NyssaW/github-issues huggingface dataset:
ImageFolder raises an error with parameters drop_metadata=True and drop_labels=False when metadata.jsonl is present

## Describe the bug

If you pass `drop_metadata=True` and `drop_labels=False` when a `data_dir` contains at least one `matadata.jsonl` file, you will get a KeyError. This is probably not a very useful case but we shouldn't get an error anyway. Asking users to move metadata files manually outside `data_dir` or pass features manually (when there is a tool that can infer them automatically) don't look like a good idea to me either.

## Steps to reproduce the bug
### Clone an example dataset from the Hub
```bash
git clone https://huggingface.co/datasets/nateraw/test-imagefolder-metadata
```
### Try to load it
```python
from datasets import load_dataset
ds = load_dataset("test-imagefolder-metadata", drop_metadata=True, drop_labels=False)
```
or even just
```python
ds = load_dataset("test-imagefolder-metadata", drop_metadata=True)
```
as `drop_labels=False` is a default value.

## Expected results
A DatasetDict object with two features: `"image"` and `"label"`.

## Actual results
```
Traceback (most recent call last):
  File "/home/polina/workspace/datasets/debug.py", line 18, in <module>
    ds = load_dataset(
  File "/home/polina/workspace/datasets/src/datasets/load.py", line 1732, in load_dataset
    builder_instance.download_and_prepare(
  File "/home/polina/workspace/datasets/src/datasets/builder.py", line 704, in download_and_prepare
    self._download_and_prepare(
  File "/home/polina/workspace/datasets/src/datasets/builder.py", line 1227, in _download_and_prepare
    super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
  File "/home/polina/workspace/datasets/src/datasets/builder.py", line 793, in _download_and_prepare
    self._prepare_split(split_generator, **prepare_split_kwargs)
  File "/home/polina/workspace/datasets/src/datasets/builder.py", line 1218, in _prepare_split
    example = self.info.features.encode_example(record)
  File "/home/polina/workspace/datasets/src/datasets/features/features.py", line 1596, in encode_example
    return encode_nested_example(self, example)
  File "/home/polina/workspace/datasets/src/datasets/features/features.py", line 1165, in encode_nested_example
    {
  File "/home/polina/workspace/datasets/src/datasets/features/features.py", line 1165, in <dictcomp>
    {
  File "/home/polina/workspace/datasets/src/datasets/utils/py_utils.py", line 249, in zip_dict
    yield key, tuple(d[key] for d in dicts)
  File "/home/polina/workspace/datasets/src/datasets/utils/py_utils.py", line 249, in <genexpr>
    yield key, tuple(d[key] for d in dicts)
KeyError: 'label'
```

## Environment info
`datasets` master branch 

- `datasets` version: 2.3.3.dev0
- Platform: Linux-5.14.0-1042-oem-x86_64-with-glibc2.17
- Python version: 3.8.12
- PyArrow version: 6.0.1
- Pandas version: 1.4.1



Comments:
Data type is not recognized when using datetime.time

## Describe the bug

Creating a dataset from a pandas dataframe with `datetime.time` format generates an error.

## Steps to reproduce the bug
```python
import pandas as pd
from datetime import time
from datasets import Dataset
df = pd.DataFrame({"feature_name": [time(1, 1, 1)]})
dataset = Dataset.from_pandas(df)
```

## Expected results

The dataset should be created.

## Actual results

```
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 823, in from_pandas
    return cls(table, info=info, split=split)
  File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 679, in __init__
    inferred_features = Features.from_arrow_schema(arrow_table.schema)
  File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1551, in from_arrow_schema
    obj = {field.name: generate_from_arrow_type(field.type) for field in pa_schema}
  File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1551, in <dictcomp>
    obj = {field.name: generate_from_arrow_type(field.type) for field in pa_schema}
  File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1315, in generate_from_arrow_type
    return Value(dtype=_arrow_to_datasets_dtype(pa_type))
  File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 83, in _arrow_to_datasets_dtype
    return f"time64[{arrow_type.unit}]"
AttributeError: 'pyarrow.lib.DataType' object has no attribute 'unit'
```

## Environment info

- `datasets` version: 2.3.3.dev0
- Platform: Linux-5.13.0-1031-aws-x86_64-with-glibc2.31
- Python version: 3.9.6
- PyArrow version: 7.0.0
- Pandas version: 1.4.2

Comments:
cc @mariosasko 

Hi, thanks for reporting! I'm investigating the issue.
np arrays get turned into native lists

## Describe the bug
When attaching an `np.array` field, it seems that it automatically gets turned into a list (see below). Why is this happening? Could it lose precision? Is there a way to make sure this doesn't happen?

## Steps to reproduce the bug
```python
>>> import datasets, numpy as np
>>> dataset = datasets.load_dataset("glue", "mrpc")["validation"]
Reusing dataset glue (...)
100%|███████████████████████████████████████████████| 3/3 [00:00<00:00, 1360.61it/s]
>>> dataset2 = dataset.map(lambda x: {"tmp": np.array([0.5])}, batched=False)
100%|██████████████████████████████████████████| 408/408 [00:00<00:00, 10819.97ex/s]
>>> dataset2[0]["tmp"]
[0.5]
>>> type(dataset2[0]["tmp"])
<class 'list'>
```

## Expected results
`dataset2[0]["tmp"]` should be an `np.ndarray`.

## Actual results
It's a list.

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: mac, though I'm pretty sure it happens on a linux machine too
- Python version: 3.9.7
- PyArrow version: 6.0.1


Comments:
If you add the line `dataset2.set_format('np')` before calling `dataset2[0]['tmp']` it should return `np.ndarray`.
I believe internally it will not store it as a list, it is only returning a list when you index it.

```
In [1]: import datasets, numpy as np
In [2]: dataset = datasets.load_dataset("glue", "mrpc")["validation"]
In [3]: dataset2 = dataset.map(lambda x: {"tmp": np.array([0.5])}, batched=False)
In [4]: dataset2[0]["tmp"]
Out[4]: [0.5]

In [5]: dataset2.set_format('np')

In [6]: dataset2[0]["tmp"]
Out[6]: array([0.5])
```

I see, thanks! Any idea if the default numpy → list conversion might cause precision loss?

I'm not super familiar with our datasets works internally, but I think your `np` array will be stored in a `pyarrow` format, and then you take a view of this as a python array. In which case, I think the precision should be preserved.
contribute data loading for object detection datasets with yolo data format

**Is your feature request related to a problem? Please describe.**
At the moment, HF datasets loads [image classification datasets](https://huggingface.co/docs/datasets/image_process) out-of-the-box. There could be a data loader for loading standard object detection datasets ([original discussion here](https://huggingface.co/datasets/jalFaizy/detect_chess_pieces/discussions/2))

**Describe the solution you'd like**
I wrote a [custom script](https://huggingface.co/datasets/jalFaizy/detect_chess_pieces/blob/main/detect_chess_pieces.py) to load dataset which has YOLO data format. 

**Describe alternatives you've considered**
The script can either be a standalone dataset builder, or a modified version of `ImageFolder`

**Additional context**
I would be happy to contribute to this, but I would do it at a very slow pace (maybe a month or two) as I have my exams approaching 😄 


Comments:
Hi! The `imagefolder` script is already quite complex, so a standalone script sounds better. Also, I suggest we create an org on the Hub (e.g. `hf-loaders`) and store such scripts there for easier maintenance rather than having them as packaged modules (IMO only very generic loaders should be packaged). WDYT @lhoestq @albertvillanova @polinaeterna?

@mariosasko sounds good to me!


Thank you for the suggestion @mariosasko . I agree with the point, but I have a few doubts

1. How would the user access the script if it's not a part of the core codebase?
2. Could you direct me as to what will be the tasks I have to do to contribute to the code? As per my understanding, it would be like
    1. Create a new org "hf-loaders" and add you (and more HF people) to the org
    2. Add data loader script as a (model?)
    3. Test it with a dataset on HF hub
3. We should maybe brainstorm as to which public datasets have this format (YOLO type) and are the most important ones to test the script with. We can even add the datasets on HF Hub alongside the script

1. Like this: `load_dataset("hf-loaders/yolo", data_files=...)`
2. The steps would be:
    1. Create a new org `hf-community-loaders` (IMO a better name than "hf-loaders") and add me (as an admin)
    2. Create a new dataset repo `yolo` and add the loading script to it (`yolo.py`)
    3. Open a discussion to request our review
4. I like this idea. Another option is to add snippets that describe how to load such datasets using the `yolo` loader.
Fix `embed_storage` on features inside lists/sequences

Add a dedicated function for embed_storage to always preserve the embedded/casted arrays (and to have more control over `embed_storage` in general).

Fix #4591 

~~(Waiting for #4608 to be merged to mark this PR as ready for review - required for fixing `xgetsize` in private repos)~~ Done!

Comments:
_The documentation is not available anymore as the PR was closed or merged._
Ensure ConcatenationTable.cast uses target_schema metadata

Currently, `ConcatenationTable.cast` does not use target_schema metadata when casting subtables. This causes an issue when using cast_column and the underlying table is a ConcatenationTable.

Code example of where issue arrises:
```
from datasets import Dataset, Image

column1 = [0, 1]
image_paths = ['/images/image1.jpg', '/images/image2.jpg']

ds = Dataset.from_dict({"column1": column1})
ds = ds.add_column("image", image_paths)
ds.cast_column("image", Image()) # Fails here
```
Output
```
...
TypeError: Couldn't cast array of type
string
to
{'bytes': Value(dtype='binary', id=None), 'path': Value(dtype='string', id=None)}
```


Comments:
Hi @lhoestq, Thanks for the detailed comment. I've tested the suggested approach and can confirm it works for the testcase outlined above! The PR is updated with the changes.

_The documentation is not available anymore as the PR was closed or merged._
Release 2.3.0 broke custom iterable datasets

## Describe the bug
Trying to iterate examples from custom iterable dataset fails to bug introduced in `torch_iterable_dataset.py` since the release of 2.3.0. 

## Steps to reproduce the bug
```python
next(iter(custom_iterable_dataset))
```

## Expected results
`next(iter(custom_iterable_dataset))` should return examples from the dataset

## Actual results
```
/usr/local/lib/python3.7/dist-packages/datasets/formatting/dataset_wrappers/torch_iterable_dataset.py in _set_fsspec_for_multiprocess()
     16     See https://github.com/fsspec/gcsfs/issues/379
     17     """
---> 18     fsspec.asyn.iothread[0] = None
     19     fsspec.asyn.loop[0] = None
     20 

AttributeError: module 'fsspec' has no attribute 'asyn'
```

## Environment info
- `datasets` version: 2.3.0
- Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- PyArrow version: 8.0.0
- Pandas version: 1.3.5


Comments:
Apparently, `fsspec` does not allow access to attribute-based modules anymore, such as `fsspec.async`.

However, this is a fairly simple fix:
- Change the import to: `from fsspec import asyn`;
- Change line 18 to: `asyn.iothread[0] = None`;
- Change line 19 to `asyn.loop[0] = None`.

Hi! I think it's easier to replace `import fsspec` with `import fsspec.asyn` and leave the rest unchanged. @gugarosa Are you interested in submitting a PR?

Perfect, it is even better!

Just submitted the PR: #4630.

Thank you!
Preserve member order by MockDownloadManager.iter_archive

Currently, `MockDownloadManager.iter_archive` yields paths to archive members in an order given by `path.rglob("*")`, which migh not be the same order as in the original archive.

See issue in:
- https://github.com/huggingface/datasets/pull/4579#issuecomment-1172135027

This PR fixes the order of the members yielded by `MockDownloadManager.iter_archive` so that it is the same as in the original archive.

Comments:
_The documentation is not available anymore as the PR was closed or merged._
librispeech dataset has to download whole subset when specifing the split to use

## Describe the bug
librispeech dataset has to download whole subset when specifing the split to use

## Steps to reproduce the bug
see below
# Sample code to reproduce the bug
```
!pip install datasets
from datasets import load_dataset
raw_dataset = load_dataset("librispeech_asr", "clean", split="train.100")
```

## Expected results
The split "train.clean.100" is downloaded.

## Actual results
All four splits in "clean" subset is downloaded.

## Environment info
- `datasets` version: 2.3.2
- Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- PyArrow version: 6.0.1
- Pandas version: 1.3.5


Comments:
Hi! You can use streaming to fetch only a subset of the data:
```python
raw_dataset = load_dataset("librispeech_asr", "clean", split="train.100", streaming=True)
```
Also, we plan to make it possible to download a particular split in the non-streaming mode, but this task is not easy due to how our dataset scripts are structured.

Hi,

That's a great help. Thank you very much.
Fix xisfile, xgetsize, xisdir, xlistdir in private repo

`xisfile` is working in a private repository when passing a chained URL to a file inside an archive, e.g. `zip://a.txt::https://huggingface/datasets/username/dataset_name/resolve/main/data.zip`. However it's not working when passing a simple file `https://huggingface/datasets/username/dataset_name/resolve/main/data.zip`.

This is because the authentication headers are not passed correctly in this case.

This is causing dataset streaming to fail in private parquet repositories, as noted in https://github.com/huggingface/datasets/issues/4605

I fixed `xisfile` and the other functions that behave the same way: xgetsize, xisdir and xlistdir

TODO:
- [x] tests

fix https://github.com/huggingface/datasets/issues/4605

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

Added tests for xisfile, xgetsize, xlistdir and xglob for private repos, and also tests for xwalk that was untested
Align more metadata with other repo types (models,spaces)

see also associated PR on the `datasets-tagging` Space: https://huggingface.co/spaces/huggingface/datasets-tagging/discussions/2 (to merge after this one is merged)

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

I just set a default value (None) for the deprecated licenses and languages fields, which should fix most of the CI failures.

Note that the CI should still be red because you edited many dataset cards and they're still missing some content - but this is unrelated to this PR so we can ignore these failures

thanks so much @lhoestq !!

There's also a follow-up PR to this one, in #4613 – I would suggest to merge all of them at the same time and hope not too many things are broken 🙀 🙀 

Alright merging this one now, let's see how broken things get
evaluation result changes after `datasets` version change

## Describe the bug
evaluation result changes after `datasets` version change

## Steps to reproduce the bug
1. Train a model on WikiAnn
2. reload the ckpt -> test accuracy becomes same as eval accuracy
3. such behavior is gone after downgrading `datasets`

https://colab.research.google.com/drive/1kYz7-aZRGdayaq-gDTt30tyEgsKlpYOw?usp=sharing

## Expected results
evaluation result shouldn't change before/after `datasets` version changes

## Actual results
evaluation result changes before/after `datasets` version changes

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: colab
- Python version: 3.7.13
- PyArrow version: 6.0.1

Q. How could the evaluation result change before/after `datasets` version changes?

Comments:
Hi! The GH/no-namespace datasets versioning is synced with the version of the `datasets` lib, which means that the `wikiann` script was modified between the two compared versions. In this scenario, you can ensure reproducibility by pinning the script version, which is done by passing `revision="x.y.z"` (e.g. `revision="2.2.0"`) to `load_dataset.`
Dataset Viewer issue for boris/gis_filtered

### Link

https://huggingface.co/datasets/boris/gis_filtered/viewer/boris--gis_filtered/train

### Description

When I try to access this from the website I get this error:

Status code:   400
Exception:     ClientResponseError
Message:       401, message='Unauthorized', url=URL('https://huggingface.co/datasets/boris/gis_filtered/resolve/80b805053ce61d4eb487b6b8d9095d775c2c466e/data/train/0000.parquet')

If I try to load with code I also get the same issue: 
```python
dataset2_train=load_dataset("boris/gis_filtered", use_auth_token=os.environ["HF_TOKEN"],split="train",streaming=True)
dataset2_validation=load_dataset("boris/gis_filtered", use_auth_token=os.environ["HF_TOKEN"], split="validation",streaming=True)
```

### Owner

No

Comments:
Yes, this dataset is "gated": you first have to go to https://huggingface.co/datasets/boris/gis_filtered and click "Access repository" (if you accept to share your contact information with the repository authors).

I already did that, it returns error when using streaming

Oh, sorry, I misread. Looking at it. Maybe @huggingface/datasets or @SBrandeis 

I could reproduce the error, even though I provided my token and accepted the gate form. It looks like an error from `datasets`

This is indeed a bug in `datasets`. Parquet datasets in gated/private repositories can't be streamed properly, which caused the viewer to fail. I opened a PR at https://github.com/huggingface/datasets/pull/4608
Smooth-BLEU bug fixed

Hi,

the current implementation of smooth-BLEU contains a bug: it smoothes unigrams as well. Consequently, when both the reference and translation consist of totally different tokens, it anyway returns a non-zero value (please see the attached image). 

This however contradicts the source paper suggesting the smooth-BLEU _(Chin-Yew Lin, Franz Josef Och. ORANGE: a method for evaluating automatic evaluation metrics for machine translation. COLING 2004.)_ :

> Add one count to the n-gram hit and total ngram count for n > 1. Therefore, for candidate translations with less than n words, they can still get a positive smoothed BLEU score from shorter n-gram matches; however if nothing matches then they will get zero scores. 

This pull request aims at fixing this bug.

I made a pull request in the target repository `tensorflow/nmt`, which implements this script, yet the last commit there is dating 19.02.2019 and I doubt whether this will be fixed promptly. Yet, this bug is critical, for instance for summarization datasets with short summaries (e.g. AESLC), since smoothing needs to be applied there. Therefore, the easiest solution that I found is to fork the repo and download this script directly from the forked fixed repo.

Kind,
Akim Tsvigun

<img width="516" alt="Снимок экрана 2022-06-29 в 17 49 27" src="https://user-images.githubusercontent.com/36672861/176466935-ac579e6d-6a93-4111-ab41-9b33056e7d47.png">
 

Comments:
Thanks @Aktsvigun for your fix.

However, metrics in `datasets` are in deprecation mode:
- #4739

You should transfer this PR to the `evaluate` library: https://github.com/huggingface/evaluate

Just for context, here the link to the PR by @Aktsvigun on tensorflow/nmt:
- https://github.com/tensorflow/nmt/pull/488
Streaming issue for financial_phrasebank

### Link

https://huggingface.co/datasets/financial_phrasebank/viewer/sentences_allagree/train

### Description

As reported by a community member using [AutoTrain Evaluate](https://huggingface.co/spaces/autoevaluate/model-evaluator/discussions/5#62bc217436d0e5d316a768f0), there seems to be a problem streaming this dataset:

```
Server error
Status code:   400
Exception:     Exception
Message:       Give up after 5 attempts with ConnectionError
```

### Owner

No

Comments:
cc @huggingface/datasets: it seems like https://www.researchgate.net/ is flaky for datasets hosting (I put the "hosted-on-google-drive" tag since it's the same kind of issue I think)

Let's see if their license allows hosting their data on the Hub.

License is Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0).

We can host their data on the Hub.
Dataset Viewer issue for universal_dependencies

### Link

https://huggingface.co/datasets/universal_dependencies

### Description

invalid json response body at https://datasets-server.huggingface.co/splits?dataset=universal_dependencies reason: Unexpected token I in JSON at position 0

### Owner

_No response_

Comments:
Thanks, looking at it!

Finally fixed! We updated the dataset viewer and it fixed the issue.

https://huggingface.co/datasets/universal_dependencies/viewer/aqz_tudet/train

<img width="1561" alt="Capture d’écran 2022-09-07 à 13 29 18" src="https://user-images.githubusercontent.com/1676121/188867795-4f7dd438-d4f2-46cd-8a92-20a37fb2d6bc.png">
Dataset Viewer issue with False positive PII redaction

### Link

https://huggingface.co/datasets/cakiki/rosetta-code

### Description

Hello, I just noticed an entry being redacted that shouldn't have been:

`RootMeanSquare@Range[10]` is being displayed as `[email protected][10]`

### Owner

_No response_

Comments:
The value is in the data, it's not an issue with the "dataset-viewer".

<img width="1161" alt="Capture d’écran 2022-06-29 à 10 25 51" src="https://user-images.githubusercontent.com/1676121/176389325-4d2a9a7f-1583-45b8-aa7a-960ffaa6a36a.png">

 Maybe open a PR: https://huggingface.co/datasets/cakiki/rosetta-code/discussions


This was indeed a scraping issue which I assumed was a display issue; sorry about that!
Fix error message when using load_from_disk to load DatasetDict

Issue #4594 
Issue: When `datasets.load_from_disk` is wrongly used to load a `DatasetDict`, the error message suggests using `datasets.load_from_disk`, which is the same function that generated the error. 
Fix: The appropriate function which should be suggested instead is `datasets.dataset_dict.load_from_disk`.
Changes: Change the suggestion to say "Please use `datasets.dataset_dict.load_from_disk` instead."

Comments:
Generalize meta_path json file creation in load.py [#4540]

# What does this PR do?

## Summary

*In function `_copy_script_and_other_resources_in_importable_dir`, using string split when generating `meta_path` throws error in the edge case raised in #4540.*

## Additions
-

## Changes
- Changed meta_path to use `os.path.splitext` instead of using `str.split` to generalize code.

## Deletions
-
## Issues Addressed : 

Fixes #4540

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

@albertvillanova,  Can you please review this PR for Issue #4540 

@lhoestq  Thank you for merging the PR . Is there a slack channel for contributing to the datasets library. I would love to work on the library and make meaningful contributions.

Hi ! Sure feel free to join our discord ^^ 
https://discuss.huggingface.co/t/join-the-hugging-face-discord/11263 so that we can discuss together mor eeasily. Otherwise everything happens on github ;)
Host head_qa data on the Hub and fix NonMatchingChecksumError

This PR:
- Hosts head_qa data on the Hub instead of Google Drive
- Fixes NonMatchingChecksumError

Fix https://huggingface.co/datasets/head_qa/discussions/1

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

Hi @albertvillanova ! Thanks for the fix ;)
Can I safely checkout from this branch to build `datasets` or it is preferable to wait until all CI tests pass?
Thanks 🙏 

@younesbelkada we have just merged this PR.
Add binary classification task IDs

As a precursor to aligning the task IDs in `datasets` and AutoTrain, we need a way to distinguish binary vs multiclass vs multilabel classification.

This PR adds binary classification to the task IDs to enable this.

Related AutoTrain issue: https://github.com/huggingface/autonlp-backend/issues/597

cc @abhishekkrthakur @SBrandeis 

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4584). All of your documentation changes will be reflected on that endpoint.

> Awesome thanks ! Can you add it to https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts first please ? This is where we define the cross libraries tasks taxonomy ;)

Thanks for the tip! Done in https://github.com/huggingface/hub-docs/pull/217

I don't think we need to update this file anymore. We should remove it IMO, and simply update the dataset [tagging app](https://huggingface.co/spaces/huggingface/datasets-tagging)

I'm closing this PR.
add_column should preserve _indexes

https://github.com/huggingface/datasets/issues/3769#issuecomment-1167146126

doing `.add_column("x",x_data)` also removed any `_indexes` on the dataset, decided this shouldn't be the case.

This was because `add_column` was creating a new `Dataset(...)` and wasn't possible to pass indexes on init.
with this PR now can pass 'indexes' on init through `IndexableMixin`

- [x] Added test

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4582). All of your documentation changes will be reflected on that endpoint.
Dataset Viewer issue for pn_summary

### Link

https://huggingface.co/datasets/pn_summary/viewer/1.0.0/validation

### Description

Getting an index error on the `validation` and `test` splits:

```
Server error
Status code:   400
Exception:     IndexError
Message:       list index out of range
```

### Owner

No

Comments:
linked to https://github.com/huggingface/datasets/issues/4580#issuecomment-1168373066?

Note that I refreshed twice this dataset, and I still have (another) error on one of the splits

```
Status code:   400
Exception:     ClientResponseError
Message:       403, message='Forbidden', url=URL('https://doc-14-4c-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/pgotjmcuh77q0lk7p44rparfrhv459kp/1656403650000/11771870722949762109/*/16OgJ_OrfzUF_i3ftLjFn9kpcyoi7UJeO?e=download')
```

Like the three splits are processed in parallel by the workers, I imagine that the Google hosting is rate-limiting us.

cc @albertvillanova 



Exactly, Google Drive bans our loading scripts.

When possible, we should host somewhere else.
Dataset Viewer issue for multi_news

### Link

https://huggingface.co/datasets/multi_news

### Description

Not sure what the index error is referring to here:

```
Status code:   400
Exception:     IndexError
Message:       list index out of range
```

### Owner

No

Comments:
Thanks for reporting, @lewtun.

I forced the refreshing of the preview and it worked OK for train and validation splits.

I guess the error has to do with the data files being hosted at Google Drive: this gives errors when requested automatically using scripts.
We should host them to fix the error. Let's see if the license allows that.

I guess we can host the data: https://github.com/Alex-Fabbri/Multi-News/blob/master/LICENSE.txt
Support streaming cfq dataset

Support streaming cfq dataset.

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

@lhoestq I've been refactoring a little the code:
- Use less RAM by loading only the required samples: only if its index is in the splits file
- Start yielding "earlier" in streaming mode: for each `split_idx`:
  - either yield from buffer
  - or iterate over samples and either yield or buffer the sample
  
  The speed gain obviously depends on how the indexes are sorted in the split file:
    - Best case: indices are [1, 2, 3]
    - Worst case (no speed gain): indices are [3, 1, 2] or [3, 2, 1]

Let me know what you think.

I have to update the dummy data so that it aligns with the real data (inside the archive, the samples file `dataset.json` is the last member).

There is an issue when testing `test_load_dataset_cfq` with dummy data:
- `MockDownloadManager.iter_archive` yields FIRST `'cfq/dataset.json'`
- [`Streaming`]`DownloadManager.iter_archive` yields LAST `'cfq/dataset.json'` when using real data tar.gz archive

Note that this issue arises only with dummy data: loading the real dataset works smoothly for all configurations: I recreated the `dataset_infos.json` file to check it (it generated the same file).

This PR should be merged first:
- #4611

Impressive, thank you ! :o 

feel free to merge master into this branch, now that the files order is respected. You can merge if the CI is green :)
[Multi Configs] Use directories to differentiate between subsets/configurations

Currently to define several subsets/configurations of your dataset, you need to use a dataset script.

However it would be nice to have a no-code way to to this. 

For example we could specify different configurations of a dataset (for example, if a dataset contains different languages) with one directory per configuration.

These structures are not supported right now, but would be nice to have:


```
my_dataset_repository/
├── README.md
├── en/
│   ├── train.csv
│   └── test.csv
└── fr/
    ├── train.csv
    └── test.csv
```

Or with one directory per split:

```
my_dataset_repository/
├── README.md
├── en/
│   ├── train/
│   │   ├── shard_0.csv
│   │   └── shard_1.csv
│   └── test/
│       ├── shard_0.csv
│       └── shard_1.csv
└── fr/
    ├── train/
    │   ├── shard_0.csv
    │   └── shard_1.csv
    └── test/
        ├── shard_0.csv
        └── shard_1.csv
```

cc @stevhliu @albertvillanova 

This can be specified in the README as YAML with

```
configs:
- config_name: en
  data_dir: en
- config_name: fr
  data_dir: fr
```

Comments:
I want to be able to create folders in a model.

How to set new split names, instead of train/test/validation? For example, I have a local dataset, consists of several subsets, named "A", "B", and "C". How can I create a huggingface dataset, with splits A/B/C ?

The document in https://huggingface.co/docs/datasets/dataset_script only tells me how to create datasets with subsets that is hosted on another server. How to do it if my datasets are local?

> The document in https://huggingface.co/docs/datasets/dataset_script only tells me how to create datasets with subsets that is hosted on another server. How to do it if my datasets are local?

It works the same - you just need to use local paths instead of URLs
Include `metadata.jsonl` in resolved data files

Include `metadata.jsonl` in resolved data files.

Fix #4548 

@lhoestq ~~https://github.com/huggingface/datasets/commit/d94336d30eef17fc9abc67f67fa1c139661f4e75 adds support for metadata files placed at the root, and https://github.com/huggingface/datasets/commit/4d20618ea7a19bc143ddc5fdff9d79e671fcbb95 accounts for nested metadata files also, but this results in more complex code. Let me know which one of these two approaches you prefer.~~ Maybe https://github.com/huggingface/datasets/commit/d94336d30eef17fc9abc67f67fa1c139661f4e75 is good enough for now (for the sake of simplicity). https://github.com/huggingface/datasets/commit/4d20618ea7a19bc143ddc5fdff9d79e671fcbb95 breaks the imagefolder tests due to duplicates in the resolved metadata files. One way to fix this would be to resolve the metadata pattern only on parent directories, but this adds even more logic to `_get_data_files_patterns`, so not sure if this is what we should do.

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

I still don't know if the way we implemented data files resolution could support the metadata.jsonl file without bad side effects for the other packaged builders. In particular here if you have a folder of csv/parquet/whatever files and a metadata.jsonl file, it would return 
```
split: patterns_dict[split] + [METADATA_PATTERN]
```
which is a bit unexpected and can lead to errors.

Maybe this logic can be specific to imagefolder somehow ? This could be an additional pattern `["metadata.jsonl", "**/metadata.jsonl"]` just for imagefolder, that is only used when `data_files=` is not specified by the user.

I guess it's ok to have patterns that lead to duplicate metadata.jsonl files for imagefolder, since the imagefolder logic only considers the closest metadata file for each image.

What do you think ?

Yes, that's indeed the problem. My solution in https://github.com/huggingface/datasets/commit/4d20618ea7a19bc143ddc5fdff9d79e671fcbb95 that accounts for that (include metadata files only if image files are present; not ideal): https://github.com/huggingface/datasets/blob/4d20618ea7a19bc143ddc5fdff9d79e671fcbb95/src/datasets/data_files.py#L119-L125.
Perhaps a cleaner approach would be to check for metadata files after the packaged module type is inferred as `imagefolder` and append metadata files to already resolved data files (if there are any). WDYT?

@lhoestq 

> Perhaps a cleaner approach would be to check for metadata files after the packaged module type is inferred as imagefolder and append metadata files to already resolved data files (if there are any). WDYT?

I decided to go with this approach.

 Not sure if you meant the same thing with this comment:

> Maybe this logic can be specific to imagefolder somehow ? This could be an additional pattern ["metadata.jsonl", "**/metadata.jsonl"] just for imagefolder, that is only used when data_files= is not specified by the user.


It adds more code but is easy to follow IMO.


The CI still struggles but you can merge since at least one of the two WIN CI succeeded
Dataset Viewer issue for mlsum

### Link

https://huggingface.co/datasets/mlsum/viewer/de/train

### Description

There's seems to be a problem with the download / streaming of this dataset:

```
Server error
Status code:   400
Exception:     BadZipFile
Message:       File is not a zip file
```

### Owner

No

Comments:
Thanks for reporting, @lewtun.

After investigation, it seems that the server https://gitlab.lip6.fr does not allow HTTP Range requests.

We are trying to find a workaround...
move under the facebook org?

### Link

https://huggingface.co/datasets/gsarti/flores_101

### Description

It seems like streaming isn't supported for this dataset:

```
Server Error
Status code:   400
Exception:     NotImplementedError
Message:       Extraction protocol for TAR archives like 'https://dl.fbaipublicfiles.com/flores101/dataset/flores101_dataset.tar.gz' is not implemented in streaming mode. Please use `dl_manager.iter_archive` instead.
```

### Owner

No

Comments:
Related to https://github.com/huggingface/datasets/issues/4562#issuecomment-1166911751

I'll assign @albertvillanova 

I'm just wondering why we don't have this dataset under:
- the `facebook` namespace
- or the canonical dataset `flores`: why does this only have 2 languages?

fwiw: the dataset viewer is working. Renaming the issue
Dataset sharding non-contiguous?

## Describe the bug
I'm not sure if this is a bug; more likely normal behavior but i wanted to double check.
Is it normal that `datasets.shard` does not produce chunks that, when concatenated produce the original ordering of the sharded dataset? 

This might be related to this pull request (https://github.com/huggingface/datasets/pull/4466) but I have to admit I did not properly look into the changes made.

## Steps to reproduce the bug
```python
max_shard_size = convert_file_size_to_int('300MB')
dataset_nbytes = dataset.data.nbytes
num_shards = int(dataset_nbytes / max_shard_size) + 1
num_shards = max(num_shards, 1)
print(f"{num_shards=}")
for shard_index in range(num_shards):
    shard = dataset.shard(num_shards=num_shards, index=shard_index)
    shard.to_parquet(f"tokenized/tokenized-{shard_index:03d}.parquet")
os.listdir('tokenized/')
```

## Expected results
I expected the shards to match the order of the data of the original dataset; i.e. `dataset[10]` being the same as `shard_1[10]` for example

## Actual results
Only the first element is the same; i.e. `dataset[0]` is the same as `shard_1[0]`

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: Linux-4.15.0-176-generic-x86_64-with-glibc2.31
- Python version: 3.10.4
- PyArrow version: 8.0.0
- Pandas version: 1.4.2



Comments:
This was silly; I was sure I'd looked for a `contiguous` argument, and was certain there wasn't one the first time I looked :smile:

Sorry about that.

Hi! You can pass `contiguous=True` to `.shard()` get contiguous shards. More info on this and the default behavior can be found in the [docs](https://huggingface.co/docs/datasets/v2.3.2/en/package_reference/main_classes#datasets.Dataset.shard).

EDIT: Answered as you closed the thread 😄 

Hahaha I'm sorry; my excuse is: it's Sunday. (Which makes me all the more grateful for your response :smiley: 

@mariosasko Sorry for reviving this, but I was curious as to why `contiguous=False` was the default. This might be a personal bias, but I feel that a user would expect the opposite to be the default. :thinking: 

This project started as a fork of TFDS, and `contiguous=False` is the default behavior [there](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#shard).
Dataset Viewer issue for sst2

### Link

https://huggingface.co/datasets/sst2

### Description

Not sure what is causing this, however it seems that `load_dataset("sst2")` also hangs (even though it downloads the files without problem):

```
Status code:   400
Exception:     Exception
Message:       Give up after 5 attempts with ConnectionError
```

### Owner

No

Comments:
Hi @lewtun, thanks for reporting.

I have checked locally and refreshed the preview and it seems working smooth now:
```python
In [8]: ds
Out[8]: 
DatasetDict({
    train: Dataset({
        features: ['idx', 'sentence', 'label'],
        num_rows: 67349
    })
    validation: Dataset({
        features: ['idx', 'sentence', 'label'],
        num_rows: 872
    })
    test: Dataset({
        features: ['idx', 'sentence', 'label'],
        num_rows: 1821
    })
})
```

Could you confirm? 

Thanks @albertvillanova - it is indeed working now (not sure what caused the error in the first place). Closing this :)
Document link #load_dataset_enhancing_performance points to nowhere

## Describe the bug
A clear and concise description of what the bug is.

![image](https://user-images.githubusercontent.com/11674033/175752806-5b066b92-9d28-4771-9112-5c8606f07741.png)


The [load_dataset_enhancing_performance](https://huggingface.co/docs/datasets/v2.3.2/en/package_reference/main_classes#load_dataset_enhancing_performance) link [here](https://huggingface.co/docs/datasets/v2.3.2/en/package_reference/main_classes#datasets.Dataset.load_from_disk.keep_in_memory) points to nowhere, I guess it should point to https://huggingface.co/docs/datasets/v2.3.2/en/cache#improve-performance?


Comments:
Hi! This is indeed the link the docstring should point to. Are you interested in submitting a PR to fix this?

https://github.com/huggingface/datasets/blame/master/docs/source/cache.mdx#L93

There seems already an anchor here. Somehow it doesn't work. I am not very familiar with how this online documentation works.
Add UFSC OCPap dataset

## Adding a Dataset
- **Name:** UFSC OCPap: Papanicolaou Stained Oral Cytology Dataset (v4)
- **Description:** The UFSC OCPap dataset comprises 9,797 labeled images of 1200x1600 pixels acquired from 5 slides of cancer diagnosed and 3 healthy of oral brush samples, from distinct patients.
- **Paper:** https://dx.doi.org/10.2139/ssrn.4119212
- **Data:**  https://data.mendeley.com/datasets/dr7ydy9xbk/1
- **Motivation:** real data of pap stained oral cytology samples

Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).


Comments:
I will add this directly on the hub (same as #4486)—in https://huggingface.co/lapix
Dataset Viewer issue for allocine

### Link

https://huggingface.co/datasets/allocine

### Description

Not sure if this is a problem with `bz2` compression, but I thought these datasets could be streamed:

```
Status code:   400
Exception:     AttributeError
Message:       'TarContainedFile' object has no attribute 'readable'
```

### Owner

No

Comments:
I removed my assignment as @huggingface/datasets should be able to answer better than me


Let me have a look...

Thanks for the quick fix @albertvillanova 

Note that the underlying issue is that datasets containing TAR files are not streamable out of the box: they need being iterated with `dl_manager.iter_archive` to avoid performance issues because they access their file content *sequentially* (no random access).

> Note that the underlying issue is that datasets containing TAR files are not streamable out of the box: they need being iterated with `dl_manager.iter_archive` to avoid performance issues because they access their file content _sequentially_ (no random access).

Ah thanks for the clarification! I'll look out for this next time and implement the fix myself :)
Add evaluation metadata to wmt16

Just to confirm: we should add this metadata via GitHub and not Hub PRs for canonical datasets right?

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4557). All of your documentation changes will be reflected on that endpoint.

> Just to confirm: we should add this metadata via GitHub and not Hub PRs for canonical datasets right?

yes :)

As discussed with @lewtun, we are closing this PR, because it requires first the task names to be aligned between AutoTrain and datasets.
Dataset Viewer issue for conll2003

### Link

https://huggingface.co/datasets/conll2003/viewer/conll2003/test

### Description

Seems like a cache problem with this config / split:

```
Server error
Status code:   400
Exception:     FileNotFoundError
Message:       [Errno 2] No such file or directory: '/cache/modules/datasets_modules/datasets/conll2003/__init__.py'
```

### Owner

No

Comments:
Fixed, thanks.
Dataset Viewer issue for xtreme

### Link

https://huggingface.co/datasets/xtreme/viewer/PAN-X.de/test

### Description

There seems to be a problem with the cache of this config / split:

```
Server error
Status code:   400
Exception:     FileNotFoundError
Message:       [Errno 2] No such file or directory: '/cache/modules/datasets_modules/datasets/xtreme/349258adc25bb45e47de193222f95e68a44f7a7ab53c4283b3f007208a11bf7e/xtreme.py'
```

### Owner

No

Comments:
Fixed, thanks.
Fix WMT dataset loading issue and docs update (Re-opened)

This PR is a fix for #4354 

Changes are made for `wmt14`, `wmt15`, `wmt16`, `wmt17`, `wmt18`, `wmt19` and `wmt_t2t`. And READMEs are updated for the corresponding datasets.

Let me know, if any additional changes are required.

Thanks

Comments:
_The documentation is not available anymore as the PR was closed or merged._
Stop dropping columns in to_tf_dataset() before we load batches

`to_tf_dataset()` dropped unnecessary columns before loading batches from the dataset, but this is causing problems when using a transform, because the dropped columns might be needed to compute the transform. Since there's no real way to check which columns the transform might need, we skip dropping columns and instead drop keys from the batch after we load it.

cc @amyeroberts and https://github.com/huggingface/notebooks/pull/202

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

@lhoestq Rebasing fixed the test failures, so this should be ready to review now! There's still a failure on Win but it seems unrelated.

Gentle ping @lhoestq ! This is a simple fix (dropping columns after loading a batch from the dataset rather than with `.remove_columns()` to make sure we don't break transforms), and tests are green so we're ready for review!

@lhoestq Test is in!
Tell users to upload on the hub directly

As noted in https://github.com/huggingface/datasets/pull/4534, it is still not clear that it is recommended to add datasets on the Hugging Face Hub directly instead of GitHub, so I updated some docs.

Moreover since users won't be able to get reviews from us on the Hub, I added a paragraph to tell users that they can open a discussion and tag `datasets` maintainers for reviews.

Finally I removed the _previous good reasons_ to add a dataset on GitHub to only keep this one:

> In some rare cases it makes more sense to open a PR on GitHub. For example when you are not the author of the dataset and there is no clear organization / namespace that you can put the dataset under.

Does it sound good to you @albertvillanova @julien-c ?

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

Thanks ! I updated the two remaining files
Perform hidden file check on relative data file path

Fix #4549 

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

I'm aware of this behavior, which is tricky to solve due to fsspec's hidden file handling (see https://github.com/huggingface/datasets/issues/4115#issuecomment-1108819538). I've tested some regex patterns to address this, and they seem to work (will push them on Monday; btw they don't break any of fsspec's tests, so maybe we can contribute this as an enhancement to them). Also, perhaps we should include the files starting with `__`  in the results again (we hadn't had issues with this pattern before). WDYT?

I see. Feel free to merge this one if it's good for you btw :)

> Also, perhaps we should include the files starting with __ in the results again (we hadn't had issues with this pattern before)

The point was mainly to ignore `__pycache__` directories for example. Also also for consistency with the iter_files/iter_archive which are already ignoring them

Very elegant solution! Feel free to merge if the CI is green after adding the tests.

CI failure is unrelated to this PR