


def 							__lowercase     (				__lowerCAmelCase							:       str      ,					__lowerCAmelCase							:       int     ):
							a__    =  [[] for _ in range(_lowerCamelCase     )]
							a__    =  key - 1

							if key <= 0:
														raise ValueError('Height of grid can\'t be 0 or negative'     )
							if key == 1 or len(_lowerCamelCase     ) <= key:
														return input_string

							for position, character in enumerate(_lowerCamelCase     ):
														a__    =  position % (lowest * 2)  # puts it in bounds
														a__    =  min(_lowerCamelCase      ,					lowest * 2 - num     )  # creates zigzag pattern
														temp_grid[num].append(_lowerCamelCase     )
							a__    =  ["""""".join(_lowerCamelCase     ) for row in temp_grid]
							a__    =  """""".join(_lowerCamelCase     )

							return output_string






def 							__lowercase     (				__lowerCAmelCase							:       str      ,					__lowerCAmelCase							:       int     ):
							a__    =  []
							a__    =  key - 1

							if key <= 0:
														raise ValueError('Height of grid can\'t be 0 or negative'     )
							if key == 1:
														return input_string

							a__    =  [[] for _ in range(_lowerCamelCase     )]  # generates template
							for position in range(len(_lowerCamelCase     )     ):
														a__    =  position % (lowest * 2)  # puts it in bounds
														a__    =  min(_lowerCamelCase      ,					lowest * 2 - num     )  # creates zigzag pattern
														temp_grid[num].append('*'     )

							a__    =  0
							for row in temp_grid:  # fills in the characters
														a__    =  input_string[counter : counter + len(_lowerCamelCase     )]
														grid.append(list(_lowerCamelCase     )     )
														counter += len(_lowerCamelCase     )

							a__    =  """"""  # reads as zigzag
							for position in range(len(_lowerCamelCase     )     ):
														a__    =  position % (lowest * 2)  # puts it in bounds
														a__    =  min(_lowerCamelCase      ,					lowest * 2 - num     )  # creates zigzag pattern
														output_string += grid[num][0]
														grid[num].pop(0     )
							return output_string






def 							__lowercase     (				__lowerCAmelCase							:       str     ):
							a__    =  {}
							for key_guess in range(1      ,					len(_lowerCamelCase     )     ):  # tries every key
														a__    =  decrypt(_lowerCamelCase      ,					_lowerCamelCase     )
							return results


if __name__ == "__main__":
						import doctest

						doctest.testmod()








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


snake_case							: Dict      =   logging.get_logger(__name__)







class  snake_case_     (lowerCamelCase_   ):
					UpperCAmelCase__      :					Dict					   =    ['''pixel_values''']
					def __init__(   self     :Optional[Any]    ,__snake_case     :bool = True    ,__snake_case     :int = 32    ,__snake_case     :Union[str, Any]=PILImageResampling.BILINEAR    ,__snake_case     :bool = True    ,**__snake_case     :Tuple    ,)      ->					None:
												a__    =  do_resize
												a__    =  do_rescale
												a__    =  size_divisor
												a__    =  resample
												super().__init__(**__snake_case   )
					def 				lowerCamelCase__(   self     :Union[str, Any]    ,__snake_case     :np.ndarray    ,__snake_case     :int    ,__snake_case     :Tuple    ,__snake_case     :Optional[ChannelDimension] = None    ,**__snake_case     :List[Any]   )      ->					np.ndarray:
												a__   ,      a__    =  get_image_size(__snake_case   )
												# Rounds the height and width down to the closest multiple of size_divisor
												a__    =  height // size_divisor * size_divisor
												a__    =  width // size_divisor * size_divisor
												a__    =  resize(__snake_case    ,(new_h, new_w)    ,resample=__snake_case    ,data_format=__snake_case    ,**__snake_case   )
												return image
					def 				lowerCamelCase__(   self     :List[str]    ,__snake_case     :np.ndarray    ,__snake_case     :float    ,__snake_case     :Optional[ChannelDimension] = None    ,**__snake_case     :str   )      ->					np.ndarray:
												return rescale(image=__snake_case    ,scale=__snake_case    ,data_format=__snake_case    ,**__snake_case   )



					def 				lowerCamelCase__(   self     :Tuple    ,__snake_case     :Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]    ,__snake_case     :Optional[bool] = None    ,__snake_case     :Optional[int] = None    ,__snake_case     :Union[str, Any]=None    ,__snake_case     :Optional[bool] = None    ,__snake_case     :Optional[Union[TensorType, str]] = None    ,__snake_case     :ChannelDimension = ChannelDimension.FIRST    ,**__snake_case     :List[Any]    ,)      ->					BatchFeature:
												a__    =  do_resize if do_resize is not None else self.do_resize
												a__    =  do_rescale if do_rescale is not None else self.do_rescale
												a__    =  size_divisor if size_divisor is not None else self.size_divisor
												a__    =  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'   )

												a__    =  make_list_of_images(__snake_case   )

												if not valid_images(__snake_case   ):
																			raise ValueError('Invalid image(s)'   )

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

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

												if do_rescale:
																			a__    =  [self.rescale(__snake_case    ,scale=1 / 2_55   ) for image in images]

												a__    =  [to_channel_dimension_format(__snake_case    ,__snake_case   ) for image in images]

												a__    =  {'pixel_values': images}
												return BatchFeature(data=__snake_case    ,tensor_type=__snake_case   )




