Datasets:

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[ "After some more tests I've realized that this \"issue\" is due to the `numpy.float64` to `float` conversion, but when defining a function named `compute_metrics` as it follows:\r\n\r\n```python\r\ndef compute_metrics(eval_preds):\r\n metric = load_metric(\"matthews_correlation\")\r\n logits, labels = eval_preds\r\n predictions = np.argmax(logits, axis=1)\r\n return metric.compute(predictions=predictions, references=labels)\r\n```\r\n\r\nIt fails when the evaluation metrics are computed in the `Trainer` with the same error code `AttributeError: 'float' object has no attribute 'item'` as the output is not a `numpy.float64`... Maybe I'm doing something wrong, not sure!", "Ok after some more experiments I've realized that it's an issue from my side, at first I thought it was due to `fp16=True` in `TrainingArguments`, but in the end that may not be the issue, so I'll close this for now and check later, since the mistake is on my side :weary: Sorry for the inconvenience!" ]

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[ "Looks great, thanks ! If the 3d ones are really rare we can remove them for now.\r\n\r\nAnd I can see that `object-detection` and `semantic-segmentation` are both task categories (top-level) and task ids (bottom-level). Maybe there's a way to group them and have less granularity for the task categories. For example `speech-processing` is a high level task category. What do you think ?\r\n\r\nWe can still update the list of tasks later if needed when we have more vision datasets\r\n", "@lhoestq @osanseviero I used the categories (there were main ones and subcategories) in the paperswithcode, I got rid of some of them that could be too granular. I can put it there if you'd like (I'll wait for your reply before committing it again)", "We can ignore the ones that are too granular IMO. What we did for audio tasks is to have them all under \"audio-processing\". Maybe we can do the same here for now until we have more comprehensive tasks/applications ?" ]

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[ "Hi Brady! I met the similar issue, it stuck in the downloading stage instead of download anything, maybe it is broken. After I change the downloading from URLs to one url of the [Multiwoz project](https://github.com/budzianowski/multiwoz/archive/44f0f8479f11721831c5591b839ad78827da197b.zip) and use dirs to get separate files, the problems gone." ]

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[ "There is an exception when running `pip install .[tests]`:\r\n```\r\nProcessing /home/circleci/datasets\r\nCollecting numpy>=1.17 (from datasets==1.12.2.dev0)\r\n Downloading https://files.pythonhosted.org/packages/45/b2/6c7545bb7a38754d63048c7696804a0d947328125d81bf12beaa692c3ae3/numpy-1.19.5-cp36-cp36m-manylinux1_x86_64.whl (13.4MB)\r\n 100% |████████████████████████████████| 13.4MB 3.9MB/s eta 0:00:011\r\n\r\n...\r\n\r\nCollecting faiss-cpu (from datasets==1.12.2.dev0)\r\n Downloading https://files.pythonhosted.org/packages/87/91/bf8ea0d42733cbb04f98d3bf27808e4919ceb5ec71102e21119398a97237/faiss-cpu-1.7.1.post2.tar.gz (41kB)\r\n 100% |████████████████████████████████| 51kB 30.9MB/s ta 0:00:01\r\n Complete output from command python setup.py egg_info:\r\n Traceback (most recent call last):\r\n File \"/home/circleci/.pyenv/versions/3.6.14/lib/python3.6/site-packages/setuptools/sandbox.py\", line 154, in save_modules\r\n yield saved\r\n File \"/home/circleci/.pyenv/versions/3.6.14/lib/python3.6/site-packages/setuptools/sandbox.py\", line 195, in setup_context\r\n yield\r\n File \"/home/circleci/.pyenv/versions/3.6.14/lib/python3.6/site-packages/setuptools/sandbox.py\", line 250, in run_setup\r\n _execfile(setup_script, ns)\r\n File \"/home/circleci/.pyenv/versions/3.6.14/lib/python3.6/site-packages/setuptools/sandbox.py\", line 45, in _execfile\r\n exec(code, globals, locals)\r\n File \"/tmp/easy_install-1pop4blm/numpy-1.21.2/setup.py\", line 34, in <module>\r\n method can be invoked.\r\n RuntimeError: Python version >= 3.7 required.\r\n```\r\n\r\nApparently, `numpy-1.21.2` tries to be installed in the temporary directory `/tmp/easy_install-1pop4blm` instead of the downloaded `numpy-1.19.5` (requirement of `datasets`).\r\n\r\nThis is caused because `pip` downloads the `.tar.gz` (instead of the `.whl`) and tries to build it in a tmp dir." ]

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[ "There is a speed up in Windows machines:\r\n- From `13m 52s` to `11m 10s`\r\n\r\nIn Linux machines, some workers crash with error message:\r\n```\r\nOSError: [Errno 12] Cannot allocate memory\r\n```", "There is also a speed up in Linux machines:\r\n- From `7m 30s` to `5m 32s`" ]

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[ "Hi @JamieSlome,\r\n\r\nThanks for reaching out. Yes, you are right: I'm opening a PR to add the `SECURITY.md` file and a contact method.\r\n\r\nIn the meantime, please feel free to report the security issue to: feedback@huggingface.co" ]

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[ "@lhoestq Let me make sure we never need it, and if not then I'll remove it entirely in a follow-up PR.", "Thanks ;) it will be less confusing and easier to maintain to not keep unused hacky features" ]

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[ "I just made the dummy data smaller :)\r\nOnce github refreshes the change I think we can merge !", "Thank you so much for reviewing and accepting my pull request!! :)\r\n\r\nI created these rather large dummy data sets to cover all different cases for the row structure. E.g. in the web configuration, it's possible that a row has evidence from wikipedia (\"EntityPages\") and the web (\"SearchResults\"). But it also might happen that either EntityPages or SearchResults is empty. Probably, I will add this thought to the dataset description in the future.", "Ok I see ! Yes feel free to mention it in the dataset card, this can be useful.\r\n\r\nFor the dummy data though we can keep the small ones, as the tests are mainly about testing the parsing from the dataset script rather than the actual content of the dataset." ]

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[ "Cool, I think we can do both :)", "@lhoestq now the 2 are implemented.\r\n\r\nPlease note that for the the second protection, finally I have chosen to protect the master branch only from **merge commits** (see update comment above), so no need to disable/re-enable the protection on each release (direct commits, different from merge commits, can be pushed to the remote master branch; and eventually reverted without messing up the repo history)." ]

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[ "Hi ! Good idea :)\r\nIf you are interested in contributing, feel free to give it a try and open a Pull Request. Also let me know if I can help you with this or if you have questions" ]

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[ "Hi ! I guess the caching mechanism should have considered the new `filter` to be different from the old one, and don't use cached results from the old `filter`.\r\nTo avoid other users from having this issue we could make the caching differentiate the two, what do you think ?", "If it's easy enough to implement, then yes please 😄 But this issue can be low-priority, since I've only encountered it in a couple of `transformers` CI tests.", "Well it can cause issue with anyone that updates `datasets` and re-run some code that uses filter, so I'm creating a PR", "I just merged a fix, let me know if you're still having this kind of issues :)\r\n\r\nWe'll do a release soon to make this fix available", "Definitely works on several manual cases with our dummy datasets, thank you @lhoestq !", "Fixed by #2947." ]

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[ "Thanks @albertvillanova for your great suggestions! I just pushed a new commit with the necessary fixes. For some reason, the test `test_metric_common` failed for `meteor` metric, which doesn't have any connection to this PR, so I'm trying to rebase and see if it helps.", "Hi @Hazoom,\r\n\r\nYou were right: the non-passing test had nothing to do with this PR.\r\n\r\nUnfortunately, you did a git rebase (instead of a git merge), which is not recommended once you have already opened a pull request because you mess up your pull request history. You can see that your pull request now contains:\r\n- your commits repeated two times\r\n- and commits which are not yours from the master branch\r\n\r\nIf you would like to clean your pull request, please make:\r\n```\r\ngit reset --hard 587b93a\r\ngit fetch upstream master\r\ngit merge upstream/master\r\ngit push --force origin sede\r\n```", "> Hi @Hazoom,\r\n> \r\n> You were right: the non-passing test had nothing to do with this PR.\r\n> \r\n> Unfortunately, you did a git rebase (instead of a git merge), which is not recommended once you have already opened a pull request because you mess up your pull request history. You can see that your pull request now contains:\r\n> \r\n> * your commits repeated two times\r\n> * and commits which are not yours from the master branch\r\n> \r\n> If you would like to clean your pull request, please make:\r\n> \r\n> ```\r\n> git reset --hard 587b93a\r\n> git fetch upstream master\r\n> git merge upstream/master\r\n> git push --force origin sede\r\n> ```\r\n\r\nThanks @albertvillanova ", "> Nice! Just one final request before approving your pull request:\r\n> \r\n> As you have updated the \"QuerySetId\" field data type, the size of the dataset is smaller now. You should regenerate the metadata. Please run:\r\n> \r\n> ```\r\n> rm datasets/sede/dataset_infos.json\r\n> datasets-cli test datasets/sede --save_infos --all_configs\r\n> ```\r\n\r\n@albertvillanova Good catch, just fixed it." ]

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[ "I tried `unshuffled_original_da` and it is also not working" ]

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[ "We might have collisions if a username and a dataset_name are the same. Maybe instead serialize the dataset name by replacing `/` with some string, eg `__SLASH__`, that will hopefully never appear in a dataset or user name (it's what I did in https://github.com/huggingface/datasets-preview-backend/blob/master/benchmark/scripts/serialize.py. That way, all the datasets are one-level deep directories", "IIRC we enforce that no repo id or username can contain `___` (exactly 3 underscores) specifically for this reason, so you can use that string (that we use in other projects)\r\n\r\ncc @Pierrci ", "> IIRC we enforce that no repo id or username can contain ___ (exactly 3 underscores) specifically for this reason, so you can use that string (that we use in other projects)\r\n\r\nout of curiosity: where is it enforced?", "> where is it enforced?\r\n\r\nNowhere yet but we should :) feel free to track in internal tracker and/or implement, as this will be useful in the future", "Thanks for the trick, I'm doing the change :)\r\nWe can use\r\n`~/.cache/huggingface/datasets/username___dataset_name` for the data\r\n`~/.cache/huggingface/modules/datasets_modules/datasets/username___dataset_name` for the python files", "Merging, though it will have to be integrated again the refactor at #2986" ]

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[ "Hi @daqieq, thanks for reporting.\r\n\r\nUnfortunately, I was not able to reproduce this bug:\r\n```ipython\r\nIn [1]: from datasets import load_dataset\r\n ...: ds = load_dataset('wiki_bio')\r\nDownloading: 7.58kB [00:00, 26.3kB/s]\r\nDownloading: 2.71kB [00:00, ?B/s]\r\nUsing custom data configuration default\r\nDownloading and preparing dataset wiki_bio/default (download: 318.53 MiB, generated: 736.94 MiB, post-processed: Unknown size, total: 1.03 GiB) to C:\\Users\\username\\.cache\\huggingface\\datasets\\wiki_bio\\default\\\r\n1.1.0\\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9...\r\nDownloading: 334MB [01:17, 4.32MB/s]\r\nDataset wiki_bio downloaded and prepared to C:\\Users\\username\\.cache\\huggingface\\datasets\\wiki_bio\\default\\1.1.0\\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9. Subsequent calls will reuse thi\r\ns data.\r\n```\r\n\r\nThis kind of error messages usually happen because:\r\n- Your running Python script hasn't write access to that directory\r\n- You have another program (the File Explorer?) already browsing inside that directory", "Thanks @albertvillanova for looking at it! I tried on my personal Windows machine and it downloaded just fine.\r\n\r\nRunning on my work machine and on a colleague's machine it is consistently hitting this error. It's not a write access issue because the `.incomplete` directory is written just fine. It just won't rename and then it deletes the directory in the `finally` step. Also the zip file is written and extracted fine in the downloads directory.\r\n\r\nThat leaves another program that might be interfering, and there are plenty of those in my work machine ... (full antivirus, data loss prevention, etc.). So the question remains, why not extend the `try` block to allow catching the error and circle back to the rename after the unknown program is finished doing its 'stuff'. This is the approach that I read about in the linked repo (see my comments above).\r\n\r\nIf it's not high priority, that's fine. However, if someone were to write an PR that solved this issue in our environment in an `except` clause, would it be reviewed for inclusion in a future release? Just wondering whether I should spend any more time on this issue." ]

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[ "Note that the tests seem to fail because of a bug in an Exception at the moment, see: https://github.com/huggingface/datasets/pull/2936 for the fix", "@lhoestq implemented your changes, I think this might be ready for another look.", "Thanks @lhoestq, implemented the changes, let me know if anything else pops up." ]

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[ "I did some investigation and, as it seems, the bug stems from [this line](https://github.com/huggingface/datasets/blob/8004d7c3e1d74b29c3e5b0d1660331cd26758363/src/datasets/arrow_dataset.py#L325). The lifecycle of the dataset from the linked line is bound to one of the returned `tf.data.Dataset`. So my (hacky) solution involves wrapping the linked dataset with `weakref.proxy` and adding a custom `__del__` to `tf.python.data.ops.dataset_ops.TensorSliceDataset` (this is the type of a dataset that is returned by `tf.data.Dataset.from_tensor_slices`; this works for TF 2.x, but I'm not sure `tf.python.data.ops.dataset_ops` is a valid path for TF 1.x) that deletes the linked dataset, which is assigned to the dataset object as a property. Will open a draft PR soon!", "Thanks a lot for investigating !" ]

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[ "I'm also fine with the removal in 1.15" ]

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[ "Why 1.9 ?\r\n\r\nhttps://anaconda.org/HuggingFace/datasets currently says 1.11", "Alright I added 1.12.0 and 1.12.1 and fixed the conda build #2952 " ]

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[ "I'm going to merge it, but yeah - hopefully the CI runner just cleans that up automatically and few other people run the tests on Windows anyway!" ]

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[ "Pushed a fix to my branch #2731 " ]

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[ "Thanks for reporting, I'm looking into it :)", "Fixed by #2950." ]

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[ "Cool, love it ! :)\r\n\r\nFeel free to add a paragraph saying how to load the dataset:\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"stevhliu/demo\")\r\n\r\n# or to separate each csv file into several splits\r\ndata_files = {\"train\": \"train.csv\", \"test\": \"test.csv\"}\r\ndataset = load_dataset(\"stevhliu/demo\", data_files=data_files)\r\nprint(dataset[\"train\"][0])\r\n```", "Perfect, feel free to mark this PR ready for review :)\r\n\r\ncc @albertvillanova do you have any comment ? You can check the tutorial here:\r\nhttps://47389-250213286-gh.circle-artifacts.com/0/docs/_build/html/no_code_upload.html\r\n\r\nMaybe we can just add a list of supported file types:\r\n- csv\r\n- json\r\n- json lines\r\n- text\r\n- parquet", "I just added a mention of the login for private datasets. Don't hesitate to edit or comment.\r\n\r\nOtherwise I think it's all good, feel free to merge it @stevhliu if you don't have other changes to make :)" ]

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[ "Hi, the filename here is less than 255\r\n```python\r\n>>> len(\"_home_garrett_.cache_huggingface_datasets_csv_test-7c856aea083a7043_0.0.0_9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff.incomplete.lock\")\r\n154\r\n```\r\nso not sure why it's considered too long for your filesystem.\r\n(also note that the lock files we use always have smaller filenames than 255)\r\n\r\nhttps://github.com/huggingface/datasets/blob/5d1a9f1e3c6c495dc0610b459e39d2eb8893f152/src/datasets/utils/filelock.py#L135-L135", "Yes, you're right! I need to get you more info here. Either there's something going with the name itself that the file system doesn't like (an encoding that blows up the name length??) or perhaps there's something with the path that's causing the entire string to be used as a name. I haven't seen this on any system before and the Internet's not forthcoming with any info.", "Snap, encountered when trying to run [this example from PyTorch Lightning Flash](https://lightning-flash.readthedocs.io/en/latest/reference/speech_recognition.html):\r\n\r\n```py\r\nimport torch\r\n\r\nimport flash\r\nfrom flash.audio import SpeechRecognition, SpeechRecognitionData\r\nfrom flash.core.data.utils import download_data\r\n\r\n# 1. Create the DataModule\r\ndownload_data(\"https://pl-flash-data.s3.amazonaws.com/timit_data.zip\", \"./data\")\r\n\r\ndatamodule = SpeechRecognitionData.from_json(\r\n input_fields=\"file\",\r\n target_fields=\"text\",\r\n train_file=\"data/timit/train.json\",\r\n test_file=\"data/timit/test.json\",\r\n)\r\n```\r\n\r\nGave this traceback:\r\n\r\n```py\r\nTraceback (most recent call last):\r\n File \"lf_ft.py\", line 10, in <module>\r\n datamodule = SpeechRecognitionData.from_json(\r\n File \"/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/flash/core/data/data_module.py\", line 1005, in from_json\r\n return cls.from_data_source(\r\n File \"/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/flash/core/data/data_module.py\", line 571, in from_data_source\r\n train_dataset, val_dataset, test_dataset, predict_dataset = data_source.to_datasets(\r\n File \"/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/flash/core/data/data_source.py\", line 307, in to_datasets\r\n train_dataset = self.generate_dataset(train_data, RunningStage.TRAINING)\r\n File \"/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/flash/core/data/data_source.py\", line 344, in generate_dataset\r\n data = load_data(data, mock_dataset)\r\n File \"/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/flash/audio/speech_recognition/data.py\", line 103, in load_data\r\n dataset_dict = load_dataset(self.filetype, data_files={stage: str(file)})\r\n File \"/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/datasets/load.py\", line 1599, in load_dataset\r\n builder_instance = load_dataset_builder(\r\n File \"/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/datasets/load.py\", line 1457, in load_dataset_builder\r\n builder_instance: DatasetBuilder = builder_cls(\r\n File \"/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/datasets/builder.py\", line 285, in __init__\r\n with FileLock(lock_path):\r\n File \"/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/datasets/utils/filelock.py\", line 323, in __enter__\r\n self.acquire()\r\n File \"/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/datasets/utils/filelock.py\", line 272, in acquire\r\n self._acquire()\r\n File \"/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/datasets/utils/filelock.py\", line 403, in _acquire\r\n fd = os.open(self._lock_file, open_mode)\r\nOSError: [Errno 36] File name too long: '/home/louis/.cache/huggingface/datasets/_home_louis_.cache_huggingface_datasets_json_default-98e6813a547f72fa_0.0.0_c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426.lock'\r\n```\r\n\r\nMy home directory is encrypted, therefore the maximum length is 143 ([source 1](https://github.com/ray-project/ray/issues/1463#issuecomment-425674521), [source 2](https://stackoverflow.com/a/6571568/2668831))\r\n\r\nFrom what I've read I think the error is in reference to the file name (just the final part of the path) which is 145 chars long:\r\n\r\n```py\r\n>>> len(\"_home_louis_.cache_huggingface_datasets_json_default-98e6813a547f72fa_0.0.0_c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426.lock\")\r\n145\r\n```\r\n\r\nI also have a file in this directory (i.e. whose length is not a problem):\r\n\r\n```py\r\n>>> len(\"_home_louis_.cache_huggingface_datasets_librispeech_asr_clean_2.1.0_468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1.lock\")\r\n137\r\n```", "Perhaps this could be exposed as a config setting so you could change it manually?\r\n\r\nhttps://github.com/huggingface/datasets/blob/5d1a9f1e3c6c495dc0610b459e39d2eb8893f152/src/datasets/utils/filelock.py#L135-L135\r\n\r\nRather than hard-code 255, default it to 255, and allow it to be changed, the same way is done for `datasets.config.IN_MEMORY_MAX_SIZE`:\r\n\r\nhttps://github.com/huggingface/datasets/blob/12b7e13bc568b9f92705f64b249e148f3bc9a9ea/src/datasets/config.py#L171-L173\r\n\r\nIn fact there already appears to be an existing variable to do so:\r\n\r\nhttps://github.com/huggingface/datasets/blob/12b7e13bc568b9f92705f64b249e148f3bc9a9ea/src/datasets/config.py#L187\r\n\r\nIt's used here:\r\n\r\nhttps://github.com/huggingface/datasets/blob/efe89edd36e4ffa562fc3eebaf07a5fec26e6dac/src/datasets/builder.py#L163-L165\r\n\r\nPerhaps it could be set based on a test (trying to create a 255 char length named lock file and seeing if it fails)", "Just fixed it, sending a PR :smile:", "Hi @lmmx @gar1t ,\r\n\r\nit would be helpful if you could run the following code and copy-paste the output here:\r\n```python\r\nimport datasets\r\nimport os\r\nos.statvfs(datasets.config.HF_DATASETS_CACHE)\r\n```", "`os.statvfs_result(f_bsize=4096, f_frsize=4096, f_blocks=240046344, f_bfree=96427610, f_bavail=84216487, f_files=61038592, f_ffree=58216027, f_favail=58216027, f_flag=4102, f_namemax=143)`", "Hi @lmmx,\r\n\r\nThanks for providing the result of the command. I've opened a PR, and it would be great if you could verify that the fix works on your system. To install the version of the datasets with the fix, please run the following command:\r\n```\r\npip install git+https://github.com/huggingface/datasets.git@fix-2924\r\n```\r\n\r\nBtw, I saw your PR, and I appreciate your effort. However, my approach is a bit simpler for the end-user, so that's why I decided to fix the issue myself.", "No problem Mario I didn't know that was where that value was recorded so I learnt something :smiley: I just wanted to get a local version working, of course you should implement whatever fix is best for HF. Yes can confirm this fixes it too. Thanks!" ]

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[ "doing a patch release now :)" ]

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[ "Hi @SBrandeis, thanks for reporting! ^^\r\n\r\nI think this is an issue with `fsspec`: https://github.com/intake/filesystem_spec/issues/389\r\n\r\nI will ask them if they are planning to fix it...", "Code to reproduce the bug: `ClientPayloadError: 400, message='Can not decode content-encoding: gzip'`\r\n```python\r\nIn [1]: import fsspec\r\n\r\nIn [2]: import json\r\n\r\nIn [3]: with fsspec.open('https://raw.githubusercontent.com/allenai/scitldr/master/SciTLDR-Data/SciTLDR-FullText/test.jsonl', encoding=\"utf-8\") as f:\r\n ...: for row in f:\r\n ...: data = json.loads(row)\r\n ...:\r\n---------------------------------------------------------------------------\r\nClientPayloadError Traceback (most recent call last)\r\n```", "Thanks for investigating @albertvillanova ! 🤗 " ]

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[ "Hi ! Is there some kind of proxy that is configured in your browser that gives you access to internet ? If it's the case it could explain why it doesn't work in the code, since the proxy wouldn't be used", "It is indeed an agency problem, thank you very, very much", "Let me know if you have other questions :)\r\n\r\nClosing this issue now" ]

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[ "> The implementation makes heavy use of multiprocessing which this PR does not touch. Is this conflicting with multiprocessing natively integrated in datasets?\r\n\r\nIt should work normally, but feel free to test it.\r\nThere is some documentation about using metrics in a distributed setup that uses multiprocessing [here](https://huggingface.co/docs/datasets/loading.html?highlight=rank#distributed-setup)\r\nYou can test to spawn several processes where each process would load the metric. Then in each process you add some references/predictions to the metric. Finally you call compute() in each process and on process 0 it should return the result on all the references/predictions\r\n\r\nLet me know if you have questions or if I can help", "Is there a good way to debug the Windows tests? I suspect it is an issue with `multiprocessing`, but I can't see the error messages.", "Indeed it has an issue on windows.\r\nIn your example it's supposed to output\r\n```python\r\n{'pass@1': 0.5, 'pass@2': 1.0}\r\n```\r\nbut it gets\r\n```python\r\n{'pass@1': 0.0, 'pass@2': 0.0}\r\n```\r\n\r\nI'm not on my windows machine today so I can't take a look at it. I can dive into it early next week if you want", "> I'm not on my windows machine today so I can't take a look at it. I can dive into it early next week if you want\r\n\r\nThat would be great - unfortunately I have no access to a windows machine at the moment. I am quite sure it is an issue with in exectue.py because of multiprocessing.\r\n" ]

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[ "Closed by #2915." ]

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[ "Hi @margotwagner, \r\nThis bug was fixed in #1995. Upgrading the datasets should work (min v1.8.0 ideally)", "Hi @margotwagner,\r\n\r\nYes, as @bhavitvyamalik has commented, this bug was fixed in `datasets` version 1.5.0. You need to update it, as your current version is 1.4.1:\r\n> Environment info\r\n> - `datasets` version: 1.4.1" ]

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[ "Included in https://github.com/huggingface/datasets/pull/2906" ]

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[ "Will create a new one, this one seems to be missed up. " ]

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[ "> Should I add a section in https://github.com/huggingface/datasets/blob/master/docs/source/load_hub.rst? (there is no section for get_dataset_infos)\r\n\r\nYes totally :) This tutorial should indeed mention this, given how fundamental it is" ]

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[ "Hi ! Thanks for reporting. The error seems to happen only if you use compressed files.\r\n\r\nThe second dataset is prepared in another dataset cache directory than the first - which is normal, since the source file is different. However, it doesn't uncompress the new data file because it finds the old uncompressed data in the extraction cache directory.\r\n\r\nIf we fix the extraction cache mechanism to uncompress a local file if it changed then it should fix the issue.\r\nCurrently the extraction cache mechanism only takes into account the path of the compressed file, which is an issue.", "Facing the same issue, is there any way to overtake this issue until it will be fixed? ", "You can clear your extraction cache in the meantime (by default at `~/.cache/huggingface/datasets/downloads/extracted`)" ]

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[ "thanks!" ]

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[ "@hassiahk is working on it #2810 ", "WikiMedia is now hosting the pixel values directly which should make it a lot easier!\r\nThe files can be found here:\r\nhttps://techblog.wikimedia.org/2021/09/09/the-wikipedia-image-caption-matching-challenge-and-a-huge-release-of-image-data-for-research/\r\nhttps://analytics.wikimedia.org/published/datasets/one-off/caption_competition/training/image_pixels/", "> @hassiahk is working on it #2810\r\n\r\nThank you @bhavitvyamalik! Added this issue so we could track progress 😄 . Just linked the PR as well for visibility. ", "Hey folks, we are now hosting the merged pixel values + embeddings + metadata ourselves. I gave it a try - [nateraw/wit](https://huggingface.co/datasets/nateraw/wit)\r\n\r\n**⚠️ - Make sure you add `streaming=True` unless you're prepared to download 400GB of data!**\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset('nateraw/wit', streaming=True)\r\nexample = next(iter(ds))\r\n```\r\n\r\n```python\r\n>>> example = next(iter(ds['train']))\r\n>>> example.keys()\r\ndict_keys(['b64_bytes', 'original_width', 'image_url', 'wit_features', 'original_height', 'metadata_url', 'mime_type', 'caption_attribution_description', 'embedding'])\r\n>>> example['wit_features'].keys()\r\ndict_keys(['hierarchical_section_title', 'language', 'attribution_passes_lang_id', 'context_section_description', 'is_main_image', 'page_title', 'caption_title_and_reference_description', 'caption_alt_text_description', 'caption_reference_description', 'page_url', 'context_page_description', 'section_title', 'page_changed_recently'])\r\n```" ]

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[ "Sorry, my bad... When implementing `xpathopen`, I just considered the use case in the COUNTER dataset... I'm fixing it!" ]

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[ "I just fixed the class name, and added `[More Information Needed]` in empty sections in case people want to complete the dataset card :)" ]

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[ "I think you can just add a test `test_dataset_to_csv_multiproc` in `tests/io/test_csv.py` and we'll be good", "Hi @lhoestq, \r\nI've added `test_dataset_to_csv` apart from `test_dataset_to_csv_multiproc` as no test was there to check generated CSV file when `num_proc=1`. Please let me know if anything is also required! " ]

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[ "I think it's fine to have the original schema" ]

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[ "This has been fixed by https://github.com/huggingface/datasets/pull/2900\r\nWe're doing a new release 1.12 today to make the fix available :)" ]

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[ "@lhoestq, thanks for your review.\r\n\r\nI added test for `to_pylist`, I didn't do that for `to_numpy` because this method shouldn't be called for dynamic dimension ArrayXD - this method will try to make a single numpy array for the whole column which cannot be done for dynamic arrays.\r\n\r\nI dig into `to_pandas()` functionality and I found it quite difficult to implement. `PandasArrayExtensionArray` takes single np.array as an argument. It might be a bit of changes to make it work with the list of arrays. Do you mind if we exclude this work from this PR. I added an error message for the case if somebody tries to use dynamic arrays with `to_pandas`", "@lhoestq, I just fixed all the tests. Let me know if there is anything else to add.", "@lhoestq, any chance you had some time to check out this PR?\r\n", "Hi ! Sorry for the delay\r\n\r\nIt looks good to me ! I think the only thing missing is the support for passing a list of numpy arrays to `map` when the first dimension is dynamic.\r\n\r\nCurrently it raises an error:\r\n```python\r\nfrom datasets import *\r\nimport numpy as np\r\n\r\nfeatures= Features({\"a\": Array3D(shape=(None, 5, 2), dtype=\"int32\")})\r\nd = Dataset.from_dict({\"a\": [np.zeros((5,5,2)), np.zeros((2,5,2))]}, features=features)\r\nd = d.map(lambda a: {\"a\": np.concatenate([a]*2)}, input_columns=\"a\")\r\nprint(d[0])\r\n```\r\nraises\r\n```python\r\nTraceback (most recent call last):\r\n File \"playground/ttest.py\", line 6, in <module>\r\n d = d.map(lambda x: {\"a\": np.concatenate([x]*2)}, input_columns=\"a\")\r\n File \"/home/truent/hf/datasets/src/datasets/arrow_dataset.py\", line 1932, in map\r\n return self._map_single(\r\n File \"/home/truent/hf/datasets/src/datasets/arrow_dataset.py\", line 426, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \"/home/truent/hf/datasets/src/datasets/fingerprint.py\", line 406, in wrapper\r\n out = func(self, *args, **kwargs)\r\n File \"/home/truent/hf/datasets/src/datasets/arrow_dataset.py\", line 2317, in _map_single\r\n writer.finalize()\r\n File \"/home/truent/hf/datasets/src/datasets/arrow_writer.py\", line 443, in finalize\r\n self.write_examples_on_file()\r\n File \"/home/truent/hf/datasets/src/datasets/arrow_writer.py\", line 312, in write_examples_on_file\r\n pa_array = pa.array(typed_sequence)\r\n File \"pyarrow/array.pxi\", line 222, in pyarrow.lib.array\r\n File \"pyarrow/array.pxi\", line 110, in pyarrow.lib._handle_arrow_array_protocol\r\n File \"/home/truent/hf/datasets/src/datasets/arrow_writer.py\", line 108, in __arrow_array__\r\n storage = pa.array(self.data, type.storage_dtype)\r\n File \"pyarrow/array.pxi\", line 305, in pyarrow.lib.array\r\n File \"pyarrow/array.pxi\", line 39, in pyarrow.lib._sequence_to_array\r\n File \"pyarrow/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n File \"pyarrow/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Can only convert 1-dimensional array values\r\n```\r\n\r\nI think the issue is that here we don't cover the case where self.data is a list of numpy arrays:\r\n\r\nhttps://github.com/huggingface/datasets/blob/55fd140a63b8f03a0e72985647e498f1fc799d3f/src/datasets/arrow_writer.py#L104-L109\r\n\r\nWe should remove the `isinstance(self.data[0], np.ndarray)` part and add these lines to cover this case:\r\n\r\nhttps://github.com/huggingface/datasets/blob/55fd140a63b8f03a0e72985647e498f1fc799d3f/src/datasets/arrow_writer.py#L112-L113", "@lhoestq, thanks, good catch!\r\nAre you able to run this check with fixed dimension ArrayXD?\r\nfor below example\r\n```\r\nimport numpy as np\r\nfrom datasets import *\r\n\r\nfeatures = Features({\"a\": Array3D(shape=(2, 5, 2), dtype=\"int32\")})\r\nd = Dataset.from_dict({\"a\": [np.zeros((2, 5, 2)), np.zeros((2, 5, 2))]}, features=features)\r\nd = d.map(lambda a: {\"a\": np.array(a) + 1}, input_columns=\"a\")\r\nprint(d[0])\r\n```\r\n\r\nI am getting:\r\n```\r\n File \"/home/ib/datasets/src/datasets/arrow_writer.py\", line 116, in __arrow_array__\r\n if trying_type and out[0].as_py() != self.data[0]:\r\nValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()\r\n```", "Nevertheless, I tried to fix that. Let me know if that works.", "@lhoestq, just resolved the conflicts. Let me know if there is anything left to do with this PR", "Hi, thanks a lot for your comments.\r\nAgree, happy to contribute to this topic in future PRs" ]

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