
"""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_						)




"""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'))


