


"""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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCAmelCase_				:			List[Any]   =	logging.get_logger(__name__)
class 			SCREAMING_SNAKE_CASE__  (    lowercase__      ):
      snake_case__    :					Optional[int]					       =						['''pixel_values''']
      def __init__(	self    :	Dict					,							SCREAMING_SNAKE_CASE__    :	bool = True					,							SCREAMING_SNAKE_CASE__    :	Optional[Dict[str, int]] = None					,							SCREAMING_SNAKE_CASE__    :	PILImageResampling = PILImageResampling.BILINEAR					,							SCREAMING_SNAKE_CASE__    :	bool = True					,							SCREAMING_SNAKE_CASE__    :	Dict[str, int] = None					,							SCREAMING_SNAKE_CASE__    :	bool = True					,							SCREAMING_SNAKE_CASE__    :	Union[int, float] = 1 / 2_5_5					,							SCREAMING_SNAKE_CASE__    :	bool = True					,							SCREAMING_SNAKE_CASE__    :	Optional[Union[float, List[float]]] = None					,							SCREAMING_SNAKE_CASE__    :	Optional[Union[float, List[float]]] = None					,							**SCREAMING_SNAKE_CASE__    :	Optional[Any]					,							)		->      None:
             super().__init__(**SCREAMING_SNAKE_CASE__					)
             a_ :		str							=  size if size is not None else {'shortest_edge': 2_5_6}
             a_ :		str							=  get_size_dict(SCREAMING_SNAKE_CASE__					,							default_to_square=SCREAMING_SNAKE_CASE__					)
             a_ :		Optional[int]							=  crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
             a_ :		str							=  get_size_dict(SCREAMING_SNAKE_CASE__					)
             a_ :		Union[str, Any]							=  do_resize
             a_ :		Union[str, Any]							=  size
             a_ :		Optional[int]							=  resample
             a_ :		Optional[Any]							=  do_center_crop
             a_ :		List[Any]							=  crop_size
             a_ :		Any							=  do_rescale
             a_ :		Union[str, Any]							=  rescale_factor
             a_ :		List[str]							=  do_normalize
             a_ :		Union[str, Any]							=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
             a_ :		Optional[Any]							=  image_std if image_std is not None else IMAGENET_STANDARD_STD
      def 	SCREAMING_SNAKE_CASE							(	self    :	Dict					,							SCREAMING_SNAKE_CASE__    :	np.ndarray					,							SCREAMING_SNAKE_CASE__    :	Dict[str, int]					,							SCREAMING_SNAKE_CASE__    :	PILImageResampling = PILImageResampling.BICUBIC					,							SCREAMING_SNAKE_CASE__    :	Optional[Union[str, ChannelDimension]] = None					,							**SCREAMING_SNAKE_CASE__    :	List[Any]					,							)		->      np.ndarray:
             a_ :		Union[str, Any]							=  get_size_dict(SCREAMING_SNAKE_CASE__					,							default_to_square=SCREAMING_SNAKE_CASE__					)
             if "shortest_edge" not in size:
                    raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""					)
             a_ :		int							=  get_resize_output_image_size(SCREAMING_SNAKE_CASE__					,							size=size['shortest_edge']					,							default_to_square=SCREAMING_SNAKE_CASE__					)
             return resize(SCREAMING_SNAKE_CASE__					,							size=SCREAMING_SNAKE_CASE__					,							resample=SCREAMING_SNAKE_CASE__					,							data_format=SCREAMING_SNAKE_CASE__					,							**SCREAMING_SNAKE_CASE__					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	str					,							SCREAMING_SNAKE_CASE__    :	np.ndarray					,							SCREAMING_SNAKE_CASE__    :	Dict[str, int]					,							SCREAMING_SNAKE_CASE__    :	Optional[Union[str, ChannelDimension]] = None					,							**SCREAMING_SNAKE_CASE__    :	str					,							)		->      np.ndarray:
             a_ :		Optional[int]							=  get_size_dict(SCREAMING_SNAKE_CASE__					)
             return center_crop(SCREAMING_SNAKE_CASE__					,							size=(size['height'], size['width'])					,							data_format=SCREAMING_SNAKE_CASE__					,							**SCREAMING_SNAKE_CASE__					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[Any]					,							SCREAMING_SNAKE_CASE__    :	np.ndarray					,							SCREAMING_SNAKE_CASE__    :	float					,							SCREAMING_SNAKE_CASE__    :	Optional[Union[str, ChannelDimension]] = None					,							**SCREAMING_SNAKE_CASE__    :	Union[str, Any]					)		->      np.ndarray:
             return rescale(SCREAMING_SNAKE_CASE__					,							scale=SCREAMING_SNAKE_CASE__					,							data_format=SCREAMING_SNAKE_CASE__					,							**SCREAMING_SNAKE_CASE__					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[int]					,							SCREAMING_SNAKE_CASE__    :	np.ndarray					,							SCREAMING_SNAKE_CASE__    :	Union[float, List[float]]					,							SCREAMING_SNAKE_CASE__    :	Union[float, List[float]]					,							SCREAMING_SNAKE_CASE__    :	Optional[Union[str, ChannelDimension]] = None					,							**SCREAMING_SNAKE_CASE__    :	Tuple					,							)		->      np.ndarray:
             return normalize(SCREAMING_SNAKE_CASE__					,							mean=SCREAMING_SNAKE_CASE__					,							std=SCREAMING_SNAKE_CASE__					,							data_format=SCREAMING_SNAKE_CASE__					,							**SCREAMING_SNAKE_CASE__					)

      def 	SCREAMING_SNAKE_CASE							(	self    :	Any					,							SCREAMING_SNAKE_CASE__    :	ImageInput					,							SCREAMING_SNAKE_CASE__    :	Optional[bool] = None					,							SCREAMING_SNAKE_CASE__    :	Dict[str, int] = None					,							SCREAMING_SNAKE_CASE__    :	PILImageResampling = None					,							SCREAMING_SNAKE_CASE__    :	bool = None					,							SCREAMING_SNAKE_CASE__    :	Dict[str, int] = None					,							SCREAMING_SNAKE_CASE__    :	Optional[bool] = None					,							SCREAMING_SNAKE_CASE__    :	Optional[float] = None					,							SCREAMING_SNAKE_CASE__    :	Optional[bool] = None					,							SCREAMING_SNAKE_CASE__    :	Optional[Union[float, List[float]]] = None					,							SCREAMING_SNAKE_CASE__    :	Optional[Union[float, List[float]]] = None					,							SCREAMING_SNAKE_CASE__    :	Optional[Union[str, TensorType]] = None					,							SCREAMING_SNAKE_CASE__    :	Union[str, ChannelDimension] = ChannelDimension.FIRST					,							**SCREAMING_SNAKE_CASE__    :	Optional[Any]					,							)		->      Union[str, Any]:
             a_ :		Dict							=  do_resize if do_resize is not None else self.do_resize
             a_ :		Any							=  size if size is not None else self.size
             a_ :		Any							=  get_size_dict(SCREAMING_SNAKE_CASE__					,							default_to_square=SCREAMING_SNAKE_CASE__					)
             a_ :		Union[str, Any]							=  resample if resample is not None else self.resample
             a_ :		Union[str, Any]							=  do_center_crop if do_center_crop is not None else self.do_center_crop
             a_ :		List[str]							=  crop_size if crop_size is not None else self.crop_size
             a_ :		str							=  get_size_dict(SCREAMING_SNAKE_CASE__					)
             a_ :		List[Any]							=  do_rescale if do_rescale is not None else self.do_rescale
             a_ :		List[str]							=  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_ :		Union[str, Any]							=  image_mean if image_mean is not None else self.image_mean
             a_ :		int							=  image_std if image_std is not None else self.image_std

             a_ :		Union[str, Any]							=  make_list_of_images(SCREAMING_SNAKE_CASE__					)

             if not valid_images(SCREAMING_SNAKE_CASE__					):
                    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.'					)

             # All transformations expect numpy arrays.
             a_ :		int							=  [to_numpy_array(SCREAMING_SNAKE_CASE__					) for image in images]

             if do_resize:
                    a_ :		Any							=  [self.resize(image=SCREAMING_SNAKE_CASE__					,							size=SCREAMING_SNAKE_CASE__					,							resample=SCREAMING_SNAKE_CASE__					) for image in images]

             if do_center_crop:
                    a_ :		Optional[Any]							=  [self.center_crop(image=SCREAMING_SNAKE_CASE__					,							size=SCREAMING_SNAKE_CASE__					) for image in images]

             if do_rescale:
                    a_ :		Any							=  [self.rescale(image=SCREAMING_SNAKE_CASE__					,							scale=SCREAMING_SNAKE_CASE__					) for image in images]

             if do_normalize:
                    a_ :		str							=  [self.normalize(image=SCREAMING_SNAKE_CASE__					,							mean=SCREAMING_SNAKE_CASE__					,							std=SCREAMING_SNAKE_CASE__					) for image in images]

             a_ :		Optional[int]							=  [to_channel_dimension_format(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					) for image in images]

             a_ :		Tuple							=  {'pixel_values': images}
             return BatchFeature(data=SCREAMING_SNAKE_CASE__					,							tensor_type=SCREAMING_SNAKE_CASE__					)






from string import ascii_uppercase

UpperCAmelCase_				:			Dict   =	{char: i for i, char in enumerate(ascii_uppercase)}
UpperCAmelCase_				:			Optional[int]   =	dict(enumerate(ascii_uppercase))







def 	SCREAMING_SNAKE_CASE_						(      __A			:      str  ,   __A			:      str    )     ->		str:

       """simple docstring"""

       a_ :		Tuple							=  len(__A    )
       a_ :		int							=  0
       while True:
              if x == i:
                     a_ :		Tuple							=  0
              if len(__A    ) == len(__A    ):
                     break
              key += key[i]
              i += 1
       return key







def 	SCREAMING_SNAKE_CASE_						(      __A			:      str  ,   __A			:      str    )     ->		str:

       """simple docstring"""

       a_ :		Optional[int]							=  ''
       a_ :		Any							=  0
       for letter in message:
              if letter == " ":
                     cipher_text += " "
              else:
                     a_ :		Optional[Any]							=  (dicta[letter] - dicta[key_new[i]]) % 26
                     i += 1
                     cipher_text += dicta[x]
       return cipher_text







def 	SCREAMING_SNAKE_CASE_						(      __A			:      str  ,   __A			:      str    )     ->		str:

       """simple docstring"""

       a_ :		Any							=  ''
       a_ :		Optional[Any]							=  0
       for letter in cipher_text:
              if letter == " ":
                     or_txt += " "
              else:
                     a_ :		Union[str, Any]							=  (dicta[letter] + dicta[key_new[i]] + 26) % 26
                     i += 1
                     or_txt += dicta[x]
       return or_txt







def 	SCREAMING_SNAKE_CASE_						(      )     ->		None:

       """simple docstring"""

       a_ :		Tuple							=  'THE GERMAN ATTACK'
       a_ :		Dict							=  'SECRET'
       a_ :		Optional[Any]							=  generate_key(__A  ,   __A    )
       a_ :		Union[str, Any]							=  cipher_text(__A  ,   __A    )
       print(F"""Encrypted Text = {s}"""    )
       print(F"""Original Text = {original_text(__A  ,   __A    )}"""    )


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   main()


