
'''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`."""  )




'''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)}')


