from typing import List

from tokenizers import NormalizedString, PreTokenizedString, normalizers




class        lowercase_  :



	"""simple docstring"""





	def __init__(							self		,      __SCREAMING_SNAKE_CASE    )						->None:
								lowerCAmelCase     		=		vocab
								lowerCAmelCase     		=		normalizers.BertNormalizer(
								    clean_text=__SCREAMING_SNAKE_CASE		,      handle_chinese_chars=__SCREAMING_SNAKE_CASE		,      strip_accents=__SCREAMING_SNAKE_CASE		,      lowercase=__SCREAMING_SNAKE_CASE		,      )
								try:
															import rjieba
								except ImportError:
															raise ImportError(
															    '''You need to install rjieba to use RoFormerTokenizer. '''
															    '''See https://pypi.org/project/rjieba/ for installation.'''    )
								lowerCAmelCase     		=		rjieba


	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )						->List[NormalizedString]:
								lowerCAmelCase     		=		[]

								# this code slice normalized_string is too slow (6s) but test_alignement_methods can pass
								for token, start, end in self.jieba.tokenize(str(__SCREAMING_SNAKE_CASE    )		,      hmm=__SCREAMING_SNAKE_CASE    ):
															if token in self.vocab:
																						splits.append(normalized_string[start:end]    )
															else:
																						lowerCAmelCase     		=		self.normalizers.normalize_str(__SCREAMING_SNAKE_CASE    ).split()
																						for token in token_list:
																													if token:
																																				lowerCAmelCase     		=		start + len(__SCREAMING_SNAKE_CASE    )
																																				splits.append(normalized_string[start:end]    )
																																				lowerCAmelCase     		=		end

        # this code test_alignement_methods can't pass but fast (300ms)
        # for token in self.jieba.cut(str(normalized_string), False):
        #     if token in self.vocab:
        #         splits.append(NormalizedString(token))
        #     else:
        #         token_list = self.normalizers.normalize_str(token).split()
        #         for token in token_list:
        #             if token:
        #                 splits.append(NormalizedString(token))

								return splits




	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE    )						->Tuple:
								pretok.split(self.jieba_split    )




import random




class        lowercase_  :



	"""simple docstring"""





	def __init__(							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )						->None:
								lowerCAmelCase     		=		x
								lowerCAmelCase     		=		y


	def       SCREAMING_SNAKE_CASE_    (							self    )						->bool:
								return (self.x**2 + self.y**2) <= 1




	@classmethod
	def       SCREAMING_SNAKE_CASE_    (							cls    )						->Tuple:
								return cls(x=random.random()		,      y=random.random()    )

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

							lowerCAmelCase     		=		0
							for _ in range(snake_case__							):
														lowerCAmelCase     		=		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

	lowercase__      :    int          =     '''Please enter the desired number of Monte Carlo simulations: '''
	lowercase__      :    Dict          =     estimate_pi(int(input(prompt).strip()))
	print(f'An estimate of PI is {my_pi} with an error of {abs(my_pi - pi)}')



