Datasets:

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Jhonatan321

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Datasets_Jhona

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I_kwDODunzps56egdJ

Impossible to save a mapped dataset to disk

open

NONE

### Describe the bug I want to play around with different hyperparameters when training but don't want to re-map my dataset with 3 million samples each time for tens of hours when I [fully fine-tune SDXL](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_sdxl.py). After I do the mapping like this: ``` train_dataset = train_dataset.map(compute_embeddings_fn, batched=True) train_dataset = train_dataset.map( compute_vae_encodings_fn, batched=True, batch_size=16, ) ``` and try to save it like this: `train_dataset.save_to_disk("test")` i get this error ([full traceback](https://pastebin.com/kq3vt739)): ``` TypeError: Object of type function is not JSON serializable The format kwargs must be JSON serializable, but key 'transform' isn't. ``` But what is interesting is that pushing to hub works like that: `train_dataset.push_to_hub("kopyl/mapped-833-icons-sdxl-1024-dataset", token=True)` Here is the link of the pushed dataset: https://huggingface.co/datasets/kopyl/mapped-833-icons-sdxl-1024-dataset ### Steps to reproduce the bug Here is the self-contained notebook: https://colab.research.google.com/drive/1RtCsEMVcwWcMwlWURk_cj_9xUBHz065M?usp=sharing ### Expected behavior It should be easily saved to disk ### Environment info NVIDIA A100, Linux (NC24ads A100 v4 from Azure), CUDA 12.2. [pip freeze](https://pastebin.com/QTNb6iru)

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I_kwDODunzps56cL-p

Impossible to only download a test split

open

NONE

I've spent a significant amount of time trying to locate the split object inside my _split_generators() custom function. Then after diving [in the code](https://github.com/huggingface/datasets/blob/5ff3670c18ed34fa8ddfa70a9aa403ae6cc9ad54/src/datasets/load.py#L2558) I realized that `download_and_prepare` is executed before! split is passed to the dataset builder in `as_dataset`. If I'm not missing something, this seems like bad design, for the following use case: > Imagine there is a huge dataset that has an evaluation test set and you want to just download and run just to compare your method. Is there a current workaround that can help me achieve the same result? Thank you,

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I_kwDODunzps56X3VX

Streaming the Pile: Missing Files

closed

NONE

### Describe the bug The pile does not stream, a "File not Found error" is returned. It looks like the Pile's files have been moved. ### Steps to reproduce the bug To reproduce run the following code: ``` from datasets import load_dataset dataset = load_dataset('EleutherAI/pile', 'en', split='train', streaming=True) next(iter(dataset)) ``` I get the following error: `FileNotFoundError: https://the-eye.eu/public/AI/pile/train/00.jsonl.zst` ### Expected behavior Return the data in a stream. ### Environment info - `datasets` version: 2.12.0 - Platform: Windows-10-10.0.22621-SP0 - Python version: 3.11.5 - Huggingface_hub version: 0.15.1 - PyArrow version: 11.0.0 - Pandas version: 2.0.3

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I_kwDODunzps56VBvw

Loading HF Hub Dataset (private org repo) fails to load all features

open

NONE

### Describe the bug When pushing a `Dataset` with multiple `Features` (`input`, `output`, `tags`) to Huggingface Hub (private org repo), and later downloading the `Dataset`, only `input` and `output` load - I believe the expected behavior is for all `Features` to be loaded by default? ### Steps to reproduce the bug Pushing the data. `data_concat` is a `list` of `dict`s. ```python for datum in data_concat: datum_tags = {d["key"]: d["value"] for d in datum["tags"]} split_fraction = # some logic that generates a train/test split number if split_faction < test_fraction: data_test.append(datum) else: data_train.append(datum) dataset = DatasetDict( { "train": Dataset.from_list(data_train), "test": Dataset.from_list(data_test), "full": Dataset.from_list(data_concat), }, ) dataset_shuffled = dataset.shuffle(seed=shuffle_seed) dataset_shuffled.push_to_hub( repo_id=hf_repo_id, private=True, config_name=m, revision=revision, token=hf_token, ) ``` Loading it later: ```python dataset = datasets.load_dataset( path=hf_repo_id, name=name, token=hf_token, ) ``` Produces: ``` DatasetDict({ train: Dataset({ features: ['input', 'output'], num_rows: <obfuscated> }) test: Dataset({ features: ['input', 'output'], num_rows: <obfuscated> }) full: Dataset({ features: ['input', 'output'], num_rows: <obfuscated> }) }) ``` ### Expected behavior The expected result is below: ``` DatasetDict({ train: Dataset({ features: ['input', 'output', 'tags'], num_rows: <obfuscated> }) test: Dataset({ features: ['input', 'output', 'tags'], num_rows: <obfuscated> }) full: Dataset({ features: ['input', 'output', 'tags'], num_rows: <obfuscated> }) }) ``` My workaround is as follows: ```python dsinfo = datasets.get_dataset_config_info( path=data_files, config_name=data_config, token=hf_token, ) allfeatures = dsinfo.features.copy() if "tags" not in allfeatures: allfeatures["tags"] = [{"key": Value(dtype="string", id=None), "value": Value(dtype="string", id=None)}] dataset = datasets.load_dataset( path=data_files, name=data_config, features=allfeatures, token=hf_token, ) ``` Interestingly enough (and perhaps a related bug?), if I don't add the `tags` to `allfeatures` above (i.e. only loading `input` and `output`), it throws an error when executing `load_dataset`: ``` ValueError: Couldn't cast tags: list<element: struct<key: string, value: string>> child 0, element: struct<key: string, value: string> child 0, key: string child 1, value: string input: <obfuscated> output: <obfuscated> -- schema metadata -- huggingface: '{"info": {"features": {"tags": [{"key": {"dtype": "string",' + 532 to {'input': <obfuscated>, 'output': <obfuscated> because column names don't match ``` Traceback for this: ``` Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/Users/bt/github/core/.venv/lib/python3.11/site-packages/datasets/load.py", line 2152, in load_dataset builder_instance.download_and_prepare( File "/Users/bt/github/core/.venv/lib/python3.11/site-packages/datasets/builder.py", line 948, in download_and_prepare self._download_and_prepare( File "/Users/bt/github/core/.venv/lib/python3.11/site-packages/datasets/builder.py", line 1043, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/Users/bt/github/core/.venv/lib/python3.11/site-packages/datasets/builder.py", line 1805, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/Users/bt/github/core/.venv/lib/python3.11/site-packages/datasets/builder.py", line 1950, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Environment info - `datasets` version: 2.15.0 - Platform: macOS-14.0-arm64-arm-64bit - Python version: 3.11.5 - `huggingface_hub` version: 0.19.4 - PyArrow version: 14.0.1 - Pandas version: 2.1.4 - `fsspec` version: 2023.10.0

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I_kwDODunzps56Uomi

The order of the splits is not preserved

closed

MEMBER

We had a regression and the order of the splits is not preserved. They are alphabetically sorted, instead of preserving original "train", "validation", "test" order. Check: In branch "main" ```python In [9]: dataset = load_dataset("adversarial_qa", '"adversarialQA") In [10]: dataset Out[10]: DatasetDict({ test: Dataset({ features: ['id', 'title', 'context', 'question', 'answers', 'metadata'], num_rows: 3000 }) train: Dataset({ features: ['id', 'title', 'context', 'question', 'answers', 'metadata'], num_rows: 30000 }) validation: Dataset({ features: ['id', 'title', 'context', 'question', 'answers', 'metadata'], num_rows: 3000 }) }) ``` Before (2.15.0) it was: ```python DatasetDict({ train: Dataset({ features: ['id', 'title', 'context', 'question', 'answers', 'metadata'], num_rows: 30000 }) validation: Dataset({ features: ['id', 'title', 'context', 'question', 'answers', 'metadata'], num_rows: 3000 }) test: Dataset({ features: ['id', 'title', 'context', 'question', 'answers', 'metadata'], num_rows: 3000 }) }) ``` See issues: - https://huggingface.co/datasets/adversarial_qa/discussions/3 - https://huggingface.co/datasets/beans/discussions/4 This is a regression because it was previously fixed. See: - #6196 - #5728

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I_kwDODunzps56Ml90

Bug get_metadata_patterns arg error

closed

CONTRIBUTOR

https://github.com/huggingface/datasets/blob/3f149204a2a5948287adcade5e90707aa5207a92/src/datasets/load.py#L1240C1-L1240C69 metadata_patterns = get_metadata_patterns(base_path, download_config=self.download_config)

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I_kwDODunzps56LE9b

Why call http_head() when fsspec_head() succeeds

closed

CONTRIBUTOR

https://github.com/huggingface/datasets/blob/a91582de288d98e94bcb5ab634ca1cfeeff544c5/src/datasets/utils/file_utils.py#L510C1-L523C14

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I_kwDODunzps56H0Mf

Support huggingface-hub 0.20.0

closed

MEMBER

CI to test the support of `huggingface-hub` 0.20.0: https://github.com/huggingface/datasets/compare/main...ci-test-huggingface-hub-v0.20.0.rc1 We need to merge: - #6510 - #6512 - #6516

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I_kwDODunzps555o6g

where is glue_metric.py> @Frankie123421 what was the resolution to this?

closed

NONE

> @Frankie123421 what was the resolution to this? use glue_metric.py instead of glue.py in load_metric _Originally posted by @Frankie123421 in https://github.com/huggingface/datasets/issues/2117#issuecomment-905093763_

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I_kwDODunzps5549e-

Incorrect test set labels for RTE and CoLA datasets via load_dataset

closed

NONE

### Describe the bug The test set labels for the RTE and CoLA datasets when loading via datasets load_dataset are all -1. Edit: It appears this is also the case for every other dataset except for MRPC (stsb, sst2, qqp, mnli (both matched and mismatched), qnli, wnli, ax). Is this intended behavior to safeguard the test set for evaluation purposes? ### Steps to reproduce the bug !pip install datasets from datasets import load_dataset rte_data = load_dataset('glue', 'rte') cola_data = load_dataset('glue', 'cola') print(rte_data['test'][0:30]['label']) print(cola_data['test'][0:30]['label']) Output: [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1] [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1] The non-label test data seems to be fine: e.g. rte_data['test'][1] is: {'sentence1': "Authorities in Brazil say that more than 200 people are being held hostage in a prison in the country's remote, Amazonian-jungle state of Rondonia.", 'sentence2': 'Authorities in Brazil hold 200 people as hostage.', 'label': -1, 'idx': 1} Training and validation data are also fine: e.g. rte_data['train][0] is: {'sentence1': 'No Weapons of Mass Destruction Found in Iraq Yet.', 'sentence2': 'Weapons of Mass Destruction Found in Iraq.', 'label': 1, 'idx': 0} ### Expected behavior Expected the labels to be binary 0/1 values; Got all -1s instead ### Environment info - `datasets` version: 2.15.0 - Platform: Linux-6.1.58+-x86_64-with-glibc2.35 - Python version: 3.10.12 - `huggingface_hub` version: 0.19.4 - PyArrow version: 10.0.1 - Pandas version: 1.5.3 - `fsspec` version: 2023.6.0

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I_kwDODunzps553_iI

Got stuck when I trying to load a dataset

open

NONE

### Describe the bug Hello, everyone. I met a problem when I am trying to load a data file using load_dataset method on a Debian 10 system. The data file is not very large, only 1.63MB with 600 records. Here is my code: from datasets import load_dataset dataset = load_dataset('json', data_files='mypath/oaast_rm_zh.json') I waited it for 20 minutes. It still no response. I cannot using Ctrl+C to cancel the command. I have to use Ctrl+Z to kill it. I also try it with a txt file, it still no response in a long time. I can load the same file successfully using my laptop (windows 10, python 3.8.5, datasets==2.14.5). I can also make it on another computer (Ubuntu 20.04.5 LTS, python 3.10.13, datasets 2.14.7). It only takes me 1-2 miniutes. Could you give me some suggestions? Thank you. ### Steps to reproduce the bug from datasets import load_dataset dataset = load_dataset('json', data_files='mypath/oaast_rm_zh.json') ### Expected behavior I hope it can load the file successfully. ### Environment info OS: Debian GNU/Linux 10 Python: Python 3.10.13 Pip list: Package Version ------------------------- ------------ accelerate 0.25.0 addict 2.4.0 aiofiles 23.2.1 aiohttp 3.9.1 aiosignal 1.3.1 aliyun-python-sdk-core 2.14.0 aliyun-python-sdk-kms 2.16.2 altair 5.2.0 annotated-types 0.6.0 anyio 3.7.1 async-timeout 4.0.3 attrs 23.1.0 certifi 2023.11.17 cffi 1.16.0 charset-normalizer 3.3.2 click 8.1.7 contourpy 1.2.0 crcmod 1.7 cryptography 41.0.7 cycler 0.12.1 datasets 2.14.7 dill 0.3.7 docstring-parser 0.15 einops 0.7.0 exceptiongroup 1.2.0 fastapi 0.105.0 ffmpy 0.3.1 filelock 3.13.1 fonttools 4.46.0 frozenlist 1.4.1 fsspec 2023.10.0 gast 0.5.4 gradio 3.50.2 gradio_client 0.6.1 h11 0.14.0 httpcore 1.0.2 httpx 0.25.2 huggingface-hub 0.19.4 idna 3.6 importlib-metadata 7.0.0 importlib-resources 6.1.1 jieba 0.42.1 Jinja2 3.1.2 jmespath 0.10.0 joblib 1.3.2 jsonschema 4.20.0 jsonschema-specifications 2023.11.2 kiwisolver 1.4.5 markdown-it-py 3.0.0 MarkupSafe 2.1.3 matplotlib 3.8.2 mdurl 0.1.2 modelscope 1.10.0 mpmath 1.3.0 multidict 6.0.4 multiprocess 0.70.15 networkx 3.2.1 nltk 3.8.1 numpy 1.26.2 nvidia-cublas-cu12 12.1.3.1 nvidia-cuda-cupti-cu12 12.1.105 nvidia-cuda-nvrtc-cu12 12.1.105 nvidia-cuda-runtime-cu12 12.1.105 nvidia-cudnn-cu12 8.9.2.26 nvidia-cufft-cu12 11.0.2.54 nvidia-curand-cu12 10.3.2.106 nvidia-cusolver-cu12 11.4.5.107 nvidia-cusparse-cu12 12.1.0.106 nvidia-nccl-cu12 2.18.1 nvidia-nvjitlink-cu12 12.3.101 nvidia-nvtx-cu12 12.1.105 orjson 3.9.10 oss2 2.18.3 packaging 23.2 pandas 2.1.4 peft 0.7.1 Pillow 10.1.0 pip 23.3.1 platformdirs 4.1.0 protobuf 4.25.1 psutil 5.9.6 pyarrow 14.0.1 pyarrow-hotfix 0.6 pycparser 2.21 pycryptodome 3.19.0 pydantic 2.5.2 pydantic_core 2.14.5 pydub 0.25.1 Pygments 2.17.2 pyparsing 3.1.1 python-dateutil 2.8.2 python-multipart 0.0.6 pytz 2023.3.post1 PyYAML 6.0.1 referencing 0.32.0 regex 2023.10.3 requests 2.31.0 rich 13.7.0 rouge-chinese 1.0.3 rpds-py 0.13.2 safetensors 0.4.1 scipy 1.11.4 semantic-version 2.10.0 sentencepiece 0.1.99 setuptools 68.2.2 shtab 1.6.5 simplejson 3.19.2 six 1.16.0 sniffio 1.3.0 sortedcontainers 2.4.0 sse-starlette 1.8.2 starlette 0.27.0 sympy 1.12 tiktoken 0.5.2 tokenizers 0.15.0 tomli 2.0.1 toolz 0.12.0 torch 2.1.2 tqdm 4.66.1 transformers 4.36.1 triton 2.1.0 trl 0.7.4 typing_extensions 4.9.0 tyro 0.6.0 tzdata 2023.3 urllib3 2.1.0 uvicorn 0.24.0.post1 websockets 11.0.3 wheel 0.41.2 xxhash 3.4.1 yapf 0.40.2 yarl 1.9.4 zipp 3.17.0

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I_kwDODunzps553Tji

Error Pushing to Hub

closed

NONE

### Describe the bug Error when trying to push a dataset in a special format to hub ### Steps to reproduce the bug ``` import datasets from datasets import Dataset dataset_dict = { "filename": ["apple", "banana"], "token": [[[1,2],[3,4]],[[1,2],[3,4]]], "label": [0, 1], } dataset = Dataset.from_dict(dataset_dict) dataset = dataset.cast_column("token", datasets.features.features.Array2D(shape=(2, 2),dtype="int16")) dataset.push_to_hub("SequenceModel/imagenet_val_256") ``` Error: ``` ... ConstructorError: could not determine a constructor for the tag 'tag:yaml.org,2002:python/tuple' in "<unicode string>", line 8, column 16: shape: !!python/tuple ^ ``` ### Expected behavior Dataset being pushed to hub ### Environment info - `datasets` version: 2.15.0 - Platform: Linux-5.19.0-1022-gcp-x86_64-with-glibc2.35 - Python version: 3.11.5 - `huggingface_hub` version: 0.19.4 - PyArrow version: 14.0.1 - Pandas version: 2.1.4 - `fsspec` version: 2023.10.0

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I_kwDODunzps55y3ZY

OverflowError: value too large to convert to int32_t

open

NONE

### Describe the bug  ### Steps to reproduce the bug just loading datasets ### Expected behavior how can I fix it ### Environment info pip install /mnt/cluster/zhangfan/study_info/LLaMA-Factory/peft-0.6.0-py3-none-any.whl pip install huggingface_hub-0.19.4-py3-none-any.whl tokenizers-0.15.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl transformers-4.36.1-py3-none-any.whl pyarrow_hotfix-0.6-py3-none-any.whl datasets-2.15.0-py3-none-any.whl tyro-0.5.18-py3-none-any.whl trl-0.7.4-py3-none-any.whl done

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I_kwDODunzps55tlwi

Support setting a default config name in push_to_hub

closed

MEMBER

In order to convert script-datasets to no-script datasets, we need to support setting a default config name for those scripts that set one.

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I_kwDODunzps55sC6K

Error when writing a dataset to HF Hub: A commit has happened since. Please refresh and try again.

open

NONE

**Describe the bug** Getting a `412 Client Error: Precondition Failed` when trying to write a dataset to the HF hub. ``` huggingface_hub.utils._errors.HfHubHTTPError: 412 Client Error: Precondition Failed for url: https://huggingface.co/api/datasets/GLorr/test-dask/commit/main (Request ID: Root=1-657ae26f-3bd92bf861bb254b2cc0826c;50a09ab7-9347-406a-ba49-69f98abee9cc) A commit has happened since. Please refresh and try again. ``` **Steps to reproduce the bug** This is a minimal reproducer: ``` import dask.dataframe as dd import pandas as pd import random import os import huggingface_hub import datasets huggingface_hub.login(token=os.getenv("HF_TOKEN")) data = {"number": [random.randint(0,10) for _ in range(1000)]} df = pd.DataFrame.from_dict(data) dataframe = dd.from_pandas(df, npartitions=1) dataframe = dataframe.repartition(npartitions=3) schema = datasets.Features({"number": datasets.Value("int64")}).arrow_schema repo_id = "GLorr/test-dask" repo_path = f"hf://datasets/{repo_id}" huggingface_hub.create_repo(repo_id=repo_id, repo_type="dataset", exist_ok=True) dd.to_parquet(dataframe, path=f"{repo_path}/data", schema=schema) ``` **Expected behavior** Would expect to write to the hub without any problem. **Environment info** ``` datasets==2.15.0 huggingface-hub==0.19.4 ```

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I_kwDODunzps55kx7n

Image Data loaded Twice

open

NONE

### Describe the bug  When I learn from https://huggingface.co/docs/datasets/image_load and try to load image data from a folder. I noticed that the image was read twice in the returned data. As you can see in the attached image, there are only four images in the train folder, but reading brings up eight images ### Steps to reproduce the bug from datasets import Dataset, load_dataset dataset = load_dataset("imagefolder", data_dir="data/", drop_labels=False) # print(dataset["train"][0]["image"] == dataset["train"][1]["image"]) print(dataset) print(dataset["train"]["image"]) print(len(dataset["train"]["image"])) ### Expected behavior DatasetDict({ train: Dataset({ features: ['image', 'label'], num_rows: 8 }) }) [<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2877x2129 at 0x1BD1D1CA8B0>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2877x2129 at 0x1BD1D2452E0>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4208x3120 at 0x1BD1D245310>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4208x3120 at 0x1BD1D2453A0>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2877x2129 at 0x1BD1D245460>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2877x2129 at 0x1BD1D245430>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4208x3120 at 0x1BD1D2454F0>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4208x3120 at 0x1BD1D245550>] 8 ### Environment info - `datasets` version: 2.14.5 - Platform: Windows-10-10.0.22621-SP0 - Python version: 3.9.17 - Huggingface_hub version: 0.19.4 - PyArrow version: 13.0.0 - Pandas version: 2.0.3

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I_kwDODunzps55k3tx

Newline characters don't behave as expected when calling dataset.info

open

NONE

### System Info - `transformers` version: 4.32.1 - Platform: Windows-10-10.0.19045-SP0 - Python version: 3.11.5 - Huggingface_hub version: 0.15.1 - Safetensors version: 0.3.2 - Accelerate version: not installed - Accelerate config: not found - PyTorch version (GPU?): 2.1.1+cpu (False) - Tensorflow version (GPU?): 2.15.0 (False) - Flax version (CPU?/GPU?/TPU?): not installed (NA) - Jax version: not installed - JaxLib version: not installed - Using GPU in script?: no - Using distributed or parallel set-up in script?: no ### Who can help? @marios ### Information - [X] The official example scripts - [ ] My own modified scripts ### Tasks - [X] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...) - [ ] My own task or dataset (give details below) ### Reproduction [Source](https://huggingface.co/docs/datasets/v2.2.1/en/access) ``` from datasets import load_dataset dataset = load_dataset('glue', 'mrpc', split='train') dataset.info ``` DatasetInfo(description='GLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n\n', citation='@inproceedings{dolan2005automatically,\n title={Automatically constructing a corpus of sentential paraphrases},\n author={Dolan, William B and Brockett, Chris},\n booktitle={Proceedings of the Third International Workshop on Paraphrasing (IWP2005)},\n year={2005}\n}\n@inproceedings{wang2019glue,\n title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n note={In the Proceedings of ICLR.},\n year={2019}\n}\n', homepage='https://www.microsoft.com/en-us/download/details.aspx?id=52398', license='', features={'sentence1': Value(dtype='string', id=None), 'sentence2': Value(dtype='string', id=None), 'label': ClassLabel(names=['not_equivalent', 'equivalent'], id=None), 'idx': Value(dtype='int32', id=None)}, post_processed=None, supervised_keys=None, task_templates=None, builder_name='glue', dataset_name=None, config_name='mrpc', version=1.0.0, splits={'train': SplitInfo(name='train', num_bytes=943843, num_examples=3668, shard_lengths=None, dataset_name='glue'), 'validation': SplitInfo(name='validation', num_bytes=105879, num_examples=408, shard_lengths=None, dataset_name='glue'), 'test': SplitInfo(name='test', num_bytes=442410, num_examples=1725, shard_lengths=None, dataset_name='glue')}, download_checksums={'https://dl.fbaipublicfiles.com/glue/data/mrpc_dev_ids.tsv': {'num_bytes': 6222, 'checksum': None}, 'https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt': {'num_bytes': 1047044, 'checksum': None}, 'https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt': {'num_bytes': 441275, 'checksum': None}}, download_size=1494541, post_processing_size=None, dataset_size=1492132, size_in_bytes=2986673) ### Expected behavior ``` from datasets import load_dataset dataset = load_dataset('glue', 'mrpc', split='train') dataset.info ``` DatasetInfo( description='GLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n\n', citation='@inproceedings{dolan2005automatically,\n title={Automatically constructing a corpus of sentential paraphrases},\n author={Dolan, William B and Brockett, Chris},\n booktitle={Proceedings of the Third International Workshop on Paraphrasing (IWP2005)},\n year={2005}\n}\n@inproceedings{wang2019glue,\n title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n note={In the Proceedings of ICLR.},\n year={2019}\n}\n', homepage='https://www.microsoft.com/en-us/download/details.aspx?id=52398', license='', features={'sentence1': Value(dtype='string', id=None), 'sentence2': Value(dtype='string', id=None), 'label': ClassLabel(num_classes=2, names=['not_equivalent', 'equivalent'], names_file=None, id=None), 'idx': Value(dtype='int32', id=None)}, post_processed=None, supervised_keys=None, builder_name='glue', config_name='mrpc', version=1.0.0, splits={'train': SplitInfo(name='train', num_bytes=943851, num_examples=3668, dataset_name='glue'), 'validation': SplitInfo(name='validation', num_bytes=105887, num_examples=408, dataset_name='glue'), 'test': SplitInfo(name='test', num_bytes=442418, num_examples=1725, dataset_name='glue')}, download_checksums={'https://dl.fbaipublicfiles.com/glue/data/mrpc_dev_ids.tsv': {'num_bytes': 6222, 'checksum': '971d7767d81b997fd9060ade0ec23c4fc31cbb226a55d1bd4a1bac474eb81dc7'}, 'https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt': {'num_bytes': 1047044, 'checksum': '60a9b09084528f0673eedee2b69cb941920f0b8cd0eeccefc464a98768457f89'}, 'https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt': {'num_bytes': 441275, 'checksum': 'a04e271090879aaba6423d65b94950c089298587d9c084bf9cd7439bd785f784'}}, download_size=1494541, post_processing_size=None, dataset_size=1492156, size_in_bytes=2986697 )

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I_kwDODunzps55bUec

`load_dataset(...,save_infos=True)` not working without loading script

open

NONE

### Describe the bug It seems that saving a dataset infos back into the card file is not working for datasets without a loading script. After tracking the problem a bit it looks like saving the infos uses `Builder.get_imported_module_dir()` as its destination directory. Internally this is a call to `inspect.getfile()` but since the actual builder class used is dynamically created (cf. `datasets.load.configure_builder_class`) this method actually return te path to the parent builder class (e.g. `datasets.packaged_modules.json.JSON`). ### Steps to reproduce the bug 1. Have a local dataset without any loading script 2. Make sure there are no dataset infos in the README.md 3. Load with `save_infos=True` 4. No change in the dataset README.md 5. A new README.md file is created in the directory of the parent builder class (e.g. for json in `.../site-packages/datasets/packaged_modules/json/README.md`) ### Expected behavior The dataset README.md should be updated and no file should be created in the python environment. ### Environment info - `datasets` version: 2.15.0 - Platform: Linux-6.2.0-37-generic-x86_64-with-glibc2.35 - Python version: 3.10.12 - `huggingface_hub` version: 0.19.4 - PyArrow version: 14.0.1 - Pandas version: 2.1.3 - `fsspec` version: 2023.6.0

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I_kwDODunzps55Zj5h

load_dataset imageflder for aws s3 path

open

NONE

### Feature request I would like to load a dataset from S3 using the imagefolder option something like `dataset = datasets.load_dataset('imagefolder', data_dir='s3://.../lsun/train/bedroom', fs=S3FileSystem(), streaming=True) ` ### Motivation no need of data_files ### Your contribution no experience with this

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I_kwDODunzps55WV36

429 Client Error

open

NONE

Hello, I was downloading the following dataset and after 20% of data was downloaded, I started getting error 429. It is not resolved since a few days. How should I resolve it? Thanks Dataset: https://huggingface.co/datasets/cerebras/SlimPajama-627B Error: `requests.exceptions.HTTPError: 429 Client Error: Too Many Requests for url: https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/train/chunk1/example_train_3300.jsonl.zst`

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I_kwDODunzps55Tcz8

FileNotFoundError: [Errno 2] No such file or directory: 'nul'

closed

NONE

### Describe the bug it seems that sth wrong with my terrible "bug body" life, When i run this code, "import datasets" i meet this error FileNotFoundError: [Errno 2] No such file or directory: 'nul'   ### Steps to reproduce the bug 1.import datasets ### Expected behavior i just run a single line code and stuct in this bug ### Environment info OS: Windows10 Datasets==2.15.0 python=3.10

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I_kwDODunzps55LE8l

Iterable Dataset: rename column clashes with remove column

closed

CONTRIBUTOR

### Describe the bug Suppose I have a two iterable datasets, one with the features: * `{"audio", "text", "column_a"}` And the other with the features: * `{"audio", "sentence", "column_b"}` I want to combine both datasets using `interleave_datasets`, which requires me to unify the column names. I would typically do this by: 1. Renaming the common columns to the same name (e.g. `"text"` -> `"sentence"`) 2. Removing the unwanted columns (e.g. `"column_a"`, `"column_b"`) However, the process of renaming and removing columns in an iterable dataset doesn't work, since we need to preserve the original text column, meaning we can't combine the datasets. ### Steps to reproduce the bug ```python from datasets import load_dataset # load LS in streaming mode dataset = load_dataset("librispeech_asr", "clean", split="validation", streaming=True) # check original features dataset_features = dataset.features.keys() print("Original features: ", dataset_features) # rename "text" -> "sentence" dataset = dataset.rename_column("text", "sentence") # remove unwanted columns COLUMNS_TO_KEEP = {"audio", "sentence"} dataset = dataset.remove_columns(set(dataset_features - COLUMNS_TO_KEEP)) # stream first sample, should return "audio" and "sentence" columns print(next(iter(dataset))) ``` Traceback: ```python --------------------------------------------------------------------------- KeyError Traceback (most recent call last) Cell In[5], line 17 14 COLUMNS_TO_KEEP = {"audio", "sentence"} 15 dataset = dataset.remove_columns(set(dataset_features - COLUMNS_TO_KEEP)) ---> 17 print(next(iter(dataset))) File ~/datasets/src/datasets/iterable_dataset.py:1353, in IterableDataset.__iter__(self) 1350 yield formatter.format_row(pa_table) 1351 return -> 1353 for key, example in ex_iterable: 1354 if self.features: 1355 # `IterableDataset` automatically fills missing columns with None. 1356 # This is done with `_apply_feature_types_on_example`. 1357 example = _apply_feature_types_on_example( 1358 example, self.features, token_per_repo_id=self._token_per_repo_id 1359 ) File ~/datasets/src/datasets/iterable_dataset.py:652, in MappedExamplesIterable.__iter__(self) 650 yield from ArrowExamplesIterable(self._iter_arrow, {}) 651 else: --> 652 yield from self._iter() File ~/datasets/src/datasets/iterable_dataset.py:729, in MappedExamplesIterable._iter(self) 727 if self.remove_columns: 728 for c in self.remove_columns: --> 729 del transformed_example[c] 730 yield key, transformed_example 731 current_idx += 1 KeyError: 'text' ``` => we see that `datasets` is looking for the column "text", even though we've renamed this to "sentence" and then removed the un-wanted "text" column from our dataset. ### Expected behavior Should be able to rename and remove columns from iterable dataset. ### Environment info - `datasets` version: 2.15.1.dev0 - Platform: macOS-13.5.1-arm64-arm-64bit - Python version: 3.11.6 - `huggingface_hub` version: 0.19.4 - PyArrow version: 14.0.1 - Pandas version: 2.1.2 - `fsspec` version: 2023.9.2

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I_kwDODunzps55MjQu

[Feature Request] Dataset versioning

open

NONE

**Is your feature request related to a problem? Please describe.** I am working on a project, where I would like to test different preprocessing methods for my ML-data. Thus, I would like to work a lot with revisions and compare them. Currently, I was not able to make it work with the revision keyword because it was not redownloading the data, it was reading in some cached data, until I put `download_mode="force_redownload"`, even though the reversion was different. Of course, I may have done something wrong or missed a setting somewhere! **Describe the solution you'd like** The solution would allow me to easily work with revisions: - create a new dataset (by combining things, different preprocessing, ..) and give it a new revision (v.1.2.3), maybe like this: `dataset_audio.push_to_hub('kenfus/xy', revision='v1.0.2')` - then, get the current revision as follows: ``` dataset = load_dataset( 'kenfus/xy', revision='v1.0.2', ) ``` this downloads the new version and does not load in a different revision and all future map, filter, .. operations are done on this dataset and not loaded from cache produced from a different revision. - if I rerun the run, the caching should be smart enough in every step to not reuse a mapping operation on a different revision. **Describe alternatives you've considered** I created my own caching, putting `download_mode="force_redownload"` and `load_from_cache_file=False,` everywhere. **Additional context** Thanks a lot for your great work! Creating NLP datasets and training a model with them is really easy and straightforward with huggingface. This is the data loading in my script: ``` ## CREATE PATHS prepared_dataset_path = os.path.join( DATA_FOLDER, str(DATA_VERSION), "prepared_dataset" ) os.makedirs(os.path.join(DATA_FOLDER, str(DATA_VERSION)), exist_ok=True) ## LOAD DATASET if os.path.exists(prepared_dataset_path): print("Loading prepared dataset from disk...") dataset_prepared = load_from_disk(prepared_dataset_path) else: print("Loading dataset from HuggingFace Datasets...") dataset = load_dataset( PATH_TO_DATASET, revision=DATA_VERSION, download_mode="force_redownload" ) print("Preparing dataset...") dataset_prepared = dataset.map( prepare_dataset, remove_columns=["audio", "transcription"], num_proc=os.cpu_count(), load_from_cache_file=False, ) dataset_prepared.save_to_disk(prepared_dataset_path) del dataset if CHECK_DATASET: ## CHECK DATASET dataset_prepared = dataset_prepared.map( check_dimensions, num_proc=os.cpu_count(), load_from_cache_file=False ) dataset_filtered = dataset_prepared.filter( lambda example: not example["incorrect_dimension"], load_from_cache_file=False, ) for example in dataset_prepared.filter( lambda example: example["incorrect_dimension"], load_from_cache_file=False ): print(example["path"]) print( f"Number of examples with incorrect dimension: {len(dataset_prepared) - len(dataset_filtered)}" ) print("Number of examples train: ", len(dataset_filtered["train"])) print("Number of examples test: ", len(dataset_filtered["test"])) ```

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I_kwDODunzps55J8cT

using torchrun, save_to_disk suddenly shows SIGTERM

open

NONE

### Describe the bug When I run my code using the "torchrun" command, when the code reaches the "save_to_disk" part, suddenly I get the following warning and error messages: Because the dataset is too large, the "save_to_disk" function splits it into 70 parts for saving. However, an error occurs suddenly when it reaches the 14th shard. WARNING: torch.distributed.elastic.multiprocessing.api: Sending process 2224968 closing signal SIGTERM ERROR: torch.distributed.elastic.multiprocessing.api: failed (exitcode: -7). traceback: Signal 7 (SIGBUS) received by PID 2224967. ### Steps to reproduce the bug ds_shard = ds_shard.map(map_fn, *args, **kwargs) ds_shard.save_to_disk(ds_shard_filepaths[rank]) Saving the dataset (14/70 shards): 20%|██ | 875350/4376702 [00:19<01:53, 30863.15 examples/s] WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 2224968 closing signal SIGTERM ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: -7) local_rank: 0 (pid: 2224967) of binary: /home/bingxing2/home/scx6964/.conda/envs/ariya235/bin/python Traceback (most recent call last): File "/home/bingxing2/home/scx6964/.conda/envs/ariya235/bin/torchrun", line 8, in <module> sys.exit(main()) File "/home/bingxing2/home/scx6964/.conda/envs/ariya235/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 346, in wrapper return f(*args, **kwargs) File "/home/bingxing2/home/scx6964/.conda/envs/ariya235/lib/python3.10/site-packages/torch/distributed/run.py", line 794, in main run(args) File "/home/bingxing2/home/scx6964/.conda/envs/ariya235/lib/python3.10/site-packages/torch/distributed/run.py", line 785, in run elastic_launch( File "/home/bingxing2/home/scx6964/.conda/envs/ariya235/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 134, in __call__ return launch_agent(self._config, self._entrypoint, list(args)) File "/home/bingxing2/home/scx6964/.conda/envs/ariya235/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 250, in launch_agent raise ChildFailedError( torch.distributed.elastic.multiprocessing.errors.ChildFailedError: ========================================================== run.py FAILED ---------------------------------------------------------- Failures: <NO_OTHER_FAILURES> ---------------------------------------------------------- Root Cause (first observed failure): [0]: time : 2023-12-08_20:09:04 rank : 0 (local_rank: 0) exitcode : -7 (pid: 2224967) error_file: <N/A> traceback : Signal 7 (SIGBUS) received by PID 2224967 ### Expected behavior I hope it can save successfully without any issues, but it seems there is a problem. ### Environment info `datasets` version: 2.14.6 - Platform: Linux-4.19.90-24.4.v2101.ky10.aarch64-aarch64-with-glibc2.28 - Python version: 3.10.11 - Huggingface_hub version: 0.17.3 - PyArrow version: 14.0.0 - Pandas version: 2.1.2

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I_kwDODunzps544eqs

How to load data from lakefs

closed

CONTRIBUTOR

My dataset is stored on the company's lakefs server. How can I write code to load the dataset? It would be great if I could provide code examples or provide some references

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I_kwDODunzps544Ruk

CI on windows is broken: PermissionError

closed

MEMBER

See: https://github.com/huggingface/datasets/actions/runs/7104781624/job/19340572394 ``` FAILED tests/test_load.py::test_loading_from_the_datasets_hub - NotADirectoryError: [WinError 267] The directory name is invalid: 'C:\\Users\\RUNNER~1\\AppData\\Local\\Temp\\tmpfcnps56i\\hf-internal-testing___dataset_with_script\\default\\0.0.0\\c240e2be3370bdbd\\dataset_with_script-train.arrow' ```

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I_kwDODunzps5410Sm

laion2B-en failed to load on Windows with PrefetchVirtualMemory failed

open

NONE

### Describe the bug I have downloaded laion2B-en, and I'm receiving the following error trying to load it: ``` Resolving data files: 100%|██████████| 128/128 [00:00<00:00, 1173.79it/s] Traceback (most recent call last): File "D:\Art-Workspace\src\artworkspace\tokeneval\compute_frequencies.py", line 31, in <module> count = compute_frequencies() ^^^^^^^^^^^^^^^^^^^^^ File "D:\Art-Workspace\src\artworkspace\tokeneval\compute_frequencies.py", line 17, in compute_frequencies laion2b_dataset = load_dataset("laion/laion2B-en", split="train", cache_dir=_CACHE_DIR, keep_in_memory=False) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\load.py", line 2165, in load_dataset ds = builder_instance.as_dataset(split=split, verification_mode=verification_mode, in_memory=keep_in_memory) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\builder.py", line 1187, in as_dataset datasets = map_nested( ^^^^^^^^^^^ File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\utils\py_utils.py", line 456, in map_nested return function(data_struct) ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\builder.py", line 1217, in _build_single_dataset ds = self._as_dataset( ^^^^^^^^^^^^^^^^^ File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\builder.py", line 1291, in _as_dataset dataset_kwargs = ArrowReader(cache_dir, self.info).read( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\arrow_reader.py", line 244, in read return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\arrow_reader.py", line 265, in read_files pa_table = self._read_files(files, in_memory=in_memory) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\arrow_reader.py", line 200, in _read_files pa_table: Table = self._get_table_from_filename(f_dict, in_memory=in_memory) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\arrow_reader.py", line 336, in _get_table_from_filename table = ArrowReader.read_table(filename, in_memory=in_memory) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\arrow_reader.py", line 357, in read_table return table_cls.from_file(filename) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\table.py", line 1059, in from_file table = _memory_mapped_arrow_table_from_file(filename) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\table.py", line 66, in _memory_mapped_arrow_table_from_file pa_table = opened_stream.read_all() ^^^^^^^^^^^^^^^^^^^^^^^^ File "pyarrow\ipc.pxi", line 757, in pyarrow.lib.RecordBatchReader.read_all File "pyarrow\error.pxi", line 91, in pyarrow.lib.check_status OSError: [WinError 8] PrefetchVirtualMemory failed. Detail: [Windows error 8] Not enough memory resources are available to process this command. ``` This error is probably a red herring: https://stackoverflow.com/questions/50263929/numpy-memmap-returns-not-enough-memory-while-there-are-plenty-available In other words, the issue is related to asking for a memory mapping of length N > M the length of the file on Windows. This gracefully succeeds on Linux. I have 1024 arrow files in my cache instead of 128 like in the repository for it. Probably related. I don't know why `datasets` reorganized/rewrote the dataset in my cache to be 1024 slices instead of the original 128. ### Steps to reproduce the bug ``` # as a huggingface developer, you may already have laion2B-en somewhere _CACHE_DIR = "." from datasets import load_dataset load_dataset("laion/laion2B-en", split="train", cache_dir=_CACHE_DIR, keep_in_memory=False) ``` ### Expected behavior This should correctly load as a memory mapped Arrow dataset. ### Environment info - `datasets` version: 2.15.0 - Platform: Windows-10-10.0.20348-SP0 (this is windows 2022) - Python version: 3.11.4 - `huggingface_hub` version: 0.19.4 - PyArrow version: 14.0.1 - Pandas version: 2.1.2 - `fsspec` version: 2023.10.0

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I_kwDODunzps54ydX_

CI quality is broken

closed

MEMBER

See: https://github.com/huggingface/datasets/actions/runs/7100835633/job/19327734359 ``` Would reformat: src/datasets/features/image.py 1 file would be reformatted, 253 files left unchanged ```

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I_kwDODunzps54rtdf

If an image in a dataset is corrupted, we get unescapable error

open

NONE

### Describe the bug Example discussed in detail here: https://huggingface.co/datasets/sasha/birdsnap/discussions/1 ### Steps to reproduce the bug ``` from datasets import load_dataset, VerificationMode dataset = load_dataset( 'sasha/birdsnap', split="train", verification_mode=VerificationMode.ALL_CHECKS, streaming=True # I recommend using streaming=True when reproducing, as this dataset is large ) for idx, row in enumerate(dataset): # Iterating to 9287 took 7 minutes for me # If you already have the data locally cached and set streaming=False, you see the same error just by with dataset[9287] pass # error at 9287 OSError: image file is truncated (45 bytes not processed) # note that we can't avoid the error using a try/except + continue inside the loop ``` ### Expected behavior Able to escape errors in casting to Image() without killing the whole loop ### Environment info - `datasets` version: 2.15.0 - Platform: Linux-5.15.0-84-generic-x86_64-with-glibc2.31 - Python version: 3.11.5 - `huggingface_hub` version: 0.19.4 - PyArrow version: 14.0.1 - Pandas version: 2.1.3 - `fsspec` version: 2023.10.0

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I_kwDODunzps54lzBZ

New version release request

closed

CONTRIBUTOR

### Feature request Hi! I am using `datasets` in library `xtuner` and am highly interested in the features introduced since v2.15.0. To avoid installation from source in our pypi wheels, we are eagerly waiting for the new release. So, Does your team have a new release plan for v2.15.1 and could you please share it with us? Thanks very much! ### Motivation . ### Your contribution .

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I_kwDODunzps54jnHY

Can't align optional features of struct

closed

CONTRIBUTOR

### Describe the bug Hello! I'm currently experiencing an issue where I can't concatenate datasets if an inner field of a Feature is Optional. I have a column named `speaker`, and this holds some information about a speaker. ```python @dataclass class Speaker: name: str email: Optional[str] ``` If I have two datasets, one happens to have `email` always None, then I get `The features can't be aligned because the key email of features` ### Steps to reproduce the bug You can run the following script: ```python ds = Dataset.from_dict({'speaker': [{'name': 'Ben', 'email': None}]}) ds2 = Dataset.from_dict({'speaker': [{'name': 'Fred', 'email': '[email protected]'}]}) concatenate_datasets([ds, ds2]) >>>The features can't be aligned because the key speaker of features {'speaker': {'email': Value(dtype='string', id=None), 'name': Value(dtype='string', id=None)}} has unexpected type - {'email': Value(dtype='string', id=None), 'name': Value(dtype='string', id=None)} (expected either {'email': Value(dtype='null', id=None), 'name': Value(dtype='string', id=None)} or Value("null"). ``` ### Expected behavior I think this should work; if two top-level columns were in the same situation it would properly cast to `string`. ```python ds = Dataset.from_dict({'email': [None, None]}) ds2 = Dataset.from_dict({'email': ['[email protected]', '[email protected]']}) concatenate_datasets([ds, ds2]) >>> # Works! ``` ### Environment info - `datasets` version: 2.15.1.dev0 - Platform: Linux-5.15.0-89-generic-x86_64-with-glibc2.35 - Python version: 3.9.13 - `huggingface_hub` version: 0.19.4 - PyArrow version: 9.0.0 - Pandas version: 1.4.4 - `fsspec` version: 2023.6.0 I would be happy to fix this issue.

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I_kwDODunzps54iIN0

`load_dataset` uses out-of-date cache instead of re-downloading a changed dataset

open

NONE

### Describe the bug When a dataset is updated on the hub, using `load_dataset` will load the locally cached dataset instead of re-downloading the updated dataset ### Steps to reproduce the bug Here is a minimal example script to 1. create an initial dataset and upload 2. download it so it is stored in cache 3. change the dataset and re-upload 4. redownload ```python import time from datasets import Dataset, DatasetDict, DownloadMode, load_dataset username = "YOUR_USERNAME_HERE" initial = Dataset.from_dict({"foo": [1, 2, 3]}) print(f"Intial {initial['foo']}") initial_ds = DatasetDict({"train": initial}) initial_ds.push_to_hub("test") time.sleep(1) download = load_dataset(f"{username}/test", split="train") changed = download.map(lambda x: {"foo": x["foo"] + 1}) print(f"Changed {changed['foo']}") changed.push_to_hub("test") time.sleep(1) download_again = load_dataset(f"{username}/test", split="train") print(f"Download Changed {download_again['foo']}") # >>> gives the out-dated [1,2,3] when it should be changed [2,3,4] ``` The redownloaded dataset should be the changed dataset but it is actually the cached, initial dataset. Force-redownloading gives the correct dataset ```python download_again_force = load_dataset(f"{username}/test", split="train", download_mode=DownloadMode.FORCE_REDOWNLOAD) print(f"Force Download Changed {download_again_force['foo']}") # >>> [2,3,4] ``` ### Expected behavior I assumed there should be some sort of hashing that should check for changes in the dataset and re-download if the hashes don't match ### Environment info - `datasets` version: 2.15.0 │ - Platform: Linux-5.15.0-1028-nvidia-x86_64-with-glibc2.17 │ - Python version: 3.8.17 │ - `huggingface_hub` version: 0.19.4 │ - PyArrow version: 13.0.0 │ - Pandas version: 2.0.3 │ - `fsspec` version: 2023.6.0

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I_kwDODunzps54P5kr

jsonlines files don't load with `load_dataset`

closed

NONE

### Describe the bug While [the docs](https://huggingface.co/docs/datasets/upload_dataset#upload-dataset) seem to state that `.jsonl` is a supported extension for `datasets`, loading the dataset results in a `JSONDecodeError`. ### Steps to reproduce the bug Code: ``` from datasets import load_dataset dset = load_dataset('slotreck/pickle') ``` Traceback: ``` Downloading readme: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 925/925 [00:00<00:00, 3.11MB/s] Downloading and preparing dataset json/slotreck--pickle to /mnt/home/lotrecks/.cache/huggingface/datasets/slotreck___json/slotreck--pickle-0c311f36ed032b04/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96... Downloading data: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 589k/589k [00:00<00:00, 18.9MB/s] Downloading data: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 104k/104k [00:00<00:00, 4.61MB/s] Downloading data: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 170k/170k [00:00<00:00, 7.71MB/s] Downloading data files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 3.77it/s] Extracting data files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 523.92it/s] Generating train split: 0 examples [00:00, ? examples/s]Failed to read file '/mnt/home/lotrecks/.cache/huggingface/datasets/downloads/6ec07bb2f279c9377036af6948532513fa8f48244c672d2644a2d7018ee5c9cb' with error <class 'pyarrow.lib.ArrowInvalid'>: JSON parse error: Column(/ner/[]/[]/[]) changed from number to string in row 0 Traceback (most recent call last): File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/site-packages/datasets/packaged_modules/json/json.py", line 144, in _generate_tables dataset = json.load(f) File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/json/__init__.py", line 296, in load parse_constant=parse_constant, object_pairs_hook=object_pairs_hook, **kw) File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/json/__init__.py", line 348, in loads return _default_decoder.decode(s) File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/json/decoder.py", line 340, in decode raise JSONDecodeError("Extra data", s, end) json.decoder.JSONDecodeError: Extra data: line 2 column 1 (char 3086) During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/site-packages/datasets/builder.py", line 1879, in _prepare_split_single for _, table in generator: File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/site-packages/datasets/packaged_modules/json/json.py", line 147, in _generate_tables raise e File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/site-packages/datasets/packaged_modules/json/json.py", line 122, in _generate_tables io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size) File "pyarrow/_json.pyx", line 259, in pyarrow._json.read_json File "pyarrow/error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 100, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: JSON parse error: Column(/ner/[]/[]/[]) changed from number to string in row 0 The above exception was the direct cause of the following exception: Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/site-packages/datasets/load.py", line 1815, in load_dataset storage_options=storage_options, File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/site-packages/datasets/builder.py", line 913, in download_and_prepare **download_and_prepare_kwargs, File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/site-packages/datasets/builder.py", line 1004, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/site-packages/datasets/builder.py", line 1768, in _prepare_split gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/site-packages/datasets/builder.py", line 1912, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Expected behavior For the dataset to be loaded without error. ### Environment info - `datasets` version: 2.13.1 - Platform: Linux-3.10.0-1160.80.1.el7.x86_64-x86_64-with-centos-7.9.2009-Core - Python version: 3.7.12 - Huggingface_hub version: 0.15.1 - PyArrow version: 8.0.0 - Pandas version: 1.3.5

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I_kwDODunzps54JGMD

`TypeError`: huggingface_hub.hf_file_system.HfFileSystem.find() got multiple values for keyword argument 'maxdepth'

closed

NONE

### Describe the bug Please see https://github.com/huggingface/huggingface_hub/issues/1872 ### Steps to reproduce the bug Please see https://github.com/huggingface/huggingface_hub/issues/1872 ### Expected behavior Please see https://github.com/huggingface/huggingface_hub/issues/1872 ### Environment info Please see https://github.com/huggingface/huggingface_hub/issues/1872

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I_kwDODunzps532MEY

Unable to read "marsyas/gtzan" data

closed

NONE

Hi, this is my code and the error: ``` from datasets import load_dataset gtzan = load_dataset("marsyas/gtzan", "all") ``` [error_trace.txt](https://github.com/huggingface/datasets/files/13464397/error_trace.txt) [audio_yml.txt](https://github.com/huggingface/datasets/files/13464410/audio_yml.txt) Python 3.11.5 Jupyter Notebook 6.5.4 Windows 10 I'm able to download and work with other datasets, but not this one. For example, both these below work fine: ``` from datasets import load_dataset dataset = load_dataset("facebook/voxpopuli", "pl", split="train", streaming=True) minds = load_dataset("PolyAI/minds14", name="en-US", split="train") ``` Thanks for your help https://huggingface.co/datasets/marsyas/gtzan/tree/main

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I_kwDODunzps53xme6

Support multiple image/audio columns in ImageFolder/AudioFolder

closed

COLLABORATOR

### Feature request Have a metadata.csv file with multiple columns that point to relative image or audio files. ### Motivation Currently, ImageFolder allows one column, called `file_name`, pointing to relative image files. On the same model, AudioFolder allows one column, called `file_name`, pointing to relative audio files. But it's not possible to have two image columns, or to have two audio column, or to have one audio column and one image column. ### Your contribution no specific contribution

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I_kwDODunzps53sqDi

Support one dataset loader per config when using YAML

open

COLLABORATOR

### Feature request See https://huggingface.co/datasets/datasets-examples/doc-unsupported-1 I would like to use CSV loader for the "csv" config, JSONL loader for the "jsonl" config, etc. ### Motivation It would be more flexible for the users ### Your contribution No specific contribution

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I_kwDODunzps53oc3k

Speech Commands v2 dataset doesn't match AST-v2 config

closed

NONE

### Describe the bug [According](https://huggingface.co/MIT/ast-finetuned-speech-commands-v2) to `MIT/ast-finetuned-speech-commands-v2`, the model was trained on the Speech Commands v2 dataset. However, while the model config says the model should have 35 class labels, the dataset itself has 36 class labels. Moreover, the class labels themselves don't match between the model config and the dataset. It is difficult to reproduce the data used to fine tune `MIT/ast-finetuned-speech-commands-v2`. ### Steps to reproduce the bug ``` >>> model = ASTForAudioClassification.from_pretrained("MIT/ast-finetuned-speech-commands-v2") >>> model.config.id2label {0: 'backward', 1: 'follow', 2: 'five', 3: 'bed', 4: 'zero', 5: 'on', 6: 'learn', 7: 'two', 8: 'house', 9: 'tree', 10: 'dog', 11: 'stop', 12: 'seven', 13: 'eight', 14: 'down', 15: 'six', 16: 'forward', 17: 'cat', 18: 'right', 19: 'visual', 20: 'four', 21: 'wow', 22: 'no', 23: 'nine', 24: 'off', 25: 'three', 26: 'left', 27: 'marvin', 28: 'yes', 29: 'up', 30: 'sheila', 31: 'happy', 32: 'bird', 33: 'go', 34: 'one'} >>> dataset = load_dataset("speech_commands", "v0.02", split="test") >>> torch.unique(torch.Tensor(dataset['label'])) tensor([ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29., 30., 31., 32., 33., 34., 35.]) ``` If you try to explore the [dataset itself](https://huggingface.co/datasets/speech_commands/viewer/v0.02/test), you can see that the id to label does not match what is provided by `model.config.id2label`. ### Expected behavior The labels should match completely and there should be the same number of label classes between the model config and the dataset itself. ### Environment info datasets = 2.14.6, transformers = 4.33.3

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I_kwDODunzps53mc2w

Trouble loading files defined in YAML explicitly

open

COLLABORATOR

Look at https://huggingface.co/datasets/severo/doc-yaml-2 It's a reproduction of the example given in the docs at https://huggingface.co/docs/hub/datasets-manual-configuration ``` You can select multiple files per split using a list of paths: my_dataset_repository/ ├── README.md ├── data/ │ ├── abc.csv │ └── def.csv └── holdout/ └── ghi.csv --- configs: - config_name: default data_files: - split: train path: - "data/abc.csv" - "data/def.csv" - split: test path: "holdout/ghi.csv" --- ``` It raises the following error: ``` Error code: ConfigNamesError Exception: FileNotFoundError Message: Couldn't find a dataset script at /src/services/worker/severo/doc-yaml-2/doc-yaml-2.py or any data file in the same directory. Couldn't find 'severo/doc-yaml-2' on the Hugging Face Hub either: FileNotFoundError: Unable to find 'hf://datasets/severo/doc-yaml-2@938a0578fb4c6bc9da7d80b06a3ba39c2834b0c2/data/def.csv' with any supported extension ['.csv', '.tsv', '.json', '.jsonl', '.parquet', '.arrow', '.txt', '.blp', '.bmp', '.dib', '.bufr', '.cur', '.pcx', '.dcx', '.dds', '.ps', '.eps', '.fit', '.fits', '.fli', '.flc', '.ftc', '.ftu', '.gbr', '.gif', '.grib', '.h5', '.hdf', '.png', '.apng', '.jp2', '.j2k', '.jpc', '.jpf', '.jpx', '.j2c', '.icns', '.ico', '.im', '.iim', '.tif', '.tiff', '.jfif', '.jpe', '.jpg', '.jpeg', '.mpg', '.mpeg', '.msp', '.pcd', '.pxr', '.pbm', '.pgm', '.ppm', '.pnm', '.psd', '.bw', '.rgb', '.rgba', '.sgi', '.ras', '.tga', '.icb', '.vda', '.vst', '.webp', '.wmf', '.emf', '.xbm', '.xpm', '.BLP', '.BMP', '.DIB', '.BUFR', '.CUR', '.PCX', '.DCX', '.DDS', '.PS', '.EPS', '.FIT', '.FITS', '.FLI', '.FLC', '.FTC', '.FTU', '.GBR', '.GIF', '.GRIB', '.H5', '.HDF', '.PNG', '.APNG', '.JP2', '.J2K', '.JPC', '.JPF', '.JPX', '.J2C', '.ICNS', '.ICO', '.IM', '.IIM', '.TIF', '.TIFF', '.JFIF', '.JPE', '.JPG', '.JPEG', '.MPG', '.MPEG', '.MSP', '.PCD', '.PXR', '.PBM', '.PGM', '.PPM', '.PNM', '.PSD', '.BW', '.RGB', '.RGBA', '.SGI', '.RAS', '.TGA', '.ICB', '.VDA', '.VST', '.WEBP', '.WMF', '.EMF', '.XBM', '.XPM', '.aiff', '.au', '.avr', '.caf', '.flac', '.htk', '.svx', '.mat4', '.mat5', '.mpc2k', '.ogg', '.paf', '.pvf', '.raw', '.rf64', '.sd2', '.sds', '.ircam', '.voc', '.w64', '.wav', '.nist', '.wavex', '.wve', '.xi', '.mp3', '.opus', '.AIFF', '.AU', '.AVR', '.CAF', '.FLAC', '.HTK', '.SVX', '.MAT4', '.MAT5', '.MPC2K', '.OGG', '.PAF', '.PVF', '.RAW', '.RF64', '.SD2', '.SDS', '.IRCAM', '.VOC', '.W64', '.WAV', '.NIST', '.WAVEX', '.WVE', '.XI', '.MP3', '.OPUS', '.zip'] Traceback: Traceback (most recent call last): File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 65, in compute_config_names_response for config in sorted(get_dataset_config_names(path=dataset, token=hf_token)) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 351, in get_dataset_config_names dataset_module = dataset_module_factory( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1507, in dataset_module_factory raise FileNotFoundError( FileNotFoundError: Couldn't find a dataset script at /src/services/worker/severo/doc-yaml-2/doc-yaml-2.py or any data file in the same directory. Couldn't find 'severo/doc-yaml-2' on the Hugging Face Hub either: FileNotFoundError: Unable to find 'hf://datasets/severo/doc-yaml-2@938a0578fb4c6bc9da7d80b06a3ba39c2834b0c2/data/def.csv' with any supported extension ['.csv', '.tsv', '.json', '.jsonl', '.parquet', '.arrow', '.txt', '.blp', '.bmp', '.dib', '.bufr', '.cur', '.pcx', '.dcx', '.dds', '.ps', '.eps', '.fit', '.fits', '.fli', '.flc', '.ftc', '.ftu', '.gbr', '.gif', '.grib', '.h5', '.hdf', '.png', '.apng', '.jp2', '.j2k', '.jpc', '.jpf', '.jpx', '.j2c', '.icns', '.ico', '.im', '.iim', '.tif', '.tiff', '.jfif', '.jpe', '.jpg', '.jpeg', '.mpg', '.mpeg', '.msp', '.pcd', '.pxr', '.pbm', '.pgm', '.ppm', '.pnm', '.psd', '.bw', '.rgb', '.rgba', '.sgi', '.ras', '.tga', '.icb', '.vda', '.vst', '.webp', '.wmf', '.emf', '.xbm', '.xpm', '.BLP', '.BMP', '.DIB', '.BUFR', '.CUR', '.PCX', '.DCX', '.DDS', '.PS', '.EPS', '.FIT', '.FITS', '.FLI', '.FLC', '.FTC', '.FTU', '.GBR', '.GIF', '.GRIB', '.H5', '.HDF', '.PNG', '.APNG', '.JP2', '.J2K', '.JPC', '.JPF', '.JPX', '.J2C', '.ICNS', '.ICO', '.IM', '.IIM', '.TIF', '.TIFF', '.JFIF', '.JPE', '.JPG', '.JPEG', '.MPG', '.MPEG', '.MSP', '.PCD', '.PXR', '.PBM', '.PGM', '.PPM', '.PNM', '.PSD', '.BW', '.RGB', '.RGBA', '.SGI', '.RAS', '.TGA', '.ICB', '.VDA', '.VST', '.WEBP', '.WMF', '.EMF', '.XBM', '.XPM', '.aiff', '.au', '.avr', '.caf', '.flac', '.htk', '.svx', '.mat4', '.mat5', '.mpc2k', '.ogg', '.paf', '.pvf', '.raw', '.rf64', '.sd2', '.sds', '.ircam', '.voc', '.w64', '.wav', '.nist', '.wavex', '.wve', '.xi', '.mp3', '.opus', '.AIFF', '.AU', '.AVR', '.CAF', '.FLAC', '.HTK', '.SVX', '.MAT4', '.MAT5', '.MPC2K', '.OGG', '.PAF', '.PVF', '.RAW', '.RF64', '.SD2', '.SDS', '.IRCAM', '.VOC', '.W64', '.WAV', '.NIST', '.WAVEX', '.WVE', '.XI', '.MP3', '.OPUS', '.zip'] ```

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I_kwDODunzps53knT7

Trouble loading image folder with additional features - metadata file ignored

closed

NONE

### Describe the bug Loading image folder with a caption column using `load_dataset(<image_folder_path>)` doesn't load the captions. When loading a local image folder with captions using `datasets==2.13.0` ``` from datasets import load_dataset data = load_dataset(<image_folder_path>) data.column_names ``` yields `{'train': ['image', 'prompt']}` but when using `datasets==2.15.0` yeilds `{'train': ['image']}` Putting the images and `metadata.jsonl` file into a nested `train` folder **or** loading with `load_dataset("imagefolder", data_dir=<image_folder_path>)` solves the issue and yields `{'train': ['image', 'prompt']}` ### Steps to reproduce the bug 1. create a folder `<image_folder_path>` that contains images and a metadata file with additional features- e.g. "prompt" 2. run: ``` from datasets import load_dataset data = load_dataset("<image_folder_path>") data.column_names ``` ### Expected behavior `{'train': ['image', 'prompt']}` ### Environment info - `datasets` version: 2.15.0 - Platform: Linux-5.15.120+-x86_64-with-glibc2.35 - Python version: 3.10.12 - `huggingface_hub` version: 0.19.4 - PyArrow version: 9.0.0 - Pandas version: 1.5.3 - `fsspec` version: 2023.6.0

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I_kwDODunzps53gagB

Trouble Loading a Gated Dataset For User with Granted Permission

closed

NONE

### Describe the bug I have granted permissions to several users to access a gated huggingface dataset. The users accepted the invite and when trying to load the dataset using their access token they get `FileNotFoundError: Couldn't find a dataset script at .....` . Also when they try to click the url link for the dataset they get a 404 error. ### Steps to reproduce the bug 1. Grant access to gated dataset for specific users 2. Users accept invitation 3. Users login to hugging face hub using cli login 4. Users run load_dataset ### Expected behavior Dataset is loaded normally for users who were granted access to the gated dataset. ### Environment info datasets==2.15.0

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I_kwDODunzps53emJ1

`.map` not hashing under python 3.9

closed

NONE

### Describe the bug The `.map` function cannot hash under python 3.9. Tried to use [the solution here](https://github.com/huggingface/datasets/issues/4521#issuecomment-1205166653), but still get the same message: `Parameter 'function'=<function map_to_pred at 0x7fa0b49ead30> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.` ### Steps to reproduce the bug ```python def map_to_pred(batch): """ Perform inference on an audio batch Parameters: batch (dict): A dictionary containing audio data and other related information. Returns: dict: The input batch dictionary with added prediction and transcription fields. """ audio = batch['audio'] input_features = processor( audio['array'], sampling_rate=audio['sampling_rate'], return_tensors="pt").input_features input_features = input_features.to('cuda') with torch.no_grad(): predicted_ids = model.generate(input_features) preds = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0] batch['prediction'] = processor.tokenizer._normalize(preds) batch["transcription"] = processor.tokenizer._normalize(batch['transcription']) return batch MODEL_CARD = "openai/whisper-small" MODEL_NAME = MODEL_CARD.rsplit('/', maxsplit=1)[-1] model = WhisperForConditionalGeneration.from_pretrained(MODEL_CARD) processor = AutoProcessor.from_pretrained( MODEL_CARD, language="english", task="transcribe") model = torch.compile(model) dt = load_dataset("audiofolder", data_dir=config['DATA']['dataset'], split="test") dt = dt.cast_column("audio", Audio(sampling_rate=16000)) result = coraal_dt.map(map_to_pred, num_proc=16) ``` ### Expected behavior Hashed and cached dataset starts inferencing ### Environment info - `transformers` version: 4.35.0 - Platform: Linux-5.14.0-284.30.1.el9_2.x86_64-x86_64-with-glibc2.34 - Python version: 3.9.18 - Huggingface_hub version: 0.17.3 - Safetensors version: 0.4.0 - Accelerate version: 0.24.1 - Accelerate config: not found - PyTorch version (GPU?): 2.1.0 (True) - Tensorflow version (GPU?): not installed (NA) - Flax version (CPU?/GPU?/TPU?): not installed (NA) - Jax version: not installed - JaxLib version: not installed - Using GPU in script?: yes - Using distributed or parallel set-up in script?: no

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I_kwDODunzps53YhSi

Download + preparation speed of datasets.load_dataset is 20x slower than huggingface hub snapshot and manual loding

open

NONE

### Describe the bug I am working with a dataset I am trying to publish. The path is Antreas/TALI. It's a fairly large dataset, and contains images, video, audio and text. I have been having multiple problems when the dataset is being downloaded using the load_dataset function -- even with 64 workers taking more than 7 days to process. With snapshot download it takes 12 hours, and that includes the dataset preparation done using load_dataset and passing the dataset parquet file paths. Find the script I am using below: ```python import multiprocessing as mp import pathlib from typing import Optional import datasets from rich import print from tqdm import tqdm def download_dataset_via_hub( dataset_name: str, dataset_download_path: pathlib.Path, num_download_workers: int = mp.cpu_count(), ): import huggingface_hub as hf_hub download_folder = hf_hub.snapshot_download( repo_id=dataset_name, repo_type="dataset", cache_dir=dataset_download_path, resume_download=True, max_workers=num_download_workers, ignore_patterns=[], ) return pathlib.Path(download_folder) / "data" def load_dataset_via_hub( dataset_download_path: pathlib.Path, num_download_workers: int = mp.cpu_count(), dataset_name: Optional[str] = None, ): from dataclasses import dataclass, field from datasets import ClassLabel, Features, Image, Sequence, Value dataset_path = download_dataset_via_hub( dataset_download_path=dataset_download_path, num_download_workers=num_download_workers, dataset_name=dataset_name, ) # Building a list of file paths for validation set train_files = [ file.as_posix() for file in pathlib.Path(dataset_path).glob("*.parquet") if "train" in file.as_posix() ] val_files = [ file.as_posix() for file in pathlib.Path(dataset_path).glob("*.parquet") if "val" in file.as_posix() ] test_files = [ file.as_posix() for file in pathlib.Path(dataset_path).glob("*.parquet") if "test" in file.as_posix() ] print( f"Found {len(test_files)} files for testing set, {len(train_files)} for training set and {len(val_files)} for validation set" ) data_files = { "test": test_files, "val": val_files, "train": train_files, } features = Features( { "image": Image( decode=True ), # Set `decode=True` if you want to decode the images, otherwise `decode=False` "image_url": Value("string"), "item_idx": Value("int64"), "wit_features": Sequence( { "attribution_passes_lang_id": Value("bool"), "caption_alt_text_description": Value("string"), "caption_reference_description": Value("string"), "caption_title_and_reference_description": Value("string"), "context_page_description": Value("string"), "context_section_description": Value("string"), "hierarchical_section_title": Value("string"), "is_main_image": Value("bool"), "language": Value("string"), "page_changed_recently": Value("bool"), "page_title": Value("string"), "page_url": Value("string"), "section_title": Value("string"), } ), "wit_idx": Value("int64"), "youtube_title_text": Value("string"), "youtube_description_text": Value("string"), "youtube_video_content": Value("binary"), "youtube_video_starting_time": Value("string"), "youtube_subtitle_text": Value("string"), "youtube_video_size": Value("int64"), "youtube_video_file_path": Value("string"), } ) dataset = datasets.load_dataset( "parquet" if dataset_name is None else dataset_name, data_files=data_files, features=features, num_proc=1, cache_dir=dataset_download_path / "cache", ) return dataset if __name__ == "__main__": dataset_cache = pathlib.Path("/disk/scratch_fast0/tali/") dataset = load_dataset_via_hub(dataset_cache, dataset_name="Antreas/TALI")[ "test" ] for sample in tqdm(dataset): print(list(sample.keys())) ``` Also, streaming this dataset has been a very painfully slow process. Streaming the train set takes 15m to start, and streaming the test and val sets takes 3 hours to start! ### Steps to reproduce the bug 1. Run the code I provided to get a sense of how fast snapshot + manual is 2. Run datasets.load_dataset("Antreas/TALI") to get a sense of the speed of that OP. 3. You should now have an appreciation of how long these things take. ### Expected behavior The load dataset function should be at least as fast as the huggingface snapshot download function in terms of downloading dataset files. Not 20 times slower. ### Environment info - `datasets` version: 2.14.5 - Platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.35 - Python version: 3.10.13 - Huggingface_hub version: 0.17.3 - PyArrow version: 13.0.0 - Pandas version: 2.1.1

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I_kwDODunzps53VJik

Support GeoParquet

open

COLLABORATOR

### Feature request Support the GeoParquet format ### Motivation GeoParquet (https://geoparquet.org/) is a common format for sharing vectorial geospatial data on the cloud, along with "traditional" data columns. It would be nice to be able to load this format with datasets, and more generally, in the Datasets Hub (see https://huggingface.co/datasets/joshuasundance/govgis_nov2023-slim-spatial/discussions/1). ### Your contribution I would be happy to help work on a PR (but I don't think I can do one on my own). Also, we have to define what we want to support: - load all the columns, but get the "geospatial" column in text-only mode for now - or, fully support the spatial features, maybe taking inspiration from (or depending upon) https://geopandas.org/en/stable/index.html (which itself depends on https://fiona.readthedocs.io/en/stable/, which requires a local install of https://gdal.org/)

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I_kwDODunzps53SP8e

Problem in training iterable dataset

open

NONE

### Describe the bug I am using PyTorch DDP (Distributed Data Parallel) to train my model. Since the data is too large to load into memory at once, I am using load_dataset to read the data as an iterable dataset. I have used datasets.distributed.split_dataset_by_node to distribute the dataset. However, I have noticed that this distribution results in different processes having different amounts of data to train on. As a result, when the earliest process finishes training and starts predicting on the test set, other processes are still training, causing the overall training speed to be very slow. ### Steps to reproduce the bug ``` def train(args, model, device, train_loader, optimizer, criterion, epoch, length): model.train() idx_length = 0 for batch_idx, data in enumerate(train_loader): s_time = time.time() X = data['X'] target = data['y'].reshape(-1, 28) X, target = X.to(device), target.to(device) optimizer.zero_grad() output = model(X) loss = criterion(output, target) loss.backward() optimizer.step() idx_length += 1 if batch_idx % args.log_interval == 0: # print('Train Epoch: {} Batch_idx: {} Process: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format( # epoch, batch_idx, torch.distributed.get_rank(), batch_idx * len(X), length / torch.distributed.get_world_size(), # 100. * batch_idx * len( # X) * torch.distributed.get_world_size() / length, loss.item())) print('Train Epoch: {} Batch_idx: {} Process: {} [{}/{} ({:.0f}%)]\t'.format( epoch, batch_idx, torch.distributed.get_rank(), batch_idx * len(X), length / torch.distributed.get_world_size(), 100. * batch_idx * len( X) * torch.distributed.get_world_size() / length)) if args.dry_run: break print('Process %s length: %s time: %s' % (torch.distributed.get_rank(), idx_length, datetime.datetime.now())) train_iterable_dataset = load_dataset("parquet", data_files=data_files, split="train", streaming=True) test_iterable_dataset = load_dataset("parquet", data_files=data_files, split="test", streaming=True) train_iterable_dataset = train_iterable_dataset.map(process_fn) test_iterable_dataset = test_iterable_dataset.map(process_fn) train_iterable_dataset = train_iterable_dataset.map(scale) test_iterable_dataset = test_iterable_dataset.map(scale) train_iterable_dataset = datasets.distributed.split_dataset_by_node(train_iterable_dataset, world_size=world_size, rank=local_rank).shuffle(seed=1234) test_iterable_dataset = datasets.distributed.split_dataset_by_node(test_iterable_dataset, world_size=world_size, rank=local_rank).shuffle(seed=1234) print(torch.distributed.get_rank(), train_iterable_dataset.n_shards, test_iterable_dataset.n_shards) train_kwargs = {'batch_size': args.batch_size} test_kwargs = {'batch_size': args.test_batch_size} if use_cuda: cuda_kwargs = {'num_workers': 3,#ngpus_per_node, 'pin_memory': True, 'shuffle': False} train_kwargs.update(cuda_kwargs) test_kwargs.update(cuda_kwargs) train_loader = torch.utils.data.DataLoader(train_iterable_dataset, **train_kwargs, # sampler=torch.utils.data.distributed.DistributedSampler( # train_iterable_dataset, # num_replicas=ngpus_per_node, # rank=0) ) test_loader = torch.utils.data.DataLoader(test_iterable_dataset, **test_kwargs, # sampler=torch.utils.data.distributed.DistributedSampler( # test_iterable_dataset, # num_replicas=ngpus_per_node, # rank=0) ) for epoch in range(1, args.epochs + 1): start_time = time.time() train_iterable_dataset.set_epoch(epoch) test_iterable_dataset.set_epoch(epoch) train(args, model, device, train_loader, optimizer, criterion, epoch, train_len) test(args, model, device, criterion2, test_loader) ``` And here’s the part of output: ``` Train Epoch: 1 Batch_idx: 5000 Process: 0 [320000/4710975.0 (7%)] Train Epoch: 1 Batch_idx: 5000 Process: 1 [320000/4710975.0 (7%)] Train Epoch: 1 Batch_idx: 5000 Process: 2 [320000/4710975.0 (7%)] Train Epoch: 1 Batch_idx: 5862 Process: 3 Data_length: 12 coststime: 0.04095172882080078 Train Epoch: 1 Batch_idx: 5862 Process: 0 Data_length: 3 coststime: 0.0751960277557373 Train Epoch: 1 Batch_idx: 5867 Process: 3 Data_length: 49 coststime: 0.0032558441162109375 Train Epoch: 1 Batch_idx: 5872 Process: 1 Data_length: 2 coststime: 0.022842884063720703 Train Epoch: 1 Batch_idx: 5876 Process: 3 Data_length: 63 coststime: 0.002694845199584961 Process 3 length: 5877 time: 2023-11-17 17:03:26.582317 Train epoch 1 costTime: 241.72063446044922s . Process 3 Start to test. 3 0 tensor(45508.8516, device='cuda:3') 3 100 tensor(45309.0469, device='cuda:3') 3 200 tensor(45675.3047, device='cuda:3') 3 300 tensor(45263.0273, device='cuda:3') Process 3 Reduce metrics. Train Epoch: 2 Batch_idx: 0 Process: 3 [0/4710975.0 (0%)] Train Epoch: 1 Batch_idx: 5882 Process: 1 Data_length: 63 coststime: 0.05185818672180176 Train Epoch: 1 Batch_idx: 5887 Process: 1 Data_length: 12 coststime: 0.006895303726196289 Process 1 length: 5888 time: 2023-11-17 17:20:48.578204 Train epoch 1 costTime: 1285.7279663085938s . Process 1 Start to test. 1 0 tensor(45265.9141, device='cuda:1') ``` ### Expected behavior I'd like to know how to fix this problem. ### Environment info ``` torch==2.0 datasets==2.14.0 ```

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I_kwDODunzps53Qna6

TypeError: <lambda>() takes 0 positional arguments but 1 was given

closed

NONE

### Describe the bug ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) [<ipython-input-35-7b6becee3685>](https://localhost:8080/#) in <cell line: 1>() ----> 1 from datasets import Dataset 9 frames [/usr/local/lib/python3.10/dist-packages/datasets/__init__.py](https://localhost:8080/#) in <module> 20 __version__ = "2.15.0" 21 ---> 22 from .arrow_dataset import Dataset 23 from .arrow_reader import ReadInstruction 24 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder [/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in <module> 61 import pyarrow.compute as pc 62 from huggingface_hub import CommitOperationAdd, CommitOperationDelete, DatasetCard, DatasetCardData, HfApi ---> 63 from multiprocess import Pool 64 from requests import HTTPError 65 [/usr/local/lib/python3.10/dist-packages/multiprocess/__init__.py](https://localhost:8080/#) in <module> 31 32 import sys ---> 33 from . import context 34 35 # [/usr/local/lib/python3.10/dist-packages/multiprocess/context.py](https://localhost:8080/#) in <module> 4 5 from . import process ----> 6 from . import reduction 7 8 __all__ = () [/usr/local/lib/python3.10/dist-packages/multiprocess/reduction.py](https://localhost:8080/#) in <module> 14 import os 15 try: ---> 16 import dill as pickle 17 except ImportError: 18 import pickle [/usr/local/lib/python3.10/dist-packages/dill/__init__.py](https://localhost:8080/#) in <module> 24 25 ---> 26 from ._dill import ( 27 dump, dumps, load, loads, copy, 28 Pickler, Unpickler, register, pickle, pickles, check, [/usr/local/lib/python3.10/dist-packages/dill/_dill.py](https://localhost:8080/#) in <module> 166 try: 167 from _pyio import open as _open --> 168 PyTextWrapperType = get_file_type('r', buffering=-1, open=_open) 169 PyBufferedRandomType = get_file_type('r+b', buffering=-1, open=_open) 170 PyBufferedReaderType = get_file_type('rb', buffering=-1, open=_open) [/usr/local/lib/python3.10/dist-packages/dill/_dill.py](https://localhost:8080/#) in get_file_type(*args, **kwargs) 154 def get_file_type(*args, **kwargs): 155 open = kwargs.pop("open", __builtin__.open) --> 156 f = open(os.devnull, *args, **kwargs) 157 t = type(f) 158 f.close() [/usr/lib/python3.10/_pyio.py](https://localhost:8080/#) in open(file, mode, buffering, encoding, errors, newline, closefd, opener) 280 return result 281 encoding = text_encoding(encoding) --> 282 text = TextIOWrapper(buffer, encoding, errors, newline, line_buffering) 283 result = text 284 text.mode = mode [/usr/lib/python3.10/_pyio.py](https://localhost:8080/#) in __init__(self, buffer, encoding, errors, newline, line_buffering, write_through) 2043 encoding = "utf-8" 2044 else: -> 2045 encoding = locale.getpreferredencoding(False) 2046 2047 if not isinstance(encoding, str): TypeError: <lambda>() takes 0 positional arguments but 1 was given ``` or ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) [<ipython-input-36-652e886d387f>](https://localhost:8080/#) in <cell line: 1>() ----> 1 import datasets 9 frames [/usr/local/lib/python3.10/dist-packages/datasets/__init__.py](https://localhost:8080/#) in <module> 20 __version__ = "2.15.0" 21 ---> 22 from .arrow_dataset import Dataset 23 from .arrow_reader import ReadInstruction 24 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder [/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in <module> 61 import pyarrow.compute as pc 62 from huggingface_hub import CommitOperationAdd, CommitOperationDelete, DatasetCard, DatasetCardData, HfApi ---> 63 from multiprocess import Pool 64 from requests import HTTPError 65 [/usr/local/lib/python3.10/dist-packages/multiprocess/__init__.py](https://localhost:8080/#) in <module> 31 32 import sys ---> 33 from . import context 34 35 # [/usr/local/lib/python3.10/dist-packages/multiprocess/context.py](https://localhost:8080/#) in <module> 4 5 from . import process ----> 6 from . import reduction 7 8 __all__ = () [/usr/local/lib/python3.10/dist-packages/multiprocess/reduction.py](https://localhost:8080/#) in <module> 14 import os 15 try: ---> 16 import dill as pickle 17 except ImportError: 18 import pickle [/usr/local/lib/python3.10/dist-packages/dill/__init__.py](https://localhost:8080/#) in <module> 24 25 ---> 26 from ._dill import ( 27 dump, dumps, load, loads, copy, 28 Pickler, Unpickler, register, pickle, pickles, check, [/usr/local/lib/python3.10/dist-packages/dill/_dill.py](https://localhost:8080/#) in <module> 166 try: 167 from _pyio import open as _open --> 168 PyTextWrapperType = get_file_type('r', buffering=-1, open=_open) 169 PyBufferedRandomType = get_file_type('r+b', buffering=-1, open=_open) 170 PyBufferedReaderType = get_file_type('rb', buffering=-1, open=_open) [/usr/local/lib/python3.10/dist-packages/dill/_dill.py](https://localhost:8080/#) in get_file_type(*args, **kwargs) 154 def get_file_type(*args, **kwargs): 155 open = kwargs.pop("open", __builtin__.open) --> 156 f = open(os.devnull, *args, **kwargs) 157 t = type(f) 158 f.close() [/usr/lib/python3.10/_pyio.py](https://localhost:8080/#) in open(file, mode, buffering, encoding, errors, newline, closefd, opener) 280 return result 281 encoding = text_encoding(encoding) --> 282 text = TextIOWrapper(buffer, encoding, errors, newline, line_buffering) 283 result = text 284 text.mode = mode [/usr/lib/python3.10/_pyio.py](https://localhost:8080/#) in __init__(self, buffer, encoding, errors, newline, line_buffering, write_through) 2043 encoding = "utf-8" 2044 else: -> 2045 encoding = locale.getpreferredencoding(False) 2046 2047 if not isinstance(encoding, str): TypeError: <lambda>() takes 0 positional arguments but 1 was given ``` ### Steps to reproduce the bug `import datasets` on colab ### Expected behavior work fine ### Environment info colab `!pip install datasets`

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I_kwDODunzps53QB1R

Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method

closed

NONE

### Describe the bug 1. I ran dataset mapping with `num_proc=6` in it and got this error: `RuntimeError: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method` I can't actually find a way to run multi-GPU dataset mapping. Can you help? ### Steps to reproduce the bug 1. Rund SDXL training with `num_proc=6`: https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_sdxl.py ### Expected behavior Should work well ### Environment info 6x A100 SXM, Linux

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I_kwDODunzps53KkIc

load_dataset does not load all of the data in my input file

open

NONE

### Describe the bug I have 127 elements in my input dataset. When I do a len on the dataset after loaded, it is only 124 elements. ### Steps to reproduce the bug train_dataset = nlp.load_dataset(data_args.dataset_path, name=data_args.qg_format, split=nlp.Split.TRAIN) valid_dataset = nlp.load_dataset(data_args.dataset_path, name=data_args.qg_format, split=nlp.Split.VALIDATION) logger.info(len(train_dataset)) logger.info(len(valid_dataset)) Both train and valid input are 127 items. However, they both only load 124 items. The input format is in json. At the end of the day, I am trying to create .pt files. ### Expected behavior I see all 127 elements in my dataset when performing len ### Environment info Python 3.10. CentOS operating system. nlp==0.40, datasets==2.14.5, transformers==4.26.1

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I_kwDODunzps524t1D

Allow to choose the `writer_batch_size` when using `save_to_disk`

open

NONE

### Feature request Add an argument in `save_to_disk` regarding batch size, which would be passed to `shard` and other methods. ### Motivation The `Dataset.save_to_disk` method currently calls `shard` without passing a `writer_batch_size` argument, thus implicitly using the default value (1000). This can result in RAM saturation when using a lot of processes on long text sequences or other modalities, or for specific IO configs. ### Your contribution I would be glad to submit a PR, as long as it does not imply extensive tests refactoring.

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I_kwDODunzps52zQvo

Bug: LayoutLMv3 finetuning on FUNSD Notebook; Arrow Error

closed

NONE

### Describe the bug Arrow issues when running the example Notebook laptop locally on Mac with M1. Works on Google Collab. **Notebook**: https://github.com/NielsRogge/Transformers-Tutorials/blob/master/LayoutLMv3/Fine_tune_LayoutLMv3_on_FUNSD_(HuggingFace_Trainer).ipynb **Error**: `ValueError: Arrow type extension<arrow.py_extension_type<pyarrow.lib.UnknownExtensionType>> does not have a datasets dtype equivalent.` **Caused by**: ``` # we need to define custom features for `set_format` (used later on) to work properly features = Features({ 'pixel_values': Array3D(dtype="float32", shape=(3, 224, 224)), 'input_ids': Sequence(feature=Value(dtype='int64')), 'attention_mask': Sequence(Value(dtype='int64')), 'bbox': Array2D(dtype="int64", shape=(512, 4)), 'labels': Sequence(feature=Value(dtype='int64')), }) ``` ### Steps to reproduce the bug Run the notebook provided, locally. If possible also on M1. ### Expected behavior The cell where features are mapped to Array2D and Array3D should work without any issues. ### Environment info Tried with Python 3.9 and 3.10 conda envs. Running Mac M1. `pip show datasets` > Name: datasets Version: 2.14.6 Summary: HuggingFace community-driven open-source library of datasets `pip list` > Package Version > ------------------------- ------------ > accelerate 0.24.1 > aiohttp 3.8.6 > aiosignal 1.3.1 > anyio 3.5.0 > appnope 0.1.2 > argon2-cffi 21.3.0 > argon2-cffi-bindings 21.2.0 > asttokens 2.0.5 > async-timeout 4.0.3 > attrs 23.1.0 > backcall 0.2.0 > beautifulsoup4 4.12.2 > bleach 4.1.0 > certifi 2023.7.22 > cffi 1.15.1 > charset-normalizer 3.3.2 > comm 0.1.2 > datasets 2.14.6 > debugpy 1.6.7 > decorator 5.1.1 > defusedxml 0.7.1 > dill 0.3.7 > entrypoints 0.4 > exceptiongroup 1.0.4 > executing 0.8.3 > fastjsonschema 2.16.2 > filelock 3.13.1 > frozenlist 1.4.0 > fsspec 2023.10.0 > huggingface-hub 0.17.3 > idna 3.4 > importlib-metadata 6.0.0 > IProgress 0.4 > ipykernel 6.25.0 > ipython 8.15.0 > ipython-genutils 0.2.0 > jedi 0.18.1 > Jinja2 3.1.2 > joblib 1.3.2 > jsonschema 4.19.2 > jsonschema-specifications 2023.7.1 > jupyter_client 7.4.9 > jupyter_core 5.5.0 > jupyter-server 1.23.4 > jupyterlab-pygments 0.1.2 > MarkupSafe 2.1.1 > matplotlib-inline 0.1.6 > mistune 2.0.4 > mpmath 1.3.0 > multidict 6.0.4 > multiprocess 0.70.15 > nbclassic 1.0.0 > nbclient 0.8.0 > nbconvert 7.10.0 > nbformat 5.9.2 > nest-asyncio 1.5.6 > networkx 3.2.1 > notebook 6.5.4 > notebook_shim 0.2.3 > numpy 1.26.1 > packaging 23.1 > pandas 2.1.3 > pandocfilters 1.5.0 > parso 0.8.3 > pexpect 4.8.0 > pickleshare 0.7.5 > Pillow 10.1.0 > pip 23.3 > platformdirs 3.10.0 > prometheus-client 0.14.1 > prompt-toolkit 3.0.36 > psutil 5.9.0 > ptyprocess 0.7.0 > pure-eval 0.2.2 > pyarrow 14.0.1 > pycparser 2.21 > Pygments 2.15.1 > python-dateutil 2.8.2 > pytz 2023.3.post1 > PyYAML 6.0.1 > pyzmq 23.2.0 > referencing 0.30.2 > regex 2023.10.3 > requests 2.31.0 > rpds-py 0.10.6 > safetensors 0.4.0 > scikit-learn 1.3.2 > scipy 1.11.3 > Send2Trash 1.8.2 > seqeval 1.2.2 > setuptools 68.0.0 > six 1.16.0 > sniffio 1.2.0 > soupsieve 2.5 > stack-data 0.2.0 > sympy 1.12 > terminado 0.17.1 > threadpoolctl 3.2.0 > tinycss2 1.2.1 > tokenizers 0.14.1 > torch 2.1.0 > tornado 6.3.3 > tqdm 4.66.1 > traitlets 5.7.1 > transformers 4.36.0.dev0 > typing_extensions 4.7.1 > tzdata 2023.3 > urllib3 2.0.7 > wcwidth 0.2.5 > webencodings 0.5.1 > websocket-client 0.58.0 > wheel 0.41.2 > xxhash 3.4.1 > yarl 1.9.2 > zipp 3.11.0

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I_kwDODunzps52waK6

User token is printed out!

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NONE

This line prints user token on command line! Is it safe? https://github.com/huggingface/datasets/blob/12ebe695b4748c5a26e08b44ed51955f74f5801d/src/datasets/load.py#L2091

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I_kwDODunzps52vQlx

Datasets does not load HuggingFace Repository properly

open

NONE

### Describe the bug Dear Datasets team, We just have published a dataset on Huggingface: https://huggingface.co/ai4privacy However, when trying to read it using the Dataset library we get an error. As I understand jsonl files are compatible, could you please clarify how we can solve the issue? Please let me know and we would be more than happy to adapt the structure of the repository or meta data so it works easier: ```python from datasets import load_dataset dataset = load_dataset("ai4privacy/pii-masking-200k") ``` ``` Downloading readme: 100% 11.8k/11.8k [00:00<00:00, 512kB/s] Downloading data files: 100% 1/1 [00:11<00:00, 11.16s/it] Downloading data: 100% 64.3M/64.3M [00:02<00:00, 32.9MB/s] Downloading data: 100% 113M/113M [00:03<00:00, 35.0MB/s] Downloading data: 100% 97.7M/97.7M [00:02<00:00, 46.1MB/s] Downloading data: 100% 90.8M/90.8M [00:02<00:00, 44.9MB/s] Downloading data: 100% 7.63k/7.63k [00:00<00:00, 41.0kB/s] Downloading data: 100% 1.03k/1.03k [00:00<00:00, 9.44kB/s] Extracting data files: 100% 1/1 [00:00<00:00, 29.26it/s] Generating train split: 209261/0 [00:05<00:00, 41201.25 examples/s] --------------------------------------------------------------------------- ValueError Traceback (most recent call last) [/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id) 1939 ) -> 1940 writer.write_table(table) 1941 num_examples_progress_update += len(table) 8 frames [/usr/local/lib/python3.10/dist-packages/datasets/arrow_writer.py](https://localhost:8080/#) in write_table(self, pa_table, writer_batch_size) 571 pa_table = pa_table.combine_chunks() --> 572 pa_table = table_cast(pa_table, self._schema) 573 if self.embed_local_files: [/usr/local/lib/python3.10/dist-packages/datasets/table.py](https://localhost:8080/#) in table_cast(table, schema) 2327 if table.schema != schema: -> 2328 return cast_table_to_schema(table, schema) 2329 elif table.schema.metadata != schema.metadata: [/usr/local/lib/python3.10/dist-packages/datasets/table.py](https://localhost:8080/#) in cast_table_to_schema(table, schema) 2285 if sorted(table.column_names) != sorted(features): -> 2286 raise ValueError(f"Couldn't cast\n{table.schema}\nto\n{features}\nbecause column names don't match") 2287 arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()] ValueError: Couldn't cast JOBTYPE: int64 PHONEIMEI: int64 ACCOUNTNAME: int64 VEHICLEVIN: int64 GENDER: int64 CURRENCYCODE: int64 CREDITCARDISSUER: int64 JOBTITLE: int64 SEX: int64 CURRENCYSYMBOL: int64 IP: int64 EYECOLOR: int64 MASKEDNUMBER: int64 SECONDARYADDRESS: int64 JOBAREA: int64 ACCOUNTNUMBER: int64 language: string BITCOINADDRESS: int64 MAC: int64 SSN: int64 EMAIL: int64 ETHEREUMADDRESS: int64 DOB: int64 VEHICLEVRM: int64 IPV6: int64 AMOUNT: int64 URL: int64 PHONENUMBER: int64 PIN: int64 TIME: int64 CREDITCARDNUMBER: int64 FIRSTNAME: int64 IBAN: int64 BIC: int64 COUNTY: int64 STATE: int64 LASTNAME: int64 ZIPCODE: int64 HEIGHT: int64 ORDINALDIRECTION: int64 MIDDLENAME: int64 STREET: int64 USERNAME: int64 CURRENCY: int64 PREFIX: int64 USERAGENT: int64 CURRENCYNAME: int64 LITECOINADDRESS: int64 CREDITCARDCVV: int64 AGE: int64 CITY: int64 PASSWORD: int64 BUILDINGNUMBER: int64 IPV4: int64 NEARBYGPSCOORDINATE: int64 DATE: int64 COMPANYNAME: int64 to {'masked_text': Value(dtype='string', id=None), 'unmasked_text': Value(dtype='string', id=None), 'privacy_mask': Value(dtype='string', id=None), 'span_labels': Value(dtype='string', id=None), 'bio_labels': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'tokenised_text': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)} because column names don't match The above exception was the direct cause of the following exception: DatasetGenerationError Traceback (most recent call last) [<ipython-input-2-f1c6811e9c83>](https://localhost:8080/#) in <cell line: 3>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("ai4privacy/pii-masking-200k") [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs) 2151 2152 # Download and prepare data -> 2153 builder_instance.download_and_prepare( 2154 download_config=download_config, 2155 download_mode=download_mode, [/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in download_and_prepare(self, output_dir, download_config, download_mode, verification_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs) 952 if num_proc is not None: 953 prepare_split_kwargs["num_proc"] = num_proc --> 954 self._download_and_prepare( 955 dl_manager=dl_manager, 956 verification_mode=verification_mode, [/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in _download_and_prepare(self, dl_manager, verification_mode, **prepare_split_kwargs) 1047 try: 1048 # Prepare split will record examples associated to the split -> 1049 self._prepare_split(split_generator, **prepare_split_kwargs) 1050 except OSError as e: 1051 raise OSError( [/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split(self, split_generator, file_format, num_proc, max_shard_size) 1811 job_id = 0 1812 with pbar: -> 1813 for job_id, done, content in self._prepare_split_single( 1814 gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args 1815 ): [/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id) 1956 if isinstance(e, SchemaInferenceError) and e.__context__ is not None: 1957 e = e.__context__ -> 1958 raise DatasetGenerationError("An error occurred while generating the dataset") from e 1959 1960 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths) DatasetGenerationError: An error occurred while generating the dataset ``` Thank you and have a great day ahead ### Steps to reproduce the bug Open Google Colab Notebook: Run command: !pip3 install datasets Run code: from datasets import load_dataset dataset = load_dataset("ai4privacy/pii-masking-200k") ### Expected behavior Download the dataset successfully from HuggingFace to the notebook so that we can start working with it ### Environment info - `datasets` version: 2.14.6 - Platform: Linux-5.15.120+-x86_64-with-glibc2.35 - Python version: 3.10.12 - Huggingface_hub version: 0.19.1 - PyArrow version: 9.0.0 - Pandas version: 1.5.3

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I_kwDODunzps52uukh

using DownloadManager to download from local filesystem and disable_progress_bar, there will be an exception

closed

NONE

### Describe the bug i'm using datasets.download.download_manager.DownloadManager to download files like "file:///a/b/c.txt", and i disable_progress_bar() to disable bar. there will be an exception as follows: `AttributeError: 'function' object has no attribute 'close' Exception ignored in: <function TqdmCallback.__del__ at 0x7fa8683d84c0> Traceback (most recent call last): File "/home/protoss.gao/.local/lib/python3.9/site-packages/fsspec/callbacks.py", line 233, in __del__ self.tqdm.close()` i check your source code in datasets/utils/file_utils.py:348 you define TqdmCallback derive from fsspec.callbacks.TqdmCallback but in the newest fsspec code [https://github.com/fsspec/filesystem_spec/blob/master/fsspec/callbacks.py](url) , line 146, in this case, _DEFAULT_CALLBACK will take effect, but in line 234, it calls "close()" function which _DEFAULT_CALLBACK don't have such thing. so i think the class "TqdmCallback" in datasets/utils/file_utils.py may override "__del__" function or report this bug to fsspec. ### Steps to reproduce the bug as i said ### Expected behavior no exception ### Environment info datasets: 2.14.4 python: 3.9 platform: x86_64

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I_kwDODunzps52ugb8

`IterableDataset` lost but not keep columns when map function adding columns with names in `remove_columns`

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### Describe the bug IterableDataset lost but not keep columns when map function adding columns with names in remove_columns, Dataset not. May be related to the code below: https://github.com/huggingface/datasets/blob/06c3ffb8d068b6307b247164b10f7c7311cefed4/src/datasets/iterable_dataset.py#L750-L756 ### Steps to reproduce the bug ```python dataset: IterableDataset = load_dataset("Anthropic/hh-rlhf", streaming=True, split="train") column_names = list(next(iter(dataset)).keys()) # ['chosen', 'rejected'] # map_fn will return dict {"chosen": xxx, "rejected": xxx, "prompt": xxx, "history": xxxx} dataset = dataset.map(map_fn, batched=True, remove_columns=column_names) next(iter(dataset)) # output # {'prompt': 'xxx, 'history': xxx} ``` ```python # when load_dataset with streaming=False, the column_names are kept: dataset: Dataset = load_dataset("Anthropic/hh-rlhf", streaming=False, split="train") column_names = list(next(iter(dataset)).keys()) # ['chosen', 'rejected'] # map_fn will return dict {"chosen": xxx, "rejected": xxx, "prompt": xxx, "history": xxxx} dataset = dataset.map(map_fn, batched=True, remove_columns=column_names) next(iter(dataset)) # output # {'prompt': 'xxx, 'history': xxx, "chosen": xxx, "rejected": xxx} ``` ### Expected behavior IterableDataset keep columns when map function adding columns with names in remove_columns ### Environment info datasets==2.14.6

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I_kwDODunzps52tBff

Loading the dataset from private S3 bucket gives "TypeError: cannot pickle '_contextvars.Context' object"

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NONE

### Describe the bug I'm trying to read the parquet file from the private s3 bucket using the `load_dataset` function, but I receive `TypeError: cannot pickle '_contextvars.Context' object` error I'm working on a machine with `~/.aws/credentials` file. I can't give credentials and the path to a file in a private bucket for obvious reasons, but I'll try to give all possible outputs. ### Steps to reproduce the bug ```python import s3fs from datasets import load_dataset from aiobotocore.session import get_session DATA_PATH = "s3://bucket_name/path/validation.parquet" fs = s3fs.S3FileSystem(session=get_session()) ``` `fs.stat` returns the data, so we can say that fs is working and we have all permissions ```python fs.stat(DATA_PATH) # Returns: # {'ETag': '"123123a-19"', # 'LastModified': datetime.datetime(2023, 11, 1, 10, 16, 57, tzinfo=tzutc()), # 'size': 312237170, # 'name': 'bucket_name/path/validation.parquet', # 'type': 'file', # 'StorageClass': 'STANDARD', # 'VersionId': 'Abc.HtmsC9h.as', # 'ContentType': 'binary/octet-stream'} ``` ```python fs.storage_options # Returns: # {'session': <aiobotocore.session.AioSession at 0x7f9193fa53c0>} ``` ```python ds = load_dataset("parquet", data_files={"train": DATA_PATH}, storage_options=fs.storage_options) ``` <details> <summary>Returns such error (expandable)</summary> ```python --------------------------------------------------------------------------- TypeError Traceback (most recent call last) Cell In[88], line 1 ----> 1 ds = load_dataset("parquet", data_files={"train": DATA_PATH}, storage_options=fs.storage_options) File ~/miniconda3/envs/test-env/lib/python3.10/site-packages/datasets/load.py:2153, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs) 2150 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES 2152 # Download and prepare data -> 2153 builder_instance.download_and_prepare( 2154 download_config=download_config, 2155 download_mode=download_mode, 2156 verification_mode=verification_mode, 2157 try_from_hf_gcs=try_from_hf_gcs, 2158 num_proc=num_proc, 2159 storage_options=storage_options, 2160 ) 2162 # Build dataset for splits 2163 keep_in_memory = ( 2164 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size) 2165 ) File ~/miniconda3/envs/test-env/lib/python3.10/site-packages/datasets/builder.py:954, in DatasetBuilder.download_and_prepare(self, output_dir, download_config, download_mode, verification_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs) 952 if num_proc is not None: 953 prepare_split_kwargs["num_proc"] = num_proc --> 954 self._download_and_prepare( 955 dl_manager=dl_manager, 956 verification_mode=verification_mode, 957 **prepare_split_kwargs, 958 **download_and_prepare_kwargs, 959 ) 960 # Sync info 961 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values()) File ~/miniconda3/envs/test-env/lib/python3.10/site-packages/datasets/builder.py:1027, in DatasetBuilder._download_and_prepare(self, dl_manager, verification_mode, **prepare_split_kwargs) 1025 split_dict = SplitDict(dataset_name=self.dataset_name) 1026 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs) -> 1027 split_generators = self._split_generators(dl_manager, **split_generators_kwargs) 1029 # Checksums verification 1030 if verification_mode == VerificationMode.ALL_CHECKS and dl_manager.record_checksums: File ~/miniconda3/envs/test-env/lib/python3.10/site-packages/datasets/packaged_modules/parquet/parquet.py:34, in Parquet._split_generators(self, dl_manager) 32 if not self.config.data_files: 33 raise ValueError(f"At least one data file must be specified, but got data_files={self.config.data_files}") ---> 34 data_files = dl_manager.download_and_extract(self.config.data_files) 35 if isinstance(data_files, (str, list, tuple)): 36 files = data_files File ~/miniconda3/envs/test-env/lib/python3.10/site-packages/datasets/download/download_manager.py:565, in DownloadManager.download_and_extract(self, url_or_urls) 549 def download_and_extract(self, url_or_urls): 550 """Download and extract given `url_or_urls`. 551 552 Is roughly equivalent to: (...) 563 extracted_path(s): `str`, extracted paths of given URL(s). 564 """ --> 565 return self.extract(self.download(url_or_urls)) File ~/miniconda3/envs/test-env/lib/python3.10/site-packages/datasets/download/download_manager.py:420, in DownloadManager.download(self, url_or_urls) 401 def download(self, url_or_urls): 402 """Download given URL(s). 403 404 By default, only one process is used for download. Pass customized `download_config.num_proc` to change this behavior. (...) 418 ``` 419 """ --> 420 download_config = self.download_config.copy() 421 download_config.extract_compressed_file = False 422 if download_config.download_desc is None: File ~/miniconda3/envs/test-env/lib/python3.10/site-packages/datasets/download/download_config.py:94, in DownloadConfig.copy(self) 93 def copy(self) -> "DownloadConfig": ---> 94 return self.__class__(**{k: copy.deepcopy(v) for k, v in self.__dict__.items()}) File ~/miniconda3/envs/test-env/lib/python3.10/site-packages/datasets/download/download_config.py:94, in <dictcomp>(.0) 93 def copy(self) -> "DownloadConfig": ---> 94 return self.__class__(**{k: copy.deepcopy(v) for k, v in self.__dict__.items()}) File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:231, in _deepcopy_dict(x, memo, deepcopy) 229 memo[id(x)] = y 230 for key, value in x.items(): --> 231 y[deepcopy(key, memo)] = deepcopy(value, memo) 232 return y File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:172, in deepcopy(x, memo, _nil) 170 y = x 171 else: --> 172 y = _reconstruct(x, memo, *rv) 174 # If is its own copy, don't memoize. 175 if y is not x: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:271, in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy) 269 if state is not None: 270 if deep: --> 271 state = deepcopy(state, memo) 272 if hasattr(y, '__setstate__'): 273 y.__setstate__(state) File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:231, in _deepcopy_dict(x, memo, deepcopy) 229 memo[id(x)] = y 230 for key, value in x.items(): --> 231 y[deepcopy(key, memo)] = deepcopy(value, memo) 232 return y File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:172, in deepcopy(x, memo, _nil) 170 y = x 171 else: --> 172 y = _reconstruct(x, memo, *rv) 174 # If is its own copy, don't memoize. 175 if y is not x: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:271, in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy) 269 if state is not None: 270 if deep: --> 271 state = deepcopy(state, memo) 272 if hasattr(y, '__setstate__'): 273 y.__setstate__(state) [... skipping similar frames: _deepcopy_dict at line 231 (2 times), deepcopy at line 146 (2 times)] File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:172, in deepcopy(x, memo, _nil) 170 y = x 171 else: --> 172 y = _reconstruct(x, memo, *rv) 174 # If is its own copy, don't memoize. 175 if y is not x: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:271, in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy) 269 if state is not None: 270 if deep: --> 271 state = deepcopy(state, memo) 272 if hasattr(y, '__setstate__'): 273 y.__setstate__(state) [... skipping similar frames: deepcopy at line 146 (1 times)] File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:231, in _deepcopy_dict(x, memo, deepcopy) 229 memo[id(x)] = y 230 for key, value in x.items(): --> 231 y[deepcopy(key, memo)] = deepcopy(value, memo) 232 return y File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:206, in _deepcopy_list(x, memo, deepcopy) 204 append = y.append 205 for a in x: --> 206 append(deepcopy(a, memo)) 207 return y File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:172, in deepcopy(x, memo, _nil) 170 y = x 171 else: --> 172 y = _reconstruct(x, memo, *rv) 174 # If is its own copy, don't memoize. 175 if y is not x: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:271, in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy) 269 if state is not None: 270 if deep: --> 271 state = deepcopy(state, memo) 272 if hasattr(y, '__setstate__'): 273 y.__setstate__(state) File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:231, in _deepcopy_dict(x, memo, deepcopy) 229 memo[id(x)] = y 230 for key, value in x.items(): --> 231 y[deepcopy(key, memo)] = deepcopy(value, memo) 232 return y File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:238, in _deepcopy_method(x, memo) 237 def _deepcopy_method(x, memo): # Copy instance methods --> 238 return type(x)(x.__func__, deepcopy(x.__self__, memo)) File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:172, in deepcopy(x, memo, _nil) 170 y = x 171 else: --> 172 y = _reconstruct(x, memo, *rv) 174 # If is its own copy, don't memoize. 175 if y is not x: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:271, in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy) 269 if state is not None: 270 if deep: --> 271 state = deepcopy(state, memo) 272 if hasattr(y, '__setstate__'): 273 y.__setstate__(state) File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:231, in _deepcopy_dict(x, memo, deepcopy) 229 memo[id(x)] = y 230 for key, value in x.items(): --> 231 y[deepcopy(key, memo)] = deepcopy(value, memo) 232 return y File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:231, in _deepcopy_dict(x, memo, deepcopy) 229 memo[id(x)] = y 230 for key, value in x.items(): --> 231 y[deepcopy(key, memo)] = deepcopy(value, memo) 232 return y File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:172, in deepcopy(x, memo, _nil) 170 y = x 171 else: --> 172 y = _reconstruct(x, memo, *rv) 174 # If is its own copy, don't memoize. 175 if y is not x: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:271, in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy) 269 if state is not None: 270 if deep: --> 271 state = deepcopy(state, memo) 272 if hasattr(y, '__setstate__'): 273 y.__setstate__(state) [... skipping similar frames: _deepcopy_dict at line 231 (3 times), deepcopy at line 146 (3 times), deepcopy at line 172 (3 times), _reconstruct at line 271 (2 times)] File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:271, in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy) 269 if state is not None: 270 if deep: --> 271 state = deepcopy(state, memo) 272 if hasattr(y, '__setstate__'): 273 y.__setstate__(state) [... skipping similar frames: _deepcopy_dict at line 231 (1 times), deepcopy at line 146 (1 times)] File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:231, in _deepcopy_dict(x, memo, deepcopy) 229 memo[id(x)] = y 230 for key, value in x.items(): --> 231 y[deepcopy(key, memo)] = deepcopy(value, memo) 232 return y File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:172, in deepcopy(x, memo, _nil) 170 y = x 171 else: --> 172 y = _reconstruct(x, memo, *rv) 174 # If is its own copy, don't memoize. 175 if y is not x: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:265, in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy) 263 if deep and args: 264 args = (deepcopy(arg, memo) for arg in args) --> 265 y = func(*args) 266 if deep: 267 memo[id(x)] = y File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:264, in <genexpr>(.0) 262 deep = memo is not None 263 if deep and args: --> 264 args = (deepcopy(arg, memo) for arg in args) 265 y = func(*args) 266 if deep: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:211, in _deepcopy_tuple(x, memo, deepcopy) 210 def _deepcopy_tuple(x, memo, deepcopy=deepcopy): --> 211 y = [deepcopy(a, memo) for a in x] 212 # We're not going to put the tuple in the memo, but it's still important we 213 # check for it, in case the tuple contains recursive mutable structures. 214 try: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:211, in <listcomp>(.0) 210 def _deepcopy_tuple(x, memo, deepcopy=deepcopy): --> 211 y = [deepcopy(a, memo) for a in x] 212 # We're not going to put the tuple in the memo, but it's still important we 213 # check for it, in case the tuple contains recursive mutable structures. 214 try: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:172, in deepcopy(x, memo, _nil) 170 y = x 171 else: --> 172 y = _reconstruct(x, memo, *rv) 174 # If is its own copy, don't memoize. 175 if y is not x: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:271, in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy) 269 if state is not None: 270 if deep: --> 271 state = deepcopy(state, memo) 272 if hasattr(y, '__setstate__'): 273 y.__setstate__(state) File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:211, in _deepcopy_tuple(x, memo, deepcopy) 210 def _deepcopy_tuple(x, memo, deepcopy=deepcopy): --> 211 y = [deepcopy(a, memo) for a in x] 212 # We're not going to put the tuple in the memo, but it's still important we 213 # check for it, in case the tuple contains recursive mutable structures. 214 try: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:211, in <listcomp>(.0) 210 def _deepcopy_tuple(x, memo, deepcopy=deepcopy): --> 211 y = [deepcopy(a, memo) for a in x] 212 # We're not going to put the tuple in the memo, but it's still important we 213 # check for it, in case the tuple contains recursive mutable structures. 214 try: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:231, in _deepcopy_dict(x, memo, deepcopy) 229 memo[id(x)] = y 230 for key, value in x.items(): --> 231 y[deepcopy(key, memo)] = deepcopy(value, memo) 232 return y File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:161, in deepcopy(x, memo, _nil) 159 reductor = getattr(x, "__reduce_ex__", None) 160 if reductor is not None: --> 161 rv = reductor(4) 162 else: 163 reductor = getattr(x, "__reduce__", None) TypeError: cannot pickle '_contextvars.Context' object ``` </details> ### Expected behavior If I choose to load the file from the public bucket with `anon=True` passed - everything works, so I expected loading from the private bucket to work as well ### Environment info - `datasets` version: 2.14.6 - Platform: macOS-10.16-x86_64-i386-64bit - Python version: 3.10.13 - Huggingface_hub version: 0.19.1 - PyArrow version: 14.0.1 - Pandas version: 1.5.3 - s3fs version: 2023.10.0 - fsspec version: 2023.10.0 - aiobotocore version: 2.7.0

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I_kwDODunzps52pCW1

CI Build PR Documentation is broken: ImportError: cannot import name 'TypeAliasType' from 'typing_extensions'

closed

MEMBER

Our CI Build PR Documentation is broken. See: https://github.com/huggingface/datasets/actions/runs/6799554060/job/18486828777?pr=6390 ``` ImportError: cannot import name 'TypeAliasType' from 'typing_extensions' ```

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I_kwDODunzps52onbX

ConfigNamesError on a simple CSV file

closed

COLLABORATOR

See https://huggingface.co/datasets/Nguyendo1999/mmath/discussions/1 ``` Error code: ConfigNamesError Exception: TypeError Message: __init__() missing 1 required positional argument: 'dtype' Traceback: Traceback (most recent call last): File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 65, in compute_config_names_response for config in sorted(get_dataset_config_names(path=dataset, token=hf_token)) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 351, in get_dataset_config_names dataset_module = dataset_module_factory( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1512, in dataset_module_factory raise e1 from None File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1489, in dataset_module_factory return HubDatasetModuleFactoryWithoutScript( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1039, in get_module dataset_infos = DatasetInfosDict.from_dataset_card_data(dataset_card_data) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/info.py", line 468, in from_dataset_card_data dataset_info = DatasetInfo._from_yaml_dict(dataset_card_data["dataset_info"]) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/info.py", line 399, in _from_yaml_dict yaml_data["features"] = Features._from_yaml_list(yaml_data["features"]) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1838, in _from_yaml_list return cls.from_dict(from_yaml_inner(yaml_data)) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1690, in from_dict obj = generate_from_dict(dic) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1345, in generate_from_dict return {key: generate_from_dict(value) for key, value in obj.items()} File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1345, in <dictcomp> return {key: generate_from_dict(value) for key, value in obj.items()} File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1353, in generate_from_dict return class_type(**{k: v for k, v in obj.items() if k in field_names}) TypeError: __init__() missing 1 required positional argument: 'dtype' ``` This is the CSV file: https://huggingface.co/datasets/Nguyendo1999/mmath/blob/dbcdd7c2c6fc447f852ec136a7532292802bb46f/math_train.csv

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I_kwDODunzps52nn-B

Cannot import datasets on google colab (python 3.10.12)

closed

NONE

### Describe the bug I'm trying A full colab demo notebook of zero-shot-distillation from https://github.com/huggingface/transformers/tree/main/examples/research_projects/zero-shot-distillation but i got this type of error when importing datasets on my google colab (python version is 3.10.12)  I found the same problem that have been solved in [#3326 ] but it seem still error on the google colab. I can't try on my local using jupyter notebook because of my laptop resource doesn't fulfill the requirements. Please can anyone help me solve this problem. Thank you 😅 ### Steps to reproduce the bug Error: ``` --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) [<ipython-input-8-b6e092f83978>](https://localhost:8080/#) in <cell line: 1>() ----> 1 from datasets import load_dataset 2 3 # Print all the available datasets 4 from huggingface_hub import list_datasets 5 print([dataset.id for dataset in list_datasets()]) 6 frames [/usr/lib/python3.10/functools.py](https://localhost:8080/#) in update_wrapper(wrapper, wrapped, assigned, updated) 59 # Issue #17482: set __wrapped__ last so we don't inadvertently copy it 60 # from the wrapped function when updating __dict__ ---> 61 wrapper.__wrapped__ = wrapped 62 # Return the wrapper so this can be used as a decorator via partial() 63 return wrapper AttributeError: readonly attribute ``` ### Expected behavior Run success on Google Colab (free) ### Environment info Windows 11 x64, Google Colab free

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I_kwDODunzps52iU6t

dataset = load_dataset("Hyperspace-Technologies/scp-wiki-text") not working

closed

NONE

### Describe the bug ``` (datasets) mruserbox@guru-X99:/media/10TB_HHD/_LLM_DATASETS$ python dataset.py Downloading readme: 100%|███████████████████████████████████| 360/360 [00:00<00:00, 2.16MB/s] Downloading data: 100%|█████████████████████████████████| 65.1M/65.1M [00:19<00:00, 3.38MB/s] Downloading data: 100%|█████████████████████████████████| 6.35k/6.35k [00:00<00:00, 20.7kB/s] Downloading data: 100%|█████████████████████████████████| 7.29M/7.29M [00:01<00:00, 3.99MB/s] Downloading data files: 100%|██████████████████████████████████| 3/3 [00:21<00:00, 7.14s/it] Extracting data files: 100%|█████████████████████████████████| 3/3 [00:00<00:00, 1624.23it/s] Generating train split: 100%|█████████████| 314294/314294 [00:00<00:00, 668186.58 examples/s] Generating validation split: 120 examples [00:00, 100422.28 examples/s] Generating test split: 100%|████████████████| 34922/34922 [00:00<00:00, 754683.41 examples/s] Traceback (most recent call last): File "/media/10TB_HHD/_LLM_DATASETS/dataset.py", line 3, in <module> dataset = load_dataset("Hyperspace-Technologies/scp-wiki-text") File "/home/mruserbox/miniconda3/envs/datasets/lib/python3.10/site-packages/datasets/load.py", line 2153, in load_dataset builder_instance.download_and_prepare( File "/home/mruserbox/miniconda3/envs/datasets/lib/python3.10/site-packages/datasets/builder.py", line 954, in download_and_prepare self._download_and_prepare( File "/home/mruserbox/miniconda3/envs/datasets/lib/python3.10/site-packages/datasets/builder.py", line 1067, in _download_and_prepare verify_splits(self.info.splits, split_dict) File "/home/mruserbox/miniconda3/envs/datasets/lib/python3.10/site-packages/datasets/utils/info_utils.py", line 93, in verify_splits raise UnexpectedSplits(str(set(recorded_splits) - set(expected_splits))) datasets.utils.info_utils.UnexpectedSplits: {'validation'} ``` ### Steps to reproduce the bug Name: `dataset.py` Code: ``` from datasets import load_dataset dataset = load_dataset("Hyperspace-Technologies/scp-wiki-text") ``` ### Expected behavior Run without errors ### Environment info ``` name: datasets channels: - defaults dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - bzip2=1.0.8=h7b6447c_0 - ca-certificates=2023.08.22=h06a4308_0 - ld_impl_linux-64=2.38=h1181459_1 - libffi=3.4.4=h6a678d5_0 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - libuuid=1.41.5=h5eee18b_0 - ncurses=6.4=h6a678d5_0 - openssl=3.0.12=h7f8727e_0 - python=3.10.13=h955ad1f_0 - readline=8.2=h5eee18b_0 - setuptools=68.0.0=py310h06a4308_0 - sqlite=3.41.2=h5eee18b_0 - tk=8.6.12=h1ccaba5_0 - wheel=0.41.2=py310h06a4308_0 - xz=5.4.2=h5eee18b_0 - zlib=1.2.13=h5eee18b_0 - pip: - aiohttp==3.8.6 - aiosignal==1.3.1 - async-timeout==4.0.3 - attrs==23.1.0 - certifi==2023.7.22 - charset-normalizer==3.3.2 - click==8.1.7 - datasets==2.14.6 - dill==0.3.7 - filelock==3.13.1 - frozenlist==1.4.0 - fsspec==2023.10.0 - huggingface-hub==0.19.0 - idna==3.4 - multidict==6.0.4 - multiprocess==0.70.15 - numpy==1.26.1 - openai==0.27.8 - packaging==23.2 - pandas==2.1.3 - pip==23.3.1 - platformdirs==4.0.0 - pyarrow==14.0.1 - python-dateutil==2.8.2 - pytz==2023.3.post1 - pyyaml==6.0.1 - requests==2.31.0 - six==1.16.0 - tomli==2.0.1 - tqdm==4.66.1 - typer==0.9.0 - typing-extensions==4.8.0 - tzdata==2023.3 - urllib3==2.0.7 - xxhash==3.4.1 - yarl==1.9.2 prefix: /home/mruserbox/miniconda3/envs/datasets ```

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I_kwDODunzps52hzC1

Safely load datasets by disabling execution of dataset loading script

closed

NONE

### Feature request Is there a way to disable execution of dataset loading script using `load_dataset`? This is a security vulnerability that could lead to arbitrary code execution. Any suggested workarounds are welcome as well. ### Motivation This is a security vulnerability that could lead to arbitrary code execution. ### Your contribution n/a

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I_kwDODunzps52hBh3

TypeError: Cannot convert pyarrow.lib.ChunkedArray to pyarrow.lib.Array

open

NONE

### Describe the bug Hi, I am preprocessing a large custom dataset with numpy arrays. I am running into this TypeError during writing in a dataset.map() function. I've tried decreasing writer batch size, but this error persists. This error does not occur for smaller datasets. Thank you! ### Steps to reproduce the bug Traceback (most recent call last): File "/n/home12/yhwang/.conda/envs/lib/python3.10/site-packages/multiprocess/pool.py", line 125, in worker result = (True, func(*args, **kwds)) File "/n/home12/yhwang/.conda/envs/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 1354, in _write_generator_to_queue for i, result in enumerate(func(**kwargs)): File "/n/home12/yhwang/.conda/envs/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 3493, in _map_single writer.write_batch(batch) File "/n/home12/yhwang/.conda/envs/lib/python3.10/site-packages/datasets/arrow_writer.py", line 555, in write_batch arrays.append(pa.array(typed_sequence)) File "pyarrow/array.pxi", line 243, in pyarrow.lib.array File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol File "/n/home12/yhwang/.conda/envs/lib/python3.10/site-packages/datasets/arrow_writer.py", line 184, in __arrow_array__ out = numpy_to_pyarrow_listarray(data) File "/n/home12/yhwang/.conda/envs/lib/python3.10/site-packages/datasets/features/features.py", line 1394, in numpy_to_pyarrow_listarray values = pa.ListArray.from_arrays(offsets, values) File "pyarrow/array.pxi", line 2004, in pyarrow.lib.ListArray.from_arrays TypeError: Cannot convert pyarrow.lib.ChunkedArray to pyarrow.lib.Array ### Expected behavior Type should not be a ChunkedArray ### Environment info datasets v2.14.5 arrow v1.2.3 pyarrow v12.0.1

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I_kwDODunzps52eLUI

Raise a different exception for inexisting dataset vs files without known extension

closed

COLLABORATOR

See https://github.com/huggingface/datasets-server/issues/2082#issuecomment-1805716557 We have the same error for: - https://huggingface.co/datasets/severo/a_dataset_that_does_not_exist: a dataset that does not exist - https://huggingface.co/datasets/severo/test_files_without_extension: a dataset with files without a known extension ``` >>> import datasets >>> datasets.get_dataset_config_names('severo/a_dataset_that_does_not_exist') Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 351, in get_dataset_config_names dataset_module = dataset_module_factory( File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1508, in dataset_module_factory raise FileNotFoundError( FileNotFoundError: Couldn't find a dataset script at /home/slesage/hf/datasets-server/services/worker/severo/a_dataset_that_does_not_exist/a_dataset_that_does_not_exist.py or any data file in the same directory. Couldn't find 'severo/a_dataset_that_does_not_exist' on the Hugging Face Hub either: FileNotFoundError: Dataset 'severo/a_dataset_that_does_not_exist' doesn't exist on the Hub. If the repo is private or gated, make sure to log in with `huggingface-cli login`. >>> datasets.get_dataset_config_names('severo/test_files_without_extension') Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 351, in get_dataset_config_names dataset_module = dataset_module_factory( File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1508, in dataset_module_factory raise FileNotFoundError( FileNotFoundError: Couldn't find a dataset script at /home/slesage/hf/datasets-server/services/worker/severo/test_files_without_extension/test_files_without_extension.py or any data file in the same directory. Couldn't find 'severo/test_files_without_extension' on the Hugging Face Hub either: FileNotFoundError: No (supported) data files or dataset script found in severo/test_files_without_extension. ``` To differentiate, we must parse the error message (only the end is different). We should have a different exception for these two errors.

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I_kwDODunzps52c-ot

Issue with pyarrow 14.0.1

closed

COLLABORATOR

See https://github.com/huggingface/datasets-server/pull/2089 for reference ``` from datasets import (Array2D, Dataset, Features) feature_type = Array2D(shape=(2, 2), dtype="float32") content = [[0.0, 0.0], [0.0, 0.0]] features = Features({"col": feature_type}) dataset = Dataset.from_dict({"col": [content]}, features=features) ``` generates ``` /home/slesage/hf/datasets-server/libs/libcommon/.venv/lib/python3.9/site-packages/datasets/features/features.py:648: FutureWarning: pyarrow.PyExtensionType is deprecated and will refuse deserialization by default. Instead, please derive from pyarrow.ExtensionType and implement your own serialization mechanism. pa.PyExtensionType.__init__(self, self.storage_dtype) /home/slesage/hf/datasets-server/libs/libcommon/.venv/lib/python3.9/site-packages/datasets/features/features.py:1661: RuntimeWarning: pickle-based deserialization of pyarrow.PyExtensionType subclasses is disabled by default; if you only ingest trusted data files, you may re-enable this using `pyarrow.PyExtensionType.set_auto_load(True)`. In the future, Python-defined extension subclasses should derive from pyarrow.ExtensionType (not pyarrow.PyExtensionType) and implement their own serialization mechanism. obj = {field.name: generate_from_arrow_type(field.type) for field in pa_schema} /home/slesage/hf/datasets-server/libs/libcommon/.venv/lib/python3.9/site-packages/datasets/features/features.py:1661: FutureWarning: pyarrow.PyExtensionType is deprecated and will refuse deserialization by default. Instead, please derive from pyarrow.ExtensionType and implement your own serialization mechanism. obj = {field.name: generate_from_arrow_type(field.type) for field in pa_schema} Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/slesage/hf/datasets-server/libs/libcommon/.venv/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 924, in from_dict return cls(pa_table, info=info, split=split) File "/home/slesage/hf/datasets-server/libs/libcommon/.venv/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 693, in __init__ inferred_features = Features.from_arrow_schema(arrow_table.schema) File "/home/slesage/hf/datasets-server/libs/libcommon/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1661, in from_arrow_schema obj = {field.name: generate_from_arrow_type(field.type) for field in pa_schema} File "/home/slesage/hf/datasets-server/libs/libcommon/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1661, in <dictcomp> obj = {field.name: generate_from_arrow_type(field.type) for field in pa_schema} File "/home/slesage/hf/datasets-server/libs/libcommon/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1381, in generate_from_arrow_type return Value(dtype=_arrow_to_datasets_dtype(pa_type)) File "/home/slesage/hf/datasets-server/libs/libcommon/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 111, in _arrow_to_datasets_dtype raise ValueError(f"Arrow type {arrow_type} does not have a datasets dtype equivalent.") ValueError: Arrow type extension<arrow.py_extension_type<pyarrow.lib.UnknownExtensionType>> does not have a datasets dtype equivalent. ```

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I_kwDODunzps52Z1ec

Add ability to set lock type

closed

NONE

### Feature request Allow setting file lock type, maybe from an environment variable Currently, it only depends on whether fnctl is available: https://github.com/huggingface/datasets/blob/12ebe695b4748c5a26e08b44ed51955f74f5801d/src/datasets/utils/filelock.py#L463-L470C16 ### Motivation In my environment, flock isn't supported on a network attached drive ### Your contribution I'll be happy to submit a pr.

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I_kwDODunzps52XyXs

TorchFormatter images (H, W, C) instead of (C, H, W) format