


"""simple docstring"""






from typing import List

from tokenizers import NormalizedString, PreTokenizedString, normalizers


class 			SCREAMING_SNAKE_CASE_  :




							'''simple docstring'''





							def __init__(						self	,					lowerCamelCase__)   ->	None:







								'''simple docstring'''




								snake_case__  :						Optional[Any]		=      vocab
								snake_case__  :						List[Any]		=      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.")
								snake_case__  :						List[str]		=      rjieba




							def        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__)   ->	List[NormalizedString]:







								'''simple docstring'''




								snake_case__  :						Any		=      []

								# 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:
										snake_case__  :						Optional[Any]		=      self.normalizers.normalize_str(lowerCamelCase__).split()
										for token in token_list:
											if token:
												snake_case__  :						Optional[Any]		=      start + len(lowerCamelCase__)
												splits.append(normalized_string[start:end])
												snake_case__  :						List[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        UpperCAmelCase (						self	,					lowerCamelCase__)   ->	str:







								'''simple docstring'''




								pretok.split(self.jieba_split)












def 				_lowerCAmelCase			(	_a :       str      )						->      int:
     lowerCAmelCase_       :							List[str]			 =		53_81
     for x in s:
          lowerCAmelCase_       :							Dict			 =		((hash_value << 5) + hash_value) + ord(_a      )
     return hash_value & 0XFFFFFFFF
