helmo/github-issues huggingface dataset:
save_to_disk() freezes when saving on s3 bucket with multiprocessing
State: open | Author: ycattan (NONE)

### Describe the bug

I'm trying to save a `Dataset` using the `save_to_disk()` function with:
- `num_proc > 1`
- `dataset_path` being a s3 bucket path e.g. "s3://{bucket_name}/{dataset_folder}/"

The hf progress bar shows up but the saving does not seem to start. 
When using one processor only (`num_proc=1`), everything works fine.
When saving the dataset on local disk (as opposed to s3 bucket) with `num_proc > 1`, everything works fine.

Thank you for your help! :)

### Steps to reproduce the bug

I tried without any storage options:

```
from datasets import load_dataset

sandbox_ds = load_dataset("openai_humaneval")
sandbox_ds["test"].save_to_disk(
    "s3://bucket-name/test_multiprocessing_saving/",
    num_proc=4,
)
```


and with the specific s3fs storage options: 
```
from datasets import load_dataset
from s3fs import S3FileSystem

def get_s3fs():
    return S3FileSystem()

sandbox_ds = load_dataset("openai_humaneval")
sandbox_ds["test"].save_to_disk(
    "s3://bucket-name/test_multiprocessing_saving/",
    num_proc=4,
    storage_options=get_s3fs().storage_options, # also tried: storage_options=S3FileSystem().storage_options
)
```

I'm guessing I might use `storage_options` parameter wrongly, but I didn't find anything online that made it work.

**NB**: Behavior is the same when trying to save the whole `DatasetDict`.

### Expected behavior

Progress bar fills in and saving is carried out.

### Environment info

`datasets==2.18.0`

Comments: I got the same issue. Any updates so far for this issue?

Same here. Any updates?

+1, experiencing this as well
Support for pathlib.Path in datasets 2.19.0
State: open | Author: lamyiowce (NONE)

### Describe the bug

After the recent update of `datasets`, Dataset.save_to_disk does not accept a pathlib.Path anymore. It was supported in 2.18.0 and previous versions. Is this intentional? Was it supported before only because of a Python dusk-typing miracle?

### Steps to reproduce the bug

```
from datasets import Dataset
import pathlib

path = pathlib.Path("./my_out_path")
Dataset.from_dict(
    {"text": ["hello world"], "label": [777], "split": ["train"]}
.save_to_disk(path)
```
This results in an error when using datasets 2.19:
```
Traceback (most recent call last):
  File "<stdin>", line 3, in <module>
  File "/Users/jb/scratch/venv/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 1515, in save_to_disk
    fs, _ = url_to_fs(dataset_path, **(storage_options or {}))
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/jb/scratch/venv/lib/python3.11/site-packages/fsspec/core.py", line 383, in url_to_fs
    chain = _un_chain(url, kwargs)
            ^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/jb/scratch/venv/lib/python3.11/site-packages/fsspec/core.py", line 323, in _un_chain
    if "::" in path
       ^^^^^^^^^^^^
TypeError: argument of type 'PosixPath' is not iterable
```

Converting to str works, however.
```
Dataset.from_dict(
     {"text": ["hello world"], "label": [777], "split": ["train"]}
).save_to_disk(str(path))
```

### Expected behavior

My dataset gets saved to disk without an error.

### Environment info

aiohttp==3.9.5
aiosignal==1.3.1
attrs==23.2.0
certifi==2024.2.2
charset-normalizer==3.3.2
datasets==2.19.0
dill==0.3.8
filelock==3.14.0
frozenlist==1.4.1
fsspec==2024.3.1
huggingface-hub==0.23.2
idna==3.7
multidict==6.0.5
multiprocess==0.70.16
numpy==1.26.4
packaging==24.0
pandas==2.2.2
pyarrow==16.1.0
pyarrow-hotfix==0.6
python-dateutil==2.9.0.post0
pytz==2024.1
PyYAML==6.0.1
requests==2.32.3
six==1.16.0
tqdm==4.66.4
typing_extensions==4.12.0
tzdata==2024.1
urllib3==2.2.1
xxhash==3.4.1
yarl==1.9.4

Comments: +1 I just noticed this when I tried to update `datasets` today.

The same issue, I also get error.
ValueError: Couldn't infer the same data file format for all splits. Got {'train': ('json', {}), 'validation': (None, {})}
State: open | Author: Polarisamoon (NONE)

### Describe the bug

When I run the code en = load_dataset("allenai/c4", "en", streaming=True), I encounter an error: raise ValueError(f"Couldn't infer the same data file format for all splits. Got {split_modules}") ValueError: Couldn't infer the same data file format for all splits. Got {'train': ('json', {}), 'validation': (None, {})}.
However, running dataset = load_dataset('allenai/c4', streaming=True, data_files={'validation': 'en/c4-validation.00003-of-00008.json.gz'}, split='validation') works fine. What is the issue here?

### Steps to reproduce the bug

run code：
import os
os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'
from datasets import load_dataset

en = load_dataset("allenai/c4", "en", streaming=True)

### Expected behavior

Successfully loaded the dataset.

### Environment info

- `datasets` version: 2.18.0
- Platform: Linux-6.5.0-28-generic-x86_64-with-glibc2.17
- Python version: 3.8.19
- `huggingface_hub` version: 0.22.2
- PyArrow version: 15.0.2
- Pandas version: 2.0.3
- `fsspec` version: 2024.2.0


Comments: How do you solve it ?


> How do you solve it ?

Please check your Python environment and dataset version. I have just resolved the issue, which was caused by a Python environment switching error
Avoid downloading the whole dataset when only README.me has been touched on hub.
State: open | Author: zinc75 (NONE)
Labels: name: enhancement

### Feature request

`datasets.load_dataset()` triggers a new download of the **whole dataset** when the README.md file has been touched on huggingface hub, even if data files / parquet files are the exact same.

I think the current behaviour of the load_dataset function is triggered whenever a change of the hash of latest commit on huggingface hub, but  is there a clever way to only download again the dataset **if and only if** data is modified ?  

### Motivation

The current behaviour is a waste of network bandwidth / disk space / research time.

### Your contribution

I don't have time to submit a PR, but I hope a simple solution will emerge from this issue ! 

Comments: you're right, we're tackling this here: https://github.com/huggingface/dataset-viewer/issues/2757

@severo : great !
Caching map result of DatasetDict.
State: open | Author: MostHumble (NONE)

Hi!

I'm currenty using the map function to tokenize a somewhat large dataset, so I need to use the cache to save ~25 mins.

Changing num_proc incduces the recomputation of the map, I'm not sure why and if this is excepted behavior? 

here it says, that cached files are loaded sequentially:

https://github.com/huggingface/datasets/blob/bb2664cf540d5ce4b066365e7c8b26e7f1ca4743/src/datasets/arrow_dataset.py#L3005-L3006

it seems like I can pass in a fingerprint, and load it directly:

https://github.com/huggingface/datasets/blob/bb2664cf540d5ce4b066365e7c8b26e7f1ca4743/src/datasets/arrow_dataset.py#L3108-L3125

**Environment Setup:**

- Python 3.11.9
- datasets  2.19.1 conda-forge
- Linux 6.1.83-1.el9.elrepo.x86_64

**MRE**
```python
fixed raw_datasets
fixed tokenize_function

tokenized_datasets = raw_datasets.map(
                        tokenize_function,
                        batched=True,
                        num_proc=9,
                        remove_columns=['text'],
                        load_from_cache_file= True,
                        desc="Running tokenizer on dataset line_by_line",
                    )


tokenized_datasets = raw_datasets.map(
                        tokenize_function,
                        batched=True,
                        num_proc=5,
                        remove_columns=['text'],
                        load_from_cache_file= True,
                        desc="Running tokenizer on dataset line_by_line",
                    )
```
Export Parquet Tablet Audio-Set is null bytes in Arrow 
State: open | Author: anioji (NONE)

### Describe the bug

Exporting the processed audio inside the table with the dataset.to_parquet function, the object pyarrow {bytes: null, path: "Some/Path"}
At the same time, the same dataset uploaded to the hub has bit arrays

![Screenshot from 2024-05-27 19-14-49](https://github.com/huggingface/datasets/assets/140120605/ddfba089-426f-4659-9df4-7a634c948b9e)
![Screenshot from 2024-05-27 19-12-51](https://github.com/huggingface/datasets/assets/140120605/4cf8c0a1-650e-491b-86c8-b475c284a021)


### Steps to reproduce the bug

1.Get dataset from audio and cast it 
2.Export and push dataset
3.It’s scary to be indignant at the difference in the uploaded dataset and the fact that it was saved locally
```py
from datasets import Dataset, Audio 
df = Dataset.from_csv("./datasets.csv")
df = df.cast_column("audio", Audio(16000))
df.to_parquet("./datasets.parquet")
df.push_to_hub(repo_id="************", token="**********************")
```

You can use "try replicate case" for this
[replicate_packet.zip](https://github.com/huggingface/datasets/files/15457114/replicate_packet.zip)


### Expected behavior

Two parquet tables identical in content. It is obvious?

### Environment info

Python 3.11+ (I try did it in 3.12 and got same result )
Invalid YAML in README.md: unknown tag !<tag:yaml.org,2002:python/tuple>
State: open | Author: juanqui (NONE)

### Describe the bug

I wrote a notebook to load an existing dataset, process it, and upload as a private dataset using `dataset.push_to_hub(...)` at the end. The push to hub is failing with:

```
ValueError: Invalid metadata in README.md.
- Invalid YAML in README.md: unknown tag !<tag:yaml.org,2002:python[/tuple](http://192.168.1.128:8888/tuple)> (50:11)

 47 |             - 4
 48 |             - 4
 49 |             - 8
 50 |           - !!binary |
----------------^
 51 |             TwAAAA==
 52 |           '1': !!python[/object/apply](http://192.168.1.128:8888/object/apply):nump ...
```

My dataset has a `train` and `validation` dataset.  These are the features:

```
{'c1': Value(dtype='string', id=None),
 'c2': Value(dtype='string', id=None),
 'c3': [{'value': Value(dtype='string', id=None),
   'start': Value(dtype='int64', id=None),
   'end': Value(dtype='int64', id=None),
   'label': Value(dtype='string', id=None)}],
 'c4': Value(dtype='string', id=None),
 'c5': Value(dtype='string', id=None),
 'c6': Value(dtype='string', id=None),
 'c7': Value(dtype='string', id=None),
 'c8': Sequence(feature=Value(dtype='int32', id=None), length=-1, id=None),
 'c9': Sequence(feature=Value(dtype='int8', id=None), length=-1, id=None),
 'c10': Sequence(feature=Value(dtype='int8', id=None), length=-1, id=None),
 'labels': Sequence(feature=ClassLabel(names=['O', 'B-ABC', 'I-ABC', ...], id=None), length=-1, id=None),
 'c12': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}
```

This used to work until I decided to cast the `labels` feature to a `Sequence(ClassLabel(...))` type with:

```
ds['train'] = ds['train'].cast_column("labels", Sequence(ClassLabel(names=list(labels))))
ds['validation'] = ds['validation'].cast_column("labels", Sequence(ClassLabel(names=list(labels))))
```

### Steps to reproduce the bug

1. Start with any token classification dataset.
2. Add a `labels` column with data such as `[0,0,0,12,13,13,13,0,0]`.
3. Cast the label column from `Sequence` to `Sequence(ClassLabel))` with:

```
labels = ['O', 'B-TEST', 'I-TEST']
ds = ds.cast_column("labels", Sequence(ClassLabel(names=labels)))
```

4. Push to hub with `ds.push_to_hub("me/awesome-stuff-dataset")`

### Expected behavior

I expected `push_to_hub` to successfully push my dataset to the hub without error.

### Environment info

Python 3.11.9

datasets==2.19.1
transformers==4.41.1
PyYAML==6.0.1
```push_to_hub()``` - Prevent Automatic Generation of Splits 
State: closed | Author: jetlime (NONE)

### Describe the bug

I currently have a dataset which has not been splited. When pushing the dataset to my hugging face dataset repository, it is split into a testing and training set. How can I prevent the split from happening?

### Steps to reproduce the bug

1. Have a unsplit dataset 

```python
Dataset({ features: ['input', 'output', 'Attack', '__index_level_0__'], num_rows: 944685 })
```

2. Push it to huggingface

```python
dataset.push_to_hub(dataset_name)
```

3. On the hugging face dataset repo, the dataset then appears to be splited:

![image](https://github.com/huggingface/datasets/assets/29337128/b4fbc141-42b0-4f49-98df-dd479648fe09)

4. Indeed, when loading the dataset from this repo, the dataset is split in two testing and training set.

```python
from datasets import load_dataset, Dataset

dataset = load_dataset("Jetlime/NF-CSE-CIC-IDS2018-v2", streaming=True)
dataset
```
output: 

```
IterableDatasetDict({
    train: IterableDataset({
        features: ['input', 'output', 'Attack', '__index_level_0__'],
        n_shards: 2
    })
    test: IterableDataset({
        features: ['input', 'output', 'Attack', '__index_level_0__'],
        n_shards: 1
    })
```

### Expected behavior

The dataset shall not be splited, as not requested.

### Environment info

- `datasets` version: 2.19.1
- Platform: Linux-6.2.0-35-generic-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.23.0
- PyArrow version: 15.0.2
- Pandas version: 2.2.2
- `fsspec` version: 2024.3.1
Column order is nondeterministic when loading from JSON
State: closed | Author: albertvillanova (MEMBER)
Labels: name: bug

As reported by @meg-huggingface, the order of the JSON object keys is not preserved while loading a dataset from a JSON file with a list of objects.

For example, when loading a JSON files with a list of objects, each with the following ordered keys:
- [ID, Language, Topic], 

the resulting dataset may have columns:
- [ID, Topic, Language], or
- [Topic, Language, ID], or
- [Topic, ID, Language],...

This issue is caused by the use of a Python set (which does not preserve the order):
https://github.com/huggingface/datasets/blob/60d21efbc01e15d0b596ac1072750cbecd91548a/src/datasets/packaged_modules/json/json.py#L168
introduced in
- #5772
Add MedImg for streaming
State: open | Author: lhallee (NONE)
Labels: name: dataset request

### Feature request

Host the MedImg dataset (similar to Imagenet but for biomedical images). 

### Motivation

There is a clear need for biomedical image foundation models and large scale biomedical datasets that are easily streamable. This would be an excellent tool for the biomedical community.

### Your contribution

MedImg can be found [here](https://www.cuilab.cn/medimg/#).

Comments: @mariosasko, @lhoestq, @albertvillanova
Hello! Can anyone help? or can you guys suggest who can help with this?

Hi ! Feel free to download the dataset and create a `Dataset` object with it.

Then your'll be able to use `push_to_hub()` to upload the dataset to HF in Parquet format and make it streamable :)

> Hi ! Feel free to download the dataset and create a `Dataset` object with it.
> 
> Then your'll be able to use `push_to_hub()` to upload the dataset to HF in Parquet format and make it streamable :)

The dataset is several TB in total, which I do not have the resources to handle.

Hi @lhoestq and @albertvillanova , just following up about this.

for big datasets you can push_to_hub one part at a time (e.g. as different splits) and merge the parts (just a simple modification in the YAML part of the README)

Sure, that makes sense. However, isn't there a size limit to what typical users can push?

Yes there is a limit, simply let us know by email at datasets [at] huggingface.co - this way we can give you a storage grant also help making sure the dataset is all good for people to use it easily

> Yes there is a limit, simply let us know by email at datasets [at] huggingface.co - this way we can give you a storage grant also help making sure the dataset is all good for people to use it easily

Got it, that would be great.
Support the deserialization of json lines files comprised of lists
State: open | Author: umarbutler (NONE)
Labels: name: enhancement

### Feature request

I manage a somewhat large and popular Hugging Face dataset known as the [Open Australian Legal Corpus](https://huggingface.co/datasets/umarbutler/open-australian-legal-corpus). I recently updated my corpus to be stored in a json lines file where each line is an array and each element represents a value at a particular column. Previously, my corpus was stored as a json lines file where each line was a dictionary and the keys were the fields.

Essentially, a line in my json lines file used to look like this:
```json
{"version_id":"","type":"","jurisdiction":"","source":"","citation":"","url":"","when_scraped":"","text":""}
```

And now it looks like this:
```json
["","","","","","","",""]
```

This saves 65 bytes per document and allows me very quickly serialise and deserialise documents via `msgspec`.

After making this change, I found that `datasets` was incapable of deserialising my Corpus without a custom loading script, even if I ensured that the `dataset_info` field in my dataset card contained the desired names of my features.

I would like to request that functionality be added to support this format which is more memory-efficent and faster than using dictionaries.

### Motivation

The [documentation](https://huggingface.co/docs/datasets/en/dataset_script) for creating dataset loading scripts asserts that:
> In the next major release, the new safety features of 🤗 Datasets will disable running dataset loading scripts by default, and you will have to pass trust_remote_code=True to load datasets that require running a dataset script.

I would rather not require my users to pass `trust_remote_code=True` which means that I will need built-in support for this format.

### Your contribution

I would be happy to submit a PR for this if this is something you would incorporate into `datasets` and if I can be pointed to where the code would need to go.

Comments: Update: I ended up deciding to go back to use lines of dictionaries instead of arrays, not because of this issue as my users would be capable of downloading my corpus without `datasets`, but the speed and storage savings are not currently worth breaking my API and harming the backwards compatibility of each new revision.

With that said, for a static dataset that is not regularly updated like mine, and particularly for extremely large datasets with millions or billions of rows, using arrays could have a meaningful impact, and so there is probably still value in supporting this structure, provided the effort is not too much.
Extraction protocol for arrow files is not defined
State: closed | Author: radulescupetru (NONE)

### Describe the bug

Passing files with `.arrow` extension into data_files argument, at least when `streaming=True` is very slow.

### Steps to reproduce the bug

 Basically it goes through the `_get_extraction_protocol` method located [here](https://github.com/huggingface/datasets/blob/main/src/datasets/utils/file_utils.py#L820)
The method then looks at some base known extensions where `arrow` is not defined so it proceeds to determine the compression with the magic number method which is slow when dealing with a lot of files which are stored in s3 and by looking at this predefined list, I don't see `arrow` in there either so in the end it return None:

```
MAGIC_NUMBER_TO_COMPRESSION_PROTOCOL = {
    bytes.fromhex("504B0304"): "zip",
    bytes.fromhex("504B0506"): "zip",  # empty archive
    bytes.fromhex("504B0708"): "zip",  # spanned archive
    bytes.fromhex("425A68"): "bz2",
    bytes.fromhex("1F8B"): "gzip",
    bytes.fromhex("FD377A585A00"): "xz",
    bytes.fromhex("04224D18"): "lz4",
    bytes.fromhex("28B52FFD"): "zstd",
}
```

### Expected behavior

My expectation is that `arrow` would be in the known lists so it would return None without going through the magic number method.

### Environment info

datasets 2.19.0

Comments: Fixed in https://github.com/huggingface/datasets/pull/7083
HTTPError 403 raised by CLI convert_to_parquet when creating script branch on 3rd party repos
State: closed | Author: albertvillanova (MEMBER)
Labels: name: bug

CLI convert_to_parquet cannot create "script" branch on 3rd party repos.

It can only create it on repos where the user executing the script has write access.

Otherwise, a 403 Forbidden HTTPError is raised:
```
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_errors.py", line 304, in hf_raise_for_status
    response.raise_for_status()
  File "/usr/local/lib/python3.10/dist-packages/requests/models.py", line 1021, in raise_for_status
    raise HTTPError(http_error_msg, response=self)
requests.exceptions.HTTPError: 403 Client Error: Forbidden for url: https://huggingface.co/api/datasets/ORG/DATASET/branch/script

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/usr/local/bin/datasets-cli", line 8, in <module>
    sys.exit(main())
  File "/usr/local/lib/python3.10/dist-packages/datasets/commands/datasets_cli.py", line 41, in main
    service.run()
  File "/usr/local/lib/python3.10/dist-packages/datasets/commands/convert_to_parquet.py", line 92, in run
    create_branch(dataset_id, branch="script", repo_type="dataset", token=token, exist_ok=True)
  File "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
    return fn(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/huggingface_hub/hf_api.py", line 5503, in create_branch
    hf_raise_for_status(response)
  File "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_errors.py", line 367, in hf_raise_for_status
    raise HfHubHTTPError(message, response=response) from e
huggingface_hub.utils._errors.HfHubHTTPError:  (Request ID: Root=1-6645ee0d-4db1ed8a1fbe04956be15897;139a6e23-df7d-4f62-b5ba-adb6d8e6e696)

403 Forbidden: Forbidden: cannot write to script.
Cannot access content at: https://huggingface.co/api/datasets/ORG/DATASET/branch/script.
If you are trying to create or update content,make sure you have a token with the `write` role.
```