
'''simple docstring'''





import os
from string import ascii_letters

_lowerCamelCase :       List[Any]     					=    ascii_letters + ''' \t\n'''






def  a_	( )				->						Tuple:
			_snake_case						=    os.path.split(os.path.realpath(__lowercase				)				)
			_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 :       List[Any]     					=    load_dictionary()






def  a_	( __lowercase     :			str				)				->						Dict:
			_snake_case						=    message.upper()
			_snake_case						=    remove_non_letters(__lowercase				)
			_snake_case						=    message.split()
			_snake_case						=    len([word for word in possible_words if word in ENGLISH_WORDS]				)
			return float(__lowercase				) / len(__lowercase				)






def  a_	( __lowercase     :			Optional[Any]				)				->						str:
			return "".join(symbol for symbol in message if symbol in LETTERS_AND_SPACE				)






def  a_	( __lowercase     :			Dict ,      __lowercase     :			Union[str, Any] = 20 ,      __lowercase     :			Union[str, Any] = 85				)				->						int:
			_snake_case						=    get_english_count(__lowercase				) * 100 >= word_percentage
			_snake_case						=    len(remove_non_letters(__lowercase				)				)
			_snake_case						=    (float(__lowercase				) / len(__lowercase				)) * 100
			_snake_case						=    message_letters_percentage >= letter_percentage
			return words_match and letters_match


if __name__ == "__main__":
			import doctest

			doctest.testmod()

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class      SCREAMING_SNAKE_CASE__  (			UpperCAmelCase      ):





	'''simple docstring'''



	_UpperCAmelCase :	int					     =      ["image_processor", "tokenizer"]
	_UpperCAmelCase :	Tuple					     =      "EfficientNetImageProcessor"
	_UpperCAmelCase :	Optional[Any]					     =      ("BertTokenizer", "BertTokenizerFast")



	def __init__(						self			:					Tuple    ,						lowercase			:					List[Any]    ,						lowercase			:					Optional[int]			):


				'''simple docstring'''


				super().__init__(lowercase    ,						lowercase			)



	def __call__(						self			:					Union[str, Any]    ,						lowercase			:					Dict=None    ,						lowercase			:					Optional[int]=None    ,						lowercase			:					List[Any]="max_length"    ,						lowercase			:					Optional[int]=64    ,						lowercase			:					int=None    ,						**lowercase			:					Tuple			):


				'''simple docstring'''


				if text is None and images is None:
							raise ValueError('You have to specify either text or images. Both cannot be none.'			)

				if text is not None:
							_snake_case						=    self.tokenizer(
							    lowercase    ,						padding=lowercase    ,						max_length=lowercase    ,						return_tensors=lowercase    ,						**lowercase			)

				if images is not None:
							_snake_case						=    self.image_processor(lowercase    ,						return_tensors=lowercase    ,						**lowercase			)

				if text is not None and images is not None:
							_snake_case						=    image_features.pixel_values
							return encoding
				elif text is not None:
							return encoding
				else:
							return BatchEncoding(data=dict(**lowercase			)    ,						tensor_type=lowercase			)



	def 				A							(						self			:					Any    ,						*lowercase			:					List[str]    ,						**lowercase			:					Optional[Any]			):


				'''simple docstring'''


				return self.tokenizer.batch_decode(*lowercase    ,						**lowercase			)



	def 				A							(						self			:					List[Any]    ,						*lowercase			:					Union[str, Any]    ,						**lowercase			:					Tuple			):


				'''simple docstring'''


				return self.tokenizer.decode(*lowercase    ,						**lowercase			)





	@property
	def 				A							(						self			:					List[str]			):


				'''simple docstring'''


				_snake_case						=    self.tokenizer.model_input_names
				_snake_case						=    self.image_processor.model_input_names
				return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)