import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import ViltImageProcessor
class 							a_					(		unittest.TestCase		):






 '''simple docstring'''






 def __init__(     self   :    Tuple     ,       lowercase__   :    Optional[Any]     ,       lowercase__   :    int=7     ,       lowercase__   :    Dict=3     ,       lowercase__   :    Optional[int]=18     ,       lowercase__   :    List[Any]=30     ,       lowercase__   :    List[str]=400     ,       lowercase__   :    Optional[int]=True     ,       lowercase__   :    List[str]=None     ,       lowercase__   :    Any=2     ,       lowercase__   :    Any=True     ,       lowercase__   :    Tuple=[0.5, 0.5, 0.5]     ,       lowercase__   :    List[str]=[0.5, 0.5, 0.5]     ,       ):







        '''simple docstring'''



        lowerCAmelCase__	      =   size if size is not None else {'shortest_edge': 30}
        lowerCAmelCase__	      =   parent
        lowerCAmelCase__	      =   batch_size
        lowerCAmelCase__	      =   num_channels
        lowerCAmelCase__	      =   image_size
        lowerCAmelCase__	      =   min_resolution
        lowerCAmelCase__	      =   max_resolution
        lowerCAmelCase__	      =   do_resize
        lowerCAmelCase__	      =   size
        lowerCAmelCase__	      =   size_divisor
        lowerCAmelCase__	      =   do_normalize
        lowerCAmelCase__	      =   image_mean
        lowerCAmelCase__	      =   image_std




 def 						__snake_case		(     self   :    Optional[Any]):







        '''simple docstring'''



        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_resize": self.do_resize,
            "size": self.size,
            "size_divisor": self.size_divisor,
        }




 def 						__snake_case		(     self   :    Optional[int]     ,       lowercase__   :    Any     ,       lowercase__   :    Optional[Any]=False):







        '''simple docstring'''



        if not batched:
               lowerCAmelCase__	      =   self.size['shortest_edge']
               lowerCAmelCase__	      =   image_inputs[0]
               if isinstance(lowercase__     ,       Image.Image):
                      lowerCAmelCase__      ,   lowerCAmelCase__	      =   image.size
               else:
                      lowerCAmelCase__      ,   lowerCAmelCase__	      =   image.shape[1], image.shape[2]
               lowerCAmelCase__	      =   size / min(lowercase__     ,       lowercase__)
               if h < w:
                      lowerCAmelCase__      ,   lowerCAmelCase__	      =   size, scale * w
               else:
                      lowerCAmelCase__      ,   lowerCAmelCase__	      =   scale * h, size

               lowerCAmelCase__	      =   int((1_333 / 800) * size)
               if max(lowercase__     ,       lowercase__) > max_size:
                      lowerCAmelCase__	      =   max_size / max(lowercase__     ,       lowercase__)
                      lowerCAmelCase__	      =   newh * scale
                      lowerCAmelCase__	      =   neww * scale

               lowerCAmelCase__      ,   lowerCAmelCase__	      =   int(newh + 0.5), int(neww + 0.5)
               lowerCAmelCase__      ,   lowerCAmelCase__	      =   (
                   newh // self.size_divisor * self.size_divisor,
                   neww // self.size_divisor * self.size_divisor,
               )

        else:
               lowerCAmelCase__	      =   []
               for image in image_inputs:
                      lowerCAmelCase__      ,   lowerCAmelCase__	      =   self.get_expected_values([image])
                      expected_values.append((expected_height, expected_width))
               lowerCAmelCase__	      =   max(lowercase__     ,       key=lambda lowercase__: item[0])[0]
               lowerCAmelCase__	      =   max(lowercase__     ,       key=lambda lowercase__: item[1])[1]

        return expected_height, expected_width






@require_torch
@require_vision
class 							a_					(		SCREAMING_SNAKE_CASE			,	unittest.TestCase		):






 '''simple docstring'''






 UpperCAmelCase_      					=   ViltImageProcessor if is_vision_available() else None




 def 						__snake_case		(     self   :    Dict):







        '''simple docstring'''



        lowerCAmelCase__	      =   ViltImageProcessingTester(self)




 @property
 def 						__snake_case		(     self   :    int):







        '''simple docstring'''



        return self.image_processor_tester.prepare_image_processor_dict()




 def 						__snake_case		(     self   :    Optional[Any]):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.image_processing_class(**self.image_processor_dict)
        self.assertTrue(hasattr(lowercase__     ,       'image_mean'))
        self.assertTrue(hasattr(lowercase__     ,       'image_std'))
        self.assertTrue(hasattr(lowercase__     ,       'do_normalize'))
        self.assertTrue(hasattr(lowercase__     ,       'do_resize'))
        self.assertTrue(hasattr(lowercase__     ,       'size'))
        self.assertTrue(hasattr(lowercase__     ,       'size_divisor'))




 def 						__snake_case		(     self   :    int):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.image_processing_class.from_dict(self.image_processor_dict)
        self.assertEqual(image_processor.size     ,       {'shortest_edge': 30})

        lowerCAmelCase__	      =   self.image_processing_class.from_dict(self.image_processor_dict     ,       size=42)
        self.assertEqual(image_processor.size     ,       {'shortest_edge': 42})




 def 						__snake_case		(     self   :    Any):







        '''simple docstring'''



        pass




 def 						__snake_case		(     self   :    List[Any]):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.image_processing_class(**self.image_processor_dict)
        # create random PIL images
        lowerCAmelCase__	      =   prepare_image_inputs(self.image_processor_tester     ,       equal_resolution=lowercase__)
        for image in image_inputs:
               self.assertIsInstance(lowercase__     ,       Image.Image)

        # Test not batched input
        lowerCAmelCase__	      =   image_processing(image_inputs[0]     ,       return_tensors='pt').pixel_values

        lowerCAmelCase__      ,   lowerCAmelCase__	      =   self.image_processor_tester.get_expected_values(lowercase__)
        self.assertEqual(
            encoded_images.shape     ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,       )

        # Test batched
        lowerCAmelCase__	      =   image_processing(lowercase__     ,       return_tensors='pt').pixel_values

        lowerCAmelCase__      ,   lowerCAmelCase__	      =   self.image_processor_tester.get_expected_values(lowercase__     ,       batched=lowercase__)
        self.assertEqual(
            encoded_images.shape     ,       (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )     ,       )




 def 						__snake_case		(     self   :    Tuple):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.image_processing_class(**self.image_processor_dict)
        # create random numpy tensors
        lowerCAmelCase__	      =   prepare_image_inputs(self.image_processor_tester     ,       equal_resolution=lowercase__     ,       numpify=lowercase__)
        for image in image_inputs:
               self.assertIsInstance(lowercase__     ,       np.ndarray)

        # Test not batched input
        lowerCAmelCase__	      =   image_processing(image_inputs[0]     ,       return_tensors='pt').pixel_values

        lowerCAmelCase__      ,   lowerCAmelCase__	      =   self.image_processor_tester.get_expected_values(lowercase__)
        self.assertEqual(
            encoded_images.shape     ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,       )

        # Test batched
        lowerCAmelCase__	      =   image_processing(lowercase__     ,       return_tensors='pt').pixel_values

        lowerCAmelCase__      ,   lowerCAmelCase__	      =   self.image_processor_tester.get_expected_values(lowercase__     ,       batched=lowercase__)
        self.assertEqual(
            encoded_images.shape     ,       (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )     ,       )




 def 						__snake_case		(     self   :    Dict):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.image_processing_class(**self.image_processor_dict)
        # create random PyTorch tensors
        lowerCAmelCase__	      =   prepare_image_inputs(self.image_processor_tester     ,       equal_resolution=lowercase__     ,       torchify=lowercase__)
        for image in image_inputs:
               self.assertIsInstance(lowercase__     ,       torch.Tensor)

        # Test not batched input
        lowerCAmelCase__	      =   image_processing(image_inputs[0]     ,       return_tensors='pt').pixel_values

        lowerCAmelCase__      ,   lowerCAmelCase__	      =   self.image_processor_tester.get_expected_values(lowercase__)
        self.assertEqual(
            encoded_images.shape     ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,       )

        # Test batched
        lowerCAmelCase__	      =   image_processing(lowercase__     ,       return_tensors='pt').pixel_values

        lowerCAmelCase__      ,   lowerCAmelCase__	      =   self.image_processor_tester.get_expected_values(lowercase__     ,       batched=lowercase__)
        self.assertEqual(
            encoded_images.shape     ,       (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )     ,       )

import random
import sys

from . import cryptomath_module as cryptomath

lowerCAmelCase__      		=					(
    r' !"#$%&\'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`'
    r'abcdefghijklmnopqrstuvwxyz{|}~'
)






def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__      ):
       if mode == "encrypt":
              if key_a == 1:
                     sys.exit(
                         'The affine cipher becomes weak when key '
                         'A is set to 1. Choose different key'      )
              if key_b == 0:
                     sys.exit(
                         'The affine cipher becomes weak when key '
                         'B is set to 0. Choose different key'      )
       if key_a < 0 or key_b < 0 or key_b > len(lowerCAmelCase__      ) - 1:
              sys.exit(
                  'Key A must be greater than 0 and key B must '
                  F"""be between 0 and {len(lowerCAmelCase__      ) - 1}."""      )
       if cryptomath.gcd(lowerCAmelCase__      ,    len(lowerCAmelCase__      )      ) != 1:
              sys.exit(
                  F"""Key A {key_a} and the symbol set size {len(lowerCAmelCase__      )} """
                  'are not relatively prime. Choose a different key.'      )






def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ):
       lowerCAmelCase__      ,   lowerCAmelCase__	      =   divmod(lowerCAmelCase__      ,    len(lowerCAmelCase__      )      )
       check_keys(lowerCAmelCase__      ,    lowerCAmelCase__      ,    'encrypt'      )
       lowerCAmelCase__	      =   ''
       for symbol in message:
              if symbol in SYMBOLS:
                     lowerCAmelCase__	      =   SYMBOLS.find(lowerCAmelCase__      )
                     cipher_text += SYMBOLS[(sym_index * key_a + key_b) % len(lowerCAmelCase__      )]
              else:
                     cipher_text += symbol
       return cipher_text






def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ):
       lowerCAmelCase__      ,   lowerCAmelCase__	      =   divmod(lowerCAmelCase__      ,    len(lowerCAmelCase__      )      )
       check_keys(lowerCAmelCase__      ,    lowerCAmelCase__      ,    'decrypt'      )
       lowerCAmelCase__	      =   ''
       lowerCAmelCase__	      =   cryptomath.find_mod_inverse(lowerCAmelCase__      ,    len(lowerCAmelCase__      )      )
       for symbol in message:
              if symbol in SYMBOLS:
                     lowerCAmelCase__	      =   SYMBOLS.find(lowerCAmelCase__      )
                     plain_text += SYMBOLS[
                         (sym_index - key_b) * mod_inverse_of_key_a % len(lowerCAmelCase__      )
                     ]
              else:
                     plain_text += symbol
       return plain_text






def  __lowerCamelCase						(		):
       while True:
              lowerCAmelCase__	      =   random.randint(2      ,    len(lowerCAmelCase__      )      )
              lowerCAmelCase__	      =   random.randint(2      ,    len(lowerCAmelCase__      )      )
              if cryptomath.gcd(lowerCAmelCase__      ,    len(lowerCAmelCase__      )      ) == 1 and key_b % len(lowerCAmelCase__      ) != 0:
                     return key_b * len(lowerCAmelCase__      ) + key_b






def  __lowerCamelCase						(		):
       lowerCAmelCase__	      =   input('Enter message: '      ).strip()
       lowerCAmelCase__	      =   int(input('Enter key [2000 - 9000]: '      ).strip()      )
       lowerCAmelCase__	      =   input('Encrypt/Decrypt [E/D]: '      ).strip().lower()

       if mode.startswith('e'      ):
              lowerCAmelCase__	      =   'encrypt'
              lowerCAmelCase__	      =   encrypt_message(lowerCAmelCase__      ,    lowerCAmelCase__      )
       elif mode.startswith('d'      ):
              lowerCAmelCase__	      =   'decrypt'
              lowerCAmelCase__	      =   decrypt_message(lowerCAmelCase__      ,    lowerCAmelCase__      )
       print(F"""\n{mode.title()}ed text: \n{translated}"""      )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    # main()
