mengta666/huggingface_github huggingface dataset:
Issue: Fix docstrings and types in documentation that appears on the website

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......

Comments:
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.
Issue: Bug in sample_by="paragraph"

### 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. 

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

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

Comments:
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
Issue: timit_asr doesn't load the test split.

### 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

Comments:
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 ?
Issue: Missing documentation build for versions 2.7.1 and 2.6.2 

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.

Comments:
- 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
Issue: Lossy json serialization - deserialization of dataset info 

### 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

Comments:
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.
Issue: Import error

https://github.com/huggingface/datasets/blob/cd3d8e637cfab62d352a3f4e5e60e96597b5f0e9/src/datasets/__init__.py#L28

Hy, 
I have error at the above line. I have python version 3.8.13, the message says I need python>=3.7, which is True, but I think the if statement not working properly (or the message wrong)

Comments:
Hi ! Can you 
```python
import platform
print(platform.python_version())
```
to see that it returns ?

Hi,

3.8.13

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________________________________
From: Quentin Lhoest ***@***.***>
Sent: Tuesday, November 22, 2022 2:37:02 PM
To: huggingface/datasets ***@***.***>
Cc: feketedavid1012 ***@***.***>; Author ***@***.***>
Subject: Re: [huggingface/datasets] Import error (Issue #5280)


Hi ! Can you

import platform
print(platform.python_version())

to see that it returns ?

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You are receiving this because you authored the thread.Message ID: ***@***.***>


Then it should work as expected if you use the same python when using `datasets`

Please make sure you're running your code in the right environment

It's the right environment. But in if statement I have
"3.8.13" < 3.7
And in the error message is Python>=3.7 which is true in my case (3.8.13 is greater then 3.7), so I don't understand my python should be below the 3.7 which case the if statement is right, but the message is wrong, or above 3.7 which case if statement is wrong, error message is right.

Get Outlook for Android<https://aka.ms/AAb9ysg>
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From: Quentin Lhoest ***@***.***>
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To: huggingface/datasets ***@***.***>
Cc: feketedavid1012 ***@***.***>; Author ***@***.***>
Subject: Re: [huggingface/datasets] Import error (Issue #5280)


Then it should work as expected if you use the same python when using datasets

Please make sure you're running your code in the right environment

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You are receiving this because you authored the thread.Message ID: ***@***.***>


If you're having an error then you're not running your code in the right environment.
Issue: load_dataset does not read jsonl metadata file properly

### Describe the bug

Hi, I'm following [this page](https://huggingface.co/docs/datasets/image_dataset) to create a dataset of images and captions via an image folder and a metadata.json file, but I can't seem to get the dataloader to recognize the "text" column. It just spits out "image" and "label" as features. 

Below is code to reproduce my exact example/problem.

### Steps to reproduce the bug

```ruby 
dataset_link="19Unu89Ih_kP6zsE7f9Mkw8dy3NwHopRF"
id = dataset_link
output = 'Godardv01.zip'
gdown.download(id=id, output=output, quiet=False)

ds = load_dataset("imagefolder", data_dir="/kaggle/working/Volumes/TOSHIBA/Godard_imgs/Volumes/TOSHIBA/Godard_imgs/Full/train", split="train", drop_labels=False)
print(ds)
```

### Expected behavior

I would expect that it returned "image" and "text" columns from the code above.

### Environment info

- `datasets` version: 2.1.0
- Platform: Linux-5.15.65+-x86_64-with-debian-bullseye-sid
- Python version: 3.7.12
- PyArrow version: 5.0.0
- Pandas version: 1.3.5

Comments:
Can you try to remove "drop_labels=false" ? It may force the loader to infer the labels instead of reading the metadata

Hi, thanks for responding. I tried that, but it does not change anything.

Can you try updating `datasets` ? Metadata support was added in `datasets` 2.4

Probably the issue, will report back asap!

Okay, now it seems to actually load the metadata and create the train_split, but it still says only returns "image" and "label", which is always 0 since all images are from same folder

> Can you try updating `datasets` ? Metadata support was added in `datasets` 2.4

Update: This was the issue.
Issue: load_dataset possibly broken for gated datasets?

### Describe the bug

When trying to download the [winoground dataset](https://huggingface.co/datasets/facebook/winoground), I get this error unless I roll back the version of huggingface-hub:

```
[/usr/local/lib/python3.7/dist-packages/huggingface_hub/utils/_validators.py](https://localhost:8080/#) in validate_repo_id(repo_id)
    165     if repo_id.count("/") > 1:
    166         raise HFValidationError(
--> 167             "Repo id must be in the form 'repo_name' or 'namespace/repo_name':"
    168             f" '{repo_id}'. Use `repo_type` argument if needed."
    169         )

HFValidationError: Repo id must be in the form 'repo_name' or 'namespace/repo_name': 'datasets/facebook/winoground'. Use `repo_type` argument if needed
```

### Steps to reproduce the bug

Install requirements:

```
pip install transformers
pip install datasets
# It works if you uncomment the following line, rolling back huggingface hub:
# pip install huggingface-hub==0.10.1
```

Then:

```
from datasets import load_dataset
auth_token = ""  # Replace with an auth token, which you can get from your huggingface account: Profile -> Settings -> Access Tokens -> New Token
winoground = load_dataset("facebook/winoground", use_auth_token=auth_token)["test"]
```





### Expected behavior

Downloading of the datset

### Environment info

Just a google colab; see here: https://colab.research.google.com/drive/15wwOSte2CjTazdnCWYUm2VPlFbk2NGc0?usp=sharing

Comments:
@BradleyHsu

Btw, thanks very much for finding the hub rollback temporary fix and bringing the issue to our attention @KhoomeiK!

I see the same issue when calling `load_dataset('poloclub/diffusiondb', 'large_random_1k')` with `datasets==2.7.1` and `huggingface-hub=0.11.0`. No issue with `datasets=2.6.1` and `huggingface_hub==0.10.1`.

https://github.com/poloclub/diffusiondb/issues/7

I fixed my issue by specifying `repo_type` in `hf_hub_url()`. https://github.com/poloclub/diffusiondb/commit/9eb91c79aaca98b0515a0ce45778b8af65b84652

I opened a PR on the Winoground's repo: https://huggingface.co/datasets/facebook/winoground/discussions/2

This is a bug in the script, indeed. The most robust fix is to use a relative path instead of `hf_hub_url`, which does not depend on `huggingface_hub`'s version 🙂. I've opened a PR here: https://huggingface.co/datasets/facebook/winoground/discussions/3.

Awesome, big thanks to both @xiaohk and @mariosasko!

so, if i reproduce the bug, what should i do ? with huggingface_hub0.13.3  dataset2.6.1

huggingface_hub.utils._validators.HFValidationError: Repo id must be in the form 'repo_name' or 'namespace/repo_name':

    tokenizer = AutoTokenizer.from_pretrained(ARGS.model_path, trust_remote_code=True)

Please handle automatically for local path and repo name inside, otherwise users always get confused about this

I think I'm also hitting this error, trying to load a model from a local path.
Issue: Get an IterableDataset from a map-style Dataset

This is useful to leverage iterable datasets specific features like:
- fast approximate shuffling
- lazy map, filter etc.

Iterating over the resulting iterable dataset should be at least as fast at iterating over the map-style dataset.

Here are some ideas regarding the API:
```python
# 1. 
# - consistency with load_dataset(..., streaming=True)
# - gives intuition that map/filter/etc. are done on-the-fly
ids = ds.stream()

# 2.
# - more explicit on the output type
# - but maybe sounds like a conversion tool rather than a step in a processing pipeline
ids = ds.as_iterable_dataset()
```

Comments:
I think `stream` could be misleading since the data is not being streamed from remote endpoints (one could think that's the case when they see `load_dataset` followed by `stream`). Hence, I prefer the second option.

PS: When we resolve https://github.com/huggingface/datasets/issues/4542, we could add `as_tf_dataset` to the API for consistency and deprecate `to_tf_dataset`.
Issue: AttributeError: 'Value' object has no attribute 'names'

Hello

I'm trying to build a model for custom token classification 
I already followed the token classification course on huggingface

while adapting the code to my work, this message occures : 
'Value' object has no attribute 'names'

Here's my code:
`raw_datasets` 
generates
DatasetDict({
    train: Dataset({
        features: ['isDisf', 'pos', 'tokens', 'id'],
        num_rows: 14
    })
})

`raw_datasets["train"][3]["isDisf"]`
generates 
['B_RM', 'I_RM', 'I_RM', 'B_RP', 'I_RP', 'O', 'O']

`dis_feature = raw_datasets["train"].features["isDisf"]
dis_feature`
generates
Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)

and
`label_names = dis_feature.feature.names
label_names`
generates
AttributeError                            Traceback (most recent call last)
[<ipython-input-28-972fd54a869a>](https://localhost:8080/#) in <module>
----> 1 label_names = dis_feature.feature.names
      2 label_names

AttributeError: 'Value' object has
AttributeError: 'Value' object has no attribute 'names'

Thank you for your help

Comments:
Hi ! It looks like your "isDif" column is a Sequence of Value("string"), not a Sequence of ClassLabel.

You can convert your Value("string") feature type to a ClassLabel feature type this way:
```python
from datasets import ClassLabel, Sequence

# provide the label_names yourself
label_names = [...]
# OR get them from the dataset
label_names = sorted(set(label for labels in raw_datasets["train"]["isDif"] for label in labels))

# Cast to ClassLabel
raw_datasets = raw_datasets.cast_column("isDif", Sequence(ClassLabel(names=label_names)))
```


thank you 
it works 💯
Issue: Add PubTables-1M

### Name

PubTables-1M

### Paper

https://openaccess.thecvf.com/content/CVPR2022/html/Smock_PubTables-1M_Towards_Comprehensive_Table_Extraction_From_Unstructured_Documents_CVPR_2022_paper.html

### Data

https://github.com/microsoft/table-transformer

### Motivation

Table Transformer is now available in 🤗 Transformer, and it was trained on PubTables-1M. It's a large dataset for table extraction and structure recognition in unstructured documents.

Comments:
cc @albertvillanova the author would like to add this dataset to the hub: https://github.com/microsoft/table-transformer/issues/68#issuecomment-1319114621. Could you help him out?
Issue: Restore order of split names in dataset_info for canonical datasets

After a bulk edit of canonical datasets to create the YAML `dataset_info` metadata, the split names were accidentally sorted alphabetically. See for example:
- https://huggingface.co/datasets/bc2gm_corpus/commit/2384629484401ecf4bb77cd808816719c424e57c

Note that this order is the one appearing in the preview of the datasets.

I'm making a bulk edit to align the order of the splits appearing in the metadata info with the order appearing in the loading script.

Related to:
- #5202

Comments:
The bulk edit is running...

See for example: 
- A single config: https://huggingface.co/datasets/acronym_identification/discussions/2
- Multiple configs: https://huggingface.co/datasets/babi_qa/discussions/1

TODO: Add "dataset_info" YAML metadata to:
- [x] "chr_en" has no metadata JSON file, nor "dataset_info" YAML tag in its card
  - Fixing PR: https://huggingface.co/datasets/chr_en/discussions/1 
- [x] "conll2000" has no metadata JSON file, but it has "dataset_info" YAML tag in its card
- [x] "crime_and_punish" has no metadata JSON file, but it has "dataset_info" YAML tag in its card
- [x] "dart" has no metadata JSON file, but it has "dataset_info" YAML tag in its card
- [x] "iwslt2017" has no metadata JSON file, but it has "dataset_info" YAML tag in its card
- [ ] "mc4" has no metadata JSON file, nor "dataset_info" YAML tag in its card
- [ ] "the_pile" has no metadata JSON file, nor "dataset_info" YAML tag in its card
- [ ] "timit_asr" has no metadata JSON file, nor "dataset_info" YAML tag in its card

The bulk edit is finished.
Issue: Docs are not generated after latest release

After the latest `datasets` release version 0.7.0, the docs were not generated.

As we have changed the release procedure (so that now we do not push directly to main branch), maybe we should also change the corresponding GitHub action:

https://github.com/huggingface/datasets/blob/edf1902f954c5568daadebcd8754bdad44b02a85/.github/workflows/build_documentation.yml#L3-L8

Related to:
- #5250

CC: @mishig25 

Comments:
After a discussion with @mishig25:
- He said that this action should be triggered if we call our release branch according to the regex `v*-release`, as transformers does
- I said that our procedure is different: our release branch is *temporary* and it is deleted just after the release PR is merged to main
  - Indeed the release tag is not yet created when we make the release PR (not event when this is merged to main), but when we make the Release itself.

I was thinking that maybe we could change the triggering event: use `release` instead of `push`.

What do you think, @huggingface/datasets?

Why is it an issue if our branch is temporary ?

He says not; but the branch has no tag yet; does the doc building require the tag? Or just the version number in `__init__.py` or setup.py?

It uses `module.__version__` (i.e. the one defined in `__init__.py`) - no need to have a tag

https://github.com/huggingface/doc-builder/blob/81575cf081964c30ea5fd39450f4820db963f18e/src/doc_builder/commands/build.py#L69

Thanks, @lhoestq.

@mishig25 has manually forced the generation of the docs, that are live for 2.7.0 version: https://huggingface.co/docs/datasets/v2.7.0/en/index 

Cool ! this can be closed then ?

I was waiting for #5250 to be merged to close this.

just to confirm, is there anything I need to do from my side ? Or is everything good here ?
Issue: Protect the main branch from inadvertent direct pushes

We have decided to implement a protection mechanism in this repository, so that nobody (not even administrators) can inadvertently push accidentally directly to the main branch.

See context here:
- d7c942228b8dcf4de64b00a3053dce59b335f618

To do:
- [x] Protect main branch
  - Settings > Branches > Branch protection rules > main > Edit
    - [x] Check: Do not allow bypassing the above settings 
      - The above settings will apply to administrators and custom roles with the "bypass branch protections" permission.
    - [x] Additionally, uncheck: Require approvals [under "Require a pull request before merging", which was already checked]
      - Before, we could exceptionally merge a non-approved PR, using Administrator bypass
      - Now that Administrator bypass is no longer possible, we would always need an approval to be able to merge; and pull request authors cannot approve their own pull requests. This could be an inconvenient in some exceptional circumstances when an urgent fix is needed
      - Nevertheless, although it is no longer enforced, it is strongly recommended to merge PRs only if they have at least one approval
- [x] #5250
  - So that direct pushes to main branch are no longer necessary

Comments:
It seems all the tasks have been addressed, meaning this issue can be closed, no?
Issue: Unable to rename columns in streaming dataset

### Describe the bug

Trying to rename column in a streaming datasets, destroys the features object.

### Steps to reproduce the bug

The following code illustrates the error:

```
from datasets import load_dataset
dataset = load_dataset('mc4', 'en', streaming=True, split='train')
dataset.info.features
# {'text': Value(dtype='string', id=None), 'timestamp': Value(dtype='string', id=None), 'url': Value(dtype='string', id=None)}
dataset = dataset.rename_column("text", "content")
dataset.info.features
# This returned object is now None!
```



### Expected behavior

This should just alter the renamed column.

### Environment info

datasets 2.6.1

Comments:
Hi @peregilk this bug is directly related to https://github.com/huggingface/datasets/issues/3888, and still not fixed... But I'll try to have a look!

Thanks @alvarobartt. It is great if you are able to fix it, but when reading the explanation it seems like it is possible to work around it.

We also tried keeping the 'info.features' and then adding a modified version back after the remove/rename. Unforutunately that leads to a dataset that is not possible to iterate over.

So if you iterate over the `IterableDataset` as `next(iter(ds))` and then run `rename_columns` when checking that data it will work, but in the end, it's just renaming the column one example/batch at a time, not renaming the column name for all the entries in the dataset, which is the ideal.

@alvarobartt Thanks. My use case was that I wanted to do multiple things, ie removing all unnecessary columns, renaming some valid columns, and then using cast (in my case checking if the audio is not 16K and casting it). It is just convenient to look into the info.features between each of these operations. Alternatively, I will just plan ahead...;) To me it seems like all the operations are working.

Thanks for the advice. It was very useful.

If we know the features before renaming, then we know the features after renaming, so we can pass the new features to the returned dataset in `rename_column` indeed ! If anyone is interested in contributing, feel free to open a PR and I'd be happy to help / give some pointers :)

Sure @lhoestq thanks! I’ll try to work on that

#self-assign
Issue: Failed Data Processing upon upload with zip file full of images

I went to autotrain and under image classification arrived where it was time to prepare my dataset. Screenshot below
![image](https://user-images.githubusercontent.com/82735473/201814099-3cc5ff8a-88dc-4f5f-8140-f19560641d83.png)


I chose the method 2 option. I have a csv file with two columns. ~23,000 files. 

I uploaded this and chose the image_relpath, and target columns. 

The image uploader said that I could only upload 10,000 singular images at a time so the 2nd option was to zip the images up and upload a zip archive which I did. 

That all uploaded. 

Now I have the message below. It appears the zip archive does just uncompress on the Hugging Face end? 
What am I missing here?



![image](https://user-images.githubusercontent.com/82735473/201813838-b50dbbbc-34e8-4d73-9c07-12f9e41c62eb.png)


Comments:
cc @abhishekkrthakur @SBrandeis
Issue: Incompatible dill versions in datasets 2.6.1

### Describe the bug

datasets version 2.6.1 has a dependency on dill<0.3.6. This causes a conflict with dill>=0.3.6 used by multiprocess dependency in datasets 2.6.1

This issue is already fixed in https://github.com/huggingface/datasets/pull/5166/files, but not yet been released. Please release a new version of the datasets library to fix this.

### Steps to reproduce the bug

1. Create requirements.in with only dependency being datasets (or datasets[s3])
2. Run pip-compile
3. The output is as follows:

```
Could not find a version that matches dill<0.3.6,>=0.3.6 (from datasets[s3]==2.6.1->-r requirements.in (line 1))
Tried: 0.2, 0.2, 0.2.1, 0.2.1, 0.2.2, 0.2.2, 0.2.3, 0.2.3, 0.2.4, 0.2.4, 0.2.5, 0.2.5, 0.2.6, 0.2.7, 0.2.7.1, 0.2.8, 0.2.8.1, 0.2.8.2, 0.2.9, 0.3.0, 0.3.1, 0.3.1.1, 0.3.2, 0.3.3, 0.3.3, 0.3.4, 0.3.4, 0.3.5, 0.3.5, 0.3.5.1, 0.3.5.1, 0.3.6, 0.3.6
Skipped pre-versions: 0.1a1, 0.2a1, 0.2a1, 0.2b1, 0.2b1
There are incompatible versions in the resolved dependencies:
  dill<0.3.6 (from datasets[s3]==2.6.1->-r requirements.in (line 1))
  dill>=0.3.6 (from multiprocess==0.70.14->datasets[s3]==2.6.1->-r requirements.in (line 1))
```

### Expected behavior

pip-compile produces requirements.txt without any conflicts

### Environment info

datasets version 2.6.1

Comments:
Thanks for reporting, @vinaykakade.

We are discussing about making a release early this week.

Please note that in the meantime, in your specific case (as we also pointed out here: https://github.com/huggingface/datasets/issues/5162#issuecomment-1291720293), you can circumvent the issue by pinning `multiprocess` to 0.70.13 version (instead of using latest 0.70.14).

Duplicate of:
- https://github.com/huggingface/datasets/issues/5162

You can also make `pip-compile` work by using the backtracking resolver (instead of the legacy one): https://pip-tools.readthedocs.io/en/latest/#a-note-on-resolvers
```
pip-compile --resolver=backtracking requirements.in
```
This resolver will automatically use `multiprocess` 0.70.13 version.
Issue: Using `set_format(type='torch', columns=columns)` makes Array2D/3D columns stop formatting correctly

I have a Dataset with two Features defined as follows:

```
'image': Array3D(dtype="int64", shape=(3, 224, 224)),
'bbox': Array2D(dtype="int64", shape=(512, 4)),
```

On said dataset, if I `dataset.set_format(type='torch')` and then use the dataset in a dataloader, these columns are correctly cast to Tensors of (batch_size, 3, 224, 244) for example.

However, if I `dataset.set_format(type='torch', columns=['image', 'bbox'])` these columns are cast to Lists of tensors and miss the batch size completely (the 3 dimension is the list length).

I'm currently digging through datasets formatting code to try and find out why, but was curious if someone knew an immediate solution for this.

Comments:
In case others find this, the problem was not with set_format, but my usages of `to_pandas()` and `from_pandas()` which I was using during dataset splitting; somewhere in the chain of converting to and from pandas the `Array2D/Array3D` types get converted to series of `Sequence()` types
Issue: Type error when calling `map` over dataset containing 0-d tensors

### Describe the bug

0-dimensional tensors in a dataset lead to `TypeError: iteration over a 0-d array` when calling `map`.  It is easy to generate such tensors by using `.with_format("...")` on the whole dataset.

### Steps to reproduce the bug

```
ds = datasets.Dataset.from_list([{"a": 1}, {"a": 1}]).with_format("torch")
ds.map(None)
```

### Expected behavior

Getting back `ds` without errors.

### Environment info

Python 3.10.8
datasets 2.6.
torch 1.13.0

Comments:
Hi! 

We could address this by calling `.item()` on such tensors to extract the value, but this would lose us the type, which could lead to storing the generated dataset in a suboptimal format. Considering this, I think the only proper fix would be implementing support for 0-D tensors on Apache Arrow's side (Arrow is the underlying format we use to store datasets on disk/in memory). WDYT @lhoestq?

I think we can just convert the item to a numpy typed scalar using `.numpy()` ?

For example this works:
```python
import numpy as np
import pyarrow as pa

assert pa.array([np.float64(1.0)]).type == pa.float64()
assert pa.array([np.float32(1.0)]).type == pa.float32()
assert pa.array([np.int32(1)]).type == pa.int32()
assert pa.array([np.int64(1)]).type == pa.int64()
```

And therefore it would work the same as for PyTorch N-D Tensors: convert to Numpy Array to keep the type in `_cast_to_python_objects`, then convert to Arrow
Issue: datasets.data_files.EmptyDatasetError: The directory at wikisql doesn't contain any data files

### Describe the bug

From these lines:

from datasets import list_datasets, load_dataset
dataset = load_dataset("wikisql","binary")

I get error message:
datasets.data_files.EmptyDatasetError: The directory at wikisql doesn't contain any data files

And yet the 'wikisql' is reported to exist via the list_datasets(). 

Any help appreciated. 



### Steps to reproduce the bug

From these lines:

from datasets import list_datasets, load_dataset
dataset = load_dataset("wikisql","binary")

I get error message:
datasets.data_files.EmptyDatasetError: The directory at wikisql doesn't contain any data files

And yet the 'wikisql' is reported to exist via the list_datasets(). 

Any help appreciated. 


### Expected behavior

Dataset should load. This same code used to work.

### Environment info

Mac OS

Comments:
Fixed. Please close.

how to fix?i need your help
Issue: Q: Memory release when removing the column?

### Describe the bug

How do I release memory when I use methods like `.remove_columns()` or `clear()` in notebooks?
```python
from datasets import load_dataset
common_voice = load_dataset("mozilla-foundation/common_voice_11_0", "ja", use_auth_token=True)
# check memory -> RAM Used (GB):  0.704 / Total (GB)  33.670
common_voice = common_voice.remove_columns(column_names=common_voice.column_names['train'])
common_voice.clear()
# check memory -> RAM Used (GB):  0.705 / Total (GB)  33.670
```

I tried `gc.collect()` but did not help

### Steps to reproduce the bug

1. load dataset
2. remove all the columns
3. check memory is reduced or not


[link to reproduce](https://www.kaggle.com/code/bayartsogtya/huggingface-dataset-memory-issue/notebook?scriptVersionId=110630567)

### Expected behavior

Memory released when I remove the column

### Environment info

- `datasets` version: 2.1.0
- Platform: Linux-5.15.65+-x86_64-with-debian-bullseye-sid
- Python version: 3.7.12
- PyArrow version: 8.0.0
- Pandas version: 1.3.5

Comments:
Hi ! Datasets are memory mapped from your disk, i.e. they're not loaded in RAM. This is possible thanks to the Arrow data format.

Therefore the column you remove is not in RAM, so removing it doesn't cause the RAM to decrease.

Thanks for the explanation! @lhoestq 
I wonder since it is memory mapped, can we reduce or remove this memory map?

Yes you can `del common_voice` for example or wait for it to be garbage collected
Issue: HuggingFace website is incorrectly reporting that my datasets are pickled

### Describe the bug

HuggingFace is incorrectly reporting that my datasets are pickled. They are not picked, they are simple ZIP files containing PNG images.

Hopefully this is the right location to report this bug.

### Steps to reproduce the bug

Inspect my dataset respository here: https://huggingface.co/datasets/ProGamerGov/StableDiffusion-v1-5-Regularization-Images

### Expected behavior

They should not be reported as being pickled.

### Environment info

N/A

Comments:
cc @McPatate maybe you know what's happening ?

Yes I think I know what is happening. We check in zips for pickles, and the UI must display the pickle jar when a scan has an associated list of imports, even when empty.
~I'll fix ASAP !~

>  I'll fix ASAP !

Actually I'd rather leave it like that for now, as it indicates that we checked for pickles and nothing dangerous appeared :)

Closing the issue with the typical "feature not a bug"
Issue: Cannot push 

### Describe the bug

I am facing the issue when I try to push the tar.gz file around 11G to HUB.

```
(venv) ╭─laptop@laptop ~/PersonalProjects/data/ulaanbal_v0 ‹main●› 
╰─$ du -sh *
4.0K	README.md
 13G	data
516K	test.jsonl
 18M	train.jsonl
4.0K	ulaanbal_v0.py
 11G	ulaanbal_v0.tar.gz
452K	validation.jsonl

(venv) ╭─laptop@laptop~/PersonalProjects/data/ulaanbal_v0 ‹main●› 
╰─$ git add ulaanbal_v0.tar.gz && git commit -m 'large version'

(venv) ╭─laptop@laptop ~/PersonalProjects/data/ulaanbal_v0 ‹main●› 
╰─$ git push 
EOFoading LFS objects:   0% (0/1), 0 B | 0 B/s                                                                                                                                                      
Uploading LFS objects:   0% (0/1), 0 B | 0 B/s, done.
error: failed to push some refs to 'https://huggingface.co/datasets/bayartsogt/ulaanbal_v0'
```

I have already tried pushing a small version of this and it was working fine. So my guess it is probably because of the big file.

Following I run before the commit:
```
╰─$ git lfs install
╰─$ huggingface-cli lfs-enable-largefiles .
```

### Steps to reproduce the bug

Create a private dataset on huggingface and push 12G tar.gz file

### Expected behavior

To be pushed with no issue

### Environment info


- `datasets` version: 2.6.1
- Platform: Darwin-21.6.0-x86_64-i386-64bit
- Python version: 3.7.11
- PyArrow version: 10.0.0
- Pandas version: 1.3.5


Comments:
Did you run `huggingface-cli lfs-enable-largefiles` before committing or before adding ? Maybe you can try before adding

Anyway I'd encourage you to split your data into several TAR archives if possible, this way the dataset can loaded faster using multiprocessing (by giving each process a subset of shards to process)

@lhoestq 
Thanks for the help!
> Maybe you can try before adding

It did not help

But I totally got your point about split into multiple TAR archives. It really helped!
Issue: Implicit type conversion of lists in to_pandas

### Describe the bug

```
ds = Dataset.from_list([{'a':[1,2,3]}])
ds.to_pandas().a.values[0]
```
Results in `array([1, 2, 3])`  -- a rather unexpected conversion of types which made downstream tools expecting lists not happy.

### Steps to reproduce the bug

See snippet

### Expected behavior

Keep the original type

### Environment info

datasets 2.6.1
python 3.8.10

Comments:
I think this behavior comes from PyArrow:
```python
import pyarrow as pa
t = pa.table({"a": [[0]]})
t.to_pandas().a.values[0]
# array([0])
```

I believe this has to do with zero-copy: you can get a pandas DataFrame without copying the buffers from arrow, and therefore end up with numpy arrays.

That's interesting, I guess not much to do here then.
Issue: Delta Tables usage using Datasets Library

### Feature request

Adding compatibility of Datasets library with Delta Format. Elevating the utilities of Datasets library from Machine Learning Scope to Data Engineering Scope as well.

### Motivation

We know datasets library can absorb csv, json, parquet, etc. file formats but it would be great if Datasets library could work with Delta Tables (with delta format) as it has different features such as time travelling, layout optimization, query performance, aids in Data Engineering.

This will help and enhance Datasets library from Machine Learning utility to Data Engineering utilities and expand horizons thereafter. I am totally using Datasets library in all my usecases and as my role expands so does the work, compatibility with Datasets library is something I don't want to lose.

### Your contribution

Would love to work on this feature, even if this has to picked up from scratch, including design paradigms and patterns.
I have basic idea about Delta Live Tables, would brush it easily for this feature.

Comments:
Hi ! Interesting :) Can you provide concrete examples of cases where it can be useful ?

Few example blogs and posts that might help on this - 

1. https://hevodata.com/learn/databricks-delta-tables/
2. https://docs.databricks.com/delta/index.html

Basically, we are looking at utility of Datasets library with Delta Lake Tables.


`datasets` can already read/write from parquet from/to a cloud storage using fsspec, if I understand correctly it's should be possible to load parquet files as delat lake tables no ? :) Or is there someting missing ?

@lhoestq Per my understanding, delta lake table is a bunch of paruqet files together with the meta to support ACID. For example file 1 contains v0.1 of record A while file 2 contains v0.2 of record A. I am assuming the Hugging face dataset would delegate the read/write delta table to 3rd party lib, maybe pyarrow. Correct me if I was wrong @reichenbch 

And I am assuming, people are asking the versioning of Hugging face datasets. But I am assuming Hugging face delegate this function to github and it is not the key requirement for Public Data set. It actually the key function of ML Ops, I am not sure whether hugging face would like expand to that area.
Issue: save_elasticsearch_index

Hi,

I am new to Dataset and elasticsearch. I was wondering is there any equivalent approach to save elasticsearch index as of save_faiss_index locally for later use, to remove the need to re-index a dataset?

Comments:
Hi ! I think there exist tools to dump and reload an index in your elastic search but I'm not super familiar with it.

Anyway after reloading an index in elastic search you can call `ds.load_elasticsearch_index` which will connect the index to the dataset without re-indexing
Issue: Use logging instead of printing to console

### Describe the bug

Some logs ([here](https://github.com/huggingface/datasets/blob/4a6e1fe2735505efc7e3a3dbd3e1835da0702575/src/datasets/builder.py#L778), [here](https://github.com/huggingface/datasets/blob/4a6e1fe2735505efc7e3a3dbd3e1835da0702575/src/datasets/builder.py#L786), and [here](https://github.com/huggingface/datasets/blob/4a6e1fe2735505efc7e3a3dbd3e1835da0702575/src/datasets/builder.py#L830)) generated by the `DatasetBuilder` are printed to the console instead of passed to `datasets` logger.


### Steps to reproduce the bug

```python
>> import datasets

>> datasets.load_dataset("some-dataset")

Downloading and preparing dataset csv/data to <path>...
Downloading data files: 100%|██████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 7729.06it/s]
Extracting data files: 100%|████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 527.23it/s]
Dataset csv downloaded and prepared to <path>. Subsequent calls will reuse this data.
```

### Expected behavior

The logs should not be printed to the console directly but passed to the logger so that the user can redirect them wherever he wants.

### Environment info

- `datasets` version: 2.6.1
- Platform: macOS-13.0-x86_64-i386-64bit
- Python version: 3.9.15
- PyArrow version: 10.0.0
- Pandas version: 1.5.1

Comments:
Actually upon closer inspection, it is documented in the code that this behavior is intentional, so I'll close this.
Issue: `push_to_hub` not propagating `token` through `DownloadConfig`

### Describe the bug

When trying to upload a new 🤗 Dataset to the Hub via Python, and providing the `token` as a parameter to the `Dataset.push_to_hub` function, it just works for the first time, assuming that the dataset didn't exist before.

But when trying to run `Dataset.push_to_hub` again over the same dataset, instead of updating it, it throws a `ConnectionError` when trying to retrieve the `README.md` that may contain some metadata about the dataset, so as to also update it, but since the `token` is not propagated, the `DownloadConfig` provided to the `datasets.utils.file_utils.get_from_cache` function doesn't contain the `use_auth_token` value set to `token`, it's just using the default one which is None/False.

So on, when uploading a dataset via Python with `push_to_hub` with the `token` as a parameter with the HuggingFace API Token as value, it can just be uploaded when the dataset is new, otherwise it fails with to `ConnectionError` due to the `token` not being propagated as `use_auth_token`.

### Steps to reproduce the bug

Let's create a new dataset in our HF account via Python as:

```python
from datasets import Dataset

data = {"a": [1, 2, 3], "b": [4, 5, 6]}
ds = Dataset.from_dict(data)
ds.push_to_hub(repo_id=<HF_USERNAME>/<HF_DATASET>, private=private, token=<HF_TOKEN_HERE>)
```

When we create the `Dataset` for the first time it works and there are no issues, but when trying to actually upload a new version of the same dataset (same name under the same username), we encounter the following issue:

```python
from datasets import Dataset

data = {"a": [1, 2, 3], "b": [4, 5, 6]}
ds = Dataset.from_dict(data)
ds.push_to_hub(repo_id=<HF_USERNAME>/<HF_DATASET>, private=private, token=<HF_TOKEN_HERE>)
>>> ConnectionError: Couldn't reach https://huggingface.co/datasets/alvarobartt/demo/resolve/main/README.md (ConnectionError('Unauthorized for URL https://huggingface.co/datasets/<HF_USERNAME>/<HF_DATASET>/resolve/main/README.md. Please use the parameter `use_auth_token=True` after logging in with `huggingface-cli login`'))
```

### Expected behavior

Ideally, the `token` parameter provided to `push_to_hub` should be propagated and used to download the `README.md` when trying to update a `Dataset`, instead of throwing that exception, so that the authentication can be done directly through code without running `huggingface-cli login`as mentioned at https://huggingface.co/docs/datasets/upload_dataset#upload-with-python.

### Environment info

- `datasets` version: 2.6.1
- Platform: macOS-13.0-arm64-arm-64bit
- Python version: 3.10.8
- PyArrow version: 10.0.0
- Pandas version: 1.5.1

Comments:
#self-assign

@lhoestq can you close this issue as part of the recent #5205 merge? Thanks 🤗 

Thank you :)
Issue: Some links to canonical datasets in the docs are outdated

As we don't have canonical datasets in the github repo anymore, some old links to them doesn't work. I don't know how many of them are there, I found link to SuperGlue here: https://huggingface.co/docs/datasets/dataset_script#multiple-configurations, probably there are more of them. These links should be replaced by links to the corresponding datasets on the Hub. 

Comments:
Thanks for catching this, I can go through the docs and replace the links to their corresponding datasets on the Hub!
Issue: `path` is `None` when downloading a custom audio dataset from the Hub

### Describe the bug

I've created an [audio dataset](https://huggingface.co/datasets/lewtun/audio-test-push) using the `audiofolder` feature desribed in the [docs](https://huggingface.co/docs/datasets/audio_dataset#audiofolder) and then pushed it to the Hub. 

Locally, I can see the `audio.path` feature is of the expected form `path/to/data_dir`, but when I download the dataset from the Hub, I see `audio.path` is `None`

Here's an example:

```python
from datasets import load_dataset

ds = load_dataset("lewtun/audio-test-push")
ds["train"][0]
# {
#     "audio": {
#         "path": None,    <-- Is this expected?
#         "array": array(
#             [
#                 3.97140226e-07,
#                 7.30310290e-07,
#                 7.56406735e-07,
#                 ...,
#                 -1.19636677e-01,
#                 -1.16811886e-01,
#                 -1.12441722e-01,
#             ]
#         ),
#         "sampling_rate": 44100,
#     },
#     "song_id": 0,
#     "genre_id": 0,
#     "genre": "Electronic",
# }
```

Is this expected behaviour? If yes, feel free to close this issue as it's not a true bug then :)

### Steps to reproduce the bug

1. Create an audio dataset with the `audiofolder` feature
2. Push the dataset to the Hub with `push_to_hub()`
3. Download the Hub dataset and inspect the `audio.path` feature

### Expected behavior

`audio.path` points to the file associated with the audio data

### Environment info

- `datasets` version: 2.6.2.dev0
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.8.13
- PyArrow version: 9.0.0
- Pandas version: 1.5.1

Comments:
Hi! Yes, this is expected behavior - we do this as a security measure to not leak local paths (this info would be useless on other users' machines anyways) and only push audio bytes.