added changes in deberta dropout
Author: avacaondata
Pull request: yes

# What does this PR do?

This PR does the foillowing. 
1- Add a task dropout to deberta config, that is, cls_dropout. This is similar to what Bert and others have. This is justified since in Deberta paper adjusting task dropout is very important, therefore I think it shouldn't be just be set to the hidden_dropout_prob. While the latter should, in general, not be changed, cls_dropout should be tuned to obtain best results, as stated in: https://arxiv.org/abs/2006.03654
2. Armonize the use of Dropout. In general, Deberta uses Stable Dropout, which was also used in Classification. However, I don't understand why, on Token Classification normal Dropout was used. In some experiments I carried out, StableDropout worked better than normal dropout also for token classification, and it is the type of dropout that is used in all other Deberta layers, therefore I think it is better to have the same type of dropout in all task layers. 
I also changed a little bit how the dropout is created to remove redundant lines, but as you see there are only a few changes, they're not many lines of code, but for clarity and simplicity I decided to change that also; e.g (instead of first getting cls_dropout if existing and then if it is None take hidden_dropout_prob, I do it all in one line in getattr).

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Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_16647). All of your documentation changes will be reflected on that endpoint.

Hi,

Just as an FYI, changing `nn.Dropout` to `StableDropout` will stop the models supporting `saved_model` for TensorFlow when trying to save them on disk for TensorFlow serving. This is already an issue in `TFDebertaV2ForSequenceClassification` which both `nn.Dropout` and `StableDropout` were tested on 4 different datasets for multi-class classification and the results were pretty much the same between these two. (I already changed it to nn.Dropout so I can save them as saved_model, the performance in production is exactly the same as StableDropout at least for me)

reference: https://github.com/huggingface/transformers/issues/16484


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