Push to hub capabilities for `Dataset` and `DatasetDict`
This PR implements a `push_to_hub` method on `Dataset` and `DatasetDict`. This does not currently work in `IterableDatasetDict` nor `IterableDataset` as those are simple dicts and I would like your opinion on how you would like to implement this before going ahead and doing it.

This implementation needs to be used with the following `huggingface_hub` branch in order to work correctly: https://github.com/huggingface/huggingface_hub/pull/415

### Implementation

The `push_to_hub` API is entirely based on HTTP requests rather than a git-based workflow:
- This allows pushing changes without firstly cloning the repository, which reduces the time in half for the `push_to_hub` method.
- Collaboration, as well as the system of branches/merges/rebases is IMO less straightforward than for models and spaces. In the situation where such collaboration is needed, I would *heavily* advocate for the `Repository` helper of the `huggingface_hub` to be used instead of the `push_to_hub` method which will always be, by design, limiting in that regard (even if based on a git-workflow instead of HTTP requests)

In order to overcome the limit of 5GB files set by the HTTP requests, dataset sharding is used.

### Testing

The test suite implemented here makes use of the moon-staging instead of the production setup. As several repositories are created and deleted, it is better to use the staging.

It does not require setting an environment variable or any kind of special attention but introduces a new decorator `with_staging_testing` which patches global variables to use the staging endpoint instead of the production endpoint.

### Examples

The tests cover a lot of examples and behavior.