




"""simple docstring"""

import warnings

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





class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''
 UpperCAmelCase   :					Any				  =     ['''image_processor''', '''tokenizer''']
 UpperCAmelCase   :					Any				  =     '''ViTImageProcessor'''
 UpperCAmelCase   :					Optional[Any]				  =     ('''CLIPTokenizer''', '''CLIPTokenizerFast''')

 def __init__(			self	:	List[Any]   ,							_UpperCAmelCase	:	str=None   ,							_UpperCAmelCase	:	str=None   ,							**_UpperCAmelCase	:	Tuple		):
       _A        =       None
       if "feature_extractor" in kwargs:
             warnings.warn(
                 'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                 ' instead.'   ,							_UpperCAmelCase   ,							)
             _A        =       kwargs.pop('feature_extractor'		)

       _A        =       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__(_UpperCAmelCase   ,							_UpperCAmelCase		)

 def __call__(			self	:	str   ,							_UpperCAmelCase	:	Optional[int]=None   ,							_UpperCAmelCase	:	List[Any]=None   ,							_UpperCAmelCase	:	Dict=None   ,							_UpperCAmelCase	:	Dict=None   ,							**_UpperCAmelCase	:	List[Any]		):
       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:
             _A        =       self.tokenizer(_UpperCAmelCase   ,							return_tensors=_UpperCAmelCase   ,							**_UpperCAmelCase		)

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

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

       if visual_prompt is not None and images is not None:
             _A        =       {
                 '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:
             _A        =       image_features.pixel_values
             return encoding
       elif text is not None:
             return encoding
       elif visual_prompt is not None:
             _A        =       {
                 'conditional_pixel_values': prompt_features.pixel_values,
             }
             return encoding
       else:
             return BatchEncoding(data=dict(**_UpperCAmelCase		)   ,							tensor_type=_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	int   ,							*_UpperCAmelCase	:	str   ,							**_UpperCAmelCase	:	Dict		):
       return self.tokenizer.batch_decode(*_UpperCAmelCase   ,							**_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	List[Any]   ,							*_UpperCAmelCase	:	Union[str, Any]   ,							**_UpperCAmelCase	:	Tuple		):
       return self.tokenizer.decode(*_UpperCAmelCase   ,							**_UpperCAmelCase		)

 @property
 def       lowerCAmelCase_							(			self	:	Tuple		):
       warnings.warn(
           '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'   ,							_UpperCAmelCase   ,							)
       return self.image_processor_class



 @property
 def       lowerCAmelCase_							(			self	:	Optional[int]		):
       warnings.warn(
           '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'   ,							_UpperCAmelCase   ,							)
       return self.image_processor







"""simple docstring"""

def 						_snake_case					( _snake_case							: str							)   -> str:






      '''simple docstring'''


      _A        =       ''
      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 						_snake_case					( _snake_case							: str							)   -> dict[str, str]:






      '''simple docstring'''


      _A        =       [chr(i + 65							) for i in range(26							)]
      # Remove duplicate characters from key
      _A        =       remove_duplicates(key.upper()							)
      _A        =       len(_snake_case							)
      # First fill cipher with key characters
      _A        =       {alphabet[i]: char for i, char in enumerate(_snake_case							)}
      # Then map remaining characters in alphabet to
      # the alphabet from the beginning
      for i in range(len(_snake_case							)    ,  26							):
            _A        =       alphabet[i - offset]
            # Ensure we are not mapping letters to letters previously mapped
            while char in key:
                  offset -= 1
                  _A        =       alphabet[i - offset]
            _A        =       char
      return cipher_alphabet






def 						_snake_case					( _snake_case							: str    ,  _snake_case							: dict[str, str]							)   -> str:






      '''simple docstring'''


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






def 						_snake_case					( _snake_case							: str    ,  _snake_case							: dict[str, str]							)   -> str:






      '''simple docstring'''


      _A        =       {v: k for k, v in cipher_map.items()}
      return "".join(rev_cipher_map.get(_snake_case    ,  _snake_case							) for ch in message.upper()							)






def 						_snake_case					( )   -> None:






      '''simple docstring'''


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


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       main()

