




"""simple docstring"""







from typing import List

from tokenizers import NormalizedString, PreTokenizedString, normalizers

class 							snake_case_ :







   """simple docstring"""




   def __init__(		self					,				lowerCamelCase_)							->      None:
       UpperCamelCase										=  vocab
       UpperCamelCase										=  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.''')
       UpperCamelCase										=  rjieba







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_)							->      List[NormalizedString]:
       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(lowerCamelCase_)					,				hmm=lowerCamelCase_):
           if token in self.vocab:
               splits.append(normalized_string[start:end])
           else:
               UpperCamelCase										=  self.normalizers.normalize_str(lowerCamelCase_).split()
               for token in token_list:
                   if token:
                       UpperCamelCase										=  start + len(lowerCamelCase_)
                       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					,				lowerCamelCase_)							->      Optional[int]:
       pretok.split(self.jieba_split)





"""simple docstring"""







import os
from string import ascii_letters

SCREAMING_SNAKE_CASE_      =					ascii_letters + ' \t\n'





def     __snake_case   (							):


    """simple docstring"""







    UpperCamelCase										=  os.path.split(os.path.realpath(_lowercase						)						)
    UpperCamelCase										=  {}
    with open(path[0] + '''/dictionary.txt'''						) as dictionary_file:
        for word in dictionary_file.read().split('''\n'''						):
            UpperCamelCase										=  None
    return english_words


SCREAMING_SNAKE_CASE_      =					load_dictionary()





def     __snake_case   (							_lowercase						):


    """simple docstring"""







    UpperCamelCase										=  message.upper()
    UpperCamelCase										=  remove_non_letters(_lowercase						)
    UpperCamelCase										=  message.split()
    UpperCamelCase										=  len([word for word in possible_words if word in ENGLISH_WORDS]						)
    return float(_lowercase						) / len(_lowercase						)





def     __snake_case   (							_lowercase						):


    """simple docstring"""







    return "".join(symbol for symbol in message if symbol in LETTERS_AND_SPACE						)





def     __snake_case   (							_lowercase ,_lowercase = 20 ,_lowercase = 85						):


    """simple docstring"""







    UpperCamelCase										=  get_english_count(_lowercase						) * 100 >= word_percentage
    UpperCamelCase										=  len(remove_non_letters(_lowercase						)						)
    UpperCamelCase										=  (float(_lowercase						) / len(_lowercase						)) * 100
    UpperCamelCase										=  message_letters_percentage >= letter_percentage
    return words_match and letters_match


if __name__ == "__main__":
  import doctest

  doctest.testmod()