


from typing import List

from tokenizers import NormalizedString, PreTokenizedString, normalizers

class 	__a   :
		def __init__(    self    ,					_SCREAMING_SNAKE_CASE		)				-> None:




									"""simple docstring"""





									_UpperCAmelCase					=    vocab
									_UpperCAmelCase					=    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.'		)
									_UpperCAmelCase					=    rjieba
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)				-> List[NormalizedString]:




									"""simple docstring"""





									_UpperCAmelCase					=    []

									# 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:
																							_UpperCAmelCase					=    self.normalizers.normalize_str(_SCREAMING_SNAKE_CASE		).split()
																							for token in token_list:
																														if token:
																																					_UpperCAmelCase					=    start + len(_SCREAMING_SNAKE_CASE		)
																																					splits.append(normalized_string[start:end]		)
																																					_UpperCAmelCase					=    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    ,					_SCREAMING_SNAKE_CASE		)				-> Optional[int]:




									"""simple docstring"""





									pretok.split(self.jieba_split		)




from __future__ import annotations

def  lowerCAmelCase__				(    a__:	list[int]    ,						a__:	int )						->	list[int]:


							'''simple docstring'''





							_UpperCAmelCase					=    {}
							for index, val in enumerate(a__ ):
														_UpperCAmelCase					=    target - val
														if compl in chk_map:
																					return [chk_map[compl], index]
														_UpperCAmelCase					=    index
							return []


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		print(f'''{two_sum([2, 7, 1_1, 1_5], 9) = }''')
