






from typing import List

from tokenizers import NormalizedString, PreTokenizedString, normalizers
class    a   :



      """simple docstring"""


      def __init__(				self :      Dict		,				__lowercase :      Dict )							->				None:
        __UpperCAmelCase							:     Optional[Any]									=  vocab
        __UpperCAmelCase							:     List[str]									=  normalizers.BertNormalizer(
            clean_text=__lowercase		,				handle_chinese_chars=__lowercase		,				strip_accents=__lowercase		,				lowercase=__lowercase		,				)
        try:
          import rjieba
        except ImportError:
          raise ImportError(
              """You need to install rjieba to use RoFormerTokenizer. """
              """See https://pypi.org/project/rjieba/ for installation.""" )
        __UpperCAmelCase							:     Union[str, Any]									=  rjieba
      def      UpperCAmelCase				(				self :      List[str]		,				__lowercase :      int		,				__lowercase :      NormalizedString )							->				List[NormalizedString]:
        __UpperCAmelCase							:     Optional[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(__lowercase )		,				hmm=__lowercase ):
          if token in self.vocab:
            splits.append(normalized_string[start:end] )
          else:
            __UpperCAmelCase							:     Any									=  self.normalizers.normalize_str(__lowercase ).split()
            for token in token_list:
              if token:
                __UpperCAmelCase							:     str									=  start + len(__lowercase )
                splits.append(normalized_string[start:end] )
                __UpperCAmelCase							:     int									=  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 :      Dict		,				__lowercase :      PreTokenizedString )							->				Tuple:
        pretok.split(self.jieba_split )










def     lowerCamelCase__				(   __lowerCamelCase      :					list  ,		__lowerCamelCase      :					int		):
  for index, item in enumerate(__lowerCamelCase		):
    if item == target:
      return index
  return -1






def     lowerCamelCase__				(   __lowerCamelCase      :					list  ,		__lowerCamelCase      :					int  ,		__lowerCamelCase      :					int  ,		__lowerCamelCase      :					int		):
  if not (0 <= high < len(__lowerCamelCase		) and 0 <= low < len(__lowerCamelCase		)):
    raise Exception("""Invalid upper or lower bound!"""		)
  if high < low:
    return -1
  if sequence[low] == target:
    return low
  if sequence[high] == target:
    return high
  return rec_linear_search(__lowerCamelCase  ,		low + 1  ,		high - 1  ,		__lowerCamelCase		)


if __name__ == "__main__":
       a		:	List[str]      					=			input("Enter numbers separated by comma:\n").strip()
       a		:	Tuple      					=			[int(item.strip()) for item in user_input.split(",")]

       a		:	List[str]      					=			int(input("Enter a single number to be found in the list:\n").strip())
       a		:	Optional[Any]      					=			linear_search(sequence, target)
       if result != -1:
              print(f"""linear_search({sequence}, {target}) = {result}""")
       else:
              print(f"""{target} was not found in {sequence}""")


