MarouaneSanhaji/github-issues huggingface dataset:
Wrong URL for the_pile dataset
State: closed

### Describe the bug

When trying to load `the_pile` dataset from the library, I get a `FileNotFound` error.

### Steps to reproduce the bug

Steps to reproduce:

Run:

```
from datasets import load_dataset

dataset = load_dataset("the_pile")
```

I get the output:


"name": "FileNotFoundError",
	"message": "Unable to resolve any data file that matches '['**']' at /storage/store/work/lgrinszt/memorization/the_pile with any supported extension ['csv', 'tsv', 'json', 'jsonl', 'parquet', 'txt', 'blp', 'bmp', 'dib', 'bufr', 'cur', 'pcx', 'dcx', 'dds', 'ps', 'eps', 'fit', 'fits', 'fli', 'flc', 'ftc', 'ftu', 'gbr', 'gif', 'grib', 'h5', 'hdf', 'png', 'apng', 'jp2', 'j2k', 'jpc', 'jpf', 'jpx', 'j2c', 'icns', 'ico', 'im', 'iim', 'tif', 'tiff', 'jfif', 'jpe', 'jpg', 'jpeg', 'mpg', 'mpeg', 'msp', 'pcd', 'pxr', 'pbm', 'pgm', 'ppm', 'pnm', 'psd', 'bw', 'rgb', 'rgba', 'sgi', 'ras', 'tga', 'icb', 'vda', 'vst', 'webp', 'wmf', 'emf', 'xbm', 'xpm', 'BLP', 'BMP', 'DIB', 'BUFR', 'CUR', 'PCX', 'DCX', 'DDS', 'PS', 'EPS', 'FIT', 'FITS', 'FLI', 'FLC', 'FTC', 'FTU', 'GBR', 'GIF', 'GRIB', 'H5', 'HDF', 'PNG', 'APNG', 'JP2', 'J2K', 'JPC', 'JPF', 'JPX', 'J2C', 'ICNS', 'ICO', 'IM', 'IIM', 'TIF', 'TIFF', 'JFIF', 'JPE', 'JPG', 'JPEG', 'MPG', 'MPEG', 'MSP', 'PCD', 'PXR', 'PBM', 'PGM', 'PPM', 'PNM', 'PSD', 'BW', 'RGB', 'RGBA', 'SGI', 'RAS', 'TGA', 'ICB', 'VDA', 'VST', 'WEBP', 'WMF', 'EMF', 'XBM', 'XPM', 'aiff', 'au', 'avr', 'caf', 'flac', 'htk', 'svx', 'mat4', 'mat5', 'mpc2k', 'ogg', 'paf', 'pvf', 'raw', 'rf64', 'sd2', 'sds', 'ircam', 'voc', 'w64', 'wav', 'nist', 'wavex', 'wve', 'xi', 'mp3', 'opus', 'AIFF', 'AU', 'AVR', 'CAF', 'FLAC', 'HTK', 'SVX', 'MAT4', 'MAT5', 'MPC2K', 'OGG', 'PAF', 'PVF', 'RAW', 'RF64', 'SD2', 'SDS', 'IRCAM', 'VOC', 'W64', 'WAV', 'NIST', 'WAVEX', 'WVE', 'XI', 'MP3', 'OPUS', 'zip']"

### Expected behavior

`the_pile` dataset should be dowloaded.

### Environment info

- `datasets` version: 2.7.1
- Platform: Linux-4.15.0-112-generic-x86_64-with-glibc2.27
- Python version: 3.10.8
- PyArrow version: 10.0.1
- Pandas version: 1.5.2

Hi! This error can happen if there is a local file/folder with the same name as the requested dataset. And to avoid it, rename the local file/folder.

Soon, it will be possible to explicitly request a Hub dataset as follows:https://github.com/huggingface/datasets/issues/5228#issuecomment-1313494020
The dataset "the_pile", subset "enron_emails" , load_dataset() failure
State: closed

### Describe the bug

When run 
"datasets.load_dataset("the_pile","enron_emails")" failure
![image](https://user-images.githubusercontent.com/52023469/208565302-cfab7b89-0b97-4fa6-a5ba-c11b0b629b1a.png)


### Steps to reproduce the bug

Run below code in python cli:

>>> import datasets
>>> datasets.load_dataset("the_pile","enron_emails")


### Expected behavior

Load dataset "the_pile", "enron_emails" successfully.

### Environment info

Copy-and-paste the text below in your GitHub issue.

- `datasets` version: 2.7.1
- Platform: Linux-5.15.0-53-generic-x86_64-with-glibc2.35
- Python version: 3.10.6
- PyArrow version: 10.0.0
- Pandas version: 1.4.3


Thanks for reporting @shaoyuta. We are investigating it.

We are transferring the issue to "the_pile" Community tab on the Hub: https://huggingface.co/datasets/the_pile/discussions/4
Add a robustness benchmark dataset for vision
State: open

### Name

ImageNet-C

### Paper

Benchmarking Neural Network Robustness to Common Corruptions and Perturbations

### Data

https://github.com/hendrycks/robustness

### Motivation

It's a known fact that vision models are brittle when they meet with slightly corrupted and perturbed data. This is also correlated to the robustness aspects of vision models. 

Researchers use different benchmark datasets to evaluate the robustness aspects of vision models. ImageNet-C is one of them. 

Having this dataset in 🤗 Datasets would allow researchers to evaluate and study the robustness aspects of vision models. Since the metric associated with these evaluations is top-1 accuracy, researchers should be able to easily take advantage of the evaluation benchmarks on the Hub and perform comprehensive reporting. 

ImageNet-C is a large dataset. Once it's in, it can act as a reference and we can also reach out to the authors of the other robustness benchmark datasets in vision, such as ObjectNet, WILDS, Metashift, etc. These datasets cater to different aspects. For example, ObjectNet is related to assessing how well a model performs under sub-population shifts. 

Related thread: https://huggingface.slack.com/archives/C036H4A5U8Z/p1669994598060499

Ccing @nazneenrajani @lvwerra @osanseviero
Fix remove columns from lazy dict
State: closed

This was introduced in https://github.com/huggingface/datasets/pull/5252 and causing the transformers CI to break: https://app.circleci.com/pipelines/github/huggingface/transformers/53886/workflows/522faf2e-a053-454c-94f8-a617fde33393/jobs/648597

Basically this code should return a dataset with only one column:

```python
from datasets import *

ds = Dataset.from_dict({"a": range(5)})

def f(x):
    x["b"] = x["a"]
    return x

ds = ds.map(f, remove_columns=["a"])

assert ds.column_names == ["b"]
```

_The documentation is not available anymore as the PR was closed or merged._
Run 'GPT-J' failure due to download dataset fail (' ConnectionError: Couldn't reach http://eaidata.bmk.sh/data/enron_emails.jsonl.zst ' ) 
State: closed

### Describe the bug

Run model "GPT-J" with dataset "the_pile" fail.
The fail out is as below:
![image](https://user-images.githubusercontent.com/52023469/207750127-118d9896-35f4-4ee9-90d4-d0ab9aae9c74.png)

Looks like which is due to "http://eaidata.bmk.sh/data/enron_emails.jsonl.zst" unreachable .

### Steps to reproduce the bug

Steps to reproduce this issue:

git clone https://github.com/huggingface/transformers
cd transformers
python examples/pytorch/language-modeling/run_clm.py --model_name_or_path EleutherAI/gpt-j-6B --dataset_name the_pile --dataset_config_name enron_emails --do_eval --output_dir /tmp/output --overwrite_output_dir

### Expected behavior

This issue looks like due to "http://eaidata.bmk.sh/data/enron_emails.jsonl.zst " couldn't be reached.
Is there another way to download the dataset "the_pile" ?
Is there another way to cache the dataset "the_pile" but not let the hg to download it when runtime ?

### Environment info

huggingface_hub version: 0.11.1
Platform: Linux-5.15.0-52-generic-x86_64-with-glibc2.35
Python version: 3.9.12
Running in iPython ?: No
Running in notebook ?: No
Running in Google Colab ?: No
Token path ?: /home/taosy/.huggingface/token
Has saved token ?: False
Configured git credential helpers:
FastAI: N/A
Tensorflow: N/A
Torch: N/A
Jinja2: N/A
Graphviz: N/A
Pydot: N/A

Thanks for reporting, @shaoyuta.

We have checked and yes, apparently there is an issue with the server hosting the data of the "enron_emails" subset of "the_pile" dataset: http://eaidata.bmk.sh/data/enron_emails.jsonl.zst
It seems to be down: The connection has timed out.

Please note that at the Hugging Face Hub, we are not hosting their data for this dataset, but only a script that downloads the data from their servers. We are updating the data URL to one in another server.

In the meantime, please note that you can train your model in the entire "the_pile" dataset, by passing the "all" config (instead of the "enron_emails" one).

We have transferred this issue to the corresponding dataset Community tab: https://huggingface.co/datasets/the_pile/discussions/2

Please, follow the updates there.
Raise error if ClassLabel names is not python list
State: closed

Checks type of names provided to ClassLabel to avoid easy and hard to debug errors (closes #5332 - see for discussion)

_The documentation is not available anymore as the PR was closed or merged._

Thanks for your proposed fix, @freddyheppell.

Currently the CI fails because in a test we pass a `tuple` instead of a `list`. I would say we should accept `tuple` as a valid input type as well...

What about checking for `Sequence` instead?

Fixed that @albertvillanova, can you approve CI again please? Had some issues related to Pytorch .so files when running tests on my M1 mac, so wasn't able to test locally first. Have got them working on my desktop now though.
Support torch dataloader without torch formatting
State: closed

In https://github.com/huggingface/datasets/pull/5084 we make the torch formatting consistent with the map-style datasets formatting: a torch formatted iterable dataset will yield torch tensors.

The previous behavior of the torch formatting for iterable dataset was simply to make the iterable dataset inherit from `torch.utils.data.Dataset` to make it work in a torch DataLoader. However ideally an unformatted dataset should also work with a DataLoader. To fix that, `datasets.IterableDataset` should inherit from `torch.utils.data.IterableDataset`.

Since we don't want to import torch on startup, I created this PR to dynamically make the `datasets.IterableDataset` class inherit form the torch one when a `datasets.IterableDataset` is instantiated and if PyTorch is available.

```python
>>> from datasets import load_dataset
>>> ds = load_dataset("c4", "en", streaming=True, split="train")
>>> import torch.utils.data
>>> isinstance(ds, torch.utils.data.IterableDataset)
True
>>> dataloader = torch.utils.data.DataLoader(ds, batch_size=32, num_workers=4)
>>> for example in dataloader:
...:     ...
```

_The documentation is not available anymore as the PR was closed or merged._

Need some more time to fix the tests, especially with pickle

> And I actually don't quite understand the idea - what's the motivation behind making only IterableDataset compatible with torch DataLoader without setting the format explicitly?

Setting the format to pytorch = set the output types of the dataset to be pytorch tensors. However sometimes your dataset is not made of tensors but you still want to be able to use a pytorch DataLoader

A bit more context. 

The arrow-backed `Dataset` supports `DataLoader(ds)` (even if the format is not "torch"), and we want to be able to do the same with `IterableDataset` for consistency. However, this is when the PyTorch internals come into play - an iterable dataset needs to be an instance of `torch.utils.data.IterableDataset` due to [this](https://github.com/pytorch/pytorch/blob/abc54f93145830b502400faa92bec86e05422fbd/torch/utils/data/dataloader.py#L276) check (notice there is no check for the map-style version). Hence the explicit subclassing in this PR.

Exactly :) Btw I just took your comments into account @polinaeterna , so feel free to review again

@lhoestq just checking, does this change still preserve the fix to the "data duplicate when setting num_works > 1 with streaming data" issue from before?

https://github.com/huggingface/datasets/issues/3423

Yes :)
Support remote file systems for `Audio`
State: closed

### Feature request

Hi there!

It would be super cool if `Audio()`, and potentially other features, could read files from a remote file system.

### Motivation

Large amounts of data is often stored in buckets. `load_from_disk` is able to retrieve data from cloud storage but to my knowledge actually copies the datasets across first, so if you're working off a system with smaller disk specs (like a VM), you can run out of space very quickly.

### Your contribution

Something like this (for Google Cloud Platform in this instance):



```python
from datasets import Dataset, Audio
import gcsfs

fs = gcsfs.GCSFileSystem()

list_of_audio_fp = {'audio': ['1', '2', '3']}
ds = Dataset.from_dict(list_of_audio_fp)

ds = ds.cast_column("audio", Audio(sampling_rate=16000, fs=fs))
```

Under the hood:
```python

import librosa
from io import BytesIO

def load_audio(fp, sampling_rate=None, fs=None):
    if fs is not None:
        with fs.open(fp, 'rb') as f:
            arr, sr = librosa.load(BytesIO(f), sr=sampling_rate)
    else:
        # Perform existing io operations
```

Written from memory so some things could be wrong.

Just seen https://github.com/huggingface/datasets/issues/5281
__init__() got an unexpected keyword argument 'input_size'
State: open

### Describe the bug

I try to define a custom configuration with a input_size attribute following the instructions by "Specifying several dataset configurations" in https://huggingface.co/docs/datasets/v1.2.1/add_dataset.html

But when I load the dataset, I got an error "__init__() got an unexpected keyword argument 'input_size'"

### Steps to reproduce the bug

Following is the code to define the dataset:
class CsvConfig(datasets.BuilderConfig):
    """BuilderConfig for CSV."""
    input_size: int = 2048

class MRF(datasets.ArrowBasedBuilder):
    """Archival MRF data"""
    BUILDER_CONFIG_CLASS = CsvConfig
    VERSION = datasets.Version("1.0.0")
    BUILDER_CONFIGS = [
        CsvConfig(name="default", version=VERSION, description="MRF data", input_size=2048),
    ]

...

    def _generate_examples(self):
        input_size = self.config.input_size
        if input_size > 1000:
           numin = 10000
        else:
           numin = 15000


Below is the code to load the dataset:
reader = load_dataset("default", input_size=1024)

### Expected behavior

I hope to pass the "input_size" parameter to MRF datasets, and change "input_size" to any value when loading the datasets. 

### Environment info

- `datasets` version: 2.5.1
- Platform: Linux-4.18.0-305.3.1.el8.x86_64-x86_64-with-glibc2.31
- Python version: 3.9.12
- PyArrow version: 9.0.0
- Pandas version: 1.5.0

Hi @J-shel, thanks for reporting.

I think the issue comes from your call to `load_dataset`. As first argument, you should pass:
- either the name of your dataset ("mrf") if this is already published on the Hub
- or the path to the loading script of your dataset ("path/to/your/local/mrf.py").

Hi, following your suggestion, I changed my call to load_dataset. Below is the latest:
reader = load_dataset('data/mrf.py',"default", input_size=1024, split=split, streaming=True, keep_in_memory=None)
However, I still got the same error.
I have one question that is if I only define input_size=2048 in BUILDER_CONFIGS, may I specify input_size=1024 when loading the dataset?  Cause I found that I could only specify name="default" since I only define  name="default" in BUILDER_CONFIGS.
Force soundfile to return float32 instead of the default float64 
State: open

(Fixes issue #5345)

cc @polinaeterna

The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5347). All of your documentation changes will be reflected on that endpoint.

Cool ! Feel free to add a comment in the code to explain that and we can merge :)

I'm not sure if this is a good change since we plan to get rid of `torchaudio` in the next couple of months...

What do you think @polinaeterna @patrickvonplaten ? Models are usually using float32 (e.g. Wev2vec2 in `transformers`) IIRC

IMO we can safely assume that float32 is always good enough when using audio models in inference or training. Nevertheless there might be use cases for audio datasets in the future where float64 is needed. 

=> I would by default always cast to float32, but then possible allow the user to cast to float64 

> I'm not sure if this is a good change since we plan to get rid of torchaudio in the next couple of months...

@mariosasko I agree but who knows how long we will have to wait until we are really able to do so (https://github.com/bastibe/libsndfile-binaries/pull/17 is a draft. so as @patrickvonplaten is okay with float32, I'd merge.




@polinaeterna Can you comment on the linked PR to see why it's still a draft? Maybe we can help somehow to get this merged finally.

I think it's weird to align `soundfile` with `torchaudio` when the latter is only used for MP3 (and prob for not much longer).
[Quick poll] Give your opinion on the future of the Hugging Face Open Source ecosystem!
State: closed

Thanks to all of you, Datasets is just about to pass 15k stars!

Since the last survey, a lot has happened: the [diffusers](https://github.com/huggingface/diffusers), [evaluate](https://github.com/huggingface/evaluate) and [skops](https://github.com/skops-dev/skops) libraries were born. `timm` joined the Hugging Face ecosystem. There were 25 new releases of `transformers`, 21 new releases of `datasets`, 13 new releases of `accelerate`.

If you have a couple of minutes and want to participate in shaping the future of the ecosystem, please share your thoughts: 

[**hf.co/oss-survey**](https://docs.google.com/forms/d/e/1FAIpQLSf4xFQKtpjr6I_l7OfNofqiR8s-WG6tcNbkchDJJf5gYD72zQ/viewform?usp=sf_link)

(please reply in the above feedback form rather than to this thread)

Thank you all on behalf of the HuggingFace team! 🤗

As the survey is finished, can we close this issue, @LysandreJik ?

Yes! I'll post a public summary on the forums shortly.

Is the summary available? I would be interested in reading your findings.
Emotion dataset cannot be downloaded
State: closed

### Describe the bug

The emotion dataset gives a FileNotFoundError. The full error is: `FileNotFoundError: Couldn't find file at https://www.dropbox.com/s/1pzkadrvffbqw6o/train.txt?dl=1`.

It was working yesterday (December 7, 2022), but stopped working today (December 8, 2022).

### Steps to reproduce the bug

```python
from datasets import load_dataset
dataset = load_dataset("emotion")
```

### Expected behavior

The dataset should load properly.

### Environment info

- `datasets` version: 2.7.1
- Platform: Windows-10-10.0.19045-SP0
- Python version: 3.9.13
- PyArrow version: 10.0.1
- Pandas version: 1.5.1

Hi @cbarond there's already an open issue at https://github.com/dair-ai/emotion_dataset/issues/5, as the data seems to be missing now, so check that issue instead 👍🏻 

Thanks @cbarond for reporting and @alvarobartt for pointing to the issue we opened in the author's repo.

Indeed, this issue was first raised in the "emotion" dataset Community tab: https://huggingface.co/datasets/emotion/discussions/3

I'm closing this issue and leave the issue above for the subsequent updates.

Duplicate of: https://huggingface.co/datasets/emotion/discussions/3

try using "SetFit/emotion" instead

> try using "SetFit/emotion" instead

I' replaced "emotion" with "SetFit/Emotion", but the code is getting stuck at

`emotions = load_dataset("SetFit/emotion")`

I pause execution using the debugger, and it takes me to filelock.py:226

`with self._thread_lock:`

Do you know a way to get past this issue?

thanks @honeyimholm - worked for me

> try using "SetFit/emotion" instead

It really helps a lot, thank you!

The dataset loading script has been fixed: https://huggingface.co/datasets/emotion/discussions/4
Add Video feature, videofolder, and video-classification task
State: closed

This PR does the following:

- Adds `Video` feature (Resolves #5225 )
- Adds `video-classification` task
- Adds `videofolder` packaged module for easy loading of local video classification datasets

TODO:
  - [ ] add tests
  - [ ] add docs

The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5339). All of your documentation changes will be reflected on that endpoint.

@lhoestq I think I need some serious help with the tests 😅...I started this locally but it got too time consuming.

One issue I remember running into is with lossless audio encoding/decoding. I started thinking of using the underlying Audio feature instead of PyAV so I didn't have to rewrite similar logic here...but assumed that would turn into a mess w/ underlying logic

Are you still planning to work on this?

I'm closing this PR. Feel free to reopen it if necessary.
`map()` stops every 1000 steps
State: closed

### Describe the bug

I am passing the following `prepare_dataset` function to `Dataset.map` (code is inspired from [here](https://github.com/huggingface/community-events/blob/main/whisper-fine-tuning-event/run_speech_recognition_seq2seq_streaming.py#L454))
```python3
def prepare_dataset(batch):
    # load and resample audio data from 48 to 16kHz
    audio = batch["audio"]

    # compute log-Mel input features from input audio array 
    batch["input_features"] = feature_extractor(audio["array"], sampling_rate=audio["sampling_rate"]).input_features[0]

    # encode target text to label ids 
    batch["labels"] = tokenizer(batch[text_column]).input_ids
    return batch
...
train_ds = train_ds.map(prepare_dataset)
```

Here is the exact code I am running https://github.com/bayartsogt-ya/whisper-multiple-hf-datasets/blob/main/train.py#L70-L71

It starts using all the cores (I am not sure why because I did not pass `num_proc`)
then progress bar stops at every 1k steps. (starts using a single core)
then come back to using all the cores again.

link to [screen record](https://youtu.be/jPQpQQGp6Gc)

Can someone explain this process and maybe provide a way to improve this pipeline? cc: @lhoestq 

### Steps to reproduce the bug

1. load the dataset
2. create a Whisper processor
3. create a `prepare_dataset` function
4. pass the function to `dataset.map(prepare_dataset)`

### Expected behavior

- Use a single core per a function
- not to stop at some point?

### Environment info

- `datasets` version: 2.7.1.dev0
- Platform: Linux-5.4.0-109-generic-x86_64-with-glibc2.27
- Python version: 3.8.10
- PyArrow version: 10.0.1
- Pandas version: 1.5.2

Hi !

> It starts using all the cores (I am not sure why because I did not pass num_proc)

The tokenizer uses Rust code that is multithreaded. And maybe the `feature_extractor` might run some things in parallel as well - but I'm not super familiar with its internals.

> then progress bar stops at every 1k steps. (starts using a single core)

Every 1000 examples we flush the processed examples to disk. It is this way because Arrow is a columnar format: you must write data chunk by chunk. The processing in on hold while writing right now - maybe this can be improved in the future.

Hi @lhoestq 
Thanks for the explanation! it was so helpful! Let me check why `feature_extractor` is running on multiple cpus.

Hey @lhoestq, any news about this flush operation ? This really really slow down most of my .map and .filter operation. I'm sometime taking 10x more time waiting for the flush than actually processing the 1k items. My current workarround is to set `writer_batch_size=len(dataset)`.
Support webdataset format
State: closed

Webdataset is an efficient format for iterable datasets. It would be nice to support it in `datasets`, as discussed in https://github.com/rom1504/img2dataset/issues/234.

In particular it would be awesome to be able to load one using `load_dataset` in streaming mode (either from a local directory, or from a dataset on the Hugging Face Hub). Some datasets on the Hub are already in webdataset format.

It terms of implementation, we can have something similar to the Parquet loader.
I also think it's fine to have webdataset as an optional dependency.

I like the idea of having `webdataset` as an optional dependency to ensure our loader generates web datasets the same way as the main project.

Webdataset is the one of the most popular dataset formats for large scale computer vision tasks. Upvote for this issue. 

Any updates on this?

We haven't had the bandwidth to implement it so far, but if someone wants to give it a shot please don't hesitate ^^

Done in #6391
Set `IterableDataset.map` param `batch_size` typing as optional
State: closed

This PR solves #5325 

~Indeed we're using the typing for optional values as `Union[type, None]` as it's similar to how Python 3.10 handles optional values as `type | None`, instead of using `Optional[type]`.~

~Do we want to start using `Union[type, None]` for type-hinting optional values or just keep on using `Optional`?~ -> Keeping `Optional` still for consistency with the rest of the code in `datasets`

Also we now allow `batch_size` to be `None` for `IterableDataset.map` and `IterableDataset.filter`e.g. `MappedExamplesIterable` as `map` is internally instantiating those and propagating the `batch_size` param so if it can be `None` for `map` it should also do so for `MappedExamplesIterable`, as well as for `FilteredExamplesIterable` when calling `IterableDataset.filter`.

## TODOs

- [x] Add integration tests
- [x] Handle scenario where `batched=True` and `batch_size=None` or `batch_size<=0`

The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5336). All of your documentation changes will be reflected on that endpoint.

Hi @mariosasko, @lhoestq I was wondering whether we should include `batched` as a `pytest.mark` param for the functions testing `IterableDataset.map` so as to ensure that the changes done in this PR work fine without breaking anything of the actual functionality.

I've pushed updated tests just for one of the unit testing functions to be run as `pytest tests/test_iterable_dataset.py::test_mapped_examples_iterable -s --durations 0`, but some are still missing `batched` param, it was just to ask you whether we're supposed to do this for the rest of the functions or not, if it's a yes I'll push the commit as it's ready, but didn't want to push extra stuff that may be discarded later!

Thanks :hugs:

Thanks for the feedback @lhoestq, I agree with keeping `Optional` instead of `Union[type, None]` for now 👍🏻
Update tasks.json
State: closed

Context:

* https://github.com/huggingface/datasets/issues/5255#issuecomment-1339107195

Cc: @osanseviero 

_The documentation is not available anymore as the PR was closed or merged._

I think the only place where we need to add it is here https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts

And I think we can remove tasks.json completely from this repo

Isn't tasks.json used anymore in this repo?

> I think the only place where we need to add it is here https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts
> 
> And I think we can remove tasks.json completely from this repo

What about the warning I mentioned in https://github.com/huggingface/datasets/issues/5255#issuecomment-1339013527? Also, the depth estimation entry is already present in https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts. 

The update is based on what I received in the output of the export job (c.f. https://github.com/huggingface/datasets/issues/5255#issuecomment-1339107195). 

Edit: Oh, are you referring to the dataset card of NYU Depth V2?

Yes, my suggestion was for the dataset card: you got the error message because you tried to set `depth-estimation` in `class_ids` instead of `class_categories`.

> What about the warning I mentioned in https://github.com/huggingface/datasets/issues/5255#issuecomment-1339013527? Also, the depth estimation entry is already present in https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts.

if you place it in `task_categories` you should be good :)

yes i would suggest rm'ing tasks.json here for clarity

Closing it. 

It's not clear if we can remove it btw, since old versions of `evaluate` rely on it (see https://github.com/huggingface/evaluate/pull/309)

cc @lvwerra 

Actually it can be removed without incidence in old versions of evaluate since we kept an hardcoded `known_task_ids` that is marked "DEPRECATED"
Clean up docstrings
State: closed

As raised by @polinaeterna in #5324, some of the docstrings are a bit of a mess because it has both Markdown and Sphinx syntax. This PR fixes the docstring for `DatasetBuilder`. 

I'll start working on cleaning up the rest of the docstrings and removing the old Sphinx syntax (let me know if you prefer one big PR with all the cleaned changes or multiple smaller ones)! 🧼 

_The documentation is not available anymore as the PR was closed or merged._

Thanks ! Let us know if we can help :)

Small pref for having multiple PRs

Awesome, thanks! Sorry this one is a little big, I'll open some smaller ones next :)
Clarify imagefolder is for small datasets
State: closed

Based on feedback from [here](https://github.com/huggingface/datasets/issues/5317#issuecomment-1334108824), this PR adds a note to the `imagefolder` loading and creating docs that `imagefolder` is designed for small scale image datasets.

_The documentation is not available anymore as the PR was closed or merged._

I think it's also reasonable to add the same note to the AudioFolder decription

Thank you ! I think "regular" is more appropriate than "small". It can easily scale to a few thousands of images - just not millions x)

Replaced "small" with "several thousand" since what is considered "regular" and even "small" can be kind of vague!
Fix docs building for main
State: closed

This PR reverts the triggering event for building documentation introduced by:
- #5250

Fix #5326.

_The documentation is not available anymore as the PR was closed or merged._

EDIT
At least the docs for ~~main~~ PR branch are now built:
- https://github.com/huggingface/datasets/actions/runs/3594847760/jobs/6053620813

Build documentation for main branch was triggered after this PR being merged: https://github.com/huggingface/datasets/actions/runs/3603370082/jobs/6071482470
map(...batch_size=None) for IterableDataset
State: closed

### Feature request

Dataset.map(...) allows batch_size to be None. It would be nice if IterableDataset did too.

### Motivation

Although it may seem a bit of a spurious request given that `IterableDataset` is meant for larger than memory datasets, but there are a couple of reasons why this might be nice.

One is that load_dataset(...) can return either IterableDataset or Dataset. mypy will then complain if batch_size=None even if we know it is Dataset. Of course we can do:

    assert isinstance(d, datasets.DatasetDict)

But it is a mild inconvenience. What's more annoying is that whenever we use something like e.g. `combine_datasets(...)`, we end up with the union again, and so have to do the assert again.

Another is that we could actually end up with an IterableDataset small enough for memory in normal/correct usage, e.g. by filtering a massive IterableDataset.

For practical usages, an alternative to this would be to convert from an iterable dataset to a map-style dataset, but it is not obvious how to do this.

### Your contribution

Not this time.

Hi! I agree it makes sense for `IterableDataset.map` to support the `batch_size=None` case. This should be super easy to fix.

@mariosasko as this is something simple maybe I can include it as part of https://github.com/huggingface/datasets/pull/5311? Let me know :+1:

#self-assign

Feel free to close this @lhoestq as part of https://github.com/huggingface/datasets/pull/5336 :hugs:

Thanks again :)

> For practical usages, an alternative to this would be to convert from an iterable dataset to a map-style dataset, but it is not obvious how to do this.

This is interesting as well, if anyone wants to explore
Fix docstrings and types in documentation that appears on the website
State: open

While I was working on https://github.com/huggingface/datasets/pull/5313 I've noticed that we have a mess in how we annotate types and format args and return values in the code. And some of it is displayed in the [Reference section](https://huggingface.co/docs/datasets/package_reference/builder_classes) of the documentation on the website.
 
Would be nice someday, maybe before releasing datasets 3.0.0, to unify it......

I agree we have a mess with docstrings...

Ok, I believe we've cleaned up most of the old syntax we were using for the user-facing docs! There are still a couple of `:obj:`'s and `:class:` floating around in the docstrings we don't expose that I'll track down :)

Hi @polinaeterna @albertvillanova @stevhliu,  I hope you all are doing well.

Is this issue still unresolved as I am interested in it?

It should be mostly fixed for the user-facing APIs, but there may be some Sphinx syntax still lurking around in the non-public APIs. Feel free to open a PR to fix those if you catch any! 🤗 

Thanks for your reply @stevhliu  :)
Sure, I will try to find out the remaining and fix that.
Raise error for `.tar` archives in the same way as for `.tar.gz` and `.tgz` in `_get_extraction_protocol`
State: closed

Currently `download_and_extract` doesn't throw an error when it is used with files with `.tar` extension in streaming mode because `_get_extraction_protocol` doesn't do it (like it does for  `tar.gz` and `tgz`). `_get_extraction_protocol` returns formatted url as if we support tar protocol but we don't.
That means that in dataset scripts `.tar` files would be attempted to load and fail during examples generation (after `download_and_extract` execution). So this PR raises error for `tar` files too.


_The documentation is not available anymore as the PR was closed or merged._
Fix loading from HF GCP cache
State: closed

As reported in https://discuss.huggingface.co/t/error-loading-wikipedia-dataset/26599/4 it's not possible to download a cached version of Wikipedia from the HF GCP cache

I fixed it and added an integration test (runs in 10sec)

_The documentation is not available anymore as the PR was closed or merged._

> Do you know why this stopped working?

It comes from the changes in https://github.com/huggingface/datasets/pull/5107/files#diff-355ae5c229f95f86895404b72378ecd6e966c41cbeebb674af6fe6e9611bc126
Origin/fix missing features error
State: closed

This fixes the problem of when the dataset_load function reads a function with "features" provided but some read batches don't have columns that later show up. For instance, the provided "features" requires columns A,B,C but only columns B,C show. This fixes this by adding the column A with nulls. 

_The documentation is not available anymore as the PR was closed or merged._

please review :) @lhoestq @ola13 thankoo

Thanks :) I just updated the test to make sure it works even when there's a column missing, and did a minor change to json.py to add the missing columns for the other kinds of JSON files as well (I moved the code to`self._cast_table`)

Thanks Unso! If @lhoestq is happy then I'm also happy :D

When I noticed the ping, this PR had already been merged...

Luckily, PyArrow's `read_json` behaves the same when `explicit_schema` is given via `ParseOptions`, so I'm okay with this change (our JSON loader doesn't use `read_json` for decoding JSON in some scenarios, so this manual approach is the right one).
Bug in sample_by="paragraph"
State: closed

### Describe the bug

I think [this line](https://github.com/huggingface/datasets/blob/main/src/datasets/packaged_modules/text/text.py#L96) is wrong and should be `batch = f.read(self.config.chunksize)`. Otherwise it will never terminate because even when `f` is finished reading, `batch` will still be truthy from the last iteration.

### Steps to reproduce the bug

```
> cat test.txt
a b c

d e f
````

```python
>>> import datasets
>>> datasets.load_dataset("text", data_files={"train":"test.txt"}, sample_by="paragraph")
```

This will go on forever.

### Expected behavior

Terminates very quickly. 

### Environment info

`version = "2.6.1"` but I think the bug is still there on main. 

Thanks for reporting, @adampauls.

We are having a look at it.
Datasets: classification_report() got an unexpected keyword argument 'suffix'
State: closed

https://github.com/huggingface/datasets/blob/main/metrics/seqeval/seqeval.py


> import datasets
predictions = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
references = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
seqeval = datasets.load_metric("seqeval")
results = seqeval.compute(predictions=predictions, references=references)
print(list(results.keys()))
print(results["overall_f1"])
print(results["PER"]["f1"])

It raises the error:

> TypeError: classification_report() got an unexpected keyword argument 'suffix'

For context, versions on my pip list -v

> datasets                 1.12.1  
seqeval                  1.2.2

This seems similar to https://github.com/huggingface/datasets/issues/2512 Can you try to update seqeval ? 

@JonathanAlis also note that the metrics are deprecated in our `datasets` library.

Please, use the new library 🤗 Evaluate instead: https://huggingface.co/docs/evaluate
Fix description of streaming in the docs
State: closed

We say that "the data is being downloaded progressively" which is not true, it's just streamed, so I fixed it. Probably I missed some other places where it is written? 

Also changed docstrings for `StreamingDownloadManager`'s `download` and `extract` to reflect the same, as these docstrings are displayed in the documentation cc @lhoestq 

_The documentation is not available anymore as the PR was closed or merged._
Add `features` param to `IterableDataset.map`
State: closed

## Description

As suggested by @lhoestq in #3888, we should be adding the param `features` to `IterableDataset.map` so that the features can be preserved (not turned into `None` as that's the default behavior) whenever the user passes those as param, so as to be consistent with `Dataset.map`, as it provides the `features` param so that those are not inferred by default, but specified by the user, and later validated by `ArrowWriter`.

This is internally handled already by the functions relying on `IterableDataset.map` such as `rename_column`, `rename_columns`, and `remove_columns` as described in #5287.

## Usage Example

```python
from datasets import load_dataset, Features

ds = load_dataset("rotten_tomatoes", split="validation", streaming=True)
print(ds.info.features)
ds = ds.map(
    lambda x: {"target": x["label"]},
    features=Features(
        {"target": ds.info.features["label"], "label": ds.info.features["label"], "text": ds.info.features["text"]}
    ),
)
print(ds.info.features)
```

_The documentation is not available anymore as the PR was closed or merged._
Support xPath for Windows pathnames
State: closed

This PR implements a string representation of `xPath`, which is valid for local paths (also windows) and remote URLs.

Additionally, some `os.path` methods are fixed for remote URLs on Windows machines.

Now, on Windows machines:
```python
In [2]: str(xPath("C:\\dir\\file.txt"))
Out[2]: 'C:\\dir\\file.txt'
In [3]: str(xPath("http://domain.com/file.txt"))
Out[3]: 'http://domain.com/file.txt'
```

_The documentation is not available anymore as the PR was closed or merged._
timit_asr doesn't load the test split.
State: closed

### Describe the bug

When I use the function ```timit = load_dataset('timit_asr', data_dir=data_dir)```, it only loads train split, not test split.

I tried to change the directory and filename to lower case to upper case for the test split, but it does not work at all.

```python
DatasetDict({
    train: Dataset({
        features: ['file', 'audio', 'text', 'phonetic_detail', 'word_detail', 'dialect_region', 'sentence_type', 'speaker_id', 'id'],
        num_rows: 4620
    })
    test: Dataset({
        features: ['file', 'audio', 'text', 'phonetic_detail', 'word_detail', 'dialect_region', 'sentence_type', 'speaker_id', 'id'],
        num_rows: 0
    })
})
```

The directory structure of both splits are same. (DIALECT_REGION / SPEAKER_CODE / DATA_FILES)

### Steps to reproduce the bug

1. just use ```timit = load_dataset('timit_asr', data_dir=data_dir)```

### Expected behavior

```python
DatasetDict({
    train: Dataset({
        features: ['file', 'audio', 'text', 'phonetic_detail', 'word_detail', 'dialect_region', 'sentence_type', 'speaker_id', 'id'],
        num_rows: 4620
    })
    test: Dataset({
        features: ['file', 'audio', 'text', 'phonetic_detail', 'word_detail', 'dialect_region', 'sentence_type', 'speaker_id', 'id'],
        num_rows: 1680
    })
})
```

### Environment info

- ubuntu 20.04
- python 3.9.13
- datasets 2.7.1

The [timit_asr.py](https://huggingface.co/datasets/timit_asr/blob/main/timit_asr.py) script iterates over the WAV files per split directory using this:
```python
wav_paths = sorted(Path(data_dir).glob(f"**/{split}/**/*.wav"))
wav_paths = wav_paths if wav_paths else sorted(Path(data_dir).glob(f"**/{split.upper()}/**/*.WAV"))
```

Can you check that there is a directory named "test" somewhere in your timit data directory ?
Return a split Dataset in load_dataset
State: closed

...instead of a DatasetDict.

```python
# now supported
ds = load_dataset("squad")
ds[0]  
for example in ds:
    pass

# still works
ds["train"]
ds["validation"]

# new
ds.splits  # Dict[str, Dataset] | None

# soon to be supported (not in this PR)
ds = load_dataset("dataset_with_no_splits")
ds[0]
for example in ds:
    pass
```

I implemented `Dataset.__getitem__` and `IterableDataset.__getitem__` to be able to get a split from a dataset.
The splits are defined by the `ds.info.splits` dictionary.

Therefore a dataset is a table that optionally has some splits defined in the dataset info. And a split dataset is the concatenation of all its splits.

I made as little breaking changes as possible. Notable breaking changes:
- `load_dataset("potato").keys() / .items() / .values() /` don't work anymore, since we don't return a dict
- same for `for split_name in load_dataset("potato")`, since we now iterate on the examples
- ..

TODO:
- [x] Update push_to_hub
- [x] Update save_to_disk/load_from_disk
- [ ] check for other breaking changes
- [ ] fix existing tests
- [ ] add new tests
- [ ] docs

This is related to https://github.com/huggingface/datasets/issues/5189, to extend `load_dataset` to return datasets without splits

The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5301). All of your documentation changes will be reflected on that endpoint.

Just noticed that now we have to deal with indexed & split datasets. The remaining tests are failing because one should be able to get an indexed dataset when accessing the split of a dataset made of indexed splits (right now the index is just trashed)
Use same `num_proc` for dataset download and generation
State: closed

Use the same `num_proc` value for data download and generation. Additionally, do not set `num_proc` to 16 in `DownloadManager` by default (`num_proc` now has to be specified explicitly).

_The documentation is not available anymore as the PR was closed or merged._

I noticed this bug the other day and was going to look into it! "Where are these processes coming from?" ;-)
Bug in xopen with Windows pathnames
State: closed

Currently, `xopen` function has a bug with local Windows pathnames:

From its implementation:
```python
def xopen(file: str, mode="r", *args, **kwargs):
    file = _as_posix(PurePath(file))
    main_hop, *rest_hops = file.split("::")
    if is_local_path(main_hop):
        return open(file, mode, *args, **kwargs)
```

On a Windows machine, if we pass the argument:
```python
xopen("C:\\Users\\USERNAME\\filename.txt")
```
it returns
```python
open("C:/Users/USERNAME/filename.txt")
```
Bug in xjoin with Windows pathnames
State: closed

Currently, `xjoin` function has a bug with local Windows pathnames: instead of returning the OS-dependent join pathname, it always returns it in POSIX format.

```python
from datasets.download.streaming_download_manager import xjoin

path = xjoin("C:\\Users\\USERNAME", "filename.txt")
```

Join path should be:
```python
"C:\\Users\\USERNAME\\filename.txt"
```
However it is:
```python
"C:/Users/USERNAME/filename.txt"
```
Missing documentation build for versions 2.7.1 and 2.6.2 
State: closed

After the patch releases [2.7.1](https://github.com/huggingface/datasets/releases/tag/2.7.1) and [2.6.2](https://github.com/huggingface/datasets/releases/tag/2.6.2), the online docs were not properly built (the build_documentation workflow was not triggered).

There was a fix by:
- #5291

However, both documentations were built from main branch, instead of their corresponding version branch.

We are rebuilding them.

- Build docs for 2.6.2:
  - Commit: a6a5a1cf4cdf1e0be65168aed5a327f543001fe8
  - Build docs GH Action: https://github.com/huggingface/datasets/actions/runs/3539470622/jobs/5941404044
- Build docs for 2.7.1:
  - Commit: 5ef1ab1cc06c2b7a574bf2df454cd9fcb071ccb2
  - Build docs GH Action: https://github.com/huggingface/datasets/actions/runs/3539574442/jobs/5941636792
[build doc] for v2.7.1 & v2.6.2
State: closed

Do NOT merge. Using this PR to build docs for [v2.7.1](https://github.com/huggingface/datasets/pull/5291/commits/f4914af20700f611b9331a9e3ba34743bbeff934) & [v2.6.2](https://github.com/huggingface/datasets/pull/5291/commits/025f85300a0874eeb90a20393c62f25ac0accaa0)

_The documentation is not available anymore as the PR was closed or merged._

doc versions are built https://huggingface.co/docs/datasets/index
Lossy json serialization - deserialization of dataset info 
State: open

### Describe the bug

Saving a dataset to disk as json (using `to_json`) and then loading it again (using `load_dataset`) results in features whose labels are not type-cast correctly. In the code snippet below, `features.label` should have a label of type `ClassLabel` but has type `Value` instead. 

### Steps to reproduce the bug

```
from datasets import load_dataset

def test_serdes_from_json(d):
    dataset = load_dataset(d, split="train")
    dataset.to_json('_test')
    dataset_loaded = load_dataset("json", data_files='_test', split='train')
    try:
        assert dataset_loaded.info.features == dataset.info.features, "features unequal!"
    except Exception as ex:
        print(f'{ex}')
        print(f'expected {dataset.info.features}, \nactual { dataset_loaded.info.features }')
    
test_serdes_from_json('rotten_tomatoes')
```

Output
```
features unequal!
expected {'text': Value(dtype='string', id=None), 'label': ClassLabel(names=['neg', 'pos'], id=None)}, 
actual {'text': Value(dtype='string', id=None), 'label': Value(dtype='int64', id=None)}
```


### Expected behavior

The deserialized `features.label` should have type `ClassLabel`.

### Environment info

- `datasets` version: 2.6.1
- Platform: Linux-5.10.144-127.601.amzn2.x86_64-x86_64-with-glibc2.17
- Python version: 3.7.13
- PyArrow version: 7.0.0
- Pandas version: 1.2.3

Hi ! JSON is a lossy format indeed. If you want to keep the feature types or other metadata I'd encourage you to store them as well. For example you can use `dataset.info.write_to_directory` and `DatasetInfo.from_directory` to store the feature types, split info, description, license etc.
Save file name in embed_storage
State: closed

Having the file name is useful in case we need to check the extension of the file (e.g. mp3), or in general in case it includes some metadata information (track id, image id etc.)

Related to https://github.com/huggingface/datasets/issues/5276

_The documentation is not available anymore as the PR was closed or merged._

I updated the tests, met le know if it sounds good to you now :)