code: 
  
  from collections import OrderedDict
  from typing import Mapping
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig
  from ...utils import logging
  
  
  UpperCAmelCase_       :				List[str]		   =      logging.get_logger(__name__)
  
  UpperCAmelCase_       :				Any		   =      {
      '''squeezebert/squeezebert-uncased''': (
          '''https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json'''
      ),
      '''squeezebert/squeezebert-mnli''': '''https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json''',
      '''squeezebert/squeezebert-mnli-headless''': (
          '''https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json'''
      ),
  }
  
  
  
  
  
  
  class     lowerCamelCase_			(  _lowercase  ):
  	_lowercase		:      Tuple    				=							SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
  	_lowercase		:      Tuple    				=							'''squeezebert'''
  
  
  
  
  	def __init__(    self	:    Union[str, Any]				,			__A	:    Union[str, Any]=3_0522				,			__A	:    Tuple=768				,			__A	:    List[str]=12				,			__A	:    Dict=12				,			__A	:    str=3072				,			__A	:    Any="gelu"				,			__A	:    Optional[Any]=0.1				,			__A	:    Optional[Any]=0.1				,			__A	:    List[Any]=512				,			__A	:    Tuple=2				,			__A	:    Optional[int]=0.0_2				,			__A	:    Tuple=1e-1_2				,			__A	:    List[str]=0				,			__A	:    Any=768				,			__A	:    Optional[int]=4				,			__A	:    Optional[Any]=4				,			__A	:    Tuple=4				,			__A	:    str=1				,			__A	:    Tuple=4				,			__A	:    List[Any]=4				,			**__A	:    Tuple				,			):
  		super().__init__(pad_token_id=__A				,			**__A       )
  
  		__A   : int								= vocab_size
  		__A   : int								= hidden_size
  		__A   : Tuple								= num_hidden_layers
  		__A   : Optional[int]								= num_attention_heads
  		__A   : List[str]								= hidden_act
  		__A   : Any								= intermediate_size
  		__A   : Union[str, Any]								= hidden_dropout_prob
  		__A   : Any								= attention_probs_dropout_prob
  		__A   : List[Any]								= max_position_embeddings
  		__A   : List[str]								= type_vocab_size
  		__A   : Optional[int]								= initializer_range
  		__A   : Union[str, Any]								= layer_norm_eps
  		__A   : Dict								= embedding_size
  		__A   : int								= q_groups
  		__A   : Dict								= k_groups
  		__A   : str								= v_groups
  		__A   : Any								= post_attention_groups
  		__A   : Union[str, Any]								= intermediate_groups
  		__A   : Optional[int]								= output_groups
  
  
  
  
  
  
  class     lowerCamelCase_			(  _lowercase  ):
  
  
  
  
  	@property
  	def 					lowerCAmelCase_					(    self	:    int       ):
  		if self.task == "multiple-choice":
  			__A   : Union[str, Any]								= {0: """batch""", 1: """choice""", 2: """sequence"""}
  		else:
  			__A   : Optional[int]								= {0: """batch""", 1: """sequence"""}
  		return OrderedDict(
  		    [
  		        ("""input_ids""", dynamic_axis),
  		        ("""attention_mask""", dynamic_axis),
  		        ("""token_type_ids""", dynamic_axis),
  		    ]       )
  
  
  
  
  
  
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()
  
  
  
  
  
  