



"""simple docstring"""





from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__lowerCAmelCase							:       Union[str, Any]          =       logging.get_logger(__name__)



class  _lowerCAmelCase     (					SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






		_lowerCamelCase      	=							['''pixel_values''']





		def __init__(       self				,     _lowercase = True				,     _lowercase = 3_2				,     _lowercase=PILImageResampling.BILINEAR				,     _lowercase = True				,     **_lowercase				,     )			->    None:







								'''simple docstring'''

								snake_case_ :			Any            =      do_resize
								snake_case_ :			Dict            =      do_rescale
								snake_case_ :			Union[str, Any]            =      size_divisor
								snake_case_ :			Tuple            =      resample
								super().__init__(**_lowercase )





		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase = None				,     **_lowercase )			->    np.ndarray:







								'''simple docstring'''

								snake_case_      ,		snake_case_ :			Any            =      get_image_size(_lowercase )
								# Rounds the height and width down to the closest multiple of size_divisor
								snake_case_ :			Any            =      height // size_divisor * size_divisor
								snake_case_ :			str            =      width // size_divisor * size_divisor
								snake_case_ :			Dict            =      resize(_lowercase				,     (new_h, new_w)				,     resample=_lowercase				,     data_format=_lowercase				,     **_lowercase )
								return image





		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase				,     _lowercase = None				,     **_lowercase )			->    np.ndarray:







								'''simple docstring'''

								return rescale(image=_lowercase				,     scale=_lowercase				,     data_format=_lowercase				,     **_lowercase )





		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase = None				,     _lowercase = None				,     _lowercase=None				,     _lowercase = None				,     _lowercase = None				,     _lowercase = ChannelDimension.FIRST				,     **_lowercase				,     )			->    BatchFeature:







								'''simple docstring'''

								snake_case_ :			Optional[int]            =      do_resize if do_resize is not None else self.do_resize
								snake_case_ :			str            =      do_rescale if do_rescale is not None else self.do_rescale
								snake_case_ :			int            =      size_divisor if size_divisor is not None else self.size_divisor
								snake_case_ :			Any            =      resample if resample is not None else self.resample

								if do_resize and size_divisor is None:
														raise ValueError("""size_divisor is required for resizing""" )

								snake_case_ :			List[str]            =      make_list_of_images(_lowercase )

								if not valid_images(_lowercase ):
														raise ValueError("""Invalid image(s)""" )

								# All transformations expect numpy arrays.
								snake_case_ :			List[Any]            =      [to_numpy_array(_lowercase ) for img in images]

								if do_resize:
														snake_case_ :			int            =      [self.resize(_lowercase				,     size_divisor=_lowercase				,     resample=_lowercase ) for image in images]

								if do_rescale:
														snake_case_ :			List[str]            =      [self.rescale(_lowercase				,     scale=1 / 2_5_5 ) for image in images]

								snake_case_ :			Tuple            =      [to_channel_dimension_format(_lowercase				,     _lowercase ) for image in images]

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




'''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()


