


import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class 			SCREAMING_SNAKE_CASE__  (    lowercase__      ):
      snake_case__    :					Any					       =						['''image_processor''', '''tokenizer''']
      snake_case__    :					Optional[Any]					       =						'''CLIPImageProcessor'''
      snake_case__    :					Tuple					       =						('''XLMRobertaTokenizer''', '''XLMRobertaTokenizerFast''')
      def __init__(	self    :	Any					,							SCREAMING_SNAKE_CASE__    :	Optional[int]=None					,							SCREAMING_SNAKE_CASE__    :	Tuple=None					,							**SCREAMING_SNAKE_CASE__    :	Dict					)		->      Union[str, Any]:
             a_ :		str							=  None
             if "feature_extractor" in kwargs:
                    warnings.warn(
                        'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                        ' instead.'					,							SCREAMING_SNAKE_CASE__					,							)
                    a_ :		Tuple							=  kwargs.pop('feature_extractor'					)

             a_ :		int							=  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__(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					)
      def __call__(	self    :	int					,							SCREAMING_SNAKE_CASE__    :	Dict=None					,							SCREAMING_SNAKE_CASE__    :	List[str]=None					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]=None					,							**SCREAMING_SNAKE_CASE__    :	int					)		->      List[str]:

             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:
                    a_ :		Dict							=  self.tokenizer(SCREAMING_SNAKE_CASE__					,							return_tensors=SCREAMING_SNAKE_CASE__					,							**SCREAMING_SNAKE_CASE__					)

             if images is not None:
                    a_ :		Tuple							=  self.image_processor(SCREAMING_SNAKE_CASE__					,							return_tensors=SCREAMING_SNAKE_CASE__					,							**SCREAMING_SNAKE_CASE__					)

             if text is not None and images is not None:
                    a_ :		str							=  image_features.pixel_values
                    return encoding
             elif text is not None:
                    return encoding
             else:
                    return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE__					)					,							tensor_type=SCREAMING_SNAKE_CASE__					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	int					,							*SCREAMING_SNAKE_CASE__    :	Optional[Any]					,							**SCREAMING_SNAKE_CASE__    :	List[Any]					)		->      Optional[Any]:
             return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE__					,							**SCREAMING_SNAKE_CASE__					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					,							*SCREAMING_SNAKE_CASE__    :	Tuple					,							**SCREAMING_SNAKE_CASE__    :	List[str]					)		->      Optional[Any]:
             return self.tokenizer.decode(*SCREAMING_SNAKE_CASE__					,							**SCREAMING_SNAKE_CASE__					)

      @property
      def 	SCREAMING_SNAKE_CASE							(	self    :	Tuple					)		->      int:
             a_ :		Optional[Any]							=  self.tokenizer.model_input_names
             a_ :		List[Any]							=  self.image_processor.model_input_names
             return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)






from string import ascii_uppercase

UpperCAmelCase_				:			Dict   =	{char: i for i, char in enumerate(ascii_uppercase)}
UpperCAmelCase_				:			Optional[int]   =	dict(enumerate(ascii_uppercase))







def 	SCREAMING_SNAKE_CASE_						(      __A			:      str  ,   __A			:      str    )     ->		str:

       """simple docstring"""

       a_ :		Tuple							=  len(__A    )
       a_ :		int							=  0
       while True:
              if x == i:
                     a_ :		Tuple							=  0
              if len(__A    ) == len(__A    ):
                     break
              key += key[i]
              i += 1
       return key







def 	SCREAMING_SNAKE_CASE_						(      __A			:      str  ,   __A			:      str    )     ->		str:

       """simple docstring"""

       a_ :		Optional[int]							=  ''
       a_ :		Any							=  0
       for letter in message:
              if letter == " ":
                     cipher_text += " "
              else:
                     a_ :		Optional[Any]							=  (dicta[letter] - dicta[key_new[i]]) % 26
                     i += 1
                     cipher_text += dicta[x]
       return cipher_text







def 	SCREAMING_SNAKE_CASE_						(      __A			:      str  ,   __A			:      str    )     ->		str:

       """simple docstring"""

       a_ :		Any							=  ''
       a_ :		Optional[Any]							=  0
       for letter in cipher_text:
              if letter == " ":
                     or_txt += " "
              else:
                     a_ :		Union[str, Any]							=  (dicta[letter] + dicta[key_new[i]] + 26) % 26
                     i += 1
                     or_txt += dicta[x]
       return or_txt







def 	SCREAMING_SNAKE_CASE_						(      )     ->		None:

       """simple docstring"""

       a_ :		Tuple							=  'THE GERMAN ATTACK'
       a_ :		Dict							=  'SECRET'
       a_ :		Optional[Any]							=  generate_key(__A  ,   __A    )
       a_ :		Union[str, Any]							=  cipher_text(__A  ,   __A    )
       print(F"""Encrypted Text = {s}"""    )
       print(F"""Original Text = {original_text(__A  ,   __A    )}"""    )


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   main()


