[Flax] Align FlaxBertForMaskedLM with BertForMaskedLM, implement from_pretrained, init
Author: patrickvonplaten
Pull request: yes

# What does this PR do?

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This PR:

1) Implements a flax from_pretrained, save_pretrained method and let's `FlaxPreTrainedModel.from_pretained()` default to Flax instead of PyTorch. Tests are added and `bert-base-cased` and `roberta-base` model weights have been uploaded to the model hub. I gave the flax model file the name `flax_model.msgpack` similar to `pytorch_model.bin`.
2) Corrects FlaxBertForMaskedLM to align it with BertForMaskedLM: Some weights were incorrectly transposed and the activation function was different to Bert.
3) Adds `FlaxBertPretrainedModel` to Bert (and Roberta resp.) as it's done in PT.
4) Refactors the tests a bit. It's relatively easy to init a FlaxModel now I think without going over PyTorch (see tests).
5) Enforces naming convention that every model has a corresponding `Module` class. As discussed with @mfuntowicz in Flax it does not seem to be possible to make `PreTrainedModel` a `nn.Module` because `nn.Module` should by design be state-less and not contain a `self.params` attribute and thus we always require a `....Module` in addition to every `....Model` class in Flax (@mfuntowicz can probably better explain why I think and I guess we should have an offline discussion about it). I started this design principle now in the PR. Let me know what you think @mfuntowicz @sgugger @LysandreJik @thomwolf 

Might be a good idea to go into the PR and play with the tests a bit.

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Comments:
Update: The init and `.from_pretrained()` should now be more aligned in Flax.
Notably, the user never has to call `init(...)` himself. This is done automatically in `FlaxBertModel(config)` just as it's done in PT.
The `from_pretrained(...)` method now yields explicit warnings which weights are randomly initialized and which ones were correctly loaded, just as it's done in PT.

Would be awesome if @mfuntowicz @sgugger @LysandreJik, you guys could do a second review. If this design is good for you, I'd be keen to merge this PR and think about a more general convert method.

```python
from transformers import FlaxBertModel, BertConfig

model = FlaxBertModel(BertConfig())
hid_states = model(np.ones((1, 1)))  # init was done automatically 

# one can also add the input shape used for the init to keep flexibility
model = FlaxBertModel(BertConfig(), input_shape=((16, 128))
hid_states = model(np.ones((1, 1)))  # init was done automatically 

# also the from_pretrained method now yields an explicit warning when weights are loaded, just as it's done in PT:
model = FlaxBertModel.from_pretrained("roberta-base")
# -> rnd initializes 'bias', 'dense.kernel', 'dense.bias', 'layer_norm.bias', 'decoder.weight', 'layer_norm.weight' with explicit warning
```