Add visual prompt to processor
Author: idilsulo
Pull request: yes

# What does this PR do?

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Currently, integrated CLIPSeg model only supports textual prompts. However, a main advantage of CLIPSeg is that one can provide visual prompts instead of textual prompts in order to do semantic segmentation. For further details, you can refer to the original _Image Segmentation Using Text and Image Prompts (CVPR 2022)_ paper [here](https://openaccess.thecvf.com/content/CVPR2022/html/Luddecke_Image_Segmentation_Using_Text_and_Image_Prompts_CVPR_2022_paper.html). 

This change can easily be adapted to current `CLIPSegProcessor` by just providing an additional parameter which processes the visual prompt via image processor and returns the embedding with an additional key, i.e. `conditional_pixel_values`.


## 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#start-contributing-pull-requests),
      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. -> Not discussed, but only requires a minor change to fully support CLIPSeg model.
- [X] 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? -> Previous tokenizer and image processor tests apply.


## 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.

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Models:

- text models: @ArthurZucker and @younesbelkada
- vision models: @amyeroberts and @NielsRogge
- speech models: @sanchit-gandhi

Library:

- flax: @sanchit-gandhi
- generate: @gante
- pipelines: @Narsil
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @sgugger

Integrations:

- deepspeed: @stas00
- ray/raytune: @richardliaw, @amogkam

Documentation: @sgugger and @stevhliu

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: @sgugger
- TensorFlow: @Rocketknight1

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Comments:
_The documentation is not available anymore as the PR was closed or merged._