"""simple docstring"""







from typing import List

from tokenizers import NormalizedString, PreTokenizedString, normalizers






class 							__snake_case:


    '''simple docstring'''





    def __init__(  self					,       __lowerCamelCase	):




         '''simple docstring'''



         __A					:					Dict								=		vocab
         __A					:					Optional[int]								=		normalizers.BertNormalizer(
             clean_text=__lowerCamelCase					,       handle_chinese_chars=__lowerCamelCase					,       strip_accents=__lowerCamelCase					,       lowercase=__lowerCamelCase					,       )
         try:
              import rjieba
         except ImportError:
              raise ImportError(
                  'You need to install rjieba to use RoFormerTokenizer. '
                  'See https://pypi.org/project/rjieba/ for installation.'	)
         __A					:					Optional[Any]								=		rjieba
    def 						_a							(  self					,       __lowerCamelCase					,       __lowerCamelCase	):




         '''simple docstring'''



         __A					:					List[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(__lowerCamelCase	)					,       hmm=__lowerCamelCase	):
              if token in self.vocab:
                   splits.append(normalized_string[start:end]	)
              else:
                   __A					:					Union[str, Any]								=		self.normalizers.normalize_str(__lowerCamelCase	).split()
                   for token in token_list:
                        if token:
                             __A					:					List[str]								=		start + len(__lowerCamelCase	)
                             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 						_a							(  self					,       __lowerCamelCase	):




         '''simple docstring'''



         pretok.split(self.jieba_split	)




"""simple docstring"""







def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				list[int]						)    ->     tuple[int, int]:







     '''simple docstring'''


     if len(_SCREAMING_SNAKE_CASE						) == 1:
          return a[0], a[0]
     else:
          # split A into half.
          __A					:					List[str]								=		a[: len(_SCREAMING_SNAKE_CASE						) // 2]
          __A					:					Optional[Any]								=		a[len(_SCREAMING_SNAKE_CASE						) // 2 :]

          # 2 sub problems, 1/2 of original size.
          __A						,		__A					:					int								=		max_difference(_SCREAMING_SNAKE_CASE						)
          __A						,		__A					:					str								=		max_difference(_SCREAMING_SNAKE_CASE						)

          # get min of first and max of second
          # linear time
          __A					:					str								=		min(_SCREAMING_SNAKE_CASE						)
          __A					:					Optional[int]								=		max(_SCREAMING_SNAKE_CASE						)

          # 3 cases, either (small1, big1),
          # (min_first, max_second), (small2, big2)
          # constant comparisons
          if biga - smalla > max_second - min_first and biga - smalla > biga - smalla:
               return smalla, biga
          elif biga - smalla > max_second - min_first:
               return smalla, biga
          else:
               return min_first, max_second


if __name__ == "__main__":
       import doctest

       doctest.testmod()



