



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 convert_to_rgb, 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


if is_vision_available():
  import PIL


lowerCamelCase      =		logging.get_logger(__name__)
class    lowercase__     (					_A   ):







       '''simple docstring'''


       UpperCamelCase		=  ['''pixel_values''']



       def __init__(							self    :						Tuple	,       _UpperCAmelCase    :						Union[str, Any] = True	,       _UpperCAmelCase    :						Optional[Any] = None	,       _UpperCAmelCase    :						List[Any] = PILImageResampling.BICUBIC	,       _UpperCAmelCase    :						str = True	,       _UpperCAmelCase    :						Tuple = 1 / 255	,       _UpperCAmelCase    :						List[Any] = True	,       _UpperCAmelCase    :						Any = None	,       _UpperCAmelCase    :						Any = None	,       _UpperCAmelCase    :						Union[str, Any] = True	,       **_UpperCAmelCase    :						str	,       )				->		None:


             '''simple docstring'''




             super().__init__(**_UpperCAmelCase					)
             UpperCAmelCase_    				=							size if size is not None else {"height": 384, "width": 384}
             UpperCAmelCase_    				=							get_size_dict(_UpperCAmelCase	,       default_to_square=_UpperCAmelCase					)

             UpperCAmelCase_    				=							do_resize
             UpperCAmelCase_    				=							size
             UpperCAmelCase_    				=							resample
             UpperCAmelCase_    				=							do_rescale
             UpperCAmelCase_    				=							rescale_factor
             UpperCAmelCase_    				=							do_normalize
             UpperCAmelCase_    				=							image_mean if image_mean is not None else OPENAI_CLIP_MEAN
             UpperCAmelCase_    				=							image_std if image_std is not None else OPENAI_CLIP_STD
             UpperCAmelCase_    				=							do_convert_rgb



       def   lowercase__       (							self    :						Any	,       _UpperCAmelCase    :						int	,       _UpperCAmelCase    :						Any	,       _UpperCAmelCase    :						Optional[int] = PILImageResampling.BICUBIC	,       _UpperCAmelCase    :						List[str] = None	,       **_UpperCAmelCase    :						Optional[Any]	,       )				->		np.ndarray:


             '''simple docstring'''




             UpperCAmelCase_    				=							get_size_dict(_UpperCAmelCase	,       default_to_square=_UpperCAmelCase					)
             if "height" not in size or "width" not in size:
                   raise ValueError(F"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"""					)
             UpperCAmelCase_    				=							(size["height"], size["width"])
             return resize(_UpperCAmelCase	,       size=_UpperCAmelCase	,       resample=_UpperCAmelCase	,       data_format=_UpperCAmelCase	,       **_UpperCAmelCase					)



       def   lowercase__       (							self    :						List[str]	,       _UpperCAmelCase    :						List[Any]	,       _UpperCAmelCase    :						Optional[int]	,       _UpperCAmelCase    :						Any = None	,       **_UpperCAmelCase    :						Tuple	,       )				->		str:


             '''simple docstring'''




             return rescale(_UpperCAmelCase	,       scale=_UpperCAmelCase	,       data_format=_UpperCAmelCase	,       **_UpperCAmelCase					)



       def   lowercase__       (							self    :						Union[str, Any]	,       _UpperCAmelCase    :						str	,       _UpperCAmelCase    :						Optional[Any]	,       _UpperCAmelCase    :						Any	,       _UpperCAmelCase    :						Any = None	,       **_UpperCAmelCase    :						List[str]	,       )				->		np.ndarray:


             '''simple docstring'''




             return normalize(_UpperCAmelCase	,       mean=_UpperCAmelCase	,       std=_UpperCAmelCase	,       data_format=_UpperCAmelCase	,       **_UpperCAmelCase					)







       def   lowercase__       (							self    :						Any	,       _UpperCAmelCase    :						Optional[Any]	,       _UpperCAmelCase    :						Dict = None	,       _UpperCAmelCase    :						List[Any] = None	,       _UpperCAmelCase    :						Tuple = None	,       _UpperCAmelCase    :						str = None	,       _UpperCAmelCase    :						Dict = None	,       _UpperCAmelCase    :						List[str] = None	,       _UpperCAmelCase    :						Optional[Any] = None	,       _UpperCAmelCase    :						Any = None	,       _UpperCAmelCase    :						str = None	,       _UpperCAmelCase    :						Tuple = None	,       _UpperCAmelCase    :						Any = ChannelDimension.FIRST	,       **_UpperCAmelCase    :						str	,       )				->		PIL.Image.Image:


             '''simple docstring'''




             UpperCAmelCase_    				=							do_resize if do_resize is not None else self.do_resize
             UpperCAmelCase_    				=							resample if resample is not None else self.resample
             UpperCAmelCase_    				=							do_rescale if do_rescale is not None else self.do_rescale
             UpperCAmelCase_    				=							rescale_factor if rescale_factor is not None else self.rescale_factor
             UpperCAmelCase_    				=							do_normalize if do_normalize is not None else self.do_normalize
             UpperCAmelCase_    				=							image_mean if image_mean is not None else self.image_mean
             UpperCAmelCase_    				=							image_std if image_std is not None else self.image_std
             UpperCAmelCase_    				=							do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

             UpperCAmelCase_    				=							size if size is not None else self.size
             UpperCAmelCase_    				=							get_size_dict(_UpperCAmelCase	,       default_to_square=_UpperCAmelCase					)

             UpperCAmelCase_    				=							make_list_of_images(_UpperCAmelCase					)

             if not valid_images(_UpperCAmelCase					):
                   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 or resample is None:
                   raise ValueError("Size and resample must be specified if do_resize 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_    				=							[convert_to_rgb(_UpperCAmelCase					) for image in images]

             # All transformations expect numpy arrays.
             UpperCAmelCase_    				=							[to_numpy_array(_UpperCAmelCase					) for image in images]

             if do_resize:
                   UpperCAmelCase_    				=							[self.resize(image=_UpperCAmelCase	,       size=_UpperCAmelCase	,       resample=_UpperCAmelCase					) for image in images]

             if do_rescale:
                   UpperCAmelCase_    				=							[self.rescale(image=_UpperCAmelCase	,       scale=_UpperCAmelCase					) for image in images]

             if do_normalize:
                   UpperCAmelCase_    				=							[self.normalize(image=_UpperCAmelCase	,       mean=_UpperCAmelCase	,       std=_UpperCAmelCase					) for image in images]

             UpperCAmelCase_    				=							[to_channel_dimension_format(_UpperCAmelCase	,       _UpperCAmelCase					) for image in images]

             UpperCAmelCase_    				=							BatchFeature(data={"pixel_values": images}	,       tensor_type=_UpperCAmelCase					)

             return encoded_outputs









"""simple docstring"""





import string
def      a__			(   lowerCAmelCase__   ):
      for key in range(len(string.ascii_uppercase   )   ):
            UpperCAmelCase_    				=							""
            for symbol in message:
                  if symbol in string.ascii_uppercase:
                        UpperCAmelCase_    				=							string.ascii_uppercase.find(lowerCAmelCase__   )
                        UpperCAmelCase_    				=							num - key
                        if num < 0:
                              UpperCAmelCase_    				=							num + len(string.ascii_uppercase   )
                        UpperCAmelCase_    				=							translated + string.ascii_uppercase[num]
                  else:
                        UpperCAmelCase_    				=							translated + symbol
            print(f"""Decryption using Key #{key}: {translated}"""   )
def      a__			(   ):
      UpperCAmelCase_    				=							input("Encrypted message: "   )
      UpperCAmelCase_    				=							message.upper()
      decrypt(lowerCAmelCase__   )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()




