






'''simple docstring'''





import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, 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 						A	(   self   :						Optional[int]	)  ->				List[Any]:
								torch.manual_seed(0	)
								UpperCAmelCase_	:    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 						A	(   self   :						Optional[int]	)  ->				Dict:
								UpperCAmelCase_	:    Union[str, Any]	  =    self.dummy_uncond_unet
								UpperCAmelCase_	:    int	  =    KarrasVeScheduler()

								UpperCAmelCase_	:    str	  =    KarrasVePipeline(unet=_A							,      scheduler=_A	)
								pipe.to(_A	)
								pipe.set_progress_bar_config(disable=_A	)

								UpperCAmelCase_	:    Tuple	  =    torch.manual_seed(0	)
								UpperCAmelCase_	:    List[str]	  =    pipe(num_inference_steps=2							,      generator=_A							,      output_type='''numpy'''	).images

								UpperCAmelCase_	:    List[str]	  =    torch.manual_seed(0	)
								UpperCAmelCase_	:    str	  =    pipe(num_inference_steps=2							,      generator=_A							,      output_type='''numpy'''							,      return_dict=_A	)[0]

								UpperCAmelCase_	:    Any	  =    image[0, -3:, -3:, -1]
								UpperCAmelCase_	:    str	  =    image_from_tuple[0, -3:, -3:, -1]

								assert image.shape == (1, 32, 32, 3)
								UpperCAmelCase_	:    int	  =    np.array([0.0, 1.0, 0.0, 0.0, 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 						A	(   self   :						List[str]	)  ->				str:
								UpperCAmelCase_	:    Dict	  =    """google/ncsnpp-celebahq-256"""
								UpperCAmelCase_	:    Optional[int]	  =    UNetaDModel.from_pretrained(_A	)
								UpperCAmelCase_	:    Union[str, Any]	  =    KarrasVeScheduler()

								UpperCAmelCase_	:    Tuple	  =    KarrasVePipeline(unet=_A							,      scheduler=_A	)
								pipe.to(_A	)
								pipe.set_progress_bar_config(disable=_A	)

								UpperCAmelCase_	:    int	  =    torch.manual_seed(0	)
								UpperCAmelCase_	:    Any	  =    pipe(num_inference_steps=20							,      generator=_A							,      output_type='''numpy'''	).images

								UpperCAmelCase_	:    List[str]	  =    image[0, -3:, -3:, -1]
								assert image.shape == (1, 2_56, 2_56, 3)
								UpperCAmelCase_	:    Union[str, Any]	  =    np.array([0.578, 0.5_811, 0.5_924, 0.5_809, 0.587, 0.5_886, 0.5_861, 0.5_802, 0.586]	)

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




def     _a					(						lowerCamelCase							):
 return " ".join(
     """""".join(word[::-1]							) if len(lowerCamelCase							) > 4 else word for word in sentence.split()							)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      print(reverse_long_words("""Hey wollef sroirraw"""))




