code: 
  
  
  import unittest
  
  import numpy as np
  import PIL
  import torch
  
  from diffusers.image_processor import VaeImageProcessor
  
  
  
  class 			a      (      unittest.TestCase   ):
  
  
  
  
  
  
  		@property
  		def       lowerCAmelCase_  (  self							)->			List[str]:
  						'''simple docstring'''
  
  
  						A__     :							Tuple   				=1
  						A__     :							Dict   				=3
  						A__     :							str   				=8
  						A__     :							Optional[int]   				=8
  
  						A__     :							Optional[Any]   				=torch.rand((batch_size, num_channels, height, width)							)
  
  						return sample
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self	,     __UpperCamelCase							)->			List[str]:
  						'''simple docstring'''
  
  
  						if isinstance(image[0]	,     PIL.Image.Image							):
  										return np.stack([np.array(__UpperCamelCase							) for i in image]	,     axis=0							)
  						elif isinstance(__UpperCamelCase	,     torch.Tensor							):
  										return image.cpu().numpy().transpose(0	,     2	,     3	,     1							)
  						return image
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			int:
  						'''simple docstring'''
  
  
  						A__     :							List[Any]   				=VaeImageProcessor(do_resize=__UpperCamelCase	,     do_normalize=__UpperCamelCase							)
  
  						A__     :							Optional[Any]   				=self.dummy_sample
  						A__     :							Union[str, Any]   				=self.to_np(__UpperCamelCase							)
  
  						for output_type in ["pt", "np", "pil"]:
  										A__     :							List[str]   				=image_processor.postprocess(
  										    image_processor.preprocess(__UpperCamelCase							)	,     output_type=__UpperCamelCase	,     )
  										A__     :							Dict   				=self.to_np(__UpperCamelCase							)
  										A__     :							Tuple   				=(input_np * 2_55).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       lowerCAmelCase_  (  self							)->			Any:
  						'''simple docstring'''
  
  
  						A__     :							str   				=VaeImageProcessor(do_resize=__UpperCamelCase	,     do_normalize=__UpperCamelCase							)
  						A__     :							Union[str, Any]   				=self.dummy_sample.cpu().numpy().transpose(0	,     2	,     3	,     1							)
  
  						for output_type in ["pt", "np", "pil"]:
  										A__     :							Union[str, Any]   				=image_processor.postprocess(image_processor.preprocess(__UpperCamelCase							)	,     output_type=__UpperCamelCase							)
  
  										A__     :							Any   				=self.to_np(__UpperCamelCase							)
  										A__     :							Optional[Any]   				=(input_np * 2_55).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       lowerCAmelCase_  (  self							)->			Union[str, Any]:
  						'''simple docstring'''
  
  
  						A__     :							Dict   				=VaeImageProcessor(do_resize=__UpperCamelCase	,     do_normalize=__UpperCamelCase							)
  
  						A__     :							str   				=self.dummy_sample.cpu().numpy().transpose(0	,     2	,     3	,     1							)
  						A__     :							Optional[int]   				=image_processor.numpy_to_pil(__UpperCamelCase							)
  
  						for output_type in ["pt", "np", "pil"]:
  										A__     :							Tuple   				=image_processor.postprocess(image_processor.preprocess(__UpperCamelCase							)	,     output_type=__UpperCamelCase							)
  										for i, o in zip(__UpperCamelCase	,     __UpperCamelCase							):
  														A__     :							Tuple   				=np.array(__UpperCamelCase							)
  														A__     :							int   				=self.to_np(__UpperCamelCase							) if output_type == '''pil''' else (self.to_np(__UpperCamelCase							) * 2_55).round()
  														assert (
  														    np.abs(in_np - out_np							).max() < 1E-6
  														), F'decoded output does not match input for output_type {output_type}'
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			Union[str, Any]:
  						'''simple docstring'''
  
  
  						A__     :							Optional[int]   				=VaeImageProcessor(do_resize=__UpperCamelCase	,     do_normalize=__UpperCamelCase							)
  
  						A__     :							Optional[Any]   				=self.dummy_sample
  						A__     :							List[Any]   				=input_pt_ad.squeeze(0							)
  
  						A__     :							Union[str, Any]   				=image_processor.postprocess(
  						    image_processor.preprocess(__UpperCamelCase							)	,     output_type='''np'''	,     )
  						A__     :							int   				=image_processor.postprocess(
  						    image_processor.preprocess(__UpperCamelCase							)	,     output_type='''np'''	,     )
  
  						A__     :							List[Any]   				=self.to_np(self.dummy_sample							)
  						A__     :							str   				=input_np_ad.squeeze(0							)
  
  						A__     :							Any   				=image_processor.postprocess(
  						    image_processor.preprocess(__UpperCamelCase							)	,     output_type='''np'''	,     )
  						A__     :							List[Any]   				=image_processor.postprocess(
  						    image_processor.preprocess(__UpperCamelCase							)	,     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       lowerCAmelCase_  (  self							)->			List[str]:
  						'''simple docstring'''
  
  
  						A__     :							Optional[int]   				=VaeImageProcessor(do_resize=__UpperCamelCase	,     do_normalize=__UpperCamelCase							)
  
  						A__     :							Any   				=self.dummy_sample
  						A__     :							Optional[Any]   				=list(__UpperCamelCase							)
  
  						A__     :							List[str]   				=image_processor.postprocess(
  						    image_processor.preprocess(__UpperCamelCase							)	,     output_type='''np'''	,     )
  
  						A__     :							Tuple   				=image_processor.postprocess(
  						    image_processor.preprocess(__UpperCamelCase							)	,     output_type='''np'''	,     )
  
  						A__     :							List[Any]   				=self.to_np(self.dummy_sample							)
  						list(__UpperCamelCase							)
  
  						A__     :							str   				=image_processor.postprocess(
  						    image_processor.preprocess(__UpperCamelCase							)	,     output_type='''np'''	,     )
  
  						A__     :							Tuple   				=image_processor.postprocess(
  						    image_processor.preprocess(__UpperCamelCase							)	,     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: 
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							int:
  				if number != int(snake_case_	):
  								raise ValueError('''factorial() only accepts integral values'''	)
  				if number < 0:
  								raise ValueError('''factorial() not defined for negative values'''	)
  				A__     :							Union[str, Any]   				=1
  				for i in range(1,	number + 1	):
  								value *= i
  				return value
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							int:
  				if not isinstance(snake_case_,	snake_case_	):
  								raise ValueError('''factorial() only accepts integral values'''	)
  				if n < 0:
  								raise ValueError('''factorial() not defined for negative values'''	)
  				return 1 if n == 0 or n == 1 else n * factorial(n - 1	)
  
  
  if __name__ == "__main__":
  						import doctest
  
  						doctest.testmod()
  
  						__lowerCamelCase       :       Any				   =			int(input("Enter a positive integer: ").strip() or 0)
  						print(F"factorial{n} is {factorial(n)}")
  
  
  
  