Repository: huggingface/transformers
Pull request: Fix typing issues with SigLip2

# What does this PR do?
Fixes #36754 


## 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?

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/image_processing_utils_fast.py
patch: @@ -691,8 +691,12 @@ def preprocess(self, images: ImageInput, **kwargs: Unpack[DefaultFastImageProces
 
         # torch resize uses interpolation instead of resample
         resample = kwargs.pop("resample")
+
+        # Check if resample is an int before checking if it's an instance of PILImageResampling
+        # because if pillow < 9.1.0, resample is an int and PILImageResampling is a module.
+        # Checking PILImageResampling will fail with error `TypeError: isinstance() arg 2 must be a type or tuple of types`.
         kwargs["interpolation"] = (
-            pil_torch_interpolation_mapping[resample] if isinstance(resample, (PILImageResampling, int)) else resample
+            pil_torch_interpolation_mapping[resample] if isinstance(resample, (int, PILImageResampling)) else resample
         )
 
         # Pop kwargs that are not needed in _preprocess

filename: src/transformers/models/siglip2/image_processing_siglip2.py
patch: @@ -163,7 +163,7 @@ class Siglip2ImageProcessor(BaseImageProcessor):
     def __init__(
         self,
         do_resize: bool = True,
-        resample: PILImageResampling = PILImageResampling.BILINEAR,
+        resample: "PILImageResampling" = PILImageResampling.BILINEAR,
         do_rescale: bool = True,
         rescale_factor: float = 1 / 255,
         do_normalize: bool = True,
@@ -195,7 +195,7 @@ def preprocess(
         self,
         images: ImageInput,
         do_resize: Optional[bool] = None,
-        resample: Optional[PILImageResampling] = None,
+        resample: Optional["PILImageResampling"] = None,
         do_rescale: Optional[bool] = None,
         rescale_factor: Optional[float] = None,
         do_normalize: Optional[bool] = None,