Immutability for data collators
Author: vasqu
Pull request: yes

# What does this PR do?
Introduces new tests that check if a data collator might introduce side effects, i.e. the given input changes after the call to the collator. Motivated by #30556 

Furthermore, fixes the seq2seq collator to not introduce side effects on the given input's labels. This is done by:
- Passing only relevant features to the tokenizer to pad.
- Manually crafting the labels afterwards.
- Reintroducing tokenizer behaviour by converting labels to the respective datatype (pt, tf, np).
As a side note, added some more checks on `None` labels, especially when given `"labels": None` in the dictionary. 

Last remarks: 
- The test handles most of the cases that are introduced in the base tests but if I missed something give me a heads up :D 
- I'm not sure how to handle it when the user introduces `None` labels. For now, I return them by `None` again.

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Comments:
Oh yea, one last thing. I've created separate classes for the immutability tests. Thought it got too convoluted otherwise. 

The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_30603). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

@Rocketknight1 With code duplications, do you mean across the different classes between pt/tf/np? I agree with that, it's more so a dependency check to see that it suddenly doesn't branch out to something unwanted and that different input datatypes are handled correctly. For example, when the default collator is used, it branches into separate np, tf, and pt calls (i.e. `numpy_default_data_collator`, `tf_default_data_collator`, `torch_default_data_collator`). I haven't deep-dived where else something like this might happen.

Yes, that's what I was referring to - and I think it's fine to keep it as-is!