code: 
  
  
  lowerCAmelCase					 =	[
      [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  _lowerCamelCase(			lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__			)		->			bool:
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		return 0 <= row < rows and 0 <= col < cols
  
  
  
  
  
  
  
  def  _lowerCamelCase(			lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__			)		->			int:
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		__lowercase=    len(lowercase__			)
  		__lowercase=    len(mat[0]			)
  		if is_safe(lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__			) and (row, col) not in seen and mat[row][col] == 1:
  				seen.add((row, col)			)
  				return (
  				    1
  				    + depth_first_search(row + 1     ,				lowercase__     ,				lowercase__     ,				lowercase__			)
  				    + depth_first_search(row - 1     ,				lowercase__     ,				lowercase__     ,				lowercase__			)
  				    + depth_first_search(lowercase__     ,				col + 1     ,				lowercase__     ,				lowercase__			)
  				    + depth_first_search(lowercase__     ,				col - 1     ,				lowercase__     ,				lowercase__			)
  				)
  		else:
  				return 0
  
  
  
  
  
  
  
  def  _lowerCamelCase(			lowercase__			)		->			int:
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		__lowercase=    set()
  
  		__lowercase=    0
  		for row, line in enumerate(lowercase__			):
  				for col, item in enumerate(lowercase__			):
  						if item == 1 and (row, col) not in seen:
  								# Maximizing the area
  								__lowercase=    max(lowercase__     ,				depth_first_search(lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__			)			)
  		return max_area
  
  
  if __name__ == "__main__":
  	import doctest
  
  	print(find_max_area(matrix))  # Output -> 6
  	doctest.testmod()
  
  
  
  
  
  
  
style_context: 
  
  
  from dataclasses import dataclass
  from typing import List, Optional, Union
  
  import numpy as np
  import PIL
  from PIL import Image
  
  from ...utils import BaseOutput, is_torch_available, is_transformers_available
  
  
  
  
  
  @dataclass
  # Copied from diffusers.pipelines.stable_diffusion.__init__.StableDiffusionPipelineOutput with Stable->Alt
  class    A					(     A_				):
  							UpperCamelCase_     :							Union[List[PIL.Image.Image], np.ndarray]
  							UpperCamelCase_     :							Optional[List[bool]]
  
  
  if is_transformers_available() and is_torch_available():
  	from .modeling_roberta_series import RobertaSeriesModelWithTransformation
  	from .pipeline_alt_diffusion import AltDiffusionPipeline
  	from .pipeline_alt_diffusion_imgaimg import AltDiffusionImgaImgPipeline
  
  
  
  
  
  
  