rIsHu009/github-issues-updated huggingface dataset:
Title: ImportError: To support decoding images, please install 'Pillow'.
Author: jamessdixon (NONE)
State: open
Created: 2025-01-08T02:22:57Z

### Describe the bug

Following this tutorial locally using a macboko and VSCode: https://huggingface.co/docs/diffusers/en/tutorials/basic_training
This line of code: for i, image in enumerate(dataset[:4]["image"]):
throws: ImportError: To support decoding images, please install 'Pillow'.

Pillow is installed.

### Steps to reproduce the bug

Run the tutorial


### Expected behavior

Images should be rendered

### Environment info

MacBook, VSCode

Comments:
what's your `pip show Pillow` output

same issue.. my pip show Pillow output as below:

```
Name: pillow
Version: 11.1.0
Summary: Python Imaging Library (Fork)
Home-page: https://python-pillow.github.io/
Author: 
Author-email: "Jeffrey A. Clark" <aclark@aclark.net>
License: MIT-CMU
Location: [/opt/homebrew/lib/python3.10/site-packages](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/lib/python3.10/site-packages)
Requires: 
Required-by:
```

I encountered the same problem on Ubuntu system, my pip show Pillow output as below:

```
Name: pillow
Version: 10.4.0
Summary: Python Imaging Library (Fork)
Home-page: https://python-pillow.org/
Author: 
Author-email: "Jeffrey A. Clark" <[aclark@aclark.net](mailto:aclark@aclark.net)>
License: HPND
Location: /home/shunying/.local/lib/python3.8/site-packages
Requires: 
Required-by: 
```

Well, solved this by specifying the pip version to my conda virtual environment :)

I have also encountered this. It's a strange thing that's happening.

Checking the code `datasets` it uses `importlib.util.find_spec("PIL")` to verify if `PIL` is installed. While both `pip show` and `importlib` work correctly, I still got the error.

In my case, restarting and redoing the `datasets` import helped. Seems weird to me.
Title: Fix lock permission
Author: cih9088 (NONE)
State: open
Created: 2025-01-07T04:15:53Z

All files except lock file have proper permission obeying `ACL` property if it is set.

If the cache directory has `ACL` property, it should be respected instead of just using `umask` for permission.
To fix it, just create a lock file and pass the created `mode`.

By creating a lock file with `touch()` before `FileLock` create it with `mode`,
- if `ACL` is not set, same as before
- if `ACL` is set, `ACL` is respected

If it is acceptable, it should be also applied to [`huggingface_hub`](https://github.com/huggingface/huggingface_hub/blob/2702ec2a2bd0124cc1fddfd72ccb1297b2478148/src/huggingface_hub/utils/_fixes.py#L95) I guess.

Comments:
Title: Fix remove_columns in the formatted case
Author: lhoestq (MEMBER)
State: open
Created: 2025-01-06T15:44:23Z

`remove_columns` had no effect when running a function in `.map()` on dataset that is formatted

This aligns the logic of `map()` with the non formatted case and also with with https://github.com/huggingface/datasets/pull/7353

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7358). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
Title: How about adding a feature to pass the key when performing map on DatasetDict?
Author: jp1924 (CONTRIBUTOR)
State: closed
Created: 2025-01-06T08:13:52Z
Labels: enhancement

### Feature request

Add a feature to pass the key of the DatasetDict when performing map

### Motivation

I often preprocess using map on DatasetDict. 
Sometimes, I need to preprocess train and valid data differently depending on the task. 
So, I thought it would be nice to pass the key (like train, valid) when performing map on DatasetDict. 

What do you think?

### Your contribution

I can submit a pull request to add the feature to pass the key of the DatasetDict when performing map.

Comments:
@lhoestq 
If it's okay with you, can I work on this?

Hi ! Can you give an example of what it would look like to use this new feature ?

Note that currently you can already do

```python
ds["train"] = ds["train"].map(process_train)
ds["test"] = ds["test"].map(process_test)
```

@lhoestq 
Thanks for the response! 
Let me clarify what I'm looking for with an example:

Currently, we need to write separate processing functions or call .map() separately:
```python
# Current approach
def process_train(example):
    # Training-specific processing
    return example

def process_valid(example):
    # Validation-specific processing
    return example

ds["train"] = ds["train"].map(process_train)
ds["valid"] = ds["valid"].map(process_valid)
```

What I'm proposing is to have a single processing function that knows which split it's processing:

```python
# Proposed feature
def process(example, split_key):
    if split_key == "train":
        # Training-specific processing
    elif split_key == "valid":
        # Validation-specific processing
    return example

# Using with_key=True to pass the split information
ds = ds.map(process, with_key=True)
```

This becomes particularly useful when:
1. The processing logic is heavily shared between splits but needs minor adjustments
2. You want to maintain the processing logic in one place for better maintainability
3. The processing function is complex and you want to avoid duplicating code

So I wanted to request this feature to achieve this kind of functionality. 
I've created a draft PR implementing this: https://github.com/huggingface/datasets/pull/7240/files


I see ! I think it makes sense, and it's more readable than doing something like this:
```python
from functools import partial
ds = DatasetDict({key: ds[key].map(partial(process, split_key=key)) for key in ds})
```

PS: you named the argument `with_key` in your example, but it might be even clearer with it's named `with_split` maybe no ?

@lhoestq I agree. 
It seems better to use `with_split`.
So can I open a PR with this change?

Sure !
Title: Not available datasets[audio] on python 3.13
Author: sergiosinlimites (NONE)
State: open
Created: 2025-01-04T18:37:08Z

### Describe the bug

This is the error I got, it seems numba package does not support python 3.13

PS C:\Users\sergi\Documents> pip install datasets[audio]
Defaulting to user installation because normal site-packages is not writeable
Collecting datasets[audio]
  Using cached datasets-3.2.0-py3-none-any.whl.metadata (20 kB)
... (OTHER PACKAGES)
Collecting numba>=0.51.0 (from librosa->datasets[audio])
  Downloading numba-0.60.0.tar.gz (2.7 MB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 2.7/2.7 MB 44.1 MB/s eta 0:00:00
  Installing build dependencies ... done
  Getting requirements to build wheel ... error
  error: subprocess-exited-with-error

  × Getting requirements to build wheel did not run successfully.
  │ exit code: 1
  ╰─> [24 lines of output]
      Traceback (most recent call last):
        File "C:\Program Files\WindowsApps\PythonSoftwareFoundation.Python.3.13_3.13.496.0_x64__qbz5n2kfra8p0\Lib\site-packages\pip\_vendor\pyproject_hooks\_in_process\_in_process.py", line 353, in <module>
          main()
          ~~~~^^
        File "C:\Program Files\WindowsApps\PythonSoftwareFoundation.Python.3.13_3.13.496.0_x64__qbz5n2kfra8p0\Lib\site-packages\pip\_vendor\pyproject_hooks\_in_process\_in_process.py", line 335, in main
          json_out['return_val'] = hook(**hook_input['kwargs'])
                                   ~~~~^^^^^^^^^^^^^^^^^^^^^^^^
        File "C:\Program Files\WindowsApps\PythonSoftwareFoundation.Python.3.13_3.13.496.0_x64__qbz5n2kfra8p0\Lib\site-packages\pip\_vendor\pyproject_hooks\_in_process\_in_process.py", line 118, in get_requires_for_build_wheel
          return hook(config_settings)
        File "C:\Users\sergi\AppData\Local\Temp\pip-build-env-yauns_qh\overlay\Lib\site-packages\setuptools\build_meta.py", line 334, in get_requires_for_build_wheel
          return self._get_build_requires(config_settings, requirements=[])
                 ~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        File "C:\Users\sergi\AppData\Local\Temp\pip-build-env-yauns_qh\overlay\Lib\site-packages\setuptools\build_meta.py", line 304, in _get_build_requires
          self.run_setup()
          ~~~~~~~~~~~~~~^^
      RuntimeError: Cannot install on Python version 3.13.1; only versions >=3.9,<3.13 are supported.
      [end of output]

  note: This error originates from a subprocess, and is likely not a problem with pip.
error: subprocess-exited-with-error

× Getting requirements to build wheel did not run successfully.
│ exit code: 1
╰─> See above for output.

### Steps to reproduce the bug

1. install python >=3.13
2. !pip install datasets[audio]

### Expected behavior

I needed datasets[audio] in the python 3.13

### Environment info

python 3.13.1

Comments:
It looks like an issue with `numba` which can't be installed on 3.13 ? `numba` is a dependency of `librosa`, used to decode audio files
Title: A module that was compiled using NumPy 1.x cannot be run in NumPy 2.0.2 as it may crash. To support both 1.x and 2.x versions of NumPy, modules must be compiled with NumPy 2.0. Some module may need to rebuild instead e.g. with 'pybind11>=2.12'.
Author: jamessdixon (NONE)
State: closed
Created: 2025-01-04T18:30:17Z

### Describe the bug

Following this tutorial: https://huggingface.co/docs/diffusers/en/tutorials/basic_training and running it locally using VSCode on my MacBook.  The first line in the tutorial fails:  from datasets import load_dataset
dataset = load_dataset('huggan/smithsonian_butterflies_subset', split="train"). with this error: 
A module that was compiled using NumPy 1.x cannot be run in
NumPy 2.0.2 as it may crash. To support both 1.x and 2.x
versions of NumPy, modules must be compiled with NumPy 2.0.
Some module may need to rebuild instead e.g. with 'pybind11>=2.12'.
If you are a user of the module, the easiest solution will be to
downgrade to 'numpy<2' or try to upgrade the affected module.
We expect that some modules will need time to support NumPy 2. and ImportError: numpy.core.multiarray failed to import.   

Does from datasets import load_dataset really use NumPy 1.x?  

### Steps to reproduce the bug

Open VSCode.  create a new venv.  Create a new ipynb file.  Import pip install diffusers[training] try to run this line of code: from datasets import load_dataset

### Expected behavior

data is loaded

### Environment info

ran this: datasets-cli env
and got A module that was compiled using NumPy 1.x cannot be run in
NumPy 2.0.2 as it may crash. To support both 1.x and 2.x
versions of NumPy, modules must be compiled with NumPy 2.0.
Some module may need to rebuild instead e.g. with 'pybind11>=2.12'.

If you are a user of the module, the easiest solution will be to
downgrade to 'numpy<2' or try to upgrade the affected module.
We expect that some modules will need time to support NumPy 2.

Comments:
recreated .venv and run this: pip install diffusers[training]==0.11.1
Title: changes to MappedExamplesIterable to resolve #7345
Author: vttrifonov (CONTRIBUTOR)
State: closed
Created: 2025-01-04T06:01:15Z

modified `MappedExamplesIterable` and `test_iterable_dataset.py::test_mapped_examples_iterable_with_indices`

fix #7345

@lhoestq

Comments:
I noticed that `Dataset.map` has a more complex output depending on `remove_columns`. In particular [this](https://github.com/huggingface/datasets/blob/6457be66e2ef88411281eddc4e7698866a3977f1/src/datasets/arrow_dataset.py#L3371) line removes columns from output if the input is being modified in place (i.e. `input_columns = None`). I tried to mimic this behaviour in `MappedExamplesIterable` by checking if the input and output point to the same dictionary object.

The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7353). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
Title: fsspec 2024.12.0
Author: lhoestq (MEMBER)
State: closed
Created: 2025-01-03T15:32:25Z


Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7352). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
Title: Bump hfh to 0.24 to fix ci
Author: lhoestq (MEMBER)
State: closed
Created: 2025-01-03T15:09:40Z


Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7350). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
Title: Webdataset special columns in last position
Author: lhoestq (MEMBER)
State: closed
Created: 2025-01-03T14:32:15Z

Place columns "__key__" and "__url__" in last position in the Dataset Viewer since they are not the main content

before:

<img width="1012" alt="image" src="https://github.com/user-attachments/assets/b556c1fe-2674-4ba0-9643-c074aa9716fd" />

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7349). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
Title: Catch OSError for arrow
Author: lhoestq (MEMBER)
State: closed
Created: 2025-01-02T14:30:00Z

fixes https://github.com/huggingface/datasets/issues/7346

(also updated `ruff` and appleid style changes)

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7348). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
Title: Different behaviour of IterableDataset.map vs Dataset.map with remove_columns
Author: vttrifonov (CONTRIBUTOR)
State: closed
Created: 2024-12-25T07:36:48Z

### Describe the bug

The following code

```python
import datasets as hf
ds1 = hf.Dataset.from_list([{'i': i} for i in [0,1]])
#ds1 = ds1.to_iterable_dataset()
ds2 = ds1.map(
    lambda i: {'i': i+1},
    input_columns = ['i'],
    remove_columns = ['i']
)   
list(ds2)
```

produces

```python
[{'i': 1}, {'i': 2}]
```

as expected. If the line that converts `ds1` to iterable is uncommented so that the `ds2` is a map of an `IterableDataset`, the result is 

```python
[{},{}]
```

I expected the output to be the same as before. It seems that in the second case the removed column is not added back into the output.

The issue seems to be [here](https://github.com/huggingface/datasets/blob/6c6a82a573f946c4a81069f56446caed15cee9c2/src/datasets/iterable_dataset.py#L1093): the columns are removed after the mapping which is not what we want (or what the [documentation says](https://github.com/huggingface/datasets/blob/6c6a82a573f946c4a81069f56446caed15cee9c2/src/datasets/iterable_dataset.py#L2370)) because we want the columns removed from the transformed example but then added if the map produced them.

This is `datasets==3.2.0` and `python==3.10`



### Steps to reproduce the bug

see above

### Expected behavior

see above

### Environment info

see above

Comments:
Good catch ! Do you think you can open a PR to fix this issue ?
Title: minor video docs on how to install
Author: lhoestq (MEMBER)
State: closed
Created: 2024-12-17T18:06:17Z


Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7341). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
Title: don't import soundfile in tests
Author: lhoestq (MEMBER)
State: closed
Created: 2024-12-17T16:49:55Z


Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7340). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
Title: Update CONTRIBUTING.md
Author: lhoestq (MEMBER)
State: closed
Created: 2024-12-17T16:45:25Z


Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7339). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
Title: One or several metadata.jsonl were found, but not in the same directory or in a parent directory of
Author: mst272 (NONE)
State: open
Created: 2024-12-17T12:58:43Z

### Describe the bug

ImageFolder with metadata.jsonl error. I downloaded liuhaotian/LLaVA-CC3M-Pretrain-595K locally from Hugging Face. According to the tutorial in https://huggingface.co/docs/datasets/image_dataset#image-captioning, only put images.zip and metadata.jsonl containing information in the same folder. However, after loading, an error was reported: One or several metadata.jsonl were found, but not in the same directory or in a parent directory of.

The data in my jsonl file is as follows:

> {"id": "GCC_train_002448550", "file_name": "GCC_train_002448550.jpg", "conversations": [{"from": "human", "value": "<image>\nProvide a brief description of the given image."}, {"from": "gpt", "value": "a view of a city , where the flyover was proposed to reduce the increasing traffic on thursday ."}]}


### Steps to reproduce the bug

from datasets import load_dataset
image = load_dataset("imagefolder",data_dir='data/opensource_data')

### Expected behavior

success

### Environment info

datasets==3.2.0

Comments:
Hmmm I double checked in the source code and I found a contradiction: in the current implementation the metadata file is ignored if it's not in the same archive as the zip image somehow:

https://github.com/huggingface/datasets/blob/caa705e8bf4bedf1a956f48b545283b2ca14170a/src/datasets/packaged_modules/folder_based_builder/folder_based_builder.py#L352-L353

in the tests suite the metadata file is placed inside the archive:

https://github.com/huggingface/datasets/blob/caa705e8bf4bedf1a956f48b545283b2ca14170a/tests/packaged_modules/test_imagefolder.py#L223-L223

Thanks for reporting this issue, it seems the documentation is wrong and we never implemented the support for zip + metadata outside zip. We might rewrite part of this code soon though to make it more flexible, it can be a good occasion to fix this. In the meantime feel free to open a PR to fix the documentation if you'd like
Title: Clarify documentation or Create DatasetCard
Author: August-murr (NONE)
State: open
Created: 2024-12-17T12:01:00Z
Labels: enhancement

### Feature request

I noticed that you can use a Model Card instead of a Dataset Card when pushing a dataset to the Hub, but this isn’t clearly mentioned in [the docs.](https://huggingface.co/docs/datasets/dataset_card)

- Update the docs to clarify that a Model Card can work for datasets too.
- It might be worth creating a dedicated DatasetCard module, similar to the ModelCard module, for consistency and better support.

Not sure if this belongs here or on the [Hub repo](https://github.com/huggingface/huggingface_hub), but thought I’d bring it up!

### Motivation

I just spent an hour like on [this issue](https://github.com/huggingface/trl/pull/2491) trying to create a `DatasetCard` for a script.

### Your contribution

might later

Comments:
Title: Too many open files: '/root/.cache/huggingface/token'
Author: kopyl (NONE)
State: open
Created: 2024-12-16T21:30:24Z

### Describe the bug

I ran this code:
```
from datasets import load_dataset
dataset = load_dataset("common-canvas/commoncatalog-cc-by", cache_dir="/datadrive/datasets/cc", num_proc=1000)
```

And got this error.
Before it was some other file though (lie something...incomplete)

runnting
```
ulimit -n 8192
```
did not help at all.

### Steps to reproduce the bug

Run the code i sent

### Expected behavior

Should be no errors 

### Environment info

linux, jupyter lab.

Comments:
Title: TypeError: Value.__init__() missing 1 required positional argument: 'dtype'
Author: ghost (NONE)
State: open
Created: 2024-12-15T04:08:46Z

### Describe the bug

ds = load_dataset(
    "./xxx.py",
    name="default",
    split="train",
)

The datasets does not support debugging locally anymore...

### Steps to reproduce the bug

```
from datasets import load_dataset

ds = load_dataset(
    "./repo.py",
    name="default",
    split="train",
)

for item in ds:
    print(item)
```

It works fine for "username/repo", but it does not work for "./repo.py" when debugging locally...
Running above code template will report TypeError: Value.__init__() missing 1 required positional argument: 'dtype'

### Expected behavior

fix this bug

### Environment info

python 3.10 datasets==2.21

Comments:
same error 
```
data = load_dataset('/opt/deepseek_R1_finetune/hf_datasets/openai/gsm8k', 'main')[split] 
```

> same error
> 
> ```
> data = load_dataset('/opt/deepseek_R1_finetune/hf_datasets/openai/gsm8k', 'main')[split] 
> ```

https://github.com/huggingface/open-r1/issues/204  this help me
Title: Fix typo in arrow_dataset
Author: AndreaFrancis (CONTRIBUTOR)
State: closed
Created: 2024-12-13T15:17:09Z


Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7328). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
Title: .map() is not caching and ram goes OOM
Author: simeneide (NONE)
State: open
Created: 2024-12-13T14:22:56Z

### Describe the bug

Im trying to run a fairly simple map that is converting a dataset into numpy arrays. however, it just piles up on memory and doesnt write to disk. Ive tried multiple cache techniques such as specifying the cache dir, setting max mem, +++ but none seem to work. What am I missing here?

### Steps to reproduce the bug

```
from pydub import AudioSegment
import io
import base64
import numpy as np
import os
CACHE_PATH = "/mnt/extdisk/cache" # "/root/.cache/huggingface/"# 
os.environ["HF_HOME"] = CACHE_PATH
import datasets
import logging
logger = logging.getLogger()
logger.setLevel(logging.INFO)
# Create a handler for Jupyter notebook
handler = logging.StreamHandler()
formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
handler.setFormatter(formatter)
logger.addHandler(handler)

#datasets.config.IN_MEMORY_MAX_SIZE= 1000#*(2**30) #50 gb
print(datasets.config.HF_CACHE_HOME)
print(datasets.config.HF_DATASETS_CACHE)
# Decode the base64 string into bytes
def convert_mp3_to_audio_segment(example):
    """
    example = ds['train'][0]
    """
    try:
        audio_data_bytes = base64.b64decode(example['audio'])
        # Use pydub to load the MP3 audio from the decoded bytes
        audio_segment = AudioSegment.from_file(io.BytesIO(audio_data_bytes), format="mp3")
        # Resample to 24_000
        audio_segment = audio_segment.set_frame_rate(24_000)
        audio = {'sampling_rate' : audio_segment.frame_rate,
        'array' : np.array(audio_segment.get_array_of_samples(), dtype="float")}
        del audio_segment
        duration = len(audio['array']) / audio['sampling_rate']
    except Exception as e:
        logger.warning(f"Failed to convert audio for {example['id']}. Error: {e}")
        audio = {'sampling_rate' : 0,
        'array' : np.array([]), duration : 0}
    return {'audio' : audio, 'duration' : duration}

ds = datasets.load_dataset("NbAiLab/nb_distil_speech_noconcat_stortinget", cache_dir=CACHE_PATH, keep_in_memory=False)

#%%
num_proc=32
ds_processed = (
    ds
    #.select(range(10))
    .map(convert_mp3_to_audio_segment, num_proc=num_proc, desc="Converting mp3 to audio segment") #, cache_file_name=f"{CACHE_PATH}/stortinget_audio" # , cache_file_name="test"
)
```

### Expected behavior

the map should write to disk

### Environment info

- `datasets` version: 3.2.0
- Platform: Linux-6.8.0-45-generic-x86_64-with-glibc2.39
- Python version: 3.12.7
- `huggingface_hub` version: 0.26.3
- PyArrow version: 18.1.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.9.0

Comments:
I have the same issue - any update on this?
Title: Remove upper bound for fsspec
Author: fellhorn (NONE)
State: open
Created: 2024-12-13T11:35:12Z

### Describe the bug

As also raised by @cyyever in https://github.com/huggingface/datasets/pull/7296 and @NeilGirdhar in https://github.com/huggingface/datasets/commit/d5468836fe94e8be1ae093397dd43d4a2503b926#commitcomment-140952162 , `datasets` has a problematic version constraint on `fsspec`.

In our case this causes (unnecessary?) troubles due to a race condition bug in that version of the corresponding `gcsfs` plugin, that causes deadlocks: https://github.com/fsspec/gcsfs/pull/643

We just use a version override to ignore the constraint from `datasets`, but imho the version constraint could just be removed in the first place?

The last few PRs bumping the upper bound were basically uneventful:

* https://github.com/huggingface/datasets/pull/7219
* https://github.com/huggingface/datasets/pull/6921
* https://github.com/huggingface/datasets/pull/6747

### Steps to reproduce the bug

-

### Expected behavior

Installing `fsspec>=2024.10.0` along `datasets` should be possible without overwriting constraints.

### Environment info

All recent datasets versions

Comments:
Unfortunately `fsspec` versioning allows breaking changes across version and there is no way we can keep it without constrains at the moment. It already broke `datasets` once in the past. Maybe one day once `fsspec` decides on a stable and future proof API but I don't think this will happen anytime soon

edit: bumped to 2024.10.0 in https://github.com/huggingface/datasets/pull/7352
Title: Introduce pdf support (#7318)
Author: yabramuvdi (CONTRIBUTOR)
State: closed
Created: 2024-12-12T18:31:18Z

First implementation of the Pdf feature to support pdfs (#7318) . Using [pdfplumber](https://github.com/jsvine/pdfplumber?tab=readme-ov-file#python-library) as the default library to work with pdfs. 
@lhoestq and @AndreaFrancis

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7325). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

Hi @AndreaFrancis and @lhoestq ! Thanks for looking at the code and for all the changes and suggestions. I have worked on all your suggestions. I need to work a bit more on the tests, but I created a first version and uploaded a simple pdf to use for testing. The pdf has a couple of pages some of which have images, figures, and tables (this is exactly the pdfs for which I think keeping the pdf format is very interesting because they have multiple types of content). Will try to finish the tests as soon as possible.

(I updated the CI a bit, I took the liberty to update your branch and re-run it on your PR @yabramuvdi)
Title: Unexpected cache behaviour using load_dataset
Author: Moritz-Wirth (NONE)
State: closed
Created: 2024-12-12T14:03:00Z

### Describe the bug

Following the (Cache management)[https://huggingface.co/docs/datasets/en/cache] docu and previous behaviour from datasets version 2.18.0, one is able to change the cache directory. Previously, all downloaded/extracted/etc files were found in this folder. As i have recently update to the latest version this is not the case anymore. Downloaded files are stored in `~/.cache/huggingface/hub`.
Providing the `cache_dir` argument in `load_dataset` the cache directory is created and there are some files but the bulk is still in `~/.cache/huggingface/hub`.

I believe this could be solved by adding the cache_dir argument [here](https://github.com/huggingface/datasets/blob/fdda5585ab18ea1292547f36c969d12c408ab842/src/datasets/utils/file_utils.py#L188)


### Steps to reproduce the bug

For example using https://huggingface.co/datasets/ashraq/esc50:
```python
from datasets import load_dataset
ds = load_dataset("ashraq/esc50", "default", cache_dir="~/custom/cache/path/esc50")
```


### Expected behavior

I would expect the bulk of files related to the dataset to be stored somewhere in `~/custom/cache/path/esc50`, but it seems they are in `~/.cache/huggingface/hub/datasets--ashraq--esc50`. 

### Environment info

- `datasets` version: 3.2.0
- Platform: Linux-5.14.0-503.15.1.el9_5.x86_64-x86_64-with-glibc2.34
- Python version: 3.10.14
- `huggingface_hub` version: 0.26.5
- PyArrow version: 17.0.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.6.1

Comments:
Hi ! Since `datasets` 3.x,  the `datasets` specific files are in `cache_dir=` and the HF files are cached using `huggingface_hub` and you can set its cache directory using the `HF_HOME` environment variable.

They are independent, for example you can delete the Hub cache (containing downloaded files) but still reload your cached datasets from the `datasets` cache (containing prepared datasets in Arrow format)
Title: ArrowInvalid: JSON parse error: Column() changed from object to array in row 0
Author: Polarisamoon (NONE)
State: open
Created: 2024-12-11T08:41:39Z

### Describe the bug

Encountering an error while loading the ```liuhaotian/LLaVA-Instruct-150K dataset```.

### Steps to reproduce the bug

```
from datasets import load_dataset
fw =load_dataset("liuhaotian/LLaVA-Instruct-150K")
```
Error:
```
ArrowInvalid                              Traceback (most recent call last)
[/usr/local/lib/python3.10/dist-packages/datasets/packaged_modules/json/json.py](https://localhost:8080/#) in _generate_tables(self, files)
    136                                 try:
--> 137                                     pa_table = paj.read_json(
    138                                         io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)

20 frames
ArrowInvalid: JSON parse error: Column() changed from object to array in row 0

During handling of the above exception, another exception occurred:

ArrowTypeError                            Traceback (most recent call last)
ArrowTypeError: ("Expected bytes, got a 'int' object", 'Conversion failed for column id with type object')

The above exception was the direct cause of the following exception:

DatasetGenerationError                    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)
   1895             if isinstance(e, DatasetGenerationError):
   1896                 raise
-> 1897             raise DatasetGenerationError("An error occurred while generating the dataset") from e
   1898 
   1899         yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths)

DatasetGenerationError: An error occurred while generating the dataset
```
### Expected behavior

I have tried loading the dataset both on my own server and on Colab, and encountered errors in both instances.

### Environment info

```
- `datasets` version: 3.2.0
- Platform: Linux-6.1.85+-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.26.3
- PyArrow version: 17.0.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.9.0
```

Comments:
Hi ! `datasets` uses Arrow under the hood which expects each column and array to have fixed types that don't change across rows of a dataset, which is why we get this error. This dataset in particular doesn't have a format compatible with Arrow unfortunately. Don't hesitate to open a discussion or PR on HF to fix the dataset
Title: ImportError: cannot import name 'set_caching_enabled' from 'datasets' 
Author: sankexin (NONE)
State: open
Created: 2024-12-11T01:58:46Z

### Describe the bug

Traceback (most recent call last):
  File "/usr/local/lib/python3.10/runpy.py", line 187, in _run_module_as_main
    mod_name, mod_spec, code = _get_module_details(mod_name, _Error)
  File "/usr/local/lib/python3.10/runpy.py", line 110, in _get_module_details
    __import__(pkg_name)
  File "/home/Medusa/axolotl/src/axolotl/cli/__init__.py", line 23, in <module>
    from axolotl.train import TrainDatasetMeta
  File "/home/Medusa/axolotl/src/axolotl/train.py", line 23, in <module>
    from axolotl.utils.trainer import setup_trainer
  File "/home/Medusa/axolotl/src/axolotl/utils/trainer.py", line 13, in <module>
    from datasets import set_caching_enabled
ImportError: cannot import name 'set_caching_enabled' from 'datasets' (/usr/local/lib/python3.10/site-packages/datasets/__init__.py)

### Steps to reproduce the bug

1、axolotl
2、accelerate launch -m axolotl.cli.train examples/medusa/qwen_lora_stage1.yml

### Expected behavior

enable datasets

### Environment info

python3.10

Comments:
pip install datasets==2.18.0

Hi ! I think you need to update axolotl
Title: Introduce support for PDFs
Author: yabramuvdi (CONTRIBUTOR)
State: open
Created: 2024-12-10T16:59:48Z
Labels: enhancement

### Feature request

The idea (discussed in the Discord server with @lhoestq ) is to have a Pdf type like Image/Audio/Video. For example [Video](https://github.com/huggingface/datasets/blob/main/src/datasets/features/video.py) was recently added and contains how to decode a video file encoded in a dictionary like {"path": ..., "bytes": ...} as a VideoReader using decord. We want to do the same with pdf and get a [pypdfium2.PdfDocument](https://pypdfium2.readthedocs.io/en/stable/_modules/pypdfium2/_helpers/document.html#PdfDocument).

### Motivation

In many cases PDFs contain very valuable information beyond text (e.g. images, figures). Support for PDFs would help create datasets where all the information is preserved.

### Your contribution

I can start the implementation of the Pdf type :)

Comments:
#self-assign

Awesome ! Let me know if you have any question or if I can help :)

cc @AndreaFrancis as well for viz

Other candidates libraries for the Pdf type: PyMuPDF pypdf and pdfplumber

EDIT: Pymupdf looks like a good choice when it comes to maturity + performance + versatility BUT the license is maybe an issue, and pypdf, pypdfium2 or pdfplumber are good options imo

Related to https://github.com/huggingface/datasets/issues/7058

PyMuPDF is AGPL licensed, so we can't use it. I will move forward with [pdfplumber](https://github.com/jsvine/pdfplumber?tab=readme-ov-file#python-library).

Hi both! I have made a pull request with a first basic implementation of the Pdf feature. I followed closely what I saw on the Video and Image features. It is my first time contributing so any comments are very welcomed. I think it would be useful to outline together what additional things we can implement (e.g. enabling parsing of the pdf). Thanks :)
Title: Release: 3.2.0
Author: lhoestq (MEMBER)
State: closed
Created: 2024-12-10T16:53:20Z


Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7317). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
Title: More docs to from_dict to mention that the result lives in RAM
Author: lhoestq (MEMBER)
State: closed
Created: 2024-12-10T13:56:01Z

following discussions at https://discuss.huggingface.co/t/how-to-load-this-simple-audio-data-set-and-use-dataset-map-without-memory-issues/17722/14

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7316). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
Title: Cannot create a dataset with relative audio path
Author: sedol1339 (NONE)
State: open
Created: 2024-12-09T07:34:20Z

### Describe the bug

Hello! I want to create a dataset of parquet files, with audios stored as separate .mp3 files. However, it says "No such file or directory" (see the reproducing code).

### Steps to reproduce the bug

Creating a dataset
```
from pathlib import Path
from datasets import Dataset, load_dataset, Audio

Path('my_dataset/audio').mkdir(parents=True, exist_ok=True)
Path('my_dataset/audio/file.mp3').touch(exist_ok=True)
Dataset.from_list(
    [{'audio': {'path': 'audio/file.mp3'}}]
).to_parquet('my_dataset/data.parquet')
```

Result:
```
# my_dataset
# ├── audio
# │   └── file.mp3
# └── data.parquet
```

Trying to load the dataset
```
dataset = (
    load_dataset('my_dataset', split='train')
    .cast_column('audio', Audio(sampling_rate=16_000))
)
dataset[0]

>>> FileNotFoundError: [Errno 2] No such file or directory: 'audio/file.mp3'
```

### Expected behavior

I expect the dataset to load correctly.

I've found 2 workarounds, but they are not very good:
1. I can specify an absolute path to the audio, however, when I move the folder or upload to HF it will stop working.
2. I can set `'path': 'file.mp3'`, and load with `load_dataset('my_dataset', data_dir='audio')` - it seems to work, but does this mean that anyone from Hugging Face who wants to use this dataset should also pass the `data_dir` argument, otherwise it won't work?

### Environment info

datasets 3.1.0, Ubuntu 24.04.1

Comments:
Hello ! when you `cast_column` you need the paths to be absolute paths or relative paths to your working directory, not the original dataset directory.

Though I'd recommend structuring your dataset as an AudioFolder which automatically links a metadata.jsonl or csv to the audio files via relative paths **within** the dataset repository: https://huggingface.co/docs/datasets/v3.2.0/en/audio_load#audiofolder

@lhoestq thank you, but there are two problems with using AudioFolder:
1. It is said that AudioFolder requires metadata.csv. However, my datset is too large and contains nested and np.ndarray fields, so I can't use csv.
2. It is said that I need to load the dataset with `load_dataset("audiofolder", ...)`. However, if possible, I want my dataset to be loaded as usual with `load_dataset(dataset_name)` after I upload if to HF.

You can use metadata.jsonl if you have nested data :)

And actually if you have a dataset structured as an AudioFolder then `load_dataset(dataset_name)` does work after uploading to HF

I have created an audio dataset. In my repo, I have explained the steps and structure. An example dataset is also available in the repo. https://github.com/pr0mila/ParquetToHuggingFace
Title: [Audio Features - DO NOT MERGE] PoC for adding an offset+sliced reading to audio file.
Author: TParcollet (NONE)
State: open
Created: 2024-12-08T10:27:31Z

This is a proof of concept for #7310 . The idea is to enable the access to others column of the dataset row when loading an audio file into a table. This is to allow sliced reading. As stated in the issue, many people have very long audio files and use start and stop slicing in this audio file.

Right now, this code work as a PoC on my dataset. However, this is **just to illustrate** the idea. Many things are messed up, the first being that the shards have wildly varying sizes. 

Could be of interest to @lhoestq and @sanchit-gandhi ? 

Happy to test better ideas locally.

Comments:
Title: How to get the original dataset name with username?
Author: npuichigo (CONTRIBUTOR)
State: open
Created: 2024-12-08T07:18:14Z
Labels: enhancement

### Feature request

The issue is related to ray data https://github.com/ray-project/ray/issues/49008 which it requires to check if the dataset is the original one just after `load_dataset` and parquet files are already available on hf hub.

The solution used now is to get the dataset name, config and split, then `load_dataset` again and check the fingerprint. But it's unable to get the correct dataset name if it contains username. So how to get the dataset name with username prefix, or is there another way to query if a dataset is the original one with parquet available?

@lhoestq 

### Motivation

https://github.com/ray-project/ray/issues/49008

### Your contribution

Would like to fix that.

Comments:
Hi ! why not pass the dataset id to Ray and let it check the parquet files ? Or pass the parquet files lists directly ?

I'm not sure why ray design an API like this to accept a `Dataset` object, so they need to verify the `Dataset` is the original one and use the `DatasetInfo` to query the huggingface hub. I'll advise the ray data team to use dataset id instead of dataset for the `HuggingFaceDatasource` API.
Title: Enable the Audio Feature to decode / read with an offset + duration
Author: TParcollet (NONE)
State: open
Created: 2024-12-07T22:01:44Z
Labels: enhancement

### Feature request

For most large speech dataset, we do not wish to generate hundreds of millions of small audio samples. Instead, it is quite common to provide larger audio files with frame offset (soundfile start and stop arguments). We should be able to pass these arguments to Audio() (column ID corresponding in the dataset row).

### Motivation

I am currently generating a fairly big dataset to .parquet(). Unfortunately, it does not work because all existing functions load the whole .wav file corresponding to the row. All my attempts at bypassing this failed. We should be able to put in the Table only the bytes corresponding to what soundfile reads with an offset (and subset of the audio file). 

### Your contribution

I can totally test whatever code on my large dataset creation script.

Comments:
Hi ! What about having audio + start + duration columns and enable something like this ?

```python
for example in ds:
    array = example["audio"].read(start=example["start"], frames=example["duration"])
```

Hi @lhoestq, this would work with a file-based dataset but would be terrible for a sharded one as it would duplicate the large audio file many times. Also, very long audio files are not embedded very well in the parquet file, even with large_binary(). It crashed a few times for me until I switched to one sample == one file :-(
Title: Faster parquet streaming + filters with predicate pushdown
Author: lhoestq (MEMBER)
State: closed
Created: 2024-12-06T18:01:54Z

ParquetFragment.to_batches uses a buffered stream to read parquet data, which makes streaming faster (x2 on my laptop).

I also added the `filters` config parameter to support filtering with predicate pushdown, e.g.

```python
from datasets import load_dataset

filters = [('problem_source', '==', 'math')]
ds = load_dataset("nvidia/OpenMathInstruct-2", streaming=True, filters=filters)
first_example = next(iter(ds["train"]))
print(first_example["problem_source"])
# 'math'
```

cc @allisonwang-db this is a nice plus for usage in spark

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7309). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
Title: Creating new dataset from list loses information. (Audio Information Lost - either Datatype or Values).
Author: ai-nikolai (NONE)
State: open
Created: 2024-12-05T09:07:53Z

### Describe the bug

When creating a dataset from a list of datapoints, information is lost of the individual items.

Specifically, when creating a dataset from a list of datapoints (from another dataset). Either the datatype is lost or the values are lost. See examples below. 

-> What is the best way to create a dataset from a list of datapoints?

---
e.g.:
**When running this code:**
```python
from datasets import load_dataset, Dataset
commonvoice_data = load_dataset("mozilla-foundation/common_voice_17_0", "it", split="test", streaming=True)
datapoint = next(iter(commonvoice_data))
out = [datapoint]
new_data = Dataset.from_list(out) #this loses datatype information
new_data2= Dataset.from_list(out,features=commonvoice_data.features) #this loses value information
```

**We get the following**:
---
1. `datapoint`: (the original datapoint)
```
'audio': {'path': 'it_test_0/common_voice_it_23606167.mp3', 'array': array([0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
       2.21619011e-05, 2.72628222e-05, 0.00000000e+00]), 'sampling_rate': 48000}
 ```
Original Dataset Features:
```
>>> commonvoice_data.features
'audio': Audio(sampling_rate=48000, mono=True, decode=True, id=None)
```
 - Here we see column "audio", has the proper values (both `path` & and `array`) and has the correct datatype (Audio).

 
 ----
 2. new_data[0]:
```
# Cannot be printed (as it prints the entire array).
```
New Dataset 1 Features:
```
>>> new_data.features
'audio': {'array': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'path': Value(dtype='string', id=None), 'sampling_rate': Value(dtype='int64', id=None)}
```
 - Here we see that the column "audio", has the correct values, but is not the Audio datatype anymore.

---
3. new_data2[0]:
```
'audio': {'path': None, 'array': array([0., 0., 0., ..., 0., 0., 0.]), 'sampling_rate': 48000},
```
New Dataset 2 Features:
```
>>> new_data2.features
'audio': Audio(sampling_rate=48000, mono=True, decode=True, id=None),
```
 - Here we see that the column "audio", has the correct datatype, but all the array & path values were lost!



### Steps to reproduce the bug

## Run:
```python
from datasets import load_dataset, Dataset
commonvoice_data = load_dataset("mozilla-foundation/common_voice_17_0", "it", split="test", streaming=True)
datapoint = next(iter(commonvoice_data))
out = [datapoint]
new_data = Dataset.from_list(out) #this loses datatype information
new_data2= Dataset.from_list(out,features=commonvoice_data.features) #this loses value information
```





### Expected behavior

## Expected:
```datapoint == new_data[0]```

AND

```datapoint == new_data2[0]```

### Environment info

- `datasets` version: 3.1.0
- Platform: Linux-6.2.0-37-generic-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.26.2
- PyArrow version: 15.0.2
- Pandas version: 2.2.2
- `fsspec` version: 2024.3.1

Comments:
Title:  Build Documentation Test Fails Due to "Bad Credentials" Error
Author: ruidazeng (CONTRIBUTOR)
State: open
Created: 2024-12-03T20:22:54Z

### Describe the bug

The `Build documentation / build / build_main_documentation (push)` job is consistently failing during the "Syncing repository" step. The error occurs when attempting to determine the default branch name, resulting in "Bad credentials" errors.

### Steps to reproduce the bug

1. Trigger the `build_main_documentation` job.
2. Observe the logs during the "Syncing repository" step.

### Expected behavior

The workflow should be able to retrieve the default branch name without encountering credential issues.

### Environment info

```plaintext
Syncing repository: huggingface/notebooks
Getting Git version info
Temporarily overriding HOME='/home/runner/work/_temp/00e62748-9940-4a4f-bbbc-eb2cda6d7ed6' before making global git config changes
Adding repository directory to the temporary git global config as a safe directory
/usr/bin/git config --global --add safe.directory /home/runner/work/datasets/datasets/notebooks
Initializing the repository
Disabling automatic garbage collection
Setting up auth
Determining the default branch
  Retrieving the default branch name
  Bad credentials - https://docs.github.com/rest
  Waiting 20 seconds before trying again
  Retrieving the default branch name
  Bad credentials - https://docs.github.com/rest
  Waiting 19 seconds before trying again
  Retrieving the default branch name
  Error: Bad credentials - https://docs.github.com/rest
```

Comments:
how were you able to fix this please?

> how were you able to fix this please?

I was not able to fix this.
Title: Update iterable_dataset.py
Author: lhoestq (MEMBER)
State: closed
Created: 2024-12-03T14:25:42Z

close https://github.com/huggingface/datasets/issues/7297

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7304). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.