[Flax] Add general conversion script
# 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 :-)

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> Great work! My only concern is to make sure we don't lose any performance by not using `nn.linen.SelfAttention`. If we are just using the same code as its implementation, there is no reason for that but it's good to double-check.
> Otherwise, I agree it's better to re-implement it than to have custom weight loading logic..

Great! Yeah, I'll talk with @avital about this next week (hopefully) :-) 