[WIP] WebDataset export
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5127). All of your documentation changes will be reflected on that endpoint.
I added a first draft of the `IterableDataset.to_wds` method.

You can use it to savea dataset loaded in streamign mode as a webdataset locally.
The API can be further improved to allow to export to a cloud storage like the HF Hub.

I also included sharding with a default max shard size of 500MB (uncompressed), and it is single-processed fo rnow.

Choosing the number of shards is not implemented yet - though if we know the size of the `IterableDataset` this is probably doable`.

For example
```python
>>> from datasets import load_dataset
>>> ds = load_dataset("rotten_tomatoes", split="train", streaming=True)
>>> ds.to_wds("output_dir", compress=True)
>>> import webdataset as wds
>>> ds = wds.WebDataset("output_dir/rotten_tomatoes-train-000000.tar.gz").decode()
>>> next(iter(ds))
{'__key__': '0',
'__url__': 'output_dir/rotten_tomatoes-train-000000.tar.gz',
'label.cls': 1,
'text.txt': 'the rock is destined to be the 21st century\'s new ..., jean-claud van damme or steven segal .'}
```

### Implementation details

The WebDataset format is made of TAR archives containing a series of files per example. For example one pair of `image.jpg` and `label.cls` for image classification.

WebDataset automatically decodes serialized data based on the extension of the files, and output a dictionary. For example `{"image.png": np.array(...), "label.cls": 0}` if you choose the numpy decoding.

To use the automatic decoding, I store each field of each example as a file with its corresponding extension (jpg, json, cls, etc.)

While this is useful to end up with a dictionary with one key per column and appropriate decoding, it can create huge TAR archives if the dataset is made of small samples of text - probably because of useless TAR metadata for each file. This also makes loading super slow: iterating on SQuAD takes 50sec vs 7sec using `datasets` in streaming mode.

I haven't taken a look at alternatives for text datasets made out of small samples, but for image datasets this can already be used to run some benchmarks.