"""simple docstring"""




from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_a				:		Optional[int]	   =       logging.get_logger(__name__)
class 						__A							(			SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      Any		 =		["pixel_values"]






     def __init__(		self    ,					a__ = True    ,					a__ = 32    ,					a__=PILImageResampling.BILINEAR    ,					a__ = True    ,					**a__    ,					):
       _lowerCAmelCase						:	str        =					do_resize
       _lowerCAmelCase						:	List[str]        =					do_rescale
       _lowerCAmelCase						:	List[str]        =					size_divisor
       _lowerCAmelCase						:	Any        =					resample
       super().__init__(**a__  )






     def    __A  (		self    ,					a__    ,					a__    ,					a__    ,					a__ = None    ,					**a__  ):
       _lowerCAmelCase      ,     _lowerCAmelCase						:	Union[str, Any]        =					get_image_size(a__  )
       # Rounds the height and width down to the closest multiple of size_divisor
       _lowerCAmelCase						:	Dict        =					height // size_divisor * size_divisor
       _lowerCAmelCase						:	int        =					width // size_divisor * size_divisor
       _lowerCAmelCase						:	List[str]        =					resize(a__    ,					(new_h, new_w)    ,					resample=a__    ,					data_format=a__    ,					**a__  )
       return image






     def    __A  (		self    ,					a__    ,					a__    ,					a__ = None    ,					**a__  ):
       return rescale(image=a__    ,					scale=a__    ,					data_format=a__    ,					**a__  )






     def    __A  (		self    ,					a__    ,					a__ = None    ,					a__ = None    ,					a__=None    ,					a__ = None    ,					a__ = None    ,					a__ = ChannelDimension.FIRST    ,					**a__    ,					):
       _lowerCAmelCase						:	int        =					do_resize if do_resize is not None else self.do_resize
       _lowerCAmelCase						:	Dict        =					do_rescale if do_rescale is not None else self.do_rescale
       _lowerCAmelCase						:	Union[str, Any]        =					size_divisor if size_divisor is not None else self.size_divisor
       _lowerCAmelCase						:	Dict        =					resample if resample is not None else self.resample

       if do_resize and size_divisor is None:
         raise ValueError("""size_divisor is required for resizing"""  )

       _lowerCAmelCase						:	Optional[Any]        =					make_list_of_images(a__  )

       if not valid_images(a__  ):
         raise ValueError("""Invalid image(s)"""  )

       # All transformations expect numpy arrays.
       _lowerCAmelCase						:	int        =					[to_numpy_array(a__  ) for img in images]

       if do_resize:
         _lowerCAmelCase						:	Any        =					[self.resize(a__    ,					size_divisor=a__    ,					resample=a__  ) for image in images]

       if do_rescale:
         _lowerCAmelCase						:	List[str]        =					[self.rescale(a__    ,					scale=1 / 255  ) for image in images]

       _lowerCAmelCase						:	Dict        =					[to_channel_dimension_format(a__    ,					a__  ) for image in images]

       _lowerCAmelCase						:	List[Any]        =					{"""pixel_values""": images}
       return BatchFeature(data=a__    ,					tensor_type=a__  )





"""simple docstring"""




import math





def 							SCREAMING_SNAKE_CASE					(      )	->				None:
  _lowerCAmelCase						:	Any        =					input("""Enter message: """     )
  _lowerCAmelCase						:	List[Any]        =					int(input(f"Enter key [2-{len(_lowerCamelCase     ) - 1}]: "     )     )
  _lowerCAmelCase						:	Optional[Any]        =					input("""Encryption/Decryption [e/d]: """     )

  if mode.lower().startswith("""e"""     ):
    _lowerCAmelCase						:	Tuple        =					encrypt_message(_lowerCamelCase      ,_lowerCamelCase     )
  elif mode.lower().startswith("""d"""     ):
    _lowerCAmelCase						:	Dict        =					decrypt_message(_lowerCamelCase      ,_lowerCamelCase     )

  # Append pipe symbol (vertical bar) to identify spaces at the end.
  print(f"Output:\n{text + '|'}"     )





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	int      ,_lowerCamelCase						:	str     )	->				str:
  _lowerCAmelCase						:	Dict        =					[""""""] * key
  for col in range(_lowerCamelCase     ):
    _lowerCAmelCase						:	List[str]        =					col
    while pointer < len(_lowerCamelCase     ):
      cipher_text[col] += message[pointer]
      pointer += key
  return "".join(_lowerCamelCase     )





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	int      ,_lowerCamelCase						:	str     )	->				str:
  _lowerCAmelCase						:	str        =					math.ceil(len(_lowerCamelCase     ) / key     )
  _lowerCAmelCase						:	Union[str, Any]        =					key
  _lowerCAmelCase						:	Any        =					(num_cols * num_rows) - len(_lowerCamelCase     )
  _lowerCAmelCase						:	Dict        =					[""""""] * num_cols
  _lowerCAmelCase						:	Optional[Any]        =					0
  _lowerCAmelCase						:	Dict        =					0

  for symbol in message:
    plain_text[col] += symbol
    col += 1

    if (
        (col == num_cols)
        or (col == num_cols - 1)
        and (row >= num_rows - num_shaded_boxes)
    ):
      _lowerCAmelCase						:	str        =					0
      row += 1

  return "".join(_lowerCamelCase     )


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     main()




