code: 
  
  import argparse
  
  from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  def        __SCREAMING_SNAKE_CASE    (      a__						:      Tuple ,a__						:      Union[str, Any]					)				->							str:
  	__A   : List[str]								= CanineConfig()
  	__A   : Union[str, Any]								= CanineModel(UpperCamelCase__					)
  	model.eval()
  
  	print(f"""Building PyTorch model from configuration: {config}"""					)
  
  	# Load weights from tf checkpoint
  	load_tf_weights_in_canine(UpperCamelCase__ ,UpperCamelCase__ ,UpperCamelCase__					)
  
  	# Save pytorch-model (weights and configuration)
  	print(f"""Save PyTorch model to {pytorch_dump_path}"""					)
  	model.save_pretrained(UpperCamelCase__					)
  
  	# Save tokenizer files
  	__A   : Optional[int]								= CanineTokenizer()
  	print(f"""Save tokenizer files to {pytorch_dump_path}"""					)
  	tokenizer.save_pretrained(UpperCamelCase__					)
  
  
  if __name__ == "__main__":
  			UpperCAmelCase_       :				List[str]		   =      argparse.ArgumentParser()
  			# Required parameters
  			parser.add_argument(
  			    '''--tf_checkpoint_path''',
  			    default=None,
  			    type=str,
  			    required=True,
  			    help='''Path to the TensorFlow checkpoint. Should end with model.ckpt''',
  			)
  			parser.add_argument(
  			    '''--pytorch_dump_path''',
  			    default=None,
  			    type=str,
  			    required=True,
  			    help='''Path to a folder where the PyTorch model will be placed.''',
  			)
  			UpperCAmelCase_       :				Optional[int]		   =      parser.parse_args()
  			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)
  
  
  
  
  
  
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()
  
  
  
  
  
  