rIsHu009/github-issues-updated huggingface dataset:
Title: Make DatasetDict.column_names return a list instead of dict
Author: albertvillanova (MEMBER)
State: open
Created: 2024-01-09T10:45:17Z
Labels: enhancement

Currently, `DatasetDict.column_names` returns a dict, with each split name as keys and the corresponding list of column names as values.

However, by construction, all splits have the same column names.

I think it makes more sense to return a single list with the column names, which is the same for all the split keys.

Comments:
Title: No online docs for 2.16 release
Author: albertvillanova (MEMBER)
State: closed
Created: 2024-01-09T07:43:30Z
Labels: bug

documentation

We do not have the online docs for the latest minor release 2.16 (2.16.0 nor 2.16.1).

In the online docs, the latest version appearing is 2.15.0: https://huggingface.co/docs/datasets/index

![Screenshot from 2024-01-09 08-43-08](https://github.com/huggingface/datasets/assets/8515462/83613222-867f-41f4-8833-7a4a76582f44)

Comments:
Though the `build / build_main_documentation` CI job ran for 2.16.0: https://github.com/huggingface/datasets/actions/runs/7300836845/job/19896275099 🤔 

Yes, I saw it. Maybe @mishig25 can give us some hint...

fixed https://huggingface.co/docs/datasets/v2.16.0/en/index

Still missing 2.16.1.

> Still missing 2.16.1.

re-running the doc-buld job for the missing ones should fix



Re-running the job for the 2.16.1 release: https://github.com/huggingface/datasets/actions/runs/7365231552/job/20310278583

Fixed for 2.16.1: https://huggingface.co/docs/datasets/v2.16.1/en/index
Title: WebDataset ignores features defined in YAML or passed to load_dataset
Author: lhoestq (MEMBER)
State: closed
Created: 2024-01-08T11:24:21Z

we should not override if the features exist already

https://github.com/huggingface/datasets/blob/d26abadce0b884db32382b92422d8a6aa997d40a/src/datasets/packaged_modules/webdataset/webdataset.py#L78-L85

Comments:
Title: keep_in_memory=True does not seem to work
Author: kopyl (NONE)
State: open
Created: 2024-01-08T08:03:58Z

UPD: [Fixed](https://github.com/huggingface/datasets/issues/6568#issuecomment-1880817794) . But a new issue came up :(

Comments:
Seems like I just used the old code which did not have `keep_in_memory=True` argument, sorry.

Although i encountered a different problem – at 97% my python process just hung for around 11 minutes with no logs (when running dataset.map without `keep_in_memory=True` over around 3 million of dataset samples)...

Can you open a new issue and provide a bit more details ? What kind of map operations did you run ?

Hey. I will try to find some free time to describe it.

(can't do it now, cause i need to reproduce it myself to be sure about everything, which requires spinning a new Azuree VM, copying a huge dataset to drive from network disk for a long time etc...)

@lhoestq loading dataset like this does not spawn 50 python processes:

```
datasets.load_dataset("/preprocessed_2256k/train", num_proc=50)
```

I have 64 vCPU so i hoped it could speed up the dataset loading...

My dataset onlly has images and metadata.csv with text column alongside image file path column

now noticed
```
'Setting num_proc from 50 back to 1 for the train split to disable multiprocessing as it only contains one shard
```

Any way to work around this?

@lhoestq thanks, [this helped](https://github.com/huggingface/datasets/blob/9d6d16117a30ba345b0236407975f701c5b288d4/src/datasets/arrow_dataset.py#L1053)
Title: AttributeError: 'str' object has no attribute 'to'
Author: andysingal (NONE)
State: closed
Created: 2024-01-08T06:40:21Z

### Describe the bug

```
--------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
[<ipython-input-6-80c6086794e8>](https://localhost:8080/#) in <cell line: 10>()
      8 		  report_to="wandb")
      9 
---> 10 trainer = Trainer(
     11     model=model,
     12     args=training_args,

1 frames
[/usr/local/lib/python3.10/dist-packages/transformers/trainer.py](https://localhost:8080/#) in _move_model_to_device(self, model, device)
    688 
    689     def _move_model_to_device(self, model, device):
--> 690         model = model.to(device)
    691         # Moving a model to an XLA device disconnects the tied weights, so we have to retie them.
    692         if self.args.parallel_mode == ParallelMode.TPU and hasattr(model, "tie_weights"):

AttributeError: 'str' object has no attribute 'to'
```

### Steps to reproduce the bug

here is the notebook:
```
https://colab.research.google.com/drive/10JDBNsLlYrQdnI2FWfDK3F5M8wvVUDXG?usp=sharing

```


### Expected behavior

run the Training 

### Environment info

Colab Notebook , T4

Comments:
I think you are reporting an issue with the `transformers` library. Note this is the repository of the `datasets` library. I recommend that you open an issue in their repository: https://github.com/huggingface/transformers/issues

EDIT: I have not the rights to transfer the issue
~~I am transferring your issue to their repository.~~

Thanks, I hope someone from transformers library addresses this issue.

On Mon, Jan 8, 2024 at 15:29 Albert Villanova del Moral <
***@***.***> wrote:

> I think you are reporting an issue with the transformers library. Note
> this is the repository of the datasets library. I am transferring your
> issue to their repository.
>
> —
> Reply to this email directly, view it on GitHub
> <https://github.com/huggingface/datasets/issues/6567#issuecomment-1880688586>,
> or unsubscribe
> <https://github.com/notifications/unsubscribe-auth/AE4LJNOYMD6WJMXFKPMH6DLYNO7PJAVCNFSM6AAAAABBQ63HWOVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTQOBQGY4DQNJYGY>
> .
> You are receiving this because you authored the thread.Message ID:
> ***@***.***>
>


@andysingal, I recommend that you open an issue in their repository: https://github.com/huggingface/transformers/issues
I don't have the rights to transfer this issue to their repo.
Title: `Dataset.filter` missing `with_rank` parameter
Author: kopyl (NONE)
State: closed
Created: 2024-01-06T23:48:13Z

### Describe the bug

The issue shall be open: https://github.com/huggingface/datasets/issues/6435

When i try to pass `with_rank` to `Dataset.filter()`, i get this:

`Dataset.filter() got an unexpected keyword argument 'with_rank'`

### Steps to reproduce the bug

Run notebook:

https://colab.research.google.com/drive/1WUNKph8BdP0on5ve3gQnh_PE0cFLQqTn?usp=sharing

### Expected behavior

Should work?

### Environment info

NVIDIA RTX 4090

Comments:
Thanks for reporting! I've opened a PR with a fix

@mariosasko thank you very much :)
Title: datasets.DownloadMode.FORCE_REDOWNLOAD use cache to download dataset features with load_dataset function
Author: LsTam91 (NONE)
State: open
Created: 2024-01-05T19:10:25Z

### Describe the bug

I have updated my dataset by adding a new feature, and push it to the hub. When I want to download it on my machine which contain the old version by using `datasets.load_dataset("your_dataset_name", download_mode=datasets.DownloadMode.FORCE_REDOWNLOAD)` I get an error (paste bellow).

Seems that the load_dataset function still use the old features schema instead of downloading everything new from the HUB.

I find a way to go around this issue by manually deleting the old dataset cache. But from my understanding of `datasets.DownloadMode.FORCE_REDOWNLOAD` option, the dataset cache should be ignored.

### Steps to reproduce the bug

1. Download your dataset in your machine using `datasets.load_dataset`
2. Create a new feature in your dataset and push it to the hub
3. On the same machine redownload your dataset using `datasets.load_dataset("your_dataset_name", download_mode=datasets.DownloadMode.FORCE_REDOWNLOAD)`

### Expected behavior

`
ValueError: Couldn't cast
id: string
level: string
context: list<element: string>
  child 0, element: string
type: string
answer: string
question: string
supporting_facts: list<element: string>
  child 0, element: string
fra_answer: string
fra_question: string
-- schema metadata --
huggingface: '{"info": {"features": {"id": {"dtype": "string", "_type": "' + 490
to
{'id': Value(dtype='string', id=None), 'level': Value(dtype='string', id=None), 'context': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'type': Value(dtype='string', id=None), 'answer': Value(dtype='string', id=None), 'question': Value(dtype='string', id=None), 'supporting_facts': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}
because column names don't match

The above exception was the direct cause of the following exception:

DatasetGenerationError
...
DatasetGenerationError: An error occurred while generating the dataset`

### Environment info

datasets-2.16.1 huggingface-hub-0.20.2

Comments:
Title: Document YAML configuration with "data_dir"
Author: severo (COLLABORATOR)
State: open
Created: 2024-01-05T14:03:33Z
Labels: documentation

See https://huggingface.co/datasets/uonlp/CulturaX/discussions/15#6597e83f185db94370d6bf50 for reference

Comments:
In particular, I would like to have an example of how to replace the following configuration (from https://huggingface.co/docs/hub/datasets-manual-configuration#splits)

```
---
configs:
- config_name: default
  data_files:
  - split: train
    path: "data/*.csv"
  - split: test
    path: "holdout/*.csv"
---
```

with the `data_dir` field.
Title: Support Video 
Author: yuvalkirstain (NONE)
State: closed
Created: 2024-01-04T13:10:58Z
Labels: duplicate

enhancement

### Feature request

HF datasets are awesome in supporting text and images. Will be great to see such a support in videos :) 

### Motivation

Video generation :) 

### Your contribution

Will probably be limited to raising this feature request ;)

Comments:
duplicate of #5225
Title: Fix imagefolder with one image
Author: lhoestq (MEMBER)
State: closed
Created: 2024-01-03T13:13:02Z

A dataset repository with one image and one metadata file was considered a JSON dataset instead of an ImageFolder dataset. This is because we pick the dataset type with the most compatible data file extensions present in the repository and it results in a tie in this case.

e.g. for https://huggingface.co/datasets/multimodalart/repro_1_image

I fixed this by deprioritizing metadata files in the count.

fix https://github.com/huggingface/datasets/issues/6545

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6556). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

Fixed in dataset viewer: https://huggingface.co/datasets/multimodalart/repro_1_image

<img width="682" alt="Capture d’écran 2024-02-12 à 22 57 08" src="https://github.com/huggingface/datasets/assets/1676121/be9a8dbc-2d78-4ffc-aed4-293a7c57bc0d">
Title: Cannot import name 'load_dataset' from .... module ‘datasets’
Author: ciaoyizhen (NONE)
State: closed
Created: 2024-01-03T08:18:21Z

### Describe the bug


use python -m pip install datasets to install

### Steps to reproduce the bug

from datasets import load_dataset

### Expected behavior

it doesn't work

### Environment info

datasets version==2.15.0
python == 3.10.12
linux  version I don't know??

Comments:
I don't know  My conpany conputer cannot work. but in my computer, it work?

Do you have a folder in your working directory called datasets?
Title: Loading a dataset from Google Colab hangs at "Resolving data files".
Author: KelSolaar (NONE)
State: closed
Created: 2024-01-03T02:18:17Z

### Describe the bug

Hello,

I'm trying to load a dataset from Google Colab but the process hangs at `Resolving data files`:

![image](https://github.com/huggingface/datasets/assets/99779/7175ad85-e571-46ed-9f87-92653985777d)

It is happening when the `_get_origin_metadata` definition is invoked:

```python
def _get_origin_metadata(
    data_files: List[str],
    max_workers=64,
    download_config: Optional[DownloadConfig] = None,
) -> Tuple[str]:
    return thread_map(
        partial(_get_single_origin_metadata, download_config=download_config),
        data_files,
        max_workers=max_workers,
        tqdm_class=hf_tqdm,
        desc="Resolving data files",
        disable=len(data_files) <= 16,
```

The thread is then stuck at `waiter.acquire()` in the builtin `threading.py` file.

I can load the dataset just fine on my machine.

Cheers,

Thomas


### Steps to reproduce the bug

In Google Colab:

```python
!pip install datasets
from datasets import load_dataset

dataset = load_dataset("colour-science/color-checker-detection-dataset")
```

### Expected behavior

The dataset should be loaded.

### Environment info

- `datasets` version: 2.16.1
- Platform: Linux-6.1.58+-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.20.1
- PyArrow version: 10.0.1
- Pandas version: 1.5.3
- `fsspec` version: 2023.6.0

Comments:
This bug comes from the `huggingface_hub` library, see: https://github.com/huggingface/huggingface_hub/issues/1952

A fix is provided at https://github.com/huggingface/huggingface_hub/pull/1953. Feel free to install `huggingface_hub` from this PR, or wait for it to be merged and the new version of `huggingface_hub` to be released

Thanks!
Title: Loading from hf hub with clearer error message
Author: thomwolf (MEMBER)
State: open
Created: 2024-01-02T13:26:34Z
Labels: enhancement

### Feature request

Shouldn't this kinda work ?
```
Dataset.from_json("hf://datasets/HuggingFaceTB/eval_data/resolve/main/eval_data_context_and_answers.json")
```
I got an error
```
File ~/miniconda3/envs/datatrove/lib/python3.10/site-packages/datasets/data_files.py:380, in resolve_pattern(pattern, base_path, allowed_extensions, download_config)
    378     if allowed_extensions is not None:
    379         error_msg += f" with any supported extension {list(allowed_extensions)}"
--> 380     raise FileNotFoundError(error_msg)
    381 return out

FileNotFoundError: Unable to find 'hf://datasets/HuggingFaceTB/eval_data/resolve/main/eval_data_context_and_answers.json'
(I'm logged in)
```

Fix: the correct path is
```
hf://datasets/HuggingFaceTB/eval_data/eval_data_context_and_answers.json
```

Proposal: raise a clearer error

### Motivation

Clearer error message

### Your contribution

Can open a PR

Comments:
Maybe we can add a helper message like `Maybe try again using "hf://path/without/resolve"` if the path contains `/resolve/` ?

e.g.

```
FileNotFoundError: Unable to find 'hf://datasets/HuggingFaceTB/eval_data/resolve/main/eval_data_context_and_answers.json'
It looks like you used parts of the URL of the file from the Hugging Face website, but you should remove the "/resolve/<revision>" part to have a valid `hf://` path.
Please try again using this path instead:
    hf://datasets/HuggingFaceTB/eval_data/eval_data_context_and_answers.json
```

and suggest `f"hf://datasets/HuggingFaceTB/eval_data@{revision}/eval_data_context_and_answers.json"` if revision != "main"

EDIT: I think this message should also be raised from the  `huggingface_hub`'s `HfFileSystem` implementation
Title: Skip if a dataset has issues
Author: hadianasliwa (NONE)
State: open
Created: 2023-12-31T12:41:26Z

### Describe the bug

Hello everyone,
I'm using **load_datasets** from **huggingface** to download the datasets and I'm facing an issue, the download starts but it reaches some state and then  fails with the following error:
Couldn't reach https://huggingface.co/datasets/wikimedia/wikipedia/resolve/4cb9b0d719291f1a10f96f67d609c5d442980dc9/20231101.ext/train-00000-of-00001.parquet

Failed to resolve \'huggingface.co\' ([Errno -3] Temporary failure in name resolution)"))')))


![image](https://github.com/huggingface/datasets/assets/143214684/8847d9cb-529e-4eda-9c76-282713dfa3af)

so I was wondering is there a parameter to be passed to load_dataset() to skip files that can't be downloaded??

### Steps to reproduce the bug

Parameter to be passed to load_dataset() of huggingface to skip files that can't be downloaded??

### Expected behavior

load_dataset() finishes without error

### Environment info

None

Comments:
It looks like a transient DNS issue. It should work fine now if you try again.

There is no parameter in load_dataset to skip failed downloads. In your case it would have skipped every single subsequent download until the DNS issue was resolved anyway.
Title: `image` column not automatically inferred if image dataset only contains 1 image
Author: apolinario (NONE)
State: closed
Created: 2023-12-30T16:17:29Z

### Describe the bug

By default, the standard Image Dataset maps out `file_name` to `image` when loading an Image Dataset. 

However, if the dataset contains only 1 image, this does not take place 

### Steps to reproduce the bug

Input
(dataset with one image `multimodalart/repro_1_image`) 
```py
from datasets import load_dataset
dataset = load_dataset("multimodalart/repro_1_image")
dataset
```

Output: 
```py
DatasetDict({
    train: Dataset({
        features: ['file_name', 'prompt'],
        num_rows: 1
    })
})
```

Input 
(dataset with 2+ images `multimodalart/repro_2_image`) 
```py
from datasets import load_dataset
dataset = load_dataset("multimodalart/repro_2_image")
dataset
```

Output:
```py
DatasetDict({
    train: Dataset({
        features: ['image', 'prompt'],
        num_rows: 2
    })
})
```

### Expected behavior

Expected to map `file_name`  → `image` for all dataset sizes, including 1. 

### Environment info

Both latest main and 2.16.0

Comments:
Title: Datasets : wikipedia 20220301.en error 
Author: ppx666 (NONE)
State: closed
Created: 2023-12-29T08:34:51Z

### Describe the bug

When I used load_dataset to download this data set, the following error occurred. The main problem was that the target data did not exist.

### Steps to reproduce the bug

1.I tried downloading directly.
```python
wiki_dataset = load_dataset("wikipedia", "20220301.en")
```
An exception occurred
```
MissingBeamOptions: Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, Spark, etc. More information about Apache Beam runners at https://beam.apache.org/documentation/runners/capability-matrix/
If you really want to run it locally because you feel like the Dataset is small enough, you can use the local beam runner called `DirectRunner` (you may run out of memory). 
Example of usage: 
	`load_dataset('wikipedia', '20220301.en', beam_runner='DirectRunner')`
```
2.I modified the code as prompted.
```python
wiki_dataset = load_dataset('wikipedia', '20220301.en', beam_runner='DirectRunner')
```
An exception occurred:
```
FileNotFoundError: Couldn't find file at https://dumps.wikimedia.org/enwiki/20220301/dumpstatus.json
```


### Expected behavior

I searched in the parent directory of the corresponding URL, but there was no corresponding "20220301" directory.
I really need this data set and hope to provide a download method.

### Environment info

python 3.8
datasets 2.16.0
apache-beam 2.52.0
dill 0.3.7

Comments:
Hi ! We now recommend using the `wikimedia/wikipedia` dataset, can you try loading this one instead ?

```python
wiki_dataset  = load_dataset("wikimedia/wikipedia", "20231101.en")
```

This bug has been fixed in `2.16.1` thanks to https://github.com/huggingface/datasets/pull/6544, feel free to update `datasets` and re-run your code :)

```
pip install -U datasets
```
Title: Extreme inefficiency for `save_to_disk` when merging datasets
Author: KatarinaYuan (NONE)
State: open
Created: 2023-12-29T00:44:35Z

### Describe the bug

Hi, I tried to merge in total 22M sequences of data, where each sequence is of maximum length 2000. I found that merging these datasets and then `save_to_disk` is extremely slow because of flattening the indices. Wondering if you have any suggestions or guidance on this. Thank you very much! 

### Steps to reproduce the bug

The source data is too big to demonstrate

### Expected behavior

The source data is too big to demonstrate

### Environment info

python                     3.9.0
datasets                  2.7.0
pytorch                   2.0.0 
tokenizers                0.13.1
transformers              4.31.0

Comments:
Concatenating datasets doesn't create any indices mapping - so flattening indices is not needed (unless you shuffle the dataset).
Can you share the snippet of code you are using to merge your datasets and save them to disk ?
Title: 'Repo card metadata block was not found' when loading a pragmeval dataset
Author: lambdaofgod (NONE)
State: open
Created: 2023-12-28T14:18:25Z

### Describe the bug

I can't load dataset subsets of 'pragmeval'.

The funny thing is I ran the dataset author's [colab notebook](https://colab.research.google.com/drive/1sg--LF4z7XR1wxAOfp0-3d4J6kQ9nj_A?usp=sharing) and it works just fine. I tried to install exactly the same packages that are installed on colab using poetry, so my environment info only differs from the one from colab in linux version - I still get the same bug outside colab.

### Steps to reproduce the bug

Install dependencies with poetry

pyproject.toml
```
[tool.poetry]
name = "project"
version = "0.1.0"
description = ""
authors = []

[tool.poetry.dependencies]
python = "^3.10"
datasets = "2.16.0"
pandas = "1.5.3"
pyarrow = "10.0.1"
huggingface-hub = "0.19.4"
fsspec = "2023.6.0"


[build-system]
requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api"
```

`poetry run python -c "import datasets; print(datasets.get_dataset_config_names('pragmeval'))`

prints ['default']

### Expected behavior

The command should print

```
['emergent',
 'emobank-arousal',
 'emobank-dominance',
 'emobank-valence',
 'gum',
 'mrda',
 'pdtb',
 'persuasiveness-claimtype',
 'persuasiveness-eloquence',
 'persuasiveness-premisetype',
 'persuasiveness-relevance',
 'persuasiveness-specificity',
 'persuasiveness-strength',
 'sarcasm',
 'squinky-formality',
 'squinky-implicature',
 'squinky-informativeness',
 'stac',
 'switchboard',
 'verifiability']
```

### Environment info

- `datasets` version: 2.16.0
- Platform: Linux-6.2.0-37-generic-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.19.4
- PyArrow version: 10.0.1
- Pandas version: 1.5.3
- `fsspec` version: 2023.6.0

Comments:
Title: Adding support for netCDF (*.nc) files
Author: shermansiu (NONE)
State: open
Created: 2023-12-27T09:27:29Z
Labels: enhancement

### Feature request

netCDF (*.nc) is a file format for storing multidimensional scientific data, which is used by packages like `xarray` (labelled multi-dimensional arrays in Python). It would be nice to have native support for netCDF in `datasets`.

### Motivation

When uploading *.nc files onto Huggingface Hub through the `datasets` API, I would like to be able to preview the dataset without converting it to another format.

### Your contribution

I can submit a PR, provided I have the time.

Comments:
Related to #3113 

Conceptually, we can use xarray to load the netCDF file, then xarray -> pandas -> pyarrow.

I'd still need to verify that such a conversion would be lossless, especially for multi-dimensional data.
Title: ted_talks_iwslt | Error: Config name is missing
Author: rayliuca (NONE)
State: closed
Created: 2023-12-26T00:38:18Z

### Describe the bug

Running load_dataset using the newest `datasets` library like below on the ted_talks_iwslt using year pair data will throw an error "Config name is missing"

see also:
https://huggingface.co/datasets/ted_talks_iwslt/discussions/3

likely caused by #6493, where the `and not config_kwargs` part in the if logic was removed 
 https://github.com/huggingface/datasets/blob/ef3b5dd3633995c95d77f35fb17f89ff44990bc4/src/datasets/builder.py#L512

### Steps to reproduce the bug

run: 
```python
load_dataset("ted_talks_iwslt", language_pair=("ja", "en"), year="2015")
```

### Expected behavior

Load the data without error

### Environment info

datasets 2.16.0

Comments:
Hi ! Thanks for reporting. I opened https://github.com/huggingface/datasets/pull/6544 to fix this

We just released 2.16.1 with a fix:

```
pip install -U datasets
```
Title: Impossible to save a mapped dataset to disk
Author: kopyl (NONE)
State: open
Created: 2023-12-23T15:18:27Z

### Describe the bug

I want to play around with different hyperparameters when training but don't want to re-map my dataset with 3 million samples each time for tens of hours when I [fully fine-tune SDXL](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_sdxl.py).

After I do the mapping like this:
```
train_dataset = train_dataset.map(compute_embeddings_fn, batched=True)
train_dataset = train_dataset.map(
    compute_vae_encodings_fn,
    batched=True,
    batch_size=16,
)
```
and try to save it like this:
`train_dataset.save_to_disk("test")`
i get this error ([full traceback](https://pastebin.com/kq3vt739)):
```
TypeError: Object of type function is not JSON serializable
The format kwargs must be JSON serializable, but key 'transform' isn't.
```

But what is interesting is that pushing to hub works like that:
`train_dataset.push_to_hub("kopyl/mapped-833-icons-sdxl-1024-dataset", token=True)`
Here is the link of the pushed dataset: https://huggingface.co/datasets/kopyl/mapped-833-icons-sdxl-1024-dataset

### Steps to reproduce the bug

Here is the self-contained notebook:

https://colab.research.google.com/drive/1RtCsEMVcwWcMwlWURk_cj_9xUBHz065M?usp=sharing

### Expected behavior

It should be easily saved to disk

### Environment info

NVIDIA A100, Linux (NC24ads A100 v4 from Azure), CUDA 12.2.

[pip freeze](https://pastebin.com/QTNb6iru)

Comments:
I solved it with `train_dataset.with_format(None)`
But then faced some more issues (which i later solved too).

Huggingface does not seem to care, so I do. Here is an updated training script which saves a pre-processed (mapped) dataset to your local directory if you specify `--save_precomputed_data_dir=DIR_NAME`. Then use `--train_precomputed_data_dir` with the same dir to load it instead of `--dataset_name`.

[Proper SDXL trainer code](https://github.com/kopyl/diffusers/blob/main/examples/text_to_image/train_text_to_image_sdxl.py)
[Notebook for pre-computing a dataset and saving locally](https://colab.research.google.com/drive/17Yo08hePx-NlHs99RecdeiNc8CQg4O7l?usp=sharing)

Example:

1st run (nothing is pre-computed yet):
```
accelerate launch train_text_to_image_sdxl.py \
    --pretrained_model_name_or_path=stabilityai/stable-diffusion-xl-base-1.0 \
    --pretrained_vae_model_name_or_path=madebyollin/sdxl-vae-fp16-fix \
    --dataset_name=lambdalabs/pokemon-blip-captions \
    --save_precomputed_data_dir="test-5"
```

2nd run (the pre-computed dataset is saved to `test-5` directory):
```
accelerate launch train_text_to_image_sdxl.py \
    --pretrained_model_name_or_path=stabilityai/stable-diffusion-xl-base-1.0 \
    --pretrained_vae_model_name_or_path=madebyollin/sdxl-vae-fp16-fix \
    --train_precomputed_data_dir test-5
```

This way when you're gonna be using your pre-computed dataset you don't need to worry about re-mapping your dataset when you change an argument for your trainer script
Title: Impossible to only download a test split
Author: ysig (NONE)
State: open
Created: 2023-12-22T16:56:32Z

I've spent a significant amount of time trying to locate the split object inside my _split_generators() custom function.
Then after diving [in the code](https://github.com/huggingface/datasets/blob/5ff3670c18ed34fa8ddfa70a9aa403ae6cc9ad54/src/datasets/load.py#L2558) I realized that `download_and_prepare` is executed before! split is passed to the dataset builder in `as_dataset`.

If I'm not missing something, this seems like bad design, for the following use case:

> Imagine there is a huge dataset that has an evaluation test set and you want to just download and run just to compare your method.

Is there a current workaround that can help me achieve the same result?

Thank you,

Comments:
The only way right now is to load with streaming=True

This feature has been proposed for a long time. I'm looking forward to the implementation. On clusters `streaming=True` is not an option since we do not have Internet on compute nodes. See: https://github.com/huggingface/datasets/discussions/1896#discussioncomment-2359593
Title: BBox type
Author: lhoestq (MEMBER)
State: closed
Created: 2023-12-21T22:13:27Z

see [internal discussion](https://huggingface.slack.com/archives/C02EK7C3SHW/p1703097195609209)

Draft to get some feedback on a possible `BBox` feature type that can be used to get object detection bounding boxes data in one format or another.

```python
>>> from datasets import load_dataset, BBox
>>> ds = load_dataset("svhn", "full_numbers", split="train")
>>> ds[0]
{
  'image': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=107x46 at 0x126409BE0>,
  'digits': {'bbox': [[38, 1, 21, 40], [57, 3, 16, 40]], 'label': [4, 6]}
}
>>> ds = ds.rename_column("digits", "annotations").cast_column("annotations", BBox(format="coco"))
>>> ds[0]
{
  'image': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=107x46 at 0x147730070>,
  'annotations': [{'bbox': [38, 1, 21, 40], 'category_id': 4}, {'bbox': [57, 3, 16, 40], 'category_id': 6}]
}
```

note that it's a type for a list of bounding boxes, not just one - which would be needed to switch from a format to another using type casting.

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6525). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

closing in favor of other ideas that would not involve any typing
Title: Streaming the Pile: Missing Files
Author: FelixLabelle (NONE)
State: closed
Created: 2023-12-21T21:25:09Z

### Describe the bug

The pile does not stream, a "File not Found error" is returned. It looks like the Pile's files have been moved.

### Steps to reproduce the bug

To reproduce run the following code:

```
from datasets import load_dataset

dataset = load_dataset('EleutherAI/pile', 'en', split='train', streaming=True)
next(iter(dataset))
```

I get the following error:
`FileNotFoundError: https://the-eye.eu/public/AI/pile/train/00.jsonl.zst`

### Expected behavior

Return the data in a stream.

### Environment info

- `datasets` version: 2.12.0
- Platform: Windows-10-10.0.22621-SP0
- Python version: 3.11.5
- Huggingface_hub version: 0.15.1
- PyArrow version: 11.0.0
- Pandas version: 2.0.3

Comments:
Hello @FelixLabelle,

As you can see in the Community tab of the corresponding dataset, it is a known issue: https://huggingface.co/datasets/EleutherAI/pile/discussions/15

The data has been taken down due to reported copyright infringement.

Feel free to continue the discussion there.
Title: The order of the splits is not preserved
Author: albertvillanova (MEMBER)
State: closed
Created: 2023-12-21T11:17:27Z
Labels: bug

We had a regression and the order of the splits is not preserved. They are alphabetically sorted, instead of preserving original "train", "validation", "test" order.

Check: In branch "main"
```python
In [9]: dataset = load_dataset("adversarial_qa", '"adversarialQA")

In [10]: dataset
Out[10]: 
DatasetDict({
    test: Dataset({
        features: ['id', 'title', 'context', 'question', 'answers', 'metadata'],
        num_rows: 3000
    })
    train: Dataset({
        features: ['id', 'title', 'context', 'question', 'answers', 'metadata'],
        num_rows: 30000
    })
    validation: Dataset({
        features: ['id', 'title', 'context', 'question', 'answers', 'metadata'],
        num_rows: 3000
    })
})
```

Before (2.15.0) it was:
```python
DatasetDict({
    train: Dataset({
        features: ['id', 'title', 'context', 'question', 'answers', 'metadata'],
        num_rows: 30000
    })
    validation: Dataset({
        features: ['id', 'title', 'context', 'question', 'answers', 'metadata'],
        num_rows: 3000
    })
    test: Dataset({
        features: ['id', 'title', 'context', 'question', 'answers', 'metadata'],
        num_rows: 3000
    })
})
```

See issues: 
- https://huggingface.co/datasets/adversarial_qa/discussions/3
- https://huggingface.co/datasets/beans/discussions/4

This is a regression because it was previously fixed. See:
- #6196
- #5728

Comments:
After investigation, I think the issue was introduced by the use of the Parquet export:
- #6448

I am proposing a fix.

CC: @lhoestq
Title: Bug get_metadata_patterns arg error
Author: d710055071 (CONTRIBUTOR)
State: closed
Created: 2023-12-20T08:56:44Z

https://github.com/huggingface/datasets/blob/3f149204a2a5948287adcade5e90707aa5207a92/src/datasets/load.py#L1240C1-L1240C69
metadata_patterns = get_metadata_patterns(base_path, download_config=self.download_config)

Comments:
Title: Why call http_head() when fsspec_head()  succeeds
Author: d710055071 (CONTRIBUTOR)
State: closed
Created: 2023-12-20T02:25:51Z

https://github.com/huggingface/datasets/blob/a91582de288d98e94bcb5ab634ca1cfeeff544c5/src/datasets/utils/file_utils.py#L510C1-L523C14

Comments:
Title: Support huggingface-hub 0.20.0
Author: albertvillanova (MEMBER)
State: closed
Created: 2023-12-19T15:15:46Z

CI to test the support of `huggingface-hub` 0.20.0: https://github.com/huggingface/datasets/compare/main...ci-test-huggingface-hub-v0.20.0.rc1

We need to merge:
- #6510 
- #6512
- #6516

Comments:
Title: where is glue_metric.py> @Frankie123421 what was the resolution to this?
Author: Mcccccc1024 (NONE)
State: closed
Created: 2023-12-17T09:58:25Z

> @Frankie123421 what was the resolution to this?

use glue_metric.py instead of glue.py in load_metric

_Originally posted by @Frankie123421 in https://github.com/huggingface/datasets/issues/2117#issuecomment-905093763_

Comments:
Title: Incorrect test set labels for RTE and CoLA datasets via load_dataset
Author: emreonal11 (NONE)
State: closed
Created: 2023-12-16T22:06:08Z

### Describe the bug

The test set labels for the RTE and CoLA datasets when loading via datasets load_dataset are all -1.
Edit: It appears this is also the case for every other dataset except for MRPC (stsb, sst2, qqp, mnli (both matched and mismatched), qnli, wnli, ax). Is this intended behavior to safeguard the test set for evaluation purposes?

### Steps to reproduce the bug

!pip install datasets
from datasets import load_dataset

rte_data = load_dataset('glue', 'rte')
cola_data = load_dataset('glue', 'cola')
print(rte_data['test'][0:30]['label'])
print(cola_data['test'][0:30]['label'])

Output:
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1]
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1]

The non-label test data seems to be fine:
e.g. rte_data['test'][1] is:
{'sentence1': "Authorities in Brazil say that more than 200 people are being held hostage in a prison in the country's remote, Amazonian-jungle state of Rondonia.",
 'sentence2': 'Authorities in Brazil hold 200 people as hostage.',
 'label': -1,
 'idx': 1}
Training and validation data are also fine:
e.g. rte_data['train][0] is:
{'sentence1': 'No Weapons of Mass Destruction Found in Iraq Yet.',
 'sentence2': 'Weapons of Mass Destruction Found in Iraq.',
 'label': 1,
 'idx': 0}


### Expected behavior

Expected the labels to be binary 0/1 values; Got all -1s instead

### Environment info

- `datasets` version: 2.15.0
- Platform: Linux-6.1.58+-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.19.4
- PyArrow version: 10.0.1
- Pandas version: 1.5.3
- `fsspec` version: 2023.6.0

Comments:
As this is a specific issue of the "glue" dataset, I have transferred it to the dataset Discussion page: https://huggingface.co/datasets/glue/discussions/15

Let's continue the discussion there!
Title: Error Pushing to Hub
Author: Jiayi-Pan (NONE)
State: closed
Created: 2023-12-16T01:05:22Z

### Describe the bug

Error when trying to push a dataset in a special format to hub

### Steps to reproduce the bug

```
import datasets
from datasets import Dataset
dataset_dict = {
    "filename": ["apple", "banana"],
    "token": [[[1,2],[3,4]],[[1,2],[3,4]]],
    "label": [0, 1],
}
dataset = Dataset.from_dict(dataset_dict)
dataset = dataset.cast_column("token", datasets.features.features.Array2D(shape=(2, 2),dtype="int16"))

dataset.push_to_hub("SequenceModel/imagenet_val_256")
```
Error:
```
...

ConstructorError: could not determine a constructor for the tag 'tag:yaml.org,2002:python/tuple'
  in "<unicode string>", line 8, column 16:
            shape: !!python/tuple
                   ^
```

### Expected behavior

Dataset being pushed to hub

### Environment info

- `datasets` version: 2.15.0
- Platform: Linux-5.19.0-1022-gcp-x86_64-with-glibc2.35
- Python version: 3.11.5
- `huggingface_hub` version: 0.19.4
- PyArrow version: 14.0.1
- Pandas version: 2.1.4
- `fsspec` version: 2023.10.0

Comments:
Title:  OverflowError: value too large to convert to int32_t 
Author: zhangfan-algo (NONE)
State: open
Created: 2023-12-15T10:10:21Z

### Describe the bug

![image](https://github.com/huggingface/datasets/assets/47747764/f58044fb-ddda-48b6-ba68-7bbfef781630)


### Steps to reproduce the bug

just loading datasets 

### Expected behavior

how can I fix it

### Environment info

pip install /mnt/cluster/zhangfan/study_info/LLaMA-Factory/peft-0.6.0-py3-none-any.whl
pip install huggingface_hub-0.19.4-py3-none-any.whl tokenizers-0.15.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl transformers-4.36.1-py3-none-any.whl pyarrow_hotfix-0.6-py3-none-any.whl datasets-2.15.0-py3-none-any.whl tyro-0.5.18-py3-none-any.whl trl-0.7.4-py3-none-any.whl

done

Comments:
Title: Error when writing a dataset to HF Hub: A commit has happened since. Please refresh and try again.
Author: GeorgesLorre (NONE)
State: open
Created: 2023-12-14T11:24:54Z

**Describe the bug**

Getting a `412 Client Error: Precondition Failed` when trying to write a dataset to the HF hub.

```
huggingface_hub.utils._errors.HfHubHTTPError: 412 Client Error: Precondition Failed for url: https://huggingface.co/api/datasets/GLorr/test-dask/commit/main (Request ID: Root=1-657ae26f-3bd92bf861bb254b2cc0826c;50a09ab7-9347-406a-ba49-69f98abee9cc)

A commit has happened since. Please refresh and try again.
```

**Steps to reproduce the bug**
This is a minimal reproducer:
```
import dask.dataframe as dd
import pandas as pd
import random
import os

import huggingface_hub

import datasets

huggingface_hub.login(token=os.getenv("HF_TOKEN"))

data = {"number": [random.randint(0,10) for _ in range(1000)]}
df = pd.DataFrame.from_dict(data)
dataframe = dd.from_pandas(df, npartitions=1)
dataframe = dataframe.repartition(npartitions=3)

schema = datasets.Features({"number": datasets.Value("int64")}).arrow_schema

repo_id = "GLorr/test-dask"
repo_path = f"hf://datasets/{repo_id}"
huggingface_hub.create_repo(repo_id=repo_id, repo_type="dataset", exist_ok=True)
dd.to_parquet(dataframe, path=f"{repo_path}/data", schema=schema)
```

**Expected behavior**
Would expect to write to the hub without any problem.

**Environment info**
```
datasets==2.15.0
huggingface-hub==0.19.4
```

Comments:
I transferred from datasets-server, since the issue is more about `datasets` and the integration with `huggingface_hub`.
Title: Image Data loaded Twice
Author: ArcaneLex (NONE)
State: open
Created: 2023-12-13T13:11:42Z

### Describe the bug

![1702472610561](https://github.com/huggingface/datasets/assets/28867010/4b7ef5e7-32c3-4b73-84cb-5de059caa0b6)
When I learn from https://huggingface.co/docs/datasets/image_load and try to load image data from a folder. I noticed that the image was read twice in the returned data. As you can see in the attached image, there are only four images in the train folder, but reading brings up eight images

### Steps to reproduce the bug

from datasets import Dataset, load_dataset

dataset = load_dataset("imagefolder", data_dir="data/", drop_labels=False)
# print(dataset["train"][0]["image"] == dataset["train"][1]["image"])

print(dataset)
print(dataset["train"]["image"])
print(len(dataset["train"]["image"]))

### Expected behavior

DatasetDict({
    train: Dataset({
        features: ['image', 'label'],
        num_rows: 8
    })
})
[<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2877x2129 at 0x1BD1D1CA8B0>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2877x2129 at 0x1BD1D2452E0>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4208x3120 at 0x1BD1D245310>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4208x3120 at 0x1BD1D2453A0>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2877x2129 at 0x1BD1D245460>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2877x2129 at 0x1BD1D245430>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4208x3120 at 0x1BD1D2454F0>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4208x3120 at 0x1BD1D245550>]
8

### Environment info

- `datasets` version: 2.14.5
- Platform: Windows-10-10.0.22621-SP0
- Python version: 3.9.17
- Huggingface_hub version: 0.19.4
- PyArrow version: 13.0.0
- Pandas version: 2.0.3

Comments:
Title: `load_dataset(...,save_infos=True)` not working without loading script
Author: morganveyret (NONE)
State: open
Created: 2023-12-12T08:09:18Z

### Describe the bug

It seems that saving a dataset infos back into the card file is not working for datasets without a loading script.

After tracking the problem a bit it looks like saving the infos uses `Builder.get_imported_module_dir()` as its destination directory.
Internally this is a call to `inspect.getfile()` but since the actual builder class used is dynamically created (cf. `datasets.load.configure_builder_class`) this method actually return te path to the parent builder class (e.g. `datasets.packaged_modules.json.JSON`). 



### Steps to reproduce the bug

1. Have a local dataset without any loading script
2. Make sure there are no dataset infos in the README.md
3. Load with `save_infos=True`
4. No change in the dataset README.md
5. A new README.md file is created in the directory of the parent builder class (e.g. for json in `.../site-packages/datasets/packaged_modules/json/README.md`)

### Expected behavior

The dataset README.md should be updated and no file should be created in the python environment.

### Environment info

- `datasets` version: 2.15.0
- Platform: Linux-6.2.0-37-generic-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.19.4
- PyArrow version: 14.0.1
- Pandas version: 2.1.3
- `fsspec` version: 2023.6.0

Comments:
Also, once the README.md exists in the python environment it is used when loading another dataset in the same format (e.g. json) since it always resolves the path to the same directory.
The consequence here is any other dataset won't load because of infos mismatch.
To reproduce this aspect:
1. Do a `load_datasets(...,save_infos=True)` with one dataset without a loading script
2. Try to load another dataset without a loading script in the same format (e.g. json) but with a different schema
Title: load_dataset imageflder for aws s3 path 
Author: segalinc (NONE)
State: open
Created: 2023-12-12T00:08:43Z
Labels: enhancement

### Feature request

I would like to load a dataset from S3 using the imagefolder option 
something like 
`dataset = datasets.load_dataset('imagefolder', data_dir='s3://.../lsun/train/bedroom', fs=S3FileSystem(), streaming=True)  `

### Motivation

no need of data_files

### Your contribution

no experience with this

Comments:
Title: 429 Client Error
Author: sasaadi (NONE)
State: open
Created: 2023-12-11T15:06:01Z

Hello, I was downloading the following dataset and after 20% of data was downloaded, I started getting error 429. It is not resolved since a few days. How should I resolve it?
Thanks

Dataset:
https://huggingface.co/datasets/cerebras/SlimPajama-627B

Error:
`requests.exceptions.HTTPError: 429 Client Error: Too Many Requests for url: https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/train/chunk1/example_train_3300.jsonl.zst`

Comments:
Transferring repos as this is a datasets issue 

I'm getting a similar issue even though I've already downloaded the dataset 😅 

```
huggingface_hub.utils._errors.HfHubHTTPError: 429 Client Error: Too Many Requests for url: https://huggingface.co/api/datasets/HuggingFaceM4/WebSight
```
Title: Update builder hash with info
Author: lhoestq (MEMBER)
State: closed
Created: 2023-12-11T11:09:16Z

Currently if you change the `dataset_info` of a dataset (e.g. in the YAML part of the README.md), the cache ignores this change.

This is problematic because you want to regenerate a dataset if you change the features or the split sizes for example (e.g. after push_to_hub)

Ideally we should take the resolved files into account as well but this will be for another PR

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6487). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

Closing this one in favor of https://github.com/huggingface/datasets/pull/6458/commits/565c294fc12bc547730a023a610ed4f92313d8fb in https://github.com/huggingface/datasets/pull/6458
Title: FileNotFoundError: [Errno 2] No such file or directory: 'nul'
Author: amanyara (NONE)
State: closed
Created: 2023-12-11T08:52:13Z

### Describe the bug

it seems that sth wrong with my terrible "bug body" life, When i run this code, "import datasets"
i meet this error FileNotFoundError: [Errno 2] No such file or directory: 'nul'
![image](https://github.com/huggingface/datasets/assets/73683903/3973c120-ebb1-42b7-bede-b9de053e861d)
![image](https://github.com/huggingface/datasets/assets/73683903/0496adff-a7a7-4dcb-929e-ec11ede71f04)


### Steps to reproduce the bug

1.import datasets

### Expected behavior

i just run a single line code and stuct in this bug 

### Environment info

OS: Windows10
Datasets==2.15.0
python=3.10

Comments:
Hi! It seems like the problem is your environment. Maybe this issue can help: https://github.com/pytest-dev/pytest/issues/9519.