code: 
  
  
  
  
  
  import unittest
  
  import numpy as np
  import PIL
  import torch
  
  from diffusers.image_processor import VaeImageProcessor
  
  
  
  
  
  
  
  class        __lowerCAmelCase (					unittest.TestCase):
  
  
  
  
  
  
  
        @property
        def      _lowercase						(					self							)     ->		Optional[Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      List[str]            =1
          a__			:      Union[str, Any]            =3
          a__			:      Dict            =8
          a__			:      Optional[Any]            =8
  
          a__			:      Any            =torch.rand((batch_size, num_channels, height, width)							)
  
          return sample
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__							)     ->		List[str]:
  
  
  
          '''simple docstring'''
  
  
  
  
          if isinstance(image[0]	,   PIL.Image.Image							):
            return np.stack([np.array(lowerCAmelCase__							) for i in image]	,   axis=0							)
          elif isinstance(lowerCAmelCase__	,   torch.Tensor							):
            return image.cpu().numpy().transpose(0	,   2	,   3	,   1							)
          return image
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		Union[str, Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      int            =VaeImageProcessor(do_resize=lowerCAmelCase__	,   do_normalize=lowerCAmelCase__							)
  
          a__			:      Optional[int]            =self.dummy_sample
          a__			:      str            =self.to_np(lowerCAmelCase__							)
  
          for output_type in ["pt", "np", "pil"]:
            a__			:      Optional[int]            =image_processor.postprocess(
                image_processor.preprocess(lowerCAmelCase__							)	,   output_type=lowerCAmelCase__	,   )
            a__			:      Dict            =self.to_np(lowerCAmelCase__							)
            a__			:      int            =(input_np * 2_5_5).round() if output_type == "pil" else input_np
            assert (
                np.abs(in_np - out_np							).max() < 1E-6
            ), F'''decoded output does not match input for output_type {output_type}'''
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		List[str]:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      int            =VaeImageProcessor(do_resize=lowerCAmelCase__	,   do_normalize=lowerCAmelCase__							)
          a__			:      Optional[int]            =self.dummy_sample.cpu().numpy().transpose(0	,   2	,   3	,   1							)
  
          for output_type in ["pt", "np", "pil"]:
            a__			:      List[Any]            =image_processor.postprocess(image_processor.preprocess(lowerCAmelCase__							)	,   output_type=lowerCAmelCase__							)
  
            a__			:      Union[str, Any]            =self.to_np(lowerCAmelCase__							)
            a__			:      Optional[int]            =(input_np * 2_5_5).round() if output_type == "pil" else input_np
            assert (
                np.abs(in_np - out_np							).max() < 1E-6
            ), F'''decoded output does not match input for output_type {output_type}'''
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		int:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      List[Any]            =VaeImageProcessor(do_resize=lowerCAmelCase__	,   do_normalize=lowerCAmelCase__							)
  
          a__			:      Union[str, Any]            =self.dummy_sample.cpu().numpy().transpose(0	,   2	,   3	,   1							)
          a__			:      List[Any]            =image_processor.numpy_to_pil(lowerCAmelCase__							)
  
          for output_type in ["pt", "np", "pil"]:
            a__			:      List[str]            =image_processor.postprocess(image_processor.preprocess(lowerCAmelCase__							)	,   output_type=lowerCAmelCase__							)
            for i, o in zip(lowerCAmelCase__	,   lowerCAmelCase__							):
              a__			:      List[str]            =np.array(lowerCAmelCase__							)
              a__			:      Optional[int]            =self.to_np(lowerCAmelCase__							) if output_type == "pil" else (self.to_np(lowerCAmelCase__							) * 2_5_5).round()
              assert (
                  np.abs(in_np - out_np							).max() < 1E-6
              ), F'''decoded output does not match input for output_type {output_type}'''
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		List[Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      Tuple            =VaeImageProcessor(do_resize=lowerCAmelCase__	,   do_normalize=lowerCAmelCase__							)
  
          a__			:      Optional[int]            =self.dummy_sample
          a__			:      List[str]            =input_pt_ad.squeeze(0							)
  
          a__			:      Any            =image_processor.postprocess(
              image_processor.preprocess(lowerCAmelCase__							)	,   output_type="np"	,   )
          a__			:      Optional[int]            =image_processor.postprocess(
              image_processor.preprocess(lowerCAmelCase__							)	,   output_type="np"	,   )
  
          a__			:      Optional[Any]            =self.to_np(self.dummy_sample							)
          a__			:      Tuple            =input_np_ad.squeeze(0							)
  
          a__			:      Any            =image_processor.postprocess(
              image_processor.preprocess(lowerCAmelCase__							)	,   output_type="np"	,   )
          a__			:      Optional[int]            =image_processor.postprocess(
              image_processor.preprocess(lowerCAmelCase__							)	,   output_type="np"	,   )
  
          assert np.abs(out_pt_ad - out_pt_ad							).max() < 1E-6
          assert np.abs(out_np_ad - out_np_ad							).max() < 1E-6
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		int:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      Dict            =VaeImageProcessor(do_resize=lowerCAmelCase__	,   do_normalize=lowerCAmelCase__							)
  
          a__			:      Union[str, Any]            =self.dummy_sample
          a__			:      Optional[int]            =list(lowerCAmelCase__							)
  
          a__			:      List[Any]            =image_processor.postprocess(
              image_processor.preprocess(lowerCAmelCase__							)	,   output_type="np"	,   )
  
          a__			:      Tuple            =image_processor.postprocess(
              image_processor.preprocess(lowerCAmelCase__							)	,   output_type="np"	,   )
  
          a__			:      Tuple            =self.to_np(self.dummy_sample							)
          list(lowerCAmelCase__							)
  
          a__			:      Optional[Any]            =image_processor.postprocess(
              image_processor.preprocess(lowerCAmelCase__							)	,   output_type="np"	,   )
  
          a__			:      Any            =image_processor.postprocess(
              image_processor.preprocess(lowerCAmelCase__							)	,   output_type="np"	,   )
  
          assert np.abs(out_pt_ad - out_pt_list							).max() < 1E-6
          assert np.abs(out_np_ad - out_np_list							).max() < 1E-6
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  import warnings
  
  from ...utils import logging
  from .image_processing_detr import DetrImageProcessor
  
  
  UpperCAmelCase			:			List[Any]	  = logging.get_logger(__name__)
  
  
  
  
  
  
  
  class        __lowerCAmelCase (					UpperCamelCase__):
  
  
  
  
  
  
  
        def __init__(					self	,   *lowerCAmelCase__	,   **lowerCAmelCase__							)     ->		None:
  
  
  
          '''simple docstring'''
  
  
  
  
          warnings.warn(
              "The class DetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
              " Please use DetrImageProcessor instead."	,   lowerCAmelCase__	,   )
          super().__init__(*lowerCAmelCase__	,   **lowerCAmelCase__							)
  
  
  
  
  
  
  