code: 
  '''simple docstring'''
  
  from typing import Callable, Optional, Union
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  UpperCamelCase_     			=       logging.get_logger(__name__)
  
  UpperCamelCase_     			=       {
      """microsoft/prophetnet-large-uncased""": (
          """https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json"""
      ),
  }
  
  
  class   a_  (_a    ):
         __lowerCAmelCase    :				Optional[Any]    =      """prophetnet"""
         __lowerCAmelCase    :				Dict    =      ["""past_key_values"""]
         __lowerCAmelCase    :				str    =      {
             """num_attention_heads""": """num_encoder_attention_heads""",
         }
  
  
  
  
         def __init__(     self   ,   snake_case_ = 0.1   ,   snake_case_ = "gelu"   ,   snake_case_ = 3_0_5_2_2   ,   snake_case_ = 1_0_2_4   ,   snake_case_ = 4_0_9_6   ,   snake_case_ = 1_2   ,   snake_case_ = 1_6   ,   snake_case_ = 4_0_9_6   ,   snake_case_ = 1_2   ,   snake_case_ = 1_6   ,   snake_case_ = 0.1   ,   snake_case_ = 0.1   ,   snake_case_ = 5_1_2   ,   snake_case_ = 0.02   ,   snake_case_ = True   ,   snake_case_ = True   ,   snake_case_ = 0   ,   snake_case_ = 2   ,   snake_case_ = 3_2   ,   snake_case_ = 1_2_8   ,   snake_case_ = False   ,   snake_case_ = 0.0   ,   snake_case_ = True   ,   snake_case_ = 0   ,   snake_case_ = 1   ,   snake_case_ = 2   ,   **snake_case_   ,   ):
           _lowerCAmelCase       :  str   				=					vocab_size
           _lowerCAmelCase       :  Optional[Any]   				=					hidden_size
           _lowerCAmelCase       :  Optional[int]   				=					encoder_ffn_dim
           _lowerCAmelCase       :  int   				=					num_encoder_layers
           _lowerCAmelCase       :  str   				=					num_encoder_attention_heads
           _lowerCAmelCase       :  Optional[Any]   				=					decoder_ffn_dim
           _lowerCAmelCase       :  Tuple   				=					num_decoder_layers
           _lowerCAmelCase       :  Tuple   				=					num_decoder_attention_heads
           _lowerCAmelCase       :  int   				=					max_position_embeddings
           _lowerCAmelCase       :  List[Any]   				=					init_std  # Normal(0, this parameter)
           _lowerCAmelCase       :  Optional[Any]   				=					activation_function
  
           # parameters for prophetnet
           _lowerCAmelCase       :  Dict   				=					ngram
           _lowerCAmelCase       :  str   				=					num_buckets
           _lowerCAmelCase       :  Dict   				=					relative_max_distance
           _lowerCAmelCase       :  Dict   				=					disable_ngram_loss
           _lowerCAmelCase       :  List[str]   				=					eps
  
           # 3 Types of Dropout
           _lowerCAmelCase       :  Optional[Any]   				=					attention_dropout
           _lowerCAmelCase       :  Dict   				=					activation_dropout
           _lowerCAmelCase       :  Dict   				=					dropout
  
           _lowerCAmelCase       :  str   				=					use_cache
  
           super().__init__(
               pad_token_id=snake_case_   ,   bos_token_id=snake_case_   ,   eos_token_id=snake_case_   ,   is_encoder_decoder=snake_case_   ,   add_cross_attention=snake_case_   ,   decoder_start_token_id=snake_case_   ,   **snake_case_   ,   )
  
  
  
  
         @property
         def 							__UpperCamelCase       (     self  ):
           return self.num_encoder_layers + self.num_decoder_layers
  
  
  
  
  
  
  
         @num_hidden_layers.setter
         def 							__UpperCamelCase       (     self   ,   snake_case_  ):
           raise NotImplementedError(
               """This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and"""
               """ `num_decoder_layers`."""  )
  
  
  
style_context: 
  '''simple docstring'''
  
  import random
  
  
  class   a_  :
  
  
  
  
         def __init__(     self   ,   snake_case_   ,   snake_case_  ):
           _lowerCAmelCase       :  List[str]   				=					x
           _lowerCAmelCase       :  List[str]   				=					y
  
  
  
  
         def 							__UpperCamelCase       (     self  ):
           return (self.x**2 + self.y**2) <= 1
  
  
  
  
  
  
  
         @classmethod
         def 							__UpperCamelCase       (     cls  ):
           return cls(x=random.random()   ,   y=random.random()  )
  
  
  
  def 	_UpperCAmelCase				(  _lowerCamelCase						:       int		)    ->						float:
    if number_of_simulations < 1:
      raise ValueError("""At least one simulation is necessary to estimate PI."""		)
  
    _lowerCAmelCase       :  Tuple   				=					0
    for _ in range(_lowerCamelCase		):
      _lowerCAmelCase       :  Union[str, Any]   				=					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
  
       UpperCamelCase_     			=       """Please enter the desired number of Monte Carlo simulations: """
       UpperCamelCase_     			=       estimate_pi(int(input(prompt).strip()))
       print(F'An estimate of PI is {my_pi} with an error of {abs(my_pi - pi)}')
  
  
  