


def   lowerCamelCase(      a__ = 1000):
				return sum(i for i in range(a__) if i % 3 == 0 or i % 5 == 0)


if __name__ == "__main__":
							print(f"""{solution() = }""")



from typing import List

from tokenizers import NormalizedString, PreTokenizedString, normalizers

class   A__							:




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


										"""simple docstring"""





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




						def 							__UpperCamelCase (				self    :					List[Any] ,			_a    :					int ,			_a    :					NormalizedString     )							->   List[NormalizedString]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =[]

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


										"""simple docstring"""





										pretok.split(self.jieba_split     )