Enabling multilingual models for translation pipelines.
# 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.


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Fixes # (issue)


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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

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@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.