Repository: huggingface/transformers
Pull request: Fix reduce-labels in BEIT Fast Image Processor

# What does this PR do?

When running `BeitImageProcessorFast` with `do_reduce_labels=True`, it not only reduces labels in segmentation map, but also applies `reduce_label` to the image itself

`reduce_label` should be applied only to segmentation maps

### Test

```py
import transformers
import torch


processor = transformers.BeitImageProcessorFast(
    do_resize=False, 
    do_center_crop=False, 
    do_rescale=False, 
    do_normalize=False, 
    do_convert_rgb=False,
)

image = torch.zeros([1, 3, 256, 256])

segmap = torch.zeros([1, 256, 256]) 

batch = processor.preprocess(
    images = image, 
    segmentation_maps = segmap, 
    return_tensors="pt", 
    do_reduce_labels=True,
)
```

pixel_values will be 255, should be 0.


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Changed files:
filename: docs/source/en/model_doc/beit.md
patch: @@ -150,6 +150,7 @@ If you're interested in submitting a resource to be included here, please feel f
 [[autodoc]] BeitImageProcessor
     - preprocess
     - post_process_semantic_segmentation
+
 ## BeitImageProcessorFast
 
 [[autodoc]] BeitImageProcessorFast

filename: docs/source/ja/model_doc/beit.md
patch: @@ -105,6 +105,7 @@ BEiT の使用を開始するのに役立つ公式 Hugging Face およびコミ
 
 [[autodoc]] BeitImageProcessor
     - preprocess
+    - post_process_semantic_segmentation
 
 ## BeitImageProcessorFast
 

filename: src/transformers/models/beit/image_processing_beit_fast.py
patch: @@ -137,12 +137,22 @@ def _preprocess(
         processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images
         return processed_images
 
+    def _preprocess_images(
+        self,
+        images,
+        **kwargs,
+    ):
+        """Preprocesses images."""
+        kwargs["do_reduce_labels"] = False
+        processed_images = self._preprocess(images=images, **kwargs)
+        return processed_images
+
     def _preprocess_segmentation_maps(
         self,
         segmentation_maps,
         **kwargs,
     ):
-        """Preprocesses a single segmentation map."""
+        """Preprocesses segmentation maps."""
         processed_segmentation_maps = []
         for segmentation_map in segmentation_maps:
             segmentation_map = self._process_image(
@@ -215,7 +225,7 @@ def preprocess(
         kwargs.pop("default_to_square")
         kwargs.pop("data_format")
 
-        images = self._preprocess(
+        images = self._preprocess_images(
             images=images,
             **kwargs,
         )