




'''simple docstring'''


from typing import List

from tokenizers import NormalizedString, PreTokenizedString, normalizers






class 	lowerCAmelCase_  :







		'''simple docstring'''

		def __init__(   self							:       Tuple				,     SCREAMING_SNAKE_CASE_							:       List[Any]  ) ->		None:







							'''simple docstring'''



							A:		Optional[Any]        =		vocab
							A:		List[str]        =		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.'''  )
							A:		Dict        =		rjieba



		def 				_snake_case    (   self							:       int				,     SCREAMING_SNAKE_CASE_							:       List[Any]				,     SCREAMING_SNAKE_CASE_							:       str  ) ->		List[NormalizedString]:







							'''simple docstring'''



							A:		str        =		[]

							# 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:
																	A:		Tuple        =		self.normalizers.normalize_str(SCREAMING_SNAKE_CASE_  ).split()
																	for token in token_list:
																						if token:
																											A:		List[str]        =		start + len(SCREAMING_SNAKE_CASE_  )
																											splits.append(normalized_string[start:end]  )
																											A:		str        =		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 				_snake_case    (   self							:       Optional[int]				,     SCREAMING_SNAKE_CASE_							:       Optional[Any]  ) ->		Optional[int]:







							'''simple docstring'''



							pretok.split(self.jieba_split  )












'''simple docstring'''


def 				SCREAMING_SNAKE_CASE(      __lowercase  ) ->   int:
     A:		str        =		str(__lowercase  ).strip()
     if not bin_string:
          raise ValueError('''Empty string was passed to the function'''  )
     A:		int        =		bin_string[0] == '''-'''
     if is_negative:
          A:		Optional[Any]        =		bin_string[1:]
     if not all(char in '''01''' for char in bin_string  ):
          raise ValueError('''Non-binary value was passed to the function'''  )
     A:		Any        =		0
     for char in bin_string:
          A:		Dict        =		2 * decimal_number + int(__lowercase  )
     return -decimal_number if is_negative else decimal_number


if __name__ == "__main__":
     from doctest import testmod

     testmod()






