

'''simple docstring'''

import warnings

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


class 	lowerCAmelCase  (   A ):
       lowerCAmelCase_												=			["image_processor", "tokenizer"]
       lowerCAmelCase_												=			"CLIPImageProcessor"
       lowerCAmelCase_												=			("CLIPTokenizer", "CLIPTokenizerFast")







       def __init__(				self      :			Any    ,   __lowercase      :			str=None    ,   __lowercase      :			List[Any]=None    ,   **__lowercase      :			Optional[Any]   ):







            """simple docstring"""







            __lowercase      		=None
            if "feature_extractor" in kwargs:
                 warnings.warn(
                     'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                     ' instead.'    ,   __lowercase    ,   )
                 __lowercase      		=kwargs.pop('feature_extractor'   )

            __lowercase      		=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__(__lowercase    ,   __lowercase   )







       def __call__(				self      :			Union[str, Any]    ,   __lowercase      :			Any=None    ,   __lowercase      :			List[str]=None    ,   __lowercase      :			List[str]=None    ,   **__lowercase      :			List[Any]   ):







            """simple docstring"""







            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:
                 __lowercase      		=self.tokenizer(__lowercase    ,   return_tensors=__lowercase    ,   **__lowercase   )

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

            if text is not None and images is not None:
                 __lowercase      		=image_features.pixel_values
                 return encoding
            elif text is not None:
                 return encoding
            else:
                 return BatchEncoding(data=dict(**__lowercase   )    ,   tensor_type=__lowercase   )







       def       snake_case (				self      :			int    ,   *__lowercase      :			Any    ,   **__lowercase      :			List[Any]   ):







            """simple docstring"""







            return self.tokenizer.batch_decode(*__lowercase    ,   **__lowercase   )







       def       snake_case (				self      :			Tuple    ,   *__lowercase      :			List[Any]    ,   **__lowercase      :			Any   ):







            """simple docstring"""







            return self.tokenizer.decode(*__lowercase    ,   **__lowercase   )







       @property
       def       snake_case (				self      :			Union[str, Any]   ):







            """simple docstring"""







            __lowercase      		=self.tokenizer.model_input_names
            __lowercase      		=self.image_processor.model_input_names
            return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )







       @property
       def       snake_case (				self      :			Dict   ):







            """simple docstring"""







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







       @property
       def       snake_case (				self      :			Optional[int]   ):







            """simple docstring"""







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





'''simple docstring'''

import string

def 					__UpperCamelCase				(						lowercase__    :	str			):






     '''simple docstring'''





     for key in range(len(string.ascii_uppercase			)			):
          __lowercase      		=''
          for symbol in message:
               if symbol in string.ascii_uppercase:
                    __lowercase      		=string.ascii_uppercase.find(lowercase__			)
                    __lowercase      		=num - key
                    if num < 0:
                         __lowercase      		=num + len(string.ascii_uppercase			)
                    __lowercase      		=translated + string.ascii_uppercase[num]
               else:
                    __lowercase      		=translated + symbol
          print(F'''Decryption using Key #{key}: {translated}'''			)

def 					__UpperCamelCase				(						):






     '''simple docstring'''





     __lowercase      		=input('Encrypted message: '			)
     __lowercase      		=message.upper()
     decrypt(lowercase__			)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       main()


