{ // 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 }); }); } })(); `","A similar issue arises when trying to stream the dataset\r\n\r\n```python\r\n>>> from datasets import load_dataset\r\n>>> iter_dset = load_dataset(\"amazon_polarity\", split=\"test\", streaming=True)\r\n>>> iter(iter_dset).__next__()\r\n\r\n---------------------------------------------------------------------------\r\nValueError Traceback (most recent call last)\r\n~\\lib\\tarfile.py in nti(s)\r\n 186 s = nts(s, \"ascii\", \"strict\")\r\n--> 187 n = int(s.strip() or \"0\", 8)\r\n 188 except ValueError:\r\n\r\nValueError: invalid literal for int() with base 8: 'e nonce='\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nInvalidHeaderError Traceback (most recent call last)\r\n~\\lib\\tarfile.py in next(self)\r\n 2288 try:\r\n-> 2289 tarinfo = self.tarinfo.fromtarfile(self)\r\n 2290 except EOFHeaderError as e:\r\n\r\n~\\lib\\tarfile.py in fromtarfile(cls, tarfile)\r\n 1094 buf = tarfile.fileobj.read(BLOCKSIZE)\r\n-> 1095 obj = cls.frombuf(buf, tarfile.encoding, tarfile.errors)\r\n 1096 obj.offset = tarfile.fileobj.tell() - BLOCKSIZE\r\n\r\n~\\lib\\tarfile.py in frombuf(cls, buf, encoding, errors)\r\n 1036\r\n-> 1037 chksum = nti(buf[148:156])\r\n 1038 if chksum not in calc_chksums(buf):\r\n\r\n~\\lib\\tarfile.py in nti(s)\r\n 188 except ValueError:\r\n--> 189 raise InvalidHeaderError(\"invalid header\")\r\n 190 return n\r\n\r\nInvalidHeaderError: invalid header\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nReadError Traceback (most recent call last)\r\n in \r\n----> 1 iter(iter_dset).__next__()\r\n\r\n~\\lib\\site-packages\\datasets\\iterable_dataset.py in __iter__(self)\r\n 363\r\n 364 def __iter__(self):\r\n--> 365 for key, example in self._iter():\r\n 366 if self.features:\r\n 367 # we encode the example for ClassLabel feature types for example\r\n\r\n~\\lib\\site-packages\\datasets\\iterable_dataset.py in _iter(self)\r\n 360 else:\r\n 361 ex_iterable = self._ex_iterable\r\n--> 362 yield from ex_iterable\r\n 363\r\n 364 def __iter__(self):\r\n\r\n~\\lib\\site-packages\\datasets\\iterable_dataset.py in __iter__(self)\r\n 77\r\n 78 def __iter__(self):\r\n---> 79 yield from self.generate_examples_fn(**self.kwargs)\r\n 80\r\n 81 def shuffle_data_sources(self, seed: Optional[int]) -> \"ExamplesIterable\":\r\n\r\n~\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\amazon_polarity\\56923eeb72030cb6c4ea30c8a4e1162c26b25973475ac1f44340f0ec0f2936f4\\amazon_polarity.py in _generate_examples(self, filepath, files)\r\n 114 def _generate_examples(self, filepath, files):\r\n 115 \"\"\"Yields examples.\"\"\"\r\n--> 116 for path, f in files:\r\n 117 if path == filepath:\r\n 118 lines = (line.decode(\"utf-8\") for line in f)\r\n\r\n~\\lib\\site-packages\\datasets\\utils\\streaming_download_manager.py in __iter__(self)\r\n 616\r\n 617 def __iter__(self):\r\n--> 618 yield from self.generator(*self.args, **self.kwargs)\r\n 619\r\n 620\r\n\r\n~\\lib\\site-packages\\datasets\\utils\\streaming_download_manager.py in _iter_from_urlpath(cls, urlpath, use_auth_token)\r\n 644 ) -> Generator[Tuple, None, None]:\r\n 645 with xopen(urlpath, \"rb\", use_auth_token=use_auth_token) as f:\r\n--> 646 yield from cls._iter_from_fileobj(f)\r\n 647\r\n 648 @classmethod\r\n\r\n~\\lib\\site-packages\\datasets\\utils\\streaming_download_manager.py in _iter_from_fileobj(cls, f)\r\n 624 @classmethod\r\n 625 def _iter_from_fileobj(cls, f) -> Generator[Tuple, None, None]:\r\n--> 626 stream = tarfile.open(fileobj=f, mode=\"r|*\")\r\n 627 for tarinfo in stream:\r\n 628 file_path = tarinfo.name\r\n\r\n~\\lib\\tarfile.py in open(cls, name, mode, fileobj, bufsize, **kwargs)\r\n 1603 stream = _Stream(name, filemode, comptype, fileobj, bufsize)\r\n 1604 try:\r\n-> 1605 t = cls(name, filemode, stream, **kwargs)\r\n 1606 except:\r\n 1607 stream.close()\r\n\r\n~\\lib\\tarfile.py in __init__(self, name, mode, fileobj, format, tarinfo, dereference, ignore_zeros, encoding, errors, pax_headers, debug, errorlevel, copybufsize)\r\n 1484 if self.mode == \"r\":\r\n 1485 self.firstmember = None\r\n-> 1486 self.firstmember = self.next()\r\n 1487\r\n 1488 if self.mode == \"a\":\r\n\r\n~\\lib\\tarfile.py in next(self)\r\n 2299 continue\r\n 2300 elif self.offset == 0:\r\n-> 2301 raise ReadError(str(e))\r\n 2302 except EmptyHeaderError:\r\n 2303 if self.offset == 0:\r\n\r\nReadError: invalid header\r\n\r\n```","This error still happens, but for a different reason now: Google Drive returns a warning instead of the dataset.","Met the same issue +1","Hi ! Thanks for reporting. Google Drive changed the way to bypass the warning message recently.\r\n\r\nThe latest release `1.18.4` fixes this for datasets loaded in a regular way.\r\n\r\nWe opened a PR to fix this recently for streaming mode at #3843 - we'll do a new release once the fix is merged :)","Fixed by:\r\n- #3787 \r\n- #3843"],"string":"[\n \"Hi ! This issue may be related to #996 \\r\\nThis comes probably from the Quota Exceeded error from Google Drive.\\r\\nCan you try again tomorrow and see if you still have the error ?\\r\\n\\r\\nOn my side I didn't get any error today with `load_dataset(\\\"amazon_polarity\\\")`\",\n \"+1 encountering this issue as well\",\n \"@lhoestq Hi! I encounter the same error when loading `yelp_review_full`.\\r\\n\\r\\n```\\r\\nfrom datasets import load_dataset\\r\\ndataset_yp = load_dataset(\\\"yelp_review_full\\\")\\r\\n```\\r\\n\\r\\nWhen you say the \\\"Quota Exceeded from Google drive\\\". Is this a quota from the dataset owner? or the quota from our (the runner) Google Drive?\",\n \"+1 Also encountering this issue\",\n \"> When you say the \\\"Quota Exceeded from Google drive\\\". Is this a quota from the dataset owner? or the quota from our (the runner) Google Drive?\\r\\n\\r\\nEach file on Google Drive can be downloaded only a certain amount of times per day because of a quota. The quota is reset every day. So if too many people download the dataset the same day, then the quota is likely to exceed.\\r\\nThat's a really bad limitations of Google Drive and we should definitely find another host for these dataset than Google Drive.\\r\\nFor now I would suggest to wait and try again later..\\r\\n\\r\\nSo far the issue happened with CNN DailyMail, Amazon Polarity and Yelp Reviews. \\r\\nAre you experiencing the issue with other datasets ? @calebchiam @dtch1997 \",\n \"@lhoestq Gotcha, that is quite problematic...for what it's worth, I've had no issues with the other datasets I tried, such as `yelp_reviews_full` and `amazon_reviews_multi`.\",\n \"Same issue today with \\\"big_patent\\\", though the symptoms are slightly different.\\r\\n\\r\\nWhen running\\r\\n\\r\\n```py\\r\\nfrom datasets import load_dataset\\r\\nload_dataset(\\\"big_patent\\\", split=\\\"validation\\\")\\r\\n```\\r\\n\\r\\nI get the following\\r\\n`FileNotFoundError: Local file \\\\huggingface\\\\datasets\\\\downloads\\\\6159313604f4f2c01e7d1cac52139343b6c07f73f6de348d09be6213478455c5\\\\bigPatentData\\\\train.tar.gz doesn't exist`\\r\\n\\r\\nI had to look into `6159313604f4f2c01e7d1cac52139343b6c07f73f6de348d09be6213478455c5` (which is a file instead of a folder) and got the following:\\r\\n\\r\\n`Google Drive - Quota exceeded

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`\",\n \"A similar issue arises when trying to stream the dataset\\r\\n\\r\\n```python\\r\\n>>> from datasets import load_dataset\\r\\n>>> iter_dset = load_dataset(\\\"amazon_polarity\\\", split=\\\"test\\\", streaming=True)\\r\\n>>> iter(iter_dset).__next__()\\r\\n\\r\\n---------------------------------------------------------------------------\\r\\nValueError Traceback (most recent call last)\\r\\n~\\\\lib\\\\tarfile.py in nti(s)\\r\\n 186 s = nts(s, \\\"ascii\\\", \\\"strict\\\")\\r\\n--> 187 n = int(s.strip() or \\\"0\\\", 8)\\r\\n 188 except ValueError:\\r\\n\\r\\nValueError: invalid literal for int() with base 8: 'e nonce='\\r\\n\\r\\nDuring handling of the above exception, another exception occurred:\\r\\n\\r\\nInvalidHeaderError Traceback (most recent call last)\\r\\n~\\\\lib\\\\tarfile.py in next(self)\\r\\n 2288 try:\\r\\n-> 2289 tarinfo = self.tarinfo.fromtarfile(self)\\r\\n 2290 except EOFHeaderError as e:\\r\\n\\r\\n~\\\\lib\\\\tarfile.py in fromtarfile(cls, tarfile)\\r\\n 1094 buf = tarfile.fileobj.read(BLOCKSIZE)\\r\\n-> 1095 obj = cls.frombuf(buf, tarfile.encoding, tarfile.errors)\\r\\n 1096 obj.offset = tarfile.fileobj.tell() - BLOCKSIZE\\r\\n\\r\\n~\\\\lib\\\\tarfile.py in frombuf(cls, buf, encoding, errors)\\r\\n 1036\\r\\n-> 1037 chksum = nti(buf[148:156])\\r\\n 1038 if chksum not in calc_chksums(buf):\\r\\n\\r\\n~\\\\lib\\\\tarfile.py in nti(s)\\r\\n 188 except ValueError:\\r\\n--> 189 raise InvalidHeaderError(\\\"invalid header\\\")\\r\\n 190 return n\\r\\n\\r\\nInvalidHeaderError: invalid header\\r\\n\\r\\nDuring handling of the above exception, another exception occurred:\\r\\n\\r\\nReadError Traceback (most recent call last)\\r\\n in \\r\\n----> 1 iter(iter_dset).__next__()\\r\\n\\r\\n~\\\\lib\\\\site-packages\\\\datasets\\\\iterable_dataset.py in __iter__(self)\\r\\n 363\\r\\n 364 def __iter__(self):\\r\\n--> 365 for key, example in self._iter():\\r\\n 366 if self.features:\\r\\n 367 # we encode the example for ClassLabel feature types for example\\r\\n\\r\\n~\\\\lib\\\\site-packages\\\\datasets\\\\iterable_dataset.py in _iter(self)\\r\\n 360 else:\\r\\n 361 ex_iterable = self._ex_iterable\\r\\n--> 362 yield from ex_iterable\\r\\n 363\\r\\n 364 def __iter__(self):\\r\\n\\r\\n~\\\\lib\\\\site-packages\\\\datasets\\\\iterable_dataset.py in __iter__(self)\\r\\n 77\\r\\n 78 def __iter__(self):\\r\\n---> 79 yield from self.generate_examples_fn(**self.kwargs)\\r\\n 80\\r\\n 81 def shuffle_data_sources(self, seed: Optional[int]) -> \\\"ExamplesIterable\\\":\\r\\n\\r\\n~\\\\.cache\\\\huggingface\\\\modules\\\\datasets_modules\\\\datasets\\\\amazon_polarity\\\\56923eeb72030cb6c4ea30c8a4e1162c26b25973475ac1f44340f0ec0f2936f4\\\\amazon_polarity.py in _generate_examples(self, filepath, files)\\r\\n 114 def _generate_examples(self, filepath, files):\\r\\n 115 \\\"\\\"\\\"Yields examples.\\\"\\\"\\\"\\r\\n--> 116 for path, f in files:\\r\\n 117 if path == filepath:\\r\\n 118 lines = (line.decode(\\\"utf-8\\\") for line in f)\\r\\n\\r\\n~\\\\lib\\\\site-packages\\\\datasets\\\\utils\\\\streaming_download_manager.py in __iter__(self)\\r\\n 616\\r\\n 617 def __iter__(self):\\r\\n--> 618 yield from self.generator(*self.args, **self.kwargs)\\r\\n 619\\r\\n 620\\r\\n\\r\\n~\\\\lib\\\\site-packages\\\\datasets\\\\utils\\\\streaming_download_manager.py in _iter_from_urlpath(cls, urlpath, use_auth_token)\\r\\n 644 ) -> Generator[Tuple, None, None]:\\r\\n 645 with xopen(urlpath, \\\"rb\\\", use_auth_token=use_auth_token) as f:\\r\\n--> 646 yield from cls._iter_from_fileobj(f)\\r\\n 647\\r\\n 648 @classmethod\\r\\n\\r\\n~\\\\lib\\\\site-packages\\\\datasets\\\\utils\\\\streaming_download_manager.py in _iter_from_fileobj(cls, f)\\r\\n 624 @classmethod\\r\\n 625 def _iter_from_fileobj(cls, f) -> Generator[Tuple, None, None]:\\r\\n--> 626 stream = tarfile.open(fileobj=f, mode=\\\"r|*\\\")\\r\\n 627 for tarinfo in stream:\\r\\n 628 file_path = tarinfo.name\\r\\n\\r\\n~\\\\lib\\\\tarfile.py in open(cls, name, mode, fileobj, bufsize, **kwargs)\\r\\n 1603 stream = _Stream(name, filemode, comptype, fileobj, bufsize)\\r\\n 1604 try:\\r\\n-> 1605 t = cls(name, filemode, stream, **kwargs)\\r\\n 1606 except:\\r\\n 1607 stream.close()\\r\\n\\r\\n~\\\\lib\\\\tarfile.py in __init__(self, name, mode, fileobj, format, tarinfo, dereference, ignore_zeros, encoding, errors, pax_headers, debug, errorlevel, copybufsize)\\r\\n 1484 if self.mode == \\\"r\\\":\\r\\n 1485 self.firstmember = None\\r\\n-> 1486 self.firstmember = self.next()\\r\\n 1487\\r\\n 1488 if self.mode == \\\"a\\\":\\r\\n\\r\\n~\\\\lib\\\\tarfile.py in next(self)\\r\\n 2299 continue\\r\\n 2300 elif self.offset == 0:\\r\\n-> 2301 raise ReadError(str(e))\\r\\n 2302 except EmptyHeaderError:\\r\\n 2303 if self.offset == 0:\\r\\n\\r\\nReadError: invalid header\\r\\n\\r\\n```\",\n \"This error still happens, but for a different reason now: Google Drive returns a warning instead of the dataset.\",\n \"Met the same issue +1\",\n \"Hi ! Thanks for reporting. Google Drive changed the way to bypass the warning message recently.\\r\\n\\r\\nThe latest release `1.18.4` fixes this for datasets loaded in a regular way.\\r\\n\\r\\nWe opened a PR to fix this recently for streaming mode at #3843 - we'll do a new release once the fix is merged :)\",\n \"Fixed by:\\r\\n- #3787 \\r\\n- #3843\"\n]"},"created_at":{"kind":"timestamp","value":"2021-02-10T10:00:56","string":"2021-02-10T10:00:56"},"updated_at":{"kind":"timestamp","value":"2022-03-15T13:55:24","string":"2022-03-15T13:55:24"},"closed_at":{"kind":"timestamp","value":"2022-03-15T13:55:23","string":"2022-03-15T13:55:23"},"author_association":{"kind":"string","value":"NONE"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"Hi, it seems that loading the amazon_polarity dataset gives a NonMatchingChecksumError.\r\n\r\nTo reproduce:\r\n```\r\nload_dataset(\"amazon_polarity\")\r\n```\r\nThis will give the following error:\r\n```\r\n---------------------------------------------------------------------------\r\nNonMatchingChecksumError Traceback (most recent call last)\r\n in ()\r\n----> 1 dataset = load_dataset(\"amazon_polarity\")\r\n\r\n3 frames\r\n/usr/local/lib/python3.6/dist-packages/datasets/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n 37 if len(bad_urls) > 0:\r\n 38 error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 39 raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n 40 logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n 41 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https://drive.google.com/u/0/uc?id=0Bz8a_Dbh9QhbaW12WVVZS2drcnM&export=download']\r\n```"},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1856/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 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\"site_admin\": false\n}"},"assignees":{"kind":"list like","value":[{"login":"albertvillanova","id":8515462,"node_id":"MDQ6VXNlcjg1MTU0NjI=","avatar_url":"https://avatars.githubusercontent.com/u/8515462?v=4","gravatar_id":"","url":"https://api.github.com/users/albertvillanova","html_url":"https://github.com/albertvillanova","followers_url":"https://api.github.com/users/albertvillanova/followers","following_url":"https://api.github.com/users/albertvillanova/following{/other_user}","gists_url":"https://api.github.com/users/albertvillanova/gists{/gist_id}","starred_url":"https://api.github.com/users/albertvillanova/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/albertvillanova/subscriptions","organizations_url":"https://api.github.com/users/albertvillanova/orgs","repos_url":"https://api.github.com/users/albertvillanova/repos","events_url":"https://api.github.com/users/albertvillanova/events{/privacy}","received_events_url":"https://api.github.com/users/albertvillanova/received_events","type":"User","site_admin":false}],"string":"[\n {\n \"login\": \"albertvillanova\",\n \"id\": 8515462,\n \"node_id\": \"MDQ6VXNlcjg1MTU0NjI=\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/8515462?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/albertvillanova\",\n \"html_url\": \"https://github.com/albertvillanova\",\n \"followers_url\": \"https://api.github.com/users/albertvillanova/followers\",\n \"following_url\": \"https://api.github.com/users/albertvillanova/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/albertvillanova/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/albertvillanova/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/albertvillanova/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/albertvillanova/orgs\",\n \"repos_url\": \"https://api.github.com/users/albertvillanova/repos\",\n \"events_url\": \"https://api.github.com/users/albertvillanova/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/albertvillanova/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n }\n]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["Hi @sshleifer.\r\n\r\nI am not sure of understanding the need of the `add_item` approach...\r\n\r\nBy just reading your \"Desired API\" section, I would say you could (nearly) get it with a 1-column Dataset:\r\n```python\r\ndata = {\"input_ids\": [np.array([4,4,2]), np.array([8,6,5,5,2]), np.array([3,3,31,5])]}\r\nds = Dataset.from_dict(data)\r\nassert (ds[\"input_ids\"][0] == np.array([4,4,2])).all()\r\n```","Hi @sshleifer :) \r\n\r\nWe don't have methods like `Dataset.add_batch` or `Dataset.add_entry/add_item` yet.\r\nBut that's something we'll add pretty soon. Would an API that looks roughly like this help ? Do you have suggestions ?\r\n```python\r\nimport numpy as np\r\nfrom datasets import Dataset\r\n\r\ntokenized = [np.array([4,4,2]), np.array([8,6,5,5,2]), np.array([3,3,31,5])\r\n\r\n# API suggestion (not available yet)\r\nd = Dataset()\r\nfor input_ids in tokenized:\r\n d.add_item({\"input_ids\": input_ids})\r\n\r\nprint(d[0][\"input_ids\"])\r\n# [4, 4, 2]\r\n```\r\n\r\nCurrently you can define a dataset with what @albertvillanova suggest, or via a generator using dataset builders. It's also possible to [concatenate datasets](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=concatenate#datasets.concatenate_datasets).","Your API looks perfect @lhoestq, thanks!"],"string":"[\n \"Hi @sshleifer.\\r\\n\\r\\nI am not sure of understanding the need of the `add_item` approach...\\r\\n\\r\\nBy just reading your \\\"Desired API\\\" section, I would say you could (nearly) get it with a 1-column Dataset:\\r\\n```python\\r\\ndata = {\\\"input_ids\\\": [np.array([4,4,2]), np.array([8,6,5,5,2]), np.array([3,3,31,5])]}\\r\\nds = Dataset.from_dict(data)\\r\\nassert (ds[\\\"input_ids\\\"][0] == np.array([4,4,2])).all()\\r\\n```\",\n \"Hi @sshleifer :) \\r\\n\\r\\nWe don't have methods like `Dataset.add_batch` or `Dataset.add_entry/add_item` yet.\\r\\nBut that's something we'll add pretty soon. Would an API that looks roughly like this help ? Do you have suggestions ?\\r\\n```python\\r\\nimport numpy as np\\r\\nfrom datasets import Dataset\\r\\n\\r\\ntokenized = [np.array([4,4,2]), np.array([8,6,5,5,2]), np.array([3,3,31,5])\\r\\n\\r\\n# API suggestion (not available yet)\\r\\nd = Dataset()\\r\\nfor input_ids in tokenized:\\r\\n d.add_item({\\\"input_ids\\\": input_ids})\\r\\n\\r\\nprint(d[0][\\\"input_ids\\\"])\\r\\n# [4, 4, 2]\\r\\n```\\r\\n\\r\\nCurrently you can define a dataset with what @albertvillanova suggest, or via a generator using dataset builders. It's also possible to [concatenate datasets](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=concatenate#datasets.concatenate_datasets).\",\n \"Your API looks perfect @lhoestq, thanks!\"\n]"},"created_at":{"kind":"timestamp","value":"2021-02-10T06:06:00","string":"2021-02-10T06:06:00"},"updated_at":{"kind":"timestamp","value":"2021-04-23T10:01:30","string":"2021-04-23T10:01:30"},"closed_at":{"kind":"timestamp","value":"2021-04-23T10:01:30","string":"2021-04-23T10:01:30"},"author_association":{"kind":"string","value":"CONTRIBUTOR"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"I'm trying to integrate `huggingface/datasets` functionality into `fairseq`, which requires (afaict) being able to build a dataset through an `add_item` method, such as https://github.com/pytorch/fairseq/blob/master/fairseq/data/indexed_dataset.py#L318, as opposed to loading all the text into arrow, and then `dataset.map(binarizer)`.\r\nIs this possible at the moment? Is there an example? I'm happy to use raw `pa.Table` but not sure whether it will support uneven length entries.\r\n\r\n### Desired API\r\n\r\n```python\r\nimport numpy as np\r\ntokenized: List[np.NDArray[np.int64]] = [np.array([4,4,2]), np.array([8,6,5,5,2]), np.array([3,3,31,5])\r\n\r\ndef build_dataset_from_tokenized(tokenized: List[np.NDArray[int]]) -> Dataset:\r\n \"\"\"FIXME\"\"\"\r\n dataset = EmptyDataset()\r\n for t in tokenized: dataset.append(t)\r\n return dataset\r\nds = build_dataset_from_tokenized(tokenized)\r\nassert (ds[0] == np.array([4,4,2])).all()\r\n```\r\n\r\n### What I tried\r\ngrep, google for \"add one entry at a time\", \"datasets.append\"\r\n\r\n### Current Code\r\nThis code achieves the same result but doesn't fit into the `add_item` abstraction.\r\n\r\n```python\r\n dataset = load_dataset('text', data_files={'train': 'train.txt'})\r\n tokenizer = RobertaTokenizerFast.from_pretrained('roberta-base', max_length=4096)\r\n def tokenize_function(examples):\r\n ids = tokenizer(examples['text'], return_attention_mask=False)['input_ids']\r\n return {'input_ids': [x[1:] for x in ids]}\r\n ds = dataset.map(tokenize_function, batched=True, num_proc=4, remove_columns=['text'], load_from_cache_file=not overwrite_cache)\r\n\tprint(ds['train'][0]) => np array\r\n```\r\n\r\nThanks in advance!"},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1854/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 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TIMIT"},"user":{"kind":"string","value":"{\n \"login\": \"patrickvonplaten\",\n \"id\": 23423619,\n \"node_id\": \"MDQ6VXNlcjIzNDIzNjE5\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/23423619?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/patrickvonplaten\",\n \"html_url\": \"https://github.com/patrickvonplaten\",\n \"followers_url\": \"https://api.github.com/users/patrickvonplaten/followers\",\n \"following_url\": \"https://api.github.com/users/patrickvonplaten/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/patrickvonplaten/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/patrickvonplaten/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/patrickvonplaten/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/patrickvonplaten/orgs\",\n \"repos_url\": \"https://api.github.com/users/patrickvonplaten/repos\",\n \"events_url\": 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},\n {\n \"id\": 2725241052,\n \"node_id\": \"MDU6TGFiZWwyNzI1MjQxMDUy\",\n \"url\": \"https://api.github.com/repos/huggingface/datasets/labels/speech\",\n \"name\": \"speech\",\n \"color\": \"d93f0b\",\n \"default\": false,\n \"description\": \"\"\n }\n]"},"state":{"kind":"string","value":"closed"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["@patrickvonplaten Could you please help me with how the output text has to be represented in the data? TIMIT has Words, Phonemes and texts. Also has lot on info on the speaker and the dialect. Could you please help me? An example of how to arrange it would be super helpful!\r\n\r\n","Hey @vrindaprabhu - sure I'll help you :-) Could you open a first PR for TIMIT where you copy-paste more or less the `librispeech_asr` script: https://github.com/huggingface/datasets/blob/28be129db862ec89a87ac9349c64df6b6118aff4/datasets/librispeech_asr/librispeech_asr.py#L93 (obviously replacing all the naming and links correctly...) and then you can list all possible outputs in the features dict: https://github.com/huggingface/datasets/blob/28be129db862ec89a87ac9349c64df6b6118aff4/datasets/librispeech_asr/librispeech_asr.py#L104 (words, phonemes should probably be of kind `datasets.Sequence(datasets.Value(\"string\"))` and texts I think should be of type `\"text\": datasets.Value(\"string\")`.\r\n\r\nWhen you've opened a first PR, I think it'll be much easier for us to take a look together :-) ","I am sorry! I created the PR [#1903](https://github.com/huggingface/datasets/pull/1903#). Requesting your comments! CircleCI tests are failing, will address them along with your comments!"],"string":"[\n \"@patrickvonplaten Could you please help me with how the output text has to be represented in the data? TIMIT has Words, Phonemes and texts. Also has lot on info on the speaker and the dialect. Could you please help me? An example of how to arrange it would be super helpful!\\r\\n\\r\\n\",\n \"Hey @vrindaprabhu - sure I'll help you :-) Could you open a first PR for TIMIT where you copy-paste more or less the `librispeech_asr` script: https://github.com/huggingface/datasets/blob/28be129db862ec89a87ac9349c64df6b6118aff4/datasets/librispeech_asr/librispeech_asr.py#L93 (obviously replacing all the naming and links correctly...) and then you can list all possible outputs in the features dict: https://github.com/huggingface/datasets/blob/28be129db862ec89a87ac9349c64df6b6118aff4/datasets/librispeech_asr/librispeech_asr.py#L104 (words, phonemes should probably be of kind `datasets.Sequence(datasets.Value(\\\"string\\\"))` and texts I think should be of type `\\\"text\\\": datasets.Value(\\\"string\\\")`.\\r\\n\\r\\nWhen you've opened a first PR, I think it'll be much easier for us to take a look together :-) \",\n \"I am sorry! I created the PR [#1903](https://github.com/huggingface/datasets/pull/1903#). Requesting your comments! CircleCI tests are failing, will address them along with your comments!\"\n]"},"created_at":{"kind":"timestamp","value":"2021-02-09T07:29:41","string":"2021-02-09T07:29:41"},"updated_at":{"kind":"timestamp","value":"2021-03-15T05:59:37","string":"2021-03-15T05:59:37"},"closed_at":{"kind":"timestamp","value":"2021-03-15T05:59:37","string":"2021-03-15T05:59:37"},"author_association":{"kind":"string","value":"CONTRIBUTOR"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"## Adding a Dataset\r\n- **Name:** *TIMIT*\r\n- **Description:** *The TIMIT corpus of read speech has been designed to provide speech data for the acquisition of acoustic-phonetic knowledge and for the development and evaluation of automatic speech recognition systems*\r\n\r\n- **Paper:** *Homepage*: http://groups.inf.ed.ac.uk/ami/corpus/ / *Wikipedia*: https://en.wikipedia.org/wiki/TIMIT\r\n- **Data:** *https://deepai.org/dataset/timit*\r\n- **Motivation:** Important speech dataset\r\n\r\n\r\nIf interested in tackling this issue, feel free to tag @patrickvonplaten\r\n\r\n\r\nInstructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).\r\n"},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1849/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 0\n}"},"timeline_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1849/timeline"},"performed_via_github_app":{"kind":"null"},"state_reason":{"kind":"string","value":"completed"}}},{"rowIdx":2224,"cells":{"url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1844"},"repository_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets"},"labels_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1844/labels{/name}"},"comments_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1844/comments"},"events_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1844/events"},"html_url":{"kind":"string","value":"https://github.com/huggingface/datasets/issues/1844"},"id":{"kind":"number","value":803588125,"string":"803,588,125"},"node_id":{"kind":"string","value":"MDU6SXNzdWU4MDM1ODgxMjU="},"number":{"kind":"number","value":1844,"string":"1,844"},"title":{"kind":"string","value":"Update Open Subtitles corpus with original sentence IDs"},"user":{"kind":"string","value":"{\n \"login\": \"Valahaar\",\n \"id\": 19476123,\n \"node_id\": \"MDQ6VXNlcjE5NDc2MTIz\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/19476123?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/Valahaar\",\n \"html_url\": \"https://github.com/Valahaar\",\n \"followers_url\": \"https://api.github.com/users/Valahaar/followers\",\n \"following_url\": \"https://api.github.com/users/Valahaar/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/Valahaar/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/Valahaar/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/Valahaar/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/Valahaar/orgs\",\n \"repos_url\": \"https://api.github.com/users/Valahaar/repos\",\n \"events_url\": \"https://api.github.com/users/Valahaar/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/Valahaar/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n}"},"labels":{"kind":"list like","value":[{"id":1935892877,"node_id":"MDU6TGFiZWwxOTM1ODkyODc3","url":"https://api.github.com/repos/huggingface/datasets/labels/good%20first%20issue","name":"good first issue","color":"7057ff","default":true,"description":"Good for newcomers"}],"string":"[\n {\n \"id\": 1935892877,\n \"node_id\": \"MDU6TGFiZWwxOTM1ODkyODc3\",\n \"url\": \"https://api.github.com/repos/huggingface/datasets/labels/good%20first%20issue\",\n \"name\": \"good first issue\",\n \"color\": \"7057ff\",\n \"default\": true,\n \"description\": \"Good for newcomers\"\n }\n]"},"state":{"kind":"string","value":"closed"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["Hi ! You're right this can can useful.\r\nThis should be easy to add, so feel free to give it a try if you want to contribute :)\r\nI think we just need to add it to the _generate_examples method of the OpenSubtitles dataset builder [here](https://github.com/huggingface/datasets/blob/master/datasets/open_subtitles/open_subtitles.py#L103)","Hey @lhoestq , absolutely yes! Just one question before I start implementing. The ids found in the zip file have this format: \r\n(the following is line `22497315` of the `ids` file of the `de-en` dump)\r\n\r\n\r\n`de/2017/7006210/7063319.xml.gz en/2017/7006210/7050201.xml.gz 335 339 340` (every space is actually a tab, aside from the space between `339` and `340`)\r\n\r\n\r\nWhere filenames encode the information like this: `lang/year/imdb_id/opensubtitles_id.xml.gz` whereas the numbers correspond to the sentence ids which are linked together (i.e. sentence `335` of the German subtitle corresponds to lines `339` and `340` of the English file)\r\n\r\nThat being said, do you think I should stick to the raw sentence id (and replace the current sequential id) or should I include more detailed metadata (or both things maybe)?\r\n\r\nGoing with raw ID is surely simpler, but including `year`, `imdbId` and `subtitleId` should save space as they're just integers; besides, any operation (like filtering or grouping) will be much easier if users don't have to manually parse the ids every time.\r\nAs for the language-specific sentenceIds, what could be the best option? A list of integers or a comma-separated string?\r\n\r\n**Note:** I did not find any official information about this encoding, but it appears to check out:\r\nhttps://www.imdb.com/title/tt7006210/, https://www.opensubtitles.org/en/subtitles/7063319 and https://www.opensubtitles.org/en/subtitles/7050201 all link to the same episode, so I guess (I hope!) it's correct.\r\n\r\n","I like the idea of having `year`, `imdbId` and `subtitleId` as columns for filtering for example.\r\nAnd for the `sentenceIds` a list of integers is fine.","Thanks for improving it @Valahaar :) ","Something like this? (adapted from [here](https://github.com/huggingface/datasets/blob/master/datasets/open_subtitles/open_subtitles.py#L114))\r\n\r\n```python\r\nresult = (\r\n sentence_counter,\r\n {\r\n \"id\": str(sentence_counter),\r\n \"meta\": {\r\n \"year\": year,\r\n \"imdbId\": imdb_id,\r\n \"subtitleId\": {l1: l1_sub_id, l2: l2_sub_id},\r\n \"sentenceIds\": {l1: [... source_sids ...], l2: [... target_sids ...]},\r\n # or maybe src/tgt? I'd go with the first one for consistency with 'translation'\r\n \"subtitleId\": {\"src\": l1_sub_id, \"tgt\": l2_sub_id},\r\n \"sentenceIds\": {\"src\": [... source_sids ...], \"tgt\": [... target_sids ...]},\r\n },\r\n \"translation\": {l1: x, l2: y},\r\n },\r\n )\r\n```\r\nOr at top level, avoiding nesting into 'meta'?","Merged in #1865, closing. Thanks :)"],"string":"[\n \"Hi ! You're right this can can useful.\\r\\nThis should be easy to add, so feel free to give it a try if you want to contribute :)\\r\\nI think we just need to add it to the _generate_examples method of the OpenSubtitles dataset builder [here](https://github.com/huggingface/datasets/blob/master/datasets/open_subtitles/open_subtitles.py#L103)\",\n \"Hey @lhoestq , absolutely yes! Just one question before I start implementing. The ids found in the zip file have this format: \\r\\n(the following is line `22497315` of the `ids` file of the `de-en` dump)\\r\\n\\r\\n\\r\\n`de/2017/7006210/7063319.xml.gz en/2017/7006210/7050201.xml.gz 335 339 340` (every space is actually a tab, aside from the space between `339` and `340`)\\r\\n\\r\\n\\r\\nWhere filenames encode the information like this: `lang/year/imdb_id/opensubtitles_id.xml.gz` whereas the numbers correspond to the sentence ids which are linked together (i.e. sentence `335` of the German subtitle corresponds to lines `339` and `340` of the English file)\\r\\n\\r\\nThat being said, do you think I should stick to the raw sentence id (and replace the current sequential id) or should I include more detailed metadata (or both things maybe)?\\r\\n\\r\\nGoing with raw ID is surely simpler, but including `year`, `imdbId` and `subtitleId` should save space as they're just integers; besides, any operation (like filtering or grouping) will be much easier if users don't have to manually parse the ids every time.\\r\\nAs for the language-specific sentenceIds, what could be the best option? A list of integers or a comma-separated string?\\r\\n\\r\\n**Note:** I did not find any official information about this encoding, but it appears to check out:\\r\\nhttps://www.imdb.com/title/tt7006210/, https://www.opensubtitles.org/en/subtitles/7063319 and https://www.opensubtitles.org/en/subtitles/7050201 all link to the same episode, so I guess (I hope!) it's correct.\\r\\n\\r\\n\",\n \"I like the idea of having `year`, `imdbId` and `subtitleId` as columns for filtering for example.\\r\\nAnd for the `sentenceIds` a list of integers is fine.\",\n \"Thanks for improving it @Valahaar :) \",\n \"Something like this? (adapted from [here](https://github.com/huggingface/datasets/blob/master/datasets/open_subtitles/open_subtitles.py#L114))\\r\\n\\r\\n```python\\r\\nresult = (\\r\\n sentence_counter,\\r\\n {\\r\\n \\\"id\\\": str(sentence_counter),\\r\\n \\\"meta\\\": {\\r\\n \\\"year\\\": year,\\r\\n \\\"imdbId\\\": imdb_id,\\r\\n \\\"subtitleId\\\": {l1: l1_sub_id, l2: l2_sub_id},\\r\\n \\\"sentenceIds\\\": {l1: [... source_sids ...], l2: [... target_sids ...]},\\r\\n # or maybe src/tgt? I'd go with the first one for consistency with 'translation'\\r\\n \\\"subtitleId\\\": {\\\"src\\\": l1_sub_id, \\\"tgt\\\": l2_sub_id},\\r\\n \\\"sentenceIds\\\": {\\\"src\\\": [... source_sids ...], \\\"tgt\\\": [... target_sids ...]},\\r\\n },\\r\\n \\\"translation\\\": {l1: x, l2: y},\\r\\n },\\r\\n )\\r\\n```\\r\\nOr at top level, avoiding nesting into 'meta'?\",\n \"Merged in #1865, closing. Thanks :)\"\n]"},"created_at":{"kind":"timestamp","value":"2021-02-08T13:55:13","string":"2021-02-08T13:55:13"},"updated_at":{"kind":"timestamp","value":"2021-02-12T17:38:58","string":"2021-02-12T17:38:58"},"closed_at":{"kind":"timestamp","value":"2021-02-12T17:38:58","string":"2021-02-12T17:38:58"},"author_association":{"kind":"string","value":"CONTRIBUTOR"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"Hi! It would be great if you could add the original sentence ids to [Open Subtitles](https://huggingface.co/datasets/open_subtitles).\r\n\r\nI can think of two reasons: first, it's possible to gather sentences for an entire document (the original ids contain media id, subtitle file id and sentence id), therefore somewhat allowing for document-level machine translation (and other document-level stuff which could be cool to have); second, it's possible to have parallel sentences in multiple languages, as they share the same ids across bitexts.\r\n\r\nI think I should tag @abhishekkrthakur as he's the one who added it in the first place.\r\n\r\nThanks!"},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1844/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 0\n}"},"timeline_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1844/timeline"},"performed_via_github_app":{"kind":"null"},"state_reason":{"kind":"string","value":"completed"}}},{"rowIdx":2225,"cells":{"url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1843"},"repository_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets"},"labels_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1843/labels{/name}"},"comments_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1843/comments"},"events_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1843/events"},"html_url":{"kind":"string","value":"https://github.com/huggingface/datasets/issues/1843"},"id":{"kind":"number","value":803565393,"string":"803,565,393"},"node_id":{"kind":"string","value":"MDU6SXNzdWU4MDM1NjUzOTM="},"number":{"kind":"number","value":1843,"string":"1,843"},"title":{"kind":"string","value":"MustC Speech Translation"},"user":{"kind":"string","value":"{\n \"login\": \"patrickvonplaten\",\n \"id\": 23423619,\n \"node_id\": \"MDQ6VXNlcjIzNDIzNjE5\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/23423619?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/patrickvonplaten\",\n \"html_url\": \"https://github.com/patrickvonplaten\",\n \"followers_url\": \"https://api.github.com/users/patrickvonplaten/followers\",\n \"following_url\": \"https://api.github.com/users/patrickvonplaten/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/patrickvonplaten/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/patrickvonplaten/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/patrickvonplaten/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/patrickvonplaten/orgs\",\n \"repos_url\": \"https://api.github.com/users/patrickvonplaten/repos\",\n \"events_url\": \"https://api.github.com/users/patrickvonplaten/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/patrickvonplaten/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n}"},"labels":{"kind":"list like","value":[{"id":2067376369,"node_id":"MDU6TGFiZWwyMDY3Mzc2MzY5","url":"https://api.github.com/repos/huggingface/datasets/labels/dataset%20request","name":"dataset request","color":"e99695","default":false,"description":"Requesting to add a new dataset"},{"id":2725241052,"node_id":"MDU6TGFiZWwyNzI1MjQxMDUy","url":"https://api.github.com/repos/huggingface/datasets/labels/speech","name":"speech","color":"d93f0b","default":false,"description":""}],"string":"[\n {\n \"id\": 2067376369,\n \"node_id\": \"MDU6TGFiZWwyMDY3Mzc2MzY5\",\n \"url\": \"https://api.github.com/repos/huggingface/datasets/labels/dataset%20request\",\n \"name\": \"dataset request\",\n \"color\": \"e99695\",\n \"default\": false,\n \"description\": \"Requesting to add a new dataset\"\n },\n {\n \"id\": 2725241052,\n \"node_id\": \"MDU6TGFiZWwyNzI1MjQxMDUy\",\n \"url\": \"https://api.github.com/repos/huggingface/datasets/labels/speech\",\n \"name\": \"speech\",\n \"color\": \"d93f0b\",\n \"default\": false,\n \"description\": \"\"\n }\n]"},"state":{"kind":"string","value":"open"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["Hi @patrickvonplaten I would like to work on this dataset. \r\n\r\nThanks! ","That's awesome! Actually, I just noticed that this dataset might become a bit too big!\r\n\r\nMuST-C is the main dataset used for IWSLT19 and should probably be added as a standalone dataset. Would you be interested also in adding `datasets/MuST-C` instead?\r\n\r\nDescription: \r\n_MuST-C is a multilingual speech translation corpus whose size and quality facilitates the training of end-to-end systems for speech translation from English into several languages. For each target language, MuST-C comprises several hundred hours of audio recordings from English TED Talks, which are automatically aligned at the sentence level with their manual transcriptions and translations._\r\n\r\nPaper: https://www.aclweb.org/anthology/N19-1202.pdf\r\n\r\nDataset: https://ict.fbk.eu/must-c/ (One needs to fill out a short from to download the data, but it's very easy).\r\n\r\nIt would be awesome if you're interested in adding this datates. I'm very happy to guide you through the PR! I think the easiest way to start would probably be to read [this README on how to add a dataset](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md) and open a PR. Think you can copy & paste some code from:\r\n\r\n- Librispeech_asr: https://github.com/huggingface/datasets/blob/master/datasets/librispeech_asr/librispeech_asr.py\r\n- Flores Translation: https://github.com/huggingface/datasets/blob/master/datasets/flores/flores.py\r\n\r\nThink all the rest can be handled on the PR :-) ","Hi @patrickvonplaten \r\nI have tried downloading this dataset, but the connection seems to reset all the time. I have tried it via the browser, wget, and using gdown . But it gives me an error message. _\"The server is busy or down, pls try again\"_ (rephrasing the message here)\r\n\r\nI have completed adding 4 datasets in the previous data sprint (including the IWSLT dataset #1676 ) ...so just checking if you are able to download it at your end. Otherwise will write to the dataset authors to update the links. \r\n\r\n\r\n\r\n\r\n","Let me check tomorrow! Thanks for leaving this message!","cc @patil-suraj for notification ","@skyprince999, I think I'm getting the same error you're getting :-/\r\n\r\n```\r\nSorry, you can't view or download this file at this time.\r\n\r\nToo many users have viewed or downloaded this file recently. Please try accessing the file again later. If the file you are trying to access is particularly large or is shared with many people, it may take up to 24 hours to be able to view or download the file. If you still can't access a file after 24 hours, contact your domain administrator.\r\n```\r\n\r\nIt would be great if you could write the authors to see whether they can fix it.\r\nAlso cc @lhoestq - do you think we could mirror the dataset? ","Also there are huge those datasets. Think downloading MuST-C v1.2 amounts to ~ 1000GB... because there are 14 possible configs each around 60-70GB. I think users mostly will only use one of the 14 configs so that they would only need, in theory, will have to download ~60GB which is ok. But I think this functionality doesn't exist yet in `datasets` no? cc @lhoestq ","> Also cc @lhoestq - do you think we could mirror the dataset?\r\n\r\nYes we can mirror it if the authors are fine with it. You can create a dataset repo on huggingface.co (possibly under the relevant org) and add the mirrored data files.\r\n\r\n> I think users mostly will only use one of the 14 configs so that they would only need, in theory, will have to download ~60GB which is ok. But I think this functionality doesn't exist yet in datasets no? cc @lhoestq\r\n\r\nIf there are different download links for each configuration we can make the dataset builder download only the files related to the requested configuration.","I have written to the dataset authors, highlighting this issue. Waiting for their response. \r\n\r\nUpdate on 25th Feb: \r\nThe authors have replied back, they are updating the download link and will revert back shortly! \r\n\r\n```\r\nfirst of all thanks a lot for being interested in MuST-C and for building the data-loader.\r\n\r\nBefore answering your request, I'd like to clarify that the creation, maintenance, and expansion of MuST-c are not supported by any funded project, so this means that we need to find economic support for all these activities. This also includes permanently moving all the data to AWS or GCP. We are working at this with the goal of facilitating the use of MuST-C, but this is not something that can happen today. We hope to have some news ASAP and you will be among the first to be informed.\r\n\r\nI hope you understand our situation.\r\n```\r\n\r\n","Awesome, actually @lhoestq let's just ask the authors if we should host the dataset no? They could just use our links then as well for their website - what do you think? Is it fine to use our AWS dataset storage also as external links? ","Yes definitely. Shall we suggest them to create a dataset repository under their org on huggingface.co ? @julien-c \r\nThe dataset is around 1TB","Sounds good! \r\n\r\nOrder of magnitude is storage costs ~$20 per TB per month (not including bandwidth). \r\n\r\nHappy to provide this to the community as I feel this is an important dataset. Let us know what the authors want to do!\r\n\r\n","Great! @skyprince999, do you think you could ping the authors here or link to this thread? I think it could be a cool idea to host the dataset on our side then","Done. They replied back, and they want to have a call over a meet/ skype. Is that possible ? \r\nBtw @patrickvonplaten you are looped in that email (_pls check you gmail account_) ","Hello! Any news on this?","@gegallego there were some concerns regarding dataset usage & attribution by a for-profit company, so couldn't take it forward. Also the download links were unstable. \r\nBut I guess if you want to test the fairseq benchmarks, you can connect with them directly for downloading the dataset. ","Yes, that dataset is not easy to download... I had to copy it to my Google Drive and use `rsync` to be able to download it.\r\nHowever, we could add the dataset with a manual download, right?","yes that is possible. I couldn't unfortunately complete this PR, If you would like to add it, please feel free to do it. "],"string":"[\n \"Hi @patrickvonplaten I would like to work on this dataset. \\r\\n\\r\\nThanks! \",\n \"That's awesome! Actually, I just noticed that this dataset might become a bit too big!\\r\\n\\r\\nMuST-C is the main dataset used for IWSLT19 and should probably be added as a standalone dataset. Would you be interested also in adding `datasets/MuST-C` instead?\\r\\n\\r\\nDescription: \\r\\n_MuST-C is a multilingual speech translation corpus whose size and quality facilitates the training of end-to-end systems for speech translation from English into several languages. For each target language, MuST-C comprises several hundred hours of audio recordings from English TED Talks, which are automatically aligned at the sentence level with their manual transcriptions and translations._\\r\\n\\r\\nPaper: https://www.aclweb.org/anthology/N19-1202.pdf\\r\\n\\r\\nDataset: https://ict.fbk.eu/must-c/ (One needs to fill out a short from to download the data, but it's very easy).\\r\\n\\r\\nIt would be awesome if you're interested in adding this datates. I'm very happy to guide you through the PR! I think the easiest way to start would probably be to read [this README on how to add a dataset](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md) and open a PR. Think you can copy & paste some code from:\\r\\n\\r\\n- Librispeech_asr: https://github.com/huggingface/datasets/blob/master/datasets/librispeech_asr/librispeech_asr.py\\r\\n- Flores Translation: https://github.com/huggingface/datasets/blob/master/datasets/flores/flores.py\\r\\n\\r\\nThink all the rest can be handled on the PR :-) \",\n \"Hi @patrickvonplaten \\r\\nI have tried downloading this dataset, but the connection seems to reset all the time. I have tried it via the browser, wget, and using gdown . But it gives me an error message. _\\\"The server is busy or down, pls try again\\\"_ (rephrasing the message here)\\r\\n\\r\\nI have completed adding 4 datasets in the previous data sprint (including the IWSLT dataset #1676 ) ...so just checking if you are able to download it at your end. Otherwise will write to the dataset authors to update the links. \\r\\n\\r\\n\\r\\n\\r\\n\\r\\n\",\n \"Let me check tomorrow! Thanks for leaving this message!\",\n \"cc @patil-suraj for notification \",\n \"@skyprince999, I think I'm getting the same error you're getting :-/\\r\\n\\r\\n```\\r\\nSorry, you can't view or download this file at this time.\\r\\n\\r\\nToo many users have viewed or downloaded this file recently. Please try accessing the file again later. If the file you are trying to access is particularly large or is shared with many people, it may take up to 24 hours to be able to view or download the file. If you still can't access a file after 24 hours, contact your domain administrator.\\r\\n```\\r\\n\\r\\nIt would be great if you could write the authors to see whether they can fix it.\\r\\nAlso cc @lhoestq - do you think we could mirror the dataset? \",\n \"Also there are huge those datasets. Think downloading MuST-C v1.2 amounts to ~ 1000GB... because there are 14 possible configs each around 60-70GB. I think users mostly will only use one of the 14 configs so that they would only need, in theory, will have to download ~60GB which is ok. But I think this functionality doesn't exist yet in `datasets` no? cc @lhoestq \",\n \"> Also cc @lhoestq - do you think we could mirror the dataset?\\r\\n\\r\\nYes we can mirror it if the authors are fine with it. You can create a dataset repo on huggingface.co (possibly under the relevant org) and add the mirrored data files.\\r\\n\\r\\n> I think users mostly will only use one of the 14 configs so that they would only need, in theory, will have to download ~60GB which is ok. But I think this functionality doesn't exist yet in datasets no? cc @lhoestq\\r\\n\\r\\nIf there are different download links for each configuration we can make the dataset builder download only the files related to the requested configuration.\",\n \"I have written to the dataset authors, highlighting this issue. Waiting for their response. \\r\\n\\r\\nUpdate on 25th Feb: \\r\\nThe authors have replied back, they are updating the download link and will revert back shortly! \\r\\n\\r\\n```\\r\\nfirst of all thanks a lot for being interested in MuST-C and for building the data-loader.\\r\\n\\r\\nBefore answering your request, I'd like to clarify that the creation, maintenance, and expansion of MuST-c are not supported by any funded project, so this means that we need to find economic support for all these activities. This also includes permanently moving all the data to AWS or GCP. We are working at this with the goal of facilitating the use of MuST-C, but this is not something that can happen today. We hope to have some news ASAP and you will be among the first to be informed.\\r\\n\\r\\nI hope you understand our situation.\\r\\n```\\r\\n\\r\\n\",\n \"Awesome, actually @lhoestq let's just ask the authors if we should host the dataset no? They could just use our links then as well for their website - what do you think? Is it fine to use our AWS dataset storage also as external links? \",\n \"Yes definitely. Shall we suggest them to create a dataset repository under their org on huggingface.co ? @julien-c \\r\\nThe dataset is around 1TB\",\n \"Sounds good! \\r\\n\\r\\nOrder of magnitude is storage costs ~$20 per TB per month (not including bandwidth). \\r\\n\\r\\nHappy to provide this to the community as I feel this is an important dataset. Let us know what the authors want to do!\\r\\n\\r\\n\",\n \"Great! @skyprince999, do you think you could ping the authors here or link to this thread? I think it could be a cool idea to host the dataset on our side then\",\n \"Done. They replied back, and they want to have a call over a meet/ skype. Is that possible ? \\r\\nBtw @patrickvonplaten you are looped in that email (_pls check you gmail account_) \",\n \"Hello! Any news on this?\",\n \"@gegallego there were some concerns regarding dataset usage & attribution by a for-profit company, so couldn't take it forward. Also the download links were unstable. \\r\\nBut I guess if you want to test the fairseq benchmarks, you can connect with them directly for downloading the dataset. \",\n \"Yes, that dataset is not easy to download... I had to copy it to my Google Drive and use `rsync` to be able to download it.\\r\\nHowever, we could add the dataset with a manual download, right?\",\n \"yes that is possible. I couldn't unfortunately complete this PR, If you would like to add it, please feel free to do it. \"\n]"},"created_at":{"kind":"timestamp","value":"2021-02-08T13:27:45","string":"2021-02-08T13:27:45"},"updated_at":{"kind":"timestamp","value":"2021-05-14T14:53:34","string":"2021-05-14T14:53:34"},"closed_at":{"kind":"null"},"author_association":{"kind":"string","value":"CONTRIBUTOR"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"## Adding a Dataset\r\n- **Name:** *IWSLT19*\r\n- **Description:** *The Speech Translation Task addresses the translation of English audio into German and Portuguese text.*\r\n- **Hompage:** *https://sites.google.com/view/iwslt-evaluation-2019/speech-translation*\r\n- **Data:** *https://sites.google.com/view/iwslt-evaluation-2019/speech-translation* - all data under \"Allowed Training Data\" and \"Development and Evalutaion Data for TED/How2\"\r\n- **Motivation:** Important speech dataset\r\n\r\nIf interested in tackling this issue, feel free to tag @patrickvonplaten\r\n\r\nInstructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).\r\n"},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1843/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 0\n}"},"timeline_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1843/timeline"},"performed_via_github_app":{"kind":"null"},"state_reason":{"kind":"null"}}},{"rowIdx":2226,"cells":{"url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1842"},"repository_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets"},"labels_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1842/labels{/name}"},"comments_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1842/comments"},"events_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1842/events"},"html_url":{"kind":"string","value":"https://github.com/huggingface/datasets/issues/1842"},"id":{"kind":"number","value":803563149,"string":"803,563,149"},"node_id":{"kind":"string","value":"MDU6SXNzdWU4MDM1NjMxNDk="},"number":{"kind":"number","value":1842,"string":"1,842"},"title":{"kind":"string","value":"Add AMI Corpus"},"user":{"kind":"string","value":"{\n \"login\": \"patrickvonplaten\",\n \"id\": 23423619,\n \"node_id\": \"MDQ6VXNlcjIzNDIzNjE5\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/23423619?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/patrickvonplaten\",\n \"html_url\": \"https://github.com/patrickvonplaten\",\n \"followers_url\": \"https://api.github.com/users/patrickvonplaten/followers\",\n \"following_url\": \"https://api.github.com/users/patrickvonplaten/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/patrickvonplaten/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/patrickvonplaten/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/patrickvonplaten/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/patrickvonplaten/orgs\",\n \"repos_url\": \"https://api.github.com/users/patrickvonplaten/repos\",\n \"events_url\": 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},\n {\n \"id\": 2725241052,\n \"node_id\": \"MDU6TGFiZWwyNzI1MjQxMDUy\",\n \"url\": \"https://api.github.com/repos/huggingface/datasets/labels/speech\",\n \"name\": \"speech\",\n \"color\": \"d93f0b\",\n \"default\": false,\n \"description\": \"\"\n }\n]"},"state":{"kind":"string","value":"closed"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["Available here: ~https://huggingface.co/datasets/ami~ https://huggingface.co/datasets/edinburghcstr/ami","@mariosasko actually the \"official\" AMI dataset can be found here: https://huggingface.co/datasets/edinburghcstr/ami -> the old one under `datasets/ami` doesn't work and should be deleted. \r\n\r\nThe new one was tested by fine-tuning a Wav2Vec2 model on it + we uploaded all the processed audio directly into it","@patrickvonplaten Thanks for correcting me! I've updated the link."],"string":"[\n \"Available here: ~https://huggingface.co/datasets/ami~ https://huggingface.co/datasets/edinburghcstr/ami\",\n \"@mariosasko actually the \\\"official\\\" AMI dataset can be found here: https://huggingface.co/datasets/edinburghcstr/ami -> the old one under `datasets/ami` doesn't work and should be deleted. \\r\\n\\r\\nThe new one was tested by fine-tuning a Wav2Vec2 model on it + we uploaded all the processed audio directly into it\",\n \"@patrickvonplaten Thanks for correcting me! I've updated the link.\"\n]"},"created_at":{"kind":"timestamp","value":"2021-02-08T13:25:00","string":"2021-02-08T13:25:00"},"updated_at":{"kind":"timestamp","value":"2023-02-28T16:29:22","string":"2023-02-28T16:29:22"},"closed_at":{"kind":"timestamp","value":"2023-02-28T16:29:22","string":"2023-02-28T16:29:22"},"author_association":{"kind":"string","value":"CONTRIBUTOR"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"## Adding a Dataset\r\n- **Name:** *AMI*\r\n- **Description:** *The AMI Meeting Corpus is a multi-modal data set consisting of 100 hours of meeting recordings. For a gentle introduction to the corpus, see the corpus overview. To access the data, follow the directions given there. Around two-thirds of the data has been elicited using a scenario in which the participants play different roles in a design team, taking a design project from kick-off to completion over the course of a day. The rest consists of naturally occurring meetings in a range of domains. Detailed information can be found in the documentation section.*\r\n\r\n- **Paper:** *Homepage*: http://groups.inf.ed.ac.uk/ami/corpus/\r\n- **Data:** *http://groups.inf.ed.ac.uk/ami/download/* - Select all cases in 1) and select \"Individual Headsets\" & \"Microphone array\" for 2)\r\n- **Motivation:** Important speech dataset\r\n\r\n\r\nIf interested in tackling this issue, feel free to tag @patrickvonplaten\r\n\r\n\r\nInstructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).\r\n"},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1842/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 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},\n {\n \"id\": 2725241052,\n \"node_id\": \"MDU6TGFiZWwyNzI1MjQxMDUy\",\n \"url\": \"https://api.github.com/repos/huggingface/datasets/labels/speech\",\n \"name\": \"speech\",\n \"color\": \"d93f0b\",\n \"default\": false,\n \"description\": \"\"\n }\n]"},"state":{"kind":"string","value":"closed"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":[],"string":"[]"},"created_at":{"kind":"timestamp","value":"2021-02-08T13:22:26","string":"2021-02-08T13:22:26"},"updated_at":{"kind":"timestamp","value":"2021-03-15T05:59:02","string":"2021-03-15T05:59:02"},"closed_at":{"kind":"timestamp","value":"2021-03-15T05:59:02","string":"2021-03-15T05:59:02"},"author_association":{"kind":"string","value":"CONTRIBUTOR"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"## Adding a Dataset\r\n- **Name:** *ljspeech*\r\n- **Description:** *This is a public domain speech dataset consisting of 13,100 short audio clips of a single speaker reading passages from 7 non-fiction books. A transcription is provided for each clip. Clips vary in length from 1 to 10 seconds and have a total length of approximately 24 hours.\r\n\r\nThe texts were published between 1884 and 1964, and are in the public domain. The audio was recorded in 2016-17 by the LibriVox project and is also in the public domain.)*\r\n- **Paper:** *Homepage*: https://keithito.com/LJ-Speech-Dataset/\r\n- **Data:** *https://keithito.com/LJ-Speech-Dataset/*\r\n- **Motivation:** Important speech dataset\r\n- **TFDatasets Implementation**: https://www.tensorflow.org/datasets/catalog/ljspeech\r\nIf interested in tackling this issue, feel free to tag @patrickvonplaten\r\n\r\n\r\nInstructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).\r\n"},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1841/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 0\n}"},"timeline_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1841/timeline"},"performed_via_github_app":{"kind":"null"},"state_reason":{"kind":"string","value":"completed"}}},{"rowIdx":2228,"cells":{"url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1840"},"repository_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets"},"labels_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1840/labels{/name}"},"comments_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1840/comments"},"events_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1840/events"},"html_url":{"kind":"string","value":"https://github.com/huggingface/datasets/issues/1840"},"id":{"kind":"number","value":803560039,"string":"803,560,039"},"node_id":{"kind":"string","value":"MDU6SXNzdWU4MDM1NjAwMzk="},"number":{"kind":"number","value":1840,"string":"1,840"},"title":{"kind":"string","value":"Add common voice"},"user":{"kind":"string","value":"{\n \"login\": \"patrickvonplaten\",\n \"id\": 23423619,\n \"node_id\": \"MDQ6VXNlcjIzNDIzNjE5\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/23423619?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/patrickvonplaten\",\n \"html_url\": \"https://github.com/patrickvonplaten\",\n \"followers_url\": \"https://api.github.com/users/patrickvonplaten/followers\",\n \"following_url\": \"https://api.github.com/users/patrickvonplaten/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/patrickvonplaten/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/patrickvonplaten/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/patrickvonplaten/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/patrickvonplaten/orgs\",\n \"repos_url\": \"https://api.github.com/users/patrickvonplaten/repos\",\n \"events_url\": \"https://api.github.com/users/patrickvonplaten/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/patrickvonplaten/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n}"},"labels":{"kind":"list like","value":[{"id":2067376369,"node_id":"MDU6TGFiZWwyMDY3Mzc2MzY5","url":"https://api.github.com/repos/huggingface/datasets/labels/dataset%20request","name":"dataset request","color":"e99695","default":false,"description":"Requesting to add a new dataset"},{"id":2725241052,"node_id":"MDU6TGFiZWwyNzI1MjQxMDUy","url":"https://api.github.com/repos/huggingface/datasets/labels/speech","name":"speech","color":"d93f0b","default":false,"description":""}],"string":"[\n {\n \"id\": 2067376369,\n \"node_id\": \"MDU6TGFiZWwyMDY3Mzc2MzY5\",\n \"url\": \"https://api.github.com/repos/huggingface/datasets/labels/dataset%20request\",\n \"name\": \"dataset request\",\n \"color\": \"e99695\",\n \"default\": false,\n \"description\": \"Requesting to add a new dataset\"\n },\n {\n \"id\": 2725241052,\n \"node_id\": \"MDU6TGFiZWwyNzI1MjQxMDUy\",\n \"url\": \"https://api.github.com/repos/huggingface/datasets/labels/speech\",\n \"name\": \"speech\",\n \"color\": \"d93f0b\",\n \"default\": false,\n \"description\": \"\"\n }\n]"},"state":{"kind":"string","value":"closed"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["I have started working on adding this dataset.","Hey @BirgerMoell - awesome that you started working on Common Voice. Common Voice is a bit special since, there is no direct download link to download the data. In these cases we usually consider two options:\r\n\r\n1) Find a hacky solution to extract the download link somehow from the XLM tree of the website \r\n2) If this doesn't work we force the user to download the data himself and add a `\"data_dir\"` as an input parameter. E.g. you can take a look at how it is done for [this](https://github.com/huggingface/datasets/blob/66f2a7eece98d2778bd22bb5034cb7c2376032d4/datasets/arxiv_dataset/arxiv_dataset.py#L66) \r\n\r\nAlso the documentation here: https://huggingface.co/docs/datasets/add_dataset.html?highlight=data_dir#downloading-data-files-and-organizing-splits (especially the \"note\") might be helpful.","Let me know if you have any other questions","I added a Work in Progress pull request (hope that is ok). I've made a card for the dataset and filled out the common_voice.py file with information about the datset (not completely).\r\n\r\nI didn't manage to get the tagging tool working locally on my machine but will look into that later.\r\n\r\nLeft to do.\r\n\r\n- Tag the dataset\r\n- Add missing information and update common_voice.py\r\n\r\nhttps://github.com/huggingface/datasets/pull/1886","Awesome! I left a longer comment on the PR :-)","I saw that this current datasets package holds common voice version 6.1, how to add the new version 7.0 that is already available?","Will me merged next week - we're working on it :-)","Common voice still appears to be a 6.1. Is the plan still to upgrade to 7.0?","We actually already have the code and everything ready to add Common Voice 7.0 to `datasets` but are still waiting for the common voice authors to give us the green light :-) \r\n\r\nAlso gently pinging @phirework and @milupo here","Common Voice 7.0 is available here now: https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0","For anyone else stumbling upon this thread, the 8.0 version is also available now: https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0"],"string":"[\n \"I have started working on adding this dataset.\",\n \"Hey @BirgerMoell - awesome that you started working on Common Voice. Common Voice is a bit special since, there is no direct download link to download the data. In these cases we usually consider two options:\\r\\n\\r\\n1) Find a hacky solution to extract the download link somehow from the XLM tree of the website \\r\\n2) If this doesn't work we force the user to download the data himself and add a `\\\"data_dir\\\"` as an input parameter. E.g. you can take a look at how it is done for [this](https://github.com/huggingface/datasets/blob/66f2a7eece98d2778bd22bb5034cb7c2376032d4/datasets/arxiv_dataset/arxiv_dataset.py#L66) \\r\\n\\r\\nAlso the documentation here: https://huggingface.co/docs/datasets/add_dataset.html?highlight=data_dir#downloading-data-files-and-organizing-splits (especially the \\\"note\\\") might be helpful.\",\n \"Let me know if you have any other questions\",\n \"I added a Work in Progress pull request (hope that is ok). I've made a card for the dataset and filled out the common_voice.py file with information about the datset (not completely).\\r\\n\\r\\nI didn't manage to get the tagging tool working locally on my machine but will look into that later.\\r\\n\\r\\nLeft to do.\\r\\n\\r\\n- Tag the dataset\\r\\n- Add missing information and update common_voice.py\\r\\n\\r\\nhttps://github.com/huggingface/datasets/pull/1886\",\n \"Awesome! I left a longer comment on the PR :-)\",\n \"I saw that this current datasets package holds common voice version 6.1, how to add the new version 7.0 that is already available?\",\n \"Will me merged next week - we're working on it :-)\",\n \"Common voice still appears to be a 6.1. Is the plan still to upgrade to 7.0?\",\n \"We actually already have the code and everything ready to add Common Voice 7.0 to `datasets` but are still waiting for the common voice authors to give us the green light :-) \\r\\n\\r\\nAlso gently pinging @phirework and @milupo here\",\n \"Common Voice 7.0 is available here now: https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0\",\n \"For anyone else stumbling upon this thread, the 8.0 version is also available now: https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0\"\n]"},"created_at":{"kind":"timestamp","value":"2021-02-08T13:21:05","string":"2021-02-08T13:21:05"},"updated_at":{"kind":"timestamp","value":"2022-03-20T15:23:40","string":"2022-03-20T15:23:40"},"closed_at":{"kind":"timestamp","value":"2021-03-15T05:56:21","string":"2021-03-15T05:56:21"},"author_association":{"kind":"string","value":"CONTRIBUTOR"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"## Adding a Dataset\r\n- **Name:** *common voice*\r\n- **Description:** *Mozilla Common Voice Dataset*\r\n- **Paper:** Homepage: https://voice.mozilla.org/en/datasets\r\n- **Data:** https://voice.mozilla.org/en/datasets\r\n- **Motivation:** Important speech dataset\r\n- **TFDatasets Implementation**: https://www.tensorflow.org/datasets/catalog/common_voice\r\nIf interested in tackling this issue, feel free to tag @patrickvonplaten\r\n\r\nInstructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).\r\n"},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1840/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 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Voxforge"},"user":{"kind":"string","value":"{\n \"login\": \"patrickvonplaten\",\n \"id\": 23423619,\n \"node_id\": \"MDQ6VXNlcjIzNDIzNjE5\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/23423619?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/patrickvonplaten\",\n \"html_url\": \"https://github.com/patrickvonplaten\",\n \"followers_url\": \"https://api.github.com/users/patrickvonplaten/followers\",\n \"following_url\": \"https://api.github.com/users/patrickvonplaten/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/patrickvonplaten/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/patrickvonplaten/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/patrickvonplaten/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/patrickvonplaten/orgs\",\n \"repos_url\": \"https://api.github.com/users/patrickvonplaten/repos\",\n \"events_url\": \"https://api.github.com/users/patrickvonplaten/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/patrickvonplaten/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n}"},"labels":{"kind":"list like","value":[{"id":2067376369,"node_id":"MDU6TGFiZWwyMDY3Mzc2MzY5","url":"https://api.github.com/repos/huggingface/datasets/labels/dataset%20request","name":"dataset request","color":"e99695","default":false,"description":"Requesting to add a new dataset"},{"id":2725241052,"node_id":"MDU6TGFiZWwyNzI1MjQxMDUy","url":"https://api.github.com/repos/huggingface/datasets/labels/speech","name":"speech","color":"d93f0b","default":false,"description":""}],"string":"[\n {\n \"id\": 2067376369,\n \"node_id\": \"MDU6TGFiZWwyMDY3Mzc2MzY5\",\n \"url\": \"https://api.github.com/repos/huggingface/datasets/labels/dataset%20request\",\n \"name\": \"dataset request\",\n \"color\": \"e99695\",\n \"default\": false,\n \"description\": \"Requesting to add a new dataset\"\n },\n {\n \"id\": 2725241052,\n \"node_id\": \"MDU6TGFiZWwyNzI1MjQxMDUy\",\n \"url\": \"https://api.github.com/repos/huggingface/datasets/labels/speech\",\n \"name\": \"speech\",\n \"color\": \"d93f0b\",\n \"default\": false,\n \"description\": \"\"\n }\n]"},"state":{"kind":"string","value":"open"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":[],"string":"[]"},"created_at":{"kind":"timestamp","value":"2021-02-08T13:19:56","string":"2021-02-08T13:19:56"},"updated_at":{"kind":"timestamp","value":"2021-02-08T13:28:31","string":"2021-02-08T13:28:31"},"closed_at":{"kind":"null"},"author_association":{"kind":"string","value":"CONTRIBUTOR"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"## Adding a Dataset\r\n- **Name:** *voxforge* \r\n- **Description:** *VoxForge is a language classification dataset. It consists of user submitted audio clips submitted to the website. In this release, data from 6 languages is collected - English, Spanish, French, German, Russian, and Italian. Since the website is constantly updated, and for the sake of reproducibility, this release contains only recordings submitted prior to 2020-01-01. The samples are splitted between train, validation and testing so that samples from each speaker belongs to exactly one split.*\r\n- **Paper:** *Homepage*: http://www.voxforge.org/\r\n- **Data:** *http://www.voxforge.org/home/downloads*\r\n- **Motivation:** Important speech dataset\r\n- **TFDatasets Implementation**: https://www.tensorflow.org/datasets/catalog/voxforge\r\nIf interested in tackling this issue, feel free to tag @patrickvonplaten\r\n\r\nInstructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).\r\n"},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1839/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 0\n}"},"timeline_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1839/timeline"},"performed_via_github_app":{"kind":"null"},"state_reason":{"kind":"null"}}},{"rowIdx":2230,"cells":{"url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1838"},"repository_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets"},"labels_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1838/labels{/name}"},"comments_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1838/comments"},"events_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1838/events"},"html_url":{"kind":"string","value":"https://github.com/huggingface/datasets/issues/1838"},"id":{"kind":"number","value":803557521,"string":"803,557,521"},"node_id":{"kind":"string","value":"MDU6SXNzdWU4MDM1NTc1MjE="},"number":{"kind":"number","value":1838,"string":"1,838"},"title":{"kind":"string","value":"Add tedlium"},"user":{"kind":"string","value":"{\n \"login\": \"patrickvonplaten\",\n \"id\": 23423619,\n \"node_id\": \"MDQ6VXNlcjIzNDIzNjE5\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/23423619?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/patrickvonplaten\",\n \"html_url\": \"https://github.com/patrickvonplaten\",\n \"followers_url\": \"https://api.github.com/users/patrickvonplaten/followers\",\n \"following_url\": \"https://api.github.com/users/patrickvonplaten/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/patrickvonplaten/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/patrickvonplaten/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/patrickvonplaten/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/patrickvonplaten/orgs\",\n \"repos_url\": \"https://api.github.com/users/patrickvonplaten/repos\",\n \"events_url\": \"https://api.github.com/users/patrickvonplaten/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/patrickvonplaten/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n}"},"labels":{"kind":"list like","value":[{"id":2067376369,"node_id":"MDU6TGFiZWwyMDY3Mzc2MzY5","url":"https://api.github.com/repos/huggingface/datasets/labels/dataset%20request","name":"dataset request","color":"e99695","default":false,"description":"Requesting to add a new dataset"},{"id":2725241052,"node_id":"MDU6TGFiZWwyNzI1MjQxMDUy","url":"https://api.github.com/repos/huggingface/datasets/labels/speech","name":"speech","color":"d93f0b","default":false,"description":""}],"string":"[\n {\n \"id\": 2067376369,\n \"node_id\": \"MDU6TGFiZWwyMDY3Mzc2MzY5\",\n \"url\": \"https://api.github.com/repos/huggingface/datasets/labels/dataset%20request\",\n \"name\": \"dataset request\",\n \"color\": \"e99695\",\n \"default\": false,\n \"description\": \"Requesting to add a new dataset\"\n },\n {\n \"id\": 2725241052,\n \"node_id\": \"MDU6TGFiZWwyNzI1MjQxMDUy\",\n \"url\": \"https://api.github.com/repos/huggingface/datasets/labels/speech\",\n \"name\": \"speech\",\n \"color\": \"d93f0b\",\n \"default\": false,\n \"description\": \"\"\n }\n]"},"state":{"kind":"string","value":"closed"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["Hi @patrickvonplaten \r\nI can have a look to this dataset later since I am trying to add the OpenSLR dataset https://github.com/huggingface/datasets/pull/2173\r\nHopefully I have enough space since the compressed file is 21GB. The release 3 is even bigger: 54GB :-0","Resolved via https://github.com/huggingface/datasets/pull/4309"],"string":"[\n \"Hi @patrickvonplaten \\r\\nI can have a look to this dataset later since I am trying to add the OpenSLR dataset https://github.com/huggingface/datasets/pull/2173\\r\\nHopefully I have enough space since the compressed file is 21GB. The release 3 is even bigger: 54GB :-0\",\n \"Resolved via https://github.com/huggingface/datasets/pull/4309\"\n]"},"created_at":{"kind":"timestamp","value":"2021-02-08T13:17:52","string":"2021-02-08T13:17:52"},"updated_at":{"kind":"timestamp","value":"2022-10-04T14:34:12","string":"2022-10-04T14:34:12"},"closed_at":{"kind":"timestamp","value":"2022-10-04T14:34:12","string":"2022-10-04T14:34:12"},"author_association":{"kind":"string","value":"CONTRIBUTOR"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"## Adding a Dataset\r\n- **Name:** *tedlium*\r\n- **Description:** *The TED-LIUM 1-3 corpus is English-language TED talks, with transcriptions, sampled at 16kHz. It contains about 118 hours of speech.*\r\n- **Paper:** Homepage: http://www.openslr.org/7/, https://lium.univ-lemans.fr/en/ted-lium2/ &, https://www.openslr.org/51/\r\n- **Data:** http://www.openslr.org/7/\r\n- **Motivation:** Important speech dataset\r\n- **TFDatasets Implementation**: https://www.tensorflow.org/datasets/catalog/tedlium\r\nIf interested in tackling this issue, feel free to tag @patrickvonplaten\r\n\r\nInstructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).\r\n"},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1838/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 0\n}"},"timeline_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1838/timeline"},"performed_via_github_app":{"kind":"null"},"state_reason":{"kind":"string","value":"completed"}}},{"rowIdx":2231,"cells":{"url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1837"},"repository_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets"},"labels_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1837/labels{/name}"},"comments_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1837/comments"},"events_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1837/events"},"html_url":{"kind":"string","value":"https://github.com/huggingface/datasets/issues/1837"},"id":{"kind":"number","value":803555650,"string":"803,555,650"},"node_id":{"kind":"string","value":"MDU6SXNzdWU4MDM1NTU2NTA="},"number":{"kind":"number","value":1837,"string":"1,837"},"title":{"kind":"string","value":"Add VCTK"},"user":{"kind":"string","value":"{\n \"login\": \"patrickvonplaten\",\n \"id\": 23423619,\n \"node_id\": \"MDQ6VXNlcjIzNDIzNjE5\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/23423619?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/patrickvonplaten\",\n \"html_url\": \"https://github.com/patrickvonplaten\",\n \"followers_url\": \"https://api.github.com/users/patrickvonplaten/followers\",\n \"following_url\": \"https://api.github.com/users/patrickvonplaten/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/patrickvonplaten/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/patrickvonplaten/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/patrickvonplaten/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/patrickvonplaten/orgs\",\n \"repos_url\": \"https://api.github.com/users/patrickvonplaten/repos\",\n \"events_url\": \"https://api.github.com/users/patrickvonplaten/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/patrickvonplaten/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n}"},"labels":{"kind":"list like","value":[{"id":2067376369,"node_id":"MDU6TGFiZWwyMDY3Mzc2MzY5","url":"https://api.github.com/repos/huggingface/datasets/labels/dataset%20request","name":"dataset request","color":"e99695","default":false,"description":"Requesting to add a new dataset"},{"id":2725241052,"node_id":"MDU6TGFiZWwyNzI1MjQxMDUy","url":"https://api.github.com/repos/huggingface/datasets/labels/speech","name":"speech","color":"d93f0b","default":false,"description":""}],"string":"[\n {\n \"id\": 2067376369,\n \"node_id\": \"MDU6TGFiZWwyMDY3Mzc2MzY5\",\n \"url\": \"https://api.github.com/repos/huggingface/datasets/labels/dataset%20request\",\n \"name\": \"dataset request\",\n \"color\": \"e99695\",\n \"default\": false,\n \"description\": \"Requesting to add a new dataset\"\n },\n {\n \"id\": 2725241052,\n \"node_id\": \"MDU6TGFiZWwyNzI1MjQxMDUy\",\n \"url\": \"https://api.github.com/repos/huggingface/datasets/labels/speech\",\n \"name\": \"speech\",\n \"color\": \"d93f0b\",\n \"default\": false,\n \"description\": \"\"\n }\n]"},"state":{"kind":"string","value":"closed"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["@patrickvonplaten I'd like to take this, if nobody has already done it. I have added datasets before through the datasets sprint, but I feel rusty on the details, so I'll look at the guide as well as similar audio PRs (#1878 in particular comes to mind). If there is any detail I should be aware of please, let me know! Otherwise, I'll try to write up a PR in the coming days.","That sounds great @jaketae - let me know if you need any help i.e. feel free to ping me on a first PR :-)"],"string":"[\n \"@patrickvonplaten I'd like to take this, if nobody has already done it. I have added datasets before through the datasets sprint, but I feel rusty on the details, so I'll look at the guide as well as similar audio PRs (#1878 in particular comes to mind). If there is any detail I should be aware of please, let me know! Otherwise, I'll try to write up a PR in the coming days.\",\n \"That sounds great @jaketae - let me know if you need any help i.e. feel free to ping me on a first PR :-)\"\n]"},"created_at":{"kind":"timestamp","value":"2021-02-08T13:15:28","string":"2021-02-08T13:15:28"},"updated_at":{"kind":"timestamp","value":"2021-12-28T15:05:08","string":"2021-12-28T15:05:08"},"closed_at":{"kind":"timestamp","value":"2021-12-28T15:05:08","string":"2021-12-28T15:05:08"},"author_association":{"kind":"string","value":"CONTRIBUTOR"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"## Adding a Dataset\r\n- **Name:** *VCTK*\r\n- **Description:** *This CSTR VCTK Corpus includes speech data uttered by 110 English speakers with various accents. Each speaker reads out about 400 sentences, which were selected from a newspaper, the rainbow passage and an elicitation paragraph used for the speech accent archive.*\r\n- **Paper:** Homepage: https://datashare.ed.ac.uk/handle/10283/3443\r\n- **Data:** https://datashare.ed.ac.uk/handle/10283/3443\r\n- **Motivation:** Important speech dataset\r\n- **TFDatasets Implementation**: https://www.tensorflow.org/datasets/catalog/vctk\r\n\r\nIf interested in tackling this issue, feel free to tag @patrickvonplaten\r\n\r\nInstructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).\r\n"},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1837/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 0\n}"},"timeline_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1837/timeline"},"performed_via_github_app":{"kind":"null"},"state_reason":{"kind":"string","value":"completed"}}},{"rowIdx":2232,"cells":{"url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1836"},"repository_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets"},"labels_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1836/labels{/name}"},"comments_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1836/comments"},"events_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1836/events"},"html_url":{"kind":"string","value":"https://github.com/huggingface/datasets/issues/1836"},"id":{"kind":"number","value":803531837,"string":"803,531,837"},"node_id":{"kind":"string","value":"MDU6SXNzdWU4MDM1MzE4Mzc="},"number":{"kind":"number","value":1836,"string":"1,836"},"title":{"kind":"string","value":"test.json has been removed from the limit dataset repo (breaks dataset)"},"user":{"kind":"string","value":"{\n \"login\": \"Paethon\",\n \"id\": 237550,\n \"node_id\": \"MDQ6VXNlcjIzNzU1MA==\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/237550?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/Paethon\",\n \"html_url\": \"https://github.com/Paethon\",\n \"followers_url\": \"https://api.github.com/users/Paethon/followers\",\n \"following_url\": \"https://api.github.com/users/Paethon/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/Paethon/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/Paethon/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/Paethon/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/Paethon/orgs\",\n \"repos_url\": \"https://api.github.com/users/Paethon/repos\",\n \"events_url\": \"https://api.github.com/users/Paethon/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/Paethon/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n}"},"labels":{"kind":"list like","value":[{"id":2067388877,"node_id":"MDU6TGFiZWwyMDY3Mzg4ODc3","url":"https://api.github.com/repos/huggingface/datasets/labels/dataset%20bug","name":"dataset bug","color":"2edb81","default":false,"description":"A bug in a dataset script provided in the library"}],"string":"[\n {\n \"id\": 2067388877,\n \"node_id\": \"MDU6TGFiZWwyMDY3Mzg4ODc3\",\n \"url\": \"https://api.github.com/repos/huggingface/datasets/labels/dataset%20bug\",\n \"name\": \"dataset bug\",\n \"color\": \"2edb81\",\n \"default\": false,\n \"description\": \"A bug in a dataset script provided in the library\"\n }\n]"},"state":{"kind":"string","value":"closed"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["Thanks for the heads up ! I'm opening a PR to fix that"],"string":"[\n \"Thanks for the heads up ! I'm opening a PR to fix that\"\n]"},"created_at":{"kind":"timestamp","value":"2021-02-08T12:45:53","string":"2021-02-08T12:45:53"},"updated_at":{"kind":"timestamp","value":"2021-02-10T16:14:58","string":"2021-02-10T16:14:58"},"closed_at":{"kind":"timestamp","value":"2021-02-10T16:14:58","string":"2021-02-10T16:14:58"},"author_association":{"kind":"string","value":"NONE"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"https://github.com/huggingface/datasets/blob/16042b233dbff2a7585110134e969204c69322c3/datasets/limit/limit.py#L51\r\n\r\nThe URL is not valid anymore since test.json has been removed in master for some reason. Directly referencing the last commit works:\r\n\r\n`https://raw.githubusercontent.com/ilmgut/limit_dataset/0707d3989cd8848f0f11527c77dcf168fefd2b23/data`"},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1836/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 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CHiME4 dataset"},"user":{"kind":"string","value":"{\n \"login\": \"patrickvonplaten\",\n \"id\": 23423619,\n \"node_id\": \"MDQ6VXNlcjIzNDIzNjE5\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/23423619?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/patrickvonplaten\",\n \"html_url\": \"https://github.com/patrickvonplaten\",\n \"followers_url\": \"https://api.github.com/users/patrickvonplaten/followers\",\n \"following_url\": \"https://api.github.com/users/patrickvonplaten/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/patrickvonplaten/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/patrickvonplaten/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/patrickvonplaten/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/patrickvonplaten/orgs\",\n \"repos_url\": \"https://api.github.com/users/patrickvonplaten/repos\",\n \"events_url\": 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},\n {\n \"id\": 2725241052,\n \"node_id\": \"MDU6TGFiZWwyNzI1MjQxMDUy\",\n \"url\": \"https://api.github.com/repos/huggingface/datasets/labels/speech\",\n \"name\": \"speech\",\n \"color\": \"d93f0b\",\n \"default\": false,\n \"description\": \"\"\n }\n]"},"state":{"kind":"string","value":"open"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["@patrickvonplaten not sure whether it is still needed, but willing to tackle this issue","Hey @patrickvonplaten, I have managed to download the zip on [here]( http://spandh.dcs.shef.ac.uk/chime_challenge/CHiME4/download.html) and successfully uploaded all the files on a hugging face dataset: \r\n\r\nhttps://huggingface.co/datasets/ksbai123/Chime4\r\n\r\nHowever I am getting this error when trying to use the dataset viewer:\r\n\r\n![Screenshot 2023-12-27 at 18 40 59](https://github.com/huggingface/datasets/assets/35923560/a5a9ed3d-8dbd-41c4-b83a-4e80728b1450)\r\n\r\nCan you take a look and let me know if I have missed any files please","@patrickvonplaten ?","Hi @KossaiSbai,\r\n\r\nThanks for your contribution.\r\n\r\nAs the issue is not strictly related to the `datasets` library, but to the specific implementation of the CHiME4 dataset, I have opened an issue in the Discussion tab of the dataset: https://huggingface.co/datasets/ksbai123/Chime4/discussions/2\r\nLet's continue the discussion there!"],"string":"[\n \"@patrickvonplaten not sure whether it is still needed, but willing to tackle this issue\",\n \"Hey @patrickvonplaten, I have managed to download the zip on [here]( http://spandh.dcs.shef.ac.uk/chime_challenge/CHiME4/download.html) and successfully uploaded all the files on a hugging face dataset: \\r\\n\\r\\nhttps://huggingface.co/datasets/ksbai123/Chime4\\r\\n\\r\\nHowever I am getting this error when trying to use the dataset viewer:\\r\\n\\r\\n![Screenshot 2023-12-27 at 18 40 59](https://github.com/huggingface/datasets/assets/35923560/a5a9ed3d-8dbd-41c4-b83a-4e80728b1450)\\r\\n\\r\\nCan you take a look and let me know if I have missed any files please\",\n \"@patrickvonplaten ?\",\n \"Hi @KossaiSbai,\\r\\n\\r\\nThanks for your contribution.\\r\\n\\r\\nAs the issue is not strictly related to the `datasets` library, but to the specific implementation of the CHiME4 dataset, I have opened an issue in the Discussion tab of the dataset: https://huggingface.co/datasets/ksbai123/Chime4/discussions/2\\r\\nLet's continue the discussion there!\"\n]"},"created_at":{"kind":"timestamp","value":"2021-02-08T12:36:38","string":"2021-02-08T12:36:38"},"updated_at":{"kind":"timestamp","value":"2024-02-01T10:25:03","string":"2024-02-01T10:25:03"},"closed_at":{"kind":"null"},"author_association":{"kind":"string","value":"CONTRIBUTOR"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"## Adding a Dataset\r\n- **Name:** Chime4\r\n- **Description:** Chime4 is a dataset for automatic speech recognition. It is especially useful for evaluating models in a noisy environment and for multi-channel ASR\r\n- **Paper:** Dataset comes from a channel: http://spandh.dcs.shef.ac.uk/chime_challenge/CHiME4/ . Results paper: \r\n- **Data:** http://spandh.dcs.shef.ac.uk/chime_challenge/CHiME4/download.html\r\n- **Motivation:** So far there are very little datasets for speech in `datasets`. Only `lbirispeech_asr` so far.\r\n\r\nIf interested in tackling this issue, feel free to tag @patrickvonplaten\r\n\r\nInstructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).\r\n"},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1835/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 0\n}"},"timeline_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1835/timeline"},"performed_via_github_app":{"kind":"null"},"state_reason":{"kind":"null"}}},{"rowIdx":2234,"cells":{"url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1832"},"repository_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets"},"labels_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1832/labels{/name}"},"comments_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1832/comments"},"events_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1832/events"},"html_url":{"kind":"string","value":"https://github.com/huggingface/datasets/issues/1832"},"id":{"kind":"number","value":802880897,"string":"802,880,897"},"node_id":{"kind":"string","value":"MDU6SXNzdWU4MDI4ODA4OTc="},"number":{"kind":"number","value":1832,"string":"1,832"},"title":{"kind":"string","value":"Looks like nokogumbo is up-to-date now, so this is no longer needed."},"user":{"kind":"string","value":"{\n \"login\": \"JimmyJim1\",\n \"id\": 68724553,\n \"node_id\": \"MDQ6VXNlcjY4NzI0NTUz\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/68724553?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/JimmyJim1\",\n \"html_url\": \"https://github.com/JimmyJim1\",\n \"followers_url\": \"https://api.github.com/users/JimmyJim1/followers\",\n \"following_url\": \"https://api.github.com/users/JimmyJim1/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/JimmyJim1/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/JimmyJim1/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/JimmyJim1/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/JimmyJim1/orgs\",\n \"repos_url\": \"https://api.github.com/users/JimmyJim1/repos\",\n \"events_url\": 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nokogumbo is up-to-date now, so this is no longer needed.\n\n__Originally posted by @dependabot in https://github.com/discourse/discourse/pull/11373#issuecomment-738993432__"},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1832/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 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question about raw dataset download info in the project ."},"user":{"kind":"string","value":"{\n \"login\": \"svjack\",\n \"id\": 27874014,\n \"node_id\": \"MDQ6VXNlcjI3ODc0MDE0\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/27874014?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/svjack\",\n \"html_url\": \"https://github.com/svjack\",\n \"followers_url\": \"https://api.github.com/users/svjack/followers\",\n \"following_url\": \"https://api.github.com/users/svjack/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/svjack/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/svjack/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/svjack/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/svjack/orgs\",\n \"repos_url\": \"https://api.github.com/users/svjack/repos\",\n \"events_url\": \"https://api.github.com/users/svjack/events{/privacy}\",\n 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{\n \"login\": \"albertvillanova\",\n \"id\": 8515462,\n \"node_id\": \"MDQ6VXNlcjg1MTU0NjI=\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/8515462?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/albertvillanova\",\n \"html_url\": \"https://github.com/albertvillanova\",\n \"followers_url\": \"https://api.github.com/users/albertvillanova/followers\",\n \"following_url\": \"https://api.github.com/users/albertvillanova/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/albertvillanova/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/albertvillanova/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/albertvillanova/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/albertvillanova/orgs\",\n \"repos_url\": \"https://api.github.com/users/albertvillanova/repos\",\n \"events_url\": \"https://api.github.com/users/albertvillanova/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/albertvillanova/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n }\n]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["Hi ! The `dl_manager` is a `DownloadManager` object and is responsible for downloading the raw data files.\r\nIt is used by dataset builders in their `_split_generators` method to download the raw data files that are necessary to build the datasets splits.\r\n\r\nThe `Conll2003` class is a dataset builder, and so you can download all the raw data files by calling `_split_generators` with a download manager:\r\n```python\r\nfrom datasets import DownloadManager\r\nfrom datasets.load import import_main_class\r\n\r\nconll2003_builder = import_main_class(...)\r\n\r\ndl_manager = DownloadManager()\r\nsplis_generators = conll2003_builder._split_generators(dl_manager)\r\n```\r\n\r\nThen you can see what files have been downloaded with\r\n```python\r\ndl_manager.get_recorded_sizes_checksums()\r\n```\r\nIt returns a dictionary with the format {url: {num_bytes: int, checksum: str}}\r\n\r\nThen you can get the actual location of the downloaded files with\r\n```python\r\nfrom datasets import cached_path\r\n\r\nlocal_path_to_downloaded_file = cached_path(url)\r\n```\r\n\r\n------------------\r\n\r\nNote that you can also get the urls from the Dataset object:\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nconll2003 = load_dataset(\"conll2003\")\r\nprint(conll2003[\"train\"].download_checksums)\r\n```\r\nIt returns the same dictionary with the format {url: {num_bytes: int, checksum: str}}","I am afraid that there is not a very straightforward way to get that location.\r\n\r\nAnother option, from _split_generators would be to use:\r\n- `dl_manager._download_config.cache_dir` to get the directory where all the raw downloaded files are:\r\n ```python\r\n download_dir = dl_manager._download_config.cache_dir\r\n ```\r\n- the function `datasets.utils.file_utils.hash_url_to_filename` to get the filenames of the raw downloaded files:\r\n ```python\r\n filenames = [hash_url_to_filename(url) for url in urls_to_download.values()]\r\n ```\r\nTherefore the complete path to the raw downloaded files would be the join of both:\r\n```python\r\ndownloaded_paths = [os.path.join(download_dir, filename) for filename in filenames]\r\n```\r\n\r\nMaybe it would be interesting to make these paths accessible more easily. I could work on this. What do you think, @lhoestq ?","Sure it would be nice to have an easier access to these paths !\r\nThe dataset builder could have a method to return those, what do you think ?\r\nFeel free to work on this @albertvillanova , it would be a nice addition :) \r\n\r\nYour suggestion does work as well @albertvillanova if you complete it by specifying `etag=` to `hash_url_to_filename`.\r\n\r\nThe ETag is obtained by a HEAD request and is used to know if the file on the remote host has changed. Therefore if a file is updated on the remote host, then the hash returned by `hash_url_to_filename` is different.","Once #1846 will be merged, the paths to the raw downloaded files will be accessible as:\r\n```python\r\nbuilder_instance.dl_manager.downloaded_paths\r\n``` "],"string":"[\n \"Hi ! The `dl_manager` is a `DownloadManager` object and is responsible for downloading the raw data files.\\r\\nIt is used by dataset builders in their `_split_generators` method to download the raw data files that are necessary to build the datasets splits.\\r\\n\\r\\nThe `Conll2003` class is a dataset builder, and so you can download all the raw data files by calling `_split_generators` with a download manager:\\r\\n```python\\r\\nfrom datasets import DownloadManager\\r\\nfrom datasets.load import import_main_class\\r\\n\\r\\nconll2003_builder = import_main_class(...)\\r\\n\\r\\ndl_manager = DownloadManager()\\r\\nsplis_generators = conll2003_builder._split_generators(dl_manager)\\r\\n```\\r\\n\\r\\nThen you can see what files have been downloaded with\\r\\n```python\\r\\ndl_manager.get_recorded_sizes_checksums()\\r\\n```\\r\\nIt returns a dictionary with the format {url: {num_bytes: int, checksum: str}}\\r\\n\\r\\nThen you can get the actual location of the downloaded files with\\r\\n```python\\r\\nfrom datasets import cached_path\\r\\n\\r\\nlocal_path_to_downloaded_file = cached_path(url)\\r\\n```\\r\\n\\r\\n------------------\\r\\n\\r\\nNote that you can also get the urls from the Dataset object:\\r\\n```python\\r\\nfrom datasets import load_dataset\\r\\n\\r\\nconll2003 = load_dataset(\\\"conll2003\\\")\\r\\nprint(conll2003[\\\"train\\\"].download_checksums)\\r\\n```\\r\\nIt returns the same dictionary with the format {url: {num_bytes: int, checksum: str}}\",\n \"I am afraid that there is not a very straightforward way to get that location.\\r\\n\\r\\nAnother option, from _split_generators would be to use:\\r\\n- `dl_manager._download_config.cache_dir` to get the directory where all the raw downloaded files are:\\r\\n ```python\\r\\n download_dir = dl_manager._download_config.cache_dir\\r\\n ```\\r\\n- the function `datasets.utils.file_utils.hash_url_to_filename` to get the filenames of the raw downloaded files:\\r\\n ```python\\r\\n filenames = [hash_url_to_filename(url) for url in urls_to_download.values()]\\r\\n ```\\r\\nTherefore the complete path to the raw downloaded files would be the join of both:\\r\\n```python\\r\\ndownloaded_paths = [os.path.join(download_dir, filename) for filename in filenames]\\r\\n```\\r\\n\\r\\nMaybe it would be interesting to make these paths accessible more easily. I could work on this. What do you think, @lhoestq ?\",\n \"Sure it would be nice to have an easier access to these paths !\\r\\nThe dataset builder could have a method to return those, what do you think ?\\r\\nFeel free to work on this @albertvillanova , it would be a nice addition :) \\r\\n\\r\\nYour suggestion does work as well @albertvillanova if you complete it by specifying `etag=` to `hash_url_to_filename`.\\r\\n\\r\\nThe ETag is obtained by a HEAD request and is used to know if the file on the remote host has changed. Therefore if a file is updated on the remote host, then the hash returned by `hash_url_to_filename` is different.\",\n \"Once #1846 will be merged, the paths to the raw downloaded files will be accessible as:\\r\\n```python\\r\\nbuilder_instance.dl_manager.downloaded_paths\\r\\n``` \"\n]"},"created_at":{"kind":"timestamp","value":"2021-02-07T05:33:36","string":"2021-02-07T05:33:36"},"updated_at":{"kind":"timestamp","value":"2021-02-25T14:10:18","string":"2021-02-25T14:10:18"},"closed_at":{"kind":"timestamp","value":"2021-02-25T14:10:18","string":"2021-02-25T14:10:18"},"author_association":{"kind":"string","value":"NONE"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"Hi , i review the code in \r\nhttps://github.com/huggingface/datasets/blob/master/datasets/conll2003/conll2003.py\r\nin the _split_generators function is the truly logic of download raw datasets with dl_manager\r\nand use Conll2003 cls by use import_main_class in load_dataset function\r\nMy question is that , with this logic it seems that i can not have the raw dataset download location\r\nin variable in downloaded_files in _split_generators.\r\nIf someone also want use huggingface datasets as raw dataset downloader,\r\nhow can he retrieve the raw dataset download path from attributes in \r\ndatasets.dataset_dict.DatasetDict ?"},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1831/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 0\n}"},"timeline_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1831/timeline"},"performed_via_github_app":{"kind":"null"},"state_reason":{"kind":"string","value":"completed"}}},{"rowIdx":2236,"cells":{"url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1830"},"repository_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets"},"labels_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1830/labels{/name}"},"comments_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1830/comments"},"events_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1830/events"},"html_url":{"kind":"string","value":"https://github.com/huggingface/datasets/issues/1830"},"id":{"kind":"number","value":802790075,"string":"802,790,075"},"node_id":{"kind":"string","value":"MDU6SXNzdWU4MDI3OTAwNzU="},"number":{"kind":"number","value":1830,"string":"1,830"},"title":{"kind":"string","value":"using map on loaded Tokenizer 10x - 100x slower than default Tokenizer?"},"user":{"kind":"string","value":"{\n \"login\": \"wumpusman\",\n \"id\": 7662740,\n \"node_id\": \"MDQ6VXNlcjc2NjI3NDA=\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/7662740?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/wumpusman\",\n \"html_url\": \"https://github.com/wumpusman\",\n \"followers_url\": \"https://api.github.com/users/wumpusman/followers\",\n \"following_url\": \"https://api.github.com/users/wumpusman/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/wumpusman/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/wumpusman/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/wumpusman/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/wumpusman/orgs\",\n \"repos_url\": \"https://api.github.com/users/wumpusman/repos\",\n \"events_url\": \"https://api.github.com/users/wumpusman/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/wumpusman/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n}"},"labels":{"kind":"list like","value":[],"string":"[]"},"state":{"kind":"string","value":"open"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["Hi @wumpusman \r\n`datasets` has a caching mechanism that allows to cache the results of `.map` so that when you want to re-run it later it doesn't recompute it again.\r\nSo when you do `.map`, what actually happens is:\r\n1. compute the hash used to identify your `map` for the cache\r\n2. apply your function on every batch\r\n\r\nThis can explain the time difference between your different experiments.\r\n\r\nThe hash computation time depends of how complex your function is. For a tokenizer, the hash computation scans the lists of the words in the tokenizer to identify this tokenizer. Usually it takes 2-3 seconds.\r\n\r\nAlso note that you can disable caching though using\r\n```python\r\nimport datasets\r\n\r\ndatasets.set_caching_enabled(False)\r\n```","Hi @lhoestq ,\r\n\r\nThanks for the reply. It's entirely possible that is the issue. Since it's a side project I won't be looking at it till later this week, but, I'll verify it by disabling caching and hopefully I'll see the same runtime. \r\n\r\nAppreciate the reference,\r\n\r\nMichael","I believe this is an actual issue, tokenizing a ~4GB txt file went from an hour and a half to ~10 minutes when I switched from my pre-trained tokenizer(on the same dataset) to the default gpt2 tokenizer.\r\nBoth were loaded using:\r\n```\r\nAutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)\r\n```\r\nI trained the tokenizer using ByteLevelBPETokenizer from the Tokenizers library and save it to a tokenizer.json file.\r\n\r\nI have tested the caching ideas above, changing the number of process, the TOKENIZERS_PARALLELISM env variable, keep_in_memory=True and batching with different sizes.\r\n\r\nApologies I can't really upload much code, but wanted to back up the finding and hopefully a fix/the problem can be found.\r\nI will comment back if I find a fix as well.","Hi @johncookds do you think this can come from one tokenizer being faster than the other one ? Can you try to compare their speed without using `datasets` just to make sure ?","Hi yes, I'm closing the loop here with some timings below. The issue seems to be at least somewhat/mainly with the tokenizer's themselves. Moreover legacy saves of the trainer tokenizer perform faster but differently than the new tokenizer.json saves(note nothing about the training process/adding of special tokens changed between the top two trained tokenizer tests, only the way it was saved). This is only a 3x slowdown vs like a 10x but I think the slowdown is most likely due to this.\r\n\r\n```\r\ntrained tokenizer - tokenizer.json save (same results for AutoTokenizer legacy_format=False):\r\nTokenizer time(seconds): 0.32767510414123535\r\nTokenized avg. length: 323.01\r\n\r\ntrained tokenizer - AutoTokenizer legacy_format=True:\r\nTokenizer time(seconds): 0.09258866310119629\r\nTokenized avg. length: 301.01\r\n\r\nGPT2 Tokenizer from huggingface\r\nTokenizer time(seconds): 0.1010282039642334\r\nTokenized avg. length: 461.21\r\n```","@lhoestq ,\r\n\r\nHi, which version of datasets has datasets.set_caching_enabled(False)? I get \r\nmodule 'datasets' has no attribute 'set_caching_enabled'. To hopefully get around this, I reran my code on a new set of data, and did so only once.\r\n\r\n@johncookds , thanks for chiming in, it looks this might be an issue of Tokenizer.\r\n\r\n**Tokenizer**: The runtime of GPT2TokenizerFast.from_pretrained(\"gpt2\") on 1000 chars is: **143 ms**\r\n**SlowTokenizer**: The runtime of a locally saved and loaded Tokenizer using the same vocab on 1000 chars is: **4.43 s**\r\n\r\nThat being said, I compared performance on the map function:\r\n\r\nRunning Tokenizer versus using it in the map function for 1000 chars goes from **141 ms** to **356 ms** \r\nRunning SlowTokenizer versus using it in the map function for 1000 chars with a single element goes from **4.43 s** to **9.76 s**\r\n\r\nI'm trying to figure out why the overhead of map would increase the time by double (figured it would be a fixed increase in time)? Though maybe this is expected behavior.\r\n\r\n@lhoestq, do you by chance know how I can redirect this issue to Tokenizer?\r\n\r\nRegards,\r\n\r\nMichael","Thanks for the experiments @johncookds and @wumpusman ! \r\n\r\n> Hi, which version of datasets has datasets.set_caching_enabled(False)?\r\n\r\nCurrently you have to install `datasets` from source to have this feature, but this will be available in the next release in a few days.\r\n\r\n> I'm trying to figure out why the overhead of map would increase the time by double (figured it would be a fixed increase in time)? Though maybe this is expected behavior.\r\n\r\nCould you also try with double the number of characters ? This should let us have an idea of the fixed cost (hashing) and the dynamic cost (actual tokenization, grows with the size of the input)\r\n\r\n> @lhoestq, do you by chance know how I can redirect this issue to Tokenizer?\r\n\r\nFeel free to post an issue on the `transformers` repo. Also I'm sure there should be related issues so you can also look for someone with the same concerns on the `transformers` repo.","@lhoestq,\r\n\r\nI just checked that previous run time was actually 3000 chars. I increased it to 6k chars, again, roughly double.\r\n\r\nSlowTokenizer **7.4 s** to **15.7 s**\r\nTokenizer: **276 ms** to **616 ms**\r\n\r\nI'll post this issue on Tokenizer, seems it hasn't quite been raised (albeit I noticed a similar issue that might relate).\r\n\r\nRegards,\r\n\r\nMichael","Hi, \r\nI'm following up here as I found my exact issue. It was with saving and re-loading the tokenizer. When I trained then processed the data without saving and reloading it, it was 10x-100x faster than when I saved and re-loaded it.\r\nBoth resulted in the exact same tokenized datasets as well. \r\nThere is additionally a bug where the older legacy tokenizer save does not preserve a learned tokenizing behavior if trained from scratch.\r\nUnderstand its not exactly Datasets related but hope it can help someone if they have the same issue.\r\nThanks!"],"string":"[\n \"Hi @wumpusman \\r\\n`datasets` has a caching mechanism that allows to cache the results of `.map` so that when you want to re-run it later it doesn't recompute it again.\\r\\nSo when you do `.map`, what actually happens is:\\r\\n1. compute the hash used to identify your `map` for the cache\\r\\n2. apply your function on every batch\\r\\n\\r\\nThis can explain the time difference between your different experiments.\\r\\n\\r\\nThe hash computation time depends of how complex your function is. For a tokenizer, the hash computation scans the lists of the words in the tokenizer to identify this tokenizer. Usually it takes 2-3 seconds.\\r\\n\\r\\nAlso note that you can disable caching though using\\r\\n```python\\r\\nimport datasets\\r\\n\\r\\ndatasets.set_caching_enabled(False)\\r\\n```\",\n \"Hi @lhoestq ,\\r\\n\\r\\nThanks for the reply. It's entirely possible that is the issue. Since it's a side project I won't be looking at it till later this week, but, I'll verify it by disabling caching and hopefully I'll see the same runtime. \\r\\n\\r\\nAppreciate the reference,\\r\\n\\r\\nMichael\",\n \"I believe this is an actual issue, tokenizing a ~4GB txt file went from an hour and a half to ~10 minutes when I switched from my pre-trained tokenizer(on the same dataset) to the default gpt2 tokenizer.\\r\\nBoth were loaded using:\\r\\n```\\r\\nAutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)\\r\\n```\\r\\nI trained the tokenizer using ByteLevelBPETokenizer from the Tokenizers library and save it to a tokenizer.json file.\\r\\n\\r\\nI have tested the caching ideas above, changing the number of process, the TOKENIZERS_PARALLELISM env variable, keep_in_memory=True and batching with different sizes.\\r\\n\\r\\nApologies I can't really upload much code, but wanted to back up the finding and hopefully a fix/the problem can be found.\\r\\nI will comment back if I find a fix as well.\",\n \"Hi @johncookds do you think this can come from one tokenizer being faster than the other one ? Can you try to compare their speed without using `datasets` just to make sure ?\",\n \"Hi yes, I'm closing the loop here with some timings below. The issue seems to be at least somewhat/mainly with the tokenizer's themselves. Moreover legacy saves of the trainer tokenizer perform faster but differently than the new tokenizer.json saves(note nothing about the training process/adding of special tokens changed between the top two trained tokenizer tests, only the way it was saved). This is only a 3x slowdown vs like a 10x but I think the slowdown is most likely due to this.\\r\\n\\r\\n```\\r\\ntrained tokenizer - tokenizer.json save (same results for AutoTokenizer legacy_format=False):\\r\\nTokenizer time(seconds): 0.32767510414123535\\r\\nTokenized avg. length: 323.01\\r\\n\\r\\ntrained tokenizer - AutoTokenizer legacy_format=True:\\r\\nTokenizer time(seconds): 0.09258866310119629\\r\\nTokenized avg. length: 301.01\\r\\n\\r\\nGPT2 Tokenizer from huggingface\\r\\nTokenizer time(seconds): 0.1010282039642334\\r\\nTokenized avg. length: 461.21\\r\\n```\",\n \"@lhoestq ,\\r\\n\\r\\nHi, which version of datasets has datasets.set_caching_enabled(False)? I get \\r\\nmodule 'datasets' has no attribute 'set_caching_enabled'. To hopefully get around this, I reran my code on a new set of data, and did so only once.\\r\\n\\r\\n@johncookds , thanks for chiming in, it looks this might be an issue of Tokenizer.\\r\\n\\r\\n**Tokenizer**: The runtime of GPT2TokenizerFast.from_pretrained(\\\"gpt2\\\") on 1000 chars is: **143 ms**\\r\\n**SlowTokenizer**: The runtime of a locally saved and loaded Tokenizer using the same vocab on 1000 chars is: **4.43 s**\\r\\n\\r\\nThat being said, I compared performance on the map function:\\r\\n\\r\\nRunning Tokenizer versus using it in the map function for 1000 chars goes from **141 ms** to **356 ms** \\r\\nRunning SlowTokenizer versus using it in the map function for 1000 chars with a single element goes from **4.43 s** to **9.76 s**\\r\\n\\r\\nI'm trying to figure out why the overhead of map would increase the time by double (figured it would be a fixed increase in time)? Though maybe this is expected behavior.\\r\\n\\r\\n@lhoestq, do you by chance know how I can redirect this issue to Tokenizer?\\r\\n\\r\\nRegards,\\r\\n\\r\\nMichael\",\n \"Thanks for the experiments @johncookds and @wumpusman ! \\r\\n\\r\\n> Hi, which version of datasets has datasets.set_caching_enabled(False)?\\r\\n\\r\\nCurrently you have to install `datasets` from source to have this feature, but this will be available in the next release in a few days.\\r\\n\\r\\n> I'm trying to figure out why the overhead of map would increase the time by double (figured it would be a fixed increase in time)? Though maybe this is expected behavior.\\r\\n\\r\\nCould you also try with double the number of characters ? This should let us have an idea of the fixed cost (hashing) and the dynamic cost (actual tokenization, grows with the size of the input)\\r\\n\\r\\n> @lhoestq, do you by chance know how I can redirect this issue to Tokenizer?\\r\\n\\r\\nFeel free to post an issue on the `transformers` repo. Also I'm sure there should be related issues so you can also look for someone with the same concerns on the `transformers` repo.\",\n \"@lhoestq,\\r\\n\\r\\nI just checked that previous run time was actually 3000 chars. I increased it to 6k chars, again, roughly double.\\r\\n\\r\\nSlowTokenizer **7.4 s** to **15.7 s**\\r\\nTokenizer: **276 ms** to **616 ms**\\r\\n\\r\\nI'll post this issue on Tokenizer, seems it hasn't quite been raised (albeit I noticed a similar issue that might relate).\\r\\n\\r\\nRegards,\\r\\n\\r\\nMichael\",\n \"Hi, \\r\\nI'm following up here as I found my exact issue. It was with saving and re-loading the tokenizer. When I trained then processed the data without saving and reloading it, it was 10x-100x faster than when I saved and re-loaded it.\\r\\nBoth resulted in the exact same tokenized datasets as well. \\r\\nThere is additionally a bug where the older legacy tokenizer save does not preserve a learned tokenizing behavior if trained from scratch.\\r\\nUnderstand its not exactly Datasets related but hope it can help someone if they have the same issue.\\r\\nThanks!\"\n]"},"created_at":{"kind":"timestamp","value":"2021-02-06T21:00:26","string":"2021-02-06T21:00:26"},"updated_at":{"kind":"timestamp","value":"2021-02-24T21:56:14","string":"2021-02-24T21:56:14"},"closed_at":{"kind":"null"},"author_association":{"kind":"string","value":"NONE"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"This could total relate to me misunderstanding particular call functions, but I added words to a GPT2Tokenizer, and saved it to disk (note I'm only showing snippets but I can share more) and the map function ran much slower: \r\n\r\n````\r\ndef save_tokenizer(original_tokenizer,text,path=\"simpledata/tokenizer\"):\r\n words_unique = set(text.split(\" \"))\r\n for i in words_unique:\r\n original_tokenizer.add_tokens(i)\r\n original_tokenizer.save_pretrained(path)\r\n\r\ntokenizer2 = GPT2Tokenizer.from_pretrained(os.path.join(experiment_path,experiment_name,\"tokenizer_squad\"))\r\n\r\ntrain_set_baby=Dataset.from_dict({\"text\":[train_set[\"text\"][0][0:50]]})\r\n````\r\n\r\nI then applied the dataset map function on a fairly small set of text:\r\n\r\n```\r\n%%time\r\ntrain_set_baby = train_set_baby.map(lambda d:tokenizer2(d[\"text\"]),batched=True)\r\n\r\n```\r\n\r\n\r\nThe run time for train_set_baby.map was 6 seconds, and the batch itself was 2.6 seconds\r\n\r\n**100% 1/1 [00:02<00:00, 2.60s/ba] CPU times: user 5.96 s, sys: 36 ms, total: 5.99 s Wall time: 5.99 s**\r\n\r\nIn comparison using (even after adding additional tokens): \r\n`\r\ntokenizer = GPT2TokenizerFast.from_pretrained(\"gpt2\")`\r\n\r\n```\r\n%%time\r\ntrain_set_baby = train_set_baby.map(lambda d:tokenizer2(d[\"text\"]),batched=True)\r\n\r\n```\r\nThe time is \r\n**100% 1/1 [00:00<00:00, 34.09ba/s] CPU times: user 68.1 ms, sys: 16 µs, total: 68.1 ms Wall time: 62.9 ms**\r\n\r\nIt seems this might relate to the tokenizer save or load function, however, the issue appears to come up when I apply the loaded tokenizer to the map function. \r\n\r\nI should also add that playing around with the amount of words I add to the tokenizer before I save it to disk and load it into memory appears to impact the time it takes to run the map function. \r\n\r\n\r\n"},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1830/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 0\n}"},"timeline_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1830/timeline"},"performed_via_github_app":{"kind":"null"},"state_reason":{"kind":"null"}}},{"rowIdx":2237,"cells":{"url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1827"},"repository_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets"},"labels_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1827/labels{/name}"},"comments_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1827/comments"},"events_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1827/events"},"html_url":{"kind":"string","value":"https://github.com/huggingface/datasets/issues/1827"},"id":{"kind":"number","value":802353974,"string":"802,353,974"},"node_id":{"kind":"string","value":"MDU6SXNzdWU4MDIzNTM5NzQ="},"number":{"kind":"number","value":1827,"string":"1,827"},"title":{"kind":"string","value":"Regarding On-the-fly Data Loading"},"user":{"kind":"string","value":"{\n \"login\": \"gchhablani\",\n \"id\": 29076344,\n \"node_id\": \"MDQ6VXNlcjI5MDc2MzQ0\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/29076344?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/gchhablani\",\n \"html_url\": \"https://github.com/gchhablani\",\n \"followers_url\": \"https://api.github.com/users/gchhablani/followers\",\n \"following_url\": \"https://api.github.com/users/gchhablani/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/gchhablani/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/gchhablani/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/gchhablani/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/gchhablani/orgs\",\n \"repos_url\": \"https://api.github.com/users/gchhablani/repos\",\n \"events_url\": \"https://api.github.com/users/gchhablani/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/gchhablani/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n}"},"labels":{"kind":"list like","value":[],"string":"[]"},"state":{"kind":"string","value":"closed"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["Possible duplicate\r\n\r\n#1776 https://github.com/huggingface/datasets/issues/\r\n\r\nreally looking PR for this feature","Hi @acul3 \r\n\r\nIssue #1776 talks about doing on-the-fly data pre-processing, which I think is solved in the next release as mentioned in the issue #1825. I also look forward to using this feature, though :)\r\n\r\nI wanted to ask about on-the-fly data loading from the cache (before pre-processing).","Hi ! Currently when you load a dataset via `load_dataset` for example, then the dataset is memory-mapped from an Arrow file on disk. Therefore there's almost no RAM usage even if your dataset contains TB of data.\r\nUsually at training time only one batch of data at a time is loaded in memory.\r\n\r\nDoes that answer your question or were you thinking about something else ?","Hi @lhoestq,\r\n\r\nI apologize for the late response. This answers my question. Thanks a lot."],"string":"[\n \"Possible duplicate\\r\\n\\r\\n#1776 https://github.com/huggingface/datasets/issues/\\r\\n\\r\\nreally looking PR for this feature\",\n \"Hi @acul3 \\r\\n\\r\\nIssue #1776 talks about doing on-the-fly data pre-processing, which I think is solved in the next release as mentioned in the issue #1825. I also look forward to using this feature, though :)\\r\\n\\r\\nI wanted to ask about on-the-fly data loading from the cache (before pre-processing).\",\n \"Hi ! Currently when you load a dataset via `load_dataset` for example, then the dataset is memory-mapped from an Arrow file on disk. Therefore there's almost no RAM usage even if your dataset contains TB of data.\\r\\nUsually at training time only one batch of data at a time is loaded in memory.\\r\\n\\r\\nDoes that answer your question or were you thinking about something else ?\",\n \"Hi @lhoestq,\\r\\n\\r\\nI apologize for the late response. This answers my question. Thanks a lot.\"\n]"},"created_at":{"kind":"timestamp","value":"2021-02-05T17:43:48","string":"2021-02-05T17:43:48"},"updated_at":{"kind":"timestamp","value":"2021-02-18T13:55:16","string":"2021-02-18T13:55:16"},"closed_at":{"kind":"timestamp","value":"2021-02-18T13:55:16","string":"2021-02-18T13:55:16"},"author_association":{"kind":"string","value":"CONTRIBUTOR"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"Hi,\r\n\r\nI was wondering if it is possible to load images/texts as a batch during the training process, without loading the entire dataset on the RAM at any given point.\r\n\r\nThanks,\r\nGunjan"},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1827/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 0\n}"},"timeline_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1827/timeline"},"performed_via_github_app":{"kind":"null"},"state_reason":{"kind":"string","value":"completed"}}},{"rowIdx":2238,"cells":{"url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1825"},"repository_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets"},"labels_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1825/labels{/name}"},"comments_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1825/comments"},"events_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1825/events"},"html_url":{"kind":"string","value":"https://github.com/huggingface/datasets/issues/1825"},"id":{"kind":"number","value":802073925,"string":"802,073,925"},"node_id":{"kind":"string","value":"MDU6SXNzdWU4MDIwNzM5MjU="},"number":{"kind":"number","value":1825,"string":"1,825"},"title":{"kind":"string","value":"Datasets library not suitable for huge text datasets."},"user":{"kind":"string","value":"{\n \"login\": \"avacaondata\",\n \"id\": 35173563,\n \"node_id\": \"MDQ6VXNlcjM1MTczNTYz\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/35173563?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/avacaondata\",\n \"html_url\": \"https://github.com/avacaondata\",\n \"followers_url\": \"https://api.github.com/users/avacaondata/followers\",\n \"following_url\": \"https://api.github.com/users/avacaondata/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/avacaondata/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/avacaondata/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/avacaondata/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/avacaondata/orgs\",\n \"repos_url\": \"https://api.github.com/users/avacaondata/repos\",\n \"events_url\": 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like","value":[{"login":"albertvillanova","id":8515462,"node_id":"MDQ6VXNlcjg1MTU0NjI=","avatar_url":"https://avatars.githubusercontent.com/u/8515462?v=4","gravatar_id":"","url":"https://api.github.com/users/albertvillanova","html_url":"https://github.com/albertvillanova","followers_url":"https://api.github.com/users/albertvillanova/followers","following_url":"https://api.github.com/users/albertvillanova/following{/other_user}","gists_url":"https://api.github.com/users/albertvillanova/gists{/gist_id}","starred_url":"https://api.github.com/users/albertvillanova/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/albertvillanova/subscriptions","organizations_url":"https://api.github.com/users/albertvillanova/orgs","repos_url":"https://api.github.com/users/albertvillanova/repos","events_url":"https://api.github.com/users/albertvillanova/events{/privacy}","received_events_url":"https://api.github.com/users/albertvillanova/received_events","type":"User","site_admin":false}],"string":"[\n {\n \"login\": \"albertvillanova\",\n \"id\": 8515462,\n \"node_id\": \"MDQ6VXNlcjg1MTU0NjI=\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/8515462?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/albertvillanova\",\n \"html_url\": \"https://github.com/albertvillanova\",\n \"followers_url\": \"https://api.github.com/users/albertvillanova/followers\",\n \"following_url\": \"https://api.github.com/users/albertvillanova/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/albertvillanova/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/albertvillanova/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/albertvillanova/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/albertvillanova/orgs\",\n \"repos_url\": \"https://api.github.com/users/albertvillanova/repos\",\n \"events_url\": \"https://api.github.com/users/albertvillanova/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/albertvillanova/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n }\n]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["Hi ! Looks related to #861 \r\n\r\nYou are right: tokenizing a dataset using map takes a lot of space since it can store `input_ids` but also `token_type_ids`, `attention_mask` and `special_tokens_mask`. Moreover if your tokenization function returns python integers then by default they'll be stored as int64 which can take a lot of space. Padding can also increase the size of the tokenized dataset.\r\n\r\nTo make things more convenient, we recently added a \"lazy map\" feature that allows to tokenize each batch at training time as you mentioned. For example you'll be able to do\r\n```python\r\nfrom transformers import BertTokenizer\r\n\r\ntokenizer = BertTokenizer.from_pretrained(\"bert-base-uncased\")\r\n\r\ndef encode(batch):\r\n return tokenizer(batch[\"text\"], padding=\"longest\", truncation=True, max_length=512, return_tensors=\"pt\")\r\n\r\ndataset.set_transform(encode)\r\nprint(dataset.format)\r\n# {'type': 'custom', 'format_kwargs': {'transform': }, 'columns': ['idx', 'label', 'sentence1', 'sentence2'], 'output_all_columns': False}\r\nprint(dataset[:2])\r\n# {'input_ids': tensor([[ 101, 2572, 3217, ... 102]]), 'token_type_ids': tensor([[0, 0, 0, ... 0]]), 'attention_mask': tensor([[1, 1, 1, ... 1]])}\r\n\r\n```\r\nIn this example the `encode` transform is applied on-the-fly on the \"text\" column.\r\n\r\nThis feature will be available in the next release 2.0 which will happen in a few days.\r\nYou can already play with it by installing `datasets` from source if you want :)\r\n\r\nHope that helps !","How recently was `set_transform` added? I am actually trying to implement it and getting an error:\r\n\r\n`AttributeError: 'Dataset' object has no attribute 'set_transform'\r\n`\r\n\r\nI'm on v.1.2.1.\r\n\r\nEDIT: Oh, wait I see now it's in the v.2.0. Whoops! This should be really useful.","Yes indeed it was added a few days ago. The code is available on master\r\nWe'll do a release next week :)\r\n\r\nFeel free to install `datasets` from source to try it out though, I would love to have some feedbacks","For information: it's now available in `datasets` 1.3.0.\r\nThe 2.0 is reserved for even cooler features ;)","Hi @alexvaca0 , we have optimized Datasets' disk usage in the latest release v1.5.\r\n\r\nFeel free to update your Datasets version\r\n```shell\r\npip install -U datasets\r\n```\r\nand see if it better suits your needs."],"string":"[\n \"Hi ! Looks related to #861 \\r\\n\\r\\nYou are right: tokenizing a dataset using map takes a lot of space since it can store `input_ids` but also `token_type_ids`, `attention_mask` and `special_tokens_mask`. Moreover if your tokenization function returns python integers then by default they'll be stored as int64 which can take a lot of space. Padding can also increase the size of the tokenized dataset.\\r\\n\\r\\nTo make things more convenient, we recently added a \\\"lazy map\\\" feature that allows to tokenize each batch at training time as you mentioned. For example you'll be able to do\\r\\n```python\\r\\nfrom transformers import BertTokenizer\\r\\n\\r\\ntokenizer = BertTokenizer.from_pretrained(\\\"bert-base-uncased\\\")\\r\\n\\r\\ndef encode(batch):\\r\\n return tokenizer(batch[\\\"text\\\"], padding=\\\"longest\\\", truncation=True, max_length=512, return_tensors=\\\"pt\\\")\\r\\n\\r\\ndataset.set_transform(encode)\\r\\nprint(dataset.format)\\r\\n# {'type': 'custom', 'format_kwargs': {'transform': }, 'columns': ['idx', 'label', 'sentence1', 'sentence2'], 'output_all_columns': False}\\r\\nprint(dataset[:2])\\r\\n# {'input_ids': tensor([[ 101, 2572, 3217, ... 102]]), 'token_type_ids': tensor([[0, 0, 0, ... 0]]), 'attention_mask': tensor([[1, 1, 1, ... 1]])}\\r\\n\\r\\n```\\r\\nIn this example the `encode` transform is applied on-the-fly on the \\\"text\\\" column.\\r\\n\\r\\nThis feature will be available in the next release 2.0 which will happen in a few days.\\r\\nYou can already play with it by installing `datasets` from source if you want :)\\r\\n\\r\\nHope that helps !\",\n \"How recently was `set_transform` added? I am actually trying to implement it and getting an error:\\r\\n\\r\\n`AttributeError: 'Dataset' object has no attribute 'set_transform'\\r\\n`\\r\\n\\r\\nI'm on v.1.2.1.\\r\\n\\r\\nEDIT: Oh, wait I see now it's in the v.2.0. Whoops! This should be really useful.\",\n \"Yes indeed it was added a few days ago. The code is available on master\\r\\nWe'll do a release next week :)\\r\\n\\r\\nFeel free to install `datasets` from source to try it out though, I would love to have some feedbacks\",\n \"For information: it's now available in `datasets` 1.3.0.\\r\\nThe 2.0 is reserved for even cooler features ;)\",\n \"Hi @alexvaca0 , we have optimized Datasets' disk usage in the latest release v1.5.\\r\\n\\r\\nFeel free to update your Datasets version\\r\\n```shell\\r\\npip install -U datasets\\r\\n```\\r\\nand see if it better suits your needs.\"\n]"},"created_at":{"kind":"timestamp","value":"2021-02-05T11:06:50","string":"2021-02-05T11:06:50"},"updated_at":{"kind":"timestamp","value":"2021-03-30T14:04:01","string":"2021-03-30T14:04:01"},"closed_at":{"kind":"timestamp","value":"2021-03-16T09:44:00","string":"2021-03-16T09:44:00"},"author_association":{"kind":"string","value":"NONE"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"Hi,\r\n\r\nI'm trying to use datasets library to load a 187GB dataset of pure text, with the intention of building a Language Model. The problem is that from the 187GB it goes to some TB when processed by Datasets. First of all, I think the pre-tokenizing step (with tokenizer.map()) is not really thought for datasets this big, but for fine-tuning datasets, as this process alone takes so much time, usually in expensive machines (due to the need of tpus - gpus) which is not being used for training. It would possibly be more efficient in such cases to tokenize each batch at training time (receive batch - tokenize batch - train with batch), so that the whole time the machine is up it's being used for training. \r\nMoreover, the pyarrow objects created from a 187 GB datasets are huge, I mean, we always receive OOM, or No Space left on device errors when only 10-12% of the dataset has been processed, and only that part occupies 2.1TB in disk, which is so many times the disk usage of the pure text (and this doesn't make sense, as tokenized texts should be lighter than pure texts).\r\n\r\nAny suggestions??"},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1825/reactions\",\n \"total_count\": 1,\n \"+1\": 1,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 0\n}"},"timeline_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1825/timeline"},"performed_via_github_app":{"kind":"null"},"state_reason":{"kind":"string","value":"completed"}}},{"rowIdx":2239,"cells":{"url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1821"},"repository_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets"},"labels_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1821/labels{/name}"},"comments_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1821/comments"},"events_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1821/events"},"html_url":{"kind":"string","value":"https://github.com/huggingface/datasets/issues/1821"},"id":{"kind":"number","value":801747647,"string":"801,747,647"},"node_id":{"kind":"string","value":"MDU6SXNzdWU4MDE3NDc2NDc="},"number":{"kind":"number","value":1821,"string":"1,821"},"title":{"kind":"string","value":"Provide better exception message when one of many files results in an exception"},"user":{"kind":"string","value":"{\n \"login\": \"david-waterworth\",\n \"id\": 5028974,\n \"node_id\": \"MDQ6VXNlcjUwMjg5NzQ=\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/5028974?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/david-waterworth\",\n \"html_url\": \"https://github.com/david-waterworth\",\n \"followers_url\": \"https://api.github.com/users/david-waterworth/followers\",\n \"following_url\": \"https://api.github.com/users/david-waterworth/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/david-waterworth/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/david-waterworth/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/david-waterworth/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/david-waterworth/orgs\",\n \"repos_url\": \"https://api.github.com/users/david-waterworth/repos\",\n \"events_url\": \"https://api.github.com/users/david-waterworth/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/david-waterworth/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n}"},"labels":{"kind":"list like","value":[],"string":"[]"},"state":{"kind":"string","value":"closed"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["Hi!\r\n\r\nThank you for reporting this issue. I agree that the information about the exception should be more clear and explicit.\r\n\r\nI could take on this issue.\r\n\r\nOn the meantime, as you can see from the exception stack trace, HF Datasets uses pandas to read the CSV files. You can pass arguments to `pandas.read_csv` by passing additional keyword arguments to `load_dataset`. For example, you may find useful this argument:\r\n- `error_bad_lines` : bool, default True\r\n Lines with too many fields (e.g. a csv line with too many commas) will by default cause an exception to be raised, and no DataFrame will be returned. If False, then these “bad lines” will be dropped from the DataFrame that is returned.\r\n\r\nYou could try:\r\n```python\r\ndatasets = load_dataset(\"csv\", data_files=dict(train=train_files, validation=validation_files), error_bad_lines=False)\r\n```\r\n"],"string":"[\n \"Hi!\\r\\n\\r\\nThank you for reporting this issue. I agree that the information about the exception should be more clear and explicit.\\r\\n\\r\\nI could take on this issue.\\r\\n\\r\\nOn the meantime, as you can see from the exception stack trace, HF Datasets uses pandas to read the CSV files. You can pass arguments to `pandas.read_csv` by passing additional keyword arguments to `load_dataset`. For example, you may find useful this argument:\\r\\n- `error_bad_lines` : bool, default True\\r\\n Lines with too many fields (e.g. a csv line with too many commas) will by default cause an exception to be raised, and no DataFrame will be returned. If False, then these “bad lines” will be dropped from the DataFrame that is returned.\\r\\n\\r\\nYou could try:\\r\\n```python\\r\\ndatasets = load_dataset(\\\"csv\\\", data_files=dict(train=train_files, validation=validation_files), error_bad_lines=False)\\r\\n```\\r\\n\"\n]"},"created_at":{"kind":"timestamp","value":"2021-02-05T00:49:03","string":"2021-02-05T00:49:03"},"updated_at":{"kind":"timestamp","value":"2021-02-09T17:39:27","string":"2021-02-09T17:39:27"},"closed_at":{"kind":"timestamp","value":"2021-02-09T17:39:27","string":"2021-02-09T17:39:27"},"author_association":{"kind":"string","value":"NONE"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"I find when I process many files, i.e.\r\n\r\n```\r\ntrain_files = glob.glob('rain*.csv')\r\nvalidation_files = glob.glob(validation*.csv')\r\ndatasets = load_dataset(\"csv\", data_files=dict(train=train_files, validation=validation_files))\r\n```\r\n\r\nI sometimes encounter an error due to one of the files being misformed (i.e. no data, or a comma in a field that isn't quoted, etc).\r\n\r\nFor example, this is the tail of an exception which I suspect is due to a stray comma.\r\n\r\n> File \"pandas/_libs/parsers.pyx\", line 756, in pandas._libs.parsers.TextReader.read\r\n> File \"pandas/_libs/parsers.pyx\", line 783, in pandas._libs.parsers.TextReader._read_low_memory\r\n> File \"pandas/_libs/parsers.pyx\", line 827, in pandas._libs.parsers.TextReader._read_rows\r\n> File \"pandas/_libs/parsers.pyx\", line 814, in pandas._libs.parsers.TextReader._tokenize_rows\r\n> File \"pandas/_libs/parsers.pyx\", line 1951, in pandas._libs.parsers.raise_parser_error\r\n> pandas.errors.ParserError: Error tokenizing data. C error: Expected 2 fields in line 559, saw 3\r\n\r\nIt would be nice if the exception trace contained the name of the file being processed (I have 250 separate files!)"},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1821/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 0\n}"},"timeline_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1821/timeline"},"performed_via_github_app":{"kind":"null"},"state_reason":{"kind":"string","value":"completed"}}},{"rowIdx":2240,"cells":{"url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1818"},"repository_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets"},"labels_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1818/labels{/name}"},"comments_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1818/comments"},"events_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1818/events"},"html_url":{"kind":"string","value":"https://github.com/huggingface/datasets/issues/1818"},"id":{"kind":"number","value":800958776,"string":"800,958,776"},"node_id":{"kind":"string","value":"MDU6SXNzdWU4MDA5NTg3NzY="},"number":{"kind":"number","value":1818,"string":"1,818"},"title":{"kind":"string","value":"Loading local dataset raise requests.exceptions.ConnectTimeout"},"user":{"kind":"string","value":"{\n \"login\": \"Alxe1\",\n \"id\": 15032072,\n \"node_id\": \"MDQ6VXNlcjE1MDMyMDcy\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/15032072?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/Alxe1\",\n \"html_url\": \"https://github.com/Alxe1\",\n \"followers_url\": \"https://api.github.com/users/Alxe1/followers\",\n \"following_url\": \"https://api.github.com/users/Alxe1/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/Alxe1/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/Alxe1/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/Alxe1/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/Alxe1/orgs\",\n \"repos_url\": \"https://api.github.com/users/Alxe1/repos\",\n \"events_url\": \"https://api.github.com/users/Alxe1/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/Alxe1/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n}"},"labels":{"kind":"list like","value":[],"string":"[]"},"state":{"kind":"string","value":"closed"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["Hi ! Thanks for reporting. This was indeed a bug introduced when we moved the `json` dataset loader inside the `datasets` package (before that, the `json` loader was fetched online, as all the other dataset scripts).\r\n\r\nThis should be fixed on master now. Feel free to install `datasets` from source to try it out.\r\nThe fix will be available in the next release of `datasets` in a few days"],"string":"[\n \"Hi ! Thanks for reporting. This was indeed a bug introduced when we moved the `json` dataset loader inside the `datasets` package (before that, the `json` loader was fetched online, as all the other dataset scripts).\\r\\n\\r\\nThis should be fixed on master now. Feel free to install `datasets` from source to try it out.\\r\\nThe fix will be available in the next release of `datasets` in a few days\"\n]"},"created_at":{"kind":"timestamp","value":"2021-02-04T05:55:23","string":"2021-02-04T05:55:23"},"updated_at":{"kind":"timestamp","value":"2022-06-01T15:38:42","string":"2022-06-01T15:38:42"},"closed_at":{"kind":"timestamp","value":"2022-06-01T15:38:42","string":"2022-06-01T15:38:42"},"author_association":{"kind":"string","value":"NONE"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"Load local dataset:\r\n```\r\ndataset = load_dataset('json', data_files=[\"../../data/json.json\"])\r\ntrain = dataset[\"train\"]\r\nprint(train.features)\r\ntrain1 = train.map(lambda x: {\"labels\": 1})\r\nprint(train1[:2])\r\n```\r\n\r\nbut it raised requests.exceptions.ConnectTimeout:\r\n\r\n```\r\n/Users/littlely/myvirtual/tf2/bin/python3.7 /Users/littlely/projects/python_projects/pytorch_learning/nlp/dataset/transformers_datasets.py\r\nTraceback (most recent call last):\r\n File \"/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/urllib3/connection.py\", line 160, in _new_conn\r\n (self._dns_host, self.port), self.timeout, **extra_kw\r\n File \"/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/urllib3/util/connection.py\", line 84, in create_connection\r\n raise err\r\n File \"/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/urllib3/util/connection.py\", line 74, in create_connection\r\n sock.connect(sa)\r\nsocket.timeout: timed out\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/urllib3/connectionpool.py\", line 677, in urlopen\r\n chunked=chunked,\r\n File \"/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/urllib3/connectionpool.py\", line 381, in _make_request\r\n self._validate_conn(conn)\r\n File \"/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/urllib3/connectionpool.py\", line 978, in _validate_conn\r\n conn.connect()\r\n File \"/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/urllib3/connection.py\", line 309, in connect\r\n conn = self._new_conn()\r\n File \"/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/urllib3/connection.py\", line 167, in _new_conn\r\n % (self.host, self.timeout),\r\nurllib3.exceptions.ConnectTimeoutError: (, 'Connection to s3.amazonaws.com timed out. (connect timeout=10)')\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/requests/adapters.py\", line 449, in send\r\n timeout=timeout\r\n File \"/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/urllib3/connectionpool.py\", line 727, in urlopen\r\n method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2]\r\n File \"/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/urllib3/util/retry.py\", line 439, in increment\r\n raise MaxRetryError(_pool, url, error or ResponseError(cause))\r\nurllib3.exceptions.MaxRetryError: HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: /datasets.huggingface.co/datasets/datasets/json/json.py (Caused by ConnectTimeoutError(, 'Connection to s3.amazonaws.com timed out. (connect timeout=10)'))\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"/Users/littlely/projects/python_projects/pytorch_learning/nlp/dataset/transformers_datasets.py\", line 12, in \r\n dataset = load_dataset('json', data_files=[\"../../data/json.json\"])\r\n File \"/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/datasets/load.py\", line 591, in load_dataset\r\n path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True\r\n File \"/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/datasets/load.py\", line 263, in prepare_module\r\n head_hf_s3(path, filename=name, dataset=dataset, max_retries=download_config.max_retries)\r\n File \"/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/datasets/utils/file_utils.py\", line 232, in head_hf_s3\r\n max_retries=max_retries,\r\n File \"/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/datasets/utils/file_utils.py\", line 523, in http_head\r\n max_retries=max_retries,\r\n File \"/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/datasets/utils/file_utils.py\", line 458, in _request_with_retry\r\n raise err\r\n File \"/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/datasets/utils/file_utils.py\", line 454, in _request_with_retry\r\n response = requests.request(verb.upper(), url, **params)\r\n File \"/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/requests/api.py\", line 61, in request\r\n return session.request(method=method, url=url, **kwargs)\r\n File \"/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/requests/sessions.py\", line 530, in request\r\n resp = self.send(prep, **send_kwargs)\r\n File \"/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/requests/sessions.py\", line 643, in send\r\n r = adapter.send(request, **kwargs)\r\n File \"/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/requests/adapters.py\", line 504, in send\r\n raise ConnectTimeout(e, request=request)\r\nrequests.exceptions.ConnectTimeout: HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: /datasets.huggingface.co/datasets/datasets/json/json.py (Caused by ConnectTimeoutError(, 'Connection to s3.amazonaws.com timed out. (connect timeout=10)'))\r\n\r\nProcess finished with exit code 1\r\n\r\n```\r\n\r\nWhy it want to connect a remote url when I load local datasets, and how can I fix it?"},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1818/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 0\n}"},"timeline_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1818/timeline"},"performed_via_github_app":{"kind":"null"},"state_reason":{"kind":"string","value":"completed"}}},{"rowIdx":2241,"cells":{"url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1817"},"repository_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets"},"labels_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1817/labels{/name}"},"comments_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1817/comments"},"events_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1817/events"},"html_url":{"kind":"string","value":"https://github.com/huggingface/datasets/issues/1817"},"id":{"kind":"number","value":800870652,"string":"800,870,652"},"node_id":{"kind":"string","value":"MDU6SXNzdWU4MDA4NzA2NTI="},"number":{"kind":"number","value":1817,"string":"1,817"},"title":{"kind":"string","value":"pyarrow.lib.ArrowInvalid: Column 1 named input_ids expected length 599 but got length 1500"},"user":{"kind":"string","value":"{\n \"login\": \"LuCeHe\",\n \"id\": 9610770,\n \"node_id\": \"MDQ6VXNlcjk2MTA3NzA=\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/9610770?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/LuCeHe\",\n \"html_url\": \"https://github.com/LuCeHe\",\n \"followers_url\": \"https://api.github.com/users/LuCeHe/followers\",\n \"following_url\": \"https://api.github.com/users/LuCeHe/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/LuCeHe/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/LuCeHe/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/LuCeHe/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/LuCeHe/orgs\",\n \"repos_url\": \"https://api.github.com/users/LuCeHe/repos\",\n \"events_url\": \"https://api.github.com/users/LuCeHe/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/LuCeHe/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n}"},"labels":{"kind":"list like","value":[],"string":"[]"},"state":{"kind":"string","value":"closed"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["Hi !\r\nThe error you have is due to the `input_ids` column not having the same number of examples as the other columns.\r\nIndeed you're concatenating the `input_ids` at this line:\r\n\r\nhttps://github.com/LuCeHe/GenericTools/blob/431835d8e13ec24dceb5ee4dc4ae58f0e873b091/KerasTools/lm_preprocessing.py#L134\r\n\r\nHowever the other columns are kept unchanged, and therefore you end up with an `input_ids` column with 599 elements while the others columns like `attention_mask` have 1500.\r\n\r\nTo fix that you can instead concatenate them all using\r\n```python\r\nconcatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}\r\n```\r\n\r\nAlso you may need to drop the \"text\" column before applying `group_texts` since strings can't be concatenated with lists. You can drop it at the tokenization step:\r\n```python\r\ndset = dset.map(\r\n tokenize_function,\r\n batched=True,\r\n remove_columns=[\"text\"]\r\n)\r\n```","You saved my life."],"string":"[\n \"Hi !\\r\\nThe error you have is due to the `input_ids` column not having the same number of examples as the other columns.\\r\\nIndeed you're concatenating the `input_ids` at this line:\\r\\n\\r\\nhttps://github.com/LuCeHe/GenericTools/blob/431835d8e13ec24dceb5ee4dc4ae58f0e873b091/KerasTools/lm_preprocessing.py#L134\\r\\n\\r\\nHowever the other columns are kept unchanged, and therefore you end up with an `input_ids` column with 599 elements while the others columns like `attention_mask` have 1500.\\r\\n\\r\\nTo fix that you can instead concatenate them all using\\r\\n```python\\r\\nconcatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}\\r\\n```\\r\\n\\r\\nAlso you may need to drop the \\\"text\\\" column before applying `group_texts` since strings can't be concatenated with lists. You can drop it at the tokenization step:\\r\\n```python\\r\\ndset = dset.map(\\r\\n tokenize_function,\\r\\n batched=True,\\r\\n remove_columns=[\\\"text\\\"]\\r\\n)\\r\\n```\",\n \"You saved my life.\"\n]"},"created_at":{"kind":"timestamp","value":"2021-02-04T02:30:23","string":"2021-02-04T02:30:23"},"updated_at":{"kind":"timestamp","value":"2022-10-05T12:42:57","string":"2022-10-05T12:42:57"},"closed_at":{"kind":"timestamp","value":"2022-10-05T12:42:57","string":"2022-10-05T12:42:57"},"author_association":{"kind":"string","value":"NONE"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"I am trying to preprocess any dataset in this package with GPT-2 tokenizer, so I need to structure the datasets as long sequences of text without padding. I've been following a couple of your tutorials and here you can find the script that is failing right at the end\r\n\r\nhttps://github.com/LuCeHe/GenericTools/blob/master/KerasTools/lm_preprocessing.py\r\n\r\nIn the last iteration of the last dset.map, it gives the error that I copied in the title. Another issue that I have, if I leave the batch_size set as 1000 in the last .map, I'm afraid it's going to lose most text, so I'm considering setting both writer_batch_size and batch_size to 300 K, but I'm not sure it's the best way to go.\r\n\r\nCan you help me?\r\nThanks!"},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1817/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 0\n}"},"timeline_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1817/timeline"},"performed_via_github_app":{"kind":"null"},"state_reason":{"kind":"string","value":"completed"}}},{"rowIdx":2242,"cells":{"url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1811"},"repository_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets"},"labels_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1811/labels{/name}"},"comments_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1811/comments"},"events_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1811/events"},"html_url":{"kind":"string","value":"https://github.com/huggingface/datasets/issues/1811"},"id":{"kind":"number","value":799211060,"string":"799,211,060"},"node_id":{"kind":"string","value":"MDU6SXNzdWU3OTkyMTEwNjA="},"number":{"kind":"number","value":1811,"string":"1,811"},"title":{"kind":"string","value":"Unable to add Multi-label Datasets"},"user":{"kind":"string","value":"{\n \"login\": \"gchhablani\",\n \"id\": 29076344,\n \"node_id\": \"MDQ6VXNlcjI5MDc2MzQ0\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/29076344?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/gchhablani\",\n \"html_url\": \"https://github.com/gchhablani\",\n \"followers_url\": \"https://api.github.com/users/gchhablani/followers\",\n \"following_url\": \"https://api.github.com/users/gchhablani/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/gchhablani/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/gchhablani/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/gchhablani/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/gchhablani/orgs\",\n \"repos_url\": \"https://api.github.com/users/gchhablani/repos\",\n \"events_url\": \"https://api.github.com/users/gchhablani/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/gchhablani/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n}"},"labels":{"kind":"list like","value":[],"string":"[]"},"state":{"kind":"string","value":"closed"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["Thanks for adding this dataset! As far as I know `supervised_keys` is mostly a holdover from TFDS, but isn't really used, so feel free to drop it (@lhoestq or @thomwolf correct me if I'm wrong). It definitely shouldn't be blocking :) ","I can confirm that it comes from TFDS and is not used at the moment.","Thanks @yjernite @lhoestq \r\n\r\nThe template for new dataset makes it slightly confusing. I suppose the comment suggesting its update can be removed.","Closing this issue since it was answered."],"string":"[\n \"Thanks for adding this dataset! As far as I know `supervised_keys` is mostly a holdover from TFDS, but isn't really used, so feel free to drop it (@lhoestq or @thomwolf correct me if I'm wrong). It definitely shouldn't be blocking :) \",\n \"I can confirm that it comes from TFDS and is not used at the moment.\",\n \"Thanks @yjernite @lhoestq \\r\\n\\r\\nThe template for new dataset makes it slightly confusing. I suppose the comment suggesting its update can be removed.\",\n \"Closing this issue since it was answered.\"\n]"},"created_at":{"kind":"timestamp","value":"2021-02-02T11:50:56","string":"2021-02-02T11:50:56"},"updated_at":{"kind":"timestamp","value":"2021-02-18T14:16:31","string":"2021-02-18T14:16:31"},"closed_at":{"kind":"timestamp","value":"2021-02-18T14:16:31","string":"2021-02-18T14:16:31"},"author_association":{"kind":"string","value":"CONTRIBUTOR"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"I am trying to add [CIFAR-100](https://www.cs.toronto.edu/~kriz/cifar.html) dataset. The dataset contains two labels per image - `fine label` and `coarse label`. Using just one label in supervised keys as \r\n`supervised_keys=(\"img\", \"fine_label\")` raises no issue. But trying `supervised_keys=(\"img\", \"fine_label\",\"coarse_label\")` leads to this error : \r\n\r\n```python\r\nTraceback (most recent call last):\r\n File \"test_script.py\", line 2, in \r\n d = load_dataset('./datasets/cifar100')\r\n File \"~/datasets/src/datasets/load.py\", line 668, in load_dataset\r\n **config_kwargs,\r\n File \"~/datasets/src/datasets/builder.py\", line 896, in __init__\r\n super(GeneratorBasedBuilder, self).__init__(*args, **kwargs)\r\n File \"~/datasets/src/datasets/builder.py\", line 247, in __init__\r\n info.update(self._info())\r\n File \"~/.cache/huggingface/modules/datasets_modules/datasets/cifar100/61d2489b2d4a4abc34201432541b7380984ec714e290817d9a1ee318e4b74e0f/cifar100.py\", line 79, in _info\r\n citation=_CITATION,\r\n File \"\", line 19, in __init__\r\n File \"~/datasets/src/datasets/info.py\", line 136, in __post_init__\r\n self.supervised_keys = SupervisedKeysData(*self.supervised_keys)\r\nTypeError: __init__() takes from 1 to 3 positional arguments but 4 were given\r\n```\r\nIs there a way I can fix this?\r\n\r\nAlso, what does adding `supervised_keys` do? Is it necessary? How would I specify `supervised_keys` for a multi-input, multi-label dataset?\r\n\r\nThanks,\r\nGunjan"},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1811/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 0\n}"},"timeline_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1811/timeline"},"performed_via_github_app":{"kind":"null"},"state_reason":{"kind":"string","value":"completed"}}},{"rowIdx":2243,"cells":{"url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1810"},"repository_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets"},"labels_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1810/labels{/name}"},"comments_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1810/comments"},"events_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1810/events"},"html_url":{"kind":"string","value":"https://github.com/huggingface/datasets/issues/1810"},"id":{"kind":"number","value":799168650,"string":"799,168,650"},"node_id":{"kind":"string","value":"MDU6SXNzdWU3OTkxNjg2NTA="},"number":{"kind":"number","value":1810,"string":"1,810"},"title":{"kind":"string","value":"Add Hateful Memes Dataset"},"user":{"kind":"string","value":"{\n \"login\": \"gchhablani\",\n \"id\": 29076344,\n \"node_id\": \"MDQ6VXNlcjI5MDc2MzQ0\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/29076344?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/gchhablani\",\n \"html_url\": \"https://github.com/gchhablani\",\n \"followers_url\": \"https://api.github.com/users/gchhablani/followers\",\n \"following_url\": \"https://api.github.com/users/gchhablani/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/gchhablani/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/gchhablani/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/gchhablani/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/gchhablani/orgs\",\n \"repos_url\": \"https://api.github.com/users/gchhablani/repos\",\n \"events_url\": \"https://api.github.com/users/gchhablani/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/gchhablani/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n}"},"labels":{"kind":"list like","value":[{"id":2067376369,"node_id":"MDU6TGFiZWwyMDY3Mzc2MzY5","url":"https://api.github.com/repos/huggingface/datasets/labels/dataset%20request","name":"dataset request","color":"e99695","default":false,"description":"Requesting to add a new dataset"},{"id":3608941089,"node_id":"LA_kwDODunzps7XHBIh","url":"https://api.github.com/repos/huggingface/datasets/labels/vision","name":"vision","color":"bfdadc","default":false,"description":"Vision datasets"}],"string":"[\n {\n \"id\": 2067376369,\n \"node_id\": \"MDU6TGFiZWwyMDY3Mzc2MzY5\",\n \"url\": \"https://api.github.com/repos/huggingface/datasets/labels/dataset%20request\",\n \"name\": \"dataset request\",\n \"color\": \"e99695\",\n \"default\": false,\n \"description\": \"Requesting to add a new dataset\"\n },\n {\n \"id\": 3608941089,\n \"node_id\": \"LA_kwDODunzps7XHBIh\",\n \"url\": \"https://api.github.com/repos/huggingface/datasets/labels/vision\",\n \"name\": \"vision\",\n \"color\": \"bfdadc\",\n \"default\": false,\n \"description\": \"Vision datasets\"\n }\n]"},"state":{"kind":"string","value":"open"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["I am not sure, but would `datasets.Sequence(datasets.Sequence(datasets.Sequence(datasets.Value(\"int\")))` work?","Also, I found the information for loading only subsets of the data [here](https://github.com/huggingface/datasets/blob/master/docs/source/splits.rst).","Hi @lhoestq,\r\n\r\nRequest you to check this once.\r\n\r\nThanks,\r\nGunjan","Hi @gchhablani since Array2D doesn't support images of different sizes, I would suggest to store in the dataset the paths to the image file instead of the image data. This has the advantage of not decompressing the data (images are often compressed using jpeg, png etc.). Users can still apply `.map` to load the images if they want to. Though it would en up being Sequences features.\r\n\r\nIn the future we'll add support for ragged tensors for this case and update the relevant dataset with this feature."],"string":"[\n \"I am not sure, but would `datasets.Sequence(datasets.Sequence(datasets.Sequence(datasets.Value(\\\"int\\\")))` work?\",\n \"Also, I found the information for loading only subsets of the data [here](https://github.com/huggingface/datasets/blob/master/docs/source/splits.rst).\",\n \"Hi @lhoestq,\\r\\n\\r\\nRequest you to check this once.\\r\\n\\r\\nThanks,\\r\\nGunjan\",\n \"Hi @gchhablani since Array2D doesn't support images of different sizes, I would suggest to store in the dataset the paths to the image file instead of the image data. This has the advantage of not decompressing the data (images are often compressed using jpeg, png etc.). Users can still apply `.map` to load the images if they want to. Though it would en up being Sequences features.\\r\\n\\r\\nIn the future we'll add support for ragged tensors for this case and update the relevant dataset with this feature.\"\n]"},"created_at":{"kind":"timestamp","value":"2021-02-02T10:53:59","string":"2021-02-02T10:53:59"},"updated_at":{"kind":"timestamp","value":"2021-12-08T12:03:59","string":"2021-12-08T12:03:59"},"closed_at":{"kind":"null"},"author_association":{"kind":"string","value":"CONTRIBUTOR"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"## Add Hateful Memes Dataset\r\n- **Name:** Hateful Memes\r\n- **Description:** [https://ai.facebook.com/blog/hateful-memes-challenge-and-data-set]( https://ai.facebook.com/blog/hateful-memes-challenge-and-data-set)\r\n- **Paper:** [https://arxiv.org/pdf/2005.04790.pdf](https://arxiv.org/pdf/2005.04790.pdf)\r\n- **Data:** [This link](https://drivendata-competition-fb-hateful-memes-data.s3.amazonaws.com/XjiOc5ycDBRRNwbhRlgH.zip?AWSAccessKeyId=AKIARVBOBDCY4MWEDJKS&Signature=DaUuGgZWUgDHzEPPbyJ2PhSJ56Q%3D&Expires=1612816874)\r\n- **Motivation:** Including multi-modal datasets to 🤗 datasets.\r\n\r\nI will be adding this dataset. It requires the user to sign an agreement on DrivenData. So, it will be used with a manual download.\r\n\r\nThe issue with this dataset is that the images are of different sizes. The image datasets added so far (CIFAR-10 and MNIST) have a uniform shape throughout.\r\nSo something like \r\n```python\r\n datasets.Array2D(shape=(28, 28), dtype=\"uint8\")\r\n```\r\nwon't work for the images. How would I add image features then? I checked `datasets/features.py` but couldn't figure out the appropriate class for this. I'm assuming I would want to avoid re-sizing at all since we want the user to be able to access the original images.\r\n\r\nAlso, in case I want to load only a subset of the data, since the actual data is around 8.8GB, how would that be possible?\r\n\r\nThanks,\r\nGunjan"},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1810/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 0\n}"},"timeline_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1810/timeline"},"performed_via_github_app":{"kind":"null"},"state_reason":{"kind":"null"}}},{"rowIdx":2244,"cells":{"url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1808"},"repository_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets"},"labels_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1808/labels{/name}"},"comments_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1808/comments"},"events_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1808/events"},"html_url":{"kind":"string","value":"https://github.com/huggingface/datasets/issues/1808"},"id":{"kind":"number","value":798879180,"string":"798,879,180"},"node_id":{"kind":"string","value":"MDU6SXNzdWU3OTg4NzkxODA="},"number":{"kind":"number","value":1808,"string":"1,808"},"title":{"kind":"string","value":"writing Datasets in a human readable format"},"user":{"kind":"string","value":"{\n \"login\": \"ghost\",\n \"id\": 10137,\n \"node_id\": \"MDQ6VXNlcjEwMTM3\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/10137?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/ghost\",\n \"html_url\": \"https://github.com/ghost\",\n \"followers_url\": \"https://api.github.com/users/ghost/followers\",\n \"following_url\": \"https://api.github.com/users/ghost/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/ghost/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/ghost/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/ghost/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/ghost/orgs\",\n \"repos_url\": \"https://api.github.com/users/ghost/repos\",\n \"events_url\": \"https://api.github.com/users/ghost/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/ghost/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n}"},"labels":{"kind":"list like","value":[{"id":1935892871,"node_id":"MDU6TGFiZWwxOTM1ODkyODcx","url":"https://api.github.com/repos/huggingface/datasets/labels/enhancement","name":"enhancement","color":"a2eeef","default":true,"description":"New feature or request"},{"id":1935892912,"node_id":"MDU6TGFiZWwxOTM1ODkyOTEy","url":"https://api.github.com/repos/huggingface/datasets/labels/question","name":"question","color":"d876e3","default":true,"description":"Further information is requested"}],"string":"[\n {\n \"id\": 1935892871,\n \"node_id\": \"MDU6TGFiZWwxOTM1ODkyODcx\",\n \"url\": \"https://api.github.com/repos/huggingface/datasets/labels/enhancement\",\n \"name\": \"enhancement\",\n \"color\": \"a2eeef\",\n \"default\": true,\n \"description\": \"New feature or request\"\n },\n {\n \"id\": 1935892912,\n \"node_id\": \"MDU6TGFiZWwxOTM1ODkyOTEy\",\n \"url\": \"https://api.github.com/repos/huggingface/datasets/labels/question\",\n \"name\": \"question\",\n \"color\": \"d876e3\",\n \"default\": true,\n \"description\": \"Further information is requested\"\n }\n]"},"state":{"kind":"string","value":"closed"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["AFAIK, there is currently no built-in method on the `Dataset` object to do this.\r\nHowever, a workaround is to directly use the Arrow table backing the dataset, **but it implies loading the whole dataset in memory** (correct me if I'm mistaken @lhoestq).\r\n\r\nYou can convert the Arrow table to a pandas dataframe to save the data as csv as follows:\r\n```python\r\narrow_table = dataset.data\r\ndataframe = arrow_table.to_pandas()\r\ndataframe.to_csv(\"/path/to/file.csv\")\r\n```\r\n\r\nSimilarly, you can convert the dataset to a Python dict and save it as JSON:\r\n```python\r\nimport json\r\narrow_table = dataset.data\r\npy_dict = arrow_table.to_pydict()\r\nwith open(\"/path/to/file.json\", \"w+\") as f:\r\n json.dump(py_dict, f)\r\n```","Indeed this works as long as you have enough memory.\r\nIt would be amazing to have export options like csv, json etc. !\r\n\r\nIt should be doable to implement something that iterates through the dataset batch by batch to write to csv for example.\r\nThere is already an `export` method but currently the only export type that is supported is `tfrecords`.","Hi! `datasets` now supports `Dataset.to_csv` and `Dataset.to_json` for saving data in a human readable format."],"string":"[\n \"AFAIK, there is currently no built-in method on the `Dataset` object to do this.\\r\\nHowever, a workaround is to directly use the Arrow table backing the dataset, **but it implies loading the whole dataset in memory** (correct me if I'm mistaken @lhoestq).\\r\\n\\r\\nYou can convert the Arrow table to a pandas dataframe to save the data as csv as follows:\\r\\n```python\\r\\narrow_table = dataset.data\\r\\ndataframe = arrow_table.to_pandas()\\r\\ndataframe.to_csv(\\\"/path/to/file.csv\\\")\\r\\n```\\r\\n\\r\\nSimilarly, you can convert the dataset to a Python dict and save it as JSON:\\r\\n```python\\r\\nimport json\\r\\narrow_table = dataset.data\\r\\npy_dict = arrow_table.to_pydict()\\r\\nwith open(\\\"/path/to/file.json\\\", \\\"w+\\\") as f:\\r\\n json.dump(py_dict, f)\\r\\n```\",\n \"Indeed this works as long as you have enough memory.\\r\\nIt would be amazing to have export options like csv, json etc. !\\r\\n\\r\\nIt should be doable to implement something that iterates through the dataset batch by batch to write to csv for example.\\r\\nThere is already an `export` method but currently the only export type that is supported is `tfrecords`.\",\n \"Hi! `datasets` now supports `Dataset.to_csv` and `Dataset.to_json` for saving data in a human readable format.\"\n]"},"created_at":{"kind":"timestamp","value":"2021-02-02T02:55:40","string":"2021-02-02T02:55:40"},"updated_at":{"kind":"timestamp","value":"2022-06-01T15:38:13","string":"2022-06-01T15:38:13"},"closed_at":{"kind":"timestamp","value":"2022-06-01T15:38:13","string":"2022-06-01T15:38:13"},"author_association":{"kind":"string","value":"NONE"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"Hi\r\nI see there is a save_to_disk function to save data, but this is not human readable format, is there a way I could save a Dataset object in a human readable format to a file like json? thanks @lhoestq "},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1808/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 0\n}"},"timeline_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1808/timeline"},"performed_via_github_app":{"kind":"null"},"state_reason":{"kind":"string","value":"completed"}}},{"rowIdx":2245,"cells":{"url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1805"},"repository_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets"},"labels_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1805/labels{/name}"},"comments_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1805/comments"},"events_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1805/events"},"html_url":{"kind":"string","value":"https://github.com/huggingface/datasets/issues/1805"},"id":{"kind":"number","value":798498053,"string":"798,498,053"},"node_id":{"kind":"string","value":"MDU6SXNzdWU3OTg0OTgwNTM="},"number":{"kind":"number","value":1805,"string":"1,805"},"title":{"kind":"string","value":"can't pickle SwigPyObject objects when calling dataset.get_nearest_examples from FAISS index"},"user":{"kind":"string","value":"{\n \"login\": \"abarbosa94\",\n \"id\": 6608232,\n \"node_id\": \"MDQ6VXNlcjY2MDgyMzI=\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/6608232?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/abarbosa94\",\n \"html_url\": \"https://github.com/abarbosa94\",\n \"followers_url\": \"https://api.github.com/users/abarbosa94/followers\",\n \"following_url\": \"https://api.github.com/users/abarbosa94/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/abarbosa94/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/abarbosa94/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/abarbosa94/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/abarbosa94/orgs\",\n \"repos_url\": \"https://api.github.com/users/abarbosa94/repos\",\n \"events_url\": \"https://api.github.com/users/abarbosa94/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/abarbosa94/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n}"},"labels":{"kind":"list like","value":[],"string":"[]"},"state":{"kind":"string","value":"closed"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["Hi ! Indeed we used to require mapping functions to be picklable with `pickle` or `dill` in order to cache the resulting datasets. And FAISS indexes are not picklable unfortunately.\r\n\r\nBut since #1703 this is no longer required (the caching will simply be disabled). This change will be available in the next release of `datasets`, or you can also install `datasets` from source.","I totally forgot to answer this issue, I'm so sorry. \r\n\r\nI was able to get it working by installing `datasets` from source. Huge thanks!"],"string":"[\n \"Hi ! Indeed we used to require mapping functions to be picklable with `pickle` or `dill` in order to cache the resulting datasets. And FAISS indexes are not picklable unfortunately.\\r\\n\\r\\nBut since #1703 this is no longer required (the caching will simply be disabled). This change will be available in the next release of `datasets`, or you can also install `datasets` from source.\",\n \"I totally forgot to answer this issue, I'm so sorry. \\r\\n\\r\\nI was able to get it working by installing `datasets` from source. Huge thanks!\"\n]"},"created_at":{"kind":"timestamp","value":"2021-02-01T16:14:17","string":"2021-02-01T16:14:17"},"updated_at":{"kind":"timestamp","value":"2021-03-06T14:32:46","string":"2021-03-06T14:32:46"},"closed_at":{"kind":"timestamp","value":"2021-03-06T14:32:46","string":"2021-03-06T14:32:46"},"author_association":{"kind":"string","value":"CONTRIBUTOR"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"So, I have the following instances in my dataset\r\n\r\n```\r\n{'question': 'An astronomer observes that a planet rotates faster after a meteorite impact. Which is the most likely effect of \r\nthis increase in rotation?', \r\n'answer': 'C', \r\n'example_id': 'ARCCH_Mercury_7175875', \r\n'options':[{'option_context': 'One effect of increased amperage in the planetary world (..)', 'option_id': 'A', 'option_text': 'Planetary density will decrease.'},\r\n (...)]}\r\n```\r\n\r\nThe `options` value is always an list with 4 options, each one is a dict with `option_context`; `option_id` and `option_text`.\r\n\r\nI would like to overwrite the `option_context` of each instance of my dataset for a dpr result that I am developing. Then, I trained a model already and save it in a FAISS index\r\n```\r\ndpr_dataset = load_dataset(\r\n \"text\",\r\n data_files=ARC_CORPUS_TEXT,\r\n cache_dir=CACHE_DIR,\r\n split=\"train[:100%]\",\r\n )\r\ndpr_dataset.load_faiss_index(\"embeddings\", f\"{ARC_CORPUS_FAISS}\")\r\ntorch.set_grad_enabled(False)\r\n```\r\n\r\nThen, as a processor of my dataset, I created a map function that calls the `dpr_dataset` for each _option_\r\n\r\n```\r\ndef generate_context(example):\r\n question_text = example['question']\r\n for option in example['options']:\r\n question_with_option = question_text + \" \" + option['option_text']\r\n tokenize_text = question_tokenizer(question_with_option, return_tensors=\"pt\").to(device)\r\n question_embed = (\r\n question_encoder(**tokenize_text)\r\n )[0][0].cpu().numpy()\r\n _, retrieved_examples = dpr_dataset.get_nearest_examples(\r\n \"embeddings\", question_embed, k=10\r\n )\r\n # option[\"option_context\"] = retrieved_examples[\"text\"]\r\n # option[\"option_context\"] = \" \".join(option[\"option_context\"]).strip()\r\n #result_dict = {\r\n # 'example_id': example['example_id'],\r\n # 'answer': example['answer'],\r\n # 'question': question_text,\r\n #options': example['options']\r\n # }\r\n return example\r\n```\r\n\r\nI intentionally commented on this portion of the code.\r\n\r\nBut when I call the `map` method, `ds_with_context = dataset.map(generate_context,load_from_cache_file=False)`\r\n\r\nIt calls the following error:\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nTypeError Traceback (most recent call last)\r\n in \r\n----> 1 ds_with_context = dataset.map(generate_context,load_from_cache_file=False)\r\n\r\n~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/dataset_dict.py in map(self, function, with_indices, input_columns, batched, batch_size, remove_columns, keep_in_memory, load_from_cache_file, cache_file_names, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc)\r\n 301 num_proc=num_proc,\r\n 302 )\r\n--> 303 for k, dataset in self.items()\r\n 304 }\r\n 305 )\r\n\r\n~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/dataset_dict.py in (.0)\r\n 301 num_proc=num_proc,\r\n 302 )\r\n--> 303 for k, dataset in self.items()\r\n 304 }\r\n 305 )\r\n\r\n~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint)\r\n 1257 fn_kwargs=fn_kwargs,\r\n 1258 new_fingerprint=new_fingerprint,\r\n-> 1259 update_data=update_data,\r\n 1260 )\r\n 1261 else:\r\n\r\n~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs)\r\n 155 }\r\n 156 # apply actual function\r\n--> 157 out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n 158 datasets: List[\"Dataset\"] = list(out.values()) if isinstance(out, dict) else [out]\r\n 159 # re-apply format to the output\r\n\r\n~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/fingerprint.py in wrapper(*args, **kwargs)\r\n 156 kwargs_for_fingerprint[\"fingerprint_name\"] = fingerprint_name\r\n 157 kwargs[fingerprint_name] = update_fingerprint(\r\n--> 158 self._fingerprint, transform, kwargs_for_fingerprint\r\n 159 )\r\n 160 \r\n\r\n~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/fingerprint.py in update_fingerprint(fingerprint, transform, transform_args)\r\n 103 for key in sorted(transform_args):\r\n 104 hasher.update(key)\r\n--> 105 hasher.update(transform_args[key])\r\n 106 return hasher.hexdigest()\r\n 107 \r\n\r\n~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/fingerprint.py in update(self, value)\r\n 55 def update(self, value):\r\n 56 self.m.update(f\"=={type(value)}==\".encode(\"utf8\"))\r\n---> 57 self.m.update(self.hash(value).encode(\"utf-8\"))\r\n 58 \r\n 59 def hexdigest(self):\r\n\r\n~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/fingerprint.py in hash(cls, value)\r\n 51 return cls.dispatch[type(value)](cls, value)\r\n 52 else:\r\n---> 53 return cls.hash_default(value)\r\n 54 \r\n 55 def update(self, value):\r\n\r\n~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/fingerprint.py in hash_default(cls, value)\r\n 44 @classmethod\r\n 45 def hash_default(cls, value):\r\n---> 46 return cls.hash_bytes(dumps(value))\r\n 47 \r\n 48 @classmethod\r\n\r\n~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/utils/py_utils.py in dumps(obj)\r\n 387 file = StringIO()\r\n 388 with _no_cache_fields(obj):\r\n--> 389 dump(obj, file)\r\n 390 return file.getvalue()\r\n 391 \r\n\r\n~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/utils/py_utils.py in dump(obj, file)\r\n 359 def dump(obj, file):\r\n 360 \"\"\"pickle an object to a file\"\"\"\r\n--> 361 Pickler(file, recurse=True).dump(obj)\r\n 362 return\r\n 363 \r\n\r\n~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/dill/_dill.py in dump(self, obj)\r\n 452 raise PicklingError(msg)\r\n 453 else:\r\n--> 454 StockPickler.dump(self, obj)\r\n 455 stack.clear() # clear record of 'recursion-sensitive' pickled objects\r\n 456 return\r\n\r\n/usr/lib/python3.7/pickle.py in dump(self, obj)\r\n 435 if self.proto >= 4:\r\n 436 self.framer.start_framing()\r\n--> 437 self.save(obj)\r\n 438 self.write(STOP)\r\n 439 self.framer.end_framing()\r\n\r\n/usr/lib/python3.7/pickle.py in save(self, obj, save_persistent_id)\r\n 502 f = self.dispatch.get(t)\r\n 503 if f is not None:\r\n--> 504 f(self, obj) # Call unbound method with explicit self\r\n 505 return\r\n 506 \r\n\r\n~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/utils/py_utils.py in save_function(pickler, obj)\r\n 554 dill._dill._create_function,\r\n 555 (obj.__code__, globs, obj.__name__, obj.__defaults__, obj.__closure__, obj.__dict__, fkwdefaults),\r\n--> 556 obj=obj,\r\n 557 )\r\n 558 else:\r\n\r\n/usr/lib/python3.7/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n 636 else:\r\n 637 save(func)\r\n--> 638 save(args)\r\n 639 write(REDUCE)\r\n 640 \r\n\r\n/usr/lib/python3.7/pickle.py in save(self, obj, save_persistent_id)\r\n 502 f = self.dispatch.get(t)\r\n 503 if f is not None:\r\n--> 504 f(self, obj) # Call unbound method with explicit self\r\n 505 return\r\n 506 \r\n\r\n/usr/lib/python3.7/pickle.py in save_tuple(self, obj)\r\n 784 write(MARK)\r\n 785 for element in obj:\r\n--> 786 save(element)\r\n 787 \r\n 788 if id(obj) in memo:\r\n\r\n/usr/lib/python3.7/pickle.py in save(self, obj, save_persistent_id)\r\n 502 f = self.dispatch.get(t)\r\n 503 if f is not None:\r\n--> 504 f(self, obj) # Call unbound method with explicit self\r\n 505 return\r\n 506 \r\n\r\n~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/dill/_dill.py in save_module_dict(pickler, obj)\r\n 939 # we only care about session the first pass thru\r\n 940 pickler._session = False\r\n--> 941 StockPickler.save_dict(pickler, obj)\r\n 942 log.info(\"# D2\")\r\n 943 return\r\n\r\n/usr/lib/python3.7/pickle.py in save_dict(self, obj)\r\n 854 \r\n 855 self.memoize(obj)\r\n--> 856 self._batch_setitems(obj.items())\r\n 857 \r\n 858 dispatch[dict] = save_dict\r\n\r\n/usr/lib/python3.7/pickle.py in _batch_setitems(self, items)\r\n 880 for k, v in tmp:\r\n 881 save(k)\r\n--> 882 save(v)\r\n 883 write(SETITEMS)\r\n 884 elif n:\r\n\r\n/usr/lib/python3.7/pickle.py in save(self, obj, save_persistent_id)\r\n 547 \r\n 548 # Save the reduce() output and finally memoize the object\r\n--> 549 self.save_reduce(obj=obj, *rv)\r\n 550 \r\n 551 def persistent_id(self, obj):\r\n\r\n/usr/lib/python3.7/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n 660 \r\n 661 if state is not None:\r\n--> 662 save(state)\r\n 663 write(BUILD)\r\n 664 \r\n\r\n/usr/lib/python3.7/pickle.py in save(self, obj, save_persistent_id)\r\n 502 f = self.dispatch.get(t)\r\n 503 if f is not None:\r\n--> 504 f(self, obj) # Call unbound method with explicit self\r\n 505 return\r\n 506 \r\n\r\n~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/dill/_dill.py in save_module_dict(pickler, obj)\r\n 939 # we only care about session the first pass thru\r\n 940 pickler._session = False\r\n--> 941 StockPickler.save_dict(pickler, obj)\r\n 942 log.info(\"# D2\")\r\n 943 return\r\n\r\n/usr/lib/python3.7/pickle.py in save_dict(self, obj)\r\n 854 \r\n 855 self.memoize(obj)\r\n--> 856 self._batch_setitems(obj.items())\r\n 857 \r\n 858 dispatch[dict] = save_dict\r\n\r\n/usr/lib/python3.7/pickle.py in _batch_setitems(self, items)\r\n 880 for k, v in tmp:\r\n 881 save(k)\r\n--> 882 save(v)\r\n 883 write(SETITEMS)\r\n 884 elif n:\r\n\r\n/usr/lib/python3.7/pickle.py in save(self, obj, save_persistent_id)\r\n 502 f = self.dispatch.get(t)\r\n 503 if f is not None:\r\n--> 504 f(self, obj) # Call unbound method with explicit self\r\n 505 return\r\n 506 \r\n\r\n~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/dill/_dill.py in save_module_dict(pickler, obj)\r\n 939 # we only care about session the first pass thru\r\n 940 pickler._session = False\r\n--> 941 StockPickler.save_dict(pickler, obj)\r\n 942 log.info(\"# D2\")\r\n 943 return\r\n\r\n/usr/lib/python3.7/pickle.py in save_dict(self, obj)\r\n 854 \r\n 855 self.memoize(obj)\r\n--> 856 self._batch_setitems(obj.items())\r\n 857 \r\n 858 dispatch[dict] = save_dict\r\n\r\n/usr/lib/python3.7/pickle.py in _batch_setitems(self, items)\r\n 885 k, v = tmp[0]\r\n 886 save(k)\r\n--> 887 save(v)\r\n 888 write(SETITEM)\r\n 889 # else tmp is empty, and we're done\r\n\r\n/usr/lib/python3.7/pickle.py in save(self, obj, save_persistent_id)\r\n 547 \r\n 548 # Save the reduce() output and finally memoize the object\r\n--> 549 self.save_reduce(obj=obj, *rv)\r\n 550 \r\n 551 def persistent_id(self, obj):\r\n\r\n/usr/lib/python3.7/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n 660 \r\n 661 if state is not None:\r\n--> 662 save(state)\r\n 663 write(BUILD)\r\n 664 \r\n\r\n/usr/lib/python3.7/pickle.py in save(self, obj, save_persistent_id)\r\n 502 f = self.dispatch.get(t)\r\n 503 if f is not None:\r\n--> 504 f(self, obj) # Call unbound method with explicit self\r\n 505 return\r\n 506 \r\n\r\n~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/dill/_dill.py in save_module_dict(pickler, obj)\r\n 939 # we only care about session the first pass thru\r\n 940 pickler._session = False\r\n--> 941 StockPickler.save_dict(pickler, obj)\r\n 942 log.info(\"# D2\")\r\n 943 return\r\n\r\n/usr/lib/python3.7/pickle.py in save_dict(self, obj)\r\n 854 \r\n 855 self.memoize(obj)\r\n--> 856 self._batch_setitems(obj.items())\r\n 857 \r\n 858 dispatch[dict] = save_dict\r\n\r\n/usr/lib/python3.7/pickle.py in _batch_setitems(self, items)\r\n 880 for k, v in tmp:\r\n 881 save(k)\r\n--> 882 save(v)\r\n 883 write(SETITEMS)\r\n 884 elif n:\r\n\r\n/usr/lib/python3.7/pickle.py in save(self, obj, save_persistent_id)\r\n 547 \r\n 548 # Save the reduce() output and finally memoize the object\r\n--> 549 self.save_reduce(obj=obj, *rv)\r\n 550 \r\n 551 def persistent_id(self, obj):\r\n\r\n/usr/lib/python3.7/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n 660 \r\n 661 if state is not None:\r\n--> 662 save(state)\r\n 663 write(BUILD)\r\n 664 \r\n\r\n/usr/lib/python3.7/pickle.py in save(self, obj, save_persistent_id)\r\n 502 f = self.dispatch.get(t)\r\n 503 if f is not None:\r\n--> 504 f(self, obj) # Call unbound method with explicit self\r\n 505 return\r\n 506 \r\n\r\n~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/dill/_dill.py in save_module_dict(pickler, obj)\r\n 939 # we only care about session the first pass thru\r\n 940 pickler._session = False\r\n--> 941 StockPickler.save_dict(pickler, obj)\r\n 942 log.info(\"# D2\")\r\n 943 return\r\n\r\n/usr/lib/python3.7/pickle.py in save_dict(self, obj)\r\n 854 \r\n 855 self.memoize(obj)\r\n--> 856 self._batch_setitems(obj.items())\r\n 857 \r\n 858 dispatch[dict] = save_dict\r\n\r\n/usr/lib/python3.7/pickle.py in _batch_setitems(self, items)\r\n 885 k, v = tmp[0]\r\n 886 save(k)\r\n--> 887 save(v)\r\n 888 write(SETITEM)\r\n 889 # else tmp is empty, and we're done\r\n\r\n/usr/lib/python3.7/pickle.py in save(self, obj, save_persistent_id)\r\n 522 reduce = getattr(obj, \"__reduce_ex__\", None)\r\n 523 if reduce is not None:\r\n--> 524 rv = reduce(self.proto)\r\n 525 else:\r\n 526 reduce = getattr(obj, \"__reduce__\", None)\r\n\r\nTypeError: can't pickle SwigPyObject objects\r\n```\r\n\r\nWhich I have no idea how to solve/deal with it\r\n\r\n"},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1805/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 0\n}"},"timeline_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1805/timeline"},"performed_via_github_app":{"kind":"null"},"state_reason":{"kind":"string","value":"completed"}}},{"rowIdx":2246,"cells":{"url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1803"},"repository_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets"},"labels_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1803/labels{/name}"},"comments_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1803/comments"},"events_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1803/events"},"html_url":{"kind":"string","value":"https://github.com/huggingface/datasets/issues/1803"},"id":{"kind":"number","value":798243904,"string":"798,243,904"},"node_id":{"kind":"string","value":"MDU6SXNzdWU3OTgyNDM5MDQ="},"number":{"kind":"number","value":1803,"string":"1,803"},"title":{"kind":"string","value":"Querying examples from big datasets is slower than small datasets"},"user":{"kind":"string","value":"{\n \"login\": \"lhoestq\",\n \"id\": 42851186,\n \"node_id\": \"MDQ6VXNlcjQyODUxMTg2\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/42851186?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/lhoestq\",\n \"html_url\": \"https://github.com/lhoestq\",\n \"followers_url\": \"https://api.github.com/users/lhoestq/followers\",\n \"following_url\": \"https://api.github.com/users/lhoestq/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/lhoestq/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/lhoestq/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/lhoestq/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/lhoestq/orgs\",\n \"repos_url\": \"https://api.github.com/users/lhoestq/repos\",\n \"events_url\": \"https://api.github.com/users/lhoestq/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/lhoestq/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n}"},"labels":{"kind":"list like","value":[],"string":"[]"},"state":{"kind":"string","value":"closed"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["Hello, @lhoestq / @gaceladri : We have been seeing similar behavior with bigger datasets, where querying time increases. Are you folks aware of any solution that fixes this problem yet? ","Hi ! I'm pretty sure that it can be fixed by using the Arrow IPC file format instead of the raw streaming format but I haven't tested yet.\r\nI'll take a look at it soon and let you know","My workaround is to shard the dataset into splits in my ssd disk and feed the data in different training sessions. But it is a bit of a pain when we need to reload the last training session with the rest of the split with the Trainer in transformers.\r\n\r\nI mean, when I split the training and then reloads the model and optimizer, it not gets the correct global_status of the optimizer, so I need to hardcode some things. I'm planning to open an issue in transformers and think about it.\r\n```\r\nfrom datasets import load_dataset\r\n\r\nbook_corpus = load_dataset(\"bookcorpus\", split=\"train[:25%]\")\r\nwikicorpus = load_dataset(\"wikicorpus\", split=\"train[:25%]\")\r\nopenwebtext = load_dataset(\"openwebtext\", split=\"train[:25%]\")\r\n\r\nbig_dataset = datasets.concatenate_datasets([wikicorpus, openwebtext, book_corpus])\r\nbig_dataset.shuffle(seed=42)\r\nbig_dataset = big_dataset.map(encode, batched=True, num_proc=20, load_from_cache_file=True, writer_batch_size=5000)\r\nbig_dataset.set_format(type='torch', columns=[\"text\", \"input_ids\", \"attention_mask\", \"token_type_ids\"])\r\n\r\n\r\ntraining_args = TrainingArguments(\r\n output_dir=\"./linear_bert\",\r\n overwrite_output_dir=True,\r\n per_device_train_batch_size=71,\r\n save_steps=500,\r\n save_total_limit=10,\r\n logging_first_step=True,\r\n logging_steps=100,\r\n gradient_accumulation_steps=9,\r\n fp16=True,\r\n dataloader_num_workers=20,\r\n warmup_steps=24000,\r\n learning_rate=0.000545205002870214,\r\n adam_epsilon=1e-6,\r\n adam_beta2=0.98,\r\n weight_decay=0.01,\r\n max_steps=138974, # the total number of steps after concatenating 100% datasets\r\n max_grad_norm=1.0,\r\n)\r\n\r\ntrainer = Trainer(\r\n model=model,\r\n args=training_args,\r\n data_collator=data_collator,\r\n train_dataset=big_dataset,\r\n tokenizer=tokenizer))\r\n```\r\n\r\nI do one training pass with the total steps of this shard and I use len(bbig)/batchsize to stop the training (hardcoded in the trainer.py) when I pass over all the examples in this split.\r\n\r\nNow Im working, I will edit the comment with a more elaborated answer when I left the work.","I just tested and using the Arrow File format doesn't improve the speed... This will need further investigation.\r\n\r\nMy guess is that it has to iterate over the record batches or chunks of a ChunkedArray in order to retrieve elements.\r\n\r\nHowever if we know in advance in which chunk the element is, and at what index it is, then we can access it instantaneously. But this requires dealing with the chunked arrays instead of the pyarrow Table directly which is not practical.","I have a dataset with about 2.7 million rows (which I'm loading via `load_from_disk`), and I need to fetch around 300k (particular) rows of it, by index. Currently this is taking a really long time (~8 hours). I tried sharding the large dataset but overall it doesn't change how long it takes to fetch the desired rows.\r\n\r\nI actually have enough RAM that I could fit the large dataset in memory. Would having the large dataset in memory speed up querying? To find out, I tried to load (a column of) the large dataset into memory like this:\r\n```\r\ncolumn_data = large_ds['column_name']\r\n```\r\nbut in itself this takes a really long time.\r\n\r\nI'm pretty stuck - do you have any ideas what I should do? ","Hi ! Feel free to post a message on the [forum](https://discuss.huggingface.co/c/datasets/10). I'd be happy to help you with this.\r\n\r\nIn your post on the forum, feel free to add more details about your setup:\r\nWhat are column names and types of your dataset ?\r\nHow was the dataset constructed ?\r\nIs the dataset shuffled ?\r\nIs the dataset tokenized ?\r\nAre you on a SSD or an HDD ?\r\n\r\nI'm sure we can figure something out.\r\nFor example on my laptop I can access the 6 millions articles from wikipedia in less than a minute.","Thanks @lhoestq, I've [posted on the forum](https://discuss.huggingface.co/t/fetching-rows-of-a-large-dataset-by-index/4271?u=abisee).","Fixed by #2122."],"string":"[\n \"Hello, @lhoestq / @gaceladri : We have been seeing similar behavior with bigger datasets, where querying time increases. Are you folks aware of any solution that fixes this problem yet? \",\n \"Hi ! I'm pretty sure that it can be fixed by using the Arrow IPC file format instead of the raw streaming format but I haven't tested yet.\\r\\nI'll take a look at it soon and let you know\",\n \"My workaround is to shard the dataset into splits in my ssd disk and feed the data in different training sessions. But it is a bit of a pain when we need to reload the last training session with the rest of the split with the Trainer in transformers.\\r\\n\\r\\nI mean, when I split the training and then reloads the model and optimizer, it not gets the correct global_status of the optimizer, so I need to hardcode some things. I'm planning to open an issue in transformers and think about it.\\r\\n```\\r\\nfrom datasets import load_dataset\\r\\n\\r\\nbook_corpus = load_dataset(\\\"bookcorpus\\\", split=\\\"train[:25%]\\\")\\r\\nwikicorpus = load_dataset(\\\"wikicorpus\\\", split=\\\"train[:25%]\\\")\\r\\nopenwebtext = load_dataset(\\\"openwebtext\\\", split=\\\"train[:25%]\\\")\\r\\n\\r\\nbig_dataset = datasets.concatenate_datasets([wikicorpus, openwebtext, book_corpus])\\r\\nbig_dataset.shuffle(seed=42)\\r\\nbig_dataset = big_dataset.map(encode, batched=True, num_proc=20, load_from_cache_file=True, writer_batch_size=5000)\\r\\nbig_dataset.set_format(type='torch', columns=[\\\"text\\\", \\\"input_ids\\\", \\\"attention_mask\\\", \\\"token_type_ids\\\"])\\r\\n\\r\\n\\r\\ntraining_args = TrainingArguments(\\r\\n output_dir=\\\"./linear_bert\\\",\\r\\n overwrite_output_dir=True,\\r\\n per_device_train_batch_size=71,\\r\\n save_steps=500,\\r\\n save_total_limit=10,\\r\\n logging_first_step=True,\\r\\n logging_steps=100,\\r\\n gradient_accumulation_steps=9,\\r\\n fp16=True,\\r\\n dataloader_num_workers=20,\\r\\n warmup_steps=24000,\\r\\n learning_rate=0.000545205002870214,\\r\\n adam_epsilon=1e-6,\\r\\n adam_beta2=0.98,\\r\\n weight_decay=0.01,\\r\\n max_steps=138974, # the total number of steps after concatenating 100% datasets\\r\\n max_grad_norm=1.0,\\r\\n)\\r\\n\\r\\ntrainer = Trainer(\\r\\n model=model,\\r\\n args=training_args,\\r\\n data_collator=data_collator,\\r\\n train_dataset=big_dataset,\\r\\n tokenizer=tokenizer))\\r\\n```\\r\\n\\r\\nI do one training pass with the total steps of this shard and I use len(bbig)/batchsize to stop the training (hardcoded in the trainer.py) when I pass over all the examples in this split.\\r\\n\\r\\nNow Im working, I will edit the comment with a more elaborated answer when I left the work.\",\n \"I just tested and using the Arrow File format doesn't improve the speed... This will need further investigation.\\r\\n\\r\\nMy guess is that it has to iterate over the record batches or chunks of a ChunkedArray in order to retrieve elements.\\r\\n\\r\\nHowever if we know in advance in which chunk the element is, and at what index it is, then we can access it instantaneously. But this requires dealing with the chunked arrays instead of the pyarrow Table directly which is not practical.\",\n \"I have a dataset with about 2.7 million rows (which I'm loading via `load_from_disk`), and I need to fetch around 300k (particular) rows of it, by index. Currently this is taking a really long time (~8 hours). I tried sharding the large dataset but overall it doesn't change how long it takes to fetch the desired rows.\\r\\n\\r\\nI actually have enough RAM that I could fit the large dataset in memory. Would having the large dataset in memory speed up querying? To find out, I tried to load (a column of) the large dataset into memory like this:\\r\\n```\\r\\ncolumn_data = large_ds['column_name']\\r\\n```\\r\\nbut in itself this takes a really long time.\\r\\n\\r\\nI'm pretty stuck - do you have any ideas what I should do? \",\n \"Hi ! Feel free to post a message on the [forum](https://discuss.huggingface.co/c/datasets/10). I'd be happy to help you with this.\\r\\n\\r\\nIn your post on the forum, feel free to add more details about your setup:\\r\\nWhat are column names and types of your dataset ?\\r\\nHow was the dataset constructed ?\\r\\nIs the dataset shuffled ?\\r\\nIs the dataset tokenized ?\\r\\nAre you on a SSD or an HDD ?\\r\\n\\r\\nI'm sure we can figure something out.\\r\\nFor example on my laptop I can access the 6 millions articles from wikipedia in less than a minute.\",\n \"Thanks @lhoestq, I've [posted on the forum](https://discuss.huggingface.co/t/fetching-rows-of-a-large-dataset-by-index/4271?u=abisee).\",\n \"Fixed by #2122.\"\n]"},"created_at":{"kind":"timestamp","value":"2021-02-01T11:08:23","string":"2021-02-01T11:08:23"},"updated_at":{"kind":"timestamp","value":"2021-08-04T18:11:01","string":"2021-08-04T18:11:01"},"closed_at":{"kind":"timestamp","value":"2021-08-04T18:10:42","string":"2021-08-04T18:10:42"},"author_association":{"kind":"string","value":"MEMBER"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"After some experiments with bookcorpus I noticed that querying examples from big datasets is slower than small datasets.\r\nFor example\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nb1 = load_dataset(\"bookcorpus\", split=\"train[:1%]\")\r\nb50 = load_dataset(\"bookcorpus\", split=\"train[:50%]\")\r\nb100 = load_dataset(\"bookcorpus\", split=\"train[:100%]\")\r\n\r\n%timeit _ = b1[-1] \r\n# 12.2 µs ± 70.4 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\r\n\r\n%timeit _ = b50[-1] \r\n# 92.5 µs ± 1.24 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\r\n\r\n%timeit _ = b100[-1] \r\n# 177 µs ± 3.13 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\r\n\r\n```\r\n\r\nIt looks like the time to fetch the example increases with the size of the dataset.\r\n\r\nThis is maybe due to the use of the Arrow streaming format to store the data on disk. I guess pyarrow needs to iterate through the file as a stream to find the queried sample.\r\n\r\nMaybe switching to the Arrow IPC file format could help fixing this issue.\r\n\r\nIndeed according to the [documentation](https://arrow.apache.org/docs/format/Columnar.html?highlight=arrow1#ipc-file-format), it's identical to the streaming format except that it contains the memory offsets of each sample, which could fix the issue:\r\n> We define a “file format” supporting random access that is build with the stream format. The file starts and ends with a magic string ARROW1 (plus padding). What follows in the file is identical to the stream format. At the end of the file, we write a footer containing a redundant copy of the schema (which is a part of the streaming format) plus memory offsets and sizes for each of the data blocks in the file. This enables random access any record batch in the file. See File.fbs for the precise details of the file footer.\r\n\r\ncc @gaceladri since it can help speed up your training when this one is fixed."},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1803/reactions\",\n \"total_count\": 3,\n \"+1\": 3,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 0\n}"},"timeline_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1803/timeline"},"performed_via_github_app":{"kind":"null"},"state_reason":{"kind":"string","value":"completed"}}},{"rowIdx":2247,"cells":{"url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1797"},"repository_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets"},"labels_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1797/labels{/name}"},"comments_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1797/comments"},"events_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1797/events"},"html_url":{"kind":"string","value":"https://github.com/huggingface/datasets/issues/1797"},"id":{"kind":"number","value":797357901,"string":"797,357,901"},"node_id":{"kind":"string","value":"MDU6SXNzdWU3OTczNTc5MDE="},"number":{"kind":"number","value":1797,"string":"1,797"},"title":{"kind":"string","value":"Connection error"},"user":{"kind":"string","value":"{\n \"login\": \"smile0925\",\n \"id\": 46243662,\n \"node_id\": \"MDQ6VXNlcjQ2MjQzNjYy\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/46243662?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/smile0925\",\n \"html_url\": \"https://github.com/smile0925\",\n \"followers_url\": \"https://api.github.com/users/smile0925/followers\",\n \"following_url\": \"https://api.github.com/users/smile0925/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/smile0925/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/smile0925/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/smile0925/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/smile0925/orgs\",\n \"repos_url\": \"https://api.github.com/users/smile0925/repos\",\n \"events_url\": \"https://api.github.com/users/smile0925/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/smile0925/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n}"},"labels":{"kind":"list like","value":[],"string":"[]"},"state":{"kind":"string","value":"closed"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["Hi ! For future references let me add a link to our discussion here : https://github.com/huggingface/datasets/issues/759#issuecomment-770684693\r\n\r\nLet me know if you manage to fix your proxy issue or if we can do something on our end to help you :)"],"string":"[\n \"Hi ! For future references let me add a link to our discussion here : https://github.com/huggingface/datasets/issues/759#issuecomment-770684693\\r\\n\\r\\nLet me know if you manage to fix your proxy issue or if we can do something on our end to help you :)\"\n]"},"created_at":{"kind":"timestamp","value":"2021-01-30T07:32:45","string":"2021-01-30T07:32:45"},"updated_at":{"kind":"timestamp","value":"2021-08-04T18:09:37","string":"2021-08-04T18:09:37"},"closed_at":{"kind":"timestamp","value":"2021-08-04T18:09:37","string":"2021-08-04T18:09:37"},"author_association":{"kind":"string","value":"NONE"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"Hi\r\nI am hitting to the error, help me and thanks.\r\n\r\n`train_data = datasets.load_dataset(\"xsum\", split=\"train\")`\r\n`ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/xsum/xsum.py`"},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1797/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 0\n}"},"timeline_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1797/timeline"},"performed_via_github_app":{"kind":"null"},"state_reason":{"kind":"string","value":"completed"}}},{"rowIdx":2248,"cells":{"url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1796"},"repository_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets"},"labels_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1796/labels{/name}"},"comments_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1796/comments"},"events_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1796/events"},"html_url":{"kind":"string","value":"https://github.com/huggingface/datasets/issues/1796"},"id":{"kind":"number","value":797329905,"string":"797,329,905"},"node_id":{"kind":"string","value":"MDU6SXNzdWU3OTczMjk5MDU="},"number":{"kind":"number","value":1796,"string":"1,796"},"title":{"kind":"string","value":"Filter on dataset too much slowww"},"user":{"kind":"string","value":"{\n \"login\": \"ayubSubhaniya\",\n \"id\": 20911334,\n \"node_id\": \"MDQ6VXNlcjIwOTExMzM0\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/20911334?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/ayubSubhaniya\",\n \"html_url\": \"https://github.com/ayubSubhaniya\",\n \"followers_url\": \"https://api.github.com/users/ayubSubhaniya/followers\",\n \"following_url\": \"https://api.github.com/users/ayubSubhaniya/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/ayubSubhaniya/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/ayubSubhaniya/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/ayubSubhaniya/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/ayubSubhaniya/orgs\",\n \"repos_url\": \"https://api.github.com/users/ayubSubhaniya/repos\",\n \"events_url\": \"https://api.github.com/users/ayubSubhaniya/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/ayubSubhaniya/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n}"},"labels":{"kind":"list like","value":[],"string":"[]"},"state":{"kind":"string","value":"open"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["When I use the filter on the arrow table directly, it works like butter. But I can't find a way to update the table in `Dataset` object.\r\n\r\n```\r\nds_table = dataset.data.filter(mask=dataset['flag'])\r\n```","@thomwolf @lhoestq can you guys please take a look and recommend some solution.","Hi ! Currently the filter method reads the dataset batch by batch to write a new, filtered, arrow file on disk. Therefore all the reading + writing can take some time.\r\nUsing a mask directly on the arrow table doesn't do any read or write operation therefore it's way quicker.\r\n\r\nReplacing the old table by the new one should do the job:\r\n```python\r\ndataset._data = dataset._data.filter(...)\r\n```\r\n\r\nNote: this is a **workaround** and in general users shouldn't have to do that. In particular if you did some `shuffle` or `select` before that then it would not work correctly since the indices mapping (index from `__getitem__` -> index in the table) would not be valid anymore. But if you haven't done any `shuffle`, `select`, `shard`, `train_test_split` etc. then it should work.\r\n\r\nIdeally it would be awesome to update the filter function to allow masking this way !\r\nIf you would like to give it a shot I will be happy to help :) ","Yes, would be happy to contribute. Thanks","Hi @lhoestq @ayubSubhaniya,\r\n\r\nIf there's no progress on this one, can I try working on it?\r\n\r\nThanks,\r\nGunjan","Sure @gchhablani feel free to start working on it, this would be very appreciated :)\r\nThis feature is would be really awesome, especially since arrow allows to mask really quickly and without having to rewrite the dataset on disk","Hi @lhoestq, any updates on this issue? The `filter` method is still veryyy slow 😕 ","No update so far, we haven't worked on this yet :/\r\n\r\nThough PyArrow is much more stable than 3 years ago so it would be a good time to dive into this","Hi @lhoestq, thanks a lot for the update! \r\n\r\nI would like to work on this(if possible). Could you please give me some steps regarding how should I approach this? Also any references would be great! ","I just played a bit with it to make sure using `table.filter()` is fine, but actually it seems to create a new table **in memory** :/\r\nThis is an issue since it can quickly fill the RAM, and `datasets`'s role is to make sure you can load bigger-than-memory datasets. Therefore I don't think it's a good idea in the end to use `table.filter()`\r\n\r\nAnyway I just ran OP's code an it runs in 20ms now on my side thanks to the I/O optimizations we did.\r\n\r\nAnother way to speed up `filter` is to add support pyarrow expressions though, using e.g. arrow formatting + dataset.filter (runs in 10ms on my side):\r\n\r\n```python\r\nimport pyarrow.dataset as pds\r\nimport pyarrow.compute as pc\r\n\r\nexpr = pc.field(\"flag\") == True\r\n\r\nfiltered = dataset.with_format(\"arrow\").filter(\r\n lambda t: pds.dataset(t).to_table(columns={\"mask\": expr})[0].to_numpy(),\r\n batched=True,\r\n).with_format(None)\r\n```"],"string":"[\n \"When I use the filter on the arrow table directly, it works like butter. But I can't find a way to update the table in `Dataset` object.\\r\\n\\r\\n```\\r\\nds_table = dataset.data.filter(mask=dataset['flag'])\\r\\n```\",\n \"@thomwolf @lhoestq can you guys please take a look and recommend some solution.\",\n \"Hi ! Currently the filter method reads the dataset batch by batch to write a new, filtered, arrow file on disk. Therefore all the reading + writing can take some time.\\r\\nUsing a mask directly on the arrow table doesn't do any read or write operation therefore it's way quicker.\\r\\n\\r\\nReplacing the old table by the new one should do the job:\\r\\n```python\\r\\ndataset._data = dataset._data.filter(...)\\r\\n```\\r\\n\\r\\nNote: this is a **workaround** and in general users shouldn't have to do that. In particular if you did some `shuffle` or `select` before that then it would not work correctly since the indices mapping (index from `__getitem__` -> index in the table) would not be valid anymore. But if you haven't done any `shuffle`, `select`, `shard`, `train_test_split` etc. then it should work.\\r\\n\\r\\nIdeally it would be awesome to update the filter function to allow masking this way !\\r\\nIf you would like to give it a shot I will be happy to help :) \",\n \"Yes, would be happy to contribute. Thanks\",\n \"Hi @lhoestq @ayubSubhaniya,\\r\\n\\r\\nIf there's no progress on this one, can I try working on it?\\r\\n\\r\\nThanks,\\r\\nGunjan\",\n \"Sure @gchhablani feel free to start working on it, this would be very appreciated :)\\r\\nThis feature is would be really awesome, especially since arrow allows to mask really quickly and without having to rewrite the dataset on disk\",\n \"Hi @lhoestq, any updates on this issue? The `filter` method is still veryyy slow 😕 \",\n \"No update so far, we haven't worked on this yet :/\\r\\n\\r\\nThough PyArrow is much more stable than 3 years ago so it would be a good time to dive into this\",\n \"Hi @lhoestq, thanks a lot for the update! \\r\\n\\r\\nI would like to work on this(if possible). Could you please give me some steps regarding how should I approach this? Also any references would be great! \",\n \"I just played a bit with it to make sure using `table.filter()` is fine, but actually it seems to create a new table **in memory** :/\\r\\nThis is an issue since it can quickly fill the RAM, and `datasets`'s role is to make sure you can load bigger-than-memory datasets. Therefore I don't think it's a good idea in the end to use `table.filter()`\\r\\n\\r\\nAnyway I just ran OP's code an it runs in 20ms now on my side thanks to the I/O optimizations we did.\\r\\n\\r\\nAnother way to speed up `filter` is to add support pyarrow expressions though, using e.g. arrow formatting + dataset.filter (runs in 10ms on my side):\\r\\n\\r\\n```python\\r\\nimport pyarrow.dataset as pds\\r\\nimport pyarrow.compute as pc\\r\\n\\r\\nexpr = pc.field(\\\"flag\\\") == True\\r\\n\\r\\nfiltered = dataset.with_format(\\\"arrow\\\").filter(\\r\\n lambda t: pds.dataset(t).to_table(columns={\\\"mask\\\": expr})[0].to_numpy(),\\r\\n batched=True,\\r\\n).with_format(None)\\r\\n```\"\n]"},"created_at":{"kind":"timestamp","value":"2021-01-30T04:09:19","string":"2021-01-30T04:09:19"},"updated_at":{"kind":"timestamp","value":"2024-01-19T13:25:21","string":"2024-01-19T13:25:21"},"closed_at":{"kind":"null"},"author_association":{"kind":"string","value":"NONE"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"I have a dataset with 50M rows.\r\nFor pre-processing, I need to tokenize this and filter rows with the large sequence.\r\n\r\nMy tokenization took roughly 12mins. I used `map()` with batch size 1024 and multi-process with 96 processes.\r\n\r\nWhen I applied the `filter()` function it is taking too much time. I need to filter sequences based on a boolean column.\r\nBelow are the variants I tried.\r\n1. filter() with batch size 1024, single process (takes roughly 3 hr)\r\n2. filter() with batch size 1024, 96 processes (takes 5-6 hrs ¯\\\\\\_(ツ)\\_/¯)\r\n3. filter() with loading all data in memory, only a single boolean column (never ends).\r\n\r\nCan someone please help?\r\n\r\nBelow is a sample code for small dataset.\r\n\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('glue', 'mrpc', split='train')\r\ndataset = dataset.map(lambda x: {'flag': random.randint(0,1)==1})\r\n\r\ndef _amplify(data):\r\n return data\r\n\r\ndataset = dataset.filter(_amplify, batch_size=1024, keep_in_memory=False, input_columns=['flag'])\r\n```\r\n"},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1796/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 0\n}"},"timeline_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1796/timeline"},"performed_via_github_app":{"kind":"null"},"state_reason":{"kind":"null"}}},{"rowIdx":2249,"cells":{"url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1790"},"repository_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets"},"labels_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1790/labels{/name}"},"comments_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1790/comments"},"events_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1790/events"},"html_url":{"kind":"string","value":"https://github.com/huggingface/datasets/issues/1790"},"id":{"kind":"number","value":796678157,"string":"796,678,157"},"node_id":{"kind":"string","value":"MDU6SXNzdWU3OTY2NzgxNTc="},"number":{"kind":"number","value":1790,"string":"1,790"},"title":{"kind":"string","value":"ModuleNotFoundError: No module named 'apache_beam', when specific languages."},"user":{"kind":"string","value":"{\n \"login\": \"miyamonz\",\n \"id\": 6331508,\n \"node_id\": \"MDQ6VXNlcjYzMzE1MDg=\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/6331508?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/miyamonz\",\n \"html_url\": \"https://github.com/miyamonz\",\n \"followers_url\": \"https://api.github.com/users/miyamonz/followers\",\n \"following_url\": \"https://api.github.com/users/miyamonz/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/miyamonz/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/miyamonz/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/miyamonz/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/miyamonz/orgs\",\n \"repos_url\": \"https://api.github.com/users/miyamonz/repos\",\n \"events_url\": \"https://api.github.com/users/miyamonz/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/miyamonz/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n}"},"labels":{"kind":"list like","value":[],"string":"[]"},"state":{"kind":"string","value":"open"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["Hi !\r\n\r\nApache Beam is a framework used to define data transformation pipelines. These pipeline can then be run in many runtimes: DataFlow, Spark, Flink, etc. There also exist a local runner called the DirectRunner.\r\nWikipedia is a dataset that requires some parsing, so to allow the processing to be run on this kind of runtime we're using Apache Beam.\r\n\r\nAt Hugging Face we've already processed certain versions of wikipedia (the `20200501.en` one for example) so that users can directly download the processed version instead of using Apache Beam to process it.\r\nHowever for the japanese language we haven't processed it so you'll have to run the processing on your side.\r\nSo you do need Apache Beam to process `20200501.ja`.\r\n\r\nYou can install Apache Beam with\r\n```\r\npip install apache-beam\r\n```\r\n\r\nI think we can probably improve the error message to let users know of this subtlety.\r\nWhat #498 implied is that Apache Beam is not needed when you process a dataset that doesn't use Apache Beam.","Thanks for your reply! \r\nI understood.\r\n\r\nI tried again with installing apache-beam, add ` beam_runner=\"DirectRunner\"` and an anther `mwparserfromhell` is also required so I installed it.\r\nbut, it also failed. It exited 1 without error message.\r\n\r\n```py\r\nimport datasets\r\n# BTW, 20200501.ja doesn't exist at wikipedia, so I specified date argument\r\nwiki = datasets.load_dataset(\"wikipedia\", language=\"ja\", date=\"20210120\", cache_dir=\"./datasets\", beam_runner=\"DirectRunner\")\r\nprint(wiki)\r\n```\r\nand its log is below\r\n```\r\nUsing custom data configuration 20210120.ja\r\nDownloading and preparing dataset wikipedia/20210120.ja-date=20210120,language=ja (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to ./datasets/wikipedia/20210120.ja-date=20210120,language=ja/0.0.0/4021357e28509391eab2f8300d9b689e7e8f3a877ebb3d354b01577d497ebc63...\r\nKilled\r\n```\r\n\r\nI also tried on another machine because it may caused by insufficient resources.\r\n```\r\n$ python main.py\r\nUsing custom data configuration 20210120.ja\r\nDownloading and preparing dataset wikipedia/20210120.ja-date=20210120,language=ja (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to ./datasets/wikipedia/20210120.ja-date=20210120,language=ja/0.0.0/4021357e28509391eab2f8300d9b689e7e8f3a877ebb3d354b01577d497ebc63...\r\n\r\nTraceback (most recent call last):\r\n File \"main.py\", line 3, in \r\n wiki = datasets.load_dataset(\"wikipedia\", language=\"ja\", date=\"20210120\", cache_dir=\"./datasets\", beam_runner=\"DirectRunner\")\r\n File \"/home/miyamonz/.cache/pypoetry/virtualenvs/try-datasets-4t4JWXxu-py3.8/lib/python3.8/site-packages/datasets/load.py\", line 609, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/home/miyamonz/.cache/pypoetry/virtualenvs/try-datasets-4t4JWXxu-py3.8/lib/python3.8/site-packages/datasets/builder.py\", line 526, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/home/miyamonz/.cache/pypoetry/virtualenvs/try-datasets-4t4JWXxu-py3.8/lib/python3.8/site-packages/datasets/builder.py\", line 1069, in _download_and_prepare\r\n pipeline_results = pipeline.run()\r\n File \"/home/miyamonz/.cache/pypoetry/virtualenvs/try-datasets-4t4JWXxu-py3.8/lib/python3.8/site-packages/apache_beam/pipeline.py\", line 561, in run\r\n return self.runner.run_pipeline(self, self._options)\r\n File \"/home/miyamonz/.cache/pypoetry/virtualenvs/try-datasets-4t4JWXxu-py3.8/lib/python3.8/site-packages/apache_beam/runners/direct/direct_runner.py\", line 126, in run_pipeline\r\n return runner.run_pipeline(pipeline, options)\r\n File \"/home/miyamonz/.cache/pypoetry/virtualenvs/try-datasets-4t4JWXxu-py3.8/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 182, in run_pipeline\r\n self._latest_run_result = self.run_via_runner_api(\r\n File \"/home/miyamonz/.cache/pypoetry/virtualenvs/try-datasets-4t4JWXxu-py3.8/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 193, in run_via_runner_api\r\n return self.run_stages(stage_context, stages)\r\n File \"/home/miyamonz/.cache/pypoetry/virtualenvs/try-datasets-4t4JWXxu-py3.8/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 358, in run_stages\r\n stage_results = self._run_stage(\r\n File \"/home/miyamonz/.cache/pypoetry/virtualenvs/try-datasets-4t4JWXxu-py3.8/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 549, in _run_stage\r\n last_result, deferred_inputs, fired_timers = self._run_bundle(\r\n File \"/home/miyamonz/.cache/pypoetry/virtualenvs/try-datasets-4t4JWXxu-py3.8/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 595, in _run_bundle\r\n result, splits = bundle_manager.process_bundle(\r\n File \"/home/miyamonz/.cache/pypoetry/virtualenvs/try-datasets-4t4JWXxu-py3.8/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 888, in process_bundle\r\n self._send_input_to_worker(process_bundle_id, transform_id, elements)\r\n File \"/home/miyamonz/.cache/pypoetry/virtualenvs/try-datasets-4t4JWXxu-py3.8/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 765, in _send_input_to_worker\r\n data_out.write(byte_stream)\r\n File \"apache_beam/coders/stream.pyx\", line 42, in apache_beam.coders.stream.OutputStream.write\r\n File \"apache_beam/coders/stream.pyx\", line 47, in apache_beam.coders.stream.OutputStream.write\r\n File \"apache_beam/coders/stream.pyx\", line 109, in apache_beam.coders.stream.OutputStream.extend\r\nAssertionError: OutputStream realloc failed.\r\n```\r\n\r\n","Hi @miyamonz,\r\n\r\nI tried replicating this issue using the same snippet used by you. I am able to download the dataset without any issues, although I stopped it in the middle because the dataset is huge.\r\n\r\nBased on a similar issue [here](https://github.com/google-research/fixmatch/issues/23), it could be related to your environment setup, although I am just guessing here. Can you share these details?","thanks for your reply and sorry for my late response.\r\n\r\n## environment\r\nmy local machine environment info\r\n- Ubuntu on WSL2\r\n\r\n`lsb_release -a`\r\n```\r\nNo LSB modules are available.\r\nDistributor ID: Ubuntu\r\nDescription: Ubuntu 20.04.2 LTS\r\nRelease: 20.04\r\nCodename: focal\r\n```\r\n\r\nRTX 2070 super\r\nInside WSL, there is no nvidia-msi command. I don't know why.\r\nBut, `torch.cuda.is_available()` is true and when I start something ML training code GPU usage is growing up, so I think it works.\r\n\r\nFrom PowerShell, there is nvidia-smi.exe and result is below.\r\n```\r\n+-----------------------------------------------------------------------------+\r\n| NVIDIA-SMI 470.05 Driver Version: 470.05 CUDA Version: 11.3 |\r\n|-------------------------------+----------------------+----------------------+\r\n| GPU Name TCC/WDDM | Bus-Id Disp.A | Volatile Uncorr. ECC |\r\n| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\r\n| | | MIG M. |\r\n|===============================+======================+======================|\r\n| 0 NVIDIA GeForce ... WDDM | 00000000:09:00.0 On | N/A |\r\n| 0% 30C P8 19W / 175W | 523MiB / 8192MiB | 3% Default |\r\n| | | N/A |\r\n+-------------------------------+----------------------+----------------------+\r\n\r\n+-----------------------------------------------------------------------------+\r\n| Processes: |\r\n| GPU GI CI PID Type Process name GPU Memory |\r\n| ID ID Usage |\r\n|=============================================================================|\r\n| 0 N/A N/A 1728 C+G Insufficient Permissions N/A |\r\n| 0 N/A N/A 3672 C+G ...ekyb3d8bbwe\\YourPhone.exe N/A |\r\n| 0 N/A N/A 6304 C+G ...2txyewy\\TextInputHost.exe N/A |\r\n| 0 N/A N/A 8648 C+G C:\\Windows\\explorer.exe N/A |\r\n| 0 N/A N/A 9536 C+G ...y\\ShellExperienceHost.exe N/A |\r\n| 0 N/A N/A 10668 C+G ...5n1h2txyewy\\SearchApp.exe N/A |\r\n| 0 N/A N/A 10948 C+G ...artMenuExperienceHost.exe N/A |\r\n| 0 N/A N/A 11988 C+G ...8wekyb3d8bbwe\\Cortana.exe N/A |\r\n| 0 N/A N/A 12464 C+G ...cw5n1h2txyewy\\LockApp.exe N/A |\r\n| 0 N/A N/A 13280 C+G ...upport\\CEF\\Max Helper.exe N/A |\r\n| 0 N/A N/A 15948 C+G ...t\\GoogleIMEJaRenderer.exe N/A |\r\n| 0 N/A N/A 16128 C+G ...ram Files\\Slack\\Slack.exe N/A |\r\n| 0 N/A N/A 19096 C+G ...8bbwe\\WindowsTerminal.exe N/A |\r\n+-----------------------------------------------------------------------------+\r\n```\r\n\r\nI don't know what should I show in such a case. If it's not enough, please tell me some commands.\r\n\r\n---\r\n## what I did\r\nI surveyed more and I found 2 issues.\r\n\r\nAbout the first one, I wrote it as a new issue.\r\nhttps://github.com/huggingface/datasets/issues/2031\r\n\r\nThe error I mentioned in the previous comment above, which occurred on my local machine, is no longer occurring.\r\n\r\nBut, it still failed. In the previous comment, I wrote `AssertionError: OutputStream realloc failed.` happen on another machine. It also happens on my local machine.\r\n\r\nHere's what I've tried.\r\n\r\nthe wikipedia.py downloads these xml.bz2 files based on dumpstatus.json\r\nIn Japanese Wikipedia dataset that I specified, it will download these 6 files.\r\n\r\n\r\n`https://dumps.wikimedia.org/jawiki/20210120/dumpstatus.json`\r\nand filtered json based on wikipedia.py is below.\r\n```json\r\n {\r\n \"jobs\": {\r\n \"articlesmultistreamdump\": {\r\n \"files\": {\r\n \"jawiki-20210120-pages-articles-multistream1.xml-p1p114794.bz2\": {\r\n \"url\": \"/jawiki/20210120/jawiki-20210120-pages-articles-multistream1.xml-p1p114794.bz2\"\r\n },\r\n \"jawiki-20210120-pages-articles-multistream2.xml-p114795p390428.bz2\": {\r\n \"url\": \"/jawiki/20210120/jawiki-20210120-pages-articles-multistream2.xml-p114795p390428.bz2\"\r\n },\r\n \"jawiki-20210120-pages-articles-multistream3.xml-p390429p902407.bz2\": {\r\n \"url\": \"/jawiki/20210120/jawiki-20210120-pages-articles-multistream3.xml-p390429p902407.bz2\"\r\n },\r\n \"jawiki-20210120-pages-articles-multistream4.xml-p902408p1721646.bz2\": {\r\n \"url\": \"/jawiki/20210120/jawiki-20210120-pages-articles-multistream4.xml-p902408p1721646.bz2\"\r\n },\r\n \"jawiki-20210120-pages-articles-multistream5.xml-p1721647p2807947.bz2\": {\r\n \"url\": \"/jawiki/20210120/jawiki-20210120-pages-articles-multistream5.xml-p1721647p2807947.bz2\"\r\n },\r\n \"jawiki-20210120-pages-articles-multistream6.xml-p2807948p4290013.bz2\": {\r\n \"url\": \"/jawiki/20210120/jawiki-20210120-pages-articles-multistream6.xml-p2807948p4290013.bz2\"\r\n }\r\n }\r\n }\r\n }\r\n }\r\n```\r\n\r\nSo, I tried running with fewer resources by modifying this line.\r\nhttps://github.com/huggingface/datasets/blob/13a5b7db992ad5cf77895e4c0f76595314390418/datasets/wikipedia/wikipedia.py#L524\r\nI changed it like this. just change filepaths list.\r\n` | \"Initialize\" >> beam.Create(filepaths[:1])`\r\n\r\nand I added a print line inside for the loop of _extract_content.\r\nlike this `if(i % 100000 == 0): print(i)`\r\n\r\nfirst, without modification, it always stops after all _extract_content is done.\r\n\r\n- `filepaths[:1]` then it succeeded.\r\n- `filepaths[:2]` then it failed.\r\nI don't try all patterns because each pattern takes a long time.\r\n\r\n### my opinion\r\nIt seems it's successful when the entire file size is small.\r\n \r\nso, at least it doesn't file-specific issue.\r\n\r\n\r\nI don't know it's true but I think when beam_writter writes into a file, it consumes memory depends on its entire file.\r\nbut It's correct Apache Beam's behavior? I'm not familiar with this library.\r\n","I don't know if this is related, but there is this issue on the wikipedia processing that you reported at #2031 (open PR is at #2037 ) .\r\nDoes the fix your proposed at #2037 helps in your case ?\r\n\r\nAnd for information, the DirectRunner of Apache Beam is not optimized for memory intensive tasks, so you must be right when you say that it uses the memory for the entire file.","the #2037 doesn't solve my problem directly, but I found the point!\r\n\r\nhttps://github.com/huggingface/datasets/blob/349ac4398a3bcae6356f14c5754483383a60e8a4/datasets/wikipedia/wikipedia.py#L523\r\nthis `beam.transforms.Reshuffle()` cause the memory error.\r\n\r\nit makes sense if I consider the shuffle means. Beam's reshuffle seems need put all data in memory.\r\nPreviously I doubt that this line causes error, but at that time another bug showed in #2037 made error, so I can't found it.\r\n\r\nAnyway, I comment out this line, and run load_dataset, then it works!\r\n\r\n```python\r\nwiki = datasets.load_dataset(\r\n \"./wikipedia.py\",\r\n cache_dir=\"./datasets\",\r\n beam_runner=\"DirectRunner\",\r\n language=\"ja\",\r\n date=\"20210120\",\r\n)[\"train\"]\r\n```\r\n![image](https://user-images.githubusercontent.com/6331508/112283369-6a9f3300-8ccb-11eb-82e5-827bf7fddfb9.png)\r\n\r\nDataset has already shuffle function. https://github.com/huggingface/datasets/blob/349ac4398a3bcae6356f14c5754483383a60e8a4/src/datasets/arrow_dataset.py#L2069\r\nSo, though I don't know it's difference correctly, but I think Beam's reshuffle isn't be needed. How do you think?","The reshuffle is needed when you use parallelism.\r\nThe objective is to redistribute the articles evenly on the workers, since the `_extract_content` step generated many articles per file. By using reshuffle, we can split the processing of the articles of one file into several workers. Without reshuffle, all the articles of one file would be processed on the same worker that read the file, making the whole process take a very long time.","Maybe the reshuffle step can be added only if the runner is not a DirectRunner ?"],"string":"[\n \"Hi !\\r\\n\\r\\nApache Beam is a framework used to define data transformation pipelines. These pipeline can then be run in many runtimes: DataFlow, Spark, Flink, etc. There also exist a local runner called the DirectRunner.\\r\\nWikipedia is a dataset that requires some parsing, so to allow the processing to be run on this kind of runtime we're using Apache Beam.\\r\\n\\r\\nAt Hugging Face we've already processed certain versions of wikipedia (the `20200501.en` one for example) so that users can directly download the processed version instead of using Apache Beam to process it.\\r\\nHowever for the japanese language we haven't processed it so you'll have to run the processing on your side.\\r\\nSo you do need Apache Beam to process `20200501.ja`.\\r\\n\\r\\nYou can install Apache Beam with\\r\\n```\\r\\npip install apache-beam\\r\\n```\\r\\n\\r\\nI think we can probably improve the error message to let users know of this subtlety.\\r\\nWhat #498 implied is that Apache Beam is not needed when you process a dataset that doesn't use Apache Beam.\",\n \"Thanks for your reply! \\r\\nI understood.\\r\\n\\r\\nI tried again with installing apache-beam, add ` beam_runner=\\\"DirectRunner\\\"` and an anther `mwparserfromhell` is also required so I installed it.\\r\\nbut, it also failed. It exited 1 without error message.\\r\\n\\r\\n```py\\r\\nimport datasets\\r\\n# BTW, 20200501.ja doesn't exist at wikipedia, so I specified date argument\\r\\nwiki = datasets.load_dataset(\\\"wikipedia\\\", language=\\\"ja\\\", date=\\\"20210120\\\", cache_dir=\\\"./datasets\\\", beam_runner=\\\"DirectRunner\\\")\\r\\nprint(wiki)\\r\\n```\\r\\nand its log is below\\r\\n```\\r\\nUsing custom data configuration 20210120.ja\\r\\nDownloading and preparing dataset wikipedia/20210120.ja-date=20210120,language=ja (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to ./datasets/wikipedia/20210120.ja-date=20210120,language=ja/0.0.0/4021357e28509391eab2f8300d9b689e7e8f3a877ebb3d354b01577d497ebc63...\\r\\nKilled\\r\\n```\\r\\n\\r\\nI also tried on another machine because it may caused by insufficient resources.\\r\\n```\\r\\n$ python main.py\\r\\nUsing custom data configuration 20210120.ja\\r\\nDownloading and preparing dataset wikipedia/20210120.ja-date=20210120,language=ja (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to ./datasets/wikipedia/20210120.ja-date=20210120,language=ja/0.0.0/4021357e28509391eab2f8300d9b689e7e8f3a877ebb3d354b01577d497ebc63...\\r\\n\\r\\nTraceback (most recent call last):\\r\\n File \\\"main.py\\\", line 3, in \\r\\n wiki = datasets.load_dataset(\\\"wikipedia\\\", language=\\\"ja\\\", date=\\\"20210120\\\", cache_dir=\\\"./datasets\\\", beam_runner=\\\"DirectRunner\\\")\\r\\n File \\\"/home/miyamonz/.cache/pypoetry/virtualenvs/try-datasets-4t4JWXxu-py3.8/lib/python3.8/site-packages/datasets/load.py\\\", line 609, in load_dataset\\r\\n builder_instance.download_and_prepare(\\r\\n File \\\"/home/miyamonz/.cache/pypoetry/virtualenvs/try-datasets-4t4JWXxu-py3.8/lib/python3.8/site-packages/datasets/builder.py\\\", line 526, in download_and_prepare\\r\\n self._download_and_prepare(\\r\\n File \\\"/home/miyamonz/.cache/pypoetry/virtualenvs/try-datasets-4t4JWXxu-py3.8/lib/python3.8/site-packages/datasets/builder.py\\\", line 1069, in _download_and_prepare\\r\\n pipeline_results = pipeline.run()\\r\\n File \\\"/home/miyamonz/.cache/pypoetry/virtualenvs/try-datasets-4t4JWXxu-py3.8/lib/python3.8/site-packages/apache_beam/pipeline.py\\\", line 561, in run\\r\\n return self.runner.run_pipeline(self, self._options)\\r\\n File \\\"/home/miyamonz/.cache/pypoetry/virtualenvs/try-datasets-4t4JWXxu-py3.8/lib/python3.8/site-packages/apache_beam/runners/direct/direct_runner.py\\\", line 126, in run_pipeline\\r\\n return runner.run_pipeline(pipeline, options)\\r\\n File \\\"/home/miyamonz/.cache/pypoetry/virtualenvs/try-datasets-4t4JWXxu-py3.8/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\\\", line 182, in run_pipeline\\r\\n self._latest_run_result = self.run_via_runner_api(\\r\\n File \\\"/home/miyamonz/.cache/pypoetry/virtualenvs/try-datasets-4t4JWXxu-py3.8/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\\\", line 193, in run_via_runner_api\\r\\n return self.run_stages(stage_context, stages)\\r\\n File \\\"/home/miyamonz/.cache/pypoetry/virtualenvs/try-datasets-4t4JWXxu-py3.8/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\\\", line 358, in run_stages\\r\\n stage_results = self._run_stage(\\r\\n File \\\"/home/miyamonz/.cache/pypoetry/virtualenvs/try-datasets-4t4JWXxu-py3.8/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\\\", line 549, in _run_stage\\r\\n last_result, deferred_inputs, fired_timers = self._run_bundle(\\r\\n File \\\"/home/miyamonz/.cache/pypoetry/virtualenvs/try-datasets-4t4JWXxu-py3.8/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\\\", line 595, in _run_bundle\\r\\n result, splits = bundle_manager.process_bundle(\\r\\n File \\\"/home/miyamonz/.cache/pypoetry/virtualenvs/try-datasets-4t4JWXxu-py3.8/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\\\", line 888, in process_bundle\\r\\n self._send_input_to_worker(process_bundle_id, transform_id, elements)\\r\\n File \\\"/home/miyamonz/.cache/pypoetry/virtualenvs/try-datasets-4t4JWXxu-py3.8/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\\\", line 765, in _send_input_to_worker\\r\\n data_out.write(byte_stream)\\r\\n File \\\"apache_beam/coders/stream.pyx\\\", line 42, in apache_beam.coders.stream.OutputStream.write\\r\\n File \\\"apache_beam/coders/stream.pyx\\\", line 47, in apache_beam.coders.stream.OutputStream.write\\r\\n File \\\"apache_beam/coders/stream.pyx\\\", line 109, in apache_beam.coders.stream.OutputStream.extend\\r\\nAssertionError: OutputStream realloc failed.\\r\\n```\\r\\n\\r\\n\",\n \"Hi @miyamonz,\\r\\n\\r\\nI tried replicating this issue using the same snippet used by you. I am able to download the dataset without any issues, although I stopped it in the middle because the dataset is huge.\\r\\n\\r\\nBased on a similar issue [here](https://github.com/google-research/fixmatch/issues/23), it could be related to your environment setup, although I am just guessing here. Can you share these details?\",\n \"thanks for your reply and sorry for my late response.\\r\\n\\r\\n## environment\\r\\nmy local machine environment info\\r\\n- Ubuntu on WSL2\\r\\n\\r\\n`lsb_release -a`\\r\\n```\\r\\nNo LSB modules are available.\\r\\nDistributor ID: Ubuntu\\r\\nDescription: Ubuntu 20.04.2 LTS\\r\\nRelease: 20.04\\r\\nCodename: focal\\r\\n```\\r\\n\\r\\nRTX 2070 super\\r\\nInside WSL, there is no nvidia-msi command. I don't know why.\\r\\nBut, `torch.cuda.is_available()` is true and when I start something ML training code GPU usage is growing up, so I think it works.\\r\\n\\r\\nFrom PowerShell, there is nvidia-smi.exe and result is below.\\r\\n```\\r\\n+-----------------------------------------------------------------------------+\\r\\n| NVIDIA-SMI 470.05 Driver Version: 470.05 CUDA Version: 11.3 |\\r\\n|-------------------------------+----------------------+----------------------+\\r\\n| GPU Name TCC/WDDM | Bus-Id Disp.A | Volatile Uncorr. ECC |\\r\\n| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\\r\\n| | | MIG M. |\\r\\n|===============================+======================+======================|\\r\\n| 0 NVIDIA GeForce ... WDDM | 00000000:09:00.0 On | N/A |\\r\\n| 0% 30C P8 19W / 175W | 523MiB / 8192MiB | 3% Default |\\r\\n| | | N/A |\\r\\n+-------------------------------+----------------------+----------------------+\\r\\n\\r\\n+-----------------------------------------------------------------------------+\\r\\n| Processes: |\\r\\n| GPU GI CI PID Type Process name GPU Memory |\\r\\n| ID ID Usage |\\r\\n|=============================================================================|\\r\\n| 0 N/A N/A 1728 C+G Insufficient Permissions N/A |\\r\\n| 0 N/A N/A 3672 C+G ...ekyb3d8bbwe\\\\YourPhone.exe N/A |\\r\\n| 0 N/A N/A 6304 C+G ...2txyewy\\\\TextInputHost.exe N/A |\\r\\n| 0 N/A N/A 8648 C+G C:\\\\Windows\\\\explorer.exe N/A |\\r\\n| 0 N/A N/A 9536 C+G ...y\\\\ShellExperienceHost.exe N/A |\\r\\n| 0 N/A N/A 10668 C+G ...5n1h2txyewy\\\\SearchApp.exe N/A |\\r\\n| 0 N/A N/A 10948 C+G ...artMenuExperienceHost.exe N/A |\\r\\n| 0 N/A N/A 11988 C+G ...8wekyb3d8bbwe\\\\Cortana.exe N/A |\\r\\n| 0 N/A N/A 12464 C+G ...cw5n1h2txyewy\\\\LockApp.exe N/A |\\r\\n| 0 N/A N/A 13280 C+G ...upport\\\\CEF\\\\Max Helper.exe N/A |\\r\\n| 0 N/A N/A 15948 C+G ...t\\\\GoogleIMEJaRenderer.exe N/A |\\r\\n| 0 N/A N/A 16128 C+G ...ram Files\\\\Slack\\\\Slack.exe N/A |\\r\\n| 0 N/A N/A 19096 C+G ...8bbwe\\\\WindowsTerminal.exe N/A |\\r\\n+-----------------------------------------------------------------------------+\\r\\n```\\r\\n\\r\\nI don't know what should I show in such a case. If it's not enough, please tell me some commands.\\r\\n\\r\\n---\\r\\n## what I did\\r\\nI surveyed more and I found 2 issues.\\r\\n\\r\\nAbout the first one, I wrote it as a new issue.\\r\\nhttps://github.com/huggingface/datasets/issues/2031\\r\\n\\r\\nThe error I mentioned in the previous comment above, which occurred on my local machine, is no longer occurring.\\r\\n\\r\\nBut, it still failed. In the previous comment, I wrote `AssertionError: OutputStream realloc failed.` happen on another machine. It also happens on my local machine.\\r\\n\\r\\nHere's what I've tried.\\r\\n\\r\\nthe wikipedia.py downloads these xml.bz2 files based on dumpstatus.json\\r\\nIn Japanese Wikipedia dataset that I specified, it will download these 6 files.\\r\\n\\r\\n\\r\\n`https://dumps.wikimedia.org/jawiki/20210120/dumpstatus.json`\\r\\nand filtered json based on wikipedia.py is below.\\r\\n```json\\r\\n {\\r\\n \\\"jobs\\\": {\\r\\n \\\"articlesmultistreamdump\\\": {\\r\\n \\\"files\\\": {\\r\\n \\\"jawiki-20210120-pages-articles-multistream1.xml-p1p114794.bz2\\\": {\\r\\n \\\"url\\\": \\\"/jawiki/20210120/jawiki-20210120-pages-articles-multistream1.xml-p1p114794.bz2\\\"\\r\\n },\\r\\n \\\"jawiki-20210120-pages-articles-multistream2.xml-p114795p390428.bz2\\\": {\\r\\n \\\"url\\\": \\\"/jawiki/20210120/jawiki-20210120-pages-articles-multistream2.xml-p114795p390428.bz2\\\"\\r\\n },\\r\\n \\\"jawiki-20210120-pages-articles-multistream3.xml-p390429p902407.bz2\\\": {\\r\\n \\\"url\\\": \\\"/jawiki/20210120/jawiki-20210120-pages-articles-multistream3.xml-p390429p902407.bz2\\\"\\r\\n },\\r\\n \\\"jawiki-20210120-pages-articles-multistream4.xml-p902408p1721646.bz2\\\": {\\r\\n \\\"url\\\": \\\"/jawiki/20210120/jawiki-20210120-pages-articles-multistream4.xml-p902408p1721646.bz2\\\"\\r\\n },\\r\\n \\\"jawiki-20210120-pages-articles-multistream5.xml-p1721647p2807947.bz2\\\": {\\r\\n \\\"url\\\": \\\"/jawiki/20210120/jawiki-20210120-pages-articles-multistream5.xml-p1721647p2807947.bz2\\\"\\r\\n },\\r\\n \\\"jawiki-20210120-pages-articles-multistream6.xml-p2807948p4290013.bz2\\\": {\\r\\n \\\"url\\\": \\\"/jawiki/20210120/jawiki-20210120-pages-articles-multistream6.xml-p2807948p4290013.bz2\\\"\\r\\n }\\r\\n }\\r\\n }\\r\\n }\\r\\n }\\r\\n```\\r\\n\\r\\nSo, I tried running with fewer resources by modifying this line.\\r\\nhttps://github.com/huggingface/datasets/blob/13a5b7db992ad5cf77895e4c0f76595314390418/datasets/wikipedia/wikipedia.py#L524\\r\\nI changed it like this. just change filepaths list.\\r\\n` | \\\"Initialize\\\" >> beam.Create(filepaths[:1])`\\r\\n\\r\\nand I added a print line inside for the loop of _extract_content.\\r\\nlike this `if(i % 100000 == 0): print(i)`\\r\\n\\r\\nfirst, without modification, it always stops after all _extract_content is done.\\r\\n\\r\\n- `filepaths[:1]` then it succeeded.\\r\\n- `filepaths[:2]` then it failed.\\r\\nI don't try all patterns because each pattern takes a long time.\\r\\n\\r\\n### my opinion\\r\\nIt seems it's successful when the entire file size is small.\\r\\n \\r\\nso, at least it doesn't file-specific issue.\\r\\n\\r\\n\\r\\nI don't know it's true but I think when beam_writter writes into a file, it consumes memory depends on its entire file.\\r\\nbut It's correct Apache Beam's behavior? I'm not familiar with this library.\\r\\n\",\n \"I don't know if this is related, but there is this issue on the wikipedia processing that you reported at #2031 (open PR is at #2037 ) .\\r\\nDoes the fix your proposed at #2037 helps in your case ?\\r\\n\\r\\nAnd for information, the DirectRunner of Apache Beam is not optimized for memory intensive tasks, so you must be right when you say that it uses the memory for the entire file.\",\n \"the #2037 doesn't solve my problem directly, but I found the point!\\r\\n\\r\\nhttps://github.com/huggingface/datasets/blob/349ac4398a3bcae6356f14c5754483383a60e8a4/datasets/wikipedia/wikipedia.py#L523\\r\\nthis `beam.transforms.Reshuffle()` cause the memory error.\\r\\n\\r\\nit makes sense if I consider the shuffle means. Beam's reshuffle seems need put all data in memory.\\r\\nPreviously I doubt that this line causes error, but at that time another bug showed in #2037 made error, so I can't found it.\\r\\n\\r\\nAnyway, I comment out this line, and run load_dataset, then it works!\\r\\n\\r\\n```python\\r\\nwiki = datasets.load_dataset(\\r\\n \\\"./wikipedia.py\\\",\\r\\n cache_dir=\\\"./datasets\\\",\\r\\n beam_runner=\\\"DirectRunner\\\",\\r\\n language=\\\"ja\\\",\\r\\n date=\\\"20210120\\\",\\r\\n)[\\\"train\\\"]\\r\\n```\\r\\n![image](https://user-images.githubusercontent.com/6331508/112283369-6a9f3300-8ccb-11eb-82e5-827bf7fddfb9.png)\\r\\n\\r\\nDataset has already shuffle function. https://github.com/huggingface/datasets/blob/349ac4398a3bcae6356f14c5754483383a60e8a4/src/datasets/arrow_dataset.py#L2069\\r\\nSo, though I don't know it's difference correctly, but I think Beam's reshuffle isn't be needed. How do you think?\",\n \"The reshuffle is needed when you use parallelism.\\r\\nThe objective is to redistribute the articles evenly on the workers, since the `_extract_content` step generated many articles per file. By using reshuffle, we can split the processing of the articles of one file into several workers. Without reshuffle, all the articles of one file would be processed on the same worker that read the file, making the whole process take a very long time.\",\n \"Maybe the reshuffle step can be added only if the runner is not a DirectRunner ?\"\n]"},"created_at":{"kind":"timestamp","value":"2021-01-29T08:17:24","string":"2021-01-29T08:17:24"},"updated_at":{"kind":"timestamp","value":"2021-03-25T12:10:51","string":"2021-03-25T12:10:51"},"closed_at":{"kind":"null"},"author_association":{"kind":"string","value":"CONTRIBUTOR"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"```py\r\nimport datasets\r\nwiki = datasets.load_dataset('wikipedia', '20200501.ja', cache_dir='./datasets')\r\n```\r\nthen `ModuleNotFoundError: No module named 'apache_beam'` happend.\r\n\r\nThe error doesn't appear when it's '20200501.en'.\r\nI don't know Apache Beam, but according to #498 it isn't necessary when it's saved to local. is it correct?"},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1790/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 0\n}"},"timeline_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1790/timeline"},"performed_via_github_app":{"kind":"null"},"state_reason":{"kind":"null"}}},{"rowIdx":2250,"cells":{"url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1786"},"repository_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets"},"labels_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1786/labels{/name}"},"comments_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1786/comments"},"events_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1786/events"},"html_url":{"kind":"string","value":"https://github.com/huggingface/datasets/issues/1786"},"id":{"kind":"number","value":795462816,"string":"795,462,816"},"node_id":{"kind":"string","value":"MDU6SXNzdWU3OTU0NjI4MTY="},"number":{"kind":"number","value":1786,"string":"1,786"},"title":{"kind":"string","value":"How to use split dataset "},"user":{"kind":"string","value":"{\n \"login\": \"kkhan188\",\n \"id\": 78090287,\n \"node_id\": \"MDQ6VXNlcjc4MDkwMjg3\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/78090287?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/kkhan188\",\n \"html_url\": \"https://github.com/kkhan188\",\n \"followers_url\": \"https://api.github.com/users/kkhan188/followers\",\n \"following_url\": \"https://api.github.com/users/kkhan188/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/kkhan188/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/kkhan188/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/kkhan188/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/kkhan188/orgs\",\n \"repos_url\": \"https://api.github.com/users/kkhan188/repos\",\n \"events_url\": \"https://api.github.com/users/kkhan188/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/kkhan188/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n}"},"labels":{"kind":"list like","value":[{"id":1935892912,"node_id":"MDU6TGFiZWwxOTM1ODkyOTEy","url":"https://api.github.com/repos/huggingface/datasets/labels/question","name":"question","color":"d876e3","default":true,"description":"Further information is requested"}],"string":"[\n {\n \"id\": 1935892912,\n \"node_id\": \"MDU6TGFiZWwxOTM1ODkyOTEy\",\n \"url\": \"https://api.github.com/repos/huggingface/datasets/labels/question\",\n \"name\": \"question\",\n \"color\": \"d876e3\",\n \"default\": true,\n \"description\": \"Further information is requested\"\n }\n]"},"state":{"kind":"string","value":"closed"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["By default, all 3 splits will be loaded if you run the following:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"lambada\")\r\nprint(dataset[\"train\"])\r\nprint(dataset[\"valid\"])\r\n\r\n```\r\n\r\nIf you wanted to do load this manually, you could do this:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndata_files = {\r\n \"train\": \"data/lambada/train.txt\",\r\n \"valid\": \"data/lambada/valid.txt\",\r\n \"test\": \"data/lambada/test.txt\",\r\n}\r\nds = load_dataset(\"text\", data_files=data_files)\r\n```","Thank you for the quick response! "],"string":"[\n \"By default, all 3 splits will be loaded if you run the following:\\r\\n\\r\\n```python\\r\\nfrom datasets import load_dataset\\r\\ndataset = load_dataset(\\\"lambada\\\")\\r\\nprint(dataset[\\\"train\\\"])\\r\\nprint(dataset[\\\"valid\\\"])\\r\\n\\r\\n```\\r\\n\\r\\nIf you wanted to do load this manually, you could do this:\\r\\n\\r\\n```python\\r\\nfrom datasets import load_dataset\\r\\ndata_files = {\\r\\n \\\"train\\\": \\\"data/lambada/train.txt\\\",\\r\\n \\\"valid\\\": \\\"data/lambada/valid.txt\\\",\\r\\n \\\"test\\\": \\\"data/lambada/test.txt\\\",\\r\\n}\\r\\nds = load_dataset(\\\"text\\\", data_files=data_files)\\r\\n```\",\n \"Thank you for the quick response! \"\n]"},"created_at":{"kind":"timestamp","value":"2021-01-27T21:37:47","string":"2021-01-27T21:37:47"},"updated_at":{"kind":"timestamp","value":"2021-04-23T15:17:39","string":"2021-04-23T15:17:39"},"closed_at":{"kind":"timestamp","value":"2021-04-23T15:17:39","string":"2021-04-23T15:17:39"},"author_association":{"kind":"string","value":"NONE"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"![Capture1](https://user-images.githubusercontent.com/78090287/106057436-cb6a1f00-6111-11eb-8c9c-3658065b1fdf.PNG)\r\n\r\nHey,\r\nI want to split the lambada dataset into corpus, test, train and valid txt files (like penn treebank) but I am not able to achieve this. What I am doing is, executing the lambada.py file in my project but its not giving desired results. Any help will be appreciated!"},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1786/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 0\n}"},"timeline_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1786/timeline"},"performed_via_github_app":{"kind":"null"},"state_reason":{"kind":"string","value":"completed"}}},{"rowIdx":2251,"cells":{"url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1785"},"repository_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets"},"labels_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1785/labels{/name}"},"comments_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1785/comments"},"events_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1785/events"},"html_url":{"kind":"string","value":"https://github.com/huggingface/datasets/issues/1785"},"id":{"kind":"number","value":795458856,"string":"795,458,856"},"node_id":{"kind":"string","value":"MDU6SXNzdWU3OTU0NTg4NTY="},"number":{"kind":"number","value":1785,"string":"1,785"},"title":{"kind":"string","value":"Not enough disk space (Needed: Unknown size) when caching on a cluster"},"user":{"kind":"string","value":"{\n \"login\": \"olinguyen\",\n \"id\": 4341867,\n \"node_id\": \"MDQ6VXNlcjQzNDE4Njc=\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/4341867?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/olinguyen\",\n \"html_url\": \"https://github.com/olinguyen\",\n \"followers_url\": \"https://api.github.com/users/olinguyen/followers\",\n \"following_url\": \"https://api.github.com/users/olinguyen/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/olinguyen/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/olinguyen/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/olinguyen/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/olinguyen/orgs\",\n \"repos_url\": \"https://api.github.com/users/olinguyen/repos\",\n \"events_url\": \"https://api.github.com/users/olinguyen/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/olinguyen/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n}"},"labels":{"kind":"list like","value":[],"string":"[]"},"state":{"kind":"string","value":"closed"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["Hi ! \r\n\r\nWhat do you mean by \"disk_usage(\".\").free` can't compute on the cluster's shared disk\" exactly ?\r\nDoes it return 0 ?","Yes, that's right. It shows 0 free space even though there is. I suspect it might have to do with permissions on the shared disk.\r\n\r\n```python\r\n>>> disk_usage(\".\")\r\nusage(total=999999, used=999999, free=0)\r\n```","That's an interesting behavior...\r\nDo you know any other way to get the free space that works in your case ?\r\nAlso if it's a permission issue could you try fix the permissions and let mus know if that helped ?","I think its an issue on the clusters end (unclear exactly why -- maybe something with docker containers?), will close the issue","Were you able to figure it out?","@philippnoah I had fixed it with a small hack where I patched `has_sufficient_disk_space` to always return `True`. you can do that with an import without having to modify the `datasets` package","@olinguyen Thanks for the suggestion, it works but I had to to edit builder.py in the installed package. Can you please explain how were you able to do this using import?","I was able to patch the builder code in my notebook before the load data call and it works. \r\n```\r\nimport datasets\r\ndatasets.builder.has_sufficient_disk_space = lambda needed_bytes, directory='.': True\r\n```"],"string":"[\n \"Hi ! \\r\\n\\r\\nWhat do you mean by \\\"disk_usage(\\\".\\\").free` can't compute on the cluster's shared disk\\\" exactly ?\\r\\nDoes it return 0 ?\",\n \"Yes, that's right. It shows 0 free space even though there is. I suspect it might have to do with permissions on the shared disk.\\r\\n\\r\\n```python\\r\\n>>> disk_usage(\\\".\\\")\\r\\nusage(total=999999, used=999999, free=0)\\r\\n```\",\n \"That's an interesting behavior...\\r\\nDo you know any other way to get the free space that works in your case ?\\r\\nAlso if it's a permission issue could you try fix the permissions and let mus know if that helped ?\",\n \"I think its an issue on the clusters end (unclear exactly why -- maybe something with docker containers?), will close the issue\",\n \"Were you able to figure it out?\",\n \"@philippnoah I had fixed it with a small hack where I patched `has_sufficient_disk_space` to always return `True`. you can do that with an import without having to modify the `datasets` package\",\n \"@olinguyen Thanks for the suggestion, it works but I had to to edit builder.py in the installed package. Can you please explain how were you able to do this using import?\",\n \"I was able to patch the builder code in my notebook before the load data call and it works. \\r\\n```\\r\\nimport datasets\\r\\ndatasets.builder.has_sufficient_disk_space = lambda needed_bytes, directory='.': True\\r\\n```\"\n]"},"created_at":{"kind":"timestamp","value":"2021-01-27T21:30:59","string":"2021-01-27T21:30:59"},"updated_at":{"kind":"timestamp","value":"2022-11-07T16:33:03","string":"2022-11-07T16:33:03"},"closed_at":{"kind":"timestamp","value":"2021-01-30T01:07:56","string":"2021-01-30T01:07:56"},"author_association":{"kind":"string","value":"CONTRIBUTOR"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"I'm running some experiments where I'm caching datasets on a cluster and accessing it through multiple compute nodes. However, I get an error when loading the cached dataset from the shared disk.\r\n\r\nThe exact error thrown:\r\n\r\n```bash\r\n>>> load_dataset(dataset, cache_dir=\"/path/to/cluster/shared/path\")\r\nOSError: Not enough disk space. Needed: Unknown size (download: Unknown size, generated: Unknown size, post-processed: Unknown size)\r\n```\r\n\r\n\r\n[`utils.has_sufficient_disk_space`](https://github.com/huggingface/datasets/blob/8a03ab7d123a76ee744304f21ce868c75f411214/src/datasets/utils/py_utils.py#L332) fails on each job because of how the cluster system is designed (`disk_usage(\".\").free` can't compute on the cluster's shared disk).\r\n\r\n\r\nThis is exactly where the error gets thrown:\r\nhttps://github.com/huggingface/datasets/blob/master/src/datasets/builder.py#L502\r\n\r\n```python\r\nif not utils.has_sufficient_disk_space(self.info.size_in_bytes or 0, directory=self._cache_dir_root):\r\n raise IOError(\r\n \"Not enough disk space. Needed: {} (download: {}, generated: {}, post-processed: {})\".format(\r\n utils.size_str(self.info.size_in_bytes or 0),\r\n utils.size_str(self.info.download_size or 0),\r\n utils.size_str(self.info.dataset_size or 0),\r\n utils.size_str(self.info.post_processing_size or 0),\r\n )\r\n )\r\n\r\n```\r\n\r\nWhat would be a good way to circumvent this? my current fix is to manually comment out that part, but that is not ideal. \r\nWould it be possible to pass a flag to skip this check on disk space?"},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1785/reactions\",\n \"total_count\": 5,\n \"+1\": 5,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 0\n}"},"timeline_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1785/timeline"},"performed_via_github_app":{"kind":"null"},"state_reason":{"kind":"string","value":"completed"}}},{"rowIdx":2252,"cells":{"url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1784"},"repository_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets"},"labels_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1784/labels{/name}"},"comments_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1784/comments"},"events_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1784/events"},"html_url":{"kind":"string","value":"https://github.com/huggingface/datasets/issues/1784"},"id":{"kind":"number","value":794659174,"string":"794,659,174"},"node_id":{"kind":"string","value":"MDU6SXNzdWU3OTQ2NTkxNzQ="},"number":{"kind":"number","value":1784,"string":"1,784"},"title":{"kind":"string","value":"JSONDecodeError on JSON with multiple lines"},"user":{"kind":"string","value":"{\n \"login\": \"gchhablani\",\n \"id\": 29076344,\n \"node_id\": \"MDQ6VXNlcjI5MDc2MzQ0\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/29076344?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/gchhablani\",\n \"html_url\": \"https://github.com/gchhablani\",\n \"followers_url\": \"https://api.github.com/users/gchhablani/followers\",\n \"following_url\": \"https://api.github.com/users/gchhablani/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/gchhablani/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/gchhablani/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/gchhablani/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/gchhablani/orgs\",\n \"repos_url\": \"https://api.github.com/users/gchhablani/repos\",\n \"events_url\": \"https://api.github.com/users/gchhablani/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/gchhablani/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n}"},"labels":{"kind":"list like","value":[],"string":"[]"},"state":{"kind":"string","value":"closed"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["Hi !\r\n\r\nThe `json` dataset script does support this format. For example loading a dataset with this format works on my side:\r\n```json\r\n{\"key1\":11, \"key2\":12, \"key3\":13}\r\n{\"key1\":21, \"key2\":22, \"key3\":23}\r\n```\r\n\r\nCan you show the full stacktrace please ? Also which version of datasets and pyarrow are you using ?\r\n\r\n","Hi Quentin!\r\n\r\nI apologize for bothering you. There was some issue with my pyarrow version as far as I understand. I don't remember the exact version I was using as I didn't check it.\r\n\r\nI repeated it with `datasets 1.2.1` and `pyarrow 2.0.0` and it worked.\r\n\r\nClosing this issue. Again, sorry for the bother.\r\n\r\nThanks,\r\nGunjan"],"string":"[\n \"Hi !\\r\\n\\r\\nThe `json` dataset script does support this format. For example loading a dataset with this format works on my side:\\r\\n```json\\r\\n{\\\"key1\\\":11, \\\"key2\\\":12, \\\"key3\\\":13}\\r\\n{\\\"key1\\\":21, \\\"key2\\\":22, \\\"key3\\\":23}\\r\\n```\\r\\n\\r\\nCan you show the full stacktrace please ? Also which version of datasets and pyarrow are you using ?\\r\\n\\r\\n\",\n \"Hi Quentin!\\r\\n\\r\\nI apologize for bothering you. There was some issue with my pyarrow version as far as I understand. I don't remember the exact version I was using as I didn't check it.\\r\\n\\r\\nI repeated it with `datasets 1.2.1` and `pyarrow 2.0.0` and it worked.\\r\\n\\r\\nClosing this issue. Again, sorry for the bother.\\r\\n\\r\\nThanks,\\r\\nGunjan\"\n]"},"created_at":{"kind":"timestamp","value":"2021-01-27T00:19:22","string":"2021-01-27T00:19:22"},"updated_at":{"kind":"timestamp","value":"2021-01-31T08:47:18","string":"2021-01-31T08:47:18"},"closed_at":{"kind":"timestamp","value":"2021-01-31T08:47:18","string":"2021-01-31T08:47:18"},"author_association":{"kind":"string","value":"CONTRIBUTOR"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"Hello :),\r\n\r\nI have been trying to load data using a JSON file. Based on the [docs](https://huggingface.co/docs/datasets/loading_datasets.html#json-files), the following format is supported:\r\n\r\n```json\r\n{\"key1\":11, \"key2\":12, \"key3\":13}\r\n{\"key1\":21, \"key2\":22, \"key3\":23}\r\n```\r\n But, when I try loading a dataset with the same format, I get a JSONDecodeError : `JSONDecodeError: Extra data: line 2 column 1 (char 7142)`. Now, this is expected when using `json` to load a JSON file. But I was wondering if there are any special arguments to pass when using `load_dataset` as the docs suggest that this format is supported.\r\n\r\nWhen I convert the JSON file to a list of dictionaries format, I get AttributeError: `AttributeError: 'list' object has no attribute 'keys'`. So, I can't convert them to list of dictionaries either.\r\n\r\nPlease let me know :)\r\n\r\nThanks,\r\nGunjan"},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1784/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 0\n}"},"timeline_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1784/timeline"},"performed_via_github_app":{"kind":"null"},"state_reason":{"kind":"string","value":"completed"}}},{"rowIdx":2253,"cells":{"url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1783"},"repository_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets"},"labels_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1783/labels{/name}"},"comments_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1783/comments"},"events_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1783/events"},"html_url":{"kind":"string","value":"https://github.com/huggingface/datasets/issues/1783"},"id":{"kind":"number","value":794544495,"string":"794,544,495"},"node_id":{"kind":"string","value":"MDU6SXNzdWU3OTQ1NDQ0OTU="},"number":{"kind":"number","value":1783,"string":"1,783"},"title":{"kind":"string","value":"Dataset Examples Explorer"},"user":{"kind":"string","value":"{\n \"login\": \"ChewKokWah\",\n \"id\": 30875246,\n \"node_id\": \"MDQ6VXNlcjMwODc1MjQ2\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/30875246?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/ChewKokWah\",\n \"html_url\": \"https://github.com/ChewKokWah\",\n \"followers_url\": \"https://api.github.com/users/ChewKokWah/followers\",\n \"following_url\": \"https://api.github.com/users/ChewKokWah/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/ChewKokWah/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/ChewKokWah/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/ChewKokWah/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/ChewKokWah/orgs\",\n \"repos_url\": \"https://api.github.com/users/ChewKokWah/repos\",\n \"events_url\": \"https://api.github.com/users/ChewKokWah/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/ChewKokWah/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n}"},"labels":{"kind":"list like","value":[],"string":"[]"},"state":{"kind":"string","value":"closed"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["Hi @ChewKokWah,\r\n\r\nWe're working on it! In the meantime, you can still find the dataset explorer at the following URL: https://huggingface.co/datasets/viewer/","Glad to see that it still exist, this existing one is more than good enough for me, it is feature rich, simple to use and concise. \r\nHope similar feature can be retain in the future version."],"string":"[\n \"Hi @ChewKokWah,\\r\\n\\r\\nWe're working on it! In the meantime, you can still find the dataset explorer at the following URL: https://huggingface.co/datasets/viewer/\",\n \"Glad to see that it still exist, this existing one is more than good enough for me, it is feature rich, simple to use and concise. \\r\\nHope similar feature can be retain in the future version.\"\n]"},"created_at":{"kind":"timestamp","value":"2021-01-26T20:39:02","string":"2021-01-26T20:39:02"},"updated_at":{"kind":"timestamp","value":"2021-02-01T13:58:44","string":"2021-02-01T13:58:44"},"closed_at":{"kind":"timestamp","value":"2021-02-01T13:58:44","string":"2021-02-01T13:58:44"},"author_association":{"kind":"string","value":"NONE"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"In the Older version of the Dataset, there are a useful Dataset Explorer that allow user to visualize the examples (training, test and validation) of a particular dataset, it is no longer there in current version.\r\n\r\nHope HuggingFace can re-enable the feature that at least allow viewing of the first 20 examples of a particular dataset, or alternatively can extract 20 examples for each datasets and make those part of the Dataset Card Documentation."},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1783/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 0\n}"},"timeline_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1783/timeline"},"performed_via_github_app":{"kind":"null"},"state_reason":{"kind":"string","value":"completed"}}},{"rowIdx":2254,"cells":{"url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1781"},"repository_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets"},"labels_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1781/labels{/name}"},"comments_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1781/comments"},"events_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1781/events"},"html_url":{"kind":"string","value":"https://github.com/huggingface/datasets/issues/1781"},"id":{"kind":"number","value":793914556,"string":"793,914,556"},"node_id":{"kind":"string","value":"MDU6SXNzdWU3OTM5MTQ1NTY="},"number":{"kind":"number","value":1781,"string":"1,781"},"title":{"kind":"string","value":"AttributeError: module 'pyarrow' has no attribute 'PyExtensionType' during import "},"user":{"kind":"string","value":"{\n \"login\": \"PalaashAgrawal\",\n \"id\": 45964869,\n \"node_id\": \"MDQ6VXNlcjQ1OTY0ODY5\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/45964869?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/PalaashAgrawal\",\n \"html_url\": \"https://github.com/PalaashAgrawal\",\n \"followers_url\": \"https://api.github.com/users/PalaashAgrawal/followers\",\n \"following_url\": \"https://api.github.com/users/PalaashAgrawal/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/PalaashAgrawal/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/PalaashAgrawal/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/PalaashAgrawal/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/PalaashAgrawal/orgs\",\n \"repos_url\": \"https://api.github.com/users/PalaashAgrawal/repos\",\n \"events_url\": \"https://api.github.com/users/PalaashAgrawal/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/PalaashAgrawal/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n}"},"labels":{"kind":"list like","value":[],"string":"[]"},"state":{"kind":"string","value":"closed"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["Hi ! I'm not able to reproduce the issue. Can you try restarting your runtime ?\r\n\r\nThe PyExtensionType is available in pyarrow starting 0.17.1 iirc. If restarting your runtime doesn't fix this, can you try updating pyarrow ?\r\n```\r\npip install pyarrow --upgrade\r\n```","We should bump up the version test of pyarrow maybe no?\r\n\r\nhttps://github.com/huggingface/datasets/blob/master/src/datasets/__init__.py#L60","Yes indeed.\r\n\r\nAlso it looks like Pyarrow 3.0.0 got released on pypi 10 hours ago. This might be related to the bug, I'll investigate\r\nEDIT: looks like the 3.0.0 release doesn't have unexpected breaking changes for us, so I don't think the issue comes from that","Maybe colab moved to pyarrow 0.16 by default (instead of 0.14 before)?","Installing datasets installs pyarrow>=0.17.1 so in theory it doesn't matter which version of pyarrow colab has by default (which is currently pyarrow 0.14.1).\r\n\r\nAlso now the colab runtime refresh the pyarrow version automatically after the update from pip (previously you needed to restart your runtime).\r\n\r\nI guess what happened is that Colab didn't refresh pyarrow for some reason, and the AttributeError was raised *before* the pyarrow version check from `datasets` at https://github.com/huggingface/datasets/blob/master/src/datasets/__init__.py#L60","Yes colab doesn’t reload preloaded library unless you restart the instance. Maybe we should move the check on top of the init ","Yes I'll do that :)","I updated the pyarrow version check in #1782"],"string":"[\n \"Hi ! I'm not able to reproduce the issue. Can you try restarting your runtime ?\\r\\n\\r\\nThe PyExtensionType is available in pyarrow starting 0.17.1 iirc. If restarting your runtime doesn't fix this, can you try updating pyarrow ?\\r\\n```\\r\\npip install pyarrow --upgrade\\r\\n```\",\n \"We should bump up the version test of pyarrow maybe no?\\r\\n\\r\\nhttps://github.com/huggingface/datasets/blob/master/src/datasets/__init__.py#L60\",\n \"Yes indeed.\\r\\n\\r\\nAlso it looks like Pyarrow 3.0.0 got released on pypi 10 hours ago. This might be related to the bug, I'll investigate\\r\\nEDIT: looks like the 3.0.0 release doesn't have unexpected breaking changes for us, so I don't think the issue comes from that\",\n \"Maybe colab moved to pyarrow 0.16 by default (instead of 0.14 before)?\",\n \"Installing datasets installs pyarrow>=0.17.1 so in theory it doesn't matter which version of pyarrow colab has by default (which is currently pyarrow 0.14.1).\\r\\n\\r\\nAlso now the colab runtime refresh the pyarrow version automatically after the update from pip (previously you needed to restart your runtime).\\r\\n\\r\\nI guess what happened is that Colab didn't refresh pyarrow for some reason, and the AttributeError was raised *before* the pyarrow version check from `datasets` at https://github.com/huggingface/datasets/blob/master/src/datasets/__init__.py#L60\",\n \"Yes colab doesn’t reload preloaded library unless you restart the instance. Maybe we should move the check on top of the init \",\n \"Yes I'll do that :)\",\n \"I updated the pyarrow version check in #1782\"\n]"},"created_at":{"kind":"timestamp","value":"2021-01-26T04:18:35","string":"2021-01-26T04:18:35"},"updated_at":{"kind":"timestamp","value":"2022-10-05T12:37:06","string":"2022-10-05T12:37:06"},"closed_at":{"kind":"timestamp","value":"2022-10-05T12:37:06","string":"2022-10-05T12:37:06"},"author_association":{"kind":"string","value":"NONE"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"I'm using Colab. And suddenly this morning, there is this error. Have a look below!\r\n\r\n![screenshot-colab research google com-2021 01 26-08-15-36](https://user-images.githubusercontent.com/45964869/105799890-fdaf3b80-5fae-11eb-8f06-11b65cdccc30.png)\r\n"},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1781/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 0\n}"},"timeline_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1781/timeline"},"performed_via_github_app":{"kind":"null"},"state_reason":{"kind":"string","value":"completed"}}},{"rowIdx":2255,"cells":{"url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1777"},"repository_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets"},"labels_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1777/labels{/name}"},"comments_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1777/comments"},"events_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1777/events"},"html_url":{"kind":"string","value":"https://github.com/huggingface/datasets/issues/1777"},"id":{"kind":"number","value":793273770,"string":"793,273,770"},"node_id":{"kind":"string","value":"MDU6SXNzdWU3OTMyNzM3NzA="},"number":{"kind":"number","value":1777,"string":"1,777"},"title":{"kind":"string","value":"GPT2 MNLI training using run_glue.py"},"user":{"kind":"string","value":"{\n \"login\": \"nlp-student\",\n \"id\": 76427077,\n \"node_id\": \"MDQ6VXNlcjc2NDI3MDc3\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/76427077?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/nlp-student\",\n \"html_url\": \"https://github.com/nlp-student\",\n \"followers_url\": \"https://api.github.com/users/nlp-student/followers\",\n \"following_url\": \"https://api.github.com/users/nlp-student/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/nlp-student/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/nlp-student/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/nlp-student/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/nlp-student/orgs\",\n \"repos_url\": \"https://api.github.com/users/nlp-student/repos\",\n \"events_url\": \"https://api.github.com/users/nlp-student/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/nlp-student/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n}"},"labels":{"kind":"list like","value":[],"string":"[]"},"state":{"kind":"string","value":"closed"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":[],"string":"[]"},"created_at":{"kind":"timestamp","value":"2021-01-25T10:53:52","string":"2021-01-25T10:53:52"},"updated_at":{"kind":"timestamp","value":"2021-01-25T11:12:53","string":"2021-01-25T11:12:53"},"closed_at":{"kind":"timestamp","value":"2021-01-25T11:12:53","string":"2021-01-25T11:12:53"},"author_association":{"kind":"string","value":"NONE"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"Edit: I'm closing this because I actually meant to post this in `transformers `not `datasets`\r\n\r\nRunning this on Google Colab,\r\n\r\n```\r\n!python run_glue.py \\\r\n --model_name_or_path gpt2 \\\r\n --task_name mnli \\\r\n --do_train \\\r\n --do_eval \\\r\n --max_seq_length 128 \\\r\n --per_gpu_train_batch_size 10 \\\r\n --gradient_accumulation_steps 32\\\r\n --learning_rate 2e-5 \\\r\n --num_train_epochs 3.0 \\\r\n --output_dir models/gpt2/mnli/\r\n```\r\n\r\nI get the following error,\r\n\r\n```\r\n \"Asking to pad but the tokenizer does not have a padding token. \"\r\nValueError: Asking to pad but the tokenizer does not have a padding token. Please select a token to use as `pad_token` `(tokenizer.pad_token = tokenizer.eos_token e.g.)` or add a new pad token via `tokenizer.add_special_tokens({'pad_token': '[PAD]'})`.\r\n```\r\n\r\nDo I need to modify the trainer to work with GPT2 ?"},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1777/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 0\n}"},"timeline_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1777/timeline"},"performed_via_github_app":{"kind":"null"},"state_reason":{"kind":"string","value":"completed"}}},{"rowIdx":2256,"cells":{"url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1776"},"repository_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets"},"labels_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1776/labels{/name}"},"comments_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1776/comments"},"events_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1776/events"},"html_url":{"kind":"string","value":"https://github.com/huggingface/datasets/issues/1776"},"id":{"kind":"number","value":792755249,"string":"792,755,249"},"node_id":{"kind":"string","value":"MDU6SXNzdWU3OTI3NTUyNDk="},"number":{"kind":"number","value":1776,"string":"1,776"},"title":{"kind":"string","value":"[Question & Bug Report] Can we preprocess a dataset on the fly?"},"user":{"kind":"string","value":"{\n \"login\": \"shuaihuaiyi\",\n \"id\": 14048129,\n \"node_id\": \"MDQ6VXNlcjE0MDQ4MTI5\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/14048129?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/shuaihuaiyi\",\n \"html_url\": \"https://github.com/shuaihuaiyi\",\n \"followers_url\": \"https://api.github.com/users/shuaihuaiyi/followers\",\n \"following_url\": \"https://api.github.com/users/shuaihuaiyi/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/shuaihuaiyi/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/shuaihuaiyi/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/shuaihuaiyi/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/shuaihuaiyi/orgs\",\n \"repos_url\": \"https://api.github.com/users/shuaihuaiyi/repos\",\n \"events_url\": \"https://api.github.com/users/shuaihuaiyi/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/shuaihuaiyi/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n}"},"labels":{"kind":"list like","value":[],"string":"[]"},"state":{"kind":"string","value":"closed"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["We are very actively working on this. How does your dataset look like in practice (number/size/type of files)?","It's a text file with many lines (about 1B) of Chinese sentences. I use it to train language model using https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_mlm_wwm.py","Indeed I will submit a PR in a fez days to enable processing on-the-fly :)\r\nThis can be useful in language modeling for tokenization, padding etc.\r\n","any update on this issue? ...really look forward to use it ","Hi @acul3,\r\n\r\nPlease look at the discussion on a related Issue #1825. I think using `set_transform` after building from source should do.","@gchhablani thank you so much\r\n\r\nwill try look at it"],"string":"[\n \"We are very actively working on this. How does your dataset look like in practice (number/size/type of files)?\",\n \"It's a text file with many lines (about 1B) of Chinese sentences. I use it to train language model using https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_mlm_wwm.py\",\n \"Indeed I will submit a PR in a fez days to enable processing on-the-fly :)\\r\\nThis can be useful in language modeling for tokenization, padding etc.\\r\\n\",\n \"any update on this issue? ...really look forward to use it \",\n \"Hi @acul3,\\r\\n\\r\\nPlease look at the discussion on a related Issue #1825. I think using `set_transform` after building from source should do.\",\n \"@gchhablani thank you so much\\r\\n\\r\\nwill try look at it\"\n]"},"created_at":{"kind":"timestamp","value":"2021-01-24T09:28:24","string":"2021-01-24T09:28:24"},"updated_at":{"kind":"timestamp","value":"2021-05-20T04:15:58","string":"2021-05-20T04:15:58"},"closed_at":{"kind":"timestamp","value":"2021-05-20T04:15:58","string":"2021-05-20T04:15:58"},"author_association":{"kind":"string","value":"NONE"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"I know we can use `Datasets.map` to preprocess a dataset, but I'm using it with very large corpus which generates huge cache file (several TB cache from a 400 GB text file). I have no disk large enough to save it. Can we preprocess a dataset on the fly without generating cache?\r\n\r\nBTW, I tried raising `writer_batch_size`. Seems that argument doesn't have any effect when it's larger than `batch_size`, because you are saving all the batch instantly after it's processed. Please check the following code:\r\n\r\nhttps://github.com/huggingface/datasets/blob/0281f9d881f3a55c89aeaa642f1ba23444b64083/src/datasets/arrow_dataset.py#L1532"},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1776/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 0\n}"},"timeline_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1776/timeline"},"performed_via_github_app":{"kind":"null"},"state_reason":{"kind":"string","value":"completed"}}},{"rowIdx":2257,"cells":{"url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1775"},"repository_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets"},"labels_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1775/labels{/name}"},"comments_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1775/comments"},"events_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1775/events"},"html_url":{"kind":"string","value":"https://github.com/huggingface/datasets/issues/1775"},"id":{"kind":"number","value":792742120,"string":"792,742,120"},"node_id":{"kind":"string","value":"MDU6SXNzdWU3OTI3NDIxMjA="},"number":{"kind":"number","value":1775,"string":"1,775"},"title":{"kind":"string","value":"Efficient ways to iterate the dataset"},"user":{"kind":"string","value":"{\n \"login\": \"zhongpeixiang\",\n \"id\": 11826803,\n \"node_id\": \"MDQ6VXNlcjExODI2ODAz\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/11826803?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/zhongpeixiang\",\n \"html_url\": \"https://github.com/zhongpeixiang\",\n \"followers_url\": \"https://api.github.com/users/zhongpeixiang/followers\",\n \"following_url\": \"https://api.github.com/users/zhongpeixiang/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/zhongpeixiang/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/zhongpeixiang/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/zhongpeixiang/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/zhongpeixiang/orgs\",\n \"repos_url\": \"https://api.github.com/users/zhongpeixiang/repos\",\n \"events_url\": \"https://api.github.com/users/zhongpeixiang/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/zhongpeixiang/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n}"},"labels":{"kind":"list like","value":[],"string":"[]"},"state":{"kind":"string","value":"closed"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["It seems that selecting a subset of colums directly from the dataset, i.e., dataset[\"column\"], is slow.","I was wrong, ```dataset[\"column\"]``` is fast."],"string":"[\n \"It seems that selecting a subset of colums directly from the dataset, i.e., dataset[\\\"column\\\"], is slow.\",\n \"I was wrong, ```dataset[\\\"column\\\"]``` is fast.\"\n]"},"created_at":{"kind":"timestamp","value":"2021-01-24T07:54:31","string":"2021-01-24T07:54:31"},"updated_at":{"kind":"timestamp","value":"2021-01-24T09:50:39","string":"2021-01-24T09:50:39"},"closed_at":{"kind":"timestamp","value":"2021-01-24T09:50:39","string":"2021-01-24T09:50:39"},"author_association":{"kind":"string","value":"CONTRIBUTOR"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"For a large dataset that does not fits the memory, how can I select only a subset of features from each example?\r\n\r\nIf I iterate over the dataset and then select the subset of features one by one, the resulted memory usage will be huge. Any ways to solve this?\r\n\r\nThanks"},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1775/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 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it possible to make slice to be more compatible like python list and numpy?"},"user":{"kind":"string","value":"{\n \"login\": \"world2vec\",\n \"id\": 7607120,\n \"node_id\": \"MDQ6VXNlcjc2MDcxMjA=\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/7607120?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/world2vec\",\n \"html_url\": \"https://github.com/world2vec\",\n \"followers_url\": \"https://api.github.com/users/world2vec/followers\",\n \"following_url\": \"https://api.github.com/users/world2vec/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/world2vec/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/world2vec/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/world2vec/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/world2vec/orgs\",\n \"repos_url\": \"https://api.github.com/users/world2vec/repos\",\n \"events_url\": 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like","value":[{"login":"mariosasko","id":47462742,"node_id":"MDQ6VXNlcjQ3NDYyNzQy","avatar_url":"https://avatars.githubusercontent.com/u/47462742?v=4","gravatar_id":"","url":"https://api.github.com/users/mariosasko","html_url":"https://github.com/mariosasko","followers_url":"https://api.github.com/users/mariosasko/followers","following_url":"https://api.github.com/users/mariosasko/following{/other_user}","gists_url":"https://api.github.com/users/mariosasko/gists{/gist_id}","starred_url":"https://api.github.com/users/mariosasko/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/mariosasko/subscriptions","organizations_url":"https://api.github.com/users/mariosasko/orgs","repos_url":"https://api.github.com/users/mariosasko/repos","events_url":"https://api.github.com/users/mariosasko/events{/privacy}","received_events_url":"https://api.github.com/users/mariosasko/received_events","type":"User","site_admin":false}],"string":"[\n {\n \"login\": \"mariosasko\",\n 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\"type\": \"User\",\n \"site_admin\": false\n }\n]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["Hi ! Thanks for reporting.\r\nI am working on changes in the way data are sliced from arrow. I can probably fix your issue with the changes I'm doing.\r\nIf you have some code to reproduce the issue it would be nice so I can make sure that this case will be supported :)\r\nI'll make a PR in a few days ","Good if you can take care at your side.\r\nHere is the [colab notebook](https://colab.research.google.com/drive/19c-abm87RTRYgW9G1D8ktfwRW95zDYBZ?usp=sharing)"],"string":"[\n \"Hi ! Thanks for reporting.\\r\\nI am working on changes in the way data are sliced from arrow. I can probably fix your issue with the changes I'm doing.\\r\\nIf you have some code to reproduce the issue it would be nice so I can make sure that this case will be supported :)\\r\\nI'll make a PR in a few days \",\n \"Good if you can take care at your side.\\r\\nHere is the [colab notebook](https://colab.research.google.com/drive/19c-abm87RTRYgW9G1D8ktfwRW95zDYBZ?usp=sharing)\"\n]"},"created_at":{"kind":"timestamp","value":"2021-01-24T06:15:52","string":"2021-01-24T06:15:52"},"updated_at":{"kind":"timestamp","value":"2024-01-31T15:54:18","string":"2024-01-31T15:54:18"},"closed_at":{"kind":"timestamp","value":"2024-01-31T15:54:18","string":"2024-01-31T15:54:18"},"author_association":{"kind":"string","value":"NONE"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"Hi,\r\nsee below error:\r\n```\r\nAssertionError: Requested slice [:10000000000000000] incompatible with 20 examples.\r\n```"},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1774/reactions\",\n \"total_count\": 2,\n \"+1\": 2,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 0\n}"},"timeline_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1774/timeline"},"performed_via_github_app":{"kind":"null"},"state_reason":{"kind":"string","value":"completed"}}},{"rowIdx":2259,"cells":{"url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1773"},"repository_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets"},"labels_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1773/labels{/name}"},"comments_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1773/comments"},"events_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1773/events"},"html_url":{"kind":"string","value":"https://github.com/huggingface/datasets/issues/1773"},"id":{"kind":"number","value":792708160,"string":"792,708,160"},"node_id":{"kind":"string","value":"MDU6SXNzdWU3OTI3MDgxNjA="},"number":{"kind":"number","value":1773,"string":"1,773"},"title":{"kind":"string","value":"bug in loading datasets "},"user":{"kind":"string","value":"{\n \"login\": \"ghost\",\n \"id\": 10137,\n \"node_id\": \"MDQ6VXNlcjEwMTM3\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/10137?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/ghost\",\n \"html_url\": \"https://github.com/ghost\",\n \"followers_url\": \"https://api.github.com/users/ghost/followers\",\n \"following_url\": \"https://api.github.com/users/ghost/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/ghost/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/ghost/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/ghost/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/ghost/orgs\",\n \"repos_url\": \"https://api.github.com/users/ghost/repos\",\n \"events_url\": \"https://api.github.com/users/ghost/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/ghost/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n}"},"labels":{"kind":"list like","value":[],"string":"[]"},"state":{"kind":"string","value":"closed"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["Looks like an issue with your csv file. Did you use the right delimiter ?\r\nApparently at line 37 the CSV reader from pandas reads 2 fields instead of 1.","Note that you can pass any argument you would pass to `pandas.read_csv` as kwargs to `load_dataset`. For example you can do\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('csv', data_files=data_files, sep=\"\\t\")\r\n```\r\n\r\nfor example to use a tab separator.\r\n\r\nYou can see the full list of arguments here: https://github.com/huggingface/datasets/blob/master/src/datasets/packaged_modules/csv/csv.py\r\n\r\n(I've not found the list in the documentation though, we definitely must add them !)","You can try to convert the file to (CSV UTF-8)"],"string":"[\n \"Looks like an issue with your csv file. Did you use the right delimiter ?\\r\\nApparently at line 37 the CSV reader from pandas reads 2 fields instead of 1.\",\n \"Note that you can pass any argument you would pass to `pandas.read_csv` as kwargs to `load_dataset`. For example you can do\\r\\n```python\\r\\nfrom datasets import load_dataset\\r\\ndataset = load_dataset('csv', data_files=data_files, sep=\\\"\\\\t\\\")\\r\\n```\\r\\n\\r\\nfor example to use a tab separator.\\r\\n\\r\\nYou can see the full list of arguments here: https://github.com/huggingface/datasets/blob/master/src/datasets/packaged_modules/csv/csv.py\\r\\n\\r\\n(I've not found the list in the documentation though, we definitely must add them !)\",\n \"You can try to convert the file to (CSV UTF-8)\"\n]"},"created_at":{"kind":"timestamp","value":"2021-01-24T02:53:45","string":"2021-01-24T02:53:45"},"updated_at":{"kind":"timestamp","value":"2021-09-06T08:54:46","string":"2021-09-06T08:54:46"},"closed_at":{"kind":"timestamp","value":"2021-08-04T18:13:01","string":"2021-08-04T18:13:01"},"author_association":{"kind":"string","value":"NONE"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"Hi,\r\nI need to load a dataset, I use these commands:\r\n\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('csv', data_files={'train': 'sick/train.csv',\r\n 'test': 'sick/test.csv',\r\n 'validation': 'sick/validation.csv'})\r\nprint(dataset['validation'])\r\n```\r\nthe dataset in sick/train.csv are simple csv files representing the data. I am getting this error, do you have an idea how I can solve this? thank you @lhoestq \r\n\r\n \r\n```\r\nUsing custom data configuration default\r\nDownloading and preparing dataset csv/default-61468fc71a743ec1 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /julia/cache_home_2/datasets/csv/default-61468fc71a743ec1/0.0.0/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2...\r\nTraceback (most recent call last):\r\n File \"/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py\", line 485, in incomplete_dir\r\n yield tmp_dir\r\n File \"/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py\", line 527, in download_and_prepare\r\n dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n File \"/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py\", line 604, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py\", line 959, in _prepare_split\r\n for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n File \"/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/tqdm-4.49.0-py3.7.egg/tqdm/std.py\", line 1133, in __iter__\r\n for obj in iterable:\r\n File \"/julia/cache_home_2/modules/datasets_modules/datasets/csv/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2/csv.py\", line 129, in _generate_tables\r\n for batch_idx, df in enumerate(csv_file_reader):\r\n File \"/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/pandas-1.2.0-py3.7-linux-x86_64.egg/pandas/io/parsers.py\", line 1029, in __next__\r\n return self.get_chunk()\r\n File \"/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/pandas-1.2.0-py3.7-linux-x86_64.egg/pandas/io/parsers.py\", line 1079, in get_chunk\r\n return self.read(nrows=size)\r\n File \"/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/pandas-1.2.0-py3.7-linux-x86_64.egg/pandas/io/parsers.py\", line 1052, in read\r\n index, columns, col_dict = self._engine.read(nrows)\r\n File \"/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/pandas-1.2.0-py3.7-linux-x86_64.egg/pandas/io/parsers.py\", line 2056, in read\r\n data = self._reader.read(nrows)\r\n File \"pandas/_libs/parsers.pyx\", line 756, in pandas._libs.parsers.TextReader.read\r\n File \"pandas/_libs/parsers.pyx\", line 783, in pandas._libs.parsers.TextReader._read_low_memory\r\n File \"pandas/_libs/parsers.pyx\", line 827, in pandas._libs.parsers.TextReader._read_rows\r\n File \"pandas/_libs/parsers.pyx\", line 814, in pandas._libs.parsers.TextReader._tokenize_rows\r\n File \"pandas/_libs/parsers.pyx\", line 1951, in pandas._libs.parsers.raise_parser_error\r\npandas.errors.ParserError: Error tokenizing data. C error: Expected 1 fields in line 37, saw 2\r\n\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"write_sick.py\", line 19, in \r\n 'validation': 'sick/validation.csv'})\r\n File \"/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/load.py\", line 612, in load_dataset\r\n ignore_verifications=ignore_verifications,\r\n File \"/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py\", line 534, in download_and_prepare\r\n self._save_info()\r\n File \"/julia/libs/anaconda3/envs/success/lib/python3.7/contextlib.py\", line 130, in __exit__\r\n self.gen.throw(type, value, traceback)\r\n File \"/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py\", line 491, in incomplete_dir\r\n shutil.rmtree(tmp_dir)\r\n File \"/julia/libs/anaconda3/envs/success/lib/python3.7/shutil.py\", line 498, in rmtree\r\n onerror(os.rmdir, path, sys.exc_info())\r\n File \"/julia/libs/anaconda3/envs/success/lib/python3.7/shutil.py\", line 496, in rmtree\r\n os.rmdir(path)\r\nOSError: [Errno 39] Directory not empty: '/julia/cache_home_2/datasets/csv/default-61468fc71a743ec1/0.0.0/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2.incomplete'\r\n```\r\n\r\n"},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1773/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 0\n}"},"timeline_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1773/timeline"},"performed_via_github_app":{"kind":"null"},"state_reason":{"kind":"string","value":"completed"}}},{"rowIdx":2260,"cells":{"url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1772"},"repository_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets"},"labels_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1772/labels{/name}"},"comments_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1772/comments"},"events_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1772/events"},"html_url":{"kind":"string","value":"https://github.com/huggingface/datasets/issues/1772"},"id":{"kind":"number","value":792703797,"string":"792,703,797"},"node_id":{"kind":"string","value":"MDU6SXNzdWU3OTI3MDM3OTc="},"number":{"kind":"number","value":1772,"string":"1,772"},"title":{"kind":"string","value":"Adding SICK dataset"},"user":{"kind":"string","value":"{\n \"login\": \"ghost\",\n \"id\": 10137,\n \"node_id\": \"MDQ6VXNlcjEwMTM3\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/10137?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/ghost\",\n \"html_url\": \"https://github.com/ghost\",\n \"followers_url\": \"https://api.github.com/users/ghost/followers\",\n \"following_url\": \"https://api.github.com/users/ghost/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/ghost/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/ghost/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/ghost/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/ghost/orgs\",\n \"repos_url\": \"https://api.github.com/users/ghost/repos\",\n \"events_url\": \"https://api.github.com/users/ghost/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/ghost/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n}"},"labels":{"kind":"list like","value":[{"id":2067376369,"node_id":"MDU6TGFiZWwyMDY3Mzc2MzY5","url":"https://api.github.com/repos/huggingface/datasets/labels/dataset%20request","name":"dataset request","color":"e99695","default":false,"description":"Requesting to add a new dataset"}],"string":"[\n {\n \"id\": 2067376369,\n \"node_id\": \"MDU6TGFiZWwyMDY3Mzc2MzY5\",\n \"url\": \"https://api.github.com/repos/huggingface/datasets/labels/dataset%20request\",\n \"name\": \"dataset request\",\n \"color\": \"e99695\",\n \"default\": false,\n \"description\": \"Requesting to add a new dataset\"\n }\n]"},"state":{"kind":"string","value":"closed"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":[],"string":"[]"},"created_at":{"kind":"timestamp","value":"2021-01-24T02:15:31","string":"2021-01-24T02:15:31"},"updated_at":{"kind":"timestamp","value":"2021-02-05T15:49:25","string":"2021-02-05T15:49:25"},"closed_at":{"kind":"timestamp","value":"2021-02-05T15:49:25","string":"2021-02-05T15:49:25"},"author_association":{"kind":"string","value":"NONE"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"Hi\r\nIt would be great to include SICK dataset.\r\n\r\n## Adding a Dataset\r\n- **Name:** SICK\r\n- **Description:** a well known entailment dataset \r\n- **Paper:** http://marcobaroni.org/composes/sick.html\r\n- **Data:** http://marcobaroni.org/composes/sick.html\r\n- **Motivation:** this is an important NLI benchmark\r\n\r\nInstructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).\r\n\r\n\r\n\r\nthanks"},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1772/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 0\n}"},"timeline_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1772/timeline"},"performed_via_github_app":{"kind":"null"},"state_reason":{"kind":"string","value":"completed"}}},{"rowIdx":2261,"cells":{"url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1771"},"repository_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets"},"labels_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1771/labels{/name}"},"comments_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1771/comments"},"events_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1771/events"},"html_url":{"kind":"string","value":"https://github.com/huggingface/datasets/issues/1771"},"id":{"kind":"number","value":792701276,"string":"792,701,276"},"node_id":{"kind":"string","value":"MDU6SXNzdWU3OTI3MDEyNzY="},"number":{"kind":"number","value":1771,"string":"1,771"},"title":{"kind":"string","value":"Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.2.1/datasets/csv/csv.py"},"user":{"kind":"string","value":"{\n \"login\": \"world2vec\",\n \"id\": 7607120,\n \"node_id\": \"MDQ6VXNlcjc2MDcxMjA=\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/7607120?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/world2vec\",\n \"html_url\": \"https://github.com/world2vec\",\n \"followers_url\": \"https://api.github.com/users/world2vec/followers\",\n \"following_url\": \"https://api.github.com/users/world2vec/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/world2vec/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/world2vec/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/world2vec/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/world2vec/orgs\",\n \"repos_url\": \"https://api.github.com/users/world2vec/repos\",\n \"events_url\": \"https://api.github.com/users/world2vec/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/world2vec/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n}"},"labels":{"kind":"list like","value":[],"string":"[]"},"state":{"kind":"string","value":"closed"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["I temporary manually download csv.py as custom dataset loading script","Indeed in 1.2.1 the script to process csv file is downloaded. Starting from the next release though we include the csv processing directly in the library.\r\nSee PR #1726 \r\nWe'll do a new release soon :)","Thanks."],"string":"[\n \"I temporary manually download csv.py as custom dataset loading script\",\n \"Indeed in 1.2.1 the script to process csv file is downloaded. Starting from the next release though we include the csv processing directly in the library.\\r\\nSee PR #1726 \\r\\nWe'll do a new release soon :)\",\n \"Thanks.\"\n]"},"created_at":{"kind":"timestamp","value":"2021-01-24T01:53:52","string":"2021-01-24T01:53:52"},"updated_at":{"kind":"timestamp","value":"2021-01-24T23:06:29","string":"2021-01-24T23:06:29"},"closed_at":{"kind":"timestamp","value":"2021-01-24T23:06:29","string":"2021-01-24T23:06:29"},"author_association":{"kind":"string","value":"NONE"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"Hi,\r\nWhen I load_dataset from local csv files, below error happened, looks raw.githubusercontent.com was blocked by the chinese government. But why it need to download csv.py? should it include when pip install the dataset?\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"/home/tom/pyenv/pystory/lib/python3.6/site-packages/datasets/load.py\", line 267, in prepare_module\r\n local_path = cached_path(file_path, download_config=download_config)\r\n File \"/home/tom/pyenv/pystory/lib/python3.6/site-packages/datasets/utils/file_utils.py\", line 343, in cached_path\r\n max_retries=download_config.max_retries,\r\n File \"/home/tom/pyenv/pystory/lib/python3.6/site-packages/datasets/utils/file_utils.py\", line 617, in get_from_cache\r\n raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.2.1/datasets/csv/csv.py\r\n\r\n```"},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1771/reactions\",\n \"total_count\": 2,\n \"+1\": 2,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 0\n}"},"timeline_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1771/timeline"},"performed_via_github_app":{"kind":"null"},"state_reason":{"kind":"string","value":"completed"}}},{"rowIdx":2262,"cells":{"url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1770"},"repository_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets"},"labels_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1770/labels{/name}"},"comments_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1770/comments"},"events_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1770/events"},"html_url":{"kind":"string","value":"https://github.com/huggingface/datasets/issues/1770"},"id":{"kind":"number","value":792698148,"string":"792,698,148"},"node_id":{"kind":"string","value":"MDU6SXNzdWU3OTI2OTgxNDg="},"number":{"kind":"number","value":1770,"string":"1,770"},"title":{"kind":"string","value":"how can I combine 2 dataset with different/same features?"},"user":{"kind":"string","value":"{\n \"login\": \"world2vec\",\n \"id\": 7607120,\n \"node_id\": \"MDQ6VXNlcjc2MDcxMjA=\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/7607120?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/world2vec\",\n \"html_url\": \"https://github.com/world2vec\",\n \"followers_url\": \"https://api.github.com/users/world2vec/followers\",\n \"following_url\": \"https://api.github.com/users/world2vec/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/world2vec/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/world2vec/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/world2vec/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/world2vec/orgs\",\n \"repos_url\": \"https://api.github.com/users/world2vec/repos\",\n \"events_url\": \"https://api.github.com/users/world2vec/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/world2vec/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n}"},"labels":{"kind":"list like","value":[],"string":"[]"},"state":{"kind":"string","value":"closed"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["Hi ! Currently we don't have a way to `zip` datasets but we plan to add this soon :)\r\nFor now you'll need to use `map` to add the fields from one dataset to the other. See the comment here for more info : https://github.com/huggingface/datasets/issues/853#issuecomment-727872188","Good to hear.\r\nCurrently I did not use map , just fetch src and tgt from the 2 dataset and merge them.\r\nIt will be a release if you can deal with it at the backend.\r\nThanks.","Hi! You can rename the columns and concatenate the datasets along `axis=1` to get the desired result as follows:\r\n```python\r\nds1 = ds1.rename_column(\"text\", \"src\")\r\nds2 = ds2.rename_column(\"text\", \"tgt\")\r\nds = datasets.concatenate_datasets([\"ds1\", \"ds2\"], axis=1)\r\n```"],"string":"[\n \"Hi ! Currently we don't have a way to `zip` datasets but we plan to add this soon :)\\r\\nFor now you'll need to use `map` to add the fields from one dataset to the other. See the comment here for more info : https://github.com/huggingface/datasets/issues/853#issuecomment-727872188\",\n \"Good to hear.\\r\\nCurrently I did not use map , just fetch src and tgt from the 2 dataset and merge them.\\r\\nIt will be a release if you can deal with it at the backend.\\r\\nThanks.\",\n \"Hi! You can rename the columns and concatenate the datasets along `axis=1` to get the desired result as follows:\\r\\n```python\\r\\nds1 = ds1.rename_column(\\\"text\\\", \\\"src\\\")\\r\\nds2 = ds2.rename_column(\\\"text\\\", \\\"tgt\\\")\\r\\nds = datasets.concatenate_datasets([\\\"ds1\\\", \\\"ds2\\\"], axis=1)\\r\\n```\"\n]"},"created_at":{"kind":"timestamp","value":"2021-01-24T01:26:06","string":"2021-01-24T01:26:06"},"updated_at":{"kind":"timestamp","value":"2022-06-01T15:43:15","string":"2022-06-01T15:43:15"},"closed_at":{"kind":"timestamp","value":"2022-06-01T15:43:15","string":"2022-06-01T15:43:15"},"author_association":{"kind":"string","value":"NONE"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"to combine 2 dataset by one-one map like ds = zip(ds1, ds2):\r\nds1: {'text'}, ds2: {'text'}, combine ds:{'src', 'tgt'} \r\nor different feature:\r\nds1: {'src'}, ds2: {'tgt'}, combine ds:{'src', 'tgt'}"},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1770/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 0\n}"},"timeline_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1770/timeline"},"performed_via_github_app":{"kind":"null"},"state_reason":{"kind":"string","value":"completed"}}},{"rowIdx":2263,"cells":{"url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1769"},"repository_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets"},"labels_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1769/labels{/name}"},"comments_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1769/comments"},"events_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1769/events"},"html_url":{"kind":"string","value":"https://github.com/huggingface/datasets/issues/1769"},"id":{"kind":"number","value":792523284,"string":"792,523,284"},"node_id":{"kind":"string","value":"MDU6SXNzdWU3OTI1MjMyODQ="},"number":{"kind":"number","value":1769,"string":"1,769"},"title":{"kind":"string","value":"_pickle.PicklingError: Can't pickle typing.Union[str, NoneType]: it's not the same object as typing.Union when calling datasets.map with num_proc=2"},"user":{"kind":"string","value":"{\n \"login\": \"shuaihuaiyi\",\n \"id\": 14048129,\n \"node_id\": \"MDQ6VXNlcjE0MDQ4MTI5\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/14048129?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/shuaihuaiyi\",\n \"html_url\": \"https://github.com/shuaihuaiyi\",\n \"followers_url\": \"https://api.github.com/users/shuaihuaiyi/followers\",\n \"following_url\": \"https://api.github.com/users/shuaihuaiyi/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/shuaihuaiyi/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/shuaihuaiyi/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/shuaihuaiyi/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/shuaihuaiyi/orgs\",\n \"repos_url\": \"https://api.github.com/users/shuaihuaiyi/repos\",\n \"events_url\": \"https://api.github.com/users/shuaihuaiyi/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/shuaihuaiyi/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n}"},"labels":{"kind":"list like","value":[],"string":"[]"},"state":{"kind":"string","value":"closed"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["More information: `run_mlm.py` will raise same error when `data_args.line_by_line==True`\r\n\r\nhttps://github.com/huggingface/transformers/blob/9152f16023b59d262b51573714b40325c8e49370/examples/language-modeling/run_mlm.py#L300\r\n","Hi ! What version of python and datasets do you have ? And also what version of dill and pickle ?","> Hi ! What version of python and datasets do you have ? And also what version of dill and pickle ?\r\n\r\npython==3.6.10\r\ndatasets==1.2.1\r\ndill==0.3.2\r\npickle.format_version==4.0","Multiprocessing in python require all the functions to be picklable. More specifically, functions need to be picklable with `dill`.\r\n\r\nHowever objects like `typing.Union[str, NoneType]` are not picklable in python <3.7.\r\nCan you try to update your python version to python>=3.7 ?\r\n"],"string":"[\n \"More information: `run_mlm.py` will raise same error when `data_args.line_by_line==True`\\r\\n\\r\\nhttps://github.com/huggingface/transformers/blob/9152f16023b59d262b51573714b40325c8e49370/examples/language-modeling/run_mlm.py#L300\\r\\n\",\n \"Hi ! What version of python and datasets do you have ? And also what version of dill and pickle ?\",\n \"> Hi ! What version of python and datasets do you have ? And also what version of dill and pickle ?\\r\\n\\r\\npython==3.6.10\\r\\ndatasets==1.2.1\\r\\ndill==0.3.2\\r\\npickle.format_version==4.0\",\n \"Multiprocessing in python require all the functions to be picklable. More specifically, functions need to be picklable with `dill`.\\r\\n\\r\\nHowever objects like `typing.Union[str, NoneType]` are not picklable in python <3.7.\\r\\nCan you try to update your python version to python>=3.7 ?\\r\\n\"\n]"},"created_at":{"kind":"timestamp","value":"2021-01-23T10:13:00","string":"2021-01-23T10:13:00"},"updated_at":{"kind":"timestamp","value":"2022-10-05T12:38:51","string":"2022-10-05T12:38:51"},"closed_at":{"kind":"timestamp","value":"2022-10-05T12:38:51","string":"2022-10-05T12:38:51"},"author_association":{"kind":"string","value":"NONE"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"It may be a bug of multiprocessing with Datasets, when I disable the multiprocessing by set num_proc to None, everything works fine.\r\n\r\nThe script I use is https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_mlm_wwm.py\r\n\r\nScript args:\r\n\r\n```\r\n--model_name_or_path\r\n../../../model/chinese-roberta-wwm-ext\r\n--train_file\r\n/nfs/volume-377-2/bert/data/test/train.txt\r\n--output_dir\r\ntest\r\n--do_train\r\n--per_device_train_batch_size\r\n2\r\n--gradient_accumulation_steps\r\n2\r\n--learning_rate\r\n1e-4\r\n--max_steps\r\n1000\r\n--warmup_steps\r\n10\r\n--save_steps\r\n1000\r\n--save_total_limit\r\n1\r\n--seed\r\n23333\r\n--max_seq_length\r\n512\r\n--preprocessing_num_workers\r\n2\r\n--cache_dir\r\n/nfs/volume-377-2/bert/data/test/cache\r\n```\r\n\r\nWhere the `/nfs/volume-377-2/bert/data/test/train.txt` is just a toy example with 10000 lines of random string, you should be able to reproduce this error esaily.\r\n\r\nFull Traceback:\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"/nfs/volume-377-2/bert/transformers/examples/language-modeling/run_mlm_wwm.py\", line 398, in \r\n main()\r\n File \"/nfs/volume-377-2/bert/transformers/examples/language-modeling/run_mlm_wwm.py\", line 325, in main\r\n load_from_cache_file=not data_args.overwrite_cache,\r\n File \"/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/datasets/dataset_dict.py\", line 303, in map\r\n for k, dataset in self.items()\r\n File \"/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/datasets/dataset_dict.py\", line 303, in \r\n for k, dataset in self.items()\r\n File \"/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/datasets/arrow_dataset.py\", line 1318, in map\r\n transformed_shards = [r.get() for r in results]\r\n File \"/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/datasets/arrow_dataset.py\", line 1318, in \r\n transformed_shards = [r.get() for r in results]\r\n File \"/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/multiprocess/pool.py\", line 644, in get\r\n raise self._value\r\n File \"/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/multiprocess/pool.py\", line 424, in _handle_tasks\r\n put(task)\r\n File \"/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/multiprocess/connection.py\", line 209, in send\r\n self._send_bytes(_ForkingPickler.dumps(obj))\r\n File \"/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/multiprocess/reduction.py\", line 54, in dumps\r\n cls(buf, protocol, *args, **kwds).dump(obj)\r\n File \"/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py\", line 446, in dump\r\n StockPickler.dump(self, obj)\r\n File \"/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py\", line 409, in dump\r\n self.save(obj)\r\n File \"/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py\", line 476, in save\r\n f(self, obj) # Call unbound method with explicit self\r\n File \"/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py\", line 751, in save_tuple\r\n save(element)\r\n File \"/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py\", line 476, in save\r\n f(self, obj) # Call unbound method with explicit self\r\n File \"/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py\", line 933, in save_module_dict\r\n StockPickler.save_dict(pickler, obj)\r\n File \"/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py\", line 821, in save_dict\r\n self._batch_setitems(obj.items())\r\n File \"/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py\", line 847, in _batch_setitems\r\n save(v)\r\n File \"/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py\", line 476, in save\r\n f(self, obj) # Call unbound method with explicit self\r\n File \"/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py\", line 1438, in save_function\r\n obj.__dict__, fkwdefaults), obj=obj)\r\n File \"/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py\", line 610, in save_reduce\r\n save(args)\r\n File \"/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py\", line 476, in save\r\n f(self, obj) # Call unbound method with explicit self\r\n File \"/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py\", line 751, in save_tuple\r\n save(element)\r\n File \"/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py\", line 476, in save\r\n f(self, obj) # Call unbound method with explicit self\r\n File \"/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py\", line 736, in save_tuple\r\n save(element)\r\n File \"/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py\", line 476, in save\r\n f(self, obj) # Call unbound method with explicit self\r\n File \"/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py\", line 1170, in save_cell\r\n pickler.save_reduce(_create_cell, (f,), obj=obj)\r\n File \"/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py\", line 610, in save_reduce\r\n save(args)\r\n File \"/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py\", line 476, in save\r\n f(self, obj) # Call unbound method with explicit self\r\n File \"/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py\", line 736, in save_tuple\r\n save(element)\r\n File \"/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py\", line 521, in save\r\n self.save_reduce(obj=obj, *rv)\r\n File \"/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py\", line 605, in save_reduce\r\n save(cls)\r\n File \"/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py\", line 476, in save\r\n f(self, obj) # Call unbound method with explicit self\r\n File \"/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py\", line 1365, in save_type\r\n obj.__bases__, _dict), obj=obj)\r\n File \"/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py\", line 610, in save_reduce\r\n save(args)\r\n File \"/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py\", line 476, in save\r\n f(self, obj) # Call unbound method with explicit self\r\n File \"/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py\", line 751, in save_tuple\r\n save(element)\r\n File \"/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py\", line 476, in save\r\n f(self, obj) # Call unbound method with explicit self\r\n File \"/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py\", line 933, in save_module_dict\r\n StockPickler.save_dict(pickler, obj)\r\n File \"/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py\", line 821, in save_dict\r\n self._batch_setitems(obj.items())\r\n File \"/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py\", line 847, in _batch_setitems\r\n save(v)\r\n File \"/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py\", line 476, in save\r\n f(self, obj) # Call unbound method with explicit self\r\n File \"/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py\", line 933, in save_module_dict\r\n StockPickler.save_dict(pickler, obj)\r\n File \"/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py\", line 821, in save_dict\r\n self._batch_setitems(obj.items())\r\n File \"/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py\", line 847, in _batch_setitems\r\n save(v)\r\n File \"/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py\", line 507, in save\r\n self.save_global(obj, rv)\r\n File \"/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py\", line 927, in save_global\r\n (obj, module_name, name))\r\n_pickle.PicklingError: Can't pickle typing.Union[str, NoneType]: it's not the same object as typing.Union\r\n```\r\n"},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1769/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 0\n}"},"timeline_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1769/timeline"},"performed_via_github_app":{"kind":"null"},"state_reason":{"kind":"string","value":"completed"}}},{"rowIdx":2264,"cells":{"url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1766"},"repository_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets"},"labels_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1766/labels{/name}"},"comments_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1766/comments"},"events_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1766/events"},"html_url":{"kind":"string","value":"https://github.com/huggingface/datasets/issues/1766"},"id":{"kind":"number","value":792044105,"string":"792,044,105"},"node_id":{"kind":"string","value":"MDU6SXNzdWU3OTIwNDQxMDU="},"number":{"kind":"number","value":1766,"string":"1,766"},"title":{"kind":"string","value":"Issues when run two programs compute the same metrics"},"user":{"kind":"string","value":"{\n \"login\": \"lamthuy\",\n \"id\": 8089862,\n \"node_id\": \"MDQ6VXNlcjgwODk4NjI=\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/8089862?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/lamthuy\",\n \"html_url\": \"https://github.com/lamthuy\",\n \"followers_url\": \"https://api.github.com/users/lamthuy/followers\",\n \"following_url\": \"https://api.github.com/users/lamthuy/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/lamthuy/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/lamthuy/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/lamthuy/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/lamthuy/orgs\",\n \"repos_url\": \"https://api.github.com/users/lamthuy/repos\",\n \"events_url\": \"https://api.github.com/users/lamthuy/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/lamthuy/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n}"},"labels":{"kind":"list like","value":[],"string":"[]"},"state":{"kind":"string","value":"closed"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["Hi ! To avoid collisions you can specify a `experiment_id` when instantiating your metric using `load_metric`. It will replace \"default_experiment\" with the experiment id that you provide in the arrow filename. \r\n\r\nAlso when two `experiment_id` collide we're supposed to detect it using our locking mechanism. Not sure why it didn't work in your case. Could you share some code that reproduces the issue ? This would help us investigate.","Thank you for your response. I fixed the issue by set \"keep_in_memory=True\" when load_metric. \r\nI cannot share the entire source code but below is the wrapper I wrote:\r\n\r\n```python\r\nclass Evaluation:\r\n def __init__(self, metric='sacrebleu'):\r\n # self.metric = load_metric(metric, keep_in_memory=True)\r\n self.metric = load_metric(metric)\r\n\r\n def add(self, predictions, references):\r\n self.metric.add_batch(predictions=predictions, references=references)\r\n\r\n def compute(self):\r\n return self.metric.compute()['score']\r\n```\r\n\r\nThen call the given wrapper as follows:\r\n\r\n```python\r\neval = Evaluation(metric='sacrebleu')\r\nfor query, candidates, labels in tqdm(dataset):\r\n predictions = net.generate(query)\r\n references = [[s] for s in labels]\r\n eval.add(predictions, references)\r\n if n % 100 == 0:\r\n bleu += eval.compute()\r\n eval = Evaluation(metric='sacrebleu')"],"string":"[\n \"Hi ! To avoid collisions you can specify a `experiment_id` when instantiating your metric using `load_metric`. It will replace \\\"default_experiment\\\" with the experiment id that you provide in the arrow filename. \\r\\n\\r\\nAlso when two `experiment_id` collide we're supposed to detect it using our locking mechanism. Not sure why it didn't work in your case. Could you share some code that reproduces the issue ? This would help us investigate.\",\n \"Thank you for your response. I fixed the issue by set \\\"keep_in_memory=True\\\" when load_metric. \\r\\nI cannot share the entire source code but below is the wrapper I wrote:\\r\\n\\r\\n```python\\r\\nclass Evaluation:\\r\\n def __init__(self, metric='sacrebleu'):\\r\\n # self.metric = load_metric(metric, keep_in_memory=True)\\r\\n self.metric = load_metric(metric)\\r\\n\\r\\n def add(self, predictions, references):\\r\\n self.metric.add_batch(predictions=predictions, references=references)\\r\\n\\r\\n def compute(self):\\r\\n return self.metric.compute()['score']\\r\\n```\\r\\n\\r\\nThen call the given wrapper as follows:\\r\\n\\r\\n```python\\r\\neval = Evaluation(metric='sacrebleu')\\r\\nfor query, candidates, labels in tqdm(dataset):\\r\\n predictions = net.generate(query)\\r\\n references = [[s] for s in labels]\\r\\n eval.add(predictions, references)\\r\\n if n % 100 == 0:\\r\\n bleu += eval.compute()\\r\\n eval = Evaluation(metric='sacrebleu')\"\n]"},"created_at":{"kind":"timestamp","value":"2021-01-22T14:22:55","string":"2021-01-22T14:22:55"},"updated_at":{"kind":"timestamp","value":"2021-02-02T10:38:06","string":"2021-02-02T10:38:06"},"closed_at":{"kind":"timestamp","value":"2021-02-02T10:38:06","string":"2021-02-02T10:38:06"},"author_association":{"kind":"string","value":"NONE"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"I got the following error when running two different programs that both compute sacreblue metrics. It seems that both read/and/write to the same location (.cache/huggingface/metrics/sacrebleu/default/default_experiment-1-0.arrow) where it caches the batches:\r\n\r\n```\r\nFile \"train_matching_min.py\", line 160, in ch_9_label\r\n avg_loss = valid(epoch, args.batch, args.validation, args.with_label)\r\n File \"train_matching_min.py\", line 93, in valid\r\n bleu += eval.compute()\r\n File \"/u/tlhoang/projects/seal/match/models/eval.py\", line 23, in compute\r\n return self.metric.compute()['score']\r\n File \"/dccstor/know/anaconda3/lib/python3.7/site-packages/datasets/metric.py\", line 387, in compute\r\n self._finalize()\r\n File \"/dccstor/know/anaconda3/lib/python3.7/site-packages/datasets/metric.py\", line 355, in _finalize\r\n self.data = Dataset(**reader.read_files([{\"filename\": f} for f in file_paths]))\r\n File \"/dccstor/know/anaconda3/lib/python3.7/site-packages/datasets/arrow_reader.py\", line 231, in read_files\r\n pa_table = self._read_files(files)\r\n File \"/dccstor/know/anaconda3/lib/python3.7/site-packages/datasets/arrow_reader.py\", line 170, in _read_files\r\n pa_table: pa.Table = self._get_dataset_from_filename(f_dict)\r\n File \"/dccstor/know/anaconda3/lib/python3.7/site-packages/datasets/arrow_reader.py\", line 299, in _get_dataset_from_filename\r\n pa_table = f.read_all()\r\n File \"pyarrow/ipc.pxi\", line 481, in pyarrow.lib.RecordBatchReader.read_all\r\n File \"pyarrow/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Expected to read 1819307375 metadata bytes, but only read 454396\r\n``` "},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1766/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 0\n}"},"timeline_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1766/timeline"},"performed_via_github_app":{"kind":"null"},"state_reason":{"kind":"string","value":"completed"}}},{"rowIdx":2265,"cells":{"url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1765"},"repository_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets"},"labels_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1765/labels{/name}"},"comments_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1765/comments"},"events_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1765/events"},"html_url":{"kind":"string","value":"https://github.com/huggingface/datasets/issues/1765"},"id":{"kind":"number","value":791553065,"string":"791,553,065"},"node_id":{"kind":"string","value":"MDU6SXNzdWU3OTE1NTMwNjU="},"number":{"kind":"number","value":1765,"string":"1,765"},"title":{"kind":"string","value":"Error iterating over Dataset with DataLoader"},"user":{"kind":"string","value":"{\n \"login\": \"EvanZ\",\n \"id\": 1295082,\n \"node_id\": \"MDQ6VXNlcjEyOTUwODI=\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/1295082?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/EvanZ\",\n \"html_url\": \"https://github.com/EvanZ\",\n \"followers_url\": \"https://api.github.com/users/EvanZ/followers\",\n \"following_url\": \"https://api.github.com/users/EvanZ/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/EvanZ/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/EvanZ/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/EvanZ/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/EvanZ/orgs\",\n \"repos_url\": \"https://api.github.com/users/EvanZ/repos\",\n \"events_url\": \"https://api.github.com/users/EvanZ/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/EvanZ/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n}"},"labels":{"kind":"list like","value":[],"string":"[]"},"state":{"kind":"string","value":"closed"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["Instead of:\r\n```python\r\ndataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32)\r\n```\r\nIt should be:\r\n```python\r\ndataloader = torch.utils.data.DataLoader(encoded_dataset, batch_size=32)\r\n```\r\n\r\n`batch_sampler` accepts a Sampler object or an Iterable, so you get an error.","@mariosasko I thought that would fix it, but now I'm getting a different error:\r\n\r\n```\r\n/usr/local/lib/python3.6/dist-packages/datasets/arrow_dataset.py:851: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:141.)\r\n return torch.tensor(x, **format_kwargs)\r\n---------------------------------------------------------------------------\r\nRuntimeError Traceback (most recent call last)\r\n in ()\r\n 1 dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_size=32)\r\n----> 2 next(iter(dataloader))\r\n\r\n5 frames\r\n/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/collate.py in default_collate(batch)\r\n 53 storage = elem.storage()._new_shared(numel)\r\n 54 out = elem.new(storage)\r\n---> 55 return torch.stack(batch, 0, out=out)\r\n 56 elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \\\r\n 57 and elem_type.__name__ != 'string_':\r\n\r\nRuntimeError: stack expects each tensor to be equal size, but got [7] at entry 0 and [10] at entry 1\r\n```\r\n\r\nAny thoughts what this means?I Do I need padding?","Yes, padding is an answer. \r\n\r\nThis can be solved easily by passing a callable to the collate_fn arg of DataLoader that adds padding. ","Padding was the fix, thanks!","dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_size=4)\r\nbatch = next(iter(dataloader))\r\n\r\ngetting \r\nValueError: cannot reshape array of size 8192 into shape (1,512,4)\r\n\r\nI had put padding as 2048 for encoded_dataset\r\nkindly help","data_loader_val = torch.utils.data.DataLoader(val_dataset, batch_size=32, shuffle=True, drop_last=False, num_workers=0)\r\ndataiter = iter(data_loader_val)\r\nimages, _ = next(dataiter)\r\n\r\ngetting -> TypeError: 'list' object is not callable\r\n\r\nCannot iterate through the data. Kindly suggest."],"string":"[\n \"Instead of:\\r\\n```python\\r\\ndataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32)\\r\\n```\\r\\nIt should be:\\r\\n```python\\r\\ndataloader = torch.utils.data.DataLoader(encoded_dataset, batch_size=32)\\r\\n```\\r\\n\\r\\n`batch_sampler` accepts a Sampler object or an Iterable, so you get an error.\",\n \"@mariosasko I thought that would fix it, but now I'm getting a different error:\\r\\n\\r\\n```\\r\\n/usr/local/lib/python3.6/dist-packages/datasets/arrow_dataset.py:851: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:141.)\\r\\n return torch.tensor(x, **format_kwargs)\\r\\n---------------------------------------------------------------------------\\r\\nRuntimeError Traceback (most recent call last)\\r\\n in ()\\r\\n 1 dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_size=32)\\r\\n----> 2 next(iter(dataloader))\\r\\n\\r\\n5 frames\\r\\n/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/collate.py in default_collate(batch)\\r\\n 53 storage = elem.storage()._new_shared(numel)\\r\\n 54 out = elem.new(storage)\\r\\n---> 55 return torch.stack(batch, 0, out=out)\\r\\n 56 elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \\\\\\r\\n 57 and elem_type.__name__ != 'string_':\\r\\n\\r\\nRuntimeError: stack expects each tensor to be equal size, but got [7] at entry 0 and [10] at entry 1\\r\\n```\\r\\n\\r\\nAny thoughts what this means?I Do I need padding?\",\n \"Yes, padding is an answer. \\r\\n\\r\\nThis can be solved easily by passing a callable to the collate_fn arg of DataLoader that adds padding. \",\n \"Padding was the fix, thanks!\",\n \"dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_size=4)\\r\\nbatch = next(iter(dataloader))\\r\\n\\r\\ngetting \\r\\nValueError: cannot reshape array of size 8192 into shape (1,512,4)\\r\\n\\r\\nI had put padding as 2048 for encoded_dataset\\r\\nkindly help\",\n \"data_loader_val = torch.utils.data.DataLoader(val_dataset, batch_size=32, shuffle=True, drop_last=False, num_workers=0)\\r\\ndataiter = iter(data_loader_val)\\r\\nimages, _ = next(dataiter)\\r\\n\\r\\ngetting -> TypeError: 'list' object is not callable\\r\\n\\r\\nCannot iterate through the data. Kindly suggest.\"\n]"},"created_at":{"kind":"timestamp","value":"2021-01-21T22:56:45","string":"2021-01-21T22:56:45"},"updated_at":{"kind":"timestamp","value":"2022-10-28T02:16:38","string":"2022-10-28T02:16:38"},"closed_at":{"kind":"timestamp","value":"2021-01-23T03:44:14","string":"2021-01-23T03:44:14"},"author_association":{"kind":"string","value":"NONE"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"I have a Dataset that I've mapped a tokenizer over:\r\n\r\n```\r\nencoded_dataset.set_format(type='torch',columns=['attention_mask','input_ids','token_type_ids'])\r\nencoded_dataset[:1]\r\n```\r\n```\r\n{'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]),\r\n 'input_ids': tensor([[ 101, 178, 1198, 1400, 1714, 22233, 21365, 4515, 8618, 1113,\r\n 102]]),\r\n 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])}\r\n```\r\n\r\nWhen I try to iterate as in the docs, I get errors:\r\n\r\n```\r\ndataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32)\r\nnext(iter(dataloader))\r\n```\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nTypeError Traceback (most recent call last)\r\n in ()\r\n 1 dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32)\r\n----> 2 next(iter(dataloader))\r\n\r\n3 frames\r\n/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py in __init__(self, loader)\r\n 411 self._timeout = loader.timeout\r\n 412 self._collate_fn = loader.collate_fn\r\n--> 413 self._sampler_iter = iter(self._index_sampler)\r\n 414 self._base_seed = torch.empty((), dtype=torch.int64).random_(generator=loader.generator).item()\r\n 415 self._persistent_workers = loader.persistent_workers\r\n\r\nTypeError: 'int' object is not iterable\r\n\r\n\r\n```"},"reactions":{"kind":"string","value":"{\n \"url\": 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0\n}"},"timeline_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1765/timeline"},"performed_via_github_app":{"kind":"null"},"state_reason":{"kind":"string","value":"completed"}}},{"rowIdx":2266,"cells":{"url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1764"},"repository_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets"},"labels_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1764/labels{/name}"},"comments_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1764/comments"},"events_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1764/events"},"html_url":{"kind":"string","value":"https://github.com/huggingface/datasets/issues/1764"},"id":{"kind":"number","value":791486860,"string":"791,486,860"},"node_id":{"kind":"string","value":"MDU6SXNzdWU3OTE0ODY4NjA="},"number":{"kind":"number","value":1764,"string":"1,764"},"title":{"kind":"string","value":"Connection Issues"},"user":{"kind":"string","value":"{\n \"login\": \"SaeedNajafi\",\n \"id\": 12455298,\n \"node_id\": \"MDQ6VXNlcjEyNDU1Mjk4\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/12455298?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/SaeedNajafi\",\n \"html_url\": \"https://github.com/SaeedNajafi\",\n \"followers_url\": \"https://api.github.com/users/SaeedNajafi/followers\",\n \"following_url\": \"https://api.github.com/users/SaeedNajafi/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/SaeedNajafi/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/SaeedNajafi/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/SaeedNajafi/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/SaeedNajafi/orgs\",\n \"repos_url\": \"https://api.github.com/users/SaeedNajafi/repos\",\n \"events_url\": \"https://api.github.com/users/SaeedNajafi/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/SaeedNajafi/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n}"},"labels":{"kind":"list like","value":[],"string":"[]"},"state":{"kind":"string","value":"closed"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["Academic WIFI was blocking."],"string":"[\n \"Academic WIFI was blocking.\"\n]"},"created_at":{"kind":"timestamp","value":"2021-01-21T20:56:09","string":"2021-01-21T20:56:09"},"updated_at":{"kind":"timestamp","value":"2021-01-21T21:00:19","string":"2021-01-21T21:00:19"},"closed_at":{"kind":"timestamp","value":"2021-01-21T21:00:02","string":"2021-01-21T21:00:02"},"author_association":{"kind":"string","value":"NONE"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"Today, I am getting connection issues while loading a dataset and the metric.\r\n```\r\nTraceback (most recent call last):\r\n File \"src/train.py\", line 180, in \r\n train_dataset, dev_dataset, test_dataset = create_race_dataset()\r\n File \"src/train.py\", line 130, in create_race_dataset\r\n train_dataset = load_dataset(\"race\", \"all\", split=\"train\")\r\n File \"/Users/saeed/Desktop/codes/repos/dreamscape-qa/env/lib/python3.7/site-packages/datasets/load.py\", line 591, in load_dataset\r\n path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True\r\n File \"/Users/saeed/Desktop/codes/repos/dreamscape-qa/env/lib/python3.7/site-packages/datasets/load.py\", line 267, in prepare_module\r\n local_path = cached_path(file_path, download_config=download_config)\r\n File \"/Users/saeed/Desktop/codes/repos/dreamscape-qa/env/lib/python3.7/site-packages/datasets/utils/file_utils.py\", line 343, in cached_path\r\n max_retries=download_config.max_retries,\r\n File \"/Users/saeed/Desktop/codes/repos/dreamscape-qa/env/lib/python3.7/site-packages/datasets/utils/file_utils.py\", line 617, in get_from_cache\r\n raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.2.1/datasets/race/race.py\r\n```\r\n\r\nOr\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"src/train.py\", line 105, in \r\n rouge = datasets.load_metric(\"rouge\")\r\n File \"/Users/saeed/Desktop/codes/repos/dreamscape-qa/env/lib/python3.7/site-packages/datasets/load.py\", line 500, in load_metric\r\n dataset=False,\r\n File \"/Users/saeed/Desktop/codes/repos/dreamscape-qa/env/lib/python3.7/site-packages/datasets/load.py\", line 267, in prepare_module\r\n local_path = cached_path(file_path, download_config=download_config)\r\n File \"/Users/saeed/Desktop/codes/repos/dreamscape-qa/env/lib/python3.7/site-packages/datasets/utils/file_utils.py\", line 343, in cached_path\r\n max_retries=download_config.max_retries,\r\n File \"/Users/saeed/Desktop/codes/repos/dreamscape-qa/env/lib/python3.7/site-packages/datasets/utils/file_utils.py\", line 617, in get_from_cache\r\n raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.2.1/metrics/rouge/rouge.py\r\n```"},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1764/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 0\n}"},"timeline_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1764/timeline"},"performed_via_github_app":{"kind":"null"},"state_reason":{"kind":"string","value":"completed"}}},{"rowIdx":2267,"cells":{"url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1762"},"repository_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets"},"labels_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1762/labels{/name}"},"comments_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1762/comments"},"events_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1762/events"},"html_url":{"kind":"string","value":"https://github.com/huggingface/datasets/issues/1762"},"id":{"kind":"number","value":791226007,"string":"791,226,007"},"node_id":{"kind":"string","value":"MDU6SXNzdWU3OTEyMjYwMDc="},"number":{"kind":"number","value":1762,"string":"1,762"},"title":{"kind":"string","value":"Unable to format dataset to CUDA Tensors"},"user":{"kind":"string","value":"{\n \"login\": \"gchhablani\",\n \"id\": 29076344,\n \"node_id\": \"MDQ6VXNlcjI5MDc2MzQ0\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/29076344?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/gchhablani\",\n \"html_url\": \"https://github.com/gchhablani\",\n \"followers_url\": \"https://api.github.com/users/gchhablani/followers\",\n \"following_url\": \"https://api.github.com/users/gchhablani/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/gchhablani/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/gchhablani/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/gchhablani/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/gchhablani/orgs\",\n \"repos_url\": \"https://api.github.com/users/gchhablani/repos\",\n \"events_url\": \"https://api.github.com/users/gchhablani/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/gchhablani/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n}"},"labels":{"kind":"list like","value":[],"string":"[]"},"state":{"kind":"string","value":"closed"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["Hi ! You can get CUDA tensors with\r\n\r\n```python\r\ndataset.set_format(\"torch\", columns=columns, device=\"cuda\")\r\n```\r\n\r\nIndeed `set_format` passes the `**kwargs` to `torch.tensor`","Hi @lhoestq,\r\n\r\nThanks a lot. Is this true for all format types?\r\n\r\nAs in, for 'torch', I can have `**kwargs` to `torch.tensor` and for 'tf' those args are passed to `tf.Tensor`, and the same for 'numpy' and 'pandas'?","Yes the keywords arguments are passed to the convert function like `np.array`, `torch.tensor` or `tensorflow.ragged.constant`.\r\nWe don't support the kwargs for pandas on the other hand.","Thanks @lhoestq,\r\nWould it be okay if I added this to the docs and made a PR?","Sure ! Feel free to open a PR to improve the documentation :) ","Closing this issue as it has been resolved."],"string":"[\n \"Hi ! You can get CUDA tensors with\\r\\n\\r\\n```python\\r\\ndataset.set_format(\\\"torch\\\", columns=columns, device=\\\"cuda\\\")\\r\\n```\\r\\n\\r\\nIndeed `set_format` passes the `**kwargs` to `torch.tensor`\",\n \"Hi @lhoestq,\\r\\n\\r\\nThanks a lot. Is this true for all format types?\\r\\n\\r\\nAs in, for 'torch', I can have `**kwargs` to `torch.tensor` and for 'tf' those args are passed to `tf.Tensor`, and the same for 'numpy' and 'pandas'?\",\n \"Yes the keywords arguments are passed to the convert function like `np.array`, `torch.tensor` or `tensorflow.ragged.constant`.\\r\\nWe don't support the kwargs for pandas on the other hand.\",\n \"Thanks @lhoestq,\\r\\nWould it be okay if I added this to the docs and made a PR?\",\n \"Sure ! Feel free to open a PR to improve the documentation :) \",\n \"Closing this issue as it has been resolved.\"\n]"},"created_at":{"kind":"timestamp","value":"2021-01-21T15:31:23","string":"2021-01-21T15:31:23"},"updated_at":{"kind":"timestamp","value":"2021-02-02T07:13:22","string":"2021-02-02T07:13:22"},"closed_at":{"kind":"timestamp","value":"2021-02-02T07:13:22","string":"2021-02-02T07:13:22"},"author_association":{"kind":"string","value":"CONTRIBUTOR"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"Hi,\r\n\r\nI came across this [link](https://huggingface.co/docs/datasets/torch_tensorflow.html) where the docs show show to convert a dataset to a particular format. I see that there is an option to convert it to tensors, but I don't see any option to convert it to CUDA tensors.\r\n\r\nI tried this, but Dataset doesn't support assignment:\r\n```\r\n columns=['input_ids', 'token_type_ids', 'attention_mask', 'start_positions','end_positions']\r\n\r\n samples.set_format(type='torch', columns = columns)\r\n for column in columns:\r\n samples[column].to(torch.device(self.config.device))\r\n```\r\nThere should be an option to do so, or if there is already a way to do this, please let me know.\r\n\r\nThanks,\r\nGunjan"},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1762/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 0\n}"},"timeline_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1762/timeline"},"performed_via_github_app":{"kind":"null"},"state_reason":{"kind":"string","value":"completed"}}},{"rowIdx":2268,"cells":{"url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1759"},"repository_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets"},"labels_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1759/labels{/name}"},"comments_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1759/comments"},"events_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1759/events"},"html_url":{"kind":"string","value":"https://github.com/huggingface/datasets/issues/1759"},"id":{"kind":"number","value":790992226,"string":"790,992,226"},"node_id":{"kind":"string","value":"MDU6SXNzdWU3OTA5OTIyMjY="},"number":{"kind":"number","value":1759,"string":"1,759"},"title":{"kind":"string","value":"wikipedia dataset incomplete"},"user":{"kind":"string","value":"{\n \"login\": \"ChrisDelClea\",\n \"id\": 19912393,\n \"node_id\": \"MDQ6VXNlcjE5OTEyMzkz\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/19912393?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/ChrisDelClea\",\n \"html_url\": \"https://github.com/ChrisDelClea\",\n \"followers_url\": \"https://api.github.com/users/ChrisDelClea/followers\",\n \"following_url\": \"https://api.github.com/users/ChrisDelClea/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/ChrisDelClea/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/ChrisDelClea/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/ChrisDelClea/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/ChrisDelClea/orgs\",\n \"repos_url\": \"https://api.github.com/users/ChrisDelClea/repos\",\n \"events_url\": \"https://api.github.com/users/ChrisDelClea/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/ChrisDelClea/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n}"},"labels":{"kind":"list like","value":[],"string":"[]"},"state":{"kind":"string","value":"closed"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["Hi !\r\nFrom what pickle file fo you get this ?\r\nI guess you mean the dataset loaded using `load_dataset` ?","yes sorry, I used the `load_dataset`function and saved the data to a pickle file so I don't always have to reload it and are able to work offline. ","The wikipedia articles are processed using the `mwparserfromhell` library. Even if it works well in most cases, such issues can happen unfortunately. You can find the repo here: https://github.com/earwig/mwparserfromhell\r\n\r\nThere also exist other datasets based on wikipedia that were processed differently (and are often cleaner) such as `wiki40b`.\r\n\r\n","ok great. Thank you, @lhoestq. "],"string":"[\n \"Hi !\\r\\nFrom what pickle file fo you get this ?\\r\\nI guess you mean the dataset loaded using `load_dataset` ?\",\n \"yes sorry, I used the `load_dataset`function and saved the data to a pickle file so I don't always have to reload it and are able to work offline. \",\n \"The wikipedia articles are processed using the `mwparserfromhell` library. Even if it works well in most cases, such issues can happen unfortunately. You can find the repo here: https://github.com/earwig/mwparserfromhell\\r\\n\\r\\nThere also exist other datasets based on wikipedia that were processed differently (and are often cleaner) such as `wiki40b`.\\r\\n\\r\\n\",\n \"ok great. Thank you, @lhoestq. \"\n]"},"created_at":{"kind":"timestamp","value":"2021-01-21T11:47:15","string":"2021-01-21T11:47:15"},"updated_at":{"kind":"timestamp","value":"2021-01-21T17:22:11","string":"2021-01-21T17:22:11"},"closed_at":{"kind":"timestamp","value":"2021-01-21T17:21:06","string":"2021-01-21T17:21:06"},"author_association":{"kind":"string","value":"NONE"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"Hey guys,\r\n\r\nI am using the https://github.com/huggingface/datasets/tree/master/datasets/wikipedia dataset.\r\nUnfortunately, I found out that there is an incompleteness for the German dataset.\r\n For reasons unknown to me, the number of inhabitants has been removed from many pages:\r\nThorey-sur-Ouche has 128 inhabitants according to the webpage (https://de.wikipedia.org/wiki/Thorey-sur-Ouche).\r\nThe pickle file however shows: französische Gemeinde mit Einwohnern (Stand).\r\n Is it possible to fix this?\r\n\r\nBest regards \r\nChris\r\n"},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1759/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 0\n}"},"timeline_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1759/timeline"},"performed_via_github_app":{"kind":"null"},"state_reason":{"kind":"string","value":"completed"}}},{"rowIdx":2269,"cells":{"url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1758"},"repository_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets"},"labels_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1758/labels{/name}"},"comments_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1758/comments"},"events_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1758/events"},"html_url":{"kind":"string","value":"https://github.com/huggingface/datasets/issues/1758"},"id":{"kind":"number","value":790626116,"string":"790,626,116"},"node_id":{"kind":"string","value":"MDU6SXNzdWU3OTA2MjYxMTY="},"number":{"kind":"number","value":1758,"string":"1,758"},"title":{"kind":"string","value":"dataset.search() (elastic) cannot reliably retrieve search results"},"user":{"kind":"string","value":"{\n \"login\": \"afogarty85\",\n \"id\": 49048309,\n \"node_id\": \"MDQ6VXNlcjQ5MDQ4MzA5\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/49048309?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/afogarty85\",\n \"html_url\": \"https://github.com/afogarty85\",\n \"followers_url\": \"https://api.github.com/users/afogarty85/followers\",\n \"following_url\": \"https://api.github.com/users/afogarty85/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/afogarty85/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/afogarty85/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/afogarty85/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/afogarty85/orgs\",\n \"repos_url\": \"https://api.github.com/users/afogarty85/repos\",\n \"events_url\": \"https://api.github.com/users/afogarty85/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/afogarty85/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n}"},"labels":{"kind":"list like","value":[],"string":"[]"},"state":{"kind":"string","value":"closed"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["Hi !\r\nI tried your code on my side and I was able to workaround this issue by waiting a few seconds before querying the index.\r\nMaybe this is because the index is not updated yet on the ElasticSearch side ?","Thanks for the feedback! I added a 30 second \"sleep\" and that seemed to work well!"],"string":"[\n \"Hi !\\r\\nI tried your code on my side and I was able to workaround this issue by waiting a few seconds before querying the index.\\r\\nMaybe this is because the index is not updated yet on the ElasticSearch side ?\",\n \"Thanks for the feedback! I added a 30 second \\\"sleep\\\" and that seemed to work well!\"\n]"},"created_at":{"kind":"timestamp","value":"2021-01-21T02:26:37","string":"2021-01-21T02:26:37"},"updated_at":{"kind":"timestamp","value":"2021-01-22T00:25:50","string":"2021-01-22T00:25:50"},"closed_at":{"kind":"timestamp","value":"2021-01-22T00:25:50","string":"2021-01-22T00:25:50"},"author_association":{"kind":"string","value":"NONE"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"I am trying to use elastic search to retrieve the indices of items in the dataset in their precise order, given shuffled training indices.\r\n\r\nThe problem I have is that I cannot retrieve reliable results with my data on my first search. I have to run the search **twice** to get the right answer.\r\n\r\nI am indexing data that looks like the following from the HF SQuAD 2.0 data set:\r\n\r\n```\r\n['57318658e6313a140071d02b',\r\n '56f7165e3d8e2e1400e3733a',\r\n '570e2f6e0b85d914000d7d21',\r\n '5727e58aff5b5019007d97d0',\r\n '5a3b5a503ff257001ab8441f',\r\n '57262fab271a42140099d725']\r\n```\r\n\r\n\r\n\r\nTo reproduce the issue, try:\r\n\r\n```\r\nfrom datasets import load_dataset, load_metric\r\nfrom transformers import BertTokenizerFast, BertForQuestionAnswering\r\nfrom elasticsearch import Elasticsearch\r\nimport numpy as np\r\nimport collections\r\nfrom tqdm.auto import tqdm\r\nimport torch\r\n\r\n# from https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb#scrollTo=941LPhDWeYv-\r\ntokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased')\r\nmax_length = 384 # The maximum length of a feature (question and context)\r\ndoc_stride = 128 # The authorized overlap between two part of the context when splitting it is needed.\r\npad_on_right = tokenizer.padding_side == \"right\"\r\nsquad_v2 = True\r\n\r\n# from https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb#scrollTo=941LPhDWeYv-\r\ndef prepare_validation_features(examples):\r\n # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results\r\n # in one example possible giving several features when a context is long, each of those features having a\r\n # context that overlaps a bit the context of the previous feature.\r\n tokenized_examples = tokenizer(\r\n examples[\"question\" if pad_on_right else \"context\"],\r\n examples[\"context\" if pad_on_right else \"question\"],\r\n truncation=\"only_second\" if pad_on_right else \"only_first\",\r\n max_length=max_length,\r\n stride=doc_stride,\r\n return_overflowing_tokens=True,\r\n return_offsets_mapping=True,\r\n padding=\"max_length\",\r\n )\r\n\r\n # Since one example might give us several features if it has a long context, we need a map from a feature to\r\n # its corresponding example. This key gives us just that.\r\n sample_mapping = tokenized_examples.pop(\"overflow_to_sample_mapping\")\r\n\r\n # We keep the example_id that gave us this feature and we will store the offset mappings.\r\n tokenized_examples[\"example_id\"] = []\r\n\r\n for i in range(len(tokenized_examples[\"input_ids\"])):\r\n # Grab the sequence corresponding to that example (to know what is the context and what is the question).\r\n sequence_ids = tokenized_examples.sequence_ids(i)\r\n context_index = 1 if pad_on_right else 0\r\n\r\n # One example can give several spans, this is the index of the example containing this span of text.\r\n sample_index = sample_mapping[i]\r\n tokenized_examples[\"example_id\"].append(examples[\"id\"][sample_index])\r\n\r\n # Set to None the offset_mapping that are not part of the context so it's easy to determine if a token\r\n # position is part of the context or not.\r\n tokenized_examples[\"offset_mapping\"][i] = [\r\n (list(o) if sequence_ids[k] == context_index else None)\r\n for k, o in enumerate(tokenized_examples[\"offset_mapping\"][i])\r\n ]\r\n\r\n return tokenized_examples\r\n\r\n\r\n\r\n\r\n\r\n# build base examples, features set of training data\r\nshuffled_idx = pd.read_csv('https://raw.githubusercontent.com/afogarty85/temp/main/idx.csv')['idx'].to_list()\r\nexamples = load_dataset(\"squad_v2\").shuffle(seed=1)['train']\r\nfeatures = load_dataset(\"squad_v2\").shuffle(seed=1)['train'].map(\r\n prepare_validation_features,\r\n batched=True,\r\n remove_columns=['answers', 'context', 'id', 'question', 'title'])\r\n# reorder features by the training process\r\nfeatures = features.select(indices=shuffled_idx)\r\n# get the example ids to match with the \"example\" data; get unique entries\r\nid_list = list(dict.fromkeys(features['example_id']))\r\n# now search for their index positions in the examples data set; load elastic search\r\nes = Elasticsearch([{'host': 'localhost'}]).ping()\r\n# add an index to the id column for the examples\r\nexamples.add_elasticsearch_index(column='id')\r\n# retrieve the example index\r\nexample_idx_k1 = [examples.search(index_name='id', query=i, k=1).indices for i in id_list]\r\nexample_idx_k1 = [item for sublist in example_idx_k1 for item in sublist]\r\n\r\nexample_idx_k2 = [examples.search(index_name='id', query=i, k=3).indices for i in id_list]\r\nexample_idx_k2 = [item for sublist in example_idx_k2 for item in sublist]\r\n\r\nlen(example_idx_k1) # should be 130319\r\nlen(example_idx_k2) # should be 130319\r\n\r\n#trial 1 lengths:\r\n# k=1: 130314\r\n# k=3: 130319\r\n\r\n# trial 2:\r\n# just run k=3 first: 130310\r\n# try k=1 after k=3: 130319\r\n```\r\n\r\n"},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1758/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 0\n}"},"timeline_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1758/timeline"},"performed_via_github_app":{"kind":"null"},"state_reason":{"kind":"string","value":"completed"}}},{"rowIdx":2270,"cells":{"url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1757"},"repository_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets"},"labels_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1757/labels{/name}"},"comments_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1757/comments"},"events_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1757/events"},"html_url":{"kind":"string","value":"https://github.com/huggingface/datasets/issues/1757"},"id":{"kind":"number","value":790466509,"string":"790,466,509"},"node_id":{"kind":"string","value":"MDU6SXNzdWU3OTA0NjY1MDk="},"number":{"kind":"number","value":1757,"string":"1,757"},"title":{"kind":"string","value":"FewRel"},"user":{"kind":"string","value":"{\n \"login\": \"dspoka\",\n \"id\": 6183050,\n \"node_id\": \"MDQ6VXNlcjYxODMwNTA=\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/6183050?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/dspoka\",\n \"html_url\": \"https://github.com/dspoka\",\n \"followers_url\": \"https://api.github.com/users/dspoka/followers\",\n \"following_url\": \"https://api.github.com/users/dspoka/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/dspoka/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/dspoka/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/dspoka/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/dspoka/orgs\",\n \"repos_url\": \"https://api.github.com/users/dspoka/repos\",\n \"events_url\": \"https://api.github.com/users/dspoka/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/dspoka/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n}"},"labels":{"kind":"list like","value":[{"id":2067376369,"node_id":"MDU6TGFiZWwyMDY3Mzc2MzY5","url":"https://api.github.com/repos/huggingface/datasets/labels/dataset%20request","name":"dataset request","color":"e99695","default":false,"description":"Requesting to add a new dataset"}],"string":"[\n {\n \"id\": 2067376369,\n \"node_id\": \"MDU6TGFiZWwyMDY3Mzc2MzY5\",\n \"url\": \"https://api.github.com/repos/huggingface/datasets/labels/dataset%20request\",\n \"name\": \"dataset request\",\n \"color\": \"e99695\",\n \"default\": false,\n \"description\": \"Requesting to add a new dataset\"\n }\n]"},"state":{"kind":"string","value":"closed"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["+1","@dspoka Please check the following link : https://github.com/thunlp/FewRel\r\nThis link mentions two versions of the datasets. Also, this one seems to be the official link.\r\n\r\nI am assuming this is the correct link and implementing based on the same.","Hi @lhoestq,\r\n\r\nThis issue can be closed, I guess.","Yes :) closing\r\nThanks again for adding FewRel !","Thanks for adding this @gchhablani ! Sorry didn't see the email notifications sooner!"],"string":"[\n \"+1\",\n \"@dspoka Please check the following link : https://github.com/thunlp/FewRel\\r\\nThis link mentions two versions of the datasets. Also, this one seems to be the official link.\\r\\n\\r\\nI am assuming this is the correct link and implementing based on the same.\",\n \"Hi @lhoestq,\\r\\n\\r\\nThis issue can be closed, I guess.\",\n \"Yes :) closing\\r\\nThanks again for adding FewRel !\",\n \"Thanks for adding this @gchhablani ! Sorry didn't see the email notifications sooner!\"\n]"},"created_at":{"kind":"timestamp","value":"2021-01-20T23:56:03","string":"2021-01-20T23:56:03"},"updated_at":{"kind":"timestamp","value":"2021-03-09T02:52:05","string":"2021-03-09T02:52:05"},"closed_at":{"kind":"timestamp","value":"2021-03-08T14:34:52","string":"2021-03-08T14:34:52"},"author_association":{"kind":"string","value":"NONE"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"## Adding a Dataset\r\n- **Name:** FewRel\r\n- **Description:** Large-Scale Supervised Few-Shot Relation Classification Dataset\r\n- **Paper:** @inproceedings{han2018fewrel,\r\n title={FewRel:A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation},\r\n author={Han, Xu and Zhu, Hao and Yu, Pengfei and Wang, Ziyun and Yao, Yuan and Liu, Zhiyuan and Sun, Maosong},\r\n booktitle={EMNLP},\r\n year={2018}}\r\n- **Data:** https://github.com/ProKil/FewRel\r\n- **Motivation:** relationship extraction dataset that's been used by some state of the art systems that should be incorporated.\r\n\r\nInstructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).\r\n"},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1757/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 0\n}"},"timeline_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1757/timeline"},"performed_via_github_app":{"kind":"null"},"state_reason":{"kind":"string","value":"completed"}}},{"rowIdx":2271,"cells":{"url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1756"},"repository_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets"},"labels_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1756/labels{/name}"},"comments_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1756/comments"},"events_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1756/events"},"html_url":{"kind":"string","value":"https://github.com/huggingface/datasets/issues/1756"},"id":{"kind":"number","value":790380028,"string":"790,380,028"},"node_id":{"kind":"string","value":"MDU6SXNzdWU3OTAzODAwMjg="},"number":{"kind":"number","value":1756,"string":"1,756"},"title":{"kind":"string","value":"Ccaligned multilingual translation dataset"},"user":{"kind":"string","value":"{\n \"login\": \"flozi00\",\n \"id\": 47894090,\n \"node_id\": \"MDQ6VXNlcjQ3ODk0MDkw\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/47894090?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/flozi00\",\n \"html_url\": \"https://github.com/flozi00\",\n \"followers_url\": \"https://api.github.com/users/flozi00/followers\",\n \"following_url\": \"https://api.github.com/users/flozi00/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/flozi00/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/flozi00/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/flozi00/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/flozi00/orgs\",\n \"repos_url\": \"https://api.github.com/users/flozi00/repos\",\n \"events_url\": \"https://api.github.com/users/flozi00/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/flozi00/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n}"},"labels":{"kind":"list like","value":[{"id":2067376369,"node_id":"MDU6TGFiZWwyMDY3Mzc2MzY5","url":"https://api.github.com/repos/huggingface/datasets/labels/dataset%20request","name":"dataset request","color":"e99695","default":false,"description":"Requesting to add a new dataset"}],"string":"[\n {\n \"id\": 2067376369,\n \"node_id\": \"MDU6TGFiZWwyMDY3Mzc2MzY5\",\n \"url\": \"https://api.github.com/repos/huggingface/datasets/labels/dataset%20request\",\n \"name\": \"dataset request\",\n \"color\": \"e99695\",\n \"default\": false,\n \"description\": \"Requesting to add a new dataset\"\n }\n]"},"state":{"kind":"string","value":"closed"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":[],"string":"[]"},"created_at":{"kind":"timestamp","value":"2021-01-20T22:18:44","string":"2021-01-20T22:18:44"},"updated_at":{"kind":"timestamp","value":"2021-03-01T10:36:21","string":"2021-03-01T10:36:21"},"closed_at":{"kind":"timestamp","value":"2021-03-01T10:36:21","string":"2021-03-01T10:36:21"},"author_association":{"kind":"string","value":"CONTRIBUTOR"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"## Adding a Dataset\r\n- **Name:** *name of the dataset*\r\n- **Description:** *short description of the dataset (or link to social media or blog post)*\r\n- CCAligned consists of parallel or comparable web-document pairs in 137 languages aligned with English. These web-document pairs were constructed by performing language identification on raw web-documents, and ensuring corresponding language codes were corresponding in the URLs of web documents. This pattern matching approach yielded more than 100 million aligned documents paired with English. Recognizing that each English document was often aligned to mulitple documents in different target language, we can join on English documents to obtain aligned documents that directly pair two non-English documents (e.g., Arabic-French).\r\n- **Paper:** *link to the dataset paper if available*\r\n- https://www.aclweb.org/anthology/2020.emnlp-main.480.pdf\r\n- **Data:** *link to the Github repository or current dataset location*\r\n- http://www.statmt.org/cc-aligned/\r\n- **Motivation:** *what are some good reasons to have this dataset*\r\n- The authors says it's an high quality dataset.\r\n- it's pretty large and includes many language pairs. It could be interesting training mt5 on this task.\r\n\r\nInstructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).\r\n"},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1756/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 0\n}"},"timeline_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1756/timeline"},"performed_via_github_app":{"kind":"null"},"state_reason":{"kind":"string","value":"completed"}}},{"rowIdx":2272,"cells":{"url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1755"},"repository_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets"},"labels_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1755/labels{/name}"},"comments_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1755/comments"},"events_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1755/events"},"html_url":{"kind":"string","value":"https://github.com/huggingface/datasets/issues/1755"},"id":{"kind":"number","value":790324734,"string":"790,324,734"},"node_id":{"kind":"string","value":"MDU6SXNzdWU3OTAzMjQ3MzQ="},"number":{"kind":"number","value":1755,"string":"1,755"},"title":{"kind":"string","value":"Using select/reordering datasets slows operations down immensely"},"user":{"kind":"string","value":"{\n \"login\": \"afogarty85\",\n \"id\": 49048309,\n \"node_id\": \"MDQ6VXNlcjQ5MDQ4MzA5\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/49048309?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/afogarty85\",\n \"html_url\": \"https://github.com/afogarty85\",\n \"followers_url\": \"https://api.github.com/users/afogarty85/followers\",\n \"following_url\": \"https://api.github.com/users/afogarty85/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/afogarty85/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/afogarty85/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/afogarty85/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/afogarty85/orgs\",\n \"repos_url\": \"https://api.github.com/users/afogarty85/repos\",\n \"events_url\": \"https://api.github.com/users/afogarty85/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/afogarty85/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n}"},"labels":{"kind":"list like","value":[],"string":"[]"},"state":{"kind":"string","value":"closed"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["You can use `Dataset.flatten_indices()` to make it fast after a select or shuffle.","Thanks for the input! I gave that a try by adding this after my selection / reordering operations, but before the big computation task of `score_squad`\r\n\r\n```\r\nexamples = examples.flatten_indices()\r\nfeatures = features.flatten_indices()\r\n```\r\n\r\nThat helped quite a bit!"],"string":"[\n \"You can use `Dataset.flatten_indices()` to make it fast after a select or shuffle.\",\n \"Thanks for the input! I gave that a try by adding this after my selection / reordering operations, but before the big computation task of `score_squad`\\r\\n\\r\\n```\\r\\nexamples = examples.flatten_indices()\\r\\nfeatures = features.flatten_indices()\\r\\n```\\r\\n\\r\\nThat helped quite a bit!\"\n]"},"created_at":{"kind":"timestamp","value":"2021-01-20T21:12:12","string":"2021-01-20T21:12:12"},"updated_at":{"kind":"timestamp","value":"2021-01-20T22:03:39","string":"2021-01-20T22:03:39"},"closed_at":{"kind":"timestamp","value":"2021-01-20T22:03:39","string":"2021-01-20T22:03:39"},"author_association":{"kind":"string","value":"NONE"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"I am using portions of HF's helpful work in preparing / scoring the SQuAD 2.0 data. The problem I have is that after using `select` to re-ordering the dataset, computations slow down immensely where the total scoring process on 131k training examples would take maybe 3 minutes, now take over an hour.\r\n\r\nThe below example should be reproducible and I have ran myself down this path because I want to use HF's scoring functions and helpful data preparation, but use my own trainer. The training process uses shuffle and therefore the order I trained on no longer matches the original data set order. So, to score my results correctly, the original data set needs to match the order of the training. This requires that I: (1) collect the index for each row of data emitted during training, and (2) use this index information to re-order the datasets correctly so the orders match when I go to score.\r\n\r\n\r\nThe problem is, the dataset class starts performing very poorly as soon as you start manipulating its order by immense magnitudes.\r\n\r\n\r\n\r\n```\r\nfrom datasets import load_dataset, load_metric\r\nfrom transformers import BertTokenizerFast, BertForQuestionAnswering\r\nfrom elasticsearch import Elasticsearch\r\nimport numpy as np\r\nimport collections\r\nfrom tqdm.auto import tqdm\r\nimport torch\r\n\r\n# from https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb#scrollTo=941LPhDWeYv-\r\ntokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased')\r\nmax_length = 384 # The maximum length of a feature (question and context)\r\ndoc_stride = 128 # The authorized overlap between two part of the context when splitting it is needed.\r\npad_on_right = tokenizer.padding_side == \"right\"\r\nsquad_v2 = True\r\n\r\n# from https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb#scrollTo=941LPhDWeYv-\r\ndef prepare_validation_features(examples):\r\n # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results\r\n # in one example possible giving several features when a context is long, each of those features having a\r\n # context that overlaps a bit the context of the previous feature.\r\n tokenized_examples = tokenizer(\r\n examples[\"question\" if pad_on_right else \"context\"],\r\n examples[\"context\" if pad_on_right else \"question\"],\r\n truncation=\"only_second\" if pad_on_right else \"only_first\",\r\n max_length=max_length,\r\n stride=doc_stride,\r\n return_overflowing_tokens=True,\r\n return_offsets_mapping=True,\r\n padding=\"max_length\",\r\n )\r\n\r\n # Since one example might give us several features if it has a long context, we need a map from a feature to\r\n # its corresponding example. This key gives us just that.\r\n sample_mapping = tokenized_examples.pop(\"overflow_to_sample_mapping\")\r\n\r\n # We keep the example_id that gave us this feature and we will store the offset mappings.\r\n tokenized_examples[\"example_id\"] = []\r\n\r\n for i in range(len(tokenized_examples[\"input_ids\"])):\r\n # Grab the sequence corresponding to that example (to know what is the context and what is the question).\r\n sequence_ids = tokenized_examples.sequence_ids(i)\r\n context_index = 1 if pad_on_right else 0\r\n\r\n # One example can give several spans, this is the index of the example containing this span of text.\r\n sample_index = sample_mapping[i]\r\n tokenized_examples[\"example_id\"].append(examples[\"id\"][sample_index])\r\n\r\n # Set to None the offset_mapping that are not part of the context so it's easy to determine if a token\r\n # position is part of the context or not.\r\n tokenized_examples[\"offset_mapping\"][i] = [\r\n (list(o) if sequence_ids[k] == context_index else None)\r\n for k, o in enumerate(tokenized_examples[\"offset_mapping\"][i])\r\n ]\r\n\r\n return tokenized_examples\r\n\r\n# from https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb#scrollTo=941LPhDWeYv-\r\ndef postprocess_qa_predictions(examples, features, starting_logits, ending_logits, n_best_size = 20, max_answer_length = 30):\r\n all_start_logits, all_end_logits = starting_logits, ending_logits\r\n # Build a map example to its corresponding features.\r\n example_id_to_index = {k: i for i, k in enumerate(examples[\"id\"])}\r\n features_per_example = collections.defaultdict(list)\r\n\r\n for i, feature in enumerate(features):\r\n features_per_example[example_id_to_index[feature[\"example_id\"]]].append(i)\r\n\r\n # The dictionaries we have to fill.\r\n predictions = collections.OrderedDict()\r\n\r\n # Logging.\r\n print(f\"Post-processing {len(examples)} example predictions split into {len(features)} features.\")\r\n\r\n # Let's loop over all the examples!\r\n for example_index, example in enumerate(tqdm(examples)):\r\n # Those are the indices of the features associated to the current example.\r\n feature_indices = features_per_example[example_index]\r\n\r\n min_null_score = None # Only used if squad_v2 is True.\r\n valid_answers = []\r\n\r\n context = example[\"context\"]\r\n # Looping through all the features associated to the current example.\r\n for feature_index in feature_indices:\r\n\r\n # We grab the predictions of the model for this feature.\r\n start_logits = all_start_logits[feature_index]\r\n end_logits = all_end_logits[feature_index]\r\n # This is what will allow us to map some the positions in our logits to span of texts in the original\r\n # context.\r\n offset_mapping = features[feature_index][\"offset_mapping\"]\r\n\r\n # Update minimum null prediction.\r\n cls_index = features[feature_index][\"input_ids\"].index(tokenizer.cls_token_id)\r\n feature_null_score = start_logits[cls_index] + end_logits[cls_index]\r\n if min_null_score is None or min_null_score < feature_null_score:\r\n min_null_score = feature_null_score\r\n\r\n # Go through all possibilities for the `n_best_size` greater start and end logits.\r\n start_indexes = np.argsort(start_logits)[-1 : -n_best_size - 1 : -1].tolist()\r\n end_indexes = np.argsort(end_logits)[-1 : -n_best_size - 1 : -1].tolist()\r\n for start_index in start_indexes:\r\n for end_index in end_indexes:\r\n # Don't consider out-of-scope answers, either because the indices are out of bounds or correspond\r\n # to part of the input_ids that are not in the context.\r\n if (\r\n start_index >= len(offset_mapping)\r\n or end_index >= len(offset_mapping)\r\n or offset_mapping[start_index] is None\r\n or offset_mapping[end_index] is None\r\n ):\r\n continue\r\n # Don't consider answers with a length that is either < 0 or > max_answer_length.\r\n if end_index < start_index or end_index - start_index + 1 > max_answer_length:\r\n continue\r\n\r\n start_char = offset_mapping[start_index][0]\r\n end_char = offset_mapping[end_index][1]\r\n valid_answers.append(\r\n {\r\n \"score\": start_logits[start_index] + end_logits[end_index],\r\n \"text\": context[start_char: end_char]\r\n }\r\n )\r\n\r\n\r\n if len(valid_answers) > 0:\r\n best_answer = sorted(valid_answers, key=lambda x: x[\"score\"], reverse=True)[0]\r\n else:\r\n # In the very rare edge case we have not a single non-null prediction, we create a fake prediction to avoid\r\n # failure.\r\n best_answer = {\"text\": \"\", \"score\": 0.0}\r\n\r\n # Let's pick our final answer: the best one or the null answer (only for squad_v2)\r\n if not squad_v2:\r\n predictions[example[\"id\"]] = best_answer[\"text\"]\r\n else:\r\n answer = best_answer[\"text\"] if best_answer[\"score\"] > min_null_score else \"\"\r\n predictions[example[\"id\"]] = answer\r\n\r\n return predictions\r\n\r\n\r\n\r\n# build base examples, features from training data\r\nexamples = load_dataset(\"squad_v2\").shuffle(seed=5)['train']\r\nfeatures = load_dataset(\"squad_v2\").shuffle(seed=5)['train'].map(\r\n prepare_validation_features,\r\n batched=True,\r\n remove_columns=['answers', 'context', 'id', 'question', 'title'])\r\n\r\n# sim some shuffled training indices that we want to use to re-order the data to compare how we did\r\nshuffle_idx = np.arange(0, 131754)\r\nnp.random.shuffle(shuffle_idx)\r\n# create a new dataset with rows selected following the training shuffle\r\nfeatures = features.select(indices=shuffle_idx)\r\n# get unique example ids to match with the \"example\" data\r\nid_list = list(dict.fromkeys(features['example_id']))\r\n# now search for their index positions; load elastic search\r\nes = Elasticsearch([{'host': 'localhost'}]).ping()\r\n# add an index to the id column for the examples\r\nexamples.add_elasticsearch_index(column='id')\r\n# search the examples for their index position\r\nexample_idx = [examples.search(index_name='id', query=i, k=1).indices for i in id_list]\r\n# drop the elastic search\r\nexamples.drop_index(index_name='id')\r\n# put examples in the right order\r\nexamples = examples.select(indices=example_idx)\r\n\r\n# generate some fake data\r\nlogits = {'starting_logits': torch.randn(131754, 384), 'ending_logits': torch.randn(131754, 384)}\r\n\r\n\r\ndef score_squad(logits, n_best_size, max_answer):\r\n # proceed with QA calculation\r\n final_predictions = postprocess_qa_predictions(examples=examples,\r\n features=features,\r\n starting_logits=logits['starting_logits'],\r\n ending_logits=logits['ending_logits'],\r\n n_best_size=20,\r\n max_answer_length=30)\r\n metric = load_metric(\"squad_v2\")\r\n formatted_predictions = [{\"id\": k, \"prediction_text\": v, \"no_answer_probability\": 0.0} for k, v in final_predictions.items()]\r\n references = [{\"id\": ex[\"id\"], \"answers\": ex[\"answers\"]} for ex in examples]\r\n metrics = metric.compute(predictions=formatted_predictions, references=references)\r\n return metrics\r\n\r\nmetrics = score_squad(logits, n_best_size=20, max_answer=30)\r\n```\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n"},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1755/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 0\n}"},"timeline_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1755/timeline"},"performed_via_github_app":{"kind":"null"},"state_reason":{"kind":"string","value":"completed"}}},{"rowIdx":2273,"cells":{"url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1747"},"repository_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets"},"labels_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1747/labels{/name}"},"comments_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1747/comments"},"events_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1747/events"},"html_url":{"kind":"string","value":"https://github.com/huggingface/datasets/issues/1747"},"id":{"kind":"number","value":788299775,"string":"788,299,775"},"node_id":{"kind":"string","value":"MDU6SXNzdWU3ODgyOTk3NzU="},"number":{"kind":"number","value":1747,"string":"1,747"},"title":{"kind":"string","value":"datasets slicing with seed "},"user":{"kind":"string","value":"{\n \"login\": \"ghost\",\n \"id\": 10137,\n \"node_id\": \"MDQ6VXNlcjEwMTM3\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/10137?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/ghost\",\n \"html_url\": \"https://github.com/ghost\",\n \"followers_url\": \"https://api.github.com/users/ghost/followers\",\n \"following_url\": \"https://api.github.com/users/ghost/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/ghost/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/ghost/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/ghost/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/ghost/orgs\",\n \"repos_url\": \"https://api.github.com/users/ghost/repos\",\n \"events_url\": \"https://api.github.com/users/ghost/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/ghost/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n}"},"labels":{"kind":"list like","value":[],"string":"[]"},"state":{"kind":"string","value":"closed"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["Hi :) \r\nThe slicing API from https://huggingface.co/docs/datasets/splits.html doesn't shuffle the data.\r\nYou can shuffle and then take a subset of your dataset with\r\n```python\r\n# shuffle and take the first 100 examples\r\ndataset = dataset.shuffle(seed=42).select(range(100))\r\n```\r\n\r\nYou can find more information about shuffling and selecting rows in the documentation: https://huggingface.co/docs/datasets/processing.html#selecting-sorting-shuffling-splitting-rows","thank you so much\n\nOn Mon, Jan 18, 2021 at 3:17 PM Quentin Lhoest \nwrote:\n\n> Hi :)\n> The slicing API doesn't shuffle the data.\n> You can shuffle and then take a subset of your dataset with\n>\n> # shuffle and take the first 100 examplesdataset = dataset.shuffle(seed=42).select(range(100))\n>\n> You can find more information about shuffling and selecting rows in the\n> documentation:\n> https://huggingface.co/docs/datasets/processing.html#selecting-sorting-shuffling-splitting-rows\n>\n> —\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub\n> ,\n> or unsubscribe\n> \n> .\n>\n"],"string":"[\n \"Hi :) \\r\\nThe slicing API from https://huggingface.co/docs/datasets/splits.html doesn't shuffle the data.\\r\\nYou can shuffle and then take a subset of your dataset with\\r\\n```python\\r\\n# shuffle and take the first 100 examples\\r\\ndataset = dataset.shuffle(seed=42).select(range(100))\\r\\n```\\r\\n\\r\\nYou can find more information about shuffling and selecting rows in the documentation: https://huggingface.co/docs/datasets/processing.html#selecting-sorting-shuffling-splitting-rows\",\n \"thank you so much\\n\\nOn Mon, Jan 18, 2021 at 3:17 PM Quentin Lhoest \\nwrote:\\n\\n> Hi :)\\n> The slicing API doesn't shuffle the data.\\n> You can shuffle and then take a subset of your dataset with\\n>\\n> # shuffle and take the first 100 examplesdataset = dataset.shuffle(seed=42).select(range(100))\\n>\\n> You can find more information about shuffling and selecting rows in the\\n> documentation:\\n> https://huggingface.co/docs/datasets/processing.html#selecting-sorting-shuffling-splitting-rows\\n>\\n> —\\n> You are receiving this because you authored the thread.\\n> Reply to this email directly, view it on GitHub\\n> ,\\n> or unsubscribe\\n> \\n> .\\n>\\n\"\n]"},"created_at":{"kind":"timestamp","value":"2021-01-18T14:08:55","string":"2021-01-18T14:08:55"},"updated_at":{"kind":"timestamp","value":"2022-10-05T12:37:27","string":"2022-10-05T12:37:27"},"closed_at":{"kind":"timestamp","value":"2022-10-05T12:37:27","string":"2022-10-05T12:37:27"},"author_association":{"kind":"string","value":"NONE"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"Hi\r\nI need to slice a dataset with random seed, I looked into documentation here https://huggingface.co/docs/datasets/splits.html \r\nI could not find a seed option, could you assist me please how I can get a slice for different seeds?\r\nthank you.\r\n@lhoestq "},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1747/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 0\n}"},"timeline_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1747/timeline"},"performed_via_github_app":{"kind":"null"},"state_reason":{"kind":"string","value":"completed"}}},{"rowIdx":2274,"cells":{"url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1745"},"repository_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets"},"labels_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1745/labels{/name}"},"comments_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1745/comments"},"events_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1745/events"},"html_url":{"kind":"string","value":"https://github.com/huggingface/datasets/issues/1745"},"id":{"kind":"number","value":787838256,"string":"787,838,256"},"node_id":{"kind":"string","value":"MDU6SXNzdWU3ODc4MzgyNTY="},"number":{"kind":"number","value":1745,"string":"1,745"},"title":{"kind":"string","value":"difference between wsc and wsc.fixed for superglue"},"user":{"kind":"string","value":"{\n \"login\": \"ghost\",\n \"id\": 10137,\n \"node_id\": \"MDQ6VXNlcjEwMTM3\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/10137?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/ghost\",\n \"html_url\": \"https://github.com/ghost\",\n \"followers_url\": \"https://api.github.com/users/ghost/followers\",\n \"following_url\": \"https://api.github.com/users/ghost/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/ghost/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/ghost/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/ghost/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/ghost/orgs\",\n \"repos_url\": \"https://api.github.com/users/ghost/repos\",\n \"events_url\": \"https://api.github.com/users/ghost/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/ghost/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n}"},"labels":{"kind":"list like","value":[],"string":"[]"},"state":{"kind":"string","value":"closed"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["From the description given in the dataset script for `wsc.fixed`:\r\n```\r\nThis version fixes issues where the spans are not actually substrings of the text.\r\n```"],"string":"[\n \"From the description given in the dataset script for `wsc.fixed`:\\r\\n```\\r\\nThis version fixes issues where the spans are not actually substrings of the text.\\r\\n```\"\n]"},"created_at":{"kind":"timestamp","value":"2021-01-18T00:50:19","string":"2021-01-18T00:50:19"},"updated_at":{"kind":"timestamp","value":"2021-01-18T11:02:43","string":"2021-01-18T11:02:43"},"closed_at":{"kind":"timestamp","value":"2021-01-18T00:59:34","string":"2021-01-18T00:59:34"},"author_association":{"kind":"string","value":"NONE"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"Hi\r\nI see two versions of wsc in superglue, and I am not sure what is the differences and which one is the original one. could you help to discuss the differences? thanks @lhoestq "},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1745/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 0\n}"},"timeline_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1745/timeline"},"performed_via_github_app":{"kind":"null"},"state_reason":{"kind":"string","value":"completed"}}},{"rowIdx":2275,"cells":{"url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1743"},"repository_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets"},"labels_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1743/labels{/name}"},"comments_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1743/comments"},"events_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1743/events"},"html_url":{"kind":"string","value":"https://github.com/huggingface/datasets/issues/1743"},"id":{"kind":"number","value":787631412,"string":"787,631,412"},"node_id":{"kind":"string","value":"MDU6SXNzdWU3ODc2MzE0MTI="},"number":{"kind":"number","value":1743,"string":"1,743"},"title":{"kind":"string","value":"Issue while Creating Custom Metric"},"user":{"kind":"string","value":"{\n \"login\": \"gchhablani\",\n \"id\": 29076344,\n \"node_id\": \"MDQ6VXNlcjI5MDc2MzQ0\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/29076344?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/gchhablani\",\n \"html_url\": \"https://github.com/gchhablani\",\n \"followers_url\": \"https://api.github.com/users/gchhablani/followers\",\n \"following_url\": \"https://api.github.com/users/gchhablani/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/gchhablani/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/gchhablani/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/gchhablani/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/gchhablani/orgs\",\n \"repos_url\": \"https://api.github.com/users/gchhablani/repos\",\n \"events_url\": \"https://api.github.com/users/gchhablani/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/gchhablani/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n}"},"labels":{"kind":"list like","value":[],"string":"[]"},"state":{"kind":"string","value":"closed"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["Currently it's only possible to define the features for the two columns `references` and `predictions`.\r\nThe data for these columns can then be passed to `metric.add_batch` and `metric.compute`.\r\nInstead of defining more columns `text`, `offset_mapping` and `ground` you must include them in either references and predictions.\r\n\r\nFor example \r\n```python\r\nfeatures = datasets.Features({\r\n 'predictions':datasets.Sequence(datasets.Value(\"int32\")),\r\n \"references\": datasets.Sequence({\r\n \"references_ids\": datasets.Value(\"int32\"),\r\n \"offset_mapping\": datasets.Value(\"int32\"),\r\n 'text': datasets.Value('string'),\r\n \"ground\": datasets.Value(\"int32\")\r\n }),\r\n})\r\n```\r\n\r\nAnother option would be to simply have the two features like \r\n```python\r\nfeatures = datasets.Features({\r\n 'predictions':datasets.Sequence(datasets.Value(\"int32\")),\r\n \"references\": datasets.Sequence(datasets.Value(\"int32\")),\r\n})\r\n```\r\nand keep `offset_mapping`, `text` and `ground` as as parameters for the computation (i.e. kwargs when calling `metric.compute`).\r\n\r\n\r\nWhat is the metric you would like to implement ?\r\n\r\nI'm asking since we consider allowing additional fields as requested in the `Comet` metric (see PR and discussion [here](https://github.com/huggingface/datasets/pull/1577)) and I'd like to know if it's something that can be interesting for users.\r\n\r\nWhat do you think ?","Hi @lhoestq,\r\n\r\nI am doing text segmentation and the metric is effectively dice score on character offsets. So I need to pass the actual spans and I want to be able to get the spans based on predictions using offset_mapping.\r\n\r\nIncluding them in references seems like a good idea. I'll try it out and get back to you. If there's a better way to write a metric function for the same, please let me know.","Resolved via https://github.com/huggingface/datasets/pull/3824."],"string":"[\n \"Currently it's only possible to define the features for the two columns `references` and `predictions`.\\r\\nThe data for these columns can then be passed to `metric.add_batch` and `metric.compute`.\\r\\nInstead of defining more columns `text`, `offset_mapping` and `ground` you must include them in either references and predictions.\\r\\n\\r\\nFor example \\r\\n```python\\r\\nfeatures = datasets.Features({\\r\\n 'predictions':datasets.Sequence(datasets.Value(\\\"int32\\\")),\\r\\n \\\"references\\\": datasets.Sequence({\\r\\n \\\"references_ids\\\": datasets.Value(\\\"int32\\\"),\\r\\n \\\"offset_mapping\\\": datasets.Value(\\\"int32\\\"),\\r\\n 'text': datasets.Value('string'),\\r\\n \\\"ground\\\": datasets.Value(\\\"int32\\\")\\r\\n }),\\r\\n})\\r\\n```\\r\\n\\r\\nAnother option would be to simply have the two features like \\r\\n```python\\r\\nfeatures = datasets.Features({\\r\\n 'predictions':datasets.Sequence(datasets.Value(\\\"int32\\\")),\\r\\n \\\"references\\\": datasets.Sequence(datasets.Value(\\\"int32\\\")),\\r\\n})\\r\\n```\\r\\nand keep `offset_mapping`, `text` and `ground` as as parameters for the computation (i.e. kwargs when calling `metric.compute`).\\r\\n\\r\\n\\r\\nWhat is the metric you would like to implement ?\\r\\n\\r\\nI'm asking since we consider allowing additional fields as requested in the `Comet` metric (see PR and discussion [here](https://github.com/huggingface/datasets/pull/1577)) and I'd like to know if it's something that can be interesting for users.\\r\\n\\r\\nWhat do you think ?\",\n \"Hi @lhoestq,\\r\\n\\r\\nI am doing text segmentation and the metric is effectively dice score on character offsets. So I need to pass the actual spans and I want to be able to get the spans based on predictions using offset_mapping.\\r\\n\\r\\nIncluding them in references seems like a good idea. I'll try it out and get back to you. If there's a better way to write a metric function for the same, please let me know.\",\n \"Resolved via https://github.com/huggingface/datasets/pull/3824.\"\n]"},"created_at":{"kind":"timestamp","value":"2021-01-17T07:01:14","string":"2021-01-17T07:01:14"},"updated_at":{"kind":"timestamp","value":"2022-06-01T15:49:34","string":"2022-06-01T15:49:34"},"closed_at":{"kind":"timestamp","value":"2022-06-01T15:49:34","string":"2022-06-01T15:49:34"},"author_association":{"kind":"string","value":"CONTRIBUTOR"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"Hi Team,\r\n\r\nI am trying to create a custom metric for my training as follows, where f1 is my own metric:\r\n\r\n```python\r\n def _info(self):\r\n # TODO: Specifies the datasets.MetricInfo object\r\n return datasets.MetricInfo(\r\n # This is the description that will appear on the metrics page.\r\n description=_DESCRIPTION,\r\n citation=_CITATION,\r\n inputs_description=_KWARGS_DESCRIPTION,\r\n # This defines the format of each prediction and reference\r\n features = datasets.Features({'predictions':datasets.Sequence(datasets.Value(\"int32\")), \"references\": datasets.Sequence(datasets.Value(\"int32\")),\"offset_mapping\":datasets.Sequence(datasets.Value(\"int32\")),'text':datasets.Sequence(datasets.Value('string')),\"ground\":datasets.Sequence(datasets.Value(\"int32\")),}),\r\n # Homepage of the metric for documentation\r\n homepage=\"http://metric.homepage\",\r\n # Additional links to the codebase or references\r\n codebase_urls=[\"http://github.com/path/to/codebase/of/new_metric\"],\r\n reference_urls=[\"http://path.to.reference.url/new_metric\"]\r\n )\r\n\r\n def _compute(self,predictions,references,text,offset_mapping,spans):\r\n\r\n pred_spans = []\r\n\r\n for i,preds in enumerate(predictions):\r\n current_preds = []\r\n for j,token_preds in enumerate(preds):\r\n if (preds>0.5):\r\n current_preds+=list(range(offset_mapping[i][j][0],offset_mapping[i][j][1]))\r\n pred_spans.append(current_spans)\r\n \r\n return {\r\n \"Token Wise F1\": f1_score(references,predictions,labels=[0,1]),\r\n \"Offset Wise F1\": np.mean([f1(preds,gold) for preds,fold in zip(pred_spans,ground)])\r\n }\r\n\r\n```\r\n\r\nI believe this is not correct. But that's not the issue I am facing right now. I get this error :\r\n```python\r\n---------------------------------------------------------------------------\r\nValueError Traceback (most recent call last)\r\n in ()\r\n----> 1 new_metric.compute(predictions=inputs[\"labels\"],references=inputs[\"labels\"], text=inputs[\"text\"], offset_mapping=inputs[\"offset_mapping\"],ground=inputs[\"ground\"] )\r\n\r\n2 frames\r\n/usr/local/lib/python3.6/dist-packages/datasets/features.py in encode_batch(self, batch)\r\n 802 encoded_batch = {}\r\n 803 if set(batch) != set(self):\r\n--> 804 print(batch)\r\n 805 print(self)\r\n 806 raise ValueError(\"Column mismatch between batch {} and features {}\".format(set(batch), set(self)))\r\n\r\nValueError: Column mismatch between batch {'references', 'predictions'} and features {'ground', 'predictions', 'offset_mapping', 'text', 'references'}\r\n```\r\nOn checking the features.py file, I see the call is made from add_batch() in metrics.py which only takes in predictions and references.\r\n\r\nHow do I make my custom metric work? Will it work with a trainer even if I am able to make this metric work?\r\n\r\nThanks,\r\nGunjan"},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1743/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 0\n}"},"timeline_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1743/timeline"},"performed_via_github_app":{"kind":"null"},"state_reason":{"kind":"string","value":"completed"}}},{"rowIdx":2276,"cells":{"url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1741"},"repository_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets"},"labels_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1741/labels{/name}"},"comments_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1741/comments"},"events_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1741/events"},"html_url":{"kind":"string","value":"https://github.com/huggingface/datasets/issues/1741"},"id":{"kind":"number","value":787327060,"string":"787,327,060"},"node_id":{"kind":"string","value":"MDU6SXNzdWU3ODczMjcwNjA="},"number":{"kind":"number","value":1741,"string":"1,741"},"title":{"kind":"string","value":"error when run fine_tuning on text_classification"},"user":{"kind":"string","value":"{\n \"login\": \"XiaoYang66\",\n \"id\": 43234824,\n \"node_id\": \"MDQ6VXNlcjQzMjM0ODI0\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/43234824?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/XiaoYang66\",\n \"html_url\": \"https://github.com/XiaoYang66\",\n \"followers_url\": \"https://api.github.com/users/XiaoYang66/followers\",\n \"following_url\": \"https://api.github.com/users/XiaoYang66/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/XiaoYang66/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/XiaoYang66/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/XiaoYang66/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/XiaoYang66/orgs\",\n \"repos_url\": \"https://api.github.com/users/XiaoYang66/repos\",\n \"events_url\": \"https://api.github.com/users/XiaoYang66/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/XiaoYang66/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n}"},"labels":{"kind":"list like","value":[],"string":"[]"},"state":{"kind":"string","value":"closed"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["none"],"string":"[\n \"none\"\n]"},"created_at":{"kind":"timestamp","value":"2021-01-16T02:23:19","string":"2021-01-16T02:23:19"},"updated_at":{"kind":"timestamp","value":"2021-01-16T02:39:28","string":"2021-01-16T02:39:28"},"closed_at":{"kind":"timestamp","value":"2021-01-16T02:39:18","string":"2021-01-16T02:39:18"},"author_association":{"kind":"string","value":"NONE"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"dataset:sem_eval_2014_task_1\r\npretrained_model:bert-base-uncased\r\n\r\nerror description:\r\nwhen i use these resoruce to train fine_tuning a text_classification on sem_eval_2014_task_1,there always be some problem(when i use other dataset ,there exist the error too). And i followed the colab code (url:https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/text_classification.ipynb#scrollTo=TlqNaB8jIrJW).\r\n\r\n\r\nthe error is like this :\r\n`File \"train.py\", line 69, in \r\n trainer.train()\r\n File \"/home/projects/anaconda3/envs/calibration/lib/python3.7/site-packages/transformers/trainer.py\", line 784, in train\r\n for step, inputs in enumerate(epoch_iterator):\r\n File \"/home/projects/anaconda3/envs/calibration/lib/python3.7/site-packages/torch/utils/data/dataloader.py\", line 435, in __next__\r\n data = self._next_data()\r\n File \"/home/projects/anaconda3/envs/calibration/lib/python3.7/site-packages/torch/utils/data/dataloader.py\", line 475, in _next_data\r\n data = self._dataset_fetcher.fetch(index) # may raise StopIteration\r\n File \"/home/projects/anaconda3/envs/calibration/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py\", line 44, in fetch\r\n data = [self.dataset[idx] for idx in possibly_batched_index]\r\n File \"/home/projects/anaconda3/envs/calibration/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py\", line 44, in \r\n data = [self.dataset[idx] for idx in possibly_batched_index]\r\nKeyError: 2`\r\n\r\nthis is my code :\r\n```dataset_name = 'sem_eval_2014_task_1'\r\nnum_labels_size = 3\r\nbatch_size = 4\r\nmodel_checkpoint = 'bert-base-uncased'\r\nnumber_train_epoch = 5\r\n\r\ndef tokenize(batch):\r\nreturn tokenizer(batch['premise'], batch['hypothesis'], truncation=True, )\r\n\r\ndef compute_metrics(pred):\r\nlabels = pred.label_ids\r\npreds = pred.predictions.argmax(-1)\r\nprecision, recall, f1, _ = precision_recall_fscore_support(labels, preds, average='micro')\r\nacc = accuracy_score(labels, preds)\r\nreturn {\r\n'accuracy': acc,\r\n'f1': f1,\r\n'precision': precision,\r\n'recall': recall\r\n}\r\n\r\nmodel = BertForSequenceClassification.from_pretrained(model_checkpoint, num_labels=num_labels_size)\r\ntokenizer = BertTokenizerFast.from_pretrained(model_checkpoint, use_fast=True)\r\n\r\ntrain_dataset = load_dataset(dataset_name, split='train')\r\ntest_dataset = load_dataset(dataset_name, split='test')\r\n\r\ntrain_encoded_dataset = train_dataset.map(tokenize, batched=True)\r\ntest_encoded_dataset = test_dataset.map(tokenize, batched=True)\r\n\r\nargs = TrainingArguments(\r\noutput_dir='./results',\r\nevaluation_strategy=\"epoch\",\r\nlearning_rate=2e-5,\r\nper_device_train_batch_size=batch_size,\r\nper_device_eval_batch_size=batch_size,\r\nnum_train_epochs=number_train_epoch,\r\nweight_decay=0.01,\r\ndo_predict=True,\r\n)\r\ntrainer = Trainer(\r\nmodel=model,\r\nargs=args,\r\ncompute_metrics=compute_metrics,\r\ntrain_dataset=train_encoded_dataset,\r\neval_dataset=test_encoded_dataset,\r\ntokenizer=tokenizer\r\n)\r\n\r\ntrainer.train()\r\ntrainer.evaluate()\r\n\r\n"},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1741/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 0\n}"},"timeline_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1741/timeline"},"performed_via_github_app":{"kind":"null"},"state_reason":{"kind":"string","value":"completed"}}},{"rowIdx":2277,"cells":{"url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1733"},"repository_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets"},"labels_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1733/labels{/name}"},"comments_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1733/comments"},"events_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1733/events"},"html_url":{"kind":"string","value":"https://github.com/huggingface/datasets/issues/1733"},"id":{"kind":"number","value":784903002,"string":"784,903,002"},"node_id":{"kind":"string","value":"MDU6SXNzdWU3ODQ5MDMwMDI="},"number":{"kind":"number","value":1733,"string":"1,733"},"title":{"kind":"string","value":"connection issue with glue, what is the data url for glue? "},"user":{"kind":"string","value":"{\n \"login\": \"ghost\",\n \"id\": 10137,\n \"node_id\": \"MDQ6VXNlcjEwMTM3\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/10137?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/ghost\",\n \"html_url\": \"https://github.com/ghost\",\n \"followers_url\": \"https://api.github.com/users/ghost/followers\",\n \"following_url\": \"https://api.github.com/users/ghost/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/ghost/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/ghost/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/ghost/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/ghost/orgs\",\n \"repos_url\": \"https://api.github.com/users/ghost/repos\",\n \"events_url\": \"https://api.github.com/users/ghost/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/ghost/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n}"},"labels":{"kind":"list like","value":[],"string":"[]"},"state":{"kind":"string","value":"closed"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["Hello @juliahane, which config of GLUE causes you trouble?\r\nThe URLs are defined in the dataset script source code: https://github.com/huggingface/datasets/blob/master/datasets/glue/glue.py"],"string":"[\n \"Hello @juliahane, which config of GLUE causes you trouble?\\r\\nThe URLs are defined in the dataset script source code: https://github.com/huggingface/datasets/blob/master/datasets/glue/glue.py\"\n]"},"created_at":{"kind":"timestamp","value":"2021-01-13T08:37:40","string":"2021-01-13T08:37:40"},"updated_at":{"kind":"timestamp","value":"2021-08-04T18:13:55","string":"2021-08-04T18:13:55"},"closed_at":{"kind":"timestamp","value":"2021-08-04T18:13:55","string":"2021-08-04T18:13:55"},"author_association":{"kind":"string","value":"NONE"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"Hi\r\nmy codes sometimes fails due to connection issue with glue, could you tell me how I can have the URL datasets library is trying to read GLUE from to test the machines I am working on if there is an issue on my side or not\r\nthanks "},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1733/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 0\n}"},"timeline_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1733/timeline"},"performed_via_github_app":{"kind":"null"},"state_reason":{"kind":"string","value":"completed"}}},{"rowIdx":2278,"cells":{"url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1731"},"repository_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets"},"labels_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1731/labels{/name}"},"comments_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1731/comments"},"events_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1731/events"},"html_url":{"kind":"string","value":"https://github.com/huggingface/datasets/issues/1731"},"id":{"kind":"number","value":784744674,"string":"784,744,674"},"node_id":{"kind":"string","value":"MDU6SXNzdWU3ODQ3NDQ2NzQ="},"number":{"kind":"number","value":1731,"string":"1,731"},"title":{"kind":"string","value":"Couldn't reach swda.py"},"user":{"kind":"string","value":"{\n \"login\": \"yangp725\",\n \"id\": 13365326,\n \"node_id\": \"MDQ6VXNlcjEzMzY1MzI2\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/13365326?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/yangp725\",\n \"html_url\": \"https://github.com/yangp725\",\n \"followers_url\": \"https://api.github.com/users/yangp725/followers\",\n \"following_url\": \"https://api.github.com/users/yangp725/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/yangp725/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/yangp725/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/yangp725/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/yangp725/orgs\",\n \"repos_url\": \"https://api.github.com/users/yangp725/repos\",\n \"events_url\": \"https://api.github.com/users/yangp725/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/yangp725/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n}"},"labels":{"kind":"list like","value":[],"string":"[]"},"state":{"kind":"string","value":"closed"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["Hi @yangp725,\r\nThe SWDA has been added very recently and has not been released yet, thus it is not available in the `1.2.0` version of 🤗`datasets`.\r\nYou can still access it by installing the latest version of the library (master branch), by following instructions in [this issue](https://github.com/huggingface/datasets/issues/1641#issuecomment-751571471).\r\nLet me know if this helps !","Thanks @SBrandeis ,\r\nProblem solved by downloading and installing the latest version datasets."],"string":"[\n \"Hi @yangp725,\\r\\nThe SWDA has been added very recently and has not been released yet, thus it is not available in the `1.2.0` version of 🤗`datasets`.\\r\\nYou can still access it by installing the latest version of the library (master branch), by following instructions in [this issue](https://github.com/huggingface/datasets/issues/1641#issuecomment-751571471).\\r\\nLet me know if this helps !\",\n \"Thanks @SBrandeis ,\\r\\nProblem solved by downloading and installing the latest version datasets.\"\n]"},"created_at":{"kind":"timestamp","value":"2021-01-13T02:57:40","string":"2021-01-13T02:57:40"},"updated_at":{"kind":"timestamp","value":"2021-01-13T11:17:40","string":"2021-01-13T11:17:40"},"closed_at":{"kind":"timestamp","value":"2021-01-13T11:17:40","string":"2021-01-13T11:17:40"},"author_association":{"kind":"string","value":"NONE"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.2.0/datasets/swda/swda.py\r\n"},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1731/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 0\n}"},"timeline_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1731/timeline"},"performed_via_github_app":{"kind":"null"},"state_reason":{"kind":"string","value":"completed"}}},{"rowIdx":2279,"cells":{"url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1729"},"repository_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets"},"labels_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1729/labels{/name}"},"comments_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1729/comments"},"events_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1729/events"},"html_url":{"kind":"string","value":"https://github.com/huggingface/datasets/issues/1729"},"id":{"kind":"number","value":784565898,"string":"784,565,898"},"node_id":{"kind":"string","value":"MDU6SXNzdWU3ODQ1NjU4OTg="},"number":{"kind":"number","value":1729,"string":"1,729"},"title":{"kind":"string","value":"Is there support for Deep learning datasets?"},"user":{"kind":"string","value":"{\n \"login\": \"pablodz\",\n \"id\": 28235457,\n \"node_id\": \"MDQ6VXNlcjI4MjM1NDU3\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/28235457?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/pablodz\",\n \"html_url\": \"https://github.com/pablodz\",\n \"followers_url\": \"https://api.github.com/users/pablodz/followers\",\n \"following_url\": \"https://api.github.com/users/pablodz/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/pablodz/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/pablodz/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/pablodz/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/pablodz/orgs\",\n \"repos_url\": \"https://api.github.com/users/pablodz/repos\",\n \"events_url\": \"https://api.github.com/users/pablodz/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/pablodz/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n}"},"labels":{"kind":"list like","value":[],"string":"[]"},"state":{"kind":"string","value":"closed"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["Hi @ZurMaD!\r\nThanks for your interest in 🤗 `datasets`. Support for image datasets is at an early stage, with CIFAR-10 added in #1617 \r\nMNIST is also on the way: #1730 \r\n\r\nIf you feel like adding another image dataset, I would advise starting by reading the [ADD_NEW_DATASET.md](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md) guide. New datasets are always very much appreciated 🚀\r\n"],"string":"[\n \"Hi @ZurMaD!\\r\\nThanks for your interest in 🤗 `datasets`. Support for image datasets is at an early stage, with CIFAR-10 added in #1617 \\r\\nMNIST is also on the way: #1730 \\r\\n\\r\\nIf you feel like adding another image dataset, I would advise starting by reading the [ADD_NEW_DATASET.md](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md) guide. New datasets are always very much appreciated 🚀\\r\\n\"\n]"},"created_at":{"kind":"timestamp","value":"2021-01-12T20:22:41","string":"2021-01-12T20:22:41"},"updated_at":{"kind":"timestamp","value":"2021-03-31T04:24:07","string":"2021-03-31T04:24:07"},"closed_at":{"kind":"timestamp","value":"2021-03-31T04:24:07","string":"2021-03-31T04:24:07"},"author_association":{"kind":"string","value":"NONE"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"I looked around this repository and looking the datasets I think that there's no support for images-datasets. Or am I missing something? For example to add a repo like this https://github.com/DZPeru/fish-datasets"},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1729/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 0\n}"},"timeline_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1729/timeline"},"performed_via_github_app":{"kind":"null"},"state_reason":{"kind":"string","value":"completed"}}},{"rowIdx":2280,"cells":{"url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1728"},"repository_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets"},"labels_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1728/labels{/name}"},"comments_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1728/comments"},"events_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1728/events"},"html_url":{"kind":"string","value":"https://github.com/huggingface/datasets/issues/1728"},"id":{"kind":"number","value":784458342,"string":"784,458,342"},"node_id":{"kind":"string","value":"MDU6SXNzdWU3ODQ0NTgzNDI="},"number":{"kind":"number","value":1728,"string":"1,728"},"title":{"kind":"string","value":"Add an entry to an arrow dataset"},"user":{"kind":"string","value":"{\n \"login\": \"ameet-1997\",\n \"id\": 18645407,\n \"node_id\": \"MDQ6VXNlcjE4NjQ1NDA3\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/18645407?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/ameet-1997\",\n \"html_url\": \"https://github.com/ameet-1997\",\n \"followers_url\": \"https://api.github.com/users/ameet-1997/followers\",\n \"following_url\": \"https://api.github.com/users/ameet-1997/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/ameet-1997/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/ameet-1997/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/ameet-1997/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/ameet-1997/orgs\",\n \"repos_url\": \"https://api.github.com/users/ameet-1997/repos\",\n \"events_url\": \"https://api.github.com/users/ameet-1997/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/ameet-1997/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n}"},"labels":{"kind":"list like","value":[],"string":"[]"},"state":{"kind":"string","value":"closed"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["Hi @ameet-1997,\r\nI think what you are looking for is the `concatenate_datasets` function: https://huggingface.co/docs/datasets/processing.html?highlight=concatenate#concatenate-several-datasets\r\n\r\nFor your use case, I would use the [`map` method](https://huggingface.co/docs/datasets/processing.html?highlight=concatenate#processing-data-with-map) to transform the SQuAD sentences and the `concatenate` the original and mapped dataset.\r\n\r\nLet me know If this helps!","That's a great idea! Thank you so much!\r\n\r\nWhen I try that solution, I get the following error when I try to concatenate `datasets` and `modified_dataset`. I have also attached the output I get when I print out those two variables. Am I missing something?\r\n\r\nCode:\r\n``` python\r\ncombined_dataset = concatenate_datasets([datasets, modified_dataset])\r\n```\r\n\r\nError:\r\n```\r\nAttributeError: 'DatasetDict' object has no attribute 'features'\r\n```\r\n\r\nOutput:\r\n```\r\n(Pdb) datasets\r\nDatasetDict({\r\n train: Dataset({\r\n features: ['attention_mask', 'input_ids', 'special_tokens_mask'],\r\n num_rows: 493\r\n })\r\n})\r\n(Pdb) modified_dataset\r\nDatasetDict({\r\n train: Dataset({\r\n features: ['attention_mask', 'input_ids', 'special_tokens_mask'],\r\n num_rows: 493\r\n })\r\n})\r\n```\r\n\r\nThe error is stemming from the fact that the attribute `datasets.features` does not exist. Would it not be possible to use `concatenate_datasets` in such a case? Is there an alternate solution?","You should do `combined_dataset = concatenate_datasets([datasets['train'], modified_dataset['train']])`\r\n\r\nDidn't we talk about returning a Dataset instead of a DatasetDict with load_dataset and no split provided @lhoestq? Not sure it's the way to go but I'm wondering if it's not simpler for some use-cases.","> Didn't we talk about returning a Dataset instead of a DatasetDict with load_dataset and no split provided @lhoestq? Not sure it's the way to go but I'm wondering if it's not simpler for some use-cases.\r\n\r\nMy opinion is that users should always know in advance what type of objects they're going to get. Otherwise the development workflow on their side is going to be pretty chaotic with sometimes unexpected behaviors.\r\nFor instance is `split=` is not specified it's currently always returning a DatasetDict. And if `split=\"train\"` is given for example it's always returning a Dataset.","Thanks @thomwolf. Your solution worked!"],"string":"[\n \"Hi @ameet-1997,\\r\\nI think what you are looking for is the `concatenate_datasets` function: https://huggingface.co/docs/datasets/processing.html?highlight=concatenate#concatenate-several-datasets\\r\\n\\r\\nFor your use case, I would use the [`map` method](https://huggingface.co/docs/datasets/processing.html?highlight=concatenate#processing-data-with-map) to transform the SQuAD sentences and the `concatenate` the original and mapped dataset.\\r\\n\\r\\nLet me know If this helps!\",\n \"That's a great idea! Thank you so much!\\r\\n\\r\\nWhen I try that solution, I get the following error when I try to concatenate `datasets` and `modified_dataset`. I have also attached the output I get when I print out those two variables. Am I missing something?\\r\\n\\r\\nCode:\\r\\n``` python\\r\\ncombined_dataset = concatenate_datasets([datasets, modified_dataset])\\r\\n```\\r\\n\\r\\nError:\\r\\n```\\r\\nAttributeError: 'DatasetDict' object has no attribute 'features'\\r\\n```\\r\\n\\r\\nOutput:\\r\\n```\\r\\n(Pdb) datasets\\r\\nDatasetDict({\\r\\n train: Dataset({\\r\\n features: ['attention_mask', 'input_ids', 'special_tokens_mask'],\\r\\n num_rows: 493\\r\\n })\\r\\n})\\r\\n(Pdb) modified_dataset\\r\\nDatasetDict({\\r\\n train: Dataset({\\r\\n features: ['attention_mask', 'input_ids', 'special_tokens_mask'],\\r\\n num_rows: 493\\r\\n })\\r\\n})\\r\\n```\\r\\n\\r\\nThe error is stemming from the fact that the attribute `datasets.features` does not exist. Would it not be possible to use `concatenate_datasets` in such a case? Is there an alternate solution?\",\n \"You should do `combined_dataset = concatenate_datasets([datasets['train'], modified_dataset['train']])`\\r\\n\\r\\nDidn't we talk about returning a Dataset instead of a DatasetDict with load_dataset and no split provided @lhoestq? Not sure it's the way to go but I'm wondering if it's not simpler for some use-cases.\",\n \"> Didn't we talk about returning a Dataset instead of a DatasetDict with load_dataset and no split provided @lhoestq? Not sure it's the way to go but I'm wondering if it's not simpler for some use-cases.\\r\\n\\r\\nMy opinion is that users should always know in advance what type of objects they're going to get. Otherwise the development workflow on their side is going to be pretty chaotic with sometimes unexpected behaviors.\\r\\nFor instance is `split=` is not specified it's currently always returning a DatasetDict. And if `split=\\\"train\\\"` is given for example it's always returning a Dataset.\",\n \"Thanks @thomwolf. Your solution worked!\"\n]"},"created_at":{"kind":"timestamp","value":"2021-01-12T18:01:47","string":"2021-01-12T18:01:47"},"updated_at":{"kind":"timestamp","value":"2021-01-18T19:15:32","string":"2021-01-18T19:15:32"},"closed_at":{"kind":"timestamp","value":"2021-01-18T19:15:32","string":"2021-01-18T19:15:32"},"author_association":{"kind":"string","value":"NONE"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"Is it possible to add an entry to a dataset object?\r\n\r\n**Motivation: I want to transform the sentences in the dataset and add them to the original dataset**\r\n\r\nFor example, say we have the following code:\r\n\r\n``` python\r\nfrom datasets import load_dataset\r\n\r\n# Load a dataset and print the first examples in the training set\r\nsquad_dataset = load_dataset('squad')\r\nprint(squad_dataset['train'][0])\r\n```\r\n\r\nIs it possible to add an entry to `squad_dataset`? Something like the following?\r\n\r\n``` python\r\nsquad_dataset.append({'text': \"This is a new sentence\"})\r\n```\r\n\r\nThe motivation for doing this is that I want to transform the sentences in the squad dataset and add them to the original dataset.\r\n\r\nIf the above doesn't work, is there any other way of achieving the motivation mentioned above? Perhaps by creating a new arrow dataset by using the older one and the transformer sentences?\r\n"},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1728/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 0\n}"},"timeline_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1728/timeline"},"performed_via_github_app":{"kind":"null"},"state_reason":{"kind":"string","value":"completed"}}},{"rowIdx":2281,"cells":{"url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1727"},"repository_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets"},"labels_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1727/labels{/name}"},"comments_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1727/comments"},"events_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1727/events"},"html_url":{"kind":"string","value":"https://github.com/huggingface/datasets/issues/1727"},"id":{"kind":"number","value":784435131,"string":"784,435,131"},"node_id":{"kind":"string","value":"MDU6SXNzdWU3ODQ0MzUxMzE="},"number":{"kind":"number","value":1727,"string":"1,727"},"title":{"kind":"string","value":"BLEURT score calculation raises UnrecognizedFlagError"},"user":{"kind":"string","value":"{\n \"login\": \"nadavo\",\n \"id\": 6603920,\n \"node_id\": \"MDQ6VXNlcjY2MDM5MjA=\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/6603920?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/nadavo\",\n \"html_url\": \"https://github.com/nadavo\",\n \"followers_url\": \"https://api.github.com/users/nadavo/followers\",\n \"following_url\": \"https://api.github.com/users/nadavo/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/nadavo/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/nadavo/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/nadavo/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/nadavo/orgs\",\n \"repos_url\": \"https://api.github.com/users/nadavo/repos\",\n \"events_url\": \"https://api.github.com/users/nadavo/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/nadavo/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n}"},"labels":{"kind":"list like","value":[],"string":"[]"},"state":{"kind":"string","value":"closed"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["Upgrading tensorflow to version 2.4.0 solved the issue.","I still have the same error even with TF 2.4.0.","And I have the same error with TF 2.4.1. I believe this issue should be reopened. Any ideas?!","I'm seeing the same issue with TF 2.4.1 when running the following in https://colab.research.google.com/github/huggingface/datasets/blob/master/notebooks/Overview.ipynb:\r\n```\r\n!pip install git+https://github.com/google-research/bleurt.git\r\nreferences = [\"foo bar baz\", \"one two three\"]\r\nbleurt_metric = load_metric('bleurt')\r\npredictions = [\"foo bar\", \"four five six\"]\r\nbleurt_metric.compute(predictions=predictions, references=references)\r\n```","@aleSuglia @oscartackstrom - Are you getting the error when running your code in a Jupyter notebook ?\r\n\r\nI tried reproducing this error again, and was unable to do so from the python command line console in a virtual environment similar to the one I originally used (and unfortunately no longer have access to) when I first got the error. \r\nHowever, I've managed to reproduce the error by running the same code in a Jupyter notebook running a kernel from the same virtual environment.\r\nThis made me suspect that the problem is somehow related to the Jupyter notebook.\r\n\r\nMore environment details:\r\n```\r\nOS: Ubuntu Linux 18.04\r\nconda==4.8.3\r\npython==3.8.5\r\ndatasets==1.3.0\r\ntensorflow==2.4.0\r\nBLEURT==0.0.1\r\nnotebook==6.2.0\r\n```","This happens when running the notebook on colab. The issue seems to be that colab populates sys.argv with arguments not handled by bleurt.\r\n\r\nRunning this before calling bleurt fixes it:\r\n```\r\nimport sys\r\nsys.argv = sys.argv[:1]\r\n```\r\n\r\nNot the most elegant solution. Perhaps it needs to be fixed in the bleurt code itself rather than huggingface?\r\n\r\nThis is the output of `print(sys.argv)` when running on colab:\r\n```\r\n['/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py', '-f', '/root/.local/share/jupyter/runtime/kernel-a857a78c-44d6-4b9d-b18a-030b858ee327.json']\r\n```","I got the error when running it from the command line. It looks more like an error that should be fixed in the BLEURT codebase.","Seems to be a known issue in the bleurt codebase: https://github.com/google-research/bleurt/issues/24.","Hi, the problem should be solved now.","Hi @tsellam! I can verify that the issue is indeed fixed now. Thanks!"],"string":"[\n \"Upgrading tensorflow to version 2.4.0 solved the issue.\",\n \"I still have the same error even with TF 2.4.0.\",\n \"And I have the same error with TF 2.4.1. I believe this issue should be reopened. Any ideas?!\",\n \"I'm seeing the same issue with TF 2.4.1 when running the following in https://colab.research.google.com/github/huggingface/datasets/blob/master/notebooks/Overview.ipynb:\\r\\n```\\r\\n!pip install git+https://github.com/google-research/bleurt.git\\r\\nreferences = [\\\"foo bar baz\\\", \\\"one two three\\\"]\\r\\nbleurt_metric = load_metric('bleurt')\\r\\npredictions = [\\\"foo bar\\\", \\\"four five six\\\"]\\r\\nbleurt_metric.compute(predictions=predictions, references=references)\\r\\n```\",\n \"@aleSuglia @oscartackstrom - Are you getting the error when running your code in a Jupyter notebook ?\\r\\n\\r\\nI tried reproducing this error again, and was unable to do so from the python command line console in a virtual environment similar to the one I originally used (and unfortunately no longer have access to) when I first got the error. \\r\\nHowever, I've managed to reproduce the error by running the same code in a Jupyter notebook running a kernel from the same virtual environment.\\r\\nThis made me suspect that the problem is somehow related to the Jupyter notebook.\\r\\n\\r\\nMore environment details:\\r\\n```\\r\\nOS: Ubuntu Linux 18.04\\r\\nconda==4.8.3\\r\\npython==3.8.5\\r\\ndatasets==1.3.0\\r\\ntensorflow==2.4.0\\r\\nBLEURT==0.0.1\\r\\nnotebook==6.2.0\\r\\n```\",\n \"This happens when running the notebook on colab. The issue seems to be that colab populates sys.argv with arguments not handled by bleurt.\\r\\n\\r\\nRunning this before calling bleurt fixes it:\\r\\n```\\r\\nimport sys\\r\\nsys.argv = sys.argv[:1]\\r\\n```\\r\\n\\r\\nNot the most elegant solution. Perhaps it needs to be fixed in the bleurt code itself rather than huggingface?\\r\\n\\r\\nThis is the output of `print(sys.argv)` when running on colab:\\r\\n```\\r\\n['/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py', '-f', '/root/.local/share/jupyter/runtime/kernel-a857a78c-44d6-4b9d-b18a-030b858ee327.json']\\r\\n```\",\n \"I got the error when running it from the command line. It looks more like an error that should be fixed in the BLEURT codebase.\",\n \"Seems to be a known issue in the bleurt codebase: https://github.com/google-research/bleurt/issues/24.\",\n \"Hi, the problem should be solved now.\",\n \"Hi @tsellam! I can verify that the issue is indeed fixed now. Thanks!\"\n]"},"created_at":{"kind":"timestamp","value":"2021-01-12T17:27:02","string":"2021-01-12T17:27:02"},"updated_at":{"kind":"timestamp","value":"2022-06-01T16:06:02","string":"2022-06-01T16:06:02"},"closed_at":{"kind":"timestamp","value":"2022-06-01T16:06:02","string":"2022-06-01T16:06:02"},"author_association":{"kind":"string","value":"NONE"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"Calling the `compute` method for **bleurt** metric fails with an `UnrecognizedFlagError` for `FLAGS.bleurt_batch_size`. \r\n\r\nMy environment:\r\n```\r\npython==3.8.5\r\ndatasets==1.2.0\r\ntensorflow==2.3.1\r\ncudatoolkit==11.0.221\r\n```\r\n\r\nTest code for reproducing the error:\r\n```\r\nfrom datasets import load_metric\r\nbleurt = load_metric('bleurt')\r\ngen_text = \"I am walking on the promenade today\"\r\nref_text = \"I am walking along the promenade on this sunny day\"\r\nbleurt.compute(predictions=[test_text], references=[test_text])\r\n```\r\n\r\nError Output:\r\n```\r\nUsing 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').\r\nINFO:tensorflow:Reading checkpoint /home/ubuntu/.cache/huggingface/metrics/bleurt/default/downloads/extracted/9aee35580225730ac5422599f35c4986e4c49cafd08082123342b1019720dac4/bleurt-base-128.\r\nINFO:tensorflow:Config file found, reading.\r\nINFO:tensorflow:Will load checkpoint bert_custom\r\nINFO:tensorflow:Performs basic checks...\r\nINFO:tensorflow:... name:bert_custom\r\nINFO:tensorflow:... vocab_file:vocab.txt\r\nINFO:tensorflow:... bert_config_file:bert_config.json\r\nINFO:tensorflow:... do_lower_case:True\r\nINFO:tensorflow:... max_seq_length:128\r\nINFO:tensorflow:Creating BLEURT scorer.\r\nINFO:tensorflow:Loading model...\r\nINFO:tensorflow:BLEURT initialized.\r\n---------------------------------------------------------------------------\r\nUnrecognizedFlagError Traceback (most recent call last)\r\n in \r\n 2 gen_text = \"I am walking on the promenade today\"\r\n 3 ref_text = \"I am walking along the promenade on this sunny day\"\r\n----> 4 bleurt.compute(predictions=[gen_text], references=[ref_text])\r\n\r\n~/anaconda3/envs/noved/lib/python3.8/site-packages/datasets/metric.py in compute(self, *args, **kwargs)\r\n 396 references = self.data[\"references\"]\r\n 397 with temp_seed(self.seed):\r\n--> 398 output = self._compute(predictions=predictions, references=references, **kwargs)\r\n 399 \r\n 400 if self.buf_writer is not None:\r\n\r\n~/.cache/huggingface/modules/datasets_modules/metrics/bleurt/b1de33e1cbbcb1dbe276c887efa1fad68c6aff913885108078fa1ad408908778/bleurt.py in _compute(self, predictions, references)\r\n 103 \r\n 104 def _compute(self, predictions, references):\r\n--> 105 scores = self.scorer.score(references=references, candidates=predictions)\r\n 106 return {\"scores\": scores}\r\n\r\n~/anaconda3/envs/noved/lib/python3.8/site-packages/bleurt/score.py in score(self, references, candidates, batch_size)\r\n 164 \"\"\"\r\n 165 if not batch_size:\r\n--> 166 batch_size = FLAGS.bleurt_batch_size\r\n 167 \r\n 168 candidates, references = list(candidates), list(references)\r\n\r\n~/anaconda3/envs/noved/lib/python3.8/site-packages/tensorflow/python/platform/flags.py in __getattr__(self, name)\r\n 83 # a flag.\r\n 84 if not wrapped.is_parsed():\r\n---> 85 wrapped(_sys.argv)\r\n 86 return wrapped.__getattr__(name)\r\n 87 \r\n\r\n~/anaconda3/envs/noved/lib/python3.8/site-packages/absl/flags/_flagvalues.py in __call__(self, argv, known_only)\r\n 643 for name, value in unknown_flags:\r\n 644 suggestions = _helpers.get_flag_suggestions(name, list(self))\r\n--> 645 raise _exceptions.UnrecognizedFlagError(\r\n 646 name, value, suggestions=suggestions)\r\n 647 \r\n\r\nUnrecognizedFlagError: Unknown command line flag 'f'\r\n```\r\n\r\nPossible Fix:\r\nModify `_compute` method https://github.com/huggingface/datasets/blob/7e64851a12263dc74d41c668167918484c8000ab/metrics/bleurt/bleurt.py#L104\r\nto receive a `batch_size` argument, for example:\r\n```\r\ndef _compute(self, predictions, references, batch_size=1):\r\n scores = self.scorer.score(references=references, candidates=predictions, batch_size=batch_size)\r\n return {\"scores\": scores}\r\n```"},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1727/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 0\n}"},"timeline_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1727/timeline"},"performed_via_github_app":{"kind":"null"},"state_reason":{"kind":"string","value":"completed"}}},{"rowIdx":2282,"cells":{"url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1725"},"repository_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets"},"labels_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1725/labels{/name}"},"comments_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1725/comments"},"events_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1725/events"},"html_url":{"kind":"string","value":"https://github.com/huggingface/datasets/issues/1725"},"id":{"kind":"number","value":784182273,"string":"784,182,273"},"node_id":{"kind":"string","value":"MDU6SXNzdWU3ODQxODIyNzM="},"number":{"kind":"number","value":1725,"string":"1,725"},"title":{"kind":"string","value":"load the local dataset"},"user":{"kind":"string","value":"{\n \"login\": \"xinjicong\",\n \"id\": 41193842,\n \"node_id\": \"MDQ6VXNlcjQxMTkzODQy\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/41193842?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/xinjicong\",\n \"html_url\": \"https://github.com/xinjicong\",\n \"followers_url\": \"https://api.github.com/users/xinjicong/followers\",\n \"following_url\": \"https://api.github.com/users/xinjicong/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/xinjicong/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/xinjicong/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/xinjicong/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/xinjicong/orgs\",\n \"repos_url\": \"https://api.github.com/users/xinjicong/repos\",\n \"events_url\": \"https://api.github.com/users/xinjicong/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/xinjicong/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n}"},"labels":{"kind":"list like","value":[],"string":"[]"},"state":{"kind":"string","value":"closed"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["You should rephrase your question or give more examples and details on what you want to do.\r\n\r\nit’s not possible to understand it and help you with only this information.","sorry for that.\r\ni want to know how could i load the train set and the test set from the local ,which api or function should i use .\r\n","Did you try to follow the instructions in the documentation?\r\nHere: https://huggingface.co/docs/datasets/loading_datasets.html#from-local-files","thanks a lot \r\ni find that the problem is i dont use vpn...\r\nso i have to keep my net work even if i want to load the local data ?","We will solve this soon (cf #1724)","thanks a lot","Hi! `json` is a packaged dataset now, which means its script comes with the library and doesn't require an internet connection."],"string":"[\n \"You should rephrase your question or give more examples and details on what you want to do.\\r\\n\\r\\nit’s not possible to understand it and help you with only this information.\",\n \"sorry for that.\\r\\ni want to know how could i load the train set and the test set from the local ,which api or function should i use .\\r\\n\",\n \"Did you try to follow the instructions in the documentation?\\r\\nHere: https://huggingface.co/docs/datasets/loading_datasets.html#from-local-files\",\n \"thanks a lot \\r\\ni find that the problem is i dont use vpn...\\r\\nso i have to keep my net work even if i want to load the local data ?\",\n \"We will solve this soon (cf #1724)\",\n \"thanks a lot\",\n \"Hi! `json` is a packaged dataset now, which means its script comes with the library and doesn't require an internet connection.\"\n]"},"created_at":{"kind":"timestamp","value":"2021-01-12T12:12:55","string":"2021-01-12T12:12:55"},"updated_at":{"kind":"timestamp","value":"2022-06-01T16:00:59","string":"2022-06-01T16:00:59"},"closed_at":{"kind":"timestamp","value":"2022-06-01T16:00:59","string":"2022-06-01T16:00:59"},"author_association":{"kind":"string","value":"NONE"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"your guidebook's example is like\r\n>>>from datasets import load_dataset\r\n>>> dataset = load_dataset('json', data_files='my_file.json')\r\nbut the first arg is path...\r\nso how should i do if i want to load the local dataset for model training?\r\ni will be grateful if you can help me handle this problem!\r\nthanks a lot!"},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1725/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 0\n}"},"timeline_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1725/timeline"},"performed_via_github_app":{"kind":"null"},"state_reason":{"kind":"string","value":"completed"}}},{"rowIdx":2283,"cells":{"url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1724"},"repository_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets"},"labels_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1724/labels{/name}"},"comments_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1724/comments"},"events_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1724/events"},"html_url":{"kind":"string","value":"https://github.com/huggingface/datasets/issues/1724"},"id":{"kind":"number","value":784023338,"string":"784,023,338"},"node_id":{"kind":"string","value":"MDU6SXNzdWU3ODQwMjMzMzg="},"number":{"kind":"number","value":1724,"string":"1,724"},"title":{"kind":"string","value":"could not run models on a offline server successfully"},"user":{"kind":"string","value":"{\n \"login\": \"lkcao\",\n \"id\": 49967236,\n \"node_id\": \"MDQ6VXNlcjQ5OTY3MjM2\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/49967236?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/lkcao\",\n \"html_url\": \"https://github.com/lkcao\",\n \"followers_url\": \"https://api.github.com/users/lkcao/followers\",\n \"following_url\": \"https://api.github.com/users/lkcao/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/lkcao/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/lkcao/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/lkcao/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/lkcao/orgs\",\n \"repos_url\": \"https://api.github.com/users/lkcao/repos\",\n \"events_url\": \"https://api.github.com/users/lkcao/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/lkcao/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n}"},"labels":{"kind":"list like","value":[],"string":"[]"},"state":{"kind":"string","value":"closed"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["Transferred to `datasets` based on the stack trace.","Hi @lkcao !\r\nYour issue is indeed related to `datasets`. In addition to installing the package manually, you will need to download the `text.py` script on your server. You'll find it (under `datasets/datasets/text`: https://github.com/huggingface/datasets/blob/master/datasets/text/text.py.\r\nThen you can change the line 221 of `run_mlm_new.py` into:\r\n```python\r\n datasets = load_dataset('/path/to/text.py', data_files=data_files)\r\n```\r\nWhere `/path/to/text.py` is the path on the server where you saved the `text.py` script.","We're working on including the local dataset builders (csv, text, json etc.) directly in the `datasets` package so that they can be used offline","The local dataset builders (csv, text , json and pandas) are now part of the `datasets` package since #1726 :)\r\nYou can now use them offline\r\n```python\r\ndatasets = load_dataset('text', data_files=data_files)\r\n```\r\n\r\nWe'll do a new release soon","> The local dataset builders (csv, text , json and pandas) are now part of the `datasets` package since #1726 :)\r\n> You can now use them offline\r\n> \r\n> ```python\r\n> datasets = load_dataset('text', data_files=data_files)\r\n> ```\r\n> \r\n> We'll do a new release soon\r\n\r\nso the new version release now?","Yes it's been available since datasets 1.3.0 !"],"string":"[\n \"Transferred to `datasets` based on the stack trace.\",\n \"Hi @lkcao !\\r\\nYour issue is indeed related to `datasets`. In addition to installing the package manually, you will need to download the `text.py` script on your server. You'll find it (under `datasets/datasets/text`: https://github.com/huggingface/datasets/blob/master/datasets/text/text.py.\\r\\nThen you can change the line 221 of `run_mlm_new.py` into:\\r\\n```python\\r\\n datasets = load_dataset('/path/to/text.py', data_files=data_files)\\r\\n```\\r\\nWhere `/path/to/text.py` is the path on the server where you saved the `text.py` script.\",\n \"We're working on including the local dataset builders (csv, text, json etc.) directly in the `datasets` package so that they can be used offline\",\n \"The local dataset builders (csv, text , json and pandas) are now part of the `datasets` package since #1726 :)\\r\\nYou can now use them offline\\r\\n```python\\r\\ndatasets = load_dataset('text', data_files=data_files)\\r\\n```\\r\\n\\r\\nWe'll do a new release soon\",\n \"> The local dataset builders (csv, text , json and pandas) are now part of the `datasets` package since #1726 :)\\r\\n> You can now use them offline\\r\\n> \\r\\n> ```python\\r\\n> datasets = load_dataset('text', data_files=data_files)\\r\\n> ```\\r\\n> \\r\\n> We'll do a new release soon\\r\\n\\r\\nso the new version release now?\",\n \"Yes it's been available since datasets 1.3.0 !\"\n]"},"created_at":{"kind":"timestamp","value":"2021-01-12T06:08:06","string":"2021-01-12T06:08:06"},"updated_at":{"kind":"timestamp","value":"2022-10-05T12:39:07","string":"2022-10-05T12:39:07"},"closed_at":{"kind":"timestamp","value":"2022-10-05T12:39:07","string":"2022-10-05T12:39:07"},"author_association":{"kind":"string","value":"NONE"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"Hi, I really need your help about this.\r\nI am trying to fine-tuning a RoBERTa on a remote server, which is strictly banning internet. I try to install all the packages by hand and try to run run_mlm.py on the server. It works well on colab, but when I try to run it on this offline server, it shows:\r\n![image](https://user-images.githubusercontent.com/49967236/104276256-25a88600-546a-11eb-9776-8ec695dfa24e.png)\r\n\r\nis there anything I can do? Is it possible to download all the things in cache and upload it to the server? Please help me out..."},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1724/reactions\",\n \"total_count\": 1,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 1\n}"},"timeline_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1724/timeline"},"performed_via_github_app":{"kind":"null"},"state_reason":{"kind":"string","value":"completed"}}},{"rowIdx":2284,"cells":{"url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1718"},"repository_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets"},"labels_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1718/labels{/name}"},"comments_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1718/comments"},"events_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1718/events"},"html_url":{"kind":"string","value":"https://github.com/huggingface/datasets/issues/1718"},"id":{"kind":"number","value":783474753,"string":"783,474,753"},"node_id":{"kind":"string","value":"MDU6SXNzdWU3ODM0NzQ3NTM="},"number":{"kind":"number","value":1718,"string":"1,718"},"title":{"kind":"string","value":"Possible cache miss in datasets"},"user":{"kind":"string","value":"{\n \"login\": \"ofirzaf\",\n \"id\": 18296312,\n \"node_id\": \"MDQ6VXNlcjE4Mjk2MzEy\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/18296312?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/ofirzaf\",\n \"html_url\": \"https://github.com/ofirzaf\",\n \"followers_url\": \"https://api.github.com/users/ofirzaf/followers\",\n \"following_url\": \"https://api.github.com/users/ofirzaf/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/ofirzaf/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/ofirzaf/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/ofirzaf/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/ofirzaf/orgs\",\n \"repos_url\": \"https://api.github.com/users/ofirzaf/repos\",\n \"events_url\": \"https://api.github.com/users/ofirzaf/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/ofirzaf/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n}"},"labels":{"kind":"list like","value":[],"string":"[]"},"state":{"kind":"string","value":"closed"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["Thanks for reporting !\r\nI was able to reproduce thanks to your code and find the origin of the bug.\r\nThe cache was not reusing the same file because one object was not deterministic. It comes from a conversion from `set` to `list` in the `datasets.arrrow_dataset.transmit_format` function, where the resulting list would not always be in the same order and therefore the function that computes the hash used by the cache would not always return the same result.\r\nI'm opening a PR to fix this.\r\n\r\nAlso we plan to do a new release in the coming days so you can expect the fix to be available soon.\r\nNote that you can still specify `cache_file_name=` in the second `map()` call to name the cache file yourself if you want to.","Thanks for the fast reply, waiting for the fix :)\r\n\r\nI tried to use `cache_file_names` and wasn't sure how, I tried to give it the following:\r\n```\r\ntokenized_datasets = tokenized_datasets.map(\r\n group_texts,\r\n batched=True,\r\n num_proc=60,\r\n load_from_cache_file=True,\r\n cache_file_names={k: f'.cache/{str(k)}' for k in tokenized_datasets}\r\n)\r\n```\r\n\r\nand got an error:\r\n```\r\nmultiprocess.pool.RemoteTraceback:\r\n\"\"\"\r\nTraceback (most recent call last):\r\n File \"/venv/lib/python3.6/site-packages/multiprocess/pool.py\", line 119, in worker\r\n result = (True, func(*args, **kwds))\r\n File \"/venv/lib/python3.6/site-packages/datasets/arrow_dataset.py\", line 157, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \"/venv/lib/python3.6/site-packages/datasets/fingerprint.py\", line 163, in wrapper\r\n out = func(self, *args, **kwargs)\r\n File \"/venv/lib/python3.6/site-packages/datasets/arrow_dataset.py\", line 1491, in _map_single\r\n tmp_file = tempfile.NamedTemporaryFile(\"wb\", dir=os.path.dirname(cache_file_name), delete=False)\r\n File \"/usr/lib/python3.6/tempfile.py\", line 690, in NamedTemporaryFile\r\n (fd, name) = _mkstemp_inner(dir, prefix, suffix, flags, output_type)\r\n File \"/usr/lib/python3.6/tempfile.py\", line 401, in _mkstemp_inner\r\n fd = _os.open(file, flags, 0o600)\r\nFileNotFoundError: [Errno 2] No such file or directory: '_00000_of_00060.cache/tmpsvszxtop'\r\n\"\"\"\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"test.py\", line 48, in \r\n cache_file_names={k: f'.cache/{str(k)}' for k in tokenized_datasets}\r\n File \"/venv/lib/python3.6/site-packages/datasets/dataset_dict.py\", line 303, in map\r\n for k, dataset in self.items()\r\n File \"/venv/lib/python3.6/site-packages/datasets/dataset_dict.py\", line 303, in \r\n for k, dataset in self.items()\r\n File \"/venv/lib/python3.6/site-packages/datasets/arrow_dataset.py\", line 1317, in map\r\n transformed_shards = [r.get() for r in results]\r\n File \"/venv/lib/python3.6/site-packages/datasets/arrow_dataset.py\", line 1317, in \r\n transformed_shards = [r.get() for r in results]\r\n File \"/venv/lib/python3.6/site-packages/multiprocess/pool.py\", line 644, in get\r\n raise self._value\r\nFileNotFoundError: [Errno 2] No such file or directory: '_00000_of_00060.cache/tmpsvszxtop'\r\n```\r\n","The documentation says\r\n```\r\ncache_file_names (`Optional[Dict[str, str]]`, defaults to `None`): Provide the name of a cache file to use to store the\r\n results of the computation instead of the automatically generated cache file name.\r\n You have to provide one :obj:`cache_file_name` per dataset in the dataset dictionary.\r\n```\r\nWhat is expected is simply the name of a file, not a path. The file will be located in the cache directory of the `wikitext` dataset. You can try again with something like\r\n```python\r\ncache_file_names = {k: f'tokenized_and_grouped_{str(k)}' for k in tokenized_datasets}\r\n```","Managed to get `cache_file_names` working and caching works well with it\r\nHad to make a small modification for it to work:\r\n```\r\ncache_file_names = {k: f'tokenized_and_grouped_{str(k)}.arrow' for k in tokenized_datasets}\r\n```","Another comment on `cache_file_names`, it doesn't save the produced cached files in the dataset's cache folder, it requires to give a path to an existing directory for it to work.\r\nI can confirm that this is how it works in `datasets==1.1.3`","Oh yes indeed ! Maybe we need to update the docstring to mention that it is a path","I fixed the docstring. Hopefully this is less confusing now: https://github.com/huggingface/datasets/commit/42ccc0012ba8864e6db1392430100f350236183a","I upgraded to the latest version and I encountered some strange behaviour, the script I posted in the OP doesn't trigger recalculation, however, if I add the following change it does trigger partial recalculation, I am not sure if its something wrong on my machine or a bug:\r\n```\r\nfrom datasets import load_dataset\r\nfrom transformers import AutoTokenizer\r\n\r\ndatasets = load_dataset('wikitext', 'wikitext-103-raw-v1')\r\ntokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)\r\n\r\ncolumn_names = datasets[\"train\"].column_names\r\ntext_column_name = \"text\" if \"text\" in column_names else column_names[0]\r\ndef tokenize_function(examples):\r\n return tokenizer(examples[text_column_name], return_special_tokens_mask=True)\r\n# CHANGE\r\nprint('hello')\r\n# CHANGE\r\n\r\ntokenized_datasets = datasets.map(\r\n tokenize_function,\r\n batched=True,\r\n...\r\n```\r\nI am using datasets in the `run_mlm.py` script in the transformers examples and I found that if I change the script without touching any of the preprocessing. it still triggers recalculation which is very weird\r\n\r\nEdit: accidently clicked the close issue button ","This is because the `group_texts` line definition changes (it is defined 3 lines later than in the previous call). Currently if a function is moved elsewhere in a script we consider it to be different.\r\n\r\nNot sure this is actually a good idea to keep this behavior though. We had this as a security in the early development of the lib but now the recursive hashing of objects is robust so we can probably remove that.\r\nMoreover we're already ignoring the line definition for lambda functions.","I opened a PR to change this, let me know what you think.","Sounds great, thank you for your quick responses and help! Looking forward for the next release.","I am having a similar issue where only the grouped files are loaded from cache while the tokenized ones aren't. I can confirm both datasets are being stored to file, but only the grouped version is loaded from cache. Not sure what might be going on. But I've tried to remove all kinds of non deterministic behaviour, but still no luck. Thanks for the help!\r\n\r\n\r\n```python\r\n # Datasets\r\n train = sorted(glob(args.data_dir + '*.{}'.format(args.ext)))\r\n if args.dev_split >= len(train):\r\n raise ValueError(\"Not enough dev files\")\r\n dev = []\r\n state = random.Random(1001)\r\n for _ in range(args.dev_split):\r\n dev.append(train.pop(state.randint(0, len(train) - 1)))\r\n\r\n max_seq_length = min(args.max_seq_length, tokenizer.model_max_length)\r\n\r\n def tokenize_function(examples):\r\n return tokenizer(examples['text'], return_special_tokens_mask=True)\r\n\r\n def group_texts(examples):\r\n # Concatenate all texts from our dataset and generate chunks of max_seq_length\r\n concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}\r\n total_length = len(concatenated_examples[list(examples.keys())[0]])\r\n # Truncate (not implementing padding)\r\n total_length = (total_length // max_seq_length) * max_seq_length\r\n # Split by chunks of max_seq_length\r\n result = {\r\n k: [t[i : i + max_seq_length] for i in range(0, total_length, max_seq_length)]\r\n for k, t in concatenated_examples.items()\r\n }\r\n return result\r\n\r\n datasets = load_dataset(\r\n 'text', name='DBNL', data_files={'train': train[:10], 'dev': dev[:5]}, \r\n cache_dir=args.data_cache_dir)\r\n datasets = datasets.map(tokenize_function, \r\n batched=True, remove_columns=['text'], \r\n cache_file_names={k: os.path.join(args.data_cache_dir, f'{k}-tokenized') for k in datasets},\r\n load_from_cache_file=not args.overwrite_cache)\r\n datasets = datasets.map(group_texts, \r\n batched=True,\r\n cache_file_names={k: os.path.join(args.data_cache_dir, f'{k}-grouped') for k in datasets},\r\n load_from_cache_file=not args.overwrite_cache)\r\n```\r\n\r\nAnd this is the log\r\n\r\n```\r\n04/26/2021 10:26:59 - WARNING - datasets.builder - Using custom data configuration DBNL-f8d988ad33ccf2c1\r\n04/26/2021 10:26:59 - WARNING - datasets.builder - Reusing dataset text (/home/manjavacasema/data/.cache/text/DBNL-f8d988ad33ccf2c1/0.0.0/e16f44aa1b321ece1f87b07977cc5d70be93d69b20486d6dacd62e12cf25c9a5)\r\n100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:00<00:00, 21.07ba/s]\r\n100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:01<00:00, 24.28ba/s]\r\n04/26/2021 10:27:01 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /home/manjavacasema/data/.cache/train-grouped\r\n04/26/2021 10:27:01 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /home/manjavacasema/data/.cache/dev-grouped\r\n```\r\n","Hi ! What tokenizer are you using ?","It's the ByteLevelBPETokenizer","This error happened to me too, when I tried to supply my own fingerprint to `map()` via the `new_fingerprint` arg.\r\n\r\nEdit: realized it was because my path was weird and had colons and brackets and slashes in it, since one of the variable values I included in the fingerprint was a dataset split like \"train[:10%]\". I fixed it with [this solution](https://stackoverflow.com/a/13593932/2287177) from StackOverflow to just remove those invalid characters from the fingerprint.","Good catch @jxmorris12, maybe we should do additional checks on the valid characters for fingerprints ! Would you like to contribute this ?\r\n\r\nI think this can be added here, when we set the fingerprint(s) that are passed `map`:\r\n\r\nhttps://github.com/huggingface/datasets/blob/25bb7c9cbf519fbbf9abf3898083b529e7762705/src/datasets/fingerprint.py#L449-L454\r\n\r\nmaybe something like\r\n```python\r\nif kwargs.get(fingerprint_name) is None:\r\n ...\r\nelse:\r\n # In this case, it's the user who specified the fingerprint manually:\r\n # we need to make sure it's a valid hash\r\n validate_fingerprint(kwargs[fingerprint_name])\r\n```\r\n\r\nOtherwise I can open a PR later","I opened a PR here to add the fingerprint validation: https://github.com/huggingface/datasets/pull/4587\r\n\r\nEDIT: merged :)","thank you!"],"string":"[\n \"Thanks for reporting !\\r\\nI was able to reproduce thanks to your code and find the origin of the bug.\\r\\nThe cache was not reusing the same file because one object was not deterministic. It comes from a conversion from `set` to `list` in the `datasets.arrrow_dataset.transmit_format` function, where the resulting list would not always be in the same order and therefore the function that computes the hash used by the cache would not always return the same result.\\r\\nI'm opening a PR to fix this.\\r\\n\\r\\nAlso we plan to do a new release in the coming days so you can expect the fix to be available soon.\\r\\nNote that you can still specify `cache_file_name=` in the second `map()` call to name the cache file yourself if you want to.\",\n \"Thanks for the fast reply, waiting for the fix :)\\r\\n\\r\\nI tried to use `cache_file_names` and wasn't sure how, I tried to give it the following:\\r\\n```\\r\\ntokenized_datasets = tokenized_datasets.map(\\r\\n group_texts,\\r\\n batched=True,\\r\\n num_proc=60,\\r\\n load_from_cache_file=True,\\r\\n cache_file_names={k: f'.cache/{str(k)}' for k in tokenized_datasets}\\r\\n)\\r\\n```\\r\\n\\r\\nand got an error:\\r\\n```\\r\\nmultiprocess.pool.RemoteTraceback:\\r\\n\\\"\\\"\\\"\\r\\nTraceback (most recent call last):\\r\\n File \\\"/venv/lib/python3.6/site-packages/multiprocess/pool.py\\\", line 119, in worker\\r\\n result = (True, func(*args, **kwds))\\r\\n File \\\"/venv/lib/python3.6/site-packages/datasets/arrow_dataset.py\\\", line 157, in wrapper\\r\\n out: Union[\\\"Dataset\\\", \\\"DatasetDict\\\"] = func(self, *args, **kwargs)\\r\\n File \\\"/venv/lib/python3.6/site-packages/datasets/fingerprint.py\\\", line 163, in wrapper\\r\\n out = func(self, *args, **kwargs)\\r\\n File \\\"/venv/lib/python3.6/site-packages/datasets/arrow_dataset.py\\\", line 1491, in _map_single\\r\\n tmp_file = tempfile.NamedTemporaryFile(\\\"wb\\\", dir=os.path.dirname(cache_file_name), delete=False)\\r\\n File \\\"/usr/lib/python3.6/tempfile.py\\\", line 690, in NamedTemporaryFile\\r\\n (fd, name) = _mkstemp_inner(dir, prefix, suffix, flags, output_type)\\r\\n File \\\"/usr/lib/python3.6/tempfile.py\\\", line 401, in _mkstemp_inner\\r\\n fd = _os.open(file, flags, 0o600)\\r\\nFileNotFoundError: [Errno 2] No such file or directory: '_00000_of_00060.cache/tmpsvszxtop'\\r\\n\\\"\\\"\\\"\\r\\n\\r\\nThe above exception was the direct cause of the following exception:\\r\\n\\r\\nTraceback (most recent call last):\\r\\n File \\\"test.py\\\", line 48, in \\r\\n cache_file_names={k: f'.cache/{str(k)}' for k in tokenized_datasets}\\r\\n File \\\"/venv/lib/python3.6/site-packages/datasets/dataset_dict.py\\\", line 303, in map\\r\\n for k, dataset in self.items()\\r\\n File \\\"/venv/lib/python3.6/site-packages/datasets/dataset_dict.py\\\", line 303, in \\r\\n for k, dataset in self.items()\\r\\n File \\\"/venv/lib/python3.6/site-packages/datasets/arrow_dataset.py\\\", line 1317, in map\\r\\n transformed_shards = [r.get() for r in results]\\r\\n File \\\"/venv/lib/python3.6/site-packages/datasets/arrow_dataset.py\\\", line 1317, in \\r\\n transformed_shards = [r.get() for r in results]\\r\\n File \\\"/venv/lib/python3.6/site-packages/multiprocess/pool.py\\\", line 644, in get\\r\\n raise self._value\\r\\nFileNotFoundError: [Errno 2] No such file or directory: '_00000_of_00060.cache/tmpsvszxtop'\\r\\n```\\r\\n\",\n \"The documentation says\\r\\n```\\r\\ncache_file_names (`Optional[Dict[str, str]]`, defaults to `None`): Provide the name of a cache file to use to store the\\r\\n results of the computation instead of the automatically generated cache file name.\\r\\n You have to provide one :obj:`cache_file_name` per dataset in the dataset dictionary.\\r\\n```\\r\\nWhat is expected is simply the name of a file, not a path. The file will be located in the cache directory of the `wikitext` dataset. You can try again with something like\\r\\n```python\\r\\ncache_file_names = {k: f'tokenized_and_grouped_{str(k)}' for k in tokenized_datasets}\\r\\n```\",\n \"Managed to get `cache_file_names` working and caching works well with it\\r\\nHad to make a small modification for it to work:\\r\\n```\\r\\ncache_file_names = {k: f'tokenized_and_grouped_{str(k)}.arrow' for k in tokenized_datasets}\\r\\n```\",\n \"Another comment on `cache_file_names`, it doesn't save the produced cached files in the dataset's cache folder, it requires to give a path to an existing directory for it to work.\\r\\nI can confirm that this is how it works in `datasets==1.1.3`\",\n \"Oh yes indeed ! Maybe we need to update the docstring to mention that it is a path\",\n \"I fixed the docstring. Hopefully this is less confusing now: https://github.com/huggingface/datasets/commit/42ccc0012ba8864e6db1392430100f350236183a\",\n \"I upgraded to the latest version and I encountered some strange behaviour, the script I posted in the OP doesn't trigger recalculation, however, if I add the following change it does trigger partial recalculation, I am not sure if its something wrong on my machine or a bug:\\r\\n```\\r\\nfrom datasets import load_dataset\\r\\nfrom transformers import AutoTokenizer\\r\\n\\r\\ndatasets = load_dataset('wikitext', 'wikitext-103-raw-v1')\\r\\ntokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)\\r\\n\\r\\ncolumn_names = datasets[\\\"train\\\"].column_names\\r\\ntext_column_name = \\\"text\\\" if \\\"text\\\" in column_names else column_names[0]\\r\\ndef tokenize_function(examples):\\r\\n return tokenizer(examples[text_column_name], return_special_tokens_mask=True)\\r\\n# CHANGE\\r\\nprint('hello')\\r\\n# CHANGE\\r\\n\\r\\ntokenized_datasets = datasets.map(\\r\\n tokenize_function,\\r\\n batched=True,\\r\\n...\\r\\n```\\r\\nI am using datasets in the `run_mlm.py` script in the transformers examples and I found that if I change the script without touching any of the preprocessing. it still triggers recalculation which is very weird\\r\\n\\r\\nEdit: accidently clicked the close issue button \",\n \"This is because the `group_texts` line definition changes (it is defined 3 lines later than in the previous call). Currently if a function is moved elsewhere in a script we consider it to be different.\\r\\n\\r\\nNot sure this is actually a good idea to keep this behavior though. We had this as a security in the early development of the lib but now the recursive hashing of objects is robust so we can probably remove that.\\r\\nMoreover we're already ignoring the line definition for lambda functions.\",\n \"I opened a PR to change this, let me know what you think.\",\n \"Sounds great, thank you for your quick responses and help! Looking forward for the next release.\",\n \"I am having a similar issue where only the grouped files are loaded from cache while the tokenized ones aren't. I can confirm both datasets are being stored to file, but only the grouped version is loaded from cache. Not sure what might be going on. But I've tried to remove all kinds of non deterministic behaviour, but still no luck. Thanks for the help!\\r\\n\\r\\n\\r\\n```python\\r\\n # Datasets\\r\\n train = sorted(glob(args.data_dir + '*.{}'.format(args.ext)))\\r\\n if args.dev_split >= len(train):\\r\\n raise ValueError(\\\"Not enough dev files\\\")\\r\\n dev = []\\r\\n state = random.Random(1001)\\r\\n for _ in range(args.dev_split):\\r\\n dev.append(train.pop(state.randint(0, len(train) - 1)))\\r\\n\\r\\n max_seq_length = min(args.max_seq_length, tokenizer.model_max_length)\\r\\n\\r\\n def tokenize_function(examples):\\r\\n return tokenizer(examples['text'], return_special_tokens_mask=True)\\r\\n\\r\\n def group_texts(examples):\\r\\n # Concatenate all texts from our dataset and generate chunks of max_seq_length\\r\\n concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}\\r\\n total_length = len(concatenated_examples[list(examples.keys())[0]])\\r\\n # Truncate (not implementing padding)\\r\\n total_length = (total_length // max_seq_length) * max_seq_length\\r\\n # Split by chunks of max_seq_length\\r\\n result = {\\r\\n k: [t[i : i + max_seq_length] for i in range(0, total_length, max_seq_length)]\\r\\n for k, t in concatenated_examples.items()\\r\\n }\\r\\n return result\\r\\n\\r\\n datasets = load_dataset(\\r\\n 'text', name='DBNL', data_files={'train': train[:10], 'dev': dev[:5]}, \\r\\n cache_dir=args.data_cache_dir)\\r\\n datasets = datasets.map(tokenize_function, \\r\\n batched=True, remove_columns=['text'], \\r\\n cache_file_names={k: os.path.join(args.data_cache_dir, f'{k}-tokenized') for k in datasets},\\r\\n load_from_cache_file=not args.overwrite_cache)\\r\\n datasets = datasets.map(group_texts, \\r\\n batched=True,\\r\\n cache_file_names={k: os.path.join(args.data_cache_dir, f'{k}-grouped') for k in datasets},\\r\\n load_from_cache_file=not args.overwrite_cache)\\r\\n```\\r\\n\\r\\nAnd this is the log\\r\\n\\r\\n```\\r\\n04/26/2021 10:26:59 - WARNING - datasets.builder - Using custom data configuration DBNL-f8d988ad33ccf2c1\\r\\n04/26/2021 10:26:59 - WARNING - datasets.builder - Reusing dataset text (/home/manjavacasema/data/.cache/text/DBNL-f8d988ad33ccf2c1/0.0.0/e16f44aa1b321ece1f87b07977cc5d70be93d69b20486d6dacd62e12cf25c9a5)\\r\\n100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:00<00:00, 21.07ba/s]\\r\\n100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:01<00:00, 24.28ba/s]\\r\\n04/26/2021 10:27:01 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /home/manjavacasema/data/.cache/train-grouped\\r\\n04/26/2021 10:27:01 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /home/manjavacasema/data/.cache/dev-grouped\\r\\n```\\r\\n\",\n \"Hi ! What tokenizer are you using ?\",\n \"It's the ByteLevelBPETokenizer\",\n \"This error happened to me too, when I tried to supply my own fingerprint to `map()` via the `new_fingerprint` arg.\\r\\n\\r\\nEdit: realized it was because my path was weird and had colons and brackets and slashes in it, since one of the variable values I included in the fingerprint was a dataset split like \\\"train[:10%]\\\". I fixed it with [this solution](https://stackoverflow.com/a/13593932/2287177) from StackOverflow to just remove those invalid characters from the fingerprint.\",\n \"Good catch @jxmorris12, maybe we should do additional checks on the valid characters for fingerprints ! Would you like to contribute this ?\\r\\n\\r\\nI think this can be added here, when we set the fingerprint(s) that are passed `map`:\\r\\n\\r\\nhttps://github.com/huggingface/datasets/blob/25bb7c9cbf519fbbf9abf3898083b529e7762705/src/datasets/fingerprint.py#L449-L454\\r\\n\\r\\nmaybe something like\\r\\n```python\\r\\nif kwargs.get(fingerprint_name) is None:\\r\\n ...\\r\\nelse:\\r\\n # In this case, it's the user who specified the fingerprint manually:\\r\\n # we need to make sure it's a valid hash\\r\\n validate_fingerprint(kwargs[fingerprint_name])\\r\\n```\\r\\n\\r\\nOtherwise I can open a PR later\",\n \"I opened a PR here to add the fingerprint validation: https://github.com/huggingface/datasets/pull/4587\\r\\n\\r\\nEDIT: merged :)\",\n \"thank you!\"\n]"},"created_at":{"kind":"timestamp","value":"2021-01-11T15:37:31","string":"2021-01-11T15:37:31"},"updated_at":{"kind":"timestamp","value":"2022-06-29T14:54:42","string":"2022-06-29T14:54:42"},"closed_at":{"kind":"timestamp","value":"2021-01-26T02:47:59","string":"2021-01-26T02:47:59"},"author_association":{"kind":"string","value":"NONE"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"Hi,\r\n\r\nI am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache.\r\nI have attached an example script that for me reproduces the problem.\r\nIn the attached example the second map function always recomputes instead of loading from cache.\r\nIs this a bug or am I doing something wrong?\r\nIs there a way for fix this and avoid all the recomputation?\r\n\r\nThanks\r\n\r\nEdit:\r\ntransformers==3.5.1\r\ndatasets==1.2.0\r\n\r\n```\r\nfrom datasets import load_dataset\r\nfrom transformers import AutoTokenizer\r\n\r\ndatasets = load_dataset('wikitext', 'wikitext-103-raw-v1')\r\ntokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)\r\n\r\n\r\ncolumn_names = datasets[\"train\"].column_names\r\ntext_column_name = \"text\" if \"text\" in column_names else column_names[0]\r\ndef tokenize_function(examples):\r\n return tokenizer(examples[text_column_name], return_special_tokens_mask=True)\r\n\r\ntokenized_datasets = datasets.map(\r\n tokenize_function,\r\n batched=True,\r\n num_proc=60,\r\n remove_columns=[text_column_name],\r\n load_from_cache_file=True,\r\n)\r\nmax_seq_length = tokenizer.model_max_length\r\ndef group_texts(examples):\r\n # Concatenate all texts.\r\n concatenated_examples = {\r\n k: sum(examples[k], []) for k in examples.keys()}\r\n total_length = len(concatenated_examples[list(examples.keys())[0]])\r\n # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can\r\n # customize this part to your needs.\r\n total_length = (total_length // max_seq_length) * max_seq_length\r\n # Split by chunks of max_len.\r\n result = {\r\n k: [t[i: i + max_seq_length]\r\n for i in range(0, total_length, max_seq_length)]\r\n for k, t in concatenated_examples.items()\r\n }\r\n return result\r\n\r\ntokenized_datasets = tokenized_datasets.map(\r\n group_texts,\r\n batched=True,\r\n num_proc=60,\r\n load_from_cache_file=True,\r\n)\r\nprint(tokenized_datasets)\r\n\r\nprint('finished')\r\n```"},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1718/reactions\",\n \"total_count\": 2,\n \"+1\": 2,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 0\n}"},"timeline_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1718/timeline"},"performed_via_github_app":{"kind":"null"},"state_reason":{"kind":"string","value":"completed"}}},{"rowIdx":2285,"cells":{"url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1717"},"repository_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets"},"labels_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1717/labels{/name}"},"comments_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1717/comments"},"events_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1717/events"},"html_url":{"kind":"string","value":"https://github.com/huggingface/datasets/issues/1717"},"id":{"kind":"number","value":783074255,"string":"783,074,255"},"node_id":{"kind":"string","value":"MDU6SXNzdWU3ODMwNzQyNTU="},"number":{"kind":"number","value":1717,"string":"1,717"},"title":{"kind":"string","value":"SciFact dataset - minor changes"},"user":{"kind":"string","value":"{\n \"login\": \"dwadden\",\n \"id\": 3091916,\n \"node_id\": \"MDQ6VXNlcjMwOTE5MTY=\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/3091916?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/dwadden\",\n \"html_url\": \"https://github.com/dwadden\",\n \"followers_url\": \"https://api.github.com/users/dwadden/followers\",\n \"following_url\": \"https://api.github.com/users/dwadden/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/dwadden/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/dwadden/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/dwadden/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/dwadden/orgs\",\n \"repos_url\": \"https://api.github.com/users/dwadden/repos\",\n \"events_url\": \"https://api.github.com/users/dwadden/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/dwadden/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n}"},"labels":{"kind":"list like","value":[],"string":"[]"},"state":{"kind":"string","value":"closed"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["Hi Dave,\r\nYou are more than welcome to open a PR to make these changes! 🤗\r\nYou will find the relevant information about opening a PR in the [contributing guide](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md) and in the [dataset addition guide](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).\r\n\r\nPinging also @lhoestq for the Google cloud matter.","> I'd like to make a few minor changes, including the citation information and the `_URL` from which to download the dataset. Can I submit a PR for this?\r\n\r\nSure ! Also feel free to ping us for reviews or if we can help :)\r\n\r\n> It also looks like the dataset is being downloaded directly from Huggingface's Google cloud account rather than via the `_URL` in [scifact.py](https://github.com/huggingface/datasets/blob/master/datasets/scifact/scifact.py). Can you help me update the version on gcloud?\r\n\r\nWhat makes you think that ?\r\nAfaik there's no scifact on our google storage\r\n","\r\n\r\n> > I'd like to make a few minor changes, including the citation information and the `_URL` from which to download the dataset. Can I submit a PR for this?\r\n> \r\n> Sure ! Also feel free to ping us for reviews or if we can help :)\r\n> \r\nOK! We're organizing a [shared task](https://sdproc.org/2021/sharedtasks.html#sciver) based on the dataset, and I made some updates and changed the download URL - so the current code points to a dead URL. I'll update appropriately once the task is finalized and make a PR.\r\n\r\n> > It also looks like the dataset is being downloaded directly from Huggingface's Google cloud account rather than via the `_URL` in [scifact.py](https://github.com/huggingface/datasets/blob/master/datasets/scifact/scifact.py). Can you help me update the version on gcloud?\r\n> \r\n> What makes you think that ?\r\n> Afaik there's no scifact on our google storage\r\n\r\nYou're right, I had the data cached on my machine somewhere. \r\n\r\n","I opened a PR about this: https://github.com/huggingface/datasets/pull/1780. Closing this issue, will continue there."],"string":"[\n \"Hi Dave,\\r\\nYou are more than welcome to open a PR to make these changes! 🤗\\r\\nYou will find the relevant information about opening a PR in the [contributing guide](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md) and in the [dataset addition guide](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).\\r\\n\\r\\nPinging also @lhoestq for the Google cloud matter.\",\n \"> I'd like to make a few minor changes, including the citation information and the `_URL` from which to download the dataset. Can I submit a PR for this?\\r\\n\\r\\nSure ! Also feel free to ping us for reviews or if we can help :)\\r\\n\\r\\n> It also looks like the dataset is being downloaded directly from Huggingface's Google cloud account rather than via the `_URL` in [scifact.py](https://github.com/huggingface/datasets/blob/master/datasets/scifact/scifact.py). Can you help me update the version on gcloud?\\r\\n\\r\\nWhat makes you think that ?\\r\\nAfaik there's no scifact on our google storage\\r\\n\",\n \"\\r\\n\\r\\n> > I'd like to make a few minor changes, including the citation information and the `_URL` from which to download the dataset. Can I submit a PR for this?\\r\\n> \\r\\n> Sure ! Also feel free to ping us for reviews or if we can help :)\\r\\n> \\r\\nOK! We're organizing a [shared task](https://sdproc.org/2021/sharedtasks.html#sciver) based on the dataset, and I made some updates and changed the download URL - so the current code points to a dead URL. I'll update appropriately once the task is finalized and make a PR.\\r\\n\\r\\n> > It also looks like the dataset is being downloaded directly from Huggingface's Google cloud account rather than via the `_URL` in [scifact.py](https://github.com/huggingface/datasets/blob/master/datasets/scifact/scifact.py). Can you help me update the version on gcloud?\\r\\n> \\r\\n> What makes you think that ?\\r\\n> Afaik there's no scifact on our google storage\\r\\n\\r\\nYou're right, I had the data cached on my machine somewhere. \\r\\n\\r\\n\",\n \"I opened a PR about this: https://github.com/huggingface/datasets/pull/1780. Closing this issue, will continue there.\"\n]"},"created_at":{"kind":"timestamp","value":"2021-01-11T05:26:40","string":"2021-01-11T05:26:40"},"updated_at":{"kind":"timestamp","value":"2021-01-26T02:52:17","string":"2021-01-26T02:52:17"},"closed_at":{"kind":"timestamp","value":"2021-01-26T02:52:17","string":"2021-01-26T02:52:17"},"author_association":{"kind":"string","value":"CONTRIBUTOR"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"Hi,\r\n\r\nSciFact dataset creator here. First of all, thanks for adding the dataset to Huggingface, much appreciated!\r\n\r\nI'd like to make a few minor changes, including the citation information and the `_URL` from which to download the dataset. Can I submit a PR for this?\r\n\r\nIt also looks like the dataset is being downloaded directly from Huggingface's Google cloud account rather than via the `_URL` in [scifact.py](https://github.com/huggingface/datasets/blob/master/datasets/scifact/scifact.py). Can you help me update the version on gcloud?\r\n\r\nThanks,\r\n\r\nDave"},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1717/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 0\n}"},"timeline_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1717/timeline"},"performed_via_github_app":{"kind":"null"},"state_reason":{"kind":"string","value":"completed"}}},{"rowIdx":2286,"cells":{"url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1713"},"repository_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets"},"labels_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1713/labels{/name}"},"comments_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1713/comments"},"events_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1713/events"},"html_url":{"kind":"string","value":"https://github.com/huggingface/datasets/issues/1713"},"id":{"kind":"number","value":782337723,"string":"782,337,723"},"node_id":{"kind":"string","value":"MDU6SXNzdWU3ODIzMzc3MjM="},"number":{"kind":"number","value":1713,"string":"1,713"},"title":{"kind":"string","value":"Installation using conda"},"user":{"kind":"string","value":"{\n \"login\": \"pranav-s\",\n \"id\": 9393002,\n \"node_id\": \"MDQ6VXNlcjkzOTMwMDI=\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/9393002?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/pranav-s\",\n \"html_url\": \"https://github.com/pranav-s\",\n \"followers_url\": \"https://api.github.com/users/pranav-s/followers\",\n \"following_url\": \"https://api.github.com/users/pranav-s/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/pranav-s/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/pranav-s/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/pranav-s/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/pranav-s/orgs\",\n \"repos_url\": \"https://api.github.com/users/pranav-s/repos\",\n \"events_url\": \"https://api.github.com/users/pranav-s/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/pranav-s/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n}"},"labels":{"kind":"list like","value":[],"string":"[]"},"state":{"kind":"string","value":"closed"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["Yes indeed the idea is to have the next release on conda cc @LysandreJik ","Great! Did you guys have a timeframe in mind for the next release?\r\n\r\nThank you for all the great work in developing this library.","I think we can have `datasets` on conda by next week. Will see what I can do!","Thank you. Looking forward to it.","`datasets` has been added to the huggingface channel thanks to @LysandreJik :)\r\nIt depends on conda-forge though\r\n\r\n```\r\nconda install -c huggingface -c conda-forge datasets\r\n```"],"string":"[\n \"Yes indeed the idea is to have the next release on conda cc @LysandreJik \",\n \"Great! Did you guys have a timeframe in mind for the next release?\\r\\n\\r\\nThank you for all the great work in developing this library.\",\n \"I think we can have `datasets` on conda by next week. Will see what I can do!\",\n \"Thank you. Looking forward to it.\",\n \"`datasets` has been added to the huggingface channel thanks to @LysandreJik :)\\r\\nIt depends on conda-forge though\\r\\n\\r\\n```\\r\\nconda install -c huggingface -c conda-forge datasets\\r\\n```\"\n]"},"created_at":{"kind":"timestamp","value":"2021-01-08T19:12:15","string":"2021-01-08T19:12:15"},"updated_at":{"kind":"timestamp","value":"2021-09-17T12:47:40","string":"2021-09-17T12:47:40"},"closed_at":{"kind":"timestamp","value":"2021-09-17T12:47:40","string":"2021-09-17T12:47:40"},"author_association":{"kind":"string","value":"NONE"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"Will a conda package for installing datasets be added to the huggingface conda channel? I have installed transformers using conda and would like to use the datasets library to use some of the scripts in the transformers/examples folder but am unable to do so at the moment as datasets can only be installed using pip and using pip in a conda environment is generally a bad idea in my experience."},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1713/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 0\n}"},"timeline_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1713/timeline"},"performed_via_github_app":{"kind":"null"},"state_reason":{"kind":"string","value":"completed"}}},{"rowIdx":2287,"cells":{"url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1710"},"repository_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets"},"labels_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1710/labels{/name}"},"comments_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1710/comments"},"events_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1710/events"},"html_url":{"kind":"string","value":"https://github.com/huggingface/datasets/issues/1710"},"id":{"kind":"number","value":781914951,"string":"781,914,951"},"node_id":{"kind":"string","value":"MDU6SXNzdWU3ODE5MTQ5NTE="},"number":{"kind":"number","value":1710,"string":"1,710"},"title":{"kind":"string","value":"IsADirectoryError when trying to download C4"},"user":{"kind":"string","value":"{\n \"login\": \"fredriko\",\n \"id\": 5771366,\n \"node_id\": \"MDQ6VXNlcjU3NzEzNjY=\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/5771366?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/fredriko\",\n \"html_url\": \"https://github.com/fredriko\",\n \"followers_url\": \"https://api.github.com/users/fredriko/followers\",\n \"following_url\": \"https://api.github.com/users/fredriko/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/fredriko/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/fredriko/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/fredriko/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/fredriko/orgs\",\n \"repos_url\": \"https://api.github.com/users/fredriko/repos\",\n \"events_url\": \"https://api.github.com/users/fredriko/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/fredriko/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n}"},"labels":{"kind":"list like","value":[],"string":"[]"},"state":{"kind":"string","value":"closed"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["I haven't tested C4 on my side so there so there may be a few bugs in the code/adjustments to make.\r\nHere it looks like in c4.py, line 190 one of the `files_to_download` is `'/'` which is invalid.\r\nValid files are paths to local files or URLs to remote files.","Fixed once processed data is used instead:\r\n- #2575"],"string":"[\n \"I haven't tested C4 on my side so there so there may be a few bugs in the code/adjustments to make.\\r\\nHere it looks like in c4.py, line 190 one of the `files_to_download` is `'/'` which is invalid.\\r\\nValid files are paths to local files or URLs to remote files.\",\n \"Fixed once processed data is used instead:\\r\\n- #2575\"\n]"},"created_at":{"kind":"timestamp","value":"2021-01-08T07:31:30","string":"2021-01-08T07:31:30"},"updated_at":{"kind":"timestamp","value":"2022-08-04T11:56:10","string":"2022-08-04T11:56:10"},"closed_at":{"kind":"timestamp","value":"2022-08-04T11:55:04","string":"2022-08-04T11:55:04"},"author_association":{"kind":"string","value":"NONE"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"**TLDR**:\r\n\r\nI fail to download C4 and see a stacktrace originating in `IsADirectoryError` as an explanation for failure.\r\n\r\nHow can the problem be fixed? \r\n\r\n**VERBOSE**:\r\n\r\nI use Python version 3.7 and have the following dependencies listed in my project:\r\n\r\n```\r\ndatasets==1.2.0\r\napache-beam==2.26.0\r\n```\r\n\r\nWhen running the following code, where `/data/huggingface/unpacked/` contains a single unzipped `wet.paths` file manually downloaded as per the instructions for C4:\r\n\r\n```\r\nfrom datasets import load_dataset\r\n\r\nload_dataset(\"c4\", \"en\", data_dir=\"/data/huggingface/unpacked\", beam_runner='DirectRunner')\r\n```\r\n\r\nI get the following stacktrace:\r\n\r\n```\r\n/Users/fredriko/venv/misc/bin/python /Users/fredriko/source/misc/main.py\r\nDownloading and preparing dataset c4/en (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /Users/fredriko/.cache/huggingface/datasets/c4/en/2.3.0/8304cf264cc42bdebcb13fca4b9cb36368a96f557d36f9dc969bebbe2568b283...\r\nTraceback (most recent call last):\r\n File \"/Users/fredriko/source/misc/main.py\", line 3, in \r\n load_dataset(\"c4\", \"en\", data_dir=\"/data/huggingface/unpacked\", beam_runner='DirectRunner')\r\n File \"/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/load.py\", line 612, in load_dataset\r\n ignore_verifications=ignore_verifications,\r\n File \"/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/builder.py\", line 527, in download_and_prepare\r\n dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n File \"/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/builder.py\", line 1066, in _download_and_prepare\r\n pipeline=pipeline,\r\n File \"/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/builder.py\", line 582, in _download_and_prepare\r\n split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n File \"/Users/fredriko/.cache/huggingface/modules/datasets_modules/datasets/c4/8304cf264cc42bdebcb13fca4b9cb36368a96f557d36f9dc969bebbe2568b283/c4.py\", line 190, in _split_generators\r\n file_paths = dl_manager.download_and_extract(files_to_download)\r\n File \"/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/utils/download_manager.py\", line 258, in download_and_extract\r\n return self.extract(self.download(url_or_urls))\r\n File \"/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/utils/download_manager.py\", line 189, in download\r\n self._record_sizes_checksums(url_or_urls, downloaded_path_or_paths)\r\n File \"/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/utils/download_manager.py\", line 117, in _record_sizes_checksums\r\n self._recorded_sizes_checksums[str(url)] = get_size_checksum_dict(path)\r\n File \"/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/utils/info_utils.py\", line 80, in get_size_checksum_dict\r\n with open(path, \"rb\") as f:\r\nIsADirectoryError: [Errno 21] Is a directory: '/'\r\n\r\nProcess finished with exit code 1\r\n```"},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1710/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n 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\"login\": \"JimmyJim1\",\n \"id\": 68724553,\n \"node_id\": \"MDQ6VXNlcjY4NzI0NTUz\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/68724553?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/JimmyJim1\",\n \"html_url\": \"https://github.com/JimmyJim1\",\n \"followers_url\": \"https://api.github.com/users/JimmyJim1/followers\",\n \"following_url\": \"https://api.github.com/users/JimmyJim1/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/JimmyJim1/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/JimmyJim1/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/JimmyJim1/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/JimmyJim1/orgs\",\n \"repos_url\": \"https://api.github.com/users/JimmyJim1/repos\",\n \"events_url\": \"https://api.github.com/users/JimmyJim1/events{/privacy}\",\n \"received_events_url\": 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dataset (or link to social media or blog post)*\n- **Paper:** *link to the dataset paper if available*\n- **Data:** *link to the Github repository or current dataset location*\n- **Motivation:** *what are some good reasons to have this dataset*\n\nInstructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md)."},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1709/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 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\"login\": \"Louiejay54\",\n \"id\": 77126849,\n \"node_id\": \"MDQ6VXNlcjc3MTI2ODQ5\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/77126849?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/Louiejay54\",\n \"html_url\": \"https://github.com/Louiejay54\",\n \"followers_url\": \"https://api.github.com/users/Louiejay54/followers\",\n \"following_url\": \"https://api.github.com/users/Louiejay54/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/Louiejay54/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/Louiejay54/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/Louiejay54/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/Louiejay54/orgs\",\n \"repos_url\": \"https://api.github.com/users/Louiejay54/repos\",\n \"events_url\": \"https://api.github.com/users/Louiejay54/events{/privacy}\",\n \"received_events_url\": 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dataset (or link to social media or blog post)*\n- **Paper:** *link to the dataset paper if available*\n- **Data:** *link to the Github repository or current dataset location*\n- **Motivation:** *what are some good reasons to have this dataset*\n\nInstructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md)."},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1708/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 0\n}"},"timeline_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1708/timeline"},"performed_via_github_app":{"kind":"null"},"state_reason":{"kind":"string","value":"completed"}}},{"rowIdx":2290,"cells":{"url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1706"},"repository_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets"},"labels_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1706/labels{/name}"},"comments_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1706/comments"},"events_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1706/events"},"html_url":{"kind":"string","value":"https://github.com/huggingface/datasets/issues/1706"},"id":{"kind":"number","value":781494476,"string":"781,494,476"},"node_id":{"kind":"string","value":"MDU6SXNzdWU3ODE0OTQ0NzY="},"number":{"kind":"number","value":1706,"string":"1,706"},"title":{"kind":"string","value":"Error when downloading a large dataset on slow connection."},"user":{"kind":"string","value":"{\n \"login\": \"lucadiliello\",\n \"id\": 23355969,\n \"node_id\": \"MDQ6VXNlcjIzMzU1OTY5\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/23355969?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/lucadiliello\",\n \"html_url\": \"https://github.com/lucadiliello\",\n \"followers_url\": \"https://api.github.com/users/lucadiliello/followers\",\n \"following_url\": \"https://api.github.com/users/lucadiliello/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/lucadiliello/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/lucadiliello/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/lucadiliello/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/lucadiliello/orgs\",\n \"repos_url\": \"https://api.github.com/users/lucadiliello/repos\",\n \"events_url\": 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Is this an issue you have with `openwebtext` specifically or also with other datasets ?\r\n\r\nIt looks like the downloaded file is corrupted and can't be extracted using `tarfile`.\r\nCould you try loading it again with \r\n```python\r\nimport datasets\r\ndatasets.load_dataset(\"openwebtext\", download_mode=\"force_redownload\")\r\n```"],"string":"[\n \"Hi ! Is this an issue you have with `openwebtext` specifically or also with other datasets ?\\r\\n\\r\\nIt looks like the downloaded file is corrupted and can't be extracted using `tarfile`.\\r\\nCould you try loading it again with \\r\\n```python\\r\\nimport datasets\\r\\ndatasets.load_dataset(\\\"openwebtext\\\", download_mode=\\\"force_redownload\\\")\\r\\n```\"\n]"},"created_at":{"kind":"timestamp","value":"2021-01-07T17:48:15","string":"2021-01-07T17:48:15"},"updated_at":{"kind":"timestamp","value":"2021-01-13T10:35:02","string":"2021-01-13T10:35:02"},"closed_at":{"kind":"null"},"author_association":{"kind":"string","value":"CONTRIBUTOR"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"I receive the following error after about an hour trying to download the `openwebtext` dataset.\r\n\r\nThe code used is:\r\n```python\r\nimport datasets\r\ndatasets.load_dataset(\"openwebtext\")\r\n```\r\n\r\n> Traceback (most recent call last): [4/28]\r\n> File \"\", line 1, in \r\n> File \"/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/site-packages/datasets/load.py\", line 610, in load_dataset\r\n> ignore_verifications=ignore_verifications,\r\n> File \"/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/site-packages/datasets/builder.py\", line 515, in download_and_prepare\r\n> dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n> File \"/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/site-packages/datasets/builder.py\", line 570, in _download_and_prepare\r\n> split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n> File \"/home/lucadiliello/.cache/huggingface/modules/datasets_modules/datasets/openwebtext/5c636399c7155da97c982d0d70ecdce30fbca66a4eb4fc768ad91f8331edac02/openwebtext.py\", line 62, in _split_generators\r\n> dl_dir = dl_manager.download_and_extract(_URL)\r\n> File \"/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/site-packages/datasets/utils/download_manager.py\", line 254, in download_and_extract\r\n> return self.extract(self.download(url_or_urls))\r\n> File \"/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/site-packages/datasets/utils/download_manager.py\", line 235, in extract\r\n> num_proc=num_proc,\r\n> File \"/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/site-packages/datasets/utils/py_utils.py\", line 225, in map_nested\r\n> return function(data_struct)\r\n> File \"/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/site-packages/datasets/utils/file_utils.py\", line 343, in cached_path\r\n> tar_file.extractall(output_path_extracted)\r\n> File \"/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/tarfile.py\", line 2000, in extractall\r\n> numeric_owner=numeric_owner)\r\n> File \"/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/tarfile.py\", line 2042, in extract\r\n> numeric_owner=numeric_owner)\r\n> File \"/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/tarfile.py\", line 2112, in _extract_member\r\n> self.makefile(tarinfo, targetpath)\r\n> File \"/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/tarfile.py\", line 2161, in makefile\r\n> copyfileobj(source, target, tarinfo.size, ReadError, bufsize)\r\n> File \"/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/tarfile.py\", line 253, in copyfileobj\r\n> buf = src.read(remainder)\r\n> File \"/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/lzma.py\", line 200, in read\r\n> return self._buffer.read(size)\r\n> File \"/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/_compression.py\", line 68, in readinto\r\n> data = self.read(len(byte_view))\r\n> File \"/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/_compression.py\", line 99, in read\r\n> raise EOFError(\"Compressed file ended before the \"\r\n> EOFError: Compressed file ended before the end-of-stream marker was reached\r\n"},"reactions":{"kind":"string","value":"{\n 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datasets miss dataset_infos.json or dummy_data.zip"},"user":{"kind":"string","value":"{\n \"login\": \"madlag\",\n \"id\": 272253,\n \"node_id\": \"MDQ6VXNlcjI3MjI1Mw==\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/272253?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/madlag\",\n \"html_url\": \"https://github.com/madlag\",\n \"followers_url\": \"https://api.github.com/users/madlag/followers\",\n \"following_url\": \"https://api.github.com/users/madlag/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/madlag/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/madlag/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/madlag/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/madlag/orgs\",\n \"repos_url\": \"https://api.github.com/users/madlag/repos\",\n \"events_url\": \"https://api.github.com/users/madlag/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/madlag/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n}"},"labels":{"kind":"list like","value":[],"string":"[]"},"state":{"kind":"string","value":"closed"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["Thanks for reporting.\r\nWe should indeed add all the missing dummy_data.zip and also the dataset_infos.json at least for lm1b, reclor and wikihow.\r\n\r\nFor c4 I haven't tested the script and I think we'll require some optimizations regarding beam datasets before processing it.\r\n","Closing since the dummy data generation is deprecated now (and the issue with missing metadata seems to be addressed)."],"string":"[\n \"Thanks for reporting.\\r\\nWe should indeed add all the missing dummy_data.zip and also the dataset_infos.json at least for lm1b, reclor and wikihow.\\r\\n\\r\\nFor c4 I haven't tested the script and I think we'll require some optimizations regarding beam datasets before processing it.\\r\\n\",\n \"Closing since the dummy data generation is deprecated now (and the issue with missing metadata seems to be addressed).\"\n]"},"created_at":{"kind":"timestamp","value":"2021-01-07T14:17:13","string":"2021-01-07T14:17:13"},"updated_at":{"kind":"timestamp","value":"2022-11-04T15:11:16","string":"2022-11-04T15:11:16"},"closed_at":{"kind":"timestamp","value":"2022-11-04T15:06:00","string":"2022-11-04T15:06:00"},"author_association":{"kind":"string","value":"CONTRIBUTOR"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"While working on dataset REAME generation script at https://github.com/madlag/datasets_readme_generator , I noticed that some datasets miss a dataset_infos.json : \r\n\r\n```\r\nc4\r\nlm1b\r\nreclor\r\nwikihow\r\n```\r\n\r\nAnd some does not have a dummy_data.zip : \r\n\r\n```\r\nkor_nli\r\nmath_dataset\r\nmlqa\r\nms_marco\r\nnewsgroup\r\nqa4mre\r\nqangaroo\r\nreddit_tifu\r\nsuper_glue\r\ntrivia_qa\r\nweb_of_science\r\nwmt14\r\nwmt15\r\nwmt16\r\nwmt17\r\nwmt18\r\nwmt19\r\nxtreme\r\n```\r\n\r\nBut it seems that some of those last do have a \"dummy\" directory .\r\n\r\n"},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1701/reactions\",\n \"total_count\": 1,\n \"+1\": 1,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 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to install datasets"},"user":{"kind":"string","value":"{\n \"login\": \"glee2429\",\n \"id\": 12635475,\n \"node_id\": \"MDQ6VXNlcjEyNjM1NDc1\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/12635475?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/glee2429\",\n \"html_url\": \"https://github.com/glee2429\",\n \"followers_url\": \"https://api.github.com/users/glee2429/followers\",\n \"following_url\": \"https://api.github.com/users/glee2429/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/glee2429/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/glee2429/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/glee2429/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/glee2429/orgs\",\n \"repos_url\": \"https://api.github.com/users/glee2429/repos\",\n \"events_url\": \"https://api.github.com/users/glee2429/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/glee2429/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n}"},"labels":{"kind":"list like","value":[],"string":"[]"},"state":{"kind":"string","value":"closed"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["Maybe try to create a virtual env with python 3.8 or 3.7","Thanks, @thomwolf! I fixed the issue by downgrading python to 3.7. ","Damn sorry","Damn sorry"],"string":"[\n \"Maybe try to create a virtual env with python 3.8 or 3.7\",\n \"Thanks, @thomwolf! I fixed the issue by downgrading python to 3.7. \",\n \"Damn sorry\",\n \"Damn sorry\"\n]"},"created_at":{"kind":"timestamp","value":"2021-01-07T07:24:37","string":"2021-01-07T07:24:37"},"updated_at":{"kind":"timestamp","value":"2021-01-08T00:33:05","string":"2021-01-08T00:33:05"},"closed_at":{"kind":"timestamp","value":"2021-01-07T22:06:05","string":"2021-01-07T22:06:05"},"author_association":{"kind":"string","value":"NONE"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"** Edit **\r\nI believe there's a bug with the package when you're installing it with Python 3.9. I recommend sticking with previous versions. Thanks, @thomwolf for the insight! \r\n\r\n**Short description**\r\n\r\nI followed the instructions for installing datasets (https://huggingface.co/docs/datasets/installation.html). However, while I tried to download datasets using `pip install datasets` I got a massive error message after getting stuck at \"Installing build dependencies...\" \r\n\r\nI was wondering if this problem can be fixed by creating a virtual environment, but it didn't help. Can anyone offer some advice on how to fix this issue? \r\n\r\nHere's an error message: \r\n\r\n`(env) Gas-MacBook-Pro:Downloads destiny$ pip install datasets\r\nCollecting datasets\r\n Using cached datasets-1.2.0-py3-none-any.whl (159 kB)\r\nCollecting numpy>=1.17\r\n Using cached numpy-1.19.5-cp39-cp39-macosx_10_9_x86_64.whl (15.6 MB)\r\nCollecting pyarrow>=0.17.1\r\n Using cached pyarrow-2.0.0.tar.gz (58.9 MB)\r\n....\r\n\r\n _configtest.c:9:5: warning: incompatible redeclaration of library function 'ceilf' [-Wincompatible-library-redeclaration]\r\n int ceilf (void);\r\n ^\r\n _configtest.c:9:5: note: 'ceilf' is a builtin with type 'float (float)'\r\n _configtest.c:10:5: warning: incompatible redeclaration of library function 'rintf' [-Wincompatible-library-redeclaration]\r\n int rintf (void);\r\n ^\r\n _configtest.c:10:5: note: 'rintf' is a builtin with type 'float (float)'\r\n _configtest.c:11:5: warning: incompatible redeclaration of library function 'truncf' [-Wincompatible-library-redeclaration]\r\n int truncf (void);\r\n ^\r\n _configtest.c:11:5: note: 'truncf' is a builtin with type 'float (float)'\r\n _configtest.c:12:5: warning: incompatible redeclaration of library function 'sqrtf' [-Wincompatible-library-redeclaration]\r\n int sqrtf (void);\r\n ^\r\n _configtest.c:12:5: note: 'sqrtf' is a builtin with type 'float (float)'\r\n _configtest.c:13:5: warning: incompatible redeclaration of library function 'log10f' [-Wincompatible-library-redeclaration]\r\n int log10f (void);\r\n ^\r\n _configtest.c:13:5: note: 'log10f' is a builtin with type 'float (float)'\r\n _configtest.c:14:5: warning: incompatible redeclaration of library function 'logf' [-Wincompatible-library-redeclaration]\r\n int logf (void);\r\n ^\r\n _configtest.c:14:5: note: 'logf' is a builtin with type 'float (float)'\r\n _configtest.c:15:5: warning: incompatible redeclaration of library function 'log1pf' [-Wincompatible-library-redeclaration]\r\n int log1pf (void);\r\n ^\r\n _configtest.c:15:5: note: 'log1pf' is a builtin with type 'float (float)'\r\n _configtest.c:16:5: warning: incompatible redeclaration of library function 'expf' [-Wincompatible-library-redeclaration]\r\n int expf (void);\r\n ^\r\n _configtest.c:16:5: note: 'expf' is a builtin with type 'float (float)'\r\n _configtest.c:17:5: warning: incompatible redeclaration of library function 'expm1f' [-Wincompatible-library-redeclaration]\r\n int expm1f (void);\r\n ^\r\n _configtest.c:17:5: note: 'expm1f' is a builtin with type 'float (float)'\r\n _configtest.c:18:5: warning: incompatible redeclaration of library function 'asinf' [-Wincompatible-library-redeclaration]\r\n int asinf (void);\r\n ^\r\n _configtest.c:18:5: note: 'asinf' is a builtin with type 'float (float)'\r\n _configtest.c:19:5: warning: incompatible redeclaration of library function 'acosf' [-Wincompatible-library-redeclaration]\r\n int acosf (void);\r\n ^\r\n _configtest.c:19:5: note: 'acosf' is a builtin with type 'float (float)'\r\n _configtest.c:20:5: warning: incompatible redeclaration of library function 'atanf' [-Wincompatible-library-redeclaration]\r\n int atanf (void);\r\n ^\r\n _configtest.c:20:5: note: 'atanf' is a builtin with type 'float (float)'\r\n _configtest.c:21:5: warning: incompatible redeclaration of library function 'asinhf' [-Wincompatible-library-redeclaration]\r\n int asinhf (void);\r\n ^\r\n _configtest.c:21:5: note: 'asinhf' is a builtin with type 'float (float)'\r\n _configtest.c:22:5: warning: incompatible redeclaration of library function 'acoshf' [-Wincompatible-library-redeclaration]\r\n int acoshf (void);\r\n ^\r\n _configtest.c:22:5: note: 'acoshf' is a builtin with type 'float (float)'\r\n _configtest.c:23:5: warning: incompatible redeclaration of library function 'atanhf' [-Wincompatible-library-redeclaration]\r\n int atanhf (void);\r\n ^\r\n _configtest.c:23:5: note: 'atanhf' is a builtin with type 'float (float)'\r\n _configtest.c:24:5: warning: incompatible redeclaration of library function 'hypotf' [-Wincompatible-library-redeclaration]\r\n int hypotf (void);\r\n ^\r\n _configtest.c:24:5: note: 'hypotf' is a builtin with type 'float (float, float)'\r\n _configtest.c:25:5: warning: incompatible redeclaration of library function 'atan2f' [-Wincompatible-library-redeclaration]\r\n int atan2f (void);\r\n ^\r\n _configtest.c:25:5: note: 'atan2f' is a builtin with type 'float (float, float)'\r\n _configtest.c:26:5: warning: incompatible redeclaration of library function 'powf' [-Wincompatible-library-redeclaration]\r\n int powf (void);\r\n ^\r\n _configtest.c:26:5: note: 'powf' is a builtin with type 'float (float, float)'\r\n _configtest.c:27:5: warning: incompatible redeclaration of library function 'fmodf' [-Wincompatible-library-redeclaration]\r\n int fmodf (void);\r\n ^\r\n _configtest.c:27:5: note: 'fmodf' is a builtin with type 'float (float, float)'\r\n _configtest.c:28:5: warning: incompatible redeclaration of library function 'modff' [-Wincompatible-library-redeclaration]\r\n int modff (void);\r\n ^\r\n _configtest.c:28:5: note: 'modff' is a builtin with type 'float (float, float *)'\r\n _configtest.c:29:5: warning: incompatible redeclaration of library function 'frexpf' [-Wincompatible-library-redeclaration]\r\n int frexpf (void);\r\n ^\r\n _configtest.c:29:5: note: 'frexpf' is a builtin with type 'float (float, int *)'\r\n _configtest.c:30:5: warning: incompatible redeclaration of library function 'ldexpf' [-Wincompatible-library-redeclaration]\r\n int ldexpf (void);\r\n ^\r\n _configtest.c:30:5: note: 'ldexpf' is a builtin with type 'float (float, int)'\r\n _configtest.c:31:5: warning: incompatible redeclaration of library function 'exp2f' [-Wincompatible-library-redeclaration]\r\n int exp2f (void);\r\n ^\r\n _configtest.c:31:5: note: 'exp2f' is a builtin with type 'float (float)'\r\n _configtest.c:32:5: warning: incompatible redeclaration of library function 'log2f' [-Wincompatible-library-redeclaration]\r\n int log2f (void);\r\n ^\r\n _configtest.c:32:5: note: 'log2f' is a builtin with type 'float (float)'\r\n _configtest.c:33:5: warning: incompatible redeclaration of library function 'copysignf' [-Wincompatible-library-redeclaration]\r\n int copysignf (void);\r\n ^\r\n _configtest.c:33:5: note: 'copysignf' is a builtin with type 'float (float, float)'\r\n _configtest.c:34:5: warning: incompatible redeclaration of library function 'nextafterf' [-Wincompatible-library-redeclaration]\r\n int nextafterf (void);\r\n ^\r\n _configtest.c:34:5: note: 'nextafterf' is a builtin with type 'float (float, float)'\r\n _configtest.c:35:5: warning: incompatible redeclaration of library function 'cbrtf' [-Wincompatible-library-redeclaration]\r\n int cbrtf (void);\r\n ^\r\n _configtest.c:35:5: note: 'cbrtf' is a builtin with type 'float (float)'\r\n 35 warnings generated.\r\n clang _configtest.o -o _configtest\r\n success!\r\n removing: _configtest.c _configtest.o _configtest.o.d _configtest\r\n C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers\r\n \r\n compile options: '-Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -c'\r\n clang: _configtest.c\r\n _configtest.c:1:5: warning: incompatible redeclaration of library function 'sinl' [-Wincompatible-library-redeclaration]\r\n int sinl (void);\r\n ^\r\n _configtest.c:1:5: note: 'sinl' is a builtin with type 'long double (long double)'\r\n _configtest.c:2:5: warning: incompatible redeclaration of library function 'cosl' [-Wincompatible-library-redeclaration]\r\n int cosl (void);\r\n ^\r\n _configtest.c:2:5: note: 'cosl' is a builtin with type 'long double (long double)'\r\n _configtest.c:3:5: warning: incompatible redeclaration of library function 'tanl' [-Wincompatible-library-redeclaration]\r\n int tanl (void);\r\n ^\r\n _configtest.c:3:5: note: 'tanl' is a builtin with type 'long double (long double)'\r\n _configtest.c:4:5: warning: incompatible redeclaration of library function 'sinhl' [-Wincompatible-library-redeclaration]\r\n int sinhl (void);\r\n ^\r\n _configtest.c:4:5: note: 'sinhl' is a builtin with type 'long double (long double)'\r\n _configtest.c:5:5: warning: incompatible redeclaration of library function 'coshl' [-Wincompatible-library-redeclaration]\r\n int coshl (void);\r\n ^\r\n _configtest.c:5:5: note: 'coshl' is a builtin with type 'long double (long double)'\r\n _configtest.c:6:5: warning: incompatible redeclaration of library function 'tanhl' [-Wincompatible-library-redeclaration]\r\n int tanhl (void);\r\n ^\r\n _configtest.c:6:5: note: 'tanhl' is a builtin with type 'long double (long double)'\r\n _configtest.c:7:5: warning: incompatible redeclaration of library function 'fabsl' [-Wincompatible-library-redeclaration]\r\n int fabsl (void);\r\n ^\r\n _configtest.c:7:5: note: 'fabsl' is a builtin with type 'long double (long double)'\r\n _configtest.c:8:5: warning: incompatible redeclaration of library function 'floorl' [-Wincompatible-library-redeclaration]\r\n int floorl (void);\r\n ^\r\n _configtest.c:8:5: note: 'floorl' is a builtin with type 'long double (long double)'\r\n _configtest.c:9:5: warning: incompatible redeclaration of library function 'ceill' [-Wincompatible-library-redeclaration]\r\n int ceill (void);\r\n ^\r\n _configtest.c:9:5: note: 'ceill' is a builtin with type 'long double (long double)'\r\n _configtest.c:10:5: warning: incompatible redeclaration of library function 'rintl' [-Wincompatible-library-redeclaration]\r\n int rintl (void);\r\n ^\r\n _configtest.c:10:5: note: 'rintl' is a builtin with type 'long double (long double)'\r\n _configtest.c:11:5: warning: incompatible redeclaration of library function 'truncl' [-Wincompatible-library-redeclaration]\r\n int truncl (void);\r\n ^\r\n _configtest.c:11:5: note: 'truncl' is a builtin with type 'long double (long double)'\r\n _configtest.c:12:5: warning: incompatible redeclaration of library function 'sqrtl' [-Wincompatible-library-redeclaration]\r\n int sqrtl (void);\r\n ^\r\n _configtest.c:12:5: note: 'sqrtl' is a builtin with type 'long double (long double)'\r\n _configtest.c:13:5: warning: incompatible redeclaration of library function 'log10l' [-Wincompatible-library-redeclaration]\r\n int log10l (void);\r\n ^\r\n _configtest.c:13:5: note: 'log10l' is a builtin with type 'long double (long double)'\r\n _configtest.c:14:5: warning: incompatible redeclaration of library function 'logl' [-Wincompatible-library-redeclaration]\r\n int logl (void);\r\n ^\r\n _configtest.c:14:5: note: 'logl' is a builtin with type 'long double (long double)'\r\n _configtest.c:15:5: warning: incompatible redeclaration of library function 'log1pl' [-Wincompatible-library-redeclaration]\r\n int log1pl (void);\r\n ^\r\n _configtest.c:15:5: note: 'log1pl' is a builtin with type 'long double (long double)'\r\n _configtest.c:16:5: warning: incompatible redeclaration of library function 'expl' [-Wincompatible-library-redeclaration]\r\n int expl (void);\r\n ^\r\n _configtest.c:16:5: note: 'expl' is a builtin with type 'long double (long double)'\r\n _configtest.c:17:5: warning: incompatible redeclaration of library function 'expm1l' [-Wincompatible-library-redeclaration]\r\n int expm1l (void);\r\n ^\r\n _configtest.c:17:5: note: 'expm1l' is a builtin with type 'long double (long double)'\r\n _configtest.c:18:5: warning: incompatible redeclaration of library function 'asinl' [-Wincompatible-library-redeclaration]\r\n int asinl (void);\r\n ^\r\n _configtest.c:18:5: note: 'asinl' is a builtin with type 'long double (long double)'\r\n _configtest.c:19:5: warning: incompatible redeclaration of library function 'acosl' [-Wincompatible-library-redeclaration]\r\n int acosl (void);\r\n ^\r\n _configtest.c:19:5: note: 'acosl' is a builtin with type 'long double (long double)'\r\n _configtest.c:20:5: warning: incompatible redeclaration of library function 'atanl' [-Wincompatible-library-redeclaration]\r\n int atanl (void);\r\n ^\r\n _configtest.c:20:5: note: 'atanl' is a builtin with type 'long double (long double)'\r\n _configtest.c:21:5: warning: incompatible redeclaration of library function 'asinhl' [-Wincompatible-library-redeclaration]\r\n int asinhl (void);\r\n ^\r\n _configtest.c:21:5: note: 'asinhl' is a builtin with type 'long double (long double)'\r\n _configtest.c:22:5: warning: incompatible redeclaration of library function 'acoshl' [-Wincompatible-library-redeclaration]\r\n int acoshl (void);\r\n ^\r\n _configtest.c:22:5: note: 'acoshl' is a builtin with type 'long double (long double)'\r\n _configtest.c:23:5: warning: incompatible redeclaration of library function 'atanhl' [-Wincompatible-library-redeclaration]\r\n int atanhl (void);\r\n ^\r\n _configtest.c:23:5: note: 'atanhl' is a builtin with type 'long double (long double)'\r\n _configtest.c:24:5: warning: incompatible redeclaration of library function 'hypotl' [-Wincompatible-library-redeclaration]\r\n int hypotl (void);\r\n ^\r\n _configtest.c:24:5: note: 'hypotl' is a builtin with type 'long double (long double, long double)'\r\n _configtest.c:25:5: warning: incompatible redeclaration of library function 'atan2l' [-Wincompatible-library-redeclaration]\r\n int atan2l (void);\r\n ^\r\n _configtest.c:25:5: note: 'atan2l' is a builtin with type 'long double (long double, long double)'\r\n _configtest.c:26:5: warning: incompatible redeclaration of library function 'powl' [-Wincompatible-library-redeclaration]\r\n int powl (void);\r\n ^\r\n _configtest.c:26:5: note: 'powl' is a builtin with type 'long double (long double, long double)'\r\n _configtest.c:27:5: warning: incompatible redeclaration of library function 'fmodl' [-Wincompatible-library-redeclaration]\r\n int fmodl (void);\r\n ^\r\n _configtest.c:27:5: note: 'fmodl' is a builtin with type 'long double (long double, long double)'\r\n _configtest.c:28:5: warning: incompatible redeclaration of library function 'modfl' [-Wincompatible-library-redeclaration]\r\n int modfl (void);\r\n ^\r\n _configtest.c:28:5: note: 'modfl' is a builtin with type 'long double (long double, long double *)'\r\n _configtest.c:29:5: warning: incompatible redeclaration of library function 'frexpl' [-Wincompatible-library-redeclaration]\r\n int frexpl (void);\r\n ^\r\n _configtest.c:29:5: note: 'frexpl' is a builtin with type 'long double (long double, int *)'\r\n _configtest.c:30:5: warning: incompatible redeclaration of library function 'ldexpl' [-Wincompatible-library-redeclaration]\r\n int ldexpl (void);\r\n ^\r\n _configtest.c:30:5: note: 'ldexpl' is a builtin with type 'long double (long double, int)'\r\n _configtest.c:31:5: warning: incompatible redeclaration of library function 'exp2l' [-Wincompatible-library-redeclaration]\r\n int exp2l (void);\r\n ^\r\n _configtest.c:31:5: note: 'exp2l' is a builtin with type 'long double (long double)'\r\n _configtest.c:32:5: warning: incompatible redeclaration of library function 'log2l' [-Wincompatible-library-redeclaration]\r\n int log2l (void);\r\n ^\r\n _configtest.c:32:5: note: 'log2l' is a builtin with type 'long double (long double)'\r\n _configtest.c:33:5: warning: incompatible redeclaration of library function 'copysignl' [-Wincompatible-library-redeclaration]\r\n int copysignl (void);\r\n ^\r\n _configtest.c:33:5: note: 'copysignl' is a builtin with type 'long double (long double, long double)'\r\n _configtest.c:34:5: warning: incompatible redeclaration of library function 'nextafterl' [-Wincompatible-library-redeclaration]\r\n int nextafterl (void);\r\n ^\r\n _configtest.c:34:5: note: 'nextafterl' is a builtin with type 'long double (long double, long double)'\r\n _configtest.c:35:5: warning: incompatible redeclaration of library function 'cbrtl' [-Wincompatible-library-redeclaration]\r\n int cbrtl (void);\r\n ^\r\n _configtest.c:35:5: note: 'cbrtl' is a builtin with type 'long double (long double)'\r\n 35 warnings generated.\r\n clang _configtest.o -o _configtest\r\n success!\r\n removing: _configtest.c _configtest.o _configtest.o.d _configtest\r\n C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers\r\n \r\n compile options: '-Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -c'\r\n clang: _configtest.c\r\n success!\r\n removing: _configtest.c _configtest.o _configtest.o.d\r\n C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers\r\n \r\n compile options: '-Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -c'\r\n clang: _configtest.c\r\n success!\r\n removing: _configtest.c _configtest.o _configtest.o.d\r\n C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers\r\n \r\n compile options: '-Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -c'\r\n clang: _configtest.c\r\n success!\r\n removing: _configtest.c _configtest.o _configtest.o.d\r\n C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers\r\n \r\n compile options: '-Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -c'\r\n clang: _configtest.c\r\n success!\r\n removing: _configtest.c _configtest.o _configtest.o.d\r\n C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers\r\n \r\n compile options: '-Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -c'\r\n clang: _configtest.c\r\n _configtest.c:8:12: error: use of undeclared identifier 'HAVE_DECL_SIGNBIT'\r\n (void) HAVE_DECL_SIGNBIT;\r\n ^\r\n 1 error generated.\r\n failure.\r\n removing: _configtest.c _configtest.o\r\n C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers\r\n \r\n compile options: '-Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -c'\r\n clang: _configtest.c\r\n success!\r\n removing: _configtest.c _configtest.o _configtest.o.d\r\n C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers\r\n \r\n compile options: '-Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -c'\r\n clang: _configtest.c\r\n success!\r\n removing: _configtest.c _configtest.o _configtest.o.d\r\n C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers\r\n \r\n compile options: '-Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -c'\r\n clang: _configtest.c\r\n success!\r\n removing: _configtest.c _configtest.o _configtest.o.d\r\n C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers\r\n \r\n compile options: '-Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -c'\r\n clang: _configtest.c\r\n success!\r\n removing: _configtest.c _configtest.o _configtest.o.d\r\n C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers\r\n \r\n compile options: '-Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -c'\r\n clang: _configtest.c\r\n removing: _configtest.c _configtest.o _configtest.o.d\r\n C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers\r\n \r\n compile options: '-Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -c'\r\n clang: _configtest.c\r\n removing: _configtest.c _configtest.o _configtest.o.d\r\n C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers\r\n \r\n compile options: '-Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -c'\r\n clang: _configtest.c\r\n removing: _configtest.c _configtest.o _configtest.o.d\r\n C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers\r\n \r\n compile options: '-Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -c'\r\n clang: _configtest.c\r\n _configtest.c:1:5: warning: incompatible redeclaration of library function 'cabs' [-Wincompatible-library-redeclaration]\r\n int cabs (void);\r\n ^\r\n _configtest.c:1:5: note: 'cabs' is a builtin with type 'double (_Complex double)'\r\n _configtest.c:2:5: warning: incompatible redeclaration of library function 'cacos' [-Wincompatible-library-redeclaration]\r\n int cacos (void);\r\n ^\r\n _configtest.c:2:5: note: 'cacos' is a builtin with type '_Complex double (_Complex double)'\r\n _configtest.c:3:5: warning: incompatible redeclaration of library function 'cacosh' [-Wincompatible-library-redeclaration]\r\n int cacosh (void);\r\n ^\r\n _configtest.c:3:5: note: 'cacosh' is a builtin with type '_Complex double (_Complex double)'\r\n _configtest.c:4:5: warning: incompatible redeclaration of library function 'carg' [-Wincompatible-library-redeclaration]\r\n int carg (void);\r\n ^\r\n _configtest.c:4:5: note: 'carg' is a builtin with type 'double (_Complex double)'\r\n _configtest.c:5:5: warning: incompatible redeclaration of library function 'casin' [-Wincompatible-library-redeclaration]\r\n int casin (void);\r\n ^\r\n _configtest.c:5:5: note: 'casin' is a builtin with type '_Complex double (_Complex double)'\r\n _configtest.c:6:5: warning: incompatible redeclaration of library function 'casinh' [-Wincompatible-library-redeclaration]\r\n int casinh (void);\r\n ^\r\n _configtest.c:6:5: note: 'casinh' is a builtin with type '_Complex double (_Complex double)'\r\n _configtest.c:7:5: warning: incompatible redeclaration of library function 'catan' [-Wincompatible-library-redeclaration]\r\n int catan (void);\r\n ^\r\n _configtest.c:7:5: note: 'catan' is a builtin with type '_Complex double (_Complex double)'\r\n _configtest.c:8:5: warning: incompatible redeclaration of library function 'catanh' [-Wincompatible-library-redeclaration]\r\n int catanh (void);\r\n ^\r\n _configtest.c:8:5: note: 'catanh' is a builtin with type '_Complex double (_Complex double)'\r\n _configtest.c:9:5: warning: incompatible redeclaration of library function 'ccos' [-Wincompatible-library-redeclaration]\r\n int ccos (void);\r\n ^\r\n _configtest.c:9:5: note: 'ccos' is a builtin with type '_Complex double (_Complex double)'\r\n _configtest.c:10:5: warning: incompatible redeclaration of library function 'ccosh' [-Wincompatible-library-redeclaration]\r\n int ccosh (void);\r\n ^\r\n _configtest.c:10:5: note: 'ccosh' is a builtin with type '_Complex double (_Complex double)'\r\n _configtest.c:11:5: warning: incompatible redeclaration of library function 'cexp' [-Wincompatible-library-redeclaration]\r\n int cexp (void);\r\n ^\r\n _configtest.c:11:5: note: 'cexp' is a builtin with type '_Complex double (_Complex double)'\r\n _configtest.c:12:5: warning: incompatible redeclaration of library function 'cimag' [-Wincompatible-library-redeclaration]\r\n int cimag (void);\r\n ^\r\n _configtest.c:12:5: note: 'cimag' is a builtin with type 'double (_Complex double)'\r\n _configtest.c:13:5: warning: incompatible redeclaration of library function 'clog' [-Wincompatible-library-redeclaration]\r\n int clog (void);\r\n ^\r\n _configtest.c:13:5: note: 'clog' is a builtin with type '_Complex double (_Complex double)'\r\n _configtest.c:14:5: warning: incompatible redeclaration of library function 'conj' [-Wincompatible-library-redeclaration]\r\n int conj (void);\r\n ^\r\n _configtest.c:14:5: note: 'conj' is a builtin with type '_Complex double (_Complex double)'\r\n _configtest.c:15:5: warning: incompatible redeclaration of library function 'cpow' [-Wincompatible-library-redeclaration]\r\n int cpow (void);\r\n ^\r\n _configtest.c:15:5: note: 'cpow' is a builtin with type '_Complex double (_Complex double, _Complex double)'\r\n _configtest.c:16:5: warning: incompatible redeclaration of library function 'cproj' [-Wincompatible-library-redeclaration]\r\n int cproj (void);\r\n ^\r\n _configtest.c:16:5: note: 'cproj' is a builtin with type '_Complex double (_Complex double)'\r\n _configtest.c:17:5: warning: incompatible redeclaration of library function 'creal' [-Wincompatible-library-redeclaration]\r\n int creal (void);\r\n ^\r\n _configtest.c:17:5: note: 'creal' is a builtin with type 'double (_Complex double)'\r\n _configtest.c:18:5: warning: incompatible redeclaration of library function 'csin' [-Wincompatible-library-redeclaration]\r\n int csin (void);\r\n ^\r\n _configtest.c:18:5: note: 'csin' is a builtin with type '_Complex double (_Complex double)'\r\n _configtest.c:19:5: warning: incompatible redeclaration of library function 'csinh' [-Wincompatible-library-redeclaration]\r\n int csinh (void);\r\n ^\r\n _configtest.c:19:5: note: 'csinh' is a builtin with type '_Complex double (_Complex double)'\r\n _configtest.c:20:5: warning: incompatible redeclaration of library function 'csqrt' [-Wincompatible-library-redeclaration]\r\n int csqrt (void);\r\n ^\r\n _configtest.c:20:5: note: 'csqrt' is a builtin with type '_Complex double (_Complex double)'\r\n _configtest.c:21:5: warning: incompatible redeclaration of library function 'ctan' [-Wincompatible-library-redeclaration]\r\n int ctan (void);\r\n ^\r\n _configtest.c:21:5: note: 'ctan' is a builtin with type '_Complex double (_Complex double)'\r\n _configtest.c:22:5: warning: incompatible redeclaration of library function 'ctanh' [-Wincompatible-library-redeclaration]\r\n int ctanh (void);\r\n ^\r\n _configtest.c:22:5: note: 'ctanh' is a builtin with type '_Complex double (_Complex double)'\r\n 22 warnings generated.\r\n clang _configtest.o -o _configtest\r\n success!\r\n removing: _configtest.c _configtest.o _configtest.o.d _configtest\r\n C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers\r\n \r\n compile options: '-Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -c'\r\n clang: _configtest.c\r\n _configtest.c:1:5: warning: incompatible redeclaration of library function 'cabsf' [-Wincompatible-library-redeclaration]\r\n int cabsf (void);\r\n ^\r\n _configtest.c:1:5: note: 'cabsf' is a builtin with type 'float (_Complex float)'\r\n _configtest.c:2:5: warning: incompatible redeclaration of library function 'cacosf' [-Wincompatible-library-redeclaration]\r\n int cacosf (void);\r\n ^\r\n _configtest.c:2:5: note: 'cacosf' is a builtin with type '_Complex float (_Complex float)'\r\n _configtest.c:3:5: warning: incompatible redeclaration of library function 'cacoshf' [-Wincompatible-library-redeclaration]\r\n int cacoshf (void);\r\n ^\r\n _configtest.c:3:5: note: 'cacoshf' is a builtin with type '_Complex float (_Complex float)'\r\n _configtest.c:4:5: warning: incompatible redeclaration of library function 'cargf' [-Wincompatible-library-redeclaration]\r\n int cargf (void);\r\n ^\r\n _configtest.c:4:5: note: 'cargf' is a builtin with type 'float (_Complex float)'\r\n _configtest.c:5:5: warning: incompatible redeclaration of library function 'casinf' [-Wincompatible-library-redeclaration]\r\n int casinf (void);\r\n ^\r\n _configtest.c:5:5: note: 'casinf' is a builtin with type '_Complex float (_Complex float)'\r\n _configtest.c:6:5: warning: incompatible redeclaration of library function 'casinhf' [-Wincompatible-library-redeclaration]\r\n int casinhf (void);\r\n ^\r\n _configtest.c:6:5: note: 'casinhf' is a builtin with type '_Complex float (_Complex float)'\r\n _configtest.c:7:5: warning: incompatible redeclaration of library function 'catanf' [-Wincompatible-library-redeclaration]\r\n int catanf (void);\r\n ^\r\n _configtest.c:7:5: note: 'catanf' is a builtin with type '_Complex float (_Complex float)'\r\n _configtest.c:8:5: warning: incompatible redeclaration of library function 'catanhf' [-Wincompatible-library-redeclaration]\r\n int catanhf (void);\r\n ^\r\n _configtest.c:8:5: note: 'catanhf' is a builtin with type '_Complex float (_Complex float)'\r\n _configtest.c:9:5: warning: incompatible redeclaration of library function 'ccosf' [-Wincompatible-library-redeclaration]\r\n int ccosf (void);\r\n ^\r\n _configtest.c:9:5: note: 'ccosf' is a builtin with type '_Complex float (_Complex float)'\r\n _configtest.c:10:5: warning: incompatible redeclaration of library function 'ccoshf' [-Wincompatible-library-redeclaration]\r\n int ccoshf (void);\r\n ^\r\n _configtest.c:10:5: note: 'ccoshf' is a builtin with type '_Complex float (_Complex float)'\r\n _configtest.c:11:5: warning: incompatible redeclaration of library function 'cexpf' [-Wincompatible-library-redeclaration]\r\n int cexpf (void);\r\n ^\r\n _configtest.c:11:5: note: 'cexpf' is a builtin with type '_Complex float (_Complex float)'\r\n _configtest.c:12:5: warning: incompatible redeclaration of library function 'cimagf' [-Wincompatible-library-redeclaration]\r\n int cimagf (void);\r\n ^\r\n _configtest.c:12:5: note: 'cimagf' is a builtin with type 'float (_Complex float)'\r\n _configtest.c:13:5: warning: incompatible redeclaration of library function 'clogf' [-Wincompatible-library-redeclaration]\r\n int clogf (void);\r\n ^\r\n _configtest.c:13:5: note: 'clogf' is a builtin with type '_Complex float (_Complex float)'\r\n _configtest.c:14:5: warning: incompatible redeclaration of library function 'conjf' [-Wincompatible-library-redeclaration]\r\n int conjf (void);\r\n ^\r\n _configtest.c:14:5: note: 'conjf' is a builtin with type '_Complex float (_Complex float)'\r\n _configtest.c:15:5: warning: incompatible redeclaration of library function 'cpowf' [-Wincompatible-library-redeclaration]\r\n int cpowf (void);\r\n ^\r\n _configtest.c:15:5: note: 'cpowf' is a builtin with type '_Complex float (_Complex float, _Complex float)'\r\n _configtest.c:16:5: warning: incompatible redeclaration of library function 'cprojf' [-Wincompatible-library-redeclaration]\r\n int cprojf (void);\r\n ^\r\n _configtest.c:16:5: note: 'cprojf' is a builtin with type '_Complex float (_Complex float)'\r\n _configtest.c:17:5: warning: incompatible redeclaration of library function 'crealf' [-Wincompatible-library-redeclaration]\r\n int crealf (void);\r\n ^\r\n _configtest.c:17:5: note: 'crealf' is a builtin with type 'float (_Complex float)'\r\n _configtest.c:18:5: warning: incompatible redeclaration of library function 'csinf' [-Wincompatible-library-redeclaration]\r\n int csinf (void);\r\n ^\r\n _configtest.c:18:5: note: 'csinf' is a builtin with type '_Complex float (_Complex float)'\r\n _configtest.c:19:5: warning: incompatible redeclaration of library function 'csinhf' [-Wincompatible-library-redeclaration]\r\n int csinhf (void);\r\n ^\r\n _configtest.c:19:5: note: 'csinhf' is a builtin with type '_Complex float (_Complex float)'\r\n _configtest.c:20:5: warning: incompatible redeclaration of library function 'csqrtf' [-Wincompatible-library-redeclaration]\r\n int csqrtf (void);\r\n ^\r\n _configtest.c:20:5: note: 'csqrtf' is a builtin with type '_Complex float (_Complex float)'\r\n _configtest.c:21:5: warning: incompatible redeclaration of library function 'ctanf' [-Wincompatible-library-redeclaration]\r\n int ctanf (void);\r\n ^\r\n _configtest.c:21:5: note: 'ctanf' is a builtin with type '_Complex float (_Complex float)'\r\n _configtest.c:22:5: warning: incompatible redeclaration of library function 'ctanhf' [-Wincompatible-library-redeclaration]\r\n int ctanhf (void);\r\n ^\r\n _configtest.c:22:5: note: 'ctanhf' is a builtin with type '_Complex float (_Complex float)'\r\n 22 warnings generated.\r\n clang _configtest.o -o _configtest\r\n success!\r\n removing: _configtest.c _configtest.o _configtest.o.d _configtest\r\n C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers\r\n \r\n compile options: '-Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -c'\r\n clang: _configtest.c\r\n _configtest.c:1:5: warning: incompatible redeclaration of library function 'cabsl' [-Wincompatible-library-redeclaration]\r\n int cabsl (void);\r\n ^\r\n _configtest.c:1:5: note: 'cabsl' is a builtin with type 'long double (_Complex long double)'\r\n _configtest.c:2:5: warning: incompatible redeclaration of library function 'cacosl' [-Wincompatible-library-redeclaration]\r\n int cacosl (void);\r\n ^\r\n _configtest.c:2:5: note: 'cacosl' is a builtin with type '_Complex long double (_Complex long double)'\r\n _configtest.c:3:5: warning: incompatible redeclaration of library function 'cacoshl' [-Wincompatible-library-redeclaration]\r\n int cacoshl (void);\r\n ^\r\n _configtest.c:3:5: note: 'cacoshl' is a builtin with type '_Complex long double (_Complex long double)'\r\n _configtest.c:4:5: warning: incompatible redeclaration of library function 'cargl' [-Wincompatible-library-redeclaration]\r\n int cargl (void);\r\n ^\r\n _configtest.c:4:5: note: 'cargl' is a builtin with type 'long double (_Complex long double)'\r\n _configtest.c:5:5: warning: incompatible redeclaration of library function 'casinl' [-Wincompatible-library-redeclaration]\r\n int casinl (void);\r\n ^\r\n _configtest.c:5:5: note: 'casinl' is a builtin with type '_Complex long double (_Complex long double)'\r\n _configtest.c:6:5: warning: incompatible redeclaration of library function 'casinhl' [-Wincompatible-library-redeclaration]\r\n int casinhl (void);\r\n ^\r\n _configtest.c:6:5: note: 'casinhl' is a builtin with type '_Complex long double (_Complex long double)'\r\n _configtest.c:7:5: warning: incompatible redeclaration of library function 'catanl' [-Wincompatible-library-redeclaration]\r\n int catanl (void);\r\n ^\r\n _configtest.c:7:5: note: 'catanl' is a builtin with type '_Complex long double (_Complex long double)'\r\n _configtest.c:8:5: warning: incompatible redeclaration of library function 'catanhl' [-Wincompatible-library-redeclaration]\r\n int catanhl (void);\r\n ^\r\n _configtest.c:8:5: note: 'catanhl' is a builtin with type '_Complex long double (_Complex long double)'\r\n _configtest.c:9:5: warning: incompatible redeclaration of library function 'ccosl' [-Wincompatible-library-redeclaration]\r\n int ccosl (void);\r\n ^\r\n _configtest.c:9:5: note: 'ccosl' is a builtin with type '_Complex long double (_Complex long double)'\r\n _configtest.c:10:5: warning: incompatible redeclaration of library function 'ccoshl' [-Wincompatible-library-redeclaration]\r\n int ccoshl (void);\r\n ^\r\n _configtest.c:10:5: note: 'ccoshl' is a builtin with type '_Complex long double (_Complex long double)'\r\n _configtest.c:11:5: warning: incompatible redeclaration of library function 'cexpl' [-Wincompatible-library-redeclaration]\r\n int cexpl (void);\r\n ^\r\n _configtest.c:11:5: note: 'cexpl' is a builtin with type '_Complex long double (_Complex long double)'\r\n _configtest.c:12:5: warning: incompatible redeclaration of library function 'cimagl' [-Wincompatible-library-redeclaration]\r\n int cimagl (void);\r\n ^\r\n _configtest.c:12:5: note: 'cimagl' is a builtin with type 'long double (_Complex long double)'\r\n _configtest.c:13:5: warning: incompatible redeclaration of library function 'clogl' [-Wincompatible-library-redeclaration]\r\n int clogl (void);\r\n ^\r\n _configtest.c:13:5: note: 'clogl' is a builtin with type '_Complex long double (_Complex long double)'\r\n _configtest.c:14:5: warning: incompatible redeclaration of library function 'conjl' [-Wincompatible-library-redeclaration]\r\n int conjl (void);\r\n ^\r\n _configtest.c:14:5: note: 'conjl' is a builtin with type '_Complex long double (_Complex long double)'\r\n _configtest.c:15:5: warning: incompatible redeclaration of library function 'cpowl' [-Wincompatible-library-redeclaration]\r\n int cpowl (void);\r\n ^\r\n _configtest.c:15:5: note: 'cpowl' is a builtin with type '_Complex long double (_Complex long double, _Complex long double)'\r\n _configtest.c:16:5: warning: incompatible redeclaration of library function 'cprojl' [-Wincompatible-library-redeclaration]\r\n int cprojl (void);\r\n ^\r\n _configtest.c:16:5: note: 'cprojl' is a builtin with type '_Complex long double (_Complex long double)'\r\n _configtest.c:17:5: warning: incompatible redeclaration of library function 'creall' [-Wincompatible-library-redeclaration]\r\n int creall (void);\r\n ^\r\n _configtest.c:17:5: note: 'creall' is a builtin with type 'long double (_Complex long double)'\r\n _configtest.c:18:5: warning: incompatible redeclaration of library function 'csinl' [-Wincompatible-library-redeclaration]\r\n int csinl (void);\r\n ^\r\n _configtest.c:18:5: note: 'csinl' is a builtin with type '_Complex long double (_Complex long double)'\r\n _configtest.c:19:5: warning: incompatible redeclaration of library function 'csinhl' [-Wincompatible-library-redeclaration]\r\n int csinhl (void);\r\n ^\r\n _configtest.c:19:5: note: 'csinhl' is a builtin with type '_Complex long double (_Complex long double)'\r\n _configtest.c:20:5: warning: incompatible redeclaration of library function 'csqrtl' [-Wincompatible-library-redeclaration]\r\n int csqrtl (void);\r\n ^\r\n _configtest.c:20:5: note: 'csqrtl' is a builtin with type '_Complex long double (_Complex long double)'\r\n _configtest.c:21:5: warning: incompatible redeclaration of library function 'ctanl' [-Wincompatible-library-redeclaration]\r\n int ctanl (void);\r\n ^\r\n _configtest.c:21:5: note: 'ctanl' is a builtin with type '_Complex long double (_Complex long double)'\r\n _configtest.c:22:5: warning: incompatible redeclaration of library function 'ctanhl' [-Wincompatible-library-redeclaration]\r\n int ctanhl (void);\r\n ^\r\n _configtest.c:22:5: note: 'ctanhl' is a builtin with type '_Complex long double (_Complex long double)'\r\n 22 warnings generated.\r\n clang _configtest.o -o _configtest\r\n success!\r\n removing: _configtest.c _configtest.o _configtest.o.d _configtest\r\n C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers\r\n \r\n compile options: '-Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -c'\r\n clang: _configtest.c\r\n _configtest.c:2:12: warning: unused function 'static_func' [-Wunused-function]\r\n static int static_func (char * restrict a)\r\n ^\r\n 1 warning generated.\r\n success!\r\n removing: _configtest.c _configtest.o _configtest.o.d\r\n C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers\r\n \r\n compile options: '-Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -c'\r\n clang: _configtest.c\r\n _configtest.c:3:19: warning: unused function 'static_func' [-Wunused-function]\r\n static inline int static_func (void)\r\n ^\r\n 1 warning generated.\r\n success!\r\n removing: _configtest.c _configtest.o _configtest.o.d\r\n C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers\r\n \r\n compile options: '-Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -c'\r\n clang: _configtest.c\r\n removing: _configtest.c _configtest.o _configtest.o.d\r\n File: build/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy/config.h\r\n #define SIZEOF_PY_INTPTR_T 8\r\n #define SIZEOF_OFF_T 8\r\n #define SIZEOF_PY_LONG_LONG 8\r\n #define MATHLIB\r\n #define HAVE_SIN 1\r\n #define HAVE_COS 1\r\n #define HAVE_TAN 1\r\n #define HAVE_SINH 1\r\n #define HAVE_COSH 1\r\n #define HAVE_TANH 1\r\n #define HAVE_FABS 1\r\n #define HAVE_FLOOR 1\r\n #define HAVE_CEIL 1\r\n #define HAVE_SQRT 1\r\n #define HAVE_LOG10 1\r\n #define HAVE_LOG 1\r\n #define HAVE_EXP 1\r\n #define HAVE_ASIN 1\r\n #define HAVE_ACOS 1\r\n #define HAVE_ATAN 1\r\n #define HAVE_FMOD 1\r\n #define HAVE_MODF 1\r\n #define HAVE_FREXP 1\r\n #define HAVE_LDEXP 1\r\n #define HAVE_RINT 1\r\n #define HAVE_TRUNC 1\r\n #define HAVE_EXP2 1\r\n #define HAVE_LOG2 1\r\n #define HAVE_ATAN2 1\r\n #define HAVE_POW 1\r\n #define HAVE_NEXTAFTER 1\r\n #define HAVE_STRTOLL 1\r\n #define HAVE_STRTOULL 1\r\n #define HAVE_CBRT 1\r\n #define HAVE_STRTOLD_L 1\r\n #define HAVE_BACKTRACE 1\r\n #define HAVE_MADVISE 1\r\n #define HAVE_XMMINTRIN_H 1\r\n #define HAVE_EMMINTRIN_H 1\r\n #define HAVE_XLOCALE_H 1\r\n #define HAVE_DLFCN_H 1\r\n #define HAVE_SYS_MMAN_H 1\r\n #define HAVE___BUILTIN_ISNAN 1\r\n #define HAVE___BUILTIN_ISINF 1\r\n #define HAVE___BUILTIN_ISFINITE 1\r\n #define HAVE___BUILTIN_BSWAP32 1\r\n #define HAVE___BUILTIN_BSWAP64 1\r\n #define HAVE___BUILTIN_EXPECT 1\r\n #define HAVE___BUILTIN_MUL_OVERFLOW 1\r\n #define HAVE___BUILTIN_CPU_SUPPORTS 1\r\n #define HAVE__M_FROM_INT64 1\r\n #define HAVE__MM_LOAD_PS 1\r\n #define HAVE__MM_PREFETCH 1\r\n #define HAVE__MM_LOAD_PD 1\r\n #define HAVE___BUILTIN_PREFETCH 1\r\n #define HAVE_LINK_AVX 1\r\n #define HAVE_LINK_AVX2 1\r\n #define HAVE_XGETBV 1\r\n #define HAVE_ATTRIBUTE_NONNULL 1\r\n #define HAVE_ATTRIBUTE_TARGET_AVX 1\r\n #define HAVE_ATTRIBUTE_TARGET_AVX2 1\r\n #define HAVE___THREAD 1\r\n #define HAVE_SINF 1\r\n #define HAVE_COSF 1\r\n #define HAVE_TANF 1\r\n #define HAVE_SINHF 1\r\n #define HAVE_COSHF 1\r\n #define HAVE_TANHF 1\r\n #define HAVE_FABSF 1\r\n #define HAVE_FLOORF 1\r\n #define HAVE_CEILF 1\r\n #define HAVE_RINTF 1\r\n #define HAVE_TRUNCF 1\r\n #define HAVE_SQRTF 1\r\n #define HAVE_LOG10F 1\r\n #define HAVE_LOGF 1\r\n #define HAVE_LOG1PF 1\r\n #define HAVE_EXPF 1\r\n #define HAVE_EXPM1F 1\r\n #define HAVE_ASINF 1\r\n #define HAVE_ACOSF 1\r\n #define HAVE_ATANF 1\r\n #define HAVE_ASINHF 1\r\n #define HAVE_ACOSHF 1\r\n #define HAVE_ATANHF 1\r\n #define HAVE_HYPOTF 1\r\n #define HAVE_ATAN2F 1\r\n #define HAVE_POWF 1\r\n #define HAVE_FMODF 1\r\n #define HAVE_MODFF 1\r\n #define HAVE_FREXPF 1\r\n #define HAVE_LDEXPF 1\r\n #define HAVE_EXP2F 1\r\n #define HAVE_LOG2F 1\r\n #define HAVE_COPYSIGNF 1\r\n #define HAVE_NEXTAFTERF 1\r\n #define HAVE_CBRTF 1\r\n #define HAVE_SINL 1\r\n #define HAVE_COSL 1\r\n #define HAVE_TANL 1\r\n #define HAVE_SINHL 1\r\n #define HAVE_COSHL 1\r\n #define HAVE_TANHL 1\r\n #define HAVE_FABSL 1\r\n #define HAVE_FLOORL 1\r\n #define HAVE_CEILL 1\r\n #define HAVE_RINTL 1\r\n #define HAVE_TRUNCL 1\r\n #define HAVE_SQRTL 1\r\n #define HAVE_LOG10L 1\r\n #define HAVE_LOGL 1\r\n #define HAVE_LOG1PL 1\r\n #define HAVE_EXPL 1\r\n #define HAVE_EXPM1L 1\r\n #define HAVE_ASINL 1\r\n #define HAVE_ACOSL 1\r\n #define HAVE_ATANL 1\r\n #define HAVE_ASINHL 1\r\n #define HAVE_ACOSHL 1\r\n #define HAVE_ATANHL 1\r\n #define HAVE_HYPOTL 1\r\n #define HAVE_ATAN2L 1\r\n #define HAVE_POWL 1\r\n #define HAVE_FMODL 1\r\n #define HAVE_MODFL 1\r\n #define HAVE_FREXPL 1\r\n #define HAVE_LDEXPL 1\r\n #define HAVE_EXP2L 1\r\n #define HAVE_LOG2L 1\r\n #define HAVE_COPYSIGNL 1\r\n #define HAVE_NEXTAFTERL 1\r\n #define HAVE_CBRTL 1\r\n #define HAVE_DECL_SIGNBIT\r\n #define HAVE_COMPLEX_H 1\r\n #define HAVE_CABS 1\r\n #define HAVE_CACOS 1\r\n #define HAVE_CACOSH 1\r\n #define HAVE_CARG 1\r\n #define HAVE_CASIN 1\r\n #define HAVE_CASINH 1\r\n #define HAVE_CATAN 1\r\n #define HAVE_CATANH 1\r\n #define HAVE_CCOS 1\r\n #define HAVE_CCOSH 1\r\n #define HAVE_CEXP 1\r\n #define HAVE_CIMAG 1\r\n #define HAVE_CLOG 1\r\n #define HAVE_CONJ 1\r\n #define HAVE_CPOW 1\r\n #define HAVE_CPROJ 1\r\n #define HAVE_CREAL 1\r\n #define HAVE_CSIN 1\r\n #define HAVE_CSINH 1\r\n #define HAVE_CSQRT 1\r\n #define HAVE_CTAN 1\r\n #define HAVE_CTANH 1\r\n #define HAVE_CABSF 1\r\n #define HAVE_CACOSF 1\r\n #define HAVE_CACOSHF 1\r\n #define HAVE_CARGF 1\r\n #define HAVE_CASINF 1\r\n #define HAVE_CASINHF 1\r\n #define HAVE_CATANF 1\r\n #define HAVE_CATANHF 1\r\n #define HAVE_CCOSF 1\r\n #define HAVE_CCOSHF 1\r\n #define HAVE_CEXPF 1\r\n #define HAVE_CIMAGF 1\r\n #define HAVE_CLOGF 1\r\n #define HAVE_CONJF 1\r\n #define HAVE_CPOWF 1\r\n #define HAVE_CPROJF 1\r\n #define HAVE_CREALF 1\r\n #define HAVE_CSINF 1\r\n #define HAVE_CSINHF 1\r\n #define HAVE_CSQRTF 1\r\n #define HAVE_CTANF 1\r\n #define HAVE_CTANHF 1\r\n #define HAVE_CABSL 1\r\n #define HAVE_CACOSL 1\r\n #define HAVE_CACOSHL 1\r\n #define HAVE_CARGL 1\r\n #define HAVE_CASINL 1\r\n #define HAVE_CASINHL 1\r\n #define HAVE_CATANL 1\r\n #define HAVE_CATANHL 1\r\n #define HAVE_CCOSL 1\r\n #define HAVE_CCOSHL 1\r\n #define HAVE_CEXPL 1\r\n #define HAVE_CIMAGL 1\r\n #define HAVE_CLOGL 1\r\n #define HAVE_CONJL 1\r\n #define HAVE_CPOWL 1\r\n #define HAVE_CPROJL 1\r\n #define HAVE_CREALL 1\r\n #define HAVE_CSINL 1\r\n #define HAVE_CSINHL 1\r\n #define HAVE_CSQRTL 1\r\n #define HAVE_CTANL 1\r\n #define HAVE_CTANHL 1\r\n #define NPY_RESTRICT restrict\r\n #define NPY_RELAXED_STRIDES_CHECKING 1\r\n #define HAVE_LDOUBLE_INTEL_EXTENDED_16_BYTES_LE 1\r\n #define NPY_PY3K 1\r\n #ifndef __cplusplus\r\n /* #undef inline */\r\n #endif\r\n \r\n #ifndef _NPY_NPY_CONFIG_H_\r\n #error config.h should never be included directly, include npy_config.h instead\r\n #endif\r\n \r\n EOF\r\n adding 'build/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy/config.h' to sources.\r\n Generating build/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy/_numpyconfig.h\r\n C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers\r\n \r\n compile options: '-Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -c'\r\n clang: _configtest.c\r\n _configtest.c:1:5: warning: incompatible redeclaration of library function 'exp' [-Wincompatible-library-redeclaration]\r\n int exp (void);\r\n ^\r\n _configtest.c:1:5: note: 'exp' is a builtin with type 'double (double)'\r\n 1 warning generated.\r\n clang _configtest.o -o _configtest\r\n success!\r\n removing: _configtest.c _configtest.o _configtest.o.d _configtest\r\n C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers\r\n \r\n compile options: '-Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -c'\r\n clang: _configtest.c\r\n success!\r\n removing: _configtest.c _configtest.o _configtest.o.d\r\n C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers\r\n \r\n compile options: '-Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -c'\r\n clang: _configtest.c\r\n success!\r\n removing: _configtest.c _configtest.o _configtest.o.d\r\n File: build/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy/_numpyconfig.h\r\n #define NPY_SIZEOF_SHORT SIZEOF_SHORT\r\n #define NPY_SIZEOF_INT SIZEOF_INT\r\n #define NPY_SIZEOF_LONG SIZEOF_LONG\r\n #define NPY_SIZEOF_FLOAT 4\r\n #define NPY_SIZEOF_COMPLEX_FLOAT 8\r\n #define NPY_SIZEOF_DOUBLE 8\r\n #define NPY_SIZEOF_COMPLEX_DOUBLE 16\r\n #define NPY_SIZEOF_LONGDOUBLE 16\r\n #define NPY_SIZEOF_COMPLEX_LONGDOUBLE 32\r\n #define NPY_SIZEOF_PY_INTPTR_T 8\r\n #define NPY_SIZEOF_OFF_T 8\r\n #define NPY_SIZEOF_PY_LONG_LONG 8\r\n #define NPY_SIZEOF_LONGLONG 8\r\n #define NPY_NO_SMP 0\r\n #define NPY_HAVE_DECL_ISNAN\r\n #define NPY_HAVE_DECL_ISINF\r\n #define NPY_HAVE_DECL_ISFINITE\r\n #define NPY_HAVE_DECL_SIGNBIT\r\n #define NPY_USE_C99_COMPLEX 1\r\n #define NPY_HAVE_COMPLEX_DOUBLE 1\r\n #define NPY_HAVE_COMPLEX_FLOAT 1\r\n #define NPY_HAVE_COMPLEX_LONG_DOUBLE 1\r\n #define NPY_RELAXED_STRIDES_CHECKING 1\r\n #define NPY_USE_C99_FORMATS 1\r\n #define NPY_VISIBILITY_HIDDEN __attribute__((visibility(\"hidden\")))\r\n #define NPY_ABI_VERSION 0x01000009\r\n #define NPY_API_VERSION 0x0000000D\r\n \r\n #ifndef __STDC_FORMAT_MACROS\r\n #define __STDC_FORMAT_MACROS 1\r\n #endif\r\n \r\n EOF\r\n adding 'build/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy/_numpyconfig.h' to sources.\r\n executing numpy/core/code_generators/generate_numpy_api.py\r\n adding 'build/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy/__multiarray_api.h' to sources.\r\n numpy.core - nothing done with h_files = ['build/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy/config.h', 'build/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy/_numpyconfig.h', 'build/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy/__multiarray_api.h']\r\n building extension \"numpy.core._multiarray_tests\" sources\r\n creating build/src.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray\r\n conv_template:> build/src.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/_multiarray_tests.c\r\n building extension \"numpy.core._multiarray_umath\" sources\r\n adding 'build/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy/config.h' to sources.\r\n adding 'build/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy/_numpyconfig.h' to sources.\r\n executing numpy/core/code_generators/generate_numpy_api.py\r\n adding 'build/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy/__multiarray_api.h' to sources.\r\n executing numpy/core/code_generators/generate_ufunc_api.py\r\n adding 'build/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy/__ufunc_api.h' to sources.\r\n conv_template:> build/src.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/arraytypes.c\r\n conv_template:> build/src.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/einsum.c\r\n conv_template:> build/src.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/lowlevel_strided_loops.c\r\n conv_template:> build/src.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/nditer_templ.c\r\n conv_template:> build/src.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/scalartypes.c\r\n creating build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath\r\n conv_template:> build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/funcs.inc\r\n adding 'build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath' to include_dirs.\r\n conv_template:> build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/simd.inc\r\n conv_template:> build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/loops.h\r\n conv_template:> build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/loops.c\r\n conv_template:> build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/matmul.h\r\n conv_template:> build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/matmul.c\r\n conv_template:> build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/scalarmath.c\r\n adding 'build/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath' to include_dirs.\r\n conv_template:> build/src.macosx-10.15-x86_64-3.9/numpy/core/src/common/templ_common.h\r\n adding 'build/src.macosx-10.15-x86_64-3.9/numpy/core/src/common' to include_dirs.\r\n numpy.core - nothing done with h_files = ['build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/funcs.inc', 'build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/simd.inc', 'build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/loops.h', 'build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/matmul.h', 'build/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath/npy_math_internal.h', 'build/src.macosx-10.15-x86_64-3.9/numpy/core/src/common/templ_common.h', 'build/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy/config.h', 'build/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy/_numpyconfig.h', 'build/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy/__multiarray_api.h', 'build/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy/__ufunc_api.h']\r\n building extension \"numpy.core._umath_tests\" sources\r\n conv_template:> build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/_umath_tests.c\r\n building extension \"numpy.core._rational_tests\" sources\r\n conv_template:> build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/_rational_tests.c\r\n building extension \"numpy.core._struct_ufunc_tests\" sources\r\n conv_template:> build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/_struct_ufunc_tests.c\r\n building extension \"numpy.core._operand_flag_tests\" sources\r\n conv_template:> build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/_operand_flag_tests.c\r\n building extension \"numpy.fft.fftpack_lite\" sources\r\n building extension \"numpy.linalg.lapack_lite\" sources\r\n creating build/src.macosx-10.15-x86_64-3.9/numpy/linalg\r\n adding 'numpy/linalg/lapack_lite/python_xerbla.c' to sources.\r\n building extension \"numpy.linalg._umath_linalg\" sources\r\n adding 'numpy/linalg/lapack_lite/python_xerbla.c' to sources.\r\n conv_template:> build/src.macosx-10.15-x86_64-3.9/numpy/linalg/umath_linalg.c\r\n building extension \"numpy.random.mtrand\" sources\r\n creating build/src.macosx-10.15-x86_64-3.9/numpy/random\r\n building data_files sources\r\n build_src: building npy-pkg config files\r\n running build_py\r\n creating build/lib.macosx-10.15-x86_64-3.9\r\n creating build/lib.macosx-10.15-x86_64-3.9/numpy\r\n copying numpy/conftest.py -> build/lib.macosx-10.15-x86_64-3.9/numpy\r\n copying numpy/version.py -> build/lib.macosx-10.15-x86_64-3.9/numpy\r\n copying numpy/_globals.py -> build/lib.macosx-10.15-x86_64-3.9/numpy\r\n copying numpy/__init__.py -> build/lib.macosx-10.15-x86_64-3.9/numpy\r\n copying numpy/dual.py -> build/lib.macosx-10.15-x86_64-3.9/numpy\r\n copying numpy/_distributor_init.py -> build/lib.macosx-10.15-x86_64-3.9/numpy\r\n copying numpy/setup.py -> build/lib.macosx-10.15-x86_64-3.9/numpy\r\n copying numpy/ctypeslib.py -> build/lib.macosx-10.15-x86_64-3.9/numpy\r\n copying numpy/matlib.py -> build/lib.macosx-10.15-x86_64-3.9/numpy\r\n copying numpy/_pytesttester.py -> build/lib.macosx-10.15-x86_64-3.9/numpy\r\n copying build/src.macosx-10.15-x86_64-3.9/numpy/__config__.py -> build/lib.macosx-10.15-x86_64-3.9/numpy\r\n creating build/lib.macosx-10.15-x86_64-3.9/numpy/compat\r\n copying numpy/compat/py3k.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/compat\r\n copying numpy/compat/__init__.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/compat\r\n copying numpy/compat/setup.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/compat\r\n copying numpy/compat/_inspect.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/compat\r\n creating build/lib.macosx-10.15-x86_64-3.9/numpy/core\r\n copying numpy/core/umath.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core\r\n copying numpy/core/fromnumeric.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core\r\n copying numpy/core/_dtype.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core\r\n copying numpy/core/_add_newdocs.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core\r\n copying numpy/core/_methods.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core\r\n copying numpy/core/_internal.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core\r\n copying numpy/core/_string_helpers.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core\r\n copying numpy/core/multiarray.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core\r\n copying numpy/core/records.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core\r\n copying numpy/core/__init__.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core\r\n copying numpy/core/setup_common.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core\r\n copying numpy/core/_aliased_types.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core\r\n copying numpy/core/memmap.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core\r\n copying numpy/core/overrides.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core\r\n copying numpy/core/getlimits.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core\r\n copying numpy/core/_dtype_ctypes.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core\r\n copying numpy/core/defchararray.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core\r\n copying numpy/core/shape_base.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core\r\n copying numpy/core/machar.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core\r\n copying numpy/core/setup.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core\r\n copying numpy/core/numeric.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core\r\n copying numpy/core/function_base.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core\r\n copying numpy/core/einsumfunc.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core\r\n copying numpy/core/umath_tests.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core\r\n copying numpy/core/info.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core\r\n copying numpy/core/numerictypes.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core\r\n copying numpy/core/_type_aliases.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core\r\n copying numpy/core/cversions.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core\r\n copying numpy/core/arrayprint.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core\r\n copying numpy/core/code_generators/generate_numpy_api.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core\r\n creating build/lib.macosx-10.15-x86_64-3.9/numpy/distutils\r\n copying numpy/distutils/unixccompiler.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils\r\n copying numpy/distutils/numpy_distribution.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils\r\n copying numpy/distutils/conv_template.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils\r\n copying numpy/distutils/cpuinfo.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils\r\n copying numpy/distutils/ccompiler.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils\r\n copying numpy/distutils/msvc9compiler.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils\r\n copying numpy/distutils/npy_pkg_config.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils\r\n copying numpy/distutils/compat.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils\r\n copying numpy/distutils/misc_util.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils\r\n copying numpy/distutils/log.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils\r\n copying numpy/distutils/line_endings.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils\r\n copying numpy/distutils/lib2def.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils\r\n copying numpy/distutils/pathccompiler.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils\r\n copying numpy/distutils/system_info.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils\r\n copying numpy/distutils/__init__.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils\r\n copying numpy/distutils/core.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils\r\n copying numpy/distutils/__version__.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils\r\n copying numpy/distutils/exec_command.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils\r\n copying numpy/distutils/from_template.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils\r\n copying numpy/distutils/mingw32ccompiler.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils\r\n copying numpy/distutils/setup.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils\r\n copying numpy/distutils/extension.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils\r\n copying numpy/distutils/msvccompiler.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils\r\n copying numpy/distutils/intelccompiler.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils\r\n copying numpy/distutils/info.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils\r\n copying build/src.macosx-10.15-x86_64-3.9/numpy/distutils/__config__.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils\r\n creating build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/command\r\n copying numpy/distutils/command/build.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/command\r\n copying numpy/distutils/command/config_compiler.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/command\r\n copying numpy/distutils/command/build_ext.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/command\r\n copying numpy/distutils/command/config.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/command\r\n copying numpy/distutils/command/install_headers.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/command\r\n copying numpy/distutils/command/build_py.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/command\r\n copying numpy/distutils/command/build_src.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/command\r\n copying numpy/distutils/command/__init__.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/command\r\n copying numpy/distutils/command/sdist.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/command\r\n copying numpy/distutils/command/build_scripts.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/command\r\n copying numpy/distutils/command/bdist_rpm.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/command\r\n copying numpy/distutils/command/install_clib.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/command\r\n copying numpy/distutils/command/build_clib.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/command\r\n copying numpy/distutils/command/autodist.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/command\r\n copying numpy/distutils/command/egg_info.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/command\r\n copying numpy/distutils/command/install.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/command\r\n copying numpy/distutils/command/develop.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/command\r\n copying numpy/distutils/command/install_data.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/command\r\n creating build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/fcompiler\r\n copying numpy/distutils/fcompiler/gnu.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/fcompiler\r\n copying numpy/distutils/fcompiler/compaq.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/fcompiler\r\n copying numpy/distutils/fcompiler/intel.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/fcompiler\r\n copying numpy/distutils/fcompiler/none.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/fcompiler\r\n copying numpy/distutils/fcompiler/nag.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/fcompiler\r\n copying numpy/distutils/fcompiler/pg.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/fcompiler\r\n copying numpy/distutils/fcompiler/ibm.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/fcompiler\r\n copying numpy/distutils/fcompiler/sun.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/fcompiler\r\n copying numpy/distutils/fcompiler/lahey.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/fcompiler\r\n copying numpy/distutils/fcompiler/__init__.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/fcompiler\r\n copying numpy/distutils/fcompiler/g95.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/fcompiler\r\n copying numpy/distutils/fcompiler/mips.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/fcompiler\r\n copying numpy/distutils/fcompiler/hpux.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/fcompiler\r\n copying numpy/distutils/fcompiler/environment.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/fcompiler\r\n copying numpy/distutils/fcompiler/pathf95.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/fcompiler\r\n copying numpy/distutils/fcompiler/absoft.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/fcompiler\r\n copying numpy/distutils/fcompiler/vast.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/fcompiler\r\n creating build/lib.macosx-10.15-x86_64-3.9/numpy/doc\r\n copying numpy/doc/misc.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/doc\r\n copying numpy/doc/internals.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/doc\r\n copying numpy/doc/creation.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/doc\r\n copying numpy/doc/constants.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/doc\r\n copying numpy/doc/ufuncs.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/doc\r\n copying numpy/doc/__init__.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/doc\r\n copying numpy/doc/broadcasting.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/doc\r\n copying numpy/doc/basics.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/doc\r\n copying numpy/doc/subclassing.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/doc\r\n copying numpy/doc/indexing.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/doc\r\n copying numpy/doc/byteswapping.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/doc\r\n copying numpy/doc/structured_arrays.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/doc\r\n copying numpy/doc/glossary.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/doc\r\n creating build/lib.macosx-10.15-x86_64-3.9/numpy/f2py\r\n copying numpy/f2py/cfuncs.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/f2py\r\n copying numpy/f2py/common_rules.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/f2py\r\n copying numpy/f2py/crackfortran.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/f2py\r\n copying numpy/f2py/cb_rules.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/f2py\r\n copying numpy/f2py/__init__.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/f2py\r\n copying numpy/f2py/rules.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/f2py\r\n copying numpy/f2py/f2py2e.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/f2py\r\n copying numpy/f2py/func2subr.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/f2py\r\n copying numpy/f2py/__version__.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/f2py\r\n copying numpy/f2py/diagnose.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/f2py\r\n copying numpy/f2py/setup.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/f2py\r\n copying numpy/f2py/capi_maps.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/f2py\r\n copying numpy/f2py/f90mod_rules.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/f2py\r\n copying numpy/f2py/f2py_testing.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/f2py\r\n copying numpy/f2py/use_rules.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/f2py\r\n copying numpy/f2py/info.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/f2py\r\n copying numpy/f2py/auxfuncs.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/f2py\r\n copying numpy/f2py/__main__.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/f2py\r\n creating build/lib.macosx-10.15-x86_64-3.9/numpy/fft\r\n copying numpy/fft/__init__.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/fft\r\n copying numpy/fft/setup.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/fft\r\n copying numpy/fft/helper.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/fft\r\n copying numpy/fft/fftpack.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/fft\r\n copying numpy/fft/info.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/fft\r\n creating build/lib.macosx-10.15-x86_64-3.9/numpy/lib\r\n copying numpy/lib/_iotools.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib\r\n copying numpy/lib/mixins.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib\r\n copying numpy/lib/nanfunctions.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib\r\n copying numpy/lib/recfunctions.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib\r\n copying numpy/lib/histograms.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib\r\n copying numpy/lib/scimath.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib\r\n copying numpy/lib/_version.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib\r\n copying numpy/lib/user_array.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib\r\n copying numpy/lib/__init__.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib\r\n copying numpy/lib/format.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib\r\n copying numpy/lib/twodim_base.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib\r\n copying numpy/lib/financial.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib\r\n copying numpy/lib/index_tricks.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib\r\n copying numpy/lib/npyio.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib\r\n copying numpy/lib/shape_base.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib\r\n copying numpy/lib/setup.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib\r\n copying numpy/lib/stride_tricks.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib\r\n copying numpy/lib/utils.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib\r\n copying numpy/lib/arrayterator.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib\r\n copying numpy/lib/function_base.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib\r\n copying numpy/lib/arraysetops.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib\r\n copying numpy/lib/arraypad.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib\r\n copying numpy/lib/type_check.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib\r\n copying numpy/lib/info.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib\r\n copying numpy/lib/polynomial.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib\r\n copying numpy/lib/_datasource.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib\r\n copying numpy/lib/ufunclike.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib\r\n creating build/lib.macosx-10.15-x86_64-3.9/numpy/linalg\r\n copying numpy/linalg/__init__.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/linalg\r\n copying numpy/linalg/setup.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/linalg\r\n copying numpy/linalg/linalg.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/linalg\r\n copying numpy/linalg/info.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/linalg\r\n creating build/lib.macosx-10.15-x86_64-3.9/numpy/ma\r\n copying numpy/ma/extras.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/ma\r\n copying numpy/ma/version.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/ma\r\n copying numpy/ma/testutils.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/ma\r\n copying numpy/ma/__init__.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/ma\r\n copying numpy/ma/core.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/ma\r\n copying numpy/ma/bench.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/ma\r\n copying numpy/ma/setup.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/ma\r\n copying numpy/ma/timer_comparison.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/ma\r\n copying numpy/ma/mrecords.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/ma\r\n creating build/lib.macosx-10.15-x86_64-3.9/numpy/matrixlib\r\n copying numpy/matrixlib/__init__.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/matrixlib\r\n copying numpy/matrixlib/setup.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/matrixlib\r\n copying numpy/matrixlib/defmatrix.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/matrixlib\r\n creating build/lib.macosx-10.15-x86_64-3.9/numpy/polynomial\r\n copying numpy/polynomial/laguerre.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/polynomial\r\n copying numpy/polynomial/_polybase.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/polynomial\r\n copying numpy/polynomial/polyutils.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/polynomial\r\n copying numpy/polynomial/__init__.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/polynomial\r\n copying numpy/polynomial/setup.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/polynomial\r\n copying numpy/polynomial/hermite_e.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/polynomial\r\n copying numpy/polynomial/chebyshev.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/polynomial\r\n copying numpy/polynomial/polynomial.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/polynomial\r\n copying numpy/polynomial/legendre.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/polynomial\r\n copying numpy/polynomial/hermite.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/polynomial\r\n creating build/lib.macosx-10.15-x86_64-3.9/numpy/random\r\n copying numpy/random/__init__.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/random\r\n copying numpy/random/setup.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/random\r\n copying numpy/random/info.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/random\r\n creating build/lib.macosx-10.15-x86_64-3.9/numpy/testing\r\n copying numpy/testing/nosetester.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/testing\r\n copying numpy/testing/__init__.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/testing\r\n copying numpy/testing/noseclasses.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/testing\r\n copying numpy/testing/setup.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/testing\r\n copying numpy/testing/utils.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/testing\r\n copying numpy/testing/print_coercion_tables.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/testing\r\n copying numpy/testing/decorators.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/testing\r\n creating build/lib.macosx-10.15-x86_64-3.9/numpy/testing/_private\r\n copying numpy/testing/_private/nosetester.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/testing/_private\r\n copying numpy/testing/_private/__init__.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/testing/_private\r\n copying numpy/testing/_private/noseclasses.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/testing/_private\r\n copying numpy/testing/_private/utils.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/testing/_private\r\n copying numpy/testing/_private/parameterized.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/testing/_private\r\n copying numpy/testing/_private/decorators.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/testing/_private\r\n running build_clib\r\n customize UnixCCompiler\r\n customize UnixCCompiler using build_clib\r\n building 'npymath' library\r\n compiling C sources\r\n C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers\r\n \r\n creating build/temp.macosx-10.15-x86_64-3.9\r\n creating build/temp.macosx-10.15-x86_64-3.9/numpy\r\n creating build/temp.macosx-10.15-x86_64-3.9/numpy/core\r\n creating build/temp.macosx-10.15-x86_64-3.9/numpy/core/src\r\n creating build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/npymath\r\n creating build/temp.macosx-10.15-x86_64-3.9/build\r\n creating build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9\r\n creating build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy\r\n creating build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/core\r\n creating build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/core/src\r\n creating build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath\r\n compile options: '-Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -Inumpy/core/include -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy -Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -c'\r\n clang: numpy/core/src/npymath/npy_math.c\r\n clang: build/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath/npy_math_complex.c\r\n clang: build/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath/ieee754.c\r\n clang: numpy/core/src/npymath/halffloat.c\r\n numpy/core/src/npymath/npy_math_complex.c.src:48:33: warning: unused variable 'tiny' [-Wunused-const-variable]\r\n static const volatile npy_float tiny = 3.9443045e-31f;\r\n ^\r\n numpy/core/src/npymath/npy_math_complex.c.src:67:25: warning: unused variable 'c_halff' [-Wunused-const-variable]\r\n static const npy_cfloat c_halff = {0.5F, 0.0};\r\n ^\r\n numpy/core/src/npymath/npy_math_complex.c.src:68:25: warning: unused variable 'c_if' [-Wunused-const-variable]\r\n static const npy_cfloat c_if = {0.0, 1.0F};\r\n ^\r\n numpy/core/src/npymath/npy_math_complex.c.src:69:25: warning: unused variable 'c_ihalff' [-Wunused-const-variable]\r\n static const npy_cfloat c_ihalff = {0.0, 0.5F};\r\n ^\r\n numpy/core/src/npymath/npy_math_complex.c.src:79:1: warning: unused function 'caddf' [-Wunused-function]\r\n caddf(npy_cfloat a, npy_cfloat b)\r\n ^\r\n numpy/core/src/npymath/npy_math_complex.c.src:87:1: warning: unused function 'csubf' [-Wunused-function]\r\n csubf(npy_cfloat a, npy_cfloat b)\r\n ^\r\n numpy/core/src/npymath/npy_math_complex.c.src:137:1: warning: unused function 'cnegf' [-Wunused-function]\r\n cnegf(npy_cfloat a)\r\n ^\r\n numpy/core/src/npymath/npy_math_complex.c.src:144:1: warning: unused function 'cmulif' [-Wunused-function]\r\n cmulif(npy_cfloat a)\r\n ^\r\n numpy/core/src/npymath/npy_math_complex.c.src:67:26: warning: unused variable 'c_half' [-Wunused-const-variable]\r\n static const npy_cdouble c_half = {0.5, 0.0};\r\n ^\r\n numpy/core/src/npymath/npy_math_complex.c.src:68:26: warning: unused variable 'c_i' [-Wunused-const-variable]\r\n static const npy_cdouble c_i = {0.0, 1.0};\r\n ^\r\n numpy/core/src/npymath/npy_math_complex.c.src:69:26: warning: unused variable 'c_ihalf' [-Wunused-const-variable]\r\n static const npy_cdouble c_ihalf = {0.0, 0.5};\r\n ^\r\n numpy/core/src/npymath/npy_math_complex.c.src:79:1: warning: unused function 'cadd' [-Wunused-function]\r\n cadd(npy_cdouble a, npy_cdouble b)\r\n ^\r\n numpy/core/src/npymath/npy_math_complex.c.src:87:1: warning: unused function 'csub' [-Wunused-function]\r\n csub(npy_cdouble a, npy_cdouble b)\r\n ^\r\n numpy/core/src/npymath/npy_math_complex.c.src:137:1: warning: unused function 'cneg' [-Wunused-function]\r\n cneg(npy_cdouble a)\r\n ^\r\n numpy/core/src/npymath/npy_math_complex.c.src:144:1: warning: unused function 'cmuli' [-Wunused-function]\r\n cmuli(npy_cdouble a)\r\n ^\r\n numpy/core/src/npymath/npy_math_complex.c.src:67:30: warning: unused variable 'c_halfl' [-Wunused-const-variable]\r\n static const npy_clongdouble c_halfl = {0.5L, 0.0};\r\n ^\r\n numpy/core/src/npymath/npy_math_complex.c.src:68:30: warning: unused variable 'c_il' [-Wunused-const-variable]\r\n static const npy_clongdouble c_il = {0.0, 1.0L};\r\n ^\r\n numpy/core/src/npymath/npy_math_complex.c.src:69:30: warning: unused variable 'c_ihalfl' [-Wunused-const-variable]\r\n static const npy_clongdouble c_ihalfl = {0.0, 0.5L};\r\n ^\r\n numpy/core/src/npymath/npy_math_complex.c.src:79:1: warning: unused function 'caddl' [-Wunused-function]\r\n caddl(npy_clongdouble a, npy_clongdouble b)\r\n ^\r\n numpy/core/src/npymath/npy_math_complex.c.src:87:1: warning: unused function 'csubl' [-Wunused-function]\r\n csubl(npy_clongdouble a, npy_clongdouble b)\r\n ^\r\n numpy/core/src/npymath/npy_math_complex.c.src:137:1: warning: unused function 'cnegl' [-Wunused-function]\r\n cnegl(npy_clongdouble a)\r\n ^\r\n numpy/core/src/npymath/npy_math_complex.c.src:144:1: warning: unused function 'cmulil' [-Wunused-function]\r\n cmulil(npy_clongdouble a)\r\n ^\r\n 22 warnings generated.\r\n ar: adding 4 object files to build/temp.macosx-10.15-x86_64-3.9/libnpymath.a\r\n ranlib:@ build/temp.macosx-10.15-x86_64-3.9/libnpymath.a\r\n building 'npysort' library\r\n compiling C sources\r\n C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers\r\n \r\n creating build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/core/src/npysort\r\n compile options: '-Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Inumpy/core/include -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy -Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -c'\r\n clang: build/src.macosx-10.15-x86_64-3.9/numpy/core/src/npysort/quicksort.c\r\n clang: build/src.macosx-10.15-x86_64-3.9/numpy/core/src/npysort/mergesort.c\r\n clang: build/src.macosx-10.15-x86_64-3.9/numpy/core/src/npysort/heapsort.c\r\n clang: build/src.macosx-10.15-x86_64-3.9/numpy/core/src/npysort/selection.c\r\n clang: build/src.macosx-10.15-x86_64-3.9/numpy/core/src/npysort/binsearch.c\r\n numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code]\r\n npy_intp k;\r\n ^~~~~~~~~~~\r\n numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead\r\n else if (0 && kth == num - 1) {\r\n ^\r\n /* DISABLES CODE */ ( )\r\n numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code]\r\n npy_intp k;\r\n ^~~~~~~~~~~\r\n numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead\r\n else if (0 && kth == num - 1) {\r\n ^\r\n /* DISABLES CODE */ ( )\r\n numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code]\r\n npy_intp k;\r\n ^~~~~~~~~~~\r\n numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead\r\n else if (0 && kth == num - 1) {\r\n ^\r\n /* DISABLES CODE */ ( )\r\n numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code]\r\n npy_intp k;\r\n ^~~~~~~~~~~\r\n numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead\r\n else if (0 && kth == num - 1) {\r\n ^\r\n /* DISABLES CODE */ ( )\r\n numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code]\r\n npy_intp k;\r\n ^~~~~~~~~~~\r\n numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead\r\n else if (0 && kth == num - 1) {\r\n ^\r\n /* DISABLES CODE */ ( )\r\n numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code]\r\n npy_intp k;\r\n ^~~~~~~~~~~\r\n numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead\r\n else if (0 && kth == num - 1) {\r\n ^\r\n /* DISABLES CODE */ ( )\r\n numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code]\r\n npy_intp k;\r\n ^~~~~~~~~~~\r\n numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead\r\n else if (0 && kth == num - 1) {\r\n ^\r\n /* DISABLES CODE */ ( )\r\n numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code]\r\n npy_intp k;\r\n ^~~~~~~~~~~\r\n numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead\r\n else if (0 && kth == num - 1) {\r\n ^\r\n /* DISABLES CODE */ ( )\r\n numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code]\r\n npy_intp k;\r\n ^~~~~~~~~~~\r\n numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead\r\n else if (0 && kth == num - 1) {\r\n ^\r\n /* DISABLES CODE */ ( )\r\n numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code]\r\n npy_intp k;\r\n ^~~~~~~~~~~\r\n numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead\r\n else if (0 && kth == num - 1) {\r\n ^\r\n /* DISABLES CODE */ ( )\r\n numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code]\r\n npy_intp k;\r\n ^~~~~~~~~~~\r\n numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead\r\n else if (0 && kth == num - 1) {\r\n ^\r\n /* DISABLES CODE */ ( )\r\n numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code]\r\n npy_intp k;\r\n ^~~~~~~~~~~\r\n numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead\r\n else if (0 && kth == num - 1) {\r\n ^\r\n /* DISABLES CODE */ ( )\r\n numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code]\r\n npy_intp k;\r\n ^~~~~~~~~~~\r\n numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead\r\n else if (0 && kth == num - 1) {\r\n ^\r\n /* DISABLES CODE */ ( )\r\n numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code]\r\n npy_intp k;\r\n ^~~~~~~~~~~\r\n numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead\r\n else if (0 && kth == num - 1) {\r\n ^\r\n /* DISABLES CODE */ ( )\r\n numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code]\r\n npy_intp k;\r\n ^~~~~~~~~~~\r\n numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead\r\n else if (0 && kth == num - 1) {\r\n ^\r\n /* DISABLES CODE */ ( )\r\n numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code]\r\n npy_intp k;\r\n ^~~~~~~~~~~\r\n numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead\r\n else if (0 && kth == num - 1) {\r\n ^\r\n /* DISABLES CODE */ ( )\r\n numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code]\r\n npy_intp k;\r\n ^~~~~~~~~~~\r\n numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead\r\n else if (0 && kth == num - 1) {\r\n ^\r\n /* DISABLES CODE */ ( )\r\n numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code]\r\n npy_intp k;\r\n ^~~~~~~~~~~\r\n numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead\r\n else if (0 && kth == num - 1) {\r\n ^\r\n /* DISABLES CODE */ ( )\r\n numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code]\r\n npy_intp k;\r\n ^~~~~~~~~~~\r\n numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead\r\n else if (0 && kth == num - 1) {\r\n ^\r\n /* DISABLES CODE */ ( )\r\n numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code]\r\n npy_intp k;\r\n ^~~~~~~~~~~\r\n numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead\r\n else if (0 && kth == num - 1) {\r\n ^\r\n /* DISABLES CODE */ ( )\r\n numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code]\r\n npy_intp k;\r\n ^~~~~~~~~~~\r\n numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead\r\n else if (0 && kth == num - 1) {\r\n ^\r\n /* DISABLES CODE */ ( )\r\n numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code]\r\n npy_intp k;\r\n ^~~~~~~~~~~\r\n numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead\r\n else if (0 && kth == num - 1) {\r\n ^\r\n /* DISABLES CODE */ ( )\r\n 22 warnings generated.\r\n ar: adding 5 object files to build/temp.macosx-10.15-x86_64-3.9/libnpysort.a\r\n ranlib:@ build/temp.macosx-10.15-x86_64-3.9/libnpysort.a\r\n running build_ext\r\n customize UnixCCompiler\r\n customize UnixCCompiler using build_ext\r\n building 'numpy.core._dummy' extension\r\n compiling C sources\r\n C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers\r\n \r\n compile options: '-DNPY_INTERNAL_BUILD=1 -DHAVE_NPY_CONFIG_H=1 -D_FILE_OFFSET_BITS=64 -D_LARGEFILE_SOURCE=1 -D_LARGEFILE64_SOURCE=1 -Inumpy/core/include -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy -Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -c'\r\n clang: numpy/core/src/dummymodule.c\r\n clang -bundle -undefined dynamic_lookup -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/dummymodule.o -L/usr/local/lib -L/usr/local/opt/openssl@1.1/lib -L/usr/local/opt/sqlite/lib -Lbuild/temp.macosx-10.15-x86_64-3.9 -o build/lib.macosx-10.15-x86_64-3.9/numpy/core/_dummy.cpython-39-darwin.so\r\n building 'numpy.core._multiarray_tests' extension\r\n compiling C sources\r\n C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers\r\n \r\n creating build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray\r\n creating build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/common\r\n compile options: '-DNPY_INTERNAL_BUILD=1 -DHAVE_NPY_CONFIG_H=1 -D_FILE_OFFSET_BITS=64 -D_LARGEFILE_SOURCE=1 -D_LARGEFILE64_SOURCE=1 -Inumpy/core/include -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy -Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -c'\r\n clang: build/src.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/_multiarray_tests.c\r\n clang: numpy/core/src/common/mem_overlap.c\r\n clang -bundle -undefined dynamic_lookup -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/_multiarray_tests.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/common/mem_overlap.o -L/usr/local/lib -L/usr/local/opt/openssl@1.1/lib -L/usr/local/opt/sqlite/lib -Lbuild/temp.macosx-10.15-x86_64-3.9 -lnpymath -o build/lib.macosx-10.15-x86_64-3.9/numpy/core/_multiarray_tests.cpython-39-darwin.so\r\n building 'numpy.core._multiarray_umath' extension\r\n compiling C sources\r\n C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers\r\n \r\n creating build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray\r\n creating build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/umath\r\n creating build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath\r\n creating build/temp.macosx-10.15-x86_64-3.9/private\r\n creating build/temp.macosx-10.15-x86_64-3.9/private/var\r\n creating build/temp.macosx-10.15-x86_64-3.9/private/var/folders\r\n creating build/temp.macosx-10.15-x86_64-3.9/private/var/folders/fz\r\n creating build/temp.macosx-10.15-x86_64-3.9/private/var/folders/fz/0j719tys48x7jlnjnwc69smr0000gn\r\n creating build/temp.macosx-10.15-x86_64-3.9/private/var/folders/fz/0j719tys48x7jlnjnwc69smr0000gn/T\r\n creating build/temp.macosx-10.15-x86_64-3.9/private/var/folders/fz/0j719tys48x7jlnjnwc69smr0000gn/T/pip-install-ufzck51l\r\n creating build/temp.macosx-10.15-x86_64-3.9/private/var/folders/fz/0j719tys48x7jlnjnwc69smr0000gn/T/pip-install-ufzck51l/numpy_b0e8a3953a1d4b46801f12bcea55536e\r\n creating build/temp.macosx-10.15-x86_64-3.9/private/var/folders/fz/0j719tys48x7jlnjnwc69smr0000gn/T/pip-install-ufzck51l/numpy_b0e8a3953a1d4b46801f12bcea55536e/numpy\r\n creating build/temp.macosx-10.15-x86_64-3.9/private/var/folders/fz/0j719tys48x7jlnjnwc69smr0000gn/T/pip-install-ufzck51l/numpy_b0e8a3953a1d4b46801f12bcea55536e/numpy/_build_utils\r\n creating build/temp.macosx-10.15-x86_64-3.9/private/var/folders/fz/0j719tys48x7jlnjnwc69smr0000gn/T/pip-install-ufzck51l/numpy_b0e8a3953a1d4b46801f12bcea55536e/numpy/_build_utils/src\r\n compile options: '-DNPY_INTERNAL_BUILD=1 -DHAVE_NPY_CONFIG_H=1 -D_FILE_OFFSET_BITS=64 -D_LARGEFILE_SOURCE=1 -D_LARGEFILE64_SOURCE=1 -DNO_ATLAS_INFO=3 -DHAVE_CBLAS -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Inumpy/core/include -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy -Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -c'\r\n extra options: '-msse3 -I/System/Library/Frameworks/vecLib.framework/Headers'\r\n clang: numpy/core/src/multiarray/alloc.c\r\n clang: numpy/core/src/multiarray/calculation.cclang: numpy/core/src/multiarray/array_assign_scalar.c\r\n clang: numpy/core/src/multiarray/convert.c\r\n \r\n clang: numpy/core/src/multiarray/ctors.c\r\n clang: numpy/core/src/multiarray/datetime_busday.c\r\n clang: numpy/core/src/multiarray/dragon4.cclang: numpy/core/src/multiarray/flagsobject.c\r\n \r\n numpy/core/src/multiarray/ctors.c:2261:36: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations]\r\n if (!(PyUString_Check(name) && PyUString_GET_SIZE(name) == 0)) {\r\n ^\r\n numpy/core/include/numpy/npy_3kcompat.h:110:28: note: expanded from macro 'PyUString_GET_SIZE'\r\n #define PyUString_GET_SIZE PyUnicode_GET_SIZE\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:261:7: note: expanded from macro 'PyUnicode_GET_SIZE'\r\n PyUnicode_WSTR_LENGTH(op) : \\\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH'\r\n #define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here\r\n Py_DEPRECATED(3.3)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'\r\n #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))\r\n ^\r\n numpy/core/src/multiarray/ctors.c:2261:36: warning: 'PyUnicode_AsUnicode' is deprecated [-Wdeprecated-declarations]\r\n if (!(PyUString_Check(name) && PyUString_GET_SIZE(name) == 0)) {\r\n ^\r\n numpy/core/include/numpy/npy_3kcompat.h:110:28: note: expanded from macro 'PyUString_GET_SIZE'\r\n #define PyUString_GET_SIZE PyUnicode_GET_SIZE\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:262:14: note: expanded from macro 'PyUnicode_GET_SIZE'\r\n ((void)PyUnicode_AsUnicode(_PyObject_CAST(op)),\\\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:580:1: note: 'PyUnicode_AsUnicode' has been explicitly marked deprecated here\r\n Py_DEPRECATED(3.3) PyAPI_FUNC(Py_UNICODE *) PyUnicode_AsUnicode(\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'\r\n #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))\r\n ^\r\n numpy/core/src/multiarray/ctors.c:2261:36: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations]\r\n if (!(PyUString_Check(name) && PyUString_GET_SIZE(name) == 0)) {\r\n ^\r\n numpy/core/include/numpy/npy_3kcompat.h:110:28: note: expanded from macro 'PyUString_GET_SIZE'\r\n #define PyUString_GET_SIZE PyUnicode_GET_SIZE\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:264:8: note: expanded from macro 'PyUnicode_GET_SIZE'\r\n PyUnicode_WSTR_LENGTH(op)))\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH'\r\n #define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here\r\n Py_DEPRECATED(3.3)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'\r\n #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))\r\n ^\r\n clang: numpy/core/src/multiarray/arrayobject.c\r\n clang: numpy/core/src/multiarray/array_assign_array.c\r\n clang: numpy/core/src/multiarray/convert_datatype.c\r\n clang: numpy/core/src/multiarray/getset.c\r\n clang: numpy/core/src/multiarray/datetime_busdaycal.c\r\n clang: numpy/core/src/multiarray/buffer.c\r\n clang: numpy/core/src/multiarray/compiled_base.c\r\n clang: numpy/core/src/multiarray/hashdescr.c\r\n clang: numpy/core/src/multiarray/descriptor.c\r\n numpy/core/src/multiarray/descriptor.c:453:13: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations]\r\n if (PyUString_GET_SIZE(name) == 0) {\r\n ^\r\n numpy/core/include/numpy/npy_3kcompat.h:110:28: note: expanded from macro 'PyUString_GET_SIZE'\r\n #define PyUString_GET_SIZE PyUnicode_GET_SIZE\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:261:7: note: expanded from macro 'PyUnicode_GET_SIZE'\r\n PyUnicode_WSTR_LENGTH(op) : \\\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH'\r\n #define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here\r\n Py_DEPRECATED(3.3)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'\r\n #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))\r\n ^\r\n numpy/core/src/multiarray/descriptor.c:453:13: warning: 'PyUnicode_AsUnicode' is deprecated [-Wdeprecated-declarations]\r\n if (PyUString_GET_SIZE(name) == 0) {\r\n ^\r\n numpy/core/include/numpy/npy_3kcompat.h:110:28: note: expanded from macro 'PyUString_GET_SIZE'\r\n #define PyUString_GET_SIZE PyUnicode_GET_SIZE\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:262:14: note: expanded from macro 'PyUnicode_GET_SIZE'\r\n ((void)PyUnicode_AsUnicode(_PyObject_CAST(op)),\\\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:580:1: note: 'PyUnicode_AsUnicode' has been explicitly marked deprecated here\r\n Py_DEPRECATED(3.3) PyAPI_FUNC(Py_UNICODE *) PyUnicode_AsUnicode(\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'\r\n #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))\r\n ^\r\n numpy/core/src/multiarray/descriptor.c:453:13: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations]\r\n if (PyUString_GET_SIZE(name) == 0) {\r\n ^\r\n numpy/core/include/numpy/npy_3kcompat.h:110:28: note: expanded from macro 'PyUString_GET_SIZE'\r\n #define PyUString_GET_SIZE PyUnicode_GET_SIZE\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:264:8: note: expanded from macro 'PyUnicode_GET_SIZE'\r\n PyUnicode_WSTR_LENGTH(op)))\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH'\r\n #define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here\r\n Py_DEPRECATED(3.3)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'\r\n #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))\r\n ^\r\n numpy/core/src/multiarray/descriptor.c:460:48: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations]\r\n else if (PyUString_Check(title) && PyUString_GET_SIZE(title) > 0) {\r\n ^\r\n numpy/core/include/numpy/npy_3kcompat.h:110:28: note: expanded from macro 'PyUString_GET_SIZE'\r\n #define PyUString_GET_SIZE PyUnicode_GET_SIZE\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:261:7: note: expanded from macro 'PyUnicode_GET_SIZE'\r\n PyUnicode_WSTR_LENGTH(op) : \\\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH'\r\n #define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here\r\n Py_DEPRECATED(3.3)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'\r\n #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))\r\n ^\r\n numpy/core/src/multiarray/descriptor.c:460:48: warning: 'PyUnicode_AsUnicode' is deprecated [-Wdeprecated-declarations]\r\n else if (PyUString_Check(title) && PyUString_GET_SIZE(title) > 0) {\r\n ^\r\n numpy/core/include/numpy/npy_3kcompat.h:110:28: note: expanded from macro 'PyUString_GET_SIZE'\r\n #define PyUString_GET_SIZE PyUnicode_GET_SIZE\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:262:14: note: expanded from macro 'PyUnicode_GET_SIZE'\r\n ((void)PyUnicode_AsUnicode(_PyObject_CAST(op)),\\\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:580:1: note: 'PyUnicode_AsUnicode' has been explicitly marked deprecated here\r\n Py_DEPRECATED(3.3) PyAPI_FUNC(Py_UNICODE *) PyUnicode_AsUnicode(\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'\r\n #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))\r\n ^\r\n numpy/core/src/multiarray/descriptor.c:460:48: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations]\r\n else if (PyUString_Check(title) && PyUString_GET_SIZE(title) > 0) {\r\n ^\r\n numpy/core/include/numpy/npy_3kcompat.h:110:28: note: expanded from macro 'PyUString_GET_SIZE'\r\n #define PyUString_GET_SIZE PyUnicode_GET_SIZE\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:264:8: note: expanded from macro 'PyUnicode_GET_SIZE'\r\n PyUnicode_WSTR_LENGTH(op)))\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH'\r\n #define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here\r\n Py_DEPRECATED(3.3)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'\r\n #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))\r\n ^\r\n clang: numpy/core/src/multiarray/conversion_utils.c\r\n clang: numpy/core/src/multiarray/item_selection.c\r\n clang: numpy/core/src/multiarray/dtype_transfer.c\r\n clang: numpy/core/src/multiarray/mapping.c\r\n clang: build/src.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/arraytypes.c\r\n clang: build/src.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/nditer_templ.c\r\n 3 warnings generated.\r\n clang: numpy/core/src/multiarray/datetime.c\r\n numpy/core/src/multiarray/arraytypes.c.src:477:11: warning: 'PyUnicode_AsUnicode' is deprecated [-Wdeprecated-declarations]\r\n ptr = PyUnicode_AS_UNICODE(temp);\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:279:7: note: expanded from macro 'PyUnicode_AS_UNICODE'\r\n PyUnicode_AsUnicode(_PyObject_CAST(op)))\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:580:1: note: 'PyUnicode_AsUnicode' has been explicitly marked deprecated here\r\n Py_DEPRECATED(3.3) PyAPI_FUNC(Py_UNICODE *) PyUnicode_AsUnicode(\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'\r\n #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))\r\n ^\r\n numpy/core/src/multiarray/arraytypes.c.src:482:15: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations]\r\n datalen = PyUnicode_GET_DATA_SIZE(temp);\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:268:6: note: expanded from macro 'PyUnicode_GET_DATA_SIZE'\r\n (PyUnicode_GET_SIZE(op) * Py_UNICODE_SIZE)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:261:7: note: expanded from macro 'PyUnicode_GET_SIZE'\r\n PyUnicode_WSTR_LENGTH(op) : \\\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH'\r\n #define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here\r\n Py_DEPRECATED(3.3)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'\r\n #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))\r\n ^\r\n numpy/core/src/multiarray/arraytypes.c.src:482:15: warning: 'PyUnicode_AsUnicode' is deprecated [-Wdeprecated-declarations]\r\n datalen = PyUnicode_GET_DATA_SIZE(temp);\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:268:6: note: expanded from macro 'PyUnicode_GET_DATA_SIZE'\r\n (PyUnicode_GET_SIZE(op) * Py_UNICODE_SIZE)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:262:14: note: expanded from macro 'PyUnicode_GET_SIZE'\r\n ((void)PyUnicode_AsUnicode(_PyObject_CAST(op)),\\\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:580:1: note: 'PyUnicode_AsUnicode' has been explicitly marked deprecated here\r\n Py_DEPRECATED(3.3) PyAPI_FUNC(Py_UNICODE *) PyUnicode_AsUnicode(\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'\r\n #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))\r\n ^\r\n numpy/core/src/multiarray/arraytypes.c.src:482:15: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations]\r\n datalen = PyUnicode_GET_DATA_SIZE(temp);\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:268:6: note: expanded from macro 'PyUnicode_GET_DATA_SIZE'\r\n (PyUnicode_GET_SIZE(op) * Py_UNICODE_SIZE)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:264:8: note: expanded from macro 'PyUnicode_GET_SIZE'\r\n PyUnicode_WSTR_LENGTH(op)))\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH'\r\n #define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here\r\n Py_DEPRECATED(3.3)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'\r\n #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))\r\n ^\r\n clang: numpy/core/src/multiarray/common.c\r\n numpy/core/src/multiarray/common.c:187:28: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations]\r\n itemsize = PyUnicode_GET_DATA_SIZE(temp);\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:268:6: note: expanded from macro 'PyUnicode_GET_DATA_SIZE'\r\n (PyUnicode_GET_SIZE(op) * Py_UNICODE_SIZE)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:261:7: note: expanded from macro 'PyUnicode_GET_SIZE'\r\n PyUnicode_WSTR_LENGTH(op) : \\\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH'\r\n #define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here\r\n Py_DEPRECATED(3.3)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'\r\n #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))\r\n ^\r\n numpy/core/src/multiarray/common.c:187:28: warning: 'PyUnicode_AsUnicode' is deprecated [-Wdeprecated-declarations]\r\n itemsize = PyUnicode_GET_DATA_SIZE(temp);\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:268:6: note: expanded from macro 'PyUnicode_GET_DATA_SIZE'\r\n (PyUnicode_GET_SIZE(op) * Py_UNICODE_SIZE)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:262:14: note: expanded from macro 'PyUnicode_GET_SIZE'\r\n ((void)PyUnicode_AsUnicode(_PyObject_CAST(op)),\\\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:580:1: note: 'PyUnicode_AsUnicode' has been explicitly marked deprecated here\r\n Py_DEPRECATED(3.3) PyAPI_FUNC(Py_UNICODE *) PyUnicode_AsUnicode(\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'\r\n #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))\r\n ^\r\n numpy/core/src/multiarray/common.c:187:28: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations]\r\n itemsize = PyUnicode_GET_DATA_SIZE(temp);\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:268:6: note: expanded from macro 'PyUnicode_GET_DATA_SIZE'\r\n (PyUnicode_GET_SIZE(op) * Py_UNICODE_SIZE)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:264:8: note: expanded from macro 'PyUnicode_GET_SIZE'\r\n PyUnicode_WSTR_LENGTH(op)))\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH'\r\n #define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here\r\n Py_DEPRECATED(3.3)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'\r\n #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))\r\n ^\r\n numpy/core/src/multiarray/common.c:239:28: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations]\r\n itemsize = PyUnicode_GET_DATA_SIZE(temp);\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:268:6: note: expanded from macro 'PyUnicode_GET_DATA_SIZE'\r\n (PyUnicode_GET_SIZE(op) * Py_UNICODE_SIZE)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:261:7: note: expanded from macro 'PyUnicode_GET_SIZE'\r\n PyUnicode_WSTR_LENGTH(op) : \\\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH'\r\n #define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here\r\n Py_DEPRECATED(3.3)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'\r\n #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))\r\n ^\r\n numpy/core/src/multiarray/common.c:239:28: warning: 'PyUnicode_AsUnicode' is deprecated [-Wdeprecated-declarations]\r\n itemsize = PyUnicode_GET_DATA_SIZE(temp);\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:268:6: note: expanded from macro 'PyUnicode_GET_DATA_SIZE'\r\n (PyUnicode_GET_SIZE(op) * Py_UNICODE_SIZE)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:262:14: note: expanded from macro 'PyUnicode_GET_SIZE'\r\n ((void)PyUnicode_AsUnicode(_PyObject_CAST(op)),\\\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:580:1: note: 'PyUnicode_AsUnicode' has been explicitly marked deprecated here\r\n Py_DEPRECATED(3.3) PyAPI_FUNC(Py_UNICODE *) PyUnicode_AsUnicode(\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'\r\n #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))\r\n ^\r\n numpy/core/src/multiarray/common.c:239:28: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations]\r\n itemsize = PyUnicode_GET_DATA_SIZE(temp);\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:268:6: note: expanded from macro 'PyUnicode_GET_DATA_SIZE'\r\n (PyUnicode_GET_SIZE(op) * Py_UNICODE_SIZE)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:264:8: note: expanded from macro 'PyUnicode_GET_SIZE'\r\n PyUnicode_WSTR_LENGTH(op)))\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH'\r\n #define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here\r\n Py_DEPRECATED(3.3)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'\r\n #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))\r\n ^\r\n numpy/core/src/multiarray/common.c:282:24: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations]\r\n int itemsize = PyUnicode_GET_DATA_SIZE(obj);\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:268:6: note: expanded from macro 'PyUnicode_GET_DATA_SIZE'\r\n (PyUnicode_GET_SIZE(op) * Py_UNICODE_SIZE)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:261:7: note: expanded from macro 'PyUnicode_GET_SIZE'\r\n PyUnicode_WSTR_LENGTH(op) : \\\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH'\r\n #define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here\r\n Py_DEPRECATED(3.3)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'\r\n #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))\r\n ^\r\n numpy/core/src/multiarray/common.c:282:24: warning: 'PyUnicode_AsUnicode' is deprecated [-Wdeprecated-declarations]\r\n int itemsize = PyUnicode_GET_DATA_SIZE(obj);\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:268:6: note: expanded from macro 'PyUnicode_GET_DATA_SIZE'\r\n (PyUnicode_GET_SIZE(op) * Py_UNICODE_SIZE)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:262:14: note: expanded from macro 'PyUnicode_GET_SIZE'\r\n ((void)PyUnicode_AsUnicode(_PyObject_CAST(op)),\\\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:580:1: note: 'PyUnicode_AsUnicode' has been explicitly marked deprecated here\r\n Py_DEPRECATED(3.3) PyAPI_FUNC(Py_UNICODE *) PyUnicode_AsUnicode(\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'\r\n #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))\r\n ^\r\n numpy/core/src/multiarray/common.c:282:24: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations]\r\n int itemsize = PyUnicode_GET_DATA_SIZE(obj);\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:268:6: note: expanded from macro 'PyUnicode_GET_DATA_SIZE'\r\n (PyUnicode_GET_SIZE(op) * Py_UNICODE_SIZE)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:264:8: note: expanded from macro 'PyUnicode_GET_SIZE'\r\n PyUnicode_WSTR_LENGTH(op)))\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH'\r\n #define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here\r\n Py_DEPRECATED(3.3)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'\r\n #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))\r\n ^\r\n 6 warnings generated.\r\n clang: numpy/core/src/multiarray/nditer_pywrap.c\r\n 9 warnings generated.\r\n clang: numpy/core/src/multiarray/sequence.c\r\n clang: numpy/core/src/multiarray/shape.c\r\n clang: build/src.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/einsum.c\r\n clang: numpy/core/src/multiarray/methods.c\r\n clang: numpy/core/src/multiarray/iterators.c\r\n clang: numpy/core/src/multiarray/datetime_strings.c\r\n clang: numpy/core/src/multiarray/number.c\r\n clang: numpy/core/src/multiarray/scalarapi.c\r\n clang: build/src.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/scalartypes.c\r\n numpy/core/src/multiarray/scalarapi.c:74:28: warning: 'PyUnicode_AsUnicode' is deprecated [-Wdeprecated-declarations]\r\n return (void *)PyUnicode_AS_DATA(scalar);\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:283:21: note: expanded from macro 'PyUnicode_AS_DATA'\r\n ((const char *)(PyUnicode_AS_UNICODE(op)))\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:279:7: note: expanded from macro 'PyUnicode_AS_UNICODE'\r\n PyUnicode_AsUnicode(_PyObject_CAST(op)))\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:580:1: note: 'PyUnicode_AsUnicode' has been explicitly marked deprecated here\r\n Py_DEPRECATED(3.3) PyAPI_FUNC(Py_UNICODE *) PyUnicode_AsUnicode(\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'\r\n #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))\r\n ^\r\n numpy/core/src/multiarray/scalarapi.c:135:28: warning: 'PyUnicode_AsUnicode' is deprecated [-Wdeprecated-declarations]\r\n return (void *)PyUnicode_AS_DATA(scalar);\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:283:21: note: expanded from macro 'PyUnicode_AS_DATA'\r\n ((const char *)(PyUnicode_AS_UNICODE(op)))\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:279:7: note: expanded from macro 'PyUnicode_AS_UNICODE'\r\n PyUnicode_AsUnicode(_PyObject_CAST(op)))\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:580:1: note: 'PyUnicode_AsUnicode' has been explicitly marked deprecated here\r\n Py_DEPRECATED(3.3) PyAPI_FUNC(Py_UNICODE *) PyUnicode_AsUnicode(\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'\r\n #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))\r\n ^\r\n numpy/core/src/multiarray/scalarapi.c:568:29: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations]\r\n descr->elsize = PyUnicode_GET_DATA_SIZE(sc);\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:268:6: note: expanded from macro 'PyUnicode_GET_DATA_SIZE'\r\n (PyUnicode_GET_SIZE(op) * Py_UNICODE_SIZE)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:261:7: note: expanded from macro 'PyUnicode_GET_SIZE'\r\n PyUnicode_WSTR_LENGTH(op) : \\\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH'\r\n #define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here\r\n Py_DEPRECATED(3.3)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'\r\n #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))\r\n ^\r\n numpy/core/src/multiarray/scalarapi.c:568:29: warning: 'PyUnicode_AsUnicode' is deprecated [-Wdeprecated-declarations]\r\n descr->elsize = PyUnicode_GET_DATA_SIZE(sc);\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:268:6: note: expanded from macro 'PyUnicode_GET_DATA_SIZE'\r\n (PyUnicode_GET_SIZE(op) * Py_UNICODE_SIZE)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:262:14: note: expanded from macro 'PyUnicode_GET_SIZE'\r\n ((void)PyUnicode_AsUnicode(_PyObject_CAST(op)),\\\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:580:1: note: 'PyUnicode_AsUnicode' has been explicitly marked deprecated here\r\n Py_DEPRECATED(3.3) PyAPI_FUNC(Py_UNICODE *) PyUnicode_AsUnicode(\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'\r\n #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))\r\n ^\r\n numpy/core/src/multiarray/scalarapi.c:568:29: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations]\r\n descr->elsize = PyUnicode_GET_DATA_SIZE(sc);\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:268:6: note: expanded from macro 'PyUnicode_GET_DATA_SIZE'\r\n (PyUnicode_GET_SIZE(op) * Py_UNICODE_SIZE)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:264:8: note: expanded from macro 'PyUnicode_GET_SIZE'\r\n PyUnicode_WSTR_LENGTH(op)))\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH'\r\n #define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here\r\n Py_DEPRECATED(3.3)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'\r\n #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))\r\n ^\r\n numpy/core/src/multiarray/scalartypes.c.src:475:17: warning: 'PyUnicode_AsUnicode' is deprecated [-Wdeprecated-declarations]\r\n ip = dptr = PyUnicode_AS_UNICODE(self);\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:279:7: note: expanded from macro 'PyUnicode_AS_UNICODE'\r\n PyUnicode_AsUnicode(_PyObject_CAST(op)))\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:580:1: note: 'PyUnicode_AsUnicode' has been explicitly marked deprecated here\r\n Py_DEPRECATED(3.3) PyAPI_FUNC(Py_UNICODE *) PyUnicode_AsUnicode(\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'\r\n #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))\r\n ^\r\n numpy/core/src/multiarray/scalartypes.c.src:476:11: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations]\r\n len = PyUnicode_GET_SIZE(self);\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:261:7: note: expanded from macro 'PyUnicode_GET_SIZE'\r\n PyUnicode_WSTR_LENGTH(op) : \\\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH'\r\n #define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here\r\n Py_DEPRECATED(3.3)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'\r\n #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))\r\n ^\r\n numpy/core/src/multiarray/scalartypes.c.src:476:11: warning: 'PyUnicode_AsUnicode' is deprecated [-Wdeprecated-declarations]\r\n len = PyUnicode_GET_SIZE(self);\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:262:14: note: expanded from macro 'PyUnicode_GET_SIZE'\r\n ((void)PyUnicode_AsUnicode(_PyObject_CAST(op)),\\\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:580:1: note: 'PyUnicode_AsUnicode' has been explicitly marked deprecated here\r\n Py_DEPRECATED(3.3) PyAPI_FUNC(Py_UNICODE *) PyUnicode_AsUnicode(\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'\r\n #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))\r\n ^\r\n numpy/core/src/multiarray/scalartypes.c.src:476:11: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations]\r\n len = PyUnicode_GET_SIZE(self);\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:264:8: note: expanded from macro 'PyUnicode_GET_SIZE'\r\n PyUnicode_WSTR_LENGTH(op)))\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH'\r\n #define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here\r\n Py_DEPRECATED(3.3)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'\r\n #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))\r\n ^\r\n numpy/core/src/multiarray/scalartypes.c.src:481:11: warning: 'PyUnicode_FromUnicode' is deprecated [-Wdeprecated-declarations]\r\n new = PyUnicode_FromUnicode(ip, len);\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:551:1: note: 'PyUnicode_FromUnicode' has been explicitly marked deprecated here\r\n Py_DEPRECATED(3.3) PyAPI_FUNC(PyObject*) PyUnicode_FromUnicode(\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'\r\n #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))\r\n ^\r\n numpy/core/src/multiarray/scalartypes.c.src:475:17: warning: 'PyUnicode_AsUnicode' is deprecated [-Wdeprecated-declarations]\r\n ip = dptr = PyUnicode_AS_UNICODE(self);\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:279:7: note: expanded from macro 'PyUnicode_AS_UNICODE'\r\n PyUnicode_AsUnicode(_PyObject_CAST(op)))\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:580:1: note: 'PyUnicode_AsUnicode' has been explicitly marked deprecated here\r\n Py_DEPRECATED(3.3) PyAPI_FUNC(Py_UNICODE *) PyUnicode_AsUnicode(\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'\r\n #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))\r\n ^\r\n numpy/core/src/multiarray/scalartypes.c.src:476:11: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations]\r\n len = PyUnicode_GET_SIZE(self);\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:261:7: note: expanded from macro 'PyUnicode_GET_SIZE'\r\n PyUnicode_WSTR_LENGTH(op) : \\\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH'\r\n #define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here\r\n Py_DEPRECATED(3.3)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'\r\n #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))\r\n ^\r\n numpy/core/src/multiarray/scalartypes.c.src:476:11: warning: 'PyUnicode_AsUnicode' is deprecated [-Wdeprecated-declarations]\r\n len = PyUnicode_GET_SIZE(self);\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:262:14: note: expanded from macro 'PyUnicode_GET_SIZE'\r\n ((void)PyUnicode_AsUnicode(_PyObject_CAST(op)),\\\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:580:1: note: 'PyUnicode_AsUnicode' has been explicitly marked deprecated here\r\n Py_DEPRECATED(3.3) PyAPI_FUNC(Py_UNICODE *) PyUnicode_AsUnicode(\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'\r\n #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))\r\n ^\r\n numpy/core/src/multiarray/scalartypes.c.src:476:11: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations]\r\n len = PyUnicode_GET_SIZE(self);\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:264:8: note: expanded from macro 'PyUnicode_GET_SIZE'\r\n PyUnicode_WSTR_LENGTH(op)))\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH'\r\n #define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here\r\n Py_DEPRECATED(3.3)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'\r\n #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))\r\n ^\r\n numpy/core/src/multiarray/scalartypes.c.src:481:11: warning: 'PyUnicode_FromUnicode' is deprecated [-Wdeprecated-declarations]\r\n new = PyUnicode_FromUnicode(ip, len);\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:551:1: note: 'PyUnicode_FromUnicode' has been explicitly marked deprecated here\r\n Py_DEPRECATED(3.3) PyAPI_FUNC(PyObject*) PyUnicode_FromUnicode(\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'\r\n #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))\r\n ^\r\n numpy/core/src/multiarray/scalartypes.c.src:1849:18: warning: 'PyUnicode_AsUnicode' is deprecated [-Wdeprecated-declarations]\r\n buffer = PyUnicode_AS_DATA(self);\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:283:21: note: expanded from macro 'PyUnicode_AS_DATA'\r\n ((const char *)(PyUnicode_AS_UNICODE(op)))\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:279:7: note: expanded from macro 'PyUnicode_AS_UNICODE'\r\n PyUnicode_AsUnicode(_PyObject_CAST(op)))\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:580:1: note: 'PyUnicode_AsUnicode' has been explicitly marked deprecated here\r\n Py_DEPRECATED(3.3) PyAPI_FUNC(Py_UNICODE *) PyUnicode_AsUnicode(\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'\r\n #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))\r\n ^\r\n numpy/core/src/multiarray/scalartypes.c.src:1850:18: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations]\r\n buflen = PyUnicode_GET_DATA_SIZE(self);\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:268:6: note: expanded from macro 'PyUnicode_GET_DATA_SIZE'\r\n (PyUnicode_GET_SIZE(op) * Py_UNICODE_SIZE)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:261:7: note: expanded from macro 'PyUnicode_GET_SIZE'\r\n PyUnicode_WSTR_LENGTH(op) : \\\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH'\r\n #define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here\r\n Py_DEPRECATED(3.3)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'\r\n #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))\r\n ^\r\n numpy/core/src/multiarray/scalartypes.c.src:1850:18: warning: 'PyUnicode_AsUnicode' is deprecated [-Wdeprecated-declarations]\r\n buflen = PyUnicode_GET_DATA_SIZE(self);\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:268:6: note: expanded from macro 'PyUnicode_GET_DATA_SIZE'\r\n (PyUnicode_GET_SIZE(op) * Py_UNICODE_SIZE)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:262:14: note: expanded from macro 'PyUnicode_GET_SIZE'\r\n ((void)PyUnicode_AsUnicode(_PyObject_CAST(op)),\\\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:580:1: note: 'PyUnicode_AsUnicode' has been explicitly marked deprecated here\r\n Py_DEPRECATED(3.3) PyAPI_FUNC(Py_UNICODE *) PyUnicode_AsUnicode(\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'\r\n #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))\r\n ^\r\n numpy/core/src/multiarray/scalartypes.c.src:1850:18: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations]\r\n buflen = PyUnicode_GET_DATA_SIZE(self);\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:268:6: note: expanded from macro 'PyUnicode_GET_DATA_SIZE'\r\n (PyUnicode_GET_SIZE(op) * Py_UNICODE_SIZE)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:264:8: note: expanded from macro 'PyUnicode_GET_SIZE'\r\n PyUnicode_WSTR_LENGTH(op)))\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH'\r\n #define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here\r\n Py_DEPRECATED(3.3)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'\r\n #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))\r\n ^\r\n 5 warnings generated.\r\n clang: numpy/core/src/multiarray/typeinfo.c\r\n clang: numpy/core/src/multiarray/refcount.c\r\n clang: numpy/core/src/multiarray/usertypes.c\r\n clang: numpy/core/src/multiarray/multiarraymodule.c\r\n clang: build/src.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/lowlevel_strided_loops.c\r\n clang: numpy/core/src/multiarray/vdot.c\r\n clang: numpy/core/src/umath/umathmodule.c\r\n clang: build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/matmul.c\r\n clang: numpy/core/src/umath/reduction.c\r\n clang: build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/loops.c\r\n clang: numpy/core/src/multiarray/nditer_api.c\r\n 14 warnings generated.\r\n clang: numpy/core/src/multiarray/strfuncs.c\r\n numpy/core/src/umath/loops.c.src:655:18: warning: 'PyEval_CallObjectWithKeywords' is deprecated [-Wdeprecated-declarations]\r\n result = PyEval_CallObject(tocall, arglist);\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/ceval.h:24:5: note: expanded from macro 'PyEval_CallObject'\r\n PyEval_CallObjectWithKeywords(callable, arg, (PyObject *)NULL)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/ceval.h:17:1: note: 'PyEval_CallObjectWithKeywords' has been explicitly marked deprecated here\r\n Py_DEPRECATED(3.9) PyAPI_FUNC(PyObject *) PyEval_CallObjectWithKeywords(\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'\r\n #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))\r\n ^\r\n numpy/core/src/multiarray/strfuncs.c:178:13: warning: 'PyEval_CallObjectWithKeywords' is deprecated [-Wdeprecated-declarations]\r\n s = PyEval_CallObject(PyArray_ReprFunction, arglist);\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/ceval.h:24:5: note: expanded from macro 'PyEval_CallObject'\r\n PyEval_CallObjectWithKeywords(callable, arg, (PyObject *)NULL)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/ceval.h:17:1: note: 'PyEval_CallObjectWithKeywords' has been explicitly marked deprecated here\r\n Py_DEPRECATED(3.9) PyAPI_FUNC(PyObject *) PyEval_CallObjectWithKeywords(\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'\r\n #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))\r\n ^\r\n numpy/core/src/multiarray/strfuncs.c:195:13: warning: 'PyEval_CallObjectWithKeywords' is deprecated [-Wdeprecated-declarations]\r\n s = PyEval_CallObject(PyArray_StrFunction, arglist);\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/ceval.h:24:5: note: expanded from macro 'PyEval_CallObject'\r\n PyEval_CallObjectWithKeywords(callable, arg, (PyObject *)NULL)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/ceval.h:17:1: note: 'PyEval_CallObjectWithKeywords' has been explicitly marked deprecated here\r\n Py_DEPRECATED(3.9) PyAPI_FUNC(PyObject *) PyEval_CallObjectWithKeywords(\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'\r\n #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))\r\n ^\r\n 2 warnings generated.\r\n clang: numpy/core/src/multiarray/temp_elide.c\r\n clang: numpy/core/src/umath/cpuid.c\r\n clang: build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/scalarmath.c\r\n clang: numpy/core/src/umath/ufunc_object.c\r\n numpy/core/src/umath/scalarmath.c.src:1449:1: warning: unused function 'byte_long' [-Wunused-function]\r\n byte_long(PyObject *obj)\r\n ^\r\n numpy/core/src/umath/scalarmath.c.src:1449:1: warning: unused function 'ubyte_long' [-Wunused-function]\r\n ubyte_long(PyObject *obj)\r\n ^\r\n numpy/core/src/umath/scalarmath.c.src:1449:1: warning: unused function 'short_long' [-Wunused-function]\r\n short_long(PyObject *obj)\r\n ^\r\n numpy/core/src/umath/scalarmath.c.src:1449:1: warning: unused function 'ushort_long' [-Wunused-function]\r\n ushort_long(PyObject *obj)\r\n ^\r\n numpy/core/src/umath/scalarmath.c.src:1449:1: warning: unused function 'int_long' [-Wunused-function]\r\n int_long(PyObject *obj)\r\n ^\r\n numpy/core/src/umath/scalarmath.c.src:1449:1: warning: unused function 'uint_long' [-Wunused-function]\r\n uint_long(PyObject *obj)\r\n ^\r\n numpy/core/src/umath/scalarmath.c.src:1449:1: warning: unused function 'long_long' [-Wunused-function]\r\n long_long(PyObject *obj)\r\n ^\r\n numpy/core/src/umath/scalarmath.c.src:1449:1: warning: unused function 'ulong_long' [-Wunused-function]\r\n ulong_long(PyObject *obj)\r\n ^\r\n numpy/core/src/umath/scalarmath.c.src:1449:1: warning: unused function 'longlong_long' [-Wunused-function]\r\n longlong_long(PyObject *obj)\r\n ^\r\n numpy/core/src/umath/scalarmath.c.src:1449:1: warning: unused function 'ulonglong_long' [-Wunused-function]\r\n ulonglong_long(PyObject *obj)\r\n ^\r\n numpy/core/src/umath/scalarmath.c.src:1449:1: warning: unused function 'half_long' [-Wunused-function]\r\n half_long(PyObject *obj)\r\n ^\r\n numpy/core/src/umath/scalarmath.c.src:1449:1: warning: unused function 'float_long' [-Wunused-function]\r\n float_long(PyObject *obj)\r\n ^\r\n numpy/core/src/umath/scalarmath.c.src:1449:1: warning: unused function 'double_long' [-Wunused-function]\r\n double_long(PyObject *obj)\r\n ^\r\n numpy/core/src/umath/scalarmath.c.src:1449:1: warning: unused function 'longdouble_long' [-Wunused-function]\r\n longdouble_long(PyObject *obj)\r\n ^\r\n numpy/core/src/umath/scalarmath.c.src:1449:1: warning: unused function 'cfloat_long' [-Wunused-function]\r\n cfloat_long(PyObject *obj)\r\n ^\r\n numpy/core/src/umath/scalarmath.c.src:1449:1: warning: unused function 'cdouble_long' [-Wunused-function]\r\n cdouble_long(PyObject *obj)\r\n ^\r\n numpy/core/src/umath/scalarmath.c.src:1449:1: warning: unused function 'clongdouble_long' [-Wunused-function]\r\n clongdouble_long(PyObject *obj)\r\n ^\r\n clang: numpy/core/src/multiarray/nditer_constr.c\r\n numpy/core/src/umath/ufunc_object.c:657:19: warning: comparison of integers of different signs: 'int' and 'size_t' (aka 'unsigned long') [-Wsign-compare]\r\n for (i = 0; i < len; i++) {\r\n ~ ^ ~~~\r\n clang: numpy/core/src/umath/override.c\r\n clang: numpy/core/src/npymath/npy_math.c\r\n clang: build/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath/ieee754.c\r\n numpy/core/src/umath/loops.c.src:2527:22: warning: code will never be executed [-Wunreachable-code]\r\n npy_intp n = dimensions[0];\r\n ^~~~~~~~~~\r\n numpy/core/src/umath/loops.c.src:2526:29: note: silence by adding parentheses to mark code as explicitly dead\r\n if (IS_BINARY_REDUCE && 0) {\r\n ^\r\n /* DISABLES CODE */ ( )\r\n numpy/core/src/umath/loops.c.src:2527:22: warning: code will never be executed [-Wunreachable-code]\r\n npy_intp n = dimensions[0];\r\n ^~~~~~~~~~\r\n numpy/core/src/umath/loops.c.src:2526:29: note: silence by adding parentheses to mark code as explicitly dead\r\n if (IS_BINARY_REDUCE && 0) {\r\n ^\r\n /* DISABLES CODE */ ( )\r\n numpy/core/src/umath/loops.c.src:2527:22: warning: code will never be executed [-Wunreachable-code]\r\n npy_intp n = dimensions[0];\r\n ^~~~~~~~~~\r\n numpy/core/src/umath/loops.c.src:2526:29: note: silence by adding parentheses to mark code as explicitly dead\r\n if (IS_BINARY_REDUCE && 0) {\r\n ^\r\n /* DISABLES CODE */ ( )\r\n clang: build/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath/npy_math_complex.c\r\n numpy/core/src/npymath/npy_math_complex.c.src:48:33: warning: unused variable 'tiny' [-Wunused-const-variable]\r\n static const volatile npy_float tiny = 3.9443045e-31f;\r\n ^\r\n numpy/core/src/npymath/npy_math_complex.c.src:67:25: warning: unused variable 'c_halff' [-Wunused-const-variable]\r\n static const npy_cfloat c_halff = {0.5F, 0.0};\r\n ^\r\n numpy/core/src/npymath/npy_math_complex.c.src:68:25: warning: unused variable 'c_if' [-Wunused-const-variable]\r\n static const npy_cfloat c_if = {0.0, 1.0F};\r\n ^\r\n numpy/core/src/npymath/npy_math_complex.c.src:69:25: warning: unused variable 'c_ihalff' [-Wunused-const-variable]\r\n static const npy_cfloat c_ihalff = {0.0, 0.5F};\r\n ^\r\n numpy/core/src/npymath/npy_math_complex.c.src:79:1: warning: unused function 'caddf' [-Wunused-function]\r\n caddf(npy_cfloat a, npy_cfloat b)\r\n ^\r\n numpy/core/src/npymath/npy_math_complex.c.src:87:1: warning: unused function 'csubf' [-Wunused-function]\r\n csubf(npy_cfloat a, npy_cfloat b)\r\n ^\r\n numpy/core/src/npymath/npy_math_complex.c.src:137:1: warning: unused function 'cnegf' [-Wunused-function]\r\n cnegf(npy_cfloat a)\r\n ^\r\n numpy/core/src/npymath/npy_math_complex.c.src:144:1: warning: unused function 'cmulif' [-Wunused-function]\r\n cmulif(npy_cfloat a)\r\n ^\r\n numpy/core/src/npymath/npy_math_complex.c.src:67:26: warning: unused variable 'c_half' [-Wunused-const-variable]\r\n static const npy_cdouble c_half = {0.5, 0.0};\r\n ^\r\n numpy/core/src/npymath/npy_math_complex.c.src:68:26: warning: unused variable 'c_i' [-Wunused-const-variable]\r\n static const npy_cdouble c_i = {0.0, 1.0};\r\n ^\r\n numpy/core/src/npymath/npy_math_complex.c.src:69:26: warning: unused variable 'c_ihalf' [-Wunused-const-variable]\r\n static const npy_cdouble c_ihalf = {0.0, 0.5};\r\n ^\r\n numpy/core/src/npymath/npy_math_complex.c.src:79:1: warning: unused function 'cadd' [-Wunused-function]\r\n cadd(npy_cdouble a, npy_cdouble b)\r\n ^\r\n numpy/core/src/npymath/npy_math_complex.c.src:87:1: warning: unused function 'csub' [-Wunused-function]\r\n csub(npy_cdouble a, npy_cdouble b)\r\n ^\r\n numpy/core/src/npymath/npy_math_complex.c.src:137:1: warning: unused function 'cneg' [-Wunused-function]\r\n cneg(npy_cdouble a)\r\n ^\r\n numpy/core/src/npymath/npy_math_complex.c.src:144:1: warning: unused function 'cmuli' [-Wunused-function]\r\n cmuli(npy_cdouble a)\r\n ^\r\n numpy/core/src/npymath/npy_math_complex.c.src:67:30: warning: unused variable 'c_halfl' [-Wunused-const-variable]\r\n static const npy_clongdouble c_halfl = {0.5L, 0.0};\r\n ^\r\n numpy/core/src/npymath/npy_math_complex.c.src:68:30: warning: unused variable 'c_il' [-Wunused-const-variable]\r\n static const npy_clongdouble c_il = {0.0, 1.0L};\r\n ^\r\n numpy/core/src/npymath/npy_math_complex.c.src:69:30: warning: unused variable 'c_ihalfl' [-Wunused-const-variable]\r\n static const npy_clongdouble c_ihalfl = {0.0, 0.5L};\r\n ^\r\n numpy/core/src/npymath/npy_math_complex.c.src:79:1: warning: unused function 'caddl' [-Wunused-function]\r\n caddl(npy_clongdouble a, npy_clongdouble b)\r\n ^\r\n numpy/core/src/npymath/npy_math_complex.c.src:87:1: warning: unused function 'csubl' [-Wunused-function]\r\n csubl(npy_clongdouble a, npy_clongdouble b)\r\n ^\r\n numpy/core/src/npymath/npy_math_complex.c.src:137:1: warning: unused function 'cnegl' [-Wunused-function]\r\n cnegl(npy_clongdouble a)\r\n ^\r\n numpy/core/src/npymath/npy_math_complex.c.src:144:1: warning: unused function 'cmulil' [-Wunused-function]\r\n cmulil(npy_clongdouble a)\r\n ^\r\n 22 warnings generated.\r\n clang: numpy/core/src/common/mem_overlap.c\r\n clang: numpy/core/src/npymath/halffloat.c\r\n clang: numpy/core/src/common/array_assign.c\r\n clang: numpy/core/src/common/ufunc_override.c\r\n clang: numpy/core/src/common/npy_longdouble.c\r\n clang: numpy/core/src/common/numpyos.c\r\n clang: numpy/core/src/common/ucsnarrow.c\r\n 1 warning generated.\r\n clang: numpy/core/src/umath/extobj.c\r\n numpy/core/src/common/ucsnarrow.c:139:34: warning: 'PyUnicode_FromUnicode' is deprecated [-Wdeprecated-declarations]\r\n ret = (PyUnicodeObject *)PyUnicode_FromUnicode((Py_UNICODE*)buf,\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:551:1: note: 'PyUnicode_FromUnicode' has been explicitly marked deprecated here\r\n Py_DEPRECATED(3.3) PyAPI_FUNC(PyObject*) PyUnicode_FromUnicode(\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'\r\n #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))\r\n ^\r\n 1 warning generated.\r\n clang: numpy/core/src/common/python_xerbla.c\r\n clang: numpy/core/src/common/cblasfuncs.c\r\n clang: /private/var/folders/fz/0j719tys48x7jlnjnwc69smr0000gn/T/pip-install-ufzck51l/numpy_b0e8a3953a1d4b46801f12bcea55536e/numpy/_build_utils/src/apple_sgemv_fix.c\r\n In file included from /private/var/folders/fz/0j719tys48x7jlnjnwc69smr0000gn/T/pip-install-ufzck51l/numpy_b0e8a3953a1d4b46801f12bcea55536e/numpy/_build_utils/src/apple_sgemv_fix.c:26:\r\n In file included from numpy/core/include/numpy/arrayobject.h:4:\r\n In file included from numpy/core/include/numpy/ndarrayobject.h:21:\r\n build/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy/__multiarray_api.h:1463:1: warning: unused function '_import_array' [-Wunused-function]\r\n _import_array(void)\r\n ^\r\n 1 warning generated.\r\n 17 warnings generated.\r\n clang: numpy/core/src/umath/ufunc_type_resolution.c\r\n 4 warnings generated.\r\n 4 warnings generated.\r\n clang -bundle -undefined dynamic_lookup -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/alloc.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/arrayobject.o build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/arraytypes.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/array_assign_scalar.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/array_assign_array.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/buffer.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/calculation.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/compiled_base.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/common.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/convert.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/convert_datatype.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/conversion_utils.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/ctors.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/datetime.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/datetime_strings.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/datetime_busday.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/datetime_busdaycal.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/descriptor.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/dragon4.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/dtype_transfer.o build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/einsum.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/flagsobject.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/getset.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/hashdescr.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/item_selection.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/iterators.o build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/lowlevel_strided_loops.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/mapping.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/methods.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/multiarraymodule.o build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/nditer_templ.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/nditer_api.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/nditer_constr.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/nditer_pywrap.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/number.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/refcount.o 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build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/matmul.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/umath/ufunc_object.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/umath/extobj.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/umath/cpuid.o build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/scalarmath.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/umath/ufunc_type_resolution.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/umath/override.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/npymath/npy_math.o build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath/ieee754.o build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath/npy_math_complex.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/npymath/halffloat.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/common/array_assign.o 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compiling C sources\r\n C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers\r\n \r\n compile options: '-DNPY_INTERNAL_BUILD=1 -DHAVE_NPY_CONFIG_H=1 -D_FILE_OFFSET_BITS=64 -D_LARGEFILE_SOURCE=1 -D_LARGEFILE64_SOURCE=1 -Inumpy/core/include -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy -Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -c'\r\n clang: build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/_umath_tests.c\r\n clang -bundle -undefined dynamic_lookup -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/_umath_tests.o -L/usr/local/lib -L/usr/local/opt/openssl@1.1/lib -L/usr/local/opt/sqlite/lib -Lbuild/temp.macosx-10.15-x86_64-3.9 -o build/lib.macosx-10.15-x86_64-3.9/numpy/core/_umath_tests.cpython-39-darwin.so\r\n building 'numpy.core._rational_tests' extension\r\n compiling C sources\r\n C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers\r\n \r\n compile options: '-DNPY_INTERNAL_BUILD=1 -DHAVE_NPY_CONFIG_H=1 -D_FILE_OFFSET_BITS=64 -D_LARGEFILE_SOURCE=1 -D_LARGEFILE64_SOURCE=1 -Inumpy/core/include -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy -Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -c'\r\n clang: build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/_rational_tests.c\r\n clang -bundle -undefined dynamic_lookup -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/_rational_tests.o -L/usr/local/lib -L/usr/local/opt/openssl@1.1/lib -L/usr/local/opt/sqlite/lib -Lbuild/temp.macosx-10.15-x86_64-3.9 -o build/lib.macosx-10.15-x86_64-3.9/numpy/core/_rational_tests.cpython-39-darwin.so\r\n building 'numpy.core._struct_ufunc_tests' extension\r\n compiling C sources\r\n C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers\r\n \r\n compile options: '-DNPY_INTERNAL_BUILD=1 -DHAVE_NPY_CONFIG_H=1 -D_FILE_OFFSET_BITS=64 -D_LARGEFILE_SOURCE=1 -D_LARGEFILE64_SOURCE=1 -Inumpy/core/include -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy -Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -c'\r\n clang: build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/_struct_ufunc_tests.c\r\n clang -bundle -undefined dynamic_lookup -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/_struct_ufunc_tests.o -L/usr/local/lib -L/usr/local/opt/openssl@1.1/lib -L/usr/local/opt/sqlite/lib -Lbuild/temp.macosx-10.15-x86_64-3.9 -o build/lib.macosx-10.15-x86_64-3.9/numpy/core/_struct_ufunc_tests.cpython-39-darwin.so\r\n building 'numpy.core._operand_flag_tests' extension\r\n compiling C sources\r\n C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers\r\n \r\n compile options: '-DNPY_INTERNAL_BUILD=1 -DHAVE_NPY_CONFIG_H=1 -D_FILE_OFFSET_BITS=64 -D_LARGEFILE_SOURCE=1 -D_LARGEFILE64_SOURCE=1 -Inumpy/core/include -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy -Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -c'\r\n clang: build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/_operand_flag_tests.c\r\n clang -bundle -undefined dynamic_lookup -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/_operand_flag_tests.o -L/usr/local/lib -L/usr/local/opt/openssl@1.1/lib -L/usr/local/opt/sqlite/lib -Lbuild/temp.macosx-10.15-x86_64-3.9 -o build/lib.macosx-10.15-x86_64-3.9/numpy/core/_operand_flag_tests.cpython-39-darwin.so\r\n building 'numpy.fft.fftpack_lite' extension\r\n compiling C sources\r\n C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers\r\n \r\n creating build/temp.macosx-10.15-x86_64-3.9/numpy/fft\r\n compile options: '-Inumpy/core/include -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy -Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -c'\r\n clang: numpy/fft/fftpack_litemodule.c\r\n clang: numpy/fft/fftpack.c\r\n clang -bundle -undefined dynamic_lookup -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk build/temp.macosx-10.15-x86_64-3.9/numpy/fft/fftpack_litemodule.o build/temp.macosx-10.15-x86_64-3.9/numpy/fft/fftpack.o -L/usr/local/lib -L/usr/local/opt/openssl@1.1/lib -L/usr/local/opt/sqlite/lib -Lbuild/temp.macosx-10.15-x86_64-3.9 -o build/lib.macosx-10.15-x86_64-3.9/numpy/fft/fftpack_lite.cpython-39-darwin.so\r\n building 'numpy.linalg.lapack_lite' extension\r\n compiling C sources\r\n C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers\r\n \r\n creating build/temp.macosx-10.15-x86_64-3.9/numpy/linalg\r\n creating build/temp.macosx-10.15-x86_64-3.9/numpy/linalg/lapack_lite\r\n compile options: '-DNO_ATLAS_INFO=3 -DHAVE_CBLAS -Inumpy/core/include -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy -Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -c'\r\n extra options: '-msse3 -I/System/Library/Frameworks/vecLib.framework/Headers'\r\n clang: numpy/linalg/lapack_litemodule.c\r\n clang: numpy/linalg/lapack_lite/python_xerbla.c\r\n clang -bundle -undefined dynamic_lookup -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk build/temp.macosx-10.15-x86_64-3.9/numpy/linalg/lapack_litemodule.o build/temp.macosx-10.15-x86_64-3.9/numpy/linalg/lapack_lite/python_xerbla.o -L/usr/local/lib -L/usr/local/opt/openssl@1.1/lib -L/usr/local/opt/sqlite/lib -Lbuild/temp.macosx-10.15-x86_64-3.9 -o build/lib.macosx-10.15-x86_64-3.9/numpy/linalg/lapack_lite.cpython-39-darwin.so -Wl,-framework -Wl,Accelerate\r\n building 'numpy.linalg._umath_linalg' extension\r\n compiling C sources\r\n C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers\r\n \r\n creating build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/linalg\r\n compile options: '-DNO_ATLAS_INFO=3 -DHAVE_CBLAS -Inumpy/core/include -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy -Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -c'\r\n extra options: '-msse3 -I/System/Library/Frameworks/vecLib.framework/Headers'\r\n clang: build/src.macosx-10.15-x86_64-3.9/numpy/linalg/umath_linalg.c\r\n numpy/linalg/umath_linalg.c.src:735:32: warning: unknown warning group '-Wmaybe-uninitialized', ignored [-Wunknown-warning-option]\r\n #pragma GCC diagnostic ignored \"-Wmaybe-uninitialized\"\r\n ^\r\n numpy/linalg/umath_linalg.c.src:541:1: warning: unused function 'dump_ufunc_object' [-Wunused-function]\r\n dump_ufunc_object(PyUFuncObject* ufunc)\r\n ^\r\n numpy/linalg/umath_linalg.c.src:566:1: warning: unused function 'dump_linearize_data' [-Wunused-function]\r\n dump_linearize_data(const char* name, const LINEARIZE_DATA_t* params)\r\n ^\r\n numpy/linalg/umath_linalg.c.src:602:1: warning: unused function 'dump_FLOAT_matrix' [-Wunused-function]\r\n dump_FLOAT_matrix(const char* name,\r\n ^\r\n numpy/linalg/umath_linalg.c.src:602:1: warning: unused function 'dump_DOUBLE_matrix' [-Wunused-function]\r\n dump_DOUBLE_matrix(const char* name,\r\n ^\r\n numpy/linalg/umath_linalg.c.src:602:1: warning: unused function 'dump_CFLOAT_matrix' [-Wunused-function]\r\n dump_CFLOAT_matrix(const char* name,\r\n ^\r\n numpy/linalg/umath_linalg.c.src:602:1: warning: unused function 'dump_CDOUBLE_matrix' [-Wunused-function]\r\n dump_CDOUBLE_matrix(const char* name,\r\n ^\r\n numpy/linalg/umath_linalg.c.src:865:1: warning: unused function 'zero_FLOAT_matrix' [-Wunused-function]\r\n zero_FLOAT_matrix(void *dst_in, const LINEARIZE_DATA_t* data)\r\n ^\r\n numpy/linalg/umath_linalg.c.src:865:1: warning: unused function 'zero_DOUBLE_matrix' [-Wunused-function]\r\n zero_DOUBLE_matrix(void *dst_in, const LINEARIZE_DATA_t* data)\r\n ^\r\n numpy/linalg/umath_linalg.c.src:865:1: warning: unused function 'zero_CFLOAT_matrix' [-Wunused-function]\r\n zero_CFLOAT_matrix(void *dst_in, const LINEARIZE_DATA_t* data)\r\n ^\r\n numpy/linalg/umath_linalg.c.src:865:1: warning: unused function 'zero_CDOUBLE_matrix' [-Wunused-function]\r\n zero_CDOUBLE_matrix(void *dst_in, const LINEARIZE_DATA_t* data)\r\n ^\r\n numpy/linalg/umath_linalg.c.src:1862:1: warning: unused function 'dump_geev_params' [-Wunused-function]\r\n dump_geev_params(const char *name, GEEV_PARAMS_t* params)\r\n ^\r\n numpy/linalg/umath_linalg.c.src:2132:1: warning: unused function 'init_cgeev' [-Wunused-function]\r\n init_cgeev(GEEV_PARAMS_t* params,\r\n ^\r\n numpy/linalg/umath_linalg.c.src:2213:1: warning: unused function 'process_cgeev_results' [-Wunused-function]\r\n process_cgeev_results(GEEV_PARAMS_t *NPY_UNUSED(params))\r\n ^\r\n numpy/linalg/umath_linalg.c.src:2376:1: warning: unused function 'dump_gesdd_params' [-Wunused-function]\r\n dump_gesdd_params(const char *name,\r\n ^\r\n numpy/linalg/umath_linalg.c.src:2864:1: warning: unused function 'dump_gelsd_params' [-Wunused-function]\r\n dump_gelsd_params(const char *name,\r\n ^\r\n 16 warnings generated.\r\n clang -bundle -undefined dynamic_lookup -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/linalg/umath_linalg.o build/temp.macosx-10.15-x86_64-3.9/numpy/linalg/lapack_lite/python_xerbla.o -L/usr/local/lib -L/usr/local/opt/openssl@1.1/lib -L/usr/local/opt/sqlite/lib -Lbuild/temp.macosx-10.15-x86_64-3.9 -lnpymath -o build/lib.macosx-10.15-x86_64-3.9/numpy/linalg/_umath_linalg.cpython-39-darwin.so -Wl,-framework -Wl,Accelerate\r\n building 'numpy.random.mtrand' extension\r\n compiling C sources\r\n C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers\r\n \r\n creating build/temp.macosx-10.15-x86_64-3.9/numpy/random\r\n creating build/temp.macosx-10.15-x86_64-3.9/numpy/random/mtrand\r\n compile options: '-D_FILE_OFFSET_BITS=64 -D_LARGEFILE_SOURCE=1 -D_LARGEFILE64_SOURCE=1 -Inumpy/core/include -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy -Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -c'\r\n clang: numpy/random/mtrand/mtrand.c\r\n clang: numpy/random/mtrand/initarray.cclang: numpy/random/mtrand/randomkit.c\r\n \r\n clang: numpy/random/mtrand/distributions.c\r\n numpy/random/mtrand/mtrand.c:40400:34: error: no member named 'tp_print' in 'struct _typeobject'\r\n __pyx_type_6mtrand_RandomState.tp_print = 0;\r\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ^\r\n numpy/random/mtrand/mtrand.c:42673:22: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations]\r\n (PyUnicode_GET_SIZE(**name) != PyUnicode_GET_SIZE(key)) ? 1 :\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:261:7: note: expanded from macro 'PyUnicode_GET_SIZE'\r\n PyUnicode_WSTR_LENGTH(op) : \\\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH'\r\n #define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here\r\n Py_DEPRECATED(3.3)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'\r\n #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))\r\n ^\r\n numpy/random/mtrand/mtrand.c:42673:22: warning: 'PyUnicode_AsUnicode' is deprecated [-Wdeprecated-declarations]\r\n (PyUnicode_GET_SIZE(**name) != PyUnicode_GET_SIZE(key)) ? 1 :\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:262:14: note: expanded from macro 'PyUnicode_GET_SIZE'\r\n ((void)PyUnicode_AsUnicode(_PyObject_CAST(op)),\\\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:580:1: note: 'PyUnicode_AsUnicode' has been explicitly marked deprecated here\r\n Py_DEPRECATED(3.3) PyAPI_FUNC(Py_UNICODE *) PyUnicode_AsUnicode(\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'\r\n #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))\r\n ^\r\n numpy/random/mtrand/mtrand.c:42673:22: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations]\r\n (PyUnicode_GET_SIZE(**name) != PyUnicode_GET_SIZE(key)) ? 1 :\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:264:8: note: expanded from macro 'PyUnicode_GET_SIZE'\r\n PyUnicode_WSTR_LENGTH(op)))\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH'\r\n #define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here\r\n Py_DEPRECATED(3.3)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'\r\n #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))\r\n ^\r\n numpy/random/mtrand/mtrand.c:42673:52: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations]\r\n (PyUnicode_GET_SIZE(**name) != PyUnicode_GET_SIZE(key)) ? 1 :\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:261:7: note: expanded from macro 'PyUnicode_GET_SIZE'\r\n PyUnicode_WSTR_LENGTH(op) : \\\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH'\r\n #define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here\r\n Py_DEPRECATED(3.3)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'\r\n #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))\r\n ^\r\n numpy/random/mtrand/mtrand.c:42673:52: warning: 'PyUnicode_AsUnicode' is deprecated [-Wdeprecated-declarations]\r\n (PyUnicode_GET_SIZE(**name) != PyUnicode_GET_SIZE(key)) ? 1 :\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:262:14: note: expanded from macro 'PyUnicode_GET_SIZE'\r\n ((void)PyUnicode_AsUnicode(_PyObject_CAST(op)),\\\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:580:1: note: 'PyUnicode_AsUnicode' has been explicitly marked deprecated here\r\n Py_DEPRECATED(3.3) PyAPI_FUNC(Py_UNICODE *) PyUnicode_AsUnicode(\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'\r\n #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))\r\n ^\r\n numpy/random/mtrand/mtrand.c:42673:52: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations]\r\n (PyUnicode_GET_SIZE(**name) != PyUnicode_GET_SIZE(key)) ? 1 :\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:264:8: note: expanded from macro 'PyUnicode_GET_SIZE'\r\n PyUnicode_WSTR_LENGTH(op)))\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH'\r\n #define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here\r\n Py_DEPRECATED(3.3)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'\r\n #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))\r\n ^\r\n numpy/random/mtrand/mtrand.c:42689:26: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations]\r\n (PyUnicode_GET_SIZE(**argname) != PyUnicode_GET_SIZE(key)) ? 1 :\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:261:7: note: expanded from macro 'PyUnicode_GET_SIZE'\r\n PyUnicode_WSTR_LENGTH(op) : \\\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH'\r\n #define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here\r\n Py_DEPRECATED(3.3)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'\r\n #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))\r\n ^\r\n numpy/random/mtrand/mtrand.c:42689:26: warning: 'PyUnicode_AsUnicode' is deprecated [-Wdeprecated-declarations]\r\n (PyUnicode_GET_SIZE(**argname) != PyUnicode_GET_SIZE(key)) ? 1 :\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:262:14: note: expanded from macro 'PyUnicode_GET_SIZE'\r\n ((void)PyUnicode_AsUnicode(_PyObject_CAST(op)),\\\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:580:1: note: 'PyUnicode_AsUnicode' has been explicitly marked deprecated here\r\n Py_DEPRECATED(3.3) PyAPI_FUNC(Py_UNICODE *) PyUnicode_AsUnicode(\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'\r\n #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))\r\n ^\r\n numpy/random/mtrand/mtrand.c:42689:26: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations]\r\n (PyUnicode_GET_SIZE(**argname) != PyUnicode_GET_SIZE(key)) ? 1 :\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:264:8: note: expanded from macro 'PyUnicode_GET_SIZE'\r\n PyUnicode_WSTR_LENGTH(op)))\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH'\r\n #define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here\r\n Py_DEPRECATED(3.3)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'\r\n #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))\r\n ^\r\n numpy/random/mtrand/mtrand.c:42689:59: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations]\r\n (PyUnicode_GET_SIZE(**argname) != PyUnicode_GET_SIZE(key)) ? 1 :\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:261:7: note: expanded from macro 'PyUnicode_GET_SIZE'\r\n PyUnicode_WSTR_LENGTH(op) : \\\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH'\r\n #define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here\r\n Py_DEPRECATED(3.3)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'\r\n #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))\r\n ^\r\n numpy/random/mtrand/mtrand.c:42689:59: warning: 'PyUnicode_AsUnicode' is deprecated [-Wdeprecated-declarations]\r\n (PyUnicode_GET_SIZE(**argname) != PyUnicode_GET_SIZE(key)) ? 1 :\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:262:14: note: expanded from macro 'PyUnicode_GET_SIZE'\r\n ((void)PyUnicode_AsUnicode(_PyObject_CAST(op)),\\\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:580:1: note: 'PyUnicode_AsUnicode' has been explicitly marked deprecated here\r\n Py_DEPRECATED(3.3) PyAPI_FUNC(Py_UNICODE *) PyUnicode_AsUnicode(\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'\r\n #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))\r\n ^\r\n numpy/random/mtrand/mtrand.c:42689:59: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations]\r\n (PyUnicode_GET_SIZE(**argname) != PyUnicode_GET_SIZE(key)) ? 1 :\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:264:8: note: expanded from macro 'PyUnicode_GET_SIZE'\r\n PyUnicode_WSTR_LENGTH(op)))\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH'\r\n #define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here\r\n Py_DEPRECATED(3.3)\r\n ^\r\n /usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'\r\n #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))\r\n ^\r\n 12 warnings and 1 error generated.\r\n error: Command \"clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers -D_FILE_OFFSET_BITS=64 -D_LARGEFILE_SOURCE=1 -D_LARGEFILE64_SOURCE=1 -Inumpy/core/include -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy -Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -c numpy/random/mtrand/mtrand.c -o build/temp.macosx-10.15-x86_64-3.9/numpy/random/mtrand/mtrand.o -MMD -MF build/temp.macosx-10.15-x86_64-3.9/numpy/random/mtrand/mtrand.o.d\" failed with exit status 1"},"reactions":{"kind":"string","value":"{\n \"url\": 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Shouldn't .info be a part of DatasetDict?"},"user":{"kind":"string","value":"{\n \"login\": \"KennethEnevoldsen\",\n \"id\": 23721977,\n \"node_id\": \"MDQ6VXNlcjIzNzIxOTc3\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/23721977?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/KennethEnevoldsen\",\n \"html_url\": \"https://github.com/KennethEnevoldsen\",\n \"followers_url\": \"https://api.github.com/users/KennethEnevoldsen/followers\",\n \"following_url\": \"https://api.github.com/users/KennethEnevoldsen/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/KennethEnevoldsen/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/KennethEnevoldsen/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/KennethEnevoldsen/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/KennethEnevoldsen/orgs\",\n \"repos_url\": \"https://api.github.com/users/KennethEnevoldsen/repos\",\n \"events_url\": \"https://api.github.com/users/KennethEnevoldsen/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/KennethEnevoldsen/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n}"},"labels":{"kind":"list like","value":[],"string":"[]"},"state":{"kind":"string","value":"open"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["We could do something. There is a part of `.info` which is split specific (cache files, split instructions) but maybe if could be made to work.","Yes this was kinda the idea I was going for. DatasetDict.info would be the shared info amongs the datasets (maybe even some info on how they differ). "],"string":"[\n \"We could do something. There is a part of `.info` which is split specific (cache files, split instructions) but maybe if could be made to work.\",\n \"Yes this was kinda the idea I was going for. DatasetDict.info would be the shared info amongs the datasets (maybe even some info on how they differ). \"\n]"},"created_at":{"kind":"timestamp","value":"2021-01-05T13:08:41","string":"2021-01-05T13:08:41"},"updated_at":{"kind":"timestamp","value":"2021-01-07T10:18:06","string":"2021-01-07T10:18:06"},"closed_at":{"kind":"null"},"author_association":{"kind":"string","value":"CONTRIBUTOR"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"Currently, only `Dataset` contains the .info or .features, but as many datasets contains standard splits (train, test) and thus the underlying information is the same (or at least should be) across the datasets. \r\n\r\nFor instance:\r\n```\r\n>>> ds = datasets.load_dataset(\"conll2002\", \"es\")\r\n>>> ds.info\r\nTraceback (most recent call last):\r\n File \"\", line 1, in \r\nAttributeError: 'DatasetDict' object has no attribute 'info'\r\n```\r\n\r\nI could imagine that this wouldn't work for datasets dicts which hold entirely different datasets (multimodal datasets), but it seems odd that splits of the same dataset is treated the same as what is essentially different datasets. \r\n\r\nIntuitively it would also make sense that if a dataset is supplied via. the load_dataset that is have a common .info which covers the entire dataset.\r\n\r\nIt is entirely possible that I am missing another perspective"},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1687/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 0\n}"},"timeline_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1687/timeline"},"performed_via_github_app":{"kind":"null"},"state_reason":{"kind":"null"}}},{"rowIdx":2294,"cells":{"url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1686"},"repository_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets"},"labels_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1686/labels{/name}"},"comments_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1686/comments"},"events_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1686/events"},"html_url":{"kind":"string","value":"https://github.com/huggingface/datasets/issues/1686"},"id":{"kind":"number","value":778921684,"string":"778,921,684"},"node_id":{"kind":"string","value":"MDU6SXNzdWU3Nzg5MjE2ODQ="},"number":{"kind":"number","value":1686,"string":"1,686"},"title":{"kind":"string","value":"Dataset Error: DaNE contains empty samples at the end"},"user":{"kind":"string","value":"{\n \"login\": \"KennethEnevoldsen\",\n \"id\": 23721977,\n \"node_id\": \"MDQ6VXNlcjIzNzIxOTc3\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/23721977?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/KennethEnevoldsen\",\n \"html_url\": \"https://github.com/KennethEnevoldsen\",\n \"followers_url\": \"https://api.github.com/users/KennethEnevoldsen/followers\",\n \"following_url\": \"https://api.github.com/users/KennethEnevoldsen/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/KennethEnevoldsen/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/KennethEnevoldsen/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/KennethEnevoldsen/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/KennethEnevoldsen/orgs\",\n \"repos_url\": \"https://api.github.com/users/KennethEnevoldsen/repos\",\n \"events_url\": \"https://api.github.com/users/KennethEnevoldsen/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/KennethEnevoldsen/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n}"},"labels":{"kind":"list like","value":[],"string":"[]"},"state":{"kind":"string","value":"closed"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["Thanks for reporting, I opened a PR to fix that","One the PR is merged the fix will be available in the next release of `datasets`.\r\n\r\nIf you don't want to wait the next release you can still load the script from the master branch with\r\n\r\n```python\r\nload_dataset(\"dane\", script_version=\"master\")\r\n```","If you have other questions feel free to reopen :) "],"string":"[\n \"Thanks for reporting, I opened a PR to fix that\",\n \"One the PR is merged the fix will be available in the next release of `datasets`.\\r\\n\\r\\nIf you don't want to wait the next release you can still load the script from the master branch with\\r\\n\\r\\n```python\\r\\nload_dataset(\\\"dane\\\", script_version=\\\"master\\\")\\r\\n```\",\n \"If you have other questions feel free to reopen :) \"\n]"},"created_at":{"kind":"timestamp","value":"2021-01-05T11:54:26","string":"2021-01-05T11:54:26"},"updated_at":{"kind":"timestamp","value":"2021-01-05T14:01:09","string":"2021-01-05T14:01:09"},"closed_at":{"kind":"timestamp","value":"2021-01-05T14:00:13","string":"2021-01-05T14:00:13"},"author_association":{"kind":"string","value":"CONTRIBUTOR"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"The dataset DaNE, contains empty samples at the end. It is naturally easy to remove using a filter but should probably not be there, to begin with as it can cause errors.\r\n\r\n```python\r\n>>> import datasets\r\n[...]\r\n>>> dataset = datasets.load_dataset(\"dane\")\r\n[...]\r\n>>> dataset[\"test\"][-1]\r\n{'dep_ids': [], 'dep_labels': [], 'lemmas': [], 'morph_tags': [], 'ner_tags': [], 'pos_tags': [], 'sent_id': '', 'text': '', 'tok_ids': [], 'tokens': []}\r\n>>> dataset[\"train\"][-1]\r\n{'dep_ids': [], 'dep_labels': [], 'lemmas': [], 'morph_tags': [], 'ner_tags': [], 'pos_tags': [], 'sent_id': '', 'text': '', 'tok_ids': [], 'tokens': []}\r\n```\r\n\r\nBest,\r\nKenneth"},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1686/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 0\n}"},"timeline_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1686/timeline"},"performed_via_github_app":{"kind":"null"},"state_reason":{"kind":"string","value":"completed"}}},{"rowIdx":2295,"cells":{"url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1683"},"repository_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets"},"labels_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1683/labels{/name}"},"comments_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1683/comments"},"events_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1683/events"},"html_url":{"kind":"string","value":"https://github.com/huggingface/datasets/issues/1683"},"id":{"kind":"number","value":778287612,"string":"778,287,612"},"node_id":{"kind":"string","value":"MDU6SXNzdWU3NzgyODc2MTI="},"number":{"kind":"number","value":1683,"string":"1,683"},"title":{"kind":"string","value":"`ArrowInvalid` occurs while running `Dataset.map()` function for DPRContext"},"user":{"kind":"string","value":"{\n \"login\": \"abarbosa94\",\n \"id\": 6608232,\n \"node_id\": \"MDQ6VXNlcjY2MDgyMzI=\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/6608232?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/abarbosa94\",\n \"html_url\": \"https://github.com/abarbosa94\",\n \"followers_url\": \"https://api.github.com/users/abarbosa94/followers\",\n \"following_url\": \"https://api.github.com/users/abarbosa94/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/abarbosa94/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/abarbosa94/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/abarbosa94/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/abarbosa94/orgs\",\n \"repos_url\": \"https://api.github.com/users/abarbosa94/repos\",\n \"events_url\": \"https://api.github.com/users/abarbosa94/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/abarbosa94/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n}"},"labels":{"kind":"list like","value":[],"string":"[]"},"state":{"kind":"string","value":"closed"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["Looks like the mapping function returns a dictionary with a 768-dim array in the `embeddings` field. Since the map is batched, we actually expect the `embeddings` field to be an array of shape (batch_size, 768) to have one embedding per example in the batch.\r\n\r\nTo fix that can you try to remove one of the `[0]` ? In my opinion you only need one of them, not two.","It makes sense :D\r\n\r\nIt seems to work! Thanks a lot :))\r\n\r\nClosing the issue"],"string":"[\n \"Looks like the mapping function returns a dictionary with a 768-dim array in the `embeddings` field. Since the map is batched, we actually expect the `embeddings` field to be an array of shape (batch_size, 768) to have one embedding per example in the batch.\\r\\n\\r\\nTo fix that can you try to remove one of the `[0]` ? In my opinion you only need one of them, not two.\",\n \"It makes sense :D\\r\\n\\r\\nIt seems to work! Thanks a lot :))\\r\\n\\r\\nClosing the issue\"\n]"},"created_at":{"kind":"timestamp","value":"2021-01-04T18:47:53","string":"2021-01-04T18:47:53"},"updated_at":{"kind":"timestamp","value":"2021-01-04T19:04:45","string":"2021-01-04T19:04:45"},"closed_at":{"kind":"timestamp","value":"2021-01-04T19:04:45","string":"2021-01-04T19:04:45"},"author_association":{"kind":"string","value":"CONTRIBUTOR"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"It seems to fail the final batch ):\r\n\r\nsteps to reproduce:\r\n```\r\nfrom datasets import load_dataset\r\nfrom elasticsearch import Elasticsearch\r\nimport torch\r\nfrom transformers import file_utils, set_seed\r\nfrom transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast\r\nMAX_SEQ_LENGTH = 256\r\nctx_encoder = DPRContextEncoder.from_pretrained(\"facebook/dpr-ctx_encoder-single-nq-base\", cache_dir=\"../datasets/\")\r\nctx_tokenizer = DPRContextEncoderTokenizerFast.from_pretrained(\r\n \"facebook/dpr-ctx_encoder-single-nq-base\", \r\n cache_dir=\"..datasets/\"\r\n)\r\n\r\ndataset = load_dataset('text', \r\n data_files='data/raw/ARC_Corpus.txt',\r\n cache_dir='../datasets')\r\n\r\ntorch.set_grad_enabled(False)\r\nds_with_embeddings = dataset.map(\r\n lambda example: {\r\n 'embeddings': ctx_encoder(\r\n **ctx_tokenizer(\r\n example[\"text\"], \r\n padding='max_length', \r\n truncation=True, \r\n max_length=MAX_SEQ_LENGTH,\r\n return_tensors=\"pt\"\r\n )\r\n )[0][0].numpy(),\r\n },\r\n batched=True,\r\n load_from_cache_file=False,\r\n batch_size=1000\r\n)\r\n```\r\nARC Corpus can be obtained from [here](https://ai2-datasets.s3-us-west-2.amazonaws.com/arc/ARC-V1-Feb2018.zip)\r\n\r\nAnd then the error:\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nArrowInvalid Traceback (most recent call last)\r\n in \r\n 14 batched=True,\r\n 15 load_from_cache_file=False,\r\n---> 16 batch_size=1000\r\n 17 )\r\n\r\n~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/dataset_dict.py in map(self, function, with_indices, input_columns, batched, batch_size, remove_columns, keep_in_memory, load_from_cache_file, cache_file_names, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc)\r\n 301 num_proc=num_proc,\r\n 302 )\r\n--> 303 for k, dataset in self.items()\r\n 304 }\r\n 305 )\r\n\r\n~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/dataset_dict.py in (.0)\r\n 301 num_proc=num_proc,\r\n 302 )\r\n--> 303 for k, dataset in self.items()\r\n 304 }\r\n 305 )\r\n\r\n~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint)\r\n 1257 fn_kwargs=fn_kwargs,\r\n 1258 new_fingerprint=new_fingerprint,\r\n-> 1259 update_data=update_data,\r\n 1260 )\r\n 1261 else:\r\n\r\n~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs)\r\n 155 }\r\n 156 # apply actual function\r\n--> 157 out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n 158 datasets: List[\"Dataset\"] = list(out.values()) if isinstance(out, dict) else [out]\r\n 159 # re-apply format to the output\r\n\r\n~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/fingerprint.py in wrapper(*args, **kwargs)\r\n 161 # Call actual function\r\n 162 \r\n--> 163 out = func(self, *args, **kwargs)\r\n 164 \r\n 165 # Update fingerprint of in-place transforms + update in-place history of transforms\r\n\r\n~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, update_data)\r\n 1526 if update_data:\r\n 1527 batch = cast_to_python_objects(batch)\r\n-> 1528 writer.write_batch(batch)\r\n 1529 if update_data:\r\n 1530 writer.finalize() # close_stream=bool(buf_writer is None)) # We only close if we are writing in a file\r\n\r\n~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size)\r\n 276 typed_sequence = TypedSequence(batch_examples[col], type=col_type, try_type=col_try_type)\r\n 277 typed_sequence_examples[col] = typed_sequence\r\n--> 278 pa_table = pa.Table.from_pydict(typed_sequence_examples)\r\n 279 self.write_table(pa_table)\r\n 280 \r\n\r\n~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/pyarrow/table.pxi in pyarrow.lib.Table.from_pydict()\r\n\r\n~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/pyarrow/table.pxi in pyarrow.lib.Table.from_arrays()\r\n\r\n~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/pyarrow/table.pxi in pyarrow.lib.Table.validate()\r\n\r\n~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowInvalid: Column 1 named text expected length 768 but got length 1000\r\n```"},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1683/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 0\n}"},"timeline_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1683/timeline"},"performed_via_github_app":{"kind":"null"},"state_reason":{"kind":"string","value":"completed"}}},{"rowIdx":2296,"cells":{"url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1681"},"repository_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets"},"labels_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1681/labels{/name}"},"comments_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1681/comments"},"events_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1681/events"},"html_url":{"kind":"string","value":"https://github.com/huggingface/datasets/issues/1681"},"id":{"kind":"number","value":777644163,"string":"777,644,163"},"node_id":{"kind":"string","value":"MDU6SXNzdWU3Nzc2NDQxNjM="},"number":{"kind":"number","value":1681,"string":"1,681"},"title":{"kind":"string","value":"Dataset \"dane\" missing"},"user":{"kind":"string","value":"{\n \"login\": \"KennethEnevoldsen\",\n \"id\": 23721977,\n \"node_id\": \"MDQ6VXNlcjIzNzIxOTc3\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/23721977?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/KennethEnevoldsen\",\n \"html_url\": \"https://github.com/KennethEnevoldsen\",\n \"followers_url\": \"https://api.github.com/users/KennethEnevoldsen/followers\",\n \"following_url\": \"https://api.github.com/users/KennethEnevoldsen/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/KennethEnevoldsen/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/KennethEnevoldsen/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/KennethEnevoldsen/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/KennethEnevoldsen/orgs\",\n \"repos_url\": \"https://api.github.com/users/KennethEnevoldsen/repos\",\n \"events_url\": \"https://api.github.com/users/KennethEnevoldsen/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/KennethEnevoldsen/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n}"},"labels":{"kind":"list like","value":[],"string":"[]"},"state":{"kind":"string","value":"closed"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["Hi @KennethEnevoldsen ,\r\nI think the issue might be that this dataset was added during the community sprint and has not been released yet. It will be available with the v2 of datasets.\r\nFor now, you should be able to load the datasets after installing the latest (master) version of datasets using pip:\r\npip install git+https://github.com/huggingface/datasets.git@master","The `dane` dataset was added recently, that's why it wasn't available yet. We did an intermediate release today just before the v2.0.\r\n\r\nTo load it you can just update `datasets`\r\n```\r\npip install --upgrade datasets\r\n```\r\n\r\nand then you can load `dane` with\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"dane\")\r\n```","Thanks. Solved the problem."],"string":"[\n \"Hi @KennethEnevoldsen ,\\r\\nI think the issue might be that this dataset was added during the community sprint and has not been released yet. It will be available with the v2 of datasets.\\r\\nFor now, you should be able to load the datasets after installing the latest (master) version of datasets using pip:\\r\\npip install git+https://github.com/huggingface/datasets.git@master\",\n \"The `dane` dataset was added recently, that's why it wasn't available yet. We did an intermediate release today just before the v2.0.\\r\\n\\r\\nTo load it you can just update `datasets`\\r\\n```\\r\\npip install --upgrade datasets\\r\\n```\\r\\n\\r\\nand then you can load `dane` with\\r\\n\\r\\n```python\\r\\nfrom datasets import load_dataset\\r\\n\\r\\ndataset = load_dataset(\\\"dane\\\")\\r\\n```\",\n \"Thanks. Solved the problem.\"\n]"},"created_at":{"kind":"timestamp","value":"2021-01-03T14:03:03","string":"2021-01-03T14:03:03"},"updated_at":{"kind":"timestamp","value":"2021-01-05T08:35:35","string":"2021-01-05T08:35:35"},"closed_at":{"kind":"timestamp","value":"2021-01-05T08:35:13","string":"2021-01-05T08:35:13"},"author_association":{"kind":"string","value":"CONTRIBUTOR"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"the `dane` dataset appear to be missing in the latest version (1.1.3).\r\n\r\n```python\r\n>>> import datasets\r\n>>> datasets.__version__\r\n'1.1.3'\r\n>>> \"dane\" in datasets.list_datasets()\r\nTrue\r\n```\r\n\r\nAs we can see it should be present, but doesn't seem to be findable when using `load_dataset`.\r\n\r\n```python\r\n>>> datasets.load_dataset(\"dane\")\r\nTraceback (most recent call last):\r\n File \"/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/load.py\", line 267, in prepare_module\r\n local_path = cached_path(file_path, download_config=download_config)\r\n File \"/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/utils/file_utils.py\", line 300, in cached_path\r\n output_path = get_from_cache(\r\n File \"/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/utils/file_utils.py\", line 486, in get_from_cache\r\n raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\nFileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dane/dane.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/load.py\", line 278, in prepare_module\r\n local_path = cached_path(file_path, download_config=download_config)\r\n File \"/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/utils/file_utils.py\", line 300, in cached_path\r\n output_path = get_from_cache(\r\n File \"/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/utils/file_utils.py\", line 486, in get_from_cache\r\n raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\nFileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dane/dane.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"\", line 1, in \r\n File \"/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/load.py\", line 588, in load_dataset\r\n module_path, hash = prepare_module(\r\n File \"/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/load.py\", line 280, in prepare_module\r\n raise FileNotFoundError(\r\nFileNotFoundError: Couldn't find file locally at dane/dane.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dane/dane.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dane/dane.py\r\n```\r\n\r\nThis issue might be relevant to @ophelielacroix from the Alexandra Institut whom created the data."},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1681/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 0\n}"},"timeline_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1681/timeline"},"performed_via_github_app":{"kind":"null"},"state_reason":{"kind":"string","value":"completed"}}},{"rowIdx":2297,"cells":{"url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1679"},"repository_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets"},"labels_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1679/labels{/name}"},"comments_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1679/comments"},"events_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1679/events"},"html_url":{"kind":"string","value":"https://github.com/huggingface/datasets/issues/1679"},"id":{"kind":"number","value":777587792,"string":"777,587,792"},"node_id":{"kind":"string","value":"MDU6SXNzdWU3Nzc1ODc3OTI="},"number":{"kind":"number","value":1679,"string":"1,679"},"title":{"kind":"string","value":"Can't import cc100 dataset"},"user":{"kind":"string","value":"{\n \"login\": \"alighofrani95\",\n \"id\": 14968123,\n \"node_id\": \"MDQ6VXNlcjE0OTY4MTIz\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/14968123?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/alighofrani95\",\n \"html_url\": \"https://github.com/alighofrani95\",\n \"followers_url\": \"https://api.github.com/users/alighofrani95/followers\",\n \"following_url\": \"https://api.github.com/users/alighofrani95/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/alighofrani95/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/alighofrani95/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/alighofrani95/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/alighofrani95/orgs\",\n \"repos_url\": \"https://api.github.com/users/alighofrani95/repos\",\n \"events_url\": \"https://api.github.com/users/alighofrani95/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/alighofrani95/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n}"},"labels":{"kind":"list like","value":[],"string":"[]"},"state":{"kind":"string","value":"closed"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["cc100 was added recently, that's why it wasn't available yet.\r\n\r\nTo load it you can just update `datasets`\r\n```\r\npip install --upgrade datasets\r\n```\r\n\r\nand then you can load `cc100` with\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nlang = \"en\"\r\ndataset = load_dataset(\"cc100\", lang=lang, split=\"train\")\r\n```"],"string":"[\n \"cc100 was added recently, that's why it wasn't available yet.\\r\\n\\r\\nTo load it you can just update `datasets`\\r\\n```\\r\\npip install --upgrade datasets\\r\\n```\\r\\n\\r\\nand then you can load `cc100` with\\r\\n\\r\\n```python\\r\\nfrom datasets import load_dataset\\r\\n\\r\\nlang = \\\"en\\\"\\r\\ndataset = load_dataset(\\\"cc100\\\", lang=lang, split=\\\"train\\\")\\r\\n```\"\n]"},"created_at":{"kind":"timestamp","value":"2021-01-03T07:12:56","string":"2021-01-03T07:12:56"},"updated_at":{"kind":"timestamp","value":"2022-10-05T12:42:25","string":"2022-10-05T12:42:25"},"closed_at":{"kind":"timestamp","value":"2022-10-05T12:42:25","string":"2022-10-05T12:42:25"},"author_association":{"kind":"string","value":"NONE"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"There is some issue to import cc100 dataset.\r\n\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"cc100\")\r\n```\r\n\r\nFileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/cc100/cc100.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nFileNotFoundError Traceback (most recent call last)\r\nFileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/cc100/cc100.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nFileNotFoundError Traceback (most recent call last)\r\n/usr/local/lib/python3.6/dist-packages/datasets/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)\r\n 280 raise FileNotFoundError(\r\n 281 \"Couldn't find file locally at {}, or remotely at {} or {}\".format(\r\n--> 282 combined_path, github_file_path, file_path\r\n 283 )\r\n 284 )\r\n\r\nFileNotFoundError: Couldn't find file locally at cc100/cc100.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/cc100/cc100.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/cc100/cc100.py"},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1679/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 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the 800GB Pile dataset?"},"user":{"kind":"string","value":"{\n \"login\": \"lewtun\",\n \"id\": 26859204,\n \"node_id\": \"MDQ6VXNlcjI2ODU5MjA0\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/26859204?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/lewtun\",\n \"html_url\": \"https://github.com/lewtun\",\n \"followers_url\": \"https://api.github.com/users/lewtun/followers\",\n \"following_url\": \"https://api.github.com/users/lewtun/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/lewtun/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/lewtun/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/lewtun/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/lewtun/orgs\",\n \"repos_url\": \"https://api.github.com/users/lewtun/repos\",\n \"events_url\": \"https://api.github.com/users/lewtun/events{/privacy}\",\n \"received_events_url\": 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\"site_admin\": false\n}"},"assignees":{"kind":"list like","value":[{"login":"albertvillanova","id":8515462,"node_id":"MDQ6VXNlcjg1MTU0NjI=","avatar_url":"https://avatars.githubusercontent.com/u/8515462?v=4","gravatar_id":"","url":"https://api.github.com/users/albertvillanova","html_url":"https://github.com/albertvillanova","followers_url":"https://api.github.com/users/albertvillanova/followers","following_url":"https://api.github.com/users/albertvillanova/following{/other_user}","gists_url":"https://api.github.com/users/albertvillanova/gists{/gist_id}","starred_url":"https://api.github.com/users/albertvillanova/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/albertvillanova/subscriptions","organizations_url":"https://api.github.com/users/albertvillanova/orgs","repos_url":"https://api.github.com/users/albertvillanova/repos","events_url":"https://api.github.com/users/albertvillanova/events{/privacy}","received_events_url":"https://api.github.com/users/albertvillanova/received_events","type":"User","site_admin":false}],"string":"[\n {\n \"login\": \"albertvillanova\",\n \"id\": 8515462,\n \"node_id\": \"MDQ6VXNlcjg1MTU0NjI=\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/8515462?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/albertvillanova\",\n \"html_url\": \"https://github.com/albertvillanova\",\n \"followers_url\": \"https://api.github.com/users/albertvillanova/followers\",\n \"following_url\": \"https://api.github.com/users/albertvillanova/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/albertvillanova/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/albertvillanova/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/albertvillanova/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/albertvillanova/orgs\",\n \"repos_url\": \"https://api.github.com/users/albertvillanova/repos\",\n \"events_url\": \"https://api.github.com/users/albertvillanova/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/albertvillanova/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n }\n]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["The pile dataset would be very nice.\r\nBenchmarks show that pile trained models achieve better results than most of actually trained models","The pile can very easily be added and adapted using this [tfds implementation](https://github.com/EleutherAI/The-Pile/blob/master/the_pile/tfds_pile.py) from the repo. \r\n\r\nHowever, the question is whether you'd be ok with 800GB+ cached in your local disk, since the tfds implementation was designed to offload the storage to Google Cloud Storage.","With the dataset streaming feature (see #2375) it will be more convenient to play with such big datasets :)\r\nI'm currently adding C4 (see #2511 ) but I can probably start working on this afterwards","Hi folks! Just wanted to follow up on this -- would be really nice to get the Pile on HF Datasets... unclear if it would be easy to also add partitions of the Pile subject to the original 22 datasets used, but that would be nice too!","Hi folks, thanks to some awesome work by @lhoestq and @albertvillanova you can now stream the Pile as follows:\r\n\r\n```python\r\n# Install master branch of `datasets`\r\npip install git+https://github.com/huggingface/datasets.git#egg=datasets[streaming]\r\npip install zstandard\r\n\r\nfrom datasets import load_dataset\r\n\r\ndset = load_dataset(\"json\", data_files=\"https://the-eye.eu/public/AI/pile/train/00.jsonl.zst\", streaming=True, split=\"train\")\r\nnext(iter(dset))\r\n# {'meta': {'pile_set_name': 'Pile-CC'},\r\n# 'text': 'It is done, and submitted. You can play “Survival of the Tastiest” on Android, and on the web ... '}\r\n```\r\n\r\nNext step is to add the Pile as a \"canonical\" dataset that can be streamed without specifying the file names explicitly :)","> Hi folks! Just wanted to follow up on this -- would be really nice to get the Pile on HF Datasets... unclear if it would be easy to also add partitions of the Pile subject to the original 22 datasets used, but that would be nice too!\r\n\r\nHi @siddk thanks to a tip from @richarddwang it seems we can access some of the partitions that EleutherAI created for the Pile [here](https://the-eye.eu/public/AI/pile_preliminary_components/). What's missing are links to the preprocessed versions of pre-existing datasets like DeepMind Mathematics and OpenSubtitles, but worst case we do the processing ourselves and host these components on the Hub.\r\n\r\nMy current idea is that we could provide 23 configs: one for each of the 22 datasets and an `all` config that links to the train / dev / test splits that EleutherAI released [here](https://the-eye.eu/public/AI/pile/), e.g.\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n# Load a single component\r\nyoutube_subtitles = load_dataset(\"the_pile\", \"youtube_subtitles\")\r\n# Load the train / dev / test splits of the whole corpus\r\ndset = load_dataset(\"the_pile\", \"all\")\r\n```\r\n\r\nIdeally we'd like everything to be compatible with the streaming API and there's ongoing work by @albertvillanova to make this happen for the various compression algorithms.\r\n\r\ncc @lhoestq ","Ah I just saw that @lhoestq is already thinking about the specifying of one or more subsets in [this PR](https://github.com/huggingface/datasets/pull/2817#issuecomment-901874049) :)"],"string":"[\n \"The pile dataset would be very nice.\\r\\nBenchmarks show that pile trained models achieve better results than most of actually trained models\",\n \"The pile can very easily be added and adapted using this [tfds implementation](https://github.com/EleutherAI/The-Pile/blob/master/the_pile/tfds_pile.py) from the repo. \\r\\n\\r\\nHowever, the question is whether you'd be ok with 800GB+ cached in your local disk, since the tfds implementation was designed to offload the storage to Google Cloud Storage.\",\n \"With the dataset streaming feature (see #2375) it will be more convenient to play with such big datasets :)\\r\\nI'm currently adding C4 (see #2511 ) but I can probably start working on this afterwards\",\n \"Hi folks! Just wanted to follow up on this -- would be really nice to get the Pile on HF Datasets... unclear if it would be easy to also add partitions of the Pile subject to the original 22 datasets used, but that would be nice too!\",\n \"Hi folks, thanks to some awesome work by @lhoestq and @albertvillanova you can now stream the Pile as follows:\\r\\n\\r\\n```python\\r\\n# Install master branch of `datasets`\\r\\npip install git+https://github.com/huggingface/datasets.git#egg=datasets[streaming]\\r\\npip install zstandard\\r\\n\\r\\nfrom datasets import load_dataset\\r\\n\\r\\ndset = load_dataset(\\\"json\\\", data_files=\\\"https://the-eye.eu/public/AI/pile/train/00.jsonl.zst\\\", streaming=True, split=\\\"train\\\")\\r\\nnext(iter(dset))\\r\\n# {'meta': {'pile_set_name': 'Pile-CC'},\\r\\n# 'text': 'It is done, and submitted. You can play “Survival of the Tastiest” on Android, and on the web ... '}\\r\\n```\\r\\n\\r\\nNext step is to add the Pile as a \\\"canonical\\\" dataset that can be streamed without specifying the file names explicitly :)\",\n \"> Hi folks! Just wanted to follow up on this -- would be really nice to get the Pile on HF Datasets... unclear if it would be easy to also add partitions of the Pile subject to the original 22 datasets used, but that would be nice too!\\r\\n\\r\\nHi @siddk thanks to a tip from @richarddwang it seems we can access some of the partitions that EleutherAI created for the Pile [here](https://the-eye.eu/public/AI/pile_preliminary_components/). What's missing are links to the preprocessed versions of pre-existing datasets like DeepMind Mathematics and OpenSubtitles, but worst case we do the processing ourselves and host these components on the Hub.\\r\\n\\r\\nMy current idea is that we could provide 23 configs: one for each of the 22 datasets and an `all` config that links to the train / dev / test splits that EleutherAI released [here](https://the-eye.eu/public/AI/pile/), e.g.\\r\\n\\r\\n```python\\r\\nfrom datasets import load_dataset\\r\\n\\r\\n# Load a single component\\r\\nyoutube_subtitles = load_dataset(\\\"the_pile\\\", \\\"youtube_subtitles\\\")\\r\\n# Load the train / dev / test splits of the whole corpus\\r\\ndset = load_dataset(\\\"the_pile\\\", \\\"all\\\")\\r\\n```\\r\\n\\r\\nIdeally we'd like everything to be compatible with the streaming API and there's ongoing work by @albertvillanova to make this happen for the various compression algorithms.\\r\\n\\r\\ncc @lhoestq \",\n \"Ah I just saw that @lhoestq is already thinking about the specifying of one or more subsets in [this PR](https://github.com/huggingface/datasets/pull/2817#issuecomment-901874049) :)\"\n]"},"created_at":{"kind":"timestamp","value":"2021-01-01T22:58:12","string":"2021-01-01T22:58:12"},"updated_at":{"kind":"timestamp","value":"2021-12-01T15:29:07","string":"2021-12-01T15:29:07"},"closed_at":{"kind":"timestamp","value":"2021-12-01T15:29:07","string":"2021-12-01T15:29:07"},"author_association":{"kind":"string","value":"MEMBER"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"## Adding a Dataset\r\n- **Name:** The Pile\r\n- **Description:** The Pile is a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality datasets combined together. See [here](https://twitter.com/nabla_theta/status/1345130408170541056?s=20) for the Twitter announcement\r\n- **Paper:** https://pile.eleuther.ai/paper.pdf\r\n- **Data:** https://pile.eleuther.ai/\r\n- **Motivation:** Enables hardcore (GPT-3 scale!) language modelling\r\n\r\n## Remarks\r\nGiven the extreme size of this dataset, I'm not sure how feasible this will be to include in `datasets` 🤯 . I'm also unsure how many `datasets` users are pretraining LMs, so the usage of this dataset may not warrant the effort to integrate it.\r\n"},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1675/reactions\",\n \"total_count\": 13,\n \"+1\": 5,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 1,\n \"heart\": 0,\n \"rocket\": 5,\n \"eyes\": 2\n}"},"timeline_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1675/timeline"},"performed_via_github_app":{"kind":"null"},"state_reason":{"kind":"string","value":"completed"}}},{"rowIdx":2299,"cells":{"url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1674"},"repository_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets"},"labels_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1674/labels{/name}"},"comments_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1674/comments"},"events_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1674/events"},"html_url":{"kind":"string","value":"https://github.com/huggingface/datasets/issues/1674"},"id":{"kind":"number","value":777321840,"string":"777,321,840"},"node_id":{"kind":"string","value":"MDU6SXNzdWU3NzczMjE4NDA="},"number":{"kind":"number","value":1674,"string":"1,674"},"title":{"kind":"string","value":"dutch_social can't be loaded"},"user":{"kind":"string","value":"{\n \"login\": \"koenvandenberge\",\n \"id\": 10134844,\n \"node_id\": \"MDQ6VXNlcjEwMTM0ODQ0\",\n \"avatar_url\": \"https://avatars.githubusercontent.com/u/10134844?v=4\",\n \"gravatar_id\": \"\",\n \"url\": \"https://api.github.com/users/koenvandenberge\",\n \"html_url\": \"https://github.com/koenvandenberge\",\n \"followers_url\": \"https://api.github.com/users/koenvandenberge/followers\",\n \"following_url\": \"https://api.github.com/users/koenvandenberge/following{/other_user}\",\n \"gists_url\": \"https://api.github.com/users/koenvandenberge/gists{/gist_id}\",\n \"starred_url\": \"https://api.github.com/users/koenvandenberge/starred{/owner}{/repo}\",\n \"subscriptions_url\": \"https://api.github.com/users/koenvandenberge/subscriptions\",\n \"organizations_url\": \"https://api.github.com/users/koenvandenberge/orgs\",\n \"repos_url\": \"https://api.github.com/users/koenvandenberge/repos\",\n \"events_url\": \"https://api.github.com/users/koenvandenberge/events{/privacy}\",\n \"received_events_url\": \"https://api.github.com/users/koenvandenberge/received_events\",\n \"type\": \"User\",\n \"site_admin\": false\n}"},"labels":{"kind":"list like","value":[],"string":"[]"},"state":{"kind":"string","value":"closed"},"locked":{"kind":"bool","value":false,"string":"false"},"assignee":{"kind":"null"},"assignees":{"kind":"list like","value":[],"string":"[]"},"milestone":{"kind":"null"},"comments":{"kind":"list like","value":["exactly the same issue in some other datasets.\r\nDid you find any solution??\r\n","Hi @koenvandenberge and @alighofrani95!\r\nThe datasets you're experiencing issues with were most likely added recently to the `datasets` library, meaning they have not been released yet. They will be released with the v2 of the library.\r\nMeanwhile, you can still load the datasets using one of the techniques described in this issue: #1641 \r\nLet me know if this helps!","Maybe we should do a small release on Monday in the meantime @lhoestq ?","Yes sure !","I just did the release :)\r\n\r\nTo load it you can just update `datasets`\r\n```\r\npip install --upgrade datasets\r\n```\r\n\r\nand then you can load `dutch_social` with\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"dutch_social\")\r\n```","@lhoestq could you also shed light on the Hindi Wikipedia Dataset for issue number #1673. Will this also be available in the new release that you committed recently?","The issue is different for this one, let me give more details in the issue","Okay. Could you comment on the #1673 thread? Actually @thomwolf had commented that if i use datasets library from source, it would allow me to download the Hindi Wikipedia Dataset but even the version 1.1.3 gave me the same issue. The details are there in the issue #1673 thread."],"string":"[\n \"exactly the same issue in some other datasets.\\r\\nDid you find any solution??\\r\\n\",\n \"Hi @koenvandenberge and @alighofrani95!\\r\\nThe datasets you're experiencing issues with were most likely added recently to the `datasets` library, meaning they have not been released yet. They will be released with the v2 of the library.\\r\\nMeanwhile, you can still load the datasets using one of the techniques described in this issue: #1641 \\r\\nLet me know if this helps!\",\n \"Maybe we should do a small release on Monday in the meantime @lhoestq ?\",\n \"Yes sure !\",\n \"I just did the release :)\\r\\n\\r\\nTo load it you can just update `datasets`\\r\\n```\\r\\npip install --upgrade datasets\\r\\n```\\r\\n\\r\\nand then you can load `dutch_social` with\\r\\n\\r\\n```python\\r\\nfrom datasets import load_dataset\\r\\n\\r\\ndataset = load_dataset(\\\"dutch_social\\\")\\r\\n```\",\n \"@lhoestq could you also shed light on the Hindi Wikipedia Dataset for issue number #1673. Will this also be available in the new release that you committed recently?\",\n \"The issue is different for this one, let me give more details in the issue\",\n \"Okay. Could you comment on the #1673 thread? Actually @thomwolf had commented that if i use datasets library from source, it would allow me to download the Hindi Wikipedia Dataset but even the version 1.1.3 gave me the same issue. The details are there in the issue #1673 thread.\"\n]"},"created_at":{"kind":"timestamp","value":"2021-01-01T17:37:08","string":"2021-01-01T17:37:08"},"updated_at":{"kind":"timestamp","value":"2022-10-05T13:03:26","string":"2022-10-05T13:03:26"},"closed_at":{"kind":"timestamp","value":"2022-10-05T13:03:26","string":"2022-10-05T13:03:26"},"author_association":{"kind":"string","value":"NONE"},"active_lock_reason":{"kind":"null"},"draft":{"kind":"null"},"pull_request":{"kind":"null"},"body":{"kind":"string","value":"Hi all,\r\n\r\nI'm trying to import the `dutch_social` dataset described [here](https://huggingface.co/datasets/dutch_social).\r\n\r\nHowever, the code that should load the data doesn't seem to be working, in particular because the corresponding files can't be found at the provided links.\r\n\r\n```\r\n(base) Koens-MacBook-Pro:~ koenvandenberge$ python\r\nPython 3.7.4 (default, Aug 13 2019, 15:17:50) \r\n[Clang 4.0.1 (tags/RELEASE_401/final)] :: Anaconda, Inc. on darwin\r\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n>>> from datasets import load_dataset\r\ndataset = load_dataset(\r\n 'dutch_social')\r\n>>> dataset = load_dataset(\r\n... 'dutch_social')\r\nTraceback (most recent call last):\r\n File \"/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py\", line 267, in prepare_module\r\n local_path = cached_path(file_path, download_config=download_config)\r\n File \"/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py\", line 308, in cached_path\r\n use_etag=download_config.use_etag,\r\n File \"/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py\", line 486, in get_from_cache\r\n raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\nFileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dutch_social/dutch_social.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py\", line 278, in prepare_module\r\n local_path = cached_path(file_path, download_config=download_config)\r\n File \"/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py\", line 308, in cached_path\r\n use_etag=download_config.use_etag,\r\n File \"/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py\", line 486, in get_from_cache\r\n raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\nFileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dutch_social/dutch_social.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"\", line 2, in \r\n File \"/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py\", line 589, in load_dataset\r\n path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True\r\n File \"/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py\", line 282, in prepare_module\r\n combined_path, github_file_path, file_path\r\nFileNotFoundError: Couldn't find file locally at dutch_social/dutch_social.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dutch_social/dutch_social.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dutch_social/dutch_social.py\r\n```"},"reactions":{"kind":"string","value":"{\n \"url\": \"https://api.github.com/repos/huggingface/datasets/issues/1674/reactions\",\n \"total_count\": 0,\n \"+1\": 0,\n \"-1\": 0,\n \"laugh\": 0,\n \"hooray\": 0,\n \"confused\": 0,\n \"heart\": 0,\n \"rocket\": 0,\n \"eyes\": 0\n}"},"timeline_url":{"kind":"string","value":"https://api.github.com/repos/huggingface/datasets/issues/1674/timeline"},"performed_via_github_app":{"kind":"null"},"state_reason":{"kind":"string","value":"completed"}}}],"truncated":false,"partial":false},"paginationData":{"pageIndex":22,"numItemsPerPage":100,"numTotalItems":2719,"offset":2200,"length":100}},"jwt":"eyJhbGciOiJFZERTQSJ9.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc1NjU0NDE4MSwic3ViIjoiL2RhdGFzZXRzL0ZhbmNlbGx1L2dpdGh1Yi1pc3N1ZXMiLCJleHAiOjE3NTY1NDc3ODEsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.bMjsFyDnkvd0R7SNLOkqY6b9npGgQEWdfCDzOLl3Ya84GnYlwFv-D9xFx7SPsLdOfBVAOIxBVmoNgK_6yhR-Bg","displayUrls":true},"discussionsStats":{"closed":0,"open":1,"total":1},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
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https://api.github.com/repos/huggingface/datasets/issues/1922
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813,140,806
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1,922
How to update the "wino_bias" dataset
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[ "Hi @JieyuZhao !\r\n\r\nYou can edit the dataset card of wino_bias to update the URL via a Pull Request. This would be really appreciated :)\r\n\r\nThe dataset card is the README.md file you can find at https://github.com/huggingface/datasets/tree/master/datasets/wino_bias\r\nAlso the homepage url is also mentioned in the wino_bias.py so feel free to update it there as well.\r\n\r\nYou can create a Pull Request directly from the github interface by editing the files you want and submit a PR, or from a local clone of the repository.\r\n\r\nThanks for noticing !" ]
2021-02-22T05:39:39
2021-02-22T10:35:59
null
CONTRIBUTOR
null
null
null
Hi all, Thanks for the efforts to collect all the datasets! But I think there is a problem with the wino_bias dataset. The current link is not correct. How can I update that? Thanks!
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812,626,872
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1,919
Failure to save with save_to_disk
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[ "Hi thanks for reporting and for proposing a fix :)\r\n\r\nI just merged a fix, feel free to try it from the master branch !", "Closing since this has been fixed by #1923" ]
2021-02-20T14:18:10
2021-03-03T17:40:27
2021-03-03T17:40:27
CONTRIBUTOR
null
null
null
When I try to save a dataset locally using the `save_to_disk` method I get the error: ```bash FileNotFoundError: [Errno 2] No such file or directory: '/content/squad/train/squad-train.arrow' ``` To replicate: 1. Install `datasets` from master 2. Run this code: ```python from datasets import load_dataset squad = load_dataset("squad") # or any other dataset squad.save_to_disk("squad") # error here ``` The problem is that the method is not creating a directory with the name `dataset_path` for saving the dataset in (i.e. it's not creating the *train* and *validation* directories in this case). After creating the directory the problem resolves. I'll open a PR soon doing that and linking this issue.
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1,917
UnicodeDecodeError: windows 10 machine
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[ "upgraded to php 3.9.2 and it works!" ]
2021-02-19T22:13:05
2021-02-19T22:41:11
2021-02-19T22:40:28
NONE
null
null
null
Windows 10 Php 3.6.8 when running ``` import datasets oscar_am = datasets.load_dataset("oscar", "unshuffled_deduplicated_am") print(oscar_am["train"][0]) ``` I get the following error ``` file "C:\PYTHON\3.6.8\lib\encodings\cp1252.py", line 23, in decode return codecs.charmap_decode(input,self.errors,decoding_table)[0] UnicodeDecodeError: 'charmap' codec can't decode byte 0x9d in position 58: character maps to <undefined> ```
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812,229,654
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1,915
Unable to download `wiki_dpr`
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null
[ "Thanks for reporting ! This is a bug. For now feel free to set `ignore_verifications=False` in `load_dataset`.\r\nI'm working on a fix", "I just merged a fix :)\r\n\r\nWe'll do a patch release soon. In the meantime feel free to try it from the master branch\r\nThanks again for reporting !", "Closing since this has been fixed by #1925" ]
2021-02-19T18:11:32
2021-03-03T17:40:48
2021-03-03T17:40:48
NONE
null
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I am trying to download the `wiki_dpr` dataset. Specifically, I want to download `psgs_w100.multiset.no_index` with no embeddings/no index. In order to do so, I ran: `curr_dataset = load_dataset("wiki_dpr", embeddings_name="multiset", index_name="no_index")` However, I got the following error: `datasets.utils.info_utils.UnexpectedDownloadedFile: {'embeddings_index'}` I tried adding in flags `with_embeddings=False` and `with_index=False`: `curr_dataset = load_dataset("wiki_dpr", with_embeddings=False, with_index=False, embeddings_name="multiset", index_name="no_index")` But I got the following error: `raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) datasets.utils.info_utils.ExpectedMoreDownloadedFiles: {‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_5’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_15’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_30’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_36’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_18’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_41’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_13’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_48’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_10’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_23’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_14’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_34’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_43’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_40’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_47’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_3’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_24’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_7’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_33’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_46’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_42’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_27’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_29’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_26’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_22’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_4’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_20’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_39’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_6’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_16’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_8’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_35’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_49’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_17’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_25’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_0’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_38’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_12’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_44’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_1’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_32’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_19’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_31’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_37’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_9’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_11’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_21’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_28’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_45’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_2’}` Is there anything else I need to set to download the dataset? **UPDATE**: just running `curr_dataset = load_dataset("wiki_dpr", with_embeddings=False, with_index=False)` gives me the same error.
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812,009,956
MDU6SXNzdWU4MTIwMDk5NTY=
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Saving processed dataset running infinitely
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[ "@thomwolf @lhoestq can you guys please take a look and recommend some solution.", "am suspicious of this thing? what's the purpose of this? pickling and unplickling\r\n`self = pickle.loads(pickle.dumps(self))`\r\n\r\n```\r\n def save_to_disk(self, dataset_path: str, fs=None):\r\n \"\"\"\r\n Saves a dataset to a dataset directory, or in a filesystem using either :class:`datasets.filesystem.S3FileSystem` or any implementation of ``fsspec.spec.AbstractFileSystem``.\r\n\r\n Args:\r\n dataset_path (``str``): path (e.g. ``dataset/train``) or remote uri (e.g. ``s3://my-bucket/dataset/train``) of the dataset directory where the dataset will be saved to\r\n fs (Optional[:class:`datasets.filesystem.S3FileSystem`,``fsspec.spec.AbstractFileSystem``], `optional`, defaults ``None``): instance of :class:`datasets.filesystem.S3FileSystem` or ``fsspec.spec.AbstractFileSystem`` used to download the files from remote filesystem.\r\n \"\"\"\r\n assert (\r\n not self.list_indexes()\r\n ), \"please remove all the indexes using `dataset.drop_index` before saving a dataset\"\r\n self = pickle.loads(pickle.dumps(self))\r\n ```", "It's been 24 hours and sadly it's still running. With not a single byte written", "Tried finding the root cause but was unsuccessful.\r\nI am using lazy tokenization with `dataset.set_transform()`, it works like a charm with almost same performance as pre-compute.", "Hi ! This very probably comes from the hack you used.\r\n\r\nThe pickling line was added an a sanity check because save_to_disk uses the same assumptions as pickling for a dataset object. The main assumption is that memory mapped pyarrow tables must be reloadable from the disk. In your case it's not possible since you altered the pyarrow table.\r\nI would suggest you to rebuild a valid Dataset object from your new pyarrow table. To do so you must first save your new table to a file, and then make a new Dataset object from that arrow file.\r\n\r\nYou can save the raw arrow table (without all the `datasets.Datasets` metadata) by calling `map` with `cache_file_name=\"path/to/outut.arrow\"` and `function=None`. Having `function=None` makes the `map` write your dataset on disk with no data transformation.\r\n\r\nOnce you have your new arrow file, load it with `datasets.Dataset.from_file` to have a brand new Dataset object :)\r\n\r\nIn the future we'll have a better support for the fast filtering method from pyarrow so you don't have to do this very unpractical workaround. Since it breaks somes assumptions regarding the core behavior of Dataset objects, this is very discouraged.", "Thanks, @lhoestq for your response. Will try your solution and let you know." ]
2021-02-19T13:09:19
2021-02-23T07:34:44
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I have a text dataset of size 220M. For pre-processing, I need to tokenize this and filter rows with the large sequence. My tokenization took roughly 3hrs. I used map() with batch size 1024 and multi-process with 96 processes. filter() function was way to slow, so I used a hack to use pyarrow filter table function, which is damm fast. Mentioned [here](https://github.com/huggingface/datasets/issues/1796) ```dataset._data = dataset._data.filter(...)``` It took 1 hr for the filter. Then i use `save_to_disk()` on processed dataset and it is running forever. I have been waiting since 8 hrs, it has not written a single byte. Infact it has actually read from disk more than 100GB, screenshot below shows the stats using `iotop`. Second process is the one. <img width="1672" alt="Screenshot 2021-02-19 at 6 36 53 PM" src="https://user-images.githubusercontent.com/20911334/108508197-7325d780-72e1-11eb-8369-7c057d137d81.png"> I am not able to figure out, whether this is some issue with dataset library or that it is due to my hack for filter() function.
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DBPedia14 Dataset Checksum bug?
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[ "Hi ! :)\r\n\r\nThis looks like the same issue as https://github.com/huggingface/datasets/issues/1856 \r\nBasically google drive has quota issues that makes it inconvenient for downloading files.\r\n\r\nIf the quota of a file is exceeded, you have to wait 24h for the quota to reset (which is painful).\r\n\r\nThe error says that the checksum of the downloaded file doesn't match because google drive returns a text file with the \"Quota Exceeded\" error instead of the actual data file.", "Thanks @lhoestq! Yes, it seems back to normal after a couple of days." ]
2021-02-18T22:25:48
2021-02-22T23:22:05
2021-02-22T23:22:04
CONTRIBUTOR
null
null
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Hi there!!! I've been using successfully the DBPedia dataset (https://huggingface.co/datasets/dbpedia_14) with my codebase in the last couple of weeks, but in the last couple of days now I get this error: ``` Traceback (most recent call last): File "./conditional_classification/basic_pipeline.py", line 178, in <module> main() File "./conditional_classification/basic_pipeline.py", line 128, in main corpus.load_data(limit_train_examples_per_class=args.data_args.train_examples_per_class, File "/home/fp/dev/conditional_classification/conditional_classification/datasets_base.py", line 83, in load_data datasets = load_dataset(self.name, split=dataset_split) File "/home/fp/anaconda3/envs/conditional/lib/python3.8/site-packages/datasets/load.py", line 609, in load_dataset builder_instance.download_and_prepare( File "/home/fp/anaconda3/envs/conditional/lib/python3.8/site-packages/datasets/builder.py", line 526, in download_and_prepare self._download_and_prepare( File "/home/fp/anaconda3/envs/conditional/lib/python3.8/site-packages/datasets/builder.py", line 586, in _download_and_prepare verify_checksums( File "/home/fp/anaconda3/envs/conditional/lib/python3.8/site-packages/datasets/utils/info_utils.py", line 39, in verify_checksums raise NonMatchingChecksumError(error_msg + str(bad_urls)) datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://drive.google.com/uc?export=download&id=0Bz8a_Dbh9QhbQ2Vic1kxMmZZQ1k'] ``` I've seen this has happened before in other datasets as reported in #537. I've tried clearing my cache and call again `load_dataset` but still is not working. My same codebase is successfully downloading and using other datasets (e.g. AGNews) without any problem, so I guess something has happened specifically to the DBPedia dataset in the last few days. Can you please check if there's a problem with the checksums? Or this is related to any other stuff? I've seen that the path in the cache for the dataset is `/home/fp/.cache/huggingface/datasets/d_bpedia14/dbpedia_14/2.0.0/a70413e39e7a716afd0e90c9e53cb053691f56f9ef5fe317bd07f2c368e8e897...` and includes `d_bpedia14` instead maybe of `dbpedia_14`. Was this maybe a bug introduced recently? Thanks!
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Feature Request: Support for Pandas `Categorical`
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[ "We already have a ClassLabel type that does this kind of mapping between the label ids (integers) and actual label values (strings).\r\n\r\nI wonder if actually we should use the DictionaryType from Arrow and the Categorical type from pandas for the `datasets` ClassLabel feature type.\r\nCurrently ClassLabel corresponds to `pa.int64()` in pyarrow and `dtype('int64')` in pandas (so the label names are lost during conversions).\r\n\r\nWhat do you think ?", "Now that I've heard you explain ClassLabel, that makes a lot of sense! While DictionaryType for Arrow (I think) can have arbitrarily typed keys, so it won't cover all potential cases, pandas' Category is *probably* the most common use for that pyarrow type, and ClassLabel should match that perfectly?\r\n\r\nOther thoughts:\r\n\r\n- changing the resulting patype on ClassLabel might be backward-incompatible? I'm not totally sure if users of the `datasets` library tend to directly access the `patype` attribute (I don't think we really do, but we haven't been using it for very long yet).\r\n- would ClassLabel's dtype change to `dict[int64, string]`? It seems like in practice a ClassLabel (when not explicitly specified) would be constructed from the DictionaryType branch of `generate_from_arrow_type`, so it's not totally clear to me that anyone ever actually accesses/uses that dtype?\r\n- I don't quite know how `.int2str` and `.str2int` are used in practice - would those be kept? Perhaps the implementation might actually be substantially smaller if we can just delegate to pyarrow's dict methods?\r\n\r\nAnother idea that just occurred to me: add a branch in here to generate a ClassLabel if the dict key is int64 and the values are string: https://github.com/huggingface/datasets/blob/master/src/datasets/features.py#L932 , and then don't touch anything else.\r\n\r\nIn practice, I don't think this would be backward-incompatible in a way anyone would care about since the current behavior just throws an exception, and this way, we could support *reading* a pandas Categorical into a `Dataset` as a ClassLabel. I *think* from there, while it would require some custom glue it wouldn't be too hard to convert the ClassLabel into a pandas Category if we want to go back - I think this would improve on the current behavior without risking changing the behavior of ClassLabel in a backward-incompat way.\r\n\r\nThoughts? I'm not sure if this is overly cautious. Whichever approach you think is better, I'd be happy to take it on!\r\n", "I think we can first keep the int64 precision but with an arrow Dictionary for ClassLabel, and focus on the connection with arrow and pandas.\r\n\r\nIn this scope, I really like the idea of checking for the dictionary type:\r\n\r\n> Another idea that just occurred to me: add a branch in here to generate a ClassLabel if the dict key is int64 and the values are string: https://github.com/huggingface/datasets/blob/master/src/datasets/features.py#L932 , and then don't touch anything else.\r\n\r\nThis looks like a great start.\r\n\r\nThen as you said we'd have to add the conversion from classlabel to the correct arrow dictionary type. Arrow is already able to convert from arrow Dictionary to pandas Categorical so it should be enough.\r\n\r\nI can see two things that we must take case of to make this change backward compatible:\r\n- first we must still be able to load an arrow file with arrow int64 dtype and `datasets` ClassLabel type without crashing. This can be fixed by casting the arrow int64 array to an arrow Dictionary array on-the-fly when loading the table in the ArrowReader.\r\n- then we still have to return integers when accessing examples from a ClassLabel column. Currently it would return the strings values since it's based on the pandas behavior for converting from pandas to python/numpy. To do so we just have to adapt the python/numpy extractors in formatting.py (it takes care of converting an arrow table to a dictionary of python objects by doing arrow table -> pandas dataframe -> python dictionary)\r\n\r\nAny help on this matter is very much welcome :)" ]
2021-02-18T19:46:05
2021-02-23T14:38:50
null
CONTRIBUTOR
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``` from datasets import Dataset import pandas as pd import pyarrow df = pd.DataFrame(pd.Series(["a", "b", "c", "a"], dtype="category")) pyarrow.Table.from_pandas(df) Dataset.from_pandas(df) # Throws NotImplementedError # TODO(thom) this will need access to the dictionary as well (for labels). I.e. to the py_table ``` I'm curious if https://github.com/huggingface/datasets/blob/master/src/datasets/features.py#L796 could be built out in a way similar to `Sequence`? e.g. a `Map` class (or whatever name the maintainers might prefer) that can accept: ``` index_type = generate_from_arrow_type(pa_type.index_type) value_type = generate_from_arrow_type(pa_type.value_type) ``` and then additional code points to modify: - FeatureType: https://github.com/huggingface/datasets/blob/master/src/datasets/features.py#L694 - A branch to handle Map in get_nested_type: https://github.com/huggingface/datasets/blob/master/src/datasets/features.py#L719 - I don't quite understand what `encode_nested_example` does but perhaps a branch there? https://github.com/huggingface/datasets/blob/master/src/datasets/features.py#L755 - Similarly, I don't quite understand why `Sequence` is used this way in `generate_from_dict`, but perhaps a branch here? https://github.com/huggingface/datasets/blob/master/src/datasets/features.py#L775 I couldn't find other usages of `Sequence` outside of defining specific datasets, so I'm not sure if that's a comprehensive set of touchpoints.
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1,898
ALT dataset has repeating instances in all splits
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null
[ "Thanks for reporting. This looks like a very bad issue. I'm looking into it", "I just merged a fix, we'll do a patch release soon. Thanks again for reporting, and sorry for the inconvenience.\r\nIn the meantime you can load `ALT` using `datasets` from the master branch", "Thanks!!! works perfectly in the bleading edge master version", "Closed by #1899" ]
2021-02-17T12:51:42
2021-02-19T06:18:46
2021-02-19T06:18:46
NONE
null
null
null
The [ALT](https://huggingface.co/datasets/alt) dataset has all the same instances within each split :/ Seemed like a great dataset for some experiments I wanted to carry out, especially since its medium-sized, and has all splits. Would be great if this could be fixed :) Added a snapshot of the contents from `explore-datset` feature, for quick reference. ![image](https://user-images.githubusercontent.com/33179372/108206321-442a2d00-714c-11eb-882f-b4b6e708ef9c.png)
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1,895
Bug Report: timestamp[ns] not recognized
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[ "Thanks for reporting !\r\n\r\nYou're right, `string_to_arrow` should be able to take `\"timestamp[ns]\"` as input and return the right pyarrow timestamp type.\r\nFeel free to suggest a fix for `string_to_arrow` and open a PR if you want to contribute ! This would be very appreciated :)\r\n\r\nTo give you more context:\r\n\r\nAs you may know we define the features types of a dataset using the `Features` object in combination with feature types like `Value`. For example\r\n```python\r\nfeatures = Features({\r\n \"age\": Value(\"int32\")\r\n})\r\n```\r\nHowever under the hood we are actually using pyarrow to store the data, and so we have a mapping between the feature types of `datasets` and the types of pyarrow.\r\n\r\nFor example, the `Value` feature types are created from a pyarrow type with `Value(str(pa_type))`.\r\nHowever it looks like the conversion back to a pyarrow type doesn't work with `\"timestamp[ns]\"`.\r\nThis is the `string_to_arrow` function you highlighted that does this conversion, so we should fix that.\r\n\r\n", "Thanks for the clarification @lhoestq !\r\n\r\nThis may be a little bit of a stupid question, but I wanted to clarify one more thing before I took a stab at this:\r\n\r\nWhen the features get inferred, I believe they already have a pyarrow schema (https://github.com/huggingface/datasets/blob/master/src/datasets/arrow_dataset.py#L234).\r\n\r\nWe then convert it to a string (https://github.com/huggingface/datasets/blob/master/src/datasets/features.py#L778) only to convert it back into the arrow type (https://github.com/huggingface/datasets/blob/master/src/datasets/features.py#L143, and https://github.com/huggingface/datasets/blob/master/src/datasets/features.py#L35). Is there a reason for this round-trip?\r\n\r\nI'll open a PR later to add `timestamp` support to `string_to_arrow`, but I'd be curious to understand since it feels like there may be some opportunities to simplify!", "The objective in terms of design is to make it easy to create Features in a pythonic way. So for example we use a string to define a Value type.\r\nThat's why when inferring the Features from an arrow schema we have to find the right string definitions for Value types. I guess we could also have a constructor `Value.from_arrow_type` to avoid recreating the arrow type, but this could create silent errors if the pyarrow type doesn't have a valid mapping with the string definition. The \"round-trip\" is used to enforce that the ground truth is the string definition, not the pyarrow type, and also as a sanity check.\r\n\r\nLet me know if that makes sense ", "OK I think I understand now:\r\n\r\nFeatures are datasets' internal representation of a schema type, distinct from pyarrow's schema.\r\nValue() corresponds to pyarrow's \"primitive\" types (e.g. `int` or `string`, but not things like `list` or `dict`).\r\n`get_nested_type()` (https://github.com/huggingface/datasets/blob/master/src/datasets/features.py#L698) and `generate_from_arrow_type()` (https://github.com/huggingface/datasets/blob/master/src/datasets/features.py#L778) *should* be inverses of each other, and similarly, for the primitive values, `string_to_arrow()` and `Value.__call__` (https://github.com/huggingface/datasets/blob/master/src/datasets/features.py#L146) should be inverses of each other?\r\n\r\nThanks for taking the time to answer - I just wanted to make sure I understood before opening a PR so I'm not disrupting anything about how the codebase is expected to work!", "Yes you're totally right :)" ]
2021-02-16T20:38:04
2021-02-19T18:27:11
2021-02-19T18:27:11
CONTRIBUTOR
null
null
null
Repro: ``` from datasets import Dataset import pandas as pd import pyarrow df = pd.DataFrame(pd.date_range("2018-01-01", periods=3, freq="H")) pyarrow.Table.from_pandas(df) Dataset.from_pandas(df) # Throws ValueError: Neither timestamp[ns] nor timestamp[ns]_ seems to be a pyarrow data type. ``` The factory function seems to be just "timestamp": https://arrow.apache.org/docs/python/generated/pyarrow.timestamp.html#pyarrow.timestamp It seems like https://github.com/huggingface/datasets/blob/master/src/datasets/features.py#L36-L43 could have a little bit of additional structure for handling these cases? I'd be happy to take a shot at opening a PR if I could receive some guidance on whether parsing something like `timestamp[ns]` and resolving it to timestamp('ns') is the goal of this method. Alternatively, if I'm using this incorrectly (e.g. is the expectation that we always provide a schema when timestamps are involved?), that would be very helpful to know as well! ``` $ pip list # only the relevant libraries/versions datasets 1.2.1 pandas 1.0.3 pyarrow 3.0.0 ```
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809,609,654
MDU6SXNzdWU4MDk2MDk2NTQ=
1,894
benchmarking against MMapIndexedDataset
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[ "Hi sam !\r\nIndeed we can expect the performances to be very close since both MMapIndexedDataset and the `datasets` implem use memory mapping. With memory mapping what determines the I/O performance is the speed of your hard drive/SSD.\r\n\r\nIn terms of performance we're pretty close to the optimal speed for reading text, even though I found recently that we could still slightly improve speed for big datasets (see [here](https://github.com/huggingface/datasets/issues/1803)).\r\n\r\nIn terms of number of examples and example sizes, the only limit is the available disk space you have.\r\n\r\nI haven't used `psrecord` yet but it seems to be a very interesting tool for benchmarking. Currently for benchmarks we only have github actions to avoid regressions in terms of speed. But it would be cool to have benchmarks with comparisons with other dataset tools ! This would be useful to many people", "Also I would be interested to know what data types `MMapIndexedDataset` supports. Is there some documentation somewhere ?", "no docs haha, it's written to support integer numpy arrays.\r\n\r\nYou can build one in fairseq with, roughly:\r\n```bash\r\n\r\nwget https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-103-raw-v1.zip\r\nunzip wikitext-103-raw-v1.zip\r\nexport dd=$HOME/fairseq-py/wikitext-103-raw\r\n\r\nexport mm_dir=$HOME/mmap_wikitext2\r\nmkdir -p gpt2_bpe\r\nwget -O gpt2_bpe/encoder.json https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/encoder.json\r\nwget -O gpt2_bpe/vocab.bpe https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/vocab.bpe\r\nwget -O gpt2_bpe/dict.txt https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/dict.txt\r\nfor SPLIT in train valid; do \\\r\n python -m examples.roberta.multiprocessing_bpe_encoder \\\r\n --encoder-json gpt2_bpe/encoder.json \\\r\n --vocab-bpe gpt2_bpe/vocab.bpe \\\r\n --inputs /scratch/stories_small/${SPLIT}.txt \\\r\n --outputs /scratch/stories_small/${SPLIT}.bpe \\\r\n --keep-empty \\\r\n --workers 60; \\\r\ndone\r\n\r\nmkdir -p $mm_dir\r\nfairseq-preprocess \\\r\n --only-source \\\r\n --srcdict gpt2_bpe/dict.txt \\\r\n --trainpref $dd/wiki.train.bpe \\\r\n --validpref $dd/wiki.valid.bpe \\\r\n --destdir $mm_dir \\\r\n --workers 60 \\\r\n --dataset-impl mmap\r\n```\r\n\r\nI'm noticing in my benchmarking that it's much smaller on disk than arrow (200mb vs 900mb), and that both incur significant cost by increasing the number of data loader workers. \r\nThis somewhat old [post](https://ray-project.github.io/2017/10/15/fast-python-serialization-with-ray-and-arrow.html) suggests there are some gains to be had from using `pyarrow.serialize(array).tobuffer()`. I haven't yet figured out how much of this stuff `pa.Table` does under the hood.\r\n\r\nThe `MMapIndexedDataset` bottlenecks we are working on improving (by using arrow) are:\r\n1) `MMapIndexedDataset`'s index, which stores offsets, basically gets read in its entirety by each dataloading process.\r\n2) we have separate, identical, `MMapIndexedDatasets` on each dataloading worker, so there's redundancy there; we wonder if there is a way that arrow can somehow dedupe these in shared memory.\r\n\r\nIt will take me a few hours to get `MMapIndexedDataset` benchmarks out of `fairseq`/onto a branch in this repo, but I'm happy to invest the time if you're interested in collaborating on some performance hacking." ]
2021-02-16T20:04:58
2021-02-17T18:52:28
null
CONTRIBUTOR
null
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null
I am trying to benchmark my datasets based implementation against fairseq's [`MMapIndexedDataset`](https://github.com/pytorch/fairseq/blob/master/fairseq/data/indexed_dataset.py#L365) and finding that, according to psrecord, my `datasets` implem uses about 3% more CPU memory and runs 1% slower for `wikitext103` (~1GB of tokens). Questions: 1) Is this (basically identical) performance expected? 2) Is there a scenario where this library will outperform `MMapIndexedDataset`? (maybe more examples/larger examples?) 3) Should I be using different benchmarking tools than `psrecord`/how do you guys do benchmarks? Thanks in advance! Sam
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809,556,503
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1,893
wmt19 is broken
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[ "This was also mentioned in https://github.com/huggingface/datasets/issues/488 \r\n\r\nThe bucket where is data was stored seems to be unavailable now. Maybe we can change the URL to the ones in https://conferences.unite.un.org/uncorpus/en/downloadoverview ?", "Closing since this has been fixed by #1912" ]
2021-02-16T18:39:58
2021-03-03T17:42:02
2021-03-03T17:42:02
CONTRIBUTOR
null
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1. Check which lang pairs we have: `--dataset_name wmt19`: Please pick one among the available configs: ['cs-en', 'de-en', 'fi-en', 'gu-en', 'kk-en', 'lt-en', 'ru-en', 'zh-en', 'fr-de'] 2. OK, let's pick `ru-en`: `--dataset_name wmt19 --dataset_config "ru-en"` no cookies: ``` Traceback (most recent call last): File "./run_seq2seq.py", line 661, in <module> main() File "./run_seq2seq.py", line 317, in main datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name) File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/load.py", line 740, in load_dataset builder_instance.download_and_prepare( File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/builder.py", line 572, in download_and_prepare self._download_and_prepare( File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/builder.py", line 628, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/home/stas/.cache/huggingface/modules/datasets_modules/datasets/wmt19/436092de5f3faaf0fc28bc84875475b384e90a5470fa6afaee11039ceddc5052/wmt_utils.py", line 755, in _split_generators downloaded_files = dl_manager.download_and_extract(urls_to_download) File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/download_manager.py", line 276, in download_and_extract return self.extract(self.download(url_or_urls)) File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/download_manager.py", line 191, in download downloaded_path_or_paths = map_nested( File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/py_utils.py", line 233, in map_nested mapped = [ File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/py_utils.py", line 234, in <listcomp> _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/py_utils.py", line 190, in _single_map_nested mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar] File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/py_utils.py", line 190, in <listcomp> mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar] File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/py_utils.py", line 172, in _single_map_nested return function(data_struct) File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/download_manager.py", line 211, in _download return cached_path(url_or_filename, download_config=download_config) File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/file_utils.py", line 274, in cached_path output_path = get_from_cache( File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/file_utils.py", line 584, in get_from_cache raise FileNotFoundError("Couldn't find file at {}".format(url)) FileNotFoundError: Couldn't find file at https://storage.googleapis.com/tfdataset-data/downloadataset/uncorpus/UNv1.0.en-ru.tar.gz ```
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MDU6SXNzdWU4MDk1NTQxNzQ=
1,892
request to mirror wmt datasets, as they are really slow to download
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[ "Yes that would be awesome. Not only the download speeds are awful, but also some files are missing.\r\nWe list all the URLs in the datasets/wmt19/wmt_utils.py so we can make a script to download them all and host on S3.\r\nAlso I think most of the materials are under the CC BY-NC-SA 3.0 license (must double check) so it should be possible to redistribute the data with no issues.\r\n\r\ncc @patrickvonplaten who knows more about the wmt scripts", "Yeah, the scripts are pretty ugly! A big refactor would make sense here...and I also remember that the datasets were veeery slow to download", "I'm downloading them.\r\nI'm starting with the ones hosted on http://data.statmt.org which are the slowest ones", "@lhoestq better to use our new git-based system than just raw S3, no? (that way we have built-in CDN etc.)", "Closing since the urls were changed to mirror urls in #1912 ", "Hi there! What about mirroring other datasets like [CCAligned](http://www.statmt.org/cc-aligned/) as well? All of them are really slow to download..." ]
2021-02-16T18:36:11
2021-10-26T06:55:42
2021-03-25T11:53:23
CONTRIBUTOR
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null
null
Would it be possible to mirror the wmt data files under hf? Some of them take hours to download and not because of the local speed. They are all quite small datasets, just extremely slow to download. Thank you!
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suggestion to improve a missing dataset error
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[ "This is the current error thrown for missing datasets:\r\n```\r\nFileNotFoundError: Couldn't find a dataset script at C:\\Users\\Mario\\Desktop\\projects\\datasets\\missing_dataset\\missing_dataset.py or any data file in the same directory. Couldn't find 'missing_dataset' on the Hugging Face Hub either: FileNotFoundError: Dataset 'missing_dataset' doesn't exist on the Hub. If the repo is private, make sure you are authenticated with `use_auth_token=True` after logging in with `huggingface-cli login`.\r\n```\r\n\r\nSeems much more informative, so I think we can close this issue." ]
2021-02-16T18:29:13
2022-10-05T12:48:38
2022-10-05T12:48:38
CONTRIBUTOR
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null
null
I was using `--dataset_name wmt19` all was good. Then thought perhaps wmt20 is out, so I tried to use `--dataset_name wmt20`, got 3 different errors (1 repeated twice), none telling me the real issue - that `wmt20` isn't in the `datasets`: ``` True, predict_with_generate=True) Traceback (most recent call last): File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/load.py", line 323, in prepare_module local_path = cached_path(file_path, download_config=download_config) File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/file_utils.py", line 274, in cached_path output_path = get_from_cache( File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/file_utils.py", line 584, in get_from_cache raise FileNotFoundError("Couldn't find file at {}".format(url)) FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/wmt20/wmt20.py During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/load.py", line 335, in prepare_module local_path = cached_path(file_path, download_config=download_config) File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/file_utils.py", line 274, in cached_path output_path = get_from_cache( File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/file_utils.py", line 584, in get_from_cache raise FileNotFoundError("Couldn't find file at {}".format(url)) FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/wmt20/wmt20.py During handling of the above exception, another exception occurred: Traceback (most recent call last): File "./run_seq2seq.py", line 661, in <module> main() File "./run_seq2seq.py", line 317, in main datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name) File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/load.py", line 706, in load_dataset module_path, hash, resolved_file_path = prepare_module( File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/load.py", line 343, in prepare_module raise FileNotFoundError( FileNotFoundError: Couldn't find file locally at wmt20/wmt20.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/wmt20/wmt20.py. The file is also not present on the master branch on github. ``` Suggestion: if it is not in a local path, check that there is an actual `https://github.com/huggingface/datasets/tree/master/datasets/wmt20` first and assert "dataset `wmt20` doesn't exist in datasets", rather than trying to find a load script - since the whole repo is not there. The error occured when running: ``` cd examples/seq2seq export BS=16; rm -r output_dir; PYTHONPATH=../../src USE_TF=0 CUDA_VISIBLE_DEVICES=0 python ./run_seq2seq.py --model_name_or_path t5-small --output_dir output_dir --adam_eps 1e-06 --do_eval --evaluation_strategy=steps --label_smoothing 0.1 --learning_rate 3e-5 --logging_first_step --logging_steps 1000 --max_source_length 128 --max_target_length 128 --num_train_epochs 1 --overwrite_output_dir --per_device_eval_batch_size $BS --predict_with_generate --eval_steps 25000 --sortish_sampler --task translation_en_to_ro --val_max_target_length 128 --warmup_steps 500 --max_val_samples 500 --dataset_name wmt20 --dataset_config "ro-en" --source_prefix "translate English to Romanian: " ``` Thanks.
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808,462,272
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1,877
Allow concatenation of both in-memory and on-disk datasets
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[ "I started working on this. My idea is to first add the pyarrow Table wrappers InMemoryTable and MemoryMappedTable that both implement what's necessary regarding copy/pickle. Then have another wrapper that takes the concatenation of InMemoryTable/MemoryMappedTable objects.\r\n\r\nWhat's important here is that concatenating two tables into one doesn't double the memory used (`total_allocated_bytes()` stays the same).", "Hi @lhoestq @albertvillanova,\r\n\r\nI checked the linked issues and PR, this seems like a great idea. Would you mind elaborating on the in-memory and memory-mapped datasets? \r\nBased on my understanding, it is something like this, please correct me if I am wrong:\r\n1. For in-memory datasets, we don't have any dataset files so the entire dataset is pickled to the cache during loading, and then whenever required it is unpickled .\r\n2. For on-disk/memory-mapped datasets, we have the data files provided, so they can be re-loaded from the paths, and only the file-paths are stored while pickling.\r\n\r\nIf this is correct, will the feature also handle pickling/unpickling of a concatenated dataset? Will this be cached?\r\n\r\nThis also leads me to ask whether datasets are chunked during pickling? \r\n\r\nThanks,\r\nGunjan", "Hi ! Yes you're totally right about your two points :)\r\n\r\nAnd in the case of a concatenated dataset, then we should reload each sub-table depending on whether it's in-memory or memory mapped. That means the dataset will be made of several blocks in order to keep track of what's from memory and what's memory mapped. This allows to pickle/unpickle concatenated datasets", "Hi @lhoestq\r\n\r\nThanks, that sounds nice. Can you explain where the issue of the double memory may arise? Also, why is the existing `concatenate_datasets` not sufficient for this purpose?", "Hi @lhoestq,\r\n\r\nWill the `add_item` feature also help with lazy writing (or no caching) during `map`/`filter`?", "> Can you explain where the issue of the double memory may arise?\r\n\r\nWe have to keep each block (in-memory vs memory mapped) separated in order to be able to reload them with pickle.\r\nOn the other hand we also need to have the full table from mixed in-memory and memory mapped data in order to iterate or extract data conveniently. That means that each block is accessible twice: once in the full table, and once in the separated blocks. But since pyarrow tables concatenation doesn't double the memory, then building the full table doesn't cost memory which is what we want :)\r\n\r\n> Also, why is the existing concatenate_datasets not sufficient for this purpose?\r\n\r\nThe existing `concatenate_datasets` doesn't support having both in-memory and memory mapped data together (there's no fancy block separation logic). It works for datasets fully in-memory or fully memory mapped but not a mix of the two.\r\n\r\n> Will the add_item feature also help with lazy writing (or no caching) during map/filter?\r\n\r\nIt will enable the implementation of the fast, masked filter from this discussion: https://github.com/huggingface/datasets/issues/1949\r\nHowever I don't think this will affect map." ]
2021-02-15T11:39:46
2021-03-26T16:51:58
2021-03-26T16:51:58
MEMBER
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null
This is a prerequisite for the addition of the `add_item` feature (see #1870). Currently there is one assumption that we would need to change: a dataset is either fully in memory (dataset._data_files is empty), or the dataset can be reloaded from disk (using the dataset._data_files). This assumption is used for pickling for example: - in-memory dataset can just be pickled/unpickled in-memory - on-disk dataset can be unloaded to only keep the filepaths when pickling, and then reloaded from the disk when unpickling Maybe let's have a design that allows a Dataset to have a Table that can be rebuilt from heterogenous sources like in-memory tables or on-disk tables ? This could also be further extended in the future One idea would be to define a list of sources and each source implements a way to reload its corresponding pyarrow Table. Then the dataset would be the concatenation of all these tables. Depending on the source type, the serialization using pickle would be different. In-memory data would be copied while on-disk data would simply be replaced by the path to these data. If you have some ideas you would like to share about the design/API feel free to do so :) cc @albertvillanova
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load_dataset("multi_woz_v22") NonMatchingChecksumError
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[ "Thanks for reporting !\r\nThis is due to the changes made in the data files in the multiwoz repo: https://github.com/budzianowski/multiwoz/pull/59\r\nI'm opening a PR to update the checksums of the data files.", "I just merged the fix. It will be available in the new release of `datasets` later today.\r\nYou'll be able to get the new version with\r\n```\r\npip install --upgrade datasets\r\n```", "Hi, I still meet the error when loading the datasets after upgradeing datasets.\r\n\r\nraise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/dialog_acts.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/test/dialogues_001.json']", "This must be related to https://github.com/budzianowski/multiwoz/pull/72\r\nThose files have changed, let me update the checksums for this dataset.\r\n\r\nFor now you can use `ignore_verifications=True` in `load_dataset` to skip the checksum verification." ]
2021-02-14T19:14:48
2021-08-04T18:08:00
2021-08-04T18:08:00
NONE
null
null
null
Hi, it seems that loading the multi_woz_v22 dataset gives a NonMatchingChecksumError. To reproduce: `dataset = load_dataset('multi_woz_v22','v2.2_active_only',split='train')` This will give the following error: ``` raise NonMatchingChecksumError(error_msg + str(bad_urls)) datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/dialog_acts.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_001.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_003.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_004.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_005.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_006.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_007.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_008.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_009.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_010.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_012.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_013.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_014.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_015.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_016.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_017.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/dev/dialogues_001.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/dev/dialogues_002.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/test/dialogues_001.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/test/dialogues_002.json'] ```
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MDU6SXNzdWU4MDc3MTE5MzU=
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Adding a new column to the dataset after set_format was called
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[ "Hi ! Indeed if you add a column to a formatted dataset, then the new dataset gets a new formatting in which:\r\n```\r\nnew formatted columns = (all columns - previously unformatted columns)\r\n```\r\nTherefore the new column is going to be formatted using the `torch` formatting.\r\n\r\nIf you want your new column to be unformatted you can re-run this line:\r\n```python\r\ndata.set_format(\"torch\", columns=[\"some_integer_column1\", \"some_integer_column2\"], output_all_columns=True)\r\n```", "Hi, thanks that solved my problem. Maybe mention that in the documentation. ", "Ok cool :) \r\nAlso I just did a PR to mention this behavior in the documentation", "Closed by #1888" ]
2021-02-13T09:14:35
2021-03-30T14:01:45
2021-03-30T14:01:45
NONE
null
null
null
Hi, thanks for the nice library. I'm in the process of creating a custom dataset, which has a mix of tensors and lists of strings. I stumbled upon an error and want to know if its a problem on my side. I load some lists of strings and integers, then call `data.set_format("torch", columns=["some_integer_column1", "some_integer_column2"], output_all_columns=True)`. This converts the integer columns into tensors, but keeps the lists of strings as they are. I then call `map` to add a new column to my dataset, which is a **list of strings**. Once I iterate through my dataset, I get an error that the new column can't be converted into a tensor (which is probably caused by `set_format`). Below some pseudo code: ```python def augment_func(sample: Dict) -> Dict: # do something return { "some_integer_column1" : augmented_data["some_integer_column1"], # <-- tensor "some_integer_column2" : augmented_data["some_integer_column2"], # <-- tensor "NEW_COLUMN": targets, # <-- list of strings } data = datasets.load_dataset(__file__, data_dir="...", split="train") data.set_format("torch", columns=["some_integer_column1", "some_integer_column2"], output_all_columns=True) augmented_dataset = data.map(augment_func, batched=False) for sample in augmented_dataset: print(sample) # fails ``` and the exception: ```python Traceback (most recent call last): File "dataset.py", line 487, in <module> main() File "dataset.py", line 471, in main for sample in augmented_dataset: File "lib/python3.8/site-packages/datasets/arrow_dataset.py", line 697, in __iter__ yield self._getitem( File "lib/python3.8/site-packages/datasets/arrow_dataset.py", line 1069, in _getitem outputs = self._convert_outputs( File "lib/python3.8/site-packages/datasets/arrow_dataset.py", line 890, in _convert_outputs v = map_nested(command, v, **map_nested_kwargs) File "lib/python3.8/site-packages/datasets/utils/py_utils.py", line 225, in map_nested return function(data_struct) File "lib/python3.8/site-packages/datasets/arrow_dataset.py", line 850, in command return [map_nested(command, i, **map_nested_kwargs) for i in x] File "lib/python3.8/site-packages/datasets/arrow_dataset.py", line 850, in <listcomp> return [map_nested(command, i, **map_nested_kwargs) for i in x] File "lib/python3.8/site-packages/datasets/utils/py_utils.py", line 225, in map_nested return function(data_struct) File "lib/python3.8/site-packages/datasets/arrow_dataset.py", line 850, in command return [map_nested(command, i, **map_nested_kwargs) for i in x] File "lib/python3.8/site-packages/datasets/arrow_dataset.py", line 850, in <listcomp> return [map_nested(command, i, **map_nested_kwargs) for i in x] File "lib/python3.8/site-packages/datasets/utils/py_utils.py", line 225, in map_nested return function(data_struct) File "lib/python3.8/site-packages/datasets/arrow_dataset.py", line 851, in command return torch.tensor(x, **format_kwargs) TypeError: new(): invalid data type 'str' ``` Thanks!
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ERROR WHEN USING SET_TRANSFORM()
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[ "Hi @alejandrocros it looks like an incompatibility with the current Trainer @sgugger \r\nIndeed currently the Trainer of `transformers` doesn't support a dataset with a transform\r\n\r\nIt looks like it comes from this line: https://github.com/huggingface/transformers/blob/f51188cbe74195c14c5b3e2e8f10c2f435f9751a/src/transformers/trainer.py#L442\r\n\r\nThis line sets the format to not return certain unused columns. But this has two issues:\r\n1. it forgets to also set the format_kwargs (this causes the error you got):\r\n```python\r\ndataset.set_format(type=dataset.format[\"type\"], columns=columns, format_kwargs=dataset.format[\"format_kwargs\"])\r\n```\r\n2. the Trainer wants to keep only the fields that are used as input for a model. However for a dataset with a transform, the output fields are often different from the columns fields. For example from a column \"text\" in the dataset, the strings can be transformed on-the-fly into \"input_ids\". If you want your dataset to only output certain fields and not other you must change your transform function.\r\n", "FYI that option can be removed with `remove_unused_columns = False` in your `TrainingArguments`, so there is a workaround @alexvaca0 while the fix in `Trainer` is underway.\r\n\r\n@lhoestq I think I will just use the line you suggested and if someone is using the columns that are removed in their transform they will need to change `remove_unused_columns` to `False`. We might switch the default of that argument in the next version if that proves too bug-proof.", "I've tried your solutions @sgugger @lhoestq and the good news is that it throws no error. However, TPU training is taking forever, in 1 hour it has only trained 1 batch of 8192 elements, which doesn't make much sense... Is it possible that \"on the fly\" tokenization of batches is slowing down TPU training to that extent?", "I'm pretty sure this is because of padding but @sgugger might know better", "I don't know what the value of `padding` is in your lines of code pasted above so I can't say for sure. The first batch will be very slow on TPU since it compiles everything, so that's normal (1 hour is long but 8192 elements is also large). Then if your batches are not of the same lengths, it will recompile everything at each step instead of using the same graph, which will be very slow, so you should double check you are using padding to make everything the exact same shape. ", "I have tried now on a GPU and it goes smooth! Amazing feature .set_transform() instead of .map()! Now I can pre-train my model without the hard disk limitation. Thanks for your work all HuggingFace team!! :clap: ", "In the end, to make it work I turned to A-100 gpus instead of TPUS, among other changes. Set_transform doesn't work as expected and slows down training very much even in GPUs, and applying map destroys the disk, as it multiplies by 100 the size of the data passed to it (due to inefficient implementation converting strings to int64 floats I guess). For that reason, I chose to use datasets to load the data as text, and then edit the Collator from Transformers to tokenize every batch it receives before processing it. That way, I'm being able to train fast, without memory breaks, without the disk being unnecessarily filled, while making use of GPUs almost all the time I'm paying for them (the map function over the whole dataset took ~15hrs, in which you're not training at all). I hope this info helps others that are looking for training a language model from scratch cheaply, I'm going to close the issue as the optimal solution I found after many experiments to the problem posted in it is explained above. ", "Great comment @alexvaca0 . I think that we could re-open the issue as a reformulation of why it takes so much space to save the arrow. Saving a 1% of oscar corpus takes more thank 600 GB (it breaks when it pass 600GB because it is the free memory that I have at this moment) when the full dataset is 1,3 TB. I have a 1TB M.2 NVMe disk that I can not train on because the saved .arrow files goes crazily big. If you can share your Collator I will be grateful. " ]
2021-02-12T10:38:31
2021-03-01T14:04:24
2021-02-24T12:00:43
NONE
null
null
null
Hi, I'm trying to use dataset.set_transform(encode) as @lhoestq told me in this issue: https://github.com/huggingface/datasets/issues/1825#issuecomment-774202797 However, when I try to use Trainer from transformers with such dataset, it throws an error: ``` TypeError: __init__() missing 1 required positional argument: 'transform' [INFO|trainer.py:357] 2021-02-12 10:18:09,893 >> The following columns in the training set don't have a corresponding argument in `AlbertForMaskedLM.forward` and have been ignored: text. Exception in device=TPU:0: __init__() missing 1 required positional argument: 'transform' Traceback (most recent call last): File "/anaconda3/envs/torch-xla-1.7/lib/python3.6/site-packages/torch_xla/distributed/xla_multiprocessing.py", line 330, in _mp_start_fn _start_fn(index, pf_cfg, fn, args) File "/anaconda3/envs/torch-xla-1.7/lib/python3.6/site-packages/torch_xla/distributed/xla_multiprocessing.py", line 324, in _start_fn fn(gindex, *args) File "/home/alejandro_vaca/transformers/examples/language-modeling/run_mlm_wwm.py", line 368, in _mp_fn main() File "/home/alejandro_vaca/transformers/examples/language-modeling/run_mlm_wwm.py", line 332, in main data_collator=data_collator, File "/anaconda3/envs/torch-xla-1.7/lib/python3.6/site-packages/transformers/trainer.py", line 286, in __init__ self._remove_unused_columns(self.train_dataset, description="training") File "/anaconda3/envs/torch-xla-1.7/lib/python3.6/site-packages/transformers/trainer.py", line 359, in _remove_unused_columns dataset.set_format(type=dataset.format["type"], columns=columns) File "/home/alejandro_vaca/datasets/src/datasets/fingerprint.py", line 312, in wrapper out = func(self, *args, **kwargs) File "/home/alejandro_vaca/datasets/src/datasets/arrow_dataset.py", line 818, in set_format _ = get_formatter(type, **format_kwargs) File "/home/alejandro_vaca/datasets/src/datasets/formatting/__init__.py", line 112, in get_formatter return _FORMAT_TYPES[format_type](**format_kwargs) TypeError: __init__() missing 1 required positional argument: 'transform' ``` The code I'm using: ```{python} def tokenize_function(examples): # Remove empty lines examples["text"] = [line for line in examples["text"] if len(line) > 0 and not line.isspace()] return tokenizer(examples["text"], padding=padding, truncation=True, max_length=data_args.max_seq_length) datasets.set_transform(tokenize_function) data_collator = DataCollatorForWholeWordMask(tokenizer=tokenizer, mlm_probability=data_args.mlm_probability) # Initialize our Trainer trainer = Trainer( model=model, args=training_args, train_dataset=datasets["train"] if training_args.do_train else None, eval_dataset=datasets["val"] if training_args.do_eval else None, tokenizer=tokenizer, data_collator=data_collator, ) ``` I've installed from source, master branch.
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MDU6SXNzdWU4MDYxNzI4NDM=
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Add Winogender Schemas
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[ "Nevermind, this one is already available on the hub under the name `'wino_bias'`: https://huggingface.co/datasets/wino_bias" ]
2021-02-11T08:18:38
2021-02-11T08:19:51
2021-02-11T08:19:51
CONTRIBUTOR
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## Adding a Dataset - **Name:** Winogender Schemas - **Description:** Winogender Schemas (inspired by Winograd Schemas) are minimal pairs of sentences that differ only by the gender of one pronoun in the sentence, designed to test for the presence of gender bias in automated coreference resolution systems. - **Paper:** https://arxiv.org/abs/1804.09301 - **Data:** https://github.com/rudinger/winogender-schemas (see data directory) - **Motivation:** Testing gender bias in automated coreference resolution systems, improve coreference resolution in general. Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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Add WikiCREM
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[ "Hi @NielsRogge I would like to work on this dataset.\r\n\r\nThanks!", "Hi @udapy, are you working on this?" ]
2021-02-11T08:16:00
2021-03-07T07:27:13
null
CONTRIBUTOR
null
null
null
## Adding a Dataset - **Name:** WikiCREM - **Description:** A large unsupervised corpus for coreference resolution. - **Paper:** https://arxiv.org/abs/1905.06290 - **Github repo:**: https://github.com/vid-koci/bert-commonsense - **Data:** https://ora.ox.ac.uk/objects/uuid:c83e94bb-7584-41a1-aef9-85b0e764d9e3 - **Motivation:** Coreference resolution, common sense reasoning Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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805,479,025
MDU6SXNzdWU4MDU0NzkwMjU=
1,859
Error "in void don't know how to serialize this type of index" when saving index to disk when device=0 (GPU)
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[ "Hi @corticalstack ! Thanks for reporting. Indeed in the recent versions of Faiss we must use `getDevice` to check if the index in on GPU.\r\n\r\nI'm opening a PR", "I fixed this issue. It should work fine now.\r\nFeel free to try it out by installing `datasets` from source.\r\nOtherwise you can wait for the next release of `datasets` (in a few days)", "Thanks for such a quick fix and merge to master, pip installed git master, tested all OK" ]
2021-02-10T12:41:00
2021-02-10T18:32:12
2021-02-10T18:17:47
NONE
null
null
null
Error serializing faiss index. Error as follows: `Error in void faiss::write_index(const faiss::Index*, faiss::IOWriter*) at /home/conda/feedstock_root/build_artifacts/faiss-split_1612472484670/work/faiss/impl/index_write.cpp:453: don't know how to serialize this type of index` Note: `torch.cuda.is_available()` reports: ``` Cuda is available cuda:0 ``` Adding index, device=0 for GPU. `dataset.add_faiss_index(column='embeddings', index_name='idx_embeddings', device=0)` However, during a quick debug, self.faiss_index has no attr "device" when checked in` search.py, method save`, so fails to transform gpu index to cpu index. If I add index without device, index is saved OK. ``` def save(self, file: str): """Serialize the FaissIndex on disk""" import faiss # noqa: F811 if ( hasattr(self.faiss_index, "device") and self.faiss_index.device is not None and self.faiss_index.device > -1 ): index = faiss.index_gpu_to_cpu(self.faiss_index) else: index = self.faiss_index faiss.write_index(index, file) ```
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805,391,107
MDU6SXNzdWU4MDUzOTExMDc=
1,857
Unable to upload "community provided" dataset - 400 Client Error
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[ "Hi ! We're in the process of switching the community datasets to git repos, exactly like what we're doing for models.\r\nYou can find an example here:\r\nhttps://huggingface.co/datasets/lhoestq/custom_squad/tree/main\r\n\r\nWe'll update the CLI in the coming days and do a new release :)\r\n\r\nAlso cc @julien-c maybe we can make improve the error message ?" ]
2021-02-10T10:39:01
2021-08-03T05:06:13
2021-08-03T05:06:13
CONTRIBUTOR
null
null
null
Hi, i'm trying to a upload a dataset as described [here](https://huggingface.co/docs/datasets/v1.2.0/share_dataset.html#sharing-a-community-provided-dataset). This is what happens: ``` $ datasets-cli login $ datasets-cli upload_dataset my_dataset About to upload file /path/to/my_dataset/dataset_infos.json to S3 under filename my_dataset/dataset_infos.json and namespace username About to upload file /path/to/my_dataset/my_dataset.py to S3 under filename my_dataset/my_dataset.py and namespace username Proceed? [Y/n] Y Uploading... This might take a while if files are large 400 Client Error: Bad Request for url: https://huggingface.co/api/datasets/presign huggingface.co migrated to a new model hosting system. You need to upgrade to transformers v3.5+ to upload new models. More info at https://discuss.hugginface.co or https://twitter.com/julien_c. Thank you! ``` I'm using the latest releases of datasets and transformers.
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load_dataset("amazon_polarity") NonMatchingChecksumError
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[ "Hi ! This issue may be related to #996 \r\nThis comes probably from the Quota Exceeded error from Google Drive.\r\nCan you try again tomorrow and see if you still have the error ?\r\n\r\nOn my side I didn't get any error today with `load_dataset(\"amazon_polarity\")`", "+1 encountering this issue as well", "@lhoestq Hi! I encounter the same error when loading `yelp_review_full`.\r\n\r\n```\r\nfrom datasets import load_dataset\r\ndataset_yp = load_dataset(\"yelp_review_full\")\r\n```\r\n\r\nWhen you say the \"Quota Exceeded from Google drive\". Is this a quota from the dataset owner? or the quota from our (the runner) Google Drive?", "+1 Also encountering this issue", "> When you say the \"Quota Exceeded from Google drive\". Is this a quota from the dataset owner? or the quota from our (the runner) Google Drive?\r\n\r\nEach file on Google Drive can be downloaded only a certain amount of times per day because of a quota. The quota is reset every day. So if too many people download the dataset the same day, then the quota is likely to exceed.\r\nThat's a really bad limitations of Google Drive and we should definitely find another host for these dataset than Google Drive.\r\nFor now I would suggest to wait and try again later..\r\n\r\nSo far the issue happened with CNN DailyMail, Amazon Polarity and Yelp Reviews. \r\nAre you experiencing the issue with other datasets ? @calebchiam @dtch1997 ", "@lhoestq Gotcha, that is quite problematic...for what it's worth, I've had no issues with the other datasets I tried, such as `yelp_reviews_full` and `amazon_reviews_multi`.", "Same issue today with \"big_patent\", though the symptoms are slightly different.\r\n\r\nWhen running\r\n\r\n```py\r\nfrom datasets import load_dataset\r\nload_dataset(\"big_patent\", split=\"validation\")\r\n```\r\n\r\nI get the following\r\n`FileNotFoundError: Local file \\huggingface\\datasets\\downloads\\6159313604f4f2c01e7d1cac52139343b6c07f73f6de348d09be6213478455c5\\bigPatentData\\train.tar.gz doesn't exist`\r\n\r\nI had to look into `6159313604f4f2c01e7d1cac52139343b6c07f73f6de348d09be6213478455c5` (which is a file instead of a folder) and got the following:\r\n\r\n`<!DOCTYPE html><html><head><title>Google Drive - Quota exceeded</title><meta http-equiv=\"content-type\" content=\"text/html; charset=utf-8\"/><link href=&#47;static&#47;doclist&#47;client&#47;css&#47;4033072956&#45;untrustedcontent.css rel=\"stylesheet\" nonce=\"JV0t61Smks2TEKdFCGAUFA\"><link rel=\"icon\" href=\"//ssl.gstatic.com/images/branding/product/1x/drive_2020q4_32dp.png\"/><style nonce=\"JV0t61Smks2TEKdFCGAUFA\">#gbar,#guser{font-size:13px;padding-top:0px !important;}#gbar{height:22px}#guser{padding-bottom:7px !important;text-align:right}.gbh,.gbd{border-top:1px solid #c9d7f1;font-size:1px}.gbh{height:0;position:absolute;top:24px;width:100%}@media all{.gb1{height:22px;margin-right:.5em;vertical-align:top}#gbar{float:left}}a.gb1,a.gb4{text-decoration:underline !important}a.gb1,a.gb4{color:#00c !important}.gbi .gb4{color:#dd8e27 !important}.gbf .gb4{color:#900 !important}\r\n</style><script nonce=\"iNUHigT+ENVQ3UZrLkFtRw\"></script></head><body><div id=gbar><nobr><a target=_blank class=gb1 href=\"https://www.google.fr/webhp?tab=ow\">Search</a> <a target=_blank class=gb1 href=\"http://www.google.fr/imghp?hl=en&tab=oi\">Images</a> <a target=_blank class=gb1 href=\"https://maps.google.fr/maps?hl=en&tab=ol\">Maps</a> <a target=_blank class=gb1 href=\"https://play.google.com/?hl=en&tab=o8\">Play</a> <a target=_blank class=gb1 href=\"https://www.youtube.com/?gl=FR&tab=o1\">YouTube</a> <a target=_blank class=gb1 href=\"https://news.google.com/?tab=on\">News</a> <a target=_blank class=gb1 href=\"https://mail.google.com/mail/?tab=om\">Gmail</a> <b class=gb1>Drive</b> <a target=_blank class=gb1 style=\"text-decoration:none\" href=\"https://www.google.fr/intl/en/about/products?tab=oh\"><u>More</u> &raquo;</a></nobr></div><div id=guser width=100%><nobr><span id=gbn class=gbi></span><span id=gbf class=gbf></span><span id=gbe></span><a target=\"_self\" href=\"/settings?hl=en_US\" class=gb4>Settings</a> | <a target=_blank href=\"//support.google.com/drive/?p=web_home&hl=en_US\" class=gb4>Help</a> | <a target=_top id=gb_70 href=\"https://accounts.google.com/ServiceLogin?hl=en&passive=true&continue=https://drive.google.com/uc%3Fexport%3Ddownload%26id%3D1J3mucMFTWrgAYa3LuBZoLRR3CzzYD3fa&service=writely&ec=GAZAMQ\" class=gb4>Sign in</a></nobr></div><div class=gbh style=left:0></div><div class=gbh style=right:0></div><div class=\"uc-main\"><div id=\"uc-text\"><p class=\"uc-error-caption\">Sorry, you can&#39;t view or download this file at this time.</p><p class=\"uc-error-subcaption\">Too many users have viewed or downloaded this file recently. Please try accessing the file again later. If the file you are trying to access is particularly large or is shared with many people, it may take up to 24 hours to be able to view or download the file. If you still can't access a file after 24 hours, contact your domain administrator.</p></div></div><div class=\"uc-footer\"><hr class=\"uc-footer-divider\">&copy; 2021 Google - <a class=\"goog-link\" href=\"//support.google.com/drive/?p=web_home\">Help</a> - <a class=\"goog-link\" href=\"//support.google.com/drive/bin/answer.py?hl=en_US&amp;answer=2450387\">Privacy & Terms</a></div></body></html>`", "A similar issue arises when trying to stream the dataset\r\n\r\n```python\r\n>>> from datasets import load_dataset\r\n>>> iter_dset = load_dataset(\"amazon_polarity\", split=\"test\", streaming=True)\r\n>>> iter(iter_dset).__next__()\r\n\r\n---------------------------------------------------------------------------\r\nValueError Traceback (most recent call last)\r\n~\\lib\\tarfile.py in nti(s)\r\n 186 s = nts(s, \"ascii\", \"strict\")\r\n--> 187 n = int(s.strip() or \"0\", 8)\r\n 188 except ValueError:\r\n\r\nValueError: invalid literal for int() with base 8: 'e nonce='\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nInvalidHeaderError Traceback (most recent call last)\r\n~\\lib\\tarfile.py in next(self)\r\n 2288 try:\r\n-> 2289 tarinfo = self.tarinfo.fromtarfile(self)\r\n 2290 except EOFHeaderError as e:\r\n\r\n~\\lib\\tarfile.py in fromtarfile(cls, tarfile)\r\n 1094 buf = tarfile.fileobj.read(BLOCKSIZE)\r\n-> 1095 obj = cls.frombuf(buf, tarfile.encoding, tarfile.errors)\r\n 1096 obj.offset = tarfile.fileobj.tell() - BLOCKSIZE\r\n\r\n~\\lib\\tarfile.py in frombuf(cls, buf, encoding, errors)\r\n 1036\r\n-> 1037 chksum = nti(buf[148:156])\r\n 1038 if chksum not in calc_chksums(buf):\r\n\r\n~\\lib\\tarfile.py in nti(s)\r\n 188 except ValueError:\r\n--> 189 raise InvalidHeaderError(\"invalid header\")\r\n 190 return n\r\n\r\nInvalidHeaderError: invalid header\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nReadError Traceback (most recent call last)\r\n<ipython-input-5-6b9058341b2b> in <module>\r\n----> 1 iter(iter_dset).__next__()\r\n\r\n~\\lib\\site-packages\\datasets\\iterable_dataset.py in __iter__(self)\r\n 363\r\n 364 def __iter__(self):\r\n--> 365 for key, example in self._iter():\r\n 366 if self.features:\r\n 367 # we encode the example for ClassLabel feature types for example\r\n\r\n~\\lib\\site-packages\\datasets\\iterable_dataset.py in _iter(self)\r\n 360 else:\r\n 361 ex_iterable = self._ex_iterable\r\n--> 362 yield from ex_iterable\r\n 363\r\n 364 def __iter__(self):\r\n\r\n~\\lib\\site-packages\\datasets\\iterable_dataset.py in __iter__(self)\r\n 77\r\n 78 def __iter__(self):\r\n---> 79 yield from self.generate_examples_fn(**self.kwargs)\r\n 80\r\n 81 def shuffle_data_sources(self, seed: Optional[int]) -> \"ExamplesIterable\":\r\n\r\n~\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\amazon_polarity\\56923eeb72030cb6c4ea30c8a4e1162c26b25973475ac1f44340f0ec0f2936f4\\amazon_polarity.py in _generate_examples(self, filepath, files)\r\n 114 def _generate_examples(self, filepath, files):\r\n 115 \"\"\"Yields examples.\"\"\"\r\n--> 116 for path, f in files:\r\n 117 if path == filepath:\r\n 118 lines = (line.decode(\"utf-8\") for line in f)\r\n\r\n~\\lib\\site-packages\\datasets\\utils\\streaming_download_manager.py in __iter__(self)\r\n 616\r\n 617 def __iter__(self):\r\n--> 618 yield from self.generator(*self.args, **self.kwargs)\r\n 619\r\n 620\r\n\r\n~\\lib\\site-packages\\datasets\\utils\\streaming_download_manager.py in _iter_from_urlpath(cls, urlpath, use_auth_token)\r\n 644 ) -> Generator[Tuple, None, None]:\r\n 645 with xopen(urlpath, \"rb\", use_auth_token=use_auth_token) as f:\r\n--> 646 yield from cls._iter_from_fileobj(f)\r\n 647\r\n 648 @classmethod\r\n\r\n~\\lib\\site-packages\\datasets\\utils\\streaming_download_manager.py in _iter_from_fileobj(cls, f)\r\n 624 @classmethod\r\n 625 def _iter_from_fileobj(cls, f) -> Generator[Tuple, None, None]:\r\n--> 626 stream = tarfile.open(fileobj=f, mode=\"r|*\")\r\n 627 for tarinfo in stream:\r\n 628 file_path = tarinfo.name\r\n\r\n~\\lib\\tarfile.py in open(cls, name, mode, fileobj, bufsize, **kwargs)\r\n 1603 stream = _Stream(name, filemode, comptype, fileobj, bufsize)\r\n 1604 try:\r\n-> 1605 t = cls(name, filemode, stream, **kwargs)\r\n 1606 except:\r\n 1607 stream.close()\r\n\r\n~\\lib\\tarfile.py in __init__(self, name, mode, fileobj, format, tarinfo, dereference, ignore_zeros, encoding, errors, pax_headers, debug, errorlevel, copybufsize)\r\n 1484 if self.mode == \"r\":\r\n 1485 self.firstmember = None\r\n-> 1486 self.firstmember = self.next()\r\n 1487\r\n 1488 if self.mode == \"a\":\r\n\r\n~\\lib\\tarfile.py in next(self)\r\n 2299 continue\r\n 2300 elif self.offset == 0:\r\n-> 2301 raise ReadError(str(e))\r\n 2302 except EmptyHeaderError:\r\n 2303 if self.offset == 0:\r\n\r\nReadError: invalid header\r\n\r\n```", "This error still happens, but for a different reason now: Google Drive returns a warning instead of the dataset.", "Met the same issue +1", "Hi ! Thanks for reporting. Google Drive changed the way to bypass the warning message recently.\r\n\r\nThe latest release `1.18.4` fixes this for datasets loaded in a regular way.\r\n\r\nWe opened a PR to fix this recently for streaming mode at #3843 - we'll do a new release once the fix is merged :)", "Fixed by:\r\n- #3787 \r\n- #3843" ]
2021-02-10T10:00:56
2022-03-15T13:55:24
2022-03-15T13:55:23
NONE
null
null
null
Hi, it seems that loading the amazon_polarity dataset gives a NonMatchingChecksumError. To reproduce: ``` load_dataset("amazon_polarity") ``` This will give the following error: ``` --------------------------------------------------------------------------- NonMatchingChecksumError Traceback (most recent call last) <ipython-input-3-8559a03fe0f8> in <module>() ----> 1 dataset = load_dataset("amazon_polarity") 3 frames /usr/local/lib/python3.6/dist-packages/datasets/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name) 37 if len(bad_urls) > 0: 38 error_msg = "Checksums didn't match" + for_verification_name + ":\n" ---> 39 raise NonMatchingChecksumError(error_msg + str(bad_urls)) 40 logger.info("All the checksums matched successfully" + for_verification_name) 41 NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://drive.google.com/u/0/uc?id=0Bz8a_Dbh9QhbaW12WVVZS2drcnM&export=download'] ```
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805,204,397
MDU6SXNzdWU4MDUyMDQzOTc=
1,854
Feature Request: Dataset.add_item
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[ "Hi @sshleifer.\r\n\r\nI am not sure of understanding the need of the `add_item` approach...\r\n\r\nBy just reading your \"Desired API\" section, I would say you could (nearly) get it with a 1-column Dataset:\r\n```python\r\ndata = {\"input_ids\": [np.array([4,4,2]), np.array([8,6,5,5,2]), np.array([3,3,31,5])]}\r\nds = Dataset.from_dict(data)\r\nassert (ds[\"input_ids\"][0] == np.array([4,4,2])).all()\r\n```", "Hi @sshleifer :) \r\n\r\nWe don't have methods like `Dataset.add_batch` or `Dataset.add_entry/add_item` yet.\r\nBut that's something we'll add pretty soon. Would an API that looks roughly like this help ? Do you have suggestions ?\r\n```python\r\nimport numpy as np\r\nfrom datasets import Dataset\r\n\r\ntokenized = [np.array([4,4,2]), np.array([8,6,5,5,2]), np.array([3,3,31,5])\r\n\r\n# API suggestion (not available yet)\r\nd = Dataset()\r\nfor input_ids in tokenized:\r\n d.add_item({\"input_ids\": input_ids})\r\n\r\nprint(d[0][\"input_ids\"])\r\n# [4, 4, 2]\r\n```\r\n\r\nCurrently you can define a dataset with what @albertvillanova suggest, or via a generator using dataset builders. It's also possible to [concatenate datasets](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=concatenate#datasets.concatenate_datasets).", "Your API looks perfect @lhoestq, thanks!" ]
2021-02-10T06:06:00
2021-04-23T10:01:30
2021-04-23T10:01:30
CONTRIBUTOR
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null
null
I'm trying to integrate `huggingface/datasets` functionality into `fairseq`, which requires (afaict) being able to build a dataset through an `add_item` method, such as https://github.com/pytorch/fairseq/blob/master/fairseq/data/indexed_dataset.py#L318, as opposed to loading all the text into arrow, and then `dataset.map(binarizer)`. Is this possible at the moment? Is there an example? I'm happy to use raw `pa.Table` but not sure whether it will support uneven length entries. ### Desired API ```python import numpy as np tokenized: List[np.NDArray[np.int64]] = [np.array([4,4,2]), np.array([8,6,5,5,2]), np.array([3,3,31,5]) def build_dataset_from_tokenized(tokenized: List[np.NDArray[int]]) -> Dataset: """FIXME""" dataset = EmptyDataset() for t in tokenized: dataset.append(t) return dataset ds = build_dataset_from_tokenized(tokenized) assert (ds[0] == np.array([4,4,2])).all() ``` ### What I tried grep, google for "add one entry at a time", "datasets.append" ### Current Code This code achieves the same result but doesn't fit into the `add_item` abstraction. ```python dataset = load_dataset('text', data_files={'train': 'train.txt'}) tokenizer = RobertaTokenizerFast.from_pretrained('roberta-base', max_length=4096) def tokenize_function(examples): ids = tokenizer(examples['text'], return_attention_mask=False)['input_ids'] return {'input_ids': [x[1:] for x in ids]} ds = dataset.map(tokenize_function, batched=True, num_proc=4, remove_columns=['text'], load_from_cache_file=not overwrite_cache) print(ds['train'][0]) => np array ``` Thanks in advance!
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Add TIMIT
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[ "@patrickvonplaten Could you please help me with how the output text has to be represented in the data? TIMIT has Words, Phonemes and texts. Also has lot on info on the speaker and the dialect. Could you please help me? An example of how to arrange it would be super helpful!\r\n\r\n", "Hey @vrindaprabhu - sure I'll help you :-) Could you open a first PR for TIMIT where you copy-paste more or less the `librispeech_asr` script: https://github.com/huggingface/datasets/blob/28be129db862ec89a87ac9349c64df6b6118aff4/datasets/librispeech_asr/librispeech_asr.py#L93 (obviously replacing all the naming and links correctly...) and then you can list all possible outputs in the features dict: https://github.com/huggingface/datasets/blob/28be129db862ec89a87ac9349c64df6b6118aff4/datasets/librispeech_asr/librispeech_asr.py#L104 (words, phonemes should probably be of kind `datasets.Sequence(datasets.Value(\"string\"))` and texts I think should be of type `\"text\": datasets.Value(\"string\")`.\r\n\r\nWhen you've opened a first PR, I think it'll be much easier for us to take a look together :-) ", "I am sorry! I created the PR [#1903](https://github.com/huggingface/datasets/pull/1903#). Requesting your comments! CircleCI tests are failing, will address them along with your comments!" ]
2021-02-09T07:29:41
2021-03-15T05:59:37
2021-03-15T05:59:37
CONTRIBUTOR
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## Adding a Dataset - **Name:** *TIMIT* - **Description:** *The TIMIT corpus of read speech has been designed to provide speech data for the acquisition of acoustic-phonetic knowledge and for the development and evaluation of automatic speech recognition systems* - **Paper:** *Homepage*: http://groups.inf.ed.ac.uk/ami/corpus/ / *Wikipedia*: https://en.wikipedia.org/wiki/TIMIT - **Data:** *https://deepai.org/dataset/timit* - **Motivation:** Important speech dataset If interested in tackling this issue, feel free to tag @patrickvonplaten Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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803,588,125
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1,844
Update Open Subtitles corpus with original sentence IDs
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[ "Hi ! You're right this can can useful.\r\nThis should be easy to add, so feel free to give it a try if you want to contribute :)\r\nI think we just need to add it to the _generate_examples method of the OpenSubtitles dataset builder [here](https://github.com/huggingface/datasets/blob/master/datasets/open_subtitles/open_subtitles.py#L103)", "Hey @lhoestq , absolutely yes! Just one question before I start implementing. The ids found in the zip file have this format: \r\n(the following is line `22497315` of the `ids` file of the `de-en` dump)\r\n\r\n\r\n`de/2017/7006210/7063319.xml.gz en/2017/7006210/7050201.xml.gz 335 339 340` (every space is actually a tab, aside from the space between `339` and `340`)\r\n\r\n\r\nWhere filenames encode the information like this: `lang/year/imdb_id/opensubtitles_id.xml.gz` whereas the numbers correspond to the sentence ids which are linked together (i.e. sentence `335` of the German subtitle corresponds to lines `339` and `340` of the English file)\r\n\r\nThat being said, do you think I should stick to the raw sentence id (and replace the current sequential id) or should I include more detailed metadata (or both things maybe)?\r\n\r\nGoing with raw ID is surely simpler, but including `year`, `imdbId` and `subtitleId` should save space as they're just integers; besides, any operation (like filtering or grouping) will be much easier if users don't have to manually parse the ids every time.\r\nAs for the language-specific sentenceIds, what could be the best option? A list of integers or a comma-separated string?\r\n\r\n**Note:** I did not find any official information about this encoding, but it appears to check out:\r\nhttps://www.imdb.com/title/tt7006210/, https://www.opensubtitles.org/en/subtitles/7063319 and https://www.opensubtitles.org/en/subtitles/7050201 all link to the same episode, so I guess (I hope!) it's correct.\r\n\r\n", "I like the idea of having `year`, `imdbId` and `subtitleId` as columns for filtering for example.\r\nAnd for the `sentenceIds` a list of integers is fine.", "Thanks for improving it @Valahaar :) ", "Something like this? (adapted from [here](https://github.com/huggingface/datasets/blob/master/datasets/open_subtitles/open_subtitles.py#L114))\r\n\r\n```python\r\nresult = (\r\n sentence_counter,\r\n {\r\n \"id\": str(sentence_counter),\r\n \"meta\": {\r\n \"year\": year,\r\n \"imdbId\": imdb_id,\r\n \"subtitleId\": {l1: l1_sub_id, l2: l2_sub_id},\r\n \"sentenceIds\": {l1: [... source_sids ...], l2: [... target_sids ...]},\r\n # or maybe src/tgt? I'd go with the first one for consistency with 'translation'\r\n \"subtitleId\": {\"src\": l1_sub_id, \"tgt\": l2_sub_id},\r\n \"sentenceIds\": {\"src\": [... source_sids ...], \"tgt\": [... target_sids ...]},\r\n },\r\n \"translation\": {l1: x, l2: y},\r\n },\r\n )\r\n```\r\nOr at top level, avoiding nesting into 'meta'?", "Merged in #1865, closing. Thanks :)" ]
2021-02-08T13:55:13
2021-02-12T17:38:58
2021-02-12T17:38:58
CONTRIBUTOR
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Hi! It would be great if you could add the original sentence ids to [Open Subtitles](https://huggingface.co/datasets/open_subtitles). I can think of two reasons: first, it's possible to gather sentences for an entire document (the original ids contain media id, subtitle file id and sentence id), therefore somewhat allowing for document-level machine translation (and other document-level stuff which could be cool to have); second, it's possible to have parallel sentences in multiple languages, as they share the same ids across bitexts. I think I should tag @abhishekkrthakur as he's the one who added it in the first place. Thanks!
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MustC Speech Translation
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[ "Hi @patrickvonplaten I would like to work on this dataset. \r\n\r\nThanks! ", "That's awesome! Actually, I just noticed that this dataset might become a bit too big!\r\n\r\nMuST-C is the main dataset used for IWSLT19 and should probably be added as a standalone dataset. Would you be interested also in adding `datasets/MuST-C` instead?\r\n\r\nDescription: \r\n_MuST-C is a multilingual speech translation corpus whose size and quality facilitates the training of end-to-end systems for speech translation from English into several languages. For each target language, MuST-C comprises several hundred hours of audio recordings from English TED Talks, which are automatically aligned at the sentence level with their manual transcriptions and translations._\r\n\r\nPaper: https://www.aclweb.org/anthology/N19-1202.pdf\r\n\r\nDataset: https://ict.fbk.eu/must-c/ (One needs to fill out a short from to download the data, but it's very easy).\r\n\r\nIt would be awesome if you're interested in adding this datates. I'm very happy to guide you through the PR! I think the easiest way to start would probably be to read [this README on how to add a dataset](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md) and open a PR. Think you can copy & paste some code from:\r\n\r\n- Librispeech_asr: https://github.com/huggingface/datasets/blob/master/datasets/librispeech_asr/librispeech_asr.py\r\n- Flores Translation: https://github.com/huggingface/datasets/blob/master/datasets/flores/flores.py\r\n\r\nThink all the rest can be handled on the PR :-) ", "Hi @patrickvonplaten \r\nI have tried downloading this dataset, but the connection seems to reset all the time. I have tried it via the browser, wget, and using gdown . But it gives me an error message. _\"The server is busy or down, pls try again\"_ (rephrasing the message here)\r\n\r\nI have completed adding 4 datasets in the previous data sprint (including the IWSLT dataset #1676 ) ...so just checking if you are able to download it at your end. Otherwise will write to the dataset authors to update the links. \r\n\r\n\r\n\r\n\r\n", "Let me check tomorrow! Thanks for leaving this message!", "cc @patil-suraj for notification ", "@skyprince999, I think I'm getting the same error you're getting :-/\r\n\r\n```\r\nSorry, you can't view or download this file at this time.\r\n\r\nToo many users have viewed or downloaded this file recently. Please try accessing the file again later. If the file you are trying to access is particularly large or is shared with many people, it may take up to 24 hours to be able to view or download the file. If you still can't access a file after 24 hours, contact your domain administrator.\r\n```\r\n\r\nIt would be great if you could write the authors to see whether they can fix it.\r\nAlso cc @lhoestq - do you think we could mirror the dataset? ", "Also there are huge those datasets. Think downloading MuST-C v1.2 amounts to ~ 1000GB... because there are 14 possible configs each around 60-70GB. I think users mostly will only use one of the 14 configs so that they would only need, in theory, will have to download ~60GB which is ok. But I think this functionality doesn't exist yet in `datasets` no? cc @lhoestq ", "> Also cc @lhoestq - do you think we could mirror the dataset?\r\n\r\nYes we can mirror it if the authors are fine with it. You can create a dataset repo on huggingface.co (possibly under the relevant org) and add the mirrored data files.\r\n\r\n> I think users mostly will only use one of the 14 configs so that they would only need, in theory, will have to download ~60GB which is ok. But I think this functionality doesn't exist yet in datasets no? cc @lhoestq\r\n\r\nIf there are different download links for each configuration we can make the dataset builder download only the files related to the requested configuration.", "I have written to the dataset authors, highlighting this issue. Waiting for their response. \r\n\r\nUpdate on 25th Feb: \r\nThe authors have replied back, they are updating the download link and will revert back shortly! \r\n\r\n```\r\nfirst of all thanks a lot for being interested in MuST-C and for building the data-loader.\r\n\r\nBefore answering your request, I'd like to clarify that the creation, maintenance, and expansion of MuST-c are not supported by any funded project, so this means that we need to find economic support for all these activities. This also includes permanently moving all the data to AWS or GCP. We are working at this with the goal of facilitating the use of MuST-C, but this is not something that can happen today. We hope to have some news ASAP and you will be among the first to be informed.\r\n\r\nI hope you understand our situation.\r\n```\r\n\r\n", "Awesome, actually @lhoestq let's just ask the authors if we should host the dataset no? They could just use our links then as well for their website - what do you think? Is it fine to use our AWS dataset storage also as external links? ", "Yes definitely. Shall we suggest them to create a dataset repository under their org on huggingface.co ? @julien-c \r\nThe dataset is around 1TB", "Sounds good! \r\n\r\nOrder of magnitude is storage costs ~$20 per TB per month (not including bandwidth). \r\n\r\nHappy to provide this to the community as I feel this is an important dataset. Let us know what the authors want to do!\r\n\r\n", "Great! @skyprince999, do you think you could ping the authors here or link to this thread? I think it could be a cool idea to host the dataset on our side then", "Done. They replied back, and they want to have a call over a meet/ skype. Is that possible ? \r\nBtw @patrickvonplaten you are looped in that email (_pls check you gmail account_) ", "Hello! Any news on this?", "@gegallego there were some concerns regarding dataset usage & attribution by a for-profit company, so couldn't take it forward. Also the download links were unstable. \r\nBut I guess if you want to test the fairseq benchmarks, you can connect with them directly for downloading the dataset. ", "Yes, that dataset is not easy to download... I had to copy it to my Google Drive and use `rsync` to be able to download it.\r\nHowever, we could add the dataset with a manual download, right?", "yes that is possible. I couldn't unfortunately complete this PR, If you would like to add it, please feel free to do it. " ]
2021-02-08T13:27:45
2021-05-14T14:53:34
null
CONTRIBUTOR
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## Adding a Dataset - **Name:** *IWSLT19* - **Description:** *The Speech Translation Task addresses the translation of English audio into German and Portuguese text.* - **Hompage:** *https://sites.google.com/view/iwslt-evaluation-2019/speech-translation* - **Data:** *https://sites.google.com/view/iwslt-evaluation-2019/speech-translation* - all data under "Allowed Training Data" and "Development and Evalutaion Data for TED/How2" - **Motivation:** Important speech dataset If interested in tackling this issue, feel free to tag @patrickvonplaten Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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803,563,149
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1,842
Add AMI Corpus
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[ "Available here: ~https://huggingface.co/datasets/ami~ https://huggingface.co/datasets/edinburghcstr/ami", "@mariosasko actually the \"official\" AMI dataset can be found here: https://huggingface.co/datasets/edinburghcstr/ami -> the old one under `datasets/ami` doesn't work and should be deleted. \r\n\r\nThe new one was tested by fine-tuning a Wav2Vec2 model on it + we uploaded all the processed audio directly into it", "@patrickvonplaten Thanks for correcting me! I've updated the link." ]
2021-02-08T13:25:00
2023-02-28T16:29:22
2023-02-28T16:29:22
CONTRIBUTOR
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## Adding a Dataset - **Name:** *AMI* - **Description:** *The AMI Meeting Corpus is a multi-modal data set consisting of 100 hours of meeting recordings. For a gentle introduction to the corpus, see the corpus overview. To access the data, follow the directions given there. Around two-thirds of the data has been elicited using a scenario in which the participants play different roles in a design team, taking a design project from kick-off to completion over the course of a day. The rest consists of naturally occurring meetings in a range of domains. Detailed information can be found in the documentation section.* - **Paper:** *Homepage*: http://groups.inf.ed.ac.uk/ami/corpus/ - **Data:** *http://groups.inf.ed.ac.uk/ami/download/* - Select all cases in 1) and select "Individual Headsets" & "Microphone array" for 2) - **Motivation:** Important speech dataset If interested in tackling this issue, feel free to tag @patrickvonplaten Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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Add ljspeech
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2021-02-08T13:22:26
2021-03-15T05:59:02
2021-03-15T05:59:02
CONTRIBUTOR
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## Adding a Dataset - **Name:** *ljspeech* - **Description:** *This is a public domain speech dataset consisting of 13,100 short audio clips of a single speaker reading passages from 7 non-fiction books. A transcription is provided for each clip. Clips vary in length from 1 to 10 seconds and have a total length of approximately 24 hours. The texts were published between 1884 and 1964, and are in the public domain. The audio was recorded in 2016-17 by the LibriVox project and is also in the public domain.)* - **Paper:** *Homepage*: https://keithito.com/LJ-Speech-Dataset/ - **Data:** *https://keithito.com/LJ-Speech-Dataset/* - **Motivation:** Important speech dataset - **TFDatasets Implementation**: https://www.tensorflow.org/datasets/catalog/ljspeech If interested in tackling this issue, feel free to tag @patrickvonplaten Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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Add common voice
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[ "I have started working on adding this dataset.", "Hey @BirgerMoell - awesome that you started working on Common Voice. Common Voice is a bit special since, there is no direct download link to download the data. In these cases we usually consider two options:\r\n\r\n1) Find a hacky solution to extract the download link somehow from the XLM tree of the website \r\n2) If this doesn't work we force the user to download the data himself and add a `\"data_dir\"` as an input parameter. E.g. you can take a look at how it is done for [this](https://github.com/huggingface/datasets/blob/66f2a7eece98d2778bd22bb5034cb7c2376032d4/datasets/arxiv_dataset/arxiv_dataset.py#L66) \r\n\r\nAlso the documentation here: https://huggingface.co/docs/datasets/add_dataset.html?highlight=data_dir#downloading-data-files-and-organizing-splits (especially the \"note\") might be helpful.", "Let me know if you have any other questions", "I added a Work in Progress pull request (hope that is ok). I've made a card for the dataset and filled out the common_voice.py file with information about the datset (not completely).\r\n\r\nI didn't manage to get the tagging tool working locally on my machine but will look into that later.\r\n\r\nLeft to do.\r\n\r\n- Tag the dataset\r\n- Add missing information and update common_voice.py\r\n\r\nhttps://github.com/huggingface/datasets/pull/1886", "Awesome! I left a longer comment on the PR :-)", "I saw that this current datasets package holds common voice version 6.1, how to add the new version 7.0 that is already available?", "Will me merged next week - we're working on it :-)", "Common voice still appears to be a 6.1. Is the plan still to upgrade to 7.0?", "We actually already have the code and everything ready to add Common Voice 7.0 to `datasets` but are still waiting for the common voice authors to give us the green light :-) \r\n\r\nAlso gently pinging @phirework and @milupo here", "Common Voice 7.0 is available here now: https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0", "For anyone else stumbling upon this thread, the 8.0 version is also available now: https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0" ]
2021-02-08T13:21:05
2022-03-20T15:23:40
2021-03-15T05:56:21
CONTRIBUTOR
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## Adding a Dataset - **Name:** *common voice* - **Description:** *Mozilla Common Voice Dataset* - **Paper:** Homepage: https://voice.mozilla.org/en/datasets - **Data:** https://voice.mozilla.org/en/datasets - **Motivation:** Important speech dataset - **TFDatasets Implementation**: https://www.tensorflow.org/datasets/catalog/common_voice If interested in tackling this issue, feel free to tag @patrickvonplaten Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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Add Voxforge
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2021-02-08T13:19:56
2021-02-08T13:28:31
null
CONTRIBUTOR
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## Adding a Dataset - **Name:** *voxforge* - **Description:** *VoxForge is a language classification dataset. It consists of user submitted audio clips submitted to the website. In this release, data from 6 languages is collected - English, Spanish, French, German, Russian, and Italian. Since the website is constantly updated, and for the sake of reproducibility, this release contains only recordings submitted prior to 2020-01-01. The samples are splitted between train, validation and testing so that samples from each speaker belongs to exactly one split.* - **Paper:** *Homepage*: http://www.voxforge.org/ - **Data:** *http://www.voxforge.org/home/downloads* - **Motivation:** Important speech dataset - **TFDatasets Implementation**: https://www.tensorflow.org/datasets/catalog/voxforge If interested in tackling this issue, feel free to tag @patrickvonplaten Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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Add tedlium
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[ "Hi @patrickvonplaten \r\nI can have a look to this dataset later since I am trying to add the OpenSLR dataset https://github.com/huggingface/datasets/pull/2173\r\nHopefully I have enough space since the compressed file is 21GB. The release 3 is even bigger: 54GB :-0", "Resolved via https://github.com/huggingface/datasets/pull/4309" ]
2021-02-08T13:17:52
2022-10-04T14:34:12
2022-10-04T14:34:12
CONTRIBUTOR
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## Adding a Dataset - **Name:** *tedlium* - **Description:** *The TED-LIUM 1-3 corpus is English-language TED talks, with transcriptions, sampled at 16kHz. It contains about 118 hours of speech.* - **Paper:** Homepage: http://www.openslr.org/7/, https://lium.univ-lemans.fr/en/ted-lium2/ &, https://www.openslr.org/51/ - **Data:** http://www.openslr.org/7/ - **Motivation:** Important speech dataset - **TFDatasets Implementation**: https://www.tensorflow.org/datasets/catalog/tedlium If interested in tackling this issue, feel free to tag @patrickvonplaten Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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803,555,650
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Add VCTK
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[ "@patrickvonplaten I'd like to take this, if nobody has already done it. I have added datasets before through the datasets sprint, but I feel rusty on the details, so I'll look at the guide as well as similar audio PRs (#1878 in particular comes to mind). If there is any detail I should be aware of please, let me know! Otherwise, I'll try to write up a PR in the coming days.", "That sounds great @jaketae - let me know if you need any help i.e. feel free to ping me on a first PR :-)" ]
2021-02-08T13:15:28
2021-12-28T15:05:08
2021-12-28T15:05:08
CONTRIBUTOR
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## Adding a Dataset - **Name:** *VCTK* - **Description:** *This CSTR VCTK Corpus includes speech data uttered by 110 English speakers with various accents. Each speaker reads out about 400 sentences, which were selected from a newspaper, the rainbow passage and an elicitation paragraph used for the speech accent archive.* - **Paper:** Homepage: https://datashare.ed.ac.uk/handle/10283/3443 - **Data:** https://datashare.ed.ac.uk/handle/10283/3443 - **Motivation:** Important speech dataset - **TFDatasets Implementation**: https://www.tensorflow.org/datasets/catalog/vctk If interested in tackling this issue, feel free to tag @patrickvonplaten Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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test.json has been removed from the limit dataset repo (breaks dataset)
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[ "Thanks for the heads up ! I'm opening a PR to fix that" ]
2021-02-08T12:45:53
2021-02-10T16:14:58
2021-02-10T16:14:58
NONE
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https://github.com/huggingface/datasets/blob/16042b233dbff2a7585110134e969204c69322c3/datasets/limit/limit.py#L51 The URL is not valid anymore since test.json has been removed in master for some reason. Directly referencing the last commit works: `https://raw.githubusercontent.com/ilmgut/limit_dataset/0707d3989cd8848f0f11527c77dcf168fefd2b23/data`
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Add CHiME4 dataset
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[ "@patrickvonplaten not sure whether it is still needed, but willing to tackle this issue", "Hey @patrickvonplaten, I have managed to download the zip on [here]( http://spandh.dcs.shef.ac.uk/chime_challenge/CHiME4/download.html) and successfully uploaded all the files on a hugging face dataset: \r\n\r\nhttps://huggingface.co/datasets/ksbai123/Chime4\r\n\r\nHowever I am getting this error when trying to use the dataset viewer:\r\n\r\n![Screenshot 2023-12-27 at 18 40 59](https://github.com/huggingface/datasets/assets/35923560/a5a9ed3d-8dbd-41c4-b83a-4e80728b1450)\r\n\r\nCan you take a look and let me know if I have missed any files please", "@patrickvonplaten ?", "Hi @KossaiSbai,\r\n\r\nThanks for your contribution.\r\n\r\nAs the issue is not strictly related to the `datasets` library, but to the specific implementation of the CHiME4 dataset, I have opened an issue in the Discussion tab of the dataset: https://huggingface.co/datasets/ksbai123/Chime4/discussions/2\r\nLet's continue the discussion there!" ]
2021-02-08T12:36:38
2024-02-01T10:25:03
null
CONTRIBUTOR
null
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## Adding a Dataset - **Name:** Chime4 - **Description:** Chime4 is a dataset for automatic speech recognition. It is especially useful for evaluating models in a noisy environment and for multi-channel ASR - **Paper:** Dataset comes from a channel: http://spandh.dcs.shef.ac.uk/chime_challenge/CHiME4/ . Results paper: - **Data:** http://spandh.dcs.shef.ac.uk/chime_challenge/CHiME4/download.html - **Motivation:** So far there are very little datasets for speech in `datasets`. Only `lbirispeech_asr` so far. If interested in tackling this issue, feel free to tag @patrickvonplaten Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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Looks like nokogumbo is up-to-date now, so this is no longer needed.
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2021-02-07T06:52:07
2021-02-08T17:27:29
2021-02-08T17:27:29
NONE
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Looks like nokogumbo is up-to-date now, so this is no longer needed. __Originally posted by @dependabot in https://github.com/discourse/discourse/pull/11373#issuecomment-738993432__
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Some question about raw dataset download info in the project .
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[ "Hi ! The `dl_manager` is a `DownloadManager` object and is responsible for downloading the raw data files.\r\nIt is used by dataset builders in their `_split_generators` method to download the raw data files that are necessary to build the datasets splits.\r\n\r\nThe `Conll2003` class is a dataset builder, and so you can download all the raw data files by calling `_split_generators` with a download manager:\r\n```python\r\nfrom datasets import DownloadManager\r\nfrom datasets.load import import_main_class\r\n\r\nconll2003_builder = import_main_class(...)\r\n\r\ndl_manager = DownloadManager()\r\nsplis_generators = conll2003_builder._split_generators(dl_manager)\r\n```\r\n\r\nThen you can see what files have been downloaded with\r\n```python\r\ndl_manager.get_recorded_sizes_checksums()\r\n```\r\nIt returns a dictionary with the format {url: {num_bytes: int, checksum: str}}\r\n\r\nThen you can get the actual location of the downloaded files with\r\n```python\r\nfrom datasets import cached_path\r\n\r\nlocal_path_to_downloaded_file = cached_path(url)\r\n```\r\n\r\n------------------\r\n\r\nNote that you can also get the urls from the Dataset object:\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nconll2003 = load_dataset(\"conll2003\")\r\nprint(conll2003[\"train\"].download_checksums)\r\n```\r\nIt returns the same dictionary with the format {url: {num_bytes: int, checksum: str}}", "I am afraid that there is not a very straightforward way to get that location.\r\n\r\nAnother option, from _split_generators would be to use:\r\n- `dl_manager._download_config.cache_dir` to get the directory where all the raw downloaded files are:\r\n ```python\r\n download_dir = dl_manager._download_config.cache_dir\r\n ```\r\n- the function `datasets.utils.file_utils.hash_url_to_filename` to get the filenames of the raw downloaded files:\r\n ```python\r\n filenames = [hash_url_to_filename(url) for url in urls_to_download.values()]\r\n ```\r\nTherefore the complete path to the raw downloaded files would be the join of both:\r\n```python\r\ndownloaded_paths = [os.path.join(download_dir, filename) for filename in filenames]\r\n```\r\n\r\nMaybe it would be interesting to make these paths accessible more easily. I could work on this. What do you think, @lhoestq ?", "Sure it would be nice to have an easier access to these paths !\r\nThe dataset builder could have a method to return those, what do you think ?\r\nFeel free to work on this @albertvillanova , it would be a nice addition :) \r\n\r\nYour suggestion does work as well @albertvillanova if you complete it by specifying `etag=` to `hash_url_to_filename`.\r\n\r\nThe ETag is obtained by a HEAD request and is used to know if the file on the remote host has changed. Therefore if a file is updated on the remote host, then the hash returned by `hash_url_to_filename` is different.", "Once #1846 will be merged, the paths to the raw downloaded files will be accessible as:\r\n```python\r\nbuilder_instance.dl_manager.downloaded_paths\r\n``` " ]
2021-02-07T05:33:36
2021-02-25T14:10:18
2021-02-25T14:10:18
NONE
null
null
null
Hi , i review the code in https://github.com/huggingface/datasets/blob/master/datasets/conll2003/conll2003.py in the _split_generators function is the truly logic of download raw datasets with dl_manager and use Conll2003 cls by use import_main_class in load_dataset function My question is that , with this logic it seems that i can not have the raw dataset download location in variable in downloaded_files in _split_generators. If someone also want use huggingface datasets as raw dataset downloader, how can he retrieve the raw dataset download path from attributes in datasets.dataset_dict.DatasetDict ?
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using map on loaded Tokenizer 10x - 100x slower than default Tokenizer?
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[ "Hi @wumpusman \r\n`datasets` has a caching mechanism that allows to cache the results of `.map` so that when you want to re-run it later it doesn't recompute it again.\r\nSo when you do `.map`, what actually happens is:\r\n1. compute the hash used to identify your `map` for the cache\r\n2. apply your function on every batch\r\n\r\nThis can explain the time difference between your different experiments.\r\n\r\nThe hash computation time depends of how complex your function is. For a tokenizer, the hash computation scans the lists of the words in the tokenizer to identify this tokenizer. Usually it takes 2-3 seconds.\r\n\r\nAlso note that you can disable caching though using\r\n```python\r\nimport datasets\r\n\r\ndatasets.set_caching_enabled(False)\r\n```", "Hi @lhoestq ,\r\n\r\nThanks for the reply. It's entirely possible that is the issue. Since it's a side project I won't be looking at it till later this week, but, I'll verify it by disabling caching and hopefully I'll see the same runtime. \r\n\r\nAppreciate the reference,\r\n\r\nMichael", "I believe this is an actual issue, tokenizing a ~4GB txt file went from an hour and a half to ~10 minutes when I switched from my pre-trained tokenizer(on the same dataset) to the default gpt2 tokenizer.\r\nBoth were loaded using:\r\n```\r\nAutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)\r\n```\r\nI trained the tokenizer using ByteLevelBPETokenizer from the Tokenizers library and save it to a tokenizer.json file.\r\n\r\nI have tested the caching ideas above, changing the number of process, the TOKENIZERS_PARALLELISM env variable, keep_in_memory=True and batching with different sizes.\r\n\r\nApologies I can't really upload much code, but wanted to back up the finding and hopefully a fix/the problem can be found.\r\nI will comment back if I find a fix as well.", "Hi @johncookds do you think this can come from one tokenizer being faster than the other one ? Can you try to compare their speed without using `datasets` just to make sure ?", "Hi yes, I'm closing the loop here with some timings below. The issue seems to be at least somewhat/mainly with the tokenizer's themselves. Moreover legacy saves of the trainer tokenizer perform faster but differently than the new tokenizer.json saves(note nothing about the training process/adding of special tokens changed between the top two trained tokenizer tests, only the way it was saved). This is only a 3x slowdown vs like a 10x but I think the slowdown is most likely due to this.\r\n\r\n```\r\ntrained tokenizer - tokenizer.json save (same results for AutoTokenizer legacy_format=False):\r\nTokenizer time(seconds): 0.32767510414123535\r\nTokenized avg. length: 323.01\r\n\r\ntrained tokenizer - AutoTokenizer legacy_format=True:\r\nTokenizer time(seconds): 0.09258866310119629\r\nTokenized avg. length: 301.01\r\n\r\nGPT2 Tokenizer from huggingface\r\nTokenizer time(seconds): 0.1010282039642334\r\nTokenized avg. length: 461.21\r\n```", "@lhoestq ,\r\n\r\nHi, which version of datasets has datasets.set_caching_enabled(False)? I get \r\nmodule 'datasets' has no attribute 'set_caching_enabled'. To hopefully get around this, I reran my code on a new set of data, and did so only once.\r\n\r\n@johncookds , thanks for chiming in, it looks this might be an issue of Tokenizer.\r\n\r\n**Tokenizer**: The runtime of GPT2TokenizerFast.from_pretrained(\"gpt2\") on 1000 chars is: **143 ms**\r\n**SlowTokenizer**: The runtime of a locally saved and loaded Tokenizer using the same vocab on 1000 chars is: **4.43 s**\r\n\r\nThat being said, I compared performance on the map function:\r\n\r\nRunning Tokenizer versus using it in the map function for 1000 chars goes from **141 ms** to **356 ms** \r\nRunning SlowTokenizer versus using it in the map function for 1000 chars with a single element goes from **4.43 s** to **9.76 s**\r\n\r\nI'm trying to figure out why the overhead of map would increase the time by double (figured it would be a fixed increase in time)? Though maybe this is expected behavior.\r\n\r\n@lhoestq, do you by chance know how I can redirect this issue to Tokenizer?\r\n\r\nRegards,\r\n\r\nMichael", "Thanks for the experiments @johncookds and @wumpusman ! \r\n\r\n> Hi, which version of datasets has datasets.set_caching_enabled(False)?\r\n\r\nCurrently you have to install `datasets` from source to have this feature, but this will be available in the next release in a few days.\r\n\r\n> I'm trying to figure out why the overhead of map would increase the time by double (figured it would be a fixed increase in time)? Though maybe this is expected behavior.\r\n\r\nCould you also try with double the number of characters ? This should let us have an idea of the fixed cost (hashing) and the dynamic cost (actual tokenization, grows with the size of the input)\r\n\r\n> @lhoestq, do you by chance know how I can redirect this issue to Tokenizer?\r\n\r\nFeel free to post an issue on the `transformers` repo. Also I'm sure there should be related issues so you can also look for someone with the same concerns on the `transformers` repo.", "@lhoestq,\r\n\r\nI just checked that previous run time was actually 3000 chars. I increased it to 6k chars, again, roughly double.\r\n\r\nSlowTokenizer **7.4 s** to **15.7 s**\r\nTokenizer: **276 ms** to **616 ms**\r\n\r\nI'll post this issue on Tokenizer, seems it hasn't quite been raised (albeit I noticed a similar issue that might relate).\r\n\r\nRegards,\r\n\r\nMichael", "Hi, \r\nI'm following up here as I found my exact issue. It was with saving and re-loading the tokenizer. When I trained then processed the data without saving and reloading it, it was 10x-100x faster than when I saved and re-loaded it.\r\nBoth resulted in the exact same tokenized datasets as well. \r\nThere is additionally a bug where the older legacy tokenizer save does not preserve a learned tokenizing behavior if trained from scratch.\r\nUnderstand its not exactly Datasets related but hope it can help someone if they have the same issue.\r\nThanks!" ]
2021-02-06T21:00:26
2021-02-24T21:56:14
null
NONE
null
null
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This could total relate to me misunderstanding particular call functions, but I added words to a GPT2Tokenizer, and saved it to disk (note I'm only showing snippets but I can share more) and the map function ran much slower: ```` def save_tokenizer(original_tokenizer,text,path="simpledata/tokenizer"): words_unique = set(text.split(" ")) for i in words_unique: original_tokenizer.add_tokens(i) original_tokenizer.save_pretrained(path) tokenizer2 = GPT2Tokenizer.from_pretrained(os.path.join(experiment_path,experiment_name,"tokenizer_squad")) train_set_baby=Dataset.from_dict({"text":[train_set["text"][0][0:50]]}) ```` I then applied the dataset map function on a fairly small set of text: ``` %%time train_set_baby = train_set_baby.map(lambda d:tokenizer2(d["text"]),batched=True) ``` The run time for train_set_baby.map was 6 seconds, and the batch itself was 2.6 seconds **100% 1/1 [00:02<00:00, 2.60s/ba] CPU times: user 5.96 s, sys: 36 ms, total: 5.99 s Wall time: 5.99 s** In comparison using (even after adding additional tokens): ` tokenizer = GPT2TokenizerFast.from_pretrained("gpt2")` ``` %%time train_set_baby = train_set_baby.map(lambda d:tokenizer2(d["text"]),batched=True) ``` The time is **100% 1/1 [00:00<00:00, 34.09ba/s] CPU times: user 68.1 ms, sys: 16 µs, total: 68.1 ms Wall time: 62.9 ms** It seems this might relate to the tokenizer save or load function, however, the issue appears to come up when I apply the loaded tokenizer to the map function. I should also add that playing around with the amount of words I add to the tokenizer before I save it to disk and load it into memory appears to impact the time it takes to run the map function.
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802,353,974
MDU6SXNzdWU4MDIzNTM5NzQ=
1,827
Regarding On-the-fly Data Loading
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[ "Possible duplicate\r\n\r\n#1776 https://github.com/huggingface/datasets/issues/\r\n\r\nreally looking PR for this feature", "Hi @acul3 \r\n\r\nIssue #1776 talks about doing on-the-fly data pre-processing, which I think is solved in the next release as mentioned in the issue #1825. I also look forward to using this feature, though :)\r\n\r\nI wanted to ask about on-the-fly data loading from the cache (before pre-processing).", "Hi ! Currently when you load a dataset via `load_dataset` for example, then the dataset is memory-mapped from an Arrow file on disk. Therefore there's almost no RAM usage even if your dataset contains TB of data.\r\nUsually at training time only one batch of data at a time is loaded in memory.\r\n\r\nDoes that answer your question or were you thinking about something else ?", "Hi @lhoestq,\r\n\r\nI apologize for the late response. This answers my question. Thanks a lot." ]
2021-02-05T17:43:48
2021-02-18T13:55:16
2021-02-18T13:55:16
CONTRIBUTOR
null
null
null
Hi, I was wondering if it is possible to load images/texts as a batch during the training process, without loading the entire dataset on the RAM at any given point. Thanks, Gunjan
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802,073,925
MDU6SXNzdWU4MDIwNzM5MjU=
1,825
Datasets library not suitable for huge text datasets.
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[ "Hi ! Looks related to #861 \r\n\r\nYou are right: tokenizing a dataset using map takes a lot of space since it can store `input_ids` but also `token_type_ids`, `attention_mask` and `special_tokens_mask`. Moreover if your tokenization function returns python integers then by default they'll be stored as int64 which can take a lot of space. Padding can also increase the size of the tokenized dataset.\r\n\r\nTo make things more convenient, we recently added a \"lazy map\" feature that allows to tokenize each batch at training time as you mentioned. For example you'll be able to do\r\n```python\r\nfrom transformers import BertTokenizer\r\n\r\ntokenizer = BertTokenizer.from_pretrained(\"bert-base-uncased\")\r\n\r\ndef encode(batch):\r\n return tokenizer(batch[\"text\"], padding=\"longest\", truncation=True, max_length=512, return_tensors=\"pt\")\r\n\r\ndataset.set_transform(encode)\r\nprint(dataset.format)\r\n# {'type': 'custom', 'format_kwargs': {'transform': <function __main__.encode(batch)>}, 'columns': ['idx', 'label', 'sentence1', 'sentence2'], 'output_all_columns': False}\r\nprint(dataset[:2])\r\n# {'input_ids': tensor([[ 101, 2572, 3217, ... 102]]), 'token_type_ids': tensor([[0, 0, 0, ... 0]]), 'attention_mask': tensor([[1, 1, 1, ... 1]])}\r\n\r\n```\r\nIn this example the `encode` transform is applied on-the-fly on the \"text\" column.\r\n\r\nThis feature will be available in the next release 2.0 which will happen in a few days.\r\nYou can already play with it by installing `datasets` from source if you want :)\r\n\r\nHope that helps !", "How recently was `set_transform` added? I am actually trying to implement it and getting an error:\r\n\r\n`AttributeError: 'Dataset' object has no attribute 'set_transform'\r\n`\r\n\r\nI'm on v.1.2.1.\r\n\r\nEDIT: Oh, wait I see now it's in the v.2.0. Whoops! This should be really useful.", "Yes indeed it was added a few days ago. The code is available on master\r\nWe'll do a release next week :)\r\n\r\nFeel free to install `datasets` from source to try it out though, I would love to have some feedbacks", "For information: it's now available in `datasets` 1.3.0.\r\nThe 2.0 is reserved for even cooler features ;)", "Hi @alexvaca0 , we have optimized Datasets' disk usage in the latest release v1.5.\r\n\r\nFeel free to update your Datasets version\r\n```shell\r\npip install -U datasets\r\n```\r\nand see if it better suits your needs." ]
2021-02-05T11:06:50
2021-03-30T14:04:01
2021-03-16T09:44:00
NONE
null
null
null
Hi, I'm trying to use datasets library to load a 187GB dataset of pure text, with the intention of building a Language Model. The problem is that from the 187GB it goes to some TB when processed by Datasets. First of all, I think the pre-tokenizing step (with tokenizer.map()) is not really thought for datasets this big, but for fine-tuning datasets, as this process alone takes so much time, usually in expensive machines (due to the need of tpus - gpus) which is not being used for training. It would possibly be more efficient in such cases to tokenize each batch at training time (receive batch - tokenize batch - train with batch), so that the whole time the machine is up it's being used for training. Moreover, the pyarrow objects created from a 187 GB datasets are huge, I mean, we always receive OOM, or No Space left on device errors when only 10-12% of the dataset has been processed, and only that part occupies 2.1TB in disk, which is so many times the disk usage of the pure text (and this doesn't make sense, as tokenized texts should be lighter than pure texts). Any suggestions??
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801,747,647
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1,821
Provide better exception message when one of many files results in an exception
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[ "Hi!\r\n\r\nThank you for reporting this issue. I agree that the information about the exception should be more clear and explicit.\r\n\r\nI could take on this issue.\r\n\r\nOn the meantime, as you can see from the exception stack trace, HF Datasets uses pandas to read the CSV files. You can pass arguments to `pandas.read_csv` by passing additional keyword arguments to `load_dataset`. For example, you may find useful this argument:\r\n- `error_bad_lines` : bool, default True\r\n Lines with too many fields (e.g. a csv line with too many commas) will by default cause an exception to be raised, and no DataFrame will be returned. If False, then these “bad lines” will be dropped from the DataFrame that is returned.\r\n\r\nYou could try:\r\n```python\r\ndatasets = load_dataset(\"csv\", data_files=dict(train=train_files, validation=validation_files), error_bad_lines=False)\r\n```\r\n" ]
2021-02-05T00:49:03
2021-02-09T17:39:27
2021-02-09T17:39:27
NONE
null
null
null
I find when I process many files, i.e. ``` train_files = glob.glob('rain*.csv') validation_files = glob.glob(validation*.csv') datasets = load_dataset("csv", data_files=dict(train=train_files, validation=validation_files)) ``` I sometimes encounter an error due to one of the files being misformed (i.e. no data, or a comma in a field that isn't quoted, etc). For example, this is the tail of an exception which I suspect is due to a stray comma. > File "pandas/_libs/parsers.pyx", line 756, in pandas._libs.parsers.TextReader.read > File "pandas/_libs/parsers.pyx", line 783, in pandas._libs.parsers.TextReader._read_low_memory > File "pandas/_libs/parsers.pyx", line 827, in pandas._libs.parsers.TextReader._read_rows > File "pandas/_libs/parsers.pyx", line 814, in pandas._libs.parsers.TextReader._tokenize_rows > File "pandas/_libs/parsers.pyx", line 1951, in pandas._libs.parsers.raise_parser_error > pandas.errors.ParserError: Error tokenizing data. C error: Expected 2 fields in line 559, saw 3 It would be nice if the exception trace contained the name of the file being processed (I have 250 separate files!)
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Loading local dataset raise requests.exceptions.ConnectTimeout
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[ "Hi ! Thanks for reporting. This was indeed a bug introduced when we moved the `json` dataset loader inside the `datasets` package (before that, the `json` loader was fetched online, as all the other dataset scripts).\r\n\r\nThis should be fixed on master now. Feel free to install `datasets` from source to try it out.\r\nThe fix will be available in the next release of `datasets` in a few days" ]
2021-02-04T05:55:23
2022-06-01T15:38:42
2022-06-01T15:38:42
NONE
null
null
null
Load local dataset: ``` dataset = load_dataset('json', data_files=["../../data/json.json"]) train = dataset["train"] print(train.features) train1 = train.map(lambda x: {"labels": 1}) print(train1[:2]) ``` but it raised requests.exceptions.ConnectTimeout: ``` /Users/littlely/myvirtual/tf2/bin/python3.7 /Users/littlely/projects/python_projects/pytorch_learning/nlp/dataset/transformers_datasets.py Traceback (most recent call last): File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/urllib3/connection.py", line 160, in _new_conn (self._dns_host, self.port), self.timeout, **extra_kw File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/urllib3/util/connection.py", line 84, in create_connection raise err File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/urllib3/util/connection.py", line 74, in create_connection sock.connect(sa) socket.timeout: timed out During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/urllib3/connectionpool.py", line 677, in urlopen chunked=chunked, File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/urllib3/connectionpool.py", line 381, in _make_request self._validate_conn(conn) File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/urllib3/connectionpool.py", line 978, in _validate_conn conn.connect() File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/urllib3/connection.py", line 309, in connect conn = self._new_conn() File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/urllib3/connection.py", line 167, in _new_conn % (self.host, self.timeout), urllib3.exceptions.ConnectTimeoutError: (<urllib3.connection.HTTPSConnection object at 0x1181e9940>, 'Connection to s3.amazonaws.com timed out. (connect timeout=10)') During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/requests/adapters.py", line 449, in send timeout=timeout File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/urllib3/connectionpool.py", line 727, in urlopen method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2] File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/urllib3/util/retry.py", line 439, in increment raise MaxRetryError(_pool, url, error or ResponseError(cause)) urllib3.exceptions.MaxRetryError: HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: /datasets.huggingface.co/datasets/datasets/json/json.py (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x1181e9940>, 'Connection to s3.amazonaws.com timed out. (connect timeout=10)')) During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/Users/littlely/projects/python_projects/pytorch_learning/nlp/dataset/transformers_datasets.py", line 12, in <module> dataset = load_dataset('json', data_files=["../../data/json.json"]) File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/datasets/load.py", line 591, in load_dataset path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/datasets/load.py", line 263, in prepare_module head_hf_s3(path, filename=name, dataset=dataset, max_retries=download_config.max_retries) File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 232, in head_hf_s3 max_retries=max_retries, File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 523, in http_head max_retries=max_retries, File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 458, in _request_with_retry raise err File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 454, in _request_with_retry response = requests.request(verb.upper(), url, **params) File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/requests/api.py", line 61, in request return session.request(method=method, url=url, **kwargs) File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/requests/sessions.py", line 530, in request resp = self.send(prep, **send_kwargs) File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/requests/sessions.py", line 643, in send r = adapter.send(request, **kwargs) File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/requests/adapters.py", line 504, in send raise ConnectTimeout(e, request=request) requests.exceptions.ConnectTimeout: HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: /datasets.huggingface.co/datasets/datasets/json/json.py (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x1181e9940>, 'Connection to s3.amazonaws.com timed out. (connect timeout=10)')) Process finished with exit code 1 ``` Why it want to connect a remote url when I load local datasets, and how can I fix it?
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pyarrow.lib.ArrowInvalid: Column 1 named input_ids expected length 599 but got length 1500
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[ "Hi !\r\nThe error you have is due to the `input_ids` column not having the same number of examples as the other columns.\r\nIndeed you're concatenating the `input_ids` at this line:\r\n\r\nhttps://github.com/LuCeHe/GenericTools/blob/431835d8e13ec24dceb5ee4dc4ae58f0e873b091/KerasTools/lm_preprocessing.py#L134\r\n\r\nHowever the other columns are kept unchanged, and therefore you end up with an `input_ids` column with 599 elements while the others columns like `attention_mask` have 1500.\r\n\r\nTo fix that you can instead concatenate them all using\r\n```python\r\nconcatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}\r\n```\r\n\r\nAlso you may need to drop the \"text\" column before applying `group_texts` since strings can't be concatenated with lists. You can drop it at the tokenization step:\r\n```python\r\ndset = dset.map(\r\n tokenize_function,\r\n batched=True,\r\n remove_columns=[\"text\"]\r\n)\r\n```", "You saved my life." ]
2021-02-04T02:30:23
2022-10-05T12:42:57
2022-10-05T12:42:57
NONE
null
null
null
I am trying to preprocess any dataset in this package with GPT-2 tokenizer, so I need to structure the datasets as long sequences of text without padding. I've been following a couple of your tutorials and here you can find the script that is failing right at the end https://github.com/LuCeHe/GenericTools/blob/master/KerasTools/lm_preprocessing.py In the last iteration of the last dset.map, it gives the error that I copied in the title. Another issue that I have, if I leave the batch_size set as 1000 in the last .map, I'm afraid it's going to lose most text, so I'm considering setting both writer_batch_size and batch_size to 300 K, but I'm not sure it's the best way to go. Can you help me? Thanks!
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Unable to add Multi-label Datasets
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[ "Thanks for adding this dataset! As far as I know `supervised_keys` is mostly a holdover from TFDS, but isn't really used, so feel free to drop it (@lhoestq or @thomwolf correct me if I'm wrong). It definitely shouldn't be blocking :) ", "I can confirm that it comes from TFDS and is not used at the moment.", "Thanks @yjernite @lhoestq \r\n\r\nThe template for new dataset makes it slightly confusing. I suppose the comment suggesting its update can be removed.", "Closing this issue since it was answered." ]
2021-02-02T11:50:56
2021-02-18T14:16:31
2021-02-18T14:16:31
CONTRIBUTOR
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I am trying to add [CIFAR-100](https://www.cs.toronto.edu/~kriz/cifar.html) dataset. The dataset contains two labels per image - `fine label` and `coarse label`. Using just one label in supervised keys as `supervised_keys=("img", "fine_label")` raises no issue. But trying `supervised_keys=("img", "fine_label","coarse_label")` leads to this error : ```python Traceback (most recent call last): File "test_script.py", line 2, in <module> d = load_dataset('./datasets/cifar100') File "~/datasets/src/datasets/load.py", line 668, in load_dataset **config_kwargs, File "~/datasets/src/datasets/builder.py", line 896, in __init__ super(GeneratorBasedBuilder, self).__init__(*args, **kwargs) File "~/datasets/src/datasets/builder.py", line 247, in __init__ info.update(self._info()) File "~/.cache/huggingface/modules/datasets_modules/datasets/cifar100/61d2489b2d4a4abc34201432541b7380984ec714e290817d9a1ee318e4b74e0f/cifar100.py", line 79, in _info citation=_CITATION, File "<string>", line 19, in __init__ File "~/datasets/src/datasets/info.py", line 136, in __post_init__ self.supervised_keys = SupervisedKeysData(*self.supervised_keys) TypeError: __init__() takes from 1 to 3 positional arguments but 4 were given ``` Is there a way I can fix this? Also, what does adding `supervised_keys` do? Is it necessary? How would I specify `supervised_keys` for a multi-input, multi-label dataset? Thanks, Gunjan
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Add Hateful Memes Dataset
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[ "I am not sure, but would `datasets.Sequence(datasets.Sequence(datasets.Sequence(datasets.Value(\"int\")))` work?", "Also, I found the information for loading only subsets of the data [here](https://github.com/huggingface/datasets/blob/master/docs/source/splits.rst).", "Hi @lhoestq,\r\n\r\nRequest you to check this once.\r\n\r\nThanks,\r\nGunjan", "Hi @gchhablani since Array2D doesn't support images of different sizes, I would suggest to store in the dataset the paths to the image file instead of the image data. This has the advantage of not decompressing the data (images are often compressed using jpeg, png etc.). Users can still apply `.map` to load the images if they want to. Though it would en up being Sequences features.\r\n\r\nIn the future we'll add support for ragged tensors for this case and update the relevant dataset with this feature." ]
2021-02-02T10:53:59
2021-12-08T12:03:59
null
CONTRIBUTOR
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## Add Hateful Memes Dataset - **Name:** Hateful Memes - **Description:** [https://ai.facebook.com/blog/hateful-memes-challenge-and-data-set]( https://ai.facebook.com/blog/hateful-memes-challenge-and-data-set) - **Paper:** [https://arxiv.org/pdf/2005.04790.pdf](https://arxiv.org/pdf/2005.04790.pdf) - **Data:** [This link](https://drivendata-competition-fb-hateful-memes-data.s3.amazonaws.com/XjiOc5ycDBRRNwbhRlgH.zip?AWSAccessKeyId=AKIARVBOBDCY4MWEDJKS&Signature=DaUuGgZWUgDHzEPPbyJ2PhSJ56Q%3D&Expires=1612816874) - **Motivation:** Including multi-modal datasets to 🤗 datasets. I will be adding this dataset. It requires the user to sign an agreement on DrivenData. So, it will be used with a manual download. The issue with this dataset is that the images are of different sizes. The image datasets added so far (CIFAR-10 and MNIST) have a uniform shape throughout. So something like ```python datasets.Array2D(shape=(28, 28), dtype="uint8") ``` won't work for the images. How would I add image features then? I checked `datasets/features.py` but couldn't figure out the appropriate class for this. I'm assuming I would want to avoid re-sizing at all since we want the user to be able to access the original images. Also, in case I want to load only a subset of the data, since the actual data is around 8.8GB, how would that be possible? Thanks, Gunjan
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writing Datasets in a human readable format
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[ "AFAIK, there is currently no built-in method on the `Dataset` object to do this.\r\nHowever, a workaround is to directly use the Arrow table backing the dataset, **but it implies loading the whole dataset in memory** (correct me if I'm mistaken @lhoestq).\r\n\r\nYou can convert the Arrow table to a pandas dataframe to save the data as csv as follows:\r\n```python\r\narrow_table = dataset.data\r\ndataframe = arrow_table.to_pandas()\r\ndataframe.to_csv(\"/path/to/file.csv\")\r\n```\r\n\r\nSimilarly, you can convert the dataset to a Python dict and save it as JSON:\r\n```python\r\nimport json\r\narrow_table = dataset.data\r\npy_dict = arrow_table.to_pydict()\r\nwith open(\"/path/to/file.json\", \"w+\") as f:\r\n json.dump(py_dict, f)\r\n```", "Indeed this works as long as you have enough memory.\r\nIt would be amazing to have export options like csv, json etc. !\r\n\r\nIt should be doable to implement something that iterates through the dataset batch by batch to write to csv for example.\r\nThere is already an `export` method but currently the only export type that is supported is `tfrecords`.", "Hi! `datasets` now supports `Dataset.to_csv` and `Dataset.to_json` for saving data in a human readable format." ]
2021-02-02T02:55:40
2022-06-01T15:38:13
2022-06-01T15:38:13
NONE
null
null
null
Hi I see there is a save_to_disk function to save data, but this is not human readable format, is there a way I could save a Dataset object in a human readable format to a file like json? thanks @lhoestq
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can't pickle SwigPyObject objects when calling dataset.get_nearest_examples from FAISS index
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[ "Hi ! Indeed we used to require mapping functions to be picklable with `pickle` or `dill` in order to cache the resulting datasets. And FAISS indexes are not picklable unfortunately.\r\n\r\nBut since #1703 this is no longer required (the caching will simply be disabled). This change will be available in the next release of `datasets`, or you can also install `datasets` from source.", "I totally forgot to answer this issue, I'm so sorry. \r\n\r\nI was able to get it working by installing `datasets` from source. Huge thanks!" ]
2021-02-01T16:14:17
2021-03-06T14:32:46
2021-03-06T14:32:46
CONTRIBUTOR
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So, I have the following instances in my dataset ``` {'question': 'An astronomer observes that a planet rotates faster after a meteorite impact. Which is the most likely effect of this increase in rotation?', 'answer': 'C', 'example_id': 'ARCCH_Mercury_7175875', 'options':[{'option_context': 'One effect of increased amperage in the planetary world (..)', 'option_id': 'A', 'option_text': 'Planetary density will decrease.'}, (...)]} ``` The `options` value is always an list with 4 options, each one is a dict with `option_context`; `option_id` and `option_text`. I would like to overwrite the `option_context` of each instance of my dataset for a dpr result that I am developing. Then, I trained a model already and save it in a FAISS index ``` dpr_dataset = load_dataset( "text", data_files=ARC_CORPUS_TEXT, cache_dir=CACHE_DIR, split="train[:100%]", ) dpr_dataset.load_faiss_index("embeddings", f"{ARC_CORPUS_FAISS}") torch.set_grad_enabled(False) ``` Then, as a processor of my dataset, I created a map function that calls the `dpr_dataset` for each _option_ ``` def generate_context(example): question_text = example['question'] for option in example['options']: question_with_option = question_text + " " + option['option_text'] tokenize_text = question_tokenizer(question_with_option, return_tensors="pt").to(device) question_embed = ( question_encoder(**tokenize_text) )[0][0].cpu().numpy() _, retrieved_examples = dpr_dataset.get_nearest_examples( "embeddings", question_embed, k=10 ) # option["option_context"] = retrieved_examples["text"] # option["option_context"] = " ".join(option["option_context"]).strip() #result_dict = { # 'example_id': example['example_id'], # 'answer': example['answer'], # 'question': question_text, #options': example['options'] # } return example ``` I intentionally commented on this portion of the code. But when I call the `map` method, `ds_with_context = dataset.map(generate_context,load_from_cache_file=False)` It calls the following error: ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-55-75a458ce205c> in <module> ----> 1 ds_with_context = dataset.map(generate_context,load_from_cache_file=False) ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/dataset_dict.py in map(self, function, with_indices, input_columns, batched, batch_size, remove_columns, keep_in_memory, load_from_cache_file, cache_file_names, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc) 301 num_proc=num_proc, 302 ) --> 303 for k, dataset in self.items() 304 } 305 ) ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/dataset_dict.py in <dictcomp>(.0) 301 num_proc=num_proc, 302 ) --> 303 for k, dataset in self.items() 304 } 305 ) ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint) 1257 fn_kwargs=fn_kwargs, 1258 new_fingerprint=new_fingerprint, -> 1259 update_data=update_data, 1260 ) 1261 else: ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs) 155 } 156 # apply actual function --> 157 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 158 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 159 # re-apply format to the output ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/fingerprint.py in wrapper(*args, **kwargs) 156 kwargs_for_fingerprint["fingerprint_name"] = fingerprint_name 157 kwargs[fingerprint_name] = update_fingerprint( --> 158 self._fingerprint, transform, kwargs_for_fingerprint 159 ) 160 ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/fingerprint.py in update_fingerprint(fingerprint, transform, transform_args) 103 for key in sorted(transform_args): 104 hasher.update(key) --> 105 hasher.update(transform_args[key]) 106 return hasher.hexdigest() 107 ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/fingerprint.py in update(self, value) 55 def update(self, value): 56 self.m.update(f"=={type(value)}==".encode("utf8")) ---> 57 self.m.update(self.hash(value).encode("utf-8")) 58 59 def hexdigest(self): ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/fingerprint.py in hash(cls, value) 51 return cls.dispatch[type(value)](cls, value) 52 else: ---> 53 return cls.hash_default(value) 54 55 def update(self, value): ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/fingerprint.py in hash_default(cls, value) 44 @classmethod 45 def hash_default(cls, value): ---> 46 return cls.hash_bytes(dumps(value)) 47 48 @classmethod ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/utils/py_utils.py in dumps(obj) 387 file = StringIO() 388 with _no_cache_fields(obj): --> 389 dump(obj, file) 390 return file.getvalue() 391 ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/utils/py_utils.py in dump(obj, file) 359 def dump(obj, file): 360 """pickle an object to a file""" --> 361 Pickler(file, recurse=True).dump(obj) 362 return 363 ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/dill/_dill.py in dump(self, obj) 452 raise PicklingError(msg) 453 else: --> 454 StockPickler.dump(self, obj) 455 stack.clear() # clear record of 'recursion-sensitive' pickled objects 456 return /usr/lib/python3.7/pickle.py in dump(self, obj) 435 if self.proto >= 4: 436 self.framer.start_framing() --> 437 self.save(obj) 438 self.write(STOP) 439 self.framer.end_framing() /usr/lib/python3.7/pickle.py in save(self, obj, save_persistent_id) 502 f = self.dispatch.get(t) 503 if f is not None: --> 504 f(self, obj) # Call unbound method with explicit self 505 return 506 ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/utils/py_utils.py in save_function(pickler, obj) 554 dill._dill._create_function, 555 (obj.__code__, globs, obj.__name__, obj.__defaults__, obj.__closure__, obj.__dict__, fkwdefaults), --> 556 obj=obj, 557 ) 558 else: /usr/lib/python3.7/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj) 636 else: 637 save(func) --> 638 save(args) 639 write(REDUCE) 640 /usr/lib/python3.7/pickle.py in save(self, obj, save_persistent_id) 502 f = self.dispatch.get(t) 503 if f is not None: --> 504 f(self, obj) # Call unbound method with explicit self 505 return 506 /usr/lib/python3.7/pickle.py in save_tuple(self, obj) 784 write(MARK) 785 for element in obj: --> 786 save(element) 787 788 if id(obj) in memo: /usr/lib/python3.7/pickle.py in save(self, obj, save_persistent_id) 502 f = self.dispatch.get(t) 503 if f is not None: --> 504 f(self, obj) # Call unbound method with explicit self 505 return 506 ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/dill/_dill.py in save_module_dict(pickler, obj) 939 # we only care about session the first pass thru 940 pickler._session = False --> 941 StockPickler.save_dict(pickler, obj) 942 log.info("# D2") 943 return /usr/lib/python3.7/pickle.py in save_dict(self, obj) 854 855 self.memoize(obj) --> 856 self._batch_setitems(obj.items()) 857 858 dispatch[dict] = save_dict /usr/lib/python3.7/pickle.py in _batch_setitems(self, items) 880 for k, v in tmp: 881 save(k) --> 882 save(v) 883 write(SETITEMS) 884 elif n: /usr/lib/python3.7/pickle.py in save(self, obj, save_persistent_id) 547 548 # Save the reduce() output and finally memoize the object --> 549 self.save_reduce(obj=obj, *rv) 550 551 def persistent_id(self, obj): /usr/lib/python3.7/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj) 660 661 if state is not None: --> 662 save(state) 663 write(BUILD) 664 /usr/lib/python3.7/pickle.py in save(self, obj, save_persistent_id) 502 f = self.dispatch.get(t) 503 if f is not None: --> 504 f(self, obj) # Call unbound method with explicit self 505 return 506 ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/dill/_dill.py in save_module_dict(pickler, obj) 939 # we only care about session the first pass thru 940 pickler._session = False --> 941 StockPickler.save_dict(pickler, obj) 942 log.info("# D2") 943 return /usr/lib/python3.7/pickle.py in save_dict(self, obj) 854 855 self.memoize(obj) --> 856 self._batch_setitems(obj.items()) 857 858 dispatch[dict] = save_dict /usr/lib/python3.7/pickle.py in _batch_setitems(self, items) 880 for k, v in tmp: 881 save(k) --> 882 save(v) 883 write(SETITEMS) 884 elif n: /usr/lib/python3.7/pickle.py in save(self, obj, save_persistent_id) 502 f = self.dispatch.get(t) 503 if f is not None: --> 504 f(self, obj) # Call unbound method with explicit self 505 return 506 ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/dill/_dill.py in save_module_dict(pickler, obj) 939 # we only care about session the first pass thru 940 pickler._session = False --> 941 StockPickler.save_dict(pickler, obj) 942 log.info("# D2") 943 return /usr/lib/python3.7/pickle.py in save_dict(self, obj) 854 855 self.memoize(obj) --> 856 self._batch_setitems(obj.items()) 857 858 dispatch[dict] = save_dict /usr/lib/python3.7/pickle.py in _batch_setitems(self, items) 885 k, v = tmp[0] 886 save(k) --> 887 save(v) 888 write(SETITEM) 889 # else tmp is empty, and we're done /usr/lib/python3.7/pickle.py in save(self, obj, save_persistent_id) 547 548 # Save the reduce() output and finally memoize the object --> 549 self.save_reduce(obj=obj, *rv) 550 551 def persistent_id(self, obj): /usr/lib/python3.7/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj) 660 661 if state is not None: --> 662 save(state) 663 write(BUILD) 664 /usr/lib/python3.7/pickle.py in save(self, obj, save_persistent_id) 502 f = self.dispatch.get(t) 503 if f is not None: --> 504 f(self, obj) # Call unbound method with explicit self 505 return 506 ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/dill/_dill.py in save_module_dict(pickler, obj) 939 # we only care about session the first pass thru 940 pickler._session = False --> 941 StockPickler.save_dict(pickler, obj) 942 log.info("# D2") 943 return /usr/lib/python3.7/pickle.py in save_dict(self, obj) 854 855 self.memoize(obj) --> 856 self._batch_setitems(obj.items()) 857 858 dispatch[dict] = save_dict /usr/lib/python3.7/pickle.py in _batch_setitems(self, items) 880 for k, v in tmp: 881 save(k) --> 882 save(v) 883 write(SETITEMS) 884 elif n: /usr/lib/python3.7/pickle.py in save(self, obj, save_persistent_id) 547 548 # Save the reduce() output and finally memoize the object --> 549 self.save_reduce(obj=obj, *rv) 550 551 def persistent_id(self, obj): /usr/lib/python3.7/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj) 660 661 if state is not None: --> 662 save(state) 663 write(BUILD) 664 /usr/lib/python3.7/pickle.py in save(self, obj, save_persistent_id) 502 f = self.dispatch.get(t) 503 if f is not None: --> 504 f(self, obj) # Call unbound method with explicit self 505 return 506 ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/dill/_dill.py in save_module_dict(pickler, obj) 939 # we only care about session the first pass thru 940 pickler._session = False --> 941 StockPickler.save_dict(pickler, obj) 942 log.info("# D2") 943 return /usr/lib/python3.7/pickle.py in save_dict(self, obj) 854 855 self.memoize(obj) --> 856 self._batch_setitems(obj.items()) 857 858 dispatch[dict] = save_dict /usr/lib/python3.7/pickle.py in _batch_setitems(self, items) 885 k, v = tmp[0] 886 save(k) --> 887 save(v) 888 write(SETITEM) 889 # else tmp is empty, and we're done /usr/lib/python3.7/pickle.py in save(self, obj, save_persistent_id) 522 reduce = getattr(obj, "__reduce_ex__", None) 523 if reduce is not None: --> 524 rv = reduce(self.proto) 525 else: 526 reduce = getattr(obj, "__reduce__", None) TypeError: can't pickle SwigPyObject objects ``` Which I have no idea how to solve/deal with it
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Querying examples from big datasets is slower than small datasets
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[ "Hello, @lhoestq / @gaceladri : We have been seeing similar behavior with bigger datasets, where querying time increases. Are you folks aware of any solution that fixes this problem yet? ", "Hi ! I'm pretty sure that it can be fixed by using the Arrow IPC file format instead of the raw streaming format but I haven't tested yet.\r\nI'll take a look at it soon and let you know", "My workaround is to shard the dataset into splits in my ssd disk and feed the data in different training sessions. But it is a bit of a pain when we need to reload the last training session with the rest of the split with the Trainer in transformers.\r\n\r\nI mean, when I split the training and then reloads the model and optimizer, it not gets the correct global_status of the optimizer, so I need to hardcode some things. I'm planning to open an issue in transformers and think about it.\r\n```\r\nfrom datasets import load_dataset\r\n\r\nbook_corpus = load_dataset(\"bookcorpus\", split=\"train[:25%]\")\r\nwikicorpus = load_dataset(\"wikicorpus\", split=\"train[:25%]\")\r\nopenwebtext = load_dataset(\"openwebtext\", split=\"train[:25%]\")\r\n\r\nbig_dataset = datasets.concatenate_datasets([wikicorpus, openwebtext, book_corpus])\r\nbig_dataset.shuffle(seed=42)\r\nbig_dataset = big_dataset.map(encode, batched=True, num_proc=20, load_from_cache_file=True, writer_batch_size=5000)\r\nbig_dataset.set_format(type='torch', columns=[\"text\", \"input_ids\", \"attention_mask\", \"token_type_ids\"])\r\n\r\n\r\ntraining_args = TrainingArguments(\r\n output_dir=\"./linear_bert\",\r\n overwrite_output_dir=True,\r\n per_device_train_batch_size=71,\r\n save_steps=500,\r\n save_total_limit=10,\r\n logging_first_step=True,\r\n logging_steps=100,\r\n gradient_accumulation_steps=9,\r\n fp16=True,\r\n dataloader_num_workers=20,\r\n warmup_steps=24000,\r\n learning_rate=0.000545205002870214,\r\n adam_epsilon=1e-6,\r\n adam_beta2=0.98,\r\n weight_decay=0.01,\r\n max_steps=138974, # the total number of steps after concatenating 100% datasets\r\n max_grad_norm=1.0,\r\n)\r\n\r\ntrainer = Trainer(\r\n model=model,\r\n args=training_args,\r\n data_collator=data_collator,\r\n train_dataset=big_dataset,\r\n tokenizer=tokenizer))\r\n```\r\n\r\nI do one training pass with the total steps of this shard and I use len(bbig)/batchsize to stop the training (hardcoded in the trainer.py) when I pass over all the examples in this split.\r\n\r\nNow Im working, I will edit the comment with a more elaborated answer when I left the work.", "I just tested and using the Arrow File format doesn't improve the speed... This will need further investigation.\r\n\r\nMy guess is that it has to iterate over the record batches or chunks of a ChunkedArray in order to retrieve elements.\r\n\r\nHowever if we know in advance in which chunk the element is, and at what index it is, then we can access it instantaneously. But this requires dealing with the chunked arrays instead of the pyarrow Table directly which is not practical.", "I have a dataset with about 2.7 million rows (which I'm loading via `load_from_disk`), and I need to fetch around 300k (particular) rows of it, by index. Currently this is taking a really long time (~8 hours). I tried sharding the large dataset but overall it doesn't change how long it takes to fetch the desired rows.\r\n\r\nI actually have enough RAM that I could fit the large dataset in memory. Would having the large dataset in memory speed up querying? To find out, I tried to load (a column of) the large dataset into memory like this:\r\n```\r\ncolumn_data = large_ds['column_name']\r\n```\r\nbut in itself this takes a really long time.\r\n\r\nI'm pretty stuck - do you have any ideas what I should do? ", "Hi ! Feel free to post a message on the [forum](https://discuss.huggingface.co/c/datasets/10). I'd be happy to help you with this.\r\n\r\nIn your post on the forum, feel free to add more details about your setup:\r\nWhat are column names and types of your dataset ?\r\nHow was the dataset constructed ?\r\nIs the dataset shuffled ?\r\nIs the dataset tokenized ?\r\nAre you on a SSD or an HDD ?\r\n\r\nI'm sure we can figure something out.\r\nFor example on my laptop I can access the 6 millions articles from wikipedia in less than a minute.", "Thanks @lhoestq, I've [posted on the forum](https://discuss.huggingface.co/t/fetching-rows-of-a-large-dataset-by-index/4271?u=abisee).", "Fixed by #2122." ]
2021-02-01T11:08:23
2021-08-04T18:11:01
2021-08-04T18:10:42
MEMBER
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null
After some experiments with bookcorpus I noticed that querying examples from big datasets is slower than small datasets. For example ```python from datasets import load_dataset b1 = load_dataset("bookcorpus", split="train[:1%]") b50 = load_dataset("bookcorpus", split="train[:50%]") b100 = load_dataset("bookcorpus", split="train[:100%]") %timeit _ = b1[-1] # 12.2 µs ± 70.4 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each) %timeit _ = b50[-1] # 92.5 µs ± 1.24 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each) %timeit _ = b100[-1] # 177 µs ± 3.13 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each) ``` It looks like the time to fetch the example increases with the size of the dataset. This is maybe due to the use of the Arrow streaming format to store the data on disk. I guess pyarrow needs to iterate through the file as a stream to find the queried sample. Maybe switching to the Arrow IPC file format could help fixing this issue. Indeed according to the [documentation](https://arrow.apache.org/docs/format/Columnar.html?highlight=arrow1#ipc-file-format), it's identical to the streaming format except that it contains the memory offsets of each sample, which could fix the issue: > We define a “file format” supporting random access that is build with the stream format. The file starts and ends with a magic string ARROW1 (plus padding). What follows in the file is identical to the stream format. At the end of the file, we write a footer containing a redundant copy of the schema (which is a part of the streaming format) plus memory offsets and sizes for each of the data blocks in the file. This enables random access any record batch in the file. See File.fbs for the precise details of the file footer. cc @gaceladri since it can help speed up your training when this one is fixed.
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Connection error
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[ "Hi ! For future references let me add a link to our discussion here : https://github.com/huggingface/datasets/issues/759#issuecomment-770684693\r\n\r\nLet me know if you manage to fix your proxy issue or if we can do something on our end to help you :)" ]
2021-01-30T07:32:45
2021-08-04T18:09:37
2021-08-04T18:09:37
NONE
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Hi I am hitting to the error, help me and thanks. `train_data = datasets.load_dataset("xsum", split="train")` `ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/xsum/xsum.py`
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1,796
Filter on dataset too much slowww
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[ "When I use the filter on the arrow table directly, it works like butter. But I can't find a way to update the table in `Dataset` object.\r\n\r\n```\r\nds_table = dataset.data.filter(mask=dataset['flag'])\r\n```", "@thomwolf @lhoestq can you guys please take a look and recommend some solution.", "Hi ! Currently the filter method reads the dataset batch by batch to write a new, filtered, arrow file on disk. Therefore all the reading + writing can take some time.\r\nUsing a mask directly on the arrow table doesn't do any read or write operation therefore it's way quicker.\r\n\r\nReplacing the old table by the new one should do the job:\r\n```python\r\ndataset._data = dataset._data.filter(...)\r\n```\r\n\r\nNote: this is a **workaround** and in general users shouldn't have to do that. In particular if you did some `shuffle` or `select` before that then it would not work correctly since the indices mapping (index from `__getitem__` -> index in the table) would not be valid anymore. But if you haven't done any `shuffle`, `select`, `shard`, `train_test_split` etc. then it should work.\r\n\r\nIdeally it would be awesome to update the filter function to allow masking this way !\r\nIf you would like to give it a shot I will be happy to help :) ", "Yes, would be happy to contribute. Thanks", "Hi @lhoestq @ayubSubhaniya,\r\n\r\nIf there's no progress on this one, can I try working on it?\r\n\r\nThanks,\r\nGunjan", "Sure @gchhablani feel free to start working on it, this would be very appreciated :)\r\nThis feature is would be really awesome, especially since arrow allows to mask really quickly and without having to rewrite the dataset on disk", "Hi @lhoestq, any updates on this issue? The `filter` method is still veryyy slow 😕 ", "No update so far, we haven't worked on this yet :/\r\n\r\nThough PyArrow is much more stable than 3 years ago so it would be a good time to dive into this", "Hi @lhoestq, thanks a lot for the update! \r\n\r\nI would like to work on this(if possible). Could you please give me some steps regarding how should I approach this? Also any references would be great! ", "I just played a bit with it to make sure using `table.filter()` is fine, but actually it seems to create a new table **in memory** :/\r\nThis is an issue since it can quickly fill the RAM, and `datasets`'s role is to make sure you can load bigger-than-memory datasets. Therefore I don't think it's a good idea in the end to use `table.filter()`\r\n\r\nAnyway I just ran OP's code an it runs in 20ms now on my side thanks to the I/O optimizations we did.\r\n\r\nAnother way to speed up `filter` is to add support pyarrow expressions though, using e.g. arrow formatting + dataset.filter (runs in 10ms on my side):\r\n\r\n```python\r\nimport pyarrow.dataset as pds\r\nimport pyarrow.compute as pc\r\n\r\nexpr = pc.field(\"flag\") == True\r\n\r\nfiltered = dataset.with_format(\"arrow\").filter(\r\n lambda t: pds.dataset(t).to_table(columns={\"mask\": expr})[0].to_numpy(),\r\n batched=True,\r\n).with_format(None)\r\n```" ]
2021-01-30T04:09:19
2024-01-19T13:25:21
null
NONE
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I have a dataset with 50M rows. For pre-processing, I need to tokenize this and filter rows with the large sequence. My tokenization took roughly 12mins. I used `map()` with batch size 1024 and multi-process with 96 processes. When I applied the `filter()` function it is taking too much time. I need to filter sequences based on a boolean column. Below are the variants I tried. 1. filter() with batch size 1024, single process (takes roughly 3 hr) 2. filter() with batch size 1024, 96 processes (takes 5-6 hrs ¯\\\_(ツ)\_/¯) 3. filter() with loading all data in memory, only a single boolean column (never ends). Can someone please help? Below is a sample code for small dataset. ``` from datasets import load_dataset dataset = load_dataset('glue', 'mrpc', split='train') dataset = dataset.map(lambda x: {'flag': random.randint(0,1)==1}) def _amplify(data): return data dataset = dataset.filter(_amplify, batch_size=1024, keep_in_memory=False, input_columns=['flag']) ```
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1,790
ModuleNotFoundError: No module named 'apache_beam', when specific languages.
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[ "Hi !\r\n\r\nApache Beam is a framework used to define data transformation pipelines. These pipeline can then be run in many runtimes: DataFlow, Spark, Flink, etc. There also exist a local runner called the DirectRunner.\r\nWikipedia is a dataset that requires some parsing, so to allow the processing to be run on this kind of runtime we're using Apache Beam.\r\n\r\nAt Hugging Face we've already processed certain versions of wikipedia (the `20200501.en` one for example) so that users can directly download the processed version instead of using Apache Beam to process it.\r\nHowever for the japanese language we haven't processed it so you'll have to run the processing on your side.\r\nSo you do need Apache Beam to process `20200501.ja`.\r\n\r\nYou can install Apache Beam with\r\n```\r\npip install apache-beam\r\n```\r\n\r\nI think we can probably improve the error message to let users know of this subtlety.\r\nWhat #498 implied is that Apache Beam is not needed when you process a dataset that doesn't use Apache Beam.", "Thanks for your reply! \r\nI understood.\r\n\r\nI tried again with installing apache-beam, add ` beam_runner=\"DirectRunner\"` and an anther `mwparserfromhell` is also required so I installed it.\r\nbut, it also failed. It exited 1 without error message.\r\n\r\n```py\r\nimport datasets\r\n# BTW, 20200501.ja doesn't exist at wikipedia, so I specified date argument\r\nwiki = datasets.load_dataset(\"wikipedia\", language=\"ja\", date=\"20210120\", cache_dir=\"./datasets\", beam_runner=\"DirectRunner\")\r\nprint(wiki)\r\n```\r\nand its log is below\r\n```\r\nUsing custom data configuration 20210120.ja\r\nDownloading and preparing dataset wikipedia/20210120.ja-date=20210120,language=ja (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to ./datasets/wikipedia/20210120.ja-date=20210120,language=ja/0.0.0/4021357e28509391eab2f8300d9b689e7e8f3a877ebb3d354b01577d497ebc63...\r\nKilled\r\n```\r\n\r\nI also tried on another machine because it may caused by insufficient resources.\r\n```\r\n$ python main.py\r\nUsing custom data configuration 20210120.ja\r\nDownloading and preparing dataset wikipedia/20210120.ja-date=20210120,language=ja (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to ./datasets/wikipedia/20210120.ja-date=20210120,language=ja/0.0.0/4021357e28509391eab2f8300d9b689e7e8f3a877ebb3d354b01577d497ebc63...\r\n\r\nTraceback (most recent call last):\r\n File \"main.py\", line 3, in <module>\r\n wiki = datasets.load_dataset(\"wikipedia\", language=\"ja\", date=\"20210120\", cache_dir=\"./datasets\", beam_runner=\"DirectRunner\")\r\n File \"/home/miyamonz/.cache/pypoetry/virtualenvs/try-datasets-4t4JWXxu-py3.8/lib/python3.8/site-packages/datasets/load.py\", line 609, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/home/miyamonz/.cache/pypoetry/virtualenvs/try-datasets-4t4JWXxu-py3.8/lib/python3.8/site-packages/datasets/builder.py\", line 526, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/home/miyamonz/.cache/pypoetry/virtualenvs/try-datasets-4t4JWXxu-py3.8/lib/python3.8/site-packages/datasets/builder.py\", line 1069, in _download_and_prepare\r\n pipeline_results = pipeline.run()\r\n File \"/home/miyamonz/.cache/pypoetry/virtualenvs/try-datasets-4t4JWXxu-py3.8/lib/python3.8/site-packages/apache_beam/pipeline.py\", line 561, in run\r\n return self.runner.run_pipeline(self, self._options)\r\n File \"/home/miyamonz/.cache/pypoetry/virtualenvs/try-datasets-4t4JWXxu-py3.8/lib/python3.8/site-packages/apache_beam/runners/direct/direct_runner.py\", line 126, in run_pipeline\r\n return runner.run_pipeline(pipeline, options)\r\n File \"/home/miyamonz/.cache/pypoetry/virtualenvs/try-datasets-4t4JWXxu-py3.8/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 182, in run_pipeline\r\n self._latest_run_result = self.run_via_runner_api(\r\n File \"/home/miyamonz/.cache/pypoetry/virtualenvs/try-datasets-4t4JWXxu-py3.8/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 193, in run_via_runner_api\r\n return self.run_stages(stage_context, stages)\r\n File \"/home/miyamonz/.cache/pypoetry/virtualenvs/try-datasets-4t4JWXxu-py3.8/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 358, in run_stages\r\n stage_results = self._run_stage(\r\n File \"/home/miyamonz/.cache/pypoetry/virtualenvs/try-datasets-4t4JWXxu-py3.8/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 549, in _run_stage\r\n last_result, deferred_inputs, fired_timers = self._run_bundle(\r\n File \"/home/miyamonz/.cache/pypoetry/virtualenvs/try-datasets-4t4JWXxu-py3.8/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 595, in _run_bundle\r\n result, splits = bundle_manager.process_bundle(\r\n File \"/home/miyamonz/.cache/pypoetry/virtualenvs/try-datasets-4t4JWXxu-py3.8/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 888, in process_bundle\r\n self._send_input_to_worker(process_bundle_id, transform_id, elements)\r\n File \"/home/miyamonz/.cache/pypoetry/virtualenvs/try-datasets-4t4JWXxu-py3.8/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 765, in _send_input_to_worker\r\n data_out.write(byte_stream)\r\n File \"apache_beam/coders/stream.pyx\", line 42, in apache_beam.coders.stream.OutputStream.write\r\n File \"apache_beam/coders/stream.pyx\", line 47, in apache_beam.coders.stream.OutputStream.write\r\n File \"apache_beam/coders/stream.pyx\", line 109, in apache_beam.coders.stream.OutputStream.extend\r\nAssertionError: OutputStream realloc failed.\r\n```\r\n\r\n", "Hi @miyamonz,\r\n\r\nI tried replicating this issue using the same snippet used by you. I am able to download the dataset without any issues, although I stopped it in the middle because the dataset is huge.\r\n\r\nBased on a similar issue [here](https://github.com/google-research/fixmatch/issues/23), it could be related to your environment setup, although I am just guessing here. Can you share these details?", "thanks for your reply and sorry for my late response.\r\n\r\n## environment\r\nmy local machine environment info\r\n- Ubuntu on WSL2\r\n\r\n`lsb_release -a`\r\n```\r\nNo LSB modules are available.\r\nDistributor ID: Ubuntu\r\nDescription: Ubuntu 20.04.2 LTS\r\nRelease: 20.04\r\nCodename: focal\r\n```\r\n\r\nRTX 2070 super\r\nInside WSL, there is no nvidia-msi command. I don't know why.\r\nBut, `torch.cuda.is_available()` is true and when I start something ML training code GPU usage is growing up, so I think it works.\r\n\r\nFrom PowerShell, there is nvidia-smi.exe and result is below.\r\n```\r\n+-----------------------------------------------------------------------------+\r\n| NVIDIA-SMI 470.05 Driver Version: 470.05 CUDA Version: 11.3 |\r\n|-------------------------------+----------------------+----------------------+\r\n| GPU Name TCC/WDDM | Bus-Id Disp.A | Volatile Uncorr. ECC |\r\n| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\r\n| | | MIG M. |\r\n|===============================+======================+======================|\r\n| 0 NVIDIA GeForce ... WDDM | 00000000:09:00.0 On | N/A |\r\n| 0% 30C P8 19W / 175W | 523MiB / 8192MiB | 3% Default |\r\n| | | N/A |\r\n+-------------------------------+----------------------+----------------------+\r\n\r\n+-----------------------------------------------------------------------------+\r\n| Processes: |\r\n| GPU GI CI PID Type Process name GPU Memory |\r\n| ID ID Usage |\r\n|=============================================================================|\r\n| 0 N/A N/A 1728 C+G Insufficient Permissions N/A |\r\n| 0 N/A N/A 3672 C+G ...ekyb3d8bbwe\\YourPhone.exe N/A |\r\n| 0 N/A N/A 6304 C+G ...2txyewy\\TextInputHost.exe N/A |\r\n| 0 N/A N/A 8648 C+G C:\\Windows\\explorer.exe N/A |\r\n| 0 N/A N/A 9536 C+G ...y\\ShellExperienceHost.exe N/A |\r\n| 0 N/A N/A 10668 C+G ...5n1h2txyewy\\SearchApp.exe N/A |\r\n| 0 N/A N/A 10948 C+G ...artMenuExperienceHost.exe N/A |\r\n| 0 N/A N/A 11988 C+G ...8wekyb3d8bbwe\\Cortana.exe N/A |\r\n| 0 N/A N/A 12464 C+G ...cw5n1h2txyewy\\LockApp.exe N/A |\r\n| 0 N/A N/A 13280 C+G ...upport\\CEF\\Max Helper.exe N/A |\r\n| 0 N/A N/A 15948 C+G ...t\\GoogleIMEJaRenderer.exe N/A |\r\n| 0 N/A N/A 16128 C+G ...ram Files\\Slack\\Slack.exe N/A |\r\n| 0 N/A N/A 19096 C+G ...8bbwe\\WindowsTerminal.exe N/A |\r\n+-----------------------------------------------------------------------------+\r\n```\r\n\r\nI don't know what should I show in such a case. If it's not enough, please tell me some commands.\r\n\r\n---\r\n## what I did\r\nI surveyed more and I found 2 issues.\r\n\r\nAbout the first one, I wrote it as a new issue.\r\nhttps://github.com/huggingface/datasets/issues/2031\r\n\r\nThe error I mentioned in the previous comment above, which occurred on my local machine, is no longer occurring.\r\n\r\nBut, it still failed. In the previous comment, I wrote `AssertionError: OutputStream realloc failed.` happen on another machine. It also happens on my local machine.\r\n\r\nHere's what I've tried.\r\n\r\nthe wikipedia.py downloads these xml.bz2 files based on dumpstatus.json\r\nIn Japanese Wikipedia dataset that I specified, it will download these 6 files.\r\n\r\n\r\n`https://dumps.wikimedia.org/jawiki/20210120/dumpstatus.json`\r\nand filtered json based on wikipedia.py is below.\r\n```json\r\n {\r\n \"jobs\": {\r\n \"articlesmultistreamdump\": {\r\n \"files\": {\r\n \"jawiki-20210120-pages-articles-multistream1.xml-p1p114794.bz2\": {\r\n \"url\": \"/jawiki/20210120/jawiki-20210120-pages-articles-multistream1.xml-p1p114794.bz2\"\r\n },\r\n \"jawiki-20210120-pages-articles-multistream2.xml-p114795p390428.bz2\": {\r\n \"url\": \"/jawiki/20210120/jawiki-20210120-pages-articles-multistream2.xml-p114795p390428.bz2\"\r\n },\r\n \"jawiki-20210120-pages-articles-multistream3.xml-p390429p902407.bz2\": {\r\n \"url\": \"/jawiki/20210120/jawiki-20210120-pages-articles-multistream3.xml-p390429p902407.bz2\"\r\n },\r\n \"jawiki-20210120-pages-articles-multistream4.xml-p902408p1721646.bz2\": {\r\n \"url\": \"/jawiki/20210120/jawiki-20210120-pages-articles-multistream4.xml-p902408p1721646.bz2\"\r\n },\r\n \"jawiki-20210120-pages-articles-multistream5.xml-p1721647p2807947.bz2\": {\r\n \"url\": \"/jawiki/20210120/jawiki-20210120-pages-articles-multistream5.xml-p1721647p2807947.bz2\"\r\n },\r\n \"jawiki-20210120-pages-articles-multistream6.xml-p2807948p4290013.bz2\": {\r\n \"url\": \"/jawiki/20210120/jawiki-20210120-pages-articles-multistream6.xml-p2807948p4290013.bz2\"\r\n }\r\n }\r\n }\r\n }\r\n }\r\n```\r\n\r\nSo, I tried running with fewer resources by modifying this line.\r\nhttps://github.com/huggingface/datasets/blob/13a5b7db992ad5cf77895e4c0f76595314390418/datasets/wikipedia/wikipedia.py#L524\r\nI changed it like this. just change filepaths list.\r\n` | \"Initialize\" >> beam.Create(filepaths[:1])`\r\n\r\nand I added a print line inside for the loop of _extract_content.\r\nlike this `if(i % 100000 == 0): print(i)`\r\n\r\nfirst, without modification, it always stops after all _extract_content is done.\r\n\r\n- `filepaths[:1]` then it succeeded.\r\n- `filepaths[:2]` then it failed.\r\nI don't try all patterns because each pattern takes a long time.\r\n\r\n### my opinion\r\nIt seems it's successful when the entire file size is small.\r\n \r\nso, at least it doesn't file-specific issue.\r\n\r\n\r\nI don't know it's true but I think when beam_writter writes into a file, it consumes memory depends on its entire file.\r\nbut It's correct Apache Beam's behavior? I'm not familiar with this library.\r\n", "I don't know if this is related, but there is this issue on the wikipedia processing that you reported at #2031 (open PR is at #2037 ) .\r\nDoes the fix your proposed at #2037 helps in your case ?\r\n\r\nAnd for information, the DirectRunner of Apache Beam is not optimized for memory intensive tasks, so you must be right when you say that it uses the memory for the entire file.", "the #2037 doesn't solve my problem directly, but I found the point!\r\n\r\nhttps://github.com/huggingface/datasets/blob/349ac4398a3bcae6356f14c5754483383a60e8a4/datasets/wikipedia/wikipedia.py#L523\r\nthis `beam.transforms.Reshuffle()` cause the memory error.\r\n\r\nit makes sense if I consider the shuffle means. Beam's reshuffle seems need put all data in memory.\r\nPreviously I doubt that this line causes error, but at that time another bug showed in #2037 made error, so I can't found it.\r\n\r\nAnyway, I comment out this line, and run load_dataset, then it works!\r\n\r\n```python\r\nwiki = datasets.load_dataset(\r\n \"./wikipedia.py\",\r\n cache_dir=\"./datasets\",\r\n beam_runner=\"DirectRunner\",\r\n language=\"ja\",\r\n date=\"20210120\",\r\n)[\"train\"]\r\n```\r\n![image](https://user-images.githubusercontent.com/6331508/112283369-6a9f3300-8ccb-11eb-82e5-827bf7fddfb9.png)\r\n\r\nDataset has already shuffle function. https://github.com/huggingface/datasets/blob/349ac4398a3bcae6356f14c5754483383a60e8a4/src/datasets/arrow_dataset.py#L2069\r\nSo, though I don't know it's difference correctly, but I think Beam's reshuffle isn't be needed. How do you think?", "The reshuffle is needed when you use parallelism.\r\nThe objective is to redistribute the articles evenly on the workers, since the `_extract_content` step generated many articles per file. By using reshuffle, we can split the processing of the articles of one file into several workers. Without reshuffle, all the articles of one file would be processed on the same worker that read the file, making the whole process take a very long time.", "Maybe the reshuffle step can be added only if the runner is not a DirectRunner ?" ]
2021-01-29T08:17:24
2021-03-25T12:10:51
null
CONTRIBUTOR
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```py import datasets wiki = datasets.load_dataset('wikipedia', '20200501.ja', cache_dir='./datasets') ``` then `ModuleNotFoundError: No module named 'apache_beam'` happend. The error doesn't appear when it's '20200501.en'. I don't know Apache Beam, but according to #498 it isn't necessary when it's saved to local. is it correct?
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How to use split dataset
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[ "By default, all 3 splits will be loaded if you run the following:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"lambada\")\r\nprint(dataset[\"train\"])\r\nprint(dataset[\"valid\"])\r\n\r\n```\r\n\r\nIf you wanted to do load this manually, you could do this:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndata_files = {\r\n \"train\": \"data/lambada/train.txt\",\r\n \"valid\": \"data/lambada/valid.txt\",\r\n \"test\": \"data/lambada/test.txt\",\r\n}\r\nds = load_dataset(\"text\", data_files=data_files)\r\n```", "Thank you for the quick response! " ]
2021-01-27T21:37:47
2021-04-23T15:17:39
2021-04-23T15:17:39
NONE
null
null
null
![Capture1](https://user-images.githubusercontent.com/78090287/106057436-cb6a1f00-6111-11eb-8c9c-3658065b1fdf.PNG) Hey, I want to split the lambada dataset into corpus, test, train and valid txt files (like penn treebank) but I am not able to achieve this. What I am doing is, executing the lambada.py file in my project but its not giving desired results. Any help will be appreciated!
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Not enough disk space (Needed: Unknown size) when caching on a cluster
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[ "Hi ! \r\n\r\nWhat do you mean by \"disk_usage(\".\").free` can't compute on the cluster's shared disk\" exactly ?\r\nDoes it return 0 ?", "Yes, that's right. It shows 0 free space even though there is. I suspect it might have to do with permissions on the shared disk.\r\n\r\n```python\r\n>>> disk_usage(\".\")\r\nusage(total=999999, used=999999, free=0)\r\n```", "That's an interesting behavior...\r\nDo you know any other way to get the free space that works in your case ?\r\nAlso if it's a permission issue could you try fix the permissions and let mus know if that helped ?", "I think its an issue on the clusters end (unclear exactly why -- maybe something with docker containers?), will close the issue", "Were you able to figure it out?", "@philippnoah I had fixed it with a small hack where I patched `has_sufficient_disk_space` to always return `True`. you can do that with an import without having to modify the `datasets` package", "@olinguyen Thanks for the suggestion, it works but I had to to edit builder.py in the installed package. Can you please explain how were you able to do this using import?", "I was able to patch the builder code in my notebook before the load data call and it works. \r\n```\r\nimport datasets\r\ndatasets.builder.has_sufficient_disk_space = lambda needed_bytes, directory='.': True\r\n```" ]
2021-01-27T21:30:59
2022-11-07T16:33:03
2021-01-30T01:07:56
CONTRIBUTOR
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I'm running some experiments where I'm caching datasets on a cluster and accessing it through multiple compute nodes. However, I get an error when loading the cached dataset from the shared disk. The exact error thrown: ```bash >>> load_dataset(dataset, cache_dir="/path/to/cluster/shared/path") OSError: Not enough disk space. Needed: Unknown size (download: Unknown size, generated: Unknown size, post-processed: Unknown size) ``` [`utils.has_sufficient_disk_space`](https://github.com/huggingface/datasets/blob/8a03ab7d123a76ee744304f21ce868c75f411214/src/datasets/utils/py_utils.py#L332) fails on each job because of how the cluster system is designed (`disk_usage(".").free` can't compute on the cluster's shared disk). This is exactly where the error gets thrown: https://github.com/huggingface/datasets/blob/master/src/datasets/builder.py#L502 ```python if not utils.has_sufficient_disk_space(self.info.size_in_bytes or 0, directory=self._cache_dir_root): raise IOError( "Not enough disk space. Needed: {} (download: {}, generated: {}, post-processed: {})".format( utils.size_str(self.info.size_in_bytes or 0), utils.size_str(self.info.download_size or 0), utils.size_str(self.info.dataset_size or 0), utils.size_str(self.info.post_processing_size or 0), ) ) ``` What would be a good way to circumvent this? my current fix is to manually comment out that part, but that is not ideal. Would it be possible to pass a flag to skip this check on disk space?
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JSONDecodeError on JSON with multiple lines
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[ "Hi !\r\n\r\nThe `json` dataset script does support this format. For example loading a dataset with this format works on my side:\r\n```json\r\n{\"key1\":11, \"key2\":12, \"key3\":13}\r\n{\"key1\":21, \"key2\":22, \"key3\":23}\r\n```\r\n\r\nCan you show the full stacktrace please ? Also which version of datasets and pyarrow are you using ?\r\n\r\n", "Hi Quentin!\r\n\r\nI apologize for bothering you. There was some issue with my pyarrow version as far as I understand. I don't remember the exact version I was using as I didn't check it.\r\n\r\nI repeated it with `datasets 1.2.1` and `pyarrow 2.0.0` and it worked.\r\n\r\nClosing this issue. Again, sorry for the bother.\r\n\r\nThanks,\r\nGunjan" ]
2021-01-27T00:19:22
2021-01-31T08:47:18
2021-01-31T08:47:18
CONTRIBUTOR
null
null
null
Hello :), I have been trying to load data using a JSON file. Based on the [docs](https://huggingface.co/docs/datasets/loading_datasets.html#json-files), the following format is supported: ```json {"key1":11, "key2":12, "key3":13} {"key1":21, "key2":22, "key3":23} ``` But, when I try loading a dataset with the same format, I get a JSONDecodeError : `JSONDecodeError: Extra data: line 2 column 1 (char 7142)`. Now, this is expected when using `json` to load a JSON file. But I was wondering if there are any special arguments to pass when using `load_dataset` as the docs suggest that this format is supported. When I convert the JSON file to a list of dictionaries format, I get AttributeError: `AttributeError: 'list' object has no attribute 'keys'`. So, I can't convert them to list of dictionaries either. Please let me know :) Thanks, Gunjan
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Dataset Examples Explorer
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[ "Hi @ChewKokWah,\r\n\r\nWe're working on it! In the meantime, you can still find the dataset explorer at the following URL: https://huggingface.co/datasets/viewer/", "Glad to see that it still exist, this existing one is more than good enough for me, it is feature rich, simple to use and concise. \r\nHope similar feature can be retain in the future version." ]
2021-01-26T20:39:02
2021-02-01T13:58:44
2021-02-01T13:58:44
NONE
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In the Older version of the Dataset, there are a useful Dataset Explorer that allow user to visualize the examples (training, test and validation) of a particular dataset, it is no longer there in current version. Hope HuggingFace can re-enable the feature that at least allow viewing of the first 20 examples of a particular dataset, or alternatively can extract 20 examples for each datasets and make those part of the Dataset Card Documentation.
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AttributeError: module 'pyarrow' has no attribute 'PyExtensionType' during import
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[ "Hi ! I'm not able to reproduce the issue. Can you try restarting your runtime ?\r\n\r\nThe PyExtensionType is available in pyarrow starting 0.17.1 iirc. If restarting your runtime doesn't fix this, can you try updating pyarrow ?\r\n```\r\npip install pyarrow --upgrade\r\n```", "We should bump up the version test of pyarrow maybe no?\r\n\r\nhttps://github.com/huggingface/datasets/blob/master/src/datasets/__init__.py#L60", "Yes indeed.\r\n\r\nAlso it looks like Pyarrow 3.0.0 got released on pypi 10 hours ago. This might be related to the bug, I'll investigate\r\nEDIT: looks like the 3.0.0 release doesn't have unexpected breaking changes for us, so I don't think the issue comes from that", "Maybe colab moved to pyarrow 0.16 by default (instead of 0.14 before)?", "Installing datasets installs pyarrow>=0.17.1 so in theory it doesn't matter which version of pyarrow colab has by default (which is currently pyarrow 0.14.1).\r\n\r\nAlso now the colab runtime refresh the pyarrow version automatically after the update from pip (previously you needed to restart your runtime).\r\n\r\nI guess what happened is that Colab didn't refresh pyarrow for some reason, and the AttributeError was raised *before* the pyarrow version check from `datasets` at https://github.com/huggingface/datasets/blob/master/src/datasets/__init__.py#L60", "Yes colab doesn’t reload preloaded library unless you restart the instance. Maybe we should move the check on top of the init ", "Yes I'll do that :)", "I updated the pyarrow version check in #1782" ]
2021-01-26T04:18:35
2022-10-05T12:37:06
2022-10-05T12:37:06
NONE
null
null
null
I'm using Colab. And suddenly this morning, there is this error. Have a look below! ![screenshot-colab research google com-2021 01 26-08-15-36](https://user-images.githubusercontent.com/45964869/105799890-fdaf3b80-5fae-11eb-8f06-11b65cdccc30.png)
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793,273,770
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GPT2 MNLI training using run_glue.py
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2021-01-25T10:53:52
2021-01-25T11:12:53
2021-01-25T11:12:53
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Edit: I'm closing this because I actually meant to post this in `transformers `not `datasets` Running this on Google Colab, ``` !python run_glue.py \ --model_name_or_path gpt2 \ --task_name mnli \ --do_train \ --do_eval \ --max_seq_length 128 \ --per_gpu_train_batch_size 10 \ --gradient_accumulation_steps 32\ --learning_rate 2e-5 \ --num_train_epochs 3.0 \ --output_dir models/gpt2/mnli/ ``` I get the following error, ``` "Asking to pad but the tokenizer does not have a padding token. " ValueError: Asking to pad but the tokenizer does not have a padding token. Please select a token to use as `pad_token` `(tokenizer.pad_token = tokenizer.eos_token e.g.)` or add a new pad token via `tokenizer.add_special_tokens({'pad_token': '[PAD]'})`. ``` Do I need to modify the trainer to work with GPT2 ?
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[Question & Bug Report] Can we preprocess a dataset on the fly?
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[ "We are very actively working on this. How does your dataset look like in practice (number/size/type of files)?", "It's a text file with many lines (about 1B) of Chinese sentences. I use it to train language model using https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_mlm_wwm.py", "Indeed I will submit a PR in a fez days to enable processing on-the-fly :)\r\nThis can be useful in language modeling for tokenization, padding etc.\r\n", "any update on this issue? ...really look forward to use it ", "Hi @acul3,\r\n\r\nPlease look at the discussion on a related Issue #1825. I think using `set_transform` after building from source should do.", "@gchhablani thank you so much\r\n\r\nwill try look at it" ]
2021-01-24T09:28:24
2021-05-20T04:15:58
2021-05-20T04:15:58
NONE
null
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I know we can use `Datasets.map` to preprocess a dataset, but I'm using it with very large corpus which generates huge cache file (several TB cache from a 400 GB text file). I have no disk large enough to save it. Can we preprocess a dataset on the fly without generating cache? BTW, I tried raising `writer_batch_size`. Seems that argument doesn't have any effect when it's larger than `batch_size`, because you are saving all the batch instantly after it's processed. Please check the following code: https://github.com/huggingface/datasets/blob/0281f9d881f3a55c89aeaa642f1ba23444b64083/src/datasets/arrow_dataset.py#L1532
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792,742,120
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Efficient ways to iterate the dataset
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[ "It seems that selecting a subset of colums directly from the dataset, i.e., dataset[\"column\"], is slow.", "I was wrong, ```dataset[\"column\"]``` is fast." ]
2021-01-24T07:54:31
2021-01-24T09:50:39
2021-01-24T09:50:39
CONTRIBUTOR
null
null
null
For a large dataset that does not fits the memory, how can I select only a subset of features from each example? If I iterate over the dataset and then select the subset of features one by one, the resulted memory usage will be huge. Any ways to solve this? Thanks
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MDU6SXNzdWU3OTI3MzA1NTk=
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is it possible to make slice to be more compatible like python list and numpy?
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[ "Hi ! Thanks for reporting.\r\nI am working on changes in the way data are sliced from arrow. I can probably fix your issue with the changes I'm doing.\r\nIf you have some code to reproduce the issue it would be nice so I can make sure that this case will be supported :)\r\nI'll make a PR in a few days ", "Good if you can take care at your side.\r\nHere is the [colab notebook](https://colab.research.google.com/drive/19c-abm87RTRYgW9G1D8ktfwRW95zDYBZ?usp=sharing)" ]
2021-01-24T06:15:52
2024-01-31T15:54:18
2024-01-31T15:54:18
NONE
null
null
null
Hi, see below error: ``` AssertionError: Requested slice [:10000000000000000] incompatible with 20 examples. ```
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bug in loading datasets
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[ "Looks like an issue with your csv file. Did you use the right delimiter ?\r\nApparently at line 37 the CSV reader from pandas reads 2 fields instead of 1.", "Note that you can pass any argument you would pass to `pandas.read_csv` as kwargs to `load_dataset`. For example you can do\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('csv', data_files=data_files, sep=\"\\t\")\r\n```\r\n\r\nfor example to use a tab separator.\r\n\r\nYou can see the full list of arguments here: https://github.com/huggingface/datasets/blob/master/src/datasets/packaged_modules/csv/csv.py\r\n\r\n(I've not found the list in the documentation though, we definitely must add them !)", "You can try to convert the file to (CSV UTF-8)" ]
2021-01-24T02:53:45
2021-09-06T08:54:46
2021-08-04T18:13:01
NONE
null
null
null
Hi, I need to load a dataset, I use these commands: ``` from datasets import load_dataset dataset = load_dataset('csv', data_files={'train': 'sick/train.csv', 'test': 'sick/test.csv', 'validation': 'sick/validation.csv'}) print(dataset['validation']) ``` the dataset in sick/train.csv are simple csv files representing the data. I am getting this error, do you have an idea how I can solve this? thank you @lhoestq ``` Using custom data configuration default Downloading and preparing dataset csv/default-61468fc71a743ec1 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /julia/cache_home_2/datasets/csv/default-61468fc71a743ec1/0.0.0/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2... Traceback (most recent call last): File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py", line 485, in incomplete_dir yield tmp_dir File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py", line 527, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py", line 604, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py", line 959, in _prepare_split for key, table in utils.tqdm(generator, unit=" tables", leave=False, disable=not_verbose): File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/tqdm-4.49.0-py3.7.egg/tqdm/std.py", line 1133, in __iter__ for obj in iterable: File "/julia/cache_home_2/modules/datasets_modules/datasets/csv/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2/csv.py", line 129, in _generate_tables for batch_idx, df in enumerate(csv_file_reader): File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/pandas-1.2.0-py3.7-linux-x86_64.egg/pandas/io/parsers.py", line 1029, in __next__ return self.get_chunk() File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/pandas-1.2.0-py3.7-linux-x86_64.egg/pandas/io/parsers.py", line 1079, in get_chunk return self.read(nrows=size) File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/pandas-1.2.0-py3.7-linux-x86_64.egg/pandas/io/parsers.py", line 1052, in read index, columns, col_dict = self._engine.read(nrows) File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/pandas-1.2.0-py3.7-linux-x86_64.egg/pandas/io/parsers.py", line 2056, in read data = self._reader.read(nrows) File "pandas/_libs/parsers.pyx", line 756, in pandas._libs.parsers.TextReader.read File "pandas/_libs/parsers.pyx", line 783, in pandas._libs.parsers.TextReader._read_low_memory File "pandas/_libs/parsers.pyx", line 827, in pandas._libs.parsers.TextReader._read_rows File "pandas/_libs/parsers.pyx", line 814, in pandas._libs.parsers.TextReader._tokenize_rows File "pandas/_libs/parsers.pyx", line 1951, in pandas._libs.parsers.raise_parser_error pandas.errors.ParserError: Error tokenizing data. C error: Expected 1 fields in line 37, saw 2 During handling of the above exception, another exception occurred: Traceback (most recent call last): File "write_sick.py", line 19, in <module> 'validation': 'sick/validation.csv'}) File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/load.py", line 612, in load_dataset ignore_verifications=ignore_verifications, File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py", line 534, in download_and_prepare self._save_info() File "/julia/libs/anaconda3/envs/success/lib/python3.7/contextlib.py", line 130, in __exit__ self.gen.throw(type, value, traceback) File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py", line 491, in incomplete_dir shutil.rmtree(tmp_dir) File "/julia/libs/anaconda3/envs/success/lib/python3.7/shutil.py", line 498, in rmtree onerror(os.rmdir, path, sys.exc_info()) File "/julia/libs/anaconda3/envs/success/lib/python3.7/shutil.py", line 496, in rmtree os.rmdir(path) OSError: [Errno 39] Directory not empty: '/julia/cache_home_2/datasets/csv/default-61468fc71a743ec1/0.0.0/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2.incomplete' ```
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Adding SICK dataset
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2021-01-24T02:15:31
2021-02-05T15:49:25
2021-02-05T15:49:25
NONE
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Hi It would be great to include SICK dataset. ## Adding a Dataset - **Name:** SICK - **Description:** a well known entailment dataset - **Paper:** http://marcobaroni.org/composes/sick.html - **Data:** http://marcobaroni.org/composes/sick.html - **Motivation:** this is an important NLI benchmark Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). thanks
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792,701,276
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Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.2.1/datasets/csv/csv.py
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[ "I temporary manually download csv.py as custom dataset loading script", "Indeed in 1.2.1 the script to process csv file is downloaded. Starting from the next release though we include the csv processing directly in the library.\r\nSee PR #1726 \r\nWe'll do a new release soon :)", "Thanks." ]
2021-01-24T01:53:52
2021-01-24T23:06:29
2021-01-24T23:06:29
NONE
null
null
null
Hi, When I load_dataset from local csv files, below error happened, looks raw.githubusercontent.com was blocked by the chinese government. But why it need to download csv.py? should it include when pip install the dataset? ``` Traceback (most recent call last): File "/home/tom/pyenv/pystory/lib/python3.6/site-packages/datasets/load.py", line 267, in prepare_module local_path = cached_path(file_path, download_config=download_config) File "/home/tom/pyenv/pystory/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 343, in cached_path max_retries=download_config.max_retries, File "/home/tom/pyenv/pystory/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 617, in get_from_cache raise ConnectionError("Couldn't reach {}".format(url)) ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.2.1/datasets/csv/csv.py ```
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792,698,148
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how can I combine 2 dataset with different/same features?
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[ "Hi ! Currently we don't have a way to `zip` datasets but we plan to add this soon :)\r\nFor now you'll need to use `map` to add the fields from one dataset to the other. See the comment here for more info : https://github.com/huggingface/datasets/issues/853#issuecomment-727872188", "Good to hear.\r\nCurrently I did not use map , just fetch src and tgt from the 2 dataset and merge them.\r\nIt will be a release if you can deal with it at the backend.\r\nThanks.", "Hi! You can rename the columns and concatenate the datasets along `axis=1` to get the desired result as follows:\r\n```python\r\nds1 = ds1.rename_column(\"text\", \"src\")\r\nds2 = ds2.rename_column(\"text\", \"tgt\")\r\nds = datasets.concatenate_datasets([\"ds1\", \"ds2\"], axis=1)\r\n```" ]
2021-01-24T01:26:06
2022-06-01T15:43:15
2022-06-01T15:43:15
NONE
null
null
null
to combine 2 dataset by one-one map like ds = zip(ds1, ds2): ds1: {'text'}, ds2: {'text'}, combine ds:{'src', 'tgt'} or different feature: ds1: {'src'}, ds2: {'tgt'}, combine ds:{'src', 'tgt'}
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_pickle.PicklingError: Can't pickle typing.Union[str, NoneType]: it's not the same object as typing.Union when calling datasets.map with num_proc=2
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[ "More information: `run_mlm.py` will raise same error when `data_args.line_by_line==True`\r\n\r\nhttps://github.com/huggingface/transformers/blob/9152f16023b59d262b51573714b40325c8e49370/examples/language-modeling/run_mlm.py#L300\r\n", "Hi ! What version of python and datasets do you have ? And also what version of dill and pickle ?", "> Hi ! What version of python and datasets do you have ? And also what version of dill and pickle ?\r\n\r\npython==3.6.10\r\ndatasets==1.2.1\r\ndill==0.3.2\r\npickle.format_version==4.0", "Multiprocessing in python require all the functions to be picklable. More specifically, functions need to be picklable with `dill`.\r\n\r\nHowever objects like `typing.Union[str, NoneType]` are not picklable in python <3.7.\r\nCan you try to update your python version to python>=3.7 ?\r\n" ]
2021-01-23T10:13:00
2022-10-05T12:38:51
2022-10-05T12:38:51
NONE
null
null
null
It may be a bug of multiprocessing with Datasets, when I disable the multiprocessing by set num_proc to None, everything works fine. The script I use is https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_mlm_wwm.py Script args: ``` --model_name_or_path ../../../model/chinese-roberta-wwm-ext --train_file /nfs/volume-377-2/bert/data/test/train.txt --output_dir test --do_train --per_device_train_batch_size 2 --gradient_accumulation_steps 2 --learning_rate 1e-4 --max_steps 1000 --warmup_steps 10 --save_steps 1000 --save_total_limit 1 --seed 23333 --max_seq_length 512 --preprocessing_num_workers 2 --cache_dir /nfs/volume-377-2/bert/data/test/cache ``` Where the `/nfs/volume-377-2/bert/data/test/train.txt` is just a toy example with 10000 lines of random string, you should be able to reproduce this error esaily. Full Traceback: ``` Traceback (most recent call last): File "/nfs/volume-377-2/bert/transformers/examples/language-modeling/run_mlm_wwm.py", line 398, in <module> main() File "/nfs/volume-377-2/bert/transformers/examples/language-modeling/run_mlm_wwm.py", line 325, in main load_from_cache_file=not data_args.overwrite_cache, File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/datasets/dataset_dict.py", line 303, in map for k, dataset in self.items() File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/datasets/dataset_dict.py", line 303, in <dictcomp> for k, dataset in self.items() File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 1318, in map transformed_shards = [r.get() for r in results] File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 1318, in <listcomp> transformed_shards = [r.get() for r in results] File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/multiprocess/pool.py", line 644, in get raise self._value File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/multiprocess/pool.py", line 424, in _handle_tasks put(task) File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/multiprocess/connection.py", line 209, in send self._send_bytes(_ForkingPickler.dumps(obj)) File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/multiprocess/reduction.py", line 54, in dumps cls(buf, protocol, *args, **kwds).dump(obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 446, in dump StockPickler.dump(self, obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 409, in dump self.save(obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 751, in save_tuple save(element) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 933, in save_module_dict StockPickler.save_dict(pickler, obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 821, in save_dict self._batch_setitems(obj.items()) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 847, in _batch_setitems save(v) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 1438, in save_function obj.__dict__, fkwdefaults), obj=obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 610, in save_reduce save(args) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 751, in save_tuple save(element) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 736, in save_tuple save(element) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 1170, in save_cell pickler.save_reduce(_create_cell, (f,), obj=obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 610, in save_reduce save(args) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 736, in save_tuple save(element) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 521, in save self.save_reduce(obj=obj, *rv) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 605, in save_reduce save(cls) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 1365, in save_type obj.__bases__, _dict), obj=obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 610, in save_reduce save(args) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 751, in save_tuple save(element) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 933, in save_module_dict StockPickler.save_dict(pickler, obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 821, in save_dict self._batch_setitems(obj.items()) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 847, in _batch_setitems save(v) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 933, in save_module_dict StockPickler.save_dict(pickler, obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 821, in save_dict self._batch_setitems(obj.items()) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 847, in _batch_setitems save(v) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 507, in save self.save_global(obj, rv) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 927, in save_global (obj, module_name, name)) _pickle.PicklingError: Can't pickle typing.Union[str, NoneType]: it's not the same object as typing.Union ```
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Issues when run two programs compute the same metrics
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[ "Hi ! To avoid collisions you can specify a `experiment_id` when instantiating your metric using `load_metric`. It will replace \"default_experiment\" with the experiment id that you provide in the arrow filename. \r\n\r\nAlso when two `experiment_id` collide we're supposed to detect it using our locking mechanism. Not sure why it didn't work in your case. Could you share some code that reproduces the issue ? This would help us investigate.", "Thank you for your response. I fixed the issue by set \"keep_in_memory=True\" when load_metric. \r\nI cannot share the entire source code but below is the wrapper I wrote:\r\n\r\n```python\r\nclass Evaluation:\r\n def __init__(self, metric='sacrebleu'):\r\n # self.metric = load_metric(metric, keep_in_memory=True)\r\n self.metric = load_metric(metric)\r\n\r\n def add(self, predictions, references):\r\n self.metric.add_batch(predictions=predictions, references=references)\r\n\r\n def compute(self):\r\n return self.metric.compute()['score']\r\n```\r\n\r\nThen call the given wrapper as follows:\r\n\r\n```python\r\neval = Evaluation(metric='sacrebleu')\r\nfor query, candidates, labels in tqdm(dataset):\r\n predictions = net.generate(query)\r\n references = [[s] for s in labels]\r\n eval.add(predictions, references)\r\n if n % 100 == 0:\r\n bleu += eval.compute()\r\n eval = Evaluation(metric='sacrebleu')" ]
2021-01-22T14:22:55
2021-02-02T10:38:06
2021-02-02T10:38:06
NONE
null
null
null
I got the following error when running two different programs that both compute sacreblue metrics. It seems that both read/and/write to the same location (.cache/huggingface/metrics/sacrebleu/default/default_experiment-1-0.arrow) where it caches the batches: ``` File "train_matching_min.py", line 160, in <module>ch_9_label avg_loss = valid(epoch, args.batch, args.validation, args.with_label) File "train_matching_min.py", line 93, in valid bleu += eval.compute() File "/u/tlhoang/projects/seal/match/models/eval.py", line 23, in compute return self.metric.compute()['score'] File "/dccstor/know/anaconda3/lib/python3.7/site-packages/datasets/metric.py", line 387, in compute self._finalize() File "/dccstor/know/anaconda3/lib/python3.7/site-packages/datasets/metric.py", line 355, in _finalize self.data = Dataset(**reader.read_files([{"filename": f} for f in file_paths])) File "/dccstor/know/anaconda3/lib/python3.7/site-packages/datasets/arrow_reader.py", line 231, in read_files pa_table = self._read_files(files) File "/dccstor/know/anaconda3/lib/python3.7/site-packages/datasets/arrow_reader.py", line 170, in _read_files pa_table: pa.Table = self._get_dataset_from_filename(f_dict) File "/dccstor/know/anaconda3/lib/python3.7/site-packages/datasets/arrow_reader.py", line 299, in _get_dataset_from_filename pa_table = f.read_all() File "pyarrow/ipc.pxi", line 481, in pyarrow.lib.RecordBatchReader.read_all File "pyarrow/error.pxi", line 84, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: Expected to read 1819307375 metadata bytes, but only read 454396 ```
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Error iterating over Dataset with DataLoader
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[ "Instead of:\r\n```python\r\ndataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32)\r\n```\r\nIt should be:\r\n```python\r\ndataloader = torch.utils.data.DataLoader(encoded_dataset, batch_size=32)\r\n```\r\n\r\n`batch_sampler` accepts a Sampler object or an Iterable, so you get an error.", "@mariosasko I thought that would fix it, but now I'm getting a different error:\r\n\r\n```\r\n/usr/local/lib/python3.6/dist-packages/datasets/arrow_dataset.py:851: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:141.)\r\n return torch.tensor(x, **format_kwargs)\r\n---------------------------------------------------------------------------\r\nRuntimeError Traceback (most recent call last)\r\n<ipython-input-20-3af1d82bf93a> in <module>()\r\n 1 dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_size=32)\r\n----> 2 next(iter(dataloader))\r\n\r\n5 frames\r\n/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/collate.py in default_collate(batch)\r\n 53 storage = elem.storage()._new_shared(numel)\r\n 54 out = elem.new(storage)\r\n---> 55 return torch.stack(batch, 0, out=out)\r\n 56 elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \\\r\n 57 and elem_type.__name__ != 'string_':\r\n\r\nRuntimeError: stack expects each tensor to be equal size, but got [7] at entry 0 and [10] at entry 1\r\n```\r\n\r\nAny thoughts what this means?I Do I need padding?", "Yes, padding is an answer. \r\n\r\nThis can be solved easily by passing a callable to the collate_fn arg of DataLoader that adds padding. ", "Padding was the fix, thanks!", "dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_size=4)\r\nbatch = next(iter(dataloader))\r\n\r\ngetting \r\nValueError: cannot reshape array of size 8192 into shape (1,512,4)\r\n\r\nI had put padding as 2048 for encoded_dataset\r\nkindly help", "data_loader_val = torch.utils.data.DataLoader(val_dataset, batch_size=32, shuffle=True, drop_last=False, num_workers=0)\r\ndataiter = iter(data_loader_val)\r\nimages, _ = next(dataiter)\r\n\r\ngetting -> TypeError: 'list' object is not callable\r\n\r\nCannot iterate through the data. Kindly suggest." ]
2021-01-21T22:56:45
2022-10-28T02:16:38
2021-01-23T03:44:14
NONE
null
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I have a Dataset that I've mapped a tokenizer over: ``` encoded_dataset.set_format(type='torch',columns=['attention_mask','input_ids','token_type_ids']) encoded_dataset[:1] ``` ``` {'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]), 'input_ids': tensor([[ 101, 178, 1198, 1400, 1714, 22233, 21365, 4515, 8618, 1113, 102]]), 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])} ``` When I try to iterate as in the docs, I get errors: ``` dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32) next(iter(dataloader)) ``` ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-45-05180ba8aa35> in <module>() 1 dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32) ----> 2 next(iter(dataloader)) 3 frames /usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py in __init__(self, loader) 411 self._timeout = loader.timeout 412 self._collate_fn = loader.collate_fn --> 413 self._sampler_iter = iter(self._index_sampler) 414 self._base_seed = torch.empty((), dtype=torch.int64).random_(generator=loader.generator).item() 415 self._persistent_workers = loader.persistent_workers TypeError: 'int' object is not iterable ```
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Connection Issues
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[ "Academic WIFI was blocking." ]
2021-01-21T20:56:09
2021-01-21T21:00:19
2021-01-21T21:00:02
NONE
null
null
null
Today, I am getting connection issues while loading a dataset and the metric. ``` Traceback (most recent call last): File "src/train.py", line 180, in <module> train_dataset, dev_dataset, test_dataset = create_race_dataset() File "src/train.py", line 130, in create_race_dataset train_dataset = load_dataset("race", "all", split="train") File "/Users/saeed/Desktop/codes/repos/dreamscape-qa/env/lib/python3.7/site-packages/datasets/load.py", line 591, in load_dataset path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True File "/Users/saeed/Desktop/codes/repos/dreamscape-qa/env/lib/python3.7/site-packages/datasets/load.py", line 267, in prepare_module local_path = cached_path(file_path, download_config=download_config) File "/Users/saeed/Desktop/codes/repos/dreamscape-qa/env/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 343, in cached_path max_retries=download_config.max_retries, File "/Users/saeed/Desktop/codes/repos/dreamscape-qa/env/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 617, in get_from_cache raise ConnectionError("Couldn't reach {}".format(url)) ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.2.1/datasets/race/race.py ``` Or ``` Traceback (most recent call last): File "src/train.py", line 105, in <module> rouge = datasets.load_metric("rouge") File "/Users/saeed/Desktop/codes/repos/dreamscape-qa/env/lib/python3.7/site-packages/datasets/load.py", line 500, in load_metric dataset=False, File "/Users/saeed/Desktop/codes/repos/dreamscape-qa/env/lib/python3.7/site-packages/datasets/load.py", line 267, in prepare_module local_path = cached_path(file_path, download_config=download_config) File "/Users/saeed/Desktop/codes/repos/dreamscape-qa/env/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 343, in cached_path max_retries=download_config.max_retries, File "/Users/saeed/Desktop/codes/repos/dreamscape-qa/env/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 617, in get_from_cache raise ConnectionError("Couldn't reach {}".format(url)) ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.2.1/metrics/rouge/rouge.py ```
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791,226,007
MDU6SXNzdWU3OTEyMjYwMDc=
1,762
Unable to format dataset to CUDA Tensors
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[ "Hi ! You can get CUDA tensors with\r\n\r\n```python\r\ndataset.set_format(\"torch\", columns=columns, device=\"cuda\")\r\n```\r\n\r\nIndeed `set_format` passes the `**kwargs` to `torch.tensor`", "Hi @lhoestq,\r\n\r\nThanks a lot. Is this true for all format types?\r\n\r\nAs in, for 'torch', I can have `**kwargs` to `torch.tensor` and for 'tf' those args are passed to `tf.Tensor`, and the same for 'numpy' and 'pandas'?", "Yes the keywords arguments are passed to the convert function like `np.array`, `torch.tensor` or `tensorflow.ragged.constant`.\r\nWe don't support the kwargs for pandas on the other hand.", "Thanks @lhoestq,\r\nWould it be okay if I added this to the docs and made a PR?", "Sure ! Feel free to open a PR to improve the documentation :) ", "Closing this issue as it has been resolved." ]
2021-01-21T15:31:23
2021-02-02T07:13:22
2021-02-02T07:13:22
CONTRIBUTOR
null
null
null
Hi, I came across this [link](https://huggingface.co/docs/datasets/torch_tensorflow.html) where the docs show show to convert a dataset to a particular format. I see that there is an option to convert it to tensors, but I don't see any option to convert it to CUDA tensors. I tried this, but Dataset doesn't support assignment: ``` columns=['input_ids', 'token_type_ids', 'attention_mask', 'start_positions','end_positions'] samples.set_format(type='torch', columns = columns) for column in columns: samples[column].to(torch.device(self.config.device)) ``` There should be an option to do so, or if there is already a way to do this, please let me know. Thanks, Gunjan
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790,992,226
MDU6SXNzdWU3OTA5OTIyMjY=
1,759
wikipedia dataset incomplete
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[ "Hi !\r\nFrom what pickle file fo you get this ?\r\nI guess you mean the dataset loaded using `load_dataset` ?", "yes sorry, I used the `load_dataset`function and saved the data to a pickle file so I don't always have to reload it and are able to work offline. ", "The wikipedia articles are processed using the `mwparserfromhell` library. Even if it works well in most cases, such issues can happen unfortunately. You can find the repo here: https://github.com/earwig/mwparserfromhell\r\n\r\nThere also exist other datasets based on wikipedia that were processed differently (and are often cleaner) such as `wiki40b`.\r\n\r\n", "ok great. Thank you, @lhoestq. " ]
2021-01-21T11:47:15
2021-01-21T17:22:11
2021-01-21T17:21:06
NONE
null
null
null
Hey guys, I am using the https://github.com/huggingface/datasets/tree/master/datasets/wikipedia dataset. Unfortunately, I found out that there is an incompleteness for the German dataset. For reasons unknown to me, the number of inhabitants has been removed from many pages: Thorey-sur-Ouche has 128 inhabitants according to the webpage (https://de.wikipedia.org/wiki/Thorey-sur-Ouche). The pickle file however shows: französische Gemeinde mit Einwohnern (Stand). Is it possible to fix this? Best regards Chris
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790,626,116
MDU6SXNzdWU3OTA2MjYxMTY=
1,758
dataset.search() (elastic) cannot reliably retrieve search results
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[ "Hi !\r\nI tried your code on my side and I was able to workaround this issue by waiting a few seconds before querying the index.\r\nMaybe this is because the index is not updated yet on the ElasticSearch side ?", "Thanks for the feedback! I added a 30 second \"sleep\" and that seemed to work well!" ]
2021-01-21T02:26:37
2021-01-22T00:25:50
2021-01-22T00:25:50
NONE
null
null
null
I am trying to use elastic search to retrieve the indices of items in the dataset in their precise order, given shuffled training indices. The problem I have is that I cannot retrieve reliable results with my data on my first search. I have to run the search **twice** to get the right answer. I am indexing data that looks like the following from the HF SQuAD 2.0 data set: ``` ['57318658e6313a140071d02b', '56f7165e3d8e2e1400e3733a', '570e2f6e0b85d914000d7d21', '5727e58aff5b5019007d97d0', '5a3b5a503ff257001ab8441f', '57262fab271a42140099d725'] ``` To reproduce the issue, try: ``` from datasets import load_dataset, load_metric from transformers import BertTokenizerFast, BertForQuestionAnswering from elasticsearch import Elasticsearch import numpy as np import collections from tqdm.auto import tqdm import torch # from https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb#scrollTo=941LPhDWeYv- tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased') max_length = 384 # The maximum length of a feature (question and context) doc_stride = 128 # The authorized overlap between two part of the context when splitting it is needed. pad_on_right = tokenizer.padding_side == "right" squad_v2 = True # from https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb#scrollTo=941LPhDWeYv- def prepare_validation_features(examples): # 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. tokenized_examples = tokenizer( examples["question" if pad_on_right else "context"], examples["context" if pad_on_right else "question"], truncation="only_second" if pad_on_right else "only_first", max_length=max_length, stride=doc_stride, return_overflowing_tokens=True, return_offsets_mapping=True, 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. sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping") # We keep the example_id that gave us this feature and we will store the offset mappings. tokenized_examples["example_id"] = [] 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). sequence_ids = tokenized_examples.sequence_ids(i) context_index = 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. sample_index = 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. tokenized_examples["offset_mapping"][i] = [ (list(o) if sequence_ids[k] == context_index else None) for k, o in enumerate(tokenized_examples["offset_mapping"][i]) ] return tokenized_examples # build base examples, features set of training data shuffled_idx = pd.read_csv('https://raw.githubusercontent.com/afogarty85/temp/main/idx.csv')['idx'].to_list() examples = load_dataset("squad_v2").shuffle(seed=1)['train'] features = load_dataset("squad_v2").shuffle(seed=1)['train'].map( prepare_validation_features, batched=True, remove_columns=['answers', 'context', 'id', 'question', 'title']) # reorder features by the training process features = features.select(indices=shuffled_idx) # get the example ids to match with the "example" data; get unique entries id_list = list(dict.fromkeys(features['example_id'])) # now search for their index positions in the examples data set; load elastic search es = Elasticsearch([{'host': 'localhost'}]).ping() # add an index to the id column for the examples examples.add_elasticsearch_index(column='id') # retrieve the example index example_idx_k1 = [examples.search(index_name='id', query=i, k=1).indices for i in id_list] example_idx_k1 = [item for sublist in example_idx_k1 for item in sublist] example_idx_k2 = [examples.search(index_name='id', query=i, k=3).indices for i in id_list] example_idx_k2 = [item for sublist in example_idx_k2 for item in sublist] len(example_idx_k1) # should be 130319 len(example_idx_k2) # should be 130319 #trial 1 lengths: # k=1: 130314 # k=3: 130319 # trial 2: # just run k=3 first: 130310 # try k=1 after k=3: 130319 ```
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790,466,509
MDU6SXNzdWU3OTA0NjY1MDk=
1,757
FewRel
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[ "+1", "@dspoka Please check the following link : https://github.com/thunlp/FewRel\r\nThis link mentions two versions of the datasets. Also, this one seems to be the official link.\r\n\r\nI am assuming this is the correct link and implementing based on the same.", "Hi @lhoestq,\r\n\r\nThis issue can be closed, I guess.", "Yes :) closing\r\nThanks again for adding FewRel !", "Thanks for adding this @gchhablani ! Sorry didn't see the email notifications sooner!" ]
2021-01-20T23:56:03
2021-03-09T02:52:05
2021-03-08T14:34:52
NONE
null
null
null
## Adding a Dataset - **Name:** FewRel - **Description:** Large-Scale Supervised Few-Shot Relation Classification Dataset - **Paper:** @inproceedings{han2018fewrel, title={FewRel:A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation}, author={Han, Xu and Zhu, Hao and Yu, Pengfei and Wang, Ziyun and Yao, Yuan and Liu, Zhiyuan and Sun, Maosong}, booktitle={EMNLP}, year={2018}} - **Data:** https://github.com/ProKil/FewRel - **Motivation:** relationship extraction dataset that's been used by some state of the art systems that should be incorporated. Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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790,380,028
MDU6SXNzdWU3OTAzODAwMjg=
1,756
Ccaligned multilingual translation dataset
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2021-01-20T22:18:44
2021-03-01T10:36:21
2021-03-01T10:36:21
CONTRIBUTOR
null
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## Adding a Dataset - **Name:** *name of the dataset* - **Description:** *short description of the dataset (or link to social media or blog post)* - CCAligned consists of parallel or comparable web-document pairs in 137 languages aligned with English. These web-document pairs were constructed by performing language identification on raw web-documents, and ensuring corresponding language codes were corresponding in the URLs of web documents. This pattern matching approach yielded more than 100 million aligned documents paired with English. Recognizing that each English document was often aligned to mulitple documents in different target language, we can join on English documents to obtain aligned documents that directly pair two non-English documents (e.g., Arabic-French). - **Paper:** *link to the dataset paper if available* - https://www.aclweb.org/anthology/2020.emnlp-main.480.pdf - **Data:** *link to the Github repository or current dataset location* - http://www.statmt.org/cc-aligned/ - **Motivation:** *what are some good reasons to have this dataset* - The authors says it's an high quality dataset. - it's pretty large and includes many language pairs. It could be interesting training mt5 on this task. Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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790,324,734
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Using select/reordering datasets slows operations down immensely
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[ "You can use `Dataset.flatten_indices()` to make it fast after a select or shuffle.", "Thanks for the input! I gave that a try by adding this after my selection / reordering operations, but before the big computation task of `score_squad`\r\n\r\n```\r\nexamples = examples.flatten_indices()\r\nfeatures = features.flatten_indices()\r\n```\r\n\r\nThat helped quite a bit!" ]
2021-01-20T21:12:12
2021-01-20T22:03:39
2021-01-20T22:03:39
NONE
null
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I am using portions of HF's helpful work in preparing / scoring the SQuAD 2.0 data. The problem I have is that after using `select` to re-ordering the dataset, computations slow down immensely where the total scoring process on 131k training examples would take maybe 3 minutes, now take over an hour. The below example should be reproducible and I have ran myself down this path because I want to use HF's scoring functions and helpful data preparation, but use my own trainer. The training process uses shuffle and therefore the order I trained on no longer matches the original data set order. So, to score my results correctly, the original data set needs to match the order of the training. This requires that I: (1) collect the index for each row of data emitted during training, and (2) use this index information to re-order the datasets correctly so the orders match when I go to score. The problem is, the dataset class starts performing very poorly as soon as you start manipulating its order by immense magnitudes. ``` from datasets import load_dataset, load_metric from transformers import BertTokenizerFast, BertForQuestionAnswering from elasticsearch import Elasticsearch import numpy as np import collections from tqdm.auto import tqdm import torch # from https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb#scrollTo=941LPhDWeYv- tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased') max_length = 384 # The maximum length of a feature (question and context) doc_stride = 128 # The authorized overlap between two part of the context when splitting it is needed. pad_on_right = tokenizer.padding_side == "right" squad_v2 = True # from https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb#scrollTo=941LPhDWeYv- def prepare_validation_features(examples): # 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. tokenized_examples = tokenizer( examples["question" if pad_on_right else "context"], examples["context" if pad_on_right else "question"], truncation="only_second" if pad_on_right else "only_first", max_length=max_length, stride=doc_stride, return_overflowing_tokens=True, return_offsets_mapping=True, 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. sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping") # We keep the example_id that gave us this feature and we will store the offset mappings. tokenized_examples["example_id"] = [] 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). sequence_ids = tokenized_examples.sequence_ids(i) context_index = 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. sample_index = 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. tokenized_examples["offset_mapping"][i] = [ (list(o) if sequence_ids[k] == context_index else None) for k, o in enumerate(tokenized_examples["offset_mapping"][i]) ] return tokenized_examples # from https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb#scrollTo=941LPhDWeYv- def postprocess_qa_predictions(examples, features, starting_logits, ending_logits, n_best_size = 20, max_answer_length = 30): all_start_logits, all_end_logits = starting_logits, ending_logits # Build a map example to its corresponding features. example_id_to_index = {k: i for i, k in enumerate(examples["id"])} features_per_example = collections.defaultdict(list) for i, feature in enumerate(features): features_per_example[example_id_to_index[feature["example_id"]]].append(i) # The dictionaries we have to fill. predictions = collections.OrderedDict() # Logging. print(f"Post-processing {len(examples)} example predictions split into {len(features)} features.") # Let's loop over all the examples! for example_index, example in enumerate(tqdm(examples)): # Those are the indices of the features associated to the current example. feature_indices = features_per_example[example_index] min_null_score = None # Only used if squad_v2 is True. valid_answers = [] context = example["context"] # Looping through all the features associated to the current example. for feature_index in feature_indices: # We grab the predictions of the model for this feature. start_logits = all_start_logits[feature_index] end_logits = all_end_logits[feature_index] # This is what will allow us to map some the positions in our logits to span of texts in the original # context. offset_mapping = features[feature_index]["offset_mapping"] # Update minimum null prediction. cls_index = features[feature_index]["input_ids"].index(tokenizer.cls_token_id) feature_null_score = start_logits[cls_index] + end_logits[cls_index] if min_null_score is None or min_null_score < feature_null_score: min_null_score = feature_null_score # Go through all possibilities for the `n_best_size` greater start and end logits. start_indexes = np.argsort(start_logits)[-1 : -n_best_size - 1 : -1].tolist() end_indexes = np.argsort(end_logits)[-1 : -n_best_size - 1 : -1].tolist() for start_index in start_indexes: for end_index in end_indexes: # Don't consider out-of-scope answers, either because the indices are out of bounds or correspond # to part of the input_ids that are not in the context. if ( start_index >= len(offset_mapping) or end_index >= len(offset_mapping) or offset_mapping[start_index] is None or offset_mapping[end_index] is None ): continue # Don't consider answers with a length that is either < 0 or > max_answer_length. if end_index < start_index or end_index - start_index + 1 > max_answer_length: continue start_char = offset_mapping[start_index][0] end_char = offset_mapping[end_index][1] valid_answers.append( { "score": start_logits[start_index] + end_logits[end_index], "text": context[start_char: end_char] } ) if len(valid_answers) > 0: best_answer = sorted(valid_answers, key=lambda x: x["score"], reverse=True)[0] else: # In the very rare edge case we have not a single non-null prediction, we create a fake prediction to avoid # failure. best_answer = {"text": "", "score": 0.0} # Let's pick our final answer: the best one or the null answer (only for squad_v2) if not squad_v2: predictions[example["id"]] = best_answer["text"] else: answer = best_answer["text"] if best_answer["score"] > min_null_score else "" predictions[example["id"]] = answer return predictions # build base examples, features from training data examples = load_dataset("squad_v2").shuffle(seed=5)['train'] features = load_dataset("squad_v2").shuffle(seed=5)['train'].map( prepare_validation_features, batched=True, remove_columns=['answers', 'context', 'id', 'question', 'title']) # sim some shuffled training indices that we want to use to re-order the data to compare how we did shuffle_idx = np.arange(0, 131754) np.random.shuffle(shuffle_idx) # create a new dataset with rows selected following the training shuffle features = features.select(indices=shuffle_idx) # get unique example ids to match with the "example" data id_list = list(dict.fromkeys(features['example_id'])) # now search for their index positions; load elastic search es = Elasticsearch([{'host': 'localhost'}]).ping() # add an index to the id column for the examples examples.add_elasticsearch_index(column='id') # search the examples for their index position example_idx = [examples.search(index_name='id', query=i, k=1).indices for i in id_list] # drop the elastic search examples.drop_index(index_name='id') # put examples in the right order examples = examples.select(indices=example_idx) # generate some fake data logits = {'starting_logits': torch.randn(131754, 384), 'ending_logits': torch.randn(131754, 384)} def score_squad(logits, n_best_size, max_answer): # proceed with QA calculation final_predictions = postprocess_qa_predictions(examples=examples, features=features, starting_logits=logits['starting_logits'], ending_logits=logits['ending_logits'], n_best_size=20, max_answer_length=30) metric = load_metric("squad_v2") formatted_predictions = [{"id": k, "prediction_text": v, "no_answer_probability": 0.0} for k, v in final_predictions.items()] references = [{"id": ex["id"], "answers": ex["answers"]} for ex in examples] metrics = metric.compute(predictions=formatted_predictions, references=references) return metrics metrics = score_squad(logits, n_best_size=20, max_answer=30) ```
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datasets slicing with seed
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[ "Hi :) \r\nThe slicing API from https://huggingface.co/docs/datasets/splits.html doesn't shuffle the data.\r\nYou can shuffle and then take a subset of your dataset with\r\n```python\r\n# shuffle and take the first 100 examples\r\ndataset = dataset.shuffle(seed=42).select(range(100))\r\n```\r\n\r\nYou can find more information about shuffling and selecting rows in the documentation: https://huggingface.co/docs/datasets/processing.html#selecting-sorting-shuffling-splitting-rows", "thank you so much\n\nOn Mon, Jan 18, 2021 at 3:17 PM Quentin Lhoest <[email protected]>\nwrote:\n\n> Hi :)\n> The slicing API doesn't shuffle the data.\n> You can shuffle and then take a subset of your dataset with\n>\n> # shuffle and take the first 100 examplesdataset = dataset.shuffle(seed=42).select(range(100))\n>\n> You can find more information about shuffling and selecting rows in the\n> documentation:\n> https://huggingface.co/docs/datasets/processing.html#selecting-sorting-shuffling-splitting-rows\n>\n> —\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/datasets/issues/1747#issuecomment-762278134>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/AM3GZM5D5MDPLJGI4IG3UADS2Q7GPANCNFSM4WHLOZJQ>\n> .\n>\n" ]
2021-01-18T14:08:55
2022-10-05T12:37:27
2022-10-05T12:37:27
NONE
null
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Hi I need to slice a dataset with random seed, I looked into documentation here https://huggingface.co/docs/datasets/splits.html I could not find a seed option, could you assist me please how I can get a slice for different seeds? thank you. @lhoestq
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difference between wsc and wsc.fixed for superglue
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[ "From the description given in the dataset script for `wsc.fixed`:\r\n```\r\nThis version fixes issues where the spans are not actually substrings of the text.\r\n```" ]
2021-01-18T00:50:19
2021-01-18T11:02:43
2021-01-18T00:59:34
NONE
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Hi I see two versions of wsc in superglue, and I am not sure what is the differences and which one is the original one. could you help to discuss the differences? thanks @lhoestq
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787,631,412
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Issue while Creating Custom Metric
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[ "Currently it's only possible to define the features for the two columns `references` and `predictions`.\r\nThe data for these columns can then be passed to `metric.add_batch` and `metric.compute`.\r\nInstead of defining more columns `text`, `offset_mapping` and `ground` you must include them in either references and predictions.\r\n\r\nFor example \r\n```python\r\nfeatures = datasets.Features({\r\n 'predictions':datasets.Sequence(datasets.Value(\"int32\")),\r\n \"references\": datasets.Sequence({\r\n \"references_ids\": datasets.Value(\"int32\"),\r\n \"offset_mapping\": datasets.Value(\"int32\"),\r\n 'text': datasets.Value('string'),\r\n \"ground\": datasets.Value(\"int32\")\r\n }),\r\n})\r\n```\r\n\r\nAnother option would be to simply have the two features like \r\n```python\r\nfeatures = datasets.Features({\r\n 'predictions':datasets.Sequence(datasets.Value(\"int32\")),\r\n \"references\": datasets.Sequence(datasets.Value(\"int32\")),\r\n})\r\n```\r\nand keep `offset_mapping`, `text` and `ground` as as parameters for the computation (i.e. kwargs when calling `metric.compute`).\r\n\r\n\r\nWhat is the metric you would like to implement ?\r\n\r\nI'm asking since we consider allowing additional fields as requested in the `Comet` metric (see PR and discussion [here](https://github.com/huggingface/datasets/pull/1577)) and I'd like to know if it's something that can be interesting for users.\r\n\r\nWhat do you think ?", "Hi @lhoestq,\r\n\r\nI am doing text segmentation and the metric is effectively dice score on character offsets. So I need to pass the actual spans and I want to be able to get the spans based on predictions using offset_mapping.\r\n\r\nIncluding them in references seems like a good idea. I'll try it out and get back to you. If there's a better way to write a metric function for the same, please let me know.", "Resolved via https://github.com/huggingface/datasets/pull/3824." ]
2021-01-17T07:01:14
2022-06-01T15:49:34
2022-06-01T15:49:34
CONTRIBUTOR
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Hi Team, I am trying to create a custom metric for my training as follows, where f1 is my own metric: ```python def _info(self): # TODO: Specifies the datasets.MetricInfo object return datasets.MetricInfo( # This is the description that will appear on the metrics page. description=_DESCRIPTION, citation=_CITATION, inputs_description=_KWARGS_DESCRIPTION, # This defines the format of each prediction and reference features = datasets.Features({'predictions':datasets.Sequence(datasets.Value("int32")), "references": datasets.Sequence(datasets.Value("int32")),"offset_mapping":datasets.Sequence(datasets.Value("int32")),'text':datasets.Sequence(datasets.Value('string')),"ground":datasets.Sequence(datasets.Value("int32")),}), # Homepage of the metric for documentation homepage="http://metric.homepage", # Additional links to the codebase or references codebase_urls=["http://github.com/path/to/codebase/of/new_metric"], reference_urls=["http://path.to.reference.url/new_metric"] ) def _compute(self,predictions,references,text,offset_mapping,spans): pred_spans = [] for i,preds in enumerate(predictions): current_preds = [] for j,token_preds in enumerate(preds): if (preds>0.5): current_preds+=list(range(offset_mapping[i][j][0],offset_mapping[i][j][1])) pred_spans.append(current_spans) return { "Token Wise F1": f1_score(references,predictions,labels=[0,1]), "Offset Wise F1": np.mean([f1(preds,gold) for preds,fold in zip(pred_spans,ground)]) } ``` I believe this is not correct. But that's not the issue I am facing right now. I get this error : ```python --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-144-ed7349b50821> in <module>() ----> 1 new_metric.compute(predictions=inputs["labels"],references=inputs["labels"], text=inputs["text"], offset_mapping=inputs["offset_mapping"],ground=inputs["ground"] ) 2 frames /usr/local/lib/python3.6/dist-packages/datasets/features.py in encode_batch(self, batch) 802 encoded_batch = {} 803 if set(batch) != set(self): --> 804 print(batch) 805 print(self) 806 raise ValueError("Column mismatch between batch {} and features {}".format(set(batch), set(self))) ValueError: Column mismatch between batch {'references', 'predictions'} and features {'ground', 'predictions', 'offset_mapping', 'text', 'references'} ``` On checking the features.py file, I see the call is made from add_batch() in metrics.py which only takes in predictions and references. How do I make my custom metric work? Will it work with a trainer even if I am able to make this metric work? Thanks, Gunjan
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error when run fine_tuning on text_classification
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2021-01-16T02:23:19
2021-01-16T02:39:28
2021-01-16T02:39:18
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dataset:sem_eval_2014_task_1 pretrained_model:bert-base-uncased error description: when i use these resoruce to train fine_tuning a text_classification on sem_eval_2014_task_1,there always be some problem(when i use other dataset ,there exist the error too). And i followed the colab code (url:https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/text_classification.ipynb#scrollTo=TlqNaB8jIrJW). the error is like this : `File "train.py", line 69, in <module> trainer.train() File "/home/projects/anaconda3/envs/calibration/lib/python3.7/site-packages/transformers/trainer.py", line 784, in train for step, inputs in enumerate(epoch_iterator): File "/home/projects/anaconda3/envs/calibration/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 435, in __next__ data = self._next_data() File "/home/projects/anaconda3/envs/calibration/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 475, in _next_data data = self._dataset_fetcher.fetch(index) # may raise StopIteration File "/home/projects/anaconda3/envs/calibration/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch data = [self.dataset[idx] for idx in possibly_batched_index] File "/home/projects/anaconda3/envs/calibration/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 44, in <listcomp> data = [self.dataset[idx] for idx in possibly_batched_index] KeyError: 2` this is my code : ```dataset_name = 'sem_eval_2014_task_1' num_labels_size = 3 batch_size = 4 model_checkpoint = 'bert-base-uncased' number_train_epoch = 5 def tokenize(batch): return tokenizer(batch['premise'], batch['hypothesis'], truncation=True, ) def compute_metrics(pred): labels = pred.label_ids preds = pred.predictions.argmax(-1) precision, recall, f1, _ = precision_recall_fscore_support(labels, preds, average='micro') acc = accuracy_score(labels, preds) return { 'accuracy': acc, 'f1': f1, 'precision': precision, 'recall': recall } model = BertForSequenceClassification.from_pretrained(model_checkpoint, num_labels=num_labels_size) tokenizer = BertTokenizerFast.from_pretrained(model_checkpoint, use_fast=True) train_dataset = load_dataset(dataset_name, split='train') test_dataset = load_dataset(dataset_name, split='test') train_encoded_dataset = train_dataset.map(tokenize, batched=True) test_encoded_dataset = test_dataset.map(tokenize, batched=True) args = TrainingArguments( output_dir='./results', evaluation_strategy="epoch", learning_rate=2e-5, per_device_train_batch_size=batch_size, per_device_eval_batch_size=batch_size, num_train_epochs=number_train_epoch, weight_decay=0.01, do_predict=True, ) trainer = Trainer( model=model, args=args, compute_metrics=compute_metrics, train_dataset=train_encoded_dataset, eval_dataset=test_encoded_dataset, tokenizer=tokenizer ) trainer.train() trainer.evaluate()
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1,733
connection issue with glue, what is the data url for glue?
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[ "Hello @juliahane, which config of GLUE causes you trouble?\r\nThe URLs are defined in the dataset script source code: https://github.com/huggingface/datasets/blob/master/datasets/glue/glue.py" ]
2021-01-13T08:37:40
2021-08-04T18:13:55
2021-08-04T18:13:55
NONE
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Hi my codes sometimes fails due to connection issue with glue, could you tell me how I can have the URL datasets library is trying to read GLUE from to test the machines I am working on if there is an issue on my side or not thanks
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Couldn't reach swda.py
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[ "Hi @yangp725,\r\nThe SWDA has been added very recently and has not been released yet, thus it is not available in the `1.2.0` version of 🤗`datasets`.\r\nYou can still access it by installing the latest version of the library (master branch), by following instructions in [this issue](https://github.com/huggingface/datasets/issues/1641#issuecomment-751571471).\r\nLet me know if this helps !", "Thanks @SBrandeis ,\r\nProblem solved by downloading and installing the latest version datasets." ]
2021-01-13T02:57:40
2021-01-13T11:17:40
2021-01-13T11:17:40
NONE
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ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.2.0/datasets/swda/swda.py
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784,565,898
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1,729
Is there support for Deep learning datasets?
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[ "Hi @ZurMaD!\r\nThanks for your interest in 🤗 `datasets`. Support for image datasets is at an early stage, with CIFAR-10 added in #1617 \r\nMNIST is also on the way: #1730 \r\n\r\nIf you feel like adding another image dataset, I would advise starting by reading the [ADD_NEW_DATASET.md](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md) guide. New datasets are always very much appreciated 🚀\r\n" ]
2021-01-12T20:22:41
2021-03-31T04:24:07
2021-03-31T04:24:07
NONE
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I looked around this repository and looking the datasets I think that there's no support for images-datasets. Or am I missing something? For example to add a repo like this https://github.com/DZPeru/fish-datasets
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784,458,342
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1,728
Add an entry to an arrow dataset
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[ "Hi @ameet-1997,\r\nI think what you are looking for is the `concatenate_datasets` function: https://huggingface.co/docs/datasets/processing.html?highlight=concatenate#concatenate-several-datasets\r\n\r\nFor your use case, I would use the [`map` method](https://huggingface.co/docs/datasets/processing.html?highlight=concatenate#processing-data-with-map) to transform the SQuAD sentences and the `concatenate` the original and mapped dataset.\r\n\r\nLet me know If this helps!", "That's a great idea! Thank you so much!\r\n\r\nWhen I try that solution, I get the following error when I try to concatenate `datasets` and `modified_dataset`. I have also attached the output I get when I print out those two variables. Am I missing something?\r\n\r\nCode:\r\n``` python\r\ncombined_dataset = concatenate_datasets([datasets, modified_dataset])\r\n```\r\n\r\nError:\r\n```\r\nAttributeError: 'DatasetDict' object has no attribute 'features'\r\n```\r\n\r\nOutput:\r\n```\r\n(Pdb) datasets\r\nDatasetDict({\r\n train: Dataset({\r\n features: ['attention_mask', 'input_ids', 'special_tokens_mask'],\r\n num_rows: 493\r\n })\r\n})\r\n(Pdb) modified_dataset\r\nDatasetDict({\r\n train: Dataset({\r\n features: ['attention_mask', 'input_ids', 'special_tokens_mask'],\r\n num_rows: 493\r\n })\r\n})\r\n```\r\n\r\nThe error is stemming from the fact that the attribute `datasets.features` does not exist. Would it not be possible to use `concatenate_datasets` in such a case? Is there an alternate solution?", "You should do `combined_dataset = concatenate_datasets([datasets['train'], modified_dataset['train']])`\r\n\r\nDidn't we talk about returning a Dataset instead of a DatasetDict with load_dataset and no split provided @lhoestq? Not sure it's the way to go but I'm wondering if it's not simpler for some use-cases.", "> Didn't we talk about returning a Dataset instead of a DatasetDict with load_dataset and no split provided @lhoestq? Not sure it's the way to go but I'm wondering if it's not simpler for some use-cases.\r\n\r\nMy opinion is that users should always know in advance what type of objects they're going to get. Otherwise the development workflow on their side is going to be pretty chaotic with sometimes unexpected behaviors.\r\nFor instance is `split=` is not specified it's currently always returning a DatasetDict. And if `split=\"train\"` is given for example it's always returning a Dataset.", "Thanks @thomwolf. Your solution worked!" ]
2021-01-12T18:01:47
2021-01-18T19:15:32
2021-01-18T19:15:32
NONE
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Is it possible to add an entry to a dataset object? **Motivation: I want to transform the sentences in the dataset and add them to the original dataset** For example, say we have the following code: ``` python from datasets import load_dataset # Load a dataset and print the first examples in the training set squad_dataset = load_dataset('squad') print(squad_dataset['train'][0]) ``` Is it possible to add an entry to `squad_dataset`? Something like the following? ``` python squad_dataset.append({'text': "This is a new sentence"}) ``` The motivation for doing this is that I want to transform the sentences in the squad dataset and add them to the original dataset. If the above doesn't work, is there any other way of achieving the motivation mentioned above? Perhaps by creating a new arrow dataset by using the older one and the transformer sentences?
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784,435,131
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1,727
BLEURT score calculation raises UnrecognizedFlagError
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[ "Upgrading tensorflow to version 2.4.0 solved the issue.", "I still have the same error even with TF 2.4.0.", "And I have the same error with TF 2.4.1. I believe this issue should be reopened. Any ideas?!", "I'm seeing the same issue with TF 2.4.1 when running the following in https://colab.research.google.com/github/huggingface/datasets/blob/master/notebooks/Overview.ipynb:\r\n```\r\n!pip install git+https://github.com/google-research/bleurt.git\r\nreferences = [\"foo bar baz\", \"one two three\"]\r\nbleurt_metric = load_metric('bleurt')\r\npredictions = [\"foo bar\", \"four five six\"]\r\nbleurt_metric.compute(predictions=predictions, references=references)\r\n```", "@aleSuglia @oscartackstrom - Are you getting the error when running your code in a Jupyter notebook ?\r\n\r\nI tried reproducing this error again, and was unable to do so from the python command line console in a virtual environment similar to the one I originally used (and unfortunately no longer have access to) when I first got the error. \r\nHowever, I've managed to reproduce the error by running the same code in a Jupyter notebook running a kernel from the same virtual environment.\r\nThis made me suspect that the problem is somehow related to the Jupyter notebook.\r\n\r\nMore environment details:\r\n```\r\nOS: Ubuntu Linux 18.04\r\nconda==4.8.3\r\npython==3.8.5\r\ndatasets==1.3.0\r\ntensorflow==2.4.0\r\nBLEURT==0.0.1\r\nnotebook==6.2.0\r\n```", "This happens when running the notebook on colab. The issue seems to be that colab populates sys.argv with arguments not handled by bleurt.\r\n\r\nRunning this before calling bleurt fixes it:\r\n```\r\nimport sys\r\nsys.argv = sys.argv[:1]\r\n```\r\n\r\nNot the most elegant solution. Perhaps it needs to be fixed in the bleurt code itself rather than huggingface?\r\n\r\nThis is the output of `print(sys.argv)` when running on colab:\r\n```\r\n['/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py', '-f', '/root/.local/share/jupyter/runtime/kernel-a857a78c-44d6-4b9d-b18a-030b858ee327.json']\r\n```", "I got the error when running it from the command line. It looks more like an error that should be fixed in the BLEURT codebase.", "Seems to be a known issue in the bleurt codebase: https://github.com/google-research/bleurt/issues/24.", "Hi, the problem should be solved now.", "Hi @tsellam! I can verify that the issue is indeed fixed now. Thanks!" ]
2021-01-12T17:27:02
2022-06-01T16:06:02
2022-06-01T16:06:02
NONE
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Calling the `compute` method for **bleurt** metric fails with an `UnrecognizedFlagError` for `FLAGS.bleurt_batch_size`. My environment: ``` python==3.8.5 datasets==1.2.0 tensorflow==2.3.1 cudatoolkit==11.0.221 ``` Test code for reproducing the error: ``` from datasets import load_metric bleurt = load_metric('bleurt') gen_text = "I am walking on the promenade today" ref_text = "I am walking along the promenade on this sunny day" bleurt.compute(predictions=[test_text], references=[test_text]) ``` Error Output: ``` 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'). INFO:tensorflow:Reading checkpoint /home/ubuntu/.cache/huggingface/metrics/bleurt/default/downloads/extracted/9aee35580225730ac5422599f35c4986e4c49cafd08082123342b1019720dac4/bleurt-base-128. INFO:tensorflow:Config file found, reading. INFO:tensorflow:Will load checkpoint bert_custom INFO:tensorflow:Performs basic checks... INFO:tensorflow:... name:bert_custom INFO:tensorflow:... vocab_file:vocab.txt INFO:tensorflow:... bert_config_file:bert_config.json INFO:tensorflow:... do_lower_case:True INFO:tensorflow:... max_seq_length:128 INFO:tensorflow:Creating BLEURT scorer. INFO:tensorflow:Loading model... INFO:tensorflow:BLEURT initialized. --------------------------------------------------------------------------- UnrecognizedFlagError Traceback (most recent call last) <ipython-input-12-8b3f4322318a> in <module> 2 gen_text = "I am walking on the promenade today" 3 ref_text = "I am walking along the promenade on this sunny day" ----> 4 bleurt.compute(predictions=[gen_text], references=[ref_text]) ~/anaconda3/envs/noved/lib/python3.8/site-packages/datasets/metric.py in compute(self, *args, **kwargs) 396 references = self.data["references"] 397 with temp_seed(self.seed): --> 398 output = self._compute(predictions=predictions, references=references, **kwargs) 399 400 if self.buf_writer is not None: ~/.cache/huggingface/modules/datasets_modules/metrics/bleurt/b1de33e1cbbcb1dbe276c887efa1fad68c6aff913885108078fa1ad408908778/bleurt.py in _compute(self, predictions, references) 103 104 def _compute(self, predictions, references): --> 105 scores = self.scorer.score(references=references, candidates=predictions) 106 return {"scores": scores} ~/anaconda3/envs/noved/lib/python3.8/site-packages/bleurt/score.py in score(self, references, candidates, batch_size) 164 """ 165 if not batch_size: --> 166 batch_size = FLAGS.bleurt_batch_size 167 168 candidates, references = list(candidates), list(references) ~/anaconda3/envs/noved/lib/python3.8/site-packages/tensorflow/python/platform/flags.py in __getattr__(self, name) 83 # a flag. 84 if not wrapped.is_parsed(): ---> 85 wrapped(_sys.argv) 86 return wrapped.__getattr__(name) 87 ~/anaconda3/envs/noved/lib/python3.8/site-packages/absl/flags/_flagvalues.py in __call__(self, argv, known_only) 643 for name, value in unknown_flags: 644 suggestions = _helpers.get_flag_suggestions(name, list(self)) --> 645 raise _exceptions.UnrecognizedFlagError( 646 name, value, suggestions=suggestions) 647 UnrecognizedFlagError: Unknown command line flag 'f' ``` Possible Fix: Modify `_compute` method https://github.com/huggingface/datasets/blob/7e64851a12263dc74d41c668167918484c8000ab/metrics/bleurt/bleurt.py#L104 to receive a `batch_size` argument, for example: ``` def _compute(self, predictions, references, batch_size=1): scores = self.scorer.score(references=references, candidates=predictions, batch_size=batch_size) return {"scores": scores} ```
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784,182,273
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1,725
load the local dataset
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[ "You should rephrase your question or give more examples and details on what you want to do.\r\n\r\nit’s not possible to understand it and help you with only this information.", "sorry for that.\r\ni want to know how could i load the train set and the test set from the local ,which api or function should i use .\r\n", "Did you try to follow the instructions in the documentation?\r\nHere: https://huggingface.co/docs/datasets/loading_datasets.html#from-local-files", "thanks a lot \r\ni find that the problem is i dont use vpn...\r\nso i have to keep my net work even if i want to load the local data ?", "We will solve this soon (cf #1724)", "thanks a lot", "Hi! `json` is a packaged dataset now, which means its script comes with the library and doesn't require an internet connection." ]
2021-01-12T12:12:55
2022-06-01T16:00:59
2022-06-01T16:00:59
NONE
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your guidebook's example is like >>>from datasets import load_dataset >>> dataset = load_dataset('json', data_files='my_file.json') but the first arg is path... so how should i do if i want to load the local dataset for model training? i will be grateful if you can help me handle this problem! thanks a lot!
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could not run models on a offline server successfully
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[ "Transferred to `datasets` based on the stack trace.", "Hi @lkcao !\r\nYour issue is indeed related to `datasets`. In addition to installing the package manually, you will need to download the `text.py` script on your server. You'll find it (under `datasets/datasets/text`: https://github.com/huggingface/datasets/blob/master/datasets/text/text.py.\r\nThen you can change the line 221 of `run_mlm_new.py` into:\r\n```python\r\n datasets = load_dataset('/path/to/text.py', data_files=data_files)\r\n```\r\nWhere `/path/to/text.py` is the path on the server where you saved the `text.py` script.", "We're working on including the local dataset builders (csv, text, json etc.) directly in the `datasets` package so that they can be used offline", "The local dataset builders (csv, text , json and pandas) are now part of the `datasets` package since #1726 :)\r\nYou can now use them offline\r\n```python\r\ndatasets = load_dataset('text', data_files=data_files)\r\n```\r\n\r\nWe'll do a new release soon", "> The local dataset builders (csv, text , json and pandas) are now part of the `datasets` package since #1726 :)\r\n> You can now use them offline\r\n> \r\n> ```python\r\n> datasets = load_dataset('text', data_files=data_files)\r\n> ```\r\n> \r\n> We'll do a new release soon\r\n\r\nso the new version release now?", "Yes it's been available since datasets 1.3.0 !" ]
2021-01-12T06:08:06
2022-10-05T12:39:07
2022-10-05T12:39:07
NONE
null
null
null
Hi, I really need your help about this. I am trying to fine-tuning a RoBERTa on a remote server, which is strictly banning internet. I try to install all the packages by hand and try to run run_mlm.py on the server. It works well on colab, but when I try to run it on this offline server, it shows: ![image](https://user-images.githubusercontent.com/49967236/104276256-25a88600-546a-11eb-9776-8ec695dfa24e.png) is there anything I can do? Is it possible to download all the things in cache and upload it to the server? Please help me out...
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Possible cache miss in datasets
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[ "Thanks for reporting !\r\nI was able to reproduce thanks to your code and find the origin of the bug.\r\nThe cache was not reusing the same file because one object was not deterministic. It comes from a conversion from `set` to `list` in the `datasets.arrrow_dataset.transmit_format` function, where the resulting list would not always be in the same order and therefore the function that computes the hash used by the cache would not always return the same result.\r\nI'm opening a PR to fix this.\r\n\r\nAlso we plan to do a new release in the coming days so you can expect the fix to be available soon.\r\nNote that you can still specify `cache_file_name=` in the second `map()` call to name the cache file yourself if you want to.", "Thanks for the fast reply, waiting for the fix :)\r\n\r\nI tried to use `cache_file_names` and wasn't sure how, I tried to give it the following:\r\n```\r\ntokenized_datasets = tokenized_datasets.map(\r\n group_texts,\r\n batched=True,\r\n num_proc=60,\r\n load_from_cache_file=True,\r\n cache_file_names={k: f'.cache/{str(k)}' for k in tokenized_datasets}\r\n)\r\n```\r\n\r\nand got an error:\r\n```\r\nmultiprocess.pool.RemoteTraceback:\r\n\"\"\"\r\nTraceback (most recent call last):\r\n File \"/venv/lib/python3.6/site-packages/multiprocess/pool.py\", line 119, in worker\r\n result = (True, func(*args, **kwds))\r\n File \"/venv/lib/python3.6/site-packages/datasets/arrow_dataset.py\", line 157, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \"/venv/lib/python3.6/site-packages/datasets/fingerprint.py\", line 163, in wrapper\r\n out = func(self, *args, **kwargs)\r\n File \"/venv/lib/python3.6/site-packages/datasets/arrow_dataset.py\", line 1491, in _map_single\r\n tmp_file = tempfile.NamedTemporaryFile(\"wb\", dir=os.path.dirname(cache_file_name), delete=False)\r\n File \"/usr/lib/python3.6/tempfile.py\", line 690, in NamedTemporaryFile\r\n (fd, name) = _mkstemp_inner(dir, prefix, suffix, flags, output_type)\r\n File \"/usr/lib/python3.6/tempfile.py\", line 401, in _mkstemp_inner\r\n fd = _os.open(file, flags, 0o600)\r\nFileNotFoundError: [Errno 2] No such file or directory: '_00000_of_00060.cache/tmpsvszxtop'\r\n\"\"\"\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"test.py\", line 48, in <module>\r\n cache_file_names={k: f'.cache/{str(k)}' for k in tokenized_datasets}\r\n File \"/venv/lib/python3.6/site-packages/datasets/dataset_dict.py\", line 303, in map\r\n for k, dataset in self.items()\r\n File \"/venv/lib/python3.6/site-packages/datasets/dataset_dict.py\", line 303, in <dictcomp>\r\n for k, dataset in self.items()\r\n File \"/venv/lib/python3.6/site-packages/datasets/arrow_dataset.py\", line 1317, in map\r\n transformed_shards = [r.get() for r in results]\r\n File \"/venv/lib/python3.6/site-packages/datasets/arrow_dataset.py\", line 1317, in <listcomp>\r\n transformed_shards = [r.get() for r in results]\r\n File \"/venv/lib/python3.6/site-packages/multiprocess/pool.py\", line 644, in get\r\n raise self._value\r\nFileNotFoundError: [Errno 2] No such file or directory: '_00000_of_00060.cache/tmpsvszxtop'\r\n```\r\n", "The documentation says\r\n```\r\ncache_file_names (`Optional[Dict[str, str]]`, defaults to `None`): Provide the name of a cache file to use to store the\r\n results of the computation instead of the automatically generated cache file name.\r\n You have to provide one :obj:`cache_file_name` per dataset in the dataset dictionary.\r\n```\r\nWhat is expected is simply the name of a file, not a path. The file will be located in the cache directory of the `wikitext` dataset. You can try again with something like\r\n```python\r\ncache_file_names = {k: f'tokenized_and_grouped_{str(k)}' for k in tokenized_datasets}\r\n```", "Managed to get `cache_file_names` working and caching works well with it\r\nHad to make a small modification for it to work:\r\n```\r\ncache_file_names = {k: f'tokenized_and_grouped_{str(k)}.arrow' for k in tokenized_datasets}\r\n```", "Another comment on `cache_file_names`, it doesn't save the produced cached files in the dataset's cache folder, it requires to give a path to an existing directory for it to work.\r\nI can confirm that this is how it works in `datasets==1.1.3`", "Oh yes indeed ! Maybe we need to update the docstring to mention that it is a path", "I fixed the docstring. Hopefully this is less confusing now: https://github.com/huggingface/datasets/commit/42ccc0012ba8864e6db1392430100f350236183a", "I upgraded to the latest version and I encountered some strange behaviour, the script I posted in the OP doesn't trigger recalculation, however, if I add the following change it does trigger partial recalculation, I am not sure if its something wrong on my machine or a bug:\r\n```\r\nfrom datasets import load_dataset\r\nfrom transformers import AutoTokenizer\r\n\r\ndatasets = load_dataset('wikitext', 'wikitext-103-raw-v1')\r\ntokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)\r\n\r\ncolumn_names = datasets[\"train\"].column_names\r\ntext_column_name = \"text\" if \"text\" in column_names else column_names[0]\r\ndef tokenize_function(examples):\r\n return tokenizer(examples[text_column_name], return_special_tokens_mask=True)\r\n# CHANGE\r\nprint('hello')\r\n# CHANGE\r\n\r\ntokenized_datasets = datasets.map(\r\n tokenize_function,\r\n batched=True,\r\n...\r\n```\r\nI am using datasets in the `run_mlm.py` script in the transformers examples and I found that if I change the script without touching any of the preprocessing. it still triggers recalculation which is very weird\r\n\r\nEdit: accidently clicked the close issue button ", "This is because the `group_texts` line definition changes (it is defined 3 lines later than in the previous call). Currently if a function is moved elsewhere in a script we consider it to be different.\r\n\r\nNot sure this is actually a good idea to keep this behavior though. We had this as a security in the early development of the lib but now the recursive hashing of objects is robust so we can probably remove that.\r\nMoreover we're already ignoring the line definition for lambda functions.", "I opened a PR to change this, let me know what you think.", "Sounds great, thank you for your quick responses and help! Looking forward for the next release.", "I am having a similar issue where only the grouped files are loaded from cache while the tokenized ones aren't. I can confirm both datasets are being stored to file, but only the grouped version is loaded from cache. Not sure what might be going on. But I've tried to remove all kinds of non deterministic behaviour, but still no luck. Thanks for the help!\r\n\r\n\r\n```python\r\n # Datasets\r\n train = sorted(glob(args.data_dir + '*.{}'.format(args.ext)))\r\n if args.dev_split >= len(train):\r\n raise ValueError(\"Not enough dev files\")\r\n dev = []\r\n state = random.Random(1001)\r\n for _ in range(args.dev_split):\r\n dev.append(train.pop(state.randint(0, len(train) - 1)))\r\n\r\n max_seq_length = min(args.max_seq_length, tokenizer.model_max_length)\r\n\r\n def tokenize_function(examples):\r\n return tokenizer(examples['text'], return_special_tokens_mask=True)\r\n\r\n def group_texts(examples):\r\n # Concatenate all texts from our dataset and generate chunks of max_seq_length\r\n concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}\r\n total_length = len(concatenated_examples[list(examples.keys())[0]])\r\n # Truncate (not implementing padding)\r\n total_length = (total_length // max_seq_length) * max_seq_length\r\n # Split by chunks of max_seq_length\r\n result = {\r\n k: [t[i : i + max_seq_length] for i in range(0, total_length, max_seq_length)]\r\n for k, t in concatenated_examples.items()\r\n }\r\n return result\r\n\r\n datasets = load_dataset(\r\n 'text', name='DBNL', data_files={'train': train[:10], 'dev': dev[:5]}, \r\n cache_dir=args.data_cache_dir)\r\n datasets = datasets.map(tokenize_function, \r\n batched=True, remove_columns=['text'], \r\n cache_file_names={k: os.path.join(args.data_cache_dir, f'{k}-tokenized') for k in datasets},\r\n load_from_cache_file=not args.overwrite_cache)\r\n datasets = datasets.map(group_texts, \r\n batched=True,\r\n cache_file_names={k: os.path.join(args.data_cache_dir, f'{k}-grouped') for k in datasets},\r\n load_from_cache_file=not args.overwrite_cache)\r\n```\r\n\r\nAnd this is the log\r\n\r\n```\r\n04/26/2021 10:26:59 - WARNING - datasets.builder - Using custom data configuration DBNL-f8d988ad33ccf2c1\r\n04/26/2021 10:26:59 - WARNING - datasets.builder - Reusing dataset text (/home/manjavacasema/data/.cache/text/DBNL-f8d988ad33ccf2c1/0.0.0/e16f44aa1b321ece1f87b07977cc5d70be93d69b20486d6dacd62e12cf25c9a5)\r\n100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:00<00:00, 21.07ba/s]\r\n100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:01<00:00, 24.28ba/s]\r\n04/26/2021 10:27:01 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /home/manjavacasema/data/.cache/train-grouped\r\n04/26/2021 10:27:01 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /home/manjavacasema/data/.cache/dev-grouped\r\n```\r\n", "Hi ! What tokenizer are you using ?", "It's the ByteLevelBPETokenizer", "This error happened to me too, when I tried to supply my own fingerprint to `map()` via the `new_fingerprint` arg.\r\n\r\nEdit: realized it was because my path was weird and had colons and brackets and slashes in it, since one of the variable values I included in the fingerprint was a dataset split like \"train[:10%]\". I fixed it with [this solution](https://stackoverflow.com/a/13593932/2287177) from StackOverflow to just remove those invalid characters from the fingerprint.", "Good catch @jxmorris12, maybe we should do additional checks on the valid characters for fingerprints ! Would you like to contribute this ?\r\n\r\nI think this can be added here, when we set the fingerprint(s) that are passed `map`:\r\n\r\nhttps://github.com/huggingface/datasets/blob/25bb7c9cbf519fbbf9abf3898083b529e7762705/src/datasets/fingerprint.py#L449-L454\r\n\r\nmaybe something like\r\n```python\r\nif kwargs.get(fingerprint_name) is None:\r\n ...\r\nelse:\r\n # In this case, it's the user who specified the fingerprint manually:\r\n # we need to make sure it's a valid hash\r\n validate_fingerprint(kwargs[fingerprint_name])\r\n```\r\n\r\nOtherwise I can open a PR later", "I opened a PR here to add the fingerprint validation: https://github.com/huggingface/datasets/pull/4587\r\n\r\nEDIT: merged :)", "thank you!" ]
2021-01-11T15:37:31
2022-06-29T14:54:42
2021-01-26T02:47:59
NONE
null
null
null
Hi, I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache. I have attached an example script that for me reproduces the problem. In the attached example the second map function always recomputes instead of loading from cache. Is this a bug or am I doing something wrong? Is there a way for fix this and avoid all the recomputation? Thanks Edit: transformers==3.5.1 datasets==1.2.0 ``` from datasets import load_dataset from transformers import AutoTokenizer datasets = load_dataset('wikitext', 'wikitext-103-raw-v1') tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True) column_names = datasets["train"].column_names text_column_name = "text" if "text" in column_names else column_names[0] def tokenize_function(examples): return tokenizer(examples[text_column_name], return_special_tokens_mask=True) tokenized_datasets = datasets.map( tokenize_function, batched=True, num_proc=60, remove_columns=[text_column_name], load_from_cache_file=True, ) max_seq_length = tokenizer.model_max_length def group_texts(examples): # Concatenate all texts. concatenated_examples = { k: sum(examples[k], []) for k in examples.keys()} total_length = len(concatenated_examples[list(examples.keys())[0]]) # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can # customize this part to your needs. total_length = (total_length // max_seq_length) * max_seq_length # Split by chunks of max_len. result = { k: [t[i: i + max_seq_length] for i in range(0, total_length, max_seq_length)] for k, t in concatenated_examples.items() } return result tokenized_datasets = tokenized_datasets.map( group_texts, batched=True, num_proc=60, load_from_cache_file=True, ) print(tokenized_datasets) print('finished') ```
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SciFact dataset - minor changes
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[ "Hi Dave,\r\nYou are more than welcome to open a PR to make these changes! 🤗\r\nYou will find the relevant information about opening a PR in the [contributing guide](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md) and in the [dataset addition guide](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).\r\n\r\nPinging also @lhoestq for the Google cloud matter.", "> I'd like to make a few minor changes, including the citation information and the `_URL` from which to download the dataset. Can I submit a PR for this?\r\n\r\nSure ! Also feel free to ping us for reviews or if we can help :)\r\n\r\n> It also looks like the dataset is being downloaded directly from Huggingface's Google cloud account rather than via the `_URL` in [scifact.py](https://github.com/huggingface/datasets/blob/master/datasets/scifact/scifact.py). Can you help me update the version on gcloud?\r\n\r\nWhat makes you think that ?\r\nAfaik there's no scifact on our google storage\r\n", "\r\n\r\n> > I'd like to make a few minor changes, including the citation information and the `_URL` from which to download the dataset. Can I submit a PR for this?\r\n> \r\n> Sure ! Also feel free to ping us for reviews or if we can help :)\r\n> \r\nOK! We're organizing a [shared task](https://sdproc.org/2021/sharedtasks.html#sciver) based on the dataset, and I made some updates and changed the download URL - so the current code points to a dead URL. I'll update appropriately once the task is finalized and make a PR.\r\n\r\n> > It also looks like the dataset is being downloaded directly from Huggingface's Google cloud account rather than via the `_URL` in [scifact.py](https://github.com/huggingface/datasets/blob/master/datasets/scifact/scifact.py). Can you help me update the version on gcloud?\r\n> \r\n> What makes you think that ?\r\n> Afaik there's no scifact on our google storage\r\n\r\nYou're right, I had the data cached on my machine somewhere. \r\n\r\n", "I opened a PR about this: https://github.com/huggingface/datasets/pull/1780. Closing this issue, will continue there." ]
2021-01-11T05:26:40
2021-01-26T02:52:17
2021-01-26T02:52:17
CONTRIBUTOR
null
null
null
Hi, SciFact dataset creator here. First of all, thanks for adding the dataset to Huggingface, much appreciated! I'd like to make a few minor changes, including the citation information and the `_URL` from which to download the dataset. Can I submit a PR for this? It also looks like the dataset is being downloaded directly from Huggingface's Google cloud account rather than via the `_URL` in [scifact.py](https://github.com/huggingface/datasets/blob/master/datasets/scifact/scifact.py). Can you help me update the version on gcloud? Thanks, Dave
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Installation using conda
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[ "Yes indeed the idea is to have the next release on conda cc @LysandreJik ", "Great! Did you guys have a timeframe in mind for the next release?\r\n\r\nThank you for all the great work in developing this library.", "I think we can have `datasets` on conda by next week. Will see what I can do!", "Thank you. Looking forward to it.", "`datasets` has been added to the huggingface channel thanks to @LysandreJik :)\r\nIt depends on conda-forge though\r\n\r\n```\r\nconda install -c huggingface -c conda-forge datasets\r\n```" ]
2021-01-08T19:12:15
2021-09-17T12:47:40
2021-09-17T12:47:40
NONE
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Will a conda package for installing datasets be added to the huggingface conda channel? I have installed transformers using conda and would like to use the datasets library to use some of the scripts in the transformers/examples folder but am unable to do so at the moment as datasets can only be installed using pip and using pip in a conda environment is generally a bad idea in my experience.
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IsADirectoryError when trying to download C4
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[ "I haven't tested C4 on my side so there so there may be a few bugs in the code/adjustments to make.\r\nHere it looks like in c4.py, line 190 one of the `files_to_download` is `'/'` which is invalid.\r\nValid files are paths to local files or URLs to remote files.", "Fixed once processed data is used instead:\r\n- #2575" ]
2021-01-08T07:31:30
2022-08-04T11:56:10
2022-08-04T11:55:04
NONE
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**TLDR**: I fail to download C4 and see a stacktrace originating in `IsADirectoryError` as an explanation for failure. How can the problem be fixed? **VERBOSE**: I use Python version 3.7 and have the following dependencies listed in my project: ``` datasets==1.2.0 apache-beam==2.26.0 ``` When running the following code, where `/data/huggingface/unpacked/` contains a single unzipped `wet.paths` file manually downloaded as per the instructions for C4: ``` from datasets import load_dataset load_dataset("c4", "en", data_dir="/data/huggingface/unpacked", beam_runner='DirectRunner') ``` I get the following stacktrace: ``` /Users/fredriko/venv/misc/bin/python /Users/fredriko/source/misc/main.py Downloading and preparing dataset c4/en (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /Users/fredriko/.cache/huggingface/datasets/c4/en/2.3.0/8304cf264cc42bdebcb13fca4b9cb36368a96f557d36f9dc969bebbe2568b283... Traceback (most recent call last): File "/Users/fredriko/source/misc/main.py", line 3, in <module> load_dataset("c4", "en", data_dir="/data/huggingface/unpacked", beam_runner='DirectRunner') File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/load.py", line 612, in load_dataset ignore_verifications=ignore_verifications, File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/builder.py", line 527, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/builder.py", line 1066, in _download_and_prepare pipeline=pipeline, File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/builder.py", line 582, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/Users/fredriko/.cache/huggingface/modules/datasets_modules/datasets/c4/8304cf264cc42bdebcb13fca4b9cb36368a96f557d36f9dc969bebbe2568b283/c4.py", line 190, in _split_generators file_paths = dl_manager.download_and_extract(files_to_download) File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 258, in download_and_extract return self.extract(self.download(url_or_urls)) File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 189, in download self._record_sizes_checksums(url_or_urls, downloaded_path_or_paths) File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 117, in _record_sizes_checksums self._recorded_sizes_checksums[str(url)] = get_size_checksum_dict(path) File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/utils/info_utils.py", line 80, in get_size_checksum_dict with open(path, "rb") as f: IsADirectoryError: [Errno 21] Is a directory: '/' Process finished with exit code 1 ```
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2021-01-08T06:14:03
2021-01-08T09:00:08
2021-01-08T09:00:08
NONE
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## Adding a Dataset - **Name:** *name of the dataset* - **Description:** *short description of the dataset (or link to social media or blog post)* - **Paper:** *link to the dataset paper if available* - **Data:** *link to the Github repository or current dataset location* - **Motivation:** *what are some good reasons to have this dataset* Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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2021-01-07T21:45:24
2021-01-08T09:00:01
2021-01-08T09:00:01
NONE
null
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## Adding a Dataset - **Name:** *name of the dataset* - **Description:** *short description of the dataset (or link to social media or blog post)* - **Paper:** *link to the dataset paper if available* - **Data:** *link to the Github repository or current dataset location* - **Motivation:** *what are some good reasons to have this dataset* Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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Error when downloading a large dataset on slow connection.
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[ "Hi ! Is this an issue you have with `openwebtext` specifically or also with other datasets ?\r\n\r\nIt looks like the downloaded file is corrupted and can't be extracted using `tarfile`.\r\nCould you try loading it again with \r\n```python\r\nimport datasets\r\ndatasets.load_dataset(\"openwebtext\", download_mode=\"force_redownload\")\r\n```" ]
2021-01-07T17:48:15
2021-01-13T10:35:02
null
CONTRIBUTOR
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I receive the following error after about an hour trying to download the `openwebtext` dataset. The code used is: ```python import datasets datasets.load_dataset("openwebtext") ``` > Traceback (most recent call last): [4/28] > File "<stdin>", line 1, in <module> > File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/site-packages/datasets/load.py", line 610, in load_dataset > ignore_verifications=ignore_verifications, > File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/site-packages/datasets/builder.py", line 515, in download_and_prepare > dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs > File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/site-packages/datasets/builder.py", line 570, in _download_and_prepare > split_generators = self._split_generators(dl_manager, **split_generators_kwargs) > File "/home/lucadiliello/.cache/huggingface/modules/datasets_modules/datasets/openwebtext/5c636399c7155da97c982d0d70ecdce30fbca66a4eb4fc768ad91f8331edac02/openwebtext.py", line 62, in _split_generators > dl_dir = dl_manager.download_and_extract(_URL) > File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 254, in download_and_extract > return self.extract(self.download(url_or_urls)) > File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 235, in extract > num_proc=num_proc, > File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 225, in map_nested > return function(data_struct) > File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 343, in cached_path > tar_file.extractall(output_path_extracted) > File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/tarfile.py", line 2000, in extractall > numeric_owner=numeric_owner) > File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/tarfile.py", line 2042, in extract > numeric_owner=numeric_owner) > File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/tarfile.py", line 2112, in _extract_member > self.makefile(tarinfo, targetpath) > File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/tarfile.py", line 2161, in makefile > copyfileobj(source, target, tarinfo.size, ReadError, bufsize) > File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/tarfile.py", line 253, in copyfileobj > buf = src.read(remainder) > File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/lzma.py", line 200, in read > return self._buffer.read(size) > File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/_compression.py", line 68, in readinto > data = self.read(len(byte_view)) > File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/_compression.py", line 99, in read > raise EOFError("Compressed file ended before the " > EOFError: Compressed file ended before the end-of-stream marker was reached
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Some datasets miss dataset_infos.json or dummy_data.zip
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[ "Thanks for reporting.\r\nWe should indeed add all the missing dummy_data.zip and also the dataset_infos.json at least for lm1b, reclor and wikihow.\r\n\r\nFor c4 I haven't tested the script and I think we'll require some optimizations regarding beam datasets before processing it.\r\n", "Closing since the dummy data generation is deprecated now (and the issue with missing metadata seems to be addressed)." ]
2021-01-07T14:17:13
2022-11-04T15:11:16
2022-11-04T15:06:00
CONTRIBUTOR
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While working on dataset REAME generation script at https://github.com/madlag/datasets_readme_generator , I noticed that some datasets miss a dataset_infos.json : ``` c4 lm1b reclor wikihow ``` And some does not have a dummy_data.zip : ``` kor_nli math_dataset mlqa ms_marco newsgroup qa4mre qangaroo reddit_tifu super_glue trivia_qa web_of_science wmt14 wmt15 wmt16 wmt17 wmt18 wmt19 xtreme ``` But it seems that some of those last do have a "dummy" directory .
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https://api.github.com/repos/huggingface/datasets/issues/1696
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Unable to install datasets
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[ "Maybe try to create a virtual env with python 3.8 or 3.7", "Thanks, @thomwolf! I fixed the issue by downgrading python to 3.7. ", "Damn sorry", "Damn sorry" ]
2021-01-07T07:24:37
2021-01-08T00:33:05
2021-01-07T22:06:05
NONE
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** Edit ** I believe there's a bug with the package when you're installing it with Python 3.9. I recommend sticking with previous versions. Thanks, @thomwolf for the insight! **Short description** I followed the instructions for installing datasets (https://huggingface.co/docs/datasets/installation.html). However, while I tried to download datasets using `pip install datasets` I got a massive error message after getting stuck at "Installing build dependencies..." I was wondering if this problem can be fixed by creating a virtual environment, but it didn't help. Can anyone offer some advice on how to fix this issue? Here's an error message: `(env) Gas-MacBook-Pro:Downloads destiny$ pip install datasets Collecting datasets Using cached datasets-1.2.0-py3-none-any.whl (159 kB) Collecting numpy>=1.17 Using cached numpy-1.19.5-cp39-cp39-macosx_10_9_x86_64.whl (15.6 MB) Collecting pyarrow>=0.17.1 Using cached pyarrow-2.0.0.tar.gz (58.9 MB) .... _configtest.c:9:5: warning: incompatible redeclaration of library function 'ceilf' [-Wincompatible-library-redeclaration] int ceilf (void); ^ _configtest.c:9:5: note: 'ceilf' is a builtin with type 'float (float)' _configtest.c:10:5: warning: incompatible redeclaration of library function 'rintf' [-Wincompatible-library-redeclaration] int rintf (void); ^ _configtest.c:10:5: note: 'rintf' is a builtin with type 'float (float)' _configtest.c:11:5: warning: incompatible redeclaration of library function 'truncf' [-Wincompatible-library-redeclaration] int truncf (void); ^ _configtest.c:11:5: note: 'truncf' is a builtin with type 'float (float)' _configtest.c:12:5: warning: incompatible redeclaration of library function 'sqrtf' [-Wincompatible-library-redeclaration] int sqrtf (void); ^ _configtest.c:12:5: note: 'sqrtf' is a builtin with type 'float (float)' _configtest.c:13:5: warning: incompatible redeclaration of library function 'log10f' [-Wincompatible-library-redeclaration] int log10f (void); ^ _configtest.c:13:5: note: 'log10f' is a builtin with type 'float (float)' _configtest.c:14:5: warning: incompatible redeclaration of library function 'logf' [-Wincompatible-library-redeclaration] int logf (void); ^ _configtest.c:14:5: note: 'logf' is a builtin with type 'float (float)' _configtest.c:15:5: warning: incompatible redeclaration of library function 'log1pf' [-Wincompatible-library-redeclaration] int log1pf (void); ^ _configtest.c:15:5: note: 'log1pf' is a builtin with type 'float (float)' _configtest.c:16:5: warning: incompatible redeclaration of library function 'expf' [-Wincompatible-library-redeclaration] int expf (void); ^ _configtest.c:16:5: note: 'expf' is a builtin with type 'float (float)' _configtest.c:17:5: warning: incompatible redeclaration of library function 'expm1f' [-Wincompatible-library-redeclaration] int expm1f (void); ^ _configtest.c:17:5: note: 'expm1f' is a builtin with type 'float (float)' _configtest.c:18:5: warning: incompatible redeclaration of library function 'asinf' [-Wincompatible-library-redeclaration] int asinf (void); ^ _configtest.c:18:5: note: 'asinf' is a builtin with type 'float (float)' _configtest.c:19:5: warning: incompatible redeclaration of library function 'acosf' [-Wincompatible-library-redeclaration] int acosf (void); ^ _configtest.c:19:5: note: 'acosf' is a builtin with type 'float (float)' _configtest.c:20:5: warning: incompatible redeclaration of library function 'atanf' [-Wincompatible-library-redeclaration] int atanf (void); ^ _configtest.c:20:5: note: 'atanf' is a builtin with type 'float (float)' _configtest.c:21:5: warning: incompatible redeclaration of library function 'asinhf' [-Wincompatible-library-redeclaration] int asinhf (void); ^ _configtest.c:21:5: note: 'asinhf' is a builtin with type 'float (float)' _configtest.c:22:5: warning: incompatible redeclaration of library function 'acoshf' [-Wincompatible-library-redeclaration] int acoshf (void); ^ _configtest.c:22:5: note: 'acoshf' is a builtin with type 'float (float)' _configtest.c:23:5: warning: incompatible redeclaration of library function 'atanhf' [-Wincompatible-library-redeclaration] int atanhf (void); ^ _configtest.c:23:5: note: 'atanhf' is a builtin with type 'float (float)' _configtest.c:24:5: warning: incompatible redeclaration of library function 'hypotf' [-Wincompatible-library-redeclaration] int hypotf (void); ^ _configtest.c:24:5: note: 'hypotf' is a builtin with type 'float (float, float)' _configtest.c:25:5: warning: incompatible redeclaration of library function 'atan2f' [-Wincompatible-library-redeclaration] int atan2f (void); ^ _configtest.c:25:5: note: 'atan2f' is a builtin with type 'float (float, float)' _configtest.c:26:5: warning: incompatible redeclaration of library function 'powf' [-Wincompatible-library-redeclaration] int powf (void); ^ _configtest.c:26:5: note: 'powf' is a builtin with type 'float (float, float)' _configtest.c:27:5: warning: incompatible redeclaration of library function 'fmodf' [-Wincompatible-library-redeclaration] int fmodf (void); ^ _configtest.c:27:5: note: 'fmodf' is a builtin with type 'float (float, float)' _configtest.c:28:5: warning: incompatible redeclaration of library function 'modff' [-Wincompatible-library-redeclaration] int modff (void); ^ _configtest.c:28:5: note: 'modff' is a builtin with type 'float (float, float *)' _configtest.c:29:5: warning: incompatible redeclaration of library function 'frexpf' [-Wincompatible-library-redeclaration] int frexpf (void); ^ _configtest.c:29:5: note: 'frexpf' is a builtin with type 'float (float, int *)' _configtest.c:30:5: warning: incompatible redeclaration of library function 'ldexpf' [-Wincompatible-library-redeclaration] int ldexpf (void); ^ _configtest.c:30:5: note: 'ldexpf' is a builtin with type 'float (float, int)' _configtest.c:31:5: warning: incompatible redeclaration of library function 'exp2f' [-Wincompatible-library-redeclaration] int exp2f (void); ^ _configtest.c:31:5: note: 'exp2f' is a builtin with type 'float (float)' _configtest.c:32:5: warning: incompatible redeclaration of library function 'log2f' [-Wincompatible-library-redeclaration] int log2f (void); ^ _configtest.c:32:5: note: 'log2f' is a builtin with type 'float (float)' _configtest.c:33:5: warning: incompatible redeclaration of library function 'copysignf' [-Wincompatible-library-redeclaration] int copysignf (void); ^ _configtest.c:33:5: note: 'copysignf' is a builtin with type 'float (float, float)' _configtest.c:34:5: warning: incompatible redeclaration of library function 'nextafterf' [-Wincompatible-library-redeclaration] int nextafterf (void); ^ _configtest.c:34:5: note: 'nextafterf' is a builtin with type 'float (float, float)' _configtest.c:35:5: warning: incompatible redeclaration of library function 'cbrtf' [-Wincompatible-library-redeclaration] int cbrtf (void); ^ _configtest.c:35:5: note: 'cbrtf' is a builtin with type 'float (float)' 35 warnings generated. clang _configtest.o -o _configtest success! removing: _configtest.c _configtest.o _configtest.o.d _configtest C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers compile options: '-Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/[email protected]/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -c' clang: _configtest.c _configtest.c:1:5: warning: incompatible redeclaration of library function 'sinl' [-Wincompatible-library-redeclaration] int sinl (void); ^ _configtest.c:1:5: note: 'sinl' is a builtin with type 'long double (long double)' _configtest.c:2:5: warning: incompatible redeclaration of library function 'cosl' [-Wincompatible-library-redeclaration] int cosl (void); ^ _configtest.c:2:5: note: 'cosl' is a builtin with type 'long double (long double)' _configtest.c:3:5: warning: incompatible redeclaration of library function 'tanl' [-Wincompatible-library-redeclaration] int tanl (void); ^ _configtest.c:3:5: note: 'tanl' is a builtin with type 'long double (long double)' _configtest.c:4:5: warning: incompatible redeclaration of library function 'sinhl' [-Wincompatible-library-redeclaration] int sinhl (void); ^ _configtest.c:4:5: note: 'sinhl' is a builtin with type 'long double (long double)' _configtest.c:5:5: warning: incompatible redeclaration of library function 'coshl' [-Wincompatible-library-redeclaration] int coshl (void); ^ _configtest.c:5:5: note: 'coshl' is a builtin with type 'long double (long double)' _configtest.c:6:5: warning: incompatible redeclaration of library function 'tanhl' [-Wincompatible-library-redeclaration] int tanhl (void); ^ _configtest.c:6:5: note: 'tanhl' is a builtin with type 'long double (long double)' _configtest.c:7:5: warning: incompatible redeclaration of library function 'fabsl' [-Wincompatible-library-redeclaration] int fabsl (void); ^ _configtest.c:7:5: note: 'fabsl' is a builtin with type 'long double (long double)' _configtest.c:8:5: warning: incompatible redeclaration of library function 'floorl' [-Wincompatible-library-redeclaration] int floorl (void); ^ _configtest.c:8:5: note: 'floorl' is a builtin with type 'long double (long double)' _configtest.c:9:5: warning: incompatible redeclaration of library function 'ceill' [-Wincompatible-library-redeclaration] int ceill (void); ^ _configtest.c:9:5: note: 'ceill' is a builtin with type 'long double (long double)' _configtest.c:10:5: warning: incompatible redeclaration of library function 'rintl' [-Wincompatible-library-redeclaration] int rintl (void); ^ _configtest.c:10:5: note: 'rintl' is a builtin with type 'long double (long double)' _configtest.c:11:5: warning: incompatible redeclaration of library function 'truncl' [-Wincompatible-library-redeclaration] int truncl (void); ^ _configtest.c:11:5: note: 'truncl' is a builtin with type 'long double (long double)' _configtest.c:12:5: warning: incompatible redeclaration of library function 'sqrtl' [-Wincompatible-library-redeclaration] int sqrtl (void); ^ _configtest.c:12:5: note: 'sqrtl' is a builtin with type 'long double (long double)' _configtest.c:13:5: warning: incompatible redeclaration of library function 'log10l' [-Wincompatible-library-redeclaration] int log10l (void); ^ _configtest.c:13:5: note: 'log10l' is a builtin with type 'long double (long double)' _configtest.c:14:5: warning: incompatible redeclaration of library function 'logl' [-Wincompatible-library-redeclaration] int logl (void); ^ _configtest.c:14:5: note: 'logl' is a builtin with type 'long double (long double)' _configtest.c:15:5: warning: incompatible redeclaration of library function 'log1pl' [-Wincompatible-library-redeclaration] int log1pl (void); ^ _configtest.c:15:5: note: 'log1pl' is a builtin with type 'long double (long double)' _configtest.c:16:5: warning: incompatible redeclaration of library function 'expl' [-Wincompatible-library-redeclaration] int expl (void); ^ _configtest.c:16:5: note: 'expl' is a builtin with type 'long double (long double)' _configtest.c:17:5: warning: incompatible redeclaration of library function 'expm1l' [-Wincompatible-library-redeclaration] int expm1l (void); ^ _configtest.c:17:5: note: 'expm1l' is a builtin with type 'long double (long double)' _configtest.c:18:5: warning: incompatible redeclaration of library function 'asinl' [-Wincompatible-library-redeclaration] int asinl (void); ^ _configtest.c:18:5: note: 'asinl' is a builtin with type 'long double (long double)' _configtest.c:19:5: warning: incompatible redeclaration of library function 'acosl' [-Wincompatible-library-redeclaration] int acosl (void); ^ _configtest.c:19:5: note: 'acosl' is a builtin with type 'long double (long double)' _configtest.c:20:5: warning: incompatible redeclaration of library function 'atanl' [-Wincompatible-library-redeclaration] int atanl (void); ^ _configtest.c:20:5: note: 'atanl' is a builtin with type 'long double (long double)' _configtest.c:21:5: warning: incompatible redeclaration of library function 'asinhl' [-Wincompatible-library-redeclaration] int asinhl (void); ^ _configtest.c:21:5: note: 'asinhl' is a builtin with type 'long double (long double)' _configtest.c:22:5: warning: incompatible redeclaration of library function 'acoshl' [-Wincompatible-library-redeclaration] int acoshl (void); ^ _configtest.c:22:5: note: 'acoshl' is a builtin with type 'long double (long double)' _configtest.c:23:5: warning: incompatible redeclaration of library function 'atanhl' [-Wincompatible-library-redeclaration] int atanhl (void); ^ _configtest.c:23:5: note: 'atanhl' is a builtin with type 'long double (long double)' _configtest.c:24:5: warning: incompatible redeclaration of library function 'hypotl' [-Wincompatible-library-redeclaration] int hypotl (void); ^ _configtest.c:24:5: note: 'hypotl' is a builtin with type 'long double (long double, long double)' _configtest.c:25:5: warning: incompatible redeclaration of library function 'atan2l' [-Wincompatible-library-redeclaration] int atan2l (void); ^ _configtest.c:25:5: note: 'atan2l' is a builtin with type 'long double (long double, long double)' _configtest.c:26:5: warning: incompatible redeclaration of library function 'powl' [-Wincompatible-library-redeclaration] int powl (void); ^ _configtest.c:26:5: note: 'powl' is a builtin with type 'long double (long double, long double)' _configtest.c:27:5: warning: incompatible redeclaration of library function 'fmodl' [-Wincompatible-library-redeclaration] int fmodl (void); ^ _configtest.c:27:5: note: 'fmodl' is a builtin with type 'long double (long double, long double)' _configtest.c:28:5: warning: incompatible redeclaration of library function 'modfl' [-Wincompatible-library-redeclaration] int modfl (void); ^ _configtest.c:28:5: note: 'modfl' is a builtin with type 'long double (long double, long double *)' _configtest.c:29:5: warning: incompatible redeclaration of library function 'frexpl' [-Wincompatible-library-redeclaration] int frexpl (void); ^ _configtest.c:29:5: note: 'frexpl' is a builtin with type 'long double (long double, int *)' _configtest.c:30:5: warning: incompatible redeclaration of library function 'ldexpl' [-Wincompatible-library-redeclaration] int ldexpl (void); ^ _configtest.c:30:5: note: 'ldexpl' is a builtin with type 'long double (long double, int)' _configtest.c:31:5: warning: incompatible redeclaration of library function 'exp2l' [-Wincompatible-library-redeclaration] int exp2l (void); ^ _configtest.c:31:5: note: 'exp2l' is a builtin with type 'long double (long double)' _configtest.c:32:5: warning: incompatible redeclaration of library function 'log2l' [-Wincompatible-library-redeclaration] int log2l (void); ^ _configtest.c:32:5: note: 'log2l' is a builtin with type 'long double (long double)' _configtest.c:33:5: warning: incompatible redeclaration of library function 'copysignl' [-Wincompatible-library-redeclaration] int copysignl (void); ^ _configtest.c:33:5: note: 'copysignl' is a builtin with type 'long double (long double, long double)' _configtest.c:34:5: warning: incompatible redeclaration of library function 'nextafterl' [-Wincompatible-library-redeclaration] int nextafterl (void); ^ _configtest.c:34:5: note: 'nextafterl' is a builtin with type 'long double (long double, long double)' _configtest.c:35:5: warning: incompatible redeclaration of library function 'cbrtl' [-Wincompatible-library-redeclaration] int cbrtl (void); ^ _configtest.c:35:5: note: 'cbrtl' is a builtin with type 'long double (long double)' 35 warnings generated. clang _configtest.o -o _configtest success! removing: _configtest.c _configtest.o _configtest.o.d _configtest C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers compile options: '-Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/[email protected]/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -c' clang: _configtest.c success! removing: _configtest.c _configtest.o _configtest.o.d C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers compile options: '-Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/[email protected]/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -c' clang: _configtest.c success! removing: _configtest.c _configtest.o _configtest.o.d C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers compile options: '-Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/[email protected]/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -c' clang: _configtest.c success! removing: _configtest.c _configtest.o _configtest.o.d C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers compile options: '-Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/[email protected]/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -c' clang: _configtest.c success! removing: _configtest.c _configtest.o _configtest.o.d C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers compile options: '-Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/[email protected]/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -c' clang: _configtest.c _configtest.c:8:12: error: use of undeclared identifier 'HAVE_DECL_SIGNBIT' (void) HAVE_DECL_SIGNBIT; ^ 1 error generated. failure. removing: _configtest.c _configtest.o C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers compile options: '-Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/[email protected]/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -c' clang: _configtest.c success! removing: _configtest.c _configtest.o _configtest.o.d C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers compile options: '-Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/[email protected]/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -c' clang: _configtest.c success! removing: _configtest.c _configtest.o _configtest.o.d C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers compile options: '-Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/[email protected]/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -c' clang: _configtest.c success! removing: _configtest.c _configtest.o _configtest.o.d C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers compile options: '-Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/[email protected]/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -c' clang: _configtest.c success! removing: _configtest.c _configtest.o _configtest.o.d C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers compile options: '-Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/[email protected]/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -c' clang: _configtest.c removing: _configtest.c _configtest.o _configtest.o.d C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers compile options: '-Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/[email protected]/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -c' clang: _configtest.c removing: _configtest.c _configtest.o _configtest.o.d C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers compile options: '-Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/[email protected]/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -c' clang: _configtest.c removing: _configtest.c _configtest.o _configtest.o.d C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers compile options: '-Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/[email protected]/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -c' clang: _configtest.c _configtest.c:1:5: warning: incompatible redeclaration of library function 'cabs' [-Wincompatible-library-redeclaration] int cabs (void); ^ _configtest.c:1:5: note: 'cabs' is a builtin with type 'double (_Complex double)' _configtest.c:2:5: warning: incompatible redeclaration of library function 'cacos' [-Wincompatible-library-redeclaration] int cacos (void); ^ _configtest.c:2:5: note: 'cacos' is a builtin with type '_Complex double (_Complex double)' _configtest.c:3:5: warning: incompatible redeclaration of library function 'cacosh' [-Wincompatible-library-redeclaration] int cacosh (void); ^ _configtest.c:3:5: note: 'cacosh' is a builtin with type '_Complex double (_Complex double)' _configtest.c:4:5: warning: incompatible redeclaration of library function 'carg' [-Wincompatible-library-redeclaration] int carg (void); ^ _configtest.c:4:5: note: 'carg' is a builtin with type 'double (_Complex double)' _configtest.c:5:5: warning: incompatible redeclaration of library function 'casin' [-Wincompatible-library-redeclaration] int casin (void); ^ _configtest.c:5:5: note: 'casin' is a builtin with type '_Complex double (_Complex double)' _configtest.c:6:5: warning: incompatible redeclaration of library function 'casinh' [-Wincompatible-library-redeclaration] int casinh (void); ^ _configtest.c:6:5: note: 'casinh' is a builtin with type '_Complex double (_Complex double)' _configtest.c:7:5: warning: incompatible redeclaration of library function 'catan' [-Wincompatible-library-redeclaration] int catan (void); ^ _configtest.c:7:5: note: 'catan' is a builtin with type '_Complex double (_Complex double)' _configtest.c:8:5: warning: incompatible redeclaration of library function 'catanh' [-Wincompatible-library-redeclaration] int catanh (void); ^ _configtest.c:8:5: note: 'catanh' is a builtin with type '_Complex double (_Complex double)' _configtest.c:9:5: warning: incompatible redeclaration of library function 'ccos' [-Wincompatible-library-redeclaration] int ccos (void); ^ _configtest.c:9:5: note: 'ccos' is a builtin with type '_Complex double (_Complex double)' _configtest.c:10:5: warning: incompatible redeclaration of library function 'ccosh' [-Wincompatible-library-redeclaration] int ccosh (void); ^ _configtest.c:10:5: note: 'ccosh' is a builtin with type '_Complex double (_Complex double)' _configtest.c:11:5: warning: incompatible redeclaration of library function 'cexp' [-Wincompatible-library-redeclaration] int cexp (void); ^ _configtest.c:11:5: note: 'cexp' is a builtin with type '_Complex double (_Complex double)' _configtest.c:12:5: warning: incompatible redeclaration of library function 'cimag' [-Wincompatible-library-redeclaration] int cimag (void); ^ _configtest.c:12:5: note: 'cimag' is a builtin with type 'double (_Complex double)' _configtest.c:13:5: warning: incompatible redeclaration of library function 'clog' [-Wincompatible-library-redeclaration] int clog (void); ^ _configtest.c:13:5: note: 'clog' is a builtin with type '_Complex double (_Complex double)' _configtest.c:14:5: warning: incompatible redeclaration of library function 'conj' [-Wincompatible-library-redeclaration] int conj (void); ^ _configtest.c:14:5: note: 'conj' is a builtin with type '_Complex double (_Complex double)' _configtest.c:15:5: warning: incompatible redeclaration of library function 'cpow' [-Wincompatible-library-redeclaration] int cpow (void); ^ _configtest.c:15:5: note: 'cpow' is a builtin with type '_Complex double (_Complex double, _Complex double)' _configtest.c:16:5: warning: incompatible redeclaration of library function 'cproj' [-Wincompatible-library-redeclaration] int cproj (void); ^ _configtest.c:16:5: note: 'cproj' is a builtin with type '_Complex double (_Complex double)' _configtest.c:17:5: warning: incompatible redeclaration of library function 'creal' [-Wincompatible-library-redeclaration] int creal (void); ^ _configtest.c:17:5: note: 'creal' is a builtin with type 'double (_Complex double)' _configtest.c:18:5: warning: incompatible redeclaration of library function 'csin' [-Wincompatible-library-redeclaration] int csin (void); ^ _configtest.c:18:5: note: 'csin' is a builtin with type '_Complex double (_Complex double)' _configtest.c:19:5: warning: incompatible redeclaration of library function 'csinh' [-Wincompatible-library-redeclaration] int csinh (void); ^ _configtest.c:19:5: note: 'csinh' is a builtin with type '_Complex double (_Complex double)' _configtest.c:20:5: warning: incompatible redeclaration of library function 'csqrt' [-Wincompatible-library-redeclaration] int csqrt (void); ^ _configtest.c:20:5: note: 'csqrt' is a builtin with type '_Complex double (_Complex double)' _configtest.c:21:5: warning: incompatible redeclaration of library function 'ctan' [-Wincompatible-library-redeclaration] int ctan (void); ^ _configtest.c:21:5: note: 'ctan' is a builtin with type '_Complex double (_Complex double)' _configtest.c:22:5: warning: incompatible redeclaration of library function 'ctanh' [-Wincompatible-library-redeclaration] int ctanh (void); ^ _configtest.c:22:5: note: 'ctanh' is a builtin with type '_Complex double (_Complex double)' 22 warnings generated. clang _configtest.o -o _configtest success! removing: _configtest.c _configtest.o _configtest.o.d _configtest C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers compile options: '-Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/[email protected]/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -c' clang: _configtest.c _configtest.c:1:5: warning: incompatible redeclaration of library function 'cabsf' [-Wincompatible-library-redeclaration] int cabsf (void); ^ _configtest.c:1:5: note: 'cabsf' is a builtin with type 'float (_Complex float)' _configtest.c:2:5: warning: incompatible redeclaration of library function 'cacosf' [-Wincompatible-library-redeclaration] int cacosf (void); ^ _configtest.c:2:5: note: 'cacosf' is a builtin with type '_Complex float (_Complex float)' _configtest.c:3:5: warning: incompatible redeclaration of library function 'cacoshf' [-Wincompatible-library-redeclaration] int cacoshf (void); ^ _configtest.c:3:5: note: 'cacoshf' is a builtin with type '_Complex float (_Complex float)' _configtest.c:4:5: warning: incompatible redeclaration of library function 'cargf' [-Wincompatible-library-redeclaration] int cargf (void); ^ _configtest.c:4:5: note: 'cargf' is a builtin with type 'float (_Complex float)' _configtest.c:5:5: warning: incompatible redeclaration of library function 'casinf' [-Wincompatible-library-redeclaration] int casinf (void); ^ _configtest.c:5:5: note: 'casinf' is a builtin with type '_Complex float (_Complex float)' _configtest.c:6:5: warning: incompatible redeclaration of library function 'casinhf' [-Wincompatible-library-redeclaration] int casinhf (void); ^ _configtest.c:6:5: note: 'casinhf' is a builtin with type '_Complex float (_Complex float)' _configtest.c:7:5: warning: incompatible redeclaration of library function 'catanf' [-Wincompatible-library-redeclaration] int catanf (void); ^ _configtest.c:7:5: note: 'catanf' is a builtin with type '_Complex float (_Complex float)' _configtest.c:8:5: warning: incompatible redeclaration of library function 'catanhf' [-Wincompatible-library-redeclaration] int catanhf (void); ^ _configtest.c:8:5: note: 'catanhf' is a builtin with type '_Complex float (_Complex float)' _configtest.c:9:5: warning: incompatible redeclaration of library function 'ccosf' [-Wincompatible-library-redeclaration] int ccosf (void); ^ _configtest.c:9:5: note: 'ccosf' is a builtin with type '_Complex float (_Complex float)' _configtest.c:10:5: warning: incompatible redeclaration of library function 'ccoshf' [-Wincompatible-library-redeclaration] int ccoshf (void); ^ _configtest.c:10:5: note: 'ccoshf' is a builtin with type '_Complex float (_Complex float)' _configtest.c:11:5: warning: incompatible redeclaration of library function 'cexpf' [-Wincompatible-library-redeclaration] int cexpf (void); ^ _configtest.c:11:5: note: 'cexpf' is a builtin with type '_Complex float (_Complex float)' _configtest.c:12:5: warning: incompatible redeclaration of library function 'cimagf' [-Wincompatible-library-redeclaration] int cimagf (void); ^ _configtest.c:12:5: note: 'cimagf' is a builtin with type 'float (_Complex float)' _configtest.c:13:5: warning: incompatible redeclaration of library function 'clogf' [-Wincompatible-library-redeclaration] int clogf (void); ^ _configtest.c:13:5: note: 'clogf' is a builtin with type '_Complex float (_Complex float)' _configtest.c:14:5: warning: incompatible redeclaration of library function 'conjf' [-Wincompatible-library-redeclaration] int conjf (void); ^ _configtest.c:14:5: note: 'conjf' is a builtin with type '_Complex float (_Complex float)' _configtest.c:15:5: warning: incompatible redeclaration of library function 'cpowf' [-Wincompatible-library-redeclaration] int cpowf (void); ^ _configtest.c:15:5: note: 'cpowf' is a builtin with type '_Complex float (_Complex float, _Complex float)' _configtest.c:16:5: warning: incompatible redeclaration of library function 'cprojf' [-Wincompatible-library-redeclaration] int cprojf (void); ^ _configtest.c:16:5: note: 'cprojf' is a builtin with type '_Complex float (_Complex float)' _configtest.c:17:5: warning: incompatible redeclaration of library function 'crealf' [-Wincompatible-library-redeclaration] int crealf (void); ^ _configtest.c:17:5: note: 'crealf' is a builtin with type 'float (_Complex float)' _configtest.c:18:5: warning: incompatible redeclaration of library function 'csinf' [-Wincompatible-library-redeclaration] int csinf (void); ^ _configtest.c:18:5: note: 'csinf' is a builtin with type '_Complex float (_Complex float)' _configtest.c:19:5: warning: incompatible redeclaration of library function 'csinhf' [-Wincompatible-library-redeclaration] int csinhf (void); ^ _configtest.c:19:5: note: 'csinhf' is a builtin with type '_Complex float (_Complex float)' _configtest.c:20:5: warning: incompatible redeclaration of library function 'csqrtf' [-Wincompatible-library-redeclaration] int csqrtf (void); ^ _configtest.c:20:5: note: 'csqrtf' is a builtin with type '_Complex float (_Complex float)' _configtest.c:21:5: warning: incompatible redeclaration of library function 'ctanf' [-Wincompatible-library-redeclaration] int ctanf (void); ^ _configtest.c:21:5: note: 'ctanf' is a builtin with type '_Complex float (_Complex float)' _configtest.c:22:5: warning: incompatible redeclaration of library function 'ctanhf' [-Wincompatible-library-redeclaration] int ctanhf (void); ^ _configtest.c:22:5: note: 'ctanhf' is a builtin with type '_Complex float (_Complex float)' 22 warnings generated. clang _configtest.o -o _configtest success! removing: _configtest.c _configtest.o _configtest.o.d _configtest C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers compile options: '-Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/[email protected]/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -c' clang: _configtest.c _configtest.c:1:5: warning: incompatible redeclaration of library function 'cabsl' [-Wincompatible-library-redeclaration] int cabsl (void); ^ _configtest.c:1:5: note: 'cabsl' is a builtin with type 'long double (_Complex long double)' _configtest.c:2:5: warning: incompatible redeclaration of library function 'cacosl' [-Wincompatible-library-redeclaration] int cacosl (void); ^ _configtest.c:2:5: note: 'cacosl' is a builtin with type '_Complex long double (_Complex long double)' _configtest.c:3:5: warning: incompatible redeclaration of library function 'cacoshl' [-Wincompatible-library-redeclaration] int cacoshl (void); ^ _configtest.c:3:5: note: 'cacoshl' is a builtin with type '_Complex long double (_Complex long double)' _configtest.c:4:5: warning: incompatible redeclaration of library function 'cargl' [-Wincompatible-library-redeclaration] int cargl (void); ^ _configtest.c:4:5: note: 'cargl' is a builtin with type 'long double (_Complex long double)' _configtest.c:5:5: warning: incompatible redeclaration of library function 'casinl' [-Wincompatible-library-redeclaration] int casinl (void); ^ _configtest.c:5:5: note: 'casinl' is a builtin with type '_Complex long double (_Complex long double)' _configtest.c:6:5: warning: incompatible redeclaration of library function 'casinhl' [-Wincompatible-library-redeclaration] int casinhl (void); ^ _configtest.c:6:5: note: 'casinhl' is a builtin with type '_Complex long double (_Complex long double)' _configtest.c:7:5: warning: incompatible redeclaration of library function 'catanl' [-Wincompatible-library-redeclaration] int catanl (void); ^ _configtest.c:7:5: note: 'catanl' is a builtin with type '_Complex long double (_Complex long double)' _configtest.c:8:5: warning: incompatible redeclaration of library function 'catanhl' [-Wincompatible-library-redeclaration] int catanhl (void); ^ _configtest.c:8:5: note: 'catanhl' is a builtin with type '_Complex long double (_Complex long double)' _configtest.c:9:5: warning: incompatible redeclaration of library function 'ccosl' [-Wincompatible-library-redeclaration] int ccosl (void); ^ _configtest.c:9:5: note: 'ccosl' is a builtin with type '_Complex long double (_Complex long double)' _configtest.c:10:5: warning: incompatible redeclaration of library function 'ccoshl' [-Wincompatible-library-redeclaration] int ccoshl (void); ^ _configtest.c:10:5: note: 'ccoshl' is a builtin with type '_Complex long double (_Complex long double)' _configtest.c:11:5: warning: incompatible redeclaration of library function 'cexpl' [-Wincompatible-library-redeclaration] int cexpl (void); ^ _configtest.c:11:5: note: 'cexpl' is a builtin with type '_Complex long double (_Complex long double)' _configtest.c:12:5: warning: incompatible redeclaration of library function 'cimagl' [-Wincompatible-library-redeclaration] int cimagl (void); ^ _configtest.c:12:5: note: 'cimagl' is a builtin with type 'long double (_Complex long double)' _configtest.c:13:5: warning: incompatible redeclaration of library function 'clogl' [-Wincompatible-library-redeclaration] int clogl (void); ^ _configtest.c:13:5: note: 'clogl' is a builtin with type '_Complex long double (_Complex long double)' _configtest.c:14:5: warning: incompatible redeclaration of library function 'conjl' [-Wincompatible-library-redeclaration] int conjl (void); ^ _configtest.c:14:5: note: 'conjl' is a builtin with type '_Complex long double (_Complex long double)' _configtest.c:15:5: warning: incompatible redeclaration of library function 'cpowl' [-Wincompatible-library-redeclaration] int cpowl (void); ^ _configtest.c:15:5: note: 'cpowl' is a builtin with type '_Complex long double (_Complex long double, _Complex long double)' _configtest.c:16:5: warning: incompatible redeclaration of library function 'cprojl' [-Wincompatible-library-redeclaration] int cprojl (void); ^ _configtest.c:16:5: note: 'cprojl' is a builtin with type '_Complex long double (_Complex long double)' _configtest.c:17:5: warning: incompatible redeclaration of library function 'creall' [-Wincompatible-library-redeclaration] int creall (void); ^ _configtest.c:17:5: note: 'creall' is a builtin with type 'long double (_Complex long double)' _configtest.c:18:5: warning: incompatible redeclaration of library function 'csinl' [-Wincompatible-library-redeclaration] int csinl (void); ^ _configtest.c:18:5: note: 'csinl' is a builtin with type '_Complex long double (_Complex long double)' _configtest.c:19:5: warning: incompatible redeclaration of library function 'csinhl' [-Wincompatible-library-redeclaration] int csinhl (void); ^ _configtest.c:19:5: note: 'csinhl' is a builtin with type '_Complex long double (_Complex long double)' _configtest.c:20:5: warning: incompatible redeclaration of library function 'csqrtl' [-Wincompatible-library-redeclaration] int csqrtl (void); ^ _configtest.c:20:5: note: 'csqrtl' is a builtin with type '_Complex long double (_Complex long double)' _configtest.c:21:5: warning: incompatible redeclaration of library function 'ctanl' [-Wincompatible-library-redeclaration] int ctanl (void); ^ _configtest.c:21:5: note: 'ctanl' is a builtin with type '_Complex long double (_Complex long double)' _configtest.c:22:5: warning: incompatible redeclaration of library function 'ctanhl' [-Wincompatible-library-redeclaration] int ctanhl (void); ^ _configtest.c:22:5: note: 'ctanhl' is a builtin with type '_Complex long double (_Complex long double)' 22 warnings generated. clang _configtest.o -o _configtest success! removing: _configtest.c _configtest.o _configtest.o.d _configtest C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers compile options: '-Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/[email protected]/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -c' clang: _configtest.c _configtest.c:2:12: warning: unused function 'static_func' [-Wunused-function] static int static_func (char * restrict a) ^ 1 warning generated. success! removing: _configtest.c _configtest.o _configtest.o.d C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers compile options: '-Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/[email protected]/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -c' clang: _configtest.c _configtest.c:3:19: warning: unused function 'static_func' [-Wunused-function] static inline int static_func (void) ^ 1 warning generated. success! removing: _configtest.c _configtest.o _configtest.o.d C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers compile options: '-Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/[email protected]/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -c' clang: _configtest.c removing: _configtest.c _configtest.o _configtest.o.d File: build/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy/config.h #define SIZEOF_PY_INTPTR_T 8 #define SIZEOF_OFF_T 8 #define SIZEOF_PY_LONG_LONG 8 #define MATHLIB #define HAVE_SIN 1 #define HAVE_COS 1 #define HAVE_TAN 1 #define HAVE_SINH 1 #define HAVE_COSH 1 #define HAVE_TANH 1 #define HAVE_FABS 1 #define HAVE_FLOOR 1 #define HAVE_CEIL 1 #define HAVE_SQRT 1 #define HAVE_LOG10 1 #define HAVE_LOG 1 #define HAVE_EXP 1 #define HAVE_ASIN 1 #define HAVE_ACOS 1 #define HAVE_ATAN 1 #define HAVE_FMOD 1 #define HAVE_MODF 1 #define HAVE_FREXP 1 #define HAVE_LDEXP 1 #define HAVE_RINT 1 #define HAVE_TRUNC 1 #define HAVE_EXP2 1 #define HAVE_LOG2 1 #define HAVE_ATAN2 1 #define HAVE_POW 1 #define HAVE_NEXTAFTER 1 #define HAVE_STRTOLL 1 #define HAVE_STRTOULL 1 #define HAVE_CBRT 1 #define HAVE_STRTOLD_L 1 #define HAVE_BACKTRACE 1 #define HAVE_MADVISE 1 #define HAVE_XMMINTRIN_H 1 #define HAVE_EMMINTRIN_H 1 #define HAVE_XLOCALE_H 1 #define HAVE_DLFCN_H 1 #define HAVE_SYS_MMAN_H 1 #define HAVE___BUILTIN_ISNAN 1 #define HAVE___BUILTIN_ISINF 1 #define HAVE___BUILTIN_ISFINITE 1 #define HAVE___BUILTIN_BSWAP32 1 #define HAVE___BUILTIN_BSWAP64 1 #define HAVE___BUILTIN_EXPECT 1 #define HAVE___BUILTIN_MUL_OVERFLOW 1 #define HAVE___BUILTIN_CPU_SUPPORTS 1 #define HAVE__M_FROM_INT64 1 #define HAVE__MM_LOAD_PS 1 #define HAVE__MM_PREFETCH 1 #define HAVE__MM_LOAD_PD 1 #define HAVE___BUILTIN_PREFETCH 1 #define HAVE_LINK_AVX 1 #define HAVE_LINK_AVX2 1 #define HAVE_XGETBV 1 #define HAVE_ATTRIBUTE_NONNULL 1 #define HAVE_ATTRIBUTE_TARGET_AVX 1 #define HAVE_ATTRIBUTE_TARGET_AVX2 1 #define HAVE___THREAD 1 #define HAVE_SINF 1 #define HAVE_COSF 1 #define HAVE_TANF 1 #define HAVE_SINHF 1 #define HAVE_COSHF 1 #define HAVE_TANHF 1 #define HAVE_FABSF 1 #define HAVE_FLOORF 1 #define HAVE_CEILF 1 #define HAVE_RINTF 1 #define HAVE_TRUNCF 1 #define HAVE_SQRTF 1 #define HAVE_LOG10F 1 #define HAVE_LOGF 1 #define HAVE_LOG1PF 1 #define HAVE_EXPF 1 #define HAVE_EXPM1F 1 #define HAVE_ASINF 1 #define HAVE_ACOSF 1 #define HAVE_ATANF 1 #define HAVE_ASINHF 1 #define HAVE_ACOSHF 1 #define HAVE_ATANHF 1 #define HAVE_HYPOTF 1 #define HAVE_ATAN2F 1 #define HAVE_POWF 1 #define HAVE_FMODF 1 #define HAVE_MODFF 1 #define HAVE_FREXPF 1 #define HAVE_LDEXPF 1 #define HAVE_EXP2F 1 #define HAVE_LOG2F 1 #define HAVE_COPYSIGNF 1 #define HAVE_NEXTAFTERF 1 #define HAVE_CBRTF 1 #define HAVE_SINL 1 #define HAVE_COSL 1 #define HAVE_TANL 1 #define HAVE_SINHL 1 #define HAVE_COSHL 1 #define HAVE_TANHL 1 #define HAVE_FABSL 1 #define HAVE_FLOORL 1 #define HAVE_CEILL 1 #define HAVE_RINTL 1 #define HAVE_TRUNCL 1 #define HAVE_SQRTL 1 #define HAVE_LOG10L 1 #define HAVE_LOGL 1 #define HAVE_LOG1PL 1 #define HAVE_EXPL 1 #define HAVE_EXPM1L 1 #define HAVE_ASINL 1 #define HAVE_ACOSL 1 #define HAVE_ATANL 1 #define HAVE_ASINHL 1 #define HAVE_ACOSHL 1 #define HAVE_ATANHL 1 #define HAVE_HYPOTL 1 #define HAVE_ATAN2L 1 #define HAVE_POWL 1 #define HAVE_FMODL 1 #define HAVE_MODFL 1 #define HAVE_FREXPL 1 #define HAVE_LDEXPL 1 #define HAVE_EXP2L 1 #define HAVE_LOG2L 1 #define HAVE_COPYSIGNL 1 #define HAVE_NEXTAFTERL 1 #define HAVE_CBRTL 1 #define HAVE_DECL_SIGNBIT #define HAVE_COMPLEX_H 1 #define HAVE_CABS 1 #define HAVE_CACOS 1 #define HAVE_CACOSH 1 #define HAVE_CARG 1 #define HAVE_CASIN 1 #define HAVE_CASINH 1 #define HAVE_CATAN 1 #define HAVE_CATANH 1 #define HAVE_CCOS 1 #define HAVE_CCOSH 1 #define HAVE_CEXP 1 #define HAVE_CIMAG 1 #define HAVE_CLOG 1 #define HAVE_CONJ 1 #define HAVE_CPOW 1 #define HAVE_CPROJ 1 #define HAVE_CREAL 1 #define HAVE_CSIN 1 #define HAVE_CSINH 1 #define HAVE_CSQRT 1 #define HAVE_CTAN 1 #define HAVE_CTANH 1 #define HAVE_CABSF 1 #define HAVE_CACOSF 1 #define HAVE_CACOSHF 1 #define HAVE_CARGF 1 #define HAVE_CASINF 1 #define HAVE_CASINHF 1 #define HAVE_CATANF 1 #define HAVE_CATANHF 1 #define HAVE_CCOSF 1 #define HAVE_CCOSHF 1 #define HAVE_CEXPF 1 #define HAVE_CIMAGF 1 #define HAVE_CLOGF 1 #define HAVE_CONJF 1 #define HAVE_CPOWF 1 #define HAVE_CPROJF 1 #define HAVE_CREALF 1 #define HAVE_CSINF 1 #define HAVE_CSINHF 1 #define HAVE_CSQRTF 1 #define HAVE_CTANF 1 #define HAVE_CTANHF 1 #define HAVE_CABSL 1 #define HAVE_CACOSL 1 #define HAVE_CACOSHL 1 #define HAVE_CARGL 1 #define HAVE_CASINL 1 #define HAVE_CASINHL 1 #define HAVE_CATANL 1 #define HAVE_CATANHL 1 #define HAVE_CCOSL 1 #define HAVE_CCOSHL 1 #define HAVE_CEXPL 1 #define HAVE_CIMAGL 1 #define HAVE_CLOGL 1 #define HAVE_CONJL 1 #define HAVE_CPOWL 1 #define HAVE_CPROJL 1 #define HAVE_CREALL 1 #define HAVE_CSINL 1 #define HAVE_CSINHL 1 #define HAVE_CSQRTL 1 #define HAVE_CTANL 1 #define HAVE_CTANHL 1 #define NPY_RESTRICT restrict #define NPY_RELAXED_STRIDES_CHECKING 1 #define HAVE_LDOUBLE_INTEL_EXTENDED_16_BYTES_LE 1 #define NPY_PY3K 1 #ifndef __cplusplus /* #undef inline */ #endif #ifndef _NPY_NPY_CONFIG_H_ #error config.h should never be included directly, include npy_config.h instead #endif EOF adding 'build/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy/config.h' to sources. Generating build/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy/_numpyconfig.h C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers compile options: '-Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/[email protected]/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -c' clang: _configtest.c _configtest.c:1:5: warning: incompatible redeclaration of library function 'exp' [-Wincompatible-library-redeclaration] int exp (void); ^ _configtest.c:1:5: note: 'exp' is a builtin with type 'double (double)' 1 warning generated. clang _configtest.o -o _configtest success! removing: _configtest.c _configtest.o _configtest.o.d _configtest C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers compile options: '-Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/[email protected]/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -c' clang: _configtest.c success! removing: _configtest.c _configtest.o _configtest.o.d C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers compile options: '-Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/[email protected]/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -c' clang: _configtest.c success! removing: _configtest.c _configtest.o _configtest.o.d File: build/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy/_numpyconfig.h #define NPY_SIZEOF_SHORT SIZEOF_SHORT #define NPY_SIZEOF_INT SIZEOF_INT #define NPY_SIZEOF_LONG SIZEOF_LONG #define NPY_SIZEOF_FLOAT 4 #define NPY_SIZEOF_COMPLEX_FLOAT 8 #define NPY_SIZEOF_DOUBLE 8 #define NPY_SIZEOF_COMPLEX_DOUBLE 16 #define NPY_SIZEOF_LONGDOUBLE 16 #define NPY_SIZEOF_COMPLEX_LONGDOUBLE 32 #define NPY_SIZEOF_PY_INTPTR_T 8 #define NPY_SIZEOF_OFF_T 8 #define NPY_SIZEOF_PY_LONG_LONG 8 #define NPY_SIZEOF_LONGLONG 8 #define NPY_NO_SMP 0 #define NPY_HAVE_DECL_ISNAN #define NPY_HAVE_DECL_ISINF #define NPY_HAVE_DECL_ISFINITE #define NPY_HAVE_DECL_SIGNBIT #define NPY_USE_C99_COMPLEX 1 #define NPY_HAVE_COMPLEX_DOUBLE 1 #define NPY_HAVE_COMPLEX_FLOAT 1 #define NPY_HAVE_COMPLEX_LONG_DOUBLE 1 #define NPY_RELAXED_STRIDES_CHECKING 1 #define NPY_USE_C99_FORMATS 1 #define NPY_VISIBILITY_HIDDEN __attribute__((visibility("hidden"))) #define NPY_ABI_VERSION 0x01000009 #define NPY_API_VERSION 0x0000000D #ifndef __STDC_FORMAT_MACROS #define __STDC_FORMAT_MACROS 1 #endif EOF adding 'build/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy/_numpyconfig.h' to sources. executing numpy/core/code_generators/generate_numpy_api.py adding 'build/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy/__multiarray_api.h' to sources. numpy.core - nothing done with h_files = ['build/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy/config.h', 'build/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy/_numpyconfig.h', 'build/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy/__multiarray_api.h'] building extension "numpy.core._multiarray_tests" sources creating build/src.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray conv_template:> build/src.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/_multiarray_tests.c building extension "numpy.core._multiarray_umath" sources adding 'build/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy/config.h' to sources. adding 'build/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy/_numpyconfig.h' to sources. executing numpy/core/code_generators/generate_numpy_api.py adding 'build/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy/__multiarray_api.h' to sources. executing numpy/core/code_generators/generate_ufunc_api.py adding 'build/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy/__ufunc_api.h' to sources. conv_template:> build/src.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/arraytypes.c conv_template:> build/src.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/einsum.c conv_template:> build/src.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/lowlevel_strided_loops.c conv_template:> build/src.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/nditer_templ.c conv_template:> build/src.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/scalartypes.c creating build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath conv_template:> build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/funcs.inc adding 'build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath' to include_dirs. conv_template:> build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/simd.inc conv_template:> build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/loops.h conv_template:> build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/loops.c conv_template:> build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/matmul.h conv_template:> build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/matmul.c conv_template:> build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/scalarmath.c adding 'build/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath' to include_dirs. conv_template:> build/src.macosx-10.15-x86_64-3.9/numpy/core/src/common/templ_common.h adding 'build/src.macosx-10.15-x86_64-3.9/numpy/core/src/common' to include_dirs. numpy.core - nothing done with h_files = ['build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/funcs.inc', 'build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/simd.inc', 'build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/loops.h', 'build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/matmul.h', 'build/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath/npy_math_internal.h', 'build/src.macosx-10.15-x86_64-3.9/numpy/core/src/common/templ_common.h', 'build/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy/config.h', 'build/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy/_numpyconfig.h', 'build/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy/__multiarray_api.h', 'build/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy/__ufunc_api.h'] building extension "numpy.core._umath_tests" sources conv_template:> build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/_umath_tests.c building extension "numpy.core._rational_tests" sources conv_template:> build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/_rational_tests.c building extension "numpy.core._struct_ufunc_tests" sources conv_template:> build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/_struct_ufunc_tests.c building extension "numpy.core._operand_flag_tests" sources conv_template:> build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/_operand_flag_tests.c building extension "numpy.fft.fftpack_lite" sources building extension "numpy.linalg.lapack_lite" sources creating build/src.macosx-10.15-x86_64-3.9/numpy/linalg adding 'numpy/linalg/lapack_lite/python_xerbla.c' to sources. building extension "numpy.linalg._umath_linalg" sources adding 'numpy/linalg/lapack_lite/python_xerbla.c' to sources. conv_template:> build/src.macosx-10.15-x86_64-3.9/numpy/linalg/umath_linalg.c building extension "numpy.random.mtrand" sources creating build/src.macosx-10.15-x86_64-3.9/numpy/random building data_files sources build_src: building npy-pkg config files running build_py creating build/lib.macosx-10.15-x86_64-3.9 creating build/lib.macosx-10.15-x86_64-3.9/numpy copying numpy/conftest.py -> build/lib.macosx-10.15-x86_64-3.9/numpy copying numpy/version.py -> build/lib.macosx-10.15-x86_64-3.9/numpy copying numpy/_globals.py -> build/lib.macosx-10.15-x86_64-3.9/numpy copying numpy/__init__.py -> build/lib.macosx-10.15-x86_64-3.9/numpy copying numpy/dual.py -> build/lib.macosx-10.15-x86_64-3.9/numpy copying numpy/_distributor_init.py -> build/lib.macosx-10.15-x86_64-3.9/numpy copying numpy/setup.py -> build/lib.macosx-10.15-x86_64-3.9/numpy copying numpy/ctypeslib.py -> build/lib.macosx-10.15-x86_64-3.9/numpy copying numpy/matlib.py -> build/lib.macosx-10.15-x86_64-3.9/numpy copying numpy/_pytesttester.py -> build/lib.macosx-10.15-x86_64-3.9/numpy copying build/src.macosx-10.15-x86_64-3.9/numpy/__config__.py -> build/lib.macosx-10.15-x86_64-3.9/numpy creating build/lib.macosx-10.15-x86_64-3.9/numpy/compat copying numpy/compat/py3k.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/compat copying numpy/compat/__init__.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/compat copying numpy/compat/setup.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/compat copying numpy/compat/_inspect.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/compat creating build/lib.macosx-10.15-x86_64-3.9/numpy/core copying numpy/core/umath.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core copying numpy/core/fromnumeric.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core copying numpy/core/_dtype.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core copying numpy/core/_add_newdocs.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core copying numpy/core/_methods.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core copying numpy/core/_internal.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core copying numpy/core/_string_helpers.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core copying numpy/core/multiarray.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core copying numpy/core/records.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core copying numpy/core/__init__.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core copying numpy/core/setup_common.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core copying numpy/core/_aliased_types.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core copying numpy/core/memmap.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core copying numpy/core/overrides.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core copying numpy/core/getlimits.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core copying numpy/core/_dtype_ctypes.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core copying numpy/core/defchararray.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core copying numpy/core/shape_base.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core copying numpy/core/machar.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core copying numpy/core/setup.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core copying numpy/core/numeric.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core copying numpy/core/function_base.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core copying numpy/core/einsumfunc.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core copying numpy/core/umath_tests.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core copying numpy/core/info.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core copying numpy/core/numerictypes.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core copying numpy/core/_type_aliases.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core copying numpy/core/cversions.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core copying numpy/core/arrayprint.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core copying numpy/core/code_generators/generate_numpy_api.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core creating build/lib.macosx-10.15-x86_64-3.9/numpy/distutils copying numpy/distutils/unixccompiler.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils copying numpy/distutils/numpy_distribution.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils copying numpy/distutils/conv_template.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils copying numpy/distutils/cpuinfo.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils copying numpy/distutils/ccompiler.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils copying numpy/distutils/msvc9compiler.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils copying numpy/distutils/npy_pkg_config.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils copying numpy/distutils/compat.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils copying numpy/distutils/misc_util.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils copying numpy/distutils/log.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils copying numpy/distutils/line_endings.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils copying numpy/distutils/lib2def.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils copying numpy/distutils/pathccompiler.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils copying numpy/distutils/system_info.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils copying numpy/distutils/__init__.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils copying numpy/distutils/core.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils copying numpy/distutils/__version__.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils copying numpy/distutils/exec_command.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils copying numpy/distutils/from_template.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils copying numpy/distutils/mingw32ccompiler.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils copying numpy/distutils/setup.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils copying numpy/distutils/extension.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils copying numpy/distutils/msvccompiler.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils copying numpy/distutils/intelccompiler.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils copying numpy/distutils/info.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils copying build/src.macosx-10.15-x86_64-3.9/numpy/distutils/__config__.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils creating build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/command copying numpy/distutils/command/build.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/command copying numpy/distutils/command/config_compiler.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/command copying numpy/distutils/command/build_ext.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/command copying numpy/distutils/command/config.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/command copying numpy/distutils/command/install_headers.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/command copying numpy/distutils/command/build_py.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/command copying numpy/distutils/command/build_src.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/command copying numpy/distutils/command/__init__.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/command copying numpy/distutils/command/sdist.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/command copying numpy/distutils/command/build_scripts.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/command copying numpy/distutils/command/bdist_rpm.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/command copying numpy/distutils/command/install_clib.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/command copying numpy/distutils/command/build_clib.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/command copying numpy/distutils/command/autodist.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/command copying numpy/distutils/command/egg_info.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/command copying numpy/distutils/command/install.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/command copying numpy/distutils/command/develop.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/command copying numpy/distutils/command/install_data.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/command creating build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/fcompiler copying numpy/distutils/fcompiler/gnu.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/fcompiler copying numpy/distutils/fcompiler/compaq.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/fcompiler copying numpy/distutils/fcompiler/intel.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/fcompiler copying numpy/distutils/fcompiler/none.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/fcompiler copying numpy/distutils/fcompiler/nag.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/fcompiler copying numpy/distutils/fcompiler/pg.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/fcompiler copying numpy/distutils/fcompiler/ibm.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/fcompiler copying numpy/distutils/fcompiler/sun.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/fcompiler copying numpy/distutils/fcompiler/lahey.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/fcompiler copying numpy/distutils/fcompiler/__init__.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/fcompiler copying numpy/distutils/fcompiler/g95.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/fcompiler copying numpy/distutils/fcompiler/mips.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/fcompiler copying numpy/distutils/fcompiler/hpux.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/fcompiler copying numpy/distutils/fcompiler/environment.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/fcompiler copying numpy/distutils/fcompiler/pathf95.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/fcompiler copying numpy/distutils/fcompiler/absoft.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/fcompiler copying numpy/distutils/fcompiler/vast.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/fcompiler creating build/lib.macosx-10.15-x86_64-3.9/numpy/doc copying numpy/doc/misc.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/doc copying numpy/doc/internals.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/doc copying numpy/doc/creation.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/doc copying numpy/doc/constants.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/doc copying numpy/doc/ufuncs.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/doc copying numpy/doc/__init__.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/doc copying numpy/doc/broadcasting.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/doc copying numpy/doc/basics.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/doc copying numpy/doc/subclassing.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/doc copying numpy/doc/indexing.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/doc copying numpy/doc/byteswapping.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/doc copying numpy/doc/structured_arrays.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/doc copying numpy/doc/glossary.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/doc creating build/lib.macosx-10.15-x86_64-3.9/numpy/f2py copying numpy/f2py/cfuncs.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/f2py copying numpy/f2py/common_rules.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/f2py copying numpy/f2py/crackfortran.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/f2py copying numpy/f2py/cb_rules.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/f2py copying numpy/f2py/__init__.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/f2py copying numpy/f2py/rules.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/f2py copying numpy/f2py/f2py2e.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/f2py copying numpy/f2py/func2subr.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/f2py copying numpy/f2py/__version__.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/f2py copying numpy/f2py/diagnose.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/f2py copying numpy/f2py/setup.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/f2py copying numpy/f2py/capi_maps.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/f2py copying numpy/f2py/f90mod_rules.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/f2py copying numpy/f2py/f2py_testing.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/f2py copying numpy/f2py/use_rules.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/f2py copying numpy/f2py/info.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/f2py copying numpy/f2py/auxfuncs.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/f2py copying numpy/f2py/__main__.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/f2py creating build/lib.macosx-10.15-x86_64-3.9/numpy/fft copying numpy/fft/__init__.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/fft copying numpy/fft/setup.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/fft copying numpy/fft/helper.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/fft copying numpy/fft/fftpack.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/fft copying numpy/fft/info.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/fft creating build/lib.macosx-10.15-x86_64-3.9/numpy/lib copying numpy/lib/_iotools.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib copying numpy/lib/mixins.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib copying numpy/lib/nanfunctions.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib copying numpy/lib/recfunctions.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib copying numpy/lib/histograms.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib copying numpy/lib/scimath.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib copying numpy/lib/_version.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib copying numpy/lib/user_array.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib copying numpy/lib/__init__.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib copying numpy/lib/format.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib copying numpy/lib/twodim_base.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib copying numpy/lib/financial.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib copying numpy/lib/index_tricks.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib copying numpy/lib/npyio.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib copying numpy/lib/shape_base.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib copying numpy/lib/setup.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib copying numpy/lib/stride_tricks.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib copying numpy/lib/utils.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib copying numpy/lib/arrayterator.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib copying numpy/lib/function_base.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib copying numpy/lib/arraysetops.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib copying numpy/lib/arraypad.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib copying numpy/lib/type_check.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib copying numpy/lib/info.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib copying numpy/lib/polynomial.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib copying numpy/lib/_datasource.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib copying numpy/lib/ufunclike.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib creating build/lib.macosx-10.15-x86_64-3.9/numpy/linalg copying numpy/linalg/__init__.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/linalg copying numpy/linalg/setup.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/linalg copying numpy/linalg/linalg.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/linalg copying numpy/linalg/info.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/linalg creating build/lib.macosx-10.15-x86_64-3.9/numpy/ma copying numpy/ma/extras.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/ma copying numpy/ma/version.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/ma copying numpy/ma/testutils.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/ma copying numpy/ma/__init__.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/ma copying numpy/ma/core.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/ma copying numpy/ma/bench.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/ma copying numpy/ma/setup.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/ma copying numpy/ma/timer_comparison.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/ma copying numpy/ma/mrecords.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/ma creating build/lib.macosx-10.15-x86_64-3.9/numpy/matrixlib copying numpy/matrixlib/__init__.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/matrixlib copying numpy/matrixlib/setup.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/matrixlib copying numpy/matrixlib/defmatrix.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/matrixlib creating build/lib.macosx-10.15-x86_64-3.9/numpy/polynomial copying numpy/polynomial/laguerre.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/polynomial copying numpy/polynomial/_polybase.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/polynomial copying numpy/polynomial/polyutils.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/polynomial copying numpy/polynomial/__init__.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/polynomial copying numpy/polynomial/setup.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/polynomial copying numpy/polynomial/hermite_e.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/polynomial copying numpy/polynomial/chebyshev.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/polynomial copying numpy/polynomial/polynomial.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/polynomial copying numpy/polynomial/legendre.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/polynomial copying numpy/polynomial/hermite.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/polynomial creating build/lib.macosx-10.15-x86_64-3.9/numpy/random copying numpy/random/__init__.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/random copying numpy/random/setup.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/random copying numpy/random/info.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/random creating build/lib.macosx-10.15-x86_64-3.9/numpy/testing copying numpy/testing/nosetester.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/testing copying numpy/testing/__init__.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/testing copying numpy/testing/noseclasses.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/testing copying numpy/testing/setup.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/testing copying numpy/testing/utils.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/testing copying numpy/testing/print_coercion_tables.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/testing copying numpy/testing/decorators.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/testing creating build/lib.macosx-10.15-x86_64-3.9/numpy/testing/_private copying numpy/testing/_private/nosetester.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/testing/_private copying numpy/testing/_private/__init__.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/testing/_private copying numpy/testing/_private/noseclasses.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/testing/_private copying numpy/testing/_private/utils.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/testing/_private copying numpy/testing/_private/parameterized.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/testing/_private copying numpy/testing/_private/decorators.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/testing/_private running build_clib customize UnixCCompiler customize UnixCCompiler using build_clib building 'npymath' library compiling C sources C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers creating build/temp.macosx-10.15-x86_64-3.9 creating build/temp.macosx-10.15-x86_64-3.9/numpy creating build/temp.macosx-10.15-x86_64-3.9/numpy/core creating build/temp.macosx-10.15-x86_64-3.9/numpy/core/src creating build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/npymath creating build/temp.macosx-10.15-x86_64-3.9/build creating build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9 creating build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy creating build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/core creating build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/core/src creating build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath compile options: '-Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -Inumpy/core/include -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy -Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/[email protected]/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -c' clang: numpy/core/src/npymath/npy_math.c clang: build/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath/npy_math_complex.c clang: build/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath/ieee754.c clang: numpy/core/src/npymath/halffloat.c numpy/core/src/npymath/npy_math_complex.c.src:48:33: warning: unused variable 'tiny' [-Wunused-const-variable] static const volatile npy_float tiny = 3.9443045e-31f; ^ numpy/core/src/npymath/npy_math_complex.c.src:67:25: warning: unused variable 'c_halff' [-Wunused-const-variable] static const npy_cfloat c_halff = {0.5F, 0.0}; ^ numpy/core/src/npymath/npy_math_complex.c.src:68:25: warning: unused variable 'c_if' [-Wunused-const-variable] static const npy_cfloat c_if = {0.0, 1.0F}; ^ numpy/core/src/npymath/npy_math_complex.c.src:69:25: warning: unused variable 'c_ihalff' [-Wunused-const-variable] static const npy_cfloat c_ihalff = {0.0, 0.5F}; ^ numpy/core/src/npymath/npy_math_complex.c.src:79:1: warning: unused function 'caddf' [-Wunused-function] caddf(npy_cfloat a, npy_cfloat b) ^ numpy/core/src/npymath/npy_math_complex.c.src:87:1: warning: unused function 'csubf' [-Wunused-function] csubf(npy_cfloat a, npy_cfloat b) ^ numpy/core/src/npymath/npy_math_complex.c.src:137:1: warning: unused function 'cnegf' [-Wunused-function] cnegf(npy_cfloat a) ^ numpy/core/src/npymath/npy_math_complex.c.src:144:1: warning: unused function 'cmulif' [-Wunused-function] cmulif(npy_cfloat a) ^ numpy/core/src/npymath/npy_math_complex.c.src:67:26: warning: unused variable 'c_half' [-Wunused-const-variable] static const npy_cdouble c_half = {0.5, 0.0}; ^ numpy/core/src/npymath/npy_math_complex.c.src:68:26: warning: unused variable 'c_i' [-Wunused-const-variable] static const npy_cdouble c_i = {0.0, 1.0}; ^ numpy/core/src/npymath/npy_math_complex.c.src:69:26: warning: unused variable 'c_ihalf' [-Wunused-const-variable] static const npy_cdouble c_ihalf = {0.0, 0.5}; ^ numpy/core/src/npymath/npy_math_complex.c.src:79:1: warning: unused function 'cadd' [-Wunused-function] cadd(npy_cdouble a, npy_cdouble b) ^ numpy/core/src/npymath/npy_math_complex.c.src:87:1: warning: unused function 'csub' [-Wunused-function] csub(npy_cdouble a, npy_cdouble b) ^ numpy/core/src/npymath/npy_math_complex.c.src:137:1: warning: unused function 'cneg' [-Wunused-function] cneg(npy_cdouble a) ^ numpy/core/src/npymath/npy_math_complex.c.src:144:1: warning: unused function 'cmuli' [-Wunused-function] cmuli(npy_cdouble a) ^ numpy/core/src/npymath/npy_math_complex.c.src:67:30: warning: unused variable 'c_halfl' [-Wunused-const-variable] static const npy_clongdouble c_halfl = {0.5L, 0.0}; ^ numpy/core/src/npymath/npy_math_complex.c.src:68:30: warning: unused variable 'c_il' [-Wunused-const-variable] static const npy_clongdouble c_il = {0.0, 1.0L}; ^ numpy/core/src/npymath/npy_math_complex.c.src:69:30: warning: unused variable 'c_ihalfl' [-Wunused-const-variable] static const npy_clongdouble c_ihalfl = {0.0, 0.5L}; ^ numpy/core/src/npymath/npy_math_complex.c.src:79:1: warning: unused function 'caddl' [-Wunused-function] caddl(npy_clongdouble a, npy_clongdouble b) ^ numpy/core/src/npymath/npy_math_complex.c.src:87:1: warning: unused function 'csubl' [-Wunused-function] csubl(npy_clongdouble a, npy_clongdouble b) ^ numpy/core/src/npymath/npy_math_complex.c.src:137:1: warning: unused function 'cnegl' [-Wunused-function] cnegl(npy_clongdouble a) ^ numpy/core/src/npymath/npy_math_complex.c.src:144:1: warning: unused function 'cmulil' [-Wunused-function] cmulil(npy_clongdouble a) ^ 22 warnings generated. ar: adding 4 object files to build/temp.macosx-10.15-x86_64-3.9/libnpymath.a ranlib:@ build/temp.macosx-10.15-x86_64-3.9/libnpymath.a building 'npysort' library compiling C sources C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers creating build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/core/src/npysort compile options: '-Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Inumpy/core/include -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy -Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/[email protected]/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -c' clang: build/src.macosx-10.15-x86_64-3.9/numpy/core/src/npysort/quicksort.c clang: build/src.macosx-10.15-x86_64-3.9/numpy/core/src/npysort/mergesort.c clang: build/src.macosx-10.15-x86_64-3.9/numpy/core/src/npysort/heapsort.c clang: build/src.macosx-10.15-x86_64-3.9/numpy/core/src/npysort/selection.c clang: build/src.macosx-10.15-x86_64-3.9/numpy/core/src/npysort/binsearch.c numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code] npy_intp k; ^~~~~~~~~~~ numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead else if (0 && kth == num - 1) { ^ /* DISABLES CODE */ ( ) numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code] npy_intp k; ^~~~~~~~~~~ numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead else if (0 && kth == num - 1) { ^ /* DISABLES CODE */ ( ) numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code] npy_intp k; ^~~~~~~~~~~ numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead else if (0 && kth == num - 1) { ^ /* DISABLES CODE */ ( ) numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code] npy_intp k; ^~~~~~~~~~~ numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead else if (0 && kth == num - 1) { ^ /* DISABLES CODE */ ( ) numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code] npy_intp k; ^~~~~~~~~~~ numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead else if (0 && kth == num - 1) { ^ /* DISABLES CODE */ ( ) numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code] npy_intp k; ^~~~~~~~~~~ numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead else if (0 && kth == num - 1) { ^ /* DISABLES CODE */ ( ) numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code] npy_intp k; ^~~~~~~~~~~ numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead else if (0 && kth == num - 1) { ^ /* DISABLES CODE */ ( ) numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code] npy_intp k; ^~~~~~~~~~~ numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead else if (0 && kth == num - 1) { ^ /* DISABLES CODE */ ( ) numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code] npy_intp k; ^~~~~~~~~~~ numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead else if (0 && kth == num - 1) { ^ /* DISABLES CODE */ ( ) numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code] npy_intp k; ^~~~~~~~~~~ numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead else if (0 && kth == num - 1) { ^ /* DISABLES CODE */ ( ) numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code] npy_intp k; ^~~~~~~~~~~ numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead else if (0 && kth == num - 1) { ^ /* DISABLES CODE */ ( ) numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code] npy_intp k; ^~~~~~~~~~~ numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead else if (0 && kth == num - 1) { ^ /* DISABLES CODE */ ( ) numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code] npy_intp k; ^~~~~~~~~~~ numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead else if (0 && kth == num - 1) { ^ /* DISABLES CODE */ ( ) numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code] npy_intp k; ^~~~~~~~~~~ numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead else if (0 && kth == num - 1) { ^ /* DISABLES CODE */ ( ) numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code] npy_intp k; ^~~~~~~~~~~ numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead else if (0 && kth == num - 1) { ^ /* DISABLES CODE */ ( ) numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code] npy_intp k; ^~~~~~~~~~~ numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead else if (0 && kth == num - 1) { ^ /* DISABLES CODE */ ( ) numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code] npy_intp k; ^~~~~~~~~~~ numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead else if (0 && kth == num - 1) { ^ /* DISABLES CODE */ ( ) numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code] npy_intp k; ^~~~~~~~~~~ numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead else if (0 && kth == num - 1) { ^ /* DISABLES CODE */ ( ) numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code] npy_intp k; ^~~~~~~~~~~ numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead else if (0 && kth == num - 1) { ^ /* DISABLES CODE */ ( ) numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code] npy_intp k; ^~~~~~~~~~~ numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead else if (0 && kth == num - 1) { ^ /* DISABLES CODE */ ( ) numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code] npy_intp k; ^~~~~~~~~~~ numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead else if (0 && kth == num - 1) { ^ /* DISABLES CODE */ ( ) numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code] npy_intp k; ^~~~~~~~~~~ numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead else if (0 && kth == num - 1) { ^ /* DISABLES CODE */ ( ) 22 warnings generated. ar: adding 5 object files to build/temp.macosx-10.15-x86_64-3.9/libnpysort.a ranlib:@ build/temp.macosx-10.15-x86_64-3.9/libnpysort.a running build_ext customize UnixCCompiler customize UnixCCompiler using build_ext building 'numpy.core._dummy' extension compiling C sources C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers compile options: '-DNPY_INTERNAL_BUILD=1 -DHAVE_NPY_CONFIG_H=1 -D_FILE_OFFSET_BITS=64 -D_LARGEFILE_SOURCE=1 -D_LARGEFILE64_SOURCE=1 -Inumpy/core/include -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy -Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/[email protected]/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -c' clang: numpy/core/src/dummymodule.c clang -bundle -undefined dynamic_lookup -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/dummymodule.o -L/usr/local/lib -L/usr/local/opt/[email protected]/lib -L/usr/local/opt/sqlite/lib -Lbuild/temp.macosx-10.15-x86_64-3.9 -o build/lib.macosx-10.15-x86_64-3.9/numpy/core/_dummy.cpython-39-darwin.so building 'numpy.core._multiarray_tests' extension compiling C sources C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers creating build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray creating build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/common compile options: '-DNPY_INTERNAL_BUILD=1 -DHAVE_NPY_CONFIG_H=1 -D_FILE_OFFSET_BITS=64 -D_LARGEFILE_SOURCE=1 -D_LARGEFILE64_SOURCE=1 -Inumpy/core/include -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy -Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/[email protected]/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -c' clang: build/src.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/_multiarray_tests.c clang: numpy/core/src/common/mem_overlap.c clang -bundle -undefined dynamic_lookup -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/_multiarray_tests.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/common/mem_overlap.o -L/usr/local/lib -L/usr/local/opt/[email protected]/lib -L/usr/local/opt/sqlite/lib -Lbuild/temp.macosx-10.15-x86_64-3.9 -lnpymath -o build/lib.macosx-10.15-x86_64-3.9/numpy/core/_multiarray_tests.cpython-39-darwin.so building 'numpy.core._multiarray_umath' extension compiling C sources C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers creating build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray creating build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/umath creating build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath creating build/temp.macosx-10.15-x86_64-3.9/private creating build/temp.macosx-10.15-x86_64-3.9/private/var creating build/temp.macosx-10.15-x86_64-3.9/private/var/folders creating build/temp.macosx-10.15-x86_64-3.9/private/var/folders/fz creating build/temp.macosx-10.15-x86_64-3.9/private/var/folders/fz/0j719tys48x7jlnjnwc69smr0000gn creating build/temp.macosx-10.15-x86_64-3.9/private/var/folders/fz/0j719tys48x7jlnjnwc69smr0000gn/T creating build/temp.macosx-10.15-x86_64-3.9/private/var/folders/fz/0j719tys48x7jlnjnwc69smr0000gn/T/pip-install-ufzck51l creating build/temp.macosx-10.15-x86_64-3.9/private/var/folders/fz/0j719tys48x7jlnjnwc69smr0000gn/T/pip-install-ufzck51l/numpy_b0e8a3953a1d4b46801f12bcea55536e creating build/temp.macosx-10.15-x86_64-3.9/private/var/folders/fz/0j719tys48x7jlnjnwc69smr0000gn/T/pip-install-ufzck51l/numpy_b0e8a3953a1d4b46801f12bcea55536e/numpy creating build/temp.macosx-10.15-x86_64-3.9/private/var/folders/fz/0j719tys48x7jlnjnwc69smr0000gn/T/pip-install-ufzck51l/numpy_b0e8a3953a1d4b46801f12bcea55536e/numpy/_build_utils creating build/temp.macosx-10.15-x86_64-3.9/private/var/folders/fz/0j719tys48x7jlnjnwc69smr0000gn/T/pip-install-ufzck51l/numpy_b0e8a3953a1d4b46801f12bcea55536e/numpy/_build_utils/src compile options: '-DNPY_INTERNAL_BUILD=1 -DHAVE_NPY_CONFIG_H=1 -D_FILE_OFFSET_BITS=64 -D_LARGEFILE_SOURCE=1 -D_LARGEFILE64_SOURCE=1 -DNO_ATLAS_INFO=3 -DHAVE_CBLAS -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Inumpy/core/include -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy -Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/[email protected]/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -c' extra options: '-msse3 -I/System/Library/Frameworks/vecLib.framework/Headers' clang: numpy/core/src/multiarray/alloc.c clang: numpy/core/src/multiarray/calculation.cclang: numpy/core/src/multiarray/array_assign_scalar.c clang: numpy/core/src/multiarray/convert.c clang: numpy/core/src/multiarray/ctors.c clang: numpy/core/src/multiarray/datetime_busday.c clang: numpy/core/src/multiarray/dragon4.cclang: numpy/core/src/multiarray/flagsobject.c numpy/core/src/multiarray/ctors.c:2261:36: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations] if (!(PyUString_Check(name) && PyUString_GET_SIZE(name) == 0)) { ^ numpy/core/include/numpy/npy_3kcompat.h:110:28: note: expanded from macro 'PyUString_GET_SIZE' #define PyUString_GET_SIZE PyUnicode_GET_SIZE ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:261:7: note: expanded from macro 'PyUnicode_GET_SIZE' PyUnicode_WSTR_LENGTH(op) : \ ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH' #define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here Py_DEPRECATED(3.3) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED' #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__)) ^ numpy/core/src/multiarray/ctors.c:2261:36: warning: 'PyUnicode_AsUnicode' is deprecated [-Wdeprecated-declarations] if (!(PyUString_Check(name) && PyUString_GET_SIZE(name) == 0)) { ^ numpy/core/include/numpy/npy_3kcompat.h:110:28: note: expanded from macro 'PyUString_GET_SIZE' #define PyUString_GET_SIZE PyUnicode_GET_SIZE ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:262:14: note: expanded from macro 'PyUnicode_GET_SIZE' ((void)PyUnicode_AsUnicode(_PyObject_CAST(op)),\ ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:580:1: note: 'PyUnicode_AsUnicode' has been explicitly marked deprecated here Py_DEPRECATED(3.3) PyAPI_FUNC(Py_UNICODE *) PyUnicode_AsUnicode( ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED' #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__)) ^ numpy/core/src/multiarray/ctors.c:2261:36: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations] if (!(PyUString_Check(name) && PyUString_GET_SIZE(name) == 0)) { ^ numpy/core/include/numpy/npy_3kcompat.h:110:28: note: expanded from macro 'PyUString_GET_SIZE' #define PyUString_GET_SIZE PyUnicode_GET_SIZE ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:264:8: note: expanded from macro 'PyUnicode_GET_SIZE' PyUnicode_WSTR_LENGTH(op))) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH' #define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here Py_DEPRECATED(3.3) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED' #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__)) ^ clang: numpy/core/src/multiarray/arrayobject.c clang: numpy/core/src/multiarray/array_assign_array.c clang: numpy/core/src/multiarray/convert_datatype.c clang: numpy/core/src/multiarray/getset.c clang: numpy/core/src/multiarray/datetime_busdaycal.c clang: numpy/core/src/multiarray/buffer.c clang: numpy/core/src/multiarray/compiled_base.c clang: numpy/core/src/multiarray/hashdescr.c clang: numpy/core/src/multiarray/descriptor.c numpy/core/src/multiarray/descriptor.c:453:13: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations] if (PyUString_GET_SIZE(name) == 0) { ^ numpy/core/include/numpy/npy_3kcompat.h:110:28: note: expanded from macro 'PyUString_GET_SIZE' #define PyUString_GET_SIZE PyUnicode_GET_SIZE ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:261:7: note: expanded from macro 'PyUnicode_GET_SIZE' PyUnicode_WSTR_LENGTH(op) : \ ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH' #define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here Py_DEPRECATED(3.3) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED' #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__)) ^ numpy/core/src/multiarray/descriptor.c:453:13: warning: 'PyUnicode_AsUnicode' is deprecated [-Wdeprecated-declarations] if (PyUString_GET_SIZE(name) == 0) { ^ numpy/core/include/numpy/npy_3kcompat.h:110:28: note: expanded from macro 'PyUString_GET_SIZE' #define PyUString_GET_SIZE PyUnicode_GET_SIZE ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:262:14: note: expanded from macro 'PyUnicode_GET_SIZE' ((void)PyUnicode_AsUnicode(_PyObject_CAST(op)),\ ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:580:1: note: 'PyUnicode_AsUnicode' has been explicitly marked deprecated here Py_DEPRECATED(3.3) PyAPI_FUNC(Py_UNICODE *) PyUnicode_AsUnicode( ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED' #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__)) ^ numpy/core/src/multiarray/descriptor.c:453:13: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations] if (PyUString_GET_SIZE(name) == 0) { ^ numpy/core/include/numpy/npy_3kcompat.h:110:28: note: expanded from macro 'PyUString_GET_SIZE' #define PyUString_GET_SIZE PyUnicode_GET_SIZE ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:264:8: note: expanded from macro 'PyUnicode_GET_SIZE' PyUnicode_WSTR_LENGTH(op))) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH' #define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here Py_DEPRECATED(3.3) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED' #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__)) ^ numpy/core/src/multiarray/descriptor.c:460:48: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations] else if (PyUString_Check(title) && PyUString_GET_SIZE(title) > 0) { ^ numpy/core/include/numpy/npy_3kcompat.h:110:28: note: expanded from macro 'PyUString_GET_SIZE' #define PyUString_GET_SIZE PyUnicode_GET_SIZE ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:261:7: note: expanded from macro 'PyUnicode_GET_SIZE' PyUnicode_WSTR_LENGTH(op) : \ ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH' #define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here Py_DEPRECATED(3.3) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED' #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__)) ^ numpy/core/src/multiarray/descriptor.c:460:48: warning: 'PyUnicode_AsUnicode' is deprecated [-Wdeprecated-declarations] else if (PyUString_Check(title) && PyUString_GET_SIZE(title) > 0) { ^ numpy/core/include/numpy/npy_3kcompat.h:110:28: note: expanded from macro 'PyUString_GET_SIZE' #define PyUString_GET_SIZE PyUnicode_GET_SIZE ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:262:14: note: expanded from macro 'PyUnicode_GET_SIZE' ((void)PyUnicode_AsUnicode(_PyObject_CAST(op)),\ ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:580:1: note: 'PyUnicode_AsUnicode' has been explicitly marked deprecated here Py_DEPRECATED(3.3) PyAPI_FUNC(Py_UNICODE *) PyUnicode_AsUnicode( ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED' #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__)) ^ numpy/core/src/multiarray/descriptor.c:460:48: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations] else if (PyUString_Check(title) && PyUString_GET_SIZE(title) > 0) { ^ numpy/core/include/numpy/npy_3kcompat.h:110:28: note: expanded from macro 'PyUString_GET_SIZE' #define PyUString_GET_SIZE PyUnicode_GET_SIZE ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:264:8: note: expanded from macro 'PyUnicode_GET_SIZE' PyUnicode_WSTR_LENGTH(op))) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH' #define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here Py_DEPRECATED(3.3) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED' #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__)) ^ clang: numpy/core/src/multiarray/conversion_utils.c clang: numpy/core/src/multiarray/item_selection.c clang: numpy/core/src/multiarray/dtype_transfer.c clang: numpy/core/src/multiarray/mapping.c clang: build/src.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/arraytypes.c clang: build/src.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/nditer_templ.c 3 warnings generated. clang: numpy/core/src/multiarray/datetime.c numpy/core/src/multiarray/arraytypes.c.src:477:11: warning: 'PyUnicode_AsUnicode' is deprecated [-Wdeprecated-declarations] ptr = PyUnicode_AS_UNICODE(temp); ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:279:7: note: expanded from macro 'PyUnicode_AS_UNICODE' PyUnicode_AsUnicode(_PyObject_CAST(op))) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:580:1: note: 'PyUnicode_AsUnicode' has been explicitly marked deprecated here Py_DEPRECATED(3.3) PyAPI_FUNC(Py_UNICODE *) PyUnicode_AsUnicode( ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED' #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__)) ^ numpy/core/src/multiarray/arraytypes.c.src:482:15: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations] datalen = PyUnicode_GET_DATA_SIZE(temp); ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:268:6: note: expanded from macro 'PyUnicode_GET_DATA_SIZE' (PyUnicode_GET_SIZE(op) * Py_UNICODE_SIZE) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:261:7: note: expanded from macro 'PyUnicode_GET_SIZE' PyUnicode_WSTR_LENGTH(op) : \ ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH' #define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here Py_DEPRECATED(3.3) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED' #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__)) ^ numpy/core/src/multiarray/arraytypes.c.src:482:15: warning: 'PyUnicode_AsUnicode' is deprecated [-Wdeprecated-declarations] datalen = PyUnicode_GET_DATA_SIZE(temp); ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:268:6: note: expanded from macro 'PyUnicode_GET_DATA_SIZE' (PyUnicode_GET_SIZE(op) * Py_UNICODE_SIZE) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:262:14: note: expanded from macro 'PyUnicode_GET_SIZE' ((void)PyUnicode_AsUnicode(_PyObject_CAST(op)),\ ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:580:1: note: 'PyUnicode_AsUnicode' has been explicitly marked deprecated here Py_DEPRECATED(3.3) PyAPI_FUNC(Py_UNICODE *) PyUnicode_AsUnicode( ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED' #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__)) ^ numpy/core/src/multiarray/arraytypes.c.src:482:15: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations] datalen = PyUnicode_GET_DATA_SIZE(temp); ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:268:6: note: expanded from macro 'PyUnicode_GET_DATA_SIZE' (PyUnicode_GET_SIZE(op) * Py_UNICODE_SIZE) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:264:8: note: expanded from macro 'PyUnicode_GET_SIZE' PyUnicode_WSTR_LENGTH(op))) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH' #define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here Py_DEPRECATED(3.3) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED' #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__)) ^ clang: numpy/core/src/multiarray/common.c numpy/core/src/multiarray/common.c:187:28: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations] itemsize = PyUnicode_GET_DATA_SIZE(temp); ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:268:6: note: expanded from macro 'PyUnicode_GET_DATA_SIZE' (PyUnicode_GET_SIZE(op) * Py_UNICODE_SIZE) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:261:7: note: expanded from macro 'PyUnicode_GET_SIZE' PyUnicode_WSTR_LENGTH(op) : \ ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH' #define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here Py_DEPRECATED(3.3) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED' #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__)) ^ numpy/core/src/multiarray/common.c:187:28: warning: 'PyUnicode_AsUnicode' is deprecated [-Wdeprecated-declarations] itemsize = PyUnicode_GET_DATA_SIZE(temp); ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:268:6: note: expanded from macro 'PyUnicode_GET_DATA_SIZE' (PyUnicode_GET_SIZE(op) * Py_UNICODE_SIZE) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:262:14: note: expanded from macro 'PyUnicode_GET_SIZE' ((void)PyUnicode_AsUnicode(_PyObject_CAST(op)),\ ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:580:1: note: 'PyUnicode_AsUnicode' has been explicitly marked deprecated here Py_DEPRECATED(3.3) PyAPI_FUNC(Py_UNICODE *) PyUnicode_AsUnicode( ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED' #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__)) ^ numpy/core/src/multiarray/common.c:187:28: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations] itemsize = PyUnicode_GET_DATA_SIZE(temp); ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:268:6: note: expanded from macro 'PyUnicode_GET_DATA_SIZE' (PyUnicode_GET_SIZE(op) * Py_UNICODE_SIZE) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:264:8: note: expanded from macro 'PyUnicode_GET_SIZE' PyUnicode_WSTR_LENGTH(op))) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH' #define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here Py_DEPRECATED(3.3) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED' #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__)) ^ numpy/core/src/multiarray/common.c:239:28: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations] itemsize = PyUnicode_GET_DATA_SIZE(temp); ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:268:6: note: expanded from macro 'PyUnicode_GET_DATA_SIZE' (PyUnicode_GET_SIZE(op) * Py_UNICODE_SIZE) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:261:7: note: expanded from macro 'PyUnicode_GET_SIZE' PyUnicode_WSTR_LENGTH(op) : \ ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH' #define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here Py_DEPRECATED(3.3) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED' #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__)) ^ numpy/core/src/multiarray/common.c:239:28: warning: 'PyUnicode_AsUnicode' is deprecated [-Wdeprecated-declarations] itemsize = PyUnicode_GET_DATA_SIZE(temp); ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:268:6: note: expanded from macro 'PyUnicode_GET_DATA_SIZE' (PyUnicode_GET_SIZE(op) * Py_UNICODE_SIZE) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:262:14: note: expanded from macro 'PyUnicode_GET_SIZE' ((void)PyUnicode_AsUnicode(_PyObject_CAST(op)),\ ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:580:1: note: 'PyUnicode_AsUnicode' has been explicitly marked deprecated here Py_DEPRECATED(3.3) PyAPI_FUNC(Py_UNICODE *) PyUnicode_AsUnicode( ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED' #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__)) ^ numpy/core/src/multiarray/common.c:239:28: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations] itemsize = PyUnicode_GET_DATA_SIZE(temp); ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:268:6: note: expanded from macro 'PyUnicode_GET_DATA_SIZE' (PyUnicode_GET_SIZE(op) * Py_UNICODE_SIZE) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:264:8: note: expanded from macro 'PyUnicode_GET_SIZE' PyUnicode_WSTR_LENGTH(op))) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH' #define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here Py_DEPRECATED(3.3) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED' #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__)) ^ numpy/core/src/multiarray/common.c:282:24: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations] int itemsize = PyUnicode_GET_DATA_SIZE(obj); ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:268:6: note: expanded from macro 'PyUnicode_GET_DATA_SIZE' (PyUnicode_GET_SIZE(op) * Py_UNICODE_SIZE) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:261:7: note: expanded from macro 'PyUnicode_GET_SIZE' PyUnicode_WSTR_LENGTH(op) : \ ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH' #define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here Py_DEPRECATED(3.3) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED' #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__)) ^ numpy/core/src/multiarray/common.c:282:24: warning: 'PyUnicode_AsUnicode' is deprecated [-Wdeprecated-declarations] int itemsize = PyUnicode_GET_DATA_SIZE(obj); ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:268:6: note: expanded from macro 'PyUnicode_GET_DATA_SIZE' (PyUnicode_GET_SIZE(op) * Py_UNICODE_SIZE) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:262:14: note: expanded from macro 'PyUnicode_GET_SIZE' ((void)PyUnicode_AsUnicode(_PyObject_CAST(op)),\ ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:580:1: note: 'PyUnicode_AsUnicode' has been explicitly marked deprecated here Py_DEPRECATED(3.3) PyAPI_FUNC(Py_UNICODE *) PyUnicode_AsUnicode( ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED' #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__)) ^ numpy/core/src/multiarray/common.c:282:24: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations] int itemsize = PyUnicode_GET_DATA_SIZE(obj); ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:268:6: note: expanded from macro 'PyUnicode_GET_DATA_SIZE' (PyUnicode_GET_SIZE(op) * Py_UNICODE_SIZE) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:264:8: note: expanded from macro 'PyUnicode_GET_SIZE' PyUnicode_WSTR_LENGTH(op))) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH' #define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here Py_DEPRECATED(3.3) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED' #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__)) ^ 6 warnings generated. clang: numpy/core/src/multiarray/nditer_pywrap.c 9 warnings generated. clang: numpy/core/src/multiarray/sequence.c clang: numpy/core/src/multiarray/shape.c clang: build/src.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/einsum.c clang: numpy/core/src/multiarray/methods.c clang: numpy/core/src/multiarray/iterators.c clang: numpy/core/src/multiarray/datetime_strings.c clang: numpy/core/src/multiarray/number.c clang: numpy/core/src/multiarray/scalarapi.c clang: build/src.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/scalartypes.c numpy/core/src/multiarray/scalarapi.c:74:28: warning: 'PyUnicode_AsUnicode' is deprecated [-Wdeprecated-declarations] return (void *)PyUnicode_AS_DATA(scalar); ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:283:21: note: expanded from macro 'PyUnicode_AS_DATA' ((const char *)(PyUnicode_AS_UNICODE(op))) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:279:7: note: expanded from macro 'PyUnicode_AS_UNICODE' PyUnicode_AsUnicode(_PyObject_CAST(op))) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:580:1: note: 'PyUnicode_AsUnicode' has been explicitly marked deprecated here Py_DEPRECATED(3.3) PyAPI_FUNC(Py_UNICODE *) PyUnicode_AsUnicode( ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED' #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__)) ^ numpy/core/src/multiarray/scalarapi.c:135:28: warning: 'PyUnicode_AsUnicode' is deprecated [-Wdeprecated-declarations] return (void *)PyUnicode_AS_DATA(scalar); ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:283:21: note: expanded from macro 'PyUnicode_AS_DATA' ((const char *)(PyUnicode_AS_UNICODE(op))) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:279:7: note: expanded from macro 'PyUnicode_AS_UNICODE' PyUnicode_AsUnicode(_PyObject_CAST(op))) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:580:1: note: 'PyUnicode_AsUnicode' has been explicitly marked deprecated here Py_DEPRECATED(3.3) PyAPI_FUNC(Py_UNICODE *) PyUnicode_AsUnicode( ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED' #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__)) ^ numpy/core/src/multiarray/scalarapi.c:568:29: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations] descr->elsize = PyUnicode_GET_DATA_SIZE(sc); ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:268:6: note: expanded from macro 'PyUnicode_GET_DATA_SIZE' (PyUnicode_GET_SIZE(op) * Py_UNICODE_SIZE) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:261:7: note: expanded from macro 'PyUnicode_GET_SIZE' PyUnicode_WSTR_LENGTH(op) : \ ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH' #define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here Py_DEPRECATED(3.3) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED' #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__)) ^ numpy/core/src/multiarray/scalarapi.c:568:29: warning: 'PyUnicode_AsUnicode' is deprecated [-Wdeprecated-declarations] descr->elsize = PyUnicode_GET_DATA_SIZE(sc); ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:268:6: note: expanded from macro 'PyUnicode_GET_DATA_SIZE' (PyUnicode_GET_SIZE(op) * Py_UNICODE_SIZE) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:262:14: note: expanded from macro 'PyUnicode_GET_SIZE' ((void)PyUnicode_AsUnicode(_PyObject_CAST(op)),\ ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:580:1: note: 'PyUnicode_AsUnicode' has been explicitly marked deprecated here Py_DEPRECATED(3.3) PyAPI_FUNC(Py_UNICODE *) PyUnicode_AsUnicode( ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED' #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__)) ^ numpy/core/src/multiarray/scalarapi.c:568:29: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations] descr->elsize = PyUnicode_GET_DATA_SIZE(sc); ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:268:6: note: expanded from macro 'PyUnicode_GET_DATA_SIZE' (PyUnicode_GET_SIZE(op) * Py_UNICODE_SIZE) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:264:8: note: expanded from macro 'PyUnicode_GET_SIZE' PyUnicode_WSTR_LENGTH(op))) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH' #define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here Py_DEPRECATED(3.3) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED' #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__)) ^ numpy/core/src/multiarray/scalartypes.c.src:475:17: warning: 'PyUnicode_AsUnicode' is deprecated [-Wdeprecated-declarations] ip = dptr = PyUnicode_AS_UNICODE(self); ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:279:7: note: expanded from macro 'PyUnicode_AS_UNICODE' PyUnicode_AsUnicode(_PyObject_CAST(op))) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:580:1: note: 'PyUnicode_AsUnicode' has been explicitly marked deprecated here Py_DEPRECATED(3.3) PyAPI_FUNC(Py_UNICODE *) PyUnicode_AsUnicode( ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED' #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__)) ^ numpy/core/src/multiarray/scalartypes.c.src:476:11: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations] len = PyUnicode_GET_SIZE(self); ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:261:7: note: expanded from macro 'PyUnicode_GET_SIZE' PyUnicode_WSTR_LENGTH(op) : \ ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH' #define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here Py_DEPRECATED(3.3) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED' #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__)) ^ numpy/core/src/multiarray/scalartypes.c.src:476:11: warning: 'PyUnicode_AsUnicode' is deprecated [-Wdeprecated-declarations] len = PyUnicode_GET_SIZE(self); ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:262:14: note: expanded from macro 'PyUnicode_GET_SIZE' ((void)PyUnicode_AsUnicode(_PyObject_CAST(op)),\ ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:580:1: note: 'PyUnicode_AsUnicode' has been explicitly marked deprecated here Py_DEPRECATED(3.3) PyAPI_FUNC(Py_UNICODE *) PyUnicode_AsUnicode( ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED' #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__)) ^ numpy/core/src/multiarray/scalartypes.c.src:476:11: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations] len = PyUnicode_GET_SIZE(self); ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:264:8: note: expanded from macro 'PyUnicode_GET_SIZE' PyUnicode_WSTR_LENGTH(op))) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH' #define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here Py_DEPRECATED(3.3) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED' #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__)) ^ numpy/core/src/multiarray/scalartypes.c.src:481:11: warning: 'PyUnicode_FromUnicode' is deprecated [-Wdeprecated-declarations] new = PyUnicode_FromUnicode(ip, len); ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:551:1: note: 'PyUnicode_FromUnicode' has been explicitly marked deprecated here Py_DEPRECATED(3.3) PyAPI_FUNC(PyObject*) PyUnicode_FromUnicode( ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED' #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__)) ^ numpy/core/src/multiarray/scalartypes.c.src:475:17: warning: 'PyUnicode_AsUnicode' is deprecated [-Wdeprecated-declarations] ip = dptr = PyUnicode_AS_UNICODE(self); ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:279:7: note: expanded from macro 'PyUnicode_AS_UNICODE' PyUnicode_AsUnicode(_PyObject_CAST(op))) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:580:1: note: 'PyUnicode_AsUnicode' has been explicitly marked deprecated here Py_DEPRECATED(3.3) PyAPI_FUNC(Py_UNICODE *) PyUnicode_AsUnicode( ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED' #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__)) ^ numpy/core/src/multiarray/scalartypes.c.src:476:11: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations] len = PyUnicode_GET_SIZE(self); ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:261:7: note: expanded from macro 'PyUnicode_GET_SIZE' PyUnicode_WSTR_LENGTH(op) : \ ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH' #define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here Py_DEPRECATED(3.3) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED' #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__)) ^ numpy/core/src/multiarray/scalartypes.c.src:476:11: warning: 'PyUnicode_AsUnicode' is deprecated [-Wdeprecated-declarations] len = PyUnicode_GET_SIZE(self); ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:262:14: note: expanded from macro 'PyUnicode_GET_SIZE' ((void)PyUnicode_AsUnicode(_PyObject_CAST(op)),\ ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:580:1: note: 'PyUnicode_AsUnicode' has been explicitly marked deprecated here Py_DEPRECATED(3.3) PyAPI_FUNC(Py_UNICODE *) PyUnicode_AsUnicode( ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED' #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__)) ^ numpy/core/src/multiarray/scalartypes.c.src:476:11: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations] len = PyUnicode_GET_SIZE(self); ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:264:8: note: expanded from macro 'PyUnicode_GET_SIZE' PyUnicode_WSTR_LENGTH(op))) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH' #define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here Py_DEPRECATED(3.3) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED' #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__)) ^ numpy/core/src/multiarray/scalartypes.c.src:481:11: warning: 'PyUnicode_FromUnicode' is deprecated [-Wdeprecated-declarations] new = PyUnicode_FromUnicode(ip, len); ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:551:1: note: 'PyUnicode_FromUnicode' has been explicitly marked deprecated here Py_DEPRECATED(3.3) PyAPI_FUNC(PyObject*) PyUnicode_FromUnicode( ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED' #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__)) ^ numpy/core/src/multiarray/scalartypes.c.src:1849:18: warning: 'PyUnicode_AsUnicode' is deprecated [-Wdeprecated-declarations] buffer = PyUnicode_AS_DATA(self); ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:283:21: note: expanded from macro 'PyUnicode_AS_DATA' ((const char *)(PyUnicode_AS_UNICODE(op))) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:279:7: note: expanded from macro 'PyUnicode_AS_UNICODE' PyUnicode_AsUnicode(_PyObject_CAST(op))) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:580:1: note: 'PyUnicode_AsUnicode' has been explicitly marked deprecated here Py_DEPRECATED(3.3) PyAPI_FUNC(Py_UNICODE *) PyUnicode_AsUnicode( ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED' #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__)) ^ numpy/core/src/multiarray/scalartypes.c.src:1850:18: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations] buflen = PyUnicode_GET_DATA_SIZE(self); ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:268:6: note: expanded from macro 'PyUnicode_GET_DATA_SIZE' (PyUnicode_GET_SIZE(op) * Py_UNICODE_SIZE) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:261:7: note: expanded from macro 'PyUnicode_GET_SIZE' PyUnicode_WSTR_LENGTH(op) : \ ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH' #define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here Py_DEPRECATED(3.3) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED' #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__)) ^ numpy/core/src/multiarray/scalartypes.c.src:1850:18: warning: 'PyUnicode_AsUnicode' is deprecated [-Wdeprecated-declarations] buflen = PyUnicode_GET_DATA_SIZE(self); ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:268:6: note: expanded from macro 'PyUnicode_GET_DATA_SIZE' (PyUnicode_GET_SIZE(op) * Py_UNICODE_SIZE) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:262:14: note: expanded from macro 'PyUnicode_GET_SIZE' ((void)PyUnicode_AsUnicode(_PyObject_CAST(op)),\ ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:580:1: note: 'PyUnicode_AsUnicode' has been explicitly marked deprecated here Py_DEPRECATED(3.3) PyAPI_FUNC(Py_UNICODE *) PyUnicode_AsUnicode( ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED' #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__)) ^ numpy/core/src/multiarray/scalartypes.c.src:1850:18: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations] buflen = PyUnicode_GET_DATA_SIZE(self); ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:268:6: note: expanded from macro 'PyUnicode_GET_DATA_SIZE' (PyUnicode_GET_SIZE(op) * Py_UNICODE_SIZE) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:264:8: note: expanded from macro 'PyUnicode_GET_SIZE' PyUnicode_WSTR_LENGTH(op))) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH' #define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here Py_DEPRECATED(3.3) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED' #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__)) ^ 5 warnings generated. clang: numpy/core/src/multiarray/typeinfo.c clang: numpy/core/src/multiarray/refcount.c clang: numpy/core/src/multiarray/usertypes.c clang: numpy/core/src/multiarray/multiarraymodule.c clang: build/src.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/lowlevel_strided_loops.c clang: numpy/core/src/multiarray/vdot.c clang: numpy/core/src/umath/umathmodule.c clang: build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/matmul.c clang: numpy/core/src/umath/reduction.c clang: build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/loops.c clang: numpy/core/src/multiarray/nditer_api.c 14 warnings generated. clang: numpy/core/src/multiarray/strfuncs.c numpy/core/src/umath/loops.c.src:655:18: warning: 'PyEval_CallObjectWithKeywords' is deprecated [-Wdeprecated-declarations] result = PyEval_CallObject(tocall, arglist); ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/ceval.h:24:5: note: expanded from macro 'PyEval_CallObject' PyEval_CallObjectWithKeywords(callable, arg, (PyObject *)NULL) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/ceval.h:17:1: note: 'PyEval_CallObjectWithKeywords' has been explicitly marked deprecated here Py_DEPRECATED(3.9) PyAPI_FUNC(PyObject *) PyEval_CallObjectWithKeywords( ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED' #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__)) ^ numpy/core/src/multiarray/strfuncs.c:178:13: warning: 'PyEval_CallObjectWithKeywords' is deprecated [-Wdeprecated-declarations] s = PyEval_CallObject(PyArray_ReprFunction, arglist); ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/ceval.h:24:5: note: expanded from macro 'PyEval_CallObject' PyEval_CallObjectWithKeywords(callable, arg, (PyObject *)NULL) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/ceval.h:17:1: note: 'PyEval_CallObjectWithKeywords' has been explicitly marked deprecated here Py_DEPRECATED(3.9) PyAPI_FUNC(PyObject *) PyEval_CallObjectWithKeywords( ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED' #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__)) ^ numpy/core/src/multiarray/strfuncs.c:195:13: warning: 'PyEval_CallObjectWithKeywords' is deprecated [-Wdeprecated-declarations] s = PyEval_CallObject(PyArray_StrFunction, arglist); ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/ceval.h:24:5: note: expanded from macro 'PyEval_CallObject' PyEval_CallObjectWithKeywords(callable, arg, (PyObject *)NULL) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/ceval.h:17:1: note: 'PyEval_CallObjectWithKeywords' has been explicitly marked deprecated here Py_DEPRECATED(3.9) PyAPI_FUNC(PyObject *) PyEval_CallObjectWithKeywords( ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED' #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__)) ^ 2 warnings generated. clang: numpy/core/src/multiarray/temp_elide.c clang: numpy/core/src/umath/cpuid.c clang: build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/scalarmath.c clang: numpy/core/src/umath/ufunc_object.c numpy/core/src/umath/scalarmath.c.src:1449:1: warning: unused function 'byte_long' [-Wunused-function] byte_long(PyObject *obj) ^ numpy/core/src/umath/scalarmath.c.src:1449:1: warning: unused function 'ubyte_long' [-Wunused-function] ubyte_long(PyObject *obj) ^ numpy/core/src/umath/scalarmath.c.src:1449:1: warning: unused function 'short_long' [-Wunused-function] short_long(PyObject *obj) ^ numpy/core/src/umath/scalarmath.c.src:1449:1: warning: unused function 'ushort_long' [-Wunused-function] ushort_long(PyObject *obj) ^ numpy/core/src/umath/scalarmath.c.src:1449:1: warning: unused function 'int_long' [-Wunused-function] int_long(PyObject *obj) ^ numpy/core/src/umath/scalarmath.c.src:1449:1: warning: unused function 'uint_long' [-Wunused-function] uint_long(PyObject *obj) ^ numpy/core/src/umath/scalarmath.c.src:1449:1: warning: unused function 'long_long' [-Wunused-function] long_long(PyObject *obj) ^ numpy/core/src/umath/scalarmath.c.src:1449:1: warning: unused function 'ulong_long' [-Wunused-function] ulong_long(PyObject *obj) ^ numpy/core/src/umath/scalarmath.c.src:1449:1: warning: unused function 'longlong_long' [-Wunused-function] longlong_long(PyObject *obj) ^ numpy/core/src/umath/scalarmath.c.src:1449:1: warning: unused function 'ulonglong_long' [-Wunused-function] ulonglong_long(PyObject *obj) ^ numpy/core/src/umath/scalarmath.c.src:1449:1: warning: unused function 'half_long' [-Wunused-function] half_long(PyObject *obj) ^ numpy/core/src/umath/scalarmath.c.src:1449:1: warning: unused function 'float_long' [-Wunused-function] float_long(PyObject *obj) ^ numpy/core/src/umath/scalarmath.c.src:1449:1: warning: unused function 'double_long' [-Wunused-function] double_long(PyObject *obj) ^ numpy/core/src/umath/scalarmath.c.src:1449:1: warning: unused function 'longdouble_long' [-Wunused-function] longdouble_long(PyObject *obj) ^ numpy/core/src/umath/scalarmath.c.src:1449:1: warning: unused function 'cfloat_long' [-Wunused-function] cfloat_long(PyObject *obj) ^ numpy/core/src/umath/scalarmath.c.src:1449:1: warning: unused function 'cdouble_long' [-Wunused-function] cdouble_long(PyObject *obj) ^ numpy/core/src/umath/scalarmath.c.src:1449:1: warning: unused function 'clongdouble_long' [-Wunused-function] clongdouble_long(PyObject *obj) ^ clang: numpy/core/src/multiarray/nditer_constr.c numpy/core/src/umath/ufunc_object.c:657:19: warning: comparison of integers of different signs: 'int' and 'size_t' (aka 'unsigned long') [-Wsign-compare] for (i = 0; i < len; i++) { ~ ^ ~~~ clang: numpy/core/src/umath/override.c clang: numpy/core/src/npymath/npy_math.c clang: build/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath/ieee754.c numpy/core/src/umath/loops.c.src:2527:22: warning: code will never be executed [-Wunreachable-code] npy_intp n = dimensions[0]; ^~~~~~~~~~ numpy/core/src/umath/loops.c.src:2526:29: note: silence by adding parentheses to mark code as explicitly dead if (IS_BINARY_REDUCE && 0) { ^ /* DISABLES CODE */ ( ) numpy/core/src/umath/loops.c.src:2527:22: warning: code will never be executed [-Wunreachable-code] npy_intp n = dimensions[0]; ^~~~~~~~~~ numpy/core/src/umath/loops.c.src:2526:29: note: silence by adding parentheses to mark code as explicitly dead if (IS_BINARY_REDUCE && 0) { ^ /* DISABLES CODE */ ( ) numpy/core/src/umath/loops.c.src:2527:22: warning: code will never be executed [-Wunreachable-code] npy_intp n = dimensions[0]; ^~~~~~~~~~ numpy/core/src/umath/loops.c.src:2526:29: note: silence by adding parentheses to mark code as explicitly dead if (IS_BINARY_REDUCE && 0) { ^ /* DISABLES CODE */ ( ) clang: build/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath/npy_math_complex.c numpy/core/src/npymath/npy_math_complex.c.src:48:33: warning: unused variable 'tiny' [-Wunused-const-variable] static const volatile npy_float tiny = 3.9443045e-31f; ^ numpy/core/src/npymath/npy_math_complex.c.src:67:25: warning: unused variable 'c_halff' [-Wunused-const-variable] static const npy_cfloat c_halff = {0.5F, 0.0}; ^ numpy/core/src/npymath/npy_math_complex.c.src:68:25: warning: unused variable 'c_if' [-Wunused-const-variable] static const npy_cfloat c_if = {0.0, 1.0F}; ^ numpy/core/src/npymath/npy_math_complex.c.src:69:25: warning: unused variable 'c_ihalff' [-Wunused-const-variable] static const npy_cfloat c_ihalff = {0.0, 0.5F}; ^ numpy/core/src/npymath/npy_math_complex.c.src:79:1: warning: unused function 'caddf' [-Wunused-function] caddf(npy_cfloat a, npy_cfloat b) ^ numpy/core/src/npymath/npy_math_complex.c.src:87:1: warning: unused function 'csubf' [-Wunused-function] csubf(npy_cfloat a, npy_cfloat b) ^ numpy/core/src/npymath/npy_math_complex.c.src:137:1: warning: unused function 'cnegf' [-Wunused-function] cnegf(npy_cfloat a) ^ numpy/core/src/npymath/npy_math_complex.c.src:144:1: warning: unused function 'cmulif' [-Wunused-function] cmulif(npy_cfloat a) ^ numpy/core/src/npymath/npy_math_complex.c.src:67:26: warning: unused variable 'c_half' [-Wunused-const-variable] static const npy_cdouble c_half = {0.5, 0.0}; ^ numpy/core/src/npymath/npy_math_complex.c.src:68:26: warning: unused variable 'c_i' [-Wunused-const-variable] static const npy_cdouble c_i = {0.0, 1.0}; ^ numpy/core/src/npymath/npy_math_complex.c.src:69:26: warning: unused variable 'c_ihalf' [-Wunused-const-variable] static const npy_cdouble c_ihalf = {0.0, 0.5}; ^ numpy/core/src/npymath/npy_math_complex.c.src:79:1: warning: unused function 'cadd' [-Wunused-function] cadd(npy_cdouble a, npy_cdouble b) ^ numpy/core/src/npymath/npy_math_complex.c.src:87:1: warning: unused function 'csub' [-Wunused-function] csub(npy_cdouble a, npy_cdouble b) ^ numpy/core/src/npymath/npy_math_complex.c.src:137:1: warning: unused function 'cneg' [-Wunused-function] cneg(npy_cdouble a) ^ numpy/core/src/npymath/npy_math_complex.c.src:144:1: warning: unused function 'cmuli' [-Wunused-function] cmuli(npy_cdouble a) ^ numpy/core/src/npymath/npy_math_complex.c.src:67:30: warning: unused variable 'c_halfl' [-Wunused-const-variable] static const npy_clongdouble c_halfl = {0.5L, 0.0}; ^ numpy/core/src/npymath/npy_math_complex.c.src:68:30: warning: unused variable 'c_il' [-Wunused-const-variable] static const npy_clongdouble c_il = {0.0, 1.0L}; ^ numpy/core/src/npymath/npy_math_complex.c.src:69:30: warning: unused variable 'c_ihalfl' [-Wunused-const-variable] static const npy_clongdouble c_ihalfl = {0.0, 0.5L}; ^ numpy/core/src/npymath/npy_math_complex.c.src:79:1: warning: unused function 'caddl' [-Wunused-function] caddl(npy_clongdouble a, npy_clongdouble b) ^ numpy/core/src/npymath/npy_math_complex.c.src:87:1: warning: unused function 'csubl' [-Wunused-function] csubl(npy_clongdouble a, npy_clongdouble b) ^ numpy/core/src/npymath/npy_math_complex.c.src:137:1: warning: unused function 'cnegl' [-Wunused-function] cnegl(npy_clongdouble a) ^ numpy/core/src/npymath/npy_math_complex.c.src:144:1: warning: unused function 'cmulil' [-Wunused-function] cmulil(npy_clongdouble a) ^ 22 warnings generated. clang: numpy/core/src/common/mem_overlap.c clang: numpy/core/src/npymath/halffloat.c clang: numpy/core/src/common/array_assign.c clang: numpy/core/src/common/ufunc_override.c clang: numpy/core/src/common/npy_longdouble.c clang: numpy/core/src/common/numpyos.c clang: numpy/core/src/common/ucsnarrow.c 1 warning generated. clang: numpy/core/src/umath/extobj.c numpy/core/src/common/ucsnarrow.c:139:34: warning: 'PyUnicode_FromUnicode' is deprecated [-Wdeprecated-declarations] ret = (PyUnicodeObject *)PyUnicode_FromUnicode((Py_UNICODE*)buf, ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:551:1: note: 'PyUnicode_FromUnicode' has been explicitly marked deprecated here Py_DEPRECATED(3.3) PyAPI_FUNC(PyObject*) PyUnicode_FromUnicode( ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED' #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__)) ^ 1 warning generated. clang: numpy/core/src/common/python_xerbla.c clang: numpy/core/src/common/cblasfuncs.c clang: /private/var/folders/fz/0j719tys48x7jlnjnwc69smr0000gn/T/pip-install-ufzck51l/numpy_b0e8a3953a1d4b46801f12bcea55536e/numpy/_build_utils/src/apple_sgemv_fix.c In file included from /private/var/folders/fz/0j719tys48x7jlnjnwc69smr0000gn/T/pip-install-ufzck51l/numpy_b0e8a3953a1d4b46801f12bcea55536e/numpy/_build_utils/src/apple_sgemv_fix.c:26: In file included from numpy/core/include/numpy/arrayobject.h:4: In file included from numpy/core/include/numpy/ndarrayobject.h:21: build/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy/__multiarray_api.h:1463:1: warning: unused function '_import_array' [-Wunused-function] _import_array(void) ^ 1 warning generated. 17 warnings generated. clang: numpy/core/src/umath/ufunc_type_resolution.c 4 warnings generated. 4 warnings generated. clang -bundle -undefined dynamic_lookup -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/alloc.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/arrayobject.o build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/arraytypes.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/array_assign_scalar.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/array_assign_array.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/buffer.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/calculation.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/compiled_base.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/common.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/convert.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/convert_datatype.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/conversion_utils.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/ctors.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/datetime.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/datetime_strings.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/datetime_busday.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/datetime_busdaycal.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/descriptor.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/dragon4.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/dtype_transfer.o build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/einsum.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/flagsobject.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/getset.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/hashdescr.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/item_selection.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/iterators.o build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/lowlevel_strided_loops.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/mapping.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/methods.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/multiarraymodule.o build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/nditer_templ.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/nditer_api.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/nditer_constr.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/nditer_pywrap.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/number.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/refcount.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/sequence.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/shape.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/scalarapi.o build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/scalartypes.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/strfuncs.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/temp_elide.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/typeinfo.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/usertypes.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/vdot.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/umath/umathmodule.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/umath/reduction.o build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/loops.o build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/matmul.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/umath/ufunc_object.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/umath/extobj.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/umath/cpuid.o build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/scalarmath.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/umath/ufunc_type_resolution.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/umath/override.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/npymath/npy_math.o build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath/ieee754.o build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath/npy_math_complex.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/npymath/halffloat.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/common/array_assign.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/common/mem_overlap.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/common/npy_longdouble.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/common/ucsnarrow.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/common/ufunc_override.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/common/numpyos.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/common/cblasfuncs.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/common/python_xerbla.o build/temp.macosx-10.15-x86_64-3.9/private/var/folders/fz/0j719tys48x7jlnjnwc69smr0000gn/T/pip-install-ufzck51l/numpy_b0e8a3953a1d4b46801f12bcea55536e/numpy/_build_utils/src/apple_sgemv_fix.o -L/usr/local/lib -L/usr/local/opt/[email protected]/lib -L/usr/local/opt/sqlite/lib -Lbuild/temp.macosx-10.15-x86_64-3.9 -lnpymath -lnpysort -o build/lib.macosx-10.15-x86_64-3.9/numpy/core/_multiarray_umath.cpython-39-darwin.so -Wl,-framework -Wl,Accelerate building 'numpy.core._umath_tests' extension compiling C sources C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers compile options: '-DNPY_INTERNAL_BUILD=1 -DHAVE_NPY_CONFIG_H=1 -D_FILE_OFFSET_BITS=64 -D_LARGEFILE_SOURCE=1 -D_LARGEFILE64_SOURCE=1 -Inumpy/core/include -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy -Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/[email protected]/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -c' clang: build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/_umath_tests.c clang -bundle -undefined dynamic_lookup -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/_umath_tests.o -L/usr/local/lib -L/usr/local/opt/[email protected]/lib -L/usr/local/opt/sqlite/lib -Lbuild/temp.macosx-10.15-x86_64-3.9 -o build/lib.macosx-10.15-x86_64-3.9/numpy/core/_umath_tests.cpython-39-darwin.so building 'numpy.core._rational_tests' extension compiling C sources C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers compile options: '-DNPY_INTERNAL_BUILD=1 -DHAVE_NPY_CONFIG_H=1 -D_FILE_OFFSET_BITS=64 -D_LARGEFILE_SOURCE=1 -D_LARGEFILE64_SOURCE=1 -Inumpy/core/include -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy -Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/[email protected]/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -c' clang: build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/_rational_tests.c clang -bundle -undefined dynamic_lookup -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/_rational_tests.o -L/usr/local/lib -L/usr/local/opt/[email protected]/lib -L/usr/local/opt/sqlite/lib -Lbuild/temp.macosx-10.15-x86_64-3.9 -o build/lib.macosx-10.15-x86_64-3.9/numpy/core/_rational_tests.cpython-39-darwin.so building 'numpy.core._struct_ufunc_tests' extension compiling C sources C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers compile options: '-DNPY_INTERNAL_BUILD=1 -DHAVE_NPY_CONFIG_H=1 -D_FILE_OFFSET_BITS=64 -D_LARGEFILE_SOURCE=1 -D_LARGEFILE64_SOURCE=1 -Inumpy/core/include -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy -Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/[email protected]/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -c' clang: build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/_struct_ufunc_tests.c clang -bundle -undefined dynamic_lookup -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/_struct_ufunc_tests.o -L/usr/local/lib -L/usr/local/opt/[email protected]/lib -L/usr/local/opt/sqlite/lib -Lbuild/temp.macosx-10.15-x86_64-3.9 -o build/lib.macosx-10.15-x86_64-3.9/numpy/core/_struct_ufunc_tests.cpython-39-darwin.so building 'numpy.core._operand_flag_tests' extension compiling C sources C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers compile options: '-DNPY_INTERNAL_BUILD=1 -DHAVE_NPY_CONFIG_H=1 -D_FILE_OFFSET_BITS=64 -D_LARGEFILE_SOURCE=1 -D_LARGEFILE64_SOURCE=1 -Inumpy/core/include -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy -Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/[email protected]/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -c' clang: build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/_operand_flag_tests.c clang -bundle -undefined dynamic_lookup -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/_operand_flag_tests.o -L/usr/local/lib -L/usr/local/opt/[email protected]/lib -L/usr/local/opt/sqlite/lib -Lbuild/temp.macosx-10.15-x86_64-3.9 -o build/lib.macosx-10.15-x86_64-3.9/numpy/core/_operand_flag_tests.cpython-39-darwin.so building 'numpy.fft.fftpack_lite' extension compiling C sources C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers creating build/temp.macosx-10.15-x86_64-3.9/numpy/fft compile options: '-Inumpy/core/include -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy -Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/[email protected]/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -c' clang: numpy/fft/fftpack_litemodule.c clang: numpy/fft/fftpack.c clang -bundle -undefined dynamic_lookup -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk build/temp.macosx-10.15-x86_64-3.9/numpy/fft/fftpack_litemodule.o build/temp.macosx-10.15-x86_64-3.9/numpy/fft/fftpack.o -L/usr/local/lib -L/usr/local/opt/[email protected]/lib -L/usr/local/opt/sqlite/lib -Lbuild/temp.macosx-10.15-x86_64-3.9 -o build/lib.macosx-10.15-x86_64-3.9/numpy/fft/fftpack_lite.cpython-39-darwin.so building 'numpy.linalg.lapack_lite' extension compiling C sources C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers creating build/temp.macosx-10.15-x86_64-3.9/numpy/linalg creating build/temp.macosx-10.15-x86_64-3.9/numpy/linalg/lapack_lite compile options: '-DNO_ATLAS_INFO=3 -DHAVE_CBLAS -Inumpy/core/include -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy -Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/[email protected]/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -c' extra options: '-msse3 -I/System/Library/Frameworks/vecLib.framework/Headers' clang: numpy/linalg/lapack_litemodule.c clang: numpy/linalg/lapack_lite/python_xerbla.c clang -bundle -undefined dynamic_lookup -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk build/temp.macosx-10.15-x86_64-3.9/numpy/linalg/lapack_litemodule.o build/temp.macosx-10.15-x86_64-3.9/numpy/linalg/lapack_lite/python_xerbla.o -L/usr/local/lib -L/usr/local/opt/[email protected]/lib -L/usr/local/opt/sqlite/lib -Lbuild/temp.macosx-10.15-x86_64-3.9 -o build/lib.macosx-10.15-x86_64-3.9/numpy/linalg/lapack_lite.cpython-39-darwin.so -Wl,-framework -Wl,Accelerate building 'numpy.linalg._umath_linalg' extension compiling C sources C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers creating build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/linalg compile options: '-DNO_ATLAS_INFO=3 -DHAVE_CBLAS -Inumpy/core/include -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy -Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/[email protected]/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -c' extra options: '-msse3 -I/System/Library/Frameworks/vecLib.framework/Headers' clang: build/src.macosx-10.15-x86_64-3.9/numpy/linalg/umath_linalg.c numpy/linalg/umath_linalg.c.src:735:32: warning: unknown warning group '-Wmaybe-uninitialized', ignored [-Wunknown-warning-option] #pragma GCC diagnostic ignored "-Wmaybe-uninitialized" ^ numpy/linalg/umath_linalg.c.src:541:1: warning: unused function 'dump_ufunc_object' [-Wunused-function] dump_ufunc_object(PyUFuncObject* ufunc) ^ numpy/linalg/umath_linalg.c.src:566:1: warning: unused function 'dump_linearize_data' [-Wunused-function] dump_linearize_data(const char* name, const LINEARIZE_DATA_t* params) ^ numpy/linalg/umath_linalg.c.src:602:1: warning: unused function 'dump_FLOAT_matrix' [-Wunused-function] dump_FLOAT_matrix(const char* name, ^ numpy/linalg/umath_linalg.c.src:602:1: warning: unused function 'dump_DOUBLE_matrix' [-Wunused-function] dump_DOUBLE_matrix(const char* name, ^ numpy/linalg/umath_linalg.c.src:602:1: warning: unused function 'dump_CFLOAT_matrix' [-Wunused-function] dump_CFLOAT_matrix(const char* name, ^ numpy/linalg/umath_linalg.c.src:602:1: warning: unused function 'dump_CDOUBLE_matrix' [-Wunused-function] dump_CDOUBLE_matrix(const char* name, ^ numpy/linalg/umath_linalg.c.src:865:1: warning: unused function 'zero_FLOAT_matrix' [-Wunused-function] zero_FLOAT_matrix(void *dst_in, const LINEARIZE_DATA_t* data) ^ numpy/linalg/umath_linalg.c.src:865:1: warning: unused function 'zero_DOUBLE_matrix' [-Wunused-function] zero_DOUBLE_matrix(void *dst_in, const LINEARIZE_DATA_t* data) ^ numpy/linalg/umath_linalg.c.src:865:1: warning: unused function 'zero_CFLOAT_matrix' [-Wunused-function] zero_CFLOAT_matrix(void *dst_in, const LINEARIZE_DATA_t* data) ^ numpy/linalg/umath_linalg.c.src:865:1: warning: unused function 'zero_CDOUBLE_matrix' [-Wunused-function] zero_CDOUBLE_matrix(void *dst_in, const LINEARIZE_DATA_t* data) ^ numpy/linalg/umath_linalg.c.src:1862:1: warning: unused function 'dump_geev_params' [-Wunused-function] dump_geev_params(const char *name, GEEV_PARAMS_t* params) ^ numpy/linalg/umath_linalg.c.src:2132:1: warning: unused function 'init_cgeev' [-Wunused-function] init_cgeev(GEEV_PARAMS_t* params, ^ numpy/linalg/umath_linalg.c.src:2213:1: warning: unused function 'process_cgeev_results' [-Wunused-function] process_cgeev_results(GEEV_PARAMS_t *NPY_UNUSED(params)) ^ numpy/linalg/umath_linalg.c.src:2376:1: warning: unused function 'dump_gesdd_params' [-Wunused-function] dump_gesdd_params(const char *name, ^ numpy/linalg/umath_linalg.c.src:2864:1: warning: unused function 'dump_gelsd_params' [-Wunused-function] dump_gelsd_params(const char *name, ^ 16 warnings generated. clang -bundle -undefined dynamic_lookup -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/linalg/umath_linalg.o build/temp.macosx-10.15-x86_64-3.9/numpy/linalg/lapack_lite/python_xerbla.o -L/usr/local/lib -L/usr/local/opt/[email protected]/lib -L/usr/local/opt/sqlite/lib -Lbuild/temp.macosx-10.15-x86_64-3.9 -lnpymath -o build/lib.macosx-10.15-x86_64-3.9/numpy/linalg/_umath_linalg.cpython-39-darwin.so -Wl,-framework -Wl,Accelerate building 'numpy.random.mtrand' extension compiling C sources C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers creating build/temp.macosx-10.15-x86_64-3.9/numpy/random creating build/temp.macosx-10.15-x86_64-3.9/numpy/random/mtrand compile options: '-D_FILE_OFFSET_BITS=64 -D_LARGEFILE_SOURCE=1 -D_LARGEFILE64_SOURCE=1 -Inumpy/core/include -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy -Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/[email protected]/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -c' clang: numpy/random/mtrand/mtrand.c clang: numpy/random/mtrand/initarray.cclang: numpy/random/mtrand/randomkit.c clang: numpy/random/mtrand/distributions.c numpy/random/mtrand/mtrand.c:40400:34: error: no member named 'tp_print' in 'struct _typeobject' __pyx_type_6mtrand_RandomState.tp_print = 0; ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ^ numpy/random/mtrand/mtrand.c:42673:22: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations] (PyUnicode_GET_SIZE(**name) != PyUnicode_GET_SIZE(key)) ? 1 : ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:261:7: note: expanded from macro 'PyUnicode_GET_SIZE' PyUnicode_WSTR_LENGTH(op) : \ ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH' #define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here Py_DEPRECATED(3.3) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED' #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__)) ^ numpy/random/mtrand/mtrand.c:42673:22: warning: 'PyUnicode_AsUnicode' is deprecated [-Wdeprecated-declarations] (PyUnicode_GET_SIZE(**name) != PyUnicode_GET_SIZE(key)) ? 1 : ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:262:14: note: expanded from macro 'PyUnicode_GET_SIZE' ((void)PyUnicode_AsUnicode(_PyObject_CAST(op)),\ ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:580:1: note: 'PyUnicode_AsUnicode' has been explicitly marked deprecated here Py_DEPRECATED(3.3) PyAPI_FUNC(Py_UNICODE *) PyUnicode_AsUnicode( ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED' #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__)) ^ numpy/random/mtrand/mtrand.c:42673:22: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations] (PyUnicode_GET_SIZE(**name) != PyUnicode_GET_SIZE(key)) ? 1 : ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:264:8: note: expanded from macro 'PyUnicode_GET_SIZE' PyUnicode_WSTR_LENGTH(op))) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH' #define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here Py_DEPRECATED(3.3) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED' #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__)) ^ numpy/random/mtrand/mtrand.c:42673:52: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations] (PyUnicode_GET_SIZE(**name) != PyUnicode_GET_SIZE(key)) ? 1 : ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:261:7: note: expanded from macro 'PyUnicode_GET_SIZE' PyUnicode_WSTR_LENGTH(op) : \ ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH' #define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here Py_DEPRECATED(3.3) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED' #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__)) ^ numpy/random/mtrand/mtrand.c:42673:52: warning: 'PyUnicode_AsUnicode' is deprecated [-Wdeprecated-declarations] (PyUnicode_GET_SIZE(**name) != PyUnicode_GET_SIZE(key)) ? 1 : ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:262:14: note: expanded from macro 'PyUnicode_GET_SIZE' ((void)PyUnicode_AsUnicode(_PyObject_CAST(op)),\ ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:580:1: note: 'PyUnicode_AsUnicode' has been explicitly marked deprecated here Py_DEPRECATED(3.3) PyAPI_FUNC(Py_UNICODE *) PyUnicode_AsUnicode( ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED' #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__)) ^ numpy/random/mtrand/mtrand.c:42673:52: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations] (PyUnicode_GET_SIZE(**name) != PyUnicode_GET_SIZE(key)) ? 1 : ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:264:8: note: expanded from macro 'PyUnicode_GET_SIZE' PyUnicode_WSTR_LENGTH(op))) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH' #define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here Py_DEPRECATED(3.3) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED' #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__)) ^ numpy/random/mtrand/mtrand.c:42689:26: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations] (PyUnicode_GET_SIZE(**argname) != PyUnicode_GET_SIZE(key)) ? 1 : ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:261:7: note: expanded from macro 'PyUnicode_GET_SIZE' PyUnicode_WSTR_LENGTH(op) : \ ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH' #define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here Py_DEPRECATED(3.3) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED' #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__)) ^ numpy/random/mtrand/mtrand.c:42689:26: warning: 'PyUnicode_AsUnicode' is deprecated [-Wdeprecated-declarations] (PyUnicode_GET_SIZE(**argname) != PyUnicode_GET_SIZE(key)) ? 1 : ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:262:14: note: expanded from macro 'PyUnicode_GET_SIZE' ((void)PyUnicode_AsUnicode(_PyObject_CAST(op)),\ ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:580:1: note: 'PyUnicode_AsUnicode' has been explicitly marked deprecated here Py_DEPRECATED(3.3) PyAPI_FUNC(Py_UNICODE *) PyUnicode_AsUnicode( ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED' #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__)) ^ numpy/random/mtrand/mtrand.c:42689:26: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations] (PyUnicode_GET_SIZE(**argname) != PyUnicode_GET_SIZE(key)) ? 1 : ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:264:8: note: expanded from macro 'PyUnicode_GET_SIZE' PyUnicode_WSTR_LENGTH(op))) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH' #define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here Py_DEPRECATED(3.3) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED' #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__)) ^ numpy/random/mtrand/mtrand.c:42689:59: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations] (PyUnicode_GET_SIZE(**argname) != PyUnicode_GET_SIZE(key)) ? 1 : ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:261:7: note: expanded from macro 'PyUnicode_GET_SIZE' PyUnicode_WSTR_LENGTH(op) : \ ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH' #define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here Py_DEPRECATED(3.3) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED' #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__)) ^ numpy/random/mtrand/mtrand.c:42689:59: warning: 'PyUnicode_AsUnicode' is deprecated [-Wdeprecated-declarations] (PyUnicode_GET_SIZE(**argname) != PyUnicode_GET_SIZE(key)) ? 1 : ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:262:14: note: expanded from macro 'PyUnicode_GET_SIZE' ((void)PyUnicode_AsUnicode(_PyObject_CAST(op)),\ ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:580:1: note: 'PyUnicode_AsUnicode' has been explicitly marked deprecated here Py_DEPRECATED(3.3) PyAPI_FUNC(Py_UNICODE *) PyUnicode_AsUnicode( ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED' #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__)) ^ numpy/random/mtrand/mtrand.c:42689:59: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations] (PyUnicode_GET_SIZE(**argname) != PyUnicode_GET_SIZE(key)) ? 1 : ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:264:8: note: expanded from macro 'PyUnicode_GET_SIZE' PyUnicode_WSTR_LENGTH(op))) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH' #define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here Py_DEPRECATED(3.3) ^ /usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED' #define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__)) ^ 12 warnings and 1 error generated. error: Command "clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers -D_FILE_OFFSET_BITS=64 -D_LARGEFILE_SOURCE=1 -D_LARGEFILE64_SOURCE=1 -Inumpy/core/include -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy -Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/[email protected]/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/[email protected]/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -c numpy/random/mtrand/mtrand.c -o build/temp.macosx-10.15-x86_64-3.9/numpy/random/mtrand/mtrand.o -MMD -MF build/temp.macosx-10.15-x86_64-3.9/numpy/random/mtrand/mtrand.o.d" failed with exit status 1
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Question: Shouldn't .info be a part of DatasetDict?
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[ "We could do something. There is a part of `.info` which is split specific (cache files, split instructions) but maybe if could be made to work.", "Yes this was kinda the idea I was going for. DatasetDict.info would be the shared info amongs the datasets (maybe even some info on how they differ). " ]
2021-01-05T13:08:41
2021-01-07T10:18:06
null
CONTRIBUTOR
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Currently, only `Dataset` contains the .info or .features, but as many datasets contains standard splits (train, test) and thus the underlying information is the same (or at least should be) across the datasets. For instance: ``` >>> ds = datasets.load_dataset("conll2002", "es") >>> ds.info Traceback (most recent call last): File "<stdin>", line 1, in <module> AttributeError: 'DatasetDict' object has no attribute 'info' ``` I could imagine that this wouldn't work for datasets dicts which hold entirely different datasets (multimodal datasets), but it seems odd that splits of the same dataset is treated the same as what is essentially different datasets. Intuitively it would also make sense that if a dataset is supplied via. the load_dataset that is have a common .info which covers the entire dataset. It is entirely possible that I am missing another perspective
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Dataset Error: DaNE contains empty samples at the end
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[ "Thanks for reporting, I opened a PR to fix that", "One the PR is merged the fix will be available in the next release of `datasets`.\r\n\r\nIf you don't want to wait the next release you can still load the script from the master branch with\r\n\r\n```python\r\nload_dataset(\"dane\", script_version=\"master\")\r\n```", "If you have other questions feel free to reopen :) " ]
2021-01-05T11:54:26
2021-01-05T14:01:09
2021-01-05T14:00:13
CONTRIBUTOR
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The dataset DaNE, contains empty samples at the end. It is naturally easy to remove using a filter but should probably not be there, to begin with as it can cause errors. ```python >>> import datasets [...] >>> dataset = datasets.load_dataset("dane") [...] >>> dataset["test"][-1] {'dep_ids': [], 'dep_labels': [], 'lemmas': [], 'morph_tags': [], 'ner_tags': [], 'pos_tags': [], 'sent_id': '', 'text': '', 'tok_ids': [], 'tokens': []} >>> dataset["train"][-1] {'dep_ids': [], 'dep_labels': [], 'lemmas': [], 'morph_tags': [], 'ner_tags': [], 'pos_tags': [], 'sent_id': '', 'text': '', 'tok_ids': [], 'tokens': []} ``` Best, Kenneth
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`ArrowInvalid` occurs while running `Dataset.map()` function for DPRContext
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[ "Looks like the mapping function returns a dictionary with a 768-dim array in the `embeddings` field. Since the map is batched, we actually expect the `embeddings` field to be an array of shape (batch_size, 768) to have one embedding per example in the batch.\r\n\r\nTo fix that can you try to remove one of the `[0]` ? In my opinion you only need one of them, not two.", "It makes sense :D\r\n\r\nIt seems to work! Thanks a lot :))\r\n\r\nClosing the issue" ]
2021-01-04T18:47:53
2021-01-04T19:04:45
2021-01-04T19:04:45
CONTRIBUTOR
null
null
null
It seems to fail the final batch ): steps to reproduce: ``` from datasets import load_dataset from elasticsearch import Elasticsearch import torch from transformers import file_utils, set_seed from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast MAX_SEQ_LENGTH = 256 ctx_encoder = DPRContextEncoder.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base", cache_dir="../datasets/") ctx_tokenizer = DPRContextEncoderTokenizerFast.from_pretrained( "facebook/dpr-ctx_encoder-single-nq-base", cache_dir="..datasets/" ) dataset = load_dataset('text', data_files='data/raw/ARC_Corpus.txt', cache_dir='../datasets') torch.set_grad_enabled(False) ds_with_embeddings = dataset.map( lambda example: { 'embeddings': ctx_encoder( **ctx_tokenizer( example["text"], padding='max_length', truncation=True, max_length=MAX_SEQ_LENGTH, return_tensors="pt" ) )[0][0].numpy(), }, batched=True, load_from_cache_file=False, batch_size=1000 ) ``` ARC Corpus can be obtained from [here](https://ai2-datasets.s3-us-west-2.amazonaws.com/arc/ARC-V1-Feb2018.zip) And then the error: ``` --------------------------------------------------------------------------- ArrowInvalid Traceback (most recent call last) <ipython-input-13-67d139bb2ed3> in <module> 14 batched=True, 15 load_from_cache_file=False, ---> 16 batch_size=1000 17 ) ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/dataset_dict.py in map(self, function, with_indices, input_columns, batched, batch_size, remove_columns, keep_in_memory, load_from_cache_file, cache_file_names, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc) 301 num_proc=num_proc, 302 ) --> 303 for k, dataset in self.items() 304 } 305 ) ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/dataset_dict.py in <dictcomp>(.0) 301 num_proc=num_proc, 302 ) --> 303 for k, dataset in self.items() 304 } 305 ) ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint) 1257 fn_kwargs=fn_kwargs, 1258 new_fingerprint=new_fingerprint, -> 1259 update_data=update_data, 1260 ) 1261 else: ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs) 155 } 156 # apply actual function --> 157 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 158 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 159 # re-apply format to the output ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/fingerprint.py in wrapper(*args, **kwargs) 161 # Call actual function 162 --> 163 out = func(self, *args, **kwargs) 164 165 # Update fingerprint of in-place transforms + update in-place history of transforms ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, update_data) 1526 if update_data: 1527 batch = cast_to_python_objects(batch) -> 1528 writer.write_batch(batch) 1529 if update_data: 1530 writer.finalize() # close_stream=bool(buf_writer is None)) # We only close if we are writing in a file ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size) 276 typed_sequence = TypedSequence(batch_examples[col], type=col_type, try_type=col_try_type) 277 typed_sequence_examples[col] = typed_sequence --> 278 pa_table = pa.Table.from_pydict(typed_sequence_examples) 279 self.write_table(pa_table) 280 ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/pyarrow/table.pxi in pyarrow.lib.Table.from_pydict() ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/pyarrow/table.pxi in pyarrow.lib.Table.from_arrays() ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/pyarrow/table.pxi in pyarrow.lib.Table.validate() ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status() ArrowInvalid: Column 1 named text expected length 768 but got length 1000 ```
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1,681
Dataset "dane" missing
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[ "Hi @KennethEnevoldsen ,\r\nI think the issue might be that this dataset was added during the community sprint and has not been released yet. It will be available with the v2 of datasets.\r\nFor now, you should be able to load the datasets after installing the latest (master) version of datasets using pip:\r\npip install git+https://github.com/huggingface/datasets.git@master", "The `dane` dataset was added recently, that's why it wasn't available yet. We did an intermediate release today just before the v2.0.\r\n\r\nTo load it you can just update `datasets`\r\n```\r\npip install --upgrade datasets\r\n```\r\n\r\nand then you can load `dane` with\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"dane\")\r\n```", "Thanks. Solved the problem." ]
2021-01-03T14:03:03
2021-01-05T08:35:35
2021-01-05T08:35:13
CONTRIBUTOR
null
null
null
the `dane` dataset appear to be missing in the latest version (1.1.3). ```python >>> import datasets >>> datasets.__version__ '1.1.3' >>> "dane" in datasets.list_datasets() True ``` As we can see it should be present, but doesn't seem to be findable when using `load_dataset`. ```python >>> datasets.load_dataset("dane") Traceback (most recent call last): File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/load.py", line 267, in prepare_module local_path = cached_path(file_path, download_config=download_config) File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 300, in cached_path output_path = get_from_cache( File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache raise FileNotFoundError("Couldn't find file at {}".format(url)) FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dane/dane.py During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/load.py", line 278, in prepare_module local_path = cached_path(file_path, download_config=download_config) File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 300, in cached_path output_path = get_from_cache( File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache raise FileNotFoundError("Couldn't find file at {}".format(url)) FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dane/dane.py During handling of the above exception, another exception occurred: Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/load.py", line 588, in load_dataset module_path, hash = prepare_module( File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/load.py", line 280, in prepare_module raise FileNotFoundError( FileNotFoundError: Couldn't find file locally at dane/dane.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dane/dane.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dane/dane.py ``` This issue might be relevant to @ophelielacroix from the Alexandra Institut whom created the data.
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MDU6SXNzdWU3Nzc1ODc3OTI=
1,679
Can't import cc100 dataset
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[ "cc100 was added recently, that's why it wasn't available yet.\r\n\r\nTo load it you can just update `datasets`\r\n```\r\npip install --upgrade datasets\r\n```\r\n\r\nand then you can load `cc100` with\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nlang = \"en\"\r\ndataset = load_dataset(\"cc100\", lang=lang, split=\"train\")\r\n```" ]
2021-01-03T07:12:56
2022-10-05T12:42:25
2022-10-05T12:42:25
NONE
null
null
null
There is some issue to import cc100 dataset. ``` from datasets import load_dataset dataset = load_dataset("cc100") ``` FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/cc100/cc100.py During handling of the above exception, another exception occurred: FileNotFoundError Traceback (most recent call last) FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/cc100/cc100.py During handling of the above exception, another exception occurred: FileNotFoundError Traceback (most recent call last) /usr/local/lib/python3.6/dist-packages/datasets/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs) 280 raise FileNotFoundError( 281 "Couldn't find file locally at {}, or remotely at {} or {}".format( --> 282 combined_path, github_file_path, file_path 283 ) 284 ) FileNotFoundError: Couldn't find file locally at cc100/cc100.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/cc100/cc100.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/cc100/cc100.py
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1,675
Add the 800GB Pile dataset?
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null
[ "The pile dataset would be very nice.\r\nBenchmarks show that pile trained models achieve better results than most of actually trained models", "The pile can very easily be added and adapted using this [tfds implementation](https://github.com/EleutherAI/The-Pile/blob/master/the_pile/tfds_pile.py) from the repo. \r\n\r\nHowever, the question is whether you'd be ok with 800GB+ cached in your local disk, since the tfds implementation was designed to offload the storage to Google Cloud Storage.", "With the dataset streaming feature (see #2375) it will be more convenient to play with such big datasets :)\r\nI'm currently adding C4 (see #2511 ) but I can probably start working on this afterwards", "Hi folks! Just wanted to follow up on this -- would be really nice to get the Pile on HF Datasets... unclear if it would be easy to also add partitions of the Pile subject to the original 22 datasets used, but that would be nice too!", "Hi folks, thanks to some awesome work by @lhoestq and @albertvillanova you can now stream the Pile as follows:\r\n\r\n```python\r\n# Install master branch of `datasets`\r\npip install git+https://github.com/huggingface/datasets.git#egg=datasets[streaming]\r\npip install zstandard\r\n\r\nfrom datasets import load_dataset\r\n\r\ndset = load_dataset(\"json\", data_files=\"https://the-eye.eu/public/AI/pile/train/00.jsonl.zst\", streaming=True, split=\"train\")\r\nnext(iter(dset))\r\n# {'meta': {'pile_set_name': 'Pile-CC'},\r\n# 'text': 'It is done, and submitted. You can play “Survival of the Tastiest” on Android, and on the web ... '}\r\n```\r\n\r\nNext step is to add the Pile as a \"canonical\" dataset that can be streamed without specifying the file names explicitly :)", "> Hi folks! Just wanted to follow up on this -- would be really nice to get the Pile on HF Datasets... unclear if it would be easy to also add partitions of the Pile subject to the original 22 datasets used, but that would be nice too!\r\n\r\nHi @siddk thanks to a tip from @richarddwang it seems we can access some of the partitions that EleutherAI created for the Pile [here](https://the-eye.eu/public/AI/pile_preliminary_components/). What's missing are links to the preprocessed versions of pre-existing datasets like DeepMind Mathematics and OpenSubtitles, but worst case we do the processing ourselves and host these components on the Hub.\r\n\r\nMy current idea is that we could provide 23 configs: one for each of the 22 datasets and an `all` config that links to the train / dev / test splits that EleutherAI released [here](https://the-eye.eu/public/AI/pile/), e.g.\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n# Load a single component\r\nyoutube_subtitles = load_dataset(\"the_pile\", \"youtube_subtitles\")\r\n# Load the train / dev / test splits of the whole corpus\r\ndset = load_dataset(\"the_pile\", \"all\")\r\n```\r\n\r\nIdeally we'd like everything to be compatible with the streaming API and there's ongoing work by @albertvillanova to make this happen for the various compression algorithms.\r\n\r\ncc @lhoestq ", "Ah I just saw that @lhoestq is already thinking about the specifying of one or more subsets in [this PR](https://github.com/huggingface/datasets/pull/2817#issuecomment-901874049) :)" ]
2021-01-01T22:58:12
2021-12-01T15:29:07
2021-12-01T15:29:07
MEMBER
null
null
null
## Adding a Dataset - **Name:** The Pile - **Description:** The Pile is a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality datasets combined together. See [here](https://twitter.com/nabla_theta/status/1345130408170541056?s=20) for the Twitter announcement - **Paper:** https://pile.eleuther.ai/paper.pdf - **Data:** https://pile.eleuther.ai/ - **Motivation:** Enables hardcore (GPT-3 scale!) language modelling ## Remarks Given the extreme size of this dataset, I'm not sure how feasible this will be to include in `datasets` 🤯 . I'm also unsure how many `datasets` users are pretraining LMs, so the usage of this dataset may not warrant the effort to integrate it.
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dutch_social can't be loaded
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[ "exactly the same issue in some other datasets.\r\nDid you find any solution??\r\n", "Hi @koenvandenberge and @alighofrani95!\r\nThe datasets you're experiencing issues with were most likely added recently to the `datasets` library, meaning they have not been released yet. They will be released with the v2 of the library.\r\nMeanwhile, you can still load the datasets using one of the techniques described in this issue: #1641 \r\nLet me know if this helps!", "Maybe we should do a small release on Monday in the meantime @lhoestq ?", "Yes sure !", "I just did the release :)\r\n\r\nTo load it you can just update `datasets`\r\n```\r\npip install --upgrade datasets\r\n```\r\n\r\nand then you can load `dutch_social` with\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"dutch_social\")\r\n```", "@lhoestq could you also shed light on the Hindi Wikipedia Dataset for issue number #1673. Will this also be available in the new release that you committed recently?", "The issue is different for this one, let me give more details in the issue", "Okay. Could you comment on the #1673 thread? Actually @thomwolf had commented that if i use datasets library from source, it would allow me to download the Hindi Wikipedia Dataset but even the version 1.1.3 gave me the same issue. The details are there in the issue #1673 thread." ]
2021-01-01T17:37:08
2022-10-05T13:03:26
2022-10-05T13:03:26
NONE
null
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Hi all, I'm trying to import the `dutch_social` dataset described [here](https://huggingface.co/datasets/dutch_social). However, the code that should load the data doesn't seem to be working, in particular because the corresponding files can't be found at the provided links. ``` (base) Koens-MacBook-Pro:~ koenvandenberge$ python Python 3.7.4 (default, Aug 13 2019, 15:17:50) [Clang 4.0.1 (tags/RELEASE_401/final)] :: Anaconda, Inc. on darwin Type "help", "copyright", "credits" or "license" for more information. >>> from datasets import load_dataset dataset = load_dataset( 'dutch_social') >>> dataset = load_dataset( ... 'dutch_social') Traceback (most recent call last): File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 267, in prepare_module local_path = cached_path(file_path, download_config=download_config) File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path use_etag=download_config.use_etag, File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache raise FileNotFoundError("Couldn't find file at {}".format(url)) FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dutch_social/dutch_social.py During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 278, in prepare_module local_path = cached_path(file_path, download_config=download_config) File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path use_etag=download_config.use_etag, File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache raise FileNotFoundError("Couldn't find file at {}".format(url)) FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dutch_social/dutch_social.py During handling of the above exception, another exception occurred: Traceback (most recent call last): File "<stdin>", line 2, in <module> File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 589, in load_dataset path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 282, in prepare_module combined_path, github_file_path, file_path FileNotFoundError: Couldn't find file locally at dutch_social/dutch_social.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dutch_social/dutch_social.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dutch_social/dutch_social.py ```
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