




import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class       _A				(		__a       ,     unittest.TestCase     ):
			__a					    =       DDIMPipeline
			__a					    =       UNCONDITIONAL_IMAGE_GENERATION_PARAMS
			__a					    =       PipelineTesterMixin.required_optional_params - {
			    "num_images_per_prompt",
			    "latents",
			    "callback",
			    "callback_steps",
			}
			__a					    =       UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
			__a					    =       False






			def 				UpperCAmelCase (    self			):
									torch.manual_seed(0			)
									_UpperCAmelCase      =			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""")						,		)
									_UpperCAmelCase      =			DDIMScheduler()
									_UpperCAmelCase      =			{"""unet""": unet, """scheduler""": scheduler}
									return components






			def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=0			):







									if str(lowerCAmelCase_			).startswith("""mps"""			):
															_UpperCAmelCase      =			torch.manual_seed(lowerCAmelCase_			)
									else:
															_UpperCAmelCase      =			torch.Generator(device=lowerCAmelCase_			).manual_seed(lowerCAmelCase_			)
									_UpperCAmelCase      =			{
									    """batch_size""": 1,
									    """generator""": generator,
									    """num_inference_steps""": 2,
									    """output_type""": """numpy""",
									}
									return inputs






			def 				UpperCAmelCase (    self			):
									_UpperCAmelCase      =			"""cpu"""

									_UpperCAmelCase      =			self.get_dummy_components()
									_UpperCAmelCase      =			self.pipeline_class(**lowerCAmelCase_			)
									pipe.to(lowerCAmelCase_			)
									pipe.set_progress_bar_config(disable=lowerCAmelCase_			)

									_UpperCAmelCase      =			self.get_dummy_inputs(lowerCAmelCase_			)
									_UpperCAmelCase      =			pipe(**lowerCAmelCase_			).images
									_UpperCAmelCase      =			image[0, -3:, -3:, -1]

									self.assertEqual(image.shape						,		(1, 32, 32, 3)			)
									_UpperCAmelCase      =			np.array(
									    [1.0_00e00, 5.7_17e-01, 4.7_17e-01, 1.0_00e00, 0.0_00e00, 1.0_00e00, 3.0_00e-04, 0.0_00e00, 9.0_00e-04]			)
									_UpperCAmelCase      =			np.abs(image_slice.flatten() - expected_slice			).max()
									self.assertLessEqual(lowerCAmelCase_						,		1e-3			)






			def 				UpperCAmelCase (    self			):
									super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3			)






			def 				UpperCAmelCase (    self			):
									super().test_save_load_local(expected_max_difference=3e-3			)






			def 				UpperCAmelCase (    self			):
									super().test_save_load_optional_components(expected_max_difference=3e-3			)






			def 				UpperCAmelCase (    self			):
									super().test_inference_batch_single_identical(expected_max_diff=3e-3			)




@slow
@require_torch_gpu
class       _A				(		unittest.TestCase     ):






			def 				UpperCAmelCase (    self			):
									_UpperCAmelCase      =			"""google/ddpm-cifar10-32"""

									_UpperCAmelCase      =			UNetaDModel.from_pretrained(lowerCAmelCase_			)
									_UpperCAmelCase      =			DDIMScheduler()

									_UpperCAmelCase      =			DDIMPipeline(unet=lowerCAmelCase_						,		scheduler=lowerCAmelCase_			)
									ddim.to(lowerCAmelCase_			)
									ddim.set_progress_bar_config(disable=lowerCAmelCase_			)

									_UpperCAmelCase      =			torch.manual_seed(0			)
									_UpperCAmelCase      =			ddim(generator=lowerCAmelCase_						,		eta=0.0						,		output_type="""numpy"""			).images

									_UpperCAmelCase      =			image[0, -3:, -3:, -1]

									assert image.shape == (1, 32, 32, 3)
									_UpperCAmelCase      =			np.array([0.1723, 0.1617, 0.1600, 0.1626, 0.1497, 0.1513, 0.1505, 0.1442, 0.1453]			)

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






			def 				UpperCAmelCase (    self			):
									_UpperCAmelCase      =			"""google/ddpm-ema-bedroom-256"""

									_UpperCAmelCase      =			UNetaDModel.from_pretrained(lowerCAmelCase_			)
									_UpperCAmelCase      =			DDIMScheduler.from_pretrained(lowerCAmelCase_			)

									_UpperCAmelCase      =			DDIMPipeline(unet=lowerCAmelCase_						,		scheduler=lowerCAmelCase_			)
									ddpm.to(lowerCAmelCase_			)
									ddpm.set_progress_bar_config(disable=lowerCAmelCase_			)

									_UpperCAmelCase      =			torch.manual_seed(0			)
									_UpperCAmelCase      =			ddpm(generator=lowerCAmelCase_						,		output_type="""numpy"""			).images

									_UpperCAmelCase      =			image[0, -3:, -3:, -1]

									assert image.shape == (1, 256, 256, 3)
									_UpperCAmelCase      =			np.array([0.0060, 0.0201, 0.0344, 0.0024, 0.0018, 0.0002, 0.0022, 0.0000, 0.0069]			)

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





import unittest

import numpy as np




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case			,     snake_case = None			,     )						->				np.ndarray:
						_UpperCAmelCase      =			np.shape(snake_case       )
						_UpperCAmelCase      =			np.shape(snake_case       )
						_UpperCAmelCase      =			np.shape(snake_case       )

						if shape_a[0] != shape_b[0]:
												_UpperCAmelCase      =			(
												    """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(snake_case       )

						if shape_b[1] != shape_c[1]:
												_UpperCAmelCase      =			(
												    """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(snake_case       )

						_UpperCAmelCase      =			pseudo_inv
						if a_inv is None:
												try:
																		_UpperCAmelCase      =			np.linalg.inv(snake_case       )
												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     ):






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]			)
								_UpperCAmelCase      =			np.array([[0, 3], [3, 0], [2, 3]]			)
								_UpperCAmelCase      =			np.array([[2, 1], [6, 3]]			)

								_UpperCAmelCase      =			schur_complement(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)

								_UpperCAmelCase      =			np.block([[a, b], [b.T, c]]			)

								_UpperCAmelCase      =			np.linalg.det(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			np.linalg.det(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			np.linalg.det(_SCREAMING_SNAKE_CASE			)

								self.assertAlmostEqual(_SCREAMING_SNAKE_CASE						,		det_a * det_s			)






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]			)
								_UpperCAmelCase      =			np.array([[0, 3], [3, 0], [2, 3]]			)
								_UpperCAmelCase      =			np.array([[2, 1], [6, 3]]			)

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






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]			)
								_UpperCAmelCase      =			np.array([[0, 3], [3, 0], [2, 3]]			)
								_UpperCAmelCase      =			np.array([[2, 1, 3], [6, 3, 5]]			)

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


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				unittest.main()