{ // 获取包含Hugging Face文本的span元素 const spans = link.querySelectorAll('span.whitespace-nowrap, span.hidden.whitespace-nowrap'); spans.forEach(span => { if (span.textContent && span.textContent.trim().match(/Hugging\s*Face/i)) { span.textContent = 'AI快站'; } }); }); // 替换logo图片的alt属性 document.querySelectorAll('img[alt*="Hugging"], img[alt*="Face"]').forEach(img => { if (img.alt.match(/Hugging\s*Face/i)) { img.alt = 'AI快站 logo'; } }); } // 替换导航栏中的链接 function replaceNavigationLinks() { // 已替换标记,防止重复运行 if (window._navLinksReplaced) { return; } // 已经替换过的链接集合,防止重复替换 const replacedLinks = new Set(); // 只在导航栏区域查找和替换链接 const headerArea = document.querySelector('header') || document.querySelector('nav'); if (!headerArea) { return; } // 在导航区域内查找链接 const navLinks = headerArea.querySelectorAll('a'); navLinks.forEach(link => { // 如果已经替换过,跳过 if (replacedLinks.has(link)) return; const linkText = link.textContent.trim(); const linkHref = link.getAttribute('href') || ''; // 替换Spaces链接 - 仅替换一次 if ( (linkHref.includes('/spaces') || linkHref === '/spaces' || linkText === 'Spaces' || linkText.match(/^s*Spacess*$/i)) && linkText !== 'OCR模型免费转Markdown' && linkText !== 'OCR模型免费转Markdown' ) { link.textContent = 'OCR模型免费转Markdown'; link.href = 'https://fast360.xyz'; link.setAttribute('target', '_blank'); link.setAttribute('rel', 'noopener noreferrer'); replacedLinks.add(link); } // 删除Posts链接 else if ( (linkHref.includes('/posts') || linkHref === '/posts' || linkText === 'Posts' || linkText.match(/^s*Postss*$/i)) ) { if (link.parentNode) { link.parentNode.removeChild(link); } replacedLinks.add(link); } // 替换Docs链接 - 仅替换一次 else if ( (linkHref.includes('/docs') || linkHref === '/docs' || linkText === 'Docs' || linkText.match(/^s*Docss*$/i)) && linkText !== '模型下载攻略' ) { link.textContent = '模型下载攻略'; link.href = '/'; replacedLinks.add(link); } // 删除Enterprise链接 else if ( (linkHref.includes('/enterprise') || linkHref === '/enterprise' || linkText === 'Enterprise' || linkText.match(/^s*Enterprises*$/i)) ) { if (link.parentNode) { link.parentNode.removeChild(link); } replacedLinks.add(link); } }); // 查找可能嵌套的Spaces和Posts文本 const textNodes = []; function findTextNodes(element) { if (element.nodeType === Node.TEXT_NODE) { const text = element.textContent.trim(); if (text === 'Spaces' || text === 'Posts' || text === 'Enterprise') { textNodes.push(element); } } else { for (const child of element.childNodes) { findTextNodes(child); } } } // 只在导航区域内查找文本节点 findTextNodes(headerArea); // 替换找到的文本节点 textNodes.forEach(node => { const text = node.textContent.trim(); if (text === 'Spaces') { node.textContent = node.textContent.replace(/Spaces/g, 'OCR模型免费转Markdown'); } else if (text === 'Posts') { // 删除Posts文本节点 if (node.parentNode) { node.parentNode.removeChild(node); } } else if (text === 'Enterprise') { // 删除Enterprise文本节点 if (node.parentNode) { node.parentNode.removeChild(node); } } }); // 标记已替换完成 window._navLinksReplaced = true; } // 替换代码区域中的域名 function replaceCodeDomains() { // 特别处理span.hljs-string和span.njs-string元素 document.querySelectorAll('span.hljs-string, span.njs-string, span[class*="hljs-string"], span[class*="njs-string"]').forEach(span => { if (span.textContent && span.textContent.includes('huggingface.co')) { span.textContent = span.textContent.replace(/huggingface.co/g, 'aifasthub.com'); } }); // 替换hljs-string类的span中的域名(移除多余的转义符号) document.querySelectorAll('span.hljs-string, span[class*="hljs-string"]').forEach(span => { if (span.textContent && span.textContent.includes('huggingface.co')) { span.textContent = span.textContent.replace(/huggingface.co/g, 'aifasthub.com'); } }); // 替换pre和code标签中包含git clone命令的域名 document.querySelectorAll('pre, code').forEach(element => { if (element.textContent && element.textContent.includes('git clone')) { const text = element.innerHTML; if (text.includes('huggingface.co')) { element.innerHTML = text.replace(/huggingface.co/g, 'aifasthub.com'); } } }); // 处理特定的命令行示例 document.querySelectorAll('pre, code').forEach(element => { const text = element.innerHTML; if (text.includes('huggingface.co')) { // 针对git clone命令的专门处理 if (text.includes('git clone') || text.includes('GIT_LFS_SKIP_SMUDGE=1')) { element.innerHTML = text.replace(/huggingface.co/g, 'aifasthub.com'); } } }); // 特别处理模型下载页面上的代码片段 document.querySelectorAll('.flex.border-t, .svelte_hydrator, .inline-block').forEach(container => { const content = container.innerHTML; if (content && content.includes('huggingface.co')) { container.innerHTML = content.replace(/huggingface.co/g, 'aifasthub.com'); } }); // 特别处理模型仓库克隆对话框中的代码片段 try { // 查找包含"Clone this model repository"标题的对话框 const cloneDialog = document.querySelector('.svelte_hydration_boundary, [data-target="MainHeader"]'); if (cloneDialog) { // 查找对话框中所有的代码片段和命令示例 const codeElements = cloneDialog.querySelectorAll('pre, code, span'); codeElements.forEach(element => { if (element.textContent && element.textContent.includes('huggingface.co')) { if (element.innerHTML.includes('huggingface.co')) { element.innerHTML = element.innerHTML.replace(/huggingface.co/g, 'aifasthub.com'); } else { element.textContent = element.textContent.replace(/huggingface.co/g, 'aifasthub.com'); } } }); } // 更精确地定位克隆命令中的域名 document.querySelectorAll('[data-target]').forEach(container => { const codeBlocks = container.querySelectorAll('pre, code, span.hljs-string'); codeBlocks.forEach(block => { if (block.textContent && block.textContent.includes('huggingface.co')) { if (block.innerHTML.includes('huggingface.co')) { block.innerHTML = block.innerHTML.replace(/huggingface.co/g, 'aifasthub.com'); } else { block.textContent = block.textContent.replace(/huggingface.co/g, 'aifasthub.com'); } } }); }); } catch (e) { // 错误处理但不打印日志 } } // 当DOM加载完成后执行替换 if (document.readyState === 'loading') { document.addEventListener('DOMContentLoaded', () => { replaceHeaderBranding(); replaceNavigationLinks(); replaceCodeDomains(); // 只在必要时执行替换 - 3秒后再次检查 setTimeout(() => { if (!window._navLinksReplaced) { console.log('[Client] 3秒后重新检查导航链接'); replaceNavigationLinks(); } }, 3000); }); } else { replaceHeaderBranding(); replaceNavigationLinks(); replaceCodeDomains(); // 只在必要时执行替换 - 3秒后再次检查 setTimeout(() => { if (!window._navLinksReplaced) { console.log('[Client] 3秒后重新检查导航链接'); replaceNavigationLinks(); } }, 3000); } // 增加一个MutationObserver来处理可能的动态元素加载 const observer = new MutationObserver(mutations => { // 检查是否导航区域有变化 const hasNavChanges = mutations.some(mutation => { // 检查是否存在header或nav元素变化 return Array.from(mutation.addedNodes).some(node => { if (node.nodeType === Node.ELEMENT_NODE) { // 检查是否是导航元素或其子元素 if (node.tagName === 'HEADER' || node.tagName === 'NAV' || node.querySelector('header, nav')) { return true; } // 检查是否在导航元素内部 let parent = node.parentElement; while (parent) { if (parent.tagName === 'HEADER' || parent.tagName === 'NAV') { return true; } parent = parent.parentElement; } } return false; }); }); // 只在导航区域有变化时执行替换 if (hasNavChanges) { // 重置替换状态,允许再次替换 window._navLinksReplaced = false; replaceHeaderBranding(); replaceNavigationLinks(); } }); // 开始观察document.body的变化,包括子节点 if (document.body) { observer.observe(document.body, { childList: true, subtree: true }); } else { document.addEventListener('DOMContentLoaded', () => { observer.observe(document.body, { childList: true, subtree: true }); }); } })(); \n\n"},"__index_level_0__":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":6949,"cells":{"repo_id":{"kind":"string","value":"hf_public_repos/tokenizers/tokenizers/examples/unstable_wasm"},"file_path":{"kind":"string","value":"hf_public_repos/tokenizers/tokenizers/examples/unstable_wasm/www/index.js"},"content":{"kind":"string","value":"import * as wasm from \"unstable_wasm\";\n\nconsole.log(wasm.tokenize(\"ab\"));\nconsole.log(wasm.tokenize(\"abc\"));\n"},"__index_level_0__":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":6950,"cells":{"repo_id":{"kind":"string","value":"hf_public_repos/tokenizers/tokenizers/examples/unstable_wasm"},"file_path":{"kind":"string","value":"hf_public_repos/tokenizers/tokenizers/examples/unstable_wasm/www/README.md"},"content":{"kind":"string","value":"
\n\n

create-wasm-app

\n\n An npm init template for kick starting a project that uses NPM packages containing Rust-generated WebAssembly and bundles them with Webpack.\n\n

\n \"Build\n

\n\n

\n Usage\n | \n Chat\n

\n\n Built with 🦀🕸 by The Rust and WebAssembly Working Group\n
\n\n## About\n\nThis template is designed for depending on NPM packages that contain\nRust-generated WebAssembly and using them to create a Website.\n\n* Want to create an NPM package with Rust and WebAssembly? [Check out\n `wasm-pack-template`.](https://github.com/rustwasm/wasm-pack-template)\n* Want to make a monorepo-style Website without publishing to NPM? Check out\n [`rust-webpack-template`](https://github.com/rustwasm/rust-webpack-template)\n and/or\n [`rust-parcel-template`](https://github.com/rustwasm/rust-parcel-template).\n\n## 🚴 Usage\n\n```\nnpm init wasm-app\n```\n\n## 🔋 Batteries Included\n\n- `.gitignore`: ignores `node_modules`\n- `LICENSE-APACHE` and `LICENSE-MIT`: most Rust projects are licensed this way, so these are included for you\n- `README.md`: the file you are reading now!\n- `index.html`: a bare bones html document that includes the webpack bundle\n- `index.js`: example js file with a comment showing how to import and use a wasm pkg\n- `package.json` and `package-lock.json`:\n - pulls in devDependencies for using webpack:\n - [`webpack`](https://www.npmjs.com/package/webpack)\n - [`webpack-cli`](https://www.npmjs.com/package/webpack-cli)\n - [`webpack-dev-server`](https://www.npmjs.com/package/webpack-dev-server)\n - defines a `start` script to run `webpack-dev-server`\n- `webpack.config.js`: configuration file for bundling your js with webpack\n\n## License\n\nLicensed under either of\n\n* Apache License, Version 2.0, ([LICENSE-APACHE](LICENSE-APACHE) or http://www.apache.org/licenses/LICENSE-2.0)\n* MIT license ([LICENSE-MIT](LICENSE-MIT) or http://opensource.org/licenses/MIT)\n\nat your option.\n\n### Contribution\n\nUnless you explicitly state otherwise, any contribution intentionally\nsubmitted for inclusion in the work by you, as defined in the Apache-2.0\nlicense, shall be dual licensed as above, without any additional terms or\nconditions.\n"},"__index_level_0__":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":6951,"cells":{"repo_id":{"kind":"string","value":"hf_public_repos/tokenizers/tokenizers/examples/unstable_wasm"},"file_path":{"kind":"string","value":"hf_public_repos/tokenizers/tokenizers/examples/unstable_wasm/www/.travis.yml"},"content":{"kind":"string","value":"language: node_js\nnode_js: \"10\"\n\nscript:\n - ./node_modules/.bin/webpack\n"},"__index_level_0__":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":6952,"cells":{"repo_id":{"kind":"string","value":"hf_public_repos/tokenizers/tokenizers/examples/unstable_wasm"},"file_path":{"kind":"string","value":"hf_public_repos/tokenizers/tokenizers/examples/unstable_wasm/www/LICENSE-APACHE"},"content":{"kind":"string","value":" Apache License\n Version 2.0, January 2004\n http://www.apache.org/licenses/\n\nTERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n\n1. 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We also recommend that a\n file or class name and description of purpose be included on the\n same \"printed page\" as the copyright notice for easier\n identification within third-party archives.\n\nCopyright [yyyy] [name of copyright owner]\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.\n"},"__index_level_0__":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":6953,"cells":{"repo_id":{"kind":"string","value":"hf_public_repos/tokenizers/tokenizers/examples/unstable_wasm"},"file_path":{"kind":"string","value":"hf_public_repos/tokenizers/tokenizers/examples/unstable_wasm/www/package.json"},"content":{"kind":"string","value":"{\n \"name\": \"create-wasm-app\",\n \"version\": \"0.1.0\",\n \"description\": \"create an app to consume rust-generated wasm packages\",\n \"main\": \"index.js\",\n \"bin\": {\n \"create-wasm-app\": \".bin/create-wasm-app.js\"\n },\n \"scripts\": {\n \"build\": \"webpack --config webpack.config.js\",\n \"start\": \"NODE_OPTIONS=--openssl-legacy-provider webpack-dev-server\"\n },\n \"repository\": {\n \"type\": \"git\",\n \"url\": \"git+https://github.com/rustwasm/create-wasm-app.git\"\n },\n \"keywords\": [\"webassembly\", \"wasm\", \"rust\", \"webpack\"],\n \"author\": \"Ashley Williams \",\n \"license\": \"(MIT OR Apache-2.0)\",\n 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],\n};\n"},"__index_level_0__":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":6956,"cells":{"repo_id":{"kind":"string","value":"hf_public_repos/tokenizers/tokenizers/examples/unstable_wasm"},"file_path":{"kind":"string","value":"hf_public_repos/tokenizers/tokenizers/examples/unstable_wasm/www/bootstrap.js"},"content":{"kind":"string","value":"// A dependency graph that contains any wasm must all be imported\n// asynchronously. This `bootstrap.js` file does the single async import, so\n// that no one else needs to worry about it again.\nimport(\"./index.js\")\n .catch(e => console.error(\"Error importing `index.js`:\", e));\n"},"__index_level_0__":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":6957,"cells":{"repo_id":{"kind":"string","value":"hf_public_repos/tokenizers/tokenizers/examples/unstable_wasm/www"},"file_path":{"kind":"string","value":"hf_public_repos/tokenizers/tokenizers/examples/unstable_wasm/www/.bin/create-wasm-app.js"},"content":{"kind":"string","value":"#!/usr/bin/env node\n\nconst { spawn } = require(\"child_process\");\nconst fs = require(\"fs\");\n\nlet folderName = '.';\n\nif (process.argv.length >= 3) {\n folderName = process.argv[2];\n if (!fs.existsSync(folderName)) {\n fs.mkdirSync(folderName);\n }\n}\n\nconst clone = spawn(\"git\", [\"clone\", \"https://github.com/rustwasm/create-wasm-app.git\", folderName]);\n\nclone.on(\"close\", code => {\n if (code !== 0) {\n console.error(\"cloning the template failed!\")\n process.exit(code);\n } else {\n console.log(\"🦀 Rust + 🕸 Wasm = ❤\");\n }\n});\n"},"__index_level_0__":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":6958,"cells":{"repo_id":{"kind":"string","value":"hf_public_repos/tokenizers/tokenizers/examples/unstable_wasm"},"file_path":{"kind":"string","value":"hf_public_repos/tokenizers/tokenizers/examples/unstable_wasm/src/lib.rs"},"content":{"kind":"string","value":"mod utils;\nuse tokenizers::models::bpe::{Vocab, BPE};\nuse tokenizers::Tokenizer;\n\nuse wasm_bindgen::prelude::*;\n\n// When the `wee_alloc` feature is enabled, use `wee_alloc` as the global\n// allocator.\n#[cfg(feature = \"wee_alloc\")]\n#[global_allocator]\nstatic ALLOC: wee_alloc::WeeAlloc = wee_alloc::WeeAlloc::INIT;\n\n#[wasm_bindgen]\npub fn tokenize(string: &str) -> Vec {\n let vocab: Vocab = vec![\n (\"a\".to_string(), 0),\n (\"##b\".to_string(), 1),\n (\"##c\".to_string(), 2),\n (\"ab\".to_string(), 3),\n (\"abc\".to_string(), 4),\n ]\n .into_iter()\n .collect();\n\n let merges = vec![\n (\"a\".to_string(), \"##b\".to_string()),\n (\"ab\".to_string(), \"##c\".to_string()),\n ];\n\n let bpe = BPE::builder()\n .vocab_and_merges(vocab, merges)\n .unk_token(\"[UNK]\".to_string())\n .continuing_subword_prefix(\"##\".to_string())\n .build()\n .unwrap();\n let tokenizer = Tokenizer::new(bpe);\n tokenizer\n .encode(string, false)\n .unwrap()\n .get_ids()\n .into_iter()\n .cloned()\n .collect()\n}\n"},"__index_level_0__":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":6959,"cells":{"repo_id":{"kind":"string","value":"hf_public_repos/tokenizers/tokenizers/examples/unstable_wasm"},"file_path":{"kind":"string","value":"hf_public_repos/tokenizers/tokenizers/examples/unstable_wasm/src/utils.rs"},"content":{"kind":"string","value":"pub fn set_panic_hook() {\n // When the `console_error_panic_hook` feature is enabled, we can call the\n // `set_panic_hook` function at least once during initialization, and then\n // we will get better error messages if our code ever panics.\n //\n // For more details see\n // https://github.com/rustwasm/console_error_panic_hook#readme\n #[cfg(feature = \"console_error_panic_hook\")]\n console_error_panic_hook::set_once();\n}\n"},"__index_level_0__":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":6960,"cells":{"repo_id":{"kind":"string","value":"hf_public_repos/tokenizers/tokenizers"},"file_path":{"kind":"string","value":"hf_public_repos/tokenizers/tokenizers/tests/from_pretrained.rs"},"content":{"kind":"string","value":"#![cfg(feature = \"http\")]\nuse tokenizers::{FromPretrainedParameters, Result, Tokenizer};\n\n#[test]\nfn test_from_pretrained() -> Result<()> {\n let tokenizer = Tokenizer::from_pretrained(\"bert-base-cased\", None)?;\n let encoding = tokenizer.encode(\"Hey there dear friend!\", false)?;\n assert_eq!(\n encoding.get_tokens(),\n &[\"Hey\", \"there\", \"dear\", \"friend\", \"!\"]\n );\n Ok(())\n}\n\n#[test]\nfn test_from_pretrained_revision() -> Result<()> {\n let tokenizer = Tokenizer::from_pretrained(\"anthony/tokenizers-test\", None)?;\n let encoding = tokenizer.encode(\"Hey there dear friend!\", false)?;\n assert_eq!(\n encoding.get_tokens(),\n &[\"hey\", \"there\", \"dear\", \"friend\", \"!\"]\n );\n\n let tokenizer = Tokenizer::from_pretrained(\n \"anthony/tokenizers-test\",\n Some(FromPretrainedParameters {\n revision: \"gpt-2\".to_string(),\n ..Default::default()\n }),\n )?;\n let encoding = tokenizer.encode(\"Hey there dear friend!\", false)?;\n assert_eq!(\n encoding.get_tokens(),\n &[\"Hey\", \"Ġthere\", \"Ġdear\", \"Ġfriend\", \"!\"]\n );\n\n Ok(())\n}\n\n#[test]\nfn test_from_pretrained_invalid_model() {\n let tokenizer = Tokenizer::from_pretrained(\"docs?\", None);\n assert!(tokenizer.is_err());\n}\n\n#[test]\nfn test_from_pretrained_invalid_revision() {\n let tokenizer = Tokenizer::from_pretrained(\n \"bert-base-cased\",\n Some(FromPretrainedParameters {\n revision: \"gpt?\".to_string(),\n ..Default::default()\n }),\n );\n assert!(tokenizer.is_err());\n}\n"},"__index_level_0__":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":6961,"cells":{"repo_id":{"kind":"string","value":"hf_public_repos/tokenizers/tokenizers"},"file_path":{"kind":"string","value":"hf_public_repos/tokenizers/tokenizers/tests/documentation.rs"},"content":{"kind":"string","value":"use tokenizers::models::bpe::{BpeTrainerBuilder, BPE};\nuse tokenizers::normalizers::{Sequence, Strip, NFC};\nuse tokenizers::pre_tokenizers::byte_level::ByteLevel;\nuse tokenizers::{AddedToken, TokenizerBuilder};\nuse tokenizers::{DecoderWrapper, NormalizerWrapper, PostProcessorWrapper, PreTokenizerWrapper};\nuse tokenizers::{Tokenizer, TokenizerImpl};\n\n#[test]\nfn train_tokenizer() {\n let vocab_size: usize = 100;\n let mut tokenizer = TokenizerBuilder::new()\n .with_model(BPE::default())\n .with_normalizer(Some(Sequence::new(vec![\n Strip::new(true, true).into(),\n NFC.into(),\n ])))\n .with_pre_tokenizer(Some(ByteLevel::default()))\n .with_post_processor(Some(ByteLevel::default()))\n .with_decoder(Some(ByteLevel::default()))\n .build()\n .unwrap();\n\n let mut trainer = BpeTrainerBuilder::new()\n .show_progress(false)\n .vocab_size(vocab_size)\n .min_frequency(0)\n .special_tokens(vec![\n AddedToken::from(String::from(\"\"), true),\n AddedToken::from(String::from(\"\"), true),\n AddedToken::from(String::from(\"\"), true),\n AddedToken::from(String::from(\"\"), true),\n AddedToken::from(String::from(\"\"), true),\n ])\n .build();\n\n let pretty = true;\n tokenizer\n .train_from_files(&mut trainer, vec![\"data/small.txt\".to_string()])\n .unwrap()\n .save(\"data/tokenizer.json\", pretty)\n .unwrap();\n}\n\n#[test]\nfn load_tokenizer() {\n let tokenizer = Tokenizer::from_file(\"data/roberta.json\").unwrap();\n\n let example = \"This is an example\";\n let ids = vec![713, 16, 41, 1246];\n let tokens = vec![\"This\", \"Ġis\", \"Ġan\", \"Ġexample\"];\n\n let encodings = tokenizer.encode(example, false).unwrap();\n\n assert_eq!(encodings.get_ids(), ids);\n assert_eq!(encodings.get_tokens(), tokens);\n\n let decoded = tokenizer.decode(&ids, false).unwrap();\n assert_eq!(decoded, example);\n}\n\n#[test]\n#[ignore]\nfn quicktour_slow_train() -> tokenizers::Result<()> {\n // START quicktour_init_tokenizer\n use tokenizers::models::bpe::BPE;\n\n let mut tokenizer: TokenizerImpl<\n BPE,\n NormalizerWrapper,\n PreTokenizerWrapper,\n PostProcessorWrapper,\n DecoderWrapper,\n > = TokenizerImpl::new(\n BPE::builder()\n .unk_token(\"[UNK]\".to_string())\n .build()\n .unwrap(),\n );\n // END quicktour_init_tokenizer\n // START quicktour_init_trainer\n use tokenizers::models::bpe::BpeTrainer;\n\n let mut trainer = BpeTrainer::builder()\n .special_tokens(vec![\n AddedToken::from(\"[UNK]\", true),\n AddedToken::from(\"[CLS]\", true),\n AddedToken::from(\"[SEP]\", true),\n AddedToken::from(\"[PAD]\", true),\n AddedToken::from(\"[MASK]\", true),\n ])\n .build();\n // END quicktour_init_trainer\n // START quicktour_init_pretok\n use tokenizers::pre_tokenizers::whitespace::Whitespace;\n\n tokenizer.with_pre_tokenizer(Whitespace {});\n // END quicktour_init_pretok\n\n // START quicktour_train\n let files = vec![\n \"data/wikitext-103-raw/wiki.train.raw\".into(),\n \"data/wikitext-103-raw/wiki.test.raw\".into(),\n \"data/wikitext-103-raw/wiki.valid.raw\".into(),\n ];\n tokenizer.train_from_files(&mut trainer, files)?;\n // END quicktour_train\n // START quicktour_save\n tokenizer.save(\"data/tokenizer-wiki.json\", false)?;\n // END quicktour_save\n\n Ok(())\n}\n\n#[test]\nfn quicktour() -> tokenizers::Result<()> {\n // START quicktour_reload_tokenizer\n let mut tokenizer = Tokenizer::from_file(\"data/tokenizer-wiki.json\")?;\n // END quicktour_reload_tokenizer\n // START quicktour_encode\n let output = tokenizer.encode(\"Hello, y'all! How are you 😁 ?\", true)?;\n // END quicktour_encode\n // START quicktour_print_tokens\n println!(\"{:?}\", output.get_tokens());\n // [\"Hello\", \",\", \"y\", \"'\", \"all\", \"!\", \"How\", \"are\", \"you\", \"[UNK]\", \"?\",]\n // END quicktour_print_tokens\n assert_eq!(\n output.get_tokens(),\n [\"Hello\", \",\", \"y\", \"'\", \"all\", \"!\", \"How\", \"are\", \"you\", \"[UNK]\", \"?\",]\n );\n // START quicktour_print_ids\n println!(\"{:?}\", output.get_ids());\n // [27253, 16, 93, 11, 5097, 5, 7961, 5112, 6218, 0, 35]\n // END quicktour_print_ids\n assert_eq!(\n output.get_ids(),\n [27253, 16, 93, 11, 5097, 5, 7961, 5112, 6218, 0, 35]\n );\n // START quicktour_print_offsets\n println!(\"{:?}\", output.get_offsets()[9]);\n // (26, 30)\n // END quicktour_print_offsets\n assert_eq!(output.get_offsets()[9], (26, 30));\n // START quicktour_use_offsets\n let sentence = \"Hello, y'all! How are you 😁 ?\";\n println!(\"{}\", &sentence[26..30]);\n // \"😁\"\n // END quicktour_use_offsets\n // START quicktour_check_sep\n println!(\"{}\", tokenizer.token_to_id(\"[SEP]\").unwrap());\n // 2\n // END quicktour_check_sep\n assert_eq!(tokenizer.token_to_id(\"[SEP]\"), Some(2));\n // START quicktour_init_template_processing\n use tokenizers::processors::template::TemplateProcessing;\n\n let special_tokens = vec![\n (\"[CLS]\", tokenizer.token_to_id(\"[CLS]\").unwrap()),\n (\"[SEP]\", tokenizer.token_to_id(\"[SEP]\").unwrap()),\n ];\n tokenizer.with_post_processor(\n TemplateProcessing::builder()\n .try_single(\"[CLS] $A [SEP]\")\n .unwrap()\n .try_pair(\"[CLS] $A [SEP] $B:1 [SEP]:1\")\n .unwrap()\n .special_tokens(special_tokens)\n .build()?,\n );\n // END quicktour_init_template_processing\n // START quicktour_print_special_tokens\n let output = tokenizer.encode(\"Hello, y'all! How are you 😁 ?\", true)?;\n println!(\"{:?}\", output.get_tokens());\n // [\"[CLS]\", \"Hello\", \",\", \"y\", \"'\", \"all\", \"!\", \"How\", \"are\", \"you\", \"[UNK]\", \"?\", \"[SEP]\"]\n // END quicktour_print_special_tokens\n assert_eq!(\n output.get_tokens(),\n [\"[CLS]\", \"Hello\", \",\", \"y\", \"'\", \"all\", \"!\", \"How\", \"are\", \"you\", \"[UNK]\", \"?\", \"[SEP]\"]\n );\n // START quicktour_print_special_tokens_pair\n let output = tokenizer.encode((\"Hello, y'all!\", \"How are you 😁 ?\"), true)?;\n println!(\"{:?}\", output.get_tokens());\n // [\"[CLS]\", \"Hello\", \",\", \"y\", \"'\", \"all\", \"!\", \"[SEP]\", \"How\", \"are\", \"you\", \"[UNK]\", \"?\", \"[SEP]\"]\n // END quicktour_print_special_tokens_pair\n assert_eq!(\n output.get_tokens(),\n [\n \"[CLS]\", \"Hello\", \",\", \"y\", \"'\", \"all\", \"!\", \"[SEP]\", \"How\", \"are\", \"you\", \"[UNK]\",\n \"?\", \"[SEP]\"\n ]\n );\n // START quicktour_print_type_ids\n println!(\"{:?}\", output.get_type_ids());\n // [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]\n // END quicktour_print_type_ids\n assert_eq!(\n output.get_type_ids(),\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]\n );\n // START quicktour_encode_batch\n let output = tokenizer.encode_batch(vec![\"Hello, y'all!\", \"How are you 😁 ?\"], true)?;\n // END quicktour_encode_batch\n println!(\"{:?}\", output);\n // START quicktour_encode_batch_pair\n let output = tokenizer.encode_batch(\n vec![\n (\"Hello, y'all!\", \"How are you 😁 ?\"),\n (\"Hello to you too!\", \"I'm fine, thank you!\"),\n ],\n true,\n )?;\n // END quicktour_encode_batch_pair\n println!(\"{:?}\", output);\n // START quicktour_enable_padding\n use tokenizers::PaddingParams;\n\n tokenizer.with_padding(Some(PaddingParams {\n pad_id: 3,\n pad_token: \"[PAD]\".to_string(),\n ..PaddingParams::default()\n }));\n // END quicktour_enable_padding\n // START quicktour_print_batch_tokens\n let output = tokenizer.encode_batch(vec![\"Hello, y'all!\", \"How are you 😁 ?\"], true)?;\n println!(\"{:?}\", output[1].get_tokens());\n // [\"[CLS]\", \"How\", \"are\", \"you\", \"[UNK]\", \"?\", \"[SEP]\", \"[PAD]\"]\n // END quicktour_print_batch_tokens\n assert_eq!(\n output[1].get_tokens(),\n [\"[CLS]\", \"How\", \"are\", \"you\", \"[UNK]\", \"?\", \"[SEP]\", \"[PAD]\"]\n );\n // START quicktour_print_attention_mask\n println!(\"{:?}\", output[1].get_attention_mask());\n // [1, 1, 1, 1, 1, 1, 1, 0]\n // END quicktour_print_attention_mask\n assert_eq!(output[1].get_attention_mask(), [1, 1, 1, 1, 1, 1, 1, 0]);\n Ok(())\n}\n\n#[test]\nfn pipeline() -> tokenizers::Result<()> {\n // START pipeline_reload_tokenizer\n use tokenizers::Tokenizer;\n\n let mut tokenizer = Tokenizer::from_file(\"data/tokenizer-wiki.json\")?;\n // END pipeline_reload_tokenizer\n // START pipeline_setup_normalizer\n use tokenizers::normalizers::{\n strip::StripAccents, unicode::NFD, utils::Sequence as NormalizerSequence,\n };\n\n let normalizer = NormalizerSequence::new(vec![NFD.into(), StripAccents.into()]);\n // END pipeline_setup_normalizer\n // START pipeline_test_normalizer\n use tokenizers::{NormalizedString, Normalizer};\n\n let mut normalized = NormalizedString::from(\"Héllò hôw are ü?\");\n normalizer.normalize(&mut normalized)?;\n\n println!(\"{}\", normalized.get());\n // \"Hello how are u?\"\n // END pipeline_test_normalizer\n assert_eq!(normalized.get(), \"Hello how are u?\");\n // START pipeline_replace_normalizer\n tokenizer.with_normalizer(normalizer);\n // END pipeline_replace_normalizer\n // START pipeline_setup_pre_tokenizer\n use tokenizers::pre_tokenizers::whitespace::Whitespace;\n use tokenizers::{OffsetReferential, OffsetType, PreTokenizedString, PreTokenizer};\n\n let pre_tokenizer = Whitespace {};\n let mut pre_tokenized = PreTokenizedString::from(\"Hello! How are you? I'm fine, thank you.\");\n\n pre_tokenizer.pre_tokenize(&mut pre_tokenized)?;\n\n println!(\n \"{:?}\",\n pre_tokenized.get_splits(OffsetReferential::Original, OffsetType::Byte)\n );\n // [(\"Hello\", (0, 5), None), (\"!\", (5, 6), None), (\"How\", (7, 10), None),\n // (\"are\", (11, 14), None), (\"you\", (15, 18), None), (\"?\", (18, 19), None),\n // (\"I\", (20, 21), None), (\"\\'\", (21, 22), None), (\"m\", (22, 23), None),\n // (\"fine\", (24, 28), None), (\",\", (28, 29), None), (\"thank\", (30, 35), None),\n // (\"you\", (36, 39), None), (\".\", (39, 40), None)]\n // END pipeline_setup_pre_tokenizer\n assert_eq!(\n pre_tokenized.get_splits(OffsetReferential::Original, OffsetType::Byte),\n vec![\n (\"Hello\", (0, 5), &None),\n (\"!\", (5, 6), &None),\n (\"How\", (7, 10), &None),\n (\"are\", (11, 14), &None),\n (\"you\", (15, 18), &None),\n (\"?\", (18, 19), &None),\n (\"I\", (20, 21), &None),\n (\"\\'\", (21, 22), &None),\n (\"m\", (22, 23), &None),\n (\"fine\", (24, 28), &None),\n (\",\", (28, 29), &None),\n (\"thank\", (30, 35), &None),\n (\"you\", (36, 39), &None),\n (\".\", (39, 40), &None)\n ]\n );\n // START pipeline_combine_pre_tokenizer\n use tokenizers::pre_tokenizers::{digits::Digits, sequence::Sequence};\n\n let pre_tokenizer = Sequence::new(vec![Whitespace {}.into(), Digits::new(true).into()]);\n let mut pre_tokenized = PreTokenizedString::from(\"Call 911!\");\n\n pre_tokenizer.pre_tokenize(&mut pre_tokenized)?;\n\n println!(\n \"{:?}\",\n pre_tokenized.get_splits(OffsetReferential::Original, OffsetType::Byte)\n );\n // END pipeline_combine_pre_tokenizer\n assert_eq!(\n pre_tokenized.get_splits(OffsetReferential::Original, OffsetType::Byte),\n vec![\n (\"Call\", (0, 4), &None),\n (\"9\", (5, 6), &None),\n (\"1\", (6, 7), &None),\n (\"1\", (7, 8), &None),\n (\"!\", (8, 9), &None)\n ]\n );\n // START pipeline_replace_pre_tokenizer\n tokenizer.with_pre_tokenizer(pre_tokenizer);\n // END pipeline_replace_pre_tokenizer\n // START pipeline_setup_processor\n use tokenizers::processors::template::TemplateProcessing;\n\n tokenizer.with_post_processor(\n TemplateProcessing::builder()\n .try_single(\"[CLS] $A [SEP]\")\n .unwrap()\n .try_pair(\"[CLS] $A [SEP] $B:1 [SEP]:1\")\n .unwrap()\n .special_tokens(vec![(\"[CLS]\", 1), (\"[SEP]\", 2)])\n .build()\n .unwrap(),\n );\n // END pipeline_setup_processor\n // START pipeline_test_decoding\n let output = tokenizer.encode(\"Hello, y'all! How are you 😁 ?\", true)?;\n println!(\"{:?}\", output.get_ids());\n // [1, 27253, 16, 93, 11, 5097, 5, 7961, 5112, 6218, 0, 35, 2]\n\n let decoded = tokenizer.decode(\n &[1, 27253, 16, 93, 11, 5097, 5, 7961, 5112, 6218, 0, 35, 2],\n true,\n )?;\n println!(\"{}\", decoded);\n // \"Hello , y ' all ! How are you ?\"\n // END pipeline_test_decoding\n\n Ok(())\n}\n\n#[test]\n#[ignore]\nfn train_pipeline_bert() -> tokenizers::Result<()> {\n // START bert_setup_tokenizer\n use tokenizers::models::wordpiece::WordPiece;\n use tokenizers::Tokenizer;\n\n let mut bert_tokenizer = Tokenizer::new(\n WordPiece::builder()\n .unk_token(\"[UNK]\".to_string())\n .build()\n .unwrap(),\n );\n // END bert_setup_tokenizer\n // START bert_setup_normalizer\n use tokenizers::normalizers::utils::Sequence as NormalizerSequence;\n use tokenizers::normalizers::{strip::StripAccents, unicode::NFD, utils::Lowercase};\n\n bert_tokenizer.with_normalizer(NormalizerSequence::new(vec![\n NFD.into(),\n Lowercase.into(),\n StripAccents.into(),\n ]));\n // END bert_setup_normalizer\n // START bert_setup_pre_tokenizer\n use tokenizers::pre_tokenizers::whitespace::Whitespace;\n\n bert_tokenizer.with_pre_tokenizer(Whitespace {});\n // END bert_setup_pre_tokenizer\n // START bert_setup_processor\n use tokenizers::processors::template::TemplateProcessing;\n\n bert_tokenizer.with_post_processor(\n TemplateProcessing::builder()\n .try_single(\"[CLS] $A [SEP]\")\n .unwrap()\n .try_pair(\"[CLS] $A [SEP] $B:1 [SEP]:1\")\n .unwrap()\n .special_tokens(vec![(\"[CLS]\", 1), (\"[SEP]\", 2)])\n .build()\n .unwrap(),\n );\n // END bert_setup_processor\n // START bert_train_tokenizer\n use tokenizers::models::{wordpiece::WordPieceTrainer, TrainerWrapper};\n\n let mut trainer: TrainerWrapper = WordPieceTrainer::builder()\n .vocab_size(30_522)\n .special_tokens(vec![\n AddedToken::from(\"[UNK]\", true),\n AddedToken::from(\"[CLS]\", true),\n AddedToken::from(\"[SEP]\", true),\n AddedToken::from(\"[PAD]\", true),\n AddedToken::from(\"[MASK]\", true),\n ])\n .build()\n .into();\n let files = vec![\n \"data/wikitext-103-raw/wiki.train.raw\".into(),\n \"data/wikitext-103-raw/wiki.test.raw\".into(),\n \"data/wikitext-103-raw/wiki.valid.raw\".into(),\n ];\n bert_tokenizer.train_from_files(&mut trainer, files)?;\n\n bert_tokenizer.save(\"data/bert-wiki.json\", false)?;\n // END bert_train_tokenizer\n Ok(())\n}\n\n#[test]\nfn pipeline_bert() -> tokenizers::Result<()> {\n let mut bert_tokenizer = Tokenizer::from_file(\"data/bert-wiki.json\")?;\n\n // START bert_test_decoding\n let output = bert_tokenizer.encode(\"Welcome to the 🤗 Tokenizers library.\", true)?;\n println!(\"{:?}\", output.get_tokens());\n // [\"[CLS]\", \"welcome\", \"to\", \"the\", \"[UNK]\", \"tok\", \"##eni\", \"##zer\", \"##s\", \"library\", \".\", \"[SEP]\"]\n\n let decoded = bert_tokenizer.decode(output.get_ids(), true)?;\n println!(\"{}\", decoded);\n // \"welcome to the tok ##eni ##zer ##s library .\"\n // END bert_test_decoding\n assert_eq!(\n output.get_tokens(),\n &[\n \"[CLS]\", \"welcome\", \"to\", \"the\", \"[UNK]\", \"tok\", \"##eni\", \"##zer\", \"##s\", \"library\",\n \".\", \"[SEP]\"\n ]\n );\n assert_eq!(decoded, \"welcome to the tok ##eni ##zer ##s library .\");\n // START bert_proper_decoding\n use tokenizers::decoders::wordpiece::WordPiece as WordPieceDecoder;\n\n bert_tokenizer.with_decoder(WordPieceDecoder::default());\n let decoded = bert_tokenizer.decode(output.get_ids(), true)?;\n // \"welcome to the tokenizers library.\"\n // END bert_proper_decoding\n assert_eq!(decoded, \"welcome to the tokenizers library.\");\n\n Ok(())\n}\n"},"__index_level_0__":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":6962,"cells":{"repo_id":{"kind":"string","value":"hf_public_repos/tokenizers/tokenizers"},"file_path":{"kind":"string","value":"hf_public_repos/tokenizers/tokenizers/tests/unigram.rs"},"content":{"kind":"string","value":"#[cfg(not(debug_assertions))]\nuse assert_approx_eq::assert_approx_eq;\nuse std::collections::HashMap;\nuse std::fs::read_to_string;\nuse std::path::Path;\n#[cfg(not(debug_assertions))]\nuse tokenizers::models::unigram::Lattice;\nuse tokenizers::models::unigram::Unigram;\nuse tokenizers::models::unigram::UnigramTrainer;\nuse tokenizers::tokenizer::Model;\n\n#[test]\nfn test_unigram_from_file() {\n let model = Unigram::load(Path::new(\"data/unigram.json\")).unwrap();\n let string = \"吾輩《わがはい》は猫である。名前はまだ無い。\";\n assert_eq!(\n model\n .tokenize(string)\n .unwrap()\n .iter()\n .map(|tok| tok.value.clone())\n .collect::>(),\n vec![\n \"吾輩\",\n \"《\",\n \"わが\",\n \"はい\",\n \"》\",\n \"は\",\n \"猫\",\n \"である\",\n \"。\",\n \"名前\",\n \"はまだ\",\n \"無い\",\n \"。\"\n ]\n );\n}\n\n#[test]\nfn test_train_unigram_from_file() {\n let content = read_to_string(\"data/small.txt\").unwrap();\n let mut word_counts = HashMap::new();\n content.split_whitespace().for_each(|word| {\n // This is important for the test of char vs u8\n let word = format!(\"▁{}\", word);\n *word_counts.entry(word).or_insert(0) += 1;\n });\n\n // println!(\"Words counts {:?}\", word_counts);\n\n let trainer = UnigramTrainer::builder()\n .show_progress(false)\n .unk_token(Some(\"\".into()))\n .build()\n .unwrap();\n let mut model = Unigram::default();\n\n let sentences: Vec<_> = word_counts\n .iter()\n .map(|(s, i)| (s.to_owned(), *i))\n .collect();\n trainer.do_train(sentences, &mut model).unwrap();\n assert_eq!(model.get_vocab_size(), 719);\n}\n\n#[cfg(not(debug_assertions))]\n#[test]\nfn test_sample() {\n let mut lattice = Lattice::from(\"ABC\", 0, 2);\n lattice.insert(0, 1, 1.0, 3); // A\n lattice.insert(1, 1, 1.2, 4); // B\n lattice.insert(2, 1, 1.5, 5); // C\n lattice.insert(0, 2, 1.6, 6); // AB\n lattice.insert(1, 2, 1.7, 7); // BC\n lattice.insert(0, 3, 1.8, 8); // ABC\n\n let thetas: Vec = vec![0.0, 0.01, 0.5, 0.7, 1.0];\n\n for theta in thetas {\n let mut probs: HashMap = HashMap::new();\n probs.insert(\"A B C\".to_string(), (theta * (1.0 + 1.2 + 1.5)).exp());\n probs.insert(\"AB C\".to_string(), (theta * (1.6 + 1.5)).exp());\n probs.insert(\"A BC\".to_string(), (theta * (1.0 + 1.7)).exp());\n probs.insert(\"ABC\".to_string(), (theta * (1.8)).exp());\n\n // Computes expected probabilities.\n let mut z = 0.0;\n\n for (_, p) in probs.iter() {\n z += p;\n }\n for (_, p) in probs.iter_mut() {\n *p /= z;\n }\n\n let n_trials = 10_000;\n let mut freq: HashMap = HashMap::new();\n for _ in 0..n_trials {\n let string = lattice.sample_token(theta).join(\" \");\n *freq.entry(string).or_insert(0) += 1;\n }\n\n assert_eq!(freq.len(), probs.len());\n for (s, p) in probs.iter() {\n assert_approx_eq!(1.0 * (freq[s] as f64) / (n_trials as f64), p, 0.03)\n }\n }\n}\n"},"__index_level_0__":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":6963,"cells":{"repo_id":{"kind":"string","value":"hf_public_repos/tokenizers/tokenizers"},"file_path":{"kind":"string","value":"hf_public_repos/tokenizers/tokenizers/tests/serialization.rs"},"content":{"kind":"string","value":"mod common;\n\nuse common::*;\nuse tokenizers::decoders::byte_level::ByteLevel;\nuse tokenizers::decoders::DecoderWrapper;\nuse tokenizers::models::bpe::BPE;\nuse tokenizers::models::wordlevel::WordLevel;\nuse tokenizers::models::wordpiece::WordPiece;\nuse tokenizers::models::ModelWrapper;\nuse tokenizers::normalizers::bert::BertNormalizer;\nuse tokenizers::normalizers::unicode::{NFC, NFKC};\nuse tokenizers::normalizers::NormalizerWrapper;\nuse tokenizers::pre_tokenizers::bert::BertPreTokenizer;\nuse tokenizers::pre_tokenizers::delimiter::CharDelimiterSplit;\nuse tokenizers::pre_tokenizers::split::{Split, SplitPattern};\nuse tokenizers::pre_tokenizers::whitespace::Whitespace;\nuse tokenizers::pre_tokenizers::PreTokenizerWrapper;\nuse tokenizers::processors::bert::BertProcessing;\nuse tokenizers::processors::PostProcessorWrapper;\nuse tokenizers::{SplitDelimiterBehavior, Tokenizer, TokenizerImpl};\n\n#[test]\nfn bpe_serde() {\n let bpe = get_byte_level_bpe();\n let ser = serde_json::to_string(&bpe).unwrap();\n let de = serde_json::from_str(&ser).unwrap();\n assert_eq!(bpe, de);\n}\n\n#[test]\nfn wordpiece_serde() {\n let wordpiece = get_bert_wordpiece();\n let ser = serde_json::to_string(&wordpiece).unwrap();\n let de = serde_json::from_str(&ser).unwrap();\n assert_eq!(wordpiece, de);\n}\n\n#[test]\nfn wordlevel_serde() {\n let wordlevel = WordLevel::from_file(\"data/gpt2-vocab.json\", \"\".into()).unwrap();\n let ser = serde_json::to_string(&wordlevel).unwrap();\n let de = serde_json::from_str(&ser).unwrap();\n assert_eq!(wordlevel, de);\n}\n\n#[test]\nfn normalizers() {\n // Test unit struct\n let nfc = NFC;\n let nfc_ser = serde_json::to_string(&nfc).unwrap();\n assert_eq!(nfc_ser, r#\"{\"type\":\"NFC\"}\"#);\n // empty struct can deserialize from self\n serde_json::from_str::(&nfc_ser).unwrap();\n let err: Result = serde_json::from_str(&nfc_ser);\n assert!(err.is_err(), \"NFKC shouldn't be deserializable from NFC\");\n // wrapper can can deserialize from inner\n let nfc_wrapped: NormalizerWrapper = serde_json::from_str(&nfc_ser).unwrap();\n match &nfc_wrapped {\n NormalizerWrapper::NFC(_) => (),\n _ => panic!(\"NFC wrapped with incorrect variant\"),\n }\n let ser_wrapped = serde_json::to_string(&nfc_wrapped).unwrap();\n assert_eq!(ser_wrapped, nfc_ser);\n\n // Test non-empty roundtrip\n let bert = BertNormalizer::default();\n let bert_ser = serde_json::to_string(&bert).unwrap();\n assert_eq!(\n bert_ser,\n r#\"{\"type\":\"BertNormalizer\",\"clean_text\":true,\"handle_chinese_chars\":true,\"strip_accents\":null,\"lowercase\":true}\"#\n );\n // make sure we can deserialize to self\n serde_json::from_str::(&bert_ser).unwrap();\n // wrapper can deserialize from inner serialization\n let bert_wrapped: NormalizerWrapper = serde_json::from_str(&bert_ser).unwrap();\n match &bert_wrapped {\n NormalizerWrapper::BertNormalizer(_) => (),\n _ => panic!(\"BertNormalizer wrapped with incorrect variant\"),\n }\n // wrapped serializes same way as inner\n let ser_wrapped = serde_json::to_string(&bert_wrapped).unwrap();\n assert_eq!(ser_wrapped, bert_ser);\n}\n\n#[test]\nfn processors() {\n let bert = BertProcessing::new((\"SEP\".into(), 0), (\"CLS\".into(), 0));\n let bert_ser = serde_json::to_string(&bert).unwrap();\n assert_eq!(\n bert_ser,\n r#\"{\"type\":\"BertProcessing\",\"sep\":[\"SEP\",0],\"cls\":[\"CLS\",0]}\"#\n );\n serde_json::from_str::(&bert_ser).unwrap();\n let bert_wrapped: PostProcessorWrapper = serde_json::from_str(&bert_ser).unwrap();\n match &bert_wrapped {\n PostProcessorWrapper::Bert(_) => (),\n _ => panic!(\"Bert wrapped with incorrect variant\"),\n }\n let ser_wrapped = serde_json::to_string(&bert_wrapped).unwrap();\n assert_eq!(ser_wrapped, bert_ser);\n}\n\n#[test]\nfn pretoks() {\n // Test unit struct\n let bert = BertPreTokenizer;\n let bert_ser = serde_json::to_string(&bert).unwrap();\n assert_eq!(bert_ser, r#\"{\"type\":\"BertPreTokenizer\"}\"#);\n // empty struct can deserialize from self\n serde_json::from_str::(&bert_ser).unwrap();\n let err: Result = serde_json::from_str(&bert_ser);\n assert!(\n err.is_err(),\n \"Whitespace shouldn't be deserializable from BertPreTokenizer\"\n );\n // wrapper can can deserialize from inner\n let bert_wrapped: PreTokenizerWrapper = serde_json::from_str(&bert_ser).unwrap();\n match &bert_wrapped {\n PreTokenizerWrapper::BertPreTokenizer(_) => (),\n _ => panic!(\"Bert wrapped with incorrect variant\"),\n }\n let ser_wrapped = serde_json::to_string(&bert_wrapped).unwrap();\n assert_eq!(ser_wrapped, bert_ser);\n\n // Test non-empty roundtrip\n let ch = CharDelimiterSplit::new(' ');\n let ch_ser = serde_json::to_string(&ch).unwrap();\n assert_eq!(ch_ser, r#\"{\"type\":\"CharDelimiterSplit\",\"delimiter\":\" \"}\"#);\n // make sure we can deserialize to self\n serde_json::from_str::(&ch_ser).unwrap();\n // wrapper can deserialize from inner serialization\n let ch_wrapped: PreTokenizerWrapper = serde_json::from_str(&ch_ser).unwrap();\n match &ch_wrapped {\n PreTokenizerWrapper::Delimiter(_) => (),\n _ => panic!(\"CharDelimiterSplit wrapped with incorrect variant\"),\n }\n // wrapped serializes same way as inner\n let ser_wrapped = serde_json::to_string(&ch_wrapped).unwrap();\n assert_eq!(ser_wrapped, ch_ser);\n\n let wsp = Whitespace {};\n let wsp_ser = serde_json::to_string(&wsp).unwrap();\n assert_eq!(wsp_ser, r#\"{\"type\":\"Whitespace\"}\"#);\n serde_json::from_str::(&wsp_ser).unwrap();\n let err: Result = serde_json::from_str(&wsp_ser);\n assert!(\n err.is_err(),\n \"BertPreTokenizer shouldn't be deserializable from Whitespace\"\n );\n\n let pattern: SplitPattern = \"[SEP]\".into();\n let pretok = Split::new(pattern, SplitDelimiterBehavior::Isolated, false).unwrap();\n let pretok_str = serde_json::to_string(&pretok).unwrap();\n assert_eq!(\n pretok_str,\n r#\"{\"type\":\"Split\",\"pattern\":{\"String\":\"[SEP]\"},\"behavior\":\"Isolated\",\"invert\":false}\"#\n );\n assert_eq!(serde_json::from_str::(&pretok_str).unwrap(), pretok);\n\n let pattern = SplitPattern::Regex(\"[SEP]\".to_string());\n let pretok = Split::new(pattern, SplitDelimiterBehavior::Isolated, false).unwrap();\n let pretok_str = serde_json::to_string(&pretok).unwrap();\n assert_eq!(\n pretok_str,\n r#\"{\"type\":\"Split\",\"pattern\":{\"Regex\":\"[SEP]\"},\"behavior\":\"Isolated\",\"invert\":false}\"#\n );\n assert_eq!(serde_json::from_str::(&pretok_str).unwrap(), pretok);\n}\n\n#[test]\nfn decoders() {\n let byte_level = ByteLevel::default();\n let byte_level_ser = serde_json::to_string(&byte_level).unwrap();\n assert_eq!(\n byte_level_ser,\n r#\"{\"type\":\"ByteLevel\",\"add_prefix_space\":true,\"trim_offsets\":true,\"use_regex\":true}\"#\n );\n serde_json::from_str::(&byte_level_ser).unwrap();\n let byte_level_wrapper: DecoderWrapper = serde_json::from_str(&byte_level_ser).unwrap();\n match &byte_level_wrapper {\n DecoderWrapper::ByteLevel(_) => (),\n _ => panic!(\"ByteLevel wrapped with incorrect variant\"),\n }\n let ser_wrapped = serde_json::to_string(&byte_level_wrapper).unwrap();\n assert_eq!(ser_wrapped, byte_level_ser);\n}\n\n#[test]\nfn models() {\n let bpe = BPE::default();\n let bpe_ser = serde_json::to_string(&bpe).unwrap();\n serde_json::from_str::(&bpe_ser).unwrap();\n let bpe_wrapper: ModelWrapper = serde_json::from_str(&bpe_ser).unwrap();\n match &bpe_wrapper {\n ModelWrapper::BPE(_) => (),\n _ => panic!(\"BPE wrapped with incorrect variant\"),\n }\n let ser_wrapped = serde_json::to_string(&bpe_wrapper).unwrap();\n assert_eq!(ser_wrapped, bpe_ser);\n}\n\n#[test]\nfn tokenizer() {\n let wordpiece = WordPiece::default();\n let mut tokenizer = Tokenizer::new(wordpiece);\n tokenizer.with_normalizer(NFC);\n let ser = serde_json::to_string(&tokenizer).unwrap();\n let _: Tokenizer = serde_json::from_str(&ser).unwrap();\n let unwrapped_nfc_tok: TokenizerImpl<\n WordPiece,\n NFC,\n PreTokenizerWrapper,\n PostProcessorWrapper,\n DecoderWrapper,\n > = serde_json::from_str(&ser).unwrap();\n assert_eq!(serde_json::to_string(&unwrapped_nfc_tok).unwrap(), ser);\n let err: Result<\n TokenizerImpl,\n _,\n > = serde_json::from_str(&ser);\n assert!(err.is_err(), \"NFKC shouldn't be deserializable from NFC\");\n let de: TokenizerImpl<\n WordPiece,\n NormalizerWrapper,\n PreTokenizerWrapper,\n PostProcessorWrapper,\n DecoderWrapper,\n > = serde_json::from_str(&ser).unwrap();\n assert_eq!(serde_json::to_string(&de).unwrap(), ser);\n}\n\n#[test]\nfn test_deserialize_long_file() {\n let _tokenizer = Tokenizer::from_file(\"data/albert-base-v1-tokenizer.json\").unwrap();\n}\n"},"__index_level_0__":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":6964,"cells":{"repo_id":{"kind":"string","value":"hf_public_repos/tokenizers/tokenizers"},"file_path":{"kind":"string","value":"hf_public_repos/tokenizers/tokenizers/tests/training.rs"},"content":{"kind":"string","value":"use tokenizers::models::bpe::BPE;\nuse tokenizers::pre_tokenizers::whitespace::Whitespace;\nuse tokenizers::{DecoderWrapper, NormalizerWrapper, PostProcessorWrapper, PreTokenizerWrapper};\nuse tokenizers::{Model, Tokenizer, TokenizerBuilder};\n\n#[test]\nfn bpe_values_after_training() {\n let mut tokenizer = TokenizerBuilder::<\n BPE,\n NormalizerWrapper,\n PreTokenizerWrapper,\n PostProcessorWrapper,\n DecoderWrapper,\n >::default()\n .with_model(\n BPE::builder()\n .unk_token(\"[UNK]\".to_string())\n .dropout(0.1)\n .build()\n .unwrap(),\n )\n .build()\n .unwrap();\n let mut trainer = tokenizer.get_model().get_trainer();\n tokenizer\n .train_from_files(&mut trainer, vec![\"./data/small.txt\".to_string()])\n .unwrap();\n assert_eq!(tokenizer.get_model().dropout, Some(0.1));\n assert_eq!(tokenizer.get_model().unk_token, Some(\"[UNK]\".to_string()));\n}\n\n#[test]\nfn bpe_continuing_subword_prefix_error() {\n let mut tokenizer = TokenizerBuilder::<\n BPE,\n NormalizerWrapper,\n PreTokenizerWrapper,\n PostProcessorWrapper,\n DecoderWrapper,\n >::default()\n .with_model(\n BPE::builder()\n .unk_token(\"[UNK]\".to_string())\n .continuing_subword_prefix(\"##\".to_string())\n .build()\n .unwrap(),\n )\n .with_pre_tokenizer(Some(PreTokenizerWrapper::Whitespace(Whitespace {})))\n .build()\n .unwrap();\n let mut trainer = tokenizer.get_model().get_trainer();\n tokenizer\n .train_from_files(&mut trainer, vec![\"./data/small.txt\".to_string()])\n .unwrap();\n tokenizer.save(\"tokenizer.json\", true).unwrap();\n let tokenizer = Tokenizer::from_file(\"tokenizer.json\").unwrap();\n assert_eq!(tokenizer.get_vocab_size(false), 1526);\n\n std::fs::remove_file(\"tokenizer.json\").unwrap();\n}\n"},"__index_level_0__":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":6965,"cells":{"repo_id":{"kind":"string","value":"hf_public_repos/tokenizers/tokenizers"},"file_path":{"kind":"string","value":"hf_public_repos/tokenizers/tokenizers/tests/offsets.rs"},"content":{"kind":"string","value":"mod common;\n\nuse common::*;\nuse tokenizers::tokenizer::AddedToken;\n\nmacro_rules! check_offsets {\n ($input: expr, $output:expr, $offset:expr, $result:expr) => {\n let offsets = $output.get_offsets()[$offset];\n assert_eq!(&$input[offsets.0..offsets.1], $result);\n };\n}\n\n#[test]\nfn byte_level_basic() {\n // Without trimming offsets\n let tokenizer = get_byte_level(true, false);\n\n let input = \"Hello there, how are you?\";\n let output = tokenizer.encode(input, false).unwrap();\n\n check_offsets!(input, output, 0, \"Hello\");\n check_offsets!(input, output, 1, \" there\");\n check_offsets!(input, output, 2, \",\");\n check_offsets!(input, output, 3, \" how\");\n check_offsets!(input, output, 4, \" are\");\n check_offsets!(input, output, 5, \" you\");\n check_offsets!(input, output, 6, \"?\");\n\n // And when trimming offsets:\n let tokenizer = get_byte_level(true, true);\n\n let input = \"Hello there, how are you?\";\n let output = tokenizer.encode(input, false).unwrap();\n\n check_offsets!(input, output, 0, \"Hello\");\n check_offsets!(input, output, 1, \"there\");\n check_offsets!(input, output, 2, \",\");\n check_offsets!(input, output, 3, \"how\");\n check_offsets!(input, output, 4, \"are\");\n check_offsets!(input, output, 5, \"you\");\n check_offsets!(input, output, 6, \"?\");\n}\n\n#[test]\nfn byte_level_unicode() {\n let tokenizer = get_byte_level(true, false);\n\n let input = \"i⭢j\";\n let output = tokenizer.encode(input, false).unwrap();\n\n check_offsets!(input, output, 1, \"⭢\");\n check_offsets!(input, output, 2, \"⭢\");\n check_offsets!(input, output, 3, \"⭢\");\n}\n\n#[test]\nfn byte_level_double_sequence() {\n let input_a = \"My name is Anthony\";\n let input_b = \"What is my name?\";\n\n // Without trimming offsets\n let tokenizer = get_byte_level(true, false);\n let output = tokenizer.encode((input_a, input_b), false).unwrap();\n\n let offsets = output.get_offsets();\n assert_eq!(\n offsets,\n &[\n (0, 2),\n (2, 7),\n (7, 10),\n (10, 18),\n (0, 4),\n (4, 7),\n (7, 10),\n (10, 15),\n (15, 16)\n ]\n );\n assert_eq!(\n output.get_word_ids(),\n &[\n Some(0),\n Some(1),\n Some(2),\n Some(3),\n Some(0),\n Some(1),\n Some(2),\n Some(3),\n Some(4)\n ]\n );\n assert_eq!(output.get_type_ids(), &[0, 0, 0, 0, 1, 1, 1, 1, 1]);\n\n // When trimming offsets\n let tokenizer = get_byte_level(true, true);\n let output = tokenizer.encode((input_a, input_b), false).unwrap();\n let offsets = output.get_offsets();\n assert_eq!(\n offsets,\n &[\n (0, 2),\n (3, 7),\n (8, 10),\n (11, 18),\n (0, 4),\n (5, 7),\n (8, 10),\n (11, 15),\n (15, 16)\n ]\n );\n}\n\n#[test]\nfn byte_level_pre_tokenized_sequence() {\n let input = [\"My\", \"name\", \"is\", \"Anthonino\"];\n\n // Without trimming offsets\n let tokenizer = get_byte_level(true, false);\n let output = tokenizer.encode(&input[..], false).unwrap();\n\n assert_eq!(\n output.get_tokens(),\n &[\"ĠMy\", \"Ġname\", \"Ġis\", \"ĠAnth\", \"on\", \"ino\"]\n );\n assert_eq!(\n output.get_word_ids(),\n &[Some(0), Some(1), Some(2), Some(3), Some(3), Some(3)]\n );\n assert_eq!(\n output.get_offsets(),\n &[(0, 2), (0, 4), (0, 2), (0, 4), (4, 6), (6, 9)]\n );\n}\n\n#[test]\n#[ignore]\nfn byte_level_pre_tokenized_sequence_with_trimming() {\n let input = [\"My\", \"name\", \"is\", \"Anthonino\"];\n\n // When trimming offsets (expect same result)\n let tokenizer = get_byte_level(true, true);\n let output = tokenizer.encode(&input[..], false).unwrap();\n\n assert_eq!(\n output.get_word_ids(),\n &[Some(0), Some(1), Some(2), Some(3), Some(3), Some(3)]\n );\n assert_eq!(\n output.get_offsets(),\n &[(0, 2), (0, 4), (0, 2), (0, 4), (4, 6), (6, 9)]\n );\n}\n\n#[test]\nfn split_on_added_tokens_bert() {\n let input = \"Yesterday I saw a [MASK] far away\";\n\n let mut tokenizer = get_bert();\n tokenizer.add_special_tokens(&[AddedToken::from(\"[MASK]\", true)]);\n let output = tokenizer.encode(input, false).unwrap();\n\n assert_eq!(\n output.get_offsets(),\n &[\n (0, 9),\n (10, 11),\n (12, 15),\n (16, 17),\n (18, 24),\n (25, 28),\n (29, 33)\n ]\n );\n assert_eq!(\n output.get_tokens(),\n &[\"yesterday\", \"i\", \"saw\", \"a\", \"[MASK]\", \"far\", \"away\"]\n );\n assert_eq!(\n output.get_word_ids(),\n &[\n Some(0),\n Some(1),\n Some(2),\n Some(3),\n Some(4),\n Some(5),\n Some(6)\n ]\n );\n}\n"},"__index_level_0__":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":6966,"cells":{"repo_id":{"kind":"string","value":"hf_public_repos/tokenizers/tokenizers"},"file_path":{"kind":"string","value":"hf_public_repos/tokenizers/tokenizers/tests/added_tokens.rs"},"content":{"kind":"string","value":"mod common;\n\nuse common::*;\nuse tokenizers::tokenizer::AddedToken;\n\n#[test]\nfn add_tokens() {\n let mut tokenizer = get_empty();\n\n assert_eq!(\n tokenizer.add_special_tokens(&[\n AddedToken::from(\"\", true),\n AddedToken::from(\"\", true)\n ]),\n 2\n );\n assert_eq!(tokenizer.token_to_id(\"\"), Some(0));\n assert_eq!(tokenizer.token_to_id(\"\"), Some(1));\n\n assert_eq!(\n tokenizer.add_tokens(&[\n AddedToken::from(\"hello\", false),\n AddedToken::from(\"world\", false)\n ]),\n 2\n );\n assert_eq!(tokenizer.token_to_id(\"hello\"), Some(2));\n assert_eq!(tokenizer.token_to_id(\"world\"), Some(3));\n}\n\n#[test]\nfn lstrip_tokens() {\n let mut tokenizer = get_byte_level(true, false);\n tokenizer.add_special_tokens(&[AddedToken::from(\"\", true).lstrip(true)]);\n\n let input = \"I saw a 😺\";\n let output = tokenizer.encode(input, false).unwrap();\n\n assert_eq!(\n output.get_tokens(),\n &[\"ĠI\", \"Ġsaw\", \"Ġa\", \" \", \"ĠðŁĺ\", \"º\"]\n );\n assert_eq!(\n output.get_offsets(),\n &[(0, 1), (1, 5), (5, 7), (7, 14), (14, 19), (15, 19)]\n );\n}\n\n#[test]\nfn rstrip_tokens() {\n let mut tokenizer = get_byte_level(false, false);\n tokenizer.add_special_tokens(&[AddedToken::from(\"\", true).rstrip(true)]);\n\n let input = \"I saw a 😺\";\n let output = tokenizer.encode(input, false).unwrap();\n\n assert_eq!(\n output.get_tokens(),\n &[\"I\", \"Ġsaw\", \"Ġa\", \"Ġ\", \" \", \"ðŁĺ\", \"º\"]\n );\n\n // When `add_prefix_space = true` rstrip cannot work as a prefix space is added\n // to the next token\n let mut tokenizer = get_byte_level(true, false);\n tokenizer.add_special_tokens(&[AddedToken::from(\"\", true).rstrip(true)]);\n\n let input = \"I saw a 😺\";\n let output = tokenizer.encode(input, false).unwrap();\n\n assert_eq!(\n output.get_tokens(),\n &[\"ĠI\", \"Ġsaw\", \"Ġa\", \"Ġ\", \" \", \"ĠðŁĺ\", \"º\"]\n );\n}\n\n#[test]\nfn single_word_tokens() {\n // If `single_word = true` it shouldn't split `dancing`\n let mut tokenizer = get_byte_level(false, false);\n tokenizer.add_special_tokens(&[AddedToken::from(\"ing\", true).single_word(true)]);\n\n let input = \"I like dancing\";\n let output = tokenizer.encode(input, false).unwrap();\n\n assert_eq!(output.get_tokens(), &[\"I\", \"Ġlike\", \"Ġdancing\"]);\n\n // If `single_word = false` it should split `dancing`\n let mut tokenizer = get_byte_level(false, false);\n tokenizer.add_special_tokens(&[AddedToken::from(\"ing\", true).single_word(false)]);\n\n let input = \"I like dancing\";\n let output = tokenizer.encode(input, false).unwrap();\n\n assert_eq!(output.get_tokens(), &[\"I\", \"Ġlike\", \"Ġd\", \"anc\", \"ing\"]);\n}\n\n#[test]\nfn overlapping_tokens() {\n let mut tokenizer = get_byte_level(false, false);\n\n tokenizer.add_special_tokens(&[AddedToken::from(\"danc\", true)]);\n tokenizer.add_special_tokens(&[AddedToken::from(\"nci\", true)]);\n tokenizer.add_special_tokens(&[AddedToken::from(\"ing\", true)]);\n\n let input = \"I like dancing\";\n let output = tokenizer.encode(input, false).unwrap();\n\n assert_eq!(output.get_tokens(), &[\"I\", \"Ġlike\", \"Ġ\", \"danc\", \"ing\"]);\n\n let mut tokenizer = get_byte_level(false, false);\n\n tokenizer.add_special_tokens(&[AddedToken::from(\"nci\", true)]);\n tokenizer.add_special_tokens(&[AddedToken::from(\"danc\", true)]);\n tokenizer.add_special_tokens(&[AddedToken::from(\"ing\", true)]);\n tokenizer.add_special_tokens(&[AddedToken::from(\"ike\", true)]);\n\n let output = tokenizer.encode(input, false).unwrap();\n\n // Breaking change but following `transformers` breaking change.\n // This behavior is deemed not used in practice:\n // https://github.com/huggingface/transformers/pull/13220\n // Order does NOT matter. (We could make it work again but the trie\n // would need to keep insertion order too)\n //\n // assert_eq!(output.get_tokens(), &[\"I\", \"Ġlike\", \"Ġda\", \"nci\", \"ng\"]);\n assert_eq!(output.get_tokens(), &[\"I\", \"Ġl\", \"ike\", \"Ġ\", \"danc\", \"ing\"]);\n}\n"},"__index_level_0__":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":6967,"cells":{"repo_id":{"kind":"string","value":"hf_public_repos/tokenizers/tokenizers/tests"},"file_path":{"kind":"string","value":"hf_public_repos/tokenizers/tokenizers/tests/common/mod.rs"},"content":{"kind":"string","value":"use tokenizers::decoders::wordpiece::WordPiece as WordPieceDecoder;\nuse tokenizers::models::bpe::BPE;\nuse tokenizers::models::wordpiece::WordPiece;\nuse tokenizers::normalizers::bert::BertNormalizer;\nuse tokenizers::pre_tokenizers::bert::BertPreTokenizer;\nuse tokenizers::pre_tokenizers::byte_level::ByteLevel;\nuse tokenizers::processors::bert::BertProcessing;\nuse tokenizers::tokenizer::{Model, Tokenizer};\n\n#[allow(dead_code)]\npub fn get_empty() -> Tokenizer {\n Tokenizer::new(BPE::default())\n}\n\n#[allow(dead_code)]\npub fn get_byte_level_bpe() -> BPE {\n BPE::from_file(\"data/gpt2-vocab.json\", \"data/gpt2-merges.txt\")\n .build()\n .expect(\"Files not found, run `make test` to download these files\")\n}\n\n#[allow(dead_code)]\npub fn get_byte_level(add_prefix_space: bool, trim_offsets: bool) -> Tokenizer {\n let mut tokenizer = Tokenizer::new(get_byte_level_bpe());\n tokenizer\n .with_pre_tokenizer(ByteLevel::default().add_prefix_space(add_prefix_space))\n .with_decoder(ByteLevel::default())\n .with_post_processor(ByteLevel::default().trim_offsets(trim_offsets));\n\n tokenizer\n}\n\n#[allow(dead_code)]\npub fn get_bert_wordpiece() -> WordPiece {\n WordPiece::from_file(\"data/bert-base-uncased-vocab.txt\")\n .build()\n .expect(\"Files not found, run `make test` to download these files\")\n}\n\n#[allow(dead_code)]\npub fn get_bert() -> Tokenizer {\n let mut tokenizer = Tokenizer::new(get_bert_wordpiece());\n let sep = tokenizer.get_model().token_to_id(\"[SEP]\").unwrap();\n let cls = tokenizer.get_model().token_to_id(\"[CLS]\").unwrap();\n tokenizer\n .with_normalizer(BertNormalizer::default())\n .with_pre_tokenizer(BertPreTokenizer)\n .with_decoder(WordPieceDecoder::default())\n .with_post_processor(BertProcessing::new(\n (String::from(\"[SEP]\"), sep),\n (String::from(\"[CLS]\"), cls),\n ));\n\n tokenizer\n}\n"},"__index_level_0__":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":6968,"cells":{"repo_id":{"kind":"string","value":"hf_public_repos/tokenizers/tokenizers"},"file_path":{"kind":"string","value":"hf_public_repos/tokenizers/tokenizers/src/lib.rs"},"content":{"kind":"string","value":"#![warn(clippy::all)]\n#![allow(clippy::upper_case_acronyms)]\n#![doc(html_favicon_url = \"https://huggingface.co/favicon.ico\")]\n#![doc(html_logo_url = \"https://huggingface.co/landing/assets/huggingface_logo.svg\")]\n\n//! The core of `tokenizers`, written in Rust.\n//! Provides an implementation of today's most used tokenizers, with a focus on performance and\n//! versatility.\n//!\n//! # What is a Tokenizer\n//!\n//! A Tokenizer works as a pipeline, it processes some raw text as input and outputs an `Encoding`.\n//! The various steps of the pipeline are:\n//!\n//! 1. The `Normalizer`: in charge of normalizing the text. Common examples of normalization are\n//! the [unicode normalization standards](https://unicode.org/reports/tr15/#Norm_Forms), such as `NFD` or `NFKC`.\n//! More details about how to use the `Normalizers` are available on the\n//! [Hugging Face blog](https://huggingface.co/docs/tokenizers/components#normalizers)\n//! 2. The `PreTokenizer`: in charge of creating initial words splits in the text. The most common way of\n//! splitting text is simply on whitespace.\n//! 3. The `Model`: in charge of doing the actual tokenization. An example of a `Model` would be\n//! `BPE` or `WordPiece`.\n//! 4. The `PostProcessor`: in charge of post-processing the `Encoding` to add anything relevant\n//! that, for example, a language model would need, such as special tokens.\n//!\n//! ## Loading a pretrained tokenizer from the Hub\n//! ```\n//! use tokenizers::tokenizer::{Result, Tokenizer};\n//!\n//! fn main() -> Result<()> {\n//! # #[cfg(feature = \"http\")]\n//! # {\n//! let tokenizer = Tokenizer::from_pretrained(\"bert-base-cased\", None)?;\n//!\n//! let encoding = tokenizer.encode(\"Hey there!\", false)?;\n//! println!(\"{:?}\", encoding.get_tokens());\n//! # }\n//! Ok(())\n//! }\n//! ```\n//!\n//! ## Deserialization and tokenization example\n//!\n//! ```no_run\n//! use tokenizers::tokenizer::{Result, Tokenizer, EncodeInput};\n//! use tokenizers::models::bpe::BPE;\n//!\n//! fn main() -> Result<()> {\n//! let bpe_builder = BPE::from_file(\"./path/to/vocab.json\", \"./path/to/merges.txt\");\n//! let bpe = bpe_builder\n//! .dropout(0.1)\n//! .unk_token(\"[UNK]\".into())\n//! .build()?;\n//!\n//! let mut tokenizer = Tokenizer::new(bpe);\n//!\n//! let encoding = tokenizer.encode(\"Hey there!\", false)?;\n//! println!(\"{:?}\", encoding.get_tokens());\n//!\n//! Ok(())\n//! }\n//! ```\n//!\n//! ## Training and serialization example\n//!\n//! ```no_run\n//! use tokenizers::decoders::DecoderWrapper;\n//! use tokenizers::models::bpe::{BpeTrainerBuilder, BPE};\n//! use tokenizers::normalizers::{strip::Strip, unicode::NFC, utils::Sequence, NormalizerWrapper};\n//! use tokenizers::pre_tokenizers::byte_level::ByteLevel;\n//! use tokenizers::pre_tokenizers::PreTokenizerWrapper;\n//! use tokenizers::processors::PostProcessorWrapper;\n//! use tokenizers::{AddedToken, Model, Result, TokenizerBuilder};\n//!\n//! use std::path::Path;\n//!\n//! fn main() -> Result<()> {\n//! let vocab_size: usize = 100;\n//!\n//! let mut trainer = BpeTrainerBuilder::new()\n//! .show_progress(true)\n//! .vocab_size(vocab_size)\n//! .min_frequency(0)\n//! .special_tokens(vec![\n//! AddedToken::from(String::from(\"\"), true),\n//! AddedToken::from(String::from(\"\"), true),\n//! AddedToken::from(String::from(\"\"), true),\n//! AddedToken::from(String::from(\"\"), true),\n//! AddedToken::from(String::from(\"\"), true),\n//! ])\n//! .build();\n//!\n//! let mut tokenizer = TokenizerBuilder::new()\n//! .with_model(BPE::default())\n//! .with_normalizer(Some(Sequence::new(vec![\n//! Strip::new(true, true).into(),\n//! NFC.into(),\n//! ])))\n//! .with_pre_tokenizer(Some(ByteLevel::default()))\n//! .with_post_processor(Some(ByteLevel::default()))\n//! .with_decoder(Some(ByteLevel::default()))\n//! .build()?;\n//!\n//! let pretty = false;\n//! tokenizer\n//! .train_from_files(\n//! &mut trainer,\n//! vec![\"path/to/vocab.txt\".to_string()],\n//! )?\n//! .save(\"tokenizer.json\", pretty)?;\n//!\n//! Ok(())\n//! }\n//! ```\n//!\n//! # Additional information\n//!\n//! - tokenizers is designed to leverage CPU parallelism when possible. The level of parallelism is determined\n//! by the total number of core/threads your CPU provides but this can be tuned by setting the `RAYON_RS_NUM_THREADS`\n//! environment variable. As an example setting `RAYON_RS_NUM_THREADS=4` will allocate a maximum of 4 threads.\n//! **_Please note this behavior may evolve in the future_**\n//!\n//! # Features\n//! **progressbar**: The progress bar visualization is enabled by default. It might be disabled if\n//! compilation for certain targets is not supported by the [termios](https://crates.io/crates/termios)\n//! dependency of the [indicatif](https://crates.io/crates/indicatif) progress bar.\n\n#[macro_use]\nextern crate log;\n#[macro_use]\nextern crate lazy_static;\n\n#[macro_use]\nextern crate derive_builder;\n\n#[macro_use]\npub mod utils;\npub mod decoders;\npub mod models;\npub mod normalizers;\npub mod pre_tokenizers;\npub mod processors;\npub mod tokenizer;\n\n// Re-export from tokenizer\npub use tokenizer::*;\n\n// Re-export also parallelism utils\npub use utils::parallelism;\n\n// Re-export for from_pretrained\n#[cfg(feature = \"http\")]\npub use utils::from_pretrained::FromPretrainedParameters;\n"},"__index_level_0__":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":6969,"cells":{"repo_id":{"kind":"string","value":"hf_public_repos/tokenizers/tokenizers"},"file_path":{"kind":"string","value":"hf_public_repos/tokenizers/tokenizers/src/cli.rs"},"content":{"kind":"string","value":"//!\n//! This is the CLI binary for the Tokenizers project\n//!\n\nuse clap::{Parser, Subcommand};\nuse std::io::{self, BufRead, Write};\nuse tokenizers::models::bpe::BPE;\nuse tokenizers::pre_tokenizers::byte_level::ByteLevel;\nuse tokenizers::tokenizer::{AddedToken, Result};\nuse tokenizers::Tokenizer;\n\n/// Generate custom Tokenizers or use existing ones\n#[derive(Parser, Debug)]\n#[command(author, version)]\nstruct Args {\n #[command(subcommand)]\n command: Command,\n}\n\n#[derive(Subcommand, Debug)]\nenum Command {\n Shell {\n /// Path to the vocab.json file\n vocab: String,\n /// Path to the merges.txt file\n merges: String,\n },\n}\n\nfn shell(vocab: &str, merges: &str) -> Result<()> {\n let bpe = BPE::from_file(vocab, merges).build()?;\n let mut tokenizer = Tokenizer::new(bpe);\n tokenizer\n .with_pre_tokenizer(ByteLevel::default())\n .with_decoder(ByteLevel::default());\n\n tokenizer.add_tokens(&[AddedToken::from(String::from(\"ing\"), false).single_word(false)]);\n tokenizer\n .add_special_tokens(&[AddedToken::from(String::from(\"[ENT]\"), true).single_word(true)]);\n\n let stdin = io::stdin();\n let mut handle = stdin.lock();\n let mut buffer = String::new();\n\n loop {\n buffer.clear();\n\n print!(\"\\nEnter some text to tokenize:\\n> \");\n io::stdout().flush()?;\n handle.read_line(&mut buffer)?;\n let buffer = buffer.trim_end();\n\n let timer = std::time::Instant::now();\n let encoded = tokenizer.encode(buffer.to_owned(), false)?;\n let elapsed = timer.elapsed();\n println!(\"\\nInput:\\t\\t{}\", buffer);\n println!(\"Tokens:\\t\\t{:?}\", encoded.get_tokens());\n println!(\"IDs:\\t\\t{:?}\", encoded.get_ids());\n println!(\"Offsets:\\t{:?}\", encoded.get_offsets());\n println!(\n \"Decoded:\\t{}\",\n tokenizer.decode(encoded.get_ids(), true).unwrap()\n );\n println!(\"Tokenized in {:?}\", elapsed);\n }\n}\n\nfn main() -> Result<()> {\n let args = Args::parse();\n match args.command {\n Command::Shell { vocab, merges } => shell(&vocab, &merges),\n }\n}\n"},"__index_level_0__":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":6970,"cells":{"repo_id":{"kind":"string","value":"hf_public_repos/tokenizers/tokenizers/src"},"file_path":{"kind":"string","value":"hf_public_repos/tokenizers/tokenizers/src/processors/sequence.rs"},"content":{"kind":"string","value":"use crate::processors::PostProcessorWrapper;\nuse crate::tokenizer::{Encoding, PostProcessor, Result};\nuse crate::utils::macro_rules_attribute;\nuse serde::{Deserialize, Serialize};\n\n#[derive(Clone, Debug, PartialEq, Eq)]\n#[macro_rules_attribute(impl_serde_type!)]\npub struct Sequence {\n processors: Vec,\n}\n\nimpl Sequence {\n pub fn new(processors: Vec) -> Self {\n Self { processors }\n }\n}\n\nimpl PostProcessor for Sequence {\n fn added_tokens(&self, is_pair: bool) -> usize {\n self.processors\n .iter()\n .map(|p| p.added_tokens(is_pair))\n .sum::()\n }\n\n fn process_encodings(\n &self,\n mut encodings: Vec,\n add_special_tokens: bool,\n ) -> Result> {\n for processor in &self.processors {\n encodings = processor.process_encodings(encodings, add_special_tokens)?;\n }\n Ok(encodings)\n }\n}\n\n#[cfg(test)]\nmod tests {\n use super::*;\n use crate::processors::{ByteLevel, PostProcessorWrapper};\n use crate::tokenizer::{Encoding, PostProcessor};\n use std::collections::HashMap;\n use std::iter::FromIterator;\n\n #[test]\n fn process_chain() {\n let start = Encoding::new(\n vec![0; 5],\n vec![0; 5],\n vec![\n \"Ġ\".into(),\n \"ĠĠĠĠHelloĠĠ\".into(),\n \"ĠĠHello\".into(),\n \"HelloĠĠ\".into(),\n \"ĠĠĠĠ\".into(),\n ],\n vec![],\n vec![(0, 1), (0, 11), (11, 18), (18, 25), (25, 29)],\n vec![],\n vec![],\n vec![],\n HashMap::new(),\n );\n\n let bytelevel = ByteLevel::default().trim_offsets(true);\n let sequence = Sequence::new(vec![PostProcessorWrapper::ByteLevel(bytelevel)]);\n let expected = Encoding::new(\n vec![0; 5],\n vec![0; 5],\n vec![\n \"Ġ\".into(),\n \"ĠĠĠĠHelloĠĠ\".into(),\n \"ĠĠHello\".into(),\n \"HelloĠĠ\".into(),\n \"ĠĠĠĠ\".into(),\n ],\n vec![],\n vec![(0, 0), (4, 9), (13, 18), (18, 23), (29, 29)],\n vec![],\n vec![],\n vec![],\n HashMap::from_iter(vec![(0, 0..5)]),\n );\n\n assert_eq!(\n expected,\n bytelevel.process(start.clone(), None, false).unwrap()\n );\n assert_eq!(\n expected,\n sequence.process(start.clone(), None, false).unwrap()\n );\n\n let pair_expected = Encoding::new(\n vec![0; 10],\n vec![0, 0, 0, 0, 0, 1, 1, 1, 1, 1],\n vec![\n \"Ġ\".into(),\n \"ĠĠĠĠHelloĠĠ\".into(),\n \"ĠĠHello\".into(),\n \"HelloĠĠ\".into(),\n \"ĠĠĠĠ\".into(),\n \"Ġ\".into(),\n \"ĠĠĠĠHelloĠĠ\".into(),\n \"ĠĠHello\".into(),\n \"HelloĠĠ\".into(),\n \"ĠĠĠĠ\".into(),\n ],\n vec![],\n vec![\n (0, 0),\n (4, 9),\n (13, 18),\n (18, 23),\n (29, 29),\n (0, 0),\n (4, 9),\n (13, 18),\n (18, 23),\n (29, 29),\n ],\n vec![],\n vec![],\n vec![],\n HashMap::from_iter(vec![(0, 0..5), (1, 5..10)]),\n );\n assert_eq!(\n pair_expected,\n bytelevel\n .process(start.clone(), Some(start.clone()), false)\n .unwrap()\n );\n assert_eq!(\n pair_expected,\n sequence.process(start.clone(), Some(start), false).unwrap()\n );\n }\n}\n"},"__index_level_0__":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":6971,"cells":{"repo_id":{"kind":"string","value":"hf_public_repos/tokenizers/tokenizers/src"},"file_path":{"kind":"string","value":"hf_public_repos/tokenizers/tokenizers/src/processors/mod.rs"},"content":{"kind":"string","value":"pub mod bert;\npub mod roberta;\npub mod sequence;\npub mod template;\n\n// Re-export these as processors\npub use super::pre_tokenizers::byte_level;\n\nuse serde::{Deserialize, Serialize};\n\nuse crate::pre_tokenizers::byte_level::ByteLevel;\nuse crate::processors::bert::BertProcessing;\nuse crate::processors::roberta::RobertaProcessing;\nuse crate::processors::sequence::Sequence;\nuse crate::processors::template::TemplateProcessing;\nuse crate::{Encoding, PostProcessor, Result};\n\n#[derive(Serialize, Deserialize, PartialEq, Debug, Clone, Eq)]\n#[serde(untagged)]\npub enum PostProcessorWrapper {\n // Roberta must be before Bert for deserialization (serde does not validate tags)\n Roberta(RobertaProcessing),\n Bert(BertProcessing),\n ByteLevel(ByteLevel),\n Template(TemplateProcessing),\n Sequence(Sequence),\n}\n\nimpl PostProcessor for PostProcessorWrapper {\n fn added_tokens(&self, is_pair: bool) -> usize {\n match self {\n Self::Bert(bert) => bert.added_tokens(is_pair),\n Self::ByteLevel(bl) => bl.added_tokens(is_pair),\n Self::Roberta(roberta) => roberta.added_tokens(is_pair),\n Self::Template(template) => template.added_tokens(is_pair),\n Self::Sequence(bl) => bl.added_tokens(is_pair),\n }\n }\n\n fn process_encodings(\n &self,\n encodings: Vec,\n add_special_tokens: bool,\n ) -> Result> {\n match self {\n Self::Bert(bert) => bert.process_encodings(encodings, add_special_tokens),\n Self::ByteLevel(bl) => bl.process_encodings(encodings, add_special_tokens),\n Self::Roberta(roberta) => roberta.process_encodings(encodings, add_special_tokens),\n Self::Template(template) => template.process_encodings(encodings, add_special_tokens),\n Self::Sequence(bl) => bl.process_encodings(encodings, add_special_tokens),\n }\n }\n}\n\nimpl_enum_from!(BertProcessing, PostProcessorWrapper, Bert);\nimpl_enum_from!(ByteLevel, PostProcessorWrapper, ByteLevel);\nimpl_enum_from!(RobertaProcessing, PostProcessorWrapper, Roberta);\nimpl_enum_from!(TemplateProcessing, PostProcessorWrapper, Template);\nimpl_enum_from!(Sequence, PostProcessorWrapper, Sequence);\n\n#[cfg(test)]\nmod tests {\n use super::*;\n\n #[test]\n fn deserialize_bert_roberta_correctly() {\n let roberta = RobertaProcessing::default();\n let roberta_r = r#\"{\n \"type\":\"RobertaProcessing\",\n \"sep\":[\"\",2],\n \"cls\":[\"\",0],\n \"trim_offsets\":true,\n \"add_prefix_space\":true\n }\"#\n .replace(char::is_whitespace, \"\");\n assert_eq!(serde_json::to_string(&roberta).unwrap(), roberta_r);\n assert_eq!(\n serde_json::from_str::(&roberta_r).unwrap(),\n PostProcessorWrapper::Roberta(roberta)\n );\n\n let bert = BertProcessing::default();\n let bert_r = r#\"{\"type\":\"BertProcessing\",\"sep\":[\"[SEP]\",102],\"cls\":[\"[CLS]\",101]}\"#;\n assert_eq!(serde_json::to_string(&bert).unwrap(), bert_r);\n assert_eq!(\n serde_json::from_str::(bert_r).unwrap(),\n PostProcessorWrapper::Bert(bert)\n );\n }\n}\n"},"__index_level_0__":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":6972,"cells":{"repo_id":{"kind":"string","value":"hf_public_repos/tokenizers/tokenizers/src"},"file_path":{"kind":"string","value":"hf_public_repos/tokenizers/tokenizers/src/processors/roberta.rs"},"content":{"kind":"string","value":"use crate::processors::byte_level::process_offsets;\nuse crate::tokenizer::{Encoding, PostProcessor, Result};\nuse serde::{Deserialize, Serialize};\nuse std::collections::HashMap;\nuse std::iter::FromIterator;\n\n#[derive(Serialize, Deserialize, Debug, Clone, PartialEq, Eq)]\n#[serde(tag = \"type\")]\npub struct RobertaProcessing {\n sep: (String, u32),\n cls: (String, u32),\n trim_offsets: bool,\n add_prefix_space: bool,\n}\n\nimpl Default for RobertaProcessing {\n fn default() -> Self {\n Self {\n sep: (\"\".into(), 2),\n cls: (\"\".into(), 0),\n trim_offsets: true,\n add_prefix_space: true,\n }\n }\n}\n\nimpl RobertaProcessing {\n pub fn new(sep: (String, u32), cls: (String, u32)) -> Self {\n Self {\n sep,\n cls,\n ..Default::default()\n }\n }\n\n #[must_use]\n pub fn trim_offsets(mut self, v: bool) -> Self {\n self.trim_offsets = v;\n self\n }\n\n #[must_use]\n pub fn add_prefix_space(mut self, v: bool) -> Self {\n self.add_prefix_space = v;\n self\n }\n}\n\nimpl PostProcessor for RobertaProcessing {\n fn added_tokens(&self, is_pair: bool) -> usize {\n if is_pair {\n 4\n } else {\n 2\n }\n }\n\n fn process_encodings(\n &self,\n mut encodings: Vec,\n add_special_tokens: bool,\n ) -> Result> {\n if self.trim_offsets {\n for encoding in encodings.iter_mut() {\n process_offsets(encoding, self.add_prefix_space);\n encoding\n .get_overflowing_mut()\n .iter_mut()\n .for_each(|encoding| process_offsets(encoding, self.add_prefix_space));\n }\n }\n\n // Roberta is weird, and every encoding is type_id=0.\n encodings\n .iter_mut()\n .for_each(|encoding| encoding.set_type_ids(vec![0; encoding.len()]));\n\n if !add_special_tokens {\n return Ok(encodings);\n }\n\n let encodings: Vec = encodings\n .iter_mut()\n .enumerate()\n .map(|(i, encoding)| {\n if i == 0 {\n let ids = [&[self.cls.1], encoding.get_ids(), &[self.sep.1]].concat();\n let type_ids = [&[0], encoding.get_type_ids(), &[0]].concat();\n let tokens = [\n &[self.cls.0.clone()],\n encoding.get_tokens(),\n &[self.sep.0.clone()],\n ]\n .concat();\n let words = [&[None], encoding.get_word_ids(), &[None]].concat();\n let offsets = [&[(0, 0)], encoding.get_offsets(), &[(0, 0)]].concat();\n let special_tokens =\n [&[1u32], &vec![0; encoding.get_ids().len()][..], &[1]].concat();\n let attention_mask = vec![1; ids.len()];\n\n // For compatibility with `TemplateProcessing`, the sequence_ranges shouldn't contain\n // the special tokens.\n let sequence_ranges = HashMap::from_iter(vec![(0, 1..ids.len() - 1)]);\n Encoding::new(\n ids,\n type_ids,\n tokens,\n words,\n offsets,\n special_tokens,\n attention_mask,\n encoding\n .take_overflowing()\n .into_iter()\n .map(|encoding| {\n let ids =\n [&[self.cls.1], encoding.get_ids(), &[self.sep.1]].concat();\n let type_ids = vec![0; encoding.get_ids().len() + 2];\n let tokens = [\n &[self.cls.0.clone()],\n encoding.get_tokens(),\n &[self.sep.0.clone()],\n ]\n .concat();\n let words = [&[None], encoding.get_word_ids(), &[None]].concat();\n let offsets =\n [&[(0, 0)], encoding.get_offsets(), &[(0, 0)]].concat();\n let special_tokens =\n [&[1u32], &vec![0; encoding.get_ids().len()][..], &[1]]\n .concat();\n let attention_mask = vec![1; ids.len()];\n\n // For compatibility with `TemplateProcessing`, the sequence_ranges shouldn't\n // contain the special tokens.\n let sequence_ranges =\n HashMap::from_iter(vec![(0, 1..ids.len() - 1)]);\n Encoding::new(\n ids,\n type_ids,\n tokens,\n words,\n offsets,\n special_tokens,\n attention_mask,\n vec![],\n sequence_ranges,\n )\n })\n .collect(),\n sequence_ranges,\n )\n } else {\n let pair_ids = [&[self.sep.1], encoding.get_ids(), &[self.sep.1]].concat();\n let pair_type_ids = vec![0; encoding.get_ids().len() + 2];\n let pair_tokens = [\n &[self.sep.0.clone()],\n encoding.get_tokens(),\n &[self.sep.0.clone()],\n ]\n .concat();\n let pair_words = [&[None], encoding.get_word_ids(), &[None]].concat();\n let pair_offsets = [&[(0, 0)], encoding.get_offsets(), &[(0, 0)]].concat();\n let pair_special_tokens =\n [&[1], &vec![0u32; encoding.get_type_ids().len()][..], &[1]].concat();\n let pair_attention_mask = vec![1; pair_ids.len()];\n\n // For compatibility with `TemplateProcessing`, the sequence_ranges shouldn't contain\n // the special tokens.\n let pair_sequence_ranges = HashMap::from_iter(vec![(1, 1..pair_ids.len() - 1)]);\n Encoding::new(\n pair_ids,\n pair_type_ids,\n pair_tokens,\n pair_words,\n pair_offsets,\n pair_special_tokens,\n pair_attention_mask,\n encoding\n .take_overflowing()\n .into_iter()\n .map(|encoding| {\n let pair_ids =\n [&[self.sep.1], encoding.get_ids(), &[self.sep.1]].concat();\n let pair_type_ids = vec![0; encoding.get_ids().len() + 2];\n let pair_tokens = [\n &[self.sep.0.clone()],\n encoding.get_tokens(),\n &[self.sep.0.clone()],\n ]\n .concat();\n let pair_words =\n [&[None], encoding.get_word_ids(), &[None]].concat();\n let pair_offsets =\n [&[(0, 0)], encoding.get_offsets(), &[(0, 0)]].concat();\n let pair_special_tokens =\n [&[1], &vec![0u32; encoding.get_type_ids().len()][..], &[1]]\n .concat();\n let pair_attention_mask = vec![1; pair_ids.len()];\n\n // For compatibility with `TemplateProcessing`, the sequence_ranges\n // shouldn't contain the special tokens.\n let pair_sequence_ranges =\n HashMap::from_iter(vec![(1, 1..pair_ids.len() - 1)]);\n Encoding::new(\n pair_ids,\n pair_type_ids,\n pair_tokens,\n pair_words,\n pair_offsets,\n pair_special_tokens,\n pair_attention_mask,\n vec![],\n pair_sequence_ranges,\n )\n })\n .collect(),\n pair_sequence_ranges,\n )\n }\n })\n .collect();\n\n Ok(encodings)\n }\n}\n\n#[cfg(test)]\nmod tests {\n use super::*;\n\n #[test]\n fn serde() {\n let roberta = RobertaProcessing::default();\n let roberta_r = r#\"{\n \"type\":\"RobertaProcessing\",\n \"sep\":[\"\",2],\n \"cls\":[\"\",0],\n \"trim_offsets\":true,\n \"add_prefix_space\":true\n }\"#\n .replace(char::is_whitespace, \"\");\n assert_eq!(serde_json::to_string(&roberta).unwrap(), roberta_r);\n assert_eq!(\n serde_json::from_str::(&roberta_r).unwrap(),\n roberta\n );\n }\n\n #[test]\n fn roberta_processing() {\n let processor = RobertaProcessing::default();\n assert_eq!(processor.added_tokens(false), 2);\n assert_eq!(processor.added_tokens(true), 4);\n\n use crate::Token;\n let encoding = Encoding::from_tokens(\n vec![\n Token::new(12, \"Hello\".into(), (0, 5)),\n Token::new(14, \"there\".into(), (6, 11)),\n ],\n 0,\n );\n let pair = Encoding::from_tokens(vec![Token::new(15, \"pair\".into(), (0, 4))], 0);\n let single_encoding = processor.process(encoding.clone(), None, true).unwrap();\n assert_eq!(\n single_encoding,\n Encoding::new(\n vec![0, 12, 14, 2],\n vec![0, 0, 0, 0],\n vec![\"\".into(), \"Hello\".into(), \"there\".into(), \"\".into()],\n vec![None, None, None, None],\n vec![(0, 0), (0, 5), (6, 11), (0, 0)],\n vec![1, 0, 0, 1],\n vec![1, 1, 1, 1],\n vec![],\n HashMap::from_iter(vec![(0, 1..3)]),\n )\n );\n assert_eq!(single_encoding.token_to_sequence(2), Some(0));\n assert_eq!(single_encoding.token_to_sequence(3), None);\n let pair_encoding = processor\n .process(encoding.clone(), Some(pair.clone()), true)\n .unwrap();\n assert_eq!(\n pair_encoding,\n Encoding::new(\n vec![0, 12, 14, 2, 2, 15, 2],\n vec![0, 0, 0, 0, 0, 0, 0],\n vec![\n \"\".into(),\n \"Hello\".into(),\n \"there\".into(),\n \"\".into(),\n \"\".into(),\n \"pair\".into(),\n \"\".into()\n ],\n vec![None, None, None, None, None, None, None],\n vec![(0, 0), (0, 5), (6, 11), (0, 0), (0, 0), (0, 4), (0, 0)],\n vec![1, 0, 0, 1, 1, 0, 1],\n vec![1, 1, 1, 1, 1, 1, 1],\n vec![],\n HashMap::from_iter(vec![(0, 1..3), (1, 5..6)]),\n )\n );\n assert_eq!(pair_encoding.token_to_sequence(2), Some(0));\n assert_eq!(pair_encoding.token_to_sequence(3), None);\n assert_eq!(pair_encoding.token_to_sequence(4), None);\n assert_eq!(pair_encoding.token_to_sequence(5), Some(1));\n assert_eq!(pair_encoding.token_to_sequence(6), None);\n\n // No special tokens\n let pair_encoding = processor.process(encoding, Some(pair), false).unwrap();\n assert_eq!(\n pair_encoding,\n Encoding::new(\n vec![12, 14, 15],\n vec![0, 0, 0],\n vec![\"Hello\".into(), \"there\".into(), \"pair\".into(),],\n vec![None, None, None],\n vec![(0, 5), (6, 11), (0, 4)],\n vec![0, 0, 0],\n vec![1, 1, 1],\n vec![],\n HashMap::from_iter(vec![(0, 0..2), (1, 2..3)]),\n )\n );\n assert_eq!(pair_encoding.token_to_sequence(0), Some(0));\n assert_eq!(pair_encoding.token_to_sequence(1), Some(0));\n assert_eq!(pair_encoding.token_to_sequence(2), Some(1));\n }\n}\n"},"__index_level_0__":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":6973,"cells":{"repo_id":{"kind":"string","value":"hf_public_repos/tokenizers/tokenizers/src"},"file_path":{"kind":"string","value":"hf_public_repos/tokenizers/tokenizers/src/processors/bert.rs"},"content":{"kind":"string","value":"use crate::tokenizer::{Encoding, PostProcessor, Result};\nuse serde::{Deserialize, Serialize};\nuse std::collections::HashMap;\nuse std::iter::FromIterator;\n\n#[derive(Serialize, Deserialize, Clone, Debug, PartialEq, Eq)]\n#[serde(tag = \"type\")]\npub struct BertProcessing {\n sep: (String, u32),\n cls: (String, u32),\n}\n\nimpl Default for BertProcessing {\n fn default() -> Self {\n Self {\n sep: (\"[SEP]\".into(), 102),\n cls: (\"[CLS]\".into(), 101),\n }\n }\n}\n\nimpl BertProcessing {\n pub fn new(sep: (String, u32), cls: (String, u32)) -> Self {\n Self { sep, cls }\n }\n}\n\n#[derive(thiserror::Error, Debug)]\npub enum BertProcessorError {\n #[error(\"encodings vector length must be either 1 or 2\")]\n InvalidEncodingsVecLength,\n}\n\nimpl PostProcessor for BertProcessing {\n fn added_tokens(&self, is_pair: bool) -> usize {\n if is_pair {\n 3\n } else {\n 2\n }\n }\n\n fn process_encodings(\n &self,\n mut encodings: Vec,\n add_special_tokens: bool,\n ) -> Result> {\n if !add_special_tokens {\n return Ok(encodings);\n }\n\n let encodings: Vec = encodings\n .iter_mut()\n .enumerate()\n .map(|(i, encoding)| {\n if i == 0 {\n let ids = [&[self.cls.1], encoding.get_ids(), &[self.sep.1]].concat();\n let type_ids = [&[0], encoding.get_type_ids(), &[0]].concat();\n let tokens = [\n &[self.cls.0.clone()],\n encoding.get_tokens(),\n &[self.sep.0.clone()],\n ]\n .concat();\n let words = [&[None], encoding.get_word_ids(), &[None]].concat();\n let offsets = [&[(0, 0)], encoding.get_offsets(), &[(0, 0)]].concat();\n let special_tokens =\n [&[1u32], &vec![0; encoding.get_ids().len()][..], &[1]].concat();\n let attention_mask = vec![1; ids.len()];\n\n // For compatibility with `TemplateProcessing`, the sequence_ranges shouldn't contain\n // the special tokens.\n let sequence_ranges = HashMap::from_iter(vec![(0, 1..ids.len() - 1)]);\n Encoding::new(\n ids,\n type_ids,\n tokens,\n words,\n offsets,\n special_tokens,\n attention_mask,\n encoding\n .take_overflowing()\n .into_iter()\n .map(|encoding| {\n let ids =\n [&[self.cls.1], encoding.get_ids(), &[self.sep.1]].concat();\n let type_ids = [&[0], encoding.get_type_ids(), &[0]].concat();\n let tokens = [\n &[self.cls.0.clone()],\n encoding.get_tokens(),\n &[self.sep.0.clone()],\n ]\n .concat();\n let words = [&[None], encoding.get_word_ids(), &[None]].concat();\n let offsets =\n [&[(0, 0)], encoding.get_offsets(), &[(0, 0)]].concat();\n let special_tokens =\n [&[1u32], &vec![0; encoding.get_ids().len()][..], &[1]]\n .concat();\n let attention_mask = vec![1; ids.len()];\n\n // For compatibility with `TemplateProcessing`, the sequence_ranges shouldn't\n // contain the special tokens.\n let sequence_ranges =\n HashMap::from_iter(vec![(0, 1..ids.len() - 1)]);\n Encoding::new(\n ids,\n type_ids,\n tokens,\n words,\n offsets,\n special_tokens,\n attention_mask,\n vec![],\n sequence_ranges,\n )\n })\n .collect(),\n sequence_ranges,\n )\n } else {\n let pair_ids = [encoding.get_ids(), &[self.sep.1]].concat();\n let pair_type_ids = [encoding.get_type_ids(), &[1]].concat();\n let pair_tokens = [encoding.get_tokens(), &[self.sep.0.clone()]].concat();\n let pair_words = [encoding.get_word_ids(), &[None]].concat();\n let pair_offsets = [encoding.get_offsets(), &[(0, 0)]].concat();\n let pair_special_tokens =\n [&vec![0u32; encoding.get_type_ids().len()][..], &[1]].concat();\n let pair_attention_mask = vec![1; pair_ids.len()];\n\n // For compatibility with `TemplateProcessing`, the sequence_ranges shouldn't contain\n // the special tokens.\n let pair_sequence_ranges = HashMap::from_iter(vec![(1, 0..pair_ids.len() - 1)]);\n Encoding::new(\n pair_ids,\n pair_type_ids,\n pair_tokens,\n pair_words,\n pair_offsets,\n pair_special_tokens,\n pair_attention_mask,\n encoding\n .take_overflowing()\n .into_iter()\n .map(|encoding| {\n let pair_ids = [encoding.get_ids(), &[self.sep.1]].concat();\n let pair_type_ids = [encoding.get_type_ids(), &[1]].concat();\n let pair_tokens =\n [encoding.get_tokens(), &[self.sep.0.clone()]].concat();\n let pair_words = [encoding.get_word_ids(), &[None]].concat();\n let pair_offsets = [encoding.get_offsets(), &[(0, 0)]].concat();\n let pair_special_tokens =\n [&vec![0u32; encoding.get_type_ids().len()][..], &[1]].concat();\n let pair_attention_mask = vec![1; pair_ids.len()];\n\n // For compatibility with `TemplateProcessing`, the sequence_ranges\n // shouldn't contain the special tokens.\n let pair_sequence_ranges =\n HashMap::from_iter(vec![(1, 0..pair_ids.len() - 1)]);\n Encoding::new(\n pair_ids,\n pair_type_ids,\n pair_tokens,\n pair_words,\n pair_offsets,\n pair_special_tokens,\n pair_attention_mask,\n vec![],\n pair_sequence_ranges,\n )\n })\n .collect(),\n pair_sequence_ranges,\n )\n }\n })\n .collect();\n\n Ok(encodings)\n }\n}\n\n#[cfg(test)]\nmod tests {\n use super::*;\n\n #[test]\n fn serde() {\n let bert = BertProcessing::default();\n let bert_r = r#\"{\"type\":\"BertProcessing\",\"sep\":[\"[SEP]\",102],\"cls\":[\"[CLS]\",101]}\"#;\n assert_eq!(serde_json::to_string(&bert).unwrap(), bert_r);\n assert_eq!(\n serde_json::from_str::(bert_r).unwrap(),\n bert\n );\n }\n\n #[test]\n fn bert_processing() {\n let processor = BertProcessing::default();\n assert_eq!(processor.added_tokens(false), 2);\n assert_eq!(processor.added_tokens(true), 3);\n\n use crate::Token;\n let encoding = Encoding::from_tokens(\n vec![\n Token::new(12, \"Hello\".into(), (0, 5)),\n Token::new(14, \"there\".into(), (6, 11)),\n ],\n 0,\n );\n let pair = Encoding::from_tokens(vec![Token::new(15, \"pair\".into(), (0, 4))], 0);\n let single_encoding = processor.process(encoding.clone(), None, true).unwrap();\n assert_eq!(\n single_encoding,\n Encoding::new(\n vec![101, 12, 14, 102],\n vec![0, 0, 0, 0],\n vec![\n \"[CLS]\".into(),\n \"Hello\".into(),\n \"there\".into(),\n \"[SEP]\".into()\n ],\n vec![None, None, None, None],\n vec![(0, 0), (0, 5), (6, 11), (0, 0)],\n vec![1, 0, 0, 1],\n vec![1, 1, 1, 1],\n vec![],\n HashMap::from_iter(vec![(0, 1..3)]),\n )\n );\n assert_eq!(single_encoding.token_to_sequence(2), Some(0));\n assert_eq!(single_encoding.token_to_sequence(3), None);\n let pair_encoding = processor\n .process(encoding.clone(), Some(pair.clone()), true)\n .unwrap();\n assert_eq!(\n pair_encoding,\n Encoding::new(\n vec![101, 12, 14, 102, 15, 102],\n vec![0, 0, 0, 0, 1, 1],\n vec![\n \"[CLS]\".into(),\n \"Hello\".into(),\n \"there\".into(),\n \"[SEP]\".into(),\n \"pair\".into(),\n \"[SEP]\".into()\n ],\n vec![None, None, None, None, None, None],\n vec![(0, 0), (0, 5), (6, 11), (0, 0), (0, 4), (0, 0)],\n vec![1, 0, 0, 1, 0, 1],\n vec![1, 1, 1, 1, 1, 1],\n vec![],\n HashMap::from_iter(vec![(0, 1..3), (1, 4..5)]),\n )\n );\n assert_eq!(pair_encoding.token_to_sequence(2), Some(0));\n assert_eq!(pair_encoding.token_to_sequence(3), None);\n assert_eq!(pair_encoding.token_to_sequence(4), Some(1));\n assert_eq!(pair_encoding.token_to_sequence(5), None);\n\n // No special tokens\n let pair_encoding = processor.process(encoding, Some(pair), false).unwrap();\n assert_eq!(\n pair_encoding,\n Encoding::new(\n vec![12, 14, 15],\n vec![0, 0, 1],\n vec![\"Hello\".into(), \"there\".into(), \"pair\".into(),],\n vec![None, None, None],\n vec![(0, 5), (6, 11), (0, 4)],\n vec![0, 0, 0],\n vec![1, 1, 1],\n vec![],\n HashMap::from_iter(vec![(0, 0..2), (1, 2..3)]),\n )\n );\n assert_eq!(pair_encoding.token_to_sequence(0), Some(0));\n assert_eq!(pair_encoding.token_to_sequence(1), Some(0));\n assert_eq!(pair_encoding.token_to_sequence(2), Some(1));\n }\n}\n"},"__index_level_0__":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":6974,"cells":{"repo_id":{"kind":"string","value":"hf_public_repos/tokenizers/tokenizers/src"},"file_path":{"kind":"string","value":"hf_public_repos/tokenizers/tokenizers/src/processors/template.rs"},"content":{"kind":"string","value":"//! # Template Processing\n//!\n//! Provides a way to specify templates in order to add the special tokens to each\n//! input sequence as relevant.\n//!\n//! ## Example\n//!\n//! Let's take `BERT` tokenizer as an example. It uses two special tokens, used to\n//! delimitate each sequence. `[CLS]` is always used at the beginning of the first\n//! sequence, and `[SEP]` is added at the end of both the first, and the pair\n//! sequences. The final result looks like this:\n//! - Single sequence: `[CLS] Hello there [SEP]`\n//! - Pair sequences: `[CLS] My name is Anthony [SEP] What is my name? [SEP]`\n//! With the type ids as following:\n//! ```markdown\n//! [CLS] ... [SEP] ... [SEP]\n//! 0 0 0 1 1\n//! ```\n//!\n//! So, we can define a [`TemplateProcessing`] that will achieve this result:\n//! ```\n//! # use tokenizers::processors::template::TemplateProcessing;\n//! let template = TemplateProcessing::builder()\n//! // The template when we only have a single sequence:\n//! .try_single(vec![\"[CLS]\", \"$0\", \"[SEP]\"]).unwrap()\n//! // Same as:\n//! .try_single(\"[CLS] $0 [SEP]\").unwrap()\n//!\n//! // The template when we have both sequences:\n//! .try_pair(vec![\"[CLS]:0\", \"$A:0\", \"[SEP]:0\", \"$B:1\", \"[SEP]:1\"]).unwrap()\n//! // Same as:\n//! .try_pair(\"[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1\").unwrap()\n//! // Or:\n//! .try_pair(\"[CLS] $0 [SEP] $B:1 [SEP]:1\").unwrap()\n//!\n//! // The list of special tokens used by each sequences\n//! .special_tokens(vec![(\"[CLS]\", 1), (\"[SEP]\", 0)])\n//! .build()\n//! .unwrap();\n//! ```\n//!\n//! In this example, each input sequence is identified using a `$` construct. This identifier\n//! lets us specify each input sequence, and the type_id to use. When nothing is specified,\n//! it uses the default values. Here are the different ways to specify it:\n//! - Specifying the sequence, with default `type_id == 0`: `$A` or `$B`\n//! - Specifying the `type_id` with default `sequence == A`: `$0`, `$1`, `$2`, ...\n//! - Specifying both: `$A:0`, `$B:1`, ...\n//!\n//! The same construct is used for special tokens: `(:)?`.\n//!\n//! **Warning**: You must ensure that you are giving the correct tokens/ids as these will\n//! be added to the `Encoding` without any further check. If the given ids correspond to\n//! something totally different in a `Tokenizer` using this `PostProcessor`, it might lead\n//! to unexpected results.\n//!\n//! [`TemplateProcessing`]: struct.TemplateProcessing.html\n//!\nuse crate::{Encoding, PostProcessor, Result};\nuse itertools::Itertools;\nuse serde::{Deserialize, Serialize};\nuse std::collections::{HashMap, HashSet};\nuse std::convert::{TryFrom, TryInto};\nuse std::result::Result as StdResult;\n\n/// Represents any sequences received as input of the PostProcessor\n#[derive(Debug, Clone, PartialEq, Serialize, Deserialize, Eq)]\npub enum Sequence {\n /// This is the first sequence, the one that is always specified\n A,\n /// This is the pair sequence, that is optional\n B,\n}\n\n/// Represents the different kind of pieces that constitute a template.\n/// It can be either the input sequence or a [`SpecialToken`]:\n///\n/// - The `Sequence` has an associated `type_id` which is used by default\n/// for any token inside this sequence. The `Sequence` corresponds to one\n/// of the input sequence given as input of the `PostProcessor`.\n///\n/// - The `SpecialToken` has an associated `id`. It corresponds to a [`SpecialToken`].\n///\n/// The easiest way to build a `Piece` is actually by converting it from a string:\n/// ```\n/// # use tokenizers::processors::template::Piece;\n/// # use std::convert::TryFrom;\n/// let sequence_with_type_id_0 = Piece::try_from(\"$0\").unwrap();\n/// let sequence_with_type_id_1 = Piece::try_from(\"$1\").unwrap();\n/// let special_token_cls = Piece::try_from(\"[CLS]\").unwrap();\n/// ```\n///\n/// [`SpecialToken`]: struct.SpecialToken.html\n///\n#[derive(Debug, Clone, PartialEq, Serialize, Deserialize, Eq)]\npub enum Piece {\n Sequence { id: Sequence, type_id: u32 },\n SpecialToken { id: String, type_id: u32 },\n}\n\nimpl Piece {\n fn extract_id(s: &str) -> Option {\n if s.starts_with('$') {\n let rest = &s['$'.len_utf8()..];\n\n // If the id is just `$`, we use 0 as type_id, and Sequence A\n match rest {\n \"\" => Some(Self::Sequence {\n id: Sequence::A,\n type_id: 0,\n }),\n \"A\" | \"a\" => Some(Self::Sequence {\n id: Sequence::A,\n type_id: 0,\n }),\n \"B\" | \"b\" => Some(Self::Sequence {\n id: Sequence::B,\n type_id: 0,\n }),\n n => {\n if let Ok(type_id) = n.parse::() {\n Some(Self::Sequence {\n id: Sequence::A,\n type_id,\n })\n } else {\n None\n }\n }\n }\n } else {\n Some(Self::SpecialToken {\n id: s.to_owned(),\n type_id: 0,\n })\n }\n }\n\n fn with_type_id(self, type_id: u32) -> Self {\n match self {\n Self::Sequence { id, .. } => Self::Sequence { id, type_id },\n Self::SpecialToken { id, .. } => Self::SpecialToken { id, type_id },\n }\n }\n}\n\nimpl TryFrom for Piece {\n type Error = String;\n\n fn try_from(s: String) -> StdResult {\n let parts = s.split(':').collect::>();\n\n let err = || format!(\"Cannot build Piece from string \\\"{}\\\"\", s);\n match parts.as_slice() {\n [id, type_id] => {\n let type_id: u32 = type_id.parse().map_err(|_| err())?;\n let piece = Self::extract_id(id).ok_or_else(err)?;\n Ok(piece.with_type_id(type_id))\n }\n [id] => Self::extract_id(id).ok_or_else(err),\n _ => Err(err()),\n }\n }\n}\n\nimpl TryFrom<&str> for Piece {\n type Error = String;\n\n fn try_from(s: &str) -> StdResult {\n Piece::try_from(s.to_owned())\n }\n}\n\n/// Represents a bunch of tokens to be used in a template.\n/// Usually, special tokens have only one associated id/token but in\n/// some cases, it might be interesting to have multiple ids/tokens.\n///\n/// # Examples\n/// ```\n/// # use tokenizers::processors::template::SpecialToken;\n/// // Simple cases, where a single id/token is necessary:\n/// let cls = SpecialToken::from((\"[CLS]\", 1));\n/// let sep = SpecialToken::from((0, \"[SEP]\")); // The order in the tuple is not important\n///\n/// // More complex case with multiple values:\n/// let complex = SpecialToken::new(\n/// \"A complex special token:\".into(),\n/// vec![0, 1, 2, 3, 4],\n/// vec![\"A\".into(), \"complex\".into(), \"special\".into(), \"token\".into(), \":\".into()]\n/// ).unwrap();\n/// ```\n#[derive(Debug, Clone, PartialEq, Serialize, Deserialize, Eq)]\npub struct SpecialToken {\n /// A unique id used to identify this SpecialToken in the template\n id: String,\n /// The list of associated ids\n ids: Vec,\n /// The list of associated tokens\n tokens: Vec,\n}\n\nimpl From<(String, u32)> for SpecialToken {\n fn from(v: (String, u32)) -> Self {\n Self {\n id: v.0.clone(),\n ids: vec![v.1],\n tokens: vec![v.0],\n }\n }\n}\nimpl From<(&str, u32)> for SpecialToken {\n fn from(v: (&str, u32)) -> Self {\n Self::from((v.0.to_owned(), v.1))\n }\n}\nimpl From<(u32, String)> for SpecialToken {\n fn from(v: (u32, String)) -> Self {\n Self::from((v.1, v.0))\n }\n}\nimpl From<(u32, &str)> for SpecialToken {\n fn from(v: (u32, &str)) -> Self {\n Self::from((v.1.to_owned(), v.0))\n }\n}\n\nimpl SpecialToken {\n pub fn new(id: String, ids: Vec, tokens: Vec) -> Result {\n if ids.len() != tokens.len() {\n Err(\"SpecialToken: ids and tokens must be of the same length\".into())\n } else {\n Ok(Self { id, ids, tokens })\n }\n }\n}\n\n/// A Template represents a Vec<[`Piece`]>.\n///\n/// We can easily build one as follows\n/// ```\n/// # use tokenizers::processors::template::Template;\n/// # use std::convert::TryFrom;\n/// // By providing a `String` or `&str`, we just split on whitespaces:\n/// let template = Template::try_from(\"[CLS] $0 [SEP]\").unwrap();\n///\n/// // By providing pieces directly:\n/// let template = Template::try_from(vec![\"[CLS]\", \"$0\", \"[SEP]\"]).unwrap();\n/// ```\n/// Both of these methods give the same result.\n///\n/// [`Piece`]: enum.Piece.html\n///\n#[derive(Debug, Clone, PartialEq, Serialize, Deserialize, Eq)]\n#[serde(transparent)]\npub struct Template(Vec);\n\nimpl TryFrom> for Template\nwhere\n T: TryInto,\n{\n type Error = String;\n\n fn try_from(v: Vec) -> StdResult {\n Ok(Self(\n v.into_iter()\n .map(|p| p.try_into())\n .collect::, Self::Error>>()?,\n ))\n }\n}\n\nimpl TryFrom for Template {\n type Error = String;\n\n fn try_from(s: String) -> StdResult {\n Self::try_from(s.as_ref())\n }\n}\n\nimpl TryFrom<&str> for Template {\n type Error = String;\n\n fn try_from(s: &str) -> StdResult {\n Self::try_from(s.split(' ').collect::>())\n }\n}\n\n/// A bunch of [`SpecialToken`] represented by their ID.\n/// Internally, `Tokens` is a `HashMap` and can be built\n/// from a HashMap or a Vec<[`SpecialToken`]>.\n///\n/// [`SpecialToken`]: struct.SpecialToken.html\n#[derive(Debug, Clone, PartialEq, Default, Serialize, Deserialize, Eq)]\n#[serde(transparent)]\npub struct Tokens(\n #[serde(serialize_with = \"crate::utils::ordered_map\")] pub HashMap,\n);\n\nimpl> From> for Tokens {\n fn from(v: Vec) -> Self {\n Self(\n v.into_iter()\n .map(|t| {\n let token: SpecialToken = t.into();\n (token.id.clone(), token)\n })\n .collect(),\n )\n }\n}\n\nimpl From> for Tokens {\n fn from(v: HashMap) -> Self {\n Self(v)\n }\n}\n\n/// This PostProcessor takes care of processing each input `Encoding` by applying\n/// the corresponding template, before merging them in the final Encoding.\n///\n/// A `Template` is actually a sequence of `Piece` that will be\n/// concatenated together in the given order. Each `Piece` represents either\n/// one of the input `Encoding` or a `SpecialToken`.\n///\n/// ## Example\n/// ```\n/// # use tokenizers::processors::template::TemplateProcessing;\n/// let template = TemplateProcessing::builder()\n/// .try_single(\"[CLS] $A [SEP]\").unwrap()\n/// .try_pair(\"[CLS] $A [SEP] $B:1 [SEP]:1\").unwrap()\n/// .special_tokens(vec![(\"[CLS]\", 1), (\"[SEP]\", 0)])\n/// .build()\n/// .unwrap();\n/// ```\n///\n#[derive(Debug, Clone, PartialEq, Builder, Serialize, Deserialize, Eq)]\n#[serde(tag = \"type\", from = \"TemplateProcessingDeserializer\")]\n#[builder(build_fn(validate = \"Self::validate\"))]\npub struct TemplateProcessing {\n #[builder(try_setter, default = \"\\\"$0\\\".try_into().unwrap()\")]\n single: Template,\n #[builder(try_setter, default = \"\\\"$A:0 $B:1\\\".try_into().unwrap()\")]\n pair: Template,\n #[builder(setter(skip), default = \"self.default_added(true)\")]\n #[serde(skip)]\n added_single: usize,\n #[builder(setter(skip), default = \"self.default_added(false)\")]\n #[serde(skip)]\n added_pair: usize,\n #[builder(setter(into), default)]\n special_tokens: Tokens,\n}\n\nimpl From<&str> for TemplateProcessingBuilderError {\n fn from(e: &str) -> Self {\n e.to_string().into()\n }\n}\n\nimpl PartialEq for TemplateProcessingBuilderError {\n fn eq(&self, other: &Self) -> bool {\n self.to_string() == other.to_string()\n }\n}\n\n/// We use this custom deserializer to provided the values for `added_single`\n/// and `added_pair` during deserialization, while not having to serialize them\n#[doc(hidden)]\n#[derive(Deserialize)]\n#[serde(tag = \"type\")]\nstruct TemplateProcessingDeserializer {\n single: Template,\n pair: Template,\n special_tokens: Tokens,\n}\nimpl From for TemplateProcessing {\n fn from(t: TemplateProcessingDeserializer) -> Self {\n let added_single = count_added(&t.single, Some(&t.special_tokens));\n let added_pair = count_added(&t.pair, Some(&t.special_tokens));\n Self {\n single: t.single,\n pair: t.pair,\n added_single,\n added_pair,\n special_tokens: t.special_tokens,\n }\n }\n}\n\n/// Count the number of added tokens in the given template\nfn count_added(container: &Template, special_tokens: Option<&Tokens>) -> usize {\n container\n .0\n .iter()\n .map(|p| match p {\n Piece::Sequence { .. } => 0,\n Piece::SpecialToken { id, .. } => {\n special_tokens.map_or(0, |spt| spt.0.get(id).map_or(0, |s| s.ids.len()))\n }\n })\n .sum()\n}\n\nimpl TemplateProcessingBuilder {\n fn default_added(&self, is_single: bool) -> usize {\n let container = if is_single {\n self.single.as_ref()\n } else {\n self.pair.as_ref()\n };\n container.map_or(0, |pieces| {\n count_added(pieces, self.special_tokens.as_ref())\n })\n }\n\n fn validate(&self) -> std::result::Result<(), String> {\n let pair_has_both = self.pair.as_ref().map_or(true, |pair| {\n let mut has_a = false;\n let mut has_b = false;\n for piece in &pair.0 {\n if let Piece::Sequence {\n id: Sequence::A, ..\n } = piece\n {\n has_a = true;\n }\n if let Piece::Sequence {\n id: Sequence::B, ..\n } = piece\n {\n has_b = true;\n }\n }\n has_a && has_b\n });\n if !pair_has_both {\n return Err(\"Template for `pair` must use both sequences\".into());\n }\n\n let check = |sp| {\n let exist = self\n .special_tokens\n .as_ref()\n .map_or(false, |map| map.0.contains_key(sp));\n\n match exist {\n false => Some(sp),\n true => None,\n }\n };\n\n let empty = vec![];\n let missing: HashSet<&str> = self\n .single\n .as_ref()\n .map_or(empty.iter(), |s| s.0.iter())\n .chain(self.pair.as_ref().map_or(empty.iter(), |s| s.0.iter()))\n .filter_map(|piece| match piece {\n Piece::Sequence { .. } => None,\n Piece::SpecialToken { id, .. } => check(id.as_ref()),\n })\n .collect::>();\n\n if missing.is_empty() {\n Ok(())\n } else {\n Err(format!(\n \"Missing SpecialToken(s) with id(s) `{}`\",\n missing.iter().join(\", \")\n ))\n }\n }\n}\n\nimpl Default for TemplateProcessing {\n fn default() -> Self {\n Self {\n single: \"$0\".try_into().unwrap(),\n pair: \"$1\".try_into().unwrap(),\n added_single: 0,\n added_pair: 0,\n special_tokens: Tokens::default(),\n }\n }\n}\n\nimpl TemplateProcessing {\n pub fn builder() -> TemplateProcessingBuilder {\n TemplateProcessingBuilder::default()\n }\n\n fn apply_template(\n &self,\n template: &[Piece],\n mut encodings: Vec,\n add_special_tokens: bool,\n ) -> Result> {\n let final_encodings: Vec = template\n .iter()\n .flat_map(|piece| {\n match piece {\n Piece::Sequence { id, type_id } => {\n let i = usize::from(*id != Sequence::A);\n let encoding = &mut encodings[i];\n encoding.set_type_ids(vec![*type_id; encoding.len()]);\n encoding.set_sequence_id(i);\n Some(encoding.clone())\n }\n Piece::SpecialToken { id, type_id } => {\n if add_special_tokens {\n let tok = &self.special_tokens.0[id]; // We already checked existance above\n let len = tok.ids.len();\n\n let encoding = Encoding::new(\n tok.ids.clone(),\n std::iter::repeat(*type_id).take(len).collect(),\n tok.tokens.clone(),\n // words\n std::iter::repeat(None).take(len).collect(),\n // offsets\n std::iter::repeat((0, 0)).take(len).collect(),\n // special_tokens_mask\n std::iter::repeat(1).take(len).collect(),\n // attention_mask\n std::iter::repeat(1).take(len).collect(),\n // overflowing\n vec![],\n // sequence_range\n HashMap::new(),\n );\n Some(encoding)\n } else {\n None\n }\n }\n }\n })\n .collect();\n\n //let mut pair = if encodings.len() > 1 {\n // Some(encodings.pop().unwrap())\n //} else {\n // None\n //};\n //let mut encoding = encodings.pop().unwrap();\n\n //let pair_overflowing = pair.as_mut().map_or(vec![], |e| e.take_overflowing());\n //let mut overflowing: Vec = encoding\n // .take_overflowing()\n // .iter()\n // .map(|encoding| -> Result> {\n // // 1. The pair itself\n // let mut overflowings = self.apply_template(\n // template,\n // if encodings.len() > 1 {\n // vec![encoding.clone(), encodings[1].clone()]\n // } else {\n // vec![encoding.clone()]\n // },\n // add_special_tokens,\n // )?;\n\n // // 2. Its overflowings\n // for other_o in &pair_overflowing {\n // overflowings.extend(self.apply_template(\n // template,\n // vec![encoding.clone(), other_o.clone()],\n // add_special_tokens,\n // )?);\n // }\n\n // Ok(overflowings)\n // })\n // .collect::>>>()?\n // .into_iter()\n // .flatten()\n // .collect();\n //// We also need to combine the first sequence with all other overflowings\n //overflowing.extend(\n // pair_overflowing\n // .into_iter()\n // .map(|pair| {\n // self.apply_template(template, vec![encoding.clone(), pair], add_special_tokens)\n // })\n // .collect::>>()?\n // .into_iter()\n // .flatten(),\n //);\n\n Ok(final_encodings)\n }\n}\n\nimpl PostProcessor for TemplateProcessing {\n fn added_tokens(&self, is_pair: bool) -> usize {\n if is_pair {\n self.added_pair\n } else {\n self.added_single\n }\n }\n\n fn process_encodings(\n &self,\n encodings: Vec,\n add_special_tokens: bool,\n ) -> Result> {\n // let (encoding, pair): (Encoding, Option) = match encodings.len() {\n // 1 => (\n // encodings\n // .pop()\n // .ok_or(ProcessorError::InvalidEncodingsVecLength)?,\n // None,\n // ),\n // 2 => {\n // let pair = encodings\n // .pop()\n // .ok_or(ProcessorError::InvalidEncodingsVecLength)?;\n // let encoding = encodings\n // .pop()\n // .ok_or(ProcessorError::InvalidEncodingsVecLength)?;\n // (encoding, Some(pair))\n // }\n // _ => return Err(Box::new(ProcessorError::InvalidEncodingsVecLength)),\n // };\n let template = match encodings.len() {\n 2 => &self.pair.0,\n 1 => &self.single.0,\n _ => todo!(),\n };\n let encodings = self.apply_template(template, encodings, add_special_tokens)?;\n Ok(encodings)\n }\n}\n\n#[cfg(test)]\nmod tests {\n use super::*;\n use std::convert::TryInto;\n use std::iter::FromIterator;\n\n #[test]\n fn piece_serde() {\n let seq_0 = Piece::Sequence {\n id: Sequence::A,\n type_id: 0,\n };\n let seq_0_s = r#\"{\"Sequence\":{\"id\":\"A\",\"type_id\":0}}\"#;\n\n assert_eq!(serde_json::to_string(&seq_0).unwrap(), seq_0_s);\n assert_eq!(serde_json::from_str::(seq_0_s).unwrap(), seq_0);\n\n let seq_1 = Piece::Sequence {\n id: Sequence::B,\n type_id: 1,\n };\n let seq_1_s = r#\"{\"Sequence\":{\"id\":\"B\",\"type_id\":1}}\"#;\n assert_eq!(serde_json::to_string(&seq_1).unwrap(), seq_1_s);\n assert_eq!(serde_json::from_str::(seq_1_s).unwrap(), seq_1);\n\n let spe = Piece::SpecialToken {\n id: \"[CLS]\".into(),\n type_id: 0,\n };\n let spe_s = r#\"{\"SpecialToken\":{\"id\":\"[CLS]\",\"type_id\":0}}\"#;\n assert_eq!(serde_json::to_string(&spe).unwrap(), spe_s);\n assert_eq!(serde_json::from_str::(spe_s).unwrap(), spe);\n }\n\n #[test]\n fn piece() {\n assert_eq!(\n Ok(Piece::Sequence {\n id: Sequence::A,\n type_id: 0\n }),\n \"$\".try_into()\n );\n assert_eq!(\n Ok(Piece::Sequence {\n id: Sequence::B,\n type_id: 0\n }),\n \"$B\".try_into()\n );\n assert_eq!(\n Ok(Piece::Sequence {\n id: Sequence::A,\n type_id: 1\n }),\n \"$1\".try_into()\n );\n assert_eq!(\n Ok(Piece::Sequence {\n id: Sequence::B,\n type_id: 2\n }),\n \"$B:2\".try_into()\n );\n assert_eq!(\n Ok(Piece::Sequence {\n id: Sequence::A,\n type_id: 1\n }),\n \"$:1\".try_into()\n );\n assert!(Piece::try_from(\"$C:1\").is_err());\n assert!(Piece::try_from(\"$A:\").is_err());\n }\n\n #[test]\n fn special_token_serde() {\n let simple = SpecialToken::from((\"[CLS]\", 0));\n let simple_s = r#\"{\"id\":\"[CLS]\",\"ids\":[0],\"tokens\":[\"[CLS]\"]}\"#;\n assert_eq!(serde_json::to_string(&simple).unwrap(), simple_s);\n assert_eq!(\n serde_json::from_str::(simple_s).unwrap(),\n simple\n );\n\n let complete = SpecialToken::new(\n \"[2FR]\".into(),\n vec![1, 2, 3],\n vec![\"convert\".into(), \"to\".into(), \"FR\".into()],\n )\n .unwrap();\n let complete_s = r#\"{\"id\":\"[2FR]\",\"ids\":[1,2,3],\"tokens\":[\"convert\",\"to\",\"FR\"]}\"#;\n assert_eq!(serde_json::to_string(&complete).unwrap(), complete_s);\n assert_eq!(\n serde_json::from_str::(complete_s).unwrap(),\n complete\n );\n\n let malformed = SpecialToken::new(\n \"[2FR]\".into(),\n vec![1, 2],\n vec![\"convert\".into(), \"to\".into(), \"FR\".into()],\n );\n assert!(malformed.is_err());\n let malformed = SpecialToken::new(\n \"[2FR]\".into(),\n vec![1, 2, 3],\n vec![\"convert\".into(), \"FR\".into()],\n );\n assert!(malformed.is_err());\n }\n\n #[test]\n fn template_serde() {\n let template = Template(vec![\n Piece::Sequence {\n id: Sequence::A,\n type_id: 0,\n },\n Piece::SpecialToken {\n id: \"[CLS]\".into(),\n type_id: 0,\n },\n ]);\n let template_s =\n r#\"[{\"Sequence\":{\"id\":\"A\",\"type_id\":0}},{\"SpecialToken\":{\"id\":\"[CLS]\",\"type_id\":0}}]\"#;\n assert_eq!(serde_json::to_string(&template).unwrap(), template_s);\n assert_eq!(\n serde_json::from_str::