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[ "To reproduce:\r\n\r\n```python\r\n>>> import datasets\r\n>>> datasets.get_dataset_split_names('MalakhovIlya/RuREBus', config_name='raw_txt')\r\nTraceback (most recent call last):\r\n File \"/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 280, in get_dataset_config_info\r\n for split_generator in builder._split_generators(\r\n File \"/home/slesage/.cache/huggingface/modules/datasets_modules/datasets/MalakhovIlya--RuREBus/21046f5f1a0cf91187d68c30918d78d934ec7113ec435e146776d4f28f12c4ed/RuREBus.py\", line 101, in _split_generators\r\n decode_file_names(folder)\r\n File \"/home/slesage/.cache/huggingface/modules/datasets_modules/datasets/MalakhovIlya--RuREBus/21046f5f1a0cf91187d68c30918d78d934ec7113ec435e146776d4f28f12c4ed/RuREBus.py\", line 26, in decode_file_names\r\n for root, dirs, files in os.walk(folder, topdown=False):\r\n File \"/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/streaming.py\", line 66, in wrapper\r\n return function(*args, use_auth_token=use_auth_token, **kwargs)\r\nTypeError: xwalk() got an unexpected keyword argument 'topdown'\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 \"<stdin>\", line 1, in <module>\r\n File \"/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 323, in get_dataset_split_names\r\n info = get_dataset_config_info(\r\n File \"/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 285, in get_dataset_config_info\r\n raise SplitsNotFoundError(\"The split names could not be parsed from the dataset config.\") from err\r\ndatasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.\r\n```\r\n\r\nIt's not related to the dataset viewer. Maybe @albertvillanova or @lhoestq could help more on this issue.", "Hi! This issue stems from the fact that `xwalk`, which is a streamable version of `os.walk`, doesn't support the `topdown` param due to `fsspec`'s `walk` also not supporting it, so fixing this issue could be tricky. \r\n\r\n@MalakhovIlyaPavlovich You can avoid the error by tweaking your data processing and not using this param. (and `Path.rename`, which also cannot be streamed) ", "@mariosasko thank you for your reply. I couldn't reproduce error showed by @severo either on Ubuntu 20.04.3 LTS, Windows 10 and Google Colab environments. But trying to avoid using os.walk(topdown=False) and Path.rename(), In _split_generators I replaced\r\n```\r\ndef decode_file_names(folder):\r\n for root, dirs, files in os.walk(folder, topdown=False):\r\n root = Path(root)\r\n for file in files:\r\n old_name = root / Path(file)\r\n new_name = root / Path(\r\n file.encode('cp437').decode('cp866'))\r\n old_name.rename(new_name)\r\n for dir in dirs:\r\n old_name = root / Path(dir)\r\n new_name = root / Path(dir.encode('cp437').decode('cp866'))\r\n old_name.rename(new_name)\r\n\r\nfolder = dl_manager.download_and_extract(self._RAW_TXT_URLS)['raw_txt']\r\ndecode_file_names(folder)\r\n```\r\nby\r\n```\r\ndef extract(zip_file_path):\r\n p = Path(zip_file_path)\r\n dest_dir = str(p.parent / 'extracted' / p.stem)\r\n os.makedirs(dest_dir, exist_ok=True)\r\n with zipfile.ZipFile(zip_file_path) as archive:\r\n for file_info in tqdm(archive.infolist(), desc='Extracting'):\r\n filename = file_info.filename.encode('cp437').decode('cp866')\r\n target = os.path.join(dest_dir, *filename.split('/'))\r\n os.makedirs(os.path.dirname(target), exist_ok=True)\r\n if not file_info.is_dir():\r\n with archive.open(file_info) as source, open(target, 'wb') as dest:\r\n shutil.copyfileobj(source, dest)\r\n return dest_dir\r\n\r\nzip_file = dl_manager.download(self._RAW_TXT_URLS)['raw_txt']\r\nif not is_url(zip_file):\r\n folder = extract(zip_file)\r\nelse:\r\n folder = None\r\n```\r\nand now everything works well except data viewer for \"raw_txt\" subset: dataset preview on hub shows \"No data.\". As far as I understand dl_manager.download returns original URL when we are calling datasets.get_dataset_split_names and my suspicions are that dataset viewer can do smth similar. I couldn't find information about how it works. I would be very grateful, if you could tell me how to fix this)", "This is what I get when I try to stream the `raw_txt` subset:\r\n```python\r\n>>> dset = load_dataset(\"MalakhovIlya/RuREBus\", \"raw_txt\", split=\"raw_txt\", streaming=True)\r\n>>> next(iter(dset))\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\nStopIteration\r\n```\r\nSo there is a bug in your script.", "streaming=True helped me to find solution. I fixed\r\n```\r\ndef extract(zip_file_path):\r\n p = Path(zip_file_path)\r\n dest_dir = str(p.parent / 'extracted' / p.stem)\r\n os.makedirs(dest_dir, exist_ok=True)\r\n with zipfile.ZipFile(zip_file_path) as archive:\r\n for file_info in tqdm(archive.infolist(), desc='Extracting'):\r\n filename = file_info.filename.encode('cp437').decode('cp866')\r\n target = os.path.join(dest_dir, *filename.split('/'))\r\n os.makedirs(os.path.dirname(target), exist_ok=True)\r\n if not file_info.is_dir():\r\n with archive.open(file_info) as source, open(target, 'wb') as dest:\r\n shutil.copyfileobj(source, dest)\r\n return dest_dir\r\n\r\nzip_file = dl_manager.download(self._RAW_TXT_URLS)['raw_txt']\r\nfolder = extract(zip_file)\r\n```\r\nby \r\n```\r\nfolder = dl_manager.download_and_extract(self._RAW_TXT_URLS)['raw_txt']\r\npath = os.path.join(folder, 'MED_txt/unparsed_txt')\r\nfor root, dirs, files in os.walk(path):\r\n decoded_root_name = Path(root).name.encode('cp437').decode('cp866')\r\n```\r\n@mariosasko thank you for your help :)" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "The `test_dataset_cards` method is failing with the error:\r\n\r\n```\r\nif error_messages:\r\n> raise ValueError(\"\\n\".join(error_messages))\r\nE ValueError: The following issues have been found in the dataset cards:\r\nE YAML tags:\r\nE The following typing errors are found: {'annotations_creators': \"(Expected `typing.List` with length > 0. Found value of type: `<class 'list'>`, with length: 0.\\n)\\nOR\\n(Expected `typing.Dict` with length > 0. Found value of type: `<class 'list'>`, with length: 0.\\n)\"}\r\n```\r\n\r\nAdding `found` as annotation creators." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "Hi ! Please post your question on the [forum](https://discuss.huggingface.co/), more people will be able to help you there ;)" ]

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[ "The dataset cannot be loaded, be it in normal or streaming mode.\r\n\r\n```python\r\n>>> import datasets\r\n>>> dataset=datasets.load_dataset(\"hans\", split=\"train\", streaming=True)\r\n>>> next(iter(dataset))\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py\", line 497, in __iter__\r\n for key, example in self._iter():\r\n File \"/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py\", line 494, in _iter\r\n yield from ex_iterable\r\n File \"/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py\", line 87, in __iter__\r\n yield from self.generate_examples_fn(**self.kwargs)\r\n File \"/home/slesage/.cache/huggingface/modules/datasets_modules/datasets/hans/1bbcb735c482acd54f2e118074b59cfd2bf5f7a5a285d4d540d1e632216672ac/hans.py\", line 121, in _generate_examples\r\n for idx, line in enumerate(open(filepath, \"rb\")):\r\n File \"/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/fsspec/spec.py\", line 1595, in __next__\r\n out = self.readline()\r\n File \"/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/fsspec/spec.py\", line 1592, in readline\r\n return self.readuntil(b\"\\n\")\r\n File \"/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/fsspec/spec.py\", line 1581, in readuntil\r\n self.seek(start + found + len(char))\r\n File \"/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/fsspec/implementations/http.py\", line 676, in seek\r\n raise ValueError(\"Cannot seek streaming HTTP file\")\r\nValueError: Cannot seek streaming HTTP file\r\n>>> dataset=datasets.load_dataset(\"hans\", split=\"train\", streaming=False)\r\nDownloading and preparing dataset hans/plain_text (download: 29.51 MiB, generated: 30.34 MiB, post-processed: Unknown size, total: 59.85 MiB) to /home/slesage/.cache/huggingface/datasets/hans/plain_text/1.0.0/1bbcb735c482acd54f2e118074b59cfd2bf5f7a5a285d4d540d1e632216672ac...\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/load.py\", line 1687, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/builder.py\", line 605, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/builder.py\", line 1104, in _download_and_prepare\r\n super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)\r\n File \"/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/builder.py\", line 694, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/builder.py\", line 1087, in _prepare_split\r\n for key, record in logging.tqdm(\r\n File \"/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/tqdm/std.py\", line 1180, in __iter__\r\n for obj in iterable:\r\n File \"/home/slesage/.cache/huggingface/modules/datasets_modules/datasets/hans/1bbcb735c482acd54f2e118074b59cfd2bf5f7a5a285d4d540d1e632216672ac/hans.py\", line 121, in _generate_examples\r\n for idx, line in enumerate(open(filepath, \"rb\")):\r\nValueError: readline of closed file\r\n```\r\n\r\n", "Hi! I've opened a PR that should make this dataset stremable. You can test it as follows:\r\n```python\r\nfrom datasets import load_dataset\r\ndset = load_dataset(\"hans\", split=\"train\", streaming=True, revision=\"49decd29839c792ecc24ac88f861cbdec30c1c40\")\r\n```\r\n\r\n@severo The current script doesn't throw an error in normal mode (only in streaming mode) on my local machine or in Colab. Can you update your installation of `datasets` and see if that fixes the issue?", "Thanks for this. It works well, thanks! The dataset viewer is using https://github.com/huggingface/datasets/releases/tag/2.0.0, I'm eager to upgrade to 2.0.1 😉" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "Yes, it's a known issue, and we expect to fix it soon.", "Fixed.\r\n\r\n<img width=\"1393\" alt=\"Capture d’écran 2022-04-25 à 15 42 05\" src=\"https://user-images.githubusercontent.com/1676121/165101176-d729d85b-efff-45a8-bad1-b69223edba5f.png\">\r\n" ]

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[ "> It looks like the CI is failing on windows because our windows CI is unable to clone the bigbench repository (maybe it has to do with filenames that are longer than 256 characters, which windows don't like). Could the smaller installation of bigbench via pip solve this issue ?\r\n> Otherwise we can see how to remove this limitation in our windows CI.\r\n\r\nI'm not sure.\r\nIf it's git's fault that it can't handle the long filenames, it will possibly be resolved by the pip install. If it's an issue with windows not liking long filenames after it's installed, then it will not be resolved.\r\nI don't have a windows computer to try it on, but I might be able to tweek this PR and do an experiment to find out. \r\nWe're waiting for a quota increase for the pip install (https://github.com/pypa/pypi-support/issues/1782). It's been pending for 2-3 weeks, and I don't have an estimate for when it will be resolved. \r\n\r\n>Regarding the dummy data zip files, I think we can just keep datasets/bigbench/dummy/abstract_narrative_understanding/1.0.0/dummy_data.zip and remove all the other ones. We just require to have at least one dummy_data.zip file.\r\n\r\nSounds great. I will trim that down. ", "Do you know what are the other tests dependencies that have conflicts with bigbench ? I can try to split the CI to end up with a compatible list of test dependencies", "Hi @lhoestq,\r\n\r\nI haven't played with eliminating requirements form the test dependencies, and I've been trying to resolve this by modifying the bigbench repo to become compatible. \r\nIn the original bigbench repo, the version requirements were strict, and specifically it had a datasets==1.17.0 requirement which was causing trouble. \r\nI'm working on PR https://github.com/google/BIG-bench/pull/766 to get some more flexible versions that might be compatible with the test dependencies in HF/datasets.\r\nWe're somewhat flexible in modifying these version numbers if we can figure out what the exact conflict is. \r\n\r\nI've spent some time experimenting with different versions, but I don't have a very efficient way of doing this debugging on my work computer (which for some reason doesn't produce the same sets of errors running python 3.9 instead of 3.6 or 3.7 in the tests). \r\nIt currently fails at \r\n> The conflict is caused by:\r\n> bert-score 0.3.6 depends on matplotlib\r\n> big-bench 0.0.1 depends on matplotlib<4.0 and >=3.5.1\r\n\r\nwhich doesn't seem like it can be the real issue. \r\n\r\nIf you have any advice for how to resolve these conflicts, that would be greatly appreciated!", "Hi again @lhoestq, \r\nAfter some more or less random guessing of conflicting packages, I've managed to find a configuration that seems to be compatible with HF/datasets. \r\n\r\nThe errors went away after removing version limits on matplotlib and scipy, and loosening numpy from 1.19 -> 1.17 in the bigbench requirements. \r\n\r\nI might do some more tweaking to see if it lets me set some minimal limits on matplotlib and scipy, but I think we at least can move forward.\r\n\r\nThe WIN tests are still failing, now because of \r\n\r\n> Did not find path entry C:\\tools\\miniconda3\\bin\r\n>C:\\tools\\miniconda3\\envs\\py37\\python.exe: No module named pytest\r\n\r\nI have no way of debugging this locally, and unless there's some way to get more verbose logs, I don't know why it's not finding pytest. Would you be able to take a quick look? \r\n\r\nUpdate: Actually, I see it's still failing because of the long filenames. So perhaps the pytest error is just because the previous steps failed. ", "One more update on the WIN errors. \r\nI think all the long filenames are in files in the github repo that does not need to be included. \r\nWe will try to remove them .", "Hi ! The remaining error seems to be a `UnicodeDecodeError` from `setup.py`. I think you can fix your setup.py:\r\n```diff\r\n- with open(os.path.join(os.path.dirname(__file__), fname)) as f:\r\n+ with open(os.path.join(os.path.dirname(__file__), fname), encoding=\"utf-8\") as f:\r\n```\r\nIndeed on windows, when you `open` a file it doesn't always use \"utf-8\" by default", "Hi @lhoestq, \r\nThe dependency issues seems to now be resolved 🎉 \r\n\r\nNow, the WIN tests are failing at\r\n> ERROR tests/test_arrow_dataset.py::test_dummy_dataset_serialize_s3 - botocore...\r\n> ERROR tests/test_dataset_dict.py::test_dummy_dataset_serialize_s3 - botocore...\r\n\r\nIs this testing the dummy dataset that's added in bigbench? If so, I might need some help getting the right format in.\r\n\r\nThe error message I'm seeing is \r\n> raise EndpointConnectionError(endpoint_url=request.url, error=e)\r\n> E botocore.exceptions.EndpointConnectionError: Could not connect to the endpoint URL: \"http://127.0.0.1:5555/test\"\r\n\r\nWhich seems unrelated, but perhaps the real issue is somewhere I'm not seeing? ", "Woohoo awesome !\r\n\r\nLet me check the CI error", "Can you try to re-run the CI, just in case CircleCI messed up ?", "Hi @lhoestq, \r\nRerunning did not seem to solve the problem. \r\nThe `test_dummy_dataset_serialize_s3` error still seems to remain.", "Hi again @lhoestq, \r\nI'm not sure if this is informative or not in terms of debugging, but I deleted the dummy data and the errors for windows still fail and the others still pass. \r\nDo you have any idea what could be causing this error on windows?", "_The documentation is not available anymore as the PR was closed or merged._", "Now the last question: let's have the dataset under`google/bigbench` @andersjohanandreassen ?\r\n\r\nI think it would be nicer, this way you and anyone in your team can update the dataset card whevener you want without going through a github PR. You just need to join the https://huggingface.co/google page using your google email :)", "Hi @lhoestq, \r\n\r\nThank you so much for the help! I really appreciate it!!!\r\n\r\nAfter some discussion with the other bigbench organizers, I think there is a slight preference for bigbench to not be under google/bigbench since this is a collaboration with researchers from many different institutions/organizations beyond Google. \r\n\r\nI see the drawback with the updates to the dataset card having to go through a PR, but hopefully that won't be very frequent. \r\n\r\nWe're finalizing putting the bigbench api on pip, so once that's finalized I just need to update the setup.py with the correct dependency and I think we are ready to merge. ", "Ok perfect, thank you !", "I noticed that in the latest windows CI run it takes forever to install the dependencies, was there any change in the bigbench dependencies recently ?", "oh, sorry! I just did a double check on the dependencies, and it seems like there is at least one left that should have been removed. There's also one new one added. \r\nLet me get those removed again. Will ping you here when it's updated. ", "It looks like there is a circular dependency in `bigbench` at https://storage.googleapis.com/public_research_data/bigbench/bigbench-0.0.1.tar.gz\r\n\r\n```python\r\n>>> import bigbench.api.util as bb_utils\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/circleci/.pyenv/versions/3.6.15/lib/python3.6/site-packages/bigbench/api/util.py\", line 29, in <module>\r\n import bigbench.models.query_logging_model as query_logging_model\r\n File \"/home/circleci/.pyenv/versions/3.6.15/lib/python3.6/site-packages/bigbench/models/query_logging_model.py\", line 23, in <module>\r\n import bigbench.api.util as util\r\nAttributeError: module 'bigbench.api' has no attribute 'util'\r\n```", "Hi @lhoestq , \r\nI think we are ready to merge! \r\n\r\nI have one minor question that I haven't been able to figure out: \r\nIs there a way to bypass the `verify_infos` from triggering? I have `max_examples` as an argument to allow for selecting a fixed subset of the datasets (some of the tasks have *very* many examples). But this is a variable that's not specified by the configs, so it raises an `NonMatchingSplitsSizesError`.\r\nI wasn't able to work my way around this, but perhaps there is a way to bypass this that I'm not seeing?\r\nIf this cannot be done, I'm happy to ignore this for now.\r\n\r\nRegarding pypi, we are working on a release there, but I'm told there is some issue that there is a problem regarding the upload, and we are not sure when it will be resolved, and it's not in my control. \r\nI think merging this PR with the GCS is a great idea, and I will open a new PR when the pypi version is ready. ", "Cool ! Merging then :D\r\n\r\n> Is there a way to bypass the verify_infos from triggering? I have max_examples as an argument to allow for selecting a fixed subset of the datasets (some of the tasks have very many examples). But this is a variable that's not specified by the configs, so it raises an NonMatchingSplitsSizesError.\r\n\r\nThis is a bug, I opened an issue [here](https://github.com/huggingface/datasets/issues/4462). It should be easy to fix :)", "The bigbench page is available here ! https://huggingface.co/datasets/bigbench\r\n\r\nI think we can update the dataset viewer to install bigbench on it, but since this is production code I'd rather use the version on pypi for bigbench when it comes out" ]

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[ "A quick hack I have found is that we can call the image first before running the transforms and it makes sure the image is decoded before being passed on.\r\n\r\nFor this I just needed to add `example['img'] = example['img']` to the top of my `generate_flipped_data` function, defined above, so that image decode in invoked.\r\n\r\nAfter this minor change this function works:\r\n```python\r\ndef generate_flipped_data(example, p=0.5):\r\n \"\"\"\r\n A Dataset mapping functions that transforms some of the image up-side-down.\r\n If the probability value (p) is 0.5 approximately half the images will be flipped upside-down\r\n Args:\r\n example: An example from the dataset containing a Python dictionary with \"img\" and \"is_flipped\" key-value pair\r\n p: probability of flipping the image up-side-down, Default 0.5\r\n\r\n Returns:\r\n example: A Dataset object\r\n\r\n \"\"\"\r\n example['img'] = example['img'] # <<< This is the only change\r\n if rng.random() > p: # the flip the image and set is_flipped column to 1\r\n example['img'] = example['img'].transpose(\r\n 1) # ImageOps.flip(example['img']) #example['img'].transpose(Image.FLIP_TOP_BOTTOM)\r\n example['is_flipped'] = 1\r\n\r\n return example\r\n```", "Hi @RafayAK, thanks for reporting.\r\n\r\nCurrent implementation of the Image feature performs the decoding only if the \"img\" field is accessed by the mapped function.\r\n\r\nIn your original `generate_flipped_data` function:\r\n- it only accesses the \"img\" field (and thus performs decoding) if `rng.random() > p`;\r\n- on the other hand, for the cases where `rng.random() <= p`, the \"img\" field is not accessed and thus no decoding is performed for those examples\r\n\r\nBy adding the code line `example['img'] = example['img']`, you make sure the \"img\" field is accessed in all cases, and the decoding is done for all examples.\r\n\r\nAlso note that there is a little bug in your implementation: `p` is not the probability of flipping, but the probability of not-flipping; the larger is `p`, the smaller is the probability of flipping.\r\n\r\nSome refactoring (fixing also `p`):\r\n```python\r\ndef generate_flipped_data(example, p=0.5):\r\n \"\"\"\r\n A Dataset mapping functions that transforms some of the image up-side-down.\r\n If the probability value (p) is 0.5 approximately half the images will be flipped upside-down.\r\n\r\n Args:\r\n example: An example from the dataset containing a Python dictionary with \"img\" and \"is_flipped\" key-value pair\r\n p: probability of flipping the image up-side-down, Default 0.5\r\n\r\n Returns:\r\n example: A Dataset object\r\n\r\n \"\"\"\r\n do_flip = rng.random() < p # Note the \"<\" sign here instead of \">\"\r\n example['img'] = example['img'].transpose(1) if do_flip else example['img'] # Note \"img\" is always accessed\r\n example['is_flipped'] = 1 if do_flip else 0\r\n return example", "@albertvillanova Thanks for letting me know this is intended behavior. The docs are severely lacking on this, if I hadn't posted this here I would have never found out how I'm actually supposed to modify images in a Dataset object.", "@albertvillanova Secondly if you check the error message it shows that around 1999 images were successfully created, I'm pretty sure some of them were also flipped during the process. Back to my main contention, sometimes the decoding takes place other times it fails. \r\n\r\nI suppose to run `map` on any dataset all the examples should be invoked even if on some of them we end up doing nothing, is that right?", "Hi @RafayAK! I've opened a PR with the fix, which adds a fallback to reattempt casting to PyArrow format with a more robust (but more expensive) procedure if the first attempt fails. Feel free to test it by installing `datasets` from the PR branch with the following command:\r\n```\r\npip install git+https://github.com/huggingface/datasets.git@fix-4124\r\n```", "@mariosasko I'll try this right away and report back.", "@mariosasko Thanks a lot for looking into this, now the `map` function at least behaves as one would expect a function to behave. \r\n\r\nLooking forward to exploring Hugging Face more and even contributing 😃.\r\n\r\n```bash\r\n $ conda list | grep datasets\r\ndatasets 2.0.1.dev0 pypi_0 pypi\r\n\r\n```\r\n\r\n```python\r\ndef preprocess_data(dataset):\r\n \"\"\"\r\n Helper funtion to pre-process HuggingFace Cifar-100 Dataset to remove fine_label and coarse_label columns and\r\n add is_flipped column\r\n Args:\r\n dataset: HuggingFace CIFAR-100 Dataset Object\r\n\r\n Returns:\r\n new_dataset: A Dataset object with \"img\" and \"is_flipped\" columns only\r\n\r\n \"\"\"\r\n # remove fine_label and coarse_label columns\r\n new_dataset = dataset.remove_columns(['fine_label', 'coarse_label'])\r\n # add the column for is_flipped\r\n new_dataset = new_dataset.add_column(name=\"is_flipped\", column=np.zeros((len(new_dataset)), dtype=np.uint8))\r\n\r\n return new_dataset\r\n\r\n\r\ndef generate_flipped_data(example, p=0.5):\r\n \"\"\"\r\n A Dataset mapping functions that transforms some of the image up-side-down.\r\n If the probability value (p) is 0.5 approximately half the images will be flipped upside-down\r\n Args:\r\n example: An example from the dataset containing a Python dictionary with \"img\" and \"is_flipped\" key-value pair\r\n p: probability of flipping the image up-side-down, Default 0.5\r\n\r\n Returns:\r\n example: A Dataset object\r\n\r\n \"\"\"\r\n # example['img'] = example['img']\r\n if rng.random() > p: # the flip the image and set is_flipped column to 1\r\n example['img'] = example['img'].transpose(\r\n 1) # ImageOps.flip(example['img']) #example['img'].transpose(Image.FLIP_TOP_BOTTOM)\r\n example['is_flipped'] = 1\r\n\r\n return example\r\n\r\nmy_test = preprocess_data(test_dataset)\r\nmy_test = my_test.map(generate_flipped_data)\r\n```\r\n\r\nThe output now show the function was applied successfully:\r\n``` bash\r\n/home/rafay/anaconda3/envs/pytorch_new/bin/python /home/rafay/Documents/you_only_live_once/upside_down_detector/create_dataset.py\r\nDownloading builder script: 5.61kB [00:00, 3.16MB/s] \r\nDownloading metadata: 4.21kB [00:00, 2.56MB/s] \r\nReusing dataset cifar100 (/home/rafay/.cache/huggingface/datasets/cifar100/cifar100/1.0.0/f365c8b725c23e8f0f8d725c3641234d9331cd2f62919d1381d1baa5b3ba3142)\r\nReusing dataset cifar100 (/home/rafay/.cache/huggingface/datasets/cifar100/cifar100/1.0.0/f365c8b725c23e8f0f8d725c3641234d9331cd2f62919d1381d1baa5b3ba3142)\r\n100%|██████████| 10000/10000 [00:01<00:00, 5149.15ex/s]\r\n```\r\n" ]

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[ "Hi @StellaAthena, thanks for reporting.\r\n\r\nPlease note, that our `datasets` library performs several operations in order to load a dataset, among them:\r\n- it downloads all the required files: for C4 \"en\", 378.69 GB of JSON GZIPped files\r\n- it parses their content to generate the dataset\r\n- it caches the dataset in an Arrow file: for C4 \"en\", this file size is 1.87 TB\r\n- it memory-maps the Arrow file\r\n\r\nIf it suits your use case, you might load this dataset in streaming mode:\r\n- no Arrow file is generated\r\n- you can iterate over elements immediately (no need to wait to download all the entire files)\r\n\r\n```python\r\nIn [45]: from datasets import load_dataset\r\n ...: ds = load_dataset(\"c4\", \"en\", split=\"train\", streaming=True)\r\n ...: for item in ds:\r\n ...: print(item)\r\n ...: break\r\n ...: \r\n{'text': 'Beginners BBQ Class Taking Place in Missoula!\\nDo you want to get better at making delicious BBQ? You will have the opportunity, put this on your calendar now. Thursday, September 22nd join World Class BBQ Champion, Tony Balay from Lonestar Smoke Rangers. He will be teaching a beginner level class for everyone who wants to get better with their culinary skills.\\nHe will teach you everything you need to know to compete in a KCBS BBQ competition, including techniques, recipes, timelines, meat selection and trimming, plus smoker and fire information.\\nThe cost to be in the class is $35 per person, and for spectators it is free. Included in the cost will be either a t-shirt or apron and you will be tasting samples of each meat that is prepared.', 'timestamp': '2019-04-25T12:57:54Z', 'url': 'https://klyq.com/beginners-bbq-class-taking-place-in-missoula/'}\r\n```\r\nI hope this is useful for your use case." ]

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[ "Hi @nbroad1881, thanks for reporting.\r\n\r\nLet me have a look to try to improve its performance. ", "Thanks @nbroad1881 for reporting! I don't recall it taking so long. I will also have a look at this. \r\n@albertvillanova please let me know if I am doing something unnecessary or time consuming.", "Hi @nbroad1881 and @vrindaprabhu,\r\n\r\nAs a workaround for the performance of the parsing of the raw data files (this could be addressed in a subsequent PR), I have found that there are also processed data files, that do not require parsing. I have added these as new configurations `processed.en` and `processed.zh`:\r\n```python\r\nds = load_dataset(\"medical_dialog\", \"processed.zh\")\r\n```" ]

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[ "Hello, could you tell me how this issue can be fixed? I'm coming across the same issue." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "Hi @arymbe, thanks for reporting.\r\n\r\nUnfortunately, I'm not able to reproduce your problem.\r\n\r\nCould you please write the complete stack trace? That way we will be able to see which package originates the exception.", "Hello, thank you for your fast replied. this is the complete error that I got\r\n\r\n---------------------------------------------------------------------------\r\n\r\nAttributeError Traceback (most recent call last)\r\n\r\n---------------------------------------------------------------------------\r\n\r\nAttributeError Traceback (most recent call last)\r\n\r\nInput In [27], in <module>\r\n----> 1 from datasets import load_dataset\r\n\r\nvenv/lib/python3.8/site-packages/datasets/__init__.py:39, in <module>\r\n 37 from .arrow_dataset import Dataset, concatenate_datasets\r\n 38 from .arrow_reader import ReadInstruction\r\n---> 39 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder\r\n 40 from .combine import interleave_datasets\r\n 41 from .dataset_dict import DatasetDict, IterableDatasetDict\r\n\r\nvenv/lib/python3.8/site-packages/datasets/builder.py:40, in <module>\r\n 32 from .arrow_reader import (\r\n 33 HF_GCP_BASE_URL,\r\n 34 ArrowReader,\r\n (...)\r\n 37 ReadInstruction,\r\n 38 )\r\n 39 from .arrow_writer import ArrowWriter, BeamWriter\r\n---> 40 from .data_files import DataFilesDict, sanitize_patterns\r\n 41 from .dataset_dict import DatasetDict, IterableDatasetDict\r\n 42 from .features import Features\r\n\r\nvenv/lib/python3.8/site-packages/datasets/data_files.py:297, in <module>\r\n 292 except FileNotFoundError:\r\n 293 raise FileNotFoundError(f\"The directory at {base_path} doesn't contain any data file\") from None\r\n 296 def _resolve_single_pattern_in_dataset_repository(\r\n--> 297 dataset_info: huggingface_hub.hf_api.DatasetInfo,\r\n 298 pattern: str,\r\n 299 allowed_extensions: Optional[list] = None,\r\n 300 ) -> List[PurePath]:\r\n 301 data_files_ignore = FILES_TO_IGNORE\r\n 302 fs = HfFileSystem(repo_info=dataset_info)\r\n\r\nAttributeError: module 'huggingface_hub' has no attribute 'hf_api'", "This is weird... It is long ago that the package `huggingface_hub` has a submodule called `hf_api`.\r\n\r\nMaybe you have a problem with your installed `huggingface_hub`...\r\n\r\nCould you please try to update it?\r\n```shell\r\npip install -U huggingface_hub\r\n```", "Yap, I've updated several times. Then, I've tried numeral combination of datasets and huggingface_hub versions. However, I think your point is right that there is a problem with my huggingface_hub installation. I'll try another way to find the solution. I'll update it later when I get the solution. Thank you :)", "I'm sorry I can't reproduce your problem.\r\n\r\nMaybe you could try to create a new Python virtual environment and install all dependencies there from scratch. You can use either:\r\n- Python venv: https://docs.python.org/3/library/venv.html\r\n- or conda venv (if you are using conda): https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html", "Facing the same issue.\r\n\r\nResponse from `pip show datasets`\r\n```\r\nName: datasets\r\nVersion: 1.15.1\r\nSummary: HuggingFace community-driven open-source library of datasets\r\nHome-page: https://github.com/huggingface/datasets\r\nAuthor: HuggingFace Inc.\r\nAuthor-email: [email protected]\r\nLicense: Apache 2.0\r\nLocation: /usr/local/lib/python3.8/dist-packages\r\nRequires: aiohttp, dill, fsspec, huggingface-hub, multiprocess, numpy, packaging, pandas, pyarrow, requests, tqdm, xxhash\r\nRequired-by: lm-eval\r\n```\r\n\r\nResponse from `pip show huggingface_hub`\r\n\r\n```\r\nName: huggingface-hub\r\nVersion: 0.8.1\r\nSummary: Client library to download and publish models, datasets and other repos on the huggingface.co hub\r\nHome-page: https://github.com/huggingface/huggingface_hub\r\nAuthor: Hugging Face, Inc.\r\nAuthor-email: [email protected]\r\nLicense: Apache\r\nLocation: /usr/local/lib/python3.8/dist-packages\r\nRequires: filelock, packaging, pyyaml, requests, tqdm, typing-extensions\r\nRequired-by: datasets\r\n```\r\n\r\nresponse from `datasets-cli env`\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"/usr/local/bin/datasets-cli\", line 5, in <module>\r\n from datasets.commands.datasets_cli import main\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/__init__.py\", line 37, in <module>\r\n from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/builder.py\", line 44, in <module>\r\n from .data_files import DataFilesDict, _sanitize_patterns\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/data_files.py\", line 120, in <module>\r\n dataset_info: huggingface_hub.hf_api.DatasetInfo,\r\n File \"/usr/local/lib/python3.8/dist-packages/huggingface_hub/__init__.py\", line 105, in __getattr__\r\n raise AttributeError(f\"No {package_name} attribute {name}\")\r\nAttributeError: No huggingface_hub attribute hf_api\r\n```", "A workaround: \r\nI changed lines around Line 125 in `__init__.py` of `huggingface_hub` to something like\r\n```\r\n__getattr__, __dir__, __all__ = _attach(\r\n __name__,\r\n submodules=['hf_api'],\r\n```\r\nand it works ( which gives `datasets` direct access to `huggingface_hub.hf_api` ).", "I was getting the same issue. After trying a few versions, following combination worked for me.\r\ndataset==2.3.2\r\nhuggingface_hub==0.7.0\r\n\r\nIn another environment, I just installed latest repos from pip through `pip install -U transformers datasets tokenizers evaluate`, resulting in following versions. This also worked. Hope it helps someone. \r\n\r\ndatasets-2.3.2 evaluate-0.1.2 huggingface-hub-0.8.1 responses-0.18.0 tokenizers-0.12.1 transformers-4.20.1", "For layoutlm_v3 finetune\r\ndatasets-2.3.2 evaluate-0.1.2 huggingface-hub-0.8.1 responses-0.18.0 tokenizers-0.12.1 transformers-4.12.5", "(For layoutlmv3 fine-tuning) In my case, modifying `requirements.txt` as below worked.\r\n\r\n- python = 3.7\r\n\r\n```\r\ndatasets==2.3.2\r\nevaluate==0.1.2\r\nhuggingface-hub==0.8.1\r\nresponse==0.5.0\r\ntokenizers==0.10.1\r\ntransformers==4.12.5\r\nseqeval==1.2.2\r\ndeepspeed==0.5.7\r\ntensorboard==2.7.0\r\nseqeval==1.2.2\r\nsentencepiece\r\ntimm==0.4.12\r\nPillow\r\neinops\r\ntextdistance\r\nshapely\r\n```", "> For layoutlm_v3 finetune datasets-2.3.2 evaluate-0.1.2 huggingface-hub-0.8.1 responses-0.18.0 tokenizers-0.12.1 transformers-4.12.5\r\n\r\nGOOD!! Thanks!" ]

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[ "maybe just do it from now on no? (i.e. not for existing `dataset_infos.json` files)", "_The documentation is not available anymore as the PR was closed or merged._", "> maybe just do it from now on no? (i.e. not for existing dataset_infos.json files)\r\n\r\nYes, or do this only for datasets created with `push_to_hub` to (always) keep the GH datasets small? \r\n", "yep sounds good too on my side! ", "I reverted the change to avoid the size increase and added the `pretty_print` flag, which pretty-prints the JSON, and that flag is only True for datasets created with `push_to_hub`. " ]

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[ "Maybe it would be nice to ignore private dirs like this one (ones starting with `.`) by default. \r\n\r\nCC @mariosasko ", "Maybe we can add a `ignore_hidden_files` flag to the builder configs of our packaged loaders (to be consistent across all of them), wdyt @lhoestq @albertvillanova? ", "I think they should always ignore them actually ! Not sure if adding a flag would be helpful", "@lhoestq But what if the user explicitly requests those files via regex?\r\n\r\n`glob.glob` ignores hidden files (files starting with \".\") by default unless they are explicitly requested, but fsspec's `glob` doesn't follow this behavior, which is probably a bug, so maybe we can raise an issue or open a PR in their repo?", "> @lhoestq But what if the user explicitly requests those files via regex?\r\n\r\nUsually hidden files are meant to be ignored. If they are data files, they must be placed outside a hidden directory in the first place right ? I think it's more sensible to explain this than adding a flag.\r\n\r\n> glob.glob ignores hidden files (files starting with \".\") by default unless they are explicitly requested, but fsspec's glob doesn't follow this behavior, which is probably a bug, so maybe we can raise an issue or open a PR in their repo?\r\n\r\nAfter globbing using `fsspec`, we already ignore files that start with a `.` in `_resolve_single_pattern_locally` and `_resolve_single_pattern_in_dataset_repository`, I guess we can just account for parent directories as well ?\r\n\r\nWe could open an issue on `fsspec` but I think they won't change this since it's an important breaking change for them." ]

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[ "In the general case you can’t always reduce the quantity of data to download, since you can’t parse CSV or JSON data without downloading the whole files right ? ^^ However we could explore this case-by-case I guess", "Actually for csv pandas has `usecols` which allows loading a subset of columns in a more efficient way afaik, but yes, you're right this might be more complex than I thought." ]

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[ "Closing this one because it must be used this way actually:\r\n```python\r\ndef main():\r\n with FileLock(\"tmp.lock\"):\r\n with Pool(2) as pool:\r\n pool.map(run, range(2))\r\n\r\nif __name__ == \"__main__\":\r\n main()\r\n```" ]

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[ "Hi! Replacing:\r\n```python\r\ntransformed_dataset = dataset.with_transform(transforms)\r\ntransformed_dataset.set_format(type=\"torch\", device=\"cuda\")\r\n```\r\n\r\nwith:\r\n```python\r\ndef transform_func(examples):\r\n examples[\"image\"] = [transforms(img).to(\"cuda\") for img in examples[\"image\"]]\r\n return examples\r\n\r\ntransformed_dataset = dataset.with_transform(transform_func)\r\n```\r\nshould fix the issue. `datasets` doesn't support chaining of transforms (you can think of `set_format`/`with_format` as a predefined transform func for `set_transform`/`with_transforms`), so the last transform (in your case, `set_format`) takes precedence over the previous ones (in your case `with_format`). And the PyTorch formatter is not supported by the Image feature, hence the error (adding support for that is on our short-term roadmap).", "Ok thanks a lot for the code snippet!\r\n\r\nI love the way `datasets` is easy to use but it made it really long to pre-process all the images (400.000 in my case) before training anything. `ImageFolder` from pytorch is faster in my case but force me to have the images on my local machine.\r\n\r\nI don't know how to speed up the process without switching to `ImageFolder` :smile: ", "You can pass `ignore_verifications=True` in `load_dataset` to skip checksum verification, which takes a lot of time if the number of files is large. We will consider making this the default behavior." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "changes made! @lhoestq let me know what you think ", "The CI fail is unrelated to this PR and fixed on master, feel free to merge :)" ]

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[ "WRT the high values, can you add some unit tests with some [string, model] pairs and their resulting perplexity code, and @TristanThrush can run the same pairs through his version of the code?", "_The documentation is not available anymore as the PR was closed or merged._", "I thought that the perplexity metric should output the average perplexity value of all the strings that it gets as input (not a perplexity value per string, as the new version does).\r\n@lhoestq , @TristanThrush thoughts?", "> I thought that the perplexity metric should output the average perplexity value of all the strings that it gets as input (not a perplexity value per string, as the new version does). @lhoestq , @TristanThrush thoughts?\r\n\r\nI support this change from Emi. If we have a perplexity function that loads GPT2 and then returns an average over all of the strings, then it is impossible to get multiple perplexities of a batch of strings efficiently. If we have this new perplexity function that is built for batching, then it is possible to get a batch of perplexities efficiently and you can still compute the average efficiently afterwards.", "Thanks a lot for working on this @emibaylor @TristanThrush :)\r\n\r\nFor consistency with the other metrics, I think it's nice if we return the mean perplexity. Though I agree that having the separate perplexities per sample can also be useful. What do you think about returning both ?\r\n```python\r\nreturn {\"perplexities\": ppls, \"mean_perplexity\": np.mean(ppls)}\r\n```\r\nwe're also doing this for the COMET metric.", "> Thanks a lot for working on this @emibaylor @TristanThrush :)\r\n> \r\n> For consistency with the other metrics, I think it's nice if we return the mean perplexity. Though I agree that having the separate perplexities per sample can also be useful. What do you think about returning both ?\r\n> \r\n> ```python\r\n> return {\"perplexities\": ppls, \"mean_perplexity\": np.mean(ppls)}\r\n> ```\r\n> \r\n> we're also doing this for the COMET metric.\r\n\r\nThanks! Sounds great to me.", "The CI fail is unrelated to your PR and has been fixed on master, feel free to merge the master branch into your PR to fix the CI ;)" ]

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[ "Thanks for reporting. I'm looking at it", " It's not related to the dataset viewer in itself. I can replicate the error with:\r\n\r\n```\r\n>>> import datasets as ds\r\n>>> d = ds.load_dataset('Pavithree/explainLikeImFive')\r\nUsing custom data configuration Pavithree--explainLikeImFive-b68b6d8112cd8a51\r\nDownloading and preparing dataset json/Pavithree--explainLikeImFive to /home/slesage/.cache/huggingface/datasets/json/Pavithree--explainLikeImFive-b68b6d8112cd8a51/0.0.0/ac0ca5f5289a6cf108e706efcf040422dbbfa8e658dee6a819f20d76bb84d26b...\r\nDownloading data: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 305M/305M [00:03<00:00, 98.6MB/s]\r\nDownloading data: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 17.9M/17.9M [00:00<00:00, 75.7MB/s]\r\nDownloading data: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 11.9M/11.9M [00:00<00:00, 70.6MB/s]\r\nDownloading data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:05<00:00, 1.92s/it]\r\nExtracting data files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 1948.42it/s]\r\nFailed to read file '/home/slesage/.cache/huggingface/datasets/downloads/5fee9c8819754df277aee6f252e4db6897d785231c21938407b8862ca871d246' with error <class 'pyarrow.lib.ArrowInvalid'>: Exceeded maximum rows\r\nTraceback (most recent call last):\r\n File \"/home/slesage/hf/datasets/src/datasets/packaged_modules/json/json.py\", line 144, in _generate_tables\r\n dataset = json.load(f)\r\n File \"/home/slesage/.pyenv/versions/3.8.11/lib/python3.8/json/__init__.py\", line 293, in load\r\n return loads(fp.read(),\r\n File \"/home/slesage/.pyenv/versions/3.8.11/lib/python3.8/json/__init__.py\", line 357, in loads\r\n return _default_decoder.decode(s)\r\n File \"/home/slesage/.pyenv/versions/3.8.11/lib/python3.8/json/decoder.py\", line 340, in decode\r\n raise JSONDecodeError(\"Extra data\", s, end)\r\njson.decoder.JSONDecodeError: Extra data: line 1 column 916 (char 915)\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/slesage/hf/datasets/src/datasets/load.py\", line 1691, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/home/slesage/hf/datasets/src/datasets/builder.py\", line 605, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/home/slesage/hf/datasets/src/datasets/builder.py\", line 694, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"/home/slesage/hf/datasets/src/datasets/builder.py\", line 1151, in _prepare_split\r\n for key, table in logging.tqdm(\r\n File \"/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/tqdm/std.py\", line 1168, in __iter__\r\n for obj in iterable:\r\n File \"/home/slesage/hf/datasets/src/datasets/packaged_modules/json/json.py\", line 146, in _generate_tables\r\n raise e\r\n File \"/home/slesage/hf/datasets/src/datasets/packaged_modules/json/json.py\", line 122, in _generate_tables\r\n pa_table = paj.read_json(\r\n File \"pyarrow/_json.pyx\", line 246, in pyarrow._json.read_json\r\n File \"pyarrow/error.pxi\", line 143, in pyarrow.lib.pyarrow_internal_check_status\r\n File \"pyarrow/error.pxi\", line 99, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Exceeded maximum rows\r\n```\r\n\r\ncc @lhoestq @albertvillanova @mariosasko ", "It seems that train.json is not a valid JSON Lines file: it has several JSON objects in the first line (the 915th character in the first line starts a new object, and there's no \"\\n\")\r\n\r\nYou need to have one JSON object per line", "I'm closing this issue.\r\n\r\n@Pavithree, please, feel free to re-open it if fixing the JSON file does not solve it.", "Thank you! that fixes the issue." ]

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[ "Looks like GH actions is not able to resolve `huggingface_hub` 0.5.0, I'm investivating", "_The documentation is not available anymore as the PR was closed or merged._", "I'm glad to see changes in `huggingface_hub` are simplifying code here.", "seems to supersede #4102, feel free to close mine :)", "maybe just cherry-pick the docstring fix", "I think I've found the issue:\r\n- https://github.com/huggingface/huggingface_hub/pull/790", "Good catch, `huggingface_hub` doesn't support python 3.6 anymore indeed, therefore we should keep support for 0.4.0. I'm reverting the requirement version bump for now.\r\n\r\nWe can update the requirement once we drop support for python 3.6 in `datasets`", "@lhoestq, I've opened this PR on `huggingface_hub`: \r\n- https://github.com/huggingface/huggingface_hub/pull/823\r\n\r\nIs there any strong reason why `huggingface_hub` no longer supports Python 3.6? ", "I think `datasets` can drop support for 3.6 soon. But for now maybe let's keep support for 0.4.0, python 3.6 users are not affected by https://github.com/huggingface/datasets/issues/4105 anyway.\r\n\r\n`huggingface_hub` doesn't not have to support 3.6 again just for the CI IMO", "@lhoestq I commented on the PR, that IMO it is not a good practice to drop support for Python 3.6 without a previous deprecation cycle.", "Re-added support for older versions. I ended up checking `huggingface_hub` version to use the old, deprecated API for <0.5.0", "I find it good practice to have all dependency version related code in a single file so that when you decide to remove support for an old version of a dependency it's easy to find and remove them, hence suggesting `utils/_fixes.py` in https://github.com/huggingface/datasets/issues/4105#issuecomment-1090041204", "good idea, thanks !", "I used your suggestion @adrinjalali , I just replace the try/except with a check on the version of `huggingface_hub`" ]

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[ "Hi ! Indeed there was a breaking change in `huggingface_hub` 0.5.0 in `HfApi.create_repo`, which is called here in `datasets` by passing the org name in both the `repo_id` and the `organization` arguments:\r\n\r\nhttps://github.com/huggingface/datasets/blob/2230f7f7d7fbaf102cff356f5a8f3bd1561bea43/src/datasets/arrow_dataset.py#L3363-L3369\r\n\r\nI think we should fix that in `huggingface_hub`, will keep you posted. In the meantime please use `huggingface_hub` 0.4.0", "I'll be sending a fix for this later today on the `huggingface_hub` side.\r\n\r\nThe error would be converted to a `FutureWarning` if `datasets` uses kwargs instead of positional, for example here: \r\n\r\nhttps://github.com/huggingface/datasets/blob/2230f7f7d7fbaf102cff356f5a8f3bd1561bea43/src/datasets/arrow_dataset.py#L3363-L3369\r\n\r\nto be:\r\n\r\n``` python\r\n api.create_repo(\r\n name=dataset_name,\r\n token=token,\r\n repo_type=\"dataset\",\r\n organization=organization,\r\n private=private,\r\n )\r\n```\r\n\r\nBut `name` and `organization` are deprecated in `huggingface_hub=0.5`, and people should pass `repo_id='org/name` instead. Note that `repo_id` was introduced in 0.5 and if `datasets` wants to support older `huggingface_hub` versions (which I encourage it to do), there needs to be a helper function to do that. It can be something like:\r\n\r\n\r\n```python\r\ndef create_repo(\r\n client,\r\n name: str,\r\n token: Optional[str] = None,\r\n organization: Optional[str] = None,\r\n private: Optional[bool] = None,\r\n repo_type: Optional[str] = None,\r\n exist_ok: Optional[bool] = False,\r\n space_sdk: Optional[str] = None,\r\n) -> str:\r\n try:\r\n return client.create_repo(\r\n repo_id=f\"{organization}/{name}\",\r\n token=token,\r\n private=private,\r\n repo_type=repo_type,\r\n exist_ok=exist_ok,\r\n space_sdk=space_sdk,\r\n )\r\n except TypeError:\r\n return client.create_repo(\r\n name=name,\r\n organization=organization,\r\n token=token,\r\n private=private,\r\n repo_type=repo_type,\r\n exist_ok=exist_ok,\r\n space_sdk=space_sdk,\r\n )\r\n```\r\n\r\nin a `utils/_fixes.py` kinda file and and be used internally.\r\n\r\nI'll be sending a patch to `huggingface_hub` to convert the error reported in this issue to a `FutureWarning`.", "PR with the hotfix on the `huggingface_hub` side: https://github.com/huggingface/huggingface_hub/pull/822", "We can definitely change `push_to_hub` to use `repo_id` in `datasets` and require `huggingface_hub>=0.5.0`.\r\n\r\nLet me open a PR :)", "`huggingface_hub` 0.5.1 just got released with a fix, feel free to update `huggingface_hub` ;)" ]

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[ "Can I use instructions present in below link for time series dataset as well? \r\nhttps://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md ", "cc'ing @kashif and @NielsRogge for visibility!", "@INF800 happy to add this dataset! I will try to set a PR by the end of the day... if you can kindly point me to the dataset? Also, note we have a bunch of time series datasets checked in e.g. `electricity_load_diagrams` or `monash_tsf`, and ideally this dataset could also be in a similar format. ", "Thankyou. This is how raw data looks like before cleaning for an individual stocks:\r\n\r\n1. https://github.com/INF800/marktech/tree/raw-data/f/data/raw\r\n2. https://github.com/INF800/marktech/tree/raw-data/t/data/raw\r\n3. https://github.com/INF800/marktech/tree/raw-data/rdfn/data/raw\r\n4. https://github.com/INF800/marktech/tree/raw-data/irbt/data/raw\r\n5. https://github.com/INF800/marktech/tree/raw-data/hll/data/raw\r\n6. https://github.com/INF800/marktech/tree/raw-data/infy/data/raw\r\n7. https://github.com/INF800/marktech/tree/raw-data/reli/data/raw\r\n8. https://github.com/INF800/marktech/tree/raw-data/hdbk/data/raw\r\n\r\n> Scraping is automated using GitHub Actions. So, everyday we will see a new file added in the above links.\r\n\r\nI can rewrite the cleaning scripts to make sure it fits HF dataset standards. (P.S I am very much new to HF dataset)\r\n\r\nThe data set above can be converted into univariate regression / multivariate regression / sequence to sequence generation dataset etc. So, do we have some kind of transformation modules that will read the dataset as some type of dataset (`GenericTimeData`) and convert it to other possible dataset relating to a specific ML task. **By having this kind of transformation module, I only have to add data once** and use transformation module whenever necessary\r\n\r\nAdditionally, having some kind of versioning for the dataset will be really helpful because it will keep on updating - especially time series datasets ", "thanks @INF800 I'll have a look. I believe it should be possible to incorporate this into the time-series format.", "Referencing https://github.com/qingsongedu/time-series-transformers-review", "@INF800 yes I am aware of the review repository and paper which is more or less a collection of abstracts etc. I am working on a unified library of implementations of these papers together with datasets to be then able to compare/contrast and build upon the research etc. but I am not ready to share them publicly just yet.\r\n\r\nIn any case regarding your dataset at the moment its seems from looking at the csv files, its mixture of textual and numerical data, sometimes in the same column etc. As you know, for time series models we would need just numeric data so I would need your help in disambiguating the dataset you have collected and also perhaps starting with just numerical data to start with... \r\n\r\nDo you think you can make a version with just numerical data?", "> @INF800 yes I am aware of the review repository and paper which is more or less a collection of abstracts etc. I am working on a unified library of implementations of these papers together with datasets to be then able to compare/contrast and build upon the research etc. but I am not ready to share them publicly just yet.\r\n> \r\n> In any case regarding your dataset at the moment its seems from looking at the csv files, its mixture of textual and numerical data, sometimes in the same column etc. As you know, for time series models we would need just numeric data so I would need your help in disambiguating the dataset you have collected and also perhaps starting with just numerical data to start with...\r\n> \r\n> Do you think you can make a version with just numerical data?\r\n\r\nWill share the numeric data and conversion script within end of this week. \r\n\r\nI am on a business trip currently - it is in my desktop." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "The CI is failing because it's outdated, but the task tags are updated on `master`, merging :)" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4102). All of your documentation changes will be reflected on that endpoint.", "Closing in favor of https://github.com/huggingface/datasets/pull/4106" ]

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[ "Hi! Can you please specify the full name of the dataset? IIRC `full_numbers` is one of the configs of the `svhn` dataset, and its generation is slow due to data being stored in binary Matlab files. Even if you specify a specific split, `datasets` downloads all of them, but we plan to fix that soon and only download the requested split.\r\n\r\nIf you are in a hurry, download the `svhn` script [here](`https://huggingface.co/datasets/svhn/blob/main/svhn.py`), remove [this code](https://huggingface.co/datasets/svhn/blob/main/svhn.py#L155-L162), and run:\r\n```python\r\nfrom datasets import load_dataset\r\ndset = load_dataset(\"path/to/your/local/script.py\", \"full_numbers\")\r\n```\r\n\r\nAnd to make loading easier in Colab, you can create a dataset repo on the Hub and upload the script there. Or push the script to Google Drive and mount the drive in Colab." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "I find this preprocessing a bit too specific to add it as a method to `datasets` as it's only useful in the context of CV (and we support multiple modalities). However, I agree it would be great to move this code to another lib to avoid code duplication. Maybe we should create a package with preprocessing functions/transforms for this purpose?" ]

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[ "Hi @andreybond, thanks for reporting.\r\n\r\nUnfortunately, I'm not able to able to reproduce your issue:\r\n```python\r\nIn [4]: from datasets import load_dataset\r\n ...: datasets = load_dataset(\"nielsr/XFUN\", \"xfun.ja\")\r\n\r\nIn [5]: datasets\r\nOut[5]: \r\nDatasetDict({\r\n train: Dataset({\r\n features: ['id', 'input_ids', 'bbox', 'labels', 'image', 'entities', 'relations'],\r\n num_rows: 194\r\n })\r\n validation: Dataset({\r\n features: ['id', 'input_ids', 'bbox', 'labels', 'image', 'entities', 'relations'],\r\n num_rows: 71\r\n })\r\n})\r\n```\r\n\r\nThe only reason I can imagine this issue may arise is if your default encoding is not \"UTF-8\" (and it is ASCII instead). This is usually the case on Windows machines; but you say your environment is a Linux machine. Maybe you change your machine default encoding?\r\n\r\nCould you please check this?\r\n```python\r\nIn [6]: import sys\r\n\r\nIn [7]: sys.getdefaultencoding()\r\nOut[7]: 'utf-8'\r\n```", "I opened a PR in the original dataset loading script:\r\n- microsoft/unilm#677\r\n\r\nand fixed the corresponding dataset script on the Hub:\r\n- https://huggingface.co/datasets/nielsr/XFUN/commit/73ba5e026621e05fb756ae0f267eb49971f70ebd", "import sys\r\nsys.getdefaultencoding()\r\n\r\nreturned: 'utf-8'\r\n\r\n---------------------\r\n\r\nI've just cloned master branch - your fix works! Thank you!" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "A quick Github tip ;) To avoid running N times the CI, you can push all the changes at once: go to Files Changed tab, and on each suggestion there's a \"add to commit batch\" and then you can do one commit for all the suggestions you want to approve ;)", "Oh thanks for the tip!! Haha I was wondering why it was running a bunch of\ntimes :P\n\nOn Tue, Apr 5, 2022 at 11:44 AM Quentin Lhoest ***@***.***>\nwrote:\n\n> A quick Github tip ;) To avoid running N times the CI, you can push all\n> the changes at once: go to Files Changed tab, and on each suggestion\n> there's a \"add to commit batch\" and then you can do one commit for all the\n> suggestions you want to approve ;)\n>\n> —\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/datasets/pull/4098#issuecomment-1088894515>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/ADMMIIRZYNVFJRWRWW4VJY3VDRNUBANCNFSM5SS7L5HA>\n> .\n> You are receiving this because you modified the open/close state.Message\n> ID: ***@***.***>\n>\n\n\n-- \nSasha Luccioni, PhD\nPostdoctoral Researcher (Mila, Université de Montréal)\nChercheure postdoctorale (Mila, Université de Montréal)\nhttps://www.sashaluccioni.com/\n [image: Image result for universite de montreal logo]\n" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "Hi @jacobbieker, thanks for your request and study of possible alternatives.\r\n\r\nWe are very interested in finding a way to make `datasets` useful to you.\r\n\r\nLooking at the Zarr docs, I saw that among its storage alternatives, there is the ZIP file format: https://zarr.readthedocs.io/en/stable/api/storage.html#zarr.storage.ZipStore\r\n\r\nThis might be convenient for many reasons:\r\n- On the one hand, we avoid the Git issue with huge number of small files: chunks files are compressed into a single ZIP file\r\n- On the other hand, the ZIP file format is specially suited for streaming data because it allows random access to its component files (i.e. it supports random access to its chunks)\r\n\r\nAnyway, I think that a Python loading script will be necessary: you need to implement additional logic to select certain chunks (based on date or other criteria).\r\n\r\nPlease, let me know if this makes sense to you.", "Ah okay, I missed the option of zip files for zarr, I'll try that with our repos and see if it works! Thanks a lot!", "Hi @jacobbieker, does the Zarr ZipStore work for your use case?", "Hi,\r\n\r\nYes, it seems to! I got it working for https://huggingface.co/datasets/openclimatefix/mrms thanks for the help! ", "On behalf of the Zarr developers, let me say THANK YOU for working to support Zarr on HF! 🙏 Zarr is a 100% open-source and community driven project (fiscally sponsored by NumFocus). We see it as an ideal format for ML training datasets, particularly in scientific domains.\r\n\r\nI think the solution of zipping the Zarr store is a reasonable way to balance the constraints of Git LFS with the structure of Zarr.\r\n\r\nIt would be amazing to get something on the [Hugging Face Datasets Docs](https://huggingface.co/docs/datasets/index) about how to best work with Zarr. Let me know if there's a way I could help with that effort.", "Also just noting here that I was able to lazily open @jacobbieker's dataset over the internet from HF hub 🚀 !\r\n\r\n```python\r\nimport xarray as xr\r\nurl = \"https://huggingface.co/datasets/openclimatefix/mrms/resolve/main/data/2016_001.zarr.zip\"\r\nzip_url = 'zip:///::' + url\r\nds = xr.open_dataset(zip_url, engine='zarr', chunks={})\r\n```\r\n\r\n<img width=\"740\" alt=\"image\" src=\"https://user-images.githubusercontent.com/1197350/164508663-bc75cdc0-734d-44f4-9562-2877ecfdf433.png\">\r\n", "However, I wasn't able to get streaming working using the Datasets api:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\nds = load_dataset(\"openclimatefix/mrms\", streaming=True, split='train')\r\nitem = next(iter(ds))\r\n```\r\n\r\n<details>\r\n<summary>FileNotFoundError traceback</summary>\r\n\r\n```\r\nNo config specified, defaulting to: mrms/2021\r\nzip://::https://huggingface.co/datasets/openclimatefix/mrms/resolve/main/data/2016_001.zarr.zip\r\ndata/2016_001.zarr.zip\r\nzip://2016_001.zarr.zip::https://huggingface.co/datasets/openclimatefix/mrms/resolve/main/data/2016_001.zarr.zip\r\n---------------------------------------------------------------------------\r\nFileNotFoundError Traceback (most recent call last)\r\nInput In [1], in <cell line: 3>()\r\n 1 from datasets import load_dataset\r\n 2 ds = load_dataset(\"openclimatefix/mrms\", streaming=True, split='train')\r\n----> 3 item = next(iter(ds))\r\n\r\nFile /opt/miniconda3/envs/hugginface/lib/python3.9/site-packages/datasets/iterable_dataset.py:497, in IterableDataset.__iter__(self)\r\n 496 def __iter__(self):\r\n--> 497 for key, example in self._iter():\r\n 498 if self.features:\r\n 499 # we encode the example for ClassLabel feature types for example\r\n 500 encoded_example = self.features.encode_example(example)\r\n\r\nFile /opt/miniconda3/envs/hugginface/lib/python3.9/site-packages/datasets/iterable_dataset.py:494, in IterableDataset._iter(self)\r\n 492 else:\r\n 493 ex_iterable = self._ex_iterable\r\n--> 494 yield from ex_iterable\r\n\r\nFile /opt/miniconda3/envs/hugginface/lib/python3.9/site-packages/datasets/iterable_dataset.py:87, in ExamplesIterable.__iter__(self)\r\n 86 def __iter__(self):\r\n---> 87 yield from self.generate_examples_fn(**self.kwargs)\r\n\r\nFile ~/.cache/huggingface/modules/datasets_modules/datasets/openclimatefix--mrms/2a6f697014d7eb3caf586ca137d47ca38785ae2fe36248611b021f8248b59936/mrms.py:150, in MRMS._generate_examples(self, filepath, split)\r\n 147 filepath = \"[https://huggingface.co/datasets/openclimatefix/mrms/resolve/main/data/2016_001.zarr.zip](https://huggingface.co/datasets/openclimatefix/mrms/resolve/main/data/2016_001.zarr.zip%3C/span%3E%3Cspan) style=\"color:rgb(175,0,0)\">\"\r\n 148 # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.\r\n 149 # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.\r\n--> 150 with zarr.storage.FSStore(fsspec.open(\"zip::\" + filepath, mode='r'), mode='r') as store:\r\n 151 data = xr.open_zarr(store)\r\n 152 for key, row in enumerate(data[\"time\"].values):\r\n\r\nFile /opt/miniconda3/envs/hugginface/lib/python3.9/site-packages/zarr/storage.py:1120, in FSStore.__init__(self, url, normalize_keys, key_separator, mode, exceptions, dimension_separator, **storage_options)\r\n 1117 import fsspec\r\n 1118 self.normalize_keys = normalize_keys\r\n-> 1120 protocol, _ = fsspec.core.split_protocol(url)\r\n 1121 # set auto_mkdir to True for local file system\r\n 1122 if protocol in (None, \"file\") and not storage_options.get(\"auto_mkdir\"):\r\n\r\nFile /opt/miniconda3/envs/hugginface/lib/python3.9/site-packages/fsspec/core.py:514, in split_protocol(urlpath)\r\n 512 def split_protocol(urlpath):\r\n 513 \"\"\"Return protocol, path pair\"\"\"\r\n--> 514 urlpath = stringify_path(urlpath)\r\n 515 if \"://\" in urlpath:\r\n 516 protocol, path = urlpath.split(\"://\", 1)\r\n\r\nFile /opt/miniconda3/envs/hugginface/lib/python3.9/site-packages/fsspec/utils.py:315, in stringify_path(filepath)\r\n 313 return filepath\r\n 314 elif hasattr(filepath, \"__fspath__\"):\r\n--> 315 return filepath.__fspath__()\r\n 316 elif isinstance(filepath, pathlib.Path):\r\n 317 return str(filepath)\r\n\r\nFile /opt/miniconda3/envs/hugginface/lib/python3.9/site-packages/fsspec/core.py:98, in OpenFile.__fspath__(self)\r\n 96 def __fspath__(self):\r\n 97 # may raise if cannot be resolved to local file\r\n---> 98 return self.open().__fspath__()\r\n\r\nFile /opt/miniconda3/envs/hugginface/lib/python3.9/site-packages/fsspec/core.py:140, in OpenFile.open(self)\r\n 132 def open(self):\r\n 133 \"\"\"Materialise this as a real open file without context\r\n 134 \r\n 135 The file should be explicitly closed to avoid enclosed file\r\n (...)\r\n 138 been deleted; but a with-context is better style.\r\n 139 \"\"\"\r\n--> 140 out = self.__enter__()\r\n 141 closer = out.close\r\n 142 fobjects = self.fobjects.copy()[:-1]\r\n\r\nFile /opt/miniconda3/envs/hugginface/lib/python3.9/site-packages/fsspec/core.py:103, in OpenFile.__enter__(self)\r\n 100 def __enter__(self):\r\n 101 mode = self.mode.replace(\"t\", \"\").replace(\"b\", \"\") + \"b\"\r\n--> 103 f = self.fs.open(self.path, mode=mode)\r\n 105 self.fobjects = [f]\r\n 107 if self.compression is not None:\r\n\r\nFile /opt/miniconda3/envs/hugginface/lib/python3.9/site-packages/fsspec/spec.py:1009, in AbstractFileSystem.open(self, path, mode, block_size, cache_options, compression, **kwargs)\r\n 1007 else:\r\n 1008 ac = kwargs.pop(\"autocommit\", not self._intrans)\r\n-> 1009 f = self._open(\r\n 1010 path,\r\n 1011 mode=mode,\r\n 1012 block_size=block_size,\r\n 1013 autocommit=ac,\r\n 1014 cache_options=cache_options,\r\n 1015 **kwargs,\r\n 1016 )\r\n 1017 if compression is not None:\r\n 1018 from fsspec.compression import compr\r\n\r\nFile /opt/miniconda3/envs/hugginface/lib/python3.9/site-packages/fsspec/implementations/zip.py:96, in ZipFileSystem._open(self, path, mode, block_size, autocommit, cache_options, **kwargs)\r\n 94 if mode != \"rb\":\r\n 95 raise NotImplementedError\r\n---> 96 info = self.info(path)\r\n 97 out = self.zip.open(path, \"r\")\r\n 98 out.size = info[\"size\"]\r\n\r\nFile /opt/miniconda3/envs/hugginface/lib/python3.9/site-packages/fsspec/archive.py:42, in AbstractArchiveFileSystem.info(self, path, **kwargs)\r\n 40 return self.dir_cache[path + \"/\"]\r\n 41 else:\r\n---> 42 raise FileNotFoundError(path)\r\n\r\nFileNotFoundError:\r\n```\r\n\r\n</details>\r\n\r\nIs this a bug? Or am I just doing it wrong...", "I'm still messing around with that dataset, so the data might have moved. I currently have each year of MRMS precipitation rate data as it's own zarr, but as they are quite large (on order of 100GB each) I'm working to split them into single days, and as such they are still being moved around, I was just trying to get a proof of concept working originally. ", "I've mostly finished rearranging the data now and uploading some more, so this works now:\r\n```python\r\nimport datasets\r\nds = datasets.load_dataset(\"openclimatefix/mrms\", streaming=True, split=\"train\")\r\nitem = next(iter(ds))\r\nprint(item.keys())\r\nprint(item[\"timestamp\"])\r\n```\r\n\r\nThe MRMS data now goes most of 2016-2022, with quite a few gaps I'm working on filling in" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4095). All of your documentation changes will be reflected on that endpoint." ]

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[ "It's a bug! Thanks for reporting, I'm looking at it.", "By the way, the error on our part is due to the huge size of every row (~90MB). The dataset viewer does not support such big dataset rows for the moment.\r\nAnyway, we're working to give a hint about this in the dataset viewer.", "Fixed. See https://huggingface.co/datasets/elena-soare/crawled-ecommerce/viewer/elena-soare--crawled-ecommerce/train.\r\n\r\n<img width=\"1552\" alt=\"Capture d’écran 2022-04-12 à 11 23 51\" src=\"https://user-images.githubusercontent.com/1676121/162929722-2e2b80e2-154a-4b61-87bd-e341bd6c46e6.png\">\r\n\r\nThanks for reporting!" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "cc: @albertvillanova just FYI" ]

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[ "Hi! Yes, the problem with `add_item` is that it keeps examples in memory, so you are left with these options:\r\n* writing a dataset loading script in which you iterate over `custom_example_dict_streamer` and yield the examples (in `_generate examples`)\r\n* storing the data in a JSON/CSV/Parquet/TXT file and using `Dataset.from_{format}`\r\n* using `add_item` + `save_to_disk` on smaller chunks: \r\n ```python\r\n from datasets import Dataset, concatenate_datasets\r\n MAX_SAMPLES_IN_MEMORY = 1000\r\n samples_in_dset = 0\r\n dset = Dataset.from_dict({\"col1\": [], \"col2\": []}) # empty dataset\r\n path_to_save_dir = \"path/to/save/dir\"\r\n num_chunks = 0\r\n for example_dict in custom_example_dict_streamer(\"/path/to/raw/data\"):\r\n dset = dset.add_item(example_dict)\r\n samples_in_dset += 1\r\n if samples_in_dset == MAX_SAMPLES_IN_MEMORY:\r\n samples_in_dset = 0\r\n dset.save_to_disk(f\"{path_to_save_dir}{num_chunks}\")\r\n num_chunks =+ 1\r\n dset = Dataset.from_dict({\"col1\": [], \"col2\": []}) # empty dataset\r\n if samples_in_dset > 0:\r\n dset.save_to_disk(f\"{path_to_save_dir}{num_chunks}\")\r\n num_chunks =+ 1\r\n loaded_dsets = [] # memory-mapped\r\n for chunk_num in range(num_chunks):\r\n dset = Dataset.load_from_disk(f\"{path_to_save_dir}{chunk_num}\") \r\n loaded_dsets.append(dset)\r\n final_dset = concatenate_datasets(dset)\r\n ```\r\n If you still have issues with this approach, you can try to delete unused datasets with `gc.collect()` to free some memory. ", "This is really elegant, thank you @mariosasko! I will try this." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "Hi @cslizc, thanks for reporting.\r\n\r\nI have just forced the refresh of the corresponding cache and the preview is working now.", "thank you so much" ]

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[ "Now, I can't find any reference to set_progress_bar_enabled in the code.\r\n\r\nI think it have been deleted", "Hi @virilo,\r\n\r\nPlease note that since `datasets` version 2.0.0, we have aligned with `transformers` the management of the progress bar (among other things):\r\n- #3897\r\n\r\nNow, you should update your code to use `datasets.logging.disable_progress_bar`.\r\n\r\nYou have more info in our docs: [Logging methods](https://huggingface.co/docs/datasets/package_reference/logging_methods)", "One important thing for beginner like me is: from datasets.utils.logging import disable_progress_bar\r\nDo not forget the 'utils' or you will waste a long time like me...." ]

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[ "Hi @blackhat-coder, thanks for reporting.\r\n\r\nPlease note that the `transformers` library updated their data collators API last year (version 4.10.0):\r\n- huggingface/transformers#13105\r\n\r\nnow requiring to pass `return_tensors` argument at Data Collator instantiation.\r\n\r\nAnd therefore, we also updated in the `datasets` library documentation all the examples using `transformers` data collators.\r\n\r\nIf you would like to follow our examples, please update your installed `transformers` version:\r\n```\r\npip install -U transformers\r\n```" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "@lhoestq / @albertvillanova / @mariosasko \r\nI am facing the same scenario. Let me explain the situation point. I have a glue ETL job\r\n\r\n1--> My files are in parquet format and stored in AWS s3.\r\n2--> I am iterating a loop for a data set where the same file name can occur with diffrent other data.\r\n3--> I read the parquet and saved it in a pandas data frame.\r\n4--> Done some operation on that data frame\r\n5--> upload the updated data frame into the S3 parquet file. Below are code snippet what I am using to save the updated \r\n data frame into parquet format and load into S3\r\n `header_name_column_list = dict(data_frame)\r\n header_list = []\r\n for col_id, col_type in header_name_column_list.items():\r\n header_list.append(pyarrow.field(col_id, pyarrow.string()))\r\n table_schema = pyarrow.schema(header_list)\r\n table = pyarrow.Table.from_pandas(data_frame, schema=table_schema, preserve_index=False)\r\n writer = parquet.ParquetWriter(b_buffer, table.schema)\r\n writer.write_table(table)\r\n writer.close()\r\n b_buffer.seek(0)\r\n .....\r\n ....\r\n self.s3_client.upload_fileobj(\r\n b_buffer,\r\n self.bucket,\r\n file_key,\r\n ExtraArgs=extra_args)`\r\n\r\nBut when I executed the glue etl job, the first time it works properly and but in the next iteration, when I try to open the same file got that error.\r\n\r\n\r\n<html>\r\n<body>\r\n<!--StartFragment-->\r\n\r\nINFO:Iot-dsip-de-duplication-job:Dataframe uploaded: s3://abc/2022/07/12/file1_ft_20220714122108.3065_12345.parquet INFO:Iot-dsip-de-duplication-job:Sleep for 60 sec\r\nINFO:Iot-dsip-de-duplication-job:start after sleep\r\n.......................\r\n..........................\r\n..........................\r\nERROR:Iot-dsip-de-duplication-job:Failed to read data from parquet file s3://abc/2022/07/12/file1_ft_20220714122108.3065_12345.parquet, error is : Invalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file.INFO:Iot-dsip-de-duplication-job:Empty dataframe found\r\n\r\n<!--EndFragment-->\r\n</body>\r\n</html>\r\n\r\nAny clue will be really helpful..I got stuck with this problem." ]

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[ "Hi @richarddwang,\r\n\r\n\r\nIndeed, we have recently updated the loading script of that dataset (and fixed that bug as well):\r\n- #4002\r\n\r\nThat fix will be available in our next `datasets` library release. In the meantime, you can incorporate that fix by:\r\n- installing `datasets` from our GitHub repo:\r\n```bash\r\npip install git+https://github.com/huggingface/datasets#egg=datasets\r\n```\r\n- forcing the data files to be redownloaded\r\n```python\r\nds = load_dataset('conll2012_ontonotesv5', 'english_v4', split=\"test\", download_mode=\"force_redownload\")\r\n```\r\n\r\nFeel free to re-open this issue if the problem persists. \r\n\r\nDuplicate of:\r\n- #4031" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "Awesome ! Let us know if you have questions or if we can help ;) I'm assigning you\r\n\r\nPS: if possible, please try to not use Google Drive links in your dataset script, since Google Drive has download quotas and is not always reliable.", "HI, I was waiting to come out in the second version to do the implementation.\r\n\r\n- Paper: https://dx.doi.org/10.2139/ssrn.4126881\r\n- Data: [Data mendelay](http://doi.org/10.17632/wg4bpm33hj.2)", "Nice ! 🚀 ", "The link of CCAgT dataset is: https://huggingface.co/datasets/lapix/CCAgT" ]

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[ "Hi @wranai, thanks for reporting.\r\n\r\nPlease note that the information about language families and groups is taken form the original paper: [XTREME-S: Evaluating Cross-lingual Speech Representations](https://arxiv.org/abs/2203.10752).\r\n\r\nIf that information is wrong, feel free to contact the paper's authors to suggest that correction.\r\n\r\nJust note that Hungarian language (contrary to their geographically surrounding neighbor languages) belongs to the Uralic (languages) family, together with (among others) Finnish, Estonian, some other languages in northern regions of Scandinavia..." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "Hi @Jun-jie-Huang,\r\n\r\nAs the error message says, \".pkl\" data files are not supported.\r\n\r\nIf you would like to share your dataset on the Hub, you would need:\r\n- either to create a Python loading script, that loads the data in any format\r\n- or to transform your data files to one of the supported formats (listed in the error message above: CSV, JSON, Parquet, TXT,...)\r\n\r\nYou can find the details in our docs: \r\n- How to share a dataset: https://huggingface.co/docs/datasets/share\r\n- How to create a dataset loading script: https://huggingface.co/docs/datasets/dataset_script\r\n\r\nFeel free to re-open this issue and ping us if you need further assistance." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "Love it !\r\n\r\n+1 to using JSON Lines rather than CSV. I've also seen image datasets for which JSON Lines was used.\r\n\r\nA `file_name` column sounds good as well, and it means we could reuse the same name for audio. And ok to check the metadata file by default :)\r\n\r\nYou suggested to name the file infos.json - since we already have a datasets_infos.json file, maybe it would be nice to have a name for the metadata/annotations that doesn't contain \"info\" ? (e.g. metadata.json, annotations.json, labels.json)", "@lhoestq I've addressed your comments and my TODOs. Additionally, I've updated `encode_nested_example`/`decode_nested_example` to support null values in place of a dictionary (if it's not top-level) since JSON Lines also supports this. ", "@lhoestq Sure, feel free to add more tests if you have the time. ", "I created a dedicated test file for `imagefolder`, moved some existing tests there from `test_packaged_modules.py`, and added an end-to-end test of `imagefolder` with metadata. I tested for train split only, and for two splits train and test.\r\n\r\nLet me know if the test looks ok to you. I'll add similar tests but with the other structures we support on tuesday", "Thanks a lot for working on this! The test looks great :). ", "Added a test for archives. Will also add a test when the metadata file is not named correctly, and see if we can raise an informative error" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "Looks good, but I think we are missing some scripts with outdated tags (RedCaps, MNIST, ...).", "Just updated the tags of vision datasets :)\r\nWe can figure out one for image datasets without labels like PASS - not sure how to name the task for this, maybe `image-fill-mask` (for consistency with language modeling for pretraining) / `masked-auto-encoding` (from ViT). Let's see that in another PR later" ]

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