



import random







class      __lowerCAmelCase       :



			def __init__(		self  :    Dict					,						A  :    float					,						A  :    float)  ->       None:



						"""simple docstring"""






						_UpperCAmelCase									=  x
						_UpperCAmelCase									=  y



			def        _lowerCamelCase			(		self  :    Optional[int])  ->       bool:



						"""simple docstring"""






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



			@classmethod
			def        _lowerCamelCase			(		cls  :    Dict)  ->       Optional[int]:



						"""simple docstring"""






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





def 				A      (	_UpperCAmelCase							:  int       )							->					float:







			'''simple docstring'''
			if number_of_simulations < 1:
						raise ValueError('At least one simulation is necessary to estimate PI.'       )

			_UpperCAmelCase									=  0
			for _ in range(_UpperCAmelCase       ):
						_UpperCAmelCase									=  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)}""")





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      __lowerCAmelCase       (     A		):
			UpperCamelCase     =	'''vilt'''



			def __init__(		self  :    List[Any]					,						A  :    Dict=3_05_22					,						A  :    Union[str, Any]=2					,						A  :    Tuple=2					,						A  :    List[Any]=40					,						A  :    Optional[int]=7_68					,						A  :    Any=12					,						A  :    Optional[int]=12					,						A  :    Optional[Any]=30_72					,						A  :    Optional[Any]="gelu"					,						A  :    str=0.0					,						A  :    Any=0.0					,						A  :    Union[str, Any]=0.0_2					,						A  :    Union[str, Any]=1E-12					,						A  :    Optional[Any]=3_84					,						A  :    Dict=32					,						A  :    int=3					,						A  :    int=True					,						A  :    Union[str, Any]=-1					,						A  :    int=False					,						A  :    int=-1					,						**A  :    Tuple					,						)  ->       Dict:



						"""simple docstring"""






						super().__init__(tie_word_embeddings=A					,						**A)

						_UpperCAmelCase									=  vocab_size
						_UpperCAmelCase									=  type_vocab_size
						_UpperCAmelCase									=  modality_type_vocab_size
						_UpperCAmelCase									=  max_position_embeddings

						_UpperCAmelCase									=  hidden_size
						_UpperCAmelCase									=  num_hidden_layers
						_UpperCAmelCase									=  num_attention_heads
						_UpperCAmelCase									=  intermediate_size
						_UpperCAmelCase									=  hidden_act
						_UpperCAmelCase									=  hidden_dropout_prob
						_UpperCAmelCase									=  attention_probs_dropout_prob
						_UpperCAmelCase									=  initializer_range
						_UpperCAmelCase									=  layer_norm_eps

						_UpperCAmelCase									=  image_size
						_UpperCAmelCase									=  patch_size
						_UpperCAmelCase									=  num_channels
						_UpperCAmelCase									=  qkv_bias
						_UpperCAmelCase									=  max_image_length
						_UpperCAmelCase									=  num_images
