code: 
  
  import unittest
  
  import numpy as np
  import PIL
  import torch
  
  from diffusers.image_processor import VaeImageProcessor
  
  
  
  
  
  
  class     lowerCamelCase_			(  unittest.TestCase  ):
  
  
  
  
  	@property
  	def 					lowerCAmelCase_					(    self	:    Any       ):
  		__A   : Optional[int]								= 1
  		__A   : Union[str, Any]								= 3
  		__A   : str								= 8
  		__A   : Optional[int]								= 8
  
  		__A   : str								= torch.rand((batch_size, num_channels, height, width)       )
  
  		return sample
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    str				,			__A	:    Tuple       ):
  		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 					lowerCAmelCase_					(    self	:    Optional[int]       ):
  		__A   : Optional[Any]								= VaeImageProcessor(do_resize=__A				,			do_normalize=__A       )
  
  		__A   : Tuple								= self.dummy_sample
  		__A   : Dict								= self.to_np(__A       )
  
  		for output_type in ["pt", "np", "pil"]:
  			__A   : List[Any]								= image_processor.postprocess(
  			    image_processor.preprocess(__A       )				,			output_type=__A				,			)
  			__A   : str								= self.to_np(__A       )
  			__A   : int								= (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 					lowerCAmelCase_					(    self	:    Optional[Any]       ):
  		__A   : Any								= VaeImageProcessor(do_resize=__A				,			do_normalize=__A       )
  		__A   : Tuple								= 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(__A       )				,			output_type=__A       )
  
  			__A   : Tuple								= self.to_np(__A       )
  			__A   : Union[str, 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 					lowerCAmelCase_					(    self	:    Optional[Any]       ):
  		__A   : Dict								= VaeImageProcessor(do_resize=__A				,			do_normalize=__A       )
  
  		__A   : Tuple								= self.dummy_sample.cpu().numpy().transpose(0				,			2				,			3				,			1       )
  		__A   : Union[str, Any]								= image_processor.numpy_to_pil(__A       )
  
  		for output_type in ["pt", "np", "pil"]:
  			__A   : List[Any]								= image_processor.postprocess(image_processor.preprocess(__A       )				,			output_type=__A       )
  			for i, o in zip(__A				,			__A       ):
  				__A   : int								= np.array(__A       )
  				__A   : Optional[int]								= 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 					lowerCAmelCase_					(    self	:    List[Any]       ):
  		__A   : Dict								= VaeImageProcessor(do_resize=__A				,			do_normalize=__A       )
  
  		__A   : int								= self.dummy_sample
  		__A   : Optional[Any]								= input_pt_ad.squeeze(0       )
  
  		__A   : int								= image_processor.postprocess(
  		    image_processor.preprocess(__A       )				,			output_type="""np"""				,			)
  		__A   : List[Any]								= image_processor.postprocess(
  		    image_processor.preprocess(__A       )				,			output_type="""np"""				,			)
  
  		__A   : str								= self.to_np(self.dummy_sample       )
  		__A   : Tuple								= input_np_ad.squeeze(0       )
  
  		__A   : Dict								= image_processor.postprocess(
  		    image_processor.preprocess(__A       )				,			output_type="""np"""				,			)
  		__A   : int								= 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 					lowerCAmelCase_					(    self	:    List[Any]       ):
  		__A   : Optional[Any]								= VaeImageProcessor(do_resize=__A				,			do_normalize=__A       )
  
  		__A   : Any								= self.dummy_sample
  		__A   : List[str]								= list(__A       )
  
  		__A   : List[str]								= image_processor.postprocess(
  		    image_processor.preprocess(__A       )				,			output_type="""np"""				,			)
  
  		__A   : Optional[Any]								= image_processor.postprocess(
  		    image_processor.preprocess(__A       )				,			output_type="""np"""				,			)
  
  		__A   : str								= self.to_np(self.dummy_sample       )
  		list(__A       )
  
  		__A   : Any								= image_processor.postprocess(
  		    image_processor.preprocess(__A       )				,			output_type="""np"""				,			)
  
  		__A   : 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: 
  
  UpperCAmelCase_       :				str		   =      [
      [0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
      [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0],
      [0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
      [0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0],
      [0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0],
      [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],
      [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0],
      [0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0],
  ]
  def        __SCREAMING_SNAKE_CASE    (      a__						:      int ,a__						:      int ,a__						:      int ,a__						:      int					)				->							bool:
  	return 0 <= row < rows and 0 <= col < cols
  def        __SCREAMING_SNAKE_CASE    (      a__						:      int ,a__						:      int ,a__						:      set ,a__						:      list[list[int]]					)				->							int:
  	__A   : Union[str, Any]								= len(a__					)
  	__A   : Any								= len(mat[0]					)
  	if is_safe(a__ ,a__ ,a__ ,a__					) and (row, col) not in seen and mat[row][col] == 1:
  		seen.add((row, col)					)
  		return (
  		    1
  		    + depth_first_search(row + 1 ,a__ ,a__ ,a__					)
  		    + depth_first_search(row - 1 ,a__ ,a__ ,a__					)
  		    + depth_first_search(a__ ,col + 1 ,a__ ,a__					)
  		    + depth_first_search(a__ ,col - 1 ,a__ ,a__					)
  		)
  	else:
  		return 0
  def        __SCREAMING_SNAKE_CASE    (      a__						:      list[list[int]]					)				->							int:
  	__A   : set								= set()
  
  	__A   : Union[str, Any]								= 0
  	for row, line in enumerate(a__					):
  		for col, item in enumerate(a__					):
  			if item == 1 and (row, col) not in seen:
  				# Maximizing the area
  				__A   : Optional[Any]								= max(a__ ,depth_first_search(a__ ,a__ ,a__ ,a__					)					)
  	return max_area
  
  
  if __name__ == "__main__":
  			import doctest
  
  			print(find_max_area(matrix))  # Output -> 6
  			doctest.testmod()
  
  
  
  
  
  