
'''simple docstring'''





from __future__ import annotations



def        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase     ):
				A_	,					A_	,					A_       	=  '''''', '''''', ''''''
				A_       	=  []

				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_  (   __UpperCamelCase ,						__UpperCamelCase     ):
				A_	,					A_       	=  '''''', ''''''
				A_       	=  []

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

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

				return result[0], result[1], result[2]



def        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase     ):
				# Validate message and alphabet, set to upper and remove spaces
				A_       	=  alphabet.replace(''' ''' ,						''''''     ).upper()
				A_       	=  message.replace(''' ''' ,						''''''     ).upper()

				# Check length and characters
				if len(__UpperCamelCase     ) != 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
				A_       	=  (
				    '''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''',
				)
				A_       	=  {}
				A_       	=  {}
				for letter, number in zip(__UpperCamelCase ,						__UpperCamelCase     ):
								A_       	=  number
								A_       	=  letter

				return message, alphabet, character_to_number, number_to_character



def        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase = "ABCDEFGHIJKLMNOPQRSTUVWXYZ." ,						__UpperCamelCase = 5     ):
				A_	,					A_	,					A_	,					A_       	=  __prepare(
				    __UpperCamelCase ,						__UpperCamelCase     )
				A_	,					A_       	=  '''''', ''''''

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

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

				return encrypted



def        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase = "ABCDEFGHIJKLMNOPQRSTUVWXYZ." ,						__UpperCamelCase = 5     ):
				A_	,					A_	,					A_	,					A_       	=  __prepare(
				    __UpperCamelCase ,						__UpperCamelCase     )
				A_       	=  []
				A_       	=  ''''''

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

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

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

				return decrypted


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE   :		Any					 =       "DEFEND THE EAST WALL OF THE CASTLE."
						SCREAMING_SNAKE_CASE   :		Optional[Any]					 =       encrypt_message(msg, "EPSDUCVWYM.ZLKXNBTFGORIJHAQ")
						SCREAMING_SNAKE_CASE   :		List[Any]					 =       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


SCREAMING_SNAKE_CASE   :		Optional[int]					 =       logging.get_logger(__name__)






class 					__lowercase		(					A       ):
					__magic_name__						:							int     	=	['''pixel_values''']


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





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

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


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__       ,       a__ = None       ,       a__ = None       ,       **a__       ,       )		->							np.ndarray:





									'''simple docstring'''
									A_       	=  self.size if size is None else size
									A_       	=  get_size_dict(a__						)
									A_       	=  get_size_dict(a__       ,       param_name='''crop_size'''						)

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


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__       ,       a__ = PIL.Image.BICUBIC       ,       a__ = None       ,       **a__       ,       )		->							np.ndarray:





									'''simple docstring'''
									A_       	=  get_size_dict(a__						)
									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(
									    a__       ,       size=(size['''height'''], size['''width'''])       ,       resample=a__       ,       data_format=a__       ,       **a__						)


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__       ,       a__ = None       ,       **a__       ,       )		->							Union[str, Any]:





									'''simple docstring'''
									return rescale(a__       ,       scale=a__       ,       data_format=a__       ,       **a__						)


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__       ,       a__       ,       a__ = None       ,       **a__       ,       )		->							np.ndarray:





									'''simple docstring'''
									return normalize(a__       ,       mean=a__       ,       std=a__       ,       data_format=a__       ,       **a__						)


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = ChannelDimension.FIRST       ,       **a__       ,       )		->							PIL.Image.Image:





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

									A_       	=  make_list_of_images(a__						)

									if not valid_images(a__						):
													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.
									A_       	=  [to_numpy_array(a__						) for image in images]

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

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

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

									if do_normalize:
													A_       	=  [self.normalize(image=a__       ,       mean=a__       ,       std=a__						) for image in images]

									A_       	=  [to_channel_dimension_format(a__       ,       a__						) for image in images]

									A_       	=  {'''pixel_values''': images}
									return BatchFeature(data=a__       ,       tensor_type=a__						)