


'''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


_UpperCAmelCase				: Dict							    =				logging.get_logger(__name__)


class       a__				( __A      ):







				"""simple docstring"""



				__UpperCamelCase    :		int	=	['pixel_values']




				def __init__(self       ,			__lowercase = True       ,			__lowercase = 32       ,			__lowercase=PILImageResampling.BILINEAR       ,			__lowercase = True       ,			**__lowercase       ,			):
										__lowerCAmelCase							       =				do_resize
										__lowerCAmelCase							       =				do_rescale
										__lowerCAmelCase							       =				size_divisor
										__lowerCAmelCase							       =				resample
										super().__init__(**__lowercase		)




				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase       ,			__lowercase       ,			__lowercase = None       ,			**__lowercase		):
										__lowerCAmelCase       ,					__lowerCAmelCase							       =				get_image_size(__lowercase		)
										# Rounds the height and width down to the closest multiple of size_divisor
										__lowerCAmelCase							       =				height // size_divisor * size_divisor
										__lowerCAmelCase							       =				width // size_divisor * size_divisor
										__lowerCAmelCase							       =				resize(__lowercase       ,			(new_h, new_w)       ,			resample=__lowercase       ,			data_format=__lowercase       ,			**__lowercase		)
										return image




				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase       ,			__lowercase = None       ,			**__lowercase		):
										return rescale(image=__lowercase       ,			scale=__lowercase       ,			data_format=__lowercase       ,			**__lowercase		)




				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase = None       ,			__lowercase = None       ,			__lowercase=None       ,			__lowercase = None       ,			__lowercase = None       ,			__lowercase = ChannelDimension.FIRST       ,			**__lowercase       ,			):
										__lowerCAmelCase							       =				do_resize if do_resize is not None else self.do_resize
										__lowerCAmelCase							       =				do_rescale if do_rescale is not None else self.do_rescale
										__lowerCAmelCase							       =				size_divisor if size_divisor is not None else self.size_divisor
										__lowerCAmelCase							       =				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'''		)

										__lowerCAmelCase							       =				make_list_of_images(__lowercase		)

										if not valid_images(__lowercase		):
																raise ValueError('''Invalid image(s)'''		)

										# All transformations expect numpy arrays.
										__lowerCAmelCase							       =				[to_numpy_array(__lowercase		) for img in images]

										if do_resize:
																__lowerCAmelCase							       =				[self.resize(__lowercase       ,			size_divisor=__lowercase       ,			resample=__lowercase		) for image in images]

										if do_rescale:
																__lowerCAmelCase							       =				[self.rescale(__lowercase       ,			scale=1 / 2_55		) for image in images]

										__lowerCAmelCase							       =				[to_channel_dimension_format(__lowercase       ,			__lowercase		) for image in images]

										__lowerCAmelCase							       =				{'''pixel_values''': images}
										return BatchFeature(data=__lowercase       ,			tensor_type=__lowercase		)





'''simple docstring'''

from string import ascii_uppercase

_UpperCAmelCase				: Optional[int]							    =				{char: i for i, char in enumerate(ascii_uppercase)}
_UpperCAmelCase				: Optional[int]							    =				dict(enumerate(ascii_uppercase))

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase):
						__lowerCAmelCase							       =				len(lowerCamelCase)
						__lowerCAmelCase							       =				0
						while True:
												if x == i:
																		__lowerCAmelCase							       =				0
												if len(lowerCamelCase) == len(lowerCamelCase):
																		break
												key += key[i]
												i += 1
						return key

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase):
						__lowerCAmelCase							       =				''''''
						__lowerCAmelCase							       =				0
						for letter in message:
												if letter == " ":
																		cipher_text += " "
												else:
																		__lowerCAmelCase							       =				(dicta[letter] - dicta[key_new[i]]) % 2_6
																		i += 1
																		cipher_text += dicta[x]
						return cipher_text

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase):
						__lowerCAmelCase							       =				''''''
						__lowerCAmelCase							       =				0
						for letter in cipher_text:
												if letter == " ":
																		or_txt += " "
												else:
																		__lowerCAmelCase							       =				(dicta[letter] + dicta[key_new[i]] + 2_6) % 2_6
																		i += 1
																		or_txt += dicta[x]
						return or_txt

def 							__magic_name__(						):
						__lowerCAmelCase							       =				'''THE GERMAN ATTACK'''
						__lowerCAmelCase							       =				'''SECRET'''
						__lowerCAmelCase							       =				generate_key(lowerCamelCase,				lowerCamelCase)
						__lowerCAmelCase							       =				cipher_text(lowerCamelCase,				lowerCamelCase)
						print(F"""Encrypted Text = {s}""")
						print(F"""Original Text = {original_text(lowerCamelCase,				lowerCamelCase)}""")


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()

