code: from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  a     :			Tuple					    =						logging.get_logger(__name__)
  
  a     :			str					    =						{
      """microsoft/mpnet-base""": """https://huggingface.co/microsoft/mpnet-base/resolve/main/config.json""",
  }
  
  class 				_lowercase						(    a__ ):
  
  
  
  
  
  
      '''simple docstring'''
      SCREAMING_SNAKE_CASE:							str        =						'mpnet'
  
  
  
  
      def __init__(						self					,	lowerCamelCase__=30_527					,	lowerCamelCase__=768					,	lowerCamelCase__=12					,	lowerCamelCase__=12					,	lowerCamelCase__=3_072					,	lowerCamelCase__="gelu"					,	lowerCamelCase__=0.1					,	lowerCamelCase__=0.1					,	lowerCamelCase__=512					,	lowerCamelCase__=0.0_2					,	lowerCamelCase__=1E-12					,	lowerCamelCase__=32					,	lowerCamelCase__=1					,	lowerCamelCase__=0					,	lowerCamelCase__=2					,	**lowerCamelCase__					,	):
         super().__init__(pad_token_id=__A					,	bos_token_id=__A					,	eos_token_id=__A					,	**__A   )
  
         lowerCAmelCase_:       Tuple         =  vocab_size
         lowerCAmelCase_:       List[Any]         =  hidden_size
         lowerCAmelCase_:       Optional[int]         =  num_hidden_layers
         lowerCAmelCase_:       List[Any]         =  num_attention_heads
         lowerCAmelCase_:       int         =  hidden_act
         lowerCAmelCase_:       str         =  intermediate_size
         lowerCAmelCase_:       Optional[int]         =  hidden_dropout_prob
         lowerCAmelCase_:       Union[str, Any]         =  attention_probs_dropout_prob
         lowerCAmelCase_:       int         =  max_position_embeddings
         lowerCAmelCase_:       str         =  initializer_range
         lowerCAmelCase_:       List[str]         =  layer_norm_eps
         lowerCAmelCase_:       List[str]         =  relative_attention_num_buckets
style_context: import random
  
  class 				_lowercase						:
  
  
  
  
  
  
      '''simple docstring'''
  
  
  
  
      def __init__(						self					,	lowerCamelCase__					,	lowerCamelCase__   ):
         lowerCAmelCase_:       int         =  x
         lowerCAmelCase_:       List[str]         =  y
  
  
  
  
      def     _a						(						self   ):
         return (self.x**2 + self.y**2) <= 1
  
  
  
  
      @classmethod
      def     _a						(						cls   ):
         return cls(x=random.random()					,	y=random.random()   )
  
  
  
  
  
  def 		snake_case__  (		lowercase				):
     if number_of_simulations < 1:
        raise ValueError("At least one simulation is necessary to estimate PI."				)
  
     lowerCAmelCase_:       List[Any]         =  0
     for _ in range(lowercase				):
        lowerCAmelCase_:       Tuple         =  Point.random_unit_square()
  
        if random_point.is_in_unit_circle():
           number_in_unit_circle += 1
  
     return 4 * number_in_unit_circle / number_of_simulations
  
  
  if __name__ == "__main__":
   # import doctest
  
   # doctest.testmod()
   from math import pi
  
   a     :			Union[str, Any]					    =						"""Please enter the desired number of Monte Carlo simulations: """
   a     :			int					    =						estimate_pi(int(input(prompt).strip()))
   print(f'''An estimate of PI is {my_pi} with an error of {abs(my_pi - pi)}''')