User-pickling with dynamic sub-classing

@lhoestq Feel free to have a look. The implementation is slightly different from what you suggested. I have opted to overwrite `save` instead of meddling with `save_global`. `save_global` is called very late down in dill/pickle so it is hard to control for what is happening there. I might be wrong. Pickling is more complex than I thought!

The linked issue (`map` with spaCy) also works now!

```python
import pickle
import spacy
from spacy import Language
from datasets import load_dataset
from datasets.utils.py_utils import dumps, pklregister

@pklregister(Language, allow_subclasses=True)
def hash_spacy_language(pickler, nlp: Language):
    pickler.save(nlp.to_bytes())

def main():
    fin = r"large/file.txt"
    nlp = spacy.load("en_core_web_sm")

    def tokenize(l):
        return {"tok": [t.text for t in nlp(l["text"])]}

    ds =  load_dataset("text", data_files=fin)
    ds = ds["train"].map(tokenize)

    # Sanity check: load NLP from pickle created with our own `dumps`
    config = nlp.config
    lang_cls = spacy.util.get_lang_class(config["nlp"]["lang"])
    nlp2 = lang_cls.from_config(config)
    nlp2.from_bytes(pickle.loads(dumps(nlp)))

    assert isinstance(nlp2, type(nlp))
    assert dumps(nlp) == dumps(nlp2)

if __name__ == '__main__':
    main()
```

If this all looks good to you, I'll start writing on some documentation and examples.

One more thing. This is a reduction function for SpaCy Language that should work with the new API:
```python
@pklregister(Language, allow_subclasses=True)
def hash_spacy_language(pickler, obj):
    def create_language(config, bytes_data):
        lang_cls = spacy.util.get_lang_class(config["nlp"]["lang"])
        nlp = lang_cls.from_config(config)
        return nlp.from_bytes(bytes_data)

    args = (obj.config, obj.to_bytes())
    pickler.save_reduce(create_language, args, obj=obj)
```
so IMO we are missing a test with `pickler.save_reduce`.

> One more thing. This is a reduction function for SpaCy Language that should work with the new API:
>
> ```python
> @pklregister(Language, allow_subclasses=True)
> def hash_spacy_language(pickler, obj):
>     def create_language(config, bytes_data):
>         lang_cls = spacy.util.get_lang_class(config["nlp"]["lang"])
>         nlp = lang_cls.from_config(config)
>         return nlp.from_bytes(bytes_data)
>
>     args = (obj.config, obj.to_bytes())
>     pickler.save_reduce(create_language, args, obj=obj)
> ```
>
> so IMO we are missing a test with `pickler.save_reduce`.

Sure that seems a good idea, but I do not quite understand what `save_reduce` does. Could you give some more info about what reduce functions do and how they differ from regular `save` and `save_global`? I've read about it but the docs nor the built-in `pickle` code seem really helpful.

I'm no pickle expect, but here is my understanding. I believe the pickler uses the reduce function when you do `loads` to reconstructs the original object from the parameters/arguments that were saved with `dumps`.

For example your sanity check could be simplified from
```python
    config = nlp.config
    lang_cls = spacy.util.get_lang_class(config["nlp"]["lang"])
    nlp2 = lang_cls.from_config(config)
    nlp2 = nlp2.from_bytes(pickle.loads(dumps(nlp)))
```
to
EDIT: <s>pickle.loads(pickle.dumps(nlp))</s>
```python
    nlp2 = loads(dumps(nlp))  # using our custom pickler
```

Though note that while it can be convenient for tests, we actually don't care about the reconstruction of the object since we're only using the pickler for `dumps` to compute hashes.

> I'm no pickle expect, but here is my understanding. I believe the pickler uses the reduce function when you do `loads` to reconstructs the original object from the parameters/arguments that were saved with `dumps`.
>
> For example your sanity check could be simplified from
>
> ```python
>     config = nlp.config
>     lang_cls = spacy.util.get_lang_class(config["nlp"]["lang"])
>     nlp2 = lang_cls.from_config(config)
>     nlp2 = nlp2.from_bytes(pickle.loads(dumps(nlp)))
> ```
>
> to
>
> ```python
>     nlp2 = pickle.loads(pickle.dumps(nlp))
> ```
>
> Though note that while it can be convenient for tests, we actually don't care about the reconstruction of the object since we're only using the pickler for `dumps` to compute hashes.

Yes, the sanity check can be simplified like that _if_ we use `pickle.dumps` - but that would not test our own `dumps` functionality and would do a naive dump instead of using `to_bytes`. It won't work if we use our own `dumps`, exactly because of the reason that we want custom pickling and being able to call `to_bytes`. To reconstruct the object from the pickled bytes from `to_bytes` we need `from_bytes`. The result of pickle/dill loads will therefore always be a `bytes` object and not a `Language` object.

But `save_reduce` is called when saving, right? Not when loading, AFAICT. I am just not sure what exactly it is saving. It is _potentially_ called [at the end of `save`](https://github.com/python/cpython/blob/24af9a40a8f85af813ea89998aa4e931fcc78cd9/Lib/pickle.py#L603) but only if we haven't returned by then. I just can't figure out what that base case is.

I don't think we expect users to write the reduce function that isn't going to be used anyway. So maybe let's stick with `save` ?

@BramVanroy
As I understand `save_reduce` is very similar to `copyreg.pickle`, so I'd suggest you to check the following links:
* https://docs.python.org/3/library/copyreg.html#copyreg.pickle
* https://docs.python.org/3/library/pickle.html#object.__reduce__

@lhoestq
> I don't think we expect users to write the reduce function that isn't going to be used anyway. So maybe let's stick with save ?

I agree.

`save_reduce` is very similar to `copyreg.pickle` and `object.__reduce__`, which are part of public API (and `save` isn't), so I expect more advanced users to know how to write their own reduction functions. But, as you say, `pklregister` should also work with `save` (even though I think `save` is a bit lower-level, and harder to understand than `save_reduce`).

All our examples in `py_utils` that use `pklregister`  also use `save_reduce` in the last step, so my reduction for SpaCy is meant to be added there, and not to be written by users (because SpaCy is very popular, so the official support by us makes sense :)).

And in the tests, let's ignore the reconstruction part of pickle/dill, because it's not important for us, and focus on the generated dumps. What do you think?

@mariosasko What exactly do you mean with "isn't part of the public API"? It is [a public method](https://github.com/python/cpython/blob/24af9a40a8f85af813ea89998aa4e931fcc78cd9/Lib/pickle.py#L535) in base pickle, just like `dump` is but maybe you mean something else.

@BramVanroy Oh sorry, it's public (not prefixed with `"_"`)  but it's not documented in the docs. `save_reduce` is also not in the docs, but its signature/functionality is similar to `copyreg.pickle` and I see it more often being used in the projects on GH, so it's seems "more public" to me.

Unfortunately I feel that pickle in general is under-documented. 😄

For the documentation, I can add a brief example, maybe under "How-to Guides"? The only thing that isn't immediately obvious to me is how I can add that doc page to the TOC?

Yes great idea ! To add that doc page to the TOC, you just have to add it to the index.rst file in the "How-to guides" TOC section

@mariosasko @lhoestq Feel free to make any edits or suggestions in the text!

Hi @mariosasko. I wish you'd told me sooner, as I spent quite some time writing on this.

I'm also not sure whether it is too advanced to have in the documentation. The spaCy use-case seems potentially frequent. Or do you wish to add that case to the defaults, and whenever new issues come up that seem like frequent/obvious cases, add those internally as well?

Documenting the internal `pklregister` is overkill IMO (and it can be kept in docstrings), but we can document something higher level like `register_hash_func` once it's implemented.

So we keep the nice documentation you've written (thank you!), except we can rename it to "Advanced caching" and show an API that is similar to
```python
>>> @register_hash_func(Language, allow_subclasses=True)
>>> def hash_spacy_language(nlp: Language):
>>>     return (nlp.to_bytes(),)
```
This way we keep the documentation centered around the public API rather than the internals that may evolve/be too complicated to fit only one section.

> Or do you wish to add that case to the defaults, and whenever new issues come up that seem like frequent/obvious cases, add those internally as well?

Let's add it to the defaults since it's a frequent use-case. And also allow users to control the hashing using the API mentioned above if they face other non-trivially-hashable objects

Sure, I can have a go at implementing spaCy as a built-in. Should it be included in the tests? (Therefore adding spaCy to the tests requirements.)

Next, from your example, it seems that the return value of `register_hash_func` will be used in pickler.save automatically (calling pklregister a bit deeper). Any reason why it returns a tuple? I can work on this as well, if needed.

> Sure, I can have a go at implementing spaCy as a built-in. Should it be included in the tests? (Therefore adding spaCy to the tests requirements.)

That would be perfect !

> Next, from your example, it seems that the return value of register_hash_func will be used in pickler.save automatically (calling pklregister a bit deeper).

Yes I think so. For example register_hash_func can call pklregister with the user's function, but wrapped to use pickler.save.

> Any reason why it returns a tuple? I can work on this as well, if needed.

It can either return an arbitrary object or a tuple. I like it a bit better if it's a tuple, so users understand more easily how to make the function take into account more than one item for the hash. It's also consistent with the streamlit caching functions, that also require a tuple. No strong opinion on this though

Let me know if I can help with anything

This is a continuation of the now closed PR in https://github.com/huggingface/datasets/pull/3206. The discussion there has shaped a new approach to do this.

In this PR, behavior of `pklregister` and `Pickler` is extended. Earlier, users were already able to register custom pickle functions. That is useful if they have objects that are not easily picklable with default methods. When one registers a custom function to a type, an object of that type will be pickled with the given function by `Pickler` which looks up the type in its `dispatch` table. The downside of this method, and of `pickle` in general, is that it is limited to direct type-matching and does not allow sub-classes. In many, default, cases that is not an issue. But when you are using external libraries where classes (e.g. parsers, models) are sub-classed this is not ideal.

```python
from datasets.fingerprint import Hasher
from datasets.utils.py_utils import pklregister

class BaseParser:
    pass

class EnglishParser(BaseParser):
    pass

@pklregister(BaseParser)
def custom_pkl_func(pickler, obj):
    print(f"Called the custom pickle function for type {type(obj)}!")
    # do something with the obj and ultimately save with the pickler

base = BaseParser()
en = EnglishParser()

# Hasher.hash uses the Pickler behind the scenes
# `custom_pkl_func` called for base
Hasher.hash(base)
# `custom_pkl_func` not called for en :-(
Hasher.hash(en)
```

In the example above we'd want to sub-class `EnglishParser` to be handled in the same way as its super-class `BaseParser`. This PR solves that by allowing for a keyword-argument `allow_subclasses` in `pklregister` (default: `False`).

```python
@pklregister(BaseParser, allow_subclasses=True)
```

When this option is enabled, we not only save the function in `Pickler.dispatch` but also save it in a custom table `Pickler.subclass_dispatch` **which allows us to dynamically add sub-classes of that class to the real dispatch table**. Then, if we want to pickle an  object `obj` with `Pickler.dump()` (which ultimately will call `Pickler.save()`) we _first_  check whether any of the object's super-classes exist in `Pickler.sublcass_dispatch` and get the related custom pickle function. If we find one, we add the type of `obj` alongside the function to `Pickler.dispatch`. All of this happens at the start of the call to `Pickler.save()`. _Only then_ dill.Pickler's `save` will be called, which in turn will call `pickle._Pickler.save` which handles everything. Here, the `Pickler.dispatch` table will be used to look up custom pickler functions - and it now also includes the function for `obj`, which was copied from its super-class, which we added at the very start of our custom `Pickler.save()`.

For edge cases and, especially, for testing, a contextmanager class `TempPickleRegistry` is included that resets the pickle registry on exit to its previous state.

```python
with TempPickleRegistry():
    @pklregister(MyObjClass)
    def pickle_registry_test_false(pickler, obj):
        pickler.save(obj.fancy_method())

    some_obj = MyObjClass()
    dumps(some_obj)
    # `MyObjClass` is in Pickler.dispatch

# ... `MyObjClass` is _not_ in Pickler.dispatch anymore
```

closes https://github.com/huggingface/datasets/issues/3178

To Do
====
- [x] Write tests
- [ ] Write documentation/examples?


Update BibTeX entry

Update BibTeX entry.


Add docs for audio processing

documentation

Improvements or additions to documentation

Nice ! love it this way. I guess you can set this PR to "ready for review" ?

This PR adds documentation for the `Audio` feature. It describes:

- The difference between loading `path` and `audio`, as well as use-cases/best practices for each of them.
- Resampling audio files with `cast_column`, and then calling `ds[0]["audio"]` to automatically decode and resample to the desired sampling rate.
- Resampling with `map`.

Preview [here](https://52969-250213286-gh.circle-artifacts.com/0/docs/_build/html/audio_process.html), let me know if I'm missing anything!


Resolve data_files by split name

Really cool!
When splitting by folder, what do we use for validation set ("valid", "validation" or both)?

> When splitting by folder, what do we use for validation set ("valid", "validation" or both)?

Both are fine :) As soon as it has "valid" in it

Merging for now, if you have comments about the documentation we can address them in subsequent PRs :)

Thanks for the comments @stevhliu :) I just opened https://github.com/huggingface/datasets/pull/3233 to take them into account

As discussed in https://github.com/huggingface/datasets/issues/3027 we should automatically infer what file is supposed to go to what split automatically, based on filenames.

I added the support for different kinds of patterns, for both dataset repositories and local directories:

```
    Input structure:

        my_dataset_repository/
        ├── README.md
        └── dataset.csv

    Output patterns:

        {"train": ["*"]}
```
```
    Input structure:

        my_dataset_repository/
        ├── README.md
        ├── train.csv
        └── test.csv

        my_dataset_repository/
        ├── README.md
        └── data/
            ├── train.csv
            └── test.csv

        my_dataset_repository/
        ├── README.md
        ├── train_0.csv
        ├── train_1.csv
        ├── train_2.csv
        ├── train_3.csv
        ├── test_0.csv
        └── test_1.csv

    Output patterns:

        {"train": ["*train*"], "test": ["*test*"]}
```
```
    Input structure:

        my_dataset_repository/
        ├── README.md
        └── data/
            ├── train/
            │   ├── shard_0.csv
            │   ├── shard_1.csv
            │   ├── shard_2.csv
            │   └── shard_3.csv
            └── test/
                ├── shard_0.csv
                └── shard_1.csv

    Output patterns:

        {"train": ["*train*/*", "*train*/**/*"], "test": ["*test*/*", "*test*/**/*"]}
```

and also this pattern that allows to have custom split names, and that is the structure used by #3098 for `push_to_hub` (cc @LysandreJik ):

```
    Input structure:

        my_dataset_repository/
        ├── README.md
        └── data/
            ├── train-00000-of-00003.csv
            ├── train-00001-of-00003.csv
            ├── train-00002-of-00003.csv
            ├── test-00000-of-00001.csv
            ├── random-00000-of-00003.csv
            ├── random-00001-of-00003.csv
            └── random-00002-of-00003.csv

    Output patterns:

        {
            "train": ["data/train-[0-9][0-9][0-9][0-9][0-9]-of-[0-9][0-9][0-9][0-9][0-9].*"],
            "test": ["data/test-[0-9][0-9][0-9][0-9][0-9]-of-[0-9][0-9][0-9][0-9][0-9].*"],
            "random": ["data/random-[0-9][0-9][0-9][0-9][0-9]-of-[0-9][0-9][0-9][0-9][0-9].*"],
        }
```

You can check the documentation about structuring your repository [here](https://52640-250213286-gh.circle-artifacts.com/0/docs/_build/html/repository_structure.html). cc @stevhliu

Fix https://github.com/huggingface/datasets/issues/3027
Fix https://github.com/huggingface/datasets/issues/3212

In the future we can also add support for dataset configurations.


Add documentation about dataset viewer feature

enhancement

New feature or request

Add to the docs more details about the dataset viewer feature in the Hub.

CC: @julien-c


Eventual Invalid Token Error at setup of private datasets

bug

Something isn't working

## Describe the bug
From time to time, there appear Invalid Token errors with private datasets:

- https://app.circleci.com/pipelines/github/huggingface/datasets/8520/workflows/d44629f2-4749-40f8-a657-50931d0b3434/jobs/52534
  ```
  ____________ ERROR at setup of test_load_streaming_private_dataset _____________
  ValueError: Invalid token passed!

  ____ ERROR at setup of test_load_streaming_private_dataset_with_zipped_data ____
  ValueError: Invalid token passed!

  =========================== short test summary info ============================
  ERROR tests/test_load.py::test_load_streaming_private_dataset - ValueError: I...
  ERROR tests/test_load.py::test_load_streaming_private_dataset_with_zipped_data
  ```

- https://app.circleci.com/pipelines/github/huggingface/datasets/8557/workflows/a8383181-ba6d-4487-9d0a-f750b6dcb936/jobs/52763
  ```
  ____ ERROR at setup of test_load_streaming_private_dataset_with_zipped_data ____
  [gw1] linux -- Python 3.6.15 /home/circleci/.pyenv/versions/3.6.15/bin/python3.6

  hf_api = <huggingface_hub.hf_api.HfApi object at 0x7f4899bab908>
  hf_token = 'vgNbyuaLNEBuGbgCEtSBCOcPjZnngJufHkTaZvHwkXKGkHpjBPwmLQuJVXRxBuaRzNlGjlMpYRPbthfHPFWXaaEDTLiqTTecYENxukRYVAAdpeApIUPxcgsowadkTkPj'
  zip_csv_path = PosixPath('/tmp/pytest-of-circleci/pytest-0/popen-gw1/data16/dataset.csv.zip')

      @pytest.fixture(scope="session")
      def hf_private_dataset_repo_zipped_txt_data_(hf_api: HfApi, hf_token, zip_csv_path):
          repo_name = "repo_zipped_txt_data-{}".format(int(time.time() * 10e3))
          hf_api.create_repo(token=hf_token, name=repo_name, repo_type="dataset", private=True)
          repo_id = f"{USER}/{repo_name}"
          hf_api.upload_file(
              token=hf_token,
              path_or_fileobj=str(zip_csv_path),
              path_in_repo="data.zip",
              repo_id=repo_id,
  >           repo_type="dataset",
          )

  tests/hub_fixtures.py:68:

  ...

  ValueError: Invalid token passed!
  =========================== short test summary info ============================
  ERROR tests/test_load.py::test_load_streaming_private_dataset_with_zipped_data
  ```


Fix code quality in riddle_sense dataset

Fix trailing whitespace.

Fix #3217.


Fix code quality bug in riddle_sense dataset

Something isn't working

To give more context: https://github.com/psf/black/issues/318. `black` doesn't treat this as a bug, but `flake8` does.

## Describe the bug
```
datasets/riddle_sense/riddle_sense.py:36:21: W291 trailing whitespace
```


Pin version exclusion for tensorflow incompatible with keras

Once `tensorflow` version 2.6.2 is released:
- https://github.com/tensorflow/tensorflow/commit/c1867f3bfdd1042f694df7a9870be51ba80543cb
- https://pypi.org/project/tensorflow/2.6.2/

with the patch:
- tensorflow/tensorflow#52927

we can remove the temporary fix we introduced in:
- #3208

Fix #3209.


Small updates to to_tf_dataset documentation

@stevhliu Accepted both suggestions, thanks for the review!

I added a little more description about `to_tf_dataset` compared to just setting the format


Add ACAV100M Dataset

dataset request

Requesting to add a new dataset

## Adding a Dataset
- **Name:** *ACAV100M*
- **Description:** *contains 100 million videos with high audio-visual correspondence, ideal for self-supervised video representation learning.*
- **Paper:** *https://arxiv.org/abs/2101.10803*
- **Data:** *https://github.com/sangho-vision/acav100m*
- **Motivation:** *The largest dataset (to date) for audio-visual learning.*

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


Fix tuple_ie download url

Fix #3204


Sort files before loading

enhancement

New feature or request

This will be fixed by https://github.com/huggingface/datasets/pull/3221

When loading a dataset that consists of several files (e.g. `my_data/data_001.json`, `my_data/data_002.json` etc.) they are not loaded in order when using `load_dataset("my_data")`.

This could lead to counter-intuitive results if, for example, the data files are sorted by date or similar since they would appear in different order in the `Dataset`.

The straightforward solution is to sort the list of files alphabetically before loading them.

cc @lhoestq


Fix disable_nullable default value to False

Currently the `disable_nullable` parameter is not consistent across all dataset transforms. For example it is `False` in `map` but `True` in `flatten_indices`.

This creates unexpected behaviors like this
```python
from datasets import Dataset, concatenate_datasets

d1 = Dataset.from_dict({"a": [0, 1, 2, 3]})
d2 = d1.filter(lambda x: x["a"] < 2).flatten_indices()
d1.data.schema == d2.data.schema  # False
```
This can cause issues when concatenating datasets for example.

For consistency I set `disable_nullable` to `False` in `flatten_indices` and I fixed some docstrings

cc @SBrandeis


dataset-viewer

Related to the dataset viewer on huggingface.co

Hi ! Do you have some kind of proxy in your browser that gives you access to internet ?

Maybe you're having this error because you don't have access to this URL from python ?

when I use python examples/pytorch/translation/run_translation.py     --model_name_or_path examples/pytorch/translation/opus-mt-en-ro     --do_train     --do_eval     --source_lang en     --target_lang ro     --dataset_name wmt16     --dataset_config_name ro-en     --output_dir /tmp/tst-translation     --per_device_train_batch_size=4     --per_device_eval_batch_size=4     --overwrite_output_dir     --predict_with_generate to finetune translation model on huggingface, I get the issue"ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.15.1/datasets/wmt16/wmt16.py".But I can open the https://raw.githubusercontent.com/huggingface/datasets/1.15.1/datasets/wmt16/wmt16.py by using website. What should I do to solve the issue?


Unpin keras once TF fixes its release

Related to:
- #3208


Pin keras version until TF fixes its release

Fix #3207.


CI error: Another metric with the same name already exists in Keras 2.7.0

bug

Something isn't working

## Describe the bug
Release of TensorFlow 2.7.0 contains an incompatibility with Keras. See:
- keras-team/keras#15579

This breaks our CI test suite: https://app.circleci.com/pipelines/github/huggingface/datasets/8493/workflows/055c7ae2-43bc-49b4-9f11-8fc71f35a25c/jobs/52363


[WIP] Allow user-defined hash functions via a registry

Hi @BramVanroy, thanks for your PR.

There was a bug in TensorFlow/Keras. We have made a temporary fix in master branch. Please, merge master into your PR branch, so that the CI tests pass.

```
git checkout registry
git fetch upstream master
git merge upstream/master
```

@albertvillanova Done. Although new tests will need to be added. I am looking for some feedback on my initial proposal in this PR. Reviews and ideas welcome!

Hi ! Thanks for diving into this :)

With this approach you get the right hash when doing `Hasher.hash(nlp)` but if you try to hash an object that has `nlp` as one of its attributes for example you will get different hashes every time.

This is because `Hasher.hash` is not recursive itself. Indeed what happens when you try to hash an object is that:
1. it is dumped with our custom `dill` pickler (which is recursive)
2. the bytes of the dump are hashed

To fix this we must integrate the custom hashing as a custom pickler dumping instead.

Note that we're only using the `pickler.dumps` method and not `pickler.loads` since we only use it to get hashes, so it doesn't matter if `loads` doesn't reconstruct the object exactly. What's important it only to capture all the necessary information that defines how the object transforms the data (here `nlp.to_bytes()` determines how the spacy pipeline transforms the text).

Our pickler already has a registry and you can register new dump functions with:
```python
import dill
import spacy
from datasets.utils.py_utils import pklregister

@pklregister(spacy.Language)
def _save_spacy_language(pickler, nlp):
    pickler.save_reduce(...)  # I think we can use nlp.to_bytes() here
    dill._dill.log.info(...)
```

You can find some examples of custom dump functions in `py_utils.py`

Ah, darn it. Completely missed that register. Time wasted, unfortunately.

To better understand what you mean, I figured I'd try the basis of your snippet and I've noticed quite an annoying side-effect of how the pickle dispatch table seems to work. It explicitly uses an object's [`type()`](https://github.com/python/cpython/blob/87032cfa3dc975d7442fd57dea2c6a56d31c911a/Lib/pickle.py#L557-L558), which makes sense for pickling some (primitive) types it is not ideal for more complex ones, I think. `Hasher.hash` has the same issue as far as I can tell.

https://github.com/huggingface/datasets/blob/d21ce54f2c2782f854f975eb1dc2be6f923b4314/src/datasets/fingerprint.py#L187-L191

This is very restrictive, and won't work for subclasses. In the case of spaCy, for instance, we register `Language`, but `nlp` is an instance of `English`, which is a _subclass_ of `Language`. These are different types, and so they will not match in the dispatch table. Maybe this is more general approach to cover such cases? Something like this is a start but too broad, but ideally a hierarchy is constructed and traversed of all classes in the table and the lowest class is selected to ensure that the most specific class function is dispatched.

```python
    def hash(cls, value: Any) -> str:
        # Try to match the exact type
        if type(value) in cls.dispatch:
            return cls.dispatch[type(value)](cls, value)

        # Try to match instance (superclass)
        for type_cls, func in cls.dispatch.items():
            if isinstance(value, type_cls):
                return cls.dispatch[type_cls](cls, value)

        return cls.hash_default(value)
```

This does not solve the problem for pickling, though. That is quite unfortunate IMO because that implies that users always have to specify the most specific class, which is not always obvious. (For instance, `spacy.load`'s signature returns `Language`, but as said before a subclass might be returned.)

Second, I am trying to understand `save_reduce` but I can find very little documentation about it, only the source code which is quite cryptic. Can you explain it a bit? The required arguments are not very clear to me and there is no docstring.

```python
    def save_reduce(self, func, args, state=None, listitems=None, dictitems=None, obj=None):
```

Here is an example illustrating the problem with sub-classes.

```python
import spacy

from spacy import Language
from spacy.lang.en import English

from datasets.utils.py_utils import Pickler, pklregister

# Only useful in the registry (matching with `nlp`)
# if you swap it out for very specific `English`
@pklregister(English)
def hash_spacy_language(pickler, nlp):
    pass

def main():
    print(Pickler.dispatch)
    nlp = spacy.load("en_core_web_sm")
    print(f"NLP type {type(nlp)} in dispatch table? ", type(nlp) in Pickler.dispatch)

if __name__ == '__main__':
    main()
```

Indeed that's not ideal.
Maybe we could integrate all the subclasses directly in `datasets`. That's simple to do but the catch is that if users have new subclasses of `Language` it won't work.

Otherwise we can see how to make the API simpler for users by allowing subclasses
```python
# if you swap it out for very specific `English`
@pklregister(Language, allow_subclasses=True)
def hash_spacy_language(pickler, nlp):
    pass
```

Here is an idea how to make this work, let me know what you think:

When `Pickler.dumps` is called, it uses `Pickler.save_global` which is a method that is going to be called recursively on all the objects. We can customize this part, and make it work as we want when it encounters a subclass of `Language`.

For example when it encounters a subclass of `Language`, we can dynamically register the hashing function for the subclass (`English` for example) in `Pickler.save_global`, right before calling the actual `dill.Pickler.save_global(self, obj, name=name)`:
```python
pklregister(type(obj))(hash_function_registered_for_parent_class)
dill.Pickler.save_global(self, obj, name=name)
```

In practice that means we can have an additional dispatch dictionary (similar to `Pickler.dispatch`) to store the hashing functions when `allow_subclasses=True`, and use this dictionary in `Pickler.save_global` to check if we need to use a hashing function registered with `allow_subclasses=True` and get `hash_function_registered_for_parent_class`.

If I understood you correctly, I do not think that that is enough because you are only doing this for a type and its direct parent class. You could do this for all superclasses (so traverse all ancestors and find the registered function for the first that is encountered). I can work on that, if you agree. The one thing that I am not sure about is how you want to create the secondary dispatch table. An empty dict as class variable in Pickler? (It doesn't have to be a true dispatcher, I think.)

I do not think that dynamic registration is the ideal situation (it feels a bit hacky). An alternative would be to subclass Pickle and Dill to make sure that instead of just type() checking in the dispatch table also superclasses are considered. But that is probably overkill.

> You could do this for all superclasses (so traverse all ancestors and find the registered function for the first that is encountered)

That makes sense indeed !

> The one thing that I am not sure about is how you want to create the secondary dispatch table. An empty dict as class variable in Pickler? (It doesn't have to be a true dispatcher, I think.)

Sure, let's try to not use too complicated stuff

> I do not think that dynamic registration is the ideal situation (it feels a bit hacky). An alternative would be to subclass Pickle and Dill to make sure that instead of just type() checking in the dispatch table also superclasses are considered. But that is probably overkill.

Indeed that would feel less hacky, but maybe it's too complex just for this. I feel like this part of the library is already hard to understand when you're not familiar with pickle. IMO having only a few changes that are simpler to understand is better than having a rewrite of `dill`'s core code.

Thanks a lot for your insights, it looks like we're going to have something that works well and that unlocks some nice flexibility for users :) Feel free to ping me anytime if I can help on this

Sure, thanks for brainstorming! I'll try to work on it this weekend. Will also revert the current changes in this PR and rename it.

It seems like this is going in the right direction :).

@BramVanroy Just one small suggestion for future contributions: instead of using `WIP` in the PR title, you can create a draft PR if you're still working on it.

Maybe I should just create a new (draft) PR then, seeing that I'll have to rename and revert the changes anyway? I'll link to this PR so that the discussion is at least referenced.

I can convert this PR to a draft PR. Let me know what would you prefer.

I think reverting my previous commits would make for a dirty (or confusing) commit history, so I'll just create a new one. Thanks.

Inspired by the discussion on hashing in https://github.com/huggingface/datasets/issues/3178#issuecomment-959016329, @lhoestq suggested that it would be neat to allow users more control over the hashing process. Specifically, it would be great if users can specify specific hashing functions depending on the **class** of the object.

As an example, we found in the linked topic that loaded spaCy models (`Language` objects) have different hashes when `dump`'d, but their byte representation with `Language.to_bytes()` _is_ deterministic. It would therefore be great if we could specify that for `Language` objects, the hasher should hash the objects `to_bytes()` return value instead of the object itself.

This PR adds a new, but tiny, dependency to manage the registry, namely [`catalogue`](https://github.com/explosion/catalogue).

Two files have been changed (apart from the added dependency in `setup.py`) and one file has been added.

**utils.registry** (added)

This file defines our custom Registry and builds a registry called "hashers". A Registry is basically dictionary from names (str) to functions. A function can be added to the registry by a decorator, e.g.

```python
@hashers.register(spacy.Language)
def hash_spacy_language(nlp):
    return Hasher.hash(nlp.to_bytes())
```

You'll notice that `spacy.Language` is not a string, even though the registry holds a str->func mapping. To accomplish this with classes in a dynamic way, catalogue.Registry needed to be subclassed and modified as `DatasetsRegistry`. All methods that use a name as an input are now modified so that classes are deterministically converted in strings in such a way that we can later retrieve the actual class from the string (below).

**utils.py_utils** (modified)

Added two functions to deal with classes and their qualified names, that is, their full descriptive name including the module. On the one hand it allows us to retrieve a string from a given class, e.g. given `Module` class, return `torch.nn.Module` str. Conversly, a function is added to convert such a full qualified name into a class. For instance, given the string `torch.nn.Module`, return the `Module` class. These straightforward methods allow us to interchangeably use classes and strings without any needed user interaction - they can just register a class, and behind the scenes `DatasetsRegistry` converts these to deterministic strings.

**fingerprint** (modified)

Updated Hasher.hash so that if the object to hash is an instance of a class in the registry, the registered function is used to hash the object instead of the default behavior. To do so we iterate over the registry `hashers` and convert its keys (strings) into classes, and then we can use `isinstance`.

```python
# Check if the current object is an instance that is
# applicable to the user-defined hashers. If so, hash
# with the user-defined function
for full_module_name, func in hashers.get_all().items():
    registered_cls = get_cls_from_qualname(full_module_name)
    if isinstance(value, registered_cls):
        return func(value)
```

**Putting it all together**

To test this, you can try the following example with spaCy. First install spaCy from source and checkout a specific commit.

```shell
git clone https://github.com/explosion/spaCy.git
cd spaCy/
git checkout cab9209c3dfcd1b75dfe5657f10e52c4d847a3cf
cd ..

git clone https://github.com/BramVanroy/datasets.git
cd datasets
git checkout registry
pip install -e .
pip install ../spaCy
spacy download en_core_web_sm
```

Now you can run the following script. By default it will use the custom hasher function for the Language object. You can enable the default behavior by commenting out `@hashers.register...`.

```python
import spacy

from datasets.fingerprint import Hasher
from datasets.utils.registry import hashers

# Register a function so that when the Hasher encounters a spacy.Language object
# it uses this custom function to hash instead of the default
@hashers.register(spacy.Language)
def hash_spacy_language(nlp):
    return Hasher.hash(nlp.to_bytes())

def main():
    print(hashers.get_all())
    nlp = spacy.load("en_core_web_sm")
    dump1 = Hasher.hash(nlp)
    nlp = spacy.load("en_core_web_sm")
    dump2 = Hasher.hash(nlp)
    print(dump1)
    # succeeds when using the registered custom function
    # fails if using the default
    assert dump1 == dump2

if __name__ == '__main__':
    main()
```

To do
====
- The above is just a proof-of-concept. I am open to changes/suggestions
- Tests still need to be written
- We should consider whether we can make `DatasetsRegistry` very restrictive and ONLY allowing classes. That would make testing easier - otherwise we also need to test for other sorts of objects.
- Maybe the `hashers` definition is better suited in `fingerprint`?
- Documentation/examples need to be updated
- Not sure why the logger is not working in `hash()`
- `get_cls_from_qualname` might need a fail-safe: is it possible for a full_qualname to not have a module, and if so how do we deal with that?


Add Multidoc2dial Dataset

Hi @sivasankalpp, thanks for your PR.

There was a bug in TensorFlow/Keras. We have made a temporary fix in our master branch. Please, merge master into your PR branch, so that the CI tests pass.

```
git checkout multidoc2dial
git fetch upstream master
git merge upstream/master
```

Hi @albertvillanova, I have merged master into my PR branch. All tests are passing.
Please take a look when you get a chance, thanks!

Thanks for your feedback @lhoestq. We addressed your comments in the latest commit. Let us know if everything looks okay :)

This PR adds the MultiDoc2Dial dataset introduced in this [paper](https://arxiv.org/pdf/2109.12595v1.pdf )


FileNotFoundError for TupleIE dataste

Something isn't working

@mariosasko @lhoestq Could you give me an update on how to load the dataset after the fix?
Thanks.

Hi @arda-vianai,

first, you can try:
```python
import datasets
dataset = datasets.load_dataset('tuple_ie', 'all', revision="master")
```
If this doesn't work, your version of `datasets` is missing some features that are required to run the dataset script, so install the master version with the following command:
```
pip install git+https://github.com/huggingface/datasets.git
```
and then:
```python
import datasets
dataset = datasets.load_dataset('tuple_ie', 'all')
```
should work (even without `revision`).

@mariosasko
Thanks, it is working now. I actually did that before but I didn't restart the kernel. I restarted it and it works now. My bad!!!
Many thanks and great job!
-arda

Hi,
`dataset = datasets.load_dataset('tuple_ie', 'all')`

returns a FileNotFound error. Is the data not available?

Many thanks.


Updated: DaNE - updated URL for download

Actually it looks like the old URL is still working, and it's also the one that is mentioned in https://github.com/alexandrainst/danlp/blob/master/docs/docs/datasets.md

What makes you think we should use the new URL ?

@lhoestq Sorry! I might have jumped to conclusions a bit too fast here...

I was working in Google Colab and got an error that it was unable to use the URL. I then forked the project, updated the URL, ran it locally and it worked. I therefore assumed that my URL update fixed the issue, however, I see now that it might rather be a Google Colab issue...

Still - this seems to be the official URL for downloading the dataset, and I think that it will be most beneficial to use. :-)

It looks like they're using these new urls for their new datasets. Maybe let's change to the new URL in case the old one stops working at one point. Thanks

It seems that DaNLP has updated their download URLs and it therefore also needs to be updated in here...


Add mIoU metric

enhancement

New feature or request

**Is your feature request related to a problem? Please describe.**
Recently, some semantic segmentation models were added to HuggingFace Transformers, including [SegFormer](https://huggingface.co/transformers/model_doc/segformer.html) and [BEiT](https://huggingface.co/transformers/model_doc/beit.html).

Semantic segmentation (which is the task of labeling every pixel of an image with a corresponding class) is typically evaluated using the Mean Intersection and Union (mIoU). Together with the upcoming Image Feature, adding this metric could be very handy when creating example scripts to fine-tune any Transformer-based model on a semantic segmentation dataset.

An implementation can be found [here](https://github.com/open-mmlab/mmsegmentation/blob/504965184c3e6bc9ec43af54237129ef21981a5f/mmseg/core/evaluation/metrics.py#L132) for instance.


Add GSM8K dataset

dataset request

Requesting to add a new dataset

## Adding a Dataset
- **Name:** GSM8K (short for Grade School Math 8k)
- **Description:** GSM8K is a dataset of 8.5K high quality linguistically diverse grade school math word problems created by human problem writers.
- **Paper:** https://openai.com/blog/grade-school-math/
- **Data:** https://github.com/openai/grade-school-math
- **Motivation:** The dataset is useful to investigate the reasoning abilities of large Transformer models, such as GPT-3.

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


Catch token invalid error in CI

The staging back end sometimes returns invalid token errors when trying to delete a repo.
I modified the fixture in the test that uses staging to ignore this error


Bump huggingface_hub

huggingface_hub just released its first minor version, so we need to update the dependency

It was supposed to be part of 1.15.0 but I'm adding it for 1.15.1


Add Multi-Lingual LibriSpeech

Add https://www.openslr.org/94/


Fix optimized encoding for arrays

Hi !

#3124 introduced a regression that made the benchmarks CI fail because of a bad array comparison when checking the first encoded element. This PR fixes this by making sure that encoding is applied on all sequence types except lists.

cc @eladsegal fyi (no big deal)


QOL improvements: auto-flatten_indices and desc in map calls

This PR:
* automatically calls `flatten_indices` where needed: in `unique` and `save_to_disk` to avoid saving the indices file
* adds descriptions to the map calls

Fix #3040


Fix temporary dataset_path creation for URIs related to remote fs

This aims to close #3295


Temporary dataset_path for remote fs URIs not built properly in arrow_dataset.py::load_from_disk

bug

Something isn't working

## Describe the bug
When trying to build a temporary dataset path from a remote URI in this block of code:

https://github.com/huggingface/datasets/blob/42f6b1d18a4a1b6009b6e62d115491be16dfca22/src/datasets/arrow_dataset.py#L1038-L1042

the result is not the expected when passing an absolute path in an URI like `hdfs:///absolute/path`.

## Steps to reproduce the bug
```python
dataset_path = "hdfs:///absolute/path"
src_dataset_path = extract_path_from_uri(dataset_path)
tmp_dir = get_temporary_cache_files_directory()
dataset_path = Path(tmp_dir, src_dataset_path)
print(dataset_path)
```

## Expected results
With the code above, we would expect a value in `dataset_path` similar to:
`/tmp/tmpnwxyvao5/absolute/path`

## Actual results
However, we get a `dataset_path` value like:
`/absolute/path`

This is because this line here: https://github.com/huggingface/datasets/blob/42f6b1d18a4a1b6009b6e62d115491be16dfca22/src/datasets/arrow_dataset.py#L1041
returns the last absolute path when two absolute paths (the one in `tmp_dir` and the one extracted from the URI in `src_dataset_path`) are passed as arguments.

## Environment info
- `datasets` version: 1.13.3
- Platform: Linux-3.10.0-1160.15.2.el7.x86_64-x86_64-with-glibc2.33
- Python version: 3.9.7
- PyArrow version: 5.0.0


Add Natural Adversarial Objects dataset

dataset request

Requesting to add a new dataset

## Adding a Dataset
- **Name:** Natural Adversarial Objects (NAO)
- **Description:** Natural Adversarial Objects (NAO) is a new dataset to evaluate the robustness of object detection models. NAO contains 7,934 images and 9,943 objects that are unmodified and representative of real-world scenarios, but cause state-of-the-art detection models to misclassify with high confidence.
- **Paper:** https://arxiv.org/abs/2111.04204v1
- **Data:** https://drive.google.com/drive/folders/15P8sOWoJku6SSEiHLEts86ORfytGezi8
- **Motivation:** interesting object detection dataset useful for miscclassifications

cc @NielsRogge

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


Pin version exclusion for Markdown

As Markdown version 3.3.5 has a bug, it is better to exclude it in case the users have it previously installed in their environment.

Related to #3289, #3286.


Not able to load 'wikipedia' dataset

bug

Something isn't working

## Describe the bug
I am following the instruction for loading the wikipedia dataset using datasets. However getting the below error.

## Steps to reproduce the bug
from datasets import load_dataset
dataset = load_dataset("wikipedia")
```

## Expected results
A clear and concise description of the expected results.

## Actual results
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/datasets/builder.py in _create_builder_config(self, name, custom_features, **config_kwargs)
    339                         "Config name is missing."
    340                         "\nPlease pick one among the available configs: %s" % list(self.builder_configs.keys())
--> 341                         + "\nExample of usage:\n\t`{}`".format(example_of_usage)
    342                     )
    343                 builder_config = self.BUILDER_CONFIGS[0]

ValueError: Config name is missing.
Please pick one among the available configs: ['20200501.aa', '20200501.ab', '20200501.ace', '20200501.ady', '20200501.af', '20200501.ak', '20200501.als', '20200501.am', '20200501.an', '20200501.ang', '20200501.ar', '20200501.arc', '20200501.arz', '20200501.as', '20200501.ast', '20200501.atj', '20200501.av', '20200501.ay', '20200501.az', '20200501.azb', '20200501.ba', '20200501.bar', '20200501.bat-smg', '20200501.bcl', '20200501.be', '20200501.be-x-old', '20200501.bg', '20200501.bh', '20200501.bi', '20200501.bjn', '20200501.bm', '20200501.bn', '20200501.bo', '20200501.bpy', '20200501.br', '20200501.bs', '20200501.bug', '20200501.bxr', '20200501.ca', '20200501.cbk-zam', '20200501.cdo', '20200501.ce', '20200501.ceb', '20200501.ch', '20200501.cho', '20200501.chr', '20200501.chy', '20200501.ckb', '20200501.co', '20200501.cr', '20200501.crh', '20200501.cs', '20200501.csb', '20200501.cu', '20200501.cv', '20200501.cy', '20200501.da', '20200501.de', '20200501.din', '20200501.diq', '20200501.dsb', '20200501.dty', '20200501.dv', '20200501.dz', '20200501.ee', '20200501.el', '20200501.eml', '20200501.en', '20200501.eo', '20200501.es', '20200501.et', '20200501.eu', '20200501.ext', '20200501.fa', '20200501.ff', '20200501.fi', '20200501.fiu-vro', '20200501.fj', '20200501.fo', '20200501.fr', '20200501.frp', '20200501.frr', '20200501.fur', '20200501.fy', '20200501.ga', '20200501.gag', '20200501.gan', '20200501.gd', '20200501.gl', '20200501.glk', '20200501.gn', '20200501.gom', '20200501.gor', '20200501.got', '20200501.gu', '20200501.gv', '20200501.ha', '20200501.hak', '20200501.haw', '20200501.he', '20200501.hi', '20200501.hif', '20200501.ho', '20200501.hr', '20200501.hsb', '20200501.ht', '20200501.hu', '20200501.hy', '20200501.ia', '20200501.id', '20200501.ie', '20200501.ig', '20200501.ii', '20200501.ik', '20200501.ilo', '20200501.inh', '20200501.io', '20200501.is', '20200501.it', '20200501.iu', '20200501.ja', '20200501.jam', '20200501.jbo', '20200501.jv', '20200501.ka', '20200501.kaa', '20200501.kab', '20200501.kbd', '20200501.kbp', '20200501.kg', '20200501.ki', '20200501.kj', '20200501.kk', '20200501.kl', '20200501.km', '20200501.kn', '20200501.ko', '20200501.koi', '20200501.krc', '20200501.ks', '20200501.ksh', '20200501.ku', '20200501.kv', '20200501.kw', '20200501.ky', '20200501.la', '20200501.lad', '20200501.lb', '20200501.lbe', '20200501.lez', '20200501.lfn', '20200501.lg', '20200501.li', '20200501.lij', '20200501.lmo', '20200501.ln', '20200501.lo', '20200501.lrc', '20200501.lt', '20200501.ltg', '20200501.lv', '20200501.mai', '20200501.map-bms', '20200501.mdf', '20200501.mg', '20200501.mh', '20200501.mhr', '20200501.mi', '20200501.min', '20200501.mk', '20200501.ml', '20200501.mn', '20200501.mr', '20200501.mrj', '20200501.ms', '20200501.mt', '20200501.mus', '20200501.mwl', '20200501.my', '20200501.myv', '20200501.mzn', '20200501.na', '20200501.nah', '20200501.nap', '20200501.nds', '20200501.nds-nl', '20200501.ne', '20200501.new', '20200501.ng', '20200501.nl', '20200501.nn', '20200501.no', '20200501.nov', '20200501.nrm', '20200501.nso', '20200501.nv', '20200501.ny', '20200501.oc', '20200501.olo', '20200501.om', '20200501.or', '20200501.os', '20200501.pa', '20200501.pag', '20200501.pam', '20200501.pap', '20200501.pcd', '20200501.pdc', '20200501.pfl', '20200501.pi', '20200501.pih', '20200501.pl', '20200501.pms', '20200501.pnb', '20200501.pnt', '20200501.ps', '20200501.pt', '20200501.qu', '20200501.rm', '20200501.rmy', '20200501.rn', '20200501.ro', '20200501.roa-rup', '20200501.roa-tara', '20200501.ru', '20200501.rue', '20200501.rw', '20200501.sa', '20200501.sah', '20200501.sat', '20200501.sc', '20200501.scn', '20200501.sco', '20200501.sd', '20200501.se', '20200501.sg', '20200501.sh', '20200501.si', '20200501.simple', '20200501.sk', '20200501.sl', '20200501.sm', '20200501.sn', '20200501.so', '20200501.sq', '20200501.sr', '20200501.srn', '20200501.ss', '20200501.st', '20200501.stq', '20200501.su', '20200501.sv', '20200501.sw', '20200501.szl', '20200501.ta', '20200501.tcy', '20200501.te', '20200501.tet', '20200501.tg', '20200501.th', '20200501.ti', '20200501.tk', '20200501.tl', '20200501.tn', '20200501.to', '20200501.tpi', '20200501.tr', '20200501.ts', '20200501.tt', '20200501.tum', '20200501.tw', '20200501.ty', '20200501.tyv', '20200501.udm', '20200501.ug', '20200501.uk', '20200501.ur', '20200501.uz', '20200501.ve', '20200501.vec', '20200501.vep', '20200501.vi', '20200501.vls', '20200501.vo', '20200501.wa', '20200501.war', '20200501.wo', '20200501.wuu', '20200501.xal', '20200501.xh', '20200501.xmf', '20200501.yi', '20200501.yo', '20200501.za', '20200501.zea', '20200501.zh', '20200501.zh-classical', '20200501.zh-min-nan', '20200501.zh-yue', '20200501.zu']
Example of usage:
	`load_dataset('wikipedia', '20200501.aa')`

I think the other parameter is missing in the load_dataset function that is not shown in the instruction.


Finishes #3257

Uses f-strings to format the .py files in the dataset folder


Make several audio datasets streamable

Reading FLAC (for `librispeech_asr`) works OK for me (`soundfile` version: `0.10.3`):
```python
In [2]: ds = load_dataset("datasets/librispeech_asr/librispeech_asr.py", "clean", streaming=True, split="train.100")

In [3]: item = next(iter(ds))

In [4]: item.keys()
Out[4]: dict_keys(['file', 'audio', 'text', 'speaker_id', 'chapter_id', 'id'])

In [5]: item["file"]
Out[5]: '374-180298-0000.flac'

In [6]: item["audio"].keys()
Out[6]: dict_keys(['path', 'array', 'sampling_rate'])

In [7]: item["audio"]["sampling_rate"]
Out[7]: 16000

In [8]: item["audio"]["path"]
Out[8]: '374-180298-0000.flac'

In [9]: item["audio"]["array"].shape
Out[9]: (232480,)
```

Oh cool ! I think this might have come from an issue with my local `soundfile` installation then

I'll do `multilingual_librispeech` in a separate PR since it requires the data to be in another format (in particular separate the train/dev/test splits in different files)

<s>Needs https://github.com/huggingface/datasets/pull/3129 to be merged first</s>

Make those audio datasets streamable:
- [x] common_voice
- [x] openslr
- [x] vivos
- [x] librispeech_asr <s>(still has some issues to read FLAC)</s> *actually it's ok*
- [ ] <s>multilingual_librispeech (yet to be converted)</S> *TODO in a separate PR*


Unpin markdown for build_docs now that it's fixed

`markdown`'s bug has been fixed, so this PR reverts #3286


Allow datasets with indices table when concatenating along axis=1

Calls `flatten_indices` on the datasets with indices table in `concatenate_datasets` to fix issues when concatenating along `axis=1`.

cc @lhoestq: I decided to flatten all the datasets instead of flattening all the datasets except the largest one in the end. The latter approach fails on the following example:
```python
a = Dataset.from_dict({"a": [10, 20, 30, 40]})
b = Dataset.from_dict({"b": [10, 20, 30, 40, 50, 60]})  # largest dataset
a = a.select([1, 2, 3])
b = b.select([1, 2, 3])
concatenate_datasets([a, b], axis=1) # fails at line concat_tables(...) because the real length of b's data is 6 and a's length is 3 after flattening (was 4 before flattening)
```

Also, it requires additional re-ordering of indices to prepare them for working with the indices table of the largest dataset. IMO not worth when we save only one `flatten_indices` call. (feel free to check the code of that approach at https://github.com/huggingface/datasets/commit/6acd10481c70950dcfdbfd2bab0bf0c74ad80bcb if you are interested)

Fixes #3273


Add The Pile dataset and PubMed Central subset

Add:
- The complete final version of The Pile dataset: "all" config
- PubMed Central subset of The Pile: "pubmed_central" config

Close #1675, close bigscience-workshop/data_tooling#74.

CC: @StellaAthena, @lewtun


Fix build_docs CI

Because of https://github.com/Python-Markdown/markdown/issues/1196 we have to temporarily pin `markdown` to 3.3.4 for the docs to build without issues


Add IEMOCAP dataset

dataset request

Requesting to add a new dataset

## Adding a Dataset
- **Name:** IEMOCAP
- **Description:** acted, multimodal and multispeaker database
- **Paper:** https://sail.usc.edu/iemocap/Busso_2008_iemocap.pdf
- **Data:** https://sail.usc.edu/iemocap/index.html
- **Motivation:** Useful multimodal dataset

cc @anton-l

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


Add VoxLingua107 dataset

Requesting to add a new dataset

## Adding a Dataset
- **Name:** VoxLingua107
- **Description:** VoxLingua107 is a speech dataset for training spoken language identification models. The dataset consists of short speech segments automatically extracted from YouTube videos and labeled according the language of the video title and description, with some post-processing steps to filter out false positives.
- **Paper:** https://arxiv.org/abs/2011.12998
- **Data:** http://bark.phon.ioc.ee/voxlingua107/
- **Motivation:** Nice audio classification dataset

cc @anton-l

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


Add Speech Commands dataset

Requesting to add a new dataset

## Adding a Dataset
- **Name:** Speech commands
- **Description:** A Dataset for Limited-Vocabulary Speech Recognition
- **Paper:** https://arxiv.org/abs/1804.03209
- **Data:** https://www.tensorflow.org/datasets/catalog/speech_commands,  Available:
http://download.tensorflow.org/data/speech_commands_v0.02.tar.gz
- **Motivation:** Nice dataset for audio classification training

cc @anton-l

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


Hi ! Thanks for reporting :)
I think this is because the dataset is behind an access page. We can fix the dataset viewer

If you also have this error when you use the `datasets` library in python, you should probably pass `use_auth_token=True` to the `load_dataset()` function to use your account to access the dataset.

Ah ok, I didn't realise about the login page. I'll try `use_auth_token=True` and see if that solves it.

Regards!

Hi,

Using `use_auth_token=True` and downloading the credentials with `huggingface-cli login` (stored in .huggingface/token) solved the issue.

Should I leave the issue open until you fix the Dataset viewer issue?

Cool ! Yes let's keep this issue open until the viewer is fixed - I'll close it when this is fixed. Thanks

## Dataset viewer issue for '*oscar-corpus/OSCAR-2109*'

**Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/oscar-corpus/OSCAR-2109)*

*The dataset library cannot download any language from the oscar-corpus/OSCAR-2109 dataset. By entering the URL in your browser I can access the file.*

```
    raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/resolve/main/OSCAR-2109.py
```

Am I the one who added this dataset ? No

Using the older version of [OSCAR](https://huggingface.co/datasets/oscar) I don't have any issues downloading languages with the dataset library.


[Datasets] Improve Covost 2

It's currently quite confusing to understand the manual data download instruction of Covost and not very user-friendly.

Currenty the user has to:

1. Go on Common Voice website
2. Find the correct dataset which is **not** mentioned in the error message
3. Download it
4. Untar it
5. Create a language id folder (why? this folder does not exist in the `.tar` downloaded file)
6. pass the folder containing the created language id folder

This PR improves this to:

1. Go on Common Voice website
2. Find the correct dataset which **is** mentioned in the error message
3. Download it
4. Untar it
5. pass the untared folder

**Note**: This PR is not at all time-critical


Fix bookcorpusopen RAM usage

Each document is a full book, so the default arrow writer batch size of 10,000 is too big, and it can fill up RAM quickly before flushing the first batch on disk. I changed its batch size to 256 to use maximum 100MB of memory

Fix #3167.


Minor Typo Fix - Precision to Recall


Proposed update to the documentation for WER

I wanted to submit a minor update to the description of WER for your consideration.

Because of the possibility of insertions, the numerator in the WER formula can be larger than N, so the value of WER can be greater than 1.0:

```
>>> from datasets import load_metric
>>> metric = load_metric("wer")
>>> metric.compute(predictions=["hello how are you"], references=["hello"])
3.0
```

and similarly from the underlying jiwer module's `wer` function:

```
>>> from jiwer import wer
>>> wer("hello", "hello how are you")
3.0
```


f-string formatting

Hello @lhoestq, ```make style``` is applied as asked. :)

**Fix #3257**

Replaced _.format()_ and _%_ by f-strings in the following modules :
- [x] **tests**
- [x] **metrics**
- [x] **benchmarks**
- [x] **utils**
- [x] **templates**
- [x] **src/Datasets/\*.py**

Modules in **_src/Datasets/_**:
- [x] **commands**
- [x] **features**
- [x] **formatting**
- [x] **io**
- [x] **tasks**
- [x] **utils**

Module **datasets** will not be edited as asked by @mariosasko

-A correction of the first PR (#3267)-


Update KILT metadata JSON

Fix #3265.


Force data files extraction if download_mode='force_redownload'

Avoids weird issues when redownloading a dataset due to cached data not being fully updated.

With this change, issues #3122 and https://github.com/huggingface/datasets/issues/2956 (not a fix, but a workaround) can be fixed as follows:
```python
dset = load_dataset(..., download_mode="force_redownload")
```


Fix some contact information formats

The CI fail are caused by some missing sections or tags, which is unrelated to this PR. Merging !

As reported in https://github.com/huggingface/datasets/issues/3188 some contact information are not displayed correctly.
This PR fixes this for CoNLL-2002 and some other datasets with the same issue


Respect row ordering when concatenating datasets along axis=1

bug

Something isn't working

Currently, there is a bug when concatenating datasets along `axis=1` if more than one dataset has the `_indices` attribute defined. In that scenario, all indices mappings except the first one get ignored.

A minimal reproducible example:
```python
>>> from datasets import Dataset, concatenate_datasets
>>> a = Dataset.from_dict({"a": [30, 20, 10]})
>>> b = Dataset.from_dict({"b": [2, 1, 3]})
>>> d = concatenate_datasets([a.sort("a"), b.sort("b")], axis=1)
>>> print(d[:3])              # expected: {'a': [10, 20, 30], 'b': [1, 2, 3]}
{'a': [10, 20, 30], 'b': [3, 1, 2]}
```

I've noticed the bug while working on #3195.


Make iter_archive work with ZIP files

enhancement

New feature or request

Currently users can use `dl_manager.iter_archive` in their dataset script to iterate over all the files of a TAR archive.
It would be nice if it could work with ZIP files too !


Decode audio from remote

Currently the Audio feature type can only decode local audio files, not remote files.

To fix this I replaced `open` with our `xopen` functoin that is compatible with remote files in audio.py

cc @albertvillanova @mariosasko


Add os.listdir for streaming

Extend `os.listdir` to support streaming data from remote files. This is often used to navigate in remote ZIP files for example


coqa NonMatchingChecksumError

bug

Something isn't working

Hi @ZhaofengWu, thanks for reporting.

Unfortunately, I'm not able to reproduce your bug:
```python
In [1]: from datasets import load_dataset

In [2]: ds = load_dataset("coqa")
Downloading: 3.82kB [00:00, 1.91MB/s]
Downloading: 1.79kB [00:00, 1.79MB/s]
Using custom data configuration default
Downloading and preparing dataset coqa/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to .cache\coqa\default\1.0.0\553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...
Downloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 49.0M/49.0M [00:06<00:00, 7.17MB/s]
Downloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 9.09M/9.09M [00:01<00:00, 6.08MB/s]
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:12<00:00,  6.48s/it]
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 333.26it/s]
Dataset coqa downloaded and prepared to .cache\coqa\default\1.0.0\553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0. Subsequent calls will reuse this data.
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 285.49it/s]

In [3]: ds
Out[3]:
DatasetDict({
    train: Dataset({
        features: ['source', 'story', 'questions', 'answers'],
        num_rows: 7199
    })
    validation: Dataset({
        features: ['source', 'story', 'questions', 'answers'],
        num_rows: 500
    })
})
```

Could you please give more details about your development environment? You can run the command `datasets-cli env` and copy-and-paste its output:
```
- `datasets` version:
- Platform:
- Python version:
- PyArrow version:
```
It might be because you are using an old version of `datasets`. Could you please update it (`pip install -U datasets`) and confirm if the problem parsists?

I'm getting the same error in two separate environments:
```
- `datasets` version: 1.15.1
- Platform: Linux-5.4.0-84-generic-x86_64-with-debian-bullseye-sid
- Python version: 3.7.11
- PyArrow version: 6.0.0
```

```
- `datasets` version: 1.15.1
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.9.5
- PyArrow version: 6.0.0
```

I'm sorry, but don't get to reproduce the error in the Linux environment.

@mariosasko @lhoestq can you reproduce it?

I also can't reproduce the error on Windows/Linux (tested both the master and the `1.15.1` version).

Maybe the file had issues during the download ? Could you try to delete your cache and try again ?
By default the downloads cache is at `~/.cache/huggingface/datasets/downloads`

Also can you check if you have a proxy that could prevent the download to succeed ? Are you able to download those files via your browser ?

I got the same error in a third environment (google cloud) as well. The internet for these three environments are all different so I don't think that's the reason.
```
- `datasets` version: 1.12.1
- Platform: Linux-5.11.0-1022-gcp-x86_64-with-glibc2.31
- Python version: 3.9.7
- PyArrow version: 6.0.0
```
I deleted the entire `~/.cache/huggingface/datasets` on my local mac, and got a different first time error.
```
Python 3.9.5 (default, May 18 2021, 12:31:01)
[Clang 10.0.0 ] :: Anaconda, Inc. on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> from datasets import load_dataset
>>> dataset = load_dataset("coqa")
Downloading: 3.82kB [00:00, 1.19MB/s]
Downloading: 1.79kB [00:00, 712kB/s]
Using custom data configuration default
Downloading and preparing dataset coqa/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to /Users/zhaofengw/.cache/huggingface/datasets/coqa/default/1.0.0/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.36MB/s]
 50%|████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                | 1/2 [00:00<00:00,  2.47it/s]Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/load.py", line 1632, in load_dataset
    builder_instance.download_and_prepare(
  File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 607, in download_and_prepare
    self._download_and_prepare(
  File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 675, in _download_and_prepare
    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
  File "/Users/zhaofengw/.cache/huggingface/modules/datasets_modules/datasets/coqa/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0/coqa.py", line 70, in _split_generators
    downloaded_files = dl_manager.download_and_extract(urls_to_download)
  File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/download_manager.py", line 284, in download_and_extract
    return self.extract(self.download(url_or_urls))
  File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/download_manager.py", line 196, in download
    downloaded_path_or_paths = map_nested(
  File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 216, in map_nested
    mapped = [
  File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 217, in <listcomp>
    _single_map_nested((function, obj, types, None, True))
  File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 152, in _single_map_nested
    return function(data_struct)
  File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/download_manager.py", line 217, in _download
    return cached_path(url_or_filename, download_config=download_config)
  File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 295, in cached_path
    output_path = get_from_cache(
  File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 594, in get_from_cache
    raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json
>>> dataset = load_dataset("coqa")
Using custom data configuration default
Downloading and preparing dataset coqa/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to /Users/zhaofengw/.cache/huggingface/datasets/coqa/default/1.0.0/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.38MB/s]
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00,  6.26it/s]
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 1087.45it/s]
 50%|████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                | 1/2 [00:45<00:45, 45.60s/it]
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/load.py", line 1632, in load_dataset
    builder_instance.download_and_prepare(
  File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 607, in download_and_prepare
    self._download_and_prepare(
  File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 679, in _download_and_prepare
    verify_checksums(
  File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums
    raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://nlp.stanford.edu/data/coqa/coqa-train-v1.0.json', 'https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json']
```
I can access the URL using my browser, though I did notice a redirection -- could that have something to do with it?

Hi @ZhaofengWu,

What about in Google Colab? Can you run this notebook without errors?
https://colab.research.google.com/drive/1CCpiiHmtNlfO_4CZ3-fW-TSShr1M0rL4?usp=sharing

I can run your notebook fine, but if I create one myself, it has that error: https://colab.research.google.com/drive/107GIdhrauPO6ZiFDY7G9S74in4qqI2Kx?usp=sharing.

It's so funny -- it's like whenever you guys run it it's fine but whenever I run it it fails, whatever the environment is.

I guess it must be some connection issue: the data owner may be blocking requests coming from your country or IP range...

I mean, I don't think google colab sends the connection from my IP. Same applies to google cloud.

```
>>> from datasets import load_dataset
>>> dataset = load_dataset("coqa")
Downloading: 3.82kB [00:00, 1.26MB/s]
Downloading: 1.79kB [00:00, 733kB/s]
Using custom data configuration default
Downloading and preparing dataset coqa/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to /Users/zhaofengw/.cache/huggingface/datasets/coqa/default/1.0.0/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.38MB/s]
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.32MB/s]
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:01<00:00,  1.91it/s]
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 1117.44it/s]
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/load.py", line 1632, in load_dataset
    builder_instance.download_and_prepare(
  File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 607, in download_and_prepare
    self._download_and_prepare(
  File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 679, in _download_and_prepare
    verify_checksums(
  File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums
    raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://nlp.stanford.edu/data/coqa/coqa-train-v1.0.json', 'https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json']
```


Dataset viewer issue for 'liweili/c4_200m'

Hi ! I think the issue comes from this [line](https://huggingface.co/datasets/liweili/c4_200m/blob/main/c4_200m.py#L87):
```python
path = filepath + "/*.tsv*"
```

You can fix this by doing this instead:
```python
path = os.path.join(filepath, "/*.tsv*")
```

Here is why:

Locally you can append `"/*.tsv*"` to your local path, however it doesn't work in streaming mode, and the dataset viewer does use the streaming mode.
In streaming mode, the download and extract part is done lazily. It means that instead of using local paths, it's still passing around URLs and [chained URLs](https://filesystem-spec.readthedocs.io/en/latest/features.html#url-chaining)

Therefore in streaming mode, `filepath` is not a local path, but instead is equal to
```python
zip://::https://huggingface.co/datasets/liweili/c4_200m/resolve/main/data.zip
```
The `zip://` part means that we navigate inside the remote ZIP file.

You must use `os.path.join` to navigate inside it and get your TSV files:
```python
>>> os.path.join(filepath, "/*.tsv*")
zip://*.tsv*::https://huggingface.co/datasets/liweili/c4_200m/resolve/main/data.zip
```

`datasets` extends `os.path.join`, `glob.glob`, etc. in your dataset scripts to work with remote files.

hi @lhoestq ! thanks for the tip! i've updated the line of code but it's still not working. am i doing something else wrong? thank you!

## Dataset viewer issue for '*liweili/c4_200m*'

**Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/liweili/c4_200m)*

*Server Error*
```
Status code:   404
Exception:     Status404Error
Message:       Not found. Maybe the cache is missing, or maybe the ressource does not exist.
```

Am I the one who added this dataset ? Yes


Replacing .format() and % by f-strings

Hi ! It looks like most of your changes are just `black` changes. All those changes are not necessary. In particular if you want to use `black`, please use the `make style` command instead. It runs `black` with additional parameters and you shouldn't end up with that many changes

Feel free to open a new PR that doesn't include all the unnecessary `black` changes that you have on your branch :)

> Hi ! It looks like most of your changes are just `black` changes. All those changes are not necessary. In particular if you want to use `black`, please use the `make style` command instead. It runs `black` with additional parameters and you shouldn't end up with that many changes
>
> Feel free to open a new PR that doesn't include all the unnecessary `black` changes that you have on your branch :)

Thank you for your answer :) , I will open a new PR with the correct changes.

Hi @lhoestq, I submitted 3 commits in a new PR (#3277) where I did not apply black.

I can apply the ```make style``` command if asked.

Cool thanks ! Yes feel free to make sure you have `black==21.4b0` and run `make style`

**Fix #3257**

Replaced _.format()_ and _%_ by f-strings in the following modules :
- [x] **tests**
- [x] **metrics**
- [x] **benchmarks**
- [x] **utils**
- [x] **templates**

Will follow in the next PR the modules left :
- [ ] **src**

Module **datasets** will not be edited as asked by @mariosasko

PS : black and isort applied to files


fix-3264-change-download-urls

There seems to be problems with datasets metadata, of which I dont have access to. I think one of the datasets is from reddit. Can anyone help?

Hello @LashaO , I think the errors were caused by `_DATA_FILES` in `definite_pronoun_resolution.py`. Here are details of the test error.
```
self = BuilderConfig(name='plain_text', version=1.0.0, data_dir=None, data_files={'train': 'train.c.txt', 'test': 'test.c.txt'}, description='Plain text import of the Definite Pronoun Resolution Dataset.')

    def __post_init__(self):
        # The config name is used to name the cache directory.
        invalid_windows_characters = r"<>:/\|?*"
        for invalid_char in invalid_windows_characters:
            if invalid_char in self.name:
                raise InvalidConfigName(
                    f"Bad characters from black list '{invalid_windows_characters}' found in '{self.name}'. "
                    f"They could create issues when creating a directory for this config on Windows filesystem."
                )
        if self.data_files is not None and not isinstance(self.data_files, DataFilesDict):
>           raise ValueError(f"Expected a DataFilesDict in data_files but got {self.data_files}")
E           ValueError: Expected a DataFilesDict in data_files but got {'train': 'train.c.txt', 'test': 'test.c.txt'}
```


Checksum error for kilt_task_wow

Something isn't working

Using `dataset = load_dataset("kilt_tasks", "wow", ignore_verifications=True)` may fix it, but I do not think it is a elegant solution.

Hi @slyviacassell, thanks for reporting.

Yes, there is an issue with the checksum verification. I'm fixing it.

And as you pointed out, in the meantime, you can circumvent the problem by passing `ignore_verifications=True`.

## Describe the bug
Checksum failed when downloads kilt_tasks_wow. See error output for details.

## Steps to reproduce the bug
```python
import datasets
datasets.load_datasets('kilt_tasks','wow')
```

## Expected results
Download successful

## Actual results
```
Downloading and preparing dataset kilt_tasks/wow (download: 72.07 MiB, generated: 61.82 MiB, post-processed: Unknown size, total: 133.89 MiB) to /root/.cache/huggingface/datasets/kilt_tasks/wow/1.0.0/57dc8b2431e76637e0c6ef79689ca4af61ed3a330e2e0cd62c8971465a35db3a...
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 5121.25it/s]
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 1527.42it/s]
Traceback (most recent call last):
  File "kilt_wow.py", line 30, in <module>
    main()
  File "kilt_wow.py", line 27, in main
    train, dev, test = dataset.generate_k_shot_data(k=32, seed=seed, path="../data/")
  File "/workspace/projects/CrossFit/tasks/fewshot_gym_dataset.py", line 79, in generate_k_shot_data
    dataset = self.load_dataset()
  File "kilt_wow.py", line 21, in load_dataset
    return datasets.load_dataset('kilt_tasks','wow')
  File "/opt/conda/lib/python3.8/site-packages/datasets/load.py", line 1632, in load_dataset
    builder_instance.download_and_prepare(
  File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 607, in download_and_prepare
    self._download_and_prepare(
  File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 679, in _download_and_prepare
    verify_checksums(
  File "/opt/conda/lib/python3.8/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums
    raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['http://dl.fbaipublicfiles.com/KILT/wow-train-kilt.jsonl', 'http://dl.fbaipublicfiles.com/KILT/wow-dev-kilt.jsonl']
```

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.15.1
- Platform: Linux-4.15.0-161-generic-x86_64-with-glibc2.10
- Python version: 3.8.3
- PyArrow version: 4.0.1


Downloading URL change for WikiAuto Manual, jeopardy and definite_pronoun_resolution

Something isn't working

#take
I am willing to fix this. Links can be replaced for WikiAuto Manual and jeopardy with new ones provided by authors.

As for the definite_pronoun_resolution URL, a certificate error seems to be preventing a download. I have the files on my local machine. I can include them in the dataset folder as the files are <1MB in size total.

> #take I am willing to fix this. Links can be replaced for WikiAuto Manual and jeopardy.
>
> As for the definite_pronoun_resolution URL, a certificate error seems to be preventing a download. I have the files on my local machine. Anyone has opinions on whether it is preferable for me to host them somewhere (e.g. personal GDrive account) or upload them to the dataset folder directly and use github raw URLs? The files are <1MB in size.

I am planning to fix it next few days. But my to-do list is full and I do not have the cache of definite_pronoun_resolution. I am glad that you can take this. Thanks a lot!

No problem, buddy! Will submit a PR over this weekend.

## Describe the bug
- WikiAuto Manual
The original manual datasets with the following downloading URL in this [repository](https://github.com/chaojiang06/wiki-auto) was [deleted](https://github.com/chaojiang06/wiki-auto/commit/0af9b066f2b4e02726fb8a9be49283c0ad25367f) by the author.
```
https://github.com/chaojiang06/wiki-auto/raw/master/wiki-manual/train.tsv
```

- jeopardy
The downloading URL for jeopardy may move from
```
http://skeeto.s3.amazonaws.com/share/JEOPARDY_QUESTIONS1.json.gz
```
 to
```
https://drive.google.com/file/d/0BwT5wj_P7BKXb2hfM3d2RHU1ckE/view?resourcekey=0-1abK4cJq-mqxFoSg86ieIg
```

- definite_pronoun_resolution
The following downloading URL for definite_pronoun_resolution cannot be reached for some reasons.
```
http://www.hlt.utdallas.edu/~vince/data/emnlp12/train.c.txt
```

## Steps to reproduce the bug
```python
import datasets
datasets.load_datasets('wiki_auto','manual')
datasets.load_datasets('jeopardy')
datasets.load_datasets('definite_pronoun_resolution')
```

## Expected results
Download successfully

## Actual results
- WikiAuto Manual
```
Downloading and preparing dataset wiki_auto/manual (download: 151.65 MiB, generated: 155.97 MiB, post-processed: Unknown size, total: 307.61 MiB) to /root/.cache/huggingface/datasets/wiki_auto/manual/1.0.0/5ffdd9fc62422d29bd02675fb9606f77c1251ee17169ac10b143ce07ef2f4db8...
  0%|                                                                                | 0/3 [00:00<?, ?it/s]Traceback (most recent call last):
  File "wiki_auto.py", line 43, in <module>
    main()
  File "wiki_auto.py", line 40, in main
    train, dev, test = dataset.generate_k_shot_data(k=16, seed=seed, path="../data/")
  File "/workspace/projects/CrossFit/tasks/fewshot_gym_dataset.py", line 24, in generate_k_shot_data
    dataset = self.load_dataset()
  File "wiki_auto.py", line 34, in load_dataset
    return datasets.load_dataset('wiki_auto', 'manual')
  File "/opt/conda/lib/python3.8/site-packages/datasets/load.py", line 1632, in load_dataset
    builder_instance.download_and_prepare(
  File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 607, in download_and_prepare
    self._download_and_prepare(
  File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 675, in _download_and_prepare
    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
  File "/root/.cache/huggingface/modules/datasets_modules/datasets/wiki_auto/5ffdd9fc62422d29bd02675fb9606f77c1251ee17169ac10b143ce07ef2f4db8/wiki_auto.py", line 193, in _split_generators
    data_dir = dl_manager.download_and_extract(my_urls)
  File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 284, in download_and_extract
    return self.extract(self.download(url_or_urls))
  File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 196, in download
    downloaded_path_or_paths = map_nested(
  File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 216, in map_nested
    mapped = [
  File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 217, in <listcomp>
    _single_map_nested((function, obj, types, None, True))
  File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 152, in _single_map_nested
    return function(data_struct)
  File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 217, in _download
    return cached_path(url_or_filename, download_config=download_config)
  File "/opt/conda/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 295, in cached_path
    output_path = get_from_cache(
  File "/opt/conda/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 592, in get_from_cache
    raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://github.com/chaojiang06/wiki-auto/raw/master/wiki-manual/train.tsv
```
- jeopardy
```
Using custom data configuration default
Downloading and preparing dataset jeopardy/default (download: 12.13 MiB, generated: 34.46 MiB, post-processed: Unknown size, total: 46.59 MiB) to /root/.cache/huggingface/datasets/jeopardy/default/0.1.0/25ee3e4a73755e637b8810f6493fd36e4523dea3ca8a540529d0a6e24c7f9810...
Traceback (most recent call last):
  File "jeopardy.py", line 45, in <module>
    main()
  File "jeopardy.py", line 42, in main
    train, dev, test = dataset.generate_k_shot_data(k=32, seed=seed, path="../data/")
  File "/workspace/projects/CrossFit/tasks/fewshot_gym_dataset.py", line 79, in generate_k_shot_data
    dataset = self.load_dataset()
  File "jeopardy.py", line 36, in load_dataset
    return datasets.load_dataset("jeopardy")
  File "/opt/conda/lib/python3.8/site-packages/datasets/load.py", line 1632, in load_dataset
    builder_instance.download_and_prepare(
  File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 607, in download_and_prepare
    self._download_and_prepare(
  File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 675, in _download_and_prepare
    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
  File "/root/.cache/huggingface/modules/datasets_modules/datasets/jeopardy/25ee3e4a73755e637b8810f6493fd36e4523dea3ca8a540529d0a6e24c7f9810/jeopardy.py", line 72, in _split_generators
    filepath = dl_manager.download_and_extract(_DATA_URL)
  File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 284, in download_and_extract
    return self.extract(self.download(url_or_urls))
  File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 196, in download
    downloaded_path_or_paths = map_nested(
  File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 206, in map_nested
    return function(data_struct)
  File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 217, in _download
    return cached_path(url_or_filename, download_config=download_config)
  File "/opt/conda/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 295, in cached_path
    output_path = get_from_cache(
  File "/opt/conda/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 594, in get_from_cache
    raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach http://skeeto.s3.amazonaws.com/share/JEOPARDY_QUESTIONS1.json.gz
```
- definite_pronoun_resolution
```
Downloading and preparing dataset definite_pronoun_resolution/plain_text (download: 222.12 KiB, generated: 239.12 KiB, post-processed: Unknown size, total: 461.24 KiB) to /root/.cache/huggingface/datasets/definite_pronoun_resolution/plain_text/1.0.0/35a1dfd4fba4afb8ba226cbbb65ac7cef0dd3cf9302d8f803740f05d2f16ceff...
  0%|                                                                                | 0/2 [00:00<?, ?it/s]Traceback (most recent call last):
  File "definite_pronoun_resolution.py", line 37, in <module>
    main()
  File "definite_pronoun_resolution.py", line 34, in main
    train, dev, test = dataset.generate_k_shot_data(k=32, seed=seed, path="../data/")
  File "/workspace/projects/CrossFit/tasks/fewshot_gym_dataset.py", line 79, in generate_k_shot_data
    dataset = self.load_dataset()
  File "definite_pronoun_resolution.py", line 28, in load_dataset
    return datasets.load_dataset('definite_pronoun_resolution')
  File "/opt/conda/lib/python3.8/site-packages/datasets/load.py", line 1632, in load_dataset
    builder_instance.download_and_prepare(
  File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 607, in download_and_prepare
    self._download_and_prepare(
  File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 675, in _download_and_prepare
    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
  File "/root/.cache/huggingface/modules/datasets_modules/datasets/definite_pronoun_resolution/35a1dfd4fba4afb8ba226cbbb65ac7cef0dd3cf9302d8f803740f05d2f16ceff/definite_pronoun_resolution.py", line 76, in _split_generators
    files = dl_manager.download_and_extract(
  File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 284, in download_and_extract
    return self.extract(self.download(url_or_urls))
  File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 196, in download
    downloaded_path_or_paths = map_nested(
  File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 216, in map_nested
    mapped = [
  File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 217, in <listcomp>
    _single_map_nested((function, obj, types, None, True))
  File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 152, in _single_map_nested
    return function(data_struct)
  File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 217, in _download
    return cached_path(url_or_filename, download_config=download_config)
  File "/opt/conda/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 295, in cached_path
    output_path = get_from_cache(
  File "/opt/conda/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 594, in get_from_cache
    raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach http://www.hlt.utdallas.edu/~vince/data/emnlp12/train.c.txt
```

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.15.1
- Platform: Linux-4.15.0-161-generic-x86_64-with-glibc2.10
- Python version: 3.8.3
- PyArrow version: 4.0.1


FET DATA

dataset request

Requesting to add a new dataset

## Adding a Dataset
- **Name:** *name of the dataset*
- **Description:** *short description of the dataset (or link to social media or blog post)*
- **Paper:** *link to the dataset paper if available*
- **Data:** *link to the Github repository or current dataset location*
- **Motivation:** *what are some good reasons to have this dataset*

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


asserts replaced with exception for image classification task, csv, json

Fixes for csv, json in io module and image_classification task with tests referenced in https://github.com/huggingface/datasets/issues/3171


Scifi_TV_Shows: Having trouble getting viewer to find appropriate files

Hi ! I think this is because `iter_archive` doesn't support ZIP files yet. See https://github.com/huggingface/datasets/issues/3272

You can navigate into the archive this way instead:
```python
# in split_generators
data_dir = dl_manager.download_and_extract(url)
train_filepath = os.path.join(data_dir, "all-sci-fi-data-train.txt")
return [
        datasets.SplitGenerator(
            name=datasets.Split.TRAIN,
            gen_kwargs={
                "filepath": train_filepath,
            },
        ),
...
])

# in generate_examples
with open(filepath, encoding="utf-8") as f:
    ...
```

## Dataset viewer issue for '*Science Fiction TV Show Plots Corpus  (Scifi_TV_Shows)*'

**Link:** [link](https://huggingface.co/datasets/lara-martin/Scifi_TV_Shows)

I tried adding both a script (https://huggingface.co/datasets/lara-martin/Scifi_TV_Shows/blob/main/Scifi_TV_Shows.py) and some dummy examples (https://huggingface.co/datasets/lara-martin/Scifi_TV_Shows/tree/main/dummy), but the viewer still has a 404 error ("Not found. Maybe the cache is missing, or maybe the ressource does not exist."). I'm not sure what to try next. Thanks in advance!

Am I the one who added this dataset? Yes


Fix ConnectionError in Scielo dataset

The CI error is unrelated to the change.

This PR:
* allows 403 status code in HEAD requests to S3 buckets to fix the connection error in the Scielo dataset (instead of `url`, uses `response.url` to check the URL of the final endpoint)
* makes the Scielo dataset streamable

Fixes #3255.


Updating details of IRC disentanglement data

I was pleasantly surprised to find that someone had already added my dataset to the huggingface library, but some details were missing or incorrect. This PR fixes the documentation.


Reload dataset that was already downloaded with `load_from_disk` from cloud storage

enhancement

New feature or request

`load_from_disk` downloads the dataset to a temporary directory without checking if the dataset has already been downloaded once.
It would be nice to have some sort of caching for datasets downloaded this way. This could leverage the fingerprint of the dataset that was saved in the `state.json` file.


Use f-strings for string formatting

good first issue

Good for newcomers

Hi, I would be glad to help with this. Is there anyone else working on it?

Hi, I would be glad to work on this too.

Hi @Carlosbogo,

would you be interested in replacing the `.format` and `%` syntax with f-strings in the modules in the `datasets` directory since @Mehdi2402 has opened a PR that does that for all the other directories?

Oh I see. I will be glad to help with the `datasets` directory then.

f-strings offer better readability/performance than `str.format` and `%`, so we should use them in all places in our codebase unless there is good reason to keep the older syntax.

> **NOTE FOR CONTRIBUTORS**: To avoid large PRs and possible merge conflicts, do 1-3 modules per PR. Also, feel free to ignore the files located under `datasets/*`.


asserts replaced by exception for text classification task with test.

Haha it looks like you got the chance of being reviewed twice at the same time and got the same suggestion twice x)
Anyway it's all good now so we can merge !

I have replaced only a single assert in text_classification.py along with a unit test to verify an exception is raised based on https://github.com/huggingface/datasets/issues/3171 .

I would like to first understand the code contribution workflow. So keeping the change to a single file rather than making too many changes. Once this gets approved, I will look into the rest.

Thanks.