





"""simple docstring"""


import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()



class   lowercase_					(  unittest.TestCase				):

       @property
       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

        torch.manual_seed(0  )
        snake_case__     				=					UNetaDModel(
            block_out_channels=(32, 64) ,       layers_per_block=2 ,       sample_size=32 ,       in_channels=3 ,       out_channels=3 ,       down_block_types=("DownBlock2D", "AttnDownBlock2D") ,       up_block_types=("AttnUpBlock2D", "UpBlock2D") ,       )
        return model

       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

        snake_case__     				=					"cpu"
        snake_case__     				=					self.dummy_uncond_unet
        snake_case__     				=					DDPMScheduler()

        snake_case__     				=					DDPMPipeline(unet=__SCREAMING_SNAKE_CASE ,       scheduler=__SCREAMING_SNAKE_CASE  )
        ddpm.to(__SCREAMING_SNAKE_CASE  )
        ddpm.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE  )

        snake_case__     				=					torch.Generator(device=__SCREAMING_SNAKE_CASE  ).manual_seed(0  )
        snake_case__     				=					ddpm(generator=__SCREAMING_SNAKE_CASE ,       num_inference_steps=2 ,       output_type="numpy"  ).images

        snake_case__     				=					torch.Generator(device=__SCREAMING_SNAKE_CASE  ).manual_seed(0  )
        snake_case__     				=					ddpm(generator=__SCREAMING_SNAKE_CASE ,       num_inference_steps=2 ,       output_type="numpy" ,       return_dict=__SCREAMING_SNAKE_CASE  )[0]

        snake_case__     				=					image[0, -3:, -3:, -1]
        snake_case__     				=					image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 32, 32, 3)
        snake_case__     				=					np.array(
            [9.956e-01, 5.785e-01, 4.675e-01, 9.930e-01, 0.0, 1.000, 1.199e-03, 2.648e-04, 5.101e-04]  )

        assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-2
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice  ).max() < 1e-2

       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

        snake_case__     				=					self.dummy_uncond_unet
        snake_case__     				=					DDPMScheduler(prediction_type="sample"  )

        snake_case__     				=					DDPMPipeline(unet=__SCREAMING_SNAKE_CASE ,       scheduler=__SCREAMING_SNAKE_CASE  )
        ddpm.to(__SCREAMING_SNAKE_CASE  )
        ddpm.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE  )

        snake_case__     				=					torch.manual_seed(0  )
        snake_case__     				=					ddpm(generator=__SCREAMING_SNAKE_CASE ,       num_inference_steps=2 ,       output_type="numpy"  ).images

        snake_case__     				=					torch.manual_seed(0  )
        snake_case__     				=					ddpm(generator=__SCREAMING_SNAKE_CASE ,       num_inference_steps=2 ,       output_type="numpy"  )[0]

        snake_case__     				=					image[0, -3:, -3:, -1]
        snake_case__     				=					image_eps[0, -3:, -3:, -1]

        assert image.shape == (1, 32, 32, 3)
        snake_case__     				=					1e-2 if torch_device != "mps" else 3e-2
        assert np.abs(image_slice.flatten() - image_eps_slice.flatten()  ).max() < tolerance



@slow
@require_torch_gpu
class   lowercase_					(  unittest.TestCase				):

       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

        snake_case__     				=					"google/ddpm-cifar10-32"

        snake_case__     				=					UNetaDModel.from_pretrained(__SCREAMING_SNAKE_CASE  )
        snake_case__     				=					DDPMScheduler.from_pretrained(__SCREAMING_SNAKE_CASE  )

        snake_case__     				=					DDPMPipeline(unet=__SCREAMING_SNAKE_CASE ,       scheduler=__SCREAMING_SNAKE_CASE  )
        ddpm.to(__SCREAMING_SNAKE_CASE  )
        ddpm.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE  )

        snake_case__     				=					torch.manual_seed(0  )
        snake_case__     				=					ddpm(generator=__SCREAMING_SNAKE_CASE ,       output_type="numpy"  ).images

        snake_case__     				=					image[0, -3:, -3:, -1]

        assert image.shape == (1, 32, 32, 3)
        snake_case__     				=					np.array([0.4200, 0.3588, 0.1939, 0.3847, 0.3382, 0.2647, 0.4155, 0.3582, 0.3385]  )
        assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-2








"""simple docstring"""


a__       :  Optional[Any]       		=					{
    """A""": ("""ABCDEFGHIJKLM""", """NOPQRSTUVWXYZ"""),
    """B""": ("""ABCDEFGHIJKLM""", """NOPQRSTUVWXYZ"""),
    """C""": ("""ABCDEFGHIJKLM""", """ZNOPQRSTUVWXY"""),
    """D""": ("""ABCDEFGHIJKLM""", """ZNOPQRSTUVWXY"""),
    """E""": ("""ABCDEFGHIJKLM""", """YZNOPQRSTUVWX"""),
    """F""": ("""ABCDEFGHIJKLM""", """YZNOPQRSTUVWX"""),
    """G""": ("""ABCDEFGHIJKLM""", """XYZNOPQRSTUVW"""),
    """H""": ("""ABCDEFGHIJKLM""", """XYZNOPQRSTUVW"""),
    """I""": ("""ABCDEFGHIJKLM""", """WXYZNOPQRSTUV"""),
    """J""": ("""ABCDEFGHIJKLM""", """WXYZNOPQRSTUV"""),
    """K""": ("""ABCDEFGHIJKLM""", """VWXYZNOPQRSTU"""),
    """L""": ("""ABCDEFGHIJKLM""", """VWXYZNOPQRSTU"""),
    """M""": ("""ABCDEFGHIJKLM""", """UVWXYZNOPQRST"""),
    """N""": ("""ABCDEFGHIJKLM""", """UVWXYZNOPQRST"""),
    """O""": ("""ABCDEFGHIJKLM""", """TUVWXYZNOPQRS"""),
    """P""": ("""ABCDEFGHIJKLM""", """TUVWXYZNOPQRS"""),
    """Q""": ("""ABCDEFGHIJKLM""", """STUVWXYZNOPQR"""),
    """R""": ("""ABCDEFGHIJKLM""", """STUVWXYZNOPQR"""),
    """S""": ("""ABCDEFGHIJKLM""", """RSTUVWXYZNOPQ"""),
    """T""": ("""ABCDEFGHIJKLM""", """RSTUVWXYZNOPQ"""),
    """U""": ("""ABCDEFGHIJKLM""", """QRSTUVWXYZNOP"""),
    """V""": ("""ABCDEFGHIJKLM""", """QRSTUVWXYZNOP"""),
    """W""": ("""ABCDEFGHIJKLM""", """PQRSTUVWXYZNO"""),
    """X""": ("""ABCDEFGHIJKLM""", """PQRSTUVWXYZNO"""),
    """Y""": ("""ABCDEFGHIJKLM""", """OPQRSTUVWXYZN"""),
    """Z""": ("""ABCDEFGHIJKLM""", """OPQRSTUVWXYZN"""),
}





def 				a__      (	_UpperCAmelCase						)     ->	list[tuple[str, str]]:

 '''simple docstring'''






 return [alphabet[char] for char in key.upper()]





def 				a__      (	_UpperCAmelCase						,  _UpperCAmelCase						)     ->	str:

 '''simple docstring'''






 snake_case__     				=					""
 snake_case__     				=					0
 snake_case__     				=					generate_table(_UpperCAmelCase						)
 for char in words.upper():
  cipher += get_opponent(table[count]						,  _UpperCAmelCase						)
  snake_case__     				=					(count + 1) % len(_UpperCAmelCase						)
 return cipher





def 				a__      (	_UpperCAmelCase						,  _UpperCAmelCase						)     ->	str:

 '''simple docstring'''






 return encrypt(_UpperCAmelCase						,  _UpperCAmelCase						)





def 				a__      (	_UpperCAmelCase						,  _UpperCAmelCase						)     ->	tuple[int, int]:

 '''simple docstring'''






 snake_case__     				=					0 if char in table[0] else 1
 snake_case__     				=					table[row].index(_UpperCAmelCase						)
 return row, col





def 				a__      (	_UpperCAmelCase						,  _UpperCAmelCase						)     ->	str:

 '''simple docstring'''






 snake_case__				, snake_case__     				=					get_position(_UpperCAmelCase						,  char.upper()						)
 if row == 1:
  return table[0][col]
 else:
  return table[1][col] if row == 0 else char


if __name__ == "__main__":
   import doctest

   doctest.testmod()  # Fist ensure that all our tests are passing...
   a__       :  int       		=					input("""Enter key: """).strip()
   a__       :  Optional[int]       		=					input("""Enter text to encrypt: """).strip()
   a__       :  Dict       		=					encrypt(key, text)

   print(f'''Encrypted: {cipher_text}''')
   print(f'''Decrypted with key: {decrypt(key, cipher_text)}''')

