code: 
  
  
  '''simple docstring'''
  
  
  
  
  
  def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   int = 400_0000   ):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			if n <= 1:
  						return 0
  			__A=  0
  			__A=  2
  			__A=  0
  			while 4 * b + a <= n:
  						__A,  __A=  b, 4 * b + a
  						count += a
  			return count + b
  
  
  if __name__ == "__main__":
  					print(F"""{solution() = }""")
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  UpperCAmelCase__      =							logging.get_logger(__name__)
  
  UpperCAmelCase__      =							{
      '''dandelin/vilt-b32-mlm''': '''https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json'''
  }
  class 	a__				(				a_      ):
  
  
  
  
  
  			'''simple docstring'''
  
  			A	:							Dict							= '''vilt'''
  			def __init__(     self       : str ,						lowerCAmelCase_       : Tuple=30_522 ,						lowerCAmelCase_       : List[Any]=2 ,						lowerCAmelCase_       : Optional[Any]=2 ,						lowerCAmelCase_       : Union[str, Any]=40 ,						lowerCAmelCase_       : str=768 ,						lowerCAmelCase_       : Tuple=12 ,						lowerCAmelCase_       : List[Any]=12 ,						lowerCAmelCase_       : int=3_072 ,						lowerCAmelCase_       : List[Any]="gelu" ,						lowerCAmelCase_       : int=0.0 ,						lowerCAmelCase_       : str=0.0 ,						lowerCAmelCase_       : Optional[Any]=0.02 ,						lowerCAmelCase_       : Union[str, Any]=1E-12 ,						lowerCAmelCase_       : Dict=384 ,						lowerCAmelCase_       : Optional[int]=32 ,						lowerCAmelCase_       : Optional[Any]=3 ,						lowerCAmelCase_       : str=True ,						lowerCAmelCase_       : List[Any]=-1 ,						lowerCAmelCase_       : Union[str, Any]=False ,						lowerCAmelCase_       : str=-1 ,						**lowerCAmelCase_       : Any ,						)			->		Tuple:
  						super().__init__(tie_word_embeddings=lowerCAmelCase_ ,						**lowerCAmelCase_  )
  
  						__A=  vocab_size
  						__A=  type_vocab_size
  						__A=  modality_type_vocab_size
  						__A=  max_position_embeddings
  
  						__A=  hidden_size
  						__A=  num_hidden_layers
  						__A=  num_attention_heads
  						__A=  intermediate_size
  						__A=  hidden_act
  						__A=  hidden_dropout_prob
  						__A=  attention_probs_dropout_prob
  						__A=  initializer_range
  						__A=  layer_norm_eps
  
  						__A=  image_size
  						__A=  patch_size
  						__A=  num_channels
  						__A=  qkv_bias
  						__A=  max_image_length
  						__A=  num_images
  
  
  
  