


'''simple docstring'''






import warnings

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






class    __magic_name__				(						lowerCAmelCase							):
       UpperCAmelCase		 =["image_processor", "tokenizer"]
       UpperCAmelCase		 ="ViTImageProcessor"
       UpperCAmelCase		 =("CLIPTokenizer", "CLIPTokenizerFast")






       def __init__(     self    ,				snake_case=None    ,				snake_case=None    ,				**snake_case)		->    str:




          '''simple docstring'''
          _UpperCAmelCase  :  Optional[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.'    ,				snake_case    ,				)
             _UpperCAmelCase  :  Optional[Any]				       =kwargs.pop('feature_extractor')

          _UpperCAmelCase  :  Tuple				       =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__(snake_case    ,				snake_case)






       def __call__(     self    ,				snake_case=None    ,				snake_case=None    ,				snake_case=None    ,				snake_case=None    ,				**snake_case)		->    Tuple:




          '''simple docstring'''
          if text is None and visual_prompt is None and images is None:
             raise ValueError('You have to specify either text, visual prompt or images.')

          if text is not None and visual_prompt is not None:
             raise ValueError('You have to specify exactly one type of prompt. Either text or visual prompt.')

          if text is not None:
             _UpperCAmelCase  :  Any				       =self.tokenizer(snake_case    ,				return_tensors=snake_case    ,				**snake_case)

          if visual_prompt is not None:
             _UpperCAmelCase  :  int				       =self.image_processor(snake_case    ,				return_tensors=snake_case    ,				**snake_case)

          if images is not None:
             _UpperCAmelCase  :  Dict				       =self.image_processor(snake_case    ,				return_tensors=snake_case    ,				**snake_case)

          if visual_prompt is not None and images is not None:
             _UpperCAmelCase  :  Union[str, Any]				       ={
                 'pixel_values': image_features.pixel_values,
                 'conditional_pixel_values': prompt_features.pixel_values,
             }
             return encoding
          elif text is not None and images is not None:
             _UpperCAmelCase  :  Union[str, Any]				       =image_features.pixel_values
             return encoding
          elif text is not None:
             return encoding
          elif visual_prompt is not None:
             _UpperCAmelCase  :  List[str]				       ={
                 'conditional_pixel_values': prompt_features.pixel_values,
             }
             return encoding
          else:
             return BatchEncoding(data=dict(**snake_case)    ,				tensor_type=snake_case)






       def      lowerCAmelCase						(     self    ,				*snake_case    ,				**snake_case)		->    List[str]:




          '''simple docstring'''
          return self.tokenizer.batch_decode(*snake_case    ,				**snake_case)






       def      lowerCAmelCase						(     self    ,				*snake_case    ,				**snake_case)		->    Dict:




          '''simple docstring'''
          return self.tokenizer.decode(*snake_case    ,				**snake_case)






       @property
       def      lowerCAmelCase						(     self)		->    List[Any]:




          '''simple docstring'''
          warnings.warn(
              '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'    ,				snake_case    ,				)
          return self.image_processor_class






       @property
       def      lowerCAmelCase						(     self)		->    int:




          '''simple docstring'''
          warnings.warn(
              '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'    ,				snake_case    ,				)
          return self.image_processor





'''simple docstring'''






def lowerCamelCase__     (		__lowerCamelCase			:				str				):







   '''simple docstring'''




   _UpperCAmelCase  :  Dict				       =''
   for ch in key:
      if ch == " " or ch not in key_no_dups and ch.isalpha():
         key_no_dups += ch
   return key_no_dups





def lowerCamelCase__     (		__lowerCamelCase			:				str				):







   '''simple docstring'''




   _UpperCAmelCase  :  List[str]				       =[chr(i + 6_5				) for i in range(2_6				)]
   # Remove duplicate characters from key
   _UpperCAmelCase  :  Any				       =remove_duplicates(key.upper()				)
   _UpperCAmelCase  :  Optional[Any]				       =len(__lowerCamelCase				)
   # First fill cipher with key characters
   _UpperCAmelCase  :  List[Any]				       ={alphabet[i]: char for i, char in enumerate(__lowerCamelCase				)}
   # Then map remaining characters in alphabet to
   # the alphabet from the beginning
   for i in range(len(__lowerCamelCase				)			,       2_6				):
      _UpperCAmelCase  :  Union[str, Any]				       =alphabet[i - offset]
      # Ensure we are not mapping letters to letters previously mapped
      while char in key:
         offset -= 1
         _UpperCAmelCase  :  str				       =alphabet[i - offset]
      _UpperCAmelCase  :  Any				       =char
   return cipher_alphabet





def lowerCamelCase__     (		__lowerCamelCase			:				str			,       __lowerCamelCase			:				dict[str, str]				):







   '''simple docstring'''




   return "".join(cipher_map.get(__lowerCamelCase			,       __lowerCamelCase				) for ch in message.upper()				)





def lowerCamelCase__     (		__lowerCamelCase			:				str			,       __lowerCamelCase			:				dict[str, str]				):







   '''simple docstring'''




   _UpperCAmelCase  :  List[str]				       ={v: k for k, v in cipher_map.items()}
   return "".join(rev_cipher_map.get(__lowerCamelCase			,       __lowerCamelCase				) for ch in message.upper()				)





def lowerCamelCase__     (		):







   '''simple docstring'''




   _UpperCAmelCase  :  Union[str, Any]				       =input('Enter message to encode or decode: '				).strip()
   _UpperCAmelCase  :  int				       =input('Enter keyword: '				).strip()
   _UpperCAmelCase  :  int				       =input('Encipher or decipher? E/D:'				).strip()[0].lower()
   try:
      _UpperCAmelCase  :  Tuple				       ={'e': encipher, 'd': decipher}[option]
   except KeyError:
      raise KeyError('invalid input option'				)
   _UpperCAmelCase  :  List[str]				       =create_cipher_map(__lowerCamelCase				)
   print(func(__lowerCamelCase			,       __lowerCamelCase				)				)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 main()

