Set `IterableDataset.map` param `batch_size` typing as optional
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The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5336). All of your documentation changes will be reflected on that endpoint.
2022-12-06T17:13:03Z
https://github.com/huggingface/datasets/pull/5336#issuecomment-1339701543
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Hi @mariosasko, @lhoestq I was wondering whether we should include `batched` as a `pytest.mark` param for the functions testing `IterableDataset.map` so as to ensure that the changes done in this PR work fine without breaking anything of the actual functionality.

I've pushed updated tests just for one of the unit testing functions to be run as `pytest tests/test_iterable_dataset.py::test_mapped_examples_iterable -s --durations 0`, but some are still missing `batched` param, it was just to ask you whether we're supposed to do this for the rest of the functions or not, if it's a yes I'll push the commit as it's ready, but didn't want to push extra stuff that may be discarded later!

Thanks :hugs:
2022-12-06T17:34:56Z
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Thanks for the feedback @lhoestq, I agree with keeping `Optional` instead of `Union[type, None]` for now 👍🏻
2022-12-07T12:19:01Z
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This PR solves #5325 

~Indeed we're using the typing for optional values as `Union[type, None]` as it's similar to how Python 3.10 handles optional values as `type | None`, instead of using `Optional[type]`.~

~Do we want to start using `Union[type, None]` for type-hinting optional values or just keep on using `Optional`?~ -> Keeping `Optional` still for consistency with the rest of the code in `datasets`

Also we now allow `batch_size` to be `None` for `IterableDataset.map` and `IterableDataset.filter`e.g. `MappedExamplesIterable` as `map` is internally instantiating those and propagating the `batch_size` param so if it can be `None` for `map` it should also do so for `MappedExamplesIterable`, as well as for `FilteredExamplesIterable` when calling `IterableDataset.filter`.

## TODOs

- [x] Add integration tests
- [x] Handle scenario where `batched=True` and `batch_size=None` or `batch_size<=0`