Multiprocessed dataset builder
I would also like to add a test, but am not sure whether it should go into `test_builder` (more natural imo) or `test_load` (which already contains a lot of the things I have to import to run my current testing setup). For reference, what I run to test that it works looks like:

```
import os
from pathlib import Path
import shutil

import datasets
from datasets.builder import DatasetBuilder
from datasets.features import Features, Value

DATASET_LOADING_SCRIPT_NAME = "__dummy_dataset1__"

DATASET_LOADING_SCRIPT_CODE = """
import os

import datasets
from datasets import DatasetInfo, Features, Split, SplitGenerator, Value


class __DummyDataset1__(datasets.GeneratorBasedBuilder):

    def _info(self) -> DatasetInfo:
        return DatasetInfo(features=Features({"text": Value("string")}))

    def _split_generators(self, dl_manager):
        return [
            SplitGenerator(Split.TRAIN, gen_kwargs={"filepaths": [os.path.join(dl_manager.manual_dir, "train1.txt"), os.path.join(dl_manager.manual_dir, "train2.txt")]}),
            SplitGenerator(Split.TEST, gen_kwargs={"filepaths": [os.path.join(dl_manager.manual_dir, "test.txt")]}),
        ]

    def _generate_examples(self, filepaths, **kwargs):
        idx = 0
        for filepath in filepaths:
            with open(filepath, "r", encoding="utf-8") as f:
                for line in f:
                    yield idx, {"text": line.strip()}
                    idx += 1
"""


def dataset_loading_script_dir(tmp_path):
    script_name = DATASET_LOADING_SCRIPT_NAME
    script_dir = tmp_path / script_name
    script_dir.mkdir()
    script_path = script_dir / f"{script_name}.py"
    with open(script_path, "w") as f:
        f.write(DATASET_LOADING_SCRIPT_CODE)
    return str(script_dir)


def data_dir(tmp_path):
    data_dir = tmp_path / "data_dir"
    data_dir.mkdir()
    with open(data_dir / "train1.txt", "w") as f:
        f.write("foo\n" * 10)
    with open(data_dir / "train2.txt", "w") as f:
        f.write("foo\n" * 10)
    with open(data_dir / "test.txt", "w") as f:
        f.write("bar\n" * 10)
    return str(data_dir)


def load_dataset_builder_multiprocessed(tmp_path):
    builder = datasets.load_dataset_builder(
        os.path.join(dataset_loading_script_dir(tmp_path), DATASET_LOADING_SCRIPT_NAME + ".py"),
        data_dir=data_dir(tmp_path),
    )
    assert isinstance(builder, DatasetBuilder)
    assert builder.name == DATASET_LOADING_SCRIPT_NAME
    assert builder.info.features == Features({"text": Value("string")})
    builder.download_and_prepare(tmp_path / "prepare_target", max_shard_size=500, num_proc=2)

if __name__ == "__main__":
    tmp_path = "tmp"
    if os.path.exists(tmp_path):
        raise FileExistsError(f"path {tmp_path} already exists")
    os.makedirs(tmp_path)
    try:
        load_dataset_builder_multiprocessed(Path(tmp_path))
    finally:
        # pass
        shutil.rmtree(tmp_path)
``` The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5107). All of your documentation changes will be reflected on that endpoint. Nice ! I think the test can go in `test_builder.py` :) I've added sharded arrow dataset loading. Two WIP items in the PR:
- ~~Order is not conserved (it seems like the sharded files are read in the wrong order)~~
- the tqdm for preparing the splits is wrong (it compares against the size of the whole split rather than against the size of the multiprocessing shard, but I am not sure how to access the latter)

Also `naming.filenames_for_dataset_split` is not very elegant imo.

@lvwerra if you don't care about order, as I do, it's functional for now but I'd still quite like to get to the bottom of this. Found the ordering bug ! (`glob.glob` returning stuff in arbitrary order) I fixed the tqdm to be less misleading, but it can't tell where to stop. I am a bit hesitant to add a top-level tqdm (on the shard iterator) since for most intents it will do 0 -> N shards straight, but I am not sure what is the best way to present that info here. I'm continuing the PR :) Did a few changes:
- make shards naming consistent:
  - use `{builder_name}-{split_name}.{file_format}` when there's only 1 shard
  - otherwise use `{builder_name}-{split_name}-{shard_idx:05d}-of-{num_shards:05d}.{file_format}`
- update the reader to support reading several shards
  - added a new `shard_lengths` field in `SplitInfo` (FYI it is saved in `dataset_info.json` next to the shards as usual)
    - it's None when there's only 1 shard
    - otherwise it's a list of integers that correspond to the number of rows per shard
  - implemented partial reading to only memory map the required shards
    - e.g. when someone asks for a partial split like `train[:10%]`
- align the sharding for beam datasets
  - no more combining into 1 big arrow file
- added a tqdm bar
  - only one single bar, handled by the main process
  - gathers progress updates from other processes using `iflatmap_unordered`
  - shows the number of examples (even for datasets prepared by generating arrow tables)
- disabled multiprocessing by default - users must pass `num_proc` explicitly
- tests
- docs Alright this is ready for review - sorry it ended up so big ^^'

If I can do anything to make it easier for your to review this PR @mariosasko let me know Multiprocessing is disabled by default but we may show a warning to encourage users to pass `num_proc` if the dataset is split in many files. Let me know what you think Hey, is this error seems to you guys natural? 

The package built from `0d4e3907` commit tag, and here is the version displayed from the import ... 
```bash
>>> datasets.__version__
'2.6.1.dev0'
>>> 
```

```bash
>>> data = load_dataset('dataset_loaders/rfw2latentplay', num_proc=14)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/somewhere//mambaforge/envs/datasets/lib/python3.8/site-packages/datasets/load.py", line 1719, in load_dataset
    builder_instance = load_dataset_builder(
  File "/somewhere//mambaforge/envs/datasets/lib/python3.8/site-packages/datasets/load.py", line 1523, in load_dataset_builder
    builder_instance: DatasetBuilder = builder_cls(
  File "/somewhere//mambaforge/envs/datasets/lib/python3.8/site-packages/datasets/builder.py", line 1292, in __init__
    super().__init__(*args, **kwargs)
  File "/somewhere//mambaforge/envs/datasets/lib/python3.8/site-packages/datasets/builder.py", line 303, in __init__
    self.config, self.config_id = self._create_builder_config(
  File "/somewhere//mambaforge/envs/datasets/lib/python3.8/site-packages/datasets/builder.py", line 456, in _create_builder_config
    builder_config = self.BUILDER_CONFIG_CLASS(**config_kwargs)
TypeError: __init__() got an unexpected keyword argument 'num_proc'
```

Let me know if I can help fixing this ... 
 > Do we have some benchmarks to see the speed-up?

On my machine running `load_dataset("oscar-corpus/OSCAR-2201", "br")` (which is split in shards) I go from 2-3k examples per sec to 4-5k examples per sec with num_proc=2 😉 > Hey, is this error seems to you guys natural?
>
> The package built from 0d4e3907 commit tag, and here is the version displayed from the import ...

I don't know where you got the `0d4e3907` commit tag from, it doesn't seem to be in this PR. You should try installing from this PR, or wait for it to be merged on `main` ## Splits vs Shards

Maybe it's a good idea to add some documentation on the `sharding` that can be achieved by passing `list` based arguments to the `SplitGenerator`s `gen_kwargs` ... 

I had to read the whole dataset generation source code to find this out ... 


 > Maybe it's a good idea to add some documentation on the sharding that can be achieved by passing list based arguments to the SplitGenerators gen_kwargs ...

This is part of this PR :) you can check the changes in docs/source/dataset_script.mdx I took your comments into account @mariosasko thanks !
Let me know if it's good for you now ;) The doc CI should be fixed by now hopefully, merging !
This PR adds the multiprocessing part of #2650 (but not the caching of already-computed arrow files). On the other side, loading of sharded arrow files still needs to be implemented (sharded parquet files can already be loaded). 