Uniformize LlavaNextVideoProcessor kwargs
Author: yonigozlan
Pull request: yes

# What does this PR do?
Adds uniformized processors following https://github.com/huggingface/transformers/issues/31911 for LlavaNextVideoProcessor .

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Fixes #35602 


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Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_35613). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

Thanks for the feedback @zucchini-nlp !
I also just noticed that there is an issue with LlavaOneVision processor test, more specifically  `test_chat_template_dict` seems to fail, with this error:
```py
src/transformers/models/llava_onevision/processing_llava_onevision.py:167: in __call__
    one_video = to_numpy_array(video_inputs.get("pixel_values_videos")[0])
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

    def to_numpy_array(img) -> np.ndarray:
        if not is_valid_image(img):
>           raise ValueError(f"Invalid image type: {type(img)}")
E           ValueError: Invalid image type: <class 'list'>

src/transformers/image_utils.py:231: ValueError
```
This is the case in main as well so seems unrelated to this PR.

Yep, will be fixed by https://github.com/huggingface/transformers/pull/35660

I had to make some modifications to llava_next_video processor tests following this PR #35953 .
@zucchini-nlp Could you confirm these modifications are fine? I had to add some `return_tensors=None` in the chat_template tests to have consistent output types when comparing.