code: 
  """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,
      convert_to_rgb,
      get_resize_output_image_size,
      normalize,
      rescale,
      resize,
      to_channel_dimension_format,
  )
  from ...image_utils import (
      OPENAI_CLIP_MEAN,
      OPENAI_CLIP_STD,
      ChannelDimension,
      ImageInput,
      PILImageResampling,
      make_list_of_images,
      to_numpy_array,
      valid_images,
  )
  from ...utils import TensorType, is_vision_available, logging
  
  
  _a	 =							logging.get_logger(__name__)
  
  
  if is_vision_available():
      import PIL
  
  
  class A_			(lowercase__   ):
  
  
  
  
    '''simple docstring'''
    SCREAMING_SNAKE_CASE__    :		Dict						  =	["""pixel_values"""]
  
  
  
  
  
  
  
    def __init__(	self       ,     lowercase_ = True       ,     lowercase_ = None       ,     lowercase_ = PILImageResampling.BICUBIC       ,     lowercase_ = True       ,     lowercase_ = None       ,     lowercase_ = True       ,     lowercase_ = 1 / 255       ,     lowercase_ = True       ,     lowercase_ = None       ,     lowercase_ = None       ,     lowercase_ = True       ,     **lowercase_       ,     ):
  
       """simple docstring"""
       super().__init__(**lowercase_						)
       UpperCAmelCase_      :      List[str]      =					size if size is not None else {"shortest_edge": 224}
       UpperCAmelCase_      :      Dict      =					get_size_dict(lowercase_       ,     default_to_square=lowercase_						)
       UpperCAmelCase_      :      Tuple      =					crop_size if crop_size is not None else {"height": 224, "width": 224}
       UpperCAmelCase_      :      Any      =					get_size_dict(lowercase_						)
  
       UpperCAmelCase_      :      str      =					do_resize
       UpperCAmelCase_      :      Any      =					size
       UpperCAmelCase_      :      Optional[int]      =					resample
       UpperCAmelCase_      :      Tuple      =					do_center_crop
       UpperCAmelCase_      :      str      =					crop_size
       UpperCAmelCase_      :      Any      =					do_rescale
       UpperCAmelCase_      :      List[Any]      =					rescale_factor
       UpperCAmelCase_      :      int      =					do_normalize
       UpperCAmelCase_      :      Union[str, Any]      =					image_mean if image_mean is not None else OPENAI_CLIP_MEAN
       UpperCAmelCase_      :      List[str]      =					image_std if image_std is not None else OPENAI_CLIP_STD
       UpperCAmelCase_      :      str      =					do_convert_rgb
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_       ,     lowercase_       ,     lowercase_ = PILImageResampling.BICUBIC       ,     lowercase_ = None       ,     **lowercase_       ,     ):
  
       """simple docstring"""
       UpperCAmelCase_      :      Optional[int]      =					get_size_dict(lowercase_       ,     default_to_square=lowercase_						)
       UpperCAmelCase_      :      List[str]      =					get_resize_output_image_size(
           lowercase_       ,     size=(size["height"], size["width"])       ,     default_to_square=lowercase_						)
       return resize(lowercase_       ,     size=lowercase_       ,     resample=lowercase_       ,     data_format=lowercase_       ,     **lowercase_						)
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_       ,     lowercase_       ,     lowercase_ = None       ,     **lowercase_       ,     ):
  
       """simple docstring"""
       UpperCAmelCase_      :      List[Any]      =					get_size_dict(lowercase_						)
       return center_crop(lowercase_       ,     size=(size["height"], size["width"])       ,     data_format=lowercase_       ,     **lowercase_						)
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_       ,     lowercase_       ,     lowercase_ = None       ,     **lowercase_       ,     ):
  
       """simple docstring"""
       return rescale(lowercase_       ,     scale=lowercase_       ,     data_format=lowercase_       ,     **lowercase_						)
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_ = None       ,     **lowercase_       ,     ):
  
       """simple docstring"""
       return normalize(lowercase_       ,     mean=lowercase_       ,     std=lowercase_       ,     data_format=lowercase_       ,     **lowercase_						)
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	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_ = None       ,     lowercase_ = ChannelDimension.FIRST       ,     **lowercase_       ,     ):
  
       """simple docstring"""
       UpperCAmelCase_      :      Dict      =					do_resize if do_resize is not None else self.do_resize
       UpperCAmelCase_      :      List[str]      =					size if size is not None else self.size
       UpperCAmelCase_      :      int      =					get_size_dict(lowercase_       ,     default_to_square=lowercase_						)
       UpperCAmelCase_      :      Optional[int]      =					resample if resample is not None else self.resample
       UpperCAmelCase_      :      int      =					do_center_crop if do_center_crop is not None else self.do_center_crop
       UpperCAmelCase_      :      Union[str, Any]      =					crop_size if crop_size is not None else self.crop_size
       UpperCAmelCase_      :      List[Any]      =					get_size_dict(lowercase_						)
       UpperCAmelCase_      :      List[Any]      =					do_rescale if do_rescale is not None else self.do_rescale
       UpperCAmelCase_      :      Optional[Any]      =					rescale_factor if rescale_factor is not None else self.rescale_factor
       UpperCAmelCase_      :      Optional[Any]      =					do_normalize if do_normalize is not None else self.do_normalize
       UpperCAmelCase_      :      int      =					image_mean if image_mean is not None else self.image_mean
       UpperCAmelCase_      :      Union[str, Any]      =					image_std if image_std is not None else self.image_std
       UpperCAmelCase_      :      Optional[int]      =					do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
  
       UpperCAmelCase_      :      Union[str, 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_resize and size is None:
          raise ValueError("Size must be specified if do_resize is True."						)
  
       if do_center_crop and crop_size is None:
          raise ValueError("Crop size must be specified if do_center_crop 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 std must be specified if do_normalize is True."						)
  
       # PIL RGBA images are converted to RGB
       if do_convert_rgb:
          UpperCAmelCase_      :      List[Any]      =					[convert_to_rgb(lowercase_						) for image in images]
  
       # All transformations expect numpy arrays.
       UpperCAmelCase_      :      Union[str, Any]      =					[to_numpy_array(lowercase_						) for image in images]
  
       if do_resize:
          UpperCAmelCase_      :      Optional[int]      =					[self.resize(image=lowercase_       ,     size=lowercase_       ,     resample=lowercase_						) for image in images]
  
       if do_center_crop:
          UpperCAmelCase_      :      Union[str, Any]      =					[self.center_crop(image=lowercase_       ,     size=lowercase_						) for image in images]
  
       if do_rescale:
          UpperCAmelCase_      :      Any      =					[self.rescale(image=lowercase_       ,     scale=lowercase_						) for image in images]
  
       if do_normalize:
          UpperCAmelCase_      :      Dict      =					[self.normalize(image=lowercase_       ,     mean=lowercase_       ,     std=lowercase_						) for image in images]
  
       UpperCAmelCase_      :      List[str]      =					[to_channel_dimension_format(lowercase_       ,     lowercase_						) for image in images]
  
       UpperCAmelCase_      :      Any      =					{"pixel_values": images}
       return BatchFeature(data=lowercase_       ,     tensor_type=lowercase_						)
  
  
  
style_context: 
  """simple docstring"""
  
  
  
  
  
  from string import ascii_uppercase
  
  
  
  
  
  
  
  def 			__a  (	__lowerCamelCase,	__lowerCamelCase,	__lowerCamelCase = False,	__lowerCamelCase = ascii_uppercase						):
     UpperCAmelCase_      :      List[str]      =					keyword.upper()
     UpperCAmelCase_      :      str      =					plaintext.upper()
     UpperCAmelCase_      :      Tuple      =					set(__lowerCamelCase						)
  
     # create a list of unique characters in the keyword - their order matters
     # it determines how we will map plaintext characters to the ciphertext
     UpperCAmelCase_      :      Tuple      =					[]
     for char in keyword:
        if char in alphabet_set and char not in unique_chars:
           unique_chars.append(__lowerCamelCase						)
      # the number of those unique characters will determine the number of rows
     UpperCAmelCase_      :      Union[str, Any]      =					len(__lowerCamelCase						)
  
     # create a shifted version of the alphabet
     UpperCAmelCase_      :      Tuple      =					unique_chars + [
         char for char in alphabet if char not in unique_chars
     ]
  
     # create a modified alphabet by splitting the shifted alphabet into rows
     UpperCAmelCase_      :      List[str]      =					[
         shifted_alphabet[k : k + num_unique_chars_in_keyword]
         for k in range(0,	26,	__lowerCamelCase						)
     ]
  
     # map the alphabet characters to the modified alphabet characters
     # going 'vertically' through the modified alphabet - consider columns first
     UpperCAmelCase_      :      Tuple      =					{}
     UpperCAmelCase_      :      Dict      =					0
     for column in range(__lowerCamelCase						):
        for row in modified_alphabet:
           # if current row (the last one) is too short, break out of loop
           if len(__lowerCamelCase						) <= column:
              break
  
           # map current letter to letter in modified alphabet
           UpperCAmelCase_      :      Tuple      =					row[column]
           letter_index += 1
  
     if verbose:
        print(__lowerCamelCase						)
     # create the encrypted text by mapping the plaintext to the modified alphabet
     return "".join(mapping[char] if char in mapping else char for char in plaintext						)
  
  
  if __name__ == "__main__":
      # example use
      print(mixed_keyword('college', 'UNIVERSITY'))
  
  
  