Enabling multilingual models for translation pipelines.
Author: Narsil
Pull request: yes

# What does this PR do?

Enables mutlilingual translation for pipelines.

Some models can target multiple languages/ language pairs. Before this PR, there was no simple way to exploit that within the Translation pipeline.

## Move away from `translation_XX_to_YY`.

Because src_lang, tgt_lang pairs can be used for a single model, we need to move away from this task naming scheme.
Currently it was done, as being able to use `src_lang` and `tgt_lang` both within `pipeline(.... src_lang=XX, tgt_lang=yy)` and at call time `translator = pipeline(...); translation("input string", src_lang=XX, tgt_lang=YY)`

 ## Rely on the model's tokenizer to build the inputs.

We now have at least 3 (m2m. mbart50, T5) different models that prepare input ids in a different manner. In order to avoid switches within a pipeline that would depend on a model, instead `tokenizer` can optionally implement `_build_translation_input_ids`.  That enables to put model specific logic within their model files and use all their custom methods over there.

This is in line with https://github.com/huggingface/transformers/pull/10002.

## Misc

- `ensure_tensor_on_device` now supports non tensor members
- Added a `deep_round` test utility to enable testing nested structures that contain tensors, floats and so on.


<!--
Congratulations! You've made it this far! You're not quite done yet though.

Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution.

Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change.

Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost.
-->

<!-- Remove if not applicable -->

Fixes # (issue)


## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md#start-contributing-pull-requests),
      Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/master/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/master/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?


## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors which may be interested in your PR.

<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @

 If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
 Please tag fewer than 3 people.

Models:

- albert, bert, xlm: @LysandreJik
- blenderbot, bart, marian, pegasus, encoderdecoder,  t5: @patrickvonplaten, @patil-suraj
- longformer, reformer, transfoxl, xlnet: @patrickvonplaten
- fsmt: @stas00
- funnel: @sgugger
- gpt2: @patrickvonplaten, @LysandreJik
- rag: @patrickvonplaten, @lhoestq
- tensorflow: @jplu

Library:

- benchmarks: @patrickvonplaten
- deepspeed: @stas00
- ray/raytune: @richardliaw, @amogkam
- text generation: @patrickvonplaten
- tokenizers: @n1t0, @LysandreJik
- trainer: @sgugger
- pipelines: @LysandreJik

Documentation: @sgugger

HF projects:

- nlp datasets: [different repo](https://github.com/huggingface/nlp)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Examples:

- maintained examples (not research project or legacy): @sgugger, @patil-suraj
- research_projects/bert-loses-patience: @JetRunner
- research_projects/distillation: @VictorSanh

 -->

Comments:
@LysandreJik 

I added a method here `deep_round` to try and make test equality a bit sane.

torchTensor(...) == torch.Tensor(..) does not work (understandably).
Any sort of float comparison is also flaky.

`deep_round` simply tries to make `assertEqual` work in a sane way for any sort of nested structure to make test comparisons simpler to read and write.

Here the tests just need to make sure that the actual output of `_build_translation_input_ids` is actually correct, writing it in that way make its much more readable IMO (than having to extract each element and call `allclose` on them). It actually allowed me to see that mbart has a different encoding scheme.

How do you feel about such a function to improve small sanity checks ?

Another route would be to create something like `assertAlmostEqual` that behaves similarly but I think it's a bit  less simple to reason about.

Finally we could stick to not using any such helper functions.

When this PR is ready, could you complete the description of the PR? It would help to understand what we're reviewing, and would be better for the release notes and posterity. Thanks!

Good catch ! Updated and completed !
I think this is ready to merge if you're ok with it.

I'm asking @patrickvonplaten and @sgugger for review as they're more acquainted with mBART-like tokenizers and their review would be helpful.