Fix seq2seq collator padding
Author: vasqu
Pull request: yes

# What does this PR do?
`DataCollatorForSeq2Seq` currently only supports the `longest` and silently the `no_padding` strategies. This PR adds some checks to ensure the padding strategy is respected if valid (e.g. `max_length` without a given max length defaults back to `longest`).

Furthermore, added tests for the collator in the style of the `DataCollatorForTokenClassification` for all kinds of supported datatypes (pt, tf, np). 

Something to note: The seq2seq collator causes side effects on the features' labels that are passed. I don't think it's a big deal but thought I'd clarify why I produce features twice within a single test.

<!--
Congratulations! You've made it this far! You're not quite done yet though.

Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution.

Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change.

Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost.
-->

<!-- Remove if not applicable -->

Fixes #30521 


## 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#create-a-pull-request),
      Pull Request section?
- [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [x] Did you write any new necessary tests?


## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.

<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @

 If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
 Please tag fewer than 3 people.

Models:

- text models: @ArthurZucker and @younesbelkada
- vision models: @amyeroberts
- speech models: @sanchit-gandhi
- graph models: @clefourrier

Library:

- flax: @sanchit-gandhi
- generate: @gante
- pipelines: @Narsil
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @muellerzr and @pacman100

Integrations:

- deepspeed: HF Trainer/Accelerate: @pacman100
- ray/raytune: @richardliaw, @amogkam
- Big Model Inference: @SunMarc
- quantization (bitsandbytes, autogpt): @SunMarc and @younesbelkada

Documentation: @stevhliu and @MKhalusova

HF projects:

- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Maintained examples (not research project or legacy):

- Flax: @sanchit-gandhi
- PyTorch: See Models above and tag the person corresponding to the modality of the example.
- TensorFlow: @Rocketknight1

 -->
@Rocketknight1 @amyeroberts 

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_30556). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

> The point you made about side-effects on the input is interesting, though - I agree that it's unlikely to be critical (because people rarely reuse the pre-collator inputs after calling the collator), but it's still suboptimal. Maybe we could consider having our collators make copies of the inputs instead so they don't overwrite them, though this would definitely be a task for a separate PR.

@Rocketknight1 @vasqu Very much aligned on this - side-effects should definitely be avoided, and it's a bigger piece of work for a follow-up PR.

@amyeroberts @Rocketknight1 I'm not sure which collators affect which inputs via side-effects so I'd suggest writing a mutability test which assures this wanted behaviour. Afaik, it currently only affects the labels when using the seq2seq collator but maybe I'll catch some others. Got some time now, will be looking into it in a bit.

@vasqu Awesome ❤️  looking forward to seeing the PR!