


"""simple docstring"""




import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()
class 			SCREAMING_SNAKE_CASE__  (    unittest.TestCase      ):
      @property
      def 	SCREAMING_SNAKE_CASE							(	self    :	Any					)		->      Union[str, Any]:
             torch.manual_seed(0					)
             a_ :		Optional[Any]							=  UNetaDModel(
                 block_out_channels=(3_2, 6_4)					,							layers_per_block=2					,							sample_size=3_2					,							in_channels=3					,							out_channels=3					,							down_block_types=('DownBlock2D', 'AttnDownBlock2D')					,							up_block_types=('AttnUpBlock2D', 'UpBlock2D')					,							)
             return model

      def 	SCREAMING_SNAKE_CASE							(	self    :	Dict					)		->      int:
             a_ :		int							=  self.dummy_uncond_unet
             a_ :		List[str]							=  PNDMScheduler()

             a_ :		Union[str, Any]							=  PNDMPipeline(unet=lowerCamelCase_					,							scheduler=lowerCamelCase_					)
             pndm.to(lowerCamelCase_					)
             pndm.set_progress_bar_config(disable=lowerCamelCase_					)

             a_ :		List[Any]							=  torch.manual_seed(0					)
             a_ :		Tuple							=  pndm(generator=lowerCamelCase_					,							num_inference_steps=2_0					,							output_type='numpy'					).images

             a_ :		int							=  torch.manual_seed(0					)
             a_ :		Dict							=  pndm(generator=lowerCamelCase_					,							num_inference_steps=2_0					,							output_type='numpy'					,							return_dict=lowerCamelCase_					)[0]

             a_ :		int							=  image[0, -3:, -3:, -1]
             a_ :		int							=  image_from_tuple[0, -3:, -3:, -1]

             assert image.shape == (1, 3_2, 3_2, 3)
             a_ :		Tuple							=  np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]					)

             assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2
             assert np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 1E-2
@slow
@require_torch
class 			SCREAMING_SNAKE_CASE__  (    unittest.TestCase      ):
      def 	SCREAMING_SNAKE_CASE							(	self    :	int					)		->      Optional[Any]:
             a_ :		Tuple							=  'google/ddpm-cifar10-32'

             a_ :		Tuple							=  UNetaDModel.from_pretrained(lowerCamelCase_					)
             a_ :		List[Any]							=  PNDMScheduler()

             a_ :		List[Any]							=  PNDMPipeline(unet=lowerCamelCase_					,							scheduler=lowerCamelCase_					)
             pndm.to(lowerCamelCase_					)
             pndm.set_progress_bar_config(disable=lowerCamelCase_					)
             a_ :		int							=  torch.manual_seed(0					)
             a_ :		Dict							=  pndm(generator=lowerCamelCase_					,							output_type='numpy'					).images

             a_ :		Any							=  image[0, -3:, -3:, -1]

             assert image.shape == (1, 3_2, 3_2, 3)
             a_ :		int							=  np.array([0.1564, 0.14645, 0.1406, 0.14715, 0.12425, 0.14045, 0.13115, 0.12175, 0.125]					)

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






from string import ascii_uppercase

UpperCAmelCase_				:			Dict   =	{char: i for i, char in enumerate(ascii_uppercase)}
UpperCAmelCase_				:			Optional[int]   =	dict(enumerate(ascii_uppercase))







def 	SCREAMING_SNAKE_CASE_						(      __A			:      str  ,   __A			:      str    )     ->		str:

       """simple docstring"""

       a_ :		Tuple							=  len(__A    )
       a_ :		int							=  0
       while True:
              if x == i:
                     a_ :		Tuple							=  0
              if len(__A    ) == len(__A    ):
                     break
              key += key[i]
              i += 1
       return key







def 	SCREAMING_SNAKE_CASE_						(      __A			:      str  ,   __A			:      str    )     ->		str:

       """simple docstring"""

       a_ :		Optional[int]							=  ''
       a_ :		Any							=  0
       for letter in message:
              if letter == " ":
                     cipher_text += " "
              else:
                     a_ :		Optional[Any]							=  (dicta[letter] - dicta[key_new[i]]) % 26
                     i += 1
                     cipher_text += dicta[x]
       return cipher_text







def 	SCREAMING_SNAKE_CASE_						(      __A			:      str  ,   __A			:      str    )     ->		str:

       """simple docstring"""

       a_ :		Any							=  ''
       a_ :		Optional[Any]							=  0
       for letter in cipher_text:
              if letter == " ":
                     or_txt += " "
              else:
                     a_ :		Union[str, Any]							=  (dicta[letter] + dicta[key_new[i]] + 26) % 26
                     i += 1
                     or_txt += dicta[x]
       return or_txt







def 	SCREAMING_SNAKE_CASE_						(      )     ->		None:

       """simple docstring"""

       a_ :		Tuple							=  'THE GERMAN ATTACK'
       a_ :		Dict							=  'SECRET'
       a_ :		Optional[Any]							=  generate_key(__A  ,   __A    )
       a_ :		Union[str, Any]							=  cipher_text(__A  ,   __A    )
       print(F"""Encrypted Text = {s}"""    )
       print(F"""Original Text = {original_text(__A  ,   __A    )}"""    )


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   main()


