deleted references of self.vocab_size and self.type_vocab_size for multiple models [TF implementation]
# What does this PR do?

This PR deletes references of  `self.vocab_size` and `self.type_vocab_size` for these models [Tensorflow implementation] : bert, albert, lxmert, electra, tapas, convbert, layoutlm, gpt2, camembert, clip, ctrl, deberta, deberta_v2, distilbert, esm, funnel, gptj, groupvit, longformer, mobilebert, mpnet, openai, rembert, roberta, roberta_prelayernorm, roformer, xlm_roberta, xlnet.
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## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
      Pull Request section?
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      to it if that's the case.([link](https://github.com/huggingface/transformers/issues/21053))
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- [ ] Did you write any new necessary tests?


## Who can review?

@gante
Hi, @gante as you said [here](https://github.com/huggingface/transformers/pull/21065#issuecomment-1385233073) I made changes for `src/transformers/models/albert/modeling_tf_albert.py`, could you please check it? If it's ok then I will push other changes of other models.

The failed test `ci/circleci: check_repository_consistency` are due to the fact that I only changed Embeddings for albert model and it is different from TFBertEmbeddings , the test will be successful when I change them too. <|||||>_The documentation is not available anymore as the PR was closed or merged._<|||||>@susnato before moving forward, let's make sure our `transformers` master agrees with the set of changes we are about to make :)

_________________________________
@sgugger this PR contains a change I want to apply across TF models, and @susnato is kindly doing the bulk of the work. In essence, some `config` attributes are not constant throughout the model's life, like `config.vocab_size`, and our TF implementation stores them as immutable class attributes (e.g. `self.vocab_size = config.vocab_size`). PT doesn't have this problem, since it simply stores `self.config = config` in the layers, which benefits from the updates the mutable `config` dictionary may receive elsewhere.

The proposed change is to make TF implementation closer to PT implementation and store `self.config` in the layers, as opposed to individual configuration parameters. This also solves the bug that triggered this discussion, where the vocabulary size was not being correctly updated and causing exceptions.

Let us know if you are okay with us making this change over most model architectures 🚀 <|||||>@susnato you might need to run `make fixup` locally, to automatically format the code and make our CI happy<|||||>Hi, @gante I added all the models I found to have self.vocab_size and removed reference to self.vocab_size and self.type_vocab_size and also all the tests are passed! Could you please check it? <|||||>> LGTM +1
> 
> Can we edit the PR title to a shorter one before merging? sweat_smile

@gante Done!<|||||>Awesome!  

Thank you for all the work you've put into fixing this, @susnato 🤗