



'''simple docstring'''






import string

import numpy
def 				lowerCamelCase__	(						_A			,      _A    ):
  return b if a == 0 else greatest_common_divisor(b % a			,      _A    )

class 							a__:
    lowercase__			=   string.ascii_uppercase + string.digits
    # This cipher takes alphanumerics into account
    # i.e. a total of 36 characters

    # take x and return x % len(key_string)
    lowercase__			=   numpy.vectorize(lambda lowerCamelCase__     : x % 36     )

    lowercase__			=   numpy.vectorize(lowerCamelCase__     )







    def __init__(				self			:  Any						,      __snake_case			:  numpy.ndarray     ):
      a						: Optional[Any]					       =					self.modulus(__snake_case     )  # mod36 calc's on the encrypt key
      self.check_determinant()  # validate the determinant of the encryption key
      a						: Union[str, Any]					       =					encrypt_key.shape[0]







    def       lowercase_	(				self			:  Optional[Any]						,      __snake_case			:  str     ):
      return self.key_string.index(__snake_case     )







    def       lowercase_	(				self			:  Dict						,      __snake_case			:  int     ):
      return self.key_string[round(__snake_case     )]







    def       lowercase_	(				self			:  Any     ):
      a						: Tuple					       =					round(numpy.linalg.det(self.encrypt_key     )     )

      if det < 0:
        a						: List[str]					       =					det % len(self.key_string     )

      a						: Any					       =					len(self.key_string     )
      if greatest_common_divisor(__snake_case						,      len(self.key_string     )     ) != 1:
        a						: Any					       =					(
            F"""determinant modular {req_l} of encryption key({det}) """
            F"""is not co prime w.r.t {req_l}.\nTry another key."""
        )
        raise ValueError(__snake_case     )







    def       lowercase_	(				self			:  int						,      __snake_case			:  str     ):
      a						: Dict					       =					[char for char in text.upper() if char in self.key_string]

      a						: Optional[int]					       =					chars[-1]
      while len(__snake_case     ) % self.break_key != 0:
        chars.append(__snake_case     )

      return "".join(__snake_case     )







    def       lowercase_	(				self			:  Optional[Any]						,      __snake_case			:  str     ):
      a						: str					       =					self.process_text(text.upper()     )
      a						: Optional[Any]					       =					''

      for i in range(0						,      len(__snake_case     ) - self.break_key + 1						,      self.break_key     ):
        a						: List[str]					       =					text[i : i + self.break_key]
        a						: Optional[Any]					       =					[self.replace_letters(__snake_case     ) for char in batch]
        a						: Dict					       =					numpy.array([vec]     ).T
        a						: str					       =					self.modulus(self.encrypt_key.dot(__snake_case     )     ).T.tolist()[
            0
        ]
        a						: Any					       =					''.join(
            self.replace_digits(__snake_case     ) for num in batch_encrypted     )
        encrypted += encrypted_batch

      return encrypted







    def       lowercase_	(				self			:  Optional[int]     ):
      a						: Tuple					       =					round(numpy.linalg.det(self.encrypt_key     )     )

      if det < 0:
        a						: Optional[int]					       =					det % len(self.key_string     )
      a						: Any					       =					None
      for i in range(len(self.key_string     )     ):
        if (det * i) % len(self.key_string     ) == 1:
          a						: Union[str, Any]					       =					i
          break

      a						: List[Any]					       =					(
          det_inv
          * numpy.linalg.det(self.encrypt_key     )
          * numpy.linalg.inv(self.encrypt_key     )
      )

      return self.to_int(self.modulus(__snake_case     )     )







    def       lowercase_	(				self			:  Optional[Any]						,      __snake_case			:  str     ):
      a						: List[str]					       =					self.make_decrypt_key()
      a						: Union[str, Any]					       =					self.process_text(text.upper()     )
      a						: Tuple					       =					''

      for i in range(0						,      len(__snake_case     ) - self.break_key + 1						,      self.break_key     ):
        a						: Union[str, Any]					       =					text[i : i + self.break_key]
        a						: int					       =					[self.replace_letters(__snake_case     ) for char in batch]
        a						: Union[str, Any]					       =					numpy.array([vec]     ).T
        a						: int					       =					self.modulus(decrypt_key.dot(__snake_case     )     ).T.tolist()[0]
        a						: List[Any]					       =					''.join(
            self.replace_digits(__snake_case     ) for num in batch_decrypted     )
        decrypted += decrypted_batch

      return decrypted
def 				lowerCamelCase__	(						):
  a						: int					       =					int(input('Enter the order of the encryption key: '    )    )
  a						: Union[str, Any]					       =					[]

  print('Enter each row of the encryption key with space separated integers'    )
  for _ in range(_A    ):
    a						: int					       =					[int(_A    ) for x in input().split()]
    hill_matrix.append(_A    )

  a						: Optional[Any]					       =					HillCipher(numpy.array(_A    )    )

  print('Would you like to encrypt or decrypt some text? (1 or 2)'    )
  a						: Optional[Any]					       =					input('\n1. Encrypt\n2. Decrypt\n'    )
  if option == "1":
    a						: Any					       =					input('What text would you like to encrypt?: '    )
    print('Your encrypted text is:'    )
    print(hc.encrypt(_A    )    )
  elif option == "2":
    a						: Optional[Any]					       =					input('What text would you like to decrypt?: '    )
    print('Your decrypted text is:'    )
    print(hc.decrypt(_A    )    )


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     main()




'''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


lowerCAmelCase: Any							=						logging.get_logger(__name__)

class 							a__( lowerCamelCase__     ):
    lowercase__			=   ["""pixel_values"""]







    def __init__(				self			:  List[str]						,      __snake_case			:  bool = True						,      __snake_case			:  int = 32						,      __snake_case			:  Union[str, Any]=PILImageResampling.BILINEAR						,      __snake_case			:  bool = True						,      **__snake_case			:  List[Any]						,      ):
      a						: Optional[Any]					       =					do_resize
      a						: Union[str, Any]					       =					do_rescale
      a						: Union[str, Any]					       =					size_divisor
      a						: List[Any]					       =					resample
      super().__init__(**__snake_case     )







    def       lowercase_	(				self			:  Optional[Any]						,      __snake_case			:  np.ndarray						,      __snake_case			:  int						,      __snake_case			:  Tuple						,      __snake_case			:  Optional[ChannelDimension] = None						,      **__snake_case			:  Tuple     ):
      a       ,     a						: Optional[int]					       =					get_image_size(__snake_case     )
      # Rounds the height and width down to the closest multiple of size_divisor
      a						: int					       =					height // size_divisor * size_divisor
      a						: int					       =					width // size_divisor * size_divisor
      a						: Any					       =					resize(__snake_case						,      (new_h, new_w)						,      resample=__snake_case						,      data_format=__snake_case						,      **__snake_case     )
      return image







    def       lowercase_	(				self			:  Optional[int]						,      __snake_case			:  np.ndarray						,      __snake_case			:  float						,      __snake_case			:  Optional[ChannelDimension] = None						,      **__snake_case			:  Optional[Any]     ):
      return rescale(image=__snake_case						,      scale=__snake_case						,      data_format=__snake_case						,      **__snake_case     )







    def       lowercase_	(				self			:  List[str]						,      __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			:  Dict=None						,      __snake_case			:  Optional[bool] = None						,      __snake_case			:  Optional[Union[TensorType, str]] = None						,      __snake_case			:  ChannelDimension = ChannelDimension.FIRST						,      **__snake_case			:  Any						,      ):
      a						: List[str]					       =					do_resize if do_resize is not None else self.do_resize
      a						: Tuple					       =					do_rescale if do_rescale is not None else self.do_rescale
      a						: Optional[Any]					       =					size_divisor if size_divisor is not None else self.size_divisor
      a						: Union[str, Any]					       =					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						: Tuple					       =					make_list_of_images(__snake_case     )

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

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

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

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

      a						: Any					       =					[to_channel_dimension_format(__snake_case						,      __snake_case     ) for image in images]

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