
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 __snake_case						( unittest.TestCase	):
    @property
    def  _SCREAMING_SNAKE_CASE			(    self ):



           '''simple docstring'''







           torch.manual_seed(0 )
           lowercase				:	int			      = 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'''







           lowercase				:	Dict			      = self.dummy_uncond_unet
           lowercase				:	Optional[int]			      = PNDMScheduler()

           lowercase				:	List[str]			      = PNDMPipeline(unet=_a						,scheduler=_a )
           pndm.to(_a )
           pndm.set_progress_bar_config(disable=_a )

           lowercase				:	Optional[int]			      = torch.manual_seed(0 )
           lowercase				:	int			      = pndm(generator=_a						,num_inference_steps=20						,output_type="""numpy""" ).images

           lowercase				:	int			      = torch.manual_seed(0 )
           lowercase				:	List[str]			      = pndm(generator=_a						,num_inference_steps=20						,output_type="""numpy"""						,return_dict=_a )[0]

           lowercase				:	int			      = image[0, -3:, -3:, -1]
           lowercase				:	Union[str, Any]			      = image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (1, 32, 32, 3)
           lowercase				:	str			      = 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 __snake_case						( unittest.TestCase	):
    def  _SCREAMING_SNAKE_CASE			(    self ):



           '''simple docstring'''







           lowercase				:	Tuple			      = 'google/ddpm-cifar10-32'

           lowercase				:	Optional[Any]			      = UNetaDModel.from_pretrained(_a )
           lowercase				:	Union[str, Any]			      = PNDMScheduler()

           lowercase				:	Dict			      = PNDMPipeline(unet=_a						,scheduler=_a )
           pndm.to(_a )
           pndm.set_progress_bar_config(disable=_a )
           lowercase				:	Union[str, Any]			      = torch.manual_seed(0 )
           lowercase				:	List[str]			      = pndm(generator=_a						,output_type="""numpy""" ).images

           lowercase				:	Optional[int]			      = image[0, -3:, -3:, -1]

           assert image.shape == (1, 32, 32, 3)
           lowercase				:	str			      = np.array([0.1_564, 0.14_645, 0.1_406, 0.14_715, 0.12_425, 0.14_045, 0.13_115, 0.12_175, 0.125] )

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





'''simple docstring'''





import string

def 			a_	(				_lowerCAmelCase			) -> str:
  __lowerCamelCase			:			Union[str, Any]       			=					''
  for i in sequence:
    __lowerCamelCase			:			Tuple       			=					ord(_lowerCAmelCase			)
    if 65 <= extract <= 90:
      output += chr(155 - extract			)
    elif 97 <= extract <= 122:
      output += chr(219 - extract			)
    else:
      output += i
  return output

def 			a_	(				_lowerCAmelCase			) -> str:
  __lowerCamelCase			:			Optional[Any]       			=					string.ascii_letters
  __lowerCamelCase			:			str       			=					string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
  return "".join(
      letters_reversed[letters.index(_lowerCAmelCase			)] if c in letters else c for c in sequence			)

def 			a_	(				) -> None:
  from timeit import timeit

  print('Running performance benchmarks...'			)
  __lowerCamelCase			:			Tuple       			=					'from string import printable ; from __main__ import atbash, atbash_slow'
  print(F'> atbash_slow(): {timeit("atbash_slow(printable)"			,setup=_lowerCAmelCase			)} seconds'			)
  print(F'>      atbash(): {timeit("atbash(printable)"			,setup=_lowerCAmelCase			)} seconds'			)


if __name__ == "__main__":
   for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
      print(f'''{example} encrypted in atbash: {atbash(example)}''')
   benchmark()






