





'''simple docstring'''


from __future__ import annotations
def 							UpperCAmelCase_						(		__lowerCamelCase      :      str			,__lowerCamelCase      :      dict[str, str]				):
   lowercase_    :Dict					    =				"", "", ""
   lowercase_    :List[str]					    =				[]

   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 							UpperCAmelCase_						(		__lowerCamelCase      :      str			,__lowerCamelCase      :      dict[str, str]				):
   lowercase_    :Union[str, Any]					    =				"", ""
   lowercase_    :str					    =				[]

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

   for digit in this_part:
      tmp += digit
      if len(__lowerCamelCase				) == len(__lowerCamelCase				):
         result.append(__lowerCamelCase				)
         lowercase_    :Any					    =				""

   return result[0], result[1], result[2]
def 							UpperCAmelCase_						(		__lowerCamelCase      :      str			,__lowerCamelCase      :      str				):
   # Validate message and alphabet, set to upper and remove spaces
   lowercase_    :List[Any]					    =				alphabet.replace(" "			,""				).upper()
   lowercase_    :Optional[Any]					    =				message.replace(" "			,""				).upper()

   # Check length and characters
   if len(__lowerCamelCase				) != 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
   lowercase_    :Optional[Any]					    =				(
       "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",
   )
   lowercase_    :Optional[int]					    =				{}
   lowercase_    :Union[str, Any]					    =				{}
   for letter, number in zip(__lowerCamelCase			,__lowerCamelCase				):
      lowercase_    :Optional[Any]					    =				number
      lowercase_    :Tuple					    =				letter

   return message, alphabet, character_to_number, number_to_character
def 							UpperCAmelCase_						(		__lowerCamelCase      :      str			,__lowerCamelCase      :      str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ."			,__lowerCamelCase      :      int = 5				):
   lowercase_    :Any					    =				__prepare(
       __lowerCamelCase			,__lowerCamelCase				)
   lowercase_    :List[Any]					    =				"", ""

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

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

   return encrypted
def 							UpperCAmelCase_						(		__lowerCamelCase      :      str			,__lowerCamelCase      :      str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ."			,__lowerCamelCase      :      int = 5				):
   lowercase_    :Optional[int]					    =				__prepare(
       __lowerCamelCase			,__lowerCamelCase				)
   lowercase_    :Union[str, Any]					    =				[]
   lowercase_    :Any					    =				""

   for i in range(0			,len(__lowerCamelCase				) + 1			,__lowerCamelCase				):
      lowercase_    :Any					    =				__decrypt_part(message[i : i + period]			,__lowerCamelCase				)

      for j in range(0			,len(__lowerCamelCase				)				):
         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      :  str      							='''DEFEND THE EAST WALL OF THE CASTLE.'''
       lowerCAmelCase      :  Any      							=encrypt_message(msg, '''EPSDUCVWYM.ZLKXNBTFGORIJHAQ''')
       lowerCAmelCase      :  Optional[Any]      							=decrypt_message(encrypted, '''EPSDUCVWYM.ZLKXNBTFGORIJHAQ''')
       print(F'''Encrypted: {encrypted}\nDecrypted: {decrypted}''')









'''simple docstring'''


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      :  Any      							=logging.get_logger(__name__)
class    a_     (       _lowerCAmelCase							):
     __A  				=    ["pixel_values"]





     def __init__(				self	:      Tuple			,   lowercase	:      bool = True			,   lowercase	:      Dict[str, int] = None			,   lowercase	:      bool = True			,   lowercase	:      Dict[str, int] = None			,   lowercase	:      PILImageResampling = PILImageResampling.BICUBIC			,   lowercase	:      bool = True			,   lowercase	:      Union[int, float] = 1 / 255			,   lowercase	:      bool = True			,   lowercase	:      Optional[Union[float, List[float]]] = None			,   lowercase	:      Optional[Union[float, List[float]]] = None			,   **lowercase	:      int			,   ):





        """simple docstring"""




        super().__init__(**lowercase		)
        lowercase_    :Dict					    =				crop_size if crop_size is not None else {"height": 256, "width": 256}
        lowercase_    :str					    =				get_size_dict(lowercase			,   param_name="crop_size"		)
        lowercase_    :Optional[Any]					    =				size if size is not None else {"height": 224, "width": 224}
        lowercase_    :Union[str, Any]					    =				get_size_dict(lowercase		)

        lowercase_    :Union[str, Any]					    =				do_center_crop
        lowercase_    :str					    =				crop_size
        lowercase_    :List[str]					    =				do_resize
        lowercase_    :str					    =				size
        lowercase_    :Tuple					    =				resample
        lowercase_    :int					    =				do_rescale
        lowercase_    :Union[str, Any]					    =				rescale_factor
        lowercase_    :Tuple					    =				do_normalize
        lowercase_    :List[Any]					    =				image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
        lowercase_    :Optional[int]					    =				image_std if image_std is not None else IMAGENET_DEFAULT_STD





     def 							lowercase__   (				self	:      Any			,   lowercase	:      np.ndarray			,   lowercase	:      Dict[str, int]			,   lowercase	:      Optional[int] = None			,   lowercase	:      Optional[Union[str, ChannelDimension]] = None			,   **lowercase	:      List[Any]			,   ):





        """simple docstring"""




        lowercase_    :Dict					    =				self.size if size is None else size
        lowercase_    :Dict					    =				get_size_dict(lowercase		)
        lowercase_    :List[Any]					    =				get_size_dict(lowercase			,   param_name="crop_size"		)

        lowercase_    ,							lowercase_    :List[str]					    =				get_image_size(lowercase		)
        lowercase_    :List[str]					    =				min(lowercase			,   lowercase		)
        lowercase_    :Tuple					    =				(size["height"] / crop_size["height"]) * min_dim
        lowercase_    :int					    =				(size["width"] / crop_size["width"]) * min_dim
        return center_crop(lowercase			,   size=(cropped_height, cropped_width)			,   data_format=lowercase			,   **lowercase		)





     def 							lowercase__   (				self	:      Union[str, Any]			,   lowercase	:      np.ndarray			,   lowercase	:      Dict[str, int]			,   lowercase	:      PILImageResampling = PIL.Image.BICUBIC			,   lowercase	:      Optional[Union[str, ChannelDimension]] = None			,   **lowercase	:      List[str]			,   ):





        """simple docstring"""




        lowercase_    :Optional[int]					    =				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	:      List[str]			,   lowercase	:      np.ndarray			,   lowercase	:      Union[int, float]			,   lowercase	:      Optional[Union[str, ChannelDimension]] = None			,   **lowercase	:      Union[str, Any]			,   ):





        """simple docstring"""




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





     def 							lowercase__   (				self	:      List[str]			,   lowercase	:      np.ndarray			,   lowercase	:      Union[float, List[float]]			,   lowercase	:      Union[float, List[float]]			,   lowercase	:      Optional[Union[str, ChannelDimension]] = None			,   **lowercase	:      str			,   ):





        """simple docstring"""




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







     def 							lowercase__   (				self	:      List[Any]			,   lowercase	:      ImageInput			,   lowercase	:      Optional[bool] = None			,   lowercase	:      Optional[Dict[str, int]] = None			,   lowercase	:      Optional[bool] = None			,   lowercase	:      Optional[Dict[str, int]] = None			,   lowercase	:      PILImageResampling = None			,   lowercase	:      Optional[bool] = None			,   lowercase	:      Optional[float] = None			,   lowercase	:      Optional[bool] = None			,   lowercase	:      Optional[Union[float, List[float]]] = None			,   lowercase	:      Optional[Union[float, List[float]]] = None			,   lowercase	:      Optional[Union[str, TensorType]] = None			,   lowercase	:      ChannelDimension = ChannelDimension.FIRST			,   **lowercase	:      Optional[Any]			,   ):





        """simple docstring"""




        lowercase_    :str					    =				do_center_crop if do_center_crop is not None else self.do_center_crop
        lowercase_    :Union[str, Any]					    =				crop_size if crop_size is not None else self.crop_size
        lowercase_    :Optional[Any]					    =				get_size_dict(lowercase			,   param_name="crop_size"		)
        lowercase_    :Union[str, Any]					    =				do_resize if do_resize is not None else self.do_resize
        lowercase_    :Any					    =				size if size is not None else self.size
        lowercase_    :List[Any]					    =				get_size_dict(lowercase		)
        lowercase_    :Optional[Any]					    =				resample if resample is not None else self.resample
        lowercase_    :Dict					    =				do_rescale if do_rescale is not None else self.do_rescale
        lowercase_    :List[Any]					    =				rescale_factor if rescale_factor is not None else self.rescale_factor
        lowercase_    :str					    =				do_normalize if do_normalize is not None else self.do_normalize
        lowercase_    :Dict					    =				image_mean if image_mean is not None else self.image_mean
        lowercase_    :List[Any]					    =				image_std if image_std is not None else self.image_std

        lowercase_    :Any					    =				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.
        lowercase_    :Optional[int]					    =				[to_numpy_array(lowercase		) for image in images]

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

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

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

        if do_normalize:
           lowercase_    :Optional[Any]					    =				[self.normalize(image=lowercase			,   mean=lowercase			,   std=lowercase		) for image in images]

        lowercase_    :Optional[Any]					    =				[to_channel_dimension_format(lowercase			,   lowercase		) for image in images]

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


