






from typing import Callable, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


A_			:List[str]	    =		logging.get_logger(__name__)

A_			:Dict	    =		{
    '''microsoft/prophetnet-large-uncased''': (
        '''https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json'''
    ),
}




class 							__A	(     a					):





				"""simple docstring"""







				UpperCamelCase__					: int     					="""prophetnet"""
				UpperCamelCase__					: Optional[int]     					=["""past_key_values"""]
				UpperCamelCase__					: Union[str, Any]     					={
				    """num_attention_heads""": """num_encoder_attention_heads""",
				}


				def __init__(					self				,    lowerCamelCase__ = 0.1				,    lowerCamelCase__ = "gelu"				,    lowerCamelCase__ = 30522				,    lowerCamelCase__ = 1024				,    lowerCamelCase__ = 4096				,    lowerCamelCase__ = 12				,    lowerCamelCase__ = 16				,    lowerCamelCase__ = 4096				,    lowerCamelCase__ = 12				,    lowerCamelCase__ = 16				,    lowerCamelCase__ = 0.1				,    lowerCamelCase__ = 0.1				,    lowerCamelCase__ = 512				,    lowerCamelCase__ = 0.02				,    lowerCamelCase__ = True				,    lowerCamelCase__ = True				,    lowerCamelCase__ = 0				,    lowerCamelCase__ = 2				,    lowerCamelCase__ = 32				,    lowerCamelCase__ = 128				,    lowerCamelCase__ = False				,    lowerCamelCase__ = 0.0				,    lowerCamelCase__ = True				,    lowerCamelCase__ = 0				,    lowerCamelCase__ = 1				,    lowerCamelCase__ = 2				,    **lowerCamelCase__				,    ):




									"""simple docstring"""

									__UpperCamelCase :   Union[str, Any]									=vocab_size
									__UpperCamelCase :   Optional[Any]									=hidden_size
									__UpperCamelCase :   List[str]									=encoder_ffn_dim
									__UpperCamelCase :   Dict									=num_encoder_layers
									__UpperCamelCase :   Tuple									=num_encoder_attention_heads
									__UpperCamelCase :   Union[str, Any]									=decoder_ffn_dim
									__UpperCamelCase :   Union[str, Any]									=num_decoder_layers
									__UpperCamelCase :   int									=num_decoder_attention_heads
									__UpperCamelCase :   Optional[Any]									=max_position_embeddings
									__UpperCamelCase :   Optional[int]									=init_std  # Normal(0, this parameter)
									__UpperCamelCase :   Dict									=activation_function

									# parameters for prophetnet
									__UpperCamelCase :   Union[str, Any]									=ngram
									__UpperCamelCase :   int									=num_buckets
									__UpperCamelCase :   Union[str, Any]									=relative_max_distance
									__UpperCamelCase :   Union[str, Any]									=disable_ngram_loss
									__UpperCamelCase :   Optional[int]									=eps

									# 3 Types of Dropout
									__UpperCamelCase :   Union[str, Any]									=attention_dropout
									__UpperCamelCase :   List[Any]									=activation_dropout
									__UpperCamelCase :   Any									=dropout

									__UpperCamelCase :   Any									=use_cache

									super().__init__(
									    pad_token_id=lowerCamelCase__				,    bos_token_id=lowerCamelCase__				,    eos_token_id=lowerCamelCase__				,    is_encoder_decoder=lowerCamelCase__				,    add_cross_attention=lowerCamelCase__				,    decoder_start_token_id=lowerCamelCase__				,    **lowerCamelCase__				,    )


				@property
				def  __lowercase				(					self       ):




									"""simple docstring"""

									return self.num_encoder_layers + self.num_decoder_layers







				@num_hidden_layers.setter
				def  __lowercase				(					self				,    lowerCamelCase__       ):




									"""simple docstring"""

									raise NotImplementedError(
									    'This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'
									    ' `num_decoder_layers`.'       )









import random




class 							__A	:





				"""simple docstring"""







				def __init__(					self				,    lowerCamelCase__				,    lowerCamelCase__       ):




									"""simple docstring"""

									__UpperCamelCase :   Optional[Any]									=x
									__UpperCamelCase :   int									=y


				def  __lowercase				(					self       ):




									"""simple docstring"""

									return (self.x**2 + self.y**2) <= 1







				@classmethod
				def  __lowercase				(					cls       ):




									"""simple docstring"""

									return cls(x=random.random()				,    y=random.random()       )






def 				A	(			a_   )	->		float:
					if number_of_simulations < 1:
										raise ValueError('At least one simulation is necessary to estimate PI.'   )

					__UpperCamelCase :   List[str]									=0
					for _ in range(a_   ):
										__UpperCamelCase :   Optional[int]									=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_			:Optional[int]	    =		'''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)}")

