






from __future__ import annotations
def      lowerCamelCase					(						a_ ,						a_	)	->			str:
							lowerCAmelCase_						,   lowerCAmelCase_						,   lowerCAmelCase_										=     '', '', ''
							lowerCAmelCase_										=     []

							for character in message_part:
														tmp.append(character_to_number[character]	)

							for each in tmp:
														one += each[0]
														two += each[1]
														three += each[2]

							return one + two + three
def      lowerCamelCase					(						a_ ,						a_	)	->			tuple[str, str, str]:
							lowerCAmelCase_						,   lowerCAmelCase_										=     '', ''
							lowerCAmelCase_										=     []

							for character in message_part:
														this_part += character_to_number[character]

							for digit in this_part:
														tmp += digit
														if len(a_	) == len(a_	):
																					result.append(a_	)
																					lowerCAmelCase_										=     ''

							return result[0], result[1], result[2]
def      lowerCamelCase					(						a_ ,						a_	)	->			tuple[str, str, dict[str, str], dict[str, str]]:
							# Validate message and alphabet, set to upper and remove spaces
							lowerCAmelCase_										=     alphabet.replace(' ' ,						''	).upper()
							lowerCAmelCase_										=     message.replace(' ' ,						''	).upper()

							# Check length and characters
							if len(a_	) != 27:
														raise KeyError('Length of alphabet has to be 27.'	)
							for each in message:
														if each not in alphabet:
																					raise ValueError('Each message character has to be included in alphabet!'	)

    # Generate dictionares
							lowerCAmelCase_										=     (
							    '111',
							    '112',
							    '113',
							    '121',
							    '122',
							    '123',
							    '131',
							    '132',
							    '133',
							    '211',
							    '212',
							    '213',
							    '221',
							    '222',
							    '223',
							    '231',
							    '232',
							    '233',
							    '311',
							    '312',
							    '313',
							    '321',
							    '322',
							    '323',
							    '331',
							    '332',
							    '333',
							)
							lowerCAmelCase_										=     {}
							lowerCAmelCase_										=     {}
							for letter, number in zip(a_ ,						a_	):
														lowerCAmelCase_										=     number
														lowerCAmelCase_										=     letter

							return message, alphabet, character_to_number, number_to_character
def      lowerCamelCase					(						a_ ,						a_ = "ABCDEFGHIJKLMNOPQRSTUVWXYZ." ,						a_ = 5	)	->			str:
							lowerCAmelCase_						,   lowerCAmelCase_						,   lowerCAmelCase_						,   lowerCAmelCase_										=     __prepare(
							    a_ ,						a_	)
							lowerCAmelCase_						,   lowerCAmelCase_										=     '', ''

							for i in range(0 ,						len(a_	) + 1 ,						a_	):
														encrypted_numeric += __encrypt_part(
														    message[i : i + period] ,						a_	)

							for i in range(0 ,						len(a_	) ,						3	):
														encrypted += number_to_character[encrypted_numeric[i : i + 3]]

							return encrypted
def      lowerCamelCase					(						a_ ,						a_ = "ABCDEFGHIJKLMNOPQRSTUVWXYZ." ,						a_ = 5	)	->			str:
							lowerCAmelCase_						,   lowerCAmelCase_						,   lowerCAmelCase_						,   lowerCAmelCase_										=     __prepare(
							    a_ ,						a_	)
							lowerCAmelCase_										=     []
							lowerCAmelCase_										=     ''

							for i in range(0 ,						len(a_	) + 1 ,						a_	):
														lowerCAmelCase_						,   lowerCAmelCase_						,   lowerCAmelCase_										=     __decrypt_part(message[i : i + period] ,						a_	)

														for j in range(0 ,						len(a_	)	):
																					decrypted_numeric.append(a[j] + b[j] + c[j]	)

							for each in decrypted_numeric:
														decrypted += number_to_character[each]

							return decrypted


if __name__ == "__main__":
		lowerCamelCase_						     =  """DEFEND THE EAST WALL OF THE CASTLE."""
		lowerCamelCase_						     =  encrypt_message(msg, """EPSDUCVWYM.ZLKXNBTFGORIJHAQ""")
		lowerCamelCase_						     =  decrypt_message(encrypted, """EPSDUCVWYM.ZLKXNBTFGORIJHAQ""")
		print(f'''Encrypted: {encrypted}\nDecrypted: {decrypted}''')











from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
		import PIL


lowerCamelCase_						     =  logging.get_logger(__name__)




class 							a_ (						a_		):


							'''simple docstring'''






							__a:	Optional[Any]     =    ['''pixel_values''']

							def __init__(		self			,			lowercase_ = True			,			lowercase_ = None			,			lowercase_ = True			,			lowercase_ = None			,			lowercase_ = PILImageResampling.BICUBIC			,			lowercase_ = True			,			lowercase_ = 1 / 2_5_5			,			lowercase_ = True			,			lowercase_ = None			,			lowercase_ = None			,			**lowercase_			,			)					->					None:
														'''simple docstring'''

														super().__init__(**lowercase_   )
														lowerCAmelCase_										=     crop_size if crop_size is not None else {'height': 2_5_6, 'width': 2_5_6}
														lowerCAmelCase_										=     get_size_dict(lowercase_			,			param_name='crop_size'   )
														lowerCAmelCase_										=     size if size is not None else {'height': 2_2_4, 'width': 2_2_4}
														lowerCAmelCase_										=     get_size_dict(lowercase_   )

														lowerCAmelCase_										=     do_center_crop
														lowerCAmelCase_										=     crop_size
														lowerCAmelCase_										=     do_resize
														lowerCAmelCase_										=     size
														lowerCAmelCase_										=     resample
														lowerCAmelCase_										=     do_rescale
														lowerCAmelCase_										=     rescale_factor
														lowerCAmelCase_										=     do_normalize
														lowerCAmelCase_										=     image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
														lowerCAmelCase_										=     image_std if image_std is not None else IMAGENET_DEFAULT_STD

							def      _lowercase       (		self			,			lowercase_			,			lowercase_			,			lowercase_ = None			,			lowercase_ = None			,			**lowercase_			,			)					->					np.ndarray:
														'''simple docstring'''

														lowerCAmelCase_										=     self.size if size is None else size
														lowerCAmelCase_										=     get_size_dict(lowercase_   )
														lowerCAmelCase_										=     get_size_dict(lowercase_			,			param_name='crop_size'   )

														lowerCAmelCase_						,   lowerCAmelCase_										=     get_image_size(lowercase_   )
														lowerCAmelCase_										=     min(lowercase_			,			lowercase_   )
														lowerCAmelCase_										=     (size['height'] / crop_size['height']) * min_dim
														lowerCAmelCase_										=     (size['width'] / crop_size['width']) * min_dim
														return center_crop(lowercase_			,			size=(cropped_height, cropped_width)			,			data_format=lowercase_			,			**lowercase_   )

							def      _lowercase       (		self			,			lowercase_			,			lowercase_			,			lowercase_ = PIL.Image.BICUBIC			,			lowercase_ = None			,			**lowercase_			,			)					->					np.ndarray:
														'''simple docstring'''

														lowerCAmelCase_										=     get_size_dict(lowercase_   )
														if "height" not in size or "width" not in size:
																					raise ValueError(f'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'''   )
														return resize(
														    lowercase_			,			size=(size['height'], size['width'])			,			resample=lowercase_			,			data_format=lowercase_			,			**lowercase_   )

							def      _lowercase       (		self			,			lowercase_			,			lowercase_			,			lowercase_ = None			,			**lowercase_			,			)					->					Any:
														'''simple docstring'''

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

							def      _lowercase       (		self			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ = None			,			**lowercase_			,			)					->					np.ndarray:
														'''simple docstring'''

														return normalize(lowercase_			,			mean=lowercase_			,			std=lowercase_			,			data_format=lowercase_			,			**lowercase_   )






							def      _lowercase       (		self			,			lowercase_			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = ChannelDimension.FIRST			,			**lowercase_			,			)					->					PIL.Image.Image:
														'''simple docstring'''

														lowerCAmelCase_										=     do_center_crop if do_center_crop is not None else self.do_center_crop
														lowerCAmelCase_										=     crop_size if crop_size is not None else self.crop_size
														lowerCAmelCase_										=     get_size_dict(lowercase_			,			param_name='crop_size'   )
														lowerCAmelCase_										=     do_resize if do_resize is not None else self.do_resize
														lowerCAmelCase_										=     size if size is not None else self.size
														lowerCAmelCase_										=     get_size_dict(lowercase_   )
														lowerCAmelCase_										=     resample if resample is not None else self.resample
														lowerCAmelCase_										=     do_rescale if do_rescale is not None else self.do_rescale
														lowerCAmelCase_										=     rescale_factor if rescale_factor is not None else self.rescale_factor
														lowerCAmelCase_										=     do_normalize if do_normalize is not None else self.do_normalize
														lowerCAmelCase_										=     image_mean if image_mean is not None else self.image_mean
														lowerCAmelCase_										=     image_std if image_std is not None else self.image_std

														lowerCAmelCase_										=     make_list_of_images(lowercase_   )

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

														if do_center_crop and crop_size is None:
																					raise ValueError('If `do_center_crop` is set to `True`, `crop_size` must be provided.'   )

														if do_resize and size is None:
																					raise ValueError('Size must be specified if do_resize is True.'   )

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

														if do_normalize and (image_mean is None or image_std is None):
																					raise ValueError('Image mean and image standard deviation must be specified if do_normalize is True.'   )

														# All transformations expect numpy arrays.
														lowerCAmelCase_										=     [to_numpy_array(lowercase_   ) for image in images]

														if do_center_crop:
																					lowerCAmelCase_										=     [self.center_crop(lowercase_			,			lowercase_			,			size=lowercase_   ) for image in images]

														if do_resize:
																					lowerCAmelCase_										=     [self.resize(image=lowercase_			,			size=lowercase_			,			resample=lowercase_   ) for image in images]

														if do_rescale:
																					lowerCAmelCase_										=     [self.rescale(image=lowercase_			,			scale=lowercase_   ) for image in images]

														if do_normalize:
																					lowerCAmelCase_										=     [self.normalize(image=lowercase_			,			mean=lowercase_			,			std=lowercase_   ) 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_   )



