

TITLE
[Flax] Add general conversion script

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# What does this PR do?

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This PR changes the weight architecture of `FlaxBertModel` so that it corresponds 1-to-1 to PyTorch's version of `BertModel`. This means that some weights had to be renamed (*e.g.* "layer_norm" -> "LayerNorm" since PyTorch uses "LayerNorm") and also some new `flax.linen.Modules`, such as `FlaxBertSelfOutput` had to be created.

As can be seen, the PT=>Flax conversion function is now kept very general and can be applied to all models so that we can fully delete any model-specific conversion logic.

The PR has one drawback however:

Flax official [SelfAttention Module](https://flax.readthedocs.io/en/latest/_autosummary/flax.linen.SelfAttention.html#flax-linen-selfattention) cannot be used anymore since it doesn't give us enough flexibility to convert PyTorch weights to flax weights without having a model-specific conversion function. FlaxBERT's new attention modules fully correspond to PyTorchBERT's attention modules and are IMO still kept quite short by relying on Flax's [`dot_product_attention` function](https://flax.readthedocs.io/en/latest/_autosummary/flax.linen.dot_product_attention.html). Another drawback is that for auto-regressive Transformers models we will have to manually add all the code corresponding to cached / auto-regressive attention to the attention module (which we do for PyTorch anyways) instead of being able to use already existing code of `nn.linen.SelfAttention` -> see here: https://github.com/google/flax/blob/e31063da71bd7a4df137b000df6a48b0cea35a2b/flax/linen/attention.py#L202.

All in all, rewriting parts of `flax.linen.SelfAttention` is the right choice here though because it allows us to have a much cleaner conversion function with very little downside IMO (slightly higher maintenance because we need to copy-paste a bit more code).

@LysandreJik @sgugger - could you check if you agree more or less with my solution here (below I left some comments to showcase the trade-offs a bit better). I'll clean the code & upload the new weight structure then :-)

## 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.

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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: @LysandreJik

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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