





'''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 		lowerCamelCase  (      unittest.TestCase       ):



	@property
	def   _lowerCamelCase    (   self	):
			torch.manual_seed(0	)
			lowerCAmelCase     :    Optional[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   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    List[Any]						=   self.dummy_uncond_unet
			lowerCAmelCase     :    int						=   KarrasVeScheduler()

			lowerCAmelCase     :    Optional[int]						=   KarrasVePipeline(unet=a_		,      scheduler=a_	)
			pipe.to(a_	)
			pipe.set_progress_bar_config(disable=a_	)

			lowerCAmelCase     :    Tuple						=   torch.manual_seed(0	)
			lowerCAmelCase     :    Dict						=   pipe(num_inference_steps=2		,      generator=a_		,      output_type="numpy"	).images

			lowerCAmelCase     :    Tuple						=   torch.manual_seed(0	)
			lowerCAmelCase     :    List[Any]						=   pipe(num_inference_steps=2		,      generator=a_		,      output_type="numpy"		,      return_dict=a_	)[0]

			lowerCAmelCase     :    List[Any]						=   image[0, -3:, -3:, -1]
			lowerCAmelCase     :    Optional[Any]						=   image_from_tuple[0, -3:, -3:, -1]

			assert image.shape == (1, 32, 32, 3)
			lowerCAmelCase     :    str						=   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 		lowerCamelCase  (      unittest.TestCase       ):



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    List[Any]						=   "google/ncsnpp-celebahq-256"
			lowerCAmelCase     :    List[str]						=   UNetaDModel.from_pretrained(a_	)
			lowerCAmelCase     :    str						=   KarrasVeScheduler()

			lowerCAmelCase     :    List[Any]						=   KarrasVePipeline(unet=a_		,      scheduler=a_	)
			pipe.to(a_	)
			pipe.set_progress_bar_config(disable=a_	)

			lowerCAmelCase     :    Tuple						=   torch.manual_seed(0	)
			lowerCAmelCase     :    Dict						=   pipe(num_inference_steps=20		,      generator=a_		,      output_type="numpy"	).images

			lowerCAmelCase     :    Any						=   image[0, -3:, -3:, -1]
			assert image.shape == (1, 256, 256, 3)
			lowerCAmelCase     :    int						=   np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]	)

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










'''simple docstring'''






import numpy as np






def      __A						(							a_						:  np.array	):
		return 1 / (1 + np.exp(-vector	))


if __name__ == "__main__":
					import doctest

					doctest.testmod()



