

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


UpperCAmelCase__ :     Tuple				    =    logging.get_logger(__name__)







class        a__       (   UpperCAmelCase       ):







  """simple docstring"""




  UpperCAmelCase__      : Any			       =["""pixel_values"""]


  def __init__(			self    : Tuple				,				UpperCAmelCase__    : bool = True				,				UpperCAmelCase__    : Dict[str, int] = None				,				UpperCAmelCase__    : PILImageResampling = PILImageResampling.BICUBIC				,				UpperCAmelCase__    : bool = True				,				UpperCAmelCase__    : Union[int, float] = 1 / 2_5_5				,				UpperCAmelCase__    : bool = True				,				UpperCAmelCase__    : Optional[Union[float, List[float]]] = None				,				UpperCAmelCase__    : Optional[Union[float, List[float]]] = None				,				UpperCAmelCase__    : bool = True				,				**UpperCAmelCase__    : int				,				)					->None:
        """simple docstring"""







        super().__init__(**UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      str       =   size if size is not None else {"""height""": 3_8_4, """width""": 3_8_4}
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   get_size_dict(UpperCAmelCase__				,				default_to_square=UpperCAmelCase__		)

        SCREAMING_SNAKE_CASE :      Optional[Any]       =   do_resize
        SCREAMING_SNAKE_CASE :      Dict       =   size
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   resample
        SCREAMING_SNAKE_CASE :      List[Any]       =   do_rescale
        SCREAMING_SNAKE_CASE :      List[str]       =   rescale_factor
        SCREAMING_SNAKE_CASE :      Any       =   do_normalize
        SCREAMING_SNAKE_CASE :      Optional[int]       =   image_mean if image_mean is not None else OPENAI_CLIP_MEAN
        SCREAMING_SNAKE_CASE :      Optional[int]       =   image_std if image_std is not None else OPENAI_CLIP_STD
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   do_convert_rgb


  def  _lowercase      (			self    : int				,				UpperCAmelCase__    : np.ndarray				,				UpperCAmelCase__    : Dict[str, int]				,				UpperCAmelCase__    : PILImageResampling = PILImageResampling.BICUBIC				,				UpperCAmelCase__    : Optional[Union[str, ChannelDimension]] = None				,				**UpperCAmelCase__    : str				,				)					->np.ndarray:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Dict       =   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()}"		)
        SCREAMING_SNAKE_CASE :      Tuple       =   (size["""height"""], size["""width"""])
        return resize(UpperCAmelCase__				,				size=UpperCAmelCase__				,				resample=UpperCAmelCase__				,				data_format=UpperCAmelCase__				,				**UpperCAmelCase__		)


  def  _lowercase      (			self    : Optional[Any]				,				UpperCAmelCase__    : np.ndarray				,				UpperCAmelCase__    : Union[int, float]				,				UpperCAmelCase__    : Optional[Union[str, ChannelDimension]] = None				,				**UpperCAmelCase__    : str				,				)					->Union[str, Any]:
        """simple docstring"""







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


  def  _lowercase      (			self    : Optional[int]				,				UpperCAmelCase__    : np.ndarray				,				UpperCAmelCase__    : Union[float, List[float]]				,				UpperCAmelCase__    : Union[float, List[float]]				,				UpperCAmelCase__    : Optional[Union[str, ChannelDimension]] = None				,				**UpperCAmelCase__    : Dict				,				)					->np.ndarray:
        """simple docstring"""







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




  def  _lowercase      (			self    : List[str]				,				UpperCAmelCase__    : ImageInput				,				UpperCAmelCase__    : Optional[bool] = None				,				UpperCAmelCase__    : Optional[Dict[str, int]] = None				,				UpperCAmelCase__    : PILImageResampling = None				,				UpperCAmelCase__    : Optional[bool] = None				,				UpperCAmelCase__    : Optional[float] = None				,				UpperCAmelCase__    : Optional[bool] = None				,				UpperCAmelCase__    : Optional[Union[float, List[float]]] = None				,				UpperCAmelCase__    : Optional[Union[float, List[float]]] = None				,				UpperCAmelCase__    : Optional[Union[str, TensorType]] = None				,				UpperCAmelCase__    : bool = None				,				UpperCAmelCase__    : ChannelDimension = ChannelDimension.FIRST				,				**UpperCAmelCase__    : List[str]				,				)					->PIL.Image.Image:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Dict       =   do_resize if do_resize is not None else self.do_resize
        SCREAMING_SNAKE_CASE :      str       =   resample if resample is not None else self.resample
        SCREAMING_SNAKE_CASE :      int       =   do_rescale if do_rescale is not None else self.do_rescale
        SCREAMING_SNAKE_CASE :      Any       =   rescale_factor if rescale_factor is not None else self.rescale_factor
        SCREAMING_SNAKE_CASE :      List[str]       =   do_normalize if do_normalize is not None else self.do_normalize
        SCREAMING_SNAKE_CASE :      int       =   image_mean if image_mean is not None else self.image_mean
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   image_std if image_std is not None else self.image_std
        SCREAMING_SNAKE_CASE :      Any       =   do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

        SCREAMING_SNAKE_CASE :      List[Any]       =   size if size is not None else self.size
        SCREAMING_SNAKE_CASE :      Dict       =   get_size_dict(UpperCAmelCase__				,				default_to_square=UpperCAmelCase__		)

        SCREAMING_SNAKE_CASE :      List[str]       =   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:
              SCREAMING_SNAKE_CASE :      Optional[Any]       =   [convert_to_rgb(UpperCAmelCase__		) for image in images]

        # All transformations expect numpy arrays.
        SCREAMING_SNAKE_CASE :      Any       =   [to_numpy_array(UpperCAmelCase__		) for image in images]

        if do_resize:
              SCREAMING_SNAKE_CASE :      Union[str, Any]       =   [self.resize(image=UpperCAmelCase__				,				size=UpperCAmelCase__				,				resample=UpperCAmelCase__		) for image in images]

        if do_rescale:
              SCREAMING_SNAKE_CASE :      List[Any]       =   [self.rescale(image=UpperCAmelCase__				,				scale=UpperCAmelCase__		) for image in images]

        if do_normalize:
              SCREAMING_SNAKE_CASE :      Optional[int]       =   [self.normalize(image=UpperCAmelCase__				,				mean=UpperCAmelCase__				,				std=UpperCAmelCase__		) for image in images]

        SCREAMING_SNAKE_CASE :      List[str]       =   [to_channel_dimension_format(UpperCAmelCase__				,				UpperCAmelCase__		) for image in images]

        SCREAMING_SNAKE_CASE :      str       =   BatchFeature(data={"""pixel_values""": images}				,				tensor_type=UpperCAmelCase__		)

        return encoded_outputs







def 					__lowercase      (     _A			)     ->						str:
      SCREAMING_SNAKE_CASE :      Optional[Any]       =   """"""
      for ch in key:
            if ch == " " or ch not in key_no_dups and ch.isalpha():
                  key_no_dups += ch
      return key_no_dups






def 					__lowercase      (     _A			)     ->						dict[str, str]:
      SCREAMING_SNAKE_CASE :      int       =   [chr(i + 65			) for i in range(26			)]
      # Remove duplicate characters from key
      SCREAMING_SNAKE_CASE :      List[str]       =   remove_duplicates(key.upper()			)
      SCREAMING_SNAKE_CASE :      Dict       =   len(_A			)
      # First fill cipher with key characters
      SCREAMING_SNAKE_CASE :      int       =   {alphabet[i]: char for i, char in enumerate(_A			)}
      # Then map remaining characters in alphabet to
      # the alphabet from the beginning
      for i in range(len(_A			)						,    26			):
            SCREAMING_SNAKE_CASE :      List[Any]       =   alphabet[i - offset]
            # Ensure we are not mapping letters to letters previously mapped
            while char in key:
                  offset -= 1
                  SCREAMING_SNAKE_CASE :      str       =   alphabet[i - offset]
            SCREAMING_SNAKE_CASE :      List[Any]       =   char
      return cipher_alphabet






def 					__lowercase      (     _A						,    _A			)     ->						str:
      return "".join(cipher_map.get(_A						,    _A			) for ch in message.upper()			)






def 					__lowercase      (     _A						,    _A			)     ->						str:
      SCREAMING_SNAKE_CASE :      Optional[int]       =   {v: k for k, v in cipher_map.items()}
      return "".join(rev_cipher_map.get(_A						,    _A			) for ch in message.upper()			)






def 					__lowercase      (     )     ->						None:
      SCREAMING_SNAKE_CASE :      List[Any]       =   input("""Enter message to encode or decode: """			).strip()
      SCREAMING_SNAKE_CASE :      str       =   input("""Enter keyword: """			).strip()
      SCREAMING_SNAKE_CASE :      Dict       =   input("""Encipher or decipher? E/D:"""			).strip()[0].lower()
      try:
            SCREAMING_SNAKE_CASE :      int       =   {"""e""": encipher, """d""": decipher}[option]
      except KeyError:
            raise KeyError("""invalid input option"""			)
      SCREAMING_SNAKE_CASE :      Optional[int]       =   create_cipher_map(_A			)
      print(func(_A						,    _A			)			)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()




