code: 
  
  import copy
  from typing import Dict
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  from ..auto.configuration_auto import CONFIG_MAPPING
  from ..bit import BitConfig
  
  
  UpperCAmelCase_       :				Union[str, Any]		   =      logging.get_logger(__name__)
  
  UpperCAmelCase_       :				List[str]		   =      {
      """google/vit-hybrid-base-bit-384""": """https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json""",
      # See all ViT hybrid models at https://huggingface.co/models?filter=vit
  }
  
  
  
  
  
  
  class     lowerCamelCase_			(  _SCREAMING_SNAKE_CASE  ):
  	_lowercase		:      List[str]    				=							'''vit-hybrid'''
  
  
  
  
  	def __init__(				self	:    Tuple				,			__A	:    Dict=None				,			__A	:    int=768				,			__A	:    int=12				,			__A	:    Any=12				,			__A	:    Dict=3072				,			__A	:    Dict="gelu"				,			__A	:    Optional[Any]=0.0				,			__A	:    List[str]=0.0				,			__A	:    Tuple=0.0_2				,			__A	:    Any=1e-1_2				,			__A	:    str=224				,			__A	:    Union[str, Any]=1				,			__A	:    str=3				,			__A	:    List[str]=[1, 1024, 24, 24]				,			__A	:    List[str]=True				,			**__A	:    Union[str, Any]				,			):
  		super().__init__(**__A       )
  
  		if backbone_config is None:
  			logger.info("""`backbone_config` is `None`. Initializing the config with a `BiT` backbone."""       )
  			__A   : Any								= {
  			    """global_padding""": """same""",
  			    """layer_type""": """bottleneck""",
  			    """depths""": [3, 4, 9],
  			    """out_features""": ["""stage3"""],
  			    """embedding_dynamic_padding""": True,
  			}
  
  		if isinstance(__A				,			__A       ):
  			if "model_type" in backbone_config:
  				__A   : List[Any]								= CONFIG_MAPPING[backbone_config["""model_type"""]]
  			else:
  				logger.info(
  				    """`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class."""       )
  				__A   : List[Any]								= BitConfig
  			__A   : List[Any]								= backbone_config_class(**__A       )
  
  		__A   : List[Any]								= backbone_featmap_shape
  		__A   : str								= backbone_config
  		__A   : List[str]								= hidden_size
  		__A   : Optional[Any]								= num_hidden_layers
  		__A   : Tuple								= num_attention_heads
  		__A   : Any								= intermediate_size
  		__A   : Tuple								= hidden_act
  		__A   : int								= hidden_dropout_prob
  		__A   : List[Any]								= attention_probs_dropout_prob
  		__A   : Any								= initializer_range
  		__A   : Dict								= layer_norm_eps
  		__A   : Optional[int]								= image_size
  		__A   : int								= patch_size
  		__A   : Union[str, Any]								= num_channels
  		__A   : Union[str, Any]								= qkv_bias
  
  
  
  
  	def 					lowerCAmelCase_					(				self	:    Tuple       ):
  		__A   : Optional[int]								= copy.deepcopy(self.__dict__       )
  		__A   : List[Any]								= self.backbone_config.to_dict()
  		__A   : Union[str, Any]								= self.__class__.model_type
  		return output
  
  
  
  
  
  
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()
  
  
  
  
  
  