"""simple docstring"""







import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, LDMSuperResolutionPipeline, UNetaDModel, VQModel
from diffusers.utils import PIL_INTERPOLATION, floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch


enable_full_determinism()






class      _SCREAMING_SNAKE_CASE							(			unittest.TestCase				):



   '''simple docstring'''



   @property
   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					1
      __lowerCamelCase    		=					3
      __lowerCamelCase    		=					(32, 32)

      __lowerCamelCase    		=					floats_tensor((batch_size, num_channels) + sizes ,				rng=random.Random(0	)	).to(_SCREAMING_SNAKE_CASE	)
      return image







   @property
   def    _A				(	self	):


      '''simple docstring'''

      torch.manual_seed(0	)
      __lowerCamelCase    		=					UNetaDModel(
          block_out_channels=(32, 64) ,				layers_per_block=2 ,				sample_size=32 ,				in_channels=6 ,				out_channels=3 ,				down_block_types=('DownBlock2D', 'AttnDownBlock2D') ,				up_block_types=('AttnUpBlock2D', 'UpBlock2D') ,				)
      return model







   @property
   def    _A				(	self	):


      '''simple docstring'''

      torch.manual_seed(0	)
      __lowerCamelCase    		=					VQModel(
          block_out_channels=[32, 64] ,				in_channels=3 ,				out_channels=3 ,				down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] ,				up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] ,				latent_channels=3 ,				)
      return model







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					'cpu'
      __lowerCamelCase    		=					self.dummy_uncond_unet
      __lowerCamelCase    		=					DDIMScheduler()
      __lowerCamelCase    		=					self.dummy_vq_model

      __lowerCamelCase    		=					LDMSuperResolutionPipeline(unet=_SCREAMING_SNAKE_CASE ,				vqvae=_SCREAMING_SNAKE_CASE ,				scheduler=_SCREAMING_SNAKE_CASE	)
      ldm.to(_SCREAMING_SNAKE_CASE	)
      ldm.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					self.dummy_image.to(_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					torch.Generator(device=_SCREAMING_SNAKE_CASE	).manual_seed(0	)
      __lowerCamelCase    		=					ldm(image=_SCREAMING_SNAKE_CASE ,				generator=_SCREAMING_SNAKE_CASE ,				num_inference_steps=2 ,				output_type='numpy'	).images

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

      assert image.shape == (1, 64, 64, 3)
      __lowerCamelCase    		=					np.array([0.8678, 0.8245, 0.6381, 0.6830, 0.4385, 0.5599, 0.4641, 0.6201, 0.5150]	)

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







   @unittest.skipIf(torch_device != 'cuda' ,				'This test requires a GPU'	)
   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.dummy_uncond_unet
      __lowerCamelCase    		=					DDIMScheduler()
      __lowerCamelCase    		=					self.dummy_vq_model

      # put models in fp16
      __lowerCamelCase    		=					unet.half()
      __lowerCamelCase    		=					vqvae.half()

      __lowerCamelCase    		=					LDMSuperResolutionPipeline(unet=_SCREAMING_SNAKE_CASE ,				vqvae=_SCREAMING_SNAKE_CASE ,				scheduler=_SCREAMING_SNAKE_CASE	)
      ldm.to(_SCREAMING_SNAKE_CASE	)
      ldm.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					self.dummy_image.to(_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					ldm(_SCREAMING_SNAKE_CASE ,				num_inference_steps=2 ,				output_type='numpy'	).images

      assert image.shape == (1, 64, 64, 3)






@slow
@require_torch
class      _SCREAMING_SNAKE_CASE							(			unittest.TestCase				):



   '''simple docstring'''



   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/vq_diffusion/teddy_bear_pool.png'	)
      __lowerCamelCase    		=					init_image.resize((64, 64) ,				resample=PIL_INTERPOLATION['lanczos']	)

      __lowerCamelCase    		=					LDMSuperResolutionPipeline.from_pretrained('duongna/ldm-super-resolution' ,				device_map='auto'	)
      ldm.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					torch.manual_seed(0	)
      __lowerCamelCase    		=					ldm(image=_SCREAMING_SNAKE_CASE ,				generator=_SCREAMING_SNAKE_CASE ,				num_inference_steps=20 ,				output_type='numpy'	).images

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

      assert image.shape == (1, 256, 256, 3)
      __lowerCamelCase    		=					np.array([0.7644, 0.7679, 0.7642, 0.7633, 0.7666, 0.7560, 0.7425, 0.7257, 0.6907]	)

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







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()
