






from typing import List

from tokenizers import NormalizedString, PreTokenizedString, normalizers
class 					a_:



     """simple docstring"""



     def __init__(						self			:					Optional[Any]       ,       lowerCAmelCase__			:					Dict)    ->					Tuple:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						vocab
      SCREAMING_SNAKE_CASE							  =						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.')
      SCREAMING_SNAKE_CASE							  =						rjieba





     def   __UpperCamelCase		(						self			:					List[str]       ,       lowerCAmelCase__			:					int       ,       lowerCAmelCase__			:					NormalizedString)    ->					List[str]:

      """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(_lowerCamelCase)       ,       hmm=_lowerCamelCase):
       if token in self.vocab:
        splits.append(normalized_string[start:end])
       else:
        SCREAMING_SNAKE_CASE							  =						self.normalizers.normalize_str(_lowerCamelCase).split()
        for token in token_list:
         if token:
          SCREAMING_SNAKE_CASE							  =						start + len(_lowerCamelCase)
          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			:					Union[str, Any]       ,       lowerCAmelCase__			:					PreTokenizedString)    ->					Tuple:

      """simple docstring"""
      pretok.split(self.jieba_split)








import math





def 		A_			(		lowercase_	)							->List[Any]:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						0
 if n % 2 == 0:
  count += 1
  while n % 2 == 0:
   SCREAMING_SNAKE_CASE							  =						int(n / 2	)
    # the n input value must be odd so that
    # we can skip one element (ie i += 2)

 SCREAMING_SNAKE_CASE							  =						3

 while i <= int(math.sqrt(lowercase_	)	):
  if n % i == 0:
   count += 1
   while n % i == 0:
    SCREAMING_SNAKE_CASE							  =						int(n / i	)
  SCREAMING_SNAKE_CASE							  =						i + 2

 # this condition checks the prime
 # number n is greater than 2

 if n > 2:
  count += 1
 return count


if __name__ == "__main__":
      __UpperCAmelCase      				=							5_1_2_4_2_1_8_3
      print(f'The number of distinct prime factors is/are {exact_prime_factor_count(n)}')
      print(f'The value of log(log(n)) is {math.log(math.log(n)):.4f}')




