KRadim/github-issues-minimal huggingface dataset:
save_to_disk() freezes when saving on s3 bucket with multiprocessing
State: open

### 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`
Support for pathlib.Path in datasets 2.19.0
State: open

### 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
ValueError: Couldn't infer the same data file format for all splits. Got {'train': ('json', {}), 'validation': (None, {})}
State: open

### 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
Avoid downloading the whole dataset when only README.me has been touched on hub.
State: open
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 !
Fix NonMatchingSplitsSizesError/ExpectedMoreSplits when passing data_dir/data_files in no-code Hub datasets
State: closed

Fix `NonMatchingSplitsSizesError` or `ExpectedMoreSplits` error for no-code Hub datasets if the user passes:
- `data_dir`
- `data_files`

The proposed solution is to avoid using exported dataset info (from Parquet exports) in these cases.
Additionally, also if the user passes `revision` other than "main" (so that no network requests are made).

This PR fixes a bug introduced by:
- #6714

Fix #6918, fix #6939.
Caching map result of DatasetDict.
State: open

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

### 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

### 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

### 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
Validate config name and data_files in packaged modules
State: closed

Validate the config attributes `name` and `data_files` in packaged modules by making the derived classes call their parent `__post_init__` method.

Note that their parent `BuilderConfig` validates its attributes `name` and `data_files` in its `__post_init__` method: https://github.com/huggingface/datasets/blob/60d21efbc01e15d0b596ac1072750cbecd91548a/src/datasets/builder.py#L128-L137

This PR makes the derived config classes call their parent `__post_init__` method to validate their `name` and `data_files` attributes.
Column order is nondeterministic when loading from JSON
State: closed
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
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/#).
Fail to load "stas/c4-en-10k" dataset since 2.16 version
State: closed

### Describe the bug

When update datasets library to version 2.16+ ( I test it on 2.16, 2.19.0 and 2.19.1), using the following code to load stas/c4-en-10k dataset 

```python
from datasets import load_dataset, Dataset
dataset = load_dataset('stas/c4-en-10k')
```

and then it raise UnicodeDecodeError like

```
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/*/conda3/envs/watermark/lib/python3.10/site-packages/datasets/load.py", line 2523, in load_dataset
    builder_instance = load_dataset_builder(
  File "/home/*/conda3/envs/watermark/lib/python3.10/site-packages/datasets/load.py", line 2195, in load_dataset_builder
    dataset_module = dataset_module_factory(
  File "/home/*/conda3/envs/watermark/lib/python3.10/site-packages/datasets/load.py", line 1846, in dataset_module_factory
    raise e1 from None
  File "/home/*/conda3/envs/watermark/lib/python3.10/site-packages/datasets/load.py", line 1798, in dataset_module_factory
    can_load_config_from_parquet_export = "DEFAULT_CONFIG_NAME" not in f.read()
  File "/home/*/conda3/envs/watermark/lib/python3.10/codecs.py", line 322, in decode
    (result, consumed) = self._buffer_decode(data, self.errors, final)
UnicodeDecodeError: 'utf-8' codec can't decode byte 0x8b in position 1: invalid start byte
```

I found that fs.open loads a gzip file and parses it like plain text using utf-8 encoder.

```python
fs = HfFileSystem('https://huggingface.co')
fs.open("datasets/stas/c4-en-10k/c4-en-10k.py", "rb")
data = fs.read()    # data is gzip bytes begin with b'\x1f\x8b\x08\x00\x00\tn\x88\x00...' 
data2 = unzip_gzip_bytes(data)    #  data2 is what we want: '# coding=utf-8\n# Copyright 2020 The HuggingFace Datasets...'
```

### Steps to reproduce the bug

1. Install datasets between version 2.16 and 2.19
2. Use `datasets.load_dataset` method to load `stas/c4-en-10k` dataset.

### Expected behavior

Load dataset normally.

### Environment info

Platform = Linux-5.4.0-159-generic-x86_64-with-glibc2.35
Python = 3.10.14
Datasets = 2.19
Support the deserialization of json lines files comprised of lists
State: open
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.
irc_disentangle - Issue with splitting data
State: closed

### Describe the bug

I am trying to access your database through python using "datasets.load_dataset("irc_disentangle")" and I am getting this error message:

ValueError: Instruction "train" corresponds to no data!

### Steps to reproduce the bug

import datasets
ds = datasets.load_dataset('irc_disentangle')
ds

### Expected behavior

The data is supposed to load into ds and be accessable as such:
ds['train'][1050], ds['train'][1055]

### Environment info

I tired Python 3.12 and 3.10
Extraction protocol for arrow files is not defined
State: closed

### 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
Add the option of saving in parquet instead of arrow 
State: open
Labels: name: enhancement

### Feature request

In dataset.save_to_disk('/path/to/save/dataset'),

add the option to save in parquet format

dataset.save_to_disk('/path/to/save/dataset', format="parquet"),

because arrow is not used for Production Big data.... (only parquet)



### Motivation

because arrow is not used for Production Big data.... (only parquet)

### Your contribution

I can do the testing !
Make CLI convert_to_parquet not raise error if no rights to create script branch
State: closed

Make CLI convert_to_parquet not raise error if no rights to create "script" branch.

Not that before this PR, the error was not critical because it was raised at the end of the script, once all the rest of the steps were already performed.

Fix #6901.

Bug introduced in datasets-2.19.0 by:
- #6809
HTTPError 403 raised by CLI convert_to_parquet when creating script branch on 3rd party repos
State: closed
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.
```
[WebDataset] KeyError with user-defined `Features` when a field is missing in an example
State: closed

reported at https://huggingface.co/datasets/ProGamerGov/synthetic-dataset-1m-dalle3-high-quality-captions/discussions/1

```
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 109, in _generate_examples
    example[field_name] = {"path": example["__key__"] + "." + field_name, "bytes": example[field_name]}
```
List of dictionary features get standardized
State: open

### Describe the bug

Hi, i’m trying to create a HF dataset from a list using Dataset.from_list.

Each sample in the list is a dict with the same keys (which will be my features). The values for each feature are a list of dictionaries, and each such dictionary has a different set of keys. However, the datasets library standardizes all dictionaries under a feature and adds all possible keys (with None value) from all the dictionaries under that feature.

How can I keep the same set of keys as in the original list for each dictionary under a feature?

### Steps to reproduce the bug

```
from datasets import Dataset

# Define a function to generate a sample with "tools" feature
def generate_sample():
    # Generate random sample data
    sample_data = {
        "text": "Sample text",
        "feature_1": []
    }
    
    # Add feature_1 with random keys for this sample
    feature_1 = [{"key1": "value1"}, {"key2": "value2"}]  # Example feature_1 with random keys
    sample_data["feature_1"].extend(feature_1)
    
    return sample_data

# Generate multiple samples
num_samples = 10
samples = [generate_sample() for _ in range(num_samples)]

# Create a Hugging Face Dataset
dataset = Dataset.from_list(samples)
dataset[0]
```

```{'text': 'Sample text', 'feature_1': [{'key1': 'value1', 'key2': None}, {'key1': None, 'key2': 'value2'}]}```

### Expected behavior

```{'text': 'Sample text', 'feature_1': [{'key1': 'value1'}, {'key2': 'value2'}]}```

### Environment info

- `datasets` version: 2.19.1
- Platform: Linux-5.15.0-1040-nvidia-x86_64-with-glibc2.35
- Python version: 3.10.13
- `huggingface_hub` version: 0.23.0
- PyArrow version: 15.0.0
- Pandas version: 2.2.0
- `fsspec` version: 2023.10.0
datasets template guide :: issue in documentation YAML
State: closed

### Describe the bug

There is a YAML error at the top of the page, and I don't think it's supposed to be there 

### Steps to reproduce the bug

1. Browse to [this tutorial document](https://github.com/huggingface/datasets/blob/main/templates/README_guide.md)
2. Observe a big red error at the top
3. The rest of the document remains functional

### Expected behavior

I think the YAML block should be displayed or ignored.

### Environment info

N/A
Add support for categorical/dictionary types
State: closed

Arrow has a very useful dictionary/categorical type (https://arrow.apache.org/docs/python/generated/pyarrow.dictionary.html). This data type has significant speed, memory and disk benefits over pa.string() when there are only a few unique text strings in a column.

Unfortunately, huggingface datasets currently does not support this type. So huggingface datasets cannot natively read many parquet files that use this datatype .This PR adds support for Huggingface Datasets to read categorical/dictionary data.

Note: This PR functions by simply converting those dictionary/categorical types to strings. This means that huggingface datasets cannot take advantage of the compute benefits of categoricals, but it significantly simplifies logic. At this time, I do not think it makes sense to optimize categorical support within huggingface datasets and that we should only try to optimize later, if necessary.

Closes #5706
Unable to load JSON saved using `to_json`
State: closed

### Describe the bug

Datasets stored in the JSON format cannot be loaded using `json.load()`

### Steps to reproduce the bug

```
import json
from datasets import load_dataset

dataset = load_dataset("squad")
train_dataset, test_dataset = dataset["train"], dataset["validation"]
test_dataset.to_json("full_dataset.json")

# This works
loaded_test = load_dataset("json", data_files="full_dataset.json")

# This fails
loaded_test = json.load(open("full_dataset.json", "r"))
```

### Expected behavior

The JSON should be correctly formatted when writing so that it can be loaded using `json.load()`.

### Environment info

Colab: https://colab.research.google.com/drive/1st1iStFUVgu9ZPvnzSzL4vDeYWDwYpUm?usp=sharing
add `with_transform` and/or `set_transform` to IterableDataset
State: open
Labels: name: enhancement

### Feature request

when working with a really large dataset it would save us a lot of time (and compute resources) to use either with_transform or the set_transform from the Dataset class instead of waiting for the entire dataset to map

### Motivation

don't want to wait for a really long dataset to map, this would give IterableDataset an extra advantage over the Dataset class.
reducing time and resources

### Your contribution

I am a little busy with my job search lately, but would post about this feature in my social media.
Apologies again (dad going to kick me out soon), if I ever have some free time I will contribute to making this a reality, but that's going to be hard
      / (┬┬﹏┬┬)\
fix bug #6877
State: closed

fix bug #6877 due to maybe f becomes invaild after yield process
the results are below:


Resolving data files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 828/828 [00:01<00:00, 420.41it/s]
Resolving data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 828/828 [00:00<00:00, 26148.48it/s]
Resolving data files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 828/828 [00:00<00:00, 409731.44it/s]
Resolving data files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 828/828 [00:00<00:00, 289720.84it/s]
Resolving data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 828/828 [00:00<00:00, 26663.42it/s]
Resolving data files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 828/828 [00:00<00:00, 434056.21it/s]
Downloading data: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 828/828 [00:00<00:00, 13222.33files/s]
Downloading data: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 828/828 [00:04<00:00, 180.67files/s]
Downloading data: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 828/828 [01:35<00:00,  8.70files/s]
Generating train split: 1571592 examples [00:08, 176736.09 examples/s]
Generating test split: 85533 examples [00:01, 48224.56 examples/s]
Generating validation split: 86246 examples [00:01, 50164.16 examples/s]

Fix https://github.com/huggingface/datasets/issues/6877.

CC: @natolambert
FAISS load to None
State: open

### Describe the bug

I've use FAISS with Datasets and save to FAISS.

Then load to save FAISS then no error, then ds to None

```python
ds.load_faiss_index('embeddings', 'my_index.faiss')
```

### Steps to reproduce the bug

# 1.
```python
ds_with_embeddings = ds.map(lambda example: {'embeddings': model(transforms(example['image']).unsqueeze(0)).squeeze()}, batch_size=64)

ds_with_embeddings.add_faiss_index(column='embeddings')

ds_with_embeddings.save_faiss_index('embeddings', 'index.faiss')
```

# 2.
```python
ds.load_faiss_index('embeddings', 'my_index.faiss')
```

### Expected behavior

Add column in Datasets.

### Environment info

Google Colab, SageMaker Notebook
load_dataset with data_dir and cache_dir set fail with not supported
State: open

### Describe the bug

with python 3.11 I execute:
```py
from transformers import Wav2Vec2Processor, Data2VecAudioModel
import torch
from torch import nn
from datasets import load_dataset, concatenate_datasets

# load demo audio and set processor
dataset_clean = load_dataset("librispeech_asr", "clean", split="validation", data_dir="data", cache_dir="cache")
```
This fails in the last line with 
```log
Found cached dataset librispeech_asr (file:///Users/as/Documents/Project/git/audio2vec/cache/librispeech_asr/clean-data_dir=data/2.1.0/cff5df6e7955c80a67f80e27e7e655de71c689e2d2364bece785b972acb37fe7)
Traceback (most recent call last):
  File "/Users/as/Documents/Project/git/audio2vec/src/music2vec-v1.py", line 7, in <module>
    dataset_clean = load_dataset("librispeech_asr", "clean", split="validation", data_dir="data", cache_dir="cache")
                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/as/anaconda3/lib/python3.11/site-packages/datasets/load.py", line 1810, in load_dataset
    ds = builder_instance.as_dataset(split=split, verification_mode=verification_mode, in_memory=keep_in_memory)
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/as/anaconda3/lib/python3.11/site-packages/datasets/builder.py", line 1113, in as_dataset
    raise NotImplementedError(f"Loading a dataset cached in a {type(self._fs).__name__} is not supported.")
NotImplementedError: Loading a dataset cached in a LocalFileSystem is not supported.
```

### Steps to reproduce the bug

I setup an venv with requirements.txt
```txt
transformers==4.40.2
torch==2.2.2
datasets==2.16.0
fsspec==2023.9.2
```

pip freeze is:
```
aiohttp==3.9.5
aiosignal==1.3.1
attrs==23.2.0
certifi==2024.2.2
charset-normalizer==3.3.2
datasets==2.16.0
dill==0.3.7
filelock==3.14.0
frozenlist==1.4.1
fsspec==2023.9.2
huggingface-hub==0.23.0
idna==3.7
Jinja2==3.1.4
MarkupSafe==2.1.5
mpmath==1.3.0
multidict==6.0.5
multiprocess==0.70.15
networkx==3.3
numpy==1.26.4
packaging==24.0
pandas==2.2.2
pyarrow==16.0.0
pyarrow-hotfix==0.6
python-dateutil==2.9.0.post0
pytz==2024.1
PyYAML==6.0.1
regex==2024.4.28
requests==2.31.0
safetensors==0.4.3
six==1.16.0
sympy==1.12
tokenizers==0.19.1
torch==2.2.2
tqdm==4.66.4
transformers==4.40.2
typing_extensions==4.11.0
tzdata==2024.1
urllib3==2.2.1
xxhash==3.4.1
yarl==1.9.4

```

I execute this on a M1 Mac.


### Expected behavior

I don't understand the error message. Why is "local" caching not supported. Would it possible to give some additional hint with the error message how to solve this issue?


### Environment info

source ....
python -u example.py
CI is broken after jax-0.4.27 release: AttributeError: 'jaxlib.xla_extension.DeviceList' object has no attribute 'device'
State: closed
Labels: name: bug

After jax-0.4.27 release (https://github.com/google/jax/releases/tag/jax-v0.4.27), our CI is broken with the error:
```Python traceback
AttributeError: 'jaxlib.xla_extension.DeviceList' object has no attribute 'device'. Did you mean: 'devices'?
```

See: https://github.com/huggingface/datasets/actions/runs/8997488610/job/24715736153
```Python traceback
___________________ FormatterTest.test_jax_formatter_device ____________________
[gw1] linux -- Python 3.10.14 /opt/hostedtoolcache/Python/3.10.14/x64/bin/python

self = <tests.test_formatting.FormatterTest testMethod=test_jax_formatter_device>

    @require_jax
    def test_jax_formatter_device(self):
        import jax
    
        from datasets.formatting import JaxFormatter
    
        pa_table = self._create_dummy_table()
        device = jax.devices()[0]
        formatter = JaxFormatter(device=str(device))
        row = formatter.format_row(pa_table)
>       assert row["a"].device() == device
E       AttributeError: 'jaxlib.xla_extension.DeviceList' object has no attribute 'device'. Did you mean: 'devices'?

tests/test_formatting.py:630: AttributeError
```
Require Pillow >= 9.4.0 to avoid AttributeError when loading image dataset
State: closed

Require Pillow >= 9.4.0 to avoid AttributeError when loading image dataset.

The `PIL.Image.ExifTags` that we use in our code was implemented in Pillow-9.4.0: https://github.com/python-pillow/Pillow/commit/24a5405a9f7ea22f28f9c98b3e407292ea5ee1d3

The bug #6881 was introduced in datasets-2.19.0 by this PR:
- #6739

Fix #6881.
AttributeError: module 'PIL.Image' has no attribute 'ExifTags'
State: closed
Labels: name: bug

When trying to load an image dataset in an old Python environment (with Pillow-8.4.0), an error is raised:
```Python traceback
AttributeError: module 'PIL.Image' has no attribute 'ExifTags'
```
The error traceback:
```Python traceback
~/huggingface/datasets/src/datasets/iterable_dataset.py in __iter__(self)
   1391                 # `IterableDataset` automatically fills missing columns with None.
   1392                 # This is done with `_apply_feature_types_on_example`.
-> 1393                 example = _apply_feature_types_on_example(
   1394                     example, self.features, token_per_repo_id=self._token_per_repo_id
   1395                 )

~/huggingface/datasets/src/datasets/iterable_dataset.py in _apply_feature_types_on_example(example, features, token_per_repo_id)
   1080     encoded_example = features.encode_example(example)
   1081     # Decode example for Audio feature, e.g.
-> 1082     decoded_example = features.decode_example(encoded_example, token_per_repo_id=token_per_repo_id)
   1083     return decoded_example
   1084 

~/huggingface/datasets/src/datasets/features/features.py in decode_example(self, example, token_per_repo_id)
   1974 
-> 1975         return {
   1976             column_name: decode_nested_example(feature, value, token_per_repo_id=token_per_repo_id)
   1977             if self._column_requires_decoding[column_name]

~/huggingface/datasets/src/datasets/features/features.py in <dictcomp>(.0)
   1974 
   1975         return {
-> 1976             column_name: decode_nested_example(feature, value, token_per_repo_id=token_per_repo_id)
   1977             if self._column_requires_decoding[column_name]
   1978             else value

~/huggingface/datasets/src/datasets/features/features.py in decode_nested_example(schema, obj, token_per_repo_id)
   1339         # we pass the token to read and decode files from private repositories in streaming mode
   1340         if obj is not None and schema.decode:
-> 1341             return schema.decode_example(obj, token_per_repo_id=token_per_repo_id)
   1342     return obj
   1343 

~/huggingface/datasets/src/datasets/features/image.py in decode_example(self, value, token_per_repo_id)
    187             image = PIL.Image.open(BytesIO(bytes_))
    188         image.load()  # to avoid "Too many open files" errors
--> 189         if image.getexif().get(PIL.Image.ExifTags.Base.Orientation) is not None:
    190             image = PIL.ImageOps.exif_transpose(image)
    191         if self.mode and self.mode != image.mode:

~/huggingface/datasets/venv/lib/python3.9/site-packages/PIL/Image.py in __getattr__(name)
     75                 )
     76                 return categories[name]
---> 77         raise AttributeError(f"module '{__name__}' has no attribute '{name}'")
     78 
     79 

AttributeError: module 'PIL.Image' has no attribute 'ExifTags'
```

### Environment info

Since datasets 2.19.0
Batched mapping does not raise an error if values for an existing column are empty
State: open

### Describe the bug

Using `Dataset.map(fn, batched=True)` allows resizing the dataset by returning a dict of lists, all of which must be the same size. If they are not the same size, an error like `pyarrow.lib.ArrowInvalid: Column 1 named x expected length 1 but got length 0` is raised.

This is not the case if the function returns an empty list for an existing column in the dataset. In that case, the dataset is silently resized to 0 rows.

### Steps to reproduce the bug

MWE:

```
import datasets
data = datasets.Dataset.from_dict({"test": [1]})

def mapping_fn(examples):
    return {"test": [], "y": [1]}

data = data.map(mapping_fn, batched=True)
print(len(data))
```

Note  that when returning `"x": []`, the error is raised correctly, also when returning `"test": [1,2]`.

### Expected behavior

Expected an exception: `pyarrow.lib.ArrowInvalid: Column 1 named test expected length 1 but got length 0` or `pyarrow.lib.ArrowInvalid: Column 2 named y expected length 0 but got length 1`.

Any exception would be acceptable.

### Environment info

- `datasets` version: 2.19.1
- Platform: Linux-5.4.0-153-generic-x86_64-with-glibc2.31
- Python version: 3.11.8
- `huggingface_hub` version: 0.22.2
- PyArrow version: 15.0.2
- Pandas version: 2.2.1
- `fsspec` version: 2024.2.0
Unpin hfh
State: closed

Needed to use those in dataset-viewer:

- dev version of hfh https://github.com/huggingface/dataset-viewer/pull/2781: don't span the hub with /paths-info requests
- dev version of datasets at https://github.com/huggingface/datasets/pull/6875: don't write too big logs in the viewer

close https://github.com/huggingface/datasets/issues/6863
datasets.BuilderConfig does not work.
State: closed

### Describe the bug

I custom a BuilderConfig and GeneratorBasedBuilder.
Here is the code for BuilderConfig
```
class UIEConfig(datasets.BuilderConfig):

    def __init__(
            self,
            *args,
            data_dir=None,
            instruction_file=None,
            instruction_strategy=None,
            task_config_dir=None,
            num_examples=None,
            max_num_instances_per_task=None,
            max_num_instances_per_eval_task=None,
            over_sampling=None,
            **kwargs
    ):
        super().__init__(*args, **kwargs)
        self.data_dir = data_dir
        self.num_examples = num_examples
        self.over_sampling = over_sampling
        self.instructions = self._parse_instruction(instruction_file)
        self.task_configs = self._parse_task_config(task_config_dir)
        self.instruction_strategy = instruction_strategy
        self.max_num_instances_per_task = max_num_instances_per_task
        self.max_num_instances_per_eval_task = max_num_instances_per_eval_task
```
Besides, here is the code for GeneratorBasedBuilder.
```
class UIEInstructions(datasets.GeneratorBasedBuilder):
    VERSION = datasets.Version("2.0.0")
    BUILDER_CONFIG_CLASS = UIEConfig
    BUILDER_CONFIGS = [
        UIEConfig(name="default", description="Default config for NaturalInstructions")
    ]
    DEFAULT_CONFIG_NAME = "default"
```

Here is the load_dataset
```
raw_datasets = load_dataset(
        os.path.join(CURRENT_DIR, "uie_dataset.py"),
        data_dir=data_args.data_dir,
        task_config_dir=data_args.task_config_dir,
        instruction_file=data_args.instruction_file,
        instruction_strategy=data_args.instruction_strategy,
        cache_dir=data_cache_dir,  # for debug, change dataset size, otherwise open it
        max_num_instances_per_task=data_args.max_num_instances_per_task,
        max_num_instances_per_eval_task=data_args.max_num_instances_per_eval_task,
        num_examples=data_args.num_examples,
        over_sampling=data_args.over_sampling
    )
```
Finally, I met the error.
```
BuilderConfig UIEConfig(name='default', version=0.0.0, data_dir=None, data_files=None, description='Default config for NaturalInstructions') doesn't have a 'task_config_dir' key.
```

I debugged the code, but I find the parameters added by me may not work.

### Steps to reproduce the bug

https://github.com/BeyonderXX/InstructUIE/blob/master/src/uie_dataset.py

### Expected behavior

```
BuilderConfig UIEConfig(name='default', version=0.0.0, data_dir=None, data_files=None, description='Default config for NaturalInstructions') doesn't have a 'task_config_dir' key.
```

### Environment info

torch                     2.3.0+cu118
transformers              4.40.1
python 3.8
Improve performance of JSON loader
State: closed
Labels: name: enhancement

As reported by @natolambert, loading regular JSON files with `datasets` shows poor performance.

The cause is that we use the `json` Python standard library instead of other faster libraries. See my old comment: https://github.com/huggingface/datasets/pull/2638#pullrequestreview-706983714
> There are benchmarks that compare different JSON packages, with the Standard Library one among the worst performant: 
> - https://github.com/ultrajson/ultrajson#benchmarks
> - https://github.com/ijl/orjson#performance

I remember having a discussion about this and it was decided that it was better not to include an additional dependency on a 3rd-party library.

However:
- We already depend on `pandas` and `pandas` depends on `ujson`: so we have an indirect dependency on `ujson`
- Even if the above were not the case, we always could include `ujson` as an optional extra dependency, and check at runtime if it is installed to decide which library to use, either json or ujson
Example on Semantic segmentation contains bug
State: open

### Describe the bug

https://huggingface.co/docs/datasets/en/semantic_segmentation shows wrong example with torchvision transforms.
Specifically, as one can see in screenshot below, the object boundaries have weird colors. 

<img width="689" alt="image" src="https://github.com/huggingface/datasets/assets/4803565/59aa0e2c-2e3e-415b-9d42-2314044c5aee">


Original example with `albumentations` is correct 
<img width="705" alt="image" src="https://github.com/huggingface/datasets/assets/4803565/27dbd725-cea5-4e48-ba59-7050c3ce17b3">


That is because `torch vision.transforms.Resize` interpolates with bilinear everything which is wrong when used for segmentation labels - you just cannot mix them. Overall, `torchvision.transforms` is designed for classification only and cannot be used to images and masks together, unless you write two separate branches of augmentations.


The correct way would be to use `v2` version of transforms and convert the segmentation labels to https://pytorch.org/vision/main/generated/torchvision.tv_tensors.Mask.html#torchvision.tv_tensors.Mask object




### Steps to reproduce the bug

Go to the website.
<img width="689" alt="image" src="https://github.com/huggingface/datasets/assets/4803565/ea1276d0-d69a-48cf-b9c2-cd61217815ef">

https://huggingface.co/docs/datasets/en/semantic_segmentation

### Expected behavior

Results, similar to `albumentation`. Or remove the torch vision part altogether. Or use `kornia` instead.

### Environment info

Irrelevant
Dataset 'rewardsignal/reddit_writing_prompts' doesn't exist on the Hub
State: closed

### Describe the bug

The dataset `rewardsignal/reddit_writing_prompts` is missing in Huggingface Hub.

### Steps to reproduce the bug

```
from datasets import load_dataset

prompt_response_dataset = load_dataset("rewardsignal/reddit_writing_prompts", data_files="prompt_responses_full.csv", split='train[:80%]')
```

### Expected behavior

DatasetNotFoundError: Dataset 'rewardsignal/reddit_writing_prompts' doesn't exist on the Hub or cannot be accessed

### Environment info

Nothing to do with versions
Fix load_dataset for data_files with protocols other than HF
State: closed

Fixes huggingface/datasets/issues/6598

I've added a new test case and a solution. Before applying the solution the test case was failing with the same error described in the linked issue.

MRE:
```
pip install "datasets[s3]"
python -c "from datasets import load_dataset; load_dataset('csv', data_files={'train': 's3://noaa-gsod-pds/2024/A5125600451.csv'})"
```