Replace input_values_processing with unpack_inputs
# What does this PR do?

Updates Hubert and Wav2Vec2 to use the repo standard `unpack_inputs` decorator instead of `input_values_processing`. Previously, TensorFlow models use `input_values_processing`, a function which processed inputs ready to be passed to the TF layers. This was removed and replaced with unpack inputs, see #16051. For some reason, wav2vec2 wasn't part of this update. [A bug was reported](https://github.com/huggingface/transformers/issues/16051#issuecomment-1067938180), but the conversation and details can't be found in the discord 😢 

This PR also updates any relevant tests: 
* Adds `unittest.skip` decorator so tests are marked as skipped rather than passed
* Modifies batch sizes for tests that were previously skipped for OOM to make sure logic is fully tested. 
* Adds back tests which shouldn't have been skipped. This caught the issue with loss not being calculated (see below)

**Note:** The loss calculation for `TFHubertForCTC` wasn't being calculated properly. Without the `unpack_inputs` decorator, when `model.fit` was being called, `labels` were passed into the model as part of the `input_values` dictionary. These were then processed and [part of the `inputs` dictionary](https://github.com/huggingface/transformers/blob/c35bb6de547f8839434c3d5772777c699e9595de/src/transformers/models/wav2vec2/modeling_tf_wav2vec2.py#L1645). However, `labels` was always [taken from the function kwargs](https://github.com/huggingface/transformers/blob/c35bb6de547f8839434c3d5772777c699e9595de/src/transformers/models/wav2vec2/modeling_tf_wav2vec2.py#L1677) and so loss calculation skipped. [This is why the previous test 'returned the wrong shape'](https://github.com/huggingface/transformers/blob/c35bb6de547f8839434c3d5772777c699e9595de/tests/models/hubert/test_modeling_tf_hubert.py#L328). The issue was resolved for `TFWav2Vec2ForCTC` in a previous PR #18014


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- [x] Did you write any new necessary tests?
_The documentation is not available anymore as the PR was closed or merged._