[to_tf_dataset] Use Feather for better compatibility with TensorFlow ?

generic discussion

Generic discussion on the library

This has so much potential to be great! Also I think you tagged some poor random dude on the internet whose name is also Joao, lol, edited that for you!

cc @sayakpaul here too, since he was interested in our new approaches to converting datasets!

Noted and I will look into the thread in detail tomorrow once I log back in.

@lhoestq I have used TFRecords with `tf.data` for both vision and text and I can say that they are quite performant. I haven't worked with Feather yet as similarly as I have with TFRecords. If you haven't started the benchmarking script yet, I can prepare a Colab notebook that loads Feather files, converts them into a `tf.data` pipeline, and does some basic preprocessing.

But in my limited understanding, Feather might be better suited for CSV files. Not yet sure if it's good for modalities like images.

> Not yet sure if it's good for modalities like images.

We store images pretty much the same way as tensorflow_datasets (i.e. storing the encoded image bytes, or a path to the local image, so that the image can be decoded on-the-fly), so as long as we use something similar as TFDS for image decoding it should be ok

So for image datasets, we could potentially store the paths in the feather format and decode and read them on the fly? But it introduces an I/O redundancy of having to read the images every time.

With caching it could be somewhat mitigated but it's not a good solution for bigger image datasets.

> So for image datasets, we could potentially store the paths in the feather format and decode and read them on the fly?

hopefully yes :)

I double-checked the TFDS source code and they always save the bytes actually, not the path. Anyway we'll see if we run into issues or not (as a first step we can require the bytes to be in the feather file)

Yes. For images, TFDS actually prepares TFRecords first for encoding and then reuses them for every subsequent call.

@lhoestq @Rocketknight1 I worked on [this PoC](https://gist.github.com/sayakpaul/f7d5cc312cd01cb31098fad3fd9c6b59) that

* Creates Feather files from a medium resolution dataset (`tf_flowers`).
* Explores different options with TensorFlow IO to load the Feather files.

I haven't benchmarked those different options yet. There's also a gotcha that I have noted in the PoC. I hope it gets us started but I'm sorry if this is redundant.

Cool thanks ! If I understand correctly in your PoC you store the flattened array of pixels in the feather file. This will take a lot of disk space.

Maybe we could just save the encoded bytes and let users apply a `map` to decode/transform them into the format they need for training ? Users can use tf.image to do so for example

@lhoestq this is what I tried:

```py
def read_image(path):
    with open(path, "rb") as f:
        return f.read()

total_images_written = 0

for step in tqdm.tnrange(int(math.ceil(len(image_paths) / batch_size))):
    batch_image_paths = image_paths[step * batch_size : (step + 1) * batch_size]
    batch_image_labels = all_integer_labels[step * batch_size : (step + 1) * batch_size]

    data = [read_image(path) for path in batch_image_paths]
    table = pa.Table.from_arrays([data, batch_image_labels], ["data", "labels"])
    write_feather(table, f"/tmp/flowers_feather_{step}.feather", chunksize=chunk_size)
    total_images_written += len(batch_image_paths)
    print(f"Total images written: {total_images_written}.")

    del data
```

I got the feather files done (no resizing required as you can see):

```sh
ls -lh /tmp/*.feather

-rw-r--r--  1 sayakpaul  wheel    64M Jun 24 09:28 /tmp/flowers_feather_0.feather
-rw-r--r--  1 sayakpaul  wheel    59M Jun 24 09:28 /tmp/flowers_feather_1.feather
-rw-r--r--  1 sayakpaul  wheel    51M Jun 24 09:28 /tmp/flowers_feather_2.feather
-rw-r--r--  1 sayakpaul  wheel    45M Jun 24 09:28 /tmp/flowers_feather_3.feather
```

Now there seems to be a problem with `tfio.arrow`:

```py
import tensorflow_io.arrow as arrow_io

dataset = arrow_io.ArrowFeatherDataset(
    ["/tmp/flowers_feather_0.feather"],
    columns=(0, 1),
    output_types=(tf.string, tf.int64),
    output_shapes=([], []),
    batch_mode="auto",
)

print(dataset.element_spec)
```

Prints:

```
(TensorSpec(shape=(None,), dtype=tf.string, name=None),
 TensorSpec(shape=(None,), dtype=tf.int64, name=None))
```

But when I do `sample = next(iter(dataset))` it goes into:

```py
InternalError                             Traceback (most recent call last)
Input In [30], in <cell line: 1>()
----> 1 sample = next(iter(dataset))

File ~/.local/bin/.virtualenvs/jax/lib/python3.8/site-packages/tensorflow/python/data/ops/iterator_ops.py:766, in OwnedIterator.__next__(self)
    764 def __next__(self):
    765   try:
--> 766     return self._next_internal()
    767   except errors.OutOfRangeError:
    768     raise StopIteration

File ~/.local/bin/.virtualenvs/jax/lib/python3.8/site-packages/tensorflow/python/data/ops/iterator_ops.py:749, in OwnedIterator._next_internal(self)
    746 # TODO(b/77291417): This runs in sync mode as iterators use an error status
    747 # to communicate that there is no more data to iterate over.
    748 with context.execution_mode(context.SYNC):
--> 749   ret = gen_dataset_ops.iterator_get_next(
    750       self._iterator_resource,
    751       output_types=self._flat_output_types,
    752       output_shapes=self._flat_output_shapes)
    754   try:
    755     # Fast path for the case `self._structure` is not a nested structure.
    756     return self._element_spec._from_compatible_tensor_list(ret)  # pylint: disable=protected-access

File ~/.local/bin/.virtualenvs/jax/lib/python3.8/site-packages/tensorflow/python/ops/gen_dataset_ops.py:3017, in iterator_get_next(iterator, output_types, output_shapes, name)
   3015   return _result
   3016 except _core._NotOkStatusException as e:
-> 3017   _ops.raise_from_not_ok_status(e, name)
   3018 except _core._FallbackException:
   3019   pass

File ~/.local/bin/.virtualenvs/jax/lib/python3.8/site-packages/tensorflow/python/framework/ops.py:7164, in raise_from_not_ok_status(e, name)
   7162 def raise_from_not_ok_status(e, name):
   7163   e.message += (" name: " + name if name is not None else "")
-> 7164   raise core._status_to_exception(e) from None

InternalError: Invalid: INVALID_ARGUMENT: arrow data type 0x7ff9899d8038 is not supported: Type error: Arrow data type is not supported [Op:IteratorGetNext]
```

Some additional notes:

* I can actually decode an image encoded with `read_image()` (shown earlier):

  ```py
  sample_image_path = image_paths[0]
  encoded_image = read_image(sample_image_path)
  image = tf.image.decode_png(encoded_image, 3)
  print(image.shape)
  ```

* If the above `tf.data.Dataset` object would have succeeded my plan was to just map the decoder like so:

  ```py
  autotune = tf.data.AUTOTUNE
  dataset = dataset.map(lambda x, y: (tf.image.decode_png(x, 3), y), num_parallel_calls=autotune)
  ```

@lhoestq I think I was able to make it work in the way you were envisioning. Here's the PoC:
https://gist.github.com/sayakpaul/f7d5cc312cd01cb31098fad3fd9c6b59#file-feather-tf-poc-bytes-ipynb

Some details:

* I am currently serializing the images as strings with `base64`). In comparison to the flattened arrays as before, the size of the individual feather files has reduced (144 MB -> 85 MB, largest).
* When decoding, I am first decoding the base64 string and then decoding that string (with `tf.io.decode_base64`) as an image with `tf.image.decode_png()`.
* The entire workflow (from generating the Feather files to loading them and preparing the batched `tf.data` pipeline) involves the following libraries: `pyarraow`, `tensorflow-io`, and `tensorflow`.

Cc: @Rocketknight1 @gante

Cool thanks ! Too bad the Arrow binary type doesn't seem to be supported in `arrow_io.ArrowFeatherDataset` :/ We would also need it to support Arrow struct type. Indeed images in `datasets` are represented using an Arrow type
```python
pa.struct({"path": pa.string(), "bytes": pa.binary()})
```
not sure yet how hard it is to support this though.

Changing the typing on our side would create concerning breaking changes, that's why it would be awesome if it could work using these types

If the ArrowFeatherDataset doesn't yet support it, I guess our hands are a bit tied at the moment.

IIUC, in my [latest PoC notebook](https://gist.github.com/sayakpaul/f7d5cc312cd01cb31098fad3fd9c6b59#file-feather-tf-poc-bytes-ipynb), you wanted to see each entry in the feather file to be represented like so?

```
pa.struct({"path": pa.string(), "bytes": pa.binary()})
```

In that case, `pa.binary()` isn't yet supported.

> IIUC, in my [latest PoC notebook](https://gist.github.com/sayakpaul/f7d5cc312cd01cb31098fad3fd9c6b59#file-feather-tf-poc-bytes-ipynb), you wanted to see each entry in the feather file to be represented like so?
>
> pa.struct({"path": pa.string(), "bytes": pa.binary()})

Yea because that's the data format we're using. If we were to use base64, then we would have to process the full dataset to convert it, which can take some time. Converting to TFRecords would be simpler than converting to base64 in Feather files.

Maybe it would take too much time to be worth exploring, but according to https://github.com/tensorflow/io/issues/1361#issuecomment-819029002 it's possible to add support for binary type in ArrowFeatherDataset. What do you think ? Any other alternative in mind ?

> Maybe it would take too much time to be worth exploring, but according to https://github.com/tensorflow/io/issues/1361#issuecomment-819029002 it's possible to add support for binary type in ArrowFeatherDataset.

Should be possible as per the comment but there hasn't been any progress and it's been more than a year.

>  If we were to use base64, then we would have to process the full dataset to convert it, which can take some time.

I don't understand this. I would think TFRecords would also need something similar but I need the context you're coming from.

> What do you think ? Any other alternative in mind ?

TFRecords since the TensorFlow ecosystem has developed good support for it over the years.

> I don't understand this. I would think TFRecords would also need something similar but I need the context you're coming from.

Users already have a copy of the dataset in Arrow format (we can change this to Feather). So to load the Arrow/feather files to a TF dataset we need TF IO or something like that. Otherwise the user has to convert all the files from Arrow to TFRecords to use TF data efficiently. But the conversion needs resources: CPU, disk, time. Converting the images to base64 require the same sort of resources.

So the issue we're trying to tackle is how to load the Arrow data in TF without having to convert anything ^^

Yeah, it looks like in its current state the tfio support for `Feather` is incomplete, so we'd end up having to write a lot of it, or do a conversion that defeats the whole point (because if we're going to convert the whole dataset we might as well convert to `TFRecord`).

Understood @lhoestq. Thanks for explaining!

Agreed with @Rocketknight1.

> @lhoestq Although I think this is a dead-end for now unfortunately, because of the limitations at TF's end, we could still explore automatic conversion to TFRecord, or I could dive into refining `to_tf_dataset()` to yield unbatched samples and/or load samples with multiprocessing to improve throughput. Do you have any preferences there?

Happy to take part there @Rocketknight1.

If `to_tf_dataset` can be unbatched, then it should be fairly easy for users to convert the TF dataset to TFRecords right ?

@lhoestq why one would convert to TFRecords after unbatching?

> If to_tf_dataset can be unbatched, then it should be fairly easy for users to convert the TF dataset to TFRecords right ?

Sort of! A `tf.data.Dataset` is more like an iterator, and does not support sample indexing. `to_tf_dataset()` creates an iterator, but to convert that to `TFRecord`, the user would have to iterate over the whole thing and manually save the stream of samples to files.

Someone would like to try to dive into tfio to fix this ? Sounds like a good opportunity to learn what are the best ways to load a dataset for TF, and also the connections between Arrow and TF.

If we can at least have the Arrow `binary` type working for TF that would be awesome already (issue https://github.com/tensorflow/io/issues/1361)

also cc @nateraw in case you'd be interested ;)

> Sounds like a good opportunity to learn what are the best ways to load a dataset for TF

The recommended way would likely be a combination of TFRecords and `tf.data`.

Exploring the connection between Arrow and TensorFlow is definitely worth pursuing though. But I am not sure about the implications of storing images in a format supported by Arrow. I guess we'll know more once we have at least figured out the support for `binary` type for TFIO. I will spend some time on it and keep this thread updated.

I am currently working on a fine-tuning notebook for the TFSegFormer model (Semantic Segmentation). The resolution is high for both the input images and the labels - (512, 512, 3). Here's the [Colab Notebook](https://colab.research.google.com/drive/1jAtR7Z0lYX6m6JsDI5VByh5vFaNhHIbP?usp=sharing) (it's a WIP so please bear that in mind).

I think the current implementation of `to_tf_dataset()` does create a bottleneck here since the GPU utilization is quite low.

Here's a notebook showing the performance difference: https://colab.research.google.com/gist/sayakpaul/d7ca67c90beb47e354942c9d8c0bd8ef/scratchpad.ipynb.

Note that I acknowledge that it's not an apples-to-apples comparison in many aspects (the dataset isn't the same, data serialization format isn't the same, etc.) but this is the best I could do.

Thanks ! I think the speed difference can be partly explained: you use ds.shuffle in your dataset, which is an exact shuffling (compared to TFDS which does buffer shuffling): it slows down query time by 2x to 10x since it has to play with data that are not contiguous.

The rest of the speed difference seems to be caused by image decoding (from 330µs/image to 30ms/image)

Fair enough. Can do one without shuffling too. But it's an important one to consider I guess.

To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.

It seems that using `tensorflow_io` we could have something similar for `to_tf_dataset` if we provide sharded Feather files: https://www.tensorflow.org/io/api_docs/python/tfio/arrow/ArrowFeatherDataset

Feather is a format almost equivalent to the Arrow IPC Stream format we're using in `datasets`: Feather V2 is equivalent to Arrow IPC File format, which is an extension of the stream format (it has an extra footer). Therefore we could store datasets as Feather instead of Arrow IPC Stream format without breaking the whole library.

Here are a few points to explore
- [ ] check the performance of ArrowFeatherDataset in tf.data
- [ ] check what would change if we were to switch to Feather if needed, in particular check that those are fine: memory mapping, typing, writing, reading to python objects, etc.

We would also need to implement sharding when loading a dataset (this will be done anyway for #546)

cc @Rocketknight1 @gante  feel free to comment in case I missed anything !

I'll share some files and scripts, so that we can benchmark performance of Feather files with tf.data


Fix timestamp conversion from Pandas to Python datetime in streaming mode

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

CI failures are unrelated to this PR, merging

Arrow accepts both pd.Timestamp and datetime.datetime objects to create timestamp arrays.
However a timestamp array is always converted to datetime.datetime objects.

This created an inconsistency between streaming in non-streaming. e.g. the `ett` dataset outputs datetime.datetime objects in non-streaming but pd.timestamp in streaming.

I fixed this by always converting pd.Timestamp to datetime.datetime during the example encoding step.
I fixed the same issue for pd.Timedelta as well. Finally I added an extra step of conversion for Series and DataFrame to take this into account in case such data are passed as Series or DataFrame.

Fix https://github.com/huggingface/datasets/issues/4533
Related to https://github.com/huggingface/datasets-server/issues/397


Avoid splitting by` .py` for the file.

good first issue

Good for newcomers

Hi @espoirMur, thanks for reporting.

You are right: that code line could be improved and made more generically valid.

On the other hand, I would suggest using `os.path.splitext` instead.

Are you willing to open a PR? :)

@albertvillanova  , Can you have a look at #4590.

Thanks

https://github.com/huggingface/datasets/blob/90b3a98065556fc66380cafd780af9b1814b9426/src/datasets/load.py#L272

Hello,
Thanks you for this library .

 I was using it and I had one edge case. my home folder name ends with `.py` it is `/home/espoir.py` so anytime I am running the code to load a local module this code here it is failing because after splitting it is trying to save the code to my home directory.

Step to reproduce.

- If you have a home folder which ends with `.py`

- load a module with a local folder
`qa_dataset = load_dataset("src/data/build_qa_dataset.py")`
it is failed
A possible workaround would be to use pathlib at the mentioned line

` meta_path = Path(importable_local_file).parent.joinpath("metadata.json")` this can alivate the issue .

Let me what are your thought on this and I can try to fix it by A PR.


Replace deprecated logging.warn with logging.warning

Replace `logging.warn` (deprecated in [Python 2.7, 2011](https://github.com/python/cpython/commit/04d5bc00a219860c69ea17eaa633d3ab9917409f)) with `logging.warning` (added in [Python 2.3, 2003](https://github.com/python/cpython/commit/6fa635df7aa88ae9fd8b41ae42743341316c90f7)).

* https://docs.python.org/3/library/logging.html#logging.Logger.warning
* https://github.com/python/cpython/issues/57444


Dataset Viewer issue for Pile of Law

dataset-viewer

Related to the dataset viewer on huggingface.co

Hi @Breakend, yes – we'll propose a solution today

Thanks so much for adding the docs. I was able to successfully hide the viewer using the
```
viewer: false
```
flag in the README.md of the dataset. I'm closing the issue because this is resolved. Thanks again!

Awesome! Thanks for confirming. cc @severo

Just for the record:

- the doc

- the dataset main page

- the dataset viewer page

### Link

https://huggingface.co/datasets/pile-of-law/pile-of-law

### Description

Hi, I would like to turn off the dataset viewer for our dataset without enabling access requests. To comply with upstream dataset creator requests/licenses, we would like to make sure that the data is not indexed by search engines and so would like to turn off dataset previews. But we do not want to collect user emails because it would violate single blind review, allowing us to deduce potential reviewers' identities. Is there a way that we can turn off the dataset viewer without collecting identity information?

Thanks so much!

### Owner

Yes


Fix WMT dataset loading issue and docs update

The PR branch now has some commits unrelated to the changes, probably due to rebasing. Can you please close this PR and open a new one from a new branch? You can use  `git cherry-pick` to preserve the relevant changes:
```bash
git checkout master
git remote add upstream git@github.com:huggingface/datasets.git
git pull --ff-only upstream master
git checkout -b wmt-datasets-fix2
git cherry-pick f2d6c995d5153131168f64fc60fe33a7813739a4 a9fdead5f435aeb88c237600be28eb8d4fde4c55
```

Closing this PR due to unwanted commit changes. Will be opening new PR for the same issue.

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.

As I am on a M1 Mac, I am not able to create a virtual `dev` environment using `pip install -e ".[dev]"`. Issue is with `tensorflow-text` not supported on M1s and there is no supporting repo by Apple or Google. So, if I was needed to perform local testing, I am not able to do that.

Let me know, if any additional changes are required.

Thanks


Properly raise FileNotFound even if the dataset is private

`tests/test_load.py::test_load_streaming_private_dataset` was failing because the hub now returns 401 when getting the HfApi.dataset_info of a dataset without authentication. `load_dataset` was raising ConnectionError, while it should be FileNoteFoundError since it first checks for local files before checking the Hub.

Moreover when use_auth_token is not set (default is False), we should not pass `token=None` to HfApi.dataset_info, or it will use the local token by default - instead it should use no token. It's currently not possible to ask for no token to be used, so as a workaround I simply set token="no-token"


Add `batch_size` parameter when calling `add_faiss_index` and `add_faiss_index_from_external_arrays`

Also, I had a doubt while checking the code related to the indices...

@lhoestq, there's a value in `config.py` named `DATASET_INDICES_FILENAME` which has the arrow extension (which I assume it should be `indices.faiss`, as the Elastic Search indices are not stored in a file, but not sure), and it's just used before actually saving an `ArrowDataset` in disk, but since those indices are never stored AFAIK, is that actually required?

https://github.com/huggingface/datasets/blob/aec86ea4b790ccccc9b2e0376a496728b1c914cc/src/datasets/config.py#L183

https://github.com/huggingface/datasets/blob/aec86ea4b790ccccc9b2e0376a496728b1c914cc/src/datasets/arrow_dataset.py#L1079-L1092

So should I also remove that?

P.S. I also edited the following code comment which I found misleading as it's not actually storing the indices.

https://github.com/huggingface/datasets/blob/8ddc4bbeb1e2bd307b21f5d21f884649aa2bf640/src/datasets/arrow_dataset.py#L1122

> @lhoestq, there's a value in config.py named DATASET_INDICES_FILENAME which has the arrow extension (which I assume it should be indices.faiss, as the Elastic Search indices are not stored in a file, but not sure), and it's just used before actually saving an ArrowDataset in disk, but since those indices are never stored AFAIK, is that actually required?

The arrow file is used to store an indices mapping (when you shuffle the dataset for example) - not for a faiss index ;)

Ok cool thanks a lot for the explanation @lhoestq I was not sure about that :+1: I'll also add it there as you suggested!

CI failures are unrelated to this PR and fixed on master, merging

Currently, even though the `batch_size` when adding vectors to the FAISS index can be tweaked in `FaissIndex.add_vectors()`, the function `ArrowDataset.add_faiss_index` doesn't have either the parameter `batch_size` to be propagated to the nested `FaissIndex.add_vectors` function or `*args, **kwargs`, so on, this PR adds the `batch_size` parameter to both `ArrowDataset.add_faiss_index` and `ArrowDataset.add_faiss_index_from_external_arrays`.

This is useful so as to tweak the `batch_size` according to the VM specifications.


Add `tldr_news` dataset

Hey @lhoestq,
Sorry for opening a PR, I was following the guide [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md)! Thanks for the review anyway, I will follow the instructions you sent 😃

This PR aims at adding support for a news dataset: `tldr news`.

This dataset is based on the daily [tldr tech newsletter](https://tldr.tech/newsletter) and contains a `headline` as well as a `content` for every piece of news contained in a newsletter.


Timestamp not returned as datetime objects in streaming mode

As reported in (internal) https://github.com/huggingface/datasets-server/issues/397

```python
>>> from datasets import load_dataset
>>> dataset = load_dataset("ett", name="h2", split="test", streaming=True)
>>> d = next(iter(dataset))
>>> d['start']
Timestamp('2016-07-01 00:00:00')
```

while loading in non-streaming mode it returns `datetime.datetime(2016, 7, 1, 0, 0)`


Add Video feature

The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4532). All of your documentation changes will be reflected on that endpoint.

The following adds a `Video` feature for encoding/decoding videos on the fly from in memory bytes. It uses my own `encoded-video` library which is basically `pytorchvideo`'s encoded video but with all the `torch` specific stuff stripped out. Because of that, and because the tool I used under the hood is not very mature, I leave this as a draft idea that we can use to build off of.


Dataset Viewer issue for CSV datasets

Confirmed, it's fixed now. Thanks for reporting, and thanks @coyotte508 for fixing it

<img width="1123" alt="Capture d’écran 2022-06-21 à 10 28 05" src="https://user-images.githubusercontent.com/1676121/174753833-1b453a5a-6a90-4717-bca1-1b5fc6b75e4a.png">

### Link

https://huggingface.co/datasets/scikit-learn/breast-cancer-wisconsin

### Description

I'm populating CSV datasets [here](https://huggingface.co/scikit-learn) but the viewer is not enabled and it looks for a dataset loading script, the datasets aren't on queue as well.

You can replicate the problem by simply uploading any CSV dataset.

### Owner

Yes


Add AudioFolder packaged loader

enhancement

New feature or request

The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4530). All of your documentation changes will be reflected on that endpoint.

@lhoestq @mariosasko I don't know what to do with the test, do you have any ideas? :)

also it's passed in `pyarrow_latest_WIN`

If the error only happens on 3.6, maybe #4460 can help ^^' It seems to work in 3.7 on the windows CI

> inferring labels is not the default behavior (drop_labels is set to True in config)

I think it a missed opportunity to have a consistent API between imagefolder and audiofolder, since they do everything the same way. Can you give more details why you think we should drop the labels by default ?

Considering audio classification in audio is not as common as image classification in image, I'm ok with having different config defaults as long as they are properly documented (check [Papers With Code](https://paperswithcode.com/datasets) for stats and compare the classification numbers to the other tasks, do this for both modalities)

Also, WDYT about creating a generic folder loader that ImageFolder and AudioFolder then subclass to avoid having to update both of them when there is something to update/fix?

@lhoestq I think it doesn't change the API itself, it just doesn't infer labels by default, but you can **still** set `drop_labels=False` to `load_dataset` and the labels will be inferred.
Suppose that one has data structured as follows:
```
data/
   train/
      audio/
          file1.wav
          file2.wav
          file3.wav
      metadata.jsonl
   test/
      audio/
          file1.wav
          file2.wav
          file3.wav
      metadata.jsonl
```
If users load this dataset with `load_dataset("audiofolder", data_dir="data")` (the most native way), they will get a `label` feature that will always be equal to 0 (= "audio"). To mitigate this, they will have to always specify `load_dataset("audiofolder", data_dir="data", drop_labels=True)` explicitly and I believe it's not convenient.

At the same time, `label` column can be added just as easy as adding one argument:` load_dataset("audiofolder", data_dir="data", drop_labels=False)`. As classification task is not as common, I think it should require more symbols to be added to the code :D

But this is definitely should be explained in the docs, which I've forgotten to update... I'll add this section soon.

Also +to the generic loader, will work on it.

If a metadata.jsonl file is present, then it doesn't have to infer the labels I agree. Note that this is already the case for imagefolder ;) in your case `load_dataset("audiofolder", data_dir="data")` won't return labels !

Labels are only inferred if there are no metadata.jsonl

Feel free to merge the `main` branch into yours after updating your fork of `datasets`: https://github.com/huggingface/datasets/issues/4629

This should fix some errors in the CI

will close  #3964

AudioFolder is almost identical to ImageFolder except for inferring labels is not the default behavior (`drop_labels` is set to True in config), the option of inferring them is preserved though.

The weird thing is happening with the `test_data_files_with_metadata_and_archives` when `streaming` is `True`. Here is the log from the CI:
```

../.pyenv/versions/3.6.15/lib/python3.6/site-packages/datasets/features/audio.py:237: in _decode_non_mp3_path_like
    array, sampling_rate = librosa.load(f, sr=self.sampling_rate, mono=self.mono)
../.pyenv/versions/3.6.15/lib/python3.6/site-packages/librosa/util/decorators.py:88: in inner_f
    return f(*args, **kwargs)
../.pyenv/versions/3.6.15/lib/python3.6/site-packages/librosa/core/audio.py:176: in load
    raise (exc)
../.pyenv/versions/3.6.15/lib/python3.6/site-packages/librosa/core/audio.py:155: in load
    context = sf.SoundFile(path)
../.pyenv/versions/3.6.15/lib/python3.6/site-packages/soundfile.py:629: in __init__
    self._file = self._open(file, mode_int, closefd)
../.pyenv/versions/3.6.15/lib/python3.6/site-packages/soundfile.py:1184: in _open
    "Error opening {0!r}: ".format(self.name))
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

err = 72
prefix = "Error opening <zipfile.ZipExtFile name='audio_file.wav' mode='r' compress_type=deflate>: "

    def _error_check(err, prefix=""):
        """Pretty-print a numerical error code if there is an error."""
        if err != 0:
            err_str = _snd.sf_error_number(err)
>           raise RuntimeError(prefix + _ffi.string(err_str).decode('utf-8', 'replace'))
E           RuntimeError: Error opening <zipfile.ZipExtFile name='audio_file.wav' mode='r' compress_type=deflate>: Error in WAV file. No 'data' chunk marker.
```
I hadn't been able to reproduce this locally until I created the same test environment (I mean with `pip install .[tests]`) with python3.6. The same env but with python3.8 passes the test! I didn't manage to figure out what's wrong, I also tried simply to replace the test wav file and still got the same error. Versions of `soundfile`, `librosa` and `libsndfile` are identical. Might it be something with zip compression? Sounds weird but I don't have any other ideas...

TODO:

- [ ] align with #4622
- [ ] documentation
- [ ] tests for AutoFolder?


Ecoset

dataset request

Requesting to add a new dataset

Hi! Very cool dataset! I answered your questions on the forum. Also, feel free to comment `#self-assign` on this issue to self-assign it.

## Adding a Dataset
- **Name:** *Ecoset*
- **Description:** *https://www.kietzmannlab.org/ecoset/*
- **Paper:** *https://doi.org/10.1073/pnas.2011417118*
- **Data:** *https://codeocean.com/capsule/9570390/tree/v1*
- **Motivation:**

**Ecoset** was created as a clean and ecologically valid alternative to **Imagenet**.

It is a large image recognition dataset, similar to Imagenet in size and structure. However, the authors of ecoset claim several improvements over Imagenet, like:
- more ecologically valid classes (e.g. not over-focussed on distinguishing different dog breeds)
- less NSFW content
- 'pre-packed image recognition models' that come with the dataset and can be used for validation of other models.

I am working for one of the authors of the paper with the aim of bringing Ecoset to huggingface datasets. Therefore I can work on this issue personally, but could use some help from devs and experienced users if the dataset is of interest to them. I phrased some of my questions on [discuss.huggingface](https://discuss.huggingface.co/t/handling-large-image-datasets/19373).


Memory leak when iterating a Dataset

bug

Something isn't working

## Describe the bug
It seems that memory never gets freed after iterating a `Dataset` (using `.map()` or a simple `for` loop)

## Steps to reproduce the bug
```python
import gc
import logging
import time
import pyarrow
from datasets import load_dataset
from tqdm import trange
import os, psutil

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
process = psutil.Process(os.getpid())

print(process.memory_info().rss)  # output: 633507840 bytes

corpus = load_dataset("BeIR/msmarco", 'corpus', keep_in_memory=False, streaming=False)['corpus']

print(process.memory_info().rss)  # output: 698601472 bytes

logger.info("Applying method to all examples in all splits")
for i in trange(0, len(corpus), 1000):
    batch = corpus[i:i+1000]
    data = pyarrow.total_allocated_bytes()
    if data > 0:
        logger.info(f"{i}/{len(corpus)}: {data}")

print(process.memory_info().rss)  # output: 3788247040 bytes

del batch
gc.collect()

print(process.memory_info().rss)  # output: 3788247040 bytes

logger.info("Done...")
time.sleep(100)
```

## Expected results
Limited memory usage, and memory to be freed after processing

## Actual results
Memory leak
![test](https://user-images.githubusercontent.com/29777165/174578276-f2c37e6c-b5d8-4985-b4d8-8413eb2b3241.png)

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: Linux-5.4.0-90-generic-x86_64-with-glibc2.31
- Python version: 3.9.7
- PyArrow version: 8.0.0
- Pandas version: 1.4.2


Dataset Viewer issue for vadis/sv-ident

Fixed, thanks!
![Uploading Capture d’écran 2022-06-21 à 18.42.40.png…]()

### Link

https://huggingface.co/datasets/vadis/sv-ident

### Description

The dataset preview does not work:
```
Server Error

Status code:   400
Exception:     Status400Error
Message:       The dataset does not exist.
```

However, the dataset is streamable and works locally:
```python
In [1]: from datasets import load_dataset; ds = load_dataset("sv-ident.py", split="train", streaming=True); item = next(iter(ds)); item
Using custom data configuration default
Out[1]:
{'sentence': 'Our point, however, is that so long as downward (favorable) comparisons overwhelm the potential for unfavorable comparisons, system justification should be a likely outcome amongst the disadvantaged.',
 'is_variable': 1,
 'variable': ['exploredata-ZA5400_VarV66', 'exploredata-ZA5400_VarV53'],
 'research_data': ['ZA5400'],
 'doc_id': '73106',
 'uuid': 'b9fbb80f-3492-4b42-b9d5-0254cc33ac10',
 'lang': 'en'}
```

CC: @e-tornike

### Owner

No


split cache used when processing different split

Something isn't working

I was not able to reproduce this behavior (I tried without using pytorch lightning though, since I don't know what code you ran in pytorch lightning to get this).

If you can provide a MWE that would be perfect ! :)

Hi, I think the issue happened because I was loading datasets under an `if` ... `else` statement and the condition would change the dataset I would need to load but instead the cached one was always returned. However, I believe that is expected behaviour, if so I'll close the issue.

Otherwise I will try to provide a MWE

## Describe the bug`
```
ds1 = load_dataset('squad', split='validation')
ds2 = load_dataset('squad', split='train')
ds1 = ds1.map(some_function)
ds2 = ds2.map(some_function)
assert ds1 == ds2
```
This happens when ds1 and ds2 are created in `pytorch_lightning.DataModule` through

```
class myDataModule:

    def train_dataloader(self):
         ds = load_dataset('squad', split='train')
         ds = ds.map(some_function)
         return [ds]

    def val_dataloader(self):
         ds = load_dataset('squad', split="validation")
         ds = ds.map(some_function)
         return [ds]
```
I don't know if it depends on `pytorch_lightning` or `datasets` but setting `ds.map(some_function, load_from_cache_file=False)` fixes the issue.

If this is not enough to replicate I will try and provide and MWE, I don't have time now so I thought I wuld open the issue first!


Out of memory error on workers while running Beam+Dataflow

Something isn't working

Some naive ideas to cope with this:
- enable more RAM on each worker
- force the spanning of more workers
- others?

@albertvillanova We were finally able to process the full NQ dataset on our machines using 600 gb with 5 workers. Maybe these numbers will work for you as well.

Thanks a lot for the hint, @seirasto.

I have one question: what runner did you use? Direct, Apache Flink/Nemo/Samza/Spark, Google Dataflow...? Thank you.

@albertvillanova  Since we have already processed the NQ dataset on our machines can we upload it to datasets so the NQ PR can be merged?

Maybe @lhoestq can give a more accurate answer as I am not sure about the authentication requirements to upload those files to our cloud bucket.

Anyway I propose to continue this discussion on the dedicated PR for Natural questions dataset:
- #4368

> I asked my colleague who ran the code and he said apache beam.

He looked into it further and he just used DirectRunner. @albertvillanova

OK, thank you @seirasto for your hint.

That explains why you did not encounter the out of memory error: this only appears when the processing is distributed (on workers memory) and DirectRunner does not distribute the processing (all is done in a single machine).

## Describe the bug
While running the preprocessing of the natural_question dataset (see PR #4368), there is an issue for the "default" config (train+dev files).

Previously we ran the preprocessing for the "dev" config (only dev files) with success.

Train data files are larger than dev ones and apparently workers run out of memory while processing them.

Any help/hint is welcome!

Error message:
```
Data channel closed, unable to receive additional data from SDK sdk-0-0
```

Info from the Diagnostics tab:
```
Out of memory: Killed process 1882 (python) total-vm:6041764kB, anon-rss:3290928kB, file-rss:0kB, shmem-rss:0kB, UID:0 pgtables:9520kB oom_score_adj:900
The worker VM had to shut down one or more processes due to lack of memory.
```

## Additional information

### Stack trace
```
Traceback (most recent call last):
  File "/home/albert_huggingface_co/natural_questions/venv/bin/datasets-cli", line 8, in <module>
    sys.exit(main())
  File "/home/albert_huggingface_co/natural_questions/venv/lib/python3.9/site-packages/datasets/commands/datasets_cli.py", line 39, in main
    service.run()
  File "/home/albert_huggingface_co/natural_questions/venv/lib/python3.9/site-packages/datasets/commands/run_beam.py", line 127, in run
    builder.download_and_prepare(
  File "/home/albert_huggingface_co/natural_questions/venv/lib/python3.9/site-packages/datasets/builder.py", line 704, in download_and_prepare
    self._download_and_prepare(
  File "/home/albert_huggingface_co/natural_questions/venv/lib/python3.9/site-packages/datasets/builder.py", line 1389, in _download_and_prepare
    pipeline_results.wait_until_finish()
  File "/home/albert_huggingface_co/natural_questions/venv/lib/python3.9/site-packages/apache_beam/runners/dataflow/dataflow_runner.py", line 1667, in wait_until_finish
    raise DataflowRuntimeException(
apache_beam.runners.dataflow.dataflow_runner.DataflowRuntimeException: Dataflow pipeline failed. State: FAILED, Error:
Data channel closed, unable to receive additional data from SDK sdk-0-0
```

### Logs
```
Error message from worker: Data channel closed, unable to receive additional data from SDK sdk-0-0

Workflow failed. Causes: S30:train/ReadAllFromText/ReadAllFiles/Reshard/ReshufflePerKey/GroupByKey/Read+train/ReadAllFromText/ReadAllFiles/Reshard/ReshufflePerKey/GroupByKey/GroupByWindow+train/ReadAllFromText/ReadAllFiles/Reshard/ReshufflePerKey/FlatMap(restore_timestamps)+train/ReadAllFromText/ReadAllFiles/Reshard/RemoveRandomKeys+train/ReadAllFromText/ReadAllFiles/ReadRange+train/Map(_parse_example)+train/Encode+train/Count N. Examples+train/Get values/Values+train/Save to parquet/Write/WriteImpl/WindowInto(WindowIntoFn)+train/Save to parquet/Write/WriteImpl/WriteBundles+train/Save to parquet/Write/WriteImpl/Pair+train/Save to parquet/Write/WriteImpl/GroupByKey/Write failed., The job failed because a work item has failed 4 times. Look in previous log entries for the cause of each one of the 4 failures. For more information, see https://cloud.google.com/dataflow/docs/guides/common-errors. The work item was attempted on these workers: beamapp-alberthuggingface-06170554-5p23-harness-t4v9 Root cause: Data channel closed, unable to receive additional data from SDK sdk-0-0, beamapp-alberthuggingface-06170554-5p23-harness-t4v9 Root cause: The worker lost contact with the service., beamapp-alberthuggingface-06170554-5p23-harness-bwsj Root cause: The worker lost contact with the service., beamapp-alberthuggingface-06170554-5p23-harness-5052 Root cause: The worker lost contact with the service.
```


Downloading via Apache Pipeline, client cancelled (org.apache.beam.vendor.grpc.v1p43p2.io.grpc.StatusRuntimeException)

Something isn't working

Hi @dan-the-meme-man, thanks for reporting.

We are investigating a similar issue but with Beam+Dataflow (instead of Beam+Flink):
- #4525

In order to go deeper into the root cause, we need as much information as possible: logs from the main process + logs from the workers are very informative.

In the case of the issue with Beam+Dataflow, the logs from the workers report an out of memory issue.

As I continued working on this today, I came to suspect that it is in fact an out of memory issue - I have a few more notebooks that I've left running, and if they produce the same error, I will try to get the logs. In the meantime, if there's any chance that there is a repo out there with those three languages already as .arrow files, or if you know about how much memory would be needed to actually download those sets, please let me know!

## Describe the bug
When downloading some `wikipedia` languages (in particular, I'm having a hard time with Spanish, Cebuano, and Russian) via FlinkRunner, I encounter the exception in the title. I have been playing with package versions a lot, because unfortunately, the different dependencies required by these packages seem to be incompatible in terms of versions (dill and requests, for instance). It should be noted that the following code runs for several hours without issue, executing the `load_dataset()` function, before the exception occurs.

## Steps to reproduce the bug
```python
# bash commands
!pip install datasets
!pip install apache-beam[interactive]
!pip install mwparserfromhell
!pip install dill==0.3.5.1
!pip install requests==2.23.0

# imports
import os
from datasets import load_dataset
import apache_beam as beam
import mwparserfromhell
from google.colab import drive
import dill
import requests

# mount drive
drive_dir = os.path.join(os.getcwd(), 'drive')
drive.mount(drive_dir)

# confirming the versions of these two packages are the ones that are suggested by the outputs from the bash commands
print(dill.__version__)
print(requests.__version__)

lang = 'es' # or 'ru' or 'ceb' - these are the ones causing the issue
lang_dir = os.path.join(drive_dir, 'path/to/my/folder', lang)

if not os.path.exists(lang_dir):
  x = None
  x = load_dataset('wikipedia', '20220301.' + lang, beam_runner='Flink',
                   split='train')
  x.save_to_disk(lang_dir)
```

## Expected results
Although some warnings are generally produced by this code (run in Colab Notebook), most languages I've tried have been successfully downloaded. It should simply go through without issue, but for these languages, I am continually encountering this error.

## Actual results
Traceback below:
```
Exception in thread run_worker_3-1:
Traceback (most recent call last):
  File "/usr/lib/python3.7/threading.py", line 926, in _bootstrap_inner
    self.run()
  File "/usr/lib/python3.7/threading.py", line 870, in run
    self._target(*self._args, **self._kwargs)
  File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 234, in run
    for work_request in self._control_stub.Control(get_responses()):
  File "/usr/local/lib/python3.7/dist-packages/grpc/_channel.py", line 426, in __next__
    return self._next()
  File "/usr/local/lib/python3.7/dist-packages/grpc/_channel.py", line 826, in _next
    raise self
grpc._channel._MultiThreadedRendezvous: <_MultiThreadedRendezvous of RPC that terminated with:
	status = StatusCode.UNAVAILABLE
	details = "Socket closed"
	debug_error_string = "{"created":"@1655593643.871830638","description":"Error received from peer ipv4:127.0.0.1:44441","file":"src/core/lib/surface/call.cc","file_line":952,"grpc_message":"Socket closed","grpc_status":14}"
>

Traceback (most recent call last):
  File "apache_beam/runners/common.py", line 1198, in apache_beam.runners.common.DoFnRunner.process
  File "apache_beam/runners/common.py", line 718, in apache_beam.runners.common.PerWindowInvoker.invoke_process
  File "apache_beam/runners/common.py", line 782, in apache_beam.runners.common.PerWindowInvoker._invoke_process_per_window
  File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/bundle_processor.py", line 426, in __getitem__
    self._cache[target_window] = self._side_input_data.view_fn(raw_view)
  File "/usr/local/lib/python3.7/dist-packages/apache_beam/pvalue.py", line 391, in <lambda>
    lambda iterable: from_runtime_iterable(iterable, view_options))
  File "/usr/local/lib/python3.7/dist-packages/apache_beam/pvalue.py", line 512, in _from_runtime_iterable
    head = list(itertools.islice(it, 2))
  File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 1228, in _lazy_iterator
    self._underlying.get_raw(state_key, continuation_token))
  File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 1019, in get_raw
    continuation_token=continuation_token)))
  File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 1060, in _blocking_request
    raise RuntimeError(response.error)
RuntimeError: Unknown process bundle instruction id '26'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 267, in _execute
    response = task()
  File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 340, in <lambda>
    lambda: self.create_worker().do_instruction(request), request)
  File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 581, in do_instruction
    getattr(request, request_type), request.instruction_id)
  File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 618, in process_bundle
    bundle_processor.process_bundle(instruction_id))
  File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/bundle_processor.py", line 996, in process_bundle
    element.data)
  File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/bundle_processor.py", line 221, in process_encoded
    self.output(decoded_value)
  File "apache_beam/runners/worker/operations.py", line 346, in apache_beam.runners.worker.operations.Operation.output
  File "apache_beam/runners/worker/operations.py", line 348, in apache_beam.runners.worker.operations.Operation.output
  File "apache_beam/runners/worker/operations.py", line 215, in apache_beam.runners.worker.operations.SingletonConsumerSet.receive
  File "apache_beam/runners/worker/operations.py", line 707, in apache_beam.runners.worker.operations.DoOperation.process
  File "apache_beam/runners/worker/operations.py", line 708, in apache_beam.runners.worker.operations.DoOperation.process
  File "apache_beam/runners/common.py", line 1200, in apache_beam.runners.common.DoFnRunner.process
  File "apache_beam/runners/common.py", line 1281, in apache_beam.runners.common.DoFnRunner._reraise_augmented
  File "apache_beam/runners/common.py", line 1198, in apache_beam.runners.common.DoFnRunner.process
  File "apache_beam/runners/common.py", line 718, in apache_beam.runners.common.PerWindowInvoker.invoke_process
  File "apache_beam/runners/common.py", line 782, in apache_beam.runners.common.PerWindowInvoker._invoke_process_per_window
  File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/bundle_processor.py", line 426, in __getitem__
    self._cache[target_window] = self._side_input_data.view_fn(raw_view)
  File "/usr/local/lib/python3.7/dist-packages/apache_beam/pvalue.py", line 391, in <lambda>
    lambda iterable: from_runtime_iterable(iterable, view_options))
  File "/usr/local/lib/python3.7/dist-packages/apache_beam/pvalue.py", line 512, in _from_runtime_iterable
    head = list(itertools.islice(it, 2))
  File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 1228, in _lazy_iterator
    self._underlying.get_raw(state_key, continuation_token))
  File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 1019, in get_raw
    continuation_token=continuation_token)))
  File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 1060, in _blocking_request
    raise RuntimeError(response.error)
RuntimeError: Unknown process bundle instruction id '26' [while running 'train/Save to parquet/Write/WriteImpl/WriteBundles']

ERROR:apache_beam.runners.worker.sdk_worker:Error processing instruction 26. Original traceback is
Traceback (most recent call last):
  File "apache_beam/runners/common.py", line 1198, in apache_beam.runners.common.DoFnRunner.process
  File "apache_beam/runners/common.py", line 718, in apache_beam.runners.common.PerWindowInvoker.invoke_process
  File "apache_beam/runners/common.py", line 782, in apache_beam.runners.common.PerWindowInvoker._invoke_process_per_window
  File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/bundle_processor.py", line 426, in __getitem__
    self._cache[target_window] = self._side_input_data.view_fn(raw_view)
  File "/usr/local/lib/python3.7/dist-packages/apache_beam/pvalue.py", line 391, in <lambda>
    lambda iterable: from_runtime_iterable(iterable, view_options))
  File "/usr/local/lib/python3.7/dist-packages/apache_beam/pvalue.py", line 512, in _from_runtime_iterable
    head = list(itertools.islice(it, 2))
  File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 1228, in _lazy_iterator
    self._underlying.get_raw(state_key, continuation_token))
  File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 1019, in get_raw
    continuation_token=continuation_token)))
  File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 1060, in _blocking_request
    raise RuntimeError(response.error)
RuntimeError: Unknown process bundle instruction id '26'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 267, in _execute
    response = task()
  File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 340, in <lambda>
    lambda: self.create_worker().do_instruction(request), request)
  File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 581, in do_instruction
    getattr(request, request_type), request.instruction_id)
  File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 618, in process_bundle
    bundle_processor.process_bundle(instruction_id))
  File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/bundle_processor.py", line 996, in process_bundle
    element.data)
  File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/bundle_processor.py", line 221, in process_encoded
    self.output(decoded_value)
  File "apache_beam/runners/worker/operations.py", line 346, in apache_beam.runners.worker.operations.Operation.output
  File "apache_beam/runners/worker/operations.py", line 348, in apache_beam.runners.worker.operations.Operation.output
  File "apache_beam/runners/worker/operations.py", line 215, in apache_beam.runners.worker.operations.SingletonConsumerSet.receive
  File "apache_beam/runners/worker/operations.py", line 707, in apache_beam.runners.worker.operations.DoOperation.process
  File "apache_beam/runners/worker/operations.py", line 708, in apache_beam.runners.worker.operations.DoOperation.process
  File "apache_beam/runners/common.py", line 1200, in apache_beam.runners.common.DoFnRunner.process
  File "apache_beam/runners/common.py", line 1281, in apache_beam.runners.common.DoFnRunner._reraise_augmented
  File "apache_beam/runners/common.py", line 1198, in apache_beam.runners.common.DoFnRunner.process
  File "apache_beam/runners/common.py", line 718, in apache_beam.runners.common.PerWindowInvoker.invoke_process
  File "apache_beam/runners/common.py", line 782, in apache_beam.runners.common.PerWindowInvoker._invoke_process_per_window
  File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/bundle_processor.py", line 426, in __getitem__
    self._cache[target_window] = self._side_input_data.view_fn(raw_view)
  File "/usr/local/lib/python3.7/dist-packages/apache_beam/pvalue.py", line 391, in <lambda>
    lambda iterable: from_runtime_iterable(iterable, view_options))
  File "/usr/local/lib/python3.7/dist-packages/apache_beam/pvalue.py", line 512, in _from_runtime_iterable
    head = list(itertools.islice(it, 2))
  File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 1228, in _lazy_iterator
    self._underlying.get_raw(state_key, continuation_token))
  File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 1019, in get_raw
    continuation_token=continuation_token)))
  File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 1060, in _blocking_request
    raise RuntimeError(response.error)
RuntimeError: Unknown process bundle instruction id '26' [while running 'train/Save to parquet/Write/WriteImpl/WriteBundles']

ERROR:root:org.apache.beam.vendor.grpc.v1p43p2.io.grpc.StatusRuntimeException: CANCELLED: client cancelled
ERROR:apache_beam.runners.worker.data_plane:Failed to read inputs in the data plane.
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/data_plane.py", line 634, in _read_inputs
    for elements in elements_iterator:
  File "/usr/local/lib/python3.7/dist-packages/grpc/_channel.py", line 426, in __next__
    return self._next()
  File "/usr/local/lib/python3.7/dist-packages/grpc/_channel.py", line 826, in _next
    raise self
grpc._channel._MultiThreadedRendezvous: <_MultiThreadedRendezvous of RPC that terminated with:
	status = StatusCode.CANCELLED
	details = "Multiplexer hanging up"
	debug_error_string = "{"created":"@1655593654.436885887","description":"Error received from peer ipv4:127.0.0.1:43263","file":"src/core/lib/surface/call.cc","file_line":952,"grpc_message":"Multiplexer hanging up","grpc_status":1}"
>
Exception in thread read_grpc_client_inputs:
Traceback (most recent call last):
  File "/usr/lib/python3.7/threading.py", line 926, in _bootstrap_inner
    self.run()
  File "/usr/lib/python3.7/threading.py", line 870, in run
    self._target(*self._args, **self._kwargs)
  File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/data_plane.py", line 651, in <lambda>
    target=lambda: self._read_inputs(elements_iterator),
  File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/data_plane.py", line 634, in _read_inputs
    for elements in elements_iterator:
  File "/usr/local/lib/python3.7/dist-packages/grpc/_channel.py", line 426, in __next__
    return self._next()
  File "/usr/local/lib/python3.7/dist-packages/grpc/_channel.py", line 826, in _next
    raise self
grpc._channel._MultiThreadedRendezvous: <_MultiThreadedRendezvous of RPC that terminated with:
	status = StatusCode.CANCELLED
	details = "Multiplexer hanging up"
	debug_error_string = "{"created":"@1655593654.436885887","description":"Error received from peer ipv4:127.0.0.1:43263","file":"src/core/lib/surface/call.cc","file_line":952,"grpc_message":"Multiplexer hanging up","grpc_status":1}"
>

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
[/tmp/ipykernel_219/3869142325.py](https://localhost:8080/#) in <module>
     18   x = None
     19   x = load_dataset('wikipedia', '20220301.' + lang, beam_runner='Flink',
---> 20                    split='train')
     21   x.save_to_disk(lang_dir)

3 frames
[/usr/local/lib/python3.7/dist-packages/apache_beam/runners/portability/portable_runner.py](https://localhost:8080/#) in wait_until_finish(self, duration)
    604
    605     if self._runtime_exception:
--> 606       raise self._runtime_exception
    607
    608     return self._state

RuntimeError: Pipeline BeamApp-root-0618220708-b3b59a0e_d8efcf67-9119-4f76-b013-70de7b29b54d failed in state FAILED: org.apache.beam.vendor.grpc.v1p43p2.io.grpc.StatusRuntimeException: CANCELLED: client cancelled
```

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `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


Update download url and improve card of `cats_vs_dogs` dataset

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

Improve the download URL (reported here: https://huggingface.co/datasets/cats_vs_dogs/discussions/1), remove the `image_file_path` column (not used in Transformers, so it should be safe) and add more info to the card.


Try to reduce the number of datasets that require manual download

> Currently, 41 canonical datasets require manual download. I checked their scripts and I'm pretty sure this number can be reduced to ≈ 30 by not relying on bash scripts to download data, hosting data directly on the Hub when the license permits, etc. Then, we will mostly be left with datasets with restricted access, which we can ignore

from https://github.com/huggingface/datasets-server/issues/12#issuecomment-1026920432


Datasets method `.map` not hashing

Something isn't working

Didn't realize it's a bug when I asked the question yesterday! Free free to post an answer if you are sure the cause has been addressed.

https://stackoverflow.com/questions/72664827/can-pickle-dill-foo-but-not-lambda-x-foox

## Describe the bug
Datasets method `.map` not hashing, even with an empty no-op function

## Steps to reproduce the bug
```python
from datasets import load_dataset

# download 9MB dummy dataset
ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean")

def prepare_dataset(batch):
     return(batch)

ds = ds.map(
    prepare_dataset,
    num_proc=1,
    desc="preprocess train dataset",
)
```

## Expected results
Hashed and cached dataset preprocessing

## Actual results
Does not hash properly:
```
Parameter 'function'=<function prepare_dataset at 0x7fccb68e9280> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
```

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.3.dev0
- Platform: Linux-5.11.0-1028-gcp-x86_64-with-glibc2.31
- Python version: 3.9.12
- PyArrow version: 8.0.0
- Pandas version: 1.4.2

cc @lhoestq


Failure to  hash `dataclasses` - results in functions that cannot be hashed or cached in `.map`

Something isn't working

I think this has been fixed by #4516, let me know if you encounter this again :)

I re-ran your code in 3.7 and 3.9 and it works fine

Dataclasses cannot be hashed. As a result, they cannot be hashed or cached if used in the `.map` method. Dataclasses are used extensively in Transformers examples scripts: (c.f. [CTC example](https://github.com/huggingface/transformers/blob/main/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py)). Since dataclasses cannot be hashed, one has to define separate variables prior to passing dataclass attributes to the `.map` method:
```python
phoneme_language = data_args.phoneme_language
```
in the example https://github.com/huggingface/transformers/blob/3c7e56fbb11f401de2528c1dcf0e282febc031cd/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py#L603-L630

## Steps to reproduce the bug
```python
from dataclasses import dataclass, field
from datasets.fingerprint import Hasher

@dataclass
class DataTrainingArguments:
    """
    Arguments pertaining to what data we are going to input our model for training and eval.
    """

    phoneme_language: str = field(
        default=None, metadata={"help": "The name of the phoneme language to use."}
    )

data_args = DataTrainingArguments(phoneme_language ="foo")

Hasher.hash(data_args)

phoneme_language = data_args.phoneme_language

Hasher.hash(phoneme_language)
```

## Expected results
A hash.
## Actual results
<details>
<summary> Traceback </summary>

```
---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
Input In [1], in <cell line: 16>()
     10     phoneme_language: str = field(
     11         default=None, metadata={"help": "The name of the phoneme language to use."}
     12     )
     14 data_args = DataTrainingArguments(phoneme_language ="foo")
---> 16 Hasher.hash(data_args)
     18 phoneme_language = data_args. phoneme_language
     20 Hasher.hash(phoneme_language)

File ~/datasets/src/datasets/fingerprint.py:237, in Hasher.hash(cls, value)
    235     return cls.dispatch[type(value)](cls, value)
    236 else:
--> 237     return cls.hash_default(value)

File ~/datasets/src/datasets/fingerprint.py:230, in Hasher.hash_default(cls, value)
    228 @classmethod
    229 def hash_default(cls, value: Any) -> str:
--> 230     return cls.hash_bytes(dumps(value))

File ~/datasets/src/datasets/utils/py_utils.py:564, in dumps(obj)
    562 file = StringIO()
    563 with _no_cache_fields(obj):
--> 564     dump(obj, file)
    565 return file.getvalue()

File ~/datasets/src/datasets/utils/py_utils.py:539, in dump(obj, file)
    537 def dump(obj, file):
    538     """pickle an object to a file"""
--> 539     Pickler(file, recurse=True).dump(obj)
    540     return

File ~/hf/lib/python3.8/site-packages/dill/_dill.py:620, in Pickler.dump(self, obj)
    618     raise PicklingError(msg)
    619 else:
--> 620     StockPickler.dump(self, obj)
    621 return

File /usr/lib/python3.8/pickle.py:487, in _Pickler.dump(self, obj)
    485 if self.proto >= 4:
    486     self.framer.start_framing()
--> 487 self.save(obj)
    488 self.write(STOP)
    489 self.framer.end_framing()

File /usr/lib/python3.8/pickle.py:603, in _Pickler.save(self, obj, save_persistent_id)
    599     raise PicklingError("Tuple returned by %s must have "
    600                         "two to six elements" % reduce)
    602 # Save the reduce() output and finally memoize the object
--> 603 self.save_reduce(obj=obj, *rv)

File /usr/lib/python3.8/pickle.py:687, in _Pickler.save_reduce(self, func, args, state, listitems, dictitems, state_setter, obj)
    684     raise PicklingError(
    685         "args[0] from __newobj__ args has the wrong class")
    686 args = args[1:]
--> 687 save(cls)
    688 save(args)
    689 write(NEWOBJ)

File /usr/lib/python3.8/pickle.py:560, in _Pickler.save(self, obj, save_persistent_id)
    558 f = self.dispatch.get(t)
    559 if f is not None:
--> 560     f(self, obj)  # Call unbound method with explicit self
    561     return
    563 # Check private dispatch table if any, or else
    564 # copyreg.dispatch_table

File ~/hf/lib/python3.8/site-packages/dill/_dill.py:1838, in save_type(pickler, obj, postproc_list)
   1836             postproc_list = []
   1837         postproc_list.append((setattr, (obj, '__qualname__', obj_name)))
-> 1838     _save_with_postproc(pickler, (_create_type, (
   1839         type(obj), obj.__name__, obj.__bases__, _dict
   1840     )), obj=obj, postproc_list=postproc_list)
   1841     log.info("# %s" % _t)
   1842 else:

File ~/hf/lib/python3.8/site-packages/dill/_dill.py:1140, in _save_with_postproc(pickler, reduction, is_pickler_dill, obj, postproc_list)
   1137     pickler._postproc[id(obj)] = postproc_list
   1139 # TODO: Use state_setter in Python 3.8 to allow for faster cPickle implementations
-> 1140 pickler.save_reduce(*reduction, obj=obj)
   1142 if is_pickler_dill:
   1143     # pickler.x -= 1
   1144     # print(pickler.x*' ', 'pop', obj, id(obj))
   1145     postproc = pickler._postproc.pop(id(obj))

File /usr/lib/python3.8/pickle.py:692, in _Pickler.save_reduce(self, func, args, state, listitems, dictitems, state_setter, obj)
    690 else:
    691     save(func)
--> 692     save(args)
    693     write(REDUCE)
    695 if obj is not None:
    696     # If the object is already in the memo, this means it is
    697     # recursive. In this case, throw away everything we put on the
    698     # stack, and fetch the object back from the memo.

File /usr/lib/python3.8/pickle.py:560, in _Pickler.save(self, obj, save_persistent_id)
    558 f = self.dispatch.get(t)
    559 if f is not None:
--> 560     f(self, obj)  # Call unbound method with explicit self
    561     return
    563 # Check private dispatch table if any, or else
    564 # copyreg.dispatch_table

File /usr/lib/python3.8/pickle.py:901, in _Pickler.save_tuple(self, obj)
    899 write(MARK)
    900 for element in obj:
--> 901     save(element)
    903 if id(obj) in memo:
    904     # Subtle.  d was not in memo when we entered save_tuple(), so
    905     # the process of saving the tuple's elements must have saved
   (...)
    909     # could have been done in the "for element" loop instead, but
    910     # recursive tuples are a rare thing.
    911     get = self.get(memo[id(obj)][0])

File /usr/lib/python3.8/pickle.py:560, in _Pickler.save(self, obj, save_persistent_id)
    558 f = self.dispatch.get(t)
    559 if f is not None:
--> 560     f(self, obj)  # Call unbound method with explicit self
    561     return
    563 # Check private dispatch table if any, or else
    564 # copyreg.dispatch_table

File ~/hf/lib/python3.8/site-packages/dill/_dill.py:1251, in save_module_dict(pickler, obj)
   1248     if is_dill(pickler, child=False) and pickler._session:
   1249         # we only care about session the first pass thru
   1250         pickler._first_pass = False
-> 1251     StockPickler.save_dict(pickler, obj)
   1252     log.info("# D2")
   1253 return

File /usr/lib/python3.8/pickle.py:971, in _Pickler.save_dict(self, obj)
    968     self.write(MARK + DICT)
    970 self.memoize(obj)
--> 971 self._batch_setitems(obj.items())

File /usr/lib/python3.8/pickle.py:997, in _Pickler._batch_setitems(self, items)
    995     for k, v in tmp:
    996         save(k)
--> 997         save(v)
    998     write(SETITEMS)
    999 elif n:

File /usr/lib/python3.8/pickle.py:560, in _Pickler.save(self, obj, save_persistent_id)
    558 f = self.dispatch.get(t)
    559 if f is not None:
--> 560     f(self, obj)  # Call unbound method with explicit self
    561     return
    563 # Check private dispatch table if any, or else
    564 # copyreg.dispatch_table

File ~/datasets/src/datasets/utils/py_utils.py:862, in save_function(pickler, obj)
    859     if state_dict:
    860         state = state, state_dict
--> 862     dill._dill._save_with_postproc(
    863         pickler,
    864         (
    865             dill._dill._create_function,
    866             (obj.__code__, globs, obj.__name__, obj.__defaults__, closure),
    867             state,
    868         ),
    869         obj=obj,
    870         postproc_list=postproc_list,
    871     )
    872 else:
    873     closure = obj.func_closure

File ~/hf/lib/python3.8/site-packages/dill/_dill.py:1153, in _save_with_postproc(pickler, reduction, is_pickler_dill, obj, postproc_list)
   1151 dest, source = reduction[1]
   1152 if source:
-> 1153     pickler.write(pickler.get(pickler.memo[id(dest)][0]))
   1154     pickler._batch_setitems(iter(source.items()))
   1155 else:
   1156     # Updating with an empty dictionary. Same as doing nothing.

KeyError: 140434581781568
```

</details>

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.3.dev0
- Platform: Linux-5.11.0-1028-gcp-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 8.0.0
- Pandas version: 1.4.2

cc @lhoestq


Create new sections for audio and vision in guides

documentation

Improvements or additions to documentation

Ready for review!

The `toctree` is a bit longer now with the sections. I think if we keep the audio/vision/text/dataset repository sections collapsed by default, and keep the general usage expanded, it may look a little cleaner and not as overwhelming. Let me know what you think! 😄

This PR creates separate sections in the guides for audio, vision, text, and general usage so it is easier for users to find loading, processing, or sharing guides specific to the dataset type they're working with. It'll also allow us to scale the docs to additional dataset types - like time series, tabular, etc. - while keeping our docs information architecture.

Some other changes include:

- ~Experimented with decorating text with some CSS to highlight guides specific to each modality. Hopefully, it'll be easier for users to find and realize that these different docs exist!~ Will experiment with this in a different PR.
- Added deprecation warning for Metrics and redirect to Evaluate.
- Updated `set_format` section to recommend using the new `to_tf_dataset` function if you need to convert to a TensorFlow dataset.
- Reorganized `toctree` to nest general usage, audio, vision, and text sections under the how-to guides.
- A quick review and edit to the Load and Process docs for clarity.


Patch tests for hfh v0.8.0

This PR patches testing utilities that would otherwise fail with hfh v0.8.0.


Add tags for task_ids:summarization-* and task_categories:summarization*

Associated community discussion is [here](https://huggingface.co/datasets/aeslc/discussions/1).
Paper referenced in the `dataset_infos.json` is [here](https://arxiv.org/pdf/1906.03497.pdf). It mentions the  _email-subject-generation_ task, which is not a tag mentioned in any other dataset so it was not added in this pull request. The _summarization_ task is mentioned as a related task.

yaml header at top of README.md file was edited to add task tags because I couldn't find the existing tags in the json
separate Pull Request will modify dataset_infos.json to add these tags

The Enron dataset (dataset id aeslc) is only tagged with:

    arxiv:1906.03497'
    languages:en
    pretty_name:AESLC

Using the email subject_line field as a label or target variable it possible to create models for the following task_ids (in order of relevance):

    'task_ids:summarization'
    'task_ids:summarization-other-conversations-summarization'
    "task_ids:other-other-query-based-multi-document-summarization"
    'task_ids:summarization-other-aspect-based-summarization'
    'task_ids:summarization--other-headline-generation'

The subject might also be used for the task_category "task_categories:summarization"

E-mail chains might be used for the task category "task_categories:dialogue-system"


Fix hashing for python 3.9

In python 3.9, pickle hashes the `glob_ids` dictionary in addition to the `globs` of a function.

Therefore the test at `tests/test_fingerprint.py::RecurseDumpTest::test_recurse_dump_for_function_with_shuffled_globals` is currently failing for python 3.9

To make hashing deterministic when the globals are not in the same order, we also need to make the order of `glob_ids` deterministic.

Right now we don't have a CI to test python 3.9 but we should definitely have one. For this PR in particular I ran the tests locally using python 3.9 and they're passing now.

Fix https://github.com/huggingface/datasets/issues/4506


Add uppercased versions of image file extensions for automatic module inference

Adds the uppercased versions of the image file extensions to the supported extensions.

Another approach would be to call `.lower()` on extensions while resolving data files, but uppercased extensions are not something we want to encourage out of the box IMO unless they are commonly used (as they are in the vision domain)

Note that there is a slight discrepancy between the image file resolution and `imagefolder` as the latter calls `.lower()` on file extensions leading to some image file extensions being ignored by the resolution but not by the loader (e.g. `pNg`). Such extensions should also be discouraged, so I'm ignoring that case too.

Fix #4514.


Allow .JPEG as a file extension

bug

Something isn't working

Hi, thanks for reporting!  I've opened a PR with the fix.

Wow, that was quick! Thank you very much 🙏

## Describe the bug
When loading image data, HF datasets seems to recognize `.jpg` and `.jpeg` file extensions, but not e.g. .JPEG. As the naming convention .JPEG is used in important datasets such as imagenet, I would welcome if according extensions like .JPEG or .JPG would be allowed.

## Steps to reproduce the bug
```python
# use bash to create 2 sham datasets with jpeg and JPEG ext
!mkdir dataset_a
!mkdir dataset_b
!wget https://upload.wikimedia.org/wikipedia/commons/7/71/Dsc_%28179253513%29.jpeg -O example_img.jpeg
!cp example_img.jpeg ./dataset_a/
!mv example_img.jpeg ./dataset_b/example_img.JPEG

from datasets import load_dataset

# working
df1 = load_dataset("./dataset_a", ignore_verifications=True)

#not working
df2 = load_dataset("./dataset_b", ignore_verifications=True)

# show
print(df1, df2)
```

## Expected results
```
DatasetDict({
    train: Dataset({
        features: ['image', 'label'],
        num_rows: 1
    })
}) DatasetDict({
    train: Dataset({
        features: ['image', 'label'],
        num_rows: 1
    })
})
```

## Actual results
```
FileNotFoundError: Unable to resolve any data file that matches '['**']' at /..PATH../dataset_b with any supported extension ['csv', 'tsv', 'json', 'jsonl', 'parquet', 'txt', 'blp', 'bmp', 'dib', 'bufr', 'cur', 'pcx', 'dcx', 'dds', 'ps', 'eps', 'fit', 'fits', 'fli', 'flc', 'ftc', 'ftu', 'gbr', 'gif', 'grib', 'h5', 'hdf', 'png', 'apng', 'jp2', 'j2k', 'jpc', 'jpf', 'jpx', 'j2c', 'icns', 'ico', 'im', 'iim', 'tif', 'tiff', 'jfif', 'jpe', 'jpg', 'jpeg', 'mpg', 'mpeg', 'msp', 'pcd', 'pxr', 'pbm', 'pgm', 'ppm', 'pnm', 'psd', 'bw', 'rgb', 'rgba', 'sgi', 'ras', 'tga', 'icb', 'vda', 'vst', 'webp', 'wmf', 'emf', 'xbm', 'xpm', 'zip']
```

I know that it can be annoying to allow seemingly arbitrary numbers of file extensions. But I think this one would be really welcome.


Update Google Cloud Storage documentation and add Azure Blob Storage example

Hi @stevhliu, I've kept the `>>>` before all the in-line code comments as it was done like that in the default S3 example that was already there, I assume that it's done like that just for readiness, let me know whether we should remove the `>>>` in the Python blocks before the in-line code comments or keep them.

![image](https://user-images.githubusercontent.com/36760800/174254663-b68d28d2-eae1-40f3-8695-dc4b0c3b479a.png)

Comments are ignored by doctest, so I think we can remove the `>>>` :)

Sure @lhoestq, I just kept that structure as that was the more similar one to the one that was already there, but we can go with that approach, just let me know whether I should change the headers so as to leave all those providers in the same level (`h2`). Thanks!

While I was going through the 🤗 Datasets documentation of the Cloud storage filesystems at https://huggingface.co/docs/datasets/filesystems, I realized that the Google Cloud Storage documentation could be improved e.g. bullet point says "Load your dataset" when the actual call was to "Save your dataset", in-line code comment was mentioning "s3 bucket" instead of "gcs bucket", and some more in-line comments could be included.

Also, I think that mixing Google Cloud Storage documentation with AWS S3's one was a little bit confusing, so I moved all those to the end of the document under an h2 tab named "Other filesystems", with an h3 for "Google Cloud Storage".

Besides that, I was currently working with Azure Blob Storage and found out that the URL to [adlfs](https://github.com/fsspec/adlfs) was common for both filesystems Azure Blob Storage and Azure DataLake Storage, as well as the URL, which was updated even though the redirect was working fine, so I decided to group those under the same row in the column of supported filesystems.

And took also the change to add a small documentation entry as for Google Cloud Storage but for Azure Blob Storage, as I assume that AWS S3, GCP Cloud Storage, and Azure Blob Storage, are the most used cloud storage providers.

Let me know if you're OK with these changes, or whether you want me to roll back some of those! :hugs:


Add links to vision tasks scripts in ADD_NEW_DATASET template

The CI failure is unrelated to the PR's changes. Merging.

Add links to vision dataset scripts in the ADD_NEW_DATASET template.


Support all negative values in ClassLabel

Thanks for this fix! I'm not sure what the release timeline is, but FYI #4508 is a breaking issue for transformer token classification using Trainer and PyTorch. PyTorch defaults to -100 as the ignored label for [negative log loss](https://pytorch.org/docs/stable/generated/torch.nn.NLLLoss.html?highlight=nllloss#torch.nn.NLLLoss), so switching labels to -1 leads to index errors using Trainer defaults.

As a workaround, I'm using master branch directly (`pip install git+https://github.com/huggingface/datasets.git@master` for anyone who needs to do the same) until this gets released.

We usually use -1 to represent a missing label, but we should also support any negative values (some users use -100 for example). This is a regression from `datasets` 2.3

Fix https://github.com/huggingface/datasets/issues/4508


Add regression test for `ArrowWriter.write_batch` when batch is empty

As mentioned by @lhoestq, the current behavior is correct and we should not expect batches with different columns, in that case, the if should fail, as the values of the batch can be empty, but not the actual `batch_examples` value.

As spotted by @cccntu in #4502, there's a logic bug in `ArrowWriter.write_batch` as the if-statement to handle the empty batches as detailed in the docstrings of the function ("Ignores the batch if it appears to be empty, preventing a potential schema update of unknown types."), the current if-statement is not handling properly `writer.write_batch({})` as an error is triggered.

Also, if we add a regression test in `test_arrow_writer.py::test_write_batch` before applying the fix, the test will fail as when trying to write an empty batch as follows:

```
=================================================================================== short test summary info ===================================================================================
FAILED tests/test_arrow_writer.py::test_write_batch[None-None] - ValueError: Schema and number of arrays unequal
FAILED tests/test_arrow_writer.py::test_write_batch[None-1] - ValueError: Schema and number of arrays unequal
FAILED tests/test_arrow_writer.py::test_write_batch[None-10] - ValueError: Schema and number of arrays unequal
FAILED tests/test_arrow_writer.py::test_write_batch[fields1-None] - ValueError: Schema and number of arrays unequal
FAILED tests/test_arrow_writer.py::test_write_batch[fields1-1] - ValueError: Schema and number of arrays unequal
FAILED tests/test_arrow_writer.py::test_write_batch[fields1-10] - ValueError: Schema and number of arrays unequal
FAILED tests/test_arrow_writer.py::test_write_batch[fields2-None] - ValueError: Schema and number of arrays unequal
FAILED tests/test_arrow_writer.py::test_write_batch[fields2-1] - ValueError: Schema and number of arrays unequal
FAILED tests/test_arrow_writer.py::test_write_batch[fields2-10] - ValueError: Schema and number of arrays unequal
======================================================================== 9 failed, 73 deselected, 7 warnings in 0.81s =========================================================================
```

So the batch is not ignored when empty, as `batch_examples={}` won't match the condition `if batch_examples: ...`.


Support skipping Parquet to Arrow conversion when using Beam

The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4509). All of your documentation changes will be reflected on that endpoint.


Something isn't working

Hi! We've recently added a check to the `ClassLabel` type to ensure the values are in the valid label range `-1, 0, ..., num_classes-1` (-1 is used for missing values). The error in your case happens only if the `labels` column is of type `Sequence(ClassLabel(...))` before the `map` call and can be avoided by calling `dataset = dataset.cast_column("labels", Sequence(Value("int")))` beforehand. The token-classification examples in Transformers introduce a new `labels` column, so their type is also `Sequence(Value("int"))`, which doesn't lead to an error as this type unbounded.

I'm fine with re-adding support for all negative values for unknown/missing labels @mariosasko, wdyt ?

## Describe the bug
A bug occurs when mapping a function to a dataset object. I ran the same code with the same data yesterday and it worked just fine. It works when i run locally on an old version of datasets.

## Steps to reproduce the bug
Steps are:
- load whatever datset
- write a preprocessing function such as "tokenize_and_align_labels" written in https://huggingface.co/docs/transformers/tasks/token_classification
- map the function on dataset and get "ValueError: Class label -100 less than -1" from cast_storage method from datasets.features

# Sample code to reproduce the bug
def tokenize_and_align_labels(examples):
    tokenized_inputs = tokenizer(examples["tokens"], truncation=True, is_split_into_words=True, max_length=38,padding="max_length")

    labels = []
    for i, label in enumerate(examples[f"labels"]):
        word_ids = tokenized_inputs.word_ids(batch_index=i)  # Map tokens to their respective word.
        previous_word_idx = None
        label_ids = []
        for word_idx in word_ids:  # Set the special tokens to -100.
            if word_idx is None:
                label_ids.append(-100)
            elif word_idx != previous_word_idx:  # Only label the first token of a given word.
                label_ids.append(label[word_idx])
            else:
                label_ids.append(-100)
            previous_word_idx = word_idx
        labels.append(label_ids)

    tokenized_inputs["labels"] = labels
    return tokenized_inputs

tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
dt = dataset.map(tokenize_and_align_labels, batched=True)

## Expected results
New dataset objects should load and do on older versions.

## Actual results
"ValueError: Class label -100 less than -1" from cast_storage method from datasets.features

## Environment info
everything works fine on older installations of datasets/transformers

Issue arises when installing datasets on google collab under python3.7
I can't manage to find the exact output you're requirering but version printed is datasets-2.3.2


How to let `load_dataset` return a `Dataset` instead of `DatasetDict` in customized loading script

enhancement

New feature or request

Hi @liyucheng09.

Users can pass the `split` parameter to `load_dataset`. For example, if your split name is "train",
```python
ds = load_dataset("dataset_name", split="train")
```
will return a `Dataset` instance.

@albertvillanova Thanks! I can't believe I didn't know this feature till now.

If the dataset does not need splits, i.e., no training and validation split, more like a table. How can I let the `load_dataset` function return a `Dataset` object directly rather than return a `DatasetDict` object with only one key-value pair.

Or I can paraphrase the question in the following way: how to skip `_split_generators` step in `DatasetBuilder` to let `as_dataset` gives a single `Dataset` rather than a list`[Dataset]`?

Many thanks for any help.


Failure to hash (and cache) a `.map(...)` (almost always) - using this method can produce incorrect results

bug

Something isn't working

Important info:

As hashes are generated randomly for functions, it leads to **false identifying some results as already hashed** (mapping function is not executed after a method update) when there's a `pytorch_lightning.seed_everything(123)`

@lhoestq
seems like quite critical stuff for me, if I'm not making a mistake

Hi ! Thanks for reporting. This bug seems to appear in python 3.9 using dill 3.5.1

As a workaround you can use an older version of dill:
```
pip install "dill<0.3.5"
```

installing `dill<0.3.5` after installing `datasets` by pip results in dependency conflict with the version required for `multiprocess`. It can be solved by installing `pip install datasets "dill<0.3.5"` (simultaneously) on a clean environment

This has been fixed in https://github.com/huggingface/datasets/pull/4516, we will do a new release soon to include the fix :)

## Describe the bug
Sometimes I get messages about not being able to hash a method:
`Parameter 'function'=<function StupidDataModule._separate_speaker_id_from_dialogue at 0x7f1b27180d30> of the transform datasets.arrow_dataset.Dataset.
_map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.`
Whilst the function looks like this:
```python
@staticmethod
def _separate_speaker_id_from_dialogue(example: arrow_dataset.Example):
    speaker_id, dialogue = tuple(zip(*(example["dialogue"])))
    example["speaker_id"] = speaker_id
    example["dialogue"] = dialogue
    return example
```
This is the first step in my preprocessing pipeline, but sometimes the message about failure to hash is not appearing on the first step, but then appears on a later step.
This error is sometimes causing a failure to use cached data, instead of re-running all steps again.

## Steps to reproduce the bug
```python
import copy
import datasets
from datasets import arrow_dataset

def main():
    dataset = datasets.load_dataset("blended_skill_talk")
    res = dataset.map(method)
    print(res)

def method(example: arrow_dataset.Example):
    example['previous_utterance_copy'] = copy.deepcopy(example['previous_utterance'])
    return example

if __name__ == '__main__':
    main()
```
Run with:
```
python -m reproduce_error
```

## Expected results
Dataset is mapped and cached correctly.

## Actual results
The code outputs this at some point:
`Parameter 'function'=<function method at 0x7faa83d2a160> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.`

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform: Ubuntu 20.04.3
- Python version: 3.9.12
- PyArrow version: 8.0.0
- Datasets version: 2.3.1


Fix double dots in data files

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

The CI fails are unrelated to this PR (apparently something related to `seqeval` on windows) - merging :)

As mentioned in https://github.com/huggingface/transformers/pull/17715 `data_files` can't find a file if the path contains double dots `/../`. This has been introduced in https://github.com/huggingface/datasets/pull/4412, by trying to ignore hidden files and directories (i.e. if they start with a dot)

I fixed this and added a test

cc @sgugger @ydshieh


Can you please add the Stanford dog dataset?

good first issue

Good for newcomers

dataset request

Requesting to add a new dataset

would you like to give it a try, @dgrnd4? (maybe with the help of the dataset author?)

@julien-c i am sorry but I have no idea about how it works: can I add the dataset by myself, following "instructions to add a new dataset"?
Can I add a dataset even if it's not mine? (it's public in the link that I wrote on the post)

Hi! The [ADD NEW DATASET](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md) instructions are indeed the best place to start. It's also perfectly fine to add a dataset if it's public, even if it's not yours. Let me know if you need some additional pointers.

If no one is working on this, I could take this up!

@khushmeeet this is the [link](https://huggingface.co/datasets/dgrnd4/stanford_dog_dataset) where I added the dataset already. If you can I would ask you to do this:
1) The dataset it's all in TRAINING SET: can you please divide it in Training,Test and Validation Set? If you can for each class, take the 80% for the Training set and the 10% for Test and 10% Validation
2) The images has different size, can you please resize all the images in 224,224,3? Look even at the last dimension "3" because some images has dimension 4!

Thank you!!

Hi @khushmeeet! Thanks for the interest. You can self-assign the issue by commenting `#self-assign` on it.

Also, I think we can skip @dgrnd4's steps as we try to avoid any custom processing on top of raw data. One can later copy the script and override `_post_process` in it to perform such processing on the generated dataset.

Thanks @mariosasko

@dgrnd4 As dataset is there on Hub, and preprocessing is not recommended. I am not sure if there is any other task to do. However, I can't seem to find relevant `.py` files for this dataset in GitHub repo.

@khushmeeet @mariosasko The point is that the images must be processed and must have the same size in order to can be used for things for example "Training".

@dgrnd4 Yes, but this can be done after loading (`map` to resize images and `train_test_split` to create extra splits)

@khushmeeet The linked version is implemented as a no-code dataset and is generated directly from the ZIP archive, but our "GitHub" datasets (these are datasets without a user/org namespace on the Hub) need a generation script, and you can find one [here](https://github.com/tensorflow/datasets/blob/master/tensorflow_datasets/image_classification/stanford_dogs.py). `datasets` started as a fork of TFDS, so we share similar script structure, which makes it trivial to adapt it.

@mariosasko The point is that if I use something like this:
x_train, x_test = train_test_split(dataset, test_size=0.1)

to get Train 90% and Test 10%, and then to get the Validation Set (10% of the whole 100%):

```
train_ratio = 0.80
validation_ratio = 0.10
test_ratio = 0.10

x_train, x_test, y_train, y_test = train_test_split(dataX, dataY, test_size=1 - train_ratio)
x_val, x_test, y_val, y_test = train_test_split(x_test, y_test, test_size=test_ratio/(test_ratio + validation_ratio))

```

The point is that the structure of the data is:
```
DatasetDict({
    train: Dataset({
        features: ['image', 'label'],
        num_rows: 20580
    })
})

```

So how to extract images and labels?

EDIT --> Split of the dataset in Train-Test-Validation:
```
import datasets
from datasets.dataset_dict import DatasetDict
from datasets import Dataset

percentage_divison_test = int(len(dataset['train'])/100 *10)       # 10%  --> 2058
percentage_divison_validation = int(len(dataset['train'])/100 *20) # 20%  --> 4116

dataset_ = datasets.DatasetDict({"train": Dataset.from_dict({

                                  'image':  dataset['train'][0 : len(dataset['train']) ]['image'],
                                  'labels': dataset['train'][0 : len(dataset['train']) ]['label'] }),

                                 "test": Dataset.from_dict({  #20580-4116 (validation) ,20580-2058 (test)
                                  'image':  dataset['train'][len(dataset['train']) - percentage_divison_validation : len(dataset['train']) - percentage_divison_test]['image'],
                                  'labels': dataset['train'][len(dataset['train']) - percentage_divison_validation : len(dataset['train']) - percentage_divison_test]['label'] }),

                                  "validation": Dataset.from_dict({ # 20580-2058 (test)
                                  'image':  dataset['train'][len(dataset['train']) - percentage_divison_test : len(dataset['train'])]['image'],
                                  'labels': dataset['train'][len(dataset['train']) - percentage_divison_test : len(dataset['train'])]['label'] }),
                                })
```

@mariosasko in order to resize images I'm trying this method:
```
for i in range(0,len(dataset['train'])): #len(dataset['train'])

  ex = dataset['train'][i] #i
  image = ex['image']
  image = image.convert("RGB") # <class 'PIL.Image.Image'> <PIL.Image.Image image mode=RGB size=500x333 at 0x7F84F1948150>
  image_resized = image.resize(size_to_resize) # <PIL.Image.Image image mode=RGB size=224x224 at 0x7F84F17885D0>

  dataset['train'][i]['image'] = image_resized
```

Because the DatasetDict is formed by arrows that are immutable, the changing assignment in the last line of code, doesn't work!
Do you have any idea in order to get a valid result?

I have raised PR for adding stanford-dog dataset. I have not added any data preprocessing code. Only dataset generation script is there. Let me know any changes required, or anything to add to README.

## Adding a Dataset
- **Name:** *Stanford dog dataset*
- **Description:** *The dataset is about 120 classes for a total of 20.580 images. You can find the dataset here http://vision.stanford.edu/aditya86/ImageNetDogs/*
- **Paper:** *http://vision.stanford.edu/aditya86/ImageNetDogs/*
- **Data:** *[link to the Github repository or current dataset location](http://vision.stanford.edu/aditya86/ImageNetDogs/)*
- **Motivation:** *The dataset has been built using images and annotation from ImageNet for the task of fine-grained image categorization. It is useful for fine-grain purpose *

Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).


Refactor and add metadata to fever dataset

But this is somehow fever v3 dataset (see this link https://fever.ai/ under the dropdown menu called Datasets). Our fever dataset already contains v1 and v2 configs. Then, I added this as if v3 config (but named feverous instead of v3 to align with the original naming by data owners).

> In any case, if you really think this should be a new dataset, then I would propose to create it on the Hub instead, as "fever/feverous".

Yea makes sense ! thanks :) let's push more datasets on the hub rather than on github from now on

I have added "feverous" dataset to the Hub: https://huggingface.co/datasets/fever/feverous

I change the name of this PR accordingly, as now it only:
- Refactors code and include for both Fever v1.0 and v2.0 specific:
  - Descriptions
  - Citations
  - Homepages
- Updates documentation card aligned with above:
  - It was missing v2.0 description and citation.
- Update metadata JSON

Related to: #4452 and #3792.