SuperPointModel -> SuperPointForKeypointDetection
Author: amyeroberts
Pull request: yes

# What does this PR do?

Renames `SuperPointModel` to `SuperPointForKeypointDetection` and adds `AutoModelForKeypointDetection`. This is more in-line with our library's pattern, where `AutoModel` returns a base model and `AutoModelForXxx` will return task specific outputs. 

Originally I had requested this, as the model doesn't have an obvious task-specific head on-top. @NielsRogge highlighted the discrepancy and it was agreed offline using the task-specific class was better. 

There is currently no `SuperPointModel` class, but we can add this later if needed

Fixes # (issue)


## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
      Pull Request section?
- [ ] 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).
- [ ] 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

 -->


Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_29757). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

cc @sbucaille As you're working on adding SuperGlue - and apologies for my previous bad advise to just use AutoModel! 

Thanks @amyeroberts for the quick fix - one minor remark is that I would've waited with adding an `AutoModelForKeypointDetection` class before we have at least 2 keypoint detection models, in order to unify their APIs. This is because Auto classes define a certain standard API which all classes that belong to that mapping should respect.

Yes, but in this case it's needed for the SuperGlue implementation 

Could you clarify why it's needed? Models can be added to the ["not auto mapped" list](https://github.com/huggingface/transformers/blob/main/utils/check_repo.py#L153) and eventually be added to an auto class

Because we need an auto model to be used within the modeling file for SuperPoint, similar to what we do in llava 

@amyeroberts I was exactly about to point the problem I would reach with SuperGlue, since it uses the outputs of models like SuperPoint (keypoints data) and not images (pixel_values). But we will be able to discuss this in the appropriate [PR](https://github.com/huggingface/transformers/pull/25697) (btw you can reopen it as I'll push new changes in the coming days). Anyway thanks for the anticipation ! :hugs: