


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin







class        __magic_name__  (					lowerCAmelCase   ):

     """simple docstring"""






     lowerCamelCase__						=							['image_processor', 'tokenizer']
     lowerCamelCase__						=							'AutoImageProcessor'
     lowerCamelCase__						=							'AutoTokenizer'


     def __init__(      self ,    lowerCamelCase=None ,    lowerCamelCase=None ,    **lowerCamelCase		):


          '''simple docstring'''





          __A  :					int						     =       None
          if "feature_extractor" in kwargs:
               warnings.warn(
                   "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                   " instead." ,    __lowerCamelCase ,    )
               __A  :					Dict						     =       kwargs.pop("feature_extractor"		)

          __A  :					str						     =       image_processor if image_processor is not None else feature_extractor
          if image_processor is None:
               raise ValueError("You need to specify an `image_processor`."		)
          if tokenizer is None:
               raise ValueError("You need to specify a `tokenizer`."		)

          super().__init__(__lowerCamelCase ,    __lowerCamelCase		)
          __A  :					Tuple						     =       self.image_processor
          __A  :					Dict						     =       False


     def __call__(      self ,    *lowerCamelCase ,    **lowerCamelCase		):


          '''simple docstring'''





          if self._in_target_context_manager:
               return self.current_processor(*__lowerCamelCase ,    **__lowerCamelCase		)

          __A  :					Optional[Any]						     =       kwargs.pop("images" ,    __lowerCamelCase		)
          __A  :					Optional[Any]						     =       kwargs.pop("text" ,    __lowerCamelCase		)
          if len(__lowerCamelCase		) > 0:
               __A  :					Dict						     =       args[0]
               __A  :					str						     =       args[1:]

          if images is None and text is None:
               raise ValueError("You need to specify either an `images` or `text` input to process."		)

          if images is not None:
               __A  :					Union[str, Any]						     =       self.image_processor(__lowerCamelCase ,    *__lowerCamelCase ,    **__lowerCamelCase		)
          if text is not None:
               __A  :					str						     =       self.tokenizer(__lowerCamelCase ,    **__lowerCamelCase		)

          if text is None:
               return inputs
          elif images is None:
               return encodings
          else:
               __A  :					List[Any]						     =       encodings["input_ids"]
               return inputs


     def lowerCAmelCase__     (      self ,    *lowerCamelCase ,    **lowerCamelCase		):


          '''simple docstring'''





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


     def lowerCAmelCase__     (      self ,    *lowerCamelCase ,    **lowerCamelCase		):


          '''simple docstring'''





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


     @contextmanager
     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          warnings.warn(
              "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
              "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
              "your images inputs, or in a separate call."		)
          __A  :					List[str]						     =       True
          __A  :					Union[str, Any]						     =       self.tokenizer
          yield
          __A  :					List[Any]						     =       self.image_processor
          __A  :					Optional[Any]						     =       False


     @property
     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          warnings.warn(
              "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead." ,    __lowerCamelCase ,    )
          return self.image_processor_class


     @property
     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          warnings.warn(
              "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." ,    __lowerCamelCase ,    )
          return self.image_processor





'''simple docstring'''


import os
from string import ascii_letters

_UpperCamelCase          =     ascii_letters + """ \t\n"""


def 		_lowercase   ():




     '''simple docstring'''





     __A  :					Any						     =       os.path.split(os.path.realpath(SCREAMING_SNAKE_CASE       )       )
     __A  :					dict[str, None]						     =       {}
     with open(path[0] + "/dictionary.txt"       ) as dictionary_file:
          for word in dictionary_file.read().split("\n"       ):
               __A  :					int						     =       None
     return english_words


_UpperCamelCase          =     load_dictionary()


def 		_lowercase   (SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





     __A  :					int						     =       message.upper()
     __A  :					int						     =       remove_non_letters(SCREAMING_SNAKE_CASE       )
     __A  :					str						     =       message.split()
     __A  :					Tuple						     =       len([word for word in possible_words if word in ENGLISH_WORDS]       )
     return float(SCREAMING_SNAKE_CASE       ) / len(SCREAMING_SNAKE_CASE       )


def 		_lowercase   (SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





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


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




     '''simple docstring'''





     __A  :					Optional[Any]						     =       get_english_count(SCREAMING_SNAKE_CASE       ) * 100 >= word_percentage
     __A  :					Optional[int]						     =       len(remove_non_letters(SCREAMING_SNAKE_CASE       )       )
     __A  :					Any						     =       (float(SCREAMING_SNAKE_CASE       ) / len(SCREAMING_SNAKE_CASE       )) * 100
     __A  :					int						     =       message_letters_percentage >= letter_percentage
     return words_match and letters_match


if __name__ == "__main__":
      import doctest

      doctest.testmod()

