


import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()

class        a	(unittest.TestCase   ):




							"""simple docstring"""


							@property
							def 					__snake_case      (							self :						Union[str, Any]       )			->					Optional[Any]:
														torch.manual_seed(0       )
														__snake_case	:    str											=     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 					__snake_case      (							self :						Optional[int]       )			->					str:
														__snake_case	:    List[str]											=     self.dummy_uncond_unet
														__snake_case	:    List[str]											=     ScoreSdeVeScheduler()

														__snake_case	:    str											=     ScoreSdeVePipeline(unet=lowerCamelCase					,			scheduler=lowerCamelCase       )
														sde_ve.to(lowerCamelCase       )
														sde_ve.set_progress_bar_config(disable=lowerCamelCase       )

														__snake_case	:    Union[str, Any]											=     torch.manual_seed(0       )
														__snake_case	:    int											=     sde_ve(num_inference_steps=2					,			output_type="numpy"					,			generator=lowerCamelCase       ).images

														__snake_case	:    List[str]											=     torch.manual_seed(0       )
														__snake_case	:    Union[str, Any]											=     sde_ve(num_inference_steps=2					,			output_type="numpy"					,			generator=lowerCamelCase					,			return_dict=lowerCamelCase       )[
														    0
														]

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

														assert image.shape == (1, 32, 32, 3)
														__snake_case	:    Optional[Any]											=     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        a	(unittest.TestCase   ):




							"""simple docstring"""


							def 					__snake_case      (							self :						Union[str, Any]       )			->					Tuple:
														__snake_case	:    Optional[int]											=     "google/ncsnpp-church-256"
														__snake_case	:    List[str]											=     UNetaDModel.from_pretrained(lowerCamelCase       )

														__snake_case	:    str											=     ScoreSdeVeScheduler.from_pretrained(lowerCamelCase       )

														__snake_case	:    Dict											=     ScoreSdeVePipeline(unet=lowerCamelCase					,			scheduler=lowerCamelCase       )
														sde_ve.to(lowerCamelCase       )
														sde_ve.set_progress_bar_config(disable=lowerCamelCase       )

														__snake_case	:    Union[str, Any]											=     torch.manual_seed(0       )
														__snake_case	:    Dict											=     sde_ve(num_inference_steps=10					,			output_type="numpy"					,			generator=lowerCamelCase       ).images

														__snake_case	:    List[str]											=     image[0, -3:, -3:, -1]

														assert image.shape == (1, 256, 256, 3)

														__snake_case	:    Union[str, Any]											=     np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]       )

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





import unittest

import numpy as np







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase = None							,			):
							__snake_case	:    List[str]											=     np.shape(__lowerCamelCase			)
							__snake_case	:    Optional[Any]											=     np.shape(__lowerCamelCase			)
							__snake_case	:    List[str]											=     np.shape(__lowerCamelCase			)

							if shape_a[0] != shape_b[0]:
														__snake_case	:    Any											=     (
														    "Expected the same number of rows for A and B. "
														    F'Instead found A of size {shape_a} and B of size {shape_b}'
														)
														raise ValueError(__lowerCamelCase			)

							if shape_b[1] != shape_c[1]:
														__snake_case	:    int											=     (
														    "Expected the same number of columns for B and C. "
														    F'Instead found B of size {shape_b} and C of size {shape_c}'
														)
														raise ValueError(__lowerCamelCase			)

							__snake_case	:    str											=     pseudo_inv
							if a_inv is None:
														try:
																					__snake_case	:    Optional[Any]											=     np.linalg.inv(__lowerCamelCase			)
														except np.linalg.LinAlgError:
																					raise ValueError(
																					    "Input matrix A is not invertible. Cannot compute Schur complement."			)

							return mat_c - mat_b.T @ a_inv @ mat_b

class        a	(unittest.TestCase   ):




							"""simple docstring"""


							def 					__snake_case      (							self :						Tuple       )			->					None:
														__snake_case	:    str											=     np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]       )
														__snake_case	:    str											=     np.array([[0, 3], [3, 0], [2, 3]]       )
														__snake_case	:    Dict											=     np.array([[2, 1], [6, 3]]       )

														__snake_case	:    Dict											=     schur_complement(lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       )

														__snake_case	:    int											=     np.block([[a, b], [b.T, c]]       )

														__snake_case	:    Optional[int]											=     np.linalg.det(lowerCamelCase       )
														__snake_case	:    Any											=     np.linalg.det(lowerCamelCase       )
														__snake_case	:    Tuple											=     np.linalg.det(lowerCamelCase       )

														self.assertAlmostEqual(lowerCamelCase					,			det_a * det_s       )

							def 					__snake_case      (							self :						int       )			->					None:
														__snake_case	:    str											=     np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]       )
														__snake_case	:    Dict											=     np.array([[0, 3], [3, 0], [2, 3]]       )
														__snake_case	:    Tuple											=     np.array([[2, 1], [6, 3]]       )

														with self.assertRaises(lowerCamelCase       ):
																					schur_complement(lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       )






							def 					__snake_case      (							self :						List[Any]       )			->					None:
														__snake_case	:    List[Any]											=     np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]       )
														__snake_case	:    Tuple											=     np.array([[0, 3], [3, 0], [2, 3]]       )
														__snake_case	:    List[Any]											=     np.array([[2, 1, 3], [6, 3, 5]]       )

														with self.assertRaises(lowerCamelCase       ):
																					schur_complement(lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       )


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			unittest.main()

