helmo/github-issues huggingface dataset:
function `load_dataset`  can't solve folder path with regex characters like "[]"
State: open | Author: Hpeox (NONE)

### Describe the bug

When using the `load_dataset` function with a folder path containing regex special characters (such as "[]"), the issue occurs due to how the path is handled in the `resolve_pattern` function. This function passes the unprocessed path directly to `AbstractFileSystem.glob`, which supports regular expressions. As a result, the globbing mechanism interprets these characters as regex patterns, leading to a traversal of the entire disk partition instead of confining the search to the intended directory.

### Steps to reproduce the bug

just create a folder like `E:\[D_DATA]\koch_test`, then `load_dataset("parquet", data_dir="E:\[D_DATA]\\test", split="train")`
it will keep searching the whole disk.

I add two `print` in `glob` and `resolve_pattern` to see the path

### Expected behavior

it should load the dataset as in normal folders

### Environment info

- `datasets` version: 3.3.2
- Platform: Windows-10-10.0.22631-SP0
- Python version: 3.10.16
- `huggingface_hub` version: 0.29.1
- PyArrow version: 19.0.1
- Pandas version: 2.2.3
- `fsspec` version: 2024.12.0

Comments: Hi ! Have you tried escaping the glob special characters `[` and `]` ?

btw note that`AbstractFileSystem.glob` doesn't support regex, instead it supports glob patterns as in the python library [glob](https://docs.python.org/3/library/glob.html)
load_dataset with streaming hangs on parquet datasets
State: open | Author: The0nix (NONE)

### Describe the bug

When I try to load a dataset with parquet files (e.g. "bigcode/the-stack") the dataset loads, but python interpreter can't exit and hangs

### Steps to reproduce the bug

```python3
import datasets

print('Start')
dataset = datasets.load_dataset("bigcode/the-stack", data_dir="data/yaml", streaming=True, split="train")
it = iter(dataset)
next(it)
print('Finish')
```

The program prints finish but doesn't exit and hangs indefinitely.
I tried this on two different machines and several datasets.

### Expected behavior

The program exits successfully

### Environment info

datasets==3.4.1
Python 3.12.9.
MacOS and Ubuntu Linux

Comments: Hi ! The issue comes from `pyarrow`, I reported it here: https://github.com/apache/arrow/issues/45214 (feel free to comment / thumb up).

Alternatively we can try to find something else than `ParquetFileFragment.to_batches()` to iterate on Parquet data and keep the option the pass `filters=`...
Fix local pdf loading
State: closed | Author: lhoestq (MEMBER)

fir this error when accessing a local pdf

```
File ~/.pyenv/versions/3.12.2/envs/hf-datasets/lib/python3.12/site-packages/pdfminer/psparser.py:220, in PSBaseParser.seek(self, pos)
    218 """Seeks the parser to the given position."""
    219 log.debug("seek: %r", pos)
--> 220 self.fp.seek(pos)
    221 # reset the status for nextline()
    222 self.bufpos = pos

ValueError: seek of closed file
```

Comments: The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7466). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
Adds EXR format to store depth images in float32
State: open | Author: ducha-aiki (NONE)

This PR adds the EXR feature to store depth images (or can be normals, etc) in float32.
It relies on [openexr_numpy](https://github.com/martinResearch/openexr_numpy/tree/main) to manipulate EXR images. 



Comments: Hi ! I'mn wondering if this shouldn't this be an `Image()` type and decoded as a `PIL.Image` ?

This would make it easier to integrate with the rest of the HF ecosystem, and you could still get a numpy array using `ds = ds.with_format("numpy")` which sets all the images to be formatted as numpy arrays

@lhoestq do you mean to add the decoder, and exr extension to the image format? Yes, that probably would be better 

yes exactly
Fix data_files filtering
State: closed | Author: lhoestq (MEMBER)

close https://github.com/huggingface/datasets/issues/7458

Comments: The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7459). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
Document the HF_DATASETS_CACHE env variable
State: closed | Author: LSerranoPEReN (NONE)
Labels: name: enhancement

### Feature request

Hello,

I have a use case where my team is sharing models and dataset in shared directory to avoid duplication.
I noticed that the [cache documentation for datasets](https://huggingface.co/docs/datasets/main/en/cache) only mention the `HF_HOME` environment variable but never the `HF_DATASETS_CACHE`.

It should be nice to add `HF_DATASETS_CACHE` to datasets documentation if it's an intended feature.
If it's not, I think a depreciation warning would be appreciated.

### Motivation

This variable is fully working and similar to what `HF_HUB_CACHE` does for models, so it's nice to know that this exists. This seems to be a quick change to implement.

### Your contribution

I could contribute since this is only affecting a small portion of the documentation

Comments: Strongly agree to this, in addition, I am also suffering to change the cache location similar to other issues (since I changed the environmental variables).
https://github.com/huggingface/datasets/issues/6886

`HF_DATASETS_CACHE` should be documented there indeed, feel free to open a PR :) 

Hey, I’d love to work on this issue! Could you assign it to me?

sure ! you can also comment #self-assign in an issue and a bot assigns you automatically :)
Problems with local dataset after upgrade from 3.3.2 to 3.4.0
State: open | Author: andjoer (NONE)

### Describe the bug

I was not able to open a local saved dataset anymore that was created using an older datasets version after the upgrade yesterday from datasets 3.3.2 to 3.4.0

The traceback is 

```
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/datasets/packaged_modules/arrow/arrow.py", line 67, in _generate_tables
    batches = pa.ipc.open_stream(f)
  File "/usr/local/lib/python3.10/dist-packages/pyarrow/ipc.py", line 190, in open_stream
    return RecordBatchStreamReader(source, options=options,
  File "/usr/local/lib/python3.10/dist-packages/pyarrow/ipc.py", line 52, in __init__
    self._open(source, options=options, memory_pool=memory_pool)
  File "pyarrow/ipc.pxi", line 1006, in pyarrow.lib._RecordBatchStreamReader._open
  File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
  File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Expected to read 538970747 metadata bytes, but only read 2126

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1855, in _prepare_split_single
    for _, table in generator:
  File "/usr/local/lib/python3.10/dist-packages/datasets/packaged_modules/arrow/arrow.py", line 69, in _generate_tables
    reader = pa.ipc.open_file(f)
  File "/usr/local/lib/python3.10/dist-packages/pyarrow/ipc.py", line 234, in open_file
    return RecordBatchFileReader(
  File "/usr/local/lib/python3.10/dist-packages/pyarrow/ipc.py", line 110, in __init__
    self._open(source, footer_offset=footer_offset,
  File "pyarrow/ipc.pxi", line 1090, in pyarrow.lib._RecordBatchFileReader._open
  File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
  File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Not an Arrow file
```

### Steps to reproduce the bug

Load a dataset from a local folder with 

```
dataset = load_dataset(
                args.train_data_dir,
                cache_dir=args.cache_dir,
            )
```
as it is done for example in the training script for SD3 controlnet.

This is the minimal script to test it: 

```
from datasets import load_dataset

def main():
    dataset = load_dataset(
        "local_dataset",  
    )
    print(dataset)
    print("Sample data:", dataset["train"][0])

if __name__ == "__main__":
    main()
````

### Expected behavior

Work in 3.4.0 like in 3.3.2

### Environment info

- `datasets` version: 3.4.0
- Platform: Linux-5.15.0-75-generic-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.29.3
- PyArrow version: 19.0.1
- Pandas version: 2.2.3
- `fsspec` version: 2024.12.0

Comments: Hi ! I just released 3.4.1 with a fix, let me know if it's working now !
Fix resuming after `ds.set_epoch(new_epoch)`
State: closed | Author: lhoestq (MEMBER)

close https://github.com/huggingface/datasets/issues/7447

Comments: The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7451). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
Add IterableDataset.decode with multithreading
State: closed | Author: lhoestq (MEMBER)

Useful for dataset streaming for multimodal datasets, and especially for lerobot.
It speeds up streaming up to 20 times.

When decoding is enabled (default), media types are decoded:

* audio -> dict of "array" and "sampling_rate" and "path"
* image -> PIL.Image
* video -> torchvision.io.VideoReader

You can enable multithreading using `num_threads`. This is especially useful to speed up remote
data streaming. However it can be slower than `num_threads=0` for local data on fast disks.

PS: Disabling decoding is useful if you want to iterate on the paths or bytes of the media files
without actually decoding their content.

Example: Speed up streaming with multithreading:

```py
>>> import os
>>> from datasets import load_dataset
>>> from tqdm import tqdm
>>> ds = load_dataset("sshh12/planet-textures", split="train", streaming=True)
>>> num_threads = min(32, (os.cpu_count() or 1) + 4)
>>> ds = ds.decode(num_threads=num_threads)
>>> for _ in tqdm(ds):  # 20 times faster !
...     ...
```

why not multiprocessing ? decoding is done with the GIL released in soundfile/PIL/torchvision so multiprocessing would just use more memory

TODO
- [x] test
- [x] add to docs

Comments: The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7450). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
`datasets.disable_caching` doesn't work
State: open | Author: UCC-team (NONE)

When I use `Dataset.from_generator(my_gen)` to load my dataset, it simply skips my changes to the generator function.
I tried `datasets.disable_caching`, but it doesn't work!

Comments: cc

Yes I have the same issue.  It's a confusingly named function.  See [here](https://github.com/huggingface/datasets/blob/main/src/datasets/fingerprint.py#L115-L130)

```
...
If disabled, the library will no longer reload cached datasets files when applying transforms to the datasets.
    More precisely, if the caching is disabled:
    - cache files are always recreated
    - cache files are written to a temporary directory that is deleted when session closes
    - cache files are named using a random hash instead of the dataset fingerprint
```

Also, unfortunately the member variable `ds.cache_files` is not populated either.

I'll let you know if I find a solution.
pyarrow.lib.ArrowTypeError: Expected dict key of type str or bytes, got 'int'
State: open | Author: rangehow (NONE)

### Describe the bug

A dict with its keys are all str but get following error
```python
test_data=[{'input_ids':[1,2,3],'labels':[[Counter({2:1})]]}]
dataset = datasets.Dataset.from_list(test_data)
```
```bash
pyarrow.lib.ArrowTypeError: Expected dict key of type str or bytes, got 'int'
```

### Steps to reproduce the bug

.

### Expected behavior

.

### Environment info

datasets 3.3.2
Fix small bugs with async map
State: closed | Author: lhoestq (MEMBER)

helpful for the next PR to enable parallel image/audio/video decoding and make multimodal datasets go brr (e.g. for lerobot)

- fix with_indices
- fix resuming with save_state_dict() / load_state_dict() - omg that wasn't easy
- remove unnecessary decoding in map() to enable parallelism in FormattedExampleIterable later

small bonus: keeping features in batch()

Comments: The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7445). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
index error when num_shards > len(dataset)
State: open | Author: eminorhan (NONE)

In `ds.push_to_hub()` and `ds.save_to_disk()`, `num_shards` must be smaller than or equal to the number of rows in the dataset, but currently this is not checked anywhere inside these functions. Attempting to invoke these functions with `num_shards > len(dataset)` should raise an informative `ValueError`.

I frequently work with datasets with a small number of rows where each row is pretty large, so I often encounter this issue, where the function runs until the shard index in `ds.shard(num_shards, indx)` goes out of bounds. Ideally, a `ValueError` should be raised before reaching this point (i.e. as soon as `ds.push_to_hub()` or `ds.save_to_disk()` is invoked with `num_shards > len(dataset)`).

It seems that adding something like:
```python
if len(self) < num_shards:
   raise ValueError(f"num_shards ({num_shards}) must be smaller than or equal to the number of rows in the dataset ({len(self)}). Please either reduce num_shards or increase max_shard_size to make sure num_shards <= len(dataset).")
```
to the beginning of the definition of the `ds.shard()` function [here](https://github.com/huggingface/datasets/blob/f693f4e93aabafa878470c80fd42ddb10ec550d6/src/datasets/arrow_dataset.py#L4728) would deal with this issue for both `ds.push_to_hub()` and `ds.save_to_disk()`, but I'm not exactly sure if this is the best place to raise the `ValueError` (it seems that a more correct way to do it would be to write separate checks for `ds.push_to_hub()` and `ds.save_to_disk()`). I'd be happy to submit a PR if you think something along these lines would be acceptable.

Comments: Actually, looking at the code a bit more carefully, maybe an even better solution is to explicitly set `num_shards=len(self)` somewhere inside both `push_to_hub()` and `save_to_disk()` when these functions are invoked with `num_shards > len(dataset)`.
Flexible Loader
State: open | Author: dipta007 (NONE)
Labels: name: enhancement

### Feature request

Can we have a utility function that will use `load_from_disk` when given the local path and `load_dataset` if given an HF dataset?

It can be something as simple as this one:

```
def load_hf_dataset(path_or_name):
    if os.path.exists(path_or_name):
        return load_from_disk(path_or_name)
    else:
        return load_dataset(path_or_name)
```

### Motivation

This can be done inside the user codebase, too, but in my experience, it becomes repetitive code.

### Your contribution

I can open a pull request.

Comments: Ideally `save_to_disk` should save in a format compatible with load_dataset, wdyt ?

> Ideally `save_to_disk` should save in a format compatible with load_dataset, wdyt ?

That would be perfect if not at least a flexible loader.

@lhoestq For now, you can use this small utility library: [nanoml](https://pypi.org/project/nanoml/)
```python
from nanoml.data import load_dataset_flexible
```

I actively develop and maintain this utility library. Open to contributors. Please open issues, PR, or feature requests.
Fix multi gpu process example
State: closed | Author: SwayStar123 (NONE)

to is not an inplace function.

But i am not sure about this code anyway, i think this is modifying the global variable `model` everytime the function is called? Which is on every batch? So it is juggling the same model on every gpu right? Isnt that very inefficient? 

Comments: Okay nevermind looks like to works both ways for models. but my doubt still remains, isnt this changing the device of the model every batch?
Allow dataset row indexing with np.int types (#7423)
State: open | Author: DavidRConnell (NONE)

@lhoestq 
Proposed fix for #7423. Added a couple simple tests as requested. I had some test failures related to Java and pyspark even when installing with dev but these don't seem to be related to the changes here and fail for me even on clean main.

The typeerror raised when using the wrong type is: "Wrong key type: '{key}' of type '{type(key)}'. Expected one of int, slice, range, str or Iterable." I think that is fine. But I could modify the int part to something more generic (although I'm not sure what) if wanted.
Refactor `string_to_dict` to return `None` if there is no match instead of raising `ValueError`
State: closed | Author: ringohoffman (NONE)

Making this change, as encouraged here:

* https://github.com/huggingface/datasets/pull/7434#discussion_r1979933054

instead of having the pattern of using `try`-`except` to handle when there is no match, we can instead check if the return value is `None`; we can also assert that the return value should not be `None` if we know that should be true

Comments: cc: @lhoestq 

I am going to rebase #7434 onto this branch. Then we can merge this one first if you approve, and then #7434.

@lhoestq any thoughts here?

The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7435). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

It looks like I was unsafely asserting that `source_url_fields is not None` in `image.py`, `video.py` and `audio.py` (which did not correspond to the `except ValueError` like was there previously). I've changed it to handle `source_url_fields is None`.

Can we re-run CI on this one?

Sweet! These failures are looking spurious due to connectivity issues. Can the failing run be retried?

@lhoestq Sorry to double ping, but can this PR be reviewed? I think it is ready!
Refactor `Dataset.map` to reuse cache files mapped with different `num_proc`
State: closed | Author: ringohoffman (NONE)

Fixes #7433

This refactor unifies `num_proc is None or num_proc == 1` and `num_proc > 1`; instead of handling them completely separately where one uses a list of kwargs and shards and the other just uses a single set of kwargs and `self`, by wrapping the `num_proc == 1` case in a list and making the difference just whether or not you use a pool, you set up either case to be able to load each other's cache files just by changing `num_shards`; `num_proc == 1` can sequentially load the shards of a dataset mapped `num_shards > 1` and map any missing shards

Other than the structural refactor, the main contribution of this PR is `existing_cache_file_map`, which uses a regex of `cache_file_name` and `suffix_template` to find existing cache files, grouped by their `num_shards`; using this data structure, we can reset `num_shards` to an existing set of cache files, and load them accordingly

Comments: @lhoestq please let me know what you think about this.

It looks like I can't change the merge target to #7435, so it will look like there is a bunch of extra stuff until #7435 is in main.

@lhoestq Thanks so much for reviewing #7435! Now that that's merged, I think this PR is ready!! Can you kick off CI when you get the chance?

The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7434). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

Do you mind kicking off CI again?

The change I made to support windows paths in 637c1600fe7dd601eff571fda446937bd96c5c84 ended up breaking causing these tests in [tests/test_data_files.py](https://github.com/huggingface/datasets/actions/runs/13858546629/job/38781008643#step:10:6991). When I removed `glob_pattern_to_regex` in 583c28e7560b9d6db2e13048731f41ec8fa11361, none of the tests failed. So I'm thinking the `unicode_escape` may be handling the what `glob_pattern_to_regex` was doing.


@lhoestq will you have a chance to review this today?

Any update?

> LGTM and sorry for the delay !
> 
> note that the CI failures are unrelated to this PR :)

Great job!

great job to @ringohoffman ! ;)
`Dataset.map` ignores existing caches and remaps when ran with different `num_proc`
State: closed | Author: ringohoffman (NONE)

### Describe the bug

If you `map` a dataset and save it to a specific `cache_file_name` with a specific `num_proc`, and then call map again with that same existing `cache_file_name` but a different `num_proc`, the dataset will be re-mapped.

### Steps to reproduce the bug

1. Download a dataset
```python
import datasets

dataset = datasets.load_dataset("ylecun/mnist")
```

```
Generating train split: 100%|██████████| 60000/60000 [00:00<00:00, 116429.85 examples/s]
Generating test split: 100%|██████████| 10000/10000 [00:00<00:00, 103310.27 examples/s]
```

2. `map` and cache it with a specific `num_proc`

```python
cache_file_name="./cache/train.map"
dataset["train"].map(lambda x: x, cache_file_name=cache_file_name, num_proc=2)
```

```
Map (num_proc=2): 100%|██████████| 60000/60000 [00:01<00:00, 53764.03 examples/s]
```

3. `map` it with a different `num_proc` and the same `cache_file_name` as before

```python
dataset["train"].map(lambda x: x, cache_file_name=cache_file_name, num_proc=3)
```

```
Map (num_proc=3): 100%|██████████| 60000/60000 [00:00<00:00, 65377.12 examples/s]
```

### Expected behavior

If I specify an existing `cache_file_name`, I don't expect using a different `num_proc` than the one that was used to generate it to cause the dataset to have be be re-mapped.

### Environment info

```console
$ datasets-cli env

- `datasets` version: 3.3.2
- Platform: Linux-5.15.0-131-generic-x86_64-with-glibc2.35
- Python version: 3.10.16
- `huggingface_hub` version: 0.29.1
- PyArrow version: 19.0.1
- Pandas version: 2.2.3
- `fsspec` version: 2024.12.0
```

Comments: This feels related: https://github.com/huggingface/datasets/issues/3044

@lhoestq This comment specifically, I agree:

* https://github.com/huggingface/datasets/issues/3044#issuecomment-1239877570

> Almost a year later and I'm in a similar boat. Using custom fingerprints and when using multiprocessing the cached datasets are saved with a template at the end of the filename (something like "000001_of_000008" for every process of num_proc). So if in the next time you run the script you set num_proc to a different number, the cache cannot be used.
> 
> Is there any way to get around this? I am processing a huge dataset so I do the processing on one machine and then transfer the processed data to another in its cache dir but currently that's not possible due to num_proc mismatch.