Repository: transformers
Issue: LlavaNextVideoProcessor -> TypeError: LlavaNextVideoProcessor.__call__() got an unexpected keyword argument 'legacy' (I have the fix)
Labels: Core: Pipeline,bug,VLM

### System Info

Problem's root cause is in `ImageTextToTextPipeline` class in the `image_text_to_text.py` pipeline.

Line `438`

```py
        model_inputs = self.processor(
            images=images, text=text, return_tensors=self.framework, legacy=False, **processing_kwargs
        ).to(dtype=self.torch_dtype)
```

Notice how legacy is always specified as False?

If you use this model (`llava-hf/LLaVA-NeXT-Video-7B-32K-hf`) on `transfomers==4.47.1`  you will get this error because its config specifies to use the class: `LlavaNextVideoProcessor` from `processing_llava_next_video.py` and it's `__call__` method is not expecting that kwarg.

The quick fix is this: 

Modify `__call__` (line `101`) in `processing_llava_next_video.py`

from this:

```py
    def __call__(
        self,
        text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]],
        images: ImageInput = None,
        videos: VideoInput = None,
        padding: Union[bool, str, PaddingStrategy] = False,
        truncation: Union[bool, str, TruncationStrategy] = None,
        max_length: int = None,
        return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
    ) -> BatchFeature:
 ```

to this:

```py
    def __call__(
        self,
        text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]],
        images: ImageInput = None,
        videos: VideoInput = None,
        padding: Union[bool, str, PaddingStrategy] = False,
        truncation: Union[bool, str, TruncationStrategy] = None,
        max_length: int = None,
        return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
        **kwargs, # <-- this guy
    ) -> BatchFeature:
 ```
 
 Notice the unused kwargs at the end. This reflects the pattern used for `__init__`
 
 which looks like this:

```py
    def __init__(
        self,
        video_processor=None,
        image_processor=None,
        tokenizer=None,
        chat_template=None,
        patch_size=None,
        vision_feature_select_strategy=None,
        video_token="<video>",
        image_token="<image>",
        num_additional_image_tokens=0,
        **kwargs, # <-- this guy
    ):
```

I ain't got time to step through the PR process, so I hope this helps the HF staff either make this quick patch, or solve the problem at a higher level in the code for `image_text_to_text.py`.

### Who can help?

HF staff

### Information

- [ ] The official example scripts
- [ ] My own modified scripts

### Tasks

- [x ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...) (`image-to-text-to-text`)

### Reproduction

```py
pipe = pipeline("image-text-to-text", model="llava-hf/LLaVA-NeXT-Video-7B-32K-hf")

messages = {'role': 'user', 'content': [{'type': 'text', 'text': "What's in this image?"}, {'type': 'video'}]}

videos = ["https://huggingface.co/datasets/raushan-testing-hf/videos-test/resolve/main/sample_demo_1.mp4"]

out = pipe(text=messages, videos=videos)
```

### Expected behavior

No exception raised due to an unexpected kwarg.