code: import warnings
  
  from ...utils import logging
  from .image_processing_maskformer import MaskFormerImageProcessor
  
  
  lowerCamelCase__           =				logging.get_logger(__name__)
  
  
  
  
  class 					UpperCamelCase	(     snake_case__      ):
  
  
  
  
  					def __init__( self :					str      ,*_lowerCAmelCase :					Union[str, Any]      ,**_lowerCAmelCase :					Optional[int]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							warnings.warn(
  							    "The class MaskFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
  							    " Please use MaskFormerImageProcessor instead."      ,_lowerCAmelCase      ,)
  							super().__init__(*_lowerCAmelCase      ,**_lowerCAmelCase						)
  
  
  
  
  
  
style_context: import os
  from string import ascii_letters
  
  lowerCamelCase__           =				ascii_letters + ' \t\n'
  
  
  
  def 							_lowerCamelCase(				)  ->   dict[str, None]:
  		__snake_case		       =     os.path.split(os.path.realpath(__snake_case	)	)
  		__snake_case		       =     {}
  		with open(path[0] + "/dictionary.txt"	) as dictionary_file:
  				for word in dictionary_file.read().split("\n"	):
  						__snake_case		       =     None
  		return english_words
  
  
  lowerCamelCase__           =				load_dictionary()
  
  
  
  def 							_lowerCamelCase(				__snake_case	)  ->   float:
  		__snake_case		       =     message.upper()
  		__snake_case		       =     remove_non_letters(__snake_case	)
  		__snake_case		       =     message.split()
  		__snake_case		       =     len([word for word in possible_words if word in ENGLISH_WORDS]	)
  		return float(__snake_case	) / len(__snake_case	)
  
  
  
  def 							_lowerCamelCase(				__snake_case	)  ->   str:
  		return "".join(symbol for symbol in message if symbol in LETTERS_AND_SPACE	)
  
  
  
  def 							_lowerCamelCase(				__snake_case						,						__snake_case = 20						,						__snake_case = 85	)  ->   bool:
  		__snake_case		       =     get_english_count(__snake_case	) * 100 >= word_percentage
  		__snake_case		       =     len(remove_non_letters(__snake_case	)	)
  		__snake_case		       =     (float(__snake_case	) / len(__snake_case	)) * 100
  		__snake_case		       =     message_letters_percentage >= letter_percentage
  		return words_match and letters_match
  
  
  if __name__ == "__main__":
  		import doctest
  
  		doctest.testmod()
  
  
  
  
  
  