import unittest

import numpy as np





def 		__lowerCamelCase							(UpperCAmelCase__       :							np.ndarray ,	UpperCAmelCase__       :							np.ndarray ,	UpperCAmelCase__       :							np.ndarray ,	UpperCAmelCase__       :							np.ndarray | None = None ,	):
					SCREAMING_SNAKE_CASE           =    np.shape(UpperCAmelCase__					)
					SCREAMING_SNAKE_CASE           =    np.shape(UpperCAmelCase__					)
					SCREAMING_SNAKE_CASE           =    np.shape(UpperCAmelCase__					)

					if shape_a[0] != shape_b[0]:
										SCREAMING_SNAKE_CASE           =    (
										    "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(UpperCAmelCase__					)

					if shape_b[1] != shape_c[1]:
										SCREAMING_SNAKE_CASE           =    (
										    "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(UpperCAmelCase__					)

					SCREAMING_SNAKE_CASE           =    pseudo_inv
					if a_inv is None:
										try:
															SCREAMING_SNAKE_CASE           =    np.linalg.inv(UpperCAmelCase__					)
										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   lowercase      (       unittest.TestCase       ):




							def 		__snake_case(  self				:   Tuple )   ->			None:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
												SCREAMING_SNAKE_CASE           =    np.array([[0, 3], [3, 0], [2, 3]] )
												SCREAMING_SNAKE_CASE           =    np.array([[2, 1], [6, 3]] )

												SCREAMING_SNAKE_CASE           =    schur_complement(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase )

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

												SCREAMING_SNAKE_CASE           =    np.linalg.det(_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    np.linalg.det(_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    np.linalg.det(_UpperCamelCase )

												self.assertAlmostEqual(_UpperCamelCase		,							det_a * det_s )




							def 		__snake_case(  self				:   Tuple )   ->			None:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
												SCREAMING_SNAKE_CASE           =    np.array([[0, 3], [3, 0], [2, 3]] )
												SCREAMING_SNAKE_CASE           =    np.array([[2, 1], [6, 3]] )

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





							def 		__snake_case(  self				:   List[Any] )   ->			None:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
												SCREAMING_SNAKE_CASE           =    np.array([[0, 3], [3, 0], [2, 3]] )
												SCREAMING_SNAKE_CASE           =    np.array([[2, 1, 3], [6, 3, 5]] )

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


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							unittest.main()





import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest


class   lowercase      (       a       ):
							lowercase__  :    Dict									=							(UniPCMultistepScheduler,)
							lowercase__  :    Optional[int]									=							(("""num_inference_steps""", 25),)




							def 		__snake_case(  self				:   List[str]		,							**_UpperCamelCase				:   Tuple )   ->			Union[str, Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    {
												    "num_train_timesteps": 1_000,
												    "beta_start": 0.0_0_0_1,
												    "beta_end": 0.0_2,
												    "beta_schedule": "linear",
												    "solver_order": 2,
												    "solver_type": "bh2",
												}

												config.update(**_UpperCamelCase )
												return config




							def 		__snake_case(  self				:   List[str]		,							_UpperCamelCase				:   Dict=0		,							**_UpperCamelCase				:   List[str] )   ->			Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    dict(self.forward_default_kwargs )
												SCREAMING_SNAKE_CASE           =    kwargs.pop("num_inference_steps"		,							_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    self.dummy_sample
												SCREAMING_SNAKE_CASE           =    0.1 * sample
												SCREAMING_SNAKE_CASE           =    [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

												for scheduler_class in self.scheduler_classes:
																	SCREAMING_SNAKE_CASE           =    self.get_scheduler_config(**_UpperCamelCase )
																	SCREAMING_SNAKE_CASE           =    scheduler_class(**_UpperCamelCase )
																	scheduler.set_timesteps(_UpperCamelCase )
																	# copy over dummy past residuals
																	SCREAMING_SNAKE_CASE           =    dummy_past_residuals[: scheduler.config.solver_order]

																	with tempfile.TemporaryDirectory() as tmpdirname:
																						scheduler.save_config(_UpperCamelCase )
																						SCREAMING_SNAKE_CASE           =    scheduler_class.from_pretrained(_UpperCamelCase )
																						new_scheduler.set_timesteps(_UpperCamelCase )
																						# copy over dummy past residuals
																						SCREAMING_SNAKE_CASE           =    dummy_past_residuals[: new_scheduler.config.solver_order]

																	SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    sample, sample
																	for t in range(_UpperCamelCase		,							time_step + scheduler.config.solver_order + 1 ):
																						SCREAMING_SNAKE_CASE           =    scheduler.step(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							**_UpperCamelCase ).prev_sample
																						SCREAMING_SNAKE_CASE           =    new_scheduler.step(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							**_UpperCamelCase ).prev_sample

																						assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"




							def 		__snake_case(  self				:   Any		,							_UpperCamelCase				:   Union[str, Any]=0		,							**_UpperCamelCase				:   Dict )   ->			Union[str, Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    dict(self.forward_default_kwargs )
												SCREAMING_SNAKE_CASE           =    kwargs.pop("num_inference_steps"		,							_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    self.dummy_sample
												SCREAMING_SNAKE_CASE           =    0.1 * sample
												SCREAMING_SNAKE_CASE           =    [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

												for scheduler_class in self.scheduler_classes:
																	SCREAMING_SNAKE_CASE           =    self.get_scheduler_config()
																	SCREAMING_SNAKE_CASE           =    scheduler_class(**_UpperCamelCase )
																	scheduler.set_timesteps(_UpperCamelCase )

																	# copy over dummy past residuals (must be after setting timesteps)
																	SCREAMING_SNAKE_CASE           =    dummy_past_residuals[: scheduler.config.solver_order]

																	with tempfile.TemporaryDirectory() as tmpdirname:
																						scheduler.save_config(_UpperCamelCase )
																						SCREAMING_SNAKE_CASE           =    scheduler_class.from_pretrained(_UpperCamelCase )
																						# copy over dummy past residuals
																						new_scheduler.set_timesteps(_UpperCamelCase )

																						# copy over dummy past residual (must be after setting timesteps)
																						SCREAMING_SNAKE_CASE           =    dummy_past_residuals[: new_scheduler.config.solver_order]

																	SCREAMING_SNAKE_CASE           =    scheduler.step(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							**_UpperCamelCase ).prev_sample
																	SCREAMING_SNAKE_CASE           =    new_scheduler.step(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							**_UpperCamelCase ).prev_sample

																	assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"




							def 		__snake_case(  self				:   List[str]		,							_UpperCamelCase				:   Tuple=None		,							**_UpperCamelCase				:   List[Any] )   ->			str:



												'''simple docstring'''

												if scheduler is None:
																	SCREAMING_SNAKE_CASE           =    self.scheduler_classes[0]
																	SCREAMING_SNAKE_CASE           =    self.get_scheduler_config(**_UpperCamelCase )
																	SCREAMING_SNAKE_CASE           =    scheduler_class(**_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    self.scheduler_classes[0]
												SCREAMING_SNAKE_CASE           =    self.get_scheduler_config(**_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    scheduler_class(**_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    10
												SCREAMING_SNAKE_CASE           =    self.dummy_model()
												SCREAMING_SNAKE_CASE           =    self.dummy_sample_deter
												scheduler.set_timesteps(_UpperCamelCase )

												for i, t in enumerate(scheduler.timesteps ):
																	SCREAMING_SNAKE_CASE           =    model(_UpperCamelCase		,							_UpperCamelCase )
																	SCREAMING_SNAKE_CASE           =    scheduler.step(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase ).prev_sample

												return sample




							def 		__snake_case(  self				:   int )   ->			Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    dict(self.forward_default_kwargs )

												SCREAMING_SNAKE_CASE           =    kwargs.pop("num_inference_steps"		,							_UpperCamelCase )

												for scheduler_class in self.scheduler_classes:
																	SCREAMING_SNAKE_CASE           =    self.get_scheduler_config()
																	SCREAMING_SNAKE_CASE           =    scheduler_class(**_UpperCamelCase )

																	SCREAMING_SNAKE_CASE           =    self.dummy_sample
																	SCREAMING_SNAKE_CASE           =    0.1 * sample

																	if num_inference_steps is not None and hasattr(_UpperCamelCase		,							"set_timesteps" ):
																						scheduler.set_timesteps(_UpperCamelCase )
																	elif num_inference_steps is not None and not hasattr(_UpperCamelCase		,							"set_timesteps" ):
																						SCREAMING_SNAKE_CASE           =    num_inference_steps

																	# copy over dummy past residuals (must be done after set_timesteps)
																	SCREAMING_SNAKE_CASE           =    [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
																	SCREAMING_SNAKE_CASE           =    dummy_past_residuals[: scheduler.config.solver_order]

																	SCREAMING_SNAKE_CASE           =    scheduler.timesteps[5]
																	SCREAMING_SNAKE_CASE           =    scheduler.timesteps[6]

																	SCREAMING_SNAKE_CASE           =    scheduler.step(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							**_UpperCamelCase ).prev_sample
																	SCREAMING_SNAKE_CASE           =    scheduler.step(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							**_UpperCamelCase ).prev_sample

																	self.assertEqual(output_a.shape		,							sample.shape )
																	self.assertEqual(output_a.shape		,							output_a.shape )




							def 		__snake_case(  self				:   str )   ->			Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    UniPCMultistepScheduler(**self.get_scheduler_config() )
												SCREAMING_SNAKE_CASE           =    self.full_loop(scheduler=_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    torch.mean(torch.abs(_UpperCamelCase ) )

												assert abs(result_mean.item() - 0.2_4_6_4 ) < 1e-3

												SCREAMING_SNAKE_CASE           =    DPMSolverSinglestepScheduler.from_config(scheduler.config )
												SCREAMING_SNAKE_CASE           =    DEISMultistepScheduler.from_config(scheduler.config )
												SCREAMING_SNAKE_CASE           =    DPMSolverMultistepScheduler.from_config(scheduler.config )
												SCREAMING_SNAKE_CASE           =    UniPCMultistepScheduler.from_config(scheduler.config )

												SCREAMING_SNAKE_CASE           =    self.full_loop(scheduler=_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    torch.mean(torch.abs(_UpperCamelCase ) )

												assert abs(result_mean.item() - 0.2_4_6_4 ) < 1e-3




							def 		__snake_case(  self				:   Optional[int] )   ->			List[Any]:



												'''simple docstring'''

												for timesteps in [25, 50, 100, 999, 1_000]:
																	self.check_over_configs(num_train_timesteps=_UpperCamelCase )




							def 		__snake_case(  self				:   Tuple )   ->			Union[str, Any]:



												'''simple docstring'''

												self.check_over_configs(thresholding=_UpperCamelCase )
												for order in [1, 2, 3]:
																	for solver_type in ["bh1", "bh2"]:
																						for threshold in [0.5, 1.0, 2.0]:
																											for prediction_type in ["epsilon", "sample"]:
																																self.check_over_configs(
																																    thresholding=_UpperCamelCase		,							prediction_type=_UpperCamelCase		,							sample_max_value=_UpperCamelCase		,							solver_order=_UpperCamelCase		,							solver_type=_UpperCamelCase		,							)




							def 		__snake_case(  self				:   Tuple )   ->			int:



												'''simple docstring'''

												for prediction_type in ["epsilon", "v_prediction"]:
																	self.check_over_configs(prediction_type=_UpperCamelCase )




							def 		__snake_case(  self				:   Dict )   ->			int:



												'''simple docstring'''

												for solver_type in ["bh1", "bh2"]:
																	for order in [1, 2, 3]:
																						for prediction_type in ["epsilon", "sample"]:
																											self.check_over_configs(
																											    solver_order=_UpperCamelCase		,							solver_type=_UpperCamelCase		,							prediction_type=_UpperCamelCase		,							)
																											SCREAMING_SNAKE_CASE           =    self.full_loop(
																											    solver_order=_UpperCamelCase		,							solver_type=_UpperCamelCase		,							prediction_type=_UpperCamelCase		,							)
																											assert not torch.isnan(_UpperCamelCase ).any(), "Samples have nan numbers"




							def 		__snake_case(  self				:   List[str] )   ->			Tuple:



												'''simple docstring'''

												self.check_over_configs(lower_order_final=_UpperCamelCase )
												self.check_over_configs(lower_order_final=_UpperCamelCase )




							def 		__snake_case(  self				:   Optional[int] )   ->			List[Any]:



												'''simple docstring'''

												for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
																	self.check_over_forward(num_inference_steps=_UpperCamelCase		,							time_step=0 )




							def 		__snake_case(  self				:   Tuple )   ->			Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.full_loop()
												SCREAMING_SNAKE_CASE           =    torch.mean(torch.abs(_UpperCamelCase ) )

												assert abs(result_mean.item() - 0.2_4_6_4 ) < 1e-3




							def 		__snake_case(  self				:   List[str] )   ->			List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.full_loop(prediction_type="v_prediction" )
												SCREAMING_SNAKE_CASE           =    torch.mean(torch.abs(_UpperCamelCase ) )

												assert abs(result_mean.item() - 0.1_0_1_4 ) < 1e-3




							def 		__snake_case(  self				:   int )   ->			Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.scheduler_classes[0]
												SCREAMING_SNAKE_CASE           =    self.get_scheduler_config(thresholding=_UpperCamelCase		,							dynamic_thresholding_ratio=0 )
												SCREAMING_SNAKE_CASE           =    scheduler_class(**_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    10
												SCREAMING_SNAKE_CASE           =    self.dummy_model()
												SCREAMING_SNAKE_CASE           =    self.dummy_sample_deter.half()
												scheduler.set_timesteps(_UpperCamelCase )

												for i, t in enumerate(scheduler.timesteps ):
																	SCREAMING_SNAKE_CASE           =    model(_UpperCamelCase		,							_UpperCamelCase )
																	SCREAMING_SNAKE_CASE           =    scheduler.step(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase ).prev_sample

												assert sample.dtype == torch.floataa





							def 		__snake_case(  self				:   List[str]		,							**_UpperCamelCase				:   Dict )   ->			Union[str, Any]:



												'''simple docstring'''

												for scheduler_class in self.scheduler_classes:
																	SCREAMING_SNAKE_CASE           =    self.get_scheduler_config(**_UpperCamelCase )
																	SCREAMING_SNAKE_CASE           =    scheduler_class(**_UpperCamelCase )

																	scheduler.set_timesteps(scheduler.config.num_train_timesteps )
																	assert len(scheduler.timesteps.unique() ) == scheduler.num_inference_steps




