
'''simple docstring'''





def 							snake_case				(		UpperCAmelCase						)->      Union[str, Any]:







      """simple docstring"""
      if not isinstance(UpperCAmelCase_   ,	UpperCAmelCase_						):
            raise ValueError('Input series is not valid, valid series - [1, 2/3, 2]'						)
      if len(UpperCAmelCase_						) == 0:
            raise ValueError('Input list must be a non empty list'						)
      if len(UpperCAmelCase_						) == 1 and series[0] != 0:
            return True
      __A													=      []
      __A													=      len(UpperCAmelCase_						)
      for i in range(0   ,	UpperCAmelCase_						):
            if series[i] == 0:
                  raise ValueError('Input series cannot have 0 as an element'						)
            rec_series.append(1 / series[i]						)
      __A													=      rec_series[1] - rec_series[0]
      for index in range(2   ,	UpperCAmelCase_						):
            if rec_series[index] - rec_series[index - 1] != common_diff:
                  return False
      return True





def 							snake_case				(		UpperCAmelCase						)->      Union[str, Any]:







      """simple docstring"""
      if not isinstance(UpperCAmelCase_   ,	UpperCAmelCase_						):
            raise ValueError('Input series is not valid, valid series - [2, 4, 6]'						)
      if len(UpperCAmelCase_						) == 0:
            raise ValueError('Input list must be a non empty list'						)
      __A													=      0
      for val in series:
            answer += 1 / val
      return len(UpperCAmelCase_						) / answer


if __name__ == "__main__":
 import doctest

 doctest.testmod()









"""simple docstring"""



from typing import List

from tokenizers import NormalizedString, PreTokenizedString, normalizers



class        a    :



    """simple docstring"""



    def __init__(  self:			List[Any]     ,    UpperCamelCase:			str							):






         """simple docstring"""


         A__				      =	vocab
         A__				      =	normalizers.BertNormalizer(
             clean_text=UpperCamelCase     ,    handle_chinese_chars=UpperCamelCase     ,    strip_accents=UpperCamelCase     ,    lowercase=UpperCamelCase     ,    )
         try:
              import rjieba
         except ImportError:
              raise ImportError(
                  """You need to install rjieba to use RoFormerTokenizer. """
                  """See https://pypi.org/project/rjieba/ for installation."""							)
         A__				      =	rjieba

    def 					UpperCamelCase						(  self:			Optional[Any]     ,    UpperCamelCase:			int     ,    UpperCamelCase:			NormalizedString							):






         """simple docstring"""


         A__				      =	[]

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






         """simple docstring"""


         pretok.split(self.jieba_split							)






