infinityofspace/python_codestyles-random-500

{ // 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 \"\"\"\t\t\t\t)\r with open(SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\"w\"\"\"\t\t\t\t) as f:\r f.write(SCREAMING_SNAKE_CASE__\t\t\t\t)\r return filename\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t[\r {'col_1': '0', 'col_2': 0, 'col_3': 0.0},\r {'col_1': '1', 'col_2': 1, 'col_3': 1.0},\r {'col_1': '2', 'col_2': 2, 'col_3': 2.0},\r {'col_1': '3', 'col_2': 3, 'col_3': 3.0},\r]\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t[\r {'col_1': '4', 'col_2': 4, 'col_3': 4.0},\r {'col_1': '5', 'col_2': 5, 'col_3': 5.0},\r]\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t{\r 'col_1': ['0', '1', '2', '3'],\r 'col_2': [0, 1, 2, 3],\r 'col_3': [0.0, 1.0, 2.0, 3.0],\r}\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t[\r {'col_3': 0.0, 'col_1': '0', 'col_2': 0},\r {'col_3': 1.0, 'col_1': '1', 'col_2': 1},\r]\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t[\r {'col_1': 's0', 'col_2': 0, 'col_3': 0.0},\r {'col_1': 's1', 'col_2': 1, 'col_3': 1.0},\r {'col_1': 's2', 'col_2': 2, 'col_3': 2.0},\r {'col_1': 's3', 'col_2': 3, 'col_3': 3.0},\r]\r\r@pytest.fixture(scope=\"\"\"session\"\"\"\t\t\t\t)\rdef _UpperCamelCase\t\t\t\t\t( ):\r '''simple docstring'''\r\r\r\r\r\r return DATA_DICT_OF_LISTS\r\r@pytest.fixture(scope=\"\"\"session\"\"\"\t\t\t\t)\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tAny\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdatasets.Dataset.from_dict(SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tstr(tmp_path_factory.mktemp(\"\"\"data\"\"\"\t\t\t\t) / \"\"\"dataset.arrow\"\"\"\t\t\t\t)\r dataset.map(cache_file_name=SCREAMING_SNAKE_CASE__\t\t\t\t)\r return path\r\r@pytest.fixture(scope=\"\"\"session\"\"\"\t\t\t\t)\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tTuple\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tstr(tmp_path_factory.mktemp(\"\"\"data\"\"\"\t\t\t\t) / \"\"\"dataset.sqlite\"\"\"\t\t\t\t)\r with contextlib.closing(sqlitea.connect(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t) as con:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tcon.cursor()\r cur.execute(\"\"\"CREATE TABLE dataset(col_1 text, col_2 int, col_3 real)\"\"\"\t\t\t\t)\r for item in DATA:\r cur.execute(\"\"\"INSERT INTO dataset(col_1, col_2, col_3) VALUES (?, ?, ?)\"\"\"\t\t\t\t, tuple(item.values()\t\t\t\t)\t\t\t\t)\r con.commit()\r return path\r\r@pytest.fixture(scope=\"\"\"session\"\"\"\t\t\t\t)\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[int]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tstr(tmp_path_factory.mktemp(\"\"\"data\"\"\"\t\t\t\t) / \"\"\"dataset.csv\"\"\"\t\t\t\t)\r with open(SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\"w\"\"\"\t\t\t\t, newline=\"\"\"\"\"\"\t\t\t\t) as f:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tcsv.DictWriter(SCREAMING_SNAKE_CASE__\t\t\t\t, fieldnames=[\"\"\"col_1\"\"\", \"\"\"col_2\"\"\", \"\"\"col_3\"\"\"]\t\t\t\t)\r writer.writeheader()\r for item in DATA:\r writer.writerow(SCREAMING_SNAKE_CASE__\t\t\t\t)\r return path\r\r@pytest.fixture(scope=\"\"\"session\"\"\"\t\t\t\t)\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tAny\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tstr(tmp_path_factory.mktemp(\"\"\"data\"\"\"\t\t\t\t) / \"\"\"dataset2.csv\"\"\"\t\t\t\t)\r with open(SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\"w\"\"\"\t\t\t\t, newline=\"\"\"\"\"\"\t\t\t\t) as f:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tcsv.DictWriter(SCREAMING_SNAKE_CASE__\t\t\t\t, fieldnames=[\"\"\"col_1\"\"\", \"\"\"col_2\"\"\", \"\"\"col_3\"\"\"]\t\t\t\t)\r writer.writeheader()\r for item in DATA:\r writer.writerow(SCREAMING_SNAKE_CASE__\t\t\t\t)\r return path\r\r@pytest.fixture(scope=\"\"\"session\"\"\"\t\t\t\t)\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tUnion[str, Any]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r import bza\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttmp_path_factory.mktemp(\"\"\"data\"\"\"\t\t\t\t) / \"\"\"dataset.csv.bz2\"\"\"\r with open(SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\"rb\"\"\"\t\t\t\t) as f:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tf.read()\r # data = bytes(FILE_CONTENT, \"utf-8\")\r with bza.open(SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\"wb\"\"\"\t\t\t\t) as f:\r f.write(SCREAMING_SNAKE_CASE__\t\t\t\t)\r return path\r\r@pytest.fixture(scope=\"\"\"session\"\"\"\t\t\t\t)\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tTuple\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[Any]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[Any]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttmp_path_factory.mktemp(\"\"\"data\"\"\"\t\t\t\t) / \"\"\"dataset.csv.zip\"\"\"\r with zipfile.ZipFile(SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\"w\"\"\"\t\t\t\t) as f:\r f.write(SCREAMING_SNAKE_CASE__\t\t\t\t, arcname=os.path.basename(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t)\r f.write(SCREAMING_SNAKE_CASE__\t\t\t\t, arcname=os.path.basename(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t)\r return path\r\r@pytest.fixture(scope=\"\"\"session\"\"\"\t\t\t\t)\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tUnion[str, Any]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tAny\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[Any]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttmp_path_factory.mktemp(\"\"\"data\"\"\"\t\t\t\t) / \"\"\"dataset.csv.zip\"\"\"\r with zipfile.ZipFile(SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\"w\"\"\"\t\t\t\t) as f:\r f.write(SCREAMING_SNAKE_CASE__\t\t\t\t, arcname=os.path.basename(csv_path.replace(\"\"\".csv\"\"\"\t\t\t\t, \"\"\".CSV\"\"\"\t\t\t\t)\t\t\t\t)\t\t\t\t)\r f.write(SCREAMING_SNAKE_CASE__\t\t\t\t, arcname=os.path.basename(csva_path.replace(\"\"\".csv\"\"\"\t\t\t\t, \"\"\".CSV\"\"\"\t\t\t\t)\t\t\t\t)\t\t\t\t)\r return path\r\r@pytest.fixture(scope=\"\"\"session\"\"\"\t\t\t\t)\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tDict\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tTuple\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttmp_path_factory.mktemp(\"\"\"data\"\"\"\t\t\t\t) / \"\"\"dataset_with_dir.csv.zip\"\"\"\r with zipfile.ZipFile(SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\"w\"\"\"\t\t\t\t) as f:\r f.write(SCREAMING_SNAKE_CASE__\t\t\t\t, arcname=os.path.join(\"\"\"main_dir\"\"\"\t\t\t\t, os.path.basename(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t)\t\t\t\t)\r f.write(SCREAMING_SNAKE_CASE__\t\t\t\t, arcname=os.path.join(\"\"\"main_dir\"\"\"\t\t\t\t, os.path.basename(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t)\t\t\t\t)\r return path\r\r@pytest.fixture(scope=\"\"\"session\"\"\"\t\t\t\t)\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tTuple\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tstr(tmp_path_factory.mktemp(\"\"\"data\"\"\"\t\t\t\t) / \"\"\"dataset.parquet\"\"\"\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tpa.schema(\r {\r \"\"\"col_1\"\"\": pa.string(),\r \"\"\"col_2\"\"\": pa.intaa(),\r \"\"\"col_3\"\"\": pa.floataa(),\r }\t\t\t\t)\r with open(SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\"wb\"\"\"\t\t\t\t) as f:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tpq.ParquetWriter(SCREAMING_SNAKE_CASE__\t\t\t\t, schema=SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tpa.Table.from_pydict({k: [DATA[i][k] for i in range(len(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t)] for k in DATA[0]}\t\t\t\t, schema=SCREAMING_SNAKE_CASE__\t\t\t\t)\r writer.write_table(SCREAMING_SNAKE_CASE__\t\t\t\t)\r writer.close()\r return path\r\r@pytest.fixture(scope=\"\"\"session\"\"\"\t\t\t\t)\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tstr(tmp_path_factory.mktemp(\"\"\"data\"\"\"\t\t\t\t) / \"\"\"dataset.json\"\"\"\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\"\"\"data\"\"\": DATA}\r with open(SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\"w\"\"\"\t\t\t\t) as f:\r json.dump(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r return path\r\r@pytest.fixture(scope=\"\"\"session\"\"\"\t\t\t\t)\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tAny\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tstr(tmp_path_factory.mktemp(\"\"\"data\"\"\"\t\t\t\t) / \"\"\"dataset.json\"\"\"\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\"\"\"data\"\"\": DATA_DICT_OF_LISTS}\r with open(SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\"w\"\"\"\t\t\t\t) as f:\r json.dump(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r return path\r\r@pytest.fixture(scope=\"\"\"session\"\"\"\t\t\t\t)\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tAny\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tstr(tmp_path_factory.mktemp(\"\"\"data\"\"\"\t\t\t\t) / \"\"\"dataset.jsonl\"\"\"\t\t\t\t)\r with open(SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\"w\"\"\"\t\t\t\t) as f:\r for item in DATA:\r f.write(json.dumps(SCREAMING_SNAKE_CASE__\t\t\t\t) + \"\"\"\\n\"\"\"\t\t\t\t)\r return path\r\r@pytest.fixture(scope=\"\"\"session\"\"\"\t\t\t\t)\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[Any]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tstr(tmp_path_factory.mktemp(\"\"\"data\"\"\"\t\t\t\t) / \"\"\"dataset2.jsonl\"\"\"\t\t\t\t)\r with open(SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\"w\"\"\"\t\t\t\t) as f:\r for item in DATA:\r f.write(json.dumps(SCREAMING_SNAKE_CASE__\t\t\t\t) + \"\"\"\\n\"\"\"\t\t\t\t)\r return path\r\r@pytest.fixture(scope=\"\"\"session\"\"\"\t\t\t\t)\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tstr(tmp_path_factory.mktemp(\"\"\"data\"\"\"\t\t\t\t) / \"\"\"dataset_312.jsonl\"\"\"\t\t\t\t)\r with open(SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\"w\"\"\"\t\t\t\t) as f:\r for item in DATA_312:\r f.write(json.dumps(SCREAMING_SNAKE_CASE__\t\t\t\t) + \"\"\"\\n\"\"\"\t\t\t\t)\r return path\r\r@pytest.fixture(scope=\"\"\"session\"\"\"\t\t\t\t)\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tTuple\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tstr(tmp_path_factory.mktemp(\"\"\"data\"\"\"\t\t\t\t) / \"\"\"dataset-str.jsonl\"\"\"\t\t\t\t)\r with open(SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\"w\"\"\"\t\t\t\t) as f:\r for item in DATA_STR:\r f.write(json.dumps(SCREAMING_SNAKE_CASE__\t\t\t\t) + \"\"\"\\n\"\"\"\t\t\t\t)\r return path\r\r@pytest.fixture(scope=\"\"\"session\"\"\"\t\t\t\t)\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tTuple\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r import gzip\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tstr(tmp_path_factory.mktemp(\"\"\"data\"\"\"\t\t\t\t) / \"\"\"dataset.txt.gz\"\"\"\t\t\t\t)\r with open(SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\"rb\"\"\"\t\t\t\t) as orig_file:\r with gzip.open(SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\"wb\"\"\"\t\t\t\t) as zipped_file:\r zipped_file.writelines(SCREAMING_SNAKE_CASE__\t\t\t\t)\r return path\r\r@pytest.fixture(scope=\"\"\"session\"\"\"\t\t\t\t)\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[Any]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r import gzip\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tstr(tmp_path_factory.mktemp(\"\"\"data\"\"\"\t\t\t\t) / \"\"\"dataset.jsonl.gz\"\"\"\t\t\t\t)\r with open(SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\"rb\"\"\"\t\t\t\t) as orig_file:\r with gzip.open(SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\"wb\"\"\"\t\t\t\t) as zipped_file:\r zipped_file.writelines(SCREAMING_SNAKE_CASE__\t\t\t\t)\r return path\r\r@pytest.fixture(scope=\"\"\"session\"\"\"\t\t\t\t)\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[Any]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tUnion[str, Any]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[Any]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttmp_path_factory.mktemp(\"\"\"data\"\"\"\t\t\t\t) / \"\"\"dataset.jsonl.zip\"\"\"\r with zipfile.ZipFile(SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\"w\"\"\"\t\t\t\t) as f:\r f.write(SCREAMING_SNAKE_CASE__\t\t\t\t, arcname=os.path.basename(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t)\r f.write(SCREAMING_SNAKE_CASE__\t\t\t\t, arcname=os.path.basename(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t)\r return path\r\r@pytest.fixture(scope=\"\"\"session\"\"\"\t\t\t\t)\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tTuple\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tAny\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tUnion[str, Any]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttmp_path_factory.mktemp(\"\"\"data\"\"\"\t\t\t\t) / \"\"\"dataset_nested.jsonl.zip\"\"\"\r with zipfile.ZipFile(SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\"w\"\"\"\t\t\t\t) as f:\r f.write(SCREAMING_SNAKE_CASE__\t\t\t\t, arcname=os.path.join(\"\"\"nested\"\"\"\t\t\t\t, os.path.basename(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t)\t\t\t\t)\r return path\r\r@pytest.fixture(scope=\"\"\"session\"\"\"\t\t\t\t)\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[int]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[int]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttmp_path_factory.mktemp(\"\"\"data\"\"\"\t\t\t\t) / \"\"\"dataset_with_dir.jsonl.zip\"\"\"\r with zipfile.ZipFile(SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\"w\"\"\"\t\t\t\t) as f:\r f.write(SCREAMING_SNAKE_CASE__\t\t\t\t, arcname=os.path.join(\"\"\"main_dir\"\"\"\t\t\t\t, os.path.basename(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t)\t\t\t\t)\r f.write(SCREAMING_SNAKE_CASE__\t\t\t\t, arcname=os.path.join(\"\"\"main_dir\"\"\"\t\t\t\t, os.path.basename(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t)\t\t\t\t)\r return path\r\r@pytest.fixture(scope=\"\"\"session\"\"\"\t\t\t\t)\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tTuple\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tUnion[str, Any]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[Any]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttmp_path_factory.mktemp(\"\"\"data\"\"\"\t\t\t\t) / \"\"\"dataset.jsonl.tar\"\"\"\r with tarfile.TarFile(SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\"w\"\"\"\t\t\t\t) as f:\r f.add(SCREAMING_SNAKE_CASE__\t\t\t\t, arcname=os.path.basename(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t)\r f.add(SCREAMING_SNAKE_CASE__\t\t\t\t, arcname=os.path.basename(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t)\r return path\r\r@pytest.fixture(scope=\"\"\"session\"\"\"\t\t\t\t)\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[Any]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tTuple\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttmp_path_factory.mktemp(\"\"\"data\"\"\"\t\t\t\t) / \"\"\"dataset_nested.jsonl.tar\"\"\"\r with tarfile.TarFile(SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\"w\"\"\"\t\t\t\t) as f:\r f.add(SCREAMING_SNAKE_CASE__\t\t\t\t, arcname=os.path.join(\"\"\"nested\"\"\"\t\t\t\t, os.path.basename(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t)\t\t\t\t)\r return path\r\r@pytest.fixture(scope=\"\"\"session\"\"\"\t\t\t\t)\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tUnion[str, Any]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[\"\"\"0\"\"\", \"\"\"1\"\"\", \"\"\"2\"\"\", \"\"\"3\"\"\"]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tstr(tmp_path_factory.mktemp(\"\"\"data\"\"\"\t\t\t\t) / \"\"\"dataset.txt\"\"\"\t\t\t\t)\r with open(SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\"w\"\"\"\t\t\t\t) as f:\r for item in data:\r f.write(item + \"\"\"\\n\"\"\"\t\t\t\t)\r return path\r\r@pytest.fixture(scope=\"\"\"session\"\"\"\t\t\t\t)\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[\"\"\"0\"\"\", \"\"\"1\"\"\", \"\"\"2\"\"\", \"\"\"3\"\"\"]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tstr(tmp_path_factory.mktemp(\"\"\"data\"\"\"\t\t\t\t) / \"\"\"dataset2.txt\"\"\"\t\t\t\t)\r with open(SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\"w\"\"\"\t\t\t\t) as f:\r for item in data:\r f.write(item + \"\"\"\\n\"\"\"\t\t\t\t)\r return path\r\r@pytest.fixture(scope=\"\"\"session\"\"\"\t\t\t\t)\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[Any]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[\"\"\"0\"\"\", \"\"\"1\"\"\", \"\"\"2\"\"\", \"\"\"3\"\"\"]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttmp_path_factory.mktemp(\"\"\"data\"\"\"\t\t\t\t) / \"\"\"dataset.abc\"\"\"\r with open(SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\"w\"\"\"\t\t\t\t) as f:\r for item in data:\r f.write(item + \"\"\"\\n\"\"\"\t\t\t\t)\r return path\r\r@pytest.fixture(scope=\"\"\"session\"\"\"\t\t\t\t)\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[Any]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttmp_path_factory.mktemp(\"\"\"data\"\"\"\t\t\t\t) / \"\"\"dataset.text.zip\"\"\"\r with zipfile.ZipFile(SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\"w\"\"\"\t\t\t\t) as f:\r f.write(SCREAMING_SNAKE_CASE__\t\t\t\t, arcname=os.path.basename(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t)\r f.write(SCREAMING_SNAKE_CASE__\t\t\t\t, arcname=os.path.basename(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t)\r return path\r\r@pytest.fixture(scope=\"\"\"session\"\"\"\t\t\t\t)\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[Any]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttmp_path_factory.mktemp(\"\"\"data\"\"\"\t\t\t\t) / \"\"\"dataset_with_dir.text.zip\"\"\"\r with zipfile.ZipFile(SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\"w\"\"\"\t\t\t\t) as f:\r f.write(SCREAMING_SNAKE_CASE__\t\t\t\t, arcname=os.path.join(\"\"\"main_dir\"\"\"\t\t\t\t, os.path.basename(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t)\t\t\t\t)\r f.write(SCREAMING_SNAKE_CASE__\t\t\t\t, arcname=os.path.join(\"\"\"main_dir\"\"\"\t\t\t\t, os.path.basename(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t)\t\t\t\t)\r return path\r\r@pytest.fixture(scope=\"\"\"session\"\"\"\t\t\t\t)\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tAny\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttmp_path_factory.mktemp(\"\"\"data\"\"\"\t\t\t\t) / \"\"\"dataset.ext.zip\"\"\"\r with zipfile.ZipFile(SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\"w\"\"\"\t\t\t\t) as f:\r f.write(SCREAMING_SNAKE_CASE__\t\t\t\t, arcname=os.path.basename(\"\"\"unsupported.ext\"\"\"\t\t\t\t)\t\t\t\t)\r f.write(SCREAMING_SNAKE_CASE__\t\t\t\t, arcname=os.path.basename(\"\"\"unsupported_2.ext\"\"\"\t\t\t\t)\t\t\t\t)\r return path\r\r@pytest.fixture(scope=\"\"\"session\"\"\"\t\t\t\t)\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"\\n\"\"\".join([\"\"\"First\"\"\", \"\"\"Second\\u2029with Unicode new line\"\"\", \"\"\"Third\"\"\"]\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tstr(tmp_path_factory.mktemp(\"\"\"data\"\"\"\t\t\t\t) / \"\"\"dataset_with_unicode_new_lines.txt\"\"\"\t\t\t\t)\r with open(SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\"w\"\"\"\t\t\t\t, encoding=\"\"\"utf-8\"\"\"\t\t\t\t) as f:\r f.write(SCREAMING_SNAKE_CASE__\t\t\t\t)\r return path\r\r@pytest.fixture(scope=\"\"\"session\"\"\"\t\t\t\t)\rdef _UpperCamelCase\t\t\t\t\t( ):\r '''simple docstring'''\r\r\r\r\r\r return os.path.join(\"\"\"tests\"\"\"\t\t\t\t, \"\"\"features\"\"\"\t\t\t\t, \"\"\"data\"\"\"\t\t\t\t, \"\"\"test_image_rgb.jpg\"\"\"\t\t\t\t)\r\r@pytest.fixture(scope=\"\"\"session\"\"\"\t\t\t\t)\rdef _UpperCamelCase\t\t\t\t\t( ):\r '''simple docstring'''\r\r\r\r\r\r return os.path.join(\"\"\"tests\"\"\"\t\t\t\t, \"\"\"features\"\"\"\t\t\t\t, \"\"\"data\"\"\"\t\t\t\t, \"\"\"test_audio_44100.wav\"\"\"\t\t\t\t)\r\r@pytest.fixture(scope=\"\"\"session\"\"\"\t\t\t\t)\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[Any]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tUnion[str, Any]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttmp_path_factory.mktemp(\"\"\"data\"\"\"\t\t\t\t) / \"\"\"dataset.img.zip\"\"\"\r with zipfile.ZipFile(SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\"w\"\"\"\t\t\t\t) as f:\r f.write(SCREAMING_SNAKE_CASE__\t\t\t\t, arcname=os.path.basename(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t)\r f.write(SCREAMING_SNAKE_CASE__\t\t\t\t, arcname=os.path.basename(SCREAMING_SNAKE_CASE__\t\t\t\t).replace(\"\"\".jpg\"\"\"\t\t\t\t, \"\"\"2.jpg\"\"\"\t\t\t\t)\t\t\t\t)\r return path\r\r\r\r\r\r\r\r@pytest.fixture(scope=\"\"\"session\"\"\"\t\t\t\t)\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttmp_path_factory.mktemp(\"\"\"data_dir\"\"\"\t\t\t\t)\r\r (data_dir / \"subdir\").mkdir()\r with open(data_dir / \"\"\"subdir\"\"\" / \"\"\"train.txt\"\"\"\t\t\t\t, \"\"\"w\"\"\"\t\t\t\t) as f:\r f.write(\"\"\"foo\\n\"\"\" * 10\t\t\t\t)\r with open(data_dir / \"\"\"subdir\"\"\" / \"\"\"test.txt\"\"\"\t\t\t\t, \"\"\"w\"\"\"\t\t\t\t) as f:\r f.write(\"\"\"bar\\n\"\"\" * 10\t\t\t\t)\r # hidden file\r with open(data_dir / \"\"\"subdir\"\"\" / \"\"\".test.txt\"\"\"\t\t\t\t, \"\"\"w\"\"\"\t\t\t\t) as f:\r f.write(\"\"\"bar\\n\"\"\" * 10\t\t\t\t)\r\r # hidden directory\r (data_dir / \".subdir\").mkdir()\r with open(data_dir / \"\"\".subdir\"\"\" / \"\"\"train.txt\"\"\"\t\t\t\t, \"\"\"w\"\"\"\t\t\t\t) as f:\r f.write(\"\"\"foo\\n\"\"\" * 10\t\t\t\t)\r with open(data_dir / \"\"\".subdir\"\"\" / \"\"\"test.txt\"\"\"\t\t\t\t, \"\"\"w\"\"\"\t\t\t\t) as f:\r f.write(\"\"\"bar\\n\"\"\" * 10\t\t\t\t)\r\r return data_dir\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport os\rimport unicodedata\rfrom shutil import copyfile\rfrom typing import Any, Dict, List, Optional, Tuple\r\rimport sentencepiece as spm\r\rfrom ...tokenization_utils import AddedToken, PreTrainedTokenizer\rfrom ...utils import SPIECE_UNDERLINE, logging\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tlogging.get_logger(__name__)\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t{'vocab_file': 'spiece.model'}\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t{\r 'vocab_file': {\r 'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model',\r }\r}\r\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def __init__( self\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny=False ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint=True ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=False ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict=\"~~\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint=\"~~\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict=\"\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple=\"\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any]=\"\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint=\"\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=\"\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str]=[\"\", \"\"] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Dict[str, Any]] = None ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tAddedToken(_UpperCAmelCase ,\t\t\t\t\t\tlstrip=_UpperCAmelCase ,\t\t\t\t\t\trstrip=_UpperCAmelCase ) if isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ) else mask_token\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{} if sp_model_kwargs is None else sp_model_kwargs\r\r super().__init__(\r do_lower_case=_UpperCAmelCase ,\t\t\t\t\t\tremove_space=_UpperCAmelCase ,\t\t\t\t\t\tkeep_accents=_UpperCAmelCase ,\t\t\t\t\t\tbos_token=_UpperCAmelCase ,\t\t\t\t\t\teos_token=_UpperCAmelCase ,\t\t\t\t\t\tunk_token=_UpperCAmelCase ,\t\t\t\t\t\tsep_token=_UpperCAmelCase ,\t\t\t\t\t\tpad_token=_UpperCAmelCase ,\t\t\t\t\t\tcls_token=_UpperCAmelCase ,\t\t\t\t\t\tmask_token=_UpperCAmelCase ,\t\t\t\t\t\tadditional_special_tokens=_UpperCAmelCase ,\t\t\t\t\t\tsp_model_kwargs=self.sp_model_kwargs ,\t\t\t\t\t\t**_UpperCAmelCase ,\t\t\t\t\t\t)\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t3\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdo_lower_case\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tremove_space\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tkeep_accents\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvocab_file\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tspm.SentencePieceProcessor(**self.sp_model_kwargs )\r self.sp_model.Load(_UpperCAmelCase )\r\r try:\r import jieba\r except ModuleNotFoundError as error:\r raise error.__class__(\r \"\"\"You need to install jieba to use CpmTokenizer or CpmTokenizerFast. \"\"\"\r \"\"\"See https://pypi.org/project/jieba/ for installation.\"\"\" )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tjieba\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tstr.maketrans(\"\"\" \\n\"\"\" ,\t\t\t\t\t\t\"\"\"\\u2582\\u2583\"\"\" )\r\r\r @property\r # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return len(self.sp_model )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{self.convert_ids_to_tokens(_UpperCAmelCase ): i for i in range(self.vocab_size )}\r vocab.update(self.added_tokens_encoder )\r return vocab\r\r\r def __getstate__( self\t\t\t:\t\tDict ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.__dict__.copy()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r return state\r\r\r def __setstate__( self\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\td\r\r # for backward compatibility\r if not hasattr(self ,\t\t\t\t\t\t\"\"\"sp_model_kwargs\"\"\" ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{}\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tspm.SentencePieceProcessor(**self.sp_model_kwargs )\r self.sp_model.Load(self.vocab_file )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if self.remove_space:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\" \"\"\".join(inputs.strip().split() )\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tinputs\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\toutputs.replace(\"\"\"``\"\"\" ,\t\t\t\t\t\t\"\"\"\\\"\"\"\" ).replace(\"\"\"''\"\"\" ,\t\t\t\t\t\t\"\"\"\\\"\"\"\" )\r\r if not self.keep_accents:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tunicodedata.normalize(\"\"\"NFKD\"\"\" ,\t\t\t\t\t\t_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"\"\"\".join([c for c in outputs if not unicodedata.combining(_UpperCAmelCase )] )\r if self.do_lower_case:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\toutputs.lower()\r\r return outputs\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tTuple ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.preprocess_text(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.sp_model.encode(_UpperCAmelCase ,\t\t\t\t\t\tout_type=_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[]\r for piece in pieces:\r if len(_UpperCAmelCase ) > 1 and piece[-1] == str(\"\"\",\"\"\" ) and piece[-2].isdigit():\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.sp_model.EncodeAsPieces(piece[:-1].replace(_UpperCAmelCase ,\t\t\t\t\t\t\"\"\"\"\"\" ) )\r if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:\r if len(cur_pieces[0] ) == 1:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tcur_pieces[1:]\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tcur_pieces[0][1:]\r cur_pieces.append(piece[-1] )\r new_pieces.extend(_UpperCAmelCase )\r else:\r new_pieces.append(_UpperCAmelCase )\r\r return new_pieces\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return self.sp_model.PieceToId(_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return self.sp_model.IdToPiece(_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"\"\"\".join(_UpperCAmelCase ).replace(_UpperCAmelCase ,\t\t\t\t\t\t\"\"\" \"\"\" ).strip()\r return out_string\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[List[int]] = None ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[self.sep_token_id]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[self.cls_token_id]\r if token_ids_a is None:\r return token_ids_a + sep + cls\r return token_ids_a + sep + token_ids_a + sep + cls\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[List[int]] = None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tbool = False ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if already_has_special_tokens:\r return super().get_special_tokens_mask(\r token_ids_a=_UpperCAmelCase ,\t\t\t\t\t\ttoken_ids_a=_UpperCAmelCase ,\t\t\t\t\t\talready_has_special_tokens=_UpperCAmelCase )\r\r if token_ids_a is not None:\r return ([0] * len(_UpperCAmelCase )) + [1] + ([0] * len(_UpperCAmelCase )) + [1, 1]\r return ([0] * len(_UpperCAmelCase )) + [1, 1]\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[List[int]] = None ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[self.sep_token_id]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[2]\r\r if token_ids_a is None:\r return len(token_ids_a + sep ) * [0] + cls_segment_id\r return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[str] = None ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if not os.path.isdir(_UpperCAmelCase ):\r logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )\r return\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tos.path.join(\r _UpperCAmelCase ,\t\t\t\t\t\t(filename_prefix + \"\"\"-\"\"\" if filename_prefix else \"\"\"\"\"\") + VOCAB_FILES_NAMES[\"\"\"vocab_file\"\"\"] )\r\r if os.path.abspath(self.vocab_file ) != os.path.abspath(_UpperCAmelCase ) and os.path.isfile(self.vocab_file ):\r copyfile(self.vocab_file ,\t\t\t\t\t\t_UpperCAmelCase )\r elif not os.path.isfile(self.vocab_file ):\r with open(_UpperCAmelCase ,\t\t\t\t\t\t\"\"\"wb\"\"\" ) as fi:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.sp_model.serialized_model_proto()\r fi.write(_UpperCAmelCase )\r\r return (out_vocab_file,)\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tTuple ,\t\t\t\t\t\t*_UpperCAmelCase\t\t\t:\t\tTuple ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tsuper()._decode(*_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttext.replace(\"\"\" \"\"\" ,\t\t\t\t\t\t\"\"\"\"\"\" ).replace(\"\"\"\\u2582\"\"\" ,\t\t\t\t\t\t\"\"\" \"\"\" ).replace(\"\"\"\\u2583\"\"\" ,\t\t\t\t\t\t\"\"\"\\n\"\"\" )\r return text\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152307,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rfrom __future__ import annotations\r\rimport unittest\r\rfrom transformers import MobileBertConfig, is_tf_available\rfrom transformers.models.auto import get_values\rfrom transformers.testing_utils import require_tf, slow\r\rfrom ...test_configuration_common import ConfigTester\rfrom ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask\rfrom ...test_pipeline_mixin import PipelineTesterMixin\r\r\rif is_tf_available():\r import tensorflow as tf\r\r from transformers import (\r TF_MODEL_FOR_PRETRAINING_MAPPING,\r TFMobileBertForMaskedLM,\r TFMobileBertForMultipleChoice,\r TFMobileBertForNextSentencePrediction,\r TFMobileBertForPreTraining,\r TFMobileBertForQuestionAnswering,\r TFMobileBertForSequenceClassification,\r TFMobileBertForTokenClassification,\r TFMobileBertModel,\r )\r\r@require_tf\rclass lowerCAmelCase_ ( lowerCamelCase_\t, lowerCamelCase_\t, unittest.TestCase ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r lowerCAmelCase_ : Tuple = (\r (\r TFMobileBertModel,\r TFMobileBertForMaskedLM,\r TFMobileBertForNextSentencePrediction,\r TFMobileBertForPreTraining,\r TFMobileBertForQuestionAnswering,\r TFMobileBertForSequenceClassification,\r TFMobileBertForTokenClassification,\r TFMobileBertForMultipleChoice,\r )\r if is_tf_available()\r else ()\r )\r lowerCAmelCase_ : Optional[int] = (\r {\r \"\"\"feature-extraction\"\"\": TFMobileBertModel,\r \"\"\"fill-mask\"\"\": TFMobileBertForMaskedLM,\r \"\"\"question-answering\"\"\": TFMobileBertForQuestionAnswering,\r \"\"\"text-classification\"\"\": TFMobileBertForSequenceClassification,\r \"\"\"token-classification\"\"\": TFMobileBertForTokenClassification,\r \"\"\"zero-shot\"\"\": TFMobileBertForSequenceClassification,\r }\r if is_tf_available()\r else {}\r )\r lowerCAmelCase_ : Any = False\r lowerCAmelCase_ : List[Any] = False\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=False ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tsuper()._prepare_for_class(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\treturn_labels=_UpperCAmelCase )\r\r if return_labels:\r if model_class in get_values(_UpperCAmelCase ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttf.zeros(self.model_tester.batch_size ,\t\t\t\t\t\tdtype=tf.intaa )\r\r return inputs_dict\r\r class lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def __init__( self\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny=13 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any]=7 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any]=True ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr=True ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str]=True ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any]=True ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple=99 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict=32 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any]=32 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=2 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint=4 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str]=37 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr=\"gelu\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=0.1 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any]=0.1 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict=5_12 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int]=16 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str]=2 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any]=0.02 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny=3 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=4 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=None ,\t\t\t\t\t\t):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tparent\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tbatch_size\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tseq_length\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tis_training\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tuse_input_mask\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tuse_token_type_ids\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tuse_labels\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvocab_size\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\thidden_size\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnum_hidden_layers\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnum_attention_heads\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tintermediate_size\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\thidden_act\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\thidden_dropout_prob\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tattention_probs_dropout_prob\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmax_position_embeddings\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttype_vocab_size\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttype_sequence_label_size\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tinitializer_range\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnum_labels\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnum_choices\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscope\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tembedding_size\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tids_tensor([self.batch_size, self.seq_length] ,\t\t\t\t\t\tself.vocab_size )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r if self.use_input_mask:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\trandom_attention_mask([self.batch_size, self.seq_length] )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r if self.use_token_type_ids:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tids_tensor([self.batch_size, self.seq_length] ,\t\t\t\t\t\tself.type_vocab_size )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r if self.use_labels:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tids_tensor([self.batch_size] ,\t\t\t\t\t\tself.type_sequence_label_size )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tids_tensor([self.batch_size, self.seq_length] ,\t\t\t\t\t\tself.num_labels )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tids_tensor([self.batch_size] ,\t\t\t\t\t\tself.num_choices )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tMobileBertConfig(\r vocab_size=self.vocab_size ,\t\t\t\t\t\thidden_size=self.hidden_size ,\t\t\t\t\t\tnum_hidden_layers=self.num_hidden_layers ,\t\t\t\t\t\tnum_attention_heads=self.num_attention_heads ,\t\t\t\t\t\tintermediate_size=self.intermediate_size ,\t\t\t\t\t\thidden_act=self.hidden_act ,\t\t\t\t\t\thidden_dropout_prob=self.hidden_dropout_prob ,\t\t\t\t\t\tattention_probs_dropout_prob=self.attention_probs_dropout_prob ,\t\t\t\t\t\tmax_position_embeddings=self.max_position_embeddings ,\t\t\t\t\t\ttype_vocab_size=self.type_vocab_size ,\t\t\t\t\t\tinitializer_range=self.initializer_range ,\t\t\t\t\t\tembedding_size=self.embedding_size ,\t\t\t\t\t\t)\r\r return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tAny ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTFMobileBertModel(config=_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\"\"\"input_ids\"\"\": input_ids, \"\"\"attention_mask\"\"\": input_mask, \"\"\"token_type_ids\"\"\": token_type_ids}\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[input_ids, input_mask]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase )\r\r self.parent.assertEqual(\r result.last_hidden_state.shape ,\t\t\t\t\t\t(self.batch_size, self.seq_length, self.hidden_size) )\r self.parent.assertEqual(result.pooler_output.shape ,\t\t\t\t\t\t(self.batch_size, self.hidden_size) )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTFMobileBertForMaskedLM(config=_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\"\"\"input_ids\"\"\": input_ids, \"\"\"attention_mask\"\"\": input_mask, \"\"\"token_type_ids\"\"\": token_type_ids}\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase )\r self.parent.assertEqual(result.logits.shape ,\t\t\t\t\t\t(self.batch_size, self.seq_length, self.vocab_size) )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tAny ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTFMobileBertForNextSentencePrediction(config=_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\"\"\"input_ids\"\"\": input_ids, \"\"\"attention_mask\"\"\": input_mask, \"\"\"token_type_ids\"\"\": token_type_ids}\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase )\r self.parent.assertEqual(result.logits.shape ,\t\t\t\t\t\t(self.batch_size, 2) )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTFMobileBertForPreTraining(config=_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\"\"\"input_ids\"\"\": input_ids, \"\"\"attention_mask\"\"\": input_mask, \"\"\"token_type_ids\"\"\": token_type_ids}\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase )\r self.parent.assertEqual(\r result.prediction_logits.shape ,\t\t\t\t\t\t(self.batch_size, self.seq_length, self.vocab_size) )\r self.parent.assertEqual(result.seq_relationship_logits.shape ,\t\t\t\t\t\t(self.batch_size, 2) )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.num_labels\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTFMobileBertForSequenceClassification(config=_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\"\"\"input_ids\"\"\": input_ids, \"\"\"attention_mask\"\"\": input_mask, \"\"\"token_type_ids\"\"\": token_type_ids}\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase )\r self.parent.assertEqual(result.logits.shape ,\t\t\t\t\t\t(self.batch_size, self.num_labels) )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.num_choices\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTFMobileBertForMultipleChoice(config=_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttf.tile(tf.expand_dims(_UpperCAmelCase ,\t\t\t\t\t\t1 ) ,\t\t\t\t\t\t(1, self.num_choices, 1) )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttf.tile(tf.expand_dims(_UpperCAmelCase ,\t\t\t\t\t\t1 ) ,\t\t\t\t\t\t(1, self.num_choices, 1) )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttf.tile(tf.expand_dims(_UpperCAmelCase ,\t\t\t\t\t\t1 ) ,\t\t\t\t\t\t(1, self.num_choices, 1) )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\r \"\"\"input_ids\"\"\": multiple_choice_inputs_ids,\r \"\"\"attention_mask\"\"\": multiple_choice_input_mask,\r \"\"\"token_type_ids\"\"\": multiple_choice_token_type_ids,\r }\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase )\r self.parent.assertEqual(result.logits.shape ,\t\t\t\t\t\t(self.batch_size, self.num_choices) )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.num_labels\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTFMobileBertForTokenClassification(config=_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\"\"\"input_ids\"\"\": input_ids, \"\"\"attention_mask\"\"\": input_mask, \"\"\"token_type_ids\"\"\": token_type_ids}\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase )\r self.parent.assertEqual(result.logits.shape ,\t\t\t\t\t\t(self.batch_size, self.seq_length, self.num_labels) )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTFMobileBertForQuestionAnswering(config=_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\"\"\"input_ids\"\"\": input_ids, \"\"\"attention_mask\"\"\": input_mask, \"\"\"token_type_ids\"\"\": token_type_ids}\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase )\r self.parent.assertEqual(result.start_logits.shape ,\t\t\t\t\t\t(self.batch_size, self.seq_length) )\r self.parent.assertEqual(result.end_logits.shape ,\t\t\t\t\t\t(self.batch_size, self.seq_length) )\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.prepare_config_and_inputs()\r (\r (\r UpperCAmelCase__\r )\t\t\t\t\t\t\t,\t(\r UpperCAmelCase__\r )\t\t\t\t\t\t\t,\t(\r UpperCAmelCase__\r )\t\t\t\t\t\t\t,\t(\r UpperCAmelCase__\r )\t\t\t\t\t\t\t,\t(\r UpperCAmelCase__\r )\t\t\t\t\t\t\t,\t(\r UpperCAmelCase__\r )\t\t\t\t\t\t\t,\t(\r UpperCAmelCase__\r )\t\t\t\t\t\t\t,\t\r )\t\t\t\t\t\t\t\t=\t\t\tconfig_and_inputs\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\"\"\"input_ids\"\"\": input_ids, \"\"\"token_type_ids\"\"\": token_type_ids, \"\"\"attention_mask\"\"\": input_mask}\r return config, inputs_dict\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTFMobileBertModelTest.TFMobileBertModelTester(self )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tConfigTester(self ,\t\t\t\t\t\tconfig_class=_UpperCAmelCase ,\t\t\t\t\t\thidden_size=37 )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r self.config_tester.run_common_tests()\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.model_tester.prepare_config_and_inputs()\r self.model_tester.create_and_check_mobilebert_model(*_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.model_tester.prepare_config_and_inputs()\r self.model_tester.create_and_check_mobilebert_for_masked_lm(*_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.model_tester.prepare_config_and_inputs()\r self.model_tester.create_and_check_mobilebert_for_multiple_choice(*_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.model_tester.prepare_config_and_inputs()\r self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.model_tester.prepare_config_and_inputs()\r self.model_tester.create_and_check_mobilebert_for_pretraining(*_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.model_tester.prepare_config_and_inputs()\r self.model_tester.create_and_check_mobilebert_for_question_answering(*_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.model_tester.prepare_config_and_inputs()\r self.model_tester.create_and_check_mobilebert_for_sequence_classification(*_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tAny ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.model_tester.prepare_config_and_inputs()\r self.model_tester.create_and_check_mobilebert_for_token_classification(*_UpperCAmelCase )\r\r\r\r @slow\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r for model_name in [\"google/mobilebert-uncased\"]:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTFMobileBertModel.from_pretrained(_UpperCAmelCase )\r self.assertIsNotNone(_UpperCAmelCase )\r\r@require_tf\rclass lowerCAmelCase_ ( unittest.TestCase ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r @slow\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTFMobileBertForPreTraining.from_pretrained(\"\"\"google/mobilebert-uncased\"\"\" )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttf.constant([[0, 1, 2, 3, 4, 5]] )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase )[0]\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[1, 6, 3_05_22]\r self.assertEqual(output.shape ,\t\t\t\t\t\t_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttf.constant(\r [\r [\r [-4.591_9547, -9.24_8295, -9.64_5256],\r [-6.730_6175, -6.44_0284, -6.605_2837],\r [-7.274_3506, -6.784_7915, -6.02_4673],\r ]\r ] )\r tf.debugging.assert_near(output[:, :3, :3] ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\tatol=1E-4 )\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport argparse\rimport logging\rimport os\r\rimport datasets\rimport tensorflow as tf\r\rfrom transformers import AutoTokenizer\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tlogging.getLogger(__name__)\r\rdef _UpperCamelCase\t\t\t\t\t( ):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\targparse.ArgumentParser(\r description=\"\"\"Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset.\"\"\"\t\t\t\t)\r parser.add_argument(\r \"\"\"--dataset_name\"\"\"\t\t\t\t, type=SCREAMING_SNAKE_CASE__\t\t\t\t, default=\"\"\"wikitext\"\"\"\t\t\t\t, help=\"\"\"Name of the training. Explore datasets at: hf.co/datasets.\"\"\"\t\t\t\t, )\r parser.add_argument(\r \"\"\"--dataset_config\"\"\"\t\t\t\t, type=SCREAMING_SNAKE_CASE__\t\t\t\t, default=\"\"\"wikitext-103-raw-v1\"\"\"\t\t\t\t, help=\"\"\"Configuration name of the dataset.\"\"\"\t\t\t\t)\r parser.add_argument(\r \"\"\"--tokenizer_name_or_path\"\"\"\t\t\t\t, type=SCREAMING_SNAKE_CASE__\t\t\t\t, default=\"\"\"sayakpaul/unigram-tokenizer-wikitext\"\"\"\t\t\t\t, help=\"\"\"Tokenizer identifier. Can be a local filepath or a Hub identifier.\"\"\"\t\t\t\t, )\r parser.add_argument(\r \"\"\"--shard_size\"\"\"\t\t\t\t, type=SCREAMING_SNAKE_CASE__\t\t\t\t, default=1000\t\t\t\t, help=\"\"\"Number of entries to go in a single shard.\"\"\"\t\t\t\t, )\r parser.add_argument(\"\"\"--split\"\"\"\t\t\t\t, type=SCREAMING_SNAKE_CASE__\t\t\t\t, default=\"\"\"train\"\"\"\t\t\t\t, choices=[\"\"\"train\"\"\", \"\"\"test\"\"\", \"\"\"validation\"\"\"]\t\t\t\t)\r parser.add_argument(\r \"\"\"--limit\"\"\"\t\t\t\t, default=SCREAMING_SNAKE_CASE__\t\t\t\t, type=SCREAMING_SNAKE_CASE__\t\t\t\t, help=\"\"\"Limit the number of shards (used for debugging).\"\"\"\t\t\t\t, )\r parser.add_argument(\r \"\"\"--max_length\"\"\"\t\t\t\t, type=SCREAMING_SNAKE_CASE__\t\t\t\t, default=512\t\t\t\t, help=\"\"\"Maximum sequence length. For training on TPUs, it helps to have a maximum\"\"\"\r \"\"\" sequence length that is a multiple of 8.\"\"\"\t\t\t\t, )\r parser.add_argument(\r \"\"\"--output_dir\"\"\"\t\t\t\t, default=\"\"\"tf-tpu\"\"\"\t\t\t\t, type=SCREAMING_SNAKE_CASE__\t\t\t\t, help=\"\"\"Output directory where the TFRecord shards will be saved. If the\"\"\"\r \"\"\" path is appended with `gs://` ('gs://tf-tpu', for example) then the TFRecord\"\"\"\r \"\"\" shards will be directly saved to a Google Cloud Storage bucket.\"\"\"\t\t\t\t, )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tparser.parse_args()\r return args\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tAny\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r def fn(SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tUnion[str, Any]\t\t\t\t):\r return tokenizer(examples[\"\"\"text\"\"\"]\t\t\t\t)\r\r return fn\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[int]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[]\r for i in range(len(tokenized_data[\"\"\"input_ids\"\"\"]\t\t\t\t)\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\r \"\"\"input_ids\"\"\": tf.train.Feature(intaa_list=tf.train.IntaaList(value=tokenized_data[\"\"\"input_ids\"\"\"][i]\t\t\t\t)\t\t\t\t),\r \"\"\"attention_mask\"\"\": tf.train.Feature(\r intaa_list=tf.train.IntaaList(value=tokenized_data[\"\"\"attention_mask\"\"\"][i]\t\t\t\t)\t\t\t\t),\r }\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttf.train.Features(feature=SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttf.train.Example(features=SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\texample.SerializeToString()\r records.append(SCREAMING_SNAKE_CASE__\t\t\t\t)\r return records\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tAny\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdatasets.load_dataset(args.dataset_name\t\t\t\t, args.dataset_config\t\t\t\t, split=args.split\t\t\t\t)\r\r if args.limit is not None:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmin(len(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t, args.limit\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdataset.select(range(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t)\r print(F'''Limiting the dataset to {args.limit} entries.'''\t\t\t\t)\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tAutoTokenizer.from_pretrained(args.tokenizer_name_or_path\t\t\t\t)\r\r # Handle output directory creation.\r # For serializing into a Google Cloud Storage Bucket, one needs to first\r # create a bucket.\r if \"gs\" not in args.output_dir:\r if not os.path.exists(args.output_dir\t\t\t\t):\r os.makedirs(args.output_dir\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tos.path.join(args.output_dir\t\t\t\t, args.split\t\t\t\t)\r if not os.path.exists(SCREAMING_SNAKE_CASE__\t\t\t\t):\r os.makedirs(SCREAMING_SNAKE_CASE__\t\t\t\t)\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tos.path.join(args.output_dir\t\t\t\t, args.split\t\t\t\t)\r\r # Tokenize the whole dataset at once.\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenize_function(SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdataset.map(SCREAMING_SNAKE_CASE__\t\t\t\t, batched=SCREAMING_SNAKE_CASE__\t\t\t\t, num_proc=4\t\t\t\t, remove_columns=[\"\"\"text\"\"\"]\t\t\t\t)\r\r # We need to concatenate all our texts together, and then split the result\r # into chunks of a fixed size, which we will call block_size. To do this, we\r # will use the map method again, with the option batched=True. When we use batched=True,\r # the function we pass to map() will be passed multiple inputs at once, allowing us\r # to group them into more or fewer examples than we had in the input.\r # This allows us to create our new fixed-length samples. The advantage of this\r # method is that we don't lose a whole lot of content from the dataset compared to the\r # case where we simply tokenize with a pre-defined max_length.\r\r def group_texts(SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t):\r # Concatenate all texts.\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{k: sum(examples[k]\t\t\t\t, []\t\t\t\t) for k in examples.keys()}\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlen(concatenated_examples[list(examples.keys()\t\t\t\t)[0]]\t\t\t\t)\r # We drop the small remainder, though you could add padding instead if the model supports it\r # In this, as in all things, we advise you to follow your heart 🫀\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t(total_length // args.max_length) * args.max_length\r # Split by chunks of max_len.\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\r k: [t[i : i + args.max_length] for i in range(0\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, args.max_length\t\t\t\t)]\r for k, t in concatenated_examples.items()\r }\r return result\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdataset_tokenized.map(SCREAMING_SNAKE_CASE__\t\t\t\t, batched=SCREAMING_SNAKE_CASE__\t\t\t\t, batch_size=1000\t\t\t\t, num_proc=4\t\t\t\t)\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0\r for shard in range(0\t\t\t\t, len(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t, args.shard_size\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tgrouped_dataset[shard : shard + args.shard_size]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlen(dataset_snapshot[\"\"\"input_ids\"\"\"]\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tos.path.join(SCREAMING_SNAKE_CASE__\t\t\t\t, F'''dataset-{shard_count}-{records_containing}.tfrecord'''\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tget_serialized_examples(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r with tf.io.TFRecordWriter(SCREAMING_SNAKE_CASE__\t\t\t\t) as out_file:\r for i in range(len(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tserialized_examples[i]\r out_file.write(SCREAMING_SNAKE_CASE__\t\t\t\t)\r print(\"\"\"Wrote file {} containing {} records\"\"\".format(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t)\r\r shard_count += 1\r total_records += records_containing\r\r with open(F'''split-{args.split}-records-count.txt'''\t\t\t\t, \"\"\"w\"\"\"\t\t\t\t) as f:\r print(F'''Total {args.split} records: {total_records}'''\t\t\t\t, file=SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r\rif __name__ == \"__main__\":\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tparse_args()\r main(args)\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152308,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r if not isinstance(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r raise ValueError(\"\"\"iterations must be defined as integers\"\"\"\t\t\t\t)\r if not isinstance(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t) or not number >= 1:\r raise ValueError(\r \"\"\"starting number must be\n and integer and be more than 0\"\"\"\t\t\t\t)\r if not iterations >= 1:\r raise ValueError(\"\"\"Iterations must be done more than 0 times to play FizzBuzz\"\"\"\t\t\t\t)\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"\"\"\"\r while number <= iterations:\r if number % 3 == 0:\r out += \"Fizz\"\r if number % 5 == 0:\r out += \"Buzz\"\r if 0 not in (number % 3, number % 5):\r out += str(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r # print(out)\r number += 1\r out += \" \"\r return out\r\r\rif __name__ == \"__main__\":\r import doctest\r\r doctest.testmod()\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport numpy as np\rimport torch\rfrom torch.nn import CrossEntropyLoss\rfrom transformers import AutoModelForCausalLM, AutoTokenizer\r\rimport datasets\rfrom datasets import logging\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'\\\\n\\n'\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'\\nPerplexity (PPL) is one of the most common metrics for evaluating language models.\\nIt is defined as the exponentiated average negative log-likelihood of a sequence.\\n\\nFor more information, see https://huggingface.co/docs/transformers/perplexity\\n'\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'\\nArgs:\\n model_id (str): model used for calculating Perplexity\\n NOTE: Perplexity can only be calculated for causal language models.\\n This includes models such as gpt2, causal variations of bert,\\n causal versions of t5, and more (the full list can be found\\n in the AutoModelForCausalLM documentation here:\\n https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )\\n\\n input_texts (list of str): input text, each separate text snippet\\n is one list entry.\\n batch_size (int): the batch size to run texts through the model. Defaults to 16.\\n add_start_token (bool): whether to add the start token to the texts,\\n so the perplexity can include the probability of the first word. Defaults to True.\\n device (str): device to run on, defaults to \\'cuda\\' when available\\nReturns:\\n perplexity: dictionary containing the perplexity scores for the texts\\n in the input list, as well as the mean perplexity. If one of the input texts is\\n longer than the max input length of the model, then it is truncated to the\\n max length for the perplexity computation.\\nExamples:\\n Example 1:\\n >>> perplexity = datasets.load_metric(\"perplexity\")\\n >>> input_texts = [\"lorem ipsum\", \"Happy Birthday!\", \"Bienvenue\"]\\n >>> results = perplexity.compute(model_id=\\'gpt2\\',\\n ... add_start_token=False,\\n ... input_texts=input_texts) # doctest:+ELLIPSIS\\n >>> print(list(results.keys()))\\n [\\'perplexities\\', \\'mean_perplexity\\']\\n >>> print(round(results[\"mean_perplexity\"], 2))\\n 78.22\\n >>> print(round(results[\"perplexities\"][0], 2))\\n 11.11\\n\\n Example 2:\\n >>> perplexity = datasets.load_metric(\"perplexity\")\\n >>> input_texts = datasets.load_dataset(\"wikitext\",\\n ... \"wikitext-2-raw-v1\",\\n ... split=\"test\")[\"text\"][:50] # doctest:+ELLIPSIS\\n [...]\\n >>> input_texts = [s for s in input_texts if s!=\\'\\']\\n >>> results = perplexity.compute(model_id=\\'gpt2\\',\\n ... input_texts=input_texts) # doctest:+ELLIPSIS\\n >>> print(list(results.keys()))\\n [\\'perplexities\\', \\'mean_perplexity\\']\\n >>> print(round(results[\"mean_perplexity\"], 2))\\n 60.35\\n >>> print(round(results[\"perplexities\"][0], 2))\\n 81.12\\n'\r\r@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION\t, _KWARGS_DESCRIPTION )\rclass lowerCAmelCase_ ( datasets.Metric ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return datasets.MetricInfo(\r description=_DESCRIPTION ,\t\t\t\t\t\tcitation=_CITATION ,\t\t\t\t\t\tinputs_description=_KWARGS_DESCRIPTION ,\t\t\t\t\t\tfeatures=datasets.Features(\r {\r \"\"\"input_texts\"\"\": datasets.Value(\"\"\"string\"\"\" ),\r } ) ,\t\t\t\t\t\treference_urls=[\"\"\"https://huggingface.co/docs/transformers/perplexity\"\"\"] ,\t\t\t\t\t\t)\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint = 16 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tbool = True ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int]=None ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if device is not None:\r assert device in [\"gpu\", \"cpu\", \"cuda\"], \"device should be either gpu or cpu.\"\r if device == \"gpu\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"cuda\"\"\"\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"cuda\"\"\" if torch.cuda.is_available() else \"\"\"cpu\"\"\"\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tAutoModelForCausalLM.from_pretrained(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel.to(_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tAutoTokenizer.from_pretrained(_UpperCAmelCase )\r\r # if batch_size > 1 (which generally leads to padding being required), and\r # if there is not an already assigned pad_token, assign an existing\r # special token to also be the padding token\r if tokenizer.pad_token is None and batch_size > 1:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlist(tokenizer.special_tokens_map_extended.values() )\r # check that the model already has at least one special token defined\r assert (\r len(_UpperCAmelCase ) > 0\r ), \"If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1.\"\r # assign one of the special tokens to also be the pad token\r tokenizer.add_special_tokens({\"\"\"pad_token\"\"\": existing_special_tokens[0]} )\r\r if add_start_token:\r # leave room for token to be added:\r assert (\r tokenizer.bos_token is not None\r ), \"Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False\"\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel.config.max_length - 1\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel.config.max_length\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenizer(\r _UpperCAmelCase ,\t\t\t\t\t\tadd_special_tokens=_UpperCAmelCase ,\t\t\t\t\t\tpadding=_UpperCAmelCase ,\t\t\t\t\t\ttruncation=_UpperCAmelCase ,\t\t\t\t\t\tmax_length=_UpperCAmelCase ,\t\t\t\t\t\treturn_tensors=\"\"\"pt\"\"\" ,\t\t\t\t\t\treturn_attention_mask=_UpperCAmelCase ,\t\t\t\t\t\t).to(_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tencodings[\"\"\"input_ids\"\"\"]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tencodings[\"\"\"attention_mask\"\"\"]\r\r # check that each input is long enough:\r if add_start_token:\r assert torch.all(torch.ge(attn_masks.sum(1 ) ,\t\t\t\t\t\t1 ) ), \"Each input text must be at least one token long.\"\r else:\r assert torch.all(\r torch.ge(attn_masks.sum(1 ) ,\t\t\t\t\t\t2 ) ), \"When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings.\"\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tCrossEntropyLoss(reduction=\"\"\"none\"\"\" )\r\r for start_index in logging.tqdm(range(0 ,\t\t\t\t\t\tlen(_UpperCAmelCase ) ,\t\t\t\t\t\t_UpperCAmelCase ) ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmin(start_index + batch_size ,\t\t\t\t\t\tlen(_UpperCAmelCase ) )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tencoded_texts[start_index:end_index]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tattn_masks[start_index:end_index]\r\r if add_start_token:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0 ) ).to(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.cat([bos_tokens_tensor, encoded_batch] ,\t\t\t\t\t\tdim=1 )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.cat(\r [torch.ones(bos_tokens_tensor.size() ,\t\t\t\t\t\tdtype=torch.intaa ).to(_UpperCAmelCase ), attn_mask] ,\t\t\t\t\t\tdim=1 )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tencoded_batch\r\r with torch.no_grad():\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase ,\t\t\t\t\t\tattention_mask=_UpperCAmelCase ).logits\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tout_logits[..., :-1, :].contiguous()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlabels[..., 1:].contiguous()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tattn_mask[..., 1:].contiguous()\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.expa(\r (loss_fct(shift_logits.transpose(1 ,\t\t\t\t\t\t2 ) ,\t\t\t\t\t\t_UpperCAmelCase ) * shift_attention_mask_batch).sum(1 )\r / shift_attention_mask_batch.sum(1 ) )\r\r ppls += perplexity_batch.tolist()\r\r return {\"perplexities\": ppls, \"mean_perplexity\": np.mean(_UpperCAmelCase )}\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152309,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rfrom typing import TYPE_CHECKING\r\rfrom ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t{\r 'configuration_groupvit': [\r 'GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP',\r 'GroupViTConfig',\r 'GroupViTOnnxConfig',\r 'GroupViTTextConfig',\r 'GroupViTVisionConfig',\r ],\r}\r\rtry:\r if not is_torch_available():\r raise OptionalDependencyNotAvailable()\rexcept OptionalDependencyNotAvailable:\r pass\relse:\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\t[\r 'GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST',\r 'GroupViTModel',\r 'GroupViTPreTrainedModel',\r 'GroupViTTextModel',\r 'GroupViTVisionModel',\r ]\r\rtry:\r if not is_tf_available():\r raise OptionalDependencyNotAvailable()\rexcept OptionalDependencyNotAvailable:\r pass\relse:\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\t[\r 'TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST',\r 'TFGroupViTModel',\r 'TFGroupViTPreTrainedModel',\r 'TFGroupViTTextModel',\r 'TFGroupViTVisionModel',\r ]\r\rif TYPE_CHECKING:\r from .configuration_groupvit import (\r GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,\r GroupViTConfig,\r GroupViTOnnxConfig,\r GroupViTTextConfig,\r GroupViTVisionConfig,\r )\r\r try:\r if not is_torch_available():\r raise OptionalDependencyNotAvailable()\r except OptionalDependencyNotAvailable:\r pass\r else:\r from .modeling_groupvit import (\r GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,\r GroupViTModel,\r GroupViTPreTrainedModel,\r GroupViTTextModel,\r GroupViTVisionModel,\r )\r\r try:\r if not is_tf_available():\r raise OptionalDependencyNotAvailable()\r except OptionalDependencyNotAvailable:\r pass\r else:\r from .modeling_tf_groupvit import (\r TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,\r TFGroupViTModel,\r TFGroupViTPreTrainedModel,\r TFGroupViTTextModel,\r TFGroupViTVisionModel,\r )\r\relse:\r import sys\r\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\t_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint = 1000000\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[i - 1 for i in range(limit + 1\t\t\t\t)]\r\r for i in range(2\t\t\t\t, limit + 1\t\t\t\t):\r if phi[i] == i - 1:\r for j in range(2 * i\t\t\t\t, limit + 1\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r phi[j] -= phi[j] // i\r\r return sum(phi[2 : limit + 1]\t\t\t\t)\r\r\rif __name__ == \"__main__\":\r print(solution())\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152310,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport tempfile\rimport unittest\r\rfrom transformers import TaConfig, is_torch_available\rfrom transformers.testing_utils import (\r require_sentencepiece,\r require_tokenizers,\r require_torch,\r slow,\r torch_device,\r)\r\rfrom ...generation.test_utils import GenerationTesterMixin\rfrom ...test_modeling_common import ModelTesterMixin, ids_tensor\rfrom ...test_pipeline_mixin import PipelineTesterMixin\r\r\rif is_torch_available():\r import torch\r\r from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel\r\rclass lowerCAmelCase_ :\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def __init__( self\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=99 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny=13 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=7 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=9 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any]=True ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny=True ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=False ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny=32 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=5 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=4 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict=37 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict=8 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict=0.1 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint=0.002 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str]=1 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint=0 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=0 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny=None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr=None ,\t\t\t\t\t\t):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tparent\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tbatch_size\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tencoder_seq_length\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdecoder_seq_length\r # For common tests\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.decoder_seq_length\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tis_training\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tuse_attention_mask\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tuse_labels\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvocab_size\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\thidden_size\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnum_hidden_layers\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnum_attention_heads\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\td_ff\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\trelative_attention_num_buckets\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdropout_rate\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tinitializer_factor\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\teos_token_id\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tpad_token_id\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdecoder_start_token_id\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdecoder_layers\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return TaConfig.from_pretrained(\"\"\"google/umt5-base\"\"\" )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tTuple ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str]=None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any]=None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str]=None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny=None ,\t\t\t\t\t\t):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if attention_mask is None:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tinput_ids.ne(config.pad_token_id )\r if decoder_attention_mask is None:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdecoder_input_ids.ne(config.pad_token_id )\r if head_mask is None:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.ones(config.num_hidden_layers ,\t\t\t\t\t\tconfig.num_attention_heads ,\t\t\t\t\t\tdevice=_UpperCAmelCase )\r if decoder_head_mask is None:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.ones(config.num_decoder_layers ,\t\t\t\t\t\tconfig.num_attention_heads ,\t\t\t\t\t\tdevice=_UpperCAmelCase )\r if cross_attn_head_mask is None:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.ones(\r config.num_decoder_layers ,\t\t\t\t\t\tconfig.num_attention_heads ,\t\t\t\t\t\tdevice=_UpperCAmelCase )\r return {\r \"input_ids\": input_ids,\r \"decoder_input_ids\": decoder_input_ids,\r \"attention_mask\": attention_mask,\r \"decoder_attention_mask\": decoder_attention_mask,\r \"head_mask\": head_mask,\r \"decoder_head_mask\": decoder_head_mask,\r \"cross_attn_head_mask\": cross_attn_head_mask,\r }\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tids_tensor([self.batch_size, self.encoder_seq_length] ,\t\t\t\t\t\tself.vocab_size )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tids_tensor([self.batch_size, self.decoder_seq_length] ,\t\t\t\t\t\tself.vocab_size )\r\r # we need to clamp the input ids here to avoid having pad token in between\r # this is because for NllbMoe the position_ids are prepared such that\r # all pad tokens have pos id = 2 and rest are between 2..seq_length\r # and the seq_length here is seq_length - num_pad_tokens\r # but when using past, there is no way of knowing if the past input ids had\r # pad tokens in them, which results in incorrect seq_lenth and which in turn results in\r # position_ids being off by num_pad_tokens in past input\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tinput_ids.clamp(self.pad_token_id + 1 )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdecoder_input_ids.clamp(self.pad_token_id + 1 )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_config()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tconfig.num_attention_heads\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.prepare_inputs_dict(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r return config, input_dict\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.prepare_config_and_inputs()\r return config, inputs_dict\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tAny ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return TaConfig(\r vocab_size=1_66 ,\t\t\t\t\t\td_model=self.hidden_size ,\t\t\t\t\t\td_ff=self.d_ff ,\t\t\t\t\t\td_kv=self.hidden_size // self.num_attention_heads ,\t\t\t\t\t\tnum_layers=self.num_hidden_layers ,\t\t\t\t\t\tnum_decoder_layers=self.decoder_layers ,\t\t\t\t\t\tnum_heads=self.num_attention_heads ,\t\t\t\t\t\trelative_attention_num_buckets=self.relative_attention_num_buckets ,\t\t\t\t\t\tdropout_rate=self.dropout_rate ,\t\t\t\t\t\tinitializer_factor=self.initializer_factor ,\t\t\t\t\t\teos_token_id=self.eos_token_id ,\t\t\t\t\t\tbos_token_id=self.pad_token_id ,\t\t\t\t\t\tpad_token_id=self.pad_token_id ,\t\t\t\t\t\tdecoder_start_token_id=self.decoder_start_token_id ,\t\t\t\t\t\t)\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return TaConfig(\r vocab_size=self.vocab_size ,\t\t\t\t\t\td_model=self.hidden_size ,\t\t\t\t\t\td_ff=self.d_ff ,\t\t\t\t\t\td_kv=self.hidden_size // self.num_attention_heads ,\t\t\t\t\t\tnum_layers=self.num_hidden_layers ,\t\t\t\t\t\tnum_decoder_layers=self.decoder_layers ,\t\t\t\t\t\tnum_heads=self.num_attention_heads ,\t\t\t\t\t\trelative_attention_num_buckets=self.relative_attention_num_buckets ,\t\t\t\t\t\tdropout_rate=self.dropout_rate ,\t\t\t\t\t\tinitializer_factor=self.initializer_factor ,\t\t\t\t\t\teos_token_id=self.eos_token_id ,\t\t\t\t\t\tbos_token_id=self.pad_token_id ,\t\t\t\t\t\tpad_token_id=self.pad_token_id ,\t\t\t\t\t\tdecoder_start_token_id=self.decoder_start_token_id ,\t\t\t\t\t\t)\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tUMTaModel(config=_UpperCAmelCase )\r model.to(_UpperCAmelCase )\r model.eval()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(\r input_ids=_UpperCAmelCase ,\t\t\t\t\t\tdecoder_input_ids=_UpperCAmelCase ,\t\t\t\t\t\tattention_mask=_UpperCAmelCase ,\t\t\t\t\t\tdecoder_attention_mask=_UpperCAmelCase ,\t\t\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(input_ids=_UpperCAmelCase ,\t\t\t\t\t\tdecoder_input_ids=_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tresult.last_hidden_state\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tresult.past_key_values\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tresult.encoder_last_hidden_state\r\r self.parent.assertEqual(encoder_output.size() ,\t\t\t\t\t\t(self.batch_size, self.encoder_seq_length, self.hidden_size) )\r self.parent.assertEqual(decoder_output.size() ,\t\t\t\t\t\t(self.batch_size, self.decoder_seq_length, self.hidden_size) )\r # There should be `num_layers` key value embeddings stored in decoder_past\r self.parent.assertEqual(len(_UpperCAmelCase ) ,\t\t\t\t\t\tconfig.num_layers )\r # There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple\r self.parent.assertEqual(len(decoder_past[0] ) ,\t\t\t\t\t\t4 )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tAny ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple ,\t\t\t\t\t\t):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tUMTaModel(config=_UpperCAmelCase ).get_decoder().to(_UpperCAmelCase ).eval()\r # first forward pass\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase ,\t\t\t\t\t\tuse_cache=_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase ,\t\t\t\t\t\tuse_cache=_UpperCAmelCase )\r\r self.parent.assertTrue(len(_UpperCAmelCase ) == len(_UpperCAmelCase ) )\r self.parent.assertTrue(len(_UpperCAmelCase ) == len(_UpperCAmelCase ) + 1 )\r\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\toutputs.to_tuple()\r\r # create hypothetical next token and extent to next_input_ids\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tids_tensor((self.batch_size, 1) ,\t\t\t\t\t\tconfig.vocab_size )\r\r # append to next input_ids and\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.cat([input_ids, next_tokens] ,\t\t\t\t\t\tdim=-1 )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase )[\"\"\"last_hidden_state\"\"\"]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase ,\t\t\t\t\t\tpast_key_values=_UpperCAmelCase )[\"\"\"last_hidden_state\"\"\"]\r\r # select random slice\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tids_tensor((1,) ,\t\t\t\t\t\toutput_from_past.shape[-1] ).item()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\toutput_from_no_past[:, -1, random_slice_idx].detach()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\toutput_from_past[:, 0, random_slice_idx].detach()\r\r # test that outputs are equal for slice\r self.parent.assertTrue(torch.allclose(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\tatol=1E-3 ) )\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tAny ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tUMTaModel(config=_UpperCAmelCase ).to(_UpperCAmelCase ).half().eval()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(**_UpperCAmelCase )[\"\"\"last_hidden_state\"\"\"]\r self.parent.assertFalse(torch.isnan(_UpperCAmelCase ).any().item() )\r\r@require_torch\rclass lowerCAmelCase_ ( lowerCamelCase_\t, lowerCamelCase_\t, lowerCamelCase_\t, unittest.TestCase ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r lowerCAmelCase_ : List[str] = (\r (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()\r )\r lowerCAmelCase_ : Tuple = (UMTaForConditionalGeneration,) if is_torch_available() else ()\r lowerCAmelCase_ : Any = (\r {\r \"\"\"conversational\"\"\": UMTaForConditionalGeneration,\r \"\"\"feature-extraction\"\"\": UMTaModel,\r \"\"\"summarization\"\"\": UMTaForConditionalGeneration,\r \"\"\"text2text-generation\"\"\": UMTaForConditionalGeneration,\r \"\"\"translation\"\"\": UMTaForConditionalGeneration,\r \"\"\"question-answering\"\"\": UMTaForQuestionAnswering,\r }\r if is_torch_available()\r else {}\r )\r lowerCAmelCase_ : List[Any] = True\r lowerCAmelCase_ : str = False\r lowerCAmelCase_ : Any = False\r lowerCAmelCase_ : Union[str, Any] = True\r lowerCAmelCase_ : Optional[int] = True\r # The small UMT5 model needs higher percentages for CPU/MP tests\r lowerCAmelCase_ : List[Any] = [0.8, 0.9]\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tUMTaModelTester(self )\r\r\r @unittest.skip(\"\"\"Test has a segmentation fault on torch 1.8.0\"\"\" )\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.model_tester.prepare_config_and_inputs()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tUMTaModel(config_and_inputs[0] ).to(_UpperCAmelCase )\r with tempfile.TemporaryDirectory() as tmpdirname:\r torch.onnx.export(\r _UpperCAmelCase ,\t\t\t\t\t\t(config_and_inputs[1], config_and_inputs[3], config_and_inputs[2]) ,\t\t\t\t\t\tf'''{tmpdirname}/t5_test.onnx''' ,\t\t\t\t\t\texport_params=_UpperCAmelCase ,\t\t\t\t\t\topset_version=9 ,\t\t\t\t\t\tinput_names=[\"\"\"input_ids\"\"\", \"\"\"decoder_input_ids\"\"\"] ,\t\t\t\t\t\t)\r\r\r @unittest.skipIf(torch_device == \"\"\"cpu\"\"\" ,\t\t\t\t\t\t\"\"\"Cant do half precision\"\"\" )\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.model_tester.prepare_config_and_inputs()\r self.model_tester.create_and_check_model_fpaa_forward(*_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[\"\"\"encoder_attentions\"\"\", \"\"\"decoder_attentions\"\"\", \"\"\"cross_attentions\"\"\"]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.model_tester.prepare_config_and_inputs()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tconfig_and_inputs[0]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tUMTaForConditionalGeneration(_UpperCAmelCase ).eval()\r model.to(_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\r \"\"\"head_mask\"\"\": torch.zeros(config.num_layers ,\t\t\t\t\t\tconfig.num_heads ,\t\t\t\t\t\tdevice=_UpperCAmelCase ),\r \"\"\"decoder_head_mask\"\"\": torch.zeros(config.num_decoder_layers ,\t\t\t\t\t\tconfig.num_heads ,\t\t\t\t\t\tdevice=_UpperCAmelCase ),\r \"\"\"cross_attn_head_mask\"\"\": torch.zeros(config.num_decoder_layers ,\t\t\t\t\t\tconfig.num_heads ,\t\t\t\t\t\tdevice=_UpperCAmelCase ),\r }\r\r for attn_name, (name, mask) in zip(_UpperCAmelCase ,\t\t\t\t\t\thead_masking.items() ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{name: mask}\r # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified\r if name == \"head_mask\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.ones(\r config.num_decoder_layers ,\t\t\t\t\t\tconfig.num_heads ,\t\t\t\t\t\tdevice=_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel.generate(\r config_and_inputs[1][\"\"\"input_ids\"\"\"] ,\t\t\t\t\t\tnum_beams=1 ,\t\t\t\t\t\tmax_length=3 ,\t\t\t\t\t\toutput_attentions=_UpperCAmelCase ,\t\t\t\t\t\treturn_dict_in_generate=_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase ,\t\t\t\t\t\t)\r # We check the state of decoder_attentions and cross_attentions just from the last step\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tout[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]\r self.assertEqual(sum([w.sum().item() for w in attn_weights] ) ,\t\t\t\t\t\t0.0 )\r\r\r\r @unittest.skip(\"\"\"Does not work on the tiny model as we keep hitting edge cases.\"\"\" )\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r pass\r\r@require_torch\r@require_sentencepiece\r@require_tokenizers\rclass lowerCAmelCase_ ( unittest.TestCase ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r @slow\r @unittest.skip(\r \"\"\"Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged\"\"\" )\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tUMTaForConditionalGeneration.from_pretrained(\"\"\"google/umt5-small\"\"\" ,\t\t\t\t\t\treturn_dict=_UpperCAmelCase ).to(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tAutoTokenizer.from_pretrained(\"\"\"google/umt5-small\"\"\" ,\t\t\t\t\t\tuse_fast=_UpperCAmelCase ,\t\t\t\t\t\tlegacy=_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[\r \"\"\"Bonjour monsieur bien .\"\"\",\r \"\"\"No se como puedo .\"\"\",\r \"\"\"This is the reason why we them.\"\"\",\r \"\"\"The walks in , seats\"\"\",\r \"\"\"A walks into a bar and orders a with pinch of .\"\"\",\r ]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenizer(_UpperCAmelCase ,\t\t\t\t\t\treturn_tensors=\"\"\"pt\"\"\" ,\t\t\t\t\t\tpadding=_UpperCAmelCase ).input_ids\r # fmt: off\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.tensor(\r [\r [ 3_85_30, 21_07_03, 25_62_99, 14_10, 25_62_98, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],\r [ 8_26, 3_21, 6_71, 2_59_22, 25_62_99, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],\r [ 14_60, 3_39, 3_12, 1_90_14, 1_06_20, 7_58, 25_62_99, 23_55,2_74, 1, 0, 0, 0, 0, 0, 0,0, 0],\r [ 5_17, 25_62_99, 1_48_69, 2_81, 3_01, 25_62_98, 2_75, 11_99_83,1, 0, 0, 0, 0, 0, 0, 0,0, 0],\r [ 3_20, 25_62_99, 1_48_69, 2_81, 22_34, 2_89, 22_75, 3_33,6_13_91, 2_89, 25_62_98, 5_43, 25_62_97, 16_87_14, 3_29, 25_62_96,2_74, 1],\r ] )\r # fmt: on\r torch.testing.assert_allclose(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel.generate(input_ids.to(_UpperCAmelCase ) )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[\r \"\"\" et [eod] .. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 ajšietostolleuxajšie\"\"\",\r \"\"\"..,<0x0A>...spech <0x0A> \"\"\",\r \"\"\" are not going to be a part of the world. We are not going to be a part of and<0x0A>.\"\"\",\r \"\"\" door, the door 피해[/\"\"\",\r \"\"\"nyone who drink a alcohol A A. This I\"\"\",\r ]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenizer.batch_decode(_UpperCAmelCase )\r self.assertEqual(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rfrom typing import List, Union\r\rfrom ..utils import (\r add_end_docstrings,\r is_tf_available,\r is_torch_available,\r is_vision_available,\r logging,\r requires_backends,\r)\rfrom .base import PIPELINE_INIT_ARGS, Pipeline\r\r\rif is_vision_available():\r from PIL import Image\r\r from ..image_utils import load_image\r\rif is_tf_available():\r import tensorflow as tf\r\r from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING\r from ..tf_utils import stable_softmax\r\rif is_torch_available():\r from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tlogging.get_logger(__name__)\r\r@add_end_docstrings(lowerCamelCase_ )\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def __init__( self\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t*_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tDict ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r super().__init__(*_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase )\r requires_backends(self ,\t\t\t\t\t\t\"\"\"vision\"\"\" )\r self.check_model_type(\r TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING\r if self.framework == \"\"\"tf\"\"\"\r else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any]=None ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{}\r if top_k is not None:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttop_k\r return {}, {}, postprocess_params\r\r\r def __call__( self\t\t\t:\t\tAny ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, List[str], \"Image.Image\", List[\"Image.Image\"]] ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return super().__call__(_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tload_image(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.image_processor(images=_UpperCAmelCase ,\t\t\t\t\t\treturn_tensors=self.framework )\r return model_inputs\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.model(**_UpperCAmelCase )\r return model_outputs\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr=5 ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if top_k > self.model.config.num_labels:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.model.config.num_labels\r\r if self.framework == \"pt\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel_outputs.logits.softmax(-1 )[0]\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tprobs.topk(_UpperCAmelCase )\r elif self.framework == \"tf\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tstable_softmax(model_outputs.logits ,\t\t\t\t\t\taxis=-1 )[0]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttf.math.top_k(_UpperCAmelCase ,\t\t\t\t\t\tk=_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttopk.values.numpy(), topk.indices.numpy()\r else:\r raise ValueError(f'''Unsupported framework: {self.framework}''' )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscores.tolist()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tids.tolist()\r return [{\"score\": score, \"label\": self.model.config.idalabel[_id]} for score, _id in zip(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )]\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152311,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint = 1000000\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[i - 1 for i in range(limit + 1\t\t\t\t)]\r\r for i in range(2\t\t\t\t, limit + 1\t\t\t\t):\r if phi[i] == i - 1:\r for j in range(2 * i\t\t\t\t, limit + 1\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r phi[j] -= phi[j] // i\r\r return sum(phi[2 : limit + 1]\t\t\t\t)\r\r\rif __name__ == \"__main__\":\r print(solution())\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rfrom math import factorial\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint = 20\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,\r # 2, 3,...\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tn // 2\r\r return int(factorial(SCREAMING_SNAKE_CASE__\t\t\t\t) / (factorial(SCREAMING_SNAKE_CASE__\t\t\t\t) * factorial(n - k\t\t\t\t))\t\t\t\t)\r\r\rif __name__ == \"__main__\":\r import sys\r\r if len(sys.argv) == 1:\r print(solution(2_0))\r else:\r try:\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tint(sys.argv[1])\r print(solution(n))\r except ValueError:\r print('Invalid entry - please enter a number.')\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152312,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport argparse\rimport json\rimport math\rimport os\rimport time\rimport traceback\rimport zipfile\rfrom collections import Counter\r\rimport requests\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[int]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tAny=None\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r if token is not None:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\"\"\"Accept\"\"\": \"\"\"application/vnd.github+json\"\"\", \"\"\"Authorization\"\"\": F'''Bearer {token}'''}\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tF'''https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'''\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\trequests.get(SCREAMING_SNAKE_CASE__\t\t\t\t, headers=SCREAMING_SNAKE_CASE__\t\t\t\t).json()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{}\r\r try:\r job_links.update({job[\"\"\"name\"\"\"]: job[\"\"\"html_url\"\"\"] for job in result[\"\"\"jobs\"\"\"]}\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmath.ceil((result[\"\"\"total_count\"\"\"] - 100) / 100\t\t\t\t)\r\r for i in range(SCREAMING_SNAKE_CASE__\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\trequests.get(url + F'''&page={i + 2}'''\t\t\t\t, headers=SCREAMING_SNAKE_CASE__\t\t\t\t).json()\r job_links.update({job[\"\"\"name\"\"\"]: job[\"\"\"html_url\"\"\"] for job in result[\"\"\"jobs\"\"\"]}\t\t\t\t)\r\r return job_links\r except Exception:\r print(F'''Unknown error, could not fetch links:\\n{traceback.format_exc()}'''\t\t\t\t)\r\r return {}\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]=None\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r if token is not None:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\"\"\"Accept\"\"\": \"\"\"application/vnd.github+json\"\"\", \"\"\"Authorization\"\"\": F'''Bearer {token}'''}\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tF'''https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100'''\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\trequests.get(SCREAMING_SNAKE_CASE__\t\t\t\t, headers=SCREAMING_SNAKE_CASE__\t\t\t\t).json()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{}\r\r try:\r artifacts.update({artifact[\"\"\"name\"\"\"]: artifact[\"\"\"archive_download_url\"\"\"] for artifact in result[\"\"\"artifacts\"\"\"]}\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmath.ceil((result[\"\"\"total_count\"\"\"] - 100) / 100\t\t\t\t)\r\r for i in range(SCREAMING_SNAKE_CASE__\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\trequests.get(url + F'''&page={i + 2}'''\t\t\t\t, headers=SCREAMING_SNAKE_CASE__\t\t\t\t).json()\r artifacts.update({artifact[\"\"\"name\"\"\"]: artifact[\"\"\"archive_download_url\"\"\"] for artifact in result[\"\"\"artifacts\"\"\"]}\t\t\t\t)\r\r return artifacts\r except Exception:\r print(F'''Unknown error, could not fetch links:\\n{traceback.format_exc()}'''\t\t\t\t)\r\r return {}\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tUnion[str, Any]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tUnion[str, Any]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r if token is not None:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\"\"\"Accept\"\"\": \"\"\"application/vnd.github+json\"\"\", \"\"\"Authorization\"\"\": F'''Bearer {token}'''}\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\trequests.get(SCREAMING_SNAKE_CASE__\t\t\t\t, headers=SCREAMING_SNAKE_CASE__\t\t\t\t, allow_redirects=SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tresult.headers[\"\"\"Location\"\"\"]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\trequests.get(SCREAMING_SNAKE_CASE__\t\t\t\t, allow_redirects=SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tos.path.join(SCREAMING_SNAKE_CASE__\t\t\t\t, F'''{artifact_name}.zip'''\t\t\t\t)\r with open(SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\"wb\"\"\"\t\t\t\t) as fp:\r fp.write(response.content\t\t\t\t)\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[int]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]=None\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r\r with zipfile.ZipFile(SCREAMING_SNAKE_CASE__\t\t\t\t) as z:\r for filename in z.namelist():\r if not os.path.isdir(SCREAMING_SNAKE_CASE__\t\t\t\t):\r # read the file\r if filename in [\"failures_line.txt\", \"summary_short.txt\", \"job_name.txt\"]:\r with z.open(SCREAMING_SNAKE_CASE__\t\t\t\t) as f:\r for line in f:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tline.decode(\"\"\"UTF-8\"\"\"\t\t\t\t).strip()\r if filename == \"failures_line.txt\":\r try:\r # `error_line` is the place where `error` occurs\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tline[: line.index(\"\"\": \"\"\"\t\t\t\t)]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tline[line.index(\"\"\": \"\"\"\t\t\t\t) + len(\"\"\": \"\"\"\t\t\t\t) :]\r errors.append([error_line, error]\t\t\t\t)\r except Exception:\r # skip un-related lines\r pass\r elif filename == \"summary_short.txt\" and line.startswith(\"\"\"FAILED \"\"\"\t\t\t\t):\r # `test` is the test method that failed\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tline[len(\"\"\"FAILED \"\"\"\t\t\t\t) :]\r failed_tests.append(SCREAMING_SNAKE_CASE__\t\t\t\t)\r elif filename == \"job_name.txt\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tline\r\r if len(SCREAMING_SNAKE_CASE__\t\t\t\t) != len(SCREAMING_SNAKE_CASE__\t\t\t\t):\r raise ValueError(\r F'''`errors` and `failed_tests` should have the same number of elements. Got {len(SCREAMING_SNAKE_CASE__\t\t\t\t)} for `errors` '''\r F'''and {len(SCREAMING_SNAKE_CASE__\t\t\t\t)} for `failed_tests` instead. The test reports in {artifact_zip_path} have some'''\r \"\"\" problem.\"\"\"\t\t\t\t)\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r if job_name and job_links:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tjob_links.get(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r # A list with elements of the form (line of error, error, failed test)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[x + [y] + [job_link] for x, y in zip(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)]\r\r return result\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tDict\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[Any]=None\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[]\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[os.path.join(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t) for p in os.listdir(SCREAMING_SNAKE_CASE__\t\t\t\t) if p.endswith(\"\"\".zip\"\"\"\t\t\t\t)]\r for p in paths:\r errors.extend(get_errors_from_single_artifact(SCREAMING_SNAKE_CASE__\t\t\t\t, job_links=SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t)\r\r return errors\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tUnion[str, Any]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr=None\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tCounter()\r counter.update([x[1] for x in logs]\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tcounter.most_common()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{}\r for error, count in counts:\r if error_filter is None or error not in error_filter:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\"\"\"count\"\"\": count, \"\"\"failed_tests\"\"\": [(x[2], x[0]) for x in logs if x[1] == error]}\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdict(sorted(r.items()\t\t\t\t, key=lambda SCREAMING_SNAKE_CASE__\t\t\t\t: item[1][\"count\"]\t\t\t\t, reverse=SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t)\r return r\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tUnion[str, Any]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttest.split(\"\"\"::\"\"\"\t\t\t\t)[0]\r if test.startswith(\"\"\"tests/models/\"\"\"\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttest.split(\"\"\"/\"\"\"\t\t\t\t)[2]\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r\r return test\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[Any]=None\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[(x[0], x[1], get_model(x[2]\t\t\t\t)) for x in logs]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[x for x in logs if x[2] is not None]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{x[2] for x in logs}\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{}\r for test in tests:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tCounter()\r # count by errors in `test`\r counter.update([x[1] for x in logs if x[2] == test]\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tcounter.most_common()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{error: count for error, count in counts if (error_filter is None or error not in error_filter)}\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tsum(error_counts.values()\t\t\t\t)\r if n_errors > 0:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\"\"\"count\"\"\": n_errors, \"\"\"errors\"\"\": error_counts}\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdict(sorted(r.items()\t\t\t\t, key=lambda SCREAMING_SNAKE_CASE__\t\t\t\t: item[1][\"count\"]\t\t\t\t, reverse=SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t)\r return r\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tDict\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"| no. | error | status |\"\"\"\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"|-:|:-|:-|\"\"\"\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[header, sep]\r for error in reduced_by_error:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\treduced_by_error[error][\"\"\"count\"\"\"]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tF'''| {count} | {error[:100]} | |'''\r lines.append(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r return \"\\n\".join(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tUnion[str, Any]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"| model | no. of errors | major error | count |\"\"\"\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"|-:|-:|-:|-:|\"\"\"\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[header, sep]\r for model in reduced_by_model:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\treduced_by_model[model][\"\"\"count\"\"\"]\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlist(reduced_by_model[model][\"\"\"errors\"\"\"].items()\t\t\t\t)[0]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tF'''| {model} | {count} | {error[:60]} | {_count} |'''\r lines.append(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r return \"\\n\".join(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r\rif __name__ == \"__main__\":\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\targparse.ArgumentParser()\r # Required parameters\r parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')\r parser.add_argument(\r '--output_dir',\r type=str,\r required=True,\r help='Where to store the downloaded artifacts and other result files.',\r )\r parser.add_argument('--token', default=None, type=str, help='A token that has actions:read permission.')\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tparser.parse_args()\r\r os.makedirs(args.output_dir, exist_ok=True)\r\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tget_job_links(args.workflow_run_id, token=args.token)\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\t{}\r # To deal with `workflow_call` event, where a job name is the combination of the job names in the caller and callee.\r # For example, `PyTorch 1.11 / Model tests (models/albert, single-gpu)`.\r if _job_links:\r for k, v in _job_links.items():\r # This is how GitHub actions combine job names.\r if \" / \" in k:\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tk.find(' / ')\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tk[index + len(' / ') :]\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tv\r with open(os.path.join(args.output_dir, 'job_links.json'), 'w', encoding='UTF-8') as fp:\r json.dump(job_links, fp, ensure_ascii=False, indent=4)\r\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tget_artifacts_links(args.workflow_run_id, token=args.token)\r with open(os.path.join(args.output_dir, 'artifacts.json'), 'w', encoding='UTF-8') as fp:\r json.dump(artifacts, fp, ensure_ascii=False, indent=4)\r\r for idx, (name, url) in enumerate(artifacts.items()):\r download_artifact(name, url, args.output_dir, args.token)\r # Be gentle to GitHub\r time.sleep(1)\r\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tget_all_errors(args.output_dir, job_links=job_links)\r\r # `e[1]` is the error\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tCounter()\r counter.update([e[1] for e in errors])\r\r # print the top 30 most common test errors\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tcounter.most_common(3_0)\r for item in most_common:\r print(item)\r\r with open(os.path.join(args.output_dir, 'errors.json'), 'w', encoding='UTF-8') as fp:\r json.dump(errors, fp, ensure_ascii=False, indent=4)\r\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\treduce_by_error(errors)\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\treduce_by_model(errors)\r\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tmake_github_table(reduced_by_error)\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tmake_github_table_per_model(reduced_by_model)\r\r with open(os.path.join(args.output_dir, 'reduced_by_error.txt'), 'w', encoding='UTF-8') as fp:\r fp.write(sa)\r with open(os.path.join(args.output_dir, 'reduced_by_model.txt'), 'w', encoding='UTF-8') as fp:\r fp.write(sa)\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport json\rimport os\rimport unittest\r\rfrom transformers import MgpstrTokenizer\rfrom transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES\rfrom transformers.testing_utils import require_tokenizers\r\rfrom ...test_tokenization_common import TokenizerTesterMixin\r\r@require_tokenizers\rclass lowerCAmelCase_ ( lowerCamelCase_\t, unittest.TestCase ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r lowerCAmelCase_ : int = MgpstrTokenizer\r lowerCAmelCase_ : List[str] = False\r lowerCAmelCase_ : Optional[int] = {}\r lowerCAmelCase_ : Any = False\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r super().setUp()\r\r # fmt: off\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[\"\"\"[GO]\"\"\", \"\"\"[s]\"\"\", \"\"\"0\"\"\", \"\"\"1\"\"\", \"\"\"2\"\"\", \"\"\"3\"\"\", \"\"\"4\"\"\", \"\"\"5\"\"\", \"\"\"6\"\"\", \"\"\"7\"\"\", \"\"\"8\"\"\", \"\"\"9\"\"\", \"\"\"a\"\"\", \"\"\"b\"\"\", \"\"\"c\"\"\", \"\"\"d\"\"\", \"\"\"e\"\"\", \"\"\"f\"\"\", \"\"\"g\"\"\", \"\"\"h\"\"\", \"\"\"i\"\"\", \"\"\"j\"\"\", \"\"\"k\"\"\", \"\"\"l\"\"\", \"\"\"m\"\"\", \"\"\"n\"\"\", \"\"\"o\"\"\", \"\"\"p\"\"\", \"\"\"q\"\"\", \"\"\"r\"\"\", \"\"\"s\"\"\", \"\"\"t\"\"\", \"\"\"u\"\"\", \"\"\"v\"\"\", \"\"\"w\"\"\", \"\"\"x\"\"\", \"\"\"y\"\"\", \"\"\"z\"\"\"]\r # fmt: on\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdict(zip(_UpperCAmelCase ,\t\t\t\t\t\trange(len(_UpperCAmelCase ) ) ) )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tos.path.join(self.tmpdirname ,\t\t\t\t\t\tVOCAB_FILES_NAMES[\"\"\"vocab_file\"\"\"] )\r with open(self.vocab_file ,\t\t\t\t\t\t\"\"\"w\"\"\" ,\t\t\t\t\t\tencoding=\"\"\"utf-8\"\"\" ) as fp:\r fp.write(json.dumps(_UpperCAmelCase ) + \"\"\"\\n\"\"\" )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return MgpstrTokenizer.from_pretrained(self.tmpdirname ,\t\t\t\t\t\t**_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"tester\"\"\"\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"tester\"\"\"\r return input_text, output_text\r\r\r @unittest.skip(\"\"\"MGP-STR always lower cases letters.\"\"\" )\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r pass\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tAny ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_tokenizers(do_lower_case=_UpperCAmelCase )\r for tokenizer in tokenizers:\r with self.subTest(f'''{tokenizer.__class__.__name__}''' ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"[SPECIAL_TOKEN]\"\"\"\r\r tokenizer.add_special_tokens({\"\"\"cls_token\"\"\": special_token} )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenizer.encode([special_token] ,\t\t\t\t\t\tadd_special_tokens=_UpperCAmelCase )\r self.assertEqual(len(_UpperCAmelCase ) ,\t\t\t\t\t\t1 )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenizer.decode(_UpperCAmelCase ,\t\t\t\t\t\tskip_special_tokens=_UpperCAmelCase )\r self.assertTrue(special_token not in decoded )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_tokenizers()\r for tokenizer in tokenizers:\r with self.subTest(f'''{tokenizer.__class__.__name__}''' ):\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_input_output_texts(_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenizer.tokenize(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenizer.convert_tokens_to_ids(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenizer.encode(_UpperCAmelCase ,\t\t\t\t\t\tadd_special_tokens=_UpperCAmelCase )\r self.assertListEqual(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenizer.convert_ids_to_tokens(_UpperCAmelCase )\r self.assertNotEqual(len(_UpperCAmelCase ) ,\t\t\t\t\t\t0 )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenizer.decode(_UpperCAmelCase )\r self.assertIsInstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r\r self.assertEqual(text_a.replace(\"\"\" \"\"\" ,\t\t\t\t\t\t\"\"\"\"\"\" ) ,\t\t\t\t\t\t_UpperCAmelCase )\r\r\r @unittest.skip(\"\"\"MGP-STR tokenizer only handles one sequence.\"\"\" )\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tAny ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r pass\r\r\r\r @unittest.skip(\"\"\"inputs cannot be pretokenized in MgpstrTokenizer\"\"\" )\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r pass\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152313,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\r# tests directory-specific settings - this file is run automatically\r# by pytest before any tests are run\r\rimport sys\rimport warnings\rfrom os.path import abspath, dirname, join\r\r\r# allow having multiple repository checkouts and not needing to remember to rerun\r# 'pip install -e .[dev]' when switching between checkouts and running tests.\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tabspath(join(dirname(dirname(__file__)), 'src'))\rsys.path.insert(1, git_repo_path)\r\r# silence FutureWarning warnings in tests since often we can't act on them until\r# they become normal warnings - i.e. the tests still need to test the current functionality\rwarnings.simplefilter(action='ignore', category=FutureWarning)\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r from diffusers.utils.testing_utils import pytest_addoption_shared\r\r pytest_addoption_shared(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r\r\r\r\r\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tUnion[str, Any]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r from diffusers.utils.testing_utils import pytest_terminal_summary_main\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tterminalreporter.config.getoption(\"\"\"--make-reports\"\"\"\t\t\t\t)\r if make_reports:\r pytest_terminal_summary_main(SCREAMING_SNAKE_CASE__\t\t\t\t, id=SCREAMING_SNAKE_CASE__\t\t\t\t)\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rfrom abc import ABC, abstractmethod\rfrom typing import List, Optional\r\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def __init__( self\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r self.test()\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r while not completed:\r if counter == 1:\r self.reset()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.advance()\r if not self.does_advance(_UpperCAmelCase ):\r raise Exception(\r \"\"\"Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true.\"\"\" )\r\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.update(_UpperCAmelCase )\r counter += 1\r\r if counter > 1_00_00:\r raise Exception(\"\"\"update() does not fulfill the constraint.\"\"\" )\r\r if self.remaining() != 0:\r raise Exception(\"\"\"Custom Constraint is not defined correctly.\"\"\" )\r\r\r @abstractmethod\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r raise NotImplementedError(\r f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )\r\r\r @abstractmethod\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r raise NotImplementedError(\r f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )\r\r\r @abstractmethod\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tAny ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r raise NotImplementedError(\r f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )\r\r\r @abstractmethod\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r raise NotImplementedError(\r f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )\r\r\r @abstractmethod\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r raise NotImplementedError(\r f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )\r\r\r\r @abstractmethod\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any]=False ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r raise NotImplementedError(\r f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )\r\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def __init__( self\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r super(_UpperCAmelCase ,\t\t\t\t\t\tself ).__init__()\r\r if not isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ) or len(_UpperCAmelCase ) == 0:\r raise ValueError(f'''`token_ids` has to be a non-empty list, but is {token_ids}.''' )\r if any((not isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ) or token_id < 0) for token_id in token_ids ):\r raise ValueError(f'''Each list in `token_ids` has to be a list of positive integers, but is {token_ids}.''' )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttoken_ids\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlen(self.token_ids )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t-1 # the index of the currently fulfilled step\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if self.completed:\r return None\r return self.token_ids[self.fulfilled_idx + 1]\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if not isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ):\r raise ValueError(f'''`token_id` has to be an `int`, but is {token_id} of type {type(_UpperCAmelCase )}''' )\r\r if self.completed:\r return False\r\r return token_id == self.token_ids[self.fulfilled_idx + 1]\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if not isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ):\r raise ValueError(f'''`token_id` has to be an `int`, but is {token_id} of type {type(_UpperCAmelCase )}''' )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r\r if self.does_advance(_UpperCAmelCase ):\r self.fulfilled_idx += 1\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTrue\r if self.fulfilled_idx == (self.seqlen - 1):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTrue\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tcompleted\r else:\r # failed to make progress.\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTrue\r self.reset()\r return stepped, completed, reset\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tTuple ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return self.seqlen - (self.fulfilled_idx + 1)\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tAny ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int]=False ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tPhrasalConstraint(self.token_ids )\r\r if stateful:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.seqlen\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.fulfilled_idx\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.completed\r\r return new_constraint\r\rclass lowerCAmelCase_ :\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def __init__( self\t\t\t:\t\tAny ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[List[int]] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str]=True ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmax([len(_UpperCAmelCase ) for one in nested_token_ids] )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{}\r for token_ids in nested_token_ids:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\troot\r for tidx, token_id in enumerate(_UpperCAmelCase ):\r if token_id not in level:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{}\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlevel[token_id]\r\r if no_subsets and self.has_subsets(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ):\r raise ValueError(\r \"\"\"Each list in `nested_token_ids` can't be a complete subset of another list, but is\"\"\"\r f''' {nested_token_ids}.''' )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\troot\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.trie\r\r for current_token in current_seq:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tstart[current_token]\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlist(start.keys() )\r\r return next_tokens\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.next_tokens(_UpperCAmelCase )\r\r return len(_UpperCAmelCase ) == 0\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlist(root.values() )\r if len(_UpperCAmelCase ) == 0:\r return 1\r else:\r return sum([self.count_leaves(_UpperCAmelCase ) for nn in next_nodes] )\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.count_leaves(_UpperCAmelCase )\r return len(_UpperCAmelCase ) != leaf_count\r\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def __init__( self\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[List[int]] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r super(_UpperCAmelCase ,\t\t\t\t\t\tself ).__init__()\r\r if not isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ) or len(_UpperCAmelCase ) == 0:\r raise ValueError(f'''`nested_token_ids` has to be a non-empty list, but is {nested_token_ids}.''' )\r if any(not isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ) for token_ids in nested_token_ids ):\r raise ValueError(f'''`nested_token_ids` has to be a list of lists, but is {nested_token_ids}.''' )\r if any(\r any((not isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ) or token_id < 0) for token_id in token_ids )\r for token_ids in nested_token_ids ):\r raise ValueError(\r f'''Each list in `nested_token_ids` has to be a list of positive integers, but is {nested_token_ids}.''' )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tDisjunctiveTrie(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnested_token_ids\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.trie.max_height\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tAny ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.trie.next_tokens(self.current_seq )\r\r if len(_UpperCAmelCase ) == 0:\r return None\r else:\r return token_list\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if not isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ):\r raise ValueError(f'''`token_id` is supposed to be type `int`, but is {token_id} of type {type(_UpperCAmelCase )}''' )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.trie.next_tokens(self.current_seq )\r\r return token_id in next_tokens\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if not isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ):\r raise ValueError(f'''`token_id` is supposed to be type `int`, but is {token_id} of type {type(_UpperCAmelCase )}''' )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r\r if self.does_advance(_UpperCAmelCase ):\r self.current_seq.append(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTrue\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTrue\r self.reset()\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.trie.reached_leaf(self.current_seq )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tcompleted\r\r return stepped, completed, reset\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[]\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if self.completed:\r # since this can be completed without reaching max height\r return 0\r else:\r return self.seqlen - len(self.current_seq )\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tTuple ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict=False ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tDisjunctiveConstraint(self.token_ids )\r\r if stateful:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.seqlen\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.current_seq\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.completed\r\r return new_constraint\r\rclass lowerCAmelCase_ :\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def __init__( self\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Constraint] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tconstraints\r\r # max # of steps required to fulfill a given constraint\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmax([c.seqlen for c in constraints] )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlen(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r\r self.init_state()\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tAny ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[constraint.copy(stateful=_UpperCAmelCase ) for constraint in self.constraints]\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0\r if self.inprogress_constraint:\r # extra points for having a constraint mid-fulfilled\r add += self.max_seqlen - self.inprogress_constraint.remaining()\r\r return (len(self.complete_constraints ) * self.max_seqlen) + add\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[]\r if self.inprogress_constraint is None:\r for constraint in self.pending_constraints: # \"pending\" == \"unfulfilled yet\"\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tconstraint.advance()\r if isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ):\r token_list.append(_UpperCAmelCase )\r elif isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ):\r token_list.extend(_UpperCAmelCase )\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.inprogress_constraint.advance()\r if isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ):\r token_list.append(_UpperCAmelCase )\r elif isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ):\r token_list.extend(_UpperCAmelCase )\r\r if len(_UpperCAmelCase ) == 0:\r return None\r else:\r return token_list\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[List[int]] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r self.init_state()\r\r if token_ids is not None:\r for token in token_ids:\r # completes or steps **one** constraint\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.add(_UpperCAmelCase )\r\r # the entire list of constraints are fulfilled\r if self.completed:\r break\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tAny ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if not isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ):\r raise ValueError(f'''`token_id` should be an `int`, but is `{token_id}`.''' )\r\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse, False\r\r if self.completed:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTrue\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r return complete, stepped\r\r if self.inprogress_constraint is not None:\r # In the middle of fulfilling a constraint. If the `token_id` *does* makes an incremental progress to current\r # job, simply update the state\r\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.inprogress_constraint.update(_UpperCAmelCase )\r if reset:\r # 1. If the next token breaks the progress, then we must restart.\r # e.g. constraint = \"I love pies\" and sequence so far is \"I love\" but `token_id` == \"books\".\r\r # But that doesn't mean we self.init_state(), since we only reset the state for this particular\r # constraint, not the full list of constraints.\r\r self.pending_constraints.append(self.inprogress_constraint.copy(stateful=_UpperCAmelCase ) )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r\r if complete:\r # 2. If the next token completes the constraint, move it to completed list, set\r # inprogress to None. If there are no pending constraints either, then this full list of constraints\r # is complete.\r\r self.complete_constraints.append(self.inprogress_constraint )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r\r if len(self.pending_constraints ) == 0:\r # we're done!\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTrue\r\r else:\r # Not in the middle of fulfilling a constraint. So does this `token_id` helps us step towards any of our list\r # of constraints?\r\r for cidx, pending_constraint in enumerate(self.pending_constraints ):\r if pending_constraint.does_advance(_UpperCAmelCase ):\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tpending_constraint.update(_UpperCAmelCase )\r\r if not stepped:\r raise Exception(\r \"\"\"`constraint.update(token_id)` is not yielding incremental progress, \"\"\"\r \"\"\"even though `constraint.does_advance(token_id)` is true.\"\"\" )\r\r if complete:\r self.complete_constraints.append(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r\r if not complete and stepped:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tpending_constraint\r\r if complete or stepped:\r # If we made any progress at all, then it's at least not a \"pending constraint\".\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t(\r self.pending_constraints[:cidx] + self.pending_constraints[cidx + 1 :]\r )\r\r if len(self.pending_constraints ) == 0 and self.inprogress_constraint is None:\r # If there's no longer any pending after this and no inprogress either, then we must be\r # complete.\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTrue\r\r break # prevent accidentally stepping through multiple constraints with just one token.\r\r return complete, stepped\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any]=True ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tConstraintListState(self.constraints ) # we actually never though self.constraints objects\r # throughout this process. So it's at initialization state.\r\r if stateful:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[\r constraint.copy(stateful=_UpperCAmelCase ) for constraint in self.complete_constraints\r ]\r if self.inprogress_constraint is not None:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.inprogress_constraint.copy(stateful=_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[constraint.copy() for constraint in self.pending_constraints]\r\r return new_state\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152314,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rfrom pathlib import Path\r\rimport cva\rimport numpy as np\rfrom matplotlib import pyplot as plt\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tnp.ndarray\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tnp.ndarray\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tnp.ndarray\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tcva.getAffineTransform(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r return cva.warpAffine(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, (rows, cols)\t\t\t\t)\r\r\rif __name__ == \"__main__\":\r # read original image\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tcva.imread(\r str(Path(__file__).resolve().parent.parent / 'image_data' / 'lena.jpg')\r )\r # turn image in gray scale value\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tcva.cvtColor(image, cva.COLOR_BGR2GRAY)\r # get image shape\r UpperCAmelCase_ ,\t\t\t\t\t\tUpperCAmelCase_\t\t\t\t =\t\t\t\t\tgray_img.shape\r\r # set different points to rotate image\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tnp.array([[5_0, 5_0], [2_0_0, 5_0], [5_0, 2_0_0]], np.floataa)\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tnp.array([[1_0, 1_0_0], [2_0_0, 5_0], [1_0_0, 2_5_0]], np.floataa)\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tnp.array([[5_0, 5_0], [1_5_0, 5_0], [1_2_0, 2_0_0]], np.floataa)\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tnp.array([[1_0, 1_0_0], [8_0, 5_0], [1_8_0, 2_5_0]], np.floataa)\r\r # add all rotated images in a list\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\t[\r gray_img,\r get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),\r get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),\r get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),\r ]\r\r # plot different image rotations\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tplt.figure(1)\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\t['Original', 'Rotation 1', 'Rotation 2', 'Rotation 3']\r for i, image in enumerate(images):\r plt.subplot(2, 2, i + 1), plt.imshow(image, 'gray')\r plt.title(titles[i])\r plt.axis('off')\r plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)\r plt.show()\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport doctest\rimport logging\rimport os\rimport unittest\rfrom pathlib import Path\rfrom typing import List, Union\r\rimport transformers\rfrom transformers.testing_utils import require_tf, require_torch, slow\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tlogging.getLogger()\r\r@unittest.skip(\"\"\"Temporarily disable the doc tests.\"\"\" )\r@require_torch\r@require_tf\r@slow\rclass lowerCAmelCase_ ( unittest.TestCase ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tPath ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, None] = None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[List[str], None] = None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, List[str], None] = None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tbool = True ,\t\t\t\t\t\t):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[file for file in os.listdir(_UpperCAmelCase ) if os.path.isfile(os.path.join(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ) )]\r\r if identifier is not None:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[file for file in files if identifier in file]\r\r if n_identifier is not None:\r if isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ):\r for n_ in n_identifier:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[file for file in files if n_ not in file]\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[file for file in files if n_identifier not in file]\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tignore_files or []\r ignore_files.append(\"\"\"__init__.py\"\"\" )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[file for file in files if file not in ignore_files]\r\r for file in files:\r # Open all files\r print(\"\"\"Testing\"\"\" ,\t\t\t\t\t\t_UpperCAmelCase )\r\r if only_modules:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tfile.split(\"\"\".\"\"\" )[0]\r try:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tgetattr(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdoctest.DocTestSuite(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tunittest.TextTestRunner().run(_UpperCAmelCase )\r self.assertIs(len(result.failures ) ,\t\t\t\t\t\t0 )\r except AttributeError:\r logger.info(f'''{module_identifier} is not a module.''' )\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdoctest.testfile(str(\"\"\"..\"\"\" / directory / file ) ,\t\t\t\t\t\toptionflags=doctest.ELLIPSIS )\r self.assertIs(result.failed ,\t\t\t\t\t\t0 )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tPath(\"\"\"src/transformers\"\"\" )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"modeling\"\"\"\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[\r \"\"\"modeling_ctrl.py\"\"\",\r \"\"\"modeling_tf_ctrl.py\"\"\",\r ]\r self.analyze_directory(_UpperCAmelCase ,\t\t\t\t\t\tidentifier=_UpperCAmelCase ,\t\t\t\t\t\tignore_files=_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tPath(\"\"\"src/transformers\"\"\" )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"tokenization\"\"\"\r self.analyze_directory(_UpperCAmelCase ,\t\t\t\t\t\tidentifier=_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tPath(\"\"\"src/transformers\"\"\" )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"configuration\"\"\"\r self.analyze_directory(_UpperCAmelCase ,\t\t\t\t\t\tidentifier=_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tPath(\"\"\"src/transformers\"\"\" )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[\"\"\"configuration\"\"\", \"\"\"modeling\"\"\", \"\"\"tokenization\"\"\"]\r self.analyze_directory(_UpperCAmelCase ,\t\t\t\t\t\tn_identifier=_UpperCAmelCase )\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tPath(\"\"\"docs/source\"\"\" )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[\"\"\"favicon.ico\"\"\"]\r self.analyze_directory(_UpperCAmelCase ,\t\t\t\t\t\tignore_files=_UpperCAmelCase ,\t\t\t\t\t\tonly_modules=_UpperCAmelCase )\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152315,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t[0, 2, 4, 6, 8]\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t[1, 3, 5, 7, 9]\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tlist[int]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r if remaining_length == 0:\r if digits[0] == 0 or digits[-1] == 0:\r return 0\r\r for i in range(length // 2 - 1\t\t\t\t, -1\t\t\t\t, -1\t\t\t\t):\r remainder += digits[i] + digits[length - i - 1]\r\r if remainder % 2 == 0:\r return 0\r\r remainder //= 10\r\r return 1\r\r if remaining_length == 1:\r if remainder % 2 == 0:\r return 0\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0\r for digit in range(10\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdigit\r result += reversible_numbers(\r 0\t\t\t\t, (remainder + 2 * digit) // 10\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r return result\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0\r for digita in range(10\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdigita\r\r if (remainder + digita) % 2 == 0:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tODD_DIGITS\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tEVEN_DIGITS\r\r for digita in other_parity_digits:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdigita\r result += reversible_numbers(\r remaining_length - 2\t\t\t\t, (remainder + digita + digita) // 10\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, )\r return result\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint = 9\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0\r for length in range(1\t\t\t\t, max_power + 1\t\t\t\t):\r result += reversible_numbers(SCREAMING_SNAKE_CASE__\t\t\t\t, 0\t\t\t\t, [0] * length\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r return result\r\r\rif __name__ == \"__main__\":\r print(f\"{solution() = }\")\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rfrom datasets.utils.patching import _PatchedModuleObj, patch_submodule\r\rfrom . import _test_patching\r\rdef _UpperCamelCase\t\t\t\t\t( ):\r '''simple docstring'''\r\r\r\r\r\r import os as original_os\r from os import path as original_path\r from os import rename as original_rename\r from os.path import dirname as original_dirname\r from os.path import join as original_join\r\r assert _test_patching.os is original_os\r assert _test_patching.path is original_path\r assert _test_patching.join is original_join\r\r assert _test_patching.renamed_os is original_os\r assert _test_patching.renamed_path is original_path\r assert _test_patching.renamed_join is original_join\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"__test_patch_submodule_mock__\"\"\"\r with patch_submodule(_test_patching\t\t\t\t, \"\"\"os.path.join\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r # Every way to access os.path.join must be patched, and the rest must stay untouched\r\r # check os.path.join\r assert isinstance(_test_patching.os\t\t\t\t, _PatchedModuleObj\t\t\t\t)\r assert isinstance(_test_patching.os.path\t\t\t\t, _PatchedModuleObj\t\t\t\t)\r assert _test_patching.os.path.join is mock\r\r # check path.join\r assert isinstance(_test_patching.path\t\t\t\t, _PatchedModuleObj\t\t\t\t)\r assert _test_patching.path.join is mock\r\r # check join\r assert _test_patching.join is mock\r\r # check that the other attributes are untouched\r assert _test_patching.os.rename is original_rename\r assert _test_patching.path.dirname is original_dirname\r assert _test_patching.os.path.dirname is original_dirname\r\r # Even renamed modules or objects must be patched\r\r # check renamed_os.path.join\r assert isinstance(_test_patching.renamed_os\t\t\t\t, _PatchedModuleObj\t\t\t\t)\r assert isinstance(_test_patching.renamed_os.path\t\t\t\t, _PatchedModuleObj\t\t\t\t)\r assert _test_patching.renamed_os.path.join is mock\r\r # check renamed_path.join\r assert isinstance(_test_patching.renamed_path\t\t\t\t, _PatchedModuleObj\t\t\t\t)\r assert _test_patching.renamed_path.join is mock\r\r # check renamed_join\r assert _test_patching.renamed_join is mock\r\r # check that the other attributes are untouched\r assert _test_patching.renamed_os.rename is original_rename\r assert _test_patching.renamed_path.dirname is original_dirname\r assert _test_patching.renamed_os.path.dirname is original_dirname\r\r # check that everthing is back to normal when the patch is over\r\r assert _test_patching.os is original_os\r assert _test_patching.path is original_path\r assert _test_patching.join is original_join\r\r assert _test_patching.renamed_os is original_os\r assert _test_patching.renamed_path is original_path\r assert _test_patching.renamed_join is original_join\r\rdef _UpperCamelCase\t\t\t\t\t( ):\r '''simple docstring'''\r\r\r\r\r\r assert _test_patching.open is open\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"__test_patch_submodule_builtin_mock__\"\"\"\r # _test_patching has \"open\" in its globals\r assert _test_patching.open is open\r with patch_submodule(_test_patching\t\t\t\t, \"\"\"open\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r assert _test_patching.open is mock\r\r # check that everthing is back to normal when the patch is over\r\r assert _test_patching.open is open\r\rdef _UpperCamelCase\t\t\t\t\t( ):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"__test_patch_submodule_missing_mock__\"\"\"\r with patch_submodule(_test_patching\t\t\t\t, \"\"\"pandas.read_csv\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r pass\r\rdef _UpperCamelCase\t\t\t\t\t( ):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"__test_patch_submodule_missing_builtin_mock__\"\"\"\r # _test_patching doesn't have \"len\" in its globals\r assert getattr(_test_patching\t\t\t\t, \"\"\"len\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t) is None\r with patch_submodule(_test_patching\t\t\t\t, \"\"\"len\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r assert _test_patching.len is mock\r assert _test_patching.len is len\r\rdef _UpperCamelCase\t\t\t\t\t( ):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"__test_patch_submodule_start_and_stop_mock__\"\"\"\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tpatch_submodule(_test_patching\t\t\t\t, \"\"\"open\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r assert _test_patching.open is open\r patch.start()\r assert _test_patching.open is mock\r patch.stop()\r assert _test_patching.open is open\r\rdef _UpperCamelCase\t\t\t\t\t( ):\r '''simple docstring'''\r\r\r\r\r\r from os import rename as original_rename\r from os.path import dirname as original_dirname\r from os.path import join as original_join\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"__test_patch_submodule_successive_join__\"\"\"\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"__test_patch_submodule_successive_dirname__\"\"\"\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"__test_patch_submodule_successive_rename__\"\"\"\r assert _test_patching.os.path.join is original_join\r assert _test_patching.os.path.dirname is original_dirname\r assert _test_patching.os.rename is original_rename\r\r with patch_submodule(_test_patching\t\t\t\t, \"\"\"os.path.join\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r with patch_submodule(_test_patching\t\t\t\t, \"\"\"os.rename\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r with patch_submodule(_test_patching\t\t\t\t, \"\"\"os.path.dirname\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r assert _test_patching.os.path.join is mock_join\r assert _test_patching.os.path.dirname is mock_dirname\r assert _test_patching.os.rename is mock_rename\r\r # try another order\r with patch_submodule(_test_patching\t\t\t\t, \"\"\"os.rename\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r with patch_submodule(_test_patching\t\t\t\t, \"\"\"os.path.join\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r with patch_submodule(_test_patching\t\t\t\t, \"\"\"os.path.dirname\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r assert _test_patching.os.path.join is mock_join\r assert _test_patching.os.path.dirname is mock_dirname\r assert _test_patching.os.rename is mock_rename\r\r assert _test_patching.os.path.join is original_join\r assert _test_patching.os.path.dirname is original_dirname\r assert _test_patching.os.rename is original_rename\r\r\r\r\r\r\r\rdef _UpperCamelCase\t\t\t\t\t( ):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"__test_patch_submodule_doesnt_exist_mock__\"\"\"\r with patch_submodule(_test_patching\t\t\t\t, \"\"\"__module_that_doesn_exist__.__attribute_that_doesn_exist__\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r pass\r with patch_submodule(_test_patching\t\t\t\t, \"\"\"os.__attribute_that_doesn_exist__\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r pass\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152316,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\r# Copyright 2023 The HuggingFace Inc. team. All rights reserved.\r#\r# Licensed under the Apache License, Version 2.0 (the \"License\");\r# you may not use this file except in compliance with the License.\r# You may obtain a copy of the License at\r#\r# http://www.apache.org/licenses/LICENSE-2.0\r#\r# Unless required by applicable law or agreed to in writing, software\r# distributed under the License is distributed on an \"AS IS\" BASIS,\r# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\r# See the License for the specific language governing permissions and\r# limitations under the License.\rimport torch\r\rfrom ..models.auto import AutoModelForSequenceClassification, AutoTokenizer\rfrom .base import PipelineTool\r\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r lowerCAmelCase_ : Any = \"\"\"facebook/bart-large-mnli\"\"\"\r lowerCAmelCase_ : str = (\r \"\"\"This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which \"\"\"\r \"\"\"should be the text to classify, and `labels`, which should be the list of labels to use for classification. \"\"\"\r \"\"\"It returns the most likely label in the list of provided `labels` for the input text.\"\"\"\r )\r lowerCAmelCase_ : Dict = \"\"\"text_classifier\"\"\"\r lowerCAmelCase_ : Optional[int] = AutoTokenizer\r lowerCAmelCase_ : List[str] = AutoModelForSequenceClassification\r\r lowerCAmelCase_ : Optional[Any] = [\"\"\"text\"\"\", [\"\"\"text\"\"\"]]\r lowerCAmelCase_ : Dict = [\"\"\"text\"\"\"]\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tTuple ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r super().setup()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.model.config\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t-1\r for idx, label in config.idalabel.items():\r if label.lower().startswith(\"\"\"entail\"\"\" ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tint(_UpperCAmelCase )\r if self.entailment_id == -1:\r raise ValueError(\"\"\"Could not determine the entailment ID from the model config, please pass it at init.\"\"\" )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlabels\r return self.pre_processor(\r [text] * len(_UpperCAmelCase ) ,\t\t\t\t\t\t[f'''This example is {label}''' for label in labels] ,\t\t\t\t\t\treturn_tensors=\"\"\"pt\"\"\" ,\t\t\t\t\t\tpadding=\"\"\"max_length\"\"\" ,\t\t\t\t\t\t)\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\toutputs.logits\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.argmax(logits[:, 2] ).item()\r return self._labels[label_id]\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rfrom timeit import timeit\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t{\r 'MALAYALAM': True,\r 'String': False,\r 'rotor': True,\r 'level': True,\r 'A': True,\r 'BB': True,\r 'ABC': False,\r 'amanaplanacanalpanama': True, # \"a man a plan a canal panama\"\r}\r# Ensure our test data is valid\rassert all((key == key[::-1]) is value for key, value in test_data.items())\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlen(SCREAMING_SNAKE_CASE__\t\t\t\t) - 1\r while start_i < end_i:\r if s[start_i] == s[end_i]:\r start_i += 1\r end_i -= 1\r else:\r return False\r return True\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlen(SCREAMING_SNAKE_CASE__\t\t\t\t) // 2\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlen(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r # We need to traverse till half of the length of string\r # as we can get access of the i'th last element from\r # i'th index.\r # eg: [0,1,2,3,4,5] => 4th index can be accessed\r # with the help of 1st index (i==n-i-1)\r # where n is length of string\r return all(s[i] == s[n - i - 1] for i in range(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t)\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r if len(SCREAMING_SNAKE_CASE__\t\t\t\t) <= 2:\r return True\r if s[0] == s[len(SCREAMING_SNAKE_CASE__\t\t\t\t) - 1]:\r return is_palindrome_recursive(s[1:-1]\t\t\t\t)\r else:\r return False\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r return s == s[::-1]\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tF'''all({name}(key) is value for key, value in test_data.items())'''\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tF'''from __main__ import test_data, {name}'''\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t500000\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttimeit(stmt=SCREAMING_SNAKE_CASE__\t\t\t\t, setup=SCREAMING_SNAKE_CASE__\t\t\t\t, number=SCREAMING_SNAKE_CASE__\t\t\t\t)\r print(F'''{name:<35} finished {number:,} runs in {result:.5f} seconds'''\t\t\t\t)\r\r\rif __name__ == \"__main__\":\r for key, value in test_data.items():\r assert is_palindrome(key) is is_palindrome_recursive(key)\r assert is_palindrome(key) is is_palindrome_slice(key)\r print(f\"{key:21} {value}\")\r print('a man a plan a canal panama')\r\r # finished 500,000 runs in 0.46793 seconds\r benchmark_function('is_palindrome_slice')\r # finished 500,000 runs in 0.85234 seconds\r benchmark_function('is_palindrome')\r # finished 500,000 runs in 1.32028 seconds\r benchmark_function('is_palindrome_recursive')\r # finished 500,000 runs in 2.08679 seconds\r benchmark_function('is_palindrome_traversal')\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152317,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tlist\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r _enforce_args(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r if n == 0:\r return 0\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tfloat(\"\"\"-inf\"\"\"\t\t\t\t)\r for i in range(1\t\t\t\t, n + 1\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmax(\r SCREAMING_SNAKE_CASE__\t\t\t\t, prices[i - 1] + naive_cut_rod_recursive(n - i\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t)\r\r return max_revue\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tlist\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r _enforce_args(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[float(\"\"\"-inf\"\"\"\t\t\t\t) for _ in range(n + 1\t\t\t\t)]\r return _top_down_cut_rod_recursive(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tlist\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tlist\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r if max_rev[n] >= 0:\r return max_rev[n]\r elif n == 0:\r return 0\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tfloat(\"\"\"-inf\"\"\"\t\t\t\t)\r for i in range(1\t\t\t\t, n + 1\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmax(\r SCREAMING_SNAKE_CASE__\t\t\t\t, prices[i - 1] + _top_down_cut_rod_recursive(n - i\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t, )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmax_revenue\r\r return max_rev[n]\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tlist\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r _enforce_args(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r # length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of\r # length 0.\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[float(\"\"\"-inf\"\"\"\t\t\t\t) for _ in range(n + 1\t\t\t\t)]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0\r\r for i in range(1\t\t\t\t, n + 1\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmax_rev[i]\r for j in range(1\t\t\t\t, i + 1\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmax(SCREAMING_SNAKE_CASE__\t\t\t\t, prices[j - 1] + max_rev[i - j]\t\t\t\t)\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmax_revenue_i\r\r return max_rev[n]\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tlist\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r if n < 0:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tF'''n must be greater than or equal to 0. Got n = {n}'''\r raise ValueError(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r if n > len(SCREAMING_SNAKE_CASE__\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t(\r \"\"\"Each integral piece of rod must have a corresponding price. \"\"\"\r F'''Got n = {n} but length of prices = {len(SCREAMING_SNAKE_CASE__\t\t\t\t)}'''\r )\r raise ValueError(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\rdef _UpperCamelCase\t\t\t\t\t( ):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[6, 10, 12, 15, 20, 23]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlen(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r # the best revenue comes from cutting the rod into 6 pieces, each\r # of length 1 resulting in a revenue of 6 * 6 = 36.\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t36\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttop_down_cut_rod(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tbottom_up_cut_rod(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnaive_cut_rod_recursive(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r assert expected_max_revenue == max_rev_top_down\r assert max_rev_top_down == max_rev_bottom_up\r assert max_rev_bottom_up == max_rev_naive\r\r\rif __name__ == \"__main__\":\r main()\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport datasets\r\rfrom .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'\\\\n@INPROCEEDINGS{Papineni02bleu:a,\\n author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\\n title = {BLEU: a Method for Automatic Evaluation of Machine Translation},\\n booktitle = {},\\n year = {2002},\\n pages = {311--318}\\n}\\n@inproceedings{lin-och-2004-orange,\\n title = \"{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation\",\\n author = \"Lin, Chin-Yew and\\n Och, Franz Josef\",\\n booktitle = \"{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics\",\\n month = \"aug 23{--}aug 27\",\\n year = \"2004\",\\n address = \"Geneva, Switzerland\",\\n publisher = \"COLING\",\\n url = \"https://www.aclweb.org/anthology/C04-1072\",\\n pages = \"501--507\",\\n}\\n'\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'\\\\nBLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.\\nQuality is considered to be the correspondence between a machine\\'s output and that of a human: \"the closer a machine translation is to a professional human translation,\\nthe better it is\" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and\\nremains one of the most popular automated and inexpensive metrics.\\n\\nScores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.\\nThose scores are then averaged over the whole corpus to reach an estimate of the translation\\'s overall quality. Intelligibility or grammatical correctness\\nare not taken into account[citation needed].\\n\\nBLEU\\'s output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1\\nrepresenting more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the\\nreference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional\\nreference translations will increase the BLEU score.\\n'\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'\\nComputes BLEU score of translated segments against one or more references.\\nArgs:\\n predictions: list of translations to score.\\n Each translation should be tokenized into a list of tokens.\\n references: list of lists of references for each translation.\\n Each reference should be tokenized into a list of tokens.\\n max_order: Maximum n-gram order to use when computing BLEU score.\\n smooth: Whether or not to apply Lin et al. 2004 smoothing.\\nReturns:\\n \\'bleu\\': bleu score,\\n \\'precisions\\': geometric mean of n-gram precisions,\\n \\'brevity_penalty\\': brevity penalty,\\n \\'length_ratio\\': ratio of lengths,\\n \\'translation_length\\': translation_length,\\n \\'reference_length\\': reference_length\\nExamples:\\n\\n >>> predictions = [\\n ... [\"hello\", \"there\", \"general\", \"kenobi\"], # tokenized prediction of the first sample\\n ... [\"foo\", \"bar\", \"foobar\"] # tokenized prediction of the second sample\\n ... ]\\n >>> references = [\\n ... [[\"hello\", \"there\", \"general\", \"kenobi\"], [\"hello\", \"there\", \"!\"]], # tokenized references for the first sample (2 references)\\n ... [[\"foo\", \"bar\", \"foobar\"]] # tokenized references for the second sample (1 reference)\\n ... ]\\n >>> bleu = datasets.load_metric(\"bleu\")\\n >>> results = bleu.compute(predictions=predictions, references=references)\\n >>> print(results[\"bleu\"])\\n 1.0\\n'\r\r@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION\t, _KWARGS_DESCRIPTION )\rclass lowerCAmelCase_ ( datasets.Metric ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return datasets.MetricInfo(\r description=_DESCRIPTION ,\t\t\t\t\t\tcitation=_CITATION ,\t\t\t\t\t\tinputs_description=_KWARGS_DESCRIPTION ,\t\t\t\t\t\tfeatures=datasets.Features(\r {\r \"\"\"predictions\"\"\": datasets.Sequence(datasets.Value(\"\"\"string\"\"\" ,\t\t\t\t\t\tid=\"\"\"token\"\"\" ) ,\t\t\t\t\t\tid=\"\"\"sequence\"\"\" ),\r \"\"\"references\"\"\": datasets.Sequence(\r datasets.Sequence(datasets.Value(\"\"\"string\"\"\" ,\t\t\t\t\t\tid=\"\"\"token\"\"\" ) ,\t\t\t\t\t\tid=\"\"\"sequence\"\"\" ) ,\t\t\t\t\t\tid=\"\"\"references\"\"\" ),\r } ) ,\t\t\t\t\t\tcodebase_urls=[\"\"\"https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py\"\"\"] ,\t\t\t\t\t\treference_urls=[\r \"\"\"https://en.wikipedia.org/wiki/BLEU\"\"\",\r \"\"\"https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213\"\"\",\r ] ,\t\t\t\t\t\t)\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any]=4 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=False ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tcompute_bleu(\r reference_corpus=_UpperCAmelCase ,\t\t\t\t\t\ttranslation_corpus=_UpperCAmelCase ,\t\t\t\t\t\tmax_order=_UpperCAmelCase ,\t\t\t\t\t\tsmooth=_UpperCAmelCase )\r ((UpperCAmelCase__)\t\t\t\t\t\t\t,\t(UpperCAmelCase__)\t\t\t\t\t\t\t,\t(UpperCAmelCase__)\t\t\t\t\t\t\t,\t(UpperCAmelCase__)\t\t\t\t\t\t\t,\t(UpperCAmelCase__)\t\t\t\t\t\t\t,\t(UpperCAmelCase__))\t\t\t\t\t\t\t\t=\t\t\tscore\r return {\r \"bleu\": bleu,\r \"precisions\": precisions,\r \"brevity_penalty\": bp,\r \"length_ratio\": ratio,\r \"translation_length\": translation_length,\r \"reference_length\": reference_length,\r }\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152318,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport fire\r\rfrom utils import calculate_rouge, save_json\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tTuple=None\t\t\t\t, **SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[x.strip() for x in open(SCREAMING_SNAKE_CASE__\t\t\t\t).readlines()]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[x.strip() for x in open(SCREAMING_SNAKE_CASE__\t\t\t\t).readlines()][: len(SCREAMING_SNAKE_CASE__\t\t\t\t)]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tcalculate_rouge(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, **SCREAMING_SNAKE_CASE__\t\t\t\t)\r if save_path is not None:\r save_json(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, indent=SCREAMING_SNAKE_CASE__\t\t\t\t)\r return metrics # these print nicely\r\r\rif __name__ == \"__main__\":\r fire.Fire(calculate_rouge_path)\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rfrom dataclasses import dataclass\rfrom typing import List, Optional, Union\r\rimport numpy as np\rimport torch\r\rfrom ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available\r\r@dataclass\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r lowerCAmelCase_ : Union[List[np.ndarray], torch.FloatTensor]\r\r\rtry:\r if not (is_transformers_available() and is_torch_available()):\r raise OptionalDependencyNotAvailable()\rexcept OptionalDependencyNotAvailable:\r from ...utils.dummy_torch_and_transformers_objects import * # noqa F403\relse:\r from .pipeline_text_to_video_synth import TextToVideoSDPipeline\r from .pipeline_text_to_video_synth_imgaimg import VideoToVideoSDPipeline # noqa: F401\r from .pipeline_text_to_video_zero import TextToVideoZeroPipeline\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152319,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport inspect\rimport unittest\r\rfrom transformers import ViTHybridConfig\rfrom transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device\rfrom transformers.utils import cached_property, is_torch_available, is_vision_available\r\rfrom ...test_configuration_common import ConfigTester\rfrom ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor\rfrom ...test_pipeline_mixin import PipelineTesterMixin\r\r\rif is_torch_available():\r import torch\r from torch import nn\r\r from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel\r from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST\r\r\rif is_vision_available():\r from PIL import Image\r\rclass lowerCAmelCase_ :\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def __init__( self\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple=13 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint=64 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple=2 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any]=3 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any]=True ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict=True ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any]=32 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int]=5 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int]=4 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any]=37 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr=\"gelu\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict=0.1 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny=0.1 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint=10 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict=0.02 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any]=[1, 16, 4, 4] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str]=None ,\t\t\t\t\t\t):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tparent\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tbatch_size\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\timage_size\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tpatch_size\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnum_channels\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tis_training\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tuse_labels\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\thidden_size\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnum_hidden_layers\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnum_attention_heads\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tintermediate_size\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\thidden_act\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\thidden_dropout_prob\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tattention_probs_dropout_prob\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttype_sequence_label_size\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tinitializer_range\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscope\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tbackbone_featmap_shape\r\r # in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)\r # the number of patches is based on the feature map of the backbone, which by default uses an output stride\r # of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t(self.image_size // 32) ** 2\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnum_patches + 1\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tfloats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r if self.use_labels:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tids_tensor([self.batch_size] ,\t\t\t\t\t\tself.type_sequence_label_size )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_config()\r\r return config, pixel_values, labels\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\r \"\"\"global_padding\"\"\": \"\"\"same\"\"\",\r \"\"\"layer_type\"\"\": \"\"\"bottleneck\"\"\",\r \"\"\"depths\"\"\": [3, 4, 9],\r \"\"\"out_features\"\"\": [\"\"\"stage1\"\"\", \"\"\"stage2\"\"\", \"\"\"stage3\"\"\"],\r \"\"\"embedding_dynamic_padding\"\"\": True,\r \"\"\"hidden_sizes\"\"\": [4, 8, 16, 32],\r \"\"\"num_groups\"\"\": 2,\r }\r\r return ViTHybridConfig(\r image_size=self.image_size ,\t\t\t\t\t\tpatch_size=self.patch_size ,\t\t\t\t\t\tnum_channels=self.num_channels ,\t\t\t\t\t\thidden_size=self.hidden_size ,\t\t\t\t\t\tnum_hidden_layers=self.num_hidden_layers ,\t\t\t\t\t\tnum_attention_heads=self.num_attention_heads ,\t\t\t\t\t\tintermediate_size=self.intermediate_size ,\t\t\t\t\t\thidden_act=self.hidden_act ,\t\t\t\t\t\thidden_dropout_prob=self.hidden_dropout_prob ,\t\t\t\t\t\tattention_probs_dropout_prob=self.attention_probs_dropout_prob ,\t\t\t\t\t\tis_decoder=_UpperCAmelCase ,\t\t\t\t\t\tinitializer_range=self.initializer_range ,\t\t\t\t\t\tbackbone_featmap_shape=self.backbone_featmap_shape ,\t\t\t\t\t\tbackbone_config=_UpperCAmelCase ,\t\t\t\t\t\t)\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tViTHybridModel(config=_UpperCAmelCase )\r model.to(_UpperCAmelCase )\r model.eval()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase )\r self.parent.assertEqual(result.last_hidden_state.shape ,\t\t\t\t\t\t(self.batch_size, self.seq_length, self.hidden_size) )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.type_sequence_label_size\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tViTHybridForImageClassification(_UpperCAmelCase )\r model.to(_UpperCAmelCase )\r model.eval()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase ,\t\t\t\t\t\tlabels=_UpperCAmelCase )\r self.parent.assertEqual(result.logits.shape ,\t\t\t\t\t\t(self.batch_size, self.type_sequence_label_size) )\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.prepare_config_and_inputs()\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tconfig_and_inputs\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\"\"\"pixel_values\"\"\": pixel_values}\r return config, inputs_dict\r\r@require_torch\rclass lowerCAmelCase_ ( lowerCamelCase_\t, lowerCamelCase_\t, unittest.TestCase ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r lowerCAmelCase_ : Union[str, Any] = (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()\r lowerCAmelCase_ : Optional[int] = (\r {\"\"\"feature-extraction\"\"\": ViTHybridModel, \"\"\"image-classification\"\"\": ViTHybridForImageClassification}\r if is_torch_available()\r else {}\r )\r lowerCAmelCase_ : Union[str, Any] = False\r lowerCAmelCase_ : Any = False\r lowerCAmelCase_ : Optional[int] = False\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tViTHybridModelTester(self )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tConfigTester(self ,\t\t\t\t\t\tconfig_class=_UpperCAmelCase ,\t\t\t\t\t\thas_text_modality=_UpperCAmelCase ,\t\t\t\t\t\thidden_size=37 )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r self.config_tester.run_common_tests()\r\r\r @unittest.skip(reason=\"\"\"ViT does not use inputs_embeds\"\"\" )\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r pass\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.model_tester.prepare_config_and_inputs_for_common()\r\r for model_class in self.all_model_classes:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel_class(_UpperCAmelCase )\r self.assertIsInstance(model.get_input_embeddings() ,\t\t\t\t\t\t(nn.Module) )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel.get_output_embeddings()\r self.assertTrue(x is None or isinstance(_UpperCAmelCase ,\t\t\t\t\t\tnn.Linear ) )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tTuple ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.model_tester.prepare_config_and_inputs_for_common()\r\r for model_class in self.all_model_classes:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel_class(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tinspect.signature(model.forward )\r # signature.parameters is an OrderedDict => so arg_names order is deterministic\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[*signature.parameters.keys()]\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[\"\"\"pixel_values\"\"\"]\r self.assertListEqual(arg_names[:1] ,\t\t\t\t\t\t_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.model_tester.prepare_config_and_inputs()\r self.model_tester.create_and_check_model(*_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.model_tester.prepare_config_and_inputs()\r self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tAny ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.model_tester.prepare_config_and_inputs_for_common()\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t_config_zero_init(_UpperCAmelCase )\r for model_class in self.all_model_classes:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel_class(config=_UpperCAmelCase )\r # Skip the check for the backbone\r for name, module in model.named_modules():\r if module.__class__.__name__ == \"ViTHybridPatchEmbeddings\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[f'''{name}.{key}''' for key in module.state_dict().keys()]\r break\r\r for name, param in model.named_parameters():\r if param.requires_grad:\r if name in backbone_params:\r continue\r self.assertIn(\r ((param.data.mean() * 1E9).round() / 1E9).item() ,\t\t\t\t\t\t[0.0, 1.0] ,\t\t\t\t\t\tmsg=f'''Parameter {name} of model {model_class} seems not properly initialized''' ,\t\t\t\t\t\t)\r\r\r\r @slow\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tViTHybridModel.from_pretrained(_UpperCAmelCase )\r self.assertIsNotNone(_UpperCAmelCase )\r\rdef _UpperCamelCase\t\t\t\t\t( ):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tImage.open(\"\"\"./tests/fixtures/tests_samples/COCO/000000039769.png\"\"\"\t\t\t\t)\r return image\r\r\r\r\r\r\r\r@require_torch\r@require_vision\rclass lowerCAmelCase_ ( unittest.TestCase ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r @cached_property\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return (\r ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0] )\r if is_vision_available()\r else None\r )\r\r\r @slow\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(\r _UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.default_image_processor\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tprepare_img()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\timage_processor(images=_UpperCAmelCase ,\t\t\t\t\t\treturn_tensors=\"\"\"pt\"\"\" ).to(_UpperCAmelCase )\r\r # forward pass\r with torch.no_grad():\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(**_UpperCAmelCase )\r\r # verify the logits\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.Size((1, 10_00) )\r self.assertEqual(outputs.logits.shape ,\t\t\t\t\t\t_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.tensor([-1.9090, -0.4993, -0.2389] ).to(_UpperCAmelCase )\r\r self.assertTrue(torch.allclose(outputs.logits[0, :3] ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\tatol=1E-4 ) )\r\r\r\r @slow\r @require_accelerate\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tViTHybridImageProcessor.from_pretrained(\"\"\"google/vit-hybrid-base-bit-384\"\"\" )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tViTHybridForImageClassification.from_pretrained(\"\"\"google/vit-hybrid-base-bit-384\"\"\" ,\t\t\t\t\t\tdevice_map=\"\"\"auto\"\"\" )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tprepare_img()\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\timage_processor(images=_UpperCAmelCase ,\t\t\t\t\t\treturn_tensors=\"\"\"pt\"\"\" )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(**_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\toutputs.logits\r # model predicts one of the 1000 ImageNet classes\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlogits.argmax(-1 ).item()\r\r self.assertTrue(model.config.idalabel[predicted_class_idx] ,\t\t\t\t\t\t\"\"\"tabby, tabby cat\"\"\" )\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport torch\rimport torch.nn as nn\rfrom transformers.modeling_utils import ModuleUtilsMixin\rfrom transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm\r\rfrom ...configuration_utils import ConfigMixin, register_to_config\rfrom ...models import ModelMixin\r\rclass lowerCAmelCase_ ( lowerCamelCase_\t, lowerCamelCase_\t, lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r @register_to_config\r def __init__( self\t\t\t:\t\tList[str] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tfloat ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tbool = False ,\t\t\t\t\t\t):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r super().__init__()\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnn.Embedding(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnn.Embedding(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnn.Dropout(p=_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTaConfig(\r vocab_size=_UpperCAmelCase ,\t\t\t\t\t\td_model=_UpperCAmelCase ,\t\t\t\t\t\tnum_heads=_UpperCAmelCase ,\t\t\t\t\t\td_kv=_UpperCAmelCase ,\t\t\t\t\t\td_ff=_UpperCAmelCase ,\t\t\t\t\t\tdropout_rate=_UpperCAmelCase ,\t\t\t\t\t\tfeed_forward_proj=_UpperCAmelCase ,\t\t\t\t\t\tis_decoder=_UpperCAmelCase ,\t\t\t\t\t\tis_encoder_decoder=_UpperCAmelCase ,\t\t\t\t\t\t)\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnn.ModuleList()\r for lyr_num in range(_UpperCAmelCase ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTaBlock(_UpperCAmelCase )\r self.encoders.append(_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTaLayerNorm(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnn.Dropout(p=_UpperCAmelCase )\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.token_embedder(_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tencoder_input_tokens.shape[1]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.arange(_UpperCAmelCase ,\t\t\t\t\t\tdevice=encoder_input_tokens.device )\r x += self.position_encoding(_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.dropout_pre(_UpperCAmelCase )\r\r # inverted the attention mask\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tencoder_input_tokens.size()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_extended_attention_mask(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r\r for lyr in self.encoders:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlyr(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )[0]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.layer_norm(_UpperCAmelCase )\r\r return self.dropout_post(_UpperCAmelCase ), encoder_inputs_mask\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152320,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rfrom typing import Callable, Optional, Union\r\rfrom ...configuration_utils import PretrainedConfig\rfrom ...utils import logging\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tlogging.get_logger(__name__)\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t{\r 'microsoft/xprophetnet-large-wiki100-cased': (\r 'https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json'\r ),\r}\r\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r lowerCAmelCase_ : Dict = \"\"\"xlm-prophetnet\"\"\"\r lowerCAmelCase_ : Any = [\"\"\"past_key_values\"\"\"]\r lowerCAmelCase_ : List[str] = {\r \"\"\"num_attention_heads\"\"\": \"\"\"num_encoder_attention_heads\"\"\",\r }\r\r\r def __init__( self\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[float] = 0.1 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Union[str, Callable]] = \"gelu\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int] = 3_05_22 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int] = 10_24 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int] = 40_96 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int] = 12 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int] = 16 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int] = 40_96 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int] = 12 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int] = 16 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[float] = 0.1 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[float] = 0.1 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int] = 5_12 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[float] = 0.02 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[bool] = True ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[bool] = True ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int] = 0 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int] = 2 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int] = 32 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int] = 1_28 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[bool] = False ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[float] = 0.0 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[bool] = True ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int] = 0 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int] = 1 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int] = 2 ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tDict ,\t\t\t\t\t\t):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvocab_size\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\thidden_size\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tencoder_ffn_dim\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnum_encoder_layers\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnum_encoder_attention_heads\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdecoder_ffn_dim\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnum_decoder_layers\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnum_decoder_attention_heads\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmax_position_embeddings\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tinit_std # Normal(0, this parameter)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tactivation_function\r\r # parameters for xlmprophetnet\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tngram\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnum_buckets\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\trelative_max_distance\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdisable_ngram_loss\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\teps\r\r # 3 Types of Dropout\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tattention_dropout\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tactivation_dropout\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdropout\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tuse_cache\r\r super().__init__(\r pad_token_id=_UpperCAmelCase ,\t\t\t\t\t\tbos_token_id=_UpperCAmelCase ,\t\t\t\t\t\teos_token_id=_UpperCAmelCase ,\t\t\t\t\t\tis_encoder_decoder=_UpperCAmelCase ,\t\t\t\t\t\tadd_cross_attention=_UpperCAmelCase ,\t\t\t\t\t\tdecoder_start_token_id=_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase ,\t\t\t\t\t\t)\r\r\r @property\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return self.num_encoder_layers + self.num_decoder_layers\r\r\r\r @num_hidden_layers.setter\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tTuple ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r raise NotImplementedError(\r \"\"\"This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and\"\"\"\r \"\"\" `num_decoder_layers`.\"\"\" )\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport argparse\rimport json\rimport os\r\rimport fairseq\rimport torch\rfrom fairseq.data import Dictionary\r\rfrom transformers import (\r WavaVecaConfig,\r WavaVecaCTCTokenizer,\r WavaVecaFeatureExtractor,\r WavaVecaForCTC,\r WavaVecaForPreTraining,\r WavaVecaProcessor,\r logging,\r)\rfrom transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification\r\r\rlogging.set_verbosity_info()\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tlogging.get_logger(__name__)\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t{\r 'post_extract_proj': 'feature_projection.projection',\r 'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',\r 'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',\r 'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',\r 'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',\r 'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',\r 'self_attn_layer_norm': 'encoder.layers.*.layer_norm',\r 'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',\r 'fc2': 'encoder.layers.*.feed_forward.output_dense',\r 'final_layer_norm': 'encoder.layers.*.final_layer_norm',\r 'encoder.layer_norm': 'encoder.layer_norm',\r 'adapter_layer': 'encoder.layers.*.adapter_layer',\r 'w2v_model.layer_norm': 'feature_projection.layer_norm',\r 'quantizer.weight_proj': 'quantizer.weight_proj',\r 'quantizer.vars': 'quantizer.codevectors',\r 'project_q': 'project_q',\r 'final_proj': 'project_hid',\r 'w2v_encoder.proj': 'lm_head',\r 'mask_emb': 'masked_spec_embed',\r 'pooling_layer.linear': 'projector',\r 'pooling_layer.projection': 'classifier',\r}\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t[\r 'lm_head',\r 'quantizer.weight_proj',\r 'quantizer.codevectors',\r 'project_q',\r 'project_hid',\r 'projector',\r 'classifier',\r]\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tTuple\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{}\r with open(SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\"r\"\"\"\t\t\t\t) as file:\r for line_number, line in enumerate(SCREAMING_SNAKE_CASE__\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tline.strip()\r if line:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tline.split()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tline_number\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\twords[0]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue\r return result\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tUnion[str, Any]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[Any]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tAny\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tUnion[str, Any]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r for attribute in key.split(\"\"\".\"\"\"\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tgetattr(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r for param_key in PARAM_MAPPING.keys():\r if full_name.endswith(SCREAMING_SNAKE_CASE__\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tPARAM_MAPPING[full_name.split(\"\"\".\"\"\"\t\t\t\t)[-1]]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"param\"\"\"\r\r if weight_type is not None and weight_type != \"param\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tgetattr(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t).shape\r elif weight_type is not None and weight_type == \"param\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\thf_pointer\r for attribute in hf_param_name.split(\"\"\".\"\"\"\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tgetattr(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tshape_pointer.shape\r\r # let's reduce dimension\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue[0]\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\thf_pointer.shape\r\r if hf_shape != value.shape:\r raise ValueError(\r F'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''\r F''' {value.shape} for {full_name}'''\t\t\t\t)\r\r if weight_type == \"weight\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue\r elif weight_type == \"weight_g\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue\r elif weight_type == \"weight_v\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue\r elif weight_type == \"bias\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue\r elif weight_type == \"param\":\r for attribute in hf_param_name.split(\"\"\".\"\"\"\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tgetattr(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue\r\r logger.info(F'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.'''\t\t\t\t)\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tUnion[str, Any]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tUnion[str, Any]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[Any]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[Any]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r for param_key in PARAM_MAPPING.keys():\r if full_name.endswith(SCREAMING_SNAKE_CASE__\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tPARAM_MAPPING[full_name.split(\"\"\".\"\"\"\t\t\t\t)[-1]]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"param\"\"\"\r\r if weight_type is not None and weight_type != \"param\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\".\"\"\".join([key, weight_type]\t\t\t\t)\r elif weight_type is not None and weight_type == \"param\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\".\"\"\".join([key, hf_param_name]\t\t\t\t)\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tkey\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue if \"\"\"lm_head\"\"\" in full_key else value[0]\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t{\r 'W_a': 'linear_1.weight',\r 'W_b': 'linear_2.weight',\r 'b_a': 'linear_1.bias',\r 'b_b': 'linear_2.bias',\r 'ln_W': 'norm.weight',\r 'ln_b': 'norm.bias',\r}\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tAny\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tDict=None\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[Any]=None\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r for key, mapped_key in MAPPING.items():\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"wav2vec2.\"\"\" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key\r if key in name or key.split(\"\"\"w2v_model.\"\"\"\t\t\t\t)[-1] == name.split(\"\"\".\"\"\"\t\t\t\t)[0]:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTrue\r if \"*\" in mapped_key:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tname.split(SCREAMING_SNAKE_CASE__\t\t\t\t)[0].split(\"\"\".\"\"\"\t\t\t\t)[-2]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmapped_key.replace(\"\"\"*\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r if \"weight_g\" in name:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"weight_g\"\"\"\r elif \"weight_v\" in name:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"weight_v\"\"\"\r elif \"bias\" in name:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"bias\"\"\"\r elif \"weight\" in name:\r # TODO: don't match quantizer.weight_proj\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"weight\"\"\"\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r if hf_dict is not None:\r rename_dict(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r else:\r set_recursively(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r return is_used\r return is_used\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tTuple\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[int]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tfairseq_model.state_dict()\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\thf_model.wavaveca.feature_extractor\r\r for name, value in fairseq_dict.items():\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r if \"conv_layers\" in name:\r load_conv_layer(\r SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, hf_model.config.feat_extract_norm == \"\"\"group\"\"\"\t\t\t\t, )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTrue\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tload_wavaveca_layer(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r if not is_used:\r unused_weights.append(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r logger.warning(F'''Unused weights: {unused_weights}'''\t\t\t\t)\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[Any]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tfull_name.split(\"\"\"conv_layers.\"\"\"\t\t\t\t)[-1]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tname.split(\"\"\".\"\"\"\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tint(items[0]\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tint(items[1]\t\t\t\t)\r\r if type_id == 0:\r if \"bias\" in name:\r if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:\r raise ValueError(\r F'''{full_name} has size {value.shape}, but'''\r F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue\r logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''\t\t\t\t)\r elif \"weight\" in name:\r if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:\r raise ValueError(\r F'''{full_name} has size {value.shape}, but'''\r F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue\r logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''\t\t\t\t)\r elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):\r if \"bias\" in name:\r if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:\r raise ValueError(\r F'''{full_name} has size {value.shape}, but'''\r F''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'''\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue\r logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''\t\t\t\t)\r elif \"weight\" in name:\r if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:\r raise ValueError(\r F'''{full_name} has size {value.shape}, but'''\r F''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'''\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue\r logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''\t\t\t\t)\r else:\r unused_weights.append(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r@torch.no_grad()\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]=None\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[int]=None\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tUnion[str, Any]=True\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tUnion[str, Any]=False\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r if config_path is not None:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tWavaVecaConfig.from_pretrained(SCREAMING_SNAKE_CASE__\t\t\t\t)\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tWavaVecaConfig()\r\r if is_seq_class:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tread_txt_into_dict(SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tidalabel\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tWavaVecaForSequenceClassification(SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tWavaVecaFeatureExtractor(\r feature_size=1\t\t\t\t, sampling_rate=16000\t\t\t\t, padding_value=0\t\t\t\t, do_normalize=SCREAMING_SNAKE_CASE__\t\t\t\t, return_attention_mask=SCREAMING_SNAKE_CASE__\t\t\t\t, )\r feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r elif is_finetuned:\r if dict_path:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tDictionary.load(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r # important change bos & pad token id since CTC symbol is and\r # not as in fairseq\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttarget_dict.pad_index\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttarget_dict.bos_index\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttarget_dict.eos_index\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlen(target_dict.symbols\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tos.path.join(SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\"vocab.json\"\"\"\t\t\t\t)\r if not os.path.isdir(SCREAMING_SNAKE_CASE__\t\t\t\t):\r logger.error(\"\"\"--pytorch_dump_folder_path ({}) should be a directory\"\"\".format(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t)\r return\r os.makedirs(SCREAMING_SNAKE_CASE__\t\t\t\t, exist_ok=SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttarget_dict.indices\r\r # fairseq has the and switched\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t1\r with open(SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\"w\"\"\"\t\t\t\t, encoding=\"\"\"utf-8\"\"\"\t\t\t\t) as vocab_handle:\r json.dump(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tWavaVecaCTCTokenizer(\r SCREAMING_SNAKE_CASE__\t\t\t\t, unk_token=target_dict.unk_word\t\t\t\t, pad_token=target_dict.pad_word\t\t\t\t, bos_token=target_dict.bos_word\t\t\t\t, eos_token=target_dict.eos_word\t\t\t\t, word_delimiter_token=\"\"\"|\"\"\"\t\t\t\t, do_lower_case=SCREAMING_SNAKE_CASE__\t\t\t\t, )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTrue if config.feat_extract_norm == \"\"\"layer\"\"\" else False\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tWavaVecaFeatureExtractor(\r feature_size=1\t\t\t\t, sampling_rate=16000\t\t\t\t, padding_value=0\t\t\t\t, do_normalize=SCREAMING_SNAKE_CASE__\t\t\t\t, return_attention_mask=SCREAMING_SNAKE_CASE__\t\t\t\t, )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tWavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE__\t\t\t\t, tokenizer=SCREAMING_SNAKE_CASE__\t\t\t\t)\r processor.save_pretrained(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tWavaVecaForCTC(SCREAMING_SNAKE_CASE__\t\t\t\t)\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tWavaVecaForPreTraining(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r if is_finetuned or is_seq_class:\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tfairseq.checkpoint_utils.load_model_ensemble_and_task(\r [checkpoint_path]\t\t\t\t, arg_overrides={\"\"\"data\"\"\": \"\"\"/\"\"\".join(dict_path.split(\"\"\"/\"\"\"\t\t\t\t)[:-1]\t\t\t\t)}\t\t\t\t)\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\targparse.Namespace(task=\"\"\"audio_pretraining\"\"\"\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tfairseq.tasks.setup_task(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tfairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]\t\t\t\t, task=SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel[0].eval()\r\r recursively_load_weights(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, not is_finetuned\t\t\t\t)\r\r hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r\rif __name__ == \"__main__\":\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\targparse.ArgumentParser()\r parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')\r parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')\r parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')\r parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')\r parser.add_argument(\r '--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'\r )\r parser.add_argument(\r '--is_seq_class',\r action='store_true',\r help='Whether the model to convert is a fine-tuned sequence classification model or not',\r )\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tparser.parse_args()\r\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tnot args.not_finetuned and not args.is_seq_class\r convert_wavaveca_checkpoint(\r args.checkpoint_path,\r args.pytorch_dump_folder_path,\r args.config_path,\r args.dict_path,\r is_finetuned,\r args.is_seq_class,\r )\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152321,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport unittest\rfrom pathlib import Path\rfrom tempfile import TemporaryDirectory\r\rfrom transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available\rfrom transformers.models.gpta.tokenization_gpta import GPTaTokenizer\rfrom transformers.testing_utils import require_keras_nlp, require_tf, slow\r\r\rif is_tf_available():\r import tensorflow as tf\r\rif is_keras_nlp_available():\r from transformers.models.gpta import TFGPTaTokenizer\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t['gpt2']\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'gpt2'\r\rif is_tf_available():\r\r class lowerCAmelCase_ ( tf.Module ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def __init__( self\t\t\t:\t\tAny ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r super().__init__()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenizer\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tAutoConfig.from_pretrained(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTFGPTaLMHeadModel.from_config(_UpperCAmelCase )\r\r\r\r @tf.function(input_signature=(tf.TensorSpec((None,) ,\t\t\t\t\t\ttf.string ,\t\t\t\t\t\tname=\"\"\"text\"\"\" ),) )\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.tokenizer(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenized[\"\"\"input_ids\"\"\"].to_tensor()\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttf.cast(input_ids_dense > 0 ,\t\t\t\t\t\ttf.intaa )\r # input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.model(input_ids=_UpperCAmelCase ,\t\t\t\t\t\tattention_mask=_UpperCAmelCase )[\"\"\"logits\"\"\"]\r\r return outputs\r\r@require_tf\r@require_keras_nlp\rclass lowerCAmelCase_ ( unittest.TestCase ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r super().setUp()\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[GPTaTokenizer.from_pretrained(_UpperCAmelCase ) for checkpoint in (TOKENIZER_CHECKPOINTS)]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[TFGPTaTokenizer.from_pretrained(_UpperCAmelCase ) for checkpoint in TOKENIZER_CHECKPOINTS]\r assert len(self.tokenizers ) == len(self.tf_tokenizers )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[\r \"\"\"This is a straightforward English test sentence.\"\"\",\r \"\"\"This one has some weird characters\\rto\\nsee\\r\\nif those\\u00E9break things.\"\"\",\r \"\"\"Now we're going to add some Chinese: 一二三一二三\"\"\",\r \"\"\"And some much more rare Chinese: 齉堃齉堃\"\"\",\r \"\"\"Je vais aussi écrire en français pour tester les accents\"\"\",\r \"\"\"Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ\"\"\",\r ]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlist(zip(self.test_sentences ,\t\t\t\t\t\tself.test_sentences[::-1] ) )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tAny ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r for tokenizer, tf_tokenizer in zip(self.tokenizers ,\t\t\t\t\t\tself.tf_tokenizers ):\r for test_inputs in self.test_sentences:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenizer([test_inputs] ,\t\t\t\t\t\treturn_tensors=\"\"\"tf\"\"\" )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttf_tokenizer([test_inputs] )\r\r for key in python_outputs.keys():\r # convert them to numpy to avoid messing with ragged tensors\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tpython_outputs[key].numpy()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttf_outputs[key].numpy()\r\r self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape ) )\r self.assertTrue(tf.reduce_all(tf.cast(_UpperCAmelCase ,\t\t\t\t\t\ttf.intaa ) == tf_outputs_values ) )\r\r\r @slow\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r for tf_tokenizer in self.tf_tokenizers:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttf.function(_UpperCAmelCase )\r for test_inputs in self.test_sentences:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttf.constant(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tcompiled_tokenizer(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttf_tokenizer(_UpperCAmelCase )\r\r for key in eager_outputs.keys():\r self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key] ) )\r\r\r @slow\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r for tf_tokenizer in self.tf_tokenizers:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tModelToSave(tokenizer=_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttf.convert_to_tensor([self.test_sentences[0]] )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel.serving(_UpperCAmelCase ) # Build model with some sample inputs\r with TemporaryDirectory() as tempdir:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tPath(_UpperCAmelCase ) / \"\"\"saved.model\"\"\"\r tf.saved_model.save(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\tsignatures={\"\"\"serving_default\"\"\": model.serving} )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttf.saved_model.load(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tloaded_model.signatures[\"\"\"serving_default\"\"\"](_UpperCAmelCase )[\"\"\"output_0\"\"\"]\r # We may see small differences because the loaded model is compiled, so we need an epsilon for the test\r self.assertTrue(tf.reduce_all(out == loaded_output ) )\r\r\r @slow\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r for tf_tokenizer in self.tf_tokenizers:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttf.convert_to_tensor([self.test_sentences[0]] )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttf_tokenizer(_UpperCAmelCase ) # Build model with some sample inputs\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttf_tokenizer.get_config()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTFGPTaTokenizer.from_config(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel_from_config(_UpperCAmelCase )\r\r for key in from_config_output.keys():\r self.assertTrue(tf.reduce_all(from_config_output[key] == out[key] ) )\r\r\r\r @slow\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r for tf_tokenizer in self.tf_tokenizers:\r # for the test to run\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t12_31_23\r\r for max_length in [3, 5, 10_24]:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttf.convert_to_tensor([self.test_sentences[0]] )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttf_tokenizer(_UpperCAmelCase ,\t\t\t\t\t\tmax_length=_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tout[\"\"\"input_ids\"\"\"].numpy().shape[1]\r\r assert out_length == max_length\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport itertools\rimport os\rfrom collections import Counter, defaultdict\rfrom concurrent.futures import ThreadPoolExecutor, as_completed\r\rimport numpy as np\r\rimport datasets\r\rfrom .execute import check_correctness\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'\\\\n@misc{chen2021evaluating,\\n title={Evaluating Large Language Models Trained on Code},\\n author={Mark Chen and Jerry Tworek and Heewoo Jun and Qiming Yuan \\\\nand Henrique Ponde de Oliveira Pinto and Jared Kaplan and Harri Edwards \\\\nand Yuri Burda and Nicholas Joseph and Greg Brockman and Alex Ray \\\\nand Raul Puri and Gretchen Krueger and Michael Petrov and Heidy Khlaaf \\\\nand Girish Sastry and Pamela Mishkin and Brooke Chan and Scott Gray \\\\nand Nick Ryder and Mikhail Pavlov and Alethea Power and Lukasz Kaiser \\\\nand Mohammad Bavarian and Clemens Winter and Philippe Tillet \\\\nand Felipe Petroski Such and Dave Cummings and Matthias Plappert \\\\nand Fotios Chantzis and Elizabeth Barnes and Ariel Herbert-Voss \\\\nand William Hebgen Guss and Alex Nichol and Alex Paino and Nikolas Tezak \\\\nand Jie Tang and Igor Babuschkin and Suchir Balaji and Shantanu Jain \\\\nand William Saunders and Christopher Hesse and Andrew N. Carr \\\\nand Jan Leike and Josh Achiam and Vedant Misra and Evan Morikawa \\\\nand Alec Radford and Matthew Knight and Miles Brundage and Mira Murati \\\\nand Katie Mayer and Peter Welinder and Bob McGrew and Dario Amodei \\\\nand Sam McCandlish and Ilya Sutskever and Wojciech Zaremba},\\n year={2021},\\n eprint={2107.03374},\\n archivePrefix={arXiv},\\n primaryClass={cs.LG}\\n}\\n'\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'\\\\nThis metric implements the evaluation harness for the HumanEval problem solving dataset\\ndescribed in the paper \"Evaluating Large Language Models Trained on Code\"\\n(https://arxiv.org/abs/2107.03374).\\n'\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'\\nCalculates how good are predictions given some references, using certain scores\\nArgs:\\n predictions: list of candidates to evaluate. Each candidates should be a list\\n of strings with several code candidates to solve the problem.\\n references: a list with a test for each prediction. Each test should evaluate the\\n correctness of a code candidate.\\n k: number of code candidates to consider in the evaluation (Default: [1, 10, 100])\\n num_workers: number of workers used to evaluate the canidate programs (Default: 4).\\n timeout:\\nReturns:\\n pass_at_k: dict with pass rates for each k\\n results: dict with granular results of each unittest\\nExamples:\\n >>> code_eval = datasets.load_metric(\"code_eval\")\\n >>> test_cases = [\"assert add(2,3)==5\"]\\n >>> candidates = [[\"def add(a,b): return a*b\", \"def add(a, b): return a+b\"]]\\n >>> pass_at_k, results = code_eval.compute(references=test_cases, predictions=candidates, k=[1, 2])\\n >>> print(pass_at_k)\\n {\\'pass@1\\': 0.5, \\'pass@2\\': 1.0}\\n'\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'\\n################################################################################\\n !!!WARNING!!!\\n################################################################################\\nThe \"code_eval\" metric executes untrusted model-generated code in Python.\\nAlthough it is highly unlikely that model-generated code will do something\\novertly malicious in response to this test suite, model-generated code may act\\ndestructively due to a lack of model capability or alignment.\\nUsers are strongly encouraged to sandbox this evaluation suite so that it\\ndoes not perform destructive actions on their host or network. For more\\ninformation on how OpenAI sandboxes its code, see the paper \"Evaluating Large\\nLanguage Models Trained on Code\" (https://arxiv.org/abs/2107.03374).\\n\\nOnce you have read this disclaimer and taken appropriate precautions,\\nset the environment variable HF_ALLOW_CODE_EVAL=\"1\". Within Python you can to this\\nwith:\\n\\n>>> import os\\n>>> os.environ[\"HF_ALLOW_CODE_EVAL\"] = \"1\"\\n\\n################################################################################\\\\n'\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'The MIT License\\n\\nCopyright (c) OpenAI (https://openai.com)\\n\\nPermission is hereby granted, free of charge, to any person obtaining a copy\\nof this software and associated documentation files (the \"Software\"), to deal\\nin the Software without restriction, including without limitation the rights\\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\\ncopies of the Software, and to permit persons to whom the Software is\\nfurnished to do so, subject to the following conditions:\\n\\nThe above copyright notice and this permission notice shall be included in\\nall copies or substantial portions of the Software.\\n\\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\\nTHE SOFTWARE.'\r\r@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION\t, _KWARGS_DESCRIPTION )\rclass lowerCAmelCase_ ( datasets.Metric ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return datasets.MetricInfo(\r # This is the description that will appear on the metrics page.\r description=_DESCRIPTION ,\t\t\t\t\t\tcitation=_CITATION ,\t\t\t\t\t\tinputs_description=_KWARGS_DESCRIPTION ,\t\t\t\t\t\tfeatures=datasets.Features(\r {\r \"\"\"predictions\"\"\": datasets.Sequence(datasets.Value(\"\"\"string\"\"\" ) ),\r \"\"\"references\"\"\": datasets.Value(\"\"\"string\"\"\" ),\r } ) ,\t\t\t\t\t\thomepage=\"\"\"https://github.com/openai/human-eval\"\"\" ,\t\t\t\t\t\tcodebase_urls=[\"\"\"https://github.com/openai/human-eval\"\"\"] ,\t\t\t\t\t\treference_urls=[\"\"\"https://github.com/openai/human-eval\"\"\"] ,\t\t\t\t\t\tlicense=_LICENSE ,\t\t\t\t\t\t)\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str]=[1, 10, 1_00] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any]=4 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny=3.0 ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if os.getenv(\"\"\"HF_ALLOW_CODE_EVAL\"\"\" ,\t\t\t\t\t\t0 ) != \"1\":\r raise ValueError(_WARNING )\r\r if os.name == \"nt\":\r raise NotImplementedError(\"\"\"This metric is currently not supported on Windows.\"\"\" )\r\r with ThreadPoolExecutor(max_workers=_UpperCAmelCase ) as executor:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tCounter()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdefaultdict(_UpperCAmelCase )\r\r for task_id, (candidates, test_case) in enumerate(zip(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ) ):\r for candidate in candidates:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tcandidate + \"\"\"\\n\"\"\" + test_case\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t(test_program, timeout, task_id, completion_id[task_id])\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\texecutor.submit(_UpperCAmelCase ,\t\t\t\t\t\t*_UpperCAmelCase )\r futures.append(_UpperCAmelCase )\r completion_id[task_id] += 1\r n_samples += 1\r\r for future in as_completed(_UpperCAmelCase ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tfuture.result()\r results[result[\"task_id\"]].append((result[\"\"\"completion_id\"\"\"], result) )\r\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[], []\r for result in results.values():\r result.sort()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[r[1][\"\"\"passed\"\"\"] for r in result]\r total.append(len(_UpperCAmelCase ) )\r correct.append(sum(_UpperCAmelCase ) )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnp.array(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnp.array(_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tk\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{f'''pass@{k}''': estimate_pass_at_k(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ).mean() for k in ks if (total >= k).all()}\r\r return pass_at_k, results\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[Any]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[Any]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r def estimator(SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t) -> float:\r if n - c < k:\r return 1.0\r return 1.0 - np.prod(1.0 - k / np.arange(n - c + 1\t\t\t\t, n + 1\t\t\t\t)\t\t\t\t)\r\r if isinstance(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\titertools.repeat(SCREAMING_SNAKE_CASE__\t\t\t\t, len(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t)\r else:\r assert len(SCREAMING_SNAKE_CASE__\t\t\t\t) == len(SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\titer(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r return np.array([estimator(int(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t, int(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t) for n, c in zip(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)]\t\t\t\t)\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152322,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rfrom random import randint, random\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tbool = False\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tbool = False\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint = 5\t\t\t\t, ):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[[-1] * number_of_cells] # Create a highway without any car\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmax(SCREAMING_SNAKE_CASE__\t\t\t\t, 0\t\t\t\t)\r while i < number_of_cells:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t(\r randint(0\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t) if random_speed else initial_speed\r ) # Place the cars\r i += (\r randint(1\t\t\t\t, max_speed * 2\t\t\t\t) if random_frequency else frequency\r ) # Arbitrary number, may need tuning\r return highway\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tlist\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\thighway_now[car_index + 1 :]\r for cell in range(len(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t): # May need a better name for this\r if cells[cell] != -1: # If the cell is not empty then\r return distance # we have the distance we wanted\r distance += 1\r # Here if the car is near the end of the highway\r return distance + get_distance(SCREAMING_SNAKE_CASE__\t\t\t\t, -1\t\t\t\t)\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tlist\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tfloat\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlen(SCREAMING_SNAKE_CASE__\t\t\t\t)\r # Beforce calculations, the highway is empty\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[-1] * number_of_cells\r\r for car_index in range(SCREAMING_SNAKE_CASE__\t\t\t\t):\r if highway_now[car_index] != -1:\r # Add 1 to the current speed of the car and cap the speed\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmin(highway_now[car_index] + 1\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r # Number of empty cell before the next car\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tget_distance(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t) - 1\r # We can't have the car causing an accident\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmin(next_highway[car_index]\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r if random() < probability:\r # Randomly, a driver will slow down\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmax(next_highway[car_index] - 1\t\t\t\t, 0\t\t\t\t)\r return next_highway\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tlist\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tfloat\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlen(highway[0]\t\t\t\t)\r\r for i in range(SCREAMING_SNAKE_CASE__\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tupdate(highway[i]\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[-1] * number_of_cells\r\r for car_index in range(SCREAMING_SNAKE_CASE__\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnext_speeds_calculated[car_index]\r if speed != -1:\r # Change the position based on the speed (with % to create the loop)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t(car_index + speed) % number_of_cells\r # Commit the change of position\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tspeed\r highway.append(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r return highway\r\r\rif __name__ == \"__main__\":\r import doctest\r\r doctest.testmod()\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport math\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r assert isinstance(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t) and (\r number >= 0\r ), \"'number' must been an int and positive\"\r\r if 1 < number < 4:\r # 2 and 3 are primes\r return True\r elif number < 2 or not number % 2:\r # Negatives, 0, 1 and all even numbers are not primes\r return False\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\trange(3\t\t\t\t, int(math.sqrt(SCREAMING_SNAKE_CASE__\t\t\t\t) + 1\t\t\t\t)\t\t\t\t, 2\t\t\t\t)\r return not any(not number % i for i in odd_numbers\t\t\t\t)\r\r\r\r\r\r\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tUnion[str, Any]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]=1\t\t\t\t, **SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tfactor * value\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue\r\r while not is_prime(SCREAMING_SNAKE_CASE__\t\t\t\t):\r value += 1 if not (\"desc\" in kwargs and kwargs[\"desc\"] is True) else -1\r\r if value == first_value_val:\r return next_prime(value + 1\t\t\t\t, **SCREAMING_SNAKE_CASE__\t\t\t\t)\r return value\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152323,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport inspect\rfrom typing import List, Optional, Tuple, Union\r\rimport torch\r\rfrom ...models import UNetaDModel, VQModel\rfrom ...schedulers import DDIMScheduler\rfrom ...utils import randn_tensor\rfrom ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput\r\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def __init__( self\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tVQModel ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUNetaDModel ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDDIMScheduler ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r super().__init__()\r self.register_modules(vqvae=_UpperCAmelCase ,\t\t\t\t\t\tunet=_UpperCAmelCase ,\t\t\t\t\t\tscheduler=_UpperCAmelCase )\r\r\r\r @torch.no_grad()\r def __call__( self\t\t\t:\t\tTuple ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint = 1 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Union[torch.Generator, List[torch.Generator]]] = None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tfloat = 0.0 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint = 50 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[str] = \"pil\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tbool = True ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\trandn_tensor(\r (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) ,\t\t\t\t\t\tgenerator=_UpperCAmelCase ,\t\t\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlatents.to(self.device )\r\r # scale the initial noise by the standard deviation required by the scheduler\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlatents * self.scheduler.init_noise_sigma\r\r self.scheduler.set_timesteps(_UpperCAmelCase )\r\r # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"eta\"\"\" in set(inspect.signature(self.scheduler.step ).parameters.keys() )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{}\r if accepts_eta:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\teta\r\r for t in self.progress_bar(self.scheduler.timesteps ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.scheduler.scale_model_input(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r # predict the noise residual\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.unet(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ).sample\r # compute the previous noisy sample x_t -> x_t-1\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.scheduler.step(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase ).prev_sample\r\r # decode the image latents with the VAE\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.vqvae.decode(_UpperCAmelCase ).sample\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t(image / 2 + 0.5).clamp(0 ,\t\t\t\t\t\t1 )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\timage.cpu().permute(0 ,\t\t\t\t\t\t2 ,\t\t\t\t\t\t3 ,\t\t\t\t\t\t1 ).numpy()\r if output_type == \"pil\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.numpy_to_pil(_UpperCAmelCase )\r\r if not return_dict:\r return (image,)\r\r return ImagePipelineOutput(images=_UpperCAmelCase )\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport string\rfrom math import logaa\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdocument.translate(\r str.maketrans(\"\"\"\"\"\"\t\t\t\t, \"\"\"\"\"\"\t\t\t\t, string.punctuation\t\t\t\t)\t\t\t\t).replace(\"\"\"\\n\"\"\"\t\t\t\t, \"\"\"\"\"\"\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdocument_without_punctuation.split(\"\"\" \"\"\"\t\t\t\t) # word tokenization\r return len([word for word in tokenize_document if word.lower() == term.lower()]\t\t\t\t)\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tcorpus.lower().translate(\r str.maketrans(\"\"\"\"\"\"\t\t\t\t, \"\"\"\"\"\"\t\t\t\t, string.punctuation\t\t\t\t)\t\t\t\t) # strip all punctuation and replace it with ''\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tcorpus_without_punctuation.split(\"\"\"\\n\"\"\"\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tterm.lower()\r return (len([doc for doc in docs if term in doc]\t\t\t\t), len(SCREAMING_SNAKE_CASE__\t\t\t\t))\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tTuple=False\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r if smoothing:\r if n == 0:\r raise ValueError(\"\"\"log10(0) is undefined.\"\"\"\t\t\t\t)\r return round(1 + logaa(n / (1 + df)\t\t\t\t)\t\t\t\t, 3\t\t\t\t)\r\r if df == 0:\r raise ZeroDivisionError(\"\"\"df must be > 0\"\"\"\t\t\t\t)\r elif n == 0:\r raise ValueError(\"\"\"log10(0) is undefined.\"\"\"\t\t\t\t)\r return round(logaa(n / df\t\t\t\t)\t\t\t\t, 3\t\t\t\t)\r\r\r\r\r\r\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r return round(tf * idf\t\t\t\t, 3\t\t\t\t)\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152324,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rfrom typing import TYPE_CHECKING\r\rfrom ...utils import (\r OptionalDependencyNotAvailable,\r _LazyModule,\r is_flax_available,\r is_tf_available,\r is_tokenizers_available,\r is_torch_available,\r)\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t{\r 'configuration_blenderbot': [\r 'BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP',\r 'BlenderbotConfig',\r 'BlenderbotOnnxConfig',\r ],\r 'tokenization_blenderbot': ['BlenderbotTokenizer'],\r}\r\rtry:\r if not is_tokenizers_available():\r raise OptionalDependencyNotAvailable()\rexcept OptionalDependencyNotAvailable:\r pass\relse:\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\t['BlenderbotTokenizerFast']\r\rtry:\r if not is_torch_available():\r raise OptionalDependencyNotAvailable()\rexcept OptionalDependencyNotAvailable:\r pass\relse:\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\t[\r 'BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST',\r 'BlenderbotForCausalLM',\r 'BlenderbotForConditionalGeneration',\r 'BlenderbotModel',\r 'BlenderbotPreTrainedModel',\r ]\r\r\rtry:\r if not is_tf_available():\r raise OptionalDependencyNotAvailable()\rexcept OptionalDependencyNotAvailable:\r pass\relse:\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\t[\r 'TFBlenderbotForConditionalGeneration',\r 'TFBlenderbotModel',\r 'TFBlenderbotPreTrainedModel',\r ]\r\r\rtry:\r if not is_flax_available():\r raise OptionalDependencyNotAvailable()\rexcept OptionalDependencyNotAvailable:\r pass\relse:\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\t[\r 'FlaxBlenderbotForConditionalGeneration',\r 'FlaxBlenderbotModel',\r 'FlaxBlenderbotPreTrainedModel',\r ]\r\r\rif TYPE_CHECKING:\r from .configuration_blenderbot import (\r BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,\r BlenderbotConfig,\r BlenderbotOnnxConfig,\r )\r from .tokenization_blenderbot import BlenderbotTokenizer\r\r try:\r if not is_tokenizers_available():\r raise OptionalDependencyNotAvailable()\r except OptionalDependencyNotAvailable:\r pass\r else:\r from .tokenization_blenderbot_fast import BlenderbotTokenizerFast\r\r try:\r if not is_torch_available():\r raise OptionalDependencyNotAvailable()\r except OptionalDependencyNotAvailable:\r pass\r else:\r from .modeling_blenderbot import (\r BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,\r BlenderbotForCausalLM,\r BlenderbotForConditionalGeneration,\r BlenderbotModel,\r BlenderbotPreTrainedModel,\r )\r\r try:\r if not is_tf_available():\r raise OptionalDependencyNotAvailable()\r except OptionalDependencyNotAvailable:\r pass\r else:\r from .modeling_tf_blenderbot import (\r TFBlenderbotForConditionalGeneration,\r TFBlenderbotModel,\r TFBlenderbotPreTrainedModel,\r )\r\r try:\r if not is_flax_available():\r raise OptionalDependencyNotAvailable()\r except OptionalDependencyNotAvailable:\r pass\r else:\r from .modeling_flax_blenderbot import (\r FlaxBlenderbotForConditionalGeneration,\r FlaxBlenderbotModel,\r FlaxBlenderbotPreTrainedModel,\r )\r\relse:\r import sys\r\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\t_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport argparse\r\rimport torch\r\rfrom transformers import BertForMaskedLM\r\r\rif __name__ == \"__main__\":\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\targparse.ArgumentParser(\r description=(\r 'Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'\r ' Distillation'\r )\r )\r parser.add_argument('--model_type', default='bert', choices=['bert'])\r parser.add_argument('--model_name', default='bert-base-uncased', type=str)\r parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_bert-base-uncased_0247911.pth', type=str)\r parser.add_argument('--vocab_transform', action='store_true')\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tparser.parse_args()\r\r if args.model_type == \"bert\":\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tBertForMaskedLM.from_pretrained(args.model_name)\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\t'bert'\r else:\r raise ValueError('args.model_type should be \"bert\".')\r\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tmodel.state_dict()\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\t{}\r\r for w in [\"word_embeddings\", \"position_embeddings\"]:\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tstate_dict[f\"{prefix}.embeddings.{w}.weight\"]\r for w in [\"weight\", \"bias\"]:\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tstate_dict[f\"{prefix}.embeddings.LayerNorm.{w}\"]\r\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\t0\r for teacher_idx in [0, 2, 4, 7, 9, 1_1]:\r for w in [\"weight\", \"bias\"]:\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tstate_dict[\r f\"{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}\"\r ]\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tstate_dict[\r f\"{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}\"\r ]\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tstate_dict[\r f\"{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}\"\r ]\r\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tstate_dict[\r f\"{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}\"\r ]\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tstate_dict[\r f\"{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}\"\r ]\r\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tstate_dict[\r f\"{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}\"\r ]\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tstate_dict[\r f\"{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}\"\r ]\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tstate_dict[\r f\"{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}\"\r ]\r std_idx += 1\r\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tstate_dict['cls.predictions.decoder.weight']\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tstate_dict['cls.predictions.bias']\r if args.vocab_transform:\r for w in [\"weight\", \"bias\"]:\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tstate_dict[f\"cls.predictions.transform.dense.{w}\"]\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tstate_dict[f\"cls.predictions.transform.LayerNorm.{w}\"]\r\r print(f\"N layers selected for distillation: {std_idx}\")\r print(f\"Number of params transferred for distillation: {len(compressed_sd.keys())}\")\r\r print(f\"Save transferred checkpoint to {args.dump_checkpoint}.\")\r torch.save(compressed_sd, args.dump_checkpoint)\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152325,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rfrom collections import Counter\r\rimport numpy as np\rfrom sklearn import datasets\rfrom sklearn.model_selection import train_test_split\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tdatasets.load_iris()\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tnp.array(data['data'])\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tnp.array(data['target'])\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tdata['target_names']\r\rUpperCAmelCase_ ,\t\t\t\t\t\tUpperCAmelCase_ ,\t\t\t\t\t\tUpperCAmelCase_ ,\t\t\t\t\t\tUpperCAmelCase_\t\t\t\t =\t\t\t\t\ttrain_test_split(X, y)\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[Any]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r return np.linalg.norm(np.array(SCREAMING_SNAKE_CASE__\t\t\t\t) - np.array(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t)\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tAny\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[Any]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tAny=5\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tzip(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r # List of distances of all points from the point to be classified\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[]\r for data_point in data:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\teuclidean_distance(data_point[0]\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r distances.append((distance, data_point[1])\t\t\t\t)\r # Choosing 'k' points with the least distances.\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[i[1] for i in sorted(SCREAMING_SNAKE_CASE__\t\t\t\t)[:k]]\r # Most commonly occurring class among them\r # is the class into which the point is classified\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tCounter(SCREAMING_SNAKE_CASE__\t\t\t\t).most_common(1\t\t\t\t)[0][0]\r return classes[result]\r\r\rif __name__ == \"__main__\":\r print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport tempfile\r\rimport torch\r\rfrom diffusers import PNDMScheduler\r\rfrom .test_schedulers import SchedulerCommonTest\r\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r lowerCAmelCase_ : Union[str, Any] = (PNDMScheduler,)\r lowerCAmelCase_ : Optional[int] = ((\"\"\"num_inference_steps\"\"\", 50),)\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tOptional[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\r \"\"\"num_train_timesteps\"\"\": 10_00,\r \"\"\"beta_start\"\"\": 0.0001,\r \"\"\"beta_end\"\"\": 0.02,\r \"\"\"beta_schedule\"\"\": \"\"\"linear\"\"\",\r }\r\r config.update(**_UpperCAmelCase )\r return config\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple=0 ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdict(self.forward_default_kwargs )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tkwargs.pop(\"\"\"num_inference_steps\"\"\" ,\t\t\t\t\t\t_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.dummy_sample\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0.1 * sample\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]\r\r for scheduler_class in self.scheduler_classes:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_scheduler_config(**_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler_class(**_UpperCAmelCase )\r scheduler.set_timesteps(_UpperCAmelCase )\r # copy over dummy past residuals\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdummy_past_residuals[:]\r\r with tempfile.TemporaryDirectory() as tmpdirname:\r scheduler.save_config(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler_class.from_pretrained(_UpperCAmelCase )\r new_scheduler.set_timesteps(_UpperCAmelCase )\r # copy over dummy past residuals\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdummy_past_residuals[:]\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler.step_prk(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase ).prev_sample\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnew_scheduler.step_prk(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase ).prev_sample\r\r assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, \"Scheduler outputs are not identical\"\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler.step_plms(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase ).prev_sample\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnew_scheduler.step_plms(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase ).prev_sample\r\r assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, \"Scheduler outputs are not identical\"\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r pass\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=0 ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tOptional[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdict(self.forward_default_kwargs )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tkwargs.pop(\"\"\"num_inference_steps\"\"\" ,\t\t\t\t\t\t_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.dummy_sample\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0.1 * sample\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]\r\r for scheduler_class in self.scheduler_classes:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_scheduler_config()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler_class(**_UpperCAmelCase )\r scheduler.set_timesteps(_UpperCAmelCase )\r\r # copy over dummy past residuals (must be after setting timesteps)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdummy_past_residuals[:]\r\r with tempfile.TemporaryDirectory() as tmpdirname:\r scheduler.save_config(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler_class.from_pretrained(_UpperCAmelCase )\r # copy over dummy past residuals\r new_scheduler.set_timesteps(_UpperCAmelCase )\r\r # copy over dummy past residual (must be after setting timesteps)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdummy_past_residuals[:]\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler.step_prk(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase ).prev_sample\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnew_scheduler.step_prk(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase ).prev_sample\r\r assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, \"Scheduler outputs are not identical\"\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler.step_plms(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase ).prev_sample\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnew_scheduler.step_plms(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase ).prev_sample\r\r assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, \"Scheduler outputs are not identical\"\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tTuple ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.scheduler_classes[0]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_scheduler_config(**_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler_class(**_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t10\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.dummy_model()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.dummy_sample_deter\r scheduler.set_timesteps(_UpperCAmelCase )\r\r for i, t in enumerate(scheduler.prk_timesteps ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler.step_prk(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ).prev_sample\r\r for i, t in enumerate(scheduler.plms_timesteps ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler.step_plms(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ).prev_sample\r\r return sample\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdict(self.forward_default_kwargs )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tkwargs.pop(\"\"\"num_inference_steps\"\"\" ,\t\t\t\t\t\t_UpperCAmelCase )\r\r for scheduler_class in self.scheduler_classes:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_scheduler_config()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler_class(**_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.dummy_sample\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0.1 * sample\r\r if num_inference_steps is not None and hasattr(_UpperCAmelCase ,\t\t\t\t\t\t\"\"\"set_timesteps\"\"\" ):\r scheduler.set_timesteps(_UpperCAmelCase )\r elif num_inference_steps is not None and not hasattr(_UpperCAmelCase ,\t\t\t\t\t\t\"\"\"set_timesteps\"\"\" ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnum_inference_steps\r\r # copy over dummy past residuals (must be done after set_timesteps)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdummy_past_residuals[:]\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler.step_prk(_UpperCAmelCase ,\t\t\t\t\t\t0 ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase ).prev_sample\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler.step_prk(_UpperCAmelCase ,\t\t\t\t\t\t1 ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase ).prev_sample\r\r self.assertEqual(output_a.shape ,\t\t\t\t\t\tsample.shape )\r self.assertEqual(output_a.shape ,\t\t\t\t\t\toutput_a.shape )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler.step_plms(_UpperCAmelCase ,\t\t\t\t\t\t0 ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase ).prev_sample\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler.step_plms(_UpperCAmelCase ,\t\t\t\t\t\t1 ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase ).prev_sample\r\r self.assertEqual(output_a.shape ,\t\t\t\t\t\tsample.shape )\r self.assertEqual(output_a.shape ,\t\t\t\t\t\toutput_a.shape )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r for timesteps in [1_00, 10_00]:\r self.check_over_configs(num_train_timesteps=_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r for steps_offset in [0, 1]:\r self.check_over_configs(steps_offset=_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.scheduler_classes[0]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_scheduler_config(steps_offset=1 )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler_class(**_UpperCAmelCase )\r scheduler.set_timesteps(10 )\r assert torch.equal(\r scheduler.timesteps ,\t\t\t\t\t\ttorch.LongTensor(\r [9_01, 8_51, 8_51, 8_01, 8_01, 7_51, 7_51, 7_01, 7_01, 6_51, 6_51, 6_01, 6_01, 5_01, 4_01, 3_01, 2_01, 1_01, 1] ) ,\t\t\t\t\t\t)\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r for beta_start, beta_end in zip([0.0001, 0.001] ,\t\t\t\t\t\t[0.002, 0.02] ):\r self.check_over_configs(beta_start=_UpperCAmelCase ,\t\t\t\t\t\tbeta_end=_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r for schedule in [\"linear\", \"squaredcos_cap_v2\"]:\r self.check_over_configs(beta_schedule=_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r for prediction_type in [\"epsilon\", \"v_prediction\"]:\r self.check_over_configs(prediction_type=_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r for t in [1, 5, 10]:\r self.check_over_forward(time_step=_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tAny ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r for t, num_inference_steps in zip([1, 5, 10] ,\t\t\t\t\t\t[10, 50, 1_00] ):\r self.check_over_forward(num_inference_steps=_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t27\r\r for scheduler_class in self.scheduler_classes:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.dummy_sample\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0.1 * sample\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_scheduler_config()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler_class(**_UpperCAmelCase )\r\r scheduler.set_timesteps(_UpperCAmelCase )\r\r # before power of 3 fix, would error on first step, so we only need to do two\r for i, t in enumerate(scheduler.prk_timesteps[:2] ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler.step_prk(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ).prev_sample\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r with self.assertRaises(_UpperCAmelCase ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.scheduler_classes[0]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_scheduler_config()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler_class(**_UpperCAmelCase )\r\r scheduler.step_plms(self.dummy_sample ,\t\t\t\t\t\t1 ,\t\t\t\t\t\tself.dummy_sample ).prev_sample\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.full_loop()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.sum(torch.abs(_UpperCAmelCase ) )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.mean(torch.abs(_UpperCAmelCase ) )\r\r assert abs(result_sum.item() - 198.1318 ) < 1E-2\r assert abs(result_mean.item() - 0.2580 ) < 1E-3\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.full_loop(prediction_type=\"\"\"v_prediction\"\"\" )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.sum(torch.abs(_UpperCAmelCase ) )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.mean(torch.abs(_UpperCAmelCase ) )\r\r assert abs(result_sum.item() - 67.3986 ) < 1E-2\r assert abs(result_mean.item() - 0.0878 ) < 1E-3\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tAny ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.full_loop(set_alpha_to_one=_UpperCAmelCase ,\t\t\t\t\t\tbeta_start=0.01 )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.sum(torch.abs(_UpperCAmelCase ) )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.mean(torch.abs(_UpperCAmelCase ) )\r\r assert abs(result_sum.item() - 230.0399 ) < 1E-2\r assert abs(result_mean.item() - 0.2995 ) < 1E-3\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.full_loop(set_alpha_to_one=_UpperCAmelCase ,\t\t\t\t\t\tbeta_start=0.01 )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.sum(torch.abs(_UpperCAmelCase ) )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.mean(torch.abs(_UpperCAmelCase ) )\r\r assert abs(result_sum.item() - 186.9482 ) < 1E-2\r assert abs(result_mean.item() - 0.2434 ) < 1E-3\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152326,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tlist\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint = 0\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlength or len(SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r for i in range(length - 1\t\t\t\t):\r if list_data[i] > list_data[i + 1]:\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlist_data[i + 1], list_data[i]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTrue\r\r return list_data if not swapped else bubble_sort(SCREAMING_SNAKE_CASE__\t\t\t\t, length - 1\t\t\t\t)\r\r\rif __name__ == \"__main__\":\r import doctest\r\r doctest.testmod()\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rfrom ...configuration_utils import PretrainedConfig\rfrom ...utils import logging\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tlogging.get_logger(__name__)\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t{\r 'google/vivit-b-16x2-kinetics400': (\r 'https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json'\r ),\r # See all Vivit models at https://huggingface.co/models?filter=vivit\r}\r\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r lowerCAmelCase_ : Optional[int] = \"\"\"vivit\"\"\"\r\r\r def __init__( self\t\t\t:\t\tList[str] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any]=2_24 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str]=32 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny=[2, 16, 16] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint=3 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any]=7_68 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=12 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict=12 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any]=30_72 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int]=\"gelu_fast\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=0.0 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple=0.0 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int]=0.02 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any]=1E-06 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str]=True ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\thidden_size\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnum_hidden_layers\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnum_attention_heads\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tintermediate_size\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\thidden_act\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\thidden_dropout_prob\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tattention_probs_dropout_prob\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tinitializer_range\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlayer_norm_eps\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\timage_size\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnum_frames\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttubelet_size\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnum_channels\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tqkv_bias\r\r super().__init__(**_UpperCAmelCase )\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152327,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport inspect\rimport unittest\r\rfrom transformers import DecisionTransformerConfig, is_torch_available\rfrom transformers.testing_utils import require_torch, slow, torch_device\r\rfrom ...generation.test_utils import GenerationTesterMixin\rfrom ...test_configuration_common import ConfigTester\rfrom ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask\rfrom ...test_pipeline_mixin import PipelineTesterMixin\r\r\rif is_torch_available():\r import torch\r\r from transformers import DecisionTransformerModel\r from transformers.models.decision_transformer.modeling_decision_transformer import (\r DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,\r )\r\rclass lowerCAmelCase_ :\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def __init__( self\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=13 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any]=7 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny=6 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int]=17 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str]=23 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int]=11 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint=True ,\t\t\t\t\t\t):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tparent\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tbatch_size\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tseq_length\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tact_dim\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tstate_dim\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\thidden_size\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmax_length\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tis_training\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tfloats_tensor((self.batch_size, self.seq_length, self.state_dim) )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tfloats_tensor((self.batch_size, self.seq_length, self.act_dim) )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tfloats_tensor((self.batch_size, self.seq_length, 1) )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tfloats_tensor((self.batch_size, self.seq_length, 1) )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tids_tensor((self.batch_size, self.seq_length) ,\t\t\t\t\t\tvocab_size=10_00 )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\trandom_attention_mask((self.batch_size, self.seq_length) )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_config()\r\r return (\r config,\r states,\r actions,\r rewards,\r returns_to_go,\r timesteps,\r attention_mask,\r )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return DecisionTransformerConfig(\r batch_size=self.batch_size ,\t\t\t\t\t\tseq_length=self.seq_length ,\t\t\t\t\t\tact_dim=self.act_dim ,\t\t\t\t\t\tstate_dim=self.state_dim ,\t\t\t\t\t\thidden_size=self.hidden_size ,\t\t\t\t\t\tmax_length=self.max_length ,\t\t\t\t\t\t)\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple ,\t\t\t\t\t\t):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tDecisionTransformerModel(config=_UpperCAmelCase )\r model.to(_UpperCAmelCase )\r model.eval()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r\r self.parent.assertEqual(result.state_preds.shape ,\t\t\t\t\t\tstates.shape )\r self.parent.assertEqual(result.action_preds.shape ,\t\t\t\t\t\tactions.shape )\r self.parent.assertEqual(result.return_preds.shape ,\t\t\t\t\t\treturns_to_go.shape )\r self.parent.assertEqual(\r result.last_hidden_state.shape ,\t\t\t\t\t\t(self.batch_size, self.seq_length * 3, self.hidden_size) ) # seq length *3 as there are 3 modelities: states, returns and actions\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tTuple ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.prepare_config_and_inputs()\r (\r (\r UpperCAmelCase__\r )\t\t\t\t\t\t\t,\t(\r UpperCAmelCase__\r )\t\t\t\t\t\t\t,\t(\r UpperCAmelCase__\r )\t\t\t\t\t\t\t,\t(\r UpperCAmelCase__\r )\t\t\t\t\t\t\t,\t(\r UpperCAmelCase__\r )\t\t\t\t\t\t\t,\t(\r UpperCAmelCase__\r )\t\t\t\t\t\t\t,\t(\r UpperCAmelCase__\r )\t\t\t\t\t\t\t,\t\r )\t\t\t\t\t\t\t\t=\t\t\tconfig_and_inputs\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\r \"\"\"states\"\"\": states,\r \"\"\"actions\"\"\": actions,\r \"\"\"rewards\"\"\": rewards,\r \"\"\"returns_to_go\"\"\": returns_to_go,\r \"\"\"timesteps\"\"\": timesteps,\r \"\"\"attention_mask\"\"\": attention_mask,\r }\r return config, inputs_dict\r\r@require_torch\rclass lowerCAmelCase_ ( lowerCamelCase_\t, lowerCamelCase_\t, lowerCamelCase_\t, unittest.TestCase ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r lowerCAmelCase_ : List[Any] = (DecisionTransformerModel,) if is_torch_available() else ()\r lowerCAmelCase_ : Optional[int] = ()\r lowerCAmelCase_ : Optional[int] = {\"\"\"feature-extraction\"\"\": DecisionTransformerModel} if is_torch_available() else {}\r\r # Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids\r lowerCAmelCase_ : Optional[int] = False\r\r # Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features\r lowerCAmelCase_ : Tuple = False\r lowerCAmelCase_ : Optional[Any] = False\r lowerCAmelCase_ : Dict = False\r lowerCAmelCase_ : List[Any] = False\r lowerCAmelCase_ : Tuple = False\r lowerCAmelCase_ : int = False\r lowerCAmelCase_ : Tuple = False\r lowerCAmelCase_ : List[Any] = False\r lowerCAmelCase_ : Union[str, Any] = False\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tDecisionTransformerModelTester(self )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tConfigTester(self ,\t\t\t\t\t\tconfig_class=_UpperCAmelCase ,\t\t\t\t\t\thidden_size=37 )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r self.config_tester.run_common_tests()\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.model_tester.prepare_config_and_inputs()\r self.model_tester.create_and_check_model(*_UpperCAmelCase )\r\r\r @slow\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tDecisionTransformerModel.from_pretrained(_UpperCAmelCase )\r self.assertIsNotNone(_UpperCAmelCase )\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.model_tester.prepare_config_and_inputs_for_common()\r\r for model_class in self.all_model_classes:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel_class(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tinspect.signature(model.forward )\r # signature.parameters is an OrderedDict => so arg_names order is deterministic\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[*signature.parameters.keys()]\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[\r \"\"\"states\"\"\",\r \"\"\"actions\"\"\",\r \"\"\"rewards\"\"\",\r \"\"\"returns_to_go\"\"\",\r \"\"\"timesteps\"\"\",\r \"\"\"attention_mask\"\"\",\r ]\r\r self.assertListEqual(arg_names[: len(_UpperCAmelCase )] ,\t\t\t\t\t\t_UpperCAmelCase )\r\r@require_torch\rclass lowerCAmelCase_ ( unittest.TestCase ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r @slow\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t2 # number of steps of autoregressive prediction we will perform\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t10 # defined by the RL environment, may be normalized\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tDecisionTransformerModel.from_pretrained(\"\"\"edbeeching/decision-transformer-gym-hopper-expert\"\"\" )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel.to(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel.config\r torch.manual_seed(0 )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.randn(1 ,\t\t\t\t\t\t1 ,\t\t\t\t\t\tconfig.state_dim ).to(device=_UpperCAmelCase ,\t\t\t\t\t\tdtype=torch.floataa ) # env.reset()\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.tensor(\r [[0.24_2793, -0.2869_3074, 0.874_2613], [0.6781_5274, -0.0810_1085, -0.1295_2147]] ,\t\t\t\t\t\tdevice=_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.tensor(_UpperCAmelCase ,\t\t\t\t\t\tdevice=_UpperCAmelCase ,\t\t\t\t\t\tdtype=torch.floataa ).reshape(1 ,\t\t\t\t\t\t1 ,\t\t\t\t\t\t1 )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tstate\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.zeros(1 ,\t\t\t\t\t\t0 ,\t\t\t\t\t\tconfig.act_dim ,\t\t\t\t\t\tdevice=_UpperCAmelCase ,\t\t\t\t\t\tdtype=torch.floataa )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.zeros(1 ,\t\t\t\t\t\t0 ,\t\t\t\t\t\tdevice=_UpperCAmelCase ,\t\t\t\t\t\tdtype=torch.floataa )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.tensor(0 ,\t\t\t\t\t\tdevice=_UpperCAmelCase ,\t\t\t\t\t\tdtype=torch.long ).reshape(1 ,\t\t\t\t\t\t1 )\r\r for step in range(_UpperCAmelCase ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.cat([actions, torch.zeros(1 ,\t\t\t\t\t\t1 ,\t\t\t\t\t\tconfig.act_dim ,\t\t\t\t\t\tdevice=_UpperCAmelCase )] ,\t\t\t\t\t\tdim=1 )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.cat([rewards, torch.zeros(1 ,\t\t\t\t\t\t1 ,\t\t\t\t\t\tdevice=_UpperCAmelCase )] ,\t\t\t\t\t\tdim=1 )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.ones(1 ,\t\t\t\t\t\tstates.shape[1] ).to(dtype=torch.long ,\t\t\t\t\t\tdevice=states.device )\r\r with torch.no_grad():\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(\r states=_UpperCAmelCase ,\t\t\t\t\t\tactions=_UpperCAmelCase ,\t\t\t\t\t\trewards=_UpperCAmelCase ,\t\t\t\t\t\treturns_to_go=_UpperCAmelCase ,\t\t\t\t\t\ttimesteps=_UpperCAmelCase ,\t\t\t\t\t\tattention_mask=_UpperCAmelCase ,\t\t\t\t\t\treturn_dict=_UpperCAmelCase ,\t\t\t\t\t\t)\r\r self.assertEqual(action_pred.shape ,\t\t\t\t\t\tactions.shape )\r self.assertTrue(torch.allclose(action_pred[0, -1] ,\t\t\t\t\t\texpected_outputs[step] ,\t\t\t\t\t\tatol=1E-4 ) )\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t( # env.step(action)\r torch.randn(1 ,\t\t\t\t\t\t1 ,\t\t\t\t\t\tconfig.state_dim ).to(device=_UpperCAmelCase ,\t\t\t\t\t\tdtype=torch.floataa ),\r 1.0,\r False,\r {},\r )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\taction_pred[0, -1]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.cat([states, state] ,\t\t\t\t\t\tdim=1 )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\treturns_to_go[0, -1] - reward\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.cat([returns_to_go, pred_return.reshape(1 ,\t\t\t\t\t\t1 ,\t\t\t\t\t\t1 )] ,\t\t\t\t\t\tdim=1 )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.cat(\r [timesteps, torch.ones((1, 1) ,\t\t\t\t\t\tdevice=_UpperCAmelCase ,\t\t\t\t\t\tdtype=torch.long ) * (step + 1)] ,\t\t\t\t\t\tdim=1 )\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport warnings\r\rfrom ...utils import logging\rfrom .image_processing_deit import DeiTImageProcessor\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tlogging.get_logger(__name__)\r\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def __init__( self\t\t\t:\t\tList[str] ,\t\t\t\t\t\t*_UpperCAmelCase\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r warnings.warn(\r \"\"\"The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please\"\"\"\r \"\"\" use DeiTImageProcessor instead.\"\"\" ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t)\r super().__init__(*_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase )\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152328,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport os\rimport unittest\r\rfrom transformers import BertTokenizerFast\rfrom transformers.models.bert.tokenization_bert import (\r VOCAB_FILES_NAMES,\r BasicTokenizer,\r BertTokenizer,\r WordpieceTokenizer,\r _is_control,\r _is_punctuation,\r _is_whitespace,\r)\rfrom transformers.testing_utils import require_tokenizers, slow\r\rfrom ...test_tokenization_common import TokenizerTesterMixin, filter_non_english\r\r@require_tokenizers\rclass lowerCAmelCase_ ( lowerCamelCase_\t, unittest.TestCase ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r lowerCAmelCase_ : List[str] = BertTokenizer\r lowerCAmelCase_ : Tuple = BertTokenizerFast\r lowerCAmelCase_ : Tuple = True\r lowerCAmelCase_ : Optional[int] = True\r lowerCAmelCase_ : str = filter_non_english\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r super().setUp()\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[\r \"\"\"[UNK]\"\"\",\r \"\"\"[CLS]\"\"\",\r \"\"\"[SEP]\"\"\",\r \"\"\"[PAD]\"\"\",\r \"\"\"[MASK]\"\"\",\r \"\"\"want\"\"\",\r \"\"\"##want\"\"\",\r \"\"\"##ed\"\"\",\r \"\"\"wa\"\"\",\r \"\"\"un\"\"\",\r \"\"\"runn\"\"\",\r \"\"\"##ing\"\"\",\r \"\"\",\"\"\",\r \"\"\"low\"\"\",\r \"\"\"lowest\"\"\",\r ]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tos.path.join(self.tmpdirname ,\t\t\t\t\t\tVOCAB_FILES_NAMES[\"\"\"vocab_file\"\"\"] )\r with open(self.vocab_file ,\t\t\t\t\t\t\"\"\"w\"\"\" ,\t\t\t\t\t\tencoding=\"\"\"utf-8\"\"\" ) as vocab_writer:\r vocab_writer.write(\"\"\"\"\"\".join([x + \"\"\"\\n\"\"\" for x in vocab_tokens] ) )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"UNwant\\u00E9d,running\"\"\"\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"unwanted, running\"\"\"\r return input_text, output_text\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.tokenizer_class(self.vocab_file )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenizer.tokenize(\"\"\"UNwant\\u00E9d,running\"\"\" )\r self.assertListEqual(_UpperCAmelCase ,\t\t\t\t\t\t[\"\"\"un\"\"\", \"\"\"##want\"\"\", \"\"\"##ed\"\"\", \"\"\",\"\"\", \"\"\"runn\"\"\", \"\"\"##ing\"\"\"] )\r self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase ) ,\t\t\t\t\t\t[9, 6, 7, 12, 10, 11] )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if not self.test_rust_tokenizer:\r return\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_tokenizer()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_rust_tokenizer()\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"UNwant\\u00E9d,running\"\"\"\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenizer.tokenize(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\trust_tokenizer.tokenize(_UpperCAmelCase )\r self.assertListEqual(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenizer.encode(_UpperCAmelCase ,\t\t\t\t\t\tadd_special_tokens=_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\trust_tokenizer.encode(_UpperCAmelCase ,\t\t\t\t\t\tadd_special_tokens=_UpperCAmelCase )\r self.assertListEqual(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_rust_tokenizer()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenizer.encode(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\trust_tokenizer.encode(_UpperCAmelCase )\r self.assertListEqual(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r\r # With lower casing\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_tokenizer(do_lower_case=_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_rust_tokenizer(do_lower_case=_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"UNwant\\u00E9d,running\"\"\"\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenizer.tokenize(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\trust_tokenizer.tokenize(_UpperCAmelCase )\r self.assertListEqual(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenizer.encode(_UpperCAmelCase ,\t\t\t\t\t\tadd_special_tokens=_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\trust_tokenizer.encode(_UpperCAmelCase ,\t\t\t\t\t\tadd_special_tokens=_UpperCAmelCase )\r self.assertListEqual(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_rust_tokenizer()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenizer.encode(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\trust_tokenizer.encode(_UpperCAmelCase )\r self.assertListEqual(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tTuple ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tBasicTokenizer()\r\r self.assertListEqual(tokenizer.tokenize(\"\"\"ah\\u535A\\u63A8zz\"\"\" ) ,\t\t\t\t\t\t[\"\"\"ah\"\"\", \"\"\"\\u535A\"\"\", \"\"\"\\u63A8\"\"\", \"\"\"zz\"\"\"] )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tBasicTokenizer(do_lower_case=_UpperCAmelCase )\r\r self.assertListEqual(\r tokenizer.tokenize(\"\"\" \\tHeLLo!how \\n Are yoU? \"\"\" ) ,\t\t\t\t\t\t[\"\"\"hello\"\"\", \"\"\"!\"\"\", \"\"\"how\"\"\", \"\"\"are\"\"\", \"\"\"you\"\"\", \"\"\"?\"\"\"] )\r self.assertListEqual(tokenizer.tokenize(\"\"\"H\\u00E9llo\"\"\" ) ,\t\t\t\t\t\t[\"\"\"hello\"\"\"] )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tBasicTokenizer(do_lower_case=_UpperCAmelCase ,\t\t\t\t\t\tstrip_accents=_UpperCAmelCase )\r\r self.assertListEqual(\r tokenizer.tokenize(\"\"\" \\tHäLLo!how \\n Are yoU? \"\"\" ) ,\t\t\t\t\t\t[\"\"\"hällo\"\"\", \"\"\"!\"\"\", \"\"\"how\"\"\", \"\"\"are\"\"\", \"\"\"you\"\"\", \"\"\"?\"\"\"] )\r self.assertListEqual(tokenizer.tokenize(\"\"\"H\\u00E9llo\"\"\" ) ,\t\t\t\t\t\t[\"\"\"h\\u00E9llo\"\"\"] )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tBasicTokenizer(do_lower_case=_UpperCAmelCase ,\t\t\t\t\t\tstrip_accents=_UpperCAmelCase )\r\r self.assertListEqual(\r tokenizer.tokenize(\"\"\" \\tHäLLo!how \\n Are yoU? \"\"\" ) ,\t\t\t\t\t\t[\"\"\"hallo\"\"\", \"\"\"!\"\"\", \"\"\"how\"\"\", \"\"\"are\"\"\", \"\"\"you\"\"\", \"\"\"?\"\"\"] )\r self.assertListEqual(tokenizer.tokenize(\"\"\"H\\u00E9llo\"\"\" ) ,\t\t\t\t\t\t[\"\"\"hello\"\"\"] )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tAny ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tBasicTokenizer(do_lower_case=_UpperCAmelCase )\r\r self.assertListEqual(\r tokenizer.tokenize(\"\"\" \\tHäLLo!how \\n Are yoU? \"\"\" ) ,\t\t\t\t\t\t[\"\"\"hallo\"\"\", \"\"\"!\"\"\", \"\"\"how\"\"\", \"\"\"are\"\"\", \"\"\"you\"\"\", \"\"\"?\"\"\"] )\r self.assertListEqual(tokenizer.tokenize(\"\"\"H\\u00E9llo\"\"\" ) ,\t\t\t\t\t\t[\"\"\"hello\"\"\"] )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tBasicTokenizer(do_lower_case=_UpperCAmelCase )\r\r self.assertListEqual(\r tokenizer.tokenize(\"\"\" \\tHeLLo!how \\n Are yoU? \"\"\" ) ,\t\t\t\t\t\t[\"\"\"HeLLo\"\"\", \"\"\"!\"\"\", \"\"\"how\"\"\", \"\"\"Are\"\"\", \"\"\"yoU\"\"\", \"\"\"?\"\"\"] )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tBasicTokenizer(do_lower_case=_UpperCAmelCase ,\t\t\t\t\t\tstrip_accents=_UpperCAmelCase )\r\r self.assertListEqual(\r tokenizer.tokenize(\"\"\" \\tHäLLo!how \\n Are yoU? \"\"\" ) ,\t\t\t\t\t\t[\"\"\"HäLLo\"\"\", \"\"\"!\"\"\", \"\"\"how\"\"\", \"\"\"Are\"\"\", \"\"\"yoU\"\"\", \"\"\"?\"\"\"] )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tBasicTokenizer(do_lower_case=_UpperCAmelCase ,\t\t\t\t\t\tstrip_accents=_UpperCAmelCase )\r\r self.assertListEqual(\r tokenizer.tokenize(\"\"\" \\tHäLLo!how \\n Are yoU? \"\"\" ) ,\t\t\t\t\t\t[\"\"\"HaLLo\"\"\", \"\"\"!\"\"\", \"\"\"how\"\"\", \"\"\"Are\"\"\", \"\"\"yoU\"\"\", \"\"\"?\"\"\"] )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tAny ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tBasicTokenizer(do_lower_case=_UpperCAmelCase ,\t\t\t\t\t\tnever_split=[\"\"\"[UNK]\"\"\"] )\r\r self.assertListEqual(\r tokenizer.tokenize(\"\"\" \\tHeLLo!how \\n Are yoU? [UNK]\"\"\" ) ,\t\t\t\t\t\t[\"\"\"HeLLo\"\"\", \"\"\"!\"\"\", \"\"\"how\"\"\", \"\"\"Are\"\"\", \"\"\"yoU\"\"\", \"\"\"?\"\"\", \"\"\"[UNK]\"\"\"] )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tTuple ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tBasicTokenizer()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"a\\n'll !!to?'d of, can't.\"\"\"\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[\"\"\"a\"\"\", \"\"\"'\"\"\", \"\"\"ll\"\"\", \"\"\"!\"\"\", \"\"\"!\"\"\", \"\"\"to\"\"\", \"\"\"?\"\"\", \"\"\"'\"\"\", \"\"\"d\"\"\", \"\"\"of\"\"\", \"\"\",\"\"\", \"\"\"can\"\"\", \"\"\"'\"\"\", \"\"\"t\"\"\", \"\"\".\"\"\"]\r self.assertListEqual(tokenizer.tokenize(_UpperCAmelCase ) ,\t\t\t\t\t\t_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[\"\"\"[UNK]\"\"\", \"\"\"[CLS]\"\"\", \"\"\"[SEP]\"\"\", \"\"\"want\"\"\", \"\"\"##want\"\"\", \"\"\"##ed\"\"\", \"\"\"wa\"\"\", \"\"\"un\"\"\", \"\"\"runn\"\"\", \"\"\"##ing\"\"\"]\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{}\r for i, token in enumerate(_UpperCAmelCase ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ti\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tWordpieceTokenizer(vocab=_UpperCAmelCase ,\t\t\t\t\t\tunk_token=\"\"\"[UNK]\"\"\" )\r\r self.assertListEqual(tokenizer.tokenize(\"\"\"\"\"\" ) ,\t\t\t\t\t\t[] )\r\r self.assertListEqual(tokenizer.tokenize(\"\"\"unwanted running\"\"\" ) ,\t\t\t\t\t\t[\"\"\"un\"\"\", \"\"\"##want\"\"\", \"\"\"##ed\"\"\", \"\"\"runn\"\"\", \"\"\"##ing\"\"\"] )\r\r self.assertListEqual(tokenizer.tokenize(\"\"\"unwantedX running\"\"\" ) ,\t\t\t\t\t\t[\"\"\"[UNK]\"\"\", \"\"\"runn\"\"\", \"\"\"##ing\"\"\"] )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r self.assertTrue(_is_whitespace(\"\"\" \"\"\" ) )\r self.assertTrue(_is_whitespace(\"\"\"\\t\"\"\" ) )\r self.assertTrue(_is_whitespace(\"\"\"\\r\"\"\" ) )\r self.assertTrue(_is_whitespace(\"\"\"\\n\"\"\" ) )\r self.assertTrue(_is_whitespace(\"\"\"\\u00A0\"\"\" ) )\r\r self.assertFalse(_is_whitespace(\"\"\"A\"\"\" ) )\r self.assertFalse(_is_whitespace(\"\"\"-\"\"\" ) )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r self.assertTrue(_is_control(\"\"\"\\u0005\"\"\" ) )\r\r self.assertFalse(_is_control(\"\"\"A\"\"\" ) )\r self.assertFalse(_is_control(\"\"\" \"\"\" ) )\r self.assertFalse(_is_control(\"\"\"\\t\"\"\" ) )\r self.assertFalse(_is_control(\"\"\"\\r\"\"\" ) )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r self.assertTrue(_is_punctuation(\"\"\"-\"\"\" ) )\r self.assertTrue(_is_punctuation(\"\"\"$\"\"\" ) )\r self.assertTrue(_is_punctuation(\"\"\"`\"\"\" ) )\r self.assertTrue(_is_punctuation(\"\"\".\"\"\" ) )\r\r self.assertFalse(_is_punctuation(\"\"\"A\"\"\" ) )\r self.assertFalse(_is_punctuation(\"\"\" \"\"\" ) )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_tokenizer()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_rust_tokenizer()\r\r # Example taken from the issue https://github.com/huggingface/tokenizers/issues/340\r self.assertListEqual([tokenizer.tokenize(_UpperCAmelCase ) for t in [\"\"\"Test\"\"\", \"\"\"\\xad\"\"\", \"\"\"test\"\"\"]] ,\t\t\t\t\t\t[[\"\"\"[UNK]\"\"\"], [], [\"\"\"[UNK]\"\"\"]] )\r\r self.assertListEqual(\r [rust_tokenizer.tokenize(_UpperCAmelCase ) for t in [\"\"\"Test\"\"\", \"\"\"\\xad\"\"\", \"\"\"test\"\"\"]] ,\t\t\t\t\t\t[[\"\"\"[UNK]\"\"\"], [], [\"\"\"[UNK]\"\"\"]] )\r\r\r @slow\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.tokenizer_class.from_pretrained(\"\"\"bert-base-uncased\"\"\" )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenizer.encode(\"\"\"sequence builders\"\"\" ,\t\t\t\t\t\tadd_special_tokens=_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenizer.encode(\"\"\"multi-sequence build\"\"\" ,\t\t\t\t\t\tadd_special_tokens=_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenizer.build_inputs_with_special_tokens(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenizer.build_inputs_with_special_tokens(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r\r assert encoded_sentence == [1_01] + text + [1_02]\r assert encoded_pair == [1_01] + text + [1_02] + text_a + [1_02]\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r for tokenizer, pretrained_name, kwargs in self.tokenizers_list:\r with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.rust_tokenizer_class.from_pretrained(_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tf'''A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'''\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenizer_r.encode_plus(\r _UpperCAmelCase ,\t\t\t\t\t\treturn_attention_mask=_UpperCAmelCase ,\t\t\t\t\t\treturn_token_type_ids=_UpperCAmelCase ,\t\t\t\t\t\treturn_offsets_mapping=_UpperCAmelCase ,\t\t\t\t\t\tadd_special_tokens=_UpperCAmelCase ,\t\t\t\t\t\t)\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenizer_r.do_lower_case if hasattr(_UpperCAmelCase ,\t\t\t\t\t\t\"\"\"do_lower_case\"\"\" ) else False\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t(\r [\r ((0, 0), tokenizer_r.cls_token),\r ((0, 1), \"\"\"A\"\"\"),\r ((1, 2), \"\"\",\"\"\"),\r ((3, 5), \"\"\"na\"\"\"),\r ((5, 6), \"\"\"##ï\"\"\"),\r ((6, 8), \"\"\"##ve\"\"\"),\r ((9, 15), tokenizer_r.mask_token),\r ((16, 21), \"\"\"Allen\"\"\"),\r ((21, 23), \"\"\"##NL\"\"\"),\r ((23, 24), \"\"\"##P\"\"\"),\r ((25, 33), \"\"\"sentence\"\"\"),\r ((33, 34), \"\"\".\"\"\"),\r ((0, 0), tokenizer_r.sep_token),\r ]\r if not do_lower_case\r else [\r ((0, 0), tokenizer_r.cls_token),\r ((0, 1), \"\"\"a\"\"\"),\r ((1, 2), \"\"\",\"\"\"),\r ((3, 8), \"\"\"naive\"\"\"),\r ((9, 15), tokenizer_r.mask_token),\r ((16, 21), \"\"\"allen\"\"\"),\r ((21, 23), \"\"\"##nl\"\"\"),\r ((23, 24), \"\"\"##p\"\"\"),\r ((25, 33), \"\"\"sentence\"\"\"),\r ((33, 34), \"\"\".\"\"\"),\r ((0, 0), tokenizer_r.sep_token),\r ]\r )\r\r self.assertEqual(\r [e[1] for e in expected_results] ,\t\t\t\t\t\ttokenizer_r.convert_ids_to_tokens(tokens[\"\"\"input_ids\"\"\"] ) )\r self.assertEqual([e[0] for e in expected_results] ,\t\t\t\t\t\ttokens[\"\"\"offset_mapping\"\"\"] )\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tTuple ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[\"\"\"的\"\"\", \"\"\"人\"\"\", \"\"\"有\"\"\"]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"\"\"\".join(_UpperCAmelCase )\r for tokenizer, pretrained_name, kwargs in self.tokenizers_list:\r with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTrue\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.tokenizer_class.from_pretrained(_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.rust_tokenizer_class.from_pretrained(_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenizer_p.encode(_UpperCAmelCase ,\t\t\t\t\t\tadd_special_tokens=_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenizer_r.encode(_UpperCAmelCase ,\t\t\t\t\t\tadd_special_tokens=_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenizer_r.convert_ids_to_tokens(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenizer_p.convert_ids_to_tokens(_UpperCAmelCase )\r\r # it is expected that each Chinese character is not preceded by \"##\"\r self.assertListEqual(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r self.assertListEqual(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.rust_tokenizer_class.from_pretrained(_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.tokenizer_class.from_pretrained(_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenizer_r.encode(_UpperCAmelCase ,\t\t\t\t\t\tadd_special_tokens=_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenizer_p.encode(_UpperCAmelCase ,\t\t\t\t\t\tadd_special_tokens=_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenizer_r.convert_ids_to_tokens(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenizer_p.convert_ids_to_tokens(_UpperCAmelCase )\r\r # it is expected that only the first Chinese character is not preceded by \"##\".\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[\r f'''##{token}''' if idx != 0 else token for idx, token in enumerate(_UpperCAmelCase )\r ]\r self.assertListEqual(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r self.assertListEqual(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport os\rimport unicodedata\rfrom shutil import copyfile\rfrom typing import Any, Dict, List, Optional, Tuple\r\rimport sentencepiece as spm\r\rfrom ...tokenization_utils import AddedToken, PreTrainedTokenizer\rfrom ...utils import SPIECE_UNDERLINE, logging\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tlogging.get_logger(__name__)\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t{'vocab_file': 'spiece.model'}\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t{\r 'vocab_file': {\r 'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model',\r }\r}\r\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def __init__( self\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny=False ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint=True ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=False ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict=\"~~\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint=\"~~\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict=\"\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple=\"\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any]=\"\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint=\"\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=\"\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str]=[\"\", \"\"] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Dict[str, Any]] = None ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tAddedToken(_UpperCAmelCase ,\t\t\t\t\t\tlstrip=_UpperCAmelCase ,\t\t\t\t\t\trstrip=_UpperCAmelCase ) if isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ) else mask_token\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{} if sp_model_kwargs is None else sp_model_kwargs\r\r super().__init__(\r do_lower_case=_UpperCAmelCase ,\t\t\t\t\t\tremove_space=_UpperCAmelCase ,\t\t\t\t\t\tkeep_accents=_UpperCAmelCase ,\t\t\t\t\t\tbos_token=_UpperCAmelCase ,\t\t\t\t\t\teos_token=_UpperCAmelCase ,\t\t\t\t\t\tunk_token=_UpperCAmelCase ,\t\t\t\t\t\tsep_token=_UpperCAmelCase ,\t\t\t\t\t\tpad_token=_UpperCAmelCase ,\t\t\t\t\t\tcls_token=_UpperCAmelCase ,\t\t\t\t\t\tmask_token=_UpperCAmelCase ,\t\t\t\t\t\tadditional_special_tokens=_UpperCAmelCase ,\t\t\t\t\t\tsp_model_kwargs=self.sp_model_kwargs ,\t\t\t\t\t\t**_UpperCAmelCase ,\t\t\t\t\t\t)\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t3\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdo_lower_case\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tremove_space\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tkeep_accents\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvocab_file\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tspm.SentencePieceProcessor(**self.sp_model_kwargs )\r self.sp_model.Load(_UpperCAmelCase )\r\r try:\r import jieba\r except ModuleNotFoundError as error:\r raise error.__class__(\r \"\"\"You need to install jieba to use CpmTokenizer or CpmTokenizerFast. \"\"\"\r \"\"\"See https://pypi.org/project/jieba/ for installation.\"\"\" )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tjieba\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tstr.maketrans(\"\"\" \\n\"\"\" ,\t\t\t\t\t\t\"\"\"\\u2582\\u2583\"\"\" )\r\r\r @property\r # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return len(self.sp_model )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{self.convert_ids_to_tokens(_UpperCAmelCase ): i for i in range(self.vocab_size )}\r vocab.update(self.added_tokens_encoder )\r return vocab\r\r\r def __getstate__( self\t\t\t:\t\tDict ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.__dict__.copy()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r return state\r\r\r def __setstate__( self\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\td\r\r # for backward compatibility\r if not hasattr(self ,\t\t\t\t\t\t\"\"\"sp_model_kwargs\"\"\" ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{}\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tspm.SentencePieceProcessor(**self.sp_model_kwargs )\r self.sp_model.Load(self.vocab_file )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if self.remove_space:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\" \"\"\".join(inputs.strip().split() )\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tinputs\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\toutputs.replace(\"\"\"``\"\"\" ,\t\t\t\t\t\t\"\"\"\\\"\"\"\" ).replace(\"\"\"''\"\"\" ,\t\t\t\t\t\t\"\"\"\\\"\"\"\" )\r\r if not self.keep_accents:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tunicodedata.normalize(\"\"\"NFKD\"\"\" ,\t\t\t\t\t\t_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"\"\"\".join([c for c in outputs if not unicodedata.combining(_UpperCAmelCase )] )\r if self.do_lower_case:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\toutputs.lower()\r\r return outputs\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tTuple ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.preprocess_text(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.sp_model.encode(_UpperCAmelCase ,\t\t\t\t\t\tout_type=_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[]\r for piece in pieces:\r if len(_UpperCAmelCase ) > 1 and piece[-1] == str(\"\"\",\"\"\" ) and piece[-2].isdigit():\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.sp_model.EncodeAsPieces(piece[:-1].replace(_UpperCAmelCase ,\t\t\t\t\t\t\"\"\"\"\"\" ) )\r if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:\r if len(cur_pieces[0] ) == 1:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tcur_pieces[1:]\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tcur_pieces[0][1:]\r cur_pieces.append(piece[-1] )\r new_pieces.extend(_UpperCAmelCase )\r else:\r new_pieces.append(_UpperCAmelCase )\r\r return new_pieces\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return self.sp_model.PieceToId(_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return self.sp_model.IdToPiece(_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"\"\"\".join(_UpperCAmelCase ).replace(_UpperCAmelCase ,\t\t\t\t\t\t\"\"\" \"\"\" ).strip()\r return out_string\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[List[int]] = None ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[self.sep_token_id]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[self.cls_token_id]\r if token_ids_a is None:\r return token_ids_a + sep + cls\r return token_ids_a + sep + token_ids_a + sep + cls\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[List[int]] = None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tbool = False ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if already_has_special_tokens:\r return super().get_special_tokens_mask(\r token_ids_a=_UpperCAmelCase ,\t\t\t\t\t\ttoken_ids_a=_UpperCAmelCase ,\t\t\t\t\t\talready_has_special_tokens=_UpperCAmelCase )\r\r if token_ids_a is not None:\r return ([0] * len(_UpperCAmelCase )) + [1] + ([0] * len(_UpperCAmelCase )) + [1, 1]\r return ([0] * len(_UpperCAmelCase )) + [1, 1]\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[List[int]] = None ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[self.sep_token_id]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[2]\r\r if token_ids_a is None:\r return len(token_ids_a + sep ) * [0] + cls_segment_id\r return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[str] = None ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if not os.path.isdir(_UpperCAmelCase ):\r logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )\r return\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tos.path.join(\r _UpperCAmelCase ,\t\t\t\t\t\t(filename_prefix + \"\"\"-\"\"\" if filename_prefix else \"\"\"\"\"\") + VOCAB_FILES_NAMES[\"\"\"vocab_file\"\"\"] )\r\r if os.path.abspath(self.vocab_file ) != os.path.abspath(_UpperCAmelCase ) and os.path.isfile(self.vocab_file ):\r copyfile(self.vocab_file ,\t\t\t\t\t\t_UpperCAmelCase )\r elif not os.path.isfile(self.vocab_file ):\r with open(_UpperCAmelCase ,\t\t\t\t\t\t\"\"\"wb\"\"\" ) as fi:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.sp_model.serialized_model_proto()\r fi.write(_UpperCAmelCase )\r\r return (out_vocab_file,)\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tTuple ,\t\t\t\t\t\t*_UpperCAmelCase\t\t\t:\t\tTuple ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tsuper()._decode(*_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttext.replace(\"\"\" \"\"\" ,\t\t\t\t\t\t\"\"\"\"\"\" ).replace(\"\"\"\\u2582\"\"\" ,\t\t\t\t\t\t\"\"\" \"\"\" ).replace(\"\"\"\\u2583\"\"\" ,\t\t\t\t\t\t\"\"\"\\n\"\"\" )\r return text\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152329,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r if a < 0:\r raise ValueError(\"\"\"Input value must be a positive integer\"\"\"\t\t\t\t)\r elif isinstance(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r raise TypeError(\"\"\"Input value must be a 'int' type\"\"\"\t\t\t\t)\r return bin(SCREAMING_SNAKE_CASE__\t\t\t\t).count(\"\"\"1\"\"\"\t\t\t\t)\r\r\rif __name__ == \"__main__\":\r import doctest\r\r doctest.testmod()\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport argparse\rimport logging\rimport os\r\rimport datasets\rimport tensorflow as tf\r\rfrom transformers import AutoTokenizer\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tlogging.getLogger(__name__)\r\rdef _UpperCamelCase\t\t\t\t\t( ):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\targparse.ArgumentParser(\r description=\"\"\"Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset.\"\"\"\t\t\t\t)\r parser.add_argument(\r \"\"\"--dataset_name\"\"\"\t\t\t\t, type=SCREAMING_SNAKE_CASE__\t\t\t\t, default=\"\"\"wikitext\"\"\"\t\t\t\t, help=\"\"\"Name of the training. Explore datasets at: hf.co/datasets.\"\"\"\t\t\t\t, )\r parser.add_argument(\r \"\"\"--dataset_config\"\"\"\t\t\t\t, type=SCREAMING_SNAKE_CASE__\t\t\t\t, default=\"\"\"wikitext-103-raw-v1\"\"\"\t\t\t\t, help=\"\"\"Configuration name of the dataset.\"\"\"\t\t\t\t)\r parser.add_argument(\r \"\"\"--tokenizer_name_or_path\"\"\"\t\t\t\t, type=SCREAMING_SNAKE_CASE__\t\t\t\t, default=\"\"\"sayakpaul/unigram-tokenizer-wikitext\"\"\"\t\t\t\t, help=\"\"\"Tokenizer identifier. Can be a local filepath or a Hub identifier.\"\"\"\t\t\t\t, )\r parser.add_argument(\r \"\"\"--shard_size\"\"\"\t\t\t\t, type=SCREAMING_SNAKE_CASE__\t\t\t\t, default=1000\t\t\t\t, help=\"\"\"Number of entries to go in a single shard.\"\"\"\t\t\t\t, )\r parser.add_argument(\"\"\"--split\"\"\"\t\t\t\t, type=SCREAMING_SNAKE_CASE__\t\t\t\t, default=\"\"\"train\"\"\"\t\t\t\t, choices=[\"\"\"train\"\"\", \"\"\"test\"\"\", \"\"\"validation\"\"\"]\t\t\t\t)\r parser.add_argument(\r \"\"\"--limit\"\"\"\t\t\t\t, default=SCREAMING_SNAKE_CASE__\t\t\t\t, type=SCREAMING_SNAKE_CASE__\t\t\t\t, help=\"\"\"Limit the number of shards (used for debugging).\"\"\"\t\t\t\t, )\r parser.add_argument(\r \"\"\"--max_length\"\"\"\t\t\t\t, type=SCREAMING_SNAKE_CASE__\t\t\t\t, default=512\t\t\t\t, help=\"\"\"Maximum sequence length. For training on TPUs, it helps to have a maximum\"\"\"\r \"\"\" sequence length that is a multiple of 8.\"\"\"\t\t\t\t, )\r parser.add_argument(\r \"\"\"--output_dir\"\"\"\t\t\t\t, default=\"\"\"tf-tpu\"\"\"\t\t\t\t, type=SCREAMING_SNAKE_CASE__\t\t\t\t, help=\"\"\"Output directory where the TFRecord shards will be saved. If the\"\"\"\r \"\"\" path is appended with `gs://` ('gs://tf-tpu', for example) then the TFRecord\"\"\"\r \"\"\" shards will be directly saved to a Google Cloud Storage bucket.\"\"\"\t\t\t\t, )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tparser.parse_args()\r return args\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tAny\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r def fn(SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tUnion[str, Any]\t\t\t\t):\r return tokenizer(examples[\"\"\"text\"\"\"]\t\t\t\t)\r\r return fn\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[int]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[]\r for i in range(len(tokenized_data[\"\"\"input_ids\"\"\"]\t\t\t\t)\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\r \"\"\"input_ids\"\"\": tf.train.Feature(intaa_list=tf.train.IntaaList(value=tokenized_data[\"\"\"input_ids\"\"\"][i]\t\t\t\t)\t\t\t\t),\r \"\"\"attention_mask\"\"\": tf.train.Feature(\r intaa_list=tf.train.IntaaList(value=tokenized_data[\"\"\"attention_mask\"\"\"][i]\t\t\t\t)\t\t\t\t),\r }\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttf.train.Features(feature=SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttf.train.Example(features=SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\texample.SerializeToString()\r records.append(SCREAMING_SNAKE_CASE__\t\t\t\t)\r return records\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tAny\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdatasets.load_dataset(args.dataset_name\t\t\t\t, args.dataset_config\t\t\t\t, split=args.split\t\t\t\t)\r\r if args.limit is not None:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmin(len(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t, args.limit\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdataset.select(range(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t)\r print(F'''Limiting the dataset to {args.limit} entries.'''\t\t\t\t)\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tAutoTokenizer.from_pretrained(args.tokenizer_name_or_path\t\t\t\t)\r\r # Handle output directory creation.\r # For serializing into a Google Cloud Storage Bucket, one needs to first\r # create a bucket.\r if \"gs\" not in args.output_dir:\r if not os.path.exists(args.output_dir\t\t\t\t):\r os.makedirs(args.output_dir\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tos.path.join(args.output_dir\t\t\t\t, args.split\t\t\t\t)\r if not os.path.exists(SCREAMING_SNAKE_CASE__\t\t\t\t):\r os.makedirs(SCREAMING_SNAKE_CASE__\t\t\t\t)\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tos.path.join(args.output_dir\t\t\t\t, args.split\t\t\t\t)\r\r # Tokenize the whole dataset at once.\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenize_function(SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdataset.map(SCREAMING_SNAKE_CASE__\t\t\t\t, batched=SCREAMING_SNAKE_CASE__\t\t\t\t, num_proc=4\t\t\t\t, remove_columns=[\"\"\"text\"\"\"]\t\t\t\t)\r\r # We need to concatenate all our texts together, and then split the result\r # into chunks of a fixed size, which we will call block_size. To do this, we\r # will use the map method again, with the option batched=True. When we use batched=True,\r # the function we pass to map() will be passed multiple inputs at once, allowing us\r # to group them into more or fewer examples than we had in the input.\r # This allows us to create our new fixed-length samples. The advantage of this\r # method is that we don't lose a whole lot of content from the dataset compared to the\r # case where we simply tokenize with a pre-defined max_length.\r\r def group_texts(SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t):\r # Concatenate all texts.\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{k: sum(examples[k]\t\t\t\t, []\t\t\t\t) for k in examples.keys()}\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlen(concatenated_examples[list(examples.keys()\t\t\t\t)[0]]\t\t\t\t)\r # We drop the small remainder, though you could add padding instead if the model supports it\r # In this, as in all things, we advise you to follow your heart 🫀\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t(total_length // args.max_length) * args.max_length\r # Split by chunks of max_len.\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\r k: [t[i : i + args.max_length] for i in range(0\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, args.max_length\t\t\t\t)]\r for k, t in concatenated_examples.items()\r }\r return result\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdataset_tokenized.map(SCREAMING_SNAKE_CASE__\t\t\t\t, batched=SCREAMING_SNAKE_CASE__\t\t\t\t, batch_size=1000\t\t\t\t, num_proc=4\t\t\t\t)\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0\r for shard in range(0\t\t\t\t, len(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t, args.shard_size\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tgrouped_dataset[shard : shard + args.shard_size]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlen(dataset_snapshot[\"\"\"input_ids\"\"\"]\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tos.path.join(SCREAMING_SNAKE_CASE__\t\t\t\t, F'''dataset-{shard_count}-{records_containing}.tfrecord'''\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tget_serialized_examples(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r with tf.io.TFRecordWriter(SCREAMING_SNAKE_CASE__\t\t\t\t) as out_file:\r for i in range(len(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tserialized_examples[i]\r out_file.write(SCREAMING_SNAKE_CASE__\t\t\t\t)\r print(\"\"\"Wrote file {} containing {} records\"\"\".format(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t)\r\r shard_count += 1\r total_records += records_containing\r\r with open(F'''split-{args.split}-records-count.txt'''\t\t\t\t, \"\"\"w\"\"\"\t\t\t\t) as f:\r print(F'''Total {args.split} records: {total_records}'''\t\t\t\t, file=SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r\rif __name__ == \"__main__\":\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tparse_args()\r main(args)\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152330,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport colorsys\r\rfrom PIL import Image # type: ignore\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tfloat\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tfloat\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tx\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ty\r for step in range(SCREAMING_SNAKE_CASE__\t\t\t\t): # noqa: B007\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ta * a - b * b + x\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t2 * a * b + y\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ta_new\r\r # divergence happens for all complex number with an absolute value\r # greater than 4\r if a * a + b * b > 4:\r break\r return step / (max_step - 1)\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tfloat\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r if distance == 1:\r return (0, 0, 0)\r else:\r return (255, 255, 255)\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tfloat\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r if distance == 1:\r return (0, 0, 0)\r else:\r return tuple(round(i * 255\t\t\t\t) for i in colorsys.hsv_to_rgb(SCREAMING_SNAKE_CASE__\t\t\t\t, 1\t\t\t\t, 1\t\t\t\t)\t\t\t\t)\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint = 800\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint = 600\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tfloat = -0.6\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tfloat = 0\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tfloat = 3.2\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint = 50\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tbool = True\t\t\t\t, ):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tImage.new(\"\"\"RGB\"\"\"\t\t\t\t, (image_width, image_height)\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\timg.load()\r\r # loop through the image-coordinates\r for image_x in range(SCREAMING_SNAKE_CASE__\t\t\t\t):\r for image_y in range(SCREAMING_SNAKE_CASE__\t\t\t\t):\r # determine the figure-coordinates based on the image-coordinates\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tfigure_width / image_width * image_height\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tfigure_center_x + (image_x / image_width - 0.5) * figure_width\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tfigure_center_y + (image_y / image_height - 0.5) * figure_height\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tget_distance(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r # color the corresponding pixel based on the selected coloring-function\r if use_distance_color_coding:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tget_color_coded_rgb(SCREAMING_SNAKE_CASE__\t\t\t\t)\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tget_black_and_white_rgb(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r return img\r\r\rif __name__ == \"__main__\":\r import doctest\r\r doctest.testmod()\r\r # colored version, full figure\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tget_image()\r\r # uncomment for colored version, different section, zoomed in\r # img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,\r # figure_width = 0.8)\r\r # uncomment for black and white version, full figure\r # img = get_image(use_distance_color_coding = False)\r\r # uncomment to save the image\r # img.save(\"mandelbrot.png\")\r\r img.show()\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport numpy as np\rimport torch\rfrom torch.nn import CrossEntropyLoss\rfrom transformers import AutoModelForCausalLM, AutoTokenizer\r\rimport datasets\rfrom datasets import logging\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'\\\\n\\n'\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'\\nPerplexity (PPL) is one of the most common metrics for evaluating language models.\\nIt is defined as the exponentiated average negative log-likelihood of a sequence.\\n\\nFor more information, see https://huggingface.co/docs/transformers/perplexity\\n'\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'\\nArgs:\\n model_id (str): model used for calculating Perplexity\\n NOTE: Perplexity can only be calculated for causal language models.\\n This includes models such as gpt2, causal variations of bert,\\n causal versions of t5, and more (the full list can be found\\n in the AutoModelForCausalLM documentation here:\\n https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )\\n\\n input_texts (list of str): input text, each separate text snippet\\n is one list entry.\\n batch_size (int): the batch size to run texts through the model. Defaults to 16.\\n add_start_token (bool): whether to add the start token to the texts,\\n so the perplexity can include the probability of the first word. Defaults to True.\\n device (str): device to run on, defaults to \\'cuda\\' when available\\nReturns:\\n perplexity: dictionary containing the perplexity scores for the texts\\n in the input list, as well as the mean perplexity. If one of the input texts is\\n longer than the max input length of the model, then it is truncated to the\\n max length for the perplexity computation.\\nExamples:\\n Example 1:\\n >>> perplexity = datasets.load_metric(\"perplexity\")\\n >>> input_texts = [\"lorem ipsum\", \"Happy Birthday!\", \"Bienvenue\"]\\n >>> results = perplexity.compute(model_id=\\'gpt2\\',\\n ... add_start_token=False,\\n ... input_texts=input_texts) # doctest:+ELLIPSIS\\n >>> print(list(results.keys()))\\n [\\'perplexities\\', \\'mean_perplexity\\']\\n >>> print(round(results[\"mean_perplexity\"], 2))\\n 78.22\\n >>> print(round(results[\"perplexities\"][0], 2))\\n 11.11\\n\\n Example 2:\\n >>> perplexity = datasets.load_metric(\"perplexity\")\\n >>> input_texts = datasets.load_dataset(\"wikitext\",\\n ... \"wikitext-2-raw-v1\",\\n ... split=\"test\")[\"text\"][:50] # doctest:+ELLIPSIS\\n [...]\\n >>> input_texts = [s for s in input_texts if s!=\\'\\']\\n >>> results = perplexity.compute(model_id=\\'gpt2\\',\\n ... input_texts=input_texts) # doctest:+ELLIPSIS\\n >>> print(list(results.keys()))\\n [\\'perplexities\\', \\'mean_perplexity\\']\\n >>> print(round(results[\"mean_perplexity\"], 2))\\n 60.35\\n >>> print(round(results[\"perplexities\"][0], 2))\\n 81.12\\n'\r\r@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION\t, _KWARGS_DESCRIPTION )\rclass lowerCAmelCase_ ( datasets.Metric ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return datasets.MetricInfo(\r description=_DESCRIPTION ,\t\t\t\t\t\tcitation=_CITATION ,\t\t\t\t\t\tinputs_description=_KWARGS_DESCRIPTION ,\t\t\t\t\t\tfeatures=datasets.Features(\r {\r \"\"\"input_texts\"\"\": datasets.Value(\"\"\"string\"\"\" ),\r } ) ,\t\t\t\t\t\treference_urls=[\"\"\"https://huggingface.co/docs/transformers/perplexity\"\"\"] ,\t\t\t\t\t\t)\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint = 16 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tbool = True ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int]=None ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if device is not None:\r assert device in [\"gpu\", \"cpu\", \"cuda\"], \"device should be either gpu or cpu.\"\r if device == \"gpu\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"cuda\"\"\"\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"cuda\"\"\" if torch.cuda.is_available() else \"\"\"cpu\"\"\"\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tAutoModelForCausalLM.from_pretrained(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel.to(_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tAutoTokenizer.from_pretrained(_UpperCAmelCase )\r\r # if batch_size > 1 (which generally leads to padding being required), and\r # if there is not an already assigned pad_token, assign an existing\r # special token to also be the padding token\r if tokenizer.pad_token is None and batch_size > 1:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlist(tokenizer.special_tokens_map_extended.values() )\r # check that the model already has at least one special token defined\r assert (\r len(_UpperCAmelCase ) > 0\r ), \"If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1.\"\r # assign one of the special tokens to also be the pad token\r tokenizer.add_special_tokens({\"\"\"pad_token\"\"\": existing_special_tokens[0]} )\r\r if add_start_token:\r # leave room for token to be added:\r assert (\r tokenizer.bos_token is not None\r ), \"Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False\"\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel.config.max_length - 1\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel.config.max_length\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenizer(\r _UpperCAmelCase ,\t\t\t\t\t\tadd_special_tokens=_UpperCAmelCase ,\t\t\t\t\t\tpadding=_UpperCAmelCase ,\t\t\t\t\t\ttruncation=_UpperCAmelCase ,\t\t\t\t\t\tmax_length=_UpperCAmelCase ,\t\t\t\t\t\treturn_tensors=\"\"\"pt\"\"\" ,\t\t\t\t\t\treturn_attention_mask=_UpperCAmelCase ,\t\t\t\t\t\t).to(_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tencodings[\"\"\"input_ids\"\"\"]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tencodings[\"\"\"attention_mask\"\"\"]\r\r # check that each input is long enough:\r if add_start_token:\r assert torch.all(torch.ge(attn_masks.sum(1 ) ,\t\t\t\t\t\t1 ) ), \"Each input text must be at least one token long.\"\r else:\r assert torch.all(\r torch.ge(attn_masks.sum(1 ) ,\t\t\t\t\t\t2 ) ), \"When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings.\"\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tCrossEntropyLoss(reduction=\"\"\"none\"\"\" )\r\r for start_index in logging.tqdm(range(0 ,\t\t\t\t\t\tlen(_UpperCAmelCase ) ,\t\t\t\t\t\t_UpperCAmelCase ) ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmin(start_index + batch_size ,\t\t\t\t\t\tlen(_UpperCAmelCase ) )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tencoded_texts[start_index:end_index]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tattn_masks[start_index:end_index]\r\r if add_start_token:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0 ) ).to(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.cat([bos_tokens_tensor, encoded_batch] ,\t\t\t\t\t\tdim=1 )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.cat(\r [torch.ones(bos_tokens_tensor.size() ,\t\t\t\t\t\tdtype=torch.intaa ).to(_UpperCAmelCase ), attn_mask] ,\t\t\t\t\t\tdim=1 )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tencoded_batch\r\r with torch.no_grad():\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase ,\t\t\t\t\t\tattention_mask=_UpperCAmelCase ).logits\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tout_logits[..., :-1, :].contiguous()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlabels[..., 1:].contiguous()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tattn_mask[..., 1:].contiguous()\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.expa(\r (loss_fct(shift_logits.transpose(1 ,\t\t\t\t\t\t2 ) ,\t\t\t\t\t\t_UpperCAmelCase ) * shift_attention_mask_batch).sum(1 )\r / shift_attention_mask_batch.sum(1 ) )\r\r ppls += perplexity_batch.tolist()\r\r return {\"perplexities\": ppls, \"mean_perplexity\": np.mean(_UpperCAmelCase )}\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152331,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\r# flake8: noqa\r# Lint as: python3\r\rfrom typing import Dict, List, Optional, Type\r\rfrom .. import config\rfrom ..utils import logging\rfrom .formatting import (\r ArrowFormatter,\r CustomFormatter,\r Formatter,\r PandasFormatter,\r PythonFormatter,\r TensorFormatter,\r format_table,\r query_table,\r)\rfrom .np_formatter import NumpyFormatter\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tlogging.get_logger(__name__)\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t{}\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t{}\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t{}\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\ttype\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[str]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[List[str]] = None\t\t\t\t, ):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\taliases if aliases is not None else []\r if format_type in _FORMAT_TYPES:\r logger.warning(\r F'''Overwriting format type \\'{format_type}\\' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})'''\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tformatter_cls\r for alias in set(aliases + [format_type]\t\t\t\t):\r if alias in _FORMAT_TYPES_ALIASES:\r logger.warning(\r F'''Overwriting format type alias \\'{alias}\\' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})'''\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tformat_type\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tException\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[str]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[List[str]] = None\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\taliases if aliases is not None else []\r for alias in set(aliases + [format_type]\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tunavailable_error\r\r\r# Here we define all the available formatting functions that can be used by `Dataset.set_format`\r_register_formatter(PythonFormatter, None, aliases=['python'])\r_register_formatter(ArrowFormatter, 'arrow', aliases=['pa', 'pyarrow'])\r_register_formatter(NumpyFormatter, 'numpy', aliases=['np'])\r_register_formatter(PandasFormatter, 'pandas', aliases=['pd'])\r_register_formatter(CustomFormatter, 'custom')\r\rif config.TORCH_AVAILABLE:\r from .torch_formatter import TorchFormatter\r\r _register_formatter(TorchFormatter, 'torch', aliases=['pt', 'pytorch'])\relse:\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tValueError('PyTorch needs to be installed to be able to return PyTorch tensors.')\r _register_unavailable_formatter(_torch_error, 'torch', aliases=['pt', 'pytorch'])\r\rif config.TF_AVAILABLE:\r from .tf_formatter import TFFormatter\r\r _register_formatter(TFFormatter, 'tensorflow', aliases=['tf'])\relse:\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tValueError('Tensorflow needs to be installed to be able to return Tensorflow tensors.')\r _register_unavailable_formatter(_tf_error, 'tensorflow', aliases=['tf'])\r\rif config.JAX_AVAILABLE:\r from .jax_formatter import JaxFormatter\r\r _register_formatter(JaxFormatter, 'jax', aliases=[])\relse:\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tValueError('JAX needs to be installed to be able to return JAX arrays.')\r _register_unavailable_formatter(_jax_error, 'jax', aliases=[])\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[str]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r if format_type in _FORMAT_TYPES_ALIASES:\r return _FORMAT_TYPES_ALIASES[format_type]\r else:\r return format_type\r\r\r\r\r\r\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[str]\t\t\t\t, **SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tUnion[str, Any]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tget_format_type_from_alias(SCREAMING_SNAKE_CASE__\t\t\t\t)\r if format_type in _FORMAT_TYPES:\r return _FORMAT_TYPES[format_type](**SCREAMING_SNAKE_CASE__\t\t\t\t)\r if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:\r raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]\r else:\r raise ValueError(\r F'''Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None\t\t\t\t)}, but got \\'{format_type}\\''''\t\t\t\t)\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint = 1000000\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[i - 1 for i in range(limit + 1\t\t\t\t)]\r\r for i in range(2\t\t\t\t, limit + 1\t\t\t\t):\r if phi[i] == i - 1:\r for j in range(2 * i\t\t\t\t, limit + 1\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r phi[j] -= phi[j] // i\r\r return sum(phi[2 : limit + 1]\t\t\t\t)\r\r\rif __name__ == \"__main__\":\r print(solution())\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152332,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tlist[list[int]]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r def update_area_of_max_square(SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t) -> int:\r # BASE CASE\r if row >= rows or col >= cols:\r return 0\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tupdate_area_of_max_square(SCREAMING_SNAKE_CASE__\t\t\t\t, col + 1\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tupdate_area_of_max_square(row + 1\t\t\t\t, col + 1\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tupdate_area_of_max_square(row + 1\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r if mat[row][col]:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t1 + min([right, diagonal, down]\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmax(largest_square_area[0]\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r return sub_problem_sol\r else:\r return 0\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[0]\r update_area_of_max_square(0\t\t\t\t, 0\t\t\t\t)\r return largest_square_area[0]\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tlist[list[int]]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r def update_area_of_max_square_using_dp_array(\r SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tlist[list[int]]\t\t\t\t) -> int:\r if row >= rows or col >= cols:\r return 0\r if dp_array[row][col] != -1:\r return dp_array[row][col]\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tupdate_area_of_max_square_using_dp_array(SCREAMING_SNAKE_CASE__\t\t\t\t, col + 1\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tupdate_area_of_max_square_using_dp_array(row + 1\t\t\t\t, col + 1\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tupdate_area_of_max_square_using_dp_array(row + 1\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r if mat[row][col]:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t1 + min([right, diagonal, down]\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmax(largest_square_area[0]\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tsub_problem_sol\r return sub_problem_sol\r else:\r return 0\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[0]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[[-1] * cols for _ in range(SCREAMING_SNAKE_CASE__\t\t\t\t)]\r update_area_of_max_square_using_dp_array(0\t\t\t\t, 0\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r return largest_square_area[0]\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tlist[list[int]]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[[0] * (cols + 1) for _ in range(rows + 1\t\t\t\t)]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0\r for row in range(rows - 1\t\t\t\t, -1\t\t\t\t, -1\t\t\t\t):\r for col in range(cols - 1\t\t\t\t, -1\t\t\t\t, -1\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdp_array[row][col + 1]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdp_array[row + 1][col + 1]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdp_array[row + 1][col]\r\r if mat[row][col] == 1:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t1 + min(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmax(dp_array[row][col]\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0\r\r return largest_square_area\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tlist[list[int]]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[0] * (cols + 1)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[0] * (cols + 1)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0\r for row in range(rows - 1\t\t\t\t, -1\t\t\t\t, -1\t\t\t\t):\r for col in range(cols - 1\t\t\t\t, -1\t\t\t\t, -1\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tcurrent_row[col + 1]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnext_row[col + 1]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnext_row[col]\r\r if mat[row][col] == 1:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t1 + min(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmax(current_row[col]\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tcurrent_row\r\r return largest_square_area\r\r\rif __name__ == \"__main__\":\r import doctest\r\r doctest.testmod()\r print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rfrom typing import List, Union\r\rfrom ..utils import (\r add_end_docstrings,\r is_tf_available,\r is_torch_available,\r is_vision_available,\r logging,\r requires_backends,\r)\rfrom .base import PIPELINE_INIT_ARGS, Pipeline\r\r\rif is_vision_available():\r from PIL import Image\r\r from ..image_utils import load_image\r\rif is_tf_available():\r import tensorflow as tf\r\r from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING\r from ..tf_utils import stable_softmax\r\rif is_torch_available():\r from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tlogging.get_logger(__name__)\r\r@add_end_docstrings(lowerCamelCase_ )\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def __init__( self\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t*_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tDict ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r super().__init__(*_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase )\r requires_backends(self ,\t\t\t\t\t\t\"\"\"vision\"\"\" )\r self.check_model_type(\r TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING\r if self.framework == \"\"\"tf\"\"\"\r else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any]=None ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{}\r if top_k is not None:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttop_k\r return {}, {}, postprocess_params\r\r\r def __call__( self\t\t\t:\t\tAny ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, List[str], \"Image.Image\", List[\"Image.Image\"]] ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return super().__call__(_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tload_image(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.image_processor(images=_UpperCAmelCase ,\t\t\t\t\t\treturn_tensors=self.framework )\r return model_inputs\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.model(**_UpperCAmelCase )\r return model_outputs\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr=5 ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if top_k > self.model.config.num_labels:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.model.config.num_labels\r\r if self.framework == \"pt\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel_outputs.logits.softmax(-1 )[0]\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tprobs.topk(_UpperCAmelCase )\r elif self.framework == \"tf\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tstable_softmax(model_outputs.logits ,\t\t\t\t\t\taxis=-1 )[0]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttf.math.top_k(_UpperCAmelCase ,\t\t\t\t\t\tk=_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttopk.values.numpy(), topk.indices.numpy()\r else:\r raise ValueError(f'''Unsupported framework: {self.framework}''' )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscores.tolist()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tids.tolist()\r return [{\"score\": score, \"label\": self.model.config.idalabel[_id]} for score, _id in zip(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )]\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152333,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rfrom dataclasses import dataclass\rfrom typing import List, Optional, Union\r\rimport numpy as np\rimport torch\r\rfrom ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available\r\r@dataclass\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r lowerCAmelCase_ : Union[List[np.ndarray], torch.FloatTensor]\r\r\rtry:\r if not (is_transformers_available() and is_torch_available()):\r raise OptionalDependencyNotAvailable()\rexcept OptionalDependencyNotAvailable:\r from ...utils.dummy_torch_and_transformers_objects import * # noqa F403\relse:\r from .pipeline_text_to_video_synth import TextToVideoSDPipeline\r from .pipeline_text_to_video_synth_imgaimg import VideoToVideoSDPipeline # noqa: F401\r from .pipeline_text_to_video_zero import TextToVideoZeroPipeline\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rfrom math import factorial\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint = 20\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,\r # 2, 3,...\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tn // 2\r\r return int(factorial(SCREAMING_SNAKE_CASE__\t\t\t\t) / (factorial(SCREAMING_SNAKE_CASE__\t\t\t\t) * factorial(n - k\t\t\t\t))\t\t\t\t)\r\r\rif __name__ == \"__main__\":\r import sys\r\r if len(sys.argv) == 1:\r print(solution(2_0))\r else:\r try:\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tint(sys.argv[1])\r print(solution(n))\r except ValueError:\r print('Invalid entry - please enter a number.')\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152334,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport unittest\r\rimport numpy as np\rimport torch\rfrom transformers import CLIPTextConfig, CLIPTextModel\r\rfrom diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel\rfrom diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device\r\r\renable_full_determinism()\r\rclass lowerCAmelCase_ ( unittest.TestCase ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r @property\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r torch.manual_seed(0 )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tUNetaDModel(\r block_out_channels=(32, 64) ,\t\t\t\t\t\tlayers_per_block=2 ,\t\t\t\t\t\tsample_size=32 ,\t\t\t\t\t\tin_channels=3 ,\t\t\t\t\t\tout_channels=3 ,\t\t\t\t\t\tdown_block_types=(\"\"\"DownBlock2D\"\"\", \"\"\"AttnDownBlock2D\"\"\") ,\t\t\t\t\t\tup_block_types=(\"\"\"AttnUpBlock2D\"\"\", \"\"\"UpBlock2D\"\"\") ,\t\t\t\t\t\t)\r return model\r\r\r @property\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r torch.manual_seed(0 )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tVQModel(\r block_out_channels=[32, 64] ,\t\t\t\t\t\tin_channels=3 ,\t\t\t\t\t\tout_channels=3 ,\t\t\t\t\t\tdown_block_types=[\"\"\"DownEncoderBlock2D\"\"\", \"\"\"DownEncoderBlock2D\"\"\"] ,\t\t\t\t\t\tup_block_types=[\"\"\"UpDecoderBlock2D\"\"\", \"\"\"UpDecoderBlock2D\"\"\"] ,\t\t\t\t\t\tlatent_channels=3 ,\t\t\t\t\t\t)\r return model\r\r\r @property\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tAny ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r torch.manual_seed(0 )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tCLIPTextConfig(\r bos_token_id=0 ,\t\t\t\t\t\teos_token_id=2 ,\t\t\t\t\t\thidden_size=32 ,\t\t\t\t\t\tintermediate_size=37 ,\t\t\t\t\t\tlayer_norm_eps=1E-05 ,\t\t\t\t\t\tnum_attention_heads=4 ,\t\t\t\t\t\tnum_hidden_layers=5 ,\t\t\t\t\t\tpad_token_id=1 ,\t\t\t\t\t\tvocab_size=10_00 ,\t\t\t\t\t\t)\r return CLIPTextModel(_UpperCAmelCase )\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tTuple ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.dummy_uncond_unet\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tDDIMScheduler()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.dummy_vq_model\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tLDMPipeline(unet=_UpperCAmelCase ,\t\t\t\t\t\tvqvae=_UpperCAmelCase ,\t\t\t\t\t\tscheduler=_UpperCAmelCase )\r ldm.to(_UpperCAmelCase )\r ldm.set_progress_bar_config(disable=_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.manual_seed(0 )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tldm(generator=_UpperCAmelCase ,\t\t\t\t\t\tnum_inference_steps=2 ,\t\t\t\t\t\toutput_type=\"\"\"numpy\"\"\" ).images\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.manual_seed(0 )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tldm(generator=_UpperCAmelCase ,\t\t\t\t\t\tnum_inference_steps=2 ,\t\t\t\t\t\toutput_type=\"\"\"numpy\"\"\" ,\t\t\t\t\t\treturn_dict=_UpperCAmelCase )[0]\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\timage[0, -3:, -3:, -1]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\timage_from_tuple[0, -3:, -3:, -1]\r\r assert image.shape == (1, 64, 64, 3)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnp.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172] )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t1E-2 if torch_device != \"\"\"mps\"\"\" else 3E-2\r\r assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance\r assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < tolerance\r\r@slow\r@require_torch\rclass lowerCAmelCase_ ( unittest.TestCase ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tLDMPipeline.from_pretrained(\"\"\"CompVis/ldm-celebahq-256\"\"\" )\r ldm.to(_UpperCAmelCase )\r ldm.set_progress_bar_config(disable=_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.manual_seed(0 )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tldm(generator=_UpperCAmelCase ,\t\t\t\t\t\tnum_inference_steps=5 ,\t\t\t\t\t\toutput_type=\"\"\"numpy\"\"\" ).images\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\timage[0, -3:, -3:, -1]\r\r assert image.shape == (1, 2_56, 2_56, 3)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnp.array([0.4399, 0.4_4975, 0.4_6825, 0.474, 0.4359, 0.4581, 0.4_5095, 0.4341, 0.4447] )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t1E-2 if torch_device != \"\"\"mps\"\"\" else 3E-2\r\r assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport json\rimport os\rimport unittest\r\rfrom transformers import MgpstrTokenizer\rfrom transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES\rfrom transformers.testing_utils import require_tokenizers\r\rfrom ...test_tokenization_common import TokenizerTesterMixin\r\r@require_tokenizers\rclass lowerCAmelCase_ ( lowerCamelCase_\t, unittest.TestCase ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r lowerCAmelCase_ : int = MgpstrTokenizer\r lowerCAmelCase_ : List[str] = False\r lowerCAmelCase_ : Optional[int] = {}\r lowerCAmelCase_ : Any = False\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r super().setUp()\r\r # fmt: off\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[\"\"\"[GO]\"\"\", \"\"\"[s]\"\"\", \"\"\"0\"\"\", \"\"\"1\"\"\", \"\"\"2\"\"\", \"\"\"3\"\"\", \"\"\"4\"\"\", \"\"\"5\"\"\", \"\"\"6\"\"\", \"\"\"7\"\"\", \"\"\"8\"\"\", \"\"\"9\"\"\", \"\"\"a\"\"\", \"\"\"b\"\"\", \"\"\"c\"\"\", \"\"\"d\"\"\", \"\"\"e\"\"\", \"\"\"f\"\"\", \"\"\"g\"\"\", \"\"\"h\"\"\", \"\"\"i\"\"\", \"\"\"j\"\"\", \"\"\"k\"\"\", \"\"\"l\"\"\", \"\"\"m\"\"\", \"\"\"n\"\"\", \"\"\"o\"\"\", \"\"\"p\"\"\", \"\"\"q\"\"\", \"\"\"r\"\"\", \"\"\"s\"\"\", \"\"\"t\"\"\", \"\"\"u\"\"\", \"\"\"v\"\"\", \"\"\"w\"\"\", \"\"\"x\"\"\", \"\"\"y\"\"\", \"\"\"z\"\"\"]\r # fmt: on\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdict(zip(_UpperCAmelCase ,\t\t\t\t\t\trange(len(_UpperCAmelCase ) ) ) )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tos.path.join(self.tmpdirname ,\t\t\t\t\t\tVOCAB_FILES_NAMES[\"\"\"vocab_file\"\"\"] )\r with open(self.vocab_file ,\t\t\t\t\t\t\"\"\"w\"\"\" ,\t\t\t\t\t\tencoding=\"\"\"utf-8\"\"\" ) as fp:\r fp.write(json.dumps(_UpperCAmelCase ) + \"\"\"\\n\"\"\" )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return MgpstrTokenizer.from_pretrained(self.tmpdirname ,\t\t\t\t\t\t**_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"tester\"\"\"\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"tester\"\"\"\r return input_text, output_text\r\r\r @unittest.skip(\"\"\"MGP-STR always lower cases letters.\"\"\" )\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r pass\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tAny ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_tokenizers(do_lower_case=_UpperCAmelCase )\r for tokenizer in tokenizers:\r with self.subTest(f'''{tokenizer.__class__.__name__}''' ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"[SPECIAL_TOKEN]\"\"\"\r\r tokenizer.add_special_tokens({\"\"\"cls_token\"\"\": special_token} )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenizer.encode([special_token] ,\t\t\t\t\t\tadd_special_tokens=_UpperCAmelCase )\r self.assertEqual(len(_UpperCAmelCase ) ,\t\t\t\t\t\t1 )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenizer.decode(_UpperCAmelCase ,\t\t\t\t\t\tskip_special_tokens=_UpperCAmelCase )\r self.assertTrue(special_token not in decoded )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_tokenizers()\r for tokenizer in tokenizers:\r with self.subTest(f'''{tokenizer.__class__.__name__}''' ):\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_input_output_texts(_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenizer.tokenize(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenizer.convert_tokens_to_ids(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenizer.encode(_UpperCAmelCase ,\t\t\t\t\t\tadd_special_tokens=_UpperCAmelCase )\r self.assertListEqual(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenizer.convert_ids_to_tokens(_UpperCAmelCase )\r self.assertNotEqual(len(_UpperCAmelCase ) ,\t\t\t\t\t\t0 )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenizer.decode(_UpperCAmelCase )\r self.assertIsInstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r\r self.assertEqual(text_a.replace(\"\"\" \"\"\" ,\t\t\t\t\t\t\"\"\"\"\"\" ) ,\t\t\t\t\t\t_UpperCAmelCase )\r\r\r @unittest.skip(\"\"\"MGP-STR tokenizer only handles one sequence.\"\"\" )\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tAny ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r pass\r\r\r\r @unittest.skip(\"\"\"inputs cannot be pretokenized in MgpstrTokenizer\"\"\" )\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r pass\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152335,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport asyncio\rimport os\rimport re\rimport sys\rimport tempfile\rimport unittest\rfrom contextlib import contextmanager\rfrom copy import deepcopy\rfrom distutils.util import strtobool\rfrom enum import Enum\rfrom importlib.util import find_spec\rfrom pathlib import Path\rfrom unittest.mock import patch\r\rimport pyarrow as pa\rimport pytest\rimport requests\rfrom packaging import version\r\rfrom datasets import config\r\r\rif config.PY_VERSION < version.parse('3.8'):\r import importlib_metadata\relse:\r import importlib.metadata as importlib_metadata\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint=False\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r try:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tos.environ[key]\r except KeyError:\r # KEY isn't set, default to `default`.\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdefault\r else:\r # KEY is set, convert it to True or False.\r try:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tstrtobool(SCREAMING_SNAKE_CASE__\t\t\t\t)\r except ValueError:\r # More values are supported, but let's keep the message simple.\r raise ValueError(F'''If set, {key} must be yes or no.'''\t\t\t\t)\r return _value\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tparse_flag_from_env('RUN_SLOW', default=False)\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tparse_flag_from_env('RUN_REMOTE', default=False)\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tparse_flag_from_env('RUN_LOCAL', default=True)\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tparse_flag_from_env('RUN_PACKAGED', default=True)\r\r# Compression\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tpytest.mark.skipif(not config.LZ4_AVAILABLE, reason='test requires lz4')\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tpytest.mark.skipif(not config.PY7ZR_AVAILABLE, reason='test requires py7zr')\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tpytest.mark.skipif(not config.ZSTANDARD_AVAILABLE, reason='test requires zstandard')\r\r# Audio\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tpytest.mark.skipif(\r # On Windows and OS X, soundfile installs sndfile\r find_spec('soundfile') is None or version.parse(importlib_metadata.version('soundfile')) < version.parse('0.12.0'),\r reason='test requires sndfile>=0.12.1: \\'pip install \\\"soundfile>=0.12.1\\\"\\'; ',\r)\r\r# Beam\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tpytest.mark.skipif(\r not config.BEAM_AVAILABLE or config.DILL_VERSION >= version.parse('0.3.2'),\r reason='test requires apache-beam and a compatible dill version',\r)\r\r# Dill-cloudpickle compatibility\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tpytest.mark.skipif(\r config.DILL_VERSION <= version.parse('0.3.2'),\r reason='test requires dill>0.3.2 for cloudpickle compatibility',\r)\r\r# Windows\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tpytest.mark.skipif(\r sys.platform == 'win32',\r reason='test should not be run on Windows',\r)\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[Any]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r try:\r import faiss # noqa\r except ImportError:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tunittest.skip(\"\"\"test requires faiss\"\"\"\t\t\t\t)(SCREAMING_SNAKE_CASE__\t\t\t\t)\r return test_case\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r try:\r import regex # noqa\r except ImportError:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tunittest.skip(\"\"\"test requires regex\"\"\"\t\t\t\t)(SCREAMING_SNAKE_CASE__\t\t\t\t)\r return test_case\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[Any]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r try:\r import elasticsearch # noqa\r except ImportError:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tunittest.skip(\"\"\"test requires elasticsearch\"\"\"\t\t\t\t)(SCREAMING_SNAKE_CASE__\t\t\t\t)\r return test_case\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r try:\r import sqlalchemy # noqa\r except ImportError:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tunittest.skip(\"\"\"test requires sqlalchemy\"\"\"\t\t\t\t)(SCREAMING_SNAKE_CASE__\t\t\t\t)\r return test_case\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[Any]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r if not config.TORCH_AVAILABLE:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tunittest.skip(\"\"\"test requires PyTorch\"\"\"\t\t\t\t)(SCREAMING_SNAKE_CASE__\t\t\t\t)\r return test_case\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tAny\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r if not config.TF_AVAILABLE:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tunittest.skip(\"\"\"test requires TensorFlow\"\"\"\t\t\t\t)(SCREAMING_SNAKE_CASE__\t\t\t\t)\r return test_case\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r if not config.JAX_AVAILABLE:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tunittest.skip(\"\"\"test requires JAX\"\"\"\t\t\t\t)(SCREAMING_SNAKE_CASE__\t\t\t\t)\r return test_case\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r if not config.PIL_AVAILABLE:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tunittest.skip(\"\"\"test requires Pillow\"\"\"\t\t\t\t)(SCREAMING_SNAKE_CASE__\t\t\t\t)\r return test_case\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r try:\r import transformers # noqa F401\r except ImportError:\r return unittest.skip(\"\"\"test requires transformers\"\"\"\t\t\t\t)(SCREAMING_SNAKE_CASE__\t\t\t\t)\r else:\r return test_case\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tTuple\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r try:\r import tiktoken # noqa F401\r except ImportError:\r return unittest.skip(\"\"\"test requires tiktoken\"\"\"\t\t\t\t)(SCREAMING_SNAKE_CASE__\t\t\t\t)\r else:\r return test_case\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tUnion[str, Any]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r try:\r import spacy # noqa F401\r except ImportError:\r return unittest.skip(\"\"\"test requires spacy\"\"\"\t\t\t\t)(SCREAMING_SNAKE_CASE__\t\t\t\t)\r else:\r return test_case\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[int]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r def _require_spacy_model(SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[Any]\t\t\t\t):\r try:\r import spacy # noqa F401\r\r spacy.load(SCREAMING_SNAKE_CASE__\t\t\t\t)\r except ImportError:\r return unittest.skip(\"\"\"test requires spacy\"\"\"\t\t\t\t)(SCREAMING_SNAKE_CASE__\t\t\t\t)\r except OSError:\r return unittest.skip(\"\"\"test requires spacy model '{}'\"\"\".format(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t)(SCREAMING_SNAKE_CASE__\t\t\t\t)\r else:\r return test_case\r\r return _require_spacy_model\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tTuple\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r try:\r import pyspark # noqa F401\r except ImportError:\r return unittest.skip(\"\"\"test requires pyspark\"\"\"\t\t\t\t)(SCREAMING_SNAKE_CASE__\t\t\t\t)\r else:\r return test_case\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r try:\r import joblibspark # noqa F401\r except ImportError:\r return unittest.skip(\"\"\"test requires joblibspark\"\"\"\t\t\t\t)(SCREAMING_SNAKE_CASE__\t\t\t\t)\r else:\r return test_case\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r if not _run_slow_tests or _run_slow_tests == 0:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tunittest.skip(\"\"\"test is slow\"\"\"\t\t\t\t)(SCREAMING_SNAKE_CASE__\t\t\t\t)\r return test_case\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r if not _run_local_tests or _run_local_tests == 0:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tunittest.skip(\"\"\"test is local\"\"\"\t\t\t\t)(SCREAMING_SNAKE_CASE__\t\t\t\t)\r return test_case\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r if not _run_packaged_tests or _run_packaged_tests == 0:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tunittest.skip(\"\"\"test is packaged\"\"\"\t\t\t\t)(SCREAMING_SNAKE_CASE__\t\t\t\t)\r return test_case\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r if not _run_remote_tests or _run_remote_tests == 0:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tunittest.skip(\"\"\"test requires remote\"\"\"\t\t\t\t)(SCREAMING_SNAKE_CASE__\t\t\t\t)\r return test_case\r\rdef _UpperCamelCase\t\t\t\t\t( *SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tTuple\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r def decorate(cls\t:\t\t\t\t\tOptional[int]\t\t\t\t):\r for name, fn in cls.__dict__.items():\r if callable(SCREAMING_SNAKE_CASE__\t\t\t\t) and name.startswith(\"\"\"test\"\"\"\t\t\t\t):\r for decorator in decorators:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdecorator(SCREAMING_SNAKE_CASE__\t\t\t\t)\r setattr(cls\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r return cls\r\r return decorate\r\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r pass\r\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r lowerCAmelCase_ : str = 0\r lowerCAmelCase_ : Dict = 1\r lowerCAmelCase_ : int = 2\r\r@contextmanager\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]=OfflineSimulationMode.CONNECTION_FAILS\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr=1e-16\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\trequests.Session().request\r\r def timeout_request(SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tAny\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, **SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tDict\t\t\t\t):\r # Change the url to an invalid url so that the connection hangs\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"https://10.255.255.1\"\"\"\r if kwargs.get(\"\"\"timeout\"\"\"\t\t\t\t) is None:\r raise RequestWouldHangIndefinitelyError(\r F'''Tried a call to {url} in offline mode with no timeout set. Please set a timeout.'''\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttimeout\r try:\r return online_request(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, **SCREAMING_SNAKE_CASE__\t\t\t\t)\r except Exception as e:\r # The following changes in the error are just here to make the offline timeout error prettier\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\turl\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\te.args[0]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t(max_retry_error.args[0].replace(\"\"\"10.255.255.1\"\"\"\t\t\t\t, F'''OfflineMock[{url}]'''\t\t\t\t),)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t(max_retry_error,)\r raise\r\r def raise_connection_error(SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tAny\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]\t\t\t\t, **SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[Any]\t\t\t\t):\r raise requests.ConnectionError(\"\"\"Offline mode is enabled.\"\"\"\t\t\t\t, request=SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r if mode is OfflineSimulationMode.CONNECTION_FAILS:\r with patch(\"\"\"requests.Session.send\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r yield\r elif mode is OfflineSimulationMode.CONNECTION_TIMES_OUT:\r # inspired from https://stackoverflow.com/a/904609\r with patch(\"\"\"requests.Session.request\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r yield\r elif mode is OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1:\r with patch(\"\"\"datasets.config.HF_DATASETS_OFFLINE\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r yield\r else:\r raise ValueError(\"\"\"Please use a value from the OfflineSimulationMode enum.\"\"\"\t\t\t\t)\r\r@contextmanager\rdef _UpperCamelCase\t\t\t\t\t( *SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tUnion[str, Any]\t\t\t\t, **SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tDict\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tstr(Path().resolve()\t\t\t\t)\r with tempfile.TemporaryDirectory(*SCREAMING_SNAKE_CASE__\t\t\t\t, **SCREAMING_SNAKE_CASE__\t\t\t\t) as tmp_dir:\r try:\r os.chdir(SCREAMING_SNAKE_CASE__\t\t\t\t)\r yield\r finally:\r os.chdir(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r@contextmanager\rdef _UpperCamelCase\t\t\t\t\t( ):\r '''simple docstring'''\r\r\r\r\r\r import gc\r\r gc.collect()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tpa.total_allocated_bytes()\r yield\r assert pa.total_allocated_bytes() - previous_allocated_memory > 0, \"Arrow memory didn't increase.\"\r\r@contextmanager\rdef _UpperCamelCase\t\t\t\t\t( ):\r '''simple docstring'''\r\r\r\r\r\r import gc\r\r gc.collect()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tpa.total_allocated_bytes()\r yield\r assert pa.total_allocated_bytes() - previous_allocated_memory <= 0, \"Arrow memory wasn't expected to increase.\"\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[Any]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r return deepcopy(SCREAMING_SNAKE_CASE__\t\t\t\t).integers(0\t\t\t\t, 100\t\t\t\t, 10\t\t\t\t).tolist() == deepcopy(SCREAMING_SNAKE_CASE__\t\t\t\t).integers(0\t\t\t\t, 100\t\t\t\t, 10\t\t\t\t).tolist()\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[Any]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r import decorator\r from requests.exceptions import HTTPError\r\r def _wrapper(SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tUnion[str, Any]\t\t\t\t, *SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tTuple\t\t\t\t, **SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tAny\t\t\t\t):\r try:\r return func(*SCREAMING_SNAKE_CASE__\t\t\t\t, **SCREAMING_SNAKE_CASE__\t\t\t\t)\r except HTTPError as err:\r if str(SCREAMING_SNAKE_CASE__\t\t\t\t).startswith(\"\"\"500\"\"\"\t\t\t\t) or str(SCREAMING_SNAKE_CASE__\t\t\t\t).startswith(\"\"\"502\"\"\"\t\t\t\t):\r pytest.xfail(str(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t)\r raise err\r\r return decorator.decorator(_wrapper\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r\rclass lowerCAmelCase_ :\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def __init__( self\t\t\t:\t\tList[str] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\treturncode\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tstdout\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tstderr\r\rasync def _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[Any]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r while True:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tawait stream.readline()\r if line:\r callback(SCREAMING_SNAKE_CASE__\t\t\t\t)\r else:\r break\r\rasync def _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint=None\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tTuple=None\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[int]=None\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[Any]=False\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tDict=False\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r if echo:\r print(\"\"\"\\nRunning: \"\"\"\t\t\t\t, \"\"\" \"\"\".join(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t)\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tawait asyncio.create_subprocess_exec(\r cmd[0]\t\t\t\t, *cmd[1:]\t\t\t\t, stdin=SCREAMING_SNAKE_CASE__\t\t\t\t, stdout=asyncio.subprocess.PIPE\t\t\t\t, stderr=asyncio.subprocess.PIPE\t\t\t\t, env=SCREAMING_SNAKE_CASE__\t\t\t\t, )\r\r # note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe\r # https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait\r #\r # If it starts hanging, will need to switch to the following code. The problem is that no data\r # will be seen until it's done and if it hangs for example there will be no debug info.\r # out, err = await p.communicate()\r # return _RunOutput(p.returncode, out, err)\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[]\r\r def tee(SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tDict\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tTuple\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tDict\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tTuple=\"\"\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tline.decode(\"\"\"utf-8\"\"\"\t\t\t\t).rstrip()\r sink.append(SCREAMING_SNAKE_CASE__\t\t\t\t)\r if not quiet:\r print(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, file=SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r # XXX: the timeout doesn't seem to make any difference here\r await asyncio.wait(\r [\r _read_stream(p.stdout\t\t\t\t, lambda SCREAMING_SNAKE_CASE__\t\t\t\t: tee(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, sys.stdout\t\t\t\t, label=\"\"\"stdout:\"\"\"\t\t\t\t)\t\t\t\t),\r _read_stream(p.stderr\t\t\t\t, lambda SCREAMING_SNAKE_CASE__\t\t\t\t: tee(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, sys.stderr\t\t\t\t, label=\"\"\"stderr:\"\"\"\t\t\t\t)\t\t\t\t),\r ]\t\t\t\t, timeout=SCREAMING_SNAKE_CASE__\t\t\t\t, )\r return _RunOutput(await p.wait()\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tTuple\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[Any]=None\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[Any]=None\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[int]=180\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tTuple=False\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tTuple=True\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tasyncio.get_event_loop()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tloop.run_until_complete(\r _stream_subprocess(SCREAMING_SNAKE_CASE__\t\t\t\t, env=SCREAMING_SNAKE_CASE__\t\t\t\t, stdin=SCREAMING_SNAKE_CASE__\t\t\t\t, timeout=SCREAMING_SNAKE_CASE__\t\t\t\t, quiet=SCREAMING_SNAKE_CASE__\t\t\t\t, echo=SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t)\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\" \"\"\".join(SCREAMING_SNAKE_CASE__\t\t\t\t)\r if result.returncode > 0:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"\\n\"\"\".join(result.stderr\t\t\t\t)\r raise RuntimeError(\r F'''\\'{cmd_str}\\' failed with returncode {result.returncode}\\n\\n'''\r F'''The combined stderr from workers follows:\\n{stderr}'''\t\t\t\t)\r\r # check that the subprocess actually did run and produced some output, should the test rely on\r # the remote side to do the testing\r if not result.stdout and not result.stderr:\r raise RuntimeError(F'''\\'{cmd_str}\\' produced no output.'''\t\t\t\t)\r\r return result\r\rdef _UpperCamelCase\t\t\t\t\t( ):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tos.environ.get(\"\"\"PYTEST_XDIST_WORKER\"\"\"\t\t\t\t, \"\"\"gw0\"\"\"\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tre.sub(r\"\"\"^gw\"\"\"\t\t\t\t, \"\"\"\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, 0\t\t\t\t, re.M\t\t\t\t)\r return int(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r\r\r\r\r\r\rdef _UpperCamelCase\t\t\t\t\t( ):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t29500\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tpytest_xdist_worker_id()\r return port + uniq_delta\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rfrom abc import ABC, abstractmethod\rfrom typing import List, Optional\r\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def __init__( self\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r self.test()\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r while not completed:\r if counter == 1:\r self.reset()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.advance()\r if not self.does_advance(_UpperCAmelCase ):\r raise Exception(\r \"\"\"Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true.\"\"\" )\r\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.update(_UpperCAmelCase )\r counter += 1\r\r if counter > 1_00_00:\r raise Exception(\"\"\"update() does not fulfill the constraint.\"\"\" )\r\r if self.remaining() != 0:\r raise Exception(\"\"\"Custom Constraint is not defined correctly.\"\"\" )\r\r\r @abstractmethod\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r raise NotImplementedError(\r f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )\r\r\r @abstractmethod\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r raise NotImplementedError(\r f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )\r\r\r @abstractmethod\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tAny ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r raise NotImplementedError(\r f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )\r\r\r @abstractmethod\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r raise NotImplementedError(\r f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )\r\r\r @abstractmethod\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r raise NotImplementedError(\r f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )\r\r\r\r @abstractmethod\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any]=False ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r raise NotImplementedError(\r f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )\r\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def __init__( self\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r super(_UpperCAmelCase ,\t\t\t\t\t\tself ).__init__()\r\r if not isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ) or len(_UpperCAmelCase ) == 0:\r raise ValueError(f'''`token_ids` has to be a non-empty list, but is {token_ids}.''' )\r if any((not isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ) or token_id < 0) for token_id in token_ids ):\r raise ValueError(f'''Each list in `token_ids` has to be a list of positive integers, but is {token_ids}.''' )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttoken_ids\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlen(self.token_ids )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t-1 # the index of the currently fulfilled step\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if self.completed:\r return None\r return self.token_ids[self.fulfilled_idx + 1]\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if not isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ):\r raise ValueError(f'''`token_id` has to be an `int`, but is {token_id} of type {type(_UpperCAmelCase )}''' )\r\r if self.completed:\r return False\r\r return token_id == self.token_ids[self.fulfilled_idx + 1]\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if not isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ):\r raise ValueError(f'''`token_id` has to be an `int`, but is {token_id} of type {type(_UpperCAmelCase )}''' )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r\r if self.does_advance(_UpperCAmelCase ):\r self.fulfilled_idx += 1\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTrue\r if self.fulfilled_idx == (self.seqlen - 1):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTrue\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tcompleted\r else:\r # failed to make progress.\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTrue\r self.reset()\r return stepped, completed, reset\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tTuple ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return self.seqlen - (self.fulfilled_idx + 1)\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tAny ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int]=False ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tPhrasalConstraint(self.token_ids )\r\r if stateful:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.seqlen\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.fulfilled_idx\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.completed\r\r return new_constraint\r\rclass lowerCAmelCase_ :\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def __init__( self\t\t\t:\t\tAny ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[List[int]] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str]=True ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmax([len(_UpperCAmelCase ) for one in nested_token_ids] )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{}\r for token_ids in nested_token_ids:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\troot\r for tidx, token_id in enumerate(_UpperCAmelCase ):\r if token_id not in level:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{}\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlevel[token_id]\r\r if no_subsets and self.has_subsets(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ):\r raise ValueError(\r \"\"\"Each list in `nested_token_ids` can't be a complete subset of another list, but is\"\"\"\r f''' {nested_token_ids}.''' )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\troot\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.trie\r\r for current_token in current_seq:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tstart[current_token]\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlist(start.keys() )\r\r return next_tokens\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.next_tokens(_UpperCAmelCase )\r\r return len(_UpperCAmelCase ) == 0\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlist(root.values() )\r if len(_UpperCAmelCase ) == 0:\r return 1\r else:\r return sum([self.count_leaves(_UpperCAmelCase ) for nn in next_nodes] )\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.count_leaves(_UpperCAmelCase )\r return len(_UpperCAmelCase ) != leaf_count\r\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def __init__( self\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[List[int]] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r super(_UpperCAmelCase ,\t\t\t\t\t\tself ).__init__()\r\r if not isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ) or len(_UpperCAmelCase ) == 0:\r raise ValueError(f'''`nested_token_ids` has to be a non-empty list, but is {nested_token_ids}.''' )\r if any(not isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ) for token_ids in nested_token_ids ):\r raise ValueError(f'''`nested_token_ids` has to be a list of lists, but is {nested_token_ids}.''' )\r if any(\r any((not isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ) or token_id < 0) for token_id in token_ids )\r for token_ids in nested_token_ids ):\r raise ValueError(\r f'''Each list in `nested_token_ids` has to be a list of positive integers, but is {nested_token_ids}.''' )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tDisjunctiveTrie(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnested_token_ids\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.trie.max_height\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tAny ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.trie.next_tokens(self.current_seq )\r\r if len(_UpperCAmelCase ) == 0:\r return None\r else:\r return token_list\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if not isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ):\r raise ValueError(f'''`token_id` is supposed to be type `int`, but is {token_id} of type {type(_UpperCAmelCase )}''' )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.trie.next_tokens(self.current_seq )\r\r return token_id in next_tokens\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if not isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ):\r raise ValueError(f'''`token_id` is supposed to be type `int`, but is {token_id} of type {type(_UpperCAmelCase )}''' )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r\r if self.does_advance(_UpperCAmelCase ):\r self.current_seq.append(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTrue\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTrue\r self.reset()\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.trie.reached_leaf(self.current_seq )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tcompleted\r\r return stepped, completed, reset\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[]\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if self.completed:\r # since this can be completed without reaching max height\r return 0\r else:\r return self.seqlen - len(self.current_seq )\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tTuple ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict=False ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tDisjunctiveConstraint(self.token_ids )\r\r if stateful:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.seqlen\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.current_seq\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.completed\r\r return new_constraint\r\rclass lowerCAmelCase_ :\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def __init__( self\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Constraint] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tconstraints\r\r # max # of steps required to fulfill a given constraint\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmax([c.seqlen for c in constraints] )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlen(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r\r self.init_state()\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tAny ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[constraint.copy(stateful=_UpperCAmelCase ) for constraint in self.constraints]\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0\r if self.inprogress_constraint:\r # extra points for having a constraint mid-fulfilled\r add += self.max_seqlen - self.inprogress_constraint.remaining()\r\r return (len(self.complete_constraints ) * self.max_seqlen) + add\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[]\r if self.inprogress_constraint is None:\r for constraint in self.pending_constraints: # \"pending\" == \"unfulfilled yet\"\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tconstraint.advance()\r if isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ):\r token_list.append(_UpperCAmelCase )\r elif isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ):\r token_list.extend(_UpperCAmelCase )\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.inprogress_constraint.advance()\r if isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ):\r token_list.append(_UpperCAmelCase )\r elif isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ):\r token_list.extend(_UpperCAmelCase )\r\r if len(_UpperCAmelCase ) == 0:\r return None\r else:\r return token_list\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[List[int]] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r self.init_state()\r\r if token_ids is not None:\r for token in token_ids:\r # completes or steps **one** constraint\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.add(_UpperCAmelCase )\r\r # the entire list of constraints are fulfilled\r if self.completed:\r break\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tAny ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if not isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ):\r raise ValueError(f'''`token_id` should be an `int`, but is `{token_id}`.''' )\r\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse, False\r\r if self.completed:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTrue\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r return complete, stepped\r\r if self.inprogress_constraint is not None:\r # In the middle of fulfilling a constraint. If the `token_id` *does* makes an incremental progress to current\r # job, simply update the state\r\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.inprogress_constraint.update(_UpperCAmelCase )\r if reset:\r # 1. If the next token breaks the progress, then we must restart.\r # e.g. constraint = \"I love pies\" and sequence so far is \"I love\" but `token_id` == \"books\".\r\r # But that doesn't mean we self.init_state(), since we only reset the state for this particular\r # constraint, not the full list of constraints.\r\r self.pending_constraints.append(self.inprogress_constraint.copy(stateful=_UpperCAmelCase ) )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r\r if complete:\r # 2. If the next token completes the constraint, move it to completed list, set\r # inprogress to None. If there are no pending constraints either, then this full list of constraints\r # is complete.\r\r self.complete_constraints.append(self.inprogress_constraint )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r\r if len(self.pending_constraints ) == 0:\r # we're done!\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTrue\r\r else:\r # Not in the middle of fulfilling a constraint. So does this `token_id` helps us step towards any of our list\r # of constraints?\r\r for cidx, pending_constraint in enumerate(self.pending_constraints ):\r if pending_constraint.does_advance(_UpperCAmelCase ):\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tpending_constraint.update(_UpperCAmelCase )\r\r if not stepped:\r raise Exception(\r \"\"\"`constraint.update(token_id)` is not yielding incremental progress, \"\"\"\r \"\"\"even though `constraint.does_advance(token_id)` is true.\"\"\" )\r\r if complete:\r self.complete_constraints.append(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r\r if not complete and stepped:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tpending_constraint\r\r if complete or stepped:\r # If we made any progress at all, then it's at least not a \"pending constraint\".\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t(\r self.pending_constraints[:cidx] + self.pending_constraints[cidx + 1 :]\r )\r\r if len(self.pending_constraints ) == 0 and self.inprogress_constraint is None:\r # If there's no longer any pending after this and no inprogress either, then we must be\r # complete.\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTrue\r\r break # prevent accidentally stepping through multiple constraints with just one token.\r\r return complete, stepped\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any]=True ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tConstraintListState(self.constraints ) # we actually never though self.constraints objects\r # throughout this process. So it's at initialization state.\r\r if stateful:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[\r constraint.copy(stateful=_UpperCAmelCase ) for constraint in self.complete_constraints\r ]\r if self.inprogress_constraint is not None:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.inprogress_constraint.copy(stateful=_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[constraint.copy() for constraint in self.pending_constraints]\r\r return new_state\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152336,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport os\rimport re\rimport shutil\rimport sys\rimport tempfile\rimport unittest\r\rimport black\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tos.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))\rsys.path.append(os.path.join(git_repo_path, 'utils'))\r\rimport check_copies # noqa: E402\r\r\r# This is the reference code that will be used in the tests.\r# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t' \\\"\"\"\\n Output class for the scheduler\\'s step function output.\\n\\n Args:\\n prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\\n Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\\n denoising loop.\\n pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\\n The predicted denoised sample (x_{0}) based on the model output from the current timestep.\\n `pred_original_sample` can be used to preview progress or for guidance.\\n \\\"\"\"\\n\\n prev_sample: torch.FloatTensor\\n pred_original_sample: Optional[torch.FloatTensor] = None\\n'\r\rclass lowerCAmelCase_ ( unittest.TestCase ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttempfile.mkdtemp()\r os.makedirs(os.path.join(self.diffusers_dir ,\t\t\t\t\t\t\"\"\"schedulers/\"\"\" ) )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.diffusers_dir\r shutil.copy(\r os.path.join(_UpperCAmelCase ,\t\t\t\t\t\t\"\"\"src/diffusers/schedulers/scheduling_ddpm.py\"\"\" ) ,\t\t\t\t\t\tos.path.join(self.diffusers_dir ,\t\t\t\t\t\t\"\"\"schedulers/scheduling_ddpm.py\"\"\" ) ,\t\t\t\t\t\t)\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tAny ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"src/diffusers\"\"\"\r shutil.rmtree(self.diffusers_dir )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr=None ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tcomment + f'''\\nclass {class_name}(nn.Module):\\n''' + class_code\r if overwrite_result is not None:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tcomment + f'''\\nclass {class_name}(nn.Module):\\n''' + overwrite_result\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tblack.Mode(target_versions={black.TargetVersion.PYaa} ,\t\t\t\t\t\tline_length=1_19 )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tblack.format_str(_UpperCAmelCase ,\t\t\t\t\t\tmode=_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tos.path.join(self.diffusers_dir ,\t\t\t\t\t\t\"\"\"new_code.py\"\"\" )\r with open(_UpperCAmelCase ,\t\t\t\t\t\t\"\"\"w\"\"\" ,\t\t\t\t\t\tnewline=\"\"\"\\n\"\"\" ) as f:\r f.write(_UpperCAmelCase )\r if overwrite_result is None:\r self.assertTrue(len(check_copies.is_copy_consistent(_UpperCAmelCase ) ) == 0 )\r else:\r check_copies.is_copy_consistent(f.name ,\t\t\t\t\t\toverwrite=_UpperCAmelCase )\r with open(_UpperCAmelCase ,\t\t\t\t\t\t\"\"\"r\"\"\" ) as f:\r self.assertTrue(f.read() ,\t\t\t\t\t\t_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tcheck_copies.find_code_in_diffusers(\"\"\"schedulers.scheduling_ddpm.DDPMSchedulerOutput\"\"\" )\r self.assertEqual(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r self.check_copy_consistency(\r \"\"\"# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput\"\"\" ,\t\t\t\t\t\t\"\"\"DDPMSchedulerOutput\"\"\" ,\t\t\t\t\t\tREFERENCE_CODE + \"\"\"\\n\"\"\" ,\t\t\t\t\t\t)\r\r # With no empty line at the end\r self.check_copy_consistency(\r \"\"\"# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput\"\"\" ,\t\t\t\t\t\t\"\"\"DDPMSchedulerOutput\"\"\" ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t)\r\r # Copy consistency with rename\r self.check_copy_consistency(\r \"\"\"# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test\"\"\" ,\t\t\t\t\t\t\"\"\"TestSchedulerOutput\"\"\" ,\t\t\t\t\t\tre.sub(\"\"\"DDPM\"\"\" ,\t\t\t\t\t\t\"\"\"Test\"\"\" ,\t\t\t\t\t\t_UpperCAmelCase ) ,\t\t\t\t\t\t)\r\r # Copy consistency with a really long name\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason\"\"\"\r self.check_copy_consistency(\r f'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}''' ,\t\t\t\t\t\tf'''{long_class_name}SchedulerOutput''' ,\t\t\t\t\t\tre.sub(\"\"\"Bert\"\"\" ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ) ,\t\t\t\t\t\t)\r\r # Copy consistency with overwrite\r self.check_copy_consistency(\r \"\"\"# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test\"\"\" ,\t\t\t\t\t\t\"\"\"TestSchedulerOutput\"\"\" ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\toverwrite_result=re.sub(\"\"\"DDPM\"\"\" ,\t\t\t\t\t\t\"\"\"Test\"\"\" ,\t\t\t\t\t\t_UpperCAmelCase ) ,\t\t\t\t\t\t)\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport doctest\rimport logging\rimport os\rimport unittest\rfrom pathlib import Path\rfrom typing import List, Union\r\rimport transformers\rfrom transformers.testing_utils import require_tf, require_torch, slow\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tlogging.getLogger()\r\r@unittest.skip(\"\"\"Temporarily disable the doc tests.\"\"\" )\r@require_torch\r@require_tf\r@slow\rclass lowerCAmelCase_ ( unittest.TestCase ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tPath ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, None] = None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[List[str], None] = None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, List[str], None] = None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tbool = True ,\t\t\t\t\t\t):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[file for file in os.listdir(_UpperCAmelCase ) if os.path.isfile(os.path.join(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ) )]\r\r if identifier is not None:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[file for file in files if identifier in file]\r\r if n_identifier is not None:\r if isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ):\r for n_ in n_identifier:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[file for file in files if n_ not in file]\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[file for file in files if n_identifier not in file]\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tignore_files or []\r ignore_files.append(\"\"\"__init__.py\"\"\" )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[file for file in files if file not in ignore_files]\r\r for file in files:\r # Open all files\r print(\"\"\"Testing\"\"\" ,\t\t\t\t\t\t_UpperCAmelCase )\r\r if only_modules:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tfile.split(\"\"\".\"\"\" )[0]\r try:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tgetattr(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdoctest.DocTestSuite(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tunittest.TextTestRunner().run(_UpperCAmelCase )\r self.assertIs(len(result.failures ) ,\t\t\t\t\t\t0 )\r except AttributeError:\r logger.info(f'''{module_identifier} is not a module.''' )\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdoctest.testfile(str(\"\"\"..\"\"\" / directory / file ) ,\t\t\t\t\t\toptionflags=doctest.ELLIPSIS )\r self.assertIs(result.failed ,\t\t\t\t\t\t0 )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tPath(\"\"\"src/transformers\"\"\" )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"modeling\"\"\"\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[\r \"\"\"modeling_ctrl.py\"\"\",\r \"\"\"modeling_tf_ctrl.py\"\"\",\r ]\r self.analyze_directory(_UpperCAmelCase ,\t\t\t\t\t\tidentifier=_UpperCAmelCase ,\t\t\t\t\t\tignore_files=_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tPath(\"\"\"src/transformers\"\"\" )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"tokenization\"\"\"\r self.analyze_directory(_UpperCAmelCase ,\t\t\t\t\t\tidentifier=_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tPath(\"\"\"src/transformers\"\"\" )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"configuration\"\"\"\r self.analyze_directory(_UpperCAmelCase ,\t\t\t\t\t\tidentifier=_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tPath(\"\"\"src/transformers\"\"\" )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[\"\"\"configuration\"\"\", \"\"\"modeling\"\"\", \"\"\"tokenization\"\"\"]\r self.analyze_directory(_UpperCAmelCase ,\t\t\t\t\t\tn_identifier=_UpperCAmelCase )\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tPath(\"\"\"docs/source\"\"\" )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[\"\"\"favicon.ico\"\"\"]\r self.analyze_directory(_UpperCAmelCase ,\t\t\t\t\t\tignore_files=_UpperCAmelCase ,\t\t\t\t\t\tonly_modules=_UpperCAmelCase )\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152337,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport numpy as np\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tnp.array\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r return 1 / (1 + np.exp(-vector\t\t\t\t))\r\r\rif __name__ == \"__main__\":\r import doctest\r\r doctest.testmod()\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rfrom datasets.utils.patching import _PatchedModuleObj, patch_submodule\r\rfrom . import _test_patching\r\rdef _UpperCamelCase\t\t\t\t\t( ):\r '''simple docstring'''\r\r\r\r\r\r import os as original_os\r from os import path as original_path\r from os import rename as original_rename\r from os.path import dirname as original_dirname\r from os.path import join as original_join\r\r assert _test_patching.os is original_os\r assert _test_patching.path is original_path\r assert _test_patching.join is original_join\r\r assert _test_patching.renamed_os is original_os\r assert _test_patching.renamed_path is original_path\r assert _test_patching.renamed_join is original_join\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"__test_patch_submodule_mock__\"\"\"\r with patch_submodule(_test_patching\t\t\t\t, \"\"\"os.path.join\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r # Every way to access os.path.join must be patched, and the rest must stay untouched\r\r # check os.path.join\r assert isinstance(_test_patching.os\t\t\t\t, _PatchedModuleObj\t\t\t\t)\r assert isinstance(_test_patching.os.path\t\t\t\t, _PatchedModuleObj\t\t\t\t)\r assert _test_patching.os.path.join is mock\r\r # check path.join\r assert isinstance(_test_patching.path\t\t\t\t, _PatchedModuleObj\t\t\t\t)\r assert _test_patching.path.join is mock\r\r # check join\r assert _test_patching.join is mock\r\r # check that the other attributes are untouched\r assert _test_patching.os.rename is original_rename\r assert _test_patching.path.dirname is original_dirname\r assert _test_patching.os.path.dirname is original_dirname\r\r # Even renamed modules or objects must be patched\r\r # check renamed_os.path.join\r assert isinstance(_test_patching.renamed_os\t\t\t\t, _PatchedModuleObj\t\t\t\t)\r assert isinstance(_test_patching.renamed_os.path\t\t\t\t, _PatchedModuleObj\t\t\t\t)\r assert _test_patching.renamed_os.path.join is mock\r\r # check renamed_path.join\r assert isinstance(_test_patching.renamed_path\t\t\t\t, _PatchedModuleObj\t\t\t\t)\r assert _test_patching.renamed_path.join is mock\r\r # check renamed_join\r assert _test_patching.renamed_join is mock\r\r # check that the other attributes are untouched\r assert _test_patching.renamed_os.rename is original_rename\r assert _test_patching.renamed_path.dirname is original_dirname\r assert _test_patching.renamed_os.path.dirname is original_dirname\r\r # check that everthing is back to normal when the patch is over\r\r assert _test_patching.os is original_os\r assert _test_patching.path is original_path\r assert _test_patching.join is original_join\r\r assert _test_patching.renamed_os is original_os\r assert _test_patching.renamed_path is original_path\r assert _test_patching.renamed_join is original_join\r\rdef _UpperCamelCase\t\t\t\t\t( ):\r '''simple docstring'''\r\r\r\r\r\r assert _test_patching.open is open\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"__test_patch_submodule_builtin_mock__\"\"\"\r # _test_patching has \"open\" in its globals\r assert _test_patching.open is open\r with patch_submodule(_test_patching\t\t\t\t, \"\"\"open\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r assert _test_patching.open is mock\r\r # check that everthing is back to normal when the patch is over\r\r assert _test_patching.open is open\r\rdef _UpperCamelCase\t\t\t\t\t( ):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"__test_patch_submodule_missing_mock__\"\"\"\r with patch_submodule(_test_patching\t\t\t\t, \"\"\"pandas.read_csv\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r pass\r\rdef _UpperCamelCase\t\t\t\t\t( ):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"__test_patch_submodule_missing_builtin_mock__\"\"\"\r # _test_patching doesn't have \"len\" in its globals\r assert getattr(_test_patching\t\t\t\t, \"\"\"len\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t) is None\r with patch_submodule(_test_patching\t\t\t\t, \"\"\"len\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r assert _test_patching.len is mock\r assert _test_patching.len is len\r\rdef _UpperCamelCase\t\t\t\t\t( ):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"__test_patch_submodule_start_and_stop_mock__\"\"\"\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tpatch_submodule(_test_patching\t\t\t\t, \"\"\"open\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r assert _test_patching.open is open\r patch.start()\r assert _test_patching.open is mock\r patch.stop()\r assert _test_patching.open is open\r\rdef _UpperCamelCase\t\t\t\t\t( ):\r '''simple docstring'''\r\r\r\r\r\r from os import rename as original_rename\r from os.path import dirname as original_dirname\r from os.path import join as original_join\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"__test_patch_submodule_successive_join__\"\"\"\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"__test_patch_submodule_successive_dirname__\"\"\"\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"__test_patch_submodule_successive_rename__\"\"\"\r assert _test_patching.os.path.join is original_join\r assert _test_patching.os.path.dirname is original_dirname\r assert _test_patching.os.rename is original_rename\r\r with patch_submodule(_test_patching\t\t\t\t, \"\"\"os.path.join\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r with patch_submodule(_test_patching\t\t\t\t, \"\"\"os.rename\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r with patch_submodule(_test_patching\t\t\t\t, \"\"\"os.path.dirname\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r assert _test_patching.os.path.join is mock_join\r assert _test_patching.os.path.dirname is mock_dirname\r assert _test_patching.os.rename is mock_rename\r\r # try another order\r with patch_submodule(_test_patching\t\t\t\t, \"\"\"os.rename\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r with patch_submodule(_test_patching\t\t\t\t, \"\"\"os.path.join\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r with patch_submodule(_test_patching\t\t\t\t, \"\"\"os.path.dirname\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r assert _test_patching.os.path.join is mock_join\r assert _test_patching.os.path.dirname is mock_dirname\r assert _test_patching.os.rename is mock_rename\r\r assert _test_patching.os.path.join is original_join\r assert _test_patching.os.path.dirname is original_dirname\r assert _test_patching.os.rename is original_rename\r\r\r\r\r\r\r\rdef _UpperCamelCase\t\t\t\t\t( ):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"__test_patch_submodule_doesnt_exist_mock__\"\"\"\r with patch_submodule(_test_patching\t\t\t\t, \"\"\"__module_that_doesn_exist__.__attribute_that_doesn_exist__\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r pass\r with patch_submodule(_test_patching\t\t\t\t, \"\"\"os.__attribute_that_doesn_exist__\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r pass\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152338,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\ttuple[float, float, float]\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\ttuple[float, float, float]\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tPointad\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tPointad\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tend_pointa[0] - end_pointa[0]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tend_pointa[1] - end_pointa[1]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tend_pointa[2] - end_pointa[2]\r return (x, y, z)\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tVectorad\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tVectorad\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tab[1] * ac[2] - ab[2] * ac[1] # *i\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t(ab[0] * ac[2] - ab[2] * ac[0]) * -1 # *j\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tab[0] * ac[1] - ab[1] * ac[0] # *k\r return (x, y, z)\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tVectorad\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r return tuple(round(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t) for x in vector\t\t\t\t) == (0, 0, 0)\r\r\r\r\r\r\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tPointad\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tPointad\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tPointad\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint = 10\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tcreate_vector(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tcreate_vector(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r return is_zero_vector(get_ad_vectors_cross(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rfrom timeit import timeit\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t{\r 'MALAYALAM': True,\r 'String': False,\r 'rotor': True,\r 'level': True,\r 'A': True,\r 'BB': True,\r 'ABC': False,\r 'amanaplanacanalpanama': True, # \"a man a plan a canal panama\"\r}\r# Ensure our test data is valid\rassert all((key == key[::-1]) is value for key, value in test_data.items())\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlen(SCREAMING_SNAKE_CASE__\t\t\t\t) - 1\r while start_i < end_i:\r if s[start_i] == s[end_i]:\r start_i += 1\r end_i -= 1\r else:\r return False\r return True\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlen(SCREAMING_SNAKE_CASE__\t\t\t\t) // 2\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlen(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r # We need to traverse till half of the length of string\r # as we can get access of the i'th last element from\r # i'th index.\r # eg: [0,1,2,3,4,5] => 4th index can be accessed\r # with the help of 1st index (i==n-i-1)\r # where n is length of string\r return all(s[i] == s[n - i - 1] for i in range(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t)\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r if len(SCREAMING_SNAKE_CASE__\t\t\t\t) <= 2:\r return True\r if s[0] == s[len(SCREAMING_SNAKE_CASE__\t\t\t\t) - 1]:\r return is_palindrome_recursive(s[1:-1]\t\t\t\t)\r else:\r return False\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r return s == s[::-1]\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tF'''all({name}(key) is value for key, value in test_data.items())'''\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tF'''from __main__ import test_data, {name}'''\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t500000\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttimeit(stmt=SCREAMING_SNAKE_CASE__\t\t\t\t, setup=SCREAMING_SNAKE_CASE__\t\t\t\t, number=SCREAMING_SNAKE_CASE__\t\t\t\t)\r print(F'''{name:<35} finished {number:,} runs in {result:.5f} seconds'''\t\t\t\t)\r\r\rif __name__ == \"__main__\":\r for key, value in test_data.items():\r assert is_palindrome(key) is is_palindrome_recursive(key)\r assert is_palindrome(key) is is_palindrome_slice(key)\r print(f\"{key:21} {value}\")\r print('a man a plan a canal panama')\r\r # finished 500,000 runs in 0.46793 seconds\r benchmark_function('is_palindrome_slice')\r # finished 500,000 runs in 0.85234 seconds\r benchmark_function('is_palindrome')\r # finished 500,000 runs in 1.32028 seconds\r benchmark_function('is_palindrome_recursive')\r # finished 500,000 runs in 2.08679 seconds\r benchmark_function('is_palindrome_traversal')\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152339,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport os\rfrom shutil import copyfile\rfrom typing import Any, Dict, List, Optional, Tuple\r\rimport sentencepiece as spm\r\rfrom ...tokenization_utils import PreTrainedTokenizer\rfrom ...utils import logging\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tlogging.get_logger(__name__)\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'▁'\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t{'vocab_file': 'sentencepiece.bpe.model'}\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t{\r 'vocab_file': {\r 'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model',\r }\r}\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t{\r 'facebook/xglm-564M': 2_0_4_8,\r}\r\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r lowerCAmelCase_ : Optional[int] = VOCAB_FILES_NAMES\r lowerCAmelCase_ : Optional[int] = PRETRAINED_VOCAB_FILES_MAP\r lowerCAmelCase_ : Optional[int] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES\r lowerCAmelCase_ : Dict = [\"\"\"input_ids\"\"\", \"\"\"attention_mask\"\"\"]\r\r\r def __init__( self\t\t\t:\t\tAny ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint=\"~~\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr=\"~~\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple=\"\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any]=\"\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=\"\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint=\"\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Dict[str, Any]] = None ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{} if sp_model_kwargs is None else sp_model_kwargs\r\r # Compatibility with the original tokenizer\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t7\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[f'''''' for i in range(self.num_madeup_words )]\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tkwargs.get(\"\"\"additional_special_tokens\"\"\" ,\t\t\t\t\t\t[] )\r kwargs[\"additional_special_tokens\"] += [\r word for word in madeup_words if word not in kwargs[\"additional_special_tokens\"]\r ]\r\r super().__init__(\r bos_token=_UpperCAmelCase ,\t\t\t\t\t\teos_token=_UpperCAmelCase ,\t\t\t\t\t\tunk_token=_UpperCAmelCase ,\t\t\t\t\t\tsep_token=_UpperCAmelCase ,\t\t\t\t\t\tcls_token=_UpperCAmelCase ,\t\t\t\t\t\tpad_token=_UpperCAmelCase ,\t\t\t\t\t\tsp_model_kwargs=self.sp_model_kwargs ,\t\t\t\t\t\t**_UpperCAmelCase ,\t\t\t\t\t\t)\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tspm.SentencePieceProcessor(**self.sp_model_kwargs )\r self.sp_model.Load(str(_UpperCAmelCase ) )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvocab_file\r\r # Original fairseq vocab and spm vocab must be \"aligned\":\r # Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9\r # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----\r # fairseq | '~~' | '' | '~~' | '' | ',' | '.' | '▁' | 's' | '▁de' | '-'\r # spm | '' | '~~' | '~~' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a'\r\r # The first \"real\" token \",\" has position 4 in the original fairseq vocab and position 3 in the spm vocab\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t1\r\r # Mimic fairseq token-to-id alignment for the first 4 token\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\"\"\"~~\"\"\": 0, \"\"\"\"\"\": 1, \"\"\"~~\"\"\": 2, \"\"\"\"\"\": 3}\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlen(self.sp_model )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{f'''''': sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words )}\r self.fairseq_tokens_to_ids.update(_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{v: k for k, v in self.fairseq_tokens_to_ids.items()}\r\r\r def __getstate__( self\t\t\t:\t\tAny ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.__dict__.copy()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.sp_model.serialized_model_proto()\r return state\r\r\r def __setstate__( self\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\td\r\r # for backward compatibility\r if not hasattr(self ,\t\t\t\t\t\t\"\"\"sp_model_kwargs\"\"\" ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{}\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tspm.SentencePieceProcessor(**self.sp_model_kwargs )\r self.sp_model.LoadFromSerializedProto(self.sp_model_proto )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tTuple ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[List[int]] = None ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if token_ids_a is None:\r return [self.sep_token_id] + token_ids_a\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[self.sep_token_id]\r return sep + token_ids_a + sep + sep + token_ids_a\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[List[int]] = None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tbool = False ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if already_has_special_tokens:\r return super().get_special_tokens_mask(\r token_ids_a=_UpperCAmelCase ,\t\t\t\t\t\ttoken_ids_a=_UpperCAmelCase ,\t\t\t\t\t\talready_has_special_tokens=_UpperCAmelCase )\r\r if token_ids_a is None:\r return [1] + ([0] * len(_UpperCAmelCase ))\r return [1] + ([0] * len(_UpperCAmelCase )) + [1, 1] + ([0] * len(_UpperCAmelCase ))\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[List[int]] = None ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[self.sep_token_id]\r\r if token_ids_a is None:\r return len(sep + token_ids_a ) * [0]\r return len(sep + token_ids_a + sep + sep + token_ids_a ) * [0]\r\r\r @property\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return len(self.sp_model ) + self.fairseq_offset + self.num_madeup_words\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{self.convert_ids_to_tokens(_UpperCAmelCase ): i for i in range(self.vocab_size )}\r vocab.update(self.added_tokens_encoder )\r return vocab\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return self.sp_model.encode(_UpperCAmelCase ,\t\t\t\t\t\tout_type=_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if token in self.fairseq_tokens_to_ids:\r return self.fairseq_tokens_to_ids[token]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.sp_model.PieceToId(_UpperCAmelCase )\r\r # Need to return unknown token if the SP model returned 0\r return spm_id + self.fairseq_offset if spm_id else self.unk_token_id\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if index in self.fairseq_ids_to_tokens:\r return self.fairseq_ids_to_tokens[index]\r return self.sp_model.IdToPiece(index - self.fairseq_offset )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"\"\"\".join(_UpperCAmelCase ).replace(_UpperCAmelCase ,\t\t\t\t\t\t\"\"\" \"\"\" ).strip()\r return out_string\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[str] = None ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if not os.path.isdir(_UpperCAmelCase ):\r logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )\r return\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tos.path.join(\r _UpperCAmelCase ,\t\t\t\t\t\t(filename_prefix + \"\"\"-\"\"\" if filename_prefix else \"\"\"\"\"\") + VOCAB_FILES_NAMES[\"\"\"vocab_file\"\"\"] )\r\r if os.path.abspath(self.vocab_file ) != os.path.abspath(_UpperCAmelCase ) and os.path.isfile(self.vocab_file ):\r copyfile(self.vocab_file ,\t\t\t\t\t\t_UpperCAmelCase )\r elif not os.path.isfile(self.vocab_file ):\r with open(_UpperCAmelCase ,\t\t\t\t\t\t\"\"\"wb\"\"\" ) as fi:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.sp_model.serialized_model_proto()\r fi.write(_UpperCAmelCase )\r\r return (out_vocab_file,)\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport datasets\r\rfrom .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'\\\\n@INPROCEEDINGS{Papineni02bleu:a,\\n author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\\n title = {BLEU: a Method for Automatic Evaluation of Machine Translation},\\n booktitle = {},\\n year = {2002},\\n pages = {311--318}\\n}\\n@inproceedings{lin-och-2004-orange,\\n title = \"{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation\",\\n author = \"Lin, Chin-Yew and\\n Och, Franz Josef\",\\n booktitle = \"{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics\",\\n month = \"aug 23{--}aug 27\",\\n year = \"2004\",\\n address = \"Geneva, Switzerland\",\\n publisher = \"COLING\",\\n url = \"https://www.aclweb.org/anthology/C04-1072\",\\n pages = \"501--507\",\\n}\\n'\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'\\\\nBLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.\\nQuality is considered to be the correspondence between a machine\\'s output and that of a human: \"the closer a machine translation is to a professional human translation,\\nthe better it is\" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and\\nremains one of the most popular automated and inexpensive metrics.\\n\\nScores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.\\nThose scores are then averaged over the whole corpus to reach an estimate of the translation\\'s overall quality. Intelligibility or grammatical correctness\\nare not taken into account[citation needed].\\n\\nBLEU\\'s output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1\\nrepresenting more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the\\nreference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional\\nreference translations will increase the BLEU score.\\n'\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'\\nComputes BLEU score of translated segments against one or more references.\\nArgs:\\n predictions: list of translations to score.\\n Each translation should be tokenized into a list of tokens.\\n references: list of lists of references for each translation.\\n Each reference should be tokenized into a list of tokens.\\n max_order: Maximum n-gram order to use when computing BLEU score.\\n smooth: Whether or not to apply Lin et al. 2004 smoothing.\\nReturns:\\n \\'bleu\\': bleu score,\\n \\'precisions\\': geometric mean of n-gram precisions,\\n \\'brevity_penalty\\': brevity penalty,\\n \\'length_ratio\\': ratio of lengths,\\n \\'translation_length\\': translation_length,\\n \\'reference_length\\': reference_length\\nExamples:\\n\\n >>> predictions = [\\n ... [\"hello\", \"there\", \"general\", \"kenobi\"], # tokenized prediction of the first sample\\n ... [\"foo\", \"bar\", \"foobar\"] # tokenized prediction of the second sample\\n ... ]\\n >>> references = [\\n ... [[\"hello\", \"there\", \"general\", \"kenobi\"], [\"hello\", \"there\", \"!\"]], # tokenized references for the first sample (2 references)\\n ... [[\"foo\", \"bar\", \"foobar\"]] # tokenized references for the second sample (1 reference)\\n ... ]\\n >>> bleu = datasets.load_metric(\"bleu\")\\n >>> results = bleu.compute(predictions=predictions, references=references)\\n >>> print(results[\"bleu\"])\\n 1.0\\n'\r\r@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION\t, _KWARGS_DESCRIPTION )\rclass lowerCAmelCase_ ( datasets.Metric ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return datasets.MetricInfo(\r description=_DESCRIPTION ,\t\t\t\t\t\tcitation=_CITATION ,\t\t\t\t\t\tinputs_description=_KWARGS_DESCRIPTION ,\t\t\t\t\t\tfeatures=datasets.Features(\r {\r \"\"\"predictions\"\"\": datasets.Sequence(datasets.Value(\"\"\"string\"\"\" ,\t\t\t\t\t\tid=\"\"\"token\"\"\" ) ,\t\t\t\t\t\tid=\"\"\"sequence\"\"\" ),\r \"\"\"references\"\"\": datasets.Sequence(\r datasets.Sequence(datasets.Value(\"\"\"string\"\"\" ,\t\t\t\t\t\tid=\"\"\"token\"\"\" ) ,\t\t\t\t\t\tid=\"\"\"sequence\"\"\" ) ,\t\t\t\t\t\tid=\"\"\"references\"\"\" ),\r } ) ,\t\t\t\t\t\tcodebase_urls=[\"\"\"https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py\"\"\"] ,\t\t\t\t\t\treference_urls=[\r \"\"\"https://en.wikipedia.org/wiki/BLEU\"\"\",\r \"\"\"https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213\"\"\",\r ] ,\t\t\t\t\t\t)\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any]=4 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=False ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tcompute_bleu(\r reference_corpus=_UpperCAmelCase ,\t\t\t\t\t\ttranslation_corpus=_UpperCAmelCase ,\t\t\t\t\t\tmax_order=_UpperCAmelCase ,\t\t\t\t\t\tsmooth=_UpperCAmelCase )\r ((UpperCAmelCase__)\t\t\t\t\t\t\t,\t(UpperCAmelCase__)\t\t\t\t\t\t\t,\t(UpperCAmelCase__)\t\t\t\t\t\t\t,\t(UpperCAmelCase__)\t\t\t\t\t\t\t,\t(UpperCAmelCase__)\t\t\t\t\t\t\t,\t(UpperCAmelCase__))\t\t\t\t\t\t\t\t=\t\t\tscore\r return {\r \"bleu\": bleu,\r \"precisions\": precisions,\r \"brevity_penalty\": bp,\r \"length_ratio\": ratio,\r \"translation_length\": translation_length,\r \"reference_length\": reference_length,\r }\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152340,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport torch\r\rfrom diffusers import DDPMScheduler\r\rfrom .test_schedulers import SchedulerCommonTest\r\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r lowerCAmelCase_ : Any = (DDPMScheduler,)\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tOptional[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\r \"\"\"num_train_timesteps\"\"\": 10_00,\r \"\"\"beta_start\"\"\": 0.0001,\r \"\"\"beta_end\"\"\": 0.02,\r \"\"\"beta_schedule\"\"\": \"\"\"linear\"\"\",\r \"\"\"variance_type\"\"\": \"\"\"fixed_small\"\"\",\r \"\"\"clip_sample\"\"\": True,\r }\r\r config.update(**_UpperCAmelCase )\r return config\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r for timesteps in [1, 5, 1_00, 10_00]:\r self.check_over_configs(num_train_timesteps=_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1] ,\t\t\t\t\t\t[0.002, 0.02, 0.2, 2] ):\r self.check_over_configs(beta_start=_UpperCAmelCase ,\t\t\t\t\t\tbeta_end=_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r for schedule in [\"linear\", \"squaredcos_cap_v2\"]:\r self.check_over_configs(beta_schedule=_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r for variance in [\"fixed_small\", \"fixed_large\", \"other\"]:\r self.check_over_configs(variance_type=_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r for clip_sample in [True, False]:\r self.check_over_configs(clip_sample=_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r self.check_over_configs(thresholding=_UpperCAmelCase )\r for threshold in [0.5, 1.0, 2.0]:\r for prediction_type in [\"epsilon\", \"sample\", \"v_prediction\"]:\r self.check_over_configs(\r thresholding=_UpperCAmelCase ,\t\t\t\t\t\tprediction_type=_UpperCAmelCase ,\t\t\t\t\t\tsample_max_value=_UpperCAmelCase ,\t\t\t\t\t\t)\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r for prediction_type in [\"epsilon\", \"sample\", \"v_prediction\"]:\r self.check_over_configs(prediction_type=_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r for t in [0, 5_00, 9_99]:\r self.check_over_forward(time_step=_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.scheduler_classes[0]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_scheduler_config()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler_class(**_UpperCAmelCase )\r\r assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1E-5\r assert torch.sum(torch.abs(scheduler._get_variance(4_87 ) - 0.0_0979 ) ) < 1E-5\r assert torch.sum(torch.abs(scheduler._get_variance(9_99 ) - 0.02 ) ) < 1E-5\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.scheduler_classes[0]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_scheduler_config()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler_class(**_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlen(_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.dummy_model()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.dummy_sample_deter\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.manual_seed(0 )\r\r for t in reversed(range(_UpperCAmelCase ) ):\r # 1. predict noise residual\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r\r # 2. predict previous mean of sample x_t-1\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler.step(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\tgenerator=_UpperCAmelCase ).prev_sample\r\r # if t > 0:\r # noise = self.dummy_sample_deter\r # variance = scheduler.get_variance(t) ** (0.5) * noise\r #\r # sample = pred_prev_sample + variance\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tpred_prev_sample\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.sum(torch.abs(_UpperCAmelCase ) )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.mean(torch.abs(_UpperCAmelCase ) )\r\r assert abs(result_sum.item() - 258.9606 ) < 1E-2\r assert abs(result_mean.item() - 0.3372 ) < 1E-3\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.scheduler_classes[0]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_scheduler_config(prediction_type=\"\"\"v_prediction\"\"\" )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler_class(**_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlen(_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.dummy_model()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.dummy_sample_deter\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.manual_seed(0 )\r\r for t in reversed(range(_UpperCAmelCase ) ):\r # 1. predict noise residual\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r\r # 2. predict previous mean of sample x_t-1\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler.step(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\tgenerator=_UpperCAmelCase ).prev_sample\r\r # if t > 0:\r # noise = self.dummy_sample_deter\r # variance = scheduler.get_variance(t) ** (0.5) * noise\r #\r # sample = pred_prev_sample + variance\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tpred_prev_sample\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.sum(torch.abs(_UpperCAmelCase ) )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.mean(torch.abs(_UpperCAmelCase ) )\r\r assert abs(result_sum.item() - 202.0296 ) < 1E-2\r assert abs(result_mean.item() - 0.2631 ) < 1E-3\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.scheduler_classes[0]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_scheduler_config()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler_class(**_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[1_00, 87, 50, 1, 0]\r\r scheduler.set_timesteps(timesteps=_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler.timesteps\r\r for i, timestep in enumerate(_UpperCAmelCase ):\r if i == len(_UpperCAmelCase ) - 1:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t-1\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttimesteps[i + 1]\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler.previous_timestep(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tprev_t.item()\r\r self.assertEqual(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.scheduler_classes[0]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_scheduler_config()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler_class(**_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[1_00, 87, 50, 51, 0]\r\r with self.assertRaises(_UpperCAmelCase ,\t\t\t\t\t\tmsg=\"\"\"`custom_timesteps` must be in descending order.\"\"\" ):\r scheduler.set_timesteps(timesteps=_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.scheduler_classes[0]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_scheduler_config()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler_class(**_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[1_00, 87, 50, 1, 0]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlen(_UpperCAmelCase )\r\r with self.assertRaises(_UpperCAmelCase ,\t\t\t\t\t\tmsg=\"\"\"Can only pass one of `num_inference_steps` or `custom_timesteps`.\"\"\" ):\r scheduler.set_timesteps(num_inference_steps=_UpperCAmelCase ,\t\t\t\t\t\ttimesteps=_UpperCAmelCase )\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.scheduler_classes[0]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_scheduler_config()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler_class(**_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[scheduler.config.num_train_timesteps]\r\r with self.assertRaises(\r _UpperCAmelCase ,\t\t\t\t\t\tmsg=\"\"\"`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}\"\"\" ,\t\t\t\t\t\t):\r scheduler.set_timesteps(timesteps=_UpperCAmelCase )\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rfrom dataclasses import dataclass\rfrom typing import List, Optional, Union\r\rimport numpy as np\rimport torch\r\rfrom ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available\r\r@dataclass\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r lowerCAmelCase_ : Union[List[np.ndarray], torch.FloatTensor]\r\r\rtry:\r if not (is_transformers_available() and is_torch_available()):\r raise OptionalDependencyNotAvailable()\rexcept OptionalDependencyNotAvailable:\r from ...utils.dummy_torch_and_transformers_objects import * # noqa F403\relse:\r from .pipeline_text_to_video_synth import TextToVideoSDPipeline\r from .pipeline_text_to_video_synth_imgaimg import VideoToVideoSDPipeline # noqa: F401\r from .pipeline_text_to_video_zero import TextToVideoZeroPipeline\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152341,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport os\r\rfrom bleurt import score # From: git+https://github.com/google-research/bleurt.git\r\rimport datasets\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tdatasets.logging.get_logger(__name__)\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'\\\\n@inproceedings{bleurt,\\n title={BLEURT: Learning Robust Metrics for Text Generation},\\n author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},\\n booktitle={ACL},\\n year={2020},\\n url={https://arxiv.org/abs/2004.04696}\\n}\\n'\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'\\\\nBLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)\\nand then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune\\nit for your specific application (the latter is expected to perform better).\\n\\nSee the project\\'s README at https://github.com/google-research/bleurt#readme for more information.\\n'\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'\\nBLEURT score.\\n\\nArgs:\\n `predictions` (list of str): prediction/candidate sentences\\n `references` (list of str): reference sentences\\n `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.\\n\\nReturns:\\n \\'scores\\': List of scores.\\nExamples:\\n\\n >>> predictions = [\"hello there\", \"general kenobi\"]\\n >>> references = [\"hello there\", \"general kenobi\"]\\n >>> bleurt = datasets.load_metric(\"bleurt\")\\n >>> results = bleurt.compute(predictions=predictions, references=references)\\n >>> print([round(v, 2) for v in results[\"scores\"]])\\n [1.03, 1.04]\\n'\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t{\r 'bleurt-tiny-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip',\r 'bleurt-tiny-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip',\r 'bleurt-base-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip',\r 'bleurt-base-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip',\r 'bleurt-large-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip',\r 'bleurt-large-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip',\r 'BLEURT-20-D3': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip',\r 'BLEURT-20-D6': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip',\r 'BLEURT-20-D12': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip',\r 'BLEURT-20': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip',\r}\r\r@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION\t, _KWARGS_DESCRIPTION )\rclass lowerCAmelCase_ ( datasets.Metric ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tTuple ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return datasets.MetricInfo(\r description=_DESCRIPTION ,\t\t\t\t\t\tcitation=_CITATION ,\t\t\t\t\t\thomepage=\"\"\"https://github.com/google-research/bleurt\"\"\" ,\t\t\t\t\t\tinputs_description=_KWARGS_DESCRIPTION ,\t\t\t\t\t\tfeatures=datasets.Features(\r {\r \"\"\"predictions\"\"\": datasets.Value(\"\"\"string\"\"\" ,\t\t\t\t\t\tid=\"\"\"sequence\"\"\" ),\r \"\"\"references\"\"\": datasets.Value(\"\"\"string\"\"\" ,\t\t\t\t\t\tid=\"\"\"sequence\"\"\" ),\r } ) ,\t\t\t\t\t\tcodebase_urls=[\"\"\"https://github.com/google-research/bleurt\"\"\"] ,\t\t\t\t\t\treference_urls=[\"\"\"https://github.com/google-research/bleurt\"\"\", \"\"\"https://arxiv.org/abs/2004.04696\"\"\"] ,\t\t\t\t\t\t)\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if self.config_name == \"default\":\r logger.warning(\r \"\"\"Using default BLEURT-Base checkpoint for sequence maximum length 128. \"\"\"\r \"\"\"You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512').\"\"\" )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"bleurt-base-128\"\"\"\r\r if self.config_name.lower() in CHECKPOINT_URLS:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.config_name.lower()\r\r elif self.config_name.upper() in CHECKPOINT_URLS:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.config_name.upper()\r\r else:\r raise KeyError(\r f'''{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}''' )\r\r # download the model checkpoint specified by self.config_name and set up the scorer\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name] )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscore.BleurtScorer(os.path.join(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ) )\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.scorer.score(references=_UpperCAmelCase ,\t\t\t\t\t\tcandidates=_UpperCAmelCase )\r return {\"scores\": scores}\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport torch\rimport torch.nn as nn\rfrom transformers.modeling_utils import ModuleUtilsMixin\rfrom transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm\r\rfrom ...configuration_utils import ConfigMixin, register_to_config\rfrom ...models import ModelMixin\r\rclass lowerCAmelCase_ ( lowerCamelCase_\t, lowerCamelCase_\t, lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r @register_to_config\r def __init__( self\t\t\t:\t\tList[str] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tfloat ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tbool = False ,\t\t\t\t\t\t):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r super().__init__()\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnn.Embedding(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnn.Embedding(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnn.Dropout(p=_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTaConfig(\r vocab_size=_UpperCAmelCase ,\t\t\t\t\t\td_model=_UpperCAmelCase ,\t\t\t\t\t\tnum_heads=_UpperCAmelCase ,\t\t\t\t\t\td_kv=_UpperCAmelCase ,\t\t\t\t\t\td_ff=_UpperCAmelCase ,\t\t\t\t\t\tdropout_rate=_UpperCAmelCase ,\t\t\t\t\t\tfeed_forward_proj=_UpperCAmelCase ,\t\t\t\t\t\tis_decoder=_UpperCAmelCase ,\t\t\t\t\t\tis_encoder_decoder=_UpperCAmelCase ,\t\t\t\t\t\t)\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnn.ModuleList()\r for lyr_num in range(_UpperCAmelCase ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTaBlock(_UpperCAmelCase )\r self.encoders.append(_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTaLayerNorm(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnn.Dropout(p=_UpperCAmelCase )\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.token_embedder(_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tencoder_input_tokens.shape[1]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.arange(_UpperCAmelCase ,\t\t\t\t\t\tdevice=encoder_input_tokens.device )\r x += self.position_encoding(_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.dropout_pre(_UpperCAmelCase )\r\r # inverted the attention mask\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tencoder_input_tokens.size()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_extended_attention_mask(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r\r for lyr in self.encoders:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlyr(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )[0]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.layer_norm(_UpperCAmelCase )\r\r return self.dropout_post(_UpperCAmelCase ), encoder_inputs_mask\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152342,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport argparse\rimport json\rfrom typing import List\r\rfrom ltp import LTP\r\rfrom transformers.models.bert.tokenization_bert import BertTokenizer\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r if (\r (cp >= 0x4e00 and cp <= 0x9fff)\r or (cp >= 0x3400 and cp <= 0x4dbf) #\r or (cp >= 0x20000 and cp <= 0x2a6df) #\r or (cp >= 0x2a700 and cp <= 0x2b73f) #\r or (cp >= 0x2b740 and cp <= 0x2b81f) #\r or (cp >= 0x2b820 and cp <= 0x2ceaf) #\r or (cp >= 0xf900 and cp <= 0xfaff)\r or (cp >= 0x2f800 and cp <= 0x2fa1f) #\r ): #\r return True\r\r return False\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r for char in word:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tord(SCREAMING_SNAKE_CASE__\t\t\t\t)\r if not _is_chinese_char(SCREAMING_SNAKE_CASE__\t\t\t\t):\r return 0\r return 1\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tset()\r\r for token in tokens:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlen(SCREAMING_SNAKE_CASE__\t\t\t\t) > 1 and is_chinese(SCREAMING_SNAKE_CASE__\t\t\t\t)\r if chinese_word:\r word_set.add(SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlist(SCREAMING_SNAKE_CASE__\t\t\t\t)\r return word_list\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tset()\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r if not chinese_word_set:\r return bert_tokens\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmax([len(SCREAMING_SNAKE_CASE__\t\t\t\t) for w in chinese_word_set]\t\t\t\t)\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tbert_tokens\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0, len(SCREAMING_SNAKE_CASE__\t\t\t\t)\r while start < end:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTrue\r if is_chinese(bert_word[start]\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmin(end - start\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r for i in range(SCREAMING_SNAKE_CASE__\t\t\t\t, 1\t\t\t\t, -1\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"\"\"\".join(bert_word[start : start + i]\t\t\t\t)\r if whole_word in chinese_word_set:\r for j in range(start + 1\t\t\t\t, start + i\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"##\"\"\" + bert_word[j]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tstart + i\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r break\r if single_word:\r start += 1\r return bert_word\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tLTP\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tBertTokenizer\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[]\r\r for i in range(0\t\t\t\t, len(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t, 100\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tltp_tokenizer.pipeline(lines[i : i + 100]\t\t\t\t, tasks=[\"\"\"cws\"\"\"]\t\t\t\t).cws\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[get_chinese_word(SCREAMING_SNAKE_CASE__\t\t\t\t) for r in res]\r ltp_res.extend(SCREAMING_SNAKE_CASE__\t\t\t\t)\r assert len(SCREAMING_SNAKE_CASE__\t\t\t\t) == len(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[]\r for i in range(0\t\t\t\t, len(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t, 100\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tbert_tokenizer(lines[i : i + 100]\t\t\t\t, add_special_tokens=SCREAMING_SNAKE_CASE__\t\t\t\t, truncation=SCREAMING_SNAKE_CASE__\t\t\t\t, max_length=512\t\t\t\t)\r bert_res.extend(res[\"\"\"input_ids\"\"\"]\t\t\t\t)\r assert len(SCREAMING_SNAKE_CASE__\t\t\t\t) == len(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[]\r for input_ids, chinese_word in zip(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[]\r for id in input_ids:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tbert_tokenizer._convert_id_to_token(SCREAMING_SNAKE_CASE__\t\t\t\t)\r input_tokens.append(SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tadd_sub_symbol(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[]\r # We only save pos of chinese subwords start with ##, which mean is part of a whole word.\r for i, token in enumerate(SCREAMING_SNAKE_CASE__\t\t\t\t):\r if token[:2] == \"##\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttoken[2:]\r # save chinese tokens' pos\r if len(SCREAMING_SNAKE_CASE__\t\t\t\t) == 1 and _is_chinese_char(ord(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t):\r ref_id.append(SCREAMING_SNAKE_CASE__\t\t\t\t)\r ref_ids.append(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r assert len(SCREAMING_SNAKE_CASE__\t\t\t\t) == len(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r return ref_ids\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tTuple\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r with open(args.file_name\t\t\t\t, \"\"\"r\"\"\"\t\t\t\t, encoding=\"\"\"utf-8\"\"\"\t\t\t\t) as f:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tf.readlines()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[line.strip() for line in data if len(SCREAMING_SNAKE_CASE__\t\t\t\t) > 0 and not line.isspace()] # avoid delimiter like '\\u2029'\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tLTP(args.ltp\t\t\t\t) # faster in GPU device\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tBertTokenizer.from_pretrained(args.bert\t\t\t\t)\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tprepare_ref(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r with open(args.save_path\t\t\t\t, \"\"\"w\"\"\"\t\t\t\t, encoding=\"\"\"utf-8\"\"\"\t\t\t\t) as f:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[json.dumps(SCREAMING_SNAKE_CASE__\t\t\t\t) + \"\"\"\\n\"\"\" for ref in ref_ids]\r f.writelines(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r\rif __name__ == \"__main__\":\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\targparse.ArgumentParser(description='prepare_chinese_ref')\r parser.add_argument(\r '--file_name',\r required=False,\r type=str,\r default='./resources/chinese-demo.txt',\r help='file need process, same as training data in lm',\r )\r parser.add_argument(\r '--ltp',\r required=False,\r type=str,\r default='./resources/ltp',\r help='resources for LTP tokenizer, usually a path',\r )\r parser.add_argument(\r '--bert',\r required=False,\r type=str,\r default='./resources/robert',\r help='resources for Bert tokenizer',\r )\r parser.add_argument(\r '--save_path',\r required=False,\r type=str,\r default='./resources/ref.txt',\r help='path to save res',\r )\r\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tparser.parse_args()\r main(args)\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport argparse\rimport json\rimport os\r\rimport fairseq\rimport torch\rfrom fairseq.data import Dictionary\r\rfrom transformers import (\r WavaVecaConfig,\r WavaVecaCTCTokenizer,\r WavaVecaFeatureExtractor,\r WavaVecaForCTC,\r WavaVecaForPreTraining,\r WavaVecaProcessor,\r logging,\r)\rfrom transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification\r\r\rlogging.set_verbosity_info()\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tlogging.get_logger(__name__)\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t{\r 'post_extract_proj': 'feature_projection.projection',\r 'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',\r 'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',\r 'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',\r 'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',\r 'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',\r 'self_attn_layer_norm': 'encoder.layers.*.layer_norm',\r 'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',\r 'fc2': 'encoder.layers.*.feed_forward.output_dense',\r 'final_layer_norm': 'encoder.layers.*.final_layer_norm',\r 'encoder.layer_norm': 'encoder.layer_norm',\r 'adapter_layer': 'encoder.layers.*.adapter_layer',\r 'w2v_model.layer_norm': 'feature_projection.layer_norm',\r 'quantizer.weight_proj': 'quantizer.weight_proj',\r 'quantizer.vars': 'quantizer.codevectors',\r 'project_q': 'project_q',\r 'final_proj': 'project_hid',\r 'w2v_encoder.proj': 'lm_head',\r 'mask_emb': 'masked_spec_embed',\r 'pooling_layer.linear': 'projector',\r 'pooling_layer.projection': 'classifier',\r}\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t[\r 'lm_head',\r 'quantizer.weight_proj',\r 'quantizer.codevectors',\r 'project_q',\r 'project_hid',\r 'projector',\r 'classifier',\r]\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tTuple\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{}\r with open(SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\"r\"\"\"\t\t\t\t) as file:\r for line_number, line in enumerate(SCREAMING_SNAKE_CASE__\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tline.strip()\r if line:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tline.split()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tline_number\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\twords[0]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue\r return result\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tUnion[str, Any]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[Any]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tAny\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tUnion[str, Any]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r for attribute in key.split(\"\"\".\"\"\"\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tgetattr(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r for param_key in PARAM_MAPPING.keys():\r if full_name.endswith(SCREAMING_SNAKE_CASE__\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tPARAM_MAPPING[full_name.split(\"\"\".\"\"\"\t\t\t\t)[-1]]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"param\"\"\"\r\r if weight_type is not None and weight_type != \"param\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tgetattr(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t).shape\r elif weight_type is not None and weight_type == \"param\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\thf_pointer\r for attribute in hf_param_name.split(\"\"\".\"\"\"\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tgetattr(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tshape_pointer.shape\r\r # let's reduce dimension\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue[0]\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\thf_pointer.shape\r\r if hf_shape != value.shape:\r raise ValueError(\r F'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''\r F''' {value.shape} for {full_name}'''\t\t\t\t)\r\r if weight_type == \"weight\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue\r elif weight_type == \"weight_g\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue\r elif weight_type == \"weight_v\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue\r elif weight_type == \"bias\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue\r elif weight_type == \"param\":\r for attribute in hf_param_name.split(\"\"\".\"\"\"\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tgetattr(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue\r\r logger.info(F'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.'''\t\t\t\t)\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tUnion[str, Any]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tUnion[str, Any]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[Any]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[Any]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r for param_key in PARAM_MAPPING.keys():\r if full_name.endswith(SCREAMING_SNAKE_CASE__\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tPARAM_MAPPING[full_name.split(\"\"\".\"\"\"\t\t\t\t)[-1]]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"param\"\"\"\r\r if weight_type is not None and weight_type != \"param\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\".\"\"\".join([key, weight_type]\t\t\t\t)\r elif weight_type is not None and weight_type == \"param\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\".\"\"\".join([key, hf_param_name]\t\t\t\t)\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tkey\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue if \"\"\"lm_head\"\"\" in full_key else value[0]\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t{\r 'W_a': 'linear_1.weight',\r 'W_b': 'linear_2.weight',\r 'b_a': 'linear_1.bias',\r 'b_b': 'linear_2.bias',\r 'ln_W': 'norm.weight',\r 'ln_b': 'norm.bias',\r}\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tAny\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tDict=None\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[Any]=None\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r for key, mapped_key in MAPPING.items():\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"wav2vec2.\"\"\" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key\r if key in name or key.split(\"\"\"w2v_model.\"\"\"\t\t\t\t)[-1] == name.split(\"\"\".\"\"\"\t\t\t\t)[0]:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTrue\r if \"*\" in mapped_key:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tname.split(SCREAMING_SNAKE_CASE__\t\t\t\t)[0].split(\"\"\".\"\"\"\t\t\t\t)[-2]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmapped_key.replace(\"\"\"*\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r if \"weight_g\" in name:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"weight_g\"\"\"\r elif \"weight_v\" in name:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"weight_v\"\"\"\r elif \"bias\" in name:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"bias\"\"\"\r elif \"weight\" in name:\r # TODO: don't match quantizer.weight_proj\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"weight\"\"\"\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r if hf_dict is not None:\r rename_dict(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r else:\r set_recursively(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r return is_used\r return is_used\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tTuple\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[int]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tfairseq_model.state_dict()\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\thf_model.wavaveca.feature_extractor\r\r for name, value in fairseq_dict.items():\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r if \"conv_layers\" in name:\r load_conv_layer(\r SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, hf_model.config.feat_extract_norm == \"\"\"group\"\"\"\t\t\t\t, )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTrue\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tload_wavaveca_layer(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r if not is_used:\r unused_weights.append(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r logger.warning(F'''Unused weights: {unused_weights}'''\t\t\t\t)\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[Any]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tfull_name.split(\"\"\"conv_layers.\"\"\"\t\t\t\t)[-1]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tname.split(\"\"\".\"\"\"\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tint(items[0]\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tint(items[1]\t\t\t\t)\r\r if type_id == 0:\r if \"bias\" in name:\r if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:\r raise ValueError(\r F'''{full_name} has size {value.shape}, but'''\r F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue\r logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''\t\t\t\t)\r elif \"weight\" in name:\r if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:\r raise ValueError(\r F'''{full_name} has size {value.shape}, but'''\r F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue\r logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''\t\t\t\t)\r elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):\r if \"bias\" in name:\r if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:\r raise ValueError(\r F'''{full_name} has size {value.shape}, but'''\r F''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'''\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue\r logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''\t\t\t\t)\r elif \"weight\" in name:\r if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:\r raise ValueError(\r F'''{full_name} has size {value.shape}, but'''\r F''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'''\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue\r logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''\t\t\t\t)\r else:\r unused_weights.append(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r@torch.no_grad()\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]=None\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[int]=None\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tUnion[str, Any]=True\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tUnion[str, Any]=False\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r if config_path is not None:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tWavaVecaConfig.from_pretrained(SCREAMING_SNAKE_CASE__\t\t\t\t)\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tWavaVecaConfig()\r\r if is_seq_class:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tread_txt_into_dict(SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tidalabel\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tWavaVecaForSequenceClassification(SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tWavaVecaFeatureExtractor(\r feature_size=1\t\t\t\t, sampling_rate=16000\t\t\t\t, padding_value=0\t\t\t\t, do_normalize=SCREAMING_SNAKE_CASE__\t\t\t\t, return_attention_mask=SCREAMING_SNAKE_CASE__\t\t\t\t, )\r feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r elif is_finetuned:\r if dict_path:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tDictionary.load(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r # important change bos & pad token id since CTC symbol is and\r # not as in fairseq\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttarget_dict.pad_index\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttarget_dict.bos_index\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttarget_dict.eos_index\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlen(target_dict.symbols\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tos.path.join(SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\"vocab.json\"\"\"\t\t\t\t)\r if not os.path.isdir(SCREAMING_SNAKE_CASE__\t\t\t\t):\r logger.error(\"\"\"--pytorch_dump_folder_path ({}) should be a directory\"\"\".format(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t)\r return\r os.makedirs(SCREAMING_SNAKE_CASE__\t\t\t\t, exist_ok=SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttarget_dict.indices\r\r # fairseq has the and switched\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t1\r with open(SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\"w\"\"\"\t\t\t\t, encoding=\"\"\"utf-8\"\"\"\t\t\t\t) as vocab_handle:\r json.dump(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tWavaVecaCTCTokenizer(\r SCREAMING_SNAKE_CASE__\t\t\t\t, unk_token=target_dict.unk_word\t\t\t\t, pad_token=target_dict.pad_word\t\t\t\t, bos_token=target_dict.bos_word\t\t\t\t, eos_token=target_dict.eos_word\t\t\t\t, word_delimiter_token=\"\"\"|\"\"\"\t\t\t\t, do_lower_case=SCREAMING_SNAKE_CASE__\t\t\t\t, )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTrue if config.feat_extract_norm == \"\"\"layer\"\"\" else False\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tWavaVecaFeatureExtractor(\r feature_size=1\t\t\t\t, sampling_rate=16000\t\t\t\t, padding_value=0\t\t\t\t, do_normalize=SCREAMING_SNAKE_CASE__\t\t\t\t, return_attention_mask=SCREAMING_SNAKE_CASE__\t\t\t\t, )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tWavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE__\t\t\t\t, tokenizer=SCREAMING_SNAKE_CASE__\t\t\t\t)\r processor.save_pretrained(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tWavaVecaForCTC(SCREAMING_SNAKE_CASE__\t\t\t\t)\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tWavaVecaForPreTraining(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r if is_finetuned or is_seq_class:\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tfairseq.checkpoint_utils.load_model_ensemble_and_task(\r [checkpoint_path]\t\t\t\t, arg_overrides={\"\"\"data\"\"\": \"\"\"/\"\"\".join(dict_path.split(\"\"\"/\"\"\"\t\t\t\t)[:-1]\t\t\t\t)}\t\t\t\t)\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\targparse.Namespace(task=\"\"\"audio_pretraining\"\"\"\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tfairseq.tasks.setup_task(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tfairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]\t\t\t\t, task=SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel[0].eval()\r\r recursively_load_weights(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, not is_finetuned\t\t\t\t)\r\r hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r\rif __name__ == \"__main__\":\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\targparse.ArgumentParser()\r parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')\r parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')\r parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')\r parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')\r parser.add_argument(\r '--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'\r )\r parser.add_argument(\r '--is_seq_class',\r action='store_true',\r help='Whether the model to convert is a fine-tuned sequence classification model or not',\r )\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tparser.parse_args()\r\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tnot args.not_finetuned and not args.is_seq_class\r convert_wavaveca_checkpoint(\r args.checkpoint_path,\r args.pytorch_dump_folder_path,\r args.config_path,\r args.dict_path,\r is_finetuned,\r args.is_seq_class,\r )\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152343,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport unittest\r\rimport numpy as np\rimport torch\r\rfrom diffusers import VersatileDiffusionImageVariationPipeline\rfrom diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tFalse\r\rclass lowerCAmelCase_ ( unittest.TestCase ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r pass\r\r@slow\r@require_torch_gpu\rclass lowerCAmelCase_ ( unittest.TestCase ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tVersatileDiffusionImageVariationPipeline.from_pretrained(\"\"\"shi-labs/versatile-diffusion\"\"\" )\r pipe.to(_UpperCAmelCase )\r pipe.set_progress_bar_config(disable=_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tload_image(\r \"\"\"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg\"\"\" )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.manual_seed(0 )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tpipe(\r image=_UpperCAmelCase ,\t\t\t\t\t\tgenerator=_UpperCAmelCase ,\t\t\t\t\t\tguidance_scale=7.5 ,\t\t\t\t\t\tnum_inference_steps=50 ,\t\t\t\t\t\toutput_type=\"\"\"numpy\"\"\" ,\t\t\t\t\t\t).images\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\timage[0, 2_53:2_56, 2_53:2_56, -1]\r\r assert image.shape == (1, 5_12, 5_12, 3)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnp.array([0.0441, 0.0469, 0.0507, 0.0575, 0.0632, 0.0650, 0.0865, 0.0909, 0.0945] )\r\r assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport itertools\rimport os\rfrom collections import Counter, defaultdict\rfrom concurrent.futures import ThreadPoolExecutor, as_completed\r\rimport numpy as np\r\rimport datasets\r\rfrom .execute import check_correctness\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'\\\\n@misc{chen2021evaluating,\\n title={Evaluating Large Language Models Trained on Code},\\n author={Mark Chen and Jerry Tworek and Heewoo Jun and Qiming Yuan \\\\nand Henrique Ponde de Oliveira Pinto and Jared Kaplan and Harri Edwards \\\\nand Yuri Burda and Nicholas Joseph and Greg Brockman and Alex Ray \\\\nand Raul Puri and Gretchen Krueger and Michael Petrov and Heidy Khlaaf \\\\nand Girish Sastry and Pamela Mishkin and Brooke Chan and Scott Gray \\\\nand Nick Ryder and Mikhail Pavlov and Alethea Power and Lukasz Kaiser \\\\nand Mohammad Bavarian and Clemens Winter and Philippe Tillet \\\\nand Felipe Petroski Such and Dave Cummings and Matthias Plappert \\\\nand Fotios Chantzis and Elizabeth Barnes and Ariel Herbert-Voss \\\\nand William Hebgen Guss and Alex Nichol and Alex Paino and Nikolas Tezak \\\\nand Jie Tang and Igor Babuschkin and Suchir Balaji and Shantanu Jain \\\\nand William Saunders and Christopher Hesse and Andrew N. Carr \\\\nand Jan Leike and Josh Achiam and Vedant Misra and Evan Morikawa \\\\nand Alec Radford and Matthew Knight and Miles Brundage and Mira Murati \\\\nand Katie Mayer and Peter Welinder and Bob McGrew and Dario Amodei \\\\nand Sam McCandlish and Ilya Sutskever and Wojciech Zaremba},\\n year={2021},\\n eprint={2107.03374},\\n archivePrefix={arXiv},\\n primaryClass={cs.LG}\\n}\\n'\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'\\\\nThis metric implements the evaluation harness for the HumanEval problem solving dataset\\ndescribed in the paper \"Evaluating Large Language Models Trained on Code\"\\n(https://arxiv.org/abs/2107.03374).\\n'\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'\\nCalculates how good are predictions given some references, using certain scores\\nArgs:\\n predictions: list of candidates to evaluate. Each candidates should be a list\\n of strings with several code candidates to solve the problem.\\n references: a list with a test for each prediction. Each test should evaluate the\\n correctness of a code candidate.\\n k: number of code candidates to consider in the evaluation (Default: [1, 10, 100])\\n num_workers: number of workers used to evaluate the canidate programs (Default: 4).\\n timeout:\\nReturns:\\n pass_at_k: dict with pass rates for each k\\n results: dict with granular results of each unittest\\nExamples:\\n >>> code_eval = datasets.load_metric(\"code_eval\")\\n >>> test_cases = [\"assert add(2,3)==5\"]\\n >>> candidates = [[\"def add(a,b): return a*b\", \"def add(a, b): return a+b\"]]\\n >>> pass_at_k, results = code_eval.compute(references=test_cases, predictions=candidates, k=[1, 2])\\n >>> print(pass_at_k)\\n {\\'pass@1\\': 0.5, \\'pass@2\\': 1.0}\\n'\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'\\n################################################################################\\n !!!WARNING!!!\\n################################################################################\\nThe \"code_eval\" metric executes untrusted model-generated code in Python.\\nAlthough it is highly unlikely that model-generated code will do something\\novertly malicious in response to this test suite, model-generated code may act\\ndestructively due to a lack of model capability or alignment.\\nUsers are strongly encouraged to sandbox this evaluation suite so that it\\ndoes not perform destructive actions on their host or network. For more\\ninformation on how OpenAI sandboxes its code, see the paper \"Evaluating Large\\nLanguage Models Trained on Code\" (https://arxiv.org/abs/2107.03374).\\n\\nOnce you have read this disclaimer and taken appropriate precautions,\\nset the environment variable HF_ALLOW_CODE_EVAL=\"1\". Within Python you can to this\\nwith:\\n\\n>>> import os\\n>>> os.environ[\"HF_ALLOW_CODE_EVAL\"] = \"1\"\\n\\n################################################################################\\\\n'\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'The MIT License\\n\\nCopyright (c) OpenAI (https://openai.com)\\n\\nPermission is hereby granted, free of charge, to any person obtaining a copy\\nof this software and associated documentation files (the \"Software\"), to deal\\nin the Software without restriction, including without limitation the rights\\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\\ncopies of the Software, and to permit persons to whom the Software is\\nfurnished to do so, subject to the following conditions:\\n\\nThe above copyright notice and this permission notice shall be included in\\nall copies or substantial portions of the Software.\\n\\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\\nTHE SOFTWARE.'\r\r@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION\t, _KWARGS_DESCRIPTION )\rclass lowerCAmelCase_ ( datasets.Metric ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return datasets.MetricInfo(\r # This is the description that will appear on the metrics page.\r description=_DESCRIPTION ,\t\t\t\t\t\tcitation=_CITATION ,\t\t\t\t\t\tinputs_description=_KWARGS_DESCRIPTION ,\t\t\t\t\t\tfeatures=datasets.Features(\r {\r \"\"\"predictions\"\"\": datasets.Sequence(datasets.Value(\"\"\"string\"\"\" ) ),\r \"\"\"references\"\"\": datasets.Value(\"\"\"string\"\"\" ),\r } ) ,\t\t\t\t\t\thomepage=\"\"\"https://github.com/openai/human-eval\"\"\" ,\t\t\t\t\t\tcodebase_urls=[\"\"\"https://github.com/openai/human-eval\"\"\"] ,\t\t\t\t\t\treference_urls=[\"\"\"https://github.com/openai/human-eval\"\"\"] ,\t\t\t\t\t\tlicense=_LICENSE ,\t\t\t\t\t\t)\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str]=[1, 10, 1_00] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any]=4 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny=3.0 ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if os.getenv(\"\"\"HF_ALLOW_CODE_EVAL\"\"\" ,\t\t\t\t\t\t0 ) != \"1\":\r raise ValueError(_WARNING )\r\r if os.name == \"nt\":\r raise NotImplementedError(\"\"\"This metric is currently not supported on Windows.\"\"\" )\r\r with ThreadPoolExecutor(max_workers=_UpperCAmelCase ) as executor:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tCounter()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdefaultdict(_UpperCAmelCase )\r\r for task_id, (candidates, test_case) in enumerate(zip(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ) ):\r for candidate in candidates:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tcandidate + \"\"\"\\n\"\"\" + test_case\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t(test_program, timeout, task_id, completion_id[task_id])\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\texecutor.submit(_UpperCAmelCase ,\t\t\t\t\t\t*_UpperCAmelCase )\r futures.append(_UpperCAmelCase )\r completion_id[task_id] += 1\r n_samples += 1\r\r for future in as_completed(_UpperCAmelCase ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tfuture.result()\r results[result[\"task_id\"]].append((result[\"\"\"completion_id\"\"\"], result) )\r\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[], []\r for result in results.values():\r result.sort()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[r[1][\"\"\"passed\"\"\"] for r in result]\r total.append(len(_UpperCAmelCase ) )\r correct.append(sum(_UpperCAmelCase ) )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnp.array(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnp.array(_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tk\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{f'''pass@{k}''': estimate_pass_at_k(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ).mean() for k in ks if (total >= k).all()}\r\r return pass_at_k, results\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[Any]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[Any]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r def estimator(SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t) -> float:\r if n - c < k:\r return 1.0\r return 1.0 - np.prod(1.0 - k / np.arange(n - c + 1\t\t\t\t, n + 1\t\t\t\t)\t\t\t\t)\r\r if isinstance(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\titertools.repeat(SCREAMING_SNAKE_CASE__\t\t\t\t, len(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t)\r else:\r assert len(SCREAMING_SNAKE_CASE__\t\t\t\t) == len(SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\titer(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r return np.array([estimator(int(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t, int(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t) for n, c in zip(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)]\t\t\t\t)\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152344,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport heapq\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tdict\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[]\r\r # for each node and his adjacency list add them and the rank of the node to queue\r # using heapq module the queue will be filled like a Priority Queue\r # heapq works with a min priority queue, so I used -1*len(v) to build it\r for key, value in graph.items():\r # O(log(n))\r heapq.heappush(SCREAMING_SNAKE_CASE__\t\t\t\t, [-1 * len(SCREAMING_SNAKE_CASE__\t\t\t\t), (key, value)]\t\t\t\t)\r\r # chosen_vertices = set of chosen vertices\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tset()\r\r # while queue isn't empty and there are still edges\r # (queue[0][0] is the rank of the node with max rank)\r while queue and queue[0][0] != 0:\r # extract vertex with max rank from queue and add it to chosen_vertices\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\theapq.heappop(SCREAMING_SNAKE_CASE__\t\t\t\t)[1][0]\r chosen_vertices.add(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r # Remove all arcs adjacent to argmax\r for elem in queue:\r # if v haven't adjacent node, skip\r if elem[0] == 0:\r continue\r # if argmax is reachable from elem\r # remove argmax from elem's adjacent list and update his rank\r if argmax in elem[1][1]:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\telem[1][1].index(SCREAMING_SNAKE_CASE__\t\t\t\t)\r del elem[1][1][index]\r elem[0] += 1\r # re-order the queue\r heapq.heapify(SCREAMING_SNAKE_CASE__\t\t\t\t)\r return chosen_vertices\r\r\rif __name__ == \"__main__\":\r import doctest\r\r doctest.testmod()\r\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\t{0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}\r print(f\"Minimum vertex cover:\\n{greedy_min_vertex_cover(graph)}\")\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport math\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r assert isinstance(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t) and (\r number >= 0\r ), \"'number' must been an int and positive\"\r\r if 1 < number < 4:\r # 2 and 3 are primes\r return True\r elif number < 2 or not number % 2:\r # Negatives, 0, 1 and all even numbers are not primes\r return False\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\trange(3\t\t\t\t, int(math.sqrt(SCREAMING_SNAKE_CASE__\t\t\t\t) + 1\t\t\t\t)\t\t\t\t, 2\t\t\t\t)\r return not any(not number % i for i in odd_numbers\t\t\t\t)\r\r\r\r\r\r\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tUnion[str, Any]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]=1\t\t\t\t, **SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tfactor * value\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue\r\r while not is_prime(SCREAMING_SNAKE_CASE__\t\t\t\t):\r value += 1 if not (\"desc\" in kwargs and kwargs[\"desc\"] is True) else -1\r\r if value == first_value_val:\r return next_prime(value + 1\t\t\t\t, **SCREAMING_SNAKE_CASE__\t\t\t\t)\r return value\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152345,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rfrom __future__ import annotations\r\rfrom fractions import Fraction\rfrom math import gcd, sqrt\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tint(number**0.5\t\t\t\t)\r return number == sq * sq\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tx_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tx_den * y_den * z_den\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tgcd(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r top //= hcf\r bottom //= hcf\r return top, bottom\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint = 35\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tset()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t42\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFraction(0\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t42\r\r for x_num in range(1\t\t\t\t, order + 1\t\t\t\t):\r for x_den in range(x_num + 1\t\t\t\t, order + 1\t\t\t\t):\r for y_num in range(1\t\t\t\t, order + 1\t\t\t\t):\r for y_den in range(y_num + 1\t\t\t\t, order + 1\t\t\t\t):\r # n=1\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tx_num * y_den + x_den * y_num\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tx_den * y_den\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tgcd(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r z_num //= hcf\r z_den //= hcf\r if 0 < z_num < z_den <= order:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tadd_three(\r SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r unique_s.add(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r # n=2\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t(\r x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num\r )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tx_den * x_den * y_den * y_den\r if is_sq(SCREAMING_SNAKE_CASE__\t\t\t\t) and is_sq(SCREAMING_SNAKE_CASE__\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tint(sqrt(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tint(sqrt(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tgcd(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r z_num //= hcf\r z_den //= hcf\r if 0 < z_num < z_den <= order:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tadd_three(\r SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r unique_s.add(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r # n=-1\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tx_num * y_num\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tx_den * y_num + x_num * y_den\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tgcd(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r z_num //= hcf\r z_den //= hcf\r if 0 < z_num < z_den <= order:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tadd_three(\r SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r unique_s.add(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r # n=2\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tx_num * x_num * y_num * y_num\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t(\r x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den\r )\r if is_sq(SCREAMING_SNAKE_CASE__\t\t\t\t) and is_sq(SCREAMING_SNAKE_CASE__\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tint(sqrt(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tint(sqrt(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tgcd(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r z_num //= hcf\r z_den //= hcf\r if 0 < z_num < z_den <= order:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tadd_three(\r SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r unique_s.add(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r for num, den in unique_s:\r total += Fraction(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r return total.denominator + total.numerator\r\r\rif __name__ == \"__main__\":\r print(f\"{solution() = }\")\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport string\rfrom math import logaa\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdocument.translate(\r str.maketrans(\"\"\"\"\"\"\t\t\t\t, \"\"\"\"\"\"\t\t\t\t, string.punctuation\t\t\t\t)\t\t\t\t).replace(\"\"\"\\n\"\"\"\t\t\t\t, \"\"\"\"\"\"\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdocument_without_punctuation.split(\"\"\" \"\"\"\t\t\t\t) # word tokenization\r return len([word for word in tokenize_document if word.lower() == term.lower()]\t\t\t\t)\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tcorpus.lower().translate(\r str.maketrans(\"\"\"\"\"\"\t\t\t\t, \"\"\"\"\"\"\t\t\t\t, string.punctuation\t\t\t\t)\t\t\t\t) # strip all punctuation and replace it with ''\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tcorpus_without_punctuation.split(\"\"\"\\n\"\"\"\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tterm.lower()\r return (len([doc for doc in docs if term in doc]\t\t\t\t), len(SCREAMING_SNAKE_CASE__\t\t\t\t))\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tTuple=False\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r if smoothing:\r if n == 0:\r raise ValueError(\"\"\"log10(0) is undefined.\"\"\"\t\t\t\t)\r return round(1 + logaa(n / (1 + df)\t\t\t\t)\t\t\t\t, 3\t\t\t\t)\r\r if df == 0:\r raise ZeroDivisionError(\"\"\"df must be > 0\"\"\"\t\t\t\t)\r elif n == 0:\r raise ValueError(\"\"\"log10(0) is undefined.\"\"\"\t\t\t\t)\r return round(logaa(n / df\t\t\t\t)\t\t\t\t, 3\t\t\t\t)\r\r\r\r\r\r\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r return round(tf * idf\t\t\t\t, 3\t\t\t\t)\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152346,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rfrom ...configuration_utils import PretrainedConfig\rfrom ...utils import logging\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tlogging.get_logger(__name__)\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t{\r 'microsoft/markuplm-base': 'https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json',\r 'microsoft/markuplm-large': 'https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json',\r}\r\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r lowerCAmelCase_ : Any = \"\"\"markuplm\"\"\"\r\r\r def __init__( self\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=3_05_22 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=7_68 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny=12 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint=12 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any]=30_72 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int]=\"gelu\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=0.1 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=0.1 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr=5_12 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr=2 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any]=0.02 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any]=1E-12 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=0 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint=0 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr=2 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny=2_56 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple=10_24 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple=2_16 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint=10_01 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any]=32 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str]=50 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny=\"absolute\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint=True ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=None ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tList[str] ,\t\t\t\t\t\t):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r super().__init__(\r pad_token_id=_UpperCAmelCase ,\t\t\t\t\t\tbos_token_id=_UpperCAmelCase ,\t\t\t\t\t\teos_token_id=_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase ,\t\t\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvocab_size\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\thidden_size\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnum_hidden_layers\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnum_attention_heads\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\thidden_act\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tintermediate_size\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\thidden_dropout_prob\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tattention_probs_dropout_prob\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmax_position_embeddings\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttype_vocab_size\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tinitializer_range\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlayer_norm_eps\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tposition_embedding_type\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tuse_cache\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tclassifier_dropout\r # additional properties\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmax_depth\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmax_xpath_tag_unit_embeddings\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmax_xpath_subs_unit_embeddings\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttag_pad_id\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tsubs_pad_id\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\txpath_unit_hidden_size\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport argparse\r\rimport torch\r\rfrom transformers import BertForMaskedLM\r\r\rif __name__ == \"__main__\":\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\targparse.ArgumentParser(\r description=(\r 'Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'\r ' Distillation'\r )\r )\r parser.add_argument('--model_type', default='bert', choices=['bert'])\r parser.add_argument('--model_name', default='bert-base-uncased', type=str)\r parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_bert-base-uncased_0247911.pth', type=str)\r parser.add_argument('--vocab_transform', action='store_true')\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tparser.parse_args()\r\r if args.model_type == \"bert\":\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tBertForMaskedLM.from_pretrained(args.model_name)\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\t'bert'\r else:\r raise ValueError('args.model_type should be \"bert\".')\r\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tmodel.state_dict()\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\t{}\r\r for w in [\"word_embeddings\", \"position_embeddings\"]:\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tstate_dict[f\"{prefix}.embeddings.{w}.weight\"]\r for w in [\"weight\", \"bias\"]:\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tstate_dict[f\"{prefix}.embeddings.LayerNorm.{w}\"]\r\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\t0\r for teacher_idx in [0, 2, 4, 7, 9, 1_1]:\r for w in [\"weight\", \"bias\"]:\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tstate_dict[\r f\"{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}\"\r ]\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tstate_dict[\r f\"{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}\"\r ]\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tstate_dict[\r f\"{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}\"\r ]\r\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tstate_dict[\r f\"{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}\"\r ]\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tstate_dict[\r f\"{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}\"\r ]\r\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tstate_dict[\r f\"{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}\"\r ]\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tstate_dict[\r f\"{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}\"\r ]\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tstate_dict[\r f\"{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}\"\r ]\r std_idx += 1\r\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tstate_dict['cls.predictions.decoder.weight']\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tstate_dict['cls.predictions.bias']\r if args.vocab_transform:\r for w in [\"weight\", \"bias\"]:\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tstate_dict[f\"cls.predictions.transform.dense.{w}\"]\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tstate_dict[f\"cls.predictions.transform.LayerNorm.{w}\"]\r\r print(f\"N layers selected for distillation: {std_idx}\")\r print(f\"Number of params transferred for distillation: {len(compressed_sd.keys())}\")\r\r print(f\"Save transferred checkpoint to {args.dump_checkpoint}.\")\r torch.save(compressed_sd, args.dump_checkpoint)\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152347,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport os\rimport tempfile\rimport unittest\r\rfrom transformers import FlaubertConfig, is_torch_available\rfrom transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device\r\rfrom ...test_configuration_common import ConfigTester\rfrom ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask\rfrom ...test_pipeline_mixin import PipelineTesterMixin\r\r\rif is_torch_available():\r import torch\r\r from transformers import (\r FlaubertForMultipleChoice,\r FlaubertForQuestionAnswering,\r FlaubertForQuestionAnsweringSimple,\r FlaubertForSequenceClassification,\r FlaubertForTokenClassification,\r FlaubertModel,\r FlaubertWithLMHeadModel,\r )\r from transformers.models.flaubert.modeling_flaubert import FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST\r\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def __init__( self\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str]=13 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=7 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint=True ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=True ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple=True ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=True ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str]=True ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any]=False ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any]=False ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple=False ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint=2 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple=99 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int]=0 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint=32 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any]=5 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any]=4 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str]=0.1 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple=0.1 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any]=5_12 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int]=12 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int]=2 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple=0.02 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr=3 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int]=4 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny=\"last\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str]=None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict=None ,\t\t\t\t\t\t):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tparent\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tbatch_size\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tseq_length\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tis_training\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tuse_input_lengths\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tuse_token_type_ids\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tuse_labels\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tgelu_activation\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tsinusoidal_embeddings\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tcausal\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tasm\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tn_langs\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvocab_size\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tn_special\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\thidden_size\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnum_hidden_layers\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnum_attention_heads\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\thidden_dropout_prob\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tattention_probs_dropout_prob\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmax_position_embeddings\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttype_vocab_size\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttype_sequence_label_size\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tinitializer_range\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnum_labels\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnum_choices\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tsummary_type\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tuse_proj\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscope\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tids_tensor([self.batch_size, self.seq_length] ,\t\t\t\t\t\tself.vocab_size )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\trandom_attention_mask([self.batch_size, self.seq_length] )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r if self.use_input_lengths:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t(\r ids_tensor([self.batch_size] ,\t\t\t\t\t\tvocab_size=2 ) + self.seq_length - 2\r ) # small variation of seq_length\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r if self.use_token_type_ids:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tids_tensor([self.batch_size, self.seq_length] ,\t\t\t\t\t\tself.n_langs )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r if self.use_labels:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tids_tensor([self.batch_size] ,\t\t\t\t\t\tself.type_sequence_label_size )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tids_tensor([self.batch_size, self.seq_length] ,\t\t\t\t\t\tself.num_labels )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tids_tensor([self.batch_size] ,\t\t\t\t\t\t2 ).float()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tids_tensor([self.batch_size] ,\t\t\t\t\t\tself.num_choices )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_config()\r\r return (\r config,\r input_ids,\r token_type_ids,\r input_lengths,\r sequence_labels,\r token_labels,\r is_impossible_labels,\r choice_labels,\r input_mask,\r )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return FlaubertConfig(\r vocab_size=self.vocab_size ,\t\t\t\t\t\tn_special=self.n_special ,\t\t\t\t\t\temb_dim=self.hidden_size ,\t\t\t\t\t\tn_layers=self.num_hidden_layers ,\t\t\t\t\t\tn_heads=self.num_attention_heads ,\t\t\t\t\t\tdropout=self.hidden_dropout_prob ,\t\t\t\t\t\tattention_dropout=self.attention_probs_dropout_prob ,\t\t\t\t\t\tgelu_activation=self.gelu_activation ,\t\t\t\t\t\tsinusoidal_embeddings=self.sinusoidal_embeddings ,\t\t\t\t\t\tasm=self.asm ,\t\t\t\t\t\tcausal=self.causal ,\t\t\t\t\t\tn_langs=self.n_langs ,\t\t\t\t\t\tmax_position_embeddings=self.max_position_embeddings ,\t\t\t\t\t\tinitializer_range=self.initializer_range ,\t\t\t\t\t\tsummary_type=self.summary_type ,\t\t\t\t\t\tuse_proj=self.use_proj ,\t\t\t\t\t\t)\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFlaubertModel(config=_UpperCAmelCase )\r model.to(_UpperCAmelCase )\r model.eval()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase ,\t\t\t\t\t\tlengths=_UpperCAmelCase ,\t\t\t\t\t\tlangs=_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase ,\t\t\t\t\t\tlangs=_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase )\r self.parent.assertEqual(result.last_hidden_state.shape ,\t\t\t\t\t\t(self.batch_size, self.seq_length, self.hidden_size) )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFlaubertWithLMHeadModel(_UpperCAmelCase )\r model.to(_UpperCAmelCase )\r model.eval()\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase ,\t\t\t\t\t\ttoken_type_ids=_UpperCAmelCase ,\t\t\t\t\t\tlabels=_UpperCAmelCase )\r self.parent.assertEqual(result.loss.shape ,\t\t\t\t\t\t() )\r self.parent.assertEqual(result.logits.shape ,\t\t\t\t\t\t(self.batch_size, self.seq_length, self.vocab_size) )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFlaubertForQuestionAnsweringSimple(_UpperCAmelCase )\r model.to(_UpperCAmelCase )\r model.eval()\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase ,\t\t\t\t\t\tstart_positions=_UpperCAmelCase ,\t\t\t\t\t\tend_positions=_UpperCAmelCase )\r self.parent.assertEqual(result.start_logits.shape ,\t\t\t\t\t\t(self.batch_size, self.seq_length) )\r self.parent.assertEqual(result.end_logits.shape ,\t\t\t\t\t\t(self.batch_size, self.seq_length) )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFlaubertForQuestionAnswering(_UpperCAmelCase )\r model.to(_UpperCAmelCase )\r model.eval()\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(\r _UpperCAmelCase ,\t\t\t\t\t\tstart_positions=_UpperCAmelCase ,\t\t\t\t\t\tend_positions=_UpperCAmelCase ,\t\t\t\t\t\tcls_index=_UpperCAmelCase ,\t\t\t\t\t\tis_impossible=_UpperCAmelCase ,\t\t\t\t\t\tp_mask=_UpperCAmelCase ,\t\t\t\t\t\t)\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(\r _UpperCAmelCase ,\t\t\t\t\t\tstart_positions=_UpperCAmelCase ,\t\t\t\t\t\tend_positions=_UpperCAmelCase ,\t\t\t\t\t\tcls_index=_UpperCAmelCase ,\t\t\t\t\t\tis_impossible=_UpperCAmelCase ,\t\t\t\t\t\t)\r\r ((UpperCAmelCase__)\t\t\t\t\t\t\t,\t)\t\t\t\t\t\t\t\t=\t\t\tresult_with_labels.to_tuple()\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase ,\t\t\t\t\t\tstart_positions=_UpperCAmelCase ,\t\t\t\t\t\tend_positions=_UpperCAmelCase )\r\r ((UpperCAmelCase__)\t\t\t\t\t\t\t,\t)\t\t\t\t\t\t\t\t=\t\t\tresult_with_labels.to_tuple()\r\r self.parent.assertEqual(result_with_labels.loss.shape ,\t\t\t\t\t\t() )\r self.parent.assertEqual(result.start_top_log_probs.shape ,\t\t\t\t\t\t(self.batch_size, model.config.start_n_top) )\r self.parent.assertEqual(result.start_top_index.shape ,\t\t\t\t\t\t(self.batch_size, model.config.start_n_top) )\r self.parent.assertEqual(\r result.end_top_log_probs.shape ,\t\t\t\t\t\t(self.batch_size, model.config.start_n_top * model.config.end_n_top) )\r self.parent.assertEqual(\r result.end_top_index.shape ,\t\t\t\t\t\t(self.batch_size, model.config.start_n_top * model.config.end_n_top) )\r self.parent.assertEqual(result.cls_logits.shape ,\t\t\t\t\t\t(self.batch_size,) )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict ,\t\t\t\t\t\t):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFlaubertForSequenceClassification(_UpperCAmelCase )\r model.to(_UpperCAmelCase )\r model.eval()\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase ,\t\t\t\t\t\tlabels=_UpperCAmelCase )\r\r self.parent.assertEqual(result.loss.shape ,\t\t\t\t\t\t() )\r self.parent.assertEqual(result.logits.shape ,\t\t\t\t\t\t(self.batch_size, self.type_sequence_label_size) )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.num_labels\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFlaubertForTokenClassification(_UpperCAmelCase )\r model.to(_UpperCAmelCase )\r model.eval()\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase ,\t\t\t\t\t\tattention_mask=_UpperCAmelCase ,\t\t\t\t\t\tlabels=_UpperCAmelCase )\r self.parent.assertEqual(result.logits.shape ,\t\t\t\t\t\t(self.batch_size, self.seq_length, self.num_labels) )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tTuple ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.num_choices\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFlaubertForMultipleChoice(config=_UpperCAmelCase )\r model.to(_UpperCAmelCase )\r model.eval()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tinput_ids.unsqueeze(1 ).expand(-1 ,\t\t\t\t\t\tself.num_choices ,\t\t\t\t\t\t-1 ).contiguous()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttoken_type_ids.unsqueeze(1 ).expand(-1 ,\t\t\t\t\t\tself.num_choices ,\t\t\t\t\t\t-1 ).contiguous()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tinput_mask.unsqueeze(1 ).expand(-1 ,\t\t\t\t\t\tself.num_choices ,\t\t\t\t\t\t-1 ).contiguous()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(\r _UpperCAmelCase ,\t\t\t\t\t\tattention_mask=_UpperCAmelCase ,\t\t\t\t\t\ttoken_type_ids=_UpperCAmelCase ,\t\t\t\t\t\tlabels=_UpperCAmelCase ,\t\t\t\t\t\t)\r self.parent.assertEqual(result.logits.shape ,\t\t\t\t\t\t(self.batch_size, self.num_choices) )\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.prepare_config_and_inputs()\r (\r (\r UpperCAmelCase__\r )\t\t\t\t\t\t\t,\t(\r UpperCAmelCase__\r )\t\t\t\t\t\t\t,\t(\r UpperCAmelCase__\r )\t\t\t\t\t\t\t,\t(\r UpperCAmelCase__\r )\t\t\t\t\t\t\t,\t(\r UpperCAmelCase__\r )\t\t\t\t\t\t\t,\t(\r UpperCAmelCase__\r )\t\t\t\t\t\t\t,\t(\r UpperCAmelCase__\r )\t\t\t\t\t\t\t,\t(\r UpperCAmelCase__\r )\t\t\t\t\t\t\t,\t(\r UpperCAmelCase__\r )\t\t\t\t\t\t\t,\t\r )\t\t\t\t\t\t\t\t=\t\t\tconfig_and_inputs\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\r \"\"\"input_ids\"\"\": input_ids,\r \"\"\"token_type_ids\"\"\": token_type_ids,\r \"\"\"lengths\"\"\": input_lengths,\r \"\"\"attention_mask\"\"\": input_mask,\r }\r return config, inputs_dict\r\r@require_torch\rclass lowerCAmelCase_ ( lowerCamelCase_\t, lowerCamelCase_\t, unittest.TestCase ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r lowerCAmelCase_ : str = (\r (\r FlaubertModel,\r FlaubertWithLMHeadModel,\r FlaubertForQuestionAnswering,\r FlaubertForQuestionAnsweringSimple,\r FlaubertForSequenceClassification,\r FlaubertForTokenClassification,\r FlaubertForMultipleChoice,\r )\r if is_torch_available()\r else ()\r )\r lowerCAmelCase_ : Tuple = (\r {\r \"\"\"feature-extraction\"\"\": FlaubertModel,\r \"\"\"fill-mask\"\"\": FlaubertWithLMHeadModel,\r \"\"\"question-answering\"\"\": FlaubertForQuestionAnsweringSimple,\r \"\"\"text-classification\"\"\": FlaubertForSequenceClassification,\r \"\"\"token-classification\"\"\": FlaubertForTokenClassification,\r \"\"\"zero-shot\"\"\": FlaubertForSequenceClassification,\r }\r if is_torch_available()\r else {}\r )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if (\r pipeline_test_casse_name == \"QAPipelineTests\"\r and tokenizer_name is not None\r and not tokenizer_name.endswith(\"\"\"Fast\"\"\" )\r ):\r # `QAPipelineTests` fails for a few models when the slower tokenizer are used.\r # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)\r # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer\r return True\r\r return False\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any]=False ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tsuper()._prepare_for_class(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\treturn_labels=_UpperCAmelCase )\r\r if return_labels:\r if model_class.__name__ == \"FlaubertForQuestionAnswering\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.zeros(\r self.model_tester.batch_size ,\t\t\t\t\t\tdtype=torch.long ,\t\t\t\t\t\tdevice=_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.zeros(\r self.model_tester.batch_size ,\t\t\t\t\t\tdtype=torch.long ,\t\t\t\t\t\tdevice=_UpperCAmelCase )\r\r return inputs_dict\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFlaubertModelTester(self )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tConfigTester(self ,\t\t\t\t\t\tconfig_class=_UpperCAmelCase ,\t\t\t\t\t\temb_dim=37 )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tTuple ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r self.config_tester.run_common_tests()\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.model_tester.prepare_config_and_inputs()\r self.model_tester.create_and_check_flaubert_model(*_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.model_tester.prepare_config_and_inputs()\r self.model_tester.create_and_check_flaubert_lm_head(*_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.model_tester.prepare_config_and_inputs()\r self.model_tester.create_and_check_flaubert_simple_qa(*_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.model_tester.prepare_config_and_inputs()\r self.model_tester.create_and_check_flaubert_qa(*_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tTuple ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.model_tester.prepare_config_and_inputs()\r self.model_tester.create_and_check_flaubert_sequence_classif(*_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.model_tester.prepare_config_and_inputs()\r self.model_tester.create_and_check_flaubert_token_classif(*_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.model_tester.prepare_config_and_inputs()\r self.model_tester.create_and_check_flaubert_multiple_choice(*_UpperCAmelCase )\r\r\r @slow\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tAny ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r for model_name in FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFlaubertModel.from_pretrained(_UpperCAmelCase )\r self.assertIsNotNone(_UpperCAmelCase )\r\r\r\r @slow\r @require_torch_gpu\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tAny ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.model_tester.prepare_config_and_inputs_for_common()\r for model_class in self.all_model_classes:\r # FlauBertForMultipleChoice behaves incorrectly in JIT environments.\r if model_class == FlaubertForMultipleChoice:\r return\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTrue\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel_class(config=_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself._prepare_for_class(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.jit.trace(\r _UpperCAmelCase ,\t\t\t\t\t\t(inputs_dict[\"\"\"input_ids\"\"\"].to(\"\"\"cpu\"\"\" ), inputs_dict[\"\"\"attention_mask\"\"\"].to(\"\"\"cpu\"\"\" )) )\r\r with tempfile.TemporaryDirectory() as tmp:\r torch.jit.save(_UpperCAmelCase ,\t\t\t\t\t\tos.path.join(_UpperCAmelCase ,\t\t\t\t\t\t\"\"\"traced_model.pt\"\"\" ) )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.jit.load(os.path.join(_UpperCAmelCase ,\t\t\t\t\t\t\"\"\"traced_model.pt\"\"\" ) ,\t\t\t\t\t\tmap_location=_UpperCAmelCase )\r loaded(inputs_dict[\"\"\"input_ids\"\"\"].to(_UpperCAmelCase ) ,\t\t\t\t\t\tinputs_dict[\"\"\"attention_mask\"\"\"].to(_UpperCAmelCase ) )\r\r@require_torch\rclass lowerCAmelCase_ ( unittest.TestCase ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r @slow\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFlaubertModel.from_pretrained(\"\"\"flaubert/flaubert_base_cased\"\"\" )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.tensor([[0, 3_45, 2_32, 3_28, 7_40, 1_40, 16_95, 69, 60_78, 15_88, 2]] )\r with torch.no_grad():\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase )[0]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.Size((1, 11, 7_68) )\r self.assertEqual(output.shape ,\t\t\t\t\t\t_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.tensor(\r [[[-2.6251, -1.4298, -0.0227], [-2.8510, -1.6387, 0.2258], [-2.8114, -1.1832, -0.3066]]] )\r\r self.assertTrue(torch.allclose(output[:, :3, :3] ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\tatol=1E-4 ) )\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport tempfile\r\rimport torch\r\rfrom diffusers import PNDMScheduler\r\rfrom .test_schedulers import SchedulerCommonTest\r\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r lowerCAmelCase_ : Union[str, Any] = (PNDMScheduler,)\r lowerCAmelCase_ : Optional[int] = ((\"\"\"num_inference_steps\"\"\", 50),)\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tOptional[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\r \"\"\"num_train_timesteps\"\"\": 10_00,\r \"\"\"beta_start\"\"\": 0.0001,\r \"\"\"beta_end\"\"\": 0.02,\r \"\"\"beta_schedule\"\"\": \"\"\"linear\"\"\",\r }\r\r config.update(**_UpperCAmelCase )\r return config\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple=0 ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdict(self.forward_default_kwargs )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tkwargs.pop(\"\"\"num_inference_steps\"\"\" ,\t\t\t\t\t\t_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.dummy_sample\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0.1 * sample\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]\r\r for scheduler_class in self.scheduler_classes:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_scheduler_config(**_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler_class(**_UpperCAmelCase )\r scheduler.set_timesteps(_UpperCAmelCase )\r # copy over dummy past residuals\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdummy_past_residuals[:]\r\r with tempfile.TemporaryDirectory() as tmpdirname:\r scheduler.save_config(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler_class.from_pretrained(_UpperCAmelCase )\r new_scheduler.set_timesteps(_UpperCAmelCase )\r # copy over dummy past residuals\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdummy_past_residuals[:]\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler.step_prk(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase ).prev_sample\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnew_scheduler.step_prk(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase ).prev_sample\r\r assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, \"Scheduler outputs are not identical\"\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler.step_plms(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase ).prev_sample\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnew_scheduler.step_plms(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase ).prev_sample\r\r assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, \"Scheduler outputs are not identical\"\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r pass\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=0 ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tOptional[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdict(self.forward_default_kwargs )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tkwargs.pop(\"\"\"num_inference_steps\"\"\" ,\t\t\t\t\t\t_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.dummy_sample\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0.1 * sample\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]\r\r for scheduler_class in self.scheduler_classes:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_scheduler_config()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler_class(**_UpperCAmelCase )\r scheduler.set_timesteps(_UpperCAmelCase )\r\r # copy over dummy past residuals (must be after setting timesteps)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdummy_past_residuals[:]\r\r with tempfile.TemporaryDirectory() as tmpdirname:\r scheduler.save_config(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler_class.from_pretrained(_UpperCAmelCase )\r # copy over dummy past residuals\r new_scheduler.set_timesteps(_UpperCAmelCase )\r\r # copy over dummy past residual (must be after setting timesteps)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdummy_past_residuals[:]\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler.step_prk(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase ).prev_sample\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnew_scheduler.step_prk(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase ).prev_sample\r\r assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, \"Scheduler outputs are not identical\"\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler.step_plms(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase ).prev_sample\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnew_scheduler.step_plms(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase ).prev_sample\r\r assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, \"Scheduler outputs are not identical\"\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tTuple ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.scheduler_classes[0]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_scheduler_config(**_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler_class(**_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t10\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.dummy_model()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.dummy_sample_deter\r scheduler.set_timesteps(_UpperCAmelCase )\r\r for i, t in enumerate(scheduler.prk_timesteps ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler.step_prk(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ).prev_sample\r\r for i, t in enumerate(scheduler.plms_timesteps ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler.step_plms(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ).prev_sample\r\r return sample\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdict(self.forward_default_kwargs )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tkwargs.pop(\"\"\"num_inference_steps\"\"\" ,\t\t\t\t\t\t_UpperCAmelCase )\r\r for scheduler_class in self.scheduler_classes:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_scheduler_config()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler_class(**_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.dummy_sample\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0.1 * sample\r\r if num_inference_steps is not None and hasattr(_UpperCAmelCase ,\t\t\t\t\t\t\"\"\"set_timesteps\"\"\" ):\r scheduler.set_timesteps(_UpperCAmelCase )\r elif num_inference_steps is not None and not hasattr(_UpperCAmelCase ,\t\t\t\t\t\t\"\"\"set_timesteps\"\"\" ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnum_inference_steps\r\r # copy over dummy past residuals (must be done after set_timesteps)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdummy_past_residuals[:]\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler.step_prk(_UpperCAmelCase ,\t\t\t\t\t\t0 ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase ).prev_sample\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler.step_prk(_UpperCAmelCase ,\t\t\t\t\t\t1 ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase ).prev_sample\r\r self.assertEqual(output_a.shape ,\t\t\t\t\t\tsample.shape )\r self.assertEqual(output_a.shape ,\t\t\t\t\t\toutput_a.shape )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler.step_plms(_UpperCAmelCase ,\t\t\t\t\t\t0 ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase ).prev_sample\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler.step_plms(_UpperCAmelCase ,\t\t\t\t\t\t1 ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase ).prev_sample\r\r self.assertEqual(output_a.shape ,\t\t\t\t\t\tsample.shape )\r self.assertEqual(output_a.shape ,\t\t\t\t\t\toutput_a.shape )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r for timesteps in [1_00, 10_00]:\r self.check_over_configs(num_train_timesteps=_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r for steps_offset in [0, 1]:\r self.check_over_configs(steps_offset=_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.scheduler_classes[0]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_scheduler_config(steps_offset=1 )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler_class(**_UpperCAmelCase )\r scheduler.set_timesteps(10 )\r assert torch.equal(\r scheduler.timesteps ,\t\t\t\t\t\ttorch.LongTensor(\r [9_01, 8_51, 8_51, 8_01, 8_01, 7_51, 7_51, 7_01, 7_01, 6_51, 6_51, 6_01, 6_01, 5_01, 4_01, 3_01, 2_01, 1_01, 1] ) ,\t\t\t\t\t\t)\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r for beta_start, beta_end in zip([0.0001, 0.001] ,\t\t\t\t\t\t[0.002, 0.02] ):\r self.check_over_configs(beta_start=_UpperCAmelCase ,\t\t\t\t\t\tbeta_end=_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r for schedule in [\"linear\", \"squaredcos_cap_v2\"]:\r self.check_over_configs(beta_schedule=_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r for prediction_type in [\"epsilon\", \"v_prediction\"]:\r self.check_over_configs(prediction_type=_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r for t in [1, 5, 10]:\r self.check_over_forward(time_step=_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tAny ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r for t, num_inference_steps in zip([1, 5, 10] ,\t\t\t\t\t\t[10, 50, 1_00] ):\r self.check_over_forward(num_inference_steps=_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t27\r\r for scheduler_class in self.scheduler_classes:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.dummy_sample\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0.1 * sample\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_scheduler_config()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler_class(**_UpperCAmelCase )\r\r scheduler.set_timesteps(_UpperCAmelCase )\r\r # before power of 3 fix, would error on first step, so we only need to do two\r for i, t in enumerate(scheduler.prk_timesteps[:2] ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler.step_prk(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ).prev_sample\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r with self.assertRaises(_UpperCAmelCase ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.scheduler_classes[0]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_scheduler_config()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler_class(**_UpperCAmelCase )\r\r scheduler.step_plms(self.dummy_sample ,\t\t\t\t\t\t1 ,\t\t\t\t\t\tself.dummy_sample ).prev_sample\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.full_loop()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.sum(torch.abs(_UpperCAmelCase ) )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.mean(torch.abs(_UpperCAmelCase ) )\r\r assert abs(result_sum.item() - 198.1318 ) < 1E-2\r assert abs(result_mean.item() - 0.2580 ) < 1E-3\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.full_loop(prediction_type=\"\"\"v_prediction\"\"\" )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.sum(torch.abs(_UpperCAmelCase ) )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.mean(torch.abs(_UpperCAmelCase ) )\r\r assert abs(result_sum.item() - 67.3986 ) < 1E-2\r assert abs(result_mean.item() - 0.0878 ) < 1E-3\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tAny ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.full_loop(set_alpha_to_one=_UpperCAmelCase ,\t\t\t\t\t\tbeta_start=0.01 )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.sum(torch.abs(_UpperCAmelCase ) )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.mean(torch.abs(_UpperCAmelCase ) )\r\r assert abs(result_sum.item() - 230.0399 ) < 1E-2\r assert abs(result_mean.item() - 0.2995 ) < 1E-3\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.full_loop(set_alpha_to_one=_UpperCAmelCase ,\t\t\t\t\t\tbeta_start=0.01 )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.sum(torch.abs(_UpperCAmelCase ) )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.mean(torch.abs(_UpperCAmelCase ) )\r\r assert abs(result_sum.item() - 186.9482 ) < 1E-2\r assert abs(result_mean.item() - 0.2434 ) < 1E-3\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152348,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport unittest\r\rfrom transformers.testing_utils import CaptureStdout\rfrom transformers.tools.python_interpreter import evaluate\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r return x + 2\r\r\r\r\r\r\r\rclass lowerCAmelCase_ ( unittest.TestCase ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"x = 3\"\"\"\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{}\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tevaluate(_UpperCAmelCase ,\t\t\t\t\t\t{} ,\t\t\t\t\t\tstate=_UpperCAmelCase )\r assert result == 3\r self.assertDictEqual(_UpperCAmelCase ,\t\t\t\t\t\t{\"\"\"x\"\"\": 3} )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"x = y\"\"\"\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\"\"\"y\"\"\": 5}\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tevaluate(_UpperCAmelCase ,\t\t\t\t\t\t{} ,\t\t\t\t\t\tstate=_UpperCAmelCase )\r # evaluate returns the value of the last assignment.\r assert result == 5\r self.assertDictEqual(_UpperCAmelCase ,\t\t\t\t\t\t{\"\"\"x\"\"\": 5, \"\"\"y\"\"\": 5} )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tTuple ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"y = add_two(x)\"\"\"\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\"\"\"x\"\"\": 3}\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tevaluate(_UpperCAmelCase ,\t\t\t\t\t\t{\"\"\"add_two\"\"\": add_two} ,\t\t\t\t\t\tstate=_UpperCAmelCase )\r assert result == 5\r self.assertDictEqual(_UpperCAmelCase ,\t\t\t\t\t\t{\"\"\"x\"\"\": 3, \"\"\"y\"\"\": 5} )\r\r # Won't work without the tool\r with CaptureStdout() as out:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tevaluate(_UpperCAmelCase ,\t\t\t\t\t\t{} ,\t\t\t\t\t\tstate=_UpperCAmelCase )\r assert result is None\r assert \"tried to execute add_two\" in out.out\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"x = 3\"\"\"\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{}\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tevaluate(_UpperCAmelCase ,\t\t\t\t\t\t{} ,\t\t\t\t\t\tstate=_UpperCAmelCase )\r assert result == 3\r self.assertDictEqual(_UpperCAmelCase ,\t\t\t\t\t\t{\"\"\"x\"\"\": 3} )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"test_dict = {'x': x, 'y': add_two(x)}\"\"\"\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\"\"\"x\"\"\": 3}\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tevaluate(_UpperCAmelCase ,\t\t\t\t\t\t{\"\"\"add_two\"\"\": add_two} ,\t\t\t\t\t\tstate=_UpperCAmelCase )\r self.assertDictEqual(_UpperCAmelCase ,\t\t\t\t\t\t{\"\"\"x\"\"\": 3, \"\"\"y\"\"\": 5} )\r self.assertDictEqual(_UpperCAmelCase ,\t\t\t\t\t\t{\"\"\"x\"\"\": 3, \"\"\"test_dict\"\"\": {\"\"\"x\"\"\": 3, \"\"\"y\"\"\": 5}} )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"x = 3\\ny = 5\"\"\"\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{}\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tevaluate(_UpperCAmelCase ,\t\t\t\t\t\t{} ,\t\t\t\t\t\tstate=_UpperCAmelCase )\r # evaluate returns the value of the last assignment.\r assert result == 5\r self.assertDictEqual(_UpperCAmelCase ,\t\t\t\t\t\t{\"\"\"x\"\"\": 3, \"\"\"y\"\"\": 5} )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"text = f'This is x: {x}.'\"\"\"\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\"\"\"x\"\"\": 3}\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tevaluate(_UpperCAmelCase ,\t\t\t\t\t\t{} ,\t\t\t\t\t\tstate=_UpperCAmelCase )\r # evaluate returns the value of the last assignment.\r assert result == \"This is x: 3.\"\r self.assertDictEqual(_UpperCAmelCase ,\t\t\t\t\t\t{\"\"\"x\"\"\": 3, \"\"\"text\"\"\": \"\"\"This is x: 3.\"\"\"} )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tAny ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"if x <= 3:\\n y = 2\\nelse:\\n y = 5\"\"\"\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\"\"\"x\"\"\": 3}\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tevaluate(_UpperCAmelCase ,\t\t\t\t\t\t{} ,\t\t\t\t\t\tstate=_UpperCAmelCase )\r # evaluate returns the value of the last assignment.\r assert result == 2\r self.assertDictEqual(_UpperCAmelCase ,\t\t\t\t\t\t{\"\"\"x\"\"\": 3, \"\"\"y\"\"\": 2} )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\"\"\"x\"\"\": 8}\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tevaluate(_UpperCAmelCase ,\t\t\t\t\t\t{} ,\t\t\t\t\t\tstate=_UpperCAmelCase )\r # evaluate returns the value of the last assignment.\r assert result == 5\r self.assertDictEqual(_UpperCAmelCase ,\t\t\t\t\t\t{\"\"\"x\"\"\": 8, \"\"\"y\"\"\": 5} )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tAny ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"test_list = [x, add_two(x)]\"\"\"\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\"\"\"x\"\"\": 3}\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tevaluate(_UpperCAmelCase ,\t\t\t\t\t\t{\"\"\"add_two\"\"\": add_two} ,\t\t\t\t\t\tstate=_UpperCAmelCase )\r self.assertListEqual(_UpperCAmelCase ,\t\t\t\t\t\t[3, 5] )\r self.assertDictEqual(_UpperCAmelCase ,\t\t\t\t\t\t{\"\"\"x\"\"\": 3, \"\"\"test_list\"\"\": [3, 5]} )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"y = x\"\"\"\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\"\"\"x\"\"\": 3}\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tevaluate(_UpperCAmelCase ,\t\t\t\t\t\t{} ,\t\t\t\t\t\tstate=_UpperCAmelCase )\r assert result == 3\r self.assertDictEqual(_UpperCAmelCase ,\t\t\t\t\t\t{\"\"\"x\"\"\": 3, \"\"\"y\"\"\": 3} )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"test_list = [x, add_two(x)]\\ntest_list[1]\"\"\"\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\"\"\"x\"\"\": 3}\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tevaluate(_UpperCAmelCase ,\t\t\t\t\t\t{\"\"\"add_two\"\"\": add_two} ,\t\t\t\t\t\tstate=_UpperCAmelCase )\r assert result == 5\r self.assertDictEqual(_UpperCAmelCase ,\t\t\t\t\t\t{\"\"\"x\"\"\": 3, \"\"\"test_list\"\"\": [3, 5]} )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"test_dict = {'x': x, 'y': add_two(x)}\\ntest_dict['y']\"\"\"\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\"\"\"x\"\"\": 3}\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tevaluate(_UpperCAmelCase ,\t\t\t\t\t\t{\"\"\"add_two\"\"\": add_two} ,\t\t\t\t\t\tstate=_UpperCAmelCase )\r assert result == 5\r self.assertDictEqual(_UpperCAmelCase ,\t\t\t\t\t\t{\"\"\"x\"\"\": 3, \"\"\"test_dict\"\"\": {\"\"\"x\"\"\": 3, \"\"\"y\"\"\": 5}} )\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"x = 0\\nfor i in range(3):\\n x = i\"\"\"\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{}\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tevaluate(_UpperCAmelCase ,\t\t\t\t\t\t{\"\"\"range\"\"\": range} ,\t\t\t\t\t\tstate=_UpperCAmelCase )\r assert result == 2\r self.assertDictEqual(_UpperCAmelCase ,\t\t\t\t\t\t{\"\"\"x\"\"\": 2, \"\"\"i\"\"\": 2} )\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rfrom ...configuration_utils import PretrainedConfig\rfrom ...utils import logging\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tlogging.get_logger(__name__)\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t{\r 'google/vivit-b-16x2-kinetics400': (\r 'https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json'\r ),\r # See all Vivit models at https://huggingface.co/models?filter=vivit\r}\r\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r lowerCAmelCase_ : Optional[int] = \"\"\"vivit\"\"\"\r\r\r def __init__( self\t\t\t:\t\tList[str] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any]=2_24 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str]=32 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny=[2, 16, 16] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint=3 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any]=7_68 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=12 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict=12 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any]=30_72 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int]=\"gelu_fast\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=0.0 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple=0.0 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int]=0.02 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any]=1E-06 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str]=True ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\thidden_size\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnum_hidden_layers\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnum_attention_heads\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tintermediate_size\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\thidden_act\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\thidden_dropout_prob\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tattention_probs_dropout_prob\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tinitializer_range\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlayer_norm_eps\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\timage_size\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnum_frames\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttubelet_size\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnum_channels\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tqkv_bias\r\r super().__init__(**_UpperCAmelCase )\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152349,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport warnings\r\rfrom ...utils import logging\rfrom .image_processing_deformable_detr import DeformableDetrImageProcessor\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tlogging.get_logger(__name__)\r\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def __init__( self\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t*_UpperCAmelCase\t\t\t:\t\tAny ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r warnings.warn(\r \"\"\"The class DeformableDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers.\"\"\"\r \"\"\" Please use DeformableDetrImageProcessor instead.\"\"\" ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t)\r super().__init__(*_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase )\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport warnings\r\rfrom ...utils import logging\rfrom .image_processing_deit import DeiTImageProcessor\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tlogging.get_logger(__name__)\r\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def __init__( self\t\t\t:\t\tList[str] ,\t\t\t\t\t\t*_UpperCAmelCase\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r warnings.warn(\r \"\"\"The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please\"\"\"\r \"\"\" use DeiTImageProcessor instead.\"\"\" ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t)\r super().__init__(*_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase )\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152350,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport datasets\rimport faiss\rimport numpy as np\rimport streamlit as st\rimport torch\rfrom elasticsearch import Elasticsearch\rfrom elia_utils import (\r embed_questions_for_retrieval,\r make_qa_sas_model,\r qa_sas_generate,\r query_es_index,\r query_qa_dense_index,\r)\r\rimport transformers\rfrom transformers import AutoModel, AutoModelForSeqaSeqLM, AutoTokenizer\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'bart'\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tTrue\r\r@st.cache(allow_output_mutation=SCREAMING_SNAKE_CASE__\t\t\t\t)\rdef _UpperCamelCase\t\t\t\t\t( ):\r '''simple docstring'''\r\r\r\r\r\r if LOAD_DENSE_INDEX:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tAutoTokenizer.from_pretrained(\"\"\"yjernite/retribert-base-uncased\"\"\"\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tAutoModel.from_pretrained(\"\"\"yjernite/retribert-base-uncased\"\"\"\t\t\t\t).to(\"\"\"cuda:0\"\"\"\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tqar_model.eval()\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t(None, None)\r if MODEL_TYPE == \"bart\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tAutoTokenizer.from_pretrained(\"\"\"yjernite/bart_eli5\"\"\"\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tAutoModelForSeqaSeqLM.from_pretrained(\"\"\"yjernite/bart_eli5\"\"\"\t\t\t\t).to(\"\"\"cuda:0\"\"\"\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.load(\"\"\"seq2seq_models/eli5_bart_model_blm_2.pth\"\"\"\t\t\t\t)\r sas_model.load_state_dict(save_dict[\"\"\"model\"\"\"]\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tsas_model.eval()\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmake_qa_sas_model(\r model_name=\"\"\"t5-small\"\"\"\t\t\t\t, from_file=\"\"\"seq2seq_models/eli5_t5_model_1024_4.pth\"\"\"\t\t\t\t, device=\"\"\"cuda:0\"\"\"\t\t\t\t)\r return (qar_tokenizer, qar_model, sas_tokenizer, sas_model)\r\r@st.cache(allow_output_mutation=SCREAMING_SNAKE_CASE__\t\t\t\t)\rdef _UpperCamelCase\t\t\t\t\t( ):\r '''simple docstring'''\r\r\r\r\r\r if LOAD_DENSE_INDEX:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tfaiss.StandardGpuResources()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdatasets.load_dataset(path=\"\"\"wiki_snippets\"\"\"\t\t\t\t, name=\"\"\"wiki40b_en_100_0\"\"\"\t\t\t\t)[\"\"\"train\"\"\"]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnp.memmap(\r \"\"\"wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat\"\"\"\t\t\t\t, dtype=\"\"\"float32\"\"\"\t\t\t\t, mode=\"\"\"r\"\"\"\t\t\t\t, shape=(wikiaab_passages.num_rows, 128)\t\t\t\t, )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tfaiss.IndexFlatIP(128\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tfaiss.index_cpu_to_gpu(SCREAMING_SNAKE_CASE__\t\t\t\t, 1\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r wikiaab_gpu_index_flat.add(SCREAMING_SNAKE_CASE__\t\t\t\t) # TODO fix for larger GPU\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t(None, None)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tElasticsearch([{\"\"\"host\"\"\": \"\"\"localhost\"\"\", \"\"\"port\"\"\": \"\"\"9200\"\"\"}]\t\t\t\t)\r return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)\r\r@st.cache(allow_output_mutation=SCREAMING_SNAKE_CASE__\t\t\t\t)\rdef _UpperCamelCase\t\t\t\t\t( ):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdatasets.load_dataset(\"\"\"eli5\"\"\"\t\t\t\t, name=\"\"\"LFQA_reddit\"\"\"\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\telia[\"\"\"train_eli5\"\"\"]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnp.memmap(\r \"\"\"eli5_questions_reps.dat\"\"\"\t\t\t\t, dtype=\"\"\"float32\"\"\"\t\t\t\t, mode=\"\"\"r\"\"\"\t\t\t\t, shape=(elia_train.num_rows, 128)\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tfaiss.IndexFlatIP(128\t\t\t\t)\r eli5_train_q_index.add(SCREAMING_SNAKE_CASE__\t\t\t\t)\r return (elia_train, eli5_train_q_index)\r\r\rUpperCAmelCase_ ,\t\t\t\t\t\tUpperCAmelCase_ ,\t\t\t\t\t\tUpperCAmelCase_\t\t\t\t =\t\t\t\t\tload_indexes()\rUpperCAmelCase_ ,\t\t\t\t\t\tUpperCAmelCase_ ,\t\t\t\t\t\tUpperCAmelCase_ ,\t\t\t\t\t\tUpperCAmelCase_\t\t\t\t =\t\t\t\t\tload_models()\rUpperCAmelCase_ ,\t\t\t\t\t\tUpperCAmelCase_\t\t\t\t =\t\t\t\t\tload_train_data()\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tTuple\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr=10\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tembed_questions_for_retrieval([question]\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\teli5_train_q_index.search(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[elia_train[int(SCREAMING_SNAKE_CASE__\t\t\t\t)] for i in I[0]]\r return nn_examples\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tTuple\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tAny=\"wiki40b\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr=\"dense\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr=10\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r if source == \"none\":\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t(\"\"\"
\"\"\".join([\"\"\"\"\"\" for _ in range(11\t\t\t\t)]\t\t\t\t).strip(), [])\r else:\r if method == \"dense\":\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tquery_qa_dense_index(\r SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tquery_es_index(\r SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, index_name=\"\"\"english_wiki40b_snippets_100w\"\"\"\t\t\t\t, n_results=SCREAMING_SNAKE_CASE__\t\t\t\t, )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[\r (res[\"\"\"article_title\"\"\"], res[\"\"\"section_title\"\"\"].strip(), res[\"\"\"score\"\"\"], res[\"\"\"passage_text\"\"\"]) for res in hit_lst\r ]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"question: {} context: {}\"\"\".format(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r return question_doc, support_list\r\r@st.cache(\r hash_funcs={\r torch.Tensor: (lambda SCREAMING_SNAKE_CASE__\t\t\t\t: None),\r transformers.models.bart.tokenization_bart.BartTokenizer: (lambda SCREAMING_SNAKE_CASE__\t\t\t\t: None),\r }\t\t\t\t)\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[int]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tTuple\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tUnion[str, Any]=64\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[int]=256\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tDict=False\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tAny=2\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr=0.95\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr=0.8\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r with torch.no_grad():\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tqa_sas_generate(\r SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, num_answers=1\t\t\t\t, num_beams=SCREAMING_SNAKE_CASE__\t\t\t\t, min_len=SCREAMING_SNAKE_CASE__\t\t\t\t, max_len=SCREAMING_SNAKE_CASE__\t\t\t\t, do_sample=SCREAMING_SNAKE_CASE__\t\t\t\t, temp=SCREAMING_SNAKE_CASE__\t\t\t\t, top_p=SCREAMING_SNAKE_CASE__\t\t\t\t, top_k=SCREAMING_SNAKE_CASE__\t\t\t\t, max_input_length=1024\t\t\t\t, device=\"\"\"cuda:0\"\"\"\t\t\t\t, )[0]\r return (answer, support_list)\r\r\rst.title('Long Form Question Answering with ELI5')\r\r# Start sidebar\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t''\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'\\n\\n \\n \\n \\n \\n \\n %s\\n \\n \\n\\n' % (\r header_html,\r)\rst.sidebar.markdown(\r header_full,\r unsafe_allow_html=True,\r)\r\r# Long Form QA with ELI5 and Wikipedia\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'\\nThis demo presents a model trained to [provide long-form answers to open-domain questions](https://yjernite.github.io/lfqa.html).\\nFirst, a document retriever fetches a set of relevant Wikipedia passages given the question from the [Wiki40b](https://research.google/pubs/pub49029/) dataset,\\na pre-processed fixed snapshot of Wikipedia.\\n'\rst.sidebar.markdown(description, unsafe_allow_html=True)\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t[\r 'Answer the question',\r 'View the retrieved document only',\r 'View the most similar ELI5 question and answer',\r 'Show me everything, please!',\r]\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tst.sidebar.checkbox('Demo options')\rif demo_options:\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tst.sidebar.selectbox(\r '',\r action_list,\r index=3,\r )\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\taction_list.index(action_st)\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tst.sidebar.selectbox(\r '',\r ['Show full text of passages', 'Show passage section titles'],\r index=0,\r )\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tshow_type == 'Show full text of passages'\relse:\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\t3\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tTrue\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tst.sidebar.checkbox('Retrieval options')\rif retrieval_options:\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\t'\\n ### Information retriever options\\n\\n The **sparse** retriever uses ElasticSearch, while the **dense** retriever uses max-inner-product search between a question and passage embedding\\n trained using the [ELI5](https://arxiv.org/abs/1907.09190) questions-answer pairs.\\n The answer is then generated by sequence to sequence model which takes the question and retrieved document as input.\\n '\r st.sidebar.markdown(retriever_info)\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tst.sidebar.selectbox('Which Wikipedia format should the model use?', ['wiki40b', 'none'])\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tst.sidebar.selectbox('Which Wikipedia indexer should the model use?', ['dense', 'sparse', 'mixed'])\relse:\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\t'wiki40b'\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\t'dense'\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'beam'\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t2\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t6_4\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t2_5_6\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tNone\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tNone\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tst.sidebar.checkbox('Generation options')\rif generate_options:\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\t'\\n ### Answer generation options\\n\\n The sequence-to-sequence model was initialized with [BART](https://huggingface.co/facebook/bart-large)\\n weights and fine-tuned on the ELI5 QA pairs and retrieved documents. You can use the model for greedy decoding with\\n **beam** search, or **sample** from the decoder\\'s output probabilities.\\n '\r st.sidebar.markdown(generate_info)\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tst.sidebar.selectbox('Would you like to use beam search or sample an answer?', ['beam', 'sampled'])\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tst.sidebar.slider(\r 'Minimum generation length', min_value=8, max_value=2_5_6, value=6_4, step=8, format=None, key=None\r )\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tst.sidebar.slider(\r 'Maximum generation length', min_value=6_4, max_value=5_1_2, value=2_5_6, step=1_6, format=None, key=None\r )\r if sampled == \"beam\":\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tst.sidebar.slider('Beam size', min_value=1, max_value=8, value=2, step=None, format=None, key=None)\r else:\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tst.sidebar.slider(\r 'Nucleus sampling p', min_value=0.1, max_value=1.0, value=0.95, step=0.01, format=None, key=None\r )\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tst.sidebar.slider(\r 'Temperature', min_value=0.1, max_value=1.0, value=0.7, step=0.01, format=None, key=None\r )\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tNone\r\r# start main text\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t[\r '',\r 'How do people make chocolate?',\r 'Why do we get a fever when we are sick?',\r 'How can different animals perceive different colors?',\r 'What is natural language processing?',\r 'What\\'s the best way to treat a sunburn?',\r 'What exactly are vitamins ?',\r 'How does nuclear energy provide electricity?',\r 'What\\'s the difference between viruses and bacteria?',\r 'Why are flutes classified as woodwinds when most of them are made out of metal ?',\r 'Why do people like drinking coffee even though it tastes so bad?',\r 'What happens when wine ages? How does it make the wine taste better?',\r 'If an animal is an herbivore, where does it get the protein that it needs to survive if it only eats grass?',\r 'How can we set a date to the beginning or end of an artistic period? Doesn\\'t the change happen gradually?',\r 'How does New Zealand have so many large bird predators?',\r]\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tst.selectbox(\r 'What would you like to ask? ---- select to enter a new query',\r questions_list,\r index=1,\r)\rif question_s == \"\":\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tst.text_input('Enter your question here:', '')\relse:\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tquestion_s\r\rif st.button('Show me!'):\r if action in [0, 1, 3]:\r if index_type == \"mixed\":\r UpperCAmelCase_ ,\t\t\t\t\t\tUpperCAmelCase_\t\t\t\t =\t\t\t\t\tmake_support(question, source=wiki_source, method='dense', n_results=1_0)\r UpperCAmelCase_ ,\t\t\t\t\t\tUpperCAmelCase_\t\t\t\t =\t\t\t\t\tmake_support(question, source=wiki_source, method='sparse', n_results=1_0)\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\t[]\r for res_d, res_s in zip(support_list_dense, support_list_sparse):\r if tuple(res_d) not in support_list:\r support_list += [tuple(res_d)]\r if tuple(res_s) not in support_list:\r support_list += [tuple(res_s)]\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tsupport_list[:1_0]\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\t'
' + '
'.join([res[-1] for res in support_list])\r else:\r UpperCAmelCase_ ,\t\t\t\t\t\tUpperCAmelCase_\t\t\t\t =\t\t\t\t\tmake_support(question, source=wiki_source, method=index_type, n_results=1_0)\r if action in [0, 3]:\r UpperCAmelCase_ ,\t\t\t\t\t\tUpperCAmelCase_\t\t\t\t =\t\t\t\t\tanswer_question(\r question_doc,\r sas_model,\r sas_tokenizer,\r min_len=min_len,\r max_len=int(max_len),\r sampling=(sampled == 'sampled'),\r n_beams=n_beams,\r top_p=top_p,\r temp=temp,\r )\r st.markdown('### The model generated answer is:')\r st.write(answer)\r if action in [0, 1, 3] and wiki_source != \"none\":\r st.markdown('--- \\n ### The model is drawing information from the following Wikipedia passages:')\r for i, res in enumerate(support_list):\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\t'https://en.wikipedia.org/wiki/{}'.format(res[0].replace(' ', '_'))\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tres[1].strip()\r if sec_titles == \"\":\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\t'[{}]({})'.format(res[0], wiki_url)\r else:\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tsec_titles.split(' & ')\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\t' & '.join(\r ['[{}]({}#{})'.format(sec.strip(), wiki_url, sec.strip().replace(' ', '_')) for sec in sec_list]\r )\r st.markdown(\r '{0:02d} - **Article**: {1:<18}
_Section_: {2}'.format(i + 1, res[0], sections),\r unsafe_allow_html=True,\r )\r if show_passages:\r st.write(\r '> ' + res[-1] + '', unsafe_allow_html=True\r )\r if action in [2, 3]:\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tfind_nearest_training(question)\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tnn_train_list[0]\r st.markdown(\r '--- \\n ### The most similar question in the ELI5 training set was: \\n\\n {}'.format(train_exple['title'])\r )\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\t[\r '{}. {}'.format(i + 1, ' \\n'.join([line.strip() for line in ans.split('\\n') if line.strip() != '']))\r for i, (ans, sc) in enumerate(zip(train_exple['answers']['text'], train_exple['answers']['score']))\r if i == 0 or sc > 2\r ]\r st.markdown('##### Its answers were: \\n\\n {}'.format('\\n'.join(answers_st)))\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'\\n---\\n\\n**Disclaimer**\\n\\n*The intent of this app is to provide some (hopefully entertaining) insights into the behavior of a current LFQA system.\\nEvaluating biases of such a model and ensuring factual generations are still very much open research problems.\\nTherefore, until some significant progress is achieved, we caution against using the generated answers for practical purposes.*\\n'\r\r\r\r\r\r\r\rst.sidebar.markdown(disclaimer, unsafe_allow_html=True)\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport os\rimport unicodedata\rfrom shutil import copyfile\rfrom typing import Any, Dict, List, Optional, Tuple\r\rimport sentencepiece as spm\r\rfrom ...tokenization_utils import AddedToken, PreTrainedTokenizer\rfrom ...utils import SPIECE_UNDERLINE, logging\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tlogging.get_logger(__name__)\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t{'vocab_file': 'spiece.model'}\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t{\r 'vocab_file': {\r 'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model',\r }\r}\r\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def __init__( self\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny=False ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint=True ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=False ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict=\"~~\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint=\"~~\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict=\"\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple=\"\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any]=\"\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint=\"\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=\"\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str]=[\"\", \"\"] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Dict[str, Any]] = None ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tAddedToken(_UpperCAmelCase ,\t\t\t\t\t\tlstrip=_UpperCAmelCase ,\t\t\t\t\t\trstrip=_UpperCAmelCase ) if isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ) else mask_token\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{} if sp_model_kwargs is None else sp_model_kwargs\r\r super().__init__(\r do_lower_case=_UpperCAmelCase ,\t\t\t\t\t\tremove_space=_UpperCAmelCase ,\t\t\t\t\t\tkeep_accents=_UpperCAmelCase ,\t\t\t\t\t\tbos_token=_UpperCAmelCase ,\t\t\t\t\t\teos_token=_UpperCAmelCase ,\t\t\t\t\t\tunk_token=_UpperCAmelCase ,\t\t\t\t\t\tsep_token=_UpperCAmelCase ,\t\t\t\t\t\tpad_token=_UpperCAmelCase ,\t\t\t\t\t\tcls_token=_UpperCAmelCase ,\t\t\t\t\t\tmask_token=_UpperCAmelCase ,\t\t\t\t\t\tadditional_special_tokens=_UpperCAmelCase ,\t\t\t\t\t\tsp_model_kwargs=self.sp_model_kwargs ,\t\t\t\t\t\t**_UpperCAmelCase ,\t\t\t\t\t\t)\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t3\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdo_lower_case\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tremove_space\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tkeep_accents\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvocab_file\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tspm.SentencePieceProcessor(**self.sp_model_kwargs )\r self.sp_model.Load(_UpperCAmelCase )\r\r try:\r import jieba\r except ModuleNotFoundError as error:\r raise error.__class__(\r \"\"\"You need to install jieba to use CpmTokenizer or CpmTokenizerFast. \"\"\"\r \"\"\"See https://pypi.org/project/jieba/ for installation.\"\"\" )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tjieba\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tstr.maketrans(\"\"\" \\n\"\"\" ,\t\t\t\t\t\t\"\"\"\\u2582\\u2583\"\"\" )\r\r\r @property\r # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return len(self.sp_model )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{self.convert_ids_to_tokens(_UpperCAmelCase ): i for i in range(self.vocab_size )}\r vocab.update(self.added_tokens_encoder )\r return vocab\r\r\r def __getstate__( self\t\t\t:\t\tDict ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.__dict__.copy()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r return state\r\r\r def __setstate__( self\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\td\r\r # for backward compatibility\r if not hasattr(self ,\t\t\t\t\t\t\"\"\"sp_model_kwargs\"\"\" ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{}\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tspm.SentencePieceProcessor(**self.sp_model_kwargs )\r self.sp_model.Load(self.vocab_file )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if self.remove_space:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\" \"\"\".join(inputs.strip().split() )\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tinputs\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\toutputs.replace(\"\"\"``\"\"\" ,\t\t\t\t\t\t\"\"\"\\\"\"\"\" ).replace(\"\"\"''\"\"\" ,\t\t\t\t\t\t\"\"\"\\\"\"\"\" )\r\r if not self.keep_accents:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tunicodedata.normalize(\"\"\"NFKD\"\"\" ,\t\t\t\t\t\t_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"\"\"\".join([c for c in outputs if not unicodedata.combining(_UpperCAmelCase )] )\r if self.do_lower_case:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\toutputs.lower()\r\r return outputs\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tTuple ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.preprocess_text(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.sp_model.encode(_UpperCAmelCase ,\t\t\t\t\t\tout_type=_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[]\r for piece in pieces:\r if len(_UpperCAmelCase ) > 1 and piece[-1] == str(\"\"\",\"\"\" ) and piece[-2].isdigit():\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.sp_model.EncodeAsPieces(piece[:-1].replace(_UpperCAmelCase ,\t\t\t\t\t\t\"\"\"\"\"\" ) )\r if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:\r if len(cur_pieces[0] ) == 1:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tcur_pieces[1:]\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tcur_pieces[0][1:]\r cur_pieces.append(piece[-1] )\r new_pieces.extend(_UpperCAmelCase )\r else:\r new_pieces.append(_UpperCAmelCase )\r\r return new_pieces\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return self.sp_model.PieceToId(_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return self.sp_model.IdToPiece(_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"\"\"\".join(_UpperCAmelCase ).replace(_UpperCAmelCase ,\t\t\t\t\t\t\"\"\" \"\"\" ).strip()\r return out_string\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[List[int]] = None ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[self.sep_token_id]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[self.cls_token_id]\r if token_ids_a is None:\r return token_ids_a + sep + cls\r return token_ids_a + sep + token_ids_a + sep + cls\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[List[int]] = None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tbool = False ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if already_has_special_tokens:\r return super().get_special_tokens_mask(\r token_ids_a=_UpperCAmelCase ,\t\t\t\t\t\ttoken_ids_a=_UpperCAmelCase ,\t\t\t\t\t\talready_has_special_tokens=_UpperCAmelCase )\r\r if token_ids_a is not None:\r return ([0] * len(_UpperCAmelCase )) + [1] + ([0] * len(_UpperCAmelCase )) + [1, 1]\r return ([0] * len(_UpperCAmelCase )) + [1, 1]\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[List[int]] = None ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[self.sep_token_id]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[2]\r\r if token_ids_a is None:\r return len(token_ids_a + sep ) * [0] + cls_segment_id\r return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[str] = None ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if not os.path.isdir(_UpperCAmelCase ):\r logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )\r return\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tos.path.join(\r _UpperCAmelCase ,\t\t\t\t\t\t(filename_prefix + \"\"\"-\"\"\" if filename_prefix else \"\"\"\"\"\") + VOCAB_FILES_NAMES[\"\"\"vocab_file\"\"\"] )\r\r if os.path.abspath(self.vocab_file ) != os.path.abspath(_UpperCAmelCase ) and os.path.isfile(self.vocab_file ):\r copyfile(self.vocab_file ,\t\t\t\t\t\t_UpperCAmelCase )\r elif not os.path.isfile(self.vocab_file ):\r with open(_UpperCAmelCase ,\t\t\t\t\t\t\"\"\"wb\"\"\" ) as fi:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.sp_model.serialized_model_proto()\r fi.write(_UpperCAmelCase )\r\r return (out_vocab_file,)\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tTuple ,\t\t\t\t\t\t*_UpperCAmelCase\t\t\t:\t\tTuple ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tsuper()._decode(*_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttext.replace(\"\"\" \"\"\" ,\t\t\t\t\t\t\"\"\"\"\"\" ).replace(\"\"\"\\u2582\"\"\" ,\t\t\t\t\t\t\"\"\" \"\"\" ).replace(\"\"\"\\u2583\"\"\" ,\t\t\t\t\t\t\"\"\"\\n\"\"\" )\r return text\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152351,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rfrom __future__ import annotations\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tlist[int | str]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r create_state_space_tree(SCREAMING_SNAKE_CASE__\t\t\t\t, []\t\t\t\t, 0\t\t\t\t, [0 for i in range(len(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t)]\t\t\t\t)\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tlist[int | str]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tlist[int | str]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tlist[int]\t\t\t\t, ):\r '''simple docstring'''\r\r\r\r\r\r if index == len(SCREAMING_SNAKE_CASE__\t\t\t\t):\r print(SCREAMING_SNAKE_CASE__\t\t\t\t)\r return\r\r for i in range(len(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t):\r if not index_used[i]:\r current_sequence.append(sequence[i]\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTrue\r create_state_space_tree(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, index + 1\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r current_sequence.pop()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t[3, 1, 2, 4]\rgenerate_all_permutations(sequence)\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t[\"A\", \"B\", \"C\"]\r\r\r\r\r\r\r\rgenerate_all_permutations(sequence_a)\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport argparse\rimport logging\rimport os\r\rimport datasets\rimport tensorflow as tf\r\rfrom transformers import AutoTokenizer\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tlogging.getLogger(__name__)\r\rdef _UpperCamelCase\t\t\t\t\t( ):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\targparse.ArgumentParser(\r description=\"\"\"Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset.\"\"\"\t\t\t\t)\r parser.add_argument(\r \"\"\"--dataset_name\"\"\"\t\t\t\t, type=SCREAMING_SNAKE_CASE__\t\t\t\t, default=\"\"\"wikitext\"\"\"\t\t\t\t, help=\"\"\"Name of the training. Explore datasets at: hf.co/datasets.\"\"\"\t\t\t\t, )\r parser.add_argument(\r \"\"\"--dataset_config\"\"\"\t\t\t\t, type=SCREAMING_SNAKE_CASE__\t\t\t\t, default=\"\"\"wikitext-103-raw-v1\"\"\"\t\t\t\t, help=\"\"\"Configuration name of the dataset.\"\"\"\t\t\t\t)\r parser.add_argument(\r \"\"\"--tokenizer_name_or_path\"\"\"\t\t\t\t, type=SCREAMING_SNAKE_CASE__\t\t\t\t, default=\"\"\"sayakpaul/unigram-tokenizer-wikitext\"\"\"\t\t\t\t, help=\"\"\"Tokenizer identifier. Can be a local filepath or a Hub identifier.\"\"\"\t\t\t\t, )\r parser.add_argument(\r \"\"\"--shard_size\"\"\"\t\t\t\t, type=SCREAMING_SNAKE_CASE__\t\t\t\t, default=1000\t\t\t\t, help=\"\"\"Number of entries to go in a single shard.\"\"\"\t\t\t\t, )\r parser.add_argument(\"\"\"--split\"\"\"\t\t\t\t, type=SCREAMING_SNAKE_CASE__\t\t\t\t, default=\"\"\"train\"\"\"\t\t\t\t, choices=[\"\"\"train\"\"\", \"\"\"test\"\"\", \"\"\"validation\"\"\"]\t\t\t\t)\r parser.add_argument(\r \"\"\"--limit\"\"\"\t\t\t\t, default=SCREAMING_SNAKE_CASE__\t\t\t\t, type=SCREAMING_SNAKE_CASE__\t\t\t\t, help=\"\"\"Limit the number of shards (used for debugging).\"\"\"\t\t\t\t, )\r parser.add_argument(\r \"\"\"--max_length\"\"\"\t\t\t\t, type=SCREAMING_SNAKE_CASE__\t\t\t\t, default=512\t\t\t\t, help=\"\"\"Maximum sequence length. For training on TPUs, it helps to have a maximum\"\"\"\r \"\"\" sequence length that is a multiple of 8.\"\"\"\t\t\t\t, )\r parser.add_argument(\r \"\"\"--output_dir\"\"\"\t\t\t\t, default=\"\"\"tf-tpu\"\"\"\t\t\t\t, type=SCREAMING_SNAKE_CASE__\t\t\t\t, help=\"\"\"Output directory where the TFRecord shards will be saved. If the\"\"\"\r \"\"\" path is appended with `gs://` ('gs://tf-tpu', for example) then the TFRecord\"\"\"\r \"\"\" shards will be directly saved to a Google Cloud Storage bucket.\"\"\"\t\t\t\t, )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tparser.parse_args()\r return args\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tAny\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r def fn(SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tUnion[str, Any]\t\t\t\t):\r return tokenizer(examples[\"\"\"text\"\"\"]\t\t\t\t)\r\r return fn\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[int]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[]\r for i in range(len(tokenized_data[\"\"\"input_ids\"\"\"]\t\t\t\t)\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\r \"\"\"input_ids\"\"\": tf.train.Feature(intaa_list=tf.train.IntaaList(value=tokenized_data[\"\"\"input_ids\"\"\"][i]\t\t\t\t)\t\t\t\t),\r \"\"\"attention_mask\"\"\": tf.train.Feature(\r intaa_list=tf.train.IntaaList(value=tokenized_data[\"\"\"attention_mask\"\"\"][i]\t\t\t\t)\t\t\t\t),\r }\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttf.train.Features(feature=SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttf.train.Example(features=SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\texample.SerializeToString()\r records.append(SCREAMING_SNAKE_CASE__\t\t\t\t)\r return records\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tAny\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdatasets.load_dataset(args.dataset_name\t\t\t\t, args.dataset_config\t\t\t\t, split=args.split\t\t\t\t)\r\r if args.limit is not None:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmin(len(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t, args.limit\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdataset.select(range(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t)\r print(F'''Limiting the dataset to {args.limit} entries.'''\t\t\t\t)\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tAutoTokenizer.from_pretrained(args.tokenizer_name_or_path\t\t\t\t)\r\r # Handle output directory creation.\r # For serializing into a Google Cloud Storage Bucket, one needs to first\r # create a bucket.\r if \"gs\" not in args.output_dir:\r if not os.path.exists(args.output_dir\t\t\t\t):\r os.makedirs(args.output_dir\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tos.path.join(args.output_dir\t\t\t\t, args.split\t\t\t\t)\r if not os.path.exists(SCREAMING_SNAKE_CASE__\t\t\t\t):\r os.makedirs(SCREAMING_SNAKE_CASE__\t\t\t\t)\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tos.path.join(args.output_dir\t\t\t\t, args.split\t\t\t\t)\r\r # Tokenize the whole dataset at once.\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenize_function(SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdataset.map(SCREAMING_SNAKE_CASE__\t\t\t\t, batched=SCREAMING_SNAKE_CASE__\t\t\t\t, num_proc=4\t\t\t\t, remove_columns=[\"\"\"text\"\"\"]\t\t\t\t)\r\r # We need to concatenate all our texts together, and then split the result\r # into chunks of a fixed size, which we will call block_size. To do this, we\r # will use the map method again, with the option batched=True. When we use batched=True,\r # the function we pass to map() will be passed multiple inputs at once, allowing us\r # to group them into more or fewer examples than we had in the input.\r # This allows us to create our new fixed-length samples. The advantage of this\r # method is that we don't lose a whole lot of content from the dataset compared to the\r # case where we simply tokenize with a pre-defined max_length.\r\r def group_texts(SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t):\r # Concatenate all texts.\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{k: sum(examples[k]\t\t\t\t, []\t\t\t\t) for k in examples.keys()}\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlen(concatenated_examples[list(examples.keys()\t\t\t\t)[0]]\t\t\t\t)\r # We drop the small remainder, though you could add padding instead if the model supports it\r # In this, as in all things, we advise you to follow your heart 🫀\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t(total_length // args.max_length) * args.max_length\r # Split by chunks of max_len.\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\r k: [t[i : i + args.max_length] for i in range(0\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, args.max_length\t\t\t\t)]\r for k, t in concatenated_examples.items()\r }\r return result\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdataset_tokenized.map(SCREAMING_SNAKE_CASE__\t\t\t\t, batched=SCREAMING_SNAKE_CASE__\t\t\t\t, batch_size=1000\t\t\t\t, num_proc=4\t\t\t\t)\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0\r for shard in range(0\t\t\t\t, len(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t, args.shard_size\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tgrouped_dataset[shard : shard + args.shard_size]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlen(dataset_snapshot[\"\"\"input_ids\"\"\"]\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tos.path.join(SCREAMING_SNAKE_CASE__\t\t\t\t, F'''dataset-{shard_count}-{records_containing}.tfrecord'''\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tget_serialized_examples(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r with tf.io.TFRecordWriter(SCREAMING_SNAKE_CASE__\t\t\t\t) as out_file:\r for i in range(len(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tserialized_examples[i]\r out_file.write(SCREAMING_SNAKE_CASE__\t\t\t\t)\r print(\"\"\"Wrote file {} containing {} records\"\"\".format(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t)\r\r shard_count += 1\r total_records += records_containing\r\r with open(F'''split-{args.split}-records-count.txt'''\t\t\t\t, \"\"\"w\"\"\"\t\t\t\t) as f:\r print(F'''Total {args.split} records: {total_records}'''\t\t\t\t, file=SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r\rif __name__ == \"__main__\":\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tparse_args()\r main(args)\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152352,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport copy\rfrom dataclasses import dataclass, field\rfrom typing import ClassVar, Dict\r\rfrom ..features import Audio, Features, Value\rfrom .base import TaskTemplate\r\r@dataclass(frozen=lowerCamelCase_ )\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r lowerCAmelCase_ : str = field(default=\"\"\"automatic-speech-recognition\"\"\"\t, metadata={\"\"\"include_in_asdict_even_if_is_default\"\"\": True} )\r lowerCAmelCase_ : ClassVar[Features] = Features({\"\"\"audio\"\"\": Audio()} )\r lowerCAmelCase_ : ClassVar[Features] = Features({\"\"\"transcription\"\"\": Value(\"\"\"string\"\"\" )} )\r lowerCAmelCase_ : str = \"audio\"\r lowerCAmelCase_ : str = \"transcription\"\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if self.audio_column not in features:\r raise ValueError(f'''Column {self.audio_column} is not present in features.''' )\r if not isinstance(features[self.audio_column] ,\t\t\t\t\t\t_UpperCAmelCase ):\r raise ValueError(f'''Column {self.audio_column} is not an Audio type.''' )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tcopy.deepcopy(self )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.input_schema.copy()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tfeatures[self.audio_column]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tinput_schema\r return task_template\r\r\r\r @property\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return {self.audio_column: \"audio\", self.transcription_column: \"transcription\"}\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport numpy as np\rimport torch\rfrom torch.nn import CrossEntropyLoss\rfrom transformers import AutoModelForCausalLM, AutoTokenizer\r\rimport datasets\rfrom datasets import logging\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'\\\\n\\n'\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'\\nPerplexity (PPL) is one of the most common metrics for evaluating language models.\\nIt is defined as the exponentiated average negative log-likelihood of a sequence.\\n\\nFor more information, see https://huggingface.co/docs/transformers/perplexity\\n'\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'\\nArgs:\\n model_id (str): model used for calculating Perplexity\\n NOTE: Perplexity can only be calculated for causal language models.\\n This includes models such as gpt2, causal variations of bert,\\n causal versions of t5, and more (the full list can be found\\n in the AutoModelForCausalLM documentation here:\\n https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )\\n\\n input_texts (list of str): input text, each separate text snippet\\n is one list entry.\\n batch_size (int): the batch size to run texts through the model. Defaults to 16.\\n add_start_token (bool): whether to add the start token to the texts,\\n so the perplexity can include the probability of the first word. Defaults to True.\\n device (str): device to run on, defaults to \\'cuda\\' when available\\nReturns:\\n perplexity: dictionary containing the perplexity scores for the texts\\n in the input list, as well as the mean perplexity. If one of the input texts is\\n longer than the max input length of the model, then it is truncated to the\\n max length for the perplexity computation.\\nExamples:\\n Example 1:\\n >>> perplexity = datasets.load_metric(\"perplexity\")\\n >>> input_texts = [\"lorem ipsum\", \"Happy Birthday!\", \"Bienvenue\"]\\n >>> results = perplexity.compute(model_id=\\'gpt2\\',\\n ... add_start_token=False,\\n ... input_texts=input_texts) # doctest:+ELLIPSIS\\n >>> print(list(results.keys()))\\n [\\'perplexities\\', \\'mean_perplexity\\']\\n >>> print(round(results[\"mean_perplexity\"], 2))\\n 78.22\\n >>> print(round(results[\"perplexities\"][0], 2))\\n 11.11\\n\\n Example 2:\\n >>> perplexity = datasets.load_metric(\"perplexity\")\\n >>> input_texts = datasets.load_dataset(\"wikitext\",\\n ... \"wikitext-2-raw-v1\",\\n ... split=\"test\")[\"text\"][:50] # doctest:+ELLIPSIS\\n [...]\\n >>> input_texts = [s for s in input_texts if s!=\\'\\']\\n >>> results = perplexity.compute(model_id=\\'gpt2\\',\\n ... input_texts=input_texts) # doctest:+ELLIPSIS\\n >>> print(list(results.keys()))\\n [\\'perplexities\\', \\'mean_perplexity\\']\\n >>> print(round(results[\"mean_perplexity\"], 2))\\n 60.35\\n >>> print(round(results[\"perplexities\"][0], 2))\\n 81.12\\n'\r\r@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION\t, _KWARGS_DESCRIPTION )\rclass lowerCAmelCase_ ( datasets.Metric ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return datasets.MetricInfo(\r description=_DESCRIPTION ,\t\t\t\t\t\tcitation=_CITATION ,\t\t\t\t\t\tinputs_description=_KWARGS_DESCRIPTION ,\t\t\t\t\t\tfeatures=datasets.Features(\r {\r \"\"\"input_texts\"\"\": datasets.Value(\"\"\"string\"\"\" ),\r } ) ,\t\t\t\t\t\treference_urls=[\"\"\"https://huggingface.co/docs/transformers/perplexity\"\"\"] ,\t\t\t\t\t\t)\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint = 16 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tbool = True ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int]=None ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if device is not None:\r assert device in [\"gpu\", \"cpu\", \"cuda\"], \"device should be either gpu or cpu.\"\r if device == \"gpu\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"cuda\"\"\"\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"cuda\"\"\" if torch.cuda.is_available() else \"\"\"cpu\"\"\"\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tAutoModelForCausalLM.from_pretrained(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel.to(_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tAutoTokenizer.from_pretrained(_UpperCAmelCase )\r\r # if batch_size > 1 (which generally leads to padding being required), and\r # if there is not an already assigned pad_token, assign an existing\r # special token to also be the padding token\r if tokenizer.pad_token is None and batch_size > 1:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlist(tokenizer.special_tokens_map_extended.values() )\r # check that the model already has at least one special token defined\r assert (\r len(_UpperCAmelCase ) > 0\r ), \"If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1.\"\r # assign one of the special tokens to also be the pad token\r tokenizer.add_special_tokens({\"\"\"pad_token\"\"\": existing_special_tokens[0]} )\r\r if add_start_token:\r # leave room for token to be added:\r assert (\r tokenizer.bos_token is not None\r ), \"Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False\"\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel.config.max_length - 1\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel.config.max_length\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenizer(\r _UpperCAmelCase ,\t\t\t\t\t\tadd_special_tokens=_UpperCAmelCase ,\t\t\t\t\t\tpadding=_UpperCAmelCase ,\t\t\t\t\t\ttruncation=_UpperCAmelCase ,\t\t\t\t\t\tmax_length=_UpperCAmelCase ,\t\t\t\t\t\treturn_tensors=\"\"\"pt\"\"\" ,\t\t\t\t\t\treturn_attention_mask=_UpperCAmelCase ,\t\t\t\t\t\t).to(_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tencodings[\"\"\"input_ids\"\"\"]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tencodings[\"\"\"attention_mask\"\"\"]\r\r # check that each input is long enough:\r if add_start_token:\r assert torch.all(torch.ge(attn_masks.sum(1 ) ,\t\t\t\t\t\t1 ) ), \"Each input text must be at least one token long.\"\r else:\r assert torch.all(\r torch.ge(attn_masks.sum(1 ) ,\t\t\t\t\t\t2 ) ), \"When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings.\"\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tCrossEntropyLoss(reduction=\"\"\"none\"\"\" )\r\r for start_index in logging.tqdm(range(0 ,\t\t\t\t\t\tlen(_UpperCAmelCase ) ,\t\t\t\t\t\t_UpperCAmelCase ) ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmin(start_index + batch_size ,\t\t\t\t\t\tlen(_UpperCAmelCase ) )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tencoded_texts[start_index:end_index]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tattn_masks[start_index:end_index]\r\r if add_start_token:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0 ) ).to(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.cat([bos_tokens_tensor, encoded_batch] ,\t\t\t\t\t\tdim=1 )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.cat(\r [torch.ones(bos_tokens_tensor.size() ,\t\t\t\t\t\tdtype=torch.intaa ).to(_UpperCAmelCase ), attn_mask] ,\t\t\t\t\t\tdim=1 )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tencoded_batch\r\r with torch.no_grad():\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase ,\t\t\t\t\t\tattention_mask=_UpperCAmelCase ).logits\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tout_logits[..., :-1, :].contiguous()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlabels[..., 1:].contiguous()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tattn_mask[..., 1:].contiguous()\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.expa(\r (loss_fct(shift_logits.transpose(1 ,\t\t\t\t\t\t2 ) ,\t\t\t\t\t\t_UpperCAmelCase ) * shift_attention_mask_batch).sum(1 )\r / shift_attention_mask_batch.sum(1 ) )\r\r ppls += perplexity_batch.tolist()\r\r return {\"perplexities\": ppls, \"mean_perplexity\": np.mean(_UpperCAmelCase )}\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152353,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rfrom __future__ import annotations\r\rfrom functools import lru_cache\rfrom math import ceil\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t1_0_0\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tset(range(3, NUM_PRIMES, 2))\rprimes.add(2)\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t42\r\rfor prime in range(3, ceil(NUM_PRIMES**0.5), 2):\r if prime not in primes:\r continue\r primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime)))\r\r@lru_cache(maxsize=100\t\t\t\t)\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r if number_to_partition < 0:\r return set()\r elif number_to_partition == 0:\r return {1}\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tset()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t42\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t42\r\r for prime in primes:\r if prime > number_to_partition:\r continue\r for sub in partition(number_to_partition - prime\t\t\t\t):\r ret.add(sub * prime\t\t\t\t)\r\r return ret\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint = 5000\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r for number_to_partition in range(1\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r if len(partition(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t) > number_unique_partitions:\r return number_to_partition\r return None\r\r\rif __name__ == \"__main__\":\r print(f\"{solution() = }\")\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint = 1000000\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[i - 1 for i in range(limit + 1\t\t\t\t)]\r\r for i in range(2\t\t\t\t, limit + 1\t\t\t\t):\r if phi[i] == i - 1:\r for j in range(2 * i\t\t\t\t, limit + 1\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r phi[j] -= phi[j] // i\r\r return sum(phi[2 : limit + 1]\t\t\t\t)\r\r\rif __name__ == \"__main__\":\r print(solution())\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152354,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport hashlib\rimport unittest\rfrom typing import Dict\r\rimport numpy as np\r\rfrom transformers import (\r MODEL_FOR_MASK_GENERATION_MAPPING,\r TF_MODEL_FOR_MASK_GENERATION_MAPPING,\r is_vision_available,\r pipeline,\r)\rfrom transformers.pipelines import MaskGenerationPipeline\rfrom transformers.testing_utils import (\r is_pipeline_test,\r nested_simplify,\r require_tf,\r require_torch,\r require_vision,\r slow,\r)\r\r\rif is_vision_available():\r from PIL import Image\relse:\r\r class lowerCAmelCase_ :\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r @staticmethod\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( *_UpperCAmelCase\t\t\t:\t\tList[str] ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tOptional[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r pass\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tImage\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\thashlib.mda(image.tobytes()\t\t\t\t)\r return m.hexdigest()[:10]\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tImage\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnp.array(SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnpimg.shape\r return {\"hash\": hashimage(SCREAMING_SNAKE_CASE__\t\t\t\t), \"shape\": shape}\r\r\r\r\r\r\r\r@is_pipeline_test\r@require_vision\r@require_torch\rclass lowerCAmelCase_ ( unittest.TestCase ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r lowerCAmelCase_ : Optional[Any] = dict(\r (list(MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if MODEL_FOR_MASK_GENERATION_MAPPING else []) )\r lowerCAmelCase_ : Dict = dict(\r (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else []) )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tMaskGenerationPipeline(model=_UpperCAmelCase ,\t\t\t\t\t\timage_processor=_UpperCAmelCase )\r return image_segmenter, [\r \"./tests/fixtures/tests_samples/COCO/000000039769.png\",\r \"./tests/fixtures/tests_samples/COCO/000000039769.png\",\r ]\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r pass\r\r\r @require_tf\r @unittest.skip(\"\"\"Image segmentation not implemented in TF\"\"\" )\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tAny ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r pass\r\r\r @slow\r @require_torch\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tpipeline(\"\"\"mask-generation\"\"\" ,\t\t\t\t\t\tmodel=\"\"\"facebook/sam-vit-huge\"\"\" )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\timage_segmenter(\"\"\"http://images.cocodataset.org/val2017/000000039769.jpg\"\"\" ,\t\t\t\t\t\tpoints_per_batch=2_56 )\r\r # Shortening by hashing\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[]\r for i, o in enumerate(outputs[\"\"\"masks\"\"\"] ):\r new_outupt += [{\"mask\": mask_to_test_readable(_UpperCAmelCase ), \"scores\": outputs[\"scores\"][i]}]\r\r # fmt: off\r self.assertEqual(\r nested_simplify(_UpperCAmelCase ,\t\t\t\t\t\tdecimals=4 ) ,\t\t\t\t\t\t[\r {\"\"\"mask\"\"\": {\"\"\"hash\"\"\": \"\"\"115ad19f5f\"\"\", \"\"\"shape\"\"\": (4_80, 6_40)}, \"\"\"scores\"\"\": 1.0444},\r {\"\"\"mask\"\"\": {\"\"\"hash\"\"\": \"\"\"6affa964c6\"\"\", \"\"\"shape\"\"\": (4_80, 6_40)}, \"\"\"scores\"\"\": 1.021},\r {\"\"\"mask\"\"\": {\"\"\"hash\"\"\": \"\"\"dfe28a0388\"\"\", \"\"\"shape\"\"\": (4_80, 6_40)}, \"\"\"scores\"\"\": 1.0167},\r {\"\"\"mask\"\"\": {\"\"\"hash\"\"\": \"\"\"c0a5f4a318\"\"\", \"\"\"shape\"\"\": (4_80, 6_40)}, \"\"\"scores\"\"\": 1.0132},\r {\"\"\"mask\"\"\": {\"\"\"hash\"\"\": \"\"\"fe8065c197\"\"\", \"\"\"shape\"\"\": (4_80, 6_40)}, \"\"\"scores\"\"\": 1.0053},\r {\"\"\"mask\"\"\": {\"\"\"hash\"\"\": \"\"\"e2d0b7a0b7\"\"\", \"\"\"shape\"\"\": (4_80, 6_40)}, \"\"\"scores\"\"\": 0.9967},\r {\"\"\"mask\"\"\": {\"\"\"hash\"\"\": \"\"\"453c7844bd\"\"\", \"\"\"shape\"\"\": (4_80, 6_40)}, \"\"\"scores\"\"\": 0.993},\r {\"\"\"mask\"\"\": {\"\"\"hash\"\"\": \"\"\"3d44f2926d\"\"\", \"\"\"shape\"\"\": (4_80, 6_40)}, \"\"\"scores\"\"\": 0.9909},\r {\"\"\"mask\"\"\": {\"\"\"hash\"\"\": \"\"\"64033ddc3f\"\"\", \"\"\"shape\"\"\": (4_80, 6_40)}, \"\"\"scores\"\"\": 0.9879},\r {\"\"\"mask\"\"\": {\"\"\"hash\"\"\": \"\"\"801064ff79\"\"\", \"\"\"shape\"\"\": (4_80, 6_40)}, \"\"\"scores\"\"\": 0.9834},\r {\"\"\"mask\"\"\": {\"\"\"hash\"\"\": \"\"\"6172f276ef\"\"\", \"\"\"shape\"\"\": (4_80, 6_40)}, \"\"\"scores\"\"\": 0.9716},\r {\"\"\"mask\"\"\": {\"\"\"hash\"\"\": \"\"\"b49e60e084\"\"\", \"\"\"shape\"\"\": (4_80, 6_40)}, \"\"\"scores\"\"\": 0.9612},\r {\"\"\"mask\"\"\": {\"\"\"hash\"\"\": \"\"\"a811e775fd\"\"\", \"\"\"shape\"\"\": (4_80, 6_40)}, \"\"\"scores\"\"\": 0.9599},\r {\"\"\"mask\"\"\": {\"\"\"hash\"\"\": \"\"\"a6a8ebcf4b\"\"\", \"\"\"shape\"\"\": (4_80, 6_40)}, \"\"\"scores\"\"\": 0.9552},\r {\"\"\"mask\"\"\": {\"\"\"hash\"\"\": \"\"\"9d8257e080\"\"\", \"\"\"shape\"\"\": (4_80, 6_40)}, \"\"\"scores\"\"\": 0.9532},\r {\"\"\"mask\"\"\": {\"\"\"hash\"\"\": \"\"\"32de6454a8\"\"\", \"\"\"shape\"\"\": (4_80, 6_40)}, \"\"\"scores\"\"\": 0.9516},\r {\"\"\"mask\"\"\": {\"\"\"hash\"\"\": \"\"\"af3d4af2c8\"\"\", \"\"\"shape\"\"\": (4_80, 6_40)}, \"\"\"scores\"\"\": 0.9499},\r {\"\"\"mask\"\"\": {\"\"\"hash\"\"\": \"\"\"3c6db475fb\"\"\", \"\"\"shape\"\"\": (4_80, 6_40)}, \"\"\"scores\"\"\": 0.9483},\r {\"\"\"mask\"\"\": {\"\"\"hash\"\"\": \"\"\"c290813fb9\"\"\", \"\"\"shape\"\"\": (4_80, 6_40)}, \"\"\"scores\"\"\": 0.9464},\r {\"\"\"mask\"\"\": {\"\"\"hash\"\"\": \"\"\"b6f0b8f606\"\"\", \"\"\"shape\"\"\": (4_80, 6_40)}, \"\"\"scores\"\"\": 0.943},\r {\"\"\"mask\"\"\": {\"\"\"hash\"\"\": \"\"\"92ce16bfdf\"\"\", \"\"\"shape\"\"\": (4_80, 6_40)}, \"\"\"scores\"\"\": 0.943},\r {\"\"\"mask\"\"\": {\"\"\"hash\"\"\": \"\"\"c749b25868\"\"\", \"\"\"shape\"\"\": (4_80, 6_40)}, \"\"\"scores\"\"\": 0.9408},\r {\"\"\"mask\"\"\": {\"\"\"hash\"\"\": \"\"\"efb6cab859\"\"\", \"\"\"shape\"\"\": (4_80, 6_40)}, \"\"\"scores\"\"\": 0.9335},\r {\"\"\"mask\"\"\": {\"\"\"hash\"\"\": \"\"\"1ff2eafb30\"\"\", \"\"\"shape\"\"\": (4_80, 6_40)}, \"\"\"scores\"\"\": 0.9326},\r {\"\"\"mask\"\"\": {\"\"\"hash\"\"\": \"\"\"788b798e24\"\"\", \"\"\"shape\"\"\": (4_80, 6_40)}, \"\"\"scores\"\"\": 0.9262},\r {\"\"\"mask\"\"\": {\"\"\"hash\"\"\": \"\"\"abea804f0e\"\"\", \"\"\"shape\"\"\": (4_80, 6_40)}, \"\"\"scores\"\"\": 0.8999},\r {\"\"\"mask\"\"\": {\"\"\"hash\"\"\": \"\"\"7b9e8ddb73\"\"\", \"\"\"shape\"\"\": (4_80, 6_40)}, \"\"\"scores\"\"\": 0.8986},\r {\"\"\"mask\"\"\": {\"\"\"hash\"\"\": \"\"\"cd24047c8a\"\"\", \"\"\"shape\"\"\": (4_80, 6_40)}, \"\"\"scores\"\"\": 0.8984},\r {\"\"\"mask\"\"\": {\"\"\"hash\"\"\": \"\"\"6943e6bcbd\"\"\", \"\"\"shape\"\"\": (4_80, 6_40)}, \"\"\"scores\"\"\": 0.8873},\r {\"\"\"mask\"\"\": {\"\"\"hash\"\"\": \"\"\"b5f47c9191\"\"\", \"\"\"shape\"\"\": (4_80, 6_40)}, \"\"\"scores\"\"\": 0.8871}\r ] ,\t\t\t\t\t\t)\r # fmt: on\r\r\r\r @require_torch\r @slow\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"facebook/sam-vit-huge\"\"\"\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tpipeline(\"\"\"mask-generation\"\"\" ,\t\t\t\t\t\tmodel=_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\timage_segmenter(\r \"\"\"http://images.cocodataset.org/val2017/000000039769.jpg\"\"\" ,\t\t\t\t\t\tpred_iou_thresh=1 ,\t\t\t\t\t\tpoints_per_batch=2_56 )\r\r # Shortening by hashing\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[]\r for i, o in enumerate(outputs[\"\"\"masks\"\"\"] ):\r new_outupt += [{\"mask\": mask_to_test_readable(_UpperCAmelCase ), \"scores\": outputs[\"scores\"][i]}]\r\r self.assertEqual(\r nested_simplify(_UpperCAmelCase ,\t\t\t\t\t\tdecimals=4 ) ,\t\t\t\t\t\t[\r {\"\"\"mask\"\"\": {\"\"\"hash\"\"\": \"\"\"115ad19f5f\"\"\", \"\"\"shape\"\"\": (4_80, 6_40)}, \"\"\"scores\"\"\": 1.0444},\r {\"\"\"mask\"\"\": {\"\"\"hash\"\"\": \"\"\"6affa964c6\"\"\", \"\"\"shape\"\"\": (4_80, 6_40)}, \"\"\"scores\"\"\": 1.0210},\r {\"\"\"mask\"\"\": {\"\"\"hash\"\"\": \"\"\"dfe28a0388\"\"\", \"\"\"shape\"\"\": (4_80, 6_40)}, \"\"\"scores\"\"\": 1.0167},\r {\"\"\"mask\"\"\": {\"\"\"hash\"\"\": \"\"\"c0a5f4a318\"\"\", \"\"\"shape\"\"\": (4_80, 6_40)}, \"\"\"scores\"\"\": 1.0132},\r {\"\"\"mask\"\"\": {\"\"\"hash\"\"\": \"\"\"fe8065c197\"\"\", \"\"\"shape\"\"\": (4_80, 6_40)}, \"\"\"scores\"\"\": 1.0053},\r ] ,\t\t\t\t\t\t)\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rfrom typing import List, Union\r\rfrom ..utils import (\r add_end_docstrings,\r is_tf_available,\r is_torch_available,\r is_vision_available,\r logging,\r requires_backends,\r)\rfrom .base import PIPELINE_INIT_ARGS, Pipeline\r\r\rif is_vision_available():\r from PIL import Image\r\r from ..image_utils import load_image\r\rif is_tf_available():\r import tensorflow as tf\r\r from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING\r from ..tf_utils import stable_softmax\r\rif is_torch_available():\r from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tlogging.get_logger(__name__)\r\r@add_end_docstrings(lowerCamelCase_ )\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def __init__( self\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t*_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tDict ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r super().__init__(*_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase )\r requires_backends(self ,\t\t\t\t\t\t\"\"\"vision\"\"\" )\r self.check_model_type(\r TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING\r if self.framework == \"\"\"tf\"\"\"\r else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any]=None ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{}\r if top_k is not None:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttop_k\r return {}, {}, postprocess_params\r\r\r def __call__( self\t\t\t:\t\tAny ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, List[str], \"Image.Image\", List[\"Image.Image\"]] ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return super().__call__(_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tload_image(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.image_processor(images=_UpperCAmelCase ,\t\t\t\t\t\treturn_tensors=self.framework )\r return model_inputs\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.model(**_UpperCAmelCase )\r return model_outputs\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr=5 ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if top_k > self.model.config.num_labels:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.model.config.num_labels\r\r if self.framework == \"pt\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel_outputs.logits.softmax(-1 )[0]\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tprobs.topk(_UpperCAmelCase )\r elif self.framework == \"tf\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tstable_softmax(model_outputs.logits ,\t\t\t\t\t\taxis=-1 )[0]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttf.math.top_k(_UpperCAmelCase ,\t\t\t\t\t\tk=_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttopk.values.numpy(), topk.indices.numpy()\r else:\r raise ValueError(f'''Unsupported framework: {self.framework}''' )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscores.tolist()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tids.tolist()\r return [{\"score\": score, \"label\": self.model.config.idalabel[_id]} for score, _id in zip(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )]\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152355,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport math\r\rimport flax.linen as nn\rimport jax.numpy as jnp\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tjnp.ndarray\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tfloat = 1\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tfloat = 1\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tfloat = 1.0e4\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tbool = False\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tfloat = 1.0\t\t\t\t, ):\r '''simple docstring'''\r\r\r\r\r\r assert timesteps.ndim == 1, \"Timesteps should be a 1d-array\"\r assert embedding_dim % 2 == 0, F'''Embedding dimension {embedding_dim} should be even'''\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tfloat(embedding_dim // 2\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmath.log(max_timescale / min_timescale\t\t\t\t) / (num_timescales - freq_shift)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmin_timescale * jnp.exp(jnp.arange(SCREAMING_SNAKE_CASE__\t\t\t\t, dtype=jnp.floataa\t\t\t\t) * -log_timescale_increment\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tjnp.expand_dims(SCREAMING_SNAKE_CASE__\t\t\t\t, 1\t\t\t\t) * jnp.expand_dims(SCREAMING_SNAKE_CASE__\t\t\t\t, 0\t\t\t\t)\r\r # scale embeddings\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscale * emb\r\r if flip_sin_to_cos:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tjnp.concatenate([jnp.cos(SCREAMING_SNAKE_CASE__\t\t\t\t), jnp.sin(SCREAMING_SNAKE_CASE__\t\t\t\t)]\t\t\t\t, axis=1\t\t\t\t)\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tjnp.concatenate([jnp.sin(SCREAMING_SNAKE_CASE__\t\t\t\t), jnp.cos(SCREAMING_SNAKE_CASE__\t\t\t\t)]\t\t\t\t, axis=1\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tjnp.reshape(SCREAMING_SNAKE_CASE__\t\t\t\t, [jnp.shape(SCREAMING_SNAKE_CASE__\t\t\t\t)[0], embedding_dim]\t\t\t\t)\r return signal\r\rclass lowerCAmelCase_ ( nn.Module ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r lowerCAmelCase_ : int = 32\r lowerCAmelCase_ : jnp.dtype = jnp.floataa\r\r\r @nn.compact\r def __call__( self\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnn.Dense(self.time_embed_dim ,\t\t\t\t\t\tdtype=self.dtype ,\t\t\t\t\t\tname=\"\"\"linear_1\"\"\" )(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnn.silu(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnn.Dense(self.time_embed_dim ,\t\t\t\t\t\tdtype=self.dtype ,\t\t\t\t\t\tname=\"\"\"linear_2\"\"\" )(_UpperCAmelCase )\r return temb\r\rclass lowerCAmelCase_ ( nn.Module ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r lowerCAmelCase_ : int = 32\r lowerCAmelCase_ : bool = False\r lowerCAmelCase_ : float = 1\r\r\r @nn.compact\r def __call__( self\t\t\t:\t\tList[str] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return get_sinusoidal_embeddings(\r _UpperCAmelCase ,\t\t\t\t\t\tembedding_dim=self.dim ,\t\t\t\t\t\tflip_sin_to_cos=self.flip_sin_to_cos ,\t\t\t\t\t\tfreq_shift=self.freq_shift )\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rfrom math import factorial\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint = 20\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,\r # 2, 3,...\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tn // 2\r\r return int(factorial(SCREAMING_SNAKE_CASE__\t\t\t\t) / (factorial(SCREAMING_SNAKE_CASE__\t\t\t\t) * factorial(n - k\t\t\t\t))\t\t\t\t)\r\r\rif __name__ == \"__main__\":\r import sys\r\r if len(sys.argv) == 1:\r print(solution(2_0))\r else:\r try:\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tint(sys.argv[1])\r print(solution(n))\r except ValueError:\r print('Invalid entry - please enter a number.')\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152356,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t8.314_4598\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tfloat\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tfloat\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r if temperature < 0:\r raise Exception(\"\"\"Temperature cannot be less than 0 K\"\"\"\t\t\t\t)\r if molar_mass <= 0:\r raise Exception(\"\"\"Molar mass cannot be less than or equal to 0 kg/mol\"\"\"\t\t\t\t)\r else:\r return (3 * UNIVERSAL_GAS_CONSTANT * temperature / molar_mass) ** 0.5\r\r\rif __name__ == \"__main__\":\r import doctest\r\r # run doctest\r doctest.testmod()\r\r # example\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\t3_0_0\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\t2_8\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\trms_speed_of_molecule(temperature, molar_mass)\r print(f\"Vrms of Nitrogen gas at 300 K is {vrms} m/s\")\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport json\rimport os\rimport unittest\r\rfrom transformers import MgpstrTokenizer\rfrom transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES\rfrom transformers.testing_utils import require_tokenizers\r\rfrom ...test_tokenization_common import TokenizerTesterMixin\r\r@require_tokenizers\rclass lowerCAmelCase_ ( lowerCamelCase_\t, unittest.TestCase ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r lowerCAmelCase_ : int = MgpstrTokenizer\r lowerCAmelCase_ : List[str] = False\r lowerCAmelCase_ : Optional[int] = {}\r lowerCAmelCase_ : Any = False\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r super().setUp()\r\r # fmt: off\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[\"\"\"[GO]\"\"\", \"\"\"[s]\"\"\", \"\"\"0\"\"\", \"\"\"1\"\"\", \"\"\"2\"\"\", \"\"\"3\"\"\", \"\"\"4\"\"\", \"\"\"5\"\"\", \"\"\"6\"\"\", \"\"\"7\"\"\", \"\"\"8\"\"\", \"\"\"9\"\"\", \"\"\"a\"\"\", \"\"\"b\"\"\", \"\"\"c\"\"\", \"\"\"d\"\"\", \"\"\"e\"\"\", \"\"\"f\"\"\", \"\"\"g\"\"\", \"\"\"h\"\"\", \"\"\"i\"\"\", \"\"\"j\"\"\", \"\"\"k\"\"\", \"\"\"l\"\"\", \"\"\"m\"\"\", \"\"\"n\"\"\", \"\"\"o\"\"\", \"\"\"p\"\"\", \"\"\"q\"\"\", \"\"\"r\"\"\", \"\"\"s\"\"\", \"\"\"t\"\"\", \"\"\"u\"\"\", \"\"\"v\"\"\", \"\"\"w\"\"\", \"\"\"x\"\"\", \"\"\"y\"\"\", \"\"\"z\"\"\"]\r # fmt: on\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdict(zip(_UpperCAmelCase ,\t\t\t\t\t\trange(len(_UpperCAmelCase ) ) ) )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tos.path.join(self.tmpdirname ,\t\t\t\t\t\tVOCAB_FILES_NAMES[\"\"\"vocab_file\"\"\"] )\r with open(self.vocab_file ,\t\t\t\t\t\t\"\"\"w\"\"\" ,\t\t\t\t\t\tencoding=\"\"\"utf-8\"\"\" ) as fp:\r fp.write(json.dumps(_UpperCAmelCase ) + \"\"\"\\n\"\"\" )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return MgpstrTokenizer.from_pretrained(self.tmpdirname ,\t\t\t\t\t\t**_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"tester\"\"\"\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"tester\"\"\"\r return input_text, output_text\r\r\r @unittest.skip(\"\"\"MGP-STR always lower cases letters.\"\"\" )\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r pass\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tAny ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_tokenizers(do_lower_case=_UpperCAmelCase )\r for tokenizer in tokenizers:\r with self.subTest(f'''{tokenizer.__class__.__name__}''' ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"[SPECIAL_TOKEN]\"\"\"\r\r tokenizer.add_special_tokens({\"\"\"cls_token\"\"\": special_token} )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenizer.encode([special_token] ,\t\t\t\t\t\tadd_special_tokens=_UpperCAmelCase )\r self.assertEqual(len(_UpperCAmelCase ) ,\t\t\t\t\t\t1 )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenizer.decode(_UpperCAmelCase ,\t\t\t\t\t\tskip_special_tokens=_UpperCAmelCase )\r self.assertTrue(special_token not in decoded )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_tokenizers()\r for tokenizer in tokenizers:\r with self.subTest(f'''{tokenizer.__class__.__name__}''' ):\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_input_output_texts(_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenizer.tokenize(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenizer.convert_tokens_to_ids(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenizer.encode(_UpperCAmelCase ,\t\t\t\t\t\tadd_special_tokens=_UpperCAmelCase )\r self.assertListEqual(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenizer.convert_ids_to_tokens(_UpperCAmelCase )\r self.assertNotEqual(len(_UpperCAmelCase ) ,\t\t\t\t\t\t0 )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenizer.decode(_UpperCAmelCase )\r self.assertIsInstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r\r self.assertEqual(text_a.replace(\"\"\" \"\"\" ,\t\t\t\t\t\t\"\"\"\"\"\" ) ,\t\t\t\t\t\t_UpperCAmelCase )\r\r\r @unittest.skip(\"\"\"MGP-STR tokenizer only handles one sequence.\"\"\" )\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tAny ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r pass\r\r\r\r @unittest.skip(\"\"\"inputs cannot be pretokenized in MgpstrTokenizer\"\"\" )\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r pass\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152357,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rfrom typing import TYPE_CHECKING\r\rfrom ...utils import (\r OptionalDependencyNotAvailable,\r _LazyModule,\r is_tf_available,\r is_tokenizers_available,\r is_torch_available,\r)\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t{\r 'configuration_longformer': [\r 'LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',\r 'LongformerConfig',\r 'LongformerOnnxConfig',\r ],\r 'tokenization_longformer': ['LongformerTokenizer'],\r}\r\rtry:\r if not is_tokenizers_available():\r raise OptionalDependencyNotAvailable()\rexcept OptionalDependencyNotAvailable:\r pass\relse:\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\t['LongformerTokenizerFast']\r\rtry:\r if not is_torch_available():\r raise OptionalDependencyNotAvailable()\rexcept OptionalDependencyNotAvailable:\r pass\relse:\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\t[\r 'LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',\r 'LongformerForMaskedLM',\r 'LongformerForMultipleChoice',\r 'LongformerForQuestionAnswering',\r 'LongformerForSequenceClassification',\r 'LongformerForTokenClassification',\r 'LongformerModel',\r 'LongformerPreTrainedModel',\r 'LongformerSelfAttention',\r ]\r\rtry:\r if not is_tf_available():\r raise OptionalDependencyNotAvailable()\rexcept OptionalDependencyNotAvailable:\r pass\relse:\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\t[\r 'TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',\r 'TFLongformerForMaskedLM',\r 'TFLongformerForMultipleChoice',\r 'TFLongformerForQuestionAnswering',\r 'TFLongformerForSequenceClassification',\r 'TFLongformerForTokenClassification',\r 'TFLongformerModel',\r 'TFLongformerPreTrainedModel',\r 'TFLongformerSelfAttention',\r ]\r\r\rif TYPE_CHECKING:\r from .configuration_longformer import (\r LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,\r LongformerConfig,\r LongformerOnnxConfig,\r )\r from .tokenization_longformer import LongformerTokenizer\r\r try:\r if not is_tokenizers_available():\r raise OptionalDependencyNotAvailable()\r except OptionalDependencyNotAvailable:\r pass\r else:\r from .tokenization_longformer_fast import LongformerTokenizerFast\r\r try:\r if not is_torch_available():\r raise OptionalDependencyNotAvailable()\r except OptionalDependencyNotAvailable:\r pass\r else:\r from .modeling_longformer import (\r LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,\r LongformerForMaskedLM,\r LongformerForMultipleChoice,\r LongformerForQuestionAnswering,\r LongformerForSequenceClassification,\r LongformerForTokenClassification,\r LongformerModel,\r LongformerPreTrainedModel,\r LongformerSelfAttention,\r )\r\r try:\r if not is_tf_available():\r raise OptionalDependencyNotAvailable()\r except OptionalDependencyNotAvailable:\r pass\r else:\r from .modeling_tf_longformer import (\r TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,\r TFLongformerForMaskedLM,\r TFLongformerForMultipleChoice,\r TFLongformerForQuestionAnswering,\r TFLongformerForSequenceClassification,\r TFLongformerForTokenClassification,\r TFLongformerModel,\r TFLongformerPreTrainedModel,\r TFLongformerSelfAttention,\r )\r\relse:\r import sys\r\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\t_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rfrom abc import ABC, abstractmethod\rfrom typing import List, Optional\r\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def __init__( self\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r self.test()\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r while not completed:\r if counter == 1:\r self.reset()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.advance()\r if not self.does_advance(_UpperCAmelCase ):\r raise Exception(\r \"\"\"Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true.\"\"\" )\r\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.update(_UpperCAmelCase )\r counter += 1\r\r if counter > 1_00_00:\r raise Exception(\"\"\"update() does not fulfill the constraint.\"\"\" )\r\r if self.remaining() != 0:\r raise Exception(\"\"\"Custom Constraint is not defined correctly.\"\"\" )\r\r\r @abstractmethod\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r raise NotImplementedError(\r f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )\r\r\r @abstractmethod\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r raise NotImplementedError(\r f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )\r\r\r @abstractmethod\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tAny ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r raise NotImplementedError(\r f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )\r\r\r @abstractmethod\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r raise NotImplementedError(\r f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )\r\r\r @abstractmethod\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r raise NotImplementedError(\r f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )\r\r\r\r @abstractmethod\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any]=False ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r raise NotImplementedError(\r f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )\r\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def __init__( self\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r super(_UpperCAmelCase ,\t\t\t\t\t\tself ).__init__()\r\r if not isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ) or len(_UpperCAmelCase ) == 0:\r raise ValueError(f'''`token_ids` has to be a non-empty list, but is {token_ids}.''' )\r if any((not isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ) or token_id < 0) for token_id in token_ids ):\r raise ValueError(f'''Each list in `token_ids` has to be a list of positive integers, but is {token_ids}.''' )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttoken_ids\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlen(self.token_ids )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t-1 # the index of the currently fulfilled step\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if self.completed:\r return None\r return self.token_ids[self.fulfilled_idx + 1]\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if not isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ):\r raise ValueError(f'''`token_id` has to be an `int`, but is {token_id} of type {type(_UpperCAmelCase )}''' )\r\r if self.completed:\r return False\r\r return token_id == self.token_ids[self.fulfilled_idx + 1]\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if not isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ):\r raise ValueError(f'''`token_id` has to be an `int`, but is {token_id} of type {type(_UpperCAmelCase )}''' )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r\r if self.does_advance(_UpperCAmelCase ):\r self.fulfilled_idx += 1\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTrue\r if self.fulfilled_idx == (self.seqlen - 1):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTrue\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tcompleted\r else:\r # failed to make progress.\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTrue\r self.reset()\r return stepped, completed, reset\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tTuple ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return self.seqlen - (self.fulfilled_idx + 1)\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tAny ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int]=False ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tPhrasalConstraint(self.token_ids )\r\r if stateful:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.seqlen\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.fulfilled_idx\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.completed\r\r return new_constraint\r\rclass lowerCAmelCase_ :\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def __init__( self\t\t\t:\t\tAny ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[List[int]] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str]=True ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmax([len(_UpperCAmelCase ) for one in nested_token_ids] )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{}\r for token_ids in nested_token_ids:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\troot\r for tidx, token_id in enumerate(_UpperCAmelCase ):\r if token_id not in level:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{}\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlevel[token_id]\r\r if no_subsets and self.has_subsets(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ):\r raise ValueError(\r \"\"\"Each list in `nested_token_ids` can't be a complete subset of another list, but is\"\"\"\r f''' {nested_token_ids}.''' )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\troot\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.trie\r\r for current_token in current_seq:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tstart[current_token]\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlist(start.keys() )\r\r return next_tokens\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.next_tokens(_UpperCAmelCase )\r\r return len(_UpperCAmelCase ) == 0\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlist(root.values() )\r if len(_UpperCAmelCase ) == 0:\r return 1\r else:\r return sum([self.count_leaves(_UpperCAmelCase ) for nn in next_nodes] )\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.count_leaves(_UpperCAmelCase )\r return len(_UpperCAmelCase ) != leaf_count\r\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def __init__( self\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[List[int]] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r super(_UpperCAmelCase ,\t\t\t\t\t\tself ).__init__()\r\r if not isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ) or len(_UpperCAmelCase ) == 0:\r raise ValueError(f'''`nested_token_ids` has to be a non-empty list, but is {nested_token_ids}.''' )\r if any(not isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ) for token_ids in nested_token_ids ):\r raise ValueError(f'''`nested_token_ids` has to be a list of lists, but is {nested_token_ids}.''' )\r if any(\r any((not isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ) or token_id < 0) for token_id in token_ids )\r for token_ids in nested_token_ids ):\r raise ValueError(\r f'''Each list in `nested_token_ids` has to be a list of positive integers, but is {nested_token_ids}.''' )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tDisjunctiveTrie(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnested_token_ids\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.trie.max_height\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tAny ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.trie.next_tokens(self.current_seq )\r\r if len(_UpperCAmelCase ) == 0:\r return None\r else:\r return token_list\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if not isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ):\r raise ValueError(f'''`token_id` is supposed to be type `int`, but is {token_id} of type {type(_UpperCAmelCase )}''' )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.trie.next_tokens(self.current_seq )\r\r return token_id in next_tokens\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if not isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ):\r raise ValueError(f'''`token_id` is supposed to be type `int`, but is {token_id} of type {type(_UpperCAmelCase )}''' )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r\r if self.does_advance(_UpperCAmelCase ):\r self.current_seq.append(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTrue\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTrue\r self.reset()\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.trie.reached_leaf(self.current_seq )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tcompleted\r\r return stepped, completed, reset\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[]\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if self.completed:\r # since this can be completed without reaching max height\r return 0\r else:\r return self.seqlen - len(self.current_seq )\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tTuple ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict=False ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tDisjunctiveConstraint(self.token_ids )\r\r if stateful:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.seqlen\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.current_seq\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.completed\r\r return new_constraint\r\rclass lowerCAmelCase_ :\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def __init__( self\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Constraint] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tconstraints\r\r # max # of steps required to fulfill a given constraint\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmax([c.seqlen for c in constraints] )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlen(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r\r self.init_state()\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tAny ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[constraint.copy(stateful=_UpperCAmelCase ) for constraint in self.constraints]\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0\r if self.inprogress_constraint:\r # extra points for having a constraint mid-fulfilled\r add += self.max_seqlen - self.inprogress_constraint.remaining()\r\r return (len(self.complete_constraints ) * self.max_seqlen) + add\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[]\r if self.inprogress_constraint is None:\r for constraint in self.pending_constraints: # \"pending\" == \"unfulfilled yet\"\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tconstraint.advance()\r if isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ):\r token_list.append(_UpperCAmelCase )\r elif isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ):\r token_list.extend(_UpperCAmelCase )\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.inprogress_constraint.advance()\r if isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ):\r token_list.append(_UpperCAmelCase )\r elif isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ):\r token_list.extend(_UpperCAmelCase )\r\r if len(_UpperCAmelCase ) == 0:\r return None\r else:\r return token_list\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[List[int]] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r self.init_state()\r\r if token_ids is not None:\r for token in token_ids:\r # completes or steps **one** constraint\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.add(_UpperCAmelCase )\r\r # the entire list of constraints are fulfilled\r if self.completed:\r break\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tAny ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if not isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ):\r raise ValueError(f'''`token_id` should be an `int`, but is `{token_id}`.''' )\r\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse, False\r\r if self.completed:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTrue\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r return complete, stepped\r\r if self.inprogress_constraint is not None:\r # In the middle of fulfilling a constraint. If the `token_id` *does* makes an incremental progress to current\r # job, simply update the state\r\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.inprogress_constraint.update(_UpperCAmelCase )\r if reset:\r # 1. If the next token breaks the progress, then we must restart.\r # e.g. constraint = \"I love pies\" and sequence so far is \"I love\" but `token_id` == \"books\".\r\r # But that doesn't mean we self.init_state(), since we only reset the state for this particular\r # constraint, not the full list of constraints.\r\r self.pending_constraints.append(self.inprogress_constraint.copy(stateful=_UpperCAmelCase ) )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r\r if complete:\r # 2. If the next token completes the constraint, move it to completed list, set\r # inprogress to None. If there are no pending constraints either, then this full list of constraints\r # is complete.\r\r self.complete_constraints.append(self.inprogress_constraint )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r\r if len(self.pending_constraints ) == 0:\r # we're done!\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTrue\r\r else:\r # Not in the middle of fulfilling a constraint. So does this `token_id` helps us step towards any of our list\r # of constraints?\r\r for cidx, pending_constraint in enumerate(self.pending_constraints ):\r if pending_constraint.does_advance(_UpperCAmelCase ):\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tpending_constraint.update(_UpperCAmelCase )\r\r if not stepped:\r raise Exception(\r \"\"\"`constraint.update(token_id)` is not yielding incremental progress, \"\"\"\r \"\"\"even though `constraint.does_advance(token_id)` is true.\"\"\" )\r\r if complete:\r self.complete_constraints.append(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r\r if not complete and stepped:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tpending_constraint\r\r if complete or stepped:\r # If we made any progress at all, then it's at least not a \"pending constraint\".\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t(\r self.pending_constraints[:cidx] + self.pending_constraints[cidx + 1 :]\r )\r\r if len(self.pending_constraints ) == 0 and self.inprogress_constraint is None:\r # If there's no longer any pending after this and no inprogress either, then we must be\r # complete.\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTrue\r\r break # prevent accidentally stepping through multiple constraints with just one token.\r\r return complete, stepped\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any]=True ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tConstraintListState(self.constraints ) # we actually never though self.constraints objects\r # throughout this process. So it's at initialization state.\r\r if stateful:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[\r constraint.copy(stateful=_UpperCAmelCase ) for constraint in self.complete_constraints\r ]\r if self.inprogress_constraint is not None:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.inprogress_constraint.copy(stateful=_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[constraint.copy() for constraint in self.pending_constraints]\r\r return new_state\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152358,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rfrom typing import List, Optional, Tuple, Union\r\rimport torch\r\rfrom ...utils import logging, randn_tensor\rfrom ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tlogging.get_logger(__name__) # pylint: disable=invalid-name\r\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def __init__( self\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r super().__init__()\r self.register_modules(unet=_UpperCAmelCase ,\t\t\t\t\t\tscheduler=_UpperCAmelCase )\r\r\r\r @torch.no_grad()\r def __call__( self\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint = 1 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint = 1_00 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Union[torch.Generator, List[torch.Generator]]] = None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[float] = None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tbool = True ,\t\t\t\t\t\t):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if audio_length_in_s is None:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.unet.config.sample_size / self.unet.config.sample_rate\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\taudio_length_in_s * self.unet.config.sample_rate\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t2 ** len(self.unet.up_blocks )\r if sample_size < 3 * down_scale_factor:\r raise ValueError(\r f'''{audio_length_in_s} is too small. Make sure it\\'s bigger or equal to'''\r f''' {3 * down_scale_factor / self.unet.config.sample_rate}.''' )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tint(_UpperCAmelCase )\r if sample_size % down_scale_factor != 0:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t(\r (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1\r ) * down_scale_factor\r logger.info(\r f'''{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled'''\r f''' by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising'''\r \"\"\" process.\"\"\" )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tint(_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnext(iter(self.unet.parameters() ) ).dtype\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t(batch_size, self.unet.config.in_channels, sample_size)\r if isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ) and len(_UpperCAmelCase ) != batch_size:\r raise ValueError(\r f'''You have passed a list of generators of length {len(_UpperCAmelCase )}, but requested an effective batch'''\r f''' size of {batch_size}. Make sure the batch size matches the length of the generators.''' )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\trandn_tensor(_UpperCAmelCase ,\t\t\t\t\t\tgenerator=_UpperCAmelCase ,\t\t\t\t\t\tdevice=self.device ,\t\t\t\t\t\tdtype=_UpperCAmelCase )\r\r # set step values\r self.scheduler.set_timesteps(_UpperCAmelCase ,\t\t\t\t\t\tdevice=audio.device )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.scheduler.timesteps.to(_UpperCAmelCase )\r\r for t in self.progress_bar(self.scheduler.timesteps ):\r # 1. predict noise model_output\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.unet(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ).sample\r\r # 2. compute previous image: x_t -> t_t-1\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.scheduler.step(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ).prev_sample\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\taudio.clamp(-1 ,\t\t\t\t\t\t1 ).float().cpu().numpy()\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\taudio[:, :, :original_sample_size]\r\r if not return_dict:\r return (audio,)\r\r return AudioPipelineOutput(audios=_UpperCAmelCase )\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport doctest\rimport logging\rimport os\rimport unittest\rfrom pathlib import Path\rfrom typing import List, Union\r\rimport transformers\rfrom transformers.testing_utils import require_tf, require_torch, slow\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tlogging.getLogger()\r\r@unittest.skip(\"\"\"Temporarily disable the doc tests.\"\"\" )\r@require_torch\r@require_tf\r@slow\rclass lowerCAmelCase_ ( unittest.TestCase ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tPath ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, None] = None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[List[str], None] = None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, List[str], None] = None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tbool = True ,\t\t\t\t\t\t):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[file for file in os.listdir(_UpperCAmelCase ) if os.path.isfile(os.path.join(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ) )]\r\r if identifier is not None:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[file for file in files if identifier in file]\r\r if n_identifier is not None:\r if isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ):\r for n_ in n_identifier:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[file for file in files if n_ not in file]\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[file for file in files if n_identifier not in file]\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tignore_files or []\r ignore_files.append(\"\"\"__init__.py\"\"\" )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[file for file in files if file not in ignore_files]\r\r for file in files:\r # Open all files\r print(\"\"\"Testing\"\"\" ,\t\t\t\t\t\t_UpperCAmelCase )\r\r if only_modules:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tfile.split(\"\"\".\"\"\" )[0]\r try:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tgetattr(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdoctest.DocTestSuite(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tunittest.TextTestRunner().run(_UpperCAmelCase )\r self.assertIs(len(result.failures ) ,\t\t\t\t\t\t0 )\r except AttributeError:\r logger.info(f'''{module_identifier} is not a module.''' )\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdoctest.testfile(str(\"\"\"..\"\"\" / directory / file ) ,\t\t\t\t\t\toptionflags=doctest.ELLIPSIS )\r self.assertIs(result.failed ,\t\t\t\t\t\t0 )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tPath(\"\"\"src/transformers\"\"\" )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"modeling\"\"\"\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[\r \"\"\"modeling_ctrl.py\"\"\",\r \"\"\"modeling_tf_ctrl.py\"\"\",\r ]\r self.analyze_directory(_UpperCAmelCase ,\t\t\t\t\t\tidentifier=_UpperCAmelCase ,\t\t\t\t\t\tignore_files=_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tPath(\"\"\"src/transformers\"\"\" )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"tokenization\"\"\"\r self.analyze_directory(_UpperCAmelCase ,\t\t\t\t\t\tidentifier=_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tPath(\"\"\"src/transformers\"\"\" )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"configuration\"\"\"\r self.analyze_directory(_UpperCAmelCase ,\t\t\t\t\t\tidentifier=_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tPath(\"\"\"src/transformers\"\"\" )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[\"\"\"configuration\"\"\", \"\"\"modeling\"\"\", \"\"\"tokenization\"\"\"]\r self.analyze_directory(_UpperCAmelCase ,\t\t\t\t\t\tn_identifier=_UpperCAmelCase )\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tPath(\"\"\"docs/source\"\"\" )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[\"\"\"favicon.ico\"\"\"]\r self.analyze_directory(_UpperCAmelCase ,\t\t\t\t\t\tignore_files=_UpperCAmelCase ,\t\t\t\t\t\tonly_modules=_UpperCAmelCase )\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152359,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport os\r\rfrom huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tHUGGINGFACE_HUB_CACHE\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'config.json'\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'diffusion_pytorch_model.bin'\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'diffusion_flax_model.msgpack'\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'model.onnx'\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'diffusion_pytorch_model.safetensors'\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'weights.pb'\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'https://huggingface.co'\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tdefault_cache_path\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'diffusers_modules'\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tos.getenv('HF_MODULES_CACHE', os.path.join(hf_cache_home, 'modules'))\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t['fp16', 'non-ema']\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'.self_attn'\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rfrom datasets.utils.patching import _PatchedModuleObj, patch_submodule\r\rfrom . import _test_patching\r\rdef _UpperCamelCase\t\t\t\t\t( ):\r '''simple docstring'''\r\r\r\r\r\r import os as original_os\r from os import path as original_path\r from os import rename as original_rename\r from os.path import dirname as original_dirname\r from os.path import join as original_join\r\r assert _test_patching.os is original_os\r assert _test_patching.path is original_path\r assert _test_patching.join is original_join\r\r assert _test_patching.renamed_os is original_os\r assert _test_patching.renamed_path is original_path\r assert _test_patching.renamed_join is original_join\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"__test_patch_submodule_mock__\"\"\"\r with patch_submodule(_test_patching\t\t\t\t, \"\"\"os.path.join\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r # Every way to access os.path.join must be patched, and the rest must stay untouched\r\r # check os.path.join\r assert isinstance(_test_patching.os\t\t\t\t, _PatchedModuleObj\t\t\t\t)\r assert isinstance(_test_patching.os.path\t\t\t\t, _PatchedModuleObj\t\t\t\t)\r assert _test_patching.os.path.join is mock\r\r # check path.join\r assert isinstance(_test_patching.path\t\t\t\t, _PatchedModuleObj\t\t\t\t)\r assert _test_patching.path.join is mock\r\r # check join\r assert _test_patching.join is mock\r\r # check that the other attributes are untouched\r assert _test_patching.os.rename is original_rename\r assert _test_patching.path.dirname is original_dirname\r assert _test_patching.os.path.dirname is original_dirname\r\r # Even renamed modules or objects must be patched\r\r # check renamed_os.path.join\r assert isinstance(_test_patching.renamed_os\t\t\t\t, _PatchedModuleObj\t\t\t\t)\r assert isinstance(_test_patching.renamed_os.path\t\t\t\t, _PatchedModuleObj\t\t\t\t)\r assert _test_patching.renamed_os.path.join is mock\r\r # check renamed_path.join\r assert isinstance(_test_patching.renamed_path\t\t\t\t, _PatchedModuleObj\t\t\t\t)\r assert _test_patching.renamed_path.join is mock\r\r # check renamed_join\r assert _test_patching.renamed_join is mock\r\r # check that the other attributes are untouched\r assert _test_patching.renamed_os.rename is original_rename\r assert _test_patching.renamed_path.dirname is original_dirname\r assert _test_patching.renamed_os.path.dirname is original_dirname\r\r # check that everthing is back to normal when the patch is over\r\r assert _test_patching.os is original_os\r assert _test_patching.path is original_path\r assert _test_patching.join is original_join\r\r assert _test_patching.renamed_os is original_os\r assert _test_patching.renamed_path is original_path\r assert _test_patching.renamed_join is original_join\r\rdef _UpperCamelCase\t\t\t\t\t( ):\r '''simple docstring'''\r\r\r\r\r\r assert _test_patching.open is open\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"__test_patch_submodule_builtin_mock__\"\"\"\r # _test_patching has \"open\" in its globals\r assert _test_patching.open is open\r with patch_submodule(_test_patching\t\t\t\t, \"\"\"open\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r assert _test_patching.open is mock\r\r # check that everthing is back to normal when the patch is over\r\r assert _test_patching.open is open\r\rdef _UpperCamelCase\t\t\t\t\t( ):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"__test_patch_submodule_missing_mock__\"\"\"\r with patch_submodule(_test_patching\t\t\t\t, \"\"\"pandas.read_csv\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r pass\r\rdef _UpperCamelCase\t\t\t\t\t( ):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"__test_patch_submodule_missing_builtin_mock__\"\"\"\r # _test_patching doesn't have \"len\" in its globals\r assert getattr(_test_patching\t\t\t\t, \"\"\"len\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t) is None\r with patch_submodule(_test_patching\t\t\t\t, \"\"\"len\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r assert _test_patching.len is mock\r assert _test_patching.len is len\r\rdef _UpperCamelCase\t\t\t\t\t( ):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"__test_patch_submodule_start_and_stop_mock__\"\"\"\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tpatch_submodule(_test_patching\t\t\t\t, \"\"\"open\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r assert _test_patching.open is open\r patch.start()\r assert _test_patching.open is mock\r patch.stop()\r assert _test_patching.open is open\r\rdef _UpperCamelCase\t\t\t\t\t( ):\r '''simple docstring'''\r\r\r\r\r\r from os import rename as original_rename\r from os.path import dirname as original_dirname\r from os.path import join as original_join\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"__test_patch_submodule_successive_join__\"\"\"\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"__test_patch_submodule_successive_dirname__\"\"\"\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"__test_patch_submodule_successive_rename__\"\"\"\r assert _test_patching.os.path.join is original_join\r assert _test_patching.os.path.dirname is original_dirname\r assert _test_patching.os.rename is original_rename\r\r with patch_submodule(_test_patching\t\t\t\t, \"\"\"os.path.join\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r with patch_submodule(_test_patching\t\t\t\t, \"\"\"os.rename\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r with patch_submodule(_test_patching\t\t\t\t, \"\"\"os.path.dirname\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r assert _test_patching.os.path.join is mock_join\r assert _test_patching.os.path.dirname is mock_dirname\r assert _test_patching.os.rename is mock_rename\r\r # try another order\r with patch_submodule(_test_patching\t\t\t\t, \"\"\"os.rename\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r with patch_submodule(_test_patching\t\t\t\t, \"\"\"os.path.join\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r with patch_submodule(_test_patching\t\t\t\t, \"\"\"os.path.dirname\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r assert _test_patching.os.path.join is mock_join\r assert _test_patching.os.path.dirname is mock_dirname\r assert _test_patching.os.rename is mock_rename\r\r assert _test_patching.os.path.join is original_join\r assert _test_patching.os.path.dirname is original_dirname\r assert _test_patching.os.rename is original_rename\r\r\r\r\r\r\r\rdef _UpperCamelCase\t\t\t\t\t( ):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"__test_patch_submodule_doesnt_exist_mock__\"\"\"\r with patch_submodule(_test_patching\t\t\t\t, \"\"\"__module_that_doesn_exist__.__attribute_that_doesn_exist__\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r pass\r with patch_submodule(_test_patching\t\t\t\t, \"\"\"os.__attribute_that_doesn_exist__\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r pass\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152360,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rfrom functools import lru_cache\r\r@lru_cache\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r if num < 0:\r raise ValueError(\"\"\"Number should not be negative.\"\"\"\t\t\t\t)\r\r return 1 if num in (0, 1) else num * factorial(num - 1\t\t\t\t)\r\r\rif __name__ == \"__main__\":\r import doctest\r\r doctest.testmod()\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rfrom timeit import timeit\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t{\r 'MALAYALAM': True,\r 'String': False,\r 'rotor': True,\r 'level': True,\r 'A': True,\r 'BB': True,\r 'ABC': False,\r 'amanaplanacanalpanama': True, # \"a man a plan a canal panama\"\r}\r# Ensure our test data is valid\rassert all((key == key[::-1]) is value for key, value in test_data.items())\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlen(SCREAMING_SNAKE_CASE__\t\t\t\t) - 1\r while start_i < end_i:\r if s[start_i] == s[end_i]:\r start_i += 1\r end_i -= 1\r else:\r return False\r return True\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlen(SCREAMING_SNAKE_CASE__\t\t\t\t) // 2\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlen(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r # We need to traverse till half of the length of string\r # as we can get access of the i'th last element from\r # i'th index.\r # eg: [0,1,2,3,4,5] => 4th index can be accessed\r # with the help of 1st index (i==n-i-1)\r # where n is length of string\r return all(s[i] == s[n - i - 1] for i in range(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t)\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r if len(SCREAMING_SNAKE_CASE__\t\t\t\t) <= 2:\r return True\r if s[0] == s[len(SCREAMING_SNAKE_CASE__\t\t\t\t) - 1]:\r return is_palindrome_recursive(s[1:-1]\t\t\t\t)\r else:\r return False\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r return s == s[::-1]\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tF'''all({name}(key) is value for key, value in test_data.items())'''\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tF'''from __main__ import test_data, {name}'''\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t500000\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttimeit(stmt=SCREAMING_SNAKE_CASE__\t\t\t\t, setup=SCREAMING_SNAKE_CASE__\t\t\t\t, number=SCREAMING_SNAKE_CASE__\t\t\t\t)\r print(F'''{name:<35} finished {number:,} runs in {result:.5f} seconds'''\t\t\t\t)\r\r\rif __name__ == \"__main__\":\r for key, value in test_data.items():\r assert is_palindrome(key) is is_palindrome_recursive(key)\r assert is_palindrome(key) is is_palindrome_slice(key)\r print(f\"{key:21} {value}\")\r print('a man a plan a canal panama')\r\r # finished 500,000 runs in 0.46793 seconds\r benchmark_function('is_palindrome_slice')\r # finished 500,000 runs in 0.85234 seconds\r benchmark_function('is_palindrome')\r # finished 500,000 runs in 1.32028 seconds\r benchmark_function('is_palindrome_recursive')\r # finished 500,000 runs in 2.08679 seconds\r benchmark_function('is_palindrome_traversal')\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152361,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rfrom ...configuration_utils import PretrainedConfig\rfrom ...utils import logging\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tlogging.get_logger(__name__)\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t{\r 'google/vivit-b-16x2-kinetics400': (\r 'https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json'\r ),\r # See all Vivit models at https://huggingface.co/models?filter=vivit\r}\r\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r lowerCAmelCase_ : Optional[int] = \"\"\"vivit\"\"\"\r\r\r def __init__( self\t\t\t:\t\tList[str] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any]=2_24 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str]=32 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny=[2, 16, 16] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint=3 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any]=7_68 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=12 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict=12 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any]=30_72 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int]=\"gelu_fast\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=0.0 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple=0.0 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int]=0.02 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any]=1E-06 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str]=True ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\thidden_size\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnum_hidden_layers\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnum_attention_heads\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tintermediate_size\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\thidden_act\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\thidden_dropout_prob\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tattention_probs_dropout_prob\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tinitializer_range\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlayer_norm_eps\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\timage_size\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnum_frames\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttubelet_size\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnum_channels\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tqkv_bias\r\r super().__init__(**_UpperCAmelCase )\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport datasets\r\rfrom .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'\\\\n@INPROCEEDINGS{Papineni02bleu:a,\\n author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\\n title = {BLEU: a Method for Automatic Evaluation of Machine Translation},\\n booktitle = {},\\n year = {2002},\\n pages = {311--318}\\n}\\n@inproceedings{lin-och-2004-orange,\\n title = \"{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation\",\\n author = \"Lin, Chin-Yew and\\n Och, Franz Josef\",\\n booktitle = \"{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics\",\\n month = \"aug 23{--}aug 27\",\\n year = \"2004\",\\n address = \"Geneva, Switzerland\",\\n publisher = \"COLING\",\\n url = \"https://www.aclweb.org/anthology/C04-1072\",\\n pages = \"501--507\",\\n}\\n'\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'\\\\nBLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.\\nQuality is considered to be the correspondence between a machine\\'s output and that of a human: \"the closer a machine translation is to a professional human translation,\\nthe better it is\" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and\\nremains one of the most popular automated and inexpensive metrics.\\n\\nScores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.\\nThose scores are then averaged over the whole corpus to reach an estimate of the translation\\'s overall quality. Intelligibility or grammatical correctness\\nare not taken into account[citation needed].\\n\\nBLEU\\'s output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1\\nrepresenting more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the\\nreference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional\\nreference translations will increase the BLEU score.\\n'\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'\\nComputes BLEU score of translated segments against one or more references.\\nArgs:\\n predictions: list of translations to score.\\n Each translation should be tokenized into a list of tokens.\\n references: list of lists of references for each translation.\\n Each reference should be tokenized into a list of tokens.\\n max_order: Maximum n-gram order to use when computing BLEU score.\\n smooth: Whether or not to apply Lin et al. 2004 smoothing.\\nReturns:\\n \\'bleu\\': bleu score,\\n \\'precisions\\': geometric mean of n-gram precisions,\\n \\'brevity_penalty\\': brevity penalty,\\n \\'length_ratio\\': ratio of lengths,\\n \\'translation_length\\': translation_length,\\n \\'reference_length\\': reference_length\\nExamples:\\n\\n >>> predictions = [\\n ... [\"hello\", \"there\", \"general\", \"kenobi\"], # tokenized prediction of the first sample\\n ... [\"foo\", \"bar\", \"foobar\"] # tokenized prediction of the second sample\\n ... ]\\n >>> references = [\\n ... [[\"hello\", \"there\", \"general\", \"kenobi\"], [\"hello\", \"there\", \"!\"]], # tokenized references for the first sample (2 references)\\n ... [[\"foo\", \"bar\", \"foobar\"]] # tokenized references for the second sample (1 reference)\\n ... ]\\n >>> bleu = datasets.load_metric(\"bleu\")\\n >>> results = bleu.compute(predictions=predictions, references=references)\\n >>> print(results[\"bleu\"])\\n 1.0\\n'\r\r@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION\t, _KWARGS_DESCRIPTION )\rclass lowerCAmelCase_ ( datasets.Metric ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return datasets.MetricInfo(\r description=_DESCRIPTION ,\t\t\t\t\t\tcitation=_CITATION ,\t\t\t\t\t\tinputs_description=_KWARGS_DESCRIPTION ,\t\t\t\t\t\tfeatures=datasets.Features(\r {\r \"\"\"predictions\"\"\": datasets.Sequence(datasets.Value(\"\"\"string\"\"\" ,\t\t\t\t\t\tid=\"\"\"token\"\"\" ) ,\t\t\t\t\t\tid=\"\"\"sequence\"\"\" ),\r \"\"\"references\"\"\": datasets.Sequence(\r datasets.Sequence(datasets.Value(\"\"\"string\"\"\" ,\t\t\t\t\t\tid=\"\"\"token\"\"\" ) ,\t\t\t\t\t\tid=\"\"\"sequence\"\"\" ) ,\t\t\t\t\t\tid=\"\"\"references\"\"\" ),\r } ) ,\t\t\t\t\t\tcodebase_urls=[\"\"\"https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py\"\"\"] ,\t\t\t\t\t\treference_urls=[\r \"\"\"https://en.wikipedia.org/wiki/BLEU\"\"\",\r \"\"\"https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213\"\"\",\r ] ,\t\t\t\t\t\t)\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any]=4 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=False ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tcompute_bleu(\r reference_corpus=_UpperCAmelCase ,\t\t\t\t\t\ttranslation_corpus=_UpperCAmelCase ,\t\t\t\t\t\tmax_order=_UpperCAmelCase ,\t\t\t\t\t\tsmooth=_UpperCAmelCase )\r ((UpperCAmelCase__)\t\t\t\t\t\t\t,\t(UpperCAmelCase__)\t\t\t\t\t\t\t,\t(UpperCAmelCase__)\t\t\t\t\t\t\t,\t(UpperCAmelCase__)\t\t\t\t\t\t\t,\t(UpperCAmelCase__)\t\t\t\t\t\t\t,\t(UpperCAmelCase__))\t\t\t\t\t\t\t\t=\t\t\tscore\r return {\r \"bleu\": bleu,\r \"precisions\": precisions,\r \"brevity_penalty\": bp,\r \"length_ratio\": ratio,\r \"translation_length\": translation_length,\r \"reference_length\": reference_length,\r }\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152362,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport logging\rfrom pathlib import Path\r\rimport numpy as np\rimport pytorch_lightning as pl\rimport torch\rfrom pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint\rfrom pytorch_lightning.utilities import rank_zero_only\rfrom utils_rag import save_json\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[int]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tfilter(lambda SCREAMING_SNAKE_CASE__\t\t\t\t: p.requires_grad\t\t\t\t, model.parameters()\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tsum([np.prod(p.size()\t\t\t\t) for p in model_parameters]\t\t\t\t)\r return params\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tlogging.getLogger(__name__)\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tUnion[str, Any]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r if metric == \"rouge2\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"{val_avg_rouge2:.4f}-{step_count}\"\"\"\r elif metric == \"bleu\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"{val_avg_bleu:.4f}-{step_count}\"\"\"\r elif metric == \"em\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"{val_avg_em:.4f}-{step_count}\"\"\"\r elif metric == \"loss\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"{val_avg_loss:.4f}-{step_count}\"\"\"\r else:\r raise NotImplementedError(\r F'''seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this'''\r \"\"\" function.\"\"\"\t\t\t\t)\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tModelCheckpoint(\r dirpath=SCREAMING_SNAKE_CASE__\t\t\t\t, filename=SCREAMING_SNAKE_CASE__\t\t\t\t, monitor=F'''val_{metric}'''\t\t\t\t, mode=\"\"\"max\"\"\"\t\t\t\t, save_top_k=1\t\t\t\t, every_n_epochs=1\t\t\t\t, )\r\r return checkpoint_callback\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r return EarlyStopping(\r monitor=F'''val_{metric}'''\t\t\t\t, mode=\"\"\"min\"\"\" if \"\"\"loss\"\"\" in metric else \"\"\"max\"\"\"\t\t\t\t, patience=SCREAMING_SNAKE_CASE__\t\t\t\t, verbose=SCREAMING_SNAKE_CASE__\t\t\t\t, )\r\r\r\r\r\r\r\rclass lowerCAmelCase_ ( pl.Callback ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{f'''lr_group_{i}''': param[\"\"\"lr\"\"\"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups )}\r pl_module.logger.log_metrics(_UpperCAmelCase )\r\r\r @rank_zero_only\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tpl.Trainer ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tpl.LightningModule ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint=True ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r logger.info(f'''***** {type_path} results at step {trainer.global_step:05d} *****''' )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttrainer.callback_metrics\r trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in [\"\"\"log\"\"\", \"\"\"progress_bar\"\"\", \"\"\"preds\"\"\"]} )\r # Log results\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tPath(pl_module.hparams.output_dir )\r if type_path == \"test\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tod / \"\"\"test_results.txt\"\"\"\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tod / \"\"\"test_generations.txt\"\"\"\r else:\r # this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json\r # If people want this it will be easy enough to add back.\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tod / f'''{type_path}_results/{trainer.global_step:05d}.txt'''\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tod / f'''{type_path}_generations/{trainer.global_step:05d}.txt'''\r results_file.parent.mkdir(exist_ok=_UpperCAmelCase )\r generations_file.parent.mkdir(exist_ok=_UpperCAmelCase )\r with open(_UpperCAmelCase ,\t\t\t\t\t\t\"\"\"a+\"\"\" ) as writer:\r for key in sorted(_UpperCAmelCase ):\r if key in [\"log\", \"progress_bar\", \"preds\"]:\r continue\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmetrics[key]\r if isinstance(_UpperCAmelCase ,\t\t\t\t\t\ttorch.Tensor ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tval.item()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tf'''{key}: {val:.6f}\\n'''\r writer.write(_UpperCAmelCase )\r\r if not save_generations:\r return\r\r if \"preds\" in metrics:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"\\n\"\"\".join(metrics[\"\"\"preds\"\"\"] )\r generations_file.open(\"\"\"w+\"\"\" ).write(_UpperCAmelCase )\r\r\r @rank_zero_only\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r try:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tpl_module.model.model.num_parameters()\r except AttributeError:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tpl_module.model.num_parameters()\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tcount_trainable_parameters(_UpperCAmelCase )\r # mp stands for million parameters\r trainer.logger.log_metrics({\"\"\"n_params\"\"\": npars, \"\"\"mp\"\"\": npars / 1E6, \"\"\"grad_mp\"\"\": n_trainable_pars / 1E6} )\r\r\r @rank_zero_only\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tpl.Trainer ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tpl.LightningModule ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r save_json(pl_module.metrics ,\t\t\t\t\t\tpl_module.metrics_save_path )\r return self._write_logs(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t\"\"\"test\"\"\" )\r\r\r\r @rank_zero_only\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tpl.Trainer ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r save_json(pl_module.metrics ,\t\t\t\t\t\tpl_module.metrics_save_path )\r # Uncommenting this will save val generations\r # return self._write_logs(trainer, pl_module, \"valid\")\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rfrom dataclasses import dataclass\rfrom typing import List, Optional, Union\r\rimport numpy as np\rimport torch\r\rfrom ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available\r\r@dataclass\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r lowerCAmelCase_ : Union[List[np.ndarray], torch.FloatTensor]\r\r\rtry:\r if not (is_transformers_available() and is_torch_available()):\r raise OptionalDependencyNotAvailable()\rexcept OptionalDependencyNotAvailable:\r from ...utils.dummy_torch_and_transformers_objects import * # noqa F403\relse:\r from .pipeline_text_to_video_synth import TextToVideoSDPipeline\r from .pipeline_text_to_video_synth_imgaimg import VideoToVideoSDPipeline # noqa: F401\r from .pipeline_text_to_video_zero import TextToVideoZeroPipeline\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152363,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport re\r\rfrom filelock import FileLock\r\r\rtry:\r import nltk\r\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tTrue\rexcept (ImportError, ModuleNotFoundError):\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tFalse\r\rif NLTK_AVAILABLE:\r with FileLock('.lock') as lock:\r nltk.download('punkt', quiet=True)\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r re.sub(\"\"\"\"\"\"\t\t\t\t, \"\"\"\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t) # remove pegasus newline char\r assert NLTK_AVAILABLE, \"nltk must be installed to separate newlines between sentences. (pip install nltk)\"\r return \"\\n\".join(nltk.sent_tokenize(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t)\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport torch\rimport torch.nn as nn\rfrom transformers.modeling_utils import ModuleUtilsMixin\rfrom transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm\r\rfrom ...configuration_utils import ConfigMixin, register_to_config\rfrom ...models import ModelMixin\r\rclass lowerCAmelCase_ ( lowerCamelCase_\t, lowerCamelCase_\t, lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r @register_to_config\r def __init__( self\t\t\t:\t\tList[str] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tfloat ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tbool = False ,\t\t\t\t\t\t):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r super().__init__()\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnn.Embedding(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnn.Embedding(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnn.Dropout(p=_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTaConfig(\r vocab_size=_UpperCAmelCase ,\t\t\t\t\t\td_model=_UpperCAmelCase ,\t\t\t\t\t\tnum_heads=_UpperCAmelCase ,\t\t\t\t\t\td_kv=_UpperCAmelCase ,\t\t\t\t\t\td_ff=_UpperCAmelCase ,\t\t\t\t\t\tdropout_rate=_UpperCAmelCase ,\t\t\t\t\t\tfeed_forward_proj=_UpperCAmelCase ,\t\t\t\t\t\tis_decoder=_UpperCAmelCase ,\t\t\t\t\t\tis_encoder_decoder=_UpperCAmelCase ,\t\t\t\t\t\t)\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnn.ModuleList()\r for lyr_num in range(_UpperCAmelCase ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTaBlock(_UpperCAmelCase )\r self.encoders.append(_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTaLayerNorm(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnn.Dropout(p=_UpperCAmelCase )\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.token_embedder(_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tencoder_input_tokens.shape[1]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.arange(_UpperCAmelCase ,\t\t\t\t\t\tdevice=encoder_input_tokens.device )\r x += self.position_encoding(_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.dropout_pre(_UpperCAmelCase )\r\r # inverted the attention mask\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tencoder_input_tokens.size()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_extended_attention_mask(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r\r for lyr in self.encoders:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlyr(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )[0]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.layer_norm(_UpperCAmelCase )\r\r return self.dropout_post(_UpperCAmelCase ), encoder_inputs_mask\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152364,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rfrom typing import Dict, List\r\rfrom nltk.translate import gleu_score\r\rimport datasets\rfrom datasets import MetricInfo\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'\\\\n@misc{wu2016googles,\\n title={Google\\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\\n author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey\\n and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin\\n Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto\\n Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and\\n Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes\\n and Jeffrey Dean},\\n year={2016},\\n eprint={1609.08144},\\n archivePrefix={arXiv},\\n primaryClass={cs.CL}\\n}\\n'\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'\\\\nThe BLEU score has some undesirable properties when used for single\\nsentences, as it was designed to be a corpus measure. We therefore\\nuse a slightly different score for our RL experiments which we call\\nthe \\'GLEU score\\'. For the GLEU score, we record all sub-sequences of\\n1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then\\ncompute a recall, which is the ratio of the number of matching n-grams\\nto the number of total n-grams in the target (ground truth) sequence,\\nand a precision, which is the ratio of the number of matching n-grams\\nto the number of total n-grams in the generated output sequence. Then\\nGLEU score is simply the minimum of recall and precision. This GLEU\\nscore\\'s range is always between 0 (no matches) and 1 (all match) and\\nit is symmetrical when switching output and target. According to\\nour experiments, GLEU score correlates quite well with the BLEU\\nmetric on a corpus level but does not have its drawbacks for our per\\nsentence reward objective.\\n'\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'\\\\nComputes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.\\nInstead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching\\ntokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.\\n\\nArgs:\\n predictions (list of str): list of translations to score.\\n Each translation should be tokenized into a list of tokens.\\n references (list of list of str): list of lists of references for each translation.\\n Each reference should be tokenized into a list of tokens.\\n min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.\\n max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.\\n\\nReturns:\\n \\'google_bleu\\': google_bleu score\\n\\nExamples:\\n Example 1:\\n >>> hyp1 = [\\'It\\', \\'is\\', \\'a\\', \\'guide\\', \\'to\\', \\'action\\', \\'which\\',\\n ... \\'ensures\\', \\'that\\', \\'the\\', \\'rubber\\', \\'duck\\', \\'always\\',\\n ... \\'disobeys\\', \\'the\\', \\'commands\\', \\'of\\', \\'the\\', \\'cat\\']\\n >>> ref1a = [\\'It\\', \\'is\\', \\'the\\', \\'guiding\\', \\'principle\\', \\'which\\',\\n ... \\'guarantees\\', \\'the\\', \\'rubber\\', \\'duck\\', \\'forces\\', \\'never\\',\\n ... \\'being\\', \\'under\\', \\'the\\', \\'command\\', \\'of\\', \\'the\\', \\'cat\\']\\n\\n >>> hyp2 = [\\'he\\', \\'read\\', \\'the\\', \\'book\\', \\'because\\', \\'he\\', \\'was\\',\\n ... \\'interested\\', \\'in\\', \\'world\\', \\'history\\']\\n >>> ref2a = [\\'he\\', \\'was\\', \\'interested\\', \\'in\\', \\'world\\', \\'history\\',\\n ... \\'because\\', \\'he\\', \\'read\\', \\'the\\', \\'book\\']\\n\\n >>> list_of_references = [[ref1a], [ref2a]]\\n >>> hypotheses = [hyp1, hyp2]\\n >>> google_bleu = datasets.load_metric(\"google_bleu\")\\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\\n >>> print(round(results[\"google_bleu\"], 2))\\n 0.44\\n\\n Example 2:\\n >>> hyp1 = [\\'It\\', \\'is\\', \\'a\\', \\'guide\\', \\'to\\', \\'action\\', \\'which\\',\\n ... \\'ensures\\', \\'that\\', \\'the\\', \\'rubber\\', \\'duck\\', \\'always\\',\\n ... \\'disobeys\\', \\'the\\', \\'commands\\', \\'of\\', \\'the\\', \\'cat\\']\\n >>> ref1a = [\\'It\\', \\'is\\', \\'the\\', \\'guiding\\', \\'principle\\', \\'which\\',\\n ... \\'guarantees\\', \\'the\\', \\'rubber\\', \\'duck\\', \\'forces\\', \\'never\\',\\n ... \\'being\\', \\'under\\', \\'the\\', \\'command\\', \\'of\\', \\'the\\', \\'cat\\']\\n >>> ref1b = [\\'It\\', \\'is\\', \\'a\\', \\'guide\\', \\'to\\', \\'action\\', \\'that\\',\\n ... \\'ensures\\', \\'that\\', \\'the\\', \\'rubber\\', \\'duck\\', \\'will\\', \\'never\\',\\n ... \\'heed\\', \\'the\\', \\'cat\\', \\'commands\\']\\n >>> ref1c = [\\'It\\', \\'is\\', \\'the\\', \\'practical\\', \\'guide\\', \\'for\\', \\'the\\',\\n ... \\'rubber\\', \\'duck\\', \\'army\\', \\'never\\', \\'to\\', \\'heed\\', \\'the\\', \\'directions\\',\\n ... \\'of\\', \\'the\\', \\'cat\\']\\n\\n >>> hyp2 = [\\'he\\', \\'read\\', \\'the\\', \\'book\\', \\'because\\', \\'he\\', \\'was\\',\\n ... \\'interested\\', \\'in\\', \\'world\\', \\'history\\']\\n >>> ref2a = [\\'he\\', \\'was\\', \\'interested\\', \\'in\\', \\'world\\', \\'history\\',\\n ... \\'because\\', \\'he\\', \\'read\\', \\'the\\', \\'book\\']\\n\\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\\n >>> hypotheses = [hyp1, hyp2]\\n >>> google_bleu = datasets.load_metric(\"google_bleu\")\\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\\n >>> print(round(results[\"google_bleu\"], 2))\\n 0.61\\n\\n Example 3:\\n >>> hyp1 = [\\'It\\', \\'is\\', \\'a\\', \\'guide\\', \\'to\\', \\'action\\', \\'which\\',\\n ... \\'ensures\\', \\'that\\', \\'the\\', \\'rubber\\', \\'duck\\', \\'always\\',\\n ... \\'disobeys\\', \\'the\\', \\'commands\\', \\'of\\', \\'the\\', \\'cat\\']\\n >>> ref1a = [\\'It\\', \\'is\\', \\'the\\', \\'guiding\\', \\'principle\\', \\'which\\',\\n ... \\'guarantees\\', \\'the\\', \\'rubber\\', \\'duck\\', \\'forces\\', \\'never\\',\\n ... \\'being\\', \\'under\\', \\'the\\', \\'command\\', \\'of\\', \\'the\\', \\'cat\\']\\n >>> ref1b = [\\'It\\', \\'is\\', \\'a\\', \\'guide\\', \\'to\\', \\'action\\', \\'that\\',\\n ... \\'ensures\\', \\'that\\', \\'the\\', \\'rubber\\', \\'duck\\', \\'will\\', \\'never\\',\\n ... \\'heed\\', \\'the\\', \\'cat\\', \\'commands\\']\\n >>> ref1c = [\\'It\\', \\'is\\', \\'the\\', \\'practical\\', \\'guide\\', \\'for\\', \\'the\\',\\n ... \\'rubber\\', \\'duck\\', \\'army\\', \\'never\\', \\'to\\', \\'heed\\', \\'the\\', \\'directions\\',\\n ... \\'of\\', \\'the\\', \\'cat\\']\\n\\n >>> hyp2 = [\\'he\\', \\'read\\', \\'the\\', \\'book\\', \\'because\\', \\'he\\', \\'was\\',\\n ... \\'interested\\', \\'in\\', \\'world\\', \\'history\\']\\n >>> ref2a = [\\'he\\', \\'was\\', \\'interested\\', \\'in\\', \\'world\\', \\'history\\',\\n ... \\'because\\', \\'he\\', \\'read\\', \\'the\\', \\'book\\']\\n\\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\\n >>> hypotheses = [hyp1, hyp2]\\n >>> google_bleu = datasets.load_metric(\"google_bleu\")\\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)\\n >>> print(round(results[\"google_bleu\"], 2))\\n 0.53\\n\\n Example 4:\\n >>> hyp1 = [\\'It\\', \\'is\\', \\'a\\', \\'guide\\', \\'to\\', \\'action\\', \\'which\\',\\n ... \\'ensures\\', \\'that\\', \\'the\\', \\'rubber\\', \\'duck\\', \\'always\\',\\n ... \\'disobeys\\', \\'the\\', \\'commands\\', \\'of\\', \\'the\\', \\'cat\\']\\n >>> ref1a = [\\'It\\', \\'is\\', \\'the\\', \\'guiding\\', \\'principle\\', \\'which\\',\\n ... \\'guarantees\\', \\'the\\', \\'rubber\\', \\'duck\\', \\'forces\\', \\'never\\',\\n ... \\'being\\', \\'under\\', \\'the\\', \\'command\\', \\'of\\', \\'the\\', \\'cat\\']\\n >>> ref1b = [\\'It\\', \\'is\\', \\'a\\', \\'guide\\', \\'to\\', \\'action\\', \\'that\\',\\n ... \\'ensures\\', \\'that\\', \\'the\\', \\'rubber\\', \\'duck\\', \\'will\\', \\'never\\',\\n ... \\'heed\\', \\'the\\', \\'cat\\', \\'commands\\']\\n >>> ref1c = [\\'It\\', \\'is\\', \\'the\\', \\'practical\\', \\'guide\\', \\'for\\', \\'the\\',\\n ... \\'rubber\\', \\'duck\\', \\'army\\', \\'never\\', \\'to\\', \\'heed\\', \\'the\\', \\'directions\\',\\n ... \\'of\\', \\'the\\', \\'cat\\']\\n\\n >>> hyp2 = [\\'he\\', \\'read\\', \\'the\\', \\'book\\', \\'because\\', \\'he\\', \\'was\\',\\n ... \\'interested\\', \\'in\\', \\'world\\', \\'history\\']\\n >>> ref2a = [\\'he\\', \\'was\\', \\'interested\\', \\'in\\', \\'world\\', \\'history\\',\\n ... \\'because\\', \\'he\\', \\'read\\', \\'the\\', \\'book\\']\\n\\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\\n >>> hypotheses = [hyp1, hyp2]\\n >>> google_bleu = datasets.load_metric(\"google_bleu\")\\n >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)\\n >>> print(round(results[\"google_bleu\"], 2))\\n 0.4\\n'\r\r@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION\t, _KWARGS_DESCRIPTION )\rclass lowerCAmelCase_ ( datasets.Metric ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return datasets.MetricInfo(\r description=_DESCRIPTION ,\t\t\t\t\t\tcitation=_CITATION ,\t\t\t\t\t\tinputs_description=_KWARGS_DESCRIPTION ,\t\t\t\t\t\tfeatures=datasets.Features(\r {\r \"\"\"predictions\"\"\": datasets.Sequence(datasets.Value(\"\"\"string\"\"\" ,\t\t\t\t\t\tid=\"\"\"token\"\"\" ) ,\t\t\t\t\t\tid=\"\"\"sequence\"\"\" ),\r \"\"\"references\"\"\": datasets.Sequence(\r datasets.Sequence(datasets.Value(\"\"\"string\"\"\" ,\t\t\t\t\t\tid=\"\"\"token\"\"\" ) ,\t\t\t\t\t\tid=\"\"\"sequence\"\"\" ) ,\t\t\t\t\t\tid=\"\"\"references\"\"\" ),\r } ) ,\t\t\t\t\t\t)\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[List[List[str]]] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[List[str]] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint = 1 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint = 4 ,\t\t\t\t\t\t):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return {\r \"google_bleu\": gleu_score.corpus_gleu(\r list_of_references=_UpperCAmelCase ,\t\t\t\t\t\thypotheses=_UpperCAmelCase ,\t\t\t\t\t\tmin_len=_UpperCAmelCase ,\t\t\t\t\t\tmax_len=_UpperCAmelCase )\r }\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport argparse\rimport json\rimport os\r\rimport fairseq\rimport torch\rfrom fairseq.data import Dictionary\r\rfrom transformers import (\r WavaVecaConfig,\r WavaVecaCTCTokenizer,\r WavaVecaFeatureExtractor,\r WavaVecaForCTC,\r WavaVecaForPreTraining,\r WavaVecaProcessor,\r logging,\r)\rfrom transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification\r\r\rlogging.set_verbosity_info()\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tlogging.get_logger(__name__)\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t{\r 'post_extract_proj': 'feature_projection.projection',\r 'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',\r 'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',\r 'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',\r 'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',\r 'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',\r 'self_attn_layer_norm': 'encoder.layers.*.layer_norm',\r 'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',\r 'fc2': 'encoder.layers.*.feed_forward.output_dense',\r 'final_layer_norm': 'encoder.layers.*.final_layer_norm',\r 'encoder.layer_norm': 'encoder.layer_norm',\r 'adapter_layer': 'encoder.layers.*.adapter_layer',\r 'w2v_model.layer_norm': 'feature_projection.layer_norm',\r 'quantizer.weight_proj': 'quantizer.weight_proj',\r 'quantizer.vars': 'quantizer.codevectors',\r 'project_q': 'project_q',\r 'final_proj': 'project_hid',\r 'w2v_encoder.proj': 'lm_head',\r 'mask_emb': 'masked_spec_embed',\r 'pooling_layer.linear': 'projector',\r 'pooling_layer.projection': 'classifier',\r}\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t[\r 'lm_head',\r 'quantizer.weight_proj',\r 'quantizer.codevectors',\r 'project_q',\r 'project_hid',\r 'projector',\r 'classifier',\r]\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tTuple\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{}\r with open(SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\"r\"\"\"\t\t\t\t) as file:\r for line_number, line in enumerate(SCREAMING_SNAKE_CASE__\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tline.strip()\r if line:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tline.split()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tline_number\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\twords[0]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue\r return result\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tUnion[str, Any]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[Any]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tAny\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tUnion[str, Any]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r for attribute in key.split(\"\"\".\"\"\"\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tgetattr(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r for param_key in PARAM_MAPPING.keys():\r if full_name.endswith(SCREAMING_SNAKE_CASE__\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tPARAM_MAPPING[full_name.split(\"\"\".\"\"\"\t\t\t\t)[-1]]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"param\"\"\"\r\r if weight_type is not None and weight_type != \"param\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tgetattr(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t).shape\r elif weight_type is not None and weight_type == \"param\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\thf_pointer\r for attribute in hf_param_name.split(\"\"\".\"\"\"\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tgetattr(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tshape_pointer.shape\r\r # let's reduce dimension\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue[0]\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\thf_pointer.shape\r\r if hf_shape != value.shape:\r raise ValueError(\r F'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''\r F''' {value.shape} for {full_name}'''\t\t\t\t)\r\r if weight_type == \"weight\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue\r elif weight_type == \"weight_g\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue\r elif weight_type == \"weight_v\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue\r elif weight_type == \"bias\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue\r elif weight_type == \"param\":\r for attribute in hf_param_name.split(\"\"\".\"\"\"\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tgetattr(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue\r\r logger.info(F'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.'''\t\t\t\t)\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tUnion[str, Any]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tUnion[str, Any]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[Any]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[Any]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r for param_key in PARAM_MAPPING.keys():\r if full_name.endswith(SCREAMING_SNAKE_CASE__\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tPARAM_MAPPING[full_name.split(\"\"\".\"\"\"\t\t\t\t)[-1]]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"param\"\"\"\r\r if weight_type is not None and weight_type != \"param\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\".\"\"\".join([key, weight_type]\t\t\t\t)\r elif weight_type is not None and weight_type == \"param\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\".\"\"\".join([key, hf_param_name]\t\t\t\t)\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tkey\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue if \"\"\"lm_head\"\"\" in full_key else value[0]\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t{\r 'W_a': 'linear_1.weight',\r 'W_b': 'linear_2.weight',\r 'b_a': 'linear_1.bias',\r 'b_b': 'linear_2.bias',\r 'ln_W': 'norm.weight',\r 'ln_b': 'norm.bias',\r}\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tAny\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tDict=None\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[Any]=None\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r for key, mapped_key in MAPPING.items():\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"wav2vec2.\"\"\" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key\r if key in name or key.split(\"\"\"w2v_model.\"\"\"\t\t\t\t)[-1] == name.split(\"\"\".\"\"\"\t\t\t\t)[0]:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTrue\r if \"*\" in mapped_key:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tname.split(SCREAMING_SNAKE_CASE__\t\t\t\t)[0].split(\"\"\".\"\"\"\t\t\t\t)[-2]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmapped_key.replace(\"\"\"*\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r if \"weight_g\" in name:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"weight_g\"\"\"\r elif \"weight_v\" in name:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"weight_v\"\"\"\r elif \"bias\" in name:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"bias\"\"\"\r elif \"weight\" in name:\r # TODO: don't match quantizer.weight_proj\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"weight\"\"\"\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r if hf_dict is not None:\r rename_dict(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r else:\r set_recursively(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r return is_used\r return is_used\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tTuple\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[int]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tfairseq_model.state_dict()\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\thf_model.wavaveca.feature_extractor\r\r for name, value in fairseq_dict.items():\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r if \"conv_layers\" in name:\r load_conv_layer(\r SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, hf_model.config.feat_extract_norm == \"\"\"group\"\"\"\t\t\t\t, )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTrue\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tload_wavaveca_layer(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r if not is_used:\r unused_weights.append(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r logger.warning(F'''Unused weights: {unused_weights}'''\t\t\t\t)\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[Any]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tfull_name.split(\"\"\"conv_layers.\"\"\"\t\t\t\t)[-1]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tname.split(\"\"\".\"\"\"\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tint(items[0]\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tint(items[1]\t\t\t\t)\r\r if type_id == 0:\r if \"bias\" in name:\r if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:\r raise ValueError(\r F'''{full_name} has size {value.shape}, but'''\r F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue\r logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''\t\t\t\t)\r elif \"weight\" in name:\r if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:\r raise ValueError(\r F'''{full_name} has size {value.shape}, but'''\r F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue\r logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''\t\t\t\t)\r elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):\r if \"bias\" in name:\r if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:\r raise ValueError(\r F'''{full_name} has size {value.shape}, but'''\r F''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'''\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue\r logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''\t\t\t\t)\r elif \"weight\" in name:\r if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:\r raise ValueError(\r F'''{full_name} has size {value.shape}, but'''\r F''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'''\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue\r logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''\t\t\t\t)\r else:\r unused_weights.append(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r@torch.no_grad()\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]=None\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[int]=None\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tUnion[str, Any]=True\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tUnion[str, Any]=False\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r if config_path is not None:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tWavaVecaConfig.from_pretrained(SCREAMING_SNAKE_CASE__\t\t\t\t)\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tWavaVecaConfig()\r\r if is_seq_class:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tread_txt_into_dict(SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tidalabel\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tWavaVecaForSequenceClassification(SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tWavaVecaFeatureExtractor(\r feature_size=1\t\t\t\t, sampling_rate=16000\t\t\t\t, padding_value=0\t\t\t\t, do_normalize=SCREAMING_SNAKE_CASE__\t\t\t\t, return_attention_mask=SCREAMING_SNAKE_CASE__\t\t\t\t, )\r feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r elif is_finetuned:\r if dict_path:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tDictionary.load(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r # important change bos & pad token id since CTC symbol is and\r # not as in fairseq\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttarget_dict.pad_index\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttarget_dict.bos_index\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttarget_dict.eos_index\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlen(target_dict.symbols\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tos.path.join(SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\"vocab.json\"\"\"\t\t\t\t)\r if not os.path.isdir(SCREAMING_SNAKE_CASE__\t\t\t\t):\r logger.error(\"\"\"--pytorch_dump_folder_path ({}) should be a directory\"\"\".format(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t)\r return\r os.makedirs(SCREAMING_SNAKE_CASE__\t\t\t\t, exist_ok=SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttarget_dict.indices\r\r # fairseq has the and switched\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t1\r with open(SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\"w\"\"\"\t\t\t\t, encoding=\"\"\"utf-8\"\"\"\t\t\t\t) as vocab_handle:\r json.dump(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tWavaVecaCTCTokenizer(\r SCREAMING_SNAKE_CASE__\t\t\t\t, unk_token=target_dict.unk_word\t\t\t\t, pad_token=target_dict.pad_word\t\t\t\t, bos_token=target_dict.bos_word\t\t\t\t, eos_token=target_dict.eos_word\t\t\t\t, word_delimiter_token=\"\"\"|\"\"\"\t\t\t\t, do_lower_case=SCREAMING_SNAKE_CASE__\t\t\t\t, )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTrue if config.feat_extract_norm == \"\"\"layer\"\"\" else False\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tWavaVecaFeatureExtractor(\r feature_size=1\t\t\t\t, sampling_rate=16000\t\t\t\t, padding_value=0\t\t\t\t, do_normalize=SCREAMING_SNAKE_CASE__\t\t\t\t, return_attention_mask=SCREAMING_SNAKE_CASE__\t\t\t\t, )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tWavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE__\t\t\t\t, tokenizer=SCREAMING_SNAKE_CASE__\t\t\t\t)\r processor.save_pretrained(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tWavaVecaForCTC(SCREAMING_SNAKE_CASE__\t\t\t\t)\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tWavaVecaForPreTraining(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r if is_finetuned or is_seq_class:\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tfairseq.checkpoint_utils.load_model_ensemble_and_task(\r [checkpoint_path]\t\t\t\t, arg_overrides={\"\"\"data\"\"\": \"\"\"/\"\"\".join(dict_path.split(\"\"\"/\"\"\"\t\t\t\t)[:-1]\t\t\t\t)}\t\t\t\t)\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\targparse.Namespace(task=\"\"\"audio_pretraining\"\"\"\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tfairseq.tasks.setup_task(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tfairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]\t\t\t\t, task=SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel[0].eval()\r\r recursively_load_weights(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, not is_finetuned\t\t\t\t)\r\r hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r\rif __name__ == \"__main__\":\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\targparse.ArgumentParser()\r parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')\r parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')\r parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')\r parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')\r parser.add_argument(\r '--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'\r )\r parser.add_argument(\r '--is_seq_class',\r action='store_true',\r help='Whether the model to convert is a fine-tuned sequence classification model or not',\r )\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tparser.parse_args()\r\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tnot args.not_finetuned and not args.is_seq_class\r convert_wavaveca_checkpoint(\r args.checkpoint_path,\r args.pytorch_dump_folder_path,\r args.config_path,\r args.dict_path,\r is_finetuned,\r args.is_seq_class,\r )\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152365,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport argparse\r\rimport fairseq\rimport torch\rfrom torch import nn\r\rfrom transformers import (\r MBartaaTokenizer,\r MBartConfig,\r MBartForCausalLM,\r SpeechEncoderDecoderConfig,\r SpeechEncoderDecoderModel,\r WavaVecaConfig,\r WavaVecaFeatureExtractor,\r WavaVecaModel,\r logging,\r)\r\r\rlogging.set_verbosity_info()\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tlogging.get_logger(__name__)\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t{\r 'post_extract_proj': 'feature_projection.projection',\r 'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',\r 'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',\r 'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',\r 'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',\r 'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',\r 'self_attn_layer_norm': 'encoder.layers.*.layer_norm',\r 'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',\r 'fc2': 'encoder.layers.*.feed_forward.output_dense',\r 'final_layer_norm': 'encoder.layers.*.final_layer_norm',\r 'encoder.layer_norm': 'encoder.layer_norm',\r 'w2v_model.layer_norm': 'feature_projection.layer_norm',\r 'quantizer.weight_proj': 'quantizer.weight_proj',\r 'quantizer.vars': 'quantizer.codevectors',\r 'project_q': 'project_q',\r 'final_proj': 'project_hid',\r 'w2v_encoder.proj': 'lm_head',\r 'mask_emb': 'masked_spec_embed',\r}\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t[\r 'lm_head',\r 'quantizer.weight_proj',\r 'quantizer.codevectors',\r 'project_q',\r 'project_hid',\r]\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[Any]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[Any]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tUnion[str, Any]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tTuple\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[Any]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r for attribute in key.split(\"\"\".\"\"\"\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tgetattr(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r if weight_type is not None:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tgetattr(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t).shape\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\thf_pointer.shape\r\r assert hf_shape == value.shape, (\r F'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''\r F''' {value.shape} for {full_name}'''\r )\r\r if weight_type == \"weight\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue\r elif weight_type == \"weight_g\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue\r elif weight_type == \"weight_v\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue\r elif weight_type == \"bias\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue\r\r logger.info(F'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.'''\t\t\t\t)\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tUnion[str, Any]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tDict\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tfairseq_model.state_dict()\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\thf_model.feature_extractor\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\thf_model.adapter\r\r for name, value in fairseq_dict.items():\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r if \"conv_layers\" in name:\r load_conv_layer(\r SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, hf_model.config.feat_extract_norm == \"\"\"group\"\"\"\t\t\t\t, )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTrue\r elif any(x in name for x in [\"\"\"adaptor\"\"\", \"\"\"w2v_encoder.proj.\"\"\", \"\"\"w2v_proj_ln.\"\"\"]\t\t\t\t):\r load_adapter(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTrue\r else:\r for key, mapped_key in MAPPING.items():\r if key in name or key.split(\"\"\"w2v_model.\"\"\"\t\t\t\t)[-1] == name.split(\"\"\".\"\"\"\t\t\t\t)[0]:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTrue\r if \"*\" in mapped_key:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tname.split(SCREAMING_SNAKE_CASE__\t\t\t\t)[0].split(\"\"\".\"\"\"\t\t\t\t)[-2]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmapped_key.replace(\"\"\"*\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r if \"weight_g\" in name:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"weight_g\"\"\"\r elif \"weight_v\" in name:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"weight_v\"\"\"\r elif \"bias\" in name:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"bias\"\"\"\r elif \"weight\" in name:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"weight\"\"\"\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r set_recursively(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r continue\r if not is_used:\r unused_weights.append(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r logger.warning(F'''Unused weights: {unused_weights}'''\t\t\t\t)\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tAny\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tAny\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tfull_name.split(\"\"\"conv_layers.\"\"\"\t\t\t\t)[-1]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tname.split(\"\"\".\"\"\"\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tint(items[0]\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tint(items[1]\t\t\t\t)\r\r if type_id == 0:\r if \"bias\" in name:\r assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (\r F'''{full_name} has size {value.shape}, but'''\r F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''\r )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue\r logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''\t\t\t\t)\r elif \"weight\" in name:\r assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (\r F'''{full_name} has size {value.shape}, but'''\r F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''\r )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue\r logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''\t\t\t\t)\r elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):\r if \"bias\" in name:\r assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (\r F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''\r \" found.\"\r )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue\r logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''\t\t\t\t)\r elif \"weight\" in name:\r assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (\r F'''{full_name} has size {value.shape}, but'''\r F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''\r )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue\r logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''\t\t\t\t)\r else:\r unused_weights.append(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tTuple\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tTuple\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tDict\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tfull_name.split(\"\"\"adaptor.\"\"\"\t\t\t\t)[-1]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tname.split(\"\"\".\"\"\"\t\t\t\t)\r\r if items[1].isdigit():\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tint(items[1]\t\t\t\t)\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r\r if \"adaptor\" not in full_name:\r if \"proj_ln\" in full_name:\r # has to be layer norm\r if \"bias\" in name:\r assert (\r value.shape == adapter.proj_layer_norm.bias.data.shape\r ), F'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found.'''\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue\r logger.info(F'''Adapter proj layer norm bias was initialized from {full_name}.'''\t\t\t\t)\r if \"weight\" in name:\r assert (\r value.shape == adapter.proj_layer_norm.weight.data.shape\r ), F'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found.'''\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue\r else:\r # has to be projection layer\r if \"bias\" in name:\r assert (\r value.shape == adapter.proj.bias.data.shape\r ), F'''{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found.'''\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue\r logger.info(F'''Adapter proj layer bias was initialized from {full_name}.'''\t\t\t\t)\r if \"weight\" in name:\r assert (\r value.shape == adapter.proj.weight.data.shape\r ), F'''{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found.'''\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue\r logger.info(F'''Adapter proj layer weight was initialized from {full_name}.'''\t\t\t\t)\r elif isinstance(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r if \"bias\" in name:\r assert (\r value.shape == adapter.layers[layer_id].conv.bias.data.shape\r ), F'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found.'''\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue\r logger.info(F'''Adapter layer {layer_id} bias was initialized from {full_name}.'''\t\t\t\t)\r elif \"weight\" in name:\r assert (\r value.shape == adapter.layers[layer_id].conv.weight.data.shape\r ), F'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found.'''\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue\r logger.info(F'''Adapter layer {layer_id} bias was initialized from {full_name}.'''\t\t\t\t)\r else:\r unused_weights.append(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[Any]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\temb.weight.shape\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnn.Linear(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, bias=SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\temb.weight.data\r return lin_layer\r\r@torch.no_grad()\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tAny\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[Any]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[Any]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[Any]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tDict\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tDict\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[Any]\t\t\t\t, ):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tWavaVecaConfig.from_pretrained(\r SCREAMING_SNAKE_CASE__\t\t\t\t, add_adapter=SCREAMING_SNAKE_CASE__\t\t\t\t, adapter_stride=SCREAMING_SNAKE_CASE__\t\t\t\t, adapter_kernel_size=SCREAMING_SNAKE_CASE__\t\t\t\t, use_auth_token=SCREAMING_SNAKE_CASE__\t\t\t\t, output_hidden_size=SCREAMING_SNAKE_CASE__\t\t\t\t, )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tMBartConfig.from_pretrained(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r # load model\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tfairseq.checkpoint_utils.load_model_ensemble_and_task(\r [checkpoint_path]\t\t\t\t, arg_overrides={\r \"\"\"config_yaml\"\"\": config_yaml_path,\r \"\"\"data\"\"\": \"\"\"/\"\"\".join(dict_path.split(\"\"\"/\"\"\"\t\t\t\t)[:-1]\t\t\t\t),\r \"\"\"w2v_path\"\"\": checkpoint_path,\r \"\"\"load_pretrained_decoder_from\"\"\": None,\r }\t\t\t\t, )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel[0].eval()\r\r # load feature extractor\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tWavaVecaFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE__\t\t\t\t, use_auth_token=SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r # set weights for wav2vec2 encoder\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tWavaVecaModel(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r recursively_load_weights_wavaveca(model.encoder\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r # load decoder weights\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tMBartForCausalLM(SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\thf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()\t\t\t\t, strict=SCREAMING_SNAKE_CASE__\t\t\t\t)\r logger.warning(F'''The following keys are missing when loading the decoder weights: {missing_keys}'''\t\t\t\t)\r logger.warning(F'''The following keys are unexpected when loading the decoder weights: {unexpected_keys}'''\t\t\t\t)\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tSpeechEncoderDecoderModel(encoder=SCREAMING_SNAKE_CASE__\t\t\t\t, decoder=SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tMBartaaTokenizer(SCREAMING_SNAKE_CASE__\t\t\t\t)\r tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\thf_wavavec.config.to_dict()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenizer.pad_token_id\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenizer.bos_token_id\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenizer.eos_token_id\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"mbart50\"\"\"\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"wav2vec2\"\"\"\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenizer.eos_token_id\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t250004\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenizer.eos_token_id\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tSpeechEncoderDecoderConfig.from_dict(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE__\t\t\t\t)\r feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r\rif __name__ == \"__main__\":\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\targparse.ArgumentParser()\r parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')\r parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')\r parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')\r parser.add_argument('--config_yaml_path', default=None, type=str, help='Path to yaml file of fine-tuned model')\r parser.add_argument(\r '--encoder_config_path',\r default='facebook/wav2vec2-xls-r-1b',\r type=str,\r help='Path to hf encoder wav2vec2 checkpoint config',\r )\r parser.add_argument(\r '--decoder_config_path',\r default='facebook/mbart-large-50-one-to-many-mmt',\r type=str,\r help='Path to hf decoder checkpoint config',\r )\r parser.add_argument('--add_adapter', default=True, type=bool, help='whethere to add model adapter layers')\r parser.add_argument('--adapter_stride', default=2, type=int, help='stride of adapter layers')\r parser.add_argument('--adapter_kernel_size', default=3, type=int, help='kernel size of adapter layers')\r parser.add_argument('--encoder_output_dim', default=1_0_2_4, type=int, help='encoder output dim')\r parser.add_argument('--start_token_id', default=2_5_0_0_0_4, type=int, help='`decoder_start_token_id` of model config')\r\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tparser.parse_args()\r convert_wavaveca_checkpoint(\r args.checkpoint_path,\r args.pytorch_dump_folder_path,\r args.dict_path,\r args.config_yaml_path,\r encoder_config_path=args.encoder_config_path,\r decoder_config_path=args.decoder_config_path,\r add_adapter=args.add_adapter,\r adapter_kernel_size=args.adapter_kernel_size,\r adapter_stride=args.adapter_stride,\r decoder_start_token_id=args.start_token_id,\r encoder_output_dim=args.encoder_output_dim,\r )\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport itertools\rimport os\rfrom collections import Counter, defaultdict\rfrom concurrent.futures import ThreadPoolExecutor, as_completed\r\rimport numpy as np\r\rimport datasets\r\rfrom .execute import check_correctness\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'\\\\n@misc{chen2021evaluating,\\n title={Evaluating Large Language Models Trained on Code},\\n author={Mark Chen and Jerry Tworek and Heewoo Jun and Qiming Yuan \\\\nand Henrique Ponde de Oliveira Pinto and Jared Kaplan and Harri Edwards \\\\nand Yuri Burda and Nicholas Joseph and Greg Brockman and Alex Ray \\\\nand Raul Puri and Gretchen Krueger and Michael Petrov and Heidy Khlaaf \\\\nand Girish Sastry and Pamela Mishkin and Brooke Chan and Scott Gray \\\\nand Nick Ryder and Mikhail Pavlov and Alethea Power and Lukasz Kaiser \\\\nand Mohammad Bavarian and Clemens Winter and Philippe Tillet \\\\nand Felipe Petroski Such and Dave Cummings and Matthias Plappert \\\\nand Fotios Chantzis and Elizabeth Barnes and Ariel Herbert-Voss \\\\nand William Hebgen Guss and Alex Nichol and Alex Paino and Nikolas Tezak \\\\nand Jie Tang and Igor Babuschkin and Suchir Balaji and Shantanu Jain \\\\nand William Saunders and Christopher Hesse and Andrew N. Carr \\\\nand Jan Leike and Josh Achiam and Vedant Misra and Evan Morikawa \\\\nand Alec Radford and Matthew Knight and Miles Brundage and Mira Murati \\\\nand Katie Mayer and Peter Welinder and Bob McGrew and Dario Amodei \\\\nand Sam McCandlish and Ilya Sutskever and Wojciech Zaremba},\\n year={2021},\\n eprint={2107.03374},\\n archivePrefix={arXiv},\\n primaryClass={cs.LG}\\n}\\n'\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'\\\\nThis metric implements the evaluation harness for the HumanEval problem solving dataset\\ndescribed in the paper \"Evaluating Large Language Models Trained on Code\"\\n(https://arxiv.org/abs/2107.03374).\\n'\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'\\nCalculates how good are predictions given some references, using certain scores\\nArgs:\\n predictions: list of candidates to evaluate. Each candidates should be a list\\n of strings with several code candidates to solve the problem.\\n references: a list with a test for each prediction. Each test should evaluate the\\n correctness of a code candidate.\\n k: number of code candidates to consider in the evaluation (Default: [1, 10, 100])\\n num_workers: number of workers used to evaluate the canidate programs (Default: 4).\\n timeout:\\nReturns:\\n pass_at_k: dict with pass rates for each k\\n results: dict with granular results of each unittest\\nExamples:\\n >>> code_eval = datasets.load_metric(\"code_eval\")\\n >>> test_cases = [\"assert add(2,3)==5\"]\\n >>> candidates = [[\"def add(a,b): return a*b\", \"def add(a, b): return a+b\"]]\\n >>> pass_at_k, results = code_eval.compute(references=test_cases, predictions=candidates, k=[1, 2])\\n >>> print(pass_at_k)\\n {\\'pass@1\\': 0.5, \\'pass@2\\': 1.0}\\n'\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'\\n################################################################################\\n !!!WARNING!!!\\n################################################################################\\nThe \"code_eval\" metric executes untrusted model-generated code in Python.\\nAlthough it is highly unlikely that model-generated code will do something\\novertly malicious in response to this test suite, model-generated code may act\\ndestructively due to a lack of model capability or alignment.\\nUsers are strongly encouraged to sandbox this evaluation suite so that it\\ndoes not perform destructive actions on their host or network. For more\\ninformation on how OpenAI sandboxes its code, see the paper \"Evaluating Large\\nLanguage Models Trained on Code\" (https://arxiv.org/abs/2107.03374).\\n\\nOnce you have read this disclaimer and taken appropriate precautions,\\nset the environment variable HF_ALLOW_CODE_EVAL=\"1\". Within Python you can to this\\nwith:\\n\\n>>> import os\\n>>> os.environ[\"HF_ALLOW_CODE_EVAL\"] = \"1\"\\n\\n################################################################################\\\\n'\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'The MIT License\\n\\nCopyright (c) OpenAI (https://openai.com)\\n\\nPermission is hereby granted, free of charge, to any person obtaining a copy\\nof this software and associated documentation files (the \"Software\"), to deal\\nin the Software without restriction, including without limitation the rights\\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\\ncopies of the Software, and to permit persons to whom the Software is\\nfurnished to do so, subject to the following conditions:\\n\\nThe above copyright notice and this permission notice shall be included in\\nall copies or substantial portions of the Software.\\n\\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\\nTHE SOFTWARE.'\r\r@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION\t, _KWARGS_DESCRIPTION )\rclass lowerCAmelCase_ ( datasets.Metric ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return datasets.MetricInfo(\r # This is the description that will appear on the metrics page.\r description=_DESCRIPTION ,\t\t\t\t\t\tcitation=_CITATION ,\t\t\t\t\t\tinputs_description=_KWARGS_DESCRIPTION ,\t\t\t\t\t\tfeatures=datasets.Features(\r {\r \"\"\"predictions\"\"\": datasets.Sequence(datasets.Value(\"\"\"string\"\"\" ) ),\r \"\"\"references\"\"\": datasets.Value(\"\"\"string\"\"\" ),\r } ) ,\t\t\t\t\t\thomepage=\"\"\"https://github.com/openai/human-eval\"\"\" ,\t\t\t\t\t\tcodebase_urls=[\"\"\"https://github.com/openai/human-eval\"\"\"] ,\t\t\t\t\t\treference_urls=[\"\"\"https://github.com/openai/human-eval\"\"\"] ,\t\t\t\t\t\tlicense=_LICENSE ,\t\t\t\t\t\t)\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str]=[1, 10, 1_00] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any]=4 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny=3.0 ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if os.getenv(\"\"\"HF_ALLOW_CODE_EVAL\"\"\" ,\t\t\t\t\t\t0 ) != \"1\":\r raise ValueError(_WARNING )\r\r if os.name == \"nt\":\r raise NotImplementedError(\"\"\"This metric is currently not supported on Windows.\"\"\" )\r\r with ThreadPoolExecutor(max_workers=_UpperCAmelCase ) as executor:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tCounter()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdefaultdict(_UpperCAmelCase )\r\r for task_id, (candidates, test_case) in enumerate(zip(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ) ):\r for candidate in candidates:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tcandidate + \"\"\"\\n\"\"\" + test_case\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t(test_program, timeout, task_id, completion_id[task_id])\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\texecutor.submit(_UpperCAmelCase ,\t\t\t\t\t\t*_UpperCAmelCase )\r futures.append(_UpperCAmelCase )\r completion_id[task_id] += 1\r n_samples += 1\r\r for future in as_completed(_UpperCAmelCase ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tfuture.result()\r results[result[\"task_id\"]].append((result[\"\"\"completion_id\"\"\"], result) )\r\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[], []\r for result in results.values():\r result.sort()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[r[1][\"\"\"passed\"\"\"] for r in result]\r total.append(len(_UpperCAmelCase ) )\r correct.append(sum(_UpperCAmelCase ) )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnp.array(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnp.array(_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tk\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{f'''pass@{k}''': estimate_pass_at_k(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ).mean() for k in ks if (total >= k).all()}\r\r return pass_at_k, results\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[Any]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[Any]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r def estimator(SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t) -> float:\r if n - c < k:\r return 1.0\r return 1.0 - np.prod(1.0 - k / np.arange(n - c + 1\t\t\t\t, n + 1\t\t\t\t)\t\t\t\t)\r\r if isinstance(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\titertools.repeat(SCREAMING_SNAKE_CASE__\t\t\t\t, len(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t)\r else:\r assert len(SCREAMING_SNAKE_CASE__\t\t\t\t) == len(SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\titer(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r return np.array([estimator(int(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t, int(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t) for n, c in zip(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)]\t\t\t\t)\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152366,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rfrom timeit import timeit\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t{\r 'MALAYALAM': True,\r 'String': False,\r 'rotor': True,\r 'level': True,\r 'A': True,\r 'BB': True,\r 'ABC': False,\r 'amanaplanacanalpanama': True, # \"a man a plan a canal panama\"\r}\r# Ensure our test data is valid\rassert all((key == key[::-1]) is value for key, value in test_data.items())\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlen(SCREAMING_SNAKE_CASE__\t\t\t\t) - 1\r while start_i < end_i:\r if s[start_i] == s[end_i]:\r start_i += 1\r end_i -= 1\r else:\r return False\r return True\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlen(SCREAMING_SNAKE_CASE__\t\t\t\t) // 2\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlen(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r # We need to traverse till half of the length of string\r # as we can get access of the i'th last element from\r # i'th index.\r # eg: [0,1,2,3,4,5] => 4th index can be accessed\r # with the help of 1st index (i==n-i-1)\r # where n is length of string\r return all(s[i] == s[n - i - 1] for i in range(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t)\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r if len(SCREAMING_SNAKE_CASE__\t\t\t\t) <= 2:\r return True\r if s[0] == s[len(SCREAMING_SNAKE_CASE__\t\t\t\t) - 1]:\r return is_palindrome_recursive(s[1:-1]\t\t\t\t)\r else:\r return False\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r return s == s[::-1]\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tF'''all({name}(key) is value for key, value in test_data.items())'''\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tF'''from __main__ import test_data, {name}'''\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t500000\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttimeit(stmt=SCREAMING_SNAKE_CASE__\t\t\t\t, setup=SCREAMING_SNAKE_CASE__\t\t\t\t, number=SCREAMING_SNAKE_CASE__\t\t\t\t)\r print(F'''{name:<35} finished {number:,} runs in {result:.5f} seconds'''\t\t\t\t)\r\r\rif __name__ == \"__main__\":\r for key, value in test_data.items():\r assert is_palindrome(key) is is_palindrome_recursive(key)\r assert is_palindrome(key) is is_palindrome_slice(key)\r print(f\"{key:21} {value}\")\r print('a man a plan a canal panama')\r\r # finished 500,000 runs in 0.46793 seconds\r benchmark_function('is_palindrome_slice')\r # finished 500,000 runs in 0.85234 seconds\r benchmark_function('is_palindrome')\r # finished 500,000 runs in 1.32028 seconds\r benchmark_function('is_palindrome_recursive')\r # finished 500,000 runs in 2.08679 seconds\r benchmark_function('is_palindrome_traversal')\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport math\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r assert isinstance(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t) and (\r number >= 0\r ), \"'number' must been an int and positive\"\r\r if 1 < number < 4:\r # 2 and 3 are primes\r return True\r elif number < 2 or not number % 2:\r # Negatives, 0, 1 and all even numbers are not primes\r return False\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\trange(3\t\t\t\t, int(math.sqrt(SCREAMING_SNAKE_CASE__\t\t\t\t) + 1\t\t\t\t)\t\t\t\t, 2\t\t\t\t)\r return not any(not number % i for i in odd_numbers\t\t\t\t)\r\r\r\r\r\r\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tUnion[str, Any]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]=1\t\t\t\t, **SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tfactor * value\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue\r\r while not is_prime(SCREAMING_SNAKE_CASE__\t\t\t\t):\r value += 1 if not (\"desc\" in kwargs and kwargs[\"desc\"] is True) else -1\r\r if value == first_value_val:\r return next_prime(value + 1\t\t\t\t, **SCREAMING_SNAKE_CASE__\t\t\t\t)\r return value\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152367,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport argparse\rfrom pathlib import Path\r\rimport fairseq\rimport torch\rfrom fairseq.models.xmod import XMODModel as FairseqXmodModel\rfrom packaging import version\r\rfrom transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification\rfrom transformers.utils import logging\r\r\rif version.parse(fairseq.__version__) < version.parse('0.12.2'):\r raise Exception('requires fairseq >= 0.12.2')\rif version.parse(fairseq.__version__) > version.parse('2'):\r raise Exception('requires fairseq < v2')\r\rlogging.set_verbosity_info()\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tlogging.get_logger(__name__)\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'Hello, World!'\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'en_XX'\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tbool\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tPath(\"\"\"data_bin\"\"\"\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFairseqXmodModel.from_pretrained(\r model_name_or_path=str(Path(SCREAMING_SNAKE_CASE__\t\t\t\t).parent\t\t\t\t)\t\t\t\t, checkpoint_file=Path(SCREAMING_SNAKE_CASE__\t\t\t\t).name\t\t\t\t, _name=\"\"\"xmod_base\"\"\"\t\t\t\t, arch=\"\"\"xmod_base\"\"\"\t\t\t\t, task=\"\"\"multilingual_masked_lm\"\"\"\t\t\t\t, data_name_or_path=str(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t, bpe=\"\"\"sentencepiece\"\"\"\t\t\t\t, sentencepiece_model=str(Path(SCREAMING_SNAKE_CASE__\t\t\t\t).parent / \"\"\"sentencepiece.bpe.model\"\"\"\t\t\t\t)\t\t\t\t, src_dict=str(data_dir / \"\"\"dict.txt\"\"\"\t\t\t\t)\t\t\t\t, )\r xmod.eval() # disable dropout\r print(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\txmod.model.encoder.sentence_encoder\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tXmodConfig(\r vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings\t\t\t\t, hidden_size=xmod.cfg.model.encoder_embed_dim\t\t\t\t, num_hidden_layers=xmod.cfg.model.encoder_layers\t\t\t\t, num_attention_heads=xmod.cfg.model.encoder_attention_heads\t\t\t\t, intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim\t\t\t\t, max_position_embeddings=514\t\t\t\t, type_vocab_size=1\t\t\t\t, layer_norm_eps=1e-5\t\t\t\t, pre_norm=xmod.cfg.model.encoder_normalize_before\t\t\t\t, adapter_reduction_factor=getattr(xmod.cfg.model\t\t\t\t, \"\"\"bottleneck\"\"\"\t\t\t\t, 2\t\t\t\t)\t\t\t\t, adapter_layer_norm=xmod.cfg.model.adapter_layer_norm\t\t\t\t, adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm\t\t\t\t, ln_before_adapter=xmod.cfg.model.ln_before_adapter\t\t\t\t, languages=xmod.cfg.model.languages\t\t\t\t, )\r if classification_head:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\txmod.model.classification_heads[\"\"\"mnli\"\"\"].out_proj.weight.shape[0]\r\r print(\"\"\"Our X-MOD config:\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tXmodForSequenceClassification(SCREAMING_SNAKE_CASE__\t\t\t\t) if classification_head else XmodForMaskedLM(SCREAMING_SNAKE_CASE__\t\t\t\t)\r model.eval()\r\r # Now let's copy all the weights.\r # Embeddings\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\txmod_sent_encoder.embed_tokens.weight\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\txmod_sent_encoder.embed_positions.weight\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.zeros_like(\r model.roberta.embeddings.token_type_embeddings.weight\t\t\t\t) # just zero them out b/c xmod doesn't use them.\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\txmod_sent_encoder.layernorm_embedding.weight\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\txmod_sent_encoder.layernorm_embedding.bias\r\r for i in range(config.num_hidden_layers\t\t\t\t):\r # Encoder: start of layer\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel.roberta.encoder.layer[i]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\txmod_sent_encoder.layers[i]\r\r # self attention\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlayer.attention.self\r if not (\r xmod_layer.self_attn.k_proj.weight.data.shape\r == xmod_layer.self_attn.q_proj.weight.data.shape\r == xmod_layer.self_attn.v_proj.weight.data.shape\r == torch.Size((config.hidden_size, config.hidden_size)\t\t\t\t)\r ):\r raise AssertionError(\"\"\"Dimensions of self-attention weights do not match.\"\"\"\t\t\t\t)\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\txmod_layer.self_attn.q_proj.weight\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\txmod_layer.self_attn.q_proj.bias\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\txmod_layer.self_attn.k_proj.weight\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\txmod_layer.self_attn.k_proj.bias\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\txmod_layer.self_attn.v_proj.weight\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\txmod_layer.self_attn.v_proj.bias\r\r # self-attention output\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlayer.attention.output\r if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:\r raise AssertionError(\"\"\"Dimensions of self-attention output weights do not match.\"\"\"\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\txmod_layer.self_attn.out_proj.weight\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\txmod_layer.self_attn.out_proj.bias\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\txmod_layer.self_attn_layer_norm.weight\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\txmod_layer.self_attn_layer_norm.bias\r\r # intermediate\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlayer.intermediate\r if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:\r raise AssertionError(\"\"\"Dimensions of intermediate weights do not match.\"\"\"\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\txmod_layer.fca.weight\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\txmod_layer.fca.bias\r\r # output\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlayer.output\r if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:\r raise AssertionError(\"\"\"Dimensions of feed-forward weights do not match.\"\"\"\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\txmod_layer.fca.weight\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\txmod_layer.fca.bias\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\txmod_layer.final_layer_norm.weight\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\txmod_layer.final_layer_norm.bias\r if bert_output.adapter_layer_norm is not None:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\txmod_layer.adapter_layer_norm.weight\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\txmod_layer.adapter_layer_norm.bias\r\r if sorted(bert_output.adapter_modules.keys()\t\t\t\t) != sorted(xmod_layer.adapter_modules.keys()\t\t\t\t):\r raise AssertionError(\"\"\"Lists of language adapters do not match.\"\"\"\t\t\t\t)\r for lang_code, adapter in xmod_layer.adapter_modules.items():\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tbert_output.adapter_modules[lang_code]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\txmod_layer.adapter_modules[lang_code]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tfrom_adapter.fca.weight\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tfrom_adapter.fca.bias\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tfrom_adapter.fca.weight\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tfrom_adapter.fca.bias\r\r # end of layer\r\r if xmod_sent_encoder.layer_norm is not None:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\txmod_sent_encoder.layer_norm.weight\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\txmod_sent_encoder.layer_norm.bias\r\r if classification_head:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\txmod.model.classification_heads[\"\"\"mnli\"\"\"].dense.weight\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\txmod.model.classification_heads[\"\"\"mnli\"\"\"].dense.bias\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\txmod.model.classification_heads[\"\"\"mnli\"\"\"].out_proj.weight\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\txmod.model.classification_heads[\"\"\"mnli\"\"\"].out_proj.bias\r else:\r # LM Head\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\txmod.model.encoder.lm_head.dense.weight\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\txmod.model.encoder.lm_head.dense.bias\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\txmod.model.encoder.lm_head.layer_norm.weight\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\txmod.model.encoder.lm_head.layer_norm.bias\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\txmod.model.encoder.lm_head.weight\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\txmod.model.encoder.lm_head.bias\r\r # Let's check that we get the same results.\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\txmod.encode(SCREAMING_SNAKE_CASE__\t\t\t\t).unsqueeze(0\t\t\t\t) # batch of size 1\r model.roberta.set_default_language(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(SCREAMING_SNAKE_CASE__\t\t\t\t)[0]\r if classification_head:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\txmod.model.classification_heads[\"\"\"mnli\"\"\"](xmod.extract_features(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t)\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\txmod.model(SCREAMING_SNAKE_CASE__\t\t\t\t, lang_id=[SAMPLE_LANGUAGE]\t\t\t\t)[0]\r print(our_output.shape\t\t\t\t, their_output.shape\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.max(torch.abs(our_output - their_output\t\t\t\t)\t\t\t\t).item()\r print(F'''max_absolute_diff = {max_absolute_diff}'''\t\t\t\t) # ~ 1e-7\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.allclose(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, atol=1e-3\t\t\t\t)\r print(\"\"\"Do both models output the same tensors?\"\"\"\t\t\t\t, \"\"\"🔥\"\"\" if success else \"\"\"💩\"\"\"\t\t\t\t)\r if not success:\r raise Exception(\"\"\"Something went wRoNg\"\"\"\t\t\t\t)\r\r Path(SCREAMING_SNAKE_CASE__\t\t\t\t).mkdir(parents=SCREAMING_SNAKE_CASE__\t\t\t\t, exist_ok=SCREAMING_SNAKE_CASE__\t\t\t\t)\r print(F'''Saving model to {pytorch_dump_folder_path}'''\t\t\t\t)\r model.save_pretrained(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r\rif __name__ == \"__main__\":\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\targparse.ArgumentParser()\r # Required parameters\r parser.add_argument(\r '--xmod_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'\r )\r parser.add_argument(\r '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'\r )\r parser.add_argument(\r '--classification_head', action='store_true', help='Whether to convert a final classification head.'\r )\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tparser.parse_args()\r convert_xmod_checkpoint_to_pytorch(\r args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head\r )\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport string\rfrom math import logaa\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdocument.translate(\r str.maketrans(\"\"\"\"\"\"\t\t\t\t, \"\"\"\"\"\"\t\t\t\t, string.punctuation\t\t\t\t)\t\t\t\t).replace(\"\"\"\\n\"\"\"\t\t\t\t, \"\"\"\"\"\"\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdocument_without_punctuation.split(\"\"\" \"\"\"\t\t\t\t) # word tokenization\r return len([word for word in tokenize_document if word.lower() == term.lower()]\t\t\t\t)\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tcorpus.lower().translate(\r str.maketrans(\"\"\"\"\"\"\t\t\t\t, \"\"\"\"\"\"\t\t\t\t, string.punctuation\t\t\t\t)\t\t\t\t) # strip all punctuation and replace it with ''\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tcorpus_without_punctuation.split(\"\"\"\\n\"\"\"\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tterm.lower()\r return (len([doc for doc in docs if term in doc]\t\t\t\t), len(SCREAMING_SNAKE_CASE__\t\t\t\t))\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tTuple=False\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r if smoothing:\r if n == 0:\r raise ValueError(\"\"\"log10(0) is undefined.\"\"\"\t\t\t\t)\r return round(1 + logaa(n / (1 + df)\t\t\t\t)\t\t\t\t, 3\t\t\t\t)\r\r if df == 0:\r raise ZeroDivisionError(\"\"\"df must be > 0\"\"\"\t\t\t\t)\r elif n == 0:\r raise ValueError(\"\"\"log10(0) is undefined.\"\"\"\t\t\t\t)\r return round(logaa(n / df\t\t\t\t)\t\t\t\t, 3\t\t\t\t)\r\r\r\r\r\r\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r return round(tf * idf\t\t\t\t, 3\t\t\t\t)\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152368,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rfrom transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments\r\rdef _UpperCamelCase\t\t\t\t\t( ):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tHfArgumentParser(SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tparser.parse_args_into_dataclasses()[0]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTensorFlowBenchmark(args=SCREAMING_SNAKE_CASE__\t\t\t\t)\r try:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tparser.parse_args_into_dataclasses()[0]\r except ValueError as e:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"Arg --no_{0} is no longer used, please use --no-{0} instead.\"\"\"\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\" \"\"\".join(str(SCREAMING_SNAKE_CASE__\t\t\t\t).split(\"\"\" \"\"\"\t\t\t\t)[:-1]\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"\"\"\"\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\teval(str(SCREAMING_SNAKE_CASE__\t\t\t\t).split(\"\"\" \"\"\"\t\t\t\t)[-1]\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[]\r for arg in depreciated_args:\r # arg[2:] removes '--'\r if arg[2:] in TensorFlowBenchmark.deprecated_args:\r # arg[5:] removes '--no_'\r full_error_msg += arg_error_msg.format(arg[5:]\t\t\t\t)\r else:\r wrong_args.append(SCREAMING_SNAKE_CASE__\t\t\t\t)\r if len(SCREAMING_SNAKE_CASE__\t\t\t\t) > 0:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tfull_error_msg + begin_error_msg + str(SCREAMING_SNAKE_CASE__\t\t\t\t)\r raise ValueError(SCREAMING_SNAKE_CASE__\t\t\t\t)\r benchmark.run()\r\r\rif __name__ == \"__main__\":\r main()\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport argparse\r\rimport torch\r\rfrom transformers import BertForMaskedLM\r\r\rif __name__ == \"__main__\":\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\targparse.ArgumentParser(\r description=(\r 'Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'\r ' Distillation'\r )\r )\r parser.add_argument('--model_type', default='bert', choices=['bert'])\r parser.add_argument('--model_name', default='bert-base-uncased', type=str)\r parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_bert-base-uncased_0247911.pth', type=str)\r parser.add_argument('--vocab_transform', action='store_true')\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tparser.parse_args()\r\r if args.model_type == \"bert\":\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tBertForMaskedLM.from_pretrained(args.model_name)\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\t'bert'\r else:\r raise ValueError('args.model_type should be \"bert\".')\r\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tmodel.state_dict()\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\t{}\r\r for w in [\"word_embeddings\", \"position_embeddings\"]:\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tstate_dict[f\"{prefix}.embeddings.{w}.weight\"]\r for w in [\"weight\", \"bias\"]:\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tstate_dict[f\"{prefix}.embeddings.LayerNorm.{w}\"]\r\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\t0\r for teacher_idx in [0, 2, 4, 7, 9, 1_1]:\r for w in [\"weight\", \"bias\"]:\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tstate_dict[\r f\"{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}\"\r ]\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tstate_dict[\r f\"{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}\"\r ]\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tstate_dict[\r f\"{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}\"\r ]\r\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tstate_dict[\r f\"{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}\"\r ]\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tstate_dict[\r f\"{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}\"\r ]\r\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tstate_dict[\r f\"{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}\"\r ]\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tstate_dict[\r f\"{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}\"\r ]\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tstate_dict[\r f\"{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}\"\r ]\r std_idx += 1\r\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tstate_dict['cls.predictions.decoder.weight']\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tstate_dict['cls.predictions.bias']\r if args.vocab_transform:\r for w in [\"weight\", \"bias\"]:\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tstate_dict[f\"cls.predictions.transform.dense.{w}\"]\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tstate_dict[f\"cls.predictions.transform.LayerNorm.{w}\"]\r\r print(f\"N layers selected for distillation: {std_idx}\")\r print(f\"Number of params transferred for distillation: {len(compressed_sd.keys())}\")\r\r print(f\"Save transferred checkpoint to {args.dump_checkpoint}.\")\r torch.save(compressed_sd, args.dump_checkpoint)\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152369,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport warnings\r\rfrom ...utils import logging\rfrom .image_processing_deit import DeiTImageProcessor\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tlogging.get_logger(__name__)\r\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def __init__( self\t\t\t:\t\tList[str] ,\t\t\t\t\t\t*_UpperCAmelCase\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r warnings.warn(\r \"\"\"The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please\"\"\"\r \"\"\" use DeiTImageProcessor instead.\"\"\" ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t)\r super().__init__(*_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase )\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport tempfile\r\rimport torch\r\rfrom diffusers import PNDMScheduler\r\rfrom .test_schedulers import SchedulerCommonTest\r\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r lowerCAmelCase_ : Union[str, Any] = (PNDMScheduler,)\r lowerCAmelCase_ : Optional[int] = ((\"\"\"num_inference_steps\"\"\", 50),)\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tOptional[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\r \"\"\"num_train_timesteps\"\"\": 10_00,\r \"\"\"beta_start\"\"\": 0.0001,\r \"\"\"beta_end\"\"\": 0.02,\r \"\"\"beta_schedule\"\"\": \"\"\"linear\"\"\",\r }\r\r config.update(**_UpperCAmelCase )\r return config\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple=0 ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdict(self.forward_default_kwargs )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tkwargs.pop(\"\"\"num_inference_steps\"\"\" ,\t\t\t\t\t\t_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.dummy_sample\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0.1 * sample\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]\r\r for scheduler_class in self.scheduler_classes:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_scheduler_config(**_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler_class(**_UpperCAmelCase )\r scheduler.set_timesteps(_UpperCAmelCase )\r # copy over dummy past residuals\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdummy_past_residuals[:]\r\r with tempfile.TemporaryDirectory() as tmpdirname:\r scheduler.save_config(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler_class.from_pretrained(_UpperCAmelCase )\r new_scheduler.set_timesteps(_UpperCAmelCase )\r # copy over dummy past residuals\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdummy_past_residuals[:]\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler.step_prk(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase ).prev_sample\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnew_scheduler.step_prk(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase ).prev_sample\r\r assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, \"Scheduler outputs are not identical\"\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler.step_plms(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase ).prev_sample\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnew_scheduler.step_plms(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase ).prev_sample\r\r assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, \"Scheduler outputs are not identical\"\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r pass\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=0 ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tOptional[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdict(self.forward_default_kwargs )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tkwargs.pop(\"\"\"num_inference_steps\"\"\" ,\t\t\t\t\t\t_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.dummy_sample\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0.1 * sample\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]\r\r for scheduler_class in self.scheduler_classes:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_scheduler_config()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler_class(**_UpperCAmelCase )\r scheduler.set_timesteps(_UpperCAmelCase )\r\r # copy over dummy past residuals (must be after setting timesteps)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdummy_past_residuals[:]\r\r with tempfile.TemporaryDirectory() as tmpdirname:\r scheduler.save_config(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler_class.from_pretrained(_UpperCAmelCase )\r # copy over dummy past residuals\r new_scheduler.set_timesteps(_UpperCAmelCase )\r\r # copy over dummy past residual (must be after setting timesteps)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdummy_past_residuals[:]\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler.step_prk(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase ).prev_sample\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnew_scheduler.step_prk(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase ).prev_sample\r\r assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, \"Scheduler outputs are not identical\"\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler.step_plms(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase ).prev_sample\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnew_scheduler.step_plms(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase ).prev_sample\r\r assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, \"Scheduler outputs are not identical\"\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tTuple ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.scheduler_classes[0]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_scheduler_config(**_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler_class(**_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t10\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.dummy_model()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.dummy_sample_deter\r scheduler.set_timesteps(_UpperCAmelCase )\r\r for i, t in enumerate(scheduler.prk_timesteps ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler.step_prk(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ).prev_sample\r\r for i, t in enumerate(scheduler.plms_timesteps ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler.step_plms(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ).prev_sample\r\r return sample\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdict(self.forward_default_kwargs )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tkwargs.pop(\"\"\"num_inference_steps\"\"\" ,\t\t\t\t\t\t_UpperCAmelCase )\r\r for scheduler_class in self.scheduler_classes:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_scheduler_config()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler_class(**_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.dummy_sample\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0.1 * sample\r\r if num_inference_steps is not None and hasattr(_UpperCAmelCase ,\t\t\t\t\t\t\"\"\"set_timesteps\"\"\" ):\r scheduler.set_timesteps(_UpperCAmelCase )\r elif num_inference_steps is not None and not hasattr(_UpperCAmelCase ,\t\t\t\t\t\t\"\"\"set_timesteps\"\"\" ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnum_inference_steps\r\r # copy over dummy past residuals (must be done after set_timesteps)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdummy_past_residuals[:]\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler.step_prk(_UpperCAmelCase ,\t\t\t\t\t\t0 ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase ).prev_sample\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler.step_prk(_UpperCAmelCase ,\t\t\t\t\t\t1 ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase ).prev_sample\r\r self.assertEqual(output_a.shape ,\t\t\t\t\t\tsample.shape )\r self.assertEqual(output_a.shape ,\t\t\t\t\t\toutput_a.shape )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler.step_plms(_UpperCAmelCase ,\t\t\t\t\t\t0 ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase ).prev_sample\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler.step_plms(_UpperCAmelCase ,\t\t\t\t\t\t1 ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase ).prev_sample\r\r self.assertEqual(output_a.shape ,\t\t\t\t\t\tsample.shape )\r self.assertEqual(output_a.shape ,\t\t\t\t\t\toutput_a.shape )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r for timesteps in [1_00, 10_00]:\r self.check_over_configs(num_train_timesteps=_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r for steps_offset in [0, 1]:\r self.check_over_configs(steps_offset=_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.scheduler_classes[0]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_scheduler_config(steps_offset=1 )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler_class(**_UpperCAmelCase )\r scheduler.set_timesteps(10 )\r assert torch.equal(\r scheduler.timesteps ,\t\t\t\t\t\ttorch.LongTensor(\r [9_01, 8_51, 8_51, 8_01, 8_01, 7_51, 7_51, 7_01, 7_01, 6_51, 6_51, 6_01, 6_01, 5_01, 4_01, 3_01, 2_01, 1_01, 1] ) ,\t\t\t\t\t\t)\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r for beta_start, beta_end in zip([0.0001, 0.001] ,\t\t\t\t\t\t[0.002, 0.02] ):\r self.check_over_configs(beta_start=_UpperCAmelCase ,\t\t\t\t\t\tbeta_end=_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r for schedule in [\"linear\", \"squaredcos_cap_v2\"]:\r self.check_over_configs(beta_schedule=_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r for prediction_type in [\"epsilon\", \"v_prediction\"]:\r self.check_over_configs(prediction_type=_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r for t in [1, 5, 10]:\r self.check_over_forward(time_step=_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tAny ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r for t, num_inference_steps in zip([1, 5, 10] ,\t\t\t\t\t\t[10, 50, 1_00] ):\r self.check_over_forward(num_inference_steps=_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t27\r\r for scheduler_class in self.scheduler_classes:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.dummy_sample\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0.1 * sample\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_scheduler_config()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler_class(**_UpperCAmelCase )\r\r scheduler.set_timesteps(_UpperCAmelCase )\r\r # before power of 3 fix, would error on first step, so we only need to do two\r for i, t in enumerate(scheduler.prk_timesteps[:2] ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler.step_prk(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ).prev_sample\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r with self.assertRaises(_UpperCAmelCase ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.scheduler_classes[0]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_scheduler_config()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler_class(**_UpperCAmelCase )\r\r scheduler.step_plms(self.dummy_sample ,\t\t\t\t\t\t1 ,\t\t\t\t\t\tself.dummy_sample ).prev_sample\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.full_loop()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.sum(torch.abs(_UpperCAmelCase ) )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.mean(torch.abs(_UpperCAmelCase ) )\r\r assert abs(result_sum.item() - 198.1318 ) < 1E-2\r assert abs(result_mean.item() - 0.2580 ) < 1E-3\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.full_loop(prediction_type=\"\"\"v_prediction\"\"\" )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.sum(torch.abs(_UpperCAmelCase ) )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.mean(torch.abs(_UpperCAmelCase ) )\r\r assert abs(result_sum.item() - 67.3986 ) < 1E-2\r assert abs(result_mean.item() - 0.0878 ) < 1E-3\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tAny ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.full_loop(set_alpha_to_one=_UpperCAmelCase ,\t\t\t\t\t\tbeta_start=0.01 )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.sum(torch.abs(_UpperCAmelCase ) )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.mean(torch.abs(_UpperCAmelCase ) )\r\r assert abs(result_sum.item() - 230.0399 ) < 1E-2\r assert abs(result_mean.item() - 0.2995 ) < 1E-3\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.full_loop(set_alpha_to_one=_UpperCAmelCase ,\t\t\t\t\t\tbeta_start=0.01 )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.sum(torch.abs(_UpperCAmelCase ) )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.mean(torch.abs(_UpperCAmelCase ) )\r\r assert abs(result_sum.item() - 186.9482 ) < 1E-2\r assert abs(result_mean.item() - 0.2434 ) < 1E-3\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152370,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r return int((input_a, input_a).count(0\t\t\t\t) != 0\t\t\t\t)\r\rdef _UpperCamelCase\t\t\t\t\t( ):\r '''simple docstring'''\r\r\r\r\r\r assert nand_gate(0\t\t\t\t, 0\t\t\t\t) == 1\r assert nand_gate(0\t\t\t\t, 1\t\t\t\t) == 1\r assert nand_gate(1\t\t\t\t, 0\t\t\t\t) == 1\r assert nand_gate(1\t\t\t\t, 1\t\t\t\t) == 0\r\r\rif __name__ == \"__main__\":\r print(nand_gate(0, 0))\r print(nand_gate(0, 1))\r print(nand_gate(1, 0))\r print(nand_gate(1, 1))\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rfrom ...configuration_utils import PretrainedConfig\rfrom ...utils import logging\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tlogging.get_logger(__name__)\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t{\r 'google/vivit-b-16x2-kinetics400': (\r 'https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json'\r ),\r # See all Vivit models at https://huggingface.co/models?filter=vivit\r}\r\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r lowerCAmelCase_ : Optional[int] = \"\"\"vivit\"\"\"\r\r\r def __init__( self\t\t\t:\t\tList[str] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any]=2_24 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str]=32 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny=[2, 16, 16] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint=3 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any]=7_68 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=12 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict=12 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any]=30_72 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int]=\"gelu_fast\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=0.0 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple=0.0 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int]=0.02 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any]=1E-06 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str]=True ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\thidden_size\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnum_hidden_layers\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnum_attention_heads\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tintermediate_size\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\thidden_act\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\thidden_dropout_prob\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tattention_probs_dropout_prob\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tinitializer_range\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlayer_norm_eps\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\timage_size\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnum_frames\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttubelet_size\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnum_channels\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tqkv_bias\r\r super().__init__(**_UpperCAmelCase )\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152371,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rfrom pathlib import PurePosixPath\rfrom typing import Optional\r\rimport fsspec\rfrom fsspec import AbstractFileSystem\rfrom huggingface_hub.hf_api import DatasetInfo\r\rfrom ..utils.file_utils import get_authentication_headers_for_url\rfrom ..utils.hub import hf_hub_url\r\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r lowerCAmelCase_ : int = \"\"\"\"\"\"\r lowerCAmelCase_ : Tuple = \"\"\"hf-legacy\"\"\" # \"hf://\"\" is reserved for hffs\r\r\r def __init__( self\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[DatasetInfo] = None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[str] = None ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tList[str] ,\t\t\t\t\t\t):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r super().__init__(self ,\t\t\t\t\t\t**_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\trepo_info\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttoken\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if self.dir_cache is None:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{}\r for hf_file in self.repo_info.siblings:\r # TODO(QL): add sizes\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\r \"\"\"name\"\"\": hf_file.rfilename,\r \"\"\"size\"\"\": None,\r \"\"\"type\"\"\": \"\"\"file\"\"\",\r }\r self.dir_cache.update(\r {\r str(_UpperCAmelCase ): {\"\"\"name\"\"\": str(_UpperCAmelCase ), \"\"\"size\"\"\": None, \"\"\"type\"\"\": \"\"\"directory\"\"\"}\r for d in list(PurePosixPath(hf_file.rfilename ).parents )[:-1]\r } )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr = \"rb\" ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if not isinstance(self.repo_info ,\t\t\t\t\t\t_UpperCAmelCase ):\r raise NotImplementedError(f'''Open is only implemented for dataset repositories, but got {self.repo_info}''' )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\thf_hub_url(self.repo_info.id ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\trevision=self.repo_info.sha )\r return fsspec.open(\r _UpperCAmelCase ,\t\t\t\t\t\tmode=_UpperCAmelCase ,\t\t\t\t\t\theaders=get_authentication_headers_for_url(_UpperCAmelCase ,\t\t\t\t\t\tuse_auth_token=self.token ) ,\t\t\t\t\t\tclient_kwargs={\"\"\"trust_env\"\"\": True} ,\t\t\t\t\t\t).open()\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str] ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tList[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r self._get_dirs()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself._strip_protocol(_UpperCAmelCase )\r if path in self.dir_cache:\r return self.dir_cache[path]\r else:\r raise FileNotFoundError(_UpperCAmelCase )\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tTuple ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny=False ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r self._get_dirs()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tPurePosixPath(path.strip(\"\"\"/\"\"\" ) )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{}\r for p, f in self.dir_cache.items():\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tPurePosixPath(p.strip(\"\"\"/\"\"\" ) )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tp.parent\r if root == path:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tf\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlist(paths.values() )\r if detail:\r return out\r else:\r return sorted(f[\"\"\"name\"\"\"] for f in out )\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport warnings\r\rfrom ...utils import logging\rfrom .image_processing_deit import DeiTImageProcessor\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tlogging.get_logger(__name__)\r\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def __init__( self\t\t\t:\t\tList[str] ,\t\t\t\t\t\t*_UpperCAmelCase\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r warnings.warn(\r \"\"\"The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please\"\"\"\r \"\"\" use DeiTImageProcessor instead.\"\"\" ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t)\r super().__init__(*_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase )\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152372,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport re\rimport warnings\rfrom contextlib import contextmanager\r\rfrom ...processing_utils import ProcessorMixin\r\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r lowerCAmelCase_ : Any = [\"\"\"image_processor\"\"\", \"\"\"tokenizer\"\"\"]\r lowerCAmelCase_ : Dict = \"\"\"AutoImageProcessor\"\"\"\r lowerCAmelCase_ : Any = \"\"\"AutoTokenizer\"\"\"\r\r\r def __init__( self\t\t\t:\t\tTuple ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any]=None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any]=None ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r if \"feature_extractor\" in kwargs:\r warnings.warn(\r \"\"\"The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`\"\"\"\r \"\"\" instead.\"\"\" ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tkwargs.pop(\"\"\"feature_extractor\"\"\" )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\timage_processor if image_processor is not None else feature_extractor\r if image_processor is None:\r raise ValueError(\"\"\"You need to specify an `image_processor`.\"\"\" )\r if tokenizer is None:\r raise ValueError(\"\"\"You need to specify a `tokenizer`.\"\"\" )\r\r super().__init__(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.image_processor\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r\r\r def __call__( self\t\t\t:\t\tDict ,\t\t\t\t\t\t*_UpperCAmelCase\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if self._in_target_context_manager:\r return self.current_processor(*_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tkwargs.pop(\"\"\"images\"\"\" ,\t\t\t\t\t\t_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tkwargs.pop(\"\"\"text\"\"\" ,\t\t\t\t\t\t_UpperCAmelCase )\r if len(_UpperCAmelCase ) > 0:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\targs[0]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\targs[1:]\r\r if images is None and text is None:\r raise ValueError(\"\"\"You need to specify either an `images` or `text` input to process.\"\"\" )\r\r if images is not None:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.image_processor(_UpperCAmelCase ,\t\t\t\t\t\t*_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase )\r if text is not None:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.tokenizer(_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase )\r\r if text is None:\r return inputs\r elif images is None:\r return encodings\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tencodings[\"\"\"input_ids\"\"\"]\r return inputs\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t*_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return self.tokenizer.batch_decode(*_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ,\t\t\t\t\t\t*_UpperCAmelCase\t\t\t:\t\tAny ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tOptional[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return self.tokenizer.decode(*_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase )\r\r\r @contextmanager\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r warnings.warn(\r \"\"\"`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your \"\"\"\r \"\"\"labels by using the argument `text` of the regular `__call__` method (either in the same call as \"\"\"\r \"\"\"your images inputs, or in a separate call.\"\"\" )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTrue\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.tokenizer\r yield\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.image_processor\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tTuple ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple=False ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any]=None ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if added_vocab is None:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.tokenizer.get_added_vocab()\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{}\r\r while tokens:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tre.search(r\"\"\"\"\"\" ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\tre.IGNORECASE )\r if start_token is None:\r break\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tstart_token.group(1 )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tre.search(rf'''''' ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\tre.IGNORECASE )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tstart_token.group()\r if end_token is None:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokens.replace(_UpperCAmelCase ,\t\t\t\t\t\t\"\"\"\"\"\" )\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tend_token.group()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tre.escape(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tre.escape(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tre.search(f'''{start_token_escaped}(.*?){end_token_escaped}''' ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\tre.IGNORECASE )\r if content is not None:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tcontent.group(1 ).strip()\r if r\"\"\"\" ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tleaf.strip()\r if leaf in added_vocab and leaf[0] == \"<\" and leaf[-2:] == \"/>\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tleaf[1:-2] # for categorical special tokens\r output[key].append(_UpperCAmelCase )\r if len(output[key] ) == 1:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\toutput[key][0]\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokens[tokens.find(_UpperCAmelCase ) + len(_UpperCAmelCase ) :].strip()\r if tokens[:6] == r\"\": # non-leaf nodes\r return [output] + self.tokenajson(tokens[6:] ,\t\t\t\t\t\tis_inner_value=_UpperCAmelCase ,\t\t\t\t\t\tadded_vocab=_UpperCAmelCase )\r\r if len(_UpperCAmelCase ):\r return [output] if is_inner_value else output\r else:\r return [] if is_inner_value else {\"text_sequence\": tokens}\r\r\r @property\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r warnings.warn(\r \"\"\"`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.\"\"\" ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t)\r return self.image_processor_class\r\r\r\r @property\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r warnings.warn(\r \"\"\"`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.\"\"\" ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t)\r return self.image_processor\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport os\rimport unicodedata\rfrom shutil import copyfile\rfrom typing import Any, Dict, List, Optional, Tuple\r\rimport sentencepiece as spm\r\rfrom ...tokenization_utils import AddedToken, PreTrainedTokenizer\rfrom ...utils import SPIECE_UNDERLINE, logging\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tlogging.get_logger(__name__)\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t{'vocab_file': 'spiece.model'}\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t{\r 'vocab_file': {\r 'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model',\r }\r}\r\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def __init__( self\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny=False ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint=True ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=False ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict=\"~~\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint=\"~~\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict=\"\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple=\"\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any]=\"\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint=\"\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=\"\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str]=[\"\", \"\"] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Dict[str, Any]] = None ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tAddedToken(_UpperCAmelCase ,\t\t\t\t\t\tlstrip=_UpperCAmelCase ,\t\t\t\t\t\trstrip=_UpperCAmelCase ) if isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ) else mask_token\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{} if sp_model_kwargs is None else sp_model_kwargs\r\r super().__init__(\r do_lower_case=_UpperCAmelCase ,\t\t\t\t\t\tremove_space=_UpperCAmelCase ,\t\t\t\t\t\tkeep_accents=_UpperCAmelCase ,\t\t\t\t\t\tbos_token=_UpperCAmelCase ,\t\t\t\t\t\teos_token=_UpperCAmelCase ,\t\t\t\t\t\tunk_token=_UpperCAmelCase ,\t\t\t\t\t\tsep_token=_UpperCAmelCase ,\t\t\t\t\t\tpad_token=_UpperCAmelCase ,\t\t\t\t\t\tcls_token=_UpperCAmelCase ,\t\t\t\t\t\tmask_token=_UpperCAmelCase ,\t\t\t\t\t\tadditional_special_tokens=_UpperCAmelCase ,\t\t\t\t\t\tsp_model_kwargs=self.sp_model_kwargs ,\t\t\t\t\t\t**_UpperCAmelCase ,\t\t\t\t\t\t)\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t3\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdo_lower_case\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tremove_space\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tkeep_accents\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvocab_file\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tspm.SentencePieceProcessor(**self.sp_model_kwargs )\r self.sp_model.Load(_UpperCAmelCase )\r\r try:\r import jieba\r except ModuleNotFoundError as error:\r raise error.__class__(\r \"\"\"You need to install jieba to use CpmTokenizer or CpmTokenizerFast. \"\"\"\r \"\"\"See https://pypi.org/project/jieba/ for installation.\"\"\" )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tjieba\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tstr.maketrans(\"\"\" \\n\"\"\" ,\t\t\t\t\t\t\"\"\"\\u2582\\u2583\"\"\" )\r\r\r @property\r # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return len(self.sp_model )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{self.convert_ids_to_tokens(_UpperCAmelCase ): i for i in range(self.vocab_size )}\r vocab.update(self.added_tokens_encoder )\r return vocab\r\r\r def __getstate__( self\t\t\t:\t\tDict ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.__dict__.copy()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r return state\r\r\r def __setstate__( self\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\td\r\r # for backward compatibility\r if not hasattr(self ,\t\t\t\t\t\t\"\"\"sp_model_kwargs\"\"\" ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{}\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tspm.SentencePieceProcessor(**self.sp_model_kwargs )\r self.sp_model.Load(self.vocab_file )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if self.remove_space:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\" \"\"\".join(inputs.strip().split() )\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tinputs\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\toutputs.replace(\"\"\"``\"\"\" ,\t\t\t\t\t\t\"\"\"\\\"\"\"\" ).replace(\"\"\"''\"\"\" ,\t\t\t\t\t\t\"\"\"\\\"\"\"\" )\r\r if not self.keep_accents:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tunicodedata.normalize(\"\"\"NFKD\"\"\" ,\t\t\t\t\t\t_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"\"\"\".join([c for c in outputs if not unicodedata.combining(_UpperCAmelCase )] )\r if self.do_lower_case:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\toutputs.lower()\r\r return outputs\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tTuple ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.preprocess_text(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.sp_model.encode(_UpperCAmelCase ,\t\t\t\t\t\tout_type=_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[]\r for piece in pieces:\r if len(_UpperCAmelCase ) > 1 and piece[-1] == str(\"\"\",\"\"\" ) and piece[-2].isdigit():\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.sp_model.EncodeAsPieces(piece[:-1].replace(_UpperCAmelCase ,\t\t\t\t\t\t\"\"\"\"\"\" ) )\r if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:\r if len(cur_pieces[0] ) == 1:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tcur_pieces[1:]\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tcur_pieces[0][1:]\r cur_pieces.append(piece[-1] )\r new_pieces.extend(_UpperCAmelCase )\r else:\r new_pieces.append(_UpperCAmelCase )\r\r return new_pieces\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return self.sp_model.PieceToId(_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return self.sp_model.IdToPiece(_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"\"\"\".join(_UpperCAmelCase ).replace(_UpperCAmelCase ,\t\t\t\t\t\t\"\"\" \"\"\" ).strip()\r return out_string\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[List[int]] = None ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[self.sep_token_id]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[self.cls_token_id]\r if token_ids_a is None:\r return token_ids_a + sep + cls\r return token_ids_a + sep + token_ids_a + sep + cls\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[List[int]] = None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tbool = False ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if already_has_special_tokens:\r return super().get_special_tokens_mask(\r token_ids_a=_UpperCAmelCase ,\t\t\t\t\t\ttoken_ids_a=_UpperCAmelCase ,\t\t\t\t\t\talready_has_special_tokens=_UpperCAmelCase )\r\r if token_ids_a is not None:\r return ([0] * len(_UpperCAmelCase )) + [1] + ([0] * len(_UpperCAmelCase )) + [1, 1]\r return ([0] * len(_UpperCAmelCase )) + [1, 1]\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[List[int]] = None ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[self.sep_token_id]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[2]\r\r if token_ids_a is None:\r return len(token_ids_a + sep ) * [0] + cls_segment_id\r return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[str] = None ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if not os.path.isdir(_UpperCAmelCase ):\r logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )\r return\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tos.path.join(\r _UpperCAmelCase ,\t\t\t\t\t\t(filename_prefix + \"\"\"-\"\"\" if filename_prefix else \"\"\"\"\"\") + VOCAB_FILES_NAMES[\"\"\"vocab_file\"\"\"] )\r\r if os.path.abspath(self.vocab_file ) != os.path.abspath(_UpperCAmelCase ) and os.path.isfile(self.vocab_file ):\r copyfile(self.vocab_file ,\t\t\t\t\t\t_UpperCAmelCase )\r elif not os.path.isfile(self.vocab_file ):\r with open(_UpperCAmelCase ,\t\t\t\t\t\t\"\"\"wb\"\"\" ) as fi:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.sp_model.serialized_model_proto()\r fi.write(_UpperCAmelCase )\r\r return (out_vocab_file,)\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tTuple ,\t\t\t\t\t\t*_UpperCAmelCase\t\t\t:\t\tTuple ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tsuper()._decode(*_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttext.replace(\"\"\" \"\"\" ,\t\t\t\t\t\t\"\"\"\"\"\" ).replace(\"\"\"\\u2582\"\"\" ,\t\t\t\t\t\t\"\"\" \"\"\" ).replace(\"\"\"\\u2583\"\"\" ,\t\t\t\t\t\t\"\"\"\\n\"\"\" )\r return text\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152373,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport datasets\r\rfrom .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'\\\\n@INPROCEEDINGS{Papineni02bleu:a,\\n author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\\n title = {BLEU: a Method for Automatic Evaluation of Machine Translation},\\n booktitle = {},\\n year = {2002},\\n pages = {311--318}\\n}\\n@inproceedings{lin-och-2004-orange,\\n title = \"{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation\",\\n author = \"Lin, Chin-Yew and\\n Och, Franz Josef\",\\n booktitle = \"{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics\",\\n month = \"aug 23{--}aug 27\",\\n year = \"2004\",\\n address = \"Geneva, Switzerland\",\\n publisher = \"COLING\",\\n url = \"https://www.aclweb.org/anthology/C04-1072\",\\n pages = \"501--507\",\\n}\\n'\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'\\\\nBLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.\\nQuality is considered to be the correspondence between a machine\\'s output and that of a human: \"the closer a machine translation is to a professional human translation,\\nthe better it is\" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and\\nremains one of the most popular automated and inexpensive metrics.\\n\\nScores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.\\nThose scores are then averaged over the whole corpus to reach an estimate of the translation\\'s overall quality. Intelligibility or grammatical correctness\\nare not taken into account[citation needed].\\n\\nBLEU\\'s output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1\\nrepresenting more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the\\nreference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional\\nreference translations will increase the BLEU score.\\n'\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'\\nComputes BLEU score of translated segments against one or more references.\\nArgs:\\n predictions: list of translations to score.\\n Each translation should be tokenized into a list of tokens.\\n references: list of lists of references for each translation.\\n Each reference should be tokenized into a list of tokens.\\n max_order: Maximum n-gram order to use when computing BLEU score.\\n smooth: Whether or not to apply Lin et al. 2004 smoothing.\\nReturns:\\n \\'bleu\\': bleu score,\\n \\'precisions\\': geometric mean of n-gram precisions,\\n \\'brevity_penalty\\': brevity penalty,\\n \\'length_ratio\\': ratio of lengths,\\n \\'translation_length\\': translation_length,\\n \\'reference_length\\': reference_length\\nExamples:\\n\\n >>> predictions = [\\n ... [\"hello\", \"there\", \"general\", \"kenobi\"], # tokenized prediction of the first sample\\n ... [\"foo\", \"bar\", \"foobar\"] # tokenized prediction of the second sample\\n ... ]\\n >>> references = [\\n ... [[\"hello\", \"there\", \"general\", \"kenobi\"], [\"hello\", \"there\", \"!\"]], # tokenized references for the first sample (2 references)\\n ... [[\"foo\", \"bar\", \"foobar\"]] # tokenized references for the second sample (1 reference)\\n ... ]\\n >>> bleu = datasets.load_metric(\"bleu\")\\n >>> results = bleu.compute(predictions=predictions, references=references)\\n >>> print(results[\"bleu\"])\\n 1.0\\n'\r\r@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION\t, _KWARGS_DESCRIPTION )\rclass lowerCAmelCase_ ( datasets.Metric ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return datasets.MetricInfo(\r description=_DESCRIPTION ,\t\t\t\t\t\tcitation=_CITATION ,\t\t\t\t\t\tinputs_description=_KWARGS_DESCRIPTION ,\t\t\t\t\t\tfeatures=datasets.Features(\r {\r \"\"\"predictions\"\"\": datasets.Sequence(datasets.Value(\"\"\"string\"\"\" ,\t\t\t\t\t\tid=\"\"\"token\"\"\" ) ,\t\t\t\t\t\tid=\"\"\"sequence\"\"\" ),\r \"\"\"references\"\"\": datasets.Sequence(\r datasets.Sequence(datasets.Value(\"\"\"string\"\"\" ,\t\t\t\t\t\tid=\"\"\"token\"\"\" ) ,\t\t\t\t\t\tid=\"\"\"sequence\"\"\" ) ,\t\t\t\t\t\tid=\"\"\"references\"\"\" ),\r } ) ,\t\t\t\t\t\tcodebase_urls=[\"\"\"https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py\"\"\"] ,\t\t\t\t\t\treference_urls=[\r \"\"\"https://en.wikipedia.org/wiki/BLEU\"\"\",\r \"\"\"https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213\"\"\",\r ] ,\t\t\t\t\t\t)\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any]=4 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=False ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tcompute_bleu(\r reference_corpus=_UpperCAmelCase ,\t\t\t\t\t\ttranslation_corpus=_UpperCAmelCase ,\t\t\t\t\t\tmax_order=_UpperCAmelCase ,\t\t\t\t\t\tsmooth=_UpperCAmelCase )\r ((UpperCAmelCase__)\t\t\t\t\t\t\t,\t(UpperCAmelCase__)\t\t\t\t\t\t\t,\t(UpperCAmelCase__)\t\t\t\t\t\t\t,\t(UpperCAmelCase__)\t\t\t\t\t\t\t,\t(UpperCAmelCase__)\t\t\t\t\t\t\t,\t(UpperCAmelCase__))\t\t\t\t\t\t\t\t=\t\t\tscore\r return {\r \"bleu\": bleu,\r \"precisions\": precisions,\r \"brevity_penalty\": bp,\r \"length_ratio\": ratio,\r \"translation_length\": translation_length,\r \"reference_length\": reference_length,\r }\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport argparse\rimport logging\rimport os\r\rimport datasets\rimport tensorflow as tf\r\rfrom transformers import AutoTokenizer\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tlogging.getLogger(__name__)\r\rdef _UpperCamelCase\t\t\t\t\t( ):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\targparse.ArgumentParser(\r description=\"\"\"Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset.\"\"\"\t\t\t\t)\r parser.add_argument(\r \"\"\"--dataset_name\"\"\"\t\t\t\t, type=SCREAMING_SNAKE_CASE__\t\t\t\t, default=\"\"\"wikitext\"\"\"\t\t\t\t, help=\"\"\"Name of the training. Explore datasets at: hf.co/datasets.\"\"\"\t\t\t\t, )\r parser.add_argument(\r \"\"\"--dataset_config\"\"\"\t\t\t\t, type=SCREAMING_SNAKE_CASE__\t\t\t\t, default=\"\"\"wikitext-103-raw-v1\"\"\"\t\t\t\t, help=\"\"\"Configuration name of the dataset.\"\"\"\t\t\t\t)\r parser.add_argument(\r \"\"\"--tokenizer_name_or_path\"\"\"\t\t\t\t, type=SCREAMING_SNAKE_CASE__\t\t\t\t, default=\"\"\"sayakpaul/unigram-tokenizer-wikitext\"\"\"\t\t\t\t, help=\"\"\"Tokenizer identifier. Can be a local filepath or a Hub identifier.\"\"\"\t\t\t\t, )\r parser.add_argument(\r \"\"\"--shard_size\"\"\"\t\t\t\t, type=SCREAMING_SNAKE_CASE__\t\t\t\t, default=1000\t\t\t\t, help=\"\"\"Number of entries to go in a single shard.\"\"\"\t\t\t\t, )\r parser.add_argument(\"\"\"--split\"\"\"\t\t\t\t, type=SCREAMING_SNAKE_CASE__\t\t\t\t, default=\"\"\"train\"\"\"\t\t\t\t, choices=[\"\"\"train\"\"\", \"\"\"test\"\"\", \"\"\"validation\"\"\"]\t\t\t\t)\r parser.add_argument(\r \"\"\"--limit\"\"\"\t\t\t\t, default=SCREAMING_SNAKE_CASE__\t\t\t\t, type=SCREAMING_SNAKE_CASE__\t\t\t\t, help=\"\"\"Limit the number of shards (used for debugging).\"\"\"\t\t\t\t, )\r parser.add_argument(\r \"\"\"--max_length\"\"\"\t\t\t\t, type=SCREAMING_SNAKE_CASE__\t\t\t\t, default=512\t\t\t\t, help=\"\"\"Maximum sequence length. For training on TPUs, it helps to have a maximum\"\"\"\r \"\"\" sequence length that is a multiple of 8.\"\"\"\t\t\t\t, )\r parser.add_argument(\r \"\"\"--output_dir\"\"\"\t\t\t\t, default=\"\"\"tf-tpu\"\"\"\t\t\t\t, type=SCREAMING_SNAKE_CASE__\t\t\t\t, help=\"\"\"Output directory where the TFRecord shards will be saved. If the\"\"\"\r \"\"\" path is appended with `gs://` ('gs://tf-tpu', for example) then the TFRecord\"\"\"\r \"\"\" shards will be directly saved to a Google Cloud Storage bucket.\"\"\"\t\t\t\t, )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tparser.parse_args()\r return args\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tAny\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r def fn(SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tUnion[str, Any]\t\t\t\t):\r return tokenizer(examples[\"\"\"text\"\"\"]\t\t\t\t)\r\r return fn\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[int]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[]\r for i in range(len(tokenized_data[\"\"\"input_ids\"\"\"]\t\t\t\t)\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\r \"\"\"input_ids\"\"\": tf.train.Feature(intaa_list=tf.train.IntaaList(value=tokenized_data[\"\"\"input_ids\"\"\"][i]\t\t\t\t)\t\t\t\t),\r \"\"\"attention_mask\"\"\": tf.train.Feature(\r intaa_list=tf.train.IntaaList(value=tokenized_data[\"\"\"attention_mask\"\"\"][i]\t\t\t\t)\t\t\t\t),\r }\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttf.train.Features(feature=SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttf.train.Example(features=SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\texample.SerializeToString()\r records.append(SCREAMING_SNAKE_CASE__\t\t\t\t)\r return records\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tAny\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdatasets.load_dataset(args.dataset_name\t\t\t\t, args.dataset_config\t\t\t\t, split=args.split\t\t\t\t)\r\r if args.limit is not None:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmin(len(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t, args.limit\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdataset.select(range(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t)\r print(F'''Limiting the dataset to {args.limit} entries.'''\t\t\t\t)\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tAutoTokenizer.from_pretrained(args.tokenizer_name_or_path\t\t\t\t)\r\r # Handle output directory creation.\r # For serializing into a Google Cloud Storage Bucket, one needs to first\r # create a bucket.\r if \"gs\" not in args.output_dir:\r if not os.path.exists(args.output_dir\t\t\t\t):\r os.makedirs(args.output_dir\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tos.path.join(args.output_dir\t\t\t\t, args.split\t\t\t\t)\r if not os.path.exists(SCREAMING_SNAKE_CASE__\t\t\t\t):\r os.makedirs(SCREAMING_SNAKE_CASE__\t\t\t\t)\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tos.path.join(args.output_dir\t\t\t\t, args.split\t\t\t\t)\r\r # Tokenize the whole dataset at once.\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenize_function(SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdataset.map(SCREAMING_SNAKE_CASE__\t\t\t\t, batched=SCREAMING_SNAKE_CASE__\t\t\t\t, num_proc=4\t\t\t\t, remove_columns=[\"\"\"text\"\"\"]\t\t\t\t)\r\r # We need to concatenate all our texts together, and then split the result\r # into chunks of a fixed size, which we will call block_size. To do this, we\r # will use the map method again, with the option batched=True. When we use batched=True,\r # the function we pass to map() will be passed multiple inputs at once, allowing us\r # to group them into more or fewer examples than we had in the input.\r # This allows us to create our new fixed-length samples. The advantage of this\r # method is that we don't lose a whole lot of content from the dataset compared to the\r # case where we simply tokenize with a pre-defined max_length.\r\r def group_texts(SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t):\r # Concatenate all texts.\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{k: sum(examples[k]\t\t\t\t, []\t\t\t\t) for k in examples.keys()}\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlen(concatenated_examples[list(examples.keys()\t\t\t\t)[0]]\t\t\t\t)\r # We drop the small remainder, though you could add padding instead if the model supports it\r # In this, as in all things, we advise you to follow your heart 🫀\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t(total_length // args.max_length) * args.max_length\r # Split by chunks of max_len.\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\r k: [t[i : i + args.max_length] for i in range(0\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, args.max_length\t\t\t\t)]\r for k, t in concatenated_examples.items()\r }\r return result\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdataset_tokenized.map(SCREAMING_SNAKE_CASE__\t\t\t\t, batched=SCREAMING_SNAKE_CASE__\t\t\t\t, batch_size=1000\t\t\t\t, num_proc=4\t\t\t\t)\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0\r for shard in range(0\t\t\t\t, len(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t, args.shard_size\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tgrouped_dataset[shard : shard + args.shard_size]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlen(dataset_snapshot[\"\"\"input_ids\"\"\"]\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tos.path.join(SCREAMING_SNAKE_CASE__\t\t\t\t, F'''dataset-{shard_count}-{records_containing}.tfrecord'''\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tget_serialized_examples(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r with tf.io.TFRecordWriter(SCREAMING_SNAKE_CASE__\t\t\t\t) as out_file:\r for i in range(len(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tserialized_examples[i]\r out_file.write(SCREAMING_SNAKE_CASE__\t\t\t\t)\r print(\"\"\"Wrote file {} containing {} records\"\"\".format(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t)\r\r shard_count += 1\r total_records += records_containing\r\r with open(F'''split-{args.split}-records-count.txt'''\t\t\t\t, \"\"\"w\"\"\"\t\t\t\t) as f:\r print(F'''Total {args.split} records: {total_records}'''\t\t\t\t, file=SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r\rif __name__ == \"__main__\":\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tparse_args()\r main(args)\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152374,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport math\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmath.loga(math.sqrt(4 * positive_integer + 1\t\t\t\t) / 2 + 1 / 2\t\t\t\t)\r\r return exponent == int(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tfloat = 1 / 12345\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t3\r while True:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t(integer**2 - 1) / 4\r # if candidate is an integer, then there is a partition for k\r if partition_candidate == int(SCREAMING_SNAKE_CASE__\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tint(SCREAMING_SNAKE_CASE__\t\t\t\t)\r total_partitions += 1\r if check_partition_perfect(SCREAMING_SNAKE_CASE__\t\t\t\t):\r perfect_partitions += 1\r if perfect_partitions > 0:\r if perfect_partitions / total_partitions < max_proportion:\r return int(SCREAMING_SNAKE_CASE__\t\t\t\t)\r integer += 1\r\r\rif __name__ == \"__main__\":\r print(f\"{solution() = }\")\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport numpy as np\rimport torch\rfrom torch.nn import CrossEntropyLoss\rfrom transformers import AutoModelForCausalLM, AutoTokenizer\r\rimport datasets\rfrom datasets import logging\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'\\\\n\\n'\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'\\nPerplexity (PPL) is one of the most common metrics for evaluating language models.\\nIt is defined as the exponentiated average negative log-likelihood of a sequence.\\n\\nFor more information, see https://huggingface.co/docs/transformers/perplexity\\n'\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'\\nArgs:\\n model_id (str): model used for calculating Perplexity\\n NOTE: Perplexity can only be calculated for causal language models.\\n This includes models such as gpt2, causal variations of bert,\\n causal versions of t5, and more (the full list can be found\\n in the AutoModelForCausalLM documentation here:\\n https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )\\n\\n input_texts (list of str): input text, each separate text snippet\\n is one list entry.\\n batch_size (int): the batch size to run texts through the model. Defaults to 16.\\n add_start_token (bool): whether to add the start token to the texts,\\n so the perplexity can include the probability of the first word. Defaults to True.\\n device (str): device to run on, defaults to \\'cuda\\' when available\\nReturns:\\n perplexity: dictionary containing the perplexity scores for the texts\\n in the input list, as well as the mean perplexity. If one of the input texts is\\n longer than the max input length of the model, then it is truncated to the\\n max length for the perplexity computation.\\nExamples:\\n Example 1:\\n >>> perplexity = datasets.load_metric(\"perplexity\")\\n >>> input_texts = [\"lorem ipsum\", \"Happy Birthday!\", \"Bienvenue\"]\\n >>> results = perplexity.compute(model_id=\\'gpt2\\',\\n ... add_start_token=False,\\n ... input_texts=input_texts) # doctest:+ELLIPSIS\\n >>> print(list(results.keys()))\\n [\\'perplexities\\', \\'mean_perplexity\\']\\n >>> print(round(results[\"mean_perplexity\"], 2))\\n 78.22\\n >>> print(round(results[\"perplexities\"][0], 2))\\n 11.11\\n\\n Example 2:\\n >>> perplexity = datasets.load_metric(\"perplexity\")\\n >>> input_texts = datasets.load_dataset(\"wikitext\",\\n ... \"wikitext-2-raw-v1\",\\n ... split=\"test\")[\"text\"][:50] # doctest:+ELLIPSIS\\n [...]\\n >>> input_texts = [s for s in input_texts if s!=\\'\\']\\n >>> results = perplexity.compute(model_id=\\'gpt2\\',\\n ... input_texts=input_texts) # doctest:+ELLIPSIS\\n >>> print(list(results.keys()))\\n [\\'perplexities\\', \\'mean_perplexity\\']\\n >>> print(round(results[\"mean_perplexity\"], 2))\\n 60.35\\n >>> print(round(results[\"perplexities\"][0], 2))\\n 81.12\\n'\r\r@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION\t, _KWARGS_DESCRIPTION )\rclass lowerCAmelCase_ ( datasets.Metric ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return datasets.MetricInfo(\r description=_DESCRIPTION ,\t\t\t\t\t\tcitation=_CITATION ,\t\t\t\t\t\tinputs_description=_KWARGS_DESCRIPTION ,\t\t\t\t\t\tfeatures=datasets.Features(\r {\r \"\"\"input_texts\"\"\": datasets.Value(\"\"\"string\"\"\" ),\r } ) ,\t\t\t\t\t\treference_urls=[\"\"\"https://huggingface.co/docs/transformers/perplexity\"\"\"] ,\t\t\t\t\t\t)\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint = 16 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tbool = True ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int]=None ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if device is not None:\r assert device in [\"gpu\", \"cpu\", \"cuda\"], \"device should be either gpu or cpu.\"\r if device == \"gpu\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"cuda\"\"\"\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"cuda\"\"\" if torch.cuda.is_available() else \"\"\"cpu\"\"\"\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tAutoModelForCausalLM.from_pretrained(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel.to(_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tAutoTokenizer.from_pretrained(_UpperCAmelCase )\r\r # if batch_size > 1 (which generally leads to padding being required), and\r # if there is not an already assigned pad_token, assign an existing\r # special token to also be the padding token\r if tokenizer.pad_token is None and batch_size > 1:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlist(tokenizer.special_tokens_map_extended.values() )\r # check that the model already has at least one special token defined\r assert (\r len(_UpperCAmelCase ) > 0\r ), \"If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1.\"\r # assign one of the special tokens to also be the pad token\r tokenizer.add_special_tokens({\"\"\"pad_token\"\"\": existing_special_tokens[0]} )\r\r if add_start_token:\r # leave room for token to be added:\r assert (\r tokenizer.bos_token is not None\r ), \"Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False\"\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel.config.max_length - 1\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel.config.max_length\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenizer(\r _UpperCAmelCase ,\t\t\t\t\t\tadd_special_tokens=_UpperCAmelCase ,\t\t\t\t\t\tpadding=_UpperCAmelCase ,\t\t\t\t\t\ttruncation=_UpperCAmelCase ,\t\t\t\t\t\tmax_length=_UpperCAmelCase ,\t\t\t\t\t\treturn_tensors=\"\"\"pt\"\"\" ,\t\t\t\t\t\treturn_attention_mask=_UpperCAmelCase ,\t\t\t\t\t\t).to(_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tencodings[\"\"\"input_ids\"\"\"]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tencodings[\"\"\"attention_mask\"\"\"]\r\r # check that each input is long enough:\r if add_start_token:\r assert torch.all(torch.ge(attn_masks.sum(1 ) ,\t\t\t\t\t\t1 ) ), \"Each input text must be at least one token long.\"\r else:\r assert torch.all(\r torch.ge(attn_masks.sum(1 ) ,\t\t\t\t\t\t2 ) ), \"When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings.\"\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tCrossEntropyLoss(reduction=\"\"\"none\"\"\" )\r\r for start_index in logging.tqdm(range(0 ,\t\t\t\t\t\tlen(_UpperCAmelCase ) ,\t\t\t\t\t\t_UpperCAmelCase ) ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmin(start_index + batch_size ,\t\t\t\t\t\tlen(_UpperCAmelCase ) )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tencoded_texts[start_index:end_index]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tattn_masks[start_index:end_index]\r\r if add_start_token:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0 ) ).to(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.cat([bos_tokens_tensor, encoded_batch] ,\t\t\t\t\t\tdim=1 )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.cat(\r [torch.ones(bos_tokens_tensor.size() ,\t\t\t\t\t\tdtype=torch.intaa ).to(_UpperCAmelCase ), attn_mask] ,\t\t\t\t\t\tdim=1 )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tencoded_batch\r\r with torch.no_grad():\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase ,\t\t\t\t\t\tattention_mask=_UpperCAmelCase ).logits\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tout_logits[..., :-1, :].contiguous()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlabels[..., 1:].contiguous()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tattn_mask[..., 1:].contiguous()\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.expa(\r (loss_fct(shift_logits.transpose(1 ,\t\t\t\t\t\t2 ) ,\t\t\t\t\t\t_UpperCAmelCase ) * shift_attention_mask_batch).sum(1 )\r / shift_attention_mask_batch.sum(1 ) )\r\r ppls += perplexity_batch.tolist()\r\r return {\"perplexities\": ppls, \"mean_perplexity\": np.mean(_UpperCAmelCase )}\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152375,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rfrom typing import Dict, List, Optional, Union\r\rimport numpy as np\r\rfrom ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict\rfrom ...image_transforms import (\r center_crop,\r get_resize_output_image_size,\r normalize,\r rescale,\r resize,\r to_channel_dimension_format,\r)\rfrom ...image_utils import (\r IMAGENET_STANDARD_MEAN,\r IMAGENET_STANDARD_STD,\r ChannelDimension,\r ImageInput,\r PILImageResampling,\r is_valid_image,\r to_numpy_array,\r valid_images,\r)\rfrom ...utils import TensorType, is_vision_available, logging\r\r\rif is_vision_available():\r import PIL\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tlogging.get_logger(__name__)\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r if isinstance(SCREAMING_SNAKE_CASE__\t\t\t\t, (list, tuple)\t\t\t\t) and isinstance(videos[0]\t\t\t\t, (list, tuple)\t\t\t\t) and is_valid_image(videos[0][0]\t\t\t\t):\r return videos\r\r elif isinstance(SCREAMING_SNAKE_CASE__\t\t\t\t, (list, tuple)\t\t\t\t) and is_valid_image(videos[0]\t\t\t\t):\r return [videos]\r\r elif is_valid_image(SCREAMING_SNAKE_CASE__\t\t\t\t):\r return [[videos]]\r\r raise ValueError(F'''Could not make batched video from {videos}'''\t\t\t\t)\r\r\r\r\r\r\r\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r lowerCAmelCase_ : int = [\"\"\"pixel_values\"\"\"]\r\r\r def __init__( self\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tbool = True ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict[str, int] = None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tPILImageResampling = PILImageResampling.BILINEAR ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tbool = True ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict[str, int] = None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tbool = True ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[int, float] = 1 / 2_55 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tbool = True ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Union[float, List[float]]] = None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Union[float, List[float]]] = None ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tstr ,\t\t\t\t\t\t):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r super().__init__(**_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tsize if size is not None else {\"\"\"shortest_edge\"\"\": 2_24}\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tget_size_dict(_UpperCAmelCase ,\t\t\t\t\t\tdefault_to_square=_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tcrop_size if crop_size is not None else {\"\"\"height\"\"\": 2_24, \"\"\"width\"\"\": 2_24}\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tget_size_dict(_UpperCAmelCase ,\t\t\t\t\t\tparam_name=\"\"\"crop_size\"\"\" )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdo_resize\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tsize\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdo_center_crop\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tcrop_size\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tresample\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdo_rescale\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\trescale_factor\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdo_normalize\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\timage_mean if image_mean is not None else IMAGENET_STANDARD_MEAN\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\timage_std if image_std is not None else IMAGENET_STANDARD_STD\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tnp.ndarray ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict[str, int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tPILImageResampling = PILImageResampling.BILINEAR ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Union[str, ChannelDimension]] = None ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tget_size_dict(_UpperCAmelCase ,\t\t\t\t\t\tdefault_to_square=_UpperCAmelCase )\r if \"shortest_edge\" in size:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tget_resize_output_image_size(_UpperCAmelCase ,\t\t\t\t\t\tsize[\"\"\"shortest_edge\"\"\"] ,\t\t\t\t\t\tdefault_to_square=_UpperCAmelCase )\r elif \"height\" in size and \"width\" in size:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t(size[\"\"\"height\"\"\"], size[\"\"\"width\"\"\"])\r else:\r raise ValueError(f'''Size must have \\'height\\' and \\'width\\' or \\'shortest_edge\\' as keys. Got {size.keys()}''' )\r return resize(_UpperCAmelCase ,\t\t\t\t\t\tsize=_UpperCAmelCase ,\t\t\t\t\t\tresample=_UpperCAmelCase ,\t\t\t\t\t\tdata_format=_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tTuple ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tnp.ndarray ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict[str, int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Union[str, ChannelDimension]] = None ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tTuple ,\t\t\t\t\t\t):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tget_size_dict(_UpperCAmelCase )\r if \"height\" not in size or \"width\" not in size:\r raise ValueError(f'''Size must have \\'height\\' and \\'width\\' as keys. Got {size.keys()}''' )\r return center_crop(_UpperCAmelCase ,\t\t\t\t\t\tsize=(size[\"\"\"height\"\"\"], size[\"\"\"width\"\"\"]) ,\t\t\t\t\t\tdata_format=_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tTuple ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tnp.ndarray ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[int, float] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Union[str, ChannelDimension]] = None ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tDict ,\t\t\t\t\t\t):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return rescale(_UpperCAmelCase ,\t\t\t\t\t\tscale=_UpperCAmelCase ,\t\t\t\t\t\tdata_format=_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tnp.ndarray ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[float, List[float]] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[float, List[float]] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Union[str, ChannelDimension]] = None ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return normalize(_UpperCAmelCase ,\t\t\t\t\t\tmean=_UpperCAmelCase ,\t\t\t\t\t\tstd=_UpperCAmelCase ,\t\t\t\t\t\tdata_format=_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tAny ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tImageInput ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tbool = None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict[str, int] = None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tPILImageResampling = None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tbool = None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict[str, int] = None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tbool = None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tfloat = None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tbool = None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Union[float, List[float]]] = None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Union[float, List[float]]] = None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[ChannelDimension] = ChannelDimension.FIRST ,\t\t\t\t\t\t):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if do_resize and size is None or resample is None:\r raise ValueError(\"\"\"Size and resample must be specified if do_resize is True.\"\"\" )\r\r if do_center_crop and crop_size is None:\r raise ValueError(\"\"\"Crop size must be specified if do_center_crop is True.\"\"\" )\r\r if do_rescale and rescale_factor is None:\r raise ValueError(\"\"\"Rescale factor must be specified if do_rescale is True.\"\"\" )\r\r if do_normalize and (image_mean is None or image_std is None):\r raise ValueError(\"\"\"Image mean and std must be specified if do_normalize is True.\"\"\" )\r\r # All transformations expect numpy arrays.\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tto_numpy_array(_UpperCAmelCase )\r\r if do_resize:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.resize(image=_UpperCAmelCase ,\t\t\t\t\t\tsize=_UpperCAmelCase ,\t\t\t\t\t\tresample=_UpperCAmelCase )\r\r if do_center_crop:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.center_crop(_UpperCAmelCase ,\t\t\t\t\t\tsize=_UpperCAmelCase )\r\r if do_rescale:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.rescale(image=_UpperCAmelCase ,\t\t\t\t\t\tscale=_UpperCAmelCase )\r\r if do_normalize:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.normalize(image=_UpperCAmelCase ,\t\t\t\t\t\tmean=_UpperCAmelCase ,\t\t\t\t\t\tstd=_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tto_channel_dimension_format(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r return image\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tImageInput ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tbool = None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict[str, int] = None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tPILImageResampling = None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tbool = None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict[str, int] = None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tbool = None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tfloat = None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tbool = None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Union[float, List[float]]] = None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Union[float, List[float]]] = None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Union[str, TensorType]] = None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tChannelDimension = ChannelDimension.FIRST ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tTuple ,\t\t\t\t\t\t):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdo_resize if do_resize is not None else self.do_resize\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tresample if resample is not None else self.resample\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdo_center_crop if do_center_crop is not None else self.do_center_crop\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdo_rescale if do_rescale is not None else self.do_rescale\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\trescale_factor if rescale_factor is not None else self.rescale_factor\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdo_normalize if do_normalize is not None else self.do_normalize\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\timage_mean if image_mean is not None else self.image_mean\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\timage_std if image_std is not None else self.image_std\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tsize if size is not None else self.size\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tget_size_dict(_UpperCAmelCase ,\t\t\t\t\t\tdefault_to_square=_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tcrop_size if crop_size is not None else self.crop_size\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tget_size_dict(_UpperCAmelCase ,\t\t\t\t\t\tparam_name=\"\"\"crop_size\"\"\" )\r\r if not valid_images(_UpperCAmelCase ):\r raise ValueError(\r \"\"\"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, \"\"\"\r \"\"\"torch.Tensor, tf.Tensor or jax.ndarray.\"\"\" )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmake_batched(_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[\r [\r self._preprocess_image(\r image=_UpperCAmelCase ,\t\t\t\t\t\tdo_resize=_UpperCAmelCase ,\t\t\t\t\t\tsize=_UpperCAmelCase ,\t\t\t\t\t\tresample=_UpperCAmelCase ,\t\t\t\t\t\tdo_center_crop=_UpperCAmelCase ,\t\t\t\t\t\tcrop_size=_UpperCAmelCase ,\t\t\t\t\t\tdo_rescale=_UpperCAmelCase ,\t\t\t\t\t\trescale_factor=_UpperCAmelCase ,\t\t\t\t\t\tdo_normalize=_UpperCAmelCase ,\t\t\t\t\t\timage_mean=_UpperCAmelCase ,\t\t\t\t\t\timage_std=_UpperCAmelCase ,\t\t\t\t\t\tdata_format=_UpperCAmelCase ,\t\t\t\t\t\t)\r for img in video\r ]\r for video in videos\r ]\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\"\"\"pixel_values\"\"\": videos}\r return BatchFeature(data=_UpperCAmelCase ,\t\t\t\t\t\ttensor_type=_UpperCAmelCase )\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint = 1000000\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[i - 1 for i in range(limit + 1\t\t\t\t)]\r\r for i in range(2\t\t\t\t, limit + 1\t\t\t\t):\r if phi[i] == i - 1:\r for j in range(2 * i\t\t\t\t, limit + 1\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r phi[j] -= phi[j] // i\r\r return sum(phi[2 : limit + 1]\t\t\t\t)\r\r\rif __name__ == \"__main__\":\r print(solution())\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152376,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t1\r for i in range(1\t\t\t\t, num + 1\t\t\t\t):\r fact *= i\r return fact\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0\r while number > 0:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnumber % 10\r sum_of_digits += last_digit\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnumber // 10 # Removing the last_digit from the given number\r return sum_of_digits\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint = 100\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tfactorial(SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tsplit_and_add(SCREAMING_SNAKE_CASE__\t\t\t\t)\r return result\r\r\rif __name__ == \"__main__\":\r print(solution(int(input('Enter the Number: ').strip())))\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rfrom typing import List, Union\r\rfrom ..utils import (\r add_end_docstrings,\r is_tf_available,\r is_torch_available,\r is_vision_available,\r logging,\r requires_backends,\r)\rfrom .base import PIPELINE_INIT_ARGS, Pipeline\r\r\rif is_vision_available():\r from PIL import Image\r\r from ..image_utils import load_image\r\rif is_tf_available():\r import tensorflow as tf\r\r from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING\r from ..tf_utils import stable_softmax\r\rif is_torch_available():\r from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tlogging.get_logger(__name__)\r\r@add_end_docstrings(lowerCamelCase_ )\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def __init__( self\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t*_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tDict ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r super().__init__(*_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase )\r requires_backends(self ,\t\t\t\t\t\t\"\"\"vision\"\"\" )\r self.check_model_type(\r TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING\r if self.framework == \"\"\"tf\"\"\"\r else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any]=None ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{}\r if top_k is not None:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttop_k\r return {}, {}, postprocess_params\r\r\r def __call__( self\t\t\t:\t\tAny ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, List[str], \"Image.Image\", List[\"Image.Image\"]] ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return super().__call__(_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tload_image(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.image_processor(images=_UpperCAmelCase ,\t\t\t\t\t\treturn_tensors=self.framework )\r return model_inputs\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.model(**_UpperCAmelCase )\r return model_outputs\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr=5 ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if top_k > self.model.config.num_labels:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.model.config.num_labels\r\r if self.framework == \"pt\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel_outputs.logits.softmax(-1 )[0]\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tprobs.topk(_UpperCAmelCase )\r elif self.framework == \"tf\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tstable_softmax(model_outputs.logits ,\t\t\t\t\t\taxis=-1 )[0]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttf.math.top_k(_UpperCAmelCase ,\t\t\t\t\t\tk=_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttopk.values.numpy(), topk.indices.numpy()\r else:\r raise ValueError(f'''Unsupported framework: {self.framework}''' )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscores.tolist()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tids.tolist()\r return [{\"score\": score, \"label\": self.model.config.idalabel[_id]} for score, _id in zip(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )]\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152377,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rfrom typing import Optional, Tuple, Union\r\rimport flax\rimport flax.linen as nn\rimport jax\rimport jax.numpy as jnp\rfrom flax.core.frozen_dict import FrozenDict\r\rfrom ..configuration_utils import ConfigMixin, flax_register_to_config\rfrom ..utils import BaseOutput\rfrom .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps\rfrom .modeling_flax_utils import FlaxModelMixin\rfrom .unet_ad_blocks_flax import (\r FlaxCrossAttnDownBlockaD,\r FlaxCrossAttnUpBlockaD,\r FlaxDownBlockaD,\r FlaxUNetMidBlockaDCrossAttn,\r FlaxUpBlockaD,\r)\r\r@flax.struct.dataclass\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r lowerCAmelCase_ : jnp.ndarray\r\r@flax_register_to_config\rclass lowerCAmelCase_ ( nn.Module\t, lowerCamelCase_\t, lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r lowerCAmelCase_ : int = 32\r lowerCAmelCase_ : int = 4\r lowerCAmelCase_ : int = 4\r lowerCAmelCase_ : Tuple[str] = (\r \"CrossAttnDownBlock2D\",\r \"CrossAttnDownBlock2D\",\r \"CrossAttnDownBlock2D\",\r \"DownBlock2D\",\r )\r lowerCAmelCase_ : Tuple[str] = (\"UpBlock2D\", \"CrossAttnUpBlock2D\", \"CrossAttnUpBlock2D\", \"CrossAttnUpBlock2D\")\r lowerCAmelCase_ : Union[bool, Tuple[bool]] = False\r lowerCAmelCase_ : Tuple[int] = (320, 640, 1_280, 1_280)\r lowerCAmelCase_ : int = 2\r lowerCAmelCase_ : Union[int, Tuple[int]] = 8\r lowerCAmelCase_ : Optional[Union[int, Tuple[int]]] = None\r lowerCAmelCase_ : int = 1_280\r lowerCAmelCase_ : float = 0.0\r lowerCAmelCase_ : bool = False\r lowerCAmelCase_ : jnp.dtype = jnp.floataa\r lowerCAmelCase_ : bool = True\r lowerCAmelCase_ : int = 0\r lowerCAmelCase_ : bool = False\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tjax.random.KeyArray ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t(1, self.in_channels, self.sample_size, self.sample_size)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tjnp.zeros(_UpperCAmelCase ,\t\t\t\t\t\tdtype=jnp.floataa )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tjnp.ones((1,) ,\t\t\t\t\t\tdtype=jnp.intaa )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tjnp.zeros((1, 1, self.cross_attention_dim) ,\t\t\t\t\t\tdtype=jnp.floataa )\r\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tjax.random.split(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\"\"\"params\"\"\": params_rng, \"\"\"dropout\"\"\": dropout_rng}\r\r return self.init(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )[\"params\"]\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.block_out_channels\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tblock_out_channels[0] * 4\r\r if self.num_attention_heads is not None:\r raise ValueError(\r \"\"\"At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19.\"\"\" )\r\r # If `num_attention_heads` is not defined (which is the case for most models)\r # it will default to `attention_head_dim`. This looks weird upon first reading it and it is.\r # The reason for this behavior is to correct for incorrectly named variables that were introduced\r # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131\r # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking\r # which is why we correct for the naming here.\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.num_attention_heads or self.attention_head_dim\r\r # input\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnn.Conv(\r block_out_channels[0] ,\t\t\t\t\t\tkernel_size=(3, 3) ,\t\t\t\t\t\tstrides=(1, 1) ,\t\t\t\t\t\tpadding=((1, 1), (1, 1)) ,\t\t\t\t\t\tdtype=self.dtype ,\t\t\t\t\t\t)\r\r # time\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFlaxTimesteps(\r block_out_channels[0] ,\t\t\t\t\t\tflip_sin_to_cos=self.flip_sin_to_cos ,\t\t\t\t\t\tfreq_shift=self.config.freq_shift )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFlaxTimestepEmbedding(_UpperCAmelCase ,\t\t\t\t\t\tdtype=self.dtype )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.only_cross_attention\r if isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t(only_cross_attention,) * len(self.down_block_types )\r\r if isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t(num_attention_heads,) * len(self.down_block_types )\r\r # down\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tblock_out_channels[0]\r for i, down_block_type in enumerate(self.down_block_types ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\toutput_channel\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tblock_out_channels[i]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ti == len(_UpperCAmelCase ) - 1\r\r if down_block_type == \"CrossAttnDownBlock2D\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFlaxCrossAttnDownBlockaD(\r in_channels=_UpperCAmelCase ,\t\t\t\t\t\tout_channels=_UpperCAmelCase ,\t\t\t\t\t\tdropout=self.dropout ,\t\t\t\t\t\tnum_layers=self.layers_per_block ,\t\t\t\t\t\tnum_attention_heads=num_attention_heads[i] ,\t\t\t\t\t\tadd_downsample=not is_final_block ,\t\t\t\t\t\tuse_linear_projection=self.use_linear_projection ,\t\t\t\t\t\tonly_cross_attention=only_cross_attention[i] ,\t\t\t\t\t\tuse_memory_efficient_attention=self.use_memory_efficient_attention ,\t\t\t\t\t\tdtype=self.dtype ,\t\t\t\t\t\t)\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFlaxDownBlockaD(\r in_channels=_UpperCAmelCase ,\t\t\t\t\t\tout_channels=_UpperCAmelCase ,\t\t\t\t\t\tdropout=self.dropout ,\t\t\t\t\t\tnum_layers=self.layers_per_block ,\t\t\t\t\t\tadd_downsample=not is_final_block ,\t\t\t\t\t\tdtype=self.dtype ,\t\t\t\t\t\t)\r\r down_blocks.append(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdown_blocks\r\r # mid\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFlaxUNetMidBlockaDCrossAttn(\r in_channels=block_out_channels[-1] ,\t\t\t\t\t\tdropout=self.dropout ,\t\t\t\t\t\tnum_attention_heads=num_attention_heads[-1] ,\t\t\t\t\t\tuse_linear_projection=self.use_linear_projection ,\t\t\t\t\t\tuse_memory_efficient_attention=self.use_memory_efficient_attention ,\t\t\t\t\t\tdtype=self.dtype ,\t\t\t\t\t\t)\r\r # up\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlist(reversed(_UpperCAmelCase ) )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlist(reversed(_UpperCAmelCase ) )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlist(reversed(_UpperCAmelCase ) )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\treversed_block_out_channels[0]\r for i, up_block_type in enumerate(self.up_block_types ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\toutput_channel\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\treversed_block_out_channels[i]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\treversed_block_out_channels[min(i + 1 ,\t\t\t\t\t\tlen(_UpperCAmelCase ) - 1 )]\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ti == len(_UpperCAmelCase ) - 1\r\r if up_block_type == \"CrossAttnUpBlock2D\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFlaxCrossAttnUpBlockaD(\r in_channels=_UpperCAmelCase ,\t\t\t\t\t\tout_channels=_UpperCAmelCase ,\t\t\t\t\t\tprev_output_channel=_UpperCAmelCase ,\t\t\t\t\t\tnum_layers=self.layers_per_block + 1 ,\t\t\t\t\t\tnum_attention_heads=reversed_num_attention_heads[i] ,\t\t\t\t\t\tadd_upsample=not is_final_block ,\t\t\t\t\t\tdropout=self.dropout ,\t\t\t\t\t\tuse_linear_projection=self.use_linear_projection ,\t\t\t\t\t\tonly_cross_attention=only_cross_attention[i] ,\t\t\t\t\t\tuse_memory_efficient_attention=self.use_memory_efficient_attention ,\t\t\t\t\t\tdtype=self.dtype ,\t\t\t\t\t\t)\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFlaxUpBlockaD(\r in_channels=_UpperCAmelCase ,\t\t\t\t\t\tout_channels=_UpperCAmelCase ,\t\t\t\t\t\tprev_output_channel=_UpperCAmelCase ,\t\t\t\t\t\tnum_layers=self.layers_per_block + 1 ,\t\t\t\t\t\tadd_upsample=not is_final_block ,\t\t\t\t\t\tdropout=self.dropout ,\t\t\t\t\t\tdtype=self.dtype ,\t\t\t\t\t\t)\r\r up_blocks.append(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\toutput_channel\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tup_blocks\r\r # out\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnn.GroupNorm(num_groups=32 ,\t\t\t\t\t\tepsilon=1E-5 )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnn.Conv(\r self.out_channels ,\t\t\t\t\t\tkernel_size=(3, 3) ,\t\t\t\t\t\tstrides=(1, 1) ,\t\t\t\t\t\tpadding=((1, 1), (1, 1)) ,\t\t\t\t\t\tdtype=self.dtype ,\t\t\t\t\t\t)\r\r\r\r def __call__( self\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict=None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple=None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tbool = True ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tbool = False ,\t\t\t\t\t\t):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if not isinstance(_UpperCAmelCase ,\t\t\t\t\t\tjnp.ndarray ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tjnp.array([timesteps] ,\t\t\t\t\t\tdtype=jnp.intaa )\r elif isinstance(_UpperCAmelCase ,\t\t\t\t\t\tjnp.ndarray ) and len(timesteps.shape ) == 0:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttimesteps.astype(dtype=jnp.floataa )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tjnp.expand_dims(_UpperCAmelCase ,\t\t\t\t\t\t0 )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.time_proj(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.time_embedding(_UpperCAmelCase )\r\r # 2. pre-process\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tjnp.transpose(_UpperCAmelCase ,\t\t\t\t\t\t(0, 2, 3, 1) )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.conv_in(_UpperCAmelCase )\r\r # 3. down\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t(sample,)\r for down_block in self.down_blocks:\r if isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ):\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdown_block(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\tdeterministic=not train )\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdown_block(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\tdeterministic=not train )\r down_block_res_samples += res_samples\r\r if down_block_additional_residuals is not None:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t()\r\r for down_block_res_sample, down_block_additional_residual in zip(\r _UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ):\r down_block_res_sample += down_block_additional_residual\r new_down_block_res_samples += (down_block_res_sample,)\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnew_down_block_res_samples\r\r # 4. mid\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.mid_block(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\tdeterministic=not train )\r\r if mid_block_additional_residual is not None:\r sample += mid_block_additional_residual\r\r # 5. up\r for up_block in self.up_blocks:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdown_block_res_samples[-(self.layers_per_block + 1) :]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdown_block_res_samples[: -(self.layers_per_block + 1)]\r if isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tup_block(\r _UpperCAmelCase ,\t\t\t\t\t\ttemb=_UpperCAmelCase ,\t\t\t\t\t\tencoder_hidden_states=_UpperCAmelCase ,\t\t\t\t\t\tres_hidden_states_tuple=_UpperCAmelCase ,\t\t\t\t\t\tdeterministic=not train ,\t\t\t\t\t\t)\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tup_block(_UpperCAmelCase ,\t\t\t\t\t\ttemb=_UpperCAmelCase ,\t\t\t\t\t\tres_hidden_states_tuple=_UpperCAmelCase ,\t\t\t\t\t\tdeterministic=not train )\r\r # 6. post-process\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.conv_norm_out(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnn.silu(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.conv_out(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tjnp.transpose(_UpperCAmelCase ,\t\t\t\t\t\t(0, 3, 1, 2) )\r\r if not return_dict:\r return (sample,)\r\r return FlaxUNetaDConditionOutput(sample=_UpperCAmelCase )\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rfrom math import factorial\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint = 20\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,\r # 2, 3,...\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tn // 2\r\r return int(factorial(SCREAMING_SNAKE_CASE__\t\t\t\t) / (factorial(SCREAMING_SNAKE_CASE__\t\t\t\t) * factorial(n - k\t\t\t\t))\t\t\t\t)\r\r\rif __name__ == \"__main__\":\r import sys\r\r if len(sys.argv) == 1:\r print(solution(2_0))\r else:\r try:\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tint(sys.argv[1])\r print(solution(n))\r except ValueError:\r print('Invalid entry - please enter a number.')\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152378,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport warnings\r\rfrom ...utils import is_sklearn_available, requires_backends\r\r\rif is_sklearn_available():\r from scipy.stats import pearsonr, spearmanr\r from sklearn.metrics import fa_score, matthews_corrcoef\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t(\r 'This metric will be removed from the library soon, metrics should be handled with the 🤗 Evaluate '\r 'library. You can have a look at this example script for pointers: '\r 'https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py'\r)\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tTuple\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tDict\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r warnings.warn(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r requires_backends(SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\"sklearn\"\"\"\t\t\t\t)\r return (preds == labels).mean()\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tUnion[str, Any]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r warnings.warn(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r requires_backends(SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\"sklearn\"\"\"\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tsimple_accuracy(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tfa_score(y_true=SCREAMING_SNAKE_CASE__\t\t\t\t, y_pred=SCREAMING_SNAKE_CASE__\t\t\t\t)\r return {\r \"acc\": acc,\r \"f1\": fa,\r \"acc_and_f1\": (acc + fa) / 2,\r }\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tTuple\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r warnings.warn(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r requires_backends(SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\"sklearn\"\"\"\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tpearsonr(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)[0]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tspearmanr(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)[0]\r return {\r \"pearson\": pearson_corr,\r \"spearmanr\": spearman_corr,\r \"corr\": (pearson_corr + spearman_corr) / 2,\r }\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[Any]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tUnion[str, Any]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tUnion[str, Any]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r warnings.warn(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r requires_backends(SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\"sklearn\"\"\"\t\t\t\t)\r assert len(SCREAMING_SNAKE_CASE__\t\t\t\t) == len(SCREAMING_SNAKE_CASE__\t\t\t\t), F'''Predictions and labels have mismatched lengths {len(SCREAMING_SNAKE_CASE__\t\t\t\t)} and {len(SCREAMING_SNAKE_CASE__\t\t\t\t)}'''\r if task_name == \"cola\":\r return {\"mcc\": matthews_corrcoef(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)}\r elif task_name == \"sst-2\":\r return {\"acc\": simple_accuracy(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)}\r elif task_name == \"mrpc\":\r return acc_and_fa(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r elif task_name == \"sts-b\":\r return pearson_and_spearman(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r elif task_name == \"qqp\":\r return acc_and_fa(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r elif task_name == \"mnli\":\r return {\"mnli/acc\": simple_accuracy(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)}\r elif task_name == \"mnli-mm\":\r return {\"mnli-mm/acc\": simple_accuracy(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)}\r elif task_name == \"qnli\":\r return {\"acc\": simple_accuracy(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)}\r elif task_name == \"rte\":\r return {\"acc\": simple_accuracy(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)}\r elif task_name == \"wnli\":\r return {\"acc\": simple_accuracy(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)}\r elif task_name == \"hans\":\r return {\"acc\": simple_accuracy(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)}\r else:\r raise KeyError(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r\r\r\r\r\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tDict\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[Any]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r warnings.warn(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r requires_backends(SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\"sklearn\"\"\"\t\t\t\t)\r if len(SCREAMING_SNAKE_CASE__\t\t\t\t) != len(SCREAMING_SNAKE_CASE__\t\t\t\t):\r raise ValueError(F'''Predictions and labels have mismatched lengths {len(SCREAMING_SNAKE_CASE__\t\t\t\t)} and {len(SCREAMING_SNAKE_CASE__\t\t\t\t)}'''\t\t\t\t)\r if task_name == \"xnli\":\r return {\"acc\": simple_accuracy(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)}\r else:\r raise KeyError(SCREAMING_SNAKE_CASE__\t\t\t\t)\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport json\rimport os\rimport unittest\r\rfrom transformers import MgpstrTokenizer\rfrom transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES\rfrom transformers.testing_utils import require_tokenizers\r\rfrom ...test_tokenization_common import TokenizerTesterMixin\r\r@require_tokenizers\rclass lowerCAmelCase_ ( lowerCamelCase_\t, unittest.TestCase ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r lowerCAmelCase_ : int = MgpstrTokenizer\r lowerCAmelCase_ : List[str] = False\r lowerCAmelCase_ : Optional[int] = {}\r lowerCAmelCase_ : Any = False\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r super().setUp()\r\r # fmt: off\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[\"\"\"[GO]\"\"\", \"\"\"[s]\"\"\", \"\"\"0\"\"\", \"\"\"1\"\"\", \"\"\"2\"\"\", \"\"\"3\"\"\", \"\"\"4\"\"\", \"\"\"5\"\"\", \"\"\"6\"\"\", \"\"\"7\"\"\", \"\"\"8\"\"\", \"\"\"9\"\"\", \"\"\"a\"\"\", \"\"\"b\"\"\", \"\"\"c\"\"\", \"\"\"d\"\"\", \"\"\"e\"\"\", \"\"\"f\"\"\", \"\"\"g\"\"\", \"\"\"h\"\"\", \"\"\"i\"\"\", \"\"\"j\"\"\", \"\"\"k\"\"\", \"\"\"l\"\"\", \"\"\"m\"\"\", \"\"\"n\"\"\", \"\"\"o\"\"\", \"\"\"p\"\"\", \"\"\"q\"\"\", \"\"\"r\"\"\", \"\"\"s\"\"\", \"\"\"t\"\"\", \"\"\"u\"\"\", \"\"\"v\"\"\", \"\"\"w\"\"\", \"\"\"x\"\"\", \"\"\"y\"\"\", \"\"\"z\"\"\"]\r # fmt: on\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdict(zip(_UpperCAmelCase ,\t\t\t\t\t\trange(len(_UpperCAmelCase ) ) ) )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tos.path.join(self.tmpdirname ,\t\t\t\t\t\tVOCAB_FILES_NAMES[\"\"\"vocab_file\"\"\"] )\r with open(self.vocab_file ,\t\t\t\t\t\t\"\"\"w\"\"\" ,\t\t\t\t\t\tencoding=\"\"\"utf-8\"\"\" ) as fp:\r fp.write(json.dumps(_UpperCAmelCase ) + \"\"\"\\n\"\"\" )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return MgpstrTokenizer.from_pretrained(self.tmpdirname ,\t\t\t\t\t\t**_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"tester\"\"\"\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"tester\"\"\"\r return input_text, output_text\r\r\r @unittest.skip(\"\"\"MGP-STR always lower cases letters.\"\"\" )\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r pass\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tAny ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_tokenizers(do_lower_case=_UpperCAmelCase )\r for tokenizer in tokenizers:\r with self.subTest(f'''{tokenizer.__class__.__name__}''' ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"[SPECIAL_TOKEN]\"\"\"\r\r tokenizer.add_special_tokens({\"\"\"cls_token\"\"\": special_token} )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenizer.encode([special_token] ,\t\t\t\t\t\tadd_special_tokens=_UpperCAmelCase )\r self.assertEqual(len(_UpperCAmelCase ) ,\t\t\t\t\t\t1 )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenizer.decode(_UpperCAmelCase ,\t\t\t\t\t\tskip_special_tokens=_UpperCAmelCase )\r self.assertTrue(special_token not in decoded )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_tokenizers()\r for tokenizer in tokenizers:\r with self.subTest(f'''{tokenizer.__class__.__name__}''' ):\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_input_output_texts(_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenizer.tokenize(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenizer.convert_tokens_to_ids(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenizer.encode(_UpperCAmelCase ,\t\t\t\t\t\tadd_special_tokens=_UpperCAmelCase )\r self.assertListEqual(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenizer.convert_ids_to_tokens(_UpperCAmelCase )\r self.assertNotEqual(len(_UpperCAmelCase ) ,\t\t\t\t\t\t0 )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenizer.decode(_UpperCAmelCase )\r self.assertIsInstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r\r self.assertEqual(text_a.replace(\"\"\" \"\"\" ,\t\t\t\t\t\t\"\"\"\"\"\" ) ,\t\t\t\t\t\t_UpperCAmelCase )\r\r\r @unittest.skip(\"\"\"MGP-STR tokenizer only handles one sequence.\"\"\" )\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tAny ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r pass\r\r\r\r @unittest.skip(\"\"\"inputs cannot be pretokenized in MgpstrTokenizer\"\"\" )\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r pass\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152379,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport random\r\rimport torch\rfrom huggingface_hub import HfApi\r\rfrom diffusers import UNetaDModel\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tHfApi()\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t{}\r# fmt: off\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\ttorch.tensor([\r -0.7515, -1.6883, 0.2420, 0.0300, 0.6347, 1.3433, -1.1743, -3.7467,\r 1.2342, -2.2485, 0.4636, 0.8076, -0.7991, 0.3969, 0.8498, 0.9189,\r -1.8887, -3.3522, 0.7639, 0.2040, 0.6271, -2.7148, -1.6316, 3.0839,\r 0.3186, 0.2721, -0.9759, -1.2461, 2.6257, 1.3557\r])\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\ttorch.tensor([\r -2.3639, -2.5344, 0.0054, -0.6674, 1.5990, 1.0158, 0.3124, -2.1436,\r 1.8795, -2.5429, -0.1566, -0.3973, 1.2490, 2.6447, 1.2283, -0.5208,\r -2.8154, -3.5119, 2.3838, 1.2033, 1.7201, -2.1256, -1.4576, 2.7948,\r 2.4204, -0.9752, -1.2546, 0.8027, 3.2758, 3.1365\r])\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\ttorch.tensor([\r -0.6531, -0.6891, -0.3172, -0.5375, -0.9140, -0.5367, -0.1175, -0.7869,\r -0.3808, -0.4513, -0.2098, -0.0083, 0.3183, 0.5140, 0.2247, -0.1304,\r -0.1302, -0.2802, -0.2084, -0.2025, -0.4967, -0.4873, -0.0861, 0.6925,\r 0.0250, 0.1290, -0.1543, 0.6316, 1.0460, 1.4943\r])\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\ttorch.tensor([\r 0.0911, 0.1107, 0.0182, 0.0435, -0.0805, -0.0608, 0.0381, 0.2172,\r -0.0280, 0.1327, -0.0299, -0.0255, -0.0050, -0.1170, -0.1046, 0.0309,\r 0.1367, 0.1728, -0.0533, -0.0748, -0.0534, 0.1624, 0.0384, -0.1805,\r -0.0707, 0.0642, 0.0220, -0.0134, -0.1333, -0.1505\r])\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\ttorch.tensor([\r 0.1321, 0.1337, 0.0440, 0.0622, -0.0591, -0.0370, 0.0503, 0.2133,\r -0.0177, 0.1415, -0.0116, -0.0112, 0.0044, -0.0980, -0.0789, 0.0395,\r 0.1502, 0.1785, -0.0488, -0.0514, -0.0404, 0.1539, 0.0454, -0.1559,\r -0.0665, 0.0659, 0.0383, -0.0005, -0.1266, -0.1386\r])\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\ttorch.tensor([\r 0.1154, 0.1218, 0.0307, 0.0526, -0.0711, -0.0541, 0.0366, 0.2078,\r -0.0267, 0.1317, -0.0226, -0.0193, -0.0014, -0.1055, -0.0902, 0.0330,\r 0.1391, 0.1709, -0.0562, -0.0693, -0.0560, 0.1482, 0.0381, -0.1683,\r -0.0681, 0.0661, 0.0331, -0.0046, -0.1268, -0.1431\r])\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\ttorch.tensor([\r 0.1192, 0.1240, 0.0414, 0.0606, -0.0557, -0.0412, 0.0430, 0.2042,\r -0.0200, 0.1385, -0.0115, -0.0132, 0.0017, -0.0965, -0.0802, 0.0398,\r 0.1433, 0.1747, -0.0458, -0.0533, -0.0407, 0.1545, 0.0419, -0.1574,\r -0.0645, 0.0626, 0.0341, -0.0010, -0.1199, -0.1390\r])\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\ttorch.tensor([\r 0.1075, 0.1074, 0.0205, 0.0431, -0.0774, -0.0607, 0.0298, 0.2042,\r -0.0320, 0.1267, -0.0281, -0.0250, -0.0064, -0.1091, -0.0946, 0.0290,\r 0.1328, 0.1650, -0.0580, -0.0738, -0.0586, 0.1440, 0.0337, -0.1746,\r -0.0712, 0.0605, 0.0250, -0.0099, -0.1316, -0.1473\r])\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\ttorch.tensor([\r -1.4572, -2.0481, -0.0414, -0.6005, 1.4136, 0.5848, 0.4028, -2.7330,\r 1.2212, -2.1228, 0.2155, 0.4039, 0.7662, 2.0535, 0.7477, -0.3243,\r -2.1758, -2.7648, 1.6947, 0.7026, 1.2338, -1.6078, -0.8682, 2.2810,\r 1.8574, -0.5718, -0.5586, -0.0186, 2.3415, 2.1251])\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\ttorch.tensor([\r -1.3690, -1.9720, -0.4090, -0.6966, 1.4660, 0.9938, -0.1385, -2.7324,\r 0.7736, -1.8917, 0.2923, 0.4293, 0.1693, 1.4112, 1.1887, -0.3181,\r -2.2160, -2.6381, 1.3170, 0.8163, 0.9240, -1.6544, -0.6099, 2.5259,\r 1.6430, -0.9090, -0.9392, -0.0126, 2.4268, 2.3266\r])\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\ttorch.tensor([\r -1.3525, -1.9628, -0.3956, -0.6860, 1.4664, 1.0014, -0.1259, -2.7212,\r 0.7772, -1.8811, 0.2996, 0.4388, 0.1704, 1.4029, 1.1701, -0.3027,\r -2.2053, -2.6287, 1.3350, 0.8131, 0.9274, -1.6292, -0.6098, 2.5131,\r 1.6505, -0.8958, -0.9298, -0.0151, 2.4257, 2.3355\r])\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\ttorch.tensor([\r -2.0585, -2.7897, -0.2850, -0.8940, 1.9052, 0.5702, 0.6345, -3.8959,\r 1.5932, -3.2319, 0.1974, 0.0287, 1.7566, 2.6543, 0.8387, -0.5351,\r -3.2736, -4.3375, 2.9029, 1.6390, 1.4640, -2.1701, -1.9013, 2.9341,\r 3.4981, -0.6255, -1.1644, -0.1591, 3.7097, 3.2066\r])\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\ttorch.tensor([\r -2.3139, -2.5594, -0.0197, -0.6785, 1.7001, 1.1606, 0.3075, -2.1740,\r 1.8071, -2.5630, -0.0926, -0.3811, 1.2116, 2.6246, 1.2731, -0.5398,\r -2.8153, -3.6140, 2.3893, 1.3262, 1.6258, -2.1856, -1.3267, 2.8395,\r 2.3779, -1.0623, -1.2468, 0.8959, 3.3367, 3.2243\r])\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\ttorch.tensor([\r -2.0628, -2.7667, -0.2089, -0.8263, 2.0539, 0.5992, 0.6495, -3.8336,\r 1.6025, -3.2817, 0.1721, -0.0633, 1.7516, 2.7039, 0.8100, -0.5908,\r -3.2113, -4.4343, 2.9257, 1.3632, 1.5562, -2.1489, -1.9894, 3.0560,\r 3.3396, -0.7328, -1.0417, 0.0383, 3.7093, 3.2343\r])\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\ttorch.tensor([\r -1.4574, -2.0569, -0.0473, -0.6117, 1.4018, 0.5769, 0.4129, -2.7344,\r 1.2241, -2.1397, 0.2000, 0.3937, 0.7616, 2.0453, 0.7324, -0.3391,\r -2.1746, -2.7744, 1.6963, 0.6921, 1.2187, -1.6172, -0.8877, 2.2439,\r 1.8471, -0.5839, -0.5605, -0.0464, 2.3250, 2.1219\r])\r# fmt: on\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tapi.list_models(filter='diffusers')\rfor mod in models:\r if \"google\" in mod.author or mod.modelId == \"CompVis/ldm-celebahq-256\":\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\t'/home/patrick/google_checkpoints/' + mod.modelId.split('/')[-1]\r\r print(f\"Started running {mod.modelId}!!!\")\r\r if mod.modelId.startswith('CompVis'):\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tUNetaDModel.from_pretrained(local_checkpoint, subfolder='unet')\r else:\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tUNetaDModel.from_pretrained(local_checkpoint)\r\r torch.manual_seed(0)\r random.seed(0)\r\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\ttorch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\ttorch.tensor([1_0] * noise.shape[0])\r with torch.no_grad():\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tmodel(noise, time_step).sample\r\r assert torch.allclose(\r logits[0, 0, 0, :3_0], results['_'.join('_'.join(mod.modelId.split('/')).split('-'))], atol=1E-3\r )\r print(f\"{mod.modelId} has passed successfully!!!\")\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rfrom abc import ABC, abstractmethod\rfrom typing import List, Optional\r\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def __init__( self\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r self.test()\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r while not completed:\r if counter == 1:\r self.reset()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.advance()\r if not self.does_advance(_UpperCAmelCase ):\r raise Exception(\r \"\"\"Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true.\"\"\" )\r\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.update(_UpperCAmelCase )\r counter += 1\r\r if counter > 1_00_00:\r raise Exception(\"\"\"update() does not fulfill the constraint.\"\"\" )\r\r if self.remaining() != 0:\r raise Exception(\"\"\"Custom Constraint is not defined correctly.\"\"\" )\r\r\r @abstractmethod\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r raise NotImplementedError(\r f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )\r\r\r @abstractmethod\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r raise NotImplementedError(\r f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )\r\r\r @abstractmethod\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tAny ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r raise NotImplementedError(\r f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )\r\r\r @abstractmethod\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r raise NotImplementedError(\r f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )\r\r\r @abstractmethod\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r raise NotImplementedError(\r f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )\r\r\r\r @abstractmethod\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any]=False ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r raise NotImplementedError(\r f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )\r\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def __init__( self\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r super(_UpperCAmelCase ,\t\t\t\t\t\tself ).__init__()\r\r if not isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ) or len(_UpperCAmelCase ) == 0:\r raise ValueError(f'''`token_ids` has to be a non-empty list, but is {token_ids}.''' )\r if any((not isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ) or token_id < 0) for token_id in token_ids ):\r raise ValueError(f'''Each list in `token_ids` has to be a list of positive integers, but is {token_ids}.''' )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttoken_ids\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlen(self.token_ids )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t-1 # the index of the currently fulfilled step\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if self.completed:\r return None\r return self.token_ids[self.fulfilled_idx + 1]\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if not isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ):\r raise ValueError(f'''`token_id` has to be an `int`, but is {token_id} of type {type(_UpperCAmelCase )}''' )\r\r if self.completed:\r return False\r\r return token_id == self.token_ids[self.fulfilled_idx + 1]\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if not isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ):\r raise ValueError(f'''`token_id` has to be an `int`, but is {token_id} of type {type(_UpperCAmelCase )}''' )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r\r if self.does_advance(_UpperCAmelCase ):\r self.fulfilled_idx += 1\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTrue\r if self.fulfilled_idx == (self.seqlen - 1):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTrue\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tcompleted\r else:\r # failed to make progress.\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTrue\r self.reset()\r return stepped, completed, reset\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tTuple ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return self.seqlen - (self.fulfilled_idx + 1)\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tAny ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int]=False ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tPhrasalConstraint(self.token_ids )\r\r if stateful:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.seqlen\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.fulfilled_idx\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.completed\r\r return new_constraint\r\rclass lowerCAmelCase_ :\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def __init__( self\t\t\t:\t\tAny ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[List[int]] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str]=True ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmax([len(_UpperCAmelCase ) for one in nested_token_ids] )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{}\r for token_ids in nested_token_ids:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\troot\r for tidx, token_id in enumerate(_UpperCAmelCase ):\r if token_id not in level:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{}\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlevel[token_id]\r\r if no_subsets and self.has_subsets(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ):\r raise ValueError(\r \"\"\"Each list in `nested_token_ids` can't be a complete subset of another list, but is\"\"\"\r f''' {nested_token_ids}.''' )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\troot\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.trie\r\r for current_token in current_seq:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tstart[current_token]\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlist(start.keys() )\r\r return next_tokens\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.next_tokens(_UpperCAmelCase )\r\r return len(_UpperCAmelCase ) == 0\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlist(root.values() )\r if len(_UpperCAmelCase ) == 0:\r return 1\r else:\r return sum([self.count_leaves(_UpperCAmelCase ) for nn in next_nodes] )\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.count_leaves(_UpperCAmelCase )\r return len(_UpperCAmelCase ) != leaf_count\r\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def __init__( self\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[List[int]] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r super(_UpperCAmelCase ,\t\t\t\t\t\tself ).__init__()\r\r if not isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ) or len(_UpperCAmelCase ) == 0:\r raise ValueError(f'''`nested_token_ids` has to be a non-empty list, but is {nested_token_ids}.''' )\r if any(not isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ) for token_ids in nested_token_ids ):\r raise ValueError(f'''`nested_token_ids` has to be a list of lists, but is {nested_token_ids}.''' )\r if any(\r any((not isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ) or token_id < 0) for token_id in token_ids )\r for token_ids in nested_token_ids ):\r raise ValueError(\r f'''Each list in `nested_token_ids` has to be a list of positive integers, but is {nested_token_ids}.''' )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tDisjunctiveTrie(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnested_token_ids\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.trie.max_height\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tAny ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.trie.next_tokens(self.current_seq )\r\r if len(_UpperCAmelCase ) == 0:\r return None\r else:\r return token_list\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if not isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ):\r raise ValueError(f'''`token_id` is supposed to be type `int`, but is {token_id} of type {type(_UpperCAmelCase )}''' )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.trie.next_tokens(self.current_seq )\r\r return token_id in next_tokens\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if not isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ):\r raise ValueError(f'''`token_id` is supposed to be type `int`, but is {token_id} of type {type(_UpperCAmelCase )}''' )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r\r if self.does_advance(_UpperCAmelCase ):\r self.current_seq.append(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTrue\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTrue\r self.reset()\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.trie.reached_leaf(self.current_seq )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tcompleted\r\r return stepped, completed, reset\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[]\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if self.completed:\r # since this can be completed without reaching max height\r return 0\r else:\r return self.seqlen - len(self.current_seq )\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tTuple ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict=False ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tDisjunctiveConstraint(self.token_ids )\r\r if stateful:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.seqlen\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.current_seq\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.completed\r\r return new_constraint\r\rclass lowerCAmelCase_ :\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def __init__( self\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Constraint] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tconstraints\r\r # max # of steps required to fulfill a given constraint\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmax([c.seqlen for c in constraints] )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlen(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r\r self.init_state()\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tAny ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[constraint.copy(stateful=_UpperCAmelCase ) for constraint in self.constraints]\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0\r if self.inprogress_constraint:\r # extra points for having a constraint mid-fulfilled\r add += self.max_seqlen - self.inprogress_constraint.remaining()\r\r return (len(self.complete_constraints ) * self.max_seqlen) + add\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[]\r if self.inprogress_constraint is None:\r for constraint in self.pending_constraints: # \"pending\" == \"unfulfilled yet\"\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tconstraint.advance()\r if isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ):\r token_list.append(_UpperCAmelCase )\r elif isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ):\r token_list.extend(_UpperCAmelCase )\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.inprogress_constraint.advance()\r if isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ):\r token_list.append(_UpperCAmelCase )\r elif isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ):\r token_list.extend(_UpperCAmelCase )\r\r if len(_UpperCAmelCase ) == 0:\r return None\r else:\r return token_list\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[List[int]] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r self.init_state()\r\r if token_ids is not None:\r for token in token_ids:\r # completes or steps **one** constraint\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.add(_UpperCAmelCase )\r\r # the entire list of constraints are fulfilled\r if self.completed:\r break\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tAny ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if not isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ):\r raise ValueError(f'''`token_id` should be an `int`, but is `{token_id}`.''' )\r\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse, False\r\r if self.completed:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTrue\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r return complete, stepped\r\r if self.inprogress_constraint is not None:\r # In the middle of fulfilling a constraint. If the `token_id` *does* makes an incremental progress to current\r # job, simply update the state\r\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.inprogress_constraint.update(_UpperCAmelCase )\r if reset:\r # 1. If the next token breaks the progress, then we must restart.\r # e.g. constraint = \"I love pies\" and sequence so far is \"I love\" but `token_id` == \"books\".\r\r # But that doesn't mean we self.init_state(), since we only reset the state for this particular\r # constraint, not the full list of constraints.\r\r self.pending_constraints.append(self.inprogress_constraint.copy(stateful=_UpperCAmelCase ) )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r\r if complete:\r # 2. If the next token completes the constraint, move it to completed list, set\r # inprogress to None. If there are no pending constraints either, then this full list of constraints\r # is complete.\r\r self.complete_constraints.append(self.inprogress_constraint )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r\r if len(self.pending_constraints ) == 0:\r # we're done!\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTrue\r\r else:\r # Not in the middle of fulfilling a constraint. So does this `token_id` helps us step towards any of our list\r # of constraints?\r\r for cidx, pending_constraint in enumerate(self.pending_constraints ):\r if pending_constraint.does_advance(_UpperCAmelCase ):\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tpending_constraint.update(_UpperCAmelCase )\r\r if not stepped:\r raise Exception(\r \"\"\"`constraint.update(token_id)` is not yielding incremental progress, \"\"\"\r \"\"\"even though `constraint.does_advance(token_id)` is true.\"\"\" )\r\r if complete:\r self.complete_constraints.append(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r\r if not complete and stepped:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tpending_constraint\r\r if complete or stepped:\r # If we made any progress at all, then it's at least not a \"pending constraint\".\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t(\r self.pending_constraints[:cidx] + self.pending_constraints[cidx + 1 :]\r )\r\r if len(self.pending_constraints ) == 0 and self.inprogress_constraint is None:\r # If there's no longer any pending after this and no inprogress either, then we must be\r # complete.\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTrue\r\r break # prevent accidentally stepping through multiple constraints with just one token.\r\r return complete, stepped\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any]=True ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tConstraintListState(self.constraints ) # we actually never though self.constraints objects\r # throughout this process. So it's at initialization state.\r\r if stateful:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[\r constraint.copy(stateful=_UpperCAmelCase ) for constraint in self.complete_constraints\r ]\r if self.inprogress_constraint is not None:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.inprogress_constraint.copy(stateful=_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[constraint.copy() for constraint in self.pending_constraints]\r\r return new_state\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152380,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport os\rimport unicodedata\rfrom shutil import copyfile\rfrom typing import Any, Dict, List, Optional, Tuple\r\rimport sentencepiece as spm\r\rfrom ...tokenization_utils import AddedToken, PreTrainedTokenizer\rfrom ...utils import SPIECE_UNDERLINE, logging\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tlogging.get_logger(__name__)\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t{'vocab_file': 'spiece.model'}\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t{\r 'vocab_file': {\r 'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model',\r }\r}\r\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def __init__( self\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny=False ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint=True ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=False ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict=\"~~\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint=\"~~\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict=\"\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple=\"\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any]=\"\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint=\"\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=\"\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str]=[\"\", \"\"] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Dict[str, Any]] = None ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tAddedToken(_UpperCAmelCase ,\t\t\t\t\t\tlstrip=_UpperCAmelCase ,\t\t\t\t\t\trstrip=_UpperCAmelCase ) if isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ) else mask_token\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{} if sp_model_kwargs is None else sp_model_kwargs\r\r super().__init__(\r do_lower_case=_UpperCAmelCase ,\t\t\t\t\t\tremove_space=_UpperCAmelCase ,\t\t\t\t\t\tkeep_accents=_UpperCAmelCase ,\t\t\t\t\t\tbos_token=_UpperCAmelCase ,\t\t\t\t\t\teos_token=_UpperCAmelCase ,\t\t\t\t\t\tunk_token=_UpperCAmelCase ,\t\t\t\t\t\tsep_token=_UpperCAmelCase ,\t\t\t\t\t\tpad_token=_UpperCAmelCase ,\t\t\t\t\t\tcls_token=_UpperCAmelCase ,\t\t\t\t\t\tmask_token=_UpperCAmelCase ,\t\t\t\t\t\tadditional_special_tokens=_UpperCAmelCase ,\t\t\t\t\t\tsp_model_kwargs=self.sp_model_kwargs ,\t\t\t\t\t\t**_UpperCAmelCase ,\t\t\t\t\t\t)\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t3\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdo_lower_case\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tremove_space\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tkeep_accents\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvocab_file\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tspm.SentencePieceProcessor(**self.sp_model_kwargs )\r self.sp_model.Load(_UpperCAmelCase )\r\r try:\r import jieba\r except ModuleNotFoundError as error:\r raise error.__class__(\r \"\"\"You need to install jieba to use CpmTokenizer or CpmTokenizerFast. \"\"\"\r \"\"\"See https://pypi.org/project/jieba/ for installation.\"\"\" )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tjieba\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tstr.maketrans(\"\"\" \\n\"\"\" ,\t\t\t\t\t\t\"\"\"\\u2582\\u2583\"\"\" )\r\r\r @property\r # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return len(self.sp_model )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{self.convert_ids_to_tokens(_UpperCAmelCase ): i for i in range(self.vocab_size )}\r vocab.update(self.added_tokens_encoder )\r return vocab\r\r\r def __getstate__( self\t\t\t:\t\tDict ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.__dict__.copy()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r return state\r\r\r def __setstate__( self\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\td\r\r # for backward compatibility\r if not hasattr(self ,\t\t\t\t\t\t\"\"\"sp_model_kwargs\"\"\" ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{}\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tspm.SentencePieceProcessor(**self.sp_model_kwargs )\r self.sp_model.Load(self.vocab_file )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if self.remove_space:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\" \"\"\".join(inputs.strip().split() )\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tinputs\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\toutputs.replace(\"\"\"``\"\"\" ,\t\t\t\t\t\t\"\"\"\\\"\"\"\" ).replace(\"\"\"''\"\"\" ,\t\t\t\t\t\t\"\"\"\\\"\"\"\" )\r\r if not self.keep_accents:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tunicodedata.normalize(\"\"\"NFKD\"\"\" ,\t\t\t\t\t\t_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"\"\"\".join([c for c in outputs if not unicodedata.combining(_UpperCAmelCase )] )\r if self.do_lower_case:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\toutputs.lower()\r\r return outputs\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tTuple ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.preprocess_text(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.sp_model.encode(_UpperCAmelCase ,\t\t\t\t\t\tout_type=_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[]\r for piece in pieces:\r if len(_UpperCAmelCase ) > 1 and piece[-1] == str(\"\"\",\"\"\" ) and piece[-2].isdigit():\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.sp_model.EncodeAsPieces(piece[:-1].replace(_UpperCAmelCase ,\t\t\t\t\t\t\"\"\"\"\"\" ) )\r if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:\r if len(cur_pieces[0] ) == 1:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tcur_pieces[1:]\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tcur_pieces[0][1:]\r cur_pieces.append(piece[-1] )\r new_pieces.extend(_UpperCAmelCase )\r else:\r new_pieces.append(_UpperCAmelCase )\r\r return new_pieces\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return self.sp_model.PieceToId(_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return self.sp_model.IdToPiece(_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"\"\"\".join(_UpperCAmelCase ).replace(_UpperCAmelCase ,\t\t\t\t\t\t\"\"\" \"\"\" ).strip()\r return out_string\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[List[int]] = None ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[self.sep_token_id]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[self.cls_token_id]\r if token_ids_a is None:\r return token_ids_a + sep + cls\r return token_ids_a + sep + token_ids_a + sep + cls\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[List[int]] = None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tbool = False ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if already_has_special_tokens:\r return super().get_special_tokens_mask(\r token_ids_a=_UpperCAmelCase ,\t\t\t\t\t\ttoken_ids_a=_UpperCAmelCase ,\t\t\t\t\t\talready_has_special_tokens=_UpperCAmelCase )\r\r if token_ids_a is not None:\r return ([0] * len(_UpperCAmelCase )) + [1] + ([0] * len(_UpperCAmelCase )) + [1, 1]\r return ([0] * len(_UpperCAmelCase )) + [1, 1]\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[List[int]] = None ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[self.sep_token_id]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[2]\r\r if token_ids_a is None:\r return len(token_ids_a + sep ) * [0] + cls_segment_id\r return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[str] = None ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if not os.path.isdir(_UpperCAmelCase ):\r logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )\r return\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tos.path.join(\r _UpperCAmelCase ,\t\t\t\t\t\t(filename_prefix + \"\"\"-\"\"\" if filename_prefix else \"\"\"\"\"\") + VOCAB_FILES_NAMES[\"\"\"vocab_file\"\"\"] )\r\r if os.path.abspath(self.vocab_file ) != os.path.abspath(_UpperCAmelCase ) and os.path.isfile(self.vocab_file ):\r copyfile(self.vocab_file ,\t\t\t\t\t\t_UpperCAmelCase )\r elif not os.path.isfile(self.vocab_file ):\r with open(_UpperCAmelCase ,\t\t\t\t\t\t\"\"\"wb\"\"\" ) as fi:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.sp_model.serialized_model_proto()\r fi.write(_UpperCAmelCase )\r\r return (out_vocab_file,)\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tTuple ,\t\t\t\t\t\t*_UpperCAmelCase\t\t\t:\t\tTuple ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tsuper()._decode(*_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttext.replace(\"\"\" \"\"\" ,\t\t\t\t\t\t\"\"\"\"\"\" ).replace(\"\"\"\\u2582\"\"\" ,\t\t\t\t\t\t\"\"\" \"\"\" ).replace(\"\"\"\\u2583\"\"\" ,\t\t\t\t\t\t\"\"\"\\n\"\"\" )\r return text\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport doctest\rimport logging\rimport os\rimport unittest\rfrom pathlib import Path\rfrom typing import List, Union\r\rimport transformers\rfrom transformers.testing_utils import require_tf, require_torch, slow\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tlogging.getLogger()\r\r@unittest.skip(\"\"\"Temporarily disable the doc tests.\"\"\" )\r@require_torch\r@require_tf\r@slow\rclass lowerCAmelCase_ ( unittest.TestCase ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tPath ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, None] = None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[List[str], None] = None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, List[str], None] = None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tbool = True ,\t\t\t\t\t\t):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[file for file in os.listdir(_UpperCAmelCase ) if os.path.isfile(os.path.join(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ) )]\r\r if identifier is not None:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[file for file in files if identifier in file]\r\r if n_identifier is not None:\r if isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ):\r for n_ in n_identifier:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[file for file in files if n_ not in file]\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[file for file in files if n_identifier not in file]\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tignore_files or []\r ignore_files.append(\"\"\"__init__.py\"\"\" )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[file for file in files if file not in ignore_files]\r\r for file in files:\r # Open all files\r print(\"\"\"Testing\"\"\" ,\t\t\t\t\t\t_UpperCAmelCase )\r\r if only_modules:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tfile.split(\"\"\".\"\"\" )[0]\r try:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tgetattr(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdoctest.DocTestSuite(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tunittest.TextTestRunner().run(_UpperCAmelCase )\r self.assertIs(len(result.failures ) ,\t\t\t\t\t\t0 )\r except AttributeError:\r logger.info(f'''{module_identifier} is not a module.''' )\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdoctest.testfile(str(\"\"\"..\"\"\" / directory / file ) ,\t\t\t\t\t\toptionflags=doctest.ELLIPSIS )\r self.assertIs(result.failed ,\t\t\t\t\t\t0 )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tPath(\"\"\"src/transformers\"\"\" )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"modeling\"\"\"\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[\r \"\"\"modeling_ctrl.py\"\"\",\r \"\"\"modeling_tf_ctrl.py\"\"\",\r ]\r self.analyze_directory(_UpperCAmelCase ,\t\t\t\t\t\tidentifier=_UpperCAmelCase ,\t\t\t\t\t\tignore_files=_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tPath(\"\"\"src/transformers\"\"\" )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"tokenization\"\"\"\r self.analyze_directory(_UpperCAmelCase ,\t\t\t\t\t\tidentifier=_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tPath(\"\"\"src/transformers\"\"\" )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"configuration\"\"\"\r self.analyze_directory(_UpperCAmelCase ,\t\t\t\t\t\tidentifier=_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tPath(\"\"\"src/transformers\"\"\" )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[\"\"\"configuration\"\"\", \"\"\"modeling\"\"\", \"\"\"tokenization\"\"\"]\r self.analyze_directory(_UpperCAmelCase ,\t\t\t\t\t\tn_identifier=_UpperCAmelCase )\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tPath(\"\"\"docs/source\"\"\" )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[\"\"\"favicon.ico\"\"\"]\r self.analyze_directory(_UpperCAmelCase ,\t\t\t\t\t\tignore_files=_UpperCAmelCase ,\t\t\t\t\t\tonly_modules=_UpperCAmelCase )\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152381,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport json\rimport os\rfrom pathlib import Path\rfrom shutil import copyfile\rfrom typing import Any, Dict, List, Optional, Tuple, Union\r\rimport sentencepiece\r\rfrom ...tokenization_utils import PreTrainedTokenizer\rfrom ...utils import logging\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tlogging.get_logger(__name__)\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'▁'\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t{\r 'vocab_file': 'vocab.json',\r 'spm_file': 'sentencepiece.bpe.model',\r}\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t{\r 'vocab_file': {\r 'facebook/s2t-small-librispeech-asr': (\r 'https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json'\r ),\r },\r 'spm_file': {\r 'facebook/s2t-small-librispeech-asr': (\r 'https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model'\r )\r },\r}\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t{\r 'facebook/s2t-small-librispeech-asr': 1_0_2_4,\r}\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t['pt', 'fr', 'ru', 'nl', 'ro', 'it', 'es', 'de']\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t{'mustc': MUSTC_LANGS}\r\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r lowerCAmelCase_ : str = VOCAB_FILES_NAMES\r lowerCAmelCase_ : Dict = PRETRAINED_VOCAB_FILES_MAP\r lowerCAmelCase_ : List[str] = MAX_MODEL_INPUT_SIZES\r lowerCAmelCase_ : Any = [\"\"\"input_ids\"\"\", \"\"\"attention_mask\"\"\"]\r\r lowerCAmelCase_ : List[int] = []\r\r\r def __init__( self\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any]=\"~~\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=\"~~\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict=\"\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny=\"\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict=False ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any]=False ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny=None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict=None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Dict[str, Any]] = None ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{} if sp_model_kwargs is None else sp_model_kwargs\r\r super().__init__(\r bos_token=_UpperCAmelCase ,\t\t\t\t\t\teos_token=_UpperCAmelCase ,\t\t\t\t\t\tunk_token=_UpperCAmelCase ,\t\t\t\t\t\tpad_token=_UpperCAmelCase ,\t\t\t\t\t\tdo_upper_case=_UpperCAmelCase ,\t\t\t\t\t\tdo_lower_case=_UpperCAmelCase ,\t\t\t\t\t\ttgt_lang=_UpperCAmelCase ,\t\t\t\t\t\tlang_codes=_UpperCAmelCase ,\t\t\t\t\t\tsp_model_kwargs=self.sp_model_kwargs ,\t\t\t\t\t\t**_UpperCAmelCase ,\t\t\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdo_upper_case\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdo_lower_case\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tload_json(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{v: k for k, v in self.encoder.items()}\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tspm_file\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tload_spm(_UpperCAmelCase ,\t\t\t\t\t\tself.sp_model_kwargs )\r\r if lang_codes is not None:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlang_codes\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tLANGUAGES[lang_codes]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[f'''''' for lang in self.langs]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{lang: self.sp_model.PieceToId(f'''''' ) for lang in self.langs}\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.lang_tokens\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttgt_lang if tgt_lang is not None else self.langs[0]\r\r self.set_tgt_lang_special_tokens(self._tgt_lang )\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{}\r\r\r @property\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return len(self.encoder )\r\r\r @property\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return self._tgt_lang\r\r\r @tgt_lang.setter\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnew_tgt_lang\r self.set_tgt_lang_special_tokens(_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.lang_code_to_id[tgt_lang]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[lang_code_id]\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return self.sp_model.encode(_UpperCAmelCase ,\t\t\t\t\t\tout_type=_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return self.encoder.get(_UpperCAmelCase ,\t\t\t\t\t\tself.encoder[self.unk_token] )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tAny ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return self.decoder.get(_UpperCAmelCase ,\t\t\t\t\t\tself.unk_token )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"\"\"\"\r for token in tokens:\r # make sure that special tokens are not decoded using sentencepiece model\r if token in self.all_special_tokens:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.sp_model.decode(_UpperCAmelCase )\r out_string += (decoded.upper() if self.do_upper_case else decoded) + token + \" \"\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[]\r else:\r current_sub_tokens.append(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.sp_model.decode(_UpperCAmelCase )\r out_string += decoded.upper() if self.do_upper_case else decoded\r return out_string.strip()\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any]=None ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if token_ids_a is None:\r return self.prefix_tokens + token_ids_a + [self.eos_token_id]\r # We don't expect to process pairs, but leave the pair logic for API consistency\r return self.prefix_tokens + token_ids_a + token_ids_a + [self.eos_token_id]\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[List[int]] = None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tbool = False ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if already_has_special_tokens:\r return super().get_special_tokens_mask(\r token_ids_a=_UpperCAmelCase ,\t\t\t\t\t\ttoken_ids_a=_UpperCAmelCase ,\t\t\t\t\t\talready_has_special_tokens=_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[1] * len(self.prefix_tokens )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[1]\r if token_ids_a is None:\r return prefix_ones + ([0] * len(_UpperCAmelCase )) + suffix_ones\r return prefix_ones + ([0] * len(_UpperCAmelCase )) + ([0] * len(_UpperCAmelCase )) + suffix_ones\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.encoder.copy()\r vocab.update(self.added_tokens_encoder )\r return vocab\r\r\r def __getstate__( self\t\t\t:\t\tOptional[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.__dict__.copy()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r return state\r\r\r def __setstate__( self\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\td\r\r # for backward compatibility\r if not hasattr(self ,\t\t\t\t\t\t\"\"\"sp_model_kwargs\"\"\" ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{}\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tload_spm(self.spm_file ,\t\t\t\t\t\tself.sp_model_kwargs )\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tTuple ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[str] = None ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tPath(_UpperCAmelCase )\r assert save_dir.is_dir(), f'''{save_directory} should be a directory'''\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tsave_dir / (\r (filename_prefix + \"\"\"-\"\"\" if filename_prefix else \"\"\"\"\"\") + self.vocab_files_names[\"\"\"vocab_file\"\"\"]\r )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tsave_dir / (\r (filename_prefix + \"\"\"-\"\"\" if filename_prefix else \"\"\"\"\"\") + self.vocab_files_names[\"\"\"spm_file\"\"\"]\r )\r\r save_json(self.encoder ,\t\t\t\t\t\t_UpperCAmelCase )\r\r if os.path.abspath(self.spm_file ) != os.path.abspath(_UpperCAmelCase ) and os.path.isfile(self.spm_file ):\r copyfile(self.spm_file ,\t\t\t\t\t\t_UpperCAmelCase )\r elif not os.path.isfile(self.spm_file ):\r with open(_UpperCAmelCase ,\t\t\t\t\t\t\"\"\"wb\"\"\" ) as fi:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.sp_model.serialized_model_proto()\r fi.write(_UpperCAmelCase )\r\r return (str(_UpperCAmelCase ), str(_UpperCAmelCase ))\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tDict[str, Any]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tsentencepiece.SentencePieceProcessor(**SCREAMING_SNAKE_CASE__\t\t\t\t)\r spm.Load(str(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t)\r return spm\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r with open(SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\"r\"\"\"\t\t\t\t) as f:\r return json.load(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r\r\r\r\r\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tDict\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r with open(SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\"w\"\"\"\t\t\t\t) as f:\r json.dump(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, indent=2\t\t\t\t)\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rfrom datasets.utils.patching import _PatchedModuleObj, patch_submodule\r\rfrom . import _test_patching\r\rdef _UpperCamelCase\t\t\t\t\t( ):\r '''simple docstring'''\r\r\r\r\r\r import os as original_os\r from os import path as original_path\r from os import rename as original_rename\r from os.path import dirname as original_dirname\r from os.path import join as original_join\r\r assert _test_patching.os is original_os\r assert _test_patching.path is original_path\r assert _test_patching.join is original_join\r\r assert _test_patching.renamed_os is original_os\r assert _test_patching.renamed_path is original_path\r assert _test_patching.renamed_join is original_join\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"__test_patch_submodule_mock__\"\"\"\r with patch_submodule(_test_patching\t\t\t\t, \"\"\"os.path.join\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r # Every way to access os.path.join must be patched, and the rest must stay untouched\r\r # check os.path.join\r assert isinstance(_test_patching.os\t\t\t\t, _PatchedModuleObj\t\t\t\t)\r assert isinstance(_test_patching.os.path\t\t\t\t, _PatchedModuleObj\t\t\t\t)\r assert _test_patching.os.path.join is mock\r\r # check path.join\r assert isinstance(_test_patching.path\t\t\t\t, _PatchedModuleObj\t\t\t\t)\r assert _test_patching.path.join is mock\r\r # check join\r assert _test_patching.join is mock\r\r # check that the other attributes are untouched\r assert _test_patching.os.rename is original_rename\r assert _test_patching.path.dirname is original_dirname\r assert _test_patching.os.path.dirname is original_dirname\r\r # Even renamed modules or objects must be patched\r\r # check renamed_os.path.join\r assert isinstance(_test_patching.renamed_os\t\t\t\t, _PatchedModuleObj\t\t\t\t)\r assert isinstance(_test_patching.renamed_os.path\t\t\t\t, _PatchedModuleObj\t\t\t\t)\r assert _test_patching.renamed_os.path.join is mock\r\r # check renamed_path.join\r assert isinstance(_test_patching.renamed_path\t\t\t\t, _PatchedModuleObj\t\t\t\t)\r assert _test_patching.renamed_path.join is mock\r\r # check renamed_join\r assert _test_patching.renamed_join is mock\r\r # check that the other attributes are untouched\r assert _test_patching.renamed_os.rename is original_rename\r assert _test_patching.renamed_path.dirname is original_dirname\r assert _test_patching.renamed_os.path.dirname is original_dirname\r\r # check that everthing is back to normal when the patch is over\r\r assert _test_patching.os is original_os\r assert _test_patching.path is original_path\r assert _test_patching.join is original_join\r\r assert _test_patching.renamed_os is original_os\r assert _test_patching.renamed_path is original_path\r assert _test_patching.renamed_join is original_join\r\rdef _UpperCamelCase\t\t\t\t\t( ):\r '''simple docstring'''\r\r\r\r\r\r assert _test_patching.open is open\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"__test_patch_submodule_builtin_mock__\"\"\"\r # _test_patching has \"open\" in its globals\r assert _test_patching.open is open\r with patch_submodule(_test_patching\t\t\t\t, \"\"\"open\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r assert _test_patching.open is mock\r\r # check that everthing is back to normal when the patch is over\r\r assert _test_patching.open is open\r\rdef _UpperCamelCase\t\t\t\t\t( ):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"__test_patch_submodule_missing_mock__\"\"\"\r with patch_submodule(_test_patching\t\t\t\t, \"\"\"pandas.read_csv\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r pass\r\rdef _UpperCamelCase\t\t\t\t\t( ):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"__test_patch_submodule_missing_builtin_mock__\"\"\"\r # _test_patching doesn't have \"len\" in its globals\r assert getattr(_test_patching\t\t\t\t, \"\"\"len\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t) is None\r with patch_submodule(_test_patching\t\t\t\t, \"\"\"len\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r assert _test_patching.len is mock\r assert _test_patching.len is len\r\rdef _UpperCamelCase\t\t\t\t\t( ):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"__test_patch_submodule_start_and_stop_mock__\"\"\"\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tpatch_submodule(_test_patching\t\t\t\t, \"\"\"open\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r assert _test_patching.open is open\r patch.start()\r assert _test_patching.open is mock\r patch.stop()\r assert _test_patching.open is open\r\rdef _UpperCamelCase\t\t\t\t\t( ):\r '''simple docstring'''\r\r\r\r\r\r from os import rename as original_rename\r from os.path import dirname as original_dirname\r from os.path import join as original_join\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"__test_patch_submodule_successive_join__\"\"\"\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"__test_patch_submodule_successive_dirname__\"\"\"\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"__test_patch_submodule_successive_rename__\"\"\"\r assert _test_patching.os.path.join is original_join\r assert _test_patching.os.path.dirname is original_dirname\r assert _test_patching.os.rename is original_rename\r\r with patch_submodule(_test_patching\t\t\t\t, \"\"\"os.path.join\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r with patch_submodule(_test_patching\t\t\t\t, \"\"\"os.rename\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r with patch_submodule(_test_patching\t\t\t\t, \"\"\"os.path.dirname\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r assert _test_patching.os.path.join is mock_join\r assert _test_patching.os.path.dirname is mock_dirname\r assert _test_patching.os.rename is mock_rename\r\r # try another order\r with patch_submodule(_test_patching\t\t\t\t, \"\"\"os.rename\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r with patch_submodule(_test_patching\t\t\t\t, \"\"\"os.path.join\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r with patch_submodule(_test_patching\t\t\t\t, \"\"\"os.path.dirname\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r assert _test_patching.os.path.join is mock_join\r assert _test_patching.os.path.dirname is mock_dirname\r assert _test_patching.os.rename is mock_rename\r\r assert _test_patching.os.path.join is original_join\r assert _test_patching.os.path.dirname is original_dirname\r assert _test_patching.os.rename is original_rename\r\r\r\r\r\r\r\rdef _UpperCamelCase\t\t\t\t\t( ):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"__test_patch_submodule_doesnt_exist_mock__\"\"\"\r with patch_submodule(_test_patching\t\t\t\t, \"\"\"__module_that_doesn_exist__.__attribute_that_doesn_exist__\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r pass\r with patch_submodule(_test_patching\t\t\t\t, \"\"\"os.__attribute_that_doesn_exist__\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r pass\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152382,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rfrom __future__ import annotations\r\rimport unittest\r\rfrom transformers import RoFormerConfig, is_tf_available\rfrom transformers.testing_utils import require_tf, slow\r\rfrom ...test_configuration_common import ConfigTester\rfrom ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask\rfrom ...test_pipeline_mixin import PipelineTesterMixin\r\r\rif is_tf_available():\r import tensorflow as tf\r\r from transformers import (\r TFRoFormerForCausalLM,\r TFRoFormerForMaskedLM,\r TFRoFormerForMultipleChoice,\r TFRoFormerForQuestionAnswering,\r TFRoFormerForSequenceClassification,\r TFRoFormerForTokenClassification,\r TFRoFormerModel,\r )\r from transformers.models.roformer.modeling_tf_roformer import (\r TFRoFormerSelfAttention,\r TFRoFormerSinusoidalPositionalEmbedding,\r )\r\rclass lowerCAmelCase_ :\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def __init__( self\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict=13 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any]=7 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=True ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict=True ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any]=True ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int]=True ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any]=99 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int]=32 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr=2 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int]=4 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int]=37 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int]=\"gelu\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int]=0.1 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any]=0.1 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any]=5_12 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str]=16 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple=2 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint=0.02 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny=3 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int]=4 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint=None ,\t\t\t\t\t\t):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tparent\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t13\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t7\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTrue\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTrue\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTrue\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTrue\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t99\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t32\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t2\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t4\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t37\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"gelu\"\"\"\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0.1\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0.1\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t5_12\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t16\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t2\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0.02\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t3\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t4\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tids_tensor([self.batch_size, self.seq_length] ,\t\t\t\t\t\tself.vocab_size )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r if self.use_input_mask:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\trandom_attention_mask([self.batch_size, self.seq_length] )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r if self.use_token_type_ids:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tids_tensor([self.batch_size, self.seq_length] ,\t\t\t\t\t\tself.type_vocab_size )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r if self.use_labels:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tids_tensor([self.batch_size] ,\t\t\t\t\t\tself.type_sequence_label_size )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tids_tensor([self.batch_size, self.seq_length] ,\t\t\t\t\t\tself.num_labels )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tids_tensor([self.batch_size] ,\t\t\t\t\t\tself.num_choices )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tRoFormerConfig(\r vocab_size=self.vocab_size ,\t\t\t\t\t\thidden_size=self.hidden_size ,\t\t\t\t\t\tnum_hidden_layers=self.num_hidden_layers ,\t\t\t\t\t\tnum_attention_heads=self.num_attention_heads ,\t\t\t\t\t\tintermediate_size=self.intermediate_size ,\t\t\t\t\t\thidden_act=self.hidden_act ,\t\t\t\t\t\thidden_dropout_prob=self.hidden_dropout_prob ,\t\t\t\t\t\tattention_probs_dropout_prob=self.attention_probs_dropout_prob ,\t\t\t\t\t\tmax_position_embeddings=self.max_position_embeddings ,\t\t\t\t\t\ttype_vocab_size=self.type_vocab_size ,\t\t\t\t\t\tinitializer_range=self.initializer_range ,\t\t\t\t\t\treturn_dict=_UpperCAmelCase ,\t\t\t\t\t\t)\r\r return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTFRoFormerModel(config=_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\"\"\"input_ids\"\"\": input_ids, \"\"\"attention_mask\"\"\": input_mask, \"\"\"token_type_ids\"\"\": token_type_ids}\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[input_ids, input_mask]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase )\r\r self.parent.assertEqual(result.last_hidden_state.shape ,\t\t\t\t\t\t(self.batch_size, self.seq_length, self.hidden_size) )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTrue\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTFRoFormerForCausalLM(config=_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\r \"\"\"input_ids\"\"\": input_ids,\r \"\"\"attention_mask\"\"\": input_mask,\r \"\"\"token_type_ids\"\"\": token_type_ids,\r }\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase )[\"\"\"logits\"\"\"]\r self.parent.assertListEqual(\r list(prediction_scores.numpy().shape ) ,\t\t\t\t\t\t[self.batch_size, self.seq_length, self.vocab_size] )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tAny ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTFRoFormerForMaskedLM(config=_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\r \"\"\"input_ids\"\"\": input_ids,\r \"\"\"attention_mask\"\"\": input_mask,\r \"\"\"token_type_ids\"\"\": token_type_ids,\r }\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase )\r self.parent.assertEqual(result.logits.shape ,\t\t\t\t\t\t(self.batch_size, self.seq_length, self.vocab_size) )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tAny ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.num_labels\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTFRoFormerForSequenceClassification(config=_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\r \"\"\"input_ids\"\"\": input_ids,\r \"\"\"attention_mask\"\"\": input_mask,\r \"\"\"token_type_ids\"\"\": token_type_ids,\r }\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase )\r self.parent.assertEqual(result.logits.shape ,\t\t\t\t\t\t(self.batch_size, self.num_labels) )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.num_choices\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTFRoFormerForMultipleChoice(config=_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttf.tile(tf.expand_dims(_UpperCAmelCase ,\t\t\t\t\t\t1 ) ,\t\t\t\t\t\t(1, self.num_choices, 1) )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttf.tile(tf.expand_dims(_UpperCAmelCase ,\t\t\t\t\t\t1 ) ,\t\t\t\t\t\t(1, self.num_choices, 1) )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttf.tile(tf.expand_dims(_UpperCAmelCase ,\t\t\t\t\t\t1 ) ,\t\t\t\t\t\t(1, self.num_choices, 1) )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\r \"\"\"input_ids\"\"\": multiple_choice_inputs_ids,\r \"\"\"attention_mask\"\"\": multiple_choice_input_mask,\r \"\"\"token_type_ids\"\"\": multiple_choice_token_type_ids,\r }\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase )\r self.parent.assertEqual(result.logits.shape ,\t\t\t\t\t\t(self.batch_size, self.num_choices) )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.num_labels\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTFRoFormerForTokenClassification(config=_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\r \"\"\"input_ids\"\"\": input_ids,\r \"\"\"attention_mask\"\"\": input_mask,\r \"\"\"token_type_ids\"\"\": token_type_ids,\r }\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase )\r self.parent.assertEqual(result.logits.shape ,\t\t\t\t\t\t(self.batch_size, self.seq_length, self.num_labels) )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTFRoFormerForQuestionAnswering(config=_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\r \"\"\"input_ids\"\"\": input_ids,\r \"\"\"attention_mask\"\"\": input_mask,\r \"\"\"token_type_ids\"\"\": token_type_ids,\r }\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase )\r self.parent.assertEqual(result.start_logits.shape ,\t\t\t\t\t\t(self.batch_size, self.seq_length) )\r self.parent.assertEqual(result.end_logits.shape ,\t\t\t\t\t\t(self.batch_size, self.seq_length) )\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.prepare_config_and_inputs()\r (\r (\r UpperCAmelCase__\r )\t\t\t\t\t\t\t,\t(\r UpperCAmelCase__\r )\t\t\t\t\t\t\t,\t(\r UpperCAmelCase__\r )\t\t\t\t\t\t\t,\t(\r UpperCAmelCase__\r )\t\t\t\t\t\t\t,\t(\r UpperCAmelCase__\r )\t\t\t\t\t\t\t,\t(\r UpperCAmelCase__\r )\t\t\t\t\t\t\t,\t(\r UpperCAmelCase__\r )\t\t\t\t\t\t\t,\t\r )\t\t\t\t\t\t\t\t=\t\t\tconfig_and_inputs\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\"\"\"input_ids\"\"\": input_ids, \"\"\"token_type_ids\"\"\": token_type_ids, \"\"\"attention_mask\"\"\": input_mask}\r return config, inputs_dict\r\r@require_tf\rclass lowerCAmelCase_ ( lowerCamelCase_\t, lowerCamelCase_\t, unittest.TestCase ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r lowerCAmelCase_ : Dict = (\r (\r TFRoFormerModel,\r TFRoFormerForCausalLM,\r TFRoFormerForMaskedLM,\r TFRoFormerForQuestionAnswering,\r TFRoFormerForSequenceClassification,\r TFRoFormerForTokenClassification,\r TFRoFormerForMultipleChoice,\r )\r if is_tf_available()\r else ()\r )\r lowerCAmelCase_ : Dict = (\r {\r \"\"\"feature-extraction\"\"\": TFRoFormerModel,\r \"\"\"fill-mask\"\"\": TFRoFormerForMaskedLM,\r \"\"\"question-answering\"\"\": TFRoFormerForQuestionAnswering,\r \"\"\"text-classification\"\"\": TFRoFormerForSequenceClassification,\r \"\"\"text-generation\"\"\": TFRoFormerForCausalLM,\r \"\"\"token-classification\"\"\": TFRoFormerForTokenClassification,\r \"\"\"zero-shot\"\"\": TFRoFormerForSequenceClassification,\r }\r if is_tf_available()\r else {}\r )\r\r lowerCAmelCase_ : Tuple = False\r lowerCAmelCase_ : Any = False\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tTuple ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if pipeline_test_casse_name == \"TextGenerationPipelineTests\":\r return True\r\r return False\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTFRoFormerModelTester(self )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tConfigTester(self ,\t\t\t\t\t\tconfig_class=_UpperCAmelCase ,\t\t\t\t\t\thidden_size=37 )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r self.config_tester.run_common_tests()\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.model_tester.prepare_config_and_inputs()\r self.model_tester.create_and_check_model(*_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.model_tester.prepare_config_and_inputs()\r self.model_tester.create_and_check_for_masked_lm(*_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.model_tester.prepare_config_and_inputs()\r self.model_tester.create_and_check_lm_head(*_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.model_tester.prepare_config_and_inputs()\r self.model_tester.create_and_check_for_multiple_choice(*_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.model_tester.prepare_config_and_inputs()\r self.model_tester.create_and_check_for_question_answering(*_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.model_tester.prepare_config_and_inputs()\r self.model_tester.create_and_check_for_sequence_classification(*_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.model_tester.prepare_config_and_inputs()\r self.model_tester.create_and_check_for_token_classification(*_UpperCAmelCase )\r\r\r\r @slow\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tAny ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTFRoFormerModel.from_pretrained(\"\"\"junnyu/roformer_chinese_base\"\"\" )\r self.assertIsNotNone(_UpperCAmelCase )\r\r@require_tf\rclass lowerCAmelCase_ ( unittest.TestCase ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r @slow\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTFRoFormerForMaskedLM.from_pretrained(\"\"\"junnyu/roformer_chinese_base\"\"\" )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttf.constant([[0, 1, 2, 3, 4, 5]] )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase )[0]\r\r # TODO Replace vocab size\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t5_00_00\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[1, 6, vocab_size]\r self.assertEqual(output.shape ,\t\t\t\t\t\t_UpperCAmelCase )\r\r print(output[:, :3, :3] )\r\r # TODO Replace values below with what was printed above.\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttf.constant(\r [\r [\r [-0.1205_3341, -1.026_4901, 0.2922_1946],\r [-1.513_3783, 0.19_7433, 0.1519_0607],\r [-5.013_5403, -3.90_0256, -0.8403_8764],\r ]\r ] )\r tf.debugging.assert_near(output[:, :3, :3] ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\tatol=1E-4 )\r\r@require_tf\rclass lowerCAmelCase_ ( unittest.TestCase ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r lowerCAmelCase_ : int = 1e-4\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttf.constant([[4, 10]] )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTFRoFormerSinusoidalPositionalEmbedding(num_positions=6 ,\t\t\t\t\t\tembedding_dim=6 )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\temba(input_ids.shape )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttf.constant(\r [[0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 1.0000], [0.8415, 0.0464, 0.0022, 0.5403, 0.9989, 1.0000]] )\r\r tf.debugging.assert_near(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\tatol=self.tolerance )\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttf.constant(\r [\r [0.0000, 0.0000, 0.0000, 0.0000, 0.0000],\r [0.8415, 0.8219, 0.8020, 0.7819, 0.7617],\r [0.9093, 0.9364, 0.9581, 0.9749, 0.9870],\r ] )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTFRoFormerSinusoidalPositionalEmbedding(num_positions=5_12 ,\t\t\t\t\t\tembedding_dim=5_12 )\r emba([2, 16, 5_12] )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\temba.weight[:3, :5]\r\r tf.debugging.assert_near(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\tatol=self.tolerance )\r\r@require_tf\rclass lowerCAmelCase_ ( unittest.TestCase ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r lowerCAmelCase_ : Union[str, Any] = 1e-4\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttf.reshape(tf.range(2 * 12 * 16 * 64 ,\t\t\t\t\t\tdtype=tf.floataa ) ,\t\t\t\t\t\tshape=(2, 12, 16, 64) ) / 1_00\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t-tf.reshape(tf.range(2 * 12 * 16 * 64 ,\t\t\t\t\t\tdtype=tf.floataa ) ,\t\t\t\t\t\tshape=(2, 12, 16, 64) ) / 1_00\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTFRoFormerSinusoidalPositionalEmbedding(num_positions=32 ,\t\t\t\t\t\tembedding_dim=64 )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tembed_positions([2, 16, 7_68] )[None, None, :, :]\r\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTFRoFormerSelfAttention.apply_rotary_position_embeddings(\r _UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttf.constant(\r [\r [0.0000, 0.0100, 0.0200, 0.0300, 0.0400, 0.0500, 0.0600, 0.0700],\r [-0.2012, 0.8897, 0.0263, 0.9401, 0.2074, 0.9463, 0.3481, 0.9343],\r [-1.7057, 0.6271, -1.2145, 1.3897, -0.6303, 1.7647, -0.1173, 1.8985],\r [-2.1731, -1.6397, -2.7358, 0.2854, -2.1840, 1.7183, -1.3018, 2.4871],\r [0.2717, -3.6173, -2.9206, -2.1988, -3.6638, 0.3858, -2.9155, 2.2980],\r [3.9859, -2.1580, -0.7984, -4.4904, -4.1181, -2.0252, -4.4782, 1.1253],\r ] )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttf.constant(\r [\r [0.0000, -0.0100, -0.0200, -0.0300, -0.0400, -0.0500, -0.0600, -0.0700],\r [0.2012, -0.8897, -0.0263, -0.9401, -0.2074, -0.9463, -0.3481, -0.9343],\r [1.7057, -0.6271, 1.2145, -1.3897, 0.6303, -1.7647, 0.1173, -1.8985],\r [2.1731, 1.6397, 2.7358, -0.2854, 2.1840, -1.7183, 1.3018, -2.4871],\r [-0.2717, 3.6173, 2.9206, 2.1988, 3.6638, -0.3858, 2.9155, -2.2980],\r [-3.9859, 2.1580, 0.7984, 4.4904, 4.1181, 2.0252, 4.4782, -1.1253],\r ] )\r\r tf.debugging.assert_near(query_layer[0, 0, :6, :8] ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\tatol=self.tolerance )\r tf.debugging.assert_near(key_layer[0, 0, :6, :8] ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\tatol=self.tolerance )\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rfrom timeit import timeit\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t{\r 'MALAYALAM': True,\r 'String': False,\r 'rotor': True,\r 'level': True,\r 'A': True,\r 'BB': True,\r 'ABC': False,\r 'amanaplanacanalpanama': True, # \"a man a plan a canal panama\"\r}\r# Ensure our test data is valid\rassert all((key == key[::-1]) is value for key, value in test_data.items())\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlen(SCREAMING_SNAKE_CASE__\t\t\t\t) - 1\r while start_i < end_i:\r if s[start_i] == s[end_i]:\r start_i += 1\r end_i -= 1\r else:\r return False\r return True\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlen(SCREAMING_SNAKE_CASE__\t\t\t\t) // 2\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlen(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r # We need to traverse till half of the length of string\r # as we can get access of the i'th last element from\r # i'th index.\r # eg: [0,1,2,3,4,5] => 4th index can be accessed\r # with the help of 1st index (i==n-i-1)\r # where n is length of string\r return all(s[i] == s[n - i - 1] for i in range(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t)\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r if len(SCREAMING_SNAKE_CASE__\t\t\t\t) <= 2:\r return True\r if s[0] == s[len(SCREAMING_SNAKE_CASE__\t\t\t\t) - 1]:\r return is_palindrome_recursive(s[1:-1]\t\t\t\t)\r else:\r return False\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r return s == s[::-1]\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tF'''all({name}(key) is value for key, value in test_data.items())'''\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tF'''from __main__ import test_data, {name}'''\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t500000\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttimeit(stmt=SCREAMING_SNAKE_CASE__\t\t\t\t, setup=SCREAMING_SNAKE_CASE__\t\t\t\t, number=SCREAMING_SNAKE_CASE__\t\t\t\t)\r print(F'''{name:<35} finished {number:,} runs in {result:.5f} seconds'''\t\t\t\t)\r\r\rif __name__ == \"__main__\":\r for key, value in test_data.items():\r assert is_palindrome(key) is is_palindrome_recursive(key)\r assert is_palindrome(key) is is_palindrome_slice(key)\r print(f\"{key:21} {value}\")\r print('a man a plan a canal panama')\r\r # finished 500,000 runs in 0.46793 seconds\r benchmark_function('is_palindrome_slice')\r # finished 500,000 runs in 0.85234 seconds\r benchmark_function('is_palindrome')\r # finished 500,000 runs in 1.32028 seconds\r benchmark_function('is_palindrome_recursive')\r # finished 500,000 runs in 2.08679 seconds\r benchmark_function('is_palindrome_traversal')\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152383,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rfrom __future__ import annotations\r\rimport unittest\r\rfrom transformers import is_tf_available\rfrom transformers.testing_utils import require_tf, slow\r\rfrom ...test_configuration_common import ConfigTester\rfrom ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask\rfrom ...test_pipeline_mixin import PipelineTesterMixin\r\r\rif is_tf_available():\r import numpy\r import tensorflow as tf\r\r from transformers import (\r TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,\r TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,\r TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,\r BertConfig,\r DPRConfig,\r TFDPRContextEncoder,\r TFDPRQuestionEncoder,\r TFDPRReader,\r )\r\rclass lowerCAmelCase_ :\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def __init__( self\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=13 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any]=7 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int]=True ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny=True ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr=True ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple=True ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint=99 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any]=32 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int]=2 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple=4 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr=37 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint=\"gelu\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny=0.1 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=0.1 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr=5_12 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple=16 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any]=2 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int]=0.02 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint=3 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=4 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int]=None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny=0 ,\t\t\t\t\t\t):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tparent\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tbatch_size\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tseq_length\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tis_training\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tuse_input_mask\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tuse_token_type_ids\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tuse_labels\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvocab_size\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\thidden_size\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnum_hidden_layers\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnum_attention_heads\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tintermediate_size\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\thidden_act\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\thidden_dropout_prob\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tattention_probs_dropout_prob\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmax_position_embeddings\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttype_vocab_size\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttype_sequence_label_size\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tinitializer_range\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnum_labels\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnum_choices\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscope\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tprojection_dim\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tids_tensor([self.batch_size, self.seq_length] ,\t\t\t\t\t\tself.vocab_size )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r if self.use_input_mask:\r # follow test_modeling_tf_ctrl.py\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\trandom_attention_mask([self.batch_size, self.seq_length] )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r if self.use_token_type_ids:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tids_tensor([self.batch_size, self.seq_length] ,\t\t\t\t\t\tself.type_vocab_size )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r if self.use_labels:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tids_tensor([self.batch_size] ,\t\t\t\t\t\tself.type_sequence_label_size )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tids_tensor([self.batch_size, self.seq_length] ,\t\t\t\t\t\tself.num_labels )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tids_tensor([self.batch_size] ,\t\t\t\t\t\tself.num_choices )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tBertConfig(\r vocab_size=self.vocab_size ,\t\t\t\t\t\thidden_size=self.hidden_size ,\t\t\t\t\t\tnum_hidden_layers=self.num_hidden_layers ,\t\t\t\t\t\tnum_attention_heads=self.num_attention_heads ,\t\t\t\t\t\tintermediate_size=self.intermediate_size ,\t\t\t\t\t\thidden_act=self.hidden_act ,\t\t\t\t\t\thidden_dropout_prob=self.hidden_dropout_prob ,\t\t\t\t\t\tattention_probs_dropout_prob=self.attention_probs_dropout_prob ,\t\t\t\t\t\tmax_position_embeddings=self.max_position_embeddings ,\t\t\t\t\t\ttype_vocab_size=self.type_vocab_size ,\t\t\t\t\t\tis_decoder=_UpperCAmelCase ,\t\t\t\t\t\tinitializer_range=self.initializer_range ,\t\t\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tDPRConfig(projection_dim=self.projection_dim ,\t\t\t\t\t\t**config.to_dict() )\r\r return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tTuple ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTFDPRContextEncoder(config=_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase ,\t\t\t\t\t\tattention_mask=_UpperCAmelCase ,\t\t\t\t\t\ttoken_type_ids=_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase ,\t\t\t\t\t\ttoken_type_ids=_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase )\r self.parent.assertEqual(result.pooler_output.shape ,\t\t\t\t\t\t(self.batch_size, self.projection_dim or self.hidden_size) )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTFDPRQuestionEncoder(config=_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase ,\t\t\t\t\t\tattention_mask=_UpperCAmelCase ,\t\t\t\t\t\ttoken_type_ids=_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase ,\t\t\t\t\t\ttoken_type_ids=_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase )\r self.parent.assertEqual(result.pooler_output.shape ,\t\t\t\t\t\t(self.batch_size, self.projection_dim or self.hidden_size) )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTFDPRReader(config=_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase ,\t\t\t\t\t\tattention_mask=_UpperCAmelCase )\r\r self.parent.assertEqual(result.start_logits.shape ,\t\t\t\t\t\t(self.batch_size, self.seq_length) )\r self.parent.assertEqual(result.end_logits.shape ,\t\t\t\t\t\t(self.batch_size, self.seq_length) )\r self.parent.assertEqual(result.relevance_logits.shape ,\t\t\t\t\t\t(self.batch_size,) )\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.prepare_config_and_inputs()\r (\r (\r UpperCAmelCase__\r )\t\t\t\t\t\t\t,\t(\r UpperCAmelCase__\r )\t\t\t\t\t\t\t,\t(\r UpperCAmelCase__\r )\t\t\t\t\t\t\t,\t(\r UpperCAmelCase__\r )\t\t\t\t\t\t\t,\t(\r UpperCAmelCase__\r )\t\t\t\t\t\t\t,\t(\r UpperCAmelCase__\r )\t\t\t\t\t\t\t,\t(\r UpperCAmelCase__\r )\t\t\t\t\t\t\t,\t\r )\t\t\t\t\t\t\t\t=\t\t\tconfig_and_inputs\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\"\"\"input_ids\"\"\": input_ids}\r return config, inputs_dict\r\r@require_tf\rclass lowerCAmelCase_ ( lowerCamelCase_\t, lowerCamelCase_\t, unittest.TestCase ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r lowerCAmelCase_ : Tuple = (\r (\r TFDPRContextEncoder,\r TFDPRQuestionEncoder,\r TFDPRReader,\r )\r if is_tf_available()\r else ()\r )\r lowerCAmelCase_ : List[str] = {\"\"\"feature-extraction\"\"\": TFDPRQuestionEncoder} if is_tf_available() else {}\r\r lowerCAmelCase_ : int = False\r lowerCAmelCase_ : Optional[int] = False\r lowerCAmelCase_ : Union[str, Any] = False\r lowerCAmelCase_ : Any = False\r lowerCAmelCase_ : Union[str, Any] = False\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTFDPRModelTester(self )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tConfigTester(self ,\t\t\t\t\t\tconfig_class=_UpperCAmelCase ,\t\t\t\t\t\thidden_size=37 )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r self.config_tester.run_common_tests()\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tAny ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.model_tester.prepare_config_and_inputs()\r self.model_tester.create_and_check_dpr_context_encoder(*_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.model_tester.prepare_config_and_inputs()\r self.model_tester.create_and_check_dpr_question_encoder(*_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.model_tester.prepare_config_and_inputs()\r self.model_tester.create_and_check_dpr_reader(*_UpperCAmelCase )\r\r\r\r @slow\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTFDPRContextEncoder.from_pretrained(_UpperCAmelCase )\r self.assertIsNotNone(_UpperCAmelCase )\r\r for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTFDPRContextEncoder.from_pretrained(_UpperCAmelCase )\r self.assertIsNotNone(_UpperCAmelCase )\r\r for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTFDPRQuestionEncoder.from_pretrained(_UpperCAmelCase )\r self.assertIsNotNone(_UpperCAmelCase )\r\r for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTFDPRReader.from_pretrained(_UpperCAmelCase )\r self.assertIsNotNone(_UpperCAmelCase )\r\r@require_tf\rclass lowerCAmelCase_ ( unittest.TestCase ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r @slow\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTFDPRQuestionEncoder.from_pretrained(\"\"\"facebook/dpr-question_encoder-single-nq-base\"\"\" )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttf.constant(\r [[1_01, 75_92, 10_10, 20_03, 20_26, 38_99, 1_01_40, 10_29, 1_02]] ) # [CLS] hello, is my dog cute? [SEP]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase )[0] # embedding shape = (1, 768)\r # compare the actual values for a slice.\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttf.constant(\r [\r [\r 0.0323_6253,\r 0.1275_3335,\r 0.1681_8509,\r 0.0027_9786,\r 0.389_6933,\r 0.2426_4945,\r 0.217_8971,\r -0.0233_5227,\r -0.0848_1959,\r -0.1432_4117,\r ]\r ] )\r self.assertTrue(numpy.allclose(output[:, :10].numpy() ,\t\t\t\t\t\texpected_slice.numpy() ,\t\t\t\t\t\tatol=1E-4 ) )\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport datasets\r\rfrom .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'\\\\n@INPROCEEDINGS{Papineni02bleu:a,\\n author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\\n title = {BLEU: a Method for Automatic Evaluation of Machine Translation},\\n booktitle = {},\\n year = {2002},\\n pages = {311--318}\\n}\\n@inproceedings{lin-och-2004-orange,\\n title = \"{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation\",\\n author = \"Lin, Chin-Yew and\\n Och, Franz Josef\",\\n booktitle = \"{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics\",\\n month = \"aug 23{--}aug 27\",\\n year = \"2004\",\\n address = \"Geneva, Switzerland\",\\n publisher = \"COLING\",\\n url = \"https://www.aclweb.org/anthology/C04-1072\",\\n pages = \"501--507\",\\n}\\n'\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'\\\\nBLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.\\nQuality is considered to be the correspondence between a machine\\'s output and that of a human: \"the closer a machine translation is to a professional human translation,\\nthe better it is\" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and\\nremains one of the most popular automated and inexpensive metrics.\\n\\nScores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.\\nThose scores are then averaged over the whole corpus to reach an estimate of the translation\\'s overall quality. Intelligibility or grammatical correctness\\nare not taken into account[citation needed].\\n\\nBLEU\\'s output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1\\nrepresenting more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the\\nreference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional\\nreference translations will increase the BLEU score.\\n'\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'\\nComputes BLEU score of translated segments against one or more references.\\nArgs:\\n predictions: list of translations to score.\\n Each translation should be tokenized into a list of tokens.\\n references: list of lists of references for each translation.\\n Each reference should be tokenized into a list of tokens.\\n max_order: Maximum n-gram order to use when computing BLEU score.\\n smooth: Whether or not to apply Lin et al. 2004 smoothing.\\nReturns:\\n \\'bleu\\': bleu score,\\n \\'precisions\\': geometric mean of n-gram precisions,\\n \\'brevity_penalty\\': brevity penalty,\\n \\'length_ratio\\': ratio of lengths,\\n \\'translation_length\\': translation_length,\\n \\'reference_length\\': reference_length\\nExamples:\\n\\n >>> predictions = [\\n ... [\"hello\", \"there\", \"general\", \"kenobi\"], # tokenized prediction of the first sample\\n ... [\"foo\", \"bar\", \"foobar\"] # tokenized prediction of the second sample\\n ... ]\\n >>> references = [\\n ... [[\"hello\", \"there\", \"general\", \"kenobi\"], [\"hello\", \"there\", \"!\"]], # tokenized references for the first sample (2 references)\\n ... [[\"foo\", \"bar\", \"foobar\"]] # tokenized references for the second sample (1 reference)\\n ... ]\\n >>> bleu = datasets.load_metric(\"bleu\")\\n >>> results = bleu.compute(predictions=predictions, references=references)\\n >>> print(results[\"bleu\"])\\n 1.0\\n'\r\r@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION\t, _KWARGS_DESCRIPTION )\rclass lowerCAmelCase_ ( datasets.Metric ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return datasets.MetricInfo(\r description=_DESCRIPTION ,\t\t\t\t\t\tcitation=_CITATION ,\t\t\t\t\t\tinputs_description=_KWARGS_DESCRIPTION ,\t\t\t\t\t\tfeatures=datasets.Features(\r {\r \"\"\"predictions\"\"\": datasets.Sequence(datasets.Value(\"\"\"string\"\"\" ,\t\t\t\t\t\tid=\"\"\"token\"\"\" ) ,\t\t\t\t\t\tid=\"\"\"sequence\"\"\" ),\r \"\"\"references\"\"\": datasets.Sequence(\r datasets.Sequence(datasets.Value(\"\"\"string\"\"\" ,\t\t\t\t\t\tid=\"\"\"token\"\"\" ) ,\t\t\t\t\t\tid=\"\"\"sequence\"\"\" ) ,\t\t\t\t\t\tid=\"\"\"references\"\"\" ),\r } ) ,\t\t\t\t\t\tcodebase_urls=[\"\"\"https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py\"\"\"] ,\t\t\t\t\t\treference_urls=[\r \"\"\"https://en.wikipedia.org/wiki/BLEU\"\"\",\r \"\"\"https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213\"\"\",\r ] ,\t\t\t\t\t\t)\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any]=4 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=False ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tcompute_bleu(\r reference_corpus=_UpperCAmelCase ,\t\t\t\t\t\ttranslation_corpus=_UpperCAmelCase ,\t\t\t\t\t\tmax_order=_UpperCAmelCase ,\t\t\t\t\t\tsmooth=_UpperCAmelCase )\r ((UpperCAmelCase__)\t\t\t\t\t\t\t,\t(UpperCAmelCase__)\t\t\t\t\t\t\t,\t(UpperCAmelCase__)\t\t\t\t\t\t\t,\t(UpperCAmelCase__)\t\t\t\t\t\t\t,\t(UpperCAmelCase__)\t\t\t\t\t\t\t,\t(UpperCAmelCase__))\t\t\t\t\t\t\t\t=\t\t\tscore\r return {\r \"bleu\": bleu,\r \"precisions\": precisions,\r \"brevity_penalty\": bp,\r \"length_ratio\": ratio,\r \"translation_length\": translation_length,\r \"reference_length\": reference_length,\r }\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152384,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'\\n# Transformers installation\\n! pip install transformers datasets\\n# To install from source instead of the last release, comment the command above and uncomment the following one.\\n# ! pip install git+https://github.com/huggingface/transformers.git\\n'\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t[{'type': 'code', 'content': INSTALL_CONTENT}]\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t{\r '{processor_class}': 'FakeProcessorClass',\r '{model_class}': 'FakeModelClass',\r '{object_class}': 'FakeObjectClass',\r}\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rfrom dataclasses import dataclass\rfrom typing import List, Optional, Union\r\rimport numpy as np\rimport torch\r\rfrom ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available\r\r@dataclass\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r lowerCAmelCase_ : Union[List[np.ndarray], torch.FloatTensor]\r\r\rtry:\r if not (is_transformers_available() and is_torch_available()):\r raise OptionalDependencyNotAvailable()\rexcept OptionalDependencyNotAvailable:\r from ...utils.dummy_torch_and_transformers_objects import * # noqa F403\relse:\r from .pipeline_text_to_video_synth import TextToVideoSDPipeline\r from .pipeline_text_to_video_synth_imgaimg import VideoToVideoSDPipeline # noqa: F401\r from .pipeline_text_to_video_zero import TextToVideoZeroPipeline\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152385,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport argparse\rimport os\rimport re\r\rimport packaging.version\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'examples/'\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t{\r 'examples': (re.compile(r'^check_min_version\$\"[^\"]+\"\$\\s*$', re.MULTILINE), 'check_min_version(\"VERSION\")\\n'),\r 'init': (re.compile(r'^__version__\\s+=\\s+\"([^\"]+)\"\\s*$', re.MULTILINE), '__version__ = \"VERSION\"\\n'),\r 'setup': (re.compile(r'^(\\s*)version\\s*=\\s*\"[^\"]+\",', re.MULTILINE), r'\\1version=\"VERSION\",'),\r 'doc': (re.compile(r'^(\\s*)release\\s*=\\s*\"[^\"]+\"$', re.MULTILINE), 'release = \"VERSION\"\\n'),\r}\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t{\r 'init': 'src/transformers/__init__.py',\r 'setup': 'setup.py',\r}\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'README.md'\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tAny\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[Any]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tAny\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r with open(SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\"r\"\"\"\t\t\t\t, encoding=\"\"\"utf-8\"\"\"\t\t\t\t, newline=\"\"\"\\n\"\"\"\t\t\t\t) as f:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tf.read()\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tREPLACE_PATTERNS[pattern]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\treplace.replace(\"\"\"VERSION\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tre_pattern.sub(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r with open(SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\"w\"\"\"\t\t\t\t, encoding=\"\"\"utf-8\"\"\"\t\t\t\t, newline=\"\"\"\\n\"\"\"\t\t\t\t) as f:\r f.write(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tAny\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r for folder, directories, fnames in os.walk(SCREAMING_SNAKE_CASE__\t\t\t\t):\r # Removing some of the folders with non-actively maintained examples from the walk\r if \"research_projects\" in directories:\r directories.remove(\"\"\"research_projects\"\"\"\t\t\t\t)\r if \"legacy\" in directories:\r directories.remove(\"\"\"legacy\"\"\"\t\t\t\t)\r for fname in fnames:\r if fname.endswith(\"\"\".py\"\"\"\t\t\t\t):\r update_version_in_file(os.path.join(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, pattern=\"\"\"examples\"\"\"\t\t\t\t)\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tDict\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint=False\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r for pattern, fname in REPLACE_FILES.items():\r update_version_in_file(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r if not patch:\r update_version_in_examples(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\rdef _UpperCamelCase\t\t\t\t\t( ):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"🤗 Transformers currently provides the following architectures\"\"\"\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"1. Want to contribute a new model?\"\"\"\r with open(SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\"r\"\"\"\t\t\t\t, encoding=\"\"\"utf-8\"\"\"\t\t\t\t, newline=\"\"\"\\n\"\"\"\t\t\t\t) as f:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tf.readlines()\r\r # Find the start of the list.\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0\r while not lines[start_index].startswith(_start_prompt\t\t\t\t):\r start_index += 1\r start_index += 1\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tstart_index\r # Update the lines in the model list.\r while not lines[index].startswith(_end_prompt\t\t\t\t):\r if lines[index].startswith(\"\"\"1.\"\"\"\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlines[index].replace(\r \"\"\"https://huggingface.co/docs/transformers/main/model_doc\"\"\"\t\t\t\t, \"\"\"https://huggingface.co/docs/transformers/model_doc\"\"\"\t\t\t\t, )\r index += 1\r\r with open(SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\"w\"\"\"\t\t\t\t, encoding=\"\"\"utf-8\"\"\"\t\t\t\t, newline=\"\"\"\\n\"\"\"\t\t\t\t) as f:\r f.writelines(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\rdef _UpperCamelCase\t\t\t\t\t( ):\r '''simple docstring'''\r\r\r\r\r\r with open(REPLACE_FILES[\"\"\"init\"\"\"]\t\t\t\t, \"\"\"r\"\"\"\t\t\t\t) as f:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tf.read()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tREPLACE_PATTERNS[\"\"\"init\"\"\"][0].search(SCREAMING_SNAKE_CASE__\t\t\t\t).groups()[0]\r return packaging.version.parse(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[Any]=False\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tget_version()\r if patch and default_version.is_devrelease:\r raise ValueError(\"\"\"Can't create a patch version from the dev branch, checkout a released version!\"\"\"\t\t\t\t)\r if default_version.is_devrelease:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdefault_version.base_version\r elif patch:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tF'''{default_version.major}.{default_version.minor}.{default_version.micro + 1}'''\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tF'''{default_version.major}.{default_version.minor + 1}.0'''\r\r # Now let's ask nicely if that's the right one.\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tinput(F'''Which version are you releasing? [{default_version}]'''\t\t\t\t)\r if len(SCREAMING_SNAKE_CASE__\t\t\t\t) == 0:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdefault_version\r\r print(F'''Updating version to {version}.'''\t\t\t\t)\r global_version_update(SCREAMING_SNAKE_CASE__\t\t\t\t, patch=SCREAMING_SNAKE_CASE__\t\t\t\t)\r if not patch:\r print(\"\"\"Cleaning main README, don't forget to run `make fix-copies`.\"\"\"\t\t\t\t)\r clean_main_ref_in_model_list()\r\rdef _UpperCamelCase\t\t\t\t\t( ):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tget_version()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tF'''{current_version.major}.{current_version.minor + 1}.0.dev0'''\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tcurrent_version.base_version\r\r # Check with the user we got that right.\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tinput(F'''Which version are we developing now? [{dev_version}]'''\t\t\t\t)\r if len(SCREAMING_SNAKE_CASE__\t\t\t\t) == 0:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdev_version\r\r print(F'''Updating version to {version}.'''\t\t\t\t)\r global_version_update(SCREAMING_SNAKE_CASE__\t\t\t\t)\r print(\"\"\"Cleaning main README, don't forget to run `make fix-copies`.\"\"\"\t\t\t\t)\r clean_main_ref_in_model_list()\r\r\rif __name__ == \"__main__\":\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\targparse.ArgumentParser()\r parser.add_argument('--post_release', action='store_true', help='Whether this is pre or post release.')\r parser.add_argument('--patch', action='store_true', help='Whether or not this is a patch release.')\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tparser.parse_args()\r if not args.post_release:\r pre_release_work(patch=args.patch)\r elif args.patch:\r print('Nothing to do after a patch :-)')\r else:\r post_release_work()\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport torch\rimport torch.nn as nn\rfrom transformers.modeling_utils import ModuleUtilsMixin\rfrom transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm\r\rfrom ...configuration_utils import ConfigMixin, register_to_config\rfrom ...models import ModelMixin\r\rclass lowerCAmelCase_ ( lowerCamelCase_\t, lowerCamelCase_\t, lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r @register_to_config\r def __init__( self\t\t\t:\t\tList[str] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tfloat ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tbool = False ,\t\t\t\t\t\t):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r super().__init__()\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnn.Embedding(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnn.Embedding(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnn.Dropout(p=_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTaConfig(\r vocab_size=_UpperCAmelCase ,\t\t\t\t\t\td_model=_UpperCAmelCase ,\t\t\t\t\t\tnum_heads=_UpperCAmelCase ,\t\t\t\t\t\td_kv=_UpperCAmelCase ,\t\t\t\t\t\td_ff=_UpperCAmelCase ,\t\t\t\t\t\tdropout_rate=_UpperCAmelCase ,\t\t\t\t\t\tfeed_forward_proj=_UpperCAmelCase ,\t\t\t\t\t\tis_decoder=_UpperCAmelCase ,\t\t\t\t\t\tis_encoder_decoder=_UpperCAmelCase ,\t\t\t\t\t\t)\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnn.ModuleList()\r for lyr_num in range(_UpperCAmelCase ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTaBlock(_UpperCAmelCase )\r self.encoders.append(_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTaLayerNorm(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnn.Dropout(p=_UpperCAmelCase )\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.token_embedder(_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tencoder_input_tokens.shape[1]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.arange(_UpperCAmelCase ,\t\t\t\t\t\tdevice=encoder_input_tokens.device )\r x += self.position_encoding(_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.dropout_pre(_UpperCAmelCase )\r\r # inverted the attention mask\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tencoder_input_tokens.size()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_extended_attention_mask(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r\r for lyr in self.encoders:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlyr(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )[0]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.layer_norm(_UpperCAmelCase )\r\r return self.dropout_post(_UpperCAmelCase ), encoder_inputs_mask\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152386,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rfrom typing import TYPE_CHECKING\r\rfrom ...utils import (\r OptionalDependencyNotAvailable,\r _LazyModule,\r is_sentencepiece_available,\r is_tokenizers_available,\r is_torch_available,\r)\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t{}\r\rtry:\r if not is_sentencepiece_available():\r raise OptionalDependencyNotAvailable()\rexcept OptionalDependencyNotAvailable:\r pass\relse:\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\t['NllbTokenizer']\r\rtry:\r if not is_tokenizers_available():\r raise OptionalDependencyNotAvailable()\rexcept OptionalDependencyNotAvailable:\r pass\relse:\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\t['NllbTokenizerFast']\r\r\rif TYPE_CHECKING:\r try:\r if not is_sentencepiece_available():\r raise OptionalDependencyNotAvailable()\r except OptionalDependencyNotAvailable:\r pass\r else:\r from .tokenization_nllb import NllbTokenizer\r\r try:\r if not is_tokenizers_available():\r raise OptionalDependencyNotAvailable()\r except OptionalDependencyNotAvailable:\r pass\r else:\r from .tokenization_nllb_fast import NllbTokenizerFast\r\relse:\r import sys\r\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\t_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport argparse\rimport json\rimport os\r\rimport fairseq\rimport torch\rfrom fairseq.data import Dictionary\r\rfrom transformers import (\r WavaVecaConfig,\r WavaVecaCTCTokenizer,\r WavaVecaFeatureExtractor,\r WavaVecaForCTC,\r WavaVecaForPreTraining,\r WavaVecaProcessor,\r logging,\r)\rfrom transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification\r\r\rlogging.set_verbosity_info()\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tlogging.get_logger(__name__)\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t{\r 'post_extract_proj': 'feature_projection.projection',\r 'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',\r 'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',\r 'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',\r 'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',\r 'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',\r 'self_attn_layer_norm': 'encoder.layers.*.layer_norm',\r 'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',\r 'fc2': 'encoder.layers.*.feed_forward.output_dense',\r 'final_layer_norm': 'encoder.layers.*.final_layer_norm',\r 'encoder.layer_norm': 'encoder.layer_norm',\r 'adapter_layer': 'encoder.layers.*.adapter_layer',\r 'w2v_model.layer_norm': 'feature_projection.layer_norm',\r 'quantizer.weight_proj': 'quantizer.weight_proj',\r 'quantizer.vars': 'quantizer.codevectors',\r 'project_q': 'project_q',\r 'final_proj': 'project_hid',\r 'w2v_encoder.proj': 'lm_head',\r 'mask_emb': 'masked_spec_embed',\r 'pooling_layer.linear': 'projector',\r 'pooling_layer.projection': 'classifier',\r}\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t[\r 'lm_head',\r 'quantizer.weight_proj',\r 'quantizer.codevectors',\r 'project_q',\r 'project_hid',\r 'projector',\r 'classifier',\r]\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tTuple\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{}\r with open(SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\"r\"\"\"\t\t\t\t) as file:\r for line_number, line in enumerate(SCREAMING_SNAKE_CASE__\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tline.strip()\r if line:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tline.split()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tline_number\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\twords[0]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue\r return result\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tUnion[str, Any]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[Any]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tAny\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tUnion[str, Any]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r for attribute in key.split(\"\"\".\"\"\"\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tgetattr(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r for param_key in PARAM_MAPPING.keys():\r if full_name.endswith(SCREAMING_SNAKE_CASE__\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tPARAM_MAPPING[full_name.split(\"\"\".\"\"\"\t\t\t\t)[-1]]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"param\"\"\"\r\r if weight_type is not None and weight_type != \"param\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tgetattr(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t).shape\r elif weight_type is not None and weight_type == \"param\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\thf_pointer\r for attribute in hf_param_name.split(\"\"\".\"\"\"\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tgetattr(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tshape_pointer.shape\r\r # let's reduce dimension\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue[0]\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\thf_pointer.shape\r\r if hf_shape != value.shape:\r raise ValueError(\r F'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''\r F''' {value.shape} for {full_name}'''\t\t\t\t)\r\r if weight_type == \"weight\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue\r elif weight_type == \"weight_g\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue\r elif weight_type == \"weight_v\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue\r elif weight_type == \"bias\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue\r elif weight_type == \"param\":\r for attribute in hf_param_name.split(\"\"\".\"\"\"\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tgetattr(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue\r\r logger.info(F'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.'''\t\t\t\t)\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tUnion[str, Any]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tUnion[str, Any]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[Any]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[Any]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r for param_key in PARAM_MAPPING.keys():\r if full_name.endswith(SCREAMING_SNAKE_CASE__\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tPARAM_MAPPING[full_name.split(\"\"\".\"\"\"\t\t\t\t)[-1]]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"param\"\"\"\r\r if weight_type is not None and weight_type != \"param\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\".\"\"\".join([key, weight_type]\t\t\t\t)\r elif weight_type is not None and weight_type == \"param\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\".\"\"\".join([key, hf_param_name]\t\t\t\t)\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tkey\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue if \"\"\"lm_head\"\"\" in full_key else value[0]\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t{\r 'W_a': 'linear_1.weight',\r 'W_b': 'linear_2.weight',\r 'b_a': 'linear_1.bias',\r 'b_b': 'linear_2.bias',\r 'ln_W': 'norm.weight',\r 'ln_b': 'norm.bias',\r}\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tAny\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tDict=None\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[Any]=None\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r for key, mapped_key in MAPPING.items():\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"wav2vec2.\"\"\" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key\r if key in name or key.split(\"\"\"w2v_model.\"\"\"\t\t\t\t)[-1] == name.split(\"\"\".\"\"\"\t\t\t\t)[0]:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTrue\r if \"*\" in mapped_key:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tname.split(SCREAMING_SNAKE_CASE__\t\t\t\t)[0].split(\"\"\".\"\"\"\t\t\t\t)[-2]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmapped_key.replace(\"\"\"*\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r if \"weight_g\" in name:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"weight_g\"\"\"\r elif \"weight_v\" in name:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"weight_v\"\"\"\r elif \"bias\" in name:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"bias\"\"\"\r elif \"weight\" in name:\r # TODO: don't match quantizer.weight_proj\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"weight\"\"\"\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r if hf_dict is not None:\r rename_dict(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r else:\r set_recursively(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r return is_used\r return is_used\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tTuple\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[int]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tfairseq_model.state_dict()\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\thf_model.wavaveca.feature_extractor\r\r for name, value in fairseq_dict.items():\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r if \"conv_layers\" in name:\r load_conv_layer(\r SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, hf_model.config.feat_extract_norm == \"\"\"group\"\"\"\t\t\t\t, )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTrue\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tload_wavaveca_layer(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r if not is_used:\r unused_weights.append(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r logger.warning(F'''Unused weights: {unused_weights}'''\t\t\t\t)\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[Any]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tfull_name.split(\"\"\"conv_layers.\"\"\"\t\t\t\t)[-1]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tname.split(\"\"\".\"\"\"\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tint(items[0]\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tint(items[1]\t\t\t\t)\r\r if type_id == 0:\r if \"bias\" in name:\r if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:\r raise ValueError(\r F'''{full_name} has size {value.shape}, but'''\r F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue\r logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''\t\t\t\t)\r elif \"weight\" in name:\r if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:\r raise ValueError(\r F'''{full_name} has size {value.shape}, but'''\r F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue\r logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''\t\t\t\t)\r elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):\r if \"bias\" in name:\r if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:\r raise ValueError(\r F'''{full_name} has size {value.shape}, but'''\r F''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'''\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue\r logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''\t\t\t\t)\r elif \"weight\" in name:\r if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:\r raise ValueError(\r F'''{full_name} has size {value.shape}, but'''\r F''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'''\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue\r logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''\t\t\t\t)\r else:\r unused_weights.append(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r@torch.no_grad()\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]=None\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[int]=None\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tUnion[str, Any]=True\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tUnion[str, Any]=False\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r if config_path is not None:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tWavaVecaConfig.from_pretrained(SCREAMING_SNAKE_CASE__\t\t\t\t)\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tWavaVecaConfig()\r\r if is_seq_class:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tread_txt_into_dict(SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tidalabel\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tWavaVecaForSequenceClassification(SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tWavaVecaFeatureExtractor(\r feature_size=1\t\t\t\t, sampling_rate=16000\t\t\t\t, padding_value=0\t\t\t\t, do_normalize=SCREAMING_SNAKE_CASE__\t\t\t\t, return_attention_mask=SCREAMING_SNAKE_CASE__\t\t\t\t, )\r feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r elif is_finetuned:\r if dict_path:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tDictionary.load(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r # important change bos & pad token id since CTC symbol is and\r # not as in fairseq\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttarget_dict.pad_index\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttarget_dict.bos_index\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttarget_dict.eos_index\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlen(target_dict.symbols\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tos.path.join(SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\"vocab.json\"\"\"\t\t\t\t)\r if not os.path.isdir(SCREAMING_SNAKE_CASE__\t\t\t\t):\r logger.error(\"\"\"--pytorch_dump_folder_path ({}) should be a directory\"\"\".format(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t)\r return\r os.makedirs(SCREAMING_SNAKE_CASE__\t\t\t\t, exist_ok=SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttarget_dict.indices\r\r # fairseq has the and switched\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t1\r with open(SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\"w\"\"\"\t\t\t\t, encoding=\"\"\"utf-8\"\"\"\t\t\t\t) as vocab_handle:\r json.dump(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tWavaVecaCTCTokenizer(\r SCREAMING_SNAKE_CASE__\t\t\t\t, unk_token=target_dict.unk_word\t\t\t\t, pad_token=target_dict.pad_word\t\t\t\t, bos_token=target_dict.bos_word\t\t\t\t, eos_token=target_dict.eos_word\t\t\t\t, word_delimiter_token=\"\"\"|\"\"\"\t\t\t\t, do_lower_case=SCREAMING_SNAKE_CASE__\t\t\t\t, )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTrue if config.feat_extract_norm == \"\"\"layer\"\"\" else False\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tWavaVecaFeatureExtractor(\r feature_size=1\t\t\t\t, sampling_rate=16000\t\t\t\t, padding_value=0\t\t\t\t, do_normalize=SCREAMING_SNAKE_CASE__\t\t\t\t, return_attention_mask=SCREAMING_SNAKE_CASE__\t\t\t\t, )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tWavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE__\t\t\t\t, tokenizer=SCREAMING_SNAKE_CASE__\t\t\t\t)\r processor.save_pretrained(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tWavaVecaForCTC(SCREAMING_SNAKE_CASE__\t\t\t\t)\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tWavaVecaForPreTraining(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r if is_finetuned or is_seq_class:\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tfairseq.checkpoint_utils.load_model_ensemble_and_task(\r [checkpoint_path]\t\t\t\t, arg_overrides={\"\"\"data\"\"\": \"\"\"/\"\"\".join(dict_path.split(\"\"\"/\"\"\"\t\t\t\t)[:-1]\t\t\t\t)}\t\t\t\t)\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\targparse.Namespace(task=\"\"\"audio_pretraining\"\"\"\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tfairseq.tasks.setup_task(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tfairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]\t\t\t\t, task=SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel[0].eval()\r\r recursively_load_weights(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, not is_finetuned\t\t\t\t)\r\r hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r\rif __name__ == \"__main__\":\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\targparse.ArgumentParser()\r parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')\r parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')\r parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')\r parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')\r parser.add_argument(\r '--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'\r )\r parser.add_argument(\r '--is_seq_class',\r action='store_true',\r help='Whether the model to convert is a fine-tuned sequence classification model or not',\r )\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tparser.parse_args()\r\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tnot args.not_finetuned and not args.is_seq_class\r convert_wavaveca_checkpoint(\r args.checkpoint_path,\r args.pytorch_dump_folder_path,\r args.config_path,\r args.dict_path,\r is_finetuned,\r args.is_seq_class,\r )\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152387,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport random\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tTuple\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[Any]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[Any]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ta[left_index]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tleft_index + 1\r for j in range(left_index + 1\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r if a[j] < pivot:\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ta[i], a[j]\r i += 1\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ta[i - 1], a[left_index]\r return i - 1\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[Any]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tDict\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[Any]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r if left < right:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\trandom.randint(SCREAMING_SNAKE_CASE__\t\t\t\t, right - 1\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t(\r a[left],\r a[pivot],\r ) # switches the pivot with the left most bound\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tpartition(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r quick_sort_random(\r SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t) # recursive quicksort to the left of the pivot point\r quick_sort_random(\r SCREAMING_SNAKE_CASE__\t\t\t\t, pivot_index + 1\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t) # recursive quicksort to the right of the pivot point\r\rdef _UpperCamelCase\t\t\t\t\t( ):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tinput(\"\"\"Enter numbers separated by a comma:\\n\"\"\"\t\t\t\t).strip()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[int(SCREAMING_SNAKE_CASE__\t\t\t\t) for item in user_input.split(\"\"\",\"\"\"\t\t\t\t)]\r\r quick_sort_random(SCREAMING_SNAKE_CASE__\t\t\t\t, 0\t\t\t\t, len(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t)\r\r print(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r\rif __name__ == \"__main__\":\r main()\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport itertools\rimport os\rfrom collections import Counter, defaultdict\rfrom concurrent.futures import ThreadPoolExecutor, as_completed\r\rimport numpy as np\r\rimport datasets\r\rfrom .execute import check_correctness\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'\\\\n@misc{chen2021evaluating,\\n title={Evaluating Large Language Models Trained on Code},\\n author={Mark Chen and Jerry Tworek and Heewoo Jun and Qiming Yuan \\\\nand Henrique Ponde de Oliveira Pinto and Jared Kaplan and Harri Edwards \\\\nand Yuri Burda and Nicholas Joseph and Greg Brockman and Alex Ray \\\\nand Raul Puri and Gretchen Krueger and Michael Petrov and Heidy Khlaaf \\\\nand Girish Sastry and Pamela Mishkin and Brooke Chan and Scott Gray \\\\nand Nick Ryder and Mikhail Pavlov and Alethea Power and Lukasz Kaiser \\\\nand Mohammad Bavarian and Clemens Winter and Philippe Tillet \\\\nand Felipe Petroski Such and Dave Cummings and Matthias Plappert \\\\nand Fotios Chantzis and Elizabeth Barnes and Ariel Herbert-Voss \\\\nand William Hebgen Guss and Alex Nichol and Alex Paino and Nikolas Tezak \\\\nand Jie Tang and Igor Babuschkin and Suchir Balaji and Shantanu Jain \\\\nand William Saunders and Christopher Hesse and Andrew N. Carr \\\\nand Jan Leike and Josh Achiam and Vedant Misra and Evan Morikawa \\\\nand Alec Radford and Matthew Knight and Miles Brundage and Mira Murati \\\\nand Katie Mayer and Peter Welinder and Bob McGrew and Dario Amodei \\\\nand Sam McCandlish and Ilya Sutskever and Wojciech Zaremba},\\n year={2021},\\n eprint={2107.03374},\\n archivePrefix={arXiv},\\n primaryClass={cs.LG}\\n}\\n'\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'\\\\nThis metric implements the evaluation harness for the HumanEval problem solving dataset\\ndescribed in the paper \"Evaluating Large Language Models Trained on Code\"\\n(https://arxiv.org/abs/2107.03374).\\n'\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'\\nCalculates how good are predictions given some references, using certain scores\\nArgs:\\n predictions: list of candidates to evaluate. Each candidates should be a list\\n of strings with several code candidates to solve the problem.\\n references: a list with a test for each prediction. Each test should evaluate the\\n correctness of a code candidate.\\n k: number of code candidates to consider in the evaluation (Default: [1, 10, 100])\\n num_workers: number of workers used to evaluate the canidate programs (Default: 4).\\n timeout:\\nReturns:\\n pass_at_k: dict with pass rates for each k\\n results: dict with granular results of each unittest\\nExamples:\\n >>> code_eval = datasets.load_metric(\"code_eval\")\\n >>> test_cases = [\"assert add(2,3)==5\"]\\n >>> candidates = [[\"def add(a,b): return a*b\", \"def add(a, b): return a+b\"]]\\n >>> pass_at_k, results = code_eval.compute(references=test_cases, predictions=candidates, k=[1, 2])\\n >>> print(pass_at_k)\\n {\\'pass@1\\': 0.5, \\'pass@2\\': 1.0}\\n'\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'\\n################################################################################\\n !!!WARNING!!!\\n################################################################################\\nThe \"code_eval\" metric executes untrusted model-generated code in Python.\\nAlthough it is highly unlikely that model-generated code will do something\\novertly malicious in response to this test suite, model-generated code may act\\ndestructively due to a lack of model capability or alignment.\\nUsers are strongly encouraged to sandbox this evaluation suite so that it\\ndoes not perform destructive actions on their host or network. For more\\ninformation on how OpenAI sandboxes its code, see the paper \"Evaluating Large\\nLanguage Models Trained on Code\" (https://arxiv.org/abs/2107.03374).\\n\\nOnce you have read this disclaimer and taken appropriate precautions,\\nset the environment variable HF_ALLOW_CODE_EVAL=\"1\". Within Python you can to this\\nwith:\\n\\n>>> import os\\n>>> os.environ[\"HF_ALLOW_CODE_EVAL\"] = \"1\"\\n\\n################################################################################\\\\n'\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'The MIT License\\n\\nCopyright (c) OpenAI (https://openai.com)\\n\\nPermission is hereby granted, free of charge, to any person obtaining a copy\\nof this software and associated documentation files (the \"Software\"), to deal\\nin the Software without restriction, including without limitation the rights\\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\\ncopies of the Software, and to permit persons to whom the Software is\\nfurnished to do so, subject to the following conditions:\\n\\nThe above copyright notice and this permission notice shall be included in\\nall copies or substantial portions of the Software.\\n\\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\\nTHE SOFTWARE.'\r\r@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION\t, _KWARGS_DESCRIPTION )\rclass lowerCAmelCase_ ( datasets.Metric ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return datasets.MetricInfo(\r # This is the description that will appear on the metrics page.\r description=_DESCRIPTION ,\t\t\t\t\t\tcitation=_CITATION ,\t\t\t\t\t\tinputs_description=_KWARGS_DESCRIPTION ,\t\t\t\t\t\tfeatures=datasets.Features(\r {\r \"\"\"predictions\"\"\": datasets.Sequence(datasets.Value(\"\"\"string\"\"\" ) ),\r \"\"\"references\"\"\": datasets.Value(\"\"\"string\"\"\" ),\r } ) ,\t\t\t\t\t\thomepage=\"\"\"https://github.com/openai/human-eval\"\"\" ,\t\t\t\t\t\tcodebase_urls=[\"\"\"https://github.com/openai/human-eval\"\"\"] ,\t\t\t\t\t\treference_urls=[\"\"\"https://github.com/openai/human-eval\"\"\"] ,\t\t\t\t\t\tlicense=_LICENSE ,\t\t\t\t\t\t)\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str]=[1, 10, 1_00] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any]=4 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny=3.0 ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if os.getenv(\"\"\"HF_ALLOW_CODE_EVAL\"\"\" ,\t\t\t\t\t\t0 ) != \"1\":\r raise ValueError(_WARNING )\r\r if os.name == \"nt\":\r raise NotImplementedError(\"\"\"This metric is currently not supported on Windows.\"\"\" )\r\r with ThreadPoolExecutor(max_workers=_UpperCAmelCase ) as executor:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tCounter()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdefaultdict(_UpperCAmelCase )\r\r for task_id, (candidates, test_case) in enumerate(zip(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ) ):\r for candidate in candidates:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tcandidate + \"\"\"\\n\"\"\" + test_case\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t(test_program, timeout, task_id, completion_id[task_id])\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\texecutor.submit(_UpperCAmelCase ,\t\t\t\t\t\t*_UpperCAmelCase )\r futures.append(_UpperCAmelCase )\r completion_id[task_id] += 1\r n_samples += 1\r\r for future in as_completed(_UpperCAmelCase ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tfuture.result()\r results[result[\"task_id\"]].append((result[\"\"\"completion_id\"\"\"], result) )\r\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[], []\r for result in results.values():\r result.sort()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[r[1][\"\"\"passed\"\"\"] for r in result]\r total.append(len(_UpperCAmelCase ) )\r correct.append(sum(_UpperCAmelCase ) )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnp.array(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnp.array(_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tk\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{f'''pass@{k}''': estimate_pass_at_k(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ).mean() for k in ks if (total >= k).all()}\r\r return pass_at_k, results\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[Any]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[Any]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r def estimator(SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t) -> float:\r if n - c < k:\r return 1.0\r return 1.0 - np.prod(1.0 - k / np.arange(n - c + 1\t\t\t\t, n + 1\t\t\t\t)\t\t\t\t)\r\r if isinstance(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\titertools.repeat(SCREAMING_SNAKE_CASE__\t\t\t\t, len(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t)\r else:\r assert len(SCREAMING_SNAKE_CASE__\t\t\t\t) == len(SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\titer(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r return np.array([estimator(int(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t, int(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t) for n, c in zip(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)]\t\t\t\t)\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152388,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\r# Lint as: python3\rimport sys\rfrom collections.abc import Mapping\rfrom typing import TYPE_CHECKING, Dict, Optional\r\rimport numpy as np\rimport pyarrow as pa\r\rfrom .. import config\rfrom ..utils.logging import get_logger\rfrom ..utils.py_utils import map_nested\rfrom .formatting import TensorFormatter\r\r\rif TYPE_CHECKING:\r import jax\r import jaxlib\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tget_logger()\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tNone\r\rclass lowerCAmelCase_ ( TensorFormatter[Mapping, \"\"\"jax.Array\"\"\", Mapping] ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def __init__( self\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny=None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=None ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tAny ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r super().__init__(features=_UpperCAmelCase )\r import jax\r from jaxlib.xla_client import Device\r\r if isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ):\r raise ValueError(\r f'''Expected {device} to be a `str` not {type(_UpperCAmelCase )}, as `jaxlib.xla_extension.Device` '''\r \"\"\"is not serializable neither with `pickle` nor with `dill`. Instead you can surround \"\"\"\r \"\"\"the device with `str()` to get its string identifier that will be internally mapped \"\"\"\r \"\"\"to the actual `jaxlib.xla_extension.Device`.\"\"\" )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdevice if isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ) else str(jax.devices()[0] )\r # using global variable since `jaxlib.xla_extension.Device` is not serializable neither\r # with `pickle` nor with `dill`, so we need to use a global variable instead\r global DEVICE_MAPPING\r if DEVICE_MAPPING is None:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself._map_devices_to_str()\r if self.device not in list(DEVICE_MAPPING.keys() ):\r logger.warning(\r f'''Device with string identifier {self.device} not listed among the available '''\r f'''devices: {list(DEVICE_MAPPING.keys() )}, so falling back to the default '''\r f'''device: {str(jax.devices()[0] )}.''' )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tstr(jax.devices()[0] )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tjnp_array_kwargs\r\r\r @staticmethod\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r import jax\r\r return {str(_UpperCAmelCase ): device for device in jax.devices()}\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tAny ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r import jax\r import jax.numpy as jnp\r\r if isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ) and column:\r if all(\r isinstance(_UpperCAmelCase ,\t\t\t\t\t\tjax.Array ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column ):\r return jnp.stack(_UpperCAmelCase ,\t\t\t\t\t\taxis=0 )\r return column\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r import jax\r import jax.numpy as jnp\r\r if isinstance(_UpperCAmelCase ,\t\t\t\t\t\t(str, bytes, type(_UpperCAmelCase )) ):\r return value\r elif isinstance(_UpperCAmelCase ,\t\t\t\t\t\t(np.character, np.ndarray) ) and np.issubdtype(value.dtype ,\t\t\t\t\t\tnp.character ):\r return value.tolist()\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{}\r\r if isinstance(_UpperCAmelCase ,\t\t\t\t\t\t(np.number, np.ndarray) ) and np.issubdtype(value.dtype ,\t\t\t\t\t\tnp.integer ):\r # the default int precision depends on the jax config\r # see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision\r if jax.config.jax_enable_xaa:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\"\"\"dtype\"\"\": jnp.intaa}\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\"\"\"dtype\"\"\": jnp.intaa}\r elif isinstance(_UpperCAmelCase ,\t\t\t\t\t\t(np.number, np.ndarray) ) and np.issubdtype(value.dtype ,\t\t\t\t\t\tnp.floating ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\"\"\"dtype\"\"\": jnp.floataa}\r elif config.PIL_AVAILABLE and \"PIL\" in sys.modules:\r import PIL.Image\r\r if isinstance(_UpperCAmelCase ,\t\t\t\t\t\tPIL.Image.Image ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnp.asarray(_UpperCAmelCase )\r\r # using global variable since `jaxlib.xla_extension.Device` is not serializable neither\r # with `pickle` nor with `dill`, so we need to use a global variable instead\r global DEVICE_MAPPING\r if DEVICE_MAPPING is None:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself._map_devices_to_str()\r\r with jax.default_device(DEVICE_MAPPING[self.device] ):\r # calling jnp.array on a np.ndarray does copy the data\r # see https://github.com/google/jax/issues/4486\r return jnp.array(_UpperCAmelCase ,\t\t\t\t\t\t**{**default_dtype, **self.jnp_array_kwargs} )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tAny ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r import jax\r\r # support for torch, tf, jax etc.\r if config.TORCH_AVAILABLE and \"torch\" in sys.modules:\r import torch\r\r if isinstance(_UpperCAmelCase ,\t\t\t\t\t\ttorch.Tensor ):\r return self._tensorize(data_struct.detach().cpu().numpy()[()] )\r if hasattr(_UpperCAmelCase ,\t\t\t\t\t\t\"\"\"__array__\"\"\" ) and not isinstance(_UpperCAmelCase ,\t\t\t\t\t\tjax.Array ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdata_struct.__array__()\r # support for nested types like struct of list of struct\r if isinstance(_UpperCAmelCase ,\t\t\t\t\t\tnp.ndarray ):\r if data_struct.dtype == object: # jax arrays cannot be instantied from an array of objects\r return self._consolidate([self.recursive_tensorize(_UpperCAmelCase ) for substruct in data_struct] )\r elif isinstance(_UpperCAmelCase ,\t\t\t\t\t\t(list, tuple) ):\r return self._consolidate([self.recursive_tensorize(_UpperCAmelCase ) for substruct in data_struct] )\r return self._tensorize(_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tdict ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return map_nested(self._recursive_tensorize ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\tmap_list=_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tpa.Table ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.numpy_arrow_extractor().extract_row(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.python_features_decoder.decode_row(_UpperCAmelCase )\r return self.recursive_tensorize(_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tpa.Table ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.numpy_arrow_extractor().extract_column(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.python_features_decoder.decode_column(_UpperCAmelCase ,\t\t\t\t\t\tpa_table.column_names[0] )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.recursive_tensorize(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself._consolidate(_UpperCAmelCase )\r return column\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tpa.Table ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.numpy_arrow_extractor().extract_batch(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.python_features_decoder.decode_batch(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.recursive_tensorize(_UpperCAmelCase )\r for column_name in batch:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself._consolidate(batch[column_name] )\r return batch\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport math\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r assert isinstance(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t) and (\r number >= 0\r ), \"'number' must been an int and positive\"\r\r if 1 < number < 4:\r # 2 and 3 are primes\r return True\r elif number < 2 or not number % 2:\r # Negatives, 0, 1 and all even numbers are not primes\r return False\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\trange(3\t\t\t\t, int(math.sqrt(SCREAMING_SNAKE_CASE__\t\t\t\t) + 1\t\t\t\t)\t\t\t\t, 2\t\t\t\t)\r return not any(not number % i for i in odd_numbers\t\t\t\t)\r\r\r\r\r\r\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tUnion[str, Any]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]=1\t\t\t\t, **SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tfactor * value\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue\r\r while not is_prime(SCREAMING_SNAKE_CASE__\t\t\t\t):\r value += 1 if not (\"desc\" in kwargs and kwargs[\"desc\"] is True) else -1\r\r if value == first_value_val:\r return next_prime(value + 1\t\t\t\t, **SCREAMING_SNAKE_CASE__\t\t\t\t)\r return value\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152389,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rfrom typing import TYPE_CHECKING\r\rfrom ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t{\r 'configuration_mask2former': [\r 'MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',\r 'Mask2FormerConfig',\r ],\r}\r\rtry:\r if not is_vision_available():\r raise OptionalDependencyNotAvailable()\rexcept OptionalDependencyNotAvailable:\r pass\relse:\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\t['Mask2FormerImageProcessor']\r\rtry:\r if not is_torch_available():\r raise OptionalDependencyNotAvailable()\rexcept OptionalDependencyNotAvailable:\r pass\relse:\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\t[\r 'MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST',\r 'Mask2FormerForUniversalSegmentation',\r 'Mask2FormerModel',\r 'Mask2FormerPreTrainedModel',\r ]\r\rif TYPE_CHECKING:\r from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig\r\r try:\r if not is_vision_available():\r raise OptionalDependencyNotAvailable()\r except OptionalDependencyNotAvailable:\r pass\r else:\r from .image_processing_maskaformer import MaskaFormerImageProcessor\r\r try:\r if not is_torch_available():\r raise OptionalDependencyNotAvailable()\r except OptionalDependencyNotAvailable:\r pass\r else:\r from .modeling_maskaformer import (\r MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,\r MaskaFormerForUniversalSegmentation,\r MaskaFormerModel,\r MaskaFormerPreTrainedModel,\r )\r\r\relse:\r import sys\r\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\t_LazyModule(__name__, globals()['__file__'], _import_structure)\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport string\rfrom math import logaa\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdocument.translate(\r str.maketrans(\"\"\"\"\"\"\t\t\t\t, \"\"\"\"\"\"\t\t\t\t, string.punctuation\t\t\t\t)\t\t\t\t).replace(\"\"\"\\n\"\"\"\t\t\t\t, \"\"\"\"\"\"\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdocument_without_punctuation.split(\"\"\" \"\"\"\t\t\t\t) # word tokenization\r return len([word for word in tokenize_document if word.lower() == term.lower()]\t\t\t\t)\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tcorpus.lower().translate(\r str.maketrans(\"\"\"\"\"\"\t\t\t\t, \"\"\"\"\"\"\t\t\t\t, string.punctuation\t\t\t\t)\t\t\t\t) # strip all punctuation and replace it with ''\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tcorpus_without_punctuation.split(\"\"\"\\n\"\"\"\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tterm.lower()\r return (len([doc for doc in docs if term in doc]\t\t\t\t), len(SCREAMING_SNAKE_CASE__\t\t\t\t))\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tTuple=False\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r if smoothing:\r if n == 0:\r raise ValueError(\"\"\"log10(0) is undefined.\"\"\"\t\t\t\t)\r return round(1 + logaa(n / (1 + df)\t\t\t\t)\t\t\t\t, 3\t\t\t\t)\r\r if df == 0:\r raise ZeroDivisionError(\"\"\"df must be > 0\"\"\"\t\t\t\t)\r elif n == 0:\r raise ValueError(\"\"\"log10(0) is undefined.\"\"\"\t\t\t\t)\r return round(logaa(n / df\t\t\t\t)\t\t\t\t, 3\t\t\t\t)\r\r\r\r\r\r\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r return round(tf * idf\t\t\t\t, 3\t\t\t\t)\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152390,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rfrom typing import Any\r\rclass lowerCAmelCase_ :\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def __init__( self\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdata\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r\rclass lowerCAmelCase_ :\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def __init__( self\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.head\r while temp is not None:\r print(temp.data ,\t\t\t\t\t\tend=\"\"\" \"\"\" )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttemp.next\r print()\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNode(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.head\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnew_node\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tAny ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if node_data_a == node_data_a:\r return\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.head\r while node_a is not None and node_a.data != node_data_a:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnode_a.next\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.head\r while node_a is not None and node_a.data != node_data_a:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnode_a.next\r\r if node_a is None or node_a is None:\r return\r\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnode_a.data, node_a.data\r\r\rif __name__ == \"__main__\":\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tLinkedList()\r for i in range(5, 0, -1):\r ll.push(i)\r\r ll.print_list()\r\r ll.swap_nodes(1, 4)\r print('After swapping')\r ll.print_list()\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport argparse\r\rimport torch\r\rfrom transformers import BertForMaskedLM\r\r\rif __name__ == \"__main__\":\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\targparse.ArgumentParser(\r description=(\r 'Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'\r ' Distillation'\r )\r )\r parser.add_argument('--model_type', default='bert', choices=['bert'])\r parser.add_argument('--model_name', default='bert-base-uncased', type=str)\r parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_bert-base-uncased_0247911.pth', type=str)\r parser.add_argument('--vocab_transform', action='store_true')\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tparser.parse_args()\r\r if args.model_type == \"bert\":\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tBertForMaskedLM.from_pretrained(args.model_name)\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\t'bert'\r else:\r raise ValueError('args.model_type should be \"bert\".')\r\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tmodel.state_dict()\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\t{}\r\r for w in [\"word_embeddings\", \"position_embeddings\"]:\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tstate_dict[f\"{prefix}.embeddings.{w}.weight\"]\r for w in [\"weight\", \"bias\"]:\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tstate_dict[f\"{prefix}.embeddings.LayerNorm.{w}\"]\r\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\t0\r for teacher_idx in [0, 2, 4, 7, 9, 1_1]:\r for w in [\"weight\", \"bias\"]:\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tstate_dict[\r f\"{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}\"\r ]\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tstate_dict[\r f\"{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}\"\r ]\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tstate_dict[\r f\"{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}\"\r ]\r\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tstate_dict[\r f\"{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}\"\r ]\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tstate_dict[\r f\"{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}\"\r ]\r\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tstate_dict[\r f\"{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}\"\r ]\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tstate_dict[\r f\"{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}\"\r ]\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tstate_dict[\r f\"{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}\"\r ]\r std_idx += 1\r\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tstate_dict['cls.predictions.decoder.weight']\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tstate_dict['cls.predictions.bias']\r if args.vocab_transform:\r for w in [\"weight\", \"bias\"]:\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tstate_dict[f\"cls.predictions.transform.dense.{w}\"]\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tstate_dict[f\"cls.predictions.transform.LayerNorm.{w}\"]\r\r print(f\"N layers selected for distillation: {std_idx}\")\r print(f\"Number of params transferred for distillation: {len(compressed_sd.keys())}\")\r\r print(f\"Save transferred checkpoint to {args.dump_checkpoint}.\")\r torch.save(compressed_sd, args.dump_checkpoint)\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152391,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport torch\r\rfrom diffusers import DPMSolverSDEScheduler\rfrom diffusers.utils import torch_device\rfrom diffusers.utils.testing_utils import require_torchsde\r\rfrom .test_schedulers import SchedulerCommonTest\r\r@require_torchsde\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r lowerCAmelCase_ : Tuple = (DPMSolverSDEScheduler,)\r lowerCAmelCase_ : Optional[Any] = 10\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tTuple ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\r \"\"\"num_train_timesteps\"\"\": 11_00,\r \"\"\"beta_start\"\"\": 0.0001,\r \"\"\"beta_end\"\"\": 0.02,\r \"\"\"beta_schedule\"\"\": \"\"\"linear\"\"\",\r \"\"\"noise_sampler_seed\"\"\": 0,\r }\r\r config.update(**_UpperCAmelCase )\r return config\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r for timesteps in [10, 50, 1_00, 10_00]:\r self.check_over_configs(num_train_timesteps=_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001] ,\t\t\t\t\t\t[0.0002, 0.002, 0.02] ):\r self.check_over_configs(beta_start=_UpperCAmelCase ,\t\t\t\t\t\tbeta_end=_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r for schedule in [\"linear\", \"scaled_linear\"]:\r self.check_over_configs(beta_schedule=_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r for prediction_type in [\"epsilon\", \"v_prediction\"]:\r self.check_over_configs(prediction_type=_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.scheduler_classes[0]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_scheduler_config()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler_class(**_UpperCAmelCase )\r\r scheduler.set_timesteps(self.num_inference_steps )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.dummy_model()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.dummy_sample_deter * scheduler.init_noise_sigma\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tsample.to(_UpperCAmelCase )\r\r for i, t in enumerate(scheduler.timesteps ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler.scale_model_input(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler.step(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\toutput.prev_sample\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.sum(torch.abs(_UpperCAmelCase ) )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.mean(torch.abs(_UpperCAmelCase ) )\r\r if torch_device in [\"mps\"]:\r assert abs(result_sum.item() - 167.47_8210_4492_1875 ) < 1E-2\r assert abs(result_mean.item() - 0.2178_7059_6456_5277 ) < 1E-3\r elif torch_device in [\"cuda\"]:\r assert abs(result_sum.item() - 171.59_3521_1181_6406 ) < 1E-2\r assert abs(result_mean.item() - 0.2_2342_9068_9229_9652 ) < 1E-3\r else:\r assert abs(result_sum.item() - 162.52_3834_2285_1562 ) < 1E-2\r assert abs(result_mean.item() - 0.211_6195_7085_1326 ) < 1E-3\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.scheduler_classes[0]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_scheduler_config(prediction_type=\"\"\"v_prediction\"\"\" )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler_class(**_UpperCAmelCase )\r\r scheduler.set_timesteps(self.num_inference_steps )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.dummy_model()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.dummy_sample_deter * scheduler.init_noise_sigma\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tsample.to(_UpperCAmelCase )\r\r for i, t in enumerate(scheduler.timesteps ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler.scale_model_input(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler.step(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\toutput.prev_sample\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.sum(torch.abs(_UpperCAmelCase ) )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.mean(torch.abs(_UpperCAmelCase ) )\r\r if torch_device in [\"mps\"]:\r assert abs(result_sum.item() - 124.77_1492_0043_9453 ) < 1E-2\r assert abs(result_mean.item() - 0.1_6226_2890_1481_6284 ) < 1E-3\r elif torch_device in [\"cuda\"]:\r assert abs(result_sum.item() - 128.1_6633_6059_5703 ) < 1E-2\r assert abs(result_mean.item() - 0.1_6688_3260_0116_7297 ) < 1E-3\r else:\r assert abs(result_sum.item() - 119.8_4875_4882_8125 ) < 1E-2\r assert abs(result_mean.item() - 0.1560_5306_6253_6621 ) < 1E-3\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.scheduler_classes[0]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_scheduler_config()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler_class(**_UpperCAmelCase )\r\r scheduler.set_timesteps(self.num_inference_steps ,\t\t\t\t\t\tdevice=_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.dummy_model()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.dummy_sample_deter.to(_UpperCAmelCase ) * scheduler.init_noise_sigma\r\r for t in scheduler.timesteps:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler.scale_model_input(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler.step(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\toutput.prev_sample\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.sum(torch.abs(_UpperCAmelCase ) )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.mean(torch.abs(_UpperCAmelCase ) )\r\r if torch_device in [\"mps\"]:\r assert abs(result_sum.item() - 167.46_9573_9746_0938 ) < 1E-2\r assert abs(result_mean.item() - 0.2_1805_9346_0798_2635 ) < 1E-3\r elif torch_device in [\"cuda\"]:\r assert abs(result_sum.item() - 171.59_3536_3769_5312 ) < 1E-2\r assert abs(result_mean.item() - 0.2_2342_9083_8241_5771 ) < 1E-3\r else:\r assert abs(result_sum.item() - 162.52_3834_2285_1562 ) < 1E-2\r assert abs(result_mean.item() - 0.211_6195_7085_1326 ) < 1E-3\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.scheduler_classes[0]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_scheduler_config()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler_class(**_UpperCAmelCase ,\t\t\t\t\t\tuse_karras_sigmas=_UpperCAmelCase )\r\r scheduler.set_timesteps(self.num_inference_steps ,\t\t\t\t\t\tdevice=_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.dummy_model()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.dummy_sample_deter.to(_UpperCAmelCase ) * scheduler.init_noise_sigma\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tsample.to(_UpperCAmelCase )\r\r for t in scheduler.timesteps:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler.scale_model_input(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler.step(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\toutput.prev_sample\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.sum(torch.abs(_UpperCAmelCase ) )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.mean(torch.abs(_UpperCAmelCase ) )\r\r if torch_device in [\"mps\"]:\r assert abs(result_sum.item() - 176.66_9741_3574_2188 ) < 1E-2\r assert abs(result_mean.item() - 0.2_3003_8727_3098_1811 ) < 1E-2\r elif torch_device in [\"cuda\"]:\r assert abs(result_sum.item() - 177.63_6535_6445_3125 ) < 1E-2\r assert abs(result_mean.item() - 0.2_3003_8727_3098_1811 ) < 1E-2\r else:\r assert abs(result_sum.item() - 170.3_1352_2338_8672 ) < 1E-2\r assert abs(result_mean.item() - 0.2_3003_8727_3098_1811 ) < 1E-2\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport tempfile\r\rimport torch\r\rfrom diffusers import PNDMScheduler\r\rfrom .test_schedulers import SchedulerCommonTest\r\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r lowerCAmelCase_ : Union[str, Any] = (PNDMScheduler,)\r lowerCAmelCase_ : Optional[int] = ((\"\"\"num_inference_steps\"\"\", 50),)\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tOptional[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\r \"\"\"num_train_timesteps\"\"\": 10_00,\r \"\"\"beta_start\"\"\": 0.0001,\r \"\"\"beta_end\"\"\": 0.02,\r \"\"\"beta_schedule\"\"\": \"\"\"linear\"\"\",\r }\r\r config.update(**_UpperCAmelCase )\r return config\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple=0 ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdict(self.forward_default_kwargs )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tkwargs.pop(\"\"\"num_inference_steps\"\"\" ,\t\t\t\t\t\t_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.dummy_sample\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0.1 * sample\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]\r\r for scheduler_class in self.scheduler_classes:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_scheduler_config(**_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler_class(**_UpperCAmelCase )\r scheduler.set_timesteps(_UpperCAmelCase )\r # copy over dummy past residuals\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdummy_past_residuals[:]\r\r with tempfile.TemporaryDirectory() as tmpdirname:\r scheduler.save_config(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler_class.from_pretrained(_UpperCAmelCase )\r new_scheduler.set_timesteps(_UpperCAmelCase )\r # copy over dummy past residuals\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdummy_past_residuals[:]\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler.step_prk(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase ).prev_sample\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnew_scheduler.step_prk(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase ).prev_sample\r\r assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, \"Scheduler outputs are not identical\"\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler.step_plms(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase ).prev_sample\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnew_scheduler.step_plms(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase ).prev_sample\r\r assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, \"Scheduler outputs are not identical\"\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r pass\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=0 ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tOptional[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdict(self.forward_default_kwargs )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tkwargs.pop(\"\"\"num_inference_steps\"\"\" ,\t\t\t\t\t\t_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.dummy_sample\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0.1 * sample\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]\r\r for scheduler_class in self.scheduler_classes:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_scheduler_config()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler_class(**_UpperCAmelCase )\r scheduler.set_timesteps(_UpperCAmelCase )\r\r # copy over dummy past residuals (must be after setting timesteps)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdummy_past_residuals[:]\r\r with tempfile.TemporaryDirectory() as tmpdirname:\r scheduler.save_config(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler_class.from_pretrained(_UpperCAmelCase )\r # copy over dummy past residuals\r new_scheduler.set_timesteps(_UpperCAmelCase )\r\r # copy over dummy past residual (must be after setting timesteps)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdummy_past_residuals[:]\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler.step_prk(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase ).prev_sample\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnew_scheduler.step_prk(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase ).prev_sample\r\r assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, \"Scheduler outputs are not identical\"\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler.step_plms(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase ).prev_sample\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnew_scheduler.step_plms(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase ).prev_sample\r\r assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, \"Scheduler outputs are not identical\"\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tTuple ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.scheduler_classes[0]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_scheduler_config(**_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler_class(**_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t10\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.dummy_model()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.dummy_sample_deter\r scheduler.set_timesteps(_UpperCAmelCase )\r\r for i, t in enumerate(scheduler.prk_timesteps ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler.step_prk(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ).prev_sample\r\r for i, t in enumerate(scheduler.plms_timesteps ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler.step_plms(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ).prev_sample\r\r return sample\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdict(self.forward_default_kwargs )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tkwargs.pop(\"\"\"num_inference_steps\"\"\" ,\t\t\t\t\t\t_UpperCAmelCase )\r\r for scheduler_class in self.scheduler_classes:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_scheduler_config()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler_class(**_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.dummy_sample\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0.1 * sample\r\r if num_inference_steps is not None and hasattr(_UpperCAmelCase ,\t\t\t\t\t\t\"\"\"set_timesteps\"\"\" ):\r scheduler.set_timesteps(_UpperCAmelCase )\r elif num_inference_steps is not None and not hasattr(_UpperCAmelCase ,\t\t\t\t\t\t\"\"\"set_timesteps\"\"\" ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnum_inference_steps\r\r # copy over dummy past residuals (must be done after set_timesteps)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdummy_past_residuals[:]\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler.step_prk(_UpperCAmelCase ,\t\t\t\t\t\t0 ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase ).prev_sample\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler.step_prk(_UpperCAmelCase ,\t\t\t\t\t\t1 ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase ).prev_sample\r\r self.assertEqual(output_a.shape ,\t\t\t\t\t\tsample.shape )\r self.assertEqual(output_a.shape ,\t\t\t\t\t\toutput_a.shape )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler.step_plms(_UpperCAmelCase ,\t\t\t\t\t\t0 ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase ).prev_sample\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler.step_plms(_UpperCAmelCase ,\t\t\t\t\t\t1 ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase ).prev_sample\r\r self.assertEqual(output_a.shape ,\t\t\t\t\t\tsample.shape )\r self.assertEqual(output_a.shape ,\t\t\t\t\t\toutput_a.shape )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r for timesteps in [1_00, 10_00]:\r self.check_over_configs(num_train_timesteps=_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r for steps_offset in [0, 1]:\r self.check_over_configs(steps_offset=_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.scheduler_classes[0]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_scheduler_config(steps_offset=1 )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler_class(**_UpperCAmelCase )\r scheduler.set_timesteps(10 )\r assert torch.equal(\r scheduler.timesteps ,\t\t\t\t\t\ttorch.LongTensor(\r [9_01, 8_51, 8_51, 8_01, 8_01, 7_51, 7_51, 7_01, 7_01, 6_51, 6_51, 6_01, 6_01, 5_01, 4_01, 3_01, 2_01, 1_01, 1] ) ,\t\t\t\t\t\t)\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r for beta_start, beta_end in zip([0.0001, 0.001] ,\t\t\t\t\t\t[0.002, 0.02] ):\r self.check_over_configs(beta_start=_UpperCAmelCase ,\t\t\t\t\t\tbeta_end=_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r for schedule in [\"linear\", \"squaredcos_cap_v2\"]:\r self.check_over_configs(beta_schedule=_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r for prediction_type in [\"epsilon\", \"v_prediction\"]:\r self.check_over_configs(prediction_type=_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r for t in [1, 5, 10]:\r self.check_over_forward(time_step=_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tAny ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r for t, num_inference_steps in zip([1, 5, 10] ,\t\t\t\t\t\t[10, 50, 1_00] ):\r self.check_over_forward(num_inference_steps=_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t27\r\r for scheduler_class in self.scheduler_classes:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.dummy_sample\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0.1 * sample\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_scheduler_config()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler_class(**_UpperCAmelCase )\r\r scheduler.set_timesteps(_UpperCAmelCase )\r\r # before power of 3 fix, would error on first step, so we only need to do two\r for i, t in enumerate(scheduler.prk_timesteps[:2] ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler.step_prk(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ).prev_sample\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r with self.assertRaises(_UpperCAmelCase ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.scheduler_classes[0]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_scheduler_config()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscheduler_class(**_UpperCAmelCase )\r\r scheduler.step_plms(self.dummy_sample ,\t\t\t\t\t\t1 ,\t\t\t\t\t\tself.dummy_sample ).prev_sample\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.full_loop()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.sum(torch.abs(_UpperCAmelCase ) )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.mean(torch.abs(_UpperCAmelCase ) )\r\r assert abs(result_sum.item() - 198.1318 ) < 1E-2\r assert abs(result_mean.item() - 0.2580 ) < 1E-3\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.full_loop(prediction_type=\"\"\"v_prediction\"\"\" )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.sum(torch.abs(_UpperCAmelCase ) )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.mean(torch.abs(_UpperCAmelCase ) )\r\r assert abs(result_sum.item() - 67.3986 ) < 1E-2\r assert abs(result_mean.item() - 0.0878 ) < 1E-3\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tAny ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.full_loop(set_alpha_to_one=_UpperCAmelCase ,\t\t\t\t\t\tbeta_start=0.01 )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.sum(torch.abs(_UpperCAmelCase ) )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.mean(torch.abs(_UpperCAmelCase ) )\r\r assert abs(result_sum.item() - 230.0399 ) < 1E-2\r assert abs(result_mean.item() - 0.2995 ) < 1E-3\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.full_loop(set_alpha_to_one=_UpperCAmelCase ,\t\t\t\t\t\tbeta_start=0.01 )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.sum(torch.abs(_UpperCAmelCase ) )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.mean(torch.abs(_UpperCAmelCase ) )\r\r assert abs(result_sum.item() - 186.9482 ) < 1E-2\r assert abs(result_mean.item() - 0.2434 ) < 1E-3\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152392,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport requests\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'YOUR API KEY'\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr = giphy_api_key\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"+\"\"\".join(query.split()\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tF'''https://api.giphy.com/v1/gifs/search?q={formatted_query}&api_key={api_key}'''\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\trequests.get(SCREAMING_SNAKE_CASE__\t\t\t\t).json()[\"\"\"data\"\"\"]\r return [gif[\"url\"] for gif in gifs]\r\r\rif __name__ == \"__main__\":\r print('\\n'.join(get_gifs('space ship')))\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rfrom ...configuration_utils import PretrainedConfig\rfrom ...utils import logging\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tlogging.get_logger(__name__)\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t{\r 'google/vivit-b-16x2-kinetics400': (\r 'https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json'\r ),\r # See all Vivit models at https://huggingface.co/models?filter=vivit\r}\r\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r lowerCAmelCase_ : Optional[int] = \"\"\"vivit\"\"\"\r\r\r def __init__( self\t\t\t:\t\tList[str] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any]=2_24 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str]=32 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny=[2, 16, 16] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint=3 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any]=7_68 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=12 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict=12 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any]=30_72 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int]=\"gelu_fast\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=0.0 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple=0.0 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int]=0.02 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any]=1E-06 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str]=True ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\thidden_size\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnum_hidden_layers\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnum_attention_heads\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tintermediate_size\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\thidden_act\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\thidden_dropout_prob\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tattention_probs_dropout_prob\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tinitializer_range\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlayer_norm_eps\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\timage_size\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnum_frames\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttubelet_size\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnum_channels\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tqkv_bias\r\r super().__init__(**_UpperCAmelCase )\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152393,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport os\rimport socket\rfrom contextlib import contextmanager\r\rimport torch\r\rfrom ..commands.config.default import write_basic_config # noqa: F401\rfrom ..state import PartialState\rfrom .dataclasses import DistributedType\rfrom .imports import is_deepspeed_available, is_tpu_available\rfrom .transformer_engine import convert_model\rfrom .versions import is_torch_version\r\r\rif is_deepspeed_available():\r from deepspeed import DeepSpeedEngine\r\rif is_tpu_available(check_device=False):\r import torch_xla.core.xla_model as xm\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tDict\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r if is_torch_version(\"\"\"<\"\"\"\t\t\t\t, \"\"\"2.0.0\"\"\"\t\t\t\t) or not hasattr(SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\"_dynamo\"\"\"\t\t\t\t):\r return False\r return isinstance(SCREAMING_SNAKE_CASE__\t\t\t\t, torch._dynamo.eval_frame.OptimizedModule\t\t\t\t)\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tbool = True\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t(torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tis_compiled_module(SCREAMING_SNAKE_CASE__\t\t\t\t)\r if is_compiled:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel._orig_mod\r\r if is_deepspeed_available():\r options += (DeepSpeedEngine,)\r\r while isinstance(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel.module\r\r if not keep_fpaa_wrapper:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tgetattr(SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\"forward\"\"\"\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel.__dict__.pop(\"\"\"_original_forward\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r if original_forward is not None:\r while hasattr(SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\"__wrapped__\"\"\"\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tforward.__wrapped__\r if forward == original_forward:\r break\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tforward\r if getattr(SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\"_converted_to_transformer_engine\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r convert_model(SCREAMING_SNAKE_CASE__\t\t\t\t, to_transformer_engine=SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r if is_compiled:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tcompiled_model\r\r return model\r\rdef _UpperCamelCase\t\t\t\t\t( ):\r '''simple docstring'''\r\r\r\r\r\r PartialState().wait_for_everyone()\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tAny\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r if PartialState().distributed_type == DistributedType.TPU:\r xm.save(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r elif PartialState().local_process_index == 0:\r torch.save(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r@contextmanager\rdef _UpperCamelCase\t\t\t\t\t( **SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r for key, value in kwargs.items():\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tstr(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r yield\r\r for key in kwargs:\r if key.upper() in os.environ:\r del os.environ[key.upper()]\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[Any]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r if not hasattr(SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\"__qualname__\"\"\"\t\t\t\t) and not hasattr(SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\"__name__\"\"\"\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tgetattr(SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\"__class__\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r if hasattr(SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\"__qualname__\"\"\"\t\t\t\t):\r return obj.__qualname__\r if hasattr(SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\"__name__\"\"\"\t\t\t\t):\r return obj.__name__\r return str(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tAny\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[int]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r for key, value in source.items():\r if isinstance(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdestination.setdefault(SCREAMING_SNAKE_CASE__\t\t\t\t, {}\t\t\t\t)\r merge_dicts(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue\r\r return destination\r\r\r\r\r\r\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint = None\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r if port is None:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t29500\r with socket.socket(socket.AF_INET\t\t\t\t, socket.SOCK_STREAM\t\t\t\t) as s:\r return s.connect_ex((\"\"\"localhost\"\"\", port)\t\t\t\t) == 0\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport warnings\r\rfrom ...utils import logging\rfrom .image_processing_deit import DeiTImageProcessor\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tlogging.get_logger(__name__)\r\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def __init__( self\t\t\t:\t\tList[str] ,\t\t\t\t\t\t*_UpperCAmelCase\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r warnings.warn(\r \"\"\"The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please\"\"\"\r \"\"\" use DeiTImageProcessor instead.\"\"\" ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t)\r super().__init__(*_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase )\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152394,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint = 10\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r if not isinstance(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t) or n < 0:\r raise ValueError(\"\"\"Invalid input\"\"\"\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t10**n\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t28433 * (pow(2\t\t\t\t, 7830457\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)) + 1\r return str(number % modulus\t\t\t\t)\r\r\rif __name__ == \"__main__\":\r from doctest import testmod\r\r testmod()\r print(f\"{solution(1_0) = }\")\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport os\rimport unicodedata\rfrom shutil import copyfile\rfrom typing import Any, Dict, List, Optional, Tuple\r\rimport sentencepiece as spm\r\rfrom ...tokenization_utils import AddedToken, PreTrainedTokenizer\rfrom ...utils import SPIECE_UNDERLINE, logging\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tlogging.get_logger(__name__)\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t{'vocab_file': 'spiece.model'}\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t{\r 'vocab_file': {\r 'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model',\r }\r}\r\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def __init__( self\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny=False ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint=True ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=False ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict=\"~~\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint=\"~~\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict=\"\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple=\"\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any]=\"\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint=\"\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=\"\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str]=[\"\", \"\"] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Dict[str, Any]] = None ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tAddedToken(_UpperCAmelCase ,\t\t\t\t\t\tlstrip=_UpperCAmelCase ,\t\t\t\t\t\trstrip=_UpperCAmelCase ) if isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ) else mask_token\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{} if sp_model_kwargs is None else sp_model_kwargs\r\r super().__init__(\r do_lower_case=_UpperCAmelCase ,\t\t\t\t\t\tremove_space=_UpperCAmelCase ,\t\t\t\t\t\tkeep_accents=_UpperCAmelCase ,\t\t\t\t\t\tbos_token=_UpperCAmelCase ,\t\t\t\t\t\teos_token=_UpperCAmelCase ,\t\t\t\t\t\tunk_token=_UpperCAmelCase ,\t\t\t\t\t\tsep_token=_UpperCAmelCase ,\t\t\t\t\t\tpad_token=_UpperCAmelCase ,\t\t\t\t\t\tcls_token=_UpperCAmelCase ,\t\t\t\t\t\tmask_token=_UpperCAmelCase ,\t\t\t\t\t\tadditional_special_tokens=_UpperCAmelCase ,\t\t\t\t\t\tsp_model_kwargs=self.sp_model_kwargs ,\t\t\t\t\t\t**_UpperCAmelCase ,\t\t\t\t\t\t)\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t3\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdo_lower_case\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tremove_space\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tkeep_accents\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvocab_file\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tspm.SentencePieceProcessor(**self.sp_model_kwargs )\r self.sp_model.Load(_UpperCAmelCase )\r\r try:\r import jieba\r except ModuleNotFoundError as error:\r raise error.__class__(\r \"\"\"You need to install jieba to use CpmTokenizer or CpmTokenizerFast. \"\"\"\r \"\"\"See https://pypi.org/project/jieba/ for installation.\"\"\" )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tjieba\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tstr.maketrans(\"\"\" \\n\"\"\" ,\t\t\t\t\t\t\"\"\"\\u2582\\u2583\"\"\" )\r\r\r @property\r # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return len(self.sp_model )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{self.convert_ids_to_tokens(_UpperCAmelCase ): i for i in range(self.vocab_size )}\r vocab.update(self.added_tokens_encoder )\r return vocab\r\r\r def __getstate__( self\t\t\t:\t\tDict ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.__dict__.copy()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r return state\r\r\r def __setstate__( self\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\td\r\r # for backward compatibility\r if not hasattr(self ,\t\t\t\t\t\t\"\"\"sp_model_kwargs\"\"\" ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{}\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tspm.SentencePieceProcessor(**self.sp_model_kwargs )\r self.sp_model.Load(self.vocab_file )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if self.remove_space:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\" \"\"\".join(inputs.strip().split() )\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tinputs\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\toutputs.replace(\"\"\"``\"\"\" ,\t\t\t\t\t\t\"\"\"\\\"\"\"\" ).replace(\"\"\"''\"\"\" ,\t\t\t\t\t\t\"\"\"\\\"\"\"\" )\r\r if not self.keep_accents:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tunicodedata.normalize(\"\"\"NFKD\"\"\" ,\t\t\t\t\t\t_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"\"\"\".join([c for c in outputs if not unicodedata.combining(_UpperCAmelCase )] )\r if self.do_lower_case:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\toutputs.lower()\r\r return outputs\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tTuple ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.preprocess_text(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.sp_model.encode(_UpperCAmelCase ,\t\t\t\t\t\tout_type=_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[]\r for piece in pieces:\r if len(_UpperCAmelCase ) > 1 and piece[-1] == str(\"\"\",\"\"\" ) and piece[-2].isdigit():\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.sp_model.EncodeAsPieces(piece[:-1].replace(_UpperCAmelCase ,\t\t\t\t\t\t\"\"\"\"\"\" ) )\r if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:\r if len(cur_pieces[0] ) == 1:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tcur_pieces[1:]\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tcur_pieces[0][1:]\r cur_pieces.append(piece[-1] )\r new_pieces.extend(_UpperCAmelCase )\r else:\r new_pieces.append(_UpperCAmelCase )\r\r return new_pieces\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return self.sp_model.PieceToId(_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return self.sp_model.IdToPiece(_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"\"\"\".join(_UpperCAmelCase ).replace(_UpperCAmelCase ,\t\t\t\t\t\t\"\"\" \"\"\" ).strip()\r return out_string\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[List[int]] = None ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[self.sep_token_id]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[self.cls_token_id]\r if token_ids_a is None:\r return token_ids_a + sep + cls\r return token_ids_a + sep + token_ids_a + sep + cls\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[List[int]] = None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tbool = False ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if already_has_special_tokens:\r return super().get_special_tokens_mask(\r token_ids_a=_UpperCAmelCase ,\t\t\t\t\t\ttoken_ids_a=_UpperCAmelCase ,\t\t\t\t\t\talready_has_special_tokens=_UpperCAmelCase )\r\r if token_ids_a is not None:\r return ([0] * len(_UpperCAmelCase )) + [1] + ([0] * len(_UpperCAmelCase )) + [1, 1]\r return ([0] * len(_UpperCAmelCase )) + [1, 1]\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[List[int]] = None ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[self.sep_token_id]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[2]\r\r if token_ids_a is None:\r return len(token_ids_a + sep ) * [0] + cls_segment_id\r return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[str] = None ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if not os.path.isdir(_UpperCAmelCase ):\r logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )\r return\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tos.path.join(\r _UpperCAmelCase ,\t\t\t\t\t\t(filename_prefix + \"\"\"-\"\"\" if filename_prefix else \"\"\"\"\"\") + VOCAB_FILES_NAMES[\"\"\"vocab_file\"\"\"] )\r\r if os.path.abspath(self.vocab_file ) != os.path.abspath(_UpperCAmelCase ) and os.path.isfile(self.vocab_file ):\r copyfile(self.vocab_file ,\t\t\t\t\t\t_UpperCAmelCase )\r elif not os.path.isfile(self.vocab_file ):\r with open(_UpperCAmelCase ,\t\t\t\t\t\t\"\"\"wb\"\"\" ) as fi:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.sp_model.serialized_model_proto()\r fi.write(_UpperCAmelCase )\r\r return (out_vocab_file,)\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tTuple ,\t\t\t\t\t\t*_UpperCAmelCase\t\t\t:\t\tTuple ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tsuper()._decode(*_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttext.replace(\"\"\" \"\"\" ,\t\t\t\t\t\t\"\"\"\"\"\" ).replace(\"\"\"\\u2582\"\"\" ,\t\t\t\t\t\t\"\"\" \"\"\" ).replace(\"\"\"\\u2583\"\"\" ,\t\t\t\t\t\t\"\"\"\\n\"\"\" )\r return text\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152395,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport argparse\rimport random\r\rimport joblib\rimport numpy as np\rimport torch\rfrom igf.igf import (\r SecondaryLearner,\r collect_objective_set,\r compute_perplexity,\r generate_datasets,\r load_gpta,\r recopy_gpta,\r set_seed,\r train_secondary_learner,\r)\rfrom torch.utils.data import DataLoader, RandomSampler\r\rfrom transformers import GPTaLMHeadModel\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[int]=32\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tDict=10\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[Any]=100\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tUnion[str, Any]=1026\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tTuple=True\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tAny=\"data/tokenized_stories_train_wikitext103.jbl\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tDict=\"igf_context_pairs.jbl\"\t\t\t\t, ):\r '''simple docstring'''\r\r\r\r\r\r set_seed(3\t\t\t\t)\r # generate train_data and objective_set\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tgenerate_datasets(\r SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, number=SCREAMING_SNAKE_CASE__\t\t\t\t, min_len=1026\t\t\t\t, trim=SCREAMING_SNAKE_CASE__\t\t\t\t)\r # keeps model same across runs\r set_seed(4\t\t\t\t)\r # model, lm_optimizer, lm_scheduler = recopy_gpt2(model, device, max_steps) # store original model weights\r # can we train on GPU?\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.device(\"\"\"cuda:0\"\"\" if torch.cuda.is_available() else \"\"\"cpu\"\"\"\t\t\t\t)\r\r # load pretrained model\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tload_gpta(\"\"\"gpt2\"\"\"\t\t\t\t).to(SCREAMING_SNAKE_CASE__\t\t\t\t)\r print(\"\"\"computing perplexity on objective set\"\"\"\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tcompute_perplexity(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t).item()\r print(\"\"\"perplexity on objective set:\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r # collect igf pairs and save to file demo.jbl\r collect_objective_set(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r # clean up, delete model and data we don't need anymore\r del model, train_data, objective_set\r torch.cuda.empty_cache()\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tDict\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[int]=15\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[Any]=128\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr=100\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr=\"igf_model.pt\"\t\t\t\t, ):\r '''simple docstring'''\r\r\r\r\r\r set_seed(42\t\t\t\t)\r\r # Load pre-trained model\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tGPTaLMHeadModel.from_pretrained(\"\"\"gpt2\"\"\"\t\t\t\t)\r\r # Initialize secondary learner to use embedding weights of model\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tSecondaryLearner(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r # Train secondary learner\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttrain_secondary_learner(\r SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, max_epochs=SCREAMING_SNAKE_CASE__\t\t\t\t, batch_size=SCREAMING_SNAKE_CASE__\t\t\t\t, eval_freq=100\t\t\t\t, igf_model_path=SCREAMING_SNAKE_CASE__\t\t\t\t, )\r\r del model, secondary_learner_train_data\r torch.cuda.empty_cache()\r\r return secondary_learner\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[Any]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[int]\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[Any]=32\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[Any]=1000\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tAny=16\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[Any]=1.0\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr=recopy_gpta\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[Any]=None\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tList[str]=10\t\t\t\t, SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tAny=\"gpt2_finetuned.pt\"\t\t\t\t, ):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.device(\"\"\"cuda:0\"\"\" if torch.cuda.is_available() else \"\"\"cpu\"\"\"\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tRandomSampler(SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tDataLoader(SCREAMING_SNAKE_CASE__\t\t\t\t, sampler=SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmax_steps // (len(SCREAMING_SNAKE_CASE__\t\t\t\t)) + 1\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.zeros((1, context_len)\t\t\t\t, dtype=torch.long\t\t\t\t, device=SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\trecopy_model(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r model.train()\r if secondary_learner is not None:\r secondary_learner.to(SCREAMING_SNAKE_CASE__\t\t\t\t)\r secondary_learner.eval()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[]\r\r # Compute the performance of the transformer model at the beginning\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tcompute_perplexity(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r test_perps.append(SCREAMING_SNAKE_CASE__\t\t\t\t)\r print(\"\"\"Test perplexity, step\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\":\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r for epoch in range(int(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t):\r for step, example in enumerate(SCREAMING_SNAKE_CASE__\t\t\t\t):\r torch.cuda.empty_cache()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\trandom.randint(0\t\t\t\t, example.size(2\t\t\t\t) - context_len - 1\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\texample[0, 0, start : start + context_len]\r lm_optimizer.zero_grad()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(SCREAMING_SNAKE_CASE__\t\t\t\t, labels=SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTrue\r\r if secondary_learner is not None:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tsecondary_learner.forward(\r torch.tensor(SCREAMING_SNAKE_CASE__\t\t\t\t, dtype=torch.long\t\t\t\t, device=SCREAMING_SNAKE_CASE__\t\t\t\t).unsqueeze(0\t\t\t\t)\t\t\t\t)[0].item()\r observed_qs.append(float(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t)\r\r # Here we implement the simple non-constant threshold for the predicted IG(X) value\r # We will decay the selectivity of our secondary learner filter from\r # 1 standard deviation above average to 1 below average after 10 batches.\r\r if global_step == 10:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t-1\r if predicted_q < threshold:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r\r # If we passed the filter, add the context to the batch!\r if do_backprop:\r contexts.append(np.array(context.cpu()\t\t\t\t)\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\toutputs[0]\r lm_loss.backward()\r examples += 1\r\r del outputs\r\r # Once the batch is filled with enough contexts, backprop on the batch.\r if examples == batch_size:\r torch.cuda.empty_cache()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0\r # Do LM backprop\r torch.nn.utils.clip_grad_norm_(model.parameters()\t\t\t\t, 3.0\t\t\t\t)\r lm_optimizer.step()\r lm_scheduler.step() # Update learning rate schedule\r global_step += 1\r # Compute the performance of the transformer model at this batch\r if global_step % eval_interval == 0:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tcompute_perplexity(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r test_perps.append(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r print(\"\"\"Test perplexity, step\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, \"\"\":\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r # Break out of the loop after 60 batches\r if max_steps > 0 and global_step > 60:\r break\r if max_steps > 0 and global_step > 60:\r break\r\r # save finetuned transformer model\r torch.save(model.state_dict()\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r torch.cuda.empty_cache()\r # Do some cleaning up so we can reinitialize for the next run of this function\r del lm_optimizer\r del lm_scheduler\r return model\r\rdef _UpperCamelCase\t\t\t\t\t( ):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\targparse.ArgumentParser(description=\"\"\"Fine-tune a transformer model with IGF on a language modeling task\"\"\"\t\t\t\t)\r\r # Required parameters\r parser.add_argument(\r \"\"\"--data_dir\"\"\"\t\t\t\t, default=SCREAMING_SNAKE_CASE__\t\t\t\t, type=SCREAMING_SNAKE_CASE__\t\t\t\t, required=SCREAMING_SNAKE_CASE__\t\t\t\t, help=\"\"\"The input data dir. Should contain data files for WikiText.\"\"\"\t\t\t\t, )\r parser.add_argument(\r \"\"\"--model_name_or_path\"\"\"\t\t\t\t, default=SCREAMING_SNAKE_CASE__\t\t\t\t, type=SCREAMING_SNAKE_CASE__\t\t\t\t, required=SCREAMING_SNAKE_CASE__\t\t\t\t, help=\"\"\"Path to pretrained model or model identifier from huggingface.co/models\"\"\"\t\t\t\t, )\r parser.add_argument(\r \"\"\"--data_file\"\"\"\t\t\t\t, type=SCREAMING_SNAKE_CASE__\t\t\t\t, default=SCREAMING_SNAKE_CASE__\t\t\t\t, help=(\r \"\"\"A jbl file containing tokenized data which can be split as objective dataset, \"\"\"\r \"\"\"train_dataset and test_dataset.\"\"\"\r )\t\t\t\t, )\r\r parser.add_argument(\r \"\"\"--igf_data_file\"\"\"\t\t\t\t, type=SCREAMING_SNAKE_CASE__\t\t\t\t, default=SCREAMING_SNAKE_CASE__\t\t\t\t, help=\"\"\"A jbl file containing the context and information gain pairs to train secondary learner.\"\"\"\t\t\t\t, )\r\r parser.add_argument(\r \"\"\"--output_dir\"\"\"\t\t\t\t, default=SCREAMING_SNAKE_CASE__\t\t\t\t, type=SCREAMING_SNAKE_CASE__\t\t\t\t, required=SCREAMING_SNAKE_CASE__\t\t\t\t, help=\"\"\"The output directory where the final fine-tuned model is stored.\"\"\"\t\t\t\t, )\r\r parser.add_argument(\r \"\"\"--tokenizer_name\"\"\"\t\t\t\t, default=SCREAMING_SNAKE_CASE__\t\t\t\t, type=SCREAMING_SNAKE_CASE__\t\t\t\t, help=\"\"\"Pretrained tokenizer name or path if not the same as model_name\"\"\"\t\t\t\t, )\r parser.add_argument(\"\"\"--seed\"\"\"\t\t\t\t, type=SCREAMING_SNAKE_CASE__\t\t\t\t, default=SCREAMING_SNAKE_CASE__\t\t\t\t, help=\"\"\"A seed for reproducible training.\"\"\"\t\t\t\t)\r\r parser.add_argument(\r \"\"\"--context_len\"\"\"\t\t\t\t, default=32\t\t\t\t, type=SCREAMING_SNAKE_CASE__\t\t\t\t, help=(\r \"\"\"The maximum total input sequence length after tokenization. Sequences longer \"\"\"\r \"\"\"than this will be truncated, sequences shorter will be padded.\"\"\"\r )\t\t\t\t, )\r\r parser.add_argument(\r \"\"\"--size_objective_set\"\"\"\t\t\t\t, default=100\t\t\t\t, type=SCREAMING_SNAKE_CASE__\t\t\t\t, help=\"\"\"number of articles that are long enough to be used as our objective set\"\"\"\t\t\t\t, )\r parser.add_argument(\r \"\"\"--eval_freq\"\"\"\t\t\t\t, default=100\t\t\t\t, type=SCREAMING_SNAKE_CASE__\t\t\t\t, help=\"\"\"secondary model evaluation is triggered at eval_freq\"\"\"\t\t\t\t)\r\r parser.add_argument(\"\"\"--max_steps\"\"\"\t\t\t\t, default=1000\t\t\t\t, type=SCREAMING_SNAKE_CASE__\t\t\t\t, help=\"\"\"To calculate training epochs\"\"\"\t\t\t\t)\r\r parser.add_argument(\r \"\"\"--secondary_learner_batch_size\"\"\"\t\t\t\t, default=128\t\t\t\t, type=SCREAMING_SNAKE_CASE__\t\t\t\t, help=\"\"\"batch size of training data for secondary learner\"\"\"\t\t\t\t, )\r\r parser.add_argument(\r \"\"\"--batch_size\"\"\"\t\t\t\t, default=16\t\t\t\t, type=SCREAMING_SNAKE_CASE__\t\t\t\t, help=\"\"\"batch size of training data of language model(gpt2) \"\"\"\t\t\t\t)\r\r parser.add_argument(\r \"\"\"--eval_interval\"\"\"\t\t\t\t, default=10\t\t\t\t, type=SCREAMING_SNAKE_CASE__\t\t\t\t, help=(\r \"\"\"decay the selectivity of our secondary learner filter from\"\"\"\r \"\"\"1 standard deviation above average to 1 below average after 10 batches\"\"\"\r )\t\t\t\t, )\r\r parser.add_argument(\r \"\"\"--number\"\"\"\t\t\t\t, default=100\t\t\t\t, type=SCREAMING_SNAKE_CASE__\t\t\t\t, help=\"\"\"The number of examples split to be used as objective_set/test_data\"\"\"\t\t\t\t)\r\r parser.add_argument(\r \"\"\"--min_len\"\"\"\t\t\t\t, default=1026\t\t\t\t, type=SCREAMING_SNAKE_CASE__\t\t\t\t, help=\"\"\"The minimum length of the article to be used as objective set\"\"\"\t\t\t\t)\r\r parser.add_argument(\r \"\"\"--secondary_learner_max_epochs\"\"\"\t\t\t\t, default=15\t\t\t\t, type=SCREAMING_SNAKE_CASE__\t\t\t\t, help=\"\"\"number of epochs to train secondary learner\"\"\"\t\t\t\t)\r\r parser.add_argument(\"\"\"--trim\"\"\"\t\t\t\t, default=SCREAMING_SNAKE_CASE__\t\t\t\t, type=SCREAMING_SNAKE_CASE__\t\t\t\t, help=\"\"\"truncate the example if it exceeds context length\"\"\"\t\t\t\t)\r\r parser.add_argument(\r \"\"\"--threshold\"\"\"\t\t\t\t, default=1.0\t\t\t\t, type=SCREAMING_SNAKE_CASE__\t\t\t\t, help=(\r \"\"\"The threshold value used by secondary learner to filter the train_data and allow only\"\"\"\r \"\"\" informative data as input to the model\"\"\"\r )\t\t\t\t, )\r\r parser.add_argument(\"\"\"--finetuned_model_name\"\"\"\t\t\t\t, default=\"\"\"gpt2_finetuned.pt\"\"\"\t\t\t\t, type=SCREAMING_SNAKE_CASE__\t\t\t\t, help=\"\"\"finetuned_model_name\"\"\"\t\t\t\t)\r\r parser.add_argument(\r \"\"\"--recopy_model\"\"\"\t\t\t\t, default=SCREAMING_SNAKE_CASE__\t\t\t\t, type=SCREAMING_SNAKE_CASE__\t\t\t\t, help=\"\"\"Reset the model to the original pretrained GPT-2 weights after each iteration\"\"\"\t\t\t\t, )\r\r # function calls\r # Collecting *n* pairs of context and information gain(X, IG(X)) for training the secondary learner\r generate_n_pairs(\r context_len=32\t\t\t\t, max_steps=10\t\t\t\t, size_objective_set=100\t\t\t\t, min_len=1026\t\t\t\t, trim=SCREAMING_SNAKE_CASE__\t\t\t\t, data_file=\"\"\"data/tokenized_stories_train_wikitext103.jbl\"\"\"\t\t\t\t, igf_data_file=\"\"\"igf_context_pairs.jbl\"\"\"\t\t\t\t, )\r\r # Load train data for secondary learner\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tjoblib.load(\"\"\"data/IGF_values.jbl\"\"\"\t\t\t\t)\r\r # Train secondary learner\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttraining_secondary_learner(\r SCREAMING_SNAKE_CASE__\t\t\t\t, secondary_learner_max_epochs=15\t\t\t\t, secondary_learner_batch_size=128\t\t\t\t, eval_freq=100\t\t\t\t, igf_model_path=\"\"\"igf_model.pt\"\"\"\t\t\t\t, )\r\r # load pretrained gpt2 model\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tGPTaLMHeadModel.from_pretrained(\"\"\"gpt2\"\"\"\t\t\t\t)\r set_seed(42\t\t\t\t)\r\r # Generate train and test data to train and evaluate gpt2 model\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tgenerate_datasets(\r context_len=32\t\t\t\t, file=\"\"\"data/tokenized_stories_train_wikitext103.jbl\"\"\"\t\t\t\t, number=100\t\t\t\t, min_len=1026\t\t\t\t, trim=SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r # fine-tuning of the gpt2 model using igf (Information Gain Filtration)\r finetune(\r SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, context_len=32\t\t\t\t, max_steps=1000\t\t\t\t, batch_size=16\t\t\t\t, threshold=1.0\t\t\t\t, recopy_model=SCREAMING_SNAKE_CASE__\t\t\t\t, secondary_learner=SCREAMING_SNAKE_CASE__\t\t\t\t, eval_interval=10\t\t\t\t, finetuned_model_name=\"\"\"gpt2_finetuned.pt\"\"\"\t\t\t\t, )\r\r\rif __name__ == \"__main__\":\r main()\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport argparse\rimport logging\rimport os\r\rimport datasets\rimport tensorflow as tf\r\rfrom transformers import AutoTokenizer\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tlogging.getLogger(__name__)\r\rdef _UpperCamelCase\t\t\t\t\t( ):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\targparse.ArgumentParser(\r description=\"\"\"Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset.\"\"\"\t\t\t\t)\r parser.add_argument(\r \"\"\"--dataset_name\"\"\"\t\t\t\t, type=SCREAMING_SNAKE_CASE__\t\t\t\t, default=\"\"\"wikitext\"\"\"\t\t\t\t, help=\"\"\"Name of the training. Explore datasets at: hf.co/datasets.\"\"\"\t\t\t\t, )\r parser.add_argument(\r \"\"\"--dataset_config\"\"\"\t\t\t\t, type=SCREAMING_SNAKE_CASE__\t\t\t\t, default=\"\"\"wikitext-103-raw-v1\"\"\"\t\t\t\t, help=\"\"\"Configuration name of the dataset.\"\"\"\t\t\t\t)\r parser.add_argument(\r \"\"\"--tokenizer_name_or_path\"\"\"\t\t\t\t, type=SCREAMING_SNAKE_CASE__\t\t\t\t, default=\"\"\"sayakpaul/unigram-tokenizer-wikitext\"\"\"\t\t\t\t, help=\"\"\"Tokenizer identifier. Can be a local filepath or a Hub identifier.\"\"\"\t\t\t\t, )\r parser.add_argument(\r \"\"\"--shard_size\"\"\"\t\t\t\t, type=SCREAMING_SNAKE_CASE__\t\t\t\t, default=1000\t\t\t\t, help=\"\"\"Number of entries to go in a single shard.\"\"\"\t\t\t\t, )\r parser.add_argument(\"\"\"--split\"\"\"\t\t\t\t, type=SCREAMING_SNAKE_CASE__\t\t\t\t, default=\"\"\"train\"\"\"\t\t\t\t, choices=[\"\"\"train\"\"\", \"\"\"test\"\"\", \"\"\"validation\"\"\"]\t\t\t\t)\r parser.add_argument(\r \"\"\"--limit\"\"\"\t\t\t\t, default=SCREAMING_SNAKE_CASE__\t\t\t\t, type=SCREAMING_SNAKE_CASE__\t\t\t\t, help=\"\"\"Limit the number of shards (used for debugging).\"\"\"\t\t\t\t, )\r parser.add_argument(\r \"\"\"--max_length\"\"\"\t\t\t\t, type=SCREAMING_SNAKE_CASE__\t\t\t\t, default=512\t\t\t\t, help=\"\"\"Maximum sequence length. For training on TPUs, it helps to have a maximum\"\"\"\r \"\"\" sequence length that is a multiple of 8.\"\"\"\t\t\t\t, )\r parser.add_argument(\r \"\"\"--output_dir\"\"\"\t\t\t\t, default=\"\"\"tf-tpu\"\"\"\t\t\t\t, type=SCREAMING_SNAKE_CASE__\t\t\t\t, help=\"\"\"Output directory where the TFRecord shards will be saved. If the\"\"\"\r \"\"\" path is appended with `gs://` ('gs://tf-tpu', for example) then the TFRecord\"\"\"\r \"\"\" shards will be directly saved to a Google Cloud Storage bucket.\"\"\"\t\t\t\t, )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tparser.parse_args()\r return args\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tAny\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r def fn(SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tUnion[str, Any]\t\t\t\t):\r return tokenizer(examples[\"\"\"text\"\"\"]\t\t\t\t)\r\r return fn\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tOptional[int]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[]\r for i in range(len(tokenized_data[\"\"\"input_ids\"\"\"]\t\t\t\t)\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\r \"\"\"input_ids\"\"\": tf.train.Feature(intaa_list=tf.train.IntaaList(value=tokenized_data[\"\"\"input_ids\"\"\"][i]\t\t\t\t)\t\t\t\t),\r \"\"\"attention_mask\"\"\": tf.train.Feature(\r intaa_list=tf.train.IntaaList(value=tokenized_data[\"\"\"attention_mask\"\"\"][i]\t\t\t\t)\t\t\t\t),\r }\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttf.train.Features(feature=SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttf.train.Example(features=SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\texample.SerializeToString()\r records.append(SCREAMING_SNAKE_CASE__\t\t\t\t)\r return records\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tAny\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdatasets.load_dataset(args.dataset_name\t\t\t\t, args.dataset_config\t\t\t\t, split=args.split\t\t\t\t)\r\r if args.limit is not None:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmin(len(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t, args.limit\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdataset.select(range(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t)\r print(F'''Limiting the dataset to {args.limit} entries.'''\t\t\t\t)\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tAutoTokenizer.from_pretrained(args.tokenizer_name_or_path\t\t\t\t)\r\r # Handle output directory creation.\r # For serializing into a Google Cloud Storage Bucket, one needs to first\r # create a bucket.\r if \"gs\" not in args.output_dir:\r if not os.path.exists(args.output_dir\t\t\t\t):\r os.makedirs(args.output_dir\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tos.path.join(args.output_dir\t\t\t\t, args.split\t\t\t\t)\r if not os.path.exists(SCREAMING_SNAKE_CASE__\t\t\t\t):\r os.makedirs(SCREAMING_SNAKE_CASE__\t\t\t\t)\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tos.path.join(args.output_dir\t\t\t\t, args.split\t\t\t\t)\r\r # Tokenize the whole dataset at once.\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenize_function(SCREAMING_SNAKE_CASE__\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdataset.map(SCREAMING_SNAKE_CASE__\t\t\t\t, batched=SCREAMING_SNAKE_CASE__\t\t\t\t, num_proc=4\t\t\t\t, remove_columns=[\"\"\"text\"\"\"]\t\t\t\t)\r\r # We need to concatenate all our texts together, and then split the result\r # into chunks of a fixed size, which we will call block_size. To do this, we\r # will use the map method again, with the option batched=True. When we use batched=True,\r # the function we pass to map() will be passed multiple inputs at once, allowing us\r # to group them into more or fewer examples than we had in the input.\r # This allows us to create our new fixed-length samples. The advantage of this\r # method is that we don't lose a whole lot of content from the dataset compared to the\r # case where we simply tokenize with a pre-defined max_length.\r\r def group_texts(SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint\t\t\t\t):\r # Concatenate all texts.\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{k: sum(examples[k]\t\t\t\t, []\t\t\t\t) for k in examples.keys()}\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlen(concatenated_examples[list(examples.keys()\t\t\t\t)[0]]\t\t\t\t)\r # We drop the small remainder, though you could add padding instead if the model supports it\r # In this, as in all things, we advise you to follow your heart 🫀\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t(total_length // args.max_length) * args.max_length\r # Split by chunks of max_len.\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\r k: [t[i : i + args.max_length] for i in range(0\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t, args.max_length\t\t\t\t)]\r for k, t in concatenated_examples.items()\r }\r return result\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdataset_tokenized.map(SCREAMING_SNAKE_CASE__\t\t\t\t, batched=SCREAMING_SNAKE_CASE__\t\t\t\t, batch_size=1000\t\t\t\t, num_proc=4\t\t\t\t)\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0\r for shard in range(0\t\t\t\t, len(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t, args.shard_size\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tgrouped_dataset[shard : shard + args.shard_size]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlen(dataset_snapshot[\"\"\"input_ids\"\"\"]\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tos.path.join(SCREAMING_SNAKE_CASE__\t\t\t\t, F'''dataset-{shard_count}-{records_containing}.tfrecord'''\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tget_serialized_examples(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r with tf.io.TFRecordWriter(SCREAMING_SNAKE_CASE__\t\t\t\t) as out_file:\r for i in range(len(SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tserialized_examples[i]\r out_file.write(SCREAMING_SNAKE_CASE__\t\t\t\t)\r print(\"\"\"Wrote file {} containing {} records\"\"\".format(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t)\r\r shard_count += 1\r total_records += records_containing\r\r with open(F'''split-{args.split}-records-count.txt'''\t\t\t\t, \"\"\"w\"\"\"\t\t\t\t) as f:\r print(F'''Total {args.split} records: {total_records}'''\t\t\t\t, file=SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r\rif __name__ == \"__main__\":\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tparse_args()\r main(args)\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152396,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rfrom __future__ import annotations\r\rimport unittest\r\rfrom transformers import DebertaVaConfig, is_tf_available\rfrom transformers.testing_utils import require_tf, slow\r\rfrom ...test_configuration_common import ConfigTester\rfrom ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask\rfrom ...test_pipeline_mixin import PipelineTesterMixin\r\r\rif is_tf_available():\r import tensorflow as tf\r\r from transformers import (\r TFDebertaVaForMaskedLM,\r TFDebertaVaForQuestionAnswering,\r TFDebertaVaForSequenceClassification,\r TFDebertaVaForTokenClassification,\r TFDebertaVaModel,\r )\r\rclass lowerCAmelCase_ :\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def __init__( self\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str]=13 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str]=7 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any]=True ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict=True ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any]=True ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint=True ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict=99 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint=32 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int]=2 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any]=4 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple=37 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int]=\"gelu\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr=0.1 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint=0.1 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=5_12 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple=16 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=2 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr=0.02 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str]=False ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr=True ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=\"None\" ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr=3 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int]=4 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any]=None ,\t\t\t\t\t\t):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tparent\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tbatch_size\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tseq_length\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tis_training\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tuse_input_mask\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tuse_token_type_ids\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tuse_labels\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvocab_size\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\thidden_size\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnum_hidden_layers\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnum_attention_heads\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tintermediate_size\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\thidden_act\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\thidden_dropout_prob\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tattention_probs_dropout_prob\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmax_position_embeddings\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttype_vocab_size\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttype_sequence_label_size\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tinitializer_range\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnum_labels\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tnum_choices\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\trelative_attention\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tposition_biased_input\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tpos_att_type\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscope\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tids_tensor([self.batch_size, self.seq_length] ,\t\t\t\t\t\tself.vocab_size )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r if self.use_input_mask:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\trandom_attention_mask([self.batch_size, self.seq_length] )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r if self.use_token_type_ids:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tids_tensor([self.batch_size, self.seq_length] ,\t\t\t\t\t\tself.type_vocab_size )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r if self.use_labels:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tids_tensor([self.batch_size] ,\t\t\t\t\t\tself.type_sequence_label_size )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tids_tensor([self.batch_size, self.seq_length] ,\t\t\t\t\t\tself.num_labels )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tDebertaVaConfig(\r vocab_size=self.vocab_size ,\t\t\t\t\t\thidden_size=self.hidden_size ,\t\t\t\t\t\tnum_hidden_layers=self.num_hidden_layers ,\t\t\t\t\t\tnum_attention_heads=self.num_attention_heads ,\t\t\t\t\t\tintermediate_size=self.intermediate_size ,\t\t\t\t\t\thidden_act=self.hidden_act ,\t\t\t\t\t\thidden_dropout_prob=self.hidden_dropout_prob ,\t\t\t\t\t\tattention_probs_dropout_prob=self.attention_probs_dropout_prob ,\t\t\t\t\t\tmax_position_embeddings=self.max_position_embeddings ,\t\t\t\t\t\ttype_vocab_size=self.type_vocab_size ,\t\t\t\t\t\trelative_attention=self.relative_attention ,\t\t\t\t\t\tposition_biased_input=self.position_biased_input ,\t\t\t\t\t\tinitializer_range=self.initializer_range ,\t\t\t\t\t\treturn_dict=_UpperCAmelCase ,\t\t\t\t\t\t)\r\r return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTFDebertaVaModel(config=_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\"\"\"input_ids\"\"\": input_ids, \"\"\"attention_mask\"\"\": input_mask, \"\"\"token_type_ids\"\"\": token_type_ids}\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[input_ids, input_mask]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase )\r\r self.parent.assertEqual(result.last_hidden_state.shape ,\t\t\t\t\t\t(self.batch_size, self.seq_length, self.hidden_size) )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTFDebertaVaForMaskedLM(config=_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\r \"\"\"input_ids\"\"\": input_ids,\r \"\"\"attention_mask\"\"\": input_mask,\r \"\"\"token_type_ids\"\"\": token_type_ids,\r }\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase )\r self.parent.assertEqual(result.logits.shape ,\t\t\t\t\t\t(self.batch_size, self.seq_length, self.vocab_size) )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.num_labels\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTFDebertaVaForSequenceClassification(config=_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\r \"\"\"input_ids\"\"\": input_ids,\r \"\"\"attention_mask\"\"\": input_mask,\r \"\"\"token_type_ids\"\"\": token_type_ids,\r }\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase )\r self.parent.assertEqual(result.logits.shape ,\t\t\t\t\t\t(self.batch_size, self.num_labels) )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.num_labels\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTFDebertaVaForTokenClassification(config=_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\r \"\"\"input_ids\"\"\": input_ids,\r \"\"\"attention_mask\"\"\": input_mask,\r \"\"\"token_type_ids\"\"\": token_type_ids,\r }\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase )\r self.parent.assertEqual(result.logits.shape ,\t\t\t\t\t\t(self.batch_size, self.seq_length, self.num_labels) )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTFDebertaVaForQuestionAnswering(config=_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\r \"\"\"input_ids\"\"\": input_ids,\r \"\"\"attention_mask\"\"\": input_mask,\r \"\"\"token_type_ids\"\"\": token_type_ids,\r }\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase )\r self.parent.assertEqual(result.start_logits.shape ,\t\t\t\t\t\t(self.batch_size, self.seq_length) )\r self.parent.assertEqual(result.end_logits.shape ,\t\t\t\t\t\t(self.batch_size, self.seq_length) )\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.prepare_config_and_inputs()\r (\r (\r UpperCAmelCase__\r )\t\t\t\t\t\t\t,\t(\r UpperCAmelCase__\r )\t\t\t\t\t\t\t,\t(\r UpperCAmelCase__\r )\t\t\t\t\t\t\t,\t(\r UpperCAmelCase__\r )\t\t\t\t\t\t\t,\t(\r UpperCAmelCase__\r )\t\t\t\t\t\t\t,\t(\r UpperCAmelCase__\r )\t\t\t\t\t\t\t,\t(\r UpperCAmelCase__\r )\t\t\t\t\t\t\t,\t\r )\t\t\t\t\t\t\t\t=\t\t\tconfig_and_inputs\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\"\"\"input_ids\"\"\": input_ids, \"\"\"token_type_ids\"\"\": token_type_ids, \"\"\"attention_mask\"\"\": input_mask}\r return config, inputs_dict\r\r@require_tf\rclass lowerCAmelCase_ ( lowerCamelCase_\t, lowerCamelCase_\t, unittest.TestCase ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r lowerCAmelCase_ : Optional[Any] = (\r (\r TFDebertaVaModel,\r TFDebertaVaForMaskedLM,\r TFDebertaVaForQuestionAnswering,\r TFDebertaVaForSequenceClassification,\r TFDebertaVaForTokenClassification,\r )\r if is_tf_available()\r else ()\r )\r lowerCAmelCase_ : Tuple = (\r {\r \"\"\"feature-extraction\"\"\": TFDebertaVaModel,\r \"\"\"fill-mask\"\"\": TFDebertaVaForMaskedLM,\r \"\"\"question-answering\"\"\": TFDebertaVaForQuestionAnswering,\r \"\"\"text-classification\"\"\": TFDebertaVaForSequenceClassification,\r \"\"\"token-classification\"\"\": TFDebertaVaForTokenClassification,\r \"\"\"zero-shot\"\"\": TFDebertaVaForSequenceClassification,\r }\r if is_tf_available()\r else {}\r )\r\r lowerCAmelCase_ : Any = False\r lowerCAmelCase_ : List[Any] = False\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTFDebertaVaModelTester(self )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tConfigTester(self ,\t\t\t\t\t\tconfig_class=_UpperCAmelCase ,\t\t\t\t\t\thidden_size=37 )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r self.config_tester.run_common_tests()\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.model_tester.prepare_config_and_inputs()\r self.model_tester.create_and_check_model(*_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tAny ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.model_tester.prepare_config_and_inputs()\r self.model_tester.create_and_check_for_masked_lm(*_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tTuple ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.model_tester.prepare_config_and_inputs()\r self.model_tester.create_and_check_for_question_answering(*_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.model_tester.prepare_config_and_inputs()\r self.model_tester.create_and_check_for_sequence_classification(*_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.model_tester.prepare_config_and_inputs()\r self.model_tester.create_and_check_for_token_classification(*_UpperCAmelCase )\r\r\r\r @slow\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTFDebertaVaModel.from_pretrained(\"\"\"kamalkraj/deberta-v2-xlarge\"\"\" )\r self.assertIsNotNone(_UpperCAmelCase )\r\r@require_tf\rclass lowerCAmelCase_ ( unittest.TestCase ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r @unittest.skip(reason=\"\"\"Model not available yet\"\"\" )\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r pass\r\r\r\r @slow\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tAny ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTFDebertaVaModel.from_pretrained(\"\"\"kamalkraj/deberta-v2-xlarge\"\"\" )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttf.constant([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]] )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase ,\t\t\t\t\t\tattention_mask=_UpperCAmelCase )[0]\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttf.constant(\r [[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]] )\r tf.debugging.assert_near(output[:, 1:4, 1:4] ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\tatol=1E-4 )\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport numpy as np\rimport torch\rfrom torch.nn import CrossEntropyLoss\rfrom transformers import AutoModelForCausalLM, AutoTokenizer\r\rimport datasets\rfrom datasets import logging\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'\\\\n\\n'\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'\\nPerplexity (PPL) is one of the most common metrics for evaluating language models.\\nIt is defined as the exponentiated average negative log-likelihood of a sequence.\\n\\nFor more information, see https://huggingface.co/docs/transformers/perplexity\\n'\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'\\nArgs:\\n model_id (str): model used for calculating Perplexity\\n NOTE: Perplexity can only be calculated for causal language models.\\n This includes models such as gpt2, causal variations of bert,\\n causal versions of t5, and more (the full list can be found\\n in the AutoModelForCausalLM documentation here:\\n https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )\\n\\n input_texts (list of str): input text, each separate text snippet\\n is one list entry.\\n batch_size (int): the batch size to run texts through the model. Defaults to 16.\\n add_start_token (bool): whether to add the start token to the texts,\\n so the perplexity can include the probability of the first word. Defaults to True.\\n device (str): device to run on, defaults to \\'cuda\\' when available\\nReturns:\\n perplexity: dictionary containing the perplexity scores for the texts\\n in the input list, as well as the mean perplexity. If one of the input texts is\\n longer than the max input length of the model, then it is truncated to the\\n max length for the perplexity computation.\\nExamples:\\n Example 1:\\n >>> perplexity = datasets.load_metric(\"perplexity\")\\n >>> input_texts = [\"lorem ipsum\", \"Happy Birthday!\", \"Bienvenue\"]\\n >>> results = perplexity.compute(model_id=\\'gpt2\\',\\n ... add_start_token=False,\\n ... input_texts=input_texts) # doctest:+ELLIPSIS\\n >>> print(list(results.keys()))\\n [\\'perplexities\\', \\'mean_perplexity\\']\\n >>> print(round(results[\"mean_perplexity\"], 2))\\n 78.22\\n >>> print(round(results[\"perplexities\"][0], 2))\\n 11.11\\n\\n Example 2:\\n >>> perplexity = datasets.load_metric(\"perplexity\")\\n >>> input_texts = datasets.load_dataset(\"wikitext\",\\n ... \"wikitext-2-raw-v1\",\\n ... split=\"test\")[\"text\"][:50] # doctest:+ELLIPSIS\\n [...]\\n >>> input_texts = [s for s in input_texts if s!=\\'\\']\\n >>> results = perplexity.compute(model_id=\\'gpt2\\',\\n ... input_texts=input_texts) # doctest:+ELLIPSIS\\n >>> print(list(results.keys()))\\n [\\'perplexities\\', \\'mean_perplexity\\']\\n >>> print(round(results[\"mean_perplexity\"], 2))\\n 60.35\\n >>> print(round(results[\"perplexities\"][0], 2))\\n 81.12\\n'\r\r@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION\t, _KWARGS_DESCRIPTION )\rclass lowerCAmelCase_ ( datasets.Metric ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return datasets.MetricInfo(\r description=_DESCRIPTION ,\t\t\t\t\t\tcitation=_CITATION ,\t\t\t\t\t\tinputs_description=_KWARGS_DESCRIPTION ,\t\t\t\t\t\tfeatures=datasets.Features(\r {\r \"\"\"input_texts\"\"\": datasets.Value(\"\"\"string\"\"\" ),\r } ) ,\t\t\t\t\t\treference_urls=[\"\"\"https://huggingface.co/docs/transformers/perplexity\"\"\"] ,\t\t\t\t\t\t)\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint = 16 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tbool = True ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int]=None ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if device is not None:\r assert device in [\"gpu\", \"cpu\", \"cuda\"], \"device should be either gpu or cpu.\"\r if device == \"gpu\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"cuda\"\"\"\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"cuda\"\"\" if torch.cuda.is_available() else \"\"\"cpu\"\"\"\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tAutoModelForCausalLM.from_pretrained(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel.to(_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tAutoTokenizer.from_pretrained(_UpperCAmelCase )\r\r # if batch_size > 1 (which generally leads to padding being required), and\r # if there is not an already assigned pad_token, assign an existing\r # special token to also be the padding token\r if tokenizer.pad_token is None and batch_size > 1:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlist(tokenizer.special_tokens_map_extended.values() )\r # check that the model already has at least one special token defined\r assert (\r len(_UpperCAmelCase ) > 0\r ), \"If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1.\"\r # assign one of the special tokens to also be the pad token\r tokenizer.add_special_tokens({\"\"\"pad_token\"\"\": existing_special_tokens[0]} )\r\r if add_start_token:\r # leave room for token to be added:\r assert (\r tokenizer.bos_token is not None\r ), \"Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False\"\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel.config.max_length - 1\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel.config.max_length\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttokenizer(\r _UpperCAmelCase ,\t\t\t\t\t\tadd_special_tokens=_UpperCAmelCase ,\t\t\t\t\t\tpadding=_UpperCAmelCase ,\t\t\t\t\t\ttruncation=_UpperCAmelCase ,\t\t\t\t\t\tmax_length=_UpperCAmelCase ,\t\t\t\t\t\treturn_tensors=\"\"\"pt\"\"\" ,\t\t\t\t\t\treturn_attention_mask=_UpperCAmelCase ,\t\t\t\t\t\t).to(_UpperCAmelCase )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tencodings[\"\"\"input_ids\"\"\"]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tencodings[\"\"\"attention_mask\"\"\"]\r\r # check that each input is long enough:\r if add_start_token:\r assert torch.all(torch.ge(attn_masks.sum(1 ) ,\t\t\t\t\t\t1 ) ), \"Each input text must be at least one token long.\"\r else:\r assert torch.all(\r torch.ge(attn_masks.sum(1 ) ,\t\t\t\t\t\t2 ) ), \"When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings.\"\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tCrossEntropyLoss(reduction=\"\"\"none\"\"\" )\r\r for start_index in logging.tqdm(range(0 ,\t\t\t\t\t\tlen(_UpperCAmelCase ) ,\t\t\t\t\t\t_UpperCAmelCase ) ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmin(start_index + batch_size ,\t\t\t\t\t\tlen(_UpperCAmelCase ) )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tencoded_texts[start_index:end_index]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tattn_masks[start_index:end_index]\r\r if add_start_token:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0 ) ).to(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.cat([bos_tokens_tensor, encoded_batch] ,\t\t\t\t\t\tdim=1 )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.cat(\r [torch.ones(bos_tokens_tensor.size() ,\t\t\t\t\t\tdtype=torch.intaa ).to(_UpperCAmelCase ), attn_mask] ,\t\t\t\t\t\tdim=1 )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tencoded_batch\r\r with torch.no_grad():\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel(_UpperCAmelCase ,\t\t\t\t\t\tattention_mask=_UpperCAmelCase ).logits\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tout_logits[..., :-1, :].contiguous()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlabels[..., 1:].contiguous()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tattn_mask[..., 1:].contiguous()\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttorch.expa(\r (loss_fct(shift_logits.transpose(1 ,\t\t\t\t\t\t2 ) ,\t\t\t\t\t\t_UpperCAmelCase ) * shift_attention_mask_batch).sum(1 )\r / shift_attention_mask_batch.sum(1 ) )\r\r ppls += perplexity_batch.tolist()\r\r return {\"perplexities\": ppls, \"mean_perplexity\": np.mean(_UpperCAmelCase )}\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152397,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport enum\rimport warnings\r\rfrom ..tokenization_utils import TruncationStrategy\rfrom ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging\rfrom .base import PIPELINE_INIT_ARGS, Pipeline\r\r\rif is_tf_available():\r import tensorflow as tf\r\r from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING\r\rif is_torch_available():\r from ..models.auto.modeling_auto import MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tlogging.get_logger(__name__)\r\rclass lowerCAmelCase_ ( enum.Enum ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r lowerCAmelCase_ : Any = 0\r lowerCAmelCase_ : List[Any] = 1\r\r@add_end_docstrings(lowerCamelCase_ )\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r lowerCAmelCase_ : Any = \"\"\"generated\"\"\"\r\r\r def __init__( self\t\t\t:\t\tstr ,\t\t\t\t\t\t*_UpperCAmelCase\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tTuple ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r super().__init__(*_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase )\r\r self.check_model_type(\r TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING\r if self.framework == \"\"\"tf\"\"\"\r else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any]=None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str]=None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint=None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple=None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any]=None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr=None ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{}\r if truncation is not None:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttruncation\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tgenerate_kwargs\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{}\r if return_tensors is not None and return_type is None:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tReturnType.TENSORS if return_tensors else ReturnType.TEXT\r if return_type is not None:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\treturn_type\r\r if clean_up_tokenization_spaces is not None:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tclean_up_tokenization_spaces\r\r if stop_sequence is not None:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.tokenizer.encode(_UpperCAmelCase ,\t\t\t\t\t\tadd_special_tokens=_UpperCAmelCase )\r if len(_UpperCAmelCase ) > 1:\r warnings.warn(\r \"\"\"Stopping on a multiple token sequence is not yet supported on transformers. The first token of\"\"\"\r \"\"\" the stop sequence will be used as the stop sequence string in the interim.\"\"\" )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tstop_sequence_ids[0]\r\r return preprocess_params, forward_params, postprocess_params\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return True\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ,\t\t\t\t\t\t*_UpperCAmelCase\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.model.config.prefix if self.model.config.prefix is not None else \"\"\"\"\"\"\r if isinstance(args[0] ,\t\t\t\t\t\t_UpperCAmelCase ):\r if self.tokenizer.pad_token_id is None:\r raise ValueError(\"\"\"Please make sure that the tokenizer has a pad_token_id when using a batch input\"\"\" )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t([prefix + arg for arg in args[0]],)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTrue\r\r elif isinstance(args[0] ,\t\t\t\t\t\t_UpperCAmelCase ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t(prefix + args[0],)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r else:\r raise ValueError(\r f''' `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`''' )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.tokenizer(*_UpperCAmelCase ,\t\t\t\t\t\tpadding=_UpperCAmelCase ,\t\t\t\t\t\ttruncation=_UpperCAmelCase ,\t\t\t\t\t\treturn_tensors=self.framework )\r # This is produced by tokenizers but is an invalid generate kwargs\r if \"token_type_ids\" in inputs:\r del inputs[\"token_type_ids\"]\r return inputs\r\r\r def __call__( self\t\t\t:\t\tstr ,\t\t\t\t\t\t*_UpperCAmelCase\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tsuper().__call__(*_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase )\r if (\r isinstance(args[0] ,\t\t\t\t\t\t_UpperCAmelCase )\r and all(isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ) for el in args[0] )\r and all(len(_UpperCAmelCase ) == 1 for res in result )\r ):\r return [res[0] for res in result]\r return result\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any]=TruncationStrategy.DO_NOT_TRUNCATE ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tList[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself._parse_and_tokenize(_UpperCAmelCase ,\t\t\t\t\t\ttruncation=_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase )\r return inputs\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tAny ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tDict ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if self.framework == \"pt\":\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel_inputs[\"\"\"input_ids\"\"\"].shape\r elif self.framework == \"tf\":\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttf.shape(model_inputs[\"\"\"input_ids\"\"\"] ).numpy()\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tgenerate_kwargs.get(\"\"\"min_length\"\"\" ,\t\t\t\t\t\tself.model.config.min_length )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tgenerate_kwargs.get(\"\"\"max_length\"\"\" ,\t\t\t\t\t\tself.model.config.max_length )\r self.check_inputs(_UpperCAmelCase ,\t\t\t\t\t\tgenerate_kwargs[\"\"\"min_length\"\"\"] ,\t\t\t\t\t\tgenerate_kwargs[\"\"\"max_length\"\"\"] )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.model.generate(**_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\toutput_ids.shape[0]\r if self.framework == \"pt\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\toutput_ids.reshape(_UpperCAmelCase ,\t\t\t\t\t\tout_b // in_b ,\t\t\t\t\t\t*output_ids.shape[1:] )\r elif self.framework == \"tf\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttf.reshape(_UpperCAmelCase ,\t\t\t\t\t\t(in_b, out_b // in_b, *output_ids.shape[1:]) )\r return {\"output_ids\": output_ids}\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tTuple ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint=ReturnType.TEXT ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple=False ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[]\r for output_ids in model_outputs[\"output_ids\"][0]:\r if return_type == ReturnType.TENSORS:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{f'''{self.return_name}_token_ids''': output_ids}\r elif return_type == ReturnType.TEXT:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\r f'''{self.return_name}_text''': self.tokenizer.decode(\r _UpperCAmelCase ,\t\t\t\t\t\tskip_special_tokens=_UpperCAmelCase ,\t\t\t\t\t\tclean_up_tokenization_spaces=_UpperCAmelCase ,\t\t\t\t\t\t)\r }\r records.append(_UpperCAmelCase )\r return records\r\r@add_end_docstrings(lowerCamelCase_ )\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r lowerCAmelCase_ : List[Any] = \"\"\"summary\"\"\"\r\r\r def __call__( self\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t*_UpperCAmelCase\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return super().__call__(*_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase )\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if max_length < min_length:\r logger.warning(f'''Your min_length={min_length} must be inferior than your max_length={max_length}.''' )\r\r if input_length < max_length:\r logger.warning(\r f'''Your max_length is set to {max_length}, but your input_length is only {input_length}. Since this is '''\r \"\"\"a summarization task, where outputs shorter than the input are typically wanted, you might \"\"\"\r f'''consider decreasing max_length manually, e.g. summarizer(\\'...\\', max_length={input_length//2})''' )\r\r@add_end_docstrings(lowerCamelCase_ )\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r lowerCAmelCase_ : Union[str, Any] = \"\"\"translation\"\"\"\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if input_length > 0.9 * max_length:\r logger.warning(\r f'''Your input_length: {input_length} is bigger than 0.9 * max_length: {max_length}. You might consider '''\r \"\"\"increasing your max_length manually, e.g. translator('...', max_length=400)\"\"\" )\r return True\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t*_UpperCAmelCase\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict=TruncationStrategy.DO_NOT_TRUNCATE ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr=None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any]=None ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if getattr(self.tokenizer ,\t\t\t\t\t\t\"\"\"_build_translation_inputs\"\"\" ,\t\t\t\t\t\t_UpperCAmelCase ):\r return self.tokenizer._build_translation_inputs(\r *_UpperCAmelCase ,\t\t\t\t\t\treturn_tensors=self.framework ,\t\t\t\t\t\ttruncation=_UpperCAmelCase ,\t\t\t\t\t\tsrc_lang=_UpperCAmelCase ,\t\t\t\t\t\ttgt_lang=_UpperCAmelCase )\r else:\r return super()._parse_and_tokenize(*_UpperCAmelCase ,\t\t\t\t\t\ttruncation=_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[int]=None ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tsuper()._sanitize_parameters(**_UpperCAmelCase )\r if src_lang is not None:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tsrc_lang\r if tgt_lang is not None:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttgt_lang\r if src_lang is None and tgt_lang is None:\r # Backward compatibility, direct arguments use is preferred.\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tkwargs.get(\"\"\"task\"\"\" ,\t\t\t\t\t\tself.task )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttask.split(\"\"\"_\"\"\" )\r if task and len(_UpperCAmelCase ) == 4:\r # translation, XX, to YY\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\titems[1]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\titems[3]\r return preprocess_params, forward_params, postprocess_params\r\r\r\r def __call__( self\t\t\t:\t\tTuple ,\t\t\t\t\t\t*_UpperCAmelCase\t\t\t:\t\tList[str] ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tOptional[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return super().__call__(*_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase )\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint = 1000000\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[i - 1 for i in range(limit + 1\t\t\t\t)]\r\r for i in range(2\t\t\t\t, limit + 1\t\t\t\t):\r if phi[i] == i - 1:\r for j in range(2 * i\t\t\t\t, limit + 1\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r phi[j] -= phi[j] // i\r\r return sum(phi[2 : limit + 1]\t\t\t\t)\r\r\rif __name__ == \"__main__\":\r print(solution())\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152398,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport logging\rimport os\rimport threading\rimport time\r\r\rtry:\r import warnings\rexcept ImportError:\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tNone\r\rtry:\r import msvcrt\rexcept ImportError:\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tNone\r\rtry:\r import fcntl\rexcept ImportError:\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tNone\r\r\r# Backward compatibility\r# ------------------------------------------------\rtry:\r TimeoutError\rexcept NameError:\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tOSError\r\r\r# Data\r# ------------------------------------------------\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t[\r 'Timeout',\r 'BaseFileLock',\r 'WindowsFileLock',\r 'UnixFileLock',\r 'SoftFileLock',\r 'FileLock',\r]\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\t'3.0.12'\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tNone\r\rdef _UpperCamelCase\t\t\t\t\t( ):\r '''simple docstring'''\r\r\r\r\r\r global _logger\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t_logger or logging.getLogger(__name__\t\t\t\t)\r return _logger\r\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def __init__( self\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlock_file\r return None\r\r\r\r def __str__( self\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tf'''The file lock \\'{self.lock_file}\\' could not be acquired.'''\r return temp\r\rclass lowerCAmelCase_ :\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def __init__( self\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlock\r return None\r\r\r def __enter__( self\t\t\t:\t\tOptional[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return self.lock\r\r\r\r def __exit__( self\t\t\t:\t\tList[str] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r self.lock.release()\r return None\r\rclass lowerCAmelCase_ :\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def __init__( self\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple=-1 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny=None ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmax_filename_length if max_filename_length is not None else 2_55\r # Hash the filename if it's too long\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.hash_filename_if_too_long(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r # The path to the lock file.\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tlock_file\r\r # The file descriptor for the *_lock_file* as it is returned by the\r # os.open() function.\r # This file lock is only NOT None, if the object currently holds the\r # lock.\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r\r # The default timeout value.\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttimeout\r\r # We use this lock primarily for the lock counter.\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tthreading.Lock()\r\r # The lock counter is used for implementing the nested locking\r # mechanism. Whenever the lock is acquired, the counter is increased and\r # the lock is only released, when this value is 0 again.\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0\r return None\r\r\r @property\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return self._lock_file\r\r\r @property\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tTuple ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return self._timeout\r\r\r @timeout.setter\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tfloat(_UpperCAmelCase )\r return None\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tTuple ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r raise NotImplementedError()\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tTuple ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r raise NotImplementedError()\r\r\r @property\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return self._lock_file_fd is not None\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict=None ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr=0.05 ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if timeout is None:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.timeout\r\r # Increment the number right at the beginning.\r # We can still undo it, if something fails.\r with self._thread_lock:\r self._lock_counter += 1\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tid(self )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself._lock_file\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttime.time()\r try:\r while True:\r with self._thread_lock:\r if not self.is_locked:\r logger().debug(f'''Attempting to acquire lock {lock_id} on {lock_filename}''' )\r self._acquire()\r\r if self.is_locked:\r logger().debug(f'''Lock {lock_id} acquired on {lock_filename}''' )\r break\r elif timeout >= 0 and time.time() - start_time > timeout:\r logger().debug(f'''Timeout on acquiring lock {lock_id} on {lock_filename}''' )\r raise Timeout(self._lock_file )\r else:\r logger().debug(\r f'''Lock {lock_id} not acquired on {lock_filename}, waiting {poll_intervall} seconds ...''' )\r time.sleep(_UpperCAmelCase )\r except: # noqa\r # Something did go wrong, so decrement the counter.\r with self._thread_lock:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmax(0 ,\t\t\t\t\t\tself._lock_counter - 1 )\r\r raise\r return _Acquire_ReturnProxy(lock=self )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict=False ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r with self._thread_lock:\r if self.is_locked:\r self._lock_counter -= 1\r\r if self._lock_counter == 0 or force:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tid(self )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself._lock_file\r\r logger().debug(f'''Attempting to release lock {lock_id} on {lock_filename}''' )\r self._release()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0\r logger().debug(f'''Lock {lock_id} released on {lock_filename}''' )\r\r return None\r\r\r def __enter__( self\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r self.acquire()\r return self\r\r\r def __exit__( self\t\t\t:\t\tTuple ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r self.release()\r return None\r\r\r def __del__( self\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r self.release(force=_UpperCAmelCase )\r return None\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tTuple ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tos.path.basename(_UpperCAmelCase )\r if len(_UpperCAmelCase ) > max_length and max_length > 0:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tos.path.dirname(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tstr(hash(_UpperCAmelCase ) )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tfilename[: max_length - len(_UpperCAmelCase ) - 8] + \"\"\"...\"\"\" + hashed_filename + \"\"\".lock\"\"\"\r return os.path.join(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )\r else:\r return path\r\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def __init__( self\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=-1 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict=None ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r from .file_utils import relative_to_absolute_path\r\r super().__init__(_UpperCAmelCase ,\t\t\t\t\t\ttimeout=_UpperCAmelCase ,\t\t\t\t\t\tmax_filename_length=_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"\\\\\\\\?\\\\\"\"\" + relative_to_absolute_path(self.lock_file )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tos.O_RDWR | os.O_CREAT | os.O_TRUNC\r\r try:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tos.open(self._lock_file ,\t\t\t\t\t\t_UpperCAmelCase )\r except OSError:\r pass\r else:\r try:\r msvcrt.locking(_UpperCAmelCase ,\t\t\t\t\t\tmsvcrt.LK_NBLCK ,\t\t\t\t\t\t1 )\r except OSError:\r os.close(_UpperCAmelCase )\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tfd\r return None\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself._lock_file_fd\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r msvcrt.locking(_UpperCAmelCase ,\t\t\t\t\t\tmsvcrt.LK_UNLCK ,\t\t\t\t\t\t1 )\r os.close(_UpperCAmelCase )\r\r try:\r os.remove(self._lock_file )\r # Probably another instance of the application\r # that acquired the file lock.\r except OSError:\r pass\r return None\r\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def __init__( self\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[str] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tAny=-1 ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, Any]=None ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tos.statvfs(os.path.dirname(_UpperCAmelCase ) ).f_namemax\r super().__init__(_UpperCAmelCase ,\t\t\t\t\t\ttimeout=_UpperCAmelCase ,\t\t\t\t\t\tmax_filename_length=_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tos.O_RDWR | os.O_CREAT | os.O_TRUNC\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tos.open(self._lock_file ,\t\t\t\t\t\t_UpperCAmelCase )\r\r try:\r fcntl.flock(_UpperCAmelCase ,\t\t\t\t\t\tfcntl.LOCK_EX | fcntl.LOCK_NB )\r except OSError:\r os.close(_UpperCAmelCase )\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tfd\r return None\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself._lock_file_fd\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r fcntl.flock(_UpperCAmelCase ,\t\t\t\t\t\tfcntl.LOCK_UN )\r os.close(_UpperCAmelCase )\r return None\r\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tos.O_WRONLY | os.O_CREAT | os.O_EXCL | os.O_TRUNC\r try:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tos.open(self._lock_file ,\t\t\t\t\t\t_UpperCAmelCase )\r except OSError:\r pass\r else:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tfd\r return None\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r os.close(self._lock_file_fd )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r\r try:\r os.remove(self._lock_file )\r # The file is already deleted and that's what we want.\r except OSError:\r pass\r return None\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tNone\r\rif msvcrt:\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tWindowsFileLock\relif fcntl:\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tUnixFileLock\relse:\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tSoftFileLock\r\r if warnings is not None:\r warnings.warn('only soft file lock is available')\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rfrom typing import List, Union\r\rfrom ..utils import (\r add_end_docstrings,\r is_tf_available,\r is_torch_available,\r is_vision_available,\r logging,\r requires_backends,\r)\rfrom .base import PIPELINE_INIT_ARGS, Pipeline\r\r\rif is_vision_available():\r from PIL import Image\r\r from ..image_utils import load_image\r\rif is_tf_available():\r import tensorflow as tf\r\r from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING\r from ..tf_utils import stable_softmax\r\rif is_torch_available():\r from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tlogging.get_logger(__name__)\r\r@add_end_docstrings(lowerCamelCase_ )\rclass lowerCAmelCase_ ( lowerCamelCase_ ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def __init__( self\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t*_UpperCAmelCase\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tDict ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r super().__init__(*_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase )\r requires_backends(self ,\t\t\t\t\t\t\"\"\"vision\"\"\" )\r self.check_model_type(\r TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING\r if self.framework == \"\"\"tf\"\"\"\r else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any]=None ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{}\r if top_k is not None:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttop_k\r return {}, {}, postprocess_params\r\r\r def __call__( self\t\t\t:\t\tAny ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tUnion[str, List[str], \"Image.Image\", List[\"Image.Image\"]] ,\t\t\t\t\t\t**_UpperCAmelCase\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return super().__call__(_UpperCAmelCase ,\t\t\t\t\t\t**_UpperCAmelCase )\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tload_image(_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.image_processor(images=_UpperCAmelCase ,\t\t\t\t\t\treturn_tensors=self.framework )\r return model_inputs\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.model(**_UpperCAmelCase )\r return model_outputs\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr=5 ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if top_k > self.model.config.num_labels:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.model.config.num_labels\r\r if self.framework == \"pt\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmodel_outputs.logits.softmax(-1 )[0]\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tprobs.topk(_UpperCAmelCase )\r elif self.framework == \"tf\":\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tstable_softmax(model_outputs.logits ,\t\t\t\t\t\taxis=-1 )[0]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttf.math.top_k(_UpperCAmelCase ,\t\t\t\t\t\tk=_UpperCAmelCase )\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttopk.values.numpy(), topk.indices.numpy()\r else:\r raise ValueError(f'''Unsupported framework: {self.framework}''' )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tscores.tolist()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tids.tolist()\r return [{\"score\": score, \"label\": self.model.config.idalabel[_id]} for score, _id in zip(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )]\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":152399,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rimport collections\rimport json\rimport math\rimport os\rimport re\rimport time\rfrom fnmatch import fnmatch\rfrom typing import Dict\r\rimport requests\rfrom slack_sdk import WebClient\r\r\rUpperCAmelCase_\t\t\t\t =\t\t\t\t\tWebClient(token=os.environ['CI_SLACK_BOT_TOKEN'])\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tUnion[str, Any]\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttest_results.split(\"\"\" \"\"\"\t\t\t\t)\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0\r\r # When the output is short enough, the output is surrounded by = signs: \"== OUTPUT ==\"\r # When it is too long, those signs are not present.\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\texpressions[-2] if \"\"\"=\"\"\" in expressions[-1] else expressions[-1]\r\r for i, expression in enumerate(SCREAMING_SNAKE_CASE__\t\t\t\t):\r if \"failed\" in expression:\r failed += int(expressions[i - 1]\t\t\t\t)\r if \"passed\" in expression:\r success += int(expressions[i - 1]\t\t\t\t)\r\r return failed, success, time_spent\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tDict\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{}\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tNone\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r for line in failures_short_lines.split(\"\"\"\\n\"\"\"\t\t\t\t):\r if re.search(r\"\"\"_ \\[doctest\\]\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tTrue\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tline.split(\"\"\" \"\"\"\t\t\t\t)[2]\r elif in_error and not line.split(\"\"\" \"\"\"\t\t\t\t)[0].isdigit():\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tline\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tFalse\r\r return failures\r\rclass lowerCAmelCase_ :\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def __init__( self\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttitle\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdoc_test_results[\"\"\"time_spent\"\"\"].split(\"\"\",\"\"\" )[0]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdoc_test_results[\"\"\"success\"\"\"]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdoc_test_results[\"\"\"failures\"\"\"]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.n_success + self.n_failures\r\r # Failures and success of the modeling tests\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdoc_test_results\r\r\r @property\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[int] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[self._time_spent]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t0\r\r for time in time_spent:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttime.split(\"\"\":\"\"\" )\r\r # Time can be formatted as xx:xx:xx, as .xx, or as x.xx if the time spent was less than a minute.\r if len(_UpperCAmelCase ) == 1:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[0, 0, time_parts[0]]\r\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tint(time_parts[0] ), int(time_parts[1] ), float(time_parts[2] )\r total_secs += hours * 36_00 + minutes * 60 + seconds\r\r UpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t,\tUpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\ttotal_secs // 36_00, (total_secs % 36_00) // 60, total_secs % 60\r return f'''{int(_UpperCAmelCase )}h{int(_UpperCAmelCase )}m{int(_UpperCAmelCase )}s'''\r\r\r @property\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return {\"type\": \"header\", \"text\": {\"type\": \"plain_text\", \"text\": self.title}}\r\r\r @property\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return {\r \"type\": \"section\",\r \"text\": {\r \"type\": \"plain_text\",\r \"text\": f'''🌞 There were no failures: all {self.n_tests} tests passed. The suite ran in {self.time}.''',\r \"emoji\": True,\r },\r \"accessory\": {\r \"type\": \"button\",\r \"text\": {\"type\": \"plain_text\", \"text\": \"Check Action results\", \"emoji\": True},\r \"url\": f'''https://github.com/huggingface/transformers/actions/runs/{os.environ['GITHUB_RUN_ID']}''',\r },\r }\r\r\r @property\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return {\r \"type\": \"section\",\r \"text\": {\r \"type\": \"plain_text\",\r \"text\": (\r f'''There were {self.n_failures} failures, out of {self.n_tests} tests.\\nThe suite ran in'''\r f''' {self.time}.'''\r ),\r \"emoji\": True,\r },\r \"accessory\": {\r \"type\": \"button\",\r \"text\": {\"type\": \"plain_text\", \"text\": \"Check Action results\", \"emoji\": True},\r \"url\": f'''https://github.com/huggingface/transformers/actions/runs/{os.environ['GITHUB_RUN_ID']}''',\r },\r }\r\r\r @property\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tTuple ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t40\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{k: v[\"\"\"failed\"\"\"] for k, v in doc_test_results.items() if isinstance(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase )}\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"\"\"\"\r for category, failures in category_failures.items():\r if len(_UpperCAmelCase ) == 0:\r continue\r\r if report != \"\":\r report += \"\\n\\n\"\r\r report += f'''*{category} failures*:'''.ljust(line_length // 2 ).rjust(line_length // 2 ) + \"\\n\"\r report += \"`\"\r report += \"`\\n`\".join(_UpperCAmelCase )\r report += \"`\"\r\r return {\r \"type\": \"section\",\r \"text\": {\r \"type\": \"mrkdwn\",\r \"text\": f'''The following examples had failures:\\n\\n\\n{report}\\n''',\r },\r }\r\r\r @property\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tDict ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[self.header]\r\r if self.n_failures > 0:\r blocks.append(self.failures )\r\r if self.n_failures > 0:\r blocks.extend([self.category_failures] )\r\r if self.n_failures == 0:\r blocks.append(self.no_failures )\r\r return json.dumps(_UpperCAmelCase )\r\r\r @staticmethod\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[\r {\r \"\"\"type\"\"\": \"\"\"section\"\"\",\r \"\"\"text\"\"\": {\r \"\"\"type\"\"\": \"\"\"plain_text\"\"\",\r \"\"\"text\"\"\": \"\"\"There was an issue running the tests.\"\"\",\r },\r \"\"\"accessory\"\"\": {\r \"\"\"type\"\"\": \"\"\"button\"\"\",\r \"\"\"text\"\"\": {\"\"\"type\"\"\": \"\"\"plain_text\"\"\", \"\"\"text\"\"\": \"\"\"Check Action results\"\"\", \"\"\"emoji\"\"\": True},\r \"\"\"url\"\"\": f'''https://github.com/huggingface/transformers/actions/runs/{os.environ['GITHUB_RUN_ID']}''',\r },\r }\r ]\r\r print(\"\"\"Sending the following payload\"\"\" )\r print(json.dumps({\"\"\"blocks\"\"\": json.loads(_UpperCAmelCase )} ) )\r\r client.chat_postMessage(\r channel=os.environ[\"\"\"CI_SLACK_CHANNEL_ID_DAILY\"\"\"] ,\t\t\t\t\t\ttext=\"\"\"There was an issue running the tests.\"\"\" ,\t\t\t\t\t\tblocks=_UpperCAmelCase ,\t\t\t\t\t\t)\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r print(\"\"\"Sending the following payload\"\"\" )\r print(json.dumps({\"\"\"blocks\"\"\": json.loads(self.payload )} ) )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tf'''{self.n_failures} failures out of {self.n_tests} tests,''' if self.n_failures else \"\"\"All tests passed.\"\"\"\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tclient.chat_postMessage(\r channel=os.environ[\"\"\"CI_SLACK_CHANNEL_ID_DAILY\"\"\"] ,\t\t\t\t\t\tblocks=self.payload ,\t\t\t\t\t\ttext=_UpperCAmelCase ,\t\t\t\t\t\t)\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tList[str] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tList[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tOptional[Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tDict ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tTuple ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t\"\"\"\"\"\"\r for key, value in failures.items():\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tvalue[:2_00] + \"\"\" [Truncated]\"\"\" if len(_UpperCAmelCase ) > 2_50 else value\r failures_text += f'''*{key}*\\n_{value}_\\n\\n'''\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tjob_name\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\"\"\"type\"\"\": \"\"\"section\"\"\", \"\"\"text\"\"\": {\"\"\"type\"\"\": \"\"\"mrkdwn\"\"\", \"\"\"text\"\"\": text}}\r\r if job_link is not None:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{\r \"\"\"type\"\"\": \"\"\"button\"\"\",\r \"\"\"text\"\"\": {\"\"\"type\"\"\": \"\"\"plain_text\"\"\", \"\"\"text\"\"\": \"\"\"GitHub Action job\"\"\", \"\"\"emoji\"\"\": True},\r \"\"\"url\"\"\": job_link,\r }\r\r return [\r {\"type\": \"header\", \"text\": {\"type\": \"plain_text\", \"text\": title.upper(), \"emoji\": True}},\r content,\r {\"type\": \"section\", \"text\": {\"type\": \"mrkdwn\", \"text\": failures_text}},\r ]\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tOptional[Any] ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r if self.thread_ts is None:\r raise ValueError(\"\"\"Can only post reply if a post has been made.\"\"\" )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.doc_test_results.pop(\"\"\"job_link\"\"\" )\r self.doc_test_results.pop(\"\"\"failures\"\"\" )\r self.doc_test_results.pop(\"\"\"success\"\"\" )\r self.doc_test_results.pop(\"\"\"time_spent\"\"\" )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tsorted(self.doc_test_results.items() ,\t\t\t\t\t\tkey=lambda _UpperCAmelCase : t[0] )\r for job, job_result in sorted_dict:\r if len(job_result[\"\"\"failures\"\"\"] ):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tf'''*Num failures* :{len(job_result['failed'] )} \\n'''\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tjob_result[\"\"\"failures\"\"\"]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tself.get_reply_blocks(_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\t_UpperCAmelCase ,\t\t\t\t\t\ttext=_UpperCAmelCase )\r\r print(\"\"\"Sending the following reply\"\"\" )\r print(json.dumps({\"\"\"blocks\"\"\": blocks} ) )\r\r client.chat_postMessage(\r channel=os.environ[\"\"\"CI_SLACK_CHANNEL_ID_DAILY\"\"\"] ,\t\t\t\t\t\ttext=f'''Results for {job}''' ,\t\t\t\t\t\tblocks=_UpperCAmelCase ,\t\t\t\t\t\tthread_ts=self.thread_ts[\"\"\"ts\"\"\"] ,\t\t\t\t\t\t)\r\r time.sleep(1 )\r\rdef _UpperCamelCase\t\t\t\t\t( ):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tos.environ[\"\"\"GITHUB_RUN_ID\"\"\"]\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tF'''https://api.github.com/repos/huggingface/transformers/actions/runs/{run_id}/jobs?per_page=100'''\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\trequests.get(SCREAMING_SNAKE_CASE__\t\t\t\t).json()\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{}\r\r try:\r jobs.update({job[\"\"\"name\"\"\"]: job[\"\"\"html_url\"\"\"] for job in result[\"\"\"jobs\"\"\"]}\t\t\t\t)\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tmath.ceil((result[\"\"\"total_count\"\"\"] - 100) / 100\t\t\t\t)\r\r for i in range(SCREAMING_SNAKE_CASE__\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\trequests.get(url + F'''&page={i + 2}'''\t\t\t\t).json()\r jobs.update({job[\"\"\"name\"\"\"]: job[\"\"\"html_url\"\"\"] for job in result[\"\"\"jobs\"\"\"]}\t\t\t\t)\r\r return jobs\r except Exception as e:\r print(\"\"\"Unknown error, could not fetch links.\"\"\"\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r return {}\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tstr\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{}\r\r if os.path.exists(SCREAMING_SNAKE_CASE__\t\t\t\t):\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tos.listdir(SCREAMING_SNAKE_CASE__\t\t\t\t)\r for file in files:\r try:\r with open(os.path.join(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)\t\t\t\t, encoding=\"\"\"utf-8\"\"\"\t\t\t\t) as f:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tf.read()\r except UnicodeDecodeError as e:\r raise ValueError(F'''Could not open {os.path.join(SCREAMING_SNAKE_CASE__\t\t\t\t, SCREAMING_SNAKE_CASE__\t\t\t\t)}.'''\t\t\t\t) from e\r\r return _artifact\r\rdef _UpperCamelCase\t\t\t\t\t( ):\r '''simple docstring'''\r\r\r\r\r\r class lowerCAmelCase_ :\r\r\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def __init__( self\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tname\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t[]\r\r\r def __str__( self\t\t\t:\t\tint ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r return self.name\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t( self\t\t\t:\t\tUnion[str, Any] ,\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\tstr ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r self.paths.append({\"\"\"name\"\"\": self.name, \"\"\"path\"\"\": path} )\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t{}\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tfilter(os.path.isdir\t\t\t\t, os.listdir()\t\t\t\t)\r for directory in directories:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tdirectory\r if artifact_name not in _available_artifacts:\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tArtifact(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r _available_artifacts[artifact_name].add_path(SCREAMING_SNAKE_CASE__\t\t\t\t)\r\r return _available_artifacts\r\r\rif __name__ == \"__main__\":\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tget_job_links()\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tretrieve_available_artifacts()\r\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tcollections.OrderedDict(\r [\r ('*.py', 'API Examples'),\r ('*.md', 'MD Examples'),\r ]\r )\r\r # This dict will contain all the information relative to each doc test category:\r # - failed: list of failed tests\r # - failures: dict in the format 'test': 'error_message'\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\t{\r v: {\r 'failed': [],\r 'failures': {},\r }\r for v in docs.values()\r }\r\r # Link to the GitHub Action job\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tgithub_actions_job_links.get('run_doctests')\r\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tavailable_artifacts['doc_tests_gpu_test_reports'].paths[0]\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tretrieve_artifact(artifact_path['name'])\r if \"stats\" in artifact:\r UpperCAmelCase_ ,\t\t\t\t\t\tUpperCAmelCase_ ,\t\t\t\t\t\tUpperCAmelCase_\t\t\t\t =\t\t\t\t\thandle_test_results(artifact['stats'])\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tfailed\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tsuccess\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\ttime_spent[1:-1] + ', '\r\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\textract_first_line_failure(artifact['failures_short'])\r for line in artifact[\"summary_short\"].split('\\n'):\r if re.search('FAILED', line):\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tline.replace('FAILED ', '')\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tline.split()[0].replace('\\n', '')\r\r if \"::\" in line:\r UpperCAmelCase_ ,\t\t\t\t\t\tUpperCAmelCase_\t\t\t\t =\t\t\t\t\tline.split('::')\r else:\r UpperCAmelCase_ ,\t\t\t\t\t\tUpperCAmelCase_\t\t\t\t =\t\t\t\t\tline, line\r\r for file_regex in docs.keys():\r if fnmatch(file_path, file_regex):\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tdocs[file_regex]\r doc_test_results[category][\"failed\"].append(test)\r\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tall_failures[test] if test in all_failures else 'N/A'\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tfailure\r break\r\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tMessage('🤗 Results of the doc tests.', doc_test_results)\r message.post()\r message.post_reply()\r"},"code_codestyle":{"kind":"number","value":346,"string":"346"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\rfrom math import factorial\r\rdef _UpperCamelCase\t\t\t\t\t( SCREAMING_SNAKE_CASE__\t:\t\t\t\t\tint = 20\t\t\t\t):\r '''simple docstring'''\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\t2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,\r # 2, 3,...\r UpperCAmelCase__\t\t\t\t\t\t\t\t=\t\t\tn // 2\r\r return int(factorial(SCREAMING_SNAKE_CASE__\t\t\t\t) / (factorial(SCREAMING_SNAKE_CASE__\t\t\t\t) * factorial(n - k\t\t\t\t))\t\t\t\t)\r\r\rif __name__ == \"__main__\":\r import sys\r\r if len(sys.argv) == 1:\r print(solution(2_0))\r else:\r try:\r UpperCAmelCase_\t\t\t\t =\t\t\t\t\tint(sys.argv[1])\r print(solution(n))\r except ValueError:\r print('Invalid entry - please enter a number.')\r"},"style_context_codestyle":{"kind":"number","value":346,"string":"346"},"label":{"kind":"number","value":1,"string":"1"}}}],"truncated":false,"partial":false},"paginationData":{"pageIndex":1523,"numItemsPerPage":100,"numTotalItems":153999,"offset":152300,"length":100}},"jwt":"eyJhbGciOiJFZERTQSJ9.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc1Njk1OTc4NCwic3ViIjoiL2RhdGFzZXRzL2luZmluaXR5b2ZzcGFjZS9weXRob25fY29kZXN0eWxlcy1yYW5kb20tNTAwIiwiZXhwIjoxNzU2OTYzMzg0LCJpc3MiOiJodHRwczovL2h1Z2dpbmdmYWNlLmNvIn0.0Qd9-Wc7uCTl2BWuf0ufrdgT_sTGOvOLmneIgQI0Ti22XFvxfRrzuX9v2hI3S4-z8TxRh02uiYsUOdi_y9KCCA","displayUrls":true},"discussionsStats":{"closed":0,"open":0,"total":0},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
Datasets:

infinityofspace
/

python_codestyles-random-500

Dataset card Data Studio Files Files and versions
xet
Community

Subset (1)
default · 182k rows

Split (2)

train · 154k rows

code

stringlengths

87

55.2k

code_codestyle

int64

0

349

style_context

stringlengths

135

49.1k

style_context_codestyle

int64

0

349

label

int64

0

1

'''simple docstring''' import itertools import os from collections import Counter, defaultdict from concurrent.futures import ThreadPoolExecutor, as_completed import numpy as np import datasets from .execute import check_correctness UpperCAmelCase_ = '\\n@misc{chen2021evaluating,\n title={Evaluating Large Language Models Trained on Code},\n author={Mark Chen and Jerry Tworek and Heewoo Jun and Qiming Yuan \\nand Henrique Ponde de Oliveira Pinto and Jared Kaplan and Harri Edwards \\nand Yuri Burda and Nicholas Joseph and Greg Brockman and Alex Ray \\nand Raul Puri and Gretchen Krueger and Michael Petrov and Heidy Khlaaf \\nand Girish Sastry and Pamela Mishkin and Brooke Chan and Scott Gray \\nand Nick Ryder and Mikhail Pavlov and Alethea Power and Lukasz Kaiser \\nand Mohammad Bavarian and Clemens Winter and Philippe Tillet \\nand Felipe Petroski Such and Dave Cummings and Matthias Plappert \\nand Fotios Chantzis and Elizabeth Barnes and Ariel Herbert-Voss \\nand William Hebgen Guss and Alex Nichol and Alex Paino and Nikolas Tezak \\nand Jie Tang and Igor Babuschkin and Suchir Balaji and Shantanu Jain \\nand William Saunders and Christopher Hesse and Andrew N. Carr \\nand Jan Leike and Josh Achiam and Vedant Misra and Evan Morikawa \\nand Alec Radford and Matthew Knight and Miles Brundage and Mira Murati \\nand Katie Mayer and Peter Welinder and Bob McGrew and Dario Amodei \\nand Sam McCandlish and Ilya Sutskever and Wojciech Zaremba},\n year={2021},\n eprint={2107.03374},\n archivePrefix={arXiv},\n primaryClass={cs.LG}\n}\n' UpperCAmelCase_ = '\\nThis metric implements the evaluation harness for the HumanEval problem solving dataset\ndescribed in the paper "Evaluating Large Language Models Trained on Code"\n(https://arxiv.org/abs/2107.03374).\n' UpperCAmelCase_ = '\nCalculates how good are predictions given some references, using certain scores\nArgs:\n predictions: list of candidates to evaluate. Each candidates should be a list\n of strings with several code candidates to solve the problem.\n references: a list with a test for each prediction. Each test should evaluate the\n correctness of a code candidate.\n k: number of code candidates to consider in the evaluation (Default: [1, 10, 100])\n num_workers: number of workers used to evaluate the canidate programs (Default: 4).\n timeout:\nReturns:\n pass_at_k: dict with pass rates for each k\n results: dict with granular results of each unittest\nExamples:\n >>> code_eval = datasets.load_metric("code_eval")\n >>> test_cases = ["assert add(2,3)==5"]\n >>> candidates = [["def add(a,b): return a*b", "def add(a, b): return a+b"]]\n >>> pass_at_k, results = code_eval.compute(references=test_cases, predictions=candidates, k=[1, 2])\n >>> print(pass_at_k)\n {\'pass@1\': 0.5, \'pass@2\': 1.0}\n' UpperCAmelCase_ = '\n################################################################################\n !!!WARNING!!!\n################################################################################\nThe "code_eval" metric executes untrusted model-generated code in Python.\nAlthough it is highly unlikely that model-generated code will do something\novertly malicious in response to this test suite, model-generated code may act\ndestructively due to a lack of model capability or alignment.\nUsers are strongly encouraged to sandbox this evaluation suite so that it\ndoes not perform destructive actions on their host or network. For more\ninformation on how OpenAI sandboxes its code, see the paper "Evaluating Large\nLanguage Models Trained on Code" (https://arxiv.org/abs/2107.03374).\n\nOnce you have read this disclaimer and taken appropriate precautions,\nset the environment variable HF_ALLOW_CODE_EVAL="1". Within Python you can to this\nwith:\n\n>>> import os\n>>> os.environ["HF_ALLOW_CODE_EVAL"] = "1"\n\n################################################################################\\n' UpperCAmelCase_ = 'The MIT License\n\nCopyright (c) OpenAI (https://openai.com)\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the "Software"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in\nall copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\nTHE SOFTWARE.' @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION ) class lowerCAmelCase_ ( datasets.Metric ): '''simple docstring''' def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ): """simple docstring""" return datasets.MetricInfo( # This is the description that will appear on the metrics page. description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features( { """predictions""": datasets.Sequence(datasets.Value("""string""" ) ), """references""": datasets.Value("""string""" ), } ) , homepage="""https://github.com/openai/human-eval""" , codebase_urls=["""https://github.com/openai/human-eval"""] , reference_urls=["""https://github.com/openai/human-eval"""] , license=_LICENSE , ) def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : List[str]=[1, 10, 1_00] , _UpperCAmelCase : Optional[Any]=4 , _UpperCAmelCase : Any=3.0 ): """simple docstring""" if os.getenv("""HF_ALLOW_CODE_EVAL""" , 0 ) != "1": raise ValueError(_WARNING ) if os.name == "nt": raise NotImplementedError("""This metric is currently not supported on Windows.""" ) with ThreadPoolExecutor(max_workers=_UpperCAmelCase ) as executor: UpperCAmelCase__ = [] UpperCAmelCase__ = Counter() UpperCAmelCase__ = 0 UpperCAmelCase__ = defaultdict(_UpperCAmelCase ) for task_id, (candidates, test_case) in enumerate(zip(_UpperCAmelCase , _UpperCAmelCase ) ): for candidate in candidates: UpperCAmelCase__ = candidate + """\n""" + test_case UpperCAmelCase__ = (test_program, timeout, task_id, completion_id[task_id]) UpperCAmelCase__ = executor.submit(_UpperCAmelCase , *_UpperCAmelCase ) futures.append(_UpperCAmelCase ) completion_id[task_id] += 1 n_samples += 1 for future in as_completed(_UpperCAmelCase ): UpperCAmelCase__ = future.result() results[result["task_id"]].append((result["""completion_id"""], result) ) UpperCAmelCase__ , UpperCAmelCase__ = [], [] for result in results.values(): result.sort() UpperCAmelCase__ = [r[1]["""passed"""] for r in result] total.append(len(_UpperCAmelCase ) ) correct.append(sum(_UpperCAmelCase ) ) UpperCAmelCase__ = np.array(_UpperCAmelCase ) UpperCAmelCase__ = np.array(_UpperCAmelCase ) UpperCAmelCase__ = k UpperCAmelCase__ = {f'''pass@{k}''': estimate_pass_at_k(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ).mean() for k in ks if (total >= k).all()} return pass_at_k, results def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : Optional[Any] ): '''simple docstring''' def estimator(SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ) -> float: if n - c < k: return 1.0 return 1.0 - np.prod(1.0 - k / np.arange(n - c + 1 , n + 1 ) ) if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ): UpperCAmelCase__ = itertools.repeat(SCREAMING_SNAKE_CASE__ , len(SCREAMING_SNAKE_CASE__ ) ) else: assert len(SCREAMING_SNAKE_CASE__ ) == len(SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = iter(SCREAMING_SNAKE_CASE__ ) return np.array([estimator(int(SCREAMING_SNAKE_CASE__ ) , int(SCREAMING_SNAKE_CASE__ ) , SCREAMING_SNAKE_CASE__ ) for n, c in zip(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )] )

346

'''simple docstring''' import math def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int ): '''simple docstring''' assert isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) and ( number >= 0 ), "'number' must been an int and positive" if 1 < number < 4: # 2 and 3 are primes return True elif number < 2 or not number % 2: # Negatives, 0, 1 and all even numbers are not primes return False UpperCAmelCase__ = range(3 , int(math.sqrt(SCREAMING_SNAKE_CASE__ ) + 1 ) , 2 ) return not any(not number % i for i in odd_numbers ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : List[str]=1 , **SCREAMING_SNAKE_CASE__ : List[str] ): '''simple docstring''' UpperCAmelCase__ = factor * value UpperCAmelCase__ = value while not is_prime(SCREAMING_SNAKE_CASE__ ): value += 1 if not ("desc" in kwargs and kwargs["desc"] is True) else -1 if value == first_value_val: return next_prime(value + 1 , **SCREAMING_SNAKE_CASE__ ) return value

346

1

'''simple docstring''' import importlib import sys from argparse import REMAINDER, ArgumentParser from pathlib import Path import torch_xla.distributed.xla_multiprocessing as xmp def _UpperCamelCase ( ): '''simple docstring''' UpperCAmelCase__ = ArgumentParser( description=( """PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes""" ) ) # Optional arguments for the launch helper parser.add_argument("""--num_cores""" , type=SCREAMING_SNAKE_CASE__ , default=1 , help="""Number of TPU cores to use (1 or 8).""" ) # positional parser.add_argument( """training_script""" , type=SCREAMING_SNAKE_CASE__ , help=( """The full path to the single TPU training """ """program/script to be launched in parallel, """ """followed by all the arguments for the """ """training script""" ) , ) # rest from the training program parser.add_argument("""training_script_args""" , nargs=SCREAMING_SNAKE_CASE__ ) return parser.parse_args() def _UpperCamelCase ( ): '''simple docstring''' UpperCAmelCase__ = parse_args() # Import training_script as a module. UpperCAmelCase__ = Path(args.training_script ) sys.path.append(str(script_fpath.parent.resolve() ) ) UpperCAmelCase__ = script_fpath.stem UpperCAmelCase__ = importlib.import_module(SCREAMING_SNAKE_CASE__ ) # Patch sys.argv UpperCAmelCase__ = [args.training_script] + args.training_script_args + ["""--tpu_num_cores""", str(args.num_cores )] xmp.spawn(mod._mp_fn , args=() , nprocs=args.num_cores ) if __name__ == "__main__": main()

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'''simple docstring''' import string from math import logaa def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : str ): '''simple docstring''' UpperCAmelCase__ = document.translate( str.maketrans("""""" , """""" , string.punctuation ) ).replace("""\n""" , """""" ) UpperCAmelCase__ = document_without_punctuation.split(""" """ ) # word tokenization return len([word for word in tokenize_document if word.lower() == term.lower()] ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : str ): '''simple docstring''' UpperCAmelCase__ = corpus.lower().translate( str.maketrans("""""" , """""" , string.punctuation ) ) # strip all punctuation and replace it with '' UpperCAmelCase__ = corpus_without_punctuation.split("""\n""" ) UpperCAmelCase__ = term.lower() return (len([doc for doc in docs if term in doc] ), len(SCREAMING_SNAKE_CASE__ )) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : Tuple=False ): '''simple docstring''' if smoothing: if n == 0: raise ValueError("""log10(0) is undefined.""" ) return round(1 + logaa(n / (1 + df) ) , 3 ) if df == 0: raise ZeroDivisionError("""df must be > 0""" ) elif n == 0: raise ValueError("""log10(0) is undefined.""" ) return round(logaa(n / df ) , 3 ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ): '''simple docstring''' return round(tf * idf , 3 )

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'''simple docstring''' import collections import json import os import re from typing import TYPE_CHECKING, List, Optional, Tuple import numpy as np from ...tokenization_utils_fast import PreTrainedTokenizer from ...utils import logging if TYPE_CHECKING: from transformers.pipelines.conversational import Conversation UpperCAmelCase_ = logging.get_logger(__name__) UpperCAmelCase_ = {'vocab_file': 'vocab.txt', 'emoji_file': 'emoji.json'} UpperCAmelCase_ = { 'vocab_file': { 'abeja/gpt-neox-japanese-2.7b': 'https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/vocab.txt', }, 'emoji_file': { 'abeja/gpt-neox-japanese-2.7b': 'https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/emoji.json', }, } UpperCAmelCase_ = { 'abeja/gpt-neox-japanese-2.7b': 2_0_4_8, } def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : Any ): '''simple docstring''' with open(SCREAMING_SNAKE_CASE__ , """r""" , encoding="""utf-8""" ) as f: UpperCAmelCase__ = json.loads(f.read() ) UpperCAmelCase__ = collections.OrderedDict() UpperCAmelCase__ = collections.OrderedDict() UpperCAmelCase__ = collections.OrderedDict() with open(SCREAMING_SNAKE_CASE__ , """r""" , encoding="""utf-8""" ) as f: UpperCAmelCase__ = f.readlines() UpperCAmelCase__ = [[t.rstrip("""\n""" )] if (t == """,""" or """,""" not in t) else t.rstrip("""\n""" ).split(""",""" ) for t in token] for idx, b in enumerate(SCREAMING_SNAKE_CASE__ ): UpperCAmelCase__ = b UpperCAmelCase__ = idx for wd in b: UpperCAmelCase__ = idx return vocab, raw_vocab, ids_to_tokens, emoji class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' lowerCAmelCase_ : Dict = VOCAB_FILES_NAMES lowerCAmelCase_ : int = PRETRAINED_VOCAB_FILES_MAP lowerCAmelCase_ : Optional[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES lowerCAmelCase_ : Any = ["""input_ids""", """attention_mask"""] def __init__( self : Dict , _UpperCAmelCase : Tuple , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : List[str]="<|endoftext|>" , _UpperCAmelCase : Optional[Any]="<|endoftext|>" , _UpperCAmelCase : str="<|startoftext|>" , _UpperCAmelCase : List[Any]="<|endoftext|>" , _UpperCAmelCase : Optional[int]=False , **_UpperCAmelCase : Union[str, Any] , ): """simple docstring""" super().__init__( unk_token=_UpperCAmelCase , pad_token=_UpperCAmelCase , bos_token=_UpperCAmelCase , eos_token=_UpperCAmelCase , do_clean_text=_UpperCAmelCase , **_UpperCAmelCase , ) if not os.path.isfile(_UpperCAmelCase ): raise ValueError( f'''Can\'t find a vocabulary file at path \'{vocab_file}\'. To load the vocabulary from a Google pretrained''' """ model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`""" ) if not os.path.isfile(_UpperCAmelCase ): raise ValueError( f'''Can\'t find a emoji file at path \'{emoji_file}\'. To load the emoji information from a Google''' """ pretrained model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`""" ) UpperCAmelCase__ = do_clean_text UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = load_vocab_and_emoji(_UpperCAmelCase , _UpperCAmelCase ) UpperCAmelCase__ = SubWordJapaneseTokenizer( vocab=self.vocab , ids_to_tokens=self.ids_to_tokens , emoji=self.emoji ) @property def SCREAMING_SNAKE_CASE__ ( self : List[Any] ): """simple docstring""" return len(self.raw_vocab ) def SCREAMING_SNAKE_CASE__ ( self : Any ): """simple docstring""" return dict(self.raw_vocab , **self.added_tokens_encoder ) def SCREAMING_SNAKE_CASE__ ( self : int , _UpperCAmelCase : Optional[Any] ): """simple docstring""" return self.subword_tokenizer.tokenize(_UpperCAmelCase , clean=self.do_clean_text ) def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , _UpperCAmelCase : List[Any] ): """simple docstring""" return self.vocab.get(_UpperCAmelCase , self.vocab.get(self.unk_token ) ) def SCREAMING_SNAKE_CASE__ ( self : Dict , _UpperCAmelCase : str ): """simple docstring""" return self.subword_tokenizer.convert_id_to_token(_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Tuple , _UpperCAmelCase : List[str] ): """simple docstring""" UpperCAmelCase__ = """""".join(_UpperCAmelCase ).strip() return out_string def SCREAMING_SNAKE_CASE__ ( self : Tuple , _UpperCAmelCase : "Conversation" ): """simple docstring""" UpperCAmelCase__ = [] for is_user, text in conversation.iter_texts(): input_ids.extend(self.encode(_UpperCAmelCase , add_special_tokens=_UpperCAmelCase ) + [self.eos_token_id] ) if len(_UpperCAmelCase ) > self.model_max_length: UpperCAmelCase__ = input_ids[-self.model_max_length :] return input_ids def SCREAMING_SNAKE_CASE__ ( self : Any , _UpperCAmelCase : str , _UpperCAmelCase : Optional[str] = None ): """simple docstring""" UpperCAmelCase__ = 0 if os.path.isdir(_UpperCAmelCase ): UpperCAmelCase__ = os.path.join( _UpperCAmelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] ) UpperCAmelCase__ = os.path.join( _UpperCAmelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""emoji_file"""] ) else: UpperCAmelCase__ = ( (filename_prefix + """-""" if filename_prefix else """""") + save_directory + VOCAB_FILES_NAMES["""vocab_file"""] ) UpperCAmelCase__ = ( (filename_prefix + """-""" if filename_prefix else """""") + save_directory + VOCAB_FILES_NAMES["""emoji_file"""] ) with open(_UpperCAmelCase , """w""" , encoding="""utf-8""" ) as writer: for token_index, token in self.ids_to_tokens.items(): if index != token_index: logger.warning( f'''Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.''' """ Please check that the vocabulary is not corrupted!""" ) UpperCAmelCase__ = token_index writer.write(""",""".join(_UpperCAmelCase ) + """\n""" ) index += 1 with open(_UpperCAmelCase , """w""" , encoding="""utf-8""" ) as writer: json.dump(self.emoji , _UpperCAmelCase ) return vocab_file, emoji_file class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' def __init__( self : Tuple , _UpperCAmelCase : Tuple , _UpperCAmelCase : Any , _UpperCAmelCase : Optional[int] ): """simple docstring""" UpperCAmelCase__ = vocab # same as swe UpperCAmelCase__ = ids_to_tokens # same as bpe UpperCAmelCase__ = emoji UpperCAmelCase__ = np.max([len(_UpperCAmelCase ) for w in self.vocab.keys()] ) UpperCAmelCase__ = re.compile(r"""(https?|ftp)(:\/\/[-_\.!~*\'()a-zA-Z0-9;\/?:\@&=\+$,%#]+)""" ) UpperCAmelCase__ = re.compile(r"""[A-Za-z0-9\._+]*@[\-_0-9A-Za-z]+(\.[A-Za-z]+)*""" ) UpperCAmelCase__ = re.compile(r"""[$]{0,1}[0-9]{2,4}[$\-$]{0,1}[0-9]{2,4}[$\-]{0,1}[0-9]{3,4}""" ) UpperCAmelCase__ = re.compile( r"""([12]\d{3}[/\-年])*(0?[1-9]|1[0-2])[/\-月]((0?[1-9]|[12][0-9]|3[01])日?)*(\d{1,2}|:|\d{1,2}時|\d{1,2}分|$日$|$月$|$火$|$水$|$木$|$金$|$土$|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*""" ) UpperCAmelCase__ = re.compile( r"""(明治|大正|昭和|平成|令和|㍾|㍽|㍼|㍻|\u32ff)\d{1,2}年(0?[1-9]|1[0-2])月(0?[1-9]|[12][0-9]|3[01])日(\d{1,2}|:|\d{1,2}時|\d{1,2}分|$日$|$月$|$火$|$水$|$木$|$金$|$土$|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*""" ) UpperCAmelCase__ = re.compile( r"""((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*億)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*万)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*千)*(0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*(千円|万円|千万円|円|千ドル|万ドル|千万ドル|ドル|千ユーロ|万ユーロ|千万ユーロ|ユーロ)+($税込$|$税抜$|\+tax)*""" ) UpperCAmelCase__ = """─━│┃┄┅┆┇┈┉┊┋┌┍┎┏┐┑┒┓└┕┖┗┘┙┚┛├┝┞┟┠┡┢┣┤┥┦┧┨┩┪┫┬┭┮┯┰┱┲┳┴┵┶┷┸┹┺┻┼┽┾┿╀╁╂╃╄╅╆╇╈╉╊╋╌╍╎╏═║╒╓╔╕╖╗╘╙╚╛╜╝╞╟╠╡╢╣╤╥╦╧╨╩╪╫╬╭╮╯╰╱╲╳╴╵╶╷╸╹╺╻╼╽╾╿""" UpperCAmelCase__ = """▀▁▂▃▄▅▆▇█▉▊▋▌▍▎▏▐░▒▓▔▕▖▗▘▙▚▛▜▝▞▟""" UpperCAmelCase__ = str.maketrans({k: """<BLOCK>""" for k in keisen + blocks} ) def __len__( self : Union[str, Any] ): """simple docstring""" return len(self.ids_to_tokens ) def SCREAMING_SNAKE_CASE__ ( self : Dict , _UpperCAmelCase : Tuple ): """simple docstring""" UpperCAmelCase__ = self.content_repattera.sub("""<URL>""" , _UpperCAmelCase ) UpperCAmelCase__ = self.content_repattera.sub("""<EMAIL>""" , _UpperCAmelCase ) UpperCAmelCase__ = self.content_repattera.sub("""<TEL>""" , _UpperCAmelCase ) UpperCAmelCase__ = self.content_repattera.sub("""<DATE>""" , _UpperCAmelCase ) UpperCAmelCase__ = self.content_repattera.sub("""<DATE>""" , _UpperCAmelCase ) UpperCAmelCase__ = self.content_repattera.sub("""<PRICE>""" , _UpperCAmelCase ) UpperCAmelCase__ = content.translate(self.content_transa ) while "<BLOCK><BLOCK>" in content: UpperCAmelCase__ = content.replace("""<BLOCK><BLOCK>""" , """<BLOCK>""" ) return content def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] , _UpperCAmelCase : int , _UpperCAmelCase : Tuple=False ): """simple docstring""" UpperCAmelCase__ = text.replace(""" """ , """<SP>""" ) UpperCAmelCase__ = text.replace("""　""" , """<SP>""" ) UpperCAmelCase__ = text.replace("""\r\n""" , """<BR>""" ) UpperCAmelCase__ = text.replace("""\n""" , """<BR>""" ) UpperCAmelCase__ = text.replace("""\r""" , """<BR>""" ) UpperCAmelCase__ = text.replace("""\t""" , """<TAB>""" ) UpperCAmelCase__ = text.replace("""—""" , """ー""" ) UpperCAmelCase__ = text.replace("""−""" , """ー""" ) for k, v in self.emoji["emoji"].items(): if k in text: UpperCAmelCase__ = text.replace(_UpperCAmelCase , _UpperCAmelCase ) if clean: UpperCAmelCase__ = self.clean_text(_UpperCAmelCase ) def check_simbol(_UpperCAmelCase : Any ): UpperCAmelCase__ = x.encode() if len(_UpperCAmelCase ) == 1 and len(_UpperCAmelCase ) == 2: UpperCAmelCase__ = (int(e[0] ) << 8) + int(e[1] ) if ( (c >= 0xC2A1 and c <= 0xC2BF) or (c >= 0xC780 and c <= 0xC783) or (c >= 0xCAB9 and c <= 0xCBBF) or (c >= 0xCC80 and c <= 0xCDA2) ): return True return False def checkuae(_UpperCAmelCase : Optional[Any] ): UpperCAmelCase__ = x.encode() if len(_UpperCAmelCase ) == 1 and len(_UpperCAmelCase ) == 3: UpperCAmelCase__ = (int(e[0] ) << 16) + (int(e[1] ) << 8) + int(e[2] ) if c >= 0xE28080 and c <= 0xE2B07F: return True return False UpperCAmelCase__ = 0 UpperCAmelCase__ = [] while pos < len(_UpperCAmelCase ): UpperCAmelCase__ = min(len(_UpperCAmelCase ) , pos + self.maxlen + 1 ) if text[pos] == """<""" else pos + 3 UpperCAmelCase__ = [] # (token_id, token, pos) for e in range(_UpperCAmelCase , _UpperCAmelCase , -1 ): UpperCAmelCase__ = text[pos:e] if wd in self.vocab: if wd[0] == "<" and len(_UpperCAmelCase ) > 2: UpperCAmelCase__ = [(self.vocab[wd], wd, e)] break else: candidates.append((self.vocab[wd], wd, e) ) if len(_UpperCAmelCase ) > 0: # the smallest token_id is adopted UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = sorted(_UpperCAmelCase , key=lambda _UpperCAmelCase : x[0] )[0] result.append(_UpperCAmelCase ) UpperCAmelCase__ = e else: UpperCAmelCase__ = pos + 1 UpperCAmelCase__ = text[pos:end] if check_simbol(_UpperCAmelCase ): result.append("""<KIGOU>""" ) elif checkuae(_UpperCAmelCase ): result.append("""<U2000U2BFF>""" ) else: for i in wd.encode("""utf-8""" ): result.append("""<|byte%d|>""" % i ) UpperCAmelCase__ = end return result def SCREAMING_SNAKE_CASE__ ( self : Any , _UpperCAmelCase : Tuple , _UpperCAmelCase : Union[str, Any]="\n" ): """simple docstring""" UpperCAmelCase__ = [] UpperCAmelCase__ = [] UpperCAmelCase__ = self.ids_to_tokens[index][0] if word[:6] == "<|byte" and word[-2:] == "|>": byte_tokens.append(int(word[6:-2] ) ) else: if len(_UpperCAmelCase ) > 0: words.append(bytearray(_UpperCAmelCase ).decode("""utf-8""" , errors="""replace""" ) ) UpperCAmelCase__ = [] if word[:7] == "<|emoji" and word[-2:] == "|>": words.append(self.emoji["""emoji_inv"""][word] ) elif word == "<SP>": words.append(""" """ ) elif word == "<BR>": words.append(_UpperCAmelCase ) elif word == "<TAB>": words.append("""\t""" ) elif word == "<BLOCK>": words.append("""▀""" ) elif word == "<KIGOU>": words.append("""ǀ""" ) elif word == "<U2000U2BFF>": words.append("""‖""" ) else: words.append(_UpperCAmelCase ) if len(_UpperCAmelCase ) > 0: words.append(bytearray(_UpperCAmelCase ).decode("""utf-8""" , errors="""replace""" ) ) UpperCAmelCase__ = """""".join(_UpperCAmelCase ) return text

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'''simple docstring''' import argparse import torch from transformers import BertForMaskedLM if __name__ == "__main__": UpperCAmelCase_ = argparse.ArgumentParser( description=( 'Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned' ' Distillation' ) ) parser.add_argument('--model_type', default='bert', choices=['bert']) parser.add_argument('--model_name', default='bert-base-uncased', type=str) parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_bert-base-uncased_0247911.pth', type=str) parser.add_argument('--vocab_transform', action='store_true') UpperCAmelCase_ = parser.parse_args() if args.model_type == "bert": UpperCAmelCase_ = BertForMaskedLM.from_pretrained(args.model_name) UpperCAmelCase_ = 'bert' else: raise ValueError('args.model_type should be "bert".') UpperCAmelCase_ = model.state_dict() UpperCAmelCase_ = {} for w in ["word_embeddings", "position_embeddings"]: UpperCAmelCase_ = state_dict[f"{prefix}.embeddings.{w}.weight"] for w in ["weight", "bias"]: UpperCAmelCase_ = state_dict[f"{prefix}.embeddings.LayerNorm.{w}"] UpperCAmelCase_ = 0 for teacher_idx in [0, 2, 4, 7, 9, 1_1]: for w in ["weight", "bias"]: UpperCAmelCase_ = state_dict[ f"{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}" ] UpperCAmelCase_ = state_dict[ f"{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}" ] UpperCAmelCase_ = state_dict[ f"{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}" ] UpperCAmelCase_ = state_dict[ f"{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}" ] UpperCAmelCase_ = state_dict[ f"{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}" ] UpperCAmelCase_ = state_dict[ f"{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}" ] UpperCAmelCase_ = state_dict[ f"{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}" ] UpperCAmelCase_ = state_dict[ f"{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}" ] std_idx += 1 UpperCAmelCase_ = state_dict['cls.predictions.decoder.weight'] UpperCAmelCase_ = state_dict['cls.predictions.bias'] if args.vocab_transform: for w in ["weight", "bias"]: UpperCAmelCase_ = state_dict[f"cls.predictions.transform.dense.{w}"] UpperCAmelCase_ = state_dict[f"cls.predictions.transform.LayerNorm.{w}"] print(f"N layers selected for distillation: {std_idx}") print(f"Number of params transferred for distillation: {len(compressed_sd.keys())}") print(f"Save transferred checkpoint to {args.dump_checkpoint}.") torch.save(compressed_sd, args.dump_checkpoint)

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'''simple docstring''' from math import ceil from typing import List, Optional, Union import numpy as np from ...audio_utils import mel_filter_bank, spectrogram, window_function from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor from ...utils import TensorType, logging UpperCAmelCase_ = logging.get_logger(__name__) class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' lowerCAmelCase_ : Optional[Any] = ["""audio_values""", """audio_mask"""] def __init__( self : List[str] , _UpperCAmelCase : List[str]=20_48 , _UpperCAmelCase : Dict=1 , _UpperCAmelCase : Tuple=[16, 16] , _UpperCAmelCase : Optional[int]=1_28 , _UpperCAmelCase : Tuple=4_41_00 , _UpperCAmelCase : Optional[Any]=86 , _UpperCAmelCase : Optional[int]=20_48 , _UpperCAmelCase : List[Any]=0.0 , **_UpperCAmelCase : Tuple , ): """simple docstring""" super().__init__( feature_size=_UpperCAmelCase , sampling_rate=_UpperCAmelCase , padding_value=_UpperCAmelCase , **_UpperCAmelCase , ) UpperCAmelCase__ = spectrogram_length UpperCAmelCase__ = num_channels UpperCAmelCase__ = patch_size UpperCAmelCase__ = feature_size // self.patch_size[1] UpperCAmelCase__ = n_fft UpperCAmelCase__ = sampling_rate // hop_length_to_sampling_rate UpperCAmelCase__ = sampling_rate UpperCAmelCase__ = padding_value UpperCAmelCase__ = mel_filter_bank( num_frequency_bins=1 + n_fft // 2 , num_mel_filters=_UpperCAmelCase , min_frequency=0.0 , max_frequency=2_2050.0 , sampling_rate=_UpperCAmelCase , norm="""slaney""" , mel_scale="""slaney""" , ).T def SCREAMING_SNAKE_CASE__ ( self : Any , _UpperCAmelCase : np.array ): """simple docstring""" UpperCAmelCase__ = spectrogram( _UpperCAmelCase , window_function(self.n_fft , """hann""" ) , frame_length=self.n_fft , hop_length=self.hop_length , power=2.0 , mel_filters=self.mel_filters.T , log_mel="""dB""" , db_range=80.0 , ) UpperCAmelCase__ = log_spec[:, :-1] UpperCAmelCase__ = log_spec - 20.0 UpperCAmelCase__ = np.clip(log_spec / 40.0 , -2.0 , 0.0 ) + 1.0 return log_spec def __call__( self : int , _UpperCAmelCase : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , _UpperCAmelCase : Optional[Union[str, TensorType]] = None , _UpperCAmelCase : Optional[bool] = True , _UpperCAmelCase : Optional[int] = None , _UpperCAmelCase : bool = False , _UpperCAmelCase : bool = False , **_UpperCAmelCase : Tuple , ): """simple docstring""" if sampling_rate is not None: if sampling_rate != self.sampling_rate: raise ValueError( """This feature extractor is set to support sampling rate""" f''' of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled''' f''' with {self.sampling_rate} and not {sampling_rate}.''' ) else: logger.warning( """It is strongly recommended to pass the `sampling_rate` argument to this function. """ """Failing to do so can result in silent errors that might be hard to debug.""" ) UpperCAmelCase__ = isinstance(_UpperCAmelCase , np.ndarray ) and len(raw_speech.shape ) > 1 if is_batched_numpy and len(raw_speech.shape ) > 2: raise ValueError(f'''Only mono-channel audio is supported for input to {self}''' ) UpperCAmelCase__ = is_batched_numpy or ( isinstance(_UpperCAmelCase , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) )) ) if is_batched: UpperCAmelCase__ = [np.asarray([speech] , dtype=np.floataa ).T for speech in raw_speech] elif not is_batched and not isinstance(_UpperCAmelCase , np.ndarray ): UpperCAmelCase__ = np.asarray(_UpperCAmelCase , dtype=np.floataa ) elif isinstance(_UpperCAmelCase , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ): UpperCAmelCase__ = raw_speech.astype(np.floataa ) # always return batch if not is_batched: UpperCAmelCase__ = [np.asarray([raw_speech] ).T] # Convert audio signals to log mel spectrograms, truncate by time axis UpperCAmelCase__ = [ self._np_extract_fbank_features(waveform.squeeze() ).T[: self.spectrogram_length] for waveform in raw_speech ] if isinstance(audio_features[0] , _UpperCAmelCase ): UpperCAmelCase__ = [np.asarray(_UpperCAmelCase , dtype=np.floataa ) for feature in audio_features] # Create audio attention mask UpperCAmelCase__ = max( [ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len for feature in audio_features] ) # The maximum number of audio patches in a batch if return_attention_mask: UpperCAmelCase__ = [ (ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len) * [1] + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len) * [0] for feature in audio_features ] UpperCAmelCase__ = np.array(_UpperCAmelCase ).astype(np.floataa ) # convert into correct format for padding UpperCAmelCase__ = max_patch_len // self.freq_len * self.patch_size[0] # The maximum audio size in a batch UpperCAmelCase__ = np.ones([len(_UpperCAmelCase ), 1, max_time_len, self.feature_size] ).astype(np.floataa ) UpperCAmelCase__ = padded_audio_features * self.padding_value for i in range(len(_UpperCAmelCase ) ): UpperCAmelCase__ = audio_features[i] UpperCAmelCase__ = feature # return as BatchFeature if return_attention_mask: UpperCAmelCase__ = {"""audio_values""": padded_audio_features, """audio_mask""": audio_mask} else: UpperCAmelCase__ = {"""audio_values""": padded_audio_features} UpperCAmelCase__ = BatchFeature(data=_UpperCAmelCase , tensor_type=_UpperCAmelCase ) return encoded_inputs

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'''simple docstring''' import tempfile import torch from diffusers import PNDMScheduler from .test_schedulers import SchedulerCommonTest class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' lowerCAmelCase_ : Union[str, Any] = (PNDMScheduler,) lowerCAmelCase_ : Optional[int] = (("""num_inference_steps""", 50),) def SCREAMING_SNAKE_CASE__ ( self : List[Any] , **_UpperCAmelCase : Optional[int] ): """simple docstring""" UpperCAmelCase__ = { """num_train_timesteps""": 10_00, """beta_start""": 0.0001, """beta_end""": 0.02, """beta_schedule""": """linear""", } config.update(**_UpperCAmelCase ) return config def SCREAMING_SNAKE_CASE__ ( self : List[Any] , _UpperCAmelCase : Tuple=0 , **_UpperCAmelCase : List[str] ): """simple docstring""" UpperCAmelCase__ = dict(self.forward_default_kwargs ) UpperCAmelCase__ = kwargs.pop("""num_inference_steps""" , _UpperCAmelCase ) UpperCAmelCase__ = self.dummy_sample UpperCAmelCase__ = 0.1 * sample UpperCAmelCase__ = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05] for scheduler_class in self.scheduler_classes: UpperCAmelCase__ = self.get_scheduler_config(**_UpperCAmelCase ) UpperCAmelCase__ = scheduler_class(**_UpperCAmelCase ) scheduler.set_timesteps(_UpperCAmelCase ) # copy over dummy past residuals UpperCAmelCase__ = dummy_past_residuals[:] with tempfile.TemporaryDirectory() as tmpdirname: scheduler.save_config(_UpperCAmelCase ) UpperCAmelCase__ = scheduler_class.from_pretrained(_UpperCAmelCase ) new_scheduler.set_timesteps(_UpperCAmelCase ) # copy over dummy past residuals UpperCAmelCase__ = dummy_past_residuals[:] UpperCAmelCase__ = scheduler.step_prk(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample UpperCAmelCase__ = new_scheduler.step_prk(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical" UpperCAmelCase__ = scheduler.step_plms(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample UpperCAmelCase__ = new_scheduler.step_plms(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical" def SCREAMING_SNAKE_CASE__ ( self : List[str] ): """simple docstring""" pass def SCREAMING_SNAKE_CASE__ ( self : int , _UpperCAmelCase : Union[str, Any]=0 , **_UpperCAmelCase : Optional[int] ): """simple docstring""" UpperCAmelCase__ = dict(self.forward_default_kwargs ) UpperCAmelCase__ = kwargs.pop("""num_inference_steps""" , _UpperCAmelCase ) UpperCAmelCase__ = self.dummy_sample UpperCAmelCase__ = 0.1 * sample UpperCAmelCase__ = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05] for scheduler_class in self.scheduler_classes: UpperCAmelCase__ = self.get_scheduler_config() UpperCAmelCase__ = scheduler_class(**_UpperCAmelCase ) scheduler.set_timesteps(_UpperCAmelCase ) # copy over dummy past residuals (must be after setting timesteps) UpperCAmelCase__ = dummy_past_residuals[:] with tempfile.TemporaryDirectory() as tmpdirname: scheduler.save_config(_UpperCAmelCase ) UpperCAmelCase__ = scheduler_class.from_pretrained(_UpperCAmelCase ) # copy over dummy past residuals new_scheduler.set_timesteps(_UpperCAmelCase ) # copy over dummy past residual (must be after setting timesteps) UpperCAmelCase__ = dummy_past_residuals[:] UpperCAmelCase__ = scheduler.step_prk(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample UpperCAmelCase__ = new_scheduler.step_prk(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical" UpperCAmelCase__ = scheduler.step_plms(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample UpperCAmelCase__ = new_scheduler.step_plms(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical" def SCREAMING_SNAKE_CASE__ ( self : int , **_UpperCAmelCase : Tuple ): """simple docstring""" UpperCAmelCase__ = self.scheduler_classes[0] UpperCAmelCase__ = self.get_scheduler_config(**_UpperCAmelCase ) UpperCAmelCase__ = scheduler_class(**_UpperCAmelCase ) UpperCAmelCase__ = 10 UpperCAmelCase__ = self.dummy_model() UpperCAmelCase__ = self.dummy_sample_deter scheduler.set_timesteps(_UpperCAmelCase ) for i, t in enumerate(scheduler.prk_timesteps ): UpperCAmelCase__ = model(_UpperCAmelCase , _UpperCAmelCase ) UpperCAmelCase__ = scheduler.step_prk(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ).prev_sample for i, t in enumerate(scheduler.plms_timesteps ): UpperCAmelCase__ = model(_UpperCAmelCase , _UpperCAmelCase ) UpperCAmelCase__ = scheduler.step_plms(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ).prev_sample return sample def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ): """simple docstring""" UpperCAmelCase__ = dict(self.forward_default_kwargs ) UpperCAmelCase__ = kwargs.pop("""num_inference_steps""" , _UpperCAmelCase ) for scheduler_class in self.scheduler_classes: UpperCAmelCase__ = self.get_scheduler_config() UpperCAmelCase__ = scheduler_class(**_UpperCAmelCase ) UpperCAmelCase__ = self.dummy_sample UpperCAmelCase__ = 0.1 * sample if num_inference_steps is not None and hasattr(_UpperCAmelCase , """set_timesteps""" ): scheduler.set_timesteps(_UpperCAmelCase ) elif num_inference_steps is not None and not hasattr(_UpperCAmelCase , """set_timesteps""" ): UpperCAmelCase__ = num_inference_steps # copy over dummy past residuals (must be done after set_timesteps) UpperCAmelCase__ = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05] UpperCAmelCase__ = dummy_past_residuals[:] UpperCAmelCase__ = scheduler.step_prk(_UpperCAmelCase , 0 , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample UpperCAmelCase__ = scheduler.step_prk(_UpperCAmelCase , 1 , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample self.assertEqual(output_a.shape , sample.shape ) self.assertEqual(output_a.shape , output_a.shape ) UpperCAmelCase__ = scheduler.step_plms(_UpperCAmelCase , 0 , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample UpperCAmelCase__ = scheduler.step_plms(_UpperCAmelCase , 1 , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample self.assertEqual(output_a.shape , sample.shape ) self.assertEqual(output_a.shape , output_a.shape ) def SCREAMING_SNAKE_CASE__ ( self : int ): """simple docstring""" for timesteps in [1_00, 10_00]: self.check_over_configs(num_train_timesteps=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ): """simple docstring""" for steps_offset in [0, 1]: self.check_over_configs(steps_offset=_UpperCAmelCase ) UpperCAmelCase__ = self.scheduler_classes[0] UpperCAmelCase__ = self.get_scheduler_config(steps_offset=1 ) UpperCAmelCase__ = scheduler_class(**_UpperCAmelCase ) scheduler.set_timesteps(10 ) assert torch.equal( scheduler.timesteps , torch.LongTensor( [9_01, 8_51, 8_51, 8_01, 8_01, 7_51, 7_51, 7_01, 7_01, 6_51, 6_51, 6_01, 6_01, 5_01, 4_01, 3_01, 2_01, 1_01, 1] ) , ) def SCREAMING_SNAKE_CASE__ ( self : List[Any] ): """simple docstring""" for beta_start, beta_end in zip([0.0001, 0.001] , [0.002, 0.02] ): self.check_over_configs(beta_start=_UpperCAmelCase , beta_end=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ): """simple docstring""" for schedule in ["linear", "squaredcos_cap_v2"]: self.check_over_configs(beta_schedule=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ): """simple docstring""" for prediction_type in ["epsilon", "v_prediction"]: self.check_over_configs(prediction_type=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : str ): """simple docstring""" for t in [1, 5, 10]: self.check_over_forward(time_step=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Any ): """simple docstring""" for t, num_inference_steps in zip([1, 5, 10] , [10, 50, 1_00] ): self.check_over_forward(num_inference_steps=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : List[str] ): """simple docstring""" UpperCAmelCase__ = 27 for scheduler_class in self.scheduler_classes: UpperCAmelCase__ = self.dummy_sample UpperCAmelCase__ = 0.1 * sample UpperCAmelCase__ = self.get_scheduler_config() UpperCAmelCase__ = scheduler_class(**_UpperCAmelCase ) scheduler.set_timesteps(_UpperCAmelCase ) # before power of 3 fix, would error on first step, so we only need to do two for i, t in enumerate(scheduler.prk_timesteps[:2] ): UpperCAmelCase__ = scheduler.step_prk(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ).prev_sample def SCREAMING_SNAKE_CASE__ ( self : Dict ): """simple docstring""" with self.assertRaises(_UpperCAmelCase ): UpperCAmelCase__ = self.scheduler_classes[0] UpperCAmelCase__ = self.get_scheduler_config() UpperCAmelCase__ = scheduler_class(**_UpperCAmelCase ) scheduler.step_plms(self.dummy_sample , 1 , self.dummy_sample ).prev_sample def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ): """simple docstring""" UpperCAmelCase__ = self.full_loop() UpperCAmelCase__ = torch.sum(torch.abs(_UpperCAmelCase ) ) UpperCAmelCase__ = torch.mean(torch.abs(_UpperCAmelCase ) ) assert abs(result_sum.item() - 198.1318 ) < 1E-2 assert abs(result_mean.item() - 0.2580 ) < 1E-3 def SCREAMING_SNAKE_CASE__ ( self : Dict ): """simple docstring""" UpperCAmelCase__ = self.full_loop(prediction_type="""v_prediction""" ) UpperCAmelCase__ = torch.sum(torch.abs(_UpperCAmelCase ) ) UpperCAmelCase__ = torch.mean(torch.abs(_UpperCAmelCase ) ) assert abs(result_sum.item() - 67.3986 ) < 1E-2 assert abs(result_mean.item() - 0.0878 ) < 1E-3 def SCREAMING_SNAKE_CASE__ ( self : Any ): """simple docstring""" UpperCAmelCase__ = self.full_loop(set_alpha_to_one=_UpperCAmelCase , beta_start=0.01 ) UpperCAmelCase__ = torch.sum(torch.abs(_UpperCAmelCase ) ) UpperCAmelCase__ = torch.mean(torch.abs(_UpperCAmelCase ) ) assert abs(result_sum.item() - 230.0399 ) < 1E-2 assert abs(result_mean.item() - 0.2995 ) < 1E-3 def SCREAMING_SNAKE_CASE__ ( self : int ): """simple docstring""" UpperCAmelCase__ = self.full_loop(set_alpha_to_one=_UpperCAmelCase , beta_start=0.01 ) UpperCAmelCase__ = torch.sum(torch.abs(_UpperCAmelCase ) ) UpperCAmelCase__ = torch.mean(torch.abs(_UpperCAmelCase ) ) assert abs(result_sum.item() - 186.9482 ) < 1E-2 assert abs(result_mean.item() - 0.2434 ) < 1E-3

346

1

'''simple docstring''' import random class lowerCAmelCase_ : '''simple docstring''' @staticmethod def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase : str ): """simple docstring""" UpperCAmelCase__ = [ord(_UpperCAmelCase ) for i in text] UpperCAmelCase__ = [] UpperCAmelCase__ = [] for i in plain: UpperCAmelCase__ = random.randint(1 , 3_00 ) UpperCAmelCase__ = (i + k) * k cipher.append(_UpperCAmelCase ) key.append(_UpperCAmelCase ) return cipher, key @staticmethod def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase : list[int] , _UpperCAmelCase : list[int] ): """simple docstring""" UpperCAmelCase__ = [] for i in range(len(_UpperCAmelCase ) ): UpperCAmelCase__ = int((cipher[i] - (key[i]) ** 2) / key[i] ) plain.append(chr(_UpperCAmelCase ) ) return "".join(_UpperCAmelCase ) if __name__ == "__main__": UpperCAmelCase_ , UpperCAmelCase_ = Onepad().encrypt('Hello') print(c, k) print(Onepad().decrypt(c, k))

346

'''simple docstring''' from ...configuration_utils import PretrainedConfig from ...utils import logging UpperCAmelCase_ = logging.get_logger(__name__) UpperCAmelCase_ = { 'google/vivit-b-16x2-kinetics400': ( 'https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json' ), # See all Vivit models at https://huggingface.co/models?filter=vivit } class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' lowerCAmelCase_ : Optional[int] = """vivit""" def __init__( self : List[str] , _UpperCAmelCase : List[Any]=2_24 , _UpperCAmelCase : List[str]=32 , _UpperCAmelCase : Any=[2, 16, 16] , _UpperCAmelCase : int=3 , _UpperCAmelCase : Optional[Any]=7_68 , _UpperCAmelCase : Union[str, Any]=12 , _UpperCAmelCase : Dict=12 , _UpperCAmelCase : Optional[Any]=30_72 , _UpperCAmelCase : Optional[int]="gelu_fast" , _UpperCAmelCase : Union[str, Any]=0.0 , _UpperCAmelCase : Tuple=0.0 , _UpperCAmelCase : Optional[int]=0.02 , _UpperCAmelCase : List[Any]=1E-06 , _UpperCAmelCase : List[str]=True , **_UpperCAmelCase : List[Any] , ): """simple docstring""" UpperCAmelCase__ = hidden_size UpperCAmelCase__ = num_hidden_layers UpperCAmelCase__ = num_attention_heads UpperCAmelCase__ = intermediate_size UpperCAmelCase__ = hidden_act UpperCAmelCase__ = hidden_dropout_prob UpperCAmelCase__ = attention_probs_dropout_prob UpperCAmelCase__ = initializer_range UpperCAmelCase__ = layer_norm_eps UpperCAmelCase__ = image_size UpperCAmelCase__ = num_frames UpperCAmelCase__ = tubelet_size UpperCAmelCase__ = num_channels UpperCAmelCase__ = qkv_bias super().__init__(**_UpperCAmelCase )

346

1

'''simple docstring''' import argparse import logging import os import time import timeit import datasets import numpy as np import pycuda.autoinit # noqa: F401 import pycuda.driver as cuda import tensorrt as trt import torch from absl import logging as absl_logging from accelerate import Accelerator from datasets import load_dataset, load_metric from torch.utils.data import DataLoader from utils_qa import postprocess_qa_predictions import transformers from transformers import AutoTokenizer, EvalPrediction, default_data_collator, set_seed from transformers.trainer_pt_utils import nested_concat, nested_truncate UpperCAmelCase_ = trt.Logger(trt.Logger.WARNING) UpperCAmelCase_ = absl_logging.get_absl_logger() absl_logger.setLevel(logging.WARNING) UpperCAmelCase_ = logging.getLogger(__name__) UpperCAmelCase_ = argparse.ArgumentParser() # Required parameters parser.add_argument( '--onnx_model_path', default=None, type=str, required=True, help='Path to ONNX model: ', ) parser.add_argument( '--output_dir', default=None, type=str, required=True, help='The output directory where the model checkpoints and predictions will be written.', ) # Other parameters parser.add_argument( '--tokenizer_name', default='', type=str, required=True, help='Pretrained tokenizer name or path if not the same as model_name', ) parser.add_argument( '--version_2_with_negative', action='store_true', help='If true, the SQuAD examples contain some that do not have an answer.', ) parser.add_argument( '--null_score_diff_threshold', type=float, default=0.0, help='If null_score - best_non_null is greater than the threshold predict null.', ) parser.add_argument( '--max_seq_length', default=3_8_4, type=int, help=( 'The maximum total input sequence length after WordPiece tokenization. Sequences ' 'longer than this will be truncated, and sequences shorter than this will be padded.' ), ) parser.add_argument( '--doc_stride', default=1_2_8, type=int, help='When splitting up a long document into chunks, how much stride to take between chunks.', ) parser.add_argument('--per_device_eval_batch_size', default=8, type=int, help='Batch size per GPU/CPU for evaluation.') parser.add_argument( '--n_best_size', default=2_0, type=int, help='The total number of n-best predictions to generate in the nbest_predictions.json output file.', ) parser.add_argument( '--max_answer_length', default=3_0, type=int, help=( 'The maximum length of an answer that can be generated. This is needed because the start ' 'and end predictions are not conditioned on one another.' ), ) parser.add_argument('--seed', type=int, default=4_2, help='random seed for initialization') parser.add_argument( '--dataset_name', type=str, default=None, required=True, help='The name of the dataset to use (via the datasets library).', ) parser.add_argument( '--dataset_config_name', type=str, default=None, help='The configuration name of the dataset to use (via the datasets library).', ) parser.add_argument( '--preprocessing_num_workers', type=int, default=4, help='A csv or a json file containing the training data.' ) parser.add_argument('--overwrite_cache', action='store_true', help='Overwrite the cached training and evaluation sets') parser.add_argument( '--fp16', action='store_true', help='Whether to use 16-bit (mixed) precision instead of 32-bit', ) parser.add_argument( '--int8', action='store_true', help='Whether to use INT8', ) UpperCAmelCase_ = parser.parse_args() if args.tokenizer_name: UpperCAmelCase_ = AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=True) else: raise ValueError( 'You are instantiating a new tokenizer from scratch. This is not supported by this script.' 'You can do it from another script, save it, and load it from here, using --tokenizer_name.' ) logger.info('Training/evaluation parameters %s', args) UpperCAmelCase_ = args.per_device_eval_batch_size UpperCAmelCase_ = (args.eval_batch_size, args.max_seq_length) # TRT Engine properties UpperCAmelCase_ = True UpperCAmelCase_ = 'temp_engine/bert-fp32.engine' if args.fpaa: UpperCAmelCase_ = 'temp_engine/bert-fp16.engine' if args.inta: UpperCAmelCase_ = 'temp_engine/bert-int8.engine' # import ONNX file if not os.path.exists('temp_engine'): os.makedirs('temp_engine') UpperCAmelCase_ = 1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH) with trt.Builder(TRT_LOGGER) as builder, builder.create_network(EXPLICIT_BATCH) as network, trt.OnnxParser( network, TRT_LOGGER ) as parser: with open(args.onnx_model_path, 'rb') as model: if not parser.parse(model.read()): for error in range(parser.num_errors): print(parser.get_error(error)) # Query input names and shapes from parsed TensorRT network UpperCAmelCase_ = [network.get_input(i) for i in range(network.num_inputs)] UpperCAmelCase_ = [_input.name for _input in network_inputs] # ex: ["actual_input1"] with builder.create_builder_config() as config: UpperCAmelCase_ = 1 << 5_0 if STRICT_TYPES: config.set_flag(trt.BuilderFlag.STRICT_TYPES) if args.fpaa: config.set_flag(trt.BuilderFlag.FPaa) if args.inta: config.set_flag(trt.BuilderFlag.INTa) UpperCAmelCase_ = builder.create_optimization_profile() config.add_optimization_profile(profile) for i in range(len(input_names)): profile.set_shape(input_names[i], INPUT_SHAPE, INPUT_SHAPE, INPUT_SHAPE) UpperCAmelCase_ = builder.build_engine(network, config) # serialize_engine and store in file (can be directly loaded and deserialized): with open(engine_name, 'wb') as f: f.write(engine.serialize()) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : List[Any] ): '''simple docstring''' UpperCAmelCase__ = np.asarray(inputs["""input_ids"""] , dtype=np.intaa ) UpperCAmelCase__ = np.asarray(inputs["""attention_mask"""] , dtype=np.intaa ) UpperCAmelCase__ = np.asarray(inputs["""token_type_ids"""] , dtype=np.intaa ) # Copy inputs cuda.memcpy_htod_async(d_inputs[0] , input_ids.ravel() , SCREAMING_SNAKE_CASE__ ) cuda.memcpy_htod_async(d_inputs[1] , attention_mask.ravel() , SCREAMING_SNAKE_CASE__ ) cuda.memcpy_htod_async(d_inputs[2] , token_type_ids.ravel() , SCREAMING_SNAKE_CASE__ ) # start time UpperCAmelCase__ = time.time() # Run inference context.execute_async( bindings=[int(SCREAMING_SNAKE_CASE__ ) for d_inp in d_inputs] + [int(SCREAMING_SNAKE_CASE__ ), int(SCREAMING_SNAKE_CASE__ )] , stream_handle=stream.handle ) # Transfer predictions back from GPU cuda.memcpy_dtoh_async(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) cuda.memcpy_dtoh_async(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) # Synchronize the stream and take time stream.synchronize() # end time UpperCAmelCase__ = time.time() UpperCAmelCase__ = end_time - start_time UpperCAmelCase__ = (h_outputa, h_outputa) # print(outputs) return outputs, infer_time # Initialize the accelerator. We will let the accelerator handle device placement for us in this example. UpperCAmelCase_ = Accelerator() # Make one log on every process with the configuration for debugging. logging.basicConfig( format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO, ) # Setup logging, we only want one process per machine to log things on the screen. # accelerator.is_local_main_process is only True for one process per machine. logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR) if accelerator.is_local_main_process: datasets.utils.logging.set_verbosity_warning() transformers.utils.logging.set_verbosity_info() else: datasets.utils.logging.set_verbosity_error() transformers.utils.logging.set_verbosity_error() # If passed along, set the training seed now. if args.seed is not None: set_seed(args.seed) # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below) # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/ # (the dataset will be downloaded automatically from the datasets Hub). # # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called # 'text' is found. You can easily tweak this behavior (see below). if args.dataset_name is not None: # Downloading and loading a dataset from the hub. UpperCAmelCase_ = load_dataset(args.dataset_name, args.dataset_config_name) else: raise ValueError('Evaluation requires a dataset name') # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at # https://huggingface.co/docs/datasets/loading_datasets.html. # Preprocessing the datasets. # Preprocessing is slighlty different for training and evaluation. UpperCAmelCase_ = raw_datasets['validation'].column_names UpperCAmelCase_ = 'question' if 'question' in column_names else column_names[0] UpperCAmelCase_ = 'context' if 'context' in column_names else column_names[1] UpperCAmelCase_ = 'answers' if 'answers' in column_names else column_names[2] # Padding side determines if we do (question|context) or (context|question). UpperCAmelCase_ = tokenizer.padding_side == 'right' if args.max_seq_length > tokenizer.model_max_length: logger.warning( f"The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the" f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}." ) UpperCAmelCase_ = min(args.max_seq_length, tokenizer.model_max_length) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Any ): '''simple docstring''' UpperCAmelCase__ = [q.lstrip() for q in examples[question_column_name]] # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results # in one example possible giving several features when a context is long, each of those features having a # context that overlaps a bit the context of the previous feature. UpperCAmelCase__ = tokenizer( examples[question_column_name if pad_on_right else context_column_name] , examples[context_column_name if pad_on_right else question_column_name] , truncation="""only_second""" if pad_on_right else """only_first""" , max_length=SCREAMING_SNAKE_CASE__ , stride=args.doc_stride , return_overflowing_tokens=SCREAMING_SNAKE_CASE__ , return_offsets_mapping=SCREAMING_SNAKE_CASE__ , padding="""max_length""" , ) # Since one example might give us several features if it has a long context, we need a map from a feature to # its corresponding example. This key gives us just that. UpperCAmelCase__ = tokenized_examples.pop("""overflow_to_sample_mapping""" ) # For evaluation, we will need to convert our predictions to substrings of the context, so we keep the # corresponding example_id and we will store the offset mappings. UpperCAmelCase__ = [] for i in range(len(tokenized_examples["""input_ids"""] ) ): # Grab the sequence corresponding to that example (to know what is the context and what is the question). UpperCAmelCase__ = tokenized_examples.sequence_ids(SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = 1 if pad_on_right else 0 # One example can give several spans, this is the index of the example containing this span of text. UpperCAmelCase__ = sample_mapping[i] tokenized_examples["example_id"].append(examples["""id"""][sample_index] ) # Set to None the offset_mapping that are not part of the context so it's easy to determine if a token # position is part of the context or not. UpperCAmelCase__ = [ (o if sequence_ids[k] == context_index else None) for k, o in enumerate(tokenized_examples["""offset_mapping"""][i] ) ] return tokenized_examples UpperCAmelCase_ = raw_datasets['validation'] # Validation Feature Creation UpperCAmelCase_ = eval_examples.map( prepare_validation_features, batched=True, num_proc=args.preprocessing_num_workers, remove_columns=column_names, load_from_cache_file=not args.overwrite_cache, desc='Running tokenizer on validation dataset', ) UpperCAmelCase_ = default_data_collator UpperCAmelCase_ = eval_dataset.remove_columns(['example_id', 'offset_mapping']) UpperCAmelCase_ = DataLoader( eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : Union[str, Any]="eval" ): '''simple docstring''' UpperCAmelCase__ = postprocess_qa_predictions( examples=SCREAMING_SNAKE_CASE__ , features=SCREAMING_SNAKE_CASE__ , predictions=SCREAMING_SNAKE_CASE__ , version_2_with_negative=args.version_2_with_negative , n_best_size=args.n_best_size , max_answer_length=args.max_answer_length , null_score_diff_threshold=args.null_score_diff_threshold , output_dir=args.output_dir , prefix=SCREAMING_SNAKE_CASE__ , ) # Format the result to the format the metric expects. if args.version_2_with_negative: UpperCAmelCase__ = [ {"""id""": k, """prediction_text""": v, """no_answer_probability""": 0.0} for k, v in predictions.items() ] else: UpperCAmelCase__ = [{"""id""": k, """prediction_text""": v} for k, v in predictions.items()] UpperCAmelCase__ = [{"""id""": ex["""id"""], """answers""": ex[answer_column_name]} for ex in examples] return EvalPrediction(predictions=SCREAMING_SNAKE_CASE__ , label_ids=SCREAMING_SNAKE_CASE__ ) UpperCAmelCase_ = load_metric('squad_v2' if args.version_2_with_negative else 'squad') # Evaluation! logger.info('Loading ONNX model %s for evaluation', args.onnx_model_path) with open(engine_name, 'rb') as f, trt.Runtime(TRT_LOGGER) as runtime, runtime.deserialize_cuda_engine( f.read() ) as engine, engine.create_execution_context() as context: # setup for TRT inferrence for i in range(len(input_names)): context.set_binding_shape(i, INPUT_SHAPE) assert context.all_binding_shapes_specified def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int ): '''simple docstring''' return trt.volume(engine.get_binding_shape(SCREAMING_SNAKE_CASE__ ) ) * engine.get_binding_dtype(SCREAMING_SNAKE_CASE__ ).itemsize # Allocate device memory for inputs and outputs. UpperCAmelCase_ = [cuda.mem_alloc(binding_nbytes(binding)) for binding in engine if engine.binding_is_input(binding)] # Allocate output buffer UpperCAmelCase_ = cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa) UpperCAmelCase_ = cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa) UpperCAmelCase_ = cuda.mem_alloc(h_outputa.nbytes) UpperCAmelCase_ = cuda.mem_alloc(h_outputa.nbytes) # Create a stream in which to copy inputs/outputs and run inference. UpperCAmelCase_ = cuda.Stream() # Evaluation logger.info('***** Running Evaluation *****') logger.info(f" Num examples = {len(eval_dataset)}") logger.info(f" Batch size = {args.per_device_eval_batch_size}") UpperCAmelCase_ = 0.0 UpperCAmelCase_ = 0 UpperCAmelCase_ = timeit.default_timer() UpperCAmelCase_ = None for step, batch in enumerate(eval_dataloader): UpperCAmelCase_ , UpperCAmelCase_ = model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream) total_time += infer_time niter += 1 UpperCAmelCase_ , UpperCAmelCase_ = outputs UpperCAmelCase_ = torch.tensor(start_logits) UpperCAmelCase_ = torch.tensor(end_logits) # necessary to pad predictions and labels for being gathered UpperCAmelCase_ = accelerator.pad_across_processes(start_logits, dim=1, pad_index=-1_0_0) UpperCAmelCase_ = accelerator.pad_across_processes(end_logits, dim=1, pad_index=-1_0_0) UpperCAmelCase_ = (accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy()) UpperCAmelCase_ = logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-1_0_0) if all_preds is not None: UpperCAmelCase_ = nested_truncate(all_preds, len(eval_dataset)) UpperCAmelCase_ = timeit.default_timer() - start_time logger.info(' Evaluation done in total %f secs (%f sec per example)', evalTime, evalTime / len(eval_dataset)) # Inference time from TRT logger.info('Average Inference Time = {:.3f} ms'.format(total_time * 1_0_0_0 / niter)) logger.info('Total Inference Time = {:.3f} ms'.format(total_time * 1_0_0_0)) logger.info('Total Number of Inference = %d', niter) UpperCAmelCase_ = post_processing_function(eval_examples, eval_dataset, all_preds) UpperCAmelCase_ = metric.compute(predictions=prediction.predictions, references=prediction.label_ids) logger.info(f"Evaluation metrics: {eval_metric}")

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'''simple docstring''' import warnings from ...utils import logging from .image_processing_deit import DeiTImageProcessor UpperCAmelCase_ = logging.get_logger(__name__) class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' def __init__( self : List[str] , *_UpperCAmelCase : Optional[int] , **_UpperCAmelCase : Optional[Any] ): """simple docstring""" warnings.warn( """The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please""" """ use DeiTImageProcessor instead.""" , _UpperCAmelCase , ) super().__init__(*_UpperCAmelCase , **_UpperCAmelCase )

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'''simple docstring''' import contextlib import csv import json import os import sqlitea import tarfile import textwrap import zipfile import pyarrow as pa import pyarrow.parquet as pq import pytest import datasets import datasets.config @pytest.fixture(scope="""session""" ) def _UpperCamelCase ( ): '''simple docstring''' UpperCAmelCase__ = 10 UpperCAmelCase__ = datasets.Features( { """tokens""": datasets.Sequence(datasets.Value("""string""" ) ), """labels""": datasets.Sequence(datasets.ClassLabel(names=["""negative""", """positive"""] ) ), """answers""": datasets.Sequence( { """text""": datasets.Value("""string""" ), """answer_start""": datasets.Value("""int32""" ), } ), """id""": datasets.Value("""int64""" ), } ) UpperCAmelCase__ = datasets.Dataset.from_dict( { """tokens""": [["""foo"""] * 5] * n, """labels""": [[1] * 5] * n, """answers""": [{"""answer_start""": [97], """text""": ["""1976"""]}] * 10, """id""": list(range(SCREAMING_SNAKE_CASE__ ) ), } , features=SCREAMING_SNAKE_CASE__ , ) return dataset @pytest.fixture(scope="""session""" ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Optional[int] ): '''simple docstring''' UpperCAmelCase__ = str(tmp_path_factory.mktemp("""data""" ) / """file.arrow""" ) dataset.map(cache_file_name=SCREAMING_SNAKE_CASE__ ) return filename # FILE_CONTENT + files UpperCAmelCase_ = '\\n Text data.\n Second line of data.' @pytest.fixture(scope="""session""" ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Union[str, Any] ): '''simple docstring''' UpperCAmelCase__ = tmp_path_factory.mktemp("""data""" ) / """file.txt""" UpperCAmelCase__ = FILE_CONTENT with open(SCREAMING_SNAKE_CASE__ , """w""" ) as f: f.write(SCREAMING_SNAKE_CASE__ ) return filename @pytest.fixture(scope="""session""" ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Union[str, Any] ): '''simple docstring''' import bza UpperCAmelCase__ = tmp_path_factory.mktemp("""data""" ) / """file.txt.bz2""" UpperCAmelCase__ = bytes(SCREAMING_SNAKE_CASE__ , """utf-8""" ) with bza.open(SCREAMING_SNAKE_CASE__ , """wb""" ) as f: f.write(SCREAMING_SNAKE_CASE__ ) return path @pytest.fixture(scope="""session""" ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int ): '''simple docstring''' import gzip UpperCAmelCase__ = str(tmp_path_factory.mktemp("""data""" ) / """file.txt.gz""" ) UpperCAmelCase__ = bytes(SCREAMING_SNAKE_CASE__ , """utf-8""" ) with gzip.open(SCREAMING_SNAKE_CASE__ , """wb""" ) as f: f.write(SCREAMING_SNAKE_CASE__ ) return path @pytest.fixture(scope="""session""" ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int ): '''simple docstring''' if datasets.config.LZ4_AVAILABLE: import lza.frame UpperCAmelCase__ = tmp_path_factory.mktemp("""data""" ) / """file.txt.lz4""" UpperCAmelCase__ = bytes(SCREAMING_SNAKE_CASE__ , """utf-8""" ) with lza.frame.open(SCREAMING_SNAKE_CASE__ , """wb""" ) as f: f.write(SCREAMING_SNAKE_CASE__ ) return path @pytest.fixture(scope="""session""" ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : Dict ): '''simple docstring''' if datasets.config.PY7ZR_AVAILABLE: import pyazr UpperCAmelCase__ = tmp_path_factory.mktemp("""data""" ) / """file.txt.7z""" with pyazr.SevenZipFile(SCREAMING_SNAKE_CASE__ , """w""" ) as archive: archive.write(SCREAMING_SNAKE_CASE__ , arcname=os.path.basename(SCREAMING_SNAKE_CASE__ ) ) return path @pytest.fixture(scope="""session""" ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : List[Any] ): '''simple docstring''' import tarfile UpperCAmelCase__ = tmp_path_factory.mktemp("""data""" ) / """file.txt.tar""" with tarfile.TarFile(SCREAMING_SNAKE_CASE__ , """w""" ) as f: f.add(SCREAMING_SNAKE_CASE__ , arcname=os.path.basename(SCREAMING_SNAKE_CASE__ ) ) return path @pytest.fixture(scope="""session""" ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Tuple ): '''simple docstring''' import lzma UpperCAmelCase__ = tmp_path_factory.mktemp("""data""" ) / """file.txt.xz""" UpperCAmelCase__ = bytes(SCREAMING_SNAKE_CASE__ , """utf-8""" ) with lzma.open(SCREAMING_SNAKE_CASE__ , """wb""" ) as f: f.write(SCREAMING_SNAKE_CASE__ ) return path @pytest.fixture(scope="""session""" ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Union[str, Any] ): '''simple docstring''' import zipfile UpperCAmelCase__ = tmp_path_factory.mktemp("""data""" ) / """file.txt.zip""" with zipfile.ZipFile(SCREAMING_SNAKE_CASE__ , """w""" ) as f: f.write(SCREAMING_SNAKE_CASE__ , arcname=os.path.basename(SCREAMING_SNAKE_CASE__ ) ) return path @pytest.fixture(scope="""session""" ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : List[str] ): '''simple docstring''' if datasets.config.ZSTANDARD_AVAILABLE: import zstandard as zstd UpperCAmelCase__ = tmp_path_factory.mktemp("""data""" ) / """file.txt.zst""" UpperCAmelCase__ = bytes(SCREAMING_SNAKE_CASE__ , """utf-8""" ) with zstd.open(SCREAMING_SNAKE_CASE__ , """wb""" ) as f: f.write(SCREAMING_SNAKE_CASE__ ) return path @pytest.fixture(scope="""session""" ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Tuple ): '''simple docstring''' UpperCAmelCase__ = tmp_path_factory.mktemp("""data""" ) / """file.xml""" UpperCAmelCase__ = textwrap.dedent( """\ <?xml version=\"1.0\" encoding=\"UTF-8\" ?> <tmx version=\"1.4\"> <header segtype=\"sentence\" srclang=\"ca\" /> <body> <tu> <tuv xml:lang=\"ca\"><seg>Contingut 1</seg></tuv> <tuv xml:lang=\"en\"><seg>Content 1</seg></tuv> </tu> <tu> <tuv xml:lang=\"ca\"><seg>Contingut 2</seg></tuv> <tuv xml:lang=\"en\"><seg>Content 2</seg></tuv> </tu> <tu> <tuv xml:lang=\"ca\"><seg>Contingut 3</seg></tuv> <tuv xml:lang=\"en\"><seg>Content 3</seg></tuv> </tu> <tu> <tuv xml:lang=\"ca\"><seg>Contingut 4</seg></tuv> <tuv xml:lang=\"en\"><seg>Content 4</seg></tuv> </tu> <tu> <tuv xml:lang=\"ca\"><seg>Contingut 5</seg></tuv> <tuv xml:lang=\"en\"><seg>Content 5</seg></tuv> </tu> </body> </tmx>""" ) with open(SCREAMING_SNAKE_CASE__ , """w""" ) as f: f.write(SCREAMING_SNAKE_CASE__ ) return filename UpperCAmelCase_ = [ {'col_1': '0', 'col_2': 0, 'col_3': 0.0}, {'col_1': '1', 'col_2': 1, 'col_3': 1.0}, {'col_1': '2', 'col_2': 2, 'col_3': 2.0}, {'col_1': '3', 'col_2': 3, 'col_3': 3.0}, ] UpperCAmelCase_ = [ {'col_1': '4', 'col_2': 4, 'col_3': 4.0}, {'col_1': '5', 'col_2': 5, 'col_3': 5.0}, ] UpperCAmelCase_ = { 'col_1': ['0', '1', '2', '3'], 'col_2': [0, 1, 2, 3], 'col_3': [0.0, 1.0, 2.0, 3.0], } UpperCAmelCase_ = [ {'col_3': 0.0, 'col_1': '0', 'col_2': 0}, {'col_3': 1.0, 'col_1': '1', 'col_2': 1}, ] UpperCAmelCase_ = [ {'col_1': 's0', 'col_2': 0, 'col_3': 0.0}, {'col_1': 's1', 'col_2': 1, 'col_3': 1.0}, {'col_1': 's2', 'col_2': 2, 'col_3': 2.0}, {'col_1': 's3', 'col_2': 3, 'col_3': 3.0}, ] @pytest.fixture(scope="""session""" ) def _UpperCamelCase ( ): '''simple docstring''' return DATA_DICT_OF_LISTS @pytest.fixture(scope="""session""" ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Any ): '''simple docstring''' UpperCAmelCase__ = datasets.Dataset.from_dict(SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = str(tmp_path_factory.mktemp("""data""" ) / """dataset.arrow""" ) dataset.map(cache_file_name=SCREAMING_SNAKE_CASE__ ) return path @pytest.fixture(scope="""session""" ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Tuple ): '''simple docstring''' UpperCAmelCase__ = str(tmp_path_factory.mktemp("""data""" ) / """dataset.sqlite""" ) with contextlib.closing(sqlitea.connect(SCREAMING_SNAKE_CASE__ ) ) as con: UpperCAmelCase__ = con.cursor() cur.execute("""CREATE TABLE dataset(col_1 text, col_2 int, col_3 real)""" ) for item in DATA: cur.execute("""INSERT INTO dataset(col_1, col_2, col_3) VALUES (?, ?, ?)""" , tuple(item.values() ) ) con.commit() return path @pytest.fixture(scope="""session""" ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Optional[int] ): '''simple docstring''' UpperCAmelCase__ = str(tmp_path_factory.mktemp("""data""" ) / """dataset.csv""" ) with open(SCREAMING_SNAKE_CASE__ , """w""" , newline="""""" ) as f: UpperCAmelCase__ = csv.DictWriter(SCREAMING_SNAKE_CASE__ , fieldnames=["""col_1""", """col_2""", """col_3"""] ) writer.writeheader() for item in DATA: writer.writerow(SCREAMING_SNAKE_CASE__ ) return path @pytest.fixture(scope="""session""" ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Any ): '''simple docstring''' UpperCAmelCase__ = str(tmp_path_factory.mktemp("""data""" ) / """dataset2.csv""" ) with open(SCREAMING_SNAKE_CASE__ , """w""" , newline="""""" ) as f: UpperCAmelCase__ = csv.DictWriter(SCREAMING_SNAKE_CASE__ , fieldnames=["""col_1""", """col_2""", """col_3"""] ) writer.writeheader() for item in DATA: writer.writerow(SCREAMING_SNAKE_CASE__ ) return path @pytest.fixture(scope="""session""" ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : Union[str, Any] ): '''simple docstring''' import bza UpperCAmelCase__ = tmp_path_factory.mktemp("""data""" ) / """dataset.csv.bz2""" with open(SCREAMING_SNAKE_CASE__ , """rb""" ) as f: UpperCAmelCase__ = f.read() # data = bytes(FILE_CONTENT, "utf-8") with bza.open(SCREAMING_SNAKE_CASE__ , """wb""" ) as f: f.write(SCREAMING_SNAKE_CASE__ ) return path @pytest.fixture(scope="""session""" ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : Optional[Any] ): '''simple docstring''' UpperCAmelCase__ = tmp_path_factory.mktemp("""data""" ) / """dataset.csv.zip""" with zipfile.ZipFile(SCREAMING_SNAKE_CASE__ , """w""" ) as f: f.write(SCREAMING_SNAKE_CASE__ , arcname=os.path.basename(SCREAMING_SNAKE_CASE__ ) ) f.write(SCREAMING_SNAKE_CASE__ , arcname=os.path.basename(SCREAMING_SNAKE_CASE__ ) ) return path @pytest.fixture(scope="""session""" ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : List[Any] ): '''simple docstring''' UpperCAmelCase__ = tmp_path_factory.mktemp("""data""" ) / """dataset.csv.zip""" with zipfile.ZipFile(SCREAMING_SNAKE_CASE__ , """w""" ) as f: f.write(SCREAMING_SNAKE_CASE__ , arcname=os.path.basename(csv_path.replace(""".csv""" , """.CSV""" ) ) ) f.write(SCREAMING_SNAKE_CASE__ , arcname=os.path.basename(csva_path.replace(""".csv""" , """.CSV""" ) ) ) return path @pytest.fixture(scope="""session""" ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : Tuple ): '''simple docstring''' UpperCAmelCase__ = tmp_path_factory.mktemp("""data""" ) / """dataset_with_dir.csv.zip""" with zipfile.ZipFile(SCREAMING_SNAKE_CASE__ , """w""" ) as f: f.write(SCREAMING_SNAKE_CASE__ , arcname=os.path.join("""main_dir""" , os.path.basename(SCREAMING_SNAKE_CASE__ ) ) ) f.write(SCREAMING_SNAKE_CASE__ , arcname=os.path.join("""main_dir""" , os.path.basename(SCREAMING_SNAKE_CASE__ ) ) ) return path @pytest.fixture(scope="""session""" ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Tuple ): '''simple docstring''' UpperCAmelCase__ = str(tmp_path_factory.mktemp("""data""" ) / """dataset.parquet""" ) UpperCAmelCase__ = pa.schema( { """col_1""": pa.string(), """col_2""": pa.intaa(), """col_3""": pa.floataa(), } ) with open(SCREAMING_SNAKE_CASE__ , """wb""" ) as f: UpperCAmelCase__ = pq.ParquetWriter(SCREAMING_SNAKE_CASE__ , schema=SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = pa.Table.from_pydict({k: [DATA[i][k] for i in range(len(SCREAMING_SNAKE_CASE__ ) )] for k in DATA[0]} , schema=SCREAMING_SNAKE_CASE__ ) writer.write_table(SCREAMING_SNAKE_CASE__ ) writer.close() return path @pytest.fixture(scope="""session""" ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : List[str] ): '''simple docstring''' UpperCAmelCase__ = str(tmp_path_factory.mktemp("""data""" ) / """dataset.json""" ) UpperCAmelCase__ = {"""data""": DATA} with open(SCREAMING_SNAKE_CASE__ , """w""" ) as f: json.dump(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) return path @pytest.fixture(scope="""session""" ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Any ): '''simple docstring''' UpperCAmelCase__ = str(tmp_path_factory.mktemp("""data""" ) / """dataset.json""" ) UpperCAmelCase__ = {"""data""": DATA_DICT_OF_LISTS} with open(SCREAMING_SNAKE_CASE__ , """w""" ) as f: json.dump(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) return path @pytest.fixture(scope="""session""" ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Any ): '''simple docstring''' UpperCAmelCase__ = str(tmp_path_factory.mktemp("""data""" ) / """dataset.jsonl""" ) with open(SCREAMING_SNAKE_CASE__ , """w""" ) as f: for item in DATA: f.write(json.dumps(SCREAMING_SNAKE_CASE__ ) + """\n""" ) return path @pytest.fixture(scope="""session""" ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Optional[Any] ): '''simple docstring''' UpperCAmelCase__ = str(tmp_path_factory.mktemp("""data""" ) / """dataset2.jsonl""" ) with open(SCREAMING_SNAKE_CASE__ , """w""" ) as f: for item in DATA: f.write(json.dumps(SCREAMING_SNAKE_CASE__ ) + """\n""" ) return path @pytest.fixture(scope="""session""" ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str ): '''simple docstring''' UpperCAmelCase__ = str(tmp_path_factory.mktemp("""data""" ) / """dataset_312.jsonl""" ) with open(SCREAMING_SNAKE_CASE__ , """w""" ) as f: for item in DATA_312: f.write(json.dumps(SCREAMING_SNAKE_CASE__ ) + """\n""" ) return path @pytest.fixture(scope="""session""" ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Tuple ): '''simple docstring''' UpperCAmelCase__ = str(tmp_path_factory.mktemp("""data""" ) / """dataset-str.jsonl""" ) with open(SCREAMING_SNAKE_CASE__ , """w""" ) as f: for item in DATA_STR: f.write(json.dumps(SCREAMING_SNAKE_CASE__ ) + """\n""" ) return path @pytest.fixture(scope="""session""" ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : int ): '''simple docstring''' import gzip UpperCAmelCase__ = str(tmp_path_factory.mktemp("""data""" ) / """dataset.txt.gz""" ) with open(SCREAMING_SNAKE_CASE__ , """rb""" ) as orig_file: with gzip.open(SCREAMING_SNAKE_CASE__ , """wb""" ) as zipped_file: zipped_file.writelines(SCREAMING_SNAKE_CASE__ ) return path @pytest.fixture(scope="""session""" ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : int ): '''simple docstring''' import gzip UpperCAmelCase__ = str(tmp_path_factory.mktemp("""data""" ) / """dataset.jsonl.gz""" ) with open(SCREAMING_SNAKE_CASE__ , """rb""" ) as orig_file: with gzip.open(SCREAMING_SNAKE_CASE__ , """wb""" ) as zipped_file: zipped_file.writelines(SCREAMING_SNAKE_CASE__ ) return path @pytest.fixture(scope="""session""" ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Optional[Any] ): '''simple docstring''' UpperCAmelCase__ = tmp_path_factory.mktemp("""data""" ) / """dataset.jsonl.zip""" with zipfile.ZipFile(SCREAMING_SNAKE_CASE__ , """w""" ) as f: f.write(SCREAMING_SNAKE_CASE__ , arcname=os.path.basename(SCREAMING_SNAKE_CASE__ ) ) f.write(SCREAMING_SNAKE_CASE__ , arcname=os.path.basename(SCREAMING_SNAKE_CASE__ ) ) return path @pytest.fixture(scope="""session""" ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : Union[str, Any] ): '''simple docstring''' UpperCAmelCase__ = tmp_path_factory.mktemp("""data""" ) / """dataset_nested.jsonl.zip""" with zipfile.ZipFile(SCREAMING_SNAKE_CASE__ , """w""" ) as f: f.write(SCREAMING_SNAKE_CASE__ , arcname=os.path.join("""nested""" , os.path.basename(SCREAMING_SNAKE_CASE__ ) ) ) return path @pytest.fixture(scope="""session""" ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CASE__ : str ): '''simple docstring''' UpperCAmelCase__ = tmp_path_factory.mktemp("""data""" ) / """dataset_with_dir.jsonl.zip""" with zipfile.ZipFile(SCREAMING_SNAKE_CASE__ , """w""" ) as f: f.write(SCREAMING_SNAKE_CASE__ , arcname=os.path.join("""main_dir""" , os.path.basename(SCREAMING_SNAKE_CASE__ ) ) ) f.write(SCREAMING_SNAKE_CASE__ , arcname=os.path.join("""main_dir""" , os.path.basename(SCREAMING_SNAKE_CASE__ ) ) ) return path @pytest.fixture(scope="""session""" ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Optional[Any] ): '''simple docstring''' UpperCAmelCase__ = tmp_path_factory.mktemp("""data""" ) / """dataset.jsonl.tar""" with tarfile.TarFile(SCREAMING_SNAKE_CASE__ , """w""" ) as f: f.add(SCREAMING_SNAKE_CASE__ , arcname=os.path.basename(SCREAMING_SNAKE_CASE__ ) ) f.add(SCREAMING_SNAKE_CASE__ , arcname=os.path.basename(SCREAMING_SNAKE_CASE__ ) ) return path @pytest.fixture(scope="""session""" ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : List[str] ): '''simple docstring''' UpperCAmelCase__ = tmp_path_factory.mktemp("""data""" ) / """dataset_nested.jsonl.tar""" with tarfile.TarFile(SCREAMING_SNAKE_CASE__ , """w""" ) as f: f.add(SCREAMING_SNAKE_CASE__ , arcname=os.path.join("""nested""" , os.path.basename(SCREAMING_SNAKE_CASE__ ) ) ) return path @pytest.fixture(scope="""session""" ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Union[str, Any] ): '''simple docstring''' UpperCAmelCase__ = ["""0""", """1""", """2""", """3"""] UpperCAmelCase__ = str(tmp_path_factory.mktemp("""data""" ) / """dataset.txt""" ) with open(SCREAMING_SNAKE_CASE__ , """w""" ) as f: for item in data: f.write(item + """\n""" ) return path @pytest.fixture(scope="""session""" ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : List[str] ): '''simple docstring''' UpperCAmelCase__ = ["""0""", """1""", """2""", """3"""] UpperCAmelCase__ = str(tmp_path_factory.mktemp("""data""" ) / """dataset2.txt""" ) with open(SCREAMING_SNAKE_CASE__ , """w""" ) as f: for item in data: f.write(item + """\n""" ) return path @pytest.fixture(scope="""session""" ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Optional[Any] ): '''simple docstring''' UpperCAmelCase__ = ["""0""", """1""", """2""", """3"""] UpperCAmelCase__ = tmp_path_factory.mktemp("""data""" ) / """dataset.abc""" with open(SCREAMING_SNAKE_CASE__ , """w""" ) as f: for item in data: f.write(item + """\n""" ) return path @pytest.fixture(scope="""session""" ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : Optional[Any] ): '''simple docstring''' UpperCAmelCase__ = tmp_path_factory.mktemp("""data""" ) / """dataset.text.zip""" with zipfile.ZipFile(SCREAMING_SNAKE_CASE__ , """w""" ) as f: f.write(SCREAMING_SNAKE_CASE__ , arcname=os.path.basename(SCREAMING_SNAKE_CASE__ ) ) f.write(SCREAMING_SNAKE_CASE__ , arcname=os.path.basename(SCREAMING_SNAKE_CASE__ ) ) return path @pytest.fixture(scope="""session""" ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : int ): '''simple docstring''' UpperCAmelCase__ = tmp_path_factory.mktemp("""data""" ) / """dataset_with_dir.text.zip""" with zipfile.ZipFile(SCREAMING_SNAKE_CASE__ , """w""" ) as f: f.write(SCREAMING_SNAKE_CASE__ , arcname=os.path.join("""main_dir""" , os.path.basename(SCREAMING_SNAKE_CASE__ ) ) ) f.write(SCREAMING_SNAKE_CASE__ , arcname=os.path.join("""main_dir""" , os.path.basename(SCREAMING_SNAKE_CASE__ ) ) ) return path @pytest.fixture(scope="""session""" ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : int ): '''simple docstring''' UpperCAmelCase__ = tmp_path_factory.mktemp("""data""" ) / """dataset.ext.zip""" with zipfile.ZipFile(SCREAMING_SNAKE_CASE__ , """w""" ) as f: f.write(SCREAMING_SNAKE_CASE__ , arcname=os.path.basename("""unsupported.ext""" ) ) f.write(SCREAMING_SNAKE_CASE__ , arcname=os.path.basename("""unsupported_2.ext""" ) ) return path @pytest.fixture(scope="""session""" ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int ): '''simple docstring''' UpperCAmelCase__ = """\n""".join(["""First""", """Second\u2029with Unicode new line""", """Third"""] ) UpperCAmelCase__ = str(tmp_path_factory.mktemp("""data""" ) / """dataset_with_unicode_new_lines.txt""" ) with open(SCREAMING_SNAKE_CASE__ , """w""" , encoding="""utf-8""" ) as f: f.write(SCREAMING_SNAKE_CASE__ ) return path @pytest.fixture(scope="""session""" ) def _UpperCamelCase ( ): '''simple docstring''' return os.path.join("""tests""" , """features""" , """data""" , """test_image_rgb.jpg""" ) @pytest.fixture(scope="""session""" ) def _UpperCamelCase ( ): '''simple docstring''' return os.path.join("""tests""" , """features""" , """data""" , """test_audio_44100.wav""" ) @pytest.fixture(scope="""session""" ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : Union[str, Any] ): '''simple docstring''' UpperCAmelCase__ = tmp_path_factory.mktemp("""data""" ) / """dataset.img.zip""" with zipfile.ZipFile(SCREAMING_SNAKE_CASE__ , """w""" ) as f: f.write(SCREAMING_SNAKE_CASE__ , arcname=os.path.basename(SCREAMING_SNAKE_CASE__ ) ) f.write(SCREAMING_SNAKE_CASE__ , arcname=os.path.basename(SCREAMING_SNAKE_CASE__ ).replace(""".jpg""" , """2.jpg""" ) ) return path @pytest.fixture(scope="""session""" ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int ): '''simple docstring''' UpperCAmelCase__ = tmp_path_factory.mktemp("""data_dir""" ) (data_dir / "subdir").mkdir() with open(data_dir / """subdir""" / """train.txt""" , """w""" ) as f: f.write("""foo\n""" * 10 ) with open(data_dir / """subdir""" / """test.txt""" , """w""" ) as f: f.write("""bar\n""" * 10 ) # hidden file with open(data_dir / """subdir""" / """.test.txt""" , """w""" ) as f: f.write("""bar\n""" * 10 ) # hidden directory (data_dir / ".subdir").mkdir() with open(data_dir / """.subdir""" / """train.txt""" , """w""" ) as f: f.write("""foo\n""" * 10 ) with open(data_dir / """.subdir""" / """test.txt""" , """w""" ) as f: f.write("""bar\n""" * 10 ) return data_dir

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'''simple docstring''' import os import unicodedata from shutil import copyfile from typing import Any, Dict, List, Optional, Tuple import sentencepiece as spm from ...tokenization_utils import AddedToken, PreTrainedTokenizer from ...utils import SPIECE_UNDERLINE, logging UpperCAmelCase_ = logging.get_logger(__name__) UpperCAmelCase_ = {'vocab_file': 'spiece.model'} UpperCAmelCase_ = { 'vocab_file': { 'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model', } } class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' def __init__( self : Dict , _UpperCAmelCase : str , _UpperCAmelCase : Any=False , _UpperCAmelCase : int=True , _UpperCAmelCase : Union[str, Any]=False , _UpperCAmelCase : Dict="<s>" , _UpperCAmelCase : int="</s>" , _UpperCAmelCase : Dict="<unk>" , _UpperCAmelCase : Tuple="<sep>" , _UpperCAmelCase : List[Any]="<pad>" , _UpperCAmelCase : int="<cls>" , _UpperCAmelCase : Union[str, Any]="<mask>" , _UpperCAmelCase : List[str]=["<eop>", "<eod>"] , _UpperCAmelCase : Optional[Dict[str, Any]] = None , **_UpperCAmelCase : int , ): """simple docstring""" UpperCAmelCase__ = AddedToken(_UpperCAmelCase , lstrip=_UpperCAmelCase , rstrip=_UpperCAmelCase ) if isinstance(_UpperCAmelCase , _UpperCAmelCase ) else mask_token UpperCAmelCase__ = {} if sp_model_kwargs is None else sp_model_kwargs super().__init__( do_lower_case=_UpperCAmelCase , remove_space=_UpperCAmelCase , keep_accents=_UpperCAmelCase , bos_token=_UpperCAmelCase , eos_token=_UpperCAmelCase , unk_token=_UpperCAmelCase , sep_token=_UpperCAmelCase , pad_token=_UpperCAmelCase , cls_token=_UpperCAmelCase , mask_token=_UpperCAmelCase , additional_special_tokens=_UpperCAmelCase , sp_model_kwargs=self.sp_model_kwargs , **_UpperCAmelCase , ) UpperCAmelCase__ = 3 UpperCAmelCase__ = do_lower_case UpperCAmelCase__ = remove_space UpperCAmelCase__ = keep_accents UpperCAmelCase__ = vocab_file UpperCAmelCase__ = spm.SentencePieceProcessor(**self.sp_model_kwargs ) self.sp_model.Load(_UpperCAmelCase ) try: import jieba except ModuleNotFoundError as error: raise error.__class__( """You need to install jieba to use CpmTokenizer or CpmTokenizerFast. """ """See https://pypi.org/project/jieba/ for installation.""" ) UpperCAmelCase__ = jieba UpperCAmelCase__ = str.maketrans(""" \n""" , """\u2582\u2583""" ) @property # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ): """simple docstring""" return len(self.sp_model ) def SCREAMING_SNAKE_CASE__ ( self : List[str] ): """simple docstring""" UpperCAmelCase__ = {self.convert_ids_to_tokens(_UpperCAmelCase ): i for i in range(self.vocab_size )} vocab.update(self.added_tokens_encoder ) return vocab def __getstate__( self : Dict ): """simple docstring""" UpperCAmelCase__ = self.__dict__.copy() UpperCAmelCase__ = None return state def __setstate__( self : Union[str, Any] , _UpperCAmelCase : Union[str, Any] ): """simple docstring""" UpperCAmelCase__ = d # for backward compatibility if not hasattr(self , """sp_model_kwargs""" ): UpperCAmelCase__ = {} UpperCAmelCase__ = spm.SentencePieceProcessor(**self.sp_model_kwargs ) self.sp_model.Load(self.vocab_file ) def SCREAMING_SNAKE_CASE__ ( self : int , _UpperCAmelCase : Optional[Any] ): """simple docstring""" if self.remove_space: UpperCAmelCase__ = """ """.join(inputs.strip().split() ) else: UpperCAmelCase__ = inputs UpperCAmelCase__ = outputs.replace("""``""" , """\"""" ).replace("""''""" , """\"""" ) if not self.keep_accents: UpperCAmelCase__ = unicodedata.normalize("""NFKD""" , _UpperCAmelCase ) UpperCAmelCase__ = """""".join([c for c in outputs if not unicodedata.combining(_UpperCAmelCase )] ) if self.do_lower_case: UpperCAmelCase__ = outputs.lower() return outputs def SCREAMING_SNAKE_CASE__ ( self : Tuple , _UpperCAmelCase : str ): """simple docstring""" UpperCAmelCase__ = self.preprocess_text(_UpperCAmelCase ) UpperCAmelCase__ = self.sp_model.encode(_UpperCAmelCase , out_type=_UpperCAmelCase ) UpperCAmelCase__ = [] for piece in pieces: if len(_UpperCAmelCase ) > 1 and piece[-1] == str(""",""" ) and piece[-2].isdigit(): UpperCAmelCase__ = self.sp_model.EncodeAsPieces(piece[:-1].replace(_UpperCAmelCase , """""" ) ) if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE: if len(cur_pieces[0] ) == 1: UpperCAmelCase__ = cur_pieces[1:] else: UpperCAmelCase__ = cur_pieces[0][1:] cur_pieces.append(piece[-1] ) new_pieces.extend(_UpperCAmelCase ) else: new_pieces.append(_UpperCAmelCase ) return new_pieces def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , _UpperCAmelCase : Union[str, Any] ): """simple docstring""" return self.sp_model.PieceToId(_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Dict , _UpperCAmelCase : Any ): """simple docstring""" return self.sp_model.IdToPiece(_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : int , _UpperCAmelCase : Dict ): """simple docstring""" UpperCAmelCase__ = """""".join(_UpperCAmelCase ).replace(_UpperCAmelCase , """ """ ).strip() return out_string def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] , _UpperCAmelCase : List[int] , _UpperCAmelCase : Optional[List[int]] = None ): """simple docstring""" UpperCAmelCase__ = [self.sep_token_id] UpperCAmelCase__ = [self.cls_token_id] if token_ids_a is None: return token_ids_a + sep + cls return token_ids_a + sep + token_ids_a + sep + cls def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] , _UpperCAmelCase : List[int] , _UpperCAmelCase : Optional[List[int]] = None , _UpperCAmelCase : bool = False ): """simple docstring""" if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_a=_UpperCAmelCase , token_ids_a=_UpperCAmelCase , already_has_special_tokens=_UpperCAmelCase ) if token_ids_a is not None: return ([0] * len(_UpperCAmelCase )) + [1] + ([0] * len(_UpperCAmelCase )) + [1, 1] return ([0] * len(_UpperCAmelCase )) + [1, 1] def SCREAMING_SNAKE_CASE__ ( self : Dict , _UpperCAmelCase : List[int] , _UpperCAmelCase : Optional[List[int]] = None ): """simple docstring""" UpperCAmelCase__ = [self.sep_token_id] UpperCAmelCase__ = [2] if token_ids_a is None: return len(token_ids_a + sep ) * [0] + cls_segment_id return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id def SCREAMING_SNAKE_CASE__ ( self : List[Any] , _UpperCAmelCase : str , _UpperCAmelCase : Optional[str] = None ): """simple docstring""" if not os.path.isdir(_UpperCAmelCase ): logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' ) return UpperCAmelCase__ = os.path.join( _UpperCAmelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] ) if os.path.abspath(self.vocab_file ) != os.path.abspath(_UpperCAmelCase ) and os.path.isfile(self.vocab_file ): copyfile(self.vocab_file , _UpperCAmelCase ) elif not os.path.isfile(self.vocab_file ): with open(_UpperCAmelCase , """wb""" ) as fi: UpperCAmelCase__ = self.sp_model.serialized_model_proto() fi.write(_UpperCAmelCase ) return (out_vocab_file,) def SCREAMING_SNAKE_CASE__ ( self : Tuple , *_UpperCAmelCase : Tuple , **_UpperCAmelCase : Optional[Any] ): """simple docstring""" UpperCAmelCase__ = super()._decode(*_UpperCAmelCase , **_UpperCAmelCase ) UpperCAmelCase__ = text.replace(""" """ , """""" ).replace("""\u2582""" , """ """ ).replace("""\u2583""" , """\n""" ) return text

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'''simple docstring''' from __future__ import annotations import unittest from transformers import MobileBertConfig, is_tf_available from transformers.models.auto import get_values from transformers.testing_utils import require_tf, slow from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_tf_available(): import tensorflow as tf from transformers import ( TF_MODEL_FOR_PRETRAINING_MAPPING, TFMobileBertForMaskedLM, TFMobileBertForMultipleChoice, TFMobileBertForNextSentencePrediction, TFMobileBertForPreTraining, TFMobileBertForQuestionAnswering, TFMobileBertForSequenceClassification, TFMobileBertForTokenClassification, TFMobileBertModel, ) @require_tf class lowerCAmelCase_ ( lowerCamelCase_ , lowerCamelCase_ , unittest.TestCase ): '''simple docstring''' lowerCAmelCase_ : Tuple = ( ( TFMobileBertModel, TFMobileBertForMaskedLM, TFMobileBertForNextSentencePrediction, TFMobileBertForPreTraining, TFMobileBertForQuestionAnswering, TFMobileBertForSequenceClassification, TFMobileBertForTokenClassification, TFMobileBertForMultipleChoice, ) if is_tf_available() else () ) lowerCAmelCase_ : Optional[int] = ( { """feature-extraction""": TFMobileBertModel, """fill-mask""": TFMobileBertForMaskedLM, """question-answering""": TFMobileBertForQuestionAnswering, """text-classification""": TFMobileBertForSequenceClassification, """token-classification""": TFMobileBertForTokenClassification, """zero-shot""": TFMobileBertForSequenceClassification, } if is_tf_available() else {} ) lowerCAmelCase_ : Any = False lowerCAmelCase_ : List[Any] = False def SCREAMING_SNAKE_CASE__ ( self : str , _UpperCAmelCase : int , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Union[str, Any]=False ): """simple docstring""" UpperCAmelCase__ = super()._prepare_for_class(_UpperCAmelCase , _UpperCAmelCase , return_labels=_UpperCAmelCase ) if return_labels: if model_class in get_values(_UpperCAmelCase ): UpperCAmelCase__ = tf.zeros(self.model_tester.batch_size , dtype=tf.intaa ) return inputs_dict class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' def __init__( self : Union[str, Any] , _UpperCAmelCase : Any , _UpperCAmelCase : Any=13 , _UpperCAmelCase : Optional[Any]=7 , _UpperCAmelCase : Optional[Any]=True , _UpperCAmelCase : str=True , _UpperCAmelCase : List[str]=True , _UpperCAmelCase : List[Any]=True , _UpperCAmelCase : Tuple=99 , _UpperCAmelCase : Dict=32 , _UpperCAmelCase : Optional[Any]=32 , _UpperCAmelCase : Union[str, Any]=2 , _UpperCAmelCase : int=4 , _UpperCAmelCase : List[str]=37 , _UpperCAmelCase : str="gelu" , _UpperCAmelCase : Union[str, Any]=0.1 , _UpperCAmelCase : Optional[Any]=0.1 , _UpperCAmelCase : Dict=5_12 , _UpperCAmelCase : Optional[int]=16 , _UpperCAmelCase : List[str]=2 , _UpperCAmelCase : List[Any]=0.02 , _UpperCAmelCase : Any=3 , _UpperCAmelCase : Union[str, Any]=4 , _UpperCAmelCase : Union[str, Any]=None , ): """simple docstring""" UpperCAmelCase__ = parent UpperCAmelCase__ = batch_size UpperCAmelCase__ = seq_length UpperCAmelCase__ = is_training UpperCAmelCase__ = use_input_mask UpperCAmelCase__ = use_token_type_ids UpperCAmelCase__ = use_labels UpperCAmelCase__ = vocab_size UpperCAmelCase__ = hidden_size UpperCAmelCase__ = num_hidden_layers UpperCAmelCase__ = num_attention_heads UpperCAmelCase__ = intermediate_size UpperCAmelCase__ = hidden_act UpperCAmelCase__ = hidden_dropout_prob UpperCAmelCase__ = attention_probs_dropout_prob UpperCAmelCase__ = max_position_embeddings UpperCAmelCase__ = type_vocab_size UpperCAmelCase__ = type_sequence_label_size UpperCAmelCase__ = initializer_range UpperCAmelCase__ = num_labels UpperCAmelCase__ = num_choices UpperCAmelCase__ = scope UpperCAmelCase__ = embedding_size def SCREAMING_SNAKE_CASE__ ( self : Dict ): """simple docstring""" UpperCAmelCase__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size ) UpperCAmelCase__ = None if self.use_input_mask: UpperCAmelCase__ = random_attention_mask([self.batch_size, self.seq_length] ) UpperCAmelCase__ = None if self.use_token_type_ids: UpperCAmelCase__ = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size ) UpperCAmelCase__ = None UpperCAmelCase__ = None UpperCAmelCase__ = None if self.use_labels: UpperCAmelCase__ = ids_tensor([self.batch_size] , self.type_sequence_label_size ) UpperCAmelCase__ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels ) UpperCAmelCase__ = ids_tensor([self.batch_size] , self.num_choices ) UpperCAmelCase__ = MobileBertConfig( vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , embedding_size=self.embedding_size , ) return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels def SCREAMING_SNAKE_CASE__ ( self : Any , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : List[Any] , _UpperCAmelCase : List[str] , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Tuple , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : List[str] ): """simple docstring""" UpperCAmelCase__ = TFMobileBertModel(config=_UpperCAmelCase ) UpperCAmelCase__ = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids} UpperCAmelCase__ = model(_UpperCAmelCase ) UpperCAmelCase__ = [input_ids, input_mask] UpperCAmelCase__ = model(_UpperCAmelCase ) UpperCAmelCase__ = model(_UpperCAmelCase ) self.parent.assertEqual( result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) ) self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) ) def SCREAMING_SNAKE_CASE__ ( self : Dict , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : str , _UpperCAmelCase : str , _UpperCAmelCase : Any , _UpperCAmelCase : Tuple , _UpperCAmelCase : Optional[Any] ): """simple docstring""" UpperCAmelCase__ = TFMobileBertForMaskedLM(config=_UpperCAmelCase ) UpperCAmelCase__ = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids} UpperCAmelCase__ = model(_UpperCAmelCase ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) ) def SCREAMING_SNAKE_CASE__ ( self : Any , _UpperCAmelCase : int , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Dict , _UpperCAmelCase : Dict , _UpperCAmelCase : List[Any] ): """simple docstring""" UpperCAmelCase__ = TFMobileBertForNextSentencePrediction(config=_UpperCAmelCase ) UpperCAmelCase__ = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids} UpperCAmelCase__ = model(_UpperCAmelCase ) self.parent.assertEqual(result.logits.shape , (self.batch_size, 2) ) def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] , _UpperCAmelCase : Any , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Any , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Optional[Any] ): """simple docstring""" UpperCAmelCase__ = TFMobileBertForPreTraining(config=_UpperCAmelCase ) UpperCAmelCase__ = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids} UpperCAmelCase__ = model(_UpperCAmelCase ) self.parent.assertEqual( result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) ) self.parent.assertEqual(result.seq_relationship_logits.shape , (self.batch_size, 2) ) def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] , _UpperCAmelCase : int , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : str , _UpperCAmelCase : List[Any] , _UpperCAmelCase : List[str] , _UpperCAmelCase : Dict , _UpperCAmelCase : Union[str, Any] ): """simple docstring""" UpperCAmelCase__ = self.num_labels UpperCAmelCase__ = TFMobileBertForSequenceClassification(config=_UpperCAmelCase ) UpperCAmelCase__ = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids} UpperCAmelCase__ = model(_UpperCAmelCase ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) ) def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Dict , _UpperCAmelCase : List[str] , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : str , _UpperCAmelCase : Any , _UpperCAmelCase : Dict ): """simple docstring""" UpperCAmelCase__ = self.num_choices UpperCAmelCase__ = TFMobileBertForMultipleChoice(config=_UpperCAmelCase ) UpperCAmelCase__ = tf.tile(tf.expand_dims(_UpperCAmelCase , 1 ) , (1, self.num_choices, 1) ) UpperCAmelCase__ = tf.tile(tf.expand_dims(_UpperCAmelCase , 1 ) , (1, self.num_choices, 1) ) UpperCAmelCase__ = tf.tile(tf.expand_dims(_UpperCAmelCase , 1 ) , (1, self.num_choices, 1) ) UpperCAmelCase__ = { """input_ids""": multiple_choice_inputs_ids, """attention_mask""": multiple_choice_input_mask, """token_type_ids""": multiple_choice_token_type_ids, } UpperCAmelCase__ = model(_UpperCAmelCase ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) ) def SCREAMING_SNAKE_CASE__ ( self : int , _UpperCAmelCase : List[str] , _UpperCAmelCase : Dict , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : int , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : List[Any] ): """simple docstring""" UpperCAmelCase__ = self.num_labels UpperCAmelCase__ = TFMobileBertForTokenClassification(config=_UpperCAmelCase ) UpperCAmelCase__ = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids} UpperCAmelCase__ = model(_UpperCAmelCase ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) ) def SCREAMING_SNAKE_CASE__ ( self : List[Any] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : int , _UpperCAmelCase : Tuple , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : List[str] , _UpperCAmelCase : Tuple ): """simple docstring""" UpperCAmelCase__ = TFMobileBertForQuestionAnswering(config=_UpperCAmelCase ) UpperCAmelCase__ = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids} UpperCAmelCase__ = model(_UpperCAmelCase ) self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) ) self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) ) def SCREAMING_SNAKE_CASE__ ( self : Dict ): """simple docstring""" UpperCAmelCase__ = self.prepare_config_and_inputs() ( ( UpperCAmelCase__ ) , ( UpperCAmelCase__ ) , ( UpperCAmelCase__ ) , ( UpperCAmelCase__ ) , ( UpperCAmelCase__ ) , ( UpperCAmelCase__ ) , ( UpperCAmelCase__ ) , ) = config_and_inputs UpperCAmelCase__ = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask} return config, inputs_dict def SCREAMING_SNAKE_CASE__ ( self : List[str] ): """simple docstring""" UpperCAmelCase__ = TFMobileBertModelTest.TFMobileBertModelTester(self ) UpperCAmelCase__ = ConfigTester(self , config_class=_UpperCAmelCase , hidden_size=37 ) def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ): """simple docstring""" self.config_tester.run_common_tests() def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ): """simple docstring""" UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_mobilebert_model(*_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : str ): """simple docstring""" UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_mobilebert_for_masked_lm(*_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ): """simple docstring""" UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_mobilebert_for_multiple_choice(*_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ): """simple docstring""" UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ): """simple docstring""" UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_mobilebert_for_pretraining(*_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ): """simple docstring""" UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_mobilebert_for_question_answering(*_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : List[str] ): """simple docstring""" UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_mobilebert_for_sequence_classification(*_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Any ): """simple docstring""" UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_mobilebert_for_token_classification(*_UpperCAmelCase ) @slow def SCREAMING_SNAKE_CASE__ ( self : List[Any] ): """simple docstring""" for model_name in ["google/mobilebert-uncased"]: UpperCAmelCase__ = TFMobileBertModel.from_pretrained(_UpperCAmelCase ) self.assertIsNotNone(_UpperCAmelCase ) @require_tf class lowerCAmelCase_ ( unittest.TestCase ): '''simple docstring''' @slow def SCREAMING_SNAKE_CASE__ ( self : int ): """simple docstring""" UpperCAmelCase__ = TFMobileBertForPreTraining.from_pretrained("""google/mobilebert-uncased""" ) UpperCAmelCase__ = tf.constant([[0, 1, 2, 3, 4, 5]] ) UpperCAmelCase__ = model(_UpperCAmelCase )[0] UpperCAmelCase__ = [1, 6, 3_05_22] self.assertEqual(output.shape , _UpperCAmelCase ) UpperCAmelCase__ = tf.constant( [ [ [-4.591_9547, -9.24_8295, -9.64_5256], [-6.730_6175, -6.44_0284, -6.605_2837], [-7.274_3506, -6.784_7915, -6.02_4673], ] ] ) tf.debugging.assert_near(output[:, :3, :3] , _UpperCAmelCase , atol=1E-4 )

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'''simple docstring''' import argparse import logging import os import datasets import tensorflow as tf from transformers import AutoTokenizer UpperCAmelCase_ = logging.getLogger(__name__) def _UpperCamelCase ( ): '''simple docstring''' UpperCAmelCase__ = argparse.ArgumentParser( description="""Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset.""" ) parser.add_argument( """--dataset_name""" , type=SCREAMING_SNAKE_CASE__ , default="""wikitext""" , help="""Name of the training. Explore datasets at: hf.co/datasets.""" , ) parser.add_argument( """--dataset_config""" , type=SCREAMING_SNAKE_CASE__ , default="""wikitext-103-raw-v1""" , help="""Configuration name of the dataset.""" ) parser.add_argument( """--tokenizer_name_or_path""" , type=SCREAMING_SNAKE_CASE__ , default="""sayakpaul/unigram-tokenizer-wikitext""" , help="""Tokenizer identifier. Can be a local filepath or a Hub identifier.""" , ) parser.add_argument( """--shard_size""" , type=SCREAMING_SNAKE_CASE__ , default=1000 , help="""Number of entries to go in a single shard.""" , ) parser.add_argument("""--split""" , type=SCREAMING_SNAKE_CASE__ , default="""train""" , choices=["""train""", """test""", """validation"""] ) parser.add_argument( """--limit""" , default=SCREAMING_SNAKE_CASE__ , type=SCREAMING_SNAKE_CASE__ , help="""Limit the number of shards (used for debugging).""" , ) parser.add_argument( """--max_length""" , type=SCREAMING_SNAKE_CASE__ , default=512 , help="""Maximum sequence length. For training on TPUs, it helps to have a maximum""" """ sequence length that is a multiple of 8.""" , ) parser.add_argument( """--output_dir""" , default="""tf-tpu""" , type=SCREAMING_SNAKE_CASE__ , help="""Output directory where the TFRecord shards will be saved. If the""" """ path is appended with `gs://` ('gs://tf-tpu', for example) then the TFRecord""" """ shards will be directly saved to a Google Cloud Storage bucket.""" , ) UpperCAmelCase__ = parser.parse_args() return args def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Any ): '''simple docstring''' def fn(SCREAMING_SNAKE_CASE__ : Union[str, Any] ): return tokenizer(examples["""text"""] ) return fn def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Optional[int] ): '''simple docstring''' UpperCAmelCase__ = [] for i in range(len(tokenized_data["""input_ids"""] ) ): UpperCAmelCase__ = { """input_ids""": tf.train.Feature(intaa_list=tf.train.IntaaList(value=tokenized_data["""input_ids"""][i] ) ), """attention_mask""": tf.train.Feature( intaa_list=tf.train.IntaaList(value=tokenized_data["""attention_mask"""][i] ) ), } UpperCAmelCase__ = tf.train.Features(feature=SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = tf.train.Example(features=SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = example.SerializeToString() records.append(SCREAMING_SNAKE_CASE__ ) return records def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Any ): '''simple docstring''' UpperCAmelCase__ = datasets.load_dataset(args.dataset_name , args.dataset_config , split=args.split ) if args.limit is not None: UpperCAmelCase__ = min(len(SCREAMING_SNAKE_CASE__ ) , args.limit ) UpperCAmelCase__ = dataset.select(range(SCREAMING_SNAKE_CASE__ ) ) print(F'''Limiting the dataset to {args.limit} entries.''' ) UpperCAmelCase__ = AutoTokenizer.from_pretrained(args.tokenizer_name_or_path ) # Handle output directory creation. # For serializing into a Google Cloud Storage Bucket, one needs to first # create a bucket. if "gs" not in args.output_dir: if not os.path.exists(args.output_dir ): os.makedirs(args.output_dir ) UpperCAmelCase__ = os.path.join(args.output_dir , args.split ) if not os.path.exists(SCREAMING_SNAKE_CASE__ ): os.makedirs(SCREAMING_SNAKE_CASE__ ) else: UpperCAmelCase__ = os.path.join(args.output_dir , args.split ) # Tokenize the whole dataset at once. UpperCAmelCase__ = tokenize_function(SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = dataset.map(SCREAMING_SNAKE_CASE__ , batched=SCREAMING_SNAKE_CASE__ , num_proc=4 , remove_columns=["""text"""] ) # We need to concatenate all our texts together, and then split the result # into chunks of a fixed size, which we will call block_size. To do this, we # will use the map method again, with the option batched=True. When we use batched=True, # the function we pass to map() will be passed multiple inputs at once, allowing us # to group them into more or fewer examples than we had in the input. # This allows us to create our new fixed-length samples. The advantage of this # method is that we don't lose a whole lot of content from the dataset compared to the # case where we simply tokenize with a pre-defined max_length. def group_texts(SCREAMING_SNAKE_CASE__ : int ): # Concatenate all texts. UpperCAmelCase__ = {k: sum(examples[k] , [] ) for k in examples.keys()} UpperCAmelCase__ = len(concatenated_examples[list(examples.keys() )[0]] ) # We drop the small remainder, though you could add padding instead if the model supports it # In this, as in all things, we advise you to follow your heart 🫀 UpperCAmelCase__ = (total_length // args.max_length) * args.max_length # Split by chunks of max_len. UpperCAmelCase__ = { k: [t[i : i + args.max_length] for i in range(0 , SCREAMING_SNAKE_CASE__ , args.max_length )] for k, t in concatenated_examples.items() } return result UpperCAmelCase__ = dataset_tokenized.map(SCREAMING_SNAKE_CASE__ , batched=SCREAMING_SNAKE_CASE__ , batch_size=1000 , num_proc=4 ) UpperCAmelCase__ = 0 UpperCAmelCase__ = 0 for shard in range(0 , len(SCREAMING_SNAKE_CASE__ ) , args.shard_size ): UpperCAmelCase__ = grouped_dataset[shard : shard + args.shard_size] UpperCAmelCase__ = len(dataset_snapshot["""input_ids"""] ) UpperCAmelCase__ = os.path.join(SCREAMING_SNAKE_CASE__ , F'''dataset-{shard_count}-{records_containing}.tfrecord''' ) UpperCAmelCase__ = get_serialized_examples(SCREAMING_SNAKE_CASE__ ) with tf.io.TFRecordWriter(SCREAMING_SNAKE_CASE__ ) as out_file: for i in range(len(SCREAMING_SNAKE_CASE__ ) ): UpperCAmelCase__ = serialized_examples[i] out_file.write(SCREAMING_SNAKE_CASE__ ) print("""Wrote file {} containing {} records""".format(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) ) shard_count += 1 total_records += records_containing with open(F'''split-{args.split}-records-count.txt''' , """w""" ) as f: print(F'''Total {args.split} records: {total_records}''' , file=SCREAMING_SNAKE_CASE__ ) if __name__ == "__main__": UpperCAmelCase_ = parse_args() main(args)

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'''simple docstring''' def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ): '''simple docstring''' if not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ): raise ValueError("""iterations must be defined as integers""" ) if not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) or not number >= 1: raise ValueError( """starting number must be and integer and be more than 0""" ) if not iterations >= 1: raise ValueError("""Iterations must be done more than 0 times to play FizzBuzz""" ) UpperCAmelCase__ = """""" while number <= iterations: if number % 3 == 0: out += "Fizz" if number % 5 == 0: out += "Buzz" if 0 not in (number % 3, number % 5): out += str(SCREAMING_SNAKE_CASE__ ) # print(out) number += 1 out += " " return out if __name__ == "__main__": import doctest doctest.testmod()

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'''simple docstring''' import numpy as np import torch from torch.nn import CrossEntropyLoss from transformers import AutoModelForCausalLM, AutoTokenizer import datasets from datasets import logging UpperCAmelCase_ = '\\n\n' UpperCAmelCase_ = '\nPerplexity (PPL) is one of the most common metrics for evaluating language models.\nIt is defined as the exponentiated average negative log-likelihood of a sequence.\n\nFor more information, see https://huggingface.co/docs/transformers/perplexity\n' UpperCAmelCase_ = '\nArgs:\n model_id (str): model used for calculating Perplexity\n NOTE: Perplexity can only be calculated for causal language models.\n This includes models such as gpt2, causal variations of bert,\n causal versions of t5, and more (the full list can be found\n in the AutoModelForCausalLM documentation here:\n https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )\n\n input_texts (list of str): input text, each separate text snippet\n is one list entry.\n batch_size (int): the batch size to run texts through the model. Defaults to 16.\n add_start_token (bool): whether to add the start token to the texts,\n so the perplexity can include the probability of the first word. Defaults to True.\n device (str): device to run on, defaults to \'cuda\' when available\nReturns:\n perplexity: dictionary containing the perplexity scores for the texts\n in the input list, as well as the mean perplexity. If one of the input texts is\n longer than the max input length of the model, then it is truncated to the\n max length for the perplexity computation.\nExamples:\n Example 1:\n >>> perplexity = datasets.load_metric("perplexity")\n >>> input_texts = ["lorem ipsum", "Happy Birthday!", "Bienvenue"]\n >>> results = perplexity.compute(model_id=\'gpt2\',\n ... add_start_token=False,\n ... input_texts=input_texts) # doctest:+ELLIPSIS\n >>> print(list(results.keys()))\n [\'perplexities\', \'mean_perplexity\']\n >>> print(round(results["mean_perplexity"], 2))\n 78.22\n >>> print(round(results["perplexities"][0], 2))\n 11.11\n\n Example 2:\n >>> perplexity = datasets.load_metric("perplexity")\n >>> input_texts = datasets.load_dataset("wikitext",\n ... "wikitext-2-raw-v1",\n ... split="test")["text"][:50] # doctest:+ELLIPSIS\n [...]\n >>> input_texts = [s for s in input_texts if s!=\'\']\n >>> results = perplexity.compute(model_id=\'gpt2\',\n ... input_texts=input_texts) # doctest:+ELLIPSIS\n >>> print(list(results.keys()))\n [\'perplexities\', \'mean_perplexity\']\n >>> print(round(results["mean_perplexity"], 2))\n 60.35\n >>> print(round(results["perplexities"][0], 2))\n 81.12\n' @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION ) class lowerCAmelCase_ ( datasets.Metric ): '''simple docstring''' def SCREAMING_SNAKE_CASE__ ( self : Dict ): """simple docstring""" return datasets.MetricInfo( description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features( { """input_texts""": datasets.Value("""string""" ), } ) , reference_urls=["""https://huggingface.co/docs/transformers/perplexity"""] , ) def SCREAMING_SNAKE_CASE__ ( self : List[Any] , _UpperCAmelCase : Tuple , _UpperCAmelCase : int , _UpperCAmelCase : int = 16 , _UpperCAmelCase : bool = True , _UpperCAmelCase : Optional[int]=None ): """simple docstring""" if device is not None: assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu." if device == "gpu": UpperCAmelCase__ = """cuda""" else: UpperCAmelCase__ = """cuda""" if torch.cuda.is_available() else """cpu""" UpperCAmelCase__ = AutoModelForCausalLM.from_pretrained(_UpperCAmelCase ) UpperCAmelCase__ = model.to(_UpperCAmelCase ) UpperCAmelCase__ = AutoTokenizer.from_pretrained(_UpperCAmelCase ) # if batch_size > 1 (which generally leads to padding being required), and # if there is not an already assigned pad_token, assign an existing # special token to also be the padding token if tokenizer.pad_token is None and batch_size > 1: UpperCAmelCase__ = list(tokenizer.special_tokens_map_extended.values() ) # check that the model already has at least one special token defined assert ( len(_UpperCAmelCase ) > 0 ), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1." # assign one of the special tokens to also be the pad token tokenizer.add_special_tokens({"""pad_token""": existing_special_tokens[0]} ) if add_start_token: # leave room for <BOS> token to be added: assert ( tokenizer.bos_token is not None ), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False" UpperCAmelCase__ = model.config.max_length - 1 else: UpperCAmelCase__ = model.config.max_length UpperCAmelCase__ = tokenizer( _UpperCAmelCase , add_special_tokens=_UpperCAmelCase , padding=_UpperCAmelCase , truncation=_UpperCAmelCase , max_length=_UpperCAmelCase , return_tensors="""pt""" , return_attention_mask=_UpperCAmelCase , ).to(_UpperCAmelCase ) UpperCAmelCase__ = encodings["""input_ids"""] UpperCAmelCase__ = encodings["""attention_mask"""] # check that each input is long enough: if add_start_token: assert torch.all(torch.ge(attn_masks.sum(1 ) , 1 ) ), "Each input text must be at least one token long." else: assert torch.all( torch.ge(attn_masks.sum(1 ) , 2 ) ), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings." UpperCAmelCase__ = [] UpperCAmelCase__ = CrossEntropyLoss(reduction="""none""" ) for start_index in logging.tqdm(range(0 , len(_UpperCAmelCase ) , _UpperCAmelCase ) ): UpperCAmelCase__ = min(start_index + batch_size , len(_UpperCAmelCase ) ) UpperCAmelCase__ = encoded_texts[start_index:end_index] UpperCAmelCase__ = attn_masks[start_index:end_index] if add_start_token: UpperCAmelCase__ = torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0 ) ).to(_UpperCAmelCase ) UpperCAmelCase__ = torch.cat([bos_tokens_tensor, encoded_batch] , dim=1 ) UpperCAmelCase__ = torch.cat( [torch.ones(bos_tokens_tensor.size() , dtype=torch.intaa ).to(_UpperCAmelCase ), attn_mask] , dim=1 ) UpperCAmelCase__ = encoded_batch with torch.no_grad(): UpperCAmelCase__ = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase ).logits UpperCAmelCase__ = out_logits[..., :-1, :].contiguous() UpperCAmelCase__ = labels[..., 1:].contiguous() UpperCAmelCase__ = attn_mask[..., 1:].contiguous() UpperCAmelCase__ = torch.expa( (loss_fct(shift_logits.transpose(1 , 2 ) , _UpperCAmelCase ) * shift_attention_mask_batch).sum(1 ) / shift_attention_mask_batch.sum(1 ) ) ppls += perplexity_batch.tolist() return {"perplexities": ppls, "mean_perplexity": np.mean(_UpperCAmelCase )}

346

1

'''simple docstring''' from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available UpperCAmelCase_ = { 'configuration_groupvit': [ 'GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GroupViTConfig', 'GroupViTOnnxConfig', 'GroupViTTextConfig', 'GroupViTVisionConfig', ], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCAmelCase_ = [ 'GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST', 'GroupViTModel', 'GroupViTPreTrainedModel', 'GroupViTTextModel', 'GroupViTVisionModel', ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCAmelCase_ = [ 'TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST', 'TFGroupViTModel', 'TFGroupViTPreTrainedModel', 'TFGroupViTTextModel', 'TFGroupViTVisionModel', ] if TYPE_CHECKING: from .configuration_groupvit import ( GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GroupViTConfig, GroupViTOnnxConfig, GroupViTTextConfig, GroupViTVisionConfig, ) try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_groupvit import ( GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST, GroupViTModel, GroupViTPreTrainedModel, GroupViTTextModel, GroupViTVisionModel, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_groupvit import ( TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST, TFGroupViTModel, TFGroupViTPreTrainedModel, TFGroupViTTextModel, TFGroupViTVisionModel, ) else: import sys UpperCAmelCase_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

346

'''simple docstring''' def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int = 1000000 ): '''simple docstring''' UpperCAmelCase__ = [i - 1 for i in range(limit + 1 )] for i in range(2 , limit + 1 ): if phi[i] == i - 1: for j in range(2 * i , limit + 1 , SCREAMING_SNAKE_CASE__ ): phi[j] -= phi[j] // i return sum(phi[2 : limit + 1] ) if __name__ == "__main__": print(solution())

346

1

'''simple docstring''' import tempfile import unittest from transformers import TaConfig, is_torch_available from transformers.testing_utils import ( require_sentencepiece, require_tokenizers, require_torch, slow, torch_device, ) from ...generation.test_utils import GenerationTesterMixin from ...test_modeling_common import ModelTesterMixin, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel class lowerCAmelCase_ : '''simple docstring''' def __init__( self : Dict , _UpperCAmelCase : Any , _UpperCAmelCase : Union[str, Any]=99 , _UpperCAmelCase : Any=13 , _UpperCAmelCase : Union[str, Any]=7 , _UpperCAmelCase : Union[str, Any]=9 , _UpperCAmelCase : List[Any]=True , _UpperCAmelCase : Any=True , _UpperCAmelCase : Union[str, Any]=False , _UpperCAmelCase : Any=32 , _UpperCAmelCase : Union[str, Any]=5 , _UpperCAmelCase : Union[str, Any]=4 , _UpperCAmelCase : Dict=37 , _UpperCAmelCase : Dict=8 , _UpperCAmelCase : Dict=0.1 , _UpperCAmelCase : int=0.002 , _UpperCAmelCase : List[str]=1 , _UpperCAmelCase : int=0 , _UpperCAmelCase : Union[str, Any]=0 , _UpperCAmelCase : Any=None , _UpperCAmelCase : str=None , ): """simple docstring""" UpperCAmelCase__ = parent UpperCAmelCase__ = batch_size UpperCAmelCase__ = encoder_seq_length UpperCAmelCase__ = decoder_seq_length # For common tests UpperCAmelCase__ = self.decoder_seq_length UpperCAmelCase__ = is_training UpperCAmelCase__ = use_attention_mask UpperCAmelCase__ = use_labels UpperCAmelCase__ = vocab_size UpperCAmelCase__ = hidden_size UpperCAmelCase__ = num_hidden_layers UpperCAmelCase__ = num_attention_heads UpperCAmelCase__ = d_ff UpperCAmelCase__ = relative_attention_num_buckets UpperCAmelCase__ = dropout_rate UpperCAmelCase__ = initializer_factor UpperCAmelCase__ = eos_token_id UpperCAmelCase__ = pad_token_id UpperCAmelCase__ = decoder_start_token_id UpperCAmelCase__ = None UpperCAmelCase__ = decoder_layers def SCREAMING_SNAKE_CASE__ ( self : List[Any] ): """simple docstring""" return TaConfig.from_pretrained("""google/umt5-base""" ) def SCREAMING_SNAKE_CASE__ ( self : Tuple , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Dict , _UpperCAmelCase : List[str] , _UpperCAmelCase : List[str]=None , _UpperCAmelCase : Optional[Any]=None , _UpperCAmelCase : Union[str, Any]=None , _UpperCAmelCase : List[str]=None , _UpperCAmelCase : Any=None , ): """simple docstring""" if attention_mask is None: UpperCAmelCase__ = input_ids.ne(config.pad_token_id ) if decoder_attention_mask is None: UpperCAmelCase__ = decoder_input_ids.ne(config.pad_token_id ) if head_mask is None: UpperCAmelCase__ = torch.ones(config.num_hidden_layers , config.num_attention_heads , device=_UpperCAmelCase ) if decoder_head_mask is None: UpperCAmelCase__ = torch.ones(config.num_decoder_layers , config.num_attention_heads , device=_UpperCAmelCase ) if cross_attn_head_mask is None: UpperCAmelCase__ = torch.ones( config.num_decoder_layers , config.num_attention_heads , device=_UpperCAmelCase ) return { "input_ids": input_ids, "decoder_input_ids": decoder_input_ids, "attention_mask": attention_mask, "decoder_attention_mask": decoder_attention_mask, "head_mask": head_mask, "decoder_head_mask": decoder_head_mask, "cross_attn_head_mask": cross_attn_head_mask, } def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ): """simple docstring""" UpperCAmelCase__ = ids_tensor([self.batch_size, self.encoder_seq_length] , self.vocab_size ) UpperCAmelCase__ = ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size ) # we need to clamp the input ids here to avoid having pad token in between # this is because for NllbMoe the position_ids are prepared such that # all pad tokens have pos id = 2 and rest are between 2..seq_length # and the seq_length here is seq_length - num_pad_tokens # but when using past, there is no way of knowing if the past input ids had # pad tokens in them, which results in incorrect seq_lenth and which in turn results in # position_ids being off by num_pad_tokens in past input UpperCAmelCase__ = input_ids.clamp(self.pad_token_id + 1 ) UpperCAmelCase__ = decoder_input_ids.clamp(self.pad_token_id + 1 ) UpperCAmelCase__ = self.get_config() UpperCAmelCase__ = config.num_attention_heads UpperCAmelCase__ = self.prepare_inputs_dict(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) return config, input_dict def SCREAMING_SNAKE_CASE__ ( self : str ): """simple docstring""" UpperCAmelCase__ , UpperCAmelCase__ = self.prepare_config_and_inputs() return config, inputs_dict def SCREAMING_SNAKE_CASE__ ( self : Any ): """simple docstring""" return TaConfig( vocab_size=1_66 , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , ) def SCREAMING_SNAKE_CASE__ ( self : str ): """simple docstring""" return TaConfig( vocab_size=self.vocab_size , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , ) def SCREAMING_SNAKE_CASE__ ( self : Dict , _UpperCAmelCase : Dict , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Any , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Optional[int] , ): """simple docstring""" UpperCAmelCase__ = UMTaModel(config=_UpperCAmelCase ) model.to(_UpperCAmelCase ) model.eval() UpperCAmelCase__ = model( input_ids=_UpperCAmelCase , decoder_input_ids=_UpperCAmelCase , attention_mask=_UpperCAmelCase , decoder_attention_mask=_UpperCAmelCase , ) UpperCAmelCase__ = model(input_ids=_UpperCAmelCase , decoder_input_ids=_UpperCAmelCase ) UpperCAmelCase__ = result.last_hidden_state UpperCAmelCase__ = result.past_key_values UpperCAmelCase__ = result.encoder_last_hidden_state self.parent.assertEqual(encoder_output.size() , (self.batch_size, self.encoder_seq_length, self.hidden_size) ) self.parent.assertEqual(decoder_output.size() , (self.batch_size, self.decoder_seq_length, self.hidden_size) ) # There should be `num_layers` key value embeddings stored in decoder_past self.parent.assertEqual(len(_UpperCAmelCase ) , config.num_layers ) # There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple self.parent.assertEqual(len(decoder_past[0] ) , 4 ) def SCREAMING_SNAKE_CASE__ ( self : Any , _UpperCAmelCase : Any , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Dict , _UpperCAmelCase : Tuple , _UpperCAmelCase : Tuple , _UpperCAmelCase : Tuple , ): """simple docstring""" UpperCAmelCase__ = UMTaModel(config=_UpperCAmelCase ).get_decoder().to(_UpperCAmelCase ).eval() # first forward pass UpperCAmelCase__ = model(_UpperCAmelCase , use_cache=_UpperCAmelCase ) UpperCAmelCase__ = model(_UpperCAmelCase ) UpperCAmelCase__ = model(_UpperCAmelCase , use_cache=_UpperCAmelCase ) self.parent.assertTrue(len(_UpperCAmelCase ) == len(_UpperCAmelCase ) ) self.parent.assertTrue(len(_UpperCAmelCase ) == len(_UpperCAmelCase ) + 1 ) UpperCAmelCase__ , UpperCAmelCase__ = outputs.to_tuple() # create hypothetical next token and extent to next_input_ids UpperCAmelCase__ = ids_tensor((self.batch_size, 1) , config.vocab_size ) # append to next input_ids and UpperCAmelCase__ = torch.cat([input_ids, next_tokens] , dim=-1 ) UpperCAmelCase__ = model(_UpperCAmelCase )["""last_hidden_state"""] UpperCAmelCase__ = model(_UpperCAmelCase , past_key_values=_UpperCAmelCase )["""last_hidden_state"""] # select random slice UpperCAmelCase__ = ids_tensor((1,) , output_from_past.shape[-1] ).item() UpperCAmelCase__ = output_from_no_past[:, -1, random_slice_idx].detach() UpperCAmelCase__ = output_from_past[:, 0, random_slice_idx].detach() # test that outputs are equal for slice self.parent.assertTrue(torch.allclose(_UpperCAmelCase , _UpperCAmelCase , atol=1E-3 ) ) def SCREAMING_SNAKE_CASE__ ( self : Any , _UpperCAmelCase : List[str] , _UpperCAmelCase : Optional[int] , ): """simple docstring""" UpperCAmelCase__ = UMTaModel(config=_UpperCAmelCase ).to(_UpperCAmelCase ).half().eval() UpperCAmelCase__ = model(**_UpperCAmelCase )["""last_hidden_state"""] self.parent.assertFalse(torch.isnan(_UpperCAmelCase ).any().item() ) @require_torch class lowerCAmelCase_ ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , unittest.TestCase ): '''simple docstring''' lowerCAmelCase_ : List[str] = ( (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else () ) lowerCAmelCase_ : Tuple = (UMTaForConditionalGeneration,) if is_torch_available() else () lowerCAmelCase_ : Any = ( { """conversational""": UMTaForConditionalGeneration, """feature-extraction""": UMTaModel, """summarization""": UMTaForConditionalGeneration, """text2text-generation""": UMTaForConditionalGeneration, """translation""": UMTaForConditionalGeneration, """question-answering""": UMTaForQuestionAnswering, } if is_torch_available() else {} ) lowerCAmelCase_ : List[Any] = True lowerCAmelCase_ : str = False lowerCAmelCase_ : Any = False lowerCAmelCase_ : Union[str, Any] = True lowerCAmelCase_ : Optional[int] = True # The small UMT5 model needs higher percentages for CPU/MP tests lowerCAmelCase_ : List[Any] = [0.8, 0.9] def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ): """simple docstring""" UpperCAmelCase__ = UMTaModelTester(self ) @unittest.skip("""Test has a segmentation fault on torch 1.8.0""" ) def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ): """simple docstring""" UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs() UpperCAmelCase__ = UMTaModel(config_and_inputs[0] ).to(_UpperCAmelCase ) with tempfile.TemporaryDirectory() as tmpdirname: torch.onnx.export( _UpperCAmelCase , (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2]) , f'''{tmpdirname}/t5_test.onnx''' , export_params=_UpperCAmelCase , opset_version=9 , input_names=["""input_ids""", """decoder_input_ids"""] , ) @unittest.skipIf(torch_device == """cpu""" , """Cant do half precision""" ) def SCREAMING_SNAKE_CASE__ ( self : List[str] ): """simple docstring""" UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model_fpaa_forward(*_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : List[Any] ): """simple docstring""" UpperCAmelCase__ = ["""encoder_attentions""", """decoder_attentions""", """cross_attentions"""] UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs() UpperCAmelCase__ = config_and_inputs[0] UpperCAmelCase__ = UMTaForConditionalGeneration(_UpperCAmelCase ).eval() model.to(_UpperCAmelCase ) UpperCAmelCase__ = { """head_mask""": torch.zeros(config.num_layers , config.num_heads , device=_UpperCAmelCase ), """decoder_head_mask""": torch.zeros(config.num_decoder_layers , config.num_heads , device=_UpperCAmelCase ), """cross_attn_head_mask""": torch.zeros(config.num_decoder_layers , config.num_heads , device=_UpperCAmelCase ), } for attn_name, (name, mask) in zip(_UpperCAmelCase , head_masking.items() ): UpperCAmelCase__ = {name: mask} # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified if name == "head_mask": UpperCAmelCase__ = torch.ones( config.num_decoder_layers , config.num_heads , device=_UpperCAmelCase ) UpperCAmelCase__ = model.generate( config_and_inputs[1]["""input_ids"""] , num_beams=1 , max_length=3 , output_attentions=_UpperCAmelCase , return_dict_in_generate=_UpperCAmelCase , **_UpperCAmelCase , ) # We check the state of decoder_attentions and cross_attentions just from the last step UpperCAmelCase__ = out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1] self.assertEqual(sum([w.sum().item() for w in attn_weights] ) , 0.0 ) @unittest.skip("""Does not work on the tiny model as we keep hitting edge cases.""" ) def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ): """simple docstring""" pass @require_torch @require_sentencepiece @require_tokenizers class lowerCAmelCase_ ( unittest.TestCase ): '''simple docstring''' @slow @unittest.skip( """Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged""" ) def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ): """simple docstring""" UpperCAmelCase__ = UMTaForConditionalGeneration.from_pretrained("""google/umt5-small""" , return_dict=_UpperCAmelCase ).to(_UpperCAmelCase ) UpperCAmelCase__ = AutoTokenizer.from_pretrained("""google/umt5-small""" , use_fast=_UpperCAmelCase , legacy=_UpperCAmelCase ) UpperCAmelCase__ = [ """Bonjour monsieur <extra_id_0> bien <extra_id_1>.""", """No se como puedo <extra_id_0>.""", """This is the reason why we <extra_id_0> them.""", """The <extra_id_0> walks in <extra_id_1>, seats""", """A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.""", ] UpperCAmelCase__ = tokenizer(_UpperCAmelCase , return_tensors="""pt""" , padding=_UpperCAmelCase ).input_ids # fmt: off UpperCAmelCase__ = torch.tensor( [ [ 3_85_30, 21_07_03, 25_62_99, 14_10, 25_62_98, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0], [ 8_26, 3_21, 6_71, 2_59_22, 25_62_99, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0], [ 14_60, 3_39, 3_12, 1_90_14, 1_06_20, 7_58, 25_62_99, 23_55,2_74, 1, 0, 0, 0, 0, 0, 0,0, 0], [ 5_17, 25_62_99, 1_48_69, 2_81, 3_01, 25_62_98, 2_75, 11_99_83,1, 0, 0, 0, 0, 0, 0, 0,0, 0], [ 3_20, 25_62_99, 1_48_69, 2_81, 22_34, 2_89, 22_75, 3_33,6_13_91, 2_89, 25_62_98, 5_43, 25_62_97, 16_87_14, 3_29, 25_62_96,2_74, 1], ] ) # fmt: on torch.testing.assert_allclose(_UpperCAmelCase , _UpperCAmelCase ) UpperCAmelCase__ = model.generate(input_ids.to(_UpperCAmelCase ) ) UpperCAmelCase__ = [ """<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>""", """<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""", """<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""", """<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""", """<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""", ] UpperCAmelCase__ = tokenizer.batch_decode(_UpperCAmelCase ) self.assertEqual(_UpperCAmelCase , _UpperCAmelCase )

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'''simple docstring''' from typing import List, Union from ..utils import ( add_end_docstrings, is_tf_available, is_torch_available, is_vision_available, logging, requires_backends, ) from .base import PIPELINE_INIT_ARGS, Pipeline if is_vision_available(): from PIL import Image from ..image_utils import load_image if is_tf_available(): import tensorflow as tf from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING from ..tf_utils import stable_softmax if is_torch_available(): from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING UpperCAmelCase_ = logging.get_logger(__name__) @add_end_docstrings(lowerCamelCase_ ) class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' def __init__( self : Optional[Any] , *_UpperCAmelCase : Union[str, Any] , **_UpperCAmelCase : Dict ): """simple docstring""" super().__init__(*_UpperCAmelCase , **_UpperCAmelCase ) requires_backends(self , """vision""" ) self.check_model_type( TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING if self.framework == """tf""" else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING ) def SCREAMING_SNAKE_CASE__ ( self : str , _UpperCAmelCase : List[Any]=None ): """simple docstring""" UpperCAmelCase__ = {} if top_k is not None: UpperCAmelCase__ = top_k return {}, {}, postprocess_params def __call__( self : Any , _UpperCAmelCase : Union[str, List[str], "Image.Image", List["Image.Image"]] , **_UpperCAmelCase : str ): """simple docstring""" return super().__call__(_UpperCAmelCase , **_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] , _UpperCAmelCase : Tuple ): """simple docstring""" UpperCAmelCase__ = load_image(_UpperCAmelCase ) UpperCAmelCase__ = self.image_processor(images=_UpperCAmelCase , return_tensors=self.framework ) return model_inputs def SCREAMING_SNAKE_CASE__ ( self : Dict , _UpperCAmelCase : Tuple ): """simple docstring""" UpperCAmelCase__ = self.model(**_UpperCAmelCase ) return model_outputs def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , _UpperCAmelCase : Dict , _UpperCAmelCase : str=5 ): """simple docstring""" if top_k > self.model.config.num_labels: UpperCAmelCase__ = self.model.config.num_labels if self.framework == "pt": UpperCAmelCase__ = model_outputs.logits.softmax(-1 )[0] UpperCAmelCase__ , UpperCAmelCase__ = probs.topk(_UpperCAmelCase ) elif self.framework == "tf": UpperCAmelCase__ = stable_softmax(model_outputs.logits , axis=-1 )[0] UpperCAmelCase__ = tf.math.top_k(_UpperCAmelCase , k=_UpperCAmelCase ) UpperCAmelCase__ , UpperCAmelCase__ = topk.values.numpy(), topk.indices.numpy() else: raise ValueError(f'''Unsupported framework: {self.framework}''' ) UpperCAmelCase__ = scores.tolist() UpperCAmelCase__ = ids.tolist() return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(_UpperCAmelCase , _UpperCAmelCase )]

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'''simple docstring''' def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int = 1000000 ): '''simple docstring''' UpperCAmelCase__ = [i - 1 for i in range(limit + 1 )] for i in range(2 , limit + 1 ): if phi[i] == i - 1: for j in range(2 * i , limit + 1 , SCREAMING_SNAKE_CASE__ ): phi[j] -= phi[j] // i return sum(phi[2 : limit + 1] ) if __name__ == "__main__": print(solution())

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'''simple docstring''' from math import factorial def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int = 20 ): '''simple docstring''' UpperCAmelCase__ = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1, # 2, 3,... UpperCAmelCase__ = n // 2 return int(factorial(SCREAMING_SNAKE_CASE__ ) / (factorial(SCREAMING_SNAKE_CASE__ ) * factorial(n - k )) ) if __name__ == "__main__": import sys if len(sys.argv) == 1: print(solution(2_0)) else: try: UpperCAmelCase_ = int(sys.argv[1]) print(solution(n)) except ValueError: print('Invalid entry - please enter a number.')

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'''simple docstring''' import argparse import json import math import os import time import traceback import zipfile from collections import Counter import requests def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CASE__ : Any=None ): '''simple docstring''' UpperCAmelCase__ = None if token is not None: UpperCAmelCase__ = {"""Accept""": """application/vnd.github+json""", """Authorization""": F'''Bearer {token}'''} UpperCAmelCase__ = F'''https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100''' UpperCAmelCase__ = requests.get(SCREAMING_SNAKE_CASE__ , headers=SCREAMING_SNAKE_CASE__ ).json() UpperCAmelCase__ = {} try: job_links.update({job["""name"""]: job["""html_url"""] for job in result["""jobs"""]} ) UpperCAmelCase__ = math.ceil((result["""total_count"""] - 100) / 100 ) for i in range(SCREAMING_SNAKE_CASE__ ): UpperCAmelCase__ = requests.get(url + F'''&page={i + 2}''' , headers=SCREAMING_SNAKE_CASE__ ).json() job_links.update({job["""name"""]: job["""html_url"""] for job in result["""jobs"""]} ) return job_links except Exception: print(F'''Unknown error, could not fetch links:\n{traceback.format_exc()}''' ) return {} def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : List[str]=None ): '''simple docstring''' UpperCAmelCase__ = None if token is not None: UpperCAmelCase__ = {"""Accept""": """application/vnd.github+json""", """Authorization""": F'''Bearer {token}'''} UpperCAmelCase__ = F'''https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100''' UpperCAmelCase__ = requests.get(SCREAMING_SNAKE_CASE__ , headers=SCREAMING_SNAKE_CASE__ ).json() UpperCAmelCase__ = {} try: artifacts.update({artifact["""name"""]: artifact["""archive_download_url"""] for artifact in result["""artifacts"""]} ) UpperCAmelCase__ = math.ceil((result["""total_count"""] - 100) / 100 ) for i in range(SCREAMING_SNAKE_CASE__ ): UpperCAmelCase__ = requests.get(url + F'''&page={i + 2}''' , headers=SCREAMING_SNAKE_CASE__ ).json() artifacts.update({artifact["""name"""]: artifact["""archive_download_url"""] for artifact in result["""artifacts"""]} ) return artifacts except Exception: print(F'''Unknown error, could not fetch links:\n{traceback.format_exc()}''' ) return {} def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : List[str] ): '''simple docstring''' UpperCAmelCase__ = None if token is not None: UpperCAmelCase__ = {"""Accept""": """application/vnd.github+json""", """Authorization""": F'''Bearer {token}'''} UpperCAmelCase__ = requests.get(SCREAMING_SNAKE_CASE__ , headers=SCREAMING_SNAKE_CASE__ , allow_redirects=SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = result.headers["""Location"""] UpperCAmelCase__ = requests.get(SCREAMING_SNAKE_CASE__ , allow_redirects=SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = os.path.join(SCREAMING_SNAKE_CASE__ , F'''{artifact_name}.zip''' ) with open(SCREAMING_SNAKE_CASE__ , """wb""" ) as fp: fp.write(response.content ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CASE__ : List[str]=None ): '''simple docstring''' UpperCAmelCase__ = [] UpperCAmelCase__ = [] UpperCAmelCase__ = None with zipfile.ZipFile(SCREAMING_SNAKE_CASE__ ) as z: for filename in z.namelist(): if not os.path.isdir(SCREAMING_SNAKE_CASE__ ): # read the file if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]: with z.open(SCREAMING_SNAKE_CASE__ ) as f: for line in f: UpperCAmelCase__ = line.decode("""UTF-8""" ).strip() if filename == "failures_line.txt": try: # `error_line` is the place where `error` occurs UpperCAmelCase__ = line[: line.index(""": """ )] UpperCAmelCase__ = line[line.index(""": """ ) + len(""": """ ) :] errors.append([error_line, error] ) except Exception: # skip un-related lines pass elif filename == "summary_short.txt" and line.startswith("""FAILED """ ): # `test` is the test method that failed UpperCAmelCase__ = line[len("""FAILED """ ) :] failed_tests.append(SCREAMING_SNAKE_CASE__ ) elif filename == "job_name.txt": UpperCAmelCase__ = line if len(SCREAMING_SNAKE_CASE__ ) != len(SCREAMING_SNAKE_CASE__ ): raise ValueError( F'''`errors` and `failed_tests` should have the same number of elements. Got {len(SCREAMING_SNAKE_CASE__ )} for `errors` ''' F'''and {len(SCREAMING_SNAKE_CASE__ )} for `failed_tests` instead. The test reports in {artifact_zip_path} have some''' """ problem.""" ) UpperCAmelCase__ = None if job_name and job_links: UpperCAmelCase__ = job_links.get(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) # A list with elements of the form (line of error, error, failed test) UpperCAmelCase__ = [x + [y] + [job_link] for x, y in zip(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )] return result def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : List[Any]=None ): '''simple docstring''' UpperCAmelCase__ = [] UpperCAmelCase__ = [os.path.join(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) for p in os.listdir(SCREAMING_SNAKE_CASE__ ) if p.endswith(""".zip""" )] for p in paths: errors.extend(get_errors_from_single_artifact(SCREAMING_SNAKE_CASE__ , job_links=SCREAMING_SNAKE_CASE__ ) ) return errors def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : str=None ): '''simple docstring''' UpperCAmelCase__ = Counter() counter.update([x[1] for x in logs] ) UpperCAmelCase__ = counter.most_common() UpperCAmelCase__ = {} for error, count in counts: if error_filter is None or error not in error_filter: UpperCAmelCase__ = {"""count""": count, """failed_tests""": [(x[2], x[0]) for x in logs if x[1] == error]} UpperCAmelCase__ = dict(sorted(r.items() , key=lambda SCREAMING_SNAKE_CASE__ : item[1]["count"] , reverse=SCREAMING_SNAKE_CASE__ ) ) return r def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Union[str, Any] ): '''simple docstring''' UpperCAmelCase__ = test.split("""::""" )[0] if test.startswith("""tests/models/""" ): UpperCAmelCase__ = test.split("""/""" )[2] else: UpperCAmelCase__ = None return test def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : Optional[Any]=None ): '''simple docstring''' UpperCAmelCase__ = [(x[0], x[1], get_model(x[2] )) for x in logs] UpperCAmelCase__ = [x for x in logs if x[2] is not None] UpperCAmelCase__ = {x[2] for x in logs} UpperCAmelCase__ = {} for test in tests: UpperCAmelCase__ = Counter() # count by errors in `test` counter.update([x[1] for x in logs if x[2] == test] ) UpperCAmelCase__ = counter.most_common() UpperCAmelCase__ = {error: count for error, count in counts if (error_filter is None or error not in error_filter)} UpperCAmelCase__ = sum(error_counts.values() ) if n_errors > 0: UpperCAmelCase__ = {"""count""": n_errors, """errors""": error_counts} UpperCAmelCase__ = dict(sorted(r.items() , key=lambda SCREAMING_SNAKE_CASE__ : item[1]["count"] , reverse=SCREAMING_SNAKE_CASE__ ) ) return r def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Dict ): '''simple docstring''' UpperCAmelCase__ = """| no. | error | status |""" UpperCAmelCase__ = """|-:|:-|:-|""" UpperCAmelCase__ = [header, sep] for error in reduced_by_error: UpperCAmelCase__ = reduced_by_error[error]["""count"""] UpperCAmelCase__ = F'''| {count} | {error[:100]} | |''' lines.append(SCREAMING_SNAKE_CASE__ ) return "\n".join(SCREAMING_SNAKE_CASE__ ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Union[str, Any] ): '''simple docstring''' UpperCAmelCase__ = """| model | no. of errors | major error | count |""" UpperCAmelCase__ = """|-:|-:|-:|-:|""" UpperCAmelCase__ = [header, sep] for model in reduced_by_model: UpperCAmelCase__ = reduced_by_model[model]["""count"""] UpperCAmelCase__ , UpperCAmelCase__ = list(reduced_by_model[model]["""errors"""].items() )[0] UpperCAmelCase__ = F'''| {model} | {count} | {error[:60]} | {_count} |''' lines.append(SCREAMING_SNAKE_CASE__ ) return "\n".join(SCREAMING_SNAKE_CASE__ ) if __name__ == "__main__": UpperCAmelCase_ = argparse.ArgumentParser() # Required parameters parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.') parser.add_argument( '--output_dir', type=str, required=True, help='Where to store the downloaded artifacts and other result files.', ) parser.add_argument('--token', default=None, type=str, help='A token that has actions:read permission.') UpperCAmelCase_ = parser.parse_args() os.makedirs(args.output_dir, exist_ok=True) UpperCAmelCase_ = get_job_links(args.workflow_run_id, token=args.token) UpperCAmelCase_ = {} # To deal with `workflow_call` event, where a job name is the combination of the job names in the caller and callee. # For example, `PyTorch 1.11 / Model tests (models/albert, single-gpu)`. if _job_links: for k, v in _job_links.items(): # This is how GitHub actions combine job names. if " / " in k: UpperCAmelCase_ = k.find(' / ') UpperCAmelCase_ = k[index + len(' / ') :] UpperCAmelCase_ = v with open(os.path.join(args.output_dir, 'job_links.json'), 'w', encoding='UTF-8') as fp: json.dump(job_links, fp, ensure_ascii=False, indent=4) UpperCAmelCase_ = get_artifacts_links(args.workflow_run_id, token=args.token) with open(os.path.join(args.output_dir, 'artifacts.json'), 'w', encoding='UTF-8') as fp: json.dump(artifacts, fp, ensure_ascii=False, indent=4) for idx, (name, url) in enumerate(artifacts.items()): download_artifact(name, url, args.output_dir, args.token) # Be gentle to GitHub time.sleep(1) UpperCAmelCase_ = get_all_errors(args.output_dir, job_links=job_links) # `e[1]` is the error UpperCAmelCase_ = Counter() counter.update([e[1] for e in errors]) # print the top 30 most common test errors UpperCAmelCase_ = counter.most_common(3_0) for item in most_common: print(item) with open(os.path.join(args.output_dir, 'errors.json'), 'w', encoding='UTF-8') as fp: json.dump(errors, fp, ensure_ascii=False, indent=4) UpperCAmelCase_ = reduce_by_error(errors) UpperCAmelCase_ = reduce_by_model(errors) UpperCAmelCase_ = make_github_table(reduced_by_error) UpperCAmelCase_ = make_github_table_per_model(reduced_by_model) with open(os.path.join(args.output_dir, 'reduced_by_error.txt'), 'w', encoding='UTF-8') as fp: fp.write(sa) with open(os.path.join(args.output_dir, 'reduced_by_model.txt'), 'w', encoding='UTF-8') as fp: fp.write(sa)

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'''simple docstring''' import json import os import unittest from transformers import MgpstrTokenizer from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES from transformers.testing_utils import require_tokenizers from ...test_tokenization_common import TokenizerTesterMixin @require_tokenizers class lowerCAmelCase_ ( lowerCamelCase_ , unittest.TestCase ): '''simple docstring''' lowerCAmelCase_ : int = MgpstrTokenizer lowerCAmelCase_ : List[str] = False lowerCAmelCase_ : Optional[int] = {} lowerCAmelCase_ : Any = False def SCREAMING_SNAKE_CASE__ ( self : List[Any] ): """simple docstring""" super().setUp() # fmt: off UpperCAmelCase__ = ["""[GO]""", """[s]""", """0""", """1""", """2""", """3""", """4""", """5""", """6""", """7""", """8""", """9""", """a""", """b""", """c""", """d""", """e""", """f""", """g""", """h""", """i""", """j""", """k""", """l""", """m""", """n""", """o""", """p""", """q""", """r""", """s""", """t""", """u""", """v""", """w""", """x""", """y""", """z"""] # fmt: on UpperCAmelCase__ = dict(zip(_UpperCAmelCase , range(len(_UpperCAmelCase ) ) ) ) UpperCAmelCase__ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] ) with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as fp: fp.write(json.dumps(_UpperCAmelCase ) + """\n""" ) def SCREAMING_SNAKE_CASE__ ( self : List[str] , **_UpperCAmelCase : Optional[Any] ): """simple docstring""" return MgpstrTokenizer.from_pretrained(self.tmpdirname , **_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] , _UpperCAmelCase : Tuple ): """simple docstring""" UpperCAmelCase__ = """tester""" UpperCAmelCase__ = """tester""" return input_text, output_text @unittest.skip("""MGP-STR always lower cases letters.""" ) def SCREAMING_SNAKE_CASE__ ( self : List[str] ): """simple docstring""" pass def SCREAMING_SNAKE_CASE__ ( self : Any ): """simple docstring""" UpperCAmelCase__ = self.get_tokenizers(do_lower_case=_UpperCAmelCase ) for tokenizer in tokenizers: with self.subTest(f'''{tokenizer.__class__.__name__}''' ): UpperCAmelCase__ = """[SPECIAL_TOKEN]""" tokenizer.add_special_tokens({"""cls_token""": special_token} ) UpperCAmelCase__ = tokenizer.encode([special_token] , add_special_tokens=_UpperCAmelCase ) self.assertEqual(len(_UpperCAmelCase ) , 1 ) UpperCAmelCase__ = tokenizer.decode(_UpperCAmelCase , skip_special_tokens=_UpperCAmelCase ) self.assertTrue(special_token not in decoded ) def SCREAMING_SNAKE_CASE__ ( self : List[str] ): """simple docstring""" UpperCAmelCase__ = self.get_tokenizers() for tokenizer in tokenizers: with self.subTest(f'''{tokenizer.__class__.__name__}''' ): UpperCAmelCase__ , UpperCAmelCase__ = self.get_input_output_texts(_UpperCAmelCase ) UpperCAmelCase__ = tokenizer.tokenize(_UpperCAmelCase ) UpperCAmelCase__ = tokenizer.convert_tokens_to_ids(_UpperCAmelCase ) UpperCAmelCase__ = tokenizer.encode(_UpperCAmelCase , add_special_tokens=_UpperCAmelCase ) self.assertListEqual(_UpperCAmelCase , _UpperCAmelCase ) UpperCAmelCase__ = tokenizer.convert_ids_to_tokens(_UpperCAmelCase ) self.assertNotEqual(len(_UpperCAmelCase ) , 0 ) UpperCAmelCase__ = tokenizer.decode(_UpperCAmelCase ) self.assertIsInstance(_UpperCAmelCase , _UpperCAmelCase ) self.assertEqual(text_a.replace(""" """ , """""" ) , _UpperCAmelCase ) @unittest.skip("""MGP-STR tokenizer only handles one sequence.""" ) def SCREAMING_SNAKE_CASE__ ( self : Any ): """simple docstring""" pass @unittest.skip("""inputs cannot be pretokenized in MgpstrTokenizer""" ) def SCREAMING_SNAKE_CASE__ ( self : str ): """simple docstring""" pass

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'''simple docstring''' # tests directory-specific settings - this file is run automatically # by pytest before any tests are run import sys import warnings from os.path import abspath, dirname, join # allow having multiple repository checkouts and not needing to remember to rerun # 'pip install -e .[dev]' when switching between checkouts and running tests. UpperCAmelCase_ = abspath(join(dirname(dirname(__file__)), 'src')) sys.path.insert(1, git_repo_path) # silence FutureWarning warnings in tests since often we can't act on them until # they become normal warnings - i.e. the tests still need to test the current functionality warnings.simplefilter(action='ignore', category=FutureWarning) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : List[str] ): '''simple docstring''' from diffusers.utils.testing_utils import pytest_addoption_shared pytest_addoption_shared(SCREAMING_SNAKE_CASE__ ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Union[str, Any] ): '''simple docstring''' from diffusers.utils.testing_utils import pytest_terminal_summary_main UpperCAmelCase__ = terminalreporter.config.getoption("""--make-reports""" ) if make_reports: pytest_terminal_summary_main(SCREAMING_SNAKE_CASE__ , id=SCREAMING_SNAKE_CASE__ )

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'''simple docstring''' from abc import ABC, abstractmethod from typing import List, Optional class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' def __init__( self : Union[str, Any] ): """simple docstring""" self.test() def SCREAMING_SNAKE_CASE__ ( self : List[str] ): """simple docstring""" UpperCAmelCase__ = 0 UpperCAmelCase__ = False while not completed: if counter == 1: self.reset() UpperCAmelCase__ = self.advance() if not self.does_advance(_UpperCAmelCase ): raise Exception( """Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true.""" ) UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = self.update(_UpperCAmelCase ) counter += 1 if counter > 1_00_00: raise Exception("""update() does not fulfill the constraint.""" ) if self.remaining() != 0: raise Exception("""Custom Constraint is not defined correctly.""" ) @abstractmethod def SCREAMING_SNAKE_CASE__ ( self : Dict ): """simple docstring""" raise NotImplementedError( f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' ) @abstractmethod def SCREAMING_SNAKE_CASE__ ( self : Dict , _UpperCAmelCase : int ): """simple docstring""" raise NotImplementedError( f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' ) @abstractmethod def SCREAMING_SNAKE_CASE__ ( self : Any , _UpperCAmelCase : int ): """simple docstring""" raise NotImplementedError( f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' ) @abstractmethod def SCREAMING_SNAKE_CASE__ ( self : List[Any] ): """simple docstring""" raise NotImplementedError( f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' ) @abstractmethod def SCREAMING_SNAKE_CASE__ ( self : List[str] ): """simple docstring""" raise NotImplementedError( f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' ) @abstractmethod def SCREAMING_SNAKE_CASE__ ( self : int , _UpperCAmelCase : List[Any]=False ): """simple docstring""" raise NotImplementedError( f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' ) class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' def __init__( self : Union[str, Any] , _UpperCAmelCase : List[int] ): """simple docstring""" super(_UpperCAmelCase , self ).__init__() if not isinstance(_UpperCAmelCase , _UpperCAmelCase ) or len(_UpperCAmelCase ) == 0: raise ValueError(f'''`token_ids` has to be a non-empty list, but is {token_ids}.''' ) if any((not isinstance(_UpperCAmelCase , _UpperCAmelCase ) or token_id < 0) for token_id in token_ids ): raise ValueError(f'''Each list in `token_ids` has to be a list of positive integers, but is {token_ids}.''' ) UpperCAmelCase__ = token_ids UpperCAmelCase__ = len(self.token_ids ) UpperCAmelCase__ = -1 # the index of the currently fulfilled step UpperCAmelCase__ = False def SCREAMING_SNAKE_CASE__ ( self : List[str] ): """simple docstring""" if self.completed: return None return self.token_ids[self.fulfilled_idx + 1] def SCREAMING_SNAKE_CASE__ ( self : List[str] , _UpperCAmelCase : int ): """simple docstring""" if not isinstance(_UpperCAmelCase , _UpperCAmelCase ): raise ValueError(f'''`token_id` has to be an `int`, but is {token_id} of type {type(_UpperCAmelCase )}''' ) if self.completed: return False return token_id == self.token_ids[self.fulfilled_idx + 1] def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] , _UpperCAmelCase : int ): """simple docstring""" if not isinstance(_UpperCAmelCase , _UpperCAmelCase ): raise ValueError(f'''`token_id` has to be an `int`, but is {token_id} of type {type(_UpperCAmelCase )}''' ) UpperCAmelCase__ = False UpperCAmelCase__ = False UpperCAmelCase__ = False if self.does_advance(_UpperCAmelCase ): self.fulfilled_idx += 1 UpperCAmelCase__ = True if self.fulfilled_idx == (self.seqlen - 1): UpperCAmelCase__ = True UpperCAmelCase__ = completed else: # failed to make progress. UpperCAmelCase__ = True self.reset() return stepped, completed, reset def SCREAMING_SNAKE_CASE__ ( self : Tuple ): """simple docstring""" UpperCAmelCase__ = False UpperCAmelCase__ = 0 def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ): """simple docstring""" return self.seqlen - (self.fulfilled_idx + 1) def SCREAMING_SNAKE_CASE__ ( self : Any , _UpperCAmelCase : Optional[int]=False ): """simple docstring""" UpperCAmelCase__ = PhrasalConstraint(self.token_ids ) if stateful: UpperCAmelCase__ = self.seqlen UpperCAmelCase__ = self.fulfilled_idx UpperCAmelCase__ = self.completed return new_constraint class lowerCAmelCase_ : '''simple docstring''' def __init__( self : Any , _UpperCAmelCase : List[List[int]] , _UpperCAmelCase : List[str]=True ): """simple docstring""" UpperCAmelCase__ = max([len(_UpperCAmelCase ) for one in nested_token_ids] ) UpperCAmelCase__ = {} for token_ids in nested_token_ids: UpperCAmelCase__ = root for tidx, token_id in enumerate(_UpperCAmelCase ): if token_id not in level: UpperCAmelCase__ = {} UpperCAmelCase__ = level[token_id] if no_subsets and self.has_subsets(_UpperCAmelCase , _UpperCAmelCase ): raise ValueError( """Each list in `nested_token_ids` can't be a complete subset of another list, but is""" f''' {nested_token_ids}.''' ) UpperCAmelCase__ = root def SCREAMING_SNAKE_CASE__ ( self : int , _UpperCAmelCase : int ): """simple docstring""" UpperCAmelCase__ = self.trie for current_token in current_seq: UpperCAmelCase__ = start[current_token] UpperCAmelCase__ = list(start.keys() ) return next_tokens def SCREAMING_SNAKE_CASE__ ( self : str , _UpperCAmelCase : Tuple ): """simple docstring""" UpperCAmelCase__ = self.next_tokens(_UpperCAmelCase ) return len(_UpperCAmelCase ) == 0 def SCREAMING_SNAKE_CASE__ ( self : List[str] , _UpperCAmelCase : Optional[int] ): """simple docstring""" UpperCAmelCase__ = list(root.values() ) if len(_UpperCAmelCase ) == 0: return 1 else: return sum([self.count_leaves(_UpperCAmelCase ) for nn in next_nodes] ) def SCREAMING_SNAKE_CASE__ ( self : List[Any] , _UpperCAmelCase : Dict , _UpperCAmelCase : Dict ): """simple docstring""" UpperCAmelCase__ = self.count_leaves(_UpperCAmelCase ) return len(_UpperCAmelCase ) != leaf_count class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' def __init__( self : Dict , _UpperCAmelCase : List[List[int]] ): """simple docstring""" super(_UpperCAmelCase , self ).__init__() if not isinstance(_UpperCAmelCase , _UpperCAmelCase ) or len(_UpperCAmelCase ) == 0: raise ValueError(f'''`nested_token_ids` has to be a non-empty list, but is {nested_token_ids}.''' ) if any(not isinstance(_UpperCAmelCase , _UpperCAmelCase ) for token_ids in nested_token_ids ): raise ValueError(f'''`nested_token_ids` has to be a list of lists, but is {nested_token_ids}.''' ) if any( any((not isinstance(_UpperCAmelCase , _UpperCAmelCase ) or token_id < 0) for token_id in token_ids ) for token_ids in nested_token_ids ): raise ValueError( f'''Each list in `nested_token_ids` has to be a list of positive integers, but is {nested_token_ids}.''' ) UpperCAmelCase__ = DisjunctiveTrie(_UpperCAmelCase ) UpperCAmelCase__ = nested_token_ids UpperCAmelCase__ = self.trie.max_height UpperCAmelCase__ = [] UpperCAmelCase__ = False def SCREAMING_SNAKE_CASE__ ( self : Any ): """simple docstring""" UpperCAmelCase__ = self.trie.next_tokens(self.current_seq ) if len(_UpperCAmelCase ) == 0: return None else: return token_list def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] , _UpperCAmelCase : int ): """simple docstring""" if not isinstance(_UpperCAmelCase , _UpperCAmelCase ): raise ValueError(f'''`token_id` is supposed to be type `int`, but is {token_id} of type {type(_UpperCAmelCase )}''' ) UpperCAmelCase__ = self.trie.next_tokens(self.current_seq ) return token_id in next_tokens def SCREAMING_SNAKE_CASE__ ( self : Dict , _UpperCAmelCase : int ): """simple docstring""" if not isinstance(_UpperCAmelCase , _UpperCAmelCase ): raise ValueError(f'''`token_id` is supposed to be type `int`, but is {token_id} of type {type(_UpperCAmelCase )}''' ) UpperCAmelCase__ = False UpperCAmelCase__ = False UpperCAmelCase__ = False if self.does_advance(_UpperCAmelCase ): self.current_seq.append(_UpperCAmelCase ) UpperCAmelCase__ = True else: UpperCAmelCase__ = True self.reset() UpperCAmelCase__ = self.trie.reached_leaf(self.current_seq ) UpperCAmelCase__ = completed return stepped, completed, reset def SCREAMING_SNAKE_CASE__ ( self : str ): """simple docstring""" UpperCAmelCase__ = False UpperCAmelCase__ = [] def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ): """simple docstring""" if self.completed: # since this can be completed without reaching max height return 0 else: return self.seqlen - len(self.current_seq ) def SCREAMING_SNAKE_CASE__ ( self : Tuple , _UpperCAmelCase : Dict=False ): """simple docstring""" UpperCAmelCase__ = DisjunctiveConstraint(self.token_ids ) if stateful: UpperCAmelCase__ = self.seqlen UpperCAmelCase__ = self.current_seq UpperCAmelCase__ = self.completed return new_constraint class lowerCAmelCase_ : '''simple docstring''' def __init__( self : Optional[int] , _UpperCAmelCase : List[Constraint] ): """simple docstring""" UpperCAmelCase__ = constraints # max # of steps required to fulfill a given constraint UpperCAmelCase__ = max([c.seqlen for c in constraints] ) UpperCAmelCase__ = len(_UpperCAmelCase ) UpperCAmelCase__ = False self.init_state() def SCREAMING_SNAKE_CASE__ ( self : Any ): """simple docstring""" UpperCAmelCase__ = [] UpperCAmelCase__ = None UpperCAmelCase__ = [constraint.copy(stateful=_UpperCAmelCase ) for constraint in self.constraints] def SCREAMING_SNAKE_CASE__ ( self : List[str] ): """simple docstring""" UpperCAmelCase__ = 0 if self.inprogress_constraint: # extra points for having a constraint mid-fulfilled add += self.max_seqlen - self.inprogress_constraint.remaining() return (len(self.complete_constraints ) * self.max_seqlen) + add def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ): """simple docstring""" UpperCAmelCase__ = [] if self.inprogress_constraint is None: for constraint in self.pending_constraints: # "pending" == "unfulfilled yet" UpperCAmelCase__ = constraint.advance() if isinstance(_UpperCAmelCase , _UpperCAmelCase ): token_list.append(_UpperCAmelCase ) elif isinstance(_UpperCAmelCase , _UpperCAmelCase ): token_list.extend(_UpperCAmelCase ) else: UpperCAmelCase__ = self.inprogress_constraint.advance() if isinstance(_UpperCAmelCase , _UpperCAmelCase ): token_list.append(_UpperCAmelCase ) elif isinstance(_UpperCAmelCase , _UpperCAmelCase ): token_list.extend(_UpperCAmelCase ) if len(_UpperCAmelCase ) == 0: return None else: return token_list def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , _UpperCAmelCase : Optional[List[int]] ): """simple docstring""" self.init_state() if token_ids is not None: for token in token_ids: # completes or steps **one** constraint UpperCAmelCase__ , UpperCAmelCase__ = self.add(_UpperCAmelCase ) # the entire list of constraints are fulfilled if self.completed: break def SCREAMING_SNAKE_CASE__ ( self : Any , _UpperCAmelCase : int ): """simple docstring""" if not isinstance(_UpperCAmelCase , _UpperCAmelCase ): raise ValueError(f'''`token_id` should be an `int`, but is `{token_id}`.''' ) UpperCAmelCase__ , UpperCAmelCase__ = False, False if self.completed: UpperCAmelCase__ = True UpperCAmelCase__ = False return complete, stepped if self.inprogress_constraint is not None: # In the middle of fulfilling a constraint. If the `token_id` *does* makes an incremental progress to current # job, simply update the state UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = self.inprogress_constraint.update(_UpperCAmelCase ) if reset: # 1. If the next token breaks the progress, then we must restart. # e.g. constraint = "I love pies" and sequence so far is "I love" but `token_id` == "books". # But that doesn't mean we self.init_state(), since we only reset the state for this particular # constraint, not the full list of constraints. self.pending_constraints.append(self.inprogress_constraint.copy(stateful=_UpperCAmelCase ) ) UpperCAmelCase__ = None if complete: # 2. If the next token completes the constraint, move it to completed list, set # inprogress to None. If there are no pending constraints either, then this full list of constraints # is complete. self.complete_constraints.append(self.inprogress_constraint ) UpperCAmelCase__ = None if len(self.pending_constraints ) == 0: # we're done! UpperCAmelCase__ = True else: # Not in the middle of fulfilling a constraint. So does this `token_id` helps us step towards any of our list # of constraints? for cidx, pending_constraint in enumerate(self.pending_constraints ): if pending_constraint.does_advance(_UpperCAmelCase ): UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = pending_constraint.update(_UpperCAmelCase ) if not stepped: raise Exception( """`constraint.update(token_id)` is not yielding incremental progress, """ """even though `constraint.does_advance(token_id)` is true.""" ) if complete: self.complete_constraints.append(_UpperCAmelCase ) UpperCAmelCase__ = None if not complete and stepped: UpperCAmelCase__ = pending_constraint if complete or stepped: # If we made any progress at all, then it's at least not a "pending constraint". UpperCAmelCase__ = ( self.pending_constraints[:cidx] + self.pending_constraints[cidx + 1 :] ) if len(self.pending_constraints ) == 0 and self.inprogress_constraint is None: # If there's no longer any pending after this and no inprogress either, then we must be # complete. UpperCAmelCase__ = True break # prevent accidentally stepping through multiple constraints with just one token. return complete, stepped def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , _UpperCAmelCase : List[Any]=True ): """simple docstring""" UpperCAmelCase__ = ConstraintListState(self.constraints ) # we actually never though self.constraints objects # throughout this process. So it's at initialization state. if stateful: UpperCAmelCase__ = [ constraint.copy(stateful=_UpperCAmelCase ) for constraint in self.complete_constraints ] if self.inprogress_constraint is not None: UpperCAmelCase__ = self.inprogress_constraint.copy(stateful=_UpperCAmelCase ) UpperCAmelCase__ = [constraint.copy() for constraint in self.pending_constraints] return new_state

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1

'''simple docstring''' from pathlib import Path import cva import numpy as np from matplotlib import pyplot as plt def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : np.ndarray , SCREAMING_SNAKE_CASE__ : np.ndarray , SCREAMING_SNAKE_CASE__ : np.ndarray , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ): '''simple docstring''' UpperCAmelCase__ = cva.getAffineTransform(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) return cva.warpAffine(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , (rows, cols) ) if __name__ == "__main__": # read original image UpperCAmelCase_ = cva.imread( str(Path(__file__).resolve().parent.parent / 'image_data' / 'lena.jpg') ) # turn image in gray scale value UpperCAmelCase_ = cva.cvtColor(image, cva.COLOR_BGR2GRAY) # get image shape UpperCAmelCase_ , UpperCAmelCase_ = gray_img.shape # set different points to rotate image UpperCAmelCase_ = np.array([[5_0, 5_0], [2_0_0, 5_0], [5_0, 2_0_0]], np.floataa) UpperCAmelCase_ = np.array([[1_0, 1_0_0], [2_0_0, 5_0], [1_0_0, 2_5_0]], np.floataa) UpperCAmelCase_ = np.array([[5_0, 5_0], [1_5_0, 5_0], [1_2_0, 2_0_0]], np.floataa) UpperCAmelCase_ = np.array([[1_0, 1_0_0], [8_0, 5_0], [1_8_0, 2_5_0]], np.floataa) # add all rotated images in a list UpperCAmelCase_ = [ gray_img, get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols), get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols), get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols), ] # plot different image rotations UpperCAmelCase_ = plt.figure(1) UpperCAmelCase_ = ['Original', 'Rotation 1', 'Rotation 2', 'Rotation 3'] for i, image in enumerate(images): plt.subplot(2, 2, i + 1), plt.imshow(image, 'gray') plt.title(titles[i]) plt.axis('off') plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95) plt.show()

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'''simple docstring''' import doctest import logging import os import unittest from pathlib import Path from typing import List, Union import transformers from transformers.testing_utils import require_tf, require_torch, slow UpperCAmelCase_ = logging.getLogger() @unittest.skip("""Temporarily disable the doc tests.""" ) @require_torch @require_tf @slow class lowerCAmelCase_ ( unittest.TestCase ): '''simple docstring''' def SCREAMING_SNAKE_CASE__ ( self : str , _UpperCAmelCase : Path , _UpperCAmelCase : Union[str, None] = None , _UpperCAmelCase : Union[List[str], None] = None , _UpperCAmelCase : Union[str, List[str], None] = None , _UpperCAmelCase : bool = True , ): """simple docstring""" UpperCAmelCase__ = [file for file in os.listdir(_UpperCAmelCase ) if os.path.isfile(os.path.join(_UpperCAmelCase , _UpperCAmelCase ) )] if identifier is not None: UpperCAmelCase__ = [file for file in files if identifier in file] if n_identifier is not None: if isinstance(_UpperCAmelCase , _UpperCAmelCase ): for n_ in n_identifier: UpperCAmelCase__ = [file for file in files if n_ not in file] else: UpperCAmelCase__ = [file for file in files if n_identifier not in file] UpperCAmelCase__ = ignore_files or [] ignore_files.append("""__init__.py""" ) UpperCAmelCase__ = [file for file in files if file not in ignore_files] for file in files: # Open all files print("""Testing""" , _UpperCAmelCase ) if only_modules: UpperCAmelCase__ = file.split(""".""" )[0] try: UpperCAmelCase__ = getattr(_UpperCAmelCase , _UpperCAmelCase ) UpperCAmelCase__ = doctest.DocTestSuite(_UpperCAmelCase ) UpperCAmelCase__ = unittest.TextTestRunner().run(_UpperCAmelCase ) self.assertIs(len(result.failures ) , 0 ) except AttributeError: logger.info(f'''{module_identifier} is not a module.''' ) else: UpperCAmelCase__ = doctest.testfile(str("""..""" / directory / file ) , optionflags=doctest.ELLIPSIS ) self.assertIs(result.failed , 0 ) def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ): """simple docstring""" UpperCAmelCase__ = Path("""src/transformers""" ) UpperCAmelCase__ = """modeling""" UpperCAmelCase__ = [ """modeling_ctrl.py""", """modeling_tf_ctrl.py""", ] self.analyze_directory(_UpperCAmelCase , identifier=_UpperCAmelCase , ignore_files=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ): """simple docstring""" UpperCAmelCase__ = Path("""src/transformers""" ) UpperCAmelCase__ = """tokenization""" self.analyze_directory(_UpperCAmelCase , identifier=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : str ): """simple docstring""" UpperCAmelCase__ = Path("""src/transformers""" ) UpperCAmelCase__ = """configuration""" self.analyze_directory(_UpperCAmelCase , identifier=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ): """simple docstring""" UpperCAmelCase__ = Path("""src/transformers""" ) UpperCAmelCase__ = ["""configuration""", """modeling""", """tokenization"""] self.analyze_directory(_UpperCAmelCase , n_identifier=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : List[str] ): """simple docstring""" UpperCAmelCase__ = Path("""docs/source""" ) UpperCAmelCase__ = ["""favicon.ico"""] self.analyze_directory(_UpperCAmelCase , ignore_files=_UpperCAmelCase , only_modules=_UpperCAmelCase )

346

1

'''simple docstring''' UpperCAmelCase_ = [0, 2, 4, 6, 8] UpperCAmelCase_ = [1, 3, 5, 7, 9] def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : list[int] , SCREAMING_SNAKE_CASE__ : int ): '''simple docstring''' if remaining_length == 0: if digits[0] == 0 or digits[-1] == 0: return 0 for i in range(length // 2 - 1 , -1 , -1 ): remainder += digits[i] + digits[length - i - 1] if remainder % 2 == 0: return 0 remainder //= 10 return 1 if remaining_length == 1: if remainder % 2 == 0: return 0 UpperCAmelCase__ = 0 for digit in range(10 ): UpperCAmelCase__ = digit result += reversible_numbers( 0 , (remainder + 2 * digit) // 10 , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) return result UpperCAmelCase__ = 0 for digita in range(10 ): UpperCAmelCase__ = digita if (remainder + digita) % 2 == 0: UpperCAmelCase__ = ODD_DIGITS else: UpperCAmelCase__ = EVEN_DIGITS for digita in other_parity_digits: UpperCAmelCase__ = digita result += reversible_numbers( remaining_length - 2 , (remainder + digita + digita) // 10 , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , ) return result def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int = 9 ): '''simple docstring''' UpperCAmelCase__ = 0 for length in range(1 , max_power + 1 ): result += reversible_numbers(SCREAMING_SNAKE_CASE__ , 0 , [0] * length , SCREAMING_SNAKE_CASE__ ) return result if __name__ == "__main__": print(f"{solution() = }")

346

'''simple docstring''' from datasets.utils.patching import _PatchedModuleObj, patch_submodule from . import _test_patching def _UpperCamelCase ( ): '''simple docstring''' import os as original_os from os import path as original_path from os import rename as original_rename from os.path import dirname as original_dirname from os.path import join as original_join assert _test_patching.os is original_os assert _test_patching.path is original_path assert _test_patching.join is original_join assert _test_patching.renamed_os is original_os assert _test_patching.renamed_path is original_path assert _test_patching.renamed_join is original_join UpperCAmelCase__ = """__test_patch_submodule_mock__""" with patch_submodule(_test_patching , """os.path.join""" , SCREAMING_SNAKE_CASE__ ): # Every way to access os.path.join must be patched, and the rest must stay untouched # check os.path.join assert isinstance(_test_patching.os , _PatchedModuleObj ) assert isinstance(_test_patching.os.path , _PatchedModuleObj ) assert _test_patching.os.path.join is mock # check path.join assert isinstance(_test_patching.path , _PatchedModuleObj ) assert _test_patching.path.join is mock # check join assert _test_patching.join is mock # check that the other attributes are untouched assert _test_patching.os.rename is original_rename assert _test_patching.path.dirname is original_dirname assert _test_patching.os.path.dirname is original_dirname # Even renamed modules or objects must be patched # check renamed_os.path.join assert isinstance(_test_patching.renamed_os , _PatchedModuleObj ) assert isinstance(_test_patching.renamed_os.path , _PatchedModuleObj ) assert _test_patching.renamed_os.path.join is mock # check renamed_path.join assert isinstance(_test_patching.renamed_path , _PatchedModuleObj ) assert _test_patching.renamed_path.join is mock # check renamed_join assert _test_patching.renamed_join is mock # check that the other attributes are untouched assert _test_patching.renamed_os.rename is original_rename assert _test_patching.renamed_path.dirname is original_dirname assert _test_patching.renamed_os.path.dirname is original_dirname # check that everthing is back to normal when the patch is over assert _test_patching.os is original_os assert _test_patching.path is original_path assert _test_patching.join is original_join assert _test_patching.renamed_os is original_os assert _test_patching.renamed_path is original_path assert _test_patching.renamed_join is original_join def _UpperCamelCase ( ): '''simple docstring''' assert _test_patching.open is open UpperCAmelCase__ = """__test_patch_submodule_builtin_mock__""" # _test_patching has "open" in its globals assert _test_patching.open is open with patch_submodule(_test_patching , """open""" , SCREAMING_SNAKE_CASE__ ): assert _test_patching.open is mock # check that everthing is back to normal when the patch is over assert _test_patching.open is open def _UpperCamelCase ( ): '''simple docstring''' UpperCAmelCase__ = """__test_patch_submodule_missing_mock__""" with patch_submodule(_test_patching , """pandas.read_csv""" , SCREAMING_SNAKE_CASE__ ): pass def _UpperCamelCase ( ): '''simple docstring''' UpperCAmelCase__ = """__test_patch_submodule_missing_builtin_mock__""" # _test_patching doesn't have "len" in its globals assert getattr(_test_patching , """len""" , SCREAMING_SNAKE_CASE__ ) is None with patch_submodule(_test_patching , """len""" , SCREAMING_SNAKE_CASE__ ): assert _test_patching.len is mock assert _test_patching.len is len def _UpperCamelCase ( ): '''simple docstring''' UpperCAmelCase__ = """__test_patch_submodule_start_and_stop_mock__""" UpperCAmelCase__ = patch_submodule(_test_patching , """open""" , SCREAMING_SNAKE_CASE__ ) assert _test_patching.open is open patch.start() assert _test_patching.open is mock patch.stop() assert _test_patching.open is open def _UpperCamelCase ( ): '''simple docstring''' from os import rename as original_rename from os.path import dirname as original_dirname from os.path import join as original_join UpperCAmelCase__ = """__test_patch_submodule_successive_join__""" UpperCAmelCase__ = """__test_patch_submodule_successive_dirname__""" UpperCAmelCase__ = """__test_patch_submodule_successive_rename__""" assert _test_patching.os.path.join is original_join assert _test_patching.os.path.dirname is original_dirname assert _test_patching.os.rename is original_rename with patch_submodule(_test_patching , """os.path.join""" , SCREAMING_SNAKE_CASE__ ): with patch_submodule(_test_patching , """os.rename""" , SCREAMING_SNAKE_CASE__ ): with patch_submodule(_test_patching , """os.path.dirname""" , SCREAMING_SNAKE_CASE__ ): assert _test_patching.os.path.join is mock_join assert _test_patching.os.path.dirname is mock_dirname assert _test_patching.os.rename is mock_rename # try another order with patch_submodule(_test_patching , """os.rename""" , SCREAMING_SNAKE_CASE__ ): with patch_submodule(_test_patching , """os.path.join""" , SCREAMING_SNAKE_CASE__ ): with patch_submodule(_test_patching , """os.path.dirname""" , SCREAMING_SNAKE_CASE__ ): assert _test_patching.os.path.join is mock_join assert _test_patching.os.path.dirname is mock_dirname assert _test_patching.os.rename is mock_rename assert _test_patching.os.path.join is original_join assert _test_patching.os.path.dirname is original_dirname assert _test_patching.os.rename is original_rename def _UpperCamelCase ( ): '''simple docstring''' UpperCAmelCase__ = """__test_patch_submodule_doesnt_exist_mock__""" with patch_submodule(_test_patching , """__module_that_doesn_exist__.__attribute_that_doesn_exist__""" , SCREAMING_SNAKE_CASE__ ): pass with patch_submodule(_test_patching , """os.__attribute_that_doesn_exist__""" , SCREAMING_SNAKE_CASE__ ): pass

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'''simple docstring''' # Copyright 2023 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import torch from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer from .base import PipelineTool class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' lowerCAmelCase_ : Any = """facebook/bart-large-mnli""" lowerCAmelCase_ : str = ( """This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which """ """should be the text to classify, and `labels`, which should be the list of labels to use for classification. """ """It returns the most likely label in the list of provided `labels` for the input text.""" ) lowerCAmelCase_ : Dict = """text_classifier""" lowerCAmelCase_ : Optional[int] = AutoTokenizer lowerCAmelCase_ : List[str] = AutoModelForSequenceClassification lowerCAmelCase_ : Optional[Any] = ["""text""", ["""text"""]] lowerCAmelCase_ : Dict = ["""text"""] def SCREAMING_SNAKE_CASE__ ( self : Tuple ): """simple docstring""" super().setup() UpperCAmelCase__ = self.model.config UpperCAmelCase__ = -1 for idx, label in config.idalabel.items(): if label.lower().startswith("""entail""" ): UpperCAmelCase__ = int(_UpperCAmelCase ) if self.entailment_id == -1: raise ValueError("""Could not determine the entailment ID from the model config, please pass it at init.""" ) def SCREAMING_SNAKE_CASE__ ( self : List[Any] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Any ): """simple docstring""" UpperCAmelCase__ = labels return self.pre_processor( [text] * len(_UpperCAmelCase ) , [f'''This example is {label}''' for label in labels] , return_tensors="""pt""" , padding="""max_length""" , ) def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] , _UpperCAmelCase : Union[str, Any] ): """simple docstring""" UpperCAmelCase__ = outputs.logits UpperCAmelCase__ = torch.argmax(logits[:, 2] ).item() return self._labels[label_id]

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'''simple docstring''' from timeit import timeit UpperCAmelCase_ = { 'MALAYALAM': True, 'String': False, 'rotor': True, 'level': True, 'A': True, 'BB': True, 'ABC': False, 'amanaplanacanalpanama': True, # "a man a plan a canal panama" } # Ensure our test data is valid assert all((key == key[::-1]) is value for key, value in test_data.items()) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str ): '''simple docstring''' UpperCAmelCase__ = 0 UpperCAmelCase__ = len(SCREAMING_SNAKE_CASE__ ) - 1 while start_i < end_i: if s[start_i] == s[end_i]: start_i += 1 end_i -= 1 else: return False return True def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str ): '''simple docstring''' UpperCAmelCase__ = len(SCREAMING_SNAKE_CASE__ ) // 2 UpperCAmelCase__ = len(SCREAMING_SNAKE_CASE__ ) # We need to traverse till half of the length of string # as we can get access of the i'th last element from # i'th index. # eg: [0,1,2,3,4,5] => 4th index can be accessed # with the help of 1st index (i==n-i-1) # where n is length of string return all(s[i] == s[n - i - 1] for i in range(SCREAMING_SNAKE_CASE__ ) ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str ): '''simple docstring''' if len(SCREAMING_SNAKE_CASE__ ) <= 2: return True if s[0] == s[len(SCREAMING_SNAKE_CASE__ ) - 1]: return is_palindrome_recursive(s[1:-1] ) else: return False def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str ): '''simple docstring''' return s == s[::-1] def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str ): '''simple docstring''' UpperCAmelCase__ = F'''all({name}(key) is value for key, value in test_data.items())''' UpperCAmelCase__ = F'''from __main__ import test_data, {name}''' UpperCAmelCase__ = 500000 UpperCAmelCase__ = timeit(stmt=SCREAMING_SNAKE_CASE__ , setup=SCREAMING_SNAKE_CASE__ , number=SCREAMING_SNAKE_CASE__ ) print(F'''{name:<35} finished {number:,} runs in {result:.5f} seconds''' ) if __name__ == "__main__": for key, value in test_data.items(): assert is_palindrome(key) is is_palindrome_recursive(key) assert is_palindrome(key) is is_palindrome_slice(key) print(f"{key:21} {value}") print('a man a plan a canal panama') # finished 500,000 runs in 0.46793 seconds benchmark_function('is_palindrome_slice') # finished 500,000 runs in 0.85234 seconds benchmark_function('is_palindrome') # finished 500,000 runs in 1.32028 seconds benchmark_function('is_palindrome_recursive') # finished 500,000 runs in 2.08679 seconds benchmark_function('is_palindrome_traversal')

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'''simple docstring''' def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : list ): '''simple docstring''' _enforce_args(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) if n == 0: return 0 UpperCAmelCase__ = float("""-inf""" ) for i in range(1 , n + 1 ): UpperCAmelCase__ = max( SCREAMING_SNAKE_CASE__ , prices[i - 1] + naive_cut_rod_recursive(n - i , SCREAMING_SNAKE_CASE__ ) ) return max_revue def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : list ): '''simple docstring''' _enforce_args(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = [float("""-inf""" ) for _ in range(n + 1 )] return _top_down_cut_rod_recursive(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : list , SCREAMING_SNAKE_CASE__ : list ): '''simple docstring''' if max_rev[n] >= 0: return max_rev[n] elif n == 0: return 0 else: UpperCAmelCase__ = float("""-inf""" ) for i in range(1 , n + 1 ): UpperCAmelCase__ = max( SCREAMING_SNAKE_CASE__ , prices[i - 1] + _top_down_cut_rod_recursive(n - i , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) , ) UpperCAmelCase__ = max_revenue return max_rev[n] def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : list ): '''simple docstring''' _enforce_args(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) # length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of # length 0. UpperCAmelCase__ = [float("""-inf""" ) for _ in range(n + 1 )] UpperCAmelCase__ = 0 for i in range(1 , n + 1 ): UpperCAmelCase__ = max_rev[i] for j in range(1 , i + 1 ): UpperCAmelCase__ = max(SCREAMING_SNAKE_CASE__ , prices[j - 1] + max_rev[i - j] ) UpperCAmelCase__ = max_revenue_i return max_rev[n] def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : list ): '''simple docstring''' if n < 0: UpperCAmelCase__ = F'''n must be greater than or equal to 0. Got n = {n}''' raise ValueError(SCREAMING_SNAKE_CASE__ ) if n > len(SCREAMING_SNAKE_CASE__ ): UpperCAmelCase__ = ( """Each integral piece of rod must have a corresponding price. """ F'''Got n = {n} but length of prices = {len(SCREAMING_SNAKE_CASE__ )}''' ) raise ValueError(SCREAMING_SNAKE_CASE__ ) def _UpperCamelCase ( ): '''simple docstring''' UpperCAmelCase__ = [6, 10, 12, 15, 20, 23] UpperCAmelCase__ = len(SCREAMING_SNAKE_CASE__ ) # the best revenue comes from cutting the rod into 6 pieces, each # of length 1 resulting in a revenue of 6 * 6 = 36. UpperCAmelCase__ = 36 UpperCAmelCase__ = top_down_cut_rod(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = bottom_up_cut_rod(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = naive_cut_rod_recursive(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) assert expected_max_revenue == max_rev_top_down assert max_rev_top_down == max_rev_bottom_up assert max_rev_bottom_up == max_rev_naive if __name__ == "__main__": main()

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'''simple docstring''' import datasets from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py UpperCAmelCase_ = '\\n@INPROCEEDINGS{Papineni02bleu:a,\n author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n title = {BLEU: a Method for Automatic Evaluation of Machine Translation},\n booktitle = {},\n year = {2002},\n pages = {311--318}\n}\n@inproceedings{lin-och-2004-orange,\n title = "{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation",\n author = "Lin, Chin-Yew and\n Och, Franz Josef",\n booktitle = "{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics",\n month = "aug 23{--}aug 27",\n year = "2004",\n address = "Geneva, Switzerland",\n publisher = "COLING",\n url = "https://www.aclweb.org/anthology/C04-1072",\n pages = "501--507",\n}\n' UpperCAmelCase_ = '\\nBLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.\nQuality is considered to be the correspondence between a machine\'s output and that of a human: "the closer a machine translation is to a professional human translation,\nthe better it is" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and\nremains one of the most popular automated and inexpensive metrics.\n\nScores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.\nThose scores are then averaged over the whole corpus to reach an estimate of the translation\'s overall quality. Intelligibility or grammatical correctness\nare not taken into account[citation needed].\n\nBLEU\'s output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1\nrepresenting more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the\nreference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional\nreference translations will increase the BLEU score.\n' UpperCAmelCase_ = '\nComputes BLEU score of translated segments against one or more references.\nArgs:\n predictions: list of translations to score.\n Each translation should be tokenized into a list of tokens.\n references: list of lists of references for each translation.\n Each reference should be tokenized into a list of tokens.\n max_order: Maximum n-gram order to use when computing BLEU score.\n smooth: Whether or not to apply Lin et al. 2004 smoothing.\nReturns:\n \'bleu\': bleu score,\n \'precisions\': geometric mean of n-gram precisions,\n \'brevity_penalty\': brevity penalty,\n \'length_ratio\': ratio of lengths,\n \'translation_length\': translation_length,\n \'reference_length\': reference_length\nExamples:\n\n >>> predictions = [\n ... ["hello", "there", "general", "kenobi"], # tokenized prediction of the first sample\n ... ["foo", "bar", "foobar"] # tokenized prediction of the second sample\n ... ]\n >>> references = [\n ... [["hello", "there", "general", "kenobi"], ["hello", "there", "!"]], # tokenized references for the first sample (2 references)\n ... [["foo", "bar", "foobar"]] # tokenized references for the second sample (1 reference)\n ... ]\n >>> bleu = datasets.load_metric("bleu")\n >>> results = bleu.compute(predictions=predictions, references=references)\n >>> print(results["bleu"])\n 1.0\n' @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION ) class lowerCAmelCase_ ( datasets.Metric ): '''simple docstring''' def SCREAMING_SNAKE_CASE__ ( self : int ): """simple docstring""" return datasets.MetricInfo( description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features( { """predictions""": datasets.Sequence(datasets.Value("""string""" , id="""token""" ) , id="""sequence""" ), """references""": datasets.Sequence( datasets.Sequence(datasets.Value("""string""" , id="""token""" ) , id="""sequence""" ) , id="""references""" ), } ) , codebase_urls=["""https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py"""] , reference_urls=[ """https://en.wikipedia.org/wiki/BLEU""", """https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213""", ] , ) def SCREAMING_SNAKE_CASE__ ( self : int , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : List[Any]=4 , _UpperCAmelCase : Union[str, Any]=False ): """simple docstring""" UpperCAmelCase__ = compute_bleu( reference_corpus=_UpperCAmelCase , translation_corpus=_UpperCAmelCase , max_order=_UpperCAmelCase , smooth=_UpperCAmelCase ) ((UpperCAmelCase__) , (UpperCAmelCase__) , (UpperCAmelCase__) , (UpperCAmelCase__) , (UpperCAmelCase__) , (UpperCAmelCase__)) = score return { "bleu": bleu, "precisions": precisions, "brevity_penalty": bp, "length_ratio": ratio, "translation_length": translation_length, "reference_length": reference_length, }

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'''simple docstring''' import fire from utils import calculate_rouge, save_json def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : Tuple=None , **SCREAMING_SNAKE_CASE__ : int ): '''simple docstring''' UpperCAmelCase__ = [x.strip() for x in open(SCREAMING_SNAKE_CASE__ ).readlines()] UpperCAmelCase__ = [x.strip() for x in open(SCREAMING_SNAKE_CASE__ ).readlines()][: len(SCREAMING_SNAKE_CASE__ )] UpperCAmelCase__ = calculate_rouge(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ) if save_path is not None: save_json(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , indent=SCREAMING_SNAKE_CASE__ ) return metrics # these print nicely if __name__ == "__main__": fire.Fire(calculate_rouge_path)

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'''simple docstring''' from dataclasses import dataclass from typing import List, Optional, Union import numpy as np import torch from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available @dataclass class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' lowerCAmelCase_ : Union[List[np.ndarray], torch.FloatTensor] try: if not (is_transformers_available() and is_torch_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from ...utils.dummy_torch_and_transformers_objects import * # noqa F403 else: from .pipeline_text_to_video_synth import TextToVideoSDPipeline from .pipeline_text_to_video_synth_imgaimg import VideoToVideoSDPipeline # noqa: F401 from .pipeline_text_to_video_zero import TextToVideoZeroPipeline

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'''simple docstring''' import inspect import unittest from transformers import ViTHybridConfig from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device from transformers.utils import cached_property, is_torch_available, is_vision_available from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from torch import nn from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST if is_vision_available(): from PIL import Image class lowerCAmelCase_ : '''simple docstring''' def __init__( self : List[Any] , _UpperCAmelCase : Tuple , _UpperCAmelCase : Tuple=13 , _UpperCAmelCase : int=64 , _UpperCAmelCase : Tuple=2 , _UpperCAmelCase : List[Any]=3 , _UpperCAmelCase : List[Any]=True , _UpperCAmelCase : Dict=True , _UpperCAmelCase : List[Any]=32 , _UpperCAmelCase : Optional[int]=5 , _UpperCAmelCase : Optional[int]=4 , _UpperCAmelCase : List[Any]=37 , _UpperCAmelCase : str="gelu" , _UpperCAmelCase : Dict=0.1 , _UpperCAmelCase : Any=0.1 , _UpperCAmelCase : int=10 , _UpperCAmelCase : Dict=0.02 , _UpperCAmelCase : List[Any]=[1, 16, 4, 4] , _UpperCAmelCase : List[str]=None , ): """simple docstring""" UpperCAmelCase__ = parent UpperCAmelCase__ = batch_size UpperCAmelCase__ = image_size UpperCAmelCase__ = patch_size UpperCAmelCase__ = num_channels UpperCAmelCase__ = is_training UpperCAmelCase__ = use_labels UpperCAmelCase__ = hidden_size UpperCAmelCase__ = num_hidden_layers UpperCAmelCase__ = num_attention_heads UpperCAmelCase__ = intermediate_size UpperCAmelCase__ = hidden_act UpperCAmelCase__ = hidden_dropout_prob UpperCAmelCase__ = attention_probs_dropout_prob UpperCAmelCase__ = type_sequence_label_size UpperCAmelCase__ = initializer_range UpperCAmelCase__ = scope UpperCAmelCase__ = backbone_featmap_shape # in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token) # the number of patches is based on the feature map of the backbone, which by default uses an output stride # of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size UpperCAmelCase__ = (self.image_size // 32) ** 2 UpperCAmelCase__ = num_patches + 1 def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ): """simple docstring""" UpperCAmelCase__ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] ) UpperCAmelCase__ = None if self.use_labels: UpperCAmelCase__ = ids_tensor([self.batch_size] , self.type_sequence_label_size ) UpperCAmelCase__ = self.get_config() return config, pixel_values, labels def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ): """simple docstring""" UpperCAmelCase__ = { """global_padding""": """same""", """layer_type""": """bottleneck""", """depths""": [3, 4, 9], """out_features""": ["""stage1""", """stage2""", """stage3"""], """embedding_dynamic_padding""": True, """hidden_sizes""": [4, 8, 16, 32], """num_groups""": 2, } return ViTHybridConfig( image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=_UpperCAmelCase , initializer_range=self.initializer_range , backbone_featmap_shape=self.backbone_featmap_shape , backbone_config=_UpperCAmelCase , ) def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] , _UpperCAmelCase : Any , _UpperCAmelCase : List[Any] , _UpperCAmelCase : int ): """simple docstring""" UpperCAmelCase__ = ViTHybridModel(config=_UpperCAmelCase ) model.to(_UpperCAmelCase ) model.eval() UpperCAmelCase__ = model(_UpperCAmelCase ) self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) ) def SCREAMING_SNAKE_CASE__ ( self : int , _UpperCAmelCase : List[str] , _UpperCAmelCase : Dict , _UpperCAmelCase : Union[str, Any] ): """simple docstring""" UpperCAmelCase__ = self.type_sequence_label_size UpperCAmelCase__ = ViTHybridForImageClassification(_UpperCAmelCase ) model.to(_UpperCAmelCase ) model.eval() UpperCAmelCase__ = model(_UpperCAmelCase , labels=_UpperCAmelCase ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) ) def SCREAMING_SNAKE_CASE__ ( self : int ): """simple docstring""" UpperCAmelCase__ = self.prepare_config_and_inputs() UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = config_and_inputs UpperCAmelCase__ = {"""pixel_values""": pixel_values} return config, inputs_dict @require_torch class lowerCAmelCase_ ( lowerCamelCase_ , lowerCamelCase_ , unittest.TestCase ): '''simple docstring''' lowerCAmelCase_ : Union[str, Any] = (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else () lowerCAmelCase_ : Optional[int] = ( {"""feature-extraction""": ViTHybridModel, """image-classification""": ViTHybridForImageClassification} if is_torch_available() else {} ) lowerCAmelCase_ : Union[str, Any] = False lowerCAmelCase_ : Any = False lowerCAmelCase_ : Optional[int] = False def SCREAMING_SNAKE_CASE__ ( self : Dict ): """simple docstring""" UpperCAmelCase__ = ViTHybridModelTester(self ) UpperCAmelCase__ = ConfigTester(self , config_class=_UpperCAmelCase , has_text_modality=_UpperCAmelCase , hidden_size=37 ) def SCREAMING_SNAKE_CASE__ ( self : Dict ): """simple docstring""" self.config_tester.run_common_tests() @unittest.skip(reason="""ViT does not use inputs_embeds""" ) def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ): """simple docstring""" pass def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ): """simple docstring""" UpperCAmelCase__ , UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: UpperCAmelCase__ = model_class(_UpperCAmelCase ) self.assertIsInstance(model.get_input_embeddings() , (nn.Module) ) UpperCAmelCase__ = model.get_output_embeddings() self.assertTrue(x is None or isinstance(_UpperCAmelCase , nn.Linear ) ) def SCREAMING_SNAKE_CASE__ ( self : Tuple ): """simple docstring""" UpperCAmelCase__ , UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: UpperCAmelCase__ = model_class(_UpperCAmelCase ) UpperCAmelCase__ = inspect.signature(model.forward ) # signature.parameters is an OrderedDict => so arg_names order is deterministic UpperCAmelCase__ = [*signature.parameters.keys()] UpperCAmelCase__ = ["""pixel_values"""] self.assertListEqual(arg_names[:1] , _UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : List[str] ): """simple docstring""" UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : int ): """simple docstring""" UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Any ): """simple docstring""" UpperCAmelCase__ , UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common() UpperCAmelCase__ = _config_zero_init(_UpperCAmelCase ) for model_class in self.all_model_classes: UpperCAmelCase__ = model_class(config=_UpperCAmelCase ) # Skip the check for the backbone for name, module in model.named_modules(): if module.__class__.__name__ == "ViTHybridPatchEmbeddings": UpperCAmelCase__ = [f'''{name}.{key}''' for key in module.state_dict().keys()] break for name, param in model.named_parameters(): if param.requires_grad: if name in backbone_params: continue self.assertIn( ((param.data.mean() * 1E9).round() / 1E9).item() , [0.0, 1.0] , msg=f'''Parameter {name} of model {model_class} seems not properly initialized''' , ) @slow def SCREAMING_SNAKE_CASE__ ( self : int ): """simple docstring""" for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: UpperCAmelCase__ = ViTHybridModel.from_pretrained(_UpperCAmelCase ) self.assertIsNotNone(_UpperCAmelCase ) def _UpperCamelCase ( ): '''simple docstring''' UpperCAmelCase__ = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" ) return image @require_torch @require_vision class lowerCAmelCase_ ( unittest.TestCase ): '''simple docstring''' @cached_property def SCREAMING_SNAKE_CASE__ ( self : str ): """simple docstring""" return ( ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0] ) if is_vision_available() else None ) @slow def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ): """simple docstring""" UpperCAmelCase__ = ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to( _UpperCAmelCase ) UpperCAmelCase__ = self.default_image_processor UpperCAmelCase__ = prepare_img() UpperCAmelCase__ = image_processor(images=_UpperCAmelCase , return_tensors="""pt""" ).to(_UpperCAmelCase ) # forward pass with torch.no_grad(): UpperCAmelCase__ = model(**_UpperCAmelCase ) # verify the logits UpperCAmelCase__ = torch.Size((1, 10_00) ) self.assertEqual(outputs.logits.shape , _UpperCAmelCase ) UpperCAmelCase__ = torch.tensor([-1.9090, -0.4993, -0.2389] ).to(_UpperCAmelCase ) self.assertTrue(torch.allclose(outputs.logits[0, :3] , _UpperCAmelCase , atol=1E-4 ) ) @slow @require_accelerate def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ): """simple docstring""" UpperCAmelCase__ = ViTHybridImageProcessor.from_pretrained("""google/vit-hybrid-base-bit-384""" ) UpperCAmelCase__ = ViTHybridForImageClassification.from_pretrained("""google/vit-hybrid-base-bit-384""" , device_map="""auto""" ) UpperCAmelCase__ = prepare_img() UpperCAmelCase__ = image_processor(images=_UpperCAmelCase , return_tensors="""pt""" ) UpperCAmelCase__ = model(**_UpperCAmelCase ) UpperCAmelCase__ = outputs.logits # model predicts one of the 1000 ImageNet classes UpperCAmelCase__ = logits.argmax(-1 ).item() self.assertTrue(model.config.idalabel[predicted_class_idx] , """tabby, tabby cat""" )

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'''simple docstring''' import torch import torch.nn as nn from transformers.modeling_utils import ModuleUtilsMixin from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm from ...configuration_utils import ConfigMixin, register_to_config from ...models import ModelMixin class lowerCAmelCase_ ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ): '''simple docstring''' @register_to_config def __init__( self : List[str] , _UpperCAmelCase : int , _UpperCAmelCase : int , _UpperCAmelCase : int , _UpperCAmelCase : float , _UpperCAmelCase : int , _UpperCAmelCase : int , _UpperCAmelCase : int , _UpperCAmelCase : int , _UpperCAmelCase : str , _UpperCAmelCase : bool = False , ): """simple docstring""" super().__init__() UpperCAmelCase__ = nn.Embedding(_UpperCAmelCase , _UpperCAmelCase ) UpperCAmelCase__ = nn.Embedding(_UpperCAmelCase , _UpperCAmelCase ) UpperCAmelCase__ = False UpperCAmelCase__ = nn.Dropout(p=_UpperCAmelCase ) UpperCAmelCase__ = TaConfig( vocab_size=_UpperCAmelCase , d_model=_UpperCAmelCase , num_heads=_UpperCAmelCase , d_kv=_UpperCAmelCase , d_ff=_UpperCAmelCase , dropout_rate=_UpperCAmelCase , feed_forward_proj=_UpperCAmelCase , is_decoder=_UpperCAmelCase , is_encoder_decoder=_UpperCAmelCase , ) UpperCAmelCase__ = nn.ModuleList() for lyr_num in range(_UpperCAmelCase ): UpperCAmelCase__ = TaBlock(_UpperCAmelCase ) self.encoders.append(_UpperCAmelCase ) UpperCAmelCase__ = TaLayerNorm(_UpperCAmelCase ) UpperCAmelCase__ = nn.Dropout(p=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Dict , _UpperCAmelCase : int , _UpperCAmelCase : str ): """simple docstring""" UpperCAmelCase__ = self.token_embedder(_UpperCAmelCase ) UpperCAmelCase__ = encoder_input_tokens.shape[1] UpperCAmelCase__ = torch.arange(_UpperCAmelCase , device=encoder_input_tokens.device ) x += self.position_encoding(_UpperCAmelCase ) UpperCAmelCase__ = self.dropout_pre(_UpperCAmelCase ) # inverted the attention mask UpperCAmelCase__ = encoder_input_tokens.size() UpperCAmelCase__ = self.get_extended_attention_mask(_UpperCAmelCase , _UpperCAmelCase ) for lyr in self.encoders: UpperCAmelCase__ = lyr(_UpperCAmelCase , _UpperCAmelCase )[0] UpperCAmelCase__ = self.layer_norm(_UpperCAmelCase ) return self.dropout_post(_UpperCAmelCase ), encoder_inputs_mask

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'''simple docstring''' from typing import Callable, Optional, Union from ...configuration_utils import PretrainedConfig from ...utils import logging UpperCAmelCase_ = logging.get_logger(__name__) UpperCAmelCase_ = { 'microsoft/xprophetnet-large-wiki100-cased': ( 'https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json' ), } class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' lowerCAmelCase_ : Dict = """xlm-prophetnet""" lowerCAmelCase_ : Any = ["""past_key_values"""] lowerCAmelCase_ : List[str] = { """num_attention_heads""": """num_encoder_attention_heads""", } def __init__( self : int , _UpperCAmelCase : Optional[float] = 0.1 , _UpperCAmelCase : Optional[Union[str, Callable]] = "gelu" , _UpperCAmelCase : Optional[int] = 3_05_22 , _UpperCAmelCase : Optional[int] = 10_24 , _UpperCAmelCase : Optional[int] = 40_96 , _UpperCAmelCase : Optional[int] = 12 , _UpperCAmelCase : Optional[int] = 16 , _UpperCAmelCase : Optional[int] = 40_96 , _UpperCAmelCase : Optional[int] = 12 , _UpperCAmelCase : Optional[int] = 16 , _UpperCAmelCase : Optional[float] = 0.1 , _UpperCAmelCase : Optional[float] = 0.1 , _UpperCAmelCase : Optional[int] = 5_12 , _UpperCAmelCase : Optional[float] = 0.02 , _UpperCAmelCase : Optional[bool] = True , _UpperCAmelCase : Optional[bool] = True , _UpperCAmelCase : Optional[int] = 0 , _UpperCAmelCase : Optional[int] = 2 , _UpperCAmelCase : Optional[int] = 32 , _UpperCAmelCase : Optional[int] = 1_28 , _UpperCAmelCase : Optional[bool] = False , _UpperCAmelCase : Optional[float] = 0.0 , _UpperCAmelCase : Optional[bool] = True , _UpperCAmelCase : Optional[int] = 0 , _UpperCAmelCase : Optional[int] = 1 , _UpperCAmelCase : Optional[int] = 2 , **_UpperCAmelCase : Dict , ): """simple docstring""" UpperCAmelCase__ = vocab_size UpperCAmelCase__ = hidden_size UpperCAmelCase__ = encoder_ffn_dim UpperCAmelCase__ = num_encoder_layers UpperCAmelCase__ = num_encoder_attention_heads UpperCAmelCase__ = decoder_ffn_dim UpperCAmelCase__ = num_decoder_layers UpperCAmelCase__ = num_decoder_attention_heads UpperCAmelCase__ = max_position_embeddings UpperCAmelCase__ = init_std # Normal(0, this parameter) UpperCAmelCase__ = activation_function # parameters for xlmprophetnet UpperCAmelCase__ = ngram UpperCAmelCase__ = num_buckets UpperCAmelCase__ = relative_max_distance UpperCAmelCase__ = disable_ngram_loss UpperCAmelCase__ = eps # 3 Types of Dropout UpperCAmelCase__ = attention_dropout UpperCAmelCase__ = activation_dropout UpperCAmelCase__ = dropout UpperCAmelCase__ = use_cache super().__init__( pad_token_id=_UpperCAmelCase , bos_token_id=_UpperCAmelCase , eos_token_id=_UpperCAmelCase , is_encoder_decoder=_UpperCAmelCase , add_cross_attention=_UpperCAmelCase , decoder_start_token_id=_UpperCAmelCase , **_UpperCAmelCase , ) @property def SCREAMING_SNAKE_CASE__ ( self : str ): """simple docstring""" return self.num_encoder_layers + self.num_decoder_layers @num_hidden_layers.setter def SCREAMING_SNAKE_CASE__ ( self : Tuple , _UpperCAmelCase : Any ): """simple docstring""" raise NotImplementedError( """This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and""" """ `num_decoder_layers`.""" )

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'''simple docstring''' import argparse import json import os import fairseq import torch from fairseq.data import Dictionary from transformers import ( WavaVecaConfig, WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaForCTC, WavaVecaForPreTraining, WavaVecaProcessor, logging, ) from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification logging.set_verbosity_info() UpperCAmelCase_ = logging.get_logger(__name__) UpperCAmelCase_ = { 'post_extract_proj': 'feature_projection.projection', 'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv', 'self_attn.k_proj': 'encoder.layers.*.attention.k_proj', 'self_attn.v_proj': 'encoder.layers.*.attention.v_proj', 'self_attn.q_proj': 'encoder.layers.*.attention.q_proj', 'self_attn.out_proj': 'encoder.layers.*.attention.out_proj', 'self_attn_layer_norm': 'encoder.layers.*.layer_norm', 'fc1': 'encoder.layers.*.feed_forward.intermediate_dense', 'fc2': 'encoder.layers.*.feed_forward.output_dense', 'final_layer_norm': 'encoder.layers.*.final_layer_norm', 'encoder.layer_norm': 'encoder.layer_norm', 'adapter_layer': 'encoder.layers.*.adapter_layer', 'w2v_model.layer_norm': 'feature_projection.layer_norm', 'quantizer.weight_proj': 'quantizer.weight_proj', 'quantizer.vars': 'quantizer.codevectors', 'project_q': 'project_q', 'final_proj': 'project_hid', 'w2v_encoder.proj': 'lm_head', 'mask_emb': 'masked_spec_embed', 'pooling_layer.linear': 'projector', 'pooling_layer.projection': 'classifier', } UpperCAmelCase_ = [ 'lm_head', 'quantizer.weight_proj', 'quantizer.codevectors', 'project_q', 'project_hid', 'projector', 'classifier', ] def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Tuple ): '''simple docstring''' UpperCAmelCase__ = {} with open(SCREAMING_SNAKE_CASE__ , """r""" ) as file: for line_number, line in enumerate(SCREAMING_SNAKE_CASE__ ): UpperCAmelCase__ = line.strip() if line: UpperCAmelCase__ = line.split() UpperCAmelCase__ = line_number UpperCAmelCase__ = words[0] UpperCAmelCase__ = value return result def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : Union[str, Any] ): '''simple docstring''' for attribute in key.split(""".""" ): UpperCAmelCase__ = getattr(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = None for param_key in PARAM_MAPPING.keys(): if full_name.endswith(SCREAMING_SNAKE_CASE__ ): UpperCAmelCase__ = PARAM_MAPPING[full_name.split(""".""" )[-1]] UpperCAmelCase__ = """param""" if weight_type is not None and weight_type != "param": UpperCAmelCase__ = getattr(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ).shape elif weight_type is not None and weight_type == "param": UpperCAmelCase__ = hf_pointer for attribute in hf_param_name.split(""".""" ): UpperCAmelCase__ = getattr(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = shape_pointer.shape # let's reduce dimension UpperCAmelCase__ = value[0] else: UpperCAmelCase__ = hf_pointer.shape if hf_shape != value.shape: raise ValueError( F'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be''' F''' {value.shape} for {full_name}''' ) if weight_type == "weight": UpperCAmelCase__ = value elif weight_type == "weight_g": UpperCAmelCase__ = value elif weight_type == "weight_v": UpperCAmelCase__ = value elif weight_type == "bias": UpperCAmelCase__ = value elif weight_type == "param": for attribute in hf_param_name.split(""".""" ): UpperCAmelCase__ = getattr(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = value else: UpperCAmelCase__ = value logger.info(F'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.''' ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : Optional[Any] ): '''simple docstring''' UpperCAmelCase__ = None for param_key in PARAM_MAPPING.keys(): if full_name.endswith(SCREAMING_SNAKE_CASE__ ): UpperCAmelCase__ = PARAM_MAPPING[full_name.split(""".""" )[-1]] UpperCAmelCase__ = """param""" if weight_type is not None and weight_type != "param": UpperCAmelCase__ = """.""".join([key, weight_type] ) elif weight_type is not None and weight_type == "param": UpperCAmelCase__ = """.""".join([key, hf_param_name] ) else: UpperCAmelCase__ = key UpperCAmelCase__ = value if """lm_head""" in full_key else value[0] UpperCAmelCase_ = { 'W_a': 'linear_1.weight', 'W_b': 'linear_2.weight', 'b_a': 'linear_1.bias', 'b_b': 'linear_2.bias', 'ln_W': 'norm.weight', 'ln_b': 'norm.bias', } def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : Dict=None , SCREAMING_SNAKE_CASE__ : Optional[Any]=None ): '''simple docstring''' UpperCAmelCase__ = False for key, mapped_key in MAPPING.items(): UpperCAmelCase__ = """wav2vec2.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key if key in name or key.split("""w2v_model.""" )[-1] == name.split(""".""" )[0]: UpperCAmelCase__ = True if "*" in mapped_key: UpperCAmelCase__ = name.split(SCREAMING_SNAKE_CASE__ )[0].split(""".""" )[-2] UpperCAmelCase__ = mapped_key.replace("""*""" , SCREAMING_SNAKE_CASE__ ) if "weight_g" in name: UpperCAmelCase__ = """weight_g""" elif "weight_v" in name: UpperCAmelCase__ = """weight_v""" elif "bias" in name: UpperCAmelCase__ = """bias""" elif "weight" in name: # TODO: don't match quantizer.weight_proj UpperCAmelCase__ = """weight""" else: UpperCAmelCase__ = None if hf_dict is not None: rename_dict(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) else: set_recursively(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) return is_used return is_used def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CASE__ : List[str] ): '''simple docstring''' UpperCAmelCase__ = [] UpperCAmelCase__ = fairseq_model.state_dict() UpperCAmelCase__ = hf_model.wavaveca.feature_extractor for name, value in fairseq_dict.items(): UpperCAmelCase__ = False if "conv_layers" in name: load_conv_layer( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , hf_model.config.feat_extract_norm == """group""" , ) UpperCAmelCase__ = True else: UpperCAmelCase__ = load_wavaveca_layer(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) if not is_used: unused_weights.append(SCREAMING_SNAKE_CASE__ ) logger.warning(F'''Unused weights: {unused_weights}''' ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : int ): '''simple docstring''' UpperCAmelCase__ = full_name.split("""conv_layers.""" )[-1] UpperCAmelCase__ = name.split(""".""" ) UpperCAmelCase__ = int(items[0] ) UpperCAmelCase__ = int(items[1] ) if type_id == 0: if "bias" in name: if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape: raise ValueError( F'''{full_name} has size {value.shape}, but''' F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.''' ) UpperCAmelCase__ = value logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' ) elif "weight" in name: if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape: raise ValueError( F'''{full_name} has size {value.shape}, but''' F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.''' ) UpperCAmelCase__ = value logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' ) elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm): if "bias" in name: if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape: raise ValueError( F'''{full_name} has size {value.shape}, but''' F''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.''' ) UpperCAmelCase__ = value logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' ) elif "weight" in name: if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape: raise ValueError( F'''{full_name} has size {value.shape}, but''' F''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.''' ) UpperCAmelCase__ = value logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' ) else: unused_weights.append(SCREAMING_SNAKE_CASE__ ) @torch.no_grad() def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : List[str]=None , SCREAMING_SNAKE_CASE__ : Optional[int]=None , SCREAMING_SNAKE_CASE__ : Union[str, Any]=True , SCREAMING_SNAKE_CASE__ : Union[str, Any]=False ): '''simple docstring''' if config_path is not None: UpperCAmelCase__ = WavaVecaConfig.from_pretrained(SCREAMING_SNAKE_CASE__ ) else: UpperCAmelCase__ = WavaVecaConfig() if is_seq_class: UpperCAmelCase__ = read_txt_into_dict(SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = idalabel UpperCAmelCase__ = WavaVecaForSequenceClassification(SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = WavaVecaFeatureExtractor( feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=SCREAMING_SNAKE_CASE__ , return_attention_mask=SCREAMING_SNAKE_CASE__ , ) feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE__ ) elif is_finetuned: if dict_path: UpperCAmelCase__ = Dictionary.load(SCREAMING_SNAKE_CASE__ ) # important change bos & pad token id since CTC symbol is <pad> and # not <s> as in fairseq UpperCAmelCase__ = target_dict.pad_index UpperCAmelCase__ = target_dict.bos_index UpperCAmelCase__ = target_dict.eos_index UpperCAmelCase__ = len(target_dict.symbols ) UpperCAmelCase__ = os.path.join(SCREAMING_SNAKE_CASE__ , """vocab.json""" ) if not os.path.isdir(SCREAMING_SNAKE_CASE__ ): logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(SCREAMING_SNAKE_CASE__ ) ) return os.makedirs(SCREAMING_SNAKE_CASE__ , exist_ok=SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = target_dict.indices # fairseq has the <pad> and <s> switched UpperCAmelCase__ = 0 UpperCAmelCase__ = 1 with open(SCREAMING_SNAKE_CASE__ , """w""" , encoding="""utf-8""" ) as vocab_handle: json.dump(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = WavaVecaCTCTokenizer( SCREAMING_SNAKE_CASE__ , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token="""|""" , do_lower_case=SCREAMING_SNAKE_CASE__ , ) UpperCAmelCase__ = True if config.feat_extract_norm == """layer""" else False UpperCAmelCase__ = WavaVecaFeatureExtractor( feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=SCREAMING_SNAKE_CASE__ , return_attention_mask=SCREAMING_SNAKE_CASE__ , ) UpperCAmelCase__ = WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE__ , tokenizer=SCREAMING_SNAKE_CASE__ ) processor.save_pretrained(SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = WavaVecaForCTC(SCREAMING_SNAKE_CASE__ ) else: UpperCAmelCase__ = WavaVecaForPreTraining(SCREAMING_SNAKE_CASE__ ) if is_finetuned or is_seq_class: UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = fairseq.checkpoint_utils.load_model_ensemble_and_task( [checkpoint_path] , arg_overrides={"""data""": """/""".join(dict_path.split("""/""" )[:-1] )} ) else: UpperCAmelCase__ = argparse.Namespace(task="""audio_pretraining""" ) UpperCAmelCase__ = fairseq.tasks.setup_task(SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] , task=SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = model[0].eval() recursively_load_weights(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , not is_finetuned ) hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE__ ) if __name__ == "__main__": UpperCAmelCase_ = argparse.ArgumentParser() parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.') parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint') parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model') parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert') parser.add_argument( '--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not' ) parser.add_argument( '--is_seq_class', action='store_true', help='Whether the model to convert is a fine-tuned sequence classification model or not', ) UpperCAmelCase_ = parser.parse_args() UpperCAmelCase_ = not args.not_finetuned and not args.is_seq_class convert_wavaveca_checkpoint( args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, is_finetuned, args.is_seq_class, )

346

1

'''simple docstring''' import unittest from pathlib import Path from tempfile import TemporaryDirectory from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available from transformers.models.gpta.tokenization_gpta import GPTaTokenizer from transformers.testing_utils import require_keras_nlp, require_tf, slow if is_tf_available(): import tensorflow as tf if is_keras_nlp_available(): from transformers.models.gpta import TFGPTaTokenizer UpperCAmelCase_ = ['gpt2'] UpperCAmelCase_ = 'gpt2' if is_tf_available(): class lowerCAmelCase_ ( tf.Module ): '''simple docstring''' def __init__( self : Any , _UpperCAmelCase : Optional[int] ): """simple docstring""" super().__init__() UpperCAmelCase__ = tokenizer UpperCAmelCase__ = AutoConfig.from_pretrained(_UpperCAmelCase ) UpperCAmelCase__ = TFGPTaLMHeadModel.from_config(_UpperCAmelCase ) @tf.function(input_signature=(tf.TensorSpec((None,) , tf.string , name="""text""" ),) ) def SCREAMING_SNAKE_CASE__ ( self : str , _UpperCAmelCase : Tuple ): """simple docstring""" UpperCAmelCase__ = self.tokenizer(_UpperCAmelCase ) UpperCAmelCase__ = tokenized["""input_ids"""].to_tensor() UpperCAmelCase__ = tf.cast(input_ids_dense > 0 , tf.intaa ) # input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN]) UpperCAmelCase__ = self.model(input_ids=_UpperCAmelCase , attention_mask=_UpperCAmelCase )["""logits"""] return outputs @require_tf @require_keras_nlp class lowerCAmelCase_ ( unittest.TestCase ): '''simple docstring''' def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ): """simple docstring""" super().setUp() UpperCAmelCase__ = [GPTaTokenizer.from_pretrained(_UpperCAmelCase ) for checkpoint in (TOKENIZER_CHECKPOINTS)] UpperCAmelCase__ = [TFGPTaTokenizer.from_pretrained(_UpperCAmelCase ) for checkpoint in TOKENIZER_CHECKPOINTS] assert len(self.tokenizers ) == len(self.tf_tokenizers ) UpperCAmelCase__ = [ """This is a straightforward English test sentence.""", """This one has some weird characters\rto\nsee\r\nif those\u00E9break things.""", """Now we're going to add some Chinese: 一二三一二三""", """And some much more rare Chinese: 齉堃齉堃""", """Je vais aussi écrire en français pour tester les accents""", """Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ""", ] UpperCAmelCase__ = list(zip(self.test_sentences , self.test_sentences[::-1] ) ) def SCREAMING_SNAKE_CASE__ ( self : Any ): """simple docstring""" for tokenizer, tf_tokenizer in zip(self.tokenizers , self.tf_tokenizers ): for test_inputs in self.test_sentences: UpperCAmelCase__ = tokenizer([test_inputs] , return_tensors="""tf""" ) UpperCAmelCase__ = tf_tokenizer([test_inputs] ) for key in python_outputs.keys(): # convert them to numpy to avoid messing with ragged tensors UpperCAmelCase__ = python_outputs[key].numpy() UpperCAmelCase__ = tf_outputs[key].numpy() self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape ) ) self.assertTrue(tf.reduce_all(tf.cast(_UpperCAmelCase , tf.intaa ) == tf_outputs_values ) ) @slow def SCREAMING_SNAKE_CASE__ ( self : List[Any] ): """simple docstring""" for tf_tokenizer in self.tf_tokenizers: UpperCAmelCase__ = tf.function(_UpperCAmelCase ) for test_inputs in self.test_sentences: UpperCAmelCase__ = tf.constant(_UpperCAmelCase ) UpperCAmelCase__ = compiled_tokenizer(_UpperCAmelCase ) UpperCAmelCase__ = tf_tokenizer(_UpperCAmelCase ) for key in eager_outputs.keys(): self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key] ) ) @slow def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ): """simple docstring""" for tf_tokenizer in self.tf_tokenizers: UpperCAmelCase__ = ModelToSave(tokenizer=_UpperCAmelCase ) UpperCAmelCase__ = tf.convert_to_tensor([self.test_sentences[0]] ) UpperCAmelCase__ = model.serving(_UpperCAmelCase ) # Build model with some sample inputs with TemporaryDirectory() as tempdir: UpperCAmelCase__ = Path(_UpperCAmelCase ) / """saved.model""" tf.saved_model.save(_UpperCAmelCase , _UpperCAmelCase , signatures={"""serving_default""": model.serving} ) UpperCAmelCase__ = tf.saved_model.load(_UpperCAmelCase ) UpperCAmelCase__ = loaded_model.signatures["""serving_default"""](_UpperCAmelCase )["""output_0"""] # We may see small differences because the loaded model is compiled, so we need an epsilon for the test self.assertTrue(tf.reduce_all(out == loaded_output ) ) @slow def SCREAMING_SNAKE_CASE__ ( self : str ): """simple docstring""" for tf_tokenizer in self.tf_tokenizers: UpperCAmelCase__ = tf.convert_to_tensor([self.test_sentences[0]] ) UpperCAmelCase__ = tf_tokenizer(_UpperCAmelCase ) # Build model with some sample inputs UpperCAmelCase__ = tf_tokenizer.get_config() UpperCAmelCase__ = TFGPTaTokenizer.from_config(_UpperCAmelCase ) UpperCAmelCase__ = model_from_config(_UpperCAmelCase ) for key in from_config_output.keys(): self.assertTrue(tf.reduce_all(from_config_output[key] == out[key] ) ) @slow def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ): """simple docstring""" for tf_tokenizer in self.tf_tokenizers: # for the test to run UpperCAmelCase__ = 12_31_23 for max_length in [3, 5, 10_24]: UpperCAmelCase__ = tf.convert_to_tensor([self.test_sentences[0]] ) UpperCAmelCase__ = tf_tokenizer(_UpperCAmelCase , max_length=_UpperCAmelCase ) UpperCAmelCase__ = out["""input_ids"""].numpy().shape[1] assert out_length == max_length

346

'''simple docstring''' import itertools import os from collections import Counter, defaultdict from concurrent.futures import ThreadPoolExecutor, as_completed import numpy as np import datasets from .execute import check_correctness UpperCAmelCase_ = '\\n@misc{chen2021evaluating,\n title={Evaluating Large Language Models Trained on Code},\n author={Mark Chen and Jerry Tworek and Heewoo Jun and Qiming Yuan \\nand Henrique Ponde de Oliveira Pinto and Jared Kaplan and Harri Edwards \\nand Yuri Burda and Nicholas Joseph and Greg Brockman and Alex Ray \\nand Raul Puri and Gretchen Krueger and Michael Petrov and Heidy Khlaaf \\nand Girish Sastry and Pamela Mishkin and Brooke Chan and Scott Gray \\nand Nick Ryder and Mikhail Pavlov and Alethea Power and Lukasz Kaiser \\nand Mohammad Bavarian and Clemens Winter and Philippe Tillet \\nand Felipe Petroski Such and Dave Cummings and Matthias Plappert \\nand Fotios Chantzis and Elizabeth Barnes and Ariel Herbert-Voss \\nand William Hebgen Guss and Alex Nichol and Alex Paino and Nikolas Tezak \\nand Jie Tang and Igor Babuschkin and Suchir Balaji and Shantanu Jain \\nand William Saunders and Christopher Hesse and Andrew N. Carr \\nand Jan Leike and Josh Achiam and Vedant Misra and Evan Morikawa \\nand Alec Radford and Matthew Knight and Miles Brundage and Mira Murati \\nand Katie Mayer and Peter Welinder and Bob McGrew and Dario Amodei \\nand Sam McCandlish and Ilya Sutskever and Wojciech Zaremba},\n year={2021},\n eprint={2107.03374},\n archivePrefix={arXiv},\n primaryClass={cs.LG}\n}\n' UpperCAmelCase_ = '\\nThis metric implements the evaluation harness for the HumanEval problem solving dataset\ndescribed in the paper "Evaluating Large Language Models Trained on Code"\n(https://arxiv.org/abs/2107.03374).\n' UpperCAmelCase_ = '\nCalculates how good are predictions given some references, using certain scores\nArgs:\n predictions: list of candidates to evaluate. Each candidates should be a list\n of strings with several code candidates to solve the problem.\n references: a list with a test for each prediction. Each test should evaluate the\n correctness of a code candidate.\n k: number of code candidates to consider in the evaluation (Default: [1, 10, 100])\n num_workers: number of workers used to evaluate the canidate programs (Default: 4).\n timeout:\nReturns:\n pass_at_k: dict with pass rates for each k\n results: dict with granular results of each unittest\nExamples:\n >>> code_eval = datasets.load_metric("code_eval")\n >>> test_cases = ["assert add(2,3)==5"]\n >>> candidates = [["def add(a,b): return a*b", "def add(a, b): return a+b"]]\n >>> pass_at_k, results = code_eval.compute(references=test_cases, predictions=candidates, k=[1, 2])\n >>> print(pass_at_k)\n {\'pass@1\': 0.5, \'pass@2\': 1.0}\n' UpperCAmelCase_ = '\n################################################################################\n !!!WARNING!!!\n################################################################################\nThe "code_eval" metric executes untrusted model-generated code in Python.\nAlthough it is highly unlikely that model-generated code will do something\novertly malicious in response to this test suite, model-generated code may act\ndestructively due to a lack of model capability or alignment.\nUsers are strongly encouraged to sandbox this evaluation suite so that it\ndoes not perform destructive actions on their host or network. For more\ninformation on how OpenAI sandboxes its code, see the paper "Evaluating Large\nLanguage Models Trained on Code" (https://arxiv.org/abs/2107.03374).\n\nOnce you have read this disclaimer and taken appropriate precautions,\nset the environment variable HF_ALLOW_CODE_EVAL="1". Within Python you can to this\nwith:\n\n>>> import os\n>>> os.environ["HF_ALLOW_CODE_EVAL"] = "1"\n\n################################################################################\\n' UpperCAmelCase_ = 'The MIT License\n\nCopyright (c) OpenAI (https://openai.com)\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the "Software"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in\nall copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\nTHE SOFTWARE.' @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION ) class lowerCAmelCase_ ( datasets.Metric ): '''simple docstring''' def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ): """simple docstring""" return datasets.MetricInfo( # This is the description that will appear on the metrics page. description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features( { """predictions""": datasets.Sequence(datasets.Value("""string""" ) ), """references""": datasets.Value("""string""" ), } ) , homepage="""https://github.com/openai/human-eval""" , codebase_urls=["""https://github.com/openai/human-eval"""] , reference_urls=["""https://github.com/openai/human-eval"""] , license=_LICENSE , ) def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : List[str]=[1, 10, 1_00] , _UpperCAmelCase : Optional[Any]=4 , _UpperCAmelCase : Any=3.0 ): """simple docstring""" if os.getenv("""HF_ALLOW_CODE_EVAL""" , 0 ) != "1": raise ValueError(_WARNING ) if os.name == "nt": raise NotImplementedError("""This metric is currently not supported on Windows.""" ) with ThreadPoolExecutor(max_workers=_UpperCAmelCase ) as executor: UpperCAmelCase__ = [] UpperCAmelCase__ = Counter() UpperCAmelCase__ = 0 UpperCAmelCase__ = defaultdict(_UpperCAmelCase ) for task_id, (candidates, test_case) in enumerate(zip(_UpperCAmelCase , _UpperCAmelCase ) ): for candidate in candidates: UpperCAmelCase__ = candidate + """\n""" + test_case UpperCAmelCase__ = (test_program, timeout, task_id, completion_id[task_id]) UpperCAmelCase__ = executor.submit(_UpperCAmelCase , *_UpperCAmelCase ) futures.append(_UpperCAmelCase ) completion_id[task_id] += 1 n_samples += 1 for future in as_completed(_UpperCAmelCase ): UpperCAmelCase__ = future.result() results[result["task_id"]].append((result["""completion_id"""], result) ) UpperCAmelCase__ , UpperCAmelCase__ = [], [] for result in results.values(): result.sort() UpperCAmelCase__ = [r[1]["""passed"""] for r in result] total.append(len(_UpperCAmelCase ) ) correct.append(sum(_UpperCAmelCase ) ) UpperCAmelCase__ = np.array(_UpperCAmelCase ) UpperCAmelCase__ = np.array(_UpperCAmelCase ) UpperCAmelCase__ = k UpperCAmelCase__ = {f'''pass@{k}''': estimate_pass_at_k(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ).mean() for k in ks if (total >= k).all()} return pass_at_k, results def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : Optional[Any] ): '''simple docstring''' def estimator(SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ) -> float: if n - c < k: return 1.0 return 1.0 - np.prod(1.0 - k / np.arange(n - c + 1 , n + 1 ) ) if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ): UpperCAmelCase__ = itertools.repeat(SCREAMING_SNAKE_CASE__ , len(SCREAMING_SNAKE_CASE__ ) ) else: assert len(SCREAMING_SNAKE_CASE__ ) == len(SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = iter(SCREAMING_SNAKE_CASE__ ) return np.array([estimator(int(SCREAMING_SNAKE_CASE__ ) , int(SCREAMING_SNAKE_CASE__ ) , SCREAMING_SNAKE_CASE__ ) for n, c in zip(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )] )

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'''simple docstring''' from random import randint, random def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : bool = False , SCREAMING_SNAKE_CASE__ : bool = False , SCREAMING_SNAKE_CASE__ : int = 5 , ): '''simple docstring''' UpperCAmelCase__ = [[-1] * number_of_cells] # Create a highway without any car UpperCAmelCase__ = 0 UpperCAmelCase__ = max(SCREAMING_SNAKE_CASE__ , 0 ) while i < number_of_cells: UpperCAmelCase__ = ( randint(0 , SCREAMING_SNAKE_CASE__ ) if random_speed else initial_speed ) # Place the cars i += ( randint(1 , max_speed * 2 ) if random_frequency else frequency ) # Arbitrary number, may need tuning return highway def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : list , SCREAMING_SNAKE_CASE__ : int ): '''simple docstring''' UpperCAmelCase__ = 0 UpperCAmelCase__ = highway_now[car_index + 1 :] for cell in range(len(SCREAMING_SNAKE_CASE__ ) ): # May need a better name for this if cells[cell] != -1: # If the cell is not empty then return distance # we have the distance we wanted distance += 1 # Here if the car is near the end of the highway return distance + get_distance(SCREAMING_SNAKE_CASE__ , -1 ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : list , SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : int ): '''simple docstring''' UpperCAmelCase__ = len(SCREAMING_SNAKE_CASE__ ) # Beforce calculations, the highway is empty UpperCAmelCase__ = [-1] * number_of_cells for car_index in range(SCREAMING_SNAKE_CASE__ ): if highway_now[car_index] != -1: # Add 1 to the current speed of the car and cap the speed UpperCAmelCase__ = min(highway_now[car_index] + 1 , SCREAMING_SNAKE_CASE__ ) # Number of empty cell before the next car UpperCAmelCase__ = get_distance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) - 1 # We can't have the car causing an accident UpperCAmelCase__ = min(next_highway[car_index] , SCREAMING_SNAKE_CASE__ ) if random() < probability: # Randomly, a driver will slow down UpperCAmelCase__ = max(next_highway[car_index] - 1 , 0 ) return next_highway def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : list , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : int ): '''simple docstring''' UpperCAmelCase__ = len(highway[0] ) for i in range(SCREAMING_SNAKE_CASE__ ): UpperCAmelCase__ = update(highway[i] , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = [-1] * number_of_cells for car_index in range(SCREAMING_SNAKE_CASE__ ): UpperCAmelCase__ = next_speeds_calculated[car_index] if speed != -1: # Change the position based on the speed (with % to create the loop) UpperCAmelCase__ = (car_index + speed) % number_of_cells # Commit the change of position UpperCAmelCase__ = speed highway.append(SCREAMING_SNAKE_CASE__ ) return highway if __name__ == "__main__": import doctest doctest.testmod()

346

'''simple docstring''' import math def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int ): '''simple docstring''' assert isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) and ( number >= 0 ), "'number' must been an int and positive" if 1 < number < 4: # 2 and 3 are primes return True elif number < 2 or not number % 2: # Negatives, 0, 1 and all even numbers are not primes return False UpperCAmelCase__ = range(3 , int(math.sqrt(SCREAMING_SNAKE_CASE__ ) + 1 ) , 2 ) return not any(not number % i for i in odd_numbers ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : List[str]=1 , **SCREAMING_SNAKE_CASE__ : List[str] ): '''simple docstring''' UpperCAmelCase__ = factor * value UpperCAmelCase__ = value while not is_prime(SCREAMING_SNAKE_CASE__ ): value += 1 if not ("desc" in kwargs and kwargs["desc"] is True) else -1 if value == first_value_val: return next_prime(value + 1 , **SCREAMING_SNAKE_CASE__ ) return value

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'''simple docstring''' import inspect from typing import List, Optional, Tuple, Union import torch from ...models import UNetaDModel, VQModel from ...schedulers import DDIMScheduler from ...utils import randn_tensor from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' def __init__( self : Dict , _UpperCAmelCase : VQModel , _UpperCAmelCase : UNetaDModel , _UpperCAmelCase : DDIMScheduler ): """simple docstring""" super().__init__() self.register_modules(vqvae=_UpperCAmelCase , unet=_UpperCAmelCase , scheduler=_UpperCAmelCase ) @torch.no_grad() def __call__( self : Tuple , _UpperCAmelCase : int = 1 , _UpperCAmelCase : Optional[Union[torch.Generator, List[torch.Generator]]] = None , _UpperCAmelCase : float = 0.0 , _UpperCAmelCase : int = 50 , _UpperCAmelCase : Optional[str] = "pil" , _UpperCAmelCase : bool = True , **_UpperCAmelCase : Optional[int] , ): """simple docstring""" UpperCAmelCase__ = randn_tensor( (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) , generator=_UpperCAmelCase , ) UpperCAmelCase__ = latents.to(self.device ) # scale the initial noise by the standard deviation required by the scheduler UpperCAmelCase__ = latents * self.scheduler.init_noise_sigma self.scheduler.set_timesteps(_UpperCAmelCase ) # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature UpperCAmelCase__ = """eta""" in set(inspect.signature(self.scheduler.step ).parameters.keys() ) UpperCAmelCase__ = {} if accepts_eta: UpperCAmelCase__ = eta for t in self.progress_bar(self.scheduler.timesteps ): UpperCAmelCase__ = self.scheduler.scale_model_input(_UpperCAmelCase , _UpperCAmelCase ) # predict the noise residual UpperCAmelCase__ = self.unet(_UpperCAmelCase , _UpperCAmelCase ).sample # compute the previous noisy sample x_t -> x_t-1 UpperCAmelCase__ = self.scheduler.step(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample # decode the image latents with the VAE UpperCAmelCase__ = self.vqvae.decode(_UpperCAmelCase ).sample UpperCAmelCase__ = (image / 2 + 0.5).clamp(0 , 1 ) UpperCAmelCase__ = image.cpu().permute(0 , 2 , 3 , 1 ).numpy() if output_type == "pil": UpperCAmelCase__ = self.numpy_to_pil(_UpperCAmelCase ) if not return_dict: return (image,) return ImagePipelineOutput(images=_UpperCAmelCase )

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'''simple docstring''' import string from math import logaa def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : str ): '''simple docstring''' UpperCAmelCase__ = document.translate( str.maketrans("""""" , """""" , string.punctuation ) ).replace("""\n""" , """""" ) UpperCAmelCase__ = document_without_punctuation.split(""" """ ) # word tokenization return len([word for word in tokenize_document if word.lower() == term.lower()] ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : str ): '''simple docstring''' UpperCAmelCase__ = corpus.lower().translate( str.maketrans("""""" , """""" , string.punctuation ) ) # strip all punctuation and replace it with '' UpperCAmelCase__ = corpus_without_punctuation.split("""\n""" ) UpperCAmelCase__ = term.lower() return (len([doc for doc in docs if term in doc] ), len(SCREAMING_SNAKE_CASE__ )) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : Tuple=False ): '''simple docstring''' if smoothing: if n == 0: raise ValueError("""log10(0) is undefined.""" ) return round(1 + logaa(n / (1 + df) ) , 3 ) if df == 0: raise ZeroDivisionError("""df must be > 0""" ) elif n == 0: raise ValueError("""log10(0) is undefined.""" ) return round(logaa(n / df ) , 3 ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ): '''simple docstring''' return round(tf * idf , 3 )

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'''simple docstring''' from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_tokenizers_available, is_torch_available, ) UpperCAmelCase_ = { 'configuration_blenderbot': [ 'BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BlenderbotConfig', 'BlenderbotOnnxConfig', ], 'tokenization_blenderbot': ['BlenderbotTokenizer'], } try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCAmelCase_ = ['BlenderbotTokenizerFast'] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCAmelCase_ = [ 'BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST', 'BlenderbotForCausalLM', 'BlenderbotForConditionalGeneration', 'BlenderbotModel', 'BlenderbotPreTrainedModel', ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCAmelCase_ = [ 'TFBlenderbotForConditionalGeneration', 'TFBlenderbotModel', 'TFBlenderbotPreTrainedModel', ] try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCAmelCase_ = [ 'FlaxBlenderbotForConditionalGeneration', 'FlaxBlenderbotModel', 'FlaxBlenderbotPreTrainedModel', ] if TYPE_CHECKING: from .configuration_blenderbot import ( BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP, BlenderbotConfig, BlenderbotOnnxConfig, ) from .tokenization_blenderbot import BlenderbotTokenizer try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tokenization_blenderbot_fast import BlenderbotTokenizerFast try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_blenderbot import ( BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST, BlenderbotForCausalLM, BlenderbotForConditionalGeneration, BlenderbotModel, BlenderbotPreTrainedModel, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_blenderbot import ( TFBlenderbotForConditionalGeneration, TFBlenderbotModel, TFBlenderbotPreTrainedModel, ) try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_flax_blenderbot import ( FlaxBlenderbotForConditionalGeneration, FlaxBlenderbotModel, FlaxBlenderbotPreTrainedModel, ) else: import sys UpperCAmelCase_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

346

'''simple docstring''' import argparse import torch from transformers import BertForMaskedLM if __name__ == "__main__": UpperCAmelCase_ = argparse.ArgumentParser( description=( 'Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned' ' Distillation' ) ) parser.add_argument('--model_type', default='bert', choices=['bert']) parser.add_argument('--model_name', default='bert-base-uncased', type=str) parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_bert-base-uncased_0247911.pth', type=str) parser.add_argument('--vocab_transform', action='store_true') UpperCAmelCase_ = parser.parse_args() if args.model_type == "bert": UpperCAmelCase_ = BertForMaskedLM.from_pretrained(args.model_name) UpperCAmelCase_ = 'bert' else: raise ValueError('args.model_type should be "bert".') UpperCAmelCase_ = model.state_dict() UpperCAmelCase_ = {} for w in ["word_embeddings", "position_embeddings"]: UpperCAmelCase_ = state_dict[f"{prefix}.embeddings.{w}.weight"] for w in ["weight", "bias"]: UpperCAmelCase_ = state_dict[f"{prefix}.embeddings.LayerNorm.{w}"] UpperCAmelCase_ = 0 for teacher_idx in [0, 2, 4, 7, 9, 1_1]: for w in ["weight", "bias"]: UpperCAmelCase_ = state_dict[ f"{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}" ] UpperCAmelCase_ = state_dict[ f"{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}" ] UpperCAmelCase_ = state_dict[ f"{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}" ] UpperCAmelCase_ = state_dict[ f"{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}" ] UpperCAmelCase_ = state_dict[ f"{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}" ] UpperCAmelCase_ = state_dict[ f"{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}" ] UpperCAmelCase_ = state_dict[ f"{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}" ] UpperCAmelCase_ = state_dict[ f"{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}" ] std_idx += 1 UpperCAmelCase_ = state_dict['cls.predictions.decoder.weight'] UpperCAmelCase_ = state_dict['cls.predictions.bias'] if args.vocab_transform: for w in ["weight", "bias"]: UpperCAmelCase_ = state_dict[f"cls.predictions.transform.dense.{w}"] UpperCAmelCase_ = state_dict[f"cls.predictions.transform.LayerNorm.{w}"] print(f"N layers selected for distillation: {std_idx}") print(f"Number of params transferred for distillation: {len(compressed_sd.keys())}") print(f"Save transferred checkpoint to {args.dump_checkpoint}.") torch.save(compressed_sd, args.dump_checkpoint)

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'''simple docstring''' from collections import Counter import numpy as np from sklearn import datasets from sklearn.model_selection import train_test_split UpperCAmelCase_ = datasets.load_iris() UpperCAmelCase_ = np.array(data['data']) UpperCAmelCase_ = np.array(data['target']) UpperCAmelCase_ = data['target_names'] UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = train_test_split(X, y) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : List[str] ): '''simple docstring''' return np.linalg.norm(np.array(SCREAMING_SNAKE_CASE__ ) - np.array(SCREAMING_SNAKE_CASE__ ) ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : Any=5 ): '''simple docstring''' UpperCAmelCase__ = zip(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) # List of distances of all points from the point to be classified UpperCAmelCase__ = [] for data_point in data: UpperCAmelCase__ = euclidean_distance(data_point[0] , SCREAMING_SNAKE_CASE__ ) distances.append((distance, data_point[1]) ) # Choosing 'k' points with the least distances. UpperCAmelCase__ = [i[1] for i in sorted(SCREAMING_SNAKE_CASE__ )[:k]] # Most commonly occurring class among them # is the class into which the point is classified UpperCAmelCase__ = Counter(SCREAMING_SNAKE_CASE__ ).most_common(1 )[0][0] return classes[result] if __name__ == "__main__": print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))

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'''simple docstring''' import tempfile import torch from diffusers import PNDMScheduler from .test_schedulers import SchedulerCommonTest class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' lowerCAmelCase_ : Union[str, Any] = (PNDMScheduler,) lowerCAmelCase_ : Optional[int] = (("""num_inference_steps""", 50),) def SCREAMING_SNAKE_CASE__ ( self : List[Any] , **_UpperCAmelCase : Optional[int] ): """simple docstring""" UpperCAmelCase__ = { """num_train_timesteps""": 10_00, """beta_start""": 0.0001, """beta_end""": 0.02, """beta_schedule""": """linear""", } config.update(**_UpperCAmelCase ) return config def SCREAMING_SNAKE_CASE__ ( self : List[Any] , _UpperCAmelCase : Tuple=0 , **_UpperCAmelCase : List[str] ): """simple docstring""" UpperCAmelCase__ = dict(self.forward_default_kwargs ) UpperCAmelCase__ = kwargs.pop("""num_inference_steps""" , _UpperCAmelCase ) UpperCAmelCase__ = self.dummy_sample UpperCAmelCase__ = 0.1 * sample UpperCAmelCase__ = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05] for scheduler_class in self.scheduler_classes: UpperCAmelCase__ = self.get_scheduler_config(**_UpperCAmelCase ) UpperCAmelCase__ = scheduler_class(**_UpperCAmelCase ) scheduler.set_timesteps(_UpperCAmelCase ) # copy over dummy past residuals UpperCAmelCase__ = dummy_past_residuals[:] with tempfile.TemporaryDirectory() as tmpdirname: scheduler.save_config(_UpperCAmelCase ) UpperCAmelCase__ = scheduler_class.from_pretrained(_UpperCAmelCase ) new_scheduler.set_timesteps(_UpperCAmelCase ) # copy over dummy past residuals UpperCAmelCase__ = dummy_past_residuals[:] UpperCAmelCase__ = scheduler.step_prk(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample UpperCAmelCase__ = new_scheduler.step_prk(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical" UpperCAmelCase__ = scheduler.step_plms(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample UpperCAmelCase__ = new_scheduler.step_plms(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical" def SCREAMING_SNAKE_CASE__ ( self : List[str] ): """simple docstring""" pass def SCREAMING_SNAKE_CASE__ ( self : int , _UpperCAmelCase : Union[str, Any]=0 , **_UpperCAmelCase : Optional[int] ): """simple docstring""" UpperCAmelCase__ = dict(self.forward_default_kwargs ) UpperCAmelCase__ = kwargs.pop("""num_inference_steps""" , _UpperCAmelCase ) UpperCAmelCase__ = self.dummy_sample UpperCAmelCase__ = 0.1 * sample UpperCAmelCase__ = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05] for scheduler_class in self.scheduler_classes: UpperCAmelCase__ = self.get_scheduler_config() UpperCAmelCase__ = scheduler_class(**_UpperCAmelCase ) scheduler.set_timesteps(_UpperCAmelCase ) # copy over dummy past residuals (must be after setting timesteps) UpperCAmelCase__ = dummy_past_residuals[:] with tempfile.TemporaryDirectory() as tmpdirname: scheduler.save_config(_UpperCAmelCase ) UpperCAmelCase__ = scheduler_class.from_pretrained(_UpperCAmelCase ) # copy over dummy past residuals new_scheduler.set_timesteps(_UpperCAmelCase ) # copy over dummy past residual (must be after setting timesteps) UpperCAmelCase__ = dummy_past_residuals[:] UpperCAmelCase__ = scheduler.step_prk(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample UpperCAmelCase__ = new_scheduler.step_prk(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical" UpperCAmelCase__ = scheduler.step_plms(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample UpperCAmelCase__ = new_scheduler.step_plms(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical" def SCREAMING_SNAKE_CASE__ ( self : int , **_UpperCAmelCase : Tuple ): """simple docstring""" UpperCAmelCase__ = self.scheduler_classes[0] UpperCAmelCase__ = self.get_scheduler_config(**_UpperCAmelCase ) UpperCAmelCase__ = scheduler_class(**_UpperCAmelCase ) UpperCAmelCase__ = 10 UpperCAmelCase__ = self.dummy_model() UpperCAmelCase__ = self.dummy_sample_deter scheduler.set_timesteps(_UpperCAmelCase ) for i, t in enumerate(scheduler.prk_timesteps ): UpperCAmelCase__ = model(_UpperCAmelCase , _UpperCAmelCase ) UpperCAmelCase__ = scheduler.step_prk(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ).prev_sample for i, t in enumerate(scheduler.plms_timesteps ): UpperCAmelCase__ = model(_UpperCAmelCase , _UpperCAmelCase ) UpperCAmelCase__ = scheduler.step_plms(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ).prev_sample return sample def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ): """simple docstring""" UpperCAmelCase__ = dict(self.forward_default_kwargs ) UpperCAmelCase__ = kwargs.pop("""num_inference_steps""" , _UpperCAmelCase ) for scheduler_class in self.scheduler_classes: UpperCAmelCase__ = self.get_scheduler_config() UpperCAmelCase__ = scheduler_class(**_UpperCAmelCase ) UpperCAmelCase__ = self.dummy_sample UpperCAmelCase__ = 0.1 * sample if num_inference_steps is not None and hasattr(_UpperCAmelCase , """set_timesteps""" ): scheduler.set_timesteps(_UpperCAmelCase ) elif num_inference_steps is not None and not hasattr(_UpperCAmelCase , """set_timesteps""" ): UpperCAmelCase__ = num_inference_steps # copy over dummy past residuals (must be done after set_timesteps) UpperCAmelCase__ = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05] UpperCAmelCase__ = dummy_past_residuals[:] UpperCAmelCase__ = scheduler.step_prk(_UpperCAmelCase , 0 , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample UpperCAmelCase__ = scheduler.step_prk(_UpperCAmelCase , 1 , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample self.assertEqual(output_a.shape , sample.shape ) self.assertEqual(output_a.shape , output_a.shape ) UpperCAmelCase__ = scheduler.step_plms(_UpperCAmelCase , 0 , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample UpperCAmelCase__ = scheduler.step_plms(_UpperCAmelCase , 1 , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample self.assertEqual(output_a.shape , sample.shape ) self.assertEqual(output_a.shape , output_a.shape ) def SCREAMING_SNAKE_CASE__ ( self : int ): """simple docstring""" for timesteps in [1_00, 10_00]: self.check_over_configs(num_train_timesteps=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ): """simple docstring""" for steps_offset in [0, 1]: self.check_over_configs(steps_offset=_UpperCAmelCase ) UpperCAmelCase__ = self.scheduler_classes[0] UpperCAmelCase__ = self.get_scheduler_config(steps_offset=1 ) UpperCAmelCase__ = scheduler_class(**_UpperCAmelCase ) scheduler.set_timesteps(10 ) assert torch.equal( scheduler.timesteps , torch.LongTensor( [9_01, 8_51, 8_51, 8_01, 8_01, 7_51, 7_51, 7_01, 7_01, 6_51, 6_51, 6_01, 6_01, 5_01, 4_01, 3_01, 2_01, 1_01, 1] ) , ) def SCREAMING_SNAKE_CASE__ ( self : List[Any] ): """simple docstring""" for beta_start, beta_end in zip([0.0001, 0.001] , [0.002, 0.02] ): self.check_over_configs(beta_start=_UpperCAmelCase , beta_end=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ): """simple docstring""" for schedule in ["linear", "squaredcos_cap_v2"]: self.check_over_configs(beta_schedule=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ): """simple docstring""" for prediction_type in ["epsilon", "v_prediction"]: self.check_over_configs(prediction_type=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : str ): """simple docstring""" for t in [1, 5, 10]: self.check_over_forward(time_step=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Any ): """simple docstring""" for t, num_inference_steps in zip([1, 5, 10] , [10, 50, 1_00] ): self.check_over_forward(num_inference_steps=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : List[str] ): """simple docstring""" UpperCAmelCase__ = 27 for scheduler_class in self.scheduler_classes: UpperCAmelCase__ = self.dummy_sample UpperCAmelCase__ = 0.1 * sample UpperCAmelCase__ = self.get_scheduler_config() UpperCAmelCase__ = scheduler_class(**_UpperCAmelCase ) scheduler.set_timesteps(_UpperCAmelCase ) # before power of 3 fix, would error on first step, so we only need to do two for i, t in enumerate(scheduler.prk_timesteps[:2] ): UpperCAmelCase__ = scheduler.step_prk(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ).prev_sample def SCREAMING_SNAKE_CASE__ ( self : Dict ): """simple docstring""" with self.assertRaises(_UpperCAmelCase ): UpperCAmelCase__ = self.scheduler_classes[0] UpperCAmelCase__ = self.get_scheduler_config() UpperCAmelCase__ = scheduler_class(**_UpperCAmelCase ) scheduler.step_plms(self.dummy_sample , 1 , self.dummy_sample ).prev_sample def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ): """simple docstring""" UpperCAmelCase__ = self.full_loop() UpperCAmelCase__ = torch.sum(torch.abs(_UpperCAmelCase ) ) UpperCAmelCase__ = torch.mean(torch.abs(_UpperCAmelCase ) ) assert abs(result_sum.item() - 198.1318 ) < 1E-2 assert abs(result_mean.item() - 0.2580 ) < 1E-3 def SCREAMING_SNAKE_CASE__ ( self : Dict ): """simple docstring""" UpperCAmelCase__ = self.full_loop(prediction_type="""v_prediction""" ) UpperCAmelCase__ = torch.sum(torch.abs(_UpperCAmelCase ) ) UpperCAmelCase__ = torch.mean(torch.abs(_UpperCAmelCase ) ) assert abs(result_sum.item() - 67.3986 ) < 1E-2 assert abs(result_mean.item() - 0.0878 ) < 1E-3 def SCREAMING_SNAKE_CASE__ ( self : Any ): """simple docstring""" UpperCAmelCase__ = self.full_loop(set_alpha_to_one=_UpperCAmelCase , beta_start=0.01 ) UpperCAmelCase__ = torch.sum(torch.abs(_UpperCAmelCase ) ) UpperCAmelCase__ = torch.mean(torch.abs(_UpperCAmelCase ) ) assert abs(result_sum.item() - 230.0399 ) < 1E-2 assert abs(result_mean.item() - 0.2995 ) < 1E-3 def SCREAMING_SNAKE_CASE__ ( self : int ): """simple docstring""" UpperCAmelCase__ = self.full_loop(set_alpha_to_one=_UpperCAmelCase , beta_start=0.01 ) UpperCAmelCase__ = torch.sum(torch.abs(_UpperCAmelCase ) ) UpperCAmelCase__ = torch.mean(torch.abs(_UpperCAmelCase ) ) assert abs(result_sum.item() - 186.9482 ) < 1E-2 assert abs(result_mean.item() - 0.2434 ) < 1E-3

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'''simple docstring''' def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : list , SCREAMING_SNAKE_CASE__ : int = 0 ): '''simple docstring''' UpperCAmelCase__ = length or len(SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = False for i in range(length - 1 ): if list_data[i] > list_data[i + 1]: UpperCAmelCase__ , UpperCAmelCase__ = list_data[i + 1], list_data[i] UpperCAmelCase__ = True return list_data if not swapped else bubble_sort(SCREAMING_SNAKE_CASE__ , length - 1 ) if __name__ == "__main__": import doctest doctest.testmod()

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'''simple docstring''' from ...configuration_utils import PretrainedConfig from ...utils import logging UpperCAmelCase_ = logging.get_logger(__name__) UpperCAmelCase_ = { 'google/vivit-b-16x2-kinetics400': ( 'https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json' ), # See all Vivit models at https://huggingface.co/models?filter=vivit } class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' lowerCAmelCase_ : Optional[int] = """vivit""" def __init__( self : List[str] , _UpperCAmelCase : List[Any]=2_24 , _UpperCAmelCase : List[str]=32 , _UpperCAmelCase : Any=[2, 16, 16] , _UpperCAmelCase : int=3 , _UpperCAmelCase : Optional[Any]=7_68 , _UpperCAmelCase : Union[str, Any]=12 , _UpperCAmelCase : Dict=12 , _UpperCAmelCase : Optional[Any]=30_72 , _UpperCAmelCase : Optional[int]="gelu_fast" , _UpperCAmelCase : Union[str, Any]=0.0 , _UpperCAmelCase : Tuple=0.0 , _UpperCAmelCase : Optional[int]=0.02 , _UpperCAmelCase : List[Any]=1E-06 , _UpperCAmelCase : List[str]=True , **_UpperCAmelCase : List[Any] , ): """simple docstring""" UpperCAmelCase__ = hidden_size UpperCAmelCase__ = num_hidden_layers UpperCAmelCase__ = num_attention_heads UpperCAmelCase__ = intermediate_size UpperCAmelCase__ = hidden_act UpperCAmelCase__ = hidden_dropout_prob UpperCAmelCase__ = attention_probs_dropout_prob UpperCAmelCase__ = initializer_range UpperCAmelCase__ = layer_norm_eps UpperCAmelCase__ = image_size UpperCAmelCase__ = num_frames UpperCAmelCase__ = tubelet_size UpperCAmelCase__ = num_channels UpperCAmelCase__ = qkv_bias super().__init__(**_UpperCAmelCase )

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'''simple docstring''' import inspect import unittest from transformers import DecisionTransformerConfig, is_torch_available from transformers.testing_utils import require_torch, slow, torch_device from ...generation.test_utils import GenerationTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import DecisionTransformerModel from transformers.models.decision_transformer.modeling_decision_transformer import ( DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, ) class lowerCAmelCase_ : '''simple docstring''' def __init__( self : Optional[Any] , _UpperCAmelCase : str , _UpperCAmelCase : Union[str, Any]=13 , _UpperCAmelCase : List[Any]=7 , _UpperCAmelCase : Any=6 , _UpperCAmelCase : Optional[int]=17 , _UpperCAmelCase : List[str]=23 , _UpperCAmelCase : Optional[int]=11 , _UpperCAmelCase : int=True , ): """simple docstring""" UpperCAmelCase__ = parent UpperCAmelCase__ = batch_size UpperCAmelCase__ = seq_length UpperCAmelCase__ = act_dim UpperCAmelCase__ = state_dim UpperCAmelCase__ = hidden_size UpperCAmelCase__ = max_length UpperCAmelCase__ = is_training def SCREAMING_SNAKE_CASE__ ( self : List[str] ): """simple docstring""" UpperCAmelCase__ = floats_tensor((self.batch_size, self.seq_length, self.state_dim) ) UpperCAmelCase__ = floats_tensor((self.batch_size, self.seq_length, self.act_dim) ) UpperCAmelCase__ = floats_tensor((self.batch_size, self.seq_length, 1) ) UpperCAmelCase__ = floats_tensor((self.batch_size, self.seq_length, 1) ) UpperCAmelCase__ = ids_tensor((self.batch_size, self.seq_length) , vocab_size=10_00 ) UpperCAmelCase__ = random_attention_mask((self.batch_size, self.seq_length) ) UpperCAmelCase__ = self.get_config() return ( config, states, actions, rewards, returns_to_go, timesteps, attention_mask, ) def SCREAMING_SNAKE_CASE__ ( self : List[Any] ): """simple docstring""" return DecisionTransformerConfig( batch_size=self.batch_size , seq_length=self.seq_length , act_dim=self.act_dim , state_dim=self.state_dim , hidden_size=self.hidden_size , max_length=self.max_length , ) def SCREAMING_SNAKE_CASE__ ( self : Dict , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Any , _UpperCAmelCase : Any , _UpperCAmelCase : Tuple , _UpperCAmelCase : str , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Tuple , ): """simple docstring""" UpperCAmelCase__ = DecisionTransformerModel(config=_UpperCAmelCase ) model.to(_UpperCAmelCase ) model.eval() UpperCAmelCase__ = model(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) self.parent.assertEqual(result.state_preds.shape , states.shape ) self.parent.assertEqual(result.action_preds.shape , actions.shape ) self.parent.assertEqual(result.return_preds.shape , returns_to_go.shape ) self.parent.assertEqual( result.last_hidden_state.shape , (self.batch_size, self.seq_length * 3, self.hidden_size) ) # seq length *3 as there are 3 modelities: states, returns and actions def SCREAMING_SNAKE_CASE__ ( self : Tuple ): """simple docstring""" UpperCAmelCase__ = self.prepare_config_and_inputs() ( ( UpperCAmelCase__ ) , ( UpperCAmelCase__ ) , ( UpperCAmelCase__ ) , ( UpperCAmelCase__ ) , ( UpperCAmelCase__ ) , ( UpperCAmelCase__ ) , ( UpperCAmelCase__ ) , ) = config_and_inputs UpperCAmelCase__ = { """states""": states, """actions""": actions, """rewards""": rewards, """returns_to_go""": returns_to_go, """timesteps""": timesteps, """attention_mask""": attention_mask, } return config, inputs_dict @require_torch class lowerCAmelCase_ ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , unittest.TestCase ): '''simple docstring''' lowerCAmelCase_ : List[Any] = (DecisionTransformerModel,) if is_torch_available() else () lowerCAmelCase_ : Optional[int] = () lowerCAmelCase_ : Optional[int] = {"""feature-extraction""": DecisionTransformerModel} if is_torch_available() else {} # Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids lowerCAmelCase_ : Optional[int] = False # Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features lowerCAmelCase_ : Tuple = False lowerCAmelCase_ : Optional[Any] = False lowerCAmelCase_ : Dict = False lowerCAmelCase_ : List[Any] = False lowerCAmelCase_ : Tuple = False lowerCAmelCase_ : int = False lowerCAmelCase_ : Tuple = False lowerCAmelCase_ : List[Any] = False lowerCAmelCase_ : Union[str, Any] = False def SCREAMING_SNAKE_CASE__ ( self : int ): """simple docstring""" UpperCAmelCase__ = DecisionTransformerModelTester(self ) UpperCAmelCase__ = ConfigTester(self , config_class=_UpperCAmelCase , hidden_size=37 ) def SCREAMING_SNAKE_CASE__ ( self : int ): """simple docstring""" self.config_tester.run_common_tests() def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ): """simple docstring""" UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*_UpperCAmelCase ) @slow def SCREAMING_SNAKE_CASE__ ( self : List[str] ): """simple docstring""" for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: UpperCAmelCase__ = DecisionTransformerModel.from_pretrained(_UpperCAmelCase ) self.assertIsNotNone(_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : str ): """simple docstring""" UpperCAmelCase__ , UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: UpperCAmelCase__ = model_class(_UpperCAmelCase ) UpperCAmelCase__ = inspect.signature(model.forward ) # signature.parameters is an OrderedDict => so arg_names order is deterministic UpperCAmelCase__ = [*signature.parameters.keys()] UpperCAmelCase__ = [ """states""", """actions""", """rewards""", """returns_to_go""", """timesteps""", """attention_mask""", ] self.assertListEqual(arg_names[: len(_UpperCAmelCase )] , _UpperCAmelCase ) @require_torch class lowerCAmelCase_ ( unittest.TestCase ): '''simple docstring''' @slow def SCREAMING_SNAKE_CASE__ ( self : str ): """simple docstring""" UpperCAmelCase__ = 2 # number of steps of autoregressive prediction we will perform UpperCAmelCase__ = 10 # defined by the RL environment, may be normalized UpperCAmelCase__ = DecisionTransformerModel.from_pretrained("""edbeeching/decision-transformer-gym-hopper-expert""" ) UpperCAmelCase__ = model.to(_UpperCAmelCase ) UpperCAmelCase__ = model.config torch.manual_seed(0 ) UpperCAmelCase__ = torch.randn(1 , 1 , config.state_dim ).to(device=_UpperCAmelCase , dtype=torch.floataa ) # env.reset() UpperCAmelCase__ = torch.tensor( [[0.24_2793, -0.2869_3074, 0.874_2613], [0.6781_5274, -0.0810_1085, -0.1295_2147]] , device=_UpperCAmelCase ) UpperCAmelCase__ = torch.tensor(_UpperCAmelCase , device=_UpperCAmelCase , dtype=torch.floataa ).reshape(1 , 1 , 1 ) UpperCAmelCase__ = state UpperCAmelCase__ = torch.zeros(1 , 0 , config.act_dim , device=_UpperCAmelCase , dtype=torch.floataa ) UpperCAmelCase__ = torch.zeros(1 , 0 , device=_UpperCAmelCase , dtype=torch.floataa ) UpperCAmelCase__ = torch.tensor(0 , device=_UpperCAmelCase , dtype=torch.long ).reshape(1 , 1 ) for step in range(_UpperCAmelCase ): UpperCAmelCase__ = torch.cat([actions, torch.zeros(1 , 1 , config.act_dim , device=_UpperCAmelCase )] , dim=1 ) UpperCAmelCase__ = torch.cat([rewards, torch.zeros(1 , 1 , device=_UpperCAmelCase )] , dim=1 ) UpperCAmelCase__ = torch.ones(1 , states.shape[1] ).to(dtype=torch.long , device=states.device ) with torch.no_grad(): UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = model( states=_UpperCAmelCase , actions=_UpperCAmelCase , rewards=_UpperCAmelCase , returns_to_go=_UpperCAmelCase , timesteps=_UpperCAmelCase , attention_mask=_UpperCAmelCase , return_dict=_UpperCAmelCase , ) self.assertEqual(action_pred.shape , actions.shape ) self.assertTrue(torch.allclose(action_pred[0, -1] , expected_outputs[step] , atol=1E-4 ) ) UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = ( # env.step(action) torch.randn(1 , 1 , config.state_dim ).to(device=_UpperCAmelCase , dtype=torch.floataa ), 1.0, False, {}, ) UpperCAmelCase__ = action_pred[0, -1] UpperCAmelCase__ = torch.cat([states, state] , dim=1 ) UpperCAmelCase__ = returns_to_go[0, -1] - reward UpperCAmelCase__ = torch.cat([returns_to_go, pred_return.reshape(1 , 1 , 1 )] , dim=1 ) UpperCAmelCase__ = torch.cat( [timesteps, torch.ones((1, 1) , device=_UpperCAmelCase , dtype=torch.long ) * (step + 1)] , dim=1 )

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'''simple docstring''' import warnings from ...utils import logging from .image_processing_deit import DeiTImageProcessor UpperCAmelCase_ = logging.get_logger(__name__) class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' def __init__( self : List[str] , *_UpperCAmelCase : Optional[int] , **_UpperCAmelCase : Optional[Any] ): """simple docstring""" warnings.warn( """The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please""" """ use DeiTImageProcessor instead.""" , _UpperCAmelCase , ) super().__init__(*_UpperCAmelCase , **_UpperCAmelCase )

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'''simple docstring''' import os import unittest from transformers import BertTokenizerFast from transformers.models.bert.tokenization_bert import ( VOCAB_FILES_NAMES, BasicTokenizer, BertTokenizer, WordpieceTokenizer, _is_control, _is_punctuation, _is_whitespace, ) from transformers.testing_utils import require_tokenizers, slow from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english @require_tokenizers class lowerCAmelCase_ ( lowerCamelCase_ , unittest.TestCase ): '''simple docstring''' lowerCAmelCase_ : List[str] = BertTokenizer lowerCAmelCase_ : Tuple = BertTokenizerFast lowerCAmelCase_ : Tuple = True lowerCAmelCase_ : Optional[int] = True lowerCAmelCase_ : str = filter_non_english def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ): """simple docstring""" super().setUp() UpperCAmelCase__ = [ """[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing""", """,""", """low""", """lowest""", ] UpperCAmelCase__ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] ) with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as vocab_writer: vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) ) def SCREAMING_SNAKE_CASE__ ( self : List[str] , _UpperCAmelCase : Any ): """simple docstring""" UpperCAmelCase__ = """UNwant\u00E9d,running""" UpperCAmelCase__ = """unwanted, running""" return input_text, output_text def SCREAMING_SNAKE_CASE__ ( self : List[str] ): """simple docstring""" UpperCAmelCase__ = self.tokenizer_class(self.vocab_file ) UpperCAmelCase__ = tokenizer.tokenize("""UNwant\u00E9d,running""" ) self.assertListEqual(_UpperCAmelCase , ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""] ) self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase ) , [9, 6, 7, 12, 10, 11] ) def SCREAMING_SNAKE_CASE__ ( self : List[Any] ): """simple docstring""" if not self.test_rust_tokenizer: return UpperCAmelCase__ = self.get_tokenizer() UpperCAmelCase__ = self.get_rust_tokenizer() UpperCAmelCase__ = """UNwant\u00E9d,running""" UpperCAmelCase__ = tokenizer.tokenize(_UpperCAmelCase ) UpperCAmelCase__ = rust_tokenizer.tokenize(_UpperCAmelCase ) self.assertListEqual(_UpperCAmelCase , _UpperCAmelCase ) UpperCAmelCase__ = tokenizer.encode(_UpperCAmelCase , add_special_tokens=_UpperCAmelCase ) UpperCAmelCase__ = rust_tokenizer.encode(_UpperCAmelCase , add_special_tokens=_UpperCAmelCase ) self.assertListEqual(_UpperCAmelCase , _UpperCAmelCase ) UpperCAmelCase__ = self.get_rust_tokenizer() UpperCAmelCase__ = tokenizer.encode(_UpperCAmelCase ) UpperCAmelCase__ = rust_tokenizer.encode(_UpperCAmelCase ) self.assertListEqual(_UpperCAmelCase , _UpperCAmelCase ) # With lower casing UpperCAmelCase__ = self.get_tokenizer(do_lower_case=_UpperCAmelCase ) UpperCAmelCase__ = self.get_rust_tokenizer(do_lower_case=_UpperCAmelCase ) UpperCAmelCase__ = """UNwant\u00E9d,running""" UpperCAmelCase__ = tokenizer.tokenize(_UpperCAmelCase ) UpperCAmelCase__ = rust_tokenizer.tokenize(_UpperCAmelCase ) self.assertListEqual(_UpperCAmelCase , _UpperCAmelCase ) UpperCAmelCase__ = tokenizer.encode(_UpperCAmelCase , add_special_tokens=_UpperCAmelCase ) UpperCAmelCase__ = rust_tokenizer.encode(_UpperCAmelCase , add_special_tokens=_UpperCAmelCase ) self.assertListEqual(_UpperCAmelCase , _UpperCAmelCase ) UpperCAmelCase__ = self.get_rust_tokenizer() UpperCAmelCase__ = tokenizer.encode(_UpperCAmelCase ) UpperCAmelCase__ = rust_tokenizer.encode(_UpperCAmelCase ) self.assertListEqual(_UpperCAmelCase , _UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Tuple ): """simple docstring""" UpperCAmelCase__ = BasicTokenizer() self.assertListEqual(tokenizer.tokenize("""ah\u535A\u63A8zz""" ) , ["""ah""", """\u535A""", """\u63A8""", """zz"""] ) def SCREAMING_SNAKE_CASE__ ( self : int ): """simple docstring""" UpperCAmelCase__ = BasicTokenizer(do_lower_case=_UpperCAmelCase ) self.assertListEqual( tokenizer.tokenize(""" \tHeLLo!how \n Are yoU? """ ) , ["""hello""", """!""", """how""", """are""", """you""", """?"""] ) self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""hello"""] ) def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ): """simple docstring""" UpperCAmelCase__ = BasicTokenizer(do_lower_case=_UpperCAmelCase , strip_accents=_UpperCAmelCase ) self.assertListEqual( tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""hällo""", """!""", """how""", """are""", """you""", """?"""] ) self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""h\u00E9llo"""] ) def SCREAMING_SNAKE_CASE__ ( self : str ): """simple docstring""" UpperCAmelCase__ = BasicTokenizer(do_lower_case=_UpperCAmelCase , strip_accents=_UpperCAmelCase ) self.assertListEqual( tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""hallo""", """!""", """how""", """are""", """you""", """?"""] ) self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""hello"""] ) def SCREAMING_SNAKE_CASE__ ( self : Any ): """simple docstring""" UpperCAmelCase__ = BasicTokenizer(do_lower_case=_UpperCAmelCase ) self.assertListEqual( tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""hallo""", """!""", """how""", """are""", """you""", """?"""] ) self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""hello"""] ) def SCREAMING_SNAKE_CASE__ ( self : int ): """simple docstring""" UpperCAmelCase__ = BasicTokenizer(do_lower_case=_UpperCAmelCase ) self.assertListEqual( tokenizer.tokenize(""" \tHeLLo!how \n Are yoU? """ ) , ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""] ) def SCREAMING_SNAKE_CASE__ ( self : str ): """simple docstring""" UpperCAmelCase__ = BasicTokenizer(do_lower_case=_UpperCAmelCase , strip_accents=_UpperCAmelCase ) self.assertListEqual( tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""HäLLo""", """!""", """how""", """Are""", """yoU""", """?"""] ) def SCREAMING_SNAKE_CASE__ ( self : str ): """simple docstring""" UpperCAmelCase__ = BasicTokenizer(do_lower_case=_UpperCAmelCase , strip_accents=_UpperCAmelCase ) self.assertListEqual( tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""HaLLo""", """!""", """how""", """Are""", """yoU""", """?"""] ) def SCREAMING_SNAKE_CASE__ ( self : Any ): """simple docstring""" UpperCAmelCase__ = BasicTokenizer(do_lower_case=_UpperCAmelCase , never_split=["""[UNK]"""] ) self.assertListEqual( tokenizer.tokenize(""" \tHeLLo!how \n Are yoU? [UNK]""" ) , ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?""", """[UNK]"""] ) def SCREAMING_SNAKE_CASE__ ( self : Tuple ): """simple docstring""" UpperCAmelCase__ = BasicTokenizer() UpperCAmelCase__ = """a\n'll !!to?'d of, can't.""" UpperCAmelCase__ = ["""a""", """'""", """ll""", """!""", """!""", """to""", """?""", """'""", """d""", """of""", """,""", """can""", """'""", """t""", """."""] self.assertListEqual(tokenizer.tokenize(_UpperCAmelCase ) , _UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ): """simple docstring""" UpperCAmelCase__ = ["""[UNK]""", """[CLS]""", """[SEP]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing"""] UpperCAmelCase__ = {} for i, token in enumerate(_UpperCAmelCase ): UpperCAmelCase__ = i UpperCAmelCase__ = WordpieceTokenizer(vocab=_UpperCAmelCase , unk_token="""[UNK]""" ) self.assertListEqual(tokenizer.tokenize("""""" ) , [] ) self.assertListEqual(tokenizer.tokenize("""unwanted running""" ) , ["""un""", """##want""", """##ed""", """runn""", """##ing"""] ) self.assertListEqual(tokenizer.tokenize("""unwantedX running""" ) , ["""[UNK]""", """runn""", """##ing"""] ) def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ): """simple docstring""" self.assertTrue(_is_whitespace(""" """ ) ) self.assertTrue(_is_whitespace("""\t""" ) ) self.assertTrue(_is_whitespace("""\r""" ) ) self.assertTrue(_is_whitespace("""\n""" ) ) self.assertTrue(_is_whitespace("""\u00A0""" ) ) self.assertFalse(_is_whitespace("""A""" ) ) self.assertFalse(_is_whitespace("""-""" ) ) def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ): """simple docstring""" self.assertTrue(_is_control("""\u0005""" ) ) self.assertFalse(_is_control("""A""" ) ) self.assertFalse(_is_control(""" """ ) ) self.assertFalse(_is_control("""\t""" ) ) self.assertFalse(_is_control("""\r""" ) ) def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ): """simple docstring""" self.assertTrue(_is_punctuation("""-""" ) ) self.assertTrue(_is_punctuation("""$""" ) ) self.assertTrue(_is_punctuation("""`""" ) ) self.assertTrue(_is_punctuation(""".""" ) ) self.assertFalse(_is_punctuation("""A""" ) ) self.assertFalse(_is_punctuation(""" """ ) ) def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ): """simple docstring""" UpperCAmelCase__ = self.get_tokenizer() UpperCAmelCase__ = self.get_rust_tokenizer() # Example taken from the issue https://github.com/huggingface/tokenizers/issues/340 self.assertListEqual([tokenizer.tokenize(_UpperCAmelCase ) for t in ["""Test""", """\xad""", """test"""]] , [["""[UNK]"""], [], ["""[UNK]"""]] ) self.assertListEqual( [rust_tokenizer.tokenize(_UpperCAmelCase ) for t in ["""Test""", """\xad""", """test"""]] , [["""[UNK]"""], [], ["""[UNK]"""]] ) @slow def SCREAMING_SNAKE_CASE__ ( self : str ): """simple docstring""" UpperCAmelCase__ = self.tokenizer_class.from_pretrained("""bert-base-uncased""" ) UpperCAmelCase__ = tokenizer.encode("""sequence builders""" , add_special_tokens=_UpperCAmelCase ) UpperCAmelCase__ = tokenizer.encode("""multi-sequence build""" , add_special_tokens=_UpperCAmelCase ) UpperCAmelCase__ = tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase ) UpperCAmelCase__ = tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase , _UpperCAmelCase ) assert encoded_sentence == [1_01] + text + [1_02] assert encoded_pair == [1_01] + text + [1_02] + text_a + [1_02] def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ): """simple docstring""" for tokenizer, pretrained_name, kwargs in self.tokenizers_list: with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ): UpperCAmelCase__ = self.rust_tokenizer_class.from_pretrained(_UpperCAmelCase , **_UpperCAmelCase ) UpperCAmelCase__ = f'''A, naïve {tokenizer_r.mask_token} AllenNLP sentence.''' UpperCAmelCase__ = tokenizer_r.encode_plus( _UpperCAmelCase , return_attention_mask=_UpperCAmelCase , return_token_type_ids=_UpperCAmelCase , return_offsets_mapping=_UpperCAmelCase , add_special_tokens=_UpperCAmelCase , ) UpperCAmelCase__ = tokenizer_r.do_lower_case if hasattr(_UpperCAmelCase , """do_lower_case""" ) else False UpperCAmelCase__ = ( [ ((0, 0), tokenizer_r.cls_token), ((0, 1), """A"""), ((1, 2), ""","""), ((3, 5), """na"""), ((5, 6), """##ï"""), ((6, 8), """##ve"""), ((9, 15), tokenizer_r.mask_token), ((16, 21), """Allen"""), ((21, 23), """##NL"""), ((23, 24), """##P"""), ((25, 33), """sentence"""), ((33, 34), """."""), ((0, 0), tokenizer_r.sep_token), ] if not do_lower_case else [ ((0, 0), tokenizer_r.cls_token), ((0, 1), """a"""), ((1, 2), ""","""), ((3, 8), """naive"""), ((9, 15), tokenizer_r.mask_token), ((16, 21), """allen"""), ((21, 23), """##nl"""), ((23, 24), """##p"""), ((25, 33), """sentence"""), ((33, 34), """."""), ((0, 0), tokenizer_r.sep_token), ] ) self.assertEqual( [e[1] for e in expected_results] , tokenizer_r.convert_ids_to_tokens(tokens["""input_ids"""] ) ) self.assertEqual([e[0] for e in expected_results] , tokens["""offset_mapping"""] ) def SCREAMING_SNAKE_CASE__ ( self : Tuple ): """simple docstring""" UpperCAmelCase__ = ["""的""", """人""", """有"""] UpperCAmelCase__ = """""".join(_UpperCAmelCase ) for tokenizer, pretrained_name, kwargs in self.tokenizers_list: with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ): UpperCAmelCase__ = True UpperCAmelCase__ = self.tokenizer_class.from_pretrained(_UpperCAmelCase , **_UpperCAmelCase ) UpperCAmelCase__ = self.rust_tokenizer_class.from_pretrained(_UpperCAmelCase , **_UpperCAmelCase ) UpperCAmelCase__ = tokenizer_p.encode(_UpperCAmelCase , add_special_tokens=_UpperCAmelCase ) UpperCAmelCase__ = tokenizer_r.encode(_UpperCAmelCase , add_special_tokens=_UpperCAmelCase ) UpperCAmelCase__ = tokenizer_r.convert_ids_to_tokens(_UpperCAmelCase ) UpperCAmelCase__ = tokenizer_p.convert_ids_to_tokens(_UpperCAmelCase ) # it is expected that each Chinese character is not preceded by "##" self.assertListEqual(_UpperCAmelCase , _UpperCAmelCase ) self.assertListEqual(_UpperCAmelCase , _UpperCAmelCase ) UpperCAmelCase__ = False UpperCAmelCase__ = self.rust_tokenizer_class.from_pretrained(_UpperCAmelCase , **_UpperCAmelCase ) UpperCAmelCase__ = self.tokenizer_class.from_pretrained(_UpperCAmelCase , **_UpperCAmelCase ) UpperCAmelCase__ = tokenizer_r.encode(_UpperCAmelCase , add_special_tokens=_UpperCAmelCase ) UpperCAmelCase__ = tokenizer_p.encode(_UpperCAmelCase , add_special_tokens=_UpperCAmelCase ) UpperCAmelCase__ = tokenizer_r.convert_ids_to_tokens(_UpperCAmelCase ) UpperCAmelCase__ = tokenizer_p.convert_ids_to_tokens(_UpperCAmelCase ) # it is expected that only the first Chinese character is not preceded by "##". UpperCAmelCase__ = [ f'''##{token}''' if idx != 0 else token for idx, token in enumerate(_UpperCAmelCase ) ] self.assertListEqual(_UpperCAmelCase , _UpperCAmelCase ) self.assertListEqual(_UpperCAmelCase , _UpperCAmelCase )

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'''simple docstring''' import os import unicodedata from shutil import copyfile from typing import Any, Dict, List, Optional, Tuple import sentencepiece as spm from ...tokenization_utils import AddedToken, PreTrainedTokenizer from ...utils import SPIECE_UNDERLINE, logging UpperCAmelCase_ = logging.get_logger(__name__) UpperCAmelCase_ = {'vocab_file': 'spiece.model'} UpperCAmelCase_ = { 'vocab_file': { 'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model', } } class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' def __init__( self : Dict , _UpperCAmelCase : str , _UpperCAmelCase : Any=False , _UpperCAmelCase : int=True , _UpperCAmelCase : Union[str, Any]=False , _UpperCAmelCase : Dict="<s>" , _UpperCAmelCase : int="</s>" , _UpperCAmelCase : Dict="<unk>" , _UpperCAmelCase : Tuple="<sep>" , _UpperCAmelCase : List[Any]="<pad>" , _UpperCAmelCase : int="<cls>" , _UpperCAmelCase : Union[str, Any]="<mask>" , _UpperCAmelCase : List[str]=["<eop>", "<eod>"] , _UpperCAmelCase : Optional[Dict[str, Any]] = None , **_UpperCAmelCase : int , ): """simple docstring""" UpperCAmelCase__ = AddedToken(_UpperCAmelCase , lstrip=_UpperCAmelCase , rstrip=_UpperCAmelCase ) if isinstance(_UpperCAmelCase , _UpperCAmelCase ) else mask_token UpperCAmelCase__ = {} if sp_model_kwargs is None else sp_model_kwargs super().__init__( do_lower_case=_UpperCAmelCase , remove_space=_UpperCAmelCase , keep_accents=_UpperCAmelCase , bos_token=_UpperCAmelCase , eos_token=_UpperCAmelCase , unk_token=_UpperCAmelCase , sep_token=_UpperCAmelCase , pad_token=_UpperCAmelCase , cls_token=_UpperCAmelCase , mask_token=_UpperCAmelCase , additional_special_tokens=_UpperCAmelCase , sp_model_kwargs=self.sp_model_kwargs , **_UpperCAmelCase , ) UpperCAmelCase__ = 3 UpperCAmelCase__ = do_lower_case UpperCAmelCase__ = remove_space UpperCAmelCase__ = keep_accents UpperCAmelCase__ = vocab_file UpperCAmelCase__ = spm.SentencePieceProcessor(**self.sp_model_kwargs ) self.sp_model.Load(_UpperCAmelCase ) try: import jieba except ModuleNotFoundError as error: raise error.__class__( """You need to install jieba to use CpmTokenizer or CpmTokenizerFast. """ """See https://pypi.org/project/jieba/ for installation.""" ) UpperCAmelCase__ = jieba UpperCAmelCase__ = str.maketrans(""" \n""" , """\u2582\u2583""" ) @property # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ): """simple docstring""" return len(self.sp_model ) def SCREAMING_SNAKE_CASE__ ( self : List[str] ): """simple docstring""" UpperCAmelCase__ = {self.convert_ids_to_tokens(_UpperCAmelCase ): i for i in range(self.vocab_size )} vocab.update(self.added_tokens_encoder ) return vocab def __getstate__( self : Dict ): """simple docstring""" UpperCAmelCase__ = self.__dict__.copy() UpperCAmelCase__ = None return state def __setstate__( self : Union[str, Any] , _UpperCAmelCase : Union[str, Any] ): """simple docstring""" UpperCAmelCase__ = d # for backward compatibility if not hasattr(self , """sp_model_kwargs""" ): UpperCAmelCase__ = {} UpperCAmelCase__ = spm.SentencePieceProcessor(**self.sp_model_kwargs ) self.sp_model.Load(self.vocab_file ) def SCREAMING_SNAKE_CASE__ ( self : int , _UpperCAmelCase : Optional[Any] ): """simple docstring""" if self.remove_space: UpperCAmelCase__ = """ """.join(inputs.strip().split() ) else: UpperCAmelCase__ = inputs UpperCAmelCase__ = outputs.replace("""``""" , """\"""" ).replace("""''""" , """\"""" ) if not self.keep_accents: UpperCAmelCase__ = unicodedata.normalize("""NFKD""" , _UpperCAmelCase ) UpperCAmelCase__ = """""".join([c for c in outputs if not unicodedata.combining(_UpperCAmelCase )] ) if self.do_lower_case: UpperCAmelCase__ = outputs.lower() return outputs def SCREAMING_SNAKE_CASE__ ( self : Tuple , _UpperCAmelCase : str ): """simple docstring""" UpperCAmelCase__ = self.preprocess_text(_UpperCAmelCase ) UpperCAmelCase__ = self.sp_model.encode(_UpperCAmelCase , out_type=_UpperCAmelCase ) UpperCAmelCase__ = [] for piece in pieces: if len(_UpperCAmelCase ) > 1 and piece[-1] == str(""",""" ) and piece[-2].isdigit(): UpperCAmelCase__ = self.sp_model.EncodeAsPieces(piece[:-1].replace(_UpperCAmelCase , """""" ) ) if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE: if len(cur_pieces[0] ) == 1: UpperCAmelCase__ = cur_pieces[1:] else: UpperCAmelCase__ = cur_pieces[0][1:] cur_pieces.append(piece[-1] ) new_pieces.extend(_UpperCAmelCase ) else: new_pieces.append(_UpperCAmelCase ) return new_pieces def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , _UpperCAmelCase : Union[str, Any] ): """simple docstring""" return self.sp_model.PieceToId(_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Dict , _UpperCAmelCase : Any ): """simple docstring""" return self.sp_model.IdToPiece(_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : int , _UpperCAmelCase : Dict ): """simple docstring""" UpperCAmelCase__ = """""".join(_UpperCAmelCase ).replace(_UpperCAmelCase , """ """ ).strip() return out_string def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] , _UpperCAmelCase : List[int] , _UpperCAmelCase : Optional[List[int]] = None ): """simple docstring""" UpperCAmelCase__ = [self.sep_token_id] UpperCAmelCase__ = [self.cls_token_id] if token_ids_a is None: return token_ids_a + sep + cls return token_ids_a + sep + token_ids_a + sep + cls def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] , _UpperCAmelCase : List[int] , _UpperCAmelCase : Optional[List[int]] = None , _UpperCAmelCase : bool = False ): """simple docstring""" if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_a=_UpperCAmelCase , token_ids_a=_UpperCAmelCase , already_has_special_tokens=_UpperCAmelCase ) if token_ids_a is not None: return ([0] * len(_UpperCAmelCase )) + [1] + ([0] * len(_UpperCAmelCase )) + [1, 1] return ([0] * len(_UpperCAmelCase )) + [1, 1] def SCREAMING_SNAKE_CASE__ ( self : Dict , _UpperCAmelCase : List[int] , _UpperCAmelCase : Optional[List[int]] = None ): """simple docstring""" UpperCAmelCase__ = [self.sep_token_id] UpperCAmelCase__ = [2] if token_ids_a is None: return len(token_ids_a + sep ) * [0] + cls_segment_id return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id def SCREAMING_SNAKE_CASE__ ( self : List[Any] , _UpperCAmelCase : str , _UpperCAmelCase : Optional[str] = None ): """simple docstring""" if not os.path.isdir(_UpperCAmelCase ): logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' ) return UpperCAmelCase__ = os.path.join( _UpperCAmelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] ) if os.path.abspath(self.vocab_file ) != os.path.abspath(_UpperCAmelCase ) and os.path.isfile(self.vocab_file ): copyfile(self.vocab_file , _UpperCAmelCase ) elif not os.path.isfile(self.vocab_file ): with open(_UpperCAmelCase , """wb""" ) as fi: UpperCAmelCase__ = self.sp_model.serialized_model_proto() fi.write(_UpperCAmelCase ) return (out_vocab_file,) def SCREAMING_SNAKE_CASE__ ( self : Tuple , *_UpperCAmelCase : Tuple , **_UpperCAmelCase : Optional[Any] ): """simple docstring""" UpperCAmelCase__ = super()._decode(*_UpperCAmelCase , **_UpperCAmelCase ) UpperCAmelCase__ = text.replace(""" """ , """""" ).replace("""\u2582""" , """ """ ).replace("""\u2583""" , """\n""" ) return text

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1

'''simple docstring''' def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int ): '''simple docstring''' if a < 0: raise ValueError("""Input value must be a positive integer""" ) elif isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ): raise TypeError("""Input value must be a 'int' type""" ) return bin(SCREAMING_SNAKE_CASE__ ).count("""1""" ) if __name__ == "__main__": import doctest doctest.testmod()

346

'''simple docstring''' import argparse import logging import os import datasets import tensorflow as tf from transformers import AutoTokenizer UpperCAmelCase_ = logging.getLogger(__name__) def _UpperCamelCase ( ): '''simple docstring''' UpperCAmelCase__ = argparse.ArgumentParser( description="""Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset.""" ) parser.add_argument( """--dataset_name""" , type=SCREAMING_SNAKE_CASE__ , default="""wikitext""" , help="""Name of the training. Explore datasets at: hf.co/datasets.""" , ) parser.add_argument( """--dataset_config""" , type=SCREAMING_SNAKE_CASE__ , default="""wikitext-103-raw-v1""" , help="""Configuration name of the dataset.""" ) parser.add_argument( """--tokenizer_name_or_path""" , type=SCREAMING_SNAKE_CASE__ , default="""sayakpaul/unigram-tokenizer-wikitext""" , help="""Tokenizer identifier. Can be a local filepath or a Hub identifier.""" , ) parser.add_argument( """--shard_size""" , type=SCREAMING_SNAKE_CASE__ , default=1000 , help="""Number of entries to go in a single shard.""" , ) parser.add_argument("""--split""" , type=SCREAMING_SNAKE_CASE__ , default="""train""" , choices=["""train""", """test""", """validation"""] ) parser.add_argument( """--limit""" , default=SCREAMING_SNAKE_CASE__ , type=SCREAMING_SNAKE_CASE__ , help="""Limit the number of shards (used for debugging).""" , ) parser.add_argument( """--max_length""" , type=SCREAMING_SNAKE_CASE__ , default=512 , help="""Maximum sequence length. For training on TPUs, it helps to have a maximum""" """ sequence length that is a multiple of 8.""" , ) parser.add_argument( """--output_dir""" , default="""tf-tpu""" , type=SCREAMING_SNAKE_CASE__ , help="""Output directory where the TFRecord shards will be saved. If the""" """ path is appended with `gs://` ('gs://tf-tpu', for example) then the TFRecord""" """ shards will be directly saved to a Google Cloud Storage bucket.""" , ) UpperCAmelCase__ = parser.parse_args() return args def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Any ): '''simple docstring''' def fn(SCREAMING_SNAKE_CASE__ : Union[str, Any] ): return tokenizer(examples["""text"""] ) return fn def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Optional[int] ): '''simple docstring''' UpperCAmelCase__ = [] for i in range(len(tokenized_data["""input_ids"""] ) ): UpperCAmelCase__ = { """input_ids""": tf.train.Feature(intaa_list=tf.train.IntaaList(value=tokenized_data["""input_ids"""][i] ) ), """attention_mask""": tf.train.Feature( intaa_list=tf.train.IntaaList(value=tokenized_data["""attention_mask"""][i] ) ), } UpperCAmelCase__ = tf.train.Features(feature=SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = tf.train.Example(features=SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = example.SerializeToString() records.append(SCREAMING_SNAKE_CASE__ ) return records def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Any ): '''simple docstring''' UpperCAmelCase__ = datasets.load_dataset(args.dataset_name , args.dataset_config , split=args.split ) if args.limit is not None: UpperCAmelCase__ = min(len(SCREAMING_SNAKE_CASE__ ) , args.limit ) UpperCAmelCase__ = dataset.select(range(SCREAMING_SNAKE_CASE__ ) ) print(F'''Limiting the dataset to {args.limit} entries.''' ) UpperCAmelCase__ = AutoTokenizer.from_pretrained(args.tokenizer_name_or_path ) # Handle output directory creation. # For serializing into a Google Cloud Storage Bucket, one needs to first # create a bucket. if "gs" not in args.output_dir: if not os.path.exists(args.output_dir ): os.makedirs(args.output_dir ) UpperCAmelCase__ = os.path.join(args.output_dir , args.split ) if not os.path.exists(SCREAMING_SNAKE_CASE__ ): os.makedirs(SCREAMING_SNAKE_CASE__ ) else: UpperCAmelCase__ = os.path.join(args.output_dir , args.split ) # Tokenize the whole dataset at once. UpperCAmelCase__ = tokenize_function(SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = dataset.map(SCREAMING_SNAKE_CASE__ , batched=SCREAMING_SNAKE_CASE__ , num_proc=4 , remove_columns=["""text"""] ) # We need to concatenate all our texts together, and then split the result # into chunks of a fixed size, which we will call block_size. To do this, we # will use the map method again, with the option batched=True. When we use batched=True, # the function we pass to map() will be passed multiple inputs at once, allowing us # to group them into more or fewer examples than we had in the input. # This allows us to create our new fixed-length samples. The advantage of this # method is that we don't lose a whole lot of content from the dataset compared to the # case where we simply tokenize with a pre-defined max_length. def group_texts(SCREAMING_SNAKE_CASE__ : int ): # Concatenate all texts. UpperCAmelCase__ = {k: sum(examples[k] , [] ) for k in examples.keys()} UpperCAmelCase__ = len(concatenated_examples[list(examples.keys() )[0]] ) # We drop the small remainder, though you could add padding instead if the model supports it # In this, as in all things, we advise you to follow your heart 🫀 UpperCAmelCase__ = (total_length // args.max_length) * args.max_length # Split by chunks of max_len. UpperCAmelCase__ = { k: [t[i : i + args.max_length] for i in range(0 , SCREAMING_SNAKE_CASE__ , args.max_length )] for k, t in concatenated_examples.items() } return result UpperCAmelCase__ = dataset_tokenized.map(SCREAMING_SNAKE_CASE__ , batched=SCREAMING_SNAKE_CASE__ , batch_size=1000 , num_proc=4 ) UpperCAmelCase__ = 0 UpperCAmelCase__ = 0 for shard in range(0 , len(SCREAMING_SNAKE_CASE__ ) , args.shard_size ): UpperCAmelCase__ = grouped_dataset[shard : shard + args.shard_size] UpperCAmelCase__ = len(dataset_snapshot["""input_ids"""] ) UpperCAmelCase__ = os.path.join(SCREAMING_SNAKE_CASE__ , F'''dataset-{shard_count}-{records_containing}.tfrecord''' ) UpperCAmelCase__ = get_serialized_examples(SCREAMING_SNAKE_CASE__ ) with tf.io.TFRecordWriter(SCREAMING_SNAKE_CASE__ ) as out_file: for i in range(len(SCREAMING_SNAKE_CASE__ ) ): UpperCAmelCase__ = serialized_examples[i] out_file.write(SCREAMING_SNAKE_CASE__ ) print("""Wrote file {} containing {} records""".format(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) ) shard_count += 1 total_records += records_containing with open(F'''split-{args.split}-records-count.txt''' , """w""" ) as f: print(F'''Total {args.split} records: {total_records}''' , file=SCREAMING_SNAKE_CASE__ ) if __name__ == "__main__": UpperCAmelCase_ = parse_args() main(args)

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'''simple docstring''' import colorsys from PIL import Image # type: ignore def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : int ): '''simple docstring''' UpperCAmelCase__ = x UpperCAmelCase__ = y for step in range(SCREAMING_SNAKE_CASE__ ): # noqa: B007 UpperCAmelCase__ = a * a - b * b + x UpperCAmelCase__ = 2 * a * b + y UpperCAmelCase__ = a_new # divergence happens for all complex number with an absolute value # greater than 4 if a * a + b * b > 4: break return step / (max_step - 1) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : float ): '''simple docstring''' if distance == 1: return (0, 0, 0) else: return (255, 255, 255) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : float ): '''simple docstring''' if distance == 1: return (0, 0, 0) else: return tuple(round(i * 255 ) for i in colorsys.hsv_to_rgb(SCREAMING_SNAKE_CASE__ , 1 , 1 ) ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int = 800 , SCREAMING_SNAKE_CASE__ : int = 600 , SCREAMING_SNAKE_CASE__ : float = -0.6 , SCREAMING_SNAKE_CASE__ : float = 0 , SCREAMING_SNAKE_CASE__ : float = 3.2 , SCREAMING_SNAKE_CASE__ : int = 50 , SCREAMING_SNAKE_CASE__ : bool = True , ): '''simple docstring''' UpperCAmelCase__ = Image.new("""RGB""" , (image_width, image_height) ) UpperCAmelCase__ = img.load() # loop through the image-coordinates for image_x in range(SCREAMING_SNAKE_CASE__ ): for image_y in range(SCREAMING_SNAKE_CASE__ ): # determine the figure-coordinates based on the image-coordinates UpperCAmelCase__ = figure_width / image_width * image_height UpperCAmelCase__ = figure_center_x + (image_x / image_width - 0.5) * figure_width UpperCAmelCase__ = figure_center_y + (image_y / image_height - 0.5) * figure_height UpperCAmelCase__ = get_distance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) # color the corresponding pixel based on the selected coloring-function if use_distance_color_coding: UpperCAmelCase__ = get_color_coded_rgb(SCREAMING_SNAKE_CASE__ ) else: UpperCAmelCase__ = get_black_and_white_rgb(SCREAMING_SNAKE_CASE__ ) return img if __name__ == "__main__": import doctest doctest.testmod() # colored version, full figure UpperCAmelCase_ = get_image() # uncomment for colored version, different section, zoomed in # img = get_image(figure_center_x = -0.6, figure_center_y = -0.4, # figure_width = 0.8) # uncomment for black and white version, full figure # img = get_image(use_distance_color_coding = False) # uncomment to save the image # img.save("mandelbrot.png") img.show()

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'''simple docstring''' import numpy as np import torch from torch.nn import CrossEntropyLoss from transformers import AutoModelForCausalLM, AutoTokenizer import datasets from datasets import logging UpperCAmelCase_ = '\\n\n' UpperCAmelCase_ = '\nPerplexity (PPL) is one of the most common metrics for evaluating language models.\nIt is defined as the exponentiated average negative log-likelihood of a sequence.\n\nFor more information, see https://huggingface.co/docs/transformers/perplexity\n' UpperCAmelCase_ = '\nArgs:\n model_id (str): model used for calculating Perplexity\n NOTE: Perplexity can only be calculated for causal language models.\n This includes models such as gpt2, causal variations of bert,\n causal versions of t5, and more (the full list can be found\n in the AutoModelForCausalLM documentation here:\n https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )\n\n input_texts (list of str): input text, each separate text snippet\n is one list entry.\n batch_size (int): the batch size to run texts through the model. Defaults to 16.\n add_start_token (bool): whether to add the start token to the texts,\n so the perplexity can include the probability of the first word. Defaults to True.\n device (str): device to run on, defaults to \'cuda\' when available\nReturns:\n perplexity: dictionary containing the perplexity scores for the texts\n in the input list, as well as the mean perplexity. If one of the input texts is\n longer than the max input length of the model, then it is truncated to the\n max length for the perplexity computation.\nExamples:\n Example 1:\n >>> perplexity = datasets.load_metric("perplexity")\n >>> input_texts = ["lorem ipsum", "Happy Birthday!", "Bienvenue"]\n >>> results = perplexity.compute(model_id=\'gpt2\',\n ... add_start_token=False,\n ... input_texts=input_texts) # doctest:+ELLIPSIS\n >>> print(list(results.keys()))\n [\'perplexities\', \'mean_perplexity\']\n >>> print(round(results["mean_perplexity"], 2))\n 78.22\n >>> print(round(results["perplexities"][0], 2))\n 11.11\n\n Example 2:\n >>> perplexity = datasets.load_metric("perplexity")\n >>> input_texts = datasets.load_dataset("wikitext",\n ... "wikitext-2-raw-v1",\n ... split="test")["text"][:50] # doctest:+ELLIPSIS\n [...]\n >>> input_texts = [s for s in input_texts if s!=\'\']\n >>> results = perplexity.compute(model_id=\'gpt2\',\n ... input_texts=input_texts) # doctest:+ELLIPSIS\n >>> print(list(results.keys()))\n [\'perplexities\', \'mean_perplexity\']\n >>> print(round(results["mean_perplexity"], 2))\n 60.35\n >>> print(round(results["perplexities"][0], 2))\n 81.12\n' @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION ) class lowerCAmelCase_ ( datasets.Metric ): '''simple docstring''' def SCREAMING_SNAKE_CASE__ ( self : Dict ): """simple docstring""" return datasets.MetricInfo( description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features( { """input_texts""": datasets.Value("""string""" ), } ) , reference_urls=["""https://huggingface.co/docs/transformers/perplexity"""] , ) def SCREAMING_SNAKE_CASE__ ( self : List[Any] , _UpperCAmelCase : Tuple , _UpperCAmelCase : int , _UpperCAmelCase : int = 16 , _UpperCAmelCase : bool = True , _UpperCAmelCase : Optional[int]=None ): """simple docstring""" if device is not None: assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu." if device == "gpu": UpperCAmelCase__ = """cuda""" else: UpperCAmelCase__ = """cuda""" if torch.cuda.is_available() else """cpu""" UpperCAmelCase__ = AutoModelForCausalLM.from_pretrained(_UpperCAmelCase ) UpperCAmelCase__ = model.to(_UpperCAmelCase ) UpperCAmelCase__ = AutoTokenizer.from_pretrained(_UpperCAmelCase ) # if batch_size > 1 (which generally leads to padding being required), and # if there is not an already assigned pad_token, assign an existing # special token to also be the padding token if tokenizer.pad_token is None and batch_size > 1: UpperCAmelCase__ = list(tokenizer.special_tokens_map_extended.values() ) # check that the model already has at least one special token defined assert ( len(_UpperCAmelCase ) > 0 ), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1." # assign one of the special tokens to also be the pad token tokenizer.add_special_tokens({"""pad_token""": existing_special_tokens[0]} ) if add_start_token: # leave room for <BOS> token to be added: assert ( tokenizer.bos_token is not None ), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False" UpperCAmelCase__ = model.config.max_length - 1 else: UpperCAmelCase__ = model.config.max_length UpperCAmelCase__ = tokenizer( _UpperCAmelCase , add_special_tokens=_UpperCAmelCase , padding=_UpperCAmelCase , truncation=_UpperCAmelCase , max_length=_UpperCAmelCase , return_tensors="""pt""" , return_attention_mask=_UpperCAmelCase , ).to(_UpperCAmelCase ) UpperCAmelCase__ = encodings["""input_ids"""] UpperCAmelCase__ = encodings["""attention_mask"""] # check that each input is long enough: if add_start_token: assert torch.all(torch.ge(attn_masks.sum(1 ) , 1 ) ), "Each input text must be at least one token long." else: assert torch.all( torch.ge(attn_masks.sum(1 ) , 2 ) ), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings." UpperCAmelCase__ = [] UpperCAmelCase__ = CrossEntropyLoss(reduction="""none""" ) for start_index in logging.tqdm(range(0 , len(_UpperCAmelCase ) , _UpperCAmelCase ) ): UpperCAmelCase__ = min(start_index + batch_size , len(_UpperCAmelCase ) ) UpperCAmelCase__ = encoded_texts[start_index:end_index] UpperCAmelCase__ = attn_masks[start_index:end_index] if add_start_token: UpperCAmelCase__ = torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0 ) ).to(_UpperCAmelCase ) UpperCAmelCase__ = torch.cat([bos_tokens_tensor, encoded_batch] , dim=1 ) UpperCAmelCase__ = torch.cat( [torch.ones(bos_tokens_tensor.size() , dtype=torch.intaa ).to(_UpperCAmelCase ), attn_mask] , dim=1 ) UpperCAmelCase__ = encoded_batch with torch.no_grad(): UpperCAmelCase__ = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase ).logits UpperCAmelCase__ = out_logits[..., :-1, :].contiguous() UpperCAmelCase__ = labels[..., 1:].contiguous() UpperCAmelCase__ = attn_mask[..., 1:].contiguous() UpperCAmelCase__ = torch.expa( (loss_fct(shift_logits.transpose(1 , 2 ) , _UpperCAmelCase ) * shift_attention_mask_batch).sum(1 ) / shift_attention_mask_batch.sum(1 ) ) ppls += perplexity_batch.tolist() return {"perplexities": ppls, "mean_perplexity": np.mean(_UpperCAmelCase )}

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'''simple docstring''' # flake8: noqa # Lint as: python3 from typing import Dict, List, Optional, Type from .. import config from ..utils import logging from .formatting import ( ArrowFormatter, CustomFormatter, Formatter, PandasFormatter, PythonFormatter, TensorFormatter, format_table, query_table, ) from .np_formatter import NumpyFormatter UpperCAmelCase_ = logging.get_logger(__name__) UpperCAmelCase_ = {} UpperCAmelCase_ = {} UpperCAmelCase_ = {} def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : type , SCREAMING_SNAKE_CASE__ : Optional[str] , SCREAMING_SNAKE_CASE__ : Optional[List[str]] = None , ): '''simple docstring''' UpperCAmelCase__ = aliases if aliases is not None else [] if format_type in _FORMAT_TYPES: logger.warning( F'''Overwriting format type \'{format_type}\' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})''' ) UpperCAmelCase__ = formatter_cls for alias in set(aliases + [format_type] ): if alias in _FORMAT_TYPES_ALIASES: logger.warning( F'''Overwriting format type alias \'{alias}\' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})''' ) UpperCAmelCase__ = format_type def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Exception , SCREAMING_SNAKE_CASE__ : Optional[str] , SCREAMING_SNAKE_CASE__ : Optional[List[str]] = None ): '''simple docstring''' UpperCAmelCase__ = aliases if aliases is not None else [] for alias in set(aliases + [format_type] ): UpperCAmelCase__ = unavailable_error # Here we define all the available formatting functions that can be used by `Dataset.set_format` _register_formatter(PythonFormatter, None, aliases=['python']) _register_formatter(ArrowFormatter, 'arrow', aliases=['pa', 'pyarrow']) _register_formatter(NumpyFormatter, 'numpy', aliases=['np']) _register_formatter(PandasFormatter, 'pandas', aliases=['pd']) _register_formatter(CustomFormatter, 'custom') if config.TORCH_AVAILABLE: from .torch_formatter import TorchFormatter _register_formatter(TorchFormatter, 'torch', aliases=['pt', 'pytorch']) else: UpperCAmelCase_ = ValueError('PyTorch needs to be installed to be able to return PyTorch tensors.') _register_unavailable_formatter(_torch_error, 'torch', aliases=['pt', 'pytorch']) if config.TF_AVAILABLE: from .tf_formatter import TFFormatter _register_formatter(TFFormatter, 'tensorflow', aliases=['tf']) else: UpperCAmelCase_ = ValueError('Tensorflow needs to be installed to be able to return Tensorflow tensors.') _register_unavailable_formatter(_tf_error, 'tensorflow', aliases=['tf']) if config.JAX_AVAILABLE: from .jax_formatter import JaxFormatter _register_formatter(JaxFormatter, 'jax', aliases=[]) else: UpperCAmelCase_ = ValueError('JAX needs to be installed to be able to return JAX arrays.') _register_unavailable_formatter(_jax_error, 'jax', aliases=[]) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Optional[str] ): '''simple docstring''' if format_type in _FORMAT_TYPES_ALIASES: return _FORMAT_TYPES_ALIASES[format_type] else: return format_type def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Optional[str] , **SCREAMING_SNAKE_CASE__ : Union[str, Any] ): '''simple docstring''' UpperCAmelCase__ = get_format_type_from_alias(SCREAMING_SNAKE_CASE__ ) if format_type in _FORMAT_TYPES: return _FORMAT_TYPES[format_type](**SCREAMING_SNAKE_CASE__ ) if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE: raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type] else: raise ValueError( F'''Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None )}, but got \'{format_type}\'''' )

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'''simple docstring''' def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int = 1000000 ): '''simple docstring''' UpperCAmelCase__ = [i - 1 for i in range(limit + 1 )] for i in range(2 , limit + 1 ): if phi[i] == i - 1: for j in range(2 * i , limit + 1 , SCREAMING_SNAKE_CASE__ ): phi[j] -= phi[j] // i return sum(phi[2 : limit + 1] ) if __name__ == "__main__": print(solution())

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'''simple docstring''' def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : list[list[int]] ): '''simple docstring''' def update_area_of_max_square(SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ) -> int: # BASE CASE if row >= rows or col >= cols: return 0 UpperCAmelCase__ = update_area_of_max_square(SCREAMING_SNAKE_CASE__ , col + 1 ) UpperCAmelCase__ = update_area_of_max_square(row + 1 , col + 1 ) UpperCAmelCase__ = update_area_of_max_square(row + 1 , SCREAMING_SNAKE_CASE__ ) if mat[row][col]: UpperCAmelCase__ = 1 + min([right, diagonal, down] ) UpperCAmelCase__ = max(largest_square_area[0] , SCREAMING_SNAKE_CASE__ ) return sub_problem_sol else: return 0 UpperCAmelCase__ = [0] update_area_of_max_square(0 , 0 ) return largest_square_area[0] def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : list[list[int]] ): '''simple docstring''' def update_area_of_max_square_using_dp_array( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : list[list[int]] ) -> int: if row >= rows or col >= cols: return 0 if dp_array[row][col] != -1: return dp_array[row][col] UpperCAmelCase__ = update_area_of_max_square_using_dp_array(SCREAMING_SNAKE_CASE__ , col + 1 , SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = update_area_of_max_square_using_dp_array(row + 1 , col + 1 , SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = update_area_of_max_square_using_dp_array(row + 1 , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) if mat[row][col]: UpperCAmelCase__ = 1 + min([right, diagonal, down] ) UpperCAmelCase__ = max(largest_square_area[0] , SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = sub_problem_sol return sub_problem_sol else: return 0 UpperCAmelCase__ = [0] UpperCAmelCase__ = [[-1] * cols for _ in range(SCREAMING_SNAKE_CASE__ )] update_area_of_max_square_using_dp_array(0 , 0 , SCREAMING_SNAKE_CASE__ ) return largest_square_area[0] def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : list[list[int]] ): '''simple docstring''' UpperCAmelCase__ = [[0] * (cols + 1) for _ in range(rows + 1 )] UpperCAmelCase__ = 0 for row in range(rows - 1 , -1 , -1 ): for col in range(cols - 1 , -1 , -1 ): UpperCAmelCase__ = dp_array[row][col + 1] UpperCAmelCase__ = dp_array[row + 1][col + 1] UpperCAmelCase__ = dp_array[row + 1][col] if mat[row][col] == 1: UpperCAmelCase__ = 1 + min(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = max(dp_array[row][col] , SCREAMING_SNAKE_CASE__ ) else: UpperCAmelCase__ = 0 return largest_square_area def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : list[list[int]] ): '''simple docstring''' UpperCAmelCase__ = [0] * (cols + 1) UpperCAmelCase__ = [0] * (cols + 1) UpperCAmelCase__ = 0 for row in range(rows - 1 , -1 , -1 ): for col in range(cols - 1 , -1 , -1 ): UpperCAmelCase__ = current_row[col + 1] UpperCAmelCase__ = next_row[col + 1] UpperCAmelCase__ = next_row[col] if mat[row][col] == 1: UpperCAmelCase__ = 1 + min(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = max(current_row[col] , SCREAMING_SNAKE_CASE__ ) else: UpperCAmelCase__ = 0 UpperCAmelCase__ = current_row return largest_square_area if __name__ == "__main__": import doctest doctest.testmod() print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))

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'''simple docstring''' from typing import List, Union from ..utils import ( add_end_docstrings, is_tf_available, is_torch_available, is_vision_available, logging, requires_backends, ) from .base import PIPELINE_INIT_ARGS, Pipeline if is_vision_available(): from PIL import Image from ..image_utils import load_image if is_tf_available(): import tensorflow as tf from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING from ..tf_utils import stable_softmax if is_torch_available(): from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING UpperCAmelCase_ = logging.get_logger(__name__) @add_end_docstrings(lowerCamelCase_ ) class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' def __init__( self : Optional[Any] , *_UpperCAmelCase : Union[str, Any] , **_UpperCAmelCase : Dict ): """simple docstring""" super().__init__(*_UpperCAmelCase , **_UpperCAmelCase ) requires_backends(self , """vision""" ) self.check_model_type( TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING if self.framework == """tf""" else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING ) def SCREAMING_SNAKE_CASE__ ( self : str , _UpperCAmelCase : List[Any]=None ): """simple docstring""" UpperCAmelCase__ = {} if top_k is not None: UpperCAmelCase__ = top_k return {}, {}, postprocess_params def __call__( self : Any , _UpperCAmelCase : Union[str, List[str], "Image.Image", List["Image.Image"]] , **_UpperCAmelCase : str ): """simple docstring""" return super().__call__(_UpperCAmelCase , **_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] , _UpperCAmelCase : Tuple ): """simple docstring""" UpperCAmelCase__ = load_image(_UpperCAmelCase ) UpperCAmelCase__ = self.image_processor(images=_UpperCAmelCase , return_tensors=self.framework ) return model_inputs def SCREAMING_SNAKE_CASE__ ( self : Dict , _UpperCAmelCase : Tuple ): """simple docstring""" UpperCAmelCase__ = self.model(**_UpperCAmelCase ) return model_outputs def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , _UpperCAmelCase : Dict , _UpperCAmelCase : str=5 ): """simple docstring""" if top_k > self.model.config.num_labels: UpperCAmelCase__ = self.model.config.num_labels if self.framework == "pt": UpperCAmelCase__ = model_outputs.logits.softmax(-1 )[0] UpperCAmelCase__ , UpperCAmelCase__ = probs.topk(_UpperCAmelCase ) elif self.framework == "tf": UpperCAmelCase__ = stable_softmax(model_outputs.logits , axis=-1 )[0] UpperCAmelCase__ = tf.math.top_k(_UpperCAmelCase , k=_UpperCAmelCase ) UpperCAmelCase__ , UpperCAmelCase__ = topk.values.numpy(), topk.indices.numpy() else: raise ValueError(f'''Unsupported framework: {self.framework}''' ) UpperCAmelCase__ = scores.tolist() UpperCAmelCase__ = ids.tolist() return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(_UpperCAmelCase , _UpperCAmelCase )]

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'''simple docstring''' from dataclasses import dataclass from typing import List, Optional, Union import numpy as np import torch from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available @dataclass class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' lowerCAmelCase_ : Union[List[np.ndarray], torch.FloatTensor] try: if not (is_transformers_available() and is_torch_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from ...utils.dummy_torch_and_transformers_objects import * # noqa F403 else: from .pipeline_text_to_video_synth import TextToVideoSDPipeline from .pipeline_text_to_video_synth_imgaimg import VideoToVideoSDPipeline # noqa: F401 from .pipeline_text_to_video_zero import TextToVideoZeroPipeline

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'''simple docstring''' from math import factorial def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int = 20 ): '''simple docstring''' UpperCAmelCase__ = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1, # 2, 3,... UpperCAmelCase__ = n // 2 return int(factorial(SCREAMING_SNAKE_CASE__ ) / (factorial(SCREAMING_SNAKE_CASE__ ) * factorial(n - k )) ) if __name__ == "__main__": import sys if len(sys.argv) == 1: print(solution(2_0)) else: try: UpperCAmelCase_ = int(sys.argv[1]) print(solution(n)) except ValueError: print('Invalid entry - please enter a number.')

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'''simple docstring''' import unittest import numpy as np import torch from transformers import CLIPTextConfig, CLIPTextModel from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device enable_full_determinism() class lowerCAmelCase_ ( unittest.TestCase ): '''simple docstring''' @property def SCREAMING_SNAKE_CASE__ ( self : str ): """simple docstring""" torch.manual_seed(0 ) UpperCAmelCase__ = UNetaDModel( block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=("""DownBlock2D""", """AttnDownBlock2D""") , up_block_types=("""AttnUpBlock2D""", """UpBlock2D""") , ) return model @property def SCREAMING_SNAKE_CASE__ ( self : int ): """simple docstring""" torch.manual_seed(0 ) UpperCAmelCase__ = VQModel( block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=3 , ) return model @property def SCREAMING_SNAKE_CASE__ ( self : Any ): """simple docstring""" torch.manual_seed(0 ) UpperCAmelCase__ = CLIPTextConfig( bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , ) return CLIPTextModel(_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Tuple ): """simple docstring""" UpperCAmelCase__ = self.dummy_uncond_unet UpperCAmelCase__ = DDIMScheduler() UpperCAmelCase__ = self.dummy_vq_model UpperCAmelCase__ = LDMPipeline(unet=_UpperCAmelCase , vqvae=_UpperCAmelCase , scheduler=_UpperCAmelCase ) ldm.to(_UpperCAmelCase ) ldm.set_progress_bar_config(disable=_UpperCAmelCase ) UpperCAmelCase__ = torch.manual_seed(0 ) UpperCAmelCase__ = ldm(generator=_UpperCAmelCase , num_inference_steps=2 , output_type="""numpy""" ).images UpperCAmelCase__ = torch.manual_seed(0 ) UpperCAmelCase__ = ldm(generator=_UpperCAmelCase , num_inference_steps=2 , output_type="""numpy""" , return_dict=_UpperCAmelCase )[0] UpperCAmelCase__ = image[0, -3:, -3:, -1] UpperCAmelCase__ = image_from_tuple[0, -3:, -3:, -1] assert image.shape == (1, 64, 64, 3) UpperCAmelCase__ = np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172] ) UpperCAmelCase__ = 1E-2 if torch_device != """mps""" else 3E-2 assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < tolerance @slow @require_torch class lowerCAmelCase_ ( unittest.TestCase ): '''simple docstring''' def SCREAMING_SNAKE_CASE__ ( self : List[Any] ): """simple docstring""" UpperCAmelCase__ = LDMPipeline.from_pretrained("""CompVis/ldm-celebahq-256""" ) ldm.to(_UpperCAmelCase ) ldm.set_progress_bar_config(disable=_UpperCAmelCase ) UpperCAmelCase__ = torch.manual_seed(0 ) UpperCAmelCase__ = ldm(generator=_UpperCAmelCase , num_inference_steps=5 , output_type="""numpy""" ).images UpperCAmelCase__ = image[0, -3:, -3:, -1] assert image.shape == (1, 2_56, 2_56, 3) UpperCAmelCase__ = np.array([0.4399, 0.4_4975, 0.4_6825, 0.474, 0.4359, 0.4581, 0.4_5095, 0.4341, 0.4447] ) UpperCAmelCase__ = 1E-2 if torch_device != """mps""" else 3E-2 assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance

346

'''simple docstring''' import json import os import unittest from transformers import MgpstrTokenizer from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES from transformers.testing_utils import require_tokenizers from ...test_tokenization_common import TokenizerTesterMixin @require_tokenizers class lowerCAmelCase_ ( lowerCamelCase_ , unittest.TestCase ): '''simple docstring''' lowerCAmelCase_ : int = MgpstrTokenizer lowerCAmelCase_ : List[str] = False lowerCAmelCase_ : Optional[int] = {} lowerCAmelCase_ : Any = False def SCREAMING_SNAKE_CASE__ ( self : List[Any] ): """simple docstring""" super().setUp() # fmt: off UpperCAmelCase__ = ["""[GO]""", """[s]""", """0""", """1""", """2""", """3""", """4""", """5""", """6""", """7""", """8""", """9""", """a""", """b""", """c""", """d""", """e""", """f""", """g""", """h""", """i""", """j""", """k""", """l""", """m""", """n""", """o""", """p""", """q""", """r""", """s""", """t""", """u""", """v""", """w""", """x""", """y""", """z"""] # fmt: on UpperCAmelCase__ = dict(zip(_UpperCAmelCase , range(len(_UpperCAmelCase ) ) ) ) UpperCAmelCase__ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] ) with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as fp: fp.write(json.dumps(_UpperCAmelCase ) + """\n""" ) def SCREAMING_SNAKE_CASE__ ( self : List[str] , **_UpperCAmelCase : Optional[Any] ): """simple docstring""" return MgpstrTokenizer.from_pretrained(self.tmpdirname , **_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] , _UpperCAmelCase : Tuple ): """simple docstring""" UpperCAmelCase__ = """tester""" UpperCAmelCase__ = """tester""" return input_text, output_text @unittest.skip("""MGP-STR always lower cases letters.""" ) def SCREAMING_SNAKE_CASE__ ( self : List[str] ): """simple docstring""" pass def SCREAMING_SNAKE_CASE__ ( self : Any ): """simple docstring""" UpperCAmelCase__ = self.get_tokenizers(do_lower_case=_UpperCAmelCase ) for tokenizer in tokenizers: with self.subTest(f'''{tokenizer.__class__.__name__}''' ): UpperCAmelCase__ = """[SPECIAL_TOKEN]""" tokenizer.add_special_tokens({"""cls_token""": special_token} ) UpperCAmelCase__ = tokenizer.encode([special_token] , add_special_tokens=_UpperCAmelCase ) self.assertEqual(len(_UpperCAmelCase ) , 1 ) UpperCAmelCase__ = tokenizer.decode(_UpperCAmelCase , skip_special_tokens=_UpperCAmelCase ) self.assertTrue(special_token not in decoded ) def SCREAMING_SNAKE_CASE__ ( self : List[str] ): """simple docstring""" UpperCAmelCase__ = self.get_tokenizers() for tokenizer in tokenizers: with self.subTest(f'''{tokenizer.__class__.__name__}''' ): UpperCAmelCase__ , UpperCAmelCase__ = self.get_input_output_texts(_UpperCAmelCase ) UpperCAmelCase__ = tokenizer.tokenize(_UpperCAmelCase ) UpperCAmelCase__ = tokenizer.convert_tokens_to_ids(_UpperCAmelCase ) UpperCAmelCase__ = tokenizer.encode(_UpperCAmelCase , add_special_tokens=_UpperCAmelCase ) self.assertListEqual(_UpperCAmelCase , _UpperCAmelCase ) UpperCAmelCase__ = tokenizer.convert_ids_to_tokens(_UpperCAmelCase ) self.assertNotEqual(len(_UpperCAmelCase ) , 0 ) UpperCAmelCase__ = tokenizer.decode(_UpperCAmelCase ) self.assertIsInstance(_UpperCAmelCase , _UpperCAmelCase ) self.assertEqual(text_a.replace(""" """ , """""" ) , _UpperCAmelCase ) @unittest.skip("""MGP-STR tokenizer only handles one sequence.""" ) def SCREAMING_SNAKE_CASE__ ( self : Any ): """simple docstring""" pass @unittest.skip("""inputs cannot be pretokenized in MgpstrTokenizer""" ) def SCREAMING_SNAKE_CASE__ ( self : str ): """simple docstring""" pass

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'''simple docstring''' import asyncio import os import re import sys import tempfile import unittest from contextlib import contextmanager from copy import deepcopy from distutils.util import strtobool from enum import Enum from importlib.util import find_spec from pathlib import Path from unittest.mock import patch import pyarrow as pa import pytest import requests from packaging import version from datasets import config if config.PY_VERSION < version.parse('3.8'): import importlib_metadata else: import importlib.metadata as importlib_metadata def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : int=False ): '''simple docstring''' try: UpperCAmelCase__ = os.environ[key] except KeyError: # KEY isn't set, default to `default`. UpperCAmelCase__ = default else: # KEY is set, convert it to True or False. try: UpperCAmelCase__ = strtobool(SCREAMING_SNAKE_CASE__ ) except ValueError: # More values are supported, but let's keep the message simple. raise ValueError(F'''If set, {key} must be yes or no.''' ) return _value UpperCAmelCase_ = parse_flag_from_env('RUN_SLOW', default=False) UpperCAmelCase_ = parse_flag_from_env('RUN_REMOTE', default=False) UpperCAmelCase_ = parse_flag_from_env('RUN_LOCAL', default=True) UpperCAmelCase_ = parse_flag_from_env('RUN_PACKAGED', default=True) # Compression UpperCAmelCase_ = pytest.mark.skipif(not config.LZ4_AVAILABLE, reason='test requires lz4') UpperCAmelCase_ = pytest.mark.skipif(not config.PY7ZR_AVAILABLE, reason='test requires py7zr') UpperCAmelCase_ = pytest.mark.skipif(not config.ZSTANDARD_AVAILABLE, reason='test requires zstandard') # Audio UpperCAmelCase_ = pytest.mark.skipif( # On Windows and OS X, soundfile installs sndfile find_spec('soundfile') is None or version.parse(importlib_metadata.version('soundfile')) < version.parse('0.12.0'), reason='test requires sndfile>=0.12.1: \'pip install \"soundfile>=0.12.1\"\'; ', ) # Beam UpperCAmelCase_ = pytest.mark.skipif( not config.BEAM_AVAILABLE or config.DILL_VERSION >= version.parse('0.3.2'), reason='test requires apache-beam and a compatible dill version', ) # Dill-cloudpickle compatibility UpperCAmelCase_ = pytest.mark.skipif( config.DILL_VERSION <= version.parse('0.3.2'), reason='test requires dill>0.3.2 for cloudpickle compatibility', ) # Windows UpperCAmelCase_ = pytest.mark.skipif( sys.platform == 'win32', reason='test should not be run on Windows', ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Optional[Any] ): '''simple docstring''' try: import faiss # noqa except ImportError: UpperCAmelCase__ = unittest.skip("""test requires faiss""" )(SCREAMING_SNAKE_CASE__ ) return test_case def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int ): '''simple docstring''' try: import regex # noqa except ImportError: UpperCAmelCase__ = unittest.skip("""test requires regex""" )(SCREAMING_SNAKE_CASE__ ) return test_case def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Optional[Any] ): '''simple docstring''' try: import elasticsearch # noqa except ImportError: UpperCAmelCase__ = unittest.skip("""test requires elasticsearch""" )(SCREAMING_SNAKE_CASE__ ) return test_case def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str ): '''simple docstring''' try: import sqlalchemy # noqa except ImportError: UpperCAmelCase__ = unittest.skip("""test requires sqlalchemy""" )(SCREAMING_SNAKE_CASE__ ) return test_case def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : List[Any] ): '''simple docstring''' if not config.TORCH_AVAILABLE: UpperCAmelCase__ = unittest.skip("""test requires PyTorch""" )(SCREAMING_SNAKE_CASE__ ) return test_case def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Any ): '''simple docstring''' if not config.TF_AVAILABLE: UpperCAmelCase__ = unittest.skip("""test requires TensorFlow""" )(SCREAMING_SNAKE_CASE__ ) return test_case def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str ): '''simple docstring''' if not config.JAX_AVAILABLE: UpperCAmelCase__ = unittest.skip("""test requires JAX""" )(SCREAMING_SNAKE_CASE__ ) return test_case def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str ): '''simple docstring''' if not config.PIL_AVAILABLE: UpperCAmelCase__ = unittest.skip("""test requires Pillow""" )(SCREAMING_SNAKE_CASE__ ) return test_case def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int ): '''simple docstring''' try: import transformers # noqa F401 except ImportError: return unittest.skip("""test requires transformers""" )(SCREAMING_SNAKE_CASE__ ) else: return test_case def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Tuple ): '''simple docstring''' try: import tiktoken # noqa F401 except ImportError: return unittest.skip("""test requires tiktoken""" )(SCREAMING_SNAKE_CASE__ ) else: return test_case def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Union[str, Any] ): '''simple docstring''' try: import spacy # noqa F401 except ImportError: return unittest.skip("""test requires spacy""" )(SCREAMING_SNAKE_CASE__ ) else: return test_case def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Optional[int] ): '''simple docstring''' def _require_spacy_model(SCREAMING_SNAKE_CASE__ : Optional[Any] ): try: import spacy # noqa F401 spacy.load(SCREAMING_SNAKE_CASE__ ) except ImportError: return unittest.skip("""test requires spacy""" )(SCREAMING_SNAKE_CASE__ ) except OSError: return unittest.skip("""test requires spacy model '{}'""".format(SCREAMING_SNAKE_CASE__ ) )(SCREAMING_SNAKE_CASE__ ) else: return test_case return _require_spacy_model def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Tuple ): '''simple docstring''' try: import pyspark # noqa F401 except ImportError: return unittest.skip("""test requires pyspark""" )(SCREAMING_SNAKE_CASE__ ) else: return test_case def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int ): '''simple docstring''' try: import joblibspark # noqa F401 except ImportError: return unittest.skip("""test requires joblibspark""" )(SCREAMING_SNAKE_CASE__ ) else: return test_case def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int ): '''simple docstring''' if not _run_slow_tests or _run_slow_tests == 0: UpperCAmelCase__ = unittest.skip("""test is slow""" )(SCREAMING_SNAKE_CASE__ ) return test_case def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : List[str] ): '''simple docstring''' if not _run_local_tests or _run_local_tests == 0: UpperCAmelCase__ = unittest.skip("""test is local""" )(SCREAMING_SNAKE_CASE__ ) return test_case def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : List[str] ): '''simple docstring''' if not _run_packaged_tests or _run_packaged_tests == 0: UpperCAmelCase__ = unittest.skip("""test is packaged""" )(SCREAMING_SNAKE_CASE__ ) return test_case def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int ): '''simple docstring''' if not _run_remote_tests or _run_remote_tests == 0: UpperCAmelCase__ = unittest.skip("""test requires remote""" )(SCREAMING_SNAKE_CASE__ ) return test_case def _UpperCamelCase ( *SCREAMING_SNAKE_CASE__ : Tuple ): '''simple docstring''' def decorate(cls : Optional[int] ): for name, fn in cls.__dict__.items(): if callable(SCREAMING_SNAKE_CASE__ ) and name.startswith("""test""" ): for decorator in decorators: UpperCAmelCase__ = decorator(SCREAMING_SNAKE_CASE__ ) setattr(cls , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) return cls return decorate class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' pass class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' lowerCAmelCase_ : str = 0 lowerCAmelCase_ : Dict = 1 lowerCAmelCase_ : int = 2 @contextmanager def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : List[str]=OfflineSimulationMode.CONNECTION_FAILS , SCREAMING_SNAKE_CASE__ : str=1e-16 ): '''simple docstring''' UpperCAmelCase__ = requests.Session().request def timeout_request(SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : int , **SCREAMING_SNAKE_CASE__ : Dict ): # Change the url to an invalid url so that the connection hangs UpperCAmelCase__ = """https://10.255.255.1""" if kwargs.get("""timeout""" ) is None: raise RequestWouldHangIndefinitelyError( F'''Tried a call to {url} in offline mode with no timeout set. Please set a timeout.''' ) UpperCAmelCase__ = timeout try: return online_request(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ) except Exception as e: # The following changes in the error are just here to make the offline timeout error prettier UpperCAmelCase__ = url UpperCAmelCase__ = e.args[0] UpperCAmelCase__ = (max_retry_error.args[0].replace("""10.255.255.1""" , F'''OfflineMock[{url}]''' ),) UpperCAmelCase__ = (max_retry_error,) raise def raise_connection_error(SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : List[str] , **SCREAMING_SNAKE_CASE__ : List[Any] ): raise requests.ConnectionError("""Offline mode is enabled.""" , request=SCREAMING_SNAKE_CASE__ ) if mode is OfflineSimulationMode.CONNECTION_FAILS: with patch("""requests.Session.send""" , SCREAMING_SNAKE_CASE__ ): yield elif mode is OfflineSimulationMode.CONNECTION_TIMES_OUT: # inspired from https://stackoverflow.com/a/904609 with patch("""requests.Session.request""" , SCREAMING_SNAKE_CASE__ ): yield elif mode is OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1: with patch("""datasets.config.HF_DATASETS_OFFLINE""" , SCREAMING_SNAKE_CASE__ ): yield else: raise ValueError("""Please use a value from the OfflineSimulationMode enum.""" ) @contextmanager def _UpperCamelCase ( *SCREAMING_SNAKE_CASE__ : Union[str, Any] , **SCREAMING_SNAKE_CASE__ : Dict ): '''simple docstring''' UpperCAmelCase__ = str(Path().resolve() ) with tempfile.TemporaryDirectory(*SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ) as tmp_dir: try: os.chdir(SCREAMING_SNAKE_CASE__ ) yield finally: os.chdir(SCREAMING_SNAKE_CASE__ ) @contextmanager def _UpperCamelCase ( ): '''simple docstring''' import gc gc.collect() UpperCAmelCase__ = pa.total_allocated_bytes() yield assert pa.total_allocated_bytes() - previous_allocated_memory > 0, "Arrow memory didn't increase." @contextmanager def _UpperCamelCase ( ): '''simple docstring''' import gc gc.collect() UpperCAmelCase__ = pa.total_allocated_bytes() yield assert pa.total_allocated_bytes() - previous_allocated_memory <= 0, "Arrow memory wasn't expected to increase." def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : List[Any] ): '''simple docstring''' return deepcopy(SCREAMING_SNAKE_CASE__ ).integers(0 , 100 , 10 ).tolist() == deepcopy(SCREAMING_SNAKE_CASE__ ).integers(0 , 100 , 10 ).tolist() def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : List[Any] ): '''simple docstring''' import decorator from requests.exceptions import HTTPError def _wrapper(SCREAMING_SNAKE_CASE__ : Union[str, Any] , *SCREAMING_SNAKE_CASE__ : Tuple , **SCREAMING_SNAKE_CASE__ : Any ): try: return func(*SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ) except HTTPError as err: if str(SCREAMING_SNAKE_CASE__ ).startswith("""500""" ) or str(SCREAMING_SNAKE_CASE__ ).startswith("""502""" ): pytest.xfail(str(SCREAMING_SNAKE_CASE__ ) ) raise err return decorator.decorator(_wrapper , SCREAMING_SNAKE_CASE__ ) class lowerCAmelCase_ : '''simple docstring''' def __init__( self : List[str] , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Tuple ): """simple docstring""" UpperCAmelCase__ = returncode UpperCAmelCase__ = stdout UpperCAmelCase__ = stderr async def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : Optional[Any] ): '''simple docstring''' while True: UpperCAmelCase__ = await stream.readline() if line: callback(SCREAMING_SNAKE_CASE__ ) else: break async def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : int=None , SCREAMING_SNAKE_CASE__ : Tuple=None , SCREAMING_SNAKE_CASE__ : Optional[int]=None , SCREAMING_SNAKE_CASE__ : List[Any]=False , SCREAMING_SNAKE_CASE__ : Dict=False ): '''simple docstring''' if echo: print("""\nRunning: """ , """ """.join(SCREAMING_SNAKE_CASE__ ) ) UpperCAmelCase__ = await asyncio.create_subprocess_exec( cmd[0] , *cmd[1:] , stdin=SCREAMING_SNAKE_CASE__ , stdout=asyncio.subprocess.PIPE , stderr=asyncio.subprocess.PIPE , env=SCREAMING_SNAKE_CASE__ , ) # note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe # https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait # # If it starts hanging, will need to switch to the following code. The problem is that no data # will be seen until it's done and if it hangs for example there will be no debug info. # out, err = await p.communicate() # return _RunOutput(p.returncode, out, err) UpperCAmelCase__ = [] UpperCAmelCase__ = [] def tee(SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : Tuple="" ): UpperCAmelCase__ = line.decode("""utf-8""" ).rstrip() sink.append(SCREAMING_SNAKE_CASE__ ) if not quiet: print(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , file=SCREAMING_SNAKE_CASE__ ) # XXX: the timeout doesn't seem to make any difference here await asyncio.wait( [ _read_stream(p.stdout , lambda SCREAMING_SNAKE_CASE__ : tee(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , sys.stdout , label="""stdout:""" ) ), _read_stream(p.stderr , lambda SCREAMING_SNAKE_CASE__ : tee(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , sys.stderr , label="""stderr:""" ) ), ] , timeout=SCREAMING_SNAKE_CASE__ , ) return _RunOutput(await p.wait() , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : List[Any]=None , SCREAMING_SNAKE_CASE__ : Optional[Any]=None , SCREAMING_SNAKE_CASE__ : Optional[int]=180 , SCREAMING_SNAKE_CASE__ : Tuple=False , SCREAMING_SNAKE_CASE__ : Tuple=True ): '''simple docstring''' UpperCAmelCase__ = asyncio.get_event_loop() UpperCAmelCase__ = loop.run_until_complete( _stream_subprocess(SCREAMING_SNAKE_CASE__ , env=SCREAMING_SNAKE_CASE__ , stdin=SCREAMING_SNAKE_CASE__ , timeout=SCREAMING_SNAKE_CASE__ , quiet=SCREAMING_SNAKE_CASE__ , echo=SCREAMING_SNAKE_CASE__ ) ) UpperCAmelCase__ = """ """.join(SCREAMING_SNAKE_CASE__ ) if result.returncode > 0: UpperCAmelCase__ = """\n""".join(result.stderr ) raise RuntimeError( F'''\'{cmd_str}\' failed with returncode {result.returncode}\n\n''' F'''The combined stderr from workers follows:\n{stderr}''' ) # check that the subprocess actually did run and produced some output, should the test rely on # the remote side to do the testing if not result.stdout and not result.stderr: raise RuntimeError(F'''\'{cmd_str}\' produced no output.''' ) return result def _UpperCamelCase ( ): '''simple docstring''' UpperCAmelCase__ = os.environ.get("""PYTEST_XDIST_WORKER""" , """gw0""" ) UpperCAmelCase__ = re.sub(r"""^gw""" , """""" , SCREAMING_SNAKE_CASE__ , 0 , re.M ) return int(SCREAMING_SNAKE_CASE__ ) def _UpperCamelCase ( ): '''simple docstring''' UpperCAmelCase__ = 29500 UpperCAmelCase__ = pytest_xdist_worker_id() return port + uniq_delta

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'''simple docstring''' from abc import ABC, abstractmethod from typing import List, Optional class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' def __init__( self : Union[str, Any] ): """simple docstring""" self.test() def SCREAMING_SNAKE_CASE__ ( self : List[str] ): """simple docstring""" UpperCAmelCase__ = 0 UpperCAmelCase__ = False while not completed: if counter == 1: self.reset() UpperCAmelCase__ = self.advance() if not self.does_advance(_UpperCAmelCase ): raise Exception( """Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true.""" ) UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = self.update(_UpperCAmelCase ) counter += 1 if counter > 1_00_00: raise Exception("""update() does not fulfill the constraint.""" ) if self.remaining() != 0: raise Exception("""Custom Constraint is not defined correctly.""" ) @abstractmethod def SCREAMING_SNAKE_CASE__ ( self : Dict ): """simple docstring""" raise NotImplementedError( f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' ) @abstractmethod def SCREAMING_SNAKE_CASE__ ( self : Dict , _UpperCAmelCase : int ): """simple docstring""" raise NotImplementedError( f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' ) @abstractmethod def SCREAMING_SNAKE_CASE__ ( self : Any , _UpperCAmelCase : int ): """simple docstring""" raise NotImplementedError( f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' ) @abstractmethod def SCREAMING_SNAKE_CASE__ ( self : List[Any] ): """simple docstring""" raise NotImplementedError( f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' ) @abstractmethod def SCREAMING_SNAKE_CASE__ ( self : List[str] ): """simple docstring""" raise NotImplementedError( f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' ) @abstractmethod def SCREAMING_SNAKE_CASE__ ( self : int , _UpperCAmelCase : List[Any]=False ): """simple docstring""" raise NotImplementedError( f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' ) class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' def __init__( self : Union[str, Any] , _UpperCAmelCase : List[int] ): """simple docstring""" super(_UpperCAmelCase , self ).__init__() if not isinstance(_UpperCAmelCase , _UpperCAmelCase ) or len(_UpperCAmelCase ) == 0: raise ValueError(f'''`token_ids` has to be a non-empty list, but is {token_ids}.''' ) if any((not isinstance(_UpperCAmelCase , _UpperCAmelCase ) or token_id < 0) for token_id in token_ids ): raise ValueError(f'''Each list in `token_ids` has to be a list of positive integers, but is {token_ids}.''' ) UpperCAmelCase__ = token_ids UpperCAmelCase__ = len(self.token_ids ) UpperCAmelCase__ = -1 # the index of the currently fulfilled step UpperCAmelCase__ = False def SCREAMING_SNAKE_CASE__ ( self : List[str] ): """simple docstring""" if self.completed: return None return self.token_ids[self.fulfilled_idx + 1] def SCREAMING_SNAKE_CASE__ ( self : List[str] , _UpperCAmelCase : int ): """simple docstring""" if not isinstance(_UpperCAmelCase , _UpperCAmelCase ): raise ValueError(f'''`token_id` has to be an `int`, but is {token_id} of type {type(_UpperCAmelCase )}''' ) if self.completed: return False return token_id == self.token_ids[self.fulfilled_idx + 1] def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] , _UpperCAmelCase : int ): """simple docstring""" if not isinstance(_UpperCAmelCase , _UpperCAmelCase ): raise ValueError(f'''`token_id` has to be an `int`, but is {token_id} of type {type(_UpperCAmelCase )}''' ) UpperCAmelCase__ = False UpperCAmelCase__ = False UpperCAmelCase__ = False if self.does_advance(_UpperCAmelCase ): self.fulfilled_idx += 1 UpperCAmelCase__ = True if self.fulfilled_idx == (self.seqlen - 1): UpperCAmelCase__ = True UpperCAmelCase__ = completed else: # failed to make progress. UpperCAmelCase__ = True self.reset() return stepped, completed, reset def SCREAMING_SNAKE_CASE__ ( self : Tuple ): """simple docstring""" UpperCAmelCase__ = False UpperCAmelCase__ = 0 def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ): """simple docstring""" return self.seqlen - (self.fulfilled_idx + 1) def SCREAMING_SNAKE_CASE__ ( self : Any , _UpperCAmelCase : Optional[int]=False ): """simple docstring""" UpperCAmelCase__ = PhrasalConstraint(self.token_ids ) if stateful: UpperCAmelCase__ = self.seqlen UpperCAmelCase__ = self.fulfilled_idx UpperCAmelCase__ = self.completed return new_constraint class lowerCAmelCase_ : '''simple docstring''' def __init__( self : Any , _UpperCAmelCase : List[List[int]] , _UpperCAmelCase : List[str]=True ): """simple docstring""" UpperCAmelCase__ = max([len(_UpperCAmelCase ) for one in nested_token_ids] ) UpperCAmelCase__ = {} for token_ids in nested_token_ids: UpperCAmelCase__ = root for tidx, token_id in enumerate(_UpperCAmelCase ): if token_id not in level: UpperCAmelCase__ = {} UpperCAmelCase__ = level[token_id] if no_subsets and self.has_subsets(_UpperCAmelCase , _UpperCAmelCase ): raise ValueError( """Each list in `nested_token_ids` can't be a complete subset of another list, but is""" f''' {nested_token_ids}.''' ) UpperCAmelCase__ = root def SCREAMING_SNAKE_CASE__ ( self : int , _UpperCAmelCase : int ): """simple docstring""" UpperCAmelCase__ = self.trie for current_token in current_seq: UpperCAmelCase__ = start[current_token] UpperCAmelCase__ = list(start.keys() ) return next_tokens def SCREAMING_SNAKE_CASE__ ( self : str , _UpperCAmelCase : Tuple ): """simple docstring""" UpperCAmelCase__ = self.next_tokens(_UpperCAmelCase ) return len(_UpperCAmelCase ) == 0 def SCREAMING_SNAKE_CASE__ ( self : List[str] , _UpperCAmelCase : Optional[int] ): """simple docstring""" UpperCAmelCase__ = list(root.values() ) if len(_UpperCAmelCase ) == 0: return 1 else: return sum([self.count_leaves(_UpperCAmelCase ) for nn in next_nodes] ) def SCREAMING_SNAKE_CASE__ ( self : List[Any] , _UpperCAmelCase : Dict , _UpperCAmelCase : Dict ): """simple docstring""" UpperCAmelCase__ = self.count_leaves(_UpperCAmelCase ) return len(_UpperCAmelCase ) != leaf_count class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' def __init__( self : Dict , _UpperCAmelCase : List[List[int]] ): """simple docstring""" super(_UpperCAmelCase , self ).__init__() if not isinstance(_UpperCAmelCase , _UpperCAmelCase ) or len(_UpperCAmelCase ) == 0: raise ValueError(f'''`nested_token_ids` has to be a non-empty list, but is {nested_token_ids}.''' ) if any(not isinstance(_UpperCAmelCase , _UpperCAmelCase ) for token_ids in nested_token_ids ): raise ValueError(f'''`nested_token_ids` has to be a list of lists, but is {nested_token_ids}.''' ) if any( any((not isinstance(_UpperCAmelCase , _UpperCAmelCase ) or token_id < 0) for token_id in token_ids ) for token_ids in nested_token_ids ): raise ValueError( f'''Each list in `nested_token_ids` has to be a list of positive integers, but is {nested_token_ids}.''' ) UpperCAmelCase__ = DisjunctiveTrie(_UpperCAmelCase ) UpperCAmelCase__ = nested_token_ids UpperCAmelCase__ = self.trie.max_height UpperCAmelCase__ = [] UpperCAmelCase__ = False def SCREAMING_SNAKE_CASE__ ( self : Any ): """simple docstring""" UpperCAmelCase__ = self.trie.next_tokens(self.current_seq ) if len(_UpperCAmelCase ) == 0: return None else: return token_list def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] , _UpperCAmelCase : int ): """simple docstring""" if not isinstance(_UpperCAmelCase , _UpperCAmelCase ): raise ValueError(f'''`token_id` is supposed to be type `int`, but is {token_id} of type {type(_UpperCAmelCase )}''' ) UpperCAmelCase__ = self.trie.next_tokens(self.current_seq ) return token_id in next_tokens def SCREAMING_SNAKE_CASE__ ( self : Dict , _UpperCAmelCase : int ): """simple docstring""" if not isinstance(_UpperCAmelCase , _UpperCAmelCase ): raise ValueError(f'''`token_id` is supposed to be type `int`, but is {token_id} of type {type(_UpperCAmelCase )}''' ) UpperCAmelCase__ = False UpperCAmelCase__ = False UpperCAmelCase__ = False if self.does_advance(_UpperCAmelCase ): self.current_seq.append(_UpperCAmelCase ) UpperCAmelCase__ = True else: UpperCAmelCase__ = True self.reset() UpperCAmelCase__ = self.trie.reached_leaf(self.current_seq ) UpperCAmelCase__ = completed return stepped, completed, reset def SCREAMING_SNAKE_CASE__ ( self : str ): """simple docstring""" UpperCAmelCase__ = False UpperCAmelCase__ = [] def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ): """simple docstring""" if self.completed: # since this can be completed without reaching max height return 0 else: return self.seqlen - len(self.current_seq ) def SCREAMING_SNAKE_CASE__ ( self : Tuple , _UpperCAmelCase : Dict=False ): """simple docstring""" UpperCAmelCase__ = DisjunctiveConstraint(self.token_ids ) if stateful: UpperCAmelCase__ = self.seqlen UpperCAmelCase__ = self.current_seq UpperCAmelCase__ = self.completed return new_constraint class lowerCAmelCase_ : '''simple docstring''' def __init__( self : Optional[int] , _UpperCAmelCase : List[Constraint] ): """simple docstring""" UpperCAmelCase__ = constraints # max # of steps required to fulfill a given constraint UpperCAmelCase__ = max([c.seqlen for c in constraints] ) UpperCAmelCase__ = len(_UpperCAmelCase ) UpperCAmelCase__ = False self.init_state() def SCREAMING_SNAKE_CASE__ ( self : Any ): """simple docstring""" UpperCAmelCase__ = [] UpperCAmelCase__ = None UpperCAmelCase__ = [constraint.copy(stateful=_UpperCAmelCase ) for constraint in self.constraints] def SCREAMING_SNAKE_CASE__ ( self : List[str] ): """simple docstring""" UpperCAmelCase__ = 0 if self.inprogress_constraint: # extra points for having a constraint mid-fulfilled add += self.max_seqlen - self.inprogress_constraint.remaining() return (len(self.complete_constraints ) * self.max_seqlen) + add def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ): """simple docstring""" UpperCAmelCase__ = [] if self.inprogress_constraint is None: for constraint in self.pending_constraints: # "pending" == "unfulfilled yet" UpperCAmelCase__ = constraint.advance() if isinstance(_UpperCAmelCase , _UpperCAmelCase ): token_list.append(_UpperCAmelCase ) elif isinstance(_UpperCAmelCase , _UpperCAmelCase ): token_list.extend(_UpperCAmelCase ) else: UpperCAmelCase__ = self.inprogress_constraint.advance() if isinstance(_UpperCAmelCase , _UpperCAmelCase ): token_list.append(_UpperCAmelCase ) elif isinstance(_UpperCAmelCase , _UpperCAmelCase ): token_list.extend(_UpperCAmelCase ) if len(_UpperCAmelCase ) == 0: return None else: return token_list def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , _UpperCAmelCase : Optional[List[int]] ): """simple docstring""" self.init_state() if token_ids is not None: for token in token_ids: # completes or steps **one** constraint UpperCAmelCase__ , UpperCAmelCase__ = self.add(_UpperCAmelCase ) # the entire list of constraints are fulfilled if self.completed: break def SCREAMING_SNAKE_CASE__ ( self : Any , _UpperCAmelCase : int ): """simple docstring""" if not isinstance(_UpperCAmelCase , _UpperCAmelCase ): raise ValueError(f'''`token_id` should be an `int`, but is `{token_id}`.''' ) UpperCAmelCase__ , UpperCAmelCase__ = False, False if self.completed: UpperCAmelCase__ = True UpperCAmelCase__ = False return complete, stepped if self.inprogress_constraint is not None: # In the middle of fulfilling a constraint. If the `token_id` *does* makes an incremental progress to current # job, simply update the state UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = self.inprogress_constraint.update(_UpperCAmelCase ) if reset: # 1. If the next token breaks the progress, then we must restart. # e.g. constraint = "I love pies" and sequence so far is "I love" but `token_id` == "books". # But that doesn't mean we self.init_state(), since we only reset the state for this particular # constraint, not the full list of constraints. self.pending_constraints.append(self.inprogress_constraint.copy(stateful=_UpperCAmelCase ) ) UpperCAmelCase__ = None if complete: # 2. If the next token completes the constraint, move it to completed list, set # inprogress to None. If there are no pending constraints either, then this full list of constraints # is complete. self.complete_constraints.append(self.inprogress_constraint ) UpperCAmelCase__ = None if len(self.pending_constraints ) == 0: # we're done! UpperCAmelCase__ = True else: # Not in the middle of fulfilling a constraint. So does this `token_id` helps us step towards any of our list # of constraints? for cidx, pending_constraint in enumerate(self.pending_constraints ): if pending_constraint.does_advance(_UpperCAmelCase ): UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = pending_constraint.update(_UpperCAmelCase ) if not stepped: raise Exception( """`constraint.update(token_id)` is not yielding incremental progress, """ """even though `constraint.does_advance(token_id)` is true.""" ) if complete: self.complete_constraints.append(_UpperCAmelCase ) UpperCAmelCase__ = None if not complete and stepped: UpperCAmelCase__ = pending_constraint if complete or stepped: # If we made any progress at all, then it's at least not a "pending constraint". UpperCAmelCase__ = ( self.pending_constraints[:cidx] + self.pending_constraints[cidx + 1 :] ) if len(self.pending_constraints ) == 0 and self.inprogress_constraint is None: # If there's no longer any pending after this and no inprogress either, then we must be # complete. UpperCAmelCase__ = True break # prevent accidentally stepping through multiple constraints with just one token. return complete, stepped def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , _UpperCAmelCase : List[Any]=True ): """simple docstring""" UpperCAmelCase__ = ConstraintListState(self.constraints ) # we actually never though self.constraints objects # throughout this process. So it's at initialization state. if stateful: UpperCAmelCase__ = [ constraint.copy(stateful=_UpperCAmelCase ) for constraint in self.complete_constraints ] if self.inprogress_constraint is not None: UpperCAmelCase__ = self.inprogress_constraint.copy(stateful=_UpperCAmelCase ) UpperCAmelCase__ = [constraint.copy() for constraint in self.pending_constraints] return new_state

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'''simple docstring''' import os import re import shutil import sys import tempfile import unittest import black UpperCAmelCase_ = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__)))) sys.path.append(os.path.join(git_repo_path, 'utils')) import check_copies # noqa: E402 # This is the reference code that will be used in the tests. # If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated. UpperCAmelCase_ = ' \"""\n Output class for the scheduler\'s step function output.\n\n Args:\n prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n denoising loop.\n pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n `pred_original_sample` can be used to preview progress or for guidance.\n \"""\n\n prev_sample: torch.FloatTensor\n pred_original_sample: Optional[torch.FloatTensor] = None\n' class lowerCAmelCase_ ( unittest.TestCase ): '''simple docstring''' def SCREAMING_SNAKE_CASE__ ( self : Dict ): """simple docstring""" UpperCAmelCase__ = tempfile.mkdtemp() os.makedirs(os.path.join(self.diffusers_dir , """schedulers/""" ) ) UpperCAmelCase__ = self.diffusers_dir shutil.copy( os.path.join(_UpperCAmelCase , """src/diffusers/schedulers/scheduling_ddpm.py""" ) , os.path.join(self.diffusers_dir , """schedulers/scheduling_ddpm.py""" ) , ) def SCREAMING_SNAKE_CASE__ ( self : Any ): """simple docstring""" UpperCAmelCase__ = """src/diffusers""" shutil.rmtree(self.diffusers_dir ) def SCREAMING_SNAKE_CASE__ ( self : List[str] , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : int , _UpperCAmelCase : str=None ): """simple docstring""" UpperCAmelCase__ = comment + f'''\nclass {class_name}(nn.Module):\n''' + class_code if overwrite_result is not None: UpperCAmelCase__ = comment + f'''\nclass {class_name}(nn.Module):\n''' + overwrite_result UpperCAmelCase__ = black.Mode(target_versions={black.TargetVersion.PYaa} , line_length=1_19 ) UpperCAmelCase__ = black.format_str(_UpperCAmelCase , mode=_UpperCAmelCase ) UpperCAmelCase__ = os.path.join(self.diffusers_dir , """new_code.py""" ) with open(_UpperCAmelCase , """w""" , newline="""\n""" ) as f: f.write(_UpperCAmelCase ) if overwrite_result is None: self.assertTrue(len(check_copies.is_copy_consistent(_UpperCAmelCase ) ) == 0 ) else: check_copies.is_copy_consistent(f.name , overwrite=_UpperCAmelCase ) with open(_UpperCAmelCase , """r""" ) as f: self.assertTrue(f.read() , _UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ): """simple docstring""" UpperCAmelCase__ = check_copies.find_code_in_diffusers("""schedulers.scheduling_ddpm.DDPMSchedulerOutput""" ) self.assertEqual(_UpperCAmelCase , _UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : str ): """simple docstring""" self.check_copy_consistency( """# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput""" , """DDPMSchedulerOutput""" , REFERENCE_CODE + """\n""" , ) # With no empty line at the end self.check_copy_consistency( """# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput""" , """DDPMSchedulerOutput""" , _UpperCAmelCase , ) # Copy consistency with rename self.check_copy_consistency( """# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test""" , """TestSchedulerOutput""" , re.sub("""DDPM""" , """Test""" , _UpperCAmelCase ) , ) # Copy consistency with a really long name UpperCAmelCase__ = """TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason""" self.check_copy_consistency( f'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}''' , f'''{long_class_name}SchedulerOutput''' , re.sub("""Bert""" , _UpperCAmelCase , _UpperCAmelCase ) , ) # Copy consistency with overwrite self.check_copy_consistency( """# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test""" , """TestSchedulerOutput""" , _UpperCAmelCase , overwrite_result=re.sub("""DDPM""" , """Test""" , _UpperCAmelCase ) , )

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'''simple docstring''' import doctest import logging import os import unittest from pathlib import Path from typing import List, Union import transformers from transformers.testing_utils import require_tf, require_torch, slow UpperCAmelCase_ = logging.getLogger() @unittest.skip("""Temporarily disable the doc tests.""" ) @require_torch @require_tf @slow class lowerCAmelCase_ ( unittest.TestCase ): '''simple docstring''' def SCREAMING_SNAKE_CASE__ ( self : str , _UpperCAmelCase : Path , _UpperCAmelCase : Union[str, None] = None , _UpperCAmelCase : Union[List[str], None] = None , _UpperCAmelCase : Union[str, List[str], None] = None , _UpperCAmelCase : bool = True , ): """simple docstring""" UpperCAmelCase__ = [file for file in os.listdir(_UpperCAmelCase ) if os.path.isfile(os.path.join(_UpperCAmelCase , _UpperCAmelCase ) )] if identifier is not None: UpperCAmelCase__ = [file for file in files if identifier in file] if n_identifier is not None: if isinstance(_UpperCAmelCase , _UpperCAmelCase ): for n_ in n_identifier: UpperCAmelCase__ = [file for file in files if n_ not in file] else: UpperCAmelCase__ = [file for file in files if n_identifier not in file] UpperCAmelCase__ = ignore_files or [] ignore_files.append("""__init__.py""" ) UpperCAmelCase__ = [file for file in files if file not in ignore_files] for file in files: # Open all files print("""Testing""" , _UpperCAmelCase ) if only_modules: UpperCAmelCase__ = file.split(""".""" )[0] try: UpperCAmelCase__ = getattr(_UpperCAmelCase , _UpperCAmelCase ) UpperCAmelCase__ = doctest.DocTestSuite(_UpperCAmelCase ) UpperCAmelCase__ = unittest.TextTestRunner().run(_UpperCAmelCase ) self.assertIs(len(result.failures ) , 0 ) except AttributeError: logger.info(f'''{module_identifier} is not a module.''' ) else: UpperCAmelCase__ = doctest.testfile(str("""..""" / directory / file ) , optionflags=doctest.ELLIPSIS ) self.assertIs(result.failed , 0 ) def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ): """simple docstring""" UpperCAmelCase__ = Path("""src/transformers""" ) UpperCAmelCase__ = """modeling""" UpperCAmelCase__ = [ """modeling_ctrl.py""", """modeling_tf_ctrl.py""", ] self.analyze_directory(_UpperCAmelCase , identifier=_UpperCAmelCase , ignore_files=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ): """simple docstring""" UpperCAmelCase__ = Path("""src/transformers""" ) UpperCAmelCase__ = """tokenization""" self.analyze_directory(_UpperCAmelCase , identifier=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : str ): """simple docstring""" UpperCAmelCase__ = Path("""src/transformers""" ) UpperCAmelCase__ = """configuration""" self.analyze_directory(_UpperCAmelCase , identifier=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ): """simple docstring""" UpperCAmelCase__ = Path("""src/transformers""" ) UpperCAmelCase__ = ["""configuration""", """modeling""", """tokenization"""] self.analyze_directory(_UpperCAmelCase , n_identifier=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : List[str] ): """simple docstring""" UpperCAmelCase__ = Path("""docs/source""" ) UpperCAmelCase__ = ["""favicon.ico"""] self.analyze_directory(_UpperCAmelCase , ignore_files=_UpperCAmelCase , only_modules=_UpperCAmelCase )

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'''simple docstring''' import numpy as np def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : np.array ): '''simple docstring''' return 1 / (1 + np.exp(-vector )) if __name__ == "__main__": import doctest doctest.testmod()

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'''simple docstring''' from datasets.utils.patching import _PatchedModuleObj, patch_submodule from . import _test_patching def _UpperCamelCase ( ): '''simple docstring''' import os as original_os from os import path as original_path from os import rename as original_rename from os.path import dirname as original_dirname from os.path import join as original_join assert _test_patching.os is original_os assert _test_patching.path is original_path assert _test_patching.join is original_join assert _test_patching.renamed_os is original_os assert _test_patching.renamed_path is original_path assert _test_patching.renamed_join is original_join UpperCAmelCase__ = """__test_patch_submodule_mock__""" with patch_submodule(_test_patching , """os.path.join""" , SCREAMING_SNAKE_CASE__ ): # Every way to access os.path.join must be patched, and the rest must stay untouched # check os.path.join assert isinstance(_test_patching.os , _PatchedModuleObj ) assert isinstance(_test_patching.os.path , _PatchedModuleObj ) assert _test_patching.os.path.join is mock # check path.join assert isinstance(_test_patching.path , _PatchedModuleObj ) assert _test_patching.path.join is mock # check join assert _test_patching.join is mock # check that the other attributes are untouched assert _test_patching.os.rename is original_rename assert _test_patching.path.dirname is original_dirname assert _test_patching.os.path.dirname is original_dirname # Even renamed modules or objects must be patched # check renamed_os.path.join assert isinstance(_test_patching.renamed_os , _PatchedModuleObj ) assert isinstance(_test_patching.renamed_os.path , _PatchedModuleObj ) assert _test_patching.renamed_os.path.join is mock # check renamed_path.join assert isinstance(_test_patching.renamed_path , _PatchedModuleObj ) assert _test_patching.renamed_path.join is mock # check renamed_join assert _test_patching.renamed_join is mock # check that the other attributes are untouched assert _test_patching.renamed_os.rename is original_rename assert _test_patching.renamed_path.dirname is original_dirname assert _test_patching.renamed_os.path.dirname is original_dirname # check that everthing is back to normal when the patch is over assert _test_patching.os is original_os assert _test_patching.path is original_path assert _test_patching.join is original_join assert _test_patching.renamed_os is original_os assert _test_patching.renamed_path is original_path assert _test_patching.renamed_join is original_join def _UpperCamelCase ( ): '''simple docstring''' assert _test_patching.open is open UpperCAmelCase__ = """__test_patch_submodule_builtin_mock__""" # _test_patching has "open" in its globals assert _test_patching.open is open with patch_submodule(_test_patching , """open""" , SCREAMING_SNAKE_CASE__ ): assert _test_patching.open is mock # check that everthing is back to normal when the patch is over assert _test_patching.open is open def _UpperCamelCase ( ): '''simple docstring''' UpperCAmelCase__ = """__test_patch_submodule_missing_mock__""" with patch_submodule(_test_patching , """pandas.read_csv""" , SCREAMING_SNAKE_CASE__ ): pass def _UpperCamelCase ( ): '''simple docstring''' UpperCAmelCase__ = """__test_patch_submodule_missing_builtin_mock__""" # _test_patching doesn't have "len" in its globals assert getattr(_test_patching , """len""" , SCREAMING_SNAKE_CASE__ ) is None with patch_submodule(_test_patching , """len""" , SCREAMING_SNAKE_CASE__ ): assert _test_patching.len is mock assert _test_patching.len is len def _UpperCamelCase ( ): '''simple docstring''' UpperCAmelCase__ = """__test_patch_submodule_start_and_stop_mock__""" UpperCAmelCase__ = patch_submodule(_test_patching , """open""" , SCREAMING_SNAKE_CASE__ ) assert _test_patching.open is open patch.start() assert _test_patching.open is mock patch.stop() assert _test_patching.open is open def _UpperCamelCase ( ): '''simple docstring''' from os import rename as original_rename from os.path import dirname as original_dirname from os.path import join as original_join UpperCAmelCase__ = """__test_patch_submodule_successive_join__""" UpperCAmelCase__ = """__test_patch_submodule_successive_dirname__""" UpperCAmelCase__ = """__test_patch_submodule_successive_rename__""" assert _test_patching.os.path.join is original_join assert _test_patching.os.path.dirname is original_dirname assert _test_patching.os.rename is original_rename with patch_submodule(_test_patching , """os.path.join""" , SCREAMING_SNAKE_CASE__ ): with patch_submodule(_test_patching , """os.rename""" , SCREAMING_SNAKE_CASE__ ): with patch_submodule(_test_patching , """os.path.dirname""" , SCREAMING_SNAKE_CASE__ ): assert _test_patching.os.path.join is mock_join assert _test_patching.os.path.dirname is mock_dirname assert _test_patching.os.rename is mock_rename # try another order with patch_submodule(_test_patching , """os.rename""" , SCREAMING_SNAKE_CASE__ ): with patch_submodule(_test_patching , """os.path.join""" , SCREAMING_SNAKE_CASE__ ): with patch_submodule(_test_patching , """os.path.dirname""" , SCREAMING_SNAKE_CASE__ ): assert _test_patching.os.path.join is mock_join assert _test_patching.os.path.dirname is mock_dirname assert _test_patching.os.rename is mock_rename assert _test_patching.os.path.join is original_join assert _test_patching.os.path.dirname is original_dirname assert _test_patching.os.rename is original_rename def _UpperCamelCase ( ): '''simple docstring''' UpperCAmelCase__ = """__test_patch_submodule_doesnt_exist_mock__""" with patch_submodule(_test_patching , """__module_that_doesn_exist__.__attribute_that_doesn_exist__""" , SCREAMING_SNAKE_CASE__ ): pass with patch_submodule(_test_patching , """os.__attribute_that_doesn_exist__""" , SCREAMING_SNAKE_CASE__ ): pass

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'''simple docstring''' UpperCAmelCase_ = tuple[float, float, float] UpperCAmelCase_ = tuple[float, float, float] def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Pointad , SCREAMING_SNAKE_CASE__ : Pointad ): '''simple docstring''' UpperCAmelCase__ = end_pointa[0] - end_pointa[0] UpperCAmelCase__ = end_pointa[1] - end_pointa[1] UpperCAmelCase__ = end_pointa[2] - end_pointa[2] return (x, y, z) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Vectorad , SCREAMING_SNAKE_CASE__ : Vectorad ): '''simple docstring''' UpperCAmelCase__ = ab[1] * ac[2] - ab[2] * ac[1] # *i UpperCAmelCase__ = (ab[0] * ac[2] - ab[2] * ac[0]) * -1 # *j UpperCAmelCase__ = ab[0] * ac[1] - ab[1] * ac[0] # *k return (x, y, z) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Vectorad , SCREAMING_SNAKE_CASE__ : int ): '''simple docstring''' return tuple(round(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) for x in vector ) == (0, 0, 0) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Pointad , SCREAMING_SNAKE_CASE__ : Pointad , SCREAMING_SNAKE_CASE__ : Pointad , SCREAMING_SNAKE_CASE__ : int = 10 ): '''simple docstring''' UpperCAmelCase__ = create_vector(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = create_vector(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) return is_zero_vector(get_ad_vectors_cross(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) , SCREAMING_SNAKE_CASE__ )

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'''simple docstring''' from timeit import timeit UpperCAmelCase_ = { 'MALAYALAM': True, 'String': False, 'rotor': True, 'level': True, 'A': True, 'BB': True, 'ABC': False, 'amanaplanacanalpanama': True, # "a man a plan a canal panama" } # Ensure our test data is valid assert all((key == key[::-1]) is value for key, value in test_data.items()) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str ): '''simple docstring''' UpperCAmelCase__ = 0 UpperCAmelCase__ = len(SCREAMING_SNAKE_CASE__ ) - 1 while start_i < end_i: if s[start_i] == s[end_i]: start_i += 1 end_i -= 1 else: return False return True def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str ): '''simple docstring''' UpperCAmelCase__ = len(SCREAMING_SNAKE_CASE__ ) // 2 UpperCAmelCase__ = len(SCREAMING_SNAKE_CASE__ ) # We need to traverse till half of the length of string # as we can get access of the i'th last element from # i'th index. # eg: [0,1,2,3,4,5] => 4th index can be accessed # with the help of 1st index (i==n-i-1) # where n is length of string return all(s[i] == s[n - i - 1] for i in range(SCREAMING_SNAKE_CASE__ ) ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str ): '''simple docstring''' if len(SCREAMING_SNAKE_CASE__ ) <= 2: return True if s[0] == s[len(SCREAMING_SNAKE_CASE__ ) - 1]: return is_palindrome_recursive(s[1:-1] ) else: return False def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str ): '''simple docstring''' return s == s[::-1] def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str ): '''simple docstring''' UpperCAmelCase__ = F'''all({name}(key) is value for key, value in test_data.items())''' UpperCAmelCase__ = F'''from __main__ import test_data, {name}''' UpperCAmelCase__ = 500000 UpperCAmelCase__ = timeit(stmt=SCREAMING_SNAKE_CASE__ , setup=SCREAMING_SNAKE_CASE__ , number=SCREAMING_SNAKE_CASE__ ) print(F'''{name:<35} finished {number:,} runs in {result:.5f} seconds''' ) if __name__ == "__main__": for key, value in test_data.items(): assert is_palindrome(key) is is_palindrome_recursive(key) assert is_palindrome(key) is is_palindrome_slice(key) print(f"{key:21} {value}") print('a man a plan a canal panama') # finished 500,000 runs in 0.46793 seconds benchmark_function('is_palindrome_slice') # finished 500,000 runs in 0.85234 seconds benchmark_function('is_palindrome') # finished 500,000 runs in 1.32028 seconds benchmark_function('is_palindrome_recursive') # finished 500,000 runs in 2.08679 seconds benchmark_function('is_palindrome_traversal')

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'''simple docstring''' import os from shutil import copyfile from typing import Any, Dict, List, Optional, Tuple import sentencepiece as spm from ...tokenization_utils import PreTrainedTokenizer from ...utils import logging UpperCAmelCase_ = logging.get_logger(__name__) UpperCAmelCase_ = '▁' UpperCAmelCase_ = {'vocab_file': 'sentencepiece.bpe.model'} UpperCAmelCase_ = { 'vocab_file': { 'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model', } } UpperCAmelCase_ = { 'facebook/xglm-564M': 2_0_4_8, } class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' lowerCAmelCase_ : Optional[int] = VOCAB_FILES_NAMES lowerCAmelCase_ : Optional[int] = PRETRAINED_VOCAB_FILES_MAP lowerCAmelCase_ : Optional[int] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES lowerCAmelCase_ : Dict = ["""input_ids""", """attention_mask"""] def __init__( self : Any , _UpperCAmelCase : Tuple , _UpperCAmelCase : int="<s>" , _UpperCAmelCase : str="</s>" , _UpperCAmelCase : Tuple="</s>" , _UpperCAmelCase : Optional[Any]="<s>" , _UpperCAmelCase : Union[str, Any]="<unk>" , _UpperCAmelCase : int="<pad>" , _UpperCAmelCase : Optional[Dict[str, Any]] = None , **_UpperCAmelCase : Optional[Any] , ): """simple docstring""" UpperCAmelCase__ = {} if sp_model_kwargs is None else sp_model_kwargs # Compatibility with the original tokenizer UpperCAmelCase__ = 7 UpperCAmelCase__ = [f'''<madeupword{i}>''' for i in range(self.num_madeup_words )] UpperCAmelCase__ = kwargs.get("""additional_special_tokens""" , [] ) kwargs["additional_special_tokens"] += [ word for word in madeup_words if word not in kwargs["additional_special_tokens"] ] super().__init__( bos_token=_UpperCAmelCase , eos_token=_UpperCAmelCase , unk_token=_UpperCAmelCase , sep_token=_UpperCAmelCase , cls_token=_UpperCAmelCase , pad_token=_UpperCAmelCase , sp_model_kwargs=self.sp_model_kwargs , **_UpperCAmelCase , ) UpperCAmelCase__ = spm.SentencePieceProcessor(**self.sp_model_kwargs ) self.sp_model.Load(str(_UpperCAmelCase ) ) UpperCAmelCase__ = vocab_file # Original fairseq vocab and spm vocab must be "aligned": # Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ---- # fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-' # spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a' # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab UpperCAmelCase__ = 1 # Mimic fairseq token-to-id alignment for the first 4 token UpperCAmelCase__ = {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3} UpperCAmelCase__ = len(self.sp_model ) UpperCAmelCase__ = {f'''<madeupword{i}>''': sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words )} self.fairseq_tokens_to_ids.update(_UpperCAmelCase ) UpperCAmelCase__ = {v: k for k, v in self.fairseq_tokens_to_ids.items()} def __getstate__( self : Any ): """simple docstring""" UpperCAmelCase__ = self.__dict__.copy() UpperCAmelCase__ = None UpperCAmelCase__ = self.sp_model.serialized_model_proto() return state def __setstate__( self : Optional[int] , _UpperCAmelCase : Optional[int] ): """simple docstring""" UpperCAmelCase__ = d # for backward compatibility if not hasattr(self , """sp_model_kwargs""" ): UpperCAmelCase__ = {} UpperCAmelCase__ = spm.SentencePieceProcessor(**self.sp_model_kwargs ) self.sp_model.LoadFromSerializedProto(self.sp_model_proto ) def SCREAMING_SNAKE_CASE__ ( self : Tuple , _UpperCAmelCase : List[int] , _UpperCAmelCase : Optional[List[int]] = None ): """simple docstring""" if token_ids_a is None: return [self.sep_token_id] + token_ids_a UpperCAmelCase__ = [self.sep_token_id] return sep + token_ids_a + sep + sep + token_ids_a def SCREAMING_SNAKE_CASE__ ( self : List[str] , _UpperCAmelCase : List[int] , _UpperCAmelCase : Optional[List[int]] = None , _UpperCAmelCase : bool = False ): """simple docstring""" if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_a=_UpperCAmelCase , token_ids_a=_UpperCAmelCase , already_has_special_tokens=_UpperCAmelCase ) if token_ids_a is None: return [1] + ([0] * len(_UpperCAmelCase )) return [1] + ([0] * len(_UpperCAmelCase )) + [1, 1] + ([0] * len(_UpperCAmelCase )) def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] , _UpperCAmelCase : List[int] , _UpperCAmelCase : Optional[List[int]] = None ): """simple docstring""" UpperCAmelCase__ = [self.sep_token_id] if token_ids_a is None: return len(sep + token_ids_a ) * [0] return len(sep + token_ids_a + sep + sep + token_ids_a ) * [0] @property def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ): """simple docstring""" return len(self.sp_model ) + self.fairseq_offset + self.num_madeup_words def SCREAMING_SNAKE_CASE__ ( self : int ): """simple docstring""" UpperCAmelCase__ = {self.convert_ids_to_tokens(_UpperCAmelCase ): i for i in range(self.vocab_size )} vocab.update(self.added_tokens_encoder ) return vocab def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , _UpperCAmelCase : str ): """simple docstring""" return self.sp_model.encode(_UpperCAmelCase , out_type=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , _UpperCAmelCase : Union[str, Any] ): """simple docstring""" if token in self.fairseq_tokens_to_ids: return self.fairseq_tokens_to_ids[token] UpperCAmelCase__ = self.sp_model.PieceToId(_UpperCAmelCase ) # Need to return unknown token if the SP model returned 0 return spm_id + self.fairseq_offset if spm_id else self.unk_token_id def SCREAMING_SNAKE_CASE__ ( self : List[str] , _UpperCAmelCase : Union[str, Any] ): """simple docstring""" if index in self.fairseq_ids_to_tokens: return self.fairseq_ids_to_tokens[index] return self.sp_model.IdToPiece(index - self.fairseq_offset ) def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] , _UpperCAmelCase : Any ): """simple docstring""" UpperCAmelCase__ = """""".join(_UpperCAmelCase ).replace(_UpperCAmelCase , """ """ ).strip() return out_string def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] , _UpperCAmelCase : str , _UpperCAmelCase : Optional[str] = None ): """simple docstring""" if not os.path.isdir(_UpperCAmelCase ): logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' ) return UpperCAmelCase__ = os.path.join( _UpperCAmelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] ) if os.path.abspath(self.vocab_file ) != os.path.abspath(_UpperCAmelCase ) and os.path.isfile(self.vocab_file ): copyfile(self.vocab_file , _UpperCAmelCase ) elif not os.path.isfile(self.vocab_file ): with open(_UpperCAmelCase , """wb""" ) as fi: UpperCAmelCase__ = self.sp_model.serialized_model_proto() fi.write(_UpperCAmelCase ) return (out_vocab_file,)

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'''simple docstring''' import datasets from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py UpperCAmelCase_ = '\\n@INPROCEEDINGS{Papineni02bleu:a,\n author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n title = {BLEU: a Method for Automatic Evaluation of Machine Translation},\n booktitle = {},\n year = {2002},\n pages = {311--318}\n}\n@inproceedings{lin-och-2004-orange,\n title = "{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation",\n author = "Lin, Chin-Yew and\n Och, Franz Josef",\n booktitle = "{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics",\n month = "aug 23{--}aug 27",\n year = "2004",\n address = "Geneva, Switzerland",\n publisher = "COLING",\n url = "https://www.aclweb.org/anthology/C04-1072",\n pages = "501--507",\n}\n' UpperCAmelCase_ = '\\nBLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.\nQuality is considered to be the correspondence between a machine\'s output and that of a human: "the closer a machine translation is to a professional human translation,\nthe better it is" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and\nremains one of the most popular automated and inexpensive metrics.\n\nScores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.\nThose scores are then averaged over the whole corpus to reach an estimate of the translation\'s overall quality. Intelligibility or grammatical correctness\nare not taken into account[citation needed].\n\nBLEU\'s output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1\nrepresenting more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the\nreference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional\nreference translations will increase the BLEU score.\n' UpperCAmelCase_ = '\nComputes BLEU score of translated segments against one or more references.\nArgs:\n predictions: list of translations to score.\n Each translation should be tokenized into a list of tokens.\n references: list of lists of references for each translation.\n Each reference should be tokenized into a list of tokens.\n max_order: Maximum n-gram order to use when computing BLEU score.\n smooth: Whether or not to apply Lin et al. 2004 smoothing.\nReturns:\n \'bleu\': bleu score,\n \'precisions\': geometric mean of n-gram precisions,\n \'brevity_penalty\': brevity penalty,\n \'length_ratio\': ratio of lengths,\n \'translation_length\': translation_length,\n \'reference_length\': reference_length\nExamples:\n\n >>> predictions = [\n ... ["hello", "there", "general", "kenobi"], # tokenized prediction of the first sample\n ... ["foo", "bar", "foobar"] # tokenized prediction of the second sample\n ... ]\n >>> references = [\n ... [["hello", "there", "general", "kenobi"], ["hello", "there", "!"]], # tokenized references for the first sample (2 references)\n ... [["foo", "bar", "foobar"]] # tokenized references for the second sample (1 reference)\n ... ]\n >>> bleu = datasets.load_metric("bleu")\n >>> results = bleu.compute(predictions=predictions, references=references)\n >>> print(results["bleu"])\n 1.0\n' @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION ) class lowerCAmelCase_ ( datasets.Metric ): '''simple docstring''' def SCREAMING_SNAKE_CASE__ ( self : int ): """simple docstring""" return datasets.MetricInfo( description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features( { """predictions""": datasets.Sequence(datasets.Value("""string""" , id="""token""" ) , id="""sequence""" ), """references""": datasets.Sequence( datasets.Sequence(datasets.Value("""string""" , id="""token""" ) , id="""sequence""" ) , id="""references""" ), } ) , codebase_urls=["""https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py"""] , reference_urls=[ """https://en.wikipedia.org/wiki/BLEU""", """https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213""", ] , ) def SCREAMING_SNAKE_CASE__ ( self : int , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : List[Any]=4 , _UpperCAmelCase : Union[str, Any]=False ): """simple docstring""" UpperCAmelCase__ = compute_bleu( reference_corpus=_UpperCAmelCase , translation_corpus=_UpperCAmelCase , max_order=_UpperCAmelCase , smooth=_UpperCAmelCase ) ((UpperCAmelCase__) , (UpperCAmelCase__) , (UpperCAmelCase__) , (UpperCAmelCase__) , (UpperCAmelCase__) , (UpperCAmelCase__)) = score return { "bleu": bleu, "precisions": precisions, "brevity_penalty": bp, "length_ratio": ratio, "translation_length": translation_length, "reference_length": reference_length, }

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'''simple docstring''' import torch from diffusers import DDPMScheduler from .test_schedulers import SchedulerCommonTest class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' lowerCAmelCase_ : Any = (DDPMScheduler,) def SCREAMING_SNAKE_CASE__ ( self : Dict , **_UpperCAmelCase : Optional[int] ): """simple docstring""" UpperCAmelCase__ = { """num_train_timesteps""": 10_00, """beta_start""": 0.0001, """beta_end""": 0.02, """beta_schedule""": """linear""", """variance_type""": """fixed_small""", """clip_sample""": True, } config.update(**_UpperCAmelCase ) return config def SCREAMING_SNAKE_CASE__ ( self : List[Any] ): """simple docstring""" for timesteps in [1, 5, 1_00, 10_00]: self.check_over_configs(num_train_timesteps=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ): """simple docstring""" for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1] , [0.002, 0.02, 0.2, 2] ): self.check_over_configs(beta_start=_UpperCAmelCase , beta_end=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ): """simple docstring""" for schedule in ["linear", "squaredcos_cap_v2"]: self.check_over_configs(beta_schedule=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ): """simple docstring""" for variance in ["fixed_small", "fixed_large", "other"]: self.check_over_configs(variance_type=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ): """simple docstring""" for clip_sample in [True, False]: self.check_over_configs(clip_sample=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ): """simple docstring""" self.check_over_configs(thresholding=_UpperCAmelCase ) for threshold in [0.5, 1.0, 2.0]: for prediction_type in ["epsilon", "sample", "v_prediction"]: self.check_over_configs( thresholding=_UpperCAmelCase , prediction_type=_UpperCAmelCase , sample_max_value=_UpperCAmelCase , ) def SCREAMING_SNAKE_CASE__ ( self : str ): """simple docstring""" for prediction_type in ["epsilon", "sample", "v_prediction"]: self.check_over_configs(prediction_type=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ): """simple docstring""" for t in [0, 5_00, 9_99]: self.check_over_forward(time_step=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ): """simple docstring""" UpperCAmelCase__ = self.scheduler_classes[0] UpperCAmelCase__ = self.get_scheduler_config() UpperCAmelCase__ = scheduler_class(**_UpperCAmelCase ) assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1E-5 assert torch.sum(torch.abs(scheduler._get_variance(4_87 ) - 0.0_0979 ) ) < 1E-5 assert torch.sum(torch.abs(scheduler._get_variance(9_99 ) - 0.02 ) ) < 1E-5 def SCREAMING_SNAKE_CASE__ ( self : int ): """simple docstring""" UpperCAmelCase__ = self.scheduler_classes[0] UpperCAmelCase__ = self.get_scheduler_config() UpperCAmelCase__ = scheduler_class(**_UpperCAmelCase ) UpperCAmelCase__ = len(_UpperCAmelCase ) UpperCAmelCase__ = self.dummy_model() UpperCAmelCase__ = self.dummy_sample_deter UpperCAmelCase__ = torch.manual_seed(0 ) for t in reversed(range(_UpperCAmelCase ) ): # 1. predict noise residual UpperCAmelCase__ = model(_UpperCAmelCase , _UpperCAmelCase ) # 2. predict previous mean of sample x_t-1 UpperCAmelCase__ = scheduler.step(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , generator=_UpperCAmelCase ).prev_sample # if t > 0: # noise = self.dummy_sample_deter # variance = scheduler.get_variance(t) ** (0.5) * noise # # sample = pred_prev_sample + variance UpperCAmelCase__ = pred_prev_sample UpperCAmelCase__ = torch.sum(torch.abs(_UpperCAmelCase ) ) UpperCAmelCase__ = torch.mean(torch.abs(_UpperCAmelCase ) ) assert abs(result_sum.item() - 258.9606 ) < 1E-2 assert abs(result_mean.item() - 0.3372 ) < 1E-3 def SCREAMING_SNAKE_CASE__ ( self : List[Any] ): """simple docstring""" UpperCAmelCase__ = self.scheduler_classes[0] UpperCAmelCase__ = self.get_scheduler_config(prediction_type="""v_prediction""" ) UpperCAmelCase__ = scheduler_class(**_UpperCAmelCase ) UpperCAmelCase__ = len(_UpperCAmelCase ) UpperCAmelCase__ = self.dummy_model() UpperCAmelCase__ = self.dummy_sample_deter UpperCAmelCase__ = torch.manual_seed(0 ) for t in reversed(range(_UpperCAmelCase ) ): # 1. predict noise residual UpperCAmelCase__ = model(_UpperCAmelCase , _UpperCAmelCase ) # 2. predict previous mean of sample x_t-1 UpperCAmelCase__ = scheduler.step(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , generator=_UpperCAmelCase ).prev_sample # if t > 0: # noise = self.dummy_sample_deter # variance = scheduler.get_variance(t) ** (0.5) * noise # # sample = pred_prev_sample + variance UpperCAmelCase__ = pred_prev_sample UpperCAmelCase__ = torch.sum(torch.abs(_UpperCAmelCase ) ) UpperCAmelCase__ = torch.mean(torch.abs(_UpperCAmelCase ) ) assert abs(result_sum.item() - 202.0296 ) < 1E-2 assert abs(result_mean.item() - 0.2631 ) < 1E-3 def SCREAMING_SNAKE_CASE__ ( self : str ): """simple docstring""" UpperCAmelCase__ = self.scheduler_classes[0] UpperCAmelCase__ = self.get_scheduler_config() UpperCAmelCase__ = scheduler_class(**_UpperCAmelCase ) UpperCAmelCase__ = [1_00, 87, 50, 1, 0] scheduler.set_timesteps(timesteps=_UpperCAmelCase ) UpperCAmelCase__ = scheduler.timesteps for i, timestep in enumerate(_UpperCAmelCase ): if i == len(_UpperCAmelCase ) - 1: UpperCAmelCase__ = -1 else: UpperCAmelCase__ = timesteps[i + 1] UpperCAmelCase__ = scheduler.previous_timestep(_UpperCAmelCase ) UpperCAmelCase__ = prev_t.item() self.assertEqual(_UpperCAmelCase , _UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Dict ): """simple docstring""" UpperCAmelCase__ = self.scheduler_classes[0] UpperCAmelCase__ = self.get_scheduler_config() UpperCAmelCase__ = scheduler_class(**_UpperCAmelCase ) UpperCAmelCase__ = [1_00, 87, 50, 51, 0] with self.assertRaises(_UpperCAmelCase , msg="""`custom_timesteps` must be in descending order.""" ): scheduler.set_timesteps(timesteps=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ): """simple docstring""" UpperCAmelCase__ = self.scheduler_classes[0] UpperCAmelCase__ = self.get_scheduler_config() UpperCAmelCase__ = scheduler_class(**_UpperCAmelCase ) UpperCAmelCase__ = [1_00, 87, 50, 1, 0] UpperCAmelCase__ = len(_UpperCAmelCase ) with self.assertRaises(_UpperCAmelCase , msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`.""" ): scheduler.set_timesteps(num_inference_steps=_UpperCAmelCase , timesteps=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ): """simple docstring""" UpperCAmelCase__ = self.scheduler_classes[0] UpperCAmelCase__ = self.get_scheduler_config() UpperCAmelCase__ = scheduler_class(**_UpperCAmelCase ) UpperCAmelCase__ = [scheduler.config.num_train_timesteps] with self.assertRaises( _UpperCAmelCase , msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}""" , ): scheduler.set_timesteps(timesteps=_UpperCAmelCase )

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'''simple docstring''' from dataclasses import dataclass from typing import List, Optional, Union import numpy as np import torch from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available @dataclass class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' lowerCAmelCase_ : Union[List[np.ndarray], torch.FloatTensor] try: if not (is_transformers_available() and is_torch_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from ...utils.dummy_torch_and_transformers_objects import * # noqa F403 else: from .pipeline_text_to_video_synth import TextToVideoSDPipeline from .pipeline_text_to_video_synth_imgaimg import VideoToVideoSDPipeline # noqa: F401 from .pipeline_text_to_video_zero import TextToVideoZeroPipeline

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'''simple docstring''' import os from bleurt import score # From: git+https://github.com/google-research/bleurt.git import datasets UpperCAmelCase_ = datasets.logging.get_logger(__name__) UpperCAmelCase_ = '\\n@inproceedings{bleurt,\n title={BLEURT: Learning Robust Metrics for Text Generation},\n author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},\n booktitle={ACL},\n year={2020},\n url={https://arxiv.org/abs/2004.04696}\n}\n' UpperCAmelCase_ = '\\nBLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)\nand then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune\nit for your specific application (the latter is expected to perform better).\n\nSee the project\'s README at https://github.com/google-research/bleurt#readme for more information.\n' UpperCAmelCase_ = '\nBLEURT score.\n\nArgs:\n `predictions` (list of str): prediction/candidate sentences\n `references` (list of str): reference sentences\n `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.\n\nReturns:\n \'scores\': List of scores.\nExamples:\n\n >>> predictions = ["hello there", "general kenobi"]\n >>> references = ["hello there", "general kenobi"]\n >>> bleurt = datasets.load_metric("bleurt")\n >>> results = bleurt.compute(predictions=predictions, references=references)\n >>> print([round(v, 2) for v in results["scores"]])\n [1.03, 1.04]\n' UpperCAmelCase_ = { 'bleurt-tiny-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip', 'bleurt-tiny-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip', 'bleurt-base-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip', 'bleurt-base-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip', 'bleurt-large-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip', 'bleurt-large-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip', 'BLEURT-20-D3': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip', 'BLEURT-20-D6': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip', 'BLEURT-20-D12': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip', 'BLEURT-20': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip', } @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION ) class lowerCAmelCase_ ( datasets.Metric ): '''simple docstring''' def SCREAMING_SNAKE_CASE__ ( self : Tuple ): """simple docstring""" return datasets.MetricInfo( description=_DESCRIPTION , citation=_CITATION , homepage="""https://github.com/google-research/bleurt""" , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features( { """predictions""": datasets.Value("""string""" , id="""sequence""" ), """references""": datasets.Value("""string""" , id="""sequence""" ), } ) , codebase_urls=["""https://github.com/google-research/bleurt"""] , reference_urls=["""https://github.com/google-research/bleurt""", """https://arxiv.org/abs/2004.04696"""] , ) def SCREAMING_SNAKE_CASE__ ( self : List[str] , _UpperCAmelCase : Optional[int] ): """simple docstring""" if self.config_name == "default": logger.warning( """Using default BLEURT-Base checkpoint for sequence maximum length 128. """ """You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512').""" ) UpperCAmelCase__ = """bleurt-base-128""" if self.config_name.lower() in CHECKPOINT_URLS: UpperCAmelCase__ = self.config_name.lower() elif self.config_name.upper() in CHECKPOINT_URLS: UpperCAmelCase__ = self.config_name.upper() else: raise KeyError( f'''{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}''' ) # download the model checkpoint specified by self.config_name and set up the scorer UpperCAmelCase__ = dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name] ) UpperCAmelCase__ = score.BleurtScorer(os.path.join(_UpperCAmelCase , _UpperCAmelCase ) ) def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Optional[Any] ): """simple docstring""" UpperCAmelCase__ = self.scorer.score(references=_UpperCAmelCase , candidates=_UpperCAmelCase ) return {"scores": scores}

346

'''simple docstring''' import torch import torch.nn as nn from transformers.modeling_utils import ModuleUtilsMixin from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm from ...configuration_utils import ConfigMixin, register_to_config from ...models import ModelMixin class lowerCAmelCase_ ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ): '''simple docstring''' @register_to_config def __init__( self : List[str] , _UpperCAmelCase : int , _UpperCAmelCase : int , _UpperCAmelCase : int , _UpperCAmelCase : float , _UpperCAmelCase : int , _UpperCAmelCase : int , _UpperCAmelCase : int , _UpperCAmelCase : int , _UpperCAmelCase : str , _UpperCAmelCase : bool = False , ): """simple docstring""" super().__init__() UpperCAmelCase__ = nn.Embedding(_UpperCAmelCase , _UpperCAmelCase ) UpperCAmelCase__ = nn.Embedding(_UpperCAmelCase , _UpperCAmelCase ) UpperCAmelCase__ = False UpperCAmelCase__ = nn.Dropout(p=_UpperCAmelCase ) UpperCAmelCase__ = TaConfig( vocab_size=_UpperCAmelCase , d_model=_UpperCAmelCase , num_heads=_UpperCAmelCase , d_kv=_UpperCAmelCase , d_ff=_UpperCAmelCase , dropout_rate=_UpperCAmelCase , feed_forward_proj=_UpperCAmelCase , is_decoder=_UpperCAmelCase , is_encoder_decoder=_UpperCAmelCase , ) UpperCAmelCase__ = nn.ModuleList() for lyr_num in range(_UpperCAmelCase ): UpperCAmelCase__ = TaBlock(_UpperCAmelCase ) self.encoders.append(_UpperCAmelCase ) UpperCAmelCase__ = TaLayerNorm(_UpperCAmelCase ) UpperCAmelCase__ = nn.Dropout(p=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Dict , _UpperCAmelCase : int , _UpperCAmelCase : str ): """simple docstring""" UpperCAmelCase__ = self.token_embedder(_UpperCAmelCase ) UpperCAmelCase__ = encoder_input_tokens.shape[1] UpperCAmelCase__ = torch.arange(_UpperCAmelCase , device=encoder_input_tokens.device ) x += self.position_encoding(_UpperCAmelCase ) UpperCAmelCase__ = self.dropout_pre(_UpperCAmelCase ) # inverted the attention mask UpperCAmelCase__ = encoder_input_tokens.size() UpperCAmelCase__ = self.get_extended_attention_mask(_UpperCAmelCase , _UpperCAmelCase ) for lyr in self.encoders: UpperCAmelCase__ = lyr(_UpperCAmelCase , _UpperCAmelCase )[0] UpperCAmelCase__ = self.layer_norm(_UpperCAmelCase ) return self.dropout_post(_UpperCAmelCase ), encoder_inputs_mask

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'''simple docstring''' import argparse import json from typing import List from ltp import LTP from transformers.models.bert.tokenization_bert import BertTokenizer def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : List[str] ): '''simple docstring''' if ( (cp >= 0x4e00 and cp <= 0x9fff) or (cp >= 0x3400 and cp <= 0x4dbf) # or (cp >= 0x20000 and cp <= 0x2a6df) # or (cp >= 0x2a700 and cp <= 0x2b73f) # or (cp >= 0x2b740 and cp <= 0x2b81f) # or (cp >= 0x2b820 and cp <= 0x2ceaf) # or (cp >= 0xf900 and cp <= 0xfaff) or (cp >= 0x2f800 and cp <= 0x2fa1f) # ): # return True return False def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str ): '''simple docstring''' for char in word: UpperCAmelCase__ = ord(SCREAMING_SNAKE_CASE__ ) if not _is_chinese_char(SCREAMING_SNAKE_CASE__ ): return 0 return 1 def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : List[str] ): '''simple docstring''' UpperCAmelCase__ = set() for token in tokens: UpperCAmelCase__ = len(SCREAMING_SNAKE_CASE__ ) > 1 and is_chinese(SCREAMING_SNAKE_CASE__ ) if chinese_word: word_set.add(SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = list(SCREAMING_SNAKE_CASE__ ) return word_list def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : set() ): '''simple docstring''' if not chinese_word_set: return bert_tokens UpperCAmelCase__ = max([len(SCREAMING_SNAKE_CASE__ ) for w in chinese_word_set] ) UpperCAmelCase__ = bert_tokens UpperCAmelCase__ , UpperCAmelCase__ = 0, len(SCREAMING_SNAKE_CASE__ ) while start < end: UpperCAmelCase__ = True if is_chinese(bert_word[start] ): UpperCAmelCase__ = min(end - start , SCREAMING_SNAKE_CASE__ ) for i in range(SCREAMING_SNAKE_CASE__ , 1 , -1 ): UpperCAmelCase__ = """""".join(bert_word[start : start + i] ) if whole_word in chinese_word_set: for j in range(start + 1 , start + i ): UpperCAmelCase__ = """##""" + bert_word[j] UpperCAmelCase__ = start + i UpperCAmelCase__ = False break if single_word: start += 1 return bert_word def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : LTP , SCREAMING_SNAKE_CASE__ : BertTokenizer ): '''simple docstring''' UpperCAmelCase__ = [] for i in range(0 , len(SCREAMING_SNAKE_CASE__ ) , 100 ): UpperCAmelCase__ = ltp_tokenizer.pipeline(lines[i : i + 100] , tasks=["""cws"""] ).cws UpperCAmelCase__ = [get_chinese_word(SCREAMING_SNAKE_CASE__ ) for r in res] ltp_res.extend(SCREAMING_SNAKE_CASE__ ) assert len(SCREAMING_SNAKE_CASE__ ) == len(SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = [] for i in range(0 , len(SCREAMING_SNAKE_CASE__ ) , 100 ): UpperCAmelCase__ = bert_tokenizer(lines[i : i + 100] , add_special_tokens=SCREAMING_SNAKE_CASE__ , truncation=SCREAMING_SNAKE_CASE__ , max_length=512 ) bert_res.extend(res["""input_ids"""] ) assert len(SCREAMING_SNAKE_CASE__ ) == len(SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = [] for input_ids, chinese_word in zip(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ): UpperCAmelCase__ = [] for id in input_ids: UpperCAmelCase__ = bert_tokenizer._convert_id_to_token(SCREAMING_SNAKE_CASE__ ) input_tokens.append(SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = add_sub_symbol(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = [] # We only save pos of chinese subwords start with ##, which mean is part of a whole word. for i, token in enumerate(SCREAMING_SNAKE_CASE__ ): if token[:2] == "##": UpperCAmelCase__ = token[2:] # save chinese tokens' pos if len(SCREAMING_SNAKE_CASE__ ) == 1 and _is_chinese_char(ord(SCREAMING_SNAKE_CASE__ ) ): ref_id.append(SCREAMING_SNAKE_CASE__ ) ref_ids.append(SCREAMING_SNAKE_CASE__ ) assert len(SCREAMING_SNAKE_CASE__ ) == len(SCREAMING_SNAKE_CASE__ ) return ref_ids def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Tuple ): '''simple docstring''' with open(args.file_name , """r""" , encoding="""utf-8""" ) as f: UpperCAmelCase__ = f.readlines() UpperCAmelCase__ = [line.strip() for line in data if len(SCREAMING_SNAKE_CASE__ ) > 0 and not line.isspace()] # avoid delimiter like '\u2029' UpperCAmelCase__ = LTP(args.ltp ) # faster in GPU device UpperCAmelCase__ = BertTokenizer.from_pretrained(args.bert ) UpperCAmelCase__ = prepare_ref(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) with open(args.save_path , """w""" , encoding="""utf-8""" ) as f: UpperCAmelCase__ = [json.dumps(SCREAMING_SNAKE_CASE__ ) + """\n""" for ref in ref_ids] f.writelines(SCREAMING_SNAKE_CASE__ ) if __name__ == "__main__": UpperCAmelCase_ = argparse.ArgumentParser(description='prepare_chinese_ref') parser.add_argument( '--file_name', required=False, type=str, default='./resources/chinese-demo.txt', help='file need process, same as training data in lm', ) parser.add_argument( '--ltp', required=False, type=str, default='./resources/ltp', help='resources for LTP tokenizer, usually a path', ) parser.add_argument( '--bert', required=False, type=str, default='./resources/robert', help='resources for Bert tokenizer', ) parser.add_argument( '--save_path', required=False, type=str, default='./resources/ref.txt', help='path to save res', ) UpperCAmelCase_ = parser.parse_args() main(args)

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'''simple docstring''' import argparse import json import os import fairseq import torch from fairseq.data import Dictionary from transformers import ( WavaVecaConfig, WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaForCTC, WavaVecaForPreTraining, WavaVecaProcessor, logging, ) from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification logging.set_verbosity_info() UpperCAmelCase_ = logging.get_logger(__name__) UpperCAmelCase_ = { 'post_extract_proj': 'feature_projection.projection', 'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv', 'self_attn.k_proj': 'encoder.layers.*.attention.k_proj', 'self_attn.v_proj': 'encoder.layers.*.attention.v_proj', 'self_attn.q_proj': 'encoder.layers.*.attention.q_proj', 'self_attn.out_proj': 'encoder.layers.*.attention.out_proj', 'self_attn_layer_norm': 'encoder.layers.*.layer_norm', 'fc1': 'encoder.layers.*.feed_forward.intermediate_dense', 'fc2': 'encoder.layers.*.feed_forward.output_dense', 'final_layer_norm': 'encoder.layers.*.final_layer_norm', 'encoder.layer_norm': 'encoder.layer_norm', 'adapter_layer': 'encoder.layers.*.adapter_layer', 'w2v_model.layer_norm': 'feature_projection.layer_norm', 'quantizer.weight_proj': 'quantizer.weight_proj', 'quantizer.vars': 'quantizer.codevectors', 'project_q': 'project_q', 'final_proj': 'project_hid', 'w2v_encoder.proj': 'lm_head', 'mask_emb': 'masked_spec_embed', 'pooling_layer.linear': 'projector', 'pooling_layer.projection': 'classifier', } UpperCAmelCase_ = [ 'lm_head', 'quantizer.weight_proj', 'quantizer.codevectors', 'project_q', 'project_hid', 'projector', 'classifier', ] def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Tuple ): '''simple docstring''' UpperCAmelCase__ = {} with open(SCREAMING_SNAKE_CASE__ , """r""" ) as file: for line_number, line in enumerate(SCREAMING_SNAKE_CASE__ ): UpperCAmelCase__ = line.strip() if line: UpperCAmelCase__ = line.split() UpperCAmelCase__ = line_number UpperCAmelCase__ = words[0] UpperCAmelCase__ = value return result def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : Union[str, Any] ): '''simple docstring''' for attribute in key.split(""".""" ): UpperCAmelCase__ = getattr(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = None for param_key in PARAM_MAPPING.keys(): if full_name.endswith(SCREAMING_SNAKE_CASE__ ): UpperCAmelCase__ = PARAM_MAPPING[full_name.split(""".""" )[-1]] UpperCAmelCase__ = """param""" if weight_type is not None and weight_type != "param": UpperCAmelCase__ = getattr(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ).shape elif weight_type is not None and weight_type == "param": UpperCAmelCase__ = hf_pointer for attribute in hf_param_name.split(""".""" ): UpperCAmelCase__ = getattr(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = shape_pointer.shape # let's reduce dimension UpperCAmelCase__ = value[0] else: UpperCAmelCase__ = hf_pointer.shape if hf_shape != value.shape: raise ValueError( F'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be''' F''' {value.shape} for {full_name}''' ) if weight_type == "weight": UpperCAmelCase__ = value elif weight_type == "weight_g": UpperCAmelCase__ = value elif weight_type == "weight_v": UpperCAmelCase__ = value elif weight_type == "bias": UpperCAmelCase__ = value elif weight_type == "param": for attribute in hf_param_name.split(""".""" ): UpperCAmelCase__ = getattr(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = value else: UpperCAmelCase__ = value logger.info(F'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.''' ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : Optional[Any] ): '''simple docstring''' UpperCAmelCase__ = None for param_key in PARAM_MAPPING.keys(): if full_name.endswith(SCREAMING_SNAKE_CASE__ ): UpperCAmelCase__ = PARAM_MAPPING[full_name.split(""".""" )[-1]] UpperCAmelCase__ = """param""" if weight_type is not None and weight_type != "param": UpperCAmelCase__ = """.""".join([key, weight_type] ) elif weight_type is not None and weight_type == "param": UpperCAmelCase__ = """.""".join([key, hf_param_name] ) else: UpperCAmelCase__ = key UpperCAmelCase__ = value if """lm_head""" in full_key else value[0] UpperCAmelCase_ = { 'W_a': 'linear_1.weight', 'W_b': 'linear_2.weight', 'b_a': 'linear_1.bias', 'b_b': 'linear_2.bias', 'ln_W': 'norm.weight', 'ln_b': 'norm.bias', } def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : Dict=None , SCREAMING_SNAKE_CASE__ : Optional[Any]=None ): '''simple docstring''' UpperCAmelCase__ = False for key, mapped_key in MAPPING.items(): UpperCAmelCase__ = """wav2vec2.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key if key in name or key.split("""w2v_model.""" )[-1] == name.split(""".""" )[0]: UpperCAmelCase__ = True if "*" in mapped_key: UpperCAmelCase__ = name.split(SCREAMING_SNAKE_CASE__ )[0].split(""".""" )[-2] UpperCAmelCase__ = mapped_key.replace("""*""" , SCREAMING_SNAKE_CASE__ ) if "weight_g" in name: UpperCAmelCase__ = """weight_g""" elif "weight_v" in name: UpperCAmelCase__ = """weight_v""" elif "bias" in name: UpperCAmelCase__ = """bias""" elif "weight" in name: # TODO: don't match quantizer.weight_proj UpperCAmelCase__ = """weight""" else: UpperCAmelCase__ = None if hf_dict is not None: rename_dict(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) else: set_recursively(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) return is_used return is_used def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CASE__ : List[str] ): '''simple docstring''' UpperCAmelCase__ = [] UpperCAmelCase__ = fairseq_model.state_dict() UpperCAmelCase__ = hf_model.wavaveca.feature_extractor for name, value in fairseq_dict.items(): UpperCAmelCase__ = False if "conv_layers" in name: load_conv_layer( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , hf_model.config.feat_extract_norm == """group""" , ) UpperCAmelCase__ = True else: UpperCAmelCase__ = load_wavaveca_layer(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) if not is_used: unused_weights.append(SCREAMING_SNAKE_CASE__ ) logger.warning(F'''Unused weights: {unused_weights}''' ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : int ): '''simple docstring''' UpperCAmelCase__ = full_name.split("""conv_layers.""" )[-1] UpperCAmelCase__ = name.split(""".""" ) UpperCAmelCase__ = int(items[0] ) UpperCAmelCase__ = int(items[1] ) if type_id == 0: if "bias" in name: if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape: raise ValueError( F'''{full_name} has size {value.shape}, but''' F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.''' ) UpperCAmelCase__ = value logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' ) elif "weight" in name: if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape: raise ValueError( F'''{full_name} has size {value.shape}, but''' F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.''' ) UpperCAmelCase__ = value logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' ) elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm): if "bias" in name: if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape: raise ValueError( F'''{full_name} has size {value.shape}, but''' F''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.''' ) UpperCAmelCase__ = value logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' ) elif "weight" in name: if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape: raise ValueError( F'''{full_name} has size {value.shape}, but''' F''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.''' ) UpperCAmelCase__ = value logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' ) else: unused_weights.append(SCREAMING_SNAKE_CASE__ ) @torch.no_grad() def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : List[str]=None , SCREAMING_SNAKE_CASE__ : Optional[int]=None , SCREAMING_SNAKE_CASE__ : Union[str, Any]=True , SCREAMING_SNAKE_CASE__ : Union[str, Any]=False ): '''simple docstring''' if config_path is not None: UpperCAmelCase__ = WavaVecaConfig.from_pretrained(SCREAMING_SNAKE_CASE__ ) else: UpperCAmelCase__ = WavaVecaConfig() if is_seq_class: UpperCAmelCase__ = read_txt_into_dict(SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = idalabel UpperCAmelCase__ = WavaVecaForSequenceClassification(SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = WavaVecaFeatureExtractor( feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=SCREAMING_SNAKE_CASE__ , return_attention_mask=SCREAMING_SNAKE_CASE__ , ) feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE__ ) elif is_finetuned: if dict_path: UpperCAmelCase__ = Dictionary.load(SCREAMING_SNAKE_CASE__ ) # important change bos & pad token id since CTC symbol is <pad> and # not <s> as in fairseq UpperCAmelCase__ = target_dict.pad_index UpperCAmelCase__ = target_dict.bos_index UpperCAmelCase__ = target_dict.eos_index UpperCAmelCase__ = len(target_dict.symbols ) UpperCAmelCase__ = os.path.join(SCREAMING_SNAKE_CASE__ , """vocab.json""" ) if not os.path.isdir(SCREAMING_SNAKE_CASE__ ): logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(SCREAMING_SNAKE_CASE__ ) ) return os.makedirs(SCREAMING_SNAKE_CASE__ , exist_ok=SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = target_dict.indices # fairseq has the <pad> and <s> switched UpperCAmelCase__ = 0 UpperCAmelCase__ = 1 with open(SCREAMING_SNAKE_CASE__ , """w""" , encoding="""utf-8""" ) as vocab_handle: json.dump(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = WavaVecaCTCTokenizer( SCREAMING_SNAKE_CASE__ , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token="""|""" , do_lower_case=SCREAMING_SNAKE_CASE__ , ) UpperCAmelCase__ = True if config.feat_extract_norm == """layer""" else False UpperCAmelCase__ = WavaVecaFeatureExtractor( feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=SCREAMING_SNAKE_CASE__ , return_attention_mask=SCREAMING_SNAKE_CASE__ , ) UpperCAmelCase__ = WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE__ , tokenizer=SCREAMING_SNAKE_CASE__ ) processor.save_pretrained(SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = WavaVecaForCTC(SCREAMING_SNAKE_CASE__ ) else: UpperCAmelCase__ = WavaVecaForPreTraining(SCREAMING_SNAKE_CASE__ ) if is_finetuned or is_seq_class: UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = fairseq.checkpoint_utils.load_model_ensemble_and_task( [checkpoint_path] , arg_overrides={"""data""": """/""".join(dict_path.split("""/""" )[:-1] )} ) else: UpperCAmelCase__ = argparse.Namespace(task="""audio_pretraining""" ) UpperCAmelCase__ = fairseq.tasks.setup_task(SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] , task=SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = model[0].eval() recursively_load_weights(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , not is_finetuned ) hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE__ ) if __name__ == "__main__": UpperCAmelCase_ = argparse.ArgumentParser() parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.') parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint') parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model') parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert') parser.add_argument( '--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not' ) parser.add_argument( '--is_seq_class', action='store_true', help='Whether the model to convert is a fine-tuned sequence classification model or not', ) UpperCAmelCase_ = parser.parse_args() UpperCAmelCase_ = not args.not_finetuned and not args.is_seq_class convert_wavaveca_checkpoint( args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, is_finetuned, args.is_seq_class, )

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'''simple docstring''' import unittest import numpy as np import torch from diffusers import VersatileDiffusionImageVariationPipeline from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device UpperCAmelCase_ = False class lowerCAmelCase_ ( unittest.TestCase ): '''simple docstring''' pass @slow @require_torch_gpu class lowerCAmelCase_ ( unittest.TestCase ): '''simple docstring''' def SCREAMING_SNAKE_CASE__ ( self : List[str] ): """simple docstring""" UpperCAmelCase__ = VersatileDiffusionImageVariationPipeline.from_pretrained("""shi-labs/versatile-diffusion""" ) pipe.to(_UpperCAmelCase ) pipe.set_progress_bar_config(disable=_UpperCAmelCase ) UpperCAmelCase__ = load_image( """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg""" ) UpperCAmelCase__ = torch.manual_seed(0 ) UpperCAmelCase__ = pipe( image=_UpperCAmelCase , generator=_UpperCAmelCase , guidance_scale=7.5 , num_inference_steps=50 , output_type="""numpy""" , ).images UpperCAmelCase__ = image[0, 2_53:2_56, 2_53:2_56, -1] assert image.shape == (1, 5_12, 5_12, 3) UpperCAmelCase__ = np.array([0.0441, 0.0469, 0.0507, 0.0575, 0.0632, 0.0650, 0.0865, 0.0909, 0.0945] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2

346

'''simple docstring''' import itertools import os from collections import Counter, defaultdict from concurrent.futures import ThreadPoolExecutor, as_completed import numpy as np import datasets from .execute import check_correctness UpperCAmelCase_ = '\\n@misc{chen2021evaluating,\n title={Evaluating Large Language Models Trained on Code},\n author={Mark Chen and Jerry Tworek and Heewoo Jun and Qiming Yuan \\nand Henrique Ponde de Oliveira Pinto and Jared Kaplan and Harri Edwards \\nand Yuri Burda and Nicholas Joseph and Greg Brockman and Alex Ray \\nand Raul Puri and Gretchen Krueger and Michael Petrov and Heidy Khlaaf \\nand Girish Sastry and Pamela Mishkin and Brooke Chan and Scott Gray \\nand Nick Ryder and Mikhail Pavlov and Alethea Power and Lukasz Kaiser \\nand Mohammad Bavarian and Clemens Winter and Philippe Tillet \\nand Felipe Petroski Such and Dave Cummings and Matthias Plappert \\nand Fotios Chantzis and Elizabeth Barnes and Ariel Herbert-Voss \\nand William Hebgen Guss and Alex Nichol and Alex Paino and Nikolas Tezak \\nand Jie Tang and Igor Babuschkin and Suchir Balaji and Shantanu Jain \\nand William Saunders and Christopher Hesse and Andrew N. Carr \\nand Jan Leike and Josh Achiam and Vedant Misra and Evan Morikawa \\nand Alec Radford and Matthew Knight and Miles Brundage and Mira Murati \\nand Katie Mayer and Peter Welinder and Bob McGrew and Dario Amodei \\nand Sam McCandlish and Ilya Sutskever and Wojciech Zaremba},\n year={2021},\n eprint={2107.03374},\n archivePrefix={arXiv},\n primaryClass={cs.LG}\n}\n' UpperCAmelCase_ = '\\nThis metric implements the evaluation harness for the HumanEval problem solving dataset\ndescribed in the paper "Evaluating Large Language Models Trained on Code"\n(https://arxiv.org/abs/2107.03374).\n' UpperCAmelCase_ = '\nCalculates how good are predictions given some references, using certain scores\nArgs:\n predictions: list of candidates to evaluate. Each candidates should be a list\n of strings with several code candidates to solve the problem.\n references: a list with a test for each prediction. Each test should evaluate the\n correctness of a code candidate.\n k: number of code candidates to consider in the evaluation (Default: [1, 10, 100])\n num_workers: number of workers used to evaluate the canidate programs (Default: 4).\n timeout:\nReturns:\n pass_at_k: dict with pass rates for each k\n results: dict with granular results of each unittest\nExamples:\n >>> code_eval = datasets.load_metric("code_eval")\n >>> test_cases = ["assert add(2,3)==5"]\n >>> candidates = [["def add(a,b): return a*b", "def add(a, b): return a+b"]]\n >>> pass_at_k, results = code_eval.compute(references=test_cases, predictions=candidates, k=[1, 2])\n >>> print(pass_at_k)\n {\'pass@1\': 0.5, \'pass@2\': 1.0}\n' UpperCAmelCase_ = '\n################################################################################\n !!!WARNING!!!\n################################################################################\nThe "code_eval" metric executes untrusted model-generated code in Python.\nAlthough it is highly unlikely that model-generated code will do something\novertly malicious in response to this test suite, model-generated code may act\ndestructively due to a lack of model capability or alignment.\nUsers are strongly encouraged to sandbox this evaluation suite so that it\ndoes not perform destructive actions on their host or network. For more\ninformation on how OpenAI sandboxes its code, see the paper "Evaluating Large\nLanguage Models Trained on Code" (https://arxiv.org/abs/2107.03374).\n\nOnce you have read this disclaimer and taken appropriate precautions,\nset the environment variable HF_ALLOW_CODE_EVAL="1". Within Python you can to this\nwith:\n\n>>> import os\n>>> os.environ["HF_ALLOW_CODE_EVAL"] = "1"\n\n################################################################################\\n' UpperCAmelCase_ = 'The MIT License\n\nCopyright (c) OpenAI (https://openai.com)\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the "Software"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in\nall copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\nTHE SOFTWARE.' @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION ) class lowerCAmelCase_ ( datasets.Metric ): '''simple docstring''' def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ): """simple docstring""" return datasets.MetricInfo( # This is the description that will appear on the metrics page. description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features( { """predictions""": datasets.Sequence(datasets.Value("""string""" ) ), """references""": datasets.Value("""string""" ), } ) , homepage="""https://github.com/openai/human-eval""" , codebase_urls=["""https://github.com/openai/human-eval"""] , reference_urls=["""https://github.com/openai/human-eval"""] , license=_LICENSE , ) def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : List[str]=[1, 10, 1_00] , _UpperCAmelCase : Optional[Any]=4 , _UpperCAmelCase : Any=3.0 ): """simple docstring""" if os.getenv("""HF_ALLOW_CODE_EVAL""" , 0 ) != "1": raise ValueError(_WARNING ) if os.name == "nt": raise NotImplementedError("""This metric is currently not supported on Windows.""" ) with ThreadPoolExecutor(max_workers=_UpperCAmelCase ) as executor: UpperCAmelCase__ = [] UpperCAmelCase__ = Counter() UpperCAmelCase__ = 0 UpperCAmelCase__ = defaultdict(_UpperCAmelCase ) for task_id, (candidates, test_case) in enumerate(zip(_UpperCAmelCase , _UpperCAmelCase ) ): for candidate in candidates: UpperCAmelCase__ = candidate + """\n""" + test_case UpperCAmelCase__ = (test_program, timeout, task_id, completion_id[task_id]) UpperCAmelCase__ = executor.submit(_UpperCAmelCase , *_UpperCAmelCase ) futures.append(_UpperCAmelCase ) completion_id[task_id] += 1 n_samples += 1 for future in as_completed(_UpperCAmelCase ): UpperCAmelCase__ = future.result() results[result["task_id"]].append((result["""completion_id"""], result) ) UpperCAmelCase__ , UpperCAmelCase__ = [], [] for result in results.values(): result.sort() UpperCAmelCase__ = [r[1]["""passed"""] for r in result] total.append(len(_UpperCAmelCase ) ) correct.append(sum(_UpperCAmelCase ) ) UpperCAmelCase__ = np.array(_UpperCAmelCase ) UpperCAmelCase__ = np.array(_UpperCAmelCase ) UpperCAmelCase__ = k UpperCAmelCase__ = {f'''pass@{k}''': estimate_pass_at_k(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ).mean() for k in ks if (total >= k).all()} return pass_at_k, results def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : Optional[Any] ): '''simple docstring''' def estimator(SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ) -> float: if n - c < k: return 1.0 return 1.0 - np.prod(1.0 - k / np.arange(n - c + 1 , n + 1 ) ) if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ): UpperCAmelCase__ = itertools.repeat(SCREAMING_SNAKE_CASE__ , len(SCREAMING_SNAKE_CASE__ ) ) else: assert len(SCREAMING_SNAKE_CASE__ ) == len(SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = iter(SCREAMING_SNAKE_CASE__ ) return np.array([estimator(int(SCREAMING_SNAKE_CASE__ ) , int(SCREAMING_SNAKE_CASE__ ) , SCREAMING_SNAKE_CASE__ ) for n, c in zip(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )] )

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'''simple docstring''' import heapq def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : dict ): '''simple docstring''' UpperCAmelCase__ = [] # for each node and his adjacency list add them and the rank of the node to queue # using heapq module the queue will be filled like a Priority Queue # heapq works with a min priority queue, so I used -1*len(v) to build it for key, value in graph.items(): # O(log(n)) heapq.heappush(SCREAMING_SNAKE_CASE__ , [-1 * len(SCREAMING_SNAKE_CASE__ ), (key, value)] ) # chosen_vertices = set of chosen vertices UpperCAmelCase__ = set() # while queue isn't empty and there are still edges # (queue[0][0] is the rank of the node with max rank) while queue and queue[0][0] != 0: # extract vertex with max rank from queue and add it to chosen_vertices UpperCAmelCase__ = heapq.heappop(SCREAMING_SNAKE_CASE__ )[1][0] chosen_vertices.add(SCREAMING_SNAKE_CASE__ ) # Remove all arcs adjacent to argmax for elem in queue: # if v haven't adjacent node, skip if elem[0] == 0: continue # if argmax is reachable from elem # remove argmax from elem's adjacent list and update his rank if argmax in elem[1][1]: UpperCAmelCase__ = elem[1][1].index(SCREAMING_SNAKE_CASE__ ) del elem[1][1][index] elem[0] += 1 # re-order the queue heapq.heapify(SCREAMING_SNAKE_CASE__ ) return chosen_vertices if __name__ == "__main__": import doctest doctest.testmod() UpperCAmelCase_ = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]} print(f"Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}")

346

'''simple docstring''' import math def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int ): '''simple docstring''' assert isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) and ( number >= 0 ), "'number' must been an int and positive" if 1 < number < 4: # 2 and 3 are primes return True elif number < 2 or not number % 2: # Negatives, 0, 1 and all even numbers are not primes return False UpperCAmelCase__ = range(3 , int(math.sqrt(SCREAMING_SNAKE_CASE__ ) + 1 ) , 2 ) return not any(not number % i for i in odd_numbers ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : List[str]=1 , **SCREAMING_SNAKE_CASE__ : List[str] ): '''simple docstring''' UpperCAmelCase__ = factor * value UpperCAmelCase__ = value while not is_prime(SCREAMING_SNAKE_CASE__ ): value += 1 if not ("desc" in kwargs and kwargs["desc"] is True) else -1 if value == first_value_val: return next_prime(value + 1 , **SCREAMING_SNAKE_CASE__ ) return value

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'''simple docstring''' from __future__ import annotations from fractions import Fraction from math import gcd, sqrt def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int ): '''simple docstring''' UpperCAmelCase__ = int(number**0.5 ) return number == sq * sq def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ): '''simple docstring''' UpperCAmelCase__ = x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den UpperCAmelCase__ = x_den * y_den * z_den UpperCAmelCase__ = gcd(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) top //= hcf bottom //= hcf return top, bottom def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int = 35 ): '''simple docstring''' UpperCAmelCase__ = set() UpperCAmelCase__ = 42 UpperCAmelCase__ = Fraction(0 ) UpperCAmelCase__ = 42 for x_num in range(1 , order + 1 ): for x_den in range(x_num + 1 , order + 1 ): for y_num in range(1 , order + 1 ): for y_den in range(y_num + 1 , order + 1 ): # n=1 UpperCAmelCase__ = x_num * y_den + x_den * y_num UpperCAmelCase__ = x_den * y_den UpperCAmelCase__ = gcd(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) z_num //= hcf z_den //= hcf if 0 < z_num < z_den <= order: UpperCAmelCase__ = add_three( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) unique_s.add(SCREAMING_SNAKE_CASE__ ) # n=2 UpperCAmelCase__ = ( x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num ) UpperCAmelCase__ = x_den * x_den * y_den * y_den if is_sq(SCREAMING_SNAKE_CASE__ ) and is_sq(SCREAMING_SNAKE_CASE__ ): UpperCAmelCase__ = int(sqrt(SCREAMING_SNAKE_CASE__ ) ) UpperCAmelCase__ = int(sqrt(SCREAMING_SNAKE_CASE__ ) ) UpperCAmelCase__ = gcd(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) z_num //= hcf z_den //= hcf if 0 < z_num < z_den <= order: UpperCAmelCase__ = add_three( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) unique_s.add(SCREAMING_SNAKE_CASE__ ) # n=-1 UpperCAmelCase__ = x_num * y_num UpperCAmelCase__ = x_den * y_num + x_num * y_den UpperCAmelCase__ = gcd(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) z_num //= hcf z_den //= hcf if 0 < z_num < z_den <= order: UpperCAmelCase__ = add_three( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) unique_s.add(SCREAMING_SNAKE_CASE__ ) # n=2 UpperCAmelCase__ = x_num * x_num * y_num * y_num UpperCAmelCase__ = ( x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den ) if is_sq(SCREAMING_SNAKE_CASE__ ) and is_sq(SCREAMING_SNAKE_CASE__ ): UpperCAmelCase__ = int(sqrt(SCREAMING_SNAKE_CASE__ ) ) UpperCAmelCase__ = int(sqrt(SCREAMING_SNAKE_CASE__ ) ) UpperCAmelCase__ = gcd(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) z_num //= hcf z_den //= hcf if 0 < z_num < z_den <= order: UpperCAmelCase__ = add_three( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) unique_s.add(SCREAMING_SNAKE_CASE__ ) for num, den in unique_s: total += Fraction(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) return total.denominator + total.numerator if __name__ == "__main__": print(f"{solution() = }")

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'''simple docstring''' import string from math import logaa def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : str ): '''simple docstring''' UpperCAmelCase__ = document.translate( str.maketrans("""""" , """""" , string.punctuation ) ).replace("""\n""" , """""" ) UpperCAmelCase__ = document_without_punctuation.split(""" """ ) # word tokenization return len([word for word in tokenize_document if word.lower() == term.lower()] ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : str ): '''simple docstring''' UpperCAmelCase__ = corpus.lower().translate( str.maketrans("""""" , """""" , string.punctuation ) ) # strip all punctuation and replace it with '' UpperCAmelCase__ = corpus_without_punctuation.split("""\n""" ) UpperCAmelCase__ = term.lower() return (len([doc for doc in docs if term in doc] ), len(SCREAMING_SNAKE_CASE__ )) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : Tuple=False ): '''simple docstring''' if smoothing: if n == 0: raise ValueError("""log10(0) is undefined.""" ) return round(1 + logaa(n / (1 + df) ) , 3 ) if df == 0: raise ZeroDivisionError("""df must be > 0""" ) elif n == 0: raise ValueError("""log10(0) is undefined.""" ) return round(logaa(n / df ) , 3 ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ): '''simple docstring''' return round(tf * idf , 3 )

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'''simple docstring''' from ...configuration_utils import PretrainedConfig from ...utils import logging UpperCAmelCase_ = logging.get_logger(__name__) UpperCAmelCase_ = { 'microsoft/markuplm-base': 'https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json', 'microsoft/markuplm-large': 'https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json', } class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' lowerCAmelCase_ : Any = """markuplm""" def __init__( self : Optional[int] , _UpperCAmelCase : Union[str, Any]=3_05_22 , _UpperCAmelCase : Union[str, Any]=7_68 , _UpperCAmelCase : Any=12 , _UpperCAmelCase : int=12 , _UpperCAmelCase : List[Any]=30_72 , _UpperCAmelCase : Optional[int]="gelu" , _UpperCAmelCase : Union[str, Any]=0.1 , _UpperCAmelCase : Union[str, Any]=0.1 , _UpperCAmelCase : str=5_12 , _UpperCAmelCase : str=2 , _UpperCAmelCase : List[Any]=0.02 , _UpperCAmelCase : Optional[Any]=1E-12 , _UpperCAmelCase : Union[str, Any]=0 , _UpperCAmelCase : int=0 , _UpperCAmelCase : str=2 , _UpperCAmelCase : Any=2_56 , _UpperCAmelCase : Tuple=10_24 , _UpperCAmelCase : Tuple=2_16 , _UpperCAmelCase : int=10_01 , _UpperCAmelCase : Optional[Any]=32 , _UpperCAmelCase : List[str]=50 , _UpperCAmelCase : Any="absolute" , _UpperCAmelCase : int=True , _UpperCAmelCase : Union[str, Any]=None , **_UpperCAmelCase : List[str] , ): """simple docstring""" super().__init__( pad_token_id=_UpperCAmelCase , bos_token_id=_UpperCAmelCase , eos_token_id=_UpperCAmelCase , **_UpperCAmelCase , ) UpperCAmelCase__ = vocab_size UpperCAmelCase__ = hidden_size UpperCAmelCase__ = num_hidden_layers UpperCAmelCase__ = num_attention_heads UpperCAmelCase__ = hidden_act UpperCAmelCase__ = intermediate_size UpperCAmelCase__ = hidden_dropout_prob UpperCAmelCase__ = attention_probs_dropout_prob UpperCAmelCase__ = max_position_embeddings UpperCAmelCase__ = type_vocab_size UpperCAmelCase__ = initializer_range UpperCAmelCase__ = layer_norm_eps UpperCAmelCase__ = position_embedding_type UpperCAmelCase__ = use_cache UpperCAmelCase__ = classifier_dropout # additional properties UpperCAmelCase__ = max_depth UpperCAmelCase__ = max_xpath_tag_unit_embeddings UpperCAmelCase__ = max_xpath_subs_unit_embeddings UpperCAmelCase__ = tag_pad_id UpperCAmelCase__ = subs_pad_id UpperCAmelCase__ = xpath_unit_hidden_size

346

'''simple docstring''' import argparse import torch from transformers import BertForMaskedLM if __name__ == "__main__": UpperCAmelCase_ = argparse.ArgumentParser( description=( 'Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned' ' Distillation' ) ) parser.add_argument('--model_type', default='bert', choices=['bert']) parser.add_argument('--model_name', default='bert-base-uncased', type=str) parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_bert-base-uncased_0247911.pth', type=str) parser.add_argument('--vocab_transform', action='store_true') UpperCAmelCase_ = parser.parse_args() if args.model_type == "bert": UpperCAmelCase_ = BertForMaskedLM.from_pretrained(args.model_name) UpperCAmelCase_ = 'bert' else: raise ValueError('args.model_type should be "bert".') UpperCAmelCase_ = model.state_dict() UpperCAmelCase_ = {} for w in ["word_embeddings", "position_embeddings"]: UpperCAmelCase_ = state_dict[f"{prefix}.embeddings.{w}.weight"] for w in ["weight", "bias"]: UpperCAmelCase_ = state_dict[f"{prefix}.embeddings.LayerNorm.{w}"] UpperCAmelCase_ = 0 for teacher_idx in [0, 2, 4, 7, 9, 1_1]: for w in ["weight", "bias"]: UpperCAmelCase_ = state_dict[ f"{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}" ] UpperCAmelCase_ = state_dict[ f"{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}" ] UpperCAmelCase_ = state_dict[ f"{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}" ] UpperCAmelCase_ = state_dict[ f"{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}" ] UpperCAmelCase_ = state_dict[ f"{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}" ] UpperCAmelCase_ = state_dict[ f"{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}" ] UpperCAmelCase_ = state_dict[ f"{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}" ] UpperCAmelCase_ = state_dict[ f"{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}" ] std_idx += 1 UpperCAmelCase_ = state_dict['cls.predictions.decoder.weight'] UpperCAmelCase_ = state_dict['cls.predictions.bias'] if args.vocab_transform: for w in ["weight", "bias"]: UpperCAmelCase_ = state_dict[f"cls.predictions.transform.dense.{w}"] UpperCAmelCase_ = state_dict[f"cls.predictions.transform.LayerNorm.{w}"] print(f"N layers selected for distillation: {std_idx}") print(f"Number of params transferred for distillation: {len(compressed_sd.keys())}") print(f"Save transferred checkpoint to {args.dump_checkpoint}.") torch.save(compressed_sd, args.dump_checkpoint)

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'''simple docstring''' import os import tempfile import unittest from transformers import FlaubertConfig, is_torch_available from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( FlaubertForMultipleChoice, FlaubertForQuestionAnswering, FlaubertForQuestionAnsweringSimple, FlaubertForSequenceClassification, FlaubertForTokenClassification, FlaubertModel, FlaubertWithLMHeadModel, ) from transformers.models.flaubert.modeling_flaubert import FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' def __init__( self : Union[str, Any] , _UpperCAmelCase : List[Any] , _UpperCAmelCase : List[str]=13 , _UpperCAmelCase : Union[str, Any]=7 , _UpperCAmelCase : int=True , _UpperCAmelCase : Union[str, Any]=True , _UpperCAmelCase : Tuple=True , _UpperCAmelCase : Union[str, Any]=True , _UpperCAmelCase : List[str]=True , _UpperCAmelCase : Optional[Any]=False , _UpperCAmelCase : List[Any]=False , _UpperCAmelCase : Tuple=False , _UpperCAmelCase : int=2 , _UpperCAmelCase : Tuple=99 , _UpperCAmelCase : Optional[int]=0 , _UpperCAmelCase : int=32 , _UpperCAmelCase : Optional[Any]=5 , _UpperCAmelCase : Optional[Any]=4 , _UpperCAmelCase : List[str]=0.1 , _UpperCAmelCase : Tuple=0.1 , _UpperCAmelCase : List[Any]=5_12 , _UpperCAmelCase : Optional[int]=12 , _UpperCAmelCase : Optional[int]=2 , _UpperCAmelCase : Tuple=0.02 , _UpperCAmelCase : str=3 , _UpperCAmelCase : Optional[int]=4 , _UpperCAmelCase : Any="last" , _UpperCAmelCase : List[str]=None , _UpperCAmelCase : Dict=None , ): """simple docstring""" UpperCAmelCase__ = parent UpperCAmelCase__ = batch_size UpperCAmelCase__ = seq_length UpperCAmelCase__ = is_training UpperCAmelCase__ = use_input_lengths UpperCAmelCase__ = use_token_type_ids UpperCAmelCase__ = use_labels UpperCAmelCase__ = gelu_activation UpperCAmelCase__ = sinusoidal_embeddings UpperCAmelCase__ = causal UpperCAmelCase__ = asm UpperCAmelCase__ = n_langs UpperCAmelCase__ = vocab_size UpperCAmelCase__ = n_special UpperCAmelCase__ = hidden_size UpperCAmelCase__ = num_hidden_layers UpperCAmelCase__ = num_attention_heads UpperCAmelCase__ = hidden_dropout_prob UpperCAmelCase__ = attention_probs_dropout_prob UpperCAmelCase__ = max_position_embeddings UpperCAmelCase__ = type_vocab_size UpperCAmelCase__ = type_sequence_label_size UpperCAmelCase__ = initializer_range UpperCAmelCase__ = num_labels UpperCAmelCase__ = num_choices UpperCAmelCase__ = summary_type UpperCAmelCase__ = use_proj UpperCAmelCase__ = scope def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ): """simple docstring""" UpperCAmelCase__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size ) UpperCAmelCase__ = random_attention_mask([self.batch_size, self.seq_length] ) UpperCAmelCase__ = None if self.use_input_lengths: UpperCAmelCase__ = ( ids_tensor([self.batch_size] , vocab_size=2 ) + self.seq_length - 2 ) # small variation of seq_length UpperCAmelCase__ = None if self.use_token_type_ids: UpperCAmelCase__ = ids_tensor([self.batch_size, self.seq_length] , self.n_langs ) UpperCAmelCase__ = None UpperCAmelCase__ = None UpperCAmelCase__ = None if self.use_labels: UpperCAmelCase__ = ids_tensor([self.batch_size] , self.type_sequence_label_size ) UpperCAmelCase__ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels ) UpperCAmelCase__ = ids_tensor([self.batch_size] , 2 ).float() UpperCAmelCase__ = ids_tensor([self.batch_size] , self.num_choices ) UpperCAmelCase__ = self.get_config() return ( config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, choice_labels, input_mask, ) def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ): """simple docstring""" return FlaubertConfig( vocab_size=self.vocab_size , n_special=self.n_special , emb_dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , gelu_activation=self.gelu_activation , sinusoidal_embeddings=self.sinusoidal_embeddings , asm=self.asm , causal=self.causal , n_langs=self.n_langs , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , summary_type=self.summary_type , use_proj=self.use_proj , ) def SCREAMING_SNAKE_CASE__ ( self : List[str] , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Dict , _UpperCAmelCase : List[str] , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : List[str] , _UpperCAmelCase : Dict , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Union[str, Any] , ): """simple docstring""" UpperCAmelCase__ = FlaubertModel(config=_UpperCAmelCase ) model.to(_UpperCAmelCase ) model.eval() UpperCAmelCase__ = model(_UpperCAmelCase , lengths=_UpperCAmelCase , langs=_UpperCAmelCase ) UpperCAmelCase__ = model(_UpperCAmelCase , langs=_UpperCAmelCase ) UpperCAmelCase__ = model(_UpperCAmelCase ) self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) ) def SCREAMING_SNAKE_CASE__ ( self : List[str] , _UpperCAmelCase : Any , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : int , _UpperCAmelCase : Dict , _UpperCAmelCase : Dict , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Optional[Any] , ): """simple docstring""" UpperCAmelCase__ = FlaubertWithLMHeadModel(_UpperCAmelCase ) model.to(_UpperCAmelCase ) model.eval() UpperCAmelCase__ = model(_UpperCAmelCase , token_type_ids=_UpperCAmelCase , labels=_UpperCAmelCase ) self.parent.assertEqual(result.loss.shape , () ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) ) def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] , _UpperCAmelCase : Tuple , _UpperCAmelCase : Dict , _UpperCAmelCase : int , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Any , _UpperCAmelCase : Union[str, Any] , ): """simple docstring""" UpperCAmelCase__ = FlaubertForQuestionAnsweringSimple(_UpperCAmelCase ) model.to(_UpperCAmelCase ) model.eval() UpperCAmelCase__ = model(_UpperCAmelCase ) UpperCAmelCase__ = model(_UpperCAmelCase , start_positions=_UpperCAmelCase , end_positions=_UpperCAmelCase ) self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) ) self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) ) def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : int , _UpperCAmelCase : Dict , _UpperCAmelCase : List[str] , _UpperCAmelCase : List[Any] , _UpperCAmelCase : int , _UpperCAmelCase : Any , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Union[str, Any] , ): """simple docstring""" UpperCAmelCase__ = FlaubertForQuestionAnswering(_UpperCAmelCase ) model.to(_UpperCAmelCase ) model.eval() UpperCAmelCase__ = model(_UpperCAmelCase ) UpperCAmelCase__ = model( _UpperCAmelCase , start_positions=_UpperCAmelCase , end_positions=_UpperCAmelCase , cls_index=_UpperCAmelCase , is_impossible=_UpperCAmelCase , p_mask=_UpperCAmelCase , ) UpperCAmelCase__ = model( _UpperCAmelCase , start_positions=_UpperCAmelCase , end_positions=_UpperCAmelCase , cls_index=_UpperCAmelCase , is_impossible=_UpperCAmelCase , ) ((UpperCAmelCase__) , ) = result_with_labels.to_tuple() UpperCAmelCase__ = model(_UpperCAmelCase , start_positions=_UpperCAmelCase , end_positions=_UpperCAmelCase ) ((UpperCAmelCase__) , ) = result_with_labels.to_tuple() self.parent.assertEqual(result_with_labels.loss.shape , () ) self.parent.assertEqual(result.start_top_log_probs.shape , (self.batch_size, model.config.start_n_top) ) self.parent.assertEqual(result.start_top_index.shape , (self.batch_size, model.config.start_n_top) ) self.parent.assertEqual( result.end_top_log_probs.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) ) self.parent.assertEqual( result.end_top_index.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) ) self.parent.assertEqual(result.cls_logits.shape , (self.batch_size,) ) def SCREAMING_SNAKE_CASE__ ( self : List[str] , _UpperCAmelCase : List[str] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : int , _UpperCAmelCase : str , _UpperCAmelCase : int , _UpperCAmelCase : List[str] , _UpperCAmelCase : List[str] , _UpperCAmelCase : Any , _UpperCAmelCase : Dict , ): """simple docstring""" UpperCAmelCase__ = FlaubertForSequenceClassification(_UpperCAmelCase ) model.to(_UpperCAmelCase ) model.eval() UpperCAmelCase__ = model(_UpperCAmelCase ) UpperCAmelCase__ = model(_UpperCAmelCase , labels=_UpperCAmelCase ) self.parent.assertEqual(result.loss.shape , () ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) ) def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] , _UpperCAmelCase : Any , _UpperCAmelCase : Tuple , _UpperCAmelCase : int , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Dict , _UpperCAmelCase : str , _UpperCAmelCase : Any , _UpperCAmelCase : str , _UpperCAmelCase : Optional[int] , ): """simple docstring""" UpperCAmelCase__ = self.num_labels UpperCAmelCase__ = FlaubertForTokenClassification(_UpperCAmelCase ) model.to(_UpperCAmelCase ) model.eval() UpperCAmelCase__ = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase , labels=_UpperCAmelCase ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) ) def SCREAMING_SNAKE_CASE__ ( self : Tuple , _UpperCAmelCase : Dict , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Any , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : List[str] , _UpperCAmelCase : str , _UpperCAmelCase : Optional[int] , ): """simple docstring""" UpperCAmelCase__ = self.num_choices UpperCAmelCase__ = FlaubertForMultipleChoice(config=_UpperCAmelCase ) model.to(_UpperCAmelCase ) model.eval() UpperCAmelCase__ = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous() UpperCAmelCase__ = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous() UpperCAmelCase__ = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous() UpperCAmelCase__ = model( _UpperCAmelCase , attention_mask=_UpperCAmelCase , token_type_ids=_UpperCAmelCase , labels=_UpperCAmelCase , ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) ) def SCREAMING_SNAKE_CASE__ ( self : int ): """simple docstring""" UpperCAmelCase__ = self.prepare_config_and_inputs() ( ( UpperCAmelCase__ ) , ( UpperCAmelCase__ ) , ( UpperCAmelCase__ ) , ( UpperCAmelCase__ ) , ( UpperCAmelCase__ ) , ( UpperCAmelCase__ ) , ( UpperCAmelCase__ ) , ( UpperCAmelCase__ ) , ( UpperCAmelCase__ ) , ) = config_and_inputs UpperCAmelCase__ = { """input_ids""": input_ids, """token_type_ids""": token_type_ids, """lengths""": input_lengths, """attention_mask""": input_mask, } return config, inputs_dict @require_torch class lowerCAmelCase_ ( lowerCamelCase_ , lowerCamelCase_ , unittest.TestCase ): '''simple docstring''' lowerCAmelCase_ : str = ( ( FlaubertModel, FlaubertWithLMHeadModel, FlaubertForQuestionAnswering, FlaubertForQuestionAnsweringSimple, FlaubertForSequenceClassification, FlaubertForTokenClassification, FlaubertForMultipleChoice, ) if is_torch_available() else () ) lowerCAmelCase_ : Tuple = ( { """feature-extraction""": FlaubertModel, """fill-mask""": FlaubertWithLMHeadModel, """question-answering""": FlaubertForQuestionAnsweringSimple, """text-classification""": FlaubertForSequenceClassification, """token-classification""": FlaubertForTokenClassification, """zero-shot""": FlaubertForSequenceClassification, } if is_torch_available() else {} ) def SCREAMING_SNAKE_CASE__ ( self : List[str] , _UpperCAmelCase : Tuple , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Tuple , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Optional[Any] ): """simple docstring""" if ( pipeline_test_casse_name == "QAPipelineTests" and tokenizer_name is not None and not tokenizer_name.endswith("""Fast""" ) ): # `QAPipelineTests` fails for a few models when the slower tokenizer are used. # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework) # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer return True return False def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : int , _UpperCAmelCase : Optional[Any]=False ): """simple docstring""" UpperCAmelCase__ = super()._prepare_for_class(_UpperCAmelCase , _UpperCAmelCase , return_labels=_UpperCAmelCase ) if return_labels: if model_class.__name__ == "FlaubertForQuestionAnswering": UpperCAmelCase__ = torch.zeros( self.model_tester.batch_size , dtype=torch.long , device=_UpperCAmelCase ) UpperCAmelCase__ = torch.zeros( self.model_tester.batch_size , dtype=torch.long , device=_UpperCAmelCase ) return inputs_dict def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ): """simple docstring""" UpperCAmelCase__ = FlaubertModelTester(self ) UpperCAmelCase__ = ConfigTester(self , config_class=_UpperCAmelCase , emb_dim=37 ) def SCREAMING_SNAKE_CASE__ ( self : Tuple ): """simple docstring""" self.config_tester.run_common_tests() def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ): """simple docstring""" UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_flaubert_model(*_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ): """simple docstring""" UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_flaubert_lm_head(*_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ): """simple docstring""" UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_flaubert_simple_qa(*_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : int ): """simple docstring""" UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_flaubert_qa(*_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Tuple ): """simple docstring""" UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_flaubert_sequence_classif(*_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : List[Any] ): """simple docstring""" UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_flaubert_token_classif(*_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ): """simple docstring""" UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_flaubert_multiple_choice(*_UpperCAmelCase ) @slow def SCREAMING_SNAKE_CASE__ ( self : Any ): """simple docstring""" for model_name in FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: UpperCAmelCase__ = FlaubertModel.from_pretrained(_UpperCAmelCase ) self.assertIsNotNone(_UpperCAmelCase ) @slow @require_torch_gpu def SCREAMING_SNAKE_CASE__ ( self : Any ): """simple docstring""" UpperCAmelCase__ , UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: # FlauBertForMultipleChoice behaves incorrectly in JIT environments. if model_class == FlaubertForMultipleChoice: return UpperCAmelCase__ = True UpperCAmelCase__ = model_class(config=_UpperCAmelCase ) UpperCAmelCase__ = self._prepare_for_class(_UpperCAmelCase , _UpperCAmelCase ) UpperCAmelCase__ = torch.jit.trace( _UpperCAmelCase , (inputs_dict["""input_ids"""].to("""cpu""" ), inputs_dict["""attention_mask"""].to("""cpu""" )) ) with tempfile.TemporaryDirectory() as tmp: torch.jit.save(_UpperCAmelCase , os.path.join(_UpperCAmelCase , """traced_model.pt""" ) ) UpperCAmelCase__ = torch.jit.load(os.path.join(_UpperCAmelCase , """traced_model.pt""" ) , map_location=_UpperCAmelCase ) loaded(inputs_dict["""input_ids"""].to(_UpperCAmelCase ) , inputs_dict["""attention_mask"""].to(_UpperCAmelCase ) ) @require_torch class lowerCAmelCase_ ( unittest.TestCase ): '''simple docstring''' @slow def SCREAMING_SNAKE_CASE__ ( self : int ): """simple docstring""" UpperCAmelCase__ = FlaubertModel.from_pretrained("""flaubert/flaubert_base_cased""" ) UpperCAmelCase__ = torch.tensor([[0, 3_45, 2_32, 3_28, 7_40, 1_40, 16_95, 69, 60_78, 15_88, 2]] ) with torch.no_grad(): UpperCAmelCase__ = model(_UpperCAmelCase )[0] UpperCAmelCase__ = torch.Size((1, 11, 7_68) ) self.assertEqual(output.shape , _UpperCAmelCase ) UpperCAmelCase__ = torch.tensor( [[[-2.6251, -1.4298, -0.0227], [-2.8510, -1.6387, 0.2258], [-2.8114, -1.1832, -0.3066]]] ) self.assertTrue(torch.allclose(output[:, :3, :3] , _UpperCAmelCase , atol=1E-4 ) )

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'''simple docstring''' import tempfile import torch from diffusers import PNDMScheduler from .test_schedulers import SchedulerCommonTest class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' lowerCAmelCase_ : Union[str, Any] = (PNDMScheduler,) lowerCAmelCase_ : Optional[int] = (("""num_inference_steps""", 50),) def SCREAMING_SNAKE_CASE__ ( self : List[Any] , **_UpperCAmelCase : Optional[int] ): """simple docstring""" UpperCAmelCase__ = { """num_train_timesteps""": 10_00, """beta_start""": 0.0001, """beta_end""": 0.02, """beta_schedule""": """linear""", } config.update(**_UpperCAmelCase ) return config def SCREAMING_SNAKE_CASE__ ( self : List[Any] , _UpperCAmelCase : Tuple=0 , **_UpperCAmelCase : List[str] ): """simple docstring""" UpperCAmelCase__ = dict(self.forward_default_kwargs ) UpperCAmelCase__ = kwargs.pop("""num_inference_steps""" , _UpperCAmelCase ) UpperCAmelCase__ = self.dummy_sample UpperCAmelCase__ = 0.1 * sample UpperCAmelCase__ = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05] for scheduler_class in self.scheduler_classes: UpperCAmelCase__ = self.get_scheduler_config(**_UpperCAmelCase ) UpperCAmelCase__ = scheduler_class(**_UpperCAmelCase ) scheduler.set_timesteps(_UpperCAmelCase ) # copy over dummy past residuals UpperCAmelCase__ = dummy_past_residuals[:] with tempfile.TemporaryDirectory() as tmpdirname: scheduler.save_config(_UpperCAmelCase ) UpperCAmelCase__ = scheduler_class.from_pretrained(_UpperCAmelCase ) new_scheduler.set_timesteps(_UpperCAmelCase ) # copy over dummy past residuals UpperCAmelCase__ = dummy_past_residuals[:] UpperCAmelCase__ = scheduler.step_prk(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample UpperCAmelCase__ = new_scheduler.step_prk(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical" UpperCAmelCase__ = scheduler.step_plms(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample UpperCAmelCase__ = new_scheduler.step_plms(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical" def SCREAMING_SNAKE_CASE__ ( self : List[str] ): """simple docstring""" pass def SCREAMING_SNAKE_CASE__ ( self : int , _UpperCAmelCase : Union[str, Any]=0 , **_UpperCAmelCase : Optional[int] ): """simple docstring""" UpperCAmelCase__ = dict(self.forward_default_kwargs ) UpperCAmelCase__ = kwargs.pop("""num_inference_steps""" , _UpperCAmelCase ) UpperCAmelCase__ = self.dummy_sample UpperCAmelCase__ = 0.1 * sample UpperCAmelCase__ = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05] for scheduler_class in self.scheduler_classes: UpperCAmelCase__ = self.get_scheduler_config() UpperCAmelCase__ = scheduler_class(**_UpperCAmelCase ) scheduler.set_timesteps(_UpperCAmelCase ) # copy over dummy past residuals (must be after setting timesteps) UpperCAmelCase__ = dummy_past_residuals[:] with tempfile.TemporaryDirectory() as tmpdirname: scheduler.save_config(_UpperCAmelCase ) UpperCAmelCase__ = scheduler_class.from_pretrained(_UpperCAmelCase ) # copy over dummy past residuals new_scheduler.set_timesteps(_UpperCAmelCase ) # copy over dummy past residual (must be after setting timesteps) UpperCAmelCase__ = dummy_past_residuals[:] UpperCAmelCase__ = scheduler.step_prk(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample UpperCAmelCase__ = new_scheduler.step_prk(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical" UpperCAmelCase__ = scheduler.step_plms(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample UpperCAmelCase__ = new_scheduler.step_plms(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical" def SCREAMING_SNAKE_CASE__ ( self : int , **_UpperCAmelCase : Tuple ): """simple docstring""" UpperCAmelCase__ = self.scheduler_classes[0] UpperCAmelCase__ = self.get_scheduler_config(**_UpperCAmelCase ) UpperCAmelCase__ = scheduler_class(**_UpperCAmelCase ) UpperCAmelCase__ = 10 UpperCAmelCase__ = self.dummy_model() UpperCAmelCase__ = self.dummy_sample_deter scheduler.set_timesteps(_UpperCAmelCase ) for i, t in enumerate(scheduler.prk_timesteps ): UpperCAmelCase__ = model(_UpperCAmelCase , _UpperCAmelCase ) UpperCAmelCase__ = scheduler.step_prk(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ).prev_sample for i, t in enumerate(scheduler.plms_timesteps ): UpperCAmelCase__ = model(_UpperCAmelCase , _UpperCAmelCase ) UpperCAmelCase__ = scheduler.step_plms(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ).prev_sample return sample def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ): """simple docstring""" UpperCAmelCase__ = dict(self.forward_default_kwargs ) UpperCAmelCase__ = kwargs.pop("""num_inference_steps""" , _UpperCAmelCase ) for scheduler_class in self.scheduler_classes: UpperCAmelCase__ = self.get_scheduler_config() UpperCAmelCase__ = scheduler_class(**_UpperCAmelCase ) UpperCAmelCase__ = self.dummy_sample UpperCAmelCase__ = 0.1 * sample if num_inference_steps is not None and hasattr(_UpperCAmelCase , """set_timesteps""" ): scheduler.set_timesteps(_UpperCAmelCase ) elif num_inference_steps is not None and not hasattr(_UpperCAmelCase , """set_timesteps""" ): UpperCAmelCase__ = num_inference_steps # copy over dummy past residuals (must be done after set_timesteps) UpperCAmelCase__ = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05] UpperCAmelCase__ = dummy_past_residuals[:] UpperCAmelCase__ = scheduler.step_prk(_UpperCAmelCase , 0 , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample UpperCAmelCase__ = scheduler.step_prk(_UpperCAmelCase , 1 , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample self.assertEqual(output_a.shape , sample.shape ) self.assertEqual(output_a.shape , output_a.shape ) UpperCAmelCase__ = scheduler.step_plms(_UpperCAmelCase , 0 , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample UpperCAmelCase__ = scheduler.step_plms(_UpperCAmelCase , 1 , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample self.assertEqual(output_a.shape , sample.shape ) self.assertEqual(output_a.shape , output_a.shape ) def SCREAMING_SNAKE_CASE__ ( self : int ): """simple docstring""" for timesteps in [1_00, 10_00]: self.check_over_configs(num_train_timesteps=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ): """simple docstring""" for steps_offset in [0, 1]: self.check_over_configs(steps_offset=_UpperCAmelCase ) UpperCAmelCase__ = self.scheduler_classes[0] UpperCAmelCase__ = self.get_scheduler_config(steps_offset=1 ) UpperCAmelCase__ = scheduler_class(**_UpperCAmelCase ) scheduler.set_timesteps(10 ) assert torch.equal( scheduler.timesteps , torch.LongTensor( [9_01, 8_51, 8_51, 8_01, 8_01, 7_51, 7_51, 7_01, 7_01, 6_51, 6_51, 6_01, 6_01, 5_01, 4_01, 3_01, 2_01, 1_01, 1] ) , ) def SCREAMING_SNAKE_CASE__ ( self : List[Any] ): """simple docstring""" for beta_start, beta_end in zip([0.0001, 0.001] , [0.002, 0.02] ): self.check_over_configs(beta_start=_UpperCAmelCase , beta_end=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ): """simple docstring""" for schedule in ["linear", "squaredcos_cap_v2"]: self.check_over_configs(beta_schedule=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ): """simple docstring""" for prediction_type in ["epsilon", "v_prediction"]: self.check_over_configs(prediction_type=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : str ): """simple docstring""" for t in [1, 5, 10]: self.check_over_forward(time_step=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Any ): """simple docstring""" for t, num_inference_steps in zip([1, 5, 10] , [10, 50, 1_00] ): self.check_over_forward(num_inference_steps=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : List[str] ): """simple docstring""" UpperCAmelCase__ = 27 for scheduler_class in self.scheduler_classes: UpperCAmelCase__ = self.dummy_sample UpperCAmelCase__ = 0.1 * sample UpperCAmelCase__ = self.get_scheduler_config() UpperCAmelCase__ = scheduler_class(**_UpperCAmelCase ) scheduler.set_timesteps(_UpperCAmelCase ) # before power of 3 fix, would error on first step, so we only need to do two for i, t in enumerate(scheduler.prk_timesteps[:2] ): UpperCAmelCase__ = scheduler.step_prk(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ).prev_sample def SCREAMING_SNAKE_CASE__ ( self : Dict ): """simple docstring""" with self.assertRaises(_UpperCAmelCase ): UpperCAmelCase__ = self.scheduler_classes[0] UpperCAmelCase__ = self.get_scheduler_config() UpperCAmelCase__ = scheduler_class(**_UpperCAmelCase ) scheduler.step_plms(self.dummy_sample , 1 , self.dummy_sample ).prev_sample def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ): """simple docstring""" UpperCAmelCase__ = self.full_loop() UpperCAmelCase__ = torch.sum(torch.abs(_UpperCAmelCase ) ) UpperCAmelCase__ = torch.mean(torch.abs(_UpperCAmelCase ) ) assert abs(result_sum.item() - 198.1318 ) < 1E-2 assert abs(result_mean.item() - 0.2580 ) < 1E-3 def SCREAMING_SNAKE_CASE__ ( self : Dict ): """simple docstring""" UpperCAmelCase__ = self.full_loop(prediction_type="""v_prediction""" ) UpperCAmelCase__ = torch.sum(torch.abs(_UpperCAmelCase ) ) UpperCAmelCase__ = torch.mean(torch.abs(_UpperCAmelCase ) ) assert abs(result_sum.item() - 67.3986 ) < 1E-2 assert abs(result_mean.item() - 0.0878 ) < 1E-3 def SCREAMING_SNAKE_CASE__ ( self : Any ): """simple docstring""" UpperCAmelCase__ = self.full_loop(set_alpha_to_one=_UpperCAmelCase , beta_start=0.01 ) UpperCAmelCase__ = torch.sum(torch.abs(_UpperCAmelCase ) ) UpperCAmelCase__ = torch.mean(torch.abs(_UpperCAmelCase ) ) assert abs(result_sum.item() - 230.0399 ) < 1E-2 assert abs(result_mean.item() - 0.2995 ) < 1E-3 def SCREAMING_SNAKE_CASE__ ( self : int ): """simple docstring""" UpperCAmelCase__ = self.full_loop(set_alpha_to_one=_UpperCAmelCase , beta_start=0.01 ) UpperCAmelCase__ = torch.sum(torch.abs(_UpperCAmelCase ) ) UpperCAmelCase__ = torch.mean(torch.abs(_UpperCAmelCase ) ) assert abs(result_sum.item() - 186.9482 ) < 1E-2 assert abs(result_mean.item() - 0.2434 ) < 1E-3

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'''simple docstring''' import unittest from transformers.testing_utils import CaptureStdout from transformers.tools.python_interpreter import evaluate def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str ): '''simple docstring''' return x + 2 class lowerCAmelCase_ ( unittest.TestCase ): '''simple docstring''' def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ): """simple docstring""" UpperCAmelCase__ = """x = 3""" UpperCAmelCase__ = {} UpperCAmelCase__ = evaluate(_UpperCAmelCase , {} , state=_UpperCAmelCase ) assert result == 3 self.assertDictEqual(_UpperCAmelCase , {"""x""": 3} ) UpperCAmelCase__ = """x = y""" UpperCAmelCase__ = {"""y""": 5} UpperCAmelCase__ = evaluate(_UpperCAmelCase , {} , state=_UpperCAmelCase ) # evaluate returns the value of the last assignment. assert result == 5 self.assertDictEqual(_UpperCAmelCase , {"""x""": 5, """y""": 5} ) def SCREAMING_SNAKE_CASE__ ( self : Tuple ): """simple docstring""" UpperCAmelCase__ = """y = add_two(x)""" UpperCAmelCase__ = {"""x""": 3} UpperCAmelCase__ = evaluate(_UpperCAmelCase , {"""add_two""": add_two} , state=_UpperCAmelCase ) assert result == 5 self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """y""": 5} ) # Won't work without the tool with CaptureStdout() as out: UpperCAmelCase__ = evaluate(_UpperCAmelCase , {} , state=_UpperCAmelCase ) assert result is None assert "tried to execute add_two" in out.out def SCREAMING_SNAKE_CASE__ ( self : str ): """simple docstring""" UpperCAmelCase__ = """x = 3""" UpperCAmelCase__ = {} UpperCAmelCase__ = evaluate(_UpperCAmelCase , {} , state=_UpperCAmelCase ) assert result == 3 self.assertDictEqual(_UpperCAmelCase , {"""x""": 3} ) def SCREAMING_SNAKE_CASE__ ( self : int ): """simple docstring""" UpperCAmelCase__ = """test_dict = {'x': x, 'y': add_two(x)}""" UpperCAmelCase__ = {"""x""": 3} UpperCAmelCase__ = evaluate(_UpperCAmelCase , {"""add_two""": add_two} , state=_UpperCAmelCase ) self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """y""": 5} ) self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """test_dict""": {"""x""": 3, """y""": 5}} ) def SCREAMING_SNAKE_CASE__ ( self : str ): """simple docstring""" UpperCAmelCase__ = """x = 3\ny = 5""" UpperCAmelCase__ = {} UpperCAmelCase__ = evaluate(_UpperCAmelCase , {} , state=_UpperCAmelCase ) # evaluate returns the value of the last assignment. assert result == 5 self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """y""": 5} ) def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ): """simple docstring""" UpperCAmelCase__ = """text = f'This is x: {x}.'""" UpperCAmelCase__ = {"""x""": 3} UpperCAmelCase__ = evaluate(_UpperCAmelCase , {} , state=_UpperCAmelCase ) # evaluate returns the value of the last assignment. assert result == "This is x: 3." self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """text""": """This is x: 3."""} ) def SCREAMING_SNAKE_CASE__ ( self : Any ): """simple docstring""" UpperCAmelCase__ = """if x <= 3:\n y = 2\nelse:\n y = 5""" UpperCAmelCase__ = {"""x""": 3} UpperCAmelCase__ = evaluate(_UpperCAmelCase , {} , state=_UpperCAmelCase ) # evaluate returns the value of the last assignment. assert result == 2 self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """y""": 2} ) UpperCAmelCase__ = {"""x""": 8} UpperCAmelCase__ = evaluate(_UpperCAmelCase , {} , state=_UpperCAmelCase ) # evaluate returns the value of the last assignment. assert result == 5 self.assertDictEqual(_UpperCAmelCase , {"""x""": 8, """y""": 5} ) def SCREAMING_SNAKE_CASE__ ( self : Any ): """simple docstring""" UpperCAmelCase__ = """test_list = [x, add_two(x)]""" UpperCAmelCase__ = {"""x""": 3} UpperCAmelCase__ = evaluate(_UpperCAmelCase , {"""add_two""": add_two} , state=_UpperCAmelCase ) self.assertListEqual(_UpperCAmelCase , [3, 5] ) self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """test_list""": [3, 5]} ) def SCREAMING_SNAKE_CASE__ ( self : Dict ): """simple docstring""" UpperCAmelCase__ = """y = x""" UpperCAmelCase__ = {"""x""": 3} UpperCAmelCase__ = evaluate(_UpperCAmelCase , {} , state=_UpperCAmelCase ) assert result == 3 self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """y""": 3} ) def SCREAMING_SNAKE_CASE__ ( self : List[Any] ): """simple docstring""" UpperCAmelCase__ = """test_list = [x, add_two(x)]\ntest_list[1]""" UpperCAmelCase__ = {"""x""": 3} UpperCAmelCase__ = evaluate(_UpperCAmelCase , {"""add_two""": add_two} , state=_UpperCAmelCase ) assert result == 5 self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """test_list""": [3, 5]} ) UpperCAmelCase__ = """test_dict = {'x': x, 'y': add_two(x)}\ntest_dict['y']""" UpperCAmelCase__ = {"""x""": 3} UpperCAmelCase__ = evaluate(_UpperCAmelCase , {"""add_two""": add_two} , state=_UpperCAmelCase ) assert result == 5 self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """test_dict""": {"""x""": 3, """y""": 5}} ) def SCREAMING_SNAKE_CASE__ ( self : Dict ): """simple docstring""" UpperCAmelCase__ = """x = 0\nfor i in range(3):\n x = i""" UpperCAmelCase__ = {} UpperCAmelCase__ = evaluate(_UpperCAmelCase , {"""range""": range} , state=_UpperCAmelCase ) assert result == 2 self.assertDictEqual(_UpperCAmelCase , {"""x""": 2, """i""": 2} )

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'''simple docstring''' from ...configuration_utils import PretrainedConfig from ...utils import logging UpperCAmelCase_ = logging.get_logger(__name__) UpperCAmelCase_ = { 'google/vivit-b-16x2-kinetics400': ( 'https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json' ), # See all Vivit models at https://huggingface.co/models?filter=vivit } class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' lowerCAmelCase_ : Optional[int] = """vivit""" def __init__( self : List[str] , _UpperCAmelCase : List[Any]=2_24 , _UpperCAmelCase : List[str]=32 , _UpperCAmelCase : Any=[2, 16, 16] , _UpperCAmelCase : int=3 , _UpperCAmelCase : Optional[Any]=7_68 , _UpperCAmelCase : Union[str, Any]=12 , _UpperCAmelCase : Dict=12 , _UpperCAmelCase : Optional[Any]=30_72 , _UpperCAmelCase : Optional[int]="gelu_fast" , _UpperCAmelCase : Union[str, Any]=0.0 , _UpperCAmelCase : Tuple=0.0 , _UpperCAmelCase : Optional[int]=0.02 , _UpperCAmelCase : List[Any]=1E-06 , _UpperCAmelCase : List[str]=True , **_UpperCAmelCase : List[Any] , ): """simple docstring""" UpperCAmelCase__ = hidden_size UpperCAmelCase__ = num_hidden_layers UpperCAmelCase__ = num_attention_heads UpperCAmelCase__ = intermediate_size UpperCAmelCase__ = hidden_act UpperCAmelCase__ = hidden_dropout_prob UpperCAmelCase__ = attention_probs_dropout_prob UpperCAmelCase__ = initializer_range UpperCAmelCase__ = layer_norm_eps UpperCAmelCase__ = image_size UpperCAmelCase__ = num_frames UpperCAmelCase__ = tubelet_size UpperCAmelCase__ = num_channels UpperCAmelCase__ = qkv_bias super().__init__(**_UpperCAmelCase )

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'''simple docstring''' import warnings from ...utils import logging from .image_processing_deformable_detr import DeformableDetrImageProcessor UpperCAmelCase_ = logging.get_logger(__name__) class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' def __init__( self : Optional[Any] , *_UpperCAmelCase : Any , **_UpperCAmelCase : str ): """simple docstring""" warnings.warn( """The class DeformableDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers.""" """ Please use DeformableDetrImageProcessor instead.""" , _UpperCAmelCase , ) super().__init__(*_UpperCAmelCase , **_UpperCAmelCase )

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'''simple docstring''' import warnings from ...utils import logging from .image_processing_deit import DeiTImageProcessor UpperCAmelCase_ = logging.get_logger(__name__) class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' def __init__( self : List[str] , *_UpperCAmelCase : Optional[int] , **_UpperCAmelCase : Optional[Any] ): """simple docstring""" warnings.warn( """The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please""" """ use DeiTImageProcessor instead.""" , _UpperCAmelCase , ) super().__init__(*_UpperCAmelCase , **_UpperCAmelCase )

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'''simple docstring''' import datasets import faiss import numpy as np import streamlit as st import torch from elasticsearch import Elasticsearch from elia_utils import ( embed_questions_for_retrieval, make_qa_sas_model, qa_sas_generate, query_es_index, query_qa_dense_index, ) import transformers from transformers import AutoModel, AutoModelForSeqaSeqLM, AutoTokenizer UpperCAmelCase_ = 'bart' UpperCAmelCase_ = True @st.cache(allow_output_mutation=SCREAMING_SNAKE_CASE__ ) def _UpperCamelCase ( ): '''simple docstring''' if LOAD_DENSE_INDEX: UpperCAmelCase__ = AutoTokenizer.from_pretrained("""yjernite/retribert-base-uncased""" ) UpperCAmelCase__ = AutoModel.from_pretrained("""yjernite/retribert-base-uncased""" ).to("""cuda:0""" ) UpperCAmelCase__ = qar_model.eval() else: UpperCAmelCase__ , UpperCAmelCase__ = (None, None) if MODEL_TYPE == "bart": UpperCAmelCase__ = AutoTokenizer.from_pretrained("""yjernite/bart_eli5""" ) UpperCAmelCase__ = AutoModelForSeqaSeqLM.from_pretrained("""yjernite/bart_eli5""" ).to("""cuda:0""" ) UpperCAmelCase__ = torch.load("""seq2seq_models/eli5_bart_model_blm_2.pth""" ) sas_model.load_state_dict(save_dict["""model"""] ) UpperCAmelCase__ = sas_model.eval() else: UpperCAmelCase__ , UpperCAmelCase__ = make_qa_sas_model( model_name="""t5-small""" , from_file="""seq2seq_models/eli5_t5_model_1024_4.pth""" , device="""cuda:0""" ) return (qar_tokenizer, qar_model, sas_tokenizer, sas_model) @st.cache(allow_output_mutation=SCREAMING_SNAKE_CASE__ ) def _UpperCamelCase ( ): '''simple docstring''' if LOAD_DENSE_INDEX: UpperCAmelCase__ = faiss.StandardGpuResources() UpperCAmelCase__ = datasets.load_dataset(path="""wiki_snippets""" , name="""wiki40b_en_100_0""" )["""train"""] UpperCAmelCase__ = np.memmap( """wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat""" , dtype="""float32""" , mode="""r""" , shape=(wikiaab_passages.num_rows, 128) , ) UpperCAmelCase__ = faiss.IndexFlatIP(128 ) UpperCAmelCase__ = faiss.index_cpu_to_gpu(SCREAMING_SNAKE_CASE__ , 1 , SCREAMING_SNAKE_CASE__ ) wikiaab_gpu_index_flat.add(SCREAMING_SNAKE_CASE__ ) # TODO fix for larger GPU else: UpperCAmelCase__ , UpperCAmelCase__ = (None, None) UpperCAmelCase__ = Elasticsearch([{"""host""": """localhost""", """port""": """9200"""}] ) return (wikiaab_passages, wikiaab_gpu_index_flat, es_client) @st.cache(allow_output_mutation=SCREAMING_SNAKE_CASE__ ) def _UpperCamelCase ( ): '''simple docstring''' UpperCAmelCase__ = datasets.load_dataset("""eli5""" , name="""LFQA_reddit""" ) UpperCAmelCase__ = elia["""train_eli5"""] UpperCAmelCase__ = np.memmap( """eli5_questions_reps.dat""" , dtype="""float32""" , mode="""r""" , shape=(elia_train.num_rows, 128) ) UpperCAmelCase__ = faiss.IndexFlatIP(128 ) eli5_train_q_index.add(SCREAMING_SNAKE_CASE__ ) return (elia_train, eli5_train_q_index) UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = load_indexes() UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = load_models() UpperCAmelCase_ , UpperCAmelCase_ = load_train_data() def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : str=10 ): '''simple docstring''' UpperCAmelCase__ = embed_questions_for_retrieval([question] , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ , UpperCAmelCase__ = eli5_train_q_index.search(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = [elia_train[int(SCREAMING_SNAKE_CASE__ )] for i in I[0]] return nn_examples def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Any="wiki40b" , SCREAMING_SNAKE_CASE__ : str="dense" , SCREAMING_SNAKE_CASE__ : str=10 ): '''simple docstring''' if source == "none": UpperCAmelCase__ , UpperCAmelCase__ = (""" <P> """.join(["""""" for _ in range(11 )] ).strip(), []) else: if method == "dense": UpperCAmelCase__ , UpperCAmelCase__ = query_qa_dense_index( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) else: UpperCAmelCase__ , UpperCAmelCase__ = query_es_index( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , index_name="""english_wiki40b_snippets_100w""" , n_results=SCREAMING_SNAKE_CASE__ , ) UpperCAmelCase__ = [ (res["""article_title"""], res["""section_title"""].strip(), res["""score"""], res["""passage_text"""]) for res in hit_lst ] UpperCAmelCase__ = """question: {} context: {}""".format(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) return question_doc, support_list @st.cache( hash_funcs={ torch.Tensor: (lambda SCREAMING_SNAKE_CASE__ : None), transformers.models.bart.tokenization_bart.BartTokenizer: (lambda SCREAMING_SNAKE_CASE__ : None), } ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Union[str, Any]=64 , SCREAMING_SNAKE_CASE__ : Optional[int]=256 , SCREAMING_SNAKE_CASE__ : Dict=False , SCREAMING_SNAKE_CASE__ : Any=2 , SCREAMING_SNAKE_CASE__ : str=0.95 , SCREAMING_SNAKE_CASE__ : str=0.8 ): '''simple docstring''' with torch.no_grad(): UpperCAmelCase__ = qa_sas_generate( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , num_answers=1 , num_beams=SCREAMING_SNAKE_CASE__ , min_len=SCREAMING_SNAKE_CASE__ , max_len=SCREAMING_SNAKE_CASE__ , do_sample=SCREAMING_SNAKE_CASE__ , temp=SCREAMING_SNAKE_CASE__ , top_p=SCREAMING_SNAKE_CASE__ , top_k=SCREAMING_SNAKE_CASE__ , max_input_length=1024 , device="""cuda:0""" , )[0] return (answer, support_list) st.title('Long Form Question Answering with ELI5') # Start sidebar UpperCAmelCase_ = '<img src=\'https://huggingface.co/front/assets/huggingface_logo.svg\'>' UpperCAmelCase_ = '\n<html>\n <head>\n <style>\n .img-container {\n padding-left: 90px;\n padding-right: 90px;\n padding-top: 50px;\n padding-bottom: 50px;\n background-color: #f0f3f9;\n }\n </style>\n </head>\n <body>\n <span class="img-container"> \n %s\n </span>\n </body>\n</html>\n' % ( header_html, ) st.sidebar.markdown( header_full, unsafe_allow_html=True, ) # Long Form QA with ELI5 and Wikipedia UpperCAmelCase_ = '\nThis demo presents a model trained to [provide long-form answers to open-domain questions](https://yjernite.github.io/lfqa.html).\nFirst, a document retriever fetches a set of relevant Wikipedia passages given the question from the [Wiki40b](https://research.google/pubs/pub49029/) dataset,\na pre-processed fixed snapshot of Wikipedia.\n' st.sidebar.markdown(description, unsafe_allow_html=True) UpperCAmelCase_ = [ 'Answer the question', 'View the retrieved document only', 'View the most similar ELI5 question and answer', 'Show me everything, please!', ] UpperCAmelCase_ = st.sidebar.checkbox('Demo options') if demo_options: UpperCAmelCase_ = st.sidebar.selectbox( '', action_list, index=3, ) UpperCAmelCase_ = action_list.index(action_st) UpperCAmelCase_ = st.sidebar.selectbox( '', ['Show full text of passages', 'Show passage section titles'], index=0, ) UpperCAmelCase_ = show_type == 'Show full text of passages' else: UpperCAmelCase_ = 3 UpperCAmelCase_ = True UpperCAmelCase_ = st.sidebar.checkbox('Retrieval options') if retrieval_options: UpperCAmelCase_ = '\n ### Information retriever options\n\n The **sparse** retriever uses ElasticSearch, while the **dense** retriever uses max-inner-product search between a question and passage embedding\n trained using the [ELI5](https://arxiv.org/abs/1907.09190) questions-answer pairs.\n The answer is then generated by sequence to sequence model which takes the question and retrieved document as input.\n ' st.sidebar.markdown(retriever_info) UpperCAmelCase_ = st.sidebar.selectbox('Which Wikipedia format should the model use?', ['wiki40b', 'none']) UpperCAmelCase_ = st.sidebar.selectbox('Which Wikipedia indexer should the model use?', ['dense', 'sparse', 'mixed']) else: UpperCAmelCase_ = 'wiki40b' UpperCAmelCase_ = 'dense' UpperCAmelCase_ = 'beam' UpperCAmelCase_ = 2 UpperCAmelCase_ = 6_4 UpperCAmelCase_ = 2_5_6 UpperCAmelCase_ = None UpperCAmelCase_ = None UpperCAmelCase_ = st.sidebar.checkbox('Generation options') if generate_options: UpperCAmelCase_ = '\n ### Answer generation options\n\n The sequence-to-sequence model was initialized with [BART](https://huggingface.co/facebook/bart-large)\n weights and fine-tuned on the ELI5 QA pairs and retrieved documents. You can use the model for greedy decoding with\n **beam** search, or **sample** from the decoder\'s output probabilities.\n ' st.sidebar.markdown(generate_info) UpperCAmelCase_ = st.sidebar.selectbox('Would you like to use beam search or sample an answer?', ['beam', 'sampled']) UpperCAmelCase_ = st.sidebar.slider( 'Minimum generation length', min_value=8, max_value=2_5_6, value=6_4, step=8, format=None, key=None ) UpperCAmelCase_ = st.sidebar.slider( 'Maximum generation length', min_value=6_4, max_value=5_1_2, value=2_5_6, step=1_6, format=None, key=None ) if sampled == "beam": UpperCAmelCase_ = st.sidebar.slider('Beam size', min_value=1, max_value=8, value=2, step=None, format=None, key=None) else: UpperCAmelCase_ = st.sidebar.slider( 'Nucleus sampling p', min_value=0.1, max_value=1.0, value=0.95, step=0.01, format=None, key=None ) UpperCAmelCase_ = st.sidebar.slider( 'Temperature', min_value=0.1, max_value=1.0, value=0.7, step=0.01, format=None, key=None ) UpperCAmelCase_ = None # start main text UpperCAmelCase_ = [ '<MY QUESTION>', 'How do people make chocolate?', 'Why do we get a fever when we are sick?', 'How can different animals perceive different colors?', 'What is natural language processing?', 'What\'s the best way to treat a sunburn?', 'What exactly are vitamins ?', 'How does nuclear energy provide electricity?', 'What\'s the difference between viruses and bacteria?', 'Why are flutes classified as woodwinds when most of them are made out of metal ?', 'Why do people like drinking coffee even though it tastes so bad?', 'What happens when wine ages? How does it make the wine taste better?', 'If an animal is an herbivore, where does it get the protein that it needs to survive if it only eats grass?', 'How can we set a date to the beginning or end of an artistic period? Doesn\'t the change happen gradually?', 'How does New Zealand have so many large bird predators?', ] UpperCAmelCase_ = st.selectbox( 'What would you like to ask? ---- select <MY QUESTION> to enter a new query', questions_list, index=1, ) if question_s == "<MY QUESTION>": UpperCAmelCase_ = st.text_input('Enter your question here:', '') else: UpperCAmelCase_ = question_s if st.button('Show me!'): if action in [0, 1, 3]: if index_type == "mixed": UpperCAmelCase_ , UpperCAmelCase_ = make_support(question, source=wiki_source, method='dense', n_results=1_0) UpperCAmelCase_ , UpperCAmelCase_ = make_support(question, source=wiki_source, method='sparse', n_results=1_0) UpperCAmelCase_ = [] for res_d, res_s in zip(support_list_dense, support_list_sparse): if tuple(res_d) not in support_list: support_list += [tuple(res_d)] if tuple(res_s) not in support_list: support_list += [tuple(res_s)] UpperCAmelCase_ = support_list[:1_0] UpperCAmelCase_ = '<P> ' + ' <P> '.join([res[-1] for res in support_list]) else: UpperCAmelCase_ , UpperCAmelCase_ = make_support(question, source=wiki_source, method=index_type, n_results=1_0) if action in [0, 3]: UpperCAmelCase_ , UpperCAmelCase_ = answer_question( question_doc, sas_model, sas_tokenizer, min_len=min_len, max_len=int(max_len), sampling=(sampled == 'sampled'), n_beams=n_beams, top_p=top_p, temp=temp, ) st.markdown('### The model generated answer is:') st.write(answer) if action in [0, 1, 3] and wiki_source != "none": st.markdown('--- \n ### The model is drawing information from the following Wikipedia passages:') for i, res in enumerate(support_list): UpperCAmelCase_ = 'https://en.wikipedia.org/wiki/{}'.format(res[0].replace(' ', '_')) UpperCAmelCase_ = res[1].strip() if sec_titles == "": UpperCAmelCase_ = '[{}]({})'.format(res[0], wiki_url) else: UpperCAmelCase_ = sec_titles.split(' & ') UpperCAmelCase_ = ' & '.join( ['[{}]({}#{})'.format(sec.strip(), wiki_url, sec.strip().replace(' ', '_')) for sec in sec_list] ) st.markdown( '{0:02d} - **Article**: {1:<18} <br> _Section_: {2}'.format(i + 1, res[0], sections), unsafe_allow_html=True, ) if show_passages: st.write( '> <span style="font-family:arial; font-size:10pt;">' + res[-1] + '</span>', unsafe_allow_html=True ) if action in [2, 3]: UpperCAmelCase_ = find_nearest_training(question) UpperCAmelCase_ = nn_train_list[0] st.markdown( '--- \n ### The most similar question in the ELI5 training set was: \n\n {}'.format(train_exple['title']) ) UpperCAmelCase_ = [ '{}. {}'.format(i + 1, ' \n'.join([line.strip() for line in ans.split('\n') if line.strip() != ''])) for i, (ans, sc) in enumerate(zip(train_exple['answers']['text'], train_exple['answers']['score'])) if i == 0 or sc > 2 ] st.markdown('##### Its answers were: \n\n {}'.format('\n'.join(answers_st))) UpperCAmelCase_ = '\n---\n\n**Disclaimer**\n\n*The intent of this app is to provide some (hopefully entertaining) insights into the behavior of a current LFQA system.\nEvaluating biases of such a model and ensuring factual generations are still very much open research problems.\nTherefore, until some significant progress is achieved, we caution against using the generated answers for practical purposes.*\n' st.sidebar.markdown(disclaimer, unsafe_allow_html=True)

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'''simple docstring''' import os import unicodedata from shutil import copyfile from typing import Any, Dict, List, Optional, Tuple import sentencepiece as spm from ...tokenization_utils import AddedToken, PreTrainedTokenizer from ...utils import SPIECE_UNDERLINE, logging UpperCAmelCase_ = logging.get_logger(__name__) UpperCAmelCase_ = {'vocab_file': 'spiece.model'} UpperCAmelCase_ = { 'vocab_file': { 'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model', } } class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' def __init__( self : Dict , _UpperCAmelCase : str , _UpperCAmelCase : Any=False , _UpperCAmelCase : int=True , _UpperCAmelCase : Union[str, Any]=False , _UpperCAmelCase : Dict="<s>" , _UpperCAmelCase : int="</s>" , _UpperCAmelCase : Dict="<unk>" , _UpperCAmelCase : Tuple="<sep>" , _UpperCAmelCase : List[Any]="<pad>" , _UpperCAmelCase : int="<cls>" , _UpperCAmelCase : Union[str, Any]="<mask>" , _UpperCAmelCase : List[str]=["<eop>", "<eod>"] , _UpperCAmelCase : Optional[Dict[str, Any]] = None , **_UpperCAmelCase : int , ): """simple docstring""" UpperCAmelCase__ = AddedToken(_UpperCAmelCase , lstrip=_UpperCAmelCase , rstrip=_UpperCAmelCase ) if isinstance(_UpperCAmelCase , _UpperCAmelCase ) else mask_token UpperCAmelCase__ = {} if sp_model_kwargs is None else sp_model_kwargs super().__init__( do_lower_case=_UpperCAmelCase , remove_space=_UpperCAmelCase , keep_accents=_UpperCAmelCase , bos_token=_UpperCAmelCase , eos_token=_UpperCAmelCase , unk_token=_UpperCAmelCase , sep_token=_UpperCAmelCase , pad_token=_UpperCAmelCase , cls_token=_UpperCAmelCase , mask_token=_UpperCAmelCase , additional_special_tokens=_UpperCAmelCase , sp_model_kwargs=self.sp_model_kwargs , **_UpperCAmelCase , ) UpperCAmelCase__ = 3 UpperCAmelCase__ = do_lower_case UpperCAmelCase__ = remove_space UpperCAmelCase__ = keep_accents UpperCAmelCase__ = vocab_file UpperCAmelCase__ = spm.SentencePieceProcessor(**self.sp_model_kwargs ) self.sp_model.Load(_UpperCAmelCase ) try: import jieba except ModuleNotFoundError as error: raise error.__class__( """You need to install jieba to use CpmTokenizer or CpmTokenizerFast. """ """See https://pypi.org/project/jieba/ for installation.""" ) UpperCAmelCase__ = jieba UpperCAmelCase__ = str.maketrans(""" \n""" , """\u2582\u2583""" ) @property # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ): """simple docstring""" return len(self.sp_model ) def SCREAMING_SNAKE_CASE__ ( self : List[str] ): """simple docstring""" UpperCAmelCase__ = {self.convert_ids_to_tokens(_UpperCAmelCase ): i for i in range(self.vocab_size )} vocab.update(self.added_tokens_encoder ) return vocab def __getstate__( self : Dict ): """simple docstring""" UpperCAmelCase__ = self.__dict__.copy() UpperCAmelCase__ = None return state def __setstate__( self : Union[str, Any] , _UpperCAmelCase : Union[str, Any] ): """simple docstring""" UpperCAmelCase__ = d # for backward compatibility if not hasattr(self , """sp_model_kwargs""" ): UpperCAmelCase__ = {} UpperCAmelCase__ = spm.SentencePieceProcessor(**self.sp_model_kwargs ) self.sp_model.Load(self.vocab_file ) def SCREAMING_SNAKE_CASE__ ( self : int , _UpperCAmelCase : Optional[Any] ): """simple docstring""" if self.remove_space: UpperCAmelCase__ = """ """.join(inputs.strip().split() ) else: UpperCAmelCase__ = inputs UpperCAmelCase__ = outputs.replace("""``""" , """\"""" ).replace("""''""" , """\"""" ) if not self.keep_accents: UpperCAmelCase__ = unicodedata.normalize("""NFKD""" , _UpperCAmelCase ) UpperCAmelCase__ = """""".join([c for c in outputs if not unicodedata.combining(_UpperCAmelCase )] ) if self.do_lower_case: UpperCAmelCase__ = outputs.lower() return outputs def SCREAMING_SNAKE_CASE__ ( self : Tuple , _UpperCAmelCase : str ): """simple docstring""" UpperCAmelCase__ = self.preprocess_text(_UpperCAmelCase ) UpperCAmelCase__ = self.sp_model.encode(_UpperCAmelCase , out_type=_UpperCAmelCase ) UpperCAmelCase__ = [] for piece in pieces: if len(_UpperCAmelCase ) > 1 and piece[-1] == str(""",""" ) and piece[-2].isdigit(): UpperCAmelCase__ = self.sp_model.EncodeAsPieces(piece[:-1].replace(_UpperCAmelCase , """""" ) ) if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE: if len(cur_pieces[0] ) == 1: UpperCAmelCase__ = cur_pieces[1:] else: UpperCAmelCase__ = cur_pieces[0][1:] cur_pieces.append(piece[-1] ) new_pieces.extend(_UpperCAmelCase ) else: new_pieces.append(_UpperCAmelCase ) return new_pieces def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , _UpperCAmelCase : Union[str, Any] ): """simple docstring""" return self.sp_model.PieceToId(_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Dict , _UpperCAmelCase : Any ): """simple docstring""" return self.sp_model.IdToPiece(_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : int , _UpperCAmelCase : Dict ): """simple docstring""" UpperCAmelCase__ = """""".join(_UpperCAmelCase ).replace(_UpperCAmelCase , """ """ ).strip() return out_string def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] , _UpperCAmelCase : List[int] , _UpperCAmelCase : Optional[List[int]] = None ): """simple docstring""" UpperCAmelCase__ = [self.sep_token_id] UpperCAmelCase__ = [self.cls_token_id] if token_ids_a is None: return token_ids_a + sep + cls return token_ids_a + sep + token_ids_a + sep + cls def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] , _UpperCAmelCase : List[int] , _UpperCAmelCase : Optional[List[int]] = None , _UpperCAmelCase : bool = False ): """simple docstring""" if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_a=_UpperCAmelCase , token_ids_a=_UpperCAmelCase , already_has_special_tokens=_UpperCAmelCase ) if token_ids_a is not None: return ([0] * len(_UpperCAmelCase )) + [1] + ([0] * len(_UpperCAmelCase )) + [1, 1] return ([0] * len(_UpperCAmelCase )) + [1, 1] def SCREAMING_SNAKE_CASE__ ( self : Dict , _UpperCAmelCase : List[int] , _UpperCAmelCase : Optional[List[int]] = None ): """simple docstring""" UpperCAmelCase__ = [self.sep_token_id] UpperCAmelCase__ = [2] if token_ids_a is None: return len(token_ids_a + sep ) * [0] + cls_segment_id return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id def SCREAMING_SNAKE_CASE__ ( self : List[Any] , _UpperCAmelCase : str , _UpperCAmelCase : Optional[str] = None ): """simple docstring""" if not os.path.isdir(_UpperCAmelCase ): logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' ) return UpperCAmelCase__ = os.path.join( _UpperCAmelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] ) if os.path.abspath(self.vocab_file ) != os.path.abspath(_UpperCAmelCase ) and os.path.isfile(self.vocab_file ): copyfile(self.vocab_file , _UpperCAmelCase ) elif not os.path.isfile(self.vocab_file ): with open(_UpperCAmelCase , """wb""" ) as fi: UpperCAmelCase__ = self.sp_model.serialized_model_proto() fi.write(_UpperCAmelCase ) return (out_vocab_file,) def SCREAMING_SNAKE_CASE__ ( self : Tuple , *_UpperCAmelCase : Tuple , **_UpperCAmelCase : Optional[Any] ): """simple docstring""" UpperCAmelCase__ = super()._decode(*_UpperCAmelCase , **_UpperCAmelCase ) UpperCAmelCase__ = text.replace(""" """ , """""" ).replace("""\u2582""" , """ """ ).replace("""\u2583""" , """\n""" ) return text

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'''simple docstring''' from __future__ import annotations def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : list[int | str] ): '''simple docstring''' create_state_space_tree(SCREAMING_SNAKE_CASE__ , [] , 0 , [0 for i in range(len(SCREAMING_SNAKE_CASE__ ) )] ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : list[int | str] , SCREAMING_SNAKE_CASE__ : list[int | str] , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : list[int] , ): '''simple docstring''' if index == len(SCREAMING_SNAKE_CASE__ ): print(SCREAMING_SNAKE_CASE__ ) return for i in range(len(SCREAMING_SNAKE_CASE__ ) ): if not index_used[i]: current_sequence.append(sequence[i] ) UpperCAmelCase__ = True create_state_space_tree(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , index + 1 , SCREAMING_SNAKE_CASE__ ) current_sequence.pop() UpperCAmelCase__ = False UpperCAmelCase_ = [3, 1, 2, 4] generate_all_permutations(sequence) UpperCAmelCase_ = ["A", "B", "C"] generate_all_permutations(sequence_a)

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'''simple docstring''' import argparse import logging import os import datasets import tensorflow as tf from transformers import AutoTokenizer UpperCAmelCase_ = logging.getLogger(__name__) def _UpperCamelCase ( ): '''simple docstring''' UpperCAmelCase__ = argparse.ArgumentParser( description="""Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset.""" ) parser.add_argument( """--dataset_name""" , type=SCREAMING_SNAKE_CASE__ , default="""wikitext""" , help="""Name of the training. Explore datasets at: hf.co/datasets.""" , ) parser.add_argument( """--dataset_config""" , type=SCREAMING_SNAKE_CASE__ , default="""wikitext-103-raw-v1""" , help="""Configuration name of the dataset.""" ) parser.add_argument( """--tokenizer_name_or_path""" , type=SCREAMING_SNAKE_CASE__ , default="""sayakpaul/unigram-tokenizer-wikitext""" , help="""Tokenizer identifier. Can be a local filepath or a Hub identifier.""" , ) parser.add_argument( """--shard_size""" , type=SCREAMING_SNAKE_CASE__ , default=1000 , help="""Number of entries to go in a single shard.""" , ) parser.add_argument("""--split""" , type=SCREAMING_SNAKE_CASE__ , default="""train""" , choices=["""train""", """test""", """validation"""] ) parser.add_argument( """--limit""" , default=SCREAMING_SNAKE_CASE__ , type=SCREAMING_SNAKE_CASE__ , help="""Limit the number of shards (used for debugging).""" , ) parser.add_argument( """--max_length""" , type=SCREAMING_SNAKE_CASE__ , default=512 , help="""Maximum sequence length. For training on TPUs, it helps to have a maximum""" """ sequence length that is a multiple of 8.""" , ) parser.add_argument( """--output_dir""" , default="""tf-tpu""" , type=SCREAMING_SNAKE_CASE__ , help="""Output directory where the TFRecord shards will be saved. If the""" """ path is appended with `gs://` ('gs://tf-tpu', for example) then the TFRecord""" """ shards will be directly saved to a Google Cloud Storage bucket.""" , ) UpperCAmelCase__ = parser.parse_args() return args def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Any ): '''simple docstring''' def fn(SCREAMING_SNAKE_CASE__ : Union[str, Any] ): return tokenizer(examples["""text"""] ) return fn def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Optional[int] ): '''simple docstring''' UpperCAmelCase__ = [] for i in range(len(tokenized_data["""input_ids"""] ) ): UpperCAmelCase__ = { """input_ids""": tf.train.Feature(intaa_list=tf.train.IntaaList(value=tokenized_data["""input_ids"""][i] ) ), """attention_mask""": tf.train.Feature( intaa_list=tf.train.IntaaList(value=tokenized_data["""attention_mask"""][i] ) ), } UpperCAmelCase__ = tf.train.Features(feature=SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = tf.train.Example(features=SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = example.SerializeToString() records.append(SCREAMING_SNAKE_CASE__ ) return records def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Any ): '''simple docstring''' UpperCAmelCase__ = datasets.load_dataset(args.dataset_name , args.dataset_config , split=args.split ) if args.limit is not None: UpperCAmelCase__ = min(len(SCREAMING_SNAKE_CASE__ ) , args.limit ) UpperCAmelCase__ = dataset.select(range(SCREAMING_SNAKE_CASE__ ) ) print(F'''Limiting the dataset to {args.limit} entries.''' ) UpperCAmelCase__ = AutoTokenizer.from_pretrained(args.tokenizer_name_or_path ) # Handle output directory creation. # For serializing into a Google Cloud Storage Bucket, one needs to first # create a bucket. if "gs" not in args.output_dir: if not os.path.exists(args.output_dir ): os.makedirs(args.output_dir ) UpperCAmelCase__ = os.path.join(args.output_dir , args.split ) if not os.path.exists(SCREAMING_SNAKE_CASE__ ): os.makedirs(SCREAMING_SNAKE_CASE__ ) else: UpperCAmelCase__ = os.path.join(args.output_dir , args.split ) # Tokenize the whole dataset at once. UpperCAmelCase__ = tokenize_function(SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = dataset.map(SCREAMING_SNAKE_CASE__ , batched=SCREAMING_SNAKE_CASE__ , num_proc=4 , remove_columns=["""text"""] ) # We need to concatenate all our texts together, and then split the result # into chunks of a fixed size, which we will call block_size. To do this, we # will use the map method again, with the option batched=True. When we use batched=True, # the function we pass to map() will be passed multiple inputs at once, allowing us # to group them into more or fewer examples than we had in the input. # This allows us to create our new fixed-length samples. The advantage of this # method is that we don't lose a whole lot of content from the dataset compared to the # case where we simply tokenize with a pre-defined max_length. def group_texts(SCREAMING_SNAKE_CASE__ : int ): # Concatenate all texts. UpperCAmelCase__ = {k: sum(examples[k] , [] ) for k in examples.keys()} UpperCAmelCase__ = len(concatenated_examples[list(examples.keys() )[0]] ) # We drop the small remainder, though you could add padding instead if the model supports it # In this, as in all things, we advise you to follow your heart 🫀 UpperCAmelCase__ = (total_length // args.max_length) * args.max_length # Split by chunks of max_len. UpperCAmelCase__ = { k: [t[i : i + args.max_length] for i in range(0 , SCREAMING_SNAKE_CASE__ , args.max_length )] for k, t in concatenated_examples.items() } return result UpperCAmelCase__ = dataset_tokenized.map(SCREAMING_SNAKE_CASE__ , batched=SCREAMING_SNAKE_CASE__ , batch_size=1000 , num_proc=4 ) UpperCAmelCase__ = 0 UpperCAmelCase__ = 0 for shard in range(0 , len(SCREAMING_SNAKE_CASE__ ) , args.shard_size ): UpperCAmelCase__ = grouped_dataset[shard : shard + args.shard_size] UpperCAmelCase__ = len(dataset_snapshot["""input_ids"""] ) UpperCAmelCase__ = os.path.join(SCREAMING_SNAKE_CASE__ , F'''dataset-{shard_count}-{records_containing}.tfrecord''' ) UpperCAmelCase__ = get_serialized_examples(SCREAMING_SNAKE_CASE__ ) with tf.io.TFRecordWriter(SCREAMING_SNAKE_CASE__ ) as out_file: for i in range(len(SCREAMING_SNAKE_CASE__ ) ): UpperCAmelCase__ = serialized_examples[i] out_file.write(SCREAMING_SNAKE_CASE__ ) print("""Wrote file {} containing {} records""".format(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) ) shard_count += 1 total_records += records_containing with open(F'''split-{args.split}-records-count.txt''' , """w""" ) as f: print(F'''Total {args.split} records: {total_records}''' , file=SCREAMING_SNAKE_CASE__ ) if __name__ == "__main__": UpperCAmelCase_ = parse_args() main(args)

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'''simple docstring''' import copy from dataclasses import dataclass, field from typing import ClassVar, Dict from ..features import Audio, Features, Value from .base import TaskTemplate @dataclass(frozen=lowerCamelCase_ ) class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' lowerCAmelCase_ : str = field(default="""automatic-speech-recognition""" , metadata={"""include_in_asdict_even_if_is_default""": True} ) lowerCAmelCase_ : ClassVar[Features] = Features({"""audio""": Audio()} ) lowerCAmelCase_ : ClassVar[Features] = Features({"""transcription""": Value("""string""" )} ) lowerCAmelCase_ : str = "audio" lowerCAmelCase_ : str = "transcription" def SCREAMING_SNAKE_CASE__ ( self : List[str] , _UpperCAmelCase : Optional[Any] ): """simple docstring""" if self.audio_column not in features: raise ValueError(f'''Column {self.audio_column} is not present in features.''' ) if not isinstance(features[self.audio_column] , _UpperCAmelCase ): raise ValueError(f'''Column {self.audio_column} is not an Audio type.''' ) UpperCAmelCase__ = copy.deepcopy(self ) UpperCAmelCase__ = self.input_schema.copy() UpperCAmelCase__ = features[self.audio_column] UpperCAmelCase__ = input_schema return task_template @property def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ): """simple docstring""" return {self.audio_column: "audio", self.transcription_column: "transcription"}

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'''simple docstring''' import numpy as np import torch from torch.nn import CrossEntropyLoss from transformers import AutoModelForCausalLM, AutoTokenizer import datasets from datasets import logging UpperCAmelCase_ = '\\n\n' UpperCAmelCase_ = '\nPerplexity (PPL) is one of the most common metrics for evaluating language models.\nIt is defined as the exponentiated average negative log-likelihood of a sequence.\n\nFor more information, see https://huggingface.co/docs/transformers/perplexity\n' UpperCAmelCase_ = '\nArgs:\n model_id (str): model used for calculating Perplexity\n NOTE: Perplexity can only be calculated for causal language models.\n This includes models such as gpt2, causal variations of bert,\n causal versions of t5, and more (the full list can be found\n in the AutoModelForCausalLM documentation here:\n https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )\n\n input_texts (list of str): input text, each separate text snippet\n is one list entry.\n batch_size (int): the batch size to run texts through the model. Defaults to 16.\n add_start_token (bool): whether to add the start token to the texts,\n so the perplexity can include the probability of the first word. Defaults to True.\n device (str): device to run on, defaults to \'cuda\' when available\nReturns:\n perplexity: dictionary containing the perplexity scores for the texts\n in the input list, as well as the mean perplexity. If one of the input texts is\n longer than the max input length of the model, then it is truncated to the\n max length for the perplexity computation.\nExamples:\n Example 1:\n >>> perplexity = datasets.load_metric("perplexity")\n >>> input_texts = ["lorem ipsum", "Happy Birthday!", "Bienvenue"]\n >>> results = perplexity.compute(model_id=\'gpt2\',\n ... add_start_token=False,\n ... input_texts=input_texts) # doctest:+ELLIPSIS\n >>> print(list(results.keys()))\n [\'perplexities\', \'mean_perplexity\']\n >>> print(round(results["mean_perplexity"], 2))\n 78.22\n >>> print(round(results["perplexities"][0], 2))\n 11.11\n\n Example 2:\n >>> perplexity = datasets.load_metric("perplexity")\n >>> input_texts = datasets.load_dataset("wikitext",\n ... "wikitext-2-raw-v1",\n ... split="test")["text"][:50] # doctest:+ELLIPSIS\n [...]\n >>> input_texts = [s for s in input_texts if s!=\'\']\n >>> results = perplexity.compute(model_id=\'gpt2\',\n ... input_texts=input_texts) # doctest:+ELLIPSIS\n >>> print(list(results.keys()))\n [\'perplexities\', \'mean_perplexity\']\n >>> print(round(results["mean_perplexity"], 2))\n 60.35\n >>> print(round(results["perplexities"][0], 2))\n 81.12\n' @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION ) class lowerCAmelCase_ ( datasets.Metric ): '''simple docstring''' def SCREAMING_SNAKE_CASE__ ( self : Dict ): """simple docstring""" return datasets.MetricInfo( description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features( { """input_texts""": datasets.Value("""string""" ), } ) , reference_urls=["""https://huggingface.co/docs/transformers/perplexity"""] , ) def SCREAMING_SNAKE_CASE__ ( self : List[Any] , _UpperCAmelCase : Tuple , _UpperCAmelCase : int , _UpperCAmelCase : int = 16 , _UpperCAmelCase : bool = True , _UpperCAmelCase : Optional[int]=None ): """simple docstring""" if device is not None: assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu." if device == "gpu": UpperCAmelCase__ = """cuda""" else: UpperCAmelCase__ = """cuda""" if torch.cuda.is_available() else """cpu""" UpperCAmelCase__ = AutoModelForCausalLM.from_pretrained(_UpperCAmelCase ) UpperCAmelCase__ = model.to(_UpperCAmelCase ) UpperCAmelCase__ = AutoTokenizer.from_pretrained(_UpperCAmelCase ) # if batch_size > 1 (which generally leads to padding being required), and # if there is not an already assigned pad_token, assign an existing # special token to also be the padding token if tokenizer.pad_token is None and batch_size > 1: UpperCAmelCase__ = list(tokenizer.special_tokens_map_extended.values() ) # check that the model already has at least one special token defined assert ( len(_UpperCAmelCase ) > 0 ), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1." # assign one of the special tokens to also be the pad token tokenizer.add_special_tokens({"""pad_token""": existing_special_tokens[0]} ) if add_start_token: # leave room for <BOS> token to be added: assert ( tokenizer.bos_token is not None ), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False" UpperCAmelCase__ = model.config.max_length - 1 else: UpperCAmelCase__ = model.config.max_length UpperCAmelCase__ = tokenizer( _UpperCAmelCase , add_special_tokens=_UpperCAmelCase , padding=_UpperCAmelCase , truncation=_UpperCAmelCase , max_length=_UpperCAmelCase , return_tensors="""pt""" , return_attention_mask=_UpperCAmelCase , ).to(_UpperCAmelCase ) UpperCAmelCase__ = encodings["""input_ids"""] UpperCAmelCase__ = encodings["""attention_mask"""] # check that each input is long enough: if add_start_token: assert torch.all(torch.ge(attn_masks.sum(1 ) , 1 ) ), "Each input text must be at least one token long." else: assert torch.all( torch.ge(attn_masks.sum(1 ) , 2 ) ), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings." UpperCAmelCase__ = [] UpperCAmelCase__ = CrossEntropyLoss(reduction="""none""" ) for start_index in logging.tqdm(range(0 , len(_UpperCAmelCase ) , _UpperCAmelCase ) ): UpperCAmelCase__ = min(start_index + batch_size , len(_UpperCAmelCase ) ) UpperCAmelCase__ = encoded_texts[start_index:end_index] UpperCAmelCase__ = attn_masks[start_index:end_index] if add_start_token: UpperCAmelCase__ = torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0 ) ).to(_UpperCAmelCase ) UpperCAmelCase__ = torch.cat([bos_tokens_tensor, encoded_batch] , dim=1 ) UpperCAmelCase__ = torch.cat( [torch.ones(bos_tokens_tensor.size() , dtype=torch.intaa ).to(_UpperCAmelCase ), attn_mask] , dim=1 ) UpperCAmelCase__ = encoded_batch with torch.no_grad(): UpperCAmelCase__ = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase ).logits UpperCAmelCase__ = out_logits[..., :-1, :].contiguous() UpperCAmelCase__ = labels[..., 1:].contiguous() UpperCAmelCase__ = attn_mask[..., 1:].contiguous() UpperCAmelCase__ = torch.expa( (loss_fct(shift_logits.transpose(1 , 2 ) , _UpperCAmelCase ) * shift_attention_mask_batch).sum(1 ) / shift_attention_mask_batch.sum(1 ) ) ppls += perplexity_batch.tolist() return {"perplexities": ppls, "mean_perplexity": np.mean(_UpperCAmelCase )}

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'''simple docstring''' from __future__ import annotations from functools import lru_cache from math import ceil UpperCAmelCase_ = 1_0_0 UpperCAmelCase_ = set(range(3, NUM_PRIMES, 2)) primes.add(2) UpperCAmelCase_ = 42 for prime in range(3, ceil(NUM_PRIMES**0.5), 2): if prime not in primes: continue primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime))) @lru_cache(maxsize=100 ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int ): '''simple docstring''' if number_to_partition < 0: return set() elif number_to_partition == 0: return {1} UpperCAmelCase__ = set() UpperCAmelCase__ = 42 UpperCAmelCase__ = 42 for prime in primes: if prime > number_to_partition: continue for sub in partition(number_to_partition - prime ): ret.add(sub * prime ) return ret def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int = 5000 ): '''simple docstring''' for number_to_partition in range(1 , SCREAMING_SNAKE_CASE__ ): if len(partition(SCREAMING_SNAKE_CASE__ ) ) > number_unique_partitions: return number_to_partition return None if __name__ == "__main__": print(f"{solution() = }")

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'''simple docstring''' def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int = 1000000 ): '''simple docstring''' UpperCAmelCase__ = [i - 1 for i in range(limit + 1 )] for i in range(2 , limit + 1 ): if phi[i] == i - 1: for j in range(2 * i , limit + 1 , SCREAMING_SNAKE_CASE__ ): phi[j] -= phi[j] // i return sum(phi[2 : limit + 1] ) if __name__ == "__main__": print(solution())

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'''simple docstring''' import hashlib import unittest from typing import Dict import numpy as np from transformers import ( MODEL_FOR_MASK_GENERATION_MAPPING, TF_MODEL_FOR_MASK_GENERATION_MAPPING, is_vision_available, pipeline, ) from transformers.pipelines import MaskGenerationPipeline from transformers.testing_utils import ( is_pipeline_test, nested_simplify, require_tf, require_torch, require_vision, slow, ) if is_vision_available(): from PIL import Image else: class lowerCAmelCase_ : '''simple docstring''' @staticmethod def SCREAMING_SNAKE_CASE__ ( *_UpperCAmelCase : List[str] , **_UpperCAmelCase : Optional[int] ): """simple docstring""" pass def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Image ): '''simple docstring''' UpperCAmelCase__ = hashlib.mda(image.tobytes() ) return m.hexdigest()[:10] def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Image ): '''simple docstring''' UpperCAmelCase__ = np.array(SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = npimg.shape return {"hash": hashimage(SCREAMING_SNAKE_CASE__ ), "shape": shape} @is_pipeline_test @require_vision @require_torch class lowerCAmelCase_ ( unittest.TestCase ): '''simple docstring''' lowerCAmelCase_ : Optional[Any] = dict( (list(MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if MODEL_FOR_MASK_GENERATION_MAPPING else []) ) lowerCAmelCase_ : Dict = dict( (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else []) ) def SCREAMING_SNAKE_CASE__ ( self : List[str] , _UpperCAmelCase : str , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : List[Any] ): """simple docstring""" UpperCAmelCase__ = MaskGenerationPipeline(model=_UpperCAmelCase , image_processor=_UpperCAmelCase ) return image_segmenter, [ "./tests/fixtures/tests_samples/COCO/000000039769.png", "./tests/fixtures/tests_samples/COCO/000000039769.png", ] def SCREAMING_SNAKE_CASE__ ( self : List[str] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Union[str, Any] ): """simple docstring""" pass @require_tf @unittest.skip("""Image segmentation not implemented in TF""" ) def SCREAMING_SNAKE_CASE__ ( self : Any ): """simple docstring""" pass @slow @require_torch def SCREAMING_SNAKE_CASE__ ( self : List[str] ): """simple docstring""" UpperCAmelCase__ = pipeline("""mask-generation""" , model="""facebook/sam-vit-huge""" ) UpperCAmelCase__ = image_segmenter("""http://images.cocodataset.org/val2017/000000039769.jpg""" , points_per_batch=2_56 ) # Shortening by hashing UpperCAmelCase__ = [] for i, o in enumerate(outputs["""masks"""] ): new_outupt += [{"mask": mask_to_test_readable(_UpperCAmelCase ), "scores": outputs["scores"][i]}] # fmt: off self.assertEqual( nested_simplify(_UpperCAmelCase , decimals=4 ) , [ {"""mask""": {"""hash""": """115ad19f5f""", """shape""": (4_80, 6_40)}, """scores""": 1.0444}, {"""mask""": {"""hash""": """6affa964c6""", """shape""": (4_80, 6_40)}, """scores""": 1.021}, {"""mask""": {"""hash""": """dfe28a0388""", """shape""": (4_80, 6_40)}, """scores""": 1.0167}, {"""mask""": {"""hash""": """c0a5f4a318""", """shape""": (4_80, 6_40)}, """scores""": 1.0132}, {"""mask""": {"""hash""": """fe8065c197""", """shape""": (4_80, 6_40)}, """scores""": 1.0053}, {"""mask""": {"""hash""": """e2d0b7a0b7""", """shape""": (4_80, 6_40)}, """scores""": 0.9967}, {"""mask""": {"""hash""": """453c7844bd""", """shape""": (4_80, 6_40)}, """scores""": 0.993}, {"""mask""": {"""hash""": """3d44f2926d""", """shape""": (4_80, 6_40)}, """scores""": 0.9909}, {"""mask""": {"""hash""": """64033ddc3f""", """shape""": (4_80, 6_40)}, """scores""": 0.9879}, {"""mask""": {"""hash""": """801064ff79""", """shape""": (4_80, 6_40)}, """scores""": 0.9834}, {"""mask""": {"""hash""": """6172f276ef""", """shape""": (4_80, 6_40)}, """scores""": 0.9716}, {"""mask""": {"""hash""": """b49e60e084""", """shape""": (4_80, 6_40)}, """scores""": 0.9612}, {"""mask""": {"""hash""": """a811e775fd""", """shape""": (4_80, 6_40)}, """scores""": 0.9599}, {"""mask""": {"""hash""": """a6a8ebcf4b""", """shape""": (4_80, 6_40)}, """scores""": 0.9552}, {"""mask""": {"""hash""": """9d8257e080""", """shape""": (4_80, 6_40)}, """scores""": 0.9532}, {"""mask""": {"""hash""": """32de6454a8""", """shape""": (4_80, 6_40)}, """scores""": 0.9516}, {"""mask""": {"""hash""": """af3d4af2c8""", """shape""": (4_80, 6_40)}, """scores""": 0.9499}, {"""mask""": {"""hash""": """3c6db475fb""", """shape""": (4_80, 6_40)}, """scores""": 0.9483}, {"""mask""": {"""hash""": """c290813fb9""", """shape""": (4_80, 6_40)}, """scores""": 0.9464}, {"""mask""": {"""hash""": """b6f0b8f606""", """shape""": (4_80, 6_40)}, """scores""": 0.943}, {"""mask""": {"""hash""": """92ce16bfdf""", """shape""": (4_80, 6_40)}, """scores""": 0.943}, {"""mask""": {"""hash""": """c749b25868""", """shape""": (4_80, 6_40)}, """scores""": 0.9408}, {"""mask""": {"""hash""": """efb6cab859""", """shape""": (4_80, 6_40)}, """scores""": 0.9335}, {"""mask""": {"""hash""": """1ff2eafb30""", """shape""": (4_80, 6_40)}, """scores""": 0.9326}, {"""mask""": {"""hash""": """788b798e24""", """shape""": (4_80, 6_40)}, """scores""": 0.9262}, {"""mask""": {"""hash""": """abea804f0e""", """shape""": (4_80, 6_40)}, """scores""": 0.8999}, {"""mask""": {"""hash""": """7b9e8ddb73""", """shape""": (4_80, 6_40)}, """scores""": 0.8986}, {"""mask""": {"""hash""": """cd24047c8a""", """shape""": (4_80, 6_40)}, """scores""": 0.8984}, {"""mask""": {"""hash""": """6943e6bcbd""", """shape""": (4_80, 6_40)}, """scores""": 0.8873}, {"""mask""": {"""hash""": """b5f47c9191""", """shape""": (4_80, 6_40)}, """scores""": 0.8871} ] , ) # fmt: on @require_torch @slow def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ): """simple docstring""" UpperCAmelCase__ = """facebook/sam-vit-huge""" UpperCAmelCase__ = pipeline("""mask-generation""" , model=_UpperCAmelCase ) UpperCAmelCase__ = image_segmenter( """http://images.cocodataset.org/val2017/000000039769.jpg""" , pred_iou_thresh=1 , points_per_batch=2_56 ) # Shortening by hashing UpperCAmelCase__ = [] for i, o in enumerate(outputs["""masks"""] ): new_outupt += [{"mask": mask_to_test_readable(_UpperCAmelCase ), "scores": outputs["scores"][i]}] self.assertEqual( nested_simplify(_UpperCAmelCase , decimals=4 ) , [ {"""mask""": {"""hash""": """115ad19f5f""", """shape""": (4_80, 6_40)}, """scores""": 1.0444}, {"""mask""": {"""hash""": """6affa964c6""", """shape""": (4_80, 6_40)}, """scores""": 1.0210}, {"""mask""": {"""hash""": """dfe28a0388""", """shape""": (4_80, 6_40)}, """scores""": 1.0167}, {"""mask""": {"""hash""": """c0a5f4a318""", """shape""": (4_80, 6_40)}, """scores""": 1.0132}, {"""mask""": {"""hash""": """fe8065c197""", """shape""": (4_80, 6_40)}, """scores""": 1.0053}, ] , )

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'''simple docstring''' from typing import List, Union from ..utils import ( add_end_docstrings, is_tf_available, is_torch_available, is_vision_available, logging, requires_backends, ) from .base import PIPELINE_INIT_ARGS, Pipeline if is_vision_available(): from PIL import Image from ..image_utils import load_image if is_tf_available(): import tensorflow as tf from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING from ..tf_utils import stable_softmax if is_torch_available(): from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING UpperCAmelCase_ = logging.get_logger(__name__) @add_end_docstrings(lowerCamelCase_ ) class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' def __init__( self : Optional[Any] , *_UpperCAmelCase : Union[str, Any] , **_UpperCAmelCase : Dict ): """simple docstring""" super().__init__(*_UpperCAmelCase , **_UpperCAmelCase ) requires_backends(self , """vision""" ) self.check_model_type( TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING if self.framework == """tf""" else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING ) def SCREAMING_SNAKE_CASE__ ( self : str , _UpperCAmelCase : List[Any]=None ): """simple docstring""" UpperCAmelCase__ = {} if top_k is not None: UpperCAmelCase__ = top_k return {}, {}, postprocess_params def __call__( self : Any , _UpperCAmelCase : Union[str, List[str], "Image.Image", List["Image.Image"]] , **_UpperCAmelCase : str ): """simple docstring""" return super().__call__(_UpperCAmelCase , **_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] , _UpperCAmelCase : Tuple ): """simple docstring""" UpperCAmelCase__ = load_image(_UpperCAmelCase ) UpperCAmelCase__ = self.image_processor(images=_UpperCAmelCase , return_tensors=self.framework ) return model_inputs def SCREAMING_SNAKE_CASE__ ( self : Dict , _UpperCAmelCase : Tuple ): """simple docstring""" UpperCAmelCase__ = self.model(**_UpperCAmelCase ) return model_outputs def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , _UpperCAmelCase : Dict , _UpperCAmelCase : str=5 ): """simple docstring""" if top_k > self.model.config.num_labels: UpperCAmelCase__ = self.model.config.num_labels if self.framework == "pt": UpperCAmelCase__ = model_outputs.logits.softmax(-1 )[0] UpperCAmelCase__ , UpperCAmelCase__ = probs.topk(_UpperCAmelCase ) elif self.framework == "tf": UpperCAmelCase__ = stable_softmax(model_outputs.logits , axis=-1 )[0] UpperCAmelCase__ = tf.math.top_k(_UpperCAmelCase , k=_UpperCAmelCase ) UpperCAmelCase__ , UpperCAmelCase__ = topk.values.numpy(), topk.indices.numpy() else: raise ValueError(f'''Unsupported framework: {self.framework}''' ) UpperCAmelCase__ = scores.tolist() UpperCAmelCase__ = ids.tolist() return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(_UpperCAmelCase , _UpperCAmelCase )]

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'''simple docstring''' import math import flax.linen as nn import jax.numpy as jnp def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : jnp.ndarray , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : float = 1 , SCREAMING_SNAKE_CASE__ : float = 1 , SCREAMING_SNAKE_CASE__ : float = 1.0e4 , SCREAMING_SNAKE_CASE__ : bool = False , SCREAMING_SNAKE_CASE__ : float = 1.0 , ): '''simple docstring''' assert timesteps.ndim == 1, "Timesteps should be a 1d-array" assert embedding_dim % 2 == 0, F'''Embedding dimension {embedding_dim} should be even''' UpperCAmelCase__ = float(embedding_dim // 2 ) UpperCAmelCase__ = math.log(max_timescale / min_timescale ) / (num_timescales - freq_shift) UpperCAmelCase__ = min_timescale * jnp.exp(jnp.arange(SCREAMING_SNAKE_CASE__ , dtype=jnp.floataa ) * -log_timescale_increment ) UpperCAmelCase__ = jnp.expand_dims(SCREAMING_SNAKE_CASE__ , 1 ) * jnp.expand_dims(SCREAMING_SNAKE_CASE__ , 0 ) # scale embeddings UpperCAmelCase__ = scale * emb if flip_sin_to_cos: UpperCAmelCase__ = jnp.concatenate([jnp.cos(SCREAMING_SNAKE_CASE__ ), jnp.sin(SCREAMING_SNAKE_CASE__ )] , axis=1 ) else: UpperCAmelCase__ = jnp.concatenate([jnp.sin(SCREAMING_SNAKE_CASE__ ), jnp.cos(SCREAMING_SNAKE_CASE__ )] , axis=1 ) UpperCAmelCase__ = jnp.reshape(SCREAMING_SNAKE_CASE__ , [jnp.shape(SCREAMING_SNAKE_CASE__ )[0], embedding_dim] ) return signal class lowerCAmelCase_ ( nn.Module ): '''simple docstring''' lowerCAmelCase_ : int = 32 lowerCAmelCase_ : jnp.dtype = jnp.floataa @nn.compact def __call__( self : List[Any] , _UpperCAmelCase : Optional[Any] ): """simple docstring""" UpperCAmelCase__ = nn.Dense(self.time_embed_dim , dtype=self.dtype , name="""linear_1""" )(_UpperCAmelCase ) UpperCAmelCase__ = nn.silu(_UpperCAmelCase ) UpperCAmelCase__ = nn.Dense(self.time_embed_dim , dtype=self.dtype , name="""linear_2""" )(_UpperCAmelCase ) return temb class lowerCAmelCase_ ( nn.Module ): '''simple docstring''' lowerCAmelCase_ : int = 32 lowerCAmelCase_ : bool = False lowerCAmelCase_ : float = 1 @nn.compact def __call__( self : List[str] , _UpperCAmelCase : int ): """simple docstring""" return get_sinusoidal_embeddings( _UpperCAmelCase , embedding_dim=self.dim , flip_sin_to_cos=self.flip_sin_to_cos , freq_shift=self.freq_shift )

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'''simple docstring''' from math import factorial def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int = 20 ): '''simple docstring''' UpperCAmelCase__ = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1, # 2, 3,... UpperCAmelCase__ = n // 2 return int(factorial(SCREAMING_SNAKE_CASE__ ) / (factorial(SCREAMING_SNAKE_CASE__ ) * factorial(n - k )) ) if __name__ == "__main__": import sys if len(sys.argv) == 1: print(solution(2_0)) else: try: UpperCAmelCase_ = int(sys.argv[1]) print(solution(n)) except ValueError: print('Invalid entry - please enter a number.')

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'''simple docstring''' UpperCAmelCase_ = 8.314_4598 def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : float ): '''simple docstring''' if temperature < 0: raise Exception("""Temperature cannot be less than 0 K""" ) if molar_mass <= 0: raise Exception("""Molar mass cannot be less than or equal to 0 kg/mol""" ) else: return (3 * UNIVERSAL_GAS_CONSTANT * temperature / molar_mass) ** 0.5 if __name__ == "__main__": import doctest # run doctest doctest.testmod() # example UpperCAmelCase_ = 3_0_0 UpperCAmelCase_ = 2_8 UpperCAmelCase_ = rms_speed_of_molecule(temperature, molar_mass) print(f"Vrms of Nitrogen gas at 300 K is {vrms} m/s")

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'''simple docstring''' import json import os import unittest from transformers import MgpstrTokenizer from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES from transformers.testing_utils import require_tokenizers from ...test_tokenization_common import TokenizerTesterMixin @require_tokenizers class lowerCAmelCase_ ( lowerCamelCase_ , unittest.TestCase ): '''simple docstring''' lowerCAmelCase_ : int = MgpstrTokenizer lowerCAmelCase_ : List[str] = False lowerCAmelCase_ : Optional[int] = {} lowerCAmelCase_ : Any = False def SCREAMING_SNAKE_CASE__ ( self : List[Any] ): """simple docstring""" super().setUp() # fmt: off UpperCAmelCase__ = ["""[GO]""", """[s]""", """0""", """1""", """2""", """3""", """4""", """5""", """6""", """7""", """8""", """9""", """a""", """b""", """c""", """d""", """e""", """f""", """g""", """h""", """i""", """j""", """k""", """l""", """m""", """n""", """o""", """p""", """q""", """r""", """s""", """t""", """u""", """v""", """w""", """x""", """y""", """z"""] # fmt: on UpperCAmelCase__ = dict(zip(_UpperCAmelCase , range(len(_UpperCAmelCase ) ) ) ) UpperCAmelCase__ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] ) with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as fp: fp.write(json.dumps(_UpperCAmelCase ) + """\n""" ) def SCREAMING_SNAKE_CASE__ ( self : List[str] , **_UpperCAmelCase : Optional[Any] ): """simple docstring""" return MgpstrTokenizer.from_pretrained(self.tmpdirname , **_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] , _UpperCAmelCase : Tuple ): """simple docstring""" UpperCAmelCase__ = """tester""" UpperCAmelCase__ = """tester""" return input_text, output_text @unittest.skip("""MGP-STR always lower cases letters.""" ) def SCREAMING_SNAKE_CASE__ ( self : List[str] ): """simple docstring""" pass def SCREAMING_SNAKE_CASE__ ( self : Any ): """simple docstring""" UpperCAmelCase__ = self.get_tokenizers(do_lower_case=_UpperCAmelCase ) for tokenizer in tokenizers: with self.subTest(f'''{tokenizer.__class__.__name__}''' ): UpperCAmelCase__ = """[SPECIAL_TOKEN]""" tokenizer.add_special_tokens({"""cls_token""": special_token} ) UpperCAmelCase__ = tokenizer.encode([special_token] , add_special_tokens=_UpperCAmelCase ) self.assertEqual(len(_UpperCAmelCase ) , 1 ) UpperCAmelCase__ = tokenizer.decode(_UpperCAmelCase , skip_special_tokens=_UpperCAmelCase ) self.assertTrue(special_token not in decoded ) def SCREAMING_SNAKE_CASE__ ( self : List[str] ): """simple docstring""" UpperCAmelCase__ = self.get_tokenizers() for tokenizer in tokenizers: with self.subTest(f'''{tokenizer.__class__.__name__}''' ): UpperCAmelCase__ , UpperCAmelCase__ = self.get_input_output_texts(_UpperCAmelCase ) UpperCAmelCase__ = tokenizer.tokenize(_UpperCAmelCase ) UpperCAmelCase__ = tokenizer.convert_tokens_to_ids(_UpperCAmelCase ) UpperCAmelCase__ = tokenizer.encode(_UpperCAmelCase , add_special_tokens=_UpperCAmelCase ) self.assertListEqual(_UpperCAmelCase , _UpperCAmelCase ) UpperCAmelCase__ = tokenizer.convert_ids_to_tokens(_UpperCAmelCase ) self.assertNotEqual(len(_UpperCAmelCase ) , 0 ) UpperCAmelCase__ = tokenizer.decode(_UpperCAmelCase ) self.assertIsInstance(_UpperCAmelCase , _UpperCAmelCase ) self.assertEqual(text_a.replace(""" """ , """""" ) , _UpperCAmelCase ) @unittest.skip("""MGP-STR tokenizer only handles one sequence.""" ) def SCREAMING_SNAKE_CASE__ ( self : Any ): """simple docstring""" pass @unittest.skip("""inputs cannot be pretokenized in MgpstrTokenizer""" ) def SCREAMING_SNAKE_CASE__ ( self : str ): """simple docstring""" pass

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'''simple docstring''' from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_tokenizers_available, is_torch_available, ) UpperCAmelCase_ = { 'configuration_longformer': [ 'LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LongformerConfig', 'LongformerOnnxConfig', ], 'tokenization_longformer': ['LongformerTokenizer'], } try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCAmelCase_ = ['LongformerTokenizerFast'] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCAmelCase_ = [ 'LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST', 'LongformerForMaskedLM', 'LongformerForMultipleChoice', 'LongformerForQuestionAnswering', 'LongformerForSequenceClassification', 'LongformerForTokenClassification', 'LongformerModel', 'LongformerPreTrainedModel', 'LongformerSelfAttention', ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCAmelCase_ = [ 'TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST', 'TFLongformerForMaskedLM', 'TFLongformerForMultipleChoice', 'TFLongformerForQuestionAnswering', 'TFLongformerForSequenceClassification', 'TFLongformerForTokenClassification', 'TFLongformerModel', 'TFLongformerPreTrainedModel', 'TFLongformerSelfAttention', ] if TYPE_CHECKING: from .configuration_longformer import ( LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, LongformerConfig, LongformerOnnxConfig, ) from .tokenization_longformer import LongformerTokenizer try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tokenization_longformer_fast import LongformerTokenizerFast try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_longformer import ( LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, LongformerForMaskedLM, LongformerForMultipleChoice, LongformerForQuestionAnswering, LongformerForSequenceClassification, LongformerForTokenClassification, LongformerModel, LongformerPreTrainedModel, LongformerSelfAttention, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_longformer import ( TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, TFLongformerForMaskedLM, TFLongformerForMultipleChoice, TFLongformerForQuestionAnswering, TFLongformerForSequenceClassification, TFLongformerForTokenClassification, TFLongformerModel, TFLongformerPreTrainedModel, TFLongformerSelfAttention, ) else: import sys UpperCAmelCase_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

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'''simple docstring''' from abc import ABC, abstractmethod from typing import List, Optional class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' def __init__( self : Union[str, Any] ): """simple docstring""" self.test() def SCREAMING_SNAKE_CASE__ ( self : List[str] ): """simple docstring""" UpperCAmelCase__ = 0 UpperCAmelCase__ = False while not completed: if counter == 1: self.reset() UpperCAmelCase__ = self.advance() if not self.does_advance(_UpperCAmelCase ): raise Exception( """Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true.""" ) UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = self.update(_UpperCAmelCase ) counter += 1 if counter > 1_00_00: raise Exception("""update() does not fulfill the constraint.""" ) if self.remaining() != 0: raise Exception("""Custom Constraint is not defined correctly.""" ) @abstractmethod def SCREAMING_SNAKE_CASE__ ( self : Dict ): """simple docstring""" raise NotImplementedError( f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' ) @abstractmethod def SCREAMING_SNAKE_CASE__ ( self : Dict , _UpperCAmelCase : int ): """simple docstring""" raise NotImplementedError( f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' ) @abstractmethod def SCREAMING_SNAKE_CASE__ ( self : Any , _UpperCAmelCase : int ): """simple docstring""" raise NotImplementedError( f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' ) @abstractmethod def SCREAMING_SNAKE_CASE__ ( self : List[Any] ): """simple docstring""" raise NotImplementedError( f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' ) @abstractmethod def SCREAMING_SNAKE_CASE__ ( self : List[str] ): """simple docstring""" raise NotImplementedError( f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' ) @abstractmethod def SCREAMING_SNAKE_CASE__ ( self : int , _UpperCAmelCase : List[Any]=False ): """simple docstring""" raise NotImplementedError( f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' ) class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' def __init__( self : Union[str, Any] , _UpperCAmelCase : List[int] ): """simple docstring""" super(_UpperCAmelCase , self ).__init__() if not isinstance(_UpperCAmelCase , _UpperCAmelCase ) or len(_UpperCAmelCase ) == 0: raise ValueError(f'''`token_ids` has to be a non-empty list, but is {token_ids}.''' ) if any((not isinstance(_UpperCAmelCase , _UpperCAmelCase ) or token_id < 0) for token_id in token_ids ): raise ValueError(f'''Each list in `token_ids` has to be a list of positive integers, but is {token_ids}.''' ) UpperCAmelCase__ = token_ids UpperCAmelCase__ = len(self.token_ids ) UpperCAmelCase__ = -1 # the index of the currently fulfilled step UpperCAmelCase__ = False def SCREAMING_SNAKE_CASE__ ( self : List[str] ): """simple docstring""" if self.completed: return None return self.token_ids[self.fulfilled_idx + 1] def SCREAMING_SNAKE_CASE__ ( self : List[str] , _UpperCAmelCase : int ): """simple docstring""" if not isinstance(_UpperCAmelCase , _UpperCAmelCase ): raise ValueError(f'''`token_id` has to be an `int`, but is {token_id} of type {type(_UpperCAmelCase )}''' ) if self.completed: return False return token_id == self.token_ids[self.fulfilled_idx + 1] def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] , _UpperCAmelCase : int ): """simple docstring""" if not isinstance(_UpperCAmelCase , _UpperCAmelCase ): raise ValueError(f'''`token_id` has to be an `int`, but is {token_id} of type {type(_UpperCAmelCase )}''' ) UpperCAmelCase__ = False UpperCAmelCase__ = False UpperCAmelCase__ = False if self.does_advance(_UpperCAmelCase ): self.fulfilled_idx += 1 UpperCAmelCase__ = True if self.fulfilled_idx == (self.seqlen - 1): UpperCAmelCase__ = True UpperCAmelCase__ = completed else: # failed to make progress. UpperCAmelCase__ = True self.reset() return stepped, completed, reset def SCREAMING_SNAKE_CASE__ ( self : Tuple ): """simple docstring""" UpperCAmelCase__ = False UpperCAmelCase__ = 0 def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ): """simple docstring""" return self.seqlen - (self.fulfilled_idx + 1) def SCREAMING_SNAKE_CASE__ ( self : Any , _UpperCAmelCase : Optional[int]=False ): """simple docstring""" UpperCAmelCase__ = PhrasalConstraint(self.token_ids ) if stateful: UpperCAmelCase__ = self.seqlen UpperCAmelCase__ = self.fulfilled_idx UpperCAmelCase__ = self.completed return new_constraint class lowerCAmelCase_ : '''simple docstring''' def __init__( self : Any , _UpperCAmelCase : List[List[int]] , _UpperCAmelCase : List[str]=True ): """simple docstring""" UpperCAmelCase__ = max([len(_UpperCAmelCase ) for one in nested_token_ids] ) UpperCAmelCase__ = {} for token_ids in nested_token_ids: UpperCAmelCase__ = root for tidx, token_id in enumerate(_UpperCAmelCase ): if token_id not in level: UpperCAmelCase__ = {} UpperCAmelCase__ = level[token_id] if no_subsets and self.has_subsets(_UpperCAmelCase , _UpperCAmelCase ): raise ValueError( """Each list in `nested_token_ids` can't be a complete subset of another list, but is""" f''' {nested_token_ids}.''' ) UpperCAmelCase__ = root def SCREAMING_SNAKE_CASE__ ( self : int , _UpperCAmelCase : int ): """simple docstring""" UpperCAmelCase__ = self.trie for current_token in current_seq: UpperCAmelCase__ = start[current_token] UpperCAmelCase__ = list(start.keys() ) return next_tokens def SCREAMING_SNAKE_CASE__ ( self : str , _UpperCAmelCase : Tuple ): """simple docstring""" UpperCAmelCase__ = self.next_tokens(_UpperCAmelCase ) return len(_UpperCAmelCase ) == 0 def SCREAMING_SNAKE_CASE__ ( self : List[str] , _UpperCAmelCase : Optional[int] ): """simple docstring""" UpperCAmelCase__ = list(root.values() ) if len(_UpperCAmelCase ) == 0: return 1 else: return sum([self.count_leaves(_UpperCAmelCase ) for nn in next_nodes] ) def SCREAMING_SNAKE_CASE__ ( self : List[Any] , _UpperCAmelCase : Dict , _UpperCAmelCase : Dict ): """simple docstring""" UpperCAmelCase__ = self.count_leaves(_UpperCAmelCase ) return len(_UpperCAmelCase ) != leaf_count class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' def __init__( self : Dict , _UpperCAmelCase : List[List[int]] ): """simple docstring""" super(_UpperCAmelCase , self ).__init__() if not isinstance(_UpperCAmelCase , _UpperCAmelCase ) or len(_UpperCAmelCase ) == 0: raise ValueError(f'''`nested_token_ids` has to be a non-empty list, but is {nested_token_ids}.''' ) if any(not isinstance(_UpperCAmelCase , _UpperCAmelCase ) for token_ids in nested_token_ids ): raise ValueError(f'''`nested_token_ids` has to be a list of lists, but is {nested_token_ids}.''' ) if any( any((not isinstance(_UpperCAmelCase , _UpperCAmelCase ) or token_id < 0) for token_id in token_ids ) for token_ids in nested_token_ids ): raise ValueError( f'''Each list in `nested_token_ids` has to be a list of positive integers, but is {nested_token_ids}.''' ) UpperCAmelCase__ = DisjunctiveTrie(_UpperCAmelCase ) UpperCAmelCase__ = nested_token_ids UpperCAmelCase__ = self.trie.max_height UpperCAmelCase__ = [] UpperCAmelCase__ = False def SCREAMING_SNAKE_CASE__ ( self : Any ): """simple docstring""" UpperCAmelCase__ = self.trie.next_tokens(self.current_seq ) if len(_UpperCAmelCase ) == 0: return None else: return token_list def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] , _UpperCAmelCase : int ): """simple docstring""" if not isinstance(_UpperCAmelCase , _UpperCAmelCase ): raise ValueError(f'''`token_id` is supposed to be type `int`, but is {token_id} of type {type(_UpperCAmelCase )}''' ) UpperCAmelCase__ = self.trie.next_tokens(self.current_seq ) return token_id in next_tokens def SCREAMING_SNAKE_CASE__ ( self : Dict , _UpperCAmelCase : int ): """simple docstring""" if not isinstance(_UpperCAmelCase , _UpperCAmelCase ): raise ValueError(f'''`token_id` is supposed to be type `int`, but is {token_id} of type {type(_UpperCAmelCase )}''' ) UpperCAmelCase__ = False UpperCAmelCase__ = False UpperCAmelCase__ = False if self.does_advance(_UpperCAmelCase ): self.current_seq.append(_UpperCAmelCase ) UpperCAmelCase__ = True else: UpperCAmelCase__ = True self.reset() UpperCAmelCase__ = self.trie.reached_leaf(self.current_seq ) UpperCAmelCase__ = completed return stepped, completed, reset def SCREAMING_SNAKE_CASE__ ( self : str ): """simple docstring""" UpperCAmelCase__ = False UpperCAmelCase__ = [] def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ): """simple docstring""" if self.completed: # since this can be completed without reaching max height return 0 else: return self.seqlen - len(self.current_seq ) def SCREAMING_SNAKE_CASE__ ( self : Tuple , _UpperCAmelCase : Dict=False ): """simple docstring""" UpperCAmelCase__ = DisjunctiveConstraint(self.token_ids ) if stateful: UpperCAmelCase__ = self.seqlen UpperCAmelCase__ = self.current_seq UpperCAmelCase__ = self.completed return new_constraint class lowerCAmelCase_ : '''simple docstring''' def __init__( self : Optional[int] , _UpperCAmelCase : List[Constraint] ): """simple docstring""" UpperCAmelCase__ = constraints # max # of steps required to fulfill a given constraint UpperCAmelCase__ = max([c.seqlen for c in constraints] ) UpperCAmelCase__ = len(_UpperCAmelCase ) UpperCAmelCase__ = False self.init_state() def SCREAMING_SNAKE_CASE__ ( self : Any ): """simple docstring""" UpperCAmelCase__ = [] UpperCAmelCase__ = None UpperCAmelCase__ = [constraint.copy(stateful=_UpperCAmelCase ) for constraint in self.constraints] def SCREAMING_SNAKE_CASE__ ( self : List[str] ): """simple docstring""" UpperCAmelCase__ = 0 if self.inprogress_constraint: # extra points for having a constraint mid-fulfilled add += self.max_seqlen - self.inprogress_constraint.remaining() return (len(self.complete_constraints ) * self.max_seqlen) + add def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ): """simple docstring""" UpperCAmelCase__ = [] if self.inprogress_constraint is None: for constraint in self.pending_constraints: # "pending" == "unfulfilled yet" UpperCAmelCase__ = constraint.advance() if isinstance(_UpperCAmelCase , _UpperCAmelCase ): token_list.append(_UpperCAmelCase ) elif isinstance(_UpperCAmelCase , _UpperCAmelCase ): token_list.extend(_UpperCAmelCase ) else: UpperCAmelCase__ = self.inprogress_constraint.advance() if isinstance(_UpperCAmelCase , _UpperCAmelCase ): token_list.append(_UpperCAmelCase ) elif isinstance(_UpperCAmelCase , _UpperCAmelCase ): token_list.extend(_UpperCAmelCase ) if len(_UpperCAmelCase ) == 0: return None else: return token_list def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , _UpperCAmelCase : Optional[List[int]] ): """simple docstring""" self.init_state() if token_ids is not None: for token in token_ids: # completes or steps **one** constraint UpperCAmelCase__ , UpperCAmelCase__ = self.add(_UpperCAmelCase ) # the entire list of constraints are fulfilled if self.completed: break def SCREAMING_SNAKE_CASE__ ( self : Any , _UpperCAmelCase : int ): """simple docstring""" if not isinstance(_UpperCAmelCase , _UpperCAmelCase ): raise ValueError(f'''`token_id` should be an `int`, but is `{token_id}`.''' ) UpperCAmelCase__ , UpperCAmelCase__ = False, False if self.completed: UpperCAmelCase__ = True UpperCAmelCase__ = False return complete, stepped if self.inprogress_constraint is not None: # In the middle of fulfilling a constraint. If the `token_id` *does* makes an incremental progress to current # job, simply update the state UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = self.inprogress_constraint.update(_UpperCAmelCase ) if reset: # 1. If the next token breaks the progress, then we must restart. # e.g. constraint = "I love pies" and sequence so far is "I love" but `token_id` == "books". # But that doesn't mean we self.init_state(), since we only reset the state for this particular # constraint, not the full list of constraints. self.pending_constraints.append(self.inprogress_constraint.copy(stateful=_UpperCAmelCase ) ) UpperCAmelCase__ = None if complete: # 2. If the next token completes the constraint, move it to completed list, set # inprogress to None. If there are no pending constraints either, then this full list of constraints # is complete. self.complete_constraints.append(self.inprogress_constraint ) UpperCAmelCase__ = None if len(self.pending_constraints ) == 0: # we're done! UpperCAmelCase__ = True else: # Not in the middle of fulfilling a constraint. So does this `token_id` helps us step towards any of our list # of constraints? for cidx, pending_constraint in enumerate(self.pending_constraints ): if pending_constraint.does_advance(_UpperCAmelCase ): UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = pending_constraint.update(_UpperCAmelCase ) if not stepped: raise Exception( """`constraint.update(token_id)` is not yielding incremental progress, """ """even though `constraint.does_advance(token_id)` is true.""" ) if complete: self.complete_constraints.append(_UpperCAmelCase ) UpperCAmelCase__ = None if not complete and stepped: UpperCAmelCase__ = pending_constraint if complete or stepped: # If we made any progress at all, then it's at least not a "pending constraint". UpperCAmelCase__ = ( self.pending_constraints[:cidx] + self.pending_constraints[cidx + 1 :] ) if len(self.pending_constraints ) == 0 and self.inprogress_constraint is None: # If there's no longer any pending after this and no inprogress either, then we must be # complete. UpperCAmelCase__ = True break # prevent accidentally stepping through multiple constraints with just one token. return complete, stepped def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , _UpperCAmelCase : List[Any]=True ): """simple docstring""" UpperCAmelCase__ = ConstraintListState(self.constraints ) # we actually never though self.constraints objects # throughout this process. So it's at initialization state. if stateful: UpperCAmelCase__ = [ constraint.copy(stateful=_UpperCAmelCase ) for constraint in self.complete_constraints ] if self.inprogress_constraint is not None: UpperCAmelCase__ = self.inprogress_constraint.copy(stateful=_UpperCAmelCase ) UpperCAmelCase__ = [constraint.copy() for constraint in self.pending_constraints] return new_state

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'''simple docstring''' from typing import List, Optional, Tuple, Union import torch from ...utils import logging, randn_tensor from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline UpperCAmelCase_ = logging.get_logger(__name__) # pylint: disable=invalid-name class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' def __init__( self : str , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Tuple ): """simple docstring""" super().__init__() self.register_modules(unet=_UpperCAmelCase , scheduler=_UpperCAmelCase ) @torch.no_grad() def __call__( self : Optional[int] , _UpperCAmelCase : int = 1 , _UpperCAmelCase : int = 1_00 , _UpperCAmelCase : Optional[Union[torch.Generator, List[torch.Generator]]] = None , _UpperCAmelCase : Optional[float] = None , _UpperCAmelCase : bool = True , ): """simple docstring""" if audio_length_in_s is None: UpperCAmelCase__ = self.unet.config.sample_size / self.unet.config.sample_rate UpperCAmelCase__ = audio_length_in_s * self.unet.config.sample_rate UpperCAmelCase__ = 2 ** len(self.unet.up_blocks ) if sample_size < 3 * down_scale_factor: raise ValueError( f'''{audio_length_in_s} is too small. Make sure it\'s bigger or equal to''' f''' {3 * down_scale_factor / self.unet.config.sample_rate}.''' ) UpperCAmelCase__ = int(_UpperCAmelCase ) if sample_size % down_scale_factor != 0: UpperCAmelCase__ = ( (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1 ) * down_scale_factor logger.info( f'''{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled''' f''' by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising''' """ process.""" ) UpperCAmelCase__ = int(_UpperCAmelCase ) UpperCAmelCase__ = next(iter(self.unet.parameters() ) ).dtype UpperCAmelCase__ = (batch_size, self.unet.config.in_channels, sample_size) if isinstance(_UpperCAmelCase , _UpperCAmelCase ) and len(_UpperCAmelCase ) != batch_size: raise ValueError( f'''You have passed a list of generators of length {len(_UpperCAmelCase )}, but requested an effective batch''' f''' size of {batch_size}. Make sure the batch size matches the length of the generators.''' ) UpperCAmelCase__ = randn_tensor(_UpperCAmelCase , generator=_UpperCAmelCase , device=self.device , dtype=_UpperCAmelCase ) # set step values self.scheduler.set_timesteps(_UpperCAmelCase , device=audio.device ) UpperCAmelCase__ = self.scheduler.timesteps.to(_UpperCAmelCase ) for t in self.progress_bar(self.scheduler.timesteps ): # 1. predict noise model_output UpperCAmelCase__ = self.unet(_UpperCAmelCase , _UpperCAmelCase ).sample # 2. compute previous image: x_t -> t_t-1 UpperCAmelCase__ = self.scheduler.step(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ).prev_sample UpperCAmelCase__ = audio.clamp(-1 , 1 ).float().cpu().numpy() UpperCAmelCase__ = audio[:, :, :original_sample_size] if not return_dict: return (audio,) return AudioPipelineOutput(audios=_UpperCAmelCase )

346

'''simple docstring''' import doctest import logging import os import unittest from pathlib import Path from typing import List, Union import transformers from transformers.testing_utils import require_tf, require_torch, slow UpperCAmelCase_ = logging.getLogger() @unittest.skip("""Temporarily disable the doc tests.""" ) @require_torch @require_tf @slow class lowerCAmelCase_ ( unittest.TestCase ): '''simple docstring''' def SCREAMING_SNAKE_CASE__ ( self : str , _UpperCAmelCase : Path , _UpperCAmelCase : Union[str, None] = None , _UpperCAmelCase : Union[List[str], None] = None , _UpperCAmelCase : Union[str, List[str], None] = None , _UpperCAmelCase : bool = True , ): """simple docstring""" UpperCAmelCase__ = [file for file in os.listdir(_UpperCAmelCase ) if os.path.isfile(os.path.join(_UpperCAmelCase , _UpperCAmelCase ) )] if identifier is not None: UpperCAmelCase__ = [file for file in files if identifier in file] if n_identifier is not None: if isinstance(_UpperCAmelCase , _UpperCAmelCase ): for n_ in n_identifier: UpperCAmelCase__ = [file for file in files if n_ not in file] else: UpperCAmelCase__ = [file for file in files if n_identifier not in file] UpperCAmelCase__ = ignore_files or [] ignore_files.append("""__init__.py""" ) UpperCAmelCase__ = [file for file in files if file not in ignore_files] for file in files: # Open all files print("""Testing""" , _UpperCAmelCase ) if only_modules: UpperCAmelCase__ = file.split(""".""" )[0] try: UpperCAmelCase__ = getattr(_UpperCAmelCase , _UpperCAmelCase ) UpperCAmelCase__ = doctest.DocTestSuite(_UpperCAmelCase ) UpperCAmelCase__ = unittest.TextTestRunner().run(_UpperCAmelCase ) self.assertIs(len(result.failures ) , 0 ) except AttributeError: logger.info(f'''{module_identifier} is not a module.''' ) else: UpperCAmelCase__ = doctest.testfile(str("""..""" / directory / file ) , optionflags=doctest.ELLIPSIS ) self.assertIs(result.failed , 0 ) def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ): """simple docstring""" UpperCAmelCase__ = Path("""src/transformers""" ) UpperCAmelCase__ = """modeling""" UpperCAmelCase__ = [ """modeling_ctrl.py""", """modeling_tf_ctrl.py""", ] self.analyze_directory(_UpperCAmelCase , identifier=_UpperCAmelCase , ignore_files=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ): """simple docstring""" UpperCAmelCase__ = Path("""src/transformers""" ) UpperCAmelCase__ = """tokenization""" self.analyze_directory(_UpperCAmelCase , identifier=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : str ): """simple docstring""" UpperCAmelCase__ = Path("""src/transformers""" ) UpperCAmelCase__ = """configuration""" self.analyze_directory(_UpperCAmelCase , identifier=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ): """simple docstring""" UpperCAmelCase__ = Path("""src/transformers""" ) UpperCAmelCase__ = ["""configuration""", """modeling""", """tokenization"""] self.analyze_directory(_UpperCAmelCase , n_identifier=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : List[str] ): """simple docstring""" UpperCAmelCase__ = Path("""docs/source""" ) UpperCAmelCase__ = ["""favicon.ico"""] self.analyze_directory(_UpperCAmelCase , ignore_files=_UpperCAmelCase , only_modules=_UpperCAmelCase )

346

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'''simple docstring''' import os from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home UpperCAmelCase_ = HUGGINGFACE_HUB_CACHE UpperCAmelCase_ = 'config.json' UpperCAmelCase_ = 'diffusion_pytorch_model.bin' UpperCAmelCase_ = 'diffusion_flax_model.msgpack' UpperCAmelCase_ = 'model.onnx' UpperCAmelCase_ = 'diffusion_pytorch_model.safetensors' UpperCAmelCase_ = 'weights.pb' UpperCAmelCase_ = 'https://huggingface.co' UpperCAmelCase_ = default_cache_path UpperCAmelCase_ = 'diffusers_modules' UpperCAmelCase_ = os.getenv('HF_MODULES_CACHE', os.path.join(hf_cache_home, 'modules')) UpperCAmelCase_ = ['fp16', 'non-ema'] UpperCAmelCase_ = '.self_attn'

346

'''simple docstring''' from datasets.utils.patching import _PatchedModuleObj, patch_submodule from . import _test_patching def _UpperCamelCase ( ): '''simple docstring''' import os as original_os from os import path as original_path from os import rename as original_rename from os.path import dirname as original_dirname from os.path import join as original_join assert _test_patching.os is original_os assert _test_patching.path is original_path assert _test_patching.join is original_join assert _test_patching.renamed_os is original_os assert _test_patching.renamed_path is original_path assert _test_patching.renamed_join is original_join UpperCAmelCase__ = """__test_patch_submodule_mock__""" with patch_submodule(_test_patching , """os.path.join""" , SCREAMING_SNAKE_CASE__ ): # Every way to access os.path.join must be patched, and the rest must stay untouched # check os.path.join assert isinstance(_test_patching.os , _PatchedModuleObj ) assert isinstance(_test_patching.os.path , _PatchedModuleObj ) assert _test_patching.os.path.join is mock # check path.join assert isinstance(_test_patching.path , _PatchedModuleObj ) assert _test_patching.path.join is mock # check join assert _test_patching.join is mock # check that the other attributes are untouched assert _test_patching.os.rename is original_rename assert _test_patching.path.dirname is original_dirname assert _test_patching.os.path.dirname is original_dirname # Even renamed modules or objects must be patched # check renamed_os.path.join assert isinstance(_test_patching.renamed_os , _PatchedModuleObj ) assert isinstance(_test_patching.renamed_os.path , _PatchedModuleObj ) assert _test_patching.renamed_os.path.join is mock # check renamed_path.join assert isinstance(_test_patching.renamed_path , _PatchedModuleObj ) assert _test_patching.renamed_path.join is mock # check renamed_join assert _test_patching.renamed_join is mock # check that the other attributes are untouched assert _test_patching.renamed_os.rename is original_rename assert _test_patching.renamed_path.dirname is original_dirname assert _test_patching.renamed_os.path.dirname is original_dirname # check that everthing is back to normal when the patch is over assert _test_patching.os is original_os assert _test_patching.path is original_path assert _test_patching.join is original_join assert _test_patching.renamed_os is original_os assert _test_patching.renamed_path is original_path assert _test_patching.renamed_join is original_join def _UpperCamelCase ( ): '''simple docstring''' assert _test_patching.open is open UpperCAmelCase__ = """__test_patch_submodule_builtin_mock__""" # _test_patching has "open" in its globals assert _test_patching.open is open with patch_submodule(_test_patching , """open""" , SCREAMING_SNAKE_CASE__ ): assert _test_patching.open is mock # check that everthing is back to normal when the patch is over assert _test_patching.open is open def _UpperCamelCase ( ): '''simple docstring''' UpperCAmelCase__ = """__test_patch_submodule_missing_mock__""" with patch_submodule(_test_patching , """pandas.read_csv""" , SCREAMING_SNAKE_CASE__ ): pass def _UpperCamelCase ( ): '''simple docstring''' UpperCAmelCase__ = """__test_patch_submodule_missing_builtin_mock__""" # _test_patching doesn't have "len" in its globals assert getattr(_test_patching , """len""" , SCREAMING_SNAKE_CASE__ ) is None with patch_submodule(_test_patching , """len""" , SCREAMING_SNAKE_CASE__ ): assert _test_patching.len is mock assert _test_patching.len is len def _UpperCamelCase ( ): '''simple docstring''' UpperCAmelCase__ = """__test_patch_submodule_start_and_stop_mock__""" UpperCAmelCase__ = patch_submodule(_test_patching , """open""" , SCREAMING_SNAKE_CASE__ ) assert _test_patching.open is open patch.start() assert _test_patching.open is mock patch.stop() assert _test_patching.open is open def _UpperCamelCase ( ): '''simple docstring''' from os import rename as original_rename from os.path import dirname as original_dirname from os.path import join as original_join UpperCAmelCase__ = """__test_patch_submodule_successive_join__""" UpperCAmelCase__ = """__test_patch_submodule_successive_dirname__""" UpperCAmelCase__ = """__test_patch_submodule_successive_rename__""" assert _test_patching.os.path.join is original_join assert _test_patching.os.path.dirname is original_dirname assert _test_patching.os.rename is original_rename with patch_submodule(_test_patching , """os.path.join""" , SCREAMING_SNAKE_CASE__ ): with patch_submodule(_test_patching , """os.rename""" , SCREAMING_SNAKE_CASE__ ): with patch_submodule(_test_patching , """os.path.dirname""" , SCREAMING_SNAKE_CASE__ ): assert _test_patching.os.path.join is mock_join assert _test_patching.os.path.dirname is mock_dirname assert _test_patching.os.rename is mock_rename # try another order with patch_submodule(_test_patching , """os.rename""" , SCREAMING_SNAKE_CASE__ ): with patch_submodule(_test_patching , """os.path.join""" , SCREAMING_SNAKE_CASE__ ): with patch_submodule(_test_patching , """os.path.dirname""" , SCREAMING_SNAKE_CASE__ ): assert _test_patching.os.path.join is mock_join assert _test_patching.os.path.dirname is mock_dirname assert _test_patching.os.rename is mock_rename assert _test_patching.os.path.join is original_join assert _test_patching.os.path.dirname is original_dirname assert _test_patching.os.rename is original_rename def _UpperCamelCase ( ): '''simple docstring''' UpperCAmelCase__ = """__test_patch_submodule_doesnt_exist_mock__""" with patch_submodule(_test_patching , """__module_that_doesn_exist__.__attribute_that_doesn_exist__""" , SCREAMING_SNAKE_CASE__ ): pass with patch_submodule(_test_patching , """os.__attribute_that_doesn_exist__""" , SCREAMING_SNAKE_CASE__ ): pass

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'''simple docstring''' from functools import lru_cache @lru_cache def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int ): '''simple docstring''' if num < 0: raise ValueError("""Number should not be negative.""" ) return 1 if num in (0, 1) else num * factorial(num - 1 ) if __name__ == "__main__": import doctest doctest.testmod()

346

'''simple docstring''' from timeit import timeit UpperCAmelCase_ = { 'MALAYALAM': True, 'String': False, 'rotor': True, 'level': True, 'A': True, 'BB': True, 'ABC': False, 'amanaplanacanalpanama': True, # "a man a plan a canal panama" } # Ensure our test data is valid assert all((key == key[::-1]) is value for key, value in test_data.items()) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str ): '''simple docstring''' UpperCAmelCase__ = 0 UpperCAmelCase__ = len(SCREAMING_SNAKE_CASE__ ) - 1 while start_i < end_i: if s[start_i] == s[end_i]: start_i += 1 end_i -= 1 else: return False return True def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str ): '''simple docstring''' UpperCAmelCase__ = len(SCREAMING_SNAKE_CASE__ ) // 2 UpperCAmelCase__ = len(SCREAMING_SNAKE_CASE__ ) # We need to traverse till half of the length of string # as we can get access of the i'th last element from # i'th index. # eg: [0,1,2,3,4,5] => 4th index can be accessed # with the help of 1st index (i==n-i-1) # where n is length of string return all(s[i] == s[n - i - 1] for i in range(SCREAMING_SNAKE_CASE__ ) ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str ): '''simple docstring''' if len(SCREAMING_SNAKE_CASE__ ) <= 2: return True if s[0] == s[len(SCREAMING_SNAKE_CASE__ ) - 1]: return is_palindrome_recursive(s[1:-1] ) else: return False def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str ): '''simple docstring''' return s == s[::-1] def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str ): '''simple docstring''' UpperCAmelCase__ = F'''all({name}(key) is value for key, value in test_data.items())''' UpperCAmelCase__ = F'''from __main__ import test_data, {name}''' UpperCAmelCase__ = 500000 UpperCAmelCase__ = timeit(stmt=SCREAMING_SNAKE_CASE__ , setup=SCREAMING_SNAKE_CASE__ , number=SCREAMING_SNAKE_CASE__ ) print(F'''{name:<35} finished {number:,} runs in {result:.5f} seconds''' ) if __name__ == "__main__": for key, value in test_data.items(): assert is_palindrome(key) is is_palindrome_recursive(key) assert is_palindrome(key) is is_palindrome_slice(key) print(f"{key:21} {value}") print('a man a plan a canal panama') # finished 500,000 runs in 0.46793 seconds benchmark_function('is_palindrome_slice') # finished 500,000 runs in 0.85234 seconds benchmark_function('is_palindrome') # finished 500,000 runs in 1.32028 seconds benchmark_function('is_palindrome_recursive') # finished 500,000 runs in 2.08679 seconds benchmark_function('is_palindrome_traversal')

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'''simple docstring''' from ...configuration_utils import PretrainedConfig from ...utils import logging UpperCAmelCase_ = logging.get_logger(__name__) UpperCAmelCase_ = { 'google/vivit-b-16x2-kinetics400': ( 'https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json' ), # See all Vivit models at https://huggingface.co/models?filter=vivit } class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' lowerCAmelCase_ : Optional[int] = """vivit""" def __init__( self : List[str] , _UpperCAmelCase : List[Any]=2_24 , _UpperCAmelCase : List[str]=32 , _UpperCAmelCase : Any=[2, 16, 16] , _UpperCAmelCase : int=3 , _UpperCAmelCase : Optional[Any]=7_68 , _UpperCAmelCase : Union[str, Any]=12 , _UpperCAmelCase : Dict=12 , _UpperCAmelCase : Optional[Any]=30_72 , _UpperCAmelCase : Optional[int]="gelu_fast" , _UpperCAmelCase : Union[str, Any]=0.0 , _UpperCAmelCase : Tuple=0.0 , _UpperCAmelCase : Optional[int]=0.02 , _UpperCAmelCase : List[Any]=1E-06 , _UpperCAmelCase : List[str]=True , **_UpperCAmelCase : List[Any] , ): """simple docstring""" UpperCAmelCase__ = hidden_size UpperCAmelCase__ = num_hidden_layers UpperCAmelCase__ = num_attention_heads UpperCAmelCase__ = intermediate_size UpperCAmelCase__ = hidden_act UpperCAmelCase__ = hidden_dropout_prob UpperCAmelCase__ = attention_probs_dropout_prob UpperCAmelCase__ = initializer_range UpperCAmelCase__ = layer_norm_eps UpperCAmelCase__ = image_size UpperCAmelCase__ = num_frames UpperCAmelCase__ = tubelet_size UpperCAmelCase__ = num_channels UpperCAmelCase__ = qkv_bias super().__init__(**_UpperCAmelCase )

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'''simple docstring''' import datasets from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py UpperCAmelCase_ = '\\n@INPROCEEDINGS{Papineni02bleu:a,\n author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n title = {BLEU: a Method for Automatic Evaluation of Machine Translation},\n booktitle = {},\n year = {2002},\n pages = {311--318}\n}\n@inproceedings{lin-och-2004-orange,\n title = "{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation",\n author = "Lin, Chin-Yew and\n Och, Franz Josef",\n booktitle = "{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics",\n month = "aug 23{--}aug 27",\n year = "2004",\n address = "Geneva, Switzerland",\n publisher = "COLING",\n url = "https://www.aclweb.org/anthology/C04-1072",\n pages = "501--507",\n}\n' UpperCAmelCase_ = '\\nBLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.\nQuality is considered to be the correspondence between a machine\'s output and that of a human: "the closer a machine translation is to a professional human translation,\nthe better it is" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and\nremains one of the most popular automated and inexpensive metrics.\n\nScores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.\nThose scores are then averaged over the whole corpus to reach an estimate of the translation\'s overall quality. Intelligibility or grammatical correctness\nare not taken into account[citation needed].\n\nBLEU\'s output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1\nrepresenting more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the\nreference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional\nreference translations will increase the BLEU score.\n' UpperCAmelCase_ = '\nComputes BLEU score of translated segments against one or more references.\nArgs:\n predictions: list of translations to score.\n Each translation should be tokenized into a list of tokens.\n references: list of lists of references for each translation.\n Each reference should be tokenized into a list of tokens.\n max_order: Maximum n-gram order to use when computing BLEU score.\n smooth: Whether or not to apply Lin et al. 2004 smoothing.\nReturns:\n \'bleu\': bleu score,\n \'precisions\': geometric mean of n-gram precisions,\n \'brevity_penalty\': brevity penalty,\n \'length_ratio\': ratio of lengths,\n \'translation_length\': translation_length,\n \'reference_length\': reference_length\nExamples:\n\n >>> predictions = [\n ... ["hello", "there", "general", "kenobi"], # tokenized prediction of the first sample\n ... ["foo", "bar", "foobar"] # tokenized prediction of the second sample\n ... ]\n >>> references = [\n ... [["hello", "there", "general", "kenobi"], ["hello", "there", "!"]], # tokenized references for the first sample (2 references)\n ... [["foo", "bar", "foobar"]] # tokenized references for the second sample (1 reference)\n ... ]\n >>> bleu = datasets.load_metric("bleu")\n >>> results = bleu.compute(predictions=predictions, references=references)\n >>> print(results["bleu"])\n 1.0\n' @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION ) class lowerCAmelCase_ ( datasets.Metric ): '''simple docstring''' def SCREAMING_SNAKE_CASE__ ( self : int ): """simple docstring""" return datasets.MetricInfo( description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features( { """predictions""": datasets.Sequence(datasets.Value("""string""" , id="""token""" ) , id="""sequence""" ), """references""": datasets.Sequence( datasets.Sequence(datasets.Value("""string""" , id="""token""" ) , id="""sequence""" ) , id="""references""" ), } ) , codebase_urls=["""https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py"""] , reference_urls=[ """https://en.wikipedia.org/wiki/BLEU""", """https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213""", ] , ) def SCREAMING_SNAKE_CASE__ ( self : int , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : List[Any]=4 , _UpperCAmelCase : Union[str, Any]=False ): """simple docstring""" UpperCAmelCase__ = compute_bleu( reference_corpus=_UpperCAmelCase , translation_corpus=_UpperCAmelCase , max_order=_UpperCAmelCase , smooth=_UpperCAmelCase ) ((UpperCAmelCase__) , (UpperCAmelCase__) , (UpperCAmelCase__) , (UpperCAmelCase__) , (UpperCAmelCase__) , (UpperCAmelCase__)) = score return { "bleu": bleu, "precisions": precisions, "brevity_penalty": bp, "length_ratio": ratio, "translation_length": translation_length, "reference_length": reference_length, }

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'''simple docstring''' import logging from pathlib import Path import numpy as np import pytorch_lightning as pl import torch from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint from pytorch_lightning.utilities import rank_zero_only from utils_rag import save_json def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Optional[int] ): '''simple docstring''' UpperCAmelCase__ = filter(lambda SCREAMING_SNAKE_CASE__ : p.requires_grad , model.parameters() ) UpperCAmelCase__ = sum([np.prod(p.size() ) for p in model_parameters] ) return params UpperCAmelCase_ = logging.getLogger(__name__) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : List[str] ): '''simple docstring''' if metric == "rouge2": UpperCAmelCase__ = """{val_avg_rouge2:.4f}-{step_count}""" elif metric == "bleu": UpperCAmelCase__ = """{val_avg_bleu:.4f}-{step_count}""" elif metric == "em": UpperCAmelCase__ = """{val_avg_em:.4f}-{step_count}""" elif metric == "loss": UpperCAmelCase__ = """{val_avg_loss:.4f}-{step_count}""" else: raise NotImplementedError( F'''seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this''' """ function.""" ) UpperCAmelCase__ = ModelCheckpoint( dirpath=SCREAMING_SNAKE_CASE__ , filename=SCREAMING_SNAKE_CASE__ , monitor=F'''val_{metric}''' , mode="""max""" , save_top_k=1 , every_n_epochs=1 , ) return checkpoint_callback def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : str ): '''simple docstring''' return EarlyStopping( monitor=F'''val_{metric}''' , mode="""min""" if """loss""" in metric else """max""" , patience=SCREAMING_SNAKE_CASE__ , verbose=SCREAMING_SNAKE_CASE__ , ) class lowerCAmelCase_ ( pl.Callback ): '''simple docstring''' def SCREAMING_SNAKE_CASE__ ( self : str , _UpperCAmelCase : int , _UpperCAmelCase : List[Any] ): """simple docstring""" UpperCAmelCase__ = {f'''lr_group_{i}''': param["""lr"""] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups )} pl_module.logger.log_metrics(_UpperCAmelCase ) @rank_zero_only def SCREAMING_SNAKE_CASE__ ( self : List[Any] , _UpperCAmelCase : pl.Trainer , _UpperCAmelCase : pl.LightningModule , _UpperCAmelCase : str , _UpperCAmelCase : int=True ): """simple docstring""" logger.info(f'''***** {type_path} results at step {trainer.global_step:05d} *****''' ) UpperCAmelCase__ = trainer.callback_metrics trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["""log""", """progress_bar""", """preds"""]} ) # Log results UpperCAmelCase__ = Path(pl_module.hparams.output_dir ) if type_path == "test": UpperCAmelCase__ = od / """test_results.txt""" UpperCAmelCase__ = od / """test_generations.txt""" else: # this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json # If people want this it will be easy enough to add back. UpperCAmelCase__ = od / f'''{type_path}_results/{trainer.global_step:05d}.txt''' UpperCAmelCase__ = od / f'''{type_path}_generations/{trainer.global_step:05d}.txt''' results_file.parent.mkdir(exist_ok=_UpperCAmelCase ) generations_file.parent.mkdir(exist_ok=_UpperCAmelCase ) with open(_UpperCAmelCase , """a+""" ) as writer: for key in sorted(_UpperCAmelCase ): if key in ["log", "progress_bar", "preds"]: continue UpperCAmelCase__ = metrics[key] if isinstance(_UpperCAmelCase , torch.Tensor ): UpperCAmelCase__ = val.item() UpperCAmelCase__ = f'''{key}: {val:.6f}\n''' writer.write(_UpperCAmelCase ) if not save_generations: return if "preds" in metrics: UpperCAmelCase__ = """\n""".join(metrics["""preds"""] ) generations_file.open("""w+""" ).write(_UpperCAmelCase ) @rank_zero_only def SCREAMING_SNAKE_CASE__ ( self : List[Any] , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Tuple ): """simple docstring""" try: UpperCAmelCase__ = pl_module.model.model.num_parameters() except AttributeError: UpperCAmelCase__ = pl_module.model.num_parameters() UpperCAmelCase__ = count_trainable_parameters(_UpperCAmelCase ) # mp stands for million parameters trainer.logger.log_metrics({"""n_params""": npars, """mp""": npars / 1E6, """grad_mp""": n_trainable_pars / 1E6} ) @rank_zero_only def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] , _UpperCAmelCase : pl.Trainer , _UpperCAmelCase : pl.LightningModule ): """simple docstring""" save_json(pl_module.metrics , pl_module.metrics_save_path ) return self._write_logs(_UpperCAmelCase , _UpperCAmelCase , """test""" ) @rank_zero_only def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , _UpperCAmelCase : pl.Trainer , _UpperCAmelCase : Any ): """simple docstring""" save_json(pl_module.metrics , pl_module.metrics_save_path ) # Uncommenting this will save val generations # return self._write_logs(trainer, pl_module, "valid")

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'''simple docstring''' from dataclasses import dataclass from typing import List, Optional, Union import numpy as np import torch from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available @dataclass class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' lowerCAmelCase_ : Union[List[np.ndarray], torch.FloatTensor] try: if not (is_transformers_available() and is_torch_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from ...utils.dummy_torch_and_transformers_objects import * # noqa F403 else: from .pipeline_text_to_video_synth import TextToVideoSDPipeline from .pipeline_text_to_video_synth_imgaimg import VideoToVideoSDPipeline # noqa: F401 from .pipeline_text_to_video_zero import TextToVideoZeroPipeline

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'''simple docstring''' import re from filelock import FileLock try: import nltk UpperCAmelCase_ = True except (ImportError, ModuleNotFoundError): UpperCAmelCase_ = False if NLTK_AVAILABLE: with FileLock('.lock') as lock: nltk.download('punkt', quiet=True) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str ): '''simple docstring''' re.sub("""<n>""" , """""" , SCREAMING_SNAKE_CASE__ ) # remove pegasus newline char assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)" return "\n".join(nltk.sent_tokenize(SCREAMING_SNAKE_CASE__ ) )

346

'''simple docstring''' import torch import torch.nn as nn from transformers.modeling_utils import ModuleUtilsMixin from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm from ...configuration_utils import ConfigMixin, register_to_config from ...models import ModelMixin class lowerCAmelCase_ ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ): '''simple docstring''' @register_to_config def __init__( self : List[str] , _UpperCAmelCase : int , _UpperCAmelCase : int , _UpperCAmelCase : int , _UpperCAmelCase : float , _UpperCAmelCase : int , _UpperCAmelCase : int , _UpperCAmelCase : int , _UpperCAmelCase : int , _UpperCAmelCase : str , _UpperCAmelCase : bool = False , ): """simple docstring""" super().__init__() UpperCAmelCase__ = nn.Embedding(_UpperCAmelCase , _UpperCAmelCase ) UpperCAmelCase__ = nn.Embedding(_UpperCAmelCase , _UpperCAmelCase ) UpperCAmelCase__ = False UpperCAmelCase__ = nn.Dropout(p=_UpperCAmelCase ) UpperCAmelCase__ = TaConfig( vocab_size=_UpperCAmelCase , d_model=_UpperCAmelCase , num_heads=_UpperCAmelCase , d_kv=_UpperCAmelCase , d_ff=_UpperCAmelCase , dropout_rate=_UpperCAmelCase , feed_forward_proj=_UpperCAmelCase , is_decoder=_UpperCAmelCase , is_encoder_decoder=_UpperCAmelCase , ) UpperCAmelCase__ = nn.ModuleList() for lyr_num in range(_UpperCAmelCase ): UpperCAmelCase__ = TaBlock(_UpperCAmelCase ) self.encoders.append(_UpperCAmelCase ) UpperCAmelCase__ = TaLayerNorm(_UpperCAmelCase ) UpperCAmelCase__ = nn.Dropout(p=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Dict , _UpperCAmelCase : int , _UpperCAmelCase : str ): """simple docstring""" UpperCAmelCase__ = self.token_embedder(_UpperCAmelCase ) UpperCAmelCase__ = encoder_input_tokens.shape[1] UpperCAmelCase__ = torch.arange(_UpperCAmelCase , device=encoder_input_tokens.device ) x += self.position_encoding(_UpperCAmelCase ) UpperCAmelCase__ = self.dropout_pre(_UpperCAmelCase ) # inverted the attention mask UpperCAmelCase__ = encoder_input_tokens.size() UpperCAmelCase__ = self.get_extended_attention_mask(_UpperCAmelCase , _UpperCAmelCase ) for lyr in self.encoders: UpperCAmelCase__ = lyr(_UpperCAmelCase , _UpperCAmelCase )[0] UpperCAmelCase__ = self.layer_norm(_UpperCAmelCase ) return self.dropout_post(_UpperCAmelCase ), encoder_inputs_mask

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'''simple docstring''' from typing import Dict, List from nltk.translate import gleu_score import datasets from datasets import MetricInfo UpperCAmelCase_ = '\\n@misc{wu2016googles,\n title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey\n and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin\n Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto\n Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and\n Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes\n and Jeffrey Dean},\n year={2016},\n eprint={1609.08144},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n' UpperCAmelCase_ = '\\nThe BLEU score has some undesirable properties when used for single\nsentences, as it was designed to be a corpus measure. We therefore\nuse a slightly different score for our RL experiments which we call\nthe \'GLEU score\'. For the GLEU score, we record all sub-sequences of\n1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then\ncompute a recall, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the target (ground truth) sequence,\nand a precision, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the generated output sequence. Then\nGLEU score is simply the minimum of recall and precision. This GLEU\nscore\'s range is always between 0 (no matches) and 1 (all match) and\nit is symmetrical when switching output and target. According to\nour experiments, GLEU score correlates quite well with the BLEU\nmetric on a corpus level but does not have its drawbacks for our per\nsentence reward objective.\n' UpperCAmelCase_ = '\\nComputes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.\nInstead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching\ntokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.\n\nArgs:\n predictions (list of str): list of translations to score.\n Each translation should be tokenized into a list of tokens.\n references (list of list of str): list of lists of references for each translation.\n Each reference should be tokenized into a list of tokens.\n min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.\n max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.\n\nReturns:\n \'google_bleu\': google_bleu score\n\nExamples:\n Example 1:\n >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n ... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n ... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n ... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n\n >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n ... \'interested\', \'in\', \'world\', \'history\']\n >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n ... \'because\', \'he\', \'read\', \'the\', \'book\']\n\n >>> list_of_references = [[ref1a], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric("google_bleu")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n >>> print(round(results["google_bleu"], 2))\n 0.44\n\n Example 2:\n >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n ... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n ... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n ... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n ... \'heed\', \'the\', \'cat\', \'commands\']\n >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n ... \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n ... \'of\', \'the\', \'cat\']\n\n >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n ... \'interested\', \'in\', \'world\', \'history\']\n >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n ... \'because\', \'he\', \'read\', \'the\', \'book\']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric("google_bleu")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n >>> print(round(results["google_bleu"], 2))\n 0.61\n\n Example 3:\n >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n ... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n ... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n ... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n ... \'heed\', \'the\', \'cat\', \'commands\']\n >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n ... \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n ... \'of\', \'the\', \'cat\']\n\n >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n ... \'interested\', \'in\', \'world\', \'history\']\n >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n ... \'because\', \'he\', \'read\', \'the\', \'book\']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric("google_bleu")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)\n >>> print(round(results["google_bleu"], 2))\n 0.53\n\n Example 4:\n >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n ... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n ... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n ... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n ... \'heed\', \'the\', \'cat\', \'commands\']\n >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n ... \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n ... \'of\', \'the\', \'cat\']\n\n >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n ... \'interested\', \'in\', \'world\', \'history\']\n >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n ... \'because\', \'he\', \'read\', \'the\', \'book\']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric("google_bleu")\n >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)\n >>> print(round(results["google_bleu"], 2))\n 0.4\n' @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION ) class lowerCAmelCase_ ( datasets.Metric ): '''simple docstring''' def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ): """simple docstring""" return datasets.MetricInfo( description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features( { """predictions""": datasets.Sequence(datasets.Value("""string""" , id="""token""" ) , id="""sequence""" ), """references""": datasets.Sequence( datasets.Sequence(datasets.Value("""string""" , id="""token""" ) , id="""sequence""" ) , id="""references""" ), } ) , ) def SCREAMING_SNAKE_CASE__ ( self : List[str] , _UpperCAmelCase : List[List[List[str]]] , _UpperCAmelCase : List[List[str]] , _UpperCAmelCase : int = 1 , _UpperCAmelCase : int = 4 , ): """simple docstring""" return { "google_bleu": gleu_score.corpus_gleu( list_of_references=_UpperCAmelCase , hypotheses=_UpperCAmelCase , min_len=_UpperCAmelCase , max_len=_UpperCAmelCase ) }

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'''simple docstring''' import argparse import json import os import fairseq import torch from fairseq.data import Dictionary from transformers import ( WavaVecaConfig, WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaForCTC, WavaVecaForPreTraining, WavaVecaProcessor, logging, ) from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification logging.set_verbosity_info() UpperCAmelCase_ = logging.get_logger(__name__) UpperCAmelCase_ = { 'post_extract_proj': 'feature_projection.projection', 'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv', 'self_attn.k_proj': 'encoder.layers.*.attention.k_proj', 'self_attn.v_proj': 'encoder.layers.*.attention.v_proj', 'self_attn.q_proj': 'encoder.layers.*.attention.q_proj', 'self_attn.out_proj': 'encoder.layers.*.attention.out_proj', 'self_attn_layer_norm': 'encoder.layers.*.layer_norm', 'fc1': 'encoder.layers.*.feed_forward.intermediate_dense', 'fc2': 'encoder.layers.*.feed_forward.output_dense', 'final_layer_norm': 'encoder.layers.*.final_layer_norm', 'encoder.layer_norm': 'encoder.layer_norm', 'adapter_layer': 'encoder.layers.*.adapter_layer', 'w2v_model.layer_norm': 'feature_projection.layer_norm', 'quantizer.weight_proj': 'quantizer.weight_proj', 'quantizer.vars': 'quantizer.codevectors', 'project_q': 'project_q', 'final_proj': 'project_hid', 'w2v_encoder.proj': 'lm_head', 'mask_emb': 'masked_spec_embed', 'pooling_layer.linear': 'projector', 'pooling_layer.projection': 'classifier', } UpperCAmelCase_ = [ 'lm_head', 'quantizer.weight_proj', 'quantizer.codevectors', 'project_q', 'project_hid', 'projector', 'classifier', ] def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Tuple ): '''simple docstring''' UpperCAmelCase__ = {} with open(SCREAMING_SNAKE_CASE__ , """r""" ) as file: for line_number, line in enumerate(SCREAMING_SNAKE_CASE__ ): UpperCAmelCase__ = line.strip() if line: UpperCAmelCase__ = line.split() UpperCAmelCase__ = line_number UpperCAmelCase__ = words[0] UpperCAmelCase__ = value return result def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : Union[str, Any] ): '''simple docstring''' for attribute in key.split(""".""" ): UpperCAmelCase__ = getattr(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = None for param_key in PARAM_MAPPING.keys(): if full_name.endswith(SCREAMING_SNAKE_CASE__ ): UpperCAmelCase__ = PARAM_MAPPING[full_name.split(""".""" )[-1]] UpperCAmelCase__ = """param""" if weight_type is not None and weight_type != "param": UpperCAmelCase__ = getattr(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ).shape elif weight_type is not None and weight_type == "param": UpperCAmelCase__ = hf_pointer for attribute in hf_param_name.split(""".""" ): UpperCAmelCase__ = getattr(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = shape_pointer.shape # let's reduce dimension UpperCAmelCase__ = value[0] else: UpperCAmelCase__ = hf_pointer.shape if hf_shape != value.shape: raise ValueError( F'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be''' F''' {value.shape} for {full_name}''' ) if weight_type == "weight": UpperCAmelCase__ = value elif weight_type == "weight_g": UpperCAmelCase__ = value elif weight_type == "weight_v": UpperCAmelCase__ = value elif weight_type == "bias": UpperCAmelCase__ = value elif weight_type == "param": for attribute in hf_param_name.split(""".""" ): UpperCAmelCase__ = getattr(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = value else: UpperCAmelCase__ = value logger.info(F'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.''' ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : Optional[Any] ): '''simple docstring''' UpperCAmelCase__ = None for param_key in PARAM_MAPPING.keys(): if full_name.endswith(SCREAMING_SNAKE_CASE__ ): UpperCAmelCase__ = PARAM_MAPPING[full_name.split(""".""" )[-1]] UpperCAmelCase__ = """param""" if weight_type is not None and weight_type != "param": UpperCAmelCase__ = """.""".join([key, weight_type] ) elif weight_type is not None and weight_type == "param": UpperCAmelCase__ = """.""".join([key, hf_param_name] ) else: UpperCAmelCase__ = key UpperCAmelCase__ = value if """lm_head""" in full_key else value[0] UpperCAmelCase_ = { 'W_a': 'linear_1.weight', 'W_b': 'linear_2.weight', 'b_a': 'linear_1.bias', 'b_b': 'linear_2.bias', 'ln_W': 'norm.weight', 'ln_b': 'norm.bias', } def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : Dict=None , SCREAMING_SNAKE_CASE__ : Optional[Any]=None ): '''simple docstring''' UpperCAmelCase__ = False for key, mapped_key in MAPPING.items(): UpperCAmelCase__ = """wav2vec2.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key if key in name or key.split("""w2v_model.""" )[-1] == name.split(""".""" )[0]: UpperCAmelCase__ = True if "*" in mapped_key: UpperCAmelCase__ = name.split(SCREAMING_SNAKE_CASE__ )[0].split(""".""" )[-2] UpperCAmelCase__ = mapped_key.replace("""*""" , SCREAMING_SNAKE_CASE__ ) if "weight_g" in name: UpperCAmelCase__ = """weight_g""" elif "weight_v" in name: UpperCAmelCase__ = """weight_v""" elif "bias" in name: UpperCAmelCase__ = """bias""" elif "weight" in name: # TODO: don't match quantizer.weight_proj UpperCAmelCase__ = """weight""" else: UpperCAmelCase__ = None if hf_dict is not None: rename_dict(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) else: set_recursively(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) return is_used return is_used def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CASE__ : List[str] ): '''simple docstring''' UpperCAmelCase__ = [] UpperCAmelCase__ = fairseq_model.state_dict() UpperCAmelCase__ = hf_model.wavaveca.feature_extractor for name, value in fairseq_dict.items(): UpperCAmelCase__ = False if "conv_layers" in name: load_conv_layer( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , hf_model.config.feat_extract_norm == """group""" , ) UpperCAmelCase__ = True else: UpperCAmelCase__ = load_wavaveca_layer(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) if not is_used: unused_weights.append(SCREAMING_SNAKE_CASE__ ) logger.warning(F'''Unused weights: {unused_weights}''' ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : int ): '''simple docstring''' UpperCAmelCase__ = full_name.split("""conv_layers.""" )[-1] UpperCAmelCase__ = name.split(""".""" ) UpperCAmelCase__ = int(items[0] ) UpperCAmelCase__ = int(items[1] ) if type_id == 0: if "bias" in name: if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape: raise ValueError( F'''{full_name} has size {value.shape}, but''' F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.''' ) UpperCAmelCase__ = value logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' ) elif "weight" in name: if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape: raise ValueError( F'''{full_name} has size {value.shape}, but''' F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.''' ) UpperCAmelCase__ = value logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' ) elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm): if "bias" in name: if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape: raise ValueError( F'''{full_name} has size {value.shape}, but''' F''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.''' ) UpperCAmelCase__ = value logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' ) elif "weight" in name: if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape: raise ValueError( F'''{full_name} has size {value.shape}, but''' F''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.''' ) UpperCAmelCase__ = value logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' ) else: unused_weights.append(SCREAMING_SNAKE_CASE__ ) @torch.no_grad() def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : List[str]=None , SCREAMING_SNAKE_CASE__ : Optional[int]=None , SCREAMING_SNAKE_CASE__ : Union[str, Any]=True , SCREAMING_SNAKE_CASE__ : Union[str, Any]=False ): '''simple docstring''' if config_path is not None: UpperCAmelCase__ = WavaVecaConfig.from_pretrained(SCREAMING_SNAKE_CASE__ ) else: UpperCAmelCase__ = WavaVecaConfig() if is_seq_class: UpperCAmelCase__ = read_txt_into_dict(SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = idalabel UpperCAmelCase__ = WavaVecaForSequenceClassification(SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = WavaVecaFeatureExtractor( feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=SCREAMING_SNAKE_CASE__ , return_attention_mask=SCREAMING_SNAKE_CASE__ , ) feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE__ ) elif is_finetuned: if dict_path: UpperCAmelCase__ = Dictionary.load(SCREAMING_SNAKE_CASE__ ) # important change bos & pad token id since CTC symbol is <pad> and # not <s> as in fairseq UpperCAmelCase__ = target_dict.pad_index UpperCAmelCase__ = target_dict.bos_index UpperCAmelCase__ = target_dict.eos_index UpperCAmelCase__ = len(target_dict.symbols ) UpperCAmelCase__ = os.path.join(SCREAMING_SNAKE_CASE__ , """vocab.json""" ) if not os.path.isdir(SCREAMING_SNAKE_CASE__ ): logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(SCREAMING_SNAKE_CASE__ ) ) return os.makedirs(SCREAMING_SNAKE_CASE__ , exist_ok=SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = target_dict.indices # fairseq has the <pad> and <s> switched UpperCAmelCase__ = 0 UpperCAmelCase__ = 1 with open(SCREAMING_SNAKE_CASE__ , """w""" , encoding="""utf-8""" ) as vocab_handle: json.dump(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = WavaVecaCTCTokenizer( SCREAMING_SNAKE_CASE__ , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token="""|""" , do_lower_case=SCREAMING_SNAKE_CASE__ , ) UpperCAmelCase__ = True if config.feat_extract_norm == """layer""" else False UpperCAmelCase__ = WavaVecaFeatureExtractor( feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=SCREAMING_SNAKE_CASE__ , return_attention_mask=SCREAMING_SNAKE_CASE__ , ) UpperCAmelCase__ = WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE__ , tokenizer=SCREAMING_SNAKE_CASE__ ) processor.save_pretrained(SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = WavaVecaForCTC(SCREAMING_SNAKE_CASE__ ) else: UpperCAmelCase__ = WavaVecaForPreTraining(SCREAMING_SNAKE_CASE__ ) if is_finetuned or is_seq_class: UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = fairseq.checkpoint_utils.load_model_ensemble_and_task( [checkpoint_path] , arg_overrides={"""data""": """/""".join(dict_path.split("""/""" )[:-1] )} ) else: UpperCAmelCase__ = argparse.Namespace(task="""audio_pretraining""" ) UpperCAmelCase__ = fairseq.tasks.setup_task(SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] , task=SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = model[0].eval() recursively_load_weights(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , not is_finetuned ) hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE__ ) if __name__ == "__main__": UpperCAmelCase_ = argparse.ArgumentParser() parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.') parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint') parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model') parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert') parser.add_argument( '--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not' ) parser.add_argument( '--is_seq_class', action='store_true', help='Whether the model to convert is a fine-tuned sequence classification model or not', ) UpperCAmelCase_ = parser.parse_args() UpperCAmelCase_ = not args.not_finetuned and not args.is_seq_class convert_wavaveca_checkpoint( args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, is_finetuned, args.is_seq_class, )

346

1

'''simple docstring''' import argparse import fairseq import torch from torch import nn from transformers import ( MBartaaTokenizer, MBartConfig, MBartForCausalLM, SpeechEncoderDecoderConfig, SpeechEncoderDecoderModel, WavaVecaConfig, WavaVecaFeatureExtractor, WavaVecaModel, logging, ) logging.set_verbosity_info() UpperCAmelCase_ = logging.get_logger(__name__) UpperCAmelCase_ = { 'post_extract_proj': 'feature_projection.projection', 'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv', 'self_attn.k_proj': 'encoder.layers.*.attention.k_proj', 'self_attn.v_proj': 'encoder.layers.*.attention.v_proj', 'self_attn.q_proj': 'encoder.layers.*.attention.q_proj', 'self_attn.out_proj': 'encoder.layers.*.attention.out_proj', 'self_attn_layer_norm': 'encoder.layers.*.layer_norm', 'fc1': 'encoder.layers.*.feed_forward.intermediate_dense', 'fc2': 'encoder.layers.*.feed_forward.output_dense', 'final_layer_norm': 'encoder.layers.*.final_layer_norm', 'encoder.layer_norm': 'encoder.layer_norm', 'w2v_model.layer_norm': 'feature_projection.layer_norm', 'quantizer.weight_proj': 'quantizer.weight_proj', 'quantizer.vars': 'quantizer.codevectors', 'project_q': 'project_q', 'final_proj': 'project_hid', 'w2v_encoder.proj': 'lm_head', 'mask_emb': 'masked_spec_embed', } UpperCAmelCase_ = [ 'lm_head', 'quantizer.weight_proj', 'quantizer.codevectors', 'project_q', 'project_hid', ] def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Optional[Any] ): '''simple docstring''' for attribute in key.split(""".""" ): UpperCAmelCase__ = getattr(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) if weight_type is not None: UpperCAmelCase__ = getattr(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ).shape else: UpperCAmelCase__ = hf_pointer.shape assert hf_shape == value.shape, ( F'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be''' F''' {value.shape} for {full_name}''' ) if weight_type == "weight": UpperCAmelCase__ = value elif weight_type == "weight_g": UpperCAmelCase__ = value elif weight_type == "weight_v": UpperCAmelCase__ = value elif weight_type == "bias": UpperCAmelCase__ = value else: UpperCAmelCase__ = value logger.info(F'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.''' ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Dict ): '''simple docstring''' UpperCAmelCase__ = [] UpperCAmelCase__ = fairseq_model.state_dict() UpperCAmelCase__ = hf_model.feature_extractor UpperCAmelCase__ = hf_model.adapter for name, value in fairseq_dict.items(): UpperCAmelCase__ = False if "conv_layers" in name: load_conv_layer( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , hf_model.config.feat_extract_norm == """group""" , ) UpperCAmelCase__ = True elif any(x in name for x in ["""adaptor""", """w2v_encoder.proj.""", """w2v_proj_ln."""] ): load_adapter(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = True else: for key, mapped_key in MAPPING.items(): if key in name or key.split("""w2v_model.""" )[-1] == name.split(""".""" )[0]: UpperCAmelCase__ = True if "*" in mapped_key: UpperCAmelCase__ = name.split(SCREAMING_SNAKE_CASE__ )[0].split(""".""" )[-2] UpperCAmelCase__ = mapped_key.replace("""*""" , SCREAMING_SNAKE_CASE__ ) if "weight_g" in name: UpperCAmelCase__ = """weight_g""" elif "weight_v" in name: UpperCAmelCase__ = """weight_v""" elif "bias" in name: UpperCAmelCase__ = """bias""" elif "weight" in name: UpperCAmelCase__ = """weight""" else: UpperCAmelCase__ = None set_recursively(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) continue if not is_used: unused_weights.append(SCREAMING_SNAKE_CASE__ ) logger.warning(F'''Unused weights: {unused_weights}''' ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : str ): '''simple docstring''' UpperCAmelCase__ = full_name.split("""conv_layers.""" )[-1] UpperCAmelCase__ = name.split(""".""" ) UpperCAmelCase__ = int(items[0] ) UpperCAmelCase__ = int(items[1] ) if type_id == 0: if "bias" in name: assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, ( F'''{full_name} has size {value.shape}, but''' F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.''' ) UpperCAmelCase__ = value logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' ) elif "weight" in name: assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, ( F'''{full_name} has size {value.shape}, but''' F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.''' ) UpperCAmelCase__ = value logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' ) elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm): if "bias" in name: assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, ( F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was''' " found." ) UpperCAmelCase__ = value logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' ) elif "weight" in name: assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, ( F'''{full_name} has size {value.shape}, but''' F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.''' ) UpperCAmelCase__ = value logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' ) else: unused_weights.append(SCREAMING_SNAKE_CASE__ ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Dict ): '''simple docstring''' UpperCAmelCase__ = full_name.split("""adaptor.""" )[-1] UpperCAmelCase__ = name.split(""".""" ) if items[1].isdigit(): UpperCAmelCase__ = int(items[1] ) else: UpperCAmelCase__ = None if "adaptor" not in full_name: if "proj_ln" in full_name: # has to be layer norm if "bias" in name: assert ( value.shape == adapter.proj_layer_norm.bias.data.shape ), F'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found.''' UpperCAmelCase__ = value logger.info(F'''Adapter proj layer norm bias was initialized from {full_name}.''' ) if "weight" in name: assert ( value.shape == adapter.proj_layer_norm.weight.data.shape ), F'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found.''' UpperCAmelCase__ = value else: # has to be projection layer if "bias" in name: assert ( value.shape == adapter.proj.bias.data.shape ), F'''{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found.''' UpperCAmelCase__ = value logger.info(F'''Adapter proj layer bias was initialized from {full_name}.''' ) if "weight" in name: assert ( value.shape == adapter.proj.weight.data.shape ), F'''{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found.''' UpperCAmelCase__ = value logger.info(F'''Adapter proj layer weight was initialized from {full_name}.''' ) elif isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ): if "bias" in name: assert ( value.shape == adapter.layers[layer_id].conv.bias.data.shape ), F'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found.''' UpperCAmelCase__ = value logger.info(F'''Adapter layer {layer_id} bias was initialized from {full_name}.''' ) elif "weight" in name: assert ( value.shape == adapter.layers[layer_id].conv.weight.data.shape ), F'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found.''' UpperCAmelCase__ = value logger.info(F'''Adapter layer {layer_id} bias was initialized from {full_name}.''' ) else: unused_weights.append(SCREAMING_SNAKE_CASE__ ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : List[Any] ): '''simple docstring''' UpperCAmelCase__ , UpperCAmelCase__ = emb.weight.shape UpperCAmelCase__ = nn.Linear(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , bias=SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = emb.weight.data return lin_layer @torch.no_grad() def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : Optional[Any] , ): '''simple docstring''' UpperCAmelCase__ = WavaVecaConfig.from_pretrained( SCREAMING_SNAKE_CASE__ , add_adapter=SCREAMING_SNAKE_CASE__ , adapter_stride=SCREAMING_SNAKE_CASE__ , adapter_kernel_size=SCREAMING_SNAKE_CASE__ , use_auth_token=SCREAMING_SNAKE_CASE__ , output_hidden_size=SCREAMING_SNAKE_CASE__ , ) UpperCAmelCase__ = MBartConfig.from_pretrained(SCREAMING_SNAKE_CASE__ ) # load model UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = fairseq.checkpoint_utils.load_model_ensemble_and_task( [checkpoint_path] , arg_overrides={ """config_yaml""": config_yaml_path, """data""": """/""".join(dict_path.split("""/""" )[:-1] ), """w2v_path""": checkpoint_path, """load_pretrained_decoder_from""": None, } , ) UpperCAmelCase__ = model[0].eval() # load feature extractor UpperCAmelCase__ = WavaVecaFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE__ , use_auth_token=SCREAMING_SNAKE_CASE__ ) # set weights for wav2vec2 encoder UpperCAmelCase__ = WavaVecaModel(SCREAMING_SNAKE_CASE__ ) recursively_load_weights_wavaveca(model.encoder , SCREAMING_SNAKE_CASE__ ) # load decoder weights UpperCAmelCase__ = MBartForCausalLM(SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ , UpperCAmelCase__ = hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict() , strict=SCREAMING_SNAKE_CASE__ ) logger.warning(F'''The following keys are missing when loading the decoder weights: {missing_keys}''' ) logger.warning(F'''The following keys are unexpected when loading the decoder weights: {unexpected_keys}''' ) UpperCAmelCase__ = SpeechEncoderDecoderModel(encoder=SCREAMING_SNAKE_CASE__ , decoder=SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = False UpperCAmelCase__ = MBartaaTokenizer(SCREAMING_SNAKE_CASE__ ) tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = hf_wavavec.config.to_dict() UpperCAmelCase__ = tokenizer.pad_token_id UpperCAmelCase__ = tokenizer.bos_token_id UpperCAmelCase__ = tokenizer.eos_token_id UpperCAmelCase__ = """mbart50""" UpperCAmelCase__ = """wav2vec2""" UpperCAmelCase__ = tokenizer.eos_token_id UpperCAmelCase__ = 250004 UpperCAmelCase__ = tokenizer.eos_token_id UpperCAmelCase__ = SpeechEncoderDecoderConfig.from_dict(SCREAMING_SNAKE_CASE__ ) hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE__ ) feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE__ ) if __name__ == "__main__": UpperCAmelCase_ = argparse.ArgumentParser() parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.') parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint') parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model') parser.add_argument('--config_yaml_path', default=None, type=str, help='Path to yaml file of fine-tuned model') parser.add_argument( '--encoder_config_path', default='facebook/wav2vec2-xls-r-1b', type=str, help='Path to hf encoder wav2vec2 checkpoint config', ) parser.add_argument( '--decoder_config_path', default='facebook/mbart-large-50-one-to-many-mmt', type=str, help='Path to hf decoder checkpoint config', ) parser.add_argument('--add_adapter', default=True, type=bool, help='whethere to add model adapter layers') parser.add_argument('--adapter_stride', default=2, type=int, help='stride of adapter layers') parser.add_argument('--adapter_kernel_size', default=3, type=int, help='kernel size of adapter layers') parser.add_argument('--encoder_output_dim', default=1_0_2_4, type=int, help='encoder output dim') parser.add_argument('--start_token_id', default=2_5_0_0_0_4, type=int, help='`decoder_start_token_id` of model config') UpperCAmelCase_ = parser.parse_args() convert_wavaveca_checkpoint( args.checkpoint_path, args.pytorch_dump_folder_path, args.dict_path, args.config_yaml_path, encoder_config_path=args.encoder_config_path, decoder_config_path=args.decoder_config_path, add_adapter=args.add_adapter, adapter_kernel_size=args.adapter_kernel_size, adapter_stride=args.adapter_stride, decoder_start_token_id=args.start_token_id, encoder_output_dim=args.encoder_output_dim, )

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'''simple docstring''' import itertools import os from collections import Counter, defaultdict from concurrent.futures import ThreadPoolExecutor, as_completed import numpy as np import datasets from .execute import check_correctness UpperCAmelCase_ = '\\n@misc{chen2021evaluating,\n title={Evaluating Large Language Models Trained on Code},\n author={Mark Chen and Jerry Tworek and Heewoo Jun and Qiming Yuan \\nand Henrique Ponde de Oliveira Pinto and Jared Kaplan and Harri Edwards \\nand Yuri Burda and Nicholas Joseph and Greg Brockman and Alex Ray \\nand Raul Puri and Gretchen Krueger and Michael Petrov and Heidy Khlaaf \\nand Girish Sastry and Pamela Mishkin and Brooke Chan and Scott Gray \\nand Nick Ryder and Mikhail Pavlov and Alethea Power and Lukasz Kaiser \\nand Mohammad Bavarian and Clemens Winter and Philippe Tillet \\nand Felipe Petroski Such and Dave Cummings and Matthias Plappert \\nand Fotios Chantzis and Elizabeth Barnes and Ariel Herbert-Voss \\nand William Hebgen Guss and Alex Nichol and Alex Paino and Nikolas Tezak \\nand Jie Tang and Igor Babuschkin and Suchir Balaji and Shantanu Jain \\nand William Saunders and Christopher Hesse and Andrew N. Carr \\nand Jan Leike and Josh Achiam and Vedant Misra and Evan Morikawa \\nand Alec Radford and Matthew Knight and Miles Brundage and Mira Murati \\nand Katie Mayer and Peter Welinder and Bob McGrew and Dario Amodei \\nand Sam McCandlish and Ilya Sutskever and Wojciech Zaremba},\n year={2021},\n eprint={2107.03374},\n archivePrefix={arXiv},\n primaryClass={cs.LG}\n}\n' UpperCAmelCase_ = '\\nThis metric implements the evaluation harness for the HumanEval problem solving dataset\ndescribed in the paper "Evaluating Large Language Models Trained on Code"\n(https://arxiv.org/abs/2107.03374).\n' UpperCAmelCase_ = '\nCalculates how good are predictions given some references, using certain scores\nArgs:\n predictions: list of candidates to evaluate. Each candidates should be a list\n of strings with several code candidates to solve the problem.\n references: a list with a test for each prediction. Each test should evaluate the\n correctness of a code candidate.\n k: number of code candidates to consider in the evaluation (Default: [1, 10, 100])\n num_workers: number of workers used to evaluate the canidate programs (Default: 4).\n timeout:\nReturns:\n pass_at_k: dict with pass rates for each k\n results: dict with granular results of each unittest\nExamples:\n >>> code_eval = datasets.load_metric("code_eval")\n >>> test_cases = ["assert add(2,3)==5"]\n >>> candidates = [["def add(a,b): return a*b", "def add(a, b): return a+b"]]\n >>> pass_at_k, results = code_eval.compute(references=test_cases, predictions=candidates, k=[1, 2])\n >>> print(pass_at_k)\n {\'pass@1\': 0.5, \'pass@2\': 1.0}\n' UpperCAmelCase_ = '\n################################################################################\n !!!WARNING!!!\n################################################################################\nThe "code_eval" metric executes untrusted model-generated code in Python.\nAlthough it is highly unlikely that model-generated code will do something\novertly malicious in response to this test suite, model-generated code may act\ndestructively due to a lack of model capability or alignment.\nUsers are strongly encouraged to sandbox this evaluation suite so that it\ndoes not perform destructive actions on their host or network. For more\ninformation on how OpenAI sandboxes its code, see the paper "Evaluating Large\nLanguage Models Trained on Code" (https://arxiv.org/abs/2107.03374).\n\nOnce you have read this disclaimer and taken appropriate precautions,\nset the environment variable HF_ALLOW_CODE_EVAL="1". Within Python you can to this\nwith:\n\n>>> import os\n>>> os.environ["HF_ALLOW_CODE_EVAL"] = "1"\n\n################################################################################\\n' UpperCAmelCase_ = 'The MIT License\n\nCopyright (c) OpenAI (https://openai.com)\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the "Software"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in\nall copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\nTHE SOFTWARE.' @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION ) class lowerCAmelCase_ ( datasets.Metric ): '''simple docstring''' def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ): """simple docstring""" return datasets.MetricInfo( # This is the description that will appear on the metrics page. description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features( { """predictions""": datasets.Sequence(datasets.Value("""string""" ) ), """references""": datasets.Value("""string""" ), } ) , homepage="""https://github.com/openai/human-eval""" , codebase_urls=["""https://github.com/openai/human-eval"""] , reference_urls=["""https://github.com/openai/human-eval"""] , license=_LICENSE , ) def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : List[str]=[1, 10, 1_00] , _UpperCAmelCase : Optional[Any]=4 , _UpperCAmelCase : Any=3.0 ): """simple docstring""" if os.getenv("""HF_ALLOW_CODE_EVAL""" , 0 ) != "1": raise ValueError(_WARNING ) if os.name == "nt": raise NotImplementedError("""This metric is currently not supported on Windows.""" ) with ThreadPoolExecutor(max_workers=_UpperCAmelCase ) as executor: UpperCAmelCase__ = [] UpperCAmelCase__ = Counter() UpperCAmelCase__ = 0 UpperCAmelCase__ = defaultdict(_UpperCAmelCase ) for task_id, (candidates, test_case) in enumerate(zip(_UpperCAmelCase , _UpperCAmelCase ) ): for candidate in candidates: UpperCAmelCase__ = candidate + """\n""" + test_case UpperCAmelCase__ = (test_program, timeout, task_id, completion_id[task_id]) UpperCAmelCase__ = executor.submit(_UpperCAmelCase , *_UpperCAmelCase ) futures.append(_UpperCAmelCase ) completion_id[task_id] += 1 n_samples += 1 for future in as_completed(_UpperCAmelCase ): UpperCAmelCase__ = future.result() results[result["task_id"]].append((result["""completion_id"""], result) ) UpperCAmelCase__ , UpperCAmelCase__ = [], [] for result in results.values(): result.sort() UpperCAmelCase__ = [r[1]["""passed"""] for r in result] total.append(len(_UpperCAmelCase ) ) correct.append(sum(_UpperCAmelCase ) ) UpperCAmelCase__ = np.array(_UpperCAmelCase ) UpperCAmelCase__ = np.array(_UpperCAmelCase ) UpperCAmelCase__ = k UpperCAmelCase__ = {f'''pass@{k}''': estimate_pass_at_k(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ).mean() for k in ks if (total >= k).all()} return pass_at_k, results def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : Optional[Any] ): '''simple docstring''' def estimator(SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ) -> float: if n - c < k: return 1.0 return 1.0 - np.prod(1.0 - k / np.arange(n - c + 1 , n + 1 ) ) if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ): UpperCAmelCase__ = itertools.repeat(SCREAMING_SNAKE_CASE__ , len(SCREAMING_SNAKE_CASE__ ) ) else: assert len(SCREAMING_SNAKE_CASE__ ) == len(SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = iter(SCREAMING_SNAKE_CASE__ ) return np.array([estimator(int(SCREAMING_SNAKE_CASE__ ) , int(SCREAMING_SNAKE_CASE__ ) , SCREAMING_SNAKE_CASE__ ) for n, c in zip(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )] )

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'''simple docstring''' from timeit import timeit UpperCAmelCase_ = { 'MALAYALAM': True, 'String': False, 'rotor': True, 'level': True, 'A': True, 'BB': True, 'ABC': False, 'amanaplanacanalpanama': True, # "a man a plan a canal panama" } # Ensure our test data is valid assert all((key == key[::-1]) is value for key, value in test_data.items()) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str ): '''simple docstring''' UpperCAmelCase__ = 0 UpperCAmelCase__ = len(SCREAMING_SNAKE_CASE__ ) - 1 while start_i < end_i: if s[start_i] == s[end_i]: start_i += 1 end_i -= 1 else: return False return True def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str ): '''simple docstring''' UpperCAmelCase__ = len(SCREAMING_SNAKE_CASE__ ) // 2 UpperCAmelCase__ = len(SCREAMING_SNAKE_CASE__ ) # We need to traverse till half of the length of string # as we can get access of the i'th last element from # i'th index. # eg: [0,1,2,3,4,5] => 4th index can be accessed # with the help of 1st index (i==n-i-1) # where n is length of string return all(s[i] == s[n - i - 1] for i in range(SCREAMING_SNAKE_CASE__ ) ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str ): '''simple docstring''' if len(SCREAMING_SNAKE_CASE__ ) <= 2: return True if s[0] == s[len(SCREAMING_SNAKE_CASE__ ) - 1]: return is_palindrome_recursive(s[1:-1] ) else: return False def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str ): '''simple docstring''' return s == s[::-1] def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str ): '''simple docstring''' UpperCAmelCase__ = F'''all({name}(key) is value for key, value in test_data.items())''' UpperCAmelCase__ = F'''from __main__ import test_data, {name}''' UpperCAmelCase__ = 500000 UpperCAmelCase__ = timeit(stmt=SCREAMING_SNAKE_CASE__ , setup=SCREAMING_SNAKE_CASE__ , number=SCREAMING_SNAKE_CASE__ ) print(F'''{name:<35} finished {number:,} runs in {result:.5f} seconds''' ) if __name__ == "__main__": for key, value in test_data.items(): assert is_palindrome(key) is is_palindrome_recursive(key) assert is_palindrome(key) is is_palindrome_slice(key) print(f"{key:21} {value}") print('a man a plan a canal panama') # finished 500,000 runs in 0.46793 seconds benchmark_function('is_palindrome_slice') # finished 500,000 runs in 0.85234 seconds benchmark_function('is_palindrome') # finished 500,000 runs in 1.32028 seconds benchmark_function('is_palindrome_recursive') # finished 500,000 runs in 2.08679 seconds benchmark_function('is_palindrome_traversal')

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'''simple docstring''' import math def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int ): '''simple docstring''' assert isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) and ( number >= 0 ), "'number' must been an int and positive" if 1 < number < 4: # 2 and 3 are primes return True elif number < 2 or not number % 2: # Negatives, 0, 1 and all even numbers are not primes return False UpperCAmelCase__ = range(3 , int(math.sqrt(SCREAMING_SNAKE_CASE__ ) + 1 ) , 2 ) return not any(not number % i for i in odd_numbers ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : List[str]=1 , **SCREAMING_SNAKE_CASE__ : List[str] ): '''simple docstring''' UpperCAmelCase__ = factor * value UpperCAmelCase__ = value while not is_prime(SCREAMING_SNAKE_CASE__ ): value += 1 if not ("desc" in kwargs and kwargs["desc"] is True) else -1 if value == first_value_val: return next_prime(value + 1 , **SCREAMING_SNAKE_CASE__ ) return value

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'''simple docstring''' import argparse from pathlib import Path import fairseq import torch from fairseq.models.xmod import XMODModel as FairseqXmodModel from packaging import version from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification from transformers.utils import logging if version.parse(fairseq.__version__) < version.parse('0.12.2'): raise Exception('requires fairseq >= 0.12.2') if version.parse(fairseq.__version__) > version.parse('2'): raise Exception('requires fairseq < v2') logging.set_verbosity_info() UpperCAmelCase_ = logging.get_logger(__name__) UpperCAmelCase_ = 'Hello, World!' UpperCAmelCase_ = 'en_XX' def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : bool ): '''simple docstring''' UpperCAmelCase__ = Path("""data_bin""" ) UpperCAmelCase__ = FairseqXmodModel.from_pretrained( model_name_or_path=str(Path(SCREAMING_SNAKE_CASE__ ).parent ) , checkpoint_file=Path(SCREAMING_SNAKE_CASE__ ).name , _name="""xmod_base""" , arch="""xmod_base""" , task="""multilingual_masked_lm""" , data_name_or_path=str(SCREAMING_SNAKE_CASE__ ) , bpe="""sentencepiece""" , sentencepiece_model=str(Path(SCREAMING_SNAKE_CASE__ ).parent / """sentencepiece.bpe.model""" ) , src_dict=str(data_dir / """dict.txt""" ) , ) xmod.eval() # disable dropout print(SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = xmod.model.encoder.sentence_encoder UpperCAmelCase__ = XmodConfig( vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings , hidden_size=xmod.cfg.model.encoder_embed_dim , num_hidden_layers=xmod.cfg.model.encoder_layers , num_attention_heads=xmod.cfg.model.encoder_attention_heads , intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim , max_position_embeddings=514 , type_vocab_size=1 , layer_norm_eps=1e-5 , pre_norm=xmod.cfg.model.encoder_normalize_before , adapter_reduction_factor=getattr(xmod.cfg.model , """bottleneck""" , 2 ) , adapter_layer_norm=xmod.cfg.model.adapter_layer_norm , adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm , ln_before_adapter=xmod.cfg.model.ln_before_adapter , languages=xmod.cfg.model.languages , ) if classification_head: UpperCAmelCase__ = xmod.model.classification_heads["""mnli"""].out_proj.weight.shape[0] print("""Our X-MOD config:""" , SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = XmodForSequenceClassification(SCREAMING_SNAKE_CASE__ ) if classification_head else XmodForMaskedLM(SCREAMING_SNAKE_CASE__ ) model.eval() # Now let's copy all the weights. # Embeddings UpperCAmelCase__ = xmod_sent_encoder.embed_tokens.weight UpperCAmelCase__ = xmod_sent_encoder.embed_positions.weight UpperCAmelCase__ = torch.zeros_like( model.roberta.embeddings.token_type_embeddings.weight ) # just zero them out b/c xmod doesn't use them. UpperCAmelCase__ = xmod_sent_encoder.layernorm_embedding.weight UpperCAmelCase__ = xmod_sent_encoder.layernorm_embedding.bias for i in range(config.num_hidden_layers ): # Encoder: start of layer UpperCAmelCase__ = model.roberta.encoder.layer[i] UpperCAmelCase__ = xmod_sent_encoder.layers[i] # self attention UpperCAmelCase__ = layer.attention.self if not ( xmod_layer.self_attn.k_proj.weight.data.shape == xmod_layer.self_attn.q_proj.weight.data.shape == xmod_layer.self_attn.v_proj.weight.data.shape == torch.Size((config.hidden_size, config.hidden_size) ) ): raise AssertionError("""Dimensions of self-attention weights do not match.""" ) UpperCAmelCase__ = xmod_layer.self_attn.q_proj.weight UpperCAmelCase__ = xmod_layer.self_attn.q_proj.bias UpperCAmelCase__ = xmod_layer.self_attn.k_proj.weight UpperCAmelCase__ = xmod_layer.self_attn.k_proj.bias UpperCAmelCase__ = xmod_layer.self_attn.v_proj.weight UpperCAmelCase__ = xmod_layer.self_attn.v_proj.bias # self-attention output UpperCAmelCase__ = layer.attention.output if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape: raise AssertionError("""Dimensions of self-attention output weights do not match.""" ) UpperCAmelCase__ = xmod_layer.self_attn.out_proj.weight UpperCAmelCase__ = xmod_layer.self_attn.out_proj.bias UpperCAmelCase__ = xmod_layer.self_attn_layer_norm.weight UpperCAmelCase__ = xmod_layer.self_attn_layer_norm.bias # intermediate UpperCAmelCase__ = layer.intermediate if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape: raise AssertionError("""Dimensions of intermediate weights do not match.""" ) UpperCAmelCase__ = xmod_layer.fca.weight UpperCAmelCase__ = xmod_layer.fca.bias # output UpperCAmelCase__ = layer.output if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape: raise AssertionError("""Dimensions of feed-forward weights do not match.""" ) UpperCAmelCase__ = xmod_layer.fca.weight UpperCAmelCase__ = xmod_layer.fca.bias UpperCAmelCase__ = xmod_layer.final_layer_norm.weight UpperCAmelCase__ = xmod_layer.final_layer_norm.bias if bert_output.adapter_layer_norm is not None: UpperCAmelCase__ = xmod_layer.adapter_layer_norm.weight UpperCAmelCase__ = xmod_layer.adapter_layer_norm.bias if sorted(bert_output.adapter_modules.keys() ) != sorted(xmod_layer.adapter_modules.keys() ): raise AssertionError("""Lists of language adapters do not match.""" ) for lang_code, adapter in xmod_layer.adapter_modules.items(): UpperCAmelCase__ = bert_output.adapter_modules[lang_code] UpperCAmelCase__ = xmod_layer.adapter_modules[lang_code] UpperCAmelCase__ = from_adapter.fca.weight UpperCAmelCase__ = from_adapter.fca.bias UpperCAmelCase__ = from_adapter.fca.weight UpperCAmelCase__ = from_adapter.fca.bias # end of layer if xmod_sent_encoder.layer_norm is not None: UpperCAmelCase__ = xmod_sent_encoder.layer_norm.weight UpperCAmelCase__ = xmod_sent_encoder.layer_norm.bias if classification_head: UpperCAmelCase__ = xmod.model.classification_heads["""mnli"""].dense.weight UpperCAmelCase__ = xmod.model.classification_heads["""mnli"""].dense.bias UpperCAmelCase__ = xmod.model.classification_heads["""mnli"""].out_proj.weight UpperCAmelCase__ = xmod.model.classification_heads["""mnli"""].out_proj.bias else: # LM Head UpperCAmelCase__ = xmod.model.encoder.lm_head.dense.weight UpperCAmelCase__ = xmod.model.encoder.lm_head.dense.bias UpperCAmelCase__ = xmod.model.encoder.lm_head.layer_norm.weight UpperCAmelCase__ = xmod.model.encoder.lm_head.layer_norm.bias UpperCAmelCase__ = xmod.model.encoder.lm_head.weight UpperCAmelCase__ = xmod.model.encoder.lm_head.bias # Let's check that we get the same results. UpperCAmelCase__ = xmod.encode(SCREAMING_SNAKE_CASE__ ).unsqueeze(0 ) # batch of size 1 model.roberta.set_default_language(SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = model(SCREAMING_SNAKE_CASE__ )[0] if classification_head: UpperCAmelCase__ = xmod.model.classification_heads["""mnli"""](xmod.extract_features(SCREAMING_SNAKE_CASE__ ) ) else: UpperCAmelCase__ = xmod.model(SCREAMING_SNAKE_CASE__ , lang_id=[SAMPLE_LANGUAGE] )[0] print(our_output.shape , their_output.shape ) UpperCAmelCase__ = torch.max(torch.abs(our_output - their_output ) ).item() print(F'''max_absolute_diff = {max_absolute_diff}''' ) # ~ 1e-7 UpperCAmelCase__ = torch.allclose(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , atol=1e-3 ) print("""Do both models output the same tensors?""" , """🔥""" if success else """💩""" ) if not success: raise Exception("""Something went wRoNg""" ) Path(SCREAMING_SNAKE_CASE__ ).mkdir(parents=SCREAMING_SNAKE_CASE__ , exist_ok=SCREAMING_SNAKE_CASE__ ) print(F'''Saving model to {pytorch_dump_folder_path}''' ) model.save_pretrained(SCREAMING_SNAKE_CASE__ ) if __name__ == "__main__": UpperCAmelCase_ = argparse.ArgumentParser() # Required parameters parser.add_argument( '--xmod_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.' ) parser.add_argument( '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.' ) parser.add_argument( '--classification_head', action='store_true', help='Whether to convert a final classification head.' ) UpperCAmelCase_ = parser.parse_args() convert_xmod_checkpoint_to_pytorch( args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head )

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'''simple docstring''' import string from math import logaa def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : str ): '''simple docstring''' UpperCAmelCase__ = document.translate( str.maketrans("""""" , """""" , string.punctuation ) ).replace("""\n""" , """""" ) UpperCAmelCase__ = document_without_punctuation.split(""" """ ) # word tokenization return len([word for word in tokenize_document if word.lower() == term.lower()] ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : str ): '''simple docstring''' UpperCAmelCase__ = corpus.lower().translate( str.maketrans("""""" , """""" , string.punctuation ) ) # strip all punctuation and replace it with '' UpperCAmelCase__ = corpus_without_punctuation.split("""\n""" ) UpperCAmelCase__ = term.lower() return (len([doc for doc in docs if term in doc] ), len(SCREAMING_SNAKE_CASE__ )) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : Tuple=False ): '''simple docstring''' if smoothing: if n == 0: raise ValueError("""log10(0) is undefined.""" ) return round(1 + logaa(n / (1 + df) ) , 3 ) if df == 0: raise ZeroDivisionError("""df must be > 0""" ) elif n == 0: raise ValueError("""log10(0) is undefined.""" ) return round(logaa(n / df ) , 3 ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ): '''simple docstring''' return round(tf * idf , 3 )

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'''simple docstring''' from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments def _UpperCamelCase ( ): '''simple docstring''' UpperCAmelCase__ = HfArgumentParser(SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = parser.parse_args_into_dataclasses()[0] UpperCAmelCase__ = TensorFlowBenchmark(args=SCREAMING_SNAKE_CASE__ ) try: UpperCAmelCase__ = parser.parse_args_into_dataclasses()[0] except ValueError as e: UpperCAmelCase__ = """Arg --no_{0} is no longer used, please use --no-{0} instead.""" UpperCAmelCase__ = """ """.join(str(SCREAMING_SNAKE_CASE__ ).split(""" """ )[:-1] ) UpperCAmelCase__ = """""" UpperCAmelCase__ = eval(str(SCREAMING_SNAKE_CASE__ ).split(""" """ )[-1] ) UpperCAmelCase__ = [] for arg in depreciated_args: # arg[2:] removes '--' if arg[2:] in TensorFlowBenchmark.deprecated_args: # arg[5:] removes '--no_' full_error_msg += arg_error_msg.format(arg[5:] ) else: wrong_args.append(SCREAMING_SNAKE_CASE__ ) if len(SCREAMING_SNAKE_CASE__ ) > 0: UpperCAmelCase__ = full_error_msg + begin_error_msg + str(SCREAMING_SNAKE_CASE__ ) raise ValueError(SCREAMING_SNAKE_CASE__ ) benchmark.run() if __name__ == "__main__": main()

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'''simple docstring''' import argparse import torch from transformers import BertForMaskedLM if __name__ == "__main__": UpperCAmelCase_ = argparse.ArgumentParser( description=( 'Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned' ' Distillation' ) ) parser.add_argument('--model_type', default='bert', choices=['bert']) parser.add_argument('--model_name', default='bert-base-uncased', type=str) parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_bert-base-uncased_0247911.pth', type=str) parser.add_argument('--vocab_transform', action='store_true') UpperCAmelCase_ = parser.parse_args() if args.model_type == "bert": UpperCAmelCase_ = BertForMaskedLM.from_pretrained(args.model_name) UpperCAmelCase_ = 'bert' else: raise ValueError('args.model_type should be "bert".') UpperCAmelCase_ = model.state_dict() UpperCAmelCase_ = {} for w in ["word_embeddings", "position_embeddings"]: UpperCAmelCase_ = state_dict[f"{prefix}.embeddings.{w}.weight"] for w in ["weight", "bias"]: UpperCAmelCase_ = state_dict[f"{prefix}.embeddings.LayerNorm.{w}"] UpperCAmelCase_ = 0 for teacher_idx in [0, 2, 4, 7, 9, 1_1]: for w in ["weight", "bias"]: UpperCAmelCase_ = state_dict[ f"{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}" ] UpperCAmelCase_ = state_dict[ f"{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}" ] UpperCAmelCase_ = state_dict[ f"{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}" ] UpperCAmelCase_ = state_dict[ f"{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}" ] UpperCAmelCase_ = state_dict[ f"{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}" ] UpperCAmelCase_ = state_dict[ f"{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}" ] UpperCAmelCase_ = state_dict[ f"{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}" ] UpperCAmelCase_ = state_dict[ f"{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}" ] std_idx += 1 UpperCAmelCase_ = state_dict['cls.predictions.decoder.weight'] UpperCAmelCase_ = state_dict['cls.predictions.bias'] if args.vocab_transform: for w in ["weight", "bias"]: UpperCAmelCase_ = state_dict[f"cls.predictions.transform.dense.{w}"] UpperCAmelCase_ = state_dict[f"cls.predictions.transform.LayerNorm.{w}"] print(f"N layers selected for distillation: {std_idx}") print(f"Number of params transferred for distillation: {len(compressed_sd.keys())}") print(f"Save transferred checkpoint to {args.dump_checkpoint}.") torch.save(compressed_sd, args.dump_checkpoint)

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'''simple docstring''' import warnings from ...utils import logging from .image_processing_deit import DeiTImageProcessor UpperCAmelCase_ = logging.get_logger(__name__) class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' def __init__( self : List[str] , *_UpperCAmelCase : Optional[int] , **_UpperCAmelCase : Optional[Any] ): """simple docstring""" warnings.warn( """The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please""" """ use DeiTImageProcessor instead.""" , _UpperCAmelCase , ) super().__init__(*_UpperCAmelCase , **_UpperCAmelCase )

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'''simple docstring''' import tempfile import torch from diffusers import PNDMScheduler from .test_schedulers import SchedulerCommonTest class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' lowerCAmelCase_ : Union[str, Any] = (PNDMScheduler,) lowerCAmelCase_ : Optional[int] = (("""num_inference_steps""", 50),) def SCREAMING_SNAKE_CASE__ ( self : List[Any] , **_UpperCAmelCase : Optional[int] ): """simple docstring""" UpperCAmelCase__ = { """num_train_timesteps""": 10_00, """beta_start""": 0.0001, """beta_end""": 0.02, """beta_schedule""": """linear""", } config.update(**_UpperCAmelCase ) return config def SCREAMING_SNAKE_CASE__ ( self : List[Any] , _UpperCAmelCase : Tuple=0 , **_UpperCAmelCase : List[str] ): """simple docstring""" UpperCAmelCase__ = dict(self.forward_default_kwargs ) UpperCAmelCase__ = kwargs.pop("""num_inference_steps""" , _UpperCAmelCase ) UpperCAmelCase__ = self.dummy_sample UpperCAmelCase__ = 0.1 * sample UpperCAmelCase__ = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05] for scheduler_class in self.scheduler_classes: UpperCAmelCase__ = self.get_scheduler_config(**_UpperCAmelCase ) UpperCAmelCase__ = scheduler_class(**_UpperCAmelCase ) scheduler.set_timesteps(_UpperCAmelCase ) # copy over dummy past residuals UpperCAmelCase__ = dummy_past_residuals[:] with tempfile.TemporaryDirectory() as tmpdirname: scheduler.save_config(_UpperCAmelCase ) UpperCAmelCase__ = scheduler_class.from_pretrained(_UpperCAmelCase ) new_scheduler.set_timesteps(_UpperCAmelCase ) # copy over dummy past residuals UpperCAmelCase__ = dummy_past_residuals[:] UpperCAmelCase__ = scheduler.step_prk(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample UpperCAmelCase__ = new_scheduler.step_prk(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical" UpperCAmelCase__ = scheduler.step_plms(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample UpperCAmelCase__ = new_scheduler.step_plms(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical" def SCREAMING_SNAKE_CASE__ ( self : List[str] ): """simple docstring""" pass def SCREAMING_SNAKE_CASE__ ( self : int , _UpperCAmelCase : Union[str, Any]=0 , **_UpperCAmelCase : Optional[int] ): """simple docstring""" UpperCAmelCase__ = dict(self.forward_default_kwargs ) UpperCAmelCase__ = kwargs.pop("""num_inference_steps""" , _UpperCAmelCase ) UpperCAmelCase__ = self.dummy_sample UpperCAmelCase__ = 0.1 * sample UpperCAmelCase__ = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05] for scheduler_class in self.scheduler_classes: UpperCAmelCase__ = self.get_scheduler_config() UpperCAmelCase__ = scheduler_class(**_UpperCAmelCase ) scheduler.set_timesteps(_UpperCAmelCase ) # copy over dummy past residuals (must be after setting timesteps) UpperCAmelCase__ = dummy_past_residuals[:] with tempfile.TemporaryDirectory() as tmpdirname: scheduler.save_config(_UpperCAmelCase ) UpperCAmelCase__ = scheduler_class.from_pretrained(_UpperCAmelCase ) # copy over dummy past residuals new_scheduler.set_timesteps(_UpperCAmelCase ) # copy over dummy past residual (must be after setting timesteps) UpperCAmelCase__ = dummy_past_residuals[:] UpperCAmelCase__ = scheduler.step_prk(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample UpperCAmelCase__ = new_scheduler.step_prk(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical" UpperCAmelCase__ = scheduler.step_plms(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample UpperCAmelCase__ = new_scheduler.step_plms(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical" def SCREAMING_SNAKE_CASE__ ( self : int , **_UpperCAmelCase : Tuple ): """simple docstring""" UpperCAmelCase__ = self.scheduler_classes[0] UpperCAmelCase__ = self.get_scheduler_config(**_UpperCAmelCase ) UpperCAmelCase__ = scheduler_class(**_UpperCAmelCase ) UpperCAmelCase__ = 10 UpperCAmelCase__ = self.dummy_model() UpperCAmelCase__ = self.dummy_sample_deter scheduler.set_timesteps(_UpperCAmelCase ) for i, t in enumerate(scheduler.prk_timesteps ): UpperCAmelCase__ = model(_UpperCAmelCase , _UpperCAmelCase ) UpperCAmelCase__ = scheduler.step_prk(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ).prev_sample for i, t in enumerate(scheduler.plms_timesteps ): UpperCAmelCase__ = model(_UpperCAmelCase , _UpperCAmelCase ) UpperCAmelCase__ = scheduler.step_plms(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ).prev_sample return sample def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ): """simple docstring""" UpperCAmelCase__ = dict(self.forward_default_kwargs ) UpperCAmelCase__ = kwargs.pop("""num_inference_steps""" , _UpperCAmelCase ) for scheduler_class in self.scheduler_classes: UpperCAmelCase__ = self.get_scheduler_config() UpperCAmelCase__ = scheduler_class(**_UpperCAmelCase ) UpperCAmelCase__ = self.dummy_sample UpperCAmelCase__ = 0.1 * sample if num_inference_steps is not None and hasattr(_UpperCAmelCase , """set_timesteps""" ): scheduler.set_timesteps(_UpperCAmelCase ) elif num_inference_steps is not None and not hasattr(_UpperCAmelCase , """set_timesteps""" ): UpperCAmelCase__ = num_inference_steps # copy over dummy past residuals (must be done after set_timesteps) UpperCAmelCase__ = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05] UpperCAmelCase__ = dummy_past_residuals[:] UpperCAmelCase__ = scheduler.step_prk(_UpperCAmelCase , 0 , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample UpperCAmelCase__ = scheduler.step_prk(_UpperCAmelCase , 1 , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample self.assertEqual(output_a.shape , sample.shape ) self.assertEqual(output_a.shape , output_a.shape ) UpperCAmelCase__ = scheduler.step_plms(_UpperCAmelCase , 0 , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample UpperCAmelCase__ = scheduler.step_plms(_UpperCAmelCase , 1 , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample self.assertEqual(output_a.shape , sample.shape ) self.assertEqual(output_a.shape , output_a.shape ) def SCREAMING_SNAKE_CASE__ ( self : int ): """simple docstring""" for timesteps in [1_00, 10_00]: self.check_over_configs(num_train_timesteps=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ): """simple docstring""" for steps_offset in [0, 1]: self.check_over_configs(steps_offset=_UpperCAmelCase ) UpperCAmelCase__ = self.scheduler_classes[0] UpperCAmelCase__ = self.get_scheduler_config(steps_offset=1 ) UpperCAmelCase__ = scheduler_class(**_UpperCAmelCase ) scheduler.set_timesteps(10 ) assert torch.equal( scheduler.timesteps , torch.LongTensor( [9_01, 8_51, 8_51, 8_01, 8_01, 7_51, 7_51, 7_01, 7_01, 6_51, 6_51, 6_01, 6_01, 5_01, 4_01, 3_01, 2_01, 1_01, 1] ) , ) def SCREAMING_SNAKE_CASE__ ( self : List[Any] ): """simple docstring""" for beta_start, beta_end in zip([0.0001, 0.001] , [0.002, 0.02] ): self.check_over_configs(beta_start=_UpperCAmelCase , beta_end=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ): """simple docstring""" for schedule in ["linear", "squaredcos_cap_v2"]: self.check_over_configs(beta_schedule=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ): """simple docstring""" for prediction_type in ["epsilon", "v_prediction"]: self.check_over_configs(prediction_type=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : str ): """simple docstring""" for t in [1, 5, 10]: self.check_over_forward(time_step=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Any ): """simple docstring""" for t, num_inference_steps in zip([1, 5, 10] , [10, 50, 1_00] ): self.check_over_forward(num_inference_steps=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : List[str] ): """simple docstring""" UpperCAmelCase__ = 27 for scheduler_class in self.scheduler_classes: UpperCAmelCase__ = self.dummy_sample UpperCAmelCase__ = 0.1 * sample UpperCAmelCase__ = self.get_scheduler_config() UpperCAmelCase__ = scheduler_class(**_UpperCAmelCase ) scheduler.set_timesteps(_UpperCAmelCase ) # before power of 3 fix, would error on first step, so we only need to do two for i, t in enumerate(scheduler.prk_timesteps[:2] ): UpperCAmelCase__ = scheduler.step_prk(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ).prev_sample def SCREAMING_SNAKE_CASE__ ( self : Dict ): """simple docstring""" with self.assertRaises(_UpperCAmelCase ): UpperCAmelCase__ = self.scheduler_classes[0] UpperCAmelCase__ = self.get_scheduler_config() UpperCAmelCase__ = scheduler_class(**_UpperCAmelCase ) scheduler.step_plms(self.dummy_sample , 1 , self.dummy_sample ).prev_sample def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ): """simple docstring""" UpperCAmelCase__ = self.full_loop() UpperCAmelCase__ = torch.sum(torch.abs(_UpperCAmelCase ) ) UpperCAmelCase__ = torch.mean(torch.abs(_UpperCAmelCase ) ) assert abs(result_sum.item() - 198.1318 ) < 1E-2 assert abs(result_mean.item() - 0.2580 ) < 1E-3 def SCREAMING_SNAKE_CASE__ ( self : Dict ): """simple docstring""" UpperCAmelCase__ = self.full_loop(prediction_type="""v_prediction""" ) UpperCAmelCase__ = torch.sum(torch.abs(_UpperCAmelCase ) ) UpperCAmelCase__ = torch.mean(torch.abs(_UpperCAmelCase ) ) assert abs(result_sum.item() - 67.3986 ) < 1E-2 assert abs(result_mean.item() - 0.0878 ) < 1E-3 def SCREAMING_SNAKE_CASE__ ( self : Any ): """simple docstring""" UpperCAmelCase__ = self.full_loop(set_alpha_to_one=_UpperCAmelCase , beta_start=0.01 ) UpperCAmelCase__ = torch.sum(torch.abs(_UpperCAmelCase ) ) UpperCAmelCase__ = torch.mean(torch.abs(_UpperCAmelCase ) ) assert abs(result_sum.item() - 230.0399 ) < 1E-2 assert abs(result_mean.item() - 0.2995 ) < 1E-3 def SCREAMING_SNAKE_CASE__ ( self : int ): """simple docstring""" UpperCAmelCase__ = self.full_loop(set_alpha_to_one=_UpperCAmelCase , beta_start=0.01 ) UpperCAmelCase__ = torch.sum(torch.abs(_UpperCAmelCase ) ) UpperCAmelCase__ = torch.mean(torch.abs(_UpperCAmelCase ) ) assert abs(result_sum.item() - 186.9482 ) < 1E-2 assert abs(result_mean.item() - 0.2434 ) < 1E-3

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'''simple docstring''' def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ): '''simple docstring''' return int((input_a, input_a).count(0 ) != 0 ) def _UpperCamelCase ( ): '''simple docstring''' assert nand_gate(0 , 0 ) == 1 assert nand_gate(0 , 1 ) == 1 assert nand_gate(1 , 0 ) == 1 assert nand_gate(1 , 1 ) == 0 if __name__ == "__main__": print(nand_gate(0, 0)) print(nand_gate(0, 1)) print(nand_gate(1, 0)) print(nand_gate(1, 1))

346

'''simple docstring''' from ...configuration_utils import PretrainedConfig from ...utils import logging UpperCAmelCase_ = logging.get_logger(__name__) UpperCAmelCase_ = { 'google/vivit-b-16x2-kinetics400': ( 'https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json' ), # See all Vivit models at https://huggingface.co/models?filter=vivit } class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' lowerCAmelCase_ : Optional[int] = """vivit""" def __init__( self : List[str] , _UpperCAmelCase : List[Any]=2_24 , _UpperCAmelCase : List[str]=32 , _UpperCAmelCase : Any=[2, 16, 16] , _UpperCAmelCase : int=3 , _UpperCAmelCase : Optional[Any]=7_68 , _UpperCAmelCase : Union[str, Any]=12 , _UpperCAmelCase : Dict=12 , _UpperCAmelCase : Optional[Any]=30_72 , _UpperCAmelCase : Optional[int]="gelu_fast" , _UpperCAmelCase : Union[str, Any]=0.0 , _UpperCAmelCase : Tuple=0.0 , _UpperCAmelCase : Optional[int]=0.02 , _UpperCAmelCase : List[Any]=1E-06 , _UpperCAmelCase : List[str]=True , **_UpperCAmelCase : List[Any] , ): """simple docstring""" UpperCAmelCase__ = hidden_size UpperCAmelCase__ = num_hidden_layers UpperCAmelCase__ = num_attention_heads UpperCAmelCase__ = intermediate_size UpperCAmelCase__ = hidden_act UpperCAmelCase__ = hidden_dropout_prob UpperCAmelCase__ = attention_probs_dropout_prob UpperCAmelCase__ = initializer_range UpperCAmelCase__ = layer_norm_eps UpperCAmelCase__ = image_size UpperCAmelCase__ = num_frames UpperCAmelCase__ = tubelet_size UpperCAmelCase__ = num_channels UpperCAmelCase__ = qkv_bias super().__init__(**_UpperCAmelCase )

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'''simple docstring''' from pathlib import PurePosixPath from typing import Optional import fsspec from fsspec import AbstractFileSystem from huggingface_hub.hf_api import DatasetInfo from ..utils.file_utils import get_authentication_headers_for_url from ..utils.hub import hf_hub_url class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' lowerCAmelCase_ : int = """""" lowerCAmelCase_ : Tuple = """hf-legacy""" # "hf://"" is reserved for hffs def __init__( self : Dict , _UpperCAmelCase : Optional[DatasetInfo] = None , _UpperCAmelCase : Optional[str] = None , **_UpperCAmelCase : List[str] , ): """simple docstring""" super().__init__(self , **_UpperCAmelCase ) UpperCAmelCase__ = repo_info UpperCAmelCase__ = token UpperCAmelCase__ = None def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ): """simple docstring""" if self.dir_cache is None: UpperCAmelCase__ = {} for hf_file in self.repo_info.siblings: # TODO(QL): add sizes UpperCAmelCase__ = { """name""": hf_file.rfilename, """size""": None, """type""": """file""", } self.dir_cache.update( { str(_UpperCAmelCase ): {"""name""": str(_UpperCAmelCase ), """size""": None, """type""": """directory"""} for d in list(PurePosixPath(hf_file.rfilename ).parents )[:-1] } ) def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] , _UpperCAmelCase : str , _UpperCAmelCase : str = "rb" , **_UpperCAmelCase : Optional[Any] , ): """simple docstring""" if not isinstance(self.repo_info , _UpperCAmelCase ): raise NotImplementedError(f'''Open is only implemented for dataset repositories, but got {self.repo_info}''' ) UpperCAmelCase__ = hf_hub_url(self.repo_info.id , _UpperCAmelCase , revision=self.repo_info.sha ) return fsspec.open( _UpperCAmelCase , mode=_UpperCAmelCase , headers=get_authentication_headers_for_url(_UpperCAmelCase , use_auth_token=self.token ) , client_kwargs={"""trust_env""": True} , ).open() def SCREAMING_SNAKE_CASE__ ( self : List[str] , _UpperCAmelCase : List[str] , **_UpperCAmelCase : List[Any] ): """simple docstring""" self._get_dirs() UpperCAmelCase__ = self._strip_protocol(_UpperCAmelCase ) if path in self.dir_cache: return self.dir_cache[path] else: raise FileNotFoundError(_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Tuple , _UpperCAmelCase : Tuple , _UpperCAmelCase : Any=False , **_UpperCAmelCase : Union[str, Any] ): """simple docstring""" self._get_dirs() UpperCAmelCase__ = PurePosixPath(path.strip("""/""" ) ) UpperCAmelCase__ = {} for p, f in self.dir_cache.items(): UpperCAmelCase__ = PurePosixPath(p.strip("""/""" ) ) UpperCAmelCase__ = p.parent if root == path: UpperCAmelCase__ = f UpperCAmelCase__ = list(paths.values() ) if detail: return out else: return sorted(f["""name"""] for f in out )

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'''simple docstring''' import warnings from ...utils import logging from .image_processing_deit import DeiTImageProcessor UpperCAmelCase_ = logging.get_logger(__name__) class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' def __init__( self : List[str] , *_UpperCAmelCase : Optional[int] , **_UpperCAmelCase : Optional[Any] ): """simple docstring""" warnings.warn( """The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please""" """ use DeiTImageProcessor instead.""" , _UpperCAmelCase , ) super().__init__(*_UpperCAmelCase , **_UpperCAmelCase )

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'''simple docstring''' import re import warnings from contextlib import contextmanager from ...processing_utils import ProcessorMixin class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' lowerCAmelCase_ : Any = ["""image_processor""", """tokenizer"""] lowerCAmelCase_ : Dict = """AutoImageProcessor""" lowerCAmelCase_ : Any = """AutoTokenizer""" def __init__( self : Tuple , _UpperCAmelCase : List[Any]=None , _UpperCAmelCase : List[Any]=None , **_UpperCAmelCase : List[str] ): """simple docstring""" UpperCAmelCase__ = None if "feature_extractor" in kwargs: warnings.warn( """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`""" """ instead.""" , _UpperCAmelCase , ) UpperCAmelCase__ = kwargs.pop("""feature_extractor""" ) UpperCAmelCase__ = image_processor if image_processor is not None else feature_extractor if image_processor is None: raise ValueError("""You need to specify an `image_processor`.""" ) if tokenizer is None: raise ValueError("""You need to specify a `tokenizer`.""" ) super().__init__(_UpperCAmelCase , _UpperCAmelCase ) UpperCAmelCase__ = self.image_processor UpperCAmelCase__ = False def __call__( self : Dict , *_UpperCAmelCase : Optional[int] , **_UpperCAmelCase : Optional[Any] ): """simple docstring""" if self._in_target_context_manager: return self.current_processor(*_UpperCAmelCase , **_UpperCAmelCase ) UpperCAmelCase__ = kwargs.pop("""images""" , _UpperCAmelCase ) UpperCAmelCase__ = kwargs.pop("""text""" , _UpperCAmelCase ) if len(_UpperCAmelCase ) > 0: UpperCAmelCase__ = args[0] UpperCAmelCase__ = args[1:] if images is None and text is None: raise ValueError("""You need to specify either an `images` or `text` input to process.""" ) if images is not None: UpperCAmelCase__ = self.image_processor(_UpperCAmelCase , *_UpperCAmelCase , **_UpperCAmelCase ) if text is not None: UpperCAmelCase__ = self.tokenizer(_UpperCAmelCase , **_UpperCAmelCase ) if text is None: return inputs elif images is None: return encodings else: UpperCAmelCase__ = encodings["""input_ids"""] return inputs def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , *_UpperCAmelCase : int , **_UpperCAmelCase : List[str] ): """simple docstring""" return self.tokenizer.batch_decode(*_UpperCAmelCase , **_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : List[str] , *_UpperCAmelCase : Any , **_UpperCAmelCase : Optional[int] ): """simple docstring""" return self.tokenizer.decode(*_UpperCAmelCase , **_UpperCAmelCase ) @contextmanager def SCREAMING_SNAKE_CASE__ ( self : List[str] ): """simple docstring""" warnings.warn( """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """ """labels by using the argument `text` of the regular `__call__` method (either in the same call as """ """your images inputs, or in a separate call.""" ) UpperCAmelCase__ = True UpperCAmelCase__ = self.tokenizer yield UpperCAmelCase__ = self.image_processor UpperCAmelCase__ = False def SCREAMING_SNAKE_CASE__ ( self : Tuple , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Tuple=False , _UpperCAmelCase : List[Any]=None ): """simple docstring""" if added_vocab is None: UpperCAmelCase__ = self.tokenizer.get_added_vocab() UpperCAmelCase__ = {} while tokens: UpperCAmelCase__ = re.search(r"""<s_(.*?)>""" , _UpperCAmelCase , re.IGNORECASE ) if start_token is None: break UpperCAmelCase__ = start_token.group(1 ) UpperCAmelCase__ = re.search(rf'''</s_{key}>''' , _UpperCAmelCase , re.IGNORECASE ) UpperCAmelCase__ = start_token.group() if end_token is None: UpperCAmelCase__ = tokens.replace(_UpperCAmelCase , """""" ) else: UpperCAmelCase__ = end_token.group() UpperCAmelCase__ = re.escape(_UpperCAmelCase ) UpperCAmelCase__ = re.escape(_UpperCAmelCase ) UpperCAmelCase__ = re.search(f'''{start_token_escaped}(.*?){end_token_escaped}''' , _UpperCAmelCase , re.IGNORECASE ) if content is not None: UpperCAmelCase__ = content.group(1 ).strip() if r"<s_" in content and r"</s_" in content: # non-leaf node UpperCAmelCase__ = self.tokenajson(_UpperCAmelCase , is_inner_value=_UpperCAmelCase , added_vocab=_UpperCAmelCase ) if value: if len(_UpperCAmelCase ) == 1: UpperCAmelCase__ = value[0] UpperCAmelCase__ = value else: # leaf nodes UpperCAmelCase__ = [] for leaf in content.split(r"""<sep/>""" ): UpperCAmelCase__ = leaf.strip() if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>": UpperCAmelCase__ = leaf[1:-2] # for categorical special tokens output[key].append(_UpperCAmelCase ) if len(output[key] ) == 1: UpperCAmelCase__ = output[key][0] UpperCAmelCase__ = tokens[tokens.find(_UpperCAmelCase ) + len(_UpperCAmelCase ) :].strip() if tokens[:6] == r"<sep/>": # non-leaf nodes return [output] + self.tokenajson(tokens[6:] , is_inner_value=_UpperCAmelCase , added_vocab=_UpperCAmelCase ) if len(_UpperCAmelCase ): return [output] if is_inner_value else output else: return [] if is_inner_value else {"text_sequence": tokens} @property def SCREAMING_SNAKE_CASE__ ( self : List[Any] ): """simple docstring""" warnings.warn( """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""" , _UpperCAmelCase , ) return self.image_processor_class @property def SCREAMING_SNAKE_CASE__ ( self : List[Any] ): """simple docstring""" warnings.warn( """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.""" , _UpperCAmelCase , ) return self.image_processor

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'''simple docstring''' import os import unicodedata from shutil import copyfile from typing import Any, Dict, List, Optional, Tuple import sentencepiece as spm from ...tokenization_utils import AddedToken, PreTrainedTokenizer from ...utils import SPIECE_UNDERLINE, logging UpperCAmelCase_ = logging.get_logger(__name__) UpperCAmelCase_ = {'vocab_file': 'spiece.model'} UpperCAmelCase_ = { 'vocab_file': { 'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model', } } class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' def __init__( self : Dict , _UpperCAmelCase : str , _UpperCAmelCase : Any=False , _UpperCAmelCase : int=True , _UpperCAmelCase : Union[str, Any]=False , _UpperCAmelCase : Dict="<s>" , _UpperCAmelCase : int="</s>" , _UpperCAmelCase : Dict="<unk>" , _UpperCAmelCase : Tuple="<sep>" , _UpperCAmelCase : List[Any]="<pad>" , _UpperCAmelCase : int="<cls>" , _UpperCAmelCase : Union[str, Any]="<mask>" , _UpperCAmelCase : List[str]=["<eop>", "<eod>"] , _UpperCAmelCase : Optional[Dict[str, Any]] = None , **_UpperCAmelCase : int , ): """simple docstring""" UpperCAmelCase__ = AddedToken(_UpperCAmelCase , lstrip=_UpperCAmelCase , rstrip=_UpperCAmelCase ) if isinstance(_UpperCAmelCase , _UpperCAmelCase ) else mask_token UpperCAmelCase__ = {} if sp_model_kwargs is None else sp_model_kwargs super().__init__( do_lower_case=_UpperCAmelCase , remove_space=_UpperCAmelCase , keep_accents=_UpperCAmelCase , bos_token=_UpperCAmelCase , eos_token=_UpperCAmelCase , unk_token=_UpperCAmelCase , sep_token=_UpperCAmelCase , pad_token=_UpperCAmelCase , cls_token=_UpperCAmelCase , mask_token=_UpperCAmelCase , additional_special_tokens=_UpperCAmelCase , sp_model_kwargs=self.sp_model_kwargs , **_UpperCAmelCase , ) UpperCAmelCase__ = 3 UpperCAmelCase__ = do_lower_case UpperCAmelCase__ = remove_space UpperCAmelCase__ = keep_accents UpperCAmelCase__ = vocab_file UpperCAmelCase__ = spm.SentencePieceProcessor(**self.sp_model_kwargs ) self.sp_model.Load(_UpperCAmelCase ) try: import jieba except ModuleNotFoundError as error: raise error.__class__( """You need to install jieba to use CpmTokenizer or CpmTokenizerFast. """ """See https://pypi.org/project/jieba/ for installation.""" ) UpperCAmelCase__ = jieba UpperCAmelCase__ = str.maketrans(""" \n""" , """\u2582\u2583""" ) @property # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ): """simple docstring""" return len(self.sp_model ) def SCREAMING_SNAKE_CASE__ ( self : List[str] ): """simple docstring""" UpperCAmelCase__ = {self.convert_ids_to_tokens(_UpperCAmelCase ): i for i in range(self.vocab_size )} vocab.update(self.added_tokens_encoder ) return vocab def __getstate__( self : Dict ): """simple docstring""" UpperCAmelCase__ = self.__dict__.copy() UpperCAmelCase__ = None return state def __setstate__( self : Union[str, Any] , _UpperCAmelCase : Union[str, Any] ): """simple docstring""" UpperCAmelCase__ = d # for backward compatibility if not hasattr(self , """sp_model_kwargs""" ): UpperCAmelCase__ = {} UpperCAmelCase__ = spm.SentencePieceProcessor(**self.sp_model_kwargs ) self.sp_model.Load(self.vocab_file ) def SCREAMING_SNAKE_CASE__ ( self : int , _UpperCAmelCase : Optional[Any] ): """simple docstring""" if self.remove_space: UpperCAmelCase__ = """ """.join(inputs.strip().split() ) else: UpperCAmelCase__ = inputs UpperCAmelCase__ = outputs.replace("""``""" , """\"""" ).replace("""''""" , """\"""" ) if not self.keep_accents: UpperCAmelCase__ = unicodedata.normalize("""NFKD""" , _UpperCAmelCase ) UpperCAmelCase__ = """""".join([c for c in outputs if not unicodedata.combining(_UpperCAmelCase )] ) if self.do_lower_case: UpperCAmelCase__ = outputs.lower() return outputs def SCREAMING_SNAKE_CASE__ ( self : Tuple , _UpperCAmelCase : str ): """simple docstring""" UpperCAmelCase__ = self.preprocess_text(_UpperCAmelCase ) UpperCAmelCase__ = self.sp_model.encode(_UpperCAmelCase , out_type=_UpperCAmelCase ) UpperCAmelCase__ = [] for piece in pieces: if len(_UpperCAmelCase ) > 1 and piece[-1] == str(""",""" ) and piece[-2].isdigit(): UpperCAmelCase__ = self.sp_model.EncodeAsPieces(piece[:-1].replace(_UpperCAmelCase , """""" ) ) if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE: if len(cur_pieces[0] ) == 1: UpperCAmelCase__ = cur_pieces[1:] else: UpperCAmelCase__ = cur_pieces[0][1:] cur_pieces.append(piece[-1] ) new_pieces.extend(_UpperCAmelCase ) else: new_pieces.append(_UpperCAmelCase ) return new_pieces def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , _UpperCAmelCase : Union[str, Any] ): """simple docstring""" return self.sp_model.PieceToId(_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Dict , _UpperCAmelCase : Any ): """simple docstring""" return self.sp_model.IdToPiece(_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : int , _UpperCAmelCase : Dict ): """simple docstring""" UpperCAmelCase__ = """""".join(_UpperCAmelCase ).replace(_UpperCAmelCase , """ """ ).strip() return out_string def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] , _UpperCAmelCase : List[int] , _UpperCAmelCase : Optional[List[int]] = None ): """simple docstring""" UpperCAmelCase__ = [self.sep_token_id] UpperCAmelCase__ = [self.cls_token_id] if token_ids_a is None: return token_ids_a + sep + cls return token_ids_a + sep + token_ids_a + sep + cls def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] , _UpperCAmelCase : List[int] , _UpperCAmelCase : Optional[List[int]] = None , _UpperCAmelCase : bool = False ): """simple docstring""" if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_a=_UpperCAmelCase , token_ids_a=_UpperCAmelCase , already_has_special_tokens=_UpperCAmelCase ) if token_ids_a is not None: return ([0] * len(_UpperCAmelCase )) + [1] + ([0] * len(_UpperCAmelCase )) + [1, 1] return ([0] * len(_UpperCAmelCase )) + [1, 1] def SCREAMING_SNAKE_CASE__ ( self : Dict , _UpperCAmelCase : List[int] , _UpperCAmelCase : Optional[List[int]] = None ): """simple docstring""" UpperCAmelCase__ = [self.sep_token_id] UpperCAmelCase__ = [2] if token_ids_a is None: return len(token_ids_a + sep ) * [0] + cls_segment_id return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id def SCREAMING_SNAKE_CASE__ ( self : List[Any] , _UpperCAmelCase : str , _UpperCAmelCase : Optional[str] = None ): """simple docstring""" if not os.path.isdir(_UpperCAmelCase ): logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' ) return UpperCAmelCase__ = os.path.join( _UpperCAmelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] ) if os.path.abspath(self.vocab_file ) != os.path.abspath(_UpperCAmelCase ) and os.path.isfile(self.vocab_file ): copyfile(self.vocab_file , _UpperCAmelCase ) elif not os.path.isfile(self.vocab_file ): with open(_UpperCAmelCase , """wb""" ) as fi: UpperCAmelCase__ = self.sp_model.serialized_model_proto() fi.write(_UpperCAmelCase ) return (out_vocab_file,) def SCREAMING_SNAKE_CASE__ ( self : Tuple , *_UpperCAmelCase : Tuple , **_UpperCAmelCase : Optional[Any] ): """simple docstring""" UpperCAmelCase__ = super()._decode(*_UpperCAmelCase , **_UpperCAmelCase ) UpperCAmelCase__ = text.replace(""" """ , """""" ).replace("""\u2582""" , """ """ ).replace("""\u2583""" , """\n""" ) return text

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'''simple docstring''' import datasets from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py UpperCAmelCase_ = '\\n@INPROCEEDINGS{Papineni02bleu:a,\n author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n title = {BLEU: a Method for Automatic Evaluation of Machine Translation},\n booktitle = {},\n year = {2002},\n pages = {311--318}\n}\n@inproceedings{lin-och-2004-orange,\n title = "{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation",\n author = "Lin, Chin-Yew and\n Och, Franz Josef",\n booktitle = "{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics",\n month = "aug 23{--}aug 27",\n year = "2004",\n address = "Geneva, Switzerland",\n publisher = "COLING",\n url = "https://www.aclweb.org/anthology/C04-1072",\n pages = "501--507",\n}\n' UpperCAmelCase_ = '\\nBLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.\nQuality is considered to be the correspondence between a machine\'s output and that of a human: "the closer a machine translation is to a professional human translation,\nthe better it is" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and\nremains one of the most popular automated and inexpensive metrics.\n\nScores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.\nThose scores are then averaged over the whole corpus to reach an estimate of the translation\'s overall quality. Intelligibility or grammatical correctness\nare not taken into account[citation needed].\n\nBLEU\'s output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1\nrepresenting more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the\nreference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional\nreference translations will increase the BLEU score.\n' UpperCAmelCase_ = '\nComputes BLEU score of translated segments against one or more references.\nArgs:\n predictions: list of translations to score.\n Each translation should be tokenized into a list of tokens.\n references: list of lists of references for each translation.\n Each reference should be tokenized into a list of tokens.\n max_order: Maximum n-gram order to use when computing BLEU score.\n smooth: Whether or not to apply Lin et al. 2004 smoothing.\nReturns:\n \'bleu\': bleu score,\n \'precisions\': geometric mean of n-gram precisions,\n \'brevity_penalty\': brevity penalty,\n \'length_ratio\': ratio of lengths,\n \'translation_length\': translation_length,\n \'reference_length\': reference_length\nExamples:\n\n >>> predictions = [\n ... ["hello", "there", "general", "kenobi"], # tokenized prediction of the first sample\n ... ["foo", "bar", "foobar"] # tokenized prediction of the second sample\n ... ]\n >>> references = [\n ... [["hello", "there", "general", "kenobi"], ["hello", "there", "!"]], # tokenized references for the first sample (2 references)\n ... [["foo", "bar", "foobar"]] # tokenized references for the second sample (1 reference)\n ... ]\n >>> bleu = datasets.load_metric("bleu")\n >>> results = bleu.compute(predictions=predictions, references=references)\n >>> print(results["bleu"])\n 1.0\n' @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION ) class lowerCAmelCase_ ( datasets.Metric ): '''simple docstring''' def SCREAMING_SNAKE_CASE__ ( self : int ): """simple docstring""" return datasets.MetricInfo( description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features( { """predictions""": datasets.Sequence(datasets.Value("""string""" , id="""token""" ) , id="""sequence""" ), """references""": datasets.Sequence( datasets.Sequence(datasets.Value("""string""" , id="""token""" ) , id="""sequence""" ) , id="""references""" ), } ) , codebase_urls=["""https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py"""] , reference_urls=[ """https://en.wikipedia.org/wiki/BLEU""", """https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213""", ] , ) def SCREAMING_SNAKE_CASE__ ( self : int , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : List[Any]=4 , _UpperCAmelCase : Union[str, Any]=False ): """simple docstring""" UpperCAmelCase__ = compute_bleu( reference_corpus=_UpperCAmelCase , translation_corpus=_UpperCAmelCase , max_order=_UpperCAmelCase , smooth=_UpperCAmelCase ) ((UpperCAmelCase__) , (UpperCAmelCase__) , (UpperCAmelCase__) , (UpperCAmelCase__) , (UpperCAmelCase__) , (UpperCAmelCase__)) = score return { "bleu": bleu, "precisions": precisions, "brevity_penalty": bp, "length_ratio": ratio, "translation_length": translation_length, "reference_length": reference_length, }

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'''simple docstring''' import argparse import logging import os import datasets import tensorflow as tf from transformers import AutoTokenizer UpperCAmelCase_ = logging.getLogger(__name__) def _UpperCamelCase ( ): '''simple docstring''' UpperCAmelCase__ = argparse.ArgumentParser( description="""Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset.""" ) parser.add_argument( """--dataset_name""" , type=SCREAMING_SNAKE_CASE__ , default="""wikitext""" , help="""Name of the training. Explore datasets at: hf.co/datasets.""" , ) parser.add_argument( """--dataset_config""" , type=SCREAMING_SNAKE_CASE__ , default="""wikitext-103-raw-v1""" , help="""Configuration name of the dataset.""" ) parser.add_argument( """--tokenizer_name_or_path""" , type=SCREAMING_SNAKE_CASE__ , default="""sayakpaul/unigram-tokenizer-wikitext""" , help="""Tokenizer identifier. Can be a local filepath or a Hub identifier.""" , ) parser.add_argument( """--shard_size""" , type=SCREAMING_SNAKE_CASE__ , default=1000 , help="""Number of entries to go in a single shard.""" , ) parser.add_argument("""--split""" , type=SCREAMING_SNAKE_CASE__ , default="""train""" , choices=["""train""", """test""", """validation"""] ) parser.add_argument( """--limit""" , default=SCREAMING_SNAKE_CASE__ , type=SCREAMING_SNAKE_CASE__ , help="""Limit the number of shards (used for debugging).""" , ) parser.add_argument( """--max_length""" , type=SCREAMING_SNAKE_CASE__ , default=512 , help="""Maximum sequence length. For training on TPUs, it helps to have a maximum""" """ sequence length that is a multiple of 8.""" , ) parser.add_argument( """--output_dir""" , default="""tf-tpu""" , type=SCREAMING_SNAKE_CASE__ , help="""Output directory where the TFRecord shards will be saved. If the""" """ path is appended with `gs://` ('gs://tf-tpu', for example) then the TFRecord""" """ shards will be directly saved to a Google Cloud Storage bucket.""" , ) UpperCAmelCase__ = parser.parse_args() return args def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Any ): '''simple docstring''' def fn(SCREAMING_SNAKE_CASE__ : Union[str, Any] ): return tokenizer(examples["""text"""] ) return fn def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Optional[int] ): '''simple docstring''' UpperCAmelCase__ = [] for i in range(len(tokenized_data["""input_ids"""] ) ): UpperCAmelCase__ = { """input_ids""": tf.train.Feature(intaa_list=tf.train.IntaaList(value=tokenized_data["""input_ids"""][i] ) ), """attention_mask""": tf.train.Feature( intaa_list=tf.train.IntaaList(value=tokenized_data["""attention_mask"""][i] ) ), } UpperCAmelCase__ = tf.train.Features(feature=SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = tf.train.Example(features=SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = example.SerializeToString() records.append(SCREAMING_SNAKE_CASE__ ) return records def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Any ): '''simple docstring''' UpperCAmelCase__ = datasets.load_dataset(args.dataset_name , args.dataset_config , split=args.split ) if args.limit is not None: UpperCAmelCase__ = min(len(SCREAMING_SNAKE_CASE__ ) , args.limit ) UpperCAmelCase__ = dataset.select(range(SCREAMING_SNAKE_CASE__ ) ) print(F'''Limiting the dataset to {args.limit} entries.''' ) UpperCAmelCase__ = AutoTokenizer.from_pretrained(args.tokenizer_name_or_path ) # Handle output directory creation. # For serializing into a Google Cloud Storage Bucket, one needs to first # create a bucket. if "gs" not in args.output_dir: if not os.path.exists(args.output_dir ): os.makedirs(args.output_dir ) UpperCAmelCase__ = os.path.join(args.output_dir , args.split ) if not os.path.exists(SCREAMING_SNAKE_CASE__ ): os.makedirs(SCREAMING_SNAKE_CASE__ ) else: UpperCAmelCase__ = os.path.join(args.output_dir , args.split ) # Tokenize the whole dataset at once. UpperCAmelCase__ = tokenize_function(SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = dataset.map(SCREAMING_SNAKE_CASE__ , batched=SCREAMING_SNAKE_CASE__ , num_proc=4 , remove_columns=["""text"""] ) # We need to concatenate all our texts together, and then split the result # into chunks of a fixed size, which we will call block_size. To do this, we # will use the map method again, with the option batched=True. When we use batched=True, # the function we pass to map() will be passed multiple inputs at once, allowing us # to group them into more or fewer examples than we had in the input. # This allows us to create our new fixed-length samples. The advantage of this # method is that we don't lose a whole lot of content from the dataset compared to the # case where we simply tokenize with a pre-defined max_length. def group_texts(SCREAMING_SNAKE_CASE__ : int ): # Concatenate all texts. UpperCAmelCase__ = {k: sum(examples[k] , [] ) for k in examples.keys()} UpperCAmelCase__ = len(concatenated_examples[list(examples.keys() )[0]] ) # We drop the small remainder, though you could add padding instead if the model supports it # In this, as in all things, we advise you to follow your heart 🫀 UpperCAmelCase__ = (total_length // args.max_length) * args.max_length # Split by chunks of max_len. UpperCAmelCase__ = { k: [t[i : i + args.max_length] for i in range(0 , SCREAMING_SNAKE_CASE__ , args.max_length )] for k, t in concatenated_examples.items() } return result UpperCAmelCase__ = dataset_tokenized.map(SCREAMING_SNAKE_CASE__ , batched=SCREAMING_SNAKE_CASE__ , batch_size=1000 , num_proc=4 ) UpperCAmelCase__ = 0 UpperCAmelCase__ = 0 for shard in range(0 , len(SCREAMING_SNAKE_CASE__ ) , args.shard_size ): UpperCAmelCase__ = grouped_dataset[shard : shard + args.shard_size] UpperCAmelCase__ = len(dataset_snapshot["""input_ids"""] ) UpperCAmelCase__ = os.path.join(SCREAMING_SNAKE_CASE__ , F'''dataset-{shard_count}-{records_containing}.tfrecord''' ) UpperCAmelCase__ = get_serialized_examples(SCREAMING_SNAKE_CASE__ ) with tf.io.TFRecordWriter(SCREAMING_SNAKE_CASE__ ) as out_file: for i in range(len(SCREAMING_SNAKE_CASE__ ) ): UpperCAmelCase__ = serialized_examples[i] out_file.write(SCREAMING_SNAKE_CASE__ ) print("""Wrote file {} containing {} records""".format(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) ) shard_count += 1 total_records += records_containing with open(F'''split-{args.split}-records-count.txt''' , """w""" ) as f: print(F'''Total {args.split} records: {total_records}''' , file=SCREAMING_SNAKE_CASE__ ) if __name__ == "__main__": UpperCAmelCase_ = parse_args() main(args)

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'''simple docstring''' import math def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int ): '''simple docstring''' UpperCAmelCase__ = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 ) return exponent == int(SCREAMING_SNAKE_CASE__ ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : float = 1 / 12345 ): '''simple docstring''' UpperCAmelCase__ = 0 UpperCAmelCase__ = 0 UpperCAmelCase__ = 3 while True: UpperCAmelCase__ = (integer**2 - 1) / 4 # if candidate is an integer, then there is a partition for k if partition_candidate == int(SCREAMING_SNAKE_CASE__ ): UpperCAmelCase__ = int(SCREAMING_SNAKE_CASE__ ) total_partitions += 1 if check_partition_perfect(SCREAMING_SNAKE_CASE__ ): perfect_partitions += 1 if perfect_partitions > 0: if perfect_partitions / total_partitions < max_proportion: return int(SCREAMING_SNAKE_CASE__ ) integer += 1 if __name__ == "__main__": print(f"{solution() = }")

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'''simple docstring''' import numpy as np import torch from torch.nn import CrossEntropyLoss from transformers import AutoModelForCausalLM, AutoTokenizer import datasets from datasets import logging UpperCAmelCase_ = '\\n\n' UpperCAmelCase_ = '\nPerplexity (PPL) is one of the most common metrics for evaluating language models.\nIt is defined as the exponentiated average negative log-likelihood of a sequence.\n\nFor more information, see https://huggingface.co/docs/transformers/perplexity\n' UpperCAmelCase_ = '\nArgs:\n model_id (str): model used for calculating Perplexity\n NOTE: Perplexity can only be calculated for causal language models.\n This includes models such as gpt2, causal variations of bert,\n causal versions of t5, and more (the full list can be found\n in the AutoModelForCausalLM documentation here:\n https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )\n\n input_texts (list of str): input text, each separate text snippet\n is one list entry.\n batch_size (int): the batch size to run texts through the model. Defaults to 16.\n add_start_token (bool): whether to add the start token to the texts,\n so the perplexity can include the probability of the first word. Defaults to True.\n device (str): device to run on, defaults to \'cuda\' when available\nReturns:\n perplexity: dictionary containing the perplexity scores for the texts\n in the input list, as well as the mean perplexity. If one of the input texts is\n longer than the max input length of the model, then it is truncated to the\n max length for the perplexity computation.\nExamples:\n Example 1:\n >>> perplexity = datasets.load_metric("perplexity")\n >>> input_texts = ["lorem ipsum", "Happy Birthday!", "Bienvenue"]\n >>> results = perplexity.compute(model_id=\'gpt2\',\n ... add_start_token=False,\n ... input_texts=input_texts) # doctest:+ELLIPSIS\n >>> print(list(results.keys()))\n [\'perplexities\', \'mean_perplexity\']\n >>> print(round(results["mean_perplexity"], 2))\n 78.22\n >>> print(round(results["perplexities"][0], 2))\n 11.11\n\n Example 2:\n >>> perplexity = datasets.load_metric("perplexity")\n >>> input_texts = datasets.load_dataset("wikitext",\n ... "wikitext-2-raw-v1",\n ... split="test")["text"][:50] # doctest:+ELLIPSIS\n [...]\n >>> input_texts = [s for s in input_texts if s!=\'\']\n >>> results = perplexity.compute(model_id=\'gpt2\',\n ... input_texts=input_texts) # doctest:+ELLIPSIS\n >>> print(list(results.keys()))\n [\'perplexities\', \'mean_perplexity\']\n >>> print(round(results["mean_perplexity"], 2))\n 60.35\n >>> print(round(results["perplexities"][0], 2))\n 81.12\n' @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION ) class lowerCAmelCase_ ( datasets.Metric ): '''simple docstring''' def SCREAMING_SNAKE_CASE__ ( self : Dict ): """simple docstring""" return datasets.MetricInfo( description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features( { """input_texts""": datasets.Value("""string""" ), } ) , reference_urls=["""https://huggingface.co/docs/transformers/perplexity"""] , ) def SCREAMING_SNAKE_CASE__ ( self : List[Any] , _UpperCAmelCase : Tuple , _UpperCAmelCase : int , _UpperCAmelCase : int = 16 , _UpperCAmelCase : bool = True , _UpperCAmelCase : Optional[int]=None ): """simple docstring""" if device is not None: assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu." if device == "gpu": UpperCAmelCase__ = """cuda""" else: UpperCAmelCase__ = """cuda""" if torch.cuda.is_available() else """cpu""" UpperCAmelCase__ = AutoModelForCausalLM.from_pretrained(_UpperCAmelCase ) UpperCAmelCase__ = model.to(_UpperCAmelCase ) UpperCAmelCase__ = AutoTokenizer.from_pretrained(_UpperCAmelCase ) # if batch_size > 1 (which generally leads to padding being required), and # if there is not an already assigned pad_token, assign an existing # special token to also be the padding token if tokenizer.pad_token is None and batch_size > 1: UpperCAmelCase__ = list(tokenizer.special_tokens_map_extended.values() ) # check that the model already has at least one special token defined assert ( len(_UpperCAmelCase ) > 0 ), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1." # assign one of the special tokens to also be the pad token tokenizer.add_special_tokens({"""pad_token""": existing_special_tokens[0]} ) if add_start_token: # leave room for <BOS> token to be added: assert ( tokenizer.bos_token is not None ), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False" UpperCAmelCase__ = model.config.max_length - 1 else: UpperCAmelCase__ = model.config.max_length UpperCAmelCase__ = tokenizer( _UpperCAmelCase , add_special_tokens=_UpperCAmelCase , padding=_UpperCAmelCase , truncation=_UpperCAmelCase , max_length=_UpperCAmelCase , return_tensors="""pt""" , return_attention_mask=_UpperCAmelCase , ).to(_UpperCAmelCase ) UpperCAmelCase__ = encodings["""input_ids"""] UpperCAmelCase__ = encodings["""attention_mask"""] # check that each input is long enough: if add_start_token: assert torch.all(torch.ge(attn_masks.sum(1 ) , 1 ) ), "Each input text must be at least one token long." else: assert torch.all( torch.ge(attn_masks.sum(1 ) , 2 ) ), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings." UpperCAmelCase__ = [] UpperCAmelCase__ = CrossEntropyLoss(reduction="""none""" ) for start_index in logging.tqdm(range(0 , len(_UpperCAmelCase ) , _UpperCAmelCase ) ): UpperCAmelCase__ = min(start_index + batch_size , len(_UpperCAmelCase ) ) UpperCAmelCase__ = encoded_texts[start_index:end_index] UpperCAmelCase__ = attn_masks[start_index:end_index] if add_start_token: UpperCAmelCase__ = torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0 ) ).to(_UpperCAmelCase ) UpperCAmelCase__ = torch.cat([bos_tokens_tensor, encoded_batch] , dim=1 ) UpperCAmelCase__ = torch.cat( [torch.ones(bos_tokens_tensor.size() , dtype=torch.intaa ).to(_UpperCAmelCase ), attn_mask] , dim=1 ) UpperCAmelCase__ = encoded_batch with torch.no_grad(): UpperCAmelCase__ = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase ).logits UpperCAmelCase__ = out_logits[..., :-1, :].contiguous() UpperCAmelCase__ = labels[..., 1:].contiguous() UpperCAmelCase__ = attn_mask[..., 1:].contiguous() UpperCAmelCase__ = torch.expa( (loss_fct(shift_logits.transpose(1 , 2 ) , _UpperCAmelCase ) * shift_attention_mask_batch).sum(1 ) / shift_attention_mask_batch.sum(1 ) ) ppls += perplexity_batch.tolist() return {"perplexities": ppls, "mean_perplexity": np.mean(_UpperCAmelCase )}

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'''simple docstring''' from typing import Dict, List, Optional, Union import numpy as np from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict from ...image_transforms import ( center_crop, get_resize_output_image_size, normalize, rescale, resize, to_channel_dimension_format, ) from ...image_utils import ( IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD, ChannelDimension, ImageInput, PILImageResampling, is_valid_image, to_numpy_array, valid_images, ) from ...utils import TensorType, is_vision_available, logging if is_vision_available(): import PIL UpperCAmelCase_ = logging.get_logger(__name__) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int ): '''simple docstring''' if isinstance(SCREAMING_SNAKE_CASE__ , (list, tuple) ) and isinstance(videos[0] , (list, tuple) ) and is_valid_image(videos[0][0] ): return videos elif isinstance(SCREAMING_SNAKE_CASE__ , (list, tuple) ) and is_valid_image(videos[0] ): return [videos] elif is_valid_image(SCREAMING_SNAKE_CASE__ ): return [[videos]] raise ValueError(F'''Could not make batched video from {videos}''' ) class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' lowerCAmelCase_ : int = ["""pixel_values"""] def __init__( self : Optional[Any] , _UpperCAmelCase : bool = True , _UpperCAmelCase : Dict[str, int] = None , _UpperCAmelCase : PILImageResampling = PILImageResampling.BILINEAR , _UpperCAmelCase : bool = True , _UpperCAmelCase : Dict[str, int] = None , _UpperCAmelCase : bool = True , _UpperCAmelCase : Union[int, float] = 1 / 2_55 , _UpperCAmelCase : bool = True , _UpperCAmelCase : Optional[Union[float, List[float]]] = None , _UpperCAmelCase : Optional[Union[float, List[float]]] = None , **_UpperCAmelCase : str , ): """simple docstring""" super().__init__(**_UpperCAmelCase ) UpperCAmelCase__ = size if size is not None else {"""shortest_edge""": 2_24} UpperCAmelCase__ = get_size_dict(_UpperCAmelCase , default_to_square=_UpperCAmelCase ) UpperCAmelCase__ = crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24} UpperCAmelCase__ = get_size_dict(_UpperCAmelCase , param_name="""crop_size""" ) UpperCAmelCase__ = do_resize UpperCAmelCase__ = size UpperCAmelCase__ = do_center_crop UpperCAmelCase__ = crop_size UpperCAmelCase__ = resample UpperCAmelCase__ = do_rescale UpperCAmelCase__ = rescale_factor UpperCAmelCase__ = do_normalize UpperCAmelCase__ = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN UpperCAmelCase__ = image_std if image_std is not None else IMAGENET_STANDARD_STD def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] , _UpperCAmelCase : np.ndarray , _UpperCAmelCase : Dict[str, int] , _UpperCAmelCase : PILImageResampling = PILImageResampling.BILINEAR , _UpperCAmelCase : Optional[Union[str, ChannelDimension]] = None , **_UpperCAmelCase : Optional[int] , ): """simple docstring""" UpperCAmelCase__ = get_size_dict(_UpperCAmelCase , default_to_square=_UpperCAmelCase ) if "shortest_edge" in size: UpperCAmelCase__ = get_resize_output_image_size(_UpperCAmelCase , size["""shortest_edge"""] , default_to_square=_UpperCAmelCase ) elif "height" in size and "width" in size: UpperCAmelCase__ = (size["""height"""], size["""width"""]) else: raise ValueError(f'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}''' ) return resize(_UpperCAmelCase , size=_UpperCAmelCase , resample=_UpperCAmelCase , data_format=_UpperCAmelCase , **_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Tuple , _UpperCAmelCase : np.ndarray , _UpperCAmelCase : Dict[str, int] , _UpperCAmelCase : Optional[Union[str, ChannelDimension]] = None , **_UpperCAmelCase : Tuple , ): """simple docstring""" UpperCAmelCase__ = get_size_dict(_UpperCAmelCase ) if "height" not in size or "width" not in size: raise ValueError(f'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}''' ) return center_crop(_UpperCAmelCase , size=(size["""height"""], size["""width"""]) , data_format=_UpperCAmelCase , **_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Tuple , _UpperCAmelCase : np.ndarray , _UpperCAmelCase : Union[int, float] , _UpperCAmelCase : Optional[Union[str, ChannelDimension]] = None , **_UpperCAmelCase : Dict , ): """simple docstring""" return rescale(_UpperCAmelCase , scale=_UpperCAmelCase , data_format=_UpperCAmelCase , **_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : List[Any] , _UpperCAmelCase : np.ndarray , _UpperCAmelCase : Union[float, List[float]] , _UpperCAmelCase : Union[float, List[float]] , _UpperCAmelCase : Optional[Union[str, ChannelDimension]] = None , **_UpperCAmelCase : Optional[Any] , ): """simple docstring""" return normalize(_UpperCAmelCase , mean=_UpperCAmelCase , std=_UpperCAmelCase , data_format=_UpperCAmelCase , **_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Any , _UpperCAmelCase : ImageInput , _UpperCAmelCase : bool = None , _UpperCAmelCase : Dict[str, int] = None , _UpperCAmelCase : PILImageResampling = None , _UpperCAmelCase : bool = None , _UpperCAmelCase : Dict[str, int] = None , _UpperCAmelCase : bool = None , _UpperCAmelCase : float = None , _UpperCAmelCase : bool = None , _UpperCAmelCase : Optional[Union[float, List[float]]] = None , _UpperCAmelCase : Optional[Union[float, List[float]]] = None , _UpperCAmelCase : Optional[ChannelDimension] = ChannelDimension.FIRST , ): """simple docstring""" if do_resize and size is None or resample is None: raise ValueError("""Size and resample must be specified if do_resize is True.""" ) if do_center_crop and crop_size is None: raise ValueError("""Crop size must be specified if do_center_crop is True.""" ) if do_rescale and rescale_factor is None: raise ValueError("""Rescale factor must be specified if do_rescale is True.""" ) if do_normalize and (image_mean is None or image_std is None): raise ValueError("""Image mean and std must be specified if do_normalize is True.""" ) # All transformations expect numpy arrays. UpperCAmelCase__ = to_numpy_array(_UpperCAmelCase ) if do_resize: UpperCAmelCase__ = self.resize(image=_UpperCAmelCase , size=_UpperCAmelCase , resample=_UpperCAmelCase ) if do_center_crop: UpperCAmelCase__ = self.center_crop(_UpperCAmelCase , size=_UpperCAmelCase ) if do_rescale: UpperCAmelCase__ = self.rescale(image=_UpperCAmelCase , scale=_UpperCAmelCase ) if do_normalize: UpperCAmelCase__ = self.normalize(image=_UpperCAmelCase , mean=_UpperCAmelCase , std=_UpperCAmelCase ) UpperCAmelCase__ = to_channel_dimension_format(_UpperCAmelCase , _UpperCAmelCase ) return image def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] , _UpperCAmelCase : ImageInput , _UpperCAmelCase : bool = None , _UpperCAmelCase : Dict[str, int] = None , _UpperCAmelCase : PILImageResampling = None , _UpperCAmelCase : bool = None , _UpperCAmelCase : Dict[str, int] = None , _UpperCAmelCase : bool = None , _UpperCAmelCase : float = None , _UpperCAmelCase : bool = None , _UpperCAmelCase : Optional[Union[float, List[float]]] = None , _UpperCAmelCase : Optional[Union[float, List[float]]] = None , _UpperCAmelCase : Optional[Union[str, TensorType]] = None , _UpperCAmelCase : ChannelDimension = ChannelDimension.FIRST , **_UpperCAmelCase : Tuple , ): """simple docstring""" UpperCAmelCase__ = do_resize if do_resize is not None else self.do_resize UpperCAmelCase__ = resample if resample is not None else self.resample UpperCAmelCase__ = do_center_crop if do_center_crop is not None else self.do_center_crop UpperCAmelCase__ = do_rescale if do_rescale is not None else self.do_rescale UpperCAmelCase__ = rescale_factor if rescale_factor is not None else self.rescale_factor UpperCAmelCase__ = do_normalize if do_normalize is not None else self.do_normalize UpperCAmelCase__ = image_mean if image_mean is not None else self.image_mean UpperCAmelCase__ = image_std if image_std is not None else self.image_std UpperCAmelCase__ = size if size is not None else self.size UpperCAmelCase__ = get_size_dict(_UpperCAmelCase , default_to_square=_UpperCAmelCase ) UpperCAmelCase__ = crop_size if crop_size is not None else self.crop_size UpperCAmelCase__ = get_size_dict(_UpperCAmelCase , param_name="""crop_size""" ) if not valid_images(_UpperCAmelCase ): raise ValueError( """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """ """torch.Tensor, tf.Tensor or jax.ndarray.""" ) UpperCAmelCase__ = make_batched(_UpperCAmelCase ) UpperCAmelCase__ = [ [ self._preprocess_image( image=_UpperCAmelCase , do_resize=_UpperCAmelCase , size=_UpperCAmelCase , resample=_UpperCAmelCase , do_center_crop=_UpperCAmelCase , crop_size=_UpperCAmelCase , do_rescale=_UpperCAmelCase , rescale_factor=_UpperCAmelCase , do_normalize=_UpperCAmelCase , image_mean=_UpperCAmelCase , image_std=_UpperCAmelCase , data_format=_UpperCAmelCase , ) for img in video ] for video in videos ] UpperCAmelCase__ = {"""pixel_values""": videos} return BatchFeature(data=_UpperCAmelCase , tensor_type=_UpperCAmelCase )

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'''simple docstring''' def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int = 1000000 ): '''simple docstring''' UpperCAmelCase__ = [i - 1 for i in range(limit + 1 )] for i in range(2 , limit + 1 ): if phi[i] == i - 1: for j in range(2 * i , limit + 1 , SCREAMING_SNAKE_CASE__ ): phi[j] -= phi[j] // i return sum(phi[2 : limit + 1] ) if __name__ == "__main__": print(solution())

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'''simple docstring''' def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int ): '''simple docstring''' UpperCAmelCase__ = 1 for i in range(1 , num + 1 ): fact *= i return fact def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int ): '''simple docstring''' UpperCAmelCase__ = 0 while number > 0: UpperCAmelCase__ = number % 10 sum_of_digits += last_digit UpperCAmelCase__ = number // 10 # Removing the last_digit from the given number return sum_of_digits def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int = 100 ): '''simple docstring''' UpperCAmelCase__ = factorial(SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = split_and_add(SCREAMING_SNAKE_CASE__ ) return result if __name__ == "__main__": print(solution(int(input('Enter the Number: ').strip())))

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'''simple docstring''' from typing import List, Union from ..utils import ( add_end_docstrings, is_tf_available, is_torch_available, is_vision_available, logging, requires_backends, ) from .base import PIPELINE_INIT_ARGS, Pipeline if is_vision_available(): from PIL import Image from ..image_utils import load_image if is_tf_available(): import tensorflow as tf from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING from ..tf_utils import stable_softmax if is_torch_available(): from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING UpperCAmelCase_ = logging.get_logger(__name__) @add_end_docstrings(lowerCamelCase_ ) class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' def __init__( self : Optional[Any] , *_UpperCAmelCase : Union[str, Any] , **_UpperCAmelCase : Dict ): """simple docstring""" super().__init__(*_UpperCAmelCase , **_UpperCAmelCase ) requires_backends(self , """vision""" ) self.check_model_type( TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING if self.framework == """tf""" else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING ) def SCREAMING_SNAKE_CASE__ ( self : str , _UpperCAmelCase : List[Any]=None ): """simple docstring""" UpperCAmelCase__ = {} if top_k is not None: UpperCAmelCase__ = top_k return {}, {}, postprocess_params def __call__( self : Any , _UpperCAmelCase : Union[str, List[str], "Image.Image", List["Image.Image"]] , **_UpperCAmelCase : str ): """simple docstring""" return super().__call__(_UpperCAmelCase , **_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] , _UpperCAmelCase : Tuple ): """simple docstring""" UpperCAmelCase__ = load_image(_UpperCAmelCase ) UpperCAmelCase__ = self.image_processor(images=_UpperCAmelCase , return_tensors=self.framework ) return model_inputs def SCREAMING_SNAKE_CASE__ ( self : Dict , _UpperCAmelCase : Tuple ): """simple docstring""" UpperCAmelCase__ = self.model(**_UpperCAmelCase ) return model_outputs def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , _UpperCAmelCase : Dict , _UpperCAmelCase : str=5 ): """simple docstring""" if top_k > self.model.config.num_labels: UpperCAmelCase__ = self.model.config.num_labels if self.framework == "pt": UpperCAmelCase__ = model_outputs.logits.softmax(-1 )[0] UpperCAmelCase__ , UpperCAmelCase__ = probs.topk(_UpperCAmelCase ) elif self.framework == "tf": UpperCAmelCase__ = stable_softmax(model_outputs.logits , axis=-1 )[0] UpperCAmelCase__ = tf.math.top_k(_UpperCAmelCase , k=_UpperCAmelCase ) UpperCAmelCase__ , UpperCAmelCase__ = topk.values.numpy(), topk.indices.numpy() else: raise ValueError(f'''Unsupported framework: {self.framework}''' ) UpperCAmelCase__ = scores.tolist() UpperCAmelCase__ = ids.tolist() return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(_UpperCAmelCase , _UpperCAmelCase )]

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'''simple docstring''' from typing import Optional, Tuple, Union import flax import flax.linen as nn import jax import jax.numpy as jnp from flax.core.frozen_dict import FrozenDict from ..configuration_utils import ConfigMixin, flax_register_to_config from ..utils import BaseOutput from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps from .modeling_flax_utils import FlaxModelMixin from .unet_ad_blocks_flax import ( FlaxCrossAttnDownBlockaD, FlaxCrossAttnUpBlockaD, FlaxDownBlockaD, FlaxUNetMidBlockaDCrossAttn, FlaxUpBlockaD, ) @flax.struct.dataclass class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' lowerCAmelCase_ : jnp.ndarray @flax_register_to_config class lowerCAmelCase_ ( nn.Module , lowerCamelCase_ , lowerCamelCase_ ): '''simple docstring''' lowerCAmelCase_ : int = 32 lowerCAmelCase_ : int = 4 lowerCAmelCase_ : int = 4 lowerCAmelCase_ : Tuple[str] = ( "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D", ) lowerCAmelCase_ : Tuple[str] = ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D") lowerCAmelCase_ : Union[bool, Tuple[bool]] = False lowerCAmelCase_ : Tuple[int] = (320, 640, 1_280, 1_280) lowerCAmelCase_ : int = 2 lowerCAmelCase_ : Union[int, Tuple[int]] = 8 lowerCAmelCase_ : Optional[Union[int, Tuple[int]]] = None lowerCAmelCase_ : int = 1_280 lowerCAmelCase_ : float = 0.0 lowerCAmelCase_ : bool = False lowerCAmelCase_ : jnp.dtype = jnp.floataa lowerCAmelCase_ : bool = True lowerCAmelCase_ : int = 0 lowerCAmelCase_ : bool = False def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , _UpperCAmelCase : jax.random.KeyArray ): """simple docstring""" UpperCAmelCase__ = (1, self.in_channels, self.sample_size, self.sample_size) UpperCAmelCase__ = jnp.zeros(_UpperCAmelCase , dtype=jnp.floataa ) UpperCAmelCase__ = jnp.ones((1,) , dtype=jnp.intaa ) UpperCAmelCase__ = jnp.zeros((1, 1, self.cross_attention_dim) , dtype=jnp.floataa ) UpperCAmelCase__ , UpperCAmelCase__ = jax.random.split(_UpperCAmelCase ) UpperCAmelCase__ = {"""params""": params_rng, """dropout""": dropout_rng} return self.init(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )["params"] def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ): """simple docstring""" UpperCAmelCase__ = self.block_out_channels UpperCAmelCase__ = block_out_channels[0] * 4 if self.num_attention_heads is not None: raise ValueError( """At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19.""" ) # If `num_attention_heads` is not defined (which is the case for most models) # it will default to `attention_head_dim`. This looks weird upon first reading it and it is. # The reason for this behavior is to correct for incorrectly named variables that were introduced # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131 # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking # which is why we correct for the naming here. UpperCAmelCase__ = self.num_attention_heads or self.attention_head_dim # input UpperCAmelCase__ = nn.Conv( block_out_channels[0] , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , ) # time UpperCAmelCase__ = FlaxTimesteps( block_out_channels[0] , flip_sin_to_cos=self.flip_sin_to_cos , freq_shift=self.config.freq_shift ) UpperCAmelCase__ = FlaxTimestepEmbedding(_UpperCAmelCase , dtype=self.dtype ) UpperCAmelCase__ = self.only_cross_attention if isinstance(_UpperCAmelCase , _UpperCAmelCase ): UpperCAmelCase__ = (only_cross_attention,) * len(self.down_block_types ) if isinstance(_UpperCAmelCase , _UpperCAmelCase ): UpperCAmelCase__ = (num_attention_heads,) * len(self.down_block_types ) # down UpperCAmelCase__ = [] UpperCAmelCase__ = block_out_channels[0] for i, down_block_type in enumerate(self.down_block_types ): UpperCAmelCase__ = output_channel UpperCAmelCase__ = block_out_channels[i] UpperCAmelCase__ = i == len(_UpperCAmelCase ) - 1 if down_block_type == "CrossAttnDownBlock2D": UpperCAmelCase__ = FlaxCrossAttnDownBlockaD( in_channels=_UpperCAmelCase , out_channels=_UpperCAmelCase , dropout=self.dropout , num_layers=self.layers_per_block , num_attention_heads=num_attention_heads[i] , add_downsample=not is_final_block , use_linear_projection=self.use_linear_projection , only_cross_attention=only_cross_attention[i] , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , ) else: UpperCAmelCase__ = FlaxDownBlockaD( in_channels=_UpperCAmelCase , out_channels=_UpperCAmelCase , dropout=self.dropout , num_layers=self.layers_per_block , add_downsample=not is_final_block , dtype=self.dtype , ) down_blocks.append(_UpperCAmelCase ) UpperCAmelCase__ = down_blocks # mid UpperCAmelCase__ = FlaxUNetMidBlockaDCrossAttn( in_channels=block_out_channels[-1] , dropout=self.dropout , num_attention_heads=num_attention_heads[-1] , use_linear_projection=self.use_linear_projection , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , ) # up UpperCAmelCase__ = [] UpperCAmelCase__ = list(reversed(_UpperCAmelCase ) ) UpperCAmelCase__ = list(reversed(_UpperCAmelCase ) ) UpperCAmelCase__ = list(reversed(_UpperCAmelCase ) ) UpperCAmelCase__ = reversed_block_out_channels[0] for i, up_block_type in enumerate(self.up_block_types ): UpperCAmelCase__ = output_channel UpperCAmelCase__ = reversed_block_out_channels[i] UpperCAmelCase__ = reversed_block_out_channels[min(i + 1 , len(_UpperCAmelCase ) - 1 )] UpperCAmelCase__ = i == len(_UpperCAmelCase ) - 1 if up_block_type == "CrossAttnUpBlock2D": UpperCAmelCase__ = FlaxCrossAttnUpBlockaD( in_channels=_UpperCAmelCase , out_channels=_UpperCAmelCase , prev_output_channel=_UpperCAmelCase , num_layers=self.layers_per_block + 1 , num_attention_heads=reversed_num_attention_heads[i] , add_upsample=not is_final_block , dropout=self.dropout , use_linear_projection=self.use_linear_projection , only_cross_attention=only_cross_attention[i] , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , ) else: UpperCAmelCase__ = FlaxUpBlockaD( in_channels=_UpperCAmelCase , out_channels=_UpperCAmelCase , prev_output_channel=_UpperCAmelCase , num_layers=self.layers_per_block + 1 , add_upsample=not is_final_block , dropout=self.dropout , dtype=self.dtype , ) up_blocks.append(_UpperCAmelCase ) UpperCAmelCase__ = output_channel UpperCAmelCase__ = up_blocks # out UpperCAmelCase__ = nn.GroupNorm(num_groups=32 , epsilon=1E-5 ) UpperCAmelCase__ = nn.Conv( self.out_channels , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , ) def __call__( self : str , _UpperCAmelCase : Any , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Dict , _UpperCAmelCase : Dict=None , _UpperCAmelCase : Tuple=None , _UpperCAmelCase : bool = True , _UpperCAmelCase : bool = False , ): """simple docstring""" if not isinstance(_UpperCAmelCase , jnp.ndarray ): UpperCAmelCase__ = jnp.array([timesteps] , dtype=jnp.intaa ) elif isinstance(_UpperCAmelCase , jnp.ndarray ) and len(timesteps.shape ) == 0: UpperCAmelCase__ = timesteps.astype(dtype=jnp.floataa ) UpperCAmelCase__ = jnp.expand_dims(_UpperCAmelCase , 0 ) UpperCAmelCase__ = self.time_proj(_UpperCAmelCase ) UpperCAmelCase__ = self.time_embedding(_UpperCAmelCase ) # 2. pre-process UpperCAmelCase__ = jnp.transpose(_UpperCAmelCase , (0, 2, 3, 1) ) UpperCAmelCase__ = self.conv_in(_UpperCAmelCase ) # 3. down UpperCAmelCase__ = (sample,) for down_block in self.down_blocks: if isinstance(_UpperCAmelCase , _UpperCAmelCase ): UpperCAmelCase__ , UpperCAmelCase__ = down_block(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , deterministic=not train ) else: UpperCAmelCase__ , UpperCAmelCase__ = down_block(_UpperCAmelCase , _UpperCAmelCase , deterministic=not train ) down_block_res_samples += res_samples if down_block_additional_residuals is not None: UpperCAmelCase__ = () for down_block_res_sample, down_block_additional_residual in zip( _UpperCAmelCase , _UpperCAmelCase ): down_block_res_sample += down_block_additional_residual new_down_block_res_samples += (down_block_res_sample,) UpperCAmelCase__ = new_down_block_res_samples # 4. mid UpperCAmelCase__ = self.mid_block(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , deterministic=not train ) if mid_block_additional_residual is not None: sample += mid_block_additional_residual # 5. up for up_block in self.up_blocks: UpperCAmelCase__ = down_block_res_samples[-(self.layers_per_block + 1) :] UpperCAmelCase__ = down_block_res_samples[: -(self.layers_per_block + 1)] if isinstance(_UpperCAmelCase , _UpperCAmelCase ): UpperCAmelCase__ = up_block( _UpperCAmelCase , temb=_UpperCAmelCase , encoder_hidden_states=_UpperCAmelCase , res_hidden_states_tuple=_UpperCAmelCase , deterministic=not train , ) else: UpperCAmelCase__ = up_block(_UpperCAmelCase , temb=_UpperCAmelCase , res_hidden_states_tuple=_UpperCAmelCase , deterministic=not train ) # 6. post-process UpperCAmelCase__ = self.conv_norm_out(_UpperCAmelCase ) UpperCAmelCase__ = nn.silu(_UpperCAmelCase ) UpperCAmelCase__ = self.conv_out(_UpperCAmelCase ) UpperCAmelCase__ = jnp.transpose(_UpperCAmelCase , (0, 3, 1, 2) ) if not return_dict: return (sample,) return FlaxUNetaDConditionOutput(sample=_UpperCAmelCase )

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'''simple docstring''' from math import factorial def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int = 20 ): '''simple docstring''' UpperCAmelCase__ = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1, # 2, 3,... UpperCAmelCase__ = n // 2 return int(factorial(SCREAMING_SNAKE_CASE__ ) / (factorial(SCREAMING_SNAKE_CASE__ ) * factorial(n - k )) ) if __name__ == "__main__": import sys if len(sys.argv) == 1: print(solution(2_0)) else: try: UpperCAmelCase_ = int(sys.argv[1]) print(solution(n)) except ValueError: print('Invalid entry - please enter a number.')

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'''simple docstring''' import warnings from ...utils import is_sklearn_available, requires_backends if is_sklearn_available(): from scipy.stats import pearsonr, spearmanr from sklearn.metrics import fa_score, matthews_corrcoef UpperCAmelCase_ = ( 'This metric will be removed from the library soon, metrics should be handled with the 🤗 Evaluate ' 'library. You can have a look at this example script for pointers: ' 'https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py' ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Dict ): '''simple docstring''' warnings.warn(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) requires_backends(SCREAMING_SNAKE_CASE__ , """sklearn""" ) return (preds == labels).mean() def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : List[str] ): '''simple docstring''' warnings.warn(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) requires_backends(SCREAMING_SNAKE_CASE__ , """sklearn""" ) UpperCAmelCase__ = simple_accuracy(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = fa_score(y_true=SCREAMING_SNAKE_CASE__ , y_pred=SCREAMING_SNAKE_CASE__ ) return { "acc": acc, "f1": fa, "acc_and_f1": (acc + fa) / 2, } def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : List[str] ): '''simple docstring''' warnings.warn(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) requires_backends(SCREAMING_SNAKE_CASE__ , """sklearn""" ) UpperCAmelCase__ = pearsonr(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )[0] UpperCAmelCase__ = spearmanr(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )[0] return { "pearson": pearson_corr, "spearmanr": spearman_corr, "corr": (pearson_corr + spearman_corr) / 2, } def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Union[str, Any] ): '''simple docstring''' warnings.warn(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) requires_backends(SCREAMING_SNAKE_CASE__ , """sklearn""" ) assert len(SCREAMING_SNAKE_CASE__ ) == len(SCREAMING_SNAKE_CASE__ ), F'''Predictions and labels have mismatched lengths {len(SCREAMING_SNAKE_CASE__ )} and {len(SCREAMING_SNAKE_CASE__ )}''' if task_name == "cola": return {"mcc": matthews_corrcoef(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )} elif task_name == "sst-2": return {"acc": simple_accuracy(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )} elif task_name == "mrpc": return acc_and_fa(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) elif task_name == "sts-b": return pearson_and_spearman(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) elif task_name == "qqp": return acc_and_fa(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) elif task_name == "mnli": return {"mnli/acc": simple_accuracy(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )} elif task_name == "mnli-mm": return {"mnli-mm/acc": simple_accuracy(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )} elif task_name == "qnli": return {"acc": simple_accuracy(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )} elif task_name == "rte": return {"acc": simple_accuracy(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )} elif task_name == "wnli": return {"acc": simple_accuracy(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )} elif task_name == "hans": return {"acc": simple_accuracy(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )} else: raise KeyError(SCREAMING_SNAKE_CASE__ ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : Optional[Any] ): '''simple docstring''' warnings.warn(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) requires_backends(SCREAMING_SNAKE_CASE__ , """sklearn""" ) if len(SCREAMING_SNAKE_CASE__ ) != len(SCREAMING_SNAKE_CASE__ ): raise ValueError(F'''Predictions and labels have mismatched lengths {len(SCREAMING_SNAKE_CASE__ )} and {len(SCREAMING_SNAKE_CASE__ )}''' ) if task_name == "xnli": return {"acc": simple_accuracy(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )} else: raise KeyError(SCREAMING_SNAKE_CASE__ )

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'''simple docstring''' import json import os import unittest from transformers import MgpstrTokenizer from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES from transformers.testing_utils import require_tokenizers from ...test_tokenization_common import TokenizerTesterMixin @require_tokenizers class lowerCAmelCase_ ( lowerCamelCase_ , unittest.TestCase ): '''simple docstring''' lowerCAmelCase_ : int = MgpstrTokenizer lowerCAmelCase_ : List[str] = False lowerCAmelCase_ : Optional[int] = {} lowerCAmelCase_ : Any = False def SCREAMING_SNAKE_CASE__ ( self : List[Any] ): """simple docstring""" super().setUp() # fmt: off UpperCAmelCase__ = ["""[GO]""", """[s]""", """0""", """1""", """2""", """3""", """4""", """5""", """6""", """7""", """8""", """9""", """a""", """b""", """c""", """d""", """e""", """f""", """g""", """h""", """i""", """j""", """k""", """l""", """m""", """n""", """o""", """p""", """q""", """r""", """s""", """t""", """u""", """v""", """w""", """x""", """y""", """z"""] # fmt: on UpperCAmelCase__ = dict(zip(_UpperCAmelCase , range(len(_UpperCAmelCase ) ) ) ) UpperCAmelCase__ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] ) with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as fp: fp.write(json.dumps(_UpperCAmelCase ) + """\n""" ) def SCREAMING_SNAKE_CASE__ ( self : List[str] , **_UpperCAmelCase : Optional[Any] ): """simple docstring""" return MgpstrTokenizer.from_pretrained(self.tmpdirname , **_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] , _UpperCAmelCase : Tuple ): """simple docstring""" UpperCAmelCase__ = """tester""" UpperCAmelCase__ = """tester""" return input_text, output_text @unittest.skip("""MGP-STR always lower cases letters.""" ) def SCREAMING_SNAKE_CASE__ ( self : List[str] ): """simple docstring""" pass def SCREAMING_SNAKE_CASE__ ( self : Any ): """simple docstring""" UpperCAmelCase__ = self.get_tokenizers(do_lower_case=_UpperCAmelCase ) for tokenizer in tokenizers: with self.subTest(f'''{tokenizer.__class__.__name__}''' ): UpperCAmelCase__ = """[SPECIAL_TOKEN]""" tokenizer.add_special_tokens({"""cls_token""": special_token} ) UpperCAmelCase__ = tokenizer.encode([special_token] , add_special_tokens=_UpperCAmelCase ) self.assertEqual(len(_UpperCAmelCase ) , 1 ) UpperCAmelCase__ = tokenizer.decode(_UpperCAmelCase , skip_special_tokens=_UpperCAmelCase ) self.assertTrue(special_token not in decoded ) def SCREAMING_SNAKE_CASE__ ( self : List[str] ): """simple docstring""" UpperCAmelCase__ = self.get_tokenizers() for tokenizer in tokenizers: with self.subTest(f'''{tokenizer.__class__.__name__}''' ): UpperCAmelCase__ , UpperCAmelCase__ = self.get_input_output_texts(_UpperCAmelCase ) UpperCAmelCase__ = tokenizer.tokenize(_UpperCAmelCase ) UpperCAmelCase__ = tokenizer.convert_tokens_to_ids(_UpperCAmelCase ) UpperCAmelCase__ = tokenizer.encode(_UpperCAmelCase , add_special_tokens=_UpperCAmelCase ) self.assertListEqual(_UpperCAmelCase , _UpperCAmelCase ) UpperCAmelCase__ = tokenizer.convert_ids_to_tokens(_UpperCAmelCase ) self.assertNotEqual(len(_UpperCAmelCase ) , 0 ) UpperCAmelCase__ = tokenizer.decode(_UpperCAmelCase ) self.assertIsInstance(_UpperCAmelCase , _UpperCAmelCase ) self.assertEqual(text_a.replace(""" """ , """""" ) , _UpperCAmelCase ) @unittest.skip("""MGP-STR tokenizer only handles one sequence.""" ) def SCREAMING_SNAKE_CASE__ ( self : Any ): """simple docstring""" pass @unittest.skip("""inputs cannot be pretokenized in MgpstrTokenizer""" ) def SCREAMING_SNAKE_CASE__ ( self : str ): """simple docstring""" pass

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'''simple docstring''' import random import torch from huggingface_hub import HfApi from diffusers import UNetaDModel UpperCAmelCase_ = HfApi() UpperCAmelCase_ = {} # fmt: off UpperCAmelCase_ = torch.tensor([ -0.7515, -1.6883, 0.2420, 0.0300, 0.6347, 1.3433, -1.1743, -3.7467, 1.2342, -2.2485, 0.4636, 0.8076, -0.7991, 0.3969, 0.8498, 0.9189, -1.8887, -3.3522, 0.7639, 0.2040, 0.6271, -2.7148, -1.6316, 3.0839, 0.3186, 0.2721, -0.9759, -1.2461, 2.6257, 1.3557 ]) UpperCAmelCase_ = torch.tensor([ -2.3639, -2.5344, 0.0054, -0.6674, 1.5990, 1.0158, 0.3124, -2.1436, 1.8795, -2.5429, -0.1566, -0.3973, 1.2490, 2.6447, 1.2283, -0.5208, -2.8154, -3.5119, 2.3838, 1.2033, 1.7201, -2.1256, -1.4576, 2.7948, 2.4204, -0.9752, -1.2546, 0.8027, 3.2758, 3.1365 ]) UpperCAmelCase_ = torch.tensor([ -0.6531, -0.6891, -0.3172, -0.5375, -0.9140, -0.5367, -0.1175, -0.7869, -0.3808, -0.4513, -0.2098, -0.0083, 0.3183, 0.5140, 0.2247, -0.1304, -0.1302, -0.2802, -0.2084, -0.2025, -0.4967, -0.4873, -0.0861, 0.6925, 0.0250, 0.1290, -0.1543, 0.6316, 1.0460, 1.4943 ]) UpperCAmelCase_ = torch.tensor([ 0.0911, 0.1107, 0.0182, 0.0435, -0.0805, -0.0608, 0.0381, 0.2172, -0.0280, 0.1327, -0.0299, -0.0255, -0.0050, -0.1170, -0.1046, 0.0309, 0.1367, 0.1728, -0.0533, -0.0748, -0.0534, 0.1624, 0.0384, -0.1805, -0.0707, 0.0642, 0.0220, -0.0134, -0.1333, -0.1505 ]) UpperCAmelCase_ = torch.tensor([ 0.1321, 0.1337, 0.0440, 0.0622, -0.0591, -0.0370, 0.0503, 0.2133, -0.0177, 0.1415, -0.0116, -0.0112, 0.0044, -0.0980, -0.0789, 0.0395, 0.1502, 0.1785, -0.0488, -0.0514, -0.0404, 0.1539, 0.0454, -0.1559, -0.0665, 0.0659, 0.0383, -0.0005, -0.1266, -0.1386 ]) UpperCAmelCase_ = torch.tensor([ 0.1154, 0.1218, 0.0307, 0.0526, -0.0711, -0.0541, 0.0366, 0.2078, -0.0267, 0.1317, -0.0226, -0.0193, -0.0014, -0.1055, -0.0902, 0.0330, 0.1391, 0.1709, -0.0562, -0.0693, -0.0560, 0.1482, 0.0381, -0.1683, -0.0681, 0.0661, 0.0331, -0.0046, -0.1268, -0.1431 ]) UpperCAmelCase_ = torch.tensor([ 0.1192, 0.1240, 0.0414, 0.0606, -0.0557, -0.0412, 0.0430, 0.2042, -0.0200, 0.1385, -0.0115, -0.0132, 0.0017, -0.0965, -0.0802, 0.0398, 0.1433, 0.1747, -0.0458, -0.0533, -0.0407, 0.1545, 0.0419, -0.1574, -0.0645, 0.0626, 0.0341, -0.0010, -0.1199, -0.1390 ]) UpperCAmelCase_ = torch.tensor([ 0.1075, 0.1074, 0.0205, 0.0431, -0.0774, -0.0607, 0.0298, 0.2042, -0.0320, 0.1267, -0.0281, -0.0250, -0.0064, -0.1091, -0.0946, 0.0290, 0.1328, 0.1650, -0.0580, -0.0738, -0.0586, 0.1440, 0.0337, -0.1746, -0.0712, 0.0605, 0.0250, -0.0099, -0.1316, -0.1473 ]) UpperCAmelCase_ = torch.tensor([ -1.4572, -2.0481, -0.0414, -0.6005, 1.4136, 0.5848, 0.4028, -2.7330, 1.2212, -2.1228, 0.2155, 0.4039, 0.7662, 2.0535, 0.7477, -0.3243, -2.1758, -2.7648, 1.6947, 0.7026, 1.2338, -1.6078, -0.8682, 2.2810, 1.8574, -0.5718, -0.5586, -0.0186, 2.3415, 2.1251]) UpperCAmelCase_ = torch.tensor([ -1.3690, -1.9720, -0.4090, -0.6966, 1.4660, 0.9938, -0.1385, -2.7324, 0.7736, -1.8917, 0.2923, 0.4293, 0.1693, 1.4112, 1.1887, -0.3181, -2.2160, -2.6381, 1.3170, 0.8163, 0.9240, -1.6544, -0.6099, 2.5259, 1.6430, -0.9090, -0.9392, -0.0126, 2.4268, 2.3266 ]) UpperCAmelCase_ = torch.tensor([ -1.3525, -1.9628, -0.3956, -0.6860, 1.4664, 1.0014, -0.1259, -2.7212, 0.7772, -1.8811, 0.2996, 0.4388, 0.1704, 1.4029, 1.1701, -0.3027, -2.2053, -2.6287, 1.3350, 0.8131, 0.9274, -1.6292, -0.6098, 2.5131, 1.6505, -0.8958, -0.9298, -0.0151, 2.4257, 2.3355 ]) UpperCAmelCase_ = torch.tensor([ -2.0585, -2.7897, -0.2850, -0.8940, 1.9052, 0.5702, 0.6345, -3.8959, 1.5932, -3.2319, 0.1974, 0.0287, 1.7566, 2.6543, 0.8387, -0.5351, -3.2736, -4.3375, 2.9029, 1.6390, 1.4640, -2.1701, -1.9013, 2.9341, 3.4981, -0.6255, -1.1644, -0.1591, 3.7097, 3.2066 ]) UpperCAmelCase_ = torch.tensor([ -2.3139, -2.5594, -0.0197, -0.6785, 1.7001, 1.1606, 0.3075, -2.1740, 1.8071, -2.5630, -0.0926, -0.3811, 1.2116, 2.6246, 1.2731, -0.5398, -2.8153, -3.6140, 2.3893, 1.3262, 1.6258, -2.1856, -1.3267, 2.8395, 2.3779, -1.0623, -1.2468, 0.8959, 3.3367, 3.2243 ]) UpperCAmelCase_ = torch.tensor([ -2.0628, -2.7667, -0.2089, -0.8263, 2.0539, 0.5992, 0.6495, -3.8336, 1.6025, -3.2817, 0.1721, -0.0633, 1.7516, 2.7039, 0.8100, -0.5908, -3.2113, -4.4343, 2.9257, 1.3632, 1.5562, -2.1489, -1.9894, 3.0560, 3.3396, -0.7328, -1.0417, 0.0383, 3.7093, 3.2343 ]) UpperCAmelCase_ = torch.tensor([ -1.4574, -2.0569, -0.0473, -0.6117, 1.4018, 0.5769, 0.4129, -2.7344, 1.2241, -2.1397, 0.2000, 0.3937, 0.7616, 2.0453, 0.7324, -0.3391, -2.1746, -2.7744, 1.6963, 0.6921, 1.2187, -1.6172, -0.8877, 2.2439, 1.8471, -0.5839, -0.5605, -0.0464, 2.3250, 2.1219 ]) # fmt: on UpperCAmelCase_ = api.list_models(filter='diffusers') for mod in models: if "google" in mod.author or mod.modelId == "CompVis/ldm-celebahq-256": UpperCAmelCase_ = '/home/patrick/google_checkpoints/' + mod.modelId.split('/')[-1] print(f"Started running {mod.modelId}!!!") if mod.modelId.startswith('CompVis'): UpperCAmelCase_ = UNetaDModel.from_pretrained(local_checkpoint, subfolder='unet') else: UpperCAmelCase_ = UNetaDModel.from_pretrained(local_checkpoint) torch.manual_seed(0) random.seed(0) UpperCAmelCase_ = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size) UpperCAmelCase_ = torch.tensor([1_0] * noise.shape[0]) with torch.no_grad(): UpperCAmelCase_ = model(noise, time_step).sample assert torch.allclose( logits[0, 0, 0, :3_0], results['_'.join('_'.join(mod.modelId.split('/')).split('-'))], atol=1E-3 ) print(f"{mod.modelId} has passed successfully!!!")

346

'''simple docstring''' from abc import ABC, abstractmethod from typing import List, Optional class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' def __init__( self : Union[str, Any] ): """simple docstring""" self.test() def SCREAMING_SNAKE_CASE__ ( self : List[str] ): """simple docstring""" UpperCAmelCase__ = 0 UpperCAmelCase__ = False while not completed: if counter == 1: self.reset() UpperCAmelCase__ = self.advance() if not self.does_advance(_UpperCAmelCase ): raise Exception( """Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true.""" ) UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = self.update(_UpperCAmelCase ) counter += 1 if counter > 1_00_00: raise Exception("""update() does not fulfill the constraint.""" ) if self.remaining() != 0: raise Exception("""Custom Constraint is not defined correctly.""" ) @abstractmethod def SCREAMING_SNAKE_CASE__ ( self : Dict ): """simple docstring""" raise NotImplementedError( f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' ) @abstractmethod def SCREAMING_SNAKE_CASE__ ( self : Dict , _UpperCAmelCase : int ): """simple docstring""" raise NotImplementedError( f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' ) @abstractmethod def SCREAMING_SNAKE_CASE__ ( self : Any , _UpperCAmelCase : int ): """simple docstring""" raise NotImplementedError( f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' ) @abstractmethod def SCREAMING_SNAKE_CASE__ ( self : List[Any] ): """simple docstring""" raise NotImplementedError( f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' ) @abstractmethod def SCREAMING_SNAKE_CASE__ ( self : List[str] ): """simple docstring""" raise NotImplementedError( f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' ) @abstractmethod def SCREAMING_SNAKE_CASE__ ( self : int , _UpperCAmelCase : List[Any]=False ): """simple docstring""" raise NotImplementedError( f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' ) class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' def __init__( self : Union[str, Any] , _UpperCAmelCase : List[int] ): """simple docstring""" super(_UpperCAmelCase , self ).__init__() if not isinstance(_UpperCAmelCase , _UpperCAmelCase ) or len(_UpperCAmelCase ) == 0: raise ValueError(f'''`token_ids` has to be a non-empty list, but is {token_ids}.''' ) if any((not isinstance(_UpperCAmelCase , _UpperCAmelCase ) or token_id < 0) for token_id in token_ids ): raise ValueError(f'''Each list in `token_ids` has to be a list of positive integers, but is {token_ids}.''' ) UpperCAmelCase__ = token_ids UpperCAmelCase__ = len(self.token_ids ) UpperCAmelCase__ = -1 # the index of the currently fulfilled step UpperCAmelCase__ = False def SCREAMING_SNAKE_CASE__ ( self : List[str] ): """simple docstring""" if self.completed: return None return self.token_ids[self.fulfilled_idx + 1] def SCREAMING_SNAKE_CASE__ ( self : List[str] , _UpperCAmelCase : int ): """simple docstring""" if not isinstance(_UpperCAmelCase , _UpperCAmelCase ): raise ValueError(f'''`token_id` has to be an `int`, but is {token_id} of type {type(_UpperCAmelCase )}''' ) if self.completed: return False return token_id == self.token_ids[self.fulfilled_idx + 1] def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] , _UpperCAmelCase : int ): """simple docstring""" if not isinstance(_UpperCAmelCase , _UpperCAmelCase ): raise ValueError(f'''`token_id` has to be an `int`, but is {token_id} of type {type(_UpperCAmelCase )}''' ) UpperCAmelCase__ = False UpperCAmelCase__ = False UpperCAmelCase__ = False if self.does_advance(_UpperCAmelCase ): self.fulfilled_idx += 1 UpperCAmelCase__ = True if self.fulfilled_idx == (self.seqlen - 1): UpperCAmelCase__ = True UpperCAmelCase__ = completed else: # failed to make progress. UpperCAmelCase__ = True self.reset() return stepped, completed, reset def SCREAMING_SNAKE_CASE__ ( self : Tuple ): """simple docstring""" UpperCAmelCase__ = False UpperCAmelCase__ = 0 def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ): """simple docstring""" return self.seqlen - (self.fulfilled_idx + 1) def SCREAMING_SNAKE_CASE__ ( self : Any , _UpperCAmelCase : Optional[int]=False ): """simple docstring""" UpperCAmelCase__ = PhrasalConstraint(self.token_ids ) if stateful: UpperCAmelCase__ = self.seqlen UpperCAmelCase__ = self.fulfilled_idx UpperCAmelCase__ = self.completed return new_constraint class lowerCAmelCase_ : '''simple docstring''' def __init__( self : Any , _UpperCAmelCase : List[List[int]] , _UpperCAmelCase : List[str]=True ): """simple docstring""" UpperCAmelCase__ = max([len(_UpperCAmelCase ) for one in nested_token_ids] ) UpperCAmelCase__ = {} for token_ids in nested_token_ids: UpperCAmelCase__ = root for tidx, token_id in enumerate(_UpperCAmelCase ): if token_id not in level: UpperCAmelCase__ = {} UpperCAmelCase__ = level[token_id] if no_subsets and self.has_subsets(_UpperCAmelCase , _UpperCAmelCase ): raise ValueError( """Each list in `nested_token_ids` can't be a complete subset of another list, but is""" f''' {nested_token_ids}.''' ) UpperCAmelCase__ = root def SCREAMING_SNAKE_CASE__ ( self : int , _UpperCAmelCase : int ): """simple docstring""" UpperCAmelCase__ = self.trie for current_token in current_seq: UpperCAmelCase__ = start[current_token] UpperCAmelCase__ = list(start.keys() ) return next_tokens def SCREAMING_SNAKE_CASE__ ( self : str , _UpperCAmelCase : Tuple ): """simple docstring""" UpperCAmelCase__ = self.next_tokens(_UpperCAmelCase ) return len(_UpperCAmelCase ) == 0 def SCREAMING_SNAKE_CASE__ ( self : List[str] , _UpperCAmelCase : Optional[int] ): """simple docstring""" UpperCAmelCase__ = list(root.values() ) if len(_UpperCAmelCase ) == 0: return 1 else: return sum([self.count_leaves(_UpperCAmelCase ) for nn in next_nodes] ) def SCREAMING_SNAKE_CASE__ ( self : List[Any] , _UpperCAmelCase : Dict , _UpperCAmelCase : Dict ): """simple docstring""" UpperCAmelCase__ = self.count_leaves(_UpperCAmelCase ) return len(_UpperCAmelCase ) != leaf_count class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' def __init__( self : Dict , _UpperCAmelCase : List[List[int]] ): """simple docstring""" super(_UpperCAmelCase , self ).__init__() if not isinstance(_UpperCAmelCase , _UpperCAmelCase ) or len(_UpperCAmelCase ) == 0: raise ValueError(f'''`nested_token_ids` has to be a non-empty list, but is {nested_token_ids}.''' ) if any(not isinstance(_UpperCAmelCase , _UpperCAmelCase ) for token_ids in nested_token_ids ): raise ValueError(f'''`nested_token_ids` has to be a list of lists, but is {nested_token_ids}.''' ) if any( any((not isinstance(_UpperCAmelCase , _UpperCAmelCase ) or token_id < 0) for token_id in token_ids ) for token_ids in nested_token_ids ): raise ValueError( f'''Each list in `nested_token_ids` has to be a list of positive integers, but is {nested_token_ids}.''' ) UpperCAmelCase__ = DisjunctiveTrie(_UpperCAmelCase ) UpperCAmelCase__ = nested_token_ids UpperCAmelCase__ = self.trie.max_height UpperCAmelCase__ = [] UpperCAmelCase__ = False def SCREAMING_SNAKE_CASE__ ( self : Any ): """simple docstring""" UpperCAmelCase__ = self.trie.next_tokens(self.current_seq ) if len(_UpperCAmelCase ) == 0: return None else: return token_list def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] , _UpperCAmelCase : int ): """simple docstring""" if not isinstance(_UpperCAmelCase , _UpperCAmelCase ): raise ValueError(f'''`token_id` is supposed to be type `int`, but is {token_id} of type {type(_UpperCAmelCase )}''' ) UpperCAmelCase__ = self.trie.next_tokens(self.current_seq ) return token_id in next_tokens def SCREAMING_SNAKE_CASE__ ( self : Dict , _UpperCAmelCase : int ): """simple docstring""" if not isinstance(_UpperCAmelCase , _UpperCAmelCase ): raise ValueError(f'''`token_id` is supposed to be type `int`, but is {token_id} of type {type(_UpperCAmelCase )}''' ) UpperCAmelCase__ = False UpperCAmelCase__ = False UpperCAmelCase__ = False if self.does_advance(_UpperCAmelCase ): self.current_seq.append(_UpperCAmelCase ) UpperCAmelCase__ = True else: UpperCAmelCase__ = True self.reset() UpperCAmelCase__ = self.trie.reached_leaf(self.current_seq ) UpperCAmelCase__ = completed return stepped, completed, reset def SCREAMING_SNAKE_CASE__ ( self : str ): """simple docstring""" UpperCAmelCase__ = False UpperCAmelCase__ = [] def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ): """simple docstring""" if self.completed: # since this can be completed without reaching max height return 0 else: return self.seqlen - len(self.current_seq ) def SCREAMING_SNAKE_CASE__ ( self : Tuple , _UpperCAmelCase : Dict=False ): """simple docstring""" UpperCAmelCase__ = DisjunctiveConstraint(self.token_ids ) if stateful: UpperCAmelCase__ = self.seqlen UpperCAmelCase__ = self.current_seq UpperCAmelCase__ = self.completed return new_constraint class lowerCAmelCase_ : '''simple docstring''' def __init__( self : Optional[int] , _UpperCAmelCase : List[Constraint] ): """simple docstring""" UpperCAmelCase__ = constraints # max # of steps required to fulfill a given constraint UpperCAmelCase__ = max([c.seqlen for c in constraints] ) UpperCAmelCase__ = len(_UpperCAmelCase ) UpperCAmelCase__ = False self.init_state() def SCREAMING_SNAKE_CASE__ ( self : Any ): """simple docstring""" UpperCAmelCase__ = [] UpperCAmelCase__ = None UpperCAmelCase__ = [constraint.copy(stateful=_UpperCAmelCase ) for constraint in self.constraints] def SCREAMING_SNAKE_CASE__ ( self : List[str] ): """simple docstring""" UpperCAmelCase__ = 0 if self.inprogress_constraint: # extra points for having a constraint mid-fulfilled add += self.max_seqlen - self.inprogress_constraint.remaining() return (len(self.complete_constraints ) * self.max_seqlen) + add def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ): """simple docstring""" UpperCAmelCase__ = [] if self.inprogress_constraint is None: for constraint in self.pending_constraints: # "pending" == "unfulfilled yet" UpperCAmelCase__ = constraint.advance() if isinstance(_UpperCAmelCase , _UpperCAmelCase ): token_list.append(_UpperCAmelCase ) elif isinstance(_UpperCAmelCase , _UpperCAmelCase ): token_list.extend(_UpperCAmelCase ) else: UpperCAmelCase__ = self.inprogress_constraint.advance() if isinstance(_UpperCAmelCase , _UpperCAmelCase ): token_list.append(_UpperCAmelCase ) elif isinstance(_UpperCAmelCase , _UpperCAmelCase ): token_list.extend(_UpperCAmelCase ) if len(_UpperCAmelCase ) == 0: return None else: return token_list def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , _UpperCAmelCase : Optional[List[int]] ): """simple docstring""" self.init_state() if token_ids is not None: for token in token_ids: # completes or steps **one** constraint UpperCAmelCase__ , UpperCAmelCase__ = self.add(_UpperCAmelCase ) # the entire list of constraints are fulfilled if self.completed: break def SCREAMING_SNAKE_CASE__ ( self : Any , _UpperCAmelCase : int ): """simple docstring""" if not isinstance(_UpperCAmelCase , _UpperCAmelCase ): raise ValueError(f'''`token_id` should be an `int`, but is `{token_id}`.''' ) UpperCAmelCase__ , UpperCAmelCase__ = False, False if self.completed: UpperCAmelCase__ = True UpperCAmelCase__ = False return complete, stepped if self.inprogress_constraint is not None: # In the middle of fulfilling a constraint. If the `token_id` *does* makes an incremental progress to current # job, simply update the state UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = self.inprogress_constraint.update(_UpperCAmelCase ) if reset: # 1. If the next token breaks the progress, then we must restart. # e.g. constraint = "I love pies" and sequence so far is "I love" but `token_id` == "books". # But that doesn't mean we self.init_state(), since we only reset the state for this particular # constraint, not the full list of constraints. self.pending_constraints.append(self.inprogress_constraint.copy(stateful=_UpperCAmelCase ) ) UpperCAmelCase__ = None if complete: # 2. If the next token completes the constraint, move it to completed list, set # inprogress to None. If there are no pending constraints either, then this full list of constraints # is complete. self.complete_constraints.append(self.inprogress_constraint ) UpperCAmelCase__ = None if len(self.pending_constraints ) == 0: # we're done! UpperCAmelCase__ = True else: # Not in the middle of fulfilling a constraint. So does this `token_id` helps us step towards any of our list # of constraints? for cidx, pending_constraint in enumerate(self.pending_constraints ): if pending_constraint.does_advance(_UpperCAmelCase ): UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = pending_constraint.update(_UpperCAmelCase ) if not stepped: raise Exception( """`constraint.update(token_id)` is not yielding incremental progress, """ """even though `constraint.does_advance(token_id)` is true.""" ) if complete: self.complete_constraints.append(_UpperCAmelCase ) UpperCAmelCase__ = None if not complete and stepped: UpperCAmelCase__ = pending_constraint if complete or stepped: # If we made any progress at all, then it's at least not a "pending constraint". UpperCAmelCase__ = ( self.pending_constraints[:cidx] + self.pending_constraints[cidx + 1 :] ) if len(self.pending_constraints ) == 0 and self.inprogress_constraint is None: # If there's no longer any pending after this and no inprogress either, then we must be # complete. UpperCAmelCase__ = True break # prevent accidentally stepping through multiple constraints with just one token. return complete, stepped def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , _UpperCAmelCase : List[Any]=True ): """simple docstring""" UpperCAmelCase__ = ConstraintListState(self.constraints ) # we actually never though self.constraints objects # throughout this process. So it's at initialization state. if stateful: UpperCAmelCase__ = [ constraint.copy(stateful=_UpperCAmelCase ) for constraint in self.complete_constraints ] if self.inprogress_constraint is not None: UpperCAmelCase__ = self.inprogress_constraint.copy(stateful=_UpperCAmelCase ) UpperCAmelCase__ = [constraint.copy() for constraint in self.pending_constraints] return new_state

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1

'''simple docstring''' import os import unicodedata from shutil import copyfile from typing import Any, Dict, List, Optional, Tuple import sentencepiece as spm from ...tokenization_utils import AddedToken, PreTrainedTokenizer from ...utils import SPIECE_UNDERLINE, logging UpperCAmelCase_ = logging.get_logger(__name__) UpperCAmelCase_ = {'vocab_file': 'spiece.model'} UpperCAmelCase_ = { 'vocab_file': { 'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model', } } class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' def __init__( self : Dict , _UpperCAmelCase : str , _UpperCAmelCase : Any=False , _UpperCAmelCase : int=True , _UpperCAmelCase : Union[str, Any]=False , _UpperCAmelCase : Dict="<s>" , _UpperCAmelCase : int="</s>" , _UpperCAmelCase : Dict="<unk>" , _UpperCAmelCase : Tuple="<sep>" , _UpperCAmelCase : List[Any]="<pad>" , _UpperCAmelCase : int="<cls>" , _UpperCAmelCase : Union[str, Any]="<mask>" , _UpperCAmelCase : List[str]=["<eop>", "<eod>"] , _UpperCAmelCase : Optional[Dict[str, Any]] = None , **_UpperCAmelCase : int , ): """simple docstring""" UpperCAmelCase__ = AddedToken(_UpperCAmelCase , lstrip=_UpperCAmelCase , rstrip=_UpperCAmelCase ) if isinstance(_UpperCAmelCase , _UpperCAmelCase ) else mask_token UpperCAmelCase__ = {} if sp_model_kwargs is None else sp_model_kwargs super().__init__( do_lower_case=_UpperCAmelCase , remove_space=_UpperCAmelCase , keep_accents=_UpperCAmelCase , bos_token=_UpperCAmelCase , eos_token=_UpperCAmelCase , unk_token=_UpperCAmelCase , sep_token=_UpperCAmelCase , pad_token=_UpperCAmelCase , cls_token=_UpperCAmelCase , mask_token=_UpperCAmelCase , additional_special_tokens=_UpperCAmelCase , sp_model_kwargs=self.sp_model_kwargs , **_UpperCAmelCase , ) UpperCAmelCase__ = 3 UpperCAmelCase__ = do_lower_case UpperCAmelCase__ = remove_space UpperCAmelCase__ = keep_accents UpperCAmelCase__ = vocab_file UpperCAmelCase__ = spm.SentencePieceProcessor(**self.sp_model_kwargs ) self.sp_model.Load(_UpperCAmelCase ) try: import jieba except ModuleNotFoundError as error: raise error.__class__( """You need to install jieba to use CpmTokenizer or CpmTokenizerFast. """ """See https://pypi.org/project/jieba/ for installation.""" ) UpperCAmelCase__ = jieba UpperCAmelCase__ = str.maketrans(""" \n""" , """\u2582\u2583""" ) @property # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ): """simple docstring""" return len(self.sp_model ) def SCREAMING_SNAKE_CASE__ ( self : List[str] ): """simple docstring""" UpperCAmelCase__ = {self.convert_ids_to_tokens(_UpperCAmelCase ): i for i in range(self.vocab_size )} vocab.update(self.added_tokens_encoder ) return vocab def __getstate__( self : Dict ): """simple docstring""" UpperCAmelCase__ = self.__dict__.copy() UpperCAmelCase__ = None return state def __setstate__( self : Union[str, Any] , _UpperCAmelCase : Union[str, Any] ): """simple docstring""" UpperCAmelCase__ = d # for backward compatibility if not hasattr(self , """sp_model_kwargs""" ): UpperCAmelCase__ = {} UpperCAmelCase__ = spm.SentencePieceProcessor(**self.sp_model_kwargs ) self.sp_model.Load(self.vocab_file ) def SCREAMING_SNAKE_CASE__ ( self : int , _UpperCAmelCase : Optional[Any] ): """simple docstring""" if self.remove_space: UpperCAmelCase__ = """ """.join(inputs.strip().split() ) else: UpperCAmelCase__ = inputs UpperCAmelCase__ = outputs.replace("""``""" , """\"""" ).replace("""''""" , """\"""" ) if not self.keep_accents: UpperCAmelCase__ = unicodedata.normalize("""NFKD""" , _UpperCAmelCase ) UpperCAmelCase__ = """""".join([c for c in outputs if not unicodedata.combining(_UpperCAmelCase )] ) if self.do_lower_case: UpperCAmelCase__ = outputs.lower() return outputs def SCREAMING_SNAKE_CASE__ ( self : Tuple , _UpperCAmelCase : str ): """simple docstring""" UpperCAmelCase__ = self.preprocess_text(_UpperCAmelCase ) UpperCAmelCase__ = self.sp_model.encode(_UpperCAmelCase , out_type=_UpperCAmelCase ) UpperCAmelCase__ = [] for piece in pieces: if len(_UpperCAmelCase ) > 1 and piece[-1] == str(""",""" ) and piece[-2].isdigit(): UpperCAmelCase__ = self.sp_model.EncodeAsPieces(piece[:-1].replace(_UpperCAmelCase , """""" ) ) if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE: if len(cur_pieces[0] ) == 1: UpperCAmelCase__ = cur_pieces[1:] else: UpperCAmelCase__ = cur_pieces[0][1:] cur_pieces.append(piece[-1] ) new_pieces.extend(_UpperCAmelCase ) else: new_pieces.append(_UpperCAmelCase ) return new_pieces def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , _UpperCAmelCase : Union[str, Any] ): """simple docstring""" return self.sp_model.PieceToId(_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Dict , _UpperCAmelCase : Any ): """simple docstring""" return self.sp_model.IdToPiece(_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : int , _UpperCAmelCase : Dict ): """simple docstring""" UpperCAmelCase__ = """""".join(_UpperCAmelCase ).replace(_UpperCAmelCase , """ """ ).strip() return out_string def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] , _UpperCAmelCase : List[int] , _UpperCAmelCase : Optional[List[int]] = None ): """simple docstring""" UpperCAmelCase__ = [self.sep_token_id] UpperCAmelCase__ = [self.cls_token_id] if token_ids_a is None: return token_ids_a + sep + cls return token_ids_a + sep + token_ids_a + sep + cls def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] , _UpperCAmelCase : List[int] , _UpperCAmelCase : Optional[List[int]] = None , _UpperCAmelCase : bool = False ): """simple docstring""" if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_a=_UpperCAmelCase , token_ids_a=_UpperCAmelCase , already_has_special_tokens=_UpperCAmelCase ) if token_ids_a is not None: return ([0] * len(_UpperCAmelCase )) + [1] + ([0] * len(_UpperCAmelCase )) + [1, 1] return ([0] * len(_UpperCAmelCase )) + [1, 1] def SCREAMING_SNAKE_CASE__ ( self : Dict , _UpperCAmelCase : List[int] , _UpperCAmelCase : Optional[List[int]] = None ): """simple docstring""" UpperCAmelCase__ = [self.sep_token_id] UpperCAmelCase__ = [2] if token_ids_a is None: return len(token_ids_a + sep ) * [0] + cls_segment_id return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id def SCREAMING_SNAKE_CASE__ ( self : List[Any] , _UpperCAmelCase : str , _UpperCAmelCase : Optional[str] = None ): """simple docstring""" if not os.path.isdir(_UpperCAmelCase ): logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' ) return UpperCAmelCase__ = os.path.join( _UpperCAmelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] ) if os.path.abspath(self.vocab_file ) != os.path.abspath(_UpperCAmelCase ) and os.path.isfile(self.vocab_file ): copyfile(self.vocab_file , _UpperCAmelCase ) elif not os.path.isfile(self.vocab_file ): with open(_UpperCAmelCase , """wb""" ) as fi: UpperCAmelCase__ = self.sp_model.serialized_model_proto() fi.write(_UpperCAmelCase ) return (out_vocab_file,) def SCREAMING_SNAKE_CASE__ ( self : Tuple , *_UpperCAmelCase : Tuple , **_UpperCAmelCase : Optional[Any] ): """simple docstring""" UpperCAmelCase__ = super()._decode(*_UpperCAmelCase , **_UpperCAmelCase ) UpperCAmelCase__ = text.replace(""" """ , """""" ).replace("""\u2582""" , """ """ ).replace("""\u2583""" , """\n""" ) return text

346

'''simple docstring''' import doctest import logging import os import unittest from pathlib import Path from typing import List, Union import transformers from transformers.testing_utils import require_tf, require_torch, slow UpperCAmelCase_ = logging.getLogger() @unittest.skip("""Temporarily disable the doc tests.""" ) @require_torch @require_tf @slow class lowerCAmelCase_ ( unittest.TestCase ): '''simple docstring''' def SCREAMING_SNAKE_CASE__ ( self : str , _UpperCAmelCase : Path , _UpperCAmelCase : Union[str, None] = None , _UpperCAmelCase : Union[List[str], None] = None , _UpperCAmelCase : Union[str, List[str], None] = None , _UpperCAmelCase : bool = True , ): """simple docstring""" UpperCAmelCase__ = [file for file in os.listdir(_UpperCAmelCase ) if os.path.isfile(os.path.join(_UpperCAmelCase , _UpperCAmelCase ) )] if identifier is not None: UpperCAmelCase__ = [file for file in files if identifier in file] if n_identifier is not None: if isinstance(_UpperCAmelCase , _UpperCAmelCase ): for n_ in n_identifier: UpperCAmelCase__ = [file for file in files if n_ not in file] else: UpperCAmelCase__ = [file for file in files if n_identifier not in file] UpperCAmelCase__ = ignore_files or [] ignore_files.append("""__init__.py""" ) UpperCAmelCase__ = [file for file in files if file not in ignore_files] for file in files: # Open all files print("""Testing""" , _UpperCAmelCase ) if only_modules: UpperCAmelCase__ = file.split(""".""" )[0] try: UpperCAmelCase__ = getattr(_UpperCAmelCase , _UpperCAmelCase ) UpperCAmelCase__ = doctest.DocTestSuite(_UpperCAmelCase ) UpperCAmelCase__ = unittest.TextTestRunner().run(_UpperCAmelCase ) self.assertIs(len(result.failures ) , 0 ) except AttributeError: logger.info(f'''{module_identifier} is not a module.''' ) else: UpperCAmelCase__ = doctest.testfile(str("""..""" / directory / file ) , optionflags=doctest.ELLIPSIS ) self.assertIs(result.failed , 0 ) def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ): """simple docstring""" UpperCAmelCase__ = Path("""src/transformers""" ) UpperCAmelCase__ = """modeling""" UpperCAmelCase__ = [ """modeling_ctrl.py""", """modeling_tf_ctrl.py""", ] self.analyze_directory(_UpperCAmelCase , identifier=_UpperCAmelCase , ignore_files=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ): """simple docstring""" UpperCAmelCase__ = Path("""src/transformers""" ) UpperCAmelCase__ = """tokenization""" self.analyze_directory(_UpperCAmelCase , identifier=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : str ): """simple docstring""" UpperCAmelCase__ = Path("""src/transformers""" ) UpperCAmelCase__ = """configuration""" self.analyze_directory(_UpperCAmelCase , identifier=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ): """simple docstring""" UpperCAmelCase__ = Path("""src/transformers""" ) UpperCAmelCase__ = ["""configuration""", """modeling""", """tokenization"""] self.analyze_directory(_UpperCAmelCase , n_identifier=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : List[str] ): """simple docstring""" UpperCAmelCase__ = Path("""docs/source""" ) UpperCAmelCase__ = ["""favicon.ico"""] self.analyze_directory(_UpperCAmelCase , ignore_files=_UpperCAmelCase , only_modules=_UpperCAmelCase )

346

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'''simple docstring''' import json import os from pathlib import Path from shutil import copyfile from typing import Any, Dict, List, Optional, Tuple, Union import sentencepiece from ...tokenization_utils import PreTrainedTokenizer from ...utils import logging UpperCAmelCase_ = logging.get_logger(__name__) UpperCAmelCase_ = '▁' UpperCAmelCase_ = { 'vocab_file': 'vocab.json', 'spm_file': 'sentencepiece.bpe.model', } UpperCAmelCase_ = { 'vocab_file': { 'facebook/s2t-small-librispeech-asr': ( 'https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json' ), }, 'spm_file': { 'facebook/s2t-small-librispeech-asr': ( 'https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model' ) }, } UpperCAmelCase_ = { 'facebook/s2t-small-librispeech-asr': 1_0_2_4, } UpperCAmelCase_ = ['pt', 'fr', 'ru', 'nl', 'ro', 'it', 'es', 'de'] UpperCAmelCase_ = {'mustc': MUSTC_LANGS} class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' lowerCAmelCase_ : str = VOCAB_FILES_NAMES lowerCAmelCase_ : Dict = PRETRAINED_VOCAB_FILES_MAP lowerCAmelCase_ : List[str] = MAX_MODEL_INPUT_SIZES lowerCAmelCase_ : Any = ["""input_ids""", """attention_mask"""] lowerCAmelCase_ : List[int] = [] def __init__( self : Optional[Any] , _UpperCAmelCase : Dict , _UpperCAmelCase : List[Any] , _UpperCAmelCase : List[Any]="<s>" , _UpperCAmelCase : Union[str, Any]="</s>" , _UpperCAmelCase : Dict="<pad>" , _UpperCAmelCase : Any="<unk>" , _UpperCAmelCase : Dict=False , _UpperCAmelCase : Optional[Any]=False , _UpperCAmelCase : Any=None , _UpperCAmelCase : Dict=None , _UpperCAmelCase : Optional[Dict[str, Any]] = None , **_UpperCAmelCase : Optional[int] , ): """simple docstring""" UpperCAmelCase__ = {} if sp_model_kwargs is None else sp_model_kwargs super().__init__( bos_token=_UpperCAmelCase , eos_token=_UpperCAmelCase , unk_token=_UpperCAmelCase , pad_token=_UpperCAmelCase , do_upper_case=_UpperCAmelCase , do_lower_case=_UpperCAmelCase , tgt_lang=_UpperCAmelCase , lang_codes=_UpperCAmelCase , sp_model_kwargs=self.sp_model_kwargs , **_UpperCAmelCase , ) UpperCAmelCase__ = do_upper_case UpperCAmelCase__ = do_lower_case UpperCAmelCase__ = load_json(_UpperCAmelCase ) UpperCAmelCase__ = {v: k for k, v in self.encoder.items()} UpperCAmelCase__ = spm_file UpperCAmelCase__ = load_spm(_UpperCAmelCase , self.sp_model_kwargs ) if lang_codes is not None: UpperCAmelCase__ = lang_codes UpperCAmelCase__ = LANGUAGES[lang_codes] UpperCAmelCase__ = [f'''<lang:{lang}>''' for lang in self.langs] UpperCAmelCase__ = {lang: self.sp_model.PieceToId(f'''<lang:{lang}>''' ) for lang in self.langs} UpperCAmelCase__ = self.lang_tokens UpperCAmelCase__ = tgt_lang if tgt_lang is not None else self.langs[0] self.set_tgt_lang_special_tokens(self._tgt_lang ) else: UpperCAmelCase__ = {} @property def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ): """simple docstring""" return len(self.encoder ) @property def SCREAMING_SNAKE_CASE__ ( self : Dict ): """simple docstring""" return self._tgt_lang @tgt_lang.setter def SCREAMING_SNAKE_CASE__ ( self : List[str] , _UpperCAmelCase : Union[str, Any] ): """simple docstring""" UpperCAmelCase__ = new_tgt_lang self.set_tgt_lang_special_tokens(_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : int , _UpperCAmelCase : str ): """simple docstring""" UpperCAmelCase__ = self.lang_code_to_id[tgt_lang] UpperCAmelCase__ = [lang_code_id] def SCREAMING_SNAKE_CASE__ ( self : List[str] , _UpperCAmelCase : str ): """simple docstring""" return self.sp_model.encode(_UpperCAmelCase , out_type=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , _UpperCAmelCase : List[str] ): """simple docstring""" return self.encoder.get(_UpperCAmelCase , self.encoder[self.unk_token] ) def SCREAMING_SNAKE_CASE__ ( self : Any , _UpperCAmelCase : int ): """simple docstring""" return self.decoder.get(_UpperCAmelCase , self.unk_token ) def SCREAMING_SNAKE_CASE__ ( self : int , _UpperCAmelCase : List[str] ): """simple docstring""" UpperCAmelCase__ = [] UpperCAmelCase__ = """""" for token in tokens: # make sure that special tokens are not decoded using sentencepiece model if token in self.all_special_tokens: UpperCAmelCase__ = self.sp_model.decode(_UpperCAmelCase ) out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " " UpperCAmelCase__ = [] else: current_sub_tokens.append(_UpperCAmelCase ) UpperCAmelCase__ = self.sp_model.decode(_UpperCAmelCase ) out_string += decoded.upper() if self.do_upper_case else decoded return out_string.strip() def SCREAMING_SNAKE_CASE__ ( self : List[Any] , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : List[Any]=None ): """simple docstring""" if token_ids_a is None: return self.prefix_tokens + token_ids_a + [self.eos_token_id] # We don't expect to process pairs, but leave the pair logic for API consistency return self.prefix_tokens + token_ids_a + token_ids_a + [self.eos_token_id] def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] , _UpperCAmelCase : List[int] , _UpperCAmelCase : Optional[List[int]] = None , _UpperCAmelCase : bool = False ): """simple docstring""" if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_a=_UpperCAmelCase , token_ids_a=_UpperCAmelCase , already_has_special_tokens=_UpperCAmelCase ) UpperCAmelCase__ = [1] * len(self.prefix_tokens ) UpperCAmelCase__ = [1] if token_ids_a is None: return prefix_ones + ([0] * len(_UpperCAmelCase )) + suffix_ones return prefix_ones + ([0] * len(_UpperCAmelCase )) + ([0] * len(_UpperCAmelCase )) + suffix_ones def SCREAMING_SNAKE_CASE__ ( self : List[Any] ): """simple docstring""" UpperCAmelCase__ = self.encoder.copy() vocab.update(self.added_tokens_encoder ) return vocab def __getstate__( self : Optional[int] ): """simple docstring""" UpperCAmelCase__ = self.__dict__.copy() UpperCAmelCase__ = None return state def __setstate__( self : int , _UpperCAmelCase : Dict ): """simple docstring""" UpperCAmelCase__ = d # for backward compatibility if not hasattr(self , """sp_model_kwargs""" ): UpperCAmelCase__ = {} UpperCAmelCase__ = load_spm(self.spm_file , self.sp_model_kwargs ) def SCREAMING_SNAKE_CASE__ ( self : Tuple , _UpperCAmelCase : str , _UpperCAmelCase : Optional[str] = None ): """simple docstring""" UpperCAmelCase__ = Path(_UpperCAmelCase ) assert save_dir.is_dir(), f'''{save_directory} should be a directory''' UpperCAmelCase__ = save_dir / ( (filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""vocab_file"""] ) UpperCAmelCase__ = save_dir / ( (filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""spm_file"""] ) save_json(self.encoder , _UpperCAmelCase ) if os.path.abspath(self.spm_file ) != os.path.abspath(_UpperCAmelCase ) and os.path.isfile(self.spm_file ): copyfile(self.spm_file , _UpperCAmelCase ) elif not os.path.isfile(self.spm_file ): with open(_UpperCAmelCase , """wb""" ) as fi: UpperCAmelCase__ = self.sp_model.serialized_model_proto() fi.write(_UpperCAmelCase ) return (str(_UpperCAmelCase ), str(_UpperCAmelCase )) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : Dict[str, Any] ): '''simple docstring''' UpperCAmelCase__ = sentencepiece.SentencePieceProcessor(**SCREAMING_SNAKE_CASE__ ) spm.Load(str(SCREAMING_SNAKE_CASE__ ) ) return spm def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str ): '''simple docstring''' with open(SCREAMING_SNAKE_CASE__ , """r""" ) as f: return json.load(SCREAMING_SNAKE_CASE__ ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : str ): '''simple docstring''' with open(SCREAMING_SNAKE_CASE__ , """w""" ) as f: json.dump(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , indent=2 )

346

'''simple docstring''' from datasets.utils.patching import _PatchedModuleObj, patch_submodule from . import _test_patching def _UpperCamelCase ( ): '''simple docstring''' import os as original_os from os import path as original_path from os import rename as original_rename from os.path import dirname as original_dirname from os.path import join as original_join assert _test_patching.os is original_os assert _test_patching.path is original_path assert _test_patching.join is original_join assert _test_patching.renamed_os is original_os assert _test_patching.renamed_path is original_path assert _test_patching.renamed_join is original_join UpperCAmelCase__ = """__test_patch_submodule_mock__""" with patch_submodule(_test_patching , """os.path.join""" , SCREAMING_SNAKE_CASE__ ): # Every way to access os.path.join must be patched, and the rest must stay untouched # check os.path.join assert isinstance(_test_patching.os , _PatchedModuleObj ) assert isinstance(_test_patching.os.path , _PatchedModuleObj ) assert _test_patching.os.path.join is mock # check path.join assert isinstance(_test_patching.path , _PatchedModuleObj ) assert _test_patching.path.join is mock # check join assert _test_patching.join is mock # check that the other attributes are untouched assert _test_patching.os.rename is original_rename assert _test_patching.path.dirname is original_dirname assert _test_patching.os.path.dirname is original_dirname # Even renamed modules or objects must be patched # check renamed_os.path.join assert isinstance(_test_patching.renamed_os , _PatchedModuleObj ) assert isinstance(_test_patching.renamed_os.path , _PatchedModuleObj ) assert _test_patching.renamed_os.path.join is mock # check renamed_path.join assert isinstance(_test_patching.renamed_path , _PatchedModuleObj ) assert _test_patching.renamed_path.join is mock # check renamed_join assert _test_patching.renamed_join is mock # check that the other attributes are untouched assert _test_patching.renamed_os.rename is original_rename assert _test_patching.renamed_path.dirname is original_dirname assert _test_patching.renamed_os.path.dirname is original_dirname # check that everthing is back to normal when the patch is over assert _test_patching.os is original_os assert _test_patching.path is original_path assert _test_patching.join is original_join assert _test_patching.renamed_os is original_os assert _test_patching.renamed_path is original_path assert _test_patching.renamed_join is original_join def _UpperCamelCase ( ): '''simple docstring''' assert _test_patching.open is open UpperCAmelCase__ = """__test_patch_submodule_builtin_mock__""" # _test_patching has "open" in its globals assert _test_patching.open is open with patch_submodule(_test_patching , """open""" , SCREAMING_SNAKE_CASE__ ): assert _test_patching.open is mock # check that everthing is back to normal when the patch is over assert _test_patching.open is open def _UpperCamelCase ( ): '''simple docstring''' UpperCAmelCase__ = """__test_patch_submodule_missing_mock__""" with patch_submodule(_test_patching , """pandas.read_csv""" , SCREAMING_SNAKE_CASE__ ): pass def _UpperCamelCase ( ): '''simple docstring''' UpperCAmelCase__ = """__test_patch_submodule_missing_builtin_mock__""" # _test_patching doesn't have "len" in its globals assert getattr(_test_patching , """len""" , SCREAMING_SNAKE_CASE__ ) is None with patch_submodule(_test_patching , """len""" , SCREAMING_SNAKE_CASE__ ): assert _test_patching.len is mock assert _test_patching.len is len def _UpperCamelCase ( ): '''simple docstring''' UpperCAmelCase__ = """__test_patch_submodule_start_and_stop_mock__""" UpperCAmelCase__ = patch_submodule(_test_patching , """open""" , SCREAMING_SNAKE_CASE__ ) assert _test_patching.open is open patch.start() assert _test_patching.open is mock patch.stop() assert _test_patching.open is open def _UpperCamelCase ( ): '''simple docstring''' from os import rename as original_rename from os.path import dirname as original_dirname from os.path import join as original_join UpperCAmelCase__ = """__test_patch_submodule_successive_join__""" UpperCAmelCase__ = """__test_patch_submodule_successive_dirname__""" UpperCAmelCase__ = """__test_patch_submodule_successive_rename__""" assert _test_patching.os.path.join is original_join assert _test_patching.os.path.dirname is original_dirname assert _test_patching.os.rename is original_rename with patch_submodule(_test_patching , """os.path.join""" , SCREAMING_SNAKE_CASE__ ): with patch_submodule(_test_patching , """os.rename""" , SCREAMING_SNAKE_CASE__ ): with patch_submodule(_test_patching , """os.path.dirname""" , SCREAMING_SNAKE_CASE__ ): assert _test_patching.os.path.join is mock_join assert _test_patching.os.path.dirname is mock_dirname assert _test_patching.os.rename is mock_rename # try another order with patch_submodule(_test_patching , """os.rename""" , SCREAMING_SNAKE_CASE__ ): with patch_submodule(_test_patching , """os.path.join""" , SCREAMING_SNAKE_CASE__ ): with patch_submodule(_test_patching , """os.path.dirname""" , SCREAMING_SNAKE_CASE__ ): assert _test_patching.os.path.join is mock_join assert _test_patching.os.path.dirname is mock_dirname assert _test_patching.os.rename is mock_rename assert _test_patching.os.path.join is original_join assert _test_patching.os.path.dirname is original_dirname assert _test_patching.os.rename is original_rename def _UpperCamelCase ( ): '''simple docstring''' UpperCAmelCase__ = """__test_patch_submodule_doesnt_exist_mock__""" with patch_submodule(_test_patching , """__module_that_doesn_exist__.__attribute_that_doesn_exist__""" , SCREAMING_SNAKE_CASE__ ): pass with patch_submodule(_test_patching , """os.__attribute_that_doesn_exist__""" , SCREAMING_SNAKE_CASE__ ): pass

346

1

'''simple docstring''' from __future__ import annotations import unittest from transformers import RoFormerConfig, is_tf_available from transformers.testing_utils import require_tf, slow from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_tf_available(): import tensorflow as tf from transformers import ( TFRoFormerForCausalLM, TFRoFormerForMaskedLM, TFRoFormerForMultipleChoice, TFRoFormerForQuestionAnswering, TFRoFormerForSequenceClassification, TFRoFormerForTokenClassification, TFRoFormerModel, ) from transformers.models.roformer.modeling_tf_roformer import ( TFRoFormerSelfAttention, TFRoFormerSinusoidalPositionalEmbedding, ) class lowerCAmelCase_ : '''simple docstring''' def __init__( self : Dict , _UpperCAmelCase : str , _UpperCAmelCase : Dict=13 , _UpperCAmelCase : List[Any]=7 , _UpperCAmelCase : Union[str, Any]=True , _UpperCAmelCase : Dict=True , _UpperCAmelCase : Optional[Any]=True , _UpperCAmelCase : Optional[int]=True , _UpperCAmelCase : List[Any]=99 , _UpperCAmelCase : Optional[int]=32 , _UpperCAmelCase : str=2 , _UpperCAmelCase : Optional[int]=4 , _UpperCAmelCase : Optional[int]=37 , _UpperCAmelCase : Optional[int]="gelu" , _UpperCAmelCase : Optional[int]=0.1 , _UpperCAmelCase : Optional[Any]=0.1 , _UpperCAmelCase : List[Any]=5_12 , _UpperCAmelCase : List[str]=16 , _UpperCAmelCase : Tuple=2 , _UpperCAmelCase : int=0.02 , _UpperCAmelCase : Any=3 , _UpperCAmelCase : Optional[int]=4 , _UpperCAmelCase : int=None , ): """simple docstring""" UpperCAmelCase__ = parent UpperCAmelCase__ = 13 UpperCAmelCase__ = 7 UpperCAmelCase__ = True UpperCAmelCase__ = True UpperCAmelCase__ = True UpperCAmelCase__ = True UpperCAmelCase__ = 99 UpperCAmelCase__ = 32 UpperCAmelCase__ = 2 UpperCAmelCase__ = 4 UpperCAmelCase__ = 37 UpperCAmelCase__ = """gelu""" UpperCAmelCase__ = 0.1 UpperCAmelCase__ = 0.1 UpperCAmelCase__ = 5_12 UpperCAmelCase__ = 16 UpperCAmelCase__ = 2 UpperCAmelCase__ = 0.02 UpperCAmelCase__ = 3 UpperCAmelCase__ = 4 UpperCAmelCase__ = None def SCREAMING_SNAKE_CASE__ ( self : int ): """simple docstring""" UpperCAmelCase__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size ) UpperCAmelCase__ = None if self.use_input_mask: UpperCAmelCase__ = random_attention_mask([self.batch_size, self.seq_length] ) UpperCAmelCase__ = None if self.use_token_type_ids: UpperCAmelCase__ = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size ) UpperCAmelCase__ = None UpperCAmelCase__ = None UpperCAmelCase__ = None if self.use_labels: UpperCAmelCase__ = ids_tensor([self.batch_size] , self.type_sequence_label_size ) UpperCAmelCase__ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels ) UpperCAmelCase__ = ids_tensor([self.batch_size] , self.num_choices ) UpperCAmelCase__ = RoFormerConfig( vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , return_dict=_UpperCAmelCase , ) return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels def SCREAMING_SNAKE_CASE__ ( self : str , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Any , _UpperCAmelCase : int , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : List[Any] , _UpperCAmelCase : List[Any] , _UpperCAmelCase : List[str] ): """simple docstring""" UpperCAmelCase__ = TFRoFormerModel(config=_UpperCAmelCase ) UpperCAmelCase__ = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids} UpperCAmelCase__ = [input_ids, input_mask] UpperCAmelCase__ = model(_UpperCAmelCase ) UpperCAmelCase__ = model(_UpperCAmelCase ) self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) ) def SCREAMING_SNAKE_CASE__ ( self : Dict , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Dict , _UpperCAmelCase : int , _UpperCAmelCase : Any , _UpperCAmelCase : str , _UpperCAmelCase : List[str] ): """simple docstring""" UpperCAmelCase__ = True UpperCAmelCase__ = TFRoFormerForCausalLM(config=_UpperCAmelCase ) UpperCAmelCase__ = { """input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids, } UpperCAmelCase__ = model(_UpperCAmelCase )["""logits"""] self.parent.assertListEqual( list(prediction_scores.numpy().shape ) , [self.batch_size, self.seq_length, self.vocab_size] ) def SCREAMING_SNAKE_CASE__ ( self : Any , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : int , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : List[Any] , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Union[str, Any] ): """simple docstring""" UpperCAmelCase__ = TFRoFormerForMaskedLM(config=_UpperCAmelCase ) UpperCAmelCase__ = { """input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids, } UpperCAmelCase__ = model(_UpperCAmelCase ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) ) def SCREAMING_SNAKE_CASE__ ( self : Any , _UpperCAmelCase : int , _UpperCAmelCase : str , _UpperCAmelCase : str , _UpperCAmelCase : List[Any] , _UpperCAmelCase : List[str] , _UpperCAmelCase : Any , _UpperCAmelCase : Union[str, Any] ): """simple docstring""" UpperCAmelCase__ = self.num_labels UpperCAmelCase__ = TFRoFormerForSequenceClassification(config=_UpperCAmelCase ) UpperCAmelCase__ = { """input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids, } UpperCAmelCase__ = model(_UpperCAmelCase ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) ) def SCREAMING_SNAKE_CASE__ ( self : str , _UpperCAmelCase : List[str] , _UpperCAmelCase : List[Any] , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Tuple , _UpperCAmelCase : List[str] , _UpperCAmelCase : Dict ): """simple docstring""" UpperCAmelCase__ = self.num_choices UpperCAmelCase__ = TFRoFormerForMultipleChoice(config=_UpperCAmelCase ) UpperCAmelCase__ = tf.tile(tf.expand_dims(_UpperCAmelCase , 1 ) , (1, self.num_choices, 1) ) UpperCAmelCase__ = tf.tile(tf.expand_dims(_UpperCAmelCase , 1 ) , (1, self.num_choices, 1) ) UpperCAmelCase__ = tf.tile(tf.expand_dims(_UpperCAmelCase , 1 ) , (1, self.num_choices, 1) ) UpperCAmelCase__ = { """input_ids""": multiple_choice_inputs_ids, """attention_mask""": multiple_choice_input_mask, """token_type_ids""": multiple_choice_token_type_ids, } UpperCAmelCase__ = model(_UpperCAmelCase ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) ) def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] , _UpperCAmelCase : Any , _UpperCAmelCase : Tuple , _UpperCAmelCase : str , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : str , _UpperCAmelCase : Any , _UpperCAmelCase : Any ): """simple docstring""" UpperCAmelCase__ = self.num_labels UpperCAmelCase__ = TFRoFormerForTokenClassification(config=_UpperCAmelCase ) UpperCAmelCase__ = { """input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids, } UpperCAmelCase__ = model(_UpperCAmelCase ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) ) def SCREAMING_SNAKE_CASE__ ( self : Dict , _UpperCAmelCase : str , _UpperCAmelCase : List[Any] , _UpperCAmelCase : str , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : str , _UpperCAmelCase : int , _UpperCAmelCase : Optional[int] ): """simple docstring""" UpperCAmelCase__ = TFRoFormerForQuestionAnswering(config=_UpperCAmelCase ) UpperCAmelCase__ = { """input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids, } UpperCAmelCase__ = model(_UpperCAmelCase ) self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) ) self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) ) def SCREAMING_SNAKE_CASE__ ( self : List[str] ): """simple docstring""" UpperCAmelCase__ = self.prepare_config_and_inputs() ( ( UpperCAmelCase__ ) , ( UpperCAmelCase__ ) , ( UpperCAmelCase__ ) , ( UpperCAmelCase__ ) , ( UpperCAmelCase__ ) , ( UpperCAmelCase__ ) , ( UpperCAmelCase__ ) , ) = config_and_inputs UpperCAmelCase__ = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask} return config, inputs_dict @require_tf class lowerCAmelCase_ ( lowerCamelCase_ , lowerCamelCase_ , unittest.TestCase ): '''simple docstring''' lowerCAmelCase_ : Dict = ( ( TFRoFormerModel, TFRoFormerForCausalLM, TFRoFormerForMaskedLM, TFRoFormerForQuestionAnswering, TFRoFormerForSequenceClassification, TFRoFormerForTokenClassification, TFRoFormerForMultipleChoice, ) if is_tf_available() else () ) lowerCAmelCase_ : Dict = ( { """feature-extraction""": TFRoFormerModel, """fill-mask""": TFRoFormerForMaskedLM, """question-answering""": TFRoFormerForQuestionAnswering, """text-classification""": TFRoFormerForSequenceClassification, """text-generation""": TFRoFormerForCausalLM, """token-classification""": TFRoFormerForTokenClassification, """zero-shot""": TFRoFormerForSequenceClassification, } if is_tf_available() else {} ) lowerCAmelCase_ : Tuple = False lowerCAmelCase_ : Any = False def SCREAMING_SNAKE_CASE__ ( self : Tuple , _UpperCAmelCase : List[Any] , _UpperCAmelCase : List[str] , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : str , _UpperCAmelCase : Union[str, Any] ): """simple docstring""" if pipeline_test_casse_name == "TextGenerationPipelineTests": return True return False def SCREAMING_SNAKE_CASE__ ( self : int ): """simple docstring""" UpperCAmelCase__ = TFRoFormerModelTester(self ) UpperCAmelCase__ = ConfigTester(self , config_class=_UpperCAmelCase , hidden_size=37 ) def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ): """simple docstring""" self.config_tester.run_common_tests() def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ): """simple docstring""" UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : List[str] ): """simple docstring""" UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_masked_lm(*_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ): """simple docstring""" UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_lm_head(*_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Dict ): """simple docstring""" UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_multiple_choice(*_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : int ): """simple docstring""" UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_question_answering(*_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ): """simple docstring""" UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_sequence_classification(*_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : List[Any] ): """simple docstring""" UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_token_classification(*_UpperCAmelCase ) @slow def SCREAMING_SNAKE_CASE__ ( self : Any ): """simple docstring""" UpperCAmelCase__ = TFRoFormerModel.from_pretrained("""junnyu/roformer_chinese_base""" ) self.assertIsNotNone(_UpperCAmelCase ) @require_tf class lowerCAmelCase_ ( unittest.TestCase ): '''simple docstring''' @slow def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ): """simple docstring""" UpperCAmelCase__ = TFRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base""" ) UpperCAmelCase__ = tf.constant([[0, 1, 2, 3, 4, 5]] ) UpperCAmelCase__ = model(_UpperCAmelCase )[0] # TODO Replace vocab size UpperCAmelCase__ = 5_00_00 UpperCAmelCase__ = [1, 6, vocab_size] self.assertEqual(output.shape , _UpperCAmelCase ) print(output[:, :3, :3] ) # TODO Replace values below with what was printed above. UpperCAmelCase__ = tf.constant( [ [ [-0.1205_3341, -1.026_4901, 0.2922_1946], [-1.513_3783, 0.19_7433, 0.1519_0607], [-5.013_5403, -3.90_0256, -0.8403_8764], ] ] ) tf.debugging.assert_near(output[:, :3, :3] , _UpperCAmelCase , atol=1E-4 ) @require_tf class lowerCAmelCase_ ( unittest.TestCase ): '''simple docstring''' lowerCAmelCase_ : int = 1e-4 def SCREAMING_SNAKE_CASE__ ( self : List[str] ): """simple docstring""" UpperCAmelCase__ = tf.constant([[4, 10]] ) UpperCAmelCase__ = TFRoFormerSinusoidalPositionalEmbedding(num_positions=6 , embedding_dim=6 ) UpperCAmelCase__ = emba(input_ids.shape ) UpperCAmelCase__ = tf.constant( [[0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 1.0000], [0.8415, 0.0464, 0.0022, 0.5403, 0.9989, 1.0000]] ) tf.debugging.assert_near(_UpperCAmelCase , _UpperCAmelCase , atol=self.tolerance ) def SCREAMING_SNAKE_CASE__ ( self : int ): """simple docstring""" UpperCAmelCase__ = tf.constant( [ [0.0000, 0.0000, 0.0000, 0.0000, 0.0000], [0.8415, 0.8219, 0.8020, 0.7819, 0.7617], [0.9093, 0.9364, 0.9581, 0.9749, 0.9870], ] ) UpperCAmelCase__ = TFRoFormerSinusoidalPositionalEmbedding(num_positions=5_12 , embedding_dim=5_12 ) emba([2, 16, 5_12] ) UpperCAmelCase__ = emba.weight[:3, :5] tf.debugging.assert_near(_UpperCAmelCase , _UpperCAmelCase , atol=self.tolerance ) @require_tf class lowerCAmelCase_ ( unittest.TestCase ): '''simple docstring''' lowerCAmelCase_ : Union[str, Any] = 1e-4 def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ): """simple docstring""" UpperCAmelCase__ = tf.reshape(tf.range(2 * 12 * 16 * 64 , dtype=tf.floataa ) , shape=(2, 12, 16, 64) ) / 1_00 UpperCAmelCase__ = -tf.reshape(tf.range(2 * 12 * 16 * 64 , dtype=tf.floataa ) , shape=(2, 12, 16, 64) ) / 1_00 UpperCAmelCase__ = TFRoFormerSinusoidalPositionalEmbedding(num_positions=32 , embedding_dim=64 ) UpperCAmelCase__ = embed_positions([2, 16, 7_68] )[None, None, :, :] UpperCAmelCase__ , UpperCAmelCase__ = TFRoFormerSelfAttention.apply_rotary_position_embeddings( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) UpperCAmelCase__ = tf.constant( [ [0.0000, 0.0100, 0.0200, 0.0300, 0.0400, 0.0500, 0.0600, 0.0700], [-0.2012, 0.8897, 0.0263, 0.9401, 0.2074, 0.9463, 0.3481, 0.9343], [-1.7057, 0.6271, -1.2145, 1.3897, -0.6303, 1.7647, -0.1173, 1.8985], [-2.1731, -1.6397, -2.7358, 0.2854, -2.1840, 1.7183, -1.3018, 2.4871], [0.2717, -3.6173, -2.9206, -2.1988, -3.6638, 0.3858, -2.9155, 2.2980], [3.9859, -2.1580, -0.7984, -4.4904, -4.1181, -2.0252, -4.4782, 1.1253], ] ) UpperCAmelCase__ = tf.constant( [ [0.0000, -0.0100, -0.0200, -0.0300, -0.0400, -0.0500, -0.0600, -0.0700], [0.2012, -0.8897, -0.0263, -0.9401, -0.2074, -0.9463, -0.3481, -0.9343], [1.7057, -0.6271, 1.2145, -1.3897, 0.6303, -1.7647, 0.1173, -1.8985], [2.1731, 1.6397, 2.7358, -0.2854, 2.1840, -1.7183, 1.3018, -2.4871], [-0.2717, 3.6173, 2.9206, 2.1988, 3.6638, -0.3858, 2.9155, -2.2980], [-3.9859, 2.1580, 0.7984, 4.4904, 4.1181, 2.0252, 4.4782, -1.1253], ] ) tf.debugging.assert_near(query_layer[0, 0, :6, :8] , _UpperCAmelCase , atol=self.tolerance ) tf.debugging.assert_near(key_layer[0, 0, :6, :8] , _UpperCAmelCase , atol=self.tolerance )

346

'''simple docstring''' from timeit import timeit UpperCAmelCase_ = { 'MALAYALAM': True, 'String': False, 'rotor': True, 'level': True, 'A': True, 'BB': True, 'ABC': False, 'amanaplanacanalpanama': True, # "a man a plan a canal panama" } # Ensure our test data is valid assert all((key == key[::-1]) is value for key, value in test_data.items()) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str ): '''simple docstring''' UpperCAmelCase__ = 0 UpperCAmelCase__ = len(SCREAMING_SNAKE_CASE__ ) - 1 while start_i < end_i: if s[start_i] == s[end_i]: start_i += 1 end_i -= 1 else: return False return True def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str ): '''simple docstring''' UpperCAmelCase__ = len(SCREAMING_SNAKE_CASE__ ) // 2 UpperCAmelCase__ = len(SCREAMING_SNAKE_CASE__ ) # We need to traverse till half of the length of string # as we can get access of the i'th last element from # i'th index. # eg: [0,1,2,3,4,5] => 4th index can be accessed # with the help of 1st index (i==n-i-1) # where n is length of string return all(s[i] == s[n - i - 1] for i in range(SCREAMING_SNAKE_CASE__ ) ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str ): '''simple docstring''' if len(SCREAMING_SNAKE_CASE__ ) <= 2: return True if s[0] == s[len(SCREAMING_SNAKE_CASE__ ) - 1]: return is_palindrome_recursive(s[1:-1] ) else: return False def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str ): '''simple docstring''' return s == s[::-1] def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str ): '''simple docstring''' UpperCAmelCase__ = F'''all({name}(key) is value for key, value in test_data.items())''' UpperCAmelCase__ = F'''from __main__ import test_data, {name}''' UpperCAmelCase__ = 500000 UpperCAmelCase__ = timeit(stmt=SCREAMING_SNAKE_CASE__ , setup=SCREAMING_SNAKE_CASE__ , number=SCREAMING_SNAKE_CASE__ ) print(F'''{name:<35} finished {number:,} runs in {result:.5f} seconds''' ) if __name__ == "__main__": for key, value in test_data.items(): assert is_palindrome(key) is is_palindrome_recursive(key) assert is_palindrome(key) is is_palindrome_slice(key) print(f"{key:21} {value}") print('a man a plan a canal panama') # finished 500,000 runs in 0.46793 seconds benchmark_function('is_palindrome_slice') # finished 500,000 runs in 0.85234 seconds benchmark_function('is_palindrome') # finished 500,000 runs in 1.32028 seconds benchmark_function('is_palindrome_recursive') # finished 500,000 runs in 2.08679 seconds benchmark_function('is_palindrome_traversal')

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1

'''simple docstring''' from __future__ import annotations import unittest from transformers import is_tf_available from transformers.testing_utils import require_tf, slow from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_tf_available(): import numpy import tensorflow as tf from transformers import ( TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST, TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST, TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST, BertConfig, DPRConfig, TFDPRContextEncoder, TFDPRQuestionEncoder, TFDPRReader, ) class lowerCAmelCase_ : '''simple docstring''' def __init__( self : str , _UpperCAmelCase : Dict , _UpperCAmelCase : Union[str, Any]=13 , _UpperCAmelCase : List[Any]=7 , _UpperCAmelCase : Optional[int]=True , _UpperCAmelCase : Any=True , _UpperCAmelCase : str=True , _UpperCAmelCase : Tuple=True , _UpperCAmelCase : int=99 , _UpperCAmelCase : Optional[Any]=32 , _UpperCAmelCase : Optional[int]=2 , _UpperCAmelCase : Tuple=4 , _UpperCAmelCase : str=37 , _UpperCAmelCase : int="gelu" , _UpperCAmelCase : Any=0.1 , _UpperCAmelCase : Union[str, Any]=0.1 , _UpperCAmelCase : str=5_12 , _UpperCAmelCase : Tuple=16 , _UpperCAmelCase : Optional[Any]=2 , _UpperCAmelCase : Optional[int]=0.02 , _UpperCAmelCase : int=3 , _UpperCAmelCase : Union[str, Any]=4 , _UpperCAmelCase : Optional[int]=None , _UpperCAmelCase : Any=0 , ): """simple docstring""" UpperCAmelCase__ = parent UpperCAmelCase__ = batch_size UpperCAmelCase__ = seq_length UpperCAmelCase__ = is_training UpperCAmelCase__ = use_input_mask UpperCAmelCase__ = use_token_type_ids UpperCAmelCase__ = use_labels UpperCAmelCase__ = vocab_size UpperCAmelCase__ = hidden_size UpperCAmelCase__ = num_hidden_layers UpperCAmelCase__ = num_attention_heads UpperCAmelCase__ = intermediate_size UpperCAmelCase__ = hidden_act UpperCAmelCase__ = hidden_dropout_prob UpperCAmelCase__ = attention_probs_dropout_prob UpperCAmelCase__ = max_position_embeddings UpperCAmelCase__ = type_vocab_size UpperCAmelCase__ = type_sequence_label_size UpperCAmelCase__ = initializer_range UpperCAmelCase__ = num_labels UpperCAmelCase__ = num_choices UpperCAmelCase__ = scope UpperCAmelCase__ = projection_dim def SCREAMING_SNAKE_CASE__ ( self : str ): """simple docstring""" UpperCAmelCase__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size ) UpperCAmelCase__ = None if self.use_input_mask: # follow test_modeling_tf_ctrl.py UpperCAmelCase__ = random_attention_mask([self.batch_size, self.seq_length] ) UpperCAmelCase__ = None if self.use_token_type_ids: UpperCAmelCase__ = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size ) UpperCAmelCase__ = None UpperCAmelCase__ = None UpperCAmelCase__ = None if self.use_labels: UpperCAmelCase__ = ids_tensor([self.batch_size] , self.type_sequence_label_size ) UpperCAmelCase__ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels ) UpperCAmelCase__ = ids_tensor([self.batch_size] , self.num_choices ) UpperCAmelCase__ = BertConfig( vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=_UpperCAmelCase , initializer_range=self.initializer_range , ) UpperCAmelCase__ = DPRConfig(projection_dim=self.projection_dim , **config.to_dict() ) return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels def SCREAMING_SNAKE_CASE__ ( self : Tuple , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Dict , _UpperCAmelCase : Dict , _UpperCAmelCase : Tuple , _UpperCAmelCase : Dict , _UpperCAmelCase : Optional[Any] ): """simple docstring""" UpperCAmelCase__ = TFDPRContextEncoder(config=_UpperCAmelCase ) UpperCAmelCase__ = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase , token_type_ids=_UpperCAmelCase ) UpperCAmelCase__ = model(_UpperCAmelCase , token_type_ids=_UpperCAmelCase ) UpperCAmelCase__ = model(_UpperCAmelCase ) self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.projection_dim or self.hidden_size) ) def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , _UpperCAmelCase : List[str] , _UpperCAmelCase : Tuple , _UpperCAmelCase : str , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Tuple , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Optional[Any] ): """simple docstring""" UpperCAmelCase__ = TFDPRQuestionEncoder(config=_UpperCAmelCase ) UpperCAmelCase__ = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase , token_type_ids=_UpperCAmelCase ) UpperCAmelCase__ = model(_UpperCAmelCase , token_type_ids=_UpperCAmelCase ) UpperCAmelCase__ = model(_UpperCAmelCase ) self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.projection_dim or self.hidden_size) ) def SCREAMING_SNAKE_CASE__ ( self : str , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Dict , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : str , _UpperCAmelCase : Optional[Any] ): """simple docstring""" UpperCAmelCase__ = TFDPRReader(config=_UpperCAmelCase ) UpperCAmelCase__ = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase ) self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) ) self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) ) self.parent.assertEqual(result.relevance_logits.shape , (self.batch_size,) ) def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ): """simple docstring""" UpperCAmelCase__ = self.prepare_config_and_inputs() ( ( UpperCAmelCase__ ) , ( UpperCAmelCase__ ) , ( UpperCAmelCase__ ) , ( UpperCAmelCase__ ) , ( UpperCAmelCase__ ) , ( UpperCAmelCase__ ) , ( UpperCAmelCase__ ) , ) = config_and_inputs UpperCAmelCase__ = {"""input_ids""": input_ids} return config, inputs_dict @require_tf class lowerCAmelCase_ ( lowerCamelCase_ , lowerCamelCase_ , unittest.TestCase ): '''simple docstring''' lowerCAmelCase_ : Tuple = ( ( TFDPRContextEncoder, TFDPRQuestionEncoder, TFDPRReader, ) if is_tf_available() else () ) lowerCAmelCase_ : List[str] = {"""feature-extraction""": TFDPRQuestionEncoder} if is_tf_available() else {} lowerCAmelCase_ : int = False lowerCAmelCase_ : Optional[int] = False lowerCAmelCase_ : Union[str, Any] = False lowerCAmelCase_ : Any = False lowerCAmelCase_ : Union[str, Any] = False def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ): """simple docstring""" UpperCAmelCase__ = TFDPRModelTester(self ) UpperCAmelCase__ = ConfigTester(self , config_class=_UpperCAmelCase , hidden_size=37 ) def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ): """simple docstring""" self.config_tester.run_common_tests() def SCREAMING_SNAKE_CASE__ ( self : Any ): """simple docstring""" UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_dpr_context_encoder(*_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : List[str] ): """simple docstring""" UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_dpr_question_encoder(*_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : List[str] ): """simple docstring""" UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_dpr_reader(*_UpperCAmelCase ) @slow def SCREAMING_SNAKE_CASE__ ( self : Dict ): """simple docstring""" for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: UpperCAmelCase__ = TFDPRContextEncoder.from_pretrained(_UpperCAmelCase ) self.assertIsNotNone(_UpperCAmelCase ) for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: UpperCAmelCase__ = TFDPRContextEncoder.from_pretrained(_UpperCAmelCase ) self.assertIsNotNone(_UpperCAmelCase ) for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: UpperCAmelCase__ = TFDPRQuestionEncoder.from_pretrained(_UpperCAmelCase ) self.assertIsNotNone(_UpperCAmelCase ) for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: UpperCAmelCase__ = TFDPRReader.from_pretrained(_UpperCAmelCase ) self.assertIsNotNone(_UpperCAmelCase ) @require_tf class lowerCAmelCase_ ( unittest.TestCase ): '''simple docstring''' @slow def SCREAMING_SNAKE_CASE__ ( self : str ): """simple docstring""" UpperCAmelCase__ = TFDPRQuestionEncoder.from_pretrained("""facebook/dpr-question_encoder-single-nq-base""" ) UpperCAmelCase__ = tf.constant( [[1_01, 75_92, 10_10, 20_03, 20_26, 38_99, 1_01_40, 10_29, 1_02]] ) # [CLS] hello, is my dog cute? [SEP] UpperCAmelCase__ = model(_UpperCAmelCase )[0] # embedding shape = (1, 768) # compare the actual values for a slice. UpperCAmelCase__ = tf.constant( [ [ 0.0323_6253, 0.1275_3335, 0.1681_8509, 0.0027_9786, 0.389_6933, 0.2426_4945, 0.217_8971, -0.0233_5227, -0.0848_1959, -0.1432_4117, ] ] ) self.assertTrue(numpy.allclose(output[:, :10].numpy() , expected_slice.numpy() , atol=1E-4 ) )

346

'''simple docstring''' import datasets from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py UpperCAmelCase_ = '\\n@INPROCEEDINGS{Papineni02bleu:a,\n author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n title = {BLEU: a Method for Automatic Evaluation of Machine Translation},\n booktitle = {},\n year = {2002},\n pages = {311--318}\n}\n@inproceedings{lin-och-2004-orange,\n title = "{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation",\n author = "Lin, Chin-Yew and\n Och, Franz Josef",\n booktitle = "{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics",\n month = "aug 23{--}aug 27",\n year = "2004",\n address = "Geneva, Switzerland",\n publisher = "COLING",\n url = "https://www.aclweb.org/anthology/C04-1072",\n pages = "501--507",\n}\n' UpperCAmelCase_ = '\\nBLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.\nQuality is considered to be the correspondence between a machine\'s output and that of a human: "the closer a machine translation is to a professional human translation,\nthe better it is" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and\nremains one of the most popular automated and inexpensive metrics.\n\nScores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.\nThose scores are then averaged over the whole corpus to reach an estimate of the translation\'s overall quality. Intelligibility or grammatical correctness\nare not taken into account[citation needed].\n\nBLEU\'s output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1\nrepresenting more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the\nreference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional\nreference translations will increase the BLEU score.\n' UpperCAmelCase_ = '\nComputes BLEU score of translated segments against one or more references.\nArgs:\n predictions: list of translations to score.\n Each translation should be tokenized into a list of tokens.\n references: list of lists of references for each translation.\n Each reference should be tokenized into a list of tokens.\n max_order: Maximum n-gram order to use when computing BLEU score.\n smooth: Whether or not to apply Lin et al. 2004 smoothing.\nReturns:\n \'bleu\': bleu score,\n \'precisions\': geometric mean of n-gram precisions,\n \'brevity_penalty\': brevity penalty,\n \'length_ratio\': ratio of lengths,\n \'translation_length\': translation_length,\n \'reference_length\': reference_length\nExamples:\n\n >>> predictions = [\n ... ["hello", "there", "general", "kenobi"], # tokenized prediction of the first sample\n ... ["foo", "bar", "foobar"] # tokenized prediction of the second sample\n ... ]\n >>> references = [\n ... [["hello", "there", "general", "kenobi"], ["hello", "there", "!"]], # tokenized references for the first sample (2 references)\n ... [["foo", "bar", "foobar"]] # tokenized references for the second sample (1 reference)\n ... ]\n >>> bleu = datasets.load_metric("bleu")\n >>> results = bleu.compute(predictions=predictions, references=references)\n >>> print(results["bleu"])\n 1.0\n' @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION ) class lowerCAmelCase_ ( datasets.Metric ): '''simple docstring''' def SCREAMING_SNAKE_CASE__ ( self : int ): """simple docstring""" return datasets.MetricInfo( description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features( { """predictions""": datasets.Sequence(datasets.Value("""string""" , id="""token""" ) , id="""sequence""" ), """references""": datasets.Sequence( datasets.Sequence(datasets.Value("""string""" , id="""token""" ) , id="""sequence""" ) , id="""references""" ), } ) , codebase_urls=["""https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py"""] , reference_urls=[ """https://en.wikipedia.org/wiki/BLEU""", """https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213""", ] , ) def SCREAMING_SNAKE_CASE__ ( self : int , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : List[Any]=4 , _UpperCAmelCase : Union[str, Any]=False ): """simple docstring""" UpperCAmelCase__ = compute_bleu( reference_corpus=_UpperCAmelCase , translation_corpus=_UpperCAmelCase , max_order=_UpperCAmelCase , smooth=_UpperCAmelCase ) ((UpperCAmelCase__) , (UpperCAmelCase__) , (UpperCAmelCase__) , (UpperCAmelCase__) , (UpperCAmelCase__) , (UpperCAmelCase__)) = score return { "bleu": bleu, "precisions": precisions, "brevity_penalty": bp, "length_ratio": ratio, "translation_length": translation_length, "reference_length": reference_length, }

346

1

'''simple docstring''' UpperCAmelCase_ = '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n' UpperCAmelCase_ = [{'type': 'code', 'content': INSTALL_CONTENT}] UpperCAmelCase_ = { '{processor_class}': 'FakeProcessorClass', '{model_class}': 'FakeModelClass', '{object_class}': 'FakeObjectClass', }

346

'''simple docstring''' from dataclasses import dataclass from typing import List, Optional, Union import numpy as np import torch from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available @dataclass class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' lowerCAmelCase_ : Union[List[np.ndarray], torch.FloatTensor] try: if not (is_transformers_available() and is_torch_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from ...utils.dummy_torch_and_transformers_objects import * # noqa F403 else: from .pipeline_text_to_video_synth import TextToVideoSDPipeline from .pipeline_text_to_video_synth_imgaimg import VideoToVideoSDPipeline # noqa: F401 from .pipeline_text_to_video_zero import TextToVideoZeroPipeline

346

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'''simple docstring''' import argparse import os import re import packaging.version UpperCAmelCase_ = 'examples/' UpperCAmelCase_ = { 'examples': (re.compile(r'^check_min_version$"[^"]+"$\s*$', re.MULTILINE), 'check_min_version("VERSION")\n'), 'init': (re.compile(r'^__version__\s+=\s+"([^"]+)"\s*$', re.MULTILINE), '__version__ = "VERSION"\n'), 'setup': (re.compile(r'^(\s*)version\s*=\s*"[^"]+",', re.MULTILINE), r'\1version="VERSION",'), 'doc': (re.compile(r'^(\s*)release\s*=\s*"[^"]+"$', re.MULTILINE), 'release = "VERSION"\n'), } UpperCAmelCase_ = { 'init': 'src/transformers/__init__.py', 'setup': 'setup.py', } UpperCAmelCase_ = 'README.md' def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : Any ): '''simple docstring''' with open(SCREAMING_SNAKE_CASE__ , """r""" , encoding="""utf-8""" , newline="""\n""" ) as f: UpperCAmelCase__ = f.read() UpperCAmelCase__ , UpperCAmelCase__ = REPLACE_PATTERNS[pattern] UpperCAmelCase__ = replace.replace("""VERSION""" , SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = re_pattern.sub(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) with open(SCREAMING_SNAKE_CASE__ , """w""" , encoding="""utf-8""" , newline="""\n""" ) as f: f.write(SCREAMING_SNAKE_CASE__ ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Any ): '''simple docstring''' for folder, directories, fnames in os.walk(SCREAMING_SNAKE_CASE__ ): # Removing some of the folders with non-actively maintained examples from the walk if "research_projects" in directories: directories.remove("""research_projects""" ) if "legacy" in directories: directories.remove("""legacy""" ) for fname in fnames: if fname.endswith(""".py""" ): update_version_in_file(os.path.join(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) , SCREAMING_SNAKE_CASE__ , pattern="""examples""" ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : int=False ): '''simple docstring''' for pattern, fname in REPLACE_FILES.items(): update_version_in_file(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) if not patch: update_version_in_examples(SCREAMING_SNAKE_CASE__ ) def _UpperCamelCase ( ): '''simple docstring''' UpperCAmelCase__ = """🤗 Transformers currently provides the following architectures""" UpperCAmelCase__ = """1. Want to contribute a new model?""" with open(SCREAMING_SNAKE_CASE__ , """r""" , encoding="""utf-8""" , newline="""\n""" ) as f: UpperCAmelCase__ = f.readlines() # Find the start of the list. UpperCAmelCase__ = 0 while not lines[start_index].startswith(_start_prompt ): start_index += 1 start_index += 1 UpperCAmelCase__ = start_index # Update the lines in the model list. while not lines[index].startswith(_end_prompt ): if lines[index].startswith("""1.""" ): UpperCAmelCase__ = lines[index].replace( """https://huggingface.co/docs/transformers/main/model_doc""" , """https://huggingface.co/docs/transformers/model_doc""" , ) index += 1 with open(SCREAMING_SNAKE_CASE__ , """w""" , encoding="""utf-8""" , newline="""\n""" ) as f: f.writelines(SCREAMING_SNAKE_CASE__ ) def _UpperCamelCase ( ): '''simple docstring''' with open(REPLACE_FILES["""init"""] , """r""" ) as f: UpperCAmelCase__ = f.read() UpperCAmelCase__ = REPLACE_PATTERNS["""init"""][0].search(SCREAMING_SNAKE_CASE__ ).groups()[0] return packaging.version.parse(SCREAMING_SNAKE_CASE__ ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Optional[Any]=False ): '''simple docstring''' UpperCAmelCase__ = get_version() if patch and default_version.is_devrelease: raise ValueError("""Can't create a patch version from the dev branch, checkout a released version!""" ) if default_version.is_devrelease: UpperCAmelCase__ = default_version.base_version elif patch: UpperCAmelCase__ = F'''{default_version.major}.{default_version.minor}.{default_version.micro + 1}''' else: UpperCAmelCase__ = F'''{default_version.major}.{default_version.minor + 1}.0''' # Now let's ask nicely if that's the right one. UpperCAmelCase__ = input(F'''Which version are you releasing? [{default_version}]''' ) if len(SCREAMING_SNAKE_CASE__ ) == 0: UpperCAmelCase__ = default_version print(F'''Updating version to {version}.''' ) global_version_update(SCREAMING_SNAKE_CASE__ , patch=SCREAMING_SNAKE_CASE__ ) if not patch: print("""Cleaning main README, don't forget to run `make fix-copies`.""" ) clean_main_ref_in_model_list() def _UpperCamelCase ( ): '''simple docstring''' UpperCAmelCase__ = get_version() UpperCAmelCase__ = F'''{current_version.major}.{current_version.minor + 1}.0.dev0''' UpperCAmelCase__ = current_version.base_version # Check with the user we got that right. UpperCAmelCase__ = input(F'''Which version are we developing now? [{dev_version}]''' ) if len(SCREAMING_SNAKE_CASE__ ) == 0: UpperCAmelCase__ = dev_version print(F'''Updating version to {version}.''' ) global_version_update(SCREAMING_SNAKE_CASE__ ) print("""Cleaning main README, don't forget to run `make fix-copies`.""" ) clean_main_ref_in_model_list() if __name__ == "__main__": UpperCAmelCase_ = argparse.ArgumentParser() parser.add_argument('--post_release', action='store_true', help='Whether this is pre or post release.') parser.add_argument('--patch', action='store_true', help='Whether or not this is a patch release.') UpperCAmelCase_ = parser.parse_args() if not args.post_release: pre_release_work(patch=args.patch) elif args.patch: print('Nothing to do after a patch :-)') else: post_release_work()

346

'''simple docstring''' import torch import torch.nn as nn from transformers.modeling_utils import ModuleUtilsMixin from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm from ...configuration_utils import ConfigMixin, register_to_config from ...models import ModelMixin class lowerCAmelCase_ ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ): '''simple docstring''' @register_to_config def __init__( self : List[str] , _UpperCAmelCase : int , _UpperCAmelCase : int , _UpperCAmelCase : int , _UpperCAmelCase : float , _UpperCAmelCase : int , _UpperCAmelCase : int , _UpperCAmelCase : int , _UpperCAmelCase : int , _UpperCAmelCase : str , _UpperCAmelCase : bool = False , ): """simple docstring""" super().__init__() UpperCAmelCase__ = nn.Embedding(_UpperCAmelCase , _UpperCAmelCase ) UpperCAmelCase__ = nn.Embedding(_UpperCAmelCase , _UpperCAmelCase ) UpperCAmelCase__ = False UpperCAmelCase__ = nn.Dropout(p=_UpperCAmelCase ) UpperCAmelCase__ = TaConfig( vocab_size=_UpperCAmelCase , d_model=_UpperCAmelCase , num_heads=_UpperCAmelCase , d_kv=_UpperCAmelCase , d_ff=_UpperCAmelCase , dropout_rate=_UpperCAmelCase , feed_forward_proj=_UpperCAmelCase , is_decoder=_UpperCAmelCase , is_encoder_decoder=_UpperCAmelCase , ) UpperCAmelCase__ = nn.ModuleList() for lyr_num in range(_UpperCAmelCase ): UpperCAmelCase__ = TaBlock(_UpperCAmelCase ) self.encoders.append(_UpperCAmelCase ) UpperCAmelCase__ = TaLayerNorm(_UpperCAmelCase ) UpperCAmelCase__ = nn.Dropout(p=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Dict , _UpperCAmelCase : int , _UpperCAmelCase : str ): """simple docstring""" UpperCAmelCase__ = self.token_embedder(_UpperCAmelCase ) UpperCAmelCase__ = encoder_input_tokens.shape[1] UpperCAmelCase__ = torch.arange(_UpperCAmelCase , device=encoder_input_tokens.device ) x += self.position_encoding(_UpperCAmelCase ) UpperCAmelCase__ = self.dropout_pre(_UpperCAmelCase ) # inverted the attention mask UpperCAmelCase__ = encoder_input_tokens.size() UpperCAmelCase__ = self.get_extended_attention_mask(_UpperCAmelCase , _UpperCAmelCase ) for lyr in self.encoders: UpperCAmelCase__ = lyr(_UpperCAmelCase , _UpperCAmelCase )[0] UpperCAmelCase__ = self.layer_norm(_UpperCAmelCase ) return self.dropout_post(_UpperCAmelCase ), encoder_inputs_mask

346

1

'''simple docstring''' from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available, is_torch_available, ) UpperCAmelCase_ = {} try: if not is_sentencepiece_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCAmelCase_ = ['NllbTokenizer'] try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCAmelCase_ = ['NllbTokenizerFast'] if TYPE_CHECKING: try: if not is_sentencepiece_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tokenization_nllb import NllbTokenizer try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tokenization_nllb_fast import NllbTokenizerFast else: import sys UpperCAmelCase_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

346

'''simple docstring''' import argparse import json import os import fairseq import torch from fairseq.data import Dictionary from transformers import ( WavaVecaConfig, WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaForCTC, WavaVecaForPreTraining, WavaVecaProcessor, logging, ) from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification logging.set_verbosity_info() UpperCAmelCase_ = logging.get_logger(__name__) UpperCAmelCase_ = { 'post_extract_proj': 'feature_projection.projection', 'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv', 'self_attn.k_proj': 'encoder.layers.*.attention.k_proj', 'self_attn.v_proj': 'encoder.layers.*.attention.v_proj', 'self_attn.q_proj': 'encoder.layers.*.attention.q_proj', 'self_attn.out_proj': 'encoder.layers.*.attention.out_proj', 'self_attn_layer_norm': 'encoder.layers.*.layer_norm', 'fc1': 'encoder.layers.*.feed_forward.intermediate_dense', 'fc2': 'encoder.layers.*.feed_forward.output_dense', 'final_layer_norm': 'encoder.layers.*.final_layer_norm', 'encoder.layer_norm': 'encoder.layer_norm', 'adapter_layer': 'encoder.layers.*.adapter_layer', 'w2v_model.layer_norm': 'feature_projection.layer_norm', 'quantizer.weight_proj': 'quantizer.weight_proj', 'quantizer.vars': 'quantizer.codevectors', 'project_q': 'project_q', 'final_proj': 'project_hid', 'w2v_encoder.proj': 'lm_head', 'mask_emb': 'masked_spec_embed', 'pooling_layer.linear': 'projector', 'pooling_layer.projection': 'classifier', } UpperCAmelCase_ = [ 'lm_head', 'quantizer.weight_proj', 'quantizer.codevectors', 'project_q', 'project_hid', 'projector', 'classifier', ] def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Tuple ): '''simple docstring''' UpperCAmelCase__ = {} with open(SCREAMING_SNAKE_CASE__ , """r""" ) as file: for line_number, line in enumerate(SCREAMING_SNAKE_CASE__ ): UpperCAmelCase__ = line.strip() if line: UpperCAmelCase__ = line.split() UpperCAmelCase__ = line_number UpperCAmelCase__ = words[0] UpperCAmelCase__ = value return result def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : Union[str, Any] ): '''simple docstring''' for attribute in key.split(""".""" ): UpperCAmelCase__ = getattr(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = None for param_key in PARAM_MAPPING.keys(): if full_name.endswith(SCREAMING_SNAKE_CASE__ ): UpperCAmelCase__ = PARAM_MAPPING[full_name.split(""".""" )[-1]] UpperCAmelCase__ = """param""" if weight_type is not None and weight_type != "param": UpperCAmelCase__ = getattr(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ).shape elif weight_type is not None and weight_type == "param": UpperCAmelCase__ = hf_pointer for attribute in hf_param_name.split(""".""" ): UpperCAmelCase__ = getattr(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = shape_pointer.shape # let's reduce dimension UpperCAmelCase__ = value[0] else: UpperCAmelCase__ = hf_pointer.shape if hf_shape != value.shape: raise ValueError( F'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be''' F''' {value.shape} for {full_name}''' ) if weight_type == "weight": UpperCAmelCase__ = value elif weight_type == "weight_g": UpperCAmelCase__ = value elif weight_type == "weight_v": UpperCAmelCase__ = value elif weight_type == "bias": UpperCAmelCase__ = value elif weight_type == "param": for attribute in hf_param_name.split(""".""" ): UpperCAmelCase__ = getattr(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = value else: UpperCAmelCase__ = value logger.info(F'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.''' ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : Optional[Any] ): '''simple docstring''' UpperCAmelCase__ = None for param_key in PARAM_MAPPING.keys(): if full_name.endswith(SCREAMING_SNAKE_CASE__ ): UpperCAmelCase__ = PARAM_MAPPING[full_name.split(""".""" )[-1]] UpperCAmelCase__ = """param""" if weight_type is not None and weight_type != "param": UpperCAmelCase__ = """.""".join([key, weight_type] ) elif weight_type is not None and weight_type == "param": UpperCAmelCase__ = """.""".join([key, hf_param_name] ) else: UpperCAmelCase__ = key UpperCAmelCase__ = value if """lm_head""" in full_key else value[0] UpperCAmelCase_ = { 'W_a': 'linear_1.weight', 'W_b': 'linear_2.weight', 'b_a': 'linear_1.bias', 'b_b': 'linear_2.bias', 'ln_W': 'norm.weight', 'ln_b': 'norm.bias', } def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : Dict=None , SCREAMING_SNAKE_CASE__ : Optional[Any]=None ): '''simple docstring''' UpperCAmelCase__ = False for key, mapped_key in MAPPING.items(): UpperCAmelCase__ = """wav2vec2.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key if key in name or key.split("""w2v_model.""" )[-1] == name.split(""".""" )[0]: UpperCAmelCase__ = True if "*" in mapped_key: UpperCAmelCase__ = name.split(SCREAMING_SNAKE_CASE__ )[0].split(""".""" )[-2] UpperCAmelCase__ = mapped_key.replace("""*""" , SCREAMING_SNAKE_CASE__ ) if "weight_g" in name: UpperCAmelCase__ = """weight_g""" elif "weight_v" in name: UpperCAmelCase__ = """weight_v""" elif "bias" in name: UpperCAmelCase__ = """bias""" elif "weight" in name: # TODO: don't match quantizer.weight_proj UpperCAmelCase__ = """weight""" else: UpperCAmelCase__ = None if hf_dict is not None: rename_dict(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) else: set_recursively(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) return is_used return is_used def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CASE__ : List[str] ): '''simple docstring''' UpperCAmelCase__ = [] UpperCAmelCase__ = fairseq_model.state_dict() UpperCAmelCase__ = hf_model.wavaveca.feature_extractor for name, value in fairseq_dict.items(): UpperCAmelCase__ = False if "conv_layers" in name: load_conv_layer( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , hf_model.config.feat_extract_norm == """group""" , ) UpperCAmelCase__ = True else: UpperCAmelCase__ = load_wavaveca_layer(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) if not is_used: unused_weights.append(SCREAMING_SNAKE_CASE__ ) logger.warning(F'''Unused weights: {unused_weights}''' ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : int ): '''simple docstring''' UpperCAmelCase__ = full_name.split("""conv_layers.""" )[-1] UpperCAmelCase__ = name.split(""".""" ) UpperCAmelCase__ = int(items[0] ) UpperCAmelCase__ = int(items[1] ) if type_id == 0: if "bias" in name: if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape: raise ValueError( F'''{full_name} has size {value.shape}, but''' F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.''' ) UpperCAmelCase__ = value logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' ) elif "weight" in name: if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape: raise ValueError( F'''{full_name} has size {value.shape}, but''' F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.''' ) UpperCAmelCase__ = value logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' ) elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm): if "bias" in name: if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape: raise ValueError( F'''{full_name} has size {value.shape}, but''' F''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.''' ) UpperCAmelCase__ = value logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' ) elif "weight" in name: if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape: raise ValueError( F'''{full_name} has size {value.shape}, but''' F''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.''' ) UpperCAmelCase__ = value logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' ) else: unused_weights.append(SCREAMING_SNAKE_CASE__ ) @torch.no_grad() def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : List[str]=None , SCREAMING_SNAKE_CASE__ : Optional[int]=None , SCREAMING_SNAKE_CASE__ : Union[str, Any]=True , SCREAMING_SNAKE_CASE__ : Union[str, Any]=False ): '''simple docstring''' if config_path is not None: UpperCAmelCase__ = WavaVecaConfig.from_pretrained(SCREAMING_SNAKE_CASE__ ) else: UpperCAmelCase__ = WavaVecaConfig() if is_seq_class: UpperCAmelCase__ = read_txt_into_dict(SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = idalabel UpperCAmelCase__ = WavaVecaForSequenceClassification(SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = WavaVecaFeatureExtractor( feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=SCREAMING_SNAKE_CASE__ , return_attention_mask=SCREAMING_SNAKE_CASE__ , ) feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE__ ) elif is_finetuned: if dict_path: UpperCAmelCase__ = Dictionary.load(SCREAMING_SNAKE_CASE__ ) # important change bos & pad token id since CTC symbol is <pad> and # not <s> as in fairseq UpperCAmelCase__ = target_dict.pad_index UpperCAmelCase__ = target_dict.bos_index UpperCAmelCase__ = target_dict.eos_index UpperCAmelCase__ = len(target_dict.symbols ) UpperCAmelCase__ = os.path.join(SCREAMING_SNAKE_CASE__ , """vocab.json""" ) if not os.path.isdir(SCREAMING_SNAKE_CASE__ ): logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(SCREAMING_SNAKE_CASE__ ) ) return os.makedirs(SCREAMING_SNAKE_CASE__ , exist_ok=SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = target_dict.indices # fairseq has the <pad> and <s> switched UpperCAmelCase__ = 0 UpperCAmelCase__ = 1 with open(SCREAMING_SNAKE_CASE__ , """w""" , encoding="""utf-8""" ) as vocab_handle: json.dump(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = WavaVecaCTCTokenizer( SCREAMING_SNAKE_CASE__ , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token="""|""" , do_lower_case=SCREAMING_SNAKE_CASE__ , ) UpperCAmelCase__ = True if config.feat_extract_norm == """layer""" else False UpperCAmelCase__ = WavaVecaFeatureExtractor( feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=SCREAMING_SNAKE_CASE__ , return_attention_mask=SCREAMING_SNAKE_CASE__ , ) UpperCAmelCase__ = WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE__ , tokenizer=SCREAMING_SNAKE_CASE__ ) processor.save_pretrained(SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = WavaVecaForCTC(SCREAMING_SNAKE_CASE__ ) else: UpperCAmelCase__ = WavaVecaForPreTraining(SCREAMING_SNAKE_CASE__ ) if is_finetuned or is_seq_class: UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = fairseq.checkpoint_utils.load_model_ensemble_and_task( [checkpoint_path] , arg_overrides={"""data""": """/""".join(dict_path.split("""/""" )[:-1] )} ) else: UpperCAmelCase__ = argparse.Namespace(task="""audio_pretraining""" ) UpperCAmelCase__ = fairseq.tasks.setup_task(SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] , task=SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = model[0].eval() recursively_load_weights(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , not is_finetuned ) hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE__ ) if __name__ == "__main__": UpperCAmelCase_ = argparse.ArgumentParser() parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.') parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint') parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model') parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert') parser.add_argument( '--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not' ) parser.add_argument( '--is_seq_class', action='store_true', help='Whether the model to convert is a fine-tuned sequence classification model or not', ) UpperCAmelCase_ = parser.parse_args() UpperCAmelCase_ = not args.not_finetuned and not args.is_seq_class convert_wavaveca_checkpoint( args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, is_finetuned, args.is_seq_class, )

346

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'''simple docstring''' import random def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : Optional[Any] ): '''simple docstring''' UpperCAmelCase__ = a[left_index] UpperCAmelCase__ = left_index + 1 for j in range(left_index + 1 , SCREAMING_SNAKE_CASE__ ): if a[j] < pivot: UpperCAmelCase__ , UpperCAmelCase__ = a[i], a[j] i += 1 UpperCAmelCase__ , UpperCAmelCase__ = a[i - 1], a[left_index] return i - 1 def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : List[Any] ): '''simple docstring''' if left < right: UpperCAmelCase__ = random.randint(SCREAMING_SNAKE_CASE__ , right - 1 ) UpperCAmelCase__ , UpperCAmelCase__ = ( a[left], a[pivot], ) # switches the pivot with the left most bound UpperCAmelCase__ = partition(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) quick_sort_random( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) # recursive quicksort to the left of the pivot point quick_sort_random( SCREAMING_SNAKE_CASE__ , pivot_index + 1 , SCREAMING_SNAKE_CASE__ ) # recursive quicksort to the right of the pivot point def _UpperCamelCase ( ): '''simple docstring''' UpperCAmelCase__ = input("""Enter numbers separated by a comma:\n""" ).strip() UpperCAmelCase__ = [int(SCREAMING_SNAKE_CASE__ ) for item in user_input.split(""",""" )] quick_sort_random(SCREAMING_SNAKE_CASE__ , 0 , len(SCREAMING_SNAKE_CASE__ ) ) print(SCREAMING_SNAKE_CASE__ ) if __name__ == "__main__": main()

346

'''simple docstring''' import itertools import os from collections import Counter, defaultdict from concurrent.futures import ThreadPoolExecutor, as_completed import numpy as np import datasets from .execute import check_correctness UpperCAmelCase_ = '\\n@misc{chen2021evaluating,\n title={Evaluating Large Language Models Trained on Code},\n author={Mark Chen and Jerry Tworek and Heewoo Jun and Qiming Yuan \\nand Henrique Ponde de Oliveira Pinto and Jared Kaplan and Harri Edwards \\nand Yuri Burda and Nicholas Joseph and Greg Brockman and Alex Ray \\nand Raul Puri and Gretchen Krueger and Michael Petrov and Heidy Khlaaf \\nand Girish Sastry and Pamela Mishkin and Brooke Chan and Scott Gray \\nand Nick Ryder and Mikhail Pavlov and Alethea Power and Lukasz Kaiser \\nand Mohammad Bavarian and Clemens Winter and Philippe Tillet \\nand Felipe Petroski Such and Dave Cummings and Matthias Plappert \\nand Fotios Chantzis and Elizabeth Barnes and Ariel Herbert-Voss \\nand William Hebgen Guss and Alex Nichol and Alex Paino and Nikolas Tezak \\nand Jie Tang and Igor Babuschkin and Suchir Balaji and Shantanu Jain \\nand William Saunders and Christopher Hesse and Andrew N. Carr \\nand Jan Leike and Josh Achiam and Vedant Misra and Evan Morikawa \\nand Alec Radford and Matthew Knight and Miles Brundage and Mira Murati \\nand Katie Mayer and Peter Welinder and Bob McGrew and Dario Amodei \\nand Sam McCandlish and Ilya Sutskever and Wojciech Zaremba},\n year={2021},\n eprint={2107.03374},\n archivePrefix={arXiv},\n primaryClass={cs.LG}\n}\n' UpperCAmelCase_ = '\\nThis metric implements the evaluation harness for the HumanEval problem solving dataset\ndescribed in the paper "Evaluating Large Language Models Trained on Code"\n(https://arxiv.org/abs/2107.03374).\n' UpperCAmelCase_ = '\nCalculates how good are predictions given some references, using certain scores\nArgs:\n predictions: list of candidates to evaluate. Each candidates should be a list\n of strings with several code candidates to solve the problem.\n references: a list with a test for each prediction. Each test should evaluate the\n correctness of a code candidate.\n k: number of code candidates to consider in the evaluation (Default: [1, 10, 100])\n num_workers: number of workers used to evaluate the canidate programs (Default: 4).\n timeout:\nReturns:\n pass_at_k: dict with pass rates for each k\n results: dict with granular results of each unittest\nExamples:\n >>> code_eval = datasets.load_metric("code_eval")\n >>> test_cases = ["assert add(2,3)==5"]\n >>> candidates = [["def add(a,b): return a*b", "def add(a, b): return a+b"]]\n >>> pass_at_k, results = code_eval.compute(references=test_cases, predictions=candidates, k=[1, 2])\n >>> print(pass_at_k)\n {\'pass@1\': 0.5, \'pass@2\': 1.0}\n' UpperCAmelCase_ = '\n################################################################################\n !!!WARNING!!!\n################################################################################\nThe "code_eval" metric executes untrusted model-generated code in Python.\nAlthough it is highly unlikely that model-generated code will do something\novertly malicious in response to this test suite, model-generated code may act\ndestructively due to a lack of model capability or alignment.\nUsers are strongly encouraged to sandbox this evaluation suite so that it\ndoes not perform destructive actions on their host or network. For more\ninformation on how OpenAI sandboxes its code, see the paper "Evaluating Large\nLanguage Models Trained on Code" (https://arxiv.org/abs/2107.03374).\n\nOnce you have read this disclaimer and taken appropriate precautions,\nset the environment variable HF_ALLOW_CODE_EVAL="1". Within Python you can to this\nwith:\n\n>>> import os\n>>> os.environ["HF_ALLOW_CODE_EVAL"] = "1"\n\n################################################################################\\n' UpperCAmelCase_ = 'The MIT License\n\nCopyright (c) OpenAI (https://openai.com)\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the "Software"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in\nall copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\nTHE SOFTWARE.' @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION ) class lowerCAmelCase_ ( datasets.Metric ): '''simple docstring''' def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ): """simple docstring""" return datasets.MetricInfo( # This is the description that will appear on the metrics page. description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features( { """predictions""": datasets.Sequence(datasets.Value("""string""" ) ), """references""": datasets.Value("""string""" ), } ) , homepage="""https://github.com/openai/human-eval""" , codebase_urls=["""https://github.com/openai/human-eval"""] , reference_urls=["""https://github.com/openai/human-eval"""] , license=_LICENSE , ) def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : List[str]=[1, 10, 1_00] , _UpperCAmelCase : Optional[Any]=4 , _UpperCAmelCase : Any=3.0 ): """simple docstring""" if os.getenv("""HF_ALLOW_CODE_EVAL""" , 0 ) != "1": raise ValueError(_WARNING ) if os.name == "nt": raise NotImplementedError("""This metric is currently not supported on Windows.""" ) with ThreadPoolExecutor(max_workers=_UpperCAmelCase ) as executor: UpperCAmelCase__ = [] UpperCAmelCase__ = Counter() UpperCAmelCase__ = 0 UpperCAmelCase__ = defaultdict(_UpperCAmelCase ) for task_id, (candidates, test_case) in enumerate(zip(_UpperCAmelCase , _UpperCAmelCase ) ): for candidate in candidates: UpperCAmelCase__ = candidate + """\n""" + test_case UpperCAmelCase__ = (test_program, timeout, task_id, completion_id[task_id]) UpperCAmelCase__ = executor.submit(_UpperCAmelCase , *_UpperCAmelCase ) futures.append(_UpperCAmelCase ) completion_id[task_id] += 1 n_samples += 1 for future in as_completed(_UpperCAmelCase ): UpperCAmelCase__ = future.result() results[result["task_id"]].append((result["""completion_id"""], result) ) UpperCAmelCase__ , UpperCAmelCase__ = [], [] for result in results.values(): result.sort() UpperCAmelCase__ = [r[1]["""passed"""] for r in result] total.append(len(_UpperCAmelCase ) ) correct.append(sum(_UpperCAmelCase ) ) UpperCAmelCase__ = np.array(_UpperCAmelCase ) UpperCAmelCase__ = np.array(_UpperCAmelCase ) UpperCAmelCase__ = k UpperCAmelCase__ = {f'''pass@{k}''': estimate_pass_at_k(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ).mean() for k in ks if (total >= k).all()} return pass_at_k, results def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : Optional[Any] ): '''simple docstring''' def estimator(SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ) -> float: if n - c < k: return 1.0 return 1.0 - np.prod(1.0 - k / np.arange(n - c + 1 , n + 1 ) ) if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ): UpperCAmelCase__ = itertools.repeat(SCREAMING_SNAKE_CASE__ , len(SCREAMING_SNAKE_CASE__ ) ) else: assert len(SCREAMING_SNAKE_CASE__ ) == len(SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = iter(SCREAMING_SNAKE_CASE__ ) return np.array([estimator(int(SCREAMING_SNAKE_CASE__ ) , int(SCREAMING_SNAKE_CASE__ ) , SCREAMING_SNAKE_CASE__ ) for n, c in zip(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )] )

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'''simple docstring''' # Lint as: python3 import sys from collections.abc import Mapping from typing import TYPE_CHECKING, Dict, Optional import numpy as np import pyarrow as pa from .. import config from ..utils.logging import get_logger from ..utils.py_utils import map_nested from .formatting import TensorFormatter if TYPE_CHECKING: import jax import jaxlib UpperCAmelCase_ = get_logger() UpperCAmelCase_ = None class lowerCAmelCase_ ( TensorFormatter[Mapping, """jax.Array""", Mapping] ): '''simple docstring''' def __init__( self : Optional[Any] , _UpperCAmelCase : Any=None , _UpperCAmelCase : Union[str, Any]=None , **_UpperCAmelCase : Any ): """simple docstring""" super().__init__(features=_UpperCAmelCase ) import jax from jaxlib.xla_client import Device if isinstance(_UpperCAmelCase , _UpperCAmelCase ): raise ValueError( f'''Expected {device} to be a `str` not {type(_UpperCAmelCase )}, as `jaxlib.xla_extension.Device` ''' """is not serializable neither with `pickle` nor with `dill`. Instead you can surround """ """the device with `str()` to get its string identifier that will be internally mapped """ """to the actual `jaxlib.xla_extension.Device`.""" ) UpperCAmelCase__ = device if isinstance(_UpperCAmelCase , _UpperCAmelCase ) else str(jax.devices()[0] ) # using global variable since `jaxlib.xla_extension.Device` is not serializable neither # with `pickle` nor with `dill`, so we need to use a global variable instead global DEVICE_MAPPING if DEVICE_MAPPING is None: UpperCAmelCase__ = self._map_devices_to_str() if self.device not in list(DEVICE_MAPPING.keys() ): logger.warning( f'''Device with string identifier {self.device} not listed among the available ''' f'''devices: {list(DEVICE_MAPPING.keys() )}, so falling back to the default ''' f'''device: {str(jax.devices()[0] )}.''' ) UpperCAmelCase__ = str(jax.devices()[0] ) UpperCAmelCase__ = jnp_array_kwargs @staticmethod def SCREAMING_SNAKE_CASE__ ( ): """simple docstring""" import jax return {str(_UpperCAmelCase ): device for device in jax.devices()} def SCREAMING_SNAKE_CASE__ ( self : Any , _UpperCAmelCase : Optional[int] ): """simple docstring""" import jax import jax.numpy as jnp if isinstance(_UpperCAmelCase , _UpperCAmelCase ) and column: if all( isinstance(_UpperCAmelCase , jax.Array ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column ): return jnp.stack(_UpperCAmelCase , axis=0 ) return column def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] , _UpperCAmelCase : List[str] ): """simple docstring""" import jax import jax.numpy as jnp if isinstance(_UpperCAmelCase , (str, bytes, type(_UpperCAmelCase )) ): return value elif isinstance(_UpperCAmelCase , (np.character, np.ndarray) ) and np.issubdtype(value.dtype , np.character ): return value.tolist() UpperCAmelCase__ = {} if isinstance(_UpperCAmelCase , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.integer ): # the default int precision depends on the jax config # see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision if jax.config.jax_enable_xaa: UpperCAmelCase__ = {"""dtype""": jnp.intaa} else: UpperCAmelCase__ = {"""dtype""": jnp.intaa} elif isinstance(_UpperCAmelCase , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.floating ): UpperCAmelCase__ = {"""dtype""": jnp.floataa} elif config.PIL_AVAILABLE and "PIL" in sys.modules: import PIL.Image if isinstance(_UpperCAmelCase , PIL.Image.Image ): UpperCAmelCase__ = np.asarray(_UpperCAmelCase ) # using global variable since `jaxlib.xla_extension.Device` is not serializable neither # with `pickle` nor with `dill`, so we need to use a global variable instead global DEVICE_MAPPING if DEVICE_MAPPING is None: UpperCAmelCase__ = self._map_devices_to_str() with jax.default_device(DEVICE_MAPPING[self.device] ): # calling jnp.array on a np.ndarray does copy the data # see https://github.com/google/jax/issues/4486 return jnp.array(_UpperCAmelCase , **{**default_dtype, **self.jnp_array_kwargs} ) def SCREAMING_SNAKE_CASE__ ( self : Any , _UpperCAmelCase : Tuple ): """simple docstring""" import jax # support for torch, tf, jax etc. if config.TORCH_AVAILABLE and "torch" in sys.modules: import torch if isinstance(_UpperCAmelCase , torch.Tensor ): return self._tensorize(data_struct.detach().cpu().numpy()[()] ) if hasattr(_UpperCAmelCase , """__array__""" ) and not isinstance(_UpperCAmelCase , jax.Array ): UpperCAmelCase__ = data_struct.__array__() # support for nested types like struct of list of struct if isinstance(_UpperCAmelCase , np.ndarray ): if data_struct.dtype == object: # jax arrays cannot be instantied from an array of objects return self._consolidate([self.recursive_tensorize(_UpperCAmelCase ) for substruct in data_struct] ) elif isinstance(_UpperCAmelCase , (list, tuple) ): return self._consolidate([self.recursive_tensorize(_UpperCAmelCase ) for substruct in data_struct] ) return self._tensorize(_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : str , _UpperCAmelCase : dict ): """simple docstring""" return map_nested(self._recursive_tensorize , _UpperCAmelCase , map_list=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Dict , _UpperCAmelCase : pa.Table ): """simple docstring""" UpperCAmelCase__ = self.numpy_arrow_extractor().extract_row(_UpperCAmelCase ) UpperCAmelCase__ = self.python_features_decoder.decode_row(_UpperCAmelCase ) return self.recursive_tensorize(_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , _UpperCAmelCase : pa.Table ): """simple docstring""" UpperCAmelCase__ = self.numpy_arrow_extractor().extract_column(_UpperCAmelCase ) UpperCAmelCase__ = self.python_features_decoder.decode_column(_UpperCAmelCase , pa_table.column_names[0] ) UpperCAmelCase__ = self.recursive_tensorize(_UpperCAmelCase ) UpperCAmelCase__ = self._consolidate(_UpperCAmelCase ) return column def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] , _UpperCAmelCase : pa.Table ): """simple docstring""" UpperCAmelCase__ = self.numpy_arrow_extractor().extract_batch(_UpperCAmelCase ) UpperCAmelCase__ = self.python_features_decoder.decode_batch(_UpperCAmelCase ) UpperCAmelCase__ = self.recursive_tensorize(_UpperCAmelCase ) for column_name in batch: UpperCAmelCase__ = self._consolidate(batch[column_name] ) return batch

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'''simple docstring''' import math def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int ): '''simple docstring''' assert isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) and ( number >= 0 ), "'number' must been an int and positive" if 1 < number < 4: # 2 and 3 are primes return True elif number < 2 or not number % 2: # Negatives, 0, 1 and all even numbers are not primes return False UpperCAmelCase__ = range(3 , int(math.sqrt(SCREAMING_SNAKE_CASE__ ) + 1 ) , 2 ) return not any(not number % i for i in odd_numbers ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : List[str]=1 , **SCREAMING_SNAKE_CASE__ : List[str] ): '''simple docstring''' UpperCAmelCase__ = factor * value UpperCAmelCase__ = value while not is_prime(SCREAMING_SNAKE_CASE__ ): value += 1 if not ("desc" in kwargs and kwargs["desc"] is True) else -1 if value == first_value_val: return next_prime(value + 1 , **SCREAMING_SNAKE_CASE__ ) return value

346

1

'''simple docstring''' from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available UpperCAmelCase_ = { 'configuration_mask2former': [ 'MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Mask2FormerConfig', ], } try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCAmelCase_ = ['Mask2FormerImageProcessor'] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCAmelCase_ = [ 'MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST', 'Mask2FormerForUniversalSegmentation', 'Mask2FormerModel', 'Mask2FormerPreTrainedModel', ] if TYPE_CHECKING: from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .image_processing_maskaformer import MaskaFormerImageProcessor try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_maskaformer import ( MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST, MaskaFormerForUniversalSegmentation, MaskaFormerModel, MaskaFormerPreTrainedModel, ) else: import sys UpperCAmelCase_ = _LazyModule(__name__, globals()['__file__'], _import_structure)

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'''simple docstring''' import string from math import logaa def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : str ): '''simple docstring''' UpperCAmelCase__ = document.translate( str.maketrans("""""" , """""" , string.punctuation ) ).replace("""\n""" , """""" ) UpperCAmelCase__ = document_without_punctuation.split(""" """ ) # word tokenization return len([word for word in tokenize_document if word.lower() == term.lower()] ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : str ): '''simple docstring''' UpperCAmelCase__ = corpus.lower().translate( str.maketrans("""""" , """""" , string.punctuation ) ) # strip all punctuation and replace it with '' UpperCAmelCase__ = corpus_without_punctuation.split("""\n""" ) UpperCAmelCase__ = term.lower() return (len([doc for doc in docs if term in doc] ), len(SCREAMING_SNAKE_CASE__ )) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : Tuple=False ): '''simple docstring''' if smoothing: if n == 0: raise ValueError("""log10(0) is undefined.""" ) return round(1 + logaa(n / (1 + df) ) , 3 ) if df == 0: raise ZeroDivisionError("""df must be > 0""" ) elif n == 0: raise ValueError("""log10(0) is undefined.""" ) return round(logaa(n / df ) , 3 ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ): '''simple docstring''' return round(tf * idf , 3 )

346

1

'''simple docstring''' from typing import Any class lowerCAmelCase_ : '''simple docstring''' def __init__( self : int , _UpperCAmelCase : Any ): """simple docstring""" UpperCAmelCase__ = data UpperCAmelCase__ = None class lowerCAmelCase_ : '''simple docstring''' def __init__( self : Optional[Any] ): """simple docstring""" UpperCAmelCase__ = None def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ): """simple docstring""" UpperCAmelCase__ = self.head while temp is not None: print(temp.data , end=""" """ ) UpperCAmelCase__ = temp.next print() def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] , _UpperCAmelCase : Any ): """simple docstring""" UpperCAmelCase__ = Node(_UpperCAmelCase ) UpperCAmelCase__ = self.head UpperCAmelCase__ = new_node def SCREAMING_SNAKE_CASE__ ( self : Any , _UpperCAmelCase : str , _UpperCAmelCase : Any ): """simple docstring""" if node_data_a == node_data_a: return else: UpperCAmelCase__ = self.head while node_a is not None and node_a.data != node_data_a: UpperCAmelCase__ = node_a.next UpperCAmelCase__ = self.head while node_a is not None and node_a.data != node_data_a: UpperCAmelCase__ = node_a.next if node_a is None or node_a is None: return UpperCAmelCase__ , UpperCAmelCase__ = node_a.data, node_a.data if __name__ == "__main__": UpperCAmelCase_ = LinkedList() for i in range(5, 0, -1): ll.push(i) ll.print_list() ll.swap_nodes(1, 4) print('After swapping') ll.print_list()

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'''simple docstring''' import argparse import torch from transformers import BertForMaskedLM if __name__ == "__main__": UpperCAmelCase_ = argparse.ArgumentParser( description=( 'Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned' ' Distillation' ) ) parser.add_argument('--model_type', default='bert', choices=['bert']) parser.add_argument('--model_name', default='bert-base-uncased', type=str) parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_bert-base-uncased_0247911.pth', type=str) parser.add_argument('--vocab_transform', action='store_true') UpperCAmelCase_ = parser.parse_args() if args.model_type == "bert": UpperCAmelCase_ = BertForMaskedLM.from_pretrained(args.model_name) UpperCAmelCase_ = 'bert' else: raise ValueError('args.model_type should be "bert".') UpperCAmelCase_ = model.state_dict() UpperCAmelCase_ = {} for w in ["word_embeddings", "position_embeddings"]: UpperCAmelCase_ = state_dict[f"{prefix}.embeddings.{w}.weight"] for w in ["weight", "bias"]: UpperCAmelCase_ = state_dict[f"{prefix}.embeddings.LayerNorm.{w}"] UpperCAmelCase_ = 0 for teacher_idx in [0, 2, 4, 7, 9, 1_1]: for w in ["weight", "bias"]: UpperCAmelCase_ = state_dict[ f"{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}" ] UpperCAmelCase_ = state_dict[ f"{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}" ] UpperCAmelCase_ = state_dict[ f"{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}" ] UpperCAmelCase_ = state_dict[ f"{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}" ] UpperCAmelCase_ = state_dict[ f"{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}" ] UpperCAmelCase_ = state_dict[ f"{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}" ] UpperCAmelCase_ = state_dict[ f"{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}" ] UpperCAmelCase_ = state_dict[ f"{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}" ] std_idx += 1 UpperCAmelCase_ = state_dict['cls.predictions.decoder.weight'] UpperCAmelCase_ = state_dict['cls.predictions.bias'] if args.vocab_transform: for w in ["weight", "bias"]: UpperCAmelCase_ = state_dict[f"cls.predictions.transform.dense.{w}"] UpperCAmelCase_ = state_dict[f"cls.predictions.transform.LayerNorm.{w}"] print(f"N layers selected for distillation: {std_idx}") print(f"Number of params transferred for distillation: {len(compressed_sd.keys())}") print(f"Save transferred checkpoint to {args.dump_checkpoint}.") torch.save(compressed_sd, args.dump_checkpoint)

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'''simple docstring''' import torch from diffusers import DPMSolverSDEScheduler from diffusers.utils import torch_device from diffusers.utils.testing_utils import require_torchsde from .test_schedulers import SchedulerCommonTest @require_torchsde class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' lowerCAmelCase_ : Tuple = (DPMSolverSDEScheduler,) lowerCAmelCase_ : Optional[Any] = 10 def SCREAMING_SNAKE_CASE__ ( self : Tuple , **_UpperCAmelCase : int ): """simple docstring""" UpperCAmelCase__ = { """num_train_timesteps""": 11_00, """beta_start""": 0.0001, """beta_end""": 0.02, """beta_schedule""": """linear""", """noise_sampler_seed""": 0, } config.update(**_UpperCAmelCase ) return config def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ): """simple docstring""" for timesteps in [10, 50, 1_00, 10_00]: self.check_over_configs(num_train_timesteps=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : List[str] ): """simple docstring""" for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001] , [0.0002, 0.002, 0.02] ): self.check_over_configs(beta_start=_UpperCAmelCase , beta_end=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : List[str] ): """simple docstring""" for schedule in ["linear", "scaled_linear"]: self.check_over_configs(beta_schedule=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : List[str] ): """simple docstring""" for prediction_type in ["epsilon", "v_prediction"]: self.check_over_configs(prediction_type=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : str ): """simple docstring""" UpperCAmelCase__ = self.scheduler_classes[0] UpperCAmelCase__ = self.get_scheduler_config() UpperCAmelCase__ = scheduler_class(**_UpperCAmelCase ) scheduler.set_timesteps(self.num_inference_steps ) UpperCAmelCase__ = self.dummy_model() UpperCAmelCase__ = self.dummy_sample_deter * scheduler.init_noise_sigma UpperCAmelCase__ = sample.to(_UpperCAmelCase ) for i, t in enumerate(scheduler.timesteps ): UpperCAmelCase__ = scheduler.scale_model_input(_UpperCAmelCase , _UpperCAmelCase ) UpperCAmelCase__ = model(_UpperCAmelCase , _UpperCAmelCase ) UpperCAmelCase__ = scheduler.step(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) UpperCAmelCase__ = output.prev_sample UpperCAmelCase__ = torch.sum(torch.abs(_UpperCAmelCase ) ) UpperCAmelCase__ = torch.mean(torch.abs(_UpperCAmelCase ) ) if torch_device in ["mps"]: assert abs(result_sum.item() - 167.47_8210_4492_1875 ) < 1E-2 assert abs(result_mean.item() - 0.2178_7059_6456_5277 ) < 1E-3 elif torch_device in ["cuda"]: assert abs(result_sum.item() - 171.59_3521_1181_6406 ) < 1E-2 assert abs(result_mean.item() - 0.2_2342_9068_9229_9652 ) < 1E-3 else: assert abs(result_sum.item() - 162.52_3834_2285_1562 ) < 1E-2 assert abs(result_mean.item() - 0.211_6195_7085_1326 ) < 1E-3 def SCREAMING_SNAKE_CASE__ ( self : str ): """simple docstring""" UpperCAmelCase__ = self.scheduler_classes[0] UpperCAmelCase__ = self.get_scheduler_config(prediction_type="""v_prediction""" ) UpperCAmelCase__ = scheduler_class(**_UpperCAmelCase ) scheduler.set_timesteps(self.num_inference_steps ) UpperCAmelCase__ = self.dummy_model() UpperCAmelCase__ = self.dummy_sample_deter * scheduler.init_noise_sigma UpperCAmelCase__ = sample.to(_UpperCAmelCase ) for i, t in enumerate(scheduler.timesteps ): UpperCAmelCase__ = scheduler.scale_model_input(_UpperCAmelCase , _UpperCAmelCase ) UpperCAmelCase__ = model(_UpperCAmelCase , _UpperCAmelCase ) UpperCAmelCase__ = scheduler.step(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) UpperCAmelCase__ = output.prev_sample UpperCAmelCase__ = torch.sum(torch.abs(_UpperCAmelCase ) ) UpperCAmelCase__ = torch.mean(torch.abs(_UpperCAmelCase ) ) if torch_device in ["mps"]: assert abs(result_sum.item() - 124.77_1492_0043_9453 ) < 1E-2 assert abs(result_mean.item() - 0.1_6226_2890_1481_6284 ) < 1E-3 elif torch_device in ["cuda"]: assert abs(result_sum.item() - 128.1_6633_6059_5703 ) < 1E-2 assert abs(result_mean.item() - 0.1_6688_3260_0116_7297 ) < 1E-3 else: assert abs(result_sum.item() - 119.8_4875_4882_8125 ) < 1E-2 assert abs(result_mean.item() - 0.1560_5306_6253_6621 ) < 1E-3 def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ): """simple docstring""" UpperCAmelCase__ = self.scheduler_classes[0] UpperCAmelCase__ = self.get_scheduler_config() UpperCAmelCase__ = scheduler_class(**_UpperCAmelCase ) scheduler.set_timesteps(self.num_inference_steps , device=_UpperCAmelCase ) UpperCAmelCase__ = self.dummy_model() UpperCAmelCase__ = self.dummy_sample_deter.to(_UpperCAmelCase ) * scheduler.init_noise_sigma for t in scheduler.timesteps: UpperCAmelCase__ = scheduler.scale_model_input(_UpperCAmelCase , _UpperCAmelCase ) UpperCAmelCase__ = model(_UpperCAmelCase , _UpperCAmelCase ) UpperCAmelCase__ = scheduler.step(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) UpperCAmelCase__ = output.prev_sample UpperCAmelCase__ = torch.sum(torch.abs(_UpperCAmelCase ) ) UpperCAmelCase__ = torch.mean(torch.abs(_UpperCAmelCase ) ) if torch_device in ["mps"]: assert abs(result_sum.item() - 167.46_9573_9746_0938 ) < 1E-2 assert abs(result_mean.item() - 0.2_1805_9346_0798_2635 ) < 1E-3 elif torch_device in ["cuda"]: assert abs(result_sum.item() - 171.59_3536_3769_5312 ) < 1E-2 assert abs(result_mean.item() - 0.2_2342_9083_8241_5771 ) < 1E-3 else: assert abs(result_sum.item() - 162.52_3834_2285_1562 ) < 1E-2 assert abs(result_mean.item() - 0.211_6195_7085_1326 ) < 1E-3 def SCREAMING_SNAKE_CASE__ ( self : str ): """simple docstring""" UpperCAmelCase__ = self.scheduler_classes[0] UpperCAmelCase__ = self.get_scheduler_config() UpperCAmelCase__ = scheduler_class(**_UpperCAmelCase , use_karras_sigmas=_UpperCAmelCase ) scheduler.set_timesteps(self.num_inference_steps , device=_UpperCAmelCase ) UpperCAmelCase__ = self.dummy_model() UpperCAmelCase__ = self.dummy_sample_deter.to(_UpperCAmelCase ) * scheduler.init_noise_sigma UpperCAmelCase__ = sample.to(_UpperCAmelCase ) for t in scheduler.timesteps: UpperCAmelCase__ = scheduler.scale_model_input(_UpperCAmelCase , _UpperCAmelCase ) UpperCAmelCase__ = model(_UpperCAmelCase , _UpperCAmelCase ) UpperCAmelCase__ = scheduler.step(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) UpperCAmelCase__ = output.prev_sample UpperCAmelCase__ = torch.sum(torch.abs(_UpperCAmelCase ) ) UpperCAmelCase__ = torch.mean(torch.abs(_UpperCAmelCase ) ) if torch_device in ["mps"]: assert abs(result_sum.item() - 176.66_9741_3574_2188 ) < 1E-2 assert abs(result_mean.item() - 0.2_3003_8727_3098_1811 ) < 1E-2 elif torch_device in ["cuda"]: assert abs(result_sum.item() - 177.63_6535_6445_3125 ) < 1E-2 assert abs(result_mean.item() - 0.2_3003_8727_3098_1811 ) < 1E-2 else: assert abs(result_sum.item() - 170.3_1352_2338_8672 ) < 1E-2 assert abs(result_mean.item() - 0.2_3003_8727_3098_1811 ) < 1E-2

346

'''simple docstring''' import tempfile import torch from diffusers import PNDMScheduler from .test_schedulers import SchedulerCommonTest class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' lowerCAmelCase_ : Union[str, Any] = (PNDMScheduler,) lowerCAmelCase_ : Optional[int] = (("""num_inference_steps""", 50),) def SCREAMING_SNAKE_CASE__ ( self : List[Any] , **_UpperCAmelCase : Optional[int] ): """simple docstring""" UpperCAmelCase__ = { """num_train_timesteps""": 10_00, """beta_start""": 0.0001, """beta_end""": 0.02, """beta_schedule""": """linear""", } config.update(**_UpperCAmelCase ) return config def SCREAMING_SNAKE_CASE__ ( self : List[Any] , _UpperCAmelCase : Tuple=0 , **_UpperCAmelCase : List[str] ): """simple docstring""" UpperCAmelCase__ = dict(self.forward_default_kwargs ) UpperCAmelCase__ = kwargs.pop("""num_inference_steps""" , _UpperCAmelCase ) UpperCAmelCase__ = self.dummy_sample UpperCAmelCase__ = 0.1 * sample UpperCAmelCase__ = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05] for scheduler_class in self.scheduler_classes: UpperCAmelCase__ = self.get_scheduler_config(**_UpperCAmelCase ) UpperCAmelCase__ = scheduler_class(**_UpperCAmelCase ) scheduler.set_timesteps(_UpperCAmelCase ) # copy over dummy past residuals UpperCAmelCase__ = dummy_past_residuals[:] with tempfile.TemporaryDirectory() as tmpdirname: scheduler.save_config(_UpperCAmelCase ) UpperCAmelCase__ = scheduler_class.from_pretrained(_UpperCAmelCase ) new_scheduler.set_timesteps(_UpperCAmelCase ) # copy over dummy past residuals UpperCAmelCase__ = dummy_past_residuals[:] UpperCAmelCase__ = scheduler.step_prk(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample UpperCAmelCase__ = new_scheduler.step_prk(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical" UpperCAmelCase__ = scheduler.step_plms(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample UpperCAmelCase__ = new_scheduler.step_plms(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical" def SCREAMING_SNAKE_CASE__ ( self : List[str] ): """simple docstring""" pass def SCREAMING_SNAKE_CASE__ ( self : int , _UpperCAmelCase : Union[str, Any]=0 , **_UpperCAmelCase : Optional[int] ): """simple docstring""" UpperCAmelCase__ = dict(self.forward_default_kwargs ) UpperCAmelCase__ = kwargs.pop("""num_inference_steps""" , _UpperCAmelCase ) UpperCAmelCase__ = self.dummy_sample UpperCAmelCase__ = 0.1 * sample UpperCAmelCase__ = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05] for scheduler_class in self.scheduler_classes: UpperCAmelCase__ = self.get_scheduler_config() UpperCAmelCase__ = scheduler_class(**_UpperCAmelCase ) scheduler.set_timesteps(_UpperCAmelCase ) # copy over dummy past residuals (must be after setting timesteps) UpperCAmelCase__ = dummy_past_residuals[:] with tempfile.TemporaryDirectory() as tmpdirname: scheduler.save_config(_UpperCAmelCase ) UpperCAmelCase__ = scheduler_class.from_pretrained(_UpperCAmelCase ) # copy over dummy past residuals new_scheduler.set_timesteps(_UpperCAmelCase ) # copy over dummy past residual (must be after setting timesteps) UpperCAmelCase__ = dummy_past_residuals[:] UpperCAmelCase__ = scheduler.step_prk(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample UpperCAmelCase__ = new_scheduler.step_prk(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical" UpperCAmelCase__ = scheduler.step_plms(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample UpperCAmelCase__ = new_scheduler.step_plms(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical" def SCREAMING_SNAKE_CASE__ ( self : int , **_UpperCAmelCase : Tuple ): """simple docstring""" UpperCAmelCase__ = self.scheduler_classes[0] UpperCAmelCase__ = self.get_scheduler_config(**_UpperCAmelCase ) UpperCAmelCase__ = scheduler_class(**_UpperCAmelCase ) UpperCAmelCase__ = 10 UpperCAmelCase__ = self.dummy_model() UpperCAmelCase__ = self.dummy_sample_deter scheduler.set_timesteps(_UpperCAmelCase ) for i, t in enumerate(scheduler.prk_timesteps ): UpperCAmelCase__ = model(_UpperCAmelCase , _UpperCAmelCase ) UpperCAmelCase__ = scheduler.step_prk(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ).prev_sample for i, t in enumerate(scheduler.plms_timesteps ): UpperCAmelCase__ = model(_UpperCAmelCase , _UpperCAmelCase ) UpperCAmelCase__ = scheduler.step_plms(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ).prev_sample return sample def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ): """simple docstring""" UpperCAmelCase__ = dict(self.forward_default_kwargs ) UpperCAmelCase__ = kwargs.pop("""num_inference_steps""" , _UpperCAmelCase ) for scheduler_class in self.scheduler_classes: UpperCAmelCase__ = self.get_scheduler_config() UpperCAmelCase__ = scheduler_class(**_UpperCAmelCase ) UpperCAmelCase__ = self.dummy_sample UpperCAmelCase__ = 0.1 * sample if num_inference_steps is not None and hasattr(_UpperCAmelCase , """set_timesteps""" ): scheduler.set_timesteps(_UpperCAmelCase ) elif num_inference_steps is not None and not hasattr(_UpperCAmelCase , """set_timesteps""" ): UpperCAmelCase__ = num_inference_steps # copy over dummy past residuals (must be done after set_timesteps) UpperCAmelCase__ = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05] UpperCAmelCase__ = dummy_past_residuals[:] UpperCAmelCase__ = scheduler.step_prk(_UpperCAmelCase , 0 , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample UpperCAmelCase__ = scheduler.step_prk(_UpperCAmelCase , 1 , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample self.assertEqual(output_a.shape , sample.shape ) self.assertEqual(output_a.shape , output_a.shape ) UpperCAmelCase__ = scheduler.step_plms(_UpperCAmelCase , 0 , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample UpperCAmelCase__ = scheduler.step_plms(_UpperCAmelCase , 1 , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample self.assertEqual(output_a.shape , sample.shape ) self.assertEqual(output_a.shape , output_a.shape ) def SCREAMING_SNAKE_CASE__ ( self : int ): """simple docstring""" for timesteps in [1_00, 10_00]: self.check_over_configs(num_train_timesteps=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ): """simple docstring""" for steps_offset in [0, 1]: self.check_over_configs(steps_offset=_UpperCAmelCase ) UpperCAmelCase__ = self.scheduler_classes[0] UpperCAmelCase__ = self.get_scheduler_config(steps_offset=1 ) UpperCAmelCase__ = scheduler_class(**_UpperCAmelCase ) scheduler.set_timesteps(10 ) assert torch.equal( scheduler.timesteps , torch.LongTensor( [9_01, 8_51, 8_51, 8_01, 8_01, 7_51, 7_51, 7_01, 7_01, 6_51, 6_51, 6_01, 6_01, 5_01, 4_01, 3_01, 2_01, 1_01, 1] ) , ) def SCREAMING_SNAKE_CASE__ ( self : List[Any] ): """simple docstring""" for beta_start, beta_end in zip([0.0001, 0.001] , [0.002, 0.02] ): self.check_over_configs(beta_start=_UpperCAmelCase , beta_end=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ): """simple docstring""" for schedule in ["linear", "squaredcos_cap_v2"]: self.check_over_configs(beta_schedule=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ): """simple docstring""" for prediction_type in ["epsilon", "v_prediction"]: self.check_over_configs(prediction_type=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : str ): """simple docstring""" for t in [1, 5, 10]: self.check_over_forward(time_step=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Any ): """simple docstring""" for t, num_inference_steps in zip([1, 5, 10] , [10, 50, 1_00] ): self.check_over_forward(num_inference_steps=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : List[str] ): """simple docstring""" UpperCAmelCase__ = 27 for scheduler_class in self.scheduler_classes: UpperCAmelCase__ = self.dummy_sample UpperCAmelCase__ = 0.1 * sample UpperCAmelCase__ = self.get_scheduler_config() UpperCAmelCase__ = scheduler_class(**_UpperCAmelCase ) scheduler.set_timesteps(_UpperCAmelCase ) # before power of 3 fix, would error on first step, so we only need to do two for i, t in enumerate(scheduler.prk_timesteps[:2] ): UpperCAmelCase__ = scheduler.step_prk(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ).prev_sample def SCREAMING_SNAKE_CASE__ ( self : Dict ): """simple docstring""" with self.assertRaises(_UpperCAmelCase ): UpperCAmelCase__ = self.scheduler_classes[0] UpperCAmelCase__ = self.get_scheduler_config() UpperCAmelCase__ = scheduler_class(**_UpperCAmelCase ) scheduler.step_plms(self.dummy_sample , 1 , self.dummy_sample ).prev_sample def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ): """simple docstring""" UpperCAmelCase__ = self.full_loop() UpperCAmelCase__ = torch.sum(torch.abs(_UpperCAmelCase ) ) UpperCAmelCase__ = torch.mean(torch.abs(_UpperCAmelCase ) ) assert abs(result_sum.item() - 198.1318 ) < 1E-2 assert abs(result_mean.item() - 0.2580 ) < 1E-3 def SCREAMING_SNAKE_CASE__ ( self : Dict ): """simple docstring""" UpperCAmelCase__ = self.full_loop(prediction_type="""v_prediction""" ) UpperCAmelCase__ = torch.sum(torch.abs(_UpperCAmelCase ) ) UpperCAmelCase__ = torch.mean(torch.abs(_UpperCAmelCase ) ) assert abs(result_sum.item() - 67.3986 ) < 1E-2 assert abs(result_mean.item() - 0.0878 ) < 1E-3 def SCREAMING_SNAKE_CASE__ ( self : Any ): """simple docstring""" UpperCAmelCase__ = self.full_loop(set_alpha_to_one=_UpperCAmelCase , beta_start=0.01 ) UpperCAmelCase__ = torch.sum(torch.abs(_UpperCAmelCase ) ) UpperCAmelCase__ = torch.mean(torch.abs(_UpperCAmelCase ) ) assert abs(result_sum.item() - 230.0399 ) < 1E-2 assert abs(result_mean.item() - 0.2995 ) < 1E-3 def SCREAMING_SNAKE_CASE__ ( self : int ): """simple docstring""" UpperCAmelCase__ = self.full_loop(set_alpha_to_one=_UpperCAmelCase , beta_start=0.01 ) UpperCAmelCase__ = torch.sum(torch.abs(_UpperCAmelCase ) ) UpperCAmelCase__ = torch.mean(torch.abs(_UpperCAmelCase ) ) assert abs(result_sum.item() - 186.9482 ) < 1E-2 assert abs(result_mean.item() - 0.2434 ) < 1E-3

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'''simple docstring''' import requests UpperCAmelCase_ = 'YOUR API KEY' def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : str = giphy_api_key ): '''simple docstring''' UpperCAmelCase__ = """+""".join(query.split() ) UpperCAmelCase__ = F'''https://api.giphy.com/v1/gifs/search?q={formatted_query}&api_key={api_key}''' UpperCAmelCase__ = requests.get(SCREAMING_SNAKE_CASE__ ).json()["""data"""] return [gif["url"] for gif in gifs] if __name__ == "__main__": print('\n'.join(get_gifs('space ship')))

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'''simple docstring''' from ...configuration_utils import PretrainedConfig from ...utils import logging UpperCAmelCase_ = logging.get_logger(__name__) UpperCAmelCase_ = { 'google/vivit-b-16x2-kinetics400': ( 'https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json' ), # See all Vivit models at https://huggingface.co/models?filter=vivit } class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' lowerCAmelCase_ : Optional[int] = """vivit""" def __init__( self : List[str] , _UpperCAmelCase : List[Any]=2_24 , _UpperCAmelCase : List[str]=32 , _UpperCAmelCase : Any=[2, 16, 16] , _UpperCAmelCase : int=3 , _UpperCAmelCase : Optional[Any]=7_68 , _UpperCAmelCase : Union[str, Any]=12 , _UpperCAmelCase : Dict=12 , _UpperCAmelCase : Optional[Any]=30_72 , _UpperCAmelCase : Optional[int]="gelu_fast" , _UpperCAmelCase : Union[str, Any]=0.0 , _UpperCAmelCase : Tuple=0.0 , _UpperCAmelCase : Optional[int]=0.02 , _UpperCAmelCase : List[Any]=1E-06 , _UpperCAmelCase : List[str]=True , **_UpperCAmelCase : List[Any] , ): """simple docstring""" UpperCAmelCase__ = hidden_size UpperCAmelCase__ = num_hidden_layers UpperCAmelCase__ = num_attention_heads UpperCAmelCase__ = intermediate_size UpperCAmelCase__ = hidden_act UpperCAmelCase__ = hidden_dropout_prob UpperCAmelCase__ = attention_probs_dropout_prob UpperCAmelCase__ = initializer_range UpperCAmelCase__ = layer_norm_eps UpperCAmelCase__ = image_size UpperCAmelCase__ = num_frames UpperCAmelCase__ = tubelet_size UpperCAmelCase__ = num_channels UpperCAmelCase__ = qkv_bias super().__init__(**_UpperCAmelCase )

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'''simple docstring''' import os import socket from contextlib import contextmanager import torch from ..commands.config.default import write_basic_config # noqa: F401 from ..state import PartialState from .dataclasses import DistributedType from .imports import is_deepspeed_available, is_tpu_available from .transformer_engine import convert_model from .versions import is_torch_version if is_deepspeed_available(): from deepspeed import DeepSpeedEngine if is_tpu_available(check_device=False): import torch_xla.core.xla_model as xm def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Dict ): '''simple docstring''' if is_torch_version("""<""" , """2.0.0""" ) or not hasattr(SCREAMING_SNAKE_CASE__ , """_dynamo""" ): return False return isinstance(SCREAMING_SNAKE_CASE__ , torch._dynamo.eval_frame.OptimizedModule ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : bool = True ): '''simple docstring''' UpperCAmelCase__ = (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel) UpperCAmelCase__ = is_compiled_module(SCREAMING_SNAKE_CASE__ ) if is_compiled: UpperCAmelCase__ = model UpperCAmelCase__ = model._orig_mod if is_deepspeed_available(): options += (DeepSpeedEngine,) while isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ): UpperCAmelCase__ = model.module if not keep_fpaa_wrapper: UpperCAmelCase__ = getattr(SCREAMING_SNAKE_CASE__ , """forward""" ) UpperCAmelCase__ = model.__dict__.pop("""_original_forward""" , SCREAMING_SNAKE_CASE__ ) if original_forward is not None: while hasattr(SCREAMING_SNAKE_CASE__ , """__wrapped__""" ): UpperCAmelCase__ = forward.__wrapped__ if forward == original_forward: break UpperCAmelCase__ = forward if getattr(SCREAMING_SNAKE_CASE__ , """_converted_to_transformer_engine""" , SCREAMING_SNAKE_CASE__ ): convert_model(SCREAMING_SNAKE_CASE__ , to_transformer_engine=SCREAMING_SNAKE_CASE__ ) if is_compiled: UpperCAmelCase__ = model UpperCAmelCase__ = compiled_model return model def _UpperCamelCase ( ): '''simple docstring''' PartialState().wait_for_everyone() def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : str ): '''simple docstring''' if PartialState().distributed_type == DistributedType.TPU: xm.save(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) elif PartialState().local_process_index == 0: torch.save(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) @contextmanager def _UpperCamelCase ( **SCREAMING_SNAKE_CASE__ : int ): '''simple docstring''' for key, value in kwargs.items(): UpperCAmelCase__ = str(SCREAMING_SNAKE_CASE__ ) yield for key in kwargs: if key.upper() in os.environ: del os.environ[key.upper()] def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Optional[Any] ): '''simple docstring''' if not hasattr(SCREAMING_SNAKE_CASE__ , """__qualname__""" ) and not hasattr(SCREAMING_SNAKE_CASE__ , """__name__""" ): UpperCAmelCase__ = getattr(SCREAMING_SNAKE_CASE__ , """__class__""" , SCREAMING_SNAKE_CASE__ ) if hasattr(SCREAMING_SNAKE_CASE__ , """__qualname__""" ): return obj.__qualname__ if hasattr(SCREAMING_SNAKE_CASE__ , """__name__""" ): return obj.__name__ return str(SCREAMING_SNAKE_CASE__ ) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : Optional[int] ): '''simple docstring''' for key, value in source.items(): if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ): UpperCAmelCase__ = destination.setdefault(SCREAMING_SNAKE_CASE__ , {} ) merge_dicts(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) else: UpperCAmelCase__ = value return destination def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int = None ): '''simple docstring''' if port is None: UpperCAmelCase__ = 29500 with socket.socket(socket.AF_INET , socket.SOCK_STREAM ) as s: return s.connect_ex(("""localhost""", port) ) == 0

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'''simple docstring''' import warnings from ...utils import logging from .image_processing_deit import DeiTImageProcessor UpperCAmelCase_ = logging.get_logger(__name__) class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' def __init__( self : List[str] , *_UpperCAmelCase : Optional[int] , **_UpperCAmelCase : Optional[Any] ): """simple docstring""" warnings.warn( """The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please""" """ use DeiTImageProcessor instead.""" , _UpperCAmelCase , ) super().__init__(*_UpperCAmelCase , **_UpperCAmelCase )

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'''simple docstring''' def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int = 10 ): '''simple docstring''' if not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) or n < 0: raise ValueError("""Invalid input""" ) UpperCAmelCase__ = 10**n UpperCAmelCase__ = 28433 * (pow(2 , 7830457 , SCREAMING_SNAKE_CASE__ )) + 1 return str(number % modulus ) if __name__ == "__main__": from doctest import testmod testmod() print(f"{solution(1_0) = }")

346

'''simple docstring''' import os import unicodedata from shutil import copyfile from typing import Any, Dict, List, Optional, Tuple import sentencepiece as spm from ...tokenization_utils import AddedToken, PreTrainedTokenizer from ...utils import SPIECE_UNDERLINE, logging UpperCAmelCase_ = logging.get_logger(__name__) UpperCAmelCase_ = {'vocab_file': 'spiece.model'} UpperCAmelCase_ = { 'vocab_file': { 'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model', } } class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' def __init__( self : Dict , _UpperCAmelCase : str , _UpperCAmelCase : Any=False , _UpperCAmelCase : int=True , _UpperCAmelCase : Union[str, Any]=False , _UpperCAmelCase : Dict="<s>" , _UpperCAmelCase : int="</s>" , _UpperCAmelCase : Dict="<unk>" , _UpperCAmelCase : Tuple="<sep>" , _UpperCAmelCase : List[Any]="<pad>" , _UpperCAmelCase : int="<cls>" , _UpperCAmelCase : Union[str, Any]="<mask>" , _UpperCAmelCase : List[str]=["<eop>", "<eod>"] , _UpperCAmelCase : Optional[Dict[str, Any]] = None , **_UpperCAmelCase : int , ): """simple docstring""" UpperCAmelCase__ = AddedToken(_UpperCAmelCase , lstrip=_UpperCAmelCase , rstrip=_UpperCAmelCase ) if isinstance(_UpperCAmelCase , _UpperCAmelCase ) else mask_token UpperCAmelCase__ = {} if sp_model_kwargs is None else sp_model_kwargs super().__init__( do_lower_case=_UpperCAmelCase , remove_space=_UpperCAmelCase , keep_accents=_UpperCAmelCase , bos_token=_UpperCAmelCase , eos_token=_UpperCAmelCase , unk_token=_UpperCAmelCase , sep_token=_UpperCAmelCase , pad_token=_UpperCAmelCase , cls_token=_UpperCAmelCase , mask_token=_UpperCAmelCase , additional_special_tokens=_UpperCAmelCase , sp_model_kwargs=self.sp_model_kwargs , **_UpperCAmelCase , ) UpperCAmelCase__ = 3 UpperCAmelCase__ = do_lower_case UpperCAmelCase__ = remove_space UpperCAmelCase__ = keep_accents UpperCAmelCase__ = vocab_file UpperCAmelCase__ = spm.SentencePieceProcessor(**self.sp_model_kwargs ) self.sp_model.Load(_UpperCAmelCase ) try: import jieba except ModuleNotFoundError as error: raise error.__class__( """You need to install jieba to use CpmTokenizer or CpmTokenizerFast. """ """See https://pypi.org/project/jieba/ for installation.""" ) UpperCAmelCase__ = jieba UpperCAmelCase__ = str.maketrans(""" \n""" , """\u2582\u2583""" ) @property # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ): """simple docstring""" return len(self.sp_model ) def SCREAMING_SNAKE_CASE__ ( self : List[str] ): """simple docstring""" UpperCAmelCase__ = {self.convert_ids_to_tokens(_UpperCAmelCase ): i for i in range(self.vocab_size )} vocab.update(self.added_tokens_encoder ) return vocab def __getstate__( self : Dict ): """simple docstring""" UpperCAmelCase__ = self.__dict__.copy() UpperCAmelCase__ = None return state def __setstate__( self : Union[str, Any] , _UpperCAmelCase : Union[str, Any] ): """simple docstring""" UpperCAmelCase__ = d # for backward compatibility if not hasattr(self , """sp_model_kwargs""" ): UpperCAmelCase__ = {} UpperCAmelCase__ = spm.SentencePieceProcessor(**self.sp_model_kwargs ) self.sp_model.Load(self.vocab_file ) def SCREAMING_SNAKE_CASE__ ( self : int , _UpperCAmelCase : Optional[Any] ): """simple docstring""" if self.remove_space: UpperCAmelCase__ = """ """.join(inputs.strip().split() ) else: UpperCAmelCase__ = inputs UpperCAmelCase__ = outputs.replace("""``""" , """\"""" ).replace("""''""" , """\"""" ) if not self.keep_accents: UpperCAmelCase__ = unicodedata.normalize("""NFKD""" , _UpperCAmelCase ) UpperCAmelCase__ = """""".join([c for c in outputs if not unicodedata.combining(_UpperCAmelCase )] ) if self.do_lower_case: UpperCAmelCase__ = outputs.lower() return outputs def SCREAMING_SNAKE_CASE__ ( self : Tuple , _UpperCAmelCase : str ): """simple docstring""" UpperCAmelCase__ = self.preprocess_text(_UpperCAmelCase ) UpperCAmelCase__ = self.sp_model.encode(_UpperCAmelCase , out_type=_UpperCAmelCase ) UpperCAmelCase__ = [] for piece in pieces: if len(_UpperCAmelCase ) > 1 and piece[-1] == str(""",""" ) and piece[-2].isdigit(): UpperCAmelCase__ = self.sp_model.EncodeAsPieces(piece[:-1].replace(_UpperCAmelCase , """""" ) ) if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE: if len(cur_pieces[0] ) == 1: UpperCAmelCase__ = cur_pieces[1:] else: UpperCAmelCase__ = cur_pieces[0][1:] cur_pieces.append(piece[-1] ) new_pieces.extend(_UpperCAmelCase ) else: new_pieces.append(_UpperCAmelCase ) return new_pieces def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , _UpperCAmelCase : Union[str, Any] ): """simple docstring""" return self.sp_model.PieceToId(_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Dict , _UpperCAmelCase : Any ): """simple docstring""" return self.sp_model.IdToPiece(_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : int , _UpperCAmelCase : Dict ): """simple docstring""" UpperCAmelCase__ = """""".join(_UpperCAmelCase ).replace(_UpperCAmelCase , """ """ ).strip() return out_string def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] , _UpperCAmelCase : List[int] , _UpperCAmelCase : Optional[List[int]] = None ): """simple docstring""" UpperCAmelCase__ = [self.sep_token_id] UpperCAmelCase__ = [self.cls_token_id] if token_ids_a is None: return token_ids_a + sep + cls return token_ids_a + sep + token_ids_a + sep + cls def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] , _UpperCAmelCase : List[int] , _UpperCAmelCase : Optional[List[int]] = None , _UpperCAmelCase : bool = False ): """simple docstring""" if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_a=_UpperCAmelCase , token_ids_a=_UpperCAmelCase , already_has_special_tokens=_UpperCAmelCase ) if token_ids_a is not None: return ([0] * len(_UpperCAmelCase )) + [1] + ([0] * len(_UpperCAmelCase )) + [1, 1] return ([0] * len(_UpperCAmelCase )) + [1, 1] def SCREAMING_SNAKE_CASE__ ( self : Dict , _UpperCAmelCase : List[int] , _UpperCAmelCase : Optional[List[int]] = None ): """simple docstring""" UpperCAmelCase__ = [self.sep_token_id] UpperCAmelCase__ = [2] if token_ids_a is None: return len(token_ids_a + sep ) * [0] + cls_segment_id return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id def SCREAMING_SNAKE_CASE__ ( self : List[Any] , _UpperCAmelCase : str , _UpperCAmelCase : Optional[str] = None ): """simple docstring""" if not os.path.isdir(_UpperCAmelCase ): logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' ) return UpperCAmelCase__ = os.path.join( _UpperCAmelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] ) if os.path.abspath(self.vocab_file ) != os.path.abspath(_UpperCAmelCase ) and os.path.isfile(self.vocab_file ): copyfile(self.vocab_file , _UpperCAmelCase ) elif not os.path.isfile(self.vocab_file ): with open(_UpperCAmelCase , """wb""" ) as fi: UpperCAmelCase__ = self.sp_model.serialized_model_proto() fi.write(_UpperCAmelCase ) return (out_vocab_file,) def SCREAMING_SNAKE_CASE__ ( self : Tuple , *_UpperCAmelCase : Tuple , **_UpperCAmelCase : Optional[Any] ): """simple docstring""" UpperCAmelCase__ = super()._decode(*_UpperCAmelCase , **_UpperCAmelCase ) UpperCAmelCase__ = text.replace(""" """ , """""" ).replace("""\u2582""" , """ """ ).replace("""\u2583""" , """\n""" ) return text

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'''simple docstring''' import argparse import random import joblib import numpy as np import torch from igf.igf import ( SecondaryLearner, collect_objective_set, compute_perplexity, generate_datasets, load_gpta, recopy_gpta, set_seed, train_secondary_learner, ) from torch.utils.data import DataLoader, RandomSampler from transformers import GPTaLMHeadModel def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Optional[int]=32 , SCREAMING_SNAKE_CASE__ : Dict=10 , SCREAMING_SNAKE_CASE__ : List[Any]=100 , SCREAMING_SNAKE_CASE__ : Union[str, Any]=1026 , SCREAMING_SNAKE_CASE__ : Tuple=True , SCREAMING_SNAKE_CASE__ : Any="data/tokenized_stories_train_wikitext103.jbl" , SCREAMING_SNAKE_CASE__ : Dict="igf_context_pairs.jbl" , ): '''simple docstring''' set_seed(3 ) # generate train_data and objective_set UpperCAmelCase__ , UpperCAmelCase__ = generate_datasets( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , number=SCREAMING_SNAKE_CASE__ , min_len=1026 , trim=SCREAMING_SNAKE_CASE__ ) # keeps model same across runs set_seed(4 ) # model, lm_optimizer, lm_scheduler = recopy_gpt2(model, device, max_steps) # store original model weights # can we train on GPU? UpperCAmelCase__ = torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu""" ) # load pretrained model UpperCAmelCase__ = load_gpta("""gpt2""" ).to(SCREAMING_SNAKE_CASE__ ) print("""computing perplexity on objective set""" ) UpperCAmelCase__ = compute_perplexity(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ).item() print("""perplexity on objective set:""" , SCREAMING_SNAKE_CASE__ ) # collect igf pairs and save to file demo.jbl collect_objective_set(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) # clean up, delete model and data we don't need anymore del model, train_data, objective_set torch.cuda.empty_cache() def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : Optional[int]=15 , SCREAMING_SNAKE_CASE__ : Optional[Any]=128 , SCREAMING_SNAKE_CASE__ : str=100 , SCREAMING_SNAKE_CASE__ : str="igf_model.pt" , ): '''simple docstring''' set_seed(42 ) # Load pre-trained model UpperCAmelCase__ = GPTaLMHeadModel.from_pretrained("""gpt2""" ) # Initialize secondary learner to use embedding weights of model UpperCAmelCase__ = SecondaryLearner(SCREAMING_SNAKE_CASE__ ) # Train secondary learner UpperCAmelCase__ = train_secondary_learner( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , max_epochs=SCREAMING_SNAKE_CASE__ , batch_size=SCREAMING_SNAKE_CASE__ , eval_freq=100 , igf_model_path=SCREAMING_SNAKE_CASE__ , ) del model, secondary_learner_train_data torch.cuda.empty_cache() return secondary_learner def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : List[Any]=32 , SCREAMING_SNAKE_CASE__ : Optional[Any]=1000 , SCREAMING_SNAKE_CASE__ : Any=16 , SCREAMING_SNAKE_CASE__ : Optional[Any]=1.0 , SCREAMING_SNAKE_CASE__ : str=recopy_gpta , SCREAMING_SNAKE_CASE__ : List[Any]=None , SCREAMING_SNAKE_CASE__ : List[str]=10 , SCREAMING_SNAKE_CASE__ : Any="gpt2_finetuned.pt" , ): '''simple docstring''' UpperCAmelCase__ = torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu""" ) UpperCAmelCase__ = RandomSampler(SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = DataLoader(SCREAMING_SNAKE_CASE__ , sampler=SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = max_steps // (len(SCREAMING_SNAKE_CASE__ )) + 1 UpperCAmelCase__ = 0 UpperCAmelCase__ = torch.zeros((1, context_len) , dtype=torch.long , device=SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = recopy_model(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) model.train() if secondary_learner is not None: secondary_learner.to(SCREAMING_SNAKE_CASE__ ) secondary_learner.eval() UpperCAmelCase__ = [] UpperCAmelCase__ = 0 UpperCAmelCase__ = [] UpperCAmelCase__ = [] # Compute the performance of the transformer model at the beginning UpperCAmelCase__ = compute_perplexity(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) test_perps.append(SCREAMING_SNAKE_CASE__ ) print("""Test perplexity, step""" , SCREAMING_SNAKE_CASE__ , """:""" , SCREAMING_SNAKE_CASE__ ) for epoch in range(int(SCREAMING_SNAKE_CASE__ ) ): for step, example in enumerate(SCREAMING_SNAKE_CASE__ ): torch.cuda.empty_cache() UpperCAmelCase__ = random.randint(0 , example.size(2 ) - context_len - 1 ) UpperCAmelCase__ = example[0, 0, start : start + context_len] lm_optimizer.zero_grad() UpperCAmelCase__ = model(SCREAMING_SNAKE_CASE__ , labels=SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = True if secondary_learner is not None: UpperCAmelCase__ = secondary_learner.forward( torch.tensor(SCREAMING_SNAKE_CASE__ , dtype=torch.long , device=SCREAMING_SNAKE_CASE__ ).unsqueeze(0 ) )[0].item() observed_qs.append(float(SCREAMING_SNAKE_CASE__ ) ) # Here we implement the simple non-constant threshold for the predicted IG(X) value # We will decay the selectivity of our secondary learner filter from # 1 standard deviation above average to 1 below average after 10 batches. if global_step == 10: UpperCAmelCase__ = -1 if predicted_q < threshold: UpperCAmelCase__ = False # If we passed the filter, add the context to the batch! if do_backprop: contexts.append(np.array(context.cpu() ) ) UpperCAmelCase__ = outputs[0] lm_loss.backward() examples += 1 del outputs # Once the batch is filled with enough contexts, backprop on the batch. if examples == batch_size: torch.cuda.empty_cache() UpperCAmelCase__ = 0 # Do LM backprop torch.nn.utils.clip_grad_norm_(model.parameters() , 3.0 ) lm_optimizer.step() lm_scheduler.step() # Update learning rate schedule global_step += 1 # Compute the performance of the transformer model at this batch if global_step % eval_interval == 0: UpperCAmelCase__ = compute_perplexity(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) test_perps.append(SCREAMING_SNAKE_CASE__ ) print("""Test perplexity, step""" , SCREAMING_SNAKE_CASE__ , """:""" , SCREAMING_SNAKE_CASE__ ) # Break out of the loop after 60 batches if max_steps > 0 and global_step > 60: break if max_steps > 0 and global_step > 60: break # save finetuned transformer model torch.save(model.state_dict() , SCREAMING_SNAKE_CASE__ ) torch.cuda.empty_cache() # Do some cleaning up so we can reinitialize for the next run of this function del lm_optimizer del lm_scheduler return model def _UpperCamelCase ( ): '''simple docstring''' UpperCAmelCase__ = argparse.ArgumentParser(description="""Fine-tune a transformer model with IGF on a language modeling task""" ) # Required parameters parser.add_argument( """--data_dir""" , default=SCREAMING_SNAKE_CASE__ , type=SCREAMING_SNAKE_CASE__ , required=SCREAMING_SNAKE_CASE__ , help="""The input data dir. Should contain data files for WikiText.""" , ) parser.add_argument( """--model_name_or_path""" , default=SCREAMING_SNAKE_CASE__ , type=SCREAMING_SNAKE_CASE__ , required=SCREAMING_SNAKE_CASE__ , help="""Path to pretrained model or model identifier from huggingface.co/models""" , ) parser.add_argument( """--data_file""" , type=SCREAMING_SNAKE_CASE__ , default=SCREAMING_SNAKE_CASE__ , help=( """A jbl file containing tokenized data which can be split as objective dataset, """ """train_dataset and test_dataset.""" ) , ) parser.add_argument( """--igf_data_file""" , type=SCREAMING_SNAKE_CASE__ , default=SCREAMING_SNAKE_CASE__ , help="""A jbl file containing the context and information gain pairs to train secondary learner.""" , ) parser.add_argument( """--output_dir""" , default=SCREAMING_SNAKE_CASE__ , type=SCREAMING_SNAKE_CASE__ , required=SCREAMING_SNAKE_CASE__ , help="""The output directory where the final fine-tuned model is stored.""" , ) parser.add_argument( """--tokenizer_name""" , default=SCREAMING_SNAKE_CASE__ , type=SCREAMING_SNAKE_CASE__ , help="""Pretrained tokenizer name or path if not the same as model_name""" , ) parser.add_argument("""--seed""" , type=SCREAMING_SNAKE_CASE__ , default=SCREAMING_SNAKE_CASE__ , help="""A seed for reproducible training.""" ) parser.add_argument( """--context_len""" , default=32 , type=SCREAMING_SNAKE_CASE__ , help=( """The maximum total input sequence length after tokenization. Sequences longer """ """than this will be truncated, sequences shorter will be padded.""" ) , ) parser.add_argument( """--size_objective_set""" , default=100 , type=SCREAMING_SNAKE_CASE__ , help="""number of articles that are long enough to be used as our objective set""" , ) parser.add_argument( """--eval_freq""" , default=100 , type=SCREAMING_SNAKE_CASE__ , help="""secondary model evaluation is triggered at eval_freq""" ) parser.add_argument("""--max_steps""" , default=1000 , type=SCREAMING_SNAKE_CASE__ , help="""To calculate training epochs""" ) parser.add_argument( """--secondary_learner_batch_size""" , default=128 , type=SCREAMING_SNAKE_CASE__ , help="""batch size of training data for secondary learner""" , ) parser.add_argument( """--batch_size""" , default=16 , type=SCREAMING_SNAKE_CASE__ , help="""batch size of training data of language model(gpt2) """ ) parser.add_argument( """--eval_interval""" , default=10 , type=SCREAMING_SNAKE_CASE__ , help=( """decay the selectivity of our secondary learner filter from""" """1 standard deviation above average to 1 below average after 10 batches""" ) , ) parser.add_argument( """--number""" , default=100 , type=SCREAMING_SNAKE_CASE__ , help="""The number of examples split to be used as objective_set/test_data""" ) parser.add_argument( """--min_len""" , default=1026 , type=SCREAMING_SNAKE_CASE__ , help="""The minimum length of the article to be used as objective set""" ) parser.add_argument( """--secondary_learner_max_epochs""" , default=15 , type=SCREAMING_SNAKE_CASE__ , help="""number of epochs to train secondary learner""" ) parser.add_argument("""--trim""" , default=SCREAMING_SNAKE_CASE__ , type=SCREAMING_SNAKE_CASE__ , help="""truncate the example if it exceeds context length""" ) parser.add_argument( """--threshold""" , default=1.0 , type=SCREAMING_SNAKE_CASE__ , help=( """The threshold value used by secondary learner to filter the train_data and allow only""" """ informative data as input to the model""" ) , ) parser.add_argument("""--finetuned_model_name""" , default="""gpt2_finetuned.pt""" , type=SCREAMING_SNAKE_CASE__ , help="""finetuned_model_name""" ) parser.add_argument( """--recopy_model""" , default=SCREAMING_SNAKE_CASE__ , type=SCREAMING_SNAKE_CASE__ , help="""Reset the model to the original pretrained GPT-2 weights after each iteration""" , ) # function calls # Collecting *n* pairs of context and information gain(X, IG(X)) for training the secondary learner generate_n_pairs( context_len=32 , max_steps=10 , size_objective_set=100 , min_len=1026 , trim=SCREAMING_SNAKE_CASE__ , data_file="""data/tokenized_stories_train_wikitext103.jbl""" , igf_data_file="""igf_context_pairs.jbl""" , ) # Load train data for secondary learner UpperCAmelCase__ = joblib.load("""data/IGF_values.jbl""" ) # Train secondary learner UpperCAmelCase__ = training_secondary_learner( SCREAMING_SNAKE_CASE__ , secondary_learner_max_epochs=15 , secondary_learner_batch_size=128 , eval_freq=100 , igf_model_path="""igf_model.pt""" , ) # load pretrained gpt2 model UpperCAmelCase__ = GPTaLMHeadModel.from_pretrained("""gpt2""" ) set_seed(42 ) # Generate train and test data to train and evaluate gpt2 model UpperCAmelCase__ , UpperCAmelCase__ = generate_datasets( context_len=32 , file="""data/tokenized_stories_train_wikitext103.jbl""" , number=100 , min_len=1026 , trim=SCREAMING_SNAKE_CASE__ ) # fine-tuning of the gpt2 model using igf (Information Gain Filtration) finetune( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , context_len=32 , max_steps=1000 , batch_size=16 , threshold=1.0 , recopy_model=SCREAMING_SNAKE_CASE__ , secondary_learner=SCREAMING_SNAKE_CASE__ , eval_interval=10 , finetuned_model_name="""gpt2_finetuned.pt""" , ) if __name__ == "__main__": main()

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'''simple docstring''' import argparse import logging import os import datasets import tensorflow as tf from transformers import AutoTokenizer UpperCAmelCase_ = logging.getLogger(__name__) def _UpperCamelCase ( ): '''simple docstring''' UpperCAmelCase__ = argparse.ArgumentParser( description="""Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset.""" ) parser.add_argument( """--dataset_name""" , type=SCREAMING_SNAKE_CASE__ , default="""wikitext""" , help="""Name of the training. Explore datasets at: hf.co/datasets.""" , ) parser.add_argument( """--dataset_config""" , type=SCREAMING_SNAKE_CASE__ , default="""wikitext-103-raw-v1""" , help="""Configuration name of the dataset.""" ) parser.add_argument( """--tokenizer_name_or_path""" , type=SCREAMING_SNAKE_CASE__ , default="""sayakpaul/unigram-tokenizer-wikitext""" , help="""Tokenizer identifier. Can be a local filepath or a Hub identifier.""" , ) parser.add_argument( """--shard_size""" , type=SCREAMING_SNAKE_CASE__ , default=1000 , help="""Number of entries to go in a single shard.""" , ) parser.add_argument("""--split""" , type=SCREAMING_SNAKE_CASE__ , default="""train""" , choices=["""train""", """test""", """validation"""] ) parser.add_argument( """--limit""" , default=SCREAMING_SNAKE_CASE__ , type=SCREAMING_SNAKE_CASE__ , help="""Limit the number of shards (used for debugging).""" , ) parser.add_argument( """--max_length""" , type=SCREAMING_SNAKE_CASE__ , default=512 , help="""Maximum sequence length. For training on TPUs, it helps to have a maximum""" """ sequence length that is a multiple of 8.""" , ) parser.add_argument( """--output_dir""" , default="""tf-tpu""" , type=SCREAMING_SNAKE_CASE__ , help="""Output directory where the TFRecord shards will be saved. If the""" """ path is appended with `gs://` ('gs://tf-tpu', for example) then the TFRecord""" """ shards will be directly saved to a Google Cloud Storage bucket.""" , ) UpperCAmelCase__ = parser.parse_args() return args def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Any ): '''simple docstring''' def fn(SCREAMING_SNAKE_CASE__ : Union[str, Any] ): return tokenizer(examples["""text"""] ) return fn def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Optional[int] ): '''simple docstring''' UpperCAmelCase__ = [] for i in range(len(tokenized_data["""input_ids"""] ) ): UpperCAmelCase__ = { """input_ids""": tf.train.Feature(intaa_list=tf.train.IntaaList(value=tokenized_data["""input_ids"""][i] ) ), """attention_mask""": tf.train.Feature( intaa_list=tf.train.IntaaList(value=tokenized_data["""attention_mask"""][i] ) ), } UpperCAmelCase__ = tf.train.Features(feature=SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = tf.train.Example(features=SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = example.SerializeToString() records.append(SCREAMING_SNAKE_CASE__ ) return records def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Any ): '''simple docstring''' UpperCAmelCase__ = datasets.load_dataset(args.dataset_name , args.dataset_config , split=args.split ) if args.limit is not None: UpperCAmelCase__ = min(len(SCREAMING_SNAKE_CASE__ ) , args.limit ) UpperCAmelCase__ = dataset.select(range(SCREAMING_SNAKE_CASE__ ) ) print(F'''Limiting the dataset to {args.limit} entries.''' ) UpperCAmelCase__ = AutoTokenizer.from_pretrained(args.tokenizer_name_or_path ) # Handle output directory creation. # For serializing into a Google Cloud Storage Bucket, one needs to first # create a bucket. if "gs" not in args.output_dir: if not os.path.exists(args.output_dir ): os.makedirs(args.output_dir ) UpperCAmelCase__ = os.path.join(args.output_dir , args.split ) if not os.path.exists(SCREAMING_SNAKE_CASE__ ): os.makedirs(SCREAMING_SNAKE_CASE__ ) else: UpperCAmelCase__ = os.path.join(args.output_dir , args.split ) # Tokenize the whole dataset at once. UpperCAmelCase__ = tokenize_function(SCREAMING_SNAKE_CASE__ ) UpperCAmelCase__ = dataset.map(SCREAMING_SNAKE_CASE__ , batched=SCREAMING_SNAKE_CASE__ , num_proc=4 , remove_columns=["""text"""] ) # We need to concatenate all our texts together, and then split the result # into chunks of a fixed size, which we will call block_size. To do this, we # will use the map method again, with the option batched=True. When we use batched=True, # the function we pass to map() will be passed multiple inputs at once, allowing us # to group them into more or fewer examples than we had in the input. # This allows us to create our new fixed-length samples. The advantage of this # method is that we don't lose a whole lot of content from the dataset compared to the # case where we simply tokenize with a pre-defined max_length. def group_texts(SCREAMING_SNAKE_CASE__ : int ): # Concatenate all texts. UpperCAmelCase__ = {k: sum(examples[k] , [] ) for k in examples.keys()} UpperCAmelCase__ = len(concatenated_examples[list(examples.keys() )[0]] ) # We drop the small remainder, though you could add padding instead if the model supports it # In this, as in all things, we advise you to follow your heart 🫀 UpperCAmelCase__ = (total_length // args.max_length) * args.max_length # Split by chunks of max_len. UpperCAmelCase__ = { k: [t[i : i + args.max_length] for i in range(0 , SCREAMING_SNAKE_CASE__ , args.max_length )] for k, t in concatenated_examples.items() } return result UpperCAmelCase__ = dataset_tokenized.map(SCREAMING_SNAKE_CASE__ , batched=SCREAMING_SNAKE_CASE__ , batch_size=1000 , num_proc=4 ) UpperCAmelCase__ = 0 UpperCAmelCase__ = 0 for shard in range(0 , len(SCREAMING_SNAKE_CASE__ ) , args.shard_size ): UpperCAmelCase__ = grouped_dataset[shard : shard + args.shard_size] UpperCAmelCase__ = len(dataset_snapshot["""input_ids"""] ) UpperCAmelCase__ = os.path.join(SCREAMING_SNAKE_CASE__ , F'''dataset-{shard_count}-{records_containing}.tfrecord''' ) UpperCAmelCase__ = get_serialized_examples(SCREAMING_SNAKE_CASE__ ) with tf.io.TFRecordWriter(SCREAMING_SNAKE_CASE__ ) as out_file: for i in range(len(SCREAMING_SNAKE_CASE__ ) ): UpperCAmelCase__ = serialized_examples[i] out_file.write(SCREAMING_SNAKE_CASE__ ) print("""Wrote file {} containing {} records""".format(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) ) shard_count += 1 total_records += records_containing with open(F'''split-{args.split}-records-count.txt''' , """w""" ) as f: print(F'''Total {args.split} records: {total_records}''' , file=SCREAMING_SNAKE_CASE__ ) if __name__ == "__main__": UpperCAmelCase_ = parse_args() main(args)

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'''simple docstring''' from __future__ import annotations import unittest from transformers import DebertaVaConfig, is_tf_available from transformers.testing_utils import require_tf, slow from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_tf_available(): import tensorflow as tf from transformers import ( TFDebertaVaForMaskedLM, TFDebertaVaForQuestionAnswering, TFDebertaVaForSequenceClassification, TFDebertaVaForTokenClassification, TFDebertaVaModel, ) class lowerCAmelCase_ : '''simple docstring''' def __init__( self : str , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : List[str]=13 , _UpperCAmelCase : List[str]=7 , _UpperCAmelCase : Optional[Any]=True , _UpperCAmelCase : Dict=True , _UpperCAmelCase : List[Any]=True , _UpperCAmelCase : int=True , _UpperCAmelCase : Dict=99 , _UpperCAmelCase : int=32 , _UpperCAmelCase : Optional[int]=2 , _UpperCAmelCase : List[Any]=4 , _UpperCAmelCase : Tuple=37 , _UpperCAmelCase : Optional[int]="gelu" , _UpperCAmelCase : str=0.1 , _UpperCAmelCase : int=0.1 , _UpperCAmelCase : Union[str, Any]=5_12 , _UpperCAmelCase : Tuple=16 , _UpperCAmelCase : Union[str, Any]=2 , _UpperCAmelCase : str=0.02 , _UpperCAmelCase : List[str]=False , _UpperCAmelCase : str=True , _UpperCAmelCase : Union[str, Any]="None" , _UpperCAmelCase : str=3 , _UpperCAmelCase : Optional[int]=4 , _UpperCAmelCase : Optional[Any]=None , ): """simple docstring""" UpperCAmelCase__ = parent UpperCAmelCase__ = batch_size UpperCAmelCase__ = seq_length UpperCAmelCase__ = is_training UpperCAmelCase__ = use_input_mask UpperCAmelCase__ = use_token_type_ids UpperCAmelCase__ = use_labels UpperCAmelCase__ = vocab_size UpperCAmelCase__ = hidden_size UpperCAmelCase__ = num_hidden_layers UpperCAmelCase__ = num_attention_heads UpperCAmelCase__ = intermediate_size UpperCAmelCase__ = hidden_act UpperCAmelCase__ = hidden_dropout_prob UpperCAmelCase__ = attention_probs_dropout_prob UpperCAmelCase__ = max_position_embeddings UpperCAmelCase__ = type_vocab_size UpperCAmelCase__ = type_sequence_label_size UpperCAmelCase__ = initializer_range UpperCAmelCase__ = num_labels UpperCAmelCase__ = num_choices UpperCAmelCase__ = relative_attention UpperCAmelCase__ = position_biased_input UpperCAmelCase__ = pos_att_type UpperCAmelCase__ = scope def SCREAMING_SNAKE_CASE__ ( self : str ): """simple docstring""" UpperCAmelCase__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size ) UpperCAmelCase__ = None if self.use_input_mask: UpperCAmelCase__ = random_attention_mask([self.batch_size, self.seq_length] ) UpperCAmelCase__ = None if self.use_token_type_ids: UpperCAmelCase__ = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size ) UpperCAmelCase__ = None UpperCAmelCase__ = None UpperCAmelCase__ = None if self.use_labels: UpperCAmelCase__ = ids_tensor([self.batch_size] , self.type_sequence_label_size ) UpperCAmelCase__ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels ) UpperCAmelCase__ = DebertaVaConfig( vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , relative_attention=self.relative_attention , position_biased_input=self.position_biased_input , initializer_range=self.initializer_range , return_dict=_UpperCAmelCase , ) return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] , _UpperCAmelCase : List[str] , _UpperCAmelCase : Dict , _UpperCAmelCase : Dict , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Tuple , _UpperCAmelCase : Tuple , _UpperCAmelCase : Tuple ): """simple docstring""" UpperCAmelCase__ = TFDebertaVaModel(config=_UpperCAmelCase ) UpperCAmelCase__ = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids} UpperCAmelCase__ = [input_ids, input_mask] UpperCAmelCase__ = model(_UpperCAmelCase ) UpperCAmelCase__ = model(_UpperCAmelCase ) self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) ) def SCREAMING_SNAKE_CASE__ ( self : str , _UpperCAmelCase : Dict , _UpperCAmelCase : List[str] , _UpperCAmelCase : List[str] , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Dict , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Optional[Any] ): """simple docstring""" UpperCAmelCase__ = TFDebertaVaForMaskedLM(config=_UpperCAmelCase ) UpperCAmelCase__ = { """input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids, } UpperCAmelCase__ = model(_UpperCAmelCase ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) ) def SCREAMING_SNAKE_CASE__ ( self : List[Any] , _UpperCAmelCase : List[str] , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Dict , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Any ): """simple docstring""" UpperCAmelCase__ = self.num_labels UpperCAmelCase__ = TFDebertaVaForSequenceClassification(config=_UpperCAmelCase ) UpperCAmelCase__ = { """input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids, } UpperCAmelCase__ = model(_UpperCAmelCase ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) ) def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , _UpperCAmelCase : List[str] , _UpperCAmelCase : Any , _UpperCAmelCase : List[str] , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Any , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Any ): """simple docstring""" UpperCAmelCase__ = self.num_labels UpperCAmelCase__ = TFDebertaVaForTokenClassification(config=_UpperCAmelCase ) UpperCAmelCase__ = { """input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids, } UpperCAmelCase__ = model(_UpperCAmelCase ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) ) def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] , _UpperCAmelCase : Dict , _UpperCAmelCase : Tuple , _UpperCAmelCase : Dict , _UpperCAmelCase : List[str] , _UpperCAmelCase : Tuple , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Optional[int] ): """simple docstring""" UpperCAmelCase__ = TFDebertaVaForQuestionAnswering(config=_UpperCAmelCase ) UpperCAmelCase__ = { """input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids, } UpperCAmelCase__ = model(_UpperCAmelCase ) self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) ) self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) ) def SCREAMING_SNAKE_CASE__ ( self : Dict ): """simple docstring""" UpperCAmelCase__ = self.prepare_config_and_inputs() ( ( UpperCAmelCase__ ) , ( UpperCAmelCase__ ) , ( UpperCAmelCase__ ) , ( UpperCAmelCase__ ) , ( UpperCAmelCase__ ) , ( UpperCAmelCase__ ) , ( UpperCAmelCase__ ) , ) = config_and_inputs UpperCAmelCase__ = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask} return config, inputs_dict @require_tf class lowerCAmelCase_ ( lowerCamelCase_ , lowerCamelCase_ , unittest.TestCase ): '''simple docstring''' lowerCAmelCase_ : Optional[Any] = ( ( TFDebertaVaModel, TFDebertaVaForMaskedLM, TFDebertaVaForQuestionAnswering, TFDebertaVaForSequenceClassification, TFDebertaVaForTokenClassification, ) if is_tf_available() else () ) lowerCAmelCase_ : Tuple = ( { """feature-extraction""": TFDebertaVaModel, """fill-mask""": TFDebertaVaForMaskedLM, """question-answering""": TFDebertaVaForQuestionAnswering, """text-classification""": TFDebertaVaForSequenceClassification, """token-classification""": TFDebertaVaForTokenClassification, """zero-shot""": TFDebertaVaForSequenceClassification, } if is_tf_available() else {} ) lowerCAmelCase_ : Any = False lowerCAmelCase_ : List[Any] = False def SCREAMING_SNAKE_CASE__ ( self : List[Any] ): """simple docstring""" UpperCAmelCase__ = TFDebertaVaModelTester(self ) UpperCAmelCase__ = ConfigTester(self , config_class=_UpperCAmelCase , hidden_size=37 ) def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ): """simple docstring""" self.config_tester.run_common_tests() def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ): """simple docstring""" UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Any ): """simple docstring""" UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_masked_lm(*_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Tuple ): """simple docstring""" UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_question_answering(*_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : List[str] ): """simple docstring""" UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_sequence_classification(*_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : List[str] ): """simple docstring""" UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_token_classification(*_UpperCAmelCase ) @slow def SCREAMING_SNAKE_CASE__ ( self : int ): """simple docstring""" UpperCAmelCase__ = TFDebertaVaModel.from_pretrained("""kamalkraj/deberta-v2-xlarge""" ) self.assertIsNotNone(_UpperCAmelCase ) @require_tf class lowerCAmelCase_ ( unittest.TestCase ): '''simple docstring''' @unittest.skip(reason="""Model not available yet""" ) def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ): """simple docstring""" pass @slow def SCREAMING_SNAKE_CASE__ ( self : Any ): """simple docstring""" UpperCAmelCase__ = TFDebertaVaModel.from_pretrained("""kamalkraj/deberta-v2-xlarge""" ) UpperCAmelCase__ = tf.constant([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]] ) UpperCAmelCase__ = tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] ) UpperCAmelCase__ = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase )[0] UpperCAmelCase__ = tf.constant( [[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]] ) tf.debugging.assert_near(output[:, 1:4, 1:4] , _UpperCAmelCase , atol=1E-4 )

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'''simple docstring''' import numpy as np import torch from torch.nn import CrossEntropyLoss from transformers import AutoModelForCausalLM, AutoTokenizer import datasets from datasets import logging UpperCAmelCase_ = '\\n\n' UpperCAmelCase_ = '\nPerplexity (PPL) is one of the most common metrics for evaluating language models.\nIt is defined as the exponentiated average negative log-likelihood of a sequence.\n\nFor more information, see https://huggingface.co/docs/transformers/perplexity\n' UpperCAmelCase_ = '\nArgs:\n model_id (str): model used for calculating Perplexity\n NOTE: Perplexity can only be calculated for causal language models.\n This includes models such as gpt2, causal variations of bert,\n causal versions of t5, and more (the full list can be found\n in the AutoModelForCausalLM documentation here:\n https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )\n\n input_texts (list of str): input text, each separate text snippet\n is one list entry.\n batch_size (int): the batch size to run texts through the model. Defaults to 16.\n add_start_token (bool): whether to add the start token to the texts,\n so the perplexity can include the probability of the first word. Defaults to True.\n device (str): device to run on, defaults to \'cuda\' when available\nReturns:\n perplexity: dictionary containing the perplexity scores for the texts\n in the input list, as well as the mean perplexity. If one of the input texts is\n longer than the max input length of the model, then it is truncated to the\n max length for the perplexity computation.\nExamples:\n Example 1:\n >>> perplexity = datasets.load_metric("perplexity")\n >>> input_texts = ["lorem ipsum", "Happy Birthday!", "Bienvenue"]\n >>> results = perplexity.compute(model_id=\'gpt2\',\n ... add_start_token=False,\n ... input_texts=input_texts) # doctest:+ELLIPSIS\n >>> print(list(results.keys()))\n [\'perplexities\', \'mean_perplexity\']\n >>> print(round(results["mean_perplexity"], 2))\n 78.22\n >>> print(round(results["perplexities"][0], 2))\n 11.11\n\n Example 2:\n >>> perplexity = datasets.load_metric("perplexity")\n >>> input_texts = datasets.load_dataset("wikitext",\n ... "wikitext-2-raw-v1",\n ... split="test")["text"][:50] # doctest:+ELLIPSIS\n [...]\n >>> input_texts = [s for s in input_texts if s!=\'\']\n >>> results = perplexity.compute(model_id=\'gpt2\',\n ... input_texts=input_texts) # doctest:+ELLIPSIS\n >>> print(list(results.keys()))\n [\'perplexities\', \'mean_perplexity\']\n >>> print(round(results["mean_perplexity"], 2))\n 60.35\n >>> print(round(results["perplexities"][0], 2))\n 81.12\n' @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION ) class lowerCAmelCase_ ( datasets.Metric ): '''simple docstring''' def SCREAMING_SNAKE_CASE__ ( self : Dict ): """simple docstring""" return datasets.MetricInfo( description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features( { """input_texts""": datasets.Value("""string""" ), } ) , reference_urls=["""https://huggingface.co/docs/transformers/perplexity"""] , ) def SCREAMING_SNAKE_CASE__ ( self : List[Any] , _UpperCAmelCase : Tuple , _UpperCAmelCase : int , _UpperCAmelCase : int = 16 , _UpperCAmelCase : bool = True , _UpperCAmelCase : Optional[int]=None ): """simple docstring""" if device is not None: assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu." if device == "gpu": UpperCAmelCase__ = """cuda""" else: UpperCAmelCase__ = """cuda""" if torch.cuda.is_available() else """cpu""" UpperCAmelCase__ = AutoModelForCausalLM.from_pretrained(_UpperCAmelCase ) UpperCAmelCase__ = model.to(_UpperCAmelCase ) UpperCAmelCase__ = AutoTokenizer.from_pretrained(_UpperCAmelCase ) # if batch_size > 1 (which generally leads to padding being required), and # if there is not an already assigned pad_token, assign an existing # special token to also be the padding token if tokenizer.pad_token is None and batch_size > 1: UpperCAmelCase__ = list(tokenizer.special_tokens_map_extended.values() ) # check that the model already has at least one special token defined assert ( len(_UpperCAmelCase ) > 0 ), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1." # assign one of the special tokens to also be the pad token tokenizer.add_special_tokens({"""pad_token""": existing_special_tokens[0]} ) if add_start_token: # leave room for <BOS> token to be added: assert ( tokenizer.bos_token is not None ), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False" UpperCAmelCase__ = model.config.max_length - 1 else: UpperCAmelCase__ = model.config.max_length UpperCAmelCase__ = tokenizer( _UpperCAmelCase , add_special_tokens=_UpperCAmelCase , padding=_UpperCAmelCase , truncation=_UpperCAmelCase , max_length=_UpperCAmelCase , return_tensors="""pt""" , return_attention_mask=_UpperCAmelCase , ).to(_UpperCAmelCase ) UpperCAmelCase__ = encodings["""input_ids"""] UpperCAmelCase__ = encodings["""attention_mask"""] # check that each input is long enough: if add_start_token: assert torch.all(torch.ge(attn_masks.sum(1 ) , 1 ) ), "Each input text must be at least one token long." else: assert torch.all( torch.ge(attn_masks.sum(1 ) , 2 ) ), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings." UpperCAmelCase__ = [] UpperCAmelCase__ = CrossEntropyLoss(reduction="""none""" ) for start_index in logging.tqdm(range(0 , len(_UpperCAmelCase ) , _UpperCAmelCase ) ): UpperCAmelCase__ = min(start_index + batch_size , len(_UpperCAmelCase ) ) UpperCAmelCase__ = encoded_texts[start_index:end_index] UpperCAmelCase__ = attn_masks[start_index:end_index] if add_start_token: UpperCAmelCase__ = torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0 ) ).to(_UpperCAmelCase ) UpperCAmelCase__ = torch.cat([bos_tokens_tensor, encoded_batch] , dim=1 ) UpperCAmelCase__ = torch.cat( [torch.ones(bos_tokens_tensor.size() , dtype=torch.intaa ).to(_UpperCAmelCase ), attn_mask] , dim=1 ) UpperCAmelCase__ = encoded_batch with torch.no_grad(): UpperCAmelCase__ = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase ).logits UpperCAmelCase__ = out_logits[..., :-1, :].contiguous() UpperCAmelCase__ = labels[..., 1:].contiguous() UpperCAmelCase__ = attn_mask[..., 1:].contiguous() UpperCAmelCase__ = torch.expa( (loss_fct(shift_logits.transpose(1 , 2 ) , _UpperCAmelCase ) * shift_attention_mask_batch).sum(1 ) / shift_attention_mask_batch.sum(1 ) ) ppls += perplexity_batch.tolist() return {"perplexities": ppls, "mean_perplexity": np.mean(_UpperCAmelCase )}

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'''simple docstring''' import enum import warnings from ..tokenization_utils import TruncationStrategy from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging from .base import PIPELINE_INIT_ARGS, Pipeline if is_tf_available(): import tensorflow as tf from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING if is_torch_available(): from ..models.auto.modeling_auto import MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING UpperCAmelCase_ = logging.get_logger(__name__) class lowerCAmelCase_ ( enum.Enum ): '''simple docstring''' lowerCAmelCase_ : Any = 0 lowerCAmelCase_ : List[Any] = 1 @add_end_docstrings(lowerCamelCase_ ) class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' lowerCAmelCase_ : Any = """generated""" def __init__( self : str , *_UpperCAmelCase : Optional[int] , **_UpperCAmelCase : Tuple ): """simple docstring""" super().__init__(*_UpperCAmelCase , **_UpperCAmelCase ) self.check_model_type( TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING if self.framework == """tf""" else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING ) def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] , _UpperCAmelCase : Optional[Any]=None , _UpperCAmelCase : List[str]=None , _UpperCAmelCase : int=None , _UpperCAmelCase : Tuple=None , _UpperCAmelCase : List[Any]=None , _UpperCAmelCase : str=None , **_UpperCAmelCase : Optional[int] , ): """simple docstring""" UpperCAmelCase__ = {} if truncation is not None: UpperCAmelCase__ = truncation UpperCAmelCase__ = generate_kwargs UpperCAmelCase__ = {} if return_tensors is not None and return_type is None: UpperCAmelCase__ = ReturnType.TENSORS if return_tensors else ReturnType.TEXT if return_type is not None: UpperCAmelCase__ = return_type if clean_up_tokenization_spaces is not None: UpperCAmelCase__ = clean_up_tokenization_spaces if stop_sequence is not None: UpperCAmelCase__ = self.tokenizer.encode(_UpperCAmelCase , add_special_tokens=_UpperCAmelCase ) if len(_UpperCAmelCase ) > 1: warnings.warn( """Stopping on a multiple token sequence is not yet supported on transformers. The first token of""" """ the stop sequence will be used as the stop sequence string in the interim.""" ) UpperCAmelCase__ = stop_sequence_ids[0] return preprocess_params, forward_params, postprocess_params def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] , _UpperCAmelCase : int , _UpperCAmelCase : int , _UpperCAmelCase : int ): """simple docstring""" return True def SCREAMING_SNAKE_CASE__ ( self : Dict , *_UpperCAmelCase : Optional[int] , _UpperCAmelCase : List[str] ): """simple docstring""" UpperCAmelCase__ = self.model.config.prefix if self.model.config.prefix is not None else """""" if isinstance(args[0] , _UpperCAmelCase ): if self.tokenizer.pad_token_id is None: raise ValueError("""Please make sure that the tokenizer has a pad_token_id when using a batch input""" ) UpperCAmelCase__ = ([prefix + arg for arg in args[0]],) UpperCAmelCase__ = True elif isinstance(args[0] , _UpperCAmelCase ): UpperCAmelCase__ = (prefix + args[0],) UpperCAmelCase__ = False else: raise ValueError( f''' `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`''' ) UpperCAmelCase__ = self.tokenizer(*_UpperCAmelCase , padding=_UpperCAmelCase , truncation=_UpperCAmelCase , return_tensors=self.framework ) # This is produced by tokenizers but is an invalid generate kwargs if "token_type_ids" in inputs: del inputs["token_type_ids"] return inputs def __call__( self : str , *_UpperCAmelCase : Optional[int] , **_UpperCAmelCase : str ): """simple docstring""" UpperCAmelCase__ = super().__call__(*_UpperCAmelCase , **_UpperCAmelCase ) if ( isinstance(args[0] , _UpperCAmelCase ) and all(isinstance(_UpperCAmelCase , _UpperCAmelCase ) for el in args[0] ) and all(len(_UpperCAmelCase ) == 1 for res in result ) ): return [res[0] for res in result] return result def SCREAMING_SNAKE_CASE__ ( self : int , _UpperCAmelCase : int , _UpperCAmelCase : List[Any]=TruncationStrategy.DO_NOT_TRUNCATE , **_UpperCAmelCase : List[Any] ): """simple docstring""" UpperCAmelCase__ = self._parse_and_tokenize(_UpperCAmelCase , truncation=_UpperCAmelCase , **_UpperCAmelCase ) return inputs def SCREAMING_SNAKE_CASE__ ( self : Any , _UpperCAmelCase : str , **_UpperCAmelCase : Dict ): """simple docstring""" if self.framework == "pt": UpperCAmelCase__ , UpperCAmelCase__ = model_inputs["""input_ids"""].shape elif self.framework == "tf": UpperCAmelCase__ , UpperCAmelCase__ = tf.shape(model_inputs["""input_ids"""] ).numpy() UpperCAmelCase__ = generate_kwargs.get("""min_length""" , self.model.config.min_length ) UpperCAmelCase__ = generate_kwargs.get("""max_length""" , self.model.config.max_length ) self.check_inputs(_UpperCAmelCase , generate_kwargs["""min_length"""] , generate_kwargs["""max_length"""] ) UpperCAmelCase__ = self.model.generate(**_UpperCAmelCase , **_UpperCAmelCase ) UpperCAmelCase__ = output_ids.shape[0] if self.framework == "pt": UpperCAmelCase__ = output_ids.reshape(_UpperCAmelCase , out_b // in_b , *output_ids.shape[1:] ) elif self.framework == "tf": UpperCAmelCase__ = tf.reshape(_UpperCAmelCase , (in_b, out_b // in_b, *output_ids.shape[1:]) ) return {"output_ids": output_ids} def SCREAMING_SNAKE_CASE__ ( self : Tuple , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : int=ReturnType.TEXT , _UpperCAmelCase : Tuple=False ): """simple docstring""" UpperCAmelCase__ = [] for output_ids in model_outputs["output_ids"][0]: if return_type == ReturnType.TENSORS: UpperCAmelCase__ = {f'''{self.return_name}_token_ids''': output_ids} elif return_type == ReturnType.TEXT: UpperCAmelCase__ = { f'''{self.return_name}_text''': self.tokenizer.decode( _UpperCAmelCase , skip_special_tokens=_UpperCAmelCase , clean_up_tokenization_spaces=_UpperCAmelCase , ) } records.append(_UpperCAmelCase ) return records @add_end_docstrings(lowerCamelCase_ ) class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' lowerCAmelCase_ : List[Any] = """summary""" def __call__( self : List[Any] , *_UpperCAmelCase : Optional[int] , **_UpperCAmelCase : str ): """simple docstring""" return super().__call__(*_UpperCAmelCase , **_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : int , _UpperCAmelCase : int , _UpperCAmelCase : int , _UpperCAmelCase : int ): """simple docstring""" if max_length < min_length: logger.warning(f'''Your min_length={min_length} must be inferior than your max_length={max_length}.''' ) if input_length < max_length: logger.warning( f'''Your max_length is set to {max_length}, but your input_length is only {input_length}. Since this is ''' """a summarization task, where outputs shorter than the input are typically wanted, you might """ f'''consider decreasing max_length manually, e.g. summarizer(\'...\', max_length={input_length//2})''' ) @add_end_docstrings(lowerCamelCase_ ) class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' lowerCAmelCase_ : Union[str, Any] = """translation""" def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , _UpperCAmelCase : int , _UpperCAmelCase : int , _UpperCAmelCase : int ): """simple docstring""" if input_length > 0.9 * max_length: logger.warning( f'''Your input_length: {input_length} is bigger than 0.9 * max_length: {max_length}. You might consider ''' """increasing your max_length manually, e.g. translator('...', max_length=400)""" ) return True def SCREAMING_SNAKE_CASE__ ( self : List[Any] , *_UpperCAmelCase : str , _UpperCAmelCase : Dict=TruncationStrategy.DO_NOT_TRUNCATE , _UpperCAmelCase : str=None , _UpperCAmelCase : List[Any]=None ): """simple docstring""" if getattr(self.tokenizer , """_build_translation_inputs""" , _UpperCAmelCase ): return self.tokenizer._build_translation_inputs( *_UpperCAmelCase , return_tensors=self.framework , truncation=_UpperCAmelCase , src_lang=_UpperCAmelCase , tgt_lang=_UpperCAmelCase ) else: return super()._parse_and_tokenize(*_UpperCAmelCase , truncation=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : int , _UpperCAmelCase : Union[str, Any]=None , _UpperCAmelCase : Optional[int]=None , **_UpperCAmelCase : str ): """simple docstring""" UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = super()._sanitize_parameters(**_UpperCAmelCase ) if src_lang is not None: UpperCAmelCase__ = src_lang if tgt_lang is not None: UpperCAmelCase__ = tgt_lang if src_lang is None and tgt_lang is None: # Backward compatibility, direct arguments use is preferred. UpperCAmelCase__ = kwargs.get("""task""" , self.task ) UpperCAmelCase__ = task.split("""_""" ) if task and len(_UpperCAmelCase ) == 4: # translation, XX, to YY UpperCAmelCase__ = items[1] UpperCAmelCase__ = items[3] return preprocess_params, forward_params, postprocess_params def __call__( self : Tuple , *_UpperCAmelCase : List[str] , **_UpperCAmelCase : Optional[int] ): """simple docstring""" return super().__call__(*_UpperCAmelCase , **_UpperCAmelCase )

346

'''simple docstring''' def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int = 1000000 ): '''simple docstring''' UpperCAmelCase__ = [i - 1 for i in range(limit + 1 )] for i in range(2 , limit + 1 ): if phi[i] == i - 1: for j in range(2 * i , limit + 1 , SCREAMING_SNAKE_CASE__ ): phi[j] -= phi[j] // i return sum(phi[2 : limit + 1] ) if __name__ == "__main__": print(solution())

346

1

'''simple docstring''' import logging import os import threading import time try: import warnings except ImportError: UpperCAmelCase_ = None try: import msvcrt except ImportError: UpperCAmelCase_ = None try: import fcntl except ImportError: UpperCAmelCase_ = None # Backward compatibility # ------------------------------------------------ try: TimeoutError except NameError: UpperCAmelCase_ = OSError # Data # ------------------------------------------------ UpperCAmelCase_ = [ 'Timeout', 'BaseFileLock', 'WindowsFileLock', 'UnixFileLock', 'SoftFileLock', 'FileLock', ] UpperCAmelCase_ = '3.0.12' UpperCAmelCase_ = None def _UpperCamelCase ( ): '''simple docstring''' global _logger UpperCAmelCase__ = _logger or logging.getLogger(__name__ ) return _logger class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' def __init__( self : str , _UpperCAmelCase : Tuple ): """simple docstring""" UpperCAmelCase__ = lock_file return None def __str__( self : Optional[Any] ): """simple docstring""" UpperCAmelCase__ = f'''The file lock \'{self.lock_file}\' could not be acquired.''' return temp class lowerCAmelCase_ : '''simple docstring''' def __init__( self : Optional[Any] , _UpperCAmelCase : Tuple ): """simple docstring""" UpperCAmelCase__ = lock return None def __enter__( self : Optional[int] ): """simple docstring""" return self.lock def __exit__( self : List[str] , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Tuple , _UpperCAmelCase : str ): """simple docstring""" self.lock.release() return None class lowerCAmelCase_ : '''simple docstring''' def __init__( self : Optional[Any] , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Tuple=-1 , _UpperCAmelCase : Any=None ): """simple docstring""" UpperCAmelCase__ = max_filename_length if max_filename_length is not None else 2_55 # Hash the filename if it's too long UpperCAmelCase__ = self.hash_filename_if_too_long(_UpperCAmelCase , _UpperCAmelCase ) # The path to the lock file. UpperCAmelCase__ = lock_file # The file descriptor for the *_lock_file* as it is returned by the # os.open() function. # This file lock is only NOT None, if the object currently holds the # lock. UpperCAmelCase__ = None # The default timeout value. UpperCAmelCase__ = timeout # We use this lock primarily for the lock counter. UpperCAmelCase__ = threading.Lock() # The lock counter is used for implementing the nested locking # mechanism. Whenever the lock is acquired, the counter is increased and # the lock is only released, when this value is 0 again. UpperCAmelCase__ = 0 return None @property def SCREAMING_SNAKE_CASE__ ( self : List[Any] ): """simple docstring""" return self._lock_file @property def SCREAMING_SNAKE_CASE__ ( self : Tuple ): """simple docstring""" return self._timeout @timeout.setter def SCREAMING_SNAKE_CASE__ ( self : Dict , _UpperCAmelCase : str ): """simple docstring""" UpperCAmelCase__ = float(_UpperCAmelCase ) return None def SCREAMING_SNAKE_CASE__ ( self : Tuple ): """simple docstring""" raise NotImplementedError() def SCREAMING_SNAKE_CASE__ ( self : Tuple ): """simple docstring""" raise NotImplementedError() @property def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ): """simple docstring""" return self._lock_file_fd is not None def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , _UpperCAmelCase : Dict=None , _UpperCAmelCase : str=0.05 ): """simple docstring""" if timeout is None: UpperCAmelCase__ = self.timeout # Increment the number right at the beginning. # We can still undo it, if something fails. with self._thread_lock: self._lock_counter += 1 UpperCAmelCase__ = id(self ) UpperCAmelCase__ = self._lock_file UpperCAmelCase__ = time.time() try: while True: with self._thread_lock: if not self.is_locked: logger().debug(f'''Attempting to acquire lock {lock_id} on {lock_filename}''' ) self._acquire() if self.is_locked: logger().debug(f'''Lock {lock_id} acquired on {lock_filename}''' ) break elif timeout >= 0 and time.time() - start_time > timeout: logger().debug(f'''Timeout on acquiring lock {lock_id} on {lock_filename}''' ) raise Timeout(self._lock_file ) else: logger().debug( f'''Lock {lock_id} not acquired on {lock_filename}, waiting {poll_intervall} seconds ...''' ) time.sleep(_UpperCAmelCase ) except: # noqa # Something did go wrong, so decrement the counter. with self._thread_lock: UpperCAmelCase__ = max(0 , self._lock_counter - 1 ) raise return _Acquire_ReturnProxy(lock=self ) def SCREAMING_SNAKE_CASE__ ( self : List[str] , _UpperCAmelCase : Dict=False ): """simple docstring""" with self._thread_lock: if self.is_locked: self._lock_counter -= 1 if self._lock_counter == 0 or force: UpperCAmelCase__ = id(self ) UpperCAmelCase__ = self._lock_file logger().debug(f'''Attempting to release lock {lock_id} on {lock_filename}''' ) self._release() UpperCAmelCase__ = 0 logger().debug(f'''Lock {lock_id} released on {lock_filename}''' ) return None def __enter__( self : Union[str, Any] ): """simple docstring""" self.acquire() return self def __exit__( self : Tuple , _UpperCAmelCase : Any , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : str ): """simple docstring""" self.release() return None def __del__( self : int ): """simple docstring""" self.release(force=_UpperCAmelCase ) return None def SCREAMING_SNAKE_CASE__ ( self : Tuple , _UpperCAmelCase : str , _UpperCAmelCase : int ): """simple docstring""" UpperCAmelCase__ = os.path.basename(_UpperCAmelCase ) if len(_UpperCAmelCase ) > max_length and max_length > 0: UpperCAmelCase__ = os.path.dirname(_UpperCAmelCase ) UpperCAmelCase__ = str(hash(_UpperCAmelCase ) ) UpperCAmelCase__ = filename[: max_length - len(_UpperCAmelCase ) - 8] + """...""" + hashed_filename + """.lock""" return os.path.join(_UpperCAmelCase , _UpperCAmelCase ) else: return path class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' def __init__( self : Union[str, Any] , _UpperCAmelCase : Any , _UpperCAmelCase : Union[str, Any]=-1 , _UpperCAmelCase : Dict=None ): """simple docstring""" from .file_utils import relative_to_absolute_path super().__init__(_UpperCAmelCase , timeout=_UpperCAmelCase , max_filename_length=_UpperCAmelCase ) UpperCAmelCase__ = """\\\\?\\""" + relative_to_absolute_path(self.lock_file ) def SCREAMING_SNAKE_CASE__ ( self : str ): """simple docstring""" UpperCAmelCase__ = os.O_RDWR | os.O_CREAT | os.O_TRUNC try: UpperCAmelCase__ = os.open(self._lock_file , _UpperCAmelCase ) except OSError: pass else: try: msvcrt.locking(_UpperCAmelCase , msvcrt.LK_NBLCK , 1 ) except OSError: os.close(_UpperCAmelCase ) else: UpperCAmelCase__ = fd return None def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ): """simple docstring""" UpperCAmelCase__ = self._lock_file_fd UpperCAmelCase__ = None msvcrt.locking(_UpperCAmelCase , msvcrt.LK_UNLCK , 1 ) os.close(_UpperCAmelCase ) try: os.remove(self._lock_file ) # Probably another instance of the application # that acquired the file lock. except OSError: pass return None class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' def __init__( self : Union[str, Any] , _UpperCAmelCase : List[str] , _UpperCAmelCase : Any=-1 , _UpperCAmelCase : Union[str, Any]=None ): """simple docstring""" UpperCAmelCase__ = os.statvfs(os.path.dirname(_UpperCAmelCase ) ).f_namemax super().__init__(_UpperCAmelCase , timeout=_UpperCAmelCase , max_filename_length=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ): """simple docstring""" UpperCAmelCase__ = os.O_RDWR | os.O_CREAT | os.O_TRUNC UpperCAmelCase__ = os.open(self._lock_file , _UpperCAmelCase ) try: fcntl.flock(_UpperCAmelCase , fcntl.LOCK_EX | fcntl.LOCK_NB ) except OSError: os.close(_UpperCAmelCase ) else: UpperCAmelCase__ = fd return None def SCREAMING_SNAKE_CASE__ ( self : Dict ): """simple docstring""" UpperCAmelCase__ = self._lock_file_fd UpperCAmelCase__ = None fcntl.flock(_UpperCAmelCase , fcntl.LOCK_UN ) os.close(_UpperCAmelCase ) return None class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' def SCREAMING_SNAKE_CASE__ ( self : str ): """simple docstring""" UpperCAmelCase__ = os.O_WRONLY | os.O_CREAT | os.O_EXCL | os.O_TRUNC try: UpperCAmelCase__ = os.open(self._lock_file , _UpperCAmelCase ) except OSError: pass else: UpperCAmelCase__ = fd return None def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ): """simple docstring""" os.close(self._lock_file_fd ) UpperCAmelCase__ = None try: os.remove(self._lock_file ) # The file is already deleted and that's what we want. except OSError: pass return None UpperCAmelCase_ = None if msvcrt: UpperCAmelCase_ = WindowsFileLock elif fcntl: UpperCAmelCase_ = UnixFileLock else: UpperCAmelCase_ = SoftFileLock if warnings is not None: warnings.warn('only soft file lock is available')

346

'''simple docstring''' from typing import List, Union from ..utils import ( add_end_docstrings, is_tf_available, is_torch_available, is_vision_available, logging, requires_backends, ) from .base import PIPELINE_INIT_ARGS, Pipeline if is_vision_available(): from PIL import Image from ..image_utils import load_image if is_tf_available(): import tensorflow as tf from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING from ..tf_utils import stable_softmax if is_torch_available(): from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING UpperCAmelCase_ = logging.get_logger(__name__) @add_end_docstrings(lowerCamelCase_ ) class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' def __init__( self : Optional[Any] , *_UpperCAmelCase : Union[str, Any] , **_UpperCAmelCase : Dict ): """simple docstring""" super().__init__(*_UpperCAmelCase , **_UpperCAmelCase ) requires_backends(self , """vision""" ) self.check_model_type( TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING if self.framework == """tf""" else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING ) def SCREAMING_SNAKE_CASE__ ( self : str , _UpperCAmelCase : List[Any]=None ): """simple docstring""" UpperCAmelCase__ = {} if top_k is not None: UpperCAmelCase__ = top_k return {}, {}, postprocess_params def __call__( self : Any , _UpperCAmelCase : Union[str, List[str], "Image.Image", List["Image.Image"]] , **_UpperCAmelCase : str ): """simple docstring""" return super().__call__(_UpperCAmelCase , **_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] , _UpperCAmelCase : Tuple ): """simple docstring""" UpperCAmelCase__ = load_image(_UpperCAmelCase ) UpperCAmelCase__ = self.image_processor(images=_UpperCAmelCase , return_tensors=self.framework ) return model_inputs def SCREAMING_SNAKE_CASE__ ( self : Dict , _UpperCAmelCase : Tuple ): """simple docstring""" UpperCAmelCase__ = self.model(**_UpperCAmelCase ) return model_outputs def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , _UpperCAmelCase : Dict , _UpperCAmelCase : str=5 ): """simple docstring""" if top_k > self.model.config.num_labels: UpperCAmelCase__ = self.model.config.num_labels if self.framework == "pt": UpperCAmelCase__ = model_outputs.logits.softmax(-1 )[0] UpperCAmelCase__ , UpperCAmelCase__ = probs.topk(_UpperCAmelCase ) elif self.framework == "tf": UpperCAmelCase__ = stable_softmax(model_outputs.logits , axis=-1 )[0] UpperCAmelCase__ = tf.math.top_k(_UpperCAmelCase , k=_UpperCAmelCase ) UpperCAmelCase__ , UpperCAmelCase__ = topk.values.numpy(), topk.indices.numpy() else: raise ValueError(f'''Unsupported framework: {self.framework}''' ) UpperCAmelCase__ = scores.tolist() UpperCAmelCase__ = ids.tolist() return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(_UpperCAmelCase , _UpperCAmelCase )]

346

1

'''simple docstring''' import collections import json import math import os import re import time from fnmatch import fnmatch from typing import Dict import requests from slack_sdk import WebClient UpperCAmelCase_ = WebClient(token=os.environ['CI_SLACK_BOT_TOKEN']) def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Union[str, Any] ): '''simple docstring''' UpperCAmelCase__ = test_results.split(""" """ ) UpperCAmelCase__ = 0 UpperCAmelCase__ = 0 # When the output is short enough, the output is surrounded by = signs: "== OUTPUT ==" # When it is too long, those signs are not present. UpperCAmelCase__ = expressions[-2] if """=""" in expressions[-1] else expressions[-1] for i, expression in enumerate(SCREAMING_SNAKE_CASE__ ): if "failed" in expression: failed += int(expressions[i - 1] ) if "passed" in expression: success += int(expressions[i - 1] ) return failed, success, time_spent def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Dict ): '''simple docstring''' UpperCAmelCase__ = {} UpperCAmelCase__ = None UpperCAmelCase__ = False for line in failures_short_lines.split("""\n""" ): if re.search(r"""_ \[doctest\]""" , SCREAMING_SNAKE_CASE__ ): UpperCAmelCase__ = True UpperCAmelCase__ = line.split(""" """ )[2] elif in_error and not line.split(""" """ )[0].isdigit(): UpperCAmelCase__ = line UpperCAmelCase__ = False return failures class lowerCAmelCase_ : '''simple docstring''' def __init__( self : Optional[Any] , _UpperCAmelCase : str , _UpperCAmelCase : Dict ): """simple docstring""" UpperCAmelCase__ = title UpperCAmelCase__ = doc_test_results["""time_spent"""].split(""",""" )[0] UpperCAmelCase__ = doc_test_results["""success"""] UpperCAmelCase__ = doc_test_results["""failures"""] UpperCAmelCase__ = self.n_success + self.n_failures # Failures and success of the modeling tests UpperCAmelCase__ = doc_test_results @property def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ): """simple docstring""" UpperCAmelCase__ = [self._time_spent] UpperCAmelCase__ = 0 for time in time_spent: UpperCAmelCase__ = time.split(""":""" ) # Time can be formatted as xx:xx:xx, as .xx, or as x.xx if the time spent was less than a minute. if len(_UpperCAmelCase ) == 1: UpperCAmelCase__ = [0, 0, time_parts[0]] UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = int(time_parts[0] ), int(time_parts[1] ), float(time_parts[2] ) total_secs += hours * 36_00 + minutes * 60 + seconds UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = total_secs // 36_00, (total_secs % 36_00) // 60, total_secs % 60 return f'''{int(_UpperCAmelCase )}h{int(_UpperCAmelCase )}m{int(_UpperCAmelCase )}s''' @property def SCREAMING_SNAKE_CASE__ ( self : List[Any] ): """simple docstring""" return {"type": "header", "text": {"type": "plain_text", "text": self.title}} @property def SCREAMING_SNAKE_CASE__ ( self : str ): """simple docstring""" return { "type": "section", "text": { "type": "plain_text", "text": f'''🌞 There were no failures: all {self.n_tests} tests passed. The suite ran in {self.time}.''', "emoji": True, }, "accessory": { "type": "button", "text": {"type": "plain_text", "text": "Check Action results", "emoji": True}, "url": f'''https://github.com/huggingface/transformers/actions/runs/{os.environ['GITHUB_RUN_ID']}''', }, } @property def SCREAMING_SNAKE_CASE__ ( self : List[str] ): """simple docstring""" return { "type": "section", "text": { "type": "plain_text", "text": ( f'''There were {self.n_failures} failures, out of {self.n_tests} tests.\nThe suite ran in''' f''' {self.time}.''' ), "emoji": True, }, "accessory": { "type": "button", "text": {"type": "plain_text", "text": "Check Action results", "emoji": True}, "url": f'''https://github.com/huggingface/transformers/actions/runs/{os.environ['GITHUB_RUN_ID']}''', }, } @property def SCREAMING_SNAKE_CASE__ ( self : Tuple ): """simple docstring""" UpperCAmelCase__ = 40 UpperCAmelCase__ = {k: v["""failed"""] for k, v in doc_test_results.items() if isinstance(_UpperCAmelCase , _UpperCAmelCase )} UpperCAmelCase__ = """""" for category, failures in category_failures.items(): if len(_UpperCAmelCase ) == 0: continue if report != "": report += "\n\n" report += f'''*{category} failures*:'''.ljust(line_length // 2 ).rjust(line_length // 2 ) + "\n" report += "`" report += "`\n`".join(_UpperCAmelCase ) report += "`" return { "type": "section", "text": { "type": "mrkdwn", "text": f'''The following examples had failures:\n\n\n{report}\n''', }, } @property def SCREAMING_SNAKE_CASE__ ( self : Dict ): """simple docstring""" UpperCAmelCase__ = [self.header] if self.n_failures > 0: blocks.append(self.failures ) if self.n_failures > 0: blocks.extend([self.category_failures] ) if self.n_failures == 0: blocks.append(self.no_failures ) return json.dumps(_UpperCAmelCase ) @staticmethod def SCREAMING_SNAKE_CASE__ ( ): """simple docstring""" UpperCAmelCase__ = [ { """type""": """section""", """text""": { """type""": """plain_text""", """text""": """There was an issue running the tests.""", }, """accessory""": { """type""": """button""", """text""": {"""type""": """plain_text""", """text""": """Check Action results""", """emoji""": True}, """url""": f'''https://github.com/huggingface/transformers/actions/runs/{os.environ['GITHUB_RUN_ID']}''', }, } ] print("""Sending the following payload""" ) print(json.dumps({"""blocks""": json.loads(_UpperCAmelCase )} ) ) client.chat_postMessage( channel=os.environ["""CI_SLACK_CHANNEL_ID_DAILY"""] , text="""There was an issue running the tests.""" , blocks=_UpperCAmelCase , ) def SCREAMING_SNAKE_CASE__ ( self : int ): """simple docstring""" print("""Sending the following payload""" ) print(json.dumps({"""blocks""": json.loads(self.payload )} ) ) UpperCAmelCase__ = f'''{self.n_failures} failures out of {self.n_tests} tests,''' if self.n_failures else """All tests passed.""" UpperCAmelCase__ = client.chat_postMessage( channel=os.environ["""CI_SLACK_CHANNEL_ID_DAILY"""] , blocks=self.payload , text=_UpperCAmelCase , ) def SCREAMING_SNAKE_CASE__ ( self : List[str] , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Dict , _UpperCAmelCase : Tuple ): """simple docstring""" UpperCAmelCase__ = """""" for key, value in failures.items(): UpperCAmelCase__ = value[:2_00] + """ [Truncated]""" if len(_UpperCAmelCase ) > 2_50 else value failures_text += f'''*{key}*\n_{value}_\n\n''' UpperCAmelCase__ = job_name UpperCAmelCase__ = {"""type""": """section""", """text""": {"""type""": """mrkdwn""", """text""": text}} if job_link is not None: UpperCAmelCase__ = { """type""": """button""", """text""": {"""type""": """plain_text""", """text""": """GitHub Action job""", """emoji""": True}, """url""": job_link, } return [ {"type": "header", "text": {"type": "plain_text", "text": title.upper(), "emoji": True}}, content, {"type": "section", "text": {"type": "mrkdwn", "text": failures_text}}, ] def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ): """simple docstring""" if self.thread_ts is None: raise ValueError("""Can only post reply if a post has been made.""" ) UpperCAmelCase__ = self.doc_test_results.pop("""job_link""" ) self.doc_test_results.pop("""failures""" ) self.doc_test_results.pop("""success""" ) self.doc_test_results.pop("""time_spent""" ) UpperCAmelCase__ = sorted(self.doc_test_results.items() , key=lambda _UpperCAmelCase : t[0] ) for job, job_result in sorted_dict: if len(job_result["""failures"""] ): UpperCAmelCase__ = f'''*Num failures* :{len(job_result['failed'] )} \n''' UpperCAmelCase__ = job_result["""failures"""] UpperCAmelCase__ = self.get_reply_blocks(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , text=_UpperCAmelCase ) print("""Sending the following reply""" ) print(json.dumps({"""blocks""": blocks} ) ) client.chat_postMessage( channel=os.environ["""CI_SLACK_CHANNEL_ID_DAILY"""] , text=f'''Results for {job}''' , blocks=_UpperCAmelCase , thread_ts=self.thread_ts["""ts"""] , ) time.sleep(1 ) def _UpperCamelCase ( ): '''simple docstring''' UpperCAmelCase__ = os.environ["""GITHUB_RUN_ID"""] UpperCAmelCase__ = F'''https://api.github.com/repos/huggingface/transformers/actions/runs/{run_id}/jobs?per_page=100''' UpperCAmelCase__ = requests.get(SCREAMING_SNAKE_CASE__ ).json() UpperCAmelCase__ = {} try: jobs.update({job["""name"""]: job["""html_url"""] for job in result["""jobs"""]} ) UpperCAmelCase__ = math.ceil((result["""total_count"""] - 100) / 100 ) for i in range(SCREAMING_SNAKE_CASE__ ): UpperCAmelCase__ = requests.get(url + F'''&page={i + 2}''' ).json() jobs.update({job["""name"""]: job["""html_url"""] for job in result["""jobs"""]} ) return jobs except Exception as e: print("""Unknown error, could not fetch links.""" , SCREAMING_SNAKE_CASE__ ) return {} def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str ): '''simple docstring''' UpperCAmelCase__ = {} if os.path.exists(SCREAMING_SNAKE_CASE__ ): UpperCAmelCase__ = os.listdir(SCREAMING_SNAKE_CASE__ ) for file in files: try: with open(os.path.join(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) , encoding="""utf-8""" ) as f: UpperCAmelCase__ = f.read() except UnicodeDecodeError as e: raise ValueError(F'''Could not open {os.path.join(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )}.''' ) from e return _artifact def _UpperCamelCase ( ): '''simple docstring''' class lowerCAmelCase_ : '''simple docstring''' def __init__( self : Union[str, Any] , _UpperCAmelCase : str ): """simple docstring""" UpperCAmelCase__ = name UpperCAmelCase__ = [] def __str__( self : int ): """simple docstring""" return self.name def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] , _UpperCAmelCase : str ): """simple docstring""" self.paths.append({"""name""": self.name, """path""": path} ) UpperCAmelCase__ = {} UpperCAmelCase__ = filter(os.path.isdir , os.listdir() ) for directory in directories: UpperCAmelCase__ = directory if artifact_name not in _available_artifacts: UpperCAmelCase__ = Artifact(SCREAMING_SNAKE_CASE__ ) _available_artifacts[artifact_name].add_path(SCREAMING_SNAKE_CASE__ ) return _available_artifacts if __name__ == "__main__": UpperCAmelCase_ = get_job_links() UpperCAmelCase_ = retrieve_available_artifacts() UpperCAmelCase_ = collections.OrderedDict( [ ('*.py', 'API Examples'), ('*.md', 'MD Examples'), ] ) # This dict will contain all the information relative to each doc test category: # - failed: list of failed tests # - failures: dict in the format 'test': 'error_message' UpperCAmelCase_ = { v: { 'failed': [], 'failures': {}, } for v in docs.values() } # Link to the GitHub Action job UpperCAmelCase_ = github_actions_job_links.get('run_doctests') UpperCAmelCase_ = available_artifacts['doc_tests_gpu_test_reports'].paths[0] UpperCAmelCase_ = retrieve_artifact(artifact_path['name']) if "stats" in artifact: UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = handle_test_results(artifact['stats']) UpperCAmelCase_ = failed UpperCAmelCase_ = success UpperCAmelCase_ = time_spent[1:-1] + ', ' UpperCAmelCase_ = extract_first_line_failure(artifact['failures_short']) for line in artifact["summary_short"].split('\n'): if re.search('FAILED', line): UpperCAmelCase_ = line.replace('FAILED ', '') UpperCAmelCase_ = line.split()[0].replace('\n', '') if "::" in line: UpperCAmelCase_ , UpperCAmelCase_ = line.split('::') else: UpperCAmelCase_ , UpperCAmelCase_ = line, line for file_regex in docs.keys(): if fnmatch(file_path, file_regex): UpperCAmelCase_ = docs[file_regex] doc_test_results[category]["failed"].append(test) UpperCAmelCase_ = all_failures[test] if test in all_failures else 'N/A' UpperCAmelCase_ = failure break UpperCAmelCase_ = Message('🤗 Results of the doc tests.', doc_test_results) message.post() message.post_reply()

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'''simple docstring''' from math import factorial def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int = 20 ): '''simple docstring''' UpperCAmelCase__ = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1, # 2, 3,... UpperCAmelCase__ = n // 2 return int(factorial(SCREAMING_SNAKE_CASE__ ) / (factorial(SCREAMING_SNAKE_CASE__ ) * factorial(n - k )) ) if __name__ == "__main__": import sys if len(sys.argv) == 1: print(solution(2_0)) else: try: UpperCAmelCase_ = int(sys.argv[1]) print(solution(n)) except ValueError: print('Invalid entry - please enter a number.')

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