






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  _UpperCAmelCase   (			unittest.TestCase      ):

				"""simple docstring"""




				@property
				def 							lowercase__  (	self		:       Dict    ):

								'''simple docstring'''
								torch.manual_seed(0    )
								lowercase__  					=     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 							lowercase__  (	self		:       Optional[Any]    ):

								'''simple docstring'''
								lowercase__  					=     self.dummy_uncond_unet
								lowercase__  					=     KarrasVeScheduler()

								lowercase__  					=     KarrasVePipeline(unet=lowerCamelCase,    scheduler=lowerCamelCase    )
								pipe.to(lowerCamelCase    )
								pipe.set_progress_bar_config(disable=lowerCamelCase    )

								lowercase__  					=     torch.manual_seed(0    )
								lowercase__  					=     pipe(num_inference_steps=2,    generator=lowerCamelCase,    output_type='''numpy'''    ).images

								lowercase__  					=     torch.manual_seed(0    )
								lowercase__  					=     pipe(num_inference_steps=2,    generator=lowerCamelCase,    output_type='''numpy''',    return_dict=lowerCamelCase    )[0]

								lowercase__  					=     image[0, -3:, -3:, -1]
								lowercase__  					=     image_from_tuple[0, -3:, -3:, -1]

								assert image.shape == (1, 32, 32, 3)
								lowercase__  					=     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  _UpperCAmelCase   (			unittest.TestCase      ):

				"""simple docstring"""




				def 							lowercase__  (	self		:       Optional[int]    ):

								'''simple docstring'''
								lowercase__  					=     '''google/ncsnpp-celebahq-256'''
								lowercase__  					=     UNetaDModel.from_pretrained(lowerCamelCase    )
								lowercase__  					=     KarrasVeScheduler()

								lowercase__  					=     KarrasVePipeline(unet=lowerCamelCase,    scheduler=lowerCamelCase    )
								pipe.to(lowerCamelCase    )
								pipe.set_progress_bar_config(disable=lowerCamelCase    )

								lowercase__  					=     torch.manual_seed(0    )
								lowercase__  					=     pipe(num_inference_steps=20,    generator=lowerCamelCase,    output_type='''numpy'''    ).images

								lowercase__  					=     image[0, -3:, -3:, -1]
								assert image.shape == (1, 256, 256, 3)
								lowercase__  					=     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












def     a     (		lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     len(lowerCamelCase_							)
				lowercase__  					=     [[False] * (required_sum + 1) for _ in range(arr_len + 1							)]

				# for each arr value, a sum of zero(0) can be formed by not taking any element
				# hence True/1
				for i in range(arr_len + 1							):
								lowercase__  					=     True

				# sum is not zero and set is empty then false
				for i in range(1						,  required_sum + 1							):
								lowercase__  					=     False

				for i in range(1						,  arr_len + 1							):
								for j in range(1						,  required_sum + 1							):
												if arr[i - 1] > j:
																lowercase__  					=     subset[i - 1][j]
												if arr[i - 1] <= j:
																lowercase__  					=     subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]

				return subset[arr_len][required_sum]


if __name__ == "__main__":
							import doctest

							doctest.testmod()




