Repository: huggingface/transformers
Pull request: Fix Qwen2.5-VL Video Processor

# What does this PR do?

This PR fixes the bug in Qwen2_5_VLProcessor, which would consistently logging "Unused or unrecognized kwargs: fps, return_tensors." during training. This issue seems to be introduced in #35206.

## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
      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.
- [ ] 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).
- [ ] Did you write any new necessary tests?


## Who can review?

@zucchini-nlp

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.

<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @

 If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
 Please tag fewer than 3 people.

Models:

- text models: @ArthurZucker
- vision models: @amyeroberts, @qubvel
- speech models: @eustlb
- graph models: @clefourrier

Library:

- flax: @gante and @Rocketknight1
- generate: @zucchini-nlp (visual-language models) or @gante (all others)
- pipelines: @Rocketknight1
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @zach-huggingface and @SunMarc
- chat templates: @Rocketknight1

Integrations:

- deepspeed: HF Trainer/Accelerate: @SunMarc @zach-huggingface
- ray/raytune: @richardliaw, @amogkam
- Big Model Inference: @SunMarc
- quantization (bitsandbytes, autogpt): @SunMarc @MekkCyber

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: @Rocketknight1
- PyTorch: See Models above and tag the person corresponding to the modality of the example.
- TensorFlow: @Rocketknight1

 -->


Changed files:
filename: src/transformers/models/qwen2_5_vl/modular_qwen2_5_vl.py
patch: @@ -1013,10 +1013,12 @@ def __call__(
             image_grid_thw = image_inputs["image_grid_thw"]
 
         if videos is not None:
+            # pop fps in advance for passing kwargs validation
+            fps = output_kwargs["videos_kwargs"].pop("fps", 2.0)
+
             videos_inputs = self.video_processor(videos=videos, **output_kwargs["videos_kwargs"])
             video_grid_thw = videos_inputs["video_grid_thw"]
 
-            fps = output_kwargs["videos_kwargs"].pop("fps", 2.0)
             if isinstance(fps, (int, float)):
                 second_per_grid_ts = [self.video_processor.temporal_patch_size / fps] * len(video_grid_thw)
             elif hasattr(fps, "__len__") and len(fps) == len(video_grid_thw):

filename: src/transformers/models/qwen2_5_vl/processing_qwen2_5_vl.py
patch: @@ -152,10 +152,12 @@ def __call__(
             image_grid_thw = image_inputs["image_grid_thw"]
 
         if videos is not None:
+            # pop fps in advance for passing kwargs validation
+            fps = output_kwargs["videos_kwargs"].pop("fps", 2.0)
+
             videos_inputs = self.video_processor(videos=videos, **output_kwargs["videos_kwargs"])
             video_grid_thw = videos_inputs["video_grid_thw"]
 
-            fps = output_kwargs["videos_kwargs"].pop("fps", 2.0)
             if isinstance(fps, (int, float)):
                 second_per_grid_ts = [self.video_processor.temporal_patch_size / fps] * len(video_grid_thw)
             elif hasattr(fps, "__len__") and len(fps) == len(video_grid_thw):

filename: src/transformers/video_processing_utils.py
patch: @@ -250,7 +250,10 @@ def preprocess(
         videos: VideoInput,
         **kwargs: Unpack[VideosKwargs],
     ) -> BatchFeature:
-        validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self.valid_kwargs.__annotations__.keys())
+        validate_kwargs(
+            captured_kwargs=kwargs.keys(),
+            valid_processor_keys=list(self.valid_kwargs.__annotations__.keys()) + ["return_tensors"],
+        )
         # Set default kwargs from self. This ensures that if a kwarg is not provided
         # by the user, it gets its default value from the instance, or is set to None.
         for kwarg_name in self.valid_kwargs.__annotations__: