Add visual prompt to processor of CLIPSeg model
# 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`.

This PR complements the work done in [this](https://github.com/huggingface/transformers/pull/20066) previous pull request.


## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
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## Who can review?

Anyone in the community is free to review the PR.

Feel free to tag members/contributors who may be interested in your PR. @NielsRogge @sgugger @alaradirik 

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_The documentation is not available anymore as the PR was closed or merged._<|||||>Hi @alaradirik, thanks for the review! Added a test to [test_processor_clipseg.py](https://github.com/huggingface/transformers/blob/main/tests/models/clipseg/test_processor_clipseg.py) as well.<|||||>> 

Hello @sgugger - I am aware that the argument can be passed at the end, but this also opens ways for faulty usage to users who do not know how CLIPSeg model processes their input.

Let's see a working example:

```
import torch
from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation
processor = CLIPSegProcessor.from_pretrained("CIDAS/clipseg-rd64-refined")
model = CLIPSegForImageSegmentation.from_pretrained("CIDAS/clipseg-rd64-refined")

from PIL import Image
import requests
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
text = ["background", "cat"]
images = [image]*2
```

```
inputs = processor(text, images, return_tensors="pt") # the processor also returns the text embedding (which should not be used)
with torch.no_grad():
    outputs = model(**inputs, conditional_pixel_values=inputs.pixel_values)
```

What did the model process in the above line? Is it visual prompt + image or text prompt + image? It seems like it is still processing the textual prompt + image pair. 

Why? Let's try to fail it:

```
inputs = processor(text, images, return_tensors="pt")
visual_prompt_input = processor(images=[image], return_tensors="pt") # Additional prompt with length 1
with torch.no_grad():
    outputs = model(**inputs, conditional_pixel_values=visual_prompt_input.pixel_values)
```

Here first processor computes `text` and `images` arguments with length 2. Second one, however, only takes a single image. This does not fail the model as it still processes a text prompt + image pair rather than visual prompt (the one passed via `conditional_pixel_values`).

**Side note:** The [processor of OWL-ViT](https://github.com/huggingface/transformers/blob/main/src/transformers/models/owlvit/processing_owlvit.py) also has an additional argument (i.e. `query_images`) in addition to `images` and `text`. An idea might be to add `visual_prompt` as the third argument (as done in OWL-ViT) so that it would not break anything as @NielsRogge suggested.

Thanks for taking your time!