Add auto model for image-text-to-text
Author: yonigozlan
Pull request: yes

# What does this PR do?

Add `AutoModelForImageTextToText` object in preparation for the image-text-to-text pipeline
Blocking PR for `image-text-to-text` pipeline.

The following models need to be added to modeling auto:

- [x]  GIT
- [x]  BLIP
- [x]  BLIP-2
- [x]  IDEFICS
- [x]  InstructBLIP
- [x]  LLaVa
- [x]  Fuyu
- [x]  Pix2Struct/DePlot/MatCha
- [x]  UDOP
- [x]  Donut
- [x]  KOSMOS-2
- [x]  Idefics2
- [x]  LLaVA-NeXT
- [x]  PaliGemma
- [x]  VipLlava
- [x] Chameleon
- [x] LlaVa OneVision
- [x] Qwen2-VL
- [x] Pixtral
- [x] Idefics3
- [x] MLlama

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## 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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      Pull Request section?
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      [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?

@molbap @amyeroberts 

<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @

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Models:

- text models: @ArthurZucker
- vision models: @amyeroberts
- speech models: @sanchit-gandhi
- graph models: @clefourrier

Library:

- flax: @sanchit-gandhi
- generate: @zucchini-nlp (visual-language models) or @gante (all others)
- pipelines: @Narsil
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @muellerzr and @SunMarc

Integrations:

- deepspeed: HF Trainer/Accelerate: @muellerzr
- ray/raytune: @richardliaw, @amogkam
- Big Model Inference: @SunMarc
- quantization (bitsandbytes, autogpt): @SunMarc

Documentation: @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: See Models above and tag the person corresponding to the modality of the example.
- TensorFlow: @Rocketknight1

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Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_32472). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

Now that all the image-text-to-text models have had their processor standardized, I think we can safely merge this PR @ArthurZucker @NielsRogge 

I replaced some tests that were using `AutoModelForVision2Seq` with `AutoModelForImageTextToText` and also swapped some instances of `[Model]ForConditionalGeneration` with `AutoModelForImageTextToText` in the documentation.

Of course, there will be more tests and documentation updates including `AutoModelForImageTextToText` once the `ImageTextToText` pipeline is added.

Did you have any other specific tests or doc in mind @ArthurZucker? I didn’t find many tests using AutoClasses except in the pipeline tests.