian": "khk_Cyrl",
-  "Hausa": "hau_Latn",
-  "Hebrew": "heb_Hebr",
-  "Hindi": "hin_Deva",
-  "Hungarian": "hun_Latn",
-  "Icelandic": "isl_Latn",
-  "Igbo": "ibo_Latn",
-  "Ilocano": "ilo_Latn",
-  "Indonesian": "ind_Latn",
-  "Irish": "gle_Latn",
-  "Italian": "ita_Latn",
-  "Japanese": "jpn_Jpan",
-  "Javanese": "jav_Latn",
-  "Jingpho": "kac_Latn",
-  "Kabiyè": "kbp_Latn",
-  "Kabuverdianu": "kea_Latn",
-  "Kabyle": "kab_Latn",
-  "Kamba": "kam_Latn",
-  "Kannada": "kan_Knda",
-  "Kashmiri (Arabic script)": "kas_Arab",
-  "Kashmiri (Devanagari script)": "kas_Deva",
-  "Kazakh": "kaz_Cyrl",
-  "Khmer": "khm_Khmr",
-  "Kikongo": "kon_Latn",
-  "Kikuyu": "kik_Latn",
-  "Kimbundu": "kmb_Latn",
-  "Kinyarwanda": "kin_Latn",
-  "Korean": "kor_Hang",
-  "Kyrgyz": "kir_Cyrl",
-  "Lao": "lao_Laoo",
-  "Latgalian": "ltg_Latn",
-  "Ligurian": "lij_Latn",
-  "Limburgish": "lim_Latn",
-  "Lingala": "lin_Latn",
-  "Lithuanian": "lit_Latn",
-  "Lombard": "lmo_Latn",
-  "Luba-Kasai": "lua_Latn",
-  "Luo": "luo_Latn",
-  "Luxembourgish": "ltz_Latn",
-  "Macedonian": "mkd_Cyrl",
-  "Magahi": "mag_Deva",
-  "Maithili": "mai_Deva",
-  "Malayalam": "mal_Mlym",
-  "Maltese": "mlt_Latn",
-  "Maori": "mri_Latn",
-  "Marathi": "mar_Deva",
-  "Meitei (Bengali script)": "mni_Beng",
-  "Mesopotamian Arabic": "acm_Arab",
-  "Minangkabau (Arabic script)": "min_Arab",
-  "Minangkabau (Latin script)": "min_Latn",
-  "Mizo": "lus_Latn",
-  "Modern Standard Arabic (Romanized)": "arb_Latn",
-  "Modern Standard Arabic": "arb_Arab",
-  "Moroccan Arabic": "ary_Arab",
-  "Mossi": "mos_Latn",
-  "Najdi Arabic": "ars_Arab",
-  "Nepali": "npi_Deva",
-  "Nigerian Fulfulde": "fuv_Latn",
-  "North Azerbaijani": "azj_Latn",
-  "North Levantine Arabic": "apc_Arab",
-  "Northern Kurdish": "kmr_Latn",
-  "Northern Sotho": "nso_Latn",
-  "Northern Uzbek": "uzn_Latn",
-  "Norwegian Bokmål": "nob_Latn",
-  "Norwegian Nynorsk": "nno_Latn",
-  "Nuer": "nus_Latn",
-  "Nyanja": "nya_Latn",
-  "Occitan": "oci_Latn",
-  "Odia": "ory_Orya",
-  "Pangasinan": "pag_Latn",
-  "Papiamento": "pap_Latn",
-  "Plateau Malagasy": "plt_Latn",
-  "Polish": "pol_Latn",
-  "Portuguese": "por_Latn",
-  "Romanian": "ron_Latn",
-  "Rundi": "run_Latn",
-  "Russian": "rus_Cyrl",
-  "Samoan": "smo_Latn",
-  "Sango": "sag_Latn",
-  "Sanskrit": "san_Deva",
-  "Santali": "sat_Olck",
-  "Sardinian": "srd_Latn",
-  "Scottish Gaelic": "gla_Latn",
-  "Serbian": "srp_Cyrl",
-  "Shan": "shn_Mymr",
-  "Shona": "sna_Latn",
-  "Sicilian": "scn_Latn",
-  "Silesian": "szl_Latn",
-  "Sindhi": "snd_Arab",
-  "Sinhala": "sin_Sinh",
-  "Slovak": "slk_Latn",
-  "Slovenian": "slv_Latn",
-  "Somali": "som_Latn",
-  "South Azerbaijani": "azb_Arab",
-  "South Levantine Arabic": "ajp_Arab",
-  "Southern Pashto": "pbt_Arab",
-  "Southern Sotho": "sot_Latn",
-  "Southwestern Dinka": "dik_Latn",
-  "Spanish": "spa_Latn",
-  "Standard Latvian": "lvs_Latn",
-  "Standard Malay": "zsm_Latn",
-  "Standard Tibetan": "bod_Tibt",
-  "Sundanese": "sun_Latn",
-  "Swahili": "swh_Latn",
-  "Swati": "ssw_Latn",
-  "Swedish": "swe_Latn",
-  "Tagalog": "tgl_Latn",
-  "Tajik": "tgk_Cyrl",
-  "Tamasheq (Latin script)": "taq_Latn",
-  "Tamasheq (Tifinagh script)": "taq_Tfng",
-  "Tamil": "tam_Taml",
-  "Tatar": "tat_Cyrl",
-  "Ta’izzi-Adeni Arabic": "acq_Arab",
-  "Telugu": "tel_Telu",
-  "Thai": "tha_Thai",
-  "Tigrinya": "tir_Ethi",
-  "Tok Pisin": "tpi_Latn",
-  "Tosk Albanian": "als_Latn",
-  "Tsonga": "tso_Latn",
-  "Tswana": "tsn_Latn",
-  "Tumbuka": "tum_Latn",
-  "Tunisian Arabic": "aeb_Arab",
-  "Turkish": "tur_Latn",
-  "Turkmen": "tuk_Latn",
-  "Twi": "twi_Latn",
-  "Ukrainian": "ukr_Cyrl",
-  "Umbundu": "umb_Latn",
-  "Urdu": "urd_Arab",
-  "Uyghur": "uig_Arab",
-  "Venetian": "vec_Latn",
-  "Vietnamese": "vie_Latn",
-  "Waray": "war_Latn",
-  "Welsh": "cym_Latn",
-  "West Central Oromo": "gaz_Latn",
-  "Western Persian": "pes_Arab",
-  "Wolof": "wol_Latn",
-  "Xhosa": "xho_Latn",
-  "Yoruba": "yor_Latn",
-  "Yue Chinese": "yue_Hant",
-  "Zulu": "zul_Latn",
-}
-
-export default function LanguageSelector({ type, onChange, defaultLanguage }) {
-  return (
-    

-      
-      
-    

-  )
-}
\ No newline at end of file
diff --git a/examples/react-translator/src/components/Progress.jsx b/examples/react-translator/src/components/Progress.jsx
deleted file mode 100644
index 58bccf18c..000000000
--- a/examples/react-translator/src/components/Progress.jsx
+++ /dev/null
@@ -1,9 +0,0 @@
-
-export default function Progress({ text, percentage }) {
-  percentage = percentage ?? 0;
-  return (
-    

-      
{text} ({`${percentage.toFixed(2)}%`})

-    

-  );
-}
diff --git a/examples/react-translator/src/index.css b/examples/react-translator/src/index.css
deleted file mode 100644
index 7ed73431a..000000000
--- a/examples/react-translator/src/index.css
+++ /dev/null
@@ -1,76 +0,0 @@
-:root {
-  font-family: Inter, system-ui, Avenir, Helvetica, Arial, sans-serif;
-  line-height: 1.5;
-  font-weight: 400;
-  color: #213547;
-  background-color: #ffffff;
-
-  font-synthesis: none;
-  text-rendering: optimizeLegibility;
-  -webkit-font-smoothing: antialiased;
-  -moz-osx-font-smoothing: grayscale;
-  -webkit-text-size-adjust: 100%;
-}
-
-body {
-  margin: 0;
-  display: flex;
-  place-items: center;
-  min-width: 320px;
-  min-height: 100vh;
-}
-
-h1 {
-  font-size: 3.2em;
-  line-height: 1;
-}
-
-h1,
-h2 {
-  margin: 8px;
-}
-
-select {
-  padding: 0.3em;
-  cursor: pointer;
-}
-
-textarea {
-  padding: 0.6em;
-}
-
-button {
-  padding: 0.6em 1.2em;
-  cursor: pointer;
-  font-weight: 500;
-}
-
-button[disabled] {
-  cursor: not-allowed;
-}
-
-select,
-textarea,
-button {
-  border-radius: 8px;
-  border: 1px solid transparent;
-  font-size: 1em;
-  font-family: inherit;
-  background-color: #f9f9f9;
-  transition: border-color 0.25s;
-}
-
-select:hover,
-textarea:hover,
-button:not([disabled]):hover {
-  border-color: #646cff;
-}
-
-select:focus,
-select:focus-visible,
-textarea:focus,
-textarea:focus-visible,
-button:focus,
-button:focus-visible {
-  outline: 4px auto -webkit-focus-ring-color;
-}
\ No newline at end of file
diff --git a/examples/react-translator/src/main.jsx b/examples/react-translator/src/main.jsx
deleted file mode 100644
index 54b39dd1d..000000000
--- a/examples/react-translator/src/main.jsx
+++ /dev/null
@@ -1,10 +0,0 @@
-import React from 'react'
-import ReactDOM from 'react-dom/client'
-import App from './App.jsx'
-import './index.css'
-
-ReactDOM.createRoot(document.getElementById('root')).render(
-  
-    
-  ,
-)
diff --git a/examples/react-translator/src/worker.js b/examples/react-translator/src/worker.js
deleted file mode 100644
index a921cdc6a..000000000
--- a/examples/react-translator/src/worker.js
+++ /dev/null
@@ -1,52 +0,0 @@
-
-import { pipeline } from '@xenova/transformers';
-
-/**
- * This class uses the Singleton pattern to ensure that only one instance of the
- * pipeline is loaded. This is because loading the pipeline is an expensive
- * operation and we don't want to do it every time we want to translate a sentence.
- */
-class MyTranslationPipeline {
-    static task = 'translation';
-    static model = 'Xenova/nllb-200-distilled-600M';
-    static instance = null;
-
-    static async getInstance(progress_callback = null) {
-        if (this.instance === null) {
-            this.instance = pipeline(this.task, this.model, { progress_callback });
-        }
-
-        return this.instance;
-    }
-}
-
-// Listen for messages from the main thread
-self.addEventListener('message', async (event) => {
-    // Retrieve the translation pipeline. When called for the first time,
-    // this will load the pipeline and save it for future use.
-    let translator = await MyTranslationPipeline.getInstance(x => {
-        // We also add a progress callback to the pipeline so that we can
-        // track model loading.
-        self.postMessage(x);
-    });
-
-    // Actually perform the translation
-    let output = await translator(event.data.text, {
-        tgt_lang: event.data.tgt_lang,
-        src_lang: event.data.src_lang,
-
-        // Allows for partial output
-        callback_function: x => {
-            self.postMessage({
-                status: 'update',
-                output: translator.tokenizer.decode(x[0].output_token_ids, { skip_special_tokens: true })
-            });
-        }
-    });
-
-    // Send the output back to the main thread
-    self.postMessage({
-        status: 'complete',
-        output: output,
-    });
-});
diff --git a/examples/react-translator/vite.config.js b/examples/react-translator/vite.config.js
deleted file mode 100644
index 5a33944a9..000000000
--- a/examples/react-translator/vite.config.js
+++ /dev/null
@@ -1,7 +0,0 @@
-import { defineConfig } from 'vite'
-import react from '@vitejs/plugin-react'
-
-// https://vitejs.dev/config/
-export default defineConfig({
-  plugins: [react()],
-})
diff --git a/examples/remove-background-client/.gitignore b/examples/remove-background-client/.gitignore
deleted file mode 100644
index a547bf36d..000000000
--- a/examples/remove-background-client/.gitignore
+++ /dev/null
@@ -1,24 +0,0 @@
-# Logs
-logs
-*.log
-npm-debug.log*
-yarn-debug.log*
-yarn-error.log*
-pnpm-debug.log*
-lerna-debug.log*
-
-node_modules
-dist
-dist-ssr
-*.local
-
-# Editor directories and files
-.vscode/*
-!.vscode/extensions.json
-.idea
-.DS_Store
-*.suo
-*.ntvs*
-*.njsproj
-*.sln
-*.sw?
diff --git a/examples/remove-background-client/index.html b/examples/remove-background-client/index.html
deleted file mode 100644
index a85cef65f..000000000
--- a/examples/remove-background-client/index.html
+++ /dev/null
@@ -1,33 +0,0 @@
-
-
-
-
-  
-  
-  Transformers.js - Background Removal
-
-
-
-  
Background Removal w/ 🤗 Transformers.js
-  

-  
Runs locally in your browser, powered by the RMBG V1.4 model from BRIA AI
-  

-  

-    
-  

-  
-  
-
-  
-
-
-
\ No newline at end of file
diff --git a/examples/remove-background-client/main.js b/examples/remove-background-client/main.js
deleted file mode 100644
index 31eb7080e..000000000
--- a/examples/remove-background-client/main.js
+++ /dev/null
@@ -1,112 +0,0 @@
-import './style.css';
-
-import { AutoModel, AutoProcessor, env, RawImage } from '@xenova/transformers';
-
-// Since we will download the model from the Hugging Face Hub, we can skip the local model check
-env.allowLocalModels = false;
-
-// Proxy the WASM backend to prevent the UI from freezing
-env.backends.onnx.wasm.proxy = true;
-
-// Constants
-const EXAMPLE_URL = 'https://images.pexels.com/photos/5965592/pexels-photo-5965592.jpeg?auto=compress&cs=tinysrgb&w=1024';
-
-// Reference the elements that we will need
-const status = document.getElementById('status');
-const fileUpload = document.getElementById('upload');
-const imageContainer = document.getElementById('container');
-const example = document.getElementById('example');
-
-// Load model and processor
-status.textContent = 'Loading model...';
-
-const model = await AutoModel.from_pretrained('briaai/RMBG-1.4', {
-    // Do not require config.json to be present in the repository
-    config: { model_type: 'custom' },
-});
-
-const processor = await AutoProcessor.from_pretrained('briaai/RMBG-1.4', {
-    // Do not require config.json to be present in the repository
-    config: {
-        do_normalize: true,
-        do_pad: false,
-        do_rescale: true,
-        do_resize: true,
-        image_mean: [0.5, 0.5, 0.5],
-        feature_extractor_type: "ImageFeatureExtractor",
-        image_std: [1, 1, 1],
-        resample: 2,
-        rescale_factor: 0.00392156862745098,
-        size: { width: 1024, height: 1024 },
-    }
-});
-
-status.textContent = 'Ready';
-
-example.addEventListener('click', (e) => {
-    e.preventDefault();
-    predict(EXAMPLE_URL);
-});
-
-fileUpload.addEventListener('change', function (e) {
-    const file = e.target.files[0];
-    if (!file) {
-        return;
-    }
-
-    const reader = new FileReader();
-
-    // Set up a callback when the file is loaded
-    reader.onload = e2 => predict(e2.target.result);
-
-    reader.readAsDataURL(file);
-});
-
-// Predict foreground of the given image
-async function predict(url) {
-    // Read image
-    const image = await RawImage.fromURL(url);
-
-    // Update UI
-    imageContainer.innerHTML = '';
-    imageContainer.style.backgroundImage = `url(${url})`;
-
-    // Set container width and height depending on the image aspect ratio
-    const ar = image.width / image.height;
-    const [cw, ch] = (ar > 720 / 480) ? [720, 720 / ar] : [480 * ar, 480];
-    imageContainer.style.width = `${cw}px`;
-    imageContainer.style.height = `${ch}px`;
-
-    status.textContent = 'Analysing...';
-
-    // Preprocess image
-    const { pixel_values } = await processor(image);
-
-    // Predict alpha matte
-    const { output } = await model({ input: pixel_values });
-
-    // Resize mask back to original size
-    const mask = await RawImage.fromTensor(output[0].mul(255).to('uint8')).resize(image.width, image.height);
-
-    // Create new canvas
-    const canvas = document.createElement('canvas');
-    canvas.width = image.width;
-    canvas.height = image.height;
-    const ctx = canvas.getContext('2d');
-
-    // Draw original image output to canvas
-    ctx.drawImage(image.toCanvas(), 0, 0);
-
-    // Update alpha channel
-    const pixelData = ctx.getImageData(0, 0, image.width, image.height);
-    for (let i = 0; i < mask.data.length; ++i) {
-        pixelData.data[4 * i + 3] = mask.data[i];
-    }
-    ctx.putImageData(pixelData, 0, 0);
-
-    // Update UI
-    imageContainer.append(canvas);
-    imageContainer.style.removeProperty('background-image');
-    imageContainer.style.background = `url("data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQBAMAAADt3eJSAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAGUExURb+/v////5nD/3QAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAAUSURBVBjTYwABQSCglEENMxgYGAAynwRB8BEAgQAAAABJRU5ErkJggg==")`;
-    status.textContent = 'Done!';
-}
diff --git a/examples/remove-background-client/package-lock.json b/examples/remove-background-client/package-lock.json
deleted file mode 100644
index 95fc5ed41..000000000
--- a/examples/remove-background-client/package-lock.json
+++ /dev/null
@@ -1,1466 +0,0 @@
-{
-  "name": "remove-background-client",
-  "version": "0.0.0",
-  "lockfileVersion": 3,
-  "requires": true,
-  "packages": {
-    "": {
-      "name": "remove-background-client",
-      "version": "0.0.0",
-      "dependencies": {
-        "@xenova/transformers": "^2.15.0"
-      },
-      "devDependencies": {
-        "vite": "^5.0.13"
-      }
-    },
-    "node_modules/@esbuild/aix-ppc64": {
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-      "cpu": [
-        "ppc64"
-      ],
-      "dev": true,
-      "optional": true,
-      "os": [
-        "aix"
-      ],
-      "engines": {
-        "node": ">=12"
-      }
-    },
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diff --git a/examples/remove-background-client/package.json b/examples/remove-background-client/package.json
deleted file mode 100644
index b5669082d..000000000
--- a/examples/remove-background-client/package.json
+++ /dev/null
@@ -1,17 +0,0 @@
-{
-  "name": "remove-background-client",
-  "private": true,
-  "version": "0.0.0",
-  "type": "module",
-  "scripts": {
-    "dev": "vite",
-    "build": "vite build",
-    "preview": "vite preview"
-  },
-  "devDependencies": {
-    "vite": "^5.0.13"
-  },
-  "dependencies": {
-    "@xenova/transformers": "^2.15.0"
-  }
-}
diff --git a/examples/remove-background-client/style.css b/examples/remove-background-client/style.css
deleted file mode 100644
index f3016c246..000000000
--- a/examples/remove-background-client/style.css
+++ /dev/null
@@ -1,83 +0,0 @@
-* {
-  box-sizing: border-box;
-  padding: 0;
-  margin: 0;
-  font-family: sans-serif;
-}
-
-html,
-body {
-  height: 100%;
-}
-
-body {
-  padding: 16px 32px;
-}
-
-body,
-#container,
-#upload-button {
-  display: flex;
-  flex-direction: column;
-  justify-content: center;
-  align-items: center;
-}
-
-h1,
-h4 {
-  text-align: center;
-}
-
-h4 {
-  margin-top: 0.5rem;
-}
-
-#container {
-  position: relative;
-  width: 720px;
-  height: 480px;
-  max-width: 100%;
-  max-height: 100%;
-  border: 2px dashed #D1D5DB;
-  border-radius: 0.75rem;
-  overflow: hidden;
-  margin-top: 1rem;
-  background-size: 100% 100%;
-  background-position: center;
-  background-repeat: no-repeat;
-}
-
-#upload-button {
-  gap: 0.4rem;
-  font-size: 18px;
-  cursor: pointer;
-}
-
-#upload {
-  display: none;
-}
-
-svg {
-  pointer-events: none;
-}
-
-#example {
-  font-size: 14px;
-  text-decoration: underline;
-  cursor: pointer;
-}
-
-#example:hover {
-  color: #2563EB;
-}
-
-canvas {
-  position: absolute;
-  width: 100%;
-  height: 100%;
-}
-
-#status {
-  min-height: 16px;
-  margin: 8px 0;
-}
\ No newline at end of file
diff --git a/examples/remove-background-client/vite.config.js b/examples/remove-background-client/vite.config.js
deleted file mode 100644
index 6c32f52df..000000000
--- a/examples/remove-background-client/vite.config.js
+++ /dev/null
@@ -1,6 +0,0 @@
-import { defineConfig } from 'vite';
-export default defineConfig({
-  build: {
-    target: 'esnext'
-  }
-});
diff --git a/examples/segment-anything-client/.gitignore b/examples/segment-anything-client/.gitignore
deleted file mode 100644
index 1521c8b76..000000000
--- a/examples/segment-anything-client/.gitignore
+++ /dev/null
@@ -1 +0,0 @@
-dist
diff --git a/examples/segment-anything-client/index.css b/examples/segment-anything-client/index.css
deleted file mode 100644
index fc556bcac..000000000
--- a/examples/segment-anything-client/index.css
+++ /dev/null
@@ -1,116 +0,0 @@
-* {
-    box-sizing: border-box;
-    padding: 0;
-    margin: 0;
-    font-family: sans-serif;
-}
-
-html,
-body {
-    height: 100%;
-}
-
-body {
-    padding: 16px 32px;
-}
-
-body,
-#container,
-#upload-button {
-    display: flex;
-    flex-direction: column;
-    justify-content: center;
-    align-items: center;
-}
-
-h1, h3 {
-    text-align: center;
-}
-
-#container {
-    position: relative;
-    width: 640px;
-    height: 420px;
-    max-width: 100%;
-    max-height: 100%;
-    border: 2px dashed #D1D5DB;
-    border-radius: 0.75rem;
-    overflow: hidden;
-    cursor: pointer;
-    margin-top: 1rem;
-    background-size: 100% 100%;
-    background-position: center;
-    background-repeat: no-repeat;
-}
-
-#mask-output {
-    position: absolute;
-    width: 100%;
-    height: 100%;
-    pointer-events: none;
-}
-
-#upload-button {
-    gap: 0.4rem;
-    font-size: 18px;
-    cursor: pointer;
-}
-
-#upload {
-    display: none;
-}
-
-svg {
-    pointer-events: none;
-}
-
-#example {
-    font-size: 14px;
-    text-decoration: underline;
-    cursor: pointer;
-}
-
-#example:hover {
-    color: #2563EB;
-}
-
-canvas {
-    position: absolute;
-    width: 100%;
-    height: 100%;
-    opacity: 0.6;
-}
-
-#status {
-    min-height: 16px;
-    margin: 8px 0;
-}
-
-.icon {
-    height: 16px;
-    width: 16px;
-    position: absolute;
-    transform: translate(-50%, -50%);
-}
-
-#controls>button {
-    padding: 6px 12px;
-    background-color: #3498db;
-    color: white;
-    border: 1px solid #2980b9;
-    border-radius: 5px;
-    cursor: pointer;
-    font-size: 16px;
-}
-
-#controls>button:disabled {
-    background-color: #d1d5db;
-    color: #6b7280;
-    border: 1px solid #9ca3af;
-    cursor: not-allowed;
-}
-
-#information {
-    margin-top: 0.25rem;
-    font-size: 15px;
-}
\ No newline at end of file
diff --git a/examples/segment-anything-client/index.html b/examples/segment-anything-client/index.html
deleted file mode 100644
index 9dba925fe..000000000
--- a/examples/segment-anything-client/index.html
+++ /dev/null
@@ -1,42 +0,0 @@
-
-
-
-
-    
-    
-
-    
-    Transformers.js - Segment Anything WebGPU
-
-
-
-    
Segment Anything WebGPU

-    
In-browser image segmentation w/ 🤗
-            Transformers.js

-    

-        
-        
-    

-    
-    

-        
-        
-        
-    

-    

-        Left click = positive points, right click = negative points.
-    

-    
-
-    
-
-
-
\ No newline at end of file
diff --git a/examples/segment-anything-client/index.js b/examples/segment-anything-client/index.js
deleted file mode 100644
index 979db0582..000000000
--- a/examples/segment-anything-client/index.js
+++ /dev/null
@@ -1,296 +0,0 @@
-
-// Reference the elements we will use
-const statusLabel = document.getElementById('status');
-const fileUpload = document.getElementById('upload');
-const imageContainer = document.getElementById('container');
-const example = document.getElementById('example');
-const maskCanvas = document.getElementById('mask-output');
-const uploadButton = document.getElementById('upload-button');
-const resetButton = document.getElementById('reset-image');
-const clearButton = document.getElementById('clear-points');
-const cutButton = document.getElementById('cut-mask');
-
-// State variables
-let lastPoints = null;
-let isEncoded = false;
-let isDecoding = false;
-let isMultiMaskMode = false;
-let modelReady = false;
-let imageDataURI = null;
-
-// Constants
-const BASE_URL = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/';
-const EXAMPLE_URL = BASE_URL + 'corgi.jpg';
-
-// Create a web worker so that the main (UI) thread is not blocked during inference.
-const worker = new Worker(
-    new URL('./worker.js', import.meta.url),
-    { type: 'module' }
-);
-
-// Preload star and cross images to avoid lag on first click
-const star = new Image();
-star.src = BASE_URL + 'star-icon.png';
-star.className = 'icon';
-
-const cross = new Image();
-cross.src = BASE_URL + 'cross-icon.png';
-cross.className = 'icon';
-
-// Set up message handler
-worker.addEventListener('message', (e) => {
-    const { type, data } = e.data;
-    if (type === 'ready') {
-        modelReady = true;
-        statusLabel.textContent = 'Ready';
-
-    } else if (type === 'decode_result') {
-        isDecoding = false;
-
-        if (!isEncoded) {
-            return; // We are not ready to decode yet
-        }
-
-        if (!isMultiMaskMode && lastPoints) {
-            // Perform decoding with the last point
-            decode();
-            lastPoints = null;
-        }
-
-        const { mask, scores } = data;
-
-        // Update canvas dimensions (if different)
-        if (maskCanvas.width !== mask.width || maskCanvas.height !== mask.height) {
-            maskCanvas.width = mask.width;
-            maskCanvas.height = mask.height;
-        }
-
-        // Create context and allocate buffer for pixel data
-        const context = maskCanvas.getContext('2d');
-        const imageData = context.createImageData(maskCanvas.width, maskCanvas.height);
-
-        // Select best mask
-        const numMasks = scores.length; // 3
-        let bestIndex = 0;
-        for (let i = 1; i < numMasks; ++i) {
-            if (scores[i] > scores[bestIndex]) {
-                bestIndex = i;
-            }
-        }
-        statusLabel.textContent = `Segment score: ${scores[bestIndex].toFixed(2)}`;
-
-        // Fill mask with colour
-        const pixelData = imageData.data;
-        for (let i = 0; i < pixelData.length; ++i) {
-            if (mask.data[numMasks * i + bestIndex] === 1) {
-                const offset = 4 * i;
-                pixelData[offset] = 0;       // red
-                pixelData[offset + 1] = 114; // green
-                pixelData[offset + 2] = 189; // blue
-                pixelData[offset + 3] = 255; // alpha
-            }
-        }
-
-        // Draw image data to context
-        context.putImageData(imageData, 0, 0);
-
-    } else if (type === 'segment_result') {
-        if (data === 'start') {
-            statusLabel.textContent = 'Extracting image embedding...';
-        } else {
-            statusLabel.textContent = 'Embedding extracted!';
-            isEncoded = true;
-        }
-    }
-});
-
-function decode() {
-    isDecoding = true;
-    worker.postMessage({ type: 'decode', data: lastPoints });
-}
-
-function clearPointsAndMask() {
-    // Reset state
-    isMultiMaskMode = false;
-    lastPoints = null;
-
-    // Remove points from previous mask (if any)
-    document.querySelectorAll('.icon').forEach(e => e.remove());
-
-    // Disable cut button
-    cutButton.disabled = true;
-
-    // Reset mask canvas
-    maskCanvas.getContext('2d').clearRect(0, 0, maskCanvas.width, maskCanvas.height);
-}
-clearButton.addEventListener('click', clearPointsAndMask);
-
-resetButton.addEventListener('click', () => {
-    // Update state
-    isEncoded = false;
-    imageDataURI = null;
-
-    // Indicate to worker that we have reset the state
-    worker.postMessage({ type: 'reset' });
-
-    // Clear points and mask (if present)
-    clearPointsAndMask();
-
-    // Update UI
-    cutButton.disabled = true;
-    imageContainer.style.backgroundImage = 'none';
-    uploadButton.style.display = 'flex';
-    statusLabel.textContent = 'Ready';
-});
-
-function segment(data) {
-    // Update state
-    isEncoded = false;
-    if (!modelReady) {
-        statusLabel.textContent = 'Loading model...';
-    }
-    imageDataURI = data;
-
-    // Update UI
-    imageContainer.style.backgroundImage = `url(${data})`;
-    uploadButton.style.display = 'none';
-    cutButton.disabled = true;
-
-    // Instruct worker to segment the image
-    worker.postMessage({ type: 'segment', data });
-}
-
-// Handle file selection
-fileUpload.addEventListener('change', function (e) {
-    const file = e.target.files[0];
-    if (!file) {
-        return;
-    }
-
-    const reader = new FileReader();
-
-    // Set up a callback when the file is loaded
-    reader.onload = e2 => segment(e2.target.result);
-
-    reader.readAsDataURL(file);
-});
-
-example.addEventListener('click', (e) => {
-    e.preventDefault();
-    segment(EXAMPLE_URL);
-});
-
-function addIcon({ point, label }) {
-    const icon = (label === 1 ? star : cross).cloneNode();
-    icon.style.left = `${point[0] * 100}%`;
-    icon.style.top = `${point[1] * 100}%`;
-    imageContainer.appendChild(icon);
-}
-
-// Attach hover event to image container
-imageContainer.addEventListener('mousedown', e => {
-    if (e.button !== 0 && e.button !== 2) {
-        return; // Ignore other buttons
-    }
-    if (!isEncoded) {
-        return; // Ignore if not encoded yet
-    }
-    if (!isMultiMaskMode) {
-        lastPoints = [];
-        isMultiMaskMode = true;
-        cutButton.disabled = false;
-    }
-
-    const point = getPoint(e);
-    lastPoints.push(point);
-
-    // add icon
-    addIcon(point);
-
-    decode();
-});
-
-
-// Clamp a value inside a range [min, max]
-function clamp(x, min = 0, max = 1) {
-    return Math.max(Math.min(x, max), min)
-}
-
-function getPoint(e) {
-    // Get bounding box
-    const bb = imageContainer.getBoundingClientRect();
-
-    // Get the mouse coordinates relative to the container
-    const mouseX = clamp((e.clientX - bb.left) / bb.width);
-    const mouseY = clamp((e.clientY - bb.top) / bb.height);
-
-    return {
-        point: [mouseX, mouseY],
-        label: e.button === 2 // right click
-            ? 0  // negative prompt
-            : 1, // positive prompt
-    }
-}
-
-// Do not show context menu on right click
-imageContainer.addEventListener('contextmenu', e => {
-    e.preventDefault();
-});
-
-// Attach hover event to image container
-imageContainer.addEventListener('mousemove', e => {
-    if (!isEncoded || isMultiMaskMode) {
-        // Ignore mousemove events if the image is not encoded yet,
-        // or we are in multi-mask mode
-        return;
-    }
-    lastPoints = [getPoint(e)];
-
-    if (!isDecoding) {
-        decode(); // Only decode if we are not already decoding
-    }
-});
-
-// Handle cut button click
-cutButton.addEventListener('click', () => {
-    const [w, h] = [maskCanvas.width, maskCanvas.height];
-
-    // Get the mask pixel data
-    const maskContext = maskCanvas.getContext('2d');
-    const maskPixelData = maskContext.getImageData(0, 0, w, h);
-
-    // Load the image
-    const image = new Image();
-    image.crossOrigin = 'anonymous';
-    image.onload = async () => {
-        // Create a new canvas to hold the image
-        const imageCanvas = new OffscreenCanvas(w, h);
-        const imageContext = imageCanvas.getContext('2d');
-        imageContext.drawImage(image, 0, 0, w, h);
-        const imagePixelData = imageContext.getImageData(0, 0, w, h);
-
-        // Create a new canvas to hold the cut-out
-        const cutCanvas = new OffscreenCanvas(w, h);
-        const cutContext = cutCanvas.getContext('2d');
-        const cutPixelData = cutContext.getImageData(0, 0, w, h);
-
-        // Copy the image pixel data to the cut canvas
-        for (let i = 3; i < maskPixelData.data.length; i += 4) {
-            if (maskPixelData.data[i] > 0) {
-                for (let j = 0; j < 4; ++j) {
-                    const offset = i - j;
-                    cutPixelData.data[offset] = imagePixelData.data[offset];
-                }
-            }
-        }
-        cutContext.putImageData(cutPixelData, 0, 0);
-
-        // Download image 
-        const link = document.createElement('a');
-        link.download = 'image.png';
-        link.href = URL.createObjectURL(await cutCanvas.convertToBlob());
-        link.click();
-        link.remove();
-    }
-    image.src = imageDataURI;
-});
diff --git a/examples/segment-anything-client/package.json b/examples/segment-anything-client/package.json
deleted file mode 100644
index aa790ea74..000000000
--- a/examples/segment-anything-client/package.json
+++ /dev/null
@@ -1,17 +0,0 @@
-{
-  "name": "segment-anything-client",
-  "private": true,
-  "version": "0.0.0",
-  "type": "module",
-  "scripts": {
-    "dev": "vite",
-    "build": "vite build",
-    "preview": "vite preview"
-  },
-  "dependencies": {
-    "@huggingface/transformers": "^3.0.0-alpha.0"
-  },
-  "devDependencies": {
-    "vite": "^5.2.9"
-  }
-}
diff --git a/examples/segment-anything-client/vite.config.js b/examples/segment-anything-client/vite.config.js
deleted file mode 100644
index b2d374d1b..000000000
--- a/examples/segment-anything-client/vite.config.js
+++ /dev/null
@@ -1,18 +0,0 @@
-import { defineConfig } from 'vite';
-export default defineConfig(env => {
-  const config = {
-    build: {
-      target: 'esnext'
-    }
-  };
-
-  // TODO: Add this back when .wasm files are served locally
-  // if (env.mode === 'development') {
-  //   // The .wasm files are not correctly served using Vite in development mode.
-  //   // This is a workaround to exclude the onnxruntime-web package from Vite's optimization.
-  //   // See also: https://github.com/vitejs/vite/issues/8427
-  //   config.optimizeDeps = { exclude: ["onnxruntime-web"] };
-  // }
-
-  return config;
-});
diff --git a/examples/segment-anything-client/worker.js b/examples/segment-anything-client/worker.js
deleted file mode 100644
index bb783e0b5..000000000
--- a/examples/segment-anything-client/worker.js
+++ /dev/null
@@ -1,101 +0,0 @@
-import { SamModel, AutoProcessor, RawImage, Tensor } from '@huggingface/transformers';
-
-// We adopt the singleton pattern to enable lazy-loading of the model and processor.
-export class SegmentAnythingSingleton {
-    static model_id = 'Xenova/slimsam-77-uniform';
-    static model;
-    static processor;
-
-    static getInstance() {
-        this.model ??= SamModel.from_pretrained(this.model_id, {
-            dtype: 'fp16',
-            device: 'webgpu',
-        });
-        this.processor ??= AutoProcessor.from_pretrained(this.model_id);
-
-        return Promise.all([this.model, this.processor]);
-    }
-}
-
-// State variables
-let imageEmbeddings = null;
-let imageInputs = null;
-let ready = false;
-
-self.onmessage = async (e) => {
-    const [model, processor] = await SegmentAnythingSingleton.getInstance();
-    if (!ready) {
-        // Indicate that we are ready to accept requests
-        ready = true;
-        self.postMessage({
-            type: 'ready',
-        });
-    }
-
-    const { type, data } = e.data;
-    if (type === 'reset') {
-        imageInputs = null;
-        imageEmbeddings = null;
-
-    } else if (type === 'segment') {
-        // Indicate that we are starting to segment the image
-        self.postMessage({
-            type: 'segment_result',
-            data: 'start',
-        });
-
-        // Read the image and recompute image embeddings
-        const image = await RawImage.read(e.data.data);
-        imageInputs = await processor(image);
-        imageEmbeddings = await model.get_image_embeddings(imageInputs)
-
-        // Indicate that we have computed the image embeddings, and we are ready to accept decoding requests
-        self.postMessage({
-            type: 'segment_result',
-            data: 'done',
-        });
-
-    } else if (type === 'decode') {
-        // Prepare inputs for decoding
-        const reshaped = imageInputs.reshaped_input_sizes[0];
-        const points = data.map(x => [x.point[0] * reshaped[1], x.point[1] * reshaped[0]])
-        const labels = data.map(x => BigInt(x.label));
-
-        const input_points = new Tensor(
-            'float32',
-            points.flat(Infinity),
-            [1, 1, points.length, 2],
-        )
-        const input_labels = new Tensor(
-            'int64',
-            labels.flat(Infinity),
-            [1, 1, labels.length],
-        )
-
-        // Generate the mask
-        const { pred_masks, iou_scores } = await model({
-            ...imageEmbeddings,
-            input_points,
-            input_labels,
-        })
-
-        // Post-process the mask
-        const masks = await processor.post_process_masks(
-            pred_masks,
-            imageInputs.original_sizes,
-            imageInputs.reshaped_input_sizes,
-        );
-
-        // Send the result back to the main thread
-        self.postMessage({
-            type: 'decode_result',
-            data: {
-                mask: RawImage.fromTensor(masks[0][0]),
-                scores: iou_scores.data,
-            },
-        });
-
-    } else {
-        throw new Error(`Unknown message type: ${type}`);
-    }
-}
diff --git a/examples/semantic-audio-search/.gitignore b/examples/semantic-audio-search/.gitignore
deleted file mode 100644
index a547bf36d..000000000
--- a/examples/semantic-audio-search/.gitignore
+++ /dev/null
@@ -1,24 +0,0 @@
-# Logs
-logs
-*.log
-npm-debug.log*
-yarn-debug.log*
-yarn-error.log*
-pnpm-debug.log*
-lerna-debug.log*
-
-node_modules
-dist
-dist-ssr
-*.local
-
-# Editor directories and files
-.vscode/*
-!.vscode/extensions.json
-.idea
-.DS_Store
-*.suo
-*.ntvs*
-*.njsproj
-*.sln
-*.sw?
diff --git a/examples/semantic-audio-search/index.html b/examples/semantic-audio-search/index.html
deleted file mode 100644
index b7b1f8af8..000000000
--- a/examples/semantic-audio-search/index.html
+++ /dev/null
@@ -1,26 +0,0 @@
-
-
-
-
-  
-  
-  Semantic Audio Search | Transformers.js
-  
-
-
-
-  
-  

-  

-  
-
-
-
-
-
\ No newline at end of file
diff --git a/examples/semantic-audio-search/index.js b/examples/semantic-audio-search/index.js
deleted file mode 100644
index 1d7b047e2..000000000
--- a/examples/semantic-audio-search/index.js
+++ /dev/null
@@ -1,129 +0,0 @@
-
-import Scatterplot from 'deepscatter';
-import { getCachedJSON } from './utils';
-
-// Start loading metadata and positions asynchronously as soon as possible.
-let metadata = {};
-getCachedJSON('https://huggingface.co/datasets/Xenova/MusicBenchEmbedded/resolve/main/metadata.json')
-    .then((data) => {
-        metadata = data;
-    })
-    .catch((e) => console.error(e));
-
-let positions = {};
-getCachedJSON('https://huggingface.co/datasets/Xenova/MusicBenchEmbedded/resolve/main/positions.json')
-    .then((data) => {
-        positions = data;
-    })
-    .catch((e) => console.error(e));
-
-const scatterplot = new Scatterplot('#deepscatter');
-window.scatterplot = scatterplot; // For debugging
-
-// Initial call
-scatterplot.plotAPI({
-    source_url: 'https://huggingface.co/datasets/Xenova/MusicBenchEmbedded/resolve/main/atlas',
-    max_points: 52768, // a full cap.
-    alpha: 35, // Target saturation for the full page.
-    zoom_balance: 0.5, // Rate at which points increase size. https://observablehq.com/@bmschmidt/zoom-strategies-for-huge-scatterplots-with-three-js
-    point_size: 3, // Default point size before application of size scaling
-    background_color: 'transparent',
-    encoding: {
-        // TODO Add colours
-        x: {
-            field: 'x',
-            transform: 'literal',
-        },
-        y: {
-            field: 'y',
-            transform: 'literal',
-        },
-        jitter_radius: {
-            method: 'uniform',
-            constant: 5,
-        },
-    },
-    tooltip_opacity: 1,
-    duration: 4000, // For slow initial transition
-}).then(_ => scatterplot.plotAPI({
-    encoding: {
-        jitter_radius: {
-            method: 'uniform',
-            constant: 0,
-        },
-    },
-}));
-
-// Custom hover function
-scatterplot.tooltip_html = (datum) => {
-    const item = metadata[datum.ix];
-    if (!item) return 'Loading...';
-    const location = item.location;
-
-    setTimeout(() => {
-        // Slight hack to append the audio element after the text
-        const tooltip = document.querySelector('.tooltip');
-        if (tooltip) {
-            tooltip.innerHTML = `
-            ${item.main_caption}
-            

-            
-            `;
-        }
-    }, 0);
-    return item.main_caption;
-};
-
-// Make references to DOM elements
-const OVERLAY = document.getElementById('overlay');
-const INPUT_ELEMENT = document.getElementById('query');
-const SEARCH_BUTTON = document.getElementById('search');
-
-// Set up worker
-const worker = new Worker(new URL('./worker.js', import.meta.url), {
-    type: 'module'
-});
-worker.addEventListener('message', (e) => {
-    switch (e.data.status) {
-        case 'initiate':
-            OVERLAY.innerText = 'Loading model and embeddings database...';
-            OVERLAY.style.display = 'flex';
-            OVERLAY.style.pointerEvents = 'all';
-            break;
-        case 'ready':
-            OVERLAY.style.display = 'none';
-            OVERLAY.style.pointerEvents = 'none';
-            break;
-        case 'complete':
-            // Output is an array of [score, index] pairs. Get top item
-            const index = e.data.output[0][1];
-            const position = positions[index];
-
-            if (position) { // Just in case the position hasn't loaded yet (this should never happen)
-                // Zoom to result
-                scatterplot.plotAPI({
-                    zoom: {
-                        bbox: {
-                            x: [position[0] - 0.5, position[0] + 0.5],
-                            y: [position[1] - 0.5, position[1] + 0.5]
-                        }
-                    },
-                    duration: 2000,
-                })
-            }
-            break;
-    }
-});
-
-const search = () => {
-    worker.postMessage({
-        status: 'search',
-        query: INPUT_ELEMENT.value,
-    });
-};
-
-// Set up event listeners
-INPUT_ELEMENT.addEventListener('keypress', (event) => {
-    if (event.keyCode === 13) search();
-});
-SEARCH_BUTTON.addEventListener('click', search);
diff --git a/examples/semantic-audio-search/package-lock.json b/examples/semantic-audio-search/package-lock.json
deleted file mode 100644
index 864183715..000000000
--- a/examples/semantic-audio-search/package-lock.json
+++ /dev/null
@@ -1,2119 +0,0 @@
-{
-  "name": "semantic-audio-search",
-  "version": "0.0.0",
-  "lockfileVersion": 3,
-  "requires": true,
-  "packages": {
-    "": {
-      "name": "semantic-audio-search",
-      "version": "0.0.0",
-      "dependencies": {
-        "@xenova/transformers": "^2.10.1",
-        "deepscatter": "github:nomic-ai/deepscatter"
-      },
-      "devDependencies": {
-        "vite": "^5.0.13"
-      }
-    },
-    "node_modules/@75lb/deep-merge": {
-      "version": "1.1.1",
-      "resolved": "https://registry.npmjs.org/@75lb/deep-merge/-/deep-merge-1.1.1.tgz",
-      "integrity": "sha512-xvgv6pkMGBA6GwdyJbNAnDmfAIR/DfWhrj9jgWh3TY7gRm3KO46x/GPjRg6wJ0nOepwqrNxFfojebh0Df4h4Tw==",
-      "peer": true,
-      "dependencies": {
-        "lodash.assignwith": "^4.2.0",
-        "typical": "^7.1.1"
-      },
-      "engines": {
-        "node": ">=12.17"
-      }
-    },
-    "node_modules/@75lb/deep-merge/node_modules/typical": {
-      "version": "7.1.1",
-      "resolved": "https://registry.npmjs.org/typical/-/typical-7.1.1.tgz",
-      "integrity": "sha512-T+tKVNs6Wu7IWiAce5BgMd7OZfNYUndHwc5MknN+UHOudi7sGZzuHdCadllRuqJ3fPtgFtIH9+lt9qRv6lmpfA==",
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-      "engines": {
-        "node": ">=12.17"
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-    },
-    "node_modules/@esbuild/android-arm": {
-      "version": "0.19.8",
-      "resolved": "https://registry.npmjs.org/@esbuild/android-arm/-/android-arm-0.19.8.tgz",
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-      "cpu": [
-        "arm"
-      ],
-      "dev": true,
-      "optional": true,
-      "os": [
-        "android"
-      ],
-      "engines": {
-        "node": ">=12"
-      }
-    },
-    "node_modules/@esbuild/android-arm64": {
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-    "node_modules/@esbuild/android-x64": {
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diff --git a/examples/semantic-audio-search/package.json b/examples/semantic-audio-search/package.json
deleted file mode 100644
index 972d964d3..000000000
--- a/examples/semantic-audio-search/package.json
+++ /dev/null
@@ -1,18 +0,0 @@
-{
-  "name": "semantic-audio-search",
-  "private": true,
-  "version": "0.0.0",
-  "type": "module",
-  "scripts": {
-    "dev": "vite",
-    "build": "vite build",
-    "preview": "vite preview"
-  },
-  "devDependencies": {
-    "vite": "^5.0.13"
-  },
-  "dependencies": {
-    "@xenova/transformers": "^2.10.1",
-    "deepscatter": "github:nomic-ai/deepscatter"
-  }
-}
diff --git a/examples/semantic-audio-search/style.css b/examples/semantic-audio-search/style.css
deleted file mode 100644
index 7d6686332..000000000
--- a/examples/semantic-audio-search/style.css
+++ /dev/null
@@ -1,109 +0,0 @@
-@import url('https://fonts.googleapis.com/css2?family=Montserrat&display=swap');
-
-* {
-  box-sizing: border-box;
-  padding: 0;
-  margin: 0;
-  font-family: 'Montserrat', sans-serif;
-}
-
-html {
-  background: radial-gradient(ellipse at center, #1b2735 0%, #090a0f 100%);
-  height: 100%;
-  width: 100%;
-}
-
-body {
-  overflow: hidden;
-  display: flex;
-  justify-content: center;
-}
-
-#deepscatter {
-  position: absolute;
-  left: 0;
-  top: 0;
-}
-
-#container-for-webgl-canvas {
-  width: 100vw;
-  display: flex;
-  justify-content: center;
-}
-
-.tooltip {
-  width: 400px;
-  margin-top: -10px;
-}
-
-.tooltip>audio {
-  margin-top: 10px;
-  width: 100%;
-}
-
-#search {
-  background: url('https://upload.wikimedia.org/wikipedia/commons/0/0b/Search_Icon.svg') no-repeat center right;
-  background-size: 50px 50px;
-  width: 56px;
-  border: none;
-  cursor: pointer;
-}
-
-#header {
-  padding: 20px 0 50px 0;
-  color: white;
-  text-align: center;
-  background: linear-gradient(to bottom, rgba(0, 0, 0, 1), rgba(0, 0, 0, 0));
-  width: 100vw;
-}
-
-#title {
-  font-size: 30px;
-  font-weight: 600;
-}
-
-#header,
-#search-bar {
-  position: absolute;
-  justify-content: center;
-  z-index: 1;
-}
-
-#search-bar {
-  padding: 0 20px;
-  max-width: 90vw;
-  overflow: hidden;
-  border-radius: 20px 20px 0px 0px;
-  bottom: 0;
-  height: 60px;
-  width: 500px;
-  background-color: white;
-  display: flex;
-}
-
-#overlay {
-  position: absolute;
-  width: 100vw;
-  height: 100vh;
-  z-index: 999999999;
-  background-color: rgba(0, 0, 0, 0.8);
-  pointer-events: none;
-  display: none;
-
-  color: white;
-  justify-content: center;
-  align-items: center;
-  font-size: 20px;
-}
-
-#query {
-  width: 100%;
-  height: 100%;
-  font-size: 1.5em;
-  border: none;
-  outline: none;
-}
-
-a {
-  color: white;
-}
\ No newline at end of file
diff --git a/examples/semantic-audio-search/utils.js b/examples/semantic-audio-search/utils.js
deleted file mode 100644
index 1aa6729c9..000000000
--- a/examples/semantic-audio-search/utils.js
+++ /dev/null
@@ -1,40 +0,0 @@
-
-// Adapted from https://github.com/xenova/transformers.js/blob/c367f9d68b809bbbf81049c808bf6d219d761d23/src/utils/hub.js#L330
-export async function getCachedFile(url) {
-    let cache;
-    try {
-        cache = await caches.open('semantic-audio-search');
-        const cachedResponse = await cache.match(url);
-        if (cachedResponse) {
-            return await cachedResponse.arrayBuffer();
-        }
-    } catch (e) {
-        console.warn('Unable to open cache', e);
-    }
-
-    // No cache, or cache failed to open. Fetch the file.
-    const response = await fetch(url);
-    const buffer = await response.arrayBuffer();
-
-    if (cache) {
-        try {
-            // NOTE: We use `new Response(buffer, ...)` instead of `response.clone()` to handle LFS files
-            await cache.put(url, new Response(buffer, {
-                headers: response.headers,
-            }));
-        } catch (e) {
-            console.warn('Unable to cache file', e);
-        }
-    }
-
-    return buffer;
-}
-
-export async function getCachedJSON(url) {
-    let buffer = await getCachedFile(url);
-
-    let decoder = new TextDecoder('utf-8');
-    let jsonData = decoder.decode(buffer);
-
-    return JSON.parse(jsonData);
-}
diff --git a/examples/semantic-audio-search/worker.js b/examples/semantic-audio-search/worker.js
deleted file mode 100644
index f095f8104..000000000
--- a/examples/semantic-audio-search/worker.js
+++ /dev/null
@@ -1,94 +0,0 @@
-
-import { env, AutoTokenizer, ClapTextModelWithProjection } from '@xenova/transformers';
-import { getCachedFile } from './utils';
-
-// Skip local model check
-env.allowLocalModels = false;
-
-class ApplicationSingleton {
-    static model_id = 'Xenova/larger_clap_music_and_speech';
-    static BASE_URL = 'https://huggingface.co/datasets/Xenova/MusicBenchEmbedded/resolve/main/';
-
-    static tokenizer = null;
-    static text_model = null;
-    static embeddings = null;
-
-    static async getInstance(progress_callback = null) {
-        this.tokenizer ??= AutoTokenizer.from_pretrained(this.model_id, { progress_callback });
-        this.text_model ??= ClapTextModelWithProjection.from_pretrained(this.model_id, {
-            progress_callback,
-            quantized: true, // TODO allow user to select quantized or not
-        });
-        this.embeddings ??= new Promise(
-            (resolve, reject) => {
-                getCachedFile(this.BASE_URL + 'audio-embeddings_52768-512_32bit.bin')
-                    .then((buffer) => {
-                        resolve(new Float32Array(buffer));
-                    })
-                    .catch(reject);
-            }
-        );
-
-        return Promise.all([this.tokenizer, this.text_model, this.embeddings]);
-    }
-}
-
-
-function cosineSimilarity(query_embeds, database_embeds) {
-    const EMBED_DIM = 512;
-
-    const numDB = database_embeds.length / EMBED_DIM;
-    const similarityScores = new Array(numDB);
-
-    for (let i = 0; i < numDB; ++i) {
-        const startOffset = i * EMBED_DIM;
-        const dbVector = database_embeds.slice(startOffset, startOffset + EMBED_DIM);
-
-        let dotProduct = 0;
-        let normEmbeds = 0;
-        let normDB = 0;
-
-        for (let j = 0; j < EMBED_DIM; ++j) {
-            const embedValue = query_embeds[j];
-            const dbValue = dbVector[j];
-
-            dotProduct += embedValue * dbValue;
-            normEmbeds += embedValue * embedValue;
-            normDB += dbValue * dbValue;
-        }
-
-        similarityScores[i] = dotProduct / (Math.sqrt(normEmbeds) * Math.sqrt(normDB));
-    }
-
-    return similarityScores;
-}
-
-// Listen for messages from the main thread
-self.addEventListener('message', async (event) => {
-    // Get the tokenizer, model, and embeddings. When called for the first time,
-    // this will load the files and cache them for future use.
-    const [tokenizer, text_model, embeddings] = await ApplicationSingleton.getInstance(self.postMessage);
-
-    // Send the output back to the main thread
-    self.postMessage({ status: 'ready' });
-
-    // Run tokenization
-    const text_inputs = tokenizer(event.data.query, { padding: true, truncation: true });
-
-    // Compute embeddings
-    const { text_embeds } = await text_model(text_inputs);
-
-    // Compute similarity scores
-    const scores = cosineSimilarity(text_embeds.data, embeddings);
-
-    const output = scores
-        .map((score, i) => [score, i]) // Save index
-        .sort((a, b) => b[0] - a[0]) // Sort by scores
-        .slice(0, 100); // Get top 100 results
-
-    // Send the output back to the main thread
-    self.postMessage({
-        status: 'complete',
-        output: output,
-    });
-});
diff --git a/examples/semantic-image-search-client/.eslintrc.json b/examples/semantic-image-search-client/.eslintrc.json
deleted file mode 100644
index bffb357a7..000000000
--- a/examples/semantic-image-search-client/.eslintrc.json
+++ /dev/null
@@ -1,3 +0,0 @@
-{
-  "extends": "next/core-web-vitals"
-}
diff --git a/examples/semantic-image-search-client/.gitignore b/examples/semantic-image-search-client/.gitignore
deleted file mode 100644
index 8f322f0d8..000000000
--- a/examples/semantic-image-search-client/.gitignore
+++ /dev/null
@@ -1,35 +0,0 @@
-# See https://help.github.com/articles/ignoring-files/ for more about ignoring files.
-
-# dependencies
-/node_modules
-/.pnp
-.pnp.js
-
-# testing
-/coverage
-
-# next.js
-/.next/
-/out/
-
-# production
-/build
-
-# misc
-.DS_Store
-*.pem
-
-# debug
-npm-debug.log*
-yarn-debug.log*
-yarn-error.log*
-
-# local env files
-.env*.local
-
-# vercel
-.vercel
-
-# typescript
-*.tsbuildinfo
-next-env.d.ts
diff --git a/examples/semantic-image-search-client/README.md b/examples/semantic-image-search-client/README.md
deleted file mode 100644
index fea12a725..000000000
--- a/examples/semantic-image-search-client/README.md
+++ /dev/null
@@ -1,34 +0,0 @@
-This is a [Next.js](https://nextjs.org/) project bootstrapped with [`create-next-app`](https://github.com/vercel/next.js/tree/canary/packages/create-next-app).
-
-## Getting Started
-
-First, run the development server:
-
-```bash
-npm run dev
-# or
-yarn dev
-# or
-pnpm dev
-```
-
-Open [http://localhost:3000](http://localhost:3000) with your browser to see the result.
-
-You can start editing the page by modifying `app/page.js`. The page auto-updates as you edit the file.
-
-This project uses [`next/font`](https://nextjs.org/docs/basic-features/font-optimization) to automatically optimize and load Inter, a custom Google Font.
-
-## Learn More
-
-To learn more about Next.js, take a look at the following resources:
-
-- [Next.js Documentation](https://nextjs.org/docs) - learn about Next.js features and API.
-- [Learn Next.js](https://nextjs.org/learn) - an interactive Next.js tutorial.
-
-You can check out [the Next.js GitHub repository](https://github.com/vercel/next.js/) - your feedback and contributions are welcome!
-
-## Deploy on Vercel
-
-The easiest way to deploy your Next.js app is to use the [Vercel Platform](https://vercel.com/new?utm_medium=default-template&filter=next.js&utm_source=create-next-app&utm_campaign=create-next-app-readme) from the creators of Next.js.
-
-Check out our [Next.js deployment documentation](https://nextjs.org/docs/deployment) for more details.
\ No newline at end of file
diff --git a/examples/semantic-image-search-client/jsconfig.json b/examples/semantic-image-search-client/jsconfig.json
deleted file mode 100644
index b8d6842d7..000000000
--- a/examples/semantic-image-search-client/jsconfig.json
+++ /dev/null
@@ -1,7 +0,0 @@
-{
-  "compilerOptions": {
-    "paths": {
-      "@/*": ["./src/*"]
-    }
-  }
-}
diff --git a/examples/semantic-image-search-client/next.config.js b/examples/semantic-image-search-client/next.config.js
deleted file mode 100644
index d1cb37318..000000000
--- a/examples/semantic-image-search-client/next.config.js
+++ /dev/null
@@ -1,20 +0,0 @@
-/** @type {import('next').NextConfig} */
-const nextConfig = {
-    // (Optional) Export as a static site
-    // See https://nextjs.org/docs/pages/building-your-application/deploying/static-exports#configuration
-    output: 'export', // Feel free to modify/remove this option
-
-    // Override the default webpack configuration
-    webpack: (config) => {
-        // Ignore node-specific modules when bundling for the browser
-        // See https://webpack.js.org/configuration/resolve/#resolvealias
-        config.resolve.alias = {
-            ...config.resolve.alias,
-            'sharp$': false,
-            'onnxruntime-node$': false,
-        }
-        return config;
-    },
-};
-
-module.exports = nextConfig;
\ No newline at end of file
diff --git a/examples/semantic-image-search-client/package-lock.json b/examples/semantic-image-search-client/package-lock.json
deleted file mode 100644
index 379527906..000000000
--- a/examples/semantic-image-search-client/package-lock.json
+++ /dev/null
@@ -1,4829 +0,0 @@
-{
-  "name": "semantic-image-search-client",
-  "version": "0.1.0",
-  "lockfileVersion": 3,
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diff --git a/examples/semantic-image-search-client/package.json b/examples/semantic-image-search-client/package.json
deleted file mode 100644
index e73aafd17..000000000
--- a/examples/semantic-image-search-client/package.json
+++ /dev/null
@@ -1,26 +0,0 @@
-{
-  "name": "semantic-image-search-client",
-  "version": "0.1.0",
-  "private": true,
-  "scripts": {
-    "dev": "next dev",
-    "build": "next build",
-    "start": "next start",
-    "lint": "next lint"
-  },
-  "dependencies": {
-    "@xenova/transformers": "^2.6.1",
-    "autoprefixer": "10.4.14",
-    "blurhash": "^2.0.5",
-    "eslint": "8.45.0",
-    "eslint-config-next": "13.4.12",
-    "next": "^14.2.3",
-    "postcss": "^8.4.31",
-    "react": "18.2.0",
-    "react-dom": "18.2.0",
-    "tailwindcss": "3.3.3"
-  },
-  "overrides": {
-    "protobufjs": "^7.2.4"
-  }
-}
diff --git a/examples/semantic-image-search-client/postcss.config.js b/examples/semantic-image-search-client/postcss.config.js
deleted file mode 100644
index 33ad091d2..000000000
--- a/examples/semantic-image-search-client/postcss.config.js
+++ /dev/null
@@ -1,6 +0,0 @@
-module.exports = {
-  plugins: {
-    tailwindcss: {},
-    autoprefixer: {},
-  },
-}
diff --git a/examples/semantic-image-search-client/public/next.svg b/examples/semantic-image-search-client/public/next.svg
deleted file mode 100644
index 5174b28c5..000000000
--- a/examples/semantic-image-search-client/public/next.svg
+++ /dev/null
@@ -1 +0,0 @@
-
\ No newline at end of file
diff --git a/examples/semantic-image-search-client/public/vercel.svg b/examples/semantic-image-search-client/public/vercel.svg
deleted file mode 100644
index d2f842227..000000000
--- a/examples/semantic-image-search-client/public/vercel.svg
+++ /dev/null
@@ -1 +0,0 @@
-
\ No newline at end of file
diff --git a/examples/semantic-image-search-client/src/app/components/ImageGrid.jsx b/examples/semantic-image-search-client/src/app/components/ImageGrid.jsx
deleted file mode 100644
index d33ab3637..000000000
--- a/examples/semantic-image-search-client/src/app/components/ImageGrid.jsx
+++ /dev/null
@@ -1,32 +0,0 @@
-'use client'
-
-import Image from 'next/image'
-import { blurHashToDataURL } from '../utils.js'
-
-export function ImageGrid({ images, setCurrentImage }) {
-    return (
-        

-            {images && images.map(({ id, url, ar, blur }) => (
-                
 {
-                        setCurrentImage({ id, url, ar, blur });
-                    }}
-                >
-                    
-                

-            ))}
-        
)
-}
\ No newline at end of file
diff --git a/examples/semantic-image-search-client/src/app/components/Modal.jsx b/examples/semantic-image-search-client/src/app/components/Modal.jsx
deleted file mode 100644
index a40cc1d0c..000000000
--- a/examples/semantic-image-search-client/src/app/components/Modal.jsx
+++ /dev/null
@@ -1,64 +0,0 @@
-'use client'
-
-import Image from 'next/image'
-import { downloadImage } from '../utils.js'
-
-export function Modal({ currentImage, setCurrentImage }) {
-    const photo_url = currentImage ? `https://unsplash.com/photos/${currentImage.id}` : null;
-    const photo_image_url = currentImage ? `https://images.unsplash.com/${currentImage.url}?auto=format&fit=crop&w=480&q=80` : null;
-    return (
-        

-            {currentImage && <>
-                
-                

-                    
-                

-                

-                    
-                        
-                    
-                    
-                

-            
-            }
-
-        
)
-}
\ No newline at end of file
diff --git a/examples/semantic-image-search-client/src/app/components/SearchBar.jsx b/examples/semantic-image-search-client/src/app/components/SearchBar.jsx
deleted file mode 100644
index aa7aaf48c..000000000
--- a/examples/semantic-image-search-client/src/app/components/SearchBar.jsx
+++ /dev/null
@@ -1,33 +0,0 @@
-'use client'
-
-export function SearchBar({ search }) {
-    return (
 {
-            e.preventDefault();
-            const formData = new FormData(e.target);
-            const text = formData.get('text');
-            search(text);
-        }}
-        className='relative mb-2'
-    >
-        

-            
-        

-        
-        
-    
)
-}
\ No newline at end of file
diff --git a/examples/semantic-image-search-client/src/app/favicon.ico b/examples/semantic-image-search-client/src/app/favicon.ico
deleted file mode 100644
index 718d6fea4..000000000
Binary files a/examples/semantic-image-search-client/src/app/favicon.ico and /dev/null differ
diff --git a/examples/semantic-image-search-client/src/app/globals.css b/examples/semantic-image-search-client/src/app/globals.css
deleted file mode 100644
index e76e81979..000000000
--- a/examples/semantic-image-search-client/src/app/globals.css
+++ /dev/null
@@ -1,19 +0,0 @@
-@tailwind base;
-@tailwind components;
-@tailwind utilities;
-
-:root {
-  --foreground-rgb: 255, 255, 255;
-  --background-start-rgb: 0, 0, 0;
-  --background-end-rgb: 0, 0, 0;
-}
-
-body {
-  color: rgb(var(--foreground-rgb));
-  background: linear-gradient(
-      to bottom,
-      transparent,
-      rgb(var(--background-end-rgb))
-    )
-    rgb(var(--background-start-rgb));
-}
diff --git a/examples/semantic-image-search-client/src/app/layout.js b/examples/semantic-image-search-client/src/app/layout.js
deleted file mode 100644
index 9d42768a1..000000000
--- a/examples/semantic-image-search-client/src/app/layout.js
+++ /dev/null
@@ -1,17 +0,0 @@
-import './globals.css'
-import { Inter } from 'next/font/google'
-
-const inter = Inter({ subsets: ['latin'] })
-
-export const metadata = {
-  title: 'In-browser Semantic Image Search',
-  description: 'Search for images using text (built w/ Transformers.js)',
-}
-
-export default function RootLayout({ children }) {
-  return (
-    
-      {children}
-    
-  )
-}
diff --git a/examples/semantic-image-search-client/src/app/page.js b/examples/semantic-image-search-client/src/app/page.js
deleted file mode 100644
index 4cb7acb17..000000000
--- a/examples/semantic-image-search-client/src/app/page.js
+++ /dev/null
@@ -1,65 +0,0 @@
-'use client'
-
-import { useState, useEffect, useCallback, useRef } from 'react'
-import { Modal } from './components/Modal';
-import { SearchBar } from './components/SearchBar';
-import { ImageGrid } from './components/ImageGrid';
-
-export default function Home() {
-
-  // Application state
-  const [ready, setReady] = useState(null);
-  const [images, setImages] = useState(null);
-  const [currentImage, setCurrentImage] = useState(null);
-
-  // Create a reference to the worker object.
-  const worker = useRef(null);
-
-  // We use the `useEffect` hook to set up the worker as soon as the `App` component is mounted.
-  useEffect(() => {
-    if (!worker.current) {
-      // Create the worker if it does not yet exist.
-      worker.current = new Worker(new URL('./worker.js', import.meta.url), {
-        type: 'module'
-      });
-    }
-    const onMessageReceived = (e) => {
-      switch (e.data.status) {
-        case 'initiate':
-          setReady(false);
-          break;
-        case 'ready':
-          setReady(true);
-          break;
-        case 'complete':
-          setImages(e.data.output);
-          break;
-      }
-    };
-
-    // Attach the callback function as an event listener.
-    worker.current.addEventListener('message', onMessageReceived);
-
-    // Define a cleanup function for when the component is unmounted.
-    return () => worker.current.removeEventListener('message', onMessageReceived);
-  });
-
-  const search = useCallback((text) => {
-    if (worker.current) {
-      worker.current.postMessage({ text });
-    }
-  }, []);
-
-  return (
-    

-      
-      
-      {ready === false && (
-        

-          
Loading model and database...

-        

-      )}
-      
-    

-  )
-}
diff --git a/examples/semantic-image-search-client/src/app/utils.js b/examples/semantic-image-search-client/src/app/utils.js
deleted file mode 100644
index 2e3a641b9..000000000
--- a/examples/semantic-image-search-client/src/app/utils.js
+++ /dev/null
@@ -1,95 +0,0 @@
-
-import { decode } from 'blurhash'
-
-const SIZE = 32;
-
-export function blurHashToDataURL(hash) {
-    if (!hash) return undefined
-
-    const pixels = decode(hash, SIZE, SIZE)
-
-    const canvas = document.createElement("canvas");
-    canvas.width = SIZE;
-    canvas.height = SIZE;
-
-    const ctx = canvas.getContext("2d");
-    const imageData = ctx.createImageData(SIZE, SIZE);
-    imageData.data.set(pixels);
-    ctx.putImageData(imageData, 0, 0);
-
-    return canvas.toDataURL();
-}
-
-function downloadData(url, filename) {
-
-    // Create an anchor element with the data URL as the href attribute
-    const downloadLink = document.createElement('a');
-    downloadLink.href = url;
-
-    // Set the download attribute to specify the desired filename for the downloaded image
-    downloadLink.download = filename;
-
-    // Trigger the download
-    downloadLink.click();
-
-    // Clean up: remove the anchor element from the DOM
-    downloadLink.remove();
-}
-
-export function downloadImage(url, filename) {
-    fetch(url, {
-        headers: new Headers({
-            Origin: location.origin,
-        }),
-        mode: 'cors',
-    })
-        .then((response) => response.blob())
-        .then((blob) => {
-            let blobUrl = window.URL.createObjectURL(blob)
-            downloadData(blobUrl, filename)
-        })
-        .catch((e) => console.error(e))
-}
-
-
-
-// Adapted from https://github.com/xenova/transformers.js/blob/c367f9d68b809bbbf81049c808bf6d219d761d23/src/utils/hub.js#L330
-export async function getCachedFile(url) {
-    let cache;
-    try {
-        cache = await caches.open('image-database');
-        const cachedResponse = await cache.match(url);
-        if (cachedResponse) {
-            return await cachedResponse.arrayBuffer();
-        }
-    } catch (e) {
-        console.warn('Unable to open cache', e);
-    }
-
-    // No cache, or cache failed to open. Fetch the file.
-    const response = await fetch(url);
-    const buffer = await response.arrayBuffer();
-
-    if (cache) {
-        try {
-            // NOTE: We use `new Response(buffer, ...)` instead of `response.clone()` to handle LFS files
-            await cache.put(url, new Response(buffer, {
-                headers: response.headers,
-            }));
-        } catch (e) {
-            console.warn('Unable to cache file', e);
-        }
-    }
-
-    return buffer;
-
-}
-
-export async function getCachedJSON(url) {
-    let buffer = await getCachedFile(url);
-
-    let decoder = new TextDecoder('utf-8');
-    let jsonData = decoder.decode(buffer);
-
-    return JSON.parse(jsonData);
-}
diff --git a/examples/semantic-image-search-client/src/app/worker.js b/examples/semantic-image-search-client/src/app/worker.js
deleted file mode 100644
index 977edf846..000000000
--- a/examples/semantic-image-search-client/src/app/worker.js
+++ /dev/null
@@ -1,110 +0,0 @@
-
-import { env, AutoTokenizer, CLIPTextModelWithProjection } from '@xenova/transformers';
-import { getCachedFile, getCachedJSON } from './utils.js';
-
-const EMBED_DIM = 512;
-
-// Skip local model check
-env.allowLocalModels = false;
-
-class ApplicationSingleton {
-    static model_id = 'Xenova/clip-vit-base-patch16';
-    static BASE_URL = 'https://huggingface.co/datasets/Xenova/semantic-image-search-assets/resolve/main/';
-
-    static tokenizer = null;
-    static text_model = null;
-    static metadata = null;
-    static embeddings = null;
-
-    static async getInstance(progress_callback = null) {
-        // Load tokenizer and text model
-        if (this.tokenizer === null) {
-            this.tokenizer = AutoTokenizer.from_pretrained(this.model_id, { progress_callback });
-        }
-        if (this.text_model === null) {
-            this.text_model = CLIPTextModelWithProjection.from_pretrained(this.model_id, { progress_callback });
-        }
-        if (this.metadata === null) {
-            this.metadata = getCachedJSON(this.BASE_URL + 'image-embeddings.json');
-        }
-        if (this.embeddings === null) {
-            this.embeddings = new Promise(
-                (resolve, reject) => {
-                    getCachedFile(this.BASE_URL + 'image-embeddings_25k-512-32bit.bin')
-                        .then((buffer) => {
-                            resolve(new Float32Array(buffer));
-                        })
-                        .catch(reject);
-                }
-            )
-        }
-
-        return Promise.all([this.tokenizer, this.text_model, this.metadata, this.embeddings]);
-    }
-}
-
-function cosineSimilarity(query_embeds, database_embeds) {
-    const numDB = database_embeds.length / EMBED_DIM;
-    const similarityScores = new Array(numDB);
-
-    for (let i = 0; i < numDB; ++i) {
-        const startOffset = i * EMBED_DIM;
-        const dbVector = database_embeds.slice(startOffset, startOffset + EMBED_DIM);
-
-        let dotProduct = 0;
-        let normEmbeds = 0;
-        let normDB = 0;
-
-        for (let j = 0; j < EMBED_DIM; ++j) {
-            const embedValue = query_embeds[j];
-            const dbValue = dbVector[j];
-
-            dotProduct += embedValue * dbValue;
-            normEmbeds += embedValue * embedValue;
-            normDB += dbValue * dbValue;
-        }
-
-        similarityScores[i] = dotProduct / (Math.sqrt(normEmbeds) * Math.sqrt(normDB));
-    }
-
-    return similarityScores;
-}
-
-// Listen for messages from the main thread
-self.addEventListener('message', async (event) => {
-    // Get the tokenizer, model, metadata, and embeddings. When called for the first time,
-    // this will load the files and cache them for future use.
-    const [tokenizer, text_model, metadata, embeddings] = await ApplicationSingleton.getInstance(self.postMessage);
-
-    // Send the output back to the main thread
-    self.postMessage({ status: 'ready' });
-
-    // Run tokenization
-    const text_inputs = tokenizer(event.data.text, { padding: true, truncation: true });
-
-    // Compute embeddings
-    const { text_embeds } = await text_model(text_inputs);
-
-    // Compute similarity scores
-    const scores = cosineSimilarity(text_embeds.data, embeddings);
-
-    // Make a copy of the metadata
-    let output = metadata.slice(0);
-
-    // Add scores to output
-    for (let i = 0; i < metadata.length; ++i) {
-        output[i].score = scores[i];
-    }
-
-    // Sort by score
-    output.sort((a, b) => b.score - a.score);
-
-    // Get top 100 results
-    output = output.slice(0, 100);
-
-    // Send the output back to the main thread
-    self.postMessage({
-        status: 'complete',
-        output: output,
-    });
-});
diff --git a/examples/semantic-image-search-client/tailwind.config.js b/examples/semantic-image-search-client/tailwind.config.js
deleted file mode 100644
index 65162f135..000000000
--- a/examples/semantic-image-search-client/tailwind.config.js
+++ /dev/null
@@ -1,16 +0,0 @@
-/** @type {import('tailwindcss').Config} */
-module.exports = {
-  content: [
-    './src/pages/**/*.{js,ts,jsx,tsx,mdx}',
-    './src/components/**/*.{js,ts,jsx,tsx,mdx}',
-    './src/app/**/*.{js,ts,jsx,tsx,mdx}',
-  ],
-  theme: {
-    extend: {
-      boxShadow: {
-        highlight: 'inset 0 0 0 1px rgba(255, 255, 255, 0.1)',
-      },
-    },
-  },
-  plugins: [],
-}
diff --git a/examples/semantic-image-search/.env.local.example b/examples/semantic-image-search/.env.local.example
deleted file mode 100644
index f7f5866a2..000000000
--- a/examples/semantic-image-search/.env.local.example
+++ /dev/null
@@ -1,3 +0,0 @@
-SUPABASE_URL=your-project-url
-SUPABASE_ANON_KEY=your-anon-key
-SUPABASE_SECRET_KEY=your-secret-key
diff --git a/examples/semantic-image-search/.eslintrc.json b/examples/semantic-image-search/.eslintrc.json
deleted file mode 100644
index bffb357a7..000000000
--- a/examples/semantic-image-search/.eslintrc.json
+++ /dev/null
@@ -1,3 +0,0 @@
-{
-  "extends": "next/core-web-vitals"
-}
diff --git a/examples/semantic-image-search/.gitignore b/examples/semantic-image-search/.gitignore
deleted file mode 100644
index 8f322f0d8..000000000
--- a/examples/semantic-image-search/.gitignore
+++ /dev/null
@@ -1,35 +0,0 @@
-# See https://help.github.com/articles/ignoring-files/ for more about ignoring files.
-
-# dependencies
-/node_modules
-/.pnp
-.pnp.js
-
-# testing
-/coverage
-
-# next.js
-/.next/
-/out/
-
-# production
-/build
-
-# misc
-.DS_Store
-*.pem
-
-# debug
-npm-debug.log*
-yarn-debug.log*
-yarn-error.log*
-
-# local env files
-.env*.local
-
-# vercel
-.vercel
-
-# typescript
-*.tsbuildinfo
-next-env.d.ts
diff --git a/examples/semantic-image-search/Dockerfile b/examples/semantic-image-search/Dockerfile
deleted file mode 100644
index 3feaf9c7e..000000000
--- a/examples/semantic-image-search/Dockerfile
+++ /dev/null
@@ -1,69 +0,0 @@
-# syntax=docker/dockerfile:1.4
-
-# Adapted from https://github.com/vercel/next.js/blob/e60a1e747c3f521fc24dfd9ee2989e13afeb0a9b/examples/with-docker/Dockerfile
-# For more information, see https://nextjs.org/docs/pages/building-your-application/deploying#docker-image
-
-FROM node:18 AS base
-
-# Install dependencies only when needed
-FROM base AS deps
-WORKDIR /app
-
-# Install dependencies based on the preferred package manager
-COPY --link package.json yarn.lock* package-lock.json* pnpm-lock.yaml* ./
-RUN \
-  if [ -f yarn.lock ]; then yarn --frozen-lockfile; \
-  elif [ -f package-lock.json ]; then npm ci; \
-  elif [ -f pnpm-lock.yaml ]; then yarn global add pnpm && pnpm i --frozen-lockfile; \
-  else echo "Lockfile not found." && exit 1; \
-  fi
-
-
-# Rebuild the source code only when needed
-FROM base AS builder
-WORKDIR /app
-COPY --from=deps --link /app/node_modules ./node_modules
-COPY --link  . .
-
-# Next.js collects completely anonymous telemetry data about general usage.
-# Learn more here: https://nextjs.org/telemetry
-# Uncomment the following line in case you want to disable telemetry during the build.
-# ENV NEXT_TELEMETRY_DISABLED 1
-
-RUN npm run build
-
-# If using yarn comment out above and use below instead
-# RUN yarn build
-
-# Production image, copy all the files and run next
-FROM base AS runner
-WORKDIR /app
-
-ENV NODE_ENV production
-# Uncomment the following line in case you want to disable telemetry during runtime.
-# ENV NEXT_TELEMETRY_DISABLED 1
-
-RUN \
-  addgroup --system --gid 1001 nodejs; \
-  adduser --system --uid 1001 nextjs
-
-COPY --from=builder --link /app/public ./public
-
-# Automatically leverage output traces to reduce image size
-# https://nextjs.org/docs/advanced-features/output-file-tracing
-COPY --from=builder --link --chown=1001:1001 /app/.next/standalone ./
-COPY --from=builder --link --chown=1001:1001 /app/.next/static ./.next/static
-
-USER nextjs
-
-EXPOSE 3000
-
-ENV PORT 3000
-ENV HOSTNAME 0.0.0.0
-
-# Allow the running process to write model files to the cache folder.
-# NOTE: In practice, you would probably want to pre-download the model files to avoid having to download them on-the-fly.
-RUN mkdir -p /app/node_modules/@xenova/.cache/
-RUN chmod 777 -R /app/node_modules/@xenova/
-
-CMD ["node", "server.js"]
diff --git a/examples/semantic-image-search/README.md b/examples/semantic-image-search/README.md
deleted file mode 100644
index 3b60a8f0f..000000000
--- a/examples/semantic-image-search/README.md
+++ /dev/null
@@ -1,97 +0,0 @@
-
-# Semantic Image Search
-
-This example shows you how to use Transformers.js to create a semantic image search engine. Check out the demo [here](https://huggingface.co/spaces/Xenova/semantic-image-search).
-
-![Semantic Image Search Demo](https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/semantic-image-search-min.png)
-
-## Getting Started
-
-### Dataset
-This application uses images from [The Unsplash Dataset](https://github.com/unsplash/datasets), which you can download [here](https://unsplash.com/data/lite/latest). All you need for this demo is the `photos.tsv000` TSV file, which contains the metadata for all the images.
-
-
-### Connecting to Supabase
-
-After creating a new [Supabase](https://supabase.com/) project, you'll need to:
-
-1. Create an `images` table and import the data from `photos.tsv000`. 
-
-2. Add a column for `image_embeddings`:
-
-    ```sql
-    -- Add a new vector column with a dimension of 512
-    alter table images add column image_embedding vector(512);
-    ```
-
-3. Add your `SUPABASE_URL`, `SUPABASE_ANON_KEY`, and `SUPABASE_SECRET_KEY` keys to a `.env.local` file (see `.env.local.example` for template).
-
-4. Update the image embeddings in your database by running the following command:
-    ```bash
-    SUPABASE_URL=your-project-url \
-    SUPABASE_SECRET_KEY=your-secret-key \
-    node scripts/update-database.mjs
-    ```
-
-    *Note:* This will take a while. Also, since queries are capped at 1000 returned rows, you'll need to run this command multiple times to insert all 25000 rows.
-
-5. Create a new `match_images` [database function](https://supabase.com/docs/guides/database/functions):
-
-    ```sql
-    -- https://supabase.com/blog/openai-embeddings-postgres-vector
-    create or replace function match_images (
-        query_embedding vector(512),
-        match_threshold float,
-        match_count int
-    )
-    returns table (
-        photo_id text,
-        photo_url text,
-        photo_image_url text,
-        photo_width int,
-        photo_height int,
-        photo_aspect_ratio float,
-        photo_description text,
-        ai_description text,
-        blur_hash text,
-        similarity float
-    )
-    language sql stable
-    as $$
-    select
-        photo_id,
-        photo_url,
-        photo_image_url,
-        photo_width,
-        photo_height,
-        photo_aspect_ratio,
-        photo_description,
-        ai_description,
-        blur_hash,
-        1 - (image_embedding <=> query_embedding) as similarity
-    from images
-    where 1 - (image_embedding <=> query_embedding) > match_threshold
-    order by similarity desc
-    limit match_count;
-    $$;
-    ```
-
-5. Add a [database policy](https://supabase.com/docs/guides/auth/row-level-security#policies) to allow users to view the database:
-
-    ```sql
-    create policy "policy_name"
-    on public.images
-    for select using (
-        true
-    );
-    ```
-
-### Development
-
-You can now run the development server with:
-
-```bash
-npm run dev
-```
-
-Open [http://localhost:3000](http://localhost:3000) with your browser to see the result.
diff --git a/examples/semantic-image-search/jsconfig.json b/examples/semantic-image-search/jsconfig.json
deleted file mode 100644
index b8d6842d7..000000000
--- a/examples/semantic-image-search/jsconfig.json
+++ /dev/null
@@ -1,7 +0,0 @@
-{
-  "compilerOptions": {
-    "paths": {
-      "@/*": ["./src/*"]
-    }
-  }
-}
diff --git a/examples/semantic-image-search/next.config.js b/examples/semantic-image-search/next.config.js
deleted file mode 100644
index 60fd1175c..000000000
--- a/examples/semantic-image-search/next.config.js
+++ /dev/null
@@ -1,18 +0,0 @@
-/** @type {import('next').NextConfig} */
-const nextConfig = {
-    // (Optional) Export as a standalone site
-    // See https://nextjs.org/docs/pages/api-reference/next-config-js/output#automatically-copying-traced-files
-    output: 'standalone', // Feel free to modify/remove this option
-
-    // Indicate that these packages should not be bundled by webpack
-    experimental: {
-        serverComponentsExternalPackages: ['sharp', 'onnxruntime-node'],
-    },
-
-    // Define which domains we are allowed to load images from
-    images: {
-        domains: ['images.unsplash.com'],
-    },
-};
-
-module.exports = nextConfig;
diff --git a/examples/semantic-image-search/package-lock.json b/examples/semantic-image-search/package-lock.json
deleted file mode 100644
index 5b6f361a2..000000000
--- a/examples/semantic-image-search/package-lock.json
+++ /dev/null
@@ -1,5122 +0,0 @@
-{
-  "name": "semantic-image-search",
-  "version": "0.1.0",
-  "lockfileVersion": 3,
-  "requires": true,
-  "packages": {
-    "": {
-      "name": "semantic-image-search",
-      "version": "0.1.0",
-      "dependencies": {
-        "@supabase/supabase-js": "^2.31.0",
-        "@xenova/transformers": "^2.5.0",
-        "autoprefixer": "10.4.14",
-        "blurhash": "^2.0.5",
-        "eslint": "8.45.0",
-        "eslint-config-next": "13.4.12",
-        "next": "^14.2.3",
-        "postcss": "^8.4.31",
-        "react": "18.2.0",
-        "react-dom": "18.2.0",
-        "tailwindcss": "3.3.3"
-      }
-    },
-    "node_modules/@aashutoshrathi/word-wrap": {
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-      }
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diff --git a/examples/semantic-image-search/package.json b/examples/semantic-image-search/package.json
deleted file mode 100644
index dd669fb24..000000000
--- a/examples/semantic-image-search/package.json
+++ /dev/null
@@ -1,27 +0,0 @@
-{
-  "name": "semantic-image-search",
-  "version": "0.1.0",
-  "private": true,
-  "scripts": {
-    "dev": "next dev",
-    "build": "next build",
-    "start": "next start",
-    "lint": "next lint"
-  },
-  "dependencies": {
-    "@supabase/supabase-js": "^2.31.0",
-    "@xenova/transformers": "^2.5.0",
-    "autoprefixer": "10.4.14",
-    "blurhash": "^2.0.5",
-    "eslint": "8.45.0",
-    "eslint-config-next": "13.4.12",
-    "next": "^14.2.3",
-    "postcss": "^8.4.31",
-    "react": "18.2.0",
-    "react-dom": "18.2.0",
-    "tailwindcss": "3.3.3"
-  },
-  "overrides": {
-    "protobufjs": "^7.2.4"
-  }
-}
diff --git a/examples/semantic-image-search/postcss.config.js b/examples/semantic-image-search/postcss.config.js
deleted file mode 100644
index 33ad091d2..000000000
--- a/examples/semantic-image-search/postcss.config.js
+++ /dev/null
@@ -1,6 +0,0 @@
-module.exports = {
-  plugins: {
-    tailwindcss: {},
-    autoprefixer: {},
-  },
-}
diff --git a/examples/semantic-image-search/public/next.svg b/examples/semantic-image-search/public/next.svg
deleted file mode 100644
index 5174b28c5..000000000
--- a/examples/semantic-image-search/public/next.svg
+++ /dev/null
@@ -1 +0,0 @@
-
\ No newline at end of file
diff --git a/examples/semantic-image-search/public/vercel.svg b/examples/semantic-image-search/public/vercel.svg
deleted file mode 100644
index d2f842227..000000000
--- a/examples/semantic-image-search/public/vercel.svg
+++ /dev/null
@@ -1 +0,0 @@
-
\ No newline at end of file
diff --git a/examples/semantic-image-search/scripts/update-database.mjs b/examples/semantic-image-search/scripts/update-database.mjs
deleted file mode 100644
index c5f245e7f..000000000
--- a/examples/semantic-image-search/scripts/update-database.mjs
+++ /dev/null
@@ -1,67 +0,0 @@
-// Helper script to update the database with image embeddings
-
-import { AutoProcessor, RawImage, CLIPVisionModelWithProjection } from '@xenova/transformers';
-import { createClient } from '@supabase/supabase-js'
-
-if (!process.env.SUPABASE_SECRET_KEY) {
-    throw new Error('Missing `SUPABASE_SECRET_KEY` environment variable.')
-}
-
-// Create a single supabase client for interacting with your database
-const supabase = createClient(
-    process.env.SUPABASE_URL,
-    process.env.SUPABASE_SECRET_KEY,
-)
-
-let { data, error } = await supabase
-    .from('images')
-    .select('*')
-    .neq('ignore', true)
-    .is('image_embedding', null);
-
-if (error) {
-    throw error;
-}
-
-// Load processor and vision model
-const model_id = 'Xenova/clip-vit-base-patch16';
-const processor = await AutoProcessor.from_pretrained(model_id);
-const vision_model = await CLIPVisionModelWithProjection.from_pretrained(model_id, {
-    quantized: false,
-});
-
-for (const image_data of data) {
-    let image;
-    try {
-        image = await RawImage.read(image_data.photo_image_url);
-    } catch (e) {
-        // Unable to load image, so we ignore it
-        console.warn('Ignoring image due to error', e)
-        await supabase
-            .from('images')
-            .update({ ignore: true })
-            .eq('photo_id', image_data.photo_id)
-            .select()
-        continue;
-    }
-
-    // Read image and run processor
-    let image_inputs = await processor(image);
-
-    // Compute embeddings
-    const { image_embeds } = await vision_model(image_inputs);
-    const embed_as_list = image_embeds.tolist()[0];
-
-    // https://supabase.com/docs/guides/ai/vector-columns#storing-a-vector--embedding
-    const { data, error } = await supabase
-        .from('images')
-        .update({ image_embedding: embed_as_list })
-        .eq('photo_id', image_data.photo_id)
-        .select()
-
-    if (error) {
-        console.error('error', error)
-    } else {
-        console.log('success', image_data.photo_id)
-    }
-}
diff --git a/examples/semantic-image-search/src/app/app.js b/examples/semantic-image-search/src/app/app.js
deleted file mode 100644
index 2fbd623f4..000000000
--- a/examples/semantic-image-search/src/app/app.js
+++ /dev/null
@@ -1,51 +0,0 @@
-import { AutoTokenizer, CLIPTextModelWithProjection } from "@xenova/transformers";
-import { createClient } from '@supabase/supabase-js'
-
-// Use the Singleton pattern to enable lazy construction of the pipeline.
-// NOTE: We wrap the class in a function to prevent code duplication (see below).
-const S = () => class ApplicationSingleton {
-    static model_id = 'Xenova/clip-vit-base-patch16';
-    static tokenizer = null;
-    static text_model = null;
-    static database = null;
-
-    static async getInstance() {
-        // Load tokenizer and text model
-        if (this.tokenizer === null) {
-            this.tokenizer = AutoTokenizer.from_pretrained(this.model_id);
-        }
-
-        if (this.text_model === null) {
-            this.text_model = CLIPTextModelWithProjection.from_pretrained(this.model_id, {
-                quantized: false,
-            });
-        }
-
-        if (this.database === null) {
-            this.database = createClient(
-                process.env.SUPABASE_URL,
-                process.env.SUPABASE_ANON_KEY,
-            )
-        }
-
-        return Promise.all([
-            this.tokenizer,
-            this.text_model,
-            this.database,
-        ]);
-    }
-}
-
-let ApplicationSingleton;
-if (process.env.NODE_ENV !== 'production') {
-    // When running in development mode, attach the pipeline to the
-    // global object so that it's preserved between hot reloads.
-    // For more information, see https://vercel.com/guides/nextjs-prisma-postgres
-    if (!global.ApplicationSingleton) {
-        global.ApplicationSingleton = S();
-    }
-    ApplicationSingleton = global.ApplicationSingleton;
-} else {
-    ApplicationSingleton = S();
-}
-export default ApplicationSingleton;
diff --git a/examples/semantic-image-search/src/app/components/ImageGrid.jsx b/examples/semantic-image-search/src/app/components/ImageGrid.jsx
deleted file mode 100644
index fcaca4537..000000000
--- a/examples/semantic-image-search/src/app/components/ImageGrid.jsx
+++ /dev/null
@@ -1,52 +0,0 @@
-import Image from 'next/image'
-import { blurHashToDataURL } from '../utils.js'
-
-export function ImageGrid({ images, setCurrentImage }) {
-    return (
-        

-            {images && images.map(({
-                photo_id,
-                photo_url,
-                photo_image_url,
-                photo_aspect_ratio,
-                photo_width,
-                photo_height,
-                blur_hash,
-                photo_description,
-                ai_description,
-                similarity,
-            }) => (
-                
 {
-                        setCurrentImage({
-                            photo_id,
-                            photo_url,
-                            photo_image_url,
-                            photo_aspect_ratio,
-                            photo_width,
-                            photo_height,
-                            blur_hash,
-                            photo_description,
-                            ai_description,
-                            similarity,
-                        });
-                    }}
-                >
-                    {photo_description
-                

-            ))}
-        
)
-}
\ No newline at end of file
diff --git a/examples/semantic-image-search/src/app/components/Modal.jsx b/examples/semantic-image-search/src/app/components/Modal.jsx
deleted file mode 100644
index fb3d16aac..000000000
--- a/examples/semantic-image-search/src/app/components/Modal.jsx
+++ /dev/null
@@ -1,60 +0,0 @@
-import { downloadImage } from '../utils.js'
-import Image from 'next/image'
-
-export function Modal({ currentImage, setCurrentImage }) {
-    return (
-        

-            {currentImage && <>
-                {currentImage.photo_description
-                

-                    
-                

-                

-                    
-                        
-                    
-                    
-                

-            
-            }
-
-        
)
-}
\ No newline at end of file
diff --git a/examples/semantic-image-search/src/app/components/SearchBar.jsx b/examples/semantic-image-search/src/app/components/SearchBar.jsx
deleted file mode 100644
index d2df1cc04..000000000
--- a/examples/semantic-image-search/src/app/components/SearchBar.jsx
+++ /dev/null
@@ -1,32 +0,0 @@
-
-export function SearchBar({ search }) {
-    return (
 {
-            e.preventDefault();
-            const formData = new FormData(e.target);
-            const text = formData.get('text');
-            search(text);
-        }}
-        className='relative mb-2'
-    >
-        

-            
-        

-        
-        
-    
)
-}
\ No newline at end of file
diff --git a/examples/semantic-image-search/src/app/favicon.ico b/examples/semantic-image-search/src/app/favicon.ico
deleted file mode 100644
index 718d6fea4..000000000
Binary files a/examples/semantic-image-search/src/app/favicon.ico and /dev/null differ
diff --git a/examples/semantic-image-search/src/app/globals.css b/examples/semantic-image-search/src/app/globals.css
deleted file mode 100644
index e76e81979..000000000
--- a/examples/semantic-image-search/src/app/globals.css
+++ /dev/null
@@ -1,19 +0,0 @@
-@tailwind base;
-@tailwind components;
-@tailwind utilities;
-
-:root {
-  --foreground-rgb: 255, 255, 255;
-  --background-start-rgb: 0, 0, 0;
-  --background-end-rgb: 0, 0, 0;
-}
-
-body {
-  color: rgb(var(--foreground-rgb));
-  background: linear-gradient(
-      to bottom,
-      transparent,
-      rgb(var(--background-end-rgb))
-    )
-    rgb(var(--background-start-rgb));
-}
diff --git a/examples/semantic-image-search/src/app/layout.js b/examples/semantic-image-search/src/app/layout.js
deleted file mode 100644
index 1748d5acb..000000000
--- a/examples/semantic-image-search/src/app/layout.js
+++ /dev/null
@@ -1,17 +0,0 @@
-import './globals.css'
-import { Inter } from 'next/font/google'
-
-const inter = Inter({ subsets: ['latin'] })
-
-export const metadata = {
-  title: 'Semantic Image Search',
-  description: 'Search for images using text (built w/ Transformers.js and Supabase)',
-}
-
-export default function RootLayout({ children }) {
-  return (
-    
-      {children}
-    
-  )
-}
diff --git a/examples/semantic-image-search/src/app/page.js b/examples/semantic-image-search/src/app/page.js
deleted file mode 100644
index cd8d393f7..000000000
--- a/examples/semantic-image-search/src/app/page.js
+++ /dev/null
@@ -1,36 +0,0 @@
-'use client'
-
-import { useState } from 'react'
-import { Modal } from './components/Modal';
-import { SearchBar } from './components/SearchBar';
-import { ImageGrid } from './components/ImageGrid';
-
-export default function Home() {
-
-  // Application state
-  const [images, setImages] = useState(null);
-  const [currentImage, setCurrentImage] = useState(null);
-
-  const search = async (text) => {
-    if (!text) return;
-
-    const params = new URLSearchParams();
-    params.append('text', text);
-    params.append('threshold', 0.1);
-    params.append('limit', 100);
-
-    // Make a request to the /classify route on the server.
-    const result = await fetch(`/search?${params.toString()}`);
-
-    const json = await result.json();
-    setImages(json);
-  };
-
-  return (
-    

-      
-      
-      
-    

-  )
-}
diff --git a/examples/semantic-image-search/src/app/search/route.js b/examples/semantic-image-search/src/app/search/route.js
deleted file mode 100644
index 4961ecfd1..000000000
--- a/examples/semantic-image-search/src/app/search/route.js
+++ /dev/null
@@ -1,73 +0,0 @@
-// Create a custom request handler for the /classify route.
-// For more information, see https://nextjs.org/docs/app/building-your-application/routing/router-handlers
-
-import { NextResponse } from 'next/server'
-import ApplicationSingleton from '../app.js'
-
-const parseInputs = (searchParams) => {
-    const text = searchParams.get('text');
-    if (!text) {
-        return {
-            error: 'Missing text parameter',
-        };
-    }
-    const threshold = searchParams.get('threshold');
-    const match_threshold = Number(threshold ?? 0.1);
-    if (isNaN(match_threshold) || match_threshold < 0 || match_threshold > 1) {
-        return {
-            error: `Invalid threshold parameter "${threshold}" (should be a number between 0 and 1)`,
-        };
-    }
-
-    const limit = searchParams.get('limit');
-    const match_count = Number(limit ?? 25);
-    if (isNaN(match_count) || !Number.isInteger(match_count) || match_count < 0 || match_count > 1000) {
-        return {
-            error: `Invalid limit parameter "${limit}" (should be an integer between 0 and 1000)`,
-        };
-    }
-
-    return { text, match_threshold, match_count }
-}
-
-// TODO: add caching
-
-export async function GET(request) {
-    const parsedInputs = parseInputs(request.nextUrl.searchParams);
-    if (parsedInputs.error) {
-        return NextResponse.json({
-            error: parsedInputs.error,
-        }, { status: 400 });
-    }
-
-    // Valid inputs, so we can proceed
-    const { text, match_threshold, match_count } = parsedInputs;
-
-    // Get the tokenizer, model, and database singletons. When called for the first time,
-    // this will load the models and cache them for future use.
-    const [tokenizer, text_model, database] = await ApplicationSingleton.getInstance();
-
-    // Run tokenization
-    let text_inputs = tokenizer(text, { padding: true, truncation: true });
-
-    // Compute embeddings
-    const { text_embeds } = await text_model(text_inputs);
-    const query_embedding = text_embeds.tolist()[0];
-
-    // TODO add pagination?
-    let { data: images, error } = await database
-        .rpc('match_images', {
-            query_embedding,
-            match_threshold,
-            match_count,
-        });
-    if (error) {
-        console.warn('Error fetching images', error);
-        return NextResponse.json({
-            error: 'An error occurred while fetching images',
-        }, { status: 500 });
-    }
-
-
-    return NextResponse.json(images);
-}
diff --git a/examples/semantic-image-search/src/app/utils.js b/examples/semantic-image-search/src/app/utils.js
deleted file mode 100644
index f0401723a..000000000
--- a/examples/semantic-image-search/src/app/utils.js
+++ /dev/null
@@ -1,52 +0,0 @@
-
-import { decode } from "blurhash"
-
-const SIZE = 32;
-
-export function blurHashToDataURL(hash) {
-    if (!hash) return undefined
-
-    const pixels = decode(hash, SIZE, SIZE)
-
-    const canvas = document.createElement("canvas");
-    canvas.width = SIZE;
-    canvas.height = SIZE;
-
-    const ctx = canvas.getContext("2d");
-    const imageData = ctx.createImageData(SIZE, SIZE);
-    imageData.data.set(pixels);
-    ctx.putImageData(imageData, 0, 0);
-
-    return canvas.toDataURL();
-}
-
-function downloadData(url, filename) {
-
-    // Create an anchor element with the data URL as the href attribute
-    const downloadLink = document.createElement('a');
-    downloadLink.href = url;
-
-    // Set the download attribute to specify the desired filename for the downloaded image
-    downloadLink.download = filename;
-
-    // Trigger the download
-    downloadLink.click();
-
-    // Clean up: remove the anchor element from the DOM
-    downloadLink.remove();
-}
-
-export function downloadImage(url, filename) {
-    fetch(url, {
-        headers: new Headers({
-            Origin: location.origin,
-        }),
-        mode: 'cors',
-    })
-        .then((response) => response.blob())
-        .then((blob) => {
-            let blobUrl = window.URL.createObjectURL(blob)
-            downloadData(blobUrl, filename)
-        })
-        .catch((e) => console.error(e))
-}
diff --git a/examples/semantic-image-search/tailwind.config.js b/examples/semantic-image-search/tailwind.config.js
deleted file mode 100644
index 65162f135..000000000
--- a/examples/semantic-image-search/tailwind.config.js
+++ /dev/null
@@ -1,16 +0,0 @@
-/** @type {import('tailwindcss').Config} */
-module.exports = {
-  content: [
-    './src/pages/**/*.{js,ts,jsx,tsx,mdx}',
-    './src/components/**/*.{js,ts,jsx,tsx,mdx}',
-    './src/app/**/*.{js,ts,jsx,tsx,mdx}',
-  ],
-  theme: {
-    extend: {
-      boxShadow: {
-        highlight: 'inset 0 0 0 1px rgba(255, 255, 255, 0.1)',
-      },
-    },
-  },
-  plugins: [],
-}
diff --git a/examples/text-to-speech-client/.eslintrc.cjs b/examples/text-to-speech-client/.eslintrc.cjs
deleted file mode 100644
index 4dcb43901..000000000
--- a/examples/text-to-speech-client/.eslintrc.cjs
+++ /dev/null
@@ -1,20 +0,0 @@
-module.exports = {
-  root: true,
-  env: { browser: true, es2020: true },
-  extends: [
-    'eslint:recommended',
-    'plugin:react/recommended',
-    'plugin:react/jsx-runtime',
-    'plugin:react-hooks/recommended',
-  ],
-  ignorePatterns: ['dist', '.eslintrc.cjs'],
-  parserOptions: { ecmaVersion: 'latest', sourceType: 'module' },
-  settings: { react: { version: '18.2' } },
-  plugins: ['react-refresh'],
-  rules: {
-    'react-refresh/only-export-components': [
-      'warn',
-      { allowConstantExport: true },
-    ],
-  },
-}
diff --git a/examples/text-to-speech-client/.gitignore b/examples/text-to-speech-client/.gitignore
deleted file mode 100644
index a547bf36d..000000000
--- a/examples/text-to-speech-client/.gitignore
+++ /dev/null
@@ -1,24 +0,0 @@
-# Logs
-logs
-*.log
-npm-debug.log*
-yarn-debug.log*
-yarn-error.log*
-pnpm-debug.log*
-lerna-debug.log*
-
-node_modules
-dist
-dist-ssr
-*.local
-
-# Editor directories and files
-.vscode/*
-!.vscode/extensions.json
-.idea
-.DS_Store
-*.suo
-*.ntvs*
-*.njsproj
-*.sln
-*.sw?
diff --git a/examples/text-to-speech-client/README.md b/examples/text-to-speech-client/README.md
deleted file mode 100644
index f768e33fc..000000000
--- a/examples/text-to-speech-client/README.md
+++ /dev/null
@@ -1,8 +0,0 @@
-# React + Vite
-
-This template provides a minimal setup to get React working in Vite with HMR and some ESLint rules.
-
-Currently, two official plugins are available:
-
-- [@vitejs/plugin-react](https://github.com/vitejs/vite-plugin-react/blob/main/packages/plugin-react/README.md) uses [Babel](https://babeljs.io/) for Fast Refresh
-- [@vitejs/plugin-react-swc](https://github.com/vitejs/vite-plugin-react-swc) uses [SWC](https://swc.rs/) for Fast Refresh
diff --git a/examples/text-to-speech-client/index.html b/examples/text-to-speech-client/index.html
deleted file mode 100644
index 26748c68f..000000000
--- a/examples/text-to-speech-client/index.html
+++ /dev/null
@@ -1,12 +0,0 @@
-
-
-  
-    
-    
-    Transformers.js - Text-to-speech demo
-  
-  
-    

-    
-  
-
diff --git a/examples/text-to-speech-client/package.json b/examples/text-to-speech-client/package.json
deleted file mode 100644
index 49949efa2..000000000
--- a/examples/text-to-speech-client/package.json
+++ /dev/null
@@ -1,30 +0,0 @@
-{
-  "name": "text-to-speech-client",
-  "private": true,
-  "version": "0.0.0",
-  "type": "module",
-  "scripts": {
-    "dev": "vite",
-    "build": "vite build",
-    "lint": "eslint . --ext js,jsx --report-unused-disable-directives --max-warnings 0",
-    "preview": "vite preview"
-  },
-  "dependencies": {
-    "@xenova/transformers": "^2.7.0",
-    "react": "^18.2.0",
-    "react-dom": "^18.2.0"
-  },
-  "devDependencies": {
-    "@types/react": "^18.2.15",
-    "@types/react-dom": "^18.2.7",
-    "@vitejs/plugin-react": "^4.0.3",
-    "autoprefixer": "^10.4.16",
-    "eslint": "^8.45.0",
-    "eslint-plugin-react": "^7.32.2",
-    "eslint-plugin-react-hooks": "^4.6.0",
-    "eslint-plugin-react-refresh": "^0.4.3",
-    "postcss": "^8.4.31",
-    "tailwindcss": "^3.3.3",
-    "vite": "^4.4.5"
-  }
-}
diff --git a/examples/text-to-speech-client/postcss.config.js b/examples/text-to-speech-client/postcss.config.js
deleted file mode 100644
index 2e7af2b7f..000000000
--- a/examples/text-to-speech-client/postcss.config.js
+++ /dev/null
@@ -1,6 +0,0 @@
-export default {
-  plugins: {
-    tailwindcss: {},
-    autoprefixer: {},
-  },
-}
diff --git a/examples/text-to-speech-client/src/App.jsx b/examples/text-to-speech-client/src/App.jsx
deleted file mode 100644
index cfa53fb9a..000000000
--- a/examples/text-to-speech-client/src/App.jsx
+++ /dev/null
@@ -1,162 +0,0 @@
-import React, { useState, useEffect, useRef } from 'react';
-
-import AudioPlayer from './components/AudioPlayer';
-import Progress from './components/Progress';
-import { SPEAKERS, DEFAULT_SPEAKER } from './constants';
-
-const App = () => {
-
-  // Model loading
-  const [ready, setReady] = useState(null);
-  const [disabled, setDisabled] = useState(false);
-  const [progressItems, setProgressItems] = useState([]);
-
-  // Inputs and outputs
-  const [text, setText] = useState('I love Hugging Face!');
-  const [selectedSpeaker, setSelectedSpeaker] = useState(DEFAULT_SPEAKER);
-  const [output, setOutput] = useState(null);
-
-  // Create a reference to the worker object.
-  const worker = useRef(null);
-
-  // We use the `useEffect` hook to setup the worker as soon as the `App` component is mounted.
-  useEffect(() => {
-    if (!worker.current) {
-      // Create the worker if it does not yet exist.
-      worker.current = new Worker(new URL('./worker.js', import.meta.url), {
-        type: 'module'
-      });
-    }
-
-    // Create a callback function for messages from the worker thread.
-    const onMessageReceived = (e) => {
-      switch (e.data.status) {
-        case 'initiate':
-          // Model file start load: add a new progress item to the list.
-          setReady(false);
-          setProgressItems(prev => [...prev, e.data]);
-          break;
-
-        case 'progress':
-          // Model file progress: update one of the progress items.
-          setProgressItems(
-            prev => prev.map(item => {
-              if (item.file === e.data.file) {
-                return { ...item, progress: e.data.progress }
-              }
-              return item;
-            })
-          );
-          break;
-
-        case 'done':
-          // Model file loaded: remove the progress item from the list.
-          setProgressItems(
-            prev => prev.filter(item => item.file !== e.data.file)
-          );
-          break;
-
-        case 'ready':
-          // Pipeline ready: the worker is ready to accept messages.
-          setReady(true);
-          break;
-
-        case 'complete':
-          // Generation complete: re-enable the "Translate" button
-          setDisabled(false);
-
-          const blobUrl = URL.createObjectURL(e.data.output);
-          setOutput(blobUrl);
-          break;
-      }
-    };
-
-    // Attach the callback function as an event listener.
-    worker.current.addEventListener('message', onMessageReceived);
-
-    // Define a cleanup function for when the component is unmounted.
-    return () => worker.current.removeEventListener('message', onMessageReceived);
-  });
-
-
-  const handleGenerateSpeech = () => {
-    setDisabled(true);
-    worker.current.postMessage({
-      text,
-      speaker_id: selectedSpeaker,
-    });
-  };
-
-  const isLoading = ready === false;
-  return (
-    

-      

-        {isLoading && (
-          
-        )}
-        {progressItems.map(data => (
-          

-            
-          

-        ))}
-      

-      

-        
In-browser Text to Speech

-        
Made with 🤗 Transformers.js

-        

-          
-          
-        

-        

-          
-          
-        

-        

-          
-        

-        {output && }
-      

-    

-  );
-};
-
-export default App;
diff --git a/examples/text-to-speech-client/src/components/AudioPlayer.jsx b/examples/text-to-speech-client/src/components/AudioPlayer.jsx
deleted file mode 100644
index a6d2daf1f..000000000
--- a/examples/text-to-speech-client/src/components/AudioPlayer.jsx
+++ /dev/null
@@ -1,26 +0,0 @@
-import { useEffect, useRef } from "react";
-
-export default function AudioPlayer({ audioUrl, mimeType }) {
-    const audioPlayer = useRef(null);
-    const audioSource = useRef(null);
-
-    // Updates src when url changes
-    useEffect(() => {
-        if (audioPlayer.current && audioSource.current) {
-            audioSource.current.src = audioUrl;
-            audioPlayer.current.load();
-        }
-    }, [audioUrl]);
-
-    return (
-        

-            
-        

-    );
-}
\ No newline at end of file
diff --git a/examples/text-to-speech-client/src/components/Progress.jsx b/examples/text-to-speech-client/src/components/Progress.jsx
deleted file mode 100644
index efaaf0a9a..000000000
--- a/examples/text-to-speech-client/src/components/Progress.jsx
+++ /dev/null
@@ -1,12 +0,0 @@
-
-export default function Progress({ text, percentage }) {
-  percentage ??= 0;
-  return (
-    

-      

-        {text} ({`${percentage.toFixed(2)}%`})
-      

-    

-  );
-}
-
diff --git a/examples/text-to-speech-client/src/constants.js b/examples/text-to-speech-client/src/constants.js
deleted file mode 100644
index ef6d848af..000000000
--- a/examples/text-to-speech-client/src/constants.js
+++ /dev/null
@@ -1,11 +0,0 @@
-export const SPEAKERS = {
-    "US female 1": "cmu_us_slt_arctic-wav-arctic_a0001",
-    "US female 2": "cmu_us_clb_arctic-wav-arctic_a0001",
-    "US male 1": "cmu_us_bdl_arctic-wav-arctic_a0003",
-    "US male 2": "cmu_us_rms_arctic-wav-arctic_a0003",
-    "Canadian male": "cmu_us_jmk_arctic-wav-arctic_a0002",
-    "Scottish male": "cmu_us_awb_arctic-wav-arctic_b0002",
-    "Indian male": "cmu_us_ksp_arctic-wav-arctic_a0007",
-}
-
-export const DEFAULT_SPEAKER = "cmu_us_slt_arctic-wav-arctic_a0001";
diff --git a/examples/text-to-speech-client/src/index.css b/examples/text-to-speech-client/src/index.css
deleted file mode 100644
index 2ea01764b..000000000
--- a/examples/text-to-speech-client/src/index.css
+++ /dev/null
@@ -1,21 +0,0 @@
-@tailwind base;
-@tailwind components;
-@tailwind utilities;
-
-:root {
-  font-family: Inter, system-ui, Avenir, Helvetica, Arial, sans-serif;
-  line-height: 1.5;
-  font-weight: 400;
-  color: #213547;
-  background-color: #ffffff;
-
-  font-synthesis: none;
-  text-rendering: optimizeLegibility;
-  -webkit-font-smoothing: antialiased;
-  -moz-osx-font-smoothing: grayscale;
-  -webkit-text-size-adjust: 100%;
-}
-
-audio::-webkit-media-controls-panel {
-  background-color: white;
-}
\ No newline at end of file
diff --git a/examples/text-to-speech-client/src/main.jsx b/examples/text-to-speech-client/src/main.jsx
deleted file mode 100644
index 54b39dd1d..000000000
--- a/examples/text-to-speech-client/src/main.jsx
+++ /dev/null
@@ -1,10 +0,0 @@
-import React from 'react'
-import ReactDOM from 'react-dom/client'
-import App from './App.jsx'
-import './index.css'
-
-ReactDOM.createRoot(document.getElementById('root')).render(
-  
-    
-  ,
-)
diff --git a/examples/text-to-speech-client/src/utils.js b/examples/text-to-speech-client/src/utils.js
deleted file mode 100644
index b23f88bba..000000000
--- a/examples/text-to-speech-client/src/utils.js
+++ /dev/null
@@ -1,47 +0,0 @@
-// Adapted from https://www.npmjs.com/package/audiobuffer-to-wav
-
-export function encodeWAV(samples) {
-    let offset = 44;
-    const buffer = new ArrayBuffer(offset + samples.length * 4);
-    const view = new DataView(buffer);
-    const sampleRate = 16000;
-
-    /* RIFF identifier */
-    writeString(view, 0, 'RIFF')
-    /* RIFF chunk length */
-    view.setUint32(4, 36 + samples.length * 4, true)
-    /* RIFF type */
-    writeString(view, 8, 'WAVE')
-    /* format chunk identifier */
-    writeString(view, 12, 'fmt ')
-    /* format chunk length */
-    view.setUint32(16, 16, true)
-    /* sample format (raw) */
-    view.setUint16(20, 3, true)
-    /* channel count */
-    view.setUint16(22, 1, true)
-    /* sample rate */
-    view.setUint32(24, sampleRate, true)
-    /* byte rate (sample rate * block align) */
-    view.setUint32(28, sampleRate * 4, true)
-    /* block align (channel count * bytes per sample) */
-    view.setUint16(32, 4, true)
-    /* bits per sample */
-    view.setUint16(34, 32, true)
-    /* data chunk identifier */
-    writeString(view, 36, 'data')
-    /* data chunk length */
-    view.setUint32(40, samples.length * 4, true)
-
-    for (let i = 0; i < samples.length; ++i, offset += 4) {
-        view.setFloat32(offset, samples[i], true)
-    }
-
-    return buffer
-}
-
-function writeString(view, offset, string) {
-    for (let i = 0; i < string.length; ++i) {
-        view.setUint8(offset + i, string.charCodeAt(i))
-    }
-}
diff --git a/examples/text-to-speech-client/src/worker.js b/examples/text-to-speech-client/src/worker.js
deleted file mode 100644
index 4644890d3..000000000
--- a/examples/text-to-speech-client/src/worker.js
+++ /dev/null
@@ -1,97 +0,0 @@
-
-import { env, Tensor, AutoTokenizer, SpeechT5ForTextToSpeech, SpeechT5HifiGan } from '@xenova/transformers';
-import { encodeWAV } from './utils';
-
-// Disable local model checks
-env.allowLocalModels = false;
-
-
-// Use the Singleton pattern to enable lazy construction of the pipeline.
-class MyTextToSpeechPipeline {
-
-    static BASE_URL = 'https://huggingface.co/datasets/Xenova/cmu-arctic-xvectors-extracted/resolve/main/';
-
-    static model_id = 'Xenova/speecht5_tts';
-    static vocoder_id = 'Xenova/speecht5_hifigan';
-
-    static tokenizer_instance = null;
-    static model_instance = null;
-    static vocoder_instance = null;
-
-    static async getInstance(progress_callback = null) {
-        if (this.tokenizer_instance === null) {
-            this.tokenizer = AutoTokenizer.from_pretrained(this.model_id, { progress_callback });
-        }
-
-        if (this.model_instance === null) {
-            this.model_instance = SpeechT5ForTextToSpeech.from_pretrained(this.model_id, {
-                dtype: 'fp32',
-                progress_callback,
-            });
-        }
-
-        if (this.vocoder_instance === null) {
-            this.vocoder_instance = SpeechT5HifiGan.from_pretrained(this.vocoder_id, {
-                dtype: 'fp32',
-                progress_callback,
-            });
-        }
-
-        return new Promise(async (resolve, reject) => {
-            const result = await Promise.all([
-                this.tokenizer,
-                this.model_instance,
-                this.vocoder_instance,
-            ]);
-            self.postMessage({
-                status: 'ready',
-            });
-            resolve(result);
-        });
-    }
-
-    static async getSpeakerEmbeddings(speaker_id) {
-        // e.g., `cmu_us_awb_arctic-wav-arctic_a0001`
-        const speaker_embeddings_url = `${this.BASE_URL}${speaker_id}.bin`;
-        const speaker_embeddings = new Tensor(
-            'float32',
-            new Float32Array(await (await fetch(speaker_embeddings_url)).arrayBuffer()),
-            [1, 512]
-        )
-        return speaker_embeddings;
-    }
-}
-
-// Mapping of cached speaker embeddings
-const speaker_embeddings_cache = new Map();
-
-// Listen for messages from the main thread
-self.addEventListener('message', async (event) => {
-    // Load the pipeline
-    const [tokenizer, model, vocoder] = await MyTextToSpeechPipeline.getInstance(x => {
-        // We also add a progress callback so that we can track model loading.
-        self.postMessage(x);
-    });
-
-    // Tokenize the input
-    const { input_ids } = tokenizer(event.data.text);
-
-    // Load the speaker embeddings
-    let speaker_embeddings = speaker_embeddings_cache.get(event.data.speaker_id);
-    if (speaker_embeddings === undefined) {
-        speaker_embeddings = await MyTextToSpeechPipeline.getSpeakerEmbeddings(event.data.speaker_id);
-        speaker_embeddings_cache.set(event.data.speaker_id, speaker_embeddings);
-    }
-
-    // Generate the waveform
-    const { waveform } = await model.generate_speech(input_ids, speaker_embeddings, { vocoder });
-
-    // Encode the waveform as a WAV file
-    const wav = encodeWAV(waveform.data);
-
-    // Send the output back to the main thread
-    self.postMessage({
-        status: 'complete',
-        output: new Blob([wav], { type: 'audio/wav' }),
-    });
-});
diff --git a/examples/text-to-speech-client/tailwind.config.js b/examples/text-to-speech-client/tailwind.config.js
deleted file mode 100644
index d37737fc0..000000000
--- a/examples/text-to-speech-client/tailwind.config.js
+++ /dev/null
@@ -1,12 +0,0 @@
-/** @type {import('tailwindcss').Config} */
-export default {
-  content: [
-    "./index.html",
-    "./src/**/*.{js,ts,jsx,tsx}",
-  ],
-  theme: {
-    extend: {},
-  },
-  plugins: [],
-}
-
diff --git a/examples/text-to-speech-client/vite.config.js b/examples/text-to-speech-client/vite.config.js
deleted file mode 100644
index 5a33944a9..000000000
--- a/examples/text-to-speech-client/vite.config.js
+++ /dev/null
@@ -1,7 +0,0 @@
-import { defineConfig } from 'vite'
-import react from '@vitejs/plugin-react'
-
-// https://vitejs.dev/config/
-export default defineConfig({
-  plugins: [react()],
-})
diff --git a/examples/tokenizer-playground/.eslintrc.cjs b/examples/tokenizer-playground/.eslintrc.cjs
deleted file mode 100644
index 4dcb43901..000000000
--- a/examples/tokenizer-playground/.eslintrc.cjs
+++ /dev/null
@@ -1,20 +0,0 @@
-module.exports = {
-  root: true,
-  env: { browser: true, es2020: true },
-  extends: [
-    'eslint:recommended',
-    'plugin:react/recommended',
-    'plugin:react/jsx-runtime',
-    'plugin:react-hooks/recommended',
-  ],
-  ignorePatterns: ['dist', '.eslintrc.cjs'],
-  parserOptions: { ecmaVersion: 'latest', sourceType: 'module' },
-  settings: { react: { version: '18.2' } },
-  plugins: ['react-refresh'],
-  rules: {
-    'react-refresh/only-export-components': [
-      'warn',
-      { allowConstantExport: true },
-    ],
-  },
-}
diff --git a/examples/tokenizer-playground/.gitignore b/examples/tokenizer-playground/.gitignore
deleted file mode 100644
index a547bf36d..000000000
--- a/examples/tokenizer-playground/.gitignore
+++ /dev/null
@@ -1,24 +0,0 @@
-# Logs
-logs
-*.log
-npm-debug.log*
-yarn-debug.log*
-yarn-error.log*
-pnpm-debug.log*
-lerna-debug.log*
-
-node_modules
-dist
-dist-ssr
-*.local
-
-# Editor directories and files
-.vscode/*
-!.vscode/extensions.json
-.idea
-.DS_Store
-*.suo
-*.ntvs*
-*.njsproj
-*.sln
-*.sw?
diff --git a/examples/tokenizer-playground/README.md b/examples/tokenizer-playground/README.md
deleted file mode 100644
index f768e33fc..000000000
--- a/examples/tokenizer-playground/README.md
+++ /dev/null
@@ -1,8 +0,0 @@
-# React + Vite
-
-This template provides a minimal setup to get React working in Vite with HMR and some ESLint rules.
-
-Currently, two official plugins are available:
-
-- [@vitejs/plugin-react](https://github.com/vitejs/vite-plugin-react/blob/main/packages/plugin-react/README.md) uses [Babel](https://babeljs.io/) for Fast Refresh
-- [@vitejs/plugin-react-swc](https://github.com/vitejs/vite-plugin-react-swc) uses [SWC](https://swc.rs/) for Fast Refresh
diff --git a/examples/tokenizer-playground/index.html b/examples/tokenizer-playground/index.html
deleted file mode 100644
index 3a73d9a50..000000000
--- a/examples/tokenizer-playground/index.html
+++ /dev/null
@@ -1,15 +0,0 @@
-
-
-
-
-  
-  
-  The Tokenizer Playground
-
-
-
-  

-  
-
-
-
\ No newline at end of file
diff --git a/examples/tokenizer-playground/package-lock.json b/examples/tokenizer-playground/package-lock.json
deleted file mode 100644
index b4abc613b..000000000
--- a/examples/tokenizer-playground/package-lock.json
+++ /dev/null
@@ -1,5154 +0,0 @@
-{
-  "name": "tokenizer-playground",
-  "version": "0.0.0",
-  "lockfileVersion": 3,
-  "requires": true,
-  "packages": {
-    "": {
-      "name": "tokenizer-playground",
-      "version": "0.0.0",
-      "dependencies": {
-        "@xenova/transformers": "^2.17.1",
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-        "react-dom": "^18.2.0"
-      },
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-        "@types/react-dom": "^18.2.7",
-        "@vitejs/plugin-react": "^4.0.3",
-        "autoprefixer": "^10.4.14",
-        "eslint": "^8.45.0",
-        "eslint-plugin-react": "^7.32.2",
-        "eslint-plugin-react-hooks": "^4.6.0",
-        "eslint-plugin-react-refresh": "^0.4.3",
-        "postcss": "^8.4.31",
-        "tailwindcss": "^3.3.3",
-        "vite": "^4.5.3"
-      }
-    },
-    "node_modules/@aashutoshrathi/word-wrap": {
-      "version": "1.2.6",
-      "resolved": "https://registry.npmjs.org/@aashutoshrathi/word-wrap/-/word-wrap-1.2.6.tgz",
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-      "dev": true,
-      "engines": {
-        "node": ">=0.10.0"
-      }
-    },
-    "node_modules/@alloc/quick-lru": {
-      "version": "5.2.0",
-      "resolved": "https://registry.npmjs.org/@alloc/quick-lru/-/quick-lru-5.2.0.tgz",
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-      "dev": true,
-      "engines": {
-        "node": ">=10"
-      },
-      "funding": {
-        "url": "https://github.com/sponsors/sindresorhus"
-      }
-    },
-    "node_modules/@ampproject/remapping": {
-      "version": "2.2.1",
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diff --git a/examples/tokenizer-playground/package.json b/examples/tokenizer-playground/package.json
deleted file mode 100644
index 0d77ce8db..000000000
--- a/examples/tokenizer-playground/package.json
+++ /dev/null
@@ -1,33 +0,0 @@
-{
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-  "version": "0.0.0",
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-    "lint": "eslint . --ext js,jsx --report-unused-disable-directives --max-warnings 0",
-    "preview": "vite preview"
-  },
-  "dependencies": {
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-    "react-dom": "^18.2.0"
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-    "eslint-plugin-react": "^7.32.2",
-    "eslint-plugin-react-hooks": "^4.6.0",
-    "eslint-plugin-react-refresh": "^0.4.3",
-    "postcss": "^8.4.31",
-    "tailwindcss": "^3.3.3",
-    "vite": "^4.5.3"
-  },
-  "overrides": {
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-  }
-}
diff --git a/examples/tokenizer-playground/postcss.config.js b/examples/tokenizer-playground/postcss.config.js
deleted file mode 100644
index 2e7af2b7f..000000000
--- a/examples/tokenizer-playground/postcss.config.js
+++ /dev/null
@@ -1,6 +0,0 @@
-export default {
-  plugins: {
-    tailwindcss: {},
-    autoprefixer: {},
-  },
-}
diff --git a/examples/tokenizer-playground/src/App.css b/examples/tokenizer-playground/src/App.css
deleted file mode 100644
index 09b73fe61..000000000
--- a/examples/tokenizer-playground/src/App.css
+++ /dev/null
@@ -1,11 +0,0 @@
-#root {
-  max-width: 1280px;
-  width: 100%;
-  margin: 0 auto;
-  padding: 2rem;
-  text-align: center;
-  display: flex;
-  justify-content: center;
-  align-items: center;
-  flex-direction: column;
-}
diff --git a/examples/tokenizer-playground/src/App.jsx b/examples/tokenizer-playground/src/App.jsx
deleted file mode 100644
index 824a1cfcd..000000000
--- a/examples/tokenizer-playground/src/App.jsx
+++ /dev/null
@@ -1,177 +0,0 @@
-import { useCallback, useEffect, useRef, useState } from 'react'
-import { Token } from './components/Token'
-import './App.css'
-
-// Define list of tokenizers and their corresponding human-readable names
-const TOKENIZER_OPTIONS = Object.freeze({
-  'Xenova/gpt-4': 'gpt-4 / gpt-3.5-turbo / text-embedding-ada-002',
-  'Xenova/text-davinci-003': 'text-davinci-003 / text-davinci-002',
-  'Xenova/gpt-3': 'gpt-3',
-  'Xenova/grok-1-tokenizer': 'Grok-1',
-  'Xenova/claude-tokenizer': 'Claude',
-  'Xenova/mistral-tokenizer-v3': 'Mistral v3',
-  'Xenova/mistral-tokenizer-v1': 'Mistral v1',
-  'Xenova/gemma-tokenizer': 'Gemma',
-  'Xenova/llama-3-tokenizer': 'Llama 3',
-  'Xenova/llama-tokenizer': 'LLaMA / Llama 2',
-  'Xenova/c4ai-command-r-v01-tokenizer': 'Cohere Command-R',
-  'Xenova/t5-small': 'T5',
-  'Xenova/bert-base-cased': 'bert-base-cased',
-  '': 'Custom',
-})
-
-function App() {
-  // Allow user to set tokenizer and text via URL query parameters
-  const urlParams = new URLSearchParams(window.location.search);
-  const tokenizerParam = urlParams.get('tokenizer');
-  const textParam = urlParams.get('text');
-
-  const [tokenIds, setTokenIds] = useState([]);
-  const [decodedTokens, setDecodedTokens] = useState([]);
-  const [margins, setMargins] = useState([]);
-  const [outputOption, setOutputOption] = useState('text');
-  const [tokenizer, setTokenizer] = useState(tokenizerParam ?? 'Xenova/gpt-4');
-  const [customTokenizer, setCustomTokenizer] = useState('');
-
-  const textareaRef = useRef(null);
-  const outputRef = useRef(null);
-
-  // Create a reference to the worker object.
-  const worker = useRef(null);
-
-  // We use the `useEffect` hook to set up the worker as soon as the `App` component is mounted.
-  useEffect(() => {
-    if (!worker.current) {
-      // Create the worker if it does not yet exist.
-      worker.current = new Worker(new URL('./worker.js', import.meta.url), {
-        type: 'module'
-      });
-    }
-
-    // Create a callback function for messages from the worker thread.
-    const onMessageReceived = (e) => {
-      setTokenIds(e.data.token_ids);
-      setDecodedTokens(e.data.decoded);
-      setMargins(e.data.margins);
-    };
-
-    // Attach the callback function as an event listener.
-    worker.current.addEventListener('message', onMessageReceived);
-
-    // Define a cleanup function for when the component is unmounted.
-    return () => worker.current.removeEventListener('message', onMessageReceived);
-  }, []);
-
-  const resetOutput = useCallback(() => {
-    setOutputOption('text');
-    setTokenIds([]);
-    setDecodedTokens([]);
-    setMargins([]);
-  }, []);
-
-  const onInputChange = useCallback((e) => {
-    const model_id = tokenizer;
-    const text = e.target.value;
-
-    if (text.length > 10000) {
-      setOutputOption(null);
-      console.log('User most likely pasted in a large body of text (> 10k chars), so we hide the output (until specifically requested by the user).');
-    }
-    worker.current.postMessage({ model_id, text });
-  }, [tokenizer]);
-
-  useEffect(() => {
-    if (textParam) {
-      onInputChange({ target: { value: textParam } });
-    }
-  }, [onInputChange, textParam]);
-
-  const onTokenizerChange = useCallback((e) => {
-    const model_id = e.target.value;
-    setTokenizer(model_id);
-    if (!model_id) return;
-    worker.current.postMessage({ model_id, text: textareaRef.current.value });
-  }, []);
-
-  return (
-    

-
-      

-        
The Tokenizer Playground

-        
Experiment with different tokenizers (running locally in your browser).

-      

-
-      

-        
-        {(!(tokenizer in TOKENIZER_OPTIONS) || customTokenizer || tokenizer === '') && (
-           {
-              setCustomTokenizer(e.target.value);
-              onTokenizerChange(e);
-            }}
-            className="bg-white border border-gray-300 text-gray-900 text-sm rounded-lg focus:ring-blue-500 focus:border-blue-500 block w-full py-1 px-2 mt-1"
-          />
-        )}
-      

-
-
-      
-
-      

-        

-          
Tokens

-          
{tokenIds.length.toLocaleString()}

-        

-        

-          
Characters

-          
{(textareaRef.current?.value.length ?? 0).toLocaleString()}

-        

-      

-
-      

-        {outputOption === 'text' ? (
-          decodedTokens.map(
-            (token, index) => 
-          )
-        ) : outputOption === 'token_ids' ? (
-          `[${tokenIds.join(', ')}]`
-        ) : null}
-      

-
-      

-        

-           setOutputOption('text')} id="output-radio-1" type="radio" value="" name="output-radio" className="w-4 h-4 text-blue-600 bg-gray-100 border-gray-300 focus:ring-blue-500" />
-          
-        

-        

-           setOutputOption('token_ids')} id="output-radio-2" type="radio" value="" name="output-radio" className="w-4 h-4 text-blue-600 bg-gray-100 border-gray-300 focus:ring-blue-500" />
-          
-        

-        

-           setOutputOption(null)} id="output-radio-3" type="radio" value="" name="output-radio" className="w-4 h-4 text-blue-600 bg-gray-100 border-gray-300 focus:ring-blue-500" />
-          
-        

-      

-    

-  )
-}
-
-export default App
diff --git a/examples/tokenizer-playground/src/components/Token.jsx b/examples/tokenizer-playground/src/components/Token.jsx
deleted file mode 100644
index 8987eb9ce..000000000
--- a/examples/tokenizer-playground/src/components/Token.jsx
+++ /dev/null
@@ -1,23 +0,0 @@
-import { Fragment } from 'react';
-
-const COLOURS = [
-    'bg-purple-300',
-    'bg-green-300',
-    'bg-yellow-300',
-    'bg-red-300',
-    'bg-blue-300',
-]
-
-export function Token({ text, position, margin }) {
-    const textWithLineBreaks = text.split('\n').map((line, index, array) => (
-        
-            {line}
-            {index !== array.length - 1 && 
}
-        
-    ));
-    return (
-        {textWithLineBreaks}
-    )
-}
\ No newline at end of file
diff --git a/examples/tokenizer-playground/src/index.css b/examples/tokenizer-playground/src/index.css
deleted file mode 100644
index 80340160d..000000000
--- a/examples/tokenizer-playground/src/index.css
+++ /dev/null
@@ -1,40 +0,0 @@
-@tailwind base;
-@tailwind components;
-@tailwind utilities;
-
-:root {
-  font-family: Inter, system-ui, Avenir, Helvetica, Arial, sans-serif;
-  line-height: 1.5;
-  font-weight: 400;
-
-  color-scheme: light dark;
-  color: rgba(255, 255, 255, 0.87);
-  background-color: #242424;
-
-  font-synthesis: none;
-  text-rendering: optimizeLegibility;
-  -webkit-font-smoothing: antialiased;
-  -moz-osx-font-smoothing: grayscale;
-  -webkit-text-size-adjust: 100%;
-}
-
-body {
-  margin: 0;
-  display: flex;
-  place-items: center;
-  min-height: 100vh;
-}
-
-
-@media (prefers-color-scheme: light) {
-  :root {
-    color: #213547;
-    background-color: #ffffff;
-  }
-  a:hover {
-    color: #747bff;
-  }
-  button {
-    background-color: #f9f9f9;
-  }
-}
diff --git a/examples/tokenizer-playground/src/main.jsx b/examples/tokenizer-playground/src/main.jsx
deleted file mode 100644
index 54b39dd1d..000000000
--- a/examples/tokenizer-playground/src/main.jsx
+++ /dev/null
@@ -1,10 +0,0 @@
-import React from 'react'
-import ReactDOM from 'react-dom/client'
-import App from './App.jsx'
-import './index.css'
-
-ReactDOM.createRoot(document.getElementById('root')).render(
-  
-    
-  ,
-)
diff --git a/examples/tokenizer-playground/src/worker.js b/examples/tokenizer-playground/src/worker.js
deleted file mode 100644
index 4db09bdc0..000000000
--- a/examples/tokenizer-playground/src/worker.js
+++ /dev/null
@@ -1,68 +0,0 @@
-// Although not strictly necessary, we delegate the tokenization to a worker thread to avoid
-// any potential issues with the tokenizer blocking the main thread (especially for large inputs).
-
-import { env, AutoTokenizer } from '@xenova/transformers'
-
-env.allowLocalModels = false;
-
-// This is a map of all the tokenizer instances that we have loaded.
-// model_id -> promise that resolves to tokenizer
-const TOKENIZER_MAPPINGS = new Map();
-
-// Listen for messages from the main thread
-self.addEventListener('message', async (event) => {
-    let tokenizerPromise = TOKENIZER_MAPPINGS.get(event.data.model_id);
-    // Load the tokenizer if it hasn't been loaded yet
-    if (!tokenizerPromise) {
-        tokenizerPromise = AutoTokenizer.from_pretrained(event.data.model_id);
-
-        TOKENIZER_MAPPINGS.set(event.data.model_id, new Promise((resolve) => {
-            // Just for visualization purposes, we may need to modify the tokenizer slightly
-            tokenizerPromise.then((tokenizer) => {
-                // NOTE: We just remove the StripDecoder from the llama tokenizer
-                switch (tokenizer.constructor.name) {
-                    case 'LlamaTokenizer':
-                    case 'Grok1Tokenizer':
-                        // tokenizer.decoder.decoders.at(-1).constructor.name === 'StripDecoder'
-                        tokenizer.decoder.decoders.pop();
-                        break;
-                    case 'T5Tokenizer':
-                        tokenizer.decoder.addPrefixSpace = false;
-                        break;
-                }
-                resolve(tokenizer);
-            });
-        }));
-    }
-
-    const tokenizer = await tokenizerPromise;
-
-    const text = event.data.text;
-
-    const start = performance.now();
-    const token_ids = tokenizer.encode(text);
-    const end = performance.now();
-    console.log('[INFO]', `Tokenized ${text.length} characters in ${(end - start).toFixed(2)}ms`)
-
-    let decoded = token_ids.map(x => tokenizer.decode([x]));
-
-    let margins = [];
-
-    // Minor post-processing for visualization purposes
-    switch (tokenizer.constructor.name) {
-        case 'BertTokenizer':
-            margins = decoded.map((x, i) => i === 0 || x.startsWith('##') ? 0 : 8);
-            decoded = decoded.map(x => x.replace('##', ''));
-            break;
-        case 'T5Tokenizer':
-            if (decoded.length > 0 && decoded.length !== ' ') {
-                decoded[0] = decoded[0].replace(/^ /, '');
-            }
-            break;
-    }
-
-    // Send the output back to the main thread
-    self.postMessage({
-        token_ids, decoded, margins
-    });
-});
\ No newline at end of file
diff --git a/examples/tokenizer-playground/tailwind.config.js b/examples/tokenizer-playground/tailwind.config.js
deleted file mode 100644
index d37737fc0..000000000
--- a/examples/tokenizer-playground/tailwind.config.js
+++ /dev/null
@@ -1,12 +0,0 @@
-/** @type {import('tailwindcss').Config} */
-export default {
-  content: [
-    "./index.html",
-    "./src/**/*.{js,ts,jsx,tsx}",
-  ],
-  theme: {
-    extend: {},
-  },
-  plugins: [],
-}
-
diff --git a/examples/tokenizer-playground/vite.config.js b/examples/tokenizer-playground/vite.config.js
deleted file mode 100644
index 5a33944a9..000000000
--- a/examples/tokenizer-playground/vite.config.js
+++ /dev/null
@@ -1,7 +0,0 @@
-import { defineConfig } from 'vite'
-import react from '@vitejs/plugin-react'
-
-// https://vitejs.dev/config/
-export default defineConfig({
-  plugins: [react()],
-})
diff --git a/examples/vanilla-js/README.md b/examples/vanilla-js/README.md
deleted file mode 100644
index e3ba3d412..000000000
--- a/examples/vanilla-js/README.md
+++ /dev/null
@@ -1,9 +0,0 @@
-# Vanilla JS Application
-
-This folder contains the source code for a simple web application that detects objects in images using Transformers.js! Check out the demo [here](https://huggingface.co/spaces/Scrimba/vanilla-js-object-detector).
-
-If you'd like to build it yourself, you can follow the [written](https://huggingface.co/docs/transformers.js/tutorials/vanilla-js) or [interactive video](https://scrimba.com/scrim/cKm9bDAg) tutorials we have made!
-
-Here's how it works: the user clicks “Upload image” and selects an image using an input dialog. After analysing the image with an object detection model, the predicted bounding boxes are overlaid on top of the image, like this:
-
-![Demo](https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/js-detection-interence-zebra.png)
diff --git a/examples/vanilla-js/index.html b/examples/vanilla-js/index.html
deleted file mode 100644
index 5ec52a3db..000000000
--- a/examples/vanilla-js/index.html
+++ /dev/null
@@ -1,24 +0,0 @@
-
-
-
-
-    
-    
-    
-    Transformers.js - Object Detection demo
-
-
-
-    

-        
-        

-        

-    

-    
-
-
-
\ No newline at end of file
diff --git a/examples/vanilla-js/index.js b/examples/vanilla-js/index.js
deleted file mode 100644
index 4fd5fe076..000000000
--- a/examples/vanilla-js/index.js
+++ /dev/null
@@ -1,73 +0,0 @@
-import { pipeline, env } from 'https://cdn.jsdelivr.net/npm/@xenova/transformers@2.6.0';
-
-// Since we will download the model from the Hugging Face Hub, we can skip the local model check
-env.allowLocalModels = false;
-
-// Reference the elements that we will need
-const status = document.getElementById('status');
-const fileUpload = document.getElementById('file-upload');
-const imageContainer = document.getElementById('image-container');
-
-// Create a new object detection pipeline
-status.textContent = 'Loading model...';
-const detector = await pipeline('object-detection', 'Xenova/detr-resnet-50');
-status.textContent = 'Ready';
-
-fileUpload.addEventListener('change', function (e) {
-    const file = e.target.files[0];
-    if (!file) {
-        return;
-    }
-
-    const reader = new FileReader();
-
-    // Set up a callback when the file is loaded
-    reader.onload = function (e2) {
-        imageContainer.innerHTML = '';
-        const image = document.createElement('img');
-        image.src = e2.target.result;
-        imageContainer.appendChild(image);
-        detect(image);
-    };
-    reader.readAsDataURL(file);
-});
-
-
-// Detect objects in the image
-async function detect(img) {
-    status.textContent = 'Analysing...';
-    const output = await detector(img.src, {
-        threshold: 0.5,
-        percentage: true,
-    });
-    status.textContent = '';
-    output.forEach(renderBox);
-}
-
-// Render a bounding box and label on the image
-function renderBox({ box, label }) {
-    const { xmax, xmin, ymax, ymin } = box;
-
-    // Generate a random color for the box
-    const color = '#' + Math.floor(Math.random() * 0xFFFFFF).toString(16).padStart(6, 0);
-
-    // Draw the box
-    const boxElement = document.createElement('div');
-    boxElement.className = 'bounding-box';
-    Object.assign(boxElement.style, {
-        borderColor: color,
-        left: 100 * xmin + '%',
-        top: 100 * ymin + '%',
-        width: 100 * (xmax - xmin) + '%',
-        height: 100 * (ymax - ymin) + '%',
-    })
-
-    // Draw label
-    const labelElement = document.createElement('span');
-    labelElement.textContent = label;
-    labelElement.className = 'bounding-box-label';
-    labelElement.style.backgroundColor = color;
-
-    boxElement.appendChild(labelElement);
-    imageContainer.appendChild(boxElement);
-}
diff --git a/examples/vanilla-js/style.css b/examples/vanilla-js/style.css
deleted file mode 100644
index a8dd91304..000000000
--- a/examples/vanilla-js/style.css
+++ /dev/null
@@ -1,58 +0,0 @@
-html,
-body {
-    font-family: Arial, Helvetica, sans-serif;
-}
-
-.container {
-    margin: 40px auto;
-    width: max(50vw, 400px);
-    display: flex;
-    flex-direction: column;
-    align-items: center;
-}
-
-
-.custom-file-upload {
-    display: flex;
-    align-items: center;
-    cursor: pointer;
-    gap: 10px;
-    border: 2px solid black;
-    padding: 8px 16px;
-    cursor: pointer;
-    border-radius: 6px;
-}
-
-#file-upload {
-    display: none;
-}
-
-.upload-icon {
-    width: 30px;
-}
-
-#image-container {
-    width: 100%;
-    margin-top: 20px;
-    position: relative;
-}
-
-#image-container>img {
-    width: 100%;
-}
-
-.bounding-box {
-    position: absolute;
-    box-sizing: border-box;
-    border-width: 2px;
-    border-style: solid;
-}
-
-.bounding-box-label {
-    color: white;
-    position: absolute;
-    font-size: 12px;
-    margin-top: -16px;
-    margin-left: -2px;
-    padding: 1px;
-}
diff --git a/examples/video-object-detection/.gitignore b/examples/video-object-detection/.gitignore
deleted file mode 100644
index a547bf36d..000000000
--- a/examples/video-object-detection/.gitignore
+++ /dev/null
@@ -1,24 +0,0 @@
-# Logs
-logs
-*.log
-npm-debug.log*
-yarn-debug.log*
-yarn-error.log*
-pnpm-debug.log*
-lerna-debug.log*
-
-node_modules
-dist
-dist-ssr
-*.local
-
-# Editor directories and files
-.vscode/*
-!.vscode/extensions.json
-.idea
-.DS_Store
-*.suo
-*.ntvs*
-*.njsproj
-*.sln
-*.sw?
diff --git a/examples/video-object-detection/index.html b/examples/video-object-detection/index.html
deleted file mode 100644
index 0c4e8dfc8..000000000
--- a/examples/video-object-detection/index.html
+++ /dev/null
@@ -1,49 +0,0 @@
-
-
-
-
-  
-  
-  Transformers.js | Real-time object detection
-
-
-
-  

-    Real-time object detection w/
-    🤗 Transformers.js
-  

-  

-    Runs locally in your browser, powered by
-    YOLOv9
-  

-  

-    
-    
-    

-  

-  

-    

-      
-      ()
-      

-      
-    

-    

-      
-      ()
-      

-      
-    

-    

-      
-      ()
-      

-      
-    

-  

-  
-
-  
-
-
-
diff --git a/examples/video-object-detection/main.js b/examples/video-object-detection/main.js
deleted file mode 100644
index 12c3552a4..000000000
--- a/examples/video-object-detection/main.js
+++ /dev/null
@@ -1,160 +0,0 @@
-import './style.css';
-
-import { env, AutoModel, AutoProcessor, RawImage } from '@xenova/transformers';
-
-// Since we will download the model from the Hugging Face Hub, we can skip the local model check
-env.allowLocalModels = false;
-
-// Proxy the WASM backend to prevent the UI from freezing
-env.backends.onnx.wasm.proxy = true;
-
-// Reference the elements that we will need
-const status = document.getElementById('status');
-const container = document.getElementById('container');
-const overlay = document.getElementById('overlay');
-const canvas = document.getElementById('canvas');
-const video = document.getElementById('video');
-const thresholdSlider = document.getElementById('threshold');
-const thresholdLabel = document.getElementById('threshold-value');
-const sizeSlider = document.getElementById('size');
-const sizeLabel = document.getElementById('size-value');
-const scaleSlider = document.getElementById('scale');
-const scaleLabel = document.getElementById('scale-value');
-
-function setStreamSize(width, height) {
-    video.width = canvas.width = Math.round(width);
-    video.height = canvas.height = Math.round(height);
-}
-
-status.textContent = 'Loading model...';
-
-// Load model and processor
-const model_id = 'Xenova/gelan-c_all';
-const model = await AutoModel.from_pretrained(model_id);
-const processor = await AutoProcessor.from_pretrained(model_id);
-
-// Set up controls
-let scale = 0.5;
-scaleSlider.addEventListener('input', () => {
-    scale = Number(scaleSlider.value);
-    setStreamSize(video.videoWidth * scale, video.videoHeight * scale);
-    scaleLabel.textContent = scale;
-});
-scaleSlider.disabled = false;
-
-let threshold = 0.25;
-thresholdSlider.addEventListener('input', () => {
-    threshold = Number(thresholdSlider.value);
-    thresholdLabel.textContent = threshold.toFixed(2);
-});
-thresholdSlider.disabled = false;
-
-let size = 128;
-processor.feature_extractor.size = { shortest_edge: size };
-sizeSlider.addEventListener('input', () => {
-    size = Number(sizeSlider.value);
-    processor.feature_extractor.size = { shortest_edge: size };
-    sizeLabel.textContent = size;
-});
-sizeSlider.disabled = false;
-
-status.textContent = 'Ready';
-
-const COLOURS = [
-    "#EF4444", "#4299E1", "#059669",
-    "#FBBF24", "#4B52B1", "#7B3AC2",
-    "#ED507A", "#1DD1A1", "#F3873A",
-    "#4B5563", "#DC2626", "#1852B4",
-    "#18A35D", "#F59E0B", "#4059BE",
-    "#6027A5", "#D63D60", "#00AC9B",
-    "#E64A19", "#272A34"
-]
-
-// Render a bounding box and label on the image
-function renderBox([xmin, ymin, xmax, ymax, score, id], [w, h]) {
-    if (score < threshold) return; // Skip boxes with low confidence
-
-    // Generate a random color for the box
-    const color = COLOURS[id % COLOURS.length];
-
-    // Draw the box
-    const boxElement = document.createElement('div');
-    boxElement.className = 'bounding-box';
-    Object.assign(boxElement.style, {
-        borderColor: color,
-        left: 100 * xmin / w + '%',
-        top: 100 * ymin / h + '%',
-        width: 100 * (xmax - xmin) / w + '%',
-        height: 100 * (ymax - ymin) / h + '%',
-    })
-
-    // Draw label
-    const labelElement = document.createElement('span');
-    labelElement.textContent = `${model.config.id2label[id]} (${(100 * score).toFixed(2)}%)`;
-    labelElement.className = 'bounding-box-label';
-    labelElement.style.backgroundColor = color;
-
-    boxElement.appendChild(labelElement);
-    overlay.appendChild(boxElement);
-}
-
-let isProcessing = false;
-let previousTime;
-const context = canvas.getContext('2d', { willReadFrequently: true });
-function updateCanvas() {
-    const { width, height } = canvas;
-    context.drawImage(video, 0, 0, width, height);
-
-    if (!isProcessing) {
-        isProcessing = true;
-        (async function () {
-            // Read the current frame from the video
-            const pixelData = context.getImageData(0, 0, width, height).data;
-            const image = new RawImage(pixelData, width, height, 4);
-
-            // Process the image and run the model
-            const inputs = await processor(image);
-            const { outputs } = await model(inputs);
-
-            // Update UI
-            overlay.innerHTML = '';
-
-            const sizes = inputs.reshaped_input_sizes[0].reverse();
-            outputs.tolist().forEach(x => renderBox(x, sizes));
-
-            if (previousTime !== undefined) {
-                const fps = 1000 / (performance.now() - previousTime);
-                status.textContent = `FPS: ${fps.toFixed(2)}`;
-            }
-            previousTime = performance.now();
-            isProcessing = false;
-        })();
-    }
-
-    window.requestAnimationFrame(updateCanvas);
-}
-
-// Start the video stream
-navigator.mediaDevices.getUserMedia(
-    { video: true }, // Ask for video
-).then((stream) => {
-    // Set up the video and canvas elements.
-    video.srcObject = stream;
-    video.play();
-
-    const videoTrack = stream.getVideoTracks()[0];
-    const { width, height } = videoTrack.getSettings();
-
-    setStreamSize(width * scale, height * scale);
-
-    // Set container width and height depending on the image aspect ratio
-    const ar = width / height;
-    const [cw, ch] = (ar > 720 / 405) ? [720, 720 / ar] : [405 * ar, 405];
-    container.style.width = `${cw}px`;
-    container.style.height = `${ch}px`;
-
-    // Start the animation loop
-    window.requestAnimationFrame(updateCanvas);
-}).catch((error) => {
-    alert(error);
-});
diff --git a/examples/video-object-detection/package-lock.json b/examples/video-object-detection/package-lock.json
deleted file mode 100644
index 9ece04cd3..000000000
--- a/examples/video-object-detection/package-lock.json
+++ /dev/null
@@ -1,1505 +0,0 @@
-{
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diff --git a/examples/video-object-detection/package.json b/examples/video-object-detection/package.json
deleted file mode 100644
index d4ac2d672..000000000
--- a/examples/video-object-detection/package.json
+++ /dev/null
@@ -1,17 +0,0 @@
-{
-  "name": "video-object-detection",
-  "private": true,
-  "version": "0.0.0",
-  "type": "module",
-  "scripts": {
-    "dev": "vite",
-    "build": "vite build",
-    "preview": "vite preview"
-  },
-  "devDependencies": {
-    "vite": "^5.1.7"
-  },
-  "dependencies": {
-    "@xenova/transformers": "^2.15.0"
-  }
-}
diff --git a/examples/video-object-detection/style.css b/examples/video-object-detection/style.css
deleted file mode 100644
index 4892e55b2..000000000
--- a/examples/video-object-detection/style.css
+++ /dev/null
@@ -1,83 +0,0 @@
-* {
-  box-sizing: border-box;
-  padding: 0;
-  margin: 0;
-  font-family: sans-serif;
-}
-
-html,
-body {
-  height: 100%;
-}
-
-body {
-  padding: 16px 32px;
-}
-
-body,
-#container {
-  display: flex;
-  flex-direction: column;
-  justify-content: center;
-  align-items: center;
-}
-
-#controls {
-  display: flex;
-  padding: 1rem;
-  gap: 1rem;
-}
-
-#controls>div {
-  text-align: center;
-}
-
-h1,
-h4 {
-  text-align: center;
-}
-
-h4 {
-  margin-top: 0.5rem;
-}
-
-#container {
-  position: relative;
-  width: 720px;
-  height: 405px;
-  max-width: 100%;
-  max-height: 100%;
-  border: 2px dashed #D1D5DB;
-  border-radius: 0.75rem;
-  overflow: hidden;
-  margin-top: 1rem;
-  background-size: 100% 100%;
-  background-position: center;
-  background-repeat: no-repeat;
-}
-
-#overlay,
-canvas {
-  position: absolute;
-  width: 100%;
-  height: 100%;
-}
-
-#status {
-  min-height: 16px;
-  margin: 8px 0;
-}
-
-.bounding-box {
-  position: absolute;
-  box-sizing: border-box;
-  border: solid 2px;
-}
-
-.bounding-box-label {
-  color: white;
-  position: absolute;
-  font-size: 12px;
-  margin: -16px 0 0 -2px;
-  padding: 1px;
-}
diff --git a/examples/video-object-detection/vite.config.js b/examples/video-object-detection/vite.config.js
deleted file mode 100644
index 6c32f52df..000000000
--- a/examples/video-object-detection/vite.config.js
+++ /dev/null
@@ -1,6 +0,0 @@
-import { defineConfig } from 'vite';
-export default defineConfig({
-  build: {
-    target: 'esnext'
-  }
-});
diff --git a/examples/webgpu-chat/.eslintrc.cjs b/examples/webgpu-chat/.eslintrc.cjs
deleted file mode 100644
index ce8fffe57..000000000
--- a/examples/webgpu-chat/.eslintrc.cjs
+++ /dev/null
@@ -1,21 +0,0 @@
-module.exports = {
-  root: true,
-  env: { browser: true, es2020: true },
-  extends: [
-    'eslint:recommended',
-    'plugin:react/recommended',
-    'plugin:react/jsx-runtime',
-    'plugin:react-hooks/recommended',
-  ],
-  ignorePatterns: ['dist', '.eslintrc.cjs'],
-  parserOptions: { ecmaVersion: 'latest', sourceType: 'module' },
-  settings: { react: { version: '18.2' } },
-  plugins: ['react-refresh'],
-  rules: {
-    'react-refresh/only-export-components': [
-      'warn',
-      { allowConstantExport: true },
-    ],
-    'react/prop-types': 'off'
-  },
-}
diff --git a/examples/webgpu-chat/.gitignore b/examples/webgpu-chat/.gitignore
deleted file mode 100644
index a547bf36d..000000000
--- a/examples/webgpu-chat/.gitignore
+++ /dev/null
@@ -1,24 +0,0 @@
-# Logs
-logs
-*.log
-npm-debug.log*
-yarn-debug.log*
-yarn-error.log*
-pnpm-debug.log*
-lerna-debug.log*
-
-node_modules
-dist
-dist-ssr
-*.local
-
-# Editor directories and files
-.vscode/*
-!.vscode/extensions.json
-.idea
-.DS_Store
-*.suo
-*.ntvs*
-*.njsproj
-*.sln
-*.sw?
diff --git a/examples/webgpu-chat/README.md b/examples/webgpu-chat/README.md
deleted file mode 100644
index f768e33fc..000000000
--- a/examples/webgpu-chat/README.md
+++ /dev/null
@@ -1,8 +0,0 @@
-# React + Vite
-
-This template provides a minimal setup to get React working in Vite with HMR and some ESLint rules.
-
-Currently, two official plugins are available:
-
-- [@vitejs/plugin-react](https://github.com/vitejs/vite-plugin-react/blob/main/packages/plugin-react/README.md) uses [Babel](https://babeljs.io/) for Fast Refresh
-- [@vitejs/plugin-react-swc](https://github.com/vitejs/vite-plugin-react-swc) uses [SWC](https://swc.rs/) for Fast Refresh
diff --git a/examples/webgpu-chat/index.html b/examples/webgpu-chat/index.html
deleted file mode 100644
index 404c33b9a..000000000
--- a/examples/webgpu-chat/index.html
+++ /dev/null
@@ -1,13 +0,0 @@
-
-
-  
-    
-    
-    
-    Phi-3 WebGPU
-  
-  
-    

-    
-  
-
diff --git a/examples/webgpu-chat/package.json b/examples/webgpu-chat/package.json
deleted file mode 100644
index 34e6e95e6..000000000
--- a/examples/webgpu-chat/package.json
+++ /dev/null
@@ -1,32 +0,0 @@
-{
-  "name": "webgpu-chat",
-  "private": true,
-  "version": "0.0.0",
-  "type": "module",
-  "scripts": {
-    "dev": "vite",
-    "build": "vite build",
-    "lint": "eslint . --ext js,jsx --report-unused-disable-directives --max-warnings 0",
-    "preview": "vite preview"
-  },
-  "dependencies": {
-    "@xenova/transformers": "github:xenova/transformers.js#v3",
-    "dompurify": "^3.1.2",
-    "marked": "^12.0.2",
-    "react": "^18.2.0",
-    "react-dom": "^18.2.0"
-  },
-  "devDependencies": {
-    "@types/react": "^18.2.43",
-    "@types/react-dom": "^18.2.17",
-    "@vitejs/plugin-react": "^4.2.1",
-    "autoprefixer": "^10.4.19",
-    "eslint": "^8.55.0",
-    "eslint-plugin-react": "^7.33.2",
-    "eslint-plugin-react-hooks": "^4.6.0",
-    "eslint-plugin-react-refresh": "^0.4.5",
-    "postcss": "^8.4.38",
-    "tailwindcss": "^3.4.3",
-    "vite": "^5.2.11"
-  }
-}
diff --git a/examples/webgpu-chat/postcss.config.js b/examples/webgpu-chat/postcss.config.js
deleted file mode 100644
index 2e7af2b7f..000000000
--- a/examples/webgpu-chat/postcss.config.js
+++ /dev/null
@@ -1,6 +0,0 @@
-export default {
-  plugins: {
-    tailwindcss: {},
-    autoprefixer: {},
-  },
-}
diff --git a/examples/webgpu-chat/public/logo.png b/examples/webgpu-chat/public/logo.png
deleted file mode 100644
index 73ecb940a..000000000
Binary files a/examples/webgpu-chat/public/logo.png and /dev/null differ
diff --git a/examples/webgpu-chat/src/App.jsx b/examples/webgpu-chat/src/App.jsx
deleted file mode 100644
index fd5e3124e..000000000
--- a/examples/webgpu-chat/src/App.jsx
+++ /dev/null
@@ -1,282 +0,0 @@
-import { useEffect, useState, useRef } from 'react';
-
-import Chat from './components/Chat';
-import ArrowRightIcon from './components/icons/ArrowRightIcon';
-import StopIcon from './components/icons/StopIcon';
-import Progress from './components/Progress';
-
-const IS_WEBGPU_AVAILABLE = !!navigator.gpu;
-const STICKY_SCROLL_THRESHOLD = 120;
-
-function App() {
-
-  // Create a reference to the worker object.
-  const worker = useRef(null);
-
-  const textareaRef = useRef(null);
-  const chatContainerRef = useRef(null);
-
-  // Model loading and progress
-  const [status, setStatus] = useState(null);
-  const [loadingMessage, setLoadingMessage] = useState('');
-  const [progressItems, setProgressItems] = useState([]);
-  const [isRunning, setIsRunning] = useState(false);
-
-  // Inputs and outputs
-  const [input, setInput] = useState('');
-  const [messages, setMessages] = useState([]);
-  const [tps, setTps] = useState(null);
-  const [numTokens, setNumTokens] = useState(null);
-
-  function onEnter(message) {
-    setMessages(prev => [
-      ...prev,
-      { "role": "user", "content": message },
-    ]);
-    setTps(null);
-    setIsRunning(true);
-    setInput('');
-  }
-
-  useEffect(() => {
-    resizeInput();
-  }, [input]);
-
-  function onInterrupt() {
-    // NOTE: We do not set isRunning to false here because the worker
-    // will send a 'complete' message when it is done.
-    worker.current.postMessage({ type: 'interrupt' });
-  }
-
-  function resizeInput() {
-    if (!textareaRef.current) return;
-
-    const target = textareaRef.current;
-    target.style.height = 'auto';
-    const newHeight = Math.min(Math.max(target.scrollHeight, 24), 200);
-    target.style.height = `${newHeight}px`;
-  }
-
-  // We use the `useEffect` hook to setup the worker as soon as the `App` component is mounted.
-  useEffect(() => {
-    if (!worker.current) {
-      // Create the worker if it does not yet exist.
-      worker.current = new Worker(new URL('./worker.js', import.meta.url), {
-        type: 'module'
-      });
-    }
-
-    // Create a callback function for messages from the worker thread.
-    const onMessageReceived = (e) => {
-      switch (e.data.status) {
-        case 'loading':
-          // Model file start load: add a new progress item to the list.
-          setStatus('loading');
-          setLoadingMessage(e.data.data);
-          break;
-
-        case 'initiate':
-          setProgressItems(prev => [...prev, e.data]);
-          break;
-
-        case 'progress':
-          // Model file progress: update one of the progress items.
-          setProgressItems(
-            prev => prev.map(item => {
-              if (item.file === e.data.file) {
-                return { ...item, ...e.data }
-              }
-              return item;
-            })
-          );
-          break;
-
-        case 'done':
-          // Model file loaded: remove the progress item from the list.
-          setProgressItems(
-            prev => prev.filter(item => item.file !== e.data.file)
-          );
-          break;
-
-        case 'ready':
-          // Pipeline ready: the worker is ready to accept messages.
-          setStatus('ready');
-          break;
-
-        case 'start': {
-          // Start generation
-          setMessages(prev => [...prev, { "role": "assistant", "content": "" }]);
-        }
-          break;
-
-        case 'update': {
-          // Generation update: update the output text.
-          // Parse messages
-          const { output, tps, numTokens } = e.data;
-          setTps(tps);
-          setNumTokens(numTokens)
-          setMessages(prev => {
-            const cloned = [...prev];
-            const last = cloned.at(-1);
-            cloned[cloned.length - 1] = { ...last, content: last.content + output };
-            return cloned;
-          });
-        }
-          break;
-
-        case 'complete':
-          // Generation complete: re-enable the "Generate" button
-          setIsRunning(false);
-          break;
-      }
-    };
-
-    // Attach the callback function as an event listener.
-    worker.current.addEventListener('message', onMessageReceived);
-
-    // Define a cleanup function for when the component is unmounted.
-    return () => {
-      worker.current.removeEventListener('message', onMessageReceived);
-    };
-  }, []);
-
-  // Send the messages to the worker thread whenever the `messages` state changes.
-  useEffect(() => {
-    if (messages.filter(x => x.role === 'user').length === 0) {
-      // No user messages yet: do nothing.
-      return;
-    }
-    if (messages.at(-1).role === 'assistant') {
-      // Do not update if the last message is from the assistant
-      return;
-    }
-    setTps(null);
-    worker.current.postMessage({ type: 'generate', data: messages });
-  }, [messages, isRunning]);
-
-  useEffect(() => {
-    if (!chatContainerRef.current) return;
-    if (isRunning) {
-      const element = chatContainerRef.current;
-      if (element.scrollHeight - element.scrollTop - element.clientHeight < STICKY_SCROLL_THRESHOLD) {
-        element.scrollTop = element.scrollHeight;
-      }
-    }
-  }, [messages, isRunning]);
-
-  return (
-    IS_WEBGPU_AVAILABLE
-      ? (

-
-        {status === null && messages.length === 0 && (
-          

-            

-              
-              
Phi-3 WebGPU

-              
A private and powerful AI chatbot that runs locally in your browser.

-            

-
-            

-              

-                

-                You are about to load Phi-3-mini-4k-instruct,
-                a 3.82 billion parameter LLM that is optimized for inference on the web. Once downloaded, the model (2.3 GB) will be cached and reused when you revisit the page.

-                

-                Everything runs directly in your browser using 🤗 Transformers.js and ONNX Runtime Web, meaning your conversations aren't sent to a server. You can even disconnect from the internet after the model has loaded!
-              

-
-              
-            

-          

-        )}
-        {status === 'loading' && (<>
-          

-            
{loadingMessage}

-            {progressItems.map(({ file, progress, total }, i) => (
-              
-            ))}
-          

-        )}
-
-        {status === 'ready' && (

-          
-          

-            {tps && messages.length > 0 && (<>
-              {!isRunning &&
-                Generated {numTokens} tokens in {(numTokens / tps).toFixed(2)} seconds (}
-              {<>
-                
-                  {tps.toFixed(2)}
-                
-                tokens/second
-              }
-              {!isRunning && <>
-                ).
-                 {
-                  worker.current.postMessage({ type: 'reset' });
-                  setMessages([]);
-                }}>Reset
-              }
-            )}
-          

-        
)}
-
-        

-          
-      

-        
-        

-          
-          
-          
-        

-      

-
-      

-        {sections.map((section, index) => (
-          

-             {
-                setSections((sections) => {
-                  const newSections = [...sections];
-                  newSections[index].title = e.target.value;
-                  return newSections;
-                })
-              }}>
-            

-              {section.items.map((item, index) => (
-                
{item}
-                

-              ))}
-            

-          

-        ))}
-      

-    

-  )
-}
-
-export default App
diff --git a/examples/zero-shot-classification/src/index.css b/examples/zero-shot-classification/src/index.css
deleted file mode 100644
index b5c61c956..000000000
--- a/examples/zero-shot-classification/src/index.css
+++ /dev/null
@@ -1,3 +0,0 @@
-@tailwind base;
-@tailwind components;
-@tailwind utilities;
diff --git a/examples/zero-shot-classification/src/main.jsx b/examples/zero-shot-classification/src/main.jsx
deleted file mode 100644
index 54b39dd1d..000000000
--- a/examples/zero-shot-classification/src/main.jsx
+++ /dev/null
@@ -1,10 +0,0 @@
-import React from 'react'
-import ReactDOM from 'react-dom/client'
-import App from './App.jsx'
-import './index.css'
-
-ReactDOM.createRoot(document.getElementById('root')).render(
-  
-    
-  ,
-)
diff --git a/examples/zero-shot-classification/src/worker.js b/examples/zero-shot-classification/src/worker.js
deleted file mode 100644
index efa61136f..000000000
--- a/examples/zero-shot-classification/src/worker.js
+++ /dev/null
@@ -1,46 +0,0 @@
-import { env, pipeline } from '@xenova/transformers';
-
-// Skip local model check since we are downloading the model from the Hugging Face Hub.
-env.allowLocalModels = false;
-
-class MyZeroShotClassificationPipeline {
-    static task = 'zero-shot-classification';
-    static model = 'MoritzLaurer/deberta-v3-xsmall-zeroshot-v1.1-all-33';
-    static instance = null;
-
-    static async getInstance(progress_callback = null) {
-        if (this.instance === null) {
-            this.instance = pipeline(this.task, this.model, {
-                quantized: true,
-                progress_callback,
-            });
-        }
-
-        return this.instance;
-    }
-}
-
-// Listen for messages from the main thread
-self.addEventListener('message', async (event) => {
-    // Retrieve the pipeline. When called for the first time,
-    // this will load the pipeline and save it for future use.
-    const classifier = await MyZeroShotClassificationPipeline.getInstance(x => {
-        // We also add a progress callback to the pipeline so that we can
-        // track model loading.
-        self.postMessage(x);
-    });
-
-    const { text, labels } = event.data;
-
-    const split = text.split('\n');
-    for (const line of split) {
-        const output = await classifier(line, labels, {
-            hypothesis_template: 'This text is about {}.',
-            multi_label: true,
-        });
-        // Send the output back to the main thread
-        self.postMessage({ status: 'output', output });
-    }
-    // Send the output back to the main thread
-    self.postMessage({ status: 'complete' });
-});
diff --git a/examples/zero-shot-classification/tailwind.config.js b/examples/zero-shot-classification/tailwind.config.js
deleted file mode 100644
index d37737fc0..000000000
--- a/examples/zero-shot-classification/tailwind.config.js
+++ /dev/null
@@ -1,12 +0,0 @@
-/** @type {import('tailwindcss').Config} */
-export default {
-  content: [
-    "./index.html",
-    "./src/**/*.{js,ts,jsx,tsx}",
-  ],
-  theme: {
-    extend: {},
-  },
-  plugins: [],
-}
-
diff --git a/examples/zero-shot-classification/vite.config.js b/examples/zero-shot-classification/vite.config.js
deleted file mode 100644
index 5a33944a9..000000000
--- a/examples/zero-shot-classification/vite.config.js
+++ /dev/null
@@ -1,7 +0,0 @@
-import { defineConfig } from 'vite'
-import react from '@vitejs/plugin-react'
-
-// https://vitejs.dev/config/
-export default defineConfig({
-  plugins: [react()],
-})
diff --git a/jest.config.mjs b/jest.config.mjs
index 0d15ce842..423f6e2ad 100644
--- a/jest.config.mjs
+++ b/jest.config.mjs
@@ -23,10 +23,7 @@ export default {
   coverageDirectory: "coverage",
 
   // An array of regexp pattern strings used to skip coverage collection
-  coveragePathIgnorePatterns: [
-    "node_modules",
-    "tests",
-  ],
+  coveragePathIgnorePatterns: ["node_modules", "tests"],
 
   // Indicates which provider should be used to instrument code for coverage
   coverageProvider: "v8",
diff --git a/package-lock.json b/package-lock.json
index 4c24b091f..a8c30d555 100644
--- a/package-lock.json
+++ b/package-lock.json
@@ -1,33 +1,32 @@
 {
   "name": "@huggingface/transformers",
-  "version": "3.7.0",
+  "version": "4.0.0-alpha.0",
   "lockfileVersion": 3,
   "requires": true,
   "packages": {
     "": {
       "name": "@huggingface/transformers",
-      "version": "3.7.0",
+      "version": "4.0.0-alpha.0",
       "license": "Apache-2.0",
       "dependencies": {
         "@huggingface/jinja": "^0.5.1",
-        "onnxruntime-node": "1.21.0",
-        "onnxruntime-web": "1.22.0-dev.20250409-89f8206ba4",
-        "sharp": "^0.34.1"
+        "onnxruntime-node": "1.23.0-dev.20250703-7fc6235861",
+        "onnxruntime-web": "1.23.0-dev.20250703-7fc6235861",
+        "sharp": "^0.34.3"
       },
       "devDependencies": {
-        "@types/jest": "^29.5.14",
-        "@types/node": "^22.10.1",
-        "@webgpu/types": "^0.1.51",
-        "catharsis": "github:xenova/catharsis",
-        "jest": "^30.0.0-alpha.6",
-        "jest-environment-node": "^30.0.0-alpha.6",
+        "@types/jest": "^30.0.0",
+        "@types/node": "^24.0.11",
+        "@webgpu/types": "^0.1.64",
+        "jest": "^30.0.4",
+        "jest-environment-node": "^30.0.4",
         "jsdoc-to-markdown": "^9.1.1",
         "prettier": "3.4.2",
-        "typescript": "^5.8.2",
+        "typescript": "^5.8.3",
         "wavefile": "11.0.0",
-        "webpack": "^5.97.1",
-        "webpack-cli": "^5.1.4",
-        "webpack-dev-server": "^5.1.0"
+        "webpack": "^5.99.9",
+        "webpack-cli": "^6.0.1",
+        "webpack-dev-server": "^5.2.2"
       }
     },
     "node_modules/@ampproject/remapping": {
@@ -45,24 +44,24 @@
       }
     },
     "node_modules/@babel/code-frame": {
-      "version": "7.26.2",
-      "resolved": "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.26.2.tgz",
-      "integrity": "sha512-RJlIHRueQgwWitWgF8OdFYGZX328Ax5BCemNGlqHfplnRT9ESi8JkFlvaVYbS+UubVY6dpv87Fs2u5M29iNFVQ==",
+      "version": "7.27.1",
+      "resolved": "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.27.1.tgz",
+      "integrity": "sha512-cjQ7ZlQ0Mv3b47hABuTevyTuYN4i+loJKGeV9flcCgIK37cCXRh+L1bd3iBHlynerhQ7BhCkn2BPbQUL+rGqFg==",
       "dev": true,
       "license": "MIT",
       "dependencies": {
-        "@babel/helper-validator-identifier": "^7.25.9",
+        "@babel/helper-validator-identifier": "^7.27.1",
         "js-tokens": "^4.0.0",
-        "picocolors": "^1.0.0"
+        "picocolors": "^1.1.1"
       },
       "engines": {
         "node": ">=6.9.0"
       }
     },
     "node_modules/@babel/compat-data": {
-      "version": "7.26.2",
-      "resolved": "https://registry.npmjs.org/@babel/compat-data/-/compat-data-7.26.2.tgz",
-      "integrity": "sha512-Z0WgzSEa+aUcdiJuCIqgujCshpMWgUpgOxXotrYPSA53hA3qopNaqcJpyr0hVb1FeWdnqFA35/fUtXgBK8srQg==",
+      "version": "7.28.0",
+      "resolved": "https://registry.npmjs.org/@babel/compat-data/-/compat-data-7.28.0.tgz",
+      "integrity": "sha512-60X7qkglvrap8mn1lh2ebxXdZYtUcpd7gsmy9kLaBJ4i/WdY8PqTSdxyA8qraikqKQK5C1KRBKXqznrVapyNaw==",
       "dev": true,
       "license": "MIT",
       "engines": {
@@ -70,22 +69,22 @@
       }
     },
     "node_modules/@babel/core": {
-      "version": "7.26.0",
-      "resolved": "https://registry.npmjs.org/@babel/core/-/core-7.26.0.tgz",
-      "integrity": "sha512-i1SLeK+DzNnQ3LL/CswPCa/E5u4lh1k6IAEphON8F+cXt0t9euTshDru0q7/IqMa1PMPz5RnHuHscF8/ZJsStg==",
+      "version": "7.28.0",
+      "resolved": "https://registry.npmjs.org/@babel/core/-/core-7.28.0.tgz",
+      "integrity": "sha512-UlLAnTPrFdNGoFtbSXwcGFQBtQZJCNjaN6hQNP3UPvuNXT1i82N26KL3dZeIpNalWywr9IuQuncaAfUaS1g6sQ==",
       "dev": true,
       "license": "MIT",
       "dependencies": {
         "@ampproject/remapping": "^2.2.0",
-        "@babel/code-frame": "^7.26.0",
-        "@babel/generator": "^7.26.0",
-        "@babel/helper-compilation-targets": "^7.25.9",
-        "@babel/helper-module-transforms": "^7.26.0",
-        "@babel/helpers": "^7.26.0",
-        "@babel/parser": "^7.26.0",
-        "@babel/template": "^7.25.9",
-        "@babel/traverse": "^7.25.9",
-        "@babel/types": "^7.26.0",
+        "@babel/code-frame": "^7.27.1",
+        "@babel/generator": "^7.28.0",
+        "@babel/helper-compilation-targets": "^7.27.2",
+        "@babel/helper-module-transforms": "^7.27.3",
+        "@babel/helpers": "^7.27.6",
+        "@babel/parser": "^7.28.0",
+        "@babel/template": "^7.27.2",
+        "@babel/traverse": "^7.28.0",
+        "@babel/types": "^7.28.0",
         "convert-source-map": "^2.0.0",
         "debug": "^4.1.0",
         "gensync": "^1.0.0-beta.2",
@@ -101,9 +100,9 @@
       }
     },
     "node_modules/@babel/core/node_modules/debug": {
-      "version": "4.3.7",
-      "resolved": "https://registry.npmjs.org/debug/-/debug-4.3.7.tgz",
-      "integrity": "sha512-Er2nc/H7RrMXZBFCEim6TCmMk02Z8vLC2Rbi1KEBggpo0fS6l0S1nnapwmIi3yW/+GOJap1Krg4w0Hg80oCqgQ==",
+      "version": "4.4.1",
+      "resolved": "https://registry.npmjs.org/debug/-/debug-4.4.1.tgz",
+      "integrity": "sha512-KcKCqiftBJcZr++7ykoDIEwSa3XWowTfNPo92BYxjXiyYEVrUQh2aLyhxBCwww+heortUFxEJYcRzosstTEBYQ==",
       "dev": true,
       "license": "MIT",
       "dependencies": {
@@ -136,16 +135,16 @@
       }
     },
     "node_modules/@babel/generator": {
-      "version": "7.26.2",
-      "resolved": "https://registry.npmjs.org/@babel/generator/-/generator-7.26.2.tgz",
-      "integrity": "sha512-zevQbhbau95nkoxSq3f/DC/SC+EEOUZd3DYqfSkMhY2/wfSeaHV1Ew4vk8e+x8lja31IbyuUa2uQ3JONqKbysw==",
+      "version": "7.28.0",
+      "resolved": "https://registry.npmjs.org/@babel/generator/-/generator-7.28.0.tgz",
+      "integrity": "sha512-lJjzvrbEeWrhB4P3QBsH7tey117PjLZnDbLiQEKjQ/fNJTjuq4HSqgFA+UNSwZT8D7dxxbnuSBMsa1lrWzKlQg==",
       "dev": true,
       "license": "MIT",
       "dependencies": {
-        "@babel/parser": "^7.26.2",
-        "@babel/types": "^7.26.0",
-        "@jridgewell/gen-mapping": "^0.3.5",
-        "@jridgewell/trace-mapping": "^0.3.25",
+        "@babel/parser": "^7.28.0",
+        "@babel/types": "^7.28.0",
+        "@jridgewell/gen-mapping": "^0.3.12",
+        "@jridgewell/trace-mapping": "^0.3.28",
         "jsesc": "^3.0.2"
       },
       "engines": {
@@ -153,14 +152,14 @@
       }
     },
     "node_modules/@babel/helper-compilation-targets": {
-      "version": "7.25.9",
-      "resolved": "https://registry.npmjs.org/@babel/helper-compilation-targets/-/helper-compilation-targets-7.25.9.tgz",
-      "integrity": "sha512-j9Db8Suy6yV/VHa4qzrj9yZfZxhLWQdVnRlXxmKLYlhWUVB1sB2G5sxuWYXk/whHD9iW76PmNzxZ4UCnTQTVEQ==",
+      "version": "7.27.2",
+      "resolved": "https://registry.npmjs.org/@babel/helper-compilation-targets/-/helper-compilation-targets-7.27.2.tgz",
+      "integrity": "sha512-2+1thGUUWWjLTYTHZWK1n8Yga0ijBz1XAhUXcKy81rd5g6yh7hGqMp45v7cadSbEHc9G3OTv45SyneRN3ps4DQ==",
       "dev": true,
       "license": "MIT",
       "dependencies": {
-        "@babel/compat-data": "^7.25.9",
-        "@babel/helper-validator-option": "^7.25.9",
+        "@babel/compat-data": "^7.27.2",
+        "@babel/helper-validator-option": "^7.27.1",
         "browserslist": "^4.24.0",
         "lru-cache": "^5.1.1",
         "semver": "^6.3.1"
@@ -179,30 +178,40 @@
         "semver": "bin/semver.js"
       }
     },
+    "node_modules/@babel/helper-globals": {
+      "version": "7.28.0",
+      "resolved": "https://registry.npmjs.org/@babel/helper-globals/-/helper-globals-7.28.0.tgz",
+      "integrity": "sha512-+W6cISkXFa1jXsDEdYA8HeevQT/FULhxzR99pxphltZcVaugps53THCeiWA8SguxxpSp3gKPiuYfSWopkLQ4hw==",
+      "dev": true,
+      "license": "MIT",
+      "engines": {
+        "node": ">=6.9.0"
+      }
+    },
     "node_modules/@babel/helper-module-imports": {
-      "version": "7.25.9",
-      "resolved": "https://registry.npmjs.org/@babel/helper-module-imports/-/helper-module-imports-7.25.9.tgz",
-      "integrity": "sha512-tnUA4RsrmflIM6W6RFTLFSXITtl0wKjgpnLgXyowocVPrbYrLUXSBXDgTs8BlbmIzIdlBySRQjINYs2BAkiLtw==",
+      "version": "7.27.1",
+      "resolved": "https://registry.npmjs.org/@babel/helper-module-imports/-/helper-module-imports-7.27.1.tgz",
+      "integrity": "sha512-0gSFWUPNXNopqtIPQvlD5WgXYI5GY2kP2cCvoT8kczjbfcfuIljTbcWrulD1CIPIX2gt1wghbDy08yE1p+/r3w==",
       "dev": true,
       "license": "MIT",
       "dependencies": {
-        "@babel/traverse": "^7.25.9",
-        "@babel/types": "^7.25.9"
+        "@babel/traverse": "^7.27.1",
+        "@babel/types": "^7.27.1"
       },
       "engines": {
         "node": ">=6.9.0"
       }
     },
     "node_modules/@babel/helper-module-transforms": {
-      "version": "7.26.0",
-      "resolved": "https://registry.npmjs.org/@babel/helper-module-transforms/-/helper-module-transforms-7.26.0.tgz",
-      "integrity": "sha512-xO+xu6B5K2czEnQye6BHA7DolFFmS3LB7stHZFaOLb1pAwO1HWLS8fXA+eh0A2yIvltPVmx3eNNDBJA2SLHXFw==",
+      "version": "7.27.3",
+      "resolved": "https://registry.npmjs.org/@babel/helper-module-transforms/-/helper-module-transforms-7.27.3.tgz",
+      "integrity": "sha512-dSOvYwvyLsWBeIRyOeHXp5vPj5l1I011r52FM1+r1jCERv+aFXYk4whgQccYEGYxK2H3ZAIA8nuPkQ0HaUo3qg==",
       "dev": true,
       "license": "MIT",
       "dependencies": {
-        "@babel/helper-module-imports": "^7.25.9",
-        "@babel/helper-validator-identifier": "^7.25.9",
-        "@babel/traverse": "^7.25.9"
+        "@babel/helper-module-imports": "^7.27.1",
+        "@babel/helper-validator-identifier": "^7.27.1",
+        "@babel/traverse": "^7.27.3"
       },
       "engines": {
         "node": ">=6.9.0"
@@ -212,9 +221,9 @@
       }
     },
     "node_modules/@babel/helper-plugin-utils": {
-      "version": "7.25.9",
-      "resolved": "https://registry.npmjs.org/@babel/helper-plugin-utils/-/helper-plugin-utils-7.25.9.tgz",
-      "integrity": "sha512-kSMlyUVdWe25rEsRGviIgOWnoT/nfABVWlqt9N19/dIPWViAOW2s9wznP5tURbs/IDuNk4gPy3YdYRgH3uxhBw==",
+      "version": "7.27.1",
+      "resolved": "https://registry.npmjs.org/@babel/helper-plugin-utils/-/helper-plugin-utils-7.27.1.tgz",
+      "integrity": "sha512-1gn1Up5YXka3YYAHGKpbideQ5Yjf1tDa9qYcgysz+cNCXukyLl6DjPXhD3VRwSb8c0J9tA4b2+rHEZtc6R0tlw==",
       "dev": true,
       "license": "MIT",
       "engines": {
@@ -222,9 +231,9 @@
       }
     },
     "node_modules/@babel/helper-string-parser": {
-      "version": "7.25.9",
-      "resolved": "https://registry.npmjs.org/@babel/helper-string-parser/-/helper-string-parser-7.25.9.tgz",
-      "integrity": "sha512-4A/SCr/2KLd5jrtOMFzaKjVtAei3+2r/NChoBNoZ3EyP/+GlhoaEGoWOZUmFmoITP7zOJyHIMm+DYRd8o3PvHA==",
+      "version": "7.27.1",
+      "resolved": "https://registry.npmjs.org/@babel/helper-string-parser/-/helper-string-parser-7.27.1.tgz",
+      "integrity": "sha512-qMlSxKbpRlAridDExk92nSobyDdpPijUq2DW6oDnUqd0iOGxmQjyqhMIihI9+zv4LPyZdRje2cavWPbCbWm3eA==",
       "dev": true,
       "license": "MIT",
       "engines": {
@@ -232,9 +241,9 @@
       }
     },
     "node_modules/@babel/helper-validator-identifier": {
-      "version": "7.25.9",
-      "resolved": "https://registry.npmjs.org/@babel/helper-validator-identifier/-/helper-validator-identifier-7.25.9.tgz",
-      "integrity": "sha512-Ed61U6XJc3CVRfkERJWDz4dJwKe7iLmmJsbOGu9wSloNSFttHV0I8g6UAgb7qnK5ly5bGLPd4oXZlxCdANBOWQ==",
+      "version": "7.27.1",
+      "resolved": "https://registry.npmjs.org/@babel/helper-validator-identifier/-/helper-validator-identifier-7.27.1.tgz",
+      "integrity": "sha512-D2hP9eA+Sqx1kBZgzxZh0y1trbuU+JoDkiEwqhQ36nodYqJwyEIhPSdMNd7lOm/4io72luTPWH20Yda0xOuUow==",
       "dev": true,
       "license": "MIT",
       "engines": {
@@ -242,9 +251,9 @@
       }
     },
     "node_modules/@babel/helper-validator-option": {
-      "version": "7.25.9",
-      "resolved": "https://registry.npmjs.org/@babel/helper-validator-option/-/helper-validator-option-7.25.9.tgz",
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+      "resolved": "https://registry.npmjs.org/webpack-sources/-/webpack-sources-3.3.3.tgz",
+      "integrity": "sha512-yd1RBzSGanHkitROoPFd6qsrxt+oFhg/129YzheDGqeustzX0vTZJZsSsQjVQC4yzBQ56K55XU8gaNCtIzOnTg==",
       "dev": true,
+      "license": "MIT",
       "engines": {
         "node": ">=10.13.0"
       }
     },
     "node_modules/websocket-driver": {
       "version": "0.7.4",
-      "resolved": "https://registry.npmjs.org/websocket-driver/-/websocket-driver-0.7.4.tgz",
-      "integrity": "sha512-b17KeDIQVjvb0ssuSDF2cYXSg2iztliJ4B9WdsuB6J952qCPKmnVq4DyW5motImXHDC1cBT/1UezrJVsKw5zjg==",
       "dev": true,
+      "license": "Apache-2.0",
       "dependencies": {
         "http-parser-js": ">=0.5.1",
         "safe-buffer": ">=5.1.0",
@@ -11291,17 +7615,16 @@
     },
     "node_modules/websocket-extensions": {
       "version": "0.1.4",
-      "resolved": "https://registry.npmjs.org/websocket-extensions/-/websocket-extensions-0.1.4.tgz",
-      "integrity": "sha512-OqedPIGOfsDlo31UNwYbCFMSaO9m9G/0faIHj5/dZFDMFqPTcx6UwqyOy3COEaEOg/9VsGIpdqn62W5KhoKSpg==",
       "dev": true,
+      "license": "Apache-2.0",
       "engines": {
         "node": ">=0.8.0"
       }
     },
     "node_modules/which": {
       "version": "2.0.2",
-      "resolved": "https://registry.npmjs.org/which/-/which-2.0.2.tgz",
-      "integrity": "sha512-BLI3Tl1TW3Pvl70l3yq3Y64i+awpwXqsGBYWkkqMtnbXgrMD+yj7rhW0kuEDxzJaYXGjEW5ogapKNMEKNMjibA==",
+      "dev": true,
+      "license": "ISC",
       "dependencies": {
         "isexe": "^2.0.0"
       },
@@ -11313,22 +7636,19 @@
       }
     },
     "node_modules/wildcard": {
-      "version": "2.0.0",
-      "resolved": "https://registry.npmjs.org/wildcard/-/wildcard-2.0.0.tgz",
-      "integrity": "sha512-JcKqAHLPxcdb9KM49dufGXn2x3ssnfjbcaQdLlfZsL9rH9wgDQjUtDxbo8NE0F6SFvydeu1VhZe7hZuHsB2/pw==",
-      "dev": true
+      "version": "2.0.1",
+      "resolved": "https://registry.npmjs.org/wildcard/-/wildcard-2.0.1.tgz",
+      "integrity": "sha512-CC1bOL87PIWSBhDcTrdeLo6eGT7mCFtrg0uIJtqJUFyK+eJnzl8A1niH56uu7KMa5XFrtiV+AQuHO3n7DsHnLQ==",
+      "dev": true,
+      "license": "MIT"
     },
     "node_modules/wordwrap": {
       "version": "1.0.0",
-      "resolved": "https://registry.npmjs.org/wordwrap/-/wordwrap-1.0.0.tgz",
-      "integrity": "sha512-gvVzJFlPycKc5dZN4yPkP8w7Dc37BtP1yczEneOb4uq34pXZcvrtRTmWV8W+Ume+XCxKgbjM+nevkyFPMybd4Q==",
       "dev": true,
       "license": "MIT"
     },
     "node_modules/wordwrapjs": {
       "version": "5.1.0",
-      "resolved": "https://registry.npmjs.org/wordwrapjs/-/wordwrapjs-5.1.0.tgz",
-      "integrity": "sha512-JNjcULU2e4KJwUNv6CHgI46UvDGitb6dGryHajXTDiLgg1/RiGoPSDw4kZfYnwGtEXf2ZMeIewDQgFGzkCB2Sg==",
       "dev": true,
       "license": "MIT",
       "engines": {
@@ -11336,18 +7656,18 @@
       }
     },
     "node_modules/wrap-ansi": {
-      "version": "7.0.0",
-      "resolved": "https://registry.npmjs.org/wrap-ansi/-/wrap-ansi-7.0.0.tgz",
-      "integrity": "sha512-YVGIj2kamLSTxw6NsZjoBxfSwsn0ycdesmc4p+Q21c5zPuZ1pl+NfxVdxPtdHvmNVOQ6XSYG4AUtyt/Fi7D16Q==",
+      "version": "8.1.0",
+      "resolved": "https://registry.npmjs.org/wrap-ansi/-/wrap-ansi-8.1.0.tgz",
+      "integrity": "sha512-si7QWI6zUMq56bESFvagtmzMdGOtoxfR+Sez11Mobfc7tm+VkUckk9bW2UeffTGVUbOksxmSw0AA2gs8g71NCQ==",
       "dev": true,
       "license": "MIT",
       "dependencies": {
-        "ansi-styles": "^4.0.0",
-        "string-width": "^4.1.0",
-        "strip-ansi": "^6.0.0"
+        "ansi-styles": "^6.1.0",
+        "string-width": "^5.0.1",
+        "strip-ansi": "^7.0.1"
       },
       "engines": {
-        "node": ">=10"
+        "node": ">=12"
       },
       "funding": {
         "url": "https://github.com/chalk/wrap-ansi?sponsor=1"
@@ -11358,6 +7678,8 @@
       "version": "7.0.0",
       "resolved": "https://registry.npmjs.org/wrap-ansi/-/wrap-ansi-7.0.0.tgz",
       "integrity": "sha512-YVGIj2kamLSTxw6NsZjoBxfSwsn0ycdesmc4p+Q21c5zPuZ1pl+NfxVdxPtdHvmNVOQ6XSYG4AUtyt/Fi7D16Q==",
+      "dev": true,
+      "license": "MIT",
       "dependencies": {
         "ansi-styles": "^4.0.0",
         "string-width": "^4.1.0",
@@ -11370,11 +7692,70 @@
         "url": "https://github.com/chalk/wrap-ansi?sponsor=1"
       }
     },
+    "node_modules/wrap-ansi-cjs/node_modules/ansi-regex": {
+      "version": "5.0.1",
+      "resolved": "https://registry.npmjs.org/ansi-regex/-/ansi-regex-5.0.1.tgz",
+      "integrity": "sha512-quJQXlTSUGL2LH9SUXo8VwsY4soanhgo6LNSm84E1LBcE8s3O0wpdiRzyR9z/ZZJMlMWv37qOOb9pdJlMUEKFQ==",
+      "dev": true,
+      "license": "MIT",
+      "engines": {
+        "node": ">=8"
+      }
+    },
+    "node_modules/wrap-ansi-cjs/node_modules/emoji-regex": {
+      "version": "8.0.0",
+      "resolved": "https://registry.npmjs.org/emoji-regex/-/emoji-regex-8.0.0.tgz",
+      "integrity": "sha512-MSjYzcWNOA0ewAHpz0MxpYFvwg6yjy1NG3xteoqz644VCo/RPgnr1/GGt+ic3iJTzQ8Eu3TdM14SawnVUmGE6A==",
+      "dev": true,
+      "license": "MIT"
+    },
+    "node_modules/wrap-ansi-cjs/node_modules/string-width": {
+      "version": "4.2.3",
+      "resolved": "https://registry.npmjs.org/string-width/-/string-width-4.2.3.tgz",
+      "integrity": "sha512-wKyQRQpjJ0sIp62ErSZdGsjMJWsap5oRNihHhu6G7JVO/9jIB6UyevL+tXuOqrng8j/cxKTWyWUwvSTriiZz/g==",
+      "dev": true,
+      "license": "MIT",
+      "dependencies": {
+        "emoji-regex": "^8.0.0",
+        "is-fullwidth-code-point": "^3.0.0",
+        "strip-ansi": "^6.0.1"
+      },
+      "engines": {
+        "node": ">=8"
+      }
+    },
+    "node_modules/wrap-ansi-cjs/node_modules/strip-ansi": {
+      "version": "6.0.1",
+      "resolved": "https://registry.npmjs.org/strip-ansi/-/strip-ansi-6.0.1.tgz",
+      "integrity": "sha512-Y38VPSHcqkFrCpFnQ9vuSXmquuv5oXOKpGeT6aGrr3o3Gc9AlVa6JBfUSOCnbxGGZF+/0ooI7KrPuUSztUdU5A==",
+      "dev": true,
+      "license": "MIT",
+      "dependencies": {
+        "ansi-regex": "^5.0.1"
+      },
+      "engines": {
+        "node": ">=8"
+      }
+    },
+    "node_modules/wrap-ansi/node_modules/ansi-styles": {
+      "version": "6.2.1",
+      "resolved": "https://registry.npmjs.org/ansi-styles/-/ansi-styles-6.2.1.tgz",
+      "integrity": "sha512-bN798gFfQX+viw3R7yrGWRqnrN2oRkEkUjjl4JNn4E8GxxbjtG3FbrEIIY3l8/hrwUwIeCZvi4QuOTP4MErVug==",
+      "dev": true,
+      "license": "MIT",
+      "engines": {
+        "node": ">=12"
+      },
+      "funding": {
+        "url": "https://github.com/chalk/ansi-styles?sponsor=1"
+      }
+    },
     "node_modules/wrappy": {
       "version": "1.0.2",
       "resolved": "https://registry.npmjs.org/wrappy/-/wrappy-1.0.2.tgz",
       "integrity": "sha512-l4Sp/DRseor9wL6EvV2+TuQn63dMkPjZ/sp9XkghTEbV9KlPS1xUsZ3u7/IQO4wxtcFB4bgpQPRcR3QCvezPcQ==",
-      "dev": true
+      "dev": true,
+      "license": "ISC"
     },
     "node_modules/write-file-atomic": {
       "version": "5.0.1",
@@ -11390,23 +7771,8 @@
         "node": "^14.17.0 || ^16.13.0 || >=18.0.0"
       }
     },
-    "node_modules/write-file-atomic/node_modules/signal-exit": {
-      "version": "4.1.0",
-      "resolved": "https://registry.npmjs.org/signal-exit/-/signal-exit-4.1.0.tgz",
-      "integrity": "sha512-bzyZ1e88w9O1iNJbKnOlvYTrWPDl46O1bG0D3XInv+9tkPrxrN8jUUTiFlDkkmKWgn1M6CfIA13SuGqOa9Korw==",
-      "dev": true,
-      "license": "ISC",
-      "engines": {
-        "node": ">=14"
-      },
-      "funding": {
-        "url": "https://github.com/sponsors/isaacs"
-      }
-    },
     "node_modules/ws": {
       "version": "8.18.0",
-      "resolved": "https://registry.npmjs.org/ws/-/ws-8.18.0.tgz",
-      "integrity": "sha512-8VbfWfHLbbwu3+N6OKsOMpBdT4kXPDDB9cJk2bJ6mh9ucxdlnNvH1e+roYkKmN9Nxw2yjz7VzeO9oOz2zJ04Pw==",
       "dev": true,
       "license": "MIT",
       "engines": {
@@ -11427,8 +7793,6 @@
     },
     "node_modules/xmlcreate": {
       "version": "2.0.4",
-      "resolved": "https://registry.npmjs.org/xmlcreate/-/xmlcreate-2.0.4.tgz",
-      "integrity": "sha512-nquOebG4sngPmGPICTS5EnxqhKbCmz5Ox5hsszI2T6U5qdrJizBc+0ilYSEjTSzU0yZcmvppztXe/5Al5fUwdg==",
       "dev": true,
       "license": "Apache-2.0"
     },
@@ -11478,6 +7842,51 @@
         "node": ">=12"
       }
     },
+    "node_modules/yargs/node_modules/ansi-regex": {
+      "version": "5.0.1",
+      "resolved": "https://registry.npmjs.org/ansi-regex/-/ansi-regex-5.0.1.tgz",
+      "integrity": "sha512-quJQXlTSUGL2LH9SUXo8VwsY4soanhgo6LNSm84E1LBcE8s3O0wpdiRzyR9z/ZZJMlMWv37qOOb9pdJlMUEKFQ==",
+      "dev": true,
+      "license": "MIT",
+      "engines": {
+        "node": ">=8"
+      }
+    },
+    "node_modules/yargs/node_modules/emoji-regex": {
+      "version": "8.0.0",
+      "resolved": "https://registry.npmjs.org/emoji-regex/-/emoji-regex-8.0.0.tgz",
+      "integrity": "sha512-MSjYzcWNOA0ewAHpz0MxpYFvwg6yjy1NG3xteoqz644VCo/RPgnr1/GGt+ic3iJTzQ8Eu3TdM14SawnVUmGE6A==",
+      "dev": true,
+      "license": "MIT"
+    },
+    "node_modules/yargs/node_modules/string-width": {
+      "version": "4.2.3",
+      "resolved": "https://registry.npmjs.org/string-width/-/string-width-4.2.3.tgz",
+      "integrity": "sha512-wKyQRQpjJ0sIp62ErSZdGsjMJWsap5oRNihHhu6G7JVO/9jIB6UyevL+tXuOqrng8j/cxKTWyWUwvSTriiZz/g==",
+      "dev": true,
+      "license": "MIT",
+      "dependencies": {
+        "emoji-regex": "^8.0.0",
+        "is-fullwidth-code-point": "^3.0.0",
+        "strip-ansi": "^6.0.1"
+      },
+      "engines": {
+        "node": ">=8"
+      }
+    },
+    "node_modules/yargs/node_modules/strip-ansi": {
+      "version": "6.0.1",
+      "resolved": "https://registry.npmjs.org/strip-ansi/-/strip-ansi-6.0.1.tgz",
+      "integrity": "sha512-Y38VPSHcqkFrCpFnQ9vuSXmquuv5oXOKpGeT6aGrr3o3Gc9AlVa6JBfUSOCnbxGGZF+/0ooI7KrPuUSztUdU5A==",
+      "dev": true,
+      "license": "MIT",
+      "dependencies": {
+        "ansi-regex": "^5.0.1"
+      },
+      "engines": {
+        "node": ">=8"
+      }
+    },
     "node_modules/yocto-queue": {
       "version": "0.1.0",
       "resolved": "https://registry.npmjs.org/yocto-queue/-/yocto-queue-0.1.0.tgz",
diff --git a/package.json b/package.json
index 2eeca1d54..10249e984 100644
--- a/package.json
+++ b/package.json
@@ -1,6 +1,6 @@
 {
   "name": "@huggingface/transformers",
-  "version": "3.7.0",
+  "version": "4.0.0-alpha.0",
   "description": "State-of-the-art Machine Learning for the web. Run 🤗 Transformers directly in your browser, with no need for a server!",
   "main": "./src/transformers.js",
   "types": "./types/transformers.d.ts",
@@ -27,7 +27,7 @@
     "typegen": "tsc --build",
     "dev": "webpack serve --no-client-overlay",
     "build": "webpack && npm run typegen",
-    "test": "node --experimental-vm-modules --expose-gc node_modules/jest/bin/jest.js --verbose",
+    "test": "node --experimental-vm-modules --expose-gc node_modules/jest/bin/jest.js --verbose --logHeapUsage",
     "readme": "python ./docs/scripts/build_readme.py",
     "docs-api": "node ./docs/scripts/generate.js",
     "docs-preview": "doc-builder preview transformers.js ./docs/source/ --not_python_module",
@@ -56,24 +56,23 @@
   "homepage": "https://github.com/huggingface/transformers.js#readme",
   "dependencies": {
     "@huggingface/jinja": "^0.5.1",
-    "onnxruntime-node": "1.21.0",
-    "onnxruntime-web": "1.22.0-dev.20250409-89f8206ba4",
-    "sharp": "^0.34.1"
+    "onnxruntime-node": "1.23.0-dev.20250703-7fc6235861",
+    "onnxruntime-web": "1.23.0-dev.20250703-7fc6235861",
+    "sharp": "^0.34.3"
   },
   "devDependencies": {
-    "@types/jest": "^29.5.14",
-    "@types/node": "^22.10.1",
-    "@webgpu/types": "^0.1.51",
-    "catharsis": "github:xenova/catharsis",
-    "jest": "^30.0.0-alpha.6",
-    "jest-environment-node": "^30.0.0-alpha.6",
+    "@types/jest": "^30.0.0",
+    "@types/node": "^24.0.11",
+    "@webgpu/types": "^0.1.64",
+    "jest": "^30.0.4",
+    "jest-environment-node": "^30.0.4",
     "jsdoc-to-markdown": "^9.1.1",
     "prettier": "3.4.2",
-    "typescript": "^5.8.2",
+    "typescript": "^5.8.3",
     "wavefile": "11.0.0",
-    "webpack": "^5.97.1",
-    "webpack-cli": "^5.1.4",
-    "webpack-dev-server": "^5.1.0"
+    "webpack": "^5.99.9",
+    "webpack-cli": "^6.0.1",
+    "webpack-dev-server": "^5.2.2"
   },
   "files": [
     "src",
diff --git a/scripts/float16.py b/scripts/float16.py
index 0be170f33..647c5df0e 100644
--- a/scripts/float16.py
+++ b/scripts/float16.py
@@ -135,13 +135,13 @@ def convert_tensor_float_to_float16(tensor, min_positive_val=1e-7, max_finite_va
         # convert raw_data (bytes type)
         if tensor.raw_data:
             # convert n.raw_data to float
-            float32_list = np.fromstring(tensor.raw_data, dtype="float32")
+            float32_list = np.frombuffer(tensor.raw_data, dtype="float32")
             # convert float to float16
             float16_list = convert_np_to_float16(
                 float32_list, min_positive_val, max_finite_val
             )
             # convert float16 to bytes and write back to raw_data
-            tensor.raw_data = float16_list.tostring()
+            tensor.raw_data = float16_list.tobytes()
     return tensor
 
 
diff --git a/scripts/quantize.py b/scripts/quantize.py
index 3f73f0916..b37e2d526 100644
--- a/scripts/quantize.py
+++ b/scripts/quantize.py
@@ -12,7 +12,7 @@
 from onnxruntime.quantization import QuantType, QuantizationMode
 from onnxruntime.quantization.onnx_quantizer import ONNXQuantizer
 from onnxruntime.quantization.registry import IntegerOpsRegistry
-from onnxruntime.quantization.matmul_4bits_quantizer import MatMul4BitsQuantizer
+from onnxruntime.quantization.matmul_nbits_quantizer import MatMulNBitsQuantizer
 from onnxruntime.quantization.matmul_bnb4_quantizer import MatMulBnb4Quantizer
 
 from . import float16
@@ -107,7 +107,7 @@ class QuantizationArguments:
         },
     )
 
-    # MatMul4BitsQuantizer
+    # MatMulNBitsQuantizer
     is_symmetric: bool = field(
         default=True,
         metadata={"help": "Indicate whether to quantize the model symmetrically"},
@@ -234,7 +234,7 @@ def quantize_q4(
     Quantize the weights of the model from float32 to 4-bit int
     """
 
-    quantizer = MatMul4BitsQuantizer(
+    quantizer = MatMulNBitsQuantizer(
         model=model,
         block_size=block_size,
         is_symmetric=is_symmetric,
diff --git a/scripts/requirements.txt b/scripts/requirements.txt
index 6d18662f4..1c8fc7fe5 100644
--- a/scripts/requirements.txt
+++ b/scripts/requirements.txt
@@ -1,7 +1,7 @@
-transformers[torch]==4.49.0
-onnxruntime==1.20.1
-optimum@git+https://github.com/huggingface/optimum.git@b04feaea78cda58d79b8da67dca3fd0c4ab33435
-onnx==1.17.0
+transformers[torch]==4.52.4
+onnxruntime==1.22.0
+optimum==1.26.1
+onnx==1.18.0
 tqdm==4.67.1
-onnxslim==0.1.48
+onnxslim==0.1.52
 numpy==2.2.6
diff --git a/src/backends/onnx.js b/src/backends/onnx.js
index a64f9d160..f964d076b 100644
--- a/src/backends/onnx.js
+++ b/src/backends/onnx.js
@@ -6,13 +6,13 @@
  * So, we just import both packages, and use the appropriate one based on the environment:
  *   - When running in node, we use `onnxruntime-node`.
  *   - When running in the browser, we use `onnxruntime-web` (`onnxruntime-node` is not bundled).
- * 
+ *
  * This module is not directly exported, but can be accessed through the environment variables:
  * ```javascript
  * import { env } from '@huggingface/transformers';
  * console.log(env.backends.onnx);
  * ```
- * 
+ *
  * @module backends/onnx
  */
 
@@ -21,7 +21,7 @@ import { env, apis } from '../env.js';
 // NOTE: Import order matters here. We need to import `onnxruntime-node` before `onnxruntime-web`.
 // In either case, we select the default export if it exists, otherwise we use the named export.
 import * as ONNX_NODE from 'onnxruntime-node';
-import * as ONNX_WEB from 'onnxruntime-web';
+import * as ONNX_WEB from 'onnxruntime-web/webgpu';
 
 export { Tensor } from 'onnxruntime-common';
 
@@ -38,6 +38,7 @@ const DEVICE_TO_EXECUTION_PROVIDER_MAPPING = Object.freeze({
     webgpu: 'webgpu', // WebGPU
     cuda: 'cuda', // CUDA
     dml: 'dml', // DirectML
+    coreml: 'coreml', // CoreML
 
     webnn: { name: 'webnn', deviceType: 'cpu' }, // WebNN (default)
     'webnn-npu': { name: 'webnn', deviceType: 'npu' }, // WebNN NPU
@@ -45,7 +46,7 @@ const DEVICE_TO_EXECUTION_PROVIDER_MAPPING = Object.freeze({
     'webnn-cpu': { name: 'webnn', deviceType: 'cpu' }, // WebNN CPU
 });
 
-/** 
+/**
  * The list of supported devices, sorted by priority/performance.
  * @type {import("../utils/devices.js").DeviceType[]}
  */
@@ -59,17 +60,18 @@ const ORT_SYMBOL = Symbol.for('onnxruntime');
 if (ORT_SYMBOL in globalThis) {
     // If the JS runtime exposes their own ONNX runtime, use it
     ONNX = globalThis[ORT_SYMBOL];
-
 } else if (apis.IS_NODE_ENV) {
     ONNX = ONNX_NODE.default ?? ONNX_NODE;
 
-    // Updated as of ONNX Runtime 1.20.1
+    // Updated as of ONNX Runtime 1.23.0-dev.20250612-70f14d7670
     // The following table lists the supported versions of ONNX Runtime Node.js binding provided with pre-built binaries.
-    // | EPs/Platforms | Windows x64 | Windows arm64 | Linux x64         | Linux arm64 | MacOS x64 | MacOS arm64 |
-    // | ------------- | ----------- | ------------- | ----------------- | ----------- | --------- | ----------- |
-    // | CPU           | ✔️          | ✔️            | ✔️                | ✔️          | ✔️        | ✔️          |
-    // | DirectML      | ✔️          | ✔️            | ❌                | ❌          | ❌        | ❌          |
-    // | CUDA          | ❌          | ❌            | ✔️ (CUDA v11.8)   | ❌          | ❌        | ❌          |
+    // | EPs/Platforms         | Windows x64        | Windows arm64      | Linux x64          | Linux arm64        | MacOS x64          | MacOS arm64        |
+    // | --------------------- | ------------------ | ------------------ | ------------------ | ------------------ | ------------------ | ------------------ |
+    // | CPU                   | ✔️                  | ✔️                  | ✔️                  | ✔️                  | ✔️                  | ✔️                  |
+    // | WebGPU (experimental) | ✔️                  | ✔️                  | ✔️                  | ❌                  | ✔️                  | ✔️                  |
+    // | DirectML              | ✔️                  | ✔️                  | ❌                  | ❌                  | ❌                  | ❌                  |
+    // | CUDA                  | ❌                  | ❌                  | ✔️ (CUDA v12)       | ❌                  | ❌                  | ❌                  |
+    // | CoreML                | ❌                  | ❌                  | ❌                  | ❌                  | ✔️                  | ✔️                  |
     switch (process.platform) {
         case 'win32': // Windows x64 and Windows arm64
             supportedDevices.push('dml');
@@ -80,9 +82,11 @@ if (ORT_SYMBOL in globalThis) {
             }
             break;
         case 'darwin': // MacOS x64 and MacOS arm64
+            supportedDevices.push('coreml');
             break;
     }
 
+    supportedDevices.push('webgpu');
     supportedDevices.push('cpu');
     defaultDevices = ['cpu'];
 } else {
@@ -115,22 +119,19 @@ export function deviceToExecutionProviders(device = null) {
 
     // Handle overloaded cases
     switch (device) {
-        case "auto":
+        case 'auto':
             return supportedDevices;
-        case "gpu":
-            return supportedDevices.filter(x =>
-                ["webgpu", "cuda", "dml", "webnn-gpu"].includes(x),
-            );
+        case 'gpu':
+            return supportedDevices.filter((x) => ['webgpu', 'cuda', 'dml', 'webnn-gpu'].includes(x));
     }
 
     if (supportedDevices.includes(device)) {
         return [DEVICE_TO_EXECUTION_PROVIDER_MAPPING[device] ?? device];
     }
 
-    throw new Error(`Unsupported device: "${device}". Should be one of: ${supportedDevices.join(', ')}.`)
+    throw new Error(`Unsupported device: "${device}". Should be one of: ${supportedDevices.join(', ')}.`);
 }
 
-
 /**
  * To prevent multiple calls to `initWasm()`, we store the first call in a Promise
  * that is resolved when the first InferenceSession is created. Subsequent calls
@@ -179,10 +180,18 @@ if (ONNX_ENV?.wasm) {
     // By default, we only do this if we are not in a service worker and the wasmPaths are not already set.
     if (
         // @ts-ignore Cannot find name 'ServiceWorkerGlobalScope'.ts(2304)
-        !(typeof ServiceWorkerGlobalScope !== 'undefined' && self instanceof ServiceWorkerGlobalScope)
-        && !ONNX_ENV.wasm.wasmPaths
+        !(typeof ServiceWorkerGlobalScope !== 'undefined' && self instanceof ServiceWorkerGlobalScope) &&
+        env.backends.onnx.versions?.web &&
+        !ONNX_ENV.wasm.wasmPaths
     ) {
-        ONNX_ENV.wasm.wasmPaths = `https://cdn.jsdelivr.net/npm/@huggingface/transformers@${env.version}/dist/`;
+        const wasmPathPrefix = `https://cdn.jsdelivr.net/npm/onnxruntime-web@${env.backends.onnx.versions.web}/dist/`;
+
+        ONNX_ENV.wasm.wasmPaths = apis.IS_SAFARI
+            ? {
+                  mjs: `${wasmPathPrefix}/ort-wasm-simd-threaded.mjs`,
+                  wasm: `${wasmPathPrefix}/ort-wasm-simd-threaded.wasm`,
+              }
+            : wasmPathPrefix;
     }
 
     // TODO: Add support for loading WASM files from cached buffer when we upgrade to onnxruntime-web@1.19.0
diff --git a/src/base/feature_extraction_utils.js b/src/base/feature_extraction_utils.js
index 257f98d57..dfcbd9134 100644
--- a/src/base/feature_extraction_utils.js
+++ b/src/base/feature_extraction_utils.js
@@ -1,6 +1,6 @@
-import { FEATURE_EXTRACTOR_NAME } from "../utils/constants.js";
-import { Callable } from "../utils/generic.js";
-import { getModelJSON } from "../utils/hub.js";
+import { FEATURE_EXTRACTOR_NAME } from '../utils/constants.js';
+import { Callable } from '../utils/generic.js';
+import { getModelJSON } from '../utils/hub.js';
 
 /**
  * Base class for feature extractors.
@@ -13,31 +13,30 @@ export class FeatureExtractor extends Callable {
      */
     constructor(config) {
         super();
-        this.config = config
+        this.config = config;
     }
 
     /**
      * Instantiate one of the feature extractor classes of the library from a pretrained model.
-     * 
+     *
      * The feature extractor class to instantiate is selected based on the `feature_extractor_type` property of
      * the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible)
-     * 
+     *
      * @param {string} pretrained_model_name_or_path The name or path of the pretrained model. Can be either:
      * - A string, the *model id* of a pretrained feature_extractor hosted inside a model repo on huggingface.co.
      *   Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a
      *   user or organization name, like `dbmdz/bert-base-german-cased`.
      * - A path to a *directory* containing feature_extractor files, e.g., `./my_model_directory/`.
      * @param {import('../utils/hub.js').PretrainedOptions} options Additional options for loading the feature_extractor.
-     * 
+     *
      * @returns {Promise} A new instance of the Feature Extractor class.
      */
-    static async from_pretrained(pretrained_model_name_or_path, options={}) {
+    static async from_pretrained(pretrained_model_name_or_path, options = {}) {
         const config = await getModelJSON(pretrained_model_name_or_path, FEATURE_EXTRACTOR_NAME, true, options);
         return new this(config);
     }
 }
 
-
 /**
  * Helper function to validate audio inputs.
  * @param {any} audio The audio data.
@@ -48,7 +47,7 @@ export function validate_audio_inputs(audio, feature_extractor) {
     if (!(audio instanceof Float32Array || audio instanceof Float64Array)) {
         throw new Error(
             `${feature_extractor} expects input to be a Float32Array or a Float64Array, but got ${audio?.constructor?.name ?? typeof audio} 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.`
-        )
+                `If using the feature extractor directly, remember to use \`read_audio(url, sampling_rate)\` to obtain the raw audio data of the file/url.`,
+        );
     }
 }
diff --git a/src/base/image_processors_utils.js b/src/base/image_processors_utils.js
index 8b98485cc..041be7f5b 100644
--- a/src/base/image_processors_utils.js
+++ b/src/base/image_processors_utils.js
@@ -1,9 +1,9 @@
-import { Callable } from "../utils/generic.js";
-import { Tensor, interpolate, stack } from "../utils/tensor.js";
-import { bankers_round, max, min, softmax } from "../utils/maths.js";
-import { RawImage } from "../utils/image.js";
-import { calculateReflectOffset } from "../utils/core.js";
-import { getModelJSON } from "../utils/hub.js";
+import { Callable } from '../utils/generic.js';
+import { Tensor, interpolate, stack } from '../utils/tensor.js';
+import { bankers_round, max, min, softmax } from '../utils/maths.js';
+import { RawImage } from '../utils/image.js';
+import { calculateReflectOffset } from '../utils/core.js';
+import { getModelJSON } from '../utils/hub.js';
 import { IMAGE_PROCESSOR_NAME } from '../utils/constants.js';
 
 /**
@@ -12,7 +12,6 @@ import { IMAGE_PROCESSOR_NAME } from '../utils/constants.js';
  * @typedef {[height: number, width: number]} HeightWidth
  */
 
-
 /**
  * @typedef {object} ImageProcessorResult
  * @property {Tensor} pixel_values The pixel values of the batched preprocessed images.
@@ -20,8 +19,6 @@ import { IMAGE_PROCESSOR_NAME } from '../utils/constants.js';
  * @property {HeightWidth[]} reshaped_input_sizes Array of two-dimensional tuples like [[1000, 1330]].
  */
 
-
-
 /**
  * Helper function to constrain a value to be a multiple of a number.
  * @param {number} val The value to constrain.
@@ -53,28 +50,19 @@ function constraint_to_multiple_of(val, multiple, minVal = 0, maxVal = null) {
  * @returns {[number, number]} The rounded size.
  */
 function enforce_size_divisibility([width, height], divisor) {
-    return [
-        Math.max(Math.floor(width / divisor), 1) * divisor,
-        Math.max(Math.floor(height / divisor), 1) * divisor
-    ];
+    return [Math.max(Math.floor(width / divisor), 1) * divisor, Math.max(Math.floor(height / divisor), 1) * divisor];
 }
 
-
 // Helper functions
 
 /**
  * Converts bounding boxes from center format to corners format.
- * 
+ *
  * @param {number[]} arr The coordinate for the center of the box and its width, height dimensions (center_x, center_y, width, height)
  * @returns {number[]} The coodinates for the top-left and bottom-right corners of the box (top_left_x, top_left_y, bottom_right_x, bottom_right_y)
  */
 export function center_to_corners_format([centerX, centerY, width, height]) {
-    return [
-        centerX - width / 2,
-        centerY - height / 2,
-        centerX + width / 2,
-        centerY + height / 2
-    ];
+    return [centerX - width / 2, centerY - height / 2, centerX + width / 2, centerY + height / 2];
 }
 
 /**
@@ -93,7 +81,7 @@ export function post_process_object_detection(outputs, threshold = 0.5, target_s
     const [batch_size, num_boxes, num_classes] = out_logits.dims;
 
     if (target_sizes !== null && target_sizes.length !== batch_size) {
-        throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits")
+        throw Error('Make sure that you pass in as many target sizes as the batch dimension of the logits');
     }
     let toReturn = [];
     for (let i = 0; i < batch_size; ++i) {
@@ -101,8 +89,8 @@ export function post_process_object_detection(outputs, threshold = 0.5, target_s
         let info = {
             boxes: [],
             classes: [],
-            scores: []
-        }
+            scores: [],
+        };
         let logits = out_logits[i];
         let bbox = out_bbox[i];
 
@@ -119,7 +107,6 @@ export function post_process_object_detection(outputs, threshold = 0.5, target_s
                         indices.push(k);
                     }
                 }
-
             } else {
                 // Get most probable class
                 let maxIndex = max(logit.data)[1];
@@ -138,15 +125,14 @@ export function post_process_object_detection(outputs, threshold = 0.5, target_s
             }
 
             for (const index of indices) {
-
                 // Some class has a high enough probability
                 /** @type {number[]} */
                 let box = bbox[j].data;
 
                 // convert to [x0, y0, x1, y1] format
-                box = center_to_corners_format(box)
+                box = center_to_corners_format(box);
                 if (target_size !== null) {
-                    box = box.map((x, i) => x * target_size[(i + 1) % 2])
+                    box = box.map((x, i) => x * target_size[(i + 1) % 2]);
                 }
 
                 info.boxes.push(box);
@@ -159,7 +145,6 @@ export function post_process_object_detection(outputs, threshold = 0.5, target_s
     return toReturn;
 }
 
-
 /**
  * Post-processes the outputs of the model (for semantic segmentation).
  * @param {*} outputs Raw outputs of the model.
@@ -168,12 +153,11 @@ export function post_process_object_detection(outputs, threshold = 0.5, target_s
  * @returns {{segmentation: Tensor; labels: number[]}[]} The semantic segmentation maps.
  */
 export function post_process_semantic_segmentation(outputs, target_sizes = null) {
-
     const logits = outputs.logits;
     const batch_size = logits.dims[0];
 
     if (target_sizes !== null && target_sizes.length !== batch_size) {
-        throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits")
+        throw Error('Make sure that you pass in as many target sizes as the batch dimension of the logits');
     }
 
     const toReturn = [];
@@ -189,11 +173,7 @@ export function post_process_semantic_segmentation(outputs, target_sizes = null)
         }
         const [height, width] = target_size ?? data.dims.slice(-2);
 
-        const segmentation = new Tensor(
-            'int32',
-            new Int32Array(height * width),
-            [height, width]
-        );
+        const segmentation = new Tensor('int32', new Int32Array(height * width), [height, width]);
 
         // Buffer to store current largest value
         const buffer = data[0].data;
@@ -216,14 +196,13 @@ export function post_process_semantic_segmentation(outputs, target_sizes = null)
             hasLabel[index] = index;
         }
         /** @type {number[]} The unique list of labels that were detected */
-        const labels = hasLabel.filter(x => x !== undefined);
+        const labels = hasLabel.filter((x) => x !== undefined);
 
         toReturn.push({ segmentation, labels });
     }
     return toReturn;
 }
 
-
 /**
  * Binarize the given masks using `object_mask_threshold`, it returns the associated values of `masks`, `scores` and `labels`.
  * @param {Tensor} class_logits The class logits.
@@ -234,7 +213,6 @@ export function post_process_semantic_segmentation(outputs, target_sizes = null)
  * @private
  */
 function remove_low_and_no_objects(class_logits, mask_logits, object_mask_threshold, num_labels) {
-
     const mask_probs_item = [];
     const pred_scores_item = [];
     const pred_labels_item = [];
@@ -271,13 +249,7 @@ function remove_low_and_no_objects(class_logits, mask_logits, object_mask_thresh
  * @returns {[boolean, number[]]} Whether the segment is valid or not, and the indices of the valid labels.
  * @private
  */
-function check_segment_validity(
-    mask_labels,
-    mask_probs,
-    k,
-    mask_threshold = 0.5,
-    overlap_mask_area_threshold = 0.8
-) {
+function check_segment_validity(mask_labels, mask_probs, k, mask_threshold = 0.5, overlap_mask_area_threshold = 0.8) {
     // mask_k is a 1D array of indices, indicating where the mask is equal to k
     const mask_k = [];
     let mask_k_area = 0;
@@ -305,7 +277,7 @@ function check_segment_validity(
         mask_exists = area_ratio > overlap_mask_area_threshold;
     }
 
-    return [mask_exists, mask_k]
+    return [mask_exists, mask_k];
 }
 
 /**
@@ -331,11 +303,7 @@ function compute_segments(
 ) {
     const [height, width] = target_size ?? mask_probs[0].dims;
 
-    const segmentation = new Tensor(
-        'int32',
-        new Int32Array(height * width),
-        [height, width]
-    );
+    const segmentation = new Tensor('int32', new Int32Array(height * width), [height, width]);
     const segments = [];
 
     // 1. If target_size is not null, we need to resize the masks to the target size
@@ -348,7 +316,7 @@ function compute_segments(
 
     // 2. Weigh each mask by its prediction score
     // NOTE: `mask_probs` is updated in-place
-    // 
+    //
     // Temporary storage for the best label/scores for each pixel ([height, width]):
     const mask_labels = new Int32Array(mask_probs[0].data.length);
     const bestScores = new Float32Array(mask_probs[0].data.length);
@@ -359,7 +327,7 @@ function compute_segments(
         const mask_probs_i_data = mask_probs[i].data;
 
         for (let j = 0; j < mask_probs_i_data.length; ++j) {
-            mask_probs_i_data[j] *= score
+            mask_probs_i_data[j] *= score;
             if (mask_probs_i_data[j] > bestScores[j]) {
                 mask_labels[j] = i;
                 bestScores[j] = mask_probs_i_data[j];
@@ -383,8 +351,8 @@ function compute_segments(
             mask_probs,
             k,
             mask_threshold,
-            overlap_mask_area_threshold
-        )
+            overlap_mask_area_threshold,
+        );
 
         if (!mask_exists) {
             // Nothing to see here
@@ -399,7 +367,6 @@ function compute_segments(
         // }
         ++current_segment_id;
 
-
         // Add current object segment to final segmentation map
         for (const index of mask_k) {
             segmentation_data[index] = current_segment_id;
@@ -410,7 +377,7 @@ function compute_segments(
             label_id: pred_class,
             // was_fused: should_fuse, TODO
             score: pred_scores[k],
-        })
+        });
 
         // TODO
         // if(should_fuse){
@@ -427,7 +394,7 @@ function compute_segments(
  * 1. Both dimensions (height and width) are divisible by 'factor'.
  * 2. The total number of pixels is within the range ['min_pixels', 'max_pixels'].
  * 3. The aspect ratio of the image is maintained as closely as possible.
- * 
+ *
  * @param {number} height The height of the image.
  * @param {number} width The width of the image.
  * @param {number} [factor=28] The factor to use for resizing.
@@ -437,12 +404,11 @@ function compute_segments(
  * @throws {Error} If the height or width is smaller than the factor.
  */
 function smart_resize(height, width, factor = 28, min_pixels = 56 * 56, max_pixels = 14 * 14 * 4 * 1280) {
-
     if (height < factor || width < factor) {
         throw new Error(`height:${height} or width:${width} must be larger than factor:${factor}`);
     } else if (Math.max(height, width) / Math.min(height, width) > 200) {
         throw new Error(
-            `absolute aspect ratio must be smaller than 200, got ${Math.max(height, width) / Math.min(height, width)}`
+            `absolute aspect ratio must be smaller than 200, got ${Math.max(height, width) / Math.min(height, width)}`,
         );
     }
 
@@ -451,8 +417,8 @@ function smart_resize(height, width, factor = 28, min_pixels = 56 * 56, max_pixe
 
     if (h_bar * w_bar > max_pixels) {
         const beta = Math.sqrt((height * width) / max_pixels);
-        h_bar = Math.floor((height / beta) / factor) * factor;
-        w_bar = Math.floor((width / beta) / factor) * factor;
+        h_bar = Math.floor(height / beta / factor) * factor;
+        w_bar = Math.floor(width / beta / factor) * factor;
     } else if (h_bar * w_bar < min_pixels) {
         const beta = Math.sqrt(min_pixels / (height * width));
         h_bar = Math.ceil((height * beta) / factor) * factor;
@@ -462,7 +428,6 @@ function smart_resize(height, width, factor = 28, min_pixels = 56 * 56, max_pixe
     return [h_bar, w_bar];
 }
 
-
 /**
  * Post-process the model output to generate the final panoptic segmentation.
  * @param {*} outputs The model output to post process
@@ -482,20 +447,20 @@ export function post_process_panoptic_segmentation(
     target_sizes = null,
 ) {
     if (label_ids_to_fuse === null) {
-        console.warn("`label_ids_to_fuse` unset. No instance will be fused.")
+        console.warn('`label_ids_to_fuse` unset. No instance will be fused.');
         label_ids_to_fuse = new Set();
     }
 
     const class_queries_logits = outputs.class_queries_logits ?? outputs.logits; // [batch_size, num_queries, num_classes+1]
     const masks_queries_logits = outputs.masks_queries_logits ?? outputs.pred_masks; // [batch_size, num_queries, height, width]
 
-    const mask_probs = masks_queries_logits.sigmoid()  // [batch_size, num_queries, height, width]
+    const mask_probs = masks_queries_logits.sigmoid(); // [batch_size, num_queries, height, width]
 
     let [batch_size, num_queries, num_labels] = class_queries_logits.dims;
     num_labels -= 1; // Remove last class (background)
 
     if (target_sizes !== null && target_sizes.length !== batch_size) {
-        throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits")
+        throw Error('Make sure that you pass in as many target sizes as the batch dimension of the logits');
     }
 
     let toReturn = [];
@@ -505,25 +470,25 @@ export function post_process_panoptic_segmentation(
         let class_logits = class_queries_logits[i];
         let mask_logits = mask_probs[i];
 
-        let [mask_probs_item, pred_scores_item, pred_labels_item] = remove_low_and_no_objects(class_logits, mask_logits, threshold, num_labels);
+        let [mask_probs_item, pred_scores_item, pred_labels_item] = remove_low_and_no_objects(
+            class_logits,
+            mask_logits,
+            threshold,
+            num_labels,
+        );
 
         if (pred_labels_item.length === 0) {
             // No mask found
             let [height, width] = target_size ?? mask_logits.dims.slice(-2);
 
-            let segmentation = new Tensor(
-                'int32',
-                new Int32Array(height * width).fill(-1),
-                [height, width]
-            )
+            let segmentation = new Tensor('int32', new Int32Array(height * width).fill(-1), [height, width]);
             toReturn.push({
                 segmentation: segmentation,
-                segments_info: []
+                segments_info: [],
             });
             continue;
         }
 
-
         // Get segmentation map and segment information of batch item
         let [segmentation, segments] = compute_segments(
             mask_probs_item,
@@ -533,18 +498,17 @@ export function post_process_panoptic_segmentation(
             overlap_mask_area_threshold,
             label_ids_to_fuse,
             target_size,
-        )
+        );
 
         toReturn.push({
             segmentation: segmentation,
-            segments_info: segments
-        })
+            segments_info: segments,
+        });
     }
 
     return toReturn;
 }
 
-
 /**
  * Post-processes the outputs of the model (for instance segmentation).
  * @param {*} outputs Raw outputs of the model.
@@ -557,9 +521,8 @@ export function post_process_instance_segmentation(outputs, threshold = 0.5, tar
     throw new Error('`post_process_instance_segmentation` is not yet implemented.');
 }
 
-
 /**
- * @typedef {Object} ImageProcessorConfig A configuration object used to create an image processor.    
+ * @typedef {Object} ImageProcessorConfig A configuration object used to create an image processor.
  * @property {function} [progress_callback=null] If specified, this function will be called during model construction, to provide the user with progress updates.
  * @property {number[]} [image_mean] The mean values for image normalization.
  * @property {number[]} [image_std] The standard deviation values for image normalization.
@@ -579,13 +542,12 @@ export function post_process_instance_segmentation(outputs, threshold = 0.5, tar
  * Can be overidden by `keep_aspect_ratio` in `preprocess`.
  * @property {number} [ensure_multiple_of] If `do_resize` is `true`, the image is resized to a size that is a multiple of this value.
  * Can be overidden by `ensure_multiple_of` in `preprocess`.
- * 
+ *
  * @property {number[]} [mean] The mean values for image normalization (same as `image_mean`).
  * @property {number[]} [std] The standard deviation values for image normalization (same as `image_std`).
  */
 
 export class ImageProcessor extends Callable {
-
     /**
      * Constructs a new `ImageProcessor`.
      * @param {ImageProcessorConfig} config The configuration object.
@@ -598,12 +560,12 @@ export class ImageProcessor extends Callable {
 
         this.resample = config.resample ?? 2; // 2 => bilinear
         this.do_rescale = config.do_rescale ?? true;
-        this.rescale_factor = config.rescale_factor ?? (1 / 255);
+        this.rescale_factor = config.rescale_factor ?? 1 / 255;
         this.do_normalize = config.do_normalize;
 
         this.do_thumbnail = config.do_thumbnail;
         this.size = config.size ?? config.image_size;
-        this.do_resize = config.do_resize ?? (this.size !== undefined);
+        this.do_resize = config.do_resize ?? this.size !== undefined;
         // @ts-expect-error TS2339
         this.size_divisibility = config.size_divisibility ?? config.size_divisor;
 
@@ -624,10 +586,16 @@ export class ImageProcessor extends Callable {
         // @ts-expect-error TS2339
         this.max_pixels = config.max_pixels;
 
-        if (this.do_pad && !this.pad_size && this.size && this.size.width !== undefined && this.size.height !== undefined) {
+        if (
+            this.do_pad &&
+            !this.pad_size &&
+            this.size &&
+            this.size.width !== undefined &&
+            this.size.height !== undefined
+        ) {
             // Should pad, but no pad size specified
             // We infer the pad size from the resize size
-            this.pad_size = this.size
+            this.pad_size = this.size;
         }
 
         this.do_flip_channel_order = config.do_flip_channel_order ?? false;
@@ -651,21 +619,20 @@ export class ImageProcessor extends Callable {
         const output_width = size.width;
 
         // We always resize to the smallest of either the input or output size.
-        let height = Math.min(input_height, output_height)
-        let width = Math.min(input_width, output_width)
+        let height = Math.min(input_height, output_height);
+        let width = Math.min(input_width, output_width);
 
         if (height === input_height && width === input_width) {
             return image;
         }
         if (input_height > input_width) {
-            width = Math.floor(input_width * height / input_height);
+            width = Math.floor((input_width * height) / input_height);
         } else if (input_width > input_height) {
-            height = Math.floor(input_height * width / input_width);
+            height = Math.floor((input_height * width) / input_width);
         }
         return await image.resize(width, height, { resample });
     }
 
-
     /**
      * Crops the margin of the image. Gray pixels are considered margin (i.e., pixels with a value below the threshold).
      * @param {RawImage} image The image to be cropped.
@@ -673,7 +640,6 @@ export class ImageProcessor extends Callable {
      * @returns {Promise} The cropped image.
      */
     async crop_margin(image, gray_threshold = 200) {
-
         const gray_image = image.clone().grayscale();
 
         const minValue = min(gray_image.data)[0];
@@ -686,7 +652,10 @@ export class ImageProcessor extends Callable {
 
         const threshold = gray_threshold / 255;
 
-        let x_min = gray_image.width, y_min = gray_image.height, x_max = 0, y_max = 0;
+        let x_min = gray_image.width,
+            y_min = gray_image.height,
+            x_max = 0,
+            y_max = 0;
         const gray_image_data = gray_image.data;
         for (let j = 0; j < gray_image.height; ++j) {
             const row = j * gray_image.width;
@@ -716,11 +685,7 @@ export class ImageProcessor extends Callable {
      * @param {number|number[]} [options.constant_values=0] The constant value to use for padding.
      * @returns {[Float32Array, number[]]} The padded pixel data and image dimensions.
      */
-    pad_image(pixelData, imgDims, padSize, {
-        mode = 'constant',
-        center = false,
-        constant_values = 0,
-    } = {}) {
+    pad_image(pixelData, imgDims, padSize, { mode = 'constant', center = false, constant_values = 0 } = {}) {
         const [imageHeight, imageWidth, imageChannels] = imgDims;
 
         let paddedImageWidth, paddedImageHeight;
@@ -787,10 +752,9 @@ export class ImageProcessor extends Callable {
                 }
             }
 
-
             // Update pixel data and image dimensions
             pixelData = paddedPixelData;
-            imgDims = [paddedImageHeight, paddedImageWidth, imageChannels]
+            imgDims = [paddedImageHeight, paddedImageWidth, imageChannels];
         }
         return [pixelData, imgDims];
     }
@@ -810,12 +774,12 @@ export class ImageProcessor extends Callable {
      * Find the target (width, height) dimension of the output image after
      * resizing given the input image and the desired size.
      * @param {RawImage} image The image to resize.
-     * @param {any} size The size to use for resizing the image. 
+     * @param {any} size The size to use for resizing the image.
      * @returns {[number, number]} The target (width, height) dimension of the output image after resizing.
      */
     get_resize_output_image_size(image, size) {
         // `size` comes in many forms, so we need to handle them all here:
-        // 1. `size` is an integer, in which case we resize the image to be a square 
+        // 1. `size` is an integer, in which case we resize the image to be a square
 
         const [srcWidth, srcHeight] = image.size;
 
@@ -825,14 +789,13 @@ export class ImageProcessor extends Callable {
         if (this.do_thumbnail) {
             // NOTE: custom logic for `Donut` models
             const { height, width } = size;
-            shortest_edge = Math.min(height, width)
+            shortest_edge = Math.min(height, width);
         }
         // Support both formats for backwards compatibility
         else if (Number.isInteger(size)) {
             shortest_edge = size;
             // @ts-expect-error TS2339
             longest_edge = this.config.max_size ?? shortest_edge;
-
         } else if (size !== undefined) {
             // Extract known properties from `size`
             shortest_edge = size.shortest_edge;
@@ -844,28 +807,32 @@ export class ImageProcessor extends Callable {
         if (shortest_edge !== undefined || longest_edge !== undefined) {
             // http://opensourcehacker.com/2011/12/01/calculate-aspect-ratio-conserving-resize-for-images-in-javascript/
             // Try resize so that shortest edge is `shortest_edge` (target)
-            const shortResizeFactor = shortest_edge === undefined
-                ? 1 // If `shortest_edge` is not set, don't upscale
-                : Math.max(shortest_edge / srcWidth, shortest_edge / srcHeight);
+            const shortResizeFactor =
+                shortest_edge === undefined
+                    ? 1 // If `shortest_edge` is not set, don't upscale
+                    : Math.max(shortest_edge / srcWidth, shortest_edge / srcHeight);
 
             const newWidth = srcWidth * shortResizeFactor;
             const newHeight = srcHeight * shortResizeFactor;
 
             // The new width and height might be greater than `longest_edge`, so
-            // we downscale again to ensure the largest dimension is `longest_edge` 
-            const longResizeFactor = longest_edge === undefined
-                ? 1 // If `longest_edge` is not set, don't downscale
-                : Math.min(longest_edge / newWidth, longest_edge / newHeight);
+            // we downscale again to ensure the largest dimension is `longest_edge`
+            const longResizeFactor =
+                longest_edge === undefined
+                    ? 1 // If `longest_edge` is not set, don't downscale
+                    : Math.min(longest_edge / newWidth, longest_edge / newHeight);
 
             // To avoid certain floating point precision issues, we round to 2 decimal places
             let finalWidth = Math.floor(Number((newWidth * longResizeFactor).toFixed(2)));
             let finalHeight = Math.floor(Number((newHeight * longResizeFactor).toFixed(2)));
 
             if (this.size_divisibility !== undefined) {
-                [finalWidth, finalHeight] = enforce_size_divisibility([finalWidth, finalHeight], this.size_divisibility)
+                [finalWidth, finalHeight] = enforce_size_divisibility(
+                    [finalWidth, finalHeight],
+                    this.size_divisibility,
+                );
             }
             return [finalWidth, finalHeight];
-
         } else if (size !== undefined && size.width !== undefined && size.height !== undefined) {
             // If `width` and `height` are set, resize to those dimensions
 
@@ -874,7 +841,6 @@ export class ImageProcessor extends Callable {
 
             // Custom for DPT models
             if (this.config.keep_aspect_ratio && this.config.ensure_multiple_of) {
-
                 // determine new height and width
                 let scale_height = newHeight / srcHeight;
                 let scale_width = newWidth / srcWidth;
@@ -893,7 +859,6 @@ export class ImageProcessor extends Callable {
             }
 
             return [newWidth, newHeight];
-
         } else if (this.size_divisibility !== undefined) {
             return enforce_size_divisibility([srcWidth, srcHeight], this.size_divisibility);
         } else if (this.min_pixels !== undefined && this.max_pixels !== undefined) {
@@ -902,7 +867,9 @@ export class ImageProcessor extends Callable {
             const factor = this.config.patch_size * this.config.merge_size;
             return smart_resize(srcHeight, srcWidth, factor, this.min_pixels, this.max_pixels);
         } else {
-            throw new Error(`Could not resize image due to unsupported \`this.size\` option in config: ${JSON.stringify(size)}`);
+            throw new Error(
+                `Could not resize image due to unsupported \`this.size\` option in config: ${JSON.stringify(size)}`,
+            );
         }
     }
 
@@ -933,13 +900,16 @@ export class ImageProcessor extends Callable {
      * @param {Object} overrides The overrides for the preprocessing options.
      * @returns {Promise} The preprocessed image.
      */
-    async preprocess(image, {
-        do_normalize = null,
-        do_pad = null,
-        do_convert_rgb = null,
-        do_convert_grayscale = null,
-        do_flip_channel_order = null,
-    } = {}) {
+    async preprocess(
+        image,
+        {
+            do_normalize = null,
+            do_pad = null,
+            do_convert_rgb = null,
+            do_convert_grayscale = null,
+            do_flip_channel_order = null,
+        } = {},
+    ) {
         if (this.do_crop_margin) {
             // NOTE: Specific to nougat processors. This is done before resizing,
             // and can be interpreted as a pre-preprocessing step.
@@ -970,7 +940,6 @@ export class ImageProcessor extends Callable {
         }
 
         if (this.do_center_crop) {
-
             let crop_width;
             let crop_height;
             if (Number.isInteger(this.crop_size)) {
@@ -988,7 +957,7 @@ export class ImageProcessor extends Callable {
         const reshaped_input_size = [image.height, image.width];
 
         // NOTE: All pixel-level manipulation (i.e., modifying `pixelData`)
-        // occurs with data in the hwc format (height, width, channels), 
+        // occurs with data in the hwc format (height, width, channels),
         // to emulate the behavior of the original Python code (w/ numpy).
         /** @type {Float32Array} */
         let pixelData = Float32Array.from(image.data);
@@ -1010,7 +979,9 @@ export class ImageProcessor extends Callable {
             }
 
             if (image_mean.length !== image.channels || image_std.length !== image.channels) {
-                throw new Error(`When set to arrays, the length of \`image_mean\` (${image_mean.length}) and \`image_std\` (${image_std.length}) must match the number of channels in the image (${image.channels}).`);
+                throw new Error(
+                    `When set to arrays, the length of \`image_mean\` (${image_mean.length}) and \`image_std\` (${image_std.length}) must match the number of channels in the image (${image.channels}).`,
+                );
             }
 
             for (let i = 0; i < pixelData.length; i += image.channels) {
@@ -1026,7 +997,10 @@ export class ImageProcessor extends Callable {
                 const padded = this.pad_image(pixelData, [image.height, image.width, image.channels], this.pad_size);
                 [pixelData, imgDims] = padded; // Update pixel data and image dimensions
             } else if (this.size_divisibility) {
-                const [paddedWidth, paddedHeight] = enforce_size_divisibility([imgDims[1], imgDims[0]], this.size_divisibility);
+                const [paddedWidth, paddedHeight] = enforce_size_divisibility(
+                    [imgDims[1], imgDims[0]],
+                    this.size_divisibility,
+                );
                 [pixelData, imgDims] = this.pad_image(pixelData, imgDims, { width: paddedWidth, height: paddedHeight });
             }
         }
@@ -1043,14 +1017,13 @@ export class ImageProcessor extends Callable {
             }
         }
 
-        const pixel_values = new Tensor('float32', pixelData, imgDims)
-            .permute(2, 0, 1); // convert to channel dimension format (hwc -> chw)
+        const pixel_values = new Tensor('float32', pixelData, imgDims).permute(2, 0, 1); // convert to channel dimension format (hwc -> chw)
 
         return {
             original_size: [srcHeight, srcWidth],
             reshaped_input_size: reshaped_input_size,
             pixel_values,
-        }
+        };
     }
 
     /**
@@ -1066,40 +1039,47 @@ export class ImageProcessor extends Callable {
             images = [images];
         }
         /** @type {PreprocessedImage[]} */
-        const imageData = await Promise.all(images.map(x => this.preprocess(x)));
+        const imageData = await Promise.all(images.map((x) => this.preprocess(x)));
 
         // Stack pixel values
-        const pixel_values = stack(imageData.map(x => x.pixel_values), 0);
+        const pixel_values = stack(
+            imageData.map((x) => x.pixel_values),
+            0,
+        );
 
         return {
             pixel_values,
 
             // Original sizes of images
-            original_sizes: imageData.map(x => x.original_size),
+            original_sizes: imageData.map((x) => x.original_size),
 
             // Reshaped sizes of images, before padding or cropping
-            reshaped_input_sizes: imageData.map(x => x.reshaped_input_size),
-        }
+            reshaped_input_sizes: imageData.map((x) => x.reshaped_input_size),
+        };
     }
 
-
     /**
      * Instantiate one of the processor classes of the library from a pretrained model.
-     * 
+     *
      * The processor class to instantiate is selected based on the `image_processor_type` (or `feature_extractor_type`; legacy)
      * property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible)
-     * 
+     *
      * @param {string} pretrained_model_name_or_path The name or path of the pretrained model. Can be either:
      * - A string, the *model id* of a pretrained processor hosted inside a model repo on huggingface.co.
      *   Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a
      *   user or organization name, like `dbmdz/bert-base-german-cased`.
      * - A path to a *directory* containing processor files, e.g., `./my_model_directory/`.
      * @param {import('../utils/hub.js').PretrainedOptions} options Additional options for loading the processor.
-     * 
+     *
      * @returns {Promise} A new instance of the Processor class.
      */
-    static async from_pretrained(pretrained_model_name_or_path, options={}) {
-        const preprocessorConfig = await getModelJSON(pretrained_model_name_or_path, IMAGE_PROCESSOR_NAME, true, options);
+    static async from_pretrained(pretrained_model_name_or_path, options = {}) {
+        const preprocessorConfig = await getModelJSON(
+            pretrained_model_name_or_path,
+            IMAGE_PROCESSOR_NAME,
+            true,
+            options,
+        );
         return new this(preprocessorConfig);
     }
 }
diff --git a/src/base/processing_utils.js b/src/base/processing_utils.js
index d0b04b083..ffa253531 100644
--- a/src/base/processing_utils.js
+++ b/src/base/processing_utils.js
@@ -1,7 +1,6 @@
-
 /**
  * @file Processors are used to prepare inputs (e.g., text, image or audio) for a model.
- * 
+ *
  * **Example:** Using a `WhisperProcessor` to prepare an audio input for a model.
  * ```javascript
  * import { AutoProcessor, read_audio } from '@huggingface/transformers';
@@ -16,13 +15,11 @@
  * //   size: 240000,
  * // }
  * ```
- * 
+ *
  * @module processors
  */
 import { PROCESSOR_NAME, CHAT_TEMPLATE_NAME } from '../utils/constants.js';
-import {
-    Callable,
-} from '../utils/generic.js';
+import { Callable } from '../utils/generic.js';
 import { getModelJSON, getModelText } from '../utils/hub.js';
 
 /**
@@ -31,23 +28,18 @@ import { getModelJSON, getModelText } from '../utils/hub.js';
  * @typedef {import('../tokenizers.js').PreTrainedTokenizer} PreTrainedTokenizer
  */
 
-
 /**
  * Represents a Processor that extracts features from an input.
  */
 export class Processor extends Callable {
-    static classes = [
-        'image_processor_class',
-        'tokenizer_class',
-        'feature_extractor_class',
-    ]
+    static classes = ['image_processor_class', 'tokenizer_class', 'feature_extractor_class'];
     static uses_processor_config = false;
     static uses_chat_template_file = false;
 
     /**
      * Creates a new Processor with the given components
-     * @param {Object} config 
-     * @param {Record} components 
+     * @param {Object} config
+     * @param {Record} components
      * @param {string} chat_template
      */
     constructor(config, components, chat_template) {
@@ -116,7 +108,6 @@ export class Processor extends Callable {
         return this.tokenizer.decode(...args);
     }
 
-
     /**
      * Calls the feature_extractor function with the given input.
      * @param {any} input The input to extract features from.
@@ -132,24 +123,22 @@ export class Processor extends Callable {
         throw new Error('No image processor, feature extractor, or tokenizer found.');
     }
 
-
     /**
      * Instantiate one of the processor classes of the library from a pretrained model.
-     * 
+     *
      * The processor class to instantiate is selected based on the `image_processor_type` (or `feature_extractor_type`; legacy)
      * property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible)
-     * 
+     *
      * @param {string} pretrained_model_name_or_path The name or path of the pretrained model. Can be either:
      * - A string, the *model id* of a pretrained processor hosted inside a model repo on huggingface.co.
      *   Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a
      *   user or organization name, like `dbmdz/bert-base-german-cased`.
      * - A path to a *directory* containing processor files, e.g., `./my_model_directory/`.
      * @param {PretrainedProcessorOptions} options Additional options for loading the processor.
-     * 
+     *
      * @returns {Promise} A new instance of the Processor class.
      */
-    static async from_pretrained(pretrained_model_name_or_path, options={}) {
-
+    static async from_pretrained(pretrained_model_name_or_path, options = {}) {
         const [config, components, chat_template] = await Promise.all([
             // TODO:
             this.uses_processor_config
@@ -161,7 +150,7 @@ export class Processor extends Callable {
                     .map(async (cls) => {
                         const component = await this[cls].from_pretrained(pretrained_model_name_or_path, options);
                         return [cls.replace(/_class$/, ''), component];
-                    })
+                    }),
             ).then(Object.fromEntries),
             this.uses_chat_template_file
                 ? getModelText(pretrained_model_name_or_path, CHAT_TEMPLATE_NAME, true, options)
diff --git a/src/configs.js b/src/configs.js
index e32594fe6..d4f21cc56 100644
--- a/src/configs.js
+++ b/src/configs.js
@@ -1,10 +1,9 @@
-
 /**
  * @file Helper module for using model configs. For more information, see the corresponding
  * [Python documentation](https://huggingface.co/docs/transformers/main/en/model_doc/auto#transformers.AutoConfig).
- * 
+ *
  * **Example:** Load an `AutoConfig`.
- * 
+ *
  * ```javascript
  * import { AutoConfig } from '@huggingface/transformers';
  * const config = await AutoConfig.from_pretrained('bert-base-uncased');
@@ -23,14 +22,12 @@
  * //   ...
  * // }
  * ```
- * 
+ *
  * @module configs
  */
 
 import { pick } from './utils/core.js';
-import {
-    getModelJSON,
-} from './utils/hub.js';
+import { getModelJSON } from './utils/hub.js';
 
 /**
  * @typedef {import('./utils/hub.js').PretrainedOptions} PretrainedOptions
@@ -55,8 +52,8 @@ async function loadConfig(pretrained_model_name_or_path, options) {
 }
 
 /**
- * 
- * @param {PretrainedConfig} config 
+ *
+ * @param {PretrainedConfig} config
  * @returns {Object} The normalized configuration.
  */
 function getNormalizedConfig(config) {
@@ -247,7 +244,6 @@ function getNormalizedConfig(config) {
                 result.hidden_size = decoderConfig.hidden_size;
             }
             return result;
-
     }
 
     // NOTE: If `num_attention_heads` is not set, it is assumed to be equal to `num_heads`
@@ -262,8 +258,8 @@ function getNormalizedConfig(config) {
 }
 
 /**
- * 
- * @param {PretrainedConfig} config 
+ *
+ * @param {PretrainedConfig} config
  * @returns {Record}
  */
 export function getCacheShapes(config, options) {
@@ -295,23 +291,22 @@ export function getCacheShapes(config, options) {
 }
 
 /** @type {typeof getKeyValueShapes} */
-function getKeyValueShapes(config, {
-    prefix = 'past_key_values',
-    batch_size = 1,
-} = {}) {
+function getKeyValueShapes(config, { prefix = 'past_key_values', batch_size = 1 } = {}) {
     /** @type {Record} */
     const decoderFeeds = {};
     const normalized_config = config.normalized_config;
 
-    if (normalized_config.is_encoder_decoder && (
-        'num_encoder_heads' in normalized_config && 'num_decoder_heads' in normalized_config
-    )) {
-        const encoder_dim_kv = normalized_config.encoder_dim_kv ?? (
-            normalized_config.encoder_hidden_size / normalized_config.num_encoder_heads
-        );
-        const decoder_dim_kv = normalized_config.decoder_dim_kv ?? (
-            normalized_config.decoder_hidden_size / normalized_config.num_decoder_heads
-        );
+    if (
+        normalized_config.is_encoder_decoder &&
+        'num_encoder_heads' in normalized_config &&
+        'num_decoder_heads' in normalized_config
+    ) {
+        const encoder_dim_kv =
+            normalized_config.encoder_dim_kv ??
+            normalized_config.encoder_hidden_size / normalized_config.num_encoder_heads;
+        const decoder_dim_kv =
+            normalized_config.decoder_dim_kv ??
+            normalized_config.decoder_hidden_size / normalized_config.num_decoder_heads;
 
         const encoder_dims = [batch_size, normalized_config.num_encoder_heads, 0, encoder_dim_kv];
         const decoder_dims = [batch_size, normalized_config.num_decoder_heads, 0, decoder_dim_kv];
@@ -321,23 +316,24 @@ function getKeyValueShapes(config, {
             decoderFeeds[`${prefix}.${i}.decoder.key`] = decoder_dims;
             decoderFeeds[`${prefix}.${i}.decoder.value`] = decoder_dims;
         }
-    } else { // Decoders
+    } else {
+        // Decoders
         const num_heads = normalized_config.num_heads;
         const num_layers = normalized_config.num_layers;
-        const dim_kv = normalized_config.dim_kv ?? (
-            normalized_config.hidden_size /
-            (normalized_config.num_attention_heads ?? num_heads)
-        );
+        const dim_kv =
+            normalized_config.dim_kv ??
+            normalized_config.hidden_size / (normalized_config.num_attention_heads ?? num_heads);
 
         if (normalized_config.model_type === 'falcon') {
             // NOTE: Custom implementation for Falcon
-            const dims = [batch_size * num_heads, 0, dim_kv]
+            const dims = [batch_size * num_heads, 0, dim_kv];
             for (let i = 0; i < num_layers; ++i) {
                 decoderFeeds[`${prefix}.${i}.key`] = dims;
                 decoderFeeds[`${prefix}.${i}.value`] = dims;
             }
-        } else if (normalized_config.multi_query) { // e.g., for `gpt_bigcode`
-            const dims = [batch_size * num_heads, 0, 2 * dim_kv]
+        } else if (normalized_config.multi_query) {
+            // e.g., for `gpt_bigcode`
+            const dims = [batch_size * num_heads, 0, 2 * dim_kv];
 
             for (let i = 0; i < num_layers; ++i) {
                 decoderFeeds[`${prefix}.${i}.key_value`] = dims;
@@ -345,21 +341,22 @@ function getKeyValueShapes(config, {
         } else if (normalized_config.model_type === 'bloom') {
             // NOTE: Custom implementation for Bloom
 
-            const keyDims = [batch_size * num_heads, dim_kv, 0] // [batch_size x num_heads,64,past_sequence_length]
-            const valueDims = [batch_size * num_heads, 0, dim_kv] // [batch_size x num_heads,past_sequence_length,64]
+            const keyDims = [batch_size * num_heads, dim_kv, 0]; // [batch_size x num_heads,64,past_sequence_length]
+            const valueDims = [batch_size * num_heads, 0, dim_kv]; // [batch_size x num_heads,past_sequence_length,64]
             for (let i = 0; i < num_layers; ++i) {
                 decoderFeeds[`${prefix}.${i}.key`] = keyDims;
                 decoderFeeds[`${prefix}.${i}.value`] = valueDims;
             }
         } else if (normalized_config.model_type === 'openelm') {
             for (let i = 0; i < num_layers; ++i) {
-                const dims = [batch_size, num_heads[i], 0, dim_kv]
+                const dims = [batch_size, num_heads[i], 0, dim_kv];
 
                 decoderFeeds[`${prefix}.${i}.key`] = dims;
                 decoderFeeds[`${prefix}.${i}.value`] = dims;
             }
-        } else { // Decoder-only
-            const dims = [batch_size, num_heads, 0, dim_kv]
+        } else {
+            // Decoder-only
+            const dims = [batch_size, num_heads, 0, dim_kv];
             for (let i = 0; i < num_layers; ++i) {
                 decoderFeeds[`${prefix}.${i}.key`] = dims;
                 decoderFeeds[`${prefix}.${i}.value`] = dims;
@@ -398,41 +395,40 @@ export class PretrainedConfig {
     }
 
     /**
-     * Loads a pre-trained config from the given `pretrained_model_name_or_path`. 
-     * 
+     * Loads a pre-trained config from the given `pretrained_model_name_or_path`.
+     *
      * @param {string} pretrained_model_name_or_path The path to the pre-trained config.
      * @param {PretrainedOptions} options Additional options for loading the config.
      * @throws {Error} Throws an error if the config.json is not found in the `pretrained_model_name_or_path`.
-     * 
+     *
      * @returns {Promise} A new instance of the `PretrainedConfig` class.
      */
-    static async from_pretrained(pretrained_model_name_or_path, {
-        progress_callback = null,
-        config = null,
-        cache_dir = null,
-        local_files_only = false,
-        revision = 'main',
-    } = {}) {
+    static async from_pretrained(
+        pretrained_model_name_or_path,
+        { progress_callback = null, config = null, cache_dir = null, local_files_only = false, revision = 'main' } = {},
+    ) {
         if (config && !(config instanceof PretrainedConfig)) {
             config = new PretrainedConfig(config);
         }
 
-        const data = config ?? await loadConfig(pretrained_model_name_or_path, {
-            progress_callback,
-            config,
-            cache_dir,
-            local_files_only,
-            revision,
-        })
+        const data =
+            config ??
+            (await loadConfig(pretrained_model_name_or_path, {
+                progress_callback,
+                config,
+                cache_dir,
+                local_files_only,
+                revision,
+            }));
         return new this(data);
     }
 }
 
 /**
  * Helper class which is used to instantiate pretrained configs with the `from_pretrained` function.
- * 
+ *
  * @example
- * const config = await AutoConfig.from_pretrained('Xenova/bert-base-uncased'); 
+ * const config = await AutoConfig.from_pretrained('Xenova/bert-base-uncased');
  */
 export class AutoConfig {
     /** @type {typeof PretrainedConfig.from_pretrained} */
diff --git a/src/env.js b/src/env.js
index 549384820..67439ed94 100644
--- a/src/env.js
+++ b/src/env.js
@@ -1,24 +1,24 @@
 /**
  * @file Module used to configure Transformers.js.
- * 
+ *
  * **Example:** Disable remote models.
  * ```javascript
  * import { env } from '@huggingface/transformers';
  * env.allowRemoteModels = false;
  * ```
- * 
+ *
  * **Example:** Set local model path.
  * ```javascript
  * import { env } from '@huggingface/transformers';
  * env.localModelPath = '/path/to/local/models/';
  * ```
- * 
+ *
  * **Example:** Set cache directory.
  * ```javascript
  * import { env } from '@huggingface/transformers';
  * env.cacheDir = '/path/to/cache/directory/';
  * ```
- * 
+ *
  * @module env
  */
 
@@ -26,14 +26,7 @@ import fs from 'node:fs';
 import path from 'node:path';
 import url from 'node:url';
 
-const VERSION = '3.7.0';
-
-// Check if various APIs are available (depends on environment)
-const IS_BROWSER_ENV = typeof window !== "undefined" && typeof window.document !== "undefined";
-const IS_WEBWORKER_ENV = typeof self !== "undefined" && (['DedicatedWorkerGlobalScope', 'ServiceWorkerGlobalScope', 'SharedWorkerGlobalScope'].includes(self.constructor?.name));
-const IS_WEB_CACHE_AVAILABLE = typeof self !== "undefined" && 'caches' in self;
-const IS_WEBGPU_AVAILABLE = typeof navigator !== 'undefined' && 'gpu' in navigator;
-const IS_WEBNN_AVAILABLE = typeof navigator !== 'undefined' && 'ml' in navigator;
+const VERSION = '4.0.0-alpha.0';
 
 const IS_PROCESS_AVAILABLE = typeof process !== 'undefined';
 const IS_NODE_ENV = IS_PROCESS_AVAILABLE && process?.release?.name === 'node';
@@ -44,6 +37,45 @@ const IS_PATH_AVAILABLE = !isEmpty(path);
 const IS_DENO_RUNTIME = typeof globalThis.Deno !== 'undefined';
 const IS_BUN_RUNTIME = typeof globalThis.Bun !== 'undefined';
 
+// Check if various APIs are available (depends on environment)
+const IS_BROWSER_ENV = typeof window !== 'undefined' && typeof window.document !== 'undefined';
+const IS_WEBWORKER_ENV =
+    typeof self !== 'undefined' &&
+    ['DedicatedWorkerGlobalScope', 'ServiceWorkerGlobalScope', 'SharedWorkerGlobalScope'].includes(
+        self.constructor?.name,
+    );
+const IS_WEB_CACHE_AVAILABLE = typeof self !== 'undefined' && 'caches' in self;
+const IS_WEBGPU_AVAILABLE = IS_NODE_ENV || (typeof navigator !== 'undefined' && 'gpu' in navigator);
+const IS_WEBNN_AVAILABLE = typeof navigator !== 'undefined' && 'ml' in navigator;
+
+/**
+ * Check if the current environment is Safari browser.
+ * Works in both browser and web worker contexts.
+ * @returns {boolean} Whether the current environment is Safari.
+ */
+const isSafari = () => {
+    // Check if we're in a browser environment
+    if (typeof navigator === 'undefined') {
+        return false;
+    }
+
+    const userAgent = navigator.userAgent;
+    const vendor = navigator.vendor || '';
+
+    // Safari has "Apple" in vendor string
+    const isAppleVendor = vendor.indexOf('Apple') > -1;
+
+    // Exclude Chrome on iOS (CriOS), Firefox on iOS (FxiOS),
+    // Edge on iOS (EdgiOS), and other browsers
+    const notOtherBrowser =
+        !userAgent.match(/CriOS|FxiOS|EdgiOS|OPiOS|mercury|brave/i) &&
+        !userAgent.includes('Chrome') &&
+        !userAgent.includes('Android');
+
+    return isAppleVendor && notOtherBrowser;
+};
+const IS_SAFARI = isSafari();
+
 /**
  * A read-only object containing information about the APIs available in the current environment.
  */
@@ -63,6 +95,9 @@ export const apis = Object.freeze({
     /** Whether the WebNN API is available */
     IS_WEBNN_AVAILABLE,
 
+    /** Whether we are running in a Safari browser */
+    IS_SAFARI,
+
     /** Whether the Node.js process API is available */
     IS_PROCESS_AVAILABLE,
 
@@ -86,22 +121,18 @@ if (RUNNING_LOCALLY) {
     const _import_meta_url = Object(import.meta).url;
 
     if (_import_meta_url) {
-        dirname__ = path.dirname(path.dirname(url.fileURLToPath(_import_meta_url))) // ESM
+        dirname__ = path.dirname(path.dirname(url.fileURLToPath(_import_meta_url))); // ESM
     } else if (typeof __dirname !== 'undefined') {
-        dirname__ = path.dirname(__dirname) // CommonJS
+        dirname__ = path.dirname(__dirname); // CommonJS
     }
 }
 
 // Only used for environments with access to file system
-const DEFAULT_CACHE_DIR = RUNNING_LOCALLY
-    ? path.join(dirname__, '/.cache/')
-    : null;
+const DEFAULT_CACHE_DIR = RUNNING_LOCALLY ? path.join(dirname__, '/.cache/') : null;
 
 // Set local model path, based on available APIs
 const DEFAULT_LOCAL_MODEL_PATH = '/models/';
-const localModelPath = RUNNING_LOCALLY
-    ? path.join(dirname__, DEFAULT_LOCAL_MODEL_PATH)
-    : DEFAULT_LOCAL_MODEL_PATH;
+const localModelPath = RUNNING_LOCALLY ? path.join(dirname__, DEFAULT_LOCAL_MODEL_PATH) : DEFAULT_LOCAL_MODEL_PATH;
 
 /**
  * Global variable given visible to users to control execution. This provides users a simple way to configure Transformers.js.
@@ -155,8 +186,7 @@ export const env = {
     useCustomCache: false,
     customCache: null,
     //////////////////////////////////////////////////////
-}
-
+};
 
 /**
  * @param {Object} obj
diff --git a/src/generation/configuration_utils.js b/src/generation/configuration_utils.js
index 57a531c75..da2789135 100644
--- a/src/generation/configuration_utils.js
+++ b/src/generation/configuration_utils.js
@@ -1,9 +1,8 @@
-
 /**
  * @module generation/configuration_utils
  */
 
-import { pick } from "../utils/core.js";
+import { pick } from '../utils/core.js';
 
 /**
  * Class that holds a configuration for a generation task.
@@ -378,11 +377,10 @@ export class GenerationConfig {
     generation_kwargs = {};
 
     /**
-     * 
-     * @param {GenerationConfig|import('../configs.js').PretrainedConfig} config 
+     *
+     * @param {GenerationConfig|import('../configs.js').PretrainedConfig} config
      */
     constructor(config) {
         Object.assign(this, pick(config, Object.getOwnPropertyNames(this)));
     }
 }
-
diff --git a/src/generation/logits_process.js b/src/generation/logits_process.js
index c20076a0a..3bdff2a2f 100644
--- a/src/generation/logits_process.js
+++ b/src/generation/logits_process.js
@@ -1,12 +1,11 @@
-
 /**
  * @module generation/logits_process
  */
 
-import { Callable } from "../utils/generic.js";
-import { Tensor } from "../utils/tensor.js";
+import { Callable } from '../utils/generic.js';
+import { Tensor } from '../utils/tensor.js';
 
-import { max, log_softmax } from "../utils/maths.js";
+import { max, log_softmax } from '../utils/maths.js';
 
 /**
  * Abstract base class for all logit processors that can be applied during generation.
@@ -21,11 +20,10 @@ export class LogitsProcessor extends Callable {
      * @throws {Error} Throws an error if `_call` is not implemented in the subclass.
      */
     _call(input_ids, logits) {
-        throw Error("`_call` should be implemented in a subclass")
+        throw Error('`_call` should be implemented in a subclass');
     }
 }
 
-
 /**
  * Abstract base class for all logit warpers that can be applied during generation with multinomial sampling.
  */
@@ -39,11 +37,10 @@ export class LogitsWarper extends Callable {
      * @throws {Error} Throws an error if `_call` is not implemented in the subclass.
      */
     _call(input_ids, logits) {
-        throw Error("`_call` should be implemented in a subclass")
+        throw Error('`_call` should be implemented in a subclass');
     }
 }
 
-
 /**
  * A class representing a list of logits processors. A logits processor is a function that modifies the logits
  * output of a language model. This class provides methods for adding new processors and applying all processors to a
@@ -103,7 +100,7 @@ export class LogitsProcessorList extends Callable {
 // export class ForceTokensLogitsProcessor extends LogitsProcessor {
 //     /**
 //      * Constructs a new instance of `ForceTokensLogitsProcessor`.
-//      * 
+//      *
 //      * @param {[number, number][]} forced_decoder_ids The ids of tokens that should be forced.
 //      */
 //     constructor(forced_decoder_ids) {
@@ -156,7 +153,7 @@ export class ForcedBOSTokenLogitsProcessor extends LogitsProcessor {
     _call(input_ids, logits) {
         for (let i = 0; i < input_ids.length; ++i) {
             if (input_ids[i].length === 1) {
-                const batch_logits_data = /** @type {Float32Array} */(logits[i].data);
+                const batch_logits_data = /** @type {Float32Array} */ (logits[i].data);
                 batch_logits_data.fill(-Infinity);
                 batch_logits_data[this.bos_token_id] = 0;
             }
@@ -182,14 +179,14 @@ export class ForcedEOSTokenLogitsProcessor extends LogitsProcessor {
 
     /**
      * Apply the processor to input_ids and logits.
-     * 
+     *
      * @param {bigint[][]} input_ids The input ids.
      * @param {Tensor} logits The logits tensor.
      */
     _call(input_ids, logits) {
         for (let i = 0; i < input_ids.length; ++i) {
             if (input_ids[i].length === this.max_length - 1) {
-                const batch_logits_data = /** @type {Float32Array} */(logits[i].data);
+                const batch_logits_data = /** @type {Float32Array} */ (logits[i].data);
                 batch_logits_data.fill(-Infinity);
                 for (const eos_token of this.eos_token_id) {
                     batch_logits_data[eos_token] = 0;
@@ -226,7 +223,7 @@ export class SuppressTokensAtBeginLogitsProcessor extends LogitsProcessor {
     _call(input_ids, logits) {
         for (let i = 0; i < input_ids.length; ++i) {
             if (input_ids[i].length === this.begin_index) {
-                const batch_logits_data = /** @type {Float32Array} */(logits[i].data);
+                const batch_logits_data = /** @type {Float32Array} */ (logits[i].data);
                 for (const token_id of this.begin_suppress_tokens) {
                     batch_logits_data[token_id] = -Infinity;
                 }
@@ -247,10 +244,9 @@ export class WhisperTimeStampLogitsProcessor extends LogitsProcessor {
      */
     constructor(generate_config, init_tokens) {
         super();
-        this.eos_token_id =
-            Array.isArray(generate_config.eos_token_id)
-                ? generate_config.eos_token_id[0]
-                : generate_config.eos_token_id;
+        this.eos_token_id = Array.isArray(generate_config.eos_token_id)
+            ? generate_config.eos_token_id[0]
+            : generate_config.eos_token_id;
 
         this.no_timestamps_token_id = generate_config.no_timestamps_token_id;
         this.timestamp_begin = this.no_timestamps_token_id + 1;
@@ -270,7 +266,7 @@ export class WhisperTimeStampLogitsProcessor extends LogitsProcessor {
      */
     _call(input_ids, logits) {
         for (let i = 0; i < input_ids.length; ++i) {
-            const batch_logits_data = /** @type {Float32Array} */(logits[i].data);
+            const batch_logits_data = /** @type {Float32Array} */ (logits[i].data);
 
             // suppress <|notimestamps|> which is handled by without_timestamps
             batch_logits_data[this.no_timestamps_token_id] = -Infinity;
@@ -287,9 +283,11 @@ export class WhisperTimeStampLogitsProcessor extends LogitsProcessor {
             const penultimate_was_timestamp = seq.length < 2 || seq[seq.length - 2] >= this.timestamp_begin;
 
             if (last_was_timestamp) {
-                if (penultimate_was_timestamp) { // has to be non-timestamp
+                if (penultimate_was_timestamp) {
+                    // has to be non-timestamp
                     batch_logits_data.subarray(this.timestamp_begin).fill(-Infinity);
-                } else { // cannot be normal text tokens
+                } else {
+                    // cannot be normal text tokens
                     batch_logits_data.subarray(0, this.eos_token_id).fill(-Infinity);
                 }
             }
@@ -302,7 +300,12 @@ export class WhisperTimeStampLogitsProcessor extends LogitsProcessor {
 
             // if sum of probability over timestamps is above any other token, sample timestamp
             const logprobs = log_softmax(batch_logits_data);
-            const timestamp_logprob = Math.log(logprobs.subarray(this.timestamp_begin).map(Math.exp).reduce((a, b) => a + b));
+            const timestamp_logprob = Math.log(
+                logprobs
+                    .subarray(this.timestamp_begin)
+                    .map(Math.exp)
+                    .reduce((a, b) => a + b),
+            );
             const max_text_token_logprob = max(logprobs.subarray(0, this.timestamp_begin))[0];
 
             if (timestamp_logprob > max_text_token_logprob) {
@@ -379,7 +382,6 @@ export class NoRepeatNGramLogitsProcessor extends LogitsProcessor {
         if (prevInputIds.length + 1 < this.no_repeat_ngram_size) {
             // return no banned tokens if we haven't generated no_repeat_ngram_size tokens yet
             return bannedTokens;
-
         } else {
             const generatedNgrams = this.getNgrams(prevInputIds);
             const bannedTokens = this.getGeneratedNgrams(generatedNgrams, prevInputIds);
@@ -395,7 +397,7 @@ export class NoRepeatNGramLogitsProcessor extends LogitsProcessor {
      */
     _call(input_ids, logits) {
         for (let i = 0; i < input_ids.length; ++i) {
-            const batch_logits_data = /** @type {Float32Array} */(logits[i].data);
+            const batch_logits_data = /** @type {Float32Array} */ (logits[i].data);
             const bannedTokens = this.calcBannedNgramTokens(input_ids[i]);
             for (const token of bannedTokens) {
                 batch_logits_data[token] = -Infinity;
@@ -409,7 +411,7 @@ export class NoRepeatNGramLogitsProcessor extends LogitsProcessor {
  * A logits processor that prevents the repetition of previous tokens through a penalty.
  * This penalty is applied at most once per token. Note that, for decoder-only models like most LLMs,
  * the considered tokens include the prompt.
- * 
+ *
  * In the original [paper](https://huggingface.co/papers/1909.05858), the authors suggest the use of a
  * penalty of around 1.2 to achieve a good balance between truthful generation and lack of repetition.
  * To penalize and reduce repetition, use `penalty` values above 1.0, where a higher value penalizes
@@ -436,7 +438,7 @@ export class RepetitionPenaltyLogitsProcessor extends LogitsProcessor {
      */
     _call(input_ids, logits) {
         for (let i = 0; i < input_ids.length; ++i) {
-            const batch_logits_data = /** @type {Float32Array} */(logits[i].data);
+            const batch_logits_data = /** @type {Float32Array} */ (logits[i].data);
             for (const input_id of new Set(input_ids[i])) {
                 const token = Number(input_id);
                 if (batch_logits_data[token] < 0) {
@@ -447,7 +449,7 @@ export class RepetitionPenaltyLogitsProcessor extends LogitsProcessor {
             }
         }
 
-        return logits
+        return logits;
     }
 }
 
@@ -475,7 +477,7 @@ export class MinLengthLogitsProcessor extends LogitsProcessor {
     _call(input_ids, logits) {
         for (let i = 0; i < input_ids.length; ++i) {
             if (input_ids[i].length < this.min_length) {
-                const batch_logits_data = /** @type {Float32Array} */(logits[i].data);
+                const batch_logits_data = /** @type {Float32Array} */ (logits[i].data);
 
                 for (const eos_token of this.eos_token_id) {
                     batch_logits_data[eos_token] = -Infinity;
@@ -483,7 +485,7 @@ export class MinLengthLogitsProcessor extends LogitsProcessor {
             }
         }
 
-        return logits
+        return logits;
     }
 }
 
@@ -514,14 +516,14 @@ export class MinNewTokensLengthLogitsProcessor extends LogitsProcessor {
         for (let i = 0; i < input_ids.length; ++i) {
             const new_tokens_length = input_ids[i].length - this.prompt_length_to_skip;
             if (new_tokens_length < this.min_new_tokens) {
-                const batch_logits_data = /** @type {Float32Array} */(logits[i].data);
+                const batch_logits_data = /** @type {Float32Array} */ (logits[i].data);
 
                 for (const eos_token of this.eos_token_id) {
                     batch_logits_data[eos_token] = -Infinity;
                 }
             }
         }
-        return logits
+        return logits;
     }
 }
 
@@ -545,7 +547,7 @@ export class NoBadWordsLogitsProcessor extends LogitsProcessor {
      */
     _call(input_ids, logits) {
         for (let i = 0; i < input_ids.length; ++i) {
-            const batch_logits_data = /** @type {Float32Array} */(logits[i].data);
+            const batch_logits_data = /** @type {Float32Array} */ (logits[i].data);
             const ids = input_ids[i];
             for (const bad_word_ids of this.bad_words_ids) {
                 // There aren't enough tokens to match the banned sequence
@@ -570,7 +572,7 @@ export class NoBadWordsLogitsProcessor extends LogitsProcessor {
                 }
             }
         }
-        return logits
+        return logits;
     }
 }
 
@@ -579,11 +581,10 @@ export class NoBadWordsLogitsProcessor extends LogitsProcessor {
  * where the first half correspond to the conditional logits (predicted from the input prompt) and the second half
  * correspond to the unconditional logits (predicted from an empty or 'null' prompt). The processor computes a
  * weighted average across the conditional and unconditional logits, parameterised by the `guidance_scale`.
- * 
+ *
  * See [the paper](https://huggingface.co/papers/2306.05284) for more information.
  */
 export class ClassifierFreeGuidanceLogitsProcessor extends LogitsProcessor {
-
     /**
      * Create a `ClassifierFreeGuidanceLogitsProcessor`.
      * @param {number} guidance_scale The guidance scale for classifier free guidance (CFG). CFG is enabled by setting `guidance_scale > 1`.
@@ -594,8 +595,8 @@ export class ClassifierFreeGuidanceLogitsProcessor extends LogitsProcessor {
         super();
         if (guidance_scale <= 1) {
             throw new Error(
-                `Require guidance scale >1 to use the classifier free guidance processor, got guidance scale ${guidance_scale}.`
-            )
+                `Require guidance scale >1 to use the classifier free guidance processor, got guidance scale ${guidance_scale}.`,
+            );
         }
         this.guidance_scale = guidance_scale;
     }
@@ -610,9 +611,9 @@ export class ClassifierFreeGuidanceLogitsProcessor extends LogitsProcessor {
         if (logits.dims[0] !== 2 * input_ids.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 ${logits.dims[0]} for the logits and ${input_ids.length} for the input ids.`
-            )
+                    `the conditional inputs, and the second half of batches corresponding to the unconditional inputs. Got ` +
+                    `batch size ${logits.dims[0]} for the logits and ${input_ids.length} for the input ids.`,
+            );
         }
 
         const unguided_bsz = input_ids.length;
@@ -644,11 +645,10 @@ export class TemperatureLogitsWarper extends LogitsWarper {
         super();
 
         if (typeof temperature !== 'number' || temperature <= 0) {
-            let errorMessage =
-                `\`temperature\` (=${temperature}) must be a strictly positive float, otherwise your next token scores will be invalid.`;
+            let errorMessage = `\`temperature\` (=${temperature}) must be a strictly positive float, otherwise your next token scores will be invalid.`;
 
             if (temperature === 0) {
-                errorMessage += " If you're looking for greedy decoding strategies, set `do_sample=false`."
+                errorMessage += " If you're looking for greedy decoding strategies, set `do_sample=false`.";
             }
         }
         this.temperature = temperature;
@@ -661,7 +661,7 @@ export class TemperatureLogitsWarper extends LogitsWarper {
      * @returns {Tensor} The processed logits.
      */
     _call(input_ids, logits) {
-        const batch_logits_data = /** @type {Float32Array} */(logits.data);
+        const batch_logits_data = /** @type {Float32Array} */ (logits.data);
         for (let i = 0; i < batch_logits_data.length; ++i) {
             batch_logits_data[i] /= this.temperature;
         }
@@ -682,21 +682,18 @@ export class TopPLogitsWarper extends LogitsWarper {
      * @param {number} [options.filter_value=-Infinity] All filtered values will be set to this float value.
      * @param {number} [options.min_tokens_to_keep=1] Minimum number of tokens that cannot be filtered.
      */
-    constructor(top_p, {
-        filter_value = -Infinity,
-        min_tokens_to_keep = 1,
-    } = {}) {
+    constructor(top_p, { filter_value = -Infinity, min_tokens_to_keep = 1 } = {}) {
         super();
         if (top_p < 0 || top_p > 1.0) {
-            throw new Error(`\`top_p\` must be a float > 0 and < 1, but is ${top_p}`)
+            throw new Error(`\`top_p\` must be a float > 0 and < 1, but is ${top_p}`);
         }
         if (!Number.isInteger(min_tokens_to_keep) || min_tokens_to_keep < 1) {
-            throw new Error(`\`min_tokens_to_keep\` must be a positive integer, but is ${min_tokens_to_keep}`)
+            throw new Error(`\`min_tokens_to_keep\` must be a positive integer, but is ${min_tokens_to_keep}`);
         }
 
-        this.top_p = top_p
-        this.filter_value = filter_value
-        this.min_tokens_to_keep = min_tokens_to_keep
+        this.top_p = top_p;
+        this.filter_value = filter_value;
+        this.min_tokens_to_keep = min_tokens_to_keep;
     }
 }
 
@@ -712,16 +709,13 @@ export class TopKLogitsWarper extends LogitsWarper {
      * @param {number} [options.filter_value=-Infinity] All filtered values will be set to this float value.
      * @param {number} [options.min_tokens_to_keep=1] Minimum number of tokens that cannot be filtered.
      */
-    constructor(top_k, {
-        filter_value = -Infinity,
-        min_tokens_to_keep = 1,
-    } = {}) {
+    constructor(top_k, { filter_value = -Infinity, min_tokens_to_keep = 1 } = {}) {
         super();
         if (!Number.isInteger(top_k) || top_k < 0) {
-            throw new Error(`\`top_k\` must be a positive integer, but is ${top_k}`)
+            throw new Error(`\`top_k\` must be a positive integer, but is ${top_k}`);
         }
 
-        this.top_k = Math.max(top_k, min_tokens_to_keep)
-        this.filter_value = filter_value
+        this.top_k = Math.max(top_k, min_tokens_to_keep);
+        this.filter_value = filter_value;
     }
-}
\ No newline at end of file
+}
diff --git a/src/generation/logits_sampler.js b/src/generation/logits_sampler.js
index 46b74e081..92e8bebb1 100644
--- a/src/generation/logits_sampler.js
+++ b/src/generation/logits_sampler.js
@@ -1,15 +1,11 @@
-
 /**
  * @module generation/logits_sampler
  */
 
-import { Callable } from "../utils/generic.js";
-import { Tensor, topk } from "../utils/tensor.js";
+import { Callable } from '../utils/generic.js';
+import { Tensor, topk } from '../utils/tensor.js';
 
-import {
-    max,
-    softmax,
-} from '../utils/maths.js';
+import { max, softmax } from '../utils/maths.js';
 import { GenerationConfig } from '../generation/configuration_utils.js';
 
 /**
@@ -43,7 +39,7 @@ export class LogitsSampler extends Callable {
      * @returns {Promise<[bigint, number][]>}
      */
     async sample(logits) {
-        throw Error("sample should be implemented in subclasses.")
+        throw Error('sample should be implemented in subclasses.');
     }
 
     /**
@@ -55,7 +51,7 @@ export class LogitsSampler extends Callable {
     getLogits(logits, index) {
         let vocabSize = logits.dims.at(-1);
 
-        let logs = /** @type {Float32Array} */(logits.data);
+        let logs = /** @type {Float32Array} */ (logits.data);
 
         if (index === -1) {
             logs = logs.slice(-vocabSize);
@@ -105,13 +101,13 @@ export class LogitsSampler extends Callable {
         // NOTE: beam search is implemented directly into the generation function
         if (generation_config.do_sample) {
             return new MultinomialSampler(generation_config);
-
         } else if (generation_config.num_beams > 1) {
             return new BeamSearchSampler(generation_config);
-
         } else {
             if (generation_config.num_return_sequences > 1) {
-                throw Error(`num_return_sequences has to be 1 when doing greedy search, but is ${generation_config.num_return_sequences}.`)
+                throw Error(
+                    `num_return_sequences has to be 1 when doing greedy search, but is ${generation_config.num_return_sequences}.`,
+                );
             }
             return new GreedySampler(generation_config);
         }
@@ -133,9 +129,7 @@ class GreedySampler extends LogitsSampler {
 
         // Note: score is meaningless in this context, since we are performing
         // greedy search (p = 1 => log(p) = 0)
-        return [
-            [BigInt(argmax), 0]
-        ];
+        return [[BigInt(argmax), 0]];
     }
 }
 
@@ -143,7 +137,6 @@ class GreedySampler extends LogitsSampler {
  * Class representing a MultinomialSampler.
  */
 class MultinomialSampler extends LogitsSampler {
-
     /**
      * Sample from the logits.
      * @param {Tensor} logits
@@ -159,7 +152,7 @@ class MultinomialSampler extends LogitsSampler {
         const [v, i] = await topk(logits, k);
 
         // Compute softmax over logits
-        const probabilities = softmax(/** @type {Float32Array} */(v.data));
+        const probabilities = softmax(/** @type {Float32Array} */ (v.data));
 
         return Array.from({ length: this.generation_config.num_beams }, () => {
             const sampledIndex = this.randomSelect(probabilities);
@@ -171,12 +164,10 @@ class MultinomialSampler extends LogitsSampler {
     }
 }
 
-
 /**
  * Class representing a BeamSearchSampler.
  */
 class BeamSearchSampler extends LogitsSampler {
-
     /**
      * Sample from the logits.
      * @param {Tensor} logits
@@ -192,7 +183,7 @@ class BeamSearchSampler extends LogitsSampler {
         const [v, i] = await topk(logits, k);
 
         // Compute softmax over logits
-        const probabilities = softmax(/** @type {Float32Array} */(v.data));
+        const probabilities = softmax(/** @type {Float32Array} */ (v.data));
 
         return Array.from({ length: this.generation_config.num_beams }, (_, x) => {
             return [
diff --git a/src/generation/parameters.js b/src/generation/parameters.js
index 1e2f2def3..d73ea642d 100644
--- a/src/generation/parameters.js
+++ b/src/generation/parameters.js
@@ -1,4 +1,3 @@
-
 /**
  * @module generation/parameters
  */
diff --git a/src/generation/stopping_criteria.js b/src/generation/stopping_criteria.js
index 08434f2b4..6451f6cd1 100644
--- a/src/generation/stopping_criteria.js
+++ b/src/generation/stopping_criteria.js
@@ -1,9 +1,8 @@
-
 /**
  * @module generation/stopping_criteria
  */
 
-import { Callable } from "../utils/generic.js";
+import { Callable } from '../utils/generic.js';
 
 // NOTE:
 // Stopping Criteria returns a list of `batch_size` booleans, indicating whether each sequence in the batch should be stopped.
@@ -13,7 +12,7 @@ import { Callable } from "../utils/generic.js";
  */
 export class StoppingCriteria extends Callable {
     /**
-     * 
+     *
      * @param {number[][]} input_ids (`number[][]` of shape `(batch_size, sequence_length)`):
      * Indices of input sequence tokens in the vocabulary.
      * @param {number[][]} scores scores (`number[][]` of shape `(batch_size, config.vocab_size)`):
@@ -22,7 +21,7 @@ export class StoppingCriteria extends Callable {
      * @returns {boolean[]} A list of booleans indicating whether each sequence should be stopped.
      */
     _call(input_ids, scores) {
-        throw Error("StoppingCriteria needs to be subclassed");
+        throw Error('StoppingCriteria needs to be subclassed');
     }
 }
 /**
@@ -80,9 +79,8 @@ export class StoppingCriteriaList extends Callable {
  * Keep in mind for decoder-only type of transformers, this will include the initial prompted tokens.
  */
 export class MaxLengthCriteria extends StoppingCriteria {
-
     /**
-     * 
+     *
      * @param {number} max_length The maximum length that the output sequence can have in number of tokens.
      * @param {number} [max_position_embeddings=null] The maximum model length, as defined by the model's `config.max_position_embeddings` attribute.
      */
@@ -93,7 +91,7 @@ export class MaxLengthCriteria extends StoppingCriteria {
     }
 
     _call(input_ids) {
-        return input_ids.map(ids => ids.length >= this.max_length);
+        return input_ids.map((ids) => ids.length >= this.max_length);
     }
 }
 
@@ -104,9 +102,8 @@ export class MaxLengthCriteria extends StoppingCriteria {
  * By default, it uses the `model.generation_config.eos_token_id`.
  */
 export class EosTokenCriteria extends StoppingCriteria {
-
     /**
-     * 
+     *
      * @param {number|number[]} eos_token_id The id of the *end-of-sequence* token.
      * Optionally, use a list to set multiple *end-of-sequence* tokens.
      */
@@ -119,16 +116,16 @@ export class EosTokenCriteria extends StoppingCriteria {
     }
 
     /**
-     * 
-     * @param {number[][]} input_ids 
-     * @param {number[][]} scores 
+     *
+     * @param {number[][]} input_ids
+     * @param {number[][]} scores
      * @returns {boolean[]}
      */
     _call(input_ids, scores) {
-        return input_ids.map(ids => {
+        return input_ids.map((ids) => {
             const last = ids.at(-1);
             // NOTE: We use == instead of === to allow for number/bigint comparison
-            return this.eos_token_id.some(eos_id => last == eos_id);
+            return this.eos_token_id.some((eos_id) => last == eos_id);
         });
     }
 }
diff --git a/src/generation/streamers.js b/src/generation/streamers.js
index 1fba94022..56ea14872 100644
--- a/src/generation/streamers.js
+++ b/src/generation/streamers.js
@@ -1,4 +1,3 @@
-
 /**
  * @module generation/streamers
  */
@@ -10,7 +9,7 @@ import { apis } from '../env.js';
 export class BaseStreamer {
     /**
      * Function that is called by `.generate()` to push new tokens
-     * @param {bigint[][]} value 
+     * @param {bigint[][]} value
      */
     put(value) {
         throw Error('Not implemented');
@@ -24,16 +23,14 @@ export class BaseStreamer {
     }
 }
 
-const stdout_write = apis.IS_PROCESS_AVAILABLE
-    ? x => process.stdout.write(x)
-    : x => console.log(x);
+const stdout_write = apis.IS_PROCESS_AVAILABLE ? (x) => process.stdout.write(x) : (x) => console.log(x);
 
 /**
  * Simple text streamer that prints the token(s) to stdout as soon as entire words are formed.
  */
 export class TextStreamer extends BaseStreamer {
     /**
-     * 
+     *
      * @param {import('../tokenizers.js').PreTrainedTokenizer} tokenizer
      * @param {Object} options
      * @param {boolean} [options.skip_prompt=false] Whether to skip the prompt tokens
@@ -42,14 +39,17 @@ export class TextStreamer extends BaseStreamer {
      * @param {function(bigint[]): void} [options.token_callback_function=null] Function to call when a new token is generated
      * @param {Object} [options.decode_kwargs={}] Additional keyword arguments to pass to the tokenizer's decode method
      */
-    constructor(tokenizer, {
-        skip_prompt = false,
-        callback_function = null,
-        token_callback_function = null,
-        skip_special_tokens = true,
-        decode_kwargs = {},
-        ...kwargs
-    } = {}) {
+    constructor(
+        tokenizer,
+        {
+            skip_prompt = false,
+            callback_function = null,
+            token_callback_function = null,
+            skip_special_tokens = true,
+            decode_kwargs = {},
+            ...kwargs
+        } = {},
+    ) {
         super();
         this.tokenizer = tokenizer;
         this.skip_prompt = skip_prompt;
@@ -65,7 +65,7 @@ export class TextStreamer extends BaseStreamer {
 
     /**
      * Receives tokens, decodes them, and prints them to stdout as soon as they form entire words.
-     * @param {bigint[][]} value 
+     * @param {bigint[][]} value
      */
     put(value) {
         if (value.length > 1) {
@@ -79,7 +79,7 @@ export class TextStreamer extends BaseStreamer {
         }
 
         const tokens = value[0];
-        this.token_callback_function?.(tokens)
+        this.token_callback_function?.(tokens);
 
         // Add the new token to the cache and decodes the entire thing.
         this.token_cache = mergeArrays(this.token_cache, tokens);
@@ -124,8 +124,8 @@ export class TextStreamer extends BaseStreamer {
 
     /**
      * Prints the new text to stdout. If the stream is ending, also prints a newline.
-     * @param {string} text 
-     * @param {boolean} stream_end 
+     * @param {string} text
+     * @param {boolean} stream_end
      */
     on_finalized_text(text, stream_end) {
         if (text.length > 0) {
@@ -159,17 +159,20 @@ export class WhisperTextStreamer extends TextStreamer {
      * @param {boolean} [options.skip_special_tokens=true] Whether to skip special tokens when decoding
      * @param {Object} [options.decode_kwargs={}] Additional keyword arguments to pass to the tokenizer's decode method
      */
-    constructor(tokenizer, {
-        skip_prompt = false,
-        callback_function = null,
-        token_callback_function = null,
-        on_chunk_start = null,
-        on_chunk_end = null,
-        on_finalize = null,
-        time_precision = 0.02,
-        skip_special_tokens = true,
-        decode_kwargs = {},
-    } = {}) {
+    constructor(
+        tokenizer,
+        {
+            skip_prompt = false,
+            callback_function = null,
+            token_callback_function = null,
+            on_chunk_start = null,
+            on_chunk_end = null,
+            on_finalize = null,
+            time_precision = 0.02,
+            skip_special_tokens = true,
+            decode_kwargs = {},
+        } = {},
+    ) {
         super(tokenizer, {
             skip_prompt,
             skip_special_tokens,
@@ -189,7 +192,7 @@ export class WhisperTextStreamer extends TextStreamer {
     }
 
     /**
-     * @param {bigint[][]} value 
+     * @param {bigint[][]} value
      */
     put(value) {
         if (value.length > 1) {
diff --git a/src/models.js b/src/models.js
index 98e6b796f..4ab4415c2 100644
--- a/src/models.js
+++ b/src/models.js
@@ -1,15 +1,14 @@
-
 /**
  * @file Definitions of all models available in Transformers.js.
- * 
+ *
  * **Example:** Load and run an `AutoModel`.
- * 
+ *
  * ```javascript
  * import { AutoModel, AutoTokenizer } from '@huggingface/transformers';
- * 
+ *
  * let tokenizer = await AutoTokenizer.from_pretrained('Xenova/bert-base-uncased');
  * let model = await AutoModel.from_pretrained('Xenova/bert-base-uncased');
- * 
+ *
  * let inputs = await tokenizer('I love transformers!');
  * let { logits } = await model(inputs);
  * // Tensor {
@@ -19,36 +18,28 @@
  * //     size: 183132,
  * // }
  * ```
- * 
+ *
  * We also provide other `AutoModel`s (listed below), which you can use in the same way as the Python library. For example:
- * 
+ *
  * **Example:** Load and run an `AutoModelForSeq2SeqLM`.
  * ```javascript
  * import { AutoModelForSeq2SeqLM, AutoTokenizer } from '@huggingface/transformers';
- * 
+ *
  * let tokenizer = await AutoTokenizer.from_pretrained('Xenova/t5-small');
  * let model = await AutoModelForSeq2SeqLM.from_pretrained('Xenova/t5-small');
- * 
+ *
  * let { input_ids } = await tokenizer('translate English to German: I love transformers!');
  * let outputs = await model.generate(input_ids);
  * let decoded = tokenizer.decode(outputs[0], { skip_special_tokens: true });
  * // 'Ich liebe Transformatoren!'
  * ```
- * 
+ *
  * @module models
  */
 
-import {
-    AutoConfig,
-    getCacheShapes,
-} from './configs.js';
+import { AutoConfig, getCacheShapes } from './configs.js';
 
-import {
-    deviceToExecutionProviders,
-    createInferenceSession,
-    isONNXTensor,
-    isONNXProxy,
-} from './backends/onnx.js';
+import { deviceToExecutionProviders, createInferenceSession, isONNXTensor, isONNXProxy } from './backends/onnx.js';
 import {
     DATA_TYPES,
     DEFAULT_DEVICE_DTYPE_MAPPING,
@@ -56,24 +47,13 @@ import {
     isWebGpuFp16Supported,
 } from './utils/dtypes.js';
 
-import {
-    Callable,
-} from './utils/generic.js';
+import { Callable } from './utils/generic.js';
 
-import {
-    mergeArrays,
-    pick,
-} from './utils/core.js';
+import { mergeArrays, pick } from './utils/core.js';
 
-import {
-    getModelFile,
-    getModelJSON,
-    MAX_EXTERNAL_DATA_CHUNKS,
-} from './utils/hub.js';
+import { getModelFile, getModelJSON, MAX_EXTERNAL_DATA_CHUNKS } from './utils/hub.js';
 
-import {
-    GITHUB_ISSUE_URL,
-} from './utils/constants.js';
+import { GITHUB_ISSUE_URL } from './utils/constants.js';
 
 import {
     LogitsProcessorList,
@@ -86,16 +66,13 @@ import {
     NoBadWordsLogitsProcessor,
     MinLengthLogitsProcessor,
     MinNewTokensLengthLogitsProcessor,
-
     TemperatureLogitsWarper,
     TopKLogitsWarper,
     TopPLogitsWarper,
     ClassifierFreeGuidanceLogitsProcessor,
 } from './generation/logits_process.js';
 
-import {
-    GenerationConfig,
-} from './generation/configuration_utils.js';
+import { GenerationConfig } from './generation/configuration_utils.js';
 
 import {
     cat,
@@ -137,10 +114,9 @@ const MODEL_TYPES = {
     AudioTextToText: 10,
     AutoEncoder: 11,
     ImageAudioTextToText: 12,
-}
+};
 //////////////////////////////////////////////////
 
-
 //////////////////////////////////////////////////
 // Helper functions
 
@@ -149,16 +125,16 @@ const MODEL_TYPE_MAPPING = new Map();
 const MODEL_NAME_TO_CLASS_MAPPING = new Map();
 const MODEL_CLASS_TO_NAME_MAPPING = new Map();
 
-
 /**
  * Constructs an InferenceSession using a model file located at the specified path.
  * @param {string} pretrained_model_name_or_path The path to the directory containing the model file.
  * @param {string} fileName The name of the model file.
  * @param {import('./utils/hub.js').PretrainedModelOptions} options Additional options for loading the model.
+ * @param {boolean} [is_decoder=false] Whether the model is a decoder model.
  * @returns {Promise<{buffer_or_path: Uint8Array|string, session_options: Object, session_config: Object}>} A Promise that resolves to the data needed to create an InferenceSession object.
  * @private
  */
-async function getSession(pretrained_model_name_or_path, fileName, options) {
+async function getSession(pretrained_model_name_or_path, fileName, options, is_decoder = false) {
     let custom_config = options.config?.['transformers.js_config'] ?? {};
 
     let device = options.device ?? custom_config.device;
@@ -172,7 +148,7 @@ async function getSession(pretrained_model_name_or_path, fileName, options) {
     }
 
     // If the device is not specified, we use the default (supported) execution providers.
-    const selectedDevice = /** @type {import("./utils/devices.js").DeviceType} */(
+    const selectedDevice = /** @type {import("./utils/devices.js").DeviceType} */ (
         device ?? (apis.IS_NODE_ENV ? 'cpu' : 'wasm')
     );
 
@@ -195,7 +171,9 @@ async function getSession(pretrained_model_name_or_path, fileName, options) {
             dtype = dtype[fileName];
         } else {
             dtype = DEFAULT_DEVICE_DTYPE_MAPPING[selectedDevice] ?? DATA_TYPES.fp32;
-            console.warn(`dtype not specified for "${fileName}". Using the default dtype (${dtype}) for this device (${selectedDevice}).`);
+            console.warn(
+                `dtype not specified for "${fileName}". Using the default dtype (${dtype}) for this device (${selectedDevice}).`,
+            );
         }
     }
 
@@ -215,20 +193,26 @@ async function getSession(pretrained_model_name_or_path, fileName, options) {
         }
     }
 
-    const selectedDtype = /** @type {import("./utils/dtypes.js").DataType} */(dtype);
+    const selectedDtype = /** @type {import("./utils/dtypes.js").DataType} */ (dtype);
 
     if (!DEFAULT_DTYPE_SUFFIX_MAPPING.hasOwnProperty(selectedDtype)) {
         throw new Error(`Invalid dtype: ${selectedDtype}. Should be one of: ${Object.keys(DATA_TYPES).join(', ')}`);
-    } else if (selectedDtype === DATA_TYPES.fp16 && selectedDevice === 'webgpu' && !(await isWebGpuFp16Supported())) {
+    } else if (
+        selectedDevice === 'webgpu' &&
+        // NOTE: Currently, we assume that the Native WebGPU EP always supports fp16. In future, we will add a check for this.
+        !apis.IS_NODE_ENV &&
+        selectedDtype === DATA_TYPES.fp16 &&
+        !(await isWebGpuFp16Supported())
+    ) {
         throw new Error(`The device (${selectedDevice}) does not support fp16.`);
     }
 
     // Only valid for models with a decoder
     const kv_cache_dtype_config = custom_config.kv_cache_dtype;
     const kv_cache_dtype = kv_cache_dtype_config
-        ? (typeof kv_cache_dtype_config === 'string'
+        ? typeof kv_cache_dtype_config === 'string'
             ? kv_cache_dtype_config
-            : kv_cache_dtype_config[selectedDtype] ?? 'float32')
+            : (kv_cache_dtype_config[selectedDtype] ?? 'float32')
         : undefined;
 
     if (kv_cache_dtype && !['float32', 'float16'].includes(kv_cache_dtype)) {
@@ -239,7 +223,7 @@ async function getSession(pretrained_model_name_or_path, fileName, options) {
         dtype: selectedDtype,
         kv_cache_dtype,
         device: selectedDevice,
-    }
+    };
 
     // Construct the model file name
     const suffix = DEFAULT_DTYPE_SUFFIX_MAPPING[selectedDtype];
@@ -258,7 +242,7 @@ async function getSession(pretrained_model_name_or_path, fileName, options) {
     } else if (selectedDevice.startsWith('webnn') && !session_options.freeDimensionOverrides) {
         console.warn(
             `WebNN does not currently support dynamic shapes and requires 'free_dimension_overrides' to be set in config.json, preferably as a field within config["transformers.js_config"]["device_config"]["${selectedDevice}"]. ` +
-            `When 'free_dimension_overrides' is not set, you may experience significant performance degradation.`
+                `When 'free_dimension_overrides' is not set, you may experience significant performance degradation.`,
         );
     }
 
@@ -285,22 +269,31 @@ async function getSession(pretrained_model_name_or_path, fileName, options) {
 
         const num_chunks = +external_data_format; // (false=0, true=1, number remains the same)
         if (num_chunks > MAX_EXTERNAL_DATA_CHUNKS) {
-            throw new Error(`The number of external data chunks (${num_chunks}) exceeds the maximum allowed value (${MAX_EXTERNAL_DATA_CHUNKS}).`);
+            throw new Error(
+                `The number of external data chunks (${num_chunks}) exceeds the maximum allowed value (${MAX_EXTERNAL_DATA_CHUNKS}).`,
+            );
         }
         for (let i = 0; i < num_chunks; ++i) {
             const path = `${baseName}_data${i === 0 ? '' : '_' + i}`;
             const fullPath = `${options.subfolder ?? ''}/${path}`;
-            externalDataPromises.push(new Promise(async (resolve, reject) => {
-                const data = await getModelFile(pretrained_model_name_or_path, fullPath, true, options, return_path);
-                resolve(data instanceof Uint8Array ? { path, data } : path);
-            }));
+            externalDataPromises.push(
+                new Promise(async (resolve, reject) => {
+                    const data = await getModelFile(
+                        pretrained_model_name_or_path,
+                        fullPath,
+                        true,
+                        options,
+                        return_path,
+                    );
+                    resolve(data instanceof Uint8Array ? { path, data } : path);
+                }),
+            );
         }
-
     } else if (session_options.externalData !== undefined) {
         externalDataPromises = session_options.externalData.map(async (ext) => {
             // if the external data is a string, fetch the file and replace the string with its content
             // @ts-expect-error TS2339
-            if (typeof ext.data === "string") {
+            if (typeof ext.data === 'string') {
                 // @ts-expect-error TS2339
                 const ext_buffer = await getModelFile(pretrained_model_name_or_path, ext.data, true, options);
                 // @ts-expect-error TS2698
@@ -317,7 +310,7 @@ async function getSession(pretrained_model_name_or_path, fileName, options) {
         }
     }
 
-    if (selectedDevice === 'webgpu') {
+    if (is_decoder && selectedDevice === 'webgpu') {
         const shapes = getCacheShapes(options.config, {
             prefix: 'present',
         });
@@ -339,21 +332,29 @@ async function getSession(pretrained_model_name_or_path, fileName, options) {
 
 /**
  * Helper function to create multiple InferenceSession objects.
- * 
+ *
  * @param {string} pretrained_model_name_or_path The path to the directory containing the model file.
  * @param {Record} names The names of the model files to load.
  * @param {import('./utils/hub.js').PretrainedModelOptions} options Additional options for loading the model.
+ * @param {string} [decoder_name] The name of the decoder model, if any.
  * @returns {Promise>} A Promise that resolves to a dictionary of InferenceSession objects.
  * @private
  */
-async function constructSessions(pretrained_model_name_or_path, names, options) {
-    return Object.fromEntries(await Promise.all(
-        Object.keys(names).map(async (name) => {
-            const { buffer_or_path, session_options, session_config } = await getSession(pretrained_model_name_or_path, names[name], options);
-            const session = await createInferenceSession(buffer_or_path, session_options, session_config);
-            return [name, session];
-        })
-    ));
+async function constructSessions(pretrained_model_name_or_path, names, options, decoder_name = undefined) {
+    return Object.fromEntries(
+        await Promise.all(
+            Object.keys(names).map(async (name) => {
+                const { buffer_or_path, session_options, session_config } = await getSession(
+                    pretrained_model_name_or_path,
+                    names[name],
+                    options,
+                    name === decoder_name,
+                );
+                const session = await createInferenceSession(buffer_or_path, session_options, session_config);
+                return [name, session];
+            }),
+        ),
+    );
 }
 
 /**
@@ -365,12 +366,14 @@ async function constructSessions(pretrained_model_name_or_path, names, options)
  * @private
  */
 async function getOptionalConfigs(pretrained_model_name_or_path, names, options) {
-    return Object.fromEntries(await Promise.all(
-        Object.keys(names).map(async (name) => {
-            const config = await getModelJSON(pretrained_model_name_or_path, names[name], false, options);
-            return [name, config];
-        })
-    ));
+    return Object.fromEntries(
+        await Promise.all(
+            Object.keys(names).map(async (name) => {
+                const config = await getModelJSON(pretrained_model_name_or_path, names[name], false, options);
+                return [name, config];
+            }),
+        ),
+    );
 }
 
 /**
@@ -404,7 +407,8 @@ function validateInputs(session, inputs) {
     }
     if (missingInputs.length > 0) {
         throw new Error(
-            `An error occurred during model execution: "Missing the following inputs: ${missingInputs.join(', ')}.`);
+            `An error occurred during model execution: "Missing the following inputs: ${missingInputs.join(', ')}.`,
+        );
     }
 
     const numInputsProvided = Object.keys(inputs).length;
@@ -412,8 +416,10 @@ function validateInputs(session, inputs) {
     if (numInputsProvided > numInputsNeeded) {
         // No missing inputs, but too many inputs were provided.
         // Warn the user and ignore the extra inputs.
-        let ignored = Object.keys(inputs).filter(inputName => !session.inputNames.includes(inputName));
-        console.warn(`WARNING: Too many inputs were provided (${numInputsProvided} > ${numInputsNeeded}). The following inputs will be ignored: "${ignored.join(', ')}".`);
+        let ignored = Object.keys(inputs).filter((inputName) => !session.inputNames.includes(inputName));
+        console.warn(
+            `WARNING: Too many inputs were provided (${numInputsProvided} > ${numInputsNeeded}). The following inputs will be ignored: "${ignored.join(', ')}".`,
+        );
     }
 
     return checkedInputs;
@@ -428,7 +434,7 @@ let webInferenceChain = Promise.resolve();
  * NOTE: `inputs` must contain at least the input names of the model.
  *  - If additional inputs are passed, they will be ignored.
  *  - If inputs are missing, an error will be thrown.
- * 
+ *
  * @param {Object} session The InferenceSession object to run.
  * @param {Object} inputs An object that maps input names to input tensors.
  * @returns {Promise} A Promise that resolves to an object that maps output names to output tensors.
@@ -440,27 +446,28 @@ async function sessionRun(session, inputs) {
         // pass the original ort tensor
         const ortFeed = Object.fromEntries(Object.entries(checkedInputs).map(([k, v]) => [k, v.ort_tensor]));
         const run = () => session.run(ortFeed);
-        const output = await ((apis.IS_BROWSER_ENV || apis.IS_WEBWORKER_ENV)
+        const output = await (apis.IS_BROWSER_ENV || apis.IS_WEBWORKER_ENV
             ? (webInferenceChain = webInferenceChain.then(run))
             : run());
         return replaceTensors(output);
     } catch (e) {
         // Error messages can be long (nested) and uninformative. For this reason,
         // we apply minor formatting to show the most important information
-        const formatted = Object.fromEntries(Object.entries(checkedInputs)
-            .map(([k, tensor]) => {
+        const formatted = Object.fromEntries(
+            Object.entries(checkedInputs).map(([k, tensor]) => {
                 // Extract these properties from the underlying ORT tensor
                 const unpacked = {
                     type: tensor.type,
                     dims: tensor.dims,
                     location: tensor.location,
-                }
-                if (unpacked.location !== "gpu-buffer") {
+                };
+                if (unpacked.location !== 'gpu-buffer') {
                     // Only return the data if it's not a GPU buffer
                     unpacked.data = tensor.data;
                 }
                 return [k, unpacked];
-            }));
+            }),
+        );
 
         // This usually occurs when the inputs are of the wrong type.
         console.error(`An error occurred during model execution: "${e}".`);
@@ -486,7 +493,6 @@ function replaceTensors(obj) {
     return obj;
 }
 
-
 /**
  * Converts an array or Tensor of integers to an int64 Tensor.
  * @param {any[]|Tensor} items The input integers to be converted.
@@ -500,25 +506,24 @@ function toI64Tensor(items) {
     }
     // items is an array
     if (items.length === 0) {
-        throw Error("items must be non-empty");
+        throw Error('items must be non-empty');
     }
 
     if (Array.isArray(items[0])) {
         // batched
-        if (items.some(x => x.length !== items[0].length)) {
-            throw Error("Unable to create tensor, you should probably activate truncation and/or padding with 'padding=True' and/or 'truncation=True' to have batched tensors with the same length.")
+        if (items.some((x) => x.length !== items[0].length)) {
+            throw Error(
+                "Unable to create tensor, you should probably activate truncation and/or padding with 'padding=True' and/or 'truncation=True' to have batched tensors with the same length.",
+            );
         }
 
-        return new Tensor('int64',
-            BigInt64Array.from(items.flat().map(x => BigInt(x))),
-            [items.length, items[0].length]
-        );
+        return new Tensor('int64', BigInt64Array.from(items.flat().map((x) => BigInt(x))), [
+            items.length,
+            items[0].length,
+        ]);
     } else {
         //flat
-        return new Tensor('int64',
-            BigInt64Array.from(items.map(x => BigInt(x))),
-            [1, items.length]
-        );
+        return new Tensor('int64', BigInt64Array.from(items.map((x) => BigInt(x))), [1, items.length]);
     }
 }
 
@@ -553,7 +558,7 @@ async function seq2seqForward(self, model_inputs) {
     other_decoder_inputs.encoder_hidden_states = encoder_outputs;
 
     if (self.sessions['decoder_model_merged'].inputNames.includes('encoder_attention_mask')) {
-        other_decoder_inputs.encoder_attention_mask = model_inputs.attention_mask
+        other_decoder_inputs.encoder_attention_mask = model_inputs.attention_mask;
     }
 
     const decoderResults = await decoderForward(self, other_decoder_inputs, true);
@@ -613,17 +618,18 @@ async function autoEncoderForward(self, model_inputs) {
  * @private
  */
 async function decoderForward(self, model_inputs, is_encoder_decoder = false) {
-
-    const session = self.sessions[
-        is_encoder_decoder ? 'decoder_model_merged' : 'model'
-    ]
+    const session = self.sessions[is_encoder_decoder ? 'decoder_model_merged' : 'model'];
 
     const { past_key_values, ...new_model_inputs } = model_inputs;
 
     if (session.inputNames.includes('use_cache_branch')) {
         new_model_inputs.use_cache_branch = boolTensor(!!past_key_values);
     }
-    if (session.inputNames.includes('position_ids') && new_model_inputs.attention_mask && !new_model_inputs.position_ids) {
+    if (
+        session.inputNames.includes('position_ids') &&
+        new_model_inputs.attention_mask &&
+        !new_model_inputs.position_ids
+    ) {
         // NOTE: Handle a special case for paligemma/gemma3 models, where positions are 1-indexed
         const start_index = ['paligemma', 'gemma3_text', 'gemma3'].includes(self.config.model_type) ? 1 : 0;
         new_model_inputs.position_ids = createPositionIds(new_model_inputs, past_key_values, start_index);
@@ -637,8 +643,6 @@ async function decoderForward(self, model_inputs, is_encoder_decoder = false) {
     return await sessionRun(session, fixed);
 }
 
-
-
 function default_merge_input_ids_with_features({
     modality_token_id,
     inputs_embeds,
@@ -646,11 +650,11 @@ function default_merge_input_ids_with_features({
     input_ids,
     attention_mask,
 }) {
-    const token_positions = input_ids.tolist().map(ids =>
+    const token_positions = input_ids.tolist().map((ids) =>
         ids.reduce((acc, x, idx) => {
             if (x == modality_token_id) acc.push(idx);
             return acc;
-        }, [])
+        }, []),
     );
     const n_tokens = token_positions.reduce((acc, x) => acc + x.length, 0);
     const n_features = modality_features.dims[0];
@@ -664,13 +668,12 @@ function default_merge_input_ids_with_features({
         const tokens = token_positions[i];
         const embeds = inputs_embeds[i];
         for (let j = 0; j < tokens.length; ++j) {
-            embeds[tokens[j]].data.set(modality_features[img++].data)
+            embeds[tokens[j]].data.set(modality_features[img++].data);
         }
     }
-    return { inputs_embeds, attention_mask }
+    return { inputs_embeds, attention_mask };
 }
 
-
 function default_merge_input_ids_with_image_features({
     image_token_id,
     inputs_embeds,
@@ -684,7 +687,7 @@ function default_merge_input_ids_with_image_features({
         modality_features: image_features,
         input_ids,
         attention_mask,
-    })
+    });
 }
 
 function default_merge_input_ids_with_audio_features({
@@ -700,7 +703,7 @@ function default_merge_input_ids_with_audio_features({
         modality_features: audio_features,
         input_ids,
         attention_mask,
-    })
+    });
 }
 
 /**
@@ -721,29 +724,32 @@ function default_merge_input_ids_with_audio_features({
  * @returns {Promise} The model's output tensor
  * @private
  */
-async function genericTextToTextForward(self, {
-    // Generic parameters:
-    encode_function,
-    merge_function,
-    modality_input_name,
-    modality_output_name,
+async function genericTextToTextForward(
+    self,
+    {
+        // Generic parameters:
+        encode_function,
+        merge_function,
+        modality_input_name,
+        modality_output_name,
 
-    // Produced by the tokenizer/processor:
-    input_ids = null,
-    attention_mask = null,
+        // Produced by the tokenizer/processor:
+        input_ids = null,
+        attention_mask = null,
 
-    // Used during generation:
-    position_ids = null,
-    inputs_embeds = null,
-    past_key_values = null,
+        // Used during generation:
+        position_ids = null,
+        inputs_embeds = null,
+        past_key_values = null,
 
-    // Generic generation parameters
-    generation_config = null,
-    logits_processor = null,
+        // Generic generation parameters
+        generation_config = null,
+        logits_processor = null,
 
-    // Additional parameters
-    ...kwargs
-}) {
+        // Additional parameters
+        ...kwargs
+    },
+) {
     const modality_values = kwargs[modality_input_name];
     if (!inputs_embeds) {
         // 1. Extract the text embeddings.
@@ -755,7 +761,7 @@ async function genericTextToTextForward(self, {
                 // Pass the modality values under its expected key.
                 // The caller knows whether this is audio or image.
                 [modality_input_name]: modality_values,
-                ...kwargs
+                ...kwargs,
             });
             ({ inputs_embeds, attention_mask } = merge_function({
                 [modality_output_name]: modality_features,
@@ -763,38 +769,43 @@ async function genericTextToTextForward(self, {
                 input_ids,
                 attention_mask,
             }));
-
         } else if (past_key_values && modality_values && input_ids.dims[1] === 1) {
             // This branch handles the cache case.
             const target_length = input_ids.dims[1]; // always 1
             const past_length = Object.values(past_key_values)[0].dims.at(-2);
 
-            attention_mask = cat([
-                ones([input_ids.dims[0], past_length]),
-                attention_mask.slice(null, [attention_mask.dims[1] - target_length, attention_mask.dims[1]]),
-            ], 1);
+            attention_mask = cat(
+                [
+                    ones([input_ids.dims[0], past_length]),
+                    attention_mask.slice(null, [attention_mask.dims[1] - target_length, attention_mask.dims[1]]),
+                ],
+                1,
+            );
         }
     }
 
     if (!position_ids) {
-
         if (self.config.model_type === 'qwen2_vl') {
             // Special case for qwen2_vl models
             // @ts-ignore
             const { image_grid_thw, video_grid_thw } = kwargs;
-            [position_ids] = self.get_rope_index(input_ids, image_grid_thw, video_grid_thw, attention_mask)
+            [position_ids] = self.get_rope_index(input_ids, image_grid_thw, video_grid_thw, attention_mask);
         }
     }
 
     // 3. Call the decoder forward using the updated inputs.
-    const outputs = await decoderForward(self, {
-        inputs_embeds,
-        past_key_values,
-        attention_mask,
-        position_ids,
-        generation_config,
-        logits_processor,
-    }, true);
+    const outputs = await decoderForward(
+        self,
+        {
+            inputs_embeds,
+            past_key_values,
+            attention_mask,
+            position_ids,
+            generation_config,
+            logits_processor,
+        },
+        true,
+    );
     return outputs;
 }
 
@@ -853,20 +864,20 @@ function cumsum_masked_fill(attention_mask, start_index = 0) {
             const index = start + j;
             if (attn_mask_data[index] === 0n) {
                 data[index] = BigInt(1);
-            } else { // === 1n
+            } else {
+                // === 1n
                 data[index] = sum;
                 sum += attn_mask_data[index];
             }
         }
     }
     return { data, dims: attention_mask.dims };
-
 }
 
 /**
  * If the model supports providing position_ids, we create position_ids on the fly for batch generation,
  * by computing the cumulative sum of the attention mask along the sequence length dimension.
- * 
+ *
  * Equivalent to:
  * ```python
  * position_ids = attention_mask.long().cumsum(-1) - 1
@@ -888,9 +899,7 @@ function createPositionIds(model_inputs, past_key_values = null, start_index = 0
 }
 
 function decoder_prepare_inputs_for_generation(self, input_ids, model_inputs, generation_config) {
-    const past_length = model_inputs.past_key_values
-        ? Object.values(model_inputs.past_key_values)[0].dims.at(-2)
-        : 0;
+    const past_length = model_inputs.past_key_values ? Object.values(model_inputs.past_key_values)[0].dims.at(-2) : 0;
 
     if (!model_inputs.attention_mask) {
         // If the attention mask is not provided, we attempt to infer based on provided inputs
@@ -902,7 +911,7 @@ function decoder_prepare_inputs_for_generation(self, input_ids, model_inputs, ge
             }
         }
         if (!dims) {
-            throw new Error("attention_mask is not provided, and unable to infer its shape from model inputs.");
+            throw new Error('attention_mask is not provided, and unable to infer its shape from model inputs.');
         }
         model_inputs.attention_mask = ones([dims[0], past_length + dims[1]]);
     }
@@ -928,7 +937,6 @@ function decoder_prepare_inputs_for_generation(self, input_ids, model_inputs, ge
         }
         // 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
         else {
-
         }
     }
 
@@ -937,7 +945,7 @@ function decoder_prepare_inputs_for_generation(self, input_ids, model_inputs, ge
 
 function encoder_decoder_prepare_inputs_for_generation(self, input_ids, model_inputs, generation_config) {
     if (model_inputs.past_key_values) {
-        input_ids = input_ids.map(x => [x.at(-1)]);
+        input_ids = input_ids.map((x) => [x.at(-1)]);
     }
 
     return {
@@ -959,20 +967,17 @@ function multimodality_prepare_inputs_for_generation(self, input_ids, model_inpu
 
     if (generation_config.guidance_scale !== null && generation_config.guidance_scale > 1) {
         if (has_past_key_values) {
-            model_inputs.input_ids = cat([
-                model_inputs.input_ids,
-                model_inputs.input_ids,
-            ], 0)
+            model_inputs.input_ids = cat([model_inputs.input_ids, model_inputs.input_ids], 0);
             // NOTE: attention_mask handled in generation
         } else {
-            model_inputs.input_ids = cat([
-                model_inputs.input_ids,
-                full_like(model_inputs.input_ids, BigInt(generation_config.pad_token_id)),
-            ], 0);
-            model_inputs.attention_mask = cat([
-                model_inputs.attention_mask,
-                full_like(model_inputs.attention_mask, 0n),
-            ], 0);
+            model_inputs.input_ids = cat(
+                [model_inputs.input_ids, full_like(model_inputs.input_ids, BigInt(generation_config.pad_token_id))],
+                0,
+            );
+            model_inputs.attention_mask = cat(
+                [model_inputs.attention_mask, full_like(model_inputs.attention_mask, 0n)],
+                0,
+            );
         }
     }
 
@@ -991,11 +996,11 @@ function multimodality_prepare_inputs_for_generation(self, input_ids, model_inpu
             new Array(num_img_tokens + num_text_tokens).fill(true).fill(false, 0, num_text_tokens),
             [batch_size, num_img_tokens + num_text_tokens],
         );
-        model_inputs.images_emb_mask = new Tensor(
-            'bool',
-            new Array(num_img_tokens).fill(!!has_image),
-            [batch_size, 1, num_img_tokens],
-        );
+        model_inputs.images_emb_mask = new Tensor('bool', new Array(num_img_tokens).fill(!!has_image), [
+            batch_size,
+            1,
+            num_img_tokens,
+        ]);
     }
     return model_inputs;
 }
@@ -1084,49 +1089,49 @@ export class PreTrainedModel extends Callable {
     }
 
     /**
-    * Disposes of all the ONNX sessions that were created during inference.
-    * @returns {Promise} An array of promises, one for each ONNX session that is being disposed.
-    * @todo Use https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/FinalizationRegistry
-    */
+     * Disposes of all the ONNX sessions that were created during inference.
+     * @returns {Promise} An array of promises, one for each ONNX session that is being disposed.
+     * @todo Use https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/FinalizationRegistry
+     */
     async dispose() {
         const promises = [];
         for (const session of Object.values(this.sessions)) {
-            if (session?.handler?.dispose) {
-                promises.push(session.handler.dispose())
-            }
+            promises.push(session.release?.());
         }
         return await Promise.all(promises);
     }
 
     /**
      * Instantiate one of the model classes of the library from a pretrained model.
-     * 
+     *
      * The model class to instantiate is selected based on the `model_type` property of the config object
      * (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible)
-     * 
+     *
      * @param {string} pretrained_model_name_or_path The name or path of the pretrained model. Can be either:
      * - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.
      *   Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a
      *   user or organization name, like `dbmdz/bert-base-german-cased`.
      * - A path to a *directory* containing model weights, e.g., `./my_model_directory/`.
      * @param {import('./utils/hub.js').PretrainedModelOptions} options Additional options for loading the model.
-     * 
+     *
      * @returns {Promise} A new instance of the `PreTrainedModel` class.
      */
-    static async from_pretrained(pretrained_model_name_or_path, {
-        progress_callback = null,
-        config = null,
-        cache_dir = null,
-        local_files_only = false,
-        revision = 'main',
-        model_file_name = null,
-        subfolder = 'onnx',
-        device = null,
-        dtype = null,
-        use_external_data_format = null,
-        session_options = {},
-    } = {}) {
-
+    static async from_pretrained(
+        pretrained_model_name_or_path,
+        {
+            progress_callback = null,
+            config = null,
+            cache_dir = null,
+            local_files_only = false,
+            revision = 'main',
+            model_file_name = null,
+            subfolder = 'onnx',
+            device = null,
+            dtype = null,
+            use_external_data_format = null,
+            session_options = {},
+        } = {},
+    ) {
         let options = {
             progress_callback,
             config,
@@ -1139,7 +1144,7 @@ export class PreTrainedModel extends Callable {
             dtype,
             use_external_data_format,
             session_options,
-        }
+        };
 
         const modelName = MODEL_CLASS_TO_NAME_MAPPING.get(this);
         const modelType = MODEL_TYPE_MAPPING.get(modelName);
@@ -1149,68 +1154,98 @@ export class PreTrainedModel extends Callable {
         let info;
         if (modelType === MODEL_TYPES.DecoderOnly) {
             info = await Promise.all([
-                constructSessions(pretrained_model_name_or_path, {
-                    model: options.model_file_name ?? 'model',
-                }, options),
-                getOptionalConfigs(pretrained_model_name_or_path, {
-                    generation_config: 'generation_config.json',
-                }, options),
+                constructSessions(
+                    pretrained_model_name_or_path,
+                    {
+                        model: options.model_file_name ?? 'model',
+                    },
+                    options,
+                    'model',
+                ),
+                getOptionalConfigs(
+                    pretrained_model_name_or_path,
+                    {
+                        generation_config: 'generation_config.json',
+                    },
+                    options,
+                ),
             ]);
-
         } else if (modelType === MODEL_TYPES.Seq2Seq || modelType === MODEL_TYPES.Vision2Seq) {
             info = await Promise.all([
-                constructSessions(pretrained_model_name_or_path, {
-                    model: 'encoder_model',
-                    decoder_model_merged: 'decoder_model_merged',
-                }, options),
-                getOptionalConfigs(pretrained_model_name_or_path, {
-                    generation_config: 'generation_config.json',
-                }, options),
+                constructSessions(
+                    pretrained_model_name_or_path,
+                    {
+                        model: 'encoder_model',
+                        decoder_model_merged: 'decoder_model_merged',
+                    },
+                    options,
+                    'decoder_model_merged',
+                ),
+                getOptionalConfigs(
+                    pretrained_model_name_or_path,
+                    {
+                        generation_config: 'generation_config.json',
+                    },
+                    options,
+                ),
             ]);
-
         } else if (modelType === MODEL_TYPES.MaskGeneration) {
             info = await Promise.all([
-                constructSessions(pretrained_model_name_or_path, {
-                    model: 'vision_encoder',
-                    prompt_encoder_mask_decoder: 'prompt_encoder_mask_decoder',
-                }, options),
+                constructSessions(
+                    pretrained_model_name_or_path,
+                    {
+                        model: 'vision_encoder',
+                        prompt_encoder_mask_decoder: 'prompt_encoder_mask_decoder',
+                    },
+                    options,
+                ),
             ]);
-
         } else if (modelType === MODEL_TYPES.EncoderDecoder) {
             info = await Promise.all([
-                constructSessions(pretrained_model_name_or_path, {
-                    model: 'encoder_model',
-                    decoder_model_merged: 'decoder_model_merged',
-                }, options),
+                constructSessions(
+                    pretrained_model_name_or_path,
+                    {
+                        model: 'encoder_model',
+                        decoder_model_merged: 'decoder_model_merged',
+                    },
+                    options,
+                    'decoder_model_merged',
+                ),
             ]);
-
         } else if (modelType === MODEL_TYPES.ImageTextToText) {
             const sessions = {
                 embed_tokens: 'embed_tokens',
                 vision_encoder: 'vision_encoder',
                 decoder_model_merged: 'decoder_model_merged',
-            }
+            };
             if (config.is_encoder_decoder) {
                 sessions['model'] = 'encoder_model';
             }
             info = await Promise.all([
-                constructSessions(pretrained_model_name_or_path, sessions, options),
-                getOptionalConfigs(pretrained_model_name_or_path, {
-                    generation_config: 'generation_config.json',
-                }, options),
+                constructSessions(pretrained_model_name_or_path, sessions, options, 'decoder_model_merged'),
+                getOptionalConfigs(
+                    pretrained_model_name_or_path,
+                    {
+                        generation_config: 'generation_config.json',
+                    },
+                    options,
+                ),
             ]);
-
         } else if (modelType === MODEL_TYPES.AudioTextToText) {
             const sessions = {
                 embed_tokens: 'embed_tokens',
                 audio_encoder: 'audio_encoder',
                 decoder_model_merged: 'decoder_model_merged',
-            }
+            };
             info = await Promise.all([
-                constructSessions(pretrained_model_name_or_path, sessions, options),
-                getOptionalConfigs(pretrained_model_name_or_path, {
-                    generation_config: 'generation_config.json',
-                }, options),
+                constructSessions(pretrained_model_name_or_path, sessions, options, 'decoder_model_merged'),
+                getOptionalConfigs(
+                    pretrained_model_name_or_path,
+                    {
+                        generation_config: 'generation_config.json',
+                    },
+                    options,
+                ),
             ]);
         } else if (modelType === MODEL_TYPES.ImageAudioTextToText) {
             const sessions = {
@@ -1218,69 +1253,109 @@ export class PreTrainedModel extends Callable {
                 audio_encoder: 'audio_encoder',
                 vision_encoder: 'vision_encoder',
                 decoder_model_merged: 'decoder_model_merged',
-            }
+            };
             info = await Promise.all([
                 constructSessions(pretrained_model_name_or_path, sessions, options),
-                getOptionalConfigs(pretrained_model_name_or_path, {
-                    generation_config: 'generation_config.json',
-                }, options),
+                getOptionalConfigs(
+                    pretrained_model_name_or_path,
+                    {
+                        generation_config: 'generation_config.json',
+                    },
+                    options,
+                ),
             ]);
         } else if (modelType === MODEL_TYPES.Musicgen) {
             info = await Promise.all([
-                constructSessions(pretrained_model_name_or_path, {
-                    model: 'text_encoder',
-                    decoder_model_merged: 'decoder_model_merged',
-                    encodec_decode: 'encodec_decode',
-                }, options),
-                getOptionalConfigs(pretrained_model_name_or_path, {
-                    generation_config: 'generation_config.json',
-                }, options),
+                constructSessions(
+                    pretrained_model_name_or_path,
+                    {
+                        model: 'text_encoder',
+                        decoder_model_merged: 'decoder_model_merged',
+                        encodec_decode: 'encodec_decode',
+                    },
+                    options,
+                    'decoder_model_merged',
+                ),
+                getOptionalConfigs(
+                    pretrained_model_name_or_path,
+                    {
+                        generation_config: 'generation_config.json',
+                    },
+                    options,
+                ),
             ]);
-
         } else if (modelType === MODEL_TYPES.MultiModality) {
             info = await Promise.all([
-                constructSessions(pretrained_model_name_or_path, {
-                    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',
-                }, options),
-                getOptionalConfigs(pretrained_model_name_or_path, {
-                    generation_config: 'generation_config.json',
-                }, options),
+                constructSessions(
+                    pretrained_model_name_or_path,
+                    {
+                        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',
+                    },
+                    options,
+                    'model',
+                ),
+                getOptionalConfigs(
+                    pretrained_model_name_or_path,
+                    {
+                        generation_config: 'generation_config.json',
+                    },
+                    options,
+                ),
             ]);
-
         } else if (modelType === MODEL_TYPES.Phi3V) {
             info = await Promise.all([
-                constructSessions(pretrained_model_name_or_path, {
-                    prepare_inputs_embeds: 'prepare_inputs_embeds',
-                    model: 'model',
-                    vision_encoder: 'vision_encoder',
-                }, options),
-                getOptionalConfigs(pretrained_model_name_or_path, {
-                    generation_config: 'generation_config.json',
-                }, options),
+                constructSessions(
+                    pretrained_model_name_or_path,
+                    {
+                        prepare_inputs_embeds: 'prepare_inputs_embeds',
+                        model: 'model',
+                        vision_encoder: 'vision_encoder',
+                    },
+                    options,
+                    'model',
+                ),
+                getOptionalConfigs(
+                    pretrained_model_name_or_path,
+                    {
+                        generation_config: 'generation_config.json',
+                    },
+                    options,
+                ),
             ]);
         } else if (modelType === MODEL_TYPES.AutoEncoder) {
             info = await Promise.all([
-                constructSessions(pretrained_model_name_or_path, {
-                    encoder_model: 'encoder_model',
-                    decoder_model: 'decoder_model',
-                }, options),
+                constructSessions(
+                    pretrained_model_name_or_path,
+                    {
+                        encoder_model: 'encoder_model',
+                        decoder_model: 'decoder_model',
+                    },
+                    options,
+                ),
             ]);
-        } else { // should be MODEL_TYPES.EncoderOnly
+        } else {
+            // should be MODEL_TYPES.EncoderOnly
             if (modelType !== MODEL_TYPES.EncoderOnly) {
                 const type = modelName ?? config?.model_type;
                 if (type !== 'custom') {
-                    console.warn(`Model type for '${type}' not found, assuming encoder-only architecture. Please report this at ${GITHUB_ISSUE_URL}.`)
+                    console.warn(
+                        `Model type for '${type}' not found, assuming encoder-only architecture. Please report this at ${GITHUB_ISSUE_URL}.`,
+                    );
                 }
             }
             info = await Promise.all([
-                constructSessions(pretrained_model_name_or_path, {
-                    model: options.model_file_name ?? 'model',
-                }, options),
+                constructSessions(
+                    pretrained_model_name_or_path,
+                    {
+                        model: options.model_file_name ?? 'model',
+                    },
+                    options,
+                ),
             ]);
         }
 
@@ -1320,10 +1395,9 @@ export class PreTrainedModel extends Callable {
      * This function returns a [`LogitsProcessorList`] list object that contains all relevant [`LogitsWarper`]
      * instances used for multinomial sampling.
      * @param {GenerationConfig} generation_config The generation config.
-     * @returns {LogitsProcessorList} generation_config 
+     * @returns {LogitsProcessorList} generation_config
      */
     _get_logits_warper(generation_config) {
-
         // instantiate warpers list
         const warpers = new LogitsProcessorList();
 
@@ -1343,7 +1417,7 @@ export class PreTrainedModel extends Callable {
     }
 
     /**
-     * @param {GenerationConfig} generation_config 
+     * @param {GenerationConfig} generation_config
      * @param {number} input_ids_seq_length The starting sequence length for the input ids.
      * @returns {LogitsProcessorList}
      * @private
@@ -1353,7 +1427,7 @@ export class PreTrainedModel extends Callable {
         input_ids_seq_length,
         // encoder_input_ids, TODO
         // prefix_allowed_tokens_fn, TODO
-        logits_processor = null
+        logits_processor = null,
     ) {
         const processors = new LogitsProcessorList();
 
@@ -1392,19 +1466,31 @@ export class PreTrainedModel extends Callable {
         // }
 
         if (generation_config.bad_words_ids !== null) {
-            processors.push(new NoBadWordsLogitsProcessor(generation_config.bad_words_ids, generation_config.eos_token_id));
+            processors.push(
+                new NoBadWordsLogitsProcessor(generation_config.bad_words_ids, generation_config.eos_token_id),
+            );
         }
 
-        if (generation_config.min_length !== null && generation_config.eos_token_id !== null && generation_config.min_length > 0) {
+        if (
+            generation_config.min_length !== null &&
+            generation_config.eos_token_id !== null &&
+            generation_config.min_length > 0
+        ) {
             processors.push(new MinLengthLogitsProcessor(generation_config.min_length, generation_config.eos_token_id));
         }
 
-        if (generation_config.min_new_tokens !== null && generation_config.eos_token_id !== null && generation_config.min_new_tokens > 0) {
-            processors.push(new MinNewTokensLengthLogitsProcessor(
-                input_ids_seq_length,
-                generation_config.min_new_tokens,
-                generation_config.eos_token_id
-            ));
+        if (
+            generation_config.min_new_tokens !== null &&
+            generation_config.eos_token_id !== null &&
+            generation_config.min_new_tokens > 0
+        ) {
+            processors.push(
+                new MinNewTokensLengthLogitsProcessor(
+                    input_ids_seq_length,
+                    generation_config.min_new_tokens,
+                    generation_config.eos_token_id,
+                ),
+            );
         }
 
         // if (prefix_allowed_tokens_fn !== null) {
@@ -1414,16 +1500,14 @@ export class PreTrainedModel extends Callable {
         //     ));
         // }
 
-
         if (generation_config.forced_bos_token_id !== null) {
             processors.push(new ForcedBOSTokenLogitsProcessor(generation_config.forced_bos_token_id));
         }
 
         if (generation_config.forced_eos_token_id !== null) {
-            processors.push(new ForcedEOSTokenLogitsProcessor(
-                generation_config.max_length,
-                generation_config.forced_eos_token_id
-            ));
+            processors.push(
+                new ForcedEOSTokenLogitsProcessor(generation_config.max_length, generation_config.forced_eos_token_id),
+            );
         }
 
         // if (generation_config.remove_invalid_values === true) {
@@ -1443,11 +1527,14 @@ export class PreTrainedModel extends Callable {
         // }
 
         if (generation_config.begin_suppress_tokens !== null) {
-            const begin_index = (input_ids_seq_length > 1 || generation_config.forced_bos_token_id === null)
-                ? input_ids_seq_length
-                : input_ids_seq_length + 1;
+            const begin_index =
+                input_ids_seq_length > 1 || generation_config.forced_bos_token_id === null
+                    ? input_ids_seq_length
+                    : input_ids_seq_length + 1;
 
-            processors.push(new SuppressTokensAtBeginLogitsProcessor(generation_config.begin_suppress_tokens, begin_index));
+            processors.push(
+                new SuppressTokensAtBeginLogitsProcessor(generation_config.begin_suppress_tokens, begin_index),
+            );
         }
 
         // DEPRECATED: https://github.com/huggingface/transformers/pull/29485
@@ -1455,14 +1542,13 @@ export class PreTrainedModel extends Callable {
         //     processors.push(new ForceTokensLogitsProcessor(generation_config.forced_decoder_ids));
         // }
 
-
         // 8. prepare batched CFG externally
         if (generation_config.guidance_scale !== null && generation_config.guidance_scale > 1) {
             processors.push(new ClassifierFreeGuidanceLogitsProcessor(generation_config.guidance_scale));
         }
 
         if (logits_processor !== null) {
-            processors.extend(logits_processor)
+            processors.extend(logits_processor);
         }
 
         // `LogitNormalization` should always be the last logit processor, when present
@@ -1484,7 +1570,7 @@ export class PreTrainedModel extends Callable {
         // Create empty generation config (contains defaults)
         // We pass `this.config` so that if `eos_token_id` or `bos_token_id` exist in the model's config, we will use them
         const config = { ...this.config };
-        for (const key of ["decoder", "generator", "text_config"]) {
+        for (const key of ['decoder', 'generator', 'text_config']) {
             // Special case: some models have generation attributes set in the decoder.
             // Use them if still unset in the generation config.
             if (key in config) {
@@ -1512,18 +1598,17 @@ export class PreTrainedModel extends Callable {
     }
 
     /**
-     * 
-     * @param {GenerationConfig} generation_config 
-     * @param {StoppingCriteriaList} [stopping_criteria=null] 
+     *
+     * @param {GenerationConfig} generation_config
+     * @param {StoppingCriteriaList} [stopping_criteria=null]
      */
     _get_stopping_criteria(generation_config, stopping_criteria = null) {
         const criteria = new StoppingCriteriaList();
 
         if (generation_config.max_length !== null) {
-            criteria.push(new MaxLengthCriteria(
-                generation_config.max_length,
-                this.config.max_position_embeddings ?? null,
-            ));
+            criteria.push(
+                new MaxLengthCriteria(generation_config.max_length, this.config.max_position_embeddings ?? null),
+            );
         }
         // if (generation_config.max_time !== null) {
         //     criteria.push(new MaxTimeCriteria(generation_config.max_time));
@@ -1536,7 +1621,6 @@ export class PreTrainedModel extends Callable {
             criteria.extend(stopping_criteria);
         }
         return criteria;
-
     }
 
     /**
@@ -1564,7 +1648,7 @@ export class PreTrainedModel extends Callable {
                 }
             }
 
-            let errorMessage = `The current model class (${modelName}) is not compatible with \`.generate()\`, as it doesn't have a language model head.`
+            let errorMessage = `The current model class (${modelName}) is not compatible with \`.generate()\`, as it doesn't have a language model head.`;
             if (generate_compatible_classes.size > 0) {
                 errorMessage += ` Please use the following class instead: ${[...generate_compatible_classes].join(', ')}`;
             }
@@ -1577,7 +1661,7 @@ export class PreTrainedModel extends Callable {
     }
 
     /**
-     * 
+     *
      * @param {Object} inputs
      * @param {bigint[][]} inputs.generated_input_ids
      * @param {Object} inputs.outputs
@@ -1595,10 +1679,8 @@ export class PreTrainedModel extends Callable {
         if (!is_encoder_decoder) {
             // update attention mask
             model_inputs.attention_mask = cat(
-                [
-                    model_inputs.attention_mask,
-                    ones([model_inputs.attention_mask.dims[0], 1]),
-                ], 1
+                [model_inputs.attention_mask, ones([model_inputs.attention_mask.dims[0], 1])],
+                1,
             );
         } else if ('decoder_attention_mask' in model_inputs) {
             // TODO: update decoder attention mask if the model requires it
@@ -1624,8 +1706,8 @@ export class PreTrainedModel extends Callable {
         if (input_name in model_inputs) {
             if (inputs) {
                 throw new Error(
-                    "`inputs`: {inputs}` were passed alongside {input_name} which is not allowed. " +
-                    "Make sure to either pass {inputs} or {input_name}=..."
+                    '`inputs`: {inputs}` were passed alongside {input_name} which is not allowed. ' +
+                        'Make sure to either pass {inputs} or {input_name}=...',
                 );
             }
         } else {
@@ -1637,11 +1719,16 @@ export class PreTrainedModel extends Callable {
         return { inputs_tensor, model_inputs, model_input_name: input_name };
     }
 
-    async _prepare_encoder_decoder_kwargs_for_generation({ inputs_tensor, model_inputs, model_input_name, generation_config }) {
+    async _prepare_encoder_decoder_kwargs_for_generation({
+        inputs_tensor,
+        model_inputs,
+        model_input_name,
+        generation_config,
+    }) {
         if (
-            this.sessions['model'].inputNames.includes('inputs_embeds')
-            && !model_inputs.inputs_embeds
-            && '_prepare_inputs_embeds' in this
+            this.sessions['model'].inputNames.includes('inputs_embeds') &&
+            !model_inputs.inputs_embeds &&
+            '_prepare_inputs_embeds' in this
         ) {
             // Encoder expects `inputs_embeds` instead of `input_ids`
             const { input_ids, pixel_values, attention_mask, ...kwargs } = model_inputs;
@@ -1656,19 +1743,14 @@ export class PreTrainedModel extends Callable {
 
         // for classifier free guidance we need to add a 'null' input to our encoder hidden states
         if (generation_config.guidance_scale !== null && generation_config.guidance_scale > 1) {
-
-            last_hidden_state = cat([
-                last_hidden_state,
-                full_like(last_hidden_state, 0.0),
-            ], 0);
+            last_hidden_state = cat([last_hidden_state, full_like(last_hidden_state, 0.0)], 0);
 
             if ('attention_mask' in model_inputs) {
-                model_inputs['attention_mask'] = cat([
-                    model_inputs['attention_mask'],
-                    zeros_like(model_inputs['attention_mask']),
-                ], 0);
+                model_inputs['attention_mask'] = cat(
+                    [model_inputs['attention_mask'], zeros_like(model_inputs['attention_mask'])],
+                    0,
+                );
             }
-
         } else if (model_inputs.decoder_input_ids) {
             // Ensure that the encoder outputs have the same batch size as the decoder inputs,
             // allowing for more efficient batched generation for single inputs
@@ -1676,10 +1758,13 @@ export class PreTrainedModel extends Callable {
             if (decoder_input_ids_batch_size !== last_hidden_state.dims[0]) {
                 if (last_hidden_state.dims[0] !== 1) {
                     throw new Error(
-                        `The encoder outputs have a different batch size (${last_hidden_state.dims[0]}) than the decoder inputs (${decoder_input_ids_batch_size}).`
-                    )
+                        `The encoder outputs have a different batch size (${last_hidden_state.dims[0]}) than the decoder inputs (${decoder_input_ids_batch_size}).`,
+                    );
                 }
-                last_hidden_state = cat(Array.from({ length: decoder_input_ids_batch_size }, () => last_hidden_state), 0);
+                last_hidden_state = cat(
+                    Array.from({ length: decoder_input_ids_batch_size }, () => last_hidden_state),
+                    0,
+                );
             }
         }
         model_inputs['encoder_outputs'] = last_hidden_state;
@@ -1689,9 +1774,16 @@ export class PreTrainedModel extends Callable {
 
     /**
      * Prepares `decoder_input_ids` for generation with encoder-decoder models
-     * @param {*} param0 
-     */
-    _prepare_decoder_input_ids_for_generation({ batch_size, model_input_name, model_kwargs, decoder_start_token_id, bos_token_id, generation_config }) {
+     * @param {*} param0
+     */
+    _prepare_decoder_input_ids_for_generation({
+        batch_size,
+        model_input_name,
+        model_kwargs,
+        decoder_start_token_id,
+        bos_token_id,
+        generation_config,
+    }) {
         let { decoder_input_ids, ...model_inputs } = model_kwargs;
 
         // Prepare input ids if the user has not defined `decoder_input_ids` manually.
@@ -1701,28 +1793,36 @@ export class PreTrainedModel extends Callable {
 
                 if (this.config.model_type === 'musicgen') {
                     // Custom logic (TODO: move to Musicgen class)
-                    decoder_input_ids = Array.from({
-                        // @ts-expect-error TS2339
-                        length: batch_size * this.config.decoder.num_codebooks
-                    }, () => [decoder_start_token_id]);
-
+                    decoder_input_ids = Array.from(
+                        {
+                            // @ts-expect-error TS2339
+                            length: batch_size * this.config.decoder.num_codebooks,
+                        },
+                        () => [decoder_start_token_id],
+                    );
                 } else if (Array.isArray(decoder_start_token_id)) {
                     if (decoder_start_token_id.length !== batch_size) {
                         throw new Error(
-                            `\`decoder_start_token_id\` expcted to have length ${batch_size} but got ${decoder_start_token_id.length}`
-                        )
+                            `\`decoder_start_token_id\` expcted to have length ${batch_size} but got ${decoder_start_token_id.length}`,
+                        );
                     }
                     decoder_input_ids = decoder_start_token_id;
                 } else {
-                    decoder_input_ids = Array.from({
-                        length: batch_size,
-                    }, () => [decoder_start_token_id]);
+                    decoder_input_ids = Array.from(
+                        {
+                            length: batch_size,
+                        },
+                        () => [decoder_start_token_id],
+                    );
                 }
             } else if (!Array.isArray(decoder_input_ids[0])) {
                 // Correct batch size
-                decoder_input_ids = Array.from({
-                    length: batch_size,
-                }, () => decoder_input_ids);
+                decoder_input_ids = Array.from(
+                    {
+                        length: batch_size,
+                    },
+                    () => decoder_input_ids,
+                );
             }
             decoder_input_ids = toI64Tensor(decoder_input_ids);
         }
@@ -1766,9 +1866,12 @@ export class PreTrainedModel extends Callable {
         } else if (!('encoder_outputs' in model_inputs)) {
             // if model is encoder decoder encoder_outputs are created
             // and added to `model_kwargs`
-            model_inputs = await this._prepare_encoder_decoder_kwargs_for_generation(
-                { inputs_tensor, model_inputs, model_input_name, generation_config }
-            )
+            model_inputs = await this._prepare_encoder_decoder_kwargs_for_generation({
+                inputs_tensor,
+                model_inputs,
+                model_input_name,
+                generation_config,
+            });
         }
 
         // 5. Prepare `input_ids` which will be used for auto-regressive generation
@@ -1785,7 +1888,7 @@ export class PreTrainedModel extends Callable {
                 generation_config,
             }));
         } else {
-            input_ids = model_inputs[model_input_name]
+            input_ids = model_inputs[model_input_name];
         }
 
         // 6. Prepare `max_length` depending on other stopping criteria.
@@ -1815,12 +1918,10 @@ export class PreTrainedModel extends Callable {
             generation_config,
             input_ids_length,
             logits_processor,
-        )
+        );
 
         // 9. prepare stopping criteria
-        const prepared_stopping_criteria = this._get_stopping_criteria(
-            generation_config, stopping_criteria
-        )
+        const prepared_stopping_criteria = this._get_stopping_criteria(generation_config, stopping_criteria);
 
         // /** @type {number[]} */
         // let eos_token_ids = generation_config.eos_token_id;
@@ -1909,12 +2010,15 @@ export class PreTrainedModel extends Callable {
             }
 
             const stop = prepared_stopping_criteria(all_input_ids);
-            if (stop.every(x => x)) {
+            if (stop.every((x) => x)) {
                 break;
             }
 
             model_inputs = this._update_model_kwargs_for_generation({
-                generated_input_ids, outputs, model_inputs, is_encoder_decoder,
+                generated_input_ids,
+                outputs,
+                model_inputs,
+                is_encoder_decoder,
             });
         }
 
@@ -1936,7 +2040,7 @@ export class PreTrainedModel extends Callable {
                 // TODO:
                 // scores,
                 // logits,
-            }
+            };
         } else {
             // Dispose all remaining tensors
             for (const tensor of Object.values(outputs)) {
@@ -1969,7 +2073,8 @@ export class PreTrainedModel extends Callable {
                     // So, we just replace the constant outputs (`decoderResults[name]`) with the previous past key values.
                     // https://github.com/huggingface/optimum/blob/0bf2c05fb7e1182b52d21b703cfc95fd9e4ea3dc/optimum/onnxruntime/base.py#L677-L704
                     pkvs[newName] = pastKeyValues[newName];
-                } else { // decoder or using first encoder PKVs
+                } else {
+                    // decoder or using first encoder PKVs
                     pkvs[newName] = decoderResults[name];
                 }
 
@@ -2016,13 +2121,13 @@ export class PreTrainedModel extends Callable {
      */
     addPastKeyValues(decoderFeeds, pastKeyValues) {
         if (pastKeyValues) {
-            Object.assign(decoderFeeds, pastKeyValues)
+            Object.assign(decoderFeeds, pastKeyValues);
         } else {
             const session = this.sessions['decoder_model_merged'] ?? this.sessions['model'];
             const batch_size = (decoderFeeds[this.main_input_name] ?? decoderFeeds.attention_mask)?.dims?.[0] ?? 1;
 
             const dtype = session?.config?.kv_cache_dtype ?? 'float32';
-            const cls = (dtype === 'float16') ? DataTypeMap.float16 : DataTypeMap.float32;
+            const cls = dtype === 'float16' ? DataTypeMap.float16 : DataTypeMap.float32;
             const shapes = getCacheShapes(this.config, { batch_size });
             for (const name in shapes) {
                 const size = shapes[name].reduce((a, b) => a * b, 1);
@@ -2049,7 +2154,7 @@ export class PreTrainedModel extends Callable {
 
 //////////////////////////////////////////////////
 // Base model output class
-export class ModelOutput { }
+export class ModelOutput {}
 
 /**
  * Base class for model's outputs, with potential hidden states and attentions.
@@ -2070,8 +2175,8 @@ export class BaseModelOutput extends ModelOutput {
 }
 //////////////////////////////////////////////////
 // Bert models
-export class BertPreTrainedModel extends PreTrainedModel { }
-export class BertModel extends BertPreTrainedModel { }
+export class BertPreTrainedModel extends PreTrainedModel {}
+export class BertModel extends BertPreTrainedModel {}
 
 /**
  * BertForMaskedLM is a class representing a BERT model for masked language modeling.
@@ -2136,8 +2241,8 @@ export class BertForQuestionAnswering extends BertPreTrainedModel {
 
 //////////////////////////////////////////////////
 // NeoBert models
-export class NeoBertPreTrainedModel extends PreTrainedModel { }
-export class NeoBertModel extends NeoBertPreTrainedModel { }
+export class NeoBertPreTrainedModel extends PreTrainedModel {}
+export class NeoBertModel extends NeoBertPreTrainedModel {}
 
 export class NeoBertForMaskedLM extends NeoBertPreTrainedModel {
     /**
@@ -2190,8 +2295,8 @@ export class NeoBertForQuestionAnswering extends NeoBertPreTrainedModel {
 
 //////////////////////////////////////////////////
 // ModernBert models
-export class ModernBertPreTrainedModel extends PreTrainedModel { }
-export class ModernBertModel extends ModernBertPreTrainedModel { }
+export class ModernBertPreTrainedModel extends PreTrainedModel {}
+export class ModernBertModel extends ModernBertPreTrainedModel {}
 
 export class ModernBertForMaskedLM extends ModernBertPreTrainedModel {
     /**
@@ -2232,25 +2337,25 @@ export class ModernBertForTokenClassification extends ModernBertPreTrainedModel
 
 //////////////////////////////////////////////////
 // ModernBERT Decoder models
-export class ModernBertDecoderPreTrainedModel extends PreTrainedModel { }
-export class ModernBertDecoderModel extends ModernBertDecoderPreTrainedModel { }
-export class ModernBertDecoderForCausalLM extends ModernBertDecoderPreTrainedModel { }
+export class ModernBertDecoderPreTrainedModel extends PreTrainedModel {}
+export class ModernBertDecoderModel extends ModernBertDecoderPreTrainedModel {}
+export class ModernBertDecoderForCausalLM extends ModernBertDecoderPreTrainedModel {}
 //////////////////////////////////////////////////
 
 //////////////////////////////////////////////////
 // NomicBert models
-export class NomicBertPreTrainedModel extends PreTrainedModel { }
-export class NomicBertModel extends NomicBertPreTrainedModel { }
+export class NomicBertPreTrainedModel extends PreTrainedModel {}
+export class NomicBertModel extends NomicBertPreTrainedModel {}
 //////////////////////////////////////////////////
 
 //////////////////////////////////////////////////
 // RoFormer models
-export class RoFormerPreTrainedModel extends PreTrainedModel { }
+export class RoFormerPreTrainedModel extends PreTrainedModel {}
 
 /**
  * The bare RoFormer Model transformer outputting raw hidden-states without any specific head on top.
  */
-export class RoFormerModel extends RoFormerPreTrainedModel { }
+export class RoFormerModel extends RoFormerPreTrainedModel {}
 
 /**
  * RoFormer Model with a `language modeling` head on top.
@@ -2318,12 +2423,12 @@ export class RoFormerForQuestionAnswering extends RoFormerPreTrainedModel {
 
 //////////////////////////////////////////////////
 // ConvBert models
-export class ConvBertPreTrainedModel extends PreTrainedModel { }
+export class ConvBertPreTrainedModel extends PreTrainedModel {}
 
 /**
  * The bare ConvBERT Model transformer outputting raw hidden-states without any specific head on top.
  */
-export class ConvBertModel extends ConvBertPreTrainedModel { }
+export class ConvBertModel extends ConvBertPreTrainedModel {}
 
 /**
  * ConvBERT Model with a language modeling head on top.
@@ -2388,17 +2493,16 @@ export class ConvBertForQuestionAnswering extends ConvBertPreTrainedModel {
 }
 //////////////////////////////////////////////////
 
-
 //////////////////////////////////////////////////
 // Electra models
-export class ElectraPreTrainedModel extends PreTrainedModel { }
+export class ElectraPreTrainedModel extends PreTrainedModel {}
 
 /**
  * The bare Electra Model transformer outputting raw hidden-states without any specific head on top.
  * Identical to the BERT model except that it uses an additional linear layer between the embedding
  * layer and the encoder if the hidden size and embedding size are different.
  */
-export class ElectraModel extends ElectraPreTrainedModel { }
+export class ElectraModel extends ElectraPreTrainedModel {}
 // TODO add ElectraForPreTraining
 /**
  * Electra model with a language modeling head on top.
@@ -2462,15 +2566,14 @@ export class ElectraForQuestionAnswering extends ElectraPreTrainedModel {
 }
 //////////////////////////////////////////////////
 
-
 //////////////////////////////////////////////////
 // CamemBERT models
-export class CamembertPreTrainedModel extends PreTrainedModel { }
+export class CamembertPreTrainedModel extends PreTrainedModel {}
 
 /**
  * The bare CamemBERT Model transformer outputting raw hidden-states without any specific head on top.
  */
-export class CamembertModel extends CamembertPreTrainedModel { }
+export class CamembertModel extends CamembertPreTrainedModel {}
 
 /**
  * CamemBERT Model with a `language modeling` head on top.
@@ -2535,12 +2638,12 @@ export class CamembertForQuestionAnswering extends CamembertPreTrainedModel {
 
 //////////////////////////////////////////////////
 // DeBERTa models
-export class DebertaPreTrainedModel extends PreTrainedModel { }
+export class DebertaPreTrainedModel extends PreTrainedModel {}
 
 /**
  * The bare DeBERTa Model transformer outputting raw hidden-states without any specific head on top.
  */
-export class DebertaModel extends DebertaPreTrainedModel { }
+export class DebertaModel extends DebertaPreTrainedModel {}
 
 /**
  * DeBERTa Model with a `language modeling` head on top.
@@ -2606,12 +2709,12 @@ export class DebertaForQuestionAnswering extends DebertaPreTrainedModel {
 
 //////////////////////////////////////////////////
 // DeBERTa-v2 models
-export class DebertaV2PreTrainedModel extends PreTrainedModel { }
+export class DebertaV2PreTrainedModel extends PreTrainedModel {}
 
 /**
  * The bare DeBERTa-V2 Model transformer outputting raw hidden-states without any specific head on top.
  */
-export class DebertaV2Model extends DebertaV2PreTrainedModel { }
+export class DebertaV2Model extends DebertaV2PreTrainedModel {}
 
 /**
  * DeBERTa-V2 Model with a `language modeling` head on top.
@@ -2677,8 +2780,8 @@ export class DebertaV2ForQuestionAnswering extends DebertaV2PreTrainedModel {
 
 //////////////////////////////////////////////////
 // DistilBert models
-export class DistilBertPreTrainedModel extends PreTrainedModel { }
-export class DistilBertModel extends DistilBertPreTrainedModel { }
+export class DistilBertPreTrainedModel extends PreTrainedModel {}
+export class DistilBertModel extends DistilBertPreTrainedModel {}
 
 /**
  * DistilBertForSequenceClassification is a class representing a DistilBERT model for sequence classification.
@@ -2710,7 +2813,6 @@ export class DistilBertForTokenClassification extends DistilBertPreTrainedModel
     }
 }
 
-
 /**
  * DistilBertForQuestionAnswering is a class representing a DistilBERT model for question answering.
  */
@@ -2742,15 +2844,14 @@ export class DistilBertForMaskedLM extends DistilBertPreTrainedModel {
 }
 //////////////////////////////////////////////////
 
-
 //////////////////////////////////////////////////
 // ESM models
-export class EsmPreTrainedModel extends PreTrainedModel { }
+export class EsmPreTrainedModel extends PreTrainedModel {}
 
 /**
  * The bare ESM Model transformer outputting raw hidden-states without any specific head on top.
  */
-export class EsmModel extends EsmPreTrainedModel { }
+export class EsmModel extends EsmPreTrainedModel {}
 
 /**
  * ESM Model with a `language modeling` head on top.
@@ -2799,11 +2900,10 @@ export class EsmForTokenClassification extends EsmPreTrainedModel {
 }
 //////////////////////////////////////////////////
 
-
 //////////////////////////////////////////////////
 // MobileBert models
-export class MobileBertPreTrainedModel extends PreTrainedModel { }
-export class MobileBertModel extends MobileBertPreTrainedModel { }
+export class MobileBertPreTrainedModel extends PreTrainedModel {}
+export class MobileBertModel extends MobileBertPreTrainedModel {}
 
 /**
  * MobileBertForMaskedLM is a class representing a MobileBERT model for masking task.
@@ -2853,12 +2953,12 @@ export class MobileBertForQuestionAnswering extends MobileBertPreTrainedModel {
 
 //////////////////////////////////////////////////
 // MPNet models
-export class MPNetPreTrainedModel extends PreTrainedModel { }
+export class MPNetPreTrainedModel extends PreTrainedModel {}
 
 /**
  * The bare MPNet Model transformer outputting raw hidden-states without any specific head on top.
  */
-export class MPNetModel extends MPNetPreTrainedModel { }
+export class MPNetModel extends MPNetPreTrainedModel {}
 
 /**
  * MPNetForMaskedLM is a class representing a MPNet model for masked language modeling.
@@ -2921,11 +3021,10 @@ export class MPNetForQuestionAnswering extends MPNetPreTrainedModel {
 }
 //////////////////////////////////////////////////
 
-
 //////////////////////////////////////////////////
 // SqueezeBert models
-export class SqueezeBertPreTrainedModel extends PreTrainedModel { }
-export class SqueezeBertModel extends SqueezeBertPreTrainedModel { }
+export class SqueezeBertPreTrainedModel extends PreTrainedModel {}
+export class SqueezeBertModel extends SqueezeBertPreTrainedModel {}
 export class SqueezeBertForMaskedLM extends SqueezeBertPreTrainedModel {
     /**
      * Calls the model on new inputs.
@@ -2961,11 +3060,10 @@ export class SqueezeBertForQuestionAnswering extends SqueezeBertPreTrainedModel
 }
 //////////////////////////////////////////////////
 
-
 //////////////////////////////////////////////////
 // Albert models
-export class AlbertPreTrainedModel extends PreTrainedModel { }
-export class AlbertModel extends AlbertPreTrainedModel { }
+export class AlbertPreTrainedModel extends PreTrainedModel {}
+export class AlbertModel extends AlbertPreTrainedModel {}
 export class AlbertForSequenceClassification extends AlbertPreTrainedModel {
     /**
      * Calls the model on new inputs.
@@ -3001,7 +3099,6 @@ export class AlbertForMaskedLM extends AlbertPreTrainedModel {
 }
 //////////////////////////////////////////////////
 
-
 //////////////////////////////////////////////////
 // T5 models
 export class T5PreTrainedModel extends PreTrainedModel {
@@ -3013,61 +3110,59 @@ export class T5PreTrainedModel extends PreTrainedModel {
         'decoder_attention_mask',
         'past_key_values',
     ];
-};
+}
 
-export class T5Model extends T5PreTrainedModel { }
+export class T5Model extends T5PreTrainedModel {}
 
 /**
  * T5Model is a class representing a T5 model for conditional generation.
  */
-export class T5ForConditionalGeneration extends T5PreTrainedModel { }
+export class T5ForConditionalGeneration extends T5PreTrainedModel {}
 //////////////////////////////////////////////////
 
-
 //////////////////////////////////////////////////
 // LONGT5 models
 /**
  * An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models.
  */
-export class LongT5PreTrainedModel extends PreTrainedModel { };
+export class LongT5PreTrainedModel extends PreTrainedModel {}
 
 /**
  * The bare LONGT5 Model transformer outputting raw hidden-states without any specific head on top.
  */
-export class LongT5Model extends LongT5PreTrainedModel { }
+export class LongT5Model extends LongT5PreTrainedModel {}
 
 /**
  * LONGT5 Model with a `language modeling` head on top.
  */
-export class LongT5ForConditionalGeneration extends LongT5PreTrainedModel { }
+export class LongT5ForConditionalGeneration extends LongT5PreTrainedModel {}
 //////////////////////////////////////////////////
 
-
 //////////////////////////////////////////////////
 // MT5 models
-export class MT5PreTrainedModel extends PreTrainedModel { };
+export class MT5PreTrainedModel extends PreTrainedModel {}
 
-export class MT5Model extends MT5PreTrainedModel { }
+export class MT5Model extends MT5PreTrainedModel {}
 
 /**
  * A class representing a conditional sequence-to-sequence model based on the MT5 architecture.
  */
-export class MT5ForConditionalGeneration extends MT5PreTrainedModel { }
+export class MT5ForConditionalGeneration extends MT5PreTrainedModel {}
 //////////////////////////////////////////////////
 
 //////////////////////////////////////////////////
 // Bart models
-export class BartPretrainedModel extends PreTrainedModel { };
+export class BartPretrainedModel extends PreTrainedModel {}
 
 /**
  * The bare BART Model outputting raw hidden-states without any specific head on top.
  */
-export class BartModel extends BartPretrainedModel { }
+export class BartModel extends BartPretrainedModel {}
 
 /**
  * The BART Model with a language modeling head. Can be used for summarization.
  */
-export class BartForConditionalGeneration extends BartPretrainedModel { }
+export class BartForConditionalGeneration extends BartPretrainedModel {}
 
 /**
  * Bart model with a sequence classification/head on top (a linear layer on top of the pooled output)
@@ -3088,17 +3183,17 @@ export class BartForSequenceClassification extends BartPretrainedModel {
 
 //////////////////////////////////////////////////
 // MBart models
-export class MBartPreTrainedModel extends PreTrainedModel { };
+export class MBartPreTrainedModel extends PreTrainedModel {}
 
 /**
  * The bare MBART Model outputting raw hidden-states without any specific head on top.
  */
-export class MBartModel extends MBartPreTrainedModel { }
+export class MBartModel extends MBartPreTrainedModel {}
 
 /**
  * The MBART Model with a language modeling head. Can be used for summarization, after fine-tuning the pretrained models.
  */
-export class MBartForConditionalGeneration extends MBartPreTrainedModel { }
+export class MBartForConditionalGeneration extends MBartPreTrainedModel {}
 
 /**
  * MBart model with a sequence classification/head on top (a linear layer on top of the pooled output).
@@ -3115,47 +3210,43 @@ export class MBartForSequenceClassification extends MBartPreTrainedModel {
     }
 }
 
-
-export class MBartForCausalLM extends MBartPreTrainedModel { }
+export class MBartForCausalLM extends MBartPreTrainedModel {}
 //////////////////////////////////////////////////
 
-
 //////////////////////////////////////////////////
 // Blenderbot models
-export class BlenderbotPreTrainedModel extends PreTrainedModel { };
+export class BlenderbotPreTrainedModel extends PreTrainedModel {}
 
 /**
  * The bare Blenderbot Model outputting raw hidden-states without any specific head on top.
  */
-export class BlenderbotModel extends BlenderbotPreTrainedModel { }
+export class BlenderbotModel extends BlenderbotPreTrainedModel {}
 
 /**
  * The Blenderbot Model with a language modeling head. Can be used for summarization.
  */
-export class BlenderbotForConditionalGeneration extends BlenderbotPreTrainedModel { }
+export class BlenderbotForConditionalGeneration extends BlenderbotPreTrainedModel {}
 //////////////////////////////////////////////////
 
-
 //////////////////////////////////////////////////
 // Blenderbot models
-export class BlenderbotSmallPreTrainedModel extends PreTrainedModel { };
+export class BlenderbotSmallPreTrainedModel extends PreTrainedModel {}
 
 /**
  * The bare BlenderbotSmall Model outputting raw hidden-states without any specific head on top.
  */
-export class BlenderbotSmallModel extends BlenderbotSmallPreTrainedModel { }
+export class BlenderbotSmallModel extends BlenderbotSmallPreTrainedModel {}
 
 /**
  * The BlenderbotSmall Model with a language modeling head. Can be used for summarization.
  */
-export class BlenderbotSmallForConditionalGeneration extends BlenderbotSmallPreTrainedModel { }
+export class BlenderbotSmallForConditionalGeneration extends BlenderbotSmallPreTrainedModel {}
 //////////////////////////////////////////////////
 
-
 //////////////////////////////////////////////////
 // Roberta models
-export class RobertaPreTrainedModel extends PreTrainedModel { }
-export class RobertaModel extends RobertaPreTrainedModel { }
+export class RobertaPreTrainedModel extends PreTrainedModel {}
+export class RobertaModel extends RobertaPreTrainedModel {}
 
 /**
  * RobertaForMaskedLM class for performing masked language modeling on Roberta models.
@@ -3218,18 +3309,17 @@ export class RobertaForQuestionAnswering extends RobertaPreTrainedModel {
 }
 //////////////////////////////////////////////////
 
-
 //////////////////////////////////////////////////
 // XLM models
 /**
  * An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models.
  */
-export class XLMPreTrainedModel extends PreTrainedModel { }
+export class XLMPreTrainedModel extends PreTrainedModel {}
 
 /**
  * The bare XLM Model transformer outputting raw hidden-states without any specific head on top.
  */
-export class XLMModel extends XLMPreTrainedModel { }
+export class XLMModel extends XLMPreTrainedModel {}
 
 /**
  * The XLM Model transformer with a language modeling head on top (linear layer with weights tied to the input embeddings).
@@ -3294,8 +3384,8 @@ export class XLMForQuestionAnswering extends XLMPreTrainedModel {
 
 //////////////////////////////////////////////////
 // XLMRoberta models
-export class XLMRobertaPreTrainedModel extends PreTrainedModel { }
-export class XLMRobertaModel extends XLMRobertaPreTrainedModel { }
+export class XLMRobertaPreTrainedModel extends PreTrainedModel {}
+export class XLMRobertaModel extends XLMRobertaPreTrainedModel {}
 
 /**
  * XLMRobertaForMaskedLM class for performing masked language modeling on XLMRoberta models.
@@ -3360,24 +3450,23 @@ export class XLMRobertaForQuestionAnswering extends XLMRobertaPreTrainedModel {
 
 //////////////////////////////////////////////////
 // Audio Spectrogram Transformer (AST) models
-export class ASTPreTrainedModel extends PreTrainedModel { };
+export class ASTPreTrainedModel extends PreTrainedModel {}
 
 /**
  * The bare AST Model transformer outputting raw hidden-states without any specific head on top.
  */
-export class ASTModel extends ASTPreTrainedModel { }
+export class ASTModel extends ASTPreTrainedModel {}
 
 /**
  * Audio Spectrogram Transformer model with an audio classification head on top
  * (a linear layer on top of the pooled output) e.g. for datasets like AudioSet, Speech Commands v2.
  */
-export class ASTForAudioClassification extends ASTPreTrainedModel { }
+export class ASTForAudioClassification extends ASTPreTrainedModel {}
 //////////////////////////////////////////////////
 
 //////////////////////////////////////////////////
 // Whisper models
 export class WhisperPreTrainedModel extends PreTrainedModel {
-
     requires_attention_mask = false;
     main_input_name = 'input_features';
     forward_params = [
@@ -3387,29 +3476,29 @@ export class WhisperPreTrainedModel extends PreTrainedModel {
         'decoder_attention_mask',
         'past_key_values',
     ];
-};
+}
 
 /**
  * WhisperModel class for training Whisper models without a language model head.
  */
-export class WhisperModel extends WhisperPreTrainedModel { }
-
+export class WhisperModel extends WhisperPreTrainedModel {}
 
 /**
  * WhisperForConditionalGeneration class for generating conditional outputs from Whisper models.
  */
 export class WhisperForConditionalGeneration extends WhisperPreTrainedModel {
-
     _prepare_generation_config(generation_config, kwargs) {
-        return /** @type {WhisperGenerationConfig} */ (super._prepare_generation_config(generation_config, kwargs, WhisperGenerationConfig));
+        return /** @type {WhisperGenerationConfig} */ (
+            super._prepare_generation_config(generation_config, kwargs, WhisperGenerationConfig)
+        );
     }
 
     /**
-     * 
-     * @param {WhisperGenerationConfig} generation_config 
+     *
+     * @param {WhisperGenerationConfig} generation_config
      */
     _retrieve_init_tokens(generation_config) {
-        // prefix tokens are of the form: 
+        // prefix tokens are of the form:
         //  - Multilingual: <|startoftranscript|> <|lang_id|> <|task|> [<|notimestamps|>]
         //  - English-only: <|startoftranscript|> [<|notimestamps|>]
 
@@ -3429,36 +3518,36 @@ export class WhisperForConditionalGeneration extends WhisperPreTrainedModel {
             // Add language token
             const language_code = whisper_language_to_code(language);
             const language_token = `<|${language_code}|>`;
-            init_tokens.push(generation_config.lang_to_id[language_token])
+            init_tokens.push(generation_config.lang_to_id[language_token]);
 
             // Add task token
             // NOTE: Defaults to 'transcribe' if no task is specified
             init_tokens.push(generation_config.task_to_id[task ?? 'transcribe']);
-
         } else if (language || task) {
             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."
-            )
+                '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.',
+            );
         }
 
         // 3. Handle <|notimestamps|> token
         if (
-            !generation_config.return_timestamps
-            && generation_config.no_timestamps_token_id
-            && init_tokens.at(-1) !== generation_config.no_timestamps_token_id
+            !generation_config.return_timestamps &&
+            generation_config.no_timestamps_token_id &&
+            init_tokens.at(-1) !== generation_config.no_timestamps_token_id
         ) {
             init_tokens.push(generation_config.no_timestamps_token_id);
         } else if (
-            generation_config.return_timestamps
-            &&
+            generation_config.return_timestamps &&
             init_tokens.at(-1) === generation_config.no_timestamps_token_id
         ) {
-            console.warn("<|notimestamps|> prompt token is removed from generation_config since `return_timestamps` is set to `true`.");
+            console.warn(
+                '<|notimestamps|> prompt token is removed from generation_config since `return_timestamps` is set to `true`.',
+            );
             init_tokens.pop();
         }
 
         // let's make sure we don't pass `null` tokens as prompt tokens
-        return init_tokens.filter(token => token != null);
+        return init_tokens.filter((token) => token != null);
     }
 
     /**
@@ -3485,28 +3574,26 @@ export class WhisperForConditionalGeneration extends WhisperPreTrainedModel {
 
         if (generation_config.return_timestamps) {
             logits_processor ??= new LogitsProcessorList();
-            logits_processor.push(
-                new WhisperTimeStampLogitsProcessor(generation_config, init_tokens)
-            );
+            logits_processor.push(new WhisperTimeStampLogitsProcessor(generation_config, init_tokens));
         }
 
         if (generation_config.begin_suppress_tokens) {
             logits_processor ??= new LogitsProcessorList();
             logits_processor.push(
-                new SuppressTokensAtBeginLogitsProcessor(generation_config.begin_suppress_tokens, init_tokens.length)
+                new SuppressTokensAtBeginLogitsProcessor(generation_config.begin_suppress_tokens, init_tokens.length),
             );
         }
 
         if (generation_config.return_token_timestamps) {
             if (!generation_config.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."
-                )
+                    '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.',
+                );
             }
 
             if (generation_config.task === 'translate') {
-                console.warn("Token-level timestamps may not be reliable for task 'translate'.")
+                console.warn("Token-level timestamps may not be reliable for task 'translate'.");
             }
 
             generation_config.output_attentions = true;
@@ -3518,11 +3605,11 @@ export class WhisperForConditionalGeneration extends WhisperPreTrainedModel {
             generation_config,
             logits_processor,
             decoder_input_ids: init_tokens,
-            ...kwargs
+            ...kwargs,
         });
 
         if (generation_config.return_token_timestamps) {
-            outputs["token_timestamps"] = this._extract_token_timestamps(
+            outputs['token_timestamps'] = this._extract_token_timestamps(
                 // @ts-expect-error TS2345
                 outputs,
                 generation_config.alignment_heads,
@@ -3548,21 +3635,21 @@ export class WhisperForConditionalGeneration extends WhisperPreTrainedModel {
     _extract_token_timestamps(generate_outputs, alignment_heads, num_frames = null, time_precision = 0.02) {
         if (!generate_outputs.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`."
-            )
+                'Model outputs must contain cross attentions to extract timestamps. ' +
+                    'This is most likely because the model was not exported with `output_attentions=True`.',
+            );
         }
         if (num_frames == null) {
             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)."
+                '`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).',
             );
         }
 
         // @ts-expect-error TS2339
         let median_filter_width = this.config.median_filter_width;
         if (median_filter_width === undefined) {
-            console.warn("Model config has no `median_filter_width`, using default value of 7.")
+            console.warn('Model config has no `median_filter_width`, using default value of 7.');
             median_filter_width = 7;
         }
 
@@ -3570,20 +3657,29 @@ export class WhisperForConditionalGeneration extends WhisperPreTrainedModel {
         const batch = generate_outputs.cross_attentions;
         // Create a list with `decoder_layers` elements, each a tensor of shape
         // (batch size, attention_heads, output length, input length).
-        // @ts-expect-error TS2339
-        const cross_attentions = Array.from({ length: this.config.decoder_layers },
+        const cross_attentions = Array.from(
+            // @ts-expect-error TS2339
+            { length: this.config.decoder_layers },
             // Concatenate the cross attentions for each layer across sequence length dimension.
-            (_, i) => cat(batch.map(x => x[i]), 2)
+            (_, i) =>
+                cat(
+                    batch.map((x) => x[i]),
+                    2,
+                ),
         );
 
-        const weights = stack(alignment_heads.map(([l, h]) => {
-            if (l >= cross_attentions.length) {
-                throw new Error(`Layer index ${l} is out of bounds for cross attentions (length ${cross_attentions.length}).`)
-            }
-            return num_frames
-                ? cross_attentions[l].slice(null, h, null, [0, num_frames])
-                : cross_attentions[l].slice(null, h);
-        })).transpose(1, 0, 2, 3);
+        const weights = stack(
+            alignment_heads.map(([l, h]) => {
+                if (l >= cross_attentions.length) {
+                    throw new Error(
+                        `Layer index ${l} is out of bounds for cross attentions (length ${cross_attentions.length}).`,
+                    );
+                }
+                return num_frames
+                    ? cross_attentions[l].slice(null, h, null, [0, num_frames])
+                    : cross_attentions[l].slice(null, h);
+            }),
+        ).transpose(1, 0, 2, 3);
 
         const [std, calculatedMean] = std_mean(weights, -2, 0, true);
 
@@ -3600,14 +3696,13 @@ export class WhisperForConditionalGeneration extends WhisperPreTrainedModel {
                 const meanTensorData = calculatedMean[a][b][0].data; // [1500]
 
                 for (let c = 0; c < bTensor.dims[0]; ++c) {
-
                     let cTensorData = bTensor[c].data; // [1500]
                     for (let d = 0; d < cTensorData.length; ++d) {
-                        cTensorData[d] = (cTensorData[d] - meanTensorData[d]) / stdTensorData[d]
+                        cTensorData[d] = (cTensorData[d] - meanTensorData[d]) / stdTensorData[d];
                     }
 
                     // Apply median filter.
-                    cTensorData.set(medianFilter(cTensorData, median_filter_width))
+                    cTensorData.set(medianFilter(cTensorData, median_filter_width));
                 }
             }
         }
@@ -3620,7 +3715,7 @@ export class WhisperForConditionalGeneration extends WhisperPreTrainedModel {
         const timestamps = new Tensor(
             'float32',
             new Float32Array(timestampsShape[0] * timestampsShape[1]),
-            timestampsShape
+            timestampsShape,
         );
 
         // Perform dynamic time warping on each element of the batch.
@@ -3630,8 +3725,11 @@ export class WhisperForConditionalGeneration extends WhisperPreTrainedModel {
             const matrix = batchedMatrices[batch_idx].neg().squeeze_(0);
             const [text_indices, time_indices] = dynamic_time_warping(matrix.tolist());
 
-            const diffs = Array.from({ length: text_indices.length - 1 }, (v, i) => text_indices[i + 1] - text_indices[i]);
-            const jumps = mergeArrays([1], diffs).map(x => !!x); // convert to boolean
+            const diffs = Array.from(
+                { length: text_indices.length - 1 },
+                (v, i) => text_indices[i + 1] - text_indices[i],
+            );
+            const jumps = mergeArrays([1], diffs).map((x) => !!x); // convert to boolean
 
             const jump_times = [];
             for (let i = 0; i < jumps.length; ++i) {
@@ -3640,7 +3738,7 @@ export class WhisperForConditionalGeneration extends WhisperPreTrainedModel {
                     jump_times.push(time_indices[i] * time_precision);
                 }
             }
-            timestamps[batch_idx].data.set(jump_times, 1)
+            timestamps[batch_idx].data.set(jump_times, 1);
         }
 
         return timestamps;
@@ -3648,30 +3746,24 @@ export class WhisperForConditionalGeneration extends WhisperPreTrainedModel {
 }
 //////////////////////////////////////////////////
 
-export class LiteWhisperForConditionalGeneration extends WhisperForConditionalGeneration { }
+export class LiteWhisperForConditionalGeneration extends WhisperForConditionalGeneration {}
 
 //////////////////////////////////////////////////
 // Moonshine models
 export class MoonshinePreTrainedModel extends PreTrainedModel {
-
     requires_attention_mask = false;
     main_input_name = 'input_values';
-    forward_params = [
-        'input_values',
-        'decoder_input_ids',
-        'past_key_values',
-    ];
-};
+    forward_params = ['input_values', 'decoder_input_ids', 'past_key_values'];
+}
 
 /**
  * MoonshineModel class for training Moonshine models without a language model head.
  */
-export class MoonshineModel extends MoonshinePreTrainedModel { }
+export class MoonshineModel extends MoonshinePreTrainedModel {}
 
-export class MoonshineForConditionalGeneration extends MoonshinePreTrainedModel { }
+export class MoonshineForConditionalGeneration extends MoonshinePreTrainedModel {}
 //////////////////////////////////////////////////
 
-
 //////////////////////////////////////////////////
 /**
  * Vision Encoder-Decoder model based on OpenAI's GPT architecture for image captioning and other vision tasks
@@ -3690,17 +3782,10 @@ export class VisionEncoderDecoderModel extends PreTrainedModel {
 }
 //////////////////////////////////////////////////
 
-
 //////////////////////////////////////////////////
 // LLaVa Models
 export class LlavaPreTrainedModel extends PreTrainedModel {
-    forward_params = [
-        'input_ids',
-        'attention_mask',
-        'pixel_values',
-        'position_ids',
-        'past_key_values',
-    ];
+    forward_params = ['input_ids', 'attention_mask', 'pixel_values', 'position_ids', 'past_key_values'];
 }
 
 /**
@@ -3716,13 +3801,13 @@ export class LlavaForConditionalGeneration extends LlavaPreTrainedModel {
             image_token_id: this.config.image_token_index,
             ...kwargs,
             image_features: reshaped_image_hidden_states,
-        })
+        });
     }
 }
 //////////////////////////////////////////////////
 
-export class LlavaOnevisionForConditionalGeneration extends LlavaForConditionalGeneration { } // NOTE: extends LlavaForConditionalGeneration
-export class Moondream1ForConditionalGeneration extends LlavaForConditionalGeneration { } // NOTE: extends LlavaForConditionalGeneration
+export class LlavaOnevisionForConditionalGeneration extends LlavaForConditionalGeneration {} // NOTE: extends LlavaForConditionalGeneration
+export class Moondream1ForConditionalGeneration extends LlavaForConditionalGeneration {} // NOTE: extends LlavaForConditionalGeneration
 
 export class Florence2PreTrainedModel extends PreTrainedModel {
     forward_params = [
@@ -3743,23 +3828,23 @@ export class Florence2PreTrainedModel extends PreTrainedModel {
 }
 
 export class Florence2ForConditionalGeneration extends Florence2PreTrainedModel {
-
-    _merge_input_ids_with_image_features({
-        inputs_embeds,
-        image_features,
-        input_ids,
-        attention_mask,
-    }) {
+    _merge_input_ids_with_image_features({ inputs_embeds, image_features, input_ids, attention_mask }) {
         return {
-            inputs_embeds: cat([
-                image_features, // image embeds
-                inputs_embeds, // task prefix embeds
-            ], 1),
-            attention_mask: cat([
-                ones(image_features.dims.slice(0, 2)), // image attention mask
-                attention_mask, // task prefix attention mask
-            ], 1),
-        }
+            inputs_embeds: cat(
+                [
+                    image_features, // image embeds
+                    inputs_embeds, // task prefix embeds
+                ],
+                1,
+            ),
+            attention_mask: cat(
+                [
+                    ones(image_features.dims.slice(0, 2)), // image attention mask
+                    attention_mask, // task prefix attention mask
+                ],
+                1,
+            ),
+        };
     }
 
     async _prepare_inputs_embeds({ input_ids, pixel_values, inputs_embeds, attention_mask }) {
@@ -3804,7 +3889,12 @@ export class Florence2ForConditionalGeneration extends Florence2PreTrainedModel
         decoder_inputs_embeds,
     }) {
         if (!inputs_embeds) {
-            ({ inputs_embeds, attention_mask } = await this._prepare_inputs_embeds({ input_ids, pixel_values, inputs_embeds, attention_mask }));
+            ({ inputs_embeds, attention_mask } = await this._prepare_inputs_embeds({
+                input_ids,
+                pixel_values,
+                inputs_embeds,
+                attention_mask,
+            }));
         }
 
         if (!encoder_outputs) {
@@ -3853,7 +3943,7 @@ export class PaliGemmaForConditionalGeneration extends PaliGemmaPreTrainedModel
             image_token_id: this.config.image_token_index,
             ...kwargs,
             image_features: reshaped_image_hidden_states,
-        })
+        });
     }
 }
 
@@ -3867,7 +3957,7 @@ export class LlavaQwen2ForCausalLM extends LlavaPreTrainedModel {
             image_token_id: this.config.image_token_index,
             ...kwargs,
             image_features: reshaped_image_hidden_states,
-        })
+        });
     }
 }
 
@@ -3886,7 +3976,6 @@ export class Gemma3nPreTrainedModel extends PreTrainedModel {
     ];
 }
 export class Gemma3nForConditionalGeneration extends Gemma3nPreTrainedModel {
-
     async forward({
         // Produced by the tokenizer/processor:
         input_ids = null,
@@ -3898,7 +3987,7 @@ export class Gemma3nForConditionalGeneration extends Gemma3nPreTrainedModel {
         // Used during generation:
         position_ids = null,
         inputs_embeds = null,
-        per_layer_inputs=null,
+        per_layer_inputs = null,
         past_key_values = null,
 
         // Generic generation parameters
@@ -3910,7 +3999,7 @@ export class Gemma3nForConditionalGeneration extends Gemma3nPreTrainedModel {
     }) {
         if (!inputs_embeds || !per_layer_inputs) {
             // 1. Extract the text embeddings.
-            ({ inputs_embeds, per_layer_inputs} = await sessionRun(this.sessions['embed_tokens'], {
+            ({ inputs_embeds, per_layer_inputs } = await sessionRun(this.sessions['embed_tokens'], {
                 input_ids,
             }));
             if (input_ids.dims[1] !== 1) {
@@ -3943,15 +4032,19 @@ export class Gemma3nForConditionalGeneration extends Gemma3nPreTrainedModel {
             }
         }
 
-        const outputs = await decoderForward(this, {
-            inputs_embeds,
-            per_layer_inputs,
-            past_key_values,
-            attention_mask,
-            position_ids,
-            generation_config,
-            logits_processor,
-        }, true);
+        const outputs = await decoderForward(
+            this,
+            {
+                inputs_embeds,
+                per_layer_inputs,
+                past_key_values,
+                attention_mask,
+                position_ids,
+                generation_config,
+                logits_processor,
+            },
+            true,
+        );
         return outputs;
     }
 
@@ -3974,10 +4067,9 @@ export class Gemma3nForConditionalGeneration extends Gemma3nPreTrainedModel {
             audio_token_id: this.config.audio_token_id,
             ...kwargs,
             audio_features: reshaped_audio_features,
-        })
+        });
     }
 }
-        
 
 //////////////////////////////////////////////////
 // Idefics3 Models
@@ -3996,9 +4088,9 @@ export class Idefics3PreTrainedModel extends PreTrainedModel {
  * The Idefics3 model which consists of a vision backbone and a language model.
  */
 export class Idefics3ForConditionalGeneration extends Idefics3PreTrainedModel {
-
     async encode_image({ pixel_values, pixel_attention_mask }) {
-        const features = (await sessionRun(this.sessions['vision_encoder'], { pixel_values, pixel_attention_mask })).image_features;
+        const features = (await sessionRun(this.sessions['vision_encoder'], { pixel_values, pixel_attention_mask }))
+            .image_features;
         return features;
     }
 
@@ -4011,7 +4103,7 @@ export class Idefics3ForConditionalGeneration extends Idefics3PreTrainedModel {
             image_token_id: this.config.image_token_id,
             ...kwargs,
             image_features: reshaped_image_hidden_states,
-        })
+        });
     }
 }
 //////////////////////////////////////////////////
@@ -4020,7 +4112,7 @@ export class Idefics3ForConditionalGeneration extends Idefics3PreTrainedModel {
  * The SmolVLM Model with a language modeling head.
  * It is made up a SigLIP vision encoder, with a language modeling head on top.
  */
-export class SmolVLMForConditionalGeneration extends Idefics3ForConditionalGeneration { }
+export class SmolVLMForConditionalGeneration extends Idefics3ForConditionalGeneration {}
 
 //////////////////////////////////////////////////
 export class Phi3VPreTrainedModel extends PreTrainedModel {
@@ -4035,7 +4127,6 @@ export class Phi3VPreTrainedModel extends PreTrainedModel {
     ];
 }
 export class Phi3VForCausalLM extends Phi3VPreTrainedModel {
-
     async forward({
         // Produced by the tokenizer/processor:
         input_ids = null,
@@ -4069,11 +4160,7 @@ export class Phi3VForCausalLM extends Phi3VPreTrainedModel {
                 }));
             } else {
                 const hidden_size = this.config.normalized_config.hidden_size;
-                image_features = new Tensor(
-                    'float32',
-                    [],
-                    [0, hidden_size],
-                );
+                image_features = new Tensor('float32', [], [0, hidden_size]);
             }
 
             ({ inputs_embeds } = await sessionRun(this.sessions['prepare_inputs_embeds'], {
@@ -4082,42 +4169,46 @@ export class Phi3VForCausalLM extends Phi3VPreTrainedModel {
             }));
         }
 
-        const outputs = await decoderForward(this, {
-            inputs_embeds,
-            past_key_values,
-            attention_mask,
-            position_ids,
-            generation_config,
-            logits_processor,
-        }, false);
+        const outputs = await decoderForward(
+            this,
+            {
+                inputs_embeds,
+                past_key_values,
+                attention_mask,
+                position_ids,
+                generation_config,
+                logits_processor,
+            },
+            false,
+        );
         return outputs;
     }
 }
 
 //////////////////////////////////////////////////
-export class CLIPPreTrainedModel extends PreTrainedModel { }
+export class CLIPPreTrainedModel extends PreTrainedModel {}
 
 /**
  * CLIP Text and Vision Model with a projection layers on top
- * 
+ *
  * **Example:** Perform zero-shot image classification with a `CLIPModel`.
- * 
+ *
  * ```javascript
  * import { AutoTokenizer, AutoProcessor, CLIPModel, RawImage } from '@huggingface/transformers';
- * 
+ *
  * // Load tokenizer, processor, and model
  * let tokenizer = await AutoTokenizer.from_pretrained('Xenova/clip-vit-base-patch16');
  * let processor = await AutoProcessor.from_pretrained('Xenova/clip-vit-base-patch16');
  * let model = await CLIPModel.from_pretrained('Xenova/clip-vit-base-patch16');
- * 
+ *
  * // Run tokenization
  * let texts = ['a photo of a car', 'a photo of a football match']
  * let text_inputs = tokenizer(texts, { padding: true, truncation: true });
- * 
+ *
  * // Read image and run processor
  * let image = await RawImage.read('https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/football-match.jpg');
  * let image_inputs = await processor(image);
- * 
+ *
  * // Run model with both text and pixel inputs
  * let output = await model({ ...text_inputs, ...image_inputs });
  * // {
@@ -4140,7 +4231,7 @@ export class CLIPPreTrainedModel extends PreTrainedModel { }
  * // }
  * ```
  */
-export class CLIPModel extends CLIPPreTrainedModel { }
+export class CLIPModel extends CLIPPreTrainedModel {}
 
 /**
  * The text model from CLIP without any head or projection on top.
@@ -4158,20 +4249,20 @@ export class CLIPTextModel extends CLIPPreTrainedModel {
 
 /**
  * CLIP Text Model with a projection layer on top (a linear layer on top of the pooled output)
- * 
+ *
  * **Example:** Compute text embeddings with `CLIPTextModelWithProjection`.
- * 
+ *
  * ```javascript
  * import { AutoTokenizer, CLIPTextModelWithProjection } from '@huggingface/transformers';
- * 
+ *
  * // Load tokenizer and text model
  * const tokenizer = await AutoTokenizer.from_pretrained('Xenova/clip-vit-base-patch16');
  * const text_model = await CLIPTextModelWithProjection.from_pretrained('Xenova/clip-vit-base-patch16');
- * 
+ *
  * // Run tokenization
  * let texts = ['a photo of a car', 'a photo of a football match'];
  * let text_inputs = tokenizer(texts, { padding: true, truncation: true });
- * 
+ *
  * // Compute embeddings
  * const { text_embeds } = await text_model(text_inputs);
  * // Tensor {
@@ -4209,20 +4300,20 @@ export class CLIPVisionModel extends CLIPPreTrainedModel {
 
 /**
  * CLIP Vision Model with a projection layer on top (a linear layer on top of the pooled output)
- * 
+ *
  * **Example:** Compute vision embeddings with `CLIPVisionModelWithProjection`.
- * 
+ *
  * ```javascript
  * import { AutoProcessor, CLIPVisionModelWithProjection, RawImage} from '@huggingface/transformers';
- * 
+ *
  * // Load processor and vision model
  * const processor = await AutoProcessor.from_pretrained('Xenova/clip-vit-base-patch16');
  * const vision_model = await CLIPVisionModelWithProjection.from_pretrained('Xenova/clip-vit-base-patch16');
- * 
+ *
  * // Read image and run processor
  * let image = await RawImage.read('https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/football-match.jpg');
  * let image_inputs = await processor(image);
- * 
+ *
  * // Compute embeddings
  * const { image_embeds } = await vision_model(image_inputs);
  * // Tensor {
@@ -4245,32 +4336,31 @@ export class CLIPVisionModelWithProjection extends CLIPPreTrainedModel {
 }
 //////////////////////////////////////////////////
 
-
 //////////////////////////////////////////////////
 // SigLIP models
-export class SiglipPreTrainedModel extends PreTrainedModel { }
+export class SiglipPreTrainedModel extends PreTrainedModel {}
 
 /**
  * SigLIP Text and Vision Model with a projection layers on top
- * 
+ *
  * **Example:** Perform zero-shot image classification with a `SiglipModel`.
- * 
+ *
  * ```javascript
  * import { AutoTokenizer, AutoProcessor, SiglipModel, RawImage } from '@huggingface/transformers';
- * 
+ *
  * // Load tokenizer, processor, and model
  * const tokenizer = await AutoTokenizer.from_pretrained('Xenova/siglip-base-patch16-224');
  * const processor = await AutoProcessor.from_pretrained('Xenova/siglip-base-patch16-224');
  * const model = await SiglipModel.from_pretrained('Xenova/siglip-base-patch16-224');
- * 
+ *
  * // Run tokenization
  * const texts = ['a photo of 2 cats', 'a photo of 2 dogs'];
  * const text_inputs = tokenizer(texts, { padding: 'max_length', truncation: true });
- * 
+ *
  * // Read image and run processor
  * const image = await RawImage.read('http://images.cocodataset.org/val2017/000000039769.jpg');
  * const image_inputs = await processor(image);
- * 
+ *
  * // Run model with both text and pixel inputs
  * const output = await model({ ...text_inputs, ...image_inputs });
  * // {
@@ -4293,24 +4383,24 @@ export class SiglipPreTrainedModel extends PreTrainedModel { }
  * // }
  * ```
  */
-export class SiglipModel extends SiglipPreTrainedModel { }
+export class SiglipModel extends SiglipPreTrainedModel {}
 
 /**
  * The text model from SigLIP without any head or projection on top.
- * 
+ *
  * **Example:** Compute text embeddings with `SiglipTextModel`.
- * 
+ *
  * ```javascript
  * import { AutoTokenizer, SiglipTextModel } from '@huggingface/transformers';
- * 
+ *
  * // Load tokenizer and text model
  * const tokenizer = await AutoTokenizer.from_pretrained('Xenova/siglip-base-patch16-224');
  * const text_model = await SiglipTextModel.from_pretrained('Xenova/siglip-base-patch16-224');
- * 
+ *
  * // Run tokenization
  * const texts = ['a photo of 2 cats', 'a photo of 2 dogs'];
  * const text_inputs = tokenizer(texts, { padding: 'max_length', truncation: true });
- * 
+ *
  * // Compute embeddings
  * const { pooler_output } = await text_model(text_inputs);
  * // Tensor {
@@ -4334,20 +4424,20 @@ export class SiglipTextModel extends SiglipPreTrainedModel {
 
 /**
  * The vision model from SigLIP without any head or projection on top.
- * 
+ *
  * **Example:** Compute vision embeddings with `SiglipVisionModel`.
- * 
+ *
  * ```javascript
  * import { AutoProcessor, SiglipVisionModel, RawImage} from '@huggingface/transformers';
- * 
+ *
  * // Load processor and vision model
  * const processor = await AutoProcessor.from_pretrained('Xenova/siglip-base-patch16-224');
  * const vision_model = await SiglipVisionModel.from_pretrained('Xenova/siglip-base-patch16-224');
- * 
+ *
  * // Read image and run processor
  * const image = await RawImage.read('https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/football-match.jpg');
  * const image_inputs = await processor(image);
- * 
+ *
  * // Compute embeddings
  * const { pooler_output } = await vision_model(image_inputs);
  * // Tensor {
@@ -4370,14 +4460,14 @@ export class SiglipVisionModel extends CLIPPreTrainedModel {
 }
 //////////////////////////////////////////////////
 // ChineseCLIP models
-export class ChineseCLIPPreTrainedModel extends PreTrainedModel { }
+export class ChineseCLIPPreTrainedModel extends PreTrainedModel {}
 
-export class ChineseCLIPModel extends ChineseCLIPPreTrainedModel { }
+export class ChineseCLIPModel extends ChineseCLIPPreTrainedModel {}
 //////////////////////////////////////////////////
 
 //////////////////////////////////////////////////
 // JinaCLIP models
-export class JinaCLIPPreTrainedModel extends PreTrainedModel { }
+export class JinaCLIPPreTrainedModel extends PreTrainedModel {}
 
 export class JinaCLIPModel extends JinaCLIPPreTrainedModel {
     async forward(model_inputs) {
@@ -4402,7 +4492,8 @@ export class JinaCLIPModel extends JinaCLIPPreTrainedModel {
             model_inputs.pixel_values = full([0, 3, image_size, image_size], 0.0); // (pass zero-dimension tensor)
         }
 
-        const { text_embeddings, image_embeddings, l2norm_text_embeddings, l2norm_image_embeddings } = await super.forward(model_inputs);
+        const { text_embeddings, image_embeddings, l2norm_text_embeddings, l2norm_image_embeddings } =
+            await super.forward(model_inputs);
 
         const result = {};
         if (!missing_text_inputs) {
@@ -4413,7 +4504,7 @@ export class JinaCLIPModel extends JinaCLIPPreTrainedModel {
             result.image_embeddings = image_embeddings;
             result.l2norm_image_embeddings = l2norm_image_embeddings;
         }
-        return result
+        return result;
     }
 }
 
@@ -4440,34 +4531,33 @@ export class JinaCLIPVisionModel extends JinaCLIPPreTrainedModel {
 }
 //////////////////////////////////////////////////
 
-
 //////////////////////////////////////////////////
 // CLIPSeg models
-export class CLIPSegPreTrainedModel extends PreTrainedModel { }
+export class CLIPSegPreTrainedModel extends PreTrainedModel {}
 
-export class CLIPSegModel extends CLIPSegPreTrainedModel { }
+export class CLIPSegModel extends CLIPSegPreTrainedModel {}
 
 /**
  * CLIPSeg model with a Transformer-based decoder on top for zero-shot and one-shot image segmentation.
- * 
+ *
  * **Example:** Perform zero-shot image segmentation with a `CLIPSegForImageSegmentation` model.
- * 
+ *
  * ```javascript
  * import { AutoTokenizer, AutoProcessor, CLIPSegForImageSegmentation, RawImage } from '@huggingface/transformers';
- * 
+ *
  * // Load tokenizer, processor, and model
  * const tokenizer = await AutoTokenizer.from_pretrained('Xenova/clipseg-rd64-refined');
  * const processor = await AutoProcessor.from_pretrained('Xenova/clipseg-rd64-refined');
  * const model = await CLIPSegForImageSegmentation.from_pretrained('Xenova/clipseg-rd64-refined');
- * 
+ *
  * // Run tokenization
  * const texts = ['a glass', 'something to fill', 'wood', 'a jar'];
  * const text_inputs = tokenizer(texts, { padding: true, truncation: true });
- * 
+ *
  * // Read image and run processor
  * const image = await RawImage.read('https://github.com/timojl/clipseg/blob/master/example_image.jpg?raw=true');
  * const image_inputs = await processor(image);
- * 
+ *
  * // Run model with both text and pixel inputs
  * const { logits } = await model({ ...text_inputs, ...image_inputs });
  * // logits: Tensor {
@@ -4477,7 +4567,7 @@ export class CLIPSegModel extends CLIPSegPreTrainedModel { }
  * //   size: 495616
  * // }
  * ```
- * 
+ *
  * You can visualize the predictions as follows:
  * ```javascript
  * const preds = logits
@@ -4486,27 +4576,26 @@ export class CLIPSegModel extends CLIPSegPreTrainedModel { }
  *   .mul_(255)
  *   .round_()
  *   .to('uint8');
- * 
+ *
  * for (let i = 0; i < preds.dims[0]; ++i) {
  *   const img = RawImage.fromTensor(preds[i]);
  *   img.save(`prediction_${i}.png`);
  * }
  * ```
  */
-export class CLIPSegForImageSegmentation extends CLIPSegPreTrainedModel { }
+export class CLIPSegForImageSegmentation extends CLIPSegPreTrainedModel {}
 //////////////////////////////////////////////////
 
-
 //////////////////////////////////////////////////
 // GPT2 models
-export class GPT2PreTrainedModel extends PreTrainedModel { }
+export class GPT2PreTrainedModel extends PreTrainedModel {}
 
-export class GPT2Model extends GPT2PreTrainedModel { }
+export class GPT2Model extends GPT2PreTrainedModel {}
 
 /**
  * GPT-2 language model head on top of the GPT-2 base model. This model is suitable for text generation tasks.
  */
-export class GPT2LMHeadModel extends GPT2PreTrainedModel { }
+export class GPT2LMHeadModel extends GPT2PreTrainedModel {}
 // export class GPT2ForSequenceClassification extends GPT2PreTrainedModel {
 // TODO
 // }
@@ -4514,170 +4603,162 @@ export class GPT2LMHeadModel extends GPT2PreTrainedModel { }
 
 //////////////////////////////////////////////////
 // JAIS models
-export class JAISPreTrainedModel extends PreTrainedModel { }
+export class JAISPreTrainedModel extends PreTrainedModel {}
 
 /**
  * The bare JAIS Model transformer outputting raw hidden-states without any specific head on top.
  */
-export class JAISModel extends JAISPreTrainedModel { }
+export class JAISModel extends JAISPreTrainedModel {}
 
 /**
  * The JAIS Model transformer with a language modeling head on top (linear layer with weights tied to the input embeddings).
  */
-export class JAISLMHeadModel extends JAISPreTrainedModel { }
+export class JAISLMHeadModel extends JAISPreTrainedModel {}
 //////////////////////////////////////////////////
 
-
 //////////////////////////////////////////////////
 // GPTNeo models
-export class GPTNeoPreTrainedModel extends PreTrainedModel { }
-export class GPTNeoModel extends GPTNeoPreTrainedModel { }
+export class GPTNeoPreTrainedModel extends PreTrainedModel {}
+export class GPTNeoModel extends GPTNeoPreTrainedModel {}
 
-export class GPTNeoForCausalLM extends GPTNeoPreTrainedModel { }
+export class GPTNeoForCausalLM extends GPTNeoPreTrainedModel {}
 //////////////////////////////////////////////////
 
 //////////////////////////////////////////////////
 // GPTNeoX models
-export class GPTNeoXPreTrainedModel extends PreTrainedModel { }
-export class GPTNeoXModel extends GPTNeoXPreTrainedModel { }
+export class GPTNeoXPreTrainedModel extends PreTrainedModel {}
+export class GPTNeoXModel extends GPTNeoXPreTrainedModel {}
 
-export class GPTNeoXForCausalLM extends GPTNeoXPreTrainedModel { }
+export class GPTNeoXForCausalLM extends GPTNeoXPreTrainedModel {}
 //////////////////////////////////////////////////
 
-
 //////////////////////////////////////////////////
 // GPT-J models
-export class GPTJPreTrainedModel extends PreTrainedModel { }
+export class GPTJPreTrainedModel extends PreTrainedModel {}
 
-export class GPTJModel extends GPTJPreTrainedModel { }
+export class GPTJModel extends GPTJPreTrainedModel {}
 
-export class GPTJForCausalLM extends GPTJPreTrainedModel { }
+export class GPTJForCausalLM extends GPTJPreTrainedModel {}
 //////////////////////////////////////////////////
 
-
 //////////////////////////////////////////////////
 // GPTBigCode models
-export class GPTBigCodePreTrainedModel extends PreTrainedModel { }
+export class GPTBigCodePreTrainedModel extends PreTrainedModel {}
 
-export class GPTBigCodeModel extends GPTBigCodePreTrainedModel { }
+export class GPTBigCodeModel extends GPTBigCodePreTrainedModel {}
 
-export class GPTBigCodeForCausalLM extends GPTBigCodePreTrainedModel { }
+export class GPTBigCodeForCausalLM extends GPTBigCodePreTrainedModel {}
 //////////////////////////////////////////////////
 
 //////////////////////////////////////////////////
 // CodeGen models
-export class CodeGenPreTrainedModel extends PreTrainedModel { }
+export class CodeGenPreTrainedModel extends PreTrainedModel {}
 /**
  * CodeGenModel is a class representing a code generation model without a language model head.
  */
-export class CodeGenModel extends CodeGenPreTrainedModel { }
+export class CodeGenModel extends CodeGenPreTrainedModel {}
 
 /**
  * CodeGenForCausalLM is a class that represents a code generation model based on the GPT-2 architecture. It extends the `CodeGenPreTrainedModel` class.
  */
-export class CodeGenForCausalLM extends CodeGenPreTrainedModel { }
+export class CodeGenForCausalLM extends CodeGenPreTrainedModel {}
 //////////////////////////////////////////////////
 
-
 //////////////////////////////////////////////////
 // LLama models
 
 /**
  * The bare LLama Model outputting raw hidden-states without any specific head on top.
  */
-export class LlamaPreTrainedModel extends PreTrainedModel { }
+export class LlamaPreTrainedModel extends PreTrainedModel {}
 /**
  * The bare LLaMA Model outputting raw hidden-states without any specific head on top.
  */
-export class LlamaModel extends LlamaPreTrainedModel { }
+export class LlamaModel extends LlamaPreTrainedModel {}
 
-export class LlamaForCausalLM extends LlamaPreTrainedModel { }
+export class LlamaForCausalLM extends LlamaPreTrainedModel {}
 //////////////////////////////////////////////////
 
 //////////////////////////////////////////////////
 // Arcee models
-export class ArceePreTrainedModel extends PreTrainedModel { }
-export class ArceeModel extends ArceePreTrainedModel { }
-export class ArceeForCausalLM extends ArceePreTrainedModel { }
+export class ArceePreTrainedModel extends PreTrainedModel {}
+export class ArceeModel extends ArceePreTrainedModel {}
+export class ArceeForCausalLM extends ArceePreTrainedModel {}
 //////////////////////////////////////////////////
 
 //////////////////////////////////////////////////
 // LFM2 models
-export class Lfm2PreTrainedModel extends PreTrainedModel { }
-export class Lfm2Model extends Lfm2PreTrainedModel { }
-export class Lfm2ForCausalLM extends Lfm2PreTrainedModel { }
+export class Lfm2PreTrainedModel extends PreTrainedModel {}
+export class Lfm2Model extends Lfm2PreTrainedModel {}
+export class Lfm2ForCausalLM extends Lfm2PreTrainedModel {}
 //////////////////////////////////////////////////
 
 //////////////////////////////////////////////////
 // SmolLM3 models
-export class SmolLM3PreTrainedModel extends PreTrainedModel { }
-export class SmolLM3Model extends SmolLM3PreTrainedModel { }
-export class SmolLM3ForCausalLM extends SmolLM3PreTrainedModel { }
+export class SmolLM3PreTrainedModel extends PreTrainedModel {}
+export class SmolLM3Model extends SmolLM3PreTrainedModel {}
+export class SmolLM3ForCausalLM extends SmolLM3PreTrainedModel {}
 //////////////////////////////////////////////////
 
 //////////////////////////////////////////////////
 // Helium models
-export class HeliumPreTrainedModel extends PreTrainedModel { }
-export class HeliumModel extends HeliumPreTrainedModel { }
-export class HeliumForCausalLM extends HeliumPreTrainedModel { }
+export class HeliumPreTrainedModel extends PreTrainedModel {}
+export class HeliumModel extends HeliumPreTrainedModel {}
+export class HeliumForCausalLM extends HeliumPreTrainedModel {}
 //////////////////////////////////////////////////
 
 //////////////////////////////////////////////////
 // Glm models
-export class GlmPreTrainedModel extends PreTrainedModel { }
-export class GlmModel extends GlmPreTrainedModel { }
-export class GlmForCausalLM extends GlmPreTrainedModel { }
+export class GlmPreTrainedModel extends PreTrainedModel {}
+export class GlmModel extends GlmPreTrainedModel {}
+export class GlmForCausalLM extends GlmPreTrainedModel {}
 //////////////////////////////////////////////////
 
 //////////////////////////////////////////////////
 // EXAONE models
-export class ExaonePreTrainedModel extends PreTrainedModel { }
-export class ExaoneModel extends ExaonePreTrainedModel { }
-export class ExaoneForCausalLM extends ExaonePreTrainedModel { }
+export class ExaonePreTrainedModel extends PreTrainedModel {}
+export class ExaoneModel extends ExaonePreTrainedModel {}
+export class ExaoneForCausalLM extends ExaonePreTrainedModel {}
 //////////////////////////////////////////////////
 
-
 //////////////////////////////////////////////////
 // MobileLLM models
-export class MobileLLMPreTrainedModel extends PreTrainedModel { }
-export class MobileLLMModel extends MobileLLMPreTrainedModel { }
-export class MobileLLMForCausalLM extends MobileLLMPreTrainedModel { }
+export class MobileLLMPreTrainedModel extends PreTrainedModel {}
+export class MobileLLMModel extends MobileLLMPreTrainedModel {}
+export class MobileLLMForCausalLM extends MobileLLMPreTrainedModel {}
 //////////////////////////////////////////////////
 
-
 //////////////////////////////////////////////////
 // OLMo models
-export class OlmoPreTrainedModel extends PreTrainedModel { }
-export class OlmoModel extends OlmoPreTrainedModel { }
-export class OlmoForCausalLM extends OlmoPreTrainedModel { }
+export class OlmoPreTrainedModel extends PreTrainedModel {}
+export class OlmoModel extends OlmoPreTrainedModel {}
+export class OlmoForCausalLM extends OlmoPreTrainedModel {}
 //////////////////////////////////////////////////
 
 //////////////////////////////////////////////////
 // OLMo2 models
-export class Olmo2PreTrainedModel extends PreTrainedModel { }
-export class Olmo2Model extends Olmo2PreTrainedModel { }
-export class Olmo2ForCausalLM extends Olmo2PreTrainedModel { }
+export class Olmo2PreTrainedModel extends PreTrainedModel {}
+export class Olmo2Model extends Olmo2PreTrainedModel {}
+export class Olmo2ForCausalLM extends Olmo2PreTrainedModel {}
 //////////////////////////////////////////////////
 
-
 //////////////////////////////////////////////////
 // Granite models
-export class GranitePreTrainedModel extends PreTrainedModel { }
-export class GraniteModel extends GranitePreTrainedModel { }
-export class GraniteForCausalLM extends GranitePreTrainedModel { }
+export class GranitePreTrainedModel extends PreTrainedModel {}
+export class GraniteModel extends GranitePreTrainedModel {}
+export class GraniteForCausalLM extends GranitePreTrainedModel {}
 //////////////////////////////////////////////////
 
-
 //////////////////////////////////////////////////
 // Cohere models
 
 /**
  * The bare Cohere Model outputting raw hidden-states without any specific head on top.
  */
-export class CoherePreTrainedModel extends PreTrainedModel { }
-export class CohereModel extends CoherePreTrainedModel { }
+export class CoherePreTrainedModel extends PreTrainedModel {}
+export class CohereModel extends CoherePreTrainedModel {}
 
-export class CohereForCausalLM extends CoherePreTrainedModel { }
+export class CohereForCausalLM extends CoherePreTrainedModel {}
 //////////////////////////////////////////////////
 
 //////////////////////////////////////////////////
@@ -4686,13 +4767,13 @@ export class CohereForCausalLM extends CoherePreTrainedModel { }
 /**
  * The bare Gemma Model outputting raw hidden-states without any specific head on top.
  */
-export class GemmaPreTrainedModel extends PreTrainedModel { }
+export class GemmaPreTrainedModel extends PreTrainedModel {}
 /**
  * The bare Gemma Model outputting raw hidden-states without any specific head on top.
  */
-export class GemmaModel extends GemmaPreTrainedModel { }
+export class GemmaModel extends GemmaPreTrainedModel {}
 
-export class GemmaForCausalLM extends GemmaPreTrainedModel { }
+export class GemmaForCausalLM extends GemmaPreTrainedModel {}
 //////////////////////////////////////////////////
 
 //////////////////////////////////////////////////
@@ -4701,38 +4782,35 @@ export class GemmaForCausalLM extends GemmaPreTrainedModel { }
 /**
  * The bare Gemma2 Model outputting raw hidden-states without any specific head on top.
  */
-export class Gemma2PreTrainedModel extends PreTrainedModel { }
+export class Gemma2PreTrainedModel extends PreTrainedModel {}
 /**
  * The bare Gemma2 Model outputting raw hidden-states without any specific head on top.
  */
-export class Gemma2Model extends Gemma2PreTrainedModel { }
+export class Gemma2Model extends Gemma2PreTrainedModel {}
 
-export class Gemma2ForCausalLM extends Gemma2PreTrainedModel { }
+export class Gemma2ForCausalLM extends Gemma2PreTrainedModel {}
 //////////////////////////////////////////////////
 
-
 //////////////////////////////////////////////////
 // Gemma3 models
 
 /**
  * The bare Gemma3 Model outputting raw hidden-states without any specific head on top.
  */
-export class Gemma3PreTrainedModel extends PreTrainedModel { }
+export class Gemma3PreTrainedModel extends PreTrainedModel {}
 /**
  * The bare Gemma3 Model outputting raw hidden-states without any specific head on top.
  */
-export class Gemma3Model extends Gemma3PreTrainedModel { }
+export class Gemma3Model extends Gemma3PreTrainedModel {}
 
-export class Gemma3ForCausalLM extends Gemma3PreTrainedModel { }
+export class Gemma3ForCausalLM extends Gemma3PreTrainedModel {}
 //////////////////////////////////////////////////
 
-
 //////////////////////////////////////////////////
-export class OpenELMPreTrainedModel extends PreTrainedModel { }
-export class OpenELMModel extends OpenELMPreTrainedModel { }
-
-export class OpenELMForCausalLM extends OpenELMPreTrainedModel { }
+export class OpenELMPreTrainedModel extends PreTrainedModel {}
+export class OpenELMModel extends OpenELMPreTrainedModel {}
 
+export class OpenELMForCausalLM extends OpenELMPreTrainedModel {}
 
 //////////////////////////////////////////////////
 // Qwen2 models
@@ -4740,29 +4818,28 @@ export class OpenELMForCausalLM extends OpenELMPreTrainedModel { }
 /**
  * The bare Qwen2 Model outputting raw hidden-states without any specific head on top.
  */
-export class Qwen2PreTrainedModel extends PreTrainedModel { }
+export class Qwen2PreTrainedModel extends PreTrainedModel {}
 /**
  * The bare Qwen2 Model outputting raw hidden-states without any specific head on top.
  */
-export class Qwen2Model extends Qwen2PreTrainedModel { }
+export class Qwen2Model extends Qwen2PreTrainedModel {}
 
-export class Qwen2ForCausalLM extends Qwen2PreTrainedModel { }
+export class Qwen2ForCausalLM extends Qwen2PreTrainedModel {}
 //////////////////////////////////////////////////
 
-
 //////////////////////////////////////////////////
 // Qwen3 models
 
 /**
  * The bare Qwen3 Model outputting raw hidden-states without any specific head on top.
  */
-export class Qwen3PreTrainedModel extends PreTrainedModel { }
+export class Qwen3PreTrainedModel extends PreTrainedModel {}
 /**
  * The bare Qwen3 Model outputting raw hidden-states without any specific head on top.
  */
-export class Qwen3Model extends Qwen3PreTrainedModel { }
+export class Qwen3Model extends Qwen3PreTrainedModel {}
 
-export class Qwen3ForCausalLM extends Qwen3PreTrainedModel { }
+export class Qwen3ForCausalLM extends Qwen3PreTrainedModel {}
 //////////////////////////////////////////////////
 
 export class Qwen2VLPreTrainedModel extends PreTrainedModel {
@@ -4779,7 +4856,6 @@ export class Qwen2VLPreTrainedModel extends PreTrainedModel {
     ];
 }
 export class Qwen2VLForConditionalGeneration extends Qwen2VLPreTrainedModel {
-
     /**
      * Calculate the 3D rope index based on image and video's temporal, height and width in LLM.
      *
@@ -4805,7 +4881,7 @@ export class Qwen2VLForConditionalGeneration extends Qwen2VLPreTrainedModel {
      *         text height position_ids: [3, 4, 5, 6, 7]
      *         text width position_ids: [3, 4, 5, 6, 7]
      *         Here we calculate the text start position_ids as the max vision position_ids plus 1.
-     * 
+     *
      * @param {Tensor} input_ids Indices of input sequence tokens in the vocabulary. Tensor of shape `(batch_size, sequence_length)`.
      * @param {Tensor} image_grid_thw (Optional) The temporal, height and width of feature shape of each image in LLM. Tensor of shape `(num_images, 3)`.
      * @param {Tensor} video_grid_thw (Optional) The temporal, height and width of feature shape of each video in LLM. Tensor of shape `(num_videos, 3)`.
@@ -4829,7 +4905,9 @@ export class Qwen2VLForConditionalGeneration extends Qwen2VLPreTrainedModel {
             }
 
             const attention_mask_list = attention_mask.tolist();
-            const position_ids_list = Array.from({ length: 3 }, _ => Array.from({ length: input_ids.dims[0] }, _ => Array.from({ length: input_ids.dims[1] }, _ => 1)));
+            const position_ids_list = Array.from({ length: 3 }, (_) =>
+                Array.from({ length: input_ids.dims[0] }, (_) => Array.from({ length: input_ids.dims[1] }, (_) => 1)),
+            );
 
             const image_grid_thw_list = image_grid_thw ? image_grid_thw.tolist() : [];
             const video_grid_thw_list = video_grid_thw ? video_grid_thw.tolist() : [];
@@ -4844,9 +4922,9 @@ export class Qwen2VLForConditionalGeneration extends Qwen2VLPreTrainedModel {
                     return acc;
                 }, []);
 
-                const vision_tokens = vision_start_indices.map(x => ids[x + 1]);
-                const image_nums = vision_tokens.filter(x => x == image_token_id).length;
-                const video_nums = vision_tokens.filter(x => x == video_token_id).length;
+                const vision_tokens = vision_start_indices.map((x) => ids[x + 1]);
+                const image_nums = vision_tokens.filter((x) => x == image_token_id).length;
+                const video_nums = vision_tokens.filter((x) => x == video_token_id).length;
 
                 /** @type {number[][]} */
                 let llm_pos_ids_list = [];
@@ -4857,23 +4935,19 @@ export class Qwen2VLForConditionalGeneration extends Qwen2VLPreTrainedModel {
                     const next_image_token = ids.findIndex((x, i) => i > st && x == image_token_id);
                     const next_video_token = ids.findIndex((x, i) => i > st && x == video_token_id);
 
-                    const ed_image = (remain_images > 0 && next_image_token !== -1)
-                        ? next_image_token
-                        : ids.length + 1;
+                    const ed_image = remain_images > 0 && next_image_token !== -1 ? next_image_token : ids.length + 1;
 
-                    const ed_video = (remain_videos > 0 && next_video_token !== -1)
-                        ? next_video_token
-                        : ids.length + 1;
+                    const ed_video = remain_videos > 0 && next_video_token !== -1 ? next_video_token : ids.length + 1;
 
                     let ed;
                     let t, h, w;
                     if (ed_image < ed_video) {
-                        ([t, h, w] = image_grid_thw_list[image_index]);
+                        [t, h, w] = image_grid_thw_list[image_index];
                         ++image_index;
                         --remain_images;
                         ed = ed_image;
                     } else {
-                        ([t, h, w] = video_grid_thw_list[video_index]);
+                        [t, h, w] = video_grid_thw_list[video_index];
                         ++video_index;
                         --remain_videos;
                         ed = ed_video;
@@ -4882,37 +4956,35 @@ export class Qwen2VLForConditionalGeneration extends Qwen2VLPreTrainedModel {
                     const [llm_grid_t, llm_grid_h, llm_grid_w] = [
                         Number(t),
                         Math.floor(Number(h) / spatial_merge_size),
-                        Math.floor(Number(w) / spatial_merge_size)
-                    ]
+                        Math.floor(Number(w) / spatial_merge_size),
+                    ];
                     const text_len = ed - st;
-                    const st_idx = llm_pos_ids_list.length > 0
-                        ? max(llm_pos_ids_list.at(-1))[0] + 1
-                        : 0;
+                    const st_idx = llm_pos_ids_list.length > 0 ? max(llm_pos_ids_list.at(-1))[0] + 1 : 0;
 
-                    llm_pos_ids_list.push(
-                        Array.from({ length: 3 * text_len }, (_, i) => st_idx + (i % text_len))
-                    )
+                    llm_pos_ids_list.push(Array.from({ length: 3 * text_len }, (_, i) => st_idx + (i % text_len)));
 
                     const offset = text_len + st_idx;
                     const grid_size = llm_grid_t * llm_grid_h * llm_grid_w;
-                    const t_index = Array.from({ length: grid_size }, (_, i) => offset + Math.floor(i / (llm_grid_h * llm_grid_w)))
-                    const h_index = Array.from({ length: grid_size }, (_, i) => offset + Math.floor(i / llm_grid_w) % llm_grid_h)
-                    const w_index = Array.from({ length: grid_size }, (_, i) => offset + i % llm_grid_w)
-
-                    llm_pos_ids_list.push([t_index, h_index, w_index].flat())
+                    const t_index = Array.from(
+                        { length: grid_size },
+                        (_, i) => offset + Math.floor(i / (llm_grid_h * llm_grid_w)),
+                    );
+                    const h_index = Array.from(
+                        { length: grid_size },
+                        (_, i) => offset + (Math.floor(i / llm_grid_w) % llm_grid_h),
+                    );
+                    const w_index = Array.from({ length: grid_size }, (_, i) => offset + (i % llm_grid_w));
+
+                    llm_pos_ids_list.push([t_index, h_index, w_index].flat());
 
                     st = ed + grid_size;
                 }
 
                 if (st < ids.length) {
-                    const st_idx = llm_pos_ids_list.length > 0
-                        ? max(llm_pos_ids_list.at(-1))[0] + 1
-                        : 0;
+                    const st_idx = llm_pos_ids_list.length > 0 ? max(llm_pos_ids_list.at(-1))[0] + 1 : 0;
                     const text_len = ids.length - st;
 
-                    llm_pos_ids_list.push(
-                        Array.from({ length: 3 * text_len }, (_, i) => (st_idx + (i % text_len)))
-                    )
+                    llm_pos_ids_list.push(Array.from({ length: 3 * text_len }, (_, i) => st_idx + (i % text_len)));
                 }
 
                 // NOTE: Each item in llm_pos_ids_list is an array of shape (3, text_len),
@@ -4936,7 +5008,7 @@ export class Qwen2VLForConditionalGeneration extends Qwen2VLPreTrainedModel {
                 for (let y = 0; y < attn_mask.length; ++y) {
                     if (attn_mask[y] == 1) {
                         for (let x = 0; x < 3; ++x) {
-                            position_ids_list[x][i][y] = llm_positions[x * num_items / 3 + count];
+                            position_ids_list[x][i][y] = llm_positions[(x * num_items) / 3 + count];
                         }
                         ++count;
                     }
@@ -4950,42 +5022,36 @@ export class Qwen2VLForConditionalGeneration extends Qwen2VLPreTrainedModel {
                 new Tensor('int64', position_ids_list.flat(Infinity), [3, input_ids.dims[0], input_ids.dims[1]]),
                 new Tensor('int64', mrope_position_deltas, [mrope_position_deltas.length, 1]),
             ];
-
-        } else { // Text-only
+        } else {
+            // Text-only
             if (attention_mask) {
                 const { data, dims } = cumsum_masked_fill(attention_mask);
 
-                const position_ids = BigInt64Array.from(
-                    { length: 3 * data.length },
-                    (_, i) => data[i % data.length]
-                );
+                const position_ids = BigInt64Array.from({ length: 3 * data.length }, (_, i) => data[i % data.length]);
                 /** @type {bigint[]} */
                 const mrope_position_deltas = Array.from(
                     { length: dims[0] },
-                    (_, i) => max(data.subarray(dims[1] * i, dims[1] * (i + 1)))[0] + 1n + BigInt(dims[1])
+                    (_, i) => max(data.subarray(dims[1] * i, dims[1] * (i + 1)))[0] + 1n + BigInt(dims[1]),
                 );
 
                 return [
                     new Tensor('int64', position_ids, [3, ...dims]),
                     new Tensor('int64', mrope_position_deltas, [mrope_position_deltas.length, 1]),
-                ]
+                ];
             } else {
                 const [batch_size, seq_length] = input_ids.dims;
-                const position_ids = BigInt64Array.from(
-                    { length: 3 * batch_size * seq_length },
-                    (_, i) => BigInt(Math.floor(i % seq_length / batch_size)),
+                const position_ids = BigInt64Array.from({ length: 3 * batch_size * seq_length }, (_, i) =>
+                    BigInt(Math.floor((i % seq_length) / batch_size)),
                 );
 
-                return [
-                    new Tensor('int64', position_ids, [3, ...input_ids.dims]),
-                    zeros([batch_size, 1]),
-                ]
+                return [new Tensor('int64', position_ids, [3, ...input_ids.dims]), zeros([batch_size, 1])];
             }
         }
     }
 
     async encode_image({ pixel_values, image_grid_thw }) {
-        const features = (await sessionRun(this.sessions['vision_encoder'], { pixel_values, grid_thw: image_grid_thw })).image_features;
+        const features = (await sessionRun(this.sessions['vision_encoder'], { pixel_values, grid_thw: image_grid_thw }))
+            .image_features;
         return features;
     }
 
@@ -4993,8 +5059,8 @@ export class Qwen2VLForConditionalGeneration extends Qwen2VLPreTrainedModel {
         return default_merge_input_ids_with_image_features({
             // @ts-ignore
             image_token_id: this.config.image_token_id,
-            ...kwargs
-        })
+            ...kwargs,
+        });
     }
 
     prepare_inputs_for_generation(input_ids, model_inputs, generation_config) {
@@ -5002,20 +5068,19 @@ export class Qwen2VLForConditionalGeneration extends Qwen2VLPreTrainedModel {
         if (model_inputs.attention_mask && !model_inputs.position_ids) {
             // Calculate position_ids and rope_deltas
             if (!model_inputs.past_key_values) {
-                ([model_inputs.position_ids, model_inputs.rope_deltas] = this.get_rope_index(
+                [model_inputs.position_ids, model_inputs.rope_deltas] = this.get_rope_index(
                     model_inputs.input_ids,
                     model_inputs.image_grid_thw,
                     model_inputs.video_grid_thw,
                     model_inputs.attention_mask,
-                ));
-
+                );
             } else {
                 model_inputs.pixel_values = null;
                 // model_inputs.pixel_values_videos = null;
 
                 const delta = BigInt(Object.values(model_inputs.past_key_values)[0].dims.at(-2));
-                const rope_deltas_list = model_inputs.rope_deltas.map(x => delta + x);
-                model_inputs.position_ids = stack([rope_deltas_list, rope_deltas_list, rope_deltas_list], 0)
+                const rope_deltas_list = model_inputs.rope_deltas.map((x) => delta + x);
+                model_inputs.position_ids = stack([rope_deltas_list, rope_deltas_list, rope_deltas_list], 0);
             }
         }
 
@@ -5023,83 +5088,80 @@ export class Qwen2VLForConditionalGeneration extends Qwen2VLPreTrainedModel {
     }
 }
 
-
 //////////////////////////////////////////////////
 // Phi models
-export class PhiPreTrainedModel extends PreTrainedModel { }
+export class PhiPreTrainedModel extends PreTrainedModel {}
 /**
  * The bare Phi Model outputting raw hidden-states without any specific head on top.
  */
-export class PhiModel extends PhiPreTrainedModel { }
+export class PhiModel extends PhiPreTrainedModel {}
 
-export class PhiForCausalLM extends PhiPreTrainedModel { }
+export class PhiForCausalLM extends PhiPreTrainedModel {}
 //////////////////////////////////////////////////
 
 //////////////////////////////////////////////////
 // Phi3 models
-export class Phi3PreTrainedModel extends PreTrainedModel { }
+export class Phi3PreTrainedModel extends PreTrainedModel {}
 
 /**
  * The bare Phi3 Model outputting raw hidden-states without any specific head on top.
  */
-export class Phi3Model extends Phi3PreTrainedModel { }
+export class Phi3Model extends Phi3PreTrainedModel {}
 
-export class Phi3ForCausalLM extends Phi3PreTrainedModel { }
+export class Phi3ForCausalLM extends Phi3PreTrainedModel {}
 //////////////////////////////////////////////////
 
-
 //////////////////////////////////////////////////
 // Bloom models
 /**
  * The Bloom Model transformer with a language modeling head on top (linear layer with weights tied to the input embeddings).
  */
-export class BloomPreTrainedModel extends PreTrainedModel { }
+export class BloomPreTrainedModel extends PreTrainedModel {}
 
 /**
  * The bare Bloom Model transformer outputting raw hidden-states without any specific head on top.
  */
-export class BloomModel extends BloomPreTrainedModel { }
+export class BloomModel extends BloomPreTrainedModel {}
 
 /**
  * The Bloom Model transformer with a language modeling head on top (linear layer with weights tied to the input embeddings).
  */
-export class BloomForCausalLM extends BloomPreTrainedModel { }
+export class BloomForCausalLM extends BloomPreTrainedModel {}
 //////////////////////////////////////////////////
 
 //////////////////////////////////////////////////
 // MPT models
-export class MptPreTrainedModel extends PreTrainedModel { }
+export class MptPreTrainedModel extends PreTrainedModel {}
 
 /**
  * The bare Mpt Model transformer outputting raw hidden-states without any specific head on top.
  */
-export class MptModel extends MptPreTrainedModel { }
+export class MptModel extends MptPreTrainedModel {}
 
 /**
  * The MPT Model transformer with a language modeling head on top (linear layer with weights tied to the input embeddings).
  */
-export class MptForCausalLM extends MptPreTrainedModel { }
+export class MptForCausalLM extends MptPreTrainedModel {}
 //////////////////////////////////////////////////
 
-
 //////////////////////////////////////////////////
 // OPT models
-export class OPTPreTrainedModel extends PreTrainedModel { }
+export class OPTPreTrainedModel extends PreTrainedModel {}
 
 /**
  * The bare OPT Model outputting raw hidden-states without any specific head on top.
  */
-export class OPTModel extends OPTPreTrainedModel { }
+export class OPTModel extends OPTPreTrainedModel {}
 
 /**
  * The OPT Model transformer with a language modeling head on top (linear layer with weights tied to the input embeddings).
  */
-export class OPTForCausalLM extends OPTPreTrainedModel { }
+export class OPTForCausalLM extends OPTPreTrainedModel {}
 //////////////////////////////////////////////////
 
 //////////////////////////////////////////////////
-export class ViTPreTrainedModel extends PreTrainedModel { }
-export class ViTModel extends ViTPreTrainedModel { }
+export class ViTPreTrainedModel extends PreTrainedModel {}
+export class ViTModel extends ViTPreTrainedModel {}
 export class ViTForImageClassification extends ViTPreTrainedModel {
     /**
      * @param {any} model_inputs
@@ -5110,10 +5172,9 @@ export class ViTForImageClassification extends ViTPreTrainedModel {
 }
 //////////////////////////////////////////////////
 
-
 //////////////////////////////////////////////////
-export class IJepaPreTrainedModel extends PreTrainedModel { }
-export class IJepaModel extends IJepaPreTrainedModel { }
+export class IJepaPreTrainedModel extends PreTrainedModel {}
+export class IJepaModel extends IJepaPreTrainedModel {}
 export class IJepaForImageClassification extends IJepaPreTrainedModel {
     /**
      * @param {any} model_inputs
@@ -5124,20 +5185,18 @@ export class IJepaForImageClassification extends IJepaPreTrainedModel {
 }
 //////////////////////////////////////////////////
 
-
 //////////////////////////////////////////////////
-export class VitPosePreTrainedModel extends PreTrainedModel { }
+export class VitPosePreTrainedModel extends PreTrainedModel {}
 
 /**
  * The VitPose model with a pose estimation head on top.
  */
-export class VitPoseForPoseEstimation extends VitPosePreTrainedModel { }
+export class VitPoseForPoseEstimation extends VitPosePreTrainedModel {}
 //////////////////////////////////////////////////
 
-
 //////////////////////////////////////////////////
-export class PvtPreTrainedModel extends PreTrainedModel { }
-export class PvtModel extends PvtPreTrainedModel { }
+export class PvtPreTrainedModel extends PreTrainedModel {}
+export class PvtModel extends PvtPreTrainedModel {}
 export class PvtForImageClassification extends PvtPreTrainedModel {
     /**
      * @param {any} model_inputs
@@ -5149,14 +5208,13 @@ export class PvtForImageClassification extends PvtPreTrainedModel {
 //////////////////////////////////////////////////
 
 //////////////////////////////////////////////////
-export class ViTMAEPreTrainedModel extends PreTrainedModel { }
-export class ViTMAEModel extends ViTMAEPreTrainedModel { }
+export class ViTMAEPreTrainedModel extends PreTrainedModel {}
+export class ViTMAEModel extends ViTMAEPreTrainedModel {}
 //////////////////////////////////////////////////
 
-
 //////////////////////////////////////////////////
-export class ViTMSNPreTrainedModel extends PreTrainedModel { }
-export class ViTMSNModel extends ViTMSNPreTrainedModel { }
+export class ViTMSNPreTrainedModel extends PreTrainedModel {}
+export class ViTMSNModel extends ViTMSNPreTrainedModel {}
 export class ViTMSNForImageClassification extends ViTMSNPreTrainedModel {
     /**
      * @param {any} model_inputs
@@ -5168,14 +5226,13 @@ export class ViTMSNForImageClassification extends ViTMSNPreTrainedModel {
 //////////////////////////////////////////////////
 
 //////////////////////////////////////////////////
-export class GroupViTPreTrainedModel extends PreTrainedModel { }
-export class GroupViTModel extends GroupViTPreTrainedModel { }
+export class GroupViTPreTrainedModel extends PreTrainedModel {}
+export class GroupViTModel extends GroupViTPreTrainedModel {}
 //////////////////////////////////////////////////
 
-
 //////////////////////////////////////////////////
-export class FastViTPreTrainedModel extends PreTrainedModel { }
-export class FastViTModel extends FastViTPreTrainedModel { }
+export class FastViTPreTrainedModel extends PreTrainedModel {}
+export class FastViTModel extends FastViTPreTrainedModel {}
 export class FastViTForImageClassification extends FastViTPreTrainedModel {
     /**
      * @param {any} model_inputs
@@ -5187,26 +5244,26 @@ export class FastViTForImageClassification extends FastViTPreTrainedModel {
 //////////////////////////////////////////////////
 
 //////////////////////////////////////////////////
-export class VitMattePreTrainedModel extends PreTrainedModel { }
+export class VitMattePreTrainedModel extends PreTrainedModel {}
 
 /**
  * ViTMatte framework leveraging any vision backbone e.g. for ADE20k, CityScapes.
- * 
+ *
  * **Example:** Perform image matting with a `VitMatteForImageMatting` model.
  * ```javascript
  * import { AutoProcessor, VitMatteForImageMatting, RawImage } from '@huggingface/transformers';
- * 
+ *
  * // Load processor and model
  * const processor = await AutoProcessor.from_pretrained('Xenova/vitmatte-small-distinctions-646');
  * const model = await VitMatteForImageMatting.from_pretrained('Xenova/vitmatte-small-distinctions-646');
- * 
+ *
  * // Load image and trimap
  * const image = await RawImage.fromURL('https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/vitmatte_image.png');
  * const trimap = await RawImage.fromURL('https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/vitmatte_trimap.png');
- * 
+ *
  * // Prepare image + trimap for the model
  * const inputs = await processor(image, trimap);
- * 
+ *
  * // Predict alpha matte
  * const { alphas } = await model(inputs);
  * // Tensor {
@@ -5216,14 +5273,14 @@ export class VitMattePreTrainedModel extends PreTrainedModel { }
  * //   data: Float32Array(614400) [ 0.9894027709960938, 0.9970508813858032, ... ]
  * // }
  * ```
- * 
+ *
  * You can visualize the alpha matte as follows:
  * ```javascript
  * import { Tensor, cat } from '@huggingface/transformers';
- * 
+ *
  * // Visualize predicted alpha matte
  * const imageTensor = image.toTensor();
- * 
+ *
  * // Convert float (0-1) alpha matte to uint8 (0-255)
  * const alphaChannel = alphas
  *   .squeeze(0)
@@ -5231,10 +5288,10 @@ export class VitMattePreTrainedModel extends PreTrainedModel { }
  *   .clamp_(0, 255)
  *   .round_()
  *   .to('uint8');
- * 
+ *
  * // Concatenate original image with predicted alpha
  * const imageData = cat([imageTensor, alphaChannel], 0);
- * 
+ *
  * // Save output image
  * const outputImage = RawImage.fromTensor(imageData);
  * outputImage.save('output.png');
@@ -5251,8 +5308,8 @@ export class VitMatteForImageMatting extends VitMattePreTrainedModel {
 //////////////////////////////////////////////////
 
 //////////////////////////////////////////////////
-export class MobileViTPreTrainedModel extends PreTrainedModel { }
-export class MobileViTModel extends MobileViTPreTrainedModel { }
+export class MobileViTPreTrainedModel extends PreTrainedModel {}
+export class MobileViTModel extends MobileViTPreTrainedModel {}
 export class MobileViTForImageClassification extends MobileViTPreTrainedModel {
     /**
      * @param {any} model_inputs
@@ -5266,8 +5323,8 @@ export class MobileViTForImageClassification extends MobileViTPreTrainedModel {
 //////////////////////////////////////////////////
 
 //////////////////////////////////////////////////
-export class MobileViTV2PreTrainedModel extends PreTrainedModel { }
-export class MobileViTV2Model extends MobileViTV2PreTrainedModel { }
+export class MobileViTV2PreTrainedModel extends PreTrainedModel {}
+export class MobileViTV2Model extends MobileViTV2PreTrainedModel {}
 export class MobileViTV2ForImageClassification extends MobileViTV2PreTrainedModel {
     /**
      * @param {any} model_inputs
@@ -5281,21 +5338,21 @@ export class MobileViTV2ForImageClassification extends MobileViTV2PreTrainedMode
 //////////////////////////////////////////////////
 
 //////////////////////////////////////////////////
-export class OwlViTPreTrainedModel extends PreTrainedModel { }
-export class OwlViTModel extends OwlViTPreTrainedModel { }
-export class OwlViTForObjectDetection extends OwlViTPreTrainedModel { }
+export class OwlViTPreTrainedModel extends PreTrainedModel {}
+export class OwlViTModel extends OwlViTPreTrainedModel {}
+export class OwlViTForObjectDetection extends OwlViTPreTrainedModel {}
 //////////////////////////////////////////////////
 
 //////////////////////////////////////////////////
-export class Owlv2PreTrainedModel extends PreTrainedModel { }
-export class Owlv2Model extends Owlv2PreTrainedModel { }
-export class Owlv2ForObjectDetection extends Owlv2PreTrainedModel { }
+export class Owlv2PreTrainedModel extends PreTrainedModel {}
+export class Owlv2Model extends Owlv2PreTrainedModel {}
+export class Owlv2ForObjectDetection extends Owlv2PreTrainedModel {}
 //////////////////////////////////////////////////
 
 //////////////////////////////////////////////////
 // Beit Models
-export class BeitPreTrainedModel extends PreTrainedModel { }
-export class BeitModel extends BeitPreTrainedModel { }
+export class BeitPreTrainedModel extends PreTrainedModel {}
+export class BeitModel extends BeitPreTrainedModel {}
 export class BeitForImageClassification extends BeitPreTrainedModel {
     /**
      * @param {any} model_inputs
@@ -5306,10 +5363,9 @@ export class BeitForImageClassification extends BeitPreTrainedModel {
 }
 //////////////////////////////////////////////////
 
-
 //////////////////////////////////////////////////
-export class DetrPreTrainedModel extends PreTrainedModel { }
-export class DetrModel extends DetrPreTrainedModel { }
+export class DetrPreTrainedModel extends PreTrainedModel {}
+export class DetrModel extends DetrPreTrainedModel {}
 export class DetrForObjectDetection extends DetrPreTrainedModel {
     /**
      * @param {any} model_inputs
@@ -5361,8 +5417,8 @@ export class DetrSegmentationOutput extends ModelOutput {
 //////////////////////////////////////////////////
 
 //////////////////////////////////////////////////
-export class RTDetrPreTrainedModel extends PreTrainedModel { }
-export class RTDetrModel extends RTDetrPreTrainedModel { }
+export class RTDetrPreTrainedModel extends PreTrainedModel {}
+export class RTDetrModel extends RTDetrPreTrainedModel {}
 export class RTDetrForObjectDetection extends RTDetrPreTrainedModel {
     /**
      * @param {any} model_inputs
@@ -5387,10 +5443,9 @@ export class RTDetrObjectDetectionOutput extends ModelOutput {
 }
 //////////////////////////////////////////////////
 
-
 //////////////////////////////////////////////////
-export class RTDetrV2PreTrainedModel extends PreTrainedModel { }
-export class RTDetrV2Model extends RTDetrV2PreTrainedModel { }
+export class RTDetrV2PreTrainedModel extends PreTrainedModel {}
+export class RTDetrV2Model extends RTDetrV2PreTrainedModel {}
 export class RTDetrV2ForObjectDetection extends RTDetrV2PreTrainedModel {
     /**
      * @param {any} model_inputs
@@ -5400,12 +5455,12 @@ export class RTDetrV2ForObjectDetection extends RTDetrV2PreTrainedModel {
     }
 }
 
-export class RTDetrV2ObjectDetectionOutput extends RTDetrObjectDetectionOutput { }
+export class RTDetrV2ObjectDetectionOutput extends RTDetrObjectDetectionOutput {}
 //////////////////////////////////////////////////
 
 //////////////////////////////////////////////////
-export class RFDetrPreTrainedModel extends PreTrainedModel { }
-export class RFDetrModel extends RFDetrPreTrainedModel { }
+export class RFDetrPreTrainedModel extends PreTrainedModel {}
+export class RFDetrModel extends RFDetrPreTrainedModel {}
 export class RFDetrForObjectDetection extends RFDetrPreTrainedModel {
     /**
      * @param {any} model_inputs
@@ -5415,12 +5470,12 @@ export class RFDetrForObjectDetection extends RFDetrPreTrainedModel {
     }
 }
 
-export class RFDetrObjectDetectionOutput extends RTDetrObjectDetectionOutput { }
+export class RFDetrObjectDetectionOutput extends RTDetrObjectDetectionOutput {}
 //////////////////////////////////////////////////
 
 //////////////////////////////////////////////////
-export class DFinePreTrainedModel extends PreTrainedModel { }
-export class DFineModel extends DFinePreTrainedModel { }
+export class DFinePreTrainedModel extends PreTrainedModel {}
+export class DFineModel extends DFinePreTrainedModel {}
 export class DFineForObjectDetection extends DFinePreTrainedModel {
     /**
      * @param {any} model_inputs
@@ -5432,13 +5487,13 @@ export class DFineForObjectDetection extends DFinePreTrainedModel {
 //////////////////////////////////////////////////
 
 //////////////////////////////////////////////////
-export class TableTransformerPreTrainedModel extends PreTrainedModel { }
+export class TableTransformerPreTrainedModel extends PreTrainedModel {}
 
 /**
  * The bare Table Transformer Model (consisting of a backbone and encoder-decoder Transformer)
  * outputting raw hidden-states without any specific head on top.
  */
-export class TableTransformerModel extends TableTransformerPreTrainedModel { }
+export class TableTransformerModel extends TableTransformerPreTrainedModel {}
 
 /**
  * Table Transformer Model (consisting of a backbone and encoder-decoder Transformer)
@@ -5452,13 +5507,12 @@ export class TableTransformerForObjectDetection extends TableTransformerPreTrain
         return new TableTransformerObjectDetectionOutput(await super._call(model_inputs));
     }
 }
-export class TableTransformerObjectDetectionOutput extends DetrObjectDetectionOutput { }
+export class TableTransformerObjectDetectionOutput extends DetrObjectDetectionOutput {}
 //////////////////////////////////////////////////
 
-
 //////////////////////////////////////////////////
-export class DeiTPreTrainedModel extends PreTrainedModel { }
-export class DeiTModel extends DeiTPreTrainedModel { }
+export class DeiTPreTrainedModel extends PreTrainedModel {}
+export class DeiTModel extends DeiTPreTrainedModel {}
 export class DeiTForImageClassification extends DeiTPreTrainedModel {
     /**
      * @param {any} model_inputs
@@ -5470,8 +5524,8 @@ export class DeiTForImageClassification extends DeiTPreTrainedModel {
 //////////////////////////////////////////////////
 
 //////////////////////////////////////////////////
-export class HieraPreTrainedModel extends PreTrainedModel { }
-export class HieraModel extends HieraPreTrainedModel { }
+export class HieraPreTrainedModel extends PreTrainedModel {}
+export class HieraModel extends HieraPreTrainedModel {}
 export class HieraForImageClassification extends HieraPreTrainedModel {
     /**
      * @param {any} model_inputs
@@ -5482,17 +5536,16 @@ export class HieraForImageClassification extends HieraPreTrainedModel {
 }
 //////////////////////////////////////////////////
 
-
 //////////////////////////////////////////////////
 /**
  * An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models.
  */
-export class ResNetPreTrainedModel extends PreTrainedModel { }
+export class ResNetPreTrainedModel extends PreTrainedModel {}
 
 /**
  * The bare ResNet model outputting raw features without any specific head on top.
  */
-export class ResNetModel extends ResNetPreTrainedModel { }
+export class ResNetModel extends ResNetPreTrainedModel {}
 
 /**
  * ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for ImageNet.
@@ -5507,10 +5560,9 @@ export class ResNetForImageClassification extends ResNetPreTrainedModel {
 }
 //////////////////////////////////////////////////
 
-
 //////////////////////////////////////////////////
-export class SwinPreTrainedModel extends PreTrainedModel { }
-export class SwinModel extends SwinPreTrainedModel { }
+export class SwinPreTrainedModel extends PreTrainedModel {}
+export class SwinModel extends SwinPreTrainedModel {}
 export class SwinForImageClassification extends SwinPreTrainedModel {
     /**
      * @param {any} model_inputs
@@ -5519,38 +5571,38 @@ export class SwinForImageClassification extends SwinPreTrainedModel {
         return new SequenceClassifierOutput(await super._call(model_inputs));
     }
 }
-export class SwinForSemanticSegmentation extends SwinPreTrainedModel { }
+export class SwinForSemanticSegmentation extends SwinPreTrainedModel {}
 //////////////////////////////////////////////////
 
 //////////////////////////////////////////////////
-export class Swin2SRPreTrainedModel extends PreTrainedModel { }
+export class Swin2SRPreTrainedModel extends PreTrainedModel {}
 
 /**
  * The bare Swin2SR Model transformer outputting raw hidden-states without any specific head on top.
  */
-export class Swin2SRModel extends Swin2SRPreTrainedModel { }
+export class Swin2SRModel extends Swin2SRPreTrainedModel {}
 
 /**
  * Swin2SR Model transformer with an upsampler head on top for image super resolution and restoration.
- * 
+ *
  * **Example:** Super-resolution w/ `Xenova/swin2SR-classical-sr-x2-64`.
- * 
+ *
  * ```javascript
  * import { AutoProcessor, Swin2SRForImageSuperResolution, RawImage } from '@huggingface/transformers';
- * 
+ *
  * // Load processor and model
  * const model_id = 'Xenova/swin2SR-classical-sr-x2-64';
  * const processor = await AutoProcessor.from_pretrained(model_id);
  * const model = await Swin2SRForImageSuperResolution.from_pretrained(model_id);
- * 
+ *
  * // Prepare model inputs
  * const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/butterfly.jpg';
  * const image = await RawImage.fromURL(url);
  * const inputs = await processor(image);
- * 
+ *
  * // Run model
  * const outputs = await model(inputs);
- * 
+ *
  * // Convert Tensor to RawImage
  * const output = outputs.reconstruction.squeeze().clamp_(0, 1).mul_(255).round_().to('uint8');
  * const outputImage = RawImage.fromTensor(output);
@@ -5562,45 +5614,45 @@ export class Swin2SRModel extends Swin2SRPreTrainedModel { }
  * // }
  * ```
  */
-export class Swin2SRForImageSuperResolution extends Swin2SRPreTrainedModel { }
+export class Swin2SRForImageSuperResolution extends Swin2SRPreTrainedModel {}
 //////////////////////////////////////////////////
 
 //////////////////////////////////////////////////
-export class DPTPreTrainedModel extends PreTrainedModel { }
+export class DPTPreTrainedModel extends PreTrainedModel {}
 
 /**
  * The bare DPT Model transformer outputting raw hidden-states without any specific head on top.
  */
-export class DPTModel extends DPTPreTrainedModel { }
+export class DPTModel extends DPTPreTrainedModel {}
 
 /**
  * DPT Model with a depth estimation head on top (consisting of 3 convolutional layers) e.g. for KITTI, NYUv2.
- * 
+ *
  * **Example:** Depth estimation w/ `Xenova/dpt-hybrid-midas`.
  * ```javascript
  * import { DPTForDepthEstimation, AutoProcessor, RawImage, interpolate_4d } from '@huggingface/transformers';
- * 
+ *
  * // Load model and processor
  * const model_id = 'Xenova/dpt-hybrid-midas';
  * const model = await DPTForDepthEstimation.from_pretrained(model_id);
  * const processor = await AutoProcessor.from_pretrained(model_id);
- * 
+ *
  * // Load image from URL
  * const url = 'http://images.cocodataset.org/val2017/000000039769.jpg';
  * const image = await RawImage.read(url);
- * 
+ *
  * // Prepare image for the model
  * const inputs = await processor(image);
- * 
+ *
  * // Run model
  * const { predicted_depth } = await model(inputs);
- * 
+ *
  * // Interpolate to original size
  * const prediction = (await interpolate_4d(predicted_depth.unsqueeze(1), {
-     * size: image.size.reverse(),
-     * mode: 'bilinear',
+ * size: image.size.reverse(),
+ * mode: 'bilinear',
  * })).squeeze(1);
- * 
+ *
  * // Visualize the prediction
  * const min = prediction.min().item();
  * const max = prediction.max().item();
@@ -5614,79 +5666,78 @@ export class DPTModel extends DPTPreTrainedModel { }
  * // }
  * ```
  */
-export class DPTForDepthEstimation extends DPTPreTrainedModel { }
+export class DPTForDepthEstimation extends DPTPreTrainedModel {}
 //////////////////////////////////////////////////
 
 //////////////////////////////////////////////////
-export class DepthAnythingPreTrainedModel extends PreTrainedModel { }
+export class DepthAnythingPreTrainedModel extends PreTrainedModel {}
 
 /**
  * Depth Anything Model with a depth estimation head on top (consisting of 3 convolutional layers) e.g. for KITTI, NYUv2.
  */
-export class DepthAnythingForDepthEstimation extends DepthAnythingPreTrainedModel { }
+export class DepthAnythingForDepthEstimation extends DepthAnythingPreTrainedModel {}
 //////////////////////////////////////////////////
 
-
 //////////////////////////////////////////////////
-export class SapiensPreTrainedModel extends PreTrainedModel { }
-export class SapiensForSemanticSegmentation extends SapiensPreTrainedModel { }
-export class SapiensForDepthEstimation extends SapiensPreTrainedModel { }
-export class SapiensForNormalEstimation extends SapiensPreTrainedModel { }
+export class SapiensPreTrainedModel extends PreTrainedModel {}
+export class SapiensForSemanticSegmentation extends SapiensPreTrainedModel {}
+export class SapiensForDepthEstimation extends SapiensPreTrainedModel {}
+export class SapiensForNormalEstimation extends SapiensPreTrainedModel {}
 //////////////////////////////////////////////////
 
 //////////////////////////////////////////////////
-export class DepthProPreTrainedModel extends PreTrainedModel { }
-export class DepthProForDepthEstimation extends DepthProPreTrainedModel { }
+export class DepthProPreTrainedModel extends PreTrainedModel {}
+export class DepthProForDepthEstimation extends DepthProPreTrainedModel {}
 //////////////////////////////////////////////////
 
 //////////////////////////////////////////////////
-export class Metric3DPreTrainedModel extends PreTrainedModel { }
-export class Metric3DForDepthEstimation extends Metric3DPreTrainedModel { }
+export class Metric3DPreTrainedModel extends PreTrainedModel {}
+export class Metric3DForDepthEstimation extends Metric3DPreTrainedModel {}
 //////////////////////////////////////////////////
 
 //////////////////////////////////////////////////
-export class Metric3Dv2PreTrainedModel extends PreTrainedModel { }
-export class Metric3Dv2ForDepthEstimation extends Metric3Dv2PreTrainedModel { }
+export class Metric3Dv2PreTrainedModel extends PreTrainedModel {}
+export class Metric3Dv2ForDepthEstimation extends Metric3Dv2PreTrainedModel {}
 //////////////////////////////////////////////////
 
 //////////////////////////////////////////////////
-export class MaskFormerPreTrainedModel extends PreTrainedModel { }
-export class MaskFormerModel extends MaskFormerPreTrainedModel { }
-export class MaskFormerForInstanceSegmentation extends MaskFormerPreTrainedModel { }
+export class MaskFormerPreTrainedModel extends PreTrainedModel {}
+export class MaskFormerModel extends MaskFormerPreTrainedModel {}
+export class MaskFormerForInstanceSegmentation extends MaskFormerPreTrainedModel {}
 //////////////////////////////////////////////////
 
 //////////////////////////////////////////////////
-export class GLPNPreTrainedModel extends PreTrainedModel { }
+export class GLPNPreTrainedModel extends PreTrainedModel {}
 
 /**
  * The bare GLPN encoder (Mix-Transformer) outputting raw hidden-states without any specific head on top.
  */
-export class GLPNModel extends GLPNPreTrainedModel { }
+export class GLPNModel extends GLPNPreTrainedModel {}
 
 /**
  * import { GLPNForDepthEstimation, AutoProcessor, RawImage, interpolate_4d } from '@huggingface/transformers';
- * 
+ *
  * // Load model and processor
  * const model_id = 'Xenova/glpn-kitti';
  * const model = await GLPNForDepthEstimation.from_pretrained(model_id);
  * const processor = await AutoProcessor.from_pretrained(model_id);
- * 
+ *
  * // Load image from URL
  * const url = 'http://images.cocodataset.org/val2017/000000039769.jpg';
  * const image = await RawImage.read(url);
- * 
+ *
  * // Prepare image for the model
  * const inputs = await processor(image);
- * 
+ *
  * // Run model
  * const { predicted_depth } = await model(inputs);
- * 
+ *
  * // Interpolate to original size
  * const prediction = (await interpolate_4d(predicted_depth.unsqueeze(1), {
-     * size: image.size.reverse(),
-     * mode: 'bilinear',
+ * size: image.size.reverse(),
+ * mode: 'bilinear',
  * })).squeeze(1);
- * 
+ *
  * // Visualize the prediction
  * const min = prediction.min().item();
  * const max = prediction.max().item();
@@ -5700,64 +5751,64 @@ export class GLPNModel extends GLPNPreTrainedModel { }
  * // }
  * ```
  */
-export class GLPNForDepthEstimation extends GLPNPreTrainedModel { }
+export class GLPNForDepthEstimation extends GLPNPreTrainedModel {}
 //////////////////////////////////////////////////
 
 //////////////////////////////////////////////////
-export class DonutSwinPreTrainedModel extends PreTrainedModel { }
+export class DonutSwinPreTrainedModel extends PreTrainedModel {}
 
 /**
  * The bare Donut Swin Model transformer outputting raw hidden-states without any specific head on top.
- * 
+ *
  * **Example:** Step-by-step Document Parsing.
- * 
+ *
  * ```javascript
  * import { AutoProcessor, AutoTokenizer, AutoModelForVision2Seq, RawImage } from '@huggingface/transformers';
- * 
+ *
  * // Choose model to use
  * const model_id = 'Xenova/donut-base-finetuned-cord-v2';
- * 
+ *
  * // Prepare image inputs
  * const processor = await AutoProcessor.from_pretrained(model_id);
  * const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/receipt.png';
  * const image = await RawImage.read(url);
  * const image_inputs = await processor(image);
- * 
+ *
  * // Prepare decoder inputs
  * const tokenizer = await AutoTokenizer.from_pretrained(model_id);
  * const task_prompt = '';
  * const decoder_input_ids = tokenizer(task_prompt, {
  *   add_special_tokens: false,
  * }).input_ids;
- * 
+ *
  * // Create the model
  * const model = await AutoModelForVision2Seq.from_pretrained(model_id);
- * 
+ *
  * // Run inference
  * const output = await model.generate(image_inputs.pixel_values, {
  *   decoder_input_ids,
  *   max_length: model.config.decoder.max_position_embeddings,
  * });
- * 
+ *
  * // Decode output
  * const decoded = tokenizer.batch_decode(output)[0];
  * //  CINNAMON SUGAR 17,000 1 x 17,000 17,000 17,000 20,000 3,000
  * ```
- * 
+ *
  * **Example:** Step-by-step Document Visual Question Answering (DocVQA)
- * 
+ *
  * ```javascript
  * import { AutoProcessor, AutoTokenizer, AutoModelForVision2Seq, RawImage } from '@huggingface/transformers';
- * 
+ *
  * // Choose model to use
  * const model_id = 'Xenova/donut-base-finetuned-docvqa';
- * 
+ *
  * // Prepare image inputs
  * const processor = await AutoProcessor.from_pretrained(model_id);
  * const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/invoice.png';
  * const image = await RawImage.read(url);
  * const image_inputs = await processor(image);
- * 
+ *
  * // Prepare decoder inputs
  * const tokenizer = await AutoTokenizer.from_pretrained(model_id);
  * const question = 'What is the invoice number?';
@@ -5765,32 +5816,31 @@ export class DonutSwinPreTrainedModel extends PreTrainedModel { }
  * const decoder_input_ids = tokenizer(task_prompt, {
  *   add_special_tokens: false,
  * }).input_ids;
- * 
+ *
  * // Create the model
  * const model = await AutoModelForVision2Seq.from_pretrained(model_id);
- * 
+ *
  * // Run inference
  * const output = await model.generate(image_inputs.pixel_values, {
  *   decoder_input_ids,
  *   max_length: model.config.decoder.max_position_embeddings,
  * });
- * 
+ *
  * // Decode output
  * const decoded = tokenizer.batch_decode(output)[0];
  * //  What is the invoice number? us-001
  * ```
  */
-export class DonutSwinModel extends DonutSwinPreTrainedModel { }
+export class DonutSwinModel extends DonutSwinPreTrainedModel {}
 //////////////////////////////////////////////////
 
-
 //////////////////////////////////////////////////
-export class ConvNextPreTrainedModel extends PreTrainedModel { }
+export class ConvNextPreTrainedModel extends PreTrainedModel {}
 
 /**
  * The bare ConvNext model outputting raw features without any specific head on top.
  */
-export class ConvNextModel extends ConvNextPreTrainedModel { }
+export class ConvNextModel extends ConvNextPreTrainedModel {}
 
 /**
  * ConvNext Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for ImageNet.
@@ -5805,14 +5855,13 @@ export class ConvNextForImageClassification extends ConvNextPreTrainedModel {
 }
 //////////////////////////////////////////////////
 
-
 //////////////////////////////////////////////////
-export class ConvNextV2PreTrainedModel extends PreTrainedModel { }
+export class ConvNextV2PreTrainedModel extends PreTrainedModel {}
 
 /**
  * The bare ConvNextV2 model outputting raw features without any specific head on top.
  */
-export class ConvNextV2Model extends ConvNextV2PreTrainedModel { }
+export class ConvNextV2Model extends ConvNextV2PreTrainedModel {}
 
 /**
  * ConvNextV2 Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for ImageNet.
@@ -5828,12 +5877,12 @@ export class ConvNextV2ForImageClassification extends ConvNextV2PreTrainedModel
 //////////////////////////////////////////////////
 
 //////////////////////////////////////////////////
-export class Dinov2PreTrainedModel extends PreTrainedModel { }
+export class Dinov2PreTrainedModel extends PreTrainedModel {}
 
 /**
  * The bare DINOv2 Model transformer outputting raw hidden-states without any specific head on top.
  */
-export class Dinov2Model extends Dinov2PreTrainedModel { }
+export class Dinov2Model extends Dinov2PreTrainedModel {}
 
 /**
  * Dinov2 Model transformer with an image classification head on top (a linear layer on top of the final hidden state of the [CLS] token) e.g. for ImageNet.
@@ -5849,12 +5898,12 @@ export class Dinov2ForImageClassification extends Dinov2PreTrainedModel {
 //////////////////////////////////////////////////
 
 //////////////////////////////////////////////////
-export class Dinov2WithRegistersPreTrainedModel extends PreTrainedModel { }
+export class Dinov2WithRegistersPreTrainedModel extends PreTrainedModel {}
 
 /**
  * The bare Dinov2WithRegisters Model transformer outputting raw hidden-states without any specific head on top.
  */
-export class Dinov2WithRegistersModel extends Dinov2WithRegistersPreTrainedModel { }
+export class Dinov2WithRegistersModel extends Dinov2WithRegistersPreTrainedModel {}
 
 /**
  * Dinov2WithRegisters Model transformer with an image classification head on top (a linear layer on top of the final hidden state of the [CLS] token) e.g. for ImageNet.
@@ -5868,12 +5917,12 @@ export class Dinov2WithRegistersForImageClassification extends Dinov2WithRegiste
     }
 }
 //////////////////////////////////////////////////
-export class GroundingDinoPreTrainedModel extends PreTrainedModel { }
-export class GroundingDinoForObjectDetection extends GroundingDinoPreTrainedModel { }
+export class GroundingDinoPreTrainedModel extends PreTrainedModel {}
+export class GroundingDinoForObjectDetection extends GroundingDinoPreTrainedModel {}
 
 //////////////////////////////////////////////////
-export class YolosPreTrainedModel extends PreTrainedModel { }
-export class YolosModel extends YolosPreTrainedModel { }
+export class YolosPreTrainedModel extends PreTrainedModel {}
+export class YolosModel extends YolosPreTrainedModel {}
 export class YolosForObjectDetection extends YolosPreTrainedModel {
     /**
      * @param {any} model_inputs
@@ -5898,30 +5947,27 @@ export class YolosObjectDetectionOutput extends ModelOutput {
 }
 //////////////////////////////////////////////////
 
-
-
-
 //////////////////////////////////////////////////
-export class SamPreTrainedModel extends PreTrainedModel { }
+export class SamPreTrainedModel extends PreTrainedModel {}
 
 /**
  * Segment Anything Model (SAM) for generating segmentation masks, given an input image
  * and optional 2D location and bounding boxes.
- * 
+ *
  * **Example:** Perform mask generation w/ `Xenova/sam-vit-base`.
  * ```javascript
  * import { SamModel, AutoProcessor, RawImage } from '@huggingface/transformers';
- * 
+ *
  * const model = await SamModel.from_pretrained('Xenova/sam-vit-base');
  * const processor = await AutoProcessor.from_pretrained('Xenova/sam-vit-base');
- * 
+ *
  * const img_url = 'https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png';
  * const raw_image = await RawImage.read(img_url);
  * const input_points = [[[450, 600]]] // 2D localization of a window
- * 
+ *
  * const inputs = await processor(raw_image, { input_points });
  * const outputs = await model(inputs);
- * 
+ *
  * const masks = await processor.post_process_masks(outputs.pred_masks, inputs.original_sizes, inputs.reshaped_input_sizes);
  * // [
  * //   Tensor {
@@ -5945,7 +5991,6 @@ export class SamPreTrainedModel extends PreTrainedModel { }
  * ```
  */
 export class SamModel extends SamPreTrainedModel {
-
     /**
      * Compute image embeddings and positional image embeddings, given the pixel values of an image.
      * @param {Object} model_inputs Object containing the model inputs.
@@ -5955,11 +6000,11 @@ export class SamModel extends SamPreTrainedModel {
     async get_image_embeddings({ pixel_values }) {
         // in:
         //  - pixel_values: tensor.float32[batch_size,3,1024,1024]
-        // 
+        //
         // out:
         //  - image_embeddings: tensor.float32[batch_size,256,64,64]
         //  - image_positional_embeddings: tensor.float32[batch_size,256,64,64]
-        return await encoderForward(this, { pixel_values })
+        return await encoderForward(this, { pixel_values });
     }
 
     /**
@@ -5988,19 +6033,15 @@ export class SamModel extends SamPreTrainedModel {
             // Compute the image embeddings if they are missing
             model_inputs = {
                 ...model_inputs,
-                ...(await this.get_image_embeddings(model_inputs))
-            }
+                ...(await this.get_image_embeddings(model_inputs)),
+            };
         }
 
         if (!model_inputs.input_labels && model_inputs.input_points) {
             // Set default input labels if they are missing
             const shape = model_inputs.input_points.dims.slice(0, -1);
             const numElements = shape.reduce((a, b) => a * b, 1);
-            model_inputs.input_labels = new Tensor(
-                'int64',
-                new BigInt64Array(numElements).fill(1n),
-                shape
-            );
+            model_inputs.input_labels = new Tensor('int64', new BigInt64Array(numElements).fill(1n), shape);
         }
 
         const decoder_inputs = {
@@ -6033,7 +6074,6 @@ export class SamModel extends SamPreTrainedModel {
     }
 }
 
-
 /**
  * Base class for Segment-Anything model's output.
  */
@@ -6051,42 +6091,41 @@ export class SamImageSegmentationOutput extends ModelOutput {
 }
 //////////////////////////////////////////////////
 
-
 //////////////////////////////////////////////////
 // MarianMT models
-export class MarianPreTrainedModel extends PreTrainedModel { };
+export class MarianPreTrainedModel extends PreTrainedModel {}
 
-export class MarianModel extends MarianPreTrainedModel { }
+export class MarianModel extends MarianPreTrainedModel {}
 
-export class MarianMTModel extends MarianPreTrainedModel { }
+export class MarianMTModel extends MarianPreTrainedModel {}
 //////////////////////////////////////////////////
 
 //////////////////////////////////////////////////
 // M2M100 models
-export class M2M100PreTrainedModel extends PreTrainedModel { };
+export class M2M100PreTrainedModel extends PreTrainedModel {}
 
-export class M2M100Model extends M2M100PreTrainedModel { }
+export class M2M100Model extends M2M100PreTrainedModel {}
 
-export class M2M100ForConditionalGeneration extends M2M100PreTrainedModel { }
+export class M2M100ForConditionalGeneration extends M2M100PreTrainedModel {}
 //////////////////////////////////////////////////
 
 //////////////////////////////////////////////////
 // Wav2Vec2 models
-export class Wav2Vec2PreTrainedModel extends PreTrainedModel { };
+export class Wav2Vec2PreTrainedModel extends PreTrainedModel {}
 
 /**
  * The bare Wav2Vec2 Model transformer outputting raw hidden-states without any specific head on top.
- * 
+ *
  * **Example:** Load and run a `Wav2Vec2Model` for feature extraction.
- * 
+ *
  * ```javascript
  * import { AutoProcessor, AutoModel, read_audio } from '@huggingface/transformers';
- * 
+ *
  * // Read and preprocess audio
  * const processor = await AutoProcessor.from_pretrained('Xenova/mms-300m');
  * const audio = await read_audio('https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/mlk.flac', 16000);
  * const inputs = await processor(audio);
- * 
+ *
  * // Run model with inputs
  * const model = await AutoModel.from_pretrained('Xenova/mms-300m');
  * const output = await model(inputs);
@@ -6100,7 +6139,7 @@ export class Wav2Vec2PreTrainedModel extends PreTrainedModel { };
  * // }
  * ```
  */
-export class Wav2Vec2Model extends Wav2Vec2PreTrainedModel { }
+export class Wav2Vec2Model extends Wav2Vec2PreTrainedModel {}
 
 export class Wav2Vec2ForCTC extends Wav2Vec2PreTrainedModel {
     /**
@@ -6139,34 +6178,33 @@ export class Wav2Vec2ForAudioFrameClassification extends Wav2Vec2PreTrainedModel
 }
 //////////////////////////////////////////////////
 
-
 //////////////////////////////////////////////////
 // PyAnnote models
-export class PyAnnotePreTrainedModel extends PreTrainedModel { };
+export class PyAnnotePreTrainedModel extends PreTrainedModel {}
 
 /**
  * The bare PyAnnote Model transformer outputting raw hidden-states without any specific head on top.
  */
-export class PyAnnoteModel extends PyAnnotePreTrainedModel { }
+export class PyAnnoteModel extends PyAnnotePreTrainedModel {}
 
 /**
  * PyAnnote Model with a frame classification head on top for tasks like Speaker Diarization.
- * 
+ *
  * **Example:** Load and run a `PyAnnoteForAudioFrameClassification` for speaker diarization.
- * 
+ *
  * ```javascript
  * import { AutoProcessor, AutoModelForAudioFrameClassification, read_audio } from '@huggingface/transformers';
- * 
+ *
  * // Load model and processor
  * const model_id = 'onnx-community/pyannote-segmentation-3.0';
  * const model = await AutoModelForAudioFrameClassification.from_pretrained(model_id);
  * const processor = await AutoProcessor.from_pretrained(model_id);
- * 
+ *
  * // Read and preprocess audio
  * const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/mlk.wav';
  * const audio = await read_audio(url, processor.feature_extractor.config.sampling_rate);
  * const inputs = await processor(audio);
- * 
+ *
  * // Run model with inputs
  * const { logits } = await model(inputs);
  * // {
@@ -6177,7 +6215,7 @@ export class PyAnnoteModel extends PyAnnotePreTrainedModel { }
  * //     size: 5369
  * //   }
  * // }
- * 
+ *
  * const result = processor.post_process_speaker_diarization(logits, audio.length);
  * // [
  * //   [
@@ -6186,7 +6224,7 @@ export class PyAnnoteModel extends PyAnnotePreTrainedModel { }
  * //     ...
  * //   ]
  * // ]
- * 
+ *
  * // Display result
  * console.table(result[0], ['start', 'end', 'id', 'confidence']);
  * // ┌─────────┬────────────────────┬────────────────────┬────┬─────────────────────┐
@@ -6220,19 +6258,18 @@ export class PyAnnoteForAudioFrameClassification extends PyAnnotePreTrainedModel
 
 //////////////////////////////////////////////////
 // WeSpeakerResNet models
-export class WeSpeakerResNetPreTrainedModel extends PreTrainedModel { };
-export class WeSpeakerResNetModel extends WeSpeakerResNetPreTrainedModel { }
+export class WeSpeakerResNetPreTrainedModel extends PreTrainedModel {}
+export class WeSpeakerResNetModel extends WeSpeakerResNetPreTrainedModel {}
 //////////////////////////////////////////////////
 
-
 //////////////////////////////////////////////////
 // UniSpeech models
-export class UniSpeechPreTrainedModel extends PreTrainedModel { };
+export class UniSpeechPreTrainedModel extends PreTrainedModel {}
 
 /**
  * The bare UniSpeech Model transformer outputting raw hidden-states without any specific head on top.
  */
-export class UniSpeechModel extends UniSpeechPreTrainedModel { }
+export class UniSpeechModel extends UniSpeechPreTrainedModel {}
 
 /**
  * UniSpeech Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC).
@@ -6265,12 +6302,12 @@ export class UniSpeechForSequenceClassification extends UniSpeechPreTrainedModel
 
 //////////////////////////////////////////////////
 // UniSpeechSat models
-export class UniSpeechSatPreTrainedModel extends PreTrainedModel { };
+export class UniSpeechSatPreTrainedModel extends PreTrainedModel {}
 
 /**
  * The bare UniSpeechSat Model transformer outputting raw hidden-states without any specific head on top.
  */
-export class UniSpeechSatModel extends UniSpeechSatPreTrainedModel { }
+export class UniSpeechSatModel extends UniSpeechSatPreTrainedModel {}
 
 /**
  * UniSpeechSat Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC).
@@ -6317,12 +6354,12 @@ export class UniSpeechSatForAudioFrameClassification extends UniSpeechSatPreTrai
 
 //////////////////////////////////////////////////
 // Wav2Vec2Bert models
-export class Wav2Vec2BertPreTrainedModel extends PreTrainedModel { };
+export class Wav2Vec2BertPreTrainedModel extends PreTrainedModel {}
 
 /**
  * The bare Wav2Vec2Bert Model transformer outputting raw hidden-states without any specific head on top.
  */
-export class Wav2Vec2BertModel extends Wav2Vec2BertPreTrainedModel { }
+export class Wav2Vec2BertModel extends Wav2Vec2BertPreTrainedModel {}
 
 /**
  * Wav2Vec2Bert Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC).
@@ -6355,21 +6392,21 @@ export class Wav2Vec2BertForSequenceClassification extends Wav2Vec2BertPreTraine
 
 //////////////////////////////////////////////////
 // Hubert models
-export class HubertPreTrainedModel extends PreTrainedModel { }
+export class HubertPreTrainedModel extends PreTrainedModel {}
 
 /**
  * The bare Hubert Model transformer outputting raw hidden-states without any specific head on top.
- * 
+ *
  * **Example:** Load and run a `HubertModel` for feature extraction.
- * 
+ *
  * ```javascript
  * import { AutoProcessor, AutoModel, read_audio } from '@huggingface/transformers';
- * 
+ *
  * // Read and preprocess audio
  * const processor = await AutoProcessor.from_pretrained('Xenova/hubert-base-ls960');
  * const audio = await read_audio('https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/jfk.wav', 16000);
  * const inputs = await processor(audio);
- * 
+ *
  * // Load and run model with inputs
  * const model = await AutoModel.from_pretrained('Xenova/hubert-base-ls960');
  * const output = await model(inputs);
@@ -6383,7 +6420,7 @@ export class HubertPreTrainedModel extends PreTrainedModel { }
  * // }
  * ```
  */
-export class HubertModel extends Wav2Vec2PreTrainedModel { }
+export class HubertModel extends Wav2Vec2PreTrainedModel {}
 
 /**
  * Hubert Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC).
@@ -6419,21 +6456,21 @@ export class HubertForSequenceClassification extends Wav2Vec2PreTrainedModel {
 /**
  * An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models.
  */
-export class WavLMPreTrainedModel extends PreTrainedModel { };
+export class WavLMPreTrainedModel extends PreTrainedModel {}
 
 /**
  * The bare WavLM Model transformer outputting raw hidden-states without any specific head on top.
- * 
+ *
  * **Example:** Load and run a `WavLMModel` for feature extraction.
- * 
+ *
  * ```javascript
  * import { AutoProcessor, AutoModel, read_audio } from '@huggingface/transformers';
- * 
+ *
  * // Read and preprocess audio
  * const processor = await AutoProcessor.from_pretrained('Xenova/wavlm-base');
  * const audio = await read_audio('https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/jfk.wav', 16000);
  * const inputs = await processor(audio);
- * 
+ *
  * // Run model with inputs
  * const model = await AutoModel.from_pretrained('Xenova/wavlm-base');
  * const output = await model(inputs);
@@ -6447,7 +6484,7 @@ export class WavLMPreTrainedModel extends PreTrainedModel { };
  * // }
  * ```
  */
-export class WavLMModel extends WavLMPreTrainedModel { }
+export class WavLMModel extends WavLMPreTrainedModel {}
 
 /**
  * WavLM Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC).
@@ -6479,17 +6516,17 @@ export class WavLMForSequenceClassification extends WavLMPreTrainedModel {
 
 /**
  * WavLM Model with an XVector feature extraction head on top for tasks like Speaker Verification.
- * 
+ *
  * **Example:** Extract speaker embeddings with `WavLMForXVector`.
  * ```javascript
  * import { AutoProcessor, AutoModel, read_audio } from '@huggingface/transformers';
- * 
+ *
  * // Read and preprocess audio
  * const processor = await AutoProcessor.from_pretrained('Xenova/wavlm-base-plus-sv');
  * const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/jfk.wav';
  * const audio = await read_audio(url, 16000);
  * const inputs = await processor(audio);
- * 
+ *
  * // Run model with inputs
  * const model = await AutoModel.from_pretrained('Xenova/wavlm-base-plus-sv');
  * const outputs = await model(inputs);
@@ -6522,17 +6559,17 @@ export class WavLMForXVector extends WavLMPreTrainedModel {
 
 /**
  * WavLM Model with a frame classification head on top for tasks like Speaker Diarization.
- * 
+ *
  * **Example:** Perform speaker diarization with `WavLMForAudioFrameClassification`.
  * ```javascript
  * import { AutoProcessor, AutoModelForAudioFrameClassification, read_audio } from '@huggingface/transformers';
- * 
+ *
  * // Read and preprocess audio
  * const processor = await AutoProcessor.from_pretrained('Xenova/wavlm-base-plus-sd');
  * const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/jfk.wav';
  * const audio = await read_audio(url, 16000);
  * const inputs = await processor(audio);
- * 
+ *
  * // Run model with inputs
  * const model = await AutoModelForAudioFrameClassification.from_pretrained('Xenova/wavlm-base-plus-sd');
  * const { logits } = await model(inputs);
@@ -6544,7 +6581,7 @@ export class WavLMForXVector extends WavLMPreTrainedModel {
  * //     size: 1098
  * //   }
  * // }
- * 
+ *
  * const labels = logits[0].sigmoid().tolist().map(
  *     frames => frames.map(speaker => speaker > 0.5 ? 1 : 0)
  * );
@@ -6568,37 +6605,37 @@ export class WavLMForAudioFrameClassification extends WavLMPreTrainedModel {
     }
 }
 
-export class StyleTextToSpeech2PreTrainedModel extends PreTrainedModel { }
-export class StyleTextToSpeech2Model extends StyleTextToSpeech2PreTrainedModel { }
+export class StyleTextToSpeech2PreTrainedModel extends PreTrainedModel {}
+export class StyleTextToSpeech2Model extends StyleTextToSpeech2PreTrainedModel {}
 
 //////////////////////////////////////////////////
 // SpeechT5 models
 /**
  * An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models.
  */
-export class SpeechT5PreTrainedModel extends PreTrainedModel { };
+export class SpeechT5PreTrainedModel extends PreTrainedModel {}
 
 /**
  * The bare SpeechT5 Encoder-Decoder Model outputting raw hidden-states without any specific pre- or post-nets.
  */
-export class SpeechT5Model extends SpeechT5PreTrainedModel { };
+export class SpeechT5Model extends SpeechT5PreTrainedModel {}
 
 /**
  * SpeechT5 Model with a speech encoder and a text decoder.
- * 
+ *
  * **Example:** Generate speech from text with `SpeechT5ForSpeechToText`.
  * ```javascript
  * import { AutoTokenizer, AutoProcessor, SpeechT5ForTextToSpeech, SpeechT5HifiGan, Tensor } from '@huggingface/transformers';
- * 
+ *
  * // Load the tokenizer and processor
  * const tokenizer = await AutoTokenizer.from_pretrained('Xenova/speecht5_tts');
  * const processor = await AutoProcessor.from_pretrained('Xenova/speecht5_tts');
- * 
+ *
  * // Load the models
  * // NOTE: We use the full-precision versions as they are more accurate
  * const model = await SpeechT5ForTextToSpeech.from_pretrained('Xenova/speecht5_tts', { dtype: 'fp32' });
  * const vocoder = await SpeechT5HifiGan.from_pretrained('Xenova/speecht5_hifigan', { dtype: 'fp32' });
- * 
+ *
  * // Load speaker embeddings from URL
  * const speaker_embeddings_data = new Float32Array(
  *     await (await fetch('https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/speaker_embeddings.bin')).arrayBuffer()
@@ -6608,10 +6645,10 @@ export class SpeechT5Model extends SpeechT5PreTrainedModel { };
  *     speaker_embeddings_data,
  *     [1, speaker_embeddings_data.length]
  * )
- * 
+ *
  * // Run tokenization
  * const { input_ids } = tokenizer('Hello, my dog is cute');
- * 
+ *
  * // Generate waveform
  * const { waveform } = await model.generate_speech(input_ids, speaker_embeddings, { vocoder });
  * console.log(waveform)
@@ -6623,13 +6660,12 @@ export class SpeechT5Model extends SpeechT5PreTrainedModel { };
  * // }
  * ```
  */
-export class SpeechT5ForSpeechToText extends SpeechT5PreTrainedModel { }
+export class SpeechT5ForSpeechToText extends SpeechT5PreTrainedModel {}
 
 /**
  * SpeechT5 Model with a text encoder and a speech decoder.
  */
 export class SpeechT5ForTextToSpeech extends SpeechT5PreTrainedModel {
-
     /**
      * @typedef {Object} SpeechOutput
      * @property {Tensor} [spectrogram] The predicted log-mel spectrogram of shape
@@ -6651,17 +6687,20 @@ export class SpeechT5ForTextToSpeech extends SpeechT5PreTrainedModel {
      * @param {boolean} [options.output_cross_attentions=false] Whether or not to return the attentions tensors of the decoder's cross-attention layers.
      * @returns {Promise} A promise which resolves to an object containing the spectrogram, waveform, and cross-attention tensors.
      */
-    async generate_speech(input_values, speaker_embeddings, {
-        threshold = 0.5,
-        minlenratio = 0.0,
-        maxlenratio = 20.0,
-        vocoder = null,
-        // output_cross_attentions = false, // TODO add
-    } = {}) {
-
+    async generate_speech(
+        input_values,
+        speaker_embeddings,
+        {
+            threshold = 0.5,
+            minlenratio = 0.0,
+            maxlenratio = 20.0,
+            vocoder = null,
+            // output_cross_attentions = false, // TODO add
+        } = {},
+    ) {
         const model_inputs = {
-            input_ids: input_values
-        }
+            input_ids: input_values,
+        };
 
         const { encoder_outputs, encoder_attention_mask } = await encoderForward(this, model_inputs);
 
@@ -6686,11 +6725,7 @@ export class SpeechT5ForTextToSpeech extends SpeechT5PreTrainedModel {
             if (decoder_outputs) {
                 output_sequence = decoder_outputs.output_sequence_out;
             } else {
-                output_sequence = new Tensor(
-                    'float32',
-                    new Float32Array(num_mel_bins),
-                    [1, 1, num_mel_bins],
-                )
+                output_sequence = new Tensor('float32', new Float32Array(num_mel_bins), [1, 1, num_mel_bins]);
             }
             let decoderFeeds = {
                 use_cache_branch,
@@ -6707,10 +6742,11 @@ export class SpeechT5ForTextToSpeech extends SpeechT5PreTrainedModel {
             const { prob, spectrum } = decoder_outputs;
             spectrogramParts.push(spectrum);
 
-            if (idx >= minlen && (
+            if (
+                idx >= minlen &&
                 // Finished when stop token or maximum length is reached.
-                Array.from(prob.data).filter(p => p >= threshold).length > 0 || idx >= maxlen
-            )) {
+                (Array.from(prob.data).filter((p) => p >= threshold).length > 0 || idx >= maxlen)
+            ) {
                 break;
             }
         }
@@ -6722,13 +6758,13 @@ export class SpeechT5ForTextToSpeech extends SpeechT5PreTrainedModel {
             spectrogram,
             waveform,
             // cross_attentions: null, // TODO add
-        }
+        };
     }
 }
 
 /**
  * HiFi-GAN vocoder.
- * 
+ *
  * See [SpeechT5ForSpeechToText](./models#module_models.SpeechT5ForSpeechToText) for example usage.
  */
 export class SpeechT5HifiGan extends PreTrainedModel {
@@ -6736,89 +6772,84 @@ export class SpeechT5HifiGan extends PreTrainedModel {
 }
 //////////////////////////////////////////////////
 
-
 //////////////////////////////////////////////////
 // TrOCR models
-export class TrOCRPreTrainedModel extends PreTrainedModel { }
+export class TrOCRPreTrainedModel extends PreTrainedModel {}
 
 /**
  * The TrOCR Decoder with a language modeling head.
  */
-export class TrOCRForCausalLM extends TrOCRPreTrainedModel { }
+export class TrOCRForCausalLM extends TrOCRPreTrainedModel {}
 
 //////////////////////////////////////////////////
 
-
 //////////////////////////////////////////////////
 // Mistral models
 /**
  * The bare Mistral Model outputting raw hidden-states without any specific head on top.
  */
-export class MistralPreTrainedModel extends PreTrainedModel { }
+export class MistralPreTrainedModel extends PreTrainedModel {}
 
-export class MistralModel extends MistralPreTrainedModel { }
+export class MistralModel extends MistralPreTrainedModel {}
 
-export class MistralForCausalLM extends MistralPreTrainedModel { }
+export class MistralForCausalLM extends MistralPreTrainedModel {}
 //////////////////////////////////////////////////
 
 //////////////////////////////////////////////////
 // ERNIE-4.5 models
-export class Ernie4_5_PretrainedModel extends PreTrainedModel { }
+export class Ernie4_5_PretrainedModel extends PreTrainedModel {}
 
-export class Ernie4_5_Model extends Ernie4_5_PretrainedModel { }
+export class Ernie4_5_Model extends Ernie4_5_PretrainedModel {}
 
-export class Ernie4_5_ForCausalLM extends Ernie4_5_PretrainedModel { }
+export class Ernie4_5_ForCausalLM extends Ernie4_5_PretrainedModel {}
 //////////////////////////////////////////////////
 
-
 //////////////////////////////////////////////////
 // Starcoder2 models
 /**
  * The bare Starcoder2 Model outputting raw hidden-states without any specific head on top.
  */
-export class Starcoder2PreTrainedModel extends PreTrainedModel { }
+export class Starcoder2PreTrainedModel extends PreTrainedModel {}
 
-export class Starcoder2Model extends Starcoder2PreTrainedModel { }
+export class Starcoder2Model extends Starcoder2PreTrainedModel {}
 
-export class Starcoder2ForCausalLM extends Starcoder2PreTrainedModel { }
+export class Starcoder2ForCausalLM extends Starcoder2PreTrainedModel {}
 //////////////////////////////////////////////////
 
-
 //////////////////////////////////////////////////
 // Falcon models
 /**
  * The bare Falcon Model outputting raw hidden-states without any specific head on top.
  */
-export class FalconPreTrainedModel extends PreTrainedModel { }
+export class FalconPreTrainedModel extends PreTrainedModel {}
 
-export class FalconModel extends FalconPreTrainedModel { }
+export class FalconModel extends FalconPreTrainedModel {}
 
-export class FalconForCausalLM extends FalconPreTrainedModel { }
+export class FalconForCausalLM extends FalconPreTrainedModel {}
 //////////////////////////////////////////////////
 
-
 //////////////////////////////////////////////////
 // CLAP models
-export class ClapPreTrainedModel extends PreTrainedModel { }
+export class ClapPreTrainedModel extends PreTrainedModel {}
 
-export class ClapModel extends ClapPreTrainedModel { }
+export class ClapModel extends ClapPreTrainedModel {}
 
 /**
  * CLAP Text Model with a projection layer on top (a linear layer on top of the pooled output).
- * 
+ *
  * **Example:** Compute text embeddings with `ClapTextModelWithProjection`.
- * 
+ *
  * ```javascript
  * import { AutoTokenizer, ClapTextModelWithProjection } from '@huggingface/transformers';
- * 
+ *
  * // Load tokenizer and text model
  * const tokenizer = await AutoTokenizer.from_pretrained('Xenova/clap-htsat-unfused');
  * const text_model = await ClapTextModelWithProjection.from_pretrained('Xenova/clap-htsat-unfused');
- * 
+ *
  * // Run tokenization
  * const texts = ['a sound of a cat', 'a sound of a dog'];
  * const text_inputs = tokenizer(texts, { padding: true, truncation: true });
- * 
+ *
  * // Compute embeddings
  * const { text_embeds } = await text_model(text_inputs);
  * // Tensor {
@@ -6842,20 +6873,20 @@ export class ClapTextModelWithProjection extends ClapPreTrainedModel {
 
 /**
  * CLAP Audio Model with a projection layer on top (a linear layer on top of the pooled output).
- * 
+ *
  * **Example:** Compute audio embeddings with `ClapAudioModelWithProjection`.
- * 
+ *
  * ```javascript
  * import { AutoProcessor, ClapAudioModelWithProjection, read_audio } from '@huggingface/transformers';
- * 
+ *
  * // Load processor and audio model
  * const processor = await AutoProcessor.from_pretrained('Xenova/clap-htsat-unfused');
  * const audio_model = await ClapAudioModelWithProjection.from_pretrained('Xenova/clap-htsat-unfused');
- * 
+ *
  * // Read audio and run processor
  * const audio = await read_audio('https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/cat_meow.wav');
  * const audio_inputs = await processor(audio);
- * 
+ *
  * // Compute embeddings
  * const { audio_embeds } = await audio_model(audio_inputs);
  * // Tensor {
@@ -6878,25 +6909,24 @@ export class ClapAudioModelWithProjection extends ClapPreTrainedModel {
 }
 //////////////////////////////////////////////////
 
-
 //////////////////////////////////////////////////
 // VITS models
-export class VitsPreTrainedModel extends PreTrainedModel { }
+export class VitsPreTrainedModel extends PreTrainedModel {}
 
 /**
  * The complete VITS model, for text-to-speech synthesis.
- * 
+ *
  * **Example:** Generate speech from text with `VitsModel`.
  * ```javascript
  * import { AutoTokenizer, VitsModel } from '@huggingface/transformers';
- * 
+ *
  * // Load the tokenizer and model
  * const tokenizer = await AutoTokenizer.from_pretrained('Xenova/mms-tts-eng');
  * const model = await VitsModel.from_pretrained('Xenova/mms-tts-eng');
- * 
+ *
  * // Run tokenization
  * const inputs = tokenizer('I love transformers');
- * 
+ *
  * // Generate waveform
  * const { waveform } = await model(inputs);
  * // Tensor {
@@ -6921,48 +6951,47 @@ export class VitsModel extends VitsPreTrainedModel {
 
 //////////////////////////////////////////////////
 // Segformer models
-export class SegformerPreTrainedModel extends PreTrainedModel { }
+export class SegformerPreTrainedModel extends PreTrainedModel {}
 
 /**
  * The bare SegFormer encoder (Mix-Transformer) outputting raw hidden-states without any specific head on top.
  */
-export class SegformerModel extends SegformerPreTrainedModel { }
+export class SegformerModel extends SegformerPreTrainedModel {}
 
 /**
  * SegFormer Model transformer with an image classification head on top (a linear layer on top of the final hidden states) e.g. for ImageNet.
  */
-export class SegformerForImageClassification extends SegformerPreTrainedModel { }
+export class SegformerForImageClassification extends SegformerPreTrainedModel {}
 
 /**
  * SegFormer Model transformer with an all-MLP decode head on top e.g. for ADE20k, CityScapes.
  */
-export class SegformerForSemanticSegmentation extends SegformerPreTrainedModel { }
+export class SegformerForSemanticSegmentation extends SegformerPreTrainedModel {}
 
 //////////////////////////////////////////////////
 
 //////////////////////////////////////////////////
 // StableLm models
-export class StableLmPreTrainedModel extends PreTrainedModel { }
+export class StableLmPreTrainedModel extends PreTrainedModel {}
 
 /**
  * The bare StableLm Model transformer outputting raw hidden-states without any specific head on top.
  */
-export class StableLmModel extends StableLmPreTrainedModel { }
+export class StableLmModel extends StableLmPreTrainedModel {}
 
 /**
  * StableLm Model with a `language modeling` head on top for Causal Language Modeling (with past).
  */
-export class StableLmForCausalLM extends StableLmPreTrainedModel { }
+export class StableLmForCausalLM extends StableLmPreTrainedModel {}
 //////////////////////////////////////////////////
 
-
 //////////////////////////////////////////////////
-export class EfficientNetPreTrainedModel extends PreTrainedModel { }
+export class EfficientNetPreTrainedModel extends PreTrainedModel {}
 
 /**
  * The bare EfficientNet model outputting raw features without any specific head on top.
  */
-export class EfficientNetModel extends EfficientNetPreTrainedModel { }
+export class EfficientNetModel extends EfficientNetPreTrainedModel {}
 
 /**
  * EfficientNet Model with an image classification head on top (a linear layer on top of the pooled features).
@@ -6979,36 +7008,36 @@ export class EfficientNetForImageClassification extends EfficientNetPreTrainedMo
 
 //////////////////////////////////////////////////
 // Musicgen models
-export class MusicgenPreTrainedModel extends PreTrainedModel { }
+export class MusicgenPreTrainedModel extends PreTrainedModel {}
 
 /**
  * The bare Musicgen decoder model outputting raw hidden-states without any specific head on top.
  */
-export class MusicgenModel extends MusicgenPreTrainedModel { }
+export class MusicgenModel extends MusicgenPreTrainedModel {}
 
 /**
  * The MusicGen decoder model with a language modelling head on top.
  */
-export class MusicgenForCausalLM extends MusicgenPreTrainedModel { }
+export class MusicgenForCausalLM extends MusicgenPreTrainedModel {}
 
 /**
  * The composite MusicGen model with a text encoder, audio encoder and Musicgen decoder,
  * for music generation tasks with one or both of text and audio prompts.
- * 
+ *
  * **Example:** Generate music from text with `Xenova/musicgen-small`.
  * ```javascript
  * import { AutoTokenizer, MusicgenForConditionalGeneration } from '@huggingface/transformers';
- * 
+ *
  * // Load tokenizer and model
  * const tokenizer = await AutoTokenizer.from_pretrained('Xenova/musicgen-small');
  * const model = await MusicgenForConditionalGeneration.from_pretrained(
  *   'Xenova/musicgen-small', { dtype: 'fp32' }
  * );
- * 
+ *
  * // Prepare text input
  * const prompt = '80s pop track with bassy drums and synth';
  * const inputs = tokenizer(prompt);
- * 
+ *
  * // Generate audio
  * const audio_values = await model.generate({
  *   ...inputs,
@@ -7016,17 +7045,18 @@ export class MusicgenForCausalLM extends MusicgenPreTrainedModel { }
  *   do_sample: true,
  *   guidance_scale: 3,
  * });
- * 
+ *
  * // (Optional) Write the output to a WAV file
  * import wavefile from 'wavefile';
  * import fs from 'fs';
- * 
+ *
  * const wav = new wavefile.WaveFile();
  * wav.fromScratch(1, model.config.audio_encoder.sampling_rate, '32f', audio_values.data);
  * fs.writeFileSync('musicgen_out.wav', wav.toBuffer());
  * ```
  */
-export class MusicgenForConditionalGeneration extends PreTrainedModel { // NOTE: not MusicgenPreTrainedModel
+export class MusicgenForConditionalGeneration extends PreTrainedModel {
+    // NOTE: not MusicgenPreTrainedModel
     forward_params = [
         'input_ids',
         'attention_mask',
@@ -7046,7 +7076,7 @@ export class MusicgenForConditionalGeneration extends PreTrainedModel { // NOTE:
         const [bs_x_codebooks, seqLength] = outputs.dims;
         // @ts-expect-error TS2339
         const num_codebooks = this.config.decoder.num_codebooks;
-        const upperBound = (seqLength - num_codebooks);
+        const upperBound = seqLength - num_codebooks;
 
         let newDataSize = 0;
         for (let i = 0; i < outputs.size; ++i) {
@@ -7055,7 +7085,7 @@ export class MusicgenForConditionalGeneration extends PreTrainedModel { // NOTE:
                 continue;
             }
 
-            const row = (i % seqLength);
+            const row = i % seqLength;
             const col = Math.floor(i / seqLength) % num_codebooks;
 
             const diff = row - col;
@@ -7067,21 +7097,16 @@ export class MusicgenForConditionalGeneration extends PreTrainedModel { // NOTE:
         const batch_size = Math.floor(bs_x_codebooks / num_codebooks);
         const inferred = newDataSize / (batch_size * num_codebooks);
         // TODO: assert `inferred` is an integer
-        return new Tensor(
-            outputs.type,
-            outputs.data.slice(0, newDataSize),
-            [batch_size, num_codebooks, inferred]
-        );
+        return new Tensor(outputs.type, outputs.data.slice(0, newDataSize), [batch_size, num_codebooks, inferred]);
     }
 
-
     prepare_inputs_for_generation(input_ids, model_inputs, generation_config) {
         // apply the delay pattern mask
         let clonedInputIds = structuredClone(input_ids);
         for (let i = 0; i < clonedInputIds.length; ++i) {
             for (let j = 0; j < clonedInputIds[i].length; ++j) {
                 // @ts-expect-error TS2339
-                if ((i % this.config.decoder.num_codebooks) >= j) {
+                if (i % this.config.decoder.num_codebooks >= j) {
                     // @ts-expect-error TS2339
                     clonedInputIds[i][j] = BigInt(this.config.decoder.pad_token_id);
                 }
@@ -7104,16 +7129,15 @@ export class MusicgenForConditionalGeneration extends PreTrainedModel { // NOTE:
      * @returns {Promise} The output of the model, which can contain the generated token ids, attentions, and scores.
      */
     async generate(options) {
-
         const output_ids = await super.generate(options);
 
         // apply the pattern mask to the final ids
         // tensor: int64[1,batch_size,4,chunk_length]
-        const audio_codes = this._apply_and_filter_by_delay_pattern_mask(
-            /** @type {Tensor} */(output_ids)
-        ).unsqueeze_(0); // append the frame dimension back to the audio codes
+        const audio_codes = this._apply_and_filter_by_delay_pattern_mask(/** @type {Tensor} */ (output_ids)).unsqueeze_(
+            0,
+        ); // append the frame dimension back to the audio codes
 
-        const { audio_values } = await sessionRun(this.sessions['encodec_decode'], { audio_codes })
+        const { audio_values } = await sessionRun(this.sessions['encodec_decode'], { audio_codes });
 
         return audio_values;
     }
@@ -7122,12 +7146,12 @@ export class MusicgenForConditionalGeneration extends PreTrainedModel { // NOTE:
 
 //////////////////////////////////////////////////
 // MobileNetV1 models
-export class MobileNetV1PreTrainedModel extends PreTrainedModel { }
+export class MobileNetV1PreTrainedModel extends PreTrainedModel {}
 
 /**
  * The bare MobileNetV1 model outputting raw hidden-states without any specific head on top.
  */
-export class MobileNetV1Model extends MobileNetV1PreTrainedModel { }
+export class MobileNetV1Model extends MobileNetV1PreTrainedModel {}
 
 /**
  * MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features),
@@ -7142,17 +7166,17 @@ export class MobileNetV1ForImageClassification extends MobileNetV1PreTrainedMode
     }
 }
 
-export class MobileNetV1ForSemanticSegmentation extends MobileNetV1PreTrainedModel { }
+export class MobileNetV1ForSemanticSegmentation extends MobileNetV1PreTrainedModel {}
 //////////////////////////////////////////////////
 
 //////////////////////////////////////////////////
 // MobileNetV2 models
-export class MobileNetV2PreTrainedModel extends PreTrainedModel { }
+export class MobileNetV2PreTrainedModel extends PreTrainedModel {}
 
 /**
  * The bare MobileNetV2 model outputting raw hidden-states without any specific head on top.
  */
-export class MobileNetV2Model extends MobileNetV2PreTrainedModel { }
+export class MobileNetV2Model extends MobileNetV2PreTrainedModel {}
 
 /**
  * MobileNetV2 model with an image classification head on top (a linear layer on top of the pooled features),
@@ -7166,17 +7190,17 @@ export class MobileNetV2ForImageClassification extends MobileNetV2PreTrainedMode
         return new SequenceClassifierOutput(await super._call(model_inputs));
     }
 }
-export class MobileNetV2ForSemanticSegmentation extends MobileNetV2PreTrainedModel { }
+export class MobileNetV2ForSemanticSegmentation extends MobileNetV2PreTrainedModel {}
 //////////////////////////////////////////////////
 
 //////////////////////////////////////////////////
 // MobileNetV3 models
-export class MobileNetV3PreTrainedModel extends PreTrainedModel { }
+export class MobileNetV3PreTrainedModel extends PreTrainedModel {}
 
 /**
  * The bare MobileNetV3 model outputting raw hidden-states without any specific head on top.
  */
-export class MobileNetV3Model extends MobileNetV3PreTrainedModel { }
+export class MobileNetV3Model extends MobileNetV3PreTrainedModel {}
 
 /**
  * MobileNetV3 model with an image classification head on top (a linear layer on top of the pooled features),
@@ -7190,17 +7214,17 @@ export class MobileNetV3ForImageClassification extends MobileNetV3PreTrainedMode
         return new SequenceClassifierOutput(await super._call(model_inputs));
     }
 }
-export class MobileNetV3ForSemanticSegmentation extends MobileNetV3PreTrainedModel { }
+export class MobileNetV3ForSemanticSegmentation extends MobileNetV3PreTrainedModel {}
 //////////////////////////////////////////////////
 
 //////////////////////////////////////////////////
 // MobileNetV4 models
-export class MobileNetV4PreTrainedModel extends PreTrainedModel { }
+export class MobileNetV4PreTrainedModel extends PreTrainedModel {}
 
 /**
  * The bare MobileNetV4 model outputting raw hidden-states without any specific head on top.
  */
-export class MobileNetV4Model extends MobileNetV4PreTrainedModel { }
+export class MobileNetV4Model extends MobileNetV4PreTrainedModel {}
 
 /**
  * MobileNetV4 model with an image classification head on top (a linear layer on top of the pooled features),
@@ -7214,22 +7238,22 @@ export class MobileNetV4ForImageClassification extends MobileNetV4PreTrainedMode
         return new SequenceClassifierOutput(await super._call(model_inputs));
     }
 }
-export class MobileNetV4ForSemanticSegmentation extends MobileNetV4PreTrainedModel { }
+export class MobileNetV4ForSemanticSegmentation extends MobileNetV4PreTrainedModel {}
 //////////////////////////////////////////////////
 
 //////////////////////////////////////////////////
 // Decision Transformer models
-export class DecisionTransformerPreTrainedModel extends PreTrainedModel { }
+export class DecisionTransformerPreTrainedModel extends PreTrainedModel {}
 
 /**
  * The model builds upon the GPT2 architecture to perform autoregressive prediction of actions in an offline RL setting.
  * Refer to the paper for more details: https://huggingface.co/papers/2106.01345
  */
-export class DecisionTransformerModel extends DecisionTransformerPreTrainedModel { }
+export class DecisionTransformerModel extends DecisionTransformerPreTrainedModel {}
 
 //////////////////////////////////////////////////
 
-export class MultiModalityPreTrainedModel extends PreTrainedModel { }
+export class MultiModalityPreTrainedModel extends PreTrainedModel {}
 export class MultiModalityCausalLM extends MultiModalityPreTrainedModel {
     forward_params = [
         // prepare_inputs_embeds
@@ -7269,25 +7293,24 @@ export class MultiModalityCausalLM extends MultiModalityPreTrainedModel {
             output_1 = await sessionRun(session, prep_inputs);
         } else {
             const session = this.sessions['gen_img_embeds'];
-            const prep_inputs = pick({
-                image_ids: model_inputs.input_ids,
-            }, session.inputNames);
+            const prep_inputs = pick(
+                {
+                    image_ids: model_inputs.input_ids,
+                },
+                session.inputNames,
+            );
             output_1 = await sessionRun(session, prep_inputs);
         }
 
-        const input_2 = { ...model_inputs, ...output_1 }
+        const input_2 = { ...model_inputs, ...output_1 };
         const output_2 = await decoderForward(this, input_2);
 
-        const head = this.sessions[
-            mode === 'text'
-                ? 'lm_head'
-                : 'gen_head'
-        ];
+        const head = this.sessions[mode === 'text' ? 'lm_head' : 'gen_head'];
         if (!head) {
             throw new Error(`Unable to find "${head}" generation head`);
         }
 
-        const output_3 = await sessionRun(head, pick(output_2, head.inputNames))
+        const output_3 = await sessionRun(head, pick(output_2, head.inputNames));
 
         return {
             ...output_1,
@@ -7313,7 +7336,7 @@ export class MultiModalityCausalLM extends MultiModalityPreTrainedModel {
         const start_num_tokens = (options.inputs ?? options[this.main_input_name]).dims[1];
         const all_tokens = await super.generate(options);
 
-        const generated_tokens = (/** @type {Tensor} */(all_tokens)).slice(null, [start_num_tokens, null])
+        const generated_tokens = /** @type {Tensor} */ (all_tokens).slice(null, [start_num_tokens, null]);
 
         const image_decode = this.sessions['image_decode'];
         const { decoded_image } = await sessionRun(image_decode, {
@@ -7350,7 +7373,7 @@ export class MgpstrModelOutput extends ModelOutput {
     }
 }
 
-export class MgpstrPreTrainedModel extends PreTrainedModel { }
+export class MgpstrPreTrainedModel extends PreTrainedModel {}
 
 /**
  * MGP-STR Model transformer with three classification heads on top
@@ -7367,47 +7390,40 @@ export class MgpstrForSceneTextRecognition extends MgpstrPreTrainedModel {
 
 //////////////////////////////////////////////////
 // PatchTST Transformer models
-export class PatchTSTPreTrainedModel extends PreTrainedModel { }
+export class PatchTSTPreTrainedModel extends PreTrainedModel {}
 
 /**
  * The bare PatchTST Model outputting raw hidden-states without any specific head.
  */
-export class PatchTSTModel extends PatchTSTPreTrainedModel { }
+export class PatchTSTModel extends PatchTSTPreTrainedModel {}
 
 /**
  * The PatchTST for prediction model.
  */
-export class PatchTSTForPrediction extends PatchTSTPreTrainedModel { }
+export class PatchTSTForPrediction extends PatchTSTPreTrainedModel {}
 //////////////////////////////////////////////////
 
 //////////////////////////////////////////////////
 // PatchTSMixer Transformer models
-export class PatchTSMixerPreTrainedModel extends PreTrainedModel { }
+export class PatchTSMixerPreTrainedModel extends PreTrainedModel {}
 
 /**
  * The bare PatchTSMixer Model outputting raw hidden-states without any specific head.
  */
-export class PatchTSMixerModel extends PatchTSMixerPreTrainedModel { }
+export class PatchTSMixerModel extends PatchTSMixerPreTrainedModel {}
 
 /**
  * The PatchTSMixer for prediction model.
  */
-export class PatchTSMixerForPrediction extends PatchTSMixerPreTrainedModel { }
+export class PatchTSMixerForPrediction extends PatchTSMixerPreTrainedModel {}
 //////////////////////////////////////////////////
 
 //////////////////////////////////////////////////
 export class UltravoxPreTrainedModel extends PreTrainedModel {
-    forward_params = [
-        'input_ids',
-        'attention_mask',
-        'position_ids',
-        'audio_values',
-        'past_key_values',
-    ];
+    forward_params = ['input_ids', 'attention_mask', 'position_ids', 'audio_values', 'past_key_values'];
 }
 
 export class UltravoxModel extends UltravoxPreTrainedModel {
-
     _merge_input_ids_with_audio_features(kwargs) {
         const audio_hidden_size = kwargs.audio_features.dims.at(-1);
         const reshaped_audio_features = kwargs.audio_features.view(-1, audio_hidden_size);
@@ -7417,12 +7433,12 @@ export class UltravoxModel extends UltravoxPreTrainedModel {
             audio_token_id: this.config.ignore_index ?? this.config.audio_token_id,
             ...kwargs,
             audio_features: reshaped_audio_features,
-        })
+        });
     }
 }
 //////////////////////////////////////////////////
 
-export class VoxtralForConditionalGeneration extends UltravoxModel { }
+export class VoxtralForConditionalGeneration extends UltravoxModel {}
 
 //////////////////////////////////////////////////
 // Mimi models
@@ -7499,7 +7515,6 @@ export class MimiDecoderModel extends MimiPreTrainedModel {
 }
 //////////////////////////////////////////////////
 
-
 //////////////////////////////////////////////////
 // Dac models
 export class DacPreTrainedModel extends PreTrainedModel {
@@ -7575,7 +7590,6 @@ export class DacDecoderModel extends DacPreTrainedModel {
 }
 //////////////////////////////////////////////////
 
-
 //////////////////////////////////////////////////
 // Snac models
 export class SnacPreTrainedModel extends PreTrainedModel {
@@ -7645,27 +7659,28 @@ export class PretrainedMixin {
     static MODEL_CLASS_MAPPINGS = null;
 
     /**
-     * Whether to attempt to instantiate the base class (`PretrainedModel`) if 
+     * Whether to attempt to instantiate the base class (`PretrainedModel`) if
      * the model type is not found in the mapping.
      */
     static BASE_IF_FAIL = false;
 
-
     /** @type {typeof PreTrainedModel.from_pretrained} */
-    static async from_pretrained(pretrained_model_name_or_path, {
-        progress_callback = null,
-        config = null,
-        cache_dir = null,
-        local_files_only = false,
-        revision = 'main',
-        model_file_name = null,
-        subfolder = 'onnx',
-        device = null,
-        dtype = null,
-        use_external_data_format = null,
-        session_options = {},
-    } = {}) {
-
+    static async from_pretrained(
+        pretrained_model_name_or_path,
+        {
+            progress_callback = null,
+            config = null,
+            cache_dir = null,
+            local_files_only = false,
+            revision = 'main',
+            model_file_name = null,
+            subfolder = 'onnx',
+            device = null,
+            dtype = null,
+            use_external_data_format = null,
+            session_options = {},
+        } = {},
+    ) {
         const options = {
             progress_callback,
             config,
@@ -7678,11 +7693,11 @@ export class PretrainedMixin {
             dtype,
             use_external_data_format,
             session_options,
-        }
+        };
         options.config = await AutoConfig.from_pretrained(pretrained_model_name_or_path, options);
 
         if (!this.MODEL_CLASS_MAPPINGS) {
-            throw new Error("`MODEL_CLASS_MAPPINGS` not implemented for this type of `AutoClass`: " + this.name);
+            throw new Error('`MODEL_CLASS_MAPPINGS` not implemented for this type of `AutoClass`: ' + this.name);
         }
         const model_type = options.config.model_type;
         for (const MODEL_CLASS_MAPPING of this.MODEL_CLASS_MAPPINGS) {
@@ -7701,12 +7716,12 @@ export class PretrainedMixin {
         }
 
         if (this.BASE_IF_FAIL) {
-            if (!(CUSTOM_ARCHITECTURES.has(model_type))) {
+            if (!CUSTOM_ARCHITECTURES.has(model_type)) {
                 console.warn(`Unknown model class "${model_type}", attempting to construct from base class.`);
             }
             return await PreTrainedModel.from_pretrained(pretrained_model_name_or_path, options);
         } else {
-            throw Error(`Unsupported model type: ${model_type}`)
+            throw Error(`Unsupported model type: ${model_type}`);
         }
     }
 }
@@ -7973,7 +7988,6 @@ const MODEL_FOR_MULTIMODALITY_MAPPING_NAMES = new Map([
     ['multi_modality', ['MultiModalityCausalLM', MultiModalityCausalLM]],
 ]);
 
-
 const MODEL_FOR_MASKED_LM_MAPPING_NAMES = new Map([
     ['bert', ['BertForMaskedLM', BertForMaskedLM]],
     ['neobert', ['NeoBertForMaskedLM', NeoBertForMaskedLM]],
@@ -8038,7 +8052,6 @@ const MODEL_FOR_AUDIO_TEXT_TO_TEXT_MAPPING_NAMES = new Map([
     ['voxtral', ['VoxtralForConditionalGeneration', VoxtralForConditionalGeneration]],
 ]);
 
-
 const MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES = new Map([
     ['vision-encoder-decoder', ['VisionEncoderDecoderModel', VisionEncoderDecoderModel]],
 ]);
@@ -8106,9 +8119,7 @@ const MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING_NAMES = new Map([
     ['maskformer', ['MaskFormerForInstanceSegmentation', MaskFormerForInstanceSegmentation]],
 ]);
 
-const MODEL_FOR_MASK_GENERATION_MAPPING_NAMES = new Map([
-    ['sam', ['SamModel', SamModel]],
-]);
+const MODEL_FOR_MASK_GENERATION_MAPPING_NAMES = new Map([['sam', ['SamModel', SamModel]]]);
 
 const MODEL_FOR_CTC_MAPPING_NAMES = new Map([
     ['wav2vec2', ['Wav2Vec2ForCTC', Wav2Vec2ForCTC]],
@@ -8129,9 +8140,7 @@ const MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES = new Map([
     ['audio-spectrogram-transformer', ['ASTForAudioClassification', ASTForAudioClassification]],
 ]);
 
-const MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES = new Map([
-    ['wavlm', ['WavLMForXVector', WavLMForXVector]],
-]);
+const MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES = new Map([['wavlm', ['WavLMForXVector', WavLMForXVector]]]);
 
 const MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES = new Map([
     ['unispeech-sat', ['UniSpeechSatForAudioFrameClassification', UniSpeechSatForAudioFrameClassification]],
@@ -8147,11 +8156,11 @@ const MODEL_FOR_IMAGE_MATTING_MAPPING_NAMES = new Map([
 const MODEL_FOR_TIME_SERIES_PREDICTION_MAPPING_NAMES = new Map([
     ['patchtst', ['PatchTSTForPrediction', PatchTSTForPrediction]],
     ['patchtsmixer', ['PatchTSMixerForPrediction', PatchTSMixerForPrediction]],
-])
+]);
 
 const MODEL_FOR_IMAGE_TO_IMAGE_MAPPING_NAMES = new Map([
     ['swin2sr', ['Swin2SRForImageSuperResolution', Swin2SRForImageSuperResolution]],
-])
+]);
 
 const MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES = new Map([
     ['dpt', ['DPTForDepthEstimation', DPTForDepthEstimation]],
@@ -8161,15 +8170,15 @@ const MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES = new Map([
     ['depth_pro', ['DepthProForDepthEstimation', DepthProForDepthEstimation]],
     ['metric3d', ['Metric3DForDepthEstimation', Metric3DForDepthEstimation]],
     ['metric3dv2', ['Metric3Dv2ForDepthEstimation', Metric3Dv2ForDepthEstimation]],
-])
+]);
 
 const MODEL_FOR_NORMAL_ESTIMATION_MAPPING_NAMES = new Map([
     ['sapiens', ['SapiensForNormalEstimation', SapiensForNormalEstimation]],
-])
+]);
 
 const MODEL_FOR_POSE_ESTIMATION_MAPPING_NAMES = new Map([
     ['vitpose', ['VitPoseForPoseEstimation', VitPoseForPoseEstimation]],
-])
+]);
 
 // NOTE: This is custom to Transformers.js, and is necessary because certain models
 // (e.g., CLIP) are split into vision and text components
@@ -8177,7 +8186,7 @@ const MODEL_FOR_IMAGE_FEATURE_EXTRACTION_MAPPING_NAMES = new Map([
     ['clip', ['CLIPVisionModelWithProjection', CLIPVisionModelWithProjection]],
     ['siglip', ['SiglipVisionModel', SiglipVisionModel]],
     ['jina_clip', ['JinaCLIPVisionModel', JinaCLIPVisionModel]],
-])
+]);
 
 const MODEL_CLASS_TYPE_MAPPING = [
     // MODEL_MAPPING_NAMES:
@@ -8250,7 +8259,7 @@ const CUSTOM_MAPPING = [
     ['SnacDecoderModel', SnacDecoderModel, MODEL_TYPES.EncoderOnly],
 
     ['Gemma3nForConditionalGeneration', Gemma3nForConditionalGeneration, MODEL_TYPES.ImageAudioTextToText],
-]
+];
 for (const [name, model, type] of CUSTOM_MAPPING) {
     MODEL_TYPE_MAPPING.set(name, type);
     MODEL_CLASS_TO_NAME_MAPPING.set(model, name);
@@ -8264,31 +8273,30 @@ const CUSTOM_ARCHITECTURES = new Map([
     ['ben', MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES],
 ]);
 for (const [name, mapping] of CUSTOM_ARCHITECTURES.entries()) {
-    mapping.set(name, ['PreTrainedModel', PreTrainedModel])
+    mapping.set(name, ['PreTrainedModel', PreTrainedModel]);
     MODEL_TYPE_MAPPING.set(name, MODEL_TYPES.EncoderOnly);
     MODEL_CLASS_TO_NAME_MAPPING.set(PreTrainedModel, name);
     MODEL_NAME_TO_CLASS_MAPPING.set(name, PreTrainedModel);
 }
 
-
 /**
  * Helper class which is used to instantiate pretrained models with the `from_pretrained` function.
  * The chosen model class is determined by the type specified in the model config.
- * 
+ *
  * @example
  * let model = await AutoModel.from_pretrained('Xenova/bert-base-uncased');
  */
 export class AutoModel extends PretrainedMixin {
     /** @type {Map[]} */
     // @ts-ignore
-    static MODEL_CLASS_MAPPINGS = MODEL_CLASS_TYPE_MAPPING.map(x => x[0]);
+    static MODEL_CLASS_MAPPINGS = MODEL_CLASS_TYPE_MAPPING.map((x) => x[0]);
     static BASE_IF_FAIL = true;
 }
 
 /**
  * Helper class which is used to instantiate pretrained sequence classification models with the `from_pretrained` function.
  * The chosen model class is determined by the type specified in the model config.
- * 
+ *
  * @example
  * let model = await AutoModelForSequenceClassification.from_pretrained('Xenova/distilbert-base-uncased-finetuned-sst-2-english');
  */
@@ -8299,7 +8307,7 @@ export class AutoModelForSequenceClassification extends PretrainedMixin {
 /**
  * Helper class which is used to instantiate pretrained token classification models with the `from_pretrained` function.
  * The chosen model class is determined by the type specified in the model config.
- * 
+ *
  * @example
  * let model = await AutoModelForTokenClassification.from_pretrained('Xenova/distilbert-base-multilingual-cased-ner-hrl');
  */
@@ -8310,7 +8318,7 @@ export class AutoModelForTokenClassification extends PretrainedMixin {
 /**
  * Helper class which is used to instantiate pretrained sequence-to-sequence models with the `from_pretrained` function.
  * The chosen model class is determined by the type specified in the model config.
- * 
+ *
  * @example
  * let model = await AutoModelForSeq2SeqLM.from_pretrained('Xenova/t5-small');
  */
@@ -8321,7 +8329,7 @@ export class AutoModelForSeq2SeqLM extends PretrainedMixin {
 /**
  * Helper class which is used to instantiate pretrained sequence-to-sequence speech-to-text models with the `from_pretrained` function.
  * The chosen model class is determined by the type specified in the model config.
- * 
+ *
  * @example
  * let model = await AutoModelForSpeechSeq2Seq.from_pretrained('openai/whisper-tiny.en');
  */
@@ -8332,7 +8340,7 @@ export class AutoModelForSpeechSeq2Seq extends PretrainedMixin {
 /**
  * Helper class which is used to instantiate pretrained sequence-to-sequence text-to-spectrogram models with the `from_pretrained` function.
  * The chosen model class is determined by the type specified in the model config.
- * 
+ *
  * @example
  * let model = await AutoModelForTextToSpectrogram.from_pretrained('microsoft/speecht5_tts');
  */
@@ -8343,7 +8351,7 @@ export class AutoModelForTextToSpectrogram extends PretrainedMixin {
 /**
  * Helper class which is used to instantiate pretrained text-to-waveform models with the `from_pretrained` function.
  * The chosen model class is determined by the type specified in the model config.
- * 
+ *
  * @example
  * let model = await AutoModelForTextToSpectrogram.from_pretrained('facebook/mms-tts-eng');
  */
@@ -8354,7 +8362,7 @@ export class AutoModelForTextToWaveform extends PretrainedMixin {
 /**
  * Helper class which is used to instantiate pretrained causal language models with the `from_pretrained` function.
  * The chosen model class is determined by the type specified in the model config.
- * 
+ *
  * @example
  * let model = await AutoModelForCausalLM.from_pretrained('Xenova/gpt2');
  */
@@ -8365,7 +8373,7 @@ export class AutoModelForCausalLM extends PretrainedMixin {
 /**
  * Helper class which is used to instantiate pretrained masked language models with the `from_pretrained` function.
  * The chosen model class is determined by the type specified in the model config.
- * 
+ *
  * @example
  * let model = await AutoModelForMaskedLM.from_pretrained('Xenova/bert-base-uncased');
  */
@@ -8376,7 +8384,7 @@ export class AutoModelForMaskedLM extends PretrainedMixin {
 /**
  * Helper class which is used to instantiate pretrained question answering models with the `from_pretrained` function.
  * The chosen model class is determined by the type specified in the model config.
- * 
+ *
  * @example
  * let model = await AutoModelForQuestionAnswering.from_pretrained('Xenova/distilbert-base-cased-distilled-squad');
  */
@@ -8387,7 +8395,7 @@ export class AutoModelForQuestionAnswering extends PretrainedMixin {
 /**
  * Helper class which is used to instantiate pretrained vision-to-sequence models with the `from_pretrained` function.
  * The chosen model class is determined by the type specified in the model config.
- * 
+ *
  * @example
  * let model = await AutoModelForVision2Seq.from_pretrained('Xenova/vit-gpt2-image-captioning');
  */
@@ -8398,7 +8406,7 @@ export class AutoModelForVision2Seq extends PretrainedMixin {
 /**
  * Helper class which is used to instantiate pretrained image classification models with the `from_pretrained` function.
  * The chosen model class is determined by the type specified in the model config.
- * 
+ *
  * @example
  * let model = await AutoModelForImageClassification.from_pretrained('Xenova/vit-base-patch16-224');
  */
@@ -8409,7 +8417,7 @@ export class AutoModelForImageClassification extends PretrainedMixin {
 /**
  * Helper class which is used to instantiate pretrained image segmentation models with the `from_pretrained` function.
  * The chosen model class is determined by the type specified in the model config.
- * 
+ *
  * @example
  * let model = await AutoModelForImageSegmentation.from_pretrained('Xenova/detr-resnet-50-panoptic');
  */
@@ -8420,7 +8428,7 @@ export class AutoModelForImageSegmentation extends PretrainedMixin {
 /**
  * Helper class which is used to instantiate pretrained image segmentation models with the `from_pretrained` function.
  * The chosen model class is determined by the type specified in the model config.
- * 
+ *
  * @example
  * let model = await AutoModelForSemanticSegmentation.from_pretrained('nvidia/segformer-b3-finetuned-cityscapes-1024-1024');
  */
@@ -8431,7 +8439,7 @@ export class AutoModelForSemanticSegmentation extends PretrainedMixin {
 /**
  * Helper class which is used to instantiate pretrained universal image segmentation models with the `from_pretrained` function.
  * The chosen model class is determined by the type specified in the model config.
- * 
+ *
  * @example
  * let model = await AutoModelForUniversalSegmentation.from_pretrained('hf-internal-testing/tiny-random-MaskFormerForInstanceSegmentation');
  */
@@ -8442,7 +8450,7 @@ export class AutoModelForUniversalSegmentation extends PretrainedMixin {
 /**
  * Helper class which is used to instantiate pretrained object detection models with the `from_pretrained` function.
  * The chosen model class is determined by the type specified in the model config.
- * 
+ *
  * @example
  * let model = await AutoModelForObjectDetection.from_pretrained('Xenova/detr-resnet-50');
  */
@@ -8454,11 +8462,10 @@ export class AutoModelForZeroShotObjectDetection extends PretrainedMixin {
     static MODEL_CLASS_MAPPINGS = [MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES];
 }
 
-
 /**
  * Helper class which is used to instantiate pretrained mask generation models with the `from_pretrained` function.
  * The chosen model class is determined by the type specified in the model config.
- * 
+ *
  * @example
  * let model = await AutoModelForMaskGeneration.from_pretrained('Xenova/sam-vit-base');
  */
@@ -8621,7 +8628,6 @@ export class QuestionAnsweringModelOutput extends ModelOutput {
     }
 }
 
-
 /**
  * Base class for causal language model (or autoregressive) outputs.
  */
diff --git a/src/models/audio_spectrogram_transformer/feature_extraction_audio_spectrogram_transformer.js b/src/models/audio_spectrogram_transformer/feature_extraction_audio_spectrogram_transformer.js
index dcd9c37d0..057e72a19 100644
--- a/src/models/audio_spectrogram_transformer/feature_extraction_audio_spectrogram_transformer.js
+++ b/src/models/audio_spectrogram_transformer/feature_extraction_audio_spectrogram_transformer.js
@@ -2,9 +2,7 @@ import { FeatureExtractor, validate_audio_inputs } from '../../base/feature_extr
 import { Tensor } from '../../utils/tensor.js';
 import { mel_filter_bank, spectrogram, window_function } from '../../utils/audio.js';
 
-
 export class ASTFeatureExtractor extends FeatureExtractor {
-
     constructor(config) {
         super(config);
 
@@ -16,14 +14,14 @@ export class ASTFeatureExtractor extends FeatureExtractor {
             Math.floor(sampling_rate / 2), // max_frequency
             sampling_rate, // sampling_rate
             null, // norm
-            "kaldi", // mel_scale
+            'kaldi', // mel_scale
             true, // triangularize_in_mel_space
         );
         this.mel_filters = mel_filters;
 
         this.window = window_function(400, 'hann', {
             periodic: false,
-        })
+        });
 
         this.mean = this.config.mean;
         this.std = this.config.std;
@@ -49,17 +47,16 @@ export class ASTFeatureExtractor extends FeatureExtractor {
                 preemphasis: 0.97,
                 mel_filters: this.mel_filters,
                 log_mel: 'log',
-                mel_floor: 1.192092955078125e-07,
+                mel_floor: 1.192092955078125e-7,
                 remove_dc_offset: true,
 
                 // Custom
                 max_num_frames: max_length,
                 transpose: true,
-            }
-        )
+            },
+        );
     }
 
-
     /**
      * Asynchronously extracts features from a given audio using the provided configuration.
      * @param {Float32Array|Float64Array} audio The audio data as a Float32Array/Float64Array.
@@ -79,7 +76,7 @@ export class ASTFeatureExtractor extends FeatureExtractor {
         }
 
         return {
-            input_values: features.unsqueeze_(0)
+            input_values: features.unsqueeze_(0),
         };
     }
 }
diff --git a/src/models/auto/feature_extraction_auto.js b/src/models/auto/feature_extraction_auto.js
index 7429d1d54..c10f5bbef 100644
--- a/src/models/auto/feature_extraction_auto.js
+++ b/src/models/auto/feature_extraction_auto.js
@@ -1,15 +1,17 @@
-
 import { FEATURE_EXTRACTOR_NAME, GITHUB_ISSUE_URL } from '../../utils/constants.js';
 import { getModelJSON } from '../../utils/hub.js';
 import { FeatureExtractor } from '../../base/feature_extraction_utils.js';
 import * as AllFeatureExtractors from '../feature_extractors.js';
 
 export class AutoFeatureExtractor {
-
     /** @type {typeof FeatureExtractor.from_pretrained} */
-    static async from_pretrained(pretrained_model_name_or_path, options={}) {
-
-        const preprocessorConfig = await getModelJSON(pretrained_model_name_or_path, FEATURE_EXTRACTOR_NAME, true, options);
+    static async from_pretrained(pretrained_model_name_or_path, options = {}) {
+        const preprocessorConfig = await getModelJSON(
+            pretrained_model_name_or_path,
+            FEATURE_EXTRACTOR_NAME,
+            true,
+            options,
+        );
 
         // Determine feature extractor class
         const key = preprocessorConfig.feature_extractor_type;
diff --git a/src/models/auto/image_processing_auto.js b/src/models/auto/image_processing_auto.js
index 1acd64fa8..1a9348dc3 100644
--- a/src/models/auto/image_processing_auto.js
+++ b/src/models/auto/image_processing_auto.js
@@ -1,15 +1,17 @@
-
 import { GITHUB_ISSUE_URL, IMAGE_PROCESSOR_NAME } from '../../utils/constants.js';
 import { getModelJSON } from '../../utils/hub.js';
 import { ImageProcessor } from '../../base/image_processors_utils.js';
 import * as AllImageProcessors from '../image_processors.js';
 
 export class AutoImageProcessor {
-
     /** @type {typeof ImageProcessor.from_pretrained} */
-    static async from_pretrained(pretrained_model_name_or_path, options={}) {
-
-        const preprocessorConfig = await getModelJSON(pretrained_model_name_or_path, IMAGE_PROCESSOR_NAME, true, options);
+    static async from_pretrained(pretrained_model_name_or_path, options = {}) {
+        const preprocessorConfig = await getModelJSON(
+            pretrained_model_name_or_path,
+            IMAGE_PROCESSOR_NAME,
+            true,
+            options,
+        );
 
         // Determine image processor class
         const key = preprocessorConfig.image_processor_type ?? preprocessorConfig.feature_extractor_type;
@@ -18,7 +20,9 @@ export class AutoImageProcessor {
         if (!image_processor_class) {
             if (key !== undefined) {
                 // Only log a warning if the class is not found and the key is set.
-                console.warn(`Image processor type '${key}' not found, assuming base ImageProcessor. Please report this at ${GITHUB_ISSUE_URL}.`)
+                console.warn(
+                    `Image processor type '${key}' not found, assuming base ImageProcessor. Please report this at ${GITHUB_ISSUE_URL}.`,
+                );
             }
             image_processor_class = ImageProcessor;
         }
diff --git a/src/models/auto/processing_auto.js b/src/models/auto/processing_auto.js
index 4f955546f..7674974e3 100644
--- a/src/models/auto/processing_auto.js
+++ b/src/models/auto/processing_auto.js
@@ -1,5 +1,3 @@
-
-
 import { IMAGE_PROCESSOR_NAME } from '../../utils/constants.js';
 import { getModelJSON } from '../../utils/hub.js';
 import { Processor } from '../../base/processing_utils.js';
@@ -11,12 +9,12 @@ import * as AllFeatureExtractors from '../feature_extractors.js';
 /**
  * Helper class which is used to instantiate pretrained processors with the `from_pretrained` function.
  * The chosen processor class is determined by the type specified in the processor config.
- * 
+ *
  * **Example:** Load a processor using `from_pretrained`.
  * ```javascript
  * let processor = await AutoProcessor.from_pretrained('openai/whisper-tiny.en');
  * ```
- * 
+ *
  * **Example:** Run an image through a processor.
  * ```javascript
  * let processor = await AutoProcessor.from_pretrained('Xenova/clip-vit-base-patch16');
@@ -39,12 +37,15 @@ import * as AllFeatureExtractors from '../feature_extractors.js';
  * ```
  */
 export class AutoProcessor {
-
     /** @type {typeof Processor.from_pretrained} */
-    static async from_pretrained(pretrained_model_name_or_path, options={}) {
-
-        // TODO: first check for processor.json 
-        const preprocessorConfig = await getModelJSON(pretrained_model_name_or_path, IMAGE_PROCESSOR_NAME, true, options);
+    static async from_pretrained(pretrained_model_name_or_path, options = {}) {
+        // TODO: first check for processor.json
+        const preprocessorConfig = await getModelJSON(
+            pretrained_model_name_or_path,
+            IMAGE_PROCESSOR_NAME,
+            true,
+            options,
+        );
 
         const { image_processor_type, feature_extractor_type, processor_class } = preprocessorConfig;
         if (processor_class && AllProcessors[processor_class]) {
diff --git a/src/models/beit/image_processing_beit.js b/src/models/beit/image_processing_beit.js
index 006399edf..5b952a248 100644
--- a/src/models/beit/image_processing_beit.js
+++ b/src/models/beit/image_processing_beit.js
@@ -1,5 +1,3 @@
-import { 
-    ImageProcessor,
-} from "../../base/image_processors_utils.js";
+import { ImageProcessor } from '../../base/image_processors_utils.js';
 
-export class BeitFeatureExtractor extends ImageProcessor { }
+export class BeitFeatureExtractor extends ImageProcessor {}
diff --git a/src/models/bit/image_processing_bit.js b/src/models/bit/image_processing_bit.js
index 66db82277..7a59a3987 100644
--- a/src/models/bit/image_processing_bit.js
+++ b/src/models/bit/image_processing_bit.js
@@ -1,5 +1,3 @@
-import { 
-    ImageProcessor,
-} from "../../base/image_processors_utils.js";
+import { ImageProcessor } from '../../base/image_processors_utils.js';
 
-export class BitImageProcessor extends ImageProcessor { }
+export class BitImageProcessor extends ImageProcessor {}
diff --git a/src/models/chinese_clip/image_processing_chinese_clip.js b/src/models/chinese_clip/image_processing_chinese_clip.js
index d720eb662..3feed9f62 100644
--- a/src/models/chinese_clip/image_processing_chinese_clip.js
+++ b/src/models/chinese_clip/image_processing_chinese_clip.js
@@ -1,5 +1,3 @@
-import { 
-    ImageProcessor,
-} from "../../base/image_processors_utils.js";
+import { ImageProcessor } from '../../base/image_processors_utils.js';
 
-export class ChineseCLIPFeatureExtractor extends ImageProcessor { }
+export class ChineseCLIPFeatureExtractor extends ImageProcessor {}
diff --git a/src/models/clap/feature_extraction_clap.js b/src/models/clap/feature_extraction_clap.js
index 5261a10b5..605748616 100644
--- a/src/models/clap/feature_extraction_clap.js
+++ b/src/models/clap/feature_extraction_clap.js
@@ -2,9 +2,7 @@ import { FeatureExtractor, validate_audio_inputs } from '../../base/feature_extr
 import { Tensor } from '../../utils/tensor.js';
 import { mel_filter_bank, spectrogram, window_function } from '../../utils/audio.js';
 
-
 export class ClapFeatureExtractor extends FeatureExtractor {
-
     constructor(config) {
         super(config);
 
@@ -15,7 +13,7 @@ export class ClapFeatureExtractor extends FeatureExtractor {
             this.config.frequency_max, // max_frequency
             this.config.sampling_rate, // sampling_rate
             null, // norm
-            "htk", // mel_scale
+            'htk', // mel_scale
         );
 
         this.mel_filters_slaney = mel_filter_bank(
@@ -24,18 +22,16 @@ export class ClapFeatureExtractor extends FeatureExtractor {
             this.config.frequency_min, // min_frequency
             this.config.frequency_max, // max_frequency
             this.config.sampling_rate, // sampling_rate
-            "slaney", // norm
-            "slaney", // mel_scale
+            'slaney', // norm
+            'slaney', // mel_scale
         );
 
-        this.window = window_function(this.config.fft_window_size, 'hann')
-
+        this.window = window_function(this.config.fft_window_size, 'hann');
     }
 
-
     /**
      * Extracts the mel spectrogram and prepares it for the mode based on the `truncation` and `padding` arguments.
-     * 
+     *
      * Four different path are possible:
      *   - `truncation="fusion"` and the length of the waveform is greater than the max length: the mel spectrogram
      *     will be computed on the entire audio. 3 random crops and a dowsampled version of the full mel spectrogram
@@ -46,7 +42,7 @@ export class ClapFeatureExtractor extends FeatureExtractor {
      *     based on `padding`, and is repeated `4` times.
      *   - `truncation="rand_trunc"` and the length of the waveform is greater than the max length: the mel
      *     spectrogram will be computed on a random crop of the waveform.
-     * 
+     *
      * @param {Float32Array|Float64Array} waveform The input waveform.
      * @param {number} max_length The maximum length of the waveform.
      * @param {string} truncation The truncation strategy to use.
@@ -55,7 +51,6 @@ export class ClapFeatureExtractor extends FeatureExtractor {
      * @private
      */
     async _get_input_mel(waveform, max_length, truncation, padding) {
-
         /** @type {Tensor} */
         let input_mel;
         let longer = false;
@@ -66,10 +61,14 @@ export class ClapFeatureExtractor extends FeatureExtractor {
                 const idx = Math.floor(Math.random() * (diff + 1));
                 waveform = waveform.subarray(idx, idx + max_length);
 
-                input_mel = await this._extract_fbank_features(waveform, this.mel_filters_slaney, this.config.nb_max_samples);
+                input_mel = await this._extract_fbank_features(
+                    waveform,
+                    this.mel_filters_slaney,
+                    this.config.nb_max_samples,
+                );
             } else {
                 // TODO implement fusion strategy
-                throw new Error(`Truncation strategy "${truncation}" not implemented`)
+                throw new Error(`Truncation strategy "${truncation}" not implemented`);
             }
         } else {
             if (diff < 0) {
@@ -89,10 +88,14 @@ export class ClapFeatureExtractor extends FeatureExtractor {
             }
 
             if (truncation === 'fusion') {
-                throw new Error(`Truncation strategy "${truncation}" not implemented`)
+                throw new Error(`Truncation strategy "${truncation}" not implemented`);
             }
 
-            input_mel = await this._extract_fbank_features(waveform, this.mel_filters_slaney, this.config.nb_max_samples);
+            input_mel = await this._extract_fbank_features(
+                waveform,
+                this.mel_filters_slaney,
+                this.config.nb_max_samples,
+            );
         }
 
         return input_mel.unsqueeze_(0);
@@ -107,7 +110,7 @@ export class ClapFeatureExtractor extends FeatureExtractor {
      *  - `self.mel_filteres_slaney` : they correspond to the default parameters of `librosa` which used
      *    `librosa.filters.mel` when computing the mel spectrogram. These filters were only used in the original
      *    implementation when the truncation mode is not `"fusion"`.
-     * 
+     *
      * @param {Float32Array|Float64Array} waveform The audio waveform to process.
      * @param {number[][]} mel_filters The mel filters to use.
      * @param {number} [max_length=null] The maximum number of frames to return.
@@ -129,19 +132,16 @@ export class ClapFeatureExtractor extends FeatureExtractor {
                 max_num_frames: max_length,
                 do_pad: false,
                 transpose: true,
-            }
-        )
+            },
+        );
     }
 
-
     /**
      * Asynchronously extracts features from a given audio using the provided configuration.
      * @param {Float32Array|Float64Array} audio The audio data as a Float32Array/Float64Array.
      * @returns {Promise<{ input_features: Tensor }>} A Promise resolving to an object containing the extracted input features as a Tensor.
      */
-    async _call(audio, {
-        max_length = null,
-    } = {}) {
+    async _call(audio, { max_length = null } = {}) {
         validate_audio_inputs(audio, 'ClapFeatureExtractor');
 
         // convert to mel spectrogram, truncate and pad if needed.
@@ -154,6 +154,6 @@ export class ClapFeatureExtractor extends FeatureExtractor {
 
         return {
             input_features: padded_inputs.unsqueeze_(0),
-        }
+        };
     }
 }
diff --git a/src/models/clip/image_processing_clip.js b/src/models/clip/image_processing_clip.js
index 3f2f9dcb0..d40bb44c4 100644
--- a/src/models/clip/image_processing_clip.js
+++ b/src/models/clip/image_processing_clip.js
@@ -1,6 +1,4 @@
-import { 
-    ImageProcessor,
-} from "../../base/image_processors_utils.js";
+import { ImageProcessor } from '../../base/image_processors_utils.js';
 
-export class CLIPImageProcessor extends ImageProcessor { }
-export class CLIPFeatureExtractor extends CLIPImageProcessor { }
+export class CLIPImageProcessor extends ImageProcessor {}
+export class CLIPFeatureExtractor extends CLIPImageProcessor {}
diff --git a/src/models/convnext/image_processing_convnext.js b/src/models/convnext/image_processing_convnext.js
index 7b867a60a..b5812c9c8 100644
--- a/src/models/convnext/image_processing_convnext.js
+++ b/src/models/convnext/image_processing_convnext.js
@@ -1,6 +1,4 @@
-import { 
-    ImageProcessor,
-} from "../../base/image_processors_utils.js";
+import { ImageProcessor } from '../../base/image_processors_utils.js';
 
 export class ConvNextImageProcessor extends ImageProcessor {
     constructor(config) {
@@ -10,7 +8,7 @@ export class ConvNextImageProcessor extends ImageProcessor {
          * Percentage of the image to crop. Only has an effect if this.size < 384.
          */
         // @ts-expect-error TS2339
-        this.crop_pct = this.config.crop_pct ?? (224 / 256);
+        this.crop_pct = this.config.crop_pct ?? 224 / 256;
     }
 
     async resize(image) {
@@ -43,4 +41,4 @@ export class ConvNextImageProcessor extends ImageProcessor {
         return image;
     }
 }
-export class ConvNextFeatureExtractor extends ConvNextImageProcessor { }
+export class ConvNextFeatureExtractor extends ConvNextImageProcessor {}
diff --git a/src/models/dac/feature_extraction_dac.js b/src/models/dac/feature_extraction_dac.js
index 238517b03..8932300d8 100644
--- a/src/models/dac/feature_extraction_dac.js
+++ b/src/models/dac/feature_extraction_dac.js
@@ -1,3 +1,3 @@
 import { EncodecFeatureExtractor } from '../encodec/feature_extraction_encodec.js';
 
-export class DacFeatureExtractor extends EncodecFeatureExtractor { }
+export class DacFeatureExtractor extends EncodecFeatureExtractor {}
diff --git a/src/models/deit/image_processing_deit.js b/src/models/deit/image_processing_deit.js
index fd3857842..7313495c7 100644
--- a/src/models/deit/image_processing_deit.js
+++ b/src/models/deit/image_processing_deit.js
@@ -1,6 +1,4 @@
-import { 
-    ImageProcessor,
-} from "../../base/image_processors_utils.js";
+import { ImageProcessor } from '../../base/image_processors_utils.js';
 
-export class DeiTImageProcessor extends ImageProcessor { }
-export class DeiTFeatureExtractor extends DeiTImageProcessor { }
\ No newline at end of file
+export class DeiTImageProcessor extends ImageProcessor {}
+export class DeiTFeatureExtractor extends DeiTImageProcessor {}
diff --git a/src/models/detr/image_processing_detr.js b/src/models/detr/image_processing_detr.js
index 40ce1232f..7c8a653b0 100644
--- a/src/models/detr/image_processing_detr.js
+++ b/src/models/detr/image_processing_detr.js
@@ -1,13 +1,12 @@
-import { 
+import {
     ImageProcessor,
     post_process_object_detection,
     post_process_panoptic_segmentation,
     post_process_instance_segmentation,
-} from "../../base/image_processors_utils.js";
+} from '../../base/image_processors_utils.js';
 
 import { full } from '../../utils/tensor.js';
 
-
 /**
  * @typedef {object} DetrFeatureExtractorResultProps
  * @property {import('../../utils/tensor.js').Tensor} pixel_mask
@@ -49,4 +48,4 @@ export class DetrImageProcessor extends ImageProcessor {
     }
 }
 
-export class DetrFeatureExtractor extends DetrImageProcessor { } // NOTE: extends DetrImageProcessor  
+export class DetrFeatureExtractor extends DetrImageProcessor {} // NOTE: extends DetrImageProcessor
diff --git a/src/models/donut/image_processing_donut.js b/src/models/donut/image_processing_donut.js
index f848a9fa5..353c0bdd4 100644
--- a/src/models/donut/image_processing_donut.js
+++ b/src/models/donut/image_processing_donut.js
@@ -1,6 +1,4 @@
-import { 
-    ImageProcessor,
-} from "../../base/image_processors_utils.js";
+import { ImageProcessor } from '../../base/image_processors_utils.js';
 
 export class DonutImageProcessor extends ImageProcessor {
     pad_image(pixelData, imgDims, padSize, options = {}) {
@@ -16,7 +14,7 @@ export class DonutImageProcessor extends ImageProcessor {
             image_std = new Array(imageChannels).fill(image_mean);
         }
 
-        const constant_values = image_mean.map((x, i) => - x / image_std[i]);
+        const constant_values = image_mean.map((x, i) => -x / image_std[i]);
 
         return super.pad_image(pixelData, imgDims, padSize, {
             center: true,
@@ -28,4 +26,4 @@ export class DonutImageProcessor extends ImageProcessor {
         });
     }
 }
-export class DonutFeatureExtractor extends DonutImageProcessor { }
+export class DonutFeatureExtractor extends DonutImageProcessor {}
diff --git a/src/models/dpt/image_processing_dpt.js b/src/models/dpt/image_processing_dpt.js
index 0c19175e7..c854ae6fd 100644
--- a/src/models/dpt/image_processing_dpt.js
+++ b/src/models/dpt/image_processing_dpt.js
@@ -1,6 +1,4 @@
-import { 
-    ImageProcessor,
-} from "../../base/image_processors_utils.js";
+import { ImageProcessor } from '../../base/image_processors_utils.js';
 
-export class DPTImageProcessor extends ImageProcessor { }
-export class DPTFeatureExtractor extends DPTImageProcessor { } // NOTE: extends DPTImageProcessor
+export class DPTImageProcessor extends ImageProcessor {}
+export class DPTFeatureExtractor extends DPTImageProcessor {} // NOTE: extends DPTImageProcessor
diff --git a/src/models/efficientnet/image_processing_efficientnet.js b/src/models/efficientnet/image_processing_efficientnet.js
index 29a594e18..837af8840 100644
--- a/src/models/efficientnet/image_processing_efficientnet.js
+++ b/src/models/efficientnet/image_processing_efficientnet.js
@@ -1,6 +1,4 @@
-import { 
-    ImageProcessor,
-} from "../../base/image_processors_utils.js";
+import { ImageProcessor } from '../../base/image_processors_utils.js';
 
 export class EfficientNetImageProcessor extends ImageProcessor {
     constructor(config) {
@@ -8,7 +6,7 @@ export class EfficientNetImageProcessor extends ImageProcessor {
         // @ts-expect-error TS2339
         this.include_top = this.config.include_top ?? true;
         if (this.include_top) {
-            this.image_std = this.image_std.map(x => x * x);
+            this.image_std = this.image_std.map((x) => x * x);
         }
     }
 }
diff --git a/src/models/encodec/feature_extraction_encodec.js b/src/models/encodec/feature_extraction_encodec.js
index 18c8041f4..bb60a5d63 100644
--- a/src/models/encodec/feature_extraction_encodec.js
+++ b/src/models/encodec/feature_extraction_encodec.js
@@ -1,7 +1,6 @@
 import { FeatureExtractor, validate_audio_inputs } from '../../base/feature_extraction_utils.js';
 import { Tensor } from '../../utils/tensor.js';
 
-
 export class EncodecFeatureExtractor extends FeatureExtractor {
     /**
      * Asynchronously extracts input values from a given audio using the provided configuration.
@@ -17,13 +16,15 @@ export class EncodecFeatureExtractor extends FeatureExtractor {
 
         const num_channels = this.config.feature_size;
         if (audio.length % num_channels !== 0) {
-            throw new Error(`The length of the audio data must be a multiple of the number of channels (${num_channels}).`);
+            throw new Error(
+                `The length of the audio data must be a multiple of the number of channels (${num_channels}).`,
+            );
         }
 
         const shape = [
-            1,                              /* batch_size */
-            num_channels,                   /* num_channels */
-            audio.length / num_channels,    /* num_samples */
+            1 /* batch_size */,
+            num_channels /* num_channels */,
+            audio.length / num_channels /* num_samples */,
         ];
         return {
             input_values: new Tensor('float32', audio, shape),
diff --git a/src/models/feature_extractors.js b/src/models/feature_extractors.js
index bf5fdac27..5adc7e115 100644
--- a/src/models/feature_extractors.js
+++ b/src/models/feature_extractors.js
@@ -1,4 +1,3 @@
-
 export * from './audio_spectrogram_transformer/feature_extraction_audio_spectrogram_transformer.js';
 export * from './encodec/feature_extraction_encodec.js';
 export * from './clap/feature_extraction_clap.js';
@@ -14,4 +13,4 @@ export * from './wespeaker/feature_extraction_wespeaker.js';
 export * from './whisper/feature_extraction_whisper.js';
 
 // For legacy support, ImageFeatureExtractor is an alias for ImageProcessor
-export { ImageProcessor as ImageFeatureExtractor } from "../base/image_processors_utils.js";
+export { ImageProcessor as ImageFeatureExtractor } from '../base/image_processors_utils.js';
diff --git a/src/models/florence2/processing_florence2.js b/src/models/florence2/processing_florence2.js
index 6ac795538..13edc10a0 100644
--- a/src/models/florence2/processing_florence2.js
+++ b/src/models/florence2/processing_florence2.js
@@ -1,10 +1,10 @@
-import { Processor } from "../../base/processing_utils.js";
-import { AutoImageProcessor } from "../auto/image_processing_auto.js";
-import { AutoTokenizer } from "../../tokenizers.js";
+import { Processor } from '../../base/processing_utils.js';
+import { AutoImageProcessor } from '../auto/image_processing_auto.js';
+import { AutoTokenizer } from '../../tokenizers.js';
 
 export class Florence2Processor extends Processor {
-    static tokenizer_class = AutoTokenizer
-    static image_processor_class = AutoImageProcessor
+    static tokenizer_class = AutoTokenizer;
+    static image_processor_class = AutoImageProcessor;
 
     constructor(config, components, chat_template) {
         super(config, components, chat_template);
@@ -28,9 +28,10 @@ export class Florence2Processor extends Processor {
         this.task_prompts_with_input = new Map(Object.entries(task_prompts_with_input ?? {}));
 
         this.regexes = {
-            quad_boxes: /(.+?)/gm,
+            quad_boxes:
+                /(.+?)/gm,
             bboxes: /([^<]+)?/gm,
-        }
+        };
         this.size_per_bin = 1000;
     }
 
@@ -50,7 +51,7 @@ export class Florence2Processor extends Processor {
             if (this.task_prompts_without_inputs.has(t)) {
                 prompts.push(this.task_prompts_without_inputs.get(t));
             }
-            // 2. task prompts with additional inputs 
+            // 2. task prompts with additional inputs
             else {
                 for (const [task, prompt] of this.task_prompts_with_input) {
                     if (t.includes(task)) {
@@ -96,10 +97,13 @@ export class Florence2Processor extends Processor {
                 const items = [];
                 for (const [_, label, ...locations] of matches) {
                     // Push new label, or duplicate the last label
-                    labels.push(label ? label.trim() : labels.at(-1) ?? '');
-                    items.push(locations.map((x, i) =>
-                        // NOTE: Add 0.5 to use the center position of the bin as the coordinate.
-                        (Number(x) + 0.5) / this.size_per_bin * image_size[i % 2])
+                    labels.push(label ? label.trim() : (labels.at(-1) ?? ''));
+                    items.push(
+                        locations.map(
+                            (x, i) =>
+                                // NOTE: Add 0.5 to use the center position of the bin as the coordinate.
+                                ((Number(x) + 0.5) / this.size_per_bin) * image_size[i % 2],
+                        ),
                     );
                 }
                 final_answer = { labels, [key]: items };
@@ -109,14 +113,13 @@ export class Florence2Processor extends Processor {
                 throw new Error(`Task "${task}" (of type "${task_answer_post_processing_type}") not yet implemented.`);
         }
 
-        return { [task]: final_answer }
+        return { [task]: final_answer };
     }
 
     // NOTE: images and text are switched from the python version
     // `images` is required, `text` is optional
-    async _call(images, text=null, kwargs = {}) {
-
-        if (!images && !text){
+    async _call(images, text = null, kwargs = {}) {
+        if (!images && !text) {
             throw new Error('Either text or images must be provided');
         }
 
@@ -126,6 +129,6 @@ export class Florence2Processor extends Processor {
         return {
             ...image_inputs,
             ...text_inputs,
-        }
+        };
     }
 }
diff --git a/src/models/gemma3n/feature_extraction_gemma3n.js b/src/models/gemma3n/feature_extraction_gemma3n.js
index 10f9a4be1..5d77ebf24 100644
--- a/src/models/gemma3n/feature_extraction_gemma3n.js
+++ b/src/models/gemma3n/feature_extraction_gemma3n.js
@@ -3,13 +3,10 @@ import { full, Tensor } from '../../utils/tensor.js';
 import { mel_filter_bank, spectrogram, window_function } from '../../utils/audio.js';
 
 export class Gemma3nAudioFeatureExtractor extends FeatureExtractor {
-
     constructor(config) {
         super(config);
 
-        const {
-            fft_length, feature_size, min_frequency, max_frequency, sampling_rate, frame_length
-        } = this.config;
+        const { fft_length, feature_size, min_frequency, max_frequency, sampling_rate, frame_length } = this.config;
 
         const mel_filters = mel_filter_bank(
             Math.floor(1 + fft_length / 2), // num_frequency_bins
@@ -18,12 +15,12 @@ export class Gemma3nAudioFeatureExtractor extends FeatureExtractor {
             max_frequency, // max_frequency
             sampling_rate, // sampling_rate
             null, // norm
-            "htk", // mel_scale
+            'htk', // mel_scale
             false, // triangularize_in_mel_space
         );
         this.mel_filters = mel_filters;
 
-        this.window = window_function(frame_length, 'hann')
+        this.window = window_function(frame_length, 'hann');
     }
 
     /**
@@ -52,8 +49,8 @@ export class Gemma3nAudioFeatureExtractor extends FeatureExtractor {
 
                 // Custom
                 transpose: true,
-            }
-        )
+            },
+        );
     }
 
     /**
@@ -67,12 +64,7 @@ export class Gemma3nAudioFeatureExtractor extends FeatureExtractor {
      * @param {number} [options.pad_to_multiple_of=128] The number to pad the sequence to a multiple of.
      * @returns {Promise<{ input_features: Tensor, input_features_mask: Tensor }>} A Promise resolving to an object containing the extracted input features and attention masks as Tensors.
      */
-    async _call(audio, {
-        max_length = 480_000,
-        truncation=true,
-        padding = true,
-        pad_to_multiple_of = 128,
-    } = {}) {
+    async _call(audio, { max_length = 480_000, truncation = true, padding = true, pad_to_multiple_of = 128 } = {}) {
         validate_audio_inputs(audio, 'Gemma3nAudioFeatureExtractor');
         if (truncation && audio.length > max_length) {
             audio = audio.slice(0, max_length);
@@ -92,6 +84,6 @@ export class Gemma3nAudioFeatureExtractor extends FeatureExtractor {
         return {
             input_features: features.unsqueeze_(0),
             input_features_mask: padded_attention_mask,
-        }
+        };
     }
 }
diff --git a/src/models/gemma3n/processing_gemma3n.js b/src/models/gemma3n/processing_gemma3n.js
index bef519a19..a3b47741d 100644
--- a/src/models/gemma3n/processing_gemma3n.js
+++ b/src/models/gemma3n/processing_gemma3n.js
@@ -1,10 +1,9 @@
-
-import { Processor } from "../../base/processing_utils.js";
-import { AutoImageProcessor } from "../auto/image_processing_auto.js";
-import { AutoFeatureExtractor } from "../auto/feature_extraction_auto.js";
-import { AutoTokenizer } from "../../tokenizers.js";
-import { RawImage } from "../../utils/image.js";
-import { RawAudio } from "../../utils/audio.js";
+import { Processor } from '../../base/processing_utils.js';
+import { AutoImageProcessor } from '../auto/image_processing_auto.js';
+import { AutoFeatureExtractor } from '../auto/feature_extraction_auto.js';
+import { AutoTokenizer } from '../../tokenizers.js';
+import { RawImage } from '../../utils/image.js';
+import { RawAudio } from '../../utils/audio.js';
 
 export class Gemma3nProcessor extends Processor {
     static image_processor_class = AutoImageProcessor;
@@ -20,34 +19,39 @@ export class Gemma3nProcessor extends Processor {
 
         const {
             // Audio tokens
-            audio_token_id, boa_token, audio_token, eoa_token,
+            audio_token_id,
+            boa_token,
+            audio_token,
+            eoa_token,
 
             // Image tokens
-            image_token_id, boi_token, image_token, eoi_token
+            image_token_id,
+            boi_token,
+            image_token,
+            eoi_token,
         } = this.tokenizer.config;
 
-        this.audio_token_id = audio_token_id
-        this.boa_token = boa_token
-        this.audio_token = audio_token
+        this.audio_token_id = audio_token_id;
+        this.boa_token = boa_token;
+        this.audio_token = audio_token;
         const audio_tokens_expanded = audio_token.repeat(this.audio_seq_length);
-        this.full_audio_sequence = `\n\n${boa_token}${audio_tokens_expanded}${eoa_token}\n\n`
+        this.full_audio_sequence = `\n\n${boa_token}${audio_tokens_expanded}${eoa_token}\n\n`;
 
-        this.image_token_id = image_token_id
-        this.boi_token = boi_token
-        this.image_token = image_token
+        this.image_token_id = image_token_id;
+        this.boi_token = boi_token;
+        this.image_token = image_token;
         const image_tokens_expanded = image_token.repeat(this.image_seq_length);
-        this.full_image_sequence = `\n\n${boi_token}${image_tokens_expanded}${eoi_token}\n\n`
+        this.full_image_sequence = `\n\n${boi_token}${image_tokens_expanded}${eoi_token}\n\n`;
     }
 
     /**
-     * 
-     * @param {string|string[]} text 
+     *
+     * @param {string|string[]} text
      * @param {RawImage|RawImage[]|RawImage[][]} images
      * @param {RawAudio|RawAudio[]|RawAudio[][]} audio
      * @returns {Promise}
      */
     async _call(text, images = null, audio = null, options = {}) {
-
         if (typeof text === 'string') {
             text = [text];
         }
@@ -56,12 +60,12 @@ export class Gemma3nProcessor extends Processor {
         if (audio) {
             audio_inputs = await this.feature_extractor(audio, options);
 
-            text = text.map(prompt => prompt.replaceAll(this.audio_token, this.full_audio_sequence));
+            text = text.map((prompt) => prompt.replaceAll(this.audio_token, this.full_audio_sequence));
         }
         let image_inputs;
         if (images) {
             image_inputs = await this.image_processor(images, options);
-            text = text.map(prompt => prompt.replaceAll(this.image_token, this.full_image_sequence));
+            text = text.map((prompt) => prompt.replaceAll(this.image_token, this.full_image_sequence));
         }
 
         let text_inputs = this.tokenizer(text, options);
@@ -69,6 +73,6 @@ export class Gemma3nProcessor extends Processor {
             ...text_inputs,
             ...image_inputs,
             ...audio_inputs,
-        }
+        };
     }
 }
diff --git a/src/models/glpn/image_processing_glpn.js b/src/models/glpn/image_processing_glpn.js
index 609f1b996..dd6b0ad4f 100644
--- a/src/models/glpn/image_processing_glpn.js
+++ b/src/models/glpn/image_processing_glpn.js
@@ -1,5 +1,3 @@
-import { 
-    ImageProcessor,
-} from "../../base/image_processors_utils.js";
+import { ImageProcessor } from '../../base/image_processors_utils.js';
 
-export class GLPNFeatureExtractor extends ImageProcessor { }
+export class GLPNFeatureExtractor extends ImageProcessor {}
diff --git a/src/models/grounding_dino/image_processing_grounding_dino.js b/src/models/grounding_dino/image_processing_grounding_dino.js
index d4bb5bd8b..0042ab763 100644
--- a/src/models/grounding_dino/image_processing_grounding_dino.js
+++ b/src/models/grounding_dino/image_processing_grounding_dino.js
@@ -1,10 +1,6 @@
-
-import { 
-    ImageProcessor,
-} from "../../base/image_processors_utils.js";
+import { ImageProcessor } from '../../base/image_processors_utils.js';
 import { ones } from '../../utils/tensor.js';
 
-
 /**
  * @typedef {object} GroundingDinoFeatureExtractorResultProps
  * @property {import('../../utils/tensor.js').Tensor} pixel_mask
diff --git a/src/models/grounding_dino/processing_grounding_dino.js b/src/models/grounding_dino/processing_grounding_dino.js
index 62d5387bf..8ad0eade0 100644
--- a/src/models/grounding_dino/processing_grounding_dino.js
+++ b/src/models/grounding_dino/processing_grounding_dino.js
@@ -1,7 +1,7 @@
-import { Processor } from "../../base/processing_utils.js";
-import { AutoImageProcessor } from "../auto/image_processing_auto.js";
-import { AutoTokenizer } from "../../tokenizers.js";
-import { center_to_corners_format } from "../../base/image_processors_utils.js";
+import { Processor } from '../../base/processing_utils.js';
+import { AutoImageProcessor } from '../auto/image_processing_auto.js';
+import { AutoTokenizer } from '../../tokenizers.js';
+import { center_to_corners_format } from '../../base/image_processors_utils.js';
 
 /**
  * Get token ids of phrases from posmaps and input_ids.
@@ -9,7 +9,6 @@ import { center_to_corners_format } from "../../base/image_processors_utils.js";
  * @param {import('../../utils/tensor.js').Tensor} input_ids A tensor of token ids of shape `(sequence_length, )`.
  */
 function get_phrases_from_posmap(posmaps, input_ids) {
-
     const left_idx = 0;
     const right_idx = posmaps.dims.at(-1) - 1;
 
@@ -19,44 +18,43 @@ function get_phrases_from_posmap(posmaps, input_ids) {
 
     const input_ids_list = input_ids.tolist();
     return posmaps_list
-        .map((val, idx) => val ? idx : null)
-        .filter(idx => idx !== null)
-        .map(i => input_ids_list[i]);
+        .map((val, idx) => (val ? idx : null))
+        .filter((idx) => idx !== null)
+        .map((i) => input_ids_list[i]);
 }
 
 export class GroundingDinoProcessor extends Processor {
-    static tokenizer_class = AutoTokenizer
-    static image_processor_class = AutoImageProcessor
+    static tokenizer_class = AutoTokenizer;
+    static image_processor_class = AutoImageProcessor;
 
     /**
      * @typedef {import('../../utils/image.js').RawImage} RawImage
      */
     /**
-     * 
-     * @param {RawImage|RawImage[]|RawImage[][]} images  
-     * @param {string|string[]} text 
+     *
+     * @param {RawImage|RawImage[]|RawImage[][]} images
+     * @param {string|string[]} text
      * @returns {Promise}
      */
     async _call(images, text, options = {}) {
-
         const image_inputs = images ? await this.image_processor(images, options) : {};
         const text_inputs = text ? this.tokenizer(text, options) : {};
 
         return {
             ...text_inputs,
             ...image_inputs,
-        }
+        };
     }
-    post_process_grounded_object_detection(outputs, input_ids, {
-        box_threshold = 0.25,
-        text_threshold = 0.25,
-        target_sizes = null
-    } = {}) {
+    post_process_grounded_object_detection(
+        outputs,
+        input_ids,
+        { box_threshold = 0.25, text_threshold = 0.25, target_sizes = null } = {},
+    ) {
         const { logits, pred_boxes } = outputs;
         const batch_size = logits.dims[0];
 
         if (target_sizes !== null && target_sizes.length !== batch_size) {
-            throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits")
+            throw Error('Make sure that you pass in as many target sizes as the batch dimension of the logits');
         }
         const num_queries = logits.dims.at(1);
 
@@ -64,8 +62,9 @@ export class GroundingDinoProcessor extends Processor {
         const scores = probs.max(-1).tolist(); // (batch_size, num_queries)
 
         // Convert to [x0, y0, x1, y1] format
-        const boxes = pred_boxes.tolist() // (batch_size, num_queries, 4)
-            .map(batch => batch.map(box => center_to_corners_format(box)));
+        const boxes = pred_boxes
+            .tolist() // (batch_size, num_queries, 4)
+            .map((batch) => batch.map((box) => center_to_corners_format(box)));
 
         const results = [];
         for (let i = 0; i < batch_size; ++i) {
@@ -73,7 +72,7 @@ export class GroundingDinoProcessor extends Processor {
 
             // Convert from relative [0, 1] to absolute [0, height] coordinates
             if (target_size !== null) {
-                boxes[i] = boxes[i].map(box => box.map((x, j) => x * target_size[(j + 1) % 2]));
+                boxes[i] = boxes[i].map((box) => box.map((x, j) => x * target_size[(j + 1) % 2]));
             }
 
             const batch_scores = scores[i];
diff --git a/src/models/idefics3/image_processing_idefics3.js b/src/models/idefics3/image_processing_idefics3.js
index 3015bff7c..a2ada459b 100644
--- a/src/models/idefics3/image_processing_idefics3.js
+++ b/src/models/idefics3/image_processing_idefics3.js
@@ -1,9 +1,5 @@
-
-
-import {
-    ImageProcessor,
-} from "../../base/image_processors_utils.js";
-import { cat, full, interpolate_4d, slice, stack } from "../../utils/tensor.js";
+import { ImageProcessor } from '../../base/image_processors_utils.js';
+import { cat, full, interpolate_4d, slice, stack } from '../../utils/tensor.js';
 
 export class Idefics3ImageProcessor extends ImageProcessor {
     constructor(config) {
@@ -41,23 +37,19 @@ export class Idefics3ImageProcessor extends ImageProcessor {
     }
 
     /** @param {RawImage|RawImage[]|RawImage[][]} images */
-    async _call(images, {
-        do_image_splitting = null,
-        return_row_col_info = false,
-    } = {}) {
-
+    async _call(images, { do_image_splitting = null, return_row_col_info = false } = {}) {
         /** @type {RawImage[][]} */
         let batched_2d_images;
         if (!Array.isArray(images)) {
             batched_2d_images = [[images]];
         } else {
             if (images.length === 0 || !images[0]) {
-                throw new Error("No images provided.");
+                throw new Error('No images provided.');
             }
             if (!Array.isArray(images[0])) {
-                batched_2d_images = [/** @type {RawImage[]} */(images)];
+                batched_2d_images = [/** @type {RawImage[]} */ (images)];
             } else {
-                batched_2d_images = /** @type {RawImage[][]} */(images);
+                batched_2d_images = /** @type {RawImage[][]} */ (images);
             }
         }
 
@@ -69,17 +61,16 @@ export class Idefics3ImageProcessor extends ImageProcessor {
         const original_sizes = [];
         const reshaped_input_sizes = [];
         for (const image_batch of batched_2d_images) {
-
-            let images_list = await Promise.all(image_batch.map(x => this.preprocess(x)));
+            let images_list = await Promise.all(image_batch.map((x) => this.preprocess(x)));
 
             // Original sizes of images
-            original_sizes.push(...images_list.map(x => x.original_size));
+            original_sizes.push(...images_list.map((x) => x.original_size));
 
             // Reshaped sizes of images, before padding or cropping
-            reshaped_input_sizes.push(...images_list.map(x => x.reshaped_input_size));
+            reshaped_input_sizes.push(...images_list.map((x) => x.reshaped_input_size));
 
             // Convert images to 4D tensors for easier processing
-            images_list.forEach(x => x.pixel_values.unsqueeze_(0));
+            images_list.forEach((x) => x.pixel_values.unsqueeze_(0));
 
             const { longest_edge } = this.max_image_size;
 
@@ -90,28 +81,30 @@ export class Idefics3ImageProcessor extends ImageProcessor {
                 let image_cols = new Array(images_list.length);
 
                 // We first resize both height and width of each image to the nearest max_image_size multiple, disregarding the aspect ratio
-                images_tensor = await Promise.all(images_list.map(async (x, i) => {
-                    const new_size = this.get_resize_for_vision_encoder(x.pixel_values, longest_edge);
-
-                    const resized = await interpolate_4d(x.pixel_values, {
-                        size: [new_size.height, new_size.width],
-                    });
-
-                    const { frames, num_splits_h, num_splits_w } = await this.split_image(resized, this.max_image_size);
-                    image_rows[i] = num_splits_h;
-                    image_cols[i] = num_splits_w;
-                    return cat(frames, 0);
-                }));
+                images_tensor = await Promise.all(
+                    images_list.map(async (x, i) => {
+                        const new_size = this.get_resize_for_vision_encoder(x.pixel_values, longest_edge);
+
+                        const resized = await interpolate_4d(x.pixel_values, {
+                            size: [new_size.height, new_size.width],
+                        });
+
+                        const { frames, num_splits_h, num_splits_w } = await this.split_image(
+                            resized,
+                            this.max_image_size,
+                        );
+                        image_rows[i] = num_splits_h;
+                        image_cols[i] = num_splits_w;
+                        return cat(frames, 0);
+                    }),
+                );
 
                 images_list_rows.push(image_rows);
                 images_list_cols.push(image_cols);
-
             } else {
                 /** @type {[number, number]} */
                 const size = [longest_edge, longest_edge];
-                images_tensor = await Promise.all(
-                    images_list.map(x => interpolate_4d(x.pixel_values, { size }))
-                );
+                images_tensor = await Promise.all(images_list.map((x) => interpolate_4d(x.pixel_values, { size })));
 
                 images_list_rows.push(new Array(images_list.length).fill(0));
                 images_list_cols.push(new Array(images_list.length).fill(0));
@@ -131,7 +124,7 @@ export class Idefics3ImageProcessor extends ImageProcessor {
             pixel_attention_mask = full([batch_size, n, h, w], true);
         } else {
             // Add padding (if necessary) to images with less patches than the maximum number of patches
-            const max_num_patches = Math.max(...all_pixel_values.map(x => x.dims.at(0)));
+            const max_num_patches = Math.max(...all_pixel_values.map((x) => x.dims.at(0)));
 
             pixel_attention_mask = full([batch_size, max_num_patches, h, w], true);
             const pixel_attention_mask_data = pixel_attention_mask.data;
@@ -139,10 +132,10 @@ export class Idefics3ImageProcessor extends ImageProcessor {
             for (let i = 0; i < batch_size; ++i) {
                 const num_patches = all_pixel_values[i].dims[0];
                 if (num_patches < max_num_patches) {
-                    all_pixel_values[i] = cat([
-                        all_pixel_values[i],
-                        full([max_num_patches - num_patches, c, h, w], 0),
-                    ], 0);
+                    all_pixel_values[i] = cat(
+                        [all_pixel_values[i], full([max_num_patches - num_patches, c, h, w], 0)],
+                        0,
+                    );
 
                     const start_offset = i * pixel_attention_mask_stride + num_patches * h * w;
                     const end_offset = (i + 1) * pixel_attention_mask_stride;
@@ -160,12 +153,8 @@ export class Idefics3ImageProcessor extends ImageProcessor {
 
             original_sizes,
             reshaped_input_sizes,
-            ...(
-                return_row_col_info
-                    ? { rows: images_list_rows, cols: images_list_cols }
-                    : {}
-            ),
-        }
+            ...(return_row_col_info ? { rows: images_list_rows, cols: images_list_cols } : {}),
+        };
     }
 
     async split_image(pixel_values, { longest_edge }) {
@@ -176,7 +165,8 @@ export class Idefics3ImageProcessor extends ImageProcessor {
 
         const [height, width] = pixel_values.dims.slice(-2);
 
-        let num_splits_h = 0, num_splits_w = 0;
+        let num_splits_h = 0,
+            num_splits_w = 0;
 
         if (height > max_height || width > max_width) {
             // Calculate the number of splits
@@ -191,14 +181,16 @@ export class Idefics3ImageProcessor extends ImageProcessor {
             for (let r = 0; r < num_splits_h; ++r) {
                 for (let c = 0; c < num_splits_w; ++c) {
                     let start_x, start_y, end_x, end_y;
-                    if (r === num_splits_h - 1) { // At bottom
+                    if (r === num_splits_h - 1) {
+                        // At bottom
                         start_y = height - optimal_height;
                         end_y = height;
                     } else {
                         start_y = r * optimal_height;
                         end_y = (r + 1) * optimal_height;
                     }
-                    if (c === num_splits_w - 1) { // At right
+                    if (c === num_splits_w - 1) {
+                        // At right
                         start_x = width - optimal_width;
                         end_x = width;
                     } else {
@@ -221,7 +213,7 @@ export class Idefics3ImageProcessor extends ImageProcessor {
             if (height !== global_image_height || width !== global_image_width) {
                 pixel_values = await interpolate_4d(pixel_values, {
                     size: [global_image_height, global_image_width],
-                })
+                });
             }
         }
 
diff --git a/src/models/idefics3/processing_idefics3.js b/src/models/idefics3/processing_idefics3.js
index 1bec97b32..fb8898cf8 100644
--- a/src/models/idefics3/processing_idefics3.js
+++ b/src/models/idefics3/processing_idefics3.js
@@ -1,33 +1,35 @@
-
-import { Processor } from "../../base/processing_utils.js";
-import { AutoImageProcessor } from "../auto/image_processing_auto.js";
-import { AutoTokenizer } from "../../tokenizers.js";
-import { RawImage } from "../../utils/image.js";
-import { count } from "../../utils/core.js";
+import { Processor } from '../../base/processing_utils.js';
+import { AutoImageProcessor } from '../auto/image_processing_auto.js';
+import { AutoTokenizer } from '../../tokenizers.js';
+import { RawImage } from '../../utils/image.js';
+import { count } from '../../utils/core.js';
 
 /**
  * Prompt with expanded image tokens for when the image is split into patches.
  * @private
  */
-function _prompt_split_image(image_seq_len, image_rows, image_cols, fake_token_around_image, image_token, global_img_token) {
-    let text_split_images = "";
+function _prompt_split_image(
+    image_seq_len,
+    image_rows,
+    image_cols,
+    fake_token_around_image,
+    image_token,
+    global_img_token,
+) {
+    let text_split_images = '';
     for (let n_h = 0; n_h < image_rows; ++n_h) {
         for (let n_w = 0; n_w < image_cols; ++n_w) {
-            text_split_images += (
-                fake_token_around_image +
-                `` +
-                image_token.repeat(image_seq_len)
-            );
+            text_split_images +=
+                fake_token_around_image + `` + image_token.repeat(image_seq_len);
         }
-        text_split_images += "\n";
+        text_split_images += '\n';
     }
 
-    text_split_images += (
+    text_split_images +=
         `\n${fake_token_around_image}` +
         `${global_img_token}` +
         image_token.repeat(image_seq_len) +
-        `${fake_token_around_image}`
-    );
+        `${fake_token_around_image}`;
     return text_split_images;
 }
 
@@ -44,34 +46,40 @@ function _prompt_single_image(image_seq_len, fake_token_around_image, image_toke
     );
 }
 
-function get_image_prompt_string(image_rows, image_cols, image_seq_len, fake_token_around_image, image_token, global_img_token) {
+function get_image_prompt_string(
+    image_rows,
+    image_cols,
+    image_seq_len,
+    fake_token_around_image,
+    image_token,
+    global_img_token,
+) {
     if (image_rows === 0 && image_cols === 0) {
-        return _prompt_single_image(
-            image_seq_len,
-            fake_token_around_image,
-            image_token,
-            global_img_token
-        );
+        return _prompt_single_image(image_seq_len, fake_token_around_image, image_token, global_img_token);
     }
     return _prompt_split_image(
-        image_seq_len, image_rows, image_cols, fake_token_around_image, image_token, global_img_token
+        image_seq_len,
+        image_rows,
+        image_cols,
+        fake_token_around_image,
+        image_token,
+        global_img_token,
     );
 }
 
-
 export class Idefics3Processor extends Processor {
-    static image_processor_class = AutoImageProcessor
-    static tokenizer_class = AutoTokenizer
+    static image_processor_class = AutoImageProcessor;
+    static tokenizer_class = AutoTokenizer;
     static uses_processor_config = true;
 
-    fake_image_token = "";
-    image_token = "";
-    global_img_token = "";
+    fake_image_token = '';
+    image_token = '';
+    global_img_token = '';
 
     /**
-     * 
-     * @param {string|string[]} text 
-     * @param {RawImage|RawImage[]|RawImage[][]} images  
+     *
+     * @param {string|string[]} text
+     * @param {RawImage|RawImage[]|RawImage[][]} images
      * @returns {Promise}
      */
     async _call(text, images = null, options = {}) {
@@ -92,7 +100,7 @@ export class Idefics3Processor extends Processor {
         const image_cols = image_inputs.cols ?? [new Array(text.length).fill(0)];
 
         const image_seq_len = this.config.image_seq_len;
-        const n_images_in_text = []
+        const n_images_in_text = [];
         const prompt_strings = [];
         for (let i = 0; i < text.length; ++i) {
             const sample = text[i];
@@ -102,20 +110,20 @@ export class Idefics3Processor extends Processor {
             n_images_in_text.push(count(sample, this.image_token));
 
             // Replace the image token with fake tokens around the expanded image token sequence of length `image_seq_len`
-            const image_prompt_strings = sample_rows.map(
-                (n_rows, j) => get_image_prompt_string(
+            const image_prompt_strings = sample_rows.map((n_rows, j) =>
+                get_image_prompt_string(
                     n_rows,
                     sample_cols[j],
                     image_seq_len,
                     this.fake_image_token,
                     this.image_token,
                     this.global_img_token,
-                )
+                ),
             );
 
             const split_sample = sample.split(this.image_token);
             if (split_sample.length === 0) {
-                throw new Error("The image token should be present in the text.");
+                throw new Error('The image token should be present in the text.');
             }
 
             // Place in the image prompt strings where the image tokens are
@@ -131,6 +139,6 @@ export class Idefics3Processor extends Processor {
         return {
             ...text_inputs,
             ...image_inputs,
-        }
+        };
     }
 }
diff --git a/src/models/image_processors.js b/src/models/image_processors.js
index 95f275893..06b1f873b 100644
--- a/src/models/image_processors.js
+++ b/src/models/image_processors.js
@@ -1,40 +1,39 @@
-
-export * from './beit/image_processing_beit.js'
-export * from './bit/image_processing_bit.js'
-export * from './chinese_clip/image_processing_chinese_clip.js'
-export * from './clip/image_processing_clip.js'
-export * from './convnext/image_processing_convnext.js'
-export * from './deit/image_processing_deit.js'
-export * from './detr/image_processing_detr.js'
-export * from './donut/image_processing_donut.js'
-export * from './dpt/image_processing_dpt.js'
-export * from './efficientnet/image_processing_efficientnet.js'
-export * from './glpn/image_processing_glpn.js'
-export * from './grounding_dino/image_processing_grounding_dino.js'
-export * from './idefics3/image_processing_idefics3.js'
-export * from './janus/image_processing_janus.js'
-export * from './jina_clip/image_processing_jina_clip.js'
-export * from './llava_onevision/image_processing_llava_onevision.js'
-export * from './mask2former/image_processing_mask2former.js'
-export * from './maskformer/image_processing_maskformer.js'
-export * from './mobilenet_v1/image_processing_mobilenet_v1.js'
-export * from './mobilenet_v2/image_processing_mobilenet_v2.js'
-export * from './mobilenet_v3/image_processing_mobilenet_v3.js'
-export * from './mobilenet_v4/image_processing_mobilenet_v4.js'
-export * from './mobilevit/image_processing_mobilevit.js'
-export * from './nougat/image_processing_nougat.js'
-export * from './owlv2/image_processing_owlv2.js'
-export * from './owlvit/image_processing_owlvit.js'
-export * from './phi3_v/image_processing_phi3_v.js'
-export * from './pvt/image_processing_pvt.js'
-export * from './qwen2_vl/image_processing_qwen2_vl.js'
-export * from './rt_detr/image_processing_rt_detr.js'
-export * from './sam/image_processing_sam.js'
-export * from './segformer/image_processing_segformer.js'
-export * from './siglip/image_processing_siglip.js'
-export * from './smolvlm/image_processing_smolvlm.js'
-export * from './swin2sr/image_processing_swin2sr.js'
-export * from './vit/image_processing_vit.js'
-export * from './vitmatte/image_processing_vitmatte.js'
-export * from './vitpose/image_processing_vitpose.js'
-export * from './yolos/image_processing_yolos.js'
+export * from './beit/image_processing_beit.js';
+export * from './bit/image_processing_bit.js';
+export * from './chinese_clip/image_processing_chinese_clip.js';
+export * from './clip/image_processing_clip.js';
+export * from './convnext/image_processing_convnext.js';
+export * from './deit/image_processing_deit.js';
+export * from './detr/image_processing_detr.js';
+export * from './donut/image_processing_donut.js';
+export * from './dpt/image_processing_dpt.js';
+export * from './efficientnet/image_processing_efficientnet.js';
+export * from './glpn/image_processing_glpn.js';
+export * from './grounding_dino/image_processing_grounding_dino.js';
+export * from './idefics3/image_processing_idefics3.js';
+export * from './janus/image_processing_janus.js';
+export * from './jina_clip/image_processing_jina_clip.js';
+export * from './llava_onevision/image_processing_llava_onevision.js';
+export * from './mask2former/image_processing_mask2former.js';
+export * from './maskformer/image_processing_maskformer.js';
+export * from './mobilenet_v1/image_processing_mobilenet_v1.js';
+export * from './mobilenet_v2/image_processing_mobilenet_v2.js';
+export * from './mobilenet_v3/image_processing_mobilenet_v3.js';
+export * from './mobilenet_v4/image_processing_mobilenet_v4.js';
+export * from './mobilevit/image_processing_mobilevit.js';
+export * from './nougat/image_processing_nougat.js';
+export * from './owlv2/image_processing_owlv2.js';
+export * from './owlvit/image_processing_owlvit.js';
+export * from './phi3_v/image_processing_phi3_v.js';
+export * from './pvt/image_processing_pvt.js';
+export * from './qwen2_vl/image_processing_qwen2_vl.js';
+export * from './rt_detr/image_processing_rt_detr.js';
+export * from './sam/image_processing_sam.js';
+export * from './segformer/image_processing_segformer.js';
+export * from './siglip/image_processing_siglip.js';
+export * from './smolvlm/image_processing_smolvlm.js';
+export * from './swin2sr/image_processing_swin2sr.js';
+export * from './vit/image_processing_vit.js';
+export * from './vitmatte/image_processing_vitmatte.js';
+export * from './vitpose/image_processing_vitpose.js';
+export * from './yolos/image_processing_yolos.js';
diff --git a/src/models/janus/image_processing_janus.js b/src/models/janus/image_processing_janus.js
index ff191855a..96d00e795 100644
--- a/src/models/janus/image_processing_janus.js
+++ b/src/models/janus/image_processing_janus.js
@@ -1,7 +1,4 @@
-
-import {
-    ImageProcessor,
-} from "../../base/image_processors_utils.js";
+import { ImageProcessor } from '../../base/image_processors_utils.js';
 
 export class VLMImageProcessor extends ImageProcessor {
     constructor(config) {
@@ -14,7 +11,7 @@ export class VLMImageProcessor extends ImageProcessor {
             ...config,
         });
         // @ts-expect-error TS2339
-        this.constant_values = this.config.background_color.map(x => x * this.rescale_factor)
+        this.constant_values = this.config.background_color.map((x) => x * this.rescale_factor);
     }
 
     pad_image(pixelData, imgDims, padSize, options) {
diff --git a/src/models/janus/processing_janus.js b/src/models/janus/processing_janus.js
index 52fd95abe..54c797f9f 100644
--- a/src/models/janus/processing_janus.js
+++ b/src/models/janus/processing_janus.js
@@ -1,14 +1,13 @@
-
-import { Processor } from "../../base/processing_utils.js";
-import { AutoImageProcessor } from "../auto/image_processing_auto.js";
-import { AutoTokenizer } from "../../tokenizers.js";
-import { mergeArrays } from "../../utils/core.js";
-import { Tensor } from "../../utils/tensor.js";
-import { RawImage } from "../../utils/image.js";
+import { Processor } from '../../base/processing_utils.js';
+import { AutoImageProcessor } from '../auto/image_processing_auto.js';
+import { AutoTokenizer } from '../../tokenizers.js';
+import { mergeArrays } from '../../utils/core.js';
+import { Tensor } from '../../utils/tensor.js';
+import { RawImage } from '../../utils/image.js';
 
 export class VLChatProcessor extends Processor {
-    static image_processor_class = AutoImageProcessor
-    static tokenizer_class = AutoTokenizer
+    static image_processor_class = AutoImageProcessor;
+    static tokenizer_class = AutoTokenizer;
     static uses_processor_config = true;
 
     constructor(config, components, chat_template) {
@@ -41,16 +40,13 @@ export class VLChatProcessor extends Processor {
      * @param {string} [options.chat_template="default"] The chat template to use.
      * @returns {Promise} The processed input.
      */
-    async _call(conversation, {
-        images = null,
-        chat_template = "default",
-    }={}) {
+    async _call(conversation, { images = null, chat_template = 'default' } = {}) {
         if (!images) {
             images = await Promise.all(
                 conversation
                     .filter((msg) => msg.images)
                     .flatMap((msg) => msg.images)
-                    .map((img) => RawImage.read(img))
+                    .map((img) => RawImage.read(img)),
             );
         } else if (!Array.isArray(images)) {
             images = [images];
@@ -64,18 +60,15 @@ export class VLChatProcessor extends Processor {
         });
 
         const encode = (text) => tokenizer.encode(text, { add_special_tokens: false });
-        const parts = (/** @type {string} */(result))
-            .split(this.image_tag);
+        const parts = /** @type {string} */ (result).split(this.image_tag);
         const num_images = parts.length - 1;
         if (images.length !== num_images) {
-            throw new Error(`Number of images provided (${images.length}) does not match number of "${this.image_tag}" image tags (${num_images})`);
+            throw new Error(
+                `Number of images provided (${images.length}) does not match number of "${this.image_tag}" image tags (${num_images})`,
+            );
         }
 
-        const [
-            image_placeholder_tag_id,
-            image_start_tag_id,
-            image_end_tag_id,
-        ] = tokenizer.model.convert_tokens_to_ids([
+        const [image_placeholder_tag_id, image_start_tag_id, image_end_tag_id] = tokenizer.model.convert_tokens_to_ids([
             this.image_tag,
             this.image_start_tag,
             this.image_end_tag,
@@ -88,13 +81,17 @@ export class VLChatProcessor extends Processor {
             const tokens = encode(parts[i]);
             input_ids = mergeArrays(
                 input_ids,
-                [image_start_tag_id], placeholder_image_tokens, [image_end_tag_id],
+                [image_start_tag_id],
+                placeholder_image_tokens,
+                [image_end_tag_id],
                 tokens,
             );
             const image_mask = new Array(this.num_image_tokens).fill(true);
             images_seq_mask = mergeArrays(
                 images_seq_mask,
-                [false], image_mask, [false],
+                [false],
+                image_mask,
+                [false],
                 new Array(tokens.length).fill(false),
             );
         }
@@ -104,12 +101,12 @@ export class VLChatProcessor extends Processor {
             input_ids: new Tensor('int64', input_ids, dims),
             attention_mask: new Tensor('int64', new Array(input_ids.length).fill(1), dims),
             images_seq_mask: new Tensor('bool', images_seq_mask, dims),
-            images_emb_mask: new Tensor(
-                'bool',
-                new Array(num_images * this.num_image_tokens).fill(true),
-                [1, num_images, this.num_image_tokens],
-            ),
-        }
+            images_emb_mask: new Tensor('bool', new Array(num_images * this.num_image_tokens).fill(true), [
+                1,
+                num_images,
+                this.num_image_tokens,
+            ]),
+        };
 
         if (images && images.length > 0) {
             const image_inputs = await this.image_processor(images);
diff --git a/src/models/jina_clip/image_processing_jina_clip.js b/src/models/jina_clip/image_processing_jina_clip.js
index fccacdd73..7b901f5ee 100644
--- a/src/models/jina_clip/image_processing_jina_clip.js
+++ b/src/models/jina_clip/image_processing_jina_clip.js
@@ -1,17 +1,16 @@
-import {
-    ImageProcessor,
-} from "../../base/image_processors_utils.js";
+import { ImageProcessor } from '../../base/image_processors_utils.js';
 
 export class JinaCLIPImageProcessor extends ImageProcessor {
     constructor(config) {
         // JinaCLIPImageProcessor uses a custom preprocessor_config.json, so we configure it here
         const { resize_mode, fill_color, interpolation, size, ...other } = config;
 
-        const new_size = resize_mode === 'squash'
-            ? { width: size, height: size }
-            : resize_mode === 'shortest'
-                ? { shortest_edge: size }
-                : { longest_edge: size };
+        const new_size =
+            resize_mode === 'squash'
+                ? { width: size, height: size }
+                : resize_mode === 'shortest'
+                  ? { shortest_edge: size }
+                  : { longest_edge: size };
 
         const resample = interpolation === 'bicubic' ? 3 : 2;
         super({
diff --git a/src/models/jina_clip/processing_jina_clip.js b/src/models/jina_clip/processing_jina_clip.js
index b5219560f..ef3d3ffd8 100644
--- a/src/models/jina_clip/processing_jina_clip.js
+++ b/src/models/jina_clip/processing_jina_clip.js
@@ -1,15 +1,13 @@
-
-import { Processor } from "../../base/processing_utils.js";
-import { AutoImageProcessor } from "../auto/image_processing_auto.js";
-import { AutoTokenizer } from "../../tokenizers.js";
+import { Processor } from '../../base/processing_utils.js';
+import { AutoImageProcessor } from '../auto/image_processing_auto.js';
+import { AutoTokenizer } from '../../tokenizers.js';
 
 export class JinaCLIPProcessor extends Processor {
-    static tokenizer_class = AutoTokenizer
-    static image_processor_class = AutoImageProcessor
-
-    async _call(text=null, images=null, kwargs = {}) {
+    static tokenizer_class = AutoTokenizer;
+    static image_processor_class = AutoImageProcessor;
 
-        if (!text && !images){
+    async _call(text = null, images = null, kwargs = {}) {
+        if (!text && !images) {
             throw new Error('Either text or images must be provided');
         }
 
@@ -19,6 +17,6 @@ export class JinaCLIPProcessor extends Processor {
         return {
             ...text_inputs,
             ...image_inputs,
-        }
+        };
     }
 }
diff --git a/src/models/llava/processing_llava.js b/src/models/llava/processing_llava.js
index b8e1a2979..4f70edca8 100644
--- a/src/models/llava/processing_llava.js
+++ b/src/models/llava/processing_llava.js
@@ -1,11 +1,10 @@
-
-import { Processor } from "../../base/processing_utils.js";
-import { AutoImageProcessor } from "../auto/image_processing_auto.js";
-import { AutoTokenizer } from "../../tokenizers.js";
+import { Processor } from '../../base/processing_utils.js';
+import { AutoImageProcessor } from '../auto/image_processing_auto.js';
+import { AutoTokenizer } from '../../tokenizers.js';
 
 export class LlavaProcessor extends Processor {
-    static tokenizer_class = AutoTokenizer
-    static image_processor_class = AutoImageProcessor
+    static tokenizer_class = AutoTokenizer;
+    static image_processor_class = AutoImageProcessor;
     static uses_processor_config = true;
 
     /**
@@ -14,16 +13,14 @@ export class LlavaProcessor extends Processor {
 
     // `images` is required, `text` is optional
     async _call(/** @type {RawImage|RawImage[]} */ images, text = null, kwargs = {}) {
-        
         const image_inputs = await this.image_processor(images, kwargs);
 
         if (text) {
             const [height, width] = image_inputs.pixel_values.dims.slice(-2);
 
-            const {image_token, patch_size, num_additional_image_tokens} = this.config;
-            const num_image_tokens = Math.floor(
-                height / patch_size
-            ) * Math.floor(width / patch_size) + num_additional_image_tokens;
+            const { image_token, patch_size, num_additional_image_tokens } = this.config;
+            const num_image_tokens =
+                Math.floor(height / patch_size) * Math.floor(width / patch_size) + num_additional_image_tokens;
 
             text = structuredClone(text); // Avoid modifying the original text input
             if (!Array.isArray(text)) {
@@ -33,12 +30,12 @@ export class LlavaProcessor extends Processor {
                 text[i] = text[i].replace(image_token, image_token.repeat(num_image_tokens));
             }
         }
-        
+
         const text_inputs = text ? this.tokenizer(text, kwargs) : {};
 
         return {
             ...image_inputs,
             ...text_inputs,
-        }
+        };
     }
 }
diff --git a/src/models/llava_onevision/image_processing_llava_onevision.js b/src/models/llava_onevision/image_processing_llava_onevision.js
index c705589de..95075666a 100644
--- a/src/models/llava_onevision/image_processing_llava_onevision.js
+++ b/src/models/llava_onevision/image_processing_llava_onevision.js
@@ -1,5 +1,3 @@
-import { 
-    ImageProcessor,
-} from "../../base/image_processors_utils.js";
+import { ImageProcessor } from '../../base/image_processors_utils.js';
 
 export class LlavaOnevisionImageProcessor extends ImageProcessor {}
diff --git a/src/models/mask2former/image_processing_mask2former.js b/src/models/mask2former/image_processing_mask2former.js
index 5e02b5c38..f8def92d9 100644
--- a/src/models/mask2former/image_processing_mask2former.js
+++ b/src/models/mask2former/image_processing_mask2former.js
@@ -1,5 +1,4 @@
-
-import { MaskFormerImageProcessor } from "../maskformer/image_processing_maskformer.js";
+import { MaskFormerImageProcessor } from '../maskformer/image_processing_maskformer.js';
 
 // NOTE: extends MaskFormerImageProcessor
-export class Mask2FormerImageProcessor extends MaskFormerImageProcessor { }
+export class Mask2FormerImageProcessor extends MaskFormerImageProcessor {}
diff --git a/src/models/maskformer/image_processing_maskformer.js b/src/models/maskformer/image_processing_maskformer.js
index 6b90b0451..95e70f6e8 100644
--- a/src/models/maskformer/image_processing_maskformer.js
+++ b/src/models/maskformer/image_processing_maskformer.js
@@ -1,11 +1,10 @@
-import { 
+import {
     ImageProcessor,
     post_process_panoptic_segmentation,
     post_process_instance_segmentation,
-} from "../../base/image_processors_utils.js";
+} from '../../base/image_processors_utils.js';
 
 export class MaskFormerImageProcessor extends ImageProcessor {
-
     /** @type {typeof post_process_panoptic_segmentation} */
     post_process_panoptic_segmentation(...args) {
         return post_process_panoptic_segmentation(...args);
@@ -15,4 +14,4 @@ export class MaskFormerImageProcessor extends ImageProcessor {
         return post_process_instance_segmentation(...args);
     }
 }
-export class MaskFormerFeatureExtractor extends MaskFormerImageProcessor { }
+export class MaskFormerFeatureExtractor extends MaskFormerImageProcessor {}
diff --git a/src/models/mgp_str/processing_mgp_str.js b/src/models/mgp_str/processing_mgp_str.js
index a4da992cc..1f16bb3d6 100644
--- a/src/models/mgp_str/processing_mgp_str.js
+++ b/src/models/mgp_str/processing_mgp_str.js
@@ -1,16 +1,16 @@
-import { Processor } from "../../base/processing_utils.js";
-import { AutoImageProcessor } from "../auto/image_processing_auto.js";
-import { AutoTokenizer } from "../../tokenizers.js";
-import { max, softmax } from "../../utils/maths.js";
+import { Processor } from '../../base/processing_utils.js';
+import { AutoImageProcessor } from '../auto/image_processing_auto.js';
+import { AutoTokenizer } from '../../tokenizers.js';
+import { max, softmax } from '../../utils/maths.js';
 
 const DECODE_TYPE_MAPPING = {
-    'char': ['char_decode', 1],
-    'bpe': ['bpe_decode', 2],
-    'wp': ['wp_decode', 102],
-}
+    char: ['char_decode', 1],
+    bpe: ['bpe_decode', 2],
+    wp: ['wp_decode', 102],
+};
 export class MgpstrProcessor extends Processor {
-    static tokenizer_class = AutoTokenizer
-    static image_processor_class = AutoImageProcessor
+    static tokenizer_class = AutoTokenizer;
+    static image_processor_class = AutoImageProcessor;
 
     /**
      * @returns {import('../../tokenizers.js').MgpstrTokenizer} The character tokenizer.
@@ -69,9 +69,7 @@ export class MgpstrProcessor extends Processor {
                 ids.push(max_prob_index);
             }
 
-            const confidence_score = scores.length > 0
-                ? scores.reduce((a, b) => a * b, 1)
-                : 0;
+            const confidence_score = scores.length > 0 ? scores.reduce((a, b) => a * b, 1) : 0;
 
             all_ids.push(ids);
             conf_scores.push(confidence_score);
@@ -87,7 +85,7 @@ export class MgpstrProcessor extends Processor {
      * @returns {string[]} The list of char decoded sentences.
      */
     char_decode(sequences) {
-        return this.char_tokenizer.batch_decode(sequences).map(str => str.replaceAll(' ', ''));
+        return this.char_tokenizer.batch_decode(sequences).map((str) => str.replaceAll(' ', ''));
     }
 
     /**
@@ -96,7 +94,7 @@ export class MgpstrProcessor extends Processor {
      * @returns {string[]} The list of BPE decoded sentences.
      */
     bpe_decode(sequences) {
-        return this.bpe_tokenizer.batch_decode(sequences)
+        return this.bpe_tokenizer.batch_decode(sequences);
     }
 
     /**
@@ -105,7 +103,7 @@ export class MgpstrProcessor extends Processor {
      * @returns {string[]} The list of wp decoded sentences.
      */
     wp_decode(sequences) {
-        return this.wp_tokenizer.batch_decode(sequences).map(str => str.replaceAll(' ', ''));
+        return this.wp_tokenizer.batch_decode(sequences).map((str) => str.replaceAll(' ', ''));
     }
 
     /**
@@ -140,15 +138,15 @@ export class MgpstrProcessor extends Processor {
             char_preds,
             bpe_preds,
             wp_preds,
-        }
+        };
     }
     /** @type {typeof Processor.from_pretrained} */
     static async from_pretrained(...args) {
         const base = await super.from_pretrained(...args);
 
         // Load Transformers.js-compatible versions of the BPE and WordPiece tokenizers
-        const bpe_tokenizer = await AutoTokenizer.from_pretrained("Xenova/gpt2") // openai-community/gpt2
-        const wp_tokenizer = await AutoTokenizer.from_pretrained("Xenova/bert-base-uncased") // google-bert/bert-base-uncased
+        const bpe_tokenizer = await AutoTokenizer.from_pretrained('Xenova/gpt2'); // openai-community/gpt2
+        const wp_tokenizer = await AutoTokenizer.from_pretrained('Xenova/bert-base-uncased'); // google-bert/bert-base-uncased
 
         // Update components
         base.components = {
@@ -156,7 +154,7 @@ export class MgpstrProcessor extends Processor {
             char_tokenizer: base.tokenizer,
             bpe_tokenizer: bpe_tokenizer,
             wp_tokenizer: wp_tokenizer,
-        }
+        };
         return base;
     }
 
@@ -164,7 +162,7 @@ export class MgpstrProcessor extends Processor {
         const result = await this.image_processor(images);
 
         if (text) {
-            result.labels = this.tokenizer(text).input_ids
+            result.labels = this.tokenizer(text).input_ids;
         }
 
         return result;
diff --git a/src/models/mobilenet_v1/image_processing_mobilenet_v1.js b/src/models/mobilenet_v1/image_processing_mobilenet_v1.js
index 61246131e..d11dbed3d 100644
--- a/src/models/mobilenet_v1/image_processing_mobilenet_v1.js
+++ b/src/models/mobilenet_v1/image_processing_mobilenet_v1.js
@@ -1,7 +1,4 @@
-import { 
-    ImageProcessor,
-} from "../../base/image_processors_utils.js";
+import { ImageProcessor } from '../../base/image_processors_utils.js';
 
-
-export class MobileNetV1ImageProcessor extends ImageProcessor { }
-export class MobileNetV1FeatureExtractor extends MobileNetV1ImageProcessor { }
+export class MobileNetV1ImageProcessor extends ImageProcessor {}
+export class MobileNetV1FeatureExtractor extends MobileNetV1ImageProcessor {}
diff --git a/src/models/mobilenet_v2/image_processing_mobilenet_v2.js b/src/models/mobilenet_v2/image_processing_mobilenet_v2.js
index 1d80a67a3..687d888ca 100644
--- a/src/models/mobilenet_v2/image_processing_mobilenet_v2.js
+++ b/src/models/mobilenet_v2/image_processing_mobilenet_v2.js
@@ -1,7 +1,4 @@
-import { 
-    ImageProcessor,
-} from "../../base/image_processors_utils.js";
+import { ImageProcessor } from '../../base/image_processors_utils.js';
 
-
-export class MobileNetV2ImageProcessor extends ImageProcessor { }
-export class MobileNetV2FeatureExtractor extends MobileNetV2ImageProcessor { }
+export class MobileNetV2ImageProcessor extends ImageProcessor {}
+export class MobileNetV2FeatureExtractor extends MobileNetV2ImageProcessor {}
diff --git a/src/models/mobilenet_v3/image_processing_mobilenet_v3.js b/src/models/mobilenet_v3/image_processing_mobilenet_v3.js
index 3a935d30d..0da34b58b 100644
--- a/src/models/mobilenet_v3/image_processing_mobilenet_v3.js
+++ b/src/models/mobilenet_v3/image_processing_mobilenet_v3.js
@@ -1,7 +1,4 @@
-import { 
-    ImageProcessor,
-} from "../../base/image_processors_utils.js";
+import { ImageProcessor } from '../../base/image_processors_utils.js';
 
-
-export class MobileNetV3ImageProcessor extends ImageProcessor { }
-export class MobileNetV3FeatureExtractor extends MobileNetV3ImageProcessor { }
+export class MobileNetV3ImageProcessor extends ImageProcessor {}
+export class MobileNetV3FeatureExtractor extends MobileNetV3ImageProcessor {}
diff --git a/src/models/mobilenet_v4/image_processing_mobilenet_v4.js b/src/models/mobilenet_v4/image_processing_mobilenet_v4.js
index fc6401f73..c838ffbdb 100644
--- a/src/models/mobilenet_v4/image_processing_mobilenet_v4.js
+++ b/src/models/mobilenet_v4/image_processing_mobilenet_v4.js
@@ -1,7 +1,4 @@
-import { 
-    ImageProcessor,
-} from "../../base/image_processors_utils.js";
+import { ImageProcessor } from '../../base/image_processors_utils.js';
 
-
-export class MobileNetV4ImageProcessor extends ImageProcessor { }
-export class MobileNetV4FeatureExtractor extends MobileNetV4ImageProcessor { }
+export class MobileNetV4ImageProcessor extends ImageProcessor {}
+export class MobileNetV4FeatureExtractor extends MobileNetV4ImageProcessor {}
diff --git a/src/models/mobilevit/image_processing_mobilevit.js b/src/models/mobilevit/image_processing_mobilevit.js
index 356570c68..df2877ca4 100644
--- a/src/models/mobilevit/image_processing_mobilevit.js
+++ b/src/models/mobilevit/image_processing_mobilevit.js
@@ -1,6 +1,4 @@
-import { 
-    ImageProcessor,
-} from "../../base/image_processors_utils.js";
+import { ImageProcessor } from '../../base/image_processors_utils.js';
 
-export class MobileViTImageProcessor extends ImageProcessor { }
-export class MobileViTFeatureExtractor extends MobileViTImageProcessor { }
+export class MobileViTImageProcessor extends ImageProcessor {}
+export class MobileViTFeatureExtractor extends MobileViTImageProcessor {}
diff --git a/src/models/moonshine/feature_extraction_moonshine.js b/src/models/moonshine/feature_extraction_moonshine.js
index 9f01ab342..6702d9021 100644
--- a/src/models/moonshine/feature_extraction_moonshine.js
+++ b/src/models/moonshine/feature_extraction_moonshine.js
@@ -1,7 +1,6 @@
 import { FeatureExtractor, validate_audio_inputs } from '../../base/feature_extraction_utils.js';
 import { Tensor } from '../../utils/tensor.js';
 
-
 export class MoonshineFeatureExtractor extends FeatureExtractor {
     /**
      * Asynchronously extracts input values from a given audio using the provided configuration.
@@ -15,10 +14,7 @@ export class MoonshineFeatureExtractor extends FeatureExtractor {
             audio = new Float32Array(audio);
         }
 
-        const shape = [
-            1,            /* batch_size */
-            audio.length, /* num_samples */
-        ];
+        const shape = [1 /* batch_size */, audio.length /* num_samples */];
         return {
             input_values: new Tensor('float32', audio, shape),
         };
diff --git a/src/models/moonshine/processing_moonshine.js b/src/models/moonshine/processing_moonshine.js
index e313976ec..895c276b6 100644
--- a/src/models/moonshine/processing_moonshine.js
+++ b/src/models/moonshine/processing_moonshine.js
@@ -1,13 +1,13 @@
-import { AutoFeatureExtractor } from "../auto/feature_extraction_auto.js"
-import { AutoTokenizer } from "../../tokenizers.js"
-import { Processor } from "../../base/processing_utils.js"
+import { AutoFeatureExtractor } from '../auto/feature_extraction_auto.js';
+import { AutoTokenizer } from '../../tokenizers.js';
+import { Processor } from '../../base/processing_utils.js';
 
 /**
  * Represents a MoonshineProcessor that extracts features from an audio input.
  */
 export class MoonshineProcessor extends Processor {
-    static tokenizer_class = AutoTokenizer
-    static feature_extractor_class = AutoFeatureExtractor
+    static tokenizer_class = AutoTokenizer;
+    static feature_extractor_class = AutoFeatureExtractor;
 
     /**
      * Calls the feature_extractor function with the given audio input.
diff --git a/src/models/nougat/image_processing_nougat.js b/src/models/nougat/image_processing_nougat.js
index c845fce3a..2b2078574 100644
--- a/src/models/nougat/image_processing_nougat.js
+++ b/src/models/nougat/image_processing_nougat.js
@@ -1,5 +1,4 @@
-
-import { DonutImageProcessor } from "../donut/image_processing_donut.js";
+import { DonutImageProcessor } from '../donut/image_processing_donut.js';
 
 // NOTE: extends DonutImageProcessor
-export class NougatImageProcessor extends DonutImageProcessor { }
+export class NougatImageProcessor extends DonutImageProcessor {}
diff --git a/src/models/owlv2/image_processing_owlv2.js b/src/models/owlv2/image_processing_owlv2.js
index 224f49cc1..9523cc18e 100644
--- a/src/models/owlv2/image_processing_owlv2.js
+++ b/src/models/owlv2/image_processing_owlv2.js
@@ -1,5 +1,4 @@
-
-import { OwlViTImageProcessor } from "../owlvit/image_processing_owlvit.js";
+import { OwlViTImageProcessor } from '../owlvit/image_processing_owlvit.js';
 
 // NOTE: extends OwlViTImageProcessor
-export class Owlv2ImageProcessor extends OwlViTImageProcessor { }
+export class Owlv2ImageProcessor extends OwlViTImageProcessor {}
diff --git a/src/models/owlvit/image_processing_owlvit.js b/src/models/owlvit/image_processing_owlvit.js
index e7c3c69cf..767c312f8 100644
--- a/src/models/owlvit/image_processing_owlvit.js
+++ b/src/models/owlvit/image_processing_owlvit.js
@@ -1,7 +1,4 @@
-import { 
-    ImageProcessor,
-    post_process_object_detection,
-} from "../../base/image_processors_utils.js";
+import { ImageProcessor, post_process_object_detection } from '../../base/image_processors_utils.js';
 
 export class OwlViTImageProcessor extends ImageProcessor {
     /** @type {typeof post_process_object_detection} */
@@ -9,4 +6,4 @@ export class OwlViTImageProcessor extends ImageProcessor {
         return post_process_object_detection(...args);
     }
 }
-export class OwlViTFeatureExtractor extends OwlViTImageProcessor { }
+export class OwlViTFeatureExtractor extends OwlViTImageProcessor {}
diff --git a/src/models/owlvit/processing_owlvit.js b/src/models/owlvit/processing_owlvit.js
index f596dbe19..6f673746e 100644
--- a/src/models/owlvit/processing_owlvit.js
+++ b/src/models/owlvit/processing_owlvit.js
@@ -1,7 +1,7 @@
-import { Processor } from "../../base/processing_utils.js";
-import { AutoImageProcessor } from "../auto/image_processing_auto.js";
-import { AutoTokenizer } from "../../tokenizers.js";
+import { Processor } from '../../base/processing_utils.js';
+import { AutoImageProcessor } from '../auto/image_processing_auto.js';
+import { AutoTokenizer } from '../../tokenizers.js';
 export class OwlViTProcessor extends Processor {
-    static tokenizer_class = AutoTokenizer
-    static image_processor_class = AutoImageProcessor
+    static tokenizer_class = AutoTokenizer;
+    static image_processor_class = AutoImageProcessor;
 }
diff --git a/src/models/paligemma/processing_paligemma.js b/src/models/paligemma/processing_paligemma.js
index 63f50b99d..1aa3b1167 100644
--- a/src/models/paligemma/processing_paligemma.js
+++ b/src/models/paligemma/processing_paligemma.js
@@ -1,22 +1,16 @@
-import { Processor } from "../../base/processing_utils.js";
-import { AutoImageProcessor } from "../auto/image_processing_auto.js";
-import { AutoTokenizer } from "../../tokenizers.js";
+import { Processor } from '../../base/processing_utils.js';
+import { AutoImageProcessor } from '../auto/image_processing_auto.js';
+import { AutoTokenizer } from '../../tokenizers.js';
 
-const IMAGE_TOKEN = "";
+const IMAGE_TOKEN = '';
 
-function build_string_from_input(
-    prompt,
-    bos_token,
-    image_seq_len,
-    image_token,
-    num_images,
-) {
-    return `${image_token.repeat(image_seq_len * num_images)}${bos_token}${prompt}\n`
+function build_string_from_input(prompt, bos_token, image_seq_len, image_token, num_images) {
+    return `${image_token.repeat(image_seq_len * num_images)}${bos_token}${prompt}\n`;
 }
 
 export class PaliGemmaProcessor extends Processor {
-    static tokenizer_class = AutoTokenizer
-    static image_processor_class = AutoImageProcessor
+    static tokenizer_class = AutoTokenizer;
+    static image_processor_class = AutoImageProcessor;
     static uses_processor_config = false;
 
     /**
@@ -27,17 +21,17 @@ export class PaliGemmaProcessor extends Processor {
     async _call(/** @type {RawImage|RawImage[]} */ images, text = null, kwargs = {}) {
         if (!text) {
             console.warn(
-                "You are using PaliGemma without a text prefix. It will perform as a picture-captioning model."
-            )
-            text = ""
+                'You are using PaliGemma without a text prefix. It will perform as a picture-captioning model.',
+            );
+            text = '';
         }
 
         if (!Array.isArray(images)) {
-            images = [images]
+            images = [images];
         }
 
         if (!Array.isArray(text)) {
-            text = [text]
+            text = [text];
         }
 
         const bos_token = this.tokenizer.bos_token;
@@ -45,31 +39,23 @@ export class PaliGemmaProcessor extends Processor {
         const image_seq_length = this.image_processor.config.image_seq_length;
         let input_strings;
         if (text.some((t) => t.includes(IMAGE_TOKEN))) {
-            input_strings = text.map(
-                sample => {
-                    const expanded_sample = sample.replaceAll(IMAGE_TOKEN, IMAGE_TOKEN.repeat(image_seq_length));
-                    const bos_rfind_index = expanded_sample.lastIndexOf(IMAGE_TOKEN);
-                    const bos_index = bos_rfind_index === -1 ? 0 : bos_rfind_index + IMAGE_TOKEN.length;
-                    return expanded_sample.slice(0, bos_index) + bos_token + expanded_sample.slice(bos_index) + "\n";
-                }
-            )
+            input_strings = text.map((sample) => {
+                const expanded_sample = sample.replaceAll(IMAGE_TOKEN, IMAGE_TOKEN.repeat(image_seq_length));
+                const bos_rfind_index = expanded_sample.lastIndexOf(IMAGE_TOKEN);
+                const bos_index = bos_rfind_index === -1 ? 0 : bos_rfind_index + IMAGE_TOKEN.length;
+                return expanded_sample.slice(0, bos_index) + bos_token + expanded_sample.slice(bos_index) + '\n';
+            });
         } else {
             console.warn(
-                "You are passing both `text` and `images` to `PaliGemmaProcessor`. The processor expects special " +
-                "image tokens in the text, as many tokens as there are images per each text. It is recommended to " +
-                "add `` tokens in the very beginning of your text. For this call, we will infer how many images " +
-                "each text has and add special tokens."
-            )
+                'You are passing both `text` and `images` to `PaliGemmaProcessor`. The processor expects special ' +
+                    'image tokens in the text, as many tokens as there are images per each text. It is recommended to ' +
+                    'add `` tokens in the very beginning of your text. For this call, we will infer how many images ' +
+                    'each text has and add special tokens.',
+            );
 
-            input_strings = text.map(
-                sample => build_string_from_input(
-                    sample,
-                    bos_token,
-                    image_seq_length,
-                    IMAGE_TOKEN,
-                    images.length,
-                )
-            )
+            input_strings = text.map((sample) =>
+                build_string_from_input(sample, bos_token, image_seq_length, IMAGE_TOKEN, images.length),
+            );
         }
 
         const text_inputs = this.tokenizer(input_strings, kwargs);
@@ -78,6 +64,6 @@ export class PaliGemmaProcessor extends Processor {
         return {
             ...image_inputs,
             ...text_inputs,
-        }
+        };
     }
 }
diff --git a/src/models/phi3_v/image_processing_phi3_v.js b/src/models/phi3_v/image_processing_phi3_v.js
index 5e032b294..50a804e98 100644
--- a/src/models/phi3_v/image_processing_phi3_v.js
+++ b/src/models/phi3_v/image_processing_phi3_v.js
@@ -1,7 +1,5 @@
-import {
-    ImageProcessor,
-} from "../../base/image_processors_utils.js";
-import { cat, interpolate_4d, slice, stack, Tensor } from "../../utils/tensor.js";
+import { ImageProcessor } from '../../base/image_processors_utils.js';
+import { cat, interpolate_4d, slice, stack, Tensor } from '../../utils/tensor.js';
 
 const IMAGE_SIZE = 336;
 const SLICE_AXES = [2, 3]; // axes to slice on
@@ -23,13 +21,17 @@ export class Phi3VImageProcessor extends ImageProcessor {
     calc_num_image_tokens_from_image_size(width, height) {
         // @ts-expect-error
         const { num_img_tokens } = this.config;
-        return floor(((floor((height / IMAGE_SIZE)) * floor((width / IMAGE_SIZE)) + 1) * num_img_tokens) + 1 + (floor(height / IMAGE_SIZE) + 1) * sqrt(num_img_tokens));
+        return floor(
+            (floor(height / IMAGE_SIZE) * floor(width / IMAGE_SIZE) + 1) * num_img_tokens +
+                1 +
+                (floor(height / IMAGE_SIZE) + 1) * sqrt(num_img_tokens),
+        );
     }
 
     /** @type {ImageProcessor['get_resize_output_image_size']} */
     get_resize_output_image_size(image, size) {
         const hd_num = this._num_crops;
-        const [width, height] = image.size
+        const [width, height] = image.size;
 
         let ratio = width / height;
         let scale = 1;
@@ -44,10 +46,9 @@ export class Phi3VImageProcessor extends ImageProcessor {
         const new_w = Math.floor(scale * 336);
         const new_h = Math.floor(new_w / ratio);
 
-        return [new_w, new_h]
+        return [new_w, new_h];
     }
 
-
     /** @type {ImageProcessor['pad_image']} */
     pad_image(pixelData, imgDims, padSize, options = {}) {
         // Phi3V uses a custom padding strategy:
@@ -59,20 +60,23 @@ export class Phi3VImageProcessor extends ImageProcessor {
 
         // NOTE: Since padding is done after normalization, we need to fill with the normalized values
         const constant_values = [1, 1, 1].map((x, i) => (x - this.image_mean[i]) / this.image_std[i]);
-        return super.pad_image(pixelData, imgDims, { width, height }, {
-            center: true,
-            constant_values,
-            ...options,
-        });
+        return super.pad_image(
+            pixelData,
+            imgDims,
+            { width, height },
+            {
+                center: true,
+                constant_values,
+                ...options,
+            },
+        );
     }
 
-    async _call(images, {
-        num_crops = null,
-    } = {}) {
+    async _call(images, { num_crops = null } = {}) {
         // @ts-expect-error
         this._num_crops = num_crops ??= this.config.num_crops;
         if (num_crops < 4 || sqrt(num_crops) % 1 !== 0) {
-            throw new Error("num_crops must be a square number >= 4");
+            throw new Error('num_crops must be a square number >= 4');
         }
 
         if (!Array.isArray(images)) {
@@ -80,10 +84,10 @@ export class Phi3VImageProcessor extends ImageProcessor {
         }
 
         const num_images = images.length;
-        const imageData = await Promise.all(images.map(x => this.preprocess(x)));
+        const imageData = await Promise.all(images.map((x) => this.preprocess(x)));
 
-        const original_sizes = imageData.map(x => x.original_size);
-        const reshaped_input_sizes = imageData.map(x => x.reshaped_input_size);
+        const original_sizes = imageData.map((x) => x.original_size);
+        const reshaped_input_sizes = imageData.map((x) => x.reshaped_input_size);
 
         // Process each image in batch
         const all_pixel_values = [];
@@ -106,14 +110,16 @@ export class Phi3VImageProcessor extends ImageProcessor {
                 for (let y = 0; y < sqrt_patches; ++y) {
                     for (let x = 0; x < sqrt_patches; ++x) {
                         let start_x, start_y, end_x, end_y;
-                        if (y === sqrt_patches - 1) { // At bottom
+                        if (y === sqrt_patches - 1) {
+                            // At bottom
                             start_y = height - patch_height;
                             end_y = height;
                         } else {
                             start_y = y * patch_height;
                             end_y = (y + 1) * patch_height;
                         }
-                        if (x === sqrt_patches - 1) { // At right
+                        if (x === sqrt_patches - 1) {
+                            // At right
                             start_x = width - patch_width;
                             end_x = width;
                         } else {
@@ -146,16 +152,12 @@ export class Phi3VImageProcessor extends ImageProcessor {
         const pixel_values = stack(all_pixel_values, 0);
 
         // Calculate padded image sizes
-        const sizes = reshaped_input_sizes.map(x => x.map(y => IMAGE_SIZE * ceil(y / IMAGE_SIZE)));
+        const sizes = reshaped_input_sizes.map((x) => x.map((y) => IMAGE_SIZE * ceil(y / IMAGE_SIZE)));
 
-        const image_sizes = new Tensor(
-            'int64',
-            sizes.flat(),
-            [num_images, 2],
-        );
+        const image_sizes = new Tensor('int64', sizes.flat(), [num_images, 2]);
 
-        const num_img_tokens = sizes.map(
-            ([height, width]) => this.calc_num_image_tokens_from_image_size(width, height),
+        const num_img_tokens = sizes.map(([height, width]) =>
+            this.calc_num_image_tokens_from_image_size(width, height),
         );
 
         return { pixel_values, original_sizes, reshaped_input_sizes, image_sizes, num_img_tokens };
diff --git a/src/models/phi3_v/processing_phi3_v.js b/src/models/phi3_v/processing_phi3_v.js
index 02adae5fe..18d3eb15a 100644
--- a/src/models/phi3_v/processing_phi3_v.js
+++ b/src/models/phi3_v/processing_phi3_v.js
@@ -1,28 +1,23 @@
-import { Processor } from "../../base/processing_utils.js";
-import { AutoImageProcessor } from "../auto/image_processing_auto.js";
-import { AutoTokenizer } from "../../tokenizers.js";
-import { RawImage } from "../../utils/image.js";
+import { Processor } from '../../base/processing_utils.js';
+import { AutoImageProcessor } from '../auto/image_processing_auto.js';
+import { AutoTokenizer } from '../../tokenizers.js';
+import { RawImage } from '../../utils/image.js';
 
-const IMAGE_TOKEN = "<|image|>";
+const IMAGE_TOKEN = '<|image|>';
 const IMAGE_TOKEN_PATTERN = /<\|image_\d+\|>/g;
 
 export class Phi3VProcessor extends Processor {
-    static image_processor_class = AutoImageProcessor
-    static tokenizer_class = AutoTokenizer
+    static image_processor_class = AutoImageProcessor;
+    static tokenizer_class = AutoTokenizer;
 
     /**
-     * 
-     * @param {string|string[]} text 
-     * @param {RawImage|RawImage[]} images 
+     *
+     * @param {string|string[]} text
+     * @param {RawImage|RawImage[]} images
      * @param  { { padding?: boolean, truncation?: boolean, num_crops?: number } | undefined } options
      * @returns {Promise}
      */
-    async _call(text, images = null, {
-        padding = true,
-        truncation = true,
-        num_crops = null,
-    } = {}) {
-
+    async _call(text, images = null, { padding = true, truncation = true, num_crops = null } = {}) {
         if (!Array.isArray(text)) {
             text = [text];
         }
@@ -34,13 +29,15 @@ export class Phi3VProcessor extends Processor {
 
             // The original implementation adds a bos_token before the image tokens
             // TODO: Check if this affects performance, since it looks like a bug in the original implementation
-            const prompt_chunks = text.map((t, i) => t.split(IMAGE_TOKEN_PATTERN).join(IMAGE_TOKEN.repeat(num_img_tokens[i])));
+            const prompt_chunks = text.map((t, i) =>
+                t.split(IMAGE_TOKEN_PATTERN).join(IMAGE_TOKEN.repeat(num_img_tokens[i])),
+            );
 
             text_inputs = this.tokenizer(prompt_chunks, { padding, truncation });
 
             // The model expects image tokens to be negative, so we negate the image token ids
             const image_token_id = this.tokenizer.model.convert_tokens_to_ids([IMAGE_TOKEN])[0];
-            text_inputs.input_ids.map_(id => (id == image_token_id) ? -id : id);
+            text_inputs.input_ids.map_((id) => (id == image_token_id ? -id : id));
         } else {
             text_inputs = this.tokenizer(text);
         }
@@ -48,6 +45,6 @@ export class Phi3VProcessor extends Processor {
         return {
             ...text_inputs,
             ...image_inputs,
-        }
+        };
     }
 }
diff --git a/src/models/pvt/image_processing_pvt.js b/src/models/pvt/image_processing_pvt.js
index 2156dfe0d..702af349e 100644
--- a/src/models/pvt/image_processing_pvt.js
+++ b/src/models/pvt/image_processing_pvt.js
@@ -1,5 +1,3 @@
-import { 
-    ImageProcessor,
-} from "../../base/image_processors_utils.js";
+import { ImageProcessor } from '../../base/image_processors_utils.js';
 
-export class PvtImageProcessor extends ImageProcessor { }
+export class PvtImageProcessor extends ImageProcessor {}
diff --git a/src/models/pyannote/feature_extraction_pyannote.js b/src/models/pyannote/feature_extraction_pyannote.js
index e563bb9c8..3231a8ea9 100644
--- a/src/models/pyannote/feature_extraction_pyannote.js
+++ b/src/models/pyannote/feature_extraction_pyannote.js
@@ -2,7 +2,6 @@ import { FeatureExtractor, validate_audio_inputs } from '../../base/feature_extr
 import { Tensor } from '../../utils/tensor.js';
 import { max, softmax } from '../../utils/maths.js';
 
-
 export class PyAnnoteFeatureExtractor extends FeatureExtractor {
     /**
      * Asynchronously extracts features from a given audio using the provided configuration.
@@ -16,11 +15,7 @@ export class PyAnnoteFeatureExtractor extends FeatureExtractor {
             audio = new Float32Array(audio);
         }
 
-        const shape = [
-            1,            /* batch_size */
-            1,            /* num_channels */
-            audio.length, /* num_samples */
-        ];
+        const shape = [1 /* batch_size */, 1 /* num_channels */, audio.length /* num_samples */];
         return {
             input_values: new Tensor('float32', audio, shape),
         };
@@ -32,7 +27,7 @@ export class PyAnnoteFeatureExtractor extends FeatureExtractor {
      * @returns {number} The number of frames in the audio.
      */
     samples_to_frames(samples) {
-        return ((samples - this.config.offset) / this.config.step);
+        return (samples - this.config.offset) / this.config.step;
     }
 
     /**
@@ -42,9 +37,7 @@ export class PyAnnoteFeatureExtractor extends FeatureExtractor {
      * @returns {Array>} The post-processed speaker diarization results.
      */
     post_process_speaker_diarization(logits, num_samples) {
-        const ratio = (
-            num_samples / this.samples_to_frames(num_samples)
-        ) / this.config.sampling_rate;
+        const ratio = num_samples / this.samples_to_frames(num_samples) / this.config.sampling_rate;
 
         const results = [];
         for (const scores of logits.tolist()) {
@@ -68,18 +61,19 @@ export class PyAnnoteFeatureExtractor extends FeatureExtractor {
                 }
             }
 
-            results.push(accumulated_segments.map(
-                // Convert frame-space to time-space
-                // and compute the confidence
-                ({ id, start, end, score }) => ({
-                    id,
-                    start: start * ratio,
-                    end: end * ratio,
-                    confidence: score / (end - start),
-                })
-            ));
+            results.push(
+                accumulated_segments.map(
+                    // Convert frame-space to time-space
+                    // and compute the confidence
+                    ({ id, start, end, score }) => ({
+                        id,
+                        start: start * ratio,
+                        end: end * ratio,
+                        confidence: score / (end - start),
+                    }),
+                ),
+            );
         }
         return results;
     }
-
 }
diff --git a/src/models/pyannote/processing_pyannote.js b/src/models/pyannote/processing_pyannote.js
index e5fff9cb6..e6f19cdb3 100644
--- a/src/models/pyannote/processing_pyannote.js
+++ b/src/models/pyannote/processing_pyannote.js
@@ -2,7 +2,7 @@ import { Processor } from '../../base/processing_utils.js';
 import { PyAnnoteFeatureExtractor } from './feature_extraction_pyannote.js';
 
 export class PyAnnoteProcessor extends Processor {
-    static feature_extractor_class = PyAnnoteFeatureExtractor
+    static feature_extractor_class = PyAnnoteFeatureExtractor;
 
     /**
      * Calls the feature_extractor function with the given audio input.
@@ -10,12 +10,14 @@ export class PyAnnoteProcessor extends Processor {
      * @returns {Promise} A Promise that resolves with the extracted features.
      */
     async _call(audio) {
-        return await this.feature_extractor(audio)
+        return await this.feature_extractor(audio);
     }
 
     /** @type {PyAnnoteFeatureExtractor['post_process_speaker_diarization']} */
     post_process_speaker_diarization(...args) {
-        return /** @type {PyAnnoteFeatureExtractor} */(this.feature_extractor).post_process_speaker_diarization(...args);
+        return /** @type {PyAnnoteFeatureExtractor} */ (this.feature_extractor).post_process_speaker_diarization(
+            ...args,
+        );
     }
 
     get sampling_rate() {
diff --git a/src/models/qwen2_vl/image_processing_qwen2_vl.js b/src/models/qwen2_vl/image_processing_qwen2_vl.js
index cb5c914f8..809b24566 100644
--- a/src/models/qwen2_vl/image_processing_qwen2_vl.js
+++ b/src/models/qwen2_vl/image_processing_qwen2_vl.js
@@ -1,7 +1,5 @@
-import {
-    ImageProcessor,
-} from "../../base/image_processors_utils.js";
-import { cat, Tensor } from "../../utils/tensor.js";
+import { ImageProcessor } from '../../base/image_processors_utils.js';
+import { cat, Tensor } from '../../utils/tensor.js';
 
 export class Qwen2VLImageProcessor extends ImageProcessor {
     async _call(images, ...args) {
@@ -13,7 +11,10 @@ export class Qwen2VLImageProcessor extends ImageProcessor {
         const { temporal_patch_size, merge_size, patch_size } = this.config;
         if (patches.dims[0] === 1) {
             // Equivalent to np.tile(patches, (self.temporal_patch_size, 1, 1, 1))
-            patches = cat(Array.from({ length: temporal_patch_size }, () => patches), 0);
+            patches = cat(
+                Array.from({ length: temporal_patch_size }, () => patches),
+                0,
+            );
         }
 
         const grid_t = patches.dims[0] / temporal_patch_size;
@@ -34,10 +35,7 @@ export class Qwen2VLImageProcessor extends ImageProcessor {
                 patch_size,
             )
             .permute(0, 3, 6, 4, 7, 2, 1, 5, 8)
-            .view(
-                grid_t * grid_h * grid_w,
-                channel * temporal_patch_size * patch_size * patch_size,
-            )
+            .view(grid_t * grid_h * grid_w, channel * temporal_patch_size * patch_size * patch_size);
 
         const image_grid_thw = new Tensor('int64', [grid_t, grid_h, grid_w], [1, 3]);
 
@@ -46,7 +44,6 @@ export class Qwen2VLImageProcessor extends ImageProcessor {
             image_grid_thw,
             original_sizes,
             reshaped_input_sizes,
-        }
+        };
     }
 }
-
diff --git a/src/models/qwen2_vl/processing_qwen2_vl.js b/src/models/qwen2_vl/processing_qwen2_vl.js
index 692987f86..dd4775721 100644
--- a/src/models/qwen2_vl/processing_qwen2_vl.js
+++ b/src/models/qwen2_vl/processing_qwen2_vl.js
@@ -1,21 +1,20 @@
-import { Processor } from "../../base/processing_utils.js";
-import { AutoImageProcessor } from "../auto/image_processing_auto.js";
-import { AutoTokenizer } from "../../tokenizers.js";
-import { RawImage } from "../../utils/image.js";
+import { Processor } from '../../base/processing_utils.js';
+import { AutoImageProcessor } from '../auto/image_processing_auto.js';
+import { AutoTokenizer } from '../../tokenizers.js';
+import { RawImage } from '../../utils/image.js';
 
 export class Qwen2VLProcessor extends Processor {
-    static image_processor_class = AutoImageProcessor
-    static tokenizer_class = AutoTokenizer
+    static image_processor_class = AutoImageProcessor;
+    static tokenizer_class = AutoTokenizer;
 
     /**
-     * 
-     * @param {string|string[]} text 
-     * @param {RawImage|RawImage[]} images 
-     * @param  {...any} args 
+     *
+     * @param {string|string[]} text
+     * @param {RawImage|RawImage[]} images
+     * @param  {...any} args
      * @returns {Promise}
      */
     async _call(text, images = null, ...args) {
-
         if (!Array.isArray(text)) {
             text = [text];
         }
@@ -33,12 +32,12 @@ export class Qwen2VLProcessor extends Processor {
             let index = 0;
 
             const image_grid_thw_list = image_grid_thw.tolist();
-            text = text.map(t => {
-                while (t.includes("<|image_pad|>")) {
+            text = text.map((t) => {
+                while (t.includes('<|image_pad|>')) {
                     const prod = Number(image_grid_thw_list[index++].reduce((a, b) => a * b, 1n));
-                    t = t.replace("<|image_pad|>", "<|placeholder|>".repeat(Math.floor(prod / merge_length)));
+                    t = t.replace('<|image_pad|>', '<|placeholder|>'.repeat(Math.floor(prod / merge_length)));
                 }
-                return t.replaceAll("<|placeholder|>", "<|image_pad|>");
+                return t.replaceAll('<|placeholder|>', '<|image_pad|>');
             });
         }
 
@@ -48,6 +47,6 @@ export class Qwen2VLProcessor extends Processor {
             ...text_inputs,
             ...image_inputs,
             // TODO: ...videos_inputs,
-        }
+        };
     }
 }
diff --git a/src/models/rt_detr/image_processing_rt_detr.js b/src/models/rt_detr/image_processing_rt_detr.js
index eef753352..1a8f8ee2d 100644
--- a/src/models/rt_detr/image_processing_rt_detr.js
+++ b/src/models/rt_detr/image_processing_rt_detr.js
@@ -1,8 +1,4 @@
-import { 
-    ImageProcessor,
-    post_process_object_detection,
-} from "../../base/image_processors_utils.js";
-
+import { ImageProcessor, post_process_object_detection } from '../../base/image_processors_utils.js';
 
 export class RTDetrImageProcessor extends ImageProcessor {
     /** @type {typeof post_process_object_detection} */
diff --git a/src/models/sam/image_processing_sam.js b/src/models/sam/image_processing_sam.js
index bd71e1f43..98ded02d3 100644
--- a/src/models/sam/image_processing_sam.js
+++ b/src/models/sam/image_processing_sam.js
@@ -1,13 +1,7 @@
-import { 
-    ImageProcessor,
-} from "../../base/image_processors_utils.js";
-import { calculateDimensions } from "../../utils/core.js";
-
-import {
-    interpolate_4d,
-    Tensor,
-} from "../../utils/tensor.js";
+import { ImageProcessor } from '../../base/image_processors_utils.js';
+import { calculateDimensions } from '../../utils/core.js';
 
+import { interpolate_4d, Tensor } from '../../utils/tensor.js';
 
 /**
  * @typedef {object} SamImageProcessorResult
@@ -20,16 +14,14 @@ import {
  */
 
 export class SamImageProcessor extends ImageProcessor {
-
     /**
-     * 
-     * @param {any} input_points 
-     * @param {import("../../base/image_processors_utils.js").HeightWidth[]} original_sizes 
-     * @param {import("../../base/image_processors_utils.js").HeightWidth[]} reshaped_input_sizes 
+     *
+     * @param {any} input_points
+     * @param {import("../../base/image_processors_utils.js").HeightWidth[]} original_sizes
+     * @param {import("../../base/image_processors_utils.js").HeightWidth[]} reshaped_input_sizes
      * @returns {Tensor}
      */
     reshape_input_points(input_points, original_sizes, reshaped_input_sizes, is_bounding_box = false) {
-
         // Make deep copy to avoid altering user's input
         input_points = structuredClone(input_points);
         let shape = calculateDimensions(input_points);
@@ -42,40 +34,41 @@ export class SamImageProcessor extends ImageProcessor {
             }
             input_points = [input_points];
         } else if (shape.length !== 4) {
-            throw Error("The input_points must be a 4D tensor of shape `batch_size`, `point_batch_size`, `nb_points_per_image`, `2`.")
+            throw Error(
+                'The input_points must be a 4D tensor of shape `batch_size`, `point_batch_size`, `nb_points_per_image`, `2`.',
+            );
         }
 
         // Reshape input points
-        for (let i = 0; i < input_points.length; ++i) { // batch_size
+        for (let i = 0; i < input_points.length; ++i) {
+            // batch_size
             let originalImageSize = original_sizes[i];
             let reshapedImageSize = reshaped_input_sizes[i];
 
             let resizeFactors = [
                 reshapedImageSize[0] / originalImageSize[0],
-                reshapedImageSize[1] / originalImageSize[1]
-            ]
-
-            for (let j = 0; j < input_points[i].length; ++j) { // point_batch_size
-                for (let k = 0; k < input_points[i][j].length; ++k) { // nb_points_per_image
-                    for (let w = 0; w < input_points[i][j][k].length; ++w) { // 2 or 4
+                reshapedImageSize[1] / originalImageSize[1],
+            ];
+
+            for (let j = 0; j < input_points[i].length; ++j) {
+                // point_batch_size
+                for (let k = 0; k < input_points[i][j].length; ++k) {
+                    // nb_points_per_image
+                    for (let w = 0; w < input_points[i][j][k].length; ++w) {
+                        // 2 or 4
                         input_points[i][j][k][w] *= resizeFactors[w % 2];
                     }
                 }
             }
         }
 
-        return new Tensor(
-            'float32',
-            Float32Array.from(input_points.flat(Infinity)),
-            shape
-        )
-
+        return new Tensor('float32', Float32Array.from(input_points.flat(Infinity)), shape);
     }
 
     /**
-     * 
-     * @param {any} input_labels 
-     * @param {Tensor} input_points 
+     *
+     * @param {any} input_labels
+     * @param {Tensor} input_points
      * @returns {Tensor}
      */
     add_input_labels(input_labels, input_points) {
@@ -85,17 +78,15 @@ export class SamImageProcessor extends ImageProcessor {
             shape = [1, ...shape];
             input_labels = [input_labels];
         } else if (shape.length !== 3) {
-            throw Error("The input_points must be a 4D tensor of shape `batch_size`, `point_batch_size`, `nb_points_per_image`, `2`.")
+            throw Error(
+                'The input_points must be a 4D tensor of shape `batch_size`, `point_batch_size`, `nb_points_per_image`, `2`.',
+            );
         }
 
         if (shape.some((x, i) => x !== input_points.dims[i])) {
-            throw Error(`The first ${shape.length} dimensions of 'input_points' and 'input_labels' must be the same.`)
+            throw Error(`The first ${shape.length} dimensions of 'input_points' and 'input_labels' must be the same.`);
         }
-        return new Tensor(
-            'int64',
-            input_labels.flat(Infinity).map(BigInt),
-            shape,
-        )
+        return new Tensor('int64', input_labels.flat(Infinity).map(BigInt), shape);
     }
     /**
      * @param {any[]} images The URL(s) of the image(s) to extract features from.
@@ -117,31 +108,32 @@ export class SamImageProcessor extends ImageProcessor {
      * - `y2`: the y coordinate of the bottom right point of the input box
      * @returns {Promise}
      */
-    async _call(images, {
-        input_points = null,
-        input_labels = null,
-        input_boxes = null
-    } = {}) {
+    async _call(images, { input_points = null, input_labels = null, input_boxes = null } = {}) {
         // TODO allow user to use preprocessed images
         /** @type {SamImageProcessorResult} */
         const processed = await super._call(images);
 
         if (input_points) {
             processed.input_points = this.reshape_input_points(
-                input_points, processed.original_sizes, processed.reshaped_input_sizes
+                input_points,
+                processed.original_sizes,
+                processed.reshaped_input_sizes,
             );
         }
 
         if (input_labels) {
             if (!processed.input_points) {
-                throw Error("`input_points` must be provided if `input_labels` are provided.")
+                throw Error('`input_points` must be provided if `input_labels` are provided.');
             }
             processed.input_labels = this.add_input_labels(input_labels, processed.input_points);
         }
 
         if (input_boxes) {
             processed.input_boxes = this.reshape_input_points(
-                input_boxes, processed.original_sizes, processed.reshaped_input_sizes, true,
+                input_boxes,
+                processed.original_sizes,
+                processed.reshaped_input_sizes,
+                true,
             );
         }
 
@@ -161,11 +153,12 @@ export class SamImageProcessor extends ImageProcessor {
      * @param {number} [options.pad_size.width] The width the images were padded to.
      * @returns {Promise} Batched masks in batch_size, num_channels, height, width) format, where (height, width) is given by original_size.
      */
-    async post_process_masks(masks, original_sizes, reshaped_input_sizes, {
-        mask_threshold = 0.0,
-        binarize = true,
-        pad_size = null,
-    } = {}) {
+    async post_process_masks(
+        masks,
+        original_sizes,
+        reshaped_input_sizes,
+        { mask_threshold = 0.0, binarize = true, pad_size = null } = {},
+    ) {
         // masks: [1, 1, 3, 256, 256]
 
         const output_masks = [];
@@ -180,19 +173,18 @@ export class SamImageProcessor extends ImageProcessor {
             const reshaped_input_size = reshaped_input_sizes[i];
 
             // Upscale mask to padded size
-            let interpolated_mask = (await interpolate_4d(
-                masks[i],
-                { mode: 'bilinear', size: target_image_size }
-            ));
+            let interpolated_mask = await interpolate_4d(masks[i], { mode: 'bilinear', size: target_image_size });
 
             // Crop mask
-            interpolated_mask = interpolated_mask.slice(null, null, [0, reshaped_input_size[0]], [0, reshaped_input_size[1]]);
+            interpolated_mask = interpolated_mask.slice(
+                null,
+                null,
+                [0, reshaped_input_size[0]],
+                [0, reshaped_input_size[1]],
+            );
 
             // Downscale mask
-            interpolated_mask = (await interpolate_4d(
-                interpolated_mask,
-                { mode: 'bilinear', size: original_size }
-            ));
+            interpolated_mask = await interpolate_4d(interpolated_mask, { mode: 'bilinear', size: original_size });
 
             if (binarize) {
                 const data = interpolated_mask.data;
@@ -202,11 +194,7 @@ export class SamImageProcessor extends ImageProcessor {
                         binarizedMaskData[i] = 1;
                     }
                 }
-                interpolated_mask = new Tensor(
-                    'bool',
-                    binarizedMaskData,
-                    interpolated_mask.dims
-                )
+                interpolated_mask = new Tensor('bool', binarizedMaskData, interpolated_mask.dims);
             }
 
             output_masks.push(interpolated_mask);
@@ -219,7 +207,7 @@ export class SamImageProcessor extends ImageProcessor {
      * Generates a list of crop boxes of different sizes. Each layer has (2**i)**2 boxes for the ith layer.
      * @param {import("../../utils/image.js").RawImage} image Input original image
      * @param {number} target_size Target size of the resized image
-     * @param {Object} options Options for generating crop boxes 
+     * @param {Object} options Options for generating crop boxes
      * @param {number} [options.crop_n_layers] If >0, mask prediction will be run again on crops of the image.
      * Sets the number of layers to run, where each layer has 2**i_layer number of image crops.
      * @param {number} [options.overlap_ratio] Sets the degree to which crops overlap. In the first crop layer,
@@ -229,14 +217,17 @@ export class SamImageProcessor extends ImageProcessor {
      * scaled down by crop_n_points_downscale_factor**n.
      * @returns {Object} An object containing the crop boxes, number of points per crop, cropped images, and input labels.
      */
-    generate_crop_boxes(image, target_size, {
-        crop_n_layers = 0,
-        overlap_ratio = 512 / 1500,
-        points_per_crop = 32,
-        crop_n_points_downscale_factor = 1,
-    } = {}) {
+    generate_crop_boxes(
+        image,
+        target_size,
+        {
+            crop_n_layers = 0,
+            overlap_ratio = 512 / 1500,
+            points_per_crop = 32,
+            crop_n_points_downscale_factor = 1,
+        } = {},
+    ) {
         // TODO: Implement
         // return { crop_boxes, points_per_crop, cropped_images, input_labels }
     }
 }
-
diff --git a/src/models/sam/processing_sam.js b/src/models/sam/processing_sam.js
index 4cc0f29e5..de9e856ce 100644
--- a/src/models/sam/processing_sam.js
+++ b/src/models/sam/processing_sam.js
@@ -1,8 +1,8 @@
-import { Processor } from "../../base/processing_utils.js";
-import { AutoImageProcessor } from "../auto/image_processing_auto.js";
+import { Processor } from '../../base/processing_utils.js';
+import { AutoImageProcessor } from '../auto/image_processing_auto.js';
 
 export class SamProcessor extends Processor {
-    static image_processor_class = AutoImageProcessor
+    static image_processor_class = AutoImageProcessor;
 
     async _call(...args) {
         return await this.image_processor(...args);
@@ -17,4 +17,4 @@ export class SamProcessor extends Processor {
         // @ts-ignore
         return this.image_processor.reshape_input_points(...args);
     }
-}
\ No newline at end of file
+}
diff --git a/src/models/sapiens/image_processing_sapiens.js b/src/models/sapiens/image_processing_sapiens.js
index df78763cf..15b755ff5 100644
--- a/src/models/sapiens/image_processing_sapiens.js
+++ b/src/models/sapiens/image_processing_sapiens.js
@@ -1,8 +1,4 @@
-import { 
-    ImageProcessor,
-    post_process_semantic_segmentation,
-} from "../../base/image_processors_utils.js";
-
+import { ImageProcessor, post_process_semantic_segmentation } from '../../base/image_processors_utils.js';
 
 export class SapiensImageProcessor extends ImageProcessor {
     /** @type {typeof post_process_semantic_segmentation} */
@@ -10,4 +6,4 @@ export class SapiensImageProcessor extends ImageProcessor {
         return post_process_semantic_segmentation(...args);
     }
 }
-export class SapiensFeatureExtractor extends SapiensImageProcessor { }
+export class SapiensFeatureExtractor extends SapiensImageProcessor {}
diff --git a/src/models/seamless_m4t/feature_extraction_seamless_m4t.js b/src/models/seamless_m4t/feature_extraction_seamless_m4t.js
index 2af54fdba..41d9b7f4e 100644
--- a/src/models/seamless_m4t/feature_extraction_seamless_m4t.js
+++ b/src/models/seamless_m4t/feature_extraction_seamless_m4t.js
@@ -3,7 +3,6 @@ import { Tensor } from '../../utils/tensor.js';
 import { mel_filter_bank, spectrogram, window_function } from '../../utils/audio.js';
 
 export class SeamlessM4TFeatureExtractor extends FeatureExtractor {
-
     constructor(config) {
         super(config);
 
@@ -15,14 +14,14 @@ export class SeamlessM4TFeatureExtractor extends FeatureExtractor {
             Math.floor(sampling_rate / 2), // max_frequency
             sampling_rate, // sampling_rate
             null, // norm
-            "kaldi", // mel_scale
+            'kaldi', // mel_scale
             true, // triangularize_in_mel_space
         );
         this.mel_filters = mel_filters;
 
         this.window = window_function(400, 'povey', {
             periodic: false,
-        })
+        });
     }
 
     /**
@@ -36,7 +35,7 @@ export class SeamlessM4TFeatureExtractor extends FeatureExtractor {
 
         // Kaldi compliance: 16-bit signed integers
         // 32768 == 2 ** 15
-        waveform = waveform.map((/** @type {number} */ x) => x * 32768)
+        waveform = waveform.map((/** @type {number} */ x) => x * 32768);
 
         return spectrogram(
             waveform,
@@ -50,14 +49,14 @@ export class SeamlessM4TFeatureExtractor extends FeatureExtractor {
                 preemphasis: 0.97,
                 mel_filters: this.mel_filters,
                 log_mel: 'log',
-                mel_floor: 1.192092955078125e-07,
+                mel_floor: 1.192092955078125e-7,
                 remove_dc_offset: true,
 
                 // Custom
                 max_num_frames: max_length,
                 transpose: true,
-            }
-        )
+            },
+        );
     }
 
     /**
@@ -70,12 +69,10 @@ export class SeamlessM4TFeatureExtractor extends FeatureExtractor {
      * @param {boolean} [options.return_attention_mask=true] Whether to return the attention mask.
      * @returns {Promise<{ input_features: Tensor, attention_mask?: Tensor }>} A Promise resolving to an object containing the extracted input features and attention masks as Tensors.
      */
-    async _call(audio, {
-        padding = true,
-        pad_to_multiple_of = 2,
-        do_normalize_per_mel_bins = true,
-        return_attention_mask = true,
-    } = {}) {
+    async _call(
+        audio,
+        { padding = true, pad_to_multiple_of = 2, do_normalize_per_mel_bins = true, return_attention_mask = true } = {},
+    ) {
         validate_audio_inputs(audio, 'SeamlessM4TFeatureExtractor');
 
         let features = await this._extract_fbank_features(audio, this.config.max_length);
@@ -108,27 +105,22 @@ export class SeamlessM4TFeatureExtractor extends FeatureExtractor {
         let padded_attention_mask;
         if (padding) {
             const [num_frames, num_channels] = features.dims;
-            const data = /** @type {Float32Array} */(features.data);
+            const data = /** @type {Float32Array} */ (features.data);
 
             const pad_size = num_frames % pad_to_multiple_of;
             if (pad_size > 0) {
                 const padded_data = new Float32Array(num_channels * (num_frames + pad_size));
-                padded_data.set(data)
-                padded_data.fill(this.config.padding_value, data.length)
+                padded_data.set(data);
+                padded_data.fill(this.config.padding_value, data.length);
 
                 const numPaddedFrames = num_frames + pad_size;
-                features = new Tensor(
-                    features.type,
-                    padded_data,
-                    [numPaddedFrames, num_channels],
-                )
+                features = new Tensor(features.type, padded_data, [numPaddedFrames, num_channels]);
 
                 if (return_attention_mask) {
-                    padded_attention_mask = new Tensor(
-                        'int64',
-                        new BigInt64Array(numPaddedFrames),
-                        [1, numPaddedFrames],
-                    );
+                    padded_attention_mask = new Tensor('int64', new BigInt64Array(numPaddedFrames), [
+                        1,
+                        numPaddedFrames,
+                    ]);
                     /** @type {BigInt64Array} */ (padded_attention_mask.data).fill(1n, 0, num_frames);
                 }
             }
@@ -139,16 +131,12 @@ export class SeamlessM4TFeatureExtractor extends FeatureExtractor {
         const stride = this.config.stride;
         const remainder = num_frames % stride;
         if (remainder !== 0) {
-            throw new Error(`The number of frames (${num_frames}) must be a multiple of the stride (${stride}).`)
+            throw new Error(`The number of frames (${num_frames}) must be a multiple of the stride (${stride}).`);
         }
 
-        const input_features = features.view(
-            1,
-            Math.floor(num_frames / stride),
-            num_channels * stride,
-        );
+        const input_features = features.view(1, Math.floor(num_frames / stride), num_channels * stride);
 
-        const result = { input_features }
+        const result = { input_features };
 
         if (return_attention_mask) {
             const reshapedNumFrames = input_features.dims[1];
@@ -163,11 +151,7 @@ export class SeamlessM4TFeatureExtractor extends FeatureExtractor {
             } else {
                 attention_mask_data.fill(1n);
             }
-            result.attention_mask = new Tensor(
-                'int64',
-                attention_mask_data,
-                [1, reshapedNumFrames],
-            );
+            result.attention_mask = new Tensor('int64', attention_mask_data, [1, reshapedNumFrames]);
         }
 
         return result;
diff --git a/src/models/segformer/image_processing_segformer.js b/src/models/segformer/image_processing_segformer.js
index fe129a05a..1d23bc045 100644
--- a/src/models/segformer/image_processing_segformer.js
+++ b/src/models/segformer/image_processing_segformer.js
@@ -1,8 +1,4 @@
-import { 
-    ImageProcessor,
-    post_process_semantic_segmentation,
-} from "../../base/image_processors_utils.js";
-
+import { ImageProcessor, post_process_semantic_segmentation } from '../../base/image_processors_utils.js';
 
 export class SegformerImageProcessor extends ImageProcessor {
     /** @type {typeof post_process_semantic_segmentation} */
@@ -10,4 +6,4 @@ export class SegformerImageProcessor extends ImageProcessor {
         return post_process_semantic_segmentation(...args);
     }
 }
-export class SegformerFeatureExtractor extends SegformerImageProcessor { }
+export class SegformerFeatureExtractor extends SegformerImageProcessor {}
diff --git a/src/models/siglip/image_processing_siglip.js b/src/models/siglip/image_processing_siglip.js
index 5e666562b..4651b9c73 100644
--- a/src/models/siglip/image_processing_siglip.js
+++ b/src/models/siglip/image_processing_siglip.js
@@ -1,5 +1,3 @@
-import { 
-    ImageProcessor,
-} from "../../base/image_processors_utils.js";
+import { ImageProcessor } from '../../base/image_processors_utils.js';
 
-export class SiglipImageProcessor extends ImageProcessor { }
+export class SiglipImageProcessor extends ImageProcessor {}
diff --git a/src/models/smolvlm/image_processing_smolvlm.js b/src/models/smolvlm/image_processing_smolvlm.js
index ca0962f3e..84bc3468d 100644
--- a/src/models/smolvlm/image_processing_smolvlm.js
+++ b/src/models/smolvlm/image_processing_smolvlm.js
@@ -1,2 +1 @@
-
-export { Idefics3ImageProcessor as SmolVLMImageProcessor } from "../idefics3/image_processing_idefics3.js";
+export { Idefics3ImageProcessor as SmolVLMImageProcessor } from '../idefics3/image_processing_idefics3.js';
diff --git a/src/models/smolvlm/processing_smolvlm.js b/src/models/smolvlm/processing_smolvlm.js
index 32bbab829..2bcd4e9bc 100644
--- a/src/models/smolvlm/processing_smolvlm.js
+++ b/src/models/smolvlm/processing_smolvlm.js
@@ -1,2 +1 @@
-
-export { Idefics3Processor as SmolVLMProcessor } from "../idefics3/processing_idefics3.js";
+export { Idefics3Processor as SmolVLMProcessor } from '../idefics3/processing_idefics3.js';
diff --git a/src/models/snac/feature_extraction_snac.js b/src/models/snac/feature_extraction_snac.js
index 9fc223004..679b4897b 100644
--- a/src/models/snac/feature_extraction_snac.js
+++ b/src/models/snac/feature_extraction_snac.js
@@ -1,3 +1,3 @@
 import { DacFeatureExtractor } from '../dac/feature_extraction_dac.js';
 
-export class SnacFeatureExtractor extends DacFeatureExtractor { }
+export class SnacFeatureExtractor extends DacFeatureExtractor {}
diff --git a/src/models/speecht5/feature_extraction_speecht5.js b/src/models/speecht5/feature_extraction_speecht5.js
index 0f3f2ab38..881f9b9ad 100644
--- a/src/models/speecht5/feature_extraction_speecht5.js
+++ b/src/models/speecht5/feature_extraction_speecht5.js
@@ -1,4 +1,3 @@
+import { FeatureExtractor } from '../../base/feature_extraction_utils.js';
 
-import { FeatureExtractor } from "../../base/feature_extraction_utils.js";
-
-export class SpeechT5FeatureExtractor extends FeatureExtractor { }
+export class SpeechT5FeatureExtractor extends FeatureExtractor {}
diff --git a/src/models/speecht5/processing_speecht5.js b/src/models/speecht5/processing_speecht5.js
index 08af8ba1a..72824b2b0 100644
--- a/src/models/speecht5/processing_speecht5.js
+++ b/src/models/speecht5/processing_speecht5.js
@@ -1,10 +1,10 @@
-import { Processor } from "../../base/processing_utils.js";
-import { AutoTokenizer } from "../../tokenizers.js";
-import { AutoFeatureExtractor } from "../auto/feature_extraction_auto.js";
+import { Processor } from '../../base/processing_utils.js';
+import { AutoTokenizer } from '../../tokenizers.js';
+import { AutoFeatureExtractor } from '../auto/feature_extraction_auto.js';
 
 export class SpeechT5Processor extends Processor {
-    static tokenizer_class = AutoTokenizer
-    static feature_extractor_class = AutoFeatureExtractor
+    static tokenizer_class = AutoTokenizer;
+    static feature_extractor_class = AutoFeatureExtractor;
 
     /**
      * Calls the feature_extractor function with the given input.
@@ -12,6 +12,6 @@ export class SpeechT5Processor extends Processor {
      * @returns {Promise} A Promise that resolves with the extracted features.
      */
     async _call(input) {
-        return await this.feature_extractor(input)
+        return await this.feature_extractor(input);
     }
 }
diff --git a/src/models/swin2sr/image_processing_swin2sr.js b/src/models/swin2sr/image_processing_swin2sr.js
index e53c5c4c1..7e5c810d1 100644
--- a/src/models/swin2sr/image_processing_swin2sr.js
+++ b/src/models/swin2sr/image_processing_swin2sr.js
@@ -1,6 +1,4 @@
-import { 
-    ImageProcessor,
-} from "../../base/image_processors_utils.js";
+import { ImageProcessor } from '../../base/image_processors_utils.js';
 
 export class Swin2SRImageProcessor extends ImageProcessor {
     pad_image(pixelData, imgDims, padSize, options = {}) {
@@ -8,17 +6,22 @@ export class Swin2SRImageProcessor extends ImageProcessor {
         // In other words, the image is padded so that its width and height are multiples of `padSize`.
         const [imageHeight, imageWidth, imageChannels] = imgDims;
 
-        return super.pad_image(pixelData, imgDims, {
-            // NOTE: For Swin2SR models, the original python implementation adds padding even when the image's width/height is already
-            // a multiple of `pad_size`. However, this is most likely a bug (PR: https://github.com/mv-lab/swin2sr/pull/19).
-            // For this reason, we only add padding when the image's width/height is not a multiple of `pad_size`.
-            width: imageWidth + (padSize - imageWidth % padSize) % padSize,
-            height: imageHeight + (padSize - imageHeight % padSize) % padSize,
-        }, {
-            mode: 'symmetric',
-            center: false,
-            constant_values: -1,
-            ...options,
-        })
+        return super.pad_image(
+            pixelData,
+            imgDims,
+            {
+                // NOTE: For Swin2SR models, the original python implementation adds padding even when the image's width/height is already
+                // a multiple of `pad_size`. However, this is most likely a bug (PR: https://github.com/mv-lab/swin2sr/pull/19).
+                // For this reason, we only add padding when the image's width/height is not a multiple of `pad_size`.
+                width: imageWidth + ((padSize - (imageWidth % padSize)) % padSize),
+                height: imageHeight + ((padSize - (imageHeight % padSize)) % padSize),
+            },
+            {
+                mode: 'symmetric',
+                center: false,
+                constant_values: -1,
+                ...options,
+            },
+        );
     }
-}
\ No newline at end of file
+}
diff --git a/src/models/ultravox/processing_ultravox.js b/src/models/ultravox/processing_ultravox.js
index b525b723f..80bee8ec8 100644
--- a/src/models/ultravox/processing_ultravox.js
+++ b/src/models/ultravox/processing_ultravox.js
@@ -1,13 +1,13 @@
-import { AutoFeatureExtractor } from "../auto/feature_extraction_auto.js"
-import { AutoTokenizer } from "../../tokenizers.js"
-import { Processor } from "../../base/processing_utils.js"
+import { AutoFeatureExtractor } from '../auto/feature_extraction_auto.js';
+import { AutoTokenizer } from '../../tokenizers.js';
+import { Processor } from '../../base/processing_utils.js';
 
 /**
  * Represents a UltravoxProcessor that extracts features from an audio input.
  */
 export class UltravoxProcessor extends Processor {
-    static tokenizer_class = AutoTokenizer
-    static feature_extractor_class = AutoFeatureExtractor
+    static tokenizer_class = AutoTokenizer;
+    static feature_extractor_class = AutoFeatureExtractor;
     static uses_processor_config = true;
 
     /**
@@ -17,7 +17,7 @@ export class UltravoxProcessor extends Processor {
     async _call(text, audio = null, kwargs = {}) {
         // TODO: Support batched inputs
         if (Array.isArray(text)) {
-            throw new Error("Batched inputs are not supported yet.");
+            throw new Error('Batched inputs are not supported yet.');
         }
 
         let audio_inputs = {};
@@ -31,8 +31,8 @@ export class UltravoxProcessor extends Processor {
 
             // NOTE: The python version appears to have an off-by-one error.
             const audio_embed_frames = 1 + Math.ceil(nb_encoder_frames / this.config.stack_factor);
-            audio_inputs["audio_token_len"] = [audio_embed_frames];
-            audio_inputs["audio_values"] = input_features;
+            audio_inputs['audio_token_len'] = [audio_embed_frames];
+            audio_inputs['audio_values'] = input_features;
 
             const image_token = this.config.audio_placeholder;
             if (!text.includes(image_token)) {
@@ -49,6 +49,6 @@ export class UltravoxProcessor extends Processor {
         return {
             ...text_inputs,
             ...audio_inputs,
-        }
+        };
     }
 }
diff --git a/src/models/vit/image_processing_vit.js b/src/models/vit/image_processing_vit.js
index ad07ca27e..63864507f 100644
--- a/src/models/vit/image_processing_vit.js
+++ b/src/models/vit/image_processing_vit.js
@@ -1,7 +1,4 @@
-import { 
-    ImageProcessor,
-} from "../../base/image_processors_utils.js";
-
-export class ViTImageProcessor extends ImageProcessor { }
-export class ViTFeatureExtractor extends ViTImageProcessor { }
+import { ImageProcessor } from '../../base/image_processors_utils.js';
 
+export class ViTImageProcessor extends ImageProcessor {}
+export class ViTFeatureExtractor extends ViTImageProcessor {}
diff --git a/src/models/vitmatte/image_processing_vitmatte.js b/src/models/vitmatte/image_processing_vitmatte.js
index 274862344..d08b9b132 100644
--- a/src/models/vitmatte/image_processing_vitmatte.js
+++ b/src/models/vitmatte/image_processing_vitmatte.js
@@ -1,11 +1,6 @@
-import { 
-    ImageProcessor,
-} from "../../base/image_processors_utils.js";
+import { ImageProcessor } from '../../base/image_processors_utils.js';
 
-import {
-    stack,
-    cat,
-} from "../../utils/tensor.js";
+import { stack, cat } from '../../utils/tensor.js';
 
 export class VitMatteImageProcessor extends ImageProcessor {
     /**
@@ -23,28 +18,34 @@ export class VitMatteImageProcessor extends ImageProcessor {
             trimaps = [trimaps];
         }
 
-        const imageData = await Promise.all(images.map(x => this.preprocess(x)));
-        const trimapData = await Promise.all(trimaps.map(x => this.preprocess(x, {
-            do_normalize: false,
-            do_convert_rgb: false,
-            do_convert_grayscale: true,
-        })));
-
+        const imageData = await Promise.all(images.map((x) => this.preprocess(x)));
+        const trimapData = await Promise.all(
+            trimaps.map((x) =>
+                this.preprocess(x, {
+                    do_normalize: false,
+                    do_convert_rgb: false,
+                    do_convert_grayscale: true,
+                }),
+            ),
+        );
 
         // Stack pixel values
-        const pixel_values = stack(imageData.map(
-            // Concatenate images and trimaps
-            (x, i) => cat([x.pixel_values, trimapData[i].pixel_values], 0)
-        ), 0);
+        const pixel_values = stack(
+            imageData.map(
+                // Concatenate images and trimaps
+                (x, i) => cat([x.pixel_values, trimapData[i].pixel_values], 0),
+            ),
+            0,
+        );
 
         return {
             pixel_values,
 
             // Original sizes of images
-            original_sizes: imageData.map(x => x.original_size),
+            original_sizes: imageData.map((x) => x.original_size),
 
             // Reshaped sizes of images, before padding or cropping
-            reshaped_input_sizes: imageData.map(x => x.reshaped_input_size),
-        }
+            reshaped_input_sizes: imageData.map((x) => x.reshaped_input_size),
+        };
     }
 }
diff --git a/src/models/vitpose/image_processing_vitpose.js b/src/models/vitpose/image_processing_vitpose.js
index daacbc4f1..c19c486cd 100644
--- a/src/models/vitpose/image_processing_vitpose.js
+++ b/src/models/vitpose/image_processing_vitpose.js
@@ -1,9 +1,6 @@
-import {
-    ImageProcessor,
-} from "../../base/image_processors_utils.js";
+import { ImageProcessor } from '../../base/image_processors_utils.js';
 
 export class VitPoseImageProcessor extends ImageProcessor {
-
     /**
      * Transform the heatmaps into keypoint predictions and transform them back to the image.
      * NOTE: This is a naive implementation and does not include advanced post-processing techniques,
@@ -18,12 +15,16 @@ export class VitPoseImageProcessor extends ImageProcessor {
      *   keypoints: [number, number][]
      * }[][]} List of keypoints predictions for each image.
      */
-    post_process_pose_estimation(outputs, boxes, {
-        threshold = null,
-        // TODO:
-        // kernel_size = 11,
-        // target_sizes = null,
-    } = {}) {
+    post_process_pose_estimation(
+        outputs,
+        boxes,
+        {
+            threshold = null,
+            // TODO:
+            // kernel_size = 11,
+            // target_sizes = null,
+        } = {},
+    ) {
         // NOTE: boxes are 3D (batch_size, num_boxes, 4)
         const heatmaps = outputs.tolist();
         const [batch_size, num_classes, height, width] = outputs.dims;
@@ -59,7 +60,7 @@ export class VitPoseImageProcessor extends ImageProcessor {
                             // Get weighted sum of positions
                             // TODO: Determine best offsets
                             xWeightedSum += (x + 0.5) * value;
-                            yWeightedSum += (y) * value;
+                            yWeightedSum += y * value;
                         }
                     }
 
@@ -67,10 +68,7 @@ export class VitPoseImageProcessor extends ImageProcessor {
                     if (threshold != null && score < threshold) continue;
 
                     /** @type {[number, number]} */
-                    const keypoint = [
-                        xScale * xWeightedSum / sum,
-                        yScale * yWeightedSum / sum,
-                    ]
+                    const keypoint = [(xScale * xWeightedSum) / sum, (yScale * yWeightedSum) / sum];
                     keypoints.push(keypoint);
                     labels.push(c);
                     scores.push(score);
diff --git a/src/models/voxtral/processing_voxtral.js b/src/models/voxtral/processing_voxtral.js
index 4fb7eb2f1..d002f4eca 100644
--- a/src/models/voxtral/processing_voxtral.js
+++ b/src/models/voxtral/processing_voxtral.js
@@ -1,16 +1,16 @@
-import { AutoFeatureExtractor } from "../auto/feature_extraction_auto.js"
-import { AutoTokenizer } from "../../tokenizers.js"
-import { Processor } from "../../base/processing_utils.js"
-import { cat } from "../../utils/tensor.js";
+import { AutoFeatureExtractor } from '../auto/feature_extraction_auto.js';
+import { AutoTokenizer } from '../../tokenizers.js';
+import { Processor } from '../../base/processing_utils.js';
+import { cat } from '../../utils/tensor.js';
 
-const AUDIO_TOKEN = "[AUDIO]";
-const BEGIN_AUDIO_TOKEN = "[BEGIN_AUDIO]";
+const AUDIO_TOKEN = '[AUDIO]';
+const BEGIN_AUDIO_TOKEN = '[BEGIN_AUDIO]';
 const NUM_AUDIO_TOKENS = 375;
 
 /**
  * Helper function to split audio into non-overlapping chunks of n_samples
- * @param {Float32Array} audio 
- * @param {number} n_samples 
+ * @param {Float32Array} audio
+ * @param {number} n_samples
  * @returns {Float32Array[]}
  */
 function chunk(audio, n_samples) {
@@ -25,8 +25,8 @@ function chunk(audio, n_samples) {
  * Represents a VoxtralProcessor that extracts features from an audio input.
  */
 export class VoxtralProcessor extends Processor {
-    static tokenizer_class = AutoTokenizer
-    static feature_extractor_class = AutoFeatureExtractor
+    static tokenizer_class = AutoTokenizer;
+    static feature_extractor_class = AutoFeatureExtractor;
     static uses_processor_config = false;
 
     /**
@@ -35,7 +35,7 @@ export class VoxtralProcessor extends Processor {
      */
     async _call(text, audio = null, kwargs = {}) {
         if (Array.isArray(text)) {
-            throw new Error("Batched inputs are not supported yet.");
+            throw new Error('Batched inputs are not supported yet.');
         }
 
         const audio_inputs = {};
@@ -49,22 +49,24 @@ export class VoxtralProcessor extends Processor {
             const text_parts = text.split(AUDIO_TOKEN);
             const num_audio_tokens = text_parts.length - 1;
             if (num_audio_tokens !== audio.length) {
-                throw new Error(`The number of audio inputs (${audio.length}) does not match the number of audio tokens in the text (${num_audio_tokens}).`);
+                throw new Error(
+                    `The number of audio inputs (${audio.length}) does not match the number of audio tokens in the text (${num_audio_tokens}).`,
+                );
             }
 
             const n_samples = this.feature_extractor.config.n_samples;
 
             // Split each audio input into chunks and keep track of chunk counts
-            const audio_chunks = audio.map(a => chunk(a, n_samples));
-            const chunk_counts = audio_chunks.map(chunks => chunks.length);
+            const audio_chunks = audio.map((a) => chunk(a, n_samples));
+            const chunk_counts = audio_chunks.map((chunks) => chunks.length);
 
             // Flatten all chunks for feature extraction
             const all_chunks = audio_chunks.flat();
-            const features = (await Promise.all(
-                all_chunks.map((audio_input) => this.feature_extractor(audio_input, kwargs))
-            )).map(x => x.input_features);
+            const features = (
+                await Promise.all(all_chunks.map((audio_input) => this.feature_extractor(audio_input, kwargs)))
+            ).map((x) => x.input_features);
 
-            audio_inputs["audio_values"] = features.length > 1 ? cat(features, 0) : features[0];
+            audio_inputs['audio_values'] = features.length > 1 ? cat(features, 0) : features[0];
 
             // Replace text tokens for each audio input, expanding for chunk count
             let new_text = text_parts[0];
@@ -86,6 +88,6 @@ export class VoxtralProcessor extends Processor {
         return {
             ...text_inputs,
             ...audio_inputs,
-        }
+        };
     }
 }
diff --git a/src/models/wav2vec2/feature_extraction_wav2vec2.js b/src/models/wav2vec2/feature_extraction_wav2vec2.js
index 51f007603..194b71359 100644
--- a/src/models/wav2vec2/feature_extraction_wav2vec2.js
+++ b/src/models/wav2vec2/feature_extraction_wav2vec2.js
@@ -1,18 +1,17 @@
-import { FeatureExtractor, validate_audio_inputs } from "../../base/feature_extraction_utils.js";
-import { Tensor } from "../../utils/tensor.js";
+import { FeatureExtractor, validate_audio_inputs } from '../../base/feature_extraction_utils.js';
+import { Tensor } from '../../utils/tensor.js';
 
 export class Wav2Vec2FeatureExtractor extends FeatureExtractor {
-
     /**
-     * @param {Float32Array} input_values 
-     * @returns {Float32Array} 
+     * @param {Float32Array} input_values
+     * @returns {Float32Array}
      */
     _zero_mean_unit_var_norm(input_values) {
         // TODO support batch?
         const sum = input_values.reduce((a, b) => a + b, 0);
         const mean = sum / input_values.length;
         const variance = input_values.reduce((a, b) => a + (b - mean) ** 2, 0) / input_values.length;
-        return input_values.map(x => (x - mean) / Math.sqrt(variance + 1e-7));
+        return input_values.map((x) => (x - mean) / Math.sqrt(variance + 1e-7));
     }
 
     /**
@@ -38,7 +37,7 @@ export class Wav2Vec2FeatureExtractor extends FeatureExtractor {
         const shape = [1, input_values.length];
         return {
             input_values: new Tensor('float32', input_values, shape),
-            attention_mask: new Tensor('int64', new BigInt64Array(input_values.length).fill(1n), shape)
+            attention_mask: new Tensor('int64', new BigInt64Array(input_values.length).fill(1n), shape),
         };
     }
 }
diff --git a/src/models/wav2vec2/processing_wav2vec2.js b/src/models/wav2vec2/processing_wav2vec2.js
index 9bc4b28f8..583eee66e 100644
--- a/src/models/wav2vec2/processing_wav2vec2.js
+++ b/src/models/wav2vec2/processing_wav2vec2.js
@@ -1,10 +1,10 @@
-import { AutoTokenizer } from "../../tokenizers.js";
-import { AutoFeatureExtractor } from "../auto/feature_extraction_auto.js";
-import { Processor } from "../../base/processing_utils.js";
+import { AutoTokenizer } from '../../tokenizers.js';
+import { AutoFeatureExtractor } from '../auto/feature_extraction_auto.js';
+import { Processor } from '../../base/processing_utils.js';
 
 export class Wav2Vec2Processor extends Processor {
-    static tokenizer_class = AutoTokenizer
-    static feature_extractor_class = AutoFeatureExtractor
+    static tokenizer_class = AutoTokenizer;
+    static feature_extractor_class = AutoFeatureExtractor;
 
     /**
      * Calls the feature_extractor function with the given audio input.
@@ -12,6 +12,6 @@ export class Wav2Vec2Processor extends Processor {
      * @returns {Promise} A Promise that resolves with the extracted features.
      */
     async _call(audio) {
-        return await this.feature_extractor(audio)
+        return await this.feature_extractor(audio);
     }
 }
diff --git a/src/models/wav2vec2_with_lm/processing_wav2vec2_with_lm.js b/src/models/wav2vec2_with_lm/processing_wav2vec2_with_lm.js
index 858f636fa..157aca474 100644
--- a/src/models/wav2vec2_with_lm/processing_wav2vec2_with_lm.js
+++ b/src/models/wav2vec2_with_lm/processing_wav2vec2_with_lm.js
@@ -1,10 +1,10 @@
-import { AutoTokenizer } from "../../tokenizers.js";
-import { AutoFeatureExtractor } from "../auto/feature_extraction_auto.js";
-import { Processor } from "../../base/processing_utils.js";
+import { AutoTokenizer } from '../../tokenizers.js';
+import { AutoFeatureExtractor } from '../auto/feature_extraction_auto.js';
+import { Processor } from '../../base/processing_utils.js';
 
 export class Wav2Vec2ProcessorWithLM extends Processor {
-    static tokenizer_class = AutoTokenizer
-    static feature_extractor_class = AutoFeatureExtractor
+    static tokenizer_class = AutoTokenizer;
+    static feature_extractor_class = AutoFeatureExtractor;
 
     /**
      * Calls the feature_extractor function with the given audio input.
@@ -12,6 +12,6 @@ export class Wav2Vec2ProcessorWithLM extends Processor {
      * @returns {Promise} A Promise that resolves with the extracted features.
      */
     async _call(audio) {
-        return await this.feature_extractor(audio)
+        return await this.feature_extractor(audio);
     }
 }
diff --git a/src/models/wespeaker/feature_extraction_wespeaker.js b/src/models/wespeaker/feature_extraction_wespeaker.js
index f8806a6b3..81145c0d6 100644
--- a/src/models/wespeaker/feature_extraction_wespeaker.js
+++ b/src/models/wespeaker/feature_extraction_wespeaker.js
@@ -2,9 +2,7 @@ import { FeatureExtractor, validate_audio_inputs } from '../../base/feature_extr
 import { Tensor } from '../../utils/tensor.js';
 import { mel_filter_bank, spectrogram, window_function } from '../../utils/audio.js';
 
-
 export class WeSpeakerFeatureExtractor extends FeatureExtractor {
-
     constructor(config) {
         super(config);
 
@@ -16,14 +14,14 @@ export class WeSpeakerFeatureExtractor extends FeatureExtractor {
             Math.floor(sampling_rate / 2), // max_frequency
             sampling_rate, // sampling_rate
             null, // norm
-            "kaldi", // mel_scale
+            'kaldi', // mel_scale
             true, // triangularize_in_mel_space
         );
         this.mel_filters = mel_filters;
 
         this.window = window_function(400, 'hamming', {
             periodic: false,
-        })
+        });
         this.min_num_frames = this.config.min_num_frames;
     }
 
@@ -35,7 +33,7 @@ export class WeSpeakerFeatureExtractor extends FeatureExtractor {
     async _extract_fbank_features(waveform) {
         // Kaldi compliance: 16-bit signed integers
         // 32768 == 2 ** 15
-        waveform = waveform.map((/** @type {number} */ x) => x * 32768)
+        waveform = waveform.map((/** @type {number} */ x) => x * 32768);
 
         return spectrogram(
             waveform,
@@ -49,17 +47,16 @@ export class WeSpeakerFeatureExtractor extends FeatureExtractor {
                 preemphasis: 0.97,
                 mel_filters: this.mel_filters,
                 log_mel: 'log',
-                mel_floor: 1.192092955078125e-07,
+                mel_floor: 1.192092955078125e-7,
                 remove_dc_offset: true,
 
                 // Custom
                 transpose: true,
                 min_num_frames: this.min_num_frames,
-            }
-        )
+            },
+        );
     }
 
-
     /**
      * Asynchronously extracts features from a given audio using the provided configuration.
      * @param {Float32Array|Float64Array} audio The audio data as a Float32Array/Float64Array.
@@ -73,7 +70,7 @@ export class WeSpeakerFeatureExtractor extends FeatureExtractor {
         if (this.config.fbank_centering_span === null) {
             // center features with global average
             const meanData = /** @type {Float32Array} */ (features.mean(1).data);
-            const featuresData = /** @type {Float32Array} */(features.data);
+            const featuresData = /** @type {Float32Array} */ (features.data);
             const [batch_size, num_frames, feature_size] = features.dims;
 
             for (let i = 0; i < batch_size; ++i) {
@@ -89,7 +86,7 @@ export class WeSpeakerFeatureExtractor extends FeatureExtractor {
         }
 
         return {
-            input_features: features
+            input_features: features,
         };
     }
 }
diff --git a/src/models/whisper/common_whisper.js b/src/models/whisper/common_whisper.js
index aa5654032..28025283c 100644
--- a/src/models/whisper/common_whisper.js
+++ b/src/models/whisper/common_whisper.js
@@ -1,106 +1,104 @@
-
-
 const WHISPER_LANGUAGES = [
-    ["en", "english"],
-    ["zh", "chinese"],
-    ["de", "german"],
-    ["es", "spanish"],
-    ["ru", "russian"],
-    ["ko", "korean"],
-    ["fr", "french"],
-    ["ja", "japanese"],
-    ["pt", "portuguese"],
-    ["tr", "turkish"],
-    ["pl", "polish"],
-    ["ca", "catalan"],
-    ["nl", "dutch"],
-    ["ar", "arabic"],
-    ["sv", "swedish"],
-    ["it", "italian"],
-    ["id", "indonesian"],
-    ["hi", "hindi"],
-    ["fi", "finnish"],
-    ["vi", "vietnamese"],
-    ["he", "hebrew"],
-    ["uk", "ukrainian"],
-    ["el", "greek"],
-    ["ms", "malay"],
-    ["cs", "czech"],
-    ["ro", "romanian"],
-    ["da", "danish"],
-    ["hu", "hungarian"],
-    ["ta", "tamil"],
-    ["no", "norwegian"],
-    ["th", "thai"],
-    ["ur", "urdu"],
-    ["hr", "croatian"],
-    ["bg", "bulgarian"],
-    ["lt", "lithuanian"],
-    ["la", "latin"],
-    ["mi", "maori"],
-    ["ml", "malayalam"],
-    ["cy", "welsh"],
-    ["sk", "slovak"],
-    ["te", "telugu"],
-    ["fa", "persian"],
-    ["lv", "latvian"],
-    ["bn", "bengali"],
-    ["sr", "serbian"],
-    ["az", "azerbaijani"],
-    ["sl", "slovenian"],
-    ["kn", "kannada"],
-    ["et", "estonian"],
-    ["mk", "macedonian"],
-    ["br", "breton"],
-    ["eu", "basque"],
-    ["is", "icelandic"],
-    ["hy", "armenian"],
-    ["ne", "nepali"],
-    ["mn", "mongolian"],
-    ["bs", "bosnian"],
-    ["kk", "kazakh"],
-    ["sq", "albanian"],
-    ["sw", "swahili"],
-    ["gl", "galician"],
-    ["mr", "marathi"],
-    ["pa", "punjabi"],
-    ["si", "sinhala"],
-    ["km", "khmer"],
-    ["sn", "shona"],
-    ["yo", "yoruba"],
-    ["so", "somali"],
-    ["af", "afrikaans"],
-    ["oc", "occitan"],
-    ["ka", "georgian"],
-    ["be", "belarusian"],
-    ["tg", "tajik"],
-    ["sd", "sindhi"],
-    ["gu", "gujarati"],
-    ["am", "amharic"],
-    ["yi", "yiddish"],
-    ["lo", "lao"],
-    ["uz", "uzbek"],
-    ["fo", "faroese"],
-    ["ht", "haitian creole"],
-    ["ps", "pashto"],
-    ["tk", "turkmen"],
-    ["nn", "nynorsk"],
-    ["mt", "maltese"],
-    ["sa", "sanskrit"],
-    ["lb", "luxembourgish"],
-    ["my", "myanmar"],
-    ["bo", "tibetan"],
-    ["tl", "tagalog"],
-    ["mg", "malagasy"],
-    ["as", "assamese"],
-    ["tt", "tatar"],
-    ["haw", "hawaiian"],
-    ["ln", "lingala"],
-    ["ha", "hausa"],
-    ["ba", "bashkir"],
-    ["jw", "javanese"],
-    ["su", "sundanese"],
-]
+    ['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'],
+];
 
 // @ts-ignore
 export const WHISPER_LANGUAGE_MAPPING = new Map(WHISPER_LANGUAGES);
@@ -108,18 +106,18 @@ export const WHISPER_LANGUAGE_MAPPING = new Map(WHISPER_LANGUAGES);
 export const WHISPER_TO_LANGUAGE_CODE_MAPPING = new Map([
     ...WHISPER_LANGUAGES.map(([k, v]) => [v, k]),
     ...[
-        ["burmese", "my"],
-        ["valencian", "ca"],
-        ["flemish", "nl"],
-        ["haitian", "ht"],
-        ["letzeburgesch", "lb"],
-        ["pushto", "ps"],
-        ["panjabi", "pa"],
-        ["moldavian", "ro"],
-        ["moldovan", "ro"],
-        ["sinhalese", "si"],
-        ["castilian", "es"],
-    ]
+        ['burmese', 'my'],
+        ['valencian', 'ca'],
+        ['flemish', 'nl'],
+        ['haitian', 'ht'],
+        ['letzeburgesch', 'lb'],
+        ['pushto', 'ps'],
+        ['panjabi', 'pa'],
+        ['moldavian', 'ro'],
+        ['moldovan', 'ro'],
+        ['sinhalese', 'si'],
+        ['castilian', 'es'],
+    ],
 ]);
 
 /**
@@ -144,13 +142,14 @@ export function whisper_language_to_code(language) {
         if (WHISPER_LANGUAGE_MAPPING.has(language)) {
             // User provided the language code directly (e.g., "en")
             language_code = language;
-
         } else {
             // User provided something that is not a language code or name
             const is_language_code = language.length === 2;
             const langs = is_language_code ? WHISPER_LANGUAGE_MAPPING.keys() : WHISPER_LANGUAGE_MAPPING.values();
 
-            throw new Error(`Language "${language}" is not supported. Must be one of: ${JSON.stringify(Array.from(langs))}`);
+            throw new Error(
+                `Language "${language}" is not supported. Must be one of: ${JSON.stringify(Array.from(langs))}`,
+            );
         }
     }
     return language_code;
diff --git a/src/models/whisper/feature_extraction_whisper.js b/src/models/whisper/feature_extraction_whisper.js
index a52a18e33..0f8c85fc7 100644
--- a/src/models/whisper/feature_extraction_whisper.js
+++ b/src/models/whisper/feature_extraction_whisper.js
@@ -4,7 +4,6 @@ import { mel_filter_bank, spectrogram, window_function } from '../../utils/audio
 import { max } from '../../utils/maths.js';
 
 export class WhisperFeatureExtractor extends FeatureExtractor {
-
     constructor(config) {
         super(config);
 
@@ -15,8 +14,8 @@ export class WhisperFeatureExtractor extends FeatureExtractor {
             0.0, // min_frequency
             8000.0, // max_frequency
             this.config.sampling_rate, // sampling_rate
-            "slaney", // norm
-            "slaney", // mel_scale
+            'slaney', // norm
+            'slaney', // mel_scale
         );
 
         this.window = window_function(this.config.n_fft, 'hann');
@@ -42,12 +41,12 @@ export class WhisperFeatureExtractor extends FeatureExtractor {
                 max_num_frames: Math.min(
                     Math.floor(waveform.length / this.config.hop_length),
                     this.config.nb_max_frames, // 3000
-                )
-            }
-        )
+                ),
+            },
+        );
 
         const data = features.data;
-        const maxValue = max(/** @type {Float32Array} */(data))[0];
+        const maxValue = max(/** @type {Float32Array} */ (data))[0];
 
         for (let i = 0; i < data.length; ++i) {
             data[i] = (Math.max(data[i], maxValue - 8.0) + 4.0) / 4.0;
@@ -61,9 +60,7 @@ export class WhisperFeatureExtractor extends FeatureExtractor {
      * @param {Float32Array|Float64Array} audio The audio data as a Float32Array/Float64Array.
      * @returns {Promise<{ input_features: Tensor }>} A Promise resolving to an object containing the extracted input features as a Tensor.
      */
-    async _call(audio, {
-        max_length = null,
-    } = {}) {
+    async _call(audio, { max_length = null } = {}) {
         validate_audio_inputs(audio, 'WhisperFeatureExtractor');
 
         let waveform;
@@ -71,9 +68,9 @@ export class WhisperFeatureExtractor extends FeatureExtractor {
         if (audio.length > length) {
             if (audio.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`."
+                    '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`.',
                 );
             }
             waveform = audio.slice(0, length);
@@ -86,7 +83,7 @@ export class WhisperFeatureExtractor extends FeatureExtractor {
         const features = await this._extract_fbank_features(waveform);
 
         return {
-            input_features: features.unsqueeze_(0)
+            input_features: features.unsqueeze_(0),
         };
     }
 }
diff --git a/src/models/whisper/generation_whisper.js b/src/models/whisper/generation_whisper.js
index 690455ff7..0fd1daa7d 100644
--- a/src/models/whisper/generation_whisper.js
+++ b/src/models/whisper/generation_whisper.js
@@ -1,7 +1,6 @@
-import { GenerationConfig } from "../../generation/configuration_utils.js";
+import { GenerationConfig } from '../../generation/configuration_utils.js';
 
 export class WhisperGenerationConfig extends GenerationConfig {
-
     /**
      * Whether to return the timestamps with the text. This enables the `WhisperTimestampsLogitsProcessor`.
      * @type {boolean}
diff --git a/src/models/whisper/processing_whisper.js b/src/models/whisper/processing_whisper.js
index b676273b8..ef18dd7ce 100644
--- a/src/models/whisper/processing_whisper.js
+++ b/src/models/whisper/processing_whisper.js
@@ -1,13 +1,13 @@
-import { AutoFeatureExtractor } from "../auto/feature_extraction_auto.js"
-import { AutoTokenizer } from "../../tokenizers.js"
-import { Processor } from "../../base/processing_utils.js"
+import { AutoFeatureExtractor } from '../auto/feature_extraction_auto.js';
+import { AutoTokenizer } from '../../tokenizers.js';
+import { Processor } from '../../base/processing_utils.js';
 
 /**
  * Represents a WhisperProcessor that extracts features from an audio input.
  */
 export class WhisperProcessor extends Processor {
-    static tokenizer_class = AutoTokenizer
-    static feature_extractor_class = AutoFeatureExtractor
+    static tokenizer_class = AutoTokenizer;
+    static feature_extractor_class = AutoFeatureExtractor;
 
     /**
      * Calls the feature_extractor function with the given audio input.
@@ -18,4 +18,3 @@ export class WhisperProcessor extends Processor {
         return await this.feature_extractor(audio);
     }
 }
-
diff --git a/src/models/yolos/image_processing_yolos.js b/src/models/yolos/image_processing_yolos.js
index f82b08984..2933536b6 100644
--- a/src/models/yolos/image_processing_yolos.js
+++ b/src/models/yolos/image_processing_yolos.js
@@ -1,7 +1,4 @@
-import { 
-    ImageProcessor,
-    post_process_object_detection,
-} from "../../base/image_processors_utils.js";
+import { ImageProcessor, post_process_object_detection } from '../../base/image_processors_utils.js';
 
 export class YolosImageProcessor extends ImageProcessor {
     /** @type {typeof post_process_object_detection} */
@@ -9,4 +6,4 @@ export class YolosImageProcessor extends ImageProcessor {
         return post_process_object_detection(...args);
     }
 }
-export class YolosFeatureExtractor extends YolosImageProcessor { }
+export class YolosFeatureExtractor extends YolosImageProcessor {}
diff --git a/src/ops/registry.js b/src/ops/registry.js
index 4f2179bec..76170af50 100644
--- a/src/ops/registry.js
+++ b/src/ops/registry.js
@@ -1,6 +1,6 @@
-import { createInferenceSession, isONNXProxy } from "../backends/onnx.js";
-import { Tensor } from "../utils/tensor.js";
-import { apis } from "../env.js";
+import { createInferenceSession, isONNXProxy } from '../backends/onnx.js';
+import { Tensor } from '../utils/tensor.js';
+import { apis } from '../env.js';
 
 const IS_WEB_ENV = apis.IS_BROWSER_ENV || apis.IS_WEBWORKER_ENV;
 /**
@@ -10,32 +10,34 @@ const IS_WEB_ENV = apis.IS_BROWSER_ENV || apis.IS_WEBWORKER_ENV;
  * @param {import('onnxruntime-common').InferenceSession.SessionOptions} session_options The options for the ONNX session.
  * @template {string | [string] | string[]} T
  * @param {T} names The name(s) of the output tensor(s).
- * 
+ *
  * @returns {Promise): Promise>}
  * The wrapper function for running the ONNX inference session.
  */
 const wrap = async (session_bytes, session_options, names) => {
-    const session = await createInferenceSession(
-        new Uint8Array(session_bytes), session_options,
-    );
+    const session = await createInferenceSession(new Uint8Array(session_bytes), session_options);
 
     /** @type {Promise} */
     let chain = Promise.resolve();
 
-    return /** @type {any} */(async (/** @type {Record} */ inputs) => {
-        const proxied = isONNXProxy();
-        const ortFeed = Object.fromEntries(Object.entries(inputs).map(([k, v]) => [k, (proxied ? v.clone() : v).ort_tensor]));
+    return /** @type {any} */ (
+        async (/** @type {Record} */ inputs) => {
+            const proxied = isONNXProxy();
+            const ortFeed = Object.fromEntries(
+                Object.entries(inputs).map(([k, v]) => [k, (proxied ? v.clone() : v).ort_tensor]),
+            );
 
-        // When running in-browser via WASM, we need to chain calls to session.run to avoid "Error: Session already started"
-        const outputs = await (chain = IS_WEB_ENV ? chain.then(() => session.run(ortFeed)) : session.run(ortFeed));
+            // When running in-browser via WASM, we need to chain calls to session.run to avoid "Error: Session already started"
+            const outputs = await (chain = IS_WEB_ENV ? chain.then(() => session.run(ortFeed)) : session.run(ortFeed));
 
-        if (Array.isArray(names)) {
-            return names.map((n) => new Tensor(outputs[n]));
-        } else {
-            return new Tensor(outputs[/** @type {string} */(names)]);
+            if (Array.isArray(names)) {
+                return names.map((n) => new Tensor(outputs[n]));
+            } else {
+                return new Tensor(outputs[/** @type {string} */ (names)]);
+            }
         }
-    })
-}
+    );
+};
 
 // In-memory registry of initialized ONNX operators
 export class TensorOpRegistry {
@@ -47,7 +49,14 @@ export class TensorOpRegistry {
     static get nearest_interpolate_4d() {
         if (!this._nearest_interpolate_4d) {
             this._nearest_interpolate_4d = wrap(
-                [8, 10, 18, 0, 58, 129, 1, 10, 41, 10, 1, 120, 10, 0, 10, 0, 10, 1, 115, 18, 1, 121, 34, 6, 82, 101, 115, 105, 122, 101, 42, 18, 10, 4, 109, 111, 100, 101, 34, 7, 110, 101, 97, 114, 101, 115, 116, 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, 21],
+                [
+                    8, 10, 18, 0, 58, 129, 1, 10, 41, 10, 1, 120, 10, 0, 10, 0, 10, 1, 115, 18, 1, 121, 34, 6, 82, 101,
+                    115, 105, 122, 101, 42, 18, 10, 4, 109, 111, 100, 101, 34, 7, 110, 101, 97, 114, 101, 115, 116, 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, 21,
+                ],
                 this.session_options,
                 'y',
             );
@@ -57,7 +66,14 @@ export class TensorOpRegistry {
     static get bilinear_interpolate_4d() {
         if (!this._bilinear_interpolate_4d) {
             this._bilinear_interpolate_4d = wrap(
-                [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],
+                [
+                    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',
             );
@@ -68,7 +84,14 @@ export class TensorOpRegistry {
     static get bicubic_interpolate_4d() {
         if (!this._bicubic_interpolate_4d) {
             this._bicubic_interpolate_4d = wrap(
-                [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],
+                [
+                    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',
             );
@@ -79,7 +102,11 @@ export class TensorOpRegistry {
     static get matmul() {
         if (!this._matmul) {
             this._matmul = wrap(
-                [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],
+                [
+                    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',
             );
@@ -90,10 +117,18 @@ export class TensorOpRegistry {
     static get stft() {
         if (!this._stft) {
             this._stft = wrap(
-                [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],
+                [
+                    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',
-            )
+            );
         }
         return this._stft;
     }
@@ -101,10 +136,16 @@ export class TensorOpRegistry {
     static get rfft() {
         if (!this._rfft) {
             this._rfft = wrap(
-                [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],
+                [
+                    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',
-            )
+            );
         }
         return this._rfft;
     }
@@ -112,10 +153,15 @@ export class TensorOpRegistry {
     static get top_k() {
         if (!this._top_k) {
             this._top_k = wrap(
-                [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],
+                [
+                    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,
-                [ /* Values */ 'v', /* Indices */ 'i']
-            )
+                [/* Values */ 'v', /* Indices */ 'i'],
+            );
         }
         return this._top_k;
     }
@@ -123,10 +169,15 @@ export class TensorOpRegistry {
     static get slice() {
         if (!this._slice) {
             this._slice = wrap(
-                [8, 7, 18, 0, 58, 96, 10, 25, 10, 1, 120, 10, 1, 115, 10, 1, 101, 10, 1, 97, 10, 1, 116, 18, 1, 121, 34, 5, 83, 108, 105, 99, 101, 18, 1, 114, 90, 9, 10, 1, 120, 18, 4, 10, 2, 8, 1, 90, 9, 10, 1, 115, 18, 4, 10, 2, 8, 7, 90, 9, 10, 1, 101, 18, 4, 10, 2, 8, 7, 90, 9, 10, 1, 97, 18, 4, 10, 2, 8, 7, 90, 9, 10, 1, 116, 18, 4, 10, 2, 8, 7, 98, 9, 10, 1, 121, 18, 4, 10, 2, 8, 1, 66, 2, 16, 13],
+                [
+                    8, 7, 18, 0, 58, 96, 10, 25, 10, 1, 120, 10, 1, 115, 10, 1, 101, 10, 1, 97, 10, 1, 116, 18, 1, 121,
+                    34, 5, 83, 108, 105, 99, 101, 18, 1, 114, 90, 9, 10, 1, 120, 18, 4, 10, 2, 8, 1, 90, 9, 10, 1, 115,
+                    18, 4, 10, 2, 8, 7, 90, 9, 10, 1, 101, 18, 4, 10, 2, 8, 7, 90, 9, 10, 1, 97, 18, 4, 10, 2, 8, 7, 90,
+                    9, 10, 1, 116, 18, 4, 10, 2, 8, 7, 98, 9, 10, 1, 121, 18, 4, 10, 2, 8, 1, 66, 2, 16, 13,
+                ],
                 this.session_options,
                 'y',
-            )
+            );
         }
         return this._slice;
     }
diff --git a/src/pipelines.js b/src/pipelines.js
index 6304ce445..1c1d8a4cd 100644
--- a/src/pipelines.js
+++ b/src/pipelines.js
@@ -13,10 +13,7 @@
  * @module pipelines
  */
 
-import {
-    AutoTokenizer,
-    PreTrainedTokenizer,
-} from './tokenizers.js';
+import { AutoTokenizer, PreTrainedTokenizer } from './tokenizers.js';
 import {
     AutoModel,
     AutoModelForSequenceClassification,
@@ -43,40 +40,17 @@ import {
     AutoModelForImageFeatureExtraction,
     PreTrainedModel,
 } from './models.js';
-import {
-    AutoProcessor,
-} from './models/auto/processing_auto.js';
-import {
-    Processor,
-} from './base/processing_utils.js';
+import { AutoProcessor } from './models/auto/processing_auto.js';
+import { Processor } from './base/processing_utils.js';
 
-import {
-    Callable,
-} from './utils/generic.js';
+import { Callable } from './utils/generic.js';
 
-import {
-    dispatchCallback,
-    product,
-} from './utils/core.js';
-import {
-    softmax,
-    max,
-    round,
-} from './utils/maths.js';
-import {
-    read_audio,
-    RawAudio
-} from './utils/audio.js';
-import {
-    Tensor,
-    mean_pooling,
-    interpolate_4d,
-    quantize_embeddings,
-    topk,
-} from './utils/tensor.js';
+import { dispatchCallback, product } from './utils/core.js';
+import { softmax, max, round } from './utils/maths.js';
+import { read_audio, RawAudio } from './utils/audio.js';
+import { Tensor, mean_pooling, interpolate_4d, quantize_embeddings, topk } from './utils/tensor.js';
 import { RawImage } from './utils/image.js';
 
-
 /**
  * @typedef {string | RawImage | URL | Blob | HTMLCanvasElement | OffscreenCanvas} ImageInput
  * @typedef {ImageInput|ImageInput[]} ImagePipelineInputs
@@ -94,7 +68,7 @@ async function prepareImages(images) {
     }
 
     // Possibly convert any non-images to images
-    return await Promise.all(images.map(x => RawImage.read(x)));
+    return await Promise.all(images.map((x) => RawImage.read(x)));
 }
 
 /**
@@ -114,14 +88,16 @@ async function prepareAudios(audios, sampling_rate) {
         audios = [audios];
     }
 
-    return await Promise.all(audios.map(x => {
-        if (typeof x === 'string' || x instanceof URL) {
-            return read_audio(x, sampling_rate);
-        } else if (x instanceof Float64Array) {
-            return new Float32Array(x);
-        }
-        return x;
-    }));
+    return await Promise.all(
+        audios.map((x) => {
+            if (typeof x === 'string' || x instanceof URL) {
+                return read_audio(x, sampling_rate);
+            } else if (x instanceof Float64Array) {
+                return new Float32Array(x);
+            }
+            return x;
+        }),
+    );
 }
 
 /**
@@ -141,14 +117,13 @@ async function prepareAudios(audios, sampling_rate) {
  */
 function get_bounding_box(box, asInteger) {
     if (asInteger) {
-        box = box.map(x => x | 0);
+        box = box.map((x) => x | 0);
     }
     const [xmin, ymin, xmax, ymax] = box;
 
     return { xmin, ymin, xmax, ymax };
 }
 
-
 /**
  * @callback DisposeType Disposes the item.
  * @returns {Promise} A promise that resolves when the item has been disposed.
@@ -203,7 +178,6 @@ export class Pipeline extends Callable {
  * @typedef {ModelProcessorConstructorArgs} ImagePipelineConstructorArgs An object used to instantiate an image-based pipeline.
  */
 
-
 /**
  * @typedef {Object} ModelTokenizerProcessorConstructorArgs
  * @property {string} task The task of the pipeline. Useful for specifying subtasks.
@@ -269,8 +243,9 @@ export class Pipeline extends Callable {
  * // ]
  * ```
  */
-export class TextClassificationPipeline extends (/** @type {new (options: TextPipelineConstructorArgs) => TextClassificationPipelineType} */ (Pipeline)) {
-
+export class TextClassificationPipeline
+    extends /** @type {new (options: TextPipelineConstructorArgs) => TextClassificationPipelineType} */ (Pipeline)
+{
     /**
      * Create a new TextClassificationPipeline.
      * @param {TextPipelineConstructorArgs} options An object used to instantiate the pipeline.
@@ -280,10 +255,7 @@ export class TextClassificationPipeline extends (/** @type {new (options: TextPi
     }
 
     /** @type {TextClassificationPipelineCallback} */
-    async _call(texts, {
-        top_k = 1
-    } = {}) {
-
+    async _call(texts, { top_k = 1 } = {}) {
         // Run tokenization
         const model_inputs = this.tokenizer(texts, {
             padding: true,
@@ -291,18 +263,14 @@ export class TextClassificationPipeline extends (/** @type {new (options: TextPi
         });
 
         // Run model
-        const outputs = await this.model(model_inputs)
+        const outputs = await this.model(model_inputs);
 
         // TODO: Use softmax tensor function
         const function_to_apply =
             // @ts-expect-error TS2339
             this.model.config.problem_type === 'multi_label_classification'
-                ? batch => batch.sigmoid()
-                : batch => new Tensor(
-                    'float32',
-                    softmax(batch.data),
-                    batch.dims,
-                ); // single_label_classification (default)
+                ? (batch) => batch.sigmoid()
+                : (batch) => new Tensor('float32', softmax(batch.data), batch.dims); // single_label_classification (default)
 
         // @ts-expect-error TS2339
         const id2label = this.model.config.id2label;
@@ -326,7 +294,9 @@ export class TextClassificationPipeline extends (/** @type {new (options: TextPi
             }
         }
 
-        return Array.isArray(texts) || top_k === 1 ? /** @type {TextClassificationOutput} */ (toReturn) : /** @type {TextClassificationOutput[]} */ (toReturn)[0];
+        return Array.isArray(texts) || top_k === 1
+            ? /** @type {TextClassificationOutput} */ (toReturn)
+            : /** @type {TextClassificationOutput[]} */ (toReturn)[0];
     }
 }
 
@@ -380,8 +350,9 @@ export class TextClassificationPipeline extends (/** @type {new (options: TextPi
  * // ]
  * ```
  */
-export class TokenClassificationPipeline extends (/** @type {new (options: TextPipelineConstructorArgs) => TokenClassificationPipelineType} */ (Pipeline)) {
-
+export class TokenClassificationPipeline
+    extends /** @type {new (options: TextPipelineConstructorArgs) => TokenClassificationPipelineType} */ (Pipeline)
+{
     /**
      * Create a new TokenClassificationPipeline.
      * @param {TextPipelineConstructorArgs} options An object used to instantiate the pipeline.
@@ -391,10 +362,7 @@ export class TokenClassificationPipeline extends (/** @type {new (options: TextP
     }
 
     /** @type {TokenClassificationPipelineCallback} */
-    async _call(texts, {
-        ignore_labels = ['O'],
-    } = {}) {
-
+    async _call(texts, { ignore_labels = ['O'] } = {}) {
         const isBatched = Array.isArray(texts);
 
         // Run tokenization
@@ -404,7 +372,7 @@ export class TokenClassificationPipeline extends (/** @type {new (options: TextP
         });
 
         // Run model
-        const outputs = await this.model(model_inputs)
+        const outputs = await this.model(model_inputs);
 
         const logits = outputs.logits;
         // @ts-expect-error TS2339
@@ -487,8 +455,9 @@ export class TokenClassificationPipeline extends (/** @type {new (options: TextP
  * // }
  * ```
  */
-export class QuestionAnsweringPipeline extends (/** @type {new (options: TextPipelineConstructorArgs) => QuestionAnsweringPipelineType} */ (Pipeline)) {
-
+export class QuestionAnsweringPipeline
+    extends /** @type {new (options: TextPipelineConstructorArgs) => QuestionAnsweringPipelineType} */ (Pipeline)
+{
     /**
      * Create a new QuestionAnsweringPipeline.
      * @param {TextPipelineConstructorArgs} options An object used to instantiate the pipeline.
@@ -498,10 +467,7 @@ export class QuestionAnsweringPipeline extends (/** @type {new (options: TextPip
     }
 
     /** @type {QuestionAnsweringPipelineCallback} */
-    async _call(question, context, {
-        top_k = 1
-    } = {}) {
-
+    async _call(question, context, { top_k = 1 } = {}) {
         // Run tokenization
         const inputs = this.tokenizer(question, {
             text_pair: context,
@@ -520,23 +486,19 @@ export class QuestionAnsweringPipeline extends (/** @type {new (options: TextPip
         const toReturn = [];
         for (let j = 0; j < start_logits.dims[0]; ++j) {
             const ids = input_ids[j];
-            const sepIndex = ids.findIndex(x =>
-                // We use == to match bigint with number
-                // @ts-ignore
-                x == this.tokenizer.sep_token_id
+            const sepIndex = ids.findIndex(
+                (x) =>
+                    // We use == to match bigint with number
+                    // @ts-ignore
+                    x == this.tokenizer.sep_token_id,
             );
 
-
-            const valid_mask = attention_mask[j].map((y, ix) => (
-                y == 1
-                && (
-                    ix === 0 // is cls_token
-                    || (
-                        ix > sepIndex
-                        && special_tokens.findIndex(x => x == ids[ix]) === -1 // token is not a special token (special_tokens_mask == 0)
-                    )
-                )
-            ));
+            const valid_mask = attention_mask[j].map(
+                (y, ix) =>
+                    y == 1 &&
+                    (ix === 0 || // is cls_token
+                        (ix > sepIndex && special_tokens.findIndex((x) => x == ids[ix]) === -1)), // token is not a special token (special_tokens_mask == 0)
+            );
 
             const start = start_logits[j].tolist();
             const end = end_logits[j].tolist();
@@ -545,9 +507,9 @@ export class QuestionAnsweringPipeline extends (/** @type {new (options: TextPip
             // NOTE: We keep the cls_token unmasked (some models use it to indicate unanswerable questions)
             for (let i = 1; i < start.length; ++i) {
                 if (
-                    attention_mask[j] == 0 // is part of padding
-                    || i <= sepIndex // is before the sep_token
-                    || special_tokens.findIndex(x => x == ids[i]) !== -1 // Is a special token
+                    attention_mask[j] == 0 || // is part of padding
+                    i <= sepIndex || // is before the sep_token
+                    special_tokens.findIndex((x) => x == ids[i]) !== -1 // Is a special token
                 ) {
                     // Make sure non-context indexes in the tensor cannot contribute to the softmax
                     start[i] = -Infinity;
@@ -565,14 +527,14 @@ export class QuestionAnsweringPipeline extends (/** @type {new (options: TextPip
 
             // Generate all valid spans and select best ones
             const options = product(start_scores, end_scores)
-                .filter(x => x[0][1] <= x[1][1])
-                .map(x => [x[0][1], x[1][1], x[0][0] * x[1][0]])
+                .filter((x) => x[0][1] <= x[1][1])
+                .map((x) => [x[0][1], x[1][1], x[0][0] * x[1][0]])
                 .sort((a, b) => b[2] - a[2]);
 
             for (let k = 0; k < Math.min(options.length, top_k); ++k) {
                 const [start, end, score] = options[k];
 
-                const answer_tokens = ids.slice(start, end + 1)
+                const answer_tokens = ids.slice(start, end + 1);
 
                 const answer = this.tokenizer.decode(answer_tokens, {
                     skip_special_tokens: true,
@@ -581,17 +543,17 @@ export class QuestionAnsweringPipeline extends (/** @type {new (options: TextPip
                 // TODO add start and end?
                 // NOTE: HF returns character index
                 toReturn.push({
-                    answer, score
+                    answer,
+                    score,
                 });
             }
         }
 
         // Mimic HF's return type based on top_k
-        return (top_k === 1) ? toReturn[0] : toReturn;
+        return top_k === 1 ? toReturn[0] : toReturn;
     }
 }
 
-
 /**
  * @typedef {Object} FillMaskSingle
  * @property {string} sequence The corresponding input with the mask token prediction.
@@ -637,8 +599,9 @@ export class QuestionAnsweringPipeline extends (/** @type {new (options: TextPip
  * // [{ token_str: 'spiral', score: 0.6299987435340881, token: 14061, sequence: 'The Milky Way is a spiral galaxy.' }]
  * ```
  */
-export class FillMaskPipeline extends (/** @type {new (options: TextPipelineConstructorArgs) => FillMaskPipelineType} */ (Pipeline)) {
-
+export class FillMaskPipeline
+    extends /** @type {new (options: TextPipelineConstructorArgs) => FillMaskPipelineType} */ (Pipeline)
+{
     /**
      * Create a new FillMaskPipeline.
      * @param {TextPipelineConstructorArgs} options An object used to instantiate the pipeline.
@@ -648,10 +611,7 @@ export class FillMaskPipeline extends (/** @type {new (options: TextPipelineCons
     }
 
     /** @type {FillMaskPipelineCallback} */
-    async _call(texts, {
-        top_k = 5
-    } = {}) {
-
+    async _call(texts, { top_k = 5 } = {}) {
         // Run tokenization
         const model_inputs = this.tokenizer(texts, {
             padding: true,
@@ -659,7 +619,7 @@ export class FillMaskPipeline extends (/** @type {new (options: TextPipelineCons
         });
 
         // Run model
-        const { logits } = await this.model(model_inputs)
+        const { logits } = await this.model(model_inputs);
 
         const toReturn = [];
 
@@ -667,41 +627,39 @@ export class FillMaskPipeline extends (/** @type {new (options: TextPipelineCons
         const input_ids = model_inputs.input_ids.tolist();
         for (let i = 0; i < input_ids.length; ++i) {
             const ids = input_ids[i];
-            const mask_token_index = ids.findIndex(x =>
-                // We use == to match bigint with number
-                // @ts-ignore
-                x == this.tokenizer.mask_token_id
+            const mask_token_index = ids.findIndex(
+                (x) =>
+                    // We use == to match bigint with number
+                    // @ts-ignore
+                    x == this.tokenizer.mask_token_id,
             );
             if (mask_token_index === -1) {
-                throw Error(`Mask token (${this.tokenizer.mask_token}) not found in text.`)
+                throw Error(`Mask token (${this.tokenizer.mask_token}) not found in text.`);
             }
             const itemLogits = logits[i][mask_token_index];
 
-            const scores = await topk(new Tensor(
-                'float32',
-                softmax(itemLogits.data),
-                itemLogits.dims,
-            ), top_k);
+            const scores = await topk(new Tensor('float32', softmax(itemLogits.data), itemLogits.dims), top_k);
             const values = scores[0].tolist();
             const indices = scores[1].tolist();
 
-            toReturn.push(indices.map((x, i) => {
-                const sequence = ids.slice();
-                sequence[mask_token_index] = x;
-
-                return {
-                    score: values[i],
-                    token: Number(x),
-                    token_str: this.tokenizer.decode([x]),
-                    sequence: this.tokenizer.decode(sequence, { skip_special_tokens: true }),
-                }
-            }));
+            toReturn.push(
+                indices.map((x, i) => {
+                    const sequence = ids.slice();
+                    sequence[mask_token_index] = x;
+
+                    return {
+                        score: values[i],
+                        token: Number(x),
+                        token_str: this.tokenizer.decode([x]),
+                        sequence: this.tokenizer.decode(sequence, { skip_special_tokens: true }),
+                    };
+                }),
+            );
         }
         return Array.isArray(texts) ? toReturn : toReturn[0];
     }
 }
 
-
 /**
  * @typedef {Object} Text2TextGenerationSingle
  * @property {string} generated_text The generated text.
@@ -727,7 +685,9 @@ export class FillMaskPipeline extends (/** @type {new (options: TextPipelineCons
  * // [{ generated_text: "To become more healthy, you can: 1. Eat a balanced diet with plenty of fruits, vegetables, whole grains, lean proteins, and healthy fats. 2. Stay hydrated by drinking plenty of water. 3. Get enough sleep and manage stress levels. 4. Avoid smoking and excessive alcohol consumption. 5. Regularly exercise and maintain a healthy weight. 6. Practice good hygiene and sanitation. 7. Seek medical attention if you experience any health issues." }]
  * ```
  */
-export class Text2TextGenerationPipeline extends (/** @type {new (options: TextPipelineConstructorArgs) => Text2TextGenerationPipelineType} */ (Pipeline)) {
+export class Text2TextGenerationPipeline
+    extends /** @type {new (options: TextPipelineConstructorArgs) => Text2TextGenerationPipelineType} */ (Pipeline)
+{
     /** @type {'generated_text'} */
     _key = 'generated_text';
 
@@ -745,21 +705,20 @@ export class Text2TextGenerationPipeline extends (/** @type {new (options: TextP
             texts = [texts];
         }
 
-
         // Add global prefix, if present
         // @ts-expect-error TS2339
         if (this.model.config.prefix) {
             // @ts-expect-error TS2339
-            texts = texts.map(x => this.model.config.prefix + x)
+            texts = texts.map((x) => this.model.config.prefix + x);
         }
 
         // Handle task specific params:
         // @ts-expect-error TS2339
-        const task_specific_params = this.model.config.task_specific_params
+        const task_specific_params = this.model.config.task_specific_params;
         if (task_specific_params && task_specific_params[this.task]) {
             // Add prefixes, if present
             if (task_specific_params[this.task].prefix) {
-                texts = texts.map(x => task_specific_params[this.task].prefix + x)
+                texts = texts.map((x) => task_specific_params[this.task].prefix + x);
             }
 
             // TODO update generation config
@@ -769,26 +728,26 @@ export class Text2TextGenerationPipeline extends (/** @type {new (options: TextP
         const tokenizer_options = {
             padding: true,
             truncation: true,
-        }
+        };
         let inputs;
         if (this instanceof TranslationPipeline && '_build_translation_inputs' in tokenizer) {
             // TODO: move to Translation pipeline?
             // Currently put here to avoid code duplication
             // @ts-ignore
             inputs = tokenizer._build_translation_inputs(texts, tokenizer_options, generate_kwargs);
-
         } else {
             inputs = tokenizer(texts, tokenizer_options);
         }
 
         const outputTokenIds = await this.model.generate({ ...inputs, ...generate_kwargs });
-        return tokenizer.batch_decode(/** @type {Tensor} */(outputTokenIds), {
-            skip_special_tokens: true,
-        }).map(text => ({ [this._key]: text }));
+        return tokenizer
+            .batch_decode(/** @type {Tensor} */ (outputTokenIds), {
+                skip_special_tokens: true,
+            })
+            .map((text) => ({ [this._key]: text }));
     }
 }
 
-
 /**
  * @typedef {Object} SummarizationSingle
  * @property {string} summary_text The summary text.
@@ -822,7 +781,11 @@ export class Text2TextGenerationPipeline extends (/** @type {new (options: TextP
  * // [{ summary_text: ' The Eiffel Tower is about the same height as an 81-storey building and the tallest structure in Paris. It is the second tallest free-standing structure in France after the Millau Viaduct.' }]
  * ```
  */
-export class SummarizationPipeline extends (/** @type {new (options: TextPipelineConstructorArgs) => SummarizationPipelineType} */ (/** @type {any} */ (Text2TextGenerationPipeline))) {
+export class SummarizationPipeline
+    extends /** @type {new (options: TextPipelineConstructorArgs) => SummarizationPipelineType} */ (
+        /** @type {any} */ (Text2TextGenerationPipeline)
+    )
+{
     /** @type {'summary_text'} */
     _key = 'summary_text';
 
@@ -835,7 +798,6 @@ export class SummarizationPipeline extends (/** @type {new (options: TextPipelin
     }
 }
 
-
 /**
  * @typedef {Object} TranslationSingle
  * @property {string} translation_text The translated text.
@@ -894,7 +856,11 @@ export class SummarizationPipeline extends (/** @type {new (options: TextPipelin
  * // [{ translation_text: 'Le chef des Nations affirme qu 'il n 'y a military solution in Syria.' }]
  * ```
  */
-export class TranslationPipeline extends (/** @type {new (options: TextPipelineConstructorArgs) => TranslationPipelineType} */ (/** @type {any} */ (Text2TextGenerationPipeline))) {
+export class TranslationPipeline
+    extends /** @type {new (options: TextPipelineConstructorArgs) => TranslationPipelineType} */ (
+        /** @type {any} */ (Text2TextGenerationPipeline)
+    )
+{
     /** @type {'translation_text'} */
     _key = 'translation_text';
 
@@ -908,7 +874,7 @@ export class TranslationPipeline extends (/** @type {new (options: TextPipelineC
 }
 
 function isChat(x) {
-    return Array.isArray(x) && x.every(x => 'role' in x && 'content' in x);
+    return Array.isArray(x) && x.every((x) => 'role' in x && 'content' in x);
 }
 
 /**
@@ -981,8 +947,9 @@ function isChat(x) {
  * // }]
  * ```
  */
-export class TextGenerationPipeline extends (/** @type {new (options: TextPipelineConstructorArgs) => TextGenerationPipelineType} */ (Pipeline)) {
-
+export class TextGenerationPipeline
+    extends /** @type {new (options: TextPipelineConstructorArgs) => TextGenerationPipelineType} */ (Pipeline)
+{
     /**
      * Create a new TextGenerationPipeline.
      * @param {TextPipelineConstructorArgs} options An object used to instantiate the pipeline.
@@ -997,21 +964,22 @@ export class TextGenerationPipeline extends (/** @type {new (options: TextPipeli
         let isChatInput = false;
 
         // By default, do not add special tokens, unless the tokenizer specifies otherwise
-        let add_special_tokens = generate_kwargs.add_special_tokens
-            ?? (this.tokenizer.add_bos_token || this.tokenizer.add_eos_token)
-            ?? false;
+        let add_special_tokens =
+            generate_kwargs.add_special_tokens ??
+            (this.tokenizer.add_bos_token || this.tokenizer.add_eos_token) ??
+            false;
 
         // Normalize inputs
         /** @type {string[]} */
         let inputs;
         if (typeof texts === 'string') {
             inputs = texts = [texts];
-        } else if (Array.isArray(texts) && texts.every(x => typeof x === 'string')) {
+        } else if (Array.isArray(texts) && texts.every((x) => typeof x === 'string')) {
             isBatched = true;
-            inputs = /** @type {string[]} */(texts);
+            inputs = /** @type {string[]} */ (texts);
         } else {
             if (isChat(texts)) {
-                texts = [/** @type {Chat} */(texts)];
+                texts = [/** @type {Chat} */ (texts)];
             } else if (Array.isArray(texts) && texts.every(isChat)) {
                 isBatched = true;
             } else {
@@ -1020,19 +988,19 @@ export class TextGenerationPipeline extends (/** @type {new (options: TextPipeli
             isChatInput = true;
 
             // If the input is a chat, we need to apply the chat template
-            inputs = /** @type {string[]} */(/** @type {Chat[]} */ (texts).map(
-                x => this.tokenizer.apply_chat_template(x, {
-                    tokenize: false,
-                    add_generation_prompt: true,
-                })
-            ));
+            inputs = /** @type {string[]} */ (
+                /** @type {Chat[]} */ (texts).map((x) =>
+                    this.tokenizer.apply_chat_template(x, {
+                        tokenize: false,
+                        add_generation_prompt: true,
+                    }),
+                )
+            );
             add_special_tokens = false; // Chat template handles this already
         }
 
         // By default, return full text
-        const return_full_text = isChatInput
-            ? false
-            : generate_kwargs.return_full_text ?? true;
+        const return_full_text = isChatInput ? false : (generate_kwargs.return_full_text ?? true);
 
         this.tokenizer.padding_side = 'left';
         const text_inputs = this.tokenizer(inputs, {
@@ -1041,10 +1009,12 @@ export class TextGenerationPipeline extends (/** @type {new (options: TextPipeli
             truncation: true,
         });
 
-        const outputTokenIds = /** @type {Tensor} */(await this.model.generate({
-            ...text_inputs,
-            ...generate_kwargs
-        }));
+        const outputTokenIds = /** @type {Tensor} */ (
+            await this.model.generate({
+                ...text_inputs,
+                ...generate_kwargs,
+            })
+        );
 
         const decoded = this.tokenizer.batch_decode(outputTokenIds, {
             skip_special_tokens: true,
@@ -1052,15 +1022,17 @@ export class TextGenerationPipeline extends (/** @type {new (options: TextPipeli
 
         let promptLengths;
         if (!return_full_text && text_inputs.input_ids.dims.at(-1) > 0) {
-            promptLengths = this.tokenizer.batch_decode(text_inputs.input_ids, {
-                skip_special_tokens: true,
-            }).map(x => x.length);
+            promptLengths = this.tokenizer
+                .batch_decode(text_inputs.input_ids, {
+                    skip_special_tokens: true,
+                })
+                .map((x) => x.length);
         }
 
         /** @type {TextGenerationOutput[]} */
-        const toReturn = Array.from({ length: texts.length }, _ => []);
+        const toReturn = Array.from({ length: texts.length }, (_) => []);
         for (let i = 0; i < decoded.length; ++i) {
-            const textIndex = Math.floor(i / outputTokenIds.dims[0] * texts.length);
+            const textIndex = Math.floor((i / outputTokenIds.dims[0]) * texts.length);
 
             if (promptLengths) {
                 // Trim the decoded text to only include the generated part
@@ -1068,14 +1040,11 @@ export class TextGenerationPipeline extends (/** @type {new (options: TextPipeli
             }
             toReturn[textIndex].push({
                 generated_text: isChatInput
-                    ? [
-                        ...((/** @type {Chat[]} */(texts)[textIndex])),
-                        { role: 'assistant', content: decoded[i] },
-                    ]
-                    : decoded[i]
+                    ? [.../** @type {Chat[]} */ (texts)[textIndex], { role: 'assistant', content: decoded[i] }]
+                    : decoded[i],
             });
         }
-        return (!isBatched && toReturn.length === 1) ? toReturn[0] : toReturn;
+        return !isBatched && toReturn.length === 1 ? toReturn[0] : toReturn;
     }
 }
 
@@ -1135,7 +1104,9 @@ export class TextGenerationPipeline extends (/** @type {new (options: TextPipeli
  * // }
  * ```
  */
-export class ZeroShotClassificationPipeline extends (/** @type {new (options: TextPipelineConstructorArgs) => ZeroShotClassificationPipelineType} */ (Pipeline)) {
+export class ZeroShotClassificationPipeline
+    extends /** @type {new (options: TextPipelineConstructorArgs) => ZeroShotClassificationPipelineType} */ (Pipeline)
+{
     /**
      * Create a new ZeroShotClassificationPipeline.
      * @param {TextPipelineConstructorArgs} options An object used to instantiate the pipeline.
@@ -1145,9 +1116,7 @@ export class ZeroShotClassificationPipeline extends (/** @type {new (options: Te
 
         // Use model config to get label2id mapping
         this.label2id = Object.fromEntries(
-            Object.entries((/** @type {any} */(this).model).config.label2id).map(
-                ([k, v]) => [k.toLowerCase(), v]
-            )
+            Object.entries(/** @type {any} */ (this).model.config.label2id).map(([k, v]) => [k.toLowerCase(), v]),
         );
 
         this.entailment_id = this.label2id['entailment'];
@@ -1164,11 +1133,7 @@ export class ZeroShotClassificationPipeline extends (/** @type {new (options: Te
     }
 
     /** @type {ZeroShotClassificationPipelineCallback} */
-    async _call(texts, candidate_labels, {
-        hypothesis_template = "This example is {}.",
-        multi_label = false,
-    } = {}) {
-
+    async _call(texts, candidate_labels, { hypothesis_template = 'This example is {}.', multi_label = false } = {}) {
         const isBatched = Array.isArray(texts);
         if (!isBatched) {
             texts = [/** @type {string} */ (texts)];
@@ -1178,9 +1143,7 @@ export class ZeroShotClassificationPipeline extends (/** @type {new (options: Te
         }
 
         // Insert labels into hypothesis template
-        const hypotheses = candidate_labels.map(
-            x => hypothesis_template.replace('{}', x)
-        );
+        const hypotheses = candidate_labels.map((x) => hypothesis_template.replace('{}', x));
 
         // How to perform the softmax over the logits:
         //  - true:  softmax over the entailment vs. contradiction dim for each label independently
@@ -1197,33 +1160,29 @@ export class ZeroShotClassificationPipeline extends (/** @type {new (options: Te
                     text_pair: hypothesis,
                     padding: true,
                     truncation: true,
-                })
-                const outputs = await this.model(inputs)
+                });
+                const outputs = await this.model(inputs);
 
                 if (softmaxEach) {
                     entails_logits.push([
                         outputs.logits.data[this.contradiction_id],
-                        outputs.logits.data[this.entailment_id]
-                    ])
+                        outputs.logits.data[this.entailment_id],
+                    ]);
                 } else {
-                    entails_logits.push(outputs.logits.data[this.entailment_id])
+                    entails_logits.push(outputs.logits.data[this.entailment_id]);
                 }
             }
 
             /** @type {number[]} */
-            const scores = softmaxEach
-                ? entails_logits.map(x => softmax(x)[1])
-                : softmax(entails_logits);
+            const scores = softmaxEach ? entails_logits.map((x) => softmax(x)[1]) : softmax(entails_logits);
 
             // Sort by scores (desc) and return scores with indices
-            const scores_sorted = scores
-                .map((x, i) => [x, i])
-                .sort((a, b) => (b[0] - a[0]));
+            const scores_sorted = scores.map((x, i) => [x, i]).sort((a, b) => b[0] - a[0]);
 
             toReturn.push({
                 sequence: premise,
-                labels: scores_sorted.map(x => candidate_labels[x[1]]),
-                scores: scores_sorted.map(x => x[0]),
+                labels: scores_sorted.map((x) => candidate_labels[x[1]]),
+                scores: scores_sorted.map((x) => x[0]),
             });
         }
         return isBatched ? toReturn : toReturn[0];
@@ -1292,7 +1251,9 @@ export class ZeroShotClassificationPipeline extends (/** @type {new (options: Te
  * // }
  * ```
  */
-export class FeatureExtractionPipeline extends (/** @type {new (options: TextPipelineConstructorArgs) => FeatureExtractionPipelineType} */ (Pipeline)) {
+export class FeatureExtractionPipeline
+    extends /** @type {new (options: TextPipelineConstructorArgs) => FeatureExtractionPipelineType} */ (Pipeline)
+{
     /**
      * Create a new FeatureExtractionPipeline.
      * @param {TextPipelineConstructorArgs} options An object used to instantiate the pipeline.
@@ -1302,13 +1263,15 @@ export class FeatureExtractionPipeline extends (/** @type {new (options: TextPip
     }
 
     /** @type {FeatureExtractionPipelineCallback} */
-    async _call(texts, {
-        pooling = /** @type {'none'} */('none'),
-        normalize = false,
-        quantize = false,
-        precision = /** @type {'binary'} */('binary'),
-    } = {}) {
-
+    async _call(
+        texts,
+        {
+            pooling = /** @type {'none'} */ ('none'),
+            normalize = false,
+            quantize = false,
+            precision = /** @type {'binary'} */ ('binary'),
+        } = {},
+    ) {
         // Run tokenization
         const model_inputs = this.tokenizer(texts, {
             padding: true,
@@ -1316,7 +1279,7 @@ export class FeatureExtractionPipeline extends (/** @type {new (options: TextPip
         });
 
         // Run model
-        const outputs = await this.model(model_inputs)
+        const outputs = await this.model(model_inputs);
 
         // TODO: Provide warning to the user that they might be using model which was not exported
         // specifically for feature extraction
@@ -1357,7 +1320,6 @@ export class FeatureExtractionPipeline extends (/** @type {new (options: TextPip
     }
 }
 
-
 /**
  * @typedef {Object} ImageFeatureExtractionPipelineOptions Parameters specific to image feature extraction pipelines.
  * @property {boolean} [pool=null] Whether or not to return the pooled output. If set to `false`, the model will return the raw hidden states.
@@ -1400,7 +1362,9 @@ export class FeatureExtractionPipeline extends (/** @type {new (options: TextPip
  * // }
  * ```
  */
-export class ImageFeatureExtractionPipeline extends (/** @type {new (options: ImagePipelineConstructorArgs) => ImageFeatureExtractionPipelineType} */ (Pipeline)) {
+export class ImageFeatureExtractionPipeline
+    extends /** @type {new (options: ImagePipelineConstructorArgs) => ImageFeatureExtractionPipelineType} */ (Pipeline)
+{
     /**
      * Create a new ImageFeatureExtractionPipeline.
      * @param {ImagePipelineConstructorArgs} options An object used to instantiate the pipeline.
@@ -1410,10 +1374,7 @@ export class ImageFeatureExtractionPipeline extends (/** @type {new (options: Im
     }
 
     /** @type {ImageFeatureExtractionPipelineCallback} */
-    async _call(images, {
-        pool = null,
-    } = {}) {
-
+    async _call(images, { pool = null } = {}) {
         const preparedImages = await prepareImages(images);
         const { pixel_values } = await this.processor(preparedImages);
         const outputs = await this.model({ pixel_values });
@@ -1422,10 +1383,11 @@ export class ImageFeatureExtractionPipeline extends (/** @type {new (options: Im
         let result;
         if (pool) {
             if (!('pooler_output' in outputs)) {
-                throw Error(`No pooled output was returned. Make sure the model has a 'pooler' layer when using the 'pool' option.`);
+                throw Error(
+                    `No pooled output was returned. Make sure the model has a 'pooler' layer when using the 'pool' option.`,
+                );
             }
             result = outputs.pooler_output;
-
         } else {
             result = outputs.last_hidden_state ?? outputs.logits ?? outputs.image_embeds;
         }
@@ -1488,8 +1450,9 @@ export class ImageFeatureExtractionPipeline extends (/** @type {new (options: Im
  * // ]
  * ```
  */
-export class AudioClassificationPipeline extends (/** @type {new (options: AudioPipelineConstructorArgs) => AudioClassificationPipelineType} */ (Pipeline)) {
-
+export class AudioClassificationPipeline
+    extends /** @type {new (options: AudioPipelineConstructorArgs) => AudioClassificationPipelineType} */ (Pipeline)
+{
     /**
      * Create a new AudioClassificationPipeline.
      * @param {AudioPipelineConstructorArgs} options An object used to instantiate the pipeline.
@@ -1499,10 +1462,7 @@ export class AudioClassificationPipeline extends (/** @type {new (options: Audio
     }
 
     /** @type {AudioClassificationPipelineCallback} */
-    async _call(audio, {
-        top_k = 5
-    } = {}) {
-
+    async _call(audio, { top_k = 5 } = {}) {
         const sampling_rate = this.processor.feature_extractor.config.sampling_rate;
         const preparedAudios = await prepareAudios(audio, sampling_rate);
 
@@ -1515,11 +1475,7 @@ export class AudioClassificationPipeline extends (/** @type {new (options: Audio
             const output = await this.model(inputs);
             const logits = output.logits[0];
 
-            const scores = await topk(new Tensor(
-                'float32',
-                softmax(logits.data),
-                logits.dims,
-            ), top_k);
+            const scores = await topk(new Tensor('float32', softmax(logits.data), logits.dims), top_k);
 
             const values = scores[0].tolist();
             const indices = scores[1].tolist();
@@ -1530,7 +1486,7 @@ export class AudioClassificationPipeline extends (/** @type {new (options: Audio
             }));
 
             toReturn.push(vals);
-        };
+        }
         return Array.isArray(audio) ? toReturn : toReturn[0];
     }
 }
@@ -1574,8 +1530,11 @@ export class AudioClassificationPipeline extends (/** @type {new (options: Audio
  * // ]
  * ```
  */
-export class ZeroShotAudioClassificationPipeline extends (/** @type {new (options: TextAudioPipelineConstructorArgs) => ZeroShotAudioClassificationPipelineType} */ (Pipeline)) {
-
+export class ZeroShotAudioClassificationPipeline
+    extends /** @type {new (options: TextAudioPipelineConstructorArgs) => ZeroShotAudioClassificationPipelineType} */ (
+        Pipeline
+    )
+{
     /**
      * Create a new ZeroShotAudioClassificationPipeline.
      * @param {TextAudioPipelineConstructorArgs} options An object used to instantiate the pipeline.
@@ -1585,19 +1544,14 @@ export class ZeroShotAudioClassificationPipeline extends (/** @type {new (option
     }
 
     /** @type {ZeroShotAudioClassificationPipelineCallback} */
-    async _call(audio, candidate_labels, {
-        hypothesis_template = "This is a sound of {}."
-    } = {}) {
-
+    async _call(audio, candidate_labels, { hypothesis_template = 'This is a sound of {}.' } = {}) {
         const single = !Array.isArray(audio);
         if (single) {
             audio = [/** @type {AudioInput} */ (audio)];
         }
 
         // Insert label into hypothesis template
-        const texts = candidate_labels.map(
-            x => hypothesis_template.replace('{}', x)
-        );
+        const texts = candidate_labels.map((x) => hypothesis_template.replace('{}', x));
 
         // Run tokenization
         const text_inputs = this.tokenizer(texts, {
@@ -1618,10 +1572,12 @@ export class ZeroShotAudioClassificationPipeline extends (/** @type {new (option
             // Compute softmax per audio
             const probs = softmax(output.logits_per_audio.data);
 
-            toReturn.push([...probs].map((x, i) => ({
-                score: x,
-                label: candidate_labels[i]
-            })));
+            toReturn.push(
+                [...probs].map((x, i) => ({
+                    score: x,
+                    label: candidate_labels[i],
+                })),
+            );
         }
         return single ? toReturn[0] : toReturn;
     }
@@ -1729,8 +1685,11 @@ export class ZeroShotAudioClassificationPipeline extends (/** @type {new (option
  * // { text: " So in college, I was a government major, which means [...] So I'd start off light and I'd bump it up" }
  * ```
  */
-export class AutomaticSpeechRecognitionPipeline extends (/** @type {new (options: TextAudioPipelineConstructorArgs) => AutomaticSpeechRecognitionPipelineType} */ (Pipeline)) {
-
+export class AutomaticSpeechRecognitionPipeline
+    extends /** @type {new (options: TextAudioPipelineConstructorArgs) => AutomaticSpeechRecognitionPipelineType} */ (
+        Pipeline
+    )
+{
     /**
      * Create a new AutomaticSpeechRecognitionPipeline.
      * @param {TextAudioPipelineConstructorArgs} options An object used to instantiate the pipeline.
@@ -1744,17 +1703,19 @@ export class AutomaticSpeechRecognitionPipeline extends (/** @type {new (options
         switch (this.model.config.model_type) {
             case 'whisper':
             case 'lite-whisper':
-                return this._call_whisper(audio, kwargs)
+                return this._call_whisper(audio, kwargs);
             case 'wav2vec2':
             case 'wav2vec2-bert':
             case 'unispeech':
             case 'unispeech-sat':
             case 'hubert':
-                return this._call_wav2vec2(audio, kwargs)
+                return this._call_wav2vec2(audio, kwargs);
             case 'moonshine':
-                return this._call_moonshine(audio, kwargs)
+                return this._call_moonshine(audio, kwargs);
             default:
-                throw new Error(`AutomaticSpeechRecognitionPipeline does not support model type '${this.model.config.model_type}'.`)
+                throw new Error(
+                    `AutomaticSpeechRecognitionPipeline does not support model type '${this.model.config.model_type}'.`,
+                );
         }
     }
 
@@ -1788,10 +1749,10 @@ export class AutomaticSpeechRecognitionPipeline extends (/** @type {new (options
 
             const predicted_ids = [];
             for (const item of logits) {
-                predicted_ids.push(max(item.data)[1])
+                predicted_ids.push(max(item.data)[1]);
             }
-            const predicted_sentences = this.tokenizer.decode(predicted_ids)
-            toReturn.push({ text: predicted_sentences })
+            const predicted_sentences = this.tokenizer.decode(predicted_ids);
+            toReturn.push({ text: predicted_sentences });
         }
         return single ? toReturn[0] : toReturn;
     }
@@ -1806,7 +1767,7 @@ export class AutomaticSpeechRecognitionPipeline extends (/** @type {new (options
         const force_full_sequences = kwargs.force_full_sequences ?? false;
         let stride_length_s = kwargs.stride_length_s ?? null;
 
-        const generation_config = { ...kwargs }
+        const generation_config = { ...kwargs };
 
         if (return_timestamps === 'word') {
             generation_config['return_token_timestamps'] = true;
@@ -1817,12 +1778,13 @@ export class AutomaticSpeechRecognitionPipeline extends (/** @type {new (options
         if (single) {
             audio = [/** @type {AudioInput} */ (audio)];
         }
+        const feature_extractor_config = this.processor.feature_extractor.config;
 
         // @ts-expect-error TS2339
-        const time_precision = this.processor.feature_extractor.config.chunk_length / this.model.config.max_source_positions;
-        const hop_length = this.processor.feature_extractor.config.hop_length;
+        const time_precision = feature_extractor_config.chunk_length / this.model.config.max_source_positions;
+        const hop_length = feature_extractor_config.hop_length;
 
-        const sampling_rate = this.processor.feature_extractor.config.sampling_rate;
+        const sampling_rate = feature_extractor_config.sampling_rate;
         const preparedAudios = await prepareAudios(audio, sampling_rate);
 
         const toReturn = [];
@@ -1833,7 +1795,7 @@ export class AutomaticSpeechRecognitionPipeline extends (/** @type {new (options
                 if (stride_length_s === null) {
                     stride_length_s = chunk_length_s / 6;
                 } else if (chunk_length_s <= stride_length_s) {
-                    throw Error("`chunk_length_s` must be larger than `stride_length_s`.")
+                    throw Error('`chunk_length_s` must be larger than `stride_length_s`.');
                 }
 
                 // TODO support different stride_length_s (for left and right)
@@ -1852,24 +1814,21 @@ export class AutomaticSpeechRecognitionPipeline extends (/** @type {new (options
                     const is_first = offset === 0;
                     const is_last = offset_end >= aud.length;
                     chunks.push({
-                        stride: [
-                            subarr.length,
-                            is_first ? 0 : stride,
-                            is_last ? 0 : stride
-                        ],
+                        stride: [subarr.length, is_first ? 0 : stride, is_last ? 0 : stride],
                         input_features: feature.input_features,
                         is_last,
-                    })
+                    });
                     if (is_last) break;
                     offset += jump;
                 }
-
             } else {
-                chunks = [{
-                    stride: [aud.length, 0, 0],
-                    input_features: (await this.processor(aud)).input_features,
-                    is_last: true
-                }]
+                chunks = [
+                    {
+                        stride: [aud.length, 0, 0],
+                        input_features: (await this.processor(aud)).input_features,
+                        is_last: true,
+                    },
+                ];
             }
 
             // Generate for each set of input features
@@ -1879,7 +1838,7 @@ export class AutomaticSpeechRecognitionPipeline extends (/** @type {new (options
                 // NOTE: doing sequentially for now
                 const data = await this.model.generate({
                     inputs: chunk.input_features,
-                    ...generation_config
+                    ...generation_config,
                 });
 
                 // TODO: Right now we only get top beam
@@ -1887,25 +1846,26 @@ export class AutomaticSpeechRecognitionPipeline extends (/** @type {new (options
                     // @ts-expect-error TS2339
                     chunk.tokens = data.sequences.tolist()[0];
                     // @ts-expect-error TS2339
-                    chunk.token_timestamps = data.token_timestamps.tolist()[0].map(
-                        (/** @type {number} */ x) => round(x, 2)
-                    );
-
+                    chunk.token_timestamps = data.token_timestamps
+                        .tolist()[0]
+                        .map((/** @type {number} */ x) => round(x, 2));
                 } else {
-                    chunk.tokens = (/** @type {Tensor} */(data))[0].tolist();
+                    chunk.tokens = /** @type {Tensor} */ (data)[0].tolist();
                 }
 
                 // convert stride to seconds
-                chunk.stride = chunk.stride.map(x => x / sampling_rate);
+                chunk.stride = chunk.stride.map((x) => x / sampling_rate);
             }
 
             // Merge text chunks
             // @ts-ignore
             const [full_text, optional] = this.tokenizer._decode_asr(chunks, {
-                time_precision, return_timestamps, force_full_sequences
+                time_precision,
+                return_timestamps,
+                force_full_sequences,
             });
 
-            toReturn.push({ text: full_text, ...optional })
+            toReturn.push({ text: full_text, ...optional });
         }
         return single ? toReturn[0] : toReturn;
     }
@@ -1931,12 +1891,11 @@ export class AutomaticSpeechRecognitionPipeline extends (/** @type {new (options
             const max_new_tokens = Math.floor(aud.length / sampling_rate) * 6;
             const outputs = await this.model.generate({ max_new_tokens, ...kwargs, ...inputs });
 
-            const text = this.processor.batch_decode(/** @type {Tensor} */(outputs), { skip_special_tokens: true })[0];
+            const text = this.processor.batch_decode(/** @type {Tensor} */ (outputs), { skip_special_tokens: true })[0];
             toReturn.push({ text });
         }
         return single ? toReturn[0] : toReturn;
     }
-
 }
 
 /**
@@ -1971,8 +1930,9 @@ export class AutomaticSpeechRecognitionPipeline extends (/** @type {new (options
  * // [{ generated_text: 'Mr. Brown commented icily.' }]
  * ```
  */
-export class ImageToTextPipeline extends (/** @type {new (options: TextImagePipelineConstructorArgs) => ImageToTextPipelineType} */ (Pipeline)) {
-
+export class ImageToTextPipeline
+    extends /** @type {new (options: TextImagePipelineConstructorArgs) => ImageToTextPipelineType} */ (Pipeline)
+{
     /**
      * Create a new ImageToTextPipeline.
      * @param {TextImagePipelineConstructorArgs} options An object used to instantiate the pipeline.
@@ -1983,7 +1943,6 @@ export class ImageToTextPipeline extends (/** @type {new (options: TextImagePipe
 
     /** @type {ImageToTextPipelineCallback} */
     async _call(images, generate_kwargs = {}) {
-
         const isBatched = Array.isArray(images);
         const preparedImages = await prepareImages(images);
 
@@ -1991,11 +1950,13 @@ export class ImageToTextPipeline extends (/** @type {new (options: TextImagePipe
 
         const toReturn = [];
         for (const batch of pixel_values) {
-            batch.dims = [1, ...batch.dims]
+            batch.dims = [1, ...batch.dims];
             const output = await this.model.generate({ inputs: batch, ...generate_kwargs });
-            const decoded = this.tokenizer.batch_decode(/** @type {Tensor} */(output), {
-                skip_special_tokens: true,
-            }).map(x => ({ generated_text: x.trim() }))
+            const decoded = this.tokenizer
+                .batch_decode(/** @type {Tensor} */ (output), {
+                    skip_special_tokens: true,
+                })
+                .map((x) => ({ generated_text: x.trim() }));
             toReturn.push(decoded);
         }
 
@@ -2060,8 +2021,9 @@ export class ImageToTextPipeline extends (/** @type {new (options: TextImagePipe
  * // ]
  * ```
  */
-export class ImageClassificationPipeline extends (/** @type {new (options: ImagePipelineConstructorArgs) => ImageClassificationPipelineType} */ (Pipeline)) {
-
+export class ImageClassificationPipeline
+    extends /** @type {new (options: ImagePipelineConstructorArgs) => ImageClassificationPipelineType} */ (Pipeline)
+{
     /**
      * Create a new ImageClassificationPipeline.
      * @param {ImagePipelineConstructorArgs} options An object used to instantiate the pipeline.
@@ -2071,10 +2033,7 @@ export class ImageClassificationPipeline extends (/** @type {new (options: Image
     }
 
     /** @type {ImageClassificationPipelineCallback} */
-    async _call(images, {
-        top_k = 5
-    } = {}) {
-
+    async _call(images, { top_k = 5 } = {}) {
         const preparedImages = await prepareImages(images);
 
         const { pixel_values } = await this.processor(preparedImages);
@@ -2086,11 +2045,7 @@ export class ImageClassificationPipeline extends (/** @type {new (options: Image
         /** @type {ImageClassificationOutput[]} */
         const toReturn = [];
         for (const batch of output.logits) {
-            const scores = await topk(new Tensor(
-                'float32',
-                softmax(batch.data),
-                batch.dims,
-            ), top_k);
+            const scores = await topk(new Tensor('float32', softmax(batch.data), batch.dims), top_k);
 
             const values = scores[0].tolist();
             const indices = scores[1].tolist();
@@ -2104,7 +2059,6 @@ export class ImageClassificationPipeline extends (/** @type {new (options: Image
 
         return Array.isArray(images) ? toReturn : toReturn[0];
     }
-
 }
 
 /**
@@ -2145,7 +2099,9 @@ export class ImageClassificationPipeline extends (/** @type {new (options: Image
  * // ]
  * ```
  */
-export class ImageSegmentationPipeline extends (/** @type {new (options: ImagePipelineConstructorArgs) => ImageSegmentationPipelineType} */ (Pipeline)) {
+export class ImageSegmentationPipeline
+    extends /** @type {new (options: ImagePipelineConstructorArgs) => ImageSegmentationPipelineType} */ (Pipeline)
+{
     /**
      * Create a new ImageSegmentationPipeline.
      * @param {ImagePipelineConstructorArgs} options An object used to instantiate the pipeline.
@@ -2157,27 +2113,30 @@ export class ImageSegmentationPipeline extends (/** @type {new (options: ImagePi
             // Mapping of subtasks to their corresponding post-processing function names.
             panoptic: 'post_process_panoptic_segmentation',
             instance: 'post_process_instance_segmentation',
-            semantic: 'post_process_semantic_segmentation'
-        }
+            semantic: 'post_process_semantic_segmentation',
+        };
     }
 
     /** @type {ImageSegmentationPipelineCallback} */
-    async _call(images, {
-        threshold = 0.5,
-        mask_threshold = 0.5,
-        overlap_mask_area_threshold = 0.8,
-        label_ids_to_fuse = null,
-        target_sizes = null,
-        subtask = null,
-    } = {}) {
+    async _call(
+        images,
+        {
+            threshold = 0.5,
+            mask_threshold = 0.5,
+            overlap_mask_area_threshold = 0.8,
+            label_ids_to_fuse = null,
+            target_sizes = null,
+            subtask = null,
+        } = {},
+    ) {
         const isBatched = Array.isArray(images);
 
         if (isBatched && images.length !== 1) {
-            throw Error("Image segmentation pipeline currently only supports a batch size of 1.");
+            throw Error('Image segmentation pipeline currently only supports a batch size of 1.');
         }
 
         const preparedImages = await prepareImages(images);
-        const imageSizes = preparedImages.map(x => [x.height, x.width]);
+        const imageSizes = preparedImages.map((x) => [x.height, x.width]);
 
         const inputs = await this.processor(preparedImages);
 
@@ -2227,14 +2186,14 @@ export class ImageSegmentationPipeline extends (/** @type {new (options: ImagePi
             for (let i = 0; i < imageSizes.length; ++i) {
                 const size = imageSizes[i];
                 const item = result[i];
-                if (item.data.some(x => x < -epsilon || x > 1 + epsilon)) {
+                if (item.data.some((x) => x < -epsilon || x > 1 + epsilon)) {
                     item.sigmoid_();
                 }
                 const mask = await RawImage.fromTensor(item.mul_(255).to('uint8')).resize(size[1], size[0]);
                 annotation.push({
                     label: null,
                     score: null,
-                    mask
+                    mask,
                 });
             }
         } else if (subtask === 'panoptic' || subtask === 'instance') {
@@ -2257,15 +2216,14 @@ export class ImageSegmentationPipeline extends (/** @type {new (options: ImagePi
                     }
                 }
 
-                const mask = new RawImage(maskData, segmentation.dims[1], segmentation.dims[0], 1)
+                const mask = new RawImage(maskData, segmentation.dims[1], segmentation.dims[0], 1);
 
                 annotation.push({
                     score: segment.score,
                     label: id2label[segment.label_id],
-                    mask: mask
-                })
+                    mask: mask,
+                });
             }
-
         } else if (subtask === 'semantic') {
             const { segmentation, labels } = fn(output, target_sizes ?? imageSizes)[0];
 
@@ -2282,7 +2240,7 @@ export class ImageSegmentationPipeline extends (/** @type {new (options: ImagePi
                 annotation.push({
                     score: null,
                     label: id2label[label],
-                    mask: mask
+                    mask: mask,
                 });
             }
         } else {
@@ -2293,7 +2251,6 @@ export class ImageSegmentationPipeline extends (/** @type {new (options: ImagePi
     }
 }
 
-
 /**
  * @typedef {Object} BackgroundRemovalPipelineOptions Parameters specific to image segmentation pipelines.
  *
@@ -2319,7 +2276,11 @@ export class ImageSegmentationPipeline extends (/** @type {new (options: ImagePi
  * // ]
  * ```
  */
-export class BackgroundRemovalPipeline extends (/** @type {new (options: ImagePipelineConstructorArgs) => BackgroundRemovalPipelineType} */ (/** @type {any} */(ImageSegmentationPipeline))) {
+export class BackgroundRemovalPipeline
+    extends /** @type {new (options: ImagePipelineConstructorArgs) => BackgroundRemovalPipelineType} */ (
+        /** @type {any} */ (ImageSegmentationPipeline)
+    )
+{
     /**
      * Create a new BackgroundRemovalPipeline.
      * @param {ImagePipelineConstructorArgs} options An object used to instantiate the pipeline.
@@ -2333,7 +2294,7 @@ export class BackgroundRemovalPipeline extends (/** @type {new (options: ImagePi
         const isBatched = Array.isArray(images);
 
         if (isBatched && images.length !== 1) {
-            throw Error("Background removal pipeline currently only supports a batch size of 1.");
+            throw Error('Background removal pipeline currently only supports a batch size of 1.');
         }
 
         const preparedImages = await prepareImages(images);
@@ -2385,7 +2346,11 @@ export class BackgroundRemovalPipeline extends (/** @type {new (options: ImagePi
  * // ]
  * ```
  */
-export class ZeroShotImageClassificationPipeline extends (/** @type {new (options: TextImagePipelineConstructorArgs) => ZeroShotImageClassificationPipelineType} */ (Pipeline)) {
+export class ZeroShotImageClassificationPipeline
+    extends /** @type {new (options: TextImagePipelineConstructorArgs) => ZeroShotImageClassificationPipelineType} */ (
+        Pipeline
+    )
+{
     /**
      * Create a new ZeroShotImageClassificationPipeline.
      * @param {TextImagePipelineConstructorArgs} options An object used to instantiate the pipeline.
@@ -2395,17 +2360,12 @@ export class ZeroShotImageClassificationPipeline extends (/** @type {new (option
     }
 
     /** @type {ZeroShotImageClassificationPipelineCallback} */
-    async _call(images, candidate_labels, {
-        hypothesis_template = "This is a photo of {}"
-    } = {}) {
-
+    async _call(images, candidate_labels, { hypothesis_template = 'This is a photo of {}' } = {}) {
         const isBatched = Array.isArray(images);
         const preparedImages = await prepareImages(images);
 
         // Insert label into hypothesis template
-        const texts = candidate_labels.map(
-            x => hypothesis_template.replace('{}', x)
-        );
+        const texts = candidate_labels.map((x) => hypothesis_template.replace('{}', x));
 
         // Run tokenization
         const text_inputs = this.tokenizer(texts, {
@@ -2421,8 +2381,8 @@ export class ZeroShotImageClassificationPipeline extends (/** @type {new (option
 
         const function_to_apply =
             this.model.config.model_type === 'siglip'
-                ? batch => batch.sigmoid().data
-                : batch => softmax(batch.data);
+                ? (batch) => batch.sigmoid().data
+                : (batch) => softmax(batch.data);
 
         // Compare each image with each candidate label
         const toReturn = [];
@@ -2432,7 +2392,7 @@ export class ZeroShotImageClassificationPipeline extends (/** @type {new (option
 
             const result = [...probs].map((x, i) => ({
                 score: x,
-                label: candidate_labels[i]
+                label: candidate_labels[i],
             }));
             result.sort((a, b) => b.score - a.score); // sort by score in descending order
             toReturn.push(result);
@@ -2442,7 +2402,6 @@ export class ZeroShotImageClassificationPipeline extends (/** @type {new (option
     }
 }
 
-
 /**
  * @typedef {Object} ObjectDetectionPipelineSingle
  * @property {string} label The class label identified by the model.
@@ -2484,8 +2443,9 @@ export class ZeroShotImageClassificationPipeline extends (/** @type {new (option
  * // }]
  * ```
  */
-export class ObjectDetectionPipeline extends (/** @type {new (options: ImagePipelineConstructorArgs) => ObjectDetectionPipelineType} */ (Pipeline)) {
-
+export class ObjectDetectionPipeline
+    extends /** @type {new (options: ImagePipelineConstructorArgs) => ObjectDetectionPipelineType} */ (Pipeline)
+{
     /**
      * Create a new ObjectDetectionPipeline.
      * @param {ImagePipelineConstructorArgs} options An object used to instantiate the pipeline.
@@ -2495,19 +2455,15 @@ export class ObjectDetectionPipeline extends (/** @type {new (options: ImagePipe
     }
 
     /** @type {ObjectDetectionPipelineCallback} */
-    async _call(images, {
-        threshold = 0.9,
-        percentage = false,
-    } = {}) {
-
+    async _call(images, { threshold = 0.9, percentage = false } = {}) {
         const isBatched = Array.isArray(images);
 
         if (isBatched && images.length !== 1) {
-            throw Error("Object detection pipeline currently only supports a batch size of 1.");
+            throw Error('Object detection pipeline currently only supports a batch size of 1.');
         }
         const preparedImages = await prepareImages(images);
 
-        const imageSizes = percentage ? null : preparedImages.map(x => [x.height, x.width]);
+        const imageSizes = percentage ? null : preparedImages.map((x) => [x.height, x.width]);
 
         const { pixel_values, pixel_mask } = await this.processor(preparedImages);
         const output = await this.model({ pixel_values, pixel_mask });
@@ -2521,19 +2477,18 @@ export class ObjectDetectionPipeline extends (/** @type {new (options: ImagePipe
 
         // Format output
         /** @type {ObjectDetectionPipelineOutput[]} */
-        const result = processed.map(batch => (
+        const result = processed.map((batch) =>
             batch.boxes.map((box, i) => ({
                 score: batch.scores[i],
                 label: id2label[batch.classes[i]],
                 box: get_bounding_box(box, !percentage),
-            }))
-        ))
+            })),
+        );
 
         return isBatched ? result : result[0];
     }
 }
 
-
 /**
  * @typedef {Object} ZeroShotObjectDetectionOutput
  * @property {string} label Text query corresponding to the found object.
@@ -2620,8 +2575,11 @@ export class ObjectDetectionPipeline extends (/** @type {new (options: ImagePipe
  * // ]
  * ```
  */
-export class ZeroShotObjectDetectionPipeline extends (/** @type {new (options: TextImagePipelineConstructorArgs) => ZeroShotObjectDetectionPipelineType} */ (Pipeline)) {
-
+export class ZeroShotObjectDetectionPipeline
+    extends /** @type {new (options: TextImagePipelineConstructorArgs) => ZeroShotObjectDetectionPipelineType} */ (
+        Pipeline
+    )
+{
     /**
      * Create a new ZeroShotObjectDetectionPipeline.
      * @param {TextImagePipelineConstructorArgs} options An object used to instantiate the pipeline.
@@ -2631,12 +2589,7 @@ export class ZeroShotObjectDetectionPipeline extends (/** @type {new (options: T
     }
 
     /** @type {ZeroShotObjectDetectionPipelineCallback} */
-    async _call(images, candidate_labels, {
-        threshold = 0.1,
-        top_k = null,
-        percentage = false,
-    } = {}) {
-
+    async _call(images, candidate_labels, { threshold = 0.1, top_k = null, percentage = false } = {}) {
         const isBatched = Array.isArray(images);
         const preparedImages = await prepareImages(images);
 
@@ -2664,36 +2617,37 @@ export class ZeroShotObjectDetectionPipeline extends (/** @type {new (options: T
             let result;
             if ('post_process_grounded_object_detection' in this.processor) {
                 // @ts-ignore
-                const processed = this.processor.post_process_grounded_object_detection(
-                    output,
-                    text_inputs.input_ids,
-                    {
-                        // TODO: support separate threshold values
-                        box_threshold: threshold,
-                        text_threshold: threshold,
-                        target_sizes: imageSize,
-                    },
-                )[0];
+                const processed = this.processor.post_process_grounded_object_detection(output, text_inputs.input_ids, {
+                    // TODO: support separate threshold values
+                    box_threshold: threshold,
+                    text_threshold: threshold,
+                    target_sizes: imageSize,
+                })[0];
                 result = processed.boxes.map((box, i) => ({
                     score: processed.scores[i],
                     label: processed.labels[i],
                     box: get_bounding_box(box, !percentage),
-                }))
+                }));
             } else {
                 // @ts-ignore
-                const processed = this.processor.image_processor.post_process_object_detection(output, threshold, imageSize, true)[0];
+                const processed = this.processor.image_processor.post_process_object_detection(
+                    output,
+                    threshold,
+                    imageSize,
+                    true,
+                )[0];
                 result = processed.boxes.map((box, i) => ({
                     score: processed.scores[i],
                     label: candidate_labels[processed.classes[i]],
                     box: get_bounding_box(box, !percentage),
-                }))
+                }));
             }
             result.sort((a, b) => b.score - a.score);
 
             if (top_k !== null) {
                 result = result.slice(0, top_k);
             }
-            toReturn.push(result)
+            toReturn.push(result);
         }
 
         return isBatched ? toReturn : toReturn[0];
@@ -2728,8 +2682,11 @@ export class ZeroShotObjectDetectionPipeline extends (/** @type {new (options: T
  * // [{ answer: 'us-001' }]
  * ```
  */
-export class DocumentQuestionAnsweringPipeline extends (/** @type {new (options: TextImagePipelineConstructorArgs) => DocumentQuestionAnsweringPipelineType} */ (Pipeline)) {
-
+export class DocumentQuestionAnsweringPipeline
+    extends /** @type {new (options: TextImagePipelineConstructorArgs) => DocumentQuestionAnsweringPipelineType} */ (
+        Pipeline
+    )
+{
     /**
      * Create a new DocumentQuestionAnsweringPipeline.
      * @param {TextImagePipelineConstructorArgs} options An object used to instantiate the pipeline.
@@ -2740,7 +2697,6 @@ export class DocumentQuestionAnsweringPipeline extends (/** @type {new (options:
 
     /** @type {DocumentQuestionAnsweringPipelineCallback} */
     async _call(image, question, generate_kwargs = {}) {
-
         // NOTE: For now, we only support a batch size of 1
 
         // Preprocess image
@@ -2765,7 +2721,7 @@ export class DocumentQuestionAnsweringPipeline extends (/** @type {new (options:
         });
 
         // Decode output
-        const decoded = this.tokenizer.batch_decode(/** @type {Tensor} */(output))[0];
+        const decoded = this.tokenizer.batch_decode(/** @type {Tensor} */ (output))[0];
 
         // Parse answer
         const match = decoded.match(/(.*?)<\/s_answer>/);
@@ -2777,7 +2733,6 @@ export class DocumentQuestionAnsweringPipeline extends (/** @type {new (options:
     }
 }
 
-
 /**
  * @typedef {Object} VocoderOptions
  * @property {PreTrainedModel} [vocoder] The vocoder used by the pipeline (if the model uses one). If not provided, use the default HifiGan vocoder.
@@ -2835,8 +2790,10 @@ export class DocumentQuestionAnsweringPipeline extends (/** @type {new (options:
  * // }
  * ```
  */
-export class TextToAudioPipeline extends (/** @type {new (options: TextToAudioPipelineConstructorArgs) => TextToAudioPipelineType} */ (Pipeline)) {
-    DEFAULT_VOCODER_ID = "Xenova/speecht5_hifigan"
+export class TextToAudioPipeline
+    extends /** @type {new (options: TextToAudioPipelineConstructorArgs) => TextToAudioPipelineType} */ (Pipeline)
+{
+    DEFAULT_VOCODER_ID = 'Xenova/speecht5_hifigan';
 
     /**
      * Create a new TextToAudioPipeline.
@@ -2849,12 +2806,8 @@ export class TextToAudioPipeline extends (/** @type {new (options: TextToAudioPi
         this.vocoder = options.vocoder ?? null;
     }
 
-
     /** @type {TextToAudioPipelineCallback} */
-    async _call(text_inputs, {
-        speaker_embeddings = null,
-    } = {}) {
-
+    async _call(text_inputs, { speaker_embeddings = null } = {}) {
         // If this.processor is not set, we are using a `AutoModelForTextToWaveform` model
         if (this.processor) {
             return this._call_text_to_spectrogram(text_inputs, { speaker_embeddings });
@@ -2864,7 +2817,6 @@ export class TextToAudioPipeline extends (/** @type {new (options: TextToAudioPi
     }
 
     async _call_text_to_waveform(text_inputs) {
-
         // Run tokenization
         const inputs = this.tokenizer(text_inputs, {
             padding: true,
@@ -2876,14 +2828,10 @@ export class TextToAudioPipeline extends (/** @type {new (options: TextToAudioPi
 
         // @ts-expect-error TS2339
         const sampling_rate = this.model.config.sampling_rate;
-        return new RawAudio(
-            waveform.data,
-            sampling_rate,
-        )
+        return new RawAudio(waveform.data, sampling_rate);
     }
 
     async _call_text_to_spectrogram(text_inputs, { speaker_embeddings }) {
-
         // Load vocoder, if not provided
         if (!this.vocoder) {
             console.log('No vocoder specified, using default HifiGan vocoder.');
@@ -2893,19 +2841,13 @@ export class TextToAudioPipeline extends (/** @type {new (options: TextToAudioPi
         // Load speaker embeddings as Float32Array from path/URL
         if (typeof speaker_embeddings === 'string' || speaker_embeddings instanceof URL) {
             // Load from URL with fetch
-            speaker_embeddings = new Float32Array(
-                await (await fetch(speaker_embeddings)).arrayBuffer()
-            );
+            speaker_embeddings = new Float32Array(await (await fetch(speaker_embeddings)).arrayBuffer());
         }
 
         if (speaker_embeddings instanceof Float32Array) {
-            speaker_embeddings = new Tensor(
-                'float32',
-                speaker_embeddings,
-                [1, speaker_embeddings.length]
-            )
+            speaker_embeddings = new Tensor('float32', speaker_embeddings, [1, speaker_embeddings.length]);
         } else if (!(speaker_embeddings instanceof Tensor)) {
-            throw new Error("Speaker embeddings must be a `Tensor`, `Float32Array`, `string`, or `URL`.")
+            throw new Error('Speaker embeddings must be a `Tensor`, `Float32Array`, `string`, or `URL`.');
         }
 
         // Run tokenization
@@ -2919,10 +2861,7 @@ export class TextToAudioPipeline extends (/** @type {new (options: TextToAudioPi
         const { waveform } = await this.model.generate_speech(input_ids, speaker_embeddings, { vocoder: this.vocoder });
 
         const sampling_rate = this.processor.feature_extractor.config.sampling_rate;
-        return new RawAudio(
-            waveform.data,
-            sampling_rate,
-        )
+        return new RawAudio(waveform.data, sampling_rate);
     }
 }
 
@@ -2950,7 +2889,9 @@ export class TextToAudioPipeline extends (/** @type {new (options: TextToAudioPi
  * // }
  * ```
  */
-export class ImageToImagePipeline extends (/** @type {new (options: ImagePipelineConstructorArgs) => ImageToImagePipelineType} */ (Pipeline)) {
+export class ImageToImagePipeline
+    extends /** @type {new (options: ImagePipelineConstructorArgs) => ImageToImagePipelineType} */ (Pipeline)
+{
     /**
      * Create a new ImageToImagePipeline.
      * @param {ImagePipelineConstructorArgs} options An object used to instantiate the pipeline.
@@ -2961,7 +2902,6 @@ export class ImageToImagePipeline extends (/** @type {new (options: ImagePipelin
 
     /** @type {ImageToImagePipelineCallback} */
     async _call(images) {
-
         const preparedImages = await prepareImages(images);
         const inputs = await this.processor(preparedImages);
         const outputs = await this.model(inputs);
@@ -3013,7 +2953,9 @@ export class ImageToImagePipeline extends (/** @type {new (options: ImagePipelin
  * // }
  * ```
  */
-export class DepthEstimationPipeline extends (/** @type {new (options: ImagePipelineConstructorArgs) => DepthEstimationPipelineType} */ (Pipeline)) {
+export class DepthEstimationPipeline
+    extends /** @type {new (options: ImagePipelineConstructorArgs) => DepthEstimationPipelineType} */ (Pipeline)
+{
     /**
      * Create a new DepthEstimationPipeline.
      * @param {ImagePipelineConstructorArgs} options An object used to instantiate the pipeline.
@@ -3024,7 +2966,6 @@ export class DepthEstimationPipeline extends (/** @type {new (options: ImagePipe
 
     /** @type {DepthEstimationPipelineCallback} */
     async _call(images) {
-
         const preparedImages = await prepareImages(images);
 
         const inputs = await this.processor(preparedImages);
@@ -3037,14 +2978,21 @@ export class DepthEstimationPipeline extends (/** @type {new (options: ImagePipe
             const [new_width, new_height] = preparedImages[i].size;
 
             // Interpolate to original size
-            const prediction = (await interpolate_4d(batch.view(1, 1, height, width), {
-                size: [new_height, new_width],
-                mode: 'bilinear',
-            })).view(new_height, new_width);
-
-            const minval = /** @type {number} */(prediction.min().item());
-            const maxval = /** @type {number} */(prediction.max().item());
-            const formatted = prediction.sub(minval).div_(maxval - minval).mul_(255).to('uint8').unsqueeze(0);
+            const prediction = (
+                await interpolate_4d(batch.view(1, 1, height, width), {
+                    size: [new_height, new_width],
+                    mode: 'bilinear',
+                })
+            ).view(new_height, new_width);
+
+            const minval = /** @type {number} */ (prediction.min().item());
+            const maxval = /** @type {number} */ (prediction.max().item());
+            const formatted = prediction
+                .sub(minval)
+                .div_(maxval - minval)
+                .mul_(255)
+                .to('uint8')
+                .unsqueeze(0);
             const depth = RawImage.fromTensor(formatted);
             toReturn.push({
                 predicted_depth: prediction,
@@ -3057,312 +3005,311 @@ export class DepthEstimationPipeline extends (/** @type {new (options: ImagePipe
 }
 
 const SUPPORTED_TASKS = Object.freeze({
-    "text-classification": {
-        "tokenizer": AutoTokenizer,
-        "pipeline": TextClassificationPipeline,
-        "model": AutoModelForSequenceClassification,
-        "default": {
+    'text-classification': {
+        tokenizer: AutoTokenizer,
+        pipeline: TextClassificationPipeline,
+        model: AutoModelForSequenceClassification,
+        default: {
             // TODO: replace with original
             // "model": "distilbert-base-uncased-finetuned-sst-2-english",
-            "model": "Xenova/distilbert-base-uncased-finetuned-sst-2-english",
+            model: 'Xenova/distilbert-base-uncased-finetuned-sst-2-english',
         },
-        "type": "text",
+        type: 'text',
     },
-    "token-classification": {
-        "tokenizer": AutoTokenizer,
-        "pipeline": TokenClassificationPipeline,
-        "model": AutoModelForTokenClassification,
-        "default": {
+    'token-classification': {
+        tokenizer: AutoTokenizer,
+        pipeline: TokenClassificationPipeline,
+        model: AutoModelForTokenClassification,
+        default: {
             // TODO: replace with original
             // "model": "Davlan/bert-base-multilingual-cased-ner-hrl",
-            "model": "Xenova/bert-base-multilingual-cased-ner-hrl",
+            model: 'Xenova/bert-base-multilingual-cased-ner-hrl',
         },
-        "type": "text",
+        type: 'text',
     },
-    "question-answering": {
-        "tokenizer": AutoTokenizer,
-        "pipeline": QuestionAnsweringPipeline,
-        "model": AutoModelForQuestionAnswering,
-        "default": {
+    'question-answering': {
+        tokenizer: AutoTokenizer,
+        pipeline: QuestionAnsweringPipeline,
+        model: AutoModelForQuestionAnswering,
+        default: {
             // TODO: replace with original
             // "model": "distilbert-base-cased-distilled-squad",
-            "model": "Xenova/distilbert-base-cased-distilled-squad",
+            model: 'Xenova/distilbert-base-cased-distilled-squad',
         },
-        "type": "text",
+        type: 'text',
     },
 
-    "fill-mask": {
-        "tokenizer": AutoTokenizer,
-        "pipeline": FillMaskPipeline,
-        "model": AutoModelForMaskedLM,
-        "default": {
+    'fill-mask': {
+        tokenizer: AutoTokenizer,
+        pipeline: FillMaskPipeline,
+        model: AutoModelForMaskedLM,
+        default: {
             // TODO: replace with original
             // "model": "bert-base-uncased",
-            "model": "Xenova/bert-base-uncased",
+            model: 'Xenova/bert-base-uncased',
         },
-        "type": "text",
+        type: 'text',
     },
-    "summarization": {
-        "tokenizer": AutoTokenizer,
-        "pipeline": SummarizationPipeline,
-        "model": AutoModelForSeq2SeqLM,
-        "default": {
+    summarization: {
+        tokenizer: AutoTokenizer,
+        pipeline: SummarizationPipeline,
+        model: AutoModelForSeq2SeqLM,
+        default: {
             // TODO: replace with original
             // "model": "sshleifer/distilbart-cnn-6-6",
-            "model": "Xenova/distilbart-cnn-6-6",
+            model: 'Xenova/distilbart-cnn-6-6',
         },
-        "type": "text",
+        type: 'text',
     },
-    "translation": {
-        "tokenizer": AutoTokenizer,
-        "pipeline": TranslationPipeline,
-        "model": AutoModelForSeq2SeqLM,
-        "default": {
+    translation: {
+        tokenizer: AutoTokenizer,
+        pipeline: TranslationPipeline,
+        model: AutoModelForSeq2SeqLM,
+        default: {
             // TODO: replace with original
             // "model": "t5-small",
-            "model": "Xenova/t5-small",
+            model: 'Xenova/t5-small',
         },
-        "type": "text",
+        type: 'text',
     },
-    "text2text-generation": {
-        "tokenizer": AutoTokenizer,
-        "pipeline": Text2TextGenerationPipeline,
-        "model": AutoModelForSeq2SeqLM,
-        "default": {
+    'text2text-generation': {
+        tokenizer: AutoTokenizer,
+        pipeline: Text2TextGenerationPipeline,
+        model: AutoModelForSeq2SeqLM,
+        default: {
             // TODO: replace with original
             // "model": "google/flan-t5-small",
-            "model": "Xenova/flan-t5-small",
+            model: 'Xenova/flan-t5-small',
         },
-        "type": "text",
+        type: 'text',
     },
-    "text-generation": {
-        "tokenizer": AutoTokenizer,
-        "pipeline": TextGenerationPipeline,
-        "model": AutoModelForCausalLM,
-        "default": {
+    'text-generation': {
+        tokenizer: AutoTokenizer,
+        pipeline: TextGenerationPipeline,
+        model: AutoModelForCausalLM,
+        default: {
             // TODO: replace with original
             // "model": "gpt2",
-            "model": "Xenova/gpt2",
+            model: 'Xenova/gpt2',
         },
-        "type": "text",
+        type: 'text',
     },
-    "zero-shot-classification": {
-        "tokenizer": AutoTokenizer,
-        "pipeline": ZeroShotClassificationPipeline,
-        "model": AutoModelForSequenceClassification,
-        "default": {
+    'zero-shot-classification': {
+        tokenizer: AutoTokenizer,
+        pipeline: ZeroShotClassificationPipeline,
+        model: AutoModelForSequenceClassification,
+        default: {
             // TODO: replace with original
             // "model": "typeform/distilbert-base-uncased-mnli",
-            "model": "Xenova/distilbert-base-uncased-mnli",
+            model: 'Xenova/distilbert-base-uncased-mnli',
         },
-        "type": "text",
+        type: 'text',
     },
-    "audio-classification": {
-        "pipeline": AudioClassificationPipeline,
-        "model": AutoModelForAudioClassification,
-        "processor": AutoProcessor,
-        "default": {
+    'audio-classification': {
+        pipeline: AudioClassificationPipeline,
+        model: AutoModelForAudioClassification,
+        processor: AutoProcessor,
+        default: {
             // TODO: replace with original
             // "model": "superb/wav2vec2-base-superb-ks",
-            "model": "Xenova/wav2vec2-base-superb-ks",
+            model: 'Xenova/wav2vec2-base-superb-ks',
         },
-        "type": "audio",
+        type: 'audio',
     },
-    "zero-shot-audio-classification": {
-        "tokenizer": AutoTokenizer,
-        "pipeline": ZeroShotAudioClassificationPipeline,
-        "model": AutoModel,
-        "processor": AutoProcessor,
-        "default": {
+    'zero-shot-audio-classification': {
+        tokenizer: AutoTokenizer,
+        pipeline: ZeroShotAudioClassificationPipeline,
+        model: AutoModel,
+        processor: AutoProcessor,
+        default: {
             // TODO: replace with original
             // "model": "laion/clap-htsat-fused",
-            "model": "Xenova/clap-htsat-unfused",
+            model: 'Xenova/clap-htsat-unfused',
         },
-        "type": "multimodal",
+        type: 'multimodal',
     },
-    "automatic-speech-recognition": {
-        "tokenizer": AutoTokenizer,
-        "pipeline": AutomaticSpeechRecognitionPipeline,
-        "model": [AutoModelForSpeechSeq2Seq, AutoModelForCTC],
-        "processor": AutoProcessor,
-        "default": {
+    'automatic-speech-recognition': {
+        tokenizer: AutoTokenizer,
+        pipeline: AutomaticSpeechRecognitionPipeline,
+        model: [AutoModelForSpeechSeq2Seq, AutoModelForCTC],
+        processor: AutoProcessor,
+        default: {
             // TODO: replace with original
             // "model": "openai/whisper-tiny.en",
-            "model": "Xenova/whisper-tiny.en",
+            model: 'Xenova/whisper-tiny.en',
         },
-        "type": "multimodal",
+        type: 'multimodal',
     },
-    "text-to-audio": {
-        "tokenizer": AutoTokenizer,
-        "pipeline": TextToAudioPipeline,
-        "model": [AutoModelForTextToWaveform, AutoModelForTextToSpectrogram],
-        "processor": [AutoProcessor, /* Some don't use a processor */ null],
-        "default": {
+    'text-to-audio': {
+        tokenizer: AutoTokenizer,
+        pipeline: TextToAudioPipeline,
+        model: [AutoModelForTextToWaveform, AutoModelForTextToSpectrogram],
+        processor: [AutoProcessor, /* Some don't use a processor */ null],
+        default: {
             // TODO: replace with original
             // "model": "microsoft/speecht5_tts",
-            "model": "Xenova/speecht5_tts",
+            model: 'Xenova/speecht5_tts',
         },
-        "type": "text",
+        type: 'text',
     },
-    "image-to-text": {
-        "tokenizer": AutoTokenizer,
-        "pipeline": ImageToTextPipeline,
-        "model": AutoModelForVision2Seq,
-        "processor": AutoProcessor,
-        "default": {
+    'image-to-text': {
+        tokenizer: AutoTokenizer,
+        pipeline: ImageToTextPipeline,
+        model: AutoModelForVision2Seq,
+        processor: AutoProcessor,
+        default: {
             // TODO: replace with original
             // "model": "nlpconnect/vit-gpt2-image-captioning",
-            "model": "Xenova/vit-gpt2-image-captioning",
+            model: 'Xenova/vit-gpt2-image-captioning',
         },
-        "type": "multimodal",
+        type: 'multimodal',
     },
 
-    "image-classification": {
+    'image-classification': {
         // no tokenizer
-        "pipeline": ImageClassificationPipeline,
-        "model": AutoModelForImageClassification,
-        "processor": AutoProcessor,
-        "default": {
+        pipeline: ImageClassificationPipeline,
+        model: AutoModelForImageClassification,
+        processor: AutoProcessor,
+        default: {
             // TODO: replace with original
             // "model": "google/vit-base-patch16-224",
-            "model": "Xenova/vit-base-patch16-224",
+            model: 'Xenova/vit-base-patch16-224',
         },
-        "type": "multimodal",
+        type: 'multimodal',
     },
 
-    "image-segmentation": {
+    'image-segmentation': {
         // no tokenizer
-        "pipeline": ImageSegmentationPipeline,
-        "model": [AutoModelForImageSegmentation, AutoModelForSemanticSegmentation, AutoModelForUniversalSegmentation],
-        "processor": AutoProcessor,
-        "default": {
+        pipeline: ImageSegmentationPipeline,
+        model: [AutoModelForImageSegmentation, AutoModelForSemanticSegmentation, AutoModelForUniversalSegmentation],
+        processor: AutoProcessor,
+        default: {
             // TODO: replace with original
             // "model": "facebook/detr-resnet-50-panoptic",
-            "model": "Xenova/detr-resnet-50-panoptic",
+            model: 'Xenova/detr-resnet-50-panoptic',
         },
-        "type": "multimodal",
+        type: 'multimodal',
     },
-    "background-removal": {
+    'background-removal': {
         // no tokenizer
-        "pipeline": BackgroundRemovalPipeline,
-        "model": [AutoModelForImageSegmentation, AutoModelForSemanticSegmentation, AutoModelForUniversalSegmentation],
-        "processor": AutoProcessor,
-        "default": {
-            "model": "Xenova/modnet",
+        pipeline: BackgroundRemovalPipeline,
+        model: [AutoModelForImageSegmentation, AutoModelForSemanticSegmentation, AutoModelForUniversalSegmentation],
+        processor: AutoProcessor,
+        default: {
+            model: 'Xenova/modnet',
         },
-        "type": "image",
+        type: 'image',
     },
 
-    "zero-shot-image-classification": {
-        "tokenizer": AutoTokenizer,
-        "pipeline": ZeroShotImageClassificationPipeline,
-        "model": AutoModel,
-        "processor": AutoProcessor,
-        "default": {
+    'zero-shot-image-classification': {
+        tokenizer: AutoTokenizer,
+        pipeline: ZeroShotImageClassificationPipeline,
+        model: AutoModel,
+        processor: AutoProcessor,
+        default: {
             // TODO: replace with original
             // "model": "openai/clip-vit-base-patch32",
-            "model": "Xenova/clip-vit-base-patch32",
+            model: 'Xenova/clip-vit-base-patch32',
         },
-        "type": "multimodal",
+        type: 'multimodal',
     },
 
-    "object-detection": {
+    'object-detection': {
         // no tokenizer
-        "pipeline": ObjectDetectionPipeline,
-        "model": AutoModelForObjectDetection,
-        "processor": AutoProcessor,
-        "default": {
+        pipeline: ObjectDetectionPipeline,
+        model: AutoModelForObjectDetection,
+        processor: AutoProcessor,
+        default: {
             // TODO: replace with original
             // "model": "facebook/detr-resnet-50",
-            "model": "Xenova/detr-resnet-50",
+            model: 'Xenova/detr-resnet-50',
         },
-        "type": "multimodal",
+        type: 'multimodal',
     },
-    "zero-shot-object-detection": {
-        "tokenizer": AutoTokenizer,
-        "pipeline": ZeroShotObjectDetectionPipeline,
-        "model": AutoModelForZeroShotObjectDetection,
-        "processor": AutoProcessor,
-        "default": {
+    'zero-shot-object-detection': {
+        tokenizer: AutoTokenizer,
+        pipeline: ZeroShotObjectDetectionPipeline,
+        model: AutoModelForZeroShotObjectDetection,
+        processor: AutoProcessor,
+        default: {
             // TODO: replace with original
             // "model": "google/owlvit-base-patch32",
-            "model": "Xenova/owlvit-base-patch32",
+            model: 'Xenova/owlvit-base-patch32',
         },
-        "type": "multimodal",
+        type: 'multimodal',
     },
-    "document-question-answering": {
-        "tokenizer": AutoTokenizer,
-        "pipeline": DocumentQuestionAnsweringPipeline,
-        "model": AutoModelForDocumentQuestionAnswering,
-        "processor": AutoProcessor,
-        "default": {
+    'document-question-answering': {
+        tokenizer: AutoTokenizer,
+        pipeline: DocumentQuestionAnsweringPipeline,
+        model: AutoModelForDocumentQuestionAnswering,
+        processor: AutoProcessor,
+        default: {
             // TODO: replace with original
             // "model": "naver-clova-ix/donut-base-finetuned-docvqa",
-            "model": "Xenova/donut-base-finetuned-docvqa",
+            model: 'Xenova/donut-base-finetuned-docvqa',
         },
-        "type": "multimodal",
+        type: 'multimodal',
     },
-    "image-to-image": {
+    'image-to-image': {
         // no tokenizer
-        "pipeline": ImageToImagePipeline,
-        "model": AutoModelForImageToImage,
-        "processor": AutoProcessor,
-        "default": {
+        pipeline: ImageToImagePipeline,
+        model: AutoModelForImageToImage,
+        processor: AutoProcessor,
+        default: {
             // TODO: replace with original
             // "model": "caidas/swin2SR-classical-sr-x2-64",
-            "model": "Xenova/swin2SR-classical-sr-x2-64",
+            model: 'Xenova/swin2SR-classical-sr-x2-64',
         },
-        "type": "image",
+        type: 'image',
     },
-    "depth-estimation": {
+    'depth-estimation': {
         // no tokenizer
-        "pipeline": DepthEstimationPipeline,
-        "model": AutoModelForDepthEstimation,
-        "processor": AutoProcessor,
-        "default": {
+        pipeline: DepthEstimationPipeline,
+        model: AutoModelForDepthEstimation,
+        processor: AutoProcessor,
+        default: {
             // TODO: replace with original
             // "model": "Intel/dpt-large",
-            "model": "Xenova/dpt-large",
+            model: 'Xenova/dpt-large',
         },
-        "type": "image",
+        type: 'image',
     },
 
     // This task serves as a useful interface for dealing with sentence-transformers (https://huggingface.co/sentence-transformers).
-    "feature-extraction": {
-        "tokenizer": AutoTokenizer,
-        "pipeline": FeatureExtractionPipeline,
-        "model": AutoModel,
-        "default": {
+    'feature-extraction': {
+        tokenizer: AutoTokenizer,
+        pipeline: FeatureExtractionPipeline,
+        model: AutoModel,
+        default: {
             // TODO: replace with original
             // "model": "sentence-transformers/all-MiniLM-L6-v2",
-            "model": "Xenova/all-MiniLM-L6-v2",
+            model: 'Xenova/all-MiniLM-L6-v2',
         },
-        "type": "text",
+        type: 'text',
     },
-    "image-feature-extraction": {
-        "processor": AutoProcessor,
-        "pipeline": ImageFeatureExtractionPipeline,
-        "model": [AutoModelForImageFeatureExtraction, AutoModel],
-        "default": {
+    'image-feature-extraction': {
+        processor: AutoProcessor,
+        pipeline: ImageFeatureExtractionPipeline,
+        model: [AutoModelForImageFeatureExtraction, AutoModel],
+        default: {
             // TODO: replace with original
             // "model": "google/vit-base-patch16-224",
-            "model": "Xenova/vit-base-patch16-224-in21k",
+            model: 'Xenova/vit-base-patch16-224-in21k',
         },
-        "type": "image",
+        type: 'image',
     },
-})
-
+});
 
 // TODO: Add types for TASK_ALIASES
 const TASK_ALIASES = Object.freeze({
-    "sentiment-analysis": "text-classification",
-    "ner": "token-classification",
+    'sentiment-analysis': 'text-classification',
+    ner: 'token-classification',
     // "vqa": "visual-question-answering", // TODO: Add
-    "asr": "automatic-speech-recognition",
-    "text-to-speech": "text-to-audio",
+    asr: 'automatic-speech-recognition',
+    'text-to-speech': 'text-to-audio',
 
     // Add for backwards compatibility
-    "embeddings": "feature-extraction",
+    embeddings: 'feature-extraction',
 });
 
 /**
@@ -3421,7 +3368,7 @@ export async function pipeline(
         use_external_data_format = null,
         model_file_name = null,
         session_options = {},
-    } = {}
+    } = {},
 ) {
     // Helper method to construct pipeline
 
@@ -3432,12 +3379,12 @@ export async function pipeline(
     // Get pipeline info
     const pipelineInfo = SUPPORTED_TASKS[task.split('_', 1)[0]];
     if (!pipelineInfo) {
-        throw Error(`Unsupported pipeline: ${task}. Must be one of [${Object.keys(SUPPORTED_TASKS)}]`)
+        throw Error(`Unsupported pipeline: ${task}. Must be one of [${Object.keys(SUPPORTED_TASKS)}]`);
     }
 
     // Use model if specified, otherwise, use default
     if (!model) {
-        model = pipelineInfo.default.model
+        model = pipelineInfo.default.model;
         console.log(`No model specified. Using default model: "${model}".`);
     }
 
@@ -3453,7 +3400,7 @@ export async function pipeline(
         use_external_data_format,
         model_file_name,
         session_options,
-    }
+    };
 
     const classes = new Map([
         ['tokenizer', pipelineInfo.tokenizer],
@@ -3466,16 +3413,15 @@ export async function pipeline(
     results.task = task;
 
     dispatchCallback(progress_callback, {
-        'status': 'ready',
-        'task': task,
-        'model': model,
+        status: 'ready',
+        task: task,
+        model: model,
     });
 
     const pipelineClass = pipelineInfo.pipeline;
     return new pipelineClass(results);
 }
 
-
 /**
  * Helper function to get applicable model, tokenizer, or processor classes for a given model.
  * @param {Map} mapping The mapping of names to classes, arrays of classes, or null.
@@ -3484,7 +3430,6 @@ export async function pipeline(
  * @private
  */
 async function loadItems(mapping, model, pretrainedOptions) {
-
     const result = Object.create(null);
 
     /**@type {Promise[]} */
@@ -3518,11 +3463,10 @@ async function loadItems(mapping, model, pretrainedOptions) {
                             reject(err);
                             return;
                         }
-
                     }
                 }
                 reject(e);
-            })
+            });
         } else {
             promise = cls.from_pretrained(model, pretrainedOptions);
         }
diff --git a/src/tokenizers.js b/src/tokenizers.js
index 2e617f93b..737eff38c 100644
--- a/src/tokenizers.js
+++ b/src/tokenizers.js
@@ -1,12 +1,11 @@
-
 /**
  * @file Tokenizers are used to prepare textual inputs for a model.
- * 
+ *
  * **Example:** Create an `AutoTokenizer` and use it to tokenize a sentence.
  * This will automatically detect the tokenizer type based on the tokenizer class defined in `tokenizer.json`.
  * ```javascript
  * import { AutoTokenizer } from '@huggingface/transformers';
- * 
+ *
  * const tokenizer = await AutoTokenizer.from_pretrained('Xenova/bert-base-uncased');
  * const { input_ids } = await tokenizer('I love transformers!');
  * // Tensor {
@@ -16,41 +15,23 @@
  * //   size: 6,
  * // }
  * ```
- * 
+ *
  * @module tokenizers
  */
-import {
-    Callable,
-} from './utils/generic.js';
-
-import {
-    reverseDictionary,
-    escapeRegExp,
-    isIntegralNumber,
-    mergeArrays,
-    len,
-} from './utils/core.js';
-
-import {
-    getModelJSON,
-} from './utils/hub.js';
+import { Callable } from './utils/generic.js';
+
+import { reverseDictionary, escapeRegExp, isIntegralNumber, mergeArrays, len } from './utils/core.js';
+
+import { getModelJSON } from './utils/hub.js';
 
 import { max, min, round } from './utils/maths.js';
 import { Tensor } from './utils/tensor.js';
 
-import {
-    PriorityQueue,
-    TokenLattice,
-    CharTrie,
-    DictionarySplitter,
-    LRUCache,
-} from './utils/data-structures.js';
+import { PriorityQueue, TokenLattice, CharTrie, DictionarySplitter, LRUCache } from './utils/data-structures.js';
 
 import { Template } from '@huggingface/jinja';
 
-import {
-    WHISPER_LANGUAGE_MAPPING
-} from './models/whisper/common_whisper.js';
+import { WHISPER_LANGUAGE_MAPPING } from './models/whisper/common_whisper.js';
 
 /**
  * @typedef {Object} TokenizerProperties Additional tokenizer-specific properties.
@@ -65,11 +46,10 @@ import {
  * @returns {Promise} A promise that resolves with information about the loaded tokenizer.
  */
 async function loadTokenizer(pretrained_model_name_or_path, options) {
-
     const info = await Promise.all([
         getModelJSON(pretrained_model_name_or_path, 'tokenizer.json', true, options),
         getModelJSON(pretrained_model_name_or_path, 'tokenizer_config.json', true, options),
-    ])
+    ]);
 
     // Override legacy option if `options.legacy` is not null
     if (options.legacy !== null) {
@@ -78,7 +58,6 @@ async function loadTokenizer(pretrained_model_name_or_path, options) {
     return info;
 }
 
-
 /**
  * Helper function to split a string on a regex, but keep the delimiters.
  * This is required, because the JavaScript `.split()` method does not keep the delimiters,
@@ -106,7 +85,6 @@ function regexSplit(text, regex) {
     return result;
 }
 
-
 /**
  * Helper method to construct a pattern from a config object.
  * @param {Object} pattern The pattern object.
@@ -114,7 +92,6 @@ function regexSplit(text, regex) {
  * @returns {RegExp|null} The compiled pattern.
  */
 function createPattern(pattern, invert = true) {
-
     if (pattern.Regex !== undefined) {
         // In certain cases, the pattern may contain unnecessary escape sequences (e.g., \# or \& or \~).
         // i.e., valid in Python (where the patterns are exported from) but invalid in JavaScript (where the patterns are parsed).
@@ -129,14 +106,12 @@ function createPattern(pattern, invert = true) {
         }
 
         return new RegExp(regex, 'gu');
-
     } else if (pattern.String !== undefined) {
         const escaped = escapeRegExp(pattern.String);
         // NOTE: if invert is true, we wrap the pattern in a group so that it is kept when performing .split()
         return new RegExp(invert ? escaped : `(${escaped})`, 'gu');
-
     } else {
-        console.warn('Unknown pattern type:', pattern)
+        console.warn('Unknown pattern type:', pattern);
         return null;
     }
 }
@@ -162,11 +137,13 @@ function prepareTensorForDecode(tensor) {
             return tensor.tolist();
         case 2:
             if (dims[0] !== 1) {
-                throw new Error('Unable to decode tensor with `batch size !== 1`. Use `tokenizer.batch_decode(...)` for batched inputs.');
+                throw new Error(
+                    'Unable to decode tensor with `batch size !== 1`. Use `tokenizer.batch_decode(...)` for batched inputs.',
+                );
             }
             return tensor.tolist()[0];
         default:
-            throw new Error(`Expected tensor to have 1-2 dimensions, got ${dims.length}.`)
+            throw new Error(`Expected tensor to have 1-2 dimensions, got ${dims.length}.`);
     }
 }
 
@@ -178,7 +155,8 @@ function prepareTensorForDecode(tensor) {
 function clean_up_tokenization(text) {
     // Clean up a list of simple English tokenization artifacts
     // like spaces before punctuations and abbreviated forms
-    return text.replace(/ \./g, '.')
+    return text
+        .replace(/ \./g, '.')
         .replace(/ \?/g, '?')
         .replace(/ \!/g, '!')
         .replace(/ ,/g, ',')
@@ -208,7 +186,6 @@ function lowercase_and_remove_accent(text) {
     return remove_accents(text.toLowerCase());
 }
 
-
 /**
  * Checks whether the given Unicode codepoint represents a CJK (Chinese, Japanese, or Korean) character.
  *
@@ -225,15 +202,15 @@ function lowercase_and_remove_accent(text) {
  */
 export function is_chinese_char(cp) {
     return (
-        (cp >= 0x4E00 && cp <= 0x9FFF)
-        || (cp >= 0x3400 && cp <= 0x4DBF)
-        || (cp >= 0x20000 && cp <= 0x2A6DF)
-        || (cp >= 0x2A700 && cp <= 0x2B73F)
-        || (cp >= 0x2B740 && cp <= 0x2B81F)
-        || (cp >= 0x2B820 && cp <= 0x2CEAF)
-        || (cp >= 0xF900 && cp <= 0xFAFF)
-        || (cp >= 0x2F800 && cp <= 0x2FA1F)
-    )
+        (cp >= 0x4e00 && cp <= 0x9fff) ||
+        (cp >= 0x3400 && cp <= 0x4dbf) ||
+        (cp >= 0x20000 && cp <= 0x2a6df) ||
+        (cp >= 0x2a700 && cp <= 0x2b73f) ||
+        (cp >= 0x2b740 && cp <= 0x2b81f) ||
+        (cp >= 0x2b820 && cp <= 0x2ceaf) ||
+        (cp >= 0xf900 && cp <= 0xfaff) ||
+        (cp >= 0x2f800 && cp <= 0x2fa1f)
+    );
 }
 
 /**
@@ -247,7 +224,7 @@ function fuse_unk(arr, tokens_to_ids, unk_token_id) {
     const fused = [];
     let i = 0;
     while (i < arr.length) {
-        fused.push(arr[i])
+        fused.push(arr[i]);
         if ((tokens_to_ids.get(arr[i]) ?? unk_token_id) !== unk_token_id) {
             ++i;
             continue;
@@ -285,8 +262,7 @@ const PROBLEMATIC_REGEX_MAP = new Map([
     // Used to override the default (invalid) regex of the bloom pretokenizer.
     // For more information, see https://github.com/huggingface/transformers.js/issues/94
     [` ?[^(\\s|[${BLOOM_SPLIT_CHARS}])]+`, ` ?[^\\s${BLOOM_SPLIT_CHARS}]+`],
-])
-
+]);
 
 /**
  * Represent a token added by the user on top of the existing Model vocabulary.
@@ -373,7 +349,10 @@ export class TokenizerModel extends Callable {
                         // config.vocab is of type `[string, number][]`
                         // @ts-ignore
                         return new Unigram(config, ...args);
-                    } else if (Object.hasOwn(config, 'continuing_subword_prefix') && Object.hasOwn(config, 'unk_token')) {
+                    } else if (
+                        Object.hasOwn(config, 'continuing_subword_prefix') &&
+                        Object.hasOwn(config, 'unk_token')
+                    ) {
                         if (Object.hasOwn(config, 'merges')) {
                             return new BPE(config);
                         } else {
@@ -409,7 +388,7 @@ export class TokenizerModel extends Callable {
      * @throws Will throw an error if not implemented in a subclass.
      */
     encode(tokens) {
-        throw Error("encode should be implemented in subclass.")
+        throw Error('encode should be implemented in subclass.');
     }
 
     /**
@@ -418,7 +397,7 @@ export class TokenizerModel extends Callable {
      * @returns {number[]} The converted token IDs.
      */
     convert_tokens_to_ids(tokens) {
-        return tokens.map(t => this.tokens_to_ids.get(t) ?? this.unk_token_id);
+        return tokens.map((t) => this.tokens_to_ids.get(t) ?? this.unk_token_id);
     }
 
     /**
@@ -427,7 +406,7 @@ export class TokenizerModel extends Callable {
      * @returns {string[]} The converted tokens.
      */
     convert_ids_to_tokens(ids) {
-        return ids.map(i => this.vocab[i] ?? this.unk_token);
+        return ids.map((i) => this.vocab[i] ?? this.unk_token);
     }
 }
 
@@ -529,7 +508,6 @@ class WordPieceTokenizer extends TokenizerModel {
 
         return outputTokens;
     }
-
 }
 
 /**
@@ -636,7 +614,6 @@ class Unigram extends TokenizerModel {
         }
         return toReturn;
     }
-
 }
 
 /**
@@ -650,9 +627,9 @@ const BYTES_TO_UNICODE = (() => {
     // the bpe code barfs on.
 
     const bs = [
-        ...Array.from({ length: "~".charCodeAt(0) - "!".charCodeAt(0) + 1 }, (_, i) => i + "!".charCodeAt(0)),
-        ...Array.from({ length: "¬".charCodeAt(0) - "¡".charCodeAt(0) + 1 }, (_, i) => i + "¡".charCodeAt(0)),
-        ...Array.from({ length: "ÿ".charCodeAt(0) - "®".charCodeAt(0) + 1 }, (_, i) => i + "®".charCodeAt(0)),
+        ...Array.from({ length: '~'.charCodeAt(0) - '!'.charCodeAt(0) + 1 }, (_, i) => i + '!'.charCodeAt(0)),
+        ...Array.from({ length: '¬'.charCodeAt(0) - '¡'.charCodeAt(0) + 1 }, (_, i) => i + '¡'.charCodeAt(0)),
+        ...Array.from({ length: 'ÿ'.charCodeAt(0) - '®'.charCodeAt(0) + 1 }, (_, i) => i + '®'.charCodeAt(0)),
     ];
     const cs = bs.slice();
     let n = 0;
@@ -663,13 +640,12 @@ const BYTES_TO_UNICODE = (() => {
             n += 1;
         }
     }
-    const ccs = cs.map(n => String.fromCharCode(n));
+    const ccs = cs.map((n) => String.fromCharCode(n));
     return Object.fromEntries(bs.map((b, i) => [b, ccs[i]]));
 })();
 
 const UNICODE_TO_BYTES = reverseDictionary(BYTES_TO_UNICODE);
 
-
 /**
  * @typedef {Object} BPENode
  * @property {string} token The token associated with the node
@@ -715,8 +691,8 @@ class BPE extends TokenizerModel {
 
         /** @type {[string, string][]} */
         this.merges = use_new_merge_format
-            ? /** @type {[string, string][]} */(config.merges)
-            : (/** @type {string[]} */(config.merges)).map(x => /** @type {[string, string]} */(x.split(' ', 2)));
+            ? /** @type {[string, string][]} */ (config.merges)
+            : /** @type {string[]} */ (config.merges).map((x) => /** @type {[string, string]} */ (x.split(' ', 2)));
         this.bpe_ranks = new Map(this.merges.map((x, i) => [JSON.stringify(x), i]));
 
         this.end_of_word_suffix = config.end_of_word_suffix;
@@ -787,19 +763,19 @@ class BPE extends TokenizerModel {
                 bias: 0,
                 prev: null,
                 next: null,
-            }
+            };
 
-            let previousNode = startingNode
+            let previousNode = startingNode;
             for (let i = 1; i < word.length; ++i) {
                 const currentNode = {
                     bias: i / word.length, // Add fractional component to break ties
                     token: word[i],
                     prev: previousNode,
                     next: null,
-                }
-                previousNode.next = currentNode
-                this._add_node(queue, previousNode)
-                previousNode = currentNode
+                };
+                previousNode.next = currentNode;
+                this._add_node(queue, previousNode);
+                previousNode = currentNode;
             }
 
             while (!queue.isEmpty()) {
@@ -816,7 +792,6 @@ class BPE extends TokenizerModel {
 
                 // Next, we fix the node that comes before the current node (i.e., left side of the merge).
                 if (node.prev) {
-
                     // Make a shallow copy of the previous node
                     const newPreviousNode = { ...node.prev };
 
@@ -841,7 +816,7 @@ class BPE extends TokenizerModel {
                     bias: node.bias,
                     prev: node.prev,
                     next: node.next.next,
-                }
+                };
 
                 // We now consider where we can add the new merged node to the priority queue:
                 // 1. prev <-> merged
@@ -884,10 +859,9 @@ class BPE extends TokenizerModel {
         return result;
     }
 
-
     /**
      * Helper function to add a node to the priority queue.
-     * @param {PriorityQueue} queue 
+     * @param {PriorityQueue} queue
      * @param {BPENode} node
      * @private
      */
@@ -921,9 +895,10 @@ class BPE extends TokenizerModel {
                 if (this.tokens_to_ids.has(t)) {
                     outputTokens.push(t);
                 } else if (this.byte_fallback) {
-                    const byteTokens = Array.from(this.text_encoder.encode(t))
-                        .map(x => `<0x${x.toString(16).toUpperCase().padStart(2, '0')}>`);
-                    if (byteTokens.every(x => this.tokens_to_ids.has(x))) {
+                    const byteTokens = Array.from(this.text_encoder.encode(t)).map(
+                        (x) => `<0x${x.toString(16).toUpperCase().padStart(2, '0')}>`,
+                    );
+                    if (byteTokens.every((x) => this.tokens_to_ids.has(x))) {
                         // Ensure the byte tokens are actually in the vocabulary, otherwise
                         // we fall back to the unknown token. For more information, see
                         // https://github.com/huggingface/transformers/issues/28096.
@@ -939,7 +914,6 @@ class BPE extends TokenizerModel {
 
         return outputTokens;
     }
-
 }
 
 /**
@@ -956,11 +930,7 @@ class LegacyTokenizerModel extends TokenizerModel {
         super(config);
 
         /**@type {Map} */
-        this.tokens_to_ids = objectToMap(
-            moreConfig.target_lang
-                ? config.vocab[moreConfig.target_lang]
-                : config.vocab
-        );
+        this.tokens_to_ids = objectToMap(moreConfig.target_lang ? config.vocab[moreConfig.target_lang] : config.vocab);
 
         this.bos_token = moreConfig.bos_token;
         this.bos_token_id = this.tokens_to_ids.get(this.bos_token);
@@ -985,7 +955,6 @@ class LegacyTokenizerModel extends TokenizerModel {
     }
 }
 
-
 /**
  * A base class for text normalization.
  * @abstract
@@ -1046,7 +1015,7 @@ class Normalizer extends Callable {
      * @throws {Error} If this method is not implemented in a subclass.
      */
     normalize(text) {
-        throw Error("normalize should be implemented in subclass.")
+        throw Error('normalize should be implemented in subclass.');
     }
 
     /**
@@ -1057,7 +1026,6 @@ class Normalizer extends Callable {
     _call(text) {
         return this.normalize(text);
     }
-
 }
 
 /**
@@ -1072,9 +1040,7 @@ class Replace extends Normalizer {
      */
     normalize(text) {
         const pattern = createPattern(this.config.pattern);
-        return pattern === null
-            ? text
-            : text.replaceAll(pattern, this.config.content);
+        return pattern === null ? text : text.replaceAll(pattern, this.config.content);
     }
 }
 
@@ -1096,7 +1062,7 @@ class UnicodeNormalizer extends Normalizer {
      * @returns {string} The normalized text.
      */
     normalize(text) {
-        text = text.normalize(this.form)
+        text = text.normalize(this.form);
         return text;
     }
 }
@@ -1216,19 +1182,19 @@ class Prepend extends Normalizer {
  */
 class NormalizerSequence extends Normalizer {
     /**
-   * Create a new instance of NormalizerSequence.
-   * @param {Object} config The configuration object.
-   * @param {Object[]} config.normalizers An array of Normalizer configuration objects.
-   */
+     * Create a new instance of NormalizerSequence.
+     * @param {Object} config The configuration object.
+     * @param {Object[]} config.normalizers An array of Normalizer configuration objects.
+     */
     constructor(config) {
         super(config);
-        this.normalizers = config.normalizers.map(x => Normalizer.fromConfig(x));
+        this.normalizers = config.normalizers.map((x) => Normalizer.fromConfig(x));
     }
     /**
-    * Apply a sequence of Normalizers to the input text.
-    * @param {string} text The text to normalize.
-    * @returns {string} The normalized text.
-    */
+     * Apply a sequence of Normalizers to the input text.
+     * @param {string} text The text to normalize.
+     * @returns {string} The normalized text.
+     */
     normalize(text) {
         return this.normalizers.reduce((t, normalizer) => {
             return normalizer.normalize(t);
@@ -1254,14 +1220,14 @@ class BertNormalizer extends Normalizer {
             const char = text[i];
             const cp = char.charCodeAt(0);
             if (is_chinese_char(cp)) {
-                output.push(" ");
+                output.push(' ');
                 output.push(char);
-                output.push(" ");
+                output.push(' ');
             } else {
                 output.push(char);
             }
         }
-        return output.join("");
+        return output.join('');
     }
 
     /**
@@ -1274,7 +1240,6 @@ class BertNormalizer extends Normalizer {
         return text.normalize('NFD').replace(/\p{Mn}/gu, '');
     }
 
-
     /**
      * Checks whether `char` is a control character.
      * @param {string} char The character to check.
@@ -1309,16 +1274,17 @@ class BertNormalizer extends Normalizer {
         const output = [];
         for (const char of text) {
             const cp = char.charCodeAt(0);
-            if (cp === 0 || cp === 0xFFFD || this._is_control(char)) {
+            if (cp === 0 || cp === 0xfffd || this._is_control(char)) {
                 continue;
             }
-            if (/^\s$/.test(char)) { // is whitespace
-                output.push(" ");
+            if (/^\s$/.test(char)) {
+                // is whitespace
+                output.push(' ');
             } else {
                 output.push(char);
             }
         }
-        return output.join("");
+        return output.join('');
     }
     /**
      * Normalizes the given text based on the configuration.
@@ -1355,13 +1321,13 @@ class BertNormalizer extends Normalizer {
  */
 class PreTokenizer extends Callable {
     /**
-   * Factory method that returns an instance of a subclass of `PreTokenizer` based on the provided configuration.
-   *
-   * @static
-   * @param {Object} config A configuration object for the pre-tokenizer.
-   * @returns {PreTokenizer} An instance of a subclass of `PreTokenizer`.
-   * @throws {Error} If the provided configuration object does not correspond to any known pre-tokenizer.
-   */
+     * Factory method that returns an instance of a subclass of `PreTokenizer` based on the provided configuration.
+     *
+     * @static
+     * @param {Object} config A configuration object for the pre-tokenizer.
+     * @returns {PreTokenizer} An instance of a subclass of `PreTokenizer`.
+     * @throws {Error} If the provided configuration object does not correspond to any known pre-tokenizer.
+     */
     static fromConfig(config) {
         if (config === null) return null;
 
@@ -1402,7 +1368,7 @@ class PreTokenizer extends Callable {
      * @throws {Error} If the method is not implemented in the subclass.
      */
     pre_tokenize_text(text, options) {
-        throw Error("pre_tokenize_text should be implemented in subclass.")
+        throw Error('pre_tokenize_text should be implemented in subclass.');
     }
 
     /**
@@ -1412,9 +1378,10 @@ class PreTokenizer extends Callable {
      * @returns {string[]} An array of pre-tokens.
      */
     pre_tokenize(text, options) {
-        return (Array.isArray(text)
-            ? text.map(x => this.pre_tokenize_text(x, options))
-            : this.pre_tokenize_text(text, options)
+        return (
+            Array.isArray(text)
+                ? text.map((x) => this.pre_tokenize_text(x, options))
+                : this.pre_tokenize_text(text, options)
         ).flat();
     }
 
@@ -1436,7 +1403,7 @@ class BertPreTokenizer extends PreTokenizer {
     /**
      * A PreTokenizer that splits text into wordpieces using a basic tokenization scheme
      * similar to that used in the original implementation of BERT.
-     * 
+     *
      * @param {Object} config The configuration object.
      */
     constructor(config) {
@@ -1448,7 +1415,7 @@ class BertPreTokenizer extends PreTokenizer {
     }
     /**
      * Tokenizes a single text using the BERT pre-tokenization scheme.
-     * 
+     *
      * @param {string} text The text to tokenize.
      * @param {Object} [options] Additional options for the pre-tokenization logic.
      * @returns {string[]} An array of tokens.
@@ -1508,11 +1475,11 @@ class ByteLevelPreTokenizer extends PreTokenizer {
         }
 
         // Split on whitespace and punctuation
-        const tokens = this.use_regex ? (text.match(this.pattern) || []) : [text];
+        const tokens = this.use_regex ? text.match(this.pattern) || [] : [text];
 
         // Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
-        return tokens.map(
-            token => Array.from(this.text_encoder.encode(token), byte => this.byte_encoder[byte]).join('')
+        return tokens.map((token) =>
+            Array.from(this.text_encoder.encode(token), (byte) => this.byte_encoder[byte]).join(''),
         );
     }
 }
@@ -1556,7 +1523,7 @@ class SplitPreTokenizer extends PreTokenizer {
         if (this.config.invert) {
             return text.match(this.pattern) || [];
         } else if (this.config.behavior?.toLowerCase() === 'removed') {
-            return text.split(this.pattern).filter(x => x);
+            return text.split(this.pattern).filter((x) => x);
         } else {
             return regexSplit(text, this.pattern);
         }
@@ -1589,7 +1556,6 @@ class PunctuationPreTokenizer extends PreTokenizer {
     }
 }
 
-
 /**
  * Splits text based on digits.
  * @extends PreTokenizer
@@ -1625,7 +1591,6 @@ class DigitsPreTokenizer extends PreTokenizer {
  * @property {number[]} [token_type_ids] List of token type ids produced by the post-processor.
  */
 
-
 /**
  * @typedef {Object} EncodingSingle
  * @property {number[]} input_ids List of token ids to be fed to a model.
@@ -1633,12 +1598,10 @@ class DigitsPreTokenizer extends PreTokenizer {
  * @property {number[]} [token_type_ids] List of indices specifying which tokens should be attended to by the model
  */
 
-
 /**
  * @extends Callable
  */
 class PostProcessor extends Callable {
-
     /**
      * @param {Object} config The configuration for the post-processor.
      */
@@ -1684,7 +1647,7 @@ class PostProcessor extends Callable {
      * @throws {Error} If the method is not implemented in subclass.
      */
     post_process(tokens, ...args) {
-        throw Error("post_process should be implemented in subclass.")
+        throw Error('post_process should be implemented in subclass.');
     }
 
     /**
@@ -1721,9 +1684,7 @@ class BertProcessing extends PostProcessor {
      * @param {string[]} [tokens_pair=null] An optional second set of input tokens.
      * @returns {PostProcessedOutput} The post-processed tokens with the special tokens added to the beginning and end.
      */
-    post_process(tokens, tokens_pair = null, {
-        add_special_tokens = true,
-    } = {}) {
+    post_process(tokens, tokens_pair = null, { add_special_tokens = true } = {}) {
         if (add_special_tokens) {
             tokens = mergeArrays([this.cls], tokens, [this.sep]);
         }
@@ -1732,18 +1693,19 @@ class BertProcessing extends PostProcessor {
         if (tokens_pair !== null) {
             // NOTE: It is intended to add 2 EOS tokens after the first set of tokens
             // https://github.com/huggingface/tokenizers/issues/983
-            const middle = (add_special_tokens && this instanceof RobertaProcessing)
-                ? [this.sep]
-                : [];
+            const middle = add_special_tokens && this instanceof RobertaProcessing ? [this.sep] : [];
             const after = add_special_tokens ? [this.sep] : [];
 
             tokens = mergeArrays(tokens, middle, tokens_pair, after);
-            token_type_ids = mergeArrays(token_type_ids, new Array(tokens_pair.length + middle.length + after.length).fill(1));
+            token_type_ids = mergeArrays(
+                token_type_ids,
+                new Array(tokens_pair.length + middle.length + after.length).fill(1),
+            );
         }
         return { tokens, token_type_ids };
     }
 }
-class RobertaProcessing extends BertProcessing { } // NOTE: extends BertProcessing
+class RobertaProcessing extends BertProcessing {} // NOTE: extends BertProcessing
 
 /**
  * Post processor that replaces special tokens in a template with actual tokens.
@@ -1769,10 +1731,8 @@ class TemplateProcessing extends PostProcessor {
      * @param {string[]} [tokens_pair=null] The list of tokens for the second sequence (optional).
      * @returns {PostProcessedOutput} An object containing the list of tokens with the special tokens replaced with actual tokens.
      */
-    post_process(tokens, tokens_pair = null, {
-        add_special_tokens = true,
-    } = {}) {
-        const type = tokens_pair === null ? this.single : this.pair
+    post_process(tokens, tokens_pair = null, { add_special_tokens = true } = {}) {
+        const type = tokens_pair === null ? this.single : this.pair;
 
         let processedTokens = [];
         let types = [];
@@ -1786,7 +1746,6 @@ class TemplateProcessing extends PostProcessor {
                 if (item.Sequence.id === 'A') {
                     processedTokens = mergeArrays(processedTokens, tokens);
                     types = mergeArrays(types, new Array(tokens.length).fill(item.Sequence.type_id));
-
                 } else if (item.Sequence.id === 'B') {
                     processedTokens = mergeArrays(processedTokens, tokens_pair);
                     types = mergeArrays(types, new Array(tokens_pair.length).fill(item.Sequence.type_id));
@@ -1816,12 +1775,10 @@ class ByteLevelPostProcessor extends PostProcessor {
     }
 }
 
-
 /**
  * A post-processor that applies multiple post-processors in sequence.
  */
 class PostProcessorSequence extends PostProcessor {
-
     /**
      * Creates a new instance of PostProcessorSequence.
      * @param {Object} config The configuration object.
@@ -1830,7 +1787,7 @@ class PostProcessorSequence extends PostProcessor {
     constructor(config) {
         super(config);
 
-        this.processors = config.processors.map(x => PostProcessor.fromConfig(x));
+        this.processors = config.processors.map((x) => PostProcessor.fromConfig(x));
     }
 
     /**
@@ -1865,12 +1822,11 @@ class PostProcessorSequence extends PostProcessor {
  * @extends Callable
  */
 class Decoder extends Callable {
-
     /**
-    * Creates an instance of `Decoder`.
-    *
-    * @param {Object} config The configuration object.
-    */
+     * Creates an instance of `Decoder`.
+     *
+     * @param {Object} config The configuration object.
+     */
     constructor(config) {
         super();
         this.config = config;
@@ -1882,12 +1838,12 @@ class Decoder extends Callable {
     }
 
     /**
-   * Creates a decoder instance based on the provided configuration.
-   *
-   * @param {Object} config The configuration object.
-   * @returns {Decoder} A decoder instance.
-   * @throws {Error} If an unknown decoder type is provided.
-   */
+     * Creates a decoder instance based on the provided configuration.
+     *
+     * @param {Object} config The configuration object.
+     * @returns {Decoder} A decoder instance.
+     * @throws {Error} If an unknown decoder type is provided.
+     */
     static fromConfig(config) {
         if (config === null) return null;
         switch (config.type) {
@@ -1920,49 +1876,44 @@ class Decoder extends Callable {
     }
 
     /**
-    * Calls the `decode` method.
-    *
-    * @param {string[]} tokens The list of tokens.
-    * @returns {string} The decoded string.
-    */
+     * Calls the `decode` method.
+     *
+     * @param {string[]} tokens The list of tokens.
+     * @returns {string} The decoded string.
+     */
     _call(tokens) {
         return this.decode(tokens);
     }
 
     /**
-    * Decodes a list of tokens.
-    * @param {string[]} tokens The list of tokens.
-    * @returns {string} The decoded string.
-    */
+     * Decodes a list of tokens.
+     * @param {string[]} tokens The list of tokens.
+     * @returns {string} The decoded string.
+     */
     decode(tokens) {
         return this.decode_chain(tokens).join('');
     }
 
     /**
      * Apply the decoder to a list of tokens.
-     * 
+     *
      * @param {string[]} tokens The list of tokens.
      * @returns {string[]} The decoded list of tokens.
      * @throws {Error} If the `decode_chain` method is not implemented in the subclass.
      */
     decode_chain(tokens) {
-        throw Error("`decode_chain` should be implemented in subclass.")
+        throw Error('`decode_chain` should be implemented in subclass.');
     }
-
 }
 
 class ReplaceDecoder extends Decoder {
-
     /** @type {Decoder['decode_chain']} */
     decode_chain(tokens) {
         const pattern = createPattern(this.config.pattern);
-        return pattern === null
-            ? tokens
-            : tokens.map(token => token.replaceAll(pattern, this.config.content))
+        return pattern === null ? tokens : tokens.map((token) => token.replaceAll(pattern, this.config.content));
     }
 }
 
-
 class ByteFallback extends Decoder {
     constructor(config) {
         super(config);
@@ -1972,7 +1923,6 @@ class ByteFallback extends Decoder {
 
     /** @type {Decoder['decode_chain']} */
     decode_chain(tokens) {
-
         const new_tokens = [];
         let previous_byte_tokens = [];
 
@@ -2011,14 +1961,12 @@ class ByteFallback extends Decoder {
  * exists incase some decoders need to happen after that step
  */
 class FuseDecoder extends Decoder {
-
     /** @type {Decoder['decode_chain']} */
     decode_chain(tokens) {
         return [tokens.join('')];
     }
 }
 
-
 class StripDecoder extends Decoder {
     constructor(config) {
         super(config);
@@ -2030,7 +1978,7 @@ class StripDecoder extends Decoder {
 
     /** @type {Decoder['decode_chain']} */
     decode_chain(tokens) {
-        return tokens.map(token => {
+        return tokens.map((token) => {
             let start_cut = 0;
             for (let i = 0; i < this.start; ++i) {
                 if (token[i] === this.content) {
@@ -2052,7 +2000,7 @@ class StripDecoder extends Decoder {
                 }
             }
 
-            return token.slice(start_cut, stop_cut)
+            return token.slice(start_cut, stop_cut);
         });
     }
 }
@@ -2062,7 +2010,6 @@ class StripDecoder extends Decoder {
  * @extends Decoder
  */
 class WordPieceDecoder extends Decoder {
-
     /**
      * Creates a new instance of WordPieceDecoder.
      * @param {Object} config The configuration object.
@@ -2086,7 +2033,7 @@ class WordPieceDecoder extends Decoder {
                 }
             }
             if (this.cleanup) {
-                token = clean_up_tokenization(token)
+                token = clean_up_tokenization(token);
             }
 
             return token;
@@ -2099,7 +2046,6 @@ class WordPieceDecoder extends Decoder {
  * @extends Decoder
  */
 class ByteLevelDecoder extends Decoder {
-
     /**
      * Create a `ByteLevelDecoder` object.
      * @param {Object} config Configuration object.
@@ -2108,7 +2054,7 @@ class ByteLevelDecoder extends Decoder {
         super(config);
 
         this.byte_decoder = UNICODE_TO_BYTES;
-        this.text_decoder = new TextDecoder("utf-8", {
+        this.text_decoder = new TextDecoder('utf-8', {
             fatal: false,
             ignoreBOM: true,
         });
@@ -2123,7 +2069,7 @@ class ByteLevelDecoder extends Decoder {
      */
     convert_tokens_to_string(tokens) {
         const text = tokens.join('');
-        const byteArray = new Uint8Array([...text].map(c => this.byte_decoder[c]));
+        const byteArray = new Uint8Array([...text].map((c) => this.byte_decoder[c]));
         const decoded_text = this.text_decoder.decode(byteArray);
         return decoded_text;
     }
@@ -2144,7 +2090,7 @@ class ByteLevelDecoder extends Decoder {
             //     continue;
             // }
 
-            if (this.added_tokens.find(x => x.content === token) !== undefined) {
+            if (this.added_tokens.find((x) => x.content === token) !== undefined) {
                 if (current_sub_text.length > 0) {
                     sub_texts.push(this.convert_tokens_to_string(current_sub_text));
                     current_sub_text = [];
@@ -2169,7 +2115,6 @@ class ByteLevelDecoder extends Decoder {
  * See https://github.com/huggingface/tokenizers/blob/bb38f390a61883fc2f29d659af696f428d1cda6b/tokenizers/src/decoders/ctc.rs
  */
 class CTCDecoder extends Decoder {
-
     constructor(config) {
         super(config);
 
@@ -2194,19 +2139,16 @@ class CTCDecoder extends Decoder {
         }
 
         // filter self.pad_token which is used as CTC-blank token
-        const filtered_tokens = grouped_tokens.filter(token => token !== this.pad_token);
+        const filtered_tokens = grouped_tokens.filter((token) => token !== this.pad_token);
 
         let text = filtered_tokens.join('');
         if (this.cleanup) {
             // cleanup and replace delimiter token
-            text = clean_up_tokenization(text)
-                .replaceAll(this.word_delimiter_token, ' ')
-                .trim();
+            text = clean_up_tokenization(text).replaceAll(this.word_delimiter_token, ' ').trim();
         }
         return text;
     }
 
-
     /** @type {Decoder['decode_chain']} */
     decode_chain(tokens) {
         return [this.convert_tokens_to_string(tokens)];
@@ -2218,7 +2160,6 @@ class CTCDecoder extends Decoder {
  * @extends Decoder
  */
 class DecoderSequence extends Decoder {
-
     /**
      * Creates a new instance of DecoderSequence.
      * @param {Object} config The configuration object.
@@ -2226,7 +2167,7 @@ class DecoderSequence extends Decoder {
      */
     constructor(config) {
         super(config);
-        this.decoders = config.decoders.map(x => Decoder.fromConfig(x));
+        this.decoders = config.decoders.map((x) => Decoder.fromConfig(x));
     }
 
     /** @type {Decoder['decode_chain']} */
@@ -2236,7 +2177,6 @@ class DecoderSequence extends Decoder {
             return decoder.decode_chain(toks);
         }, tokens);
     }
-
 }
 
 class BPEDecoder extends Decoder {
@@ -2248,7 +2188,7 @@ class BPEDecoder extends Decoder {
     /** @type {Decoder['decode_chain']} */
     decode_chain(tokens) {
         return tokens.map((token, i) => {
-            return token.replaceAll(this.suffix, (i === tokens.length - 1) ? '' : ' ')
+            return token.replaceAll(this.suffix, i === tokens.length - 1 ? '' : ' ');
         });
     }
 }
@@ -2265,7 +2205,6 @@ class VitsDecoder extends Decoder {
     }
 }
 
-
 /**
  * This PreTokenizer replaces spaces with the given replacement character, adds a prefix space if requested,
  * and returns a list of tokens.
@@ -2296,25 +2235,19 @@ class MetaspacePreTokenizer extends PreTokenizer {
      * @param {number} [options.section_index] The index of the section to pre-tokenize.
      * @returns {string[]} A new list of pre-tokenized tokens.
      */
-    pre_tokenize_text(text, {
-        section_index = undefined,
-    } = {}) {
-
+    pre_tokenize_text(text, { section_index = undefined } = {}) {
         let normalized = text.replaceAll(' ', this.strRep);
 
         if (
             // We add a prefix space if:
             //  (1) The addPrefixSpace option is enabled and the normalized
             //      token does not already start with the replacement character.
-            (this.addPrefixSpace && !normalized.startsWith(this.replacement))
-
+            this.addPrefixSpace &&
+            !normalized.startsWith(this.replacement) &&
             // and (2) either:
             //  (a) prepend_scheme is 'always'
             //  (b) prepend_scheme is 'first' and this is the first section
-            && (
-                this.prepend_scheme === 'always' ||
-                (this.prepend_scheme === 'first' && section_index === 0)
-            )
+            (this.prepend_scheme === 'always' || (this.prepend_scheme === 'first' && section_index === 0))
         ) {
             normalized = this.strRep + normalized;
         }
@@ -2385,13 +2318,16 @@ class Precompiled extends Normalizer {
         //  3. nmt_nfkc_cf: nmt_nfkc + Unicode case folding (mostly lower casing)
         //  4. nfkc_cf: nfkc + Unicode case folding.
         //  5. identity: no normalization
-        // 
+        //
         // For now, we only implement the default (nmt_nfkc).
         // See https://raw.githubusercontent.com/google/sentencepiece/master/data/nmt_nfkc.tsv for the full list of rules.
         // TODO: detect when a different `this.charsmap` is used.
 
         text = text.replace(/[\u0001-\u0008\u000B\u000E-\u001F\u007F\u008F\u009F]/gm, ''); // Remove control characters
-        text = text.replace(/[\u0009\u000A\u000C\u000D\u00A0\u1680\u2000-\u200F\u2028\u2029\u202F\u205F\u2581\u3000\uFEFF\uFFFD]/gm, '\u0020'); // Replace certain characters with a space
+        text = text.replace(
+            /[\u0009\u000A\u000C\u000D\u00A0\u1680\u2000-\u200F\u2028\u2029\u202F\u205F\u2581\u3000\uFEFF\uFFFD]/gm,
+            '\u0020',
+        ); // Replace certain characters with a space
 
         if (text.includes('\uFF5E')) {
             // To match the sentencepiece implementation 100%, we must handle a very strange edge-case.
@@ -2399,7 +2335,7 @@ class Precompiled extends Normalizer {
             // However, NFKC normalization does do this conversion. As a result, we split the string on the Fullwidth Tilde character,
             // perform NFKC normalization on each substring, and then join them back together with the Fullwidth Tilde character.
             const parts = text.split('\uFF5E');
-            text = parts.map(part => part.normalize('NFKC')).join('\uFF5E');
+            text = parts.map((part) => part.normalize('NFKC')).join('\uFF5E');
         } else {
             text = text.normalize('NFKC');
         }
@@ -2420,7 +2356,7 @@ class PreTokenizerSequence extends PreTokenizer {
      */
     constructor(config) {
         super();
-        this.tokenizers = config.pretokenizers.map(x => PreTokenizer.fromConfig(x));
+        this.tokenizers = config.pretokenizers.map((x) => PreTokenizer.fromConfig(x));
     }
 
     /**
@@ -2431,9 +2367,12 @@ class PreTokenizerSequence extends PreTokenizer {
      */
     pre_tokenize_text(text, options) {
         // Use reduce to apply each tokenizer to the text
-        return this.tokenizers.reduce((preTokenizedText, tokenizer) => {
-            return tokenizer.pre_tokenize(preTokenizedText, options);
-        }, [text]);
+        return this.tokenizers.reduce(
+            (preTokenizedText, tokenizer) => {
+                return tokenizer.pre_tokenize(preTokenizedText, options);
+            },
+            [text],
+        );
     }
 }
 
@@ -2519,10 +2458,10 @@ const SPECIAL_TOKEN_ATTRIBUTES = [
     'cls_token',
     'mask_token',
     // additional_special_tokens (TODO)
-]
+];
 
 /**
- * 
+ *
  * Helper function for padding values of an object, which are each arrays.
  * NOTE: No additional checks are made here for validity of arguments.
  * @param {Record} item The input object.
@@ -2537,9 +2476,7 @@ function padHelper(item, length, value_fn, side) {
         const value = value_fn(key);
 
         const padData = new Array(diff).fill(value);
-        item[key] = side === 'right'
-            ? mergeArrays(item[key], padData)
-            : mergeArrays(padData, item[key]);
+        item[key] = side === 'right' ? mergeArrays(item[key], padData) : mergeArrays(padData, item[key]);
     }
 }
 
@@ -2558,7 +2495,6 @@ function truncateHelper(item, length) {
     }
 }
 
-
 /**
  * @typedef {Object} Message
  * @property {string} role The role of the message (e.g., "user" or "assistant" or "system").
@@ -2621,12 +2557,10 @@ export class PreTrainedTokenizer extends Callable {
             this.decoder.end_of_word_suffix = this.model.end_of_word_suffix;
         }
 
-        this.added_tokens_splitter = new DictionarySplitter(
-            this.added_tokens.map(x => x.content),
-        );
+        this.added_tokens_splitter = new DictionarySplitter(this.added_tokens.map((x) => x.content));
 
         /** @type {Map} */
-        this.added_tokens_map = new Map(this.added_tokens.map(x => [x.content, x]))
+        this.added_tokens_map = new Map(this.added_tokens.map((x) => [x.content, x]));
 
         // Set mask token if present (otherwise will be undefined, which is fine)
         this.mask_token = this.getToken('mask_token');
@@ -2707,23 +2641,25 @@ export class PreTrainedTokenizer extends Callable {
     }
 
     /**
-     * Loads a pre-trained tokenizer from the given `pretrained_model_name_or_path`. 
-     * 
+     * Loads a pre-trained tokenizer from the given `pretrained_model_name_or_path`.
+     *
      * @param {string} pretrained_model_name_or_path The path to the pre-trained tokenizer.
      * @param {PretrainedTokenizerOptions} options Additional options for loading the tokenizer.
-     * 
+     *
      * @throws {Error} Throws an error if the tokenizer.json or tokenizer_config.json files are not found in the `pretrained_model_name_or_path`.
      * @returns {Promise} A new instance of the `PreTrainedTokenizer` class.
      */
-    static async from_pretrained(pretrained_model_name_or_path, {
-        progress_callback = null,
-        config = null,
-        cache_dir = null,
-        local_files_only = false,
-        revision = 'main',
-        legacy = null,
-    } = {}) {
-
+    static async from_pretrained(
+        pretrained_model_name_or_path,
+        {
+            progress_callback = null,
+            config = null,
+            cache_dir = null,
+            local_files_only = false,
+            revision = 'main',
+            legacy = null,
+        } = {},
+    ) {
         const info = await loadTokenizer(pretrained_model_name_or_path, {
             progress_callback,
             config,
@@ -2731,7 +2667,7 @@ export class PreTrainedTokenizer extends Callable {
             local_files_only,
             revision,
             legacy,
-        })
+        });
 
         // @ts-ignore
         return new this(...info);
@@ -2739,7 +2675,7 @@ export class PreTrainedTokenizer extends Callable {
 
     /**
      * @typedef {number[]|number[][]|Tensor} BatchEncodingItem
-     * 
+     *
      * @typedef {Object} BatchEncoding Holds the output of the tokenizer's call function.
      * @property {BatchEncodingItem} input_ids List of token ids to be fed to a model.
      * @property {BatchEncodingItem} attention_mask List of indices specifying which tokens should be attended to by the model.
@@ -2774,7 +2710,6 @@ export class PreTrainedTokenizer extends Callable {
             return_token_type_ids = null,
         } = {},
     ) {
-
         const isBatched = Array.isArray(text);
 
         /** @type {EncodingSingle[]} */
@@ -2782,32 +2717,31 @@ export class PreTrainedTokenizer extends Callable {
 
         if (isBatched) {
             if (text.length === 0) {
-                throw Error('text array must be non-empty')
+                throw Error('text array must be non-empty');
             }
 
             if (text_pair !== null) {
                 if (!Array.isArray(text_pair)) {
-                    throw Error('text_pair must also be an array')
-
+                    throw Error('text_pair must also be an array');
                 } else if (text.length !== text_pair.length) {
-                    throw Error('text and text_pair must have the same length')
+                    throw Error('text and text_pair must have the same length');
                 }
 
-                encodedTokens = text.map(
-                    (t, i) => this._encode_plus(t, { text_pair: text_pair[i], add_special_tokens, return_token_type_ids })
-                )
-
+                encodedTokens = text.map((t, i) =>
+                    this._encode_plus(t, { text_pair: text_pair[i], add_special_tokens, return_token_type_ids }),
+                );
             } else {
-                encodedTokens = text.map(x => this._encode_plus(x, { add_special_tokens, return_token_type_ids }));
+                encodedTokens = text.map((x) => this._encode_plus(x, { add_special_tokens, return_token_type_ids }));
             }
-
         } else {
             if (text === null || text === undefined) {
-                throw Error('text may not be null or undefined')
+                throw Error('text may not be null or undefined');
             }
 
             if (Array.isArray(text_pair)) {
-                throw Error('When specifying `text_pair`, since `text` is a string, `text_pair` must also be a string (i.e., not an array).')
+                throw Error(
+                    'When specifying `text_pair`, since `text` is a string, `text_pair` must also be a string (i.e., not an array).',
+                );
             }
 
             // For single input, we just wrap in an array, and then unwrap later.
@@ -2820,12 +2754,14 @@ export class PreTrainedTokenizer extends Callable {
         } else if (truncation === null) {
             if (padding === true) {
                 console.warn(
-                    "`max_length` is ignored when `padding: true` and there is no truncation strategy. " +
-                    "To pad to max length, use `padding: 'max_length'`."
-                )
+                    '`max_length` is ignored when `padding: true` and there is no truncation strategy. ' +
+                        "To pad to max length, use `padding: 'max_length'`.",
+                );
                 max_length = this.model_max_length;
             } else if (padding === false) {
-                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.");
+                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.',
+                );
                 truncation = true;
             }
         }
@@ -2833,33 +2769,31 @@ export class PreTrainedTokenizer extends Callable {
         // padding: 'max_length' doesn't require any additional calculation
         // but padding: true has to calculate max_length from the sequences
         if (padding === true) {
-            max_length = Math.min(max(encodedTokens.map(x => x.input_ids.length))[0], max_length ?? Infinity);
+            max_length = Math.min(max(encodedTokens.map((x) => x.input_ids.length))[0], max_length ?? Infinity);
         }
 
         // Ensure it is less than model max length
         max_length = Math.min(max_length, this.model_max_length ?? Infinity);
 
         if (padding || truncation) {
-
             // Perform padding and/or truncation
             for (let i = 0; i < encodedTokens.length; ++i) {
                 if (encodedTokens[i].input_ids.length === max_length) {
                     continue;
-
                 } else if (encodedTokens[i].input_ids.length > max_length) {
                     // possibly truncate
                     if (truncation) {
                         truncateHelper(encodedTokens[i], max_length);
                     }
-
-                } else { // t.length < max_length
+                } else {
+                    // t.length < max_length
                     // possibly pad
                     if (padding) {
                         padHelper(
                             encodedTokens[i],
                             max_length,
-                            key => key === 'input_ids' ? this.pad_token_id : 0,
-                            this.padding_side
+                            (key) => (key === 'input_ids' ? this.pad_token_id : 0),
+                            this.padding_side,
                         );
                     }
                 }
@@ -2874,7 +2808,7 @@ export class PreTrainedTokenizer extends Callable {
                 // we perform additional check
 
                 if (
-                    encodedTokens.some(x => {
+                    encodedTokens.some((x) => {
                         for (const key of Object.keys(x)) {
                             if (x[key].length !== encodedTokens[0][key]?.length) {
                                 return true;
@@ -2884,9 +2818,9 @@ export class PreTrainedTokenizer extends Callable {
                     })
                 ) {
                     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."
-                    )
+                        '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.",
+                    );
                 }
             }
 
@@ -2896,15 +2830,15 @@ export class PreTrainedTokenizer extends Callable {
             const dims = [encodedTokens.length, encodedTokens[0].input_ids.length];
 
             for (const key of Object.keys(encodedTokens[0])) {
-                result[key] = new Tensor('int64',
-                    BigInt64Array.from(encodedTokens.flatMap(x => x[key]).map(BigInt)),
-                    dims
+                result[key] = new Tensor(
+                    'int64',
+                    BigInt64Array.from(encodedTokens.flatMap((x) => x[key]).map(BigInt)),
+                    dims,
                 );
             }
-
         } else {
             for (const key of Object.keys(encodedTokens[0])) {
-                result[key] = encodedTokens.map(x => x[key]);
+                result[key] = encodedTokens.map((x) => x[key]);
             }
 
             // If not returning a tensor, we match the input type
@@ -2916,7 +2850,7 @@ export class PreTrainedTokenizer extends Callable {
             }
         }
 
-        return /** @type {BatchEncoding} */(result);
+        return /** @type {BatchEncoding} */ (result);
     }
 
     /**
@@ -2967,9 +2901,12 @@ export class PreTrainedTokenizer extends Callable {
                 return [];
             }
 
-            const sectionTokens = (this.pre_tokenizer !== null) ? this.pre_tokenizer(x, {
-                section_index,
-            }) : [x];
+            const sectionTokens =
+                this.pre_tokenizer !== null
+                    ? this.pre_tokenizer(x, {
+                          section_index,
+                      })
+                    : [x];
 
             const tokens = this.model(sectionTokens);
 
@@ -2990,12 +2927,7 @@ export class PreTrainedTokenizer extends Callable {
      * @returns {EncodingSingle} An object containing the encoded text.
      * @private
      */
-    _encode_plus(text, {
-        text_pair = null,
-        add_special_tokens = true,
-        return_token_type_ids = null,
-    } = {}) {
-
+    _encode_plus(text, { text_pair = null, add_special_tokens = true, return_token_type_ids = null } = {}) {
         const { tokens, token_type_ids } = this._tokenize_helper(text, { pair: text_pair, add_special_tokens });
 
         const input_ids = this.model.convert_tokens_to_ids(tokens);
@@ -3003,7 +2935,7 @@ export class PreTrainedTokenizer extends Callable {
         const result = {
             input_ids,
             attention_mask: new Array(input_ids.length).fill(1),
-        }
+        };
         if ((return_token_type_ids ?? this.return_token_type_ids) && token_type_ids) {
             result.token_type_ids = token_type_ids;
         }
@@ -3018,10 +2950,7 @@ export class PreTrainedTokenizer extends Callable {
      * @param {boolean} [options.add_special_tokens=false] Whether or not to add the special tokens associated with the corresponding model.
      * @returns {{tokens: string[], token_type_ids?: number[]}} An object containing the tokens and optionally the token type IDs.
      */
-    _tokenize_helper(text, {
-        pair = null,
-        add_special_tokens = false,
-    } = {}) {
+    _tokenize_helper(text, { pair = null, add_special_tokens = false } = {}) {
         const tokens = this._encode_text(text);
         const tokens2 = this._encode_text(pair);
 
@@ -3038,10 +2967,7 @@ export class PreTrainedTokenizer extends Callable {
      * @param {boolean} [options.add_special_tokens=false] Whether or not to add the special tokens associated with the corresponding model.
      * @returns {string[]} The list of tokens.
      */
-    tokenize(text, {
-        pair = null,
-        add_special_tokens = false,
-    } = {}) {
+    tokenize(text, { pair = null, add_special_tokens = false } = {}) {
         return this._tokenize_helper(text, { pair, add_special_tokens }).tokens;
     }
 
@@ -3055,11 +2981,7 @@ export class PreTrainedTokenizer extends Callable {
      * @param {boolean} [options.return_token_type_ids=null] Whether to return token_type_ids.
      * @returns {number[]} An array of token IDs representing the encoded text(s).
      */
-    encode(text, {
-        text_pair = null,
-        add_special_tokens = true,
-        return_token_type_ids = null,
-    } = {}) {
+    encode(text, { text_pair = null, add_special_tokens = true, return_token_type_ids = null } = {}) {
         return this._encode_plus(text, {
             text_pair,
             add_special_tokens,
@@ -3077,7 +2999,7 @@ export class PreTrainedTokenizer extends Callable {
         if (batch instanceof Tensor) {
             batch = batch.tolist();
         }
-        return batch.map(x => this.decode(x, decode_args));
+        return batch.map((x) => this.decode(x, decode_args));
     }
 
     /**
@@ -3091,19 +3013,16 @@ export class PreTrainedTokenizer extends Callable {
      * @returns {string} The decoded string.
      * @throws {Error} If `token_ids` is not a non-empty array of integers.
      */
-    decode(
-        token_ids,
-        decode_args = {},
-    ) {
+    decode(token_ids, decode_args = {}) {
         if (token_ids instanceof Tensor) {
             token_ids = prepareTensorForDecode(token_ids);
         }
 
         if (!Array.isArray(token_ids) || token_ids.length === 0 || !isIntegralNumber(token_ids[0])) {
-            throw Error("token_ids must be a non-empty array of integers.");
+            throw Error('token_ids must be a non-empty array of integers.');
         }
 
-        return this.decode_single(token_ids, decode_args)
+        return this.decode_single(token_ids, decode_args);
     }
 
     /**
@@ -3115,16 +3034,10 @@ export class PreTrainedTokenizer extends Callable {
      * If null, the value is set to `this.decoder.cleanup` if it exists, falling back to `this.clean_up_tokenization_spaces` if it exists, falling back to `true`.
      * @returns {string} The decoded string
      */
-    decode_single(
-        token_ids,
-        {
-            skip_special_tokens = false,
-            clean_up_tokenization_spaces = null,
-        }
-    ) {
+    decode_single(token_ids, { skip_special_tokens = false, clean_up_tokenization_spaces = null }) {
         let tokens = this.model.convert_ids_to_tokens(token_ids);
         if (skip_special_tokens) {
-            tokens = tokens.filter(x => !this.special_tokens.includes(x));
+            tokens = tokens.filter((x) => !this.special_tokens.includes(x));
         }
 
         // If `this.decoder` is null, we just join tokens with a space:
@@ -3152,7 +3065,7 @@ export class PreTrainedTokenizer extends Callable {
      * Retrieve the chat template string used for tokenizing chat messages. This template is used
      * internally by the `apply_chat_template` method and can also be used externally to retrieve the model's chat
      * template for better generation tracking.
-     * 
+     *
      * @param {Object} options An optional object containing the following properties:
      * @param {string} [options.chat_template=null]
      * A Jinja template or the name of a template to use for this conversion.
@@ -3166,11 +3079,7 @@ export class PreTrainedTokenizer extends Callable {
      * for more information.
      * @returns {string} The chat template string.
      */
-    get_chat_template({
-        chat_template = null,
-        tools = null,
-    } = {}) {
-
+    get_chat_template({ chat_template = null, tools = null } = {}) {
         // First, handle the cases when the model has a dict of multiple templates
         if (this.chat_template && typeof this.chat_template === 'object') {
             const template_dict = this.chat_template;
@@ -3186,9 +3095,9 @@ export class PreTrainedTokenizer extends Callable {
                 } else {
                     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(template_dict).sort()}.`
-                    )
+                            `template or the name of the template you wish to use to the 'chat_template' argument. Available ` +
+                            `template names are ${Object.keys(template_dict).sort()}.`,
+                    );
                 }
             }
         } else if (chat_template === null) {
@@ -3198,11 +3107,11 @@ export class PreTrainedTokenizer extends Callable {
                 chat_template = this.chat_template;
             } else {
                 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"
-                )
+                    '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',
+                );
             }
         }
         return chat_template;
@@ -3212,29 +3121,29 @@ export class PreTrainedTokenizer extends Callable {
      * Converts a list of message objects with `"role"` and `"content"` keys to a list of token
      * ids. This method is intended for use with chat models, and will read the tokenizer's chat_template attribute to
      * determine the format and control tokens to use when converting.
-     * 
+     *
      * See [here](https://huggingface.co/docs/transformers/chat_templating) for more information.
-     * 
+     *
      * **Example:** Applying a chat template to a conversation.
-     * 
+     *
      * ```javascript
      * import { AutoTokenizer } from "@huggingface/transformers";
-     * 
+     *
      * const tokenizer = await AutoTokenizer.from_pretrained("Xenova/mistral-tokenizer-v1");
-     * 
+     *
      * const chat = [
      *   { "role": "user", "content": "Hello, how are you?" },
      *   { "role": "assistant", "content": "I'm doing great. How can I help you today?" },
      *   { "role": "user", "content": "I'd like to show off how chat templating works!" },
      * ]
-     * 
+     *
      * const text = tokenizer.apply_chat_template(chat, { tokenize: false });
      * // "[INST] Hello, how are you? [/INST]I'm doing great. How can I help you today? [INST] I'd like to show off how chat templating works! [/INST]"
-     * 
+     *
      * const input_ids = tokenizer.apply_chat_template(chat, { tokenize: true, return_tensor: false });
      * // [1, 733, 16289, 28793, 22557, 28725, 910, 460, 368, 28804, 733, 28748, 16289, 28793, 28737, 28742, 28719, 2548, 1598, 28723, 1602, 541, 315, 1316, 368, 3154, 28804, 2, 28705, 733, 16289, 28793, 315, 28742, 28715, 737, 298, 1347, 805, 910, 10706, 5752, 1077, 3791, 28808, 733, 28748, 16289, 28793]
      * ```
-     * 
+     *
      * @param {Message[]} conversation A list of message objects with `"role"` and `"content"` keys,
      * representing the chat history so far.
      * @param {Object} options An optional object containing the following properties:
@@ -3266,21 +3175,23 @@ export class PreTrainedTokenizer extends Callable {
      * @param {Object} [options.tokenizer_kwargs={}] Additional options to pass to the tokenizer.
      * @returns {string | Tensor | number[]| number[][]|BatchEncoding} The tokenized output.
      */
-    apply_chat_template(conversation, {
-        tools = null,
-        documents = null,
-        chat_template = null,
-        add_generation_prompt = false,
-        tokenize = true,
-        padding = false,
-        truncation = false,
-        max_length = null,
-        return_tensor = true,
-        return_dict = false,
-        tokenizer_kwargs = {},
-        ...kwargs
-    } = {}) {
-
+    apply_chat_template(
+        conversation,
+        {
+            tools = null,
+            documents = null,
+            chat_template = null,
+            add_generation_prompt = false,
+            tokenize = true,
+            padding = false,
+            truncation = false,
+            max_length = null,
+            return_tensor = true,
+            return_dict = false,
+            tokenizer_kwargs = {},
+            ...kwargs
+        } = {},
+    ) {
         chat_template = this.get_chat_template({ chat_template, tools });
 
         if (typeof chat_template !== 'string') {
@@ -3362,30 +3273,32 @@ export class ConvBertTokenizer extends PreTrainedTokenizer {
 export class RoFormerTokenizer extends PreTrainedTokenizer {
     return_token_type_ids = true;
 }
-export class DistilBertTokenizer extends PreTrainedTokenizer { }
-export class CamembertTokenizer extends PreTrainedTokenizer { }
+export class DistilBertTokenizer extends PreTrainedTokenizer {}
+export class CamembertTokenizer extends PreTrainedTokenizer {}
 export class XLMTokenizer extends PreTrainedTokenizer {
     return_token_type_ids = true;
 
     constructor(tokenizerJSON, tokenizerConfig) {
         super(tokenizerJSON, tokenizerConfig);
-        console.warn('WARNING: `XLMTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')
+        console.warn(
+            'WARNING: `XLMTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.',
+        );
     }
 }
 export class ElectraTokenizer extends PreTrainedTokenizer {
     return_token_type_ids = true;
 }
 
-export class T5Tokenizer extends PreTrainedTokenizer { }
-export class GPT2Tokenizer extends PreTrainedTokenizer { }
-export class BartTokenizer extends PreTrainedTokenizer { }
+export class T5Tokenizer extends PreTrainedTokenizer {}
+export class GPT2Tokenizer extends PreTrainedTokenizer {}
+export class BartTokenizer extends PreTrainedTokenizer {}
 export class MBartTokenizer extends PreTrainedTokenizer {
     constructor(tokenizerJSON, tokenizerConfig) {
         super(tokenizerJSON, tokenizerConfig);
 
         this.languageRegex = /^[a-z]{2}_[A-Z]{2}$/;
-        this.language_codes = this.special_tokens.filter(x => this.languageRegex.test(x));
-        this.lang_to_token = x => x; // Identity function
+        this.language_codes = this.special_tokens.filter((x) => this.languageRegex.test(x));
+        this.lang_to_token = (x) => x; // Identity function
     }
 
     /**
@@ -3399,16 +3312,15 @@ export class MBartTokenizer extends PreTrainedTokenizer {
         return _build_translation_inputs(this, raw_inputs, tokenizer_options, generate_kwargs);
     }
 }
-export class MBart50Tokenizer extends MBartTokenizer { } // NOTE: extends MBartTokenizer
+export class MBart50Tokenizer extends MBartTokenizer {} // NOTE: extends MBartTokenizer
 
-export class RobertaTokenizer extends PreTrainedTokenizer { }
+export class RobertaTokenizer extends PreTrainedTokenizer {}
 
-export class BloomTokenizer extends PreTrainedTokenizer { }
+export class BloomTokenizer extends PreTrainedTokenizer {}
 
-const SPIECE_UNDERLINE = "▁";
+const SPIECE_UNDERLINE = '▁';
 
 export class LlamaTokenizer extends PreTrainedTokenizer {
-
     padding_side = 'left';
 
     constructor(tokenizerJSON, tokenizerConfig) {
@@ -3421,7 +3333,7 @@ export class LlamaTokenizer extends PreTrainedTokenizer {
             this.pre_tokenizer = new MetaspacePreTokenizer({
                 replacement: SPIECE_UNDERLINE,
                 add_prefix_space: true,
-                prepend_scheme: "first",
+                prepend_scheme: 'first',
             });
         }
     }
@@ -3439,29 +3351,29 @@ export class LlamaTokenizer extends PreTrainedTokenizer {
             return super._encode_text(text);
         }
 
-        let tokens = super._encode_text(SPIECE_UNDERLINE + text.replaceAll(SPIECE_UNDERLINE, " "));
+        let tokens = super._encode_text(SPIECE_UNDERLINE + text.replaceAll(SPIECE_UNDERLINE, ' '));
         if (tokens.length > 1 && tokens[0] === SPIECE_UNDERLINE && this.special_tokens.includes(tokens[1])) {
             tokens = tokens.slice(1);
         }
         return tokens;
     }
 }
-export class CodeLlamaTokenizer extends PreTrainedTokenizer { }
+export class CodeLlamaTokenizer extends PreTrainedTokenizer {}
 
-export class XLMRobertaTokenizer extends PreTrainedTokenizer { }
-export class MPNetTokenizer extends PreTrainedTokenizer { }
+export class XLMRobertaTokenizer extends PreTrainedTokenizer {}
+export class MPNetTokenizer extends PreTrainedTokenizer {}
 
-export class FalconTokenizer extends PreTrainedTokenizer { }
+export class FalconTokenizer extends PreTrainedTokenizer {}
 
-export class GPTNeoXTokenizer extends PreTrainedTokenizer { }
+export class GPTNeoXTokenizer extends PreTrainedTokenizer {}
 
-export class EsmTokenizer extends PreTrainedTokenizer { }
+export class EsmTokenizer extends PreTrainedTokenizer {}
 
-export class Qwen2Tokenizer extends PreTrainedTokenizer { }
+export class Qwen2Tokenizer extends PreTrainedTokenizer {}
 
-export class GemmaTokenizer extends PreTrainedTokenizer { }
+export class GemmaTokenizer extends PreTrainedTokenizer {}
 
-export class Grok1Tokenizer extends PreTrainedTokenizer { }
+export class Grok1Tokenizer extends PreTrainedTokenizer {}
 
 /**
  * Helper function to build translation inputs for an `NllbTokenizer` or `M2M100Tokenizer`.
@@ -3474,27 +3386,33 @@ export class Grok1Tokenizer extends PreTrainedTokenizer { }
  */
 function _build_translation_inputs(self, raw_inputs, tokenizer_options, generate_kwargs) {
     if (!('language_codes' in self) || !Array.isArray(self.language_codes)) {
-        throw new Error('Tokenizer must have `language_codes` attribute set and it should be an array of language ids.')
+        throw new Error(
+            'Tokenizer must have `language_codes` attribute set and it should be an array of language ids.',
+        );
     }
     if (!('languageRegex' in self) || !(self.languageRegex instanceof RegExp)) {
-        throw new Error('Tokenizer must have `languageRegex` attribute set and it should be a regular expression.')
+        throw new Error('Tokenizer must have `languageRegex` attribute set and it should be a regular expression.');
     }
     if (!('lang_to_token' in self) || typeof self.lang_to_token !== 'function') {
-        throw new Error('Tokenizer must have `lang_to_token` attribute set and it should be a function.')
+        throw new Error('Tokenizer must have `lang_to_token` attribute set and it should be a function.');
     }
     const src_lang_token = generate_kwargs.src_lang;
     const tgt_lang_token = generate_kwargs.tgt_lang;
 
     // Check that the target language is valid:
     if (!self.language_codes.includes(tgt_lang_token)) {
-        throw new Error(`Target language code "${tgt_lang_token}" is not valid. Must be one of: {${self.language_codes.join(', ')}}`);
+        throw new Error(
+            `Target language code "${tgt_lang_token}" is not valid. Must be one of: {${self.language_codes.join(', ')}}`,
+        );
     }
 
     // Allow `src_lang` to be optional. If not set, we'll use the tokenizer's default.
     if (src_lang_token !== undefined) {
         // Check that the source language is valid:
         if (!self.language_codes.includes(src_lang_token)) {
-            throw new Error(`Source language code "${src_lang_token}" is not valid. Must be one of: {${self.language_codes.join(', ')}}`);
+            throw new Error(
+                `Source language code "${src_lang_token}" is not valid. Must be one of: {${self.language_codes.join(', ')}}`,
+            );
         }
 
         // In the same way as the Python library, we override the post-processor
@@ -3516,25 +3434,24 @@ function _build_translation_inputs(self, raw_inputs, tokenizer_options, generate
 
 /**
  * The NllbTokenizer class is used to tokenize text for NLLB ("No Language Left Behind") models.
- * 
+ *
  * No Language Left Behind (NLLB) is a first-of-its-kind, AI breakthrough project
  * that open-sources models capable of delivering high-quality translations directly
  * between any pair of 200+ languages — including low-resource languages like Asturian,
  * Luganda, Urdu and more. It aims to help people communicate with anyone, anywhere,
  * regardless of their language preferences. For more information, check out their
  * [paper](https://huggingface.co/papers/2207.04672).
- * 
+ *
  * For a list of supported languages (along with their language codes),
  * @see {@link https://github.com/facebookresearch/flores/blob/main/flores200/README.md#languages-in-flores-200}
  */
 export class NllbTokenizer extends PreTrainedTokenizer {
-
     constructor(tokenizerJSON, tokenizerConfig) {
         super(tokenizerJSON, tokenizerConfig);
 
         this.languageRegex = /^[a-z]{3}_[A-Z][a-z]{3}$/;
-        this.language_codes = this.special_tokens.filter(x => this.languageRegex.test(x));
-        this.lang_to_token = x => x; // Identity function
+        this.language_codes = this.special_tokens.filter((x) => this.languageRegex.test(x));
+        this.lang_to_token = (x) => x; // Identity function
     }
 
     /**
@@ -3551,11 +3468,11 @@ export class NllbTokenizer extends PreTrainedTokenizer {
 
 /**
  * The M2M100Tokenizer class is used to tokenize text for M2M100 ("Many-to-Many") models.
- * 
+ *
  * M2M100 is a multilingual encoder-decoder (seq-to-seq) model trained for Many-to-Many
  * multilingual translation. It was introduced in this [paper](https://huggingface.co/papers/2010.11125)
  * and first released in [this](https://github.com/pytorch/fairseq/tree/master/examples/m2m_100) repository.
- * 
+ *
  * For a list of supported languages (along with their language codes),
  * @see {@link https://huggingface.co/facebook/m2m100_418M#languages-covered}
  */
@@ -3564,10 +3481,8 @@ export class M2M100Tokenizer extends PreTrainedTokenizer {
         super(tokenizerJSON, tokenizerConfig);
 
         this.languageRegex = /^__[a-z]{2,3}__$/;
-        this.language_codes = this.special_tokens
-            .filter(x => this.languageRegex.test(x))
-            .map(x => x.slice(2, -2));
-        this.lang_to_token = x => `__${x}__`;
+        this.language_codes = this.special_tokens.filter((x) => this.languageRegex.test(x)).map((x) => x.slice(2, -2));
+        this.lang_to_token = (x) => `__${x}__`;
     }
 
     /**
@@ -3587,9 +3502,8 @@ export class M2M100Tokenizer extends PreTrainedTokenizer {
  * @extends PreTrainedTokenizer
  */
 export class WhisperTokenizer extends PreTrainedTokenizer {
-
     get timestamp_begin() {
-        return this.model.convert_tokens_to_ids(["<|notimestamps|>"])[0] + 1;
+        return this.model.convert_tokens_to_ids(['<|notimestamps|>'])[0] + 1;
     }
 
     /**
@@ -3598,12 +3512,10 @@ export class WhisperTokenizer extends PreTrainedTokenizer {
      * @param {Object} options The options to use for decoding.
      * @returns {Array, text: string}>}>} The decoded sequences.
      */
-    _decode_asr(sequences, {
-        return_timestamps = false,
-        return_language = false,
-        time_precision = null,
-        force_full_sequences = true
-    } = {}) {
+    _decode_asr(
+        sequences,
+        { return_timestamps = false, return_language = false, time_precision = null, force_full_sequences = true } = {},
+    ) {
         // Set force_full_sequences=false if you want streaming
         // TODO add support for `return_language`
 
@@ -3624,14 +3536,14 @@ export class WhisperTokenizer extends PreTrainedTokenizer {
         // - Lots of complexity comes from stride and timestamps
 
         if (time_precision === null) {
-            throw Error("Must specify time_precision")
+            throw Error('Must specify time_precision');
         }
         let last_language = null;
 
-        const returnWordTimestamps = return_timestamps === "word";
+        const returnWordTimestamps = return_timestamps === 'word';
 
         function new_chunk() {
-            return { "language": last_language, "timestamp": [null, null], "text": "" };
+            return { language: last_language, timestamp: [null, null], text: '' };
         }
 
         // Welcome to the state machine!
@@ -3639,9 +3551,9 @@ export class WhisperTokenizer extends PreTrainedTokenizer {
         let chunk = new_chunk();
         let time_offset = 0.0;
         const timestamp_begin = this.timestamp_begin;
-        // Whisper timestamp tokens start from 0.00 and go to timestamp 30.00 in 0.02 increments. 
-        // We can calculate the last time stamp token as timestamp_begin plus the number of tokens 
-        // tokens from 0.00 to 30.00 which is 1500. 
+        // Whisper timestamp tokens start from 0.00 and go to timestamp 30.00 in 0.02 increments.
+        // We can calculate the last time stamp token as timestamp_begin plus the number of tokens
+        // tokens from 0.00 to 30.00 which is 1500.
         const total_timestamp_tokens = 1500; // (30.00 - 0.00) / 0.02
         const timestamp_end = timestamp_begin + total_timestamp_tokens;
 
@@ -3651,7 +3563,6 @@ export class WhisperTokenizer extends PreTrainedTokenizer {
         let skip = false;
         let right_stride_start = null;
 
-
         const all_special_ids = new Set(this.all_special_ids);
 
         for (const output of sequences) {
@@ -3664,7 +3575,7 @@ export class WhisperTokenizer extends PreTrainedTokenizer {
             let last_timestamp = null;
             let first_timestamp = timestamp_begin;
 
-            if ("stride" in output) {
+            if ('stride' in output) {
                 const [chunk_len, stride_left, stride_right] = output.stride;
 
                 // Offset the timings to account for the other `model_outputs`.
@@ -3685,7 +3596,10 @@ export class WhisperTokenizer extends PreTrainedTokenizer {
                         if (token >= timestamp_begin) {
                             // There can be several token in the right stride
                             // But the last one is ALWAYS going to be skipped
-                            if (last_timestamp !== null && (token - timestamp_begin) * time_precision < right_stride_start) {
+                            if (
+                                last_timestamp !== null &&
+                                (token - timestamp_begin) * time_precision < right_stride_start
+                            ) {
                                 break;
                             }
                             last_timestamp = token;
@@ -3759,40 +3673,42 @@ export class WhisperTokenizer extends PreTrainedTokenizer {
                             chunk.timestamp[1] = rounded_time;
 
                             // Handling merges
-                            previous_tokens.push(current_tokens)
+                            previous_tokens.push(current_tokens);
 
                             if (returnWordTimestamps) {
                                 previous_token_timestamps.push(current_token_timestamps);
                             }
                             const [resolved_tokens, resolved_token_timestamps] = this.findLongestCommonSequence(
-                                previous_tokens, previous_token_timestamps
-                            )
+                                previous_tokens,
+                                previous_token_timestamps,
+                            );
 
-                            const resolved_text = this.decode(resolved_tokens)
-                            chunk.text = resolved_text
+                            const resolved_text = this.decode(resolved_tokens);
+                            chunk.text = resolved_text;
 
                             if (returnWordTimestamps) {
                                 chunk.words = this.collateWordTimestamps(
-                                    resolved_tokens, resolved_token_timestamps, last_language,
-                                )
+                                    resolved_tokens,
+                                    resolved_token_timestamps,
+                                    last_language,
+                                );
                             }
 
-                            chunks.push(chunk)
+                            chunks.push(chunk);
 
                             // Flush all our temporary context
-                            previous_tokens = []
-                            current_tokens = []
-                            previous_token_timestamps = []
-                            current_token_timestamps = []
-                            chunk = new_chunk()
+                            previous_tokens = [];
+                            current_tokens = [];
+                            previous_token_timestamps = [];
+                            current_token_timestamps = [];
+                            chunk = new_chunk();
                         }
                     }
-
                 } else {
                     // 4/ Regular token
                     // We just append to the list of all tokens so we can handle
                     // merges later and decode into text.
-                    current_tokens.push(token)
+                    current_tokens.push(token);
 
                     if (returnWordTimestamps) {
                         let start_time = round(token_timestamps[i] + time_offset, 2);
@@ -3814,30 +3730,28 @@ export class WhisperTokenizer extends PreTrainedTokenizer {
                         }
                         current_token_timestamps.push([start_time, end_time]);
                     }
-
                 }
             }
 
             if ('stride' in output) {
                 const [chunk_len, stride_left, stride_right] = output.stride;
-                time_offset += chunk_len - stride_right
+                time_offset += chunk_len - stride_right;
             }
 
             // Leftover tokens
             if (current_tokens.length > 0) {
-                previous_tokens.push(current_tokens)
+                previous_tokens.push(current_tokens);
                 if (returnWordTimestamps) {
                     previous_token_timestamps.push(current_token_timestamps);
                 }
-            } else if (previous_tokens.every(p => p.length === 0)) {
+            } else if (previous_tokens.every((p) => p.length === 0)) {
                 // Flushing previous tokens (END)"
-                chunk = new_chunk()
-                previous_tokens = []
-                current_tokens = []
+                chunk = new_chunk();
+                previous_tokens = [];
+                current_tokens = [];
                 previous_token_timestamps = [];
                 current_token_timestamps = [];
             }
-
         }
 
         if (previous_tokens.length > 0) {
@@ -3845,21 +3759,22 @@ export class WhisperTokenizer extends PreTrainedTokenizer {
                 // Last token should always be timestamps, so there shouldn't be
                 // leftover
                 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."
+                    '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.',
                 );
             }
 
             // Happens when we don't use timestamps
-            const [resolved_tokens, resolved_token_timestamps] = this.findLongestCommonSequence(previous_tokens, previous_token_timestamps);
+            const [resolved_tokens, resolved_token_timestamps] = this.findLongestCommonSequence(
+                previous_tokens,
+                previous_token_timestamps,
+            );
 
             // Flushing previous tokens (FINAL)
             const resolved_text = this.decode(resolved_tokens);
             chunk.text = resolved_text;
             if (returnWordTimestamps) {
-                chunk.words = this.collateWordTimestamps(
-                    resolved_tokens, resolved_token_timestamps, last_language,
-                )
+                chunk.words = this.collateWordTimestamps(resolved_tokens, resolved_token_timestamps, last_language);
             }
             chunks.push(chunk);
         }
@@ -3867,16 +3782,16 @@ export class WhisperTokenizer extends PreTrainedTokenizer {
         let optional = Object.create(null);
 
         // Preparing and cleaning up the pipeline output
-        const full_text = chunks.map(chunk => chunk.text).join('');
+        const full_text = chunks.map((chunk) => chunk.text).join('');
         if (return_timestamps || return_language) {
             for (let i = 0; i < chunks.length; ++i) {
                 const chunk = chunks[i];
                 if (!return_timestamps) {
-                    delete chunk["timestamp"];
+                    delete chunk['timestamp'];
                 }
 
                 if (!return_language) {
-                    delete chunk["language"];
+                    delete chunk['language'];
                 }
             }
             if (returnWordTimestamps) {
@@ -3886,13 +3801,12 @@ export class WhisperTokenizer extends PreTrainedTokenizer {
                         new_chunks.push(word);
                     }
                 }
-                optional = { "chunks": new_chunks };
+                optional = { chunks: new_chunks };
             } else {
-                optional = { "chunks": chunks };
+                optional = { chunks: chunks };
             }
         }
         return [full_text, optional];
-
     }
 
     /**
@@ -3912,7 +3826,8 @@ export class WhisperTokenizer extends PreTrainedTokenizer {
         let leftLength = leftSequence.length;
         let totalSequence = [];
 
-        const use_token_timestamp_sequences = Array.isArray(token_timestamp_sequences) && token_timestamp_sequences.length > 0;
+        const use_token_timestamp_sequences =
+            Array.isArray(token_timestamp_sequences) && token_timestamp_sequences.length > 0;
         let total_token_timestamp_sequence = use_token_timestamp_sequences ? [] : null;
         let left_token_timestamp_sequence = use_token_timestamp_sequences ? token_timestamp_sequences[0] : null;
         for (let i = 1; i < sequences.length; ++i) {
@@ -3928,7 +3843,6 @@ export class WhisperTokenizer extends PreTrainedTokenizer {
             //       [c, d, f]
             // =     [c, d] == [c, d]
 
-
             // [a, b, c, d]
             //    [c, d, f]
 
@@ -3961,17 +3875,21 @@ export class WhisperTokenizer extends PreTrainedTokenizer {
                 const rightStop = Math.min(rightLength, j);
                 const right = rightSequence.slice(rightStart, rightStop);
                 if (left.length !== right.length) {
-                    throw new Error("There is a bug within whisper `decode_asr` function, please report it. Dropping to prevent bad inference.");
+                    throw new Error(
+                        'There is a bug within whisper `decode_asr` function, please report it. Dropping to prevent bad inference.',
+                    );
                 }
 
                 let matches;
                 if (use_token_timestamp_sequences) {
                     // Get length of longest subsequence of tokens that match
                     // and have timestamps that are in order
-                    matches = left.filter((elem, idx) => (
-                        elem === right[idx]
-                        && left_token_timestamp_sequence[leftStart + idx] <= token_timestamp_sequences[i][rightStart + idx]
-                    )).length;
+                    matches = left.filter(
+                        (elem, idx) =>
+                            elem === right[idx] &&
+                            left_token_timestamp_sequence[leftStart + idx] <=
+                                token_timestamp_sequences[i][rightStart + idx],
+                    ).length;
                 } else {
                     matches = left.filter((elem, idx) => elem === right[idx]).length;
                 }
@@ -4008,7 +3926,6 @@ export class WhisperTokenizer extends PreTrainedTokenizer {
 
     /** @private */
     collateWordTimestamps(tokens, token_timestamps, language) {
-
         const [words, _, token_indices] = this.combineTokensIntoWords(tokens, language);
 
         const timings = [];
@@ -4016,10 +3933,7 @@ export class WhisperTokenizer extends PreTrainedTokenizer {
             const indices = token_indices[i];
             timings.push({
                 text: words[i],
-                timestamp: [
-                    token_timestamps[indices.at(0)][0],
-                    token_timestamps[indices.at(-1)][1],
-                ],
+                timestamp: [token_timestamps[indices.at(0)][0], token_timestamps[indices.at(-1)][1]],
             });
         }
         return timings;
@@ -4028,33 +3942,35 @@ export class WhisperTokenizer extends PreTrainedTokenizer {
     /**
      * Groups tokens by word. Returns a tuple containing a list of strings with the words,
      * and a list of `token_id` sequences with the tokens making up each word.
-     * @param {number[]} tokens 
-     * @param {string} [language] 
-     * @param {string} prepend_punctionations 
-     * @param {string} append_punctuations 
-     * 
+     * @param {number[]} tokens
+     * @param {string} [language]
+     * @param {string} prepend_punctionations
+     * @param {string} append_punctuations
+     *
      * @private
      */
-    combineTokensIntoWords(tokens, language, prepend_punctionations = "\"'“¡¿([{-", append_punctuations = "\"'.。,，!！?？:：”)]}、") {
+    combineTokensIntoWords(
+        tokens,
+        language,
+        prepend_punctionations = '"\'“¡¿([{-',
+        append_punctuations = '"\'.。,，!！?？:：”)]}、',
+    ) {
         language = language ?? 'english';
 
         let words, word_tokens, token_indices;
 
-        if (["chinese", "japanese", "thai", "lao", "myanmar"].includes(language)) {
+        if (['chinese', 'japanese', 'thai', 'lao', 'myanmar'].includes(language)) {
             // These languages don't typically use spaces.
-            [words, word_tokens, token_indices] = this.splitTokensOnUnicode(tokens)
+            [words, word_tokens, token_indices] = this.splitTokensOnUnicode(tokens);
         } else {
-            [words, word_tokens, token_indices] = this.splitTokensOnSpaces(tokens)
+            [words, word_tokens, token_indices] = this.splitTokensOnSpaces(tokens);
         }
 
         return this.mergePunctuations(words, word_tokens, token_indices, prepend_punctionations, append_punctuations);
     }
 
     /** @type {PreTrainedTokenizer['decode']} */
-    decode(
-        token_ids,
-        decode_args,
-    ) {
+    decode(token_ids, decode_args) {
         let text;
         // @ts-ignore
         if (decode_args?.decode_with_timestamps) {
@@ -4093,16 +4009,14 @@ export class WhisperTokenizer extends PreTrainedTokenizer {
                 outputs[outputs.length - 1].push(token);
             }
         }
-        outputs = outputs.map(
-            s => typeof s === 'string' ? s : super.decode(s, decode_args)
-        )
+        outputs = outputs.map((s) => (typeof s === 'string' ? s : super.decode(s, decode_args)));
 
         return outputs.join('');
     }
 
     /**
      * Combine tokens into words by splitting at any position where the tokens are decoded as valid unicode points.
-     * @param {number[]} tokens 
+     * @param {number[]} tokens
      * @returns {*}
      * @private
      */
@@ -4113,12 +4027,12 @@ export class WhisperTokenizer extends PreTrainedTokenizer {
         });
         const replacement_char = '\uFFFD';
 
-        const words = []
-        const word_tokens = []
-        const token_indices = []
-        let current_tokens = []
-        let current_indices = []
-        let unicode_offset = 0
+        const words = [];
+        const word_tokens = [];
+        const token_indices = [];
+        let current_tokens = [];
+        let current_indices = [];
+        let unicode_offset = 0;
 
         for (let token_idx = 0; token_idx < tokens.length; ++token_idx) {
             const token = tokens[token_idx];
@@ -4131,37 +4045,37 @@ export class WhisperTokenizer extends PreTrainedTokenizer {
                 decode_with_timestamps: true,
             });
 
-            if (!decoded.includes(replacement_char) || decoded_full[unicode_offset + decoded.indexOf(replacement_char)] === replacement_char) {
-                words.push(decoded)
-                word_tokens.push(current_tokens)
-                token_indices.push(current_indices)
-                current_tokens = []
-                current_indices = []
+            if (
+                !decoded.includes(replacement_char) ||
+                decoded_full[unicode_offset + decoded.indexOf(replacement_char)] === replacement_char
+            ) {
+                words.push(decoded);
+                word_tokens.push(current_tokens);
+                token_indices.push(current_indices);
+                current_tokens = [];
+                current_indices = [];
                 unicode_offset += decoded.length;
             }
-
         }
 
-        return [words, word_tokens, token_indices]
+        return [words, word_tokens, token_indices];
     }
 
     /**
      * Combine tokens into words by splitting at whitespace and punctuation tokens.
-     * @param {number[]} tokens 
+     * @param {number[]} tokens
      * @private
      */
     splitTokensOnSpaces(tokens) {
-
         const [subwords, subword_tokens_list, subword_indices_list] = this.splitTokensOnUnicode(tokens);
 
-        const words = []
-        const word_tokens = []
-        const token_indices = []
+        const words = [];
+        const word_tokens = [];
+        const token_indices = [];
 
         const punctuationRegex = new RegExp(`^[${PUNCTUATION_REGEX}]$`, 'gu');
 
         for (let i = 0; i < subwords.length; ++i) {
-
             const subword = subwords[i];
             const subword_tokens = subword_tokens_list[i];
             const subword_indices = subword_indices_list[i];
@@ -4185,25 +4099,22 @@ export class WhisperTokenizer extends PreTrainedTokenizer {
         }
 
         return [words, word_tokens, token_indices];
-
     }
 
     /**
      * Merges punctuation tokens with neighboring words.
-     * @param {string[]} words 
-     * @param {number[][]} tokens 
-     * @param {number[][]} indices 
-     * @param {string} prepended 
-     * @param {string} appended 
+     * @param {string[]} words
+     * @param {number[][]} tokens
+     * @param {number[][]} indices
+     * @param {string} prepended
+     * @param {string} appended
      * @private
      */
     mergePunctuations(words, tokens, indices, prepended, appended) {
-
         const newWords = structuredClone(words);
         const newTokens = structuredClone(tokens);
         const newIndices = structuredClone(indices);
 
-
         // prepend punctuations
         let i = newWords.length - 2;
         let j = newWords.length - 1;
@@ -4240,15 +4151,15 @@ export class WhisperTokenizer extends PreTrainedTokenizer {
         }
 
         return [
-            newWords.filter(x => x),
-            newTokens.filter(x => x.length > 0),
-            newIndices.filter(x => x.length > 0),
-        ]
+            newWords.filter((x) => x),
+            newTokens.filter((x) => x.length > 0),
+            newIndices.filter((x) => x.length > 0),
+        ];
     }
 }
-export class CodeGenTokenizer extends PreTrainedTokenizer { }
-export class CLIPTokenizer extends PreTrainedTokenizer { }
-export class SiglipTokenizer extends PreTrainedTokenizer { }
+export class CodeGenTokenizer extends PreTrainedTokenizer {}
+export class CLIPTokenizer extends PreTrainedTokenizer {}
+export class SiglipTokenizer extends PreTrainedTokenizer {}
 
 /**
  * @todo This model is not yet supported by Hugging Face's "fast" tokenizers library (https://github.com/huggingface/tokenizers).
@@ -4265,11 +4176,11 @@ export class MarianTokenizer extends PreTrainedTokenizer {
 
         this.languageRegex = /^(>>\w+<<)\s*/g;
 
-        this.supported_language_codes = this.model.vocab.filter(
-            x => this.languageRegex.test(x)
-        );
+        this.supported_language_codes = this.model.vocab.filter((x) => this.languageRegex.test(x));
 
-        console.warn('WARNING: `MarianTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')
+        console.warn(
+            'WARNING: `MarianTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.',
+        );
     }
 
     /**
@@ -4289,31 +4200,30 @@ export class MarianTokenizer extends PreTrainedTokenizer {
         if (remainder.length === 0) {
             // No language code, encode normally
             return super._encode_text(matchInfo);
-
         } else if (remainder.length === 2) {
             // Text starts with language code, so we do not encode it with sentencepiece.
             const [language, text] = remainder;
 
             if (!this.supported_language_codes.includes(language)) {
-                console.warn(`Unsupported language code "${language}" detected, which may lead to unexpected behavior. Should be one of: ${JSON.stringify(this.supported_language_codes)}`)
+                console.warn(
+                    `Unsupported language code "${language}" detected, which may lead to unexpected behavior. Should be one of: ${JSON.stringify(this.supported_language_codes)}`,
+                );
             }
             return mergeArrays([language], super._encode_text(text));
         }
     }
-
 }
 
-export class Wav2Vec2CTCTokenizer extends PreTrainedTokenizer { }
+export class Wav2Vec2CTCTokenizer extends PreTrainedTokenizer {}
 
-export class BlenderbotTokenizer extends PreTrainedTokenizer { }
-export class BlenderbotSmallTokenizer extends PreTrainedTokenizer { }
+export class BlenderbotTokenizer extends PreTrainedTokenizer {}
+export class BlenderbotSmallTokenizer extends PreTrainedTokenizer {}
 
-export class SpeechT5Tokenizer extends PreTrainedTokenizer { }
+export class SpeechT5Tokenizer extends PreTrainedTokenizer {}
 
-export class NougatTokenizer extends PreTrainedTokenizer { }
+export class NougatTokenizer extends PreTrainedTokenizer {}
 
 export class VitsTokenizer extends PreTrainedTokenizer {
-
     constructor(tokenizerJSON, tokenizerConfig) {
         super(tokenizerJSON, tokenizerConfig);
 
@@ -4322,16 +4232,16 @@ export class VitsTokenizer extends PreTrainedTokenizer {
     }
 }
 
-export class CohereTokenizer extends PreTrainedTokenizer { }
+export class CohereTokenizer extends PreTrainedTokenizer {}
 
-export class MgpstrTokenizer extends PreTrainedTokenizer { }
+export class MgpstrTokenizer extends PreTrainedTokenizer {}
 
-export class Ernie4_5_Tokenizer extends PreTrainedTokenizer { }
+export class Ernie4_5_Tokenizer extends PreTrainedTokenizer {}
 
 /**
  * Helper class which is used to instantiate pretrained tokenizers with the `from_pretrained` function.
  * The chosen tokenizer class is determined by the type specified in the tokenizer config.
- * 
+ *
  * @example
  * const tokenizer = await AutoTokenizer.from_pretrained('Xenova/bert-base-uncased');
  */
@@ -4386,33 +4296,34 @@ export class AutoTokenizer {
 
         // Base case:
         PreTrainedTokenizer,
-    }
-
+    };
 
     /**
      * Instantiate one of the tokenizer classes of the library from a pretrained model.
-     * 
+     *
      * The tokenizer class to instantiate is selected based on the `tokenizer_class` property of the config object
      * (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible)
-     * 
+     *
      * @param {string} pretrained_model_name_or_path The name or path of the pretrained model. Can be either:
      * - A string, the *model id* of a pretrained tokenizer hosted inside a model repo on huggingface.co.
      *   Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a
      *   user or organization name, like `dbmdz/bert-base-german-cased`.
      * - A path to a *directory* containing tokenizer files, e.g., `./my_model_directory/`.
      * @param {PretrainedTokenizerOptions} options Additional options for loading the tokenizer.
-     * 
+     *
      * @returns {Promise} A new instance of the PreTrainedTokenizer class.
      */
-    static async from_pretrained(pretrained_model_name_or_path, {
-        progress_callback = null,
-        config = null,
-        cache_dir = null,
-        local_files_only = false,
-        revision = 'main',
-        legacy = null,
-    } = {}) {
-
+    static async from_pretrained(
+        pretrained_model_name_or_path,
+        {
+            progress_callback = null,
+            config = null,
+            cache_dir = null,
+            local_files_only = false,
+            revision = 'main',
+            legacy = null,
+        } = {},
+    ) {
         const [tokenizerJSON, tokenizerConfig] = await loadTokenizer(pretrained_model_name_or_path, {
             progress_callback,
             config,
@@ -4420,7 +4331,7 @@ export class AutoTokenizer {
             local_files_only,
             revision,
             legacy,
-        })
+        });
 
         // Some tokenizers are saved with the "Fast" suffix, so we remove that if present.
         const tokenizerName = tokenizerConfig.tokenizer_class?.replace(/Fast$/, '') ?? 'PreTrainedTokenizer';
diff --git a/src/transformers.js b/src/transformers.js
index 779e662aa..2212cb251 100644
--- a/src/transformers.js
+++ b/src/transformers.js
@@ -1,13 +1,13 @@
 /**
  * @file Entry point for the Transformers.js library. Only the exports from this file
  * are available to the end user, and are grouped as follows:
- * 
+ *
  * 1. [Pipelines](./pipelines)
  * 2. [Environment variables](./env)
  * 3. [Models](./models)
  * 4. [Tokenizers](./tokenizers)
  * 5. [Processors](./processors)
- * 
+ *
  * @module transformers
  */
 
@@ -24,7 +24,6 @@ export * from './utils/video.js';
 export * from './utils/tensor.js';
 export * from './utils/maths.js';
 
-
 export { FeatureExtractor } from './base/feature_extraction_utils.js';
 export * from './models/feature_extractors.js';
 export * from './models/auto/feature_extraction_auto.js';
diff --git a/src/utils/audio.js b/src/utils/audio.js
index 548e2b0d8..5163bc875 100644
--- a/src/utils/audio.js
+++ b/src/utils/audio.js
@@ -1,19 +1,15 @@
 /**
- * @file Helper module for audio processing. 
- * 
- * These functions and classes are only used internally, 
+ * @file Helper module for audio processing.
+ *
+ * These functions and classes are only used internally,
  * meaning an end-user shouldn't need to access anything here.
- * 
+ *
  * @module utils/audio
  */
 
-import {
-    getFile,
-} from './hub.js';
+import { getFile } from './hub.js';
 import { FFT, max } from './maths.js';
-import {
-    calculateReflectOffset, saveBlob,
-} from './core.js';
+import { calculateReflectOffset, saveBlob } from './core.js';
 import { apis } from '../env.js';
 import { Tensor, matmul } from './tensor.js';
 import fs from 'node:fs';
@@ -28,16 +24,16 @@ export async function read_audio(url, sampling_rate) {
     if (typeof AudioContext === 'undefined') {
         // Running in node or an environment without 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."
-        )
+            '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 response = await (await getFile(url)).arrayBuffer();
     const audioCTX = new AudioContext({ sampleRate: sampling_rate });
     if (typeof sampling_rate === 'undefined') {
-        console.warn(`No sampling rate provided, using default of ${audioCTX.sampleRate}Hz.`)
+        console.warn(`No sampling rate provided, using default of ${audioCTX.sampleRate}Hz.`);
     }
     const decoded = await audioCTX.decodeAudioData(response);
 
@@ -68,9 +64,8 @@ export async function read_audio(url, sampling_rate) {
 
         audio = new Float32Array(left.length);
         for (let i = 0; i < decoded.length; ++i) {
-            audio[i] = SCALING_FACTOR * (left[i] + right[i]) / 2;
+            audio[i] = (SCALING_FACTOR * (left[i] + right[i])) / 2;
         }
-
     } else {
         // If the audio is not stereo, we can just use the first channel:
         audio = decoded.getChannelData(0);
@@ -95,7 +90,7 @@ function generalized_cosine_window(M, a_0) {
     }
 
     const a_1 = 1 - a_0;
-    const factor = 2 * Math.PI / (M - 1);
+    const factor = (2 * Math.PI) / (M - 1);
 
     const cos_vals = new Float64Array(M);
     for (let i = 0; i < M; ++i) {
@@ -115,7 +110,6 @@ export function hanning(M) {
     return generalized_cosine_window(M, 0.5);
 }
 
-
 /**
  * Generates a Hamming window of length M.
  * See https://numpy.org/doc/stable/reference/generated/numpy.hamming.html for more information.
@@ -127,74 +121,73 @@ export function hamming(M) {
     return generalized_cosine_window(M, 0.54);
 }
 
-
 const HERTZ_TO_MEL_MAPPING = {
-    "htk": (/** @type {number} */ freq) => 2595.0 * Math.log10(1.0 + (freq / 700.0)),
-    "kaldi": (/** @type {number} */ freq) => 1127.0 * Math.log(1.0 + (freq / 700.0)),
-    "slaney": (/** @type {number} */ freq, min_log_hertz = 1000.0, min_log_mel = 15.0, logstep = 27.0 / Math.log(6.4)) =>
-        freq >= min_log_hertz
-            ? min_log_mel + Math.log(freq / min_log_hertz) * logstep
-            : 3.0 * freq / 200.0,
-}
+    htk: (/** @type {number} */ freq) => 2595.0 * Math.log10(1.0 + freq / 700.0),
+    kaldi: (/** @type {number} */ freq) => 1127.0 * Math.log(1.0 + freq / 700.0),
+    slaney: (/** @type {number} */ freq, min_log_hertz = 1000.0, min_log_mel = 15.0, logstep = 27.0 / Math.log(6.4)) =>
+        freq >= min_log_hertz ? min_log_mel + Math.log(freq / min_log_hertz) * logstep : (3.0 * freq) / 200.0,
+};
 
 /**
- * @template {Float32Array|Float64Array|number} T 
- * @param {T} freq 
+ * @template {Float32Array|Float64Array|number} T
+ * @param {T} freq
  * @param {string} [mel_scale]
  * @returns {T}
  */
-function hertz_to_mel(freq, mel_scale = "htk") {
+function hertz_to_mel(freq, mel_scale = 'htk') {
     const fn = HERTZ_TO_MEL_MAPPING[mel_scale];
     if (!fn) {
         throw new Error('mel_scale should be one of "htk", "slaney" or "kaldi".');
     }
 
     // @ts-expect-error ts(2322)
-    return typeof freq === 'number' ? fn(freq) : freq.map(x => fn(x));
+    return typeof freq === 'number' ? fn(freq) : freq.map((x) => fn(x));
 }
 
 const MEL_TO_HERTZ_MAPPING = {
-    "htk": (/** @type {number} */ mels) => 700.0 * (10.0 ** (mels / 2595.0) - 1.0),
-    "kaldi": (/** @type {number} */ mels) => 700.0 * (Math.exp(mels / 1127.0) - 1.0),
-    "slaney": (/** @type {number} */ mels, min_log_hertz = 1000.0, min_log_mel = 15.0, logstep = Math.log(6.4) / 27.0) => mels >= min_log_mel
-        ? min_log_hertz * Math.exp(logstep * (mels - min_log_mel))
-        : 200.0 * mels / 3.0,
-}
+    htk: (/** @type {number} */ mels) => 700.0 * (10.0 ** (mels / 2595.0) - 1.0),
+    kaldi: (/** @type {number} */ mels) => 700.0 * (Math.exp(mels / 1127.0) - 1.0),
+    slaney: (/** @type {number} */ mels, min_log_hertz = 1000.0, min_log_mel = 15.0, logstep = Math.log(6.4) / 27.0) =>
+        mels >= min_log_mel ? min_log_hertz * Math.exp(logstep * (mels - min_log_mel)) : (200.0 * mels) / 3.0,
+};
 
 /**
- * @template {Float32Array|Float64Array|number} T 
- * @param {T} mels 
+ * @template {Float32Array|Float64Array|number} T
+ * @param {T} mels
  * @param {string} [mel_scale]
  * @returns {T}
  */
-function mel_to_hertz(mels, mel_scale = "htk") {
+function mel_to_hertz(mels, mel_scale = 'htk') {
     const fn = MEL_TO_HERTZ_MAPPING[mel_scale];
     if (!fn) {
         throw new Error('mel_scale should be one of "htk", "slaney" or "kaldi".');
     }
 
     // @ts-expect-error ts(2322)
-    return typeof mels === 'number' ? fn(mels) : mels.map(x => fn(x));
+    return typeof mels === 'number' ? fn(mels) : mels.map((x) => fn(x));
 }
 
 /**
-* Creates a triangular filter bank.
-*
-* Adapted from torchaudio and librosa.
-*
-* @param {Float64Array} fft_freqs Discrete frequencies of the FFT bins in Hz, of shape `(num_frequency_bins,)`.
-* @param {Float64Array} filter_freqs Center frequencies of the triangular filters to create, in Hz, of shape `(num_mel_filters,)`.
-* @returns {number[][]} of shape `(num_frequency_bins, num_mel_filters)`.
-*/
+ * Creates a triangular filter bank.
+ *
+ * Adapted from torchaudio and librosa.
+ *
+ * @param {Float64Array} fft_freqs Discrete frequencies of the FFT bins in Hz, of shape `(num_frequency_bins,)`.
+ * @param {Float64Array} filter_freqs Center frequencies of the triangular filters to create, in Hz, of shape `(num_mel_filters,)`.
+ * @returns {number[][]} of shape `(num_frequency_bins, num_mel_filters)`.
+ */
 function _create_triangular_filter_bank(fft_freqs, filter_freqs) {
     const filter_diff = Float64Array.from(
         { length: filter_freqs.length - 1 },
-        (_, i) => filter_freqs[i + 1] - filter_freqs[i]
+        (_, i) => filter_freqs[i + 1] - filter_freqs[i],
     );
 
-    const slopes = Array.from({
-        length: fft_freqs.length
-    }, () => new Array(filter_freqs.length));
+    const slopes = Array.from(
+        {
+            length: fft_freqs.length,
+        },
+        () => new Array(filter_freqs.length),
+    );
 
     for (let j = 0; j < fft_freqs.length; ++j) {
         const slope = slopes[j];
@@ -206,9 +199,11 @@ function _create_triangular_filter_bank(fft_freqs, filter_freqs) {
     const numFreqs = filter_freqs.length - 2;
     const ret = Array.from({ length: numFreqs }, () => new Array(fft_freqs.length));
 
-    for (let j = 0; j < fft_freqs.length; ++j) { // 201
+    for (let j = 0; j < fft_freqs.length; ++j) {
+        // 201
         const slope = slopes[j];
-        for (let i = 0; i < numFreqs; ++i) { // 80
+        for (let i = 0; i < numFreqs; ++i) {
+            // 80
             const down = -slope[i] / filter_diff[i];
             const up = slope[i + 2] / filter_diff[i + 1];
             ret[i][j] = Math.max(0, Math.min(down, up));
@@ -254,10 +249,10 @@ export function mel_filter_bank(
     max_frequency,
     sampling_rate,
     norm = null,
-    mel_scale = "htk",
+    mel_scale = 'htk',
     triangularize_in_mel_space = false,
 ) {
-    if (norm !== null && norm !== "slaney") {
+    if (norm !== null && norm !== 'slaney') {
         throw new Error('norm must be one of null or "slaney"');
     }
 
@@ -278,7 +273,10 @@ export function mel_filter_bank(
 
     if (triangularize_in_mel_space) {
         const fft_bin_width = sampling_rate / ((num_frequency_bins - 1) * 2);
-        fft_freqs = hertz_to_mel(Float64Array.from({ length: num_frequency_bins }, (_, i) => i * fft_bin_width), mel_scale);
+        fft_freqs = hertz_to_mel(
+            Float64Array.from({ length: num_frequency_bins }, (_, i) => i * fft_bin_width),
+            mel_scale,
+        );
         filter_freqs = mel_freqs;
     } else {
         fft_freqs = linspace(0, Math.floor(sampling_rate / 2), num_frequency_bins);
@@ -286,7 +284,7 @@ export function mel_filter_bank(
 
     const mel_filters = _create_triangular_filter_bank(fft_freqs, filter_freqs);
 
-    if (norm !== null && norm === "slaney") {
+    if (norm !== null && norm === 'slaney') {
         // Slaney-style mel is scaled to be approx constant energy per channel
         for (let i = 0; i < num_mel_filters; ++i) {
             const filter = mel_filters[i];
@@ -301,7 +299,6 @@ export function mel_filter_bank(
     // TODO warn if there is a zero row
 
     return mel_filters;
-
 }
 
 /**
@@ -335,11 +332,11 @@ function padReflect(array, left, right) {
 /**
  * Helper function to compute `amplitude_to_db` and `power_to_db`.
  * @template {Float32Array|Float64Array} T
- * @param {T} spectrogram 
- * @param {number} factor 
- * @param {number} reference 
- * @param {number} min_value 
- * @param {number} db_range 
+ * @param {T} spectrogram
+ * @param {number} factor
+ * @param {number} reference
+ * @param {number} min_value
+ * @param {number} db_range
  * @returns {T}
  */
 function _db_conversion_helper(spectrogram, factor, reference, min_value, db_range) {
@@ -355,7 +352,7 @@ function _db_conversion_helper(spectrogram, factor, reference, min_value, db_ran
 
     const logReference = Math.log10(reference);
     for (let i = 0; i < spectrogram.length; ++i) {
-        spectrogram[i] = factor * Math.log10(Math.max(min_value, spectrogram[i]) - logReference)
+        spectrogram[i] = factor * Math.log10(Math.max(min_value, spectrogram[i]) - logReference);
     }
 
     if (db_range !== null) {
@@ -374,12 +371,12 @@ function _db_conversion_helper(spectrogram, factor, reference, min_value, db_ran
 /**
  * Converts an amplitude spectrogram to the decibel scale. This computes `20 * log10(spectrogram / reference)`,
  * using basic logarithm properties for numerical stability. NOTE: Operates in-place.
- * 
+ *
  * The motivation behind applying the log function on the (mel) spectrogram is that humans do not hear loudness on a
  * linear scale. Generally to double the perceived volume of a sound we need to put 8 times as much energy into it.
  * This means that large variations in energy may not sound all that different if the sound is loud to begin with.
  * This compression operation makes the (mel) spectrogram features match more closely what humans actually hear.
- * 
+ *
  * @template {Float32Array|Float64Array} T
  * @param {T} spectrogram The input amplitude (mel) spectrogram.
  * @param {number} [reference=1.0] Sets the input spectrogram value that corresponds to 0 dB.
@@ -397,14 +394,14 @@ function amplitude_to_db(spectrogram, reference = 1.0, min_value = 1e-5, db_rang
 /**
  * Converts a power spectrogram to the decibel scale. This computes `10 * log10(spectrogram / reference)`,
  * using basic logarithm properties for numerical stability. NOTE: Operates in-place.
- * 
+ *
  * The motivation behind applying the log function on the (mel) spectrogram is that humans do not hear loudness on a
  * linear scale. Generally to double the perceived volume of a sound we need to put 8 times as much energy into it.
  * This means that large variations in energy may not sound all that different if the sound is loud to begin with.
  * This compression operation makes the (mel) spectrogram features match more closely what humans actually hear.
- * 
+ *
  * Based on the implementation of `librosa.power_to_db`.
- * 
+ *
  * @template {Float32Array|Float64Array} T
  * @param {T} spectrogram The input power (mel) spectrogram. Note that a power spectrogram has the amplitudes squared!
  * @param {number} [reference=1.0] Sets the input spectrogram value that corresponds to 0 dB.
@@ -421,7 +418,7 @@ function power_to_db(spectrogram, reference = 1.0, min_value = 1e-10, db_range =
 
 /**
  * Calculates a spectrogram over one waveform using the Short-Time Fourier Transform.
- * 
+ *
  * This function can create the following kinds of spectrograms:
  *   - amplitude spectrogram (`power = 1.0`)
  *   - power spectrogram (`power = 2.0`)
@@ -431,9 +428,9 @@ function power_to_db(spectrogram, reference = 1.0, min_value = 1e-10, db_range =
  *   - log-mel spectrogram (provide `mel_filters` and `log_mel`)
  *
  * In this implementation, the window is assumed to be zero-padded to have the same size as the analysis frame.
- * A padded window can be obtained from `window_function()`. The FFT input buffer may be larger than the analysis frame, 
+ * A padded window can be obtained from `window_function()`. The FFT input buffer may be larger than the analysis frame,
  * typically the next power of two.
- * 
+ *
  * @param {Float32Array|Float64Array} waveform The input waveform of shape `(length,)`. This must be a single real-valued, mono waveform.
  * @param {Float32Array|Float64Array} window The windowing function to apply of shape `(frame_length,)`, including zero-padding if necessary. The actual window length may be
  * shorter than `frame_length`, but we're assuming the array has already been zero-padded.
@@ -481,7 +478,7 @@ export async function spectrogram(
         fft_length = null,
         power = 1.0,
         center = true,
-        pad_mode = "reflect",
+        pad_mode = 'reflect',
         onesided = true,
         preemphasis = null,
         preemphasis_htk_flavor = true,
@@ -498,14 +495,14 @@ export async function spectrogram(
         max_num_frames = null,
         do_pad = true,
         transpose = false,
-    } = {}
+    } = {},
 ) {
     const window_length = window.length;
     if (fft_length === null) {
         fft_length = frame_length;
     }
     if (frame_length > fft_length) {
-        throw Error(`frame_length (${frame_length}) may not be larger than fft_length (${fft_length})`)
+        throw Error(`frame_length (${frame_length}) may not be larger than fft_length (${fft_length})`);
     }
 
     if (window_length !== frame_length) {
@@ -513,47 +510,47 @@ export async function spectrogram(
     }
 
     if (hop_length <= 0) {
-        throw new Error("hop_length must be greater than zero");
+        throw new Error('hop_length must be greater than zero');
     }
 
     if (power === null && mel_filters !== null) {
         throw new Error(
-            "You have provided `mel_filters` but `power` is `None`. Mel spectrogram computation is not yet supported for complex-valued spectrogram. " +
-            "Specify `power` to fix this issue."
+            'You have provided `mel_filters` but `power` is `None`. Mel spectrogram computation is not yet supported for complex-valued spectrogram. ' +
+                'Specify `power` to fix this issue.',
         );
     }
 
     if (!preemphasis_htk_flavor) {
-        throw new Error(
-            "`preemphasis_htk_flavor=false` is not currently supported."
-        );
+        throw new Error('`preemphasis_htk_flavor=false` is not currently supported.');
     }
 
     if (center) {
         if (pad_mode !== 'reflect') {
-            throw new Error(`pad_mode="${pad_mode}" not implemented yet.`)
+            throw new Error(`pad_mode="${pad_mode}" not implemented yet.`);
         }
         const half_window = Math.floor((fft_length - 1) / 2) + 1;
         waveform = padReflect(waveform, half_window, half_window);
     }
 
     // split waveform into frames of frame_length size
-    let num_frames = Math.floor(1 + Math.floor((waveform.length - frame_length) / hop_length))
+    let num_frames = Math.floor(1 + Math.floor((waveform.length - frame_length) / hop_length));
     if (min_num_frames !== null && num_frames < min_num_frames) {
-        num_frames = min_num_frames
+        num_frames = min_num_frames;
     }
-    const num_frequency_bins = onesided ? Math.floor(fft_length / 2) + 1 : fft_length
+    const num_frequency_bins = onesided ? Math.floor(fft_length / 2) + 1 : fft_length;
 
     let d1 = num_frames;
     let d1Max = num_frames;
 
     // If maximum number of frames is provided, we must either pad or truncate
     if (max_num_frames !== null) {
-        if (max_num_frames > num_frames) { // input is too short, so we pad
+        if (max_num_frames > num_frames) {
+            // input is too short, so we pad
             if (do_pad) {
                 d1Max = max_num_frames;
             }
-        } else { // input is too long, so we truncate
+        } else {
+            // input is too long, so we truncate
             d1Max = d1 = max_num_frames;
         }
     }
@@ -639,7 +636,7 @@ export async function spectrogram(
         mel_spec = mel_spec.transpose(1, 0);
     }
 
-    const mel_spec_data = /** @type {Float32Array} */(mel_spec.data);
+    const mel_spec_data = /** @type {Float32Array} */ (mel_spec.data);
     for (let i = 0; i < mel_spec_data.length; ++i) {
         mel_spec_data[i] = Math.max(mel_floor, mel_spec_data[i]);
     }
@@ -664,7 +661,7 @@ export async function spectrogram(
                 } else if (power === 2.0) {
                     power_to_db(mel_spec_data, reference, min_value, db_range);
                 } else {
-                    throw new Error(`Cannot use log_mel option '${log_mel}' with power ${power}`)
+                    throw new Error(`Cannot use log_mel option '${log_mel}' with power ${power}`);
                 }
                 break;
             default:
@@ -686,11 +683,7 @@ export async function spectrogram(
  * @param {boolean} [options.center=true] Whether to center the window inside the FFT buffer. Only used when `frame_length` is provided.
  * @returns {Float64Array} The window of shape `(window_length,)` or `(frame_length,)`.
  */
-export function window_function(window_length, name, {
-    periodic = true,
-    frame_length = null,
-    center = true,
-} = {}) {
+export function window_function(window_length, name, { periodic = true, frame_length = null, center = true } = {}) {
     const length = periodic ? window_length + 1 : window_length;
     let window;
     switch (name) {
@@ -705,7 +698,7 @@ export function window_function(window_length, name, {
             window = hamming(length);
             break;
         case 'povey':
-            window = hanning(length).map(x => Math.pow(x, 0.85));
+            window = hanning(length).map((x) => Math.pow(x, 0.85));
             break;
         default:
             throw new Error(`Unknown window type ${name}.`);
@@ -717,7 +710,9 @@ export function window_function(window_length, name, {
         return window;
     }
     if (window_length > frame_length) {
-        throw new Error(`Length of the window (${window_length}) may not be larger than frame_length (${frame_length})`);
+        throw new Error(
+            `Length of the window (${window_length}) may not be larger than frame_length (${frame_length})`,
+        );
     }
 
     return window;
@@ -726,7 +721,7 @@ export function window_function(window_length, name, {
 /**
  * Encode audio data to a WAV file.
  * WAV file specs : https://en.wikipedia.org/wiki/WAV#WAV_File_header
- * 
+ *
  * Adapted from https://www.npmjs.com/package/audiobuffer-to-wav
  * @param {Float32Array} samples The audio samples.
  * @param {number} rate The sample rate.
@@ -738,13 +733,13 @@ function encodeWAV(samples, rate) {
     const view = new DataView(buffer);
 
     /* RIFF identifier */
-    writeString(view, 0, "RIFF");
+    writeString(view, 0, 'RIFF');
     /* RIFF chunk length */
     view.setUint32(4, 36 + samples.length * 4, true);
     /* RIFF type */
-    writeString(view, 8, "WAVE");
+    writeString(view, 8, 'WAVE');
     /* format chunk identifier */
-    writeString(view, 12, "fmt ");
+    writeString(view, 12, 'fmt ');
     /* format chunk length */
     view.setUint32(16, 16, true);
     /* sample format (raw) */
@@ -760,7 +755,7 @@ function encodeWAV(samples, rate) {
     /* bits per sample */
     view.setUint16(34, 32, true);
     /* data chunk identifier */
-    writeString(view, 36, "data");
+    writeString(view, 36, 'data');
     /* data chunk length */
     view.setUint32(40, samples.length * 4, true);
 
@@ -777,17 +772,15 @@ function writeString(view, offset, string) {
     }
 }
 
-
 export class RawAudio {
-
     /**
      * Create a new `RawAudio` object.
      * @param {Float32Array} audio Audio data
      * @param {number} sampling_rate Sampling rate of the audio data
      */
     constructor(audio, sampling_rate) {
-        this.audio = audio
-        this.sampling_rate = sampling_rate
+        this.audio = audio;
+        this.sampling_rate = sampling_rate;
     }
 
     /**
@@ -795,7 +788,7 @@ export class RawAudio {
      * @returns {ArrayBuffer} The WAV file.
      */
     toWav() {
-        return encodeWAV(this.audio, this.sampling_rate)
+        return encodeWAV(this.audio, this.sampling_rate);
     }
 
     /**
@@ -817,18 +810,18 @@ export class RawAudio {
 
         if (apis.IS_BROWSER_ENV) {
             if (apis.IS_WEBWORKER_ENV) {
-                throw new Error('Unable to save a file from a Web Worker.')
+                throw new Error('Unable to save a file from a Web Worker.');
             }
             fn = saveBlob;
         } else if (apis.IS_FS_AVAILABLE) {
             fn = async (/** @type {string} */ path, /** @type {Blob} */ blob) => {
                 let buffer = await blob.arrayBuffer();
                 fs.writeFileSync(path, Buffer.from(buffer));
-            }
+            };
         } else {
-            throw new Error('Unable to save because filesystem is disabled in this environment.')
+            throw new Error('Unable to save because filesystem is disabled in this environment.');
         }
 
-        await fn(path, this.toBlob())
+        await fn(path, this.toBlob());
     }
 }
diff --git a/src/utils/constants.js b/src/utils/constants.js
index 0fb1a2247..2acf16c35 100644
--- a/src/utils/constants.js
+++ b/src/utils/constants.js
@@ -1,9 +1,8 @@
-
 export const GITHUB_ISSUE_URL = 'https://github.com/huggingface/transformers.js/issues/new/choose';
 
-export const CONFIG_NAME = "config.json"
-export const FEATURE_EXTRACTOR_NAME = "preprocessor_config.json"
-export const IMAGE_PROCESSOR_NAME = FEATURE_EXTRACTOR_NAME
-export const PROCESSOR_NAME = "processor_config.json"
-export const CHAT_TEMPLATE_NAME = "chat_template.jinja"
-export const GENERATION_CONFIG_NAME = "generation_config.json"
+export const CONFIG_NAME = 'config.json';
+export const FEATURE_EXTRACTOR_NAME = 'preprocessor_config.json';
+export const IMAGE_PROCESSOR_NAME = FEATURE_EXTRACTOR_NAME;
+export const PROCESSOR_NAME = 'processor_config.json';
+export const CHAT_TEMPLATE_NAME = 'chat_template.jinja';
+export const GENERATION_CONFIG_NAME = 'generation_config.json';
diff --git a/src/utils/core.js b/src/utils/core.js
index a74ee123c..3be87926f 100644
--- a/src/utils/core.js
+++ b/src/utils/core.js
@@ -1,4 +1,3 @@
-
 /**
  * @file Core utility functions/classes for Transformers.js.
  *
@@ -102,14 +101,13 @@ export function isTypedArray(val) {
     return val?.prototype?.__proto__?.constructor?.name === 'TypedArray';
 }
 
-
 /**
  * Check if a value is an integer.
  * @param {*} x The value to check.
  * @returns {boolean} True if the value is a string, false otherwise.
  */
 export function isIntegralNumber(x) {
-    return Number.isInteger(x) || typeof x === 'bigint'
+    return Number.isInteger(x) || typeof x === 'bigint';
 }
 
 /**
@@ -152,7 +150,7 @@ export function pop(obj, key, defaultValue = undefined) {
         return value;
     }
     if (defaultValue === undefined) {
-        throw Error(`Key ${key} does not exist in object.`)
+        throw Error(`Key ${key} does not exist in object.`);
     }
     return defaultValue;
 }
@@ -176,7 +174,7 @@ export function mergeArrays(...arrs) {
 export function product(...a) {
     // Cartesian product of items
     // Adapted from https://stackoverflow.com/a/43053803
-    return a.reduce((a, b) => a.flatMap(d => b.map(e => [d, e])));
+    return a.reduce((a, b) => a.flatMap((d) => b.map((e) => [d, e])));
 }
 
 /**
@@ -186,7 +184,7 @@ export function product(...a) {
  * @returns {number} The index offset.
  */
 export function calculateReflectOffset(i, w) {
-    return Math.abs((i + w) % (2 * w) - w);
+    return Math.abs(((i + w) % (2 * w)) - w);
 }
 
 /**
@@ -194,7 +192,7 @@ export function calculateReflectOffset(i, w) {
  * @param {string} path The path to save the blob to
  * @param {Blob} blob The blob to save
  */
-export function saveBlob(path, blob){
+export function saveBlob(path, blob) {
     // Convert the canvas content to a data URL
     const dataURL = URL.createObjectURL(blob);
 
@@ -228,7 +226,7 @@ export function pick(o, props) {
             if (o[prop] !== undefined) {
                 return { [prop]: o[prop] };
             }
-        })
+        }),
     );
 }
 
diff --git a/src/utils/data-structures.js b/src/utils/data-structures.js
index f9eb04913..98e238741 100644
--- a/src/utils/data-structures.js
+++ b/src/utils/data-structures.js
@@ -1,25 +1,22 @@
-
 /**
  * @file Custom data structures.
- * 
+ *
  * These are only used internally, meaning an end-user shouldn't
  * need to access anything here.
- * 
+ *
  * @module utils/data-structures
  */
 
-
 /**
  * Efficient Heap-based Implementation of a Priority Queue.
  * It uses an array-based binary heap, where the root is at index `0`, and the
  * children of node `i` are located at indices `2i + 1` and `2i + 2`, respectively.
- * 
+ *
  * Adapted from the following sources:
  * - https://stackoverflow.com/a/42919752/13989043 (original)
  * - https://github.com/belladoreai/llama-tokenizer-js (minor improvements)
  */
 export class PriorityQueue {
-
     /**
      * Create a new PriorityQueue.
      * @param {function(any, any): boolean} comparator Comparator function to determine priority. Defaults to a MaxHeap.
@@ -198,9 +195,10 @@ export class PriorityQueue {
             (this._left(node) < this.size && this._greater(this._left(node), node)) ||
             (this._right(node) < this.size && this._greater(this._right(node), node))
         ) {
-            const maxChild = (this._right(node) < this.size && this._greater(this._right(node), this._left(node)))
-                ? this._right(node)
-                : this._left(node);
+            const maxChild =
+                this._right(node) < this.size && this._greater(this._right(node), this._left(node))
+                    ? this._right(node)
+                    : this._left(node);
             this._swap(node, maxChild);
             node = maxChild;
         }
@@ -212,7 +210,7 @@ export class PriorityQueue {
      * @private
      */
     _smallest() {
-        return (2 ** (Math.floor(Math.log2(this.size))) - 1);
+        return 2 ** Math.floor(Math.log2(this.size)) - 1;
     }
 }
 
@@ -260,7 +258,7 @@ export class CharTrie {
         let node = this.root;
         if (node === undefined) return;
 
-        let prefix = "";
+        let prefix = '';
         for (const ch of text) {
             prefix += ch;
             node = node.children.get(ch);
@@ -404,7 +402,7 @@ export class TokenLattice {
      */
     tokens() {
         const nodes = this.viterbi();
-        return nodes.map(x => this.piece(x));
+        return nodes.map((x) => this.piece(x));
     }
 
     /**
@@ -412,7 +410,7 @@ export class TokenLattice {
      */
     tokenIds() {
         const nodes = this.viterbi();
-        return nodes.map(x => x.tokenId);
+        return nodes.map((x) => x.tokenId);
     }
 }
 class TokenLatticeNode {
@@ -449,7 +447,7 @@ class TokenLatticeNode {
 /**
  * A data structure which uses a trie to split a string into tokens based on a dictionary.
  * It can also use a regular expression to preprocess the input text before splitting.
- * 
+ *
  * NOTE: To ensure multi-byte characters are handled correctly, we operate at byte-level instead of character-level.
  */
 export class DictionarySplitter {
@@ -471,7 +469,7 @@ export class DictionarySplitter {
         for (const word of dictionary) {
             let node = trie;
             for (let i = 0; i < word.length; ++i) {
-                node = (node[word[i]] ??= Object.create(null));
+                node = node[word[i]] ??= Object.create(null);
             }
             node.end = word;
         }
@@ -521,10 +519,10 @@ export class DictionarySplitter {
 }
 
 /**
-* A simple Least Recently Used (LRU) cache implementation in JavaScript.
-* This cache stores key-value pairs and evicts the least recently used item
-* when the capacity is exceeded.
-*/
+ * A simple Least Recently Used (LRU) cache implementation in JavaScript.
+ * This cache stores key-value pairs and evicts the least recently used item
+ * when the capacity is exceeded.
+ */
 export class LRUCache {
     /**
      * Creates an LRUCache instance.
diff --git a/src/utils/devices.js b/src/utils/devices.js
index 1086b33e4..d66786b54 100644
--- a/src/utils/devices.js
+++ b/src/utils/devices.js
@@ -1,4 +1,3 @@
-
 /**
  * The list of devices supported by Transformers.js
  */
@@ -10,6 +9,7 @@ export const DEVICE_TYPES = Object.freeze({
     webgpu: 'webgpu', // WebGPU
     cuda: 'cuda', // CUDA
     dml: 'dml', // DirectML
+    coreml: 'coreml', // CoreML
 
     webnn: 'webnn', // WebNN (default)
     'webnn-npu': 'webnn-npu', // WebNN NPU
diff --git a/src/utils/dtypes.js b/src/utils/dtypes.js
index 845eef5e0..c30d35b7c 100644
--- a/src/utils/dtypes.js
+++ b/src/utils/dtypes.js
@@ -1,8 +1,8 @@
 /// 
 
-import { apis } from "../env.js";
+import { apis } from '../env.js';
 
-import { DEVICE_TYPES } from "./devices.js";
+import { DEVICE_TYPES } from './devices.js';
 
 // TODO: Use the adapter from `env.backends.onnx.webgpu.adapter` to check for `shader-f16` support,
 // when available in https://github.com/microsoft/onnxruntime/pull/19940.
diff --git a/src/utils/generic.js b/src/utils/generic.js
index 5ccd467ad..3e8fc290f 100644
--- a/src/utils/generic.js
+++ b/src/utils/generic.js
@@ -1,35 +1,36 @@
-
 /**
  * A base class for creating callable objects.
  * See [here](https://stackoverflow.com/q/76073890) for more information.
- * 
+ *
  * @type {new () => {(...args: any[]): any, _call(...args: any[]): any}}
  */
-export const Callable = /** @type {any} */ (class {
-    /**
-    * Creates a new instance of the Callable class.
-    */
-    constructor() {
+export const Callable = /** @type {any} */ (
+    class {
         /**
-         * Creates a closure that delegates to a private method '_call' with the given arguments.
-         * @type {any}
-         * @param {...any} args Zero or more arguments to pass to the '_call' method.
-         * @returns {*} The result of calling the '_call' method.
+         * Creates a new instance of the Callable class.
          */
-        let closure = function (...args) {
-            return closure._call(...args)
+        constructor() {
+            /**
+             * Creates a closure that delegates to a private method '_call' with the given arguments.
+             * @type {any}
+             * @param {...any} args Zero or more arguments to pass to the '_call' method.
+             * @returns {*} The result of calling the '_call' method.
+             */
+            let closure = function (...args) {
+                return closure._call(...args);
+            };
+            return Object.setPrototypeOf(closure, new.target.prototype);
         }
-        return Object.setPrototypeOf(closure, new.target.prototype)
-    }
 
-    /**
-     * This method should be implemented in subclasses to provide the
-     * functionality of the callable object.
-     *
-     * @param {any[]} args
-     * @throws {Error} If the subclass does not implement the `_call` method.
-     */
-    _call(...args) {
-        throw Error('Must implement _call method in subclass')
+        /**
+         * This method should be implemented in subclasses to provide the
+         * functionality of the callable object.
+         *
+         * @param {any[]} args
+         * @throws {Error} If the subclass does not implement the `_call` method.
+         */
+        _call(...args) {
+            throw Error('Must implement _call method in subclass');
+        }
     }
-});
+);
diff --git a/src/utils/hub.js b/src/utils/hub.js
index 56c2a643c..421a0658f 100755
--- a/src/utils/hub.js
+++ b/src/utils/hub.js
@@ -1,4 +1,3 @@
-
 /**
  * @file Utility functions to interact with the Hugging Face Hub (https://huggingface.co/models)
  *
@@ -50,18 +49,17 @@ export const MAX_EXTERNAL_DATA_CHUNKS = 100;
  * Mapping from file extensions to MIME types.
  */
 const CONTENT_TYPE_MAP = {
-    'txt': 'text/plain',
-    'html': 'text/html',
-    'css': 'text/css',
-    'js': 'text/javascript',
-    'json': 'application/json',
-    'png': 'image/png',
-    'jpg': 'image/jpeg',
-    'jpeg': 'image/jpeg',
-    'gif': 'image/gif',
-}
+    txt: 'text/plain',
+    html: 'text/html',
+    css: 'text/css',
+    js: 'text/javascript',
+    json: 'application/json',
+    png: 'image/png',
+    jpg: 'image/jpeg',
+    jpeg: 'image/jpeg',
+    gif: 'image/gif',
+};
 class FileResponse {
-
     /**
      * Creates a new `FileResponse` object.
      * @param {string} filePath
@@ -89,7 +87,7 @@ class FileResponse {
                 },
                 cancel() {
                     stream.destroy();
-                }
+                },
             });
         } else {
             this.status = 404;
@@ -201,8 +199,8 @@ const REPO_ID_REGEX = /^(\b[\w\-.]+\b\/)?\b[\w\-.]{1,96}\b$/;
  */
 function isValidHfModelId(string) {
     if (!REPO_ID_REGEX.test(string)) return false;
-    if (string.includes("..") || string.includes("--")) return false;
-    if (string.endsWith(".git") || string.endsWith(".ipynb")) return false;
+    if (string.includes('..') || string.includes('--')) return false;
+    if (string.endsWith('.git') || string.endsWith('.ipynb')) return false;
     return true;
 }
 
@@ -213,14 +211,13 @@ function isValidHfModelId(string) {
  * @returns {Promise} A promise that resolves to a FileResponse object (if the file is retrieved using the FileSystem API), or a Response object (if the file is retrieved using the Fetch API).
  */
 export async function getFile(urlOrPath) {
-
-    if (env.useFS && !isValidUrl(urlOrPath, ["http:", "https:", "blob:"])) {
+    if (env.useFS && !isValidUrl(urlOrPath, ['http:', 'https:', 'blob:'])) {
         return new FileResponse(
-          urlOrPath instanceof URL
-            ? urlOrPath.protocol === "file:"
-              ? urlOrPath.pathname
-              : urlOrPath.toString()
-            : urlOrPath,
+            urlOrPath instanceof URL
+                ? urlOrPath.protocol === 'file:'
+                    ? urlOrPath.pathname
+                    : urlOrPath.toString()
+                : urlOrPath,
         );
     } else if (typeof process !== 'undefined' && process?.release?.name === 'node') {
         const IS_CI = !!process.env?.TESTING_REMOTELY;
@@ -262,7 +259,7 @@ const ERROR_MAPPING = {
     502: 'Bad gateway error occurred while trying to load file',
     503: 'Service unavailable error occurred while trying to load file',
     504: 'Gateway timeout error occurred while trying to load file',
-}
+};
 /**
  * Helper method to handle fatal errors that occur while trying to load a file from the Hugging Face Hub.
  * @param {number} status The HTTP status code of the error.
@@ -297,7 +294,6 @@ class FileCache {
      * @returns {Promise}
      */
     async match(request) {
-
         let filePath = path.join(this.path, request);
         let file = new FileResponse(filePath);
 
@@ -355,7 +351,7 @@ class FileCache {
             // Clean up the file if an error occurred during download
             try {
                 await fs.promises.unlink(filePath);
-            } catch { }
+            } catch {}
             throw error;
         }
     }
@@ -402,14 +398,17 @@ async function tryCache(cache, ...names) {
  * @returns {Promise} A Promise that resolves with the file content as a Uint8Array if `return_path` is false, or the file path as a string if `return_path` is true.
  */
 export async function getModelFile(path_or_repo_id, filename, fatal = true, options = {}, return_path = false) {
-
     if (!env.allowLocalModels) {
         // User has disabled local models, so we just make sure other settings are correct.
 
         if (options.local_files_only) {
-            throw Error("Invalid configuration detected: local models are disabled (`env.allowLocalModels=false`) but you have requested to only use local models (`local_files_only=true`).")
+            throw Error(
+                'Invalid configuration detected: local models are disabled (`env.allowLocalModels=false`) but you have requested to only use local models (`local_files_only=true`).',
+            );
         } else if (!env.allowRemoteModels) {
-            throw Error("Invalid configuration detected: both local and remote models are disabled. Fix by setting `env.allowLocalModels` or `env.allowRemoteModels` to `true`.")
+            throw Error(
+                'Invalid configuration detected: both local and remote models are disabled. Fix by setting `env.allowLocalModels` or `env.allowRemoteModels` to `true`.',
+            );
         }
     }
 
@@ -417,8 +416,8 @@ export async function getModelFile(path_or_repo_id, filename, fatal = true, opti
     dispatchCallback(options.progress_callback, {
         status: 'initiate',
         name: path_or_repo_id,
-        file: filename
-    })
+        file: filename,
+    });
 
     // First, check if the a caching backend is available
     // If no caching mechanism available, will download the file every time
@@ -426,22 +425,22 @@ export async function getModelFile(path_or_repo_id, filename, fatal = true, opti
     if (!cache && env.useCustomCache) {
         // Allow the user to specify a custom cache system.
         if (!env.customCache) {
-            throw Error('`env.useCustomCache=true`, but `env.customCache` is not defined.')
+            throw Error('`env.useCustomCache=true`, but `env.customCache` is not defined.');
         }
 
         // Check that the required methods are defined:
         if (!env.customCache.match || !env.customCache.put) {
             throw new Error(
-                "`env.customCache` must be an object which implements the `match` and `put` functions of the Web Cache API. " +
-                "For more information, see https://developer.mozilla.org/en-US/docs/Web/API/Cache"
-            )
+                '`env.customCache` must be an object which implements the `match` and `put` functions of the Web Cache API. ' +
+                    'For more information, see https://developer.mozilla.org/en-US/docs/Web/API/Cache',
+            );
         }
         cache = env.customCache;
     }
 
     if (!cache && env.useBrowserCache) {
         if (typeof caches === 'undefined') {
-            throw Error('Browser cache is not available in this environment.')
+            throw Error('Browser cache is not available in this environment.');
         }
         try {
             // In some cases, the browser cache may be visible, but not accessible due to security restrictions.
@@ -468,25 +467,26 @@ export async function getModelFile(path_or_repo_id, filename, fatal = true, opti
     const requestURL = pathJoin(path_or_repo_id, filename);
 
     const validModelId = isValidHfModelId(path_or_repo_id);
-    const localPath = validModelId
-        ? pathJoin(env.localModelPath, requestURL)
-        : requestURL;
+    const localPath = validModelId ? pathJoin(env.localModelPath, requestURL) : requestURL;
     const remoteURL = pathJoin(
         env.remoteHost,
         env.remotePathTemplate
             .replaceAll('{model}', path_or_repo_id)
             .replaceAll('{revision}', encodeURIComponent(revision)),
-        filename
+        filename,
     );
 
     /** @type {string} */
     let cacheKey;
-    const proposedCacheKey = cache instanceof FileCache
-        // Choose cache key for filesystem cache
-        // When using the main revision (default), we use the request URL as the cache key.
-        // If a specific revision is requested, we account for this in the cache key.
-        ? revision === 'main' ? requestURL : pathJoin(path_or_repo_id, revision, filename)
-        : remoteURL;
+    const proposedCacheKey =
+        cache instanceof FileCache
+            ? // Choose cache key for filesystem cache
+              // When using the main revision (default), we use the request URL as the cache key.
+              // If a specific revision is requested, we account for this in the cache key.
+              revision === 'main'
+                ? requestURL
+                : pathJoin(path_or_repo_id, revision, filename)
+            : remoteURL;
 
     // Whether to cache the final response in the end.
     let toCacheResponse = false;
@@ -522,7 +522,9 @@ export async function getModelFile(path_or_repo_id, filename, fatal = true, opti
             } else if (options.local_files_only) {
                 throw new Error(`\`local_files_only=true\`, but attempted to load a remote file from: ${requestURL}.`);
             } else if (!env.allowRemoteModels) {
-                throw new Error(`\`env.allowRemoteModels=false\`, but attempted to load a remote file from: ${requestURL}.`);
+                throw new Error(
+                    `\`env.allowRemoteModels=false\`, but attempted to load a remote file from: ${requestURL}.`,
+                );
             }
         }
 
@@ -535,7 +537,9 @@ export async function getModelFile(path_or_repo_id, filename, fatal = true, opti
             if (options.local_files_only || !env.allowRemoteModels) {
                 // User requested local files only, but the file is not found locally.
                 if (fatal) {
-                    throw Error(`\`local_files_only=true\` or \`env.allowRemoteModels=false\` and file was not found locally at "${localPath}".`);
+                    throw Error(
+                        `\`local_files_only=true\` or \`env.allowRemoteModels=false\` and file was not found locally at "${localPath}".`,
+                    );
                 } else {
                     // File not found, but this file is optional.
                     // TODO in future, cache the response?
@@ -545,7 +549,9 @@ export async function getModelFile(path_or_repo_id, filename, fatal = true, opti
             if (!validModelId) {
                 // Before making any requests to the remote server, we check if the model ID is valid.
                 // This prevents unnecessary network requests for invalid model IDs.
-                throw Error(`Local file missing at "${localPath}" and download aborted due to invalid model ID "${path_or_repo_id}".`);
+                throw Error(
+                    `Local file missing at "${localPath}" and download aborted due to invalid model ID "${path_or_repo_id}".`,
+                );
             }
 
             // File not found locally, so we try to download it from the remote server
@@ -561,18 +567,18 @@ export async function getModelFile(path_or_repo_id, filename, fatal = true, opti
 
         // Only cache the response if:
         toCacheResponse =
-            cache                              // 1. A caching system is available
-            && typeof Response !== 'undefined' // 2. `Response` is defined (i.e., we are in a browser-like environment)
-            && response instanceof Response    // 3. result is a `Response` object (i.e., not a `FileResponse`)
-            && response.status === 200         // 4. request was successful (status code 200)
+            cache && // 1. A caching system is available
+            typeof Response !== 'undefined' && // 2. `Response` is defined (i.e., we are in a browser-like environment)
+            response instanceof Response && // 3. result is a `Response` object (i.e., not a `FileResponse`)
+            response.status === 200; // 4. request was successful (status code 200)
     }
 
     // Start downloading
     dispatchCallback(options.progress_callback, {
         status: 'download',
         name: path_or_repo_id,
-        file: filename
-    })
+        file: filename,
+    });
 
     let result;
     if (!(apis.IS_NODE_ENV && return_path)) {
@@ -583,11 +589,10 @@ export async function getModelFile(path_or_repo_id, filename, fatal = true, opti
             // If no progress callback is specified, we can use the `.arrayBuffer()`
             // method to read the response.
             buffer = new Uint8Array(await response.arrayBuffer());
-
         } else if (
-            cacheHit // The item is being read from the cache
-            &&
-            typeof navigator !== 'undefined' && /firefox/i.test(navigator.userAgent) // We are in Firefox
+            cacheHit && // The item is being read from the cache
+            typeof navigator !== 'undefined' &&
+            /firefox/i.test(navigator.userAgent) // We are in Firefox
         ) {
             // Due to bug in Firefox, we cannot display progress when loading from cache.
             // Fortunately, since this should be instantaneous, this should not impact users too much.
@@ -601,16 +606,16 @@ export async function getModelFile(path_or_repo_id, filename, fatal = true, opti
                 progress: 100,
                 loaded: buffer.length,
                 total: buffer.length,
-            })
+            });
         } else {
-            buffer = await readResponse(response, data => {
+            buffer = await readResponse(response, (data) => {
                 dispatchCallback(options.progress_callback, {
                     status: 'progress',
                     name: path_or_repo_id,
                     file: filename,
                     ...data,
-                })
-            })
+                });
+            });
         }
         result = buffer;
     }
@@ -618,20 +623,24 @@ export async function getModelFile(path_or_repo_id, filename, fatal = true, opti
     if (
         // Only cache web responses
         // i.e., do not cache FileResponses (prevents duplication)
-        toCacheResponse && cacheKey
-        &&
+        toCacheResponse &&
+        cacheKey &&
         // Check again whether request is in cache. If not, we add the response to the cache
-        (await cache.match(cacheKey) === undefined)
+        (await cache.match(cacheKey)) === undefined
     ) {
         if (!result) {
             // We haven't yet read the response body, so we need to do so now.
-            await cache.put(cacheKey, /** @type {Response} */(response), options.progress_callback);
+            await cache.put(cacheKey, /** @type {Response} */ (response), options.progress_callback);
         } else {
             // NOTE: We use `new Response(buffer, ...)` instead of `response.clone()` to handle LFS files
-            await cache.put(cacheKey, new Response(result, {
-                headers: response.headers
-            }))
-                .catch(err => {
+            await cache
+                .put(
+                    cacheKey,
+                    new Response(result, {
+                        headers: response.headers,
+                    }),
+                )
+                .catch((err) => {
                     // Do not crash if unable to add to cache (e.g., QuotaExceededError).
                     // Rather, log a warning and proceed with execution.
                     console.warn(`Unable to add response to browser cache: ${err}.`);
@@ -641,12 +650,12 @@ export async function getModelFile(path_or_repo_id, filename, fatal = true, opti
     dispatchCallback(options.progress_callback, {
         status: 'done',
         name: path_or_repo_id,
-        file: filename
+        file: filename,
     });
 
     if (result) {
         if (!apis.IS_NODE_ENV && return_path) {
-            throw new Error("Cannot return path in a browser environment.")
+            throw new Error('Cannot return path in a browser environment.');
         }
         return result;
     }
@@ -665,7 +674,7 @@ export async function getModelFile(path_or_repo_id, filename, fatal = true, opti
         return cachedResponse;
     }
 
-    throw new Error("Unable to get model file path or buffer.");
+    throw new Error('Unable to get model file path or buffer.');
 }
 
 /**
@@ -685,7 +694,7 @@ export async function getModelText(modelPath, fileName, fatal = true, options =
     }
 
     const decoder = new TextDecoder('utf-8');
-    return decoder.decode(/** @type {Uint8Array} */(buffer));
+    return decoder.decode(/** @type {Uint8Array} */ (buffer));
 }
 
 /**
@@ -715,10 +724,9 @@ export async function getModelJSON(modelPath, fileName, fatal = true, options =
  * @returns {Promise} A Promise that resolves with the Uint8Array buffer
  */
 async function readResponse(response, progress_callback) {
-
     const contentLength = response.headers.get('Content-Length');
     if (contentLength === null) {
-        console.warn('Unable to determine content-length from response headers. Will expand buffer when needed.')
+        console.warn('Unable to determine content-length from response headers. Will expand buffer when needed.');
     }
     let total = parseInt(contentLength ?? '0');
     let buffer = new Uint8Array(total);
@@ -775,6 +783,6 @@ function pathJoin(...parts) {
             part = part.replace(new RegExp('/$'), '');
         }
         return part;
-    })
+    });
     return parts.join('/');
 }
diff --git a/src/utils/image.js b/src/utils/image.js
index 54db7938f..51990bb33 100644
--- a/src/utils/image.js
+++ b/src/utils/image.js
@@ -1,4 +1,3 @@
-
 /**
  * @file Helper module for image processing.
  *
@@ -26,15 +25,14 @@ if (IS_BROWSER_OR_WEBWORKER) {
         if (!self.OffscreenCanvas) {
             throw new Error('OffscreenCanvas not supported by this browser.');
         }
-        return new self.OffscreenCanvas(width, height)
+        return new self.OffscreenCanvas(width, height);
     };
     loadImageFunction = self.createImageBitmap;
     ImageDataClass = self.ImageData;
-
 } else if (sharp) {
     // Running in Node.js, electron, or other non-browser environment
 
-    loadImageFunction = async (/**@type {sharp.Sharp}*/img) => {
+    loadImageFunction = async (/**@type {sharp.Sharp}*/ img) => {
         const metadata = await img.metadata();
         const rawChannels = metadata.channels;
 
@@ -47,13 +45,11 @@ if (IS_BROWSER_OR_WEBWORKER) {
             newImage.convert(rawChannels);
         }
         return newImage;
-    }
-
+    };
 } else {
     throw new Error('Unable to load image processing library.');
 }
 
-
 // Defined here: https://github.com/python-pillow/Pillow/blob/a405e8406b83f8bfb8916e93971edc7407b8b1ff/src/libImaging/Imaging.h#L262-L268
 const RESAMPLING_MAPPING = {
     0: 'nearest',
@@ -62,7 +58,7 @@ const RESAMPLING_MAPPING = {
     3: 'bicubic',
     4: 'box',
     5: 'hamming',
-}
+};
 
 /**
  * Mapping from file extensions to MIME types.
@@ -75,7 +71,6 @@ const CONTENT_TYPE_MAP = new Map([
 ]);
 
 export class RawImage {
-
     /**
      * Create a new `RawImage` object.
      * @param {Uint8ClampedArray|Uint8Array} data The pixel data.
@@ -122,9 +117,8 @@ export class RawImage {
         } else if (input instanceof Blob) {
             return await this.fromBlob(input);
         } else if (
-            (typeof HTMLCanvasElement !== "undefined" && input instanceof HTMLCanvasElement)
-            ||
-            (typeof OffscreenCanvas !== "undefined" && input instanceof OffscreenCanvas)
+            (typeof HTMLCanvasElement !== 'undefined' && input instanceof HTMLCanvasElement) ||
+            (typeof OffscreenCanvas !== 'undefined' && input instanceof OffscreenCanvas)
         ) {
             return this.fromCanvas(input);
         } else {
@@ -139,10 +133,12 @@ export class RawImage {
      */
     static fromCanvas(canvas) {
         if (!IS_BROWSER_OR_WEBWORKER) {
-            throw new Error('fromCanvas() is only supported in browser environments.')
+            throw new Error('fromCanvas() is only supported in browser environments.');
         }
 
-        const ctx = /** @type {CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D} */ (canvas.getContext('2d'));
+        const ctx = /** @type {CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D} */ (
+            canvas.getContext('2d')
+        );
         const data = ctx.getImageData(0, 0, canvas.width, canvas.height).data;
         return new RawImage(data, canvas.width, canvas.height, 4);
     }
@@ -177,7 +173,6 @@ export class RawImage {
             ctx.drawImage(img, 0, 0);
 
             return new this(ctx.getImageData(0, 0, img.width, img.height).data, img.width, img.height, 4);
-
         } else {
             // Use sharp.js to read (and possible resize) the image.
             const img = sharp(await blob.arrayBuffer());
@@ -234,7 +229,7 @@ export class RawImage {
                     const green = this.data[i + 1];
                     const blue = this.data[i + 2];
 
-                    newData[offset++] = Math.round(0.2989 * red + 0.5870 * green + 0.1140 * blue);
+                    newData[offset++] = Math.round(0.2989 * red + 0.587 * green + 0.114 * blue);
                 }
                 break;
             default:
@@ -273,7 +268,6 @@ export class RawImage {
                 throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`);
         }
         return this._update(newData, this.width, this.height, 3);
-
     }
 
     /**
@@ -321,7 +315,9 @@ export class RawImage {
      */
     putAlpha(mask) {
         if (mask.width !== this.width || mask.height !== this.height) {
-            throw new Error(`Expected mask size to be ${this.width}x${this.height}, but got ${mask.width}x${mask.height}`);
+            throw new Error(
+                `Expected mask size to be ${this.width}x${this.height}, but got ${mask.width}x${mask.height}`,
+            );
         }
         if (mask.channels !== 1) {
             throw new Error(`Expected mask to have 1 channel, but got ${mask.channels}`);
@@ -340,7 +336,6 @@ export class RawImage {
                 newData[out_offset++] = mask_data[i];
             }
             return this._update(newData, this.width, this.height, 4);
-
         } else if (this.channels === 4) {
             // Apply mask to alpha channel in place
             for (let i = 0; i < num_pixels; ++i) {
@@ -359,10 +354,7 @@ export class RawImage {
      * @param {0|1|2|3|4|5|string} [options.resample] The resampling method to use.
      * @returns {Promise} `this` to support chaining.
      */
-    async resize(width, height, {
-        resample = 2,
-    } = {}) {
-
+    async resize(width, height, { resample = 2 } = {}) {
         // Do nothing if the image already has the desired size
         if (this.width === width && this.height === height) {
             return this;
@@ -405,7 +397,6 @@ export class RawImage {
 
             // Convert back so that image has the same number of channels as before
             return resizedImage.convert(numChannels);
-
         } else {
             // Create sharp image from raw data, and resize
             let img = this.toSharp();
@@ -414,7 +405,9 @@ export class RawImage {
                 case 'box':
                 case 'hamming':
                     if (resampleMethod === 'box' || resampleMethod === 'hamming') {
-                        console.warn(`Resampling method ${resampleMethod} is not yet supported. Using bilinear instead.`);
+                        console.warn(
+                            `Resampling method ${resampleMethod} is not yet supported. Using bilinear instead.`,
+                        );
                         resampleMethod = 'bilinear';
                     }
 
@@ -424,7 +417,7 @@ export class RawImage {
                     // Perform resizing using affine transform.
                     // This matches how the python Pillow library does it.
                     img = img.affine([width / this.width, 0, 0, height / this.height], {
-                        interpolator: resampleMethod
+                        interpolator: resampleMethod,
                     });
                     break;
 
@@ -432,7 +425,8 @@ export class RawImage {
                     // https://github.com/python-pillow/Pillow/discussions/5519
                     // https://github.com/lovell/sharp/blob/main/docs/api-resize.md
                     img = img.resize({
-                        width, height,
+                        width,
+                        height,
                         fit: 'fill',
                         kernel: 'lanczos3', // PIL Lanczos uses a kernel size of 3
                     });
@@ -444,7 +438,6 @@ export class RawImage {
 
             return await loadImageFunction(img);
         }
-
     }
 
     async pad([left, right, top, bottom]) {
@@ -472,20 +465,13 @@ export class RawImage {
             const ctx = createCanvasFunction(newWidth, newHeight).getContext('2d');
 
             // Draw image to context, padding in the process
-            ctx.drawImage(canvas,
-                0, 0, this.width, this.height,
-                left, top, this.width, this.height
-            );
+            ctx.drawImage(canvas, 0, 0, this.width, this.height, left, top, this.width, this.height);
 
             // Create image from the padded data
-            const paddedImage = new RawImage(
-                ctx.getImageData(0, 0, newWidth, newHeight).data,
-                newWidth, newHeight, 4
-            );
+            const paddedImage = new RawImage(ctx.getImageData(0, 0, newWidth, newHeight).data, newWidth, newHeight, 4);
 
             // Convert back so that image has the same number of channels as before
             return paddedImage.convert(numChannels);
-
         } else {
             const img = this.toSharp().extend({ left, right, top, bottom });
             return await loadImageFunction(img);
@@ -519,17 +505,18 @@ export class RawImage {
             const ctx = createCanvasFunction(crop_width, crop_height).getContext('2d');
 
             // Draw image to context, cropping in the process
-            ctx.drawImage(canvas,
-                x_min, y_min, crop_width, crop_height,
-                0, 0, crop_width, crop_height
-            );
+            ctx.drawImage(canvas, x_min, y_min, crop_width, crop_height, 0, 0, crop_width, crop_height);
 
             // Create image from the resized data
-            const resizedImage = new RawImage(ctx.getImageData(0, 0, crop_width, crop_height).data, crop_width, crop_height, 4);
+            const resizedImage = new RawImage(
+                ctx.getImageData(0, 0, crop_width, crop_height).data,
+                crop_width,
+                crop_height,
+                4,
+            );
 
             // Convert back so that image has the same number of channels as before
             return resizedImage.convert(numChannels);
-
         } else {
             // Create sharp image from raw data
             const img = this.toSharp().extract({
@@ -541,7 +528,6 @@ export class RawImage {
 
             return await loadImageFunction(img);
         }
-
     }
 
     async center_crop(crop_width, crop_height) {
@@ -554,7 +540,6 @@ export class RawImage {
         const width_offset = (this.width - crop_width) / 2;
         const height_offset = (this.height - crop_height) / 2;
 
-
         if (IS_BROWSER_OR_WEBWORKER) {
             // Store number of channels before resizing
             const numChannels = this.channels;
@@ -584,17 +569,18 @@ export class RawImage {
             }
 
             // Draw image to context, cropping in the process
-            ctx.drawImage(canvas,
-                sourceX, sourceY, crop_width, crop_height,
-                destX, destY, crop_width, crop_height
-            );
+            ctx.drawImage(canvas, sourceX, sourceY, crop_width, crop_height, destX, destY, crop_width, crop_height);
 
             // Create image from the resized data
-            const resizedImage = new RawImage(ctx.getImageData(0, 0, crop_width, crop_height).data, crop_width, crop_height, 4);
+            const resizedImage = new RawImage(
+                ctx.getImageData(0, 0, crop_width, crop_height).data,
+                crop_width,
+                crop_height,
+                4,
+            );
 
             // Convert back so that image has the same number of channels as before
             return resizedImage.convert(numChannels);
-
         } else {
             // Create sharp image from raw data
             let img = this.toSharp();
@@ -606,7 +592,7 @@ export class RawImage {
                     top: Math.floor(height_offset),
                     width: crop_width,
                     height: crop_height,
-                })
+                });
             } else if (width_offset <= 0 && height_offset <= 0) {
                 // Cropped image lies entirely outside the original image,
                 // so we add padding
@@ -642,17 +628,19 @@ export class RawImage {
                     x_extract = Math.floor(width_offset);
                 }
 
-                img = img.extend({
-                    top: y_padding[0],
-                    bottom: y_padding[1],
-                    left: x_padding[0],
-                    right: x_padding[1],
-                }).extract({
-                    left: x_extract,
-                    top: y_extract,
-                    width: crop_width,
-                    height: crop_height,
-                })
+                img = img
+                    .extend({
+                        top: y_padding[0],
+                        bottom: y_padding[1],
+                        left: x_padding[0],
+                        right: x_padding[1],
+                    })
+                    .extract({
+                        left: x_extract,
+                        top: y_extract,
+                        width: crop_width,
+                        height: crop_height,
+                    });
             }
 
             return await loadImageFunction(img);
@@ -661,7 +649,7 @@ export class RawImage {
 
     async toBlob(type = 'image/png', quality = 1) {
         if (!IS_BROWSER_OR_WEBWORKER) {
-            throw new Error('toBlob() is only supported in browser environments.')
+            throw new Error('toBlob() is only supported in browser environments.');
         }
 
         const canvas = this.toCanvas();
@@ -669,15 +657,12 @@ export class RawImage {
     }
 
     toTensor(channel_format = 'CHW') {
-        let tensor = new Tensor(
-            'uint8',
-            new Uint8Array(this.data),
-            [this.height, this.width, this.channels]
-        );
+        let tensor = new Tensor('uint8', new Uint8Array(this.data), [this.height, this.width, this.channels]);
 
         if (channel_format === 'HWC') {
             // Do nothing
-        } else if (channel_format === 'CHW') { // hwc -> chw
+        } else if (channel_format === 'CHW') {
+            // hwc -> chw
             tensor = tensor.permute(2, 0, 1);
         } else {
             throw new Error(`Unsupported channel format: ${channel_format}`);
@@ -687,7 +672,7 @@ export class RawImage {
 
     toCanvas() {
         if (!IS_BROWSER_OR_WEBWORKER) {
-            throw new Error('toCanvas() is only supported in browser environments.')
+            throw new Error('toCanvas() is only supported in browser environments.');
         }
 
         // Clone, and convert data to RGBA before drawing to canvas.
@@ -707,7 +692,7 @@ export class RawImage {
     /**
      * Split this image into individual bands. This method returns an array of individual image bands from an image.
      * For example, splitting an "RGB" image creates three new images each containing a copy of one of the original bands (red, green, blue).
-     * 
+     *
      * Inspired by PIL's `Image.split()` [function](https://pillow.readthedocs.io/en/latest/reference/Image.html#PIL.Image.Image.split).
      * @returns {RawImage[]} An array containing bands.
      */
@@ -715,14 +700,11 @@ export class RawImage {
         const { data, width, height, channels } = this;
 
         /** @type {typeof Uint8Array | typeof Uint8ClampedArray} */
-        const data_type = /** @type {any} */(data.constructor);
+        const data_type = /** @type {any} */ (data.constructor);
         const per_channel_length = data.length / channels;
 
         // Pre-allocate buffers for each channel
-        const split_data = Array.from(
-            { length: channels },
-            () => new data_type(per_channel_length),
-        );
+        const split_data = Array.from({ length: channels }, () => new data_type(per_channel_length));
 
         // Write pixel data
         for (let i = 0; i < per_channel_length; ++i) {
@@ -789,10 +771,9 @@ export class RawImage {
      * @param {string} path The path to save the image to.
      */
     async save(path) {
-
         if (IS_BROWSER_OR_WEBWORKER) {
             if (apis.IS_WEBWORKER_ENV) {
-                throw new Error('Unable to save an image from a Web Worker.')
+                throw new Error('Unable to save an image from a Web Worker.');
             }
 
             const extension = path.split('.').pop().toLowerCase();
@@ -801,11 +782,9 @@ export class RawImage {
             // Convert image to Blob
             const blob = await this.toBlob(mime);
 
-            saveBlob(path, blob)
-
+            saveBlob(path, blob);
         } else if (!apis.IS_FS_AVAILABLE) {
-            throw new Error('Unable to save the image because filesystem is disabled in this environment.')
-
+            throw new Error('Unable to save the image because filesystem is disabled in this environment.');
         } else {
             const img = this.toSharp();
             return await img.toFile(path);
@@ -814,15 +793,15 @@ export class RawImage {
 
     toSharp() {
         if (IS_BROWSER_OR_WEBWORKER) {
-            throw new Error('toSharp() is only supported in server-side environments.')
+            throw new Error('toSharp() is only supported in server-side environments.');
         }
 
         return sharp(this.data, {
             raw: {
                 width: this.width,
                 height: this.height,
-                channels: this.channels
-            }
+                channels: this.channels,
+            },
         });
     }
 }
@@ -831,4 +810,3 @@ export class RawImage {
  * Helper function to load an image from a URL, path, etc.
  */
 export const load_image = RawImage.read.bind(RawImage);
-
diff --git a/src/utils/maths.js b/src/utils/maths.js
index 4bbe709b8..7d52ba42b 100644
--- a/src/utils/maths.js
+++ b/src/utils/maths.js
@@ -1,10 +1,9 @@
-
 /**
- * @file Helper module for mathematical processing. 
- * 
- * These functions and classes are only used internally, 
+ * @file Helper module for mathematical processing.
+ *
+ * These functions and classes are only used internally,
  * meaning an end-user shouldn't need to access anything here.
- * 
+ *
  * @module utils/maths
  */
 
@@ -17,7 +16,13 @@
 /**
  * @param {TypedArray} input
  */
-export function interpolate_data(input, [in_channels, in_height, in_width], [out_height, out_width], mode = 'bilinear', align_corners = false) {
+export function interpolate_data(
+    input,
+    [in_channels, in_height, in_width],
+    [out_height, out_width],
+    mode = 'bilinear',
+    align_corners = false,
+) {
     // TODO use mode and align_corners
 
     // Output image dimensions
@@ -51,7 +56,6 @@ export function interpolate_data(input, [in_channels, in_height, in_width], [out
             x1 = Math.max(x1, 0);
             y1 = Math.max(y1, 0);
 
-
             // Calculate the fractional distances between the input pixel and the four nearest pixels
             const s = x - x1;
             const t = y - y1;
@@ -86,13 +90,12 @@ export function interpolate_data(input, [in_channels, in_height, in_width], [out
     return out_img;
 }
 
-
 /**
  * Helper method to permute a `AnyTypedArray` directly
- * @template {AnyTypedArray} T 
- * @param {T} array 
- * @param {number[]} dims 
- * @param {number[]} axes 
+ * @template {AnyTypedArray} T
+ * @param {T} array
+ * @param {number[]} dims
+ * @param {number[]} axes
  * @returns {[T, number[]]} The permuted array and the new shape.
  */
 export function permute_data(array, dims, axes) {
@@ -127,7 +130,6 @@ export function permute_data(array, dims, axes) {
     return [permutedData, shape];
 }
 
-
 /**
  * Compute the softmax of an array of numbers.
  * @template {TypedArray|number[]} T
@@ -139,16 +141,16 @@ export function softmax(arr) {
     const maxVal = max(arr)[0];
 
     // Compute the exponentials of the array values
-    const exps = arr.map(x => Math.exp(x - maxVal));
+    const exps = arr.map((x) => Math.exp(x - maxVal));
 
     // Compute the sum of the exponentials
     // @ts-ignore
     const sumExps = exps.reduce((acc, val) => acc + val, 0);
 
     // Compute the softmax values
-    const softmaxArr = exps.map(x => x / sumExps);
+    const softmaxArr = exps.map((x) => x / sumExps);
 
-    return /** @type {T} */(softmaxArr);
+    return /** @type {T} */ (softmaxArr);
 }
 
 /**
@@ -163,7 +165,7 @@ export function log_softmax(arr) {
 
     // Compute the sum of the exponentials
     let sumExps = 0;
-    for(let i = 0; i < arr.length; ++i) {
+    for (let i = 0; i < arr.length; ++i) {
         sumExps += Math.exp(arr[i] - maxVal);
     }
 
@@ -171,9 +173,9 @@ export function log_softmax(arr) {
     const logSum = Math.log(sumExps);
 
     // Compute the softmax values
-    const logSoftmaxArr = arr.map(x => x - maxVal - logSum);
+    const logSoftmaxArr = arr.map((x) => x - maxVal - logSum);
 
-    return /** @type {T} */(logSoftmaxArr);
+    return /** @type {T} */ (logSoftmaxArr);
 }
 
 /**
@@ -222,7 +224,6 @@ export function magnitude(arr) {
     return Math.sqrt(arr.reduce((acc, val) => acc + val * val, 0));
 }
 
-
 /**
  * Returns the value and index of the minimum element in an array.
  * @template {number[]|bigint[]|AnyTypedArray} T
@@ -240,10 +241,9 @@ export function min(arr) {
             indexOfMin = i;
         }
     }
-    return /** @type {T extends bigint[]|BigTypedArray ? [bigint, number] : [number, number]} */([min, indexOfMin]);
+    return /** @type {T extends bigint[]|BigTypedArray ? [bigint, number] : [number, number]} */ ([min, indexOfMin]);
 }
 
-
 /**
  * Returns the value and index of the maximum element in an array.
  * @template {number[]|bigint[]|AnyTypedArray} T
@@ -261,17 +261,17 @@ export function max(arr) {
             indexOfMax = i;
         }
     }
-    return /** @type {T extends bigint[]|BigTypedArray ? [bigint, number] : [number, number]} */([max, indexOfMax]);
+    return /** @type {T extends bigint[]|BigTypedArray ? [bigint, number] : [number, number]} */ ([max, indexOfMax]);
 }
 
 function isPowerOfTwo(number) {
     // Check if the number is greater than 0 and has only one bit set to 1
-    return (number > 0) && ((number & (number - 1)) === 0);
+    return number > 0 && (number & (number - 1)) === 0;
 }
 
 /**
  * Implementation of Radix-4 FFT.
- * 
+ *
  * P2FFT class provides functionality for performing Fast Fourier Transform on arrays
  * which are a power of two in length.
  * Code adapted from https://www.npmjs.com/package/fft.js
@@ -290,15 +290,14 @@ class P2FFT {
 
         this.table = new Float64Array(this.size * 2);
         for (let i = 0; i < this.table.length; i += 2) {
-            const angle = Math.PI * i / this.size;
+            const angle = (Math.PI * i) / this.size;
             this.table[i] = Math.cos(angle);
             this.table[i + 1] = -Math.sin(angle);
         }
 
         // Find size's power of two
         let power = 0;
-        for (let t = 1; this.size > t; t <<= 1)
-            ++power;
+        for (let t = 1; this.size > t; t <<= 1) ++power;
 
         // Calculate initial step's width:
         //   * If we are full radix-4, it is 2x smaller to give inital len=8
@@ -327,15 +326,14 @@ class P2FFT {
 
     /**
      * Converts a complex number representation stored in a Float64Array to an array of real numbers.
-     * 
+     *
      * @param {Float64Array} complex The complex number representation to be converted.
      * @param {number[]} [storage] An optional array to store the result in.
      * @returns {number[]} An array of real numbers representing the input complex number representation.
      */
     fromComplexArray(complex, storage) {
         const res = storage || new Array(complex.length >>> 1);
-        for (let i = 0; i < complex.length; i += 2)
-            res[i >>> 1] = complex[i];
+        for (let i = 0; i < complex.length; i += 2) res[i >>> 1] = complex[i];
         return res;
     }
 
@@ -356,17 +354,16 @@ class P2FFT {
 
     /**
      * Performs a Fast Fourier Transform (FFT) on the given input data and stores the result in the output buffer.
-     * 
+     *
      * @param {Float64Array} out The output buffer to store the result.
      * @param {Float64Array} data The input data to transform.
-     * 
+     *
      * @throws {Error} Input and output buffers must be different.
-     * 
+     *
      * @returns {void}
      */
     transform(out, data) {
-        if (out === data)
-            throw new Error('Input and output buffers must be different');
+        if (out === data) throw new Error('Input and output buffers must be different');
 
         this._transform4(out, data, 1 /* DONE */);
     }
@@ -382,8 +379,7 @@ class P2FFT {
      * @throws {Error} If the input and output buffers are the same.
      */
     realTransform(out, data) {
-        if (out === data)
-            throw new Error('Input and output buffers must be different');
+        if (out === data) throw new Error('Input and output buffers must be different');
 
         this._realTransform4(out, data, 1 /* DONE */);
     }
@@ -392,19 +388,17 @@ class P2FFT {
      * Performs an inverse FFT transformation on the given `data` array, and stores the result in `out`.
      * The `out` array must be a different buffer than the `data` array. The `out` array will contain the
      * result of the transformation. The `data` array will not be modified.
-     * 
+     *
      * @param {Float64Array} out The output buffer for the transformed data.
      * @param {Float64Array} data The input data to transform.
      * @throws {Error} If `out` and `data` refer to the same buffer.
      * @returns {void}
      */
     inverseTransform(out, data) {
-        if (out === data)
-            throw new Error('Input and output buffers must be different');
+        if (out === data) throw new Error('Input and output buffers must be different');
 
         this._transform4(out, data, -1 /* DONE */);
-        for (let i = 0; i < out.length; ++i)
-            out[i] /= this.size;
+        for (let i = 0; i < out.length; ++i) out[i] /= this.size;
     }
 
     /**
@@ -540,7 +534,7 @@ class P2FFT {
      * @param {number} off Index of input array to start reading from
      * @param {number} step Step size between elements in input array
      * @param {number} inv Scaling factor for inverse transform
-     * 
+     *
      * @returns {void}
      */
     _singleTransform4(data, out, outOff, off, step, inv) {
@@ -680,8 +674,7 @@ class P2FFT {
                     }
 
                     // Do not overwrite ourselves
-                    if (i === hquarterLen)
-                        continue;
+                    if (i === hquarterLen) continue;
 
                     const SA = outOff + quarterLen - i;
                     const SB = outOff + halfLen - i;
@@ -704,13 +697,13 @@ class P2FFT {
 
     /**
      * Performs a single real input radix-2 transformation on the provided data
-     * 
+     *
      * @param {Float64Array} data The input data array
      * @param {Float64Array} out The output data array
      * @param {number} outOff The output offset
      * @param {number} off The input offset
      * @param {number} step The step
-     * 
+     *
      * @returns {void}
      */
     _singleRealTransform2(data, out, outOff, off, step) {
@@ -770,11 +763,10 @@ class P2FFT {
 /**
  * NP2FFT class provides functionality for performing Fast Fourier Transform on arrays
  * which are not a power of two in length. In such cases, the chirp-z transform is used.
- * 
+ *
  * For more information, see: https://math.stackexchange.com/questions/77118/non-power-of-2-ffts/77156#77156
  */
 class NP2FFT {
-
     /**
      * Constructs a new NP2FFT object.
      * @param {number} fft_length The length of the FFT
@@ -783,7 +775,7 @@ class NP2FFT {
         // Helper variables
         const a = 2 * (fft_length - 1);
         const b = 2 * (2 * fft_length - 1);
-        const nextP2 = 2 ** (Math.ceil(Math.log2(b)))
+        const nextP2 = 2 ** Math.ceil(Math.log2(b));
         this.bufferSize = nextP2;
         this._a = a;
 
@@ -798,7 +790,7 @@ class NP2FFT {
         this._outBuffer2 = new Float64Array(nextP2);
 
         // Compute complex exponentiation
-        const theta = -2 * Math.PI / fft_length;
+        const theta = (-2 * Math.PI) / fft_length;
         const baseR = Math.cos(theta);
         const baseI = Math.sin(theta);
 
@@ -819,7 +811,7 @@ class NP2FFT {
 
             // conjugate
             ichirp[i2] = chirp[i2];
-            ichirp[i2 + 1] = - chirp[i2 + 1];
+            ichirp[i2 + 1] = -chirp[i2 + 1];
         }
         this._slicedChirpBuffer = chirp.subarray(a, b);
 
@@ -841,7 +833,7 @@ class NP2FFT {
         if (real) {
             // Real multiplication
             for (let j = 0; j < sb.length; j += 2) {
-                const j2 = j + 1
+                const j2 = j + 1;
                 const j3 = j >> 1;
 
                 const a_real = input[j3];
@@ -851,7 +843,7 @@ class NP2FFT {
         } else {
             // Complex multiplication
             for (let j = 0; j < sb.length; j += 2) {
-                const j2 = j + 1
+                const j2 = j + 1;
                 ib1[j] = input[j] * sb[j] - input[j2] * sb[j2];
                 ib1[j2] = input[j] * sb[j2] + input[j2] * sb[j];
             }
@@ -908,14 +900,12 @@ export class FFT {
     }
 }
 
-
 /**
  * Performs median filter on the provided data. Padding is done by mirroring the data.
  * @param {AnyTypedArray} data The input array
  * @param {number} windowSize The window size
  */
 export function medianFilter(data, windowSize) {
-
     if (windowSize % 2 === 0 || windowSize <= 0) {
         throw new Error('Window size must be a positive odd number');
     }
@@ -964,7 +954,7 @@ export function round(num, decimals) {
  * Helper function to round a number to the nearest integer, with ties rounded to the nearest even number.
  * Also known as "bankers' rounding". This is the default rounding mode in python. For example:
  * 1.5 rounds to 2 and 2.5 rounds to 2.
- * 
+ *
  * @param {number} x The number to round
  * @returns {number} The rounded number
  */
@@ -974,11 +964,10 @@ export function bankers_round(x) {
     return br;
 }
 
-
 /**
  * Measures similarity between two temporal sequences (e.g., input audio and output tokens
  * to generate token-level timestamps).
- * @param {number[][]} matrix 
+ * @param {number[][]} matrix
  * @returns {number[][]}
  */
 export function dynamic_time_warping(matrix) {
@@ -987,16 +976,10 @@ export function dynamic_time_warping(matrix) {
 
     const outputShape = [output_length + 1, input_length + 1];
 
-    const cost = Array.from(
-        { length: outputShape[0] },
-        () => Array(outputShape[1]).fill(Infinity)
-    );
+    const cost = Array.from({ length: outputShape[0] }, () => Array(outputShape[1]).fill(Infinity));
     cost[0][0] = 0;
 
-    const trace = Array.from(
-        { length: outputShape[0] },
-        () => Array(outputShape[1]).fill(-1)
-    );
+    const trace = Array.from({ length: outputShape[0] }, () => Array(outputShape[1]).fill(-1));
 
     for (let j = 1; j < outputShape[1]; ++j) {
         for (let i = 1; i < outputShape[0]; ++i) {
@@ -1020,10 +1003,12 @@ export function dynamic_time_warping(matrix) {
         }
     }
 
-    for (let i = 0; i < outputShape[1]; ++i) { // trace[0, :] = 2
+    for (let i = 0; i < outputShape[1]; ++i) {
+        // trace[0, :] = 2
         trace[0][i] = 2;
     }
-    for (let i = 0; i < outputShape[0]; ++i) { // trace[:, 0] = 1
+    for (let i = 0; i < outputShape[0]; ++i) {
+        // trace[:, 0] = 1
         trace[i][0] = 1;
     }
 
@@ -1038,7 +1023,8 @@ export function dynamic_time_warping(matrix) {
 
         switch (trace[i][j]) {
             case 0:
-                --i; --j;
+                --i;
+                --j;
                 break;
             case 1:
                 --i;
@@ -1048,8 +1034,8 @@ export function dynamic_time_warping(matrix) {
                 break;
             default:
                 throw new Error(
-                    `Internal error in dynamic time warping. Unexpected trace[${i}, ${j}]. Please file a bug report.`
-                )
+                    `Internal error in dynamic time warping. Unexpected trace[${i}, ${j}]. Please file a bug report.`,
+                );
         }
     }
 
@@ -1057,5 +1043,4 @@ export function dynamic_time_warping(matrix) {
     time_indices.reverse();
 
     return [text_indices, time_indices];
-
 }
diff --git a/src/utils/tensor.js b/src/utils/tensor.js
index ea822b6c6..77473c95b 100644
--- a/src/utils/tensor.js
+++ b/src/utils/tensor.js
@@ -7,16 +7,9 @@
  * @module utils/tensor
  */
 
-import {
-    interpolate_data,
-    max,
-    min,
-    permute_data
-} from './maths.js';
+import { interpolate_data, max, min, permute_data } from './maths.js';
 
-import {
-    Tensor as ONNXTensor, isONNXTensor,
-} from '../backends/onnx.js';
+import { Tensor as ONNXTensor, isONNXTensor } from '../backends/onnx.js';
 
 import { TensorOpRegistry } from '../ops/registry.js';
 
@@ -24,7 +17,7 @@ export const DataTypeMap = Object.freeze({
     float32: Float32Array,
     // @ts-ignore ts(2552) Limited availability of Float16Array across browsers:
     // https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Float16Array
-    float16: typeof Float16Array !== "undefined" ? Float16Array: Uint16Array,
+    float16: typeof Float16Array !== 'undefined' ? Float16Array : Uint16Array,
     float64: Float64Array,
     string: Array, // string[]
     int8: Int8Array,
@@ -45,7 +38,6 @@ export const DataTypeMap = Object.freeze({
  * @typedef {import('./maths.js').AnyTypedArray | any[]} DataArray
  */
 
-
 export class Tensor {
     /** @type {number[]} Dimensions of the tensor. */
     get dims() {
@@ -61,7 +53,7 @@ export class Tensor {
     /** @type {DataType} Type of the tensor. */
     get type() {
         return this.ort_tensor.type;
-    };
+    }
 
     /** @type {DataArray} The data stored in the tensor. */
     get data() {
@@ -71,12 +63,12 @@ export class Tensor {
     /** @type {number} The number of elements in the tensor. */
     get size() {
         return this.ort_tensor.size;
-    };
+    }
 
     /** @type {string} The location of the tensor data. */
     get location() {
         return this.ort_tensor.location;
-    };
+    }
 
     ort_tensor;
 
@@ -90,9 +82,9 @@ export class Tensor {
         } else {
             // Create new tensor
             this.ort_tensor = new ONNXTensor(
-                /** @type {DataType} */(args[0]),
+                /** @type {DataType} */ (args[0]),
                 // @ts-expect-error ts(2769) Type 'number' is not assignable to type 'bigint'.
-                /** @type {Exclude} */(args[1]),
+                /** @type {Exclude} */ (args[1]),
                 args[2],
             );
         }
@@ -113,8 +105,8 @@ export class Tensor {
                 // TODO allow setting of data
 
                 // @ts-ignore
-                return obj[key] = value;
-            }
+                return (obj[key] = value);
+            },
         });
     }
 
@@ -137,9 +129,8 @@ export class Tensor {
                 yield this._subarray(i, iterSize, iterDims);
             }
         } else {
-            yield* this.data
+            yield* this.data;
         }
-
     }
 
     /**
@@ -186,10 +177,7 @@ export class Tensor {
         const o2 = (index + 1) * iterSize;
 
         // We use subarray if available (typed array), otherwise we use slice (normal array)
-        const data =
-            ('subarray' in this.data)
-                ? this.data.subarray(o1, o2)
-                : this.data.slice(o1, o2);
+        const data = 'subarray' in this.data ? this.data.subarray(o1, o2) : this.data.slice(o1, o2);
         return new Tensor(this.type, data, iterDims);
     }
 
@@ -212,7 +200,7 @@ export class Tensor {
      * @returns {Array}
      */
     tolist() {
-        return reshape(this.data, this.dims)
+        return reshape(this.data, this.dims);
     }
 
     /**
@@ -398,35 +386,25 @@ export class Tensor {
                 // null or undefined means take the whole dimension
                 newOffsets.push([0, this.dims[sliceIndex]]);
                 newTensorDims.push(this.dims[sliceIndex]);
-
             } else if (typeof slice === 'number') {
                 slice = safeIndex(slice, this.dims[sliceIndex], sliceIndex);
 
                 // A number means take a single element
                 newOffsets.push([slice, slice + 1]);
-
             } else if (Array.isArray(slice) && slice.length === 2) {
                 // An array of length 2 means take a range of elements
                 let [start, end] = slice;
-                start = start === null
-                    ? 0
-                    : safeIndex(start, this.dims[sliceIndex], sliceIndex, false);
-                end = end === null
-                    ? this.dims[sliceIndex]
-                    : safeIndex(end, this.dims[sliceIndex], sliceIndex, false);
+                start = start === null ? 0 : safeIndex(start, this.dims[sliceIndex], sliceIndex, false);
+                end = end === null ? this.dims[sliceIndex] : safeIndex(end, this.dims[sliceIndex], sliceIndex, false);
 
                 if (start > end) {
                     throw new Error(`Invalid slice: ${slice}`);
                 }
 
-                const offsets = [
-                    Math.max(start, 0),
-                    Math.min(end, this.dims[sliceIndex])
-                ];
+                const offsets = [Math.max(start, 0), Math.min(end, this.dims[sliceIndex])];
 
                 newOffsets.push(offsets);
                 newTensorDims.push(offsets[1] - offsets[0]);
-
             } else {
                 throw new Error(`Invalid slice: ${slice}`);
             }
@@ -468,7 +446,7 @@ export class Tensor {
                     data[i] = slicedData[i];
                 }
             } else {
-                throw new Error("Unsupported data type for slicing");
+                throw new Error('Unsupported data type for slicing');
             }
         } else {
             // Fallback to manual copying for non-contiguous slices
@@ -528,7 +506,7 @@ export class Tensor {
         }
 
         const this_data = this.data;
-        const fn = (a, b) => a + (b ** p);
+        const fn = (a, b) => a + b ** p;
 
         if (dim === null) {
             // @ts-ignore
@@ -560,7 +538,6 @@ export class Tensor {
         const this_data = this.data;
         const norm_data = norm.data;
         for (let i = 0; i < this_data.length; ++i) {
-
             // Calculate the index in the resulting array
             let resultIndex = 0;
 
@@ -610,11 +587,7 @@ export class Tensor {
      * @returns {Tensor} The squeezed tensor
      */
     squeeze(dim = null) {
-        return new Tensor(
-            this.type,
-            this.data,
-            calc_squeeze_dims(this.dims, dim)
-        )
+        return new Tensor(this.type, this.data, calc_squeeze_dims(this.dims, dim));
     }
 
     /**
@@ -634,11 +607,7 @@ export class Tensor {
      * @returns {Tensor} The unsqueezed tensor
      */
     unsqueeze(dim = null) {
-        return new Tensor(
-            this.type,
-            this.data,
-            calc_unsqueeze_dims(this.dims, dim)
-        );
+        return new Tensor(this.type, this.data, calc_unsqueeze_dims(this.dims, dim));
     }
 
     /**
@@ -660,7 +629,7 @@ export class Tensor {
         let dimsToFlatten = this.dims.slice(start_dim, end_dim + 1);
         let dimsToKeepAfter = this.dims.slice(end_dim + 1);
 
-        this.dims = [...dimsToKeepBefore, dimsToFlatten.reduce((a, b) => a * b, 1), ...dimsToKeepAfter]
+        this.dims = [...dimsToKeepBefore, dimsToFlatten.reduce((a, b) => a * b, 1), ...dimsToKeepAfter];
         return this;
     }
 
@@ -687,7 +656,7 @@ export class Tensor {
         for (let i = 0; i < dims.length; ++i) {
             if (dims[i] === -1) {
                 if (inferredIndex !== -1) {
-                    throw new Error("Only one dimension can be inferred");
+                    throw new Error('Only one dimension can be inferred');
                 }
                 inferredIndex = i;
             }
@@ -697,7 +666,7 @@ export class Tensor {
         if (inferredIndex !== -1) {
             // Some dimension must be inferred
             const productOther = dims.reduce((product, curr, index) => {
-                return index !== inferredIndex ? product * curr : product
+                return index !== inferredIndex ? product * curr : product;
             }, 1);
 
             dims[inferredIndex] = this_data.length / productOther;
@@ -792,7 +761,13 @@ export class Tensor {
         if (dim === null) {
             // None to reduce over all dimensions.
             const val = min(this.data)[0];
-            return new Tensor(this.type, [val], [/* scalar */]);
+            return new Tensor(
+                this.type,
+                [val],
+                [
+                    /* scalar */
+                ],
+            );
         }
         const [type, result, resultDims] = reduce_helper((a, b) => Math.min(a, b), this, dim, keepdim, Infinity);
         return new Tensor(type, result, resultDims);
@@ -802,7 +777,13 @@ export class Tensor {
         if (dim === null) {
             // None to reduce over all dimensions.
             const val = max(this.data)[0];
-            return new Tensor(this.type, [val], [/* scalar */]);
+            return new Tensor(
+                this.type,
+                [val],
+                [
+                    /* scalar */
+                ],
+            );
         }
         const [type, result, resultDims] = reduce_helper((a, b) => Math.max(a, b), this, dim, keepdim, -Infinity);
         return new Tensor(type, result, resultDims);
@@ -810,14 +791,14 @@ export class Tensor {
 
     argmin(dim = null, keepdim = false) {
         if (dim !== null) {
-            throw new Error("`dim !== null` not yet implemented.");
+            throw new Error('`dim !== null` not yet implemented.');
         }
         const index = min(this.data)[1];
         return new Tensor('int64', [BigInt(index)], []);
     }
     argmax(dim = null, keepdim = false) {
         if (dim !== null) {
-            throw new Error("`dim !== null` not yet implemented.");
+            throw new Error('`dim !== null` not yet implemented.');
         }
         const index = max(this.data)[1];
         return new Tensor('int64', [BigInt(index)], []);
@@ -884,7 +865,6 @@ export class Tensor {
  * @returns {NestArray} The reshaped array.
  */
 function reshape(data, dimensions) {
-
     const totalElements = data.length;
     const dimensionSize = dimensions.reduce((a, b) => a * b);
 
@@ -896,17 +876,20 @@ function reshape(data, dimensions) {
     let reshapedArray = data;
 
     for (let i = dimensions.length - 1; i >= 0; i--) {
-        reshapedArray = reshapedArray.reduce((acc, val) => {
-            let lastArray = acc[acc.length - 1];
-
-            if (lastArray.length < dimensions[i]) {
-                lastArray.push(val);
-            } else {
-                acc.push([val]);
-            }
+        reshapedArray = reshapedArray.reduce(
+            (acc, val) => {
+                let lastArray = acc[acc.length - 1];
+
+                if (lastArray.length < dimensions[i]) {
+                    lastArray.push(val);
+                } else {
+                    acc.push([val]);
+                }
 
-            return acc;
-        }, [[]]);
+                return acc;
+            },
+            [[]],
+        );
     }
 
     return reshapedArray[0];
@@ -923,7 +906,6 @@ export function permute(tensor, axes) {
     return new Tensor(tensor.type, permutedData, shape);
 }
 
-
 /**
  * Interpolates an Tensor to the given size.
  * @param {Tensor} input The input tensor to interpolate. Data must be channel-first (i.e., [c, h, w])
@@ -933,23 +915,21 @@ export function permute(tensor, axes) {
  * @returns {Tensor} The interpolated tensor.
  */
 export function interpolate(input, [out_height, out_width], mode = 'bilinear', align_corners = false) {
-
     // Input image dimensions
     const in_channels = input.dims.at(-3) ?? 1;
     const in_height = input.dims.at(-2);
     const in_width = input.dims.at(-1);
 
     let output = interpolate_data(
-        /** @type {import('./maths.js').TypedArray}*/(input.data),
+        /** @type {import('./maths.js').TypedArray}*/ (input.data),
         [in_channels, in_height, in_width],
         [out_height, out_width],
         mode,
-        align_corners
+        align_corners,
     );
     return new Tensor(input.type, output, [in_channels, out_height, out_width]);
 }
 
-
 /**
  * Down/up samples the input.
  * Inspired by https://pytorch.org/docs/stable/generated/torch.nn.functional.interpolate.html.
@@ -959,11 +939,7 @@ export function interpolate(input, [out_height, out_width], mode = 'bilinear', a
  * @param {"nearest"|"bilinear"|"bicubic"} [options.mode='bilinear'] algorithm used for upsampling
  * @returns {Promise} The interpolated tensor.
  */
-export async function interpolate_4d(input, {
-    size = null,
-    mode = 'bilinear',
-} = {}) {
-
+export async function interpolate_4d(input, { size = null, mode = 'bilinear' } = {}) {
     // Error checking
     if (input.dims.length !== 4) {
         throw new Error('`interpolate_4d` currently only supports 4D input.');
@@ -1024,7 +1000,6 @@ export async function rfft(x, a) {
     return await op({ x, a });
 }
 
-
 /**
  * Returns the k largest elements of the given input tensor.
  * Inspired by https://pytorch.org/docs/stable/generated/torch.topk.html
@@ -1042,15 +1017,10 @@ export async function topk(x, k) {
     }
     return await op({
         x,
-        k: new Tensor(
-            'int64',
-            [BigInt(k)],
-            [1]
-        )
+        k: new Tensor('int64', [BigInt(k)], [1]),
     });
 }
 
-
 const arrayToIndexTensor = (array) => new Tensor('int64', array, [array.length]);
 /**
  * Slice a multidimensional float32 tensor.
@@ -1072,7 +1042,6 @@ export async function slice(data, starts, ends, axes, steps) {
     });
 }
 
-
 /**
  * Perform mean pooling of the last hidden state followed by a normalization step.
  * @param {Tensor} last_hidden_state Tensor of shape [batchSize, seqLength, embedDim]
@@ -1115,11 +1084,7 @@ export function mean_pooling(last_hidden_state, attention_mask) {
         }
     }
 
-    return new Tensor(
-        last_hidden_state.type,
-        returnedData,
-        shape
-    )
+    return new Tensor(last_hidden_state.type, returnedData, shape);
 }
 
 /**
@@ -1130,9 +1095,7 @@ export function mean_pooling(last_hidden_state, attention_mask) {
  * @param {number} [options.eps=1e-5] A value added to the denominator for numerical stability.
  * @returns {Tensor} The normalized tensor.
  */
-export function layer_norm(input, normalized_shape, {
-    eps = 1e-5,
-} = {}) {
+export function layer_norm(input, normalized_shape, { eps = 1e-5 } = {}) {
     if (input.dims.length !== 2) {
         throw new Error('`layer_norm` currently only supports 2D input.');
     }
@@ -1144,10 +1107,10 @@ export function layer_norm(input, normalized_shape, {
     }
 
     const [std, mean] = std_mean(input, 1, 0, true);
-    const stdData = /** @type {Float32Array} */(std.data);
-    const meanData = /** @type {Float32Array} */(mean.data);
+    const stdData = /** @type {Float32Array} */ (std.data);
+    const meanData = /** @type {Float32Array} */ (mean.data);
 
-    const inputData = /** @type {Float32Array} */(input.data);
+    const inputData = /** @type {Float32Array} */ (input.data);
 
     // @ts-ignore
     const returnedData = new inputData.constructor(inputData.length);
@@ -1215,7 +1178,9 @@ function calc_unsqueeze_dims(dims, dim) {
 function safeIndex(index, size, dimension = null, boundsCheck = true) {
     if (index < -size || index >= size) {
         if (boundsCheck) {
-            throw new Error(`IndexError: index ${index} is out of bounds for dimension${dimension === null ? '' : ' ' + dimension} with size ${size}`);
+            throw new Error(
+                `IndexError: index ${index} is out of bounds for dimension${dimension === null ? '' : ' ' + dimension} with size ${size}`,
+            );
         } else {
             return index < -size ? 0 : size;
         }
@@ -1259,9 +1224,7 @@ export function cat(tensors, dim = 0) {
             result.set(tensorData, offset);
             offset += tensorData.length;
         }
-
     } else {
-
         let currentDim = 0;
 
         for (let t = 0; t < tensors.length; ++t) {
@@ -1301,10 +1264,12 @@ export function cat(tensors, dim = 0) {
 export function stack(tensors, dim = 0) {
     // TODO do validation of shapes
     // NOTE: stack expects each tensor to be equal size
-    return cat(tensors.map(t => t.unsqueeze(dim)), dim);
+    return cat(
+        tensors.map((t) => t.unsqueeze(dim)),
+        dim,
+    );
 }
 
-
 /**
  * @param {(previousValue: any, currentValue: any, currentIndex?: number, resultIndex?: number) => any} callbackfn
  * @param {Tensor} input the input tensor.
@@ -1332,7 +1297,6 @@ function reduce_helper(callbackfn, input, dim = null, keepdim = false, initialVa
 
     // Iterate over the data array
     for (let i = 0; i < inputData.length; ++i) {
-
         // Calculate the index in the resulting array
         let resultIndex = 0;
 
@@ -1355,7 +1319,6 @@ function reduce_helper(callbackfn, input, dim = null, keepdim = false, initialVa
     return [input.type, result, resultDims];
 }
 
-
 /**
  * Calculates the standard deviation and mean over the dimensions specified by dim. dim can be a single dimension or `null` to reduce over all dimensions.
  * @param {Tensor} input the input tenso
@@ -1365,7 +1328,7 @@ function reduce_helper(callbackfn, input, dim = null, keepdim = false, initialVa
  * @returns {Tensor[]} A tuple of (std, mean) tensors.
  */
 export function std_mean(input, dim = null, correction = 1, keepdim = false) {
-    const inputData = /** @type {Float32Array} */(input.data);
+    const inputData = /** @type {Float32Array} */ (input.data);
     const inputDims = input.dims;
 
     if (dim === null) {
@@ -1374,8 +1337,20 @@ export function std_mean(input, dim = null, correction = 1, keepdim = false) {
         const mean = sum / inputData.length;
         const std = Math.sqrt(inputData.reduce((a, b) => a + (b - mean) ** 2, 0) / (inputData.length - correction));
 
-        const meanTensor = new Tensor(input.type, [mean], [/* scalar */]);
-        const stdTensor = new Tensor(input.type, [std], [/* scalar */]);
+        const meanTensor = new Tensor(
+            input.type,
+            [mean],
+            [
+                /* scalar */
+            ],
+        );
+        const stdTensor = new Tensor(
+            input.type,
+            [std],
+            [
+                /* scalar */
+            ],
+        );
 
         return [stdTensor, meanTensor];
     }
@@ -1384,7 +1359,12 @@ export function std_mean(input, dim = null, correction = 1, keepdim = false) {
     const meanTensorData = meanTensor.data;
 
     // Compute squared sum
-    const [type, result, resultDims] = reduce_helper((a, b, i, j) => a + (b - meanTensorData[j]) ** 2, input, dim, keepdim);
+    const [type, result, resultDims] = reduce_helper(
+        (a, b, i, j) => a + (b - meanTensorData[j]) ** 2,
+        input,
+        dim,
+        keepdim,
+    );
 
     // Square root of the squared sum
     for (let i = 0; i < result.length; ++i) {
@@ -1405,12 +1385,18 @@ export function std_mean(input, dim = null, correction = 1, keepdim = false) {
  */
 export function mean(input, dim = null, keepdim = false) {
     const inputDims = input.dims;
-    const inputData = /** @type {Float32Array} */(input.data);
+    const inputData = /** @type {Float32Array} */ (input.data);
 
     if (dim === null) {
         // None to reduce over all dimensions.
         const val = inputData.reduce((a, b) => a + b, 0);
-        return new Tensor(input.type, [val / inputData.length], [/* scalar */]);
+        return new Tensor(
+            input.type,
+            [val / inputData.length],
+            [
+                /* scalar */
+            ],
+        );
     }
     dim = safeIndex(dim, inputDims.length);
 
@@ -1427,7 +1413,6 @@ export function mean(input, dim = null, keepdim = false) {
     return new Tensor(type, result, resultDims);
 }
 
-
 function dimsToStride(dims) {
     const stride = new Array(dims.length);
     for (let i = dims.length - 1, s2 = 1; i >= 0; --i) {
@@ -1439,11 +1424,7 @@ function dimsToStride(dims) {
 
 function fullHelper(size, fill_value, dtype, cls) {
     const numElements = size.reduce((a, b) => a * b, 1);
-    return new Tensor(
-        dtype,
-        new cls(numElements).fill(fill_value),
-        size
-    )
+    return new Tensor(dtype, new cls(numElements).fill(fill_value), size);
 }
 
 /**
@@ -1519,10 +1500,10 @@ export function zeros_like(tensor) {
 export function rand(size) {
     const length = size.reduce((a, b) => a * b, 1);
     return new Tensor(
-        "float32",
+        'float32',
         Float32Array.from({ length }, () => Math.random()),
         size,
-    )
+    );
 }
 
 /**
@@ -1533,10 +1514,10 @@ export function rand(size) {
  */
 export function quantize_embeddings(tensor, precision) {
     if (tensor.dims.length !== 2) {
-        throw new Error("The tensor must have 2 dimensions");
+        throw new Error('The tensor must have 2 dimensions');
     }
     if (tensor.dims.at(-1) % 8 !== 0) {
-        throw new Error("The last dimension of the tensor must be a multiple of 8");
+        throw new Error('The last dimension of the tensor must be a multiple of 8');
     }
     if (!['binary', 'ubinary'].includes(precision)) {
         throw new Error("The precision must be either 'binary' or 'ubinary'");
@@ -1564,7 +1545,7 @@ export function quantize_embeddings(tensor, precision) {
         if (signed && bitPosition === 0) {
             outputData[arrayIndex] -= 128;
         }
-    };
+    }
 
     return new Tensor(dtype, outputData, [tensor.dims[0], tensor.dims[1] / 8]);
 }
diff --git a/src/utils/video.js b/src/utils/video.js
index 9bf2d6aee..7917911a8 100644
--- a/src/utils/video.js
+++ b/src/utils/video.js
@@ -1,8 +1,7 @@
-import { RawImage } from "./image.js";
-import { apis } from "../env.js";
+import { RawImage } from './image.js';
+import { apis } from '../env.js';
 
 export class RawVideoFrame {
-
     /**
      * @param {RawImage} image
      * @param {number} timestamp
@@ -21,7 +20,7 @@ export class RawVideo {
     constructor(frames, duration) {
         if (frames.length > 0 && frames[0] instanceof RawImage) {
             // Assume uniform timestamps
-            frames = frames.map((image, i) => new RawVideoFrame(image, (i + 1) / (frames.length + 1) * duration));
+            frames = frames.map((image, i) => new RawVideoFrame(image, ((i + 1) / (frames.length + 1)) * duration));
         }
         this.frames = /** @type {RawVideoFrame[]} */ (frames);
         this.duration = duration;
@@ -39,7 +38,6 @@ export class RawVideo {
     }
 }
 
-
 /**
  * Loads a video.
  *
@@ -52,19 +50,19 @@ export class RawVideo {
  */
 export async function load_video(src, { num_frames = null, fps = null } = {}) {
     if (!apis.IS_BROWSER_ENV) {
-        throw new Error("`load_video` is currently only supported in browser environments.");
+        throw new Error('`load_video` is currently only supported in browser environments.');
     }
 
     // TODO: Support efficiently loading all frames using the WebCodecs API.
     // Specfically, https://developer.mozilla.org/en-US/docs/Web/API/VideoDecoder
     if (num_frames == null && fps == null) {
-        throw new Error("Either num_frames or fps must be provided.");
+        throw new Error('Either num_frames or fps must be provided.');
     }
 
     const frames = [];
 
-    const video = document.createElement("video");
-    video.crossOrigin = "anonymous";
+    const video = document.createElement('video');
+    video.crossOrigin = 'anonymous';
     video.muted = true; // mute to allow autoplay and seeking
 
     if (typeof src === 'string') {
@@ -74,17 +72,17 @@ export async function load_video(src, { num_frames = null, fps = null } = {}) {
     } else if (src instanceof HTMLVideoElement) {
         video.src = src.src;
     } else {
-        throw new Error("Invalid URL or video element provided.");
+        throw new Error('Invalid URL or video element provided.');
     }
     // Wait for metadata to load to obtain duration
-    await new Promise((resolve) => video.onloadedmetadata = resolve);
+    await new Promise((resolve) => (video.onloadedmetadata = resolve));
 
     if (video.seekable.start(0) === video.seekable.end(0)) {
         // Fallback: Download entire video if not seekable
         const response = await fetch(video.src);
         const blob = await response.blob();
         video.src = URL.createObjectURL(blob);
-        await new Promise((resolve) => video.onloadedmetadata = resolve);
+        await new Promise((resolve) => (video.onloadedmetadata = resolve));
     }
 
     const duration = video.duration;
@@ -104,10 +102,10 @@ export async function load_video(src, { num_frames = null, fps = null } = {}) {
         sampleTimes.push(num_frames === 1 ? duration / 2 : i * step);
     }
 
-    const canvas = document.createElement("canvas");
+    const canvas = document.createElement('canvas');
     canvas.width = video.videoWidth;
     canvas.height = video.videoHeight;
-    const ctx = canvas.getContext("2d", { willReadFrequently: true });
+    const ctx = canvas.getContext('2d', { willReadFrequently: true });
     for (const t of sampleTimes) {
         video.currentTime = t;
         await new Promise((resolve) => {
diff --git a/tests/models/grounding_dino/test_modeling_grounding_dino.js b/tests/models/grounding_dino/test_modeling_grounding_dino.js
index 77cad0711..177f62aff 100644
--- a/tests/models/grounding_dino/test_modeling_grounding_dino.js
+++ b/tests/models/grounding_dino/test_modeling_grounding_dino.js
@@ -32,7 +32,7 @@ export default () => {
         expect(pred_boxes.dims).toEqual([1, num_queries, 4]);
         expect(logits.max().item()).toBeCloseTo(56.237613677978516, 2);
         expect(logits.min().item()).toEqual(-Infinity);
-        expect(pred_boxes.mean().item()).toBeCloseTo(0.2500016987323761, 6);
+        expect(pred_boxes.mean().item()).toBeCloseTo(0.2500016987323761, 4);
       },
       MAX_TEST_EXECUTION_TIME,
     );
diff --git a/tests/pipelines/test_pipelines_audio_classification.js b/tests/pipelines/test_pipelines_audio_classification.js
index e9e4ac703..f62f200f5 100644
--- a/tests/pipelines/test_pipelines_audio_classification.js
+++ b/tests/pipelines/test_pipelines_audio_classification.js
@@ -74,7 +74,7 @@ export default () => {
     });
 
     afterAll(async () => {
-      await pipe.dispose();
+      await pipe?.dispose();
     }, MAX_MODEL_DISPOSE_TIME);
   });
 };
diff --git a/tests/pipelines/test_pipelines_automatic_speech_recognition.js b/tests/pipelines/test_pipelines_automatic_speech_recognition.js
index da9dd88b4..bd03430dc 100644
--- a/tests/pipelines/test_pipelines_automatic_speech_recognition.js
+++ b/tests/pipelines/test_pipelines_automatic_speech_recognition.js
@@ -88,7 +88,7 @@ export default () => {
       });
 
       afterAll(async () => {
-        await pipe.dispose();
+        await pipe?.dispose();
       }, MAX_MODEL_DISPOSE_TIME);
     });
 
@@ -122,7 +122,7 @@ export default () => {
       });
 
       afterAll(async () => {
-        await pipe.dispose();
+        await pipe?.dispose();
       }, MAX_MODEL_DISPOSE_TIME);
     });
   });
diff --git a/tests/pipelines/test_pipelines_background_removal.js b/tests/pipelines/test_pipelines_background_removal.js
index a8fcfc9e7..e7b518a49 100644
--- a/tests/pipelines/test_pipelines_background_removal.js
+++ b/tests/pipelines/test_pipelines_background_removal.js
@@ -35,7 +35,7 @@ export default () => {
       );
 
       afterAll(async () => {
-        await pipe.dispose();
+        await pipe?.dispose();
       }, MAX_MODEL_DISPOSE_TIME);
     });
 
@@ -63,7 +63,7 @@ export default () => {
       );
 
       afterAll(async () => {
-        await pipe.dispose();
+        await pipe?.dispose();
       }, MAX_MODEL_DISPOSE_TIME);
     });
   });
diff --git a/tests/pipelines/test_pipelines_depth_estimation.js b/tests/pipelines/test_pipelines_depth_estimation.js
index 534e91030..61d4c50d1 100644
--- a/tests/pipelines/test_pipelines_depth_estimation.js
+++ b/tests/pipelines/test_pipelines_depth_estimation.js
@@ -26,7 +26,7 @@ export default () => {
         async () => {
           const output = await pipe(images[0]);
           expect(output.predicted_depth.dims).toEqual([224, 224]);
-          expect(output.predicted_depth.mean().item()).toBeCloseTo(0.000006106501587055391, 6);
+          expect(output.predicted_depth.mean().item()).toBeCloseTo(0.000006106501587055391, 4);
           expect(output.depth.size).toEqual(images[0].size);
         },
         MAX_TEST_EXECUTION_TIME,
@@ -40,10 +40,10 @@ export default () => {
           const output = await pipe(images);
           expect(output).toHaveLength(images.length);
           expect(output[0].predicted_depth.dims).toEqual([224, 224]);
-          expect(output[0].predicted_depth.mean().item()).toBeCloseTo(0.000006106501587055391, 6);
+          expect(output[0].predicted_depth.mean().item()).toBeCloseTo(0.000006106501587055391, 4);
           expect(output[0].depth.size).toEqual(images[0].size);
           expect(output[1].predicted_depth.dims).toEqual([224, 224]);
-          expect(output[1].predicted_depth.mean().item()).toBeCloseTo(0.0000014548650142387487, 6);
+          expect(output[1].predicted_depth.mean().item()).toBeCloseTo(0.0000014548650142387487, 4);
           expect(output[1].depth.size).toEqual(images[1].size);
         },
         MAX_TEST_EXECUTION_TIME,
@@ -51,7 +51,7 @@ export default () => {
     });
 
     afterAll(async () => {
-      await pipe.dispose();
+      await pipe?.dispose();
     }, MAX_MODEL_DISPOSE_TIME);
   });
 };
diff --git a/tests/pipelines/test_pipelines_document_question_answering.js b/tests/pipelines/test_pipelines_document_question_answering.js
index 3ebb1e436..015627fec 100644
--- a/tests/pipelines/test_pipelines_document_question_answering.js
+++ b/tests/pipelines/test_pipelines_document_question_answering.js
@@ -35,7 +35,7 @@ export default () => {
     });
 
     afterAll(async () => {
-      await pipe.dispose();
+      await pipe?.dispose();
     }, MAX_MODEL_DISPOSE_TIME);
   });
 };
diff --git a/tests/pipelines/test_pipelines_feature_extraction.js b/tests/pipelines/test_pipelines_feature_extraction.js
index b7bb79e59..b113b9edf 100644
--- a/tests/pipelines/test_pipelines_feature_extraction.js
+++ b/tests/pipelines/test_pipelines_feature_extraction.js
@@ -115,7 +115,7 @@ export default () => {
     });
 
     afterAll(async () => {
-      await pipe.dispose();
+      await pipe?.dispose();
     }, MAX_MODEL_DISPOSE_TIME);
   });
 };
diff --git a/tests/pipelines/test_pipelines_fill_mask.js b/tests/pipelines/test_pipelines_fill_mask.js
index 608fb54da..5a72ea893 100644
--- a/tests/pipelines/test_pipelines_fill_mask.js
+++ b/tests/pipelines/test_pipelines_fill_mask.js
@@ -95,7 +95,7 @@ export default () => {
       });
 
       afterAll(async () => {
-        await pipe.dispose();
+        await pipe?.dispose();
       }, MAX_MODEL_DISPOSE_TIME);
     });
 
@@ -158,7 +158,7 @@ export default () => {
       });
 
       afterAll(async () => {
-        await pipe.dispose();
+        await pipe?.dispose();
       }, MAX_MODEL_DISPOSE_TIME);
     });
   });
diff --git a/tests/pipelines/test_pipelines_image_classification.js b/tests/pipelines/test_pipelines_image_classification.js
index 93b693a94..2940000b1 100644
--- a/tests/pipelines/test_pipelines_image_classification.js
+++ b/tests/pipelines/test_pipelines_image_classification.js
@@ -75,7 +75,7 @@ export default () => {
     });
 
     afterAll(async () => {
-      await pipe.dispose();
+      await pipe?.dispose();
     }, MAX_MODEL_DISPOSE_TIME);
   });
 };
diff --git a/tests/pipelines/test_pipelines_image_feature_extraction.js b/tests/pipelines/test_pipelines_image_feature_extraction.js
index ae7a6fce4..14b36716b 100644
--- a/tests/pipelines/test_pipelines_image_feature_extraction.js
+++ b/tests/pipelines/test_pipelines_image_feature_extraction.js
@@ -46,7 +46,7 @@ export default () => {
       });
 
       afterAll(async () => {
-        await pipe.dispose();
+        await pipe?.dispose();
       }, MAX_MODEL_DISPOSE_TIME);
     });
     describe("CLIP-like", () => {
@@ -88,7 +88,7 @@ export default () => {
       });
 
       afterAll(async () => {
-        await pipe.dispose();
+        await pipe?.dispose();
       }, MAX_MODEL_DISPOSE_TIME);
     });
   });
diff --git a/tests/pipelines/test_pipelines_image_segmentation.js b/tests/pipelines/test_pipelines_image_segmentation.js
index 7358601ea..7e0202437 100644
--- a/tests/pipelines/test_pipelines_image_segmentation.js
+++ b/tests/pipelines/test_pipelines_image_segmentation.js
@@ -64,7 +64,7 @@ export default () => {
       );
 
       afterAll(async () => {
-        await pipe.dispose();
+        await pipe?.dispose();
       }, MAX_MODEL_DISPOSE_TIME);
     });
 
@@ -112,7 +112,7 @@ export default () => {
       );
 
       afterAll(async () => {
-        await pipe.dispose();
+        await pipe?.dispose();
       }, MAX_MODEL_DISPOSE_TIME);
     });
   });
diff --git a/tests/pipelines/test_pipelines_image_to_image.js b/tests/pipelines/test_pipelines_image_to_image.js
index c7b9a00d2..1e06c119b 100644
--- a/tests/pipelines/test_pipelines_image_to_image.js
+++ b/tests/pipelines/test_pipelines_image_to_image.js
@@ -50,7 +50,7 @@ export default () => {
     });
 
     afterAll(async () => {
-      await pipe.dispose();
+      await pipe?.dispose();
     }, MAX_MODEL_DISPOSE_TIME);
   });
 };
diff --git a/tests/pipelines/test_pipelines_image_to_text.js b/tests/pipelines/test_pipelines_image_to_text.js
index f7d951811..3ef8a29c2 100644
--- a/tests/pipelines/test_pipelines_image_to_text.js
+++ b/tests/pipelines/test_pipelines_image_to_text.js
@@ -45,7 +45,7 @@ export default () => {
     });
 
     afterAll(async () => {
-      await pipe.dispose();
+      await pipe?.dispose();
     }, MAX_MODEL_DISPOSE_TIME);
   });
 };
diff --git a/tests/pipelines/test_pipelines_object_detection.js b/tests/pipelines/test_pipelines_object_detection.js
index e9b0375d0..1573597c7 100644
--- a/tests/pipelines/test_pipelines_object_detection.js
+++ b/tests/pipelines/test_pipelines_object_detection.js
@@ -53,7 +53,7 @@ export default () => {
       );
 
       afterAll(async () => {
-        await pipe.dispose();
+        await pipe?.dispose();
       }, MAX_MODEL_DISPOSE_TIME);
     });
 
@@ -124,7 +124,7 @@ export default () => {
       // });
 
       afterAll(async () => {
-        await pipe.dispose();
+        await pipe?.dispose();
       }, MAX_MODEL_DISPOSE_TIME);
     });
   });
diff --git a/tests/pipelines/test_pipelines_question_answering.js b/tests/pipelines/test_pipelines_question_answering.js
index ff346c03b..fdee014ba 100644
--- a/tests/pipelines/test_pipelines_question_answering.js
+++ b/tests/pipelines/test_pipelines_question_answering.js
@@ -43,7 +43,7 @@ export default () => {
     });
 
     afterAll(async () => {
-      await pipe.dispose();
+      await pipe?.dispose();
     }, MAX_MODEL_DISPOSE_TIME);
   });
 };
diff --git a/tests/pipelines/test_pipelines_summarization.js b/tests/pipelines/test_pipelines_summarization.js
index 877fd81e9..0fc18ea6d 100644
--- a/tests/pipelines/test_pipelines_summarization.js
+++ b/tests/pipelines/test_pipelines_summarization.js
@@ -34,7 +34,7 @@ export default () => {
     });
 
     afterAll(async () => {
-      await pipe.dispose();
+      await pipe?.dispose();
     }, MAX_MODEL_DISPOSE_TIME);
   });
 };
diff --git a/tests/pipelines/test_pipelines_text2text_generation.js b/tests/pipelines/test_pipelines_text2text_generation.js
index 0084fbbd2..d8d756f34 100644
--- a/tests/pipelines/test_pipelines_text2text_generation.js
+++ b/tests/pipelines/test_pipelines_text2text_generation.js
@@ -34,7 +34,7 @@ export default () => {
     });
 
     afterAll(async () => {
-      await pipe.dispose();
+      await pipe?.dispose();
     }, MAX_MODEL_DISPOSE_TIME);
   });
 };
diff --git a/tests/pipelines/test_pipelines_text_classification.js b/tests/pipelines/test_pipelines_text_classification.js
index 13a78f1a6..91c855d30 100644
--- a/tests/pipelines/test_pipelines_text_classification.js
+++ b/tests/pipelines/test_pipelines_text_classification.js
@@ -101,7 +101,7 @@ export default () => {
     });
 
     afterAll(async () => {
-      await pipe.dispose();
+      await pipe?.dispose();
     }, MAX_MODEL_DISPOSE_TIME);
   });
 };
diff --git a/tests/pipelines/test_pipelines_text_generation.js b/tests/pipelines/test_pipelines_text_generation.js
index 42f23e910..3366d7beb 100644
--- a/tests/pipelines/test_pipelines_text_generation.js
+++ b/tests/pipelines/test_pipelines_text_generation.js
@@ -103,7 +103,7 @@ export default () => {
     // });
 
     afterAll(async () => {
-      await pipe.dispose();
+      await pipe?.dispose();
     }, MAX_MODEL_DISPOSE_TIME);
   });
 };
diff --git a/tests/pipelines/test_pipelines_text_to_audio.js b/tests/pipelines/test_pipelines_text_to_audio.js
index d37f0203e..8f0a4d3a5 100644
--- a/tests/pipelines/test_pipelines_text_to_audio.js
+++ b/tests/pipelines/test_pipelines_text_to_audio.js
@@ -31,7 +31,7 @@ export default () => {
     );
 
     afterAll(async () => {
-      await pipe.dispose();
+      await pipe?.dispose();
     }, MAX_MODEL_DISPOSE_TIME);
   });
 };
diff --git a/tests/pipelines/test_pipelines_token_classification.js b/tests/pipelines/test_pipelines_token_classification.js
index 9d91813d4..6bcbeb21e 100644
--- a/tests/pipelines/test_pipelines_token_classification.js
+++ b/tests/pipelines/test_pipelines_token_classification.js
@@ -151,7 +151,7 @@ export default () => {
     });
 
     afterAll(async () => {
-      await pipe.dispose();
+      await pipe?.dispose();
     }, MAX_MODEL_DISPOSE_TIME);
   });
 };
diff --git a/tests/pipelines/test_pipelines_translation.js b/tests/pipelines/test_pipelines_translation.js
index 97afce8d6..c1dc5d954 100644
--- a/tests/pipelines/test_pipelines_translation.js
+++ b/tests/pipelines/test_pipelines_translation.js
@@ -36,7 +36,7 @@ export default () => {
     });
 
     afterAll(async () => {
-      await pipe.dispose();
+      await pipe?.dispose();
     }, MAX_MODEL_DISPOSE_TIME);
   });
 };
diff --git a/tests/pipelines/test_pipelines_zero_shot.js b/tests/pipelines/test_pipelines_zero_shot.js
index 1c30db9ea..49c8cf538 100644
--- a/tests/pipelines/test_pipelines_zero_shot.js
+++ b/tests/pipelines/test_pipelines_zero_shot.js
@@ -94,7 +94,7 @@ export default () => {
     );
 
     afterAll(async () => {
-      await pipe.dispose();
+      await pipe?.dispose();
     }, MAX_MODEL_DISPOSE_TIME);
   });
 };
diff --git a/tests/pipelines/test_pipelines_zero_shot_audio_classification.js b/tests/pipelines/test_pipelines_zero_shot_audio_classification.js
index 00dd328ea..ba7a6007a 100644
--- a/tests/pipelines/test_pipelines_zero_shot_audio_classification.js
+++ b/tests/pipelines/test_pipelines_zero_shot_audio_classification.js
@@ -52,7 +52,7 @@ export default () => {
     });
 
     afterAll(async () => {
-      await pipe.dispose();
+      await pipe?.dispose();
     }, MAX_MODEL_DISPOSE_TIME);
   });
 };
diff --git a/tests/pipelines/test_pipelines_zero_shot_image_classification.js b/tests/pipelines/test_pipelines_zero_shot_image_classification.js
index dfa1e23e8..dee7202e0 100644
--- a/tests/pipelines/test_pipelines_zero_shot_image_classification.js
+++ b/tests/pipelines/test_pipelines_zero_shot_image_classification.js
@@ -92,7 +92,7 @@ export default () => {
     });
 
     afterAll(async () => {
-      await pipe.dispose();
+      await pipe?.dispose();
     }, MAX_MODEL_DISPOSE_TIME);
   });
 };
diff --git a/tests/pipelines/test_pipelines_zero_shot_object_detection.js b/tests/pipelines/test_pipelines_zero_shot_object_detection.js
index 294ba7d39..4883b1ace 100644
--- a/tests/pipelines/test_pipelines_zero_shot_object_detection.js
+++ b/tests/pipelines/test_pipelines_zero_shot_object_detection.js
@@ -129,7 +129,7 @@ export default () => {
       });
 
       afterAll(async () => {
-        await pipe.dispose();
+        await pipe?.dispose();
       }, MAX_MODEL_DISPOSE_TIME);
     });
 
@@ -179,7 +179,7 @@ export default () => {
       });
 
       afterAll(async () => {
-        await pipe.dispose();
+        await pipe?.dispose();
       }, MAX_MODEL_DISPOSE_TIME);
     });
   });
diff --git a/tests/tokenizers.test.js b/tests/tokenizers.test.js
index 5f7a4a138..8113e9205 100644
--- a/tests/tokenizers.test.js
+++ b/tests/tokenizers.test.js
@@ -34,6 +34,9 @@ describe("Tokenizers (model-specific)", () => {
               }
             });
           }
+          afterAll(() => {
+            global.gc?.();
+          });
         });
       }
       // Run custom tests, if they exist
diff --git a/webpack.config.js b/webpack.config.js
index 53b5ccd9d..4d8edb24f 100644
--- a/webpack.config.js
+++ b/webpack.config.js
@@ -8,7 +8,7 @@ const __dirname = path.dirname(fileURLToPath(import.meta.url));
 
 /**
  * Plugin to strip the "node:" prefix from module requests.
- * 
+ *
  * This is necessary to ensure both web and node builds work correctly,
  * otherwise we would get an error like:
  * ```
@@ -16,46 +16,53 @@ const __dirname = path.dirname(fileURLToPath(import.meta.url));
  * Webpack supports "data:" and "file:" URIs by default.
  * You may need an additional plugin to handle "node:" URIs.
  * ```
- * 
+ *
  * NOTE: We then do not need to use the `node:` prefix in the resolve.alias configuration.
  */
 class StripNodePrefixPlugin extends webpack.NormalModuleReplacementPlugin {
   constructor() {
-    super(
-      /^node:(.+)$/,
-      resource => {
-        resource.request = resource.request.replace(/^node:/, '');
-      }
-    );
+    super(/^node:(.+)$/, (resource) => {
+      resource.request = resource.request.replace(/^node:/, "");
+    });
   }
 }
 
 /**
  * Plugin to post-process build files. Required to solve certain issues with ESM module output.
  * See https://github.com/webpack/webpack/issues/17121 for more information.
- * 
+ *
  * @see https://webpack.js.org/contribute/writing-a-plugin/
  */
 class PostBuildPlugin {
+  static completed = false;
 
   apply(compiler) {
-    compiler.hooks.done.tap('PostBuildPlugin', () => {
-      const dist = path.join(__dirname, 'dist');
-      const ORT_JSEP_FILE = 'ort-wasm-simd-threaded.jsep.mjs';
-      const ORT_BUNDLE_FILE = 'ort.bundle.min.mjs';
-
-      // 1. Remove unnecessary files
-      {
-        const file = path.join(dist, ORT_BUNDLE_FILE);
-        if (fs.existsSync(file)) fs.unlinkSync(file);
+    compiler.hooks.done.tap("PostBuildPlugin", () => {
+      if (!process.env.WEBPACK_SERVE && !PostBuildPlugin.completed) {
+        // Ensure we only run this once
+        PostBuildPlugin.completed = true;
+        return;
       }
+      const dist = path.join(__dirname, "dist");
+      const ORT_JSEP_FILE = "ort-wasm-simd-threaded.asyncify.mjs";
+      const ORT_BUNDLE_FILE = "ort.webgpu.bundle.min.mjs";
 
-      // 2. Copy unbundled JSEP file
+      // 1. Copy unbundled asyncify file
       {
-        const src = path.join(__dirname, 'node_modules/onnxruntime-web/dist', ORT_JSEP_FILE);
+        const src = path.join(
+          __dirname,
+          "node_modules/onnxruntime-web/dist",
+          ORT_JSEP_FILE,
+        );
         const dest = path.join(dist, ORT_JSEP_FILE);
         fs.copyFileSync(src, dest);
       }
+
+      // 2. Remove unnecessary files
+      {
+        const file = path.join(dist, ORT_BUNDLE_FILE);
+        if (fs.existsSync(file)) fs.unlinkSync(file);
+      }
     });
   }
 }
@@ -172,10 +179,7 @@ const NODE_EXTERNAL_MODULES = [
 const WEB_IGNORE_MODULES = ["onnxruntime-node", "sharp", "fs", "path", "url"];
 
 // Do not bundle the following modules with webpack (mark as external)
-const WEB_EXTERNAL_MODULES = [
-  "onnxruntime-common",
-  "onnxruntime-web",
-];
+const WEB_EXTERNAL_MODULES = ["onnxruntime-common", "onnxruntime-web"];
 
 // Web-only build
 const WEB_BUILD = buildConfig({
@@ -183,19 +187,14 @@ const WEB_BUILD = buildConfig({
   type: "module",
   ignoreModules: WEB_IGNORE_MODULES,
   externalModules: WEB_EXTERNAL_MODULES,
-  plugins: [
-    new StripNodePrefixPlugin()
-  ]
+  plugins: [new StripNodePrefixPlugin(), new PostBuildPlugin()],
 });
 
 // Web-only build, bundled with onnxruntime-web
 const BUNDLE_BUILD = buildConfig({
   type: "module",
   ignoreModules: WEB_IGNORE_MODULES,
-  plugins: [
-    new StripNodePrefixPlugin(),
-    new PostBuildPlugin(),
-  ],
+  plugins: [new StripNodePrefixPlugin(), new PostBuildPlugin()],
 });
 
 // Node-compatible builds