

TITLE
TF: clearer model variable naming

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37

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### This issue is part of our **Great Code Cleanup 2022**. If you're interested in helping out, take a look at [this thread](https://twitter.com/carrigmat/status/1502319813510766599), or come [join us on Discord](https://t.co/kS42XBvpWH) and talk with other contributors!

As introduced in https://github.com/huggingface/transformers/issues/15908 and implemented in https://github.com/huggingface/transformers/pull/15907, we now have a new `@unpack_inputs` decorator to unpack TensorFlow model `call()` arguments. In essence, if we apply the decorator, we can replace `inputs["foo"]` with `foo`, making the code for the layer/model much shorter and clearer.

This issue is a call for contributors, to implement the new decorator in the architectures below. If you wish to contribute, reply in this thread which architectures you'd like to take :)

### Guide to contributing:
1. Ensure you've read our contributing [guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) 📜 
2. Claim your architecture(s) in this thread (confirm no one is working on it) 🎯 
3. Implement the changes as in https://github.com/huggingface/transformers/pull/15907 (see the diff on the model architectures for a few examples) 💪 
    - The file you want to look at is in `src/transformers/models/[model_name]/modeling_tf_[model_name].py`
    - In functions that have an `input_processing` call, remove it and add the `@unpack_inputs` decorator
    - Replace any use of the `inputs` variable in those functions (e.g. `inputs["foo"]` -> `foo`)
5. Test your changes on the architecture with `RUN_SLOW=1 py.test -vv tests/[model_name]/test_modeling_tf_[model_name].py` 💻 
6. Open the PR and tag me in it (don't forget to run `make fixup` before your final commit) 🎊 
    - Note that some code is copied across our codebase. If you see a line like `# Copied from transformers.models.bert...`, this means that the code is copied from that source, and our scripts will automatically keep that in sync. If you see that, you should not edit the copied method! Instead, edit the original method it's copied from, and run make fixup to synchronize that across all the copies. Be sure you installed the development dependencies with `pip install -e ".[dev]"`, as described in the contributor guidelines above, to ensure that the code quality tools in `make fixup` can run.

### Models updated:
- [x] (the templates)
- [x] albert
- [x] auto
- [x] bart
- [x] bert
- [x] blenderbot
- [x] blenderbot_small
- [x] camembert
- [x] clip
- [x] convbert
- [x] convnext
- [x] ctrl
- [x] deberta
- [x] deberta_v2
- [x] distilbert
- [x] dpr
- [x] electra
- [x] encoder_decoder
- [x] flaubert
- [x] funnel
- [x] gpt2
- [x] hubert
- [x] layoutlm
- [x] led
- [x] longformer
- [x] lxmert
- [x] marian
- [x] mbart
- [x] mobilebert
- [x] mpnet
- [x] mt5
- [x] openai
- [x] pegasus
- [x] rag
- [x] rembert
- [x] roberta
- [x] roformer
- [x] speech_to_text
- [x] t5
- [x] tapas
- [x] transfo_xl
- [x] vision_encoder_decoder
- [x] vit
- [x] wave2vec2 👉  lgnore this one for now, looking into the issue @infinite-Joy raised
- [x] xlm
- [x] xlm_roberta
- [x] xlnet