


"""simple docstring"""



from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


__snake_case       =       logging.get_logger(__name__)







class _lowerCAmelCase							( snake_case_      ):
   __UpperCAmelCase					:			List[str]				    =    ['''pixel_values''']







   def __init__(      self		,      UpperCamelCase__ = True		,      UpperCamelCase__ = 1 / 255		,      UpperCamelCase__ = True		,      UpperCamelCase__ = 8		,      **UpperCamelCase__		,      )      ->						None:

       '''simple docstring'''




       super().__init__(**UpperCamelCase__		)

       snake_case       : Optional[int]    =       do_rescale
       snake_case       : Optional[int]    =       rescale_factor
       snake_case       : Union[str, Any]    =       do_pad
       snake_case       : Optional[Any]    =       pad_size







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__ = None		,      **UpperCamelCase__		)      ->						np.ndarray:

       '''simple docstring'''




       return rescale(UpperCamelCase__		,      scale=UpperCamelCase__		,      data_format=UpperCamelCase__		,      **UpperCamelCase__		)







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__ = None		)      ->						List[str]:

       '''simple docstring'''




       snake_case   ,snake_case       : List[Any]    =       get_image_size(UpperCamelCase__		)
       snake_case       : Optional[int]    =       (old_height // size + 1) * size - old_height
       snake_case       : Union[str, Any]    =       (old_width // size + 1) * size - old_width

       return pad(UpperCamelCase__		,      ((0, pad_height), (0, pad_width))		,      mode="symmetric"		,      data_format=UpperCamelCase__		)







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__ = None		,      UpperCamelCase__ = None		,      UpperCamelCase__ = None		,      UpperCamelCase__ = None		,      UpperCamelCase__ = None		,      UpperCamelCase__ = ChannelDimension.FIRST		,      **UpperCamelCase__		,      )      ->						Union[str, Any]:

       '''simple docstring'''




       snake_case       : Union[str, Any]    =       do_rescale if do_rescale is not None else self.do_rescale
       snake_case       : int    =       rescale_factor if rescale_factor is not None else self.rescale_factor
       snake_case       : Tuple    =       do_pad if do_pad is not None else self.do_pad
       snake_case       : List[Any]    =       pad_size if pad_size is not None else self.pad_size

       snake_case       : int    =       make_list_of_images(UpperCamelCase__		)

       if not valid_images(UpperCamelCase__		):
           raise ValueError(
               "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
               "torch.Tensor, tf.Tensor or jax.ndarray."		)

       if do_rescale and rescale_factor is None:
           raise ValueError("Rescale factor must be specified if do_rescale is True."		)

       # All transformations expect numpy arrays.
       snake_case       : Optional[int]    =       [to_numpy_array(UpperCamelCase__		) for image in images]

       if do_rescale:
           snake_case       : List[Any]    =       [self.rescale(image=UpperCamelCase__		,      scale=UpperCamelCase__		) for image in images]

       if do_pad:
           snake_case       : List[str]    =       [self.pad(UpperCamelCase__		,      size=UpperCamelCase__		) for image in images]

       snake_case       : str    =       [to_channel_dimension_format(UpperCamelCase__		,      UpperCamelCase__		) for image in images]

       snake_case       : Union[str, Any]    =       {"pixel_values": images}
       return BatchFeature(data=UpperCamelCase__		,      tensor_type=UpperCamelCase__		)









"""simple docstring"""



import string







def     __lowerCAmelCase		(		lowercase    :    str					)      ->		None:


    """simple docstring"""





    for key in range(len(string.ascii_uppercase					)					):
        snake_case       : Dict    =       ""
        for symbol in message:
            if symbol in string.ascii_uppercase:
                snake_case       : Dict    =       string.ascii_uppercase.find(lowercase					)
                snake_case       : Optional[int]    =       num - key
                if num < 0:
                    snake_case       : Optional[int]    =       num + len(string.ascii_uppercase					)
                snake_case       : Dict    =       translated + string.ascii_uppercase[num]
            else:
                snake_case       : Any    =       translated + symbol
        print(F'Decryption using Key #{key}: {translated}'					)







def     __lowerCAmelCase		(		)      ->		None:


    """simple docstring"""





    snake_case       : Dict    =       input("Encrypted message: "					)
    snake_case       : Any    =       message.upper()
    decrypt(lowercase					)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()





