code: 
  
  
  
  
  
  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 (
      get_resize_output_image_size,
      normalize,
      pad,
      rescale,
      resize,
      to_channel_dimension_format,
  )
  from ...image_utils import (
      IMAGENET_STANDARD_MEAN,
      IMAGENET_STANDARD_STD,
      ChannelDimension,
      ImageInput,
      PILImageResampling,
      get_image_size,
      make_list_of_images,
      to_numpy_array,
      valid_images,
  )
  from ...utils import TensorType, logging
  from ...utils.import_utils import is_vision_available
  
  
  UpperCAmelCase			:			Optional[int]	  = logging.get_logger(__name__)
  
  
  if is_vision_available():
       import PIL
  
  
  
  
  
  
  
  class        __lowerCAmelCase (					UpperCamelCase__):
        _lowercase       :      Dict        =    ["""pixel_values"""]
  
  
  
  
  
  
  
        def __init__(					self	,   lowerCAmelCase__ = True	,   lowerCAmelCase__ = None	,   lowerCAmelCase__ = PILImageResampling.BILINEAR	,   lowerCAmelCase__ = True	,   lowerCAmelCase__ = False	,   lowerCAmelCase__ = True	,   lowerCAmelCase__ = True	,   lowerCAmelCase__ = 1 / 2_5_5	,   lowerCAmelCase__ = True	,   lowerCAmelCase__ = None	,   lowerCAmelCase__ = None	,   **lowerCAmelCase__	,   )     ->		None:
  
  
  
          '''simple docstring'''
  
  
  
  
          super().__init__(**lowerCAmelCase__							)
  
          a__			:      Dict            =size if size is not None else {"height": 2_5_6_0, "width": 1_9_2_0}
          if isinstance(lowerCAmelCase__	,   (tuple, list)							):
            # The previous feature extractor size parameter was in (width, height) format
            a__			:      Optional[Any]            =size[::-1]
          a__			:      str            =get_size_dict(lowerCAmelCase__							)
  
          a__			:      List[str]            =do_resize
          a__			:      Tuple            =size
          a__			:      str            =resample
          a__			:      Dict            =do_thumbnail
          a__			:      Union[str, Any]            =do_align_long_axis
          a__			:      Tuple            =do_pad
          a__			:      List[Any]            =do_rescale
          a__			:      List[str]            =rescale_factor
          a__			:      int            =do_normalize
          a__			:      Any            =image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          a__			:      Union[str, Any]            =image_std if image_std is not None else IMAGENET_STANDARD_STD
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__ = None							)     ->		np.ndarray:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__      ,							a__			:      str            =get_image_size(lowerCAmelCase__							)
          a__      ,							a__			:      List[str]            =size["height"], size["width"]
  
          if (output_width < output_height and input_width > input_height) or (
              output_width > output_height and input_width < input_height
          ):
            a__			:      int            =np.rotaa(lowerCAmelCase__	,   3							)
  
          if data_format is not None:
            a__			:      Any            =to_channel_dimension_format(lowerCAmelCase__	,   lowerCAmelCase__							)
  
          return image
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__ = False	,   lowerCAmelCase__ = None	,   )     ->		np.ndarray:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__      ,							a__			:      str            =size["height"], size["width"]
          a__      ,							a__			:      Optional[int]            =get_image_size(lowerCAmelCase__							)
  
          a__			:      int            =output_width - input_width
          a__			:      List[Any]            =output_height - input_height
  
          if random_padding:
            a__			:      Any            =np.random.randint(low=0	,   high=delta_height + 1							)
            a__			:      Union[str, Any]            =np.random.randint(low=0	,   high=delta_width + 1							)
          else:
            a__			:      Dict            =delta_height // 2
            a__			:      Tuple            =delta_width // 2
  
          a__			:      str            =delta_height - pad_top
          a__			:      Any            =delta_width - pad_left
  
          a__			:      List[Any]            =((pad_top, pad_bottom), (pad_left, pad_right))
          return pad(lowerCAmelCase__	,   lowerCAmelCase__	,   data_format=lowerCAmelCase__							)
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   *lowerCAmelCase__	,   **lowerCAmelCase__							)     ->		str:
  
  
  
          '''simple docstring'''
  
  
  
  
          logger.info("pad is deprecated and will be removed in version 4.27. Please use pad_image instead."							)
          return self.pad_image(*lowerCAmelCase__	,   **lowerCAmelCase__							)
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__ = PILImageResampling.BICUBIC	,   lowerCAmelCase__ = None	,   **lowerCAmelCase__	,   )     ->		np.ndarray:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__      ,							a__			:      int            =get_image_size(lowerCAmelCase__							)
          a__      ,							a__			:      Any            =size["height"], size["width"]
  
          # We always resize to the smallest of either the input or output size.
          a__			:      List[Any]            =min(lowerCAmelCase__	,   lowerCAmelCase__							)
          a__			:      Dict            =min(lowerCAmelCase__	,   lowerCAmelCase__							)
  
          if height == input_height and width == input_width:
            return image
  
          if input_height > input_width:
            a__			:      Optional[Any]            =int(input_width * height / input_height							)
          elif input_width > input_height:
            a__			:      Any            =int(input_height * width / input_width							)
  
          return resize(
              lowerCAmelCase__	,   size=(height, width)	,   resample=lowerCAmelCase__	,   reducing_gap=2.0	,   data_format=lowerCAmelCase__	,   **lowerCAmelCase__							)
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__ = PILImageResampling.BICUBIC	,   lowerCAmelCase__ = None	,   **lowerCAmelCase__	,   )     ->		np.ndarray:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      Dict            =get_size_dict(lowerCAmelCase__							)
          a__			:      Dict            =min(size["height"]	,   size["width"]							)
          a__			:      Any            =get_resize_output_image_size(lowerCAmelCase__	,   size=lowerCAmelCase__	,   default_to_square=lowerCAmelCase__							)
          a__			:      Any            =resize(lowerCAmelCase__	,   size=lowerCAmelCase__	,   resample=lowerCAmelCase__	,   data_format=lowerCAmelCase__	,   **lowerCAmelCase__							)
          return resized_image
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__ = None	,   **lowerCAmelCase__	,   )     ->		List[str]:
  
  
  
          '''simple docstring'''
  
  
  
  
          return rescale(lowerCAmelCase__	,   scale=lowerCAmelCase__	,   data_format=lowerCAmelCase__	,   **lowerCAmelCase__							)
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__ = None	,   **lowerCAmelCase__	,   )     ->		np.ndarray:
  
  
  
          '''simple docstring'''
  
  
  
  
          return normalize(lowerCAmelCase__	,   mean=lowerCAmelCase__	,   std=lowerCAmelCase__	,   data_format=lowerCAmelCase__	,   **lowerCAmelCase__							)
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__ = None	,   lowerCAmelCase__ = None	,   lowerCAmelCase__ = None	,   lowerCAmelCase__ = None	,   lowerCAmelCase__ = None	,   lowerCAmelCase__ = None	,   lowerCAmelCase__ = False	,   lowerCAmelCase__ = None	,   lowerCAmelCase__ = None	,   lowerCAmelCase__ = None	,   lowerCAmelCase__ = None	,   lowerCAmelCase__ = None	,   lowerCAmelCase__ = None	,   lowerCAmelCase__ = ChannelDimension.FIRST	,   **lowerCAmelCase__	,   )     ->		PIL.Image.Image:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      List[Any]            =do_resize if do_resize is not None else self.do_resize
          a__			:      Union[str, Any]            =size if size is not None else self.size
          if isinstance(lowerCAmelCase__	,   (tuple, list)							):
            # Previous feature extractor had size in (width, height) format
            a__			:      Optional[Any]            =size[::-1]
          a__			:      Optional[int]            =get_size_dict(lowerCAmelCase__							)
          a__			:      int            =resample if resample is not None else self.resample
          a__			:      List[Any]            =do_thumbnail if do_thumbnail is not None else self.do_thumbnail
          a__			:      str            =do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
          a__			:      Optional[Any]            =do_pad if do_pad is not None else self.do_pad
          a__			:      Optional[int]            =do_rescale if do_rescale is not None else self.do_rescale
          a__			:      List[Any]            =rescale_factor if rescale_factor is not None else self.rescale_factor
          a__			:      List[str]            =do_normalize if do_normalize is not None else self.do_normalize
          a__			:      Dict            =image_mean if image_mean is not None else self.image_mean
          a__			:      Optional[int]            =image_std if image_std is not None else self.image_std
  
          a__			:      Union[str, Any]            =make_list_of_images(lowerCAmelCase__							)
  
          if not valid_images(lowerCAmelCase__							):
            raise ValueError(
                "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                "torch.Tensor, tf.Tensor or jax.ndarray."							)
  
          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_pad and size is None:
            raise ValueError("Size must be specified if do_pad is True."							)
  
          if do_normalize and (image_mean is None or image_std is None):
            raise ValueError("Image mean and std must be specified if do_normalize is True."							)
  
          # All transformations expect numpy arrays.
          a__			:      Optional[int]            =[to_numpy_array(lowerCAmelCase__							) for image in images]
  
          if do_align_long_axis:
            a__			:      Optional[Any]            =[self.align_long_axis(lowerCAmelCase__	,   size=lowerCAmelCase__							) for image in images]
  
          if do_resize:
            a__			:      List[str]            =[self.resize(image=lowerCAmelCase__	,   size=lowerCAmelCase__	,   resample=lowerCAmelCase__							) for image in images]
  
          if do_thumbnail:
            a__			:      List[str]            =[self.thumbnail(image=lowerCAmelCase__	,   size=lowerCAmelCase__							) for image in images]
  
          if do_pad:
            a__			:      Optional[int]            =[self.pad_image(image=lowerCAmelCase__	,   size=lowerCAmelCase__	,   random_padding=lowerCAmelCase__							) for image in images]
  
          if do_rescale:
            a__			:      List[str]            =[self.rescale(image=lowerCAmelCase__	,   scale=lowerCAmelCase__							) for image in images]
  
          if do_normalize:
            a__			:      List[str]            =[self.normalize(image=lowerCAmelCase__	,   mean=lowerCAmelCase__	,   std=lowerCAmelCase__							) for image in images]
  
          a__			:      List[str]            =[to_channel_dimension_format(lowerCAmelCase__	,   lowerCAmelCase__							) for image in images]
  
          a__			:      str            ={"pixel_values": images}
          return BatchFeature(data=lowerCAmelCase__	,   tensor_type=lowerCAmelCase__							)
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  from __future__ import annotations
  def   _A (	SCREAMING_SNAKE_CASE  :     str , SCREAMING_SNAKE_CASE  :     dict[str, str]		):
  
  
  
  
  
    """simple docstring"""
  
  
  
    a__      ,							a__      ,							a__			:      Optional[int]            ="", "", ""
    a__			:      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   _A (	SCREAMING_SNAKE_CASE  :     str , SCREAMING_SNAKE_CASE  :     dict[str, str]		):
  
  
  
  
  
    """simple docstring"""
  
  
  
    a__      ,							a__			:      Any            ="", ""
    a__			:      Optional[int]            =[]
  
    for character in message_part:
      this_part += character_to_number[character]
  
    for digit in this_part:
      tmp += digit
      if len(SCREAMING_SNAKE_CASE		) == len(SCREAMING_SNAKE_CASE		):
        result.append(SCREAMING_SNAKE_CASE		)
        a__			:      str            =""
  
    return result[0], result[1], result[2]
  def   _A (	SCREAMING_SNAKE_CASE  :     str , SCREAMING_SNAKE_CASE  :     str		):
  
  
  
  
  
    """simple docstring"""
  
  
  
    a__			:      Optional[Any]            =alphabet.replace(" " , ""		).upper()
    a__			:      List[str]            =message.replace(" " , ""		).upper()
  
    # Check length and characters
    if len(SCREAMING_SNAKE_CASE		) != 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__			:      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",
    )
    a__			:      Tuple            ={}
    a__			:      List[str]            ={}
    for letter, number in zip(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE		):
      a__			:      Dict            =number
      a__			:      Union[str, Any]            =letter
  
    return message, alphabet, character_to_number, number_to_character
  def   _A (	SCREAMING_SNAKE_CASE  :     str , SCREAMING_SNAKE_CASE  :     str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ." , SCREAMING_SNAKE_CASE  :     int = 5		):
  
  
  
  
  
    """simple docstring"""
  
  
  
    a__      ,							a__      ,							a__      ,							a__			:      Union[str, Any]            =__prepare(
        SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE		)
    a__      ,							a__			:      Union[str, Any]            ="", ""
  
    for i in range(0 , len(SCREAMING_SNAKE_CASE		) + 1 , SCREAMING_SNAKE_CASE		):
      encrypted_numeric += __encrypt_part(
          message[i : i + period] , SCREAMING_SNAKE_CASE		)
  
    for i in range(0 , len(SCREAMING_SNAKE_CASE		) , 3		):
      encrypted += number_to_character[encrypted_numeric[i : i + 3]]
  
    return encrypted
  def   _A (	SCREAMING_SNAKE_CASE  :     str , SCREAMING_SNAKE_CASE  :     str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ." , SCREAMING_SNAKE_CASE  :     int = 5		):
  
  
  
  
  
    """simple docstring"""
  
  
  
    a__      ,							a__      ,							a__      ,							a__			:      Dict            =__prepare(
        SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE		)
    a__			:      Optional[Any]            =[]
    a__			:      Any            =""
  
    for i in range(0 , len(SCREAMING_SNAKE_CASE		) + 1 , SCREAMING_SNAKE_CASE		):
      a__      ,							a__      ,							a__			:      Union[str, Any]            =__decrypt_part(message[i : i + period] , SCREAMING_SNAKE_CASE		)
  
      for j in range(0 , len(SCREAMING_SNAKE_CASE		)		):
        decrypted_numeric.append(a[j] + b[j] + c[j]		)
  
    for each in decrypted_numeric:
      decrypted += number_to_character[each]
  
    return decrypted
  
  
  if __name__ == "__main__":
       UpperCAmelCase			:			List[str]	  = """DEFEND THE EAST WALL OF THE CASTLE."""
       UpperCAmelCase			:			Optional[Any]	  = encrypt_message(msg, """EPSDUCVWYM.ZLKXNBTFGORIJHAQ""")
       UpperCAmelCase			:			Optional[int]	  = decrypt_message(encrypted, """EPSDUCVWYM.ZLKXNBTFGORIJHAQ""")
       print(F"""Encrypted: {encrypted}\nDecrypted: {decrypted}""")
  
  
  
  
  
  
  