



'''simple docstring'''






import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin



class 							lowercase (						_lowerCamelCase       ):


 """simple docstring"""


 UpperCAmelCase					=						["""image_processor""", """tokenizer"""]
 UpperCAmelCase					=						"""AutoImageProcessor"""
 UpperCAmelCase					=						"""AutoTokenizer"""







 def __init__(							self						,a_=None						,a_=None						,**a_							)	->     str:
        _UpperCAmelCase						:		List[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."""						,a_						,)
               _UpperCAmelCase						:		Optional[Any]      =				kwargs.pop("""feature_extractor"""							)

        _UpperCAmelCase						:		List[Any]      =				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_							)
        _UpperCAmelCase						:		Dict      =				self.image_processor
        _UpperCAmelCase						:		Optional[int]      =				False







 def __call__(							self						,*a_						,**a_							)	->     Dict:
        # For backward compatibility
        if self._in_target_context_manager:
               return self.current_processor(*a_						,**a_							)

        _UpperCAmelCase						:		Union[str, Any]      =				kwargs.pop("""images"""						,a_							)
        _UpperCAmelCase						:		str      =				kwargs.pop("""text"""						,a_							)
        if len(a_							) > 0:
               _UpperCAmelCase						:		Dict      =				args[0]
               _UpperCAmelCase						:		int      =				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:
               _UpperCAmelCase						:		Optional[Any]      =				self.image_processor(a_						,*a_						,**a_							)
        if text is not None:
               _UpperCAmelCase						:		Union[str, Any]      =				self.tokenizer(a_						,**a_							)

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







 def _snake_case				(							self						,*a_						,**a_							)	->     Any:
        return self.tokenizer.batch_decode(*a_						,**a_							)







 def _snake_case				(							self						,*a_						,**a_							)	->     Dict:
        return self.tokenizer.decode(*a_						,**a_							)







 @contextmanager
 def _snake_case				(							self							)	->     Optional[int]:
        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."""							)
        _UpperCAmelCase						:		Dict      =				True
        _UpperCAmelCase						:		Optional[Any]      =				self.tokenizer
        yield
        _UpperCAmelCase						:		List[Any]      =				self.image_processor
        _UpperCAmelCase						:		str      =				False







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








'''simple docstring'''






from string import ascii_uppercase




def 					snake_case_      (  lowerCAmelCase_ ,     lowerCAmelCase_ ,     lowerCAmelCase_ = False ,     lowerCAmelCase_ = ascii_uppercase	)->	str:





       '''simple docstring'''


       _UpperCAmelCase						:		List[str]      =				keyword.upper()
       _UpperCAmelCase						:		Optional[int]      =				plaintext.upper()
       _UpperCAmelCase						:		Optional[int]      =				set(lowerCAmelCase_	)

       # create a list of unique characters in the keyword - their order matters
       # it determines how we will map plaintext characters to the ciphertext
       _UpperCAmelCase						:		str      =				[]
       for char in keyword:
              if char in alphabet_set and char not in unique_chars:
                     unique_chars.append(lowerCAmelCase_	)
    # the number of those unique characters will determine the number of rows
       _UpperCAmelCase						:		Optional[Any]      =				len(lowerCAmelCase_	)

       # create a shifted version of the alphabet
       _UpperCAmelCase						:		Optional[Any]      =				unique_chars + [
           char for char in alphabet if char not in unique_chars
       ]

       # create a modified alphabet by splitting the shifted alphabet into rows
       _UpperCAmelCase						:		List[str]      =				[
           shifted_alphabet[k : k + num_unique_chars_in_keyword]
           for k in range(0 ,     26 ,     lowerCAmelCase_	)
       ]

       # map the alphabet characters to the modified alphabet characters
       # going 'vertically' through the modified alphabet - consider columns first
       _UpperCAmelCase						:		int      =				{}
       _UpperCAmelCase						:		int      =				0
       for column in range(lowerCAmelCase_	):
              for row in modified_alphabet:
                     # if current row (the last one) is too short, break out of loop
                     if len(lowerCAmelCase_	) <= column:
                            break

                     # map current letter to letter in modified alphabet
                     _UpperCAmelCase						:		Optional[int]      =				row[column]
                     letter_index += 1

       if verbose:
              print(lowerCAmelCase_	)
       # create the encrypted text by mapping the plaintext to the modified alphabet
       return "".join(mapping[char] if char in mapping else char for char in plaintext	)


if __name__ == "__main__":
   # example use
   print(mixed_keyword("""college""", """UNIVERSITY"""))



