Download and prepare as Parquet for cloud storage
State: closed

Download a dataset as Parquet in a cloud storage can be useful for streaming mode and to use with spark/dask/ray.

This PR adds support for `fsspec` URIs like `s3://...`, `gcs://...` etc. and ads the `file_format` to save as parquet instead of arrow:
```python
from datasets import *

cache_dir = "s3://..."
builder = load_dataset_builder("crime_and_punish", cache_dir=cache_dir)
builder.download_and_prepare(file_format="parquet")
```

EDIT: actually changed the API to

```python
from datasets import *

builder = load_dataset_builder("crime_and_punish")
builder.download_and_prepare("s3://...", file_format="parquet")
```

credentials to cloud storage can be passed using the `storage_options` argument in

For consistency with the BeamBasedBuilder, I name the parquet files `{builder.name}-{split}-xxxxx-of-xxxxx.parquet`. I think this is fine since we'll need to implement parquet sharding after this PR, so that a dataset can be used efficiently with dask for example.

Note that images/audio files are not embedded yet in the parquet files, this will added in a subsequent PR

TODO:
- [x] docs
- [x] tests

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

Added some docs for dask and took your comments into account

cc @philschmid if you also want to take a look :)

Just noticed that it would be more convenient to pass the output dir to download_and_prepare directly, to bypass the caching logic which prepares the dataset at `<cache_dir>/<name>/<version>/<hash>/`. And this way the cache is only used for the downloaded files. What do you think ?

```python 

builder = load_datadet_builder("squad")
# or with a custom cache
builder = load_datadet_builder("squad", cache_dir="path/to/local/cache/for/downloaded/files")

# download and prepare to s3
builder.download_and_prepare("s3://my_bucket/squad")
```

Might be of interest: 
PyTorch and AWS introduced better support for S3 streaming in `torchtext`. 
![image](https://user-images.githubusercontent.com/32632186/183354186-a7f005e3-4167-4d80-ad1a-c62dd51ad7b6.png)


Having thought about it a bit more, I also agree with @philschmid in that it's important to follow the existing APIs (pandas/dask), which means we should support the following at some point:

* remote data files resolution for the packaged modules to support `load_dataset("<format>", data_files="<fs_url>")`
* `to_<format>("<fs_url>")`
* `load_from_disk` and `save_to_disk` already expose the `fs` param, but it would be cool to support specifying `fsspec` URLs directly as the source/destination path (perhaps we can then deprecate `fs` to be fully aligned with pandas/dask)

IMO these are the two main issues with the current approach:
* relying on the builder API to generate the formatted files results in a non-friendly format due to how our caching works (a lot of nested subdirectories)
* this approach still downloads the files needed to generate a dataset locally. Considering one of our goals is to align the streaming API with the non-streaming one, this could be avoided by running `to_<format>` on streamed/iterable datasets

Alright I did the last change I wanted to do, here is the final API:

```python
builder = load_dataset_builder(...)
builder.download_and_prepare("s3://...", storage_options={"token": ...})
```

and it creates the arrow files directly in the specified directory, not in a nested subdirectory structure as we do in the cache !

> this approach still downloads the files needed to generate a dataset locally. Considering one of our goals is to align the streaming API with the non-streaming one, this could be avoided by running to_<format> on streamed/iterable datasets

Yup this can be explored in some future work I think. Though to keep things simple and clear I would keep the streaming behaviors only when you load a dataset in streaming mode, and not include it in `download_and_prepare` (because it wouldn't be aligned with the name of the function, which imply to 1. download and 2. prepare ^^). Maybe an API like that can make sense for those who need full streaming

```python
ds = load_dataset(..., streaming=True)
ds.to_parquet("s3://...")
```

totally agree with your comment on the meaning of "loading", I'll update the docs

I took your comments into account and reverted all the changes related to `cache_dir` to keep the support for remote `cache_dir` for beam datasets. I also updated the wording in the docs to not use "load" when it's not appropriate :)