Minimartzz/github-issues huggingface dataset:
ArrayXD with None as leading dim incompatible with DatasetCardData
State: open

### Describe the bug

Creating a dataset with ArrayXD features leads to errors when downloading from hub due to DatasetCardData removing the Nones

@lhoestq 

### Steps to reproduce the bug

```python
import numpy as np
from datasets import Array2D, Dataset, Features, load_dataset

def examples_generator():
    for i in range(4):
        yield {
            "array_1d": np.zeros((10,1), dtype="uint16"),
            "array_2d": np.zeros((10, 1), dtype="uint16"),
        }

features = Features(array_1d=Array2D((None,1), "uint16"), array_2d=Array2D((None, 1), "uint16"))
dataset = Dataset.from_generator(examples_generator, features=features)
dataset.push_to_hub("alex-hh/test_array_1d2d")
ds = load_dataset("alex-hh/test_array_1d2d")
```

Source of error appears to be DatasetCardData.to_dict invoking DatasetCardData._remove_none
```python
from huggingface_hub import DatasetCardData
from datasets.info import DatasetInfosDict

dataset_card_data = DatasetCardData()
DatasetInfosDict({"default": dataset.info.copy()}).to_dataset_card_data(dataset_card_data)
print(dataset_card_data.to_dict())  # removes Nones in shape
```

### Expected behavior

Should be possible to load datasets saved with shape None in leading dimension

### Environment info

3.0.2 and latest huggingface_hub

Comments:
It looks like `CardData` in `huggingface_hub` removes None values where it shouldn't. Indeed it calls `_remove_none` on the return of `to_dict()`:

```python
    def to_dict(self) -> Dict[str, Any]:
        """Converts CardData to a dict.

        Returns:
            `dict`: CardData represented as a dictionary ready to be dumped to a YAML
            block for inclusion in a README.md file.
        """

        data_dict = copy.deepcopy(self.__dict__)
        self._to_dict(data_dict)
        return _remove_none(data_dict)
```

Would it be ok to remove `list()` from being scanned in `_remove_none` ? it could also be a specific behavior to DatasetCardData if necessary @Wauplin 

I have actually no idea why none values are removed in model and dataset card data... :see_no_evil:
Looks like `_remove_none` has been introduced at the same time as the entire repocard module (see https://github.com/huggingface/huggingface_hub/pull/940). I would be tempted to remove `_remove_none` entirely actually and only remove "top-level" None values (i.e. if something like `pipeline_tag=None` due to a default value in kwargs => we remove it). Hard to tell what could be the side effects but I'm not against trying.


However, I'm not really in favor in making an exception only for lists. It would mean that tuples, sets and dicts are filtered but not lists, which is pretty inconsistent.

let's do it for top level attributes yes

I opened https://github.com/huggingface/huggingface_hub/pull/2626 to address it :)

thanks !
`push_to_hub` overwrite argument
State: closed
Labels: description: New feature or request
name: enhancement

### Feature request

Add an `overwrite` argument to the `push_to_hub` method.

### Motivation

I want to overwrite a repo without deleting it on Hugging Face. Is this possible? I couldn't find anything in the documentation or tutorials.

### Your contribution

I can create a PR.

Comments:
Hi ! Do you mean deleting all the files ? or erasing the repository git history before push_to_hub ?

Hi! I meant the latter.

I don't think there is a `huggingface_hub` utility to erase the git history, cc @Wauplin maybe ?

What is the goal exactly of deleting all the git history without deleting the repo? 

You can use [`super_squash_commit`](https://huggingface.co/docs/huggingface_hub/main/en/package_reference/hf_api#huggingface_hub.HfApi.super_squash_history) to squash all the commits into a single one, hence deleting the git history. This is not exactly what you asked for since it squashes the commits for a specific revision (example: "all commits on main"). This means that if other branches exists, they are kept the same. Also if some PRs are already opened on the repo, they will become unmergeable since the commits will have diverted.

So the solution is:

```python
from huggingface_hub import HfApi
repo_id = "username/dataset_name"
ds.push_to_hub(repo_id)
HfApi().super_squash_commit(repo_id)
```

This way you erase previous git history to end up with only 1 commit containing your dataset.
Still, I'd be curious why it's important in your case. Is it to save storage space ? or to disallow loading old versions of the data ?

Thanks, everyone! I am building a new dataset and playing around with column names, splits, etc. Sometimes I push to the hub to share it with other teammates, I don't want those variations to be part of the repo. Deleting the repo from the website takes a little time, but it also loses repo settings that I have set, since I always set it to public with manually approved requests.

BTW, I had to write `HfApi().super_squash_history(repo_id, repo_type="dataset")`, but otherwise it works.

@ceferisbarov just to let you know, recreating a gated repo + granting access to your teammates is something that you can automate with something like this (not fully tested but should work):

```py
from huggingface_hub import HfApi

api = HfApi()
api.delete_repo(repo_id, repo_type="dataset", missing_ok=True)
api.create_repo(repo_id, repo_type="dataset", private=False)
api.update_repo_settings(repo_id, repo_type="dataset", gated="manual")
for user in ["user1", "user2"]  # list of teammates
    api.grant_access(repo_id, user, repo_type="dataset")
```

I think it'd be a better solution than squashing commits (which is more of a hack), typically if you are using the dataset viewer.

This is great, @Wauplin. If we can achieve this with HfApi, then we probably don't need to add another parameter to push_to_hub. I am closing the issue.
Feature Request: Add functionality to pass split types like train, test in DatasetDict.map
State: closed

Hello datasets!

We often encounter situations where we need to preprocess data differently depending on split types such as train, valid, and test. 

However, while DatasetDict.map has features to pass rank or index, there's no functionality to pass split types. 
Therefore, I propose adding a 'with_splits' parameter to DatasetDict, which would allow passing the split type through fn_kwargs.

Comments:
incompatibily issue when using load_dataset with datasets==3.0.1
State: open

### Describe the bug

There is a bug when using load_dataset with dataset version at 3.0.1 .
Please see below in the "steps to reproduce the bug".
To resolve the bug, I had to downgrade to version 2.21.0
OS: Ubuntu 24 (AWS instance)
Python: same bug under 3.12 and 3.10


The error I had was:
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/ubuntu/miniconda3/envs/maxence_env/lib/python3.10/site-packages/datasets/load.py", line 2096, in load_dataset
    builder_instance.download_and_prepare(
  File "/home/ubuntu/miniconda3/envs/maxence_env/lib/python3.10/site-packages/datasets/builder.py", line 924, in download_and_prepare
    self._download_and_prepare(
  File "/home/ubuntu/miniconda3/envs/maxence_env/lib/python3.10/site-packages/datasets/builder.py", line 1647, in _download_and_prepare
    super()._download_and_prepare(
  File "/home/ubuntu/miniconda3/envs/maxence_env/lib/python3.10/site-packages/datasets/builder.py", line 977, in _download_and_prepare
    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
  File "/home/ubuntu/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_6_0/cb17afd34f5799f97e8f48398748f83006335b702bd785f9880797838d541b81/common_voice_6_0.py", line 159, in _split_generators
    archive_path = dl_manager.download(self._get_bundle_url(self.config.name, bundle_url_template))
  File "/home/ubuntu/miniconda3/envs/maxence_env/lib/python3.10/site-packages/datasets/download/download_manager.py", line 150, in download
    download_config = self.download_config.copy()
  File "/home/ubuntu/miniconda3/envs/maxence_env/lib/python3.10/site-packages/datasets/download/download_config.py", line 73, in copy
    return self.__class__(**{k: copy.deepcopy(v) for k, v in self.__dict__.items()})
TypeError: DownloadConfig.__init__() got an unexpected keyword argument 'ignore_url_params'

### Steps to reproduce the bug

1. install dataset with ```pip install datasets --upgrade```
2. launch python; from datasets import loaad_dataset
3. run load_dataset("mozilla-foundation/common_voice_6_0")
4. exit python
5. uninstall datasets; then ```pip install datasets==2.21.0```
6. launch python; from datasets import loaad_dataset
7. run load_dataset("mozilla-foundation/common_voice_6_0")
8. Everything runs great now

### Expected behavior

Be able to download a dataset without error

### Environment info

Copy-and-paste the text below in your GitHub issue.

- `datasets` version: 3.0.1
- Platform: Linux-6.8.0-1017-aws-x86_64-with-glibc2.39
- Python version: 3.12.4
- `huggingface_hub` version: 0.26.0
- PyArrow version: 17.0.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.6.1

Comments:
Hi! I'm also getting the same issue - have you been able to find a solution to this? 

From what I remember, I stayed at the "downgraded" version of dataset (2.21.0)
No need for dataset_info
State: closed

save a useless call to /api/datasets/repo_id

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7234). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

merging this one for now, let me know if you'd like to see additional changes for error handling (I'll take care of them before doing a release)
Video support
State: closed

(wip and experimental)

adding the `Video` type based on `VideoReader` from `decord`

```python
>>>from datasets import load_dataset
>>> ds = load_dataset("path/to/videos", split="train").with_format("torch")
>>> print(ds[0]["video"])
<decord.video_reader.VideoReader object at 0x337a47910>
>>> print(ds[0]["video"][0])
tensor([[[73, 73, 73],
         [73, 73, 73],
         [73, 73, 73],
         ...,
         [23, 23, 23],
         [23, 23, 23],
         [23, 23, 23]]], dtype=torch.uint8)
```

the storage is the same as for audio and images: `{"path": pa.string(), "bytes": pa.binary()}` and I did a small to keep the hf:// URL in the "path" field if possible, this way the viewer can link to fiels on the hub if possible

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7230). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
Composite (multi-column) features
State: open
Labels: description: New feature or request
name: enhancement

### Feature request

Structured data types (graphs etc.) might often be most efficiently stored as multiple columns, which then need to be combined during feature decoding

Although it is currently possible to nest features as structs, my impression is that in particular when dealing with e.g. a feature composed of multiple numpy array / ArrayXD's, it would be more efficient to store each ArrayXD as a separate column (though I'm not sure by how much)

Perhaps specification / implementation could be supported by something like:

```
features=Features(**{("feature0", "feature1")=Features(feature0=Array2D((None,10), dtype="float32"), feature1=Array2D((None,10), dtype="float32"))
```

### Motivation

Defining efficient composite feature types based on numpy arrays for representing data such as graphs with multiple node and edge attributes is currently challenging.

### Your contribution

Possibly able to contribute

Comments:
Add R as a How to use from the Polars (R) Library as an option
State: open
Labels: description: New feature or request
name: enhancement

### Feature request

The boiler plate code to access a dataset via the hugging face file system is very useful. Please addd 


## Add Polars (R) option
The equivailent code works, because the [Polars-R](https://github.com/pola-rs/r-polars) wrapper has hugging faces funcitonaliy as well.

```r
library(polars)

df <- pl$read_parquet("hf://datasets/SALURBAL/core__admin_cube_public/core__admin_cube_public.parquet")
```

## Polars (python) option
![image](https://github.com/user-attachments/assets/8f1bcd19-e578-4b18-b324-7cc00b80ac0a)


## Libraries Currently

![image](https://github.com/user-attachments/assets/0cf50063-f9db-443c-97b4-3ef0664b6e6e)




### Motivation

There are many data/analysis/research/statistics teams (particularly in academia and pharma) that use R as the default language. R has great integration with most of the newer data techs (arrow, parquet, polars) and having this included could really help in bringing this community into the hugging faces ecosystem.

**This is a small/low-hanging-fruit front end change but would make a big impact expanding the community**

### Your contribution

I am not sure which repositroy this should be in, but I have experience in R, Python and JS and happy to submit a PR in the appropriate repository. 

Comments:
Huggingface GIT returns null as Content-Type instead of application/x-git-receive-pack-result
State: open

### Describe the bug

We push changes to our datasets programmatically. Our git client jGit reports that the hf git server returns null as Content-Type after a push.

### Steps to reproduce the bug

A basic kotlin application:
```
   val person = PersonIdent(
        "padmalcom",
        "padmalcom@sth.com"
    )

    val cp = UsernamePasswordCredentialsProvider(
        "padmalcom",
        "mysecrettoken"
    )

    val git =
        KGit.cloneRepository {
            setURI("https://huggingface.co/datasets/sth/images")
            setTimeout(60)
            setProgressMonitor(TextProgressMonitor())
            setCredentialsProvider(cp)
        }

    FileOutputStream("./images/images.csv").apply { writeCsv(images) }
    git.add {
        addFilepattern("images.csv")
    }

    for (i in images) {
        FileUtils.copyFile(
            File("./files/${i.id}"),
            File("./images/${i.id + File(i.fileName).extension }")
        )
        git.add {
            addFilepattern("${i.id + File(i.fileName).extension }")
        }
    }

    val revCommit = git.commit {
        author = person
        message = "Uploading images at " + LocalDateTime.now()
            .format(DateTimeFormatter.ISO_DATE_TIME)
        setCredentialsProvider(cp)
    }

    val push = git.push {
        setCredentialsProvider(cp)
    }
```

### Expected behavior

The git server is expected to return the Content-Type _application/x-git-receive-pack-result_.

### Environment info

It is independent from the datasets library.

Comments:
Fallback to arrow defaults when loading dataset with custom features that aren't registered locally
State: open

### Describe the bug

Datasets allows users to create and register custom features.

However if datasets are then pushed to the hub, this means that anyone calling load_dataset without registering the custom Features in the same way as the dataset creator will get an error message.

It would be nice to offer a fallback in this case.

### Steps to reproduce the bug

```python
load_dataset("alex-hh/custom-features-example")
```
(Dataset creation process - must be run in separate session so that NewFeature isn't registered in session in which download is attempted:)

```python
from dataclasses import dataclass, field
import pyarrow as pa
from datasets.features.features import register_feature

from datasets import Dataset, Features, Value, load_dataset
from datasets import Feature

@dataclass
class NewFeature(Feature):
    _type: str = field(default="NewFeature", init=False, repr=False)
    def __call__(self):
        return pa.int32()

def examples_generator():
    for i in range(5):
        yield {"feature": i}

ds = Dataset.from_generator(examples_generator, features=Features(feature=NewFeature()))
ds.push_to_hub("alex-hh/custom-features-example")
register_feature(NewFeature, "NewFeature")
```



### Expected behavior

It would be nice, and offer greater extensibility, if there was some kind of graceful fallback mechanism in place for cases where user-defined features are stored in the dataset but not available locally.

### Environment info

3.0.2

Comments:
add CustomFeature base class to support user-defined features with encoding/decoding logic
State: closed

intended as fix for #7220 if this kind of extensibility is something that datasets is willing to support!


```python
from datasets.features.features import CustomFeature


class ListOfStrs(CustomFeature):
    requires_encoding = True
    def _encode_example(self, value):
        if isinstance(value, str):
            return [str]
        else:
            return value
feats = Features(strlist=ListOfStrs())
feats.encode_example({"strlist": "a"})["strlist"] == feats["strlist"].encode_example("a")
```

Comments:
@lhoestq would you be open to supporting this kind of extensibility?

I suggested a fix in https://github.com/huggingface/datasets/issues/7220 that would not necessarily require a parent class for custom features, lmk what you think
Custom features not compatible with special encoding/decoding logic
State: open

### Describe the bug

It is possible to register custom features using datasets.features.features.register_feature (https://github.com/huggingface/datasets/pull/6727)

However such features are not compatible with Features.encode_example/decode_example if they require special encoding / decoding logic because encode_nested_example / decode_nested_example checks whether the feature is in a fixed list of encodable types:

https://github.com/huggingface/datasets/blob/16a121d7821a7691815a966270f577e2c503473f/src/datasets/features/features.py#L1349

This prevents the extensibility of features to complex cases

### Steps to reproduce the bug

```python
class ListOfStrs:
    def encode_example(self, value):
        if isinstance(value, str):
            return [str]
        else:
            return value
feats = Features(strlist=ListOfStrs())
assert feats.encode_example({"strlist": "a"})["strlist"] = feats["strlist"].encode_example("a")}
```

### Expected behavior

Registered feature types should be encoded based on some property of the feature (e.g. requires_encoding)?

### Environment info

3.0.2

Comments:
I think you can fix this simply by replacing the line with hardcoded features with `hastattr(schema, "encode_example")` actually

#7284
ds.map(f, num_proc=10) is slower than df.apply
State: open

### Describe the bug

pandas columns: song_id, song_name
ds = Dataset.from_pandas(df)

def has_cover(song_name):
    if song_name is None or pd.isna(song_name):
        return False
    return 'cover' in song_name.lower()

df['has_cover'] = df.song_name.progress_apply(has_cover)
ds = ds.map(lambda x: {'has_cover': has_cover(x['song_name'])}, num_proc=10)

time cost: 
1. df.apply: 100%|██████████| 12500592/12500592 [00:13<00:00, 959825.47it/s]
2. ds.map: Map (num_proc=10):  31%
 3899028/12500592 [00:28<00:38, 222532.89 examples/s]

### Steps to reproduce the bug

pandas columns: song_id, song_name
ds = Dataset.from_pandas(df)

def has_cover(song_name):
    if song_name is None or pd.isna(song_name):
        return False
    return 'cover' in song_name.lower()

df['has_cover'] = df.song_name.progress_apply(has_cover)
ds = ds.map(lambda x: {'has_cover': has_cover(x['song_name'])}, num_proc=10)

### Expected behavior

ds.map is ~num_proc faster than df.apply

### Environment info

pandas: 2.2.2
datasets: 2.19.1

Comments:
Hi ! `map()` reads all the columns and writes the resulting dataset with all the columns as well, while df.column_name.apply only reads and writes one column and does it in memory. So this is speed difference is actually expected.

Moreover using multiprocessing on a dataset that lives in memory (from_pandas uses the same in-memory data as the pandas DataFrame while load_dataset or from_generator load from disk) requires to copy the data to each subprocess which can also be slow. Data loaded from disk don't need to be copied though since they work as a form of shared memory thanks to memory mapping.

However you can make you map() call much faster by making it read and write only the column you want:

```python
has_cover_ds = ds.map(lambda song_name: {'has_cover': has_cover(song_name)}, input_columns=["song_name"], remove_columns=ds.column_names)  # outputs a dataset with 1 column
ds = ds.concatenate_datasets([ds, has_cover_ds], axis=1)
```

and if your dataset is loaded from disk you can pass num_proc=10 and get a nice speed up as well (no need to copy the data to subprocesses)

Isn't there a way to do memory mapping with the in-memory dataset without saving it to disk?

Maybe saving it to a memory-mapped filesystem? It'd be like a trick to make datasets save to "disk" but actually it's memory. But it feels like there should be a better "automatic" way provided by `datasets`.
Iterable dataset map with explicit features causes slowdown for Sequence features
State: open

### Describe the bug

When performing map, it's nice to be able to pass the new feature type, and indeed required by interleave and concatenate datasets.

However, this can cause a major slowdown for certain types of array features due to the features being re-encoded.

This is separate to the slowdown reported in #7206 

### Steps to reproduce the bug

```
from datasets import Dataset, Features, Array3D, Sequence, Value
import numpy as np
import time
features=Features(**{"array0": Sequence(feature=Value("float32"), length=-1), "array1": Sequence(feature=Value("float32"), length=-1)})
dataset = Dataset.from_dict({f"array{i}": [np.zeros((x,), dtype=np.float32) for x in [5000,10000]*25] for i in range(2)}, features=features)
```

```
ds = dataset.to_iterable_dataset()
ds = ds.with_format("numpy").map(lambda x: x)
t0 = time.time()
for ex in ds:
    pass
t1 = time.time()
```
~1.5 s on main


```
ds = dataset.to_iterable_dataset()
ds = ds.with_format("numpy").map(lambda x: x, features=features)
t0 = time.time()
for ex in ds:
    pass
t1 = time.time()
```
~ 3 s on main

### Expected behavior

I'm not 100% sure whether passing new feature types to formatted outputs of map should be supported or not, but assuming it should, then there should be a cost-free way to specify the new feature type - knowing feature type is required by interleave_datasets and concatenate_datasets for example

### Environment info

3.0.2

Comments: