





"""simple docstring"""





import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin

class 		lowercase					(					__a					):
      _SCREAMING_SNAKE_CASE					=      ['''image_processor''', '''tokenizer''']
      _SCREAMING_SNAKE_CASE					=      '''AutoImageProcessor'''
      _SCREAMING_SNAKE_CASE					=      '''AutoTokenizer'''





      def __init__(       self      ,						lowercase=None      ,						lowercase=None      ,						**lowercase )		->     Any:
            lowerCAmelCase  						=       None
            if "feature_extractor" in kwargs:
                  warnings.warn(
                      """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                      """ instead."""      ,						__a      ,						)
                  lowerCAmelCase  						=       kwargs.pop("""feature_extractor""" )

            lowerCAmelCase  						=       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__(__a      ,						__a )
            lowerCAmelCase  						=       self.image_processor
            lowerCAmelCase  						=       False





      def __call__(       self      ,						*lowercase      ,						**lowercase )		->     Dict:

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

            lowerCAmelCase  						=       kwargs.pop("""images"""      ,						__a )
            lowerCAmelCase  						=       kwargs.pop("""text"""      ,						__a )
            if len(__a ) > 0:
                  lowerCAmelCase  						=       args[0]
                  lowerCAmelCase  						=       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:
                  lowerCAmelCase  						=       self.image_processor(__a      ,						*__a      ,						**__a )
            if text is not None:
                  lowerCAmelCase  						=       self.tokenizer(__a      ,						**__a )

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





      def     _snake_case			(       self      ,						*lowercase      ,						**lowercase )		->     Union[str, Any]:
            return self.tokenizer.batch_decode(*__a      ,						**__a )





      def     _snake_case			(       self      ,						*lowercase      ,						**lowercase )		->     Any:
            return self.tokenizer.decode(*__a      ,						**__a )





      @contextmanager
      def     _snake_case			(       self )		->     Tuple:
            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.""" )
            lowerCAmelCase  						=       True
            lowerCAmelCase  						=       self.tokenizer
            yield
            lowerCAmelCase  						=       self.image_processor
            lowerCAmelCase  						=       False





      def     _snake_case			(       self      ,						lowercase      ,						lowercase=False      ,						lowercase=None )		->     Union[str, Any]:

            if added_vocab is None:
                  lowerCAmelCase  						=       self.tokenizer.get_added_vocab()

            lowerCAmelCase  						=       {}

            while tokens:
                  lowerCAmelCase  						=       re.search(r"""<s_(.*?)>"""      ,						__a      ,						re.IGNORECASE )
                  if start_token is None:
                        break
                  lowerCAmelCase  						=       start_token.group(1 )
                  lowerCAmelCase  						=       re.search(rf'</s_{key}>'      ,						__a      ,						re.IGNORECASE )
                  lowerCAmelCase  						=       start_token.group()
                  if end_token is None:
                        lowerCAmelCase  						=       tokens.replace(__a      ,						"""""" )
                  else:
                        lowerCAmelCase  						=       end_token.group()
                        lowerCAmelCase  						=       re.escape(__a )
                        lowerCAmelCase  						=       re.escape(__a )
                        lowerCAmelCase  						=       re.search(f'{start_token_escaped}(.*?){end_token_escaped}'      ,						__a      ,						re.IGNORECASE )
                        if content is not None:
                              lowerCAmelCase  						=       content.group(1 ).strip()
                              if r"<s_" in content and r"</s_" in content:  # non-leaf node
                                    lowerCAmelCase  						=       self.tokenajson(__a      ,						is_inner_value=__a      ,						added_vocab=__a )
                                    if value:
                                          if len(__a ) == 1:
                                                lowerCAmelCase  						=       value[0]
                                          lowerCAmelCase  						=       value
                              else:  # leaf nodes
                                    lowerCAmelCase  						=       []
                                    for leaf in content.split(r"""<sep/>""" ):
                                          lowerCAmelCase  						=       leaf.strip()
                                          if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
                                                lowerCAmelCase  						=       leaf[1:-2]  # for categorical special tokens
                                          output[key].append(__a )
                                    if len(output[key] ) == 1:
                                          lowerCAmelCase  						=       output[key][0]

                        lowerCAmelCase  						=       tokens[tokens.find(__a ) + len(__a ) :].strip()
                        if tokens[:6] == r"<sep/>":  # non-leaf nodes
                              return [output] + self.tokenajson(tokens[6:]      ,						is_inner_value=__a      ,						added_vocab=__a )

            if len(__a ):
                  return [output] if is_inner_value else output
            else:
                  return [] if is_inner_value else {"text_sequence": tokens}





      @property
      def     _snake_case			(       self )		->     Optional[int]:
            warnings.warn(
                """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""      ,						__a      ,						)
            return self.image_processor_class





      @property
      def     _snake_case			(       self )		->     Any:
            warnings.warn(
                """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""      ,						__a      ,						)
            return self.image_processor














from maths.prime_check import is_prime
def 					snake_case_						(   lowerCAmelCase_				:  int					):
							if not isinstance(lowerCAmelCase_				,				lowerCAmelCase_					):
														__lowercase					:							Dict			=   F"Input value of [number={number}] must be an integer"
														raise TypeError(lowerCAmelCase_					)
							if is_prime(lowerCAmelCase_					) and is_prime(number + 2					):
														return number + 2
							else:
														return -1


if __name__ == "__main__":
	import doctest

	doctest.testmod()