Datasets:

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Djarnis

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test-huggingface-datasets

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[ "`datasets` doesn't seem to urlencode the directory names here\r\n\r\nhttps://github.com/huggingface/datasets/blob/7feeb5648a63b6135a8259dedc3b1e19185ee4c7/src/datasets/utils/file_utils.py#L109-L111\r\n\r\nfor example we should have\r\n```python\r\nfrom datasets.utils.file_utils import hf_hub_url\r\n\r\nurl = hf_hub_url(\"loubnabnl/bigcode_csharp\", \"data/c#/data_0003.jsonl\")\r\nprint(url)\r\n# Currently returns\r\n# https://huggingface.co/datasets/loubnabnl/bigcode_csharp/resolve/main/data/c#/data_0003.jsonl\r\n# while it should be \r\n# https://huggingface.co/datasets/loubnabnl/bigcode_csharp/resolve/main/data/c%23/data_0003.jsonl\r\n```", "I'll work on this :)", "@loubnabnl The dataset you linked in the description of the bug does not work and returns a 404. Where can I find the dataset to reproduce the bug?", "I think you can create a dataset repository on the Hub with a dummy file containing a `#`", "Ah sorry it was private I just made it public, I can also help with this if needed", "@lhoestq Should I url encode also repo_id and revision parameters? I'm not sure what are the valid characters there.\r\n\r\nPersonally, I would be cautious and only url encode the path parameter.", "These are possible solutions (assuming `from urllib.parse import quote`):\r\n\r\n1) url encode only the path parameter:\r\n```\r\n# src/datasets/utils/file_utils.py\r\ndef hf_hub_url(repo_id: str, path: str, revision: Optional[str] = None) -> str:\r\n revision = revision or config.HUB_DEFAULT_VERSION\r\n return config.HUB_DATASETS_URL.format(repo_id=repo_id, path=quote(path), revision=revision)\r\n```\r\n2) url encode all parameters:\r\n```\r\n# src/datasets/utils/file_utils.py\r\ndef hf_hub_url(repo_id: str, path: str, revision: Optional[str] = None) -> str:\r\n revision = revision or config.HUB_DEFAULT_VERSION\r\n return config.HUB_DATASETS_URL.format(repo_id=quote(repo_id), path=quote(path), revision=quote(revision))\r\n```\r\n3) url encode the whole url:\r\n```\r\n# src/datasets/config.py\r\nHUB_DATASETS_PATH = \"/datasets/{repo_id}/resolve/{revision}/{path}\"\r\nHUB_DATASETS_URL = HF_ENDPOINT + HUB_DATASETS_PATH\r\n```\r\n```\r\n# src/datasets/utils/file_utils.py\r\ndef hf_hub_url(repo_id: str, path: str, revision: Optional[str] = None) -> str:\r\n revision = revision or config.HUB_DEFAULT_VERSION\r\n return config.HF_ENDPOINT + quote(config.HUB_DATASETS_PATH.format(repo_id=repo_id, path=path, revision=revision))\r\n```", "repo_id can only contain alphanumeric characters and _- so it doesn't need to be encoded.\r\n\r\nHowever I agree it's a good idea to also apply `quote` to the revision as well as in 2. !", "Should be fixed by https://github.com/huggingface/datasets/issues/5099 - we'll do a release later today" ]

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[ "It can be solved temporarily by remove `resolve` in \r\nhttps://github.com/huggingface/datasets/blob/bef23be3d9543b1ca2da87ab2f05070201044ddc/src/datasets/data_files.py#L278", "Hi, thanks for reporting and suggesting a fix! We still need to account for `.`/`..` in the file path, so a more robust fix would be `Path(os.path.abspath(filepath))`.", "> Hi, thanks for reporting and suggesting a fix! We still need to account for `.`/`..` in the file path, so a more robust fix would be `Path(os.path.abspath(filepath))`.\r\n\r\nThanks for your reply!" ]

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[ "Thanks for reporting, @catalys1.\r\n\r\nThis seems a duplicate of:\r\n- #3310 \r\n\r\nThe source of the problem is in PyArrow:\r\n- [ARROW-15141: [C++] Fatal error condition occurred in aws_thread_launch](https://issues.apache.org/jira/browse/ARROW-15141)\r\n- [ARROW-17501: [C++] Fatal error condition occurred in aws_thread_launch](https://issues.apache.org/jira/browse/ARROW-17501)\r\n\r\nThe bug in their dependency is still unresolved:\r\n- https://github.com/aws/aws-sdk-cpp/issues/1809\r\n\r\nApparently, the `aws-sdk-cpp` PyArrow dependency needs to be pinned at version `1.8.186` if using conda. Have you updated it after installing PyArrow?\r\n```shell\r\nconda list aws-sdk-cpp\r\n```\r\n\r\nMaybe you should try to downgrade it to that version:\r\n```shell\r\nconda install -c conda-forge aws-sdk-cpp=1.8.186\r\n```" ]

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[ "The transfer of the dummy dataset to the dummy org works as expected:\r\n```python\r\nIn [1]: from datasets import load_dataset; ds = load_dataset(\"dummy_canonical_dataset\", download_mode=\"force_redownload\"); ds\r\nDownloading builder script: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2.98k/2.98k [00:00<00:00, 2.01MB/s]\r\nDownloading and preparing dataset dummy_canonical_dataset/default (download: 411 bytes, generated: 385 bytes, post-processed: Unknown size, total: 796 bytes) to .../.cache/huggingface/datasets/dummy_canonical_dataset/default/1.0.0/100870c358637e269fee140585e61e1472d5075a9bf6f866719934c725e55fb4...\r\nDownloading data: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 411/411 [00:00<00:00, 293kB/s]\r\nDataset dummy_canonical_dataset downloaded and prepared to .../.cache/huggingface/datasets/dummy_canonical_dataset/default/1.0.0/100870c358637e269fee140585e61e1472d5075a9bf6f866719934c725e55fb4. Subsequent calls will reuse this data.\r\n100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 304.16it/s]\r\nOut[1]: \r\nDatasetDict({\r\n train: Dataset({\r\n features: ['langs', 'ner_tags', 'tokens'],\r\n num_rows: 3\r\n })\r\n})\r\n\r\nIn [2]: from datasets import load_dataset; ds = load_dataset(\"dummy-canonical-org/dummy_canonical_dataset\"); ds\r\nDownloading builder script: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2.98k/2.98k [00:00<00:00, 1.57MB/s]\r\nDownloading and preparing dataset dummy_canonical_dataset/default to .../.cache/huggingface/datasets/dummy-canonical-org___dummy_canonical_dataset/default/1.0.0/100870c358637e269fee140585e61e1472d5075a9bf6f866719934c725e55fb4...\r\nDataset dummy_canonical_dataset downloaded and prepared to .../.cache/huggingface/datasets/dummy-canonical-org___dummy_canonical_dataset/default/1.0.0/100870c358637e269fee140585e61e1472d5075a9bf6f866719934c725e55fb4. Subsequent calls will reuse this data.\r\n100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 362.48it/s]\r\nOut[2]: \r\nDatasetDict({\r\n train: Dataset({\r\n features: ['langs', 'ner_tags', 'tokens'],\r\n num_rows: 3\r\n })\r\n})\r\n```", "Cool ! 🚀 " ]

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[ "Oops I merged without linking to the hacktoberfest issue - not sure if it counts in this case\r\n\r\nsorry about that..\r\n\r\nNext time you can just mention \"Close #XXXX\" in your issue to link it", "It should :) (the `hacktoberfest` repo topic is all that matters)" ]

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[ "Hi ! Could it be an Out of Memory issue that could have killed one of the processes ? can you check your memory ?", "Hi! I don't think it is a memory issue. I'm monitoring the main and spawn python processes and threads with `htop` and the memory does not peak. Besides, the example I've posted above should not be that demanding in terms of memory, right? (I have 32GB of RAM). ", "Indeed it should be fine. I couldn't reproduce the error though - I ran your script on my side and it works fine. What version of pytorch are you using ?", "Interesting.. I'm using `torch 1.12.1`", "I also tried on colab and it works fine 🤔 \r\nMaybe something is wrong with your installation of pytorch ?", "Oh actually I just saw that you're using python 3.9\r\n\r\nThis could be related to https://github.com/huggingface/datasets/issues/4113\r\n\r\nWe'll fix that as soon as we can, in the meantime you can try to use use single process, or use an older version of python maybe ?", "I tried with python 3.7 and the issue persists. In collab, which also uses 3.7 I don't get the issue, so yes I guess is something on mu side... will post it here if I manage to fix it", "Hi! Which version of transformers are you using? I test the code on Colab (so python 3.7) with transformers 4.23.1, torch 1.12.1 and pyarrow 9.0.0 (also 6.x), it worked without stuck." ]

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[ "Hi, thanks for reporting! This line should be replaced with \r\n```python\r\ndataset = dataset.map(lambda examples: tokenizer(examples[\"text\"], return_tensors=\"np\"), batched=True)\r\n```\r\nfor it to work (the `return_tensors` part inside the `tokenizer` call).", "Can I work on this?", "Fixed in https://github.com/huggingface/datasets/pull/5095" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "> Good catch, @lewtun. Thanks for the fix.\r\n> \r\n> Do you know if there are other absolute paths in the docs that should be fixed as well?\r\n\r\nI found a few more in [0d4796b](https://github.com/huggingface/datasets/pull/5092/commits/0d4796b747e6620d9fcc17a8f74acc5cf4bba7be).\r\n\r\nHowever, I noticed that none of the cross-references (e.g. to API classes / methods) work locally, but that is probably just a limitation of the local build", "Thanks." ]

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

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[ "Nice!!" ]

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[ "Hi @junwang-wish, thanks for reporting.\r\n\r\nUnfortunately, I'm not able to reproduce the bug. Which version of `datasets` are you using? Does the problem persist if you update `datasets`?\r\n```shell\r\npip install -U datasets\r\n``` ", "Thanks @albertvillanova I updated `datasets` from `2.5.1` to `2.5.2` and tested copying the `json.gz` to a different directory and my mind was blown:\r\n\r\n```python\r\nfpath = '/data/junwang/.cache/general/57b6f2314cbe0bc45dda5b78f0871df2/test.json.gz'\r\nds_panda = DatasetDict(\r\n test=Dataset.from_pandas(\r\n pd.read_json(fpath, lines=True)\r\n )\r\n)\r\nds_direct = load_dataset(\r\n 'json', data_files={\r\n 'test': fpath\r\n }, features=Features(\r\n text_input=Value(dtype=\"string\", id=None),\r\n text_output=Value(dtype=\"string\", id=None)\r\n )\r\n)\r\nlen(ds_panda['test']), len(ds_direct['test'])\r\n```\r\nproduces \r\n```python\r\nUsing custom data configuration default-0e6cf24134163e8b\r\nFound cached dataset json (/data/junwang/.cache/huggingface/datasets/json/default-0e6cf24134163e8b/0.0.0/e6070c77f18f01a5ad4551a8b7edfba20b8438b7cad4d94e6ad9378022ce4aab)\r\n(1, 0)\r\n```\r\nbut then I ran below command to see if the same file in a different directory leads to same discrepancy\r\n```shell\r\ncp /data/junwang/.cache/general/57b6f2314cbe0bc45dda5b78f0871df2/test.json.gz tmp_test.json.gz\r\n```\r\nand so I ran\r\n```python\r\nfpath = 'tmp_test.json.gz'\r\nds_panda = DatasetDict(\r\n test=Dataset.from_pandas(\r\n pd.read_json(fpath, lines=True)\r\n )\r\n)\r\nds_direct = load_dataset(\r\n 'json', data_files={\r\n 'test': fpath\r\n }, features=Features(\r\n text_input=Value(dtype=\"string\", id=None),\r\n text_output=Value(dtype=\"string\", id=None)\r\n )\r\n)\r\nlen(ds_panda['test']), len(ds_direct['test'])\r\n```\r\nand behold, I get \r\n```python\r\nUsing custom data configuration default-f679b32ab0008520\r\nDownloading and preparing dataset json/default to /data/junwang/.cache/huggingface/datasets/json/default-f679b32ab0008520/0.0.0/e6070c77f18f01a5ad4551a8b7edfba20b8438b7cad4d94e6ad9378022ce4aab...\r\nDataset json downloaded and prepared to /data/junwang/.cache/huggingface/datasets/json/default-f679b32ab0008520/0.0.0/e6070c77f18f01a5ad4551a8b7edfba20b8438b7cad4d94e6ad9378022ce4aab. Subsequent calls will reuse this data.\r\n(1, 1)\r\n```\r\nThey match now !\r\n\r\nThis problem happens regardless of the shell I use (VScode jupyter extension or plain old Python REPL). \r\n\r\nI attached the `json.gz` here for reference: [test.json.gz](https://github.com/huggingface/datasets/files/9734843/test.json.gz)\r\n\r\n" ]

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

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[ "FYI @lewtun ", "Hi @km5ar, thanks for reporting.\r\n\r\nThis should be fixed in the notebook:\r\n- the filename `datasets-issues-with-hf-doc-builder.jsonl` no longer exists on the repo; instead, current filename is `datasets-issues-with-comments.jsonl`\r\n- see: https://huggingface.co/datasets/lewtun/github-issues/tree/main\r\n\r\nAnyway, depending on your version of `datasets`, you can now use:\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nissues_dataset = load_dataset(\"lewtun/github-issues\")\r\nissues_dataset\r\n```\r\ninstead of:\r\n```python\r\nfrom huggingface_hub import hf_hub_url\r\n\r\ndata_files = hf_hub_url(\r\n repo_id=\"lewtun/github-issues\",\r\n filename=\"datasets-issues-with-hf-doc-builder.jsonl\",\r\n repo_type=\"dataset\",\r\n)\r\nfrom datasets import load_dataset\r\n\r\nissues_dataset = load_dataset(\"json\", data_files=data_files, split=\"train\")\r\nissues_dataset\r\n```\r\n\r\nOutput:\r\n```python\r\nIn [25]: ds = load_dataset(\"lewtun/github-issues\")\r\nDownloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10.5k/10.5k [00:00<00:00, 5.75MB/s]\r\nUsing custom data configuration lewtun--github-issues-cff5093ecc410ea2\r\nDownloading and preparing dataset json/lewtun--github-issues to .../.cache/huggingface/datasets/lewtun___json/lewtun--github-issues-cff5093ecc410ea2/0.0.0/e6070c77f18f01a5ad4551a8b7edfba20b8438b7cad4d94e6ad9378022ce4aab...\r\nDownloading data: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12.2M/12.2M [00:00<00:00, 26.5MB/s]\r\nDownloading data files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:02<00:00, 2.70s/it]\r\nExtracting data files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1589.96it/s]\r\nDataset json downloaded and prepared to .../.cache/huggingface/datasets/lewtun___json/lewtun--github-issues-cff5093ecc410ea2/0.0.0/e6070c77f18f01a5ad4551a8b7edfba20b8438b7cad4d94e6ad9378022ce4aab. Subsequent calls will reuse this data.\r\n100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 133.95it/s]\r\n\r\nIn [26]: ds\r\nOut[26]: \r\nDatasetDict({\r\n train: Dataset({\r\n features: ['url', 'repository_url', 'labels_url', 'comments_url', 'events_url', 'html_url', 'id', 'node_id', 'number', 'title', 'user', 'labels', 'state', 'locked', 'assignee', 'assignees', 'milestone', 'comments', 'created_at', 'updated_at', 'closed_at', 'author_association', 'active_lock_reason', 'pull_request', 'body', 'timeline_url', 'performed_via_github_app', 'is_pull_request'],\r\n num_rows: 3019\r\n })\r\n})\r\n```", "Thanks for reporting @km5ar and thank you @albertvillanova for the quick solution! I'll post a fix on the source too" ]

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[ "~~It seems like #5043 fix (merged recently) is the root cause of such behaviour. When we empty indices mapping (because the dataset length equals to zero), we can no longer get column item like: `ds_filter_2['sentence']` which uses\r\n`ds_filter_1._indices.column(0)`~~\r\n\r\n**UPDATE:**\r\nEmpty datasets are returned without going through partial function on `map` method, which will not work to get indices for `filter`: we need to run `get_indices_from_mask_function` partial function on the dataset to get output = `{\"indices\": []}`. But this is complicated since functions used in args, in particular `get_indices_from_mask_function`, do not support empty datasets.\r\nWe can just handle empty datasets aside on filter method.", "#self-assign", "Thank you for solving this amazingly quickly!" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5084). All of your documentation changes will be reflected on that endpoint.", "Actually I'm not happy with this implementation. It always require the iterable dataset to have definite `features`, which removes a lot of flexibility. So I think we need an actual formatting from python objects, not from arrow data.", "Closing this one since it has too many conflicts and still require some work - it will be easier to open a new PR" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "Hi @mariosasko , I have added a test for the `keep_in_memory` version. I have also removed the `Compatible with temp_seed` part in the scope of `dset_shuffled`, please verify if that makes sense." ]

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[ "tagging @nreimers ", "The dataset is sadly not really compatible to be loaded with `load_dataset`. So far it is better to git clone it and to use the files directly.\r\n\r\nA data loading script would be needed to be added to this dataset. But this was too much overhead / not really intuitive how to create it.", "Since the dataset is a bunch of TSVs we should not need a dataset script I think.\r\n\r\nBy default it tries to load all the TSVs at once, which fails here because they don't all have the same columns (pd.read_csv uses the first line as header by default). But those files have no header ! So, to properly load any TSV file in this repo, one has to pass `names=[...]` for pd.read_csv to know which column names to use.\r\n\r\nTo fix this situation, we can either do\r\n1. replace the TSVs by TSV with column names\r\n2. OR specify the pd.read_csv kwargs as YAML in the dataset card - and `datasets` would use that by default\r\n\r\nWDTY ?", "There are more issues in the dataset.\r\nTo load OpenSubtitles I have to provide this (see `skiprows`):\r\n\r\n```python\r\ndf_os = pd.read_csv(\r\n \"./parallel-sentences/OpenSubtitles/OpenSubtitles-en-de-train.tsv.gz\", \r\n sep=\"\\t\", \r\n quoting=csv.QUOTE_NONE,\r\n header=None,\r\n names=[\"en\", \"de\"],\r\n skiprows=[540344, 9151700, 10040173, 10040199, 11314673, 11338258, 11869223, 12159297, 12251078, 12303334],\r\n)\r\n```", "What's wrong with those lines exactly ?\r\nMaybe passing `error_bad_lines=False` (and maybe `warn_bad_lines=True`) can be helpful", "> What's wrong with those lines exactly ? \r\n\r\nStuff like this: `ParserError: Error tokenizing data. C error: Expected 2 fields in line 540345, saw 3`\r\n\r\n", "> Maybe passing error_bad_lines=False (and maybe warn_bad_lines=True) can be helpful\r\n\r\nYes. That would hide the issue but not solve it.", "@nreimers WDYT about the two options mentioned above ?" ]

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[ "There is some discussion in https://github.com/huggingface/huggingface_hub/pull/1088 if it can help :)" ]

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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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[ "@mariosasko I've raised a PR #5076 against this issue. Please help to review. Thanks." ]

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[ "Hi, can I pick up this issue?", "#self-assign", "Looks like the top-level `datasource` directory was removed when https://github.com/huggingface/datasets/pull/4974 was merged, so there are 3 source files to fix." ]

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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._", "I just added a consolidation step so that numpy arrays or tensors of images are stacked together if the shapes match, instead of having lists of tensors\r\n\r\nFeel free to review @mariosasko :)", "I added a few lines in the docs and reverted the ragged numpy array change :)\r\n\r\nready for another review @mariosasko !" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "Super, thanks a lot for adding this support, Albert!" ]

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[ "Thank you for creating this feature request, Albert.\r\n\r\nFor context this is the datatest where Albert has been helping me to switch to on-the-fly split config https://huggingface.co/datasets/HuggingFaceM4/cm4-synthetic-testing\r\n\r\nand the attempt to switch on-the-fly splits was here: https://huggingface.co/datasets/HuggingFaceM4/cm4-synthetic-testing/discussions/2/files\r\n\r\nbut which I had to revert since providing no split breaks at run time.\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._", "Does it sound good to you @albertvillanova ?" ]

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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._", "good catch !" ]

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[ "This is maybe related to python 3.10, do you think you could try on 3.8 ?\r\n\r\nIn the meantime we'll keep improving the support for 3.10. Let me add a dedicated CI", "I did some binary search and seems like the root cause is either `multiprocess` or `dill`. python 3.10 is fine. Specifically:\r\n- `multiprocess==0.70.12.2, dill==0.3.4`: works\r\n- `multiprocess==0.70.12.2, dill==0.3.5.1`: doesn't work\r\n- `multiprocess==0.70.13, dill==0.3.5.1`: doesn't work\r\n- `multiprocess==0.70.13, dill==0.3.4`: can't test, `multiprocess==0.70.13` requires `dill>=0.3.5.1`\r\n\r\nI will pin their versions on my end. I don't have enough knowledge of how python multiprocessing works to debug this, but ideally there could be a fix. It's also possible that I'm doing something wrong in my code, but again the `.name` of the logger that failed to pickle is `datasets.fingerprint`, which I'm not using directly.", "Do you know which logger fails at being pickled ?", "I'm not 100% sure how to figure it out -- the stack trace above doesn't clearly give me a place where I can print out who owns the logger, etc. I only found out its `.name` is `datasets.fingerprint` by printing right before\r\n```\r\n File \".../logging/__init__.py\", line 1774, in __reduce__\r\n raise pickle.PicklingError('logger cannot be pickled')\r\n```\r\nIf you have any idea on how to find it out, please let me know.", "Ok I see, not sure why it triggers this error though, in `logging.py` the code is\r\n\r\nhttps://github.com/python/cpython/blob/c9da063e32725a66495e4047b8a5ed13e72d9e8e/Lib/logging/__init__.py#L1769-L1775\r\n\r\nand on my side it works on 3.10 with dill 0.3.5.1 and multiprocess 0.70.13\r\n```python\r\n>>> datasets.fingerprint.logger.__reduce__() \r\n(<function logging.getLogger(name=None)>, ('datasets.fingerprint',))\r\n```\r\nCould you try to run this code ?\r\n\r\nAre you in an environment where the loggers are instantiated differently ? Can you check the source code of `logging.Logger.__reduce__` in `\".../logging/__init__.py\", line 1774` ?" ]

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[ "Hi ! I opened a PR in your repo to fix this :)\r\nhttps://huggingface.co/datasets/zpn/pubchem_selfies/discussions/7\r\n\r\nbasically you need to use `open` for streaming to work properly", "Thank you so much for this! Naive question, is this a feature of `open` or have you all overloaded it to be able to read from a URL? Any links to code/documentation would be greatly appreciated, I'd love to learn more", "`datasets` extends `open` in dataset scripts to work with URLs. The builtin `open` from python only works with local files.\r\n\r\nYou can find the extension here: https://github.com/huggingface/datasets/blob/6ad430ba0cdeeb601170f732d4bd977f5c04594d/src/datasets/download/streaming_download_manager.py#L435-L451\r\n\r\nI think we can create a docs section dedicated to streaming to explain how this works", "Closing this one - feel free to reopen if you have more questions" ]

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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 docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5056). All of your documentation changes will be reflected on that endpoint.", "Thanks, @manandey. We have removed all dataset scripts from this repo. Subsequent PRs should be opened directly on the Hugging Face Hub." ]

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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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[ "Maybe #2838 can help. In this PR we allow to skip bad chunks of JSON data to not crash the training\r\n\r\nDid you have warning messages before the error ?\r\n\r\nsomething like this maybe ?\r\n```\r\n03/24/2022 02:19:46 - WARNING - datasets.utils.streaming_download_manager - Got disconnected from remote data host. Retrying in 5sec [1/20]\r\n03/24/2022 02:20:01 - WARNING - datasets.utils.streaming_download_manager - Got disconnected from remote data host. Retrying in 5sec [2/20]\r\n03/24/2022 02:20:09 - ERROR - datasets.packaged_modules.json.json - Failed to read file 'gzip://file-000000000007.json::https://huggingface.co/datasets/lvwerra/codeparrot-clean-train/resolve/1d740acb9d09cf7a3307553323e2c677a6535407/file-000000000007.json.gz' with error <class 'pyarrow.lib.ArrowInvalid'>: JSON parse error: Invalid value. in row 0\r\n```", "Ah, thanks! I did get errors like that. Sad that PR wasn't merged in! \r\n\r\nI'm currently just downloading 200GB of the Pile locally to avoid streaming (I have space and it's faster anyway), but that's really useful! I can probably apply the dumb patch of just commenting out the bits that raise the JSON Parse Error lol, based on your code - if I continue the loop should it be fine?", "Yup you can get some inspiration from this PR. It simply ignores the bad chunks (a chunk is ~a few MBs of data).\r\nWe'll try to merge this PR soon" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "I added a test and moved the `streaming` param from `read` to `__init_`. Then, I also decided to update the `read` method of the rest of the packaged modules to account for this param. \r\n\r\n@hamid-vakilzadeh Are you OK with these changes? ", "@mariosasko these all look great! Thanks for the updates." ]

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

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[ "Hi, can I work on this?", "Hi, sure! Let us know if you need some pointers/help." ]

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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._", "@JamesLYC88 here is the fix! Thanks again!", "Thanks, @albertvillanova. When do you expect that this change will take effect when someone downloads the dataset?", "The change is immediately available now, since this change we made to our library:\r\n- #4059" ]

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

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[ "Hi! Unfortunately, I can't reproduce this behavior. Instead, I get `ValueError: audio at 2063_fe9936e7-62b2-4e62-a276-acbd344480ce_1.wav doesn't have metadata in /audio-data/metadata.csv`, which can be fixed by removing the `./` from the file name.\r\n\r\n(Link to a Colab that tries to reproduce this behavior: https://colab.research.google.com/drive/1IhQzULYi0Van1xLrN_SddBX1JF7mLZZK?usp=sharing)", "I think we can make the file name matching part more robust by replacing `file_name` with `os.path.normpath(file_name)`, to ignore \"./\" among other things, in these two places:\r\n* https://github.com/huggingface/datasets/blob/85cd129bde605cd9acacdff0d065fc02e39e09b1/src/datasets/packaged_modules/folder_based_builder/folder_based_builder.py#L319\r\n* https://github.com/huggingface/datasets/blob/85cd129bde605cd9acacdff0d065fc02e39e09b1/src/datasets/packaged_modules/folder_based_builder/folder_based_builder.py#L388", "@mariosasko Some tests failed (see my PR). Any thoughts on that?", "Yes, I mentioned the solution in my review.", "I realized what I was doing wrong.\r\n\r\nThe documentation puts the files in a subfolder.\r\nOnce I have done that, it worked.\r\n\r\nBut l agree that this should be handled better if possible." ]

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[ "Could you share the error you got please ? Maybe the full stack trace if you have it ?\r\n\r\nMaybe `push_to_hub` be implemented as a single commit @Wauplin ? This way if it fails, the repo is still at the previous (valid) state instead of ending-up in an invalid/incimplete state.", "> Maybe push_to_hub be implemented as a single commit ? \r\n\r\nI think that would definitely be the way to go. Do you know the reasons why not implementing it like this in the first place ? I guess it is because of not been able to upload all at once with `huggingface_hub` but if there was another reason, please let me know.\r\nAbout pushing all at once, it seems to be a more and more requested feature. I have created this issue https://github.com/huggingface/huggingface_hub/issues/1085 recently but other discussions already happened in the past. The `moon-landing` team is working on it (cc @coyotte508). The `huggingface_hub` integration will come afterwards.\r\n\r\nFor now, maybe it's best to wait for a proper implementation instead of creating a temporary workaround :)\r\n", "> I think that would definitely be the way to go. Do you know the reasons why not implementing it like this in the first place ? I guess it is because of not been able to upload all at once with huggingface_hub but if there was another reason, please let me know.\r\n\r\nIdeally we would want to upload the files iteratively - and then once everything is uploaded we proceed to commit. When we implemented `push_to_hub`, using `upload_file` for each shard was the only option.\r\n\r\nFor more context: for each shard to upload we do:\r\n1. load the arrow shard in memory\r\n2. convert to parquet\r\n3. upload\r\n\r\nSo to avoid OOM we need to upload the files iteratively.\r\n\r\n> For now, maybe it's best to wait for a proper implementation instead of creating a temporary workaround :)\r\n\r\nLet us know if we can help !", "> Ideally we would want to upload the files iteratively - and then once everything is uploaded we proceed to commit. \r\n\r\nOh I see. So maybe this has to be done in an implementation specific to `datasets/` as it is not a very common case (upload a bunch of files on the fly).\r\n\r\nYou can maybe have a look at how `huggingface_hub` is implemented for LFS files (arrow shards are LFS anyway, right?).\r\nIn [`upload_lfs_files`](https://github.com/huggingface/huggingface_hub/blob/e28646c977fc9304a4c3576ce61ff07f9778950b/src/huggingface_hub/_commit_api.py#L164) LFS files are uploaded 1 by 1 (multithreaded) and then [the commit is pushed](https://github.com/huggingface/huggingface_hub/blob/e28646c977fc9304a4c3576ce61ff07f9778950b/src/huggingface_hub/hf_api.py#L1926) to the Hub once all files have been uploaded. This is pretty much what you need, right ?\r\n\r\nI can help you if you have questions how to do it in `datasets`. If that makes sense we could then move the implementation from `datasets` to `huggingface_hub` once it's mature. Next week I'm on holidays but feel free to start without my input.\r\n\r\n(also cc @coyotte508 and @SBrandeis who implemented LFS upload in `hfh`)", "> Could you share the error you got please ? Maybe the full stack trace if you have it ?\r\n\r\nHere’s part of the stack trace, that I can reproduce at the moment from a photo I took (potential typos from OCR):\r\n```\r\nValueError\r\nTraceback (most recent call last)\r\n<ipython-input-4-274613b7d3f5> in <module>\r\nfrom datasets import load dataset\r\nds = load_dataset('jrahn/chessv6', use_auth_token-True)\r\n\r\n/us/local/1ib/python3.7/dist-packages/datasets/table.py in cast_table _to_schema (table, schema)\r\nLine 2005 raise ValueError()\r\n\r\nValueError: Couldn't cast \r\nfen: string \r\nmove: string \r\nres: string \r\neco: string \r\nmove_id: int64\r\nres_num: int64 to\r\n{ 'fen': Value(dtype='string', id=None), \r\n'move': Value(dtype=' string', id=None),\r\n'res': Value(dtype='string', id=None),\r\n'eco': Value(dtype='string', id=None), \r\n'hc': Value(dtype='string', id=None), \r\n'move_ id': Value(dtype='int64', id=None),\r\n'res_num': Value(dtype= 'int64' , id=None) }\r\nbecause column names don't match \r\n```\r\n\r\nThe column 'hc' was removed before the interrupted push_to_hub(). It appears in the column list in curly brackets but not in the column list above.\r\n\r\nLet me know, if I can be of any help." ]

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[ "I agree the situation is not ideal and it would be awesome to use `load_dataset` to reload a dataset saved locally !\r\n\r\nFor context:\r\n\r\n- `load_dataset` works in three steps: download the dataset, then prepare it as an arrow dataset, and finally return a memory mapped arrow dataset. In particular it creates a cache directory to store the arrow data and the subsequent cache files for `map`.\r\n\r\n- `load_from_disk` directly returns a memory mapped dataset from the arrow file (similar to `Dataset.from_file`). It doesn't create a cache diretory, instead all the subsequent `map` calls write in the same directory as the original data. \r\n\r\nIf we want to keep the download_and_prepare step for consistency, it would unnecessarily copy the arrow data into the datasets cache. On the other hand if we don't do this step, the cache directory doesn't exist which is inconsistent.\r\n\r\nI'm curious, what would you expect to happen in this situation ?", "Thank you for the detailed breakdown, @lhoestq \r\n\r\n> I'm curious, what would you expect to happen in this situation ?\r\n\r\n1. the simplest solution is to add a flag to the dataset saved by `save_to_disk` and have `load_dataset` check that flag - if it's set simply switch control to `load_from_disk` behind the scenes. So `load_dataset` detects it's a local filesystem, looks inside to see whether it's something it can cache or whether it should use it directly as is and continues accordingly with one of the 2 dataset-type specific APIs.\r\n\r\n2. the more evolved solution is to look at a dataset produced by `save_to_disk` as a remote resource like hub. So the first time `load_dataset` sees it, it'll take a fingerprint and create a normal cached dataset. On subsequent uses it'll again discover it as a remote resource, validate that it has it cached via the fingerprint and serve as a normal dataset. \r\n\r\nAs you said the cons of approach 2 is that if the dataset is huge it'll make 2 copies on the same machine. So it's possible that both approaches can be integrated. Say if `save_to_disc(do_not_cache=True)` is passed it'll use solution 1, otherwise solution 2. or could even symlink the huge arrow files to the cache instead? or perhaps it's more intuitive to use `load_dataset(do_not_cache=True)` instead. So that one can choose whether to make a cached copy or not for the locally saved dataset. i.e. a simple at use point user control.\r\n\r\nSurely there are other ways to handle it, this is just one possibility.\r\n", "I think the simplest is to always memory map the local file without copy, but still have a cached directory in the cache at `~/.cache/huggingface` instead of saving `map` results next to the original data.\r\n\r\nIn practice we can even use symlinks if it makes the implementation simpler", "Yes, so that you always have the cached entry for any dataset, but the \"payload\" doesn't have to be physically in the cache if it's already on the local filesystem. As you said a symlink will do. " ]

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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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