code: 
  
  import unittest
  
  import numpy as np
  import PIL
  import torch
  
  from diffusers.image_processor import VaeImageProcessor
  
  
  class    SCREAMING_SNAKE_CASE__      (unittest.TestCase  ):
  
  
  
  
  
      @property
      def    snake_case_       (   self):
           lowercase__					:     int   = 1
           lowercase__					:     Dict   = 3
           lowercase__					:     int   = 8
           lowercase__					:     int   = 8
  
           lowercase__					:     List[str]   = torch.rand((batch_size, num_channels, height, width))
  
           return sample
  
  
  
  
  
      def    snake_case_       (   self		,     a):
  
  
  
  
  
  
  
           if isinstance(image[0]		,     PIL.Image.Image):
                return np.stack([np.array(a) for i in image]		,     axis=0)
           elif isinstance(a		,     torch.Tensor):
                return image.cpu().numpy().transpose(0		,     2		,     3		,     1)
           return image
  
  
  
  
  
      def    snake_case_       (   self):
           lowercase__					:     Optional[int]   = VaeImageProcessor(do_resize=a		,     do_normalize=a)
  
           lowercase__					:     Any   = self.dummy_sample
           lowercase__					:     Optional[Any]   = self.to_np(a)
  
           for output_type in ["pt", "np", "pil"]:
                lowercase__					:     Optional[Any]   = image_processor.postprocess(
                    image_processor.preprocess(a)		,     output_type=a		,     )
                lowercase__					:     Optional[Any]   = self.to_np(a)
                lowercase__					:     Any   = (input_np * 255).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    snake_case_       (   self):
           lowercase__					:     Dict   = VaeImageProcessor(do_resize=a		,     do_normalize=a)
           lowercase__					:     Tuple   = self.dummy_sample.cpu().numpy().transpose(0		,     2		,     3		,     1)
  
           for output_type in ["pt", "np", "pil"]:
                lowercase__					:     Dict   = image_processor.postprocess(image_processor.preprocess(a)		,     output_type=a)
  
                lowercase__					:     Union[str, Any]   = self.to_np(a)
                lowercase__					:     Tuple   = (input_np * 255).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    snake_case_       (   self):
           lowercase__					:     Optional[Any]   = VaeImageProcessor(do_resize=a		,     do_normalize=a)
  
           lowercase__					:     str   = self.dummy_sample.cpu().numpy().transpose(0		,     2		,     3		,     1)
           lowercase__					:     int   = image_processor.numpy_to_pil(a)
  
           for output_type in ["pt", "np", "pil"]:
                lowercase__					:     Dict   = image_processor.postprocess(image_processor.preprocess(a)		,     output_type=a)
                for i, o in zip(a		,     a):
                     lowercase__					:     Dict   = np.array(a)
                     lowercase__					:     Tuple   = self.to_np(a) if output_type == "pil" else (self.to_np(a) * 255).round()
                     assert (
                         np.abs(in_np - out_np).max() < 1e-6
                     ), f"""decoded output does not match input for output_type {output_type}"""
  
  
  
  
  
      def    snake_case_       (   self):
           lowercase__					:     Tuple   = VaeImageProcessor(do_resize=a		,     do_normalize=a)
  
           lowercase__					:     List[str]   = self.dummy_sample
           lowercase__					:     Optional[int]   = input_pt_ad.squeeze(0)
  
           lowercase__					:     Any   = image_processor.postprocess(
               image_processor.preprocess(a)		,     output_type='np'		,     )
           lowercase__					:     Union[str, Any]   = image_processor.postprocess(
               image_processor.preprocess(a)		,     output_type='np'		,     )
  
           lowercase__					:     Union[str, Any]   = self.to_np(self.dummy_sample)
           lowercase__					:     Tuple   = input_np_ad.squeeze(0)
  
           lowercase__					:     str   = image_processor.postprocess(
               image_processor.preprocess(a)		,     output_type='np'		,     )
           lowercase__					:     Union[str, Any]   = image_processor.postprocess(
               image_processor.preprocess(a)		,     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    snake_case_       (   self):
           lowercase__					:     Optional[Any]   = VaeImageProcessor(do_resize=a		,     do_normalize=a)
  
           lowercase__					:     Union[str, Any]   = self.dummy_sample
           lowercase__					:     List[str]   = list(a)
  
           lowercase__					:     str   = image_processor.postprocess(
               image_processor.preprocess(a)		,     output_type='np'		,     )
  
           lowercase__					:     Any   = image_processor.postprocess(
               image_processor.preprocess(a)		,     output_type='np'		,     )
  
           lowercase__					:     Dict   = self.to_np(self.dummy_sample)
           list(a)
  
           lowercase__					:     str   = image_processor.postprocess(
               image_processor.preprocess(a)		,     output_type='np'		,     )
  
           lowercase__					:     Union[str, Any]   = image_processor.postprocess(
               image_processor.preprocess(a)		,     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: 
  
  from __future__ import annotations
  
  
  class    SCREAMING_SNAKE_CASE__      :
  
  
  
  
  
      def __init__(   self		,     a = 0):
           lowercase__					:     int   = value
           lowercase__					:     Node | None   = None
           lowercase__					:     Node | None   = None
  
  
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Node | None			,  SCREAMING_SNAKE_CASE_	:					Node | None    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       if treea is None:
            return treea
       if treea is None:
            return treea
  
       lowercase__					:     Optional[Any]   = treea.value + treea.value
       lowercase__					:     Union[str, Any]   = merge_two_binary_trees(treea.left			,  treea.left    )
       lowercase__					:     Optional[Any]   = merge_two_binary_trees(treea.right			,  treea.right    )
       return treea
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Node | None    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       if root:
            print(root.value    )
            print_preorder(root.left    )
            print_preorder(root.right    )
  
  
  if __name__ == "__main__":
         snake_case_    						=				Node(1)
         snake_case_    						=				Node(2)
         snake_case_    						=				Node(3)
         snake_case_    						=				Node(4)
  
         snake_case_    						=				Node(2)
         snake_case_    						=				Node(4)
         snake_case_    						=				Node(6)
         snake_case_    						=				Node(9)
         snake_case_    						=				Node(5)
  
         print('''Tree1 is: ''')
         print_preorder(treea)
         print('''Tree2 is: ''')
         print_preorder(treea)
         snake_case_    						=				merge_two_binary_trees(treea, treea)
         print('''Merged Tree is: ''')
         print_preorder(merged_tree)
  
  