
"""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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCamelCase			:       List[str]									=				logging.get_logger(__name__)

class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ):
     lowercase		     =		["pixel_values"]





     def __init__(			self			,  __UpperCAmelCase = True			,  __UpperCAmelCase = None			,  __UpperCAmelCase = PILImageResampling.BICUBIC			,  __UpperCAmelCase = True			,  __UpperCAmelCase = True			,  __UpperCAmelCase = 1 / 255			,  __UpperCAmelCase = None			,  __UpperCAmelCase = True			,  __UpperCAmelCase = None			,  __UpperCAmelCase = None			,  **__UpperCAmelCase			,  ):

      '''simple docstring'''




      super().__init__(**__UpperCAmelCase   )
      __UpperCamelCase       						= size if size is not None else {'height': 224, 'width': 224}
      __UpperCamelCase       						= get_size_dict(__UpperCAmelCase   )
      __UpperCamelCase       						= crop_size if crop_size is not None else {'height': 224, 'width': 224}
      __UpperCamelCase       						= get_size_dict(__UpperCAmelCase			,  default_to_square=__UpperCAmelCase			,  param_name='crop_size'   )

      __UpperCamelCase       						= do_resize
      __UpperCamelCase       						= do_rescale
      __UpperCamelCase       						= do_normalize
      __UpperCamelCase       						= do_center_crop
      __UpperCamelCase       						= crop_size
      __UpperCamelCase       						= size
      __UpperCamelCase       						= resample
      __UpperCamelCase       						= rescale_factor
      __UpperCamelCase       						= image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
      __UpperCamelCase       						= image_std if image_std is not None else IMAGENET_DEFAULT_STD





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase = PILImageResampling.BILINEAR			,  __UpperCAmelCase = None			,  **__UpperCAmelCase			,  ):

      '''simple docstring'''




      __UpperCamelCase       						= get_size_dict(__UpperCAmelCase   )

      if "shortest_edge" in size:
       __UpperCamelCase       						= get_resize_output_image_size(__UpperCAmelCase			,  size=size['shortest_edge']			,  default_to_square=__UpperCAmelCase   )
       # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
      elif "height" in size and "width" in size:
       __UpperCamelCase       						= (size['height'], size['width'])
      else:
       raise ValueError(F'Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'   )
      return resize(__UpperCAmelCase			,  size=__UpperCAmelCase			,  resample=__UpperCAmelCase			,  data_format=__UpperCAmelCase			,  **__UpperCAmelCase   )





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase = None			,  **__UpperCAmelCase			,  ):

      '''simple docstring'''




      __UpperCamelCase       						= get_size_dict(__UpperCAmelCase   )
      if "height" not in size or "width" not in size:
       raise ValueError(F'The `size` parameter must contain the keys (height, width). Got {size.keys()}'   )
      return center_crop(__UpperCAmelCase			,  size=(size['height'], size['width'])			,  data_format=__UpperCAmelCase			,  **__UpperCAmelCase   )





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase = None			,  **__UpperCAmelCase   ):

      '''simple docstring'''




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





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase = None			,  **__UpperCAmelCase			,  ):

      '''simple docstring'''




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







     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase = None			,  __UpperCAmelCase = None			,  __UpperCAmelCase = None			,  __UpperCAmelCase = None			,  __UpperCAmelCase = None			,  __UpperCAmelCase = None			,  __UpperCAmelCase = None			,  __UpperCAmelCase = None			,  __UpperCAmelCase = None			,  __UpperCAmelCase = None			,  __UpperCAmelCase = None			,  __UpperCAmelCase = ChannelDimension.FIRST			,  **__UpperCAmelCase			,  ):

      '''simple docstring'''




      __UpperCamelCase       						= do_resize if do_resize is not None else self.do_resize
      __UpperCamelCase       						= do_rescale if do_rescale is not None else self.do_rescale
      __UpperCamelCase       						= do_normalize if do_normalize is not None else self.do_normalize
      __UpperCamelCase       						= do_center_crop if do_center_crop is not None else self.do_center_crop
      __UpperCamelCase       						= crop_size if crop_size is not None else self.crop_size
      __UpperCamelCase       						= get_size_dict(__UpperCAmelCase			,  param_name='crop_size'			,  default_to_square=__UpperCAmelCase   )
      __UpperCamelCase       						= resample if resample is not None else self.resample
      __UpperCamelCase       						= rescale_factor if rescale_factor is not None else self.rescale_factor
      __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       						= size if size is not None else self.size
      __UpperCamelCase       						= get_size_dict(__UpperCAmelCase   )

      if not is_batched(__UpperCAmelCase   ):
       __UpperCamelCase       						= [images]

      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:
       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.'   )

      # 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_center_crop:
       __UpperCamelCase       						= [self.center_crop(image=__UpperCAmelCase			,  size=__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       						= {'pixel_values': images}
      return BatchFeature(data=__UpperCAmelCase			,  tensor_type=__UpperCAmelCase   )





"""simple docstring"""

import string

import numpy






def 							A		(       snake_case   :int       ,      snake_case   :int	)   ->			int:
 return b if a == 0 else greatest_common_divisor(b % a       ,      snake_case	)

class __lowerCAmelCase	:
     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 __SCREAMING_SNAKE_CASE    : x % 36    )

     lowercase		     =		numpy.vectorize(__SCREAMING_SNAKE_CASE    )





     def __init__(			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.modulus(__UpperCAmelCase   )  # mod36 calc's on the encrypt key
      self.check_determinant()  # validate the determinant of the encryption key
      __UpperCamelCase       						= encrypt_key.shape[0]





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      return self.key_string.index(__UpperCAmelCase   )





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      return self.key_string[round(__UpperCAmelCase   )]





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= round(numpy.linalg.det(self.encrypt_key   )   )

      if det < 0:
       __UpperCamelCase       						= det % len(self.key_string   )

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





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= [char for char in text.upper() if char in self.key_string]

      __UpperCamelCase       						= chars[-1]
      while len(__UpperCAmelCase   ) % self.break_key != 0:
       chars.append(__UpperCAmelCase   )

      return "".join(__UpperCAmelCase   )





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.process_text(text.upper()   )
      __UpperCamelCase       						= ''

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

      return encrypted





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= round(numpy.linalg.det(self.encrypt_key   )   )

      if det < 0:
       __UpperCamelCase       						= det % len(self.key_string   )
      __UpperCamelCase       						= None
      for i in range(len(self.key_string   )   ):
       if (det * i) % len(self.key_string   ) == 1:
        __UpperCamelCase       						= i
        break

      __UpperCamelCase       						= (
          det_inv
          * numpy.linalg.det(self.encrypt_key   )
          * numpy.linalg.inv(self.encrypt_key   )
      )

      return self.to_int(self.modulus(__UpperCAmelCase   )   )







     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.make_decrypt_key()
      __UpperCamelCase       						= self.process_text(text.upper()   )
      __UpperCamelCase       						= ''

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

      return decrypted






def 							A		(       )   ->			None:
 __UpperCamelCase       						= int(input('Enter the order of the encryption key: '	)	)
 __UpperCamelCase       						= []

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

 __UpperCamelCase       						= HillCipher(numpy.array(snake_case	)	)

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


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      main()



