
import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest




@require_torchsde
class 		__A      (			__lowercase			):
						UpperCamelCase     						=					(DPMSolverSDEScheduler,)
						UpperCamelCase     						=					10

						def     A__						(		self   :Tuple    , **__snake_case   :Union[str, Any] ):
										'''simple docstring'''

										__magic_name__	:	str								={
										    """num_train_timesteps""": 11_00,
										    """beta_start""": 0.0001,
										    """beta_end""": 0.02,
										    """beta_schedule""": """linear""",
										    """noise_sampler_seed""": 0,
										}

										config.update(**_A )
										return config

						def     A__						(		self   :List[Any] ):
										'''simple docstring'''

										for timesteps in [10, 50, 1_00, 10_00]:
														self.check_over_configs(num_train_timesteps=_A )

						def     A__						(		self   :List[Any] ):
										'''simple docstring'''

										for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]    , [0.0002, 0.002, 0.02] ):
														self.check_over_configs(beta_start=_A    , beta_end=_A )

						def     A__						(		self   :List[str] ):
										'''simple docstring'''

										for schedule in ["linear", "scaled_linear"]:
														self.check_over_configs(beta_schedule=_A )

						def     A__						(		self   :Union[str, Any] ):
										'''simple docstring'''

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

						def     A__						(		self   :int ):
										'''simple docstring'''

										__magic_name__	:	Optional[Any]								=self.scheduler_classes[0]
										__magic_name__	:	Dict								=self.get_scheduler_config()
										__magic_name__	:	Dict								=scheduler_class(**_A )

										scheduler.set_timesteps(self.num_inference_steps )

										__magic_name__	:	Optional[int]								=self.dummy_model()
										__magic_name__	:	str								=self.dummy_sample_deter * scheduler.init_noise_sigma
										__magic_name__	:	str								=sample.to(_A )

										for i, t in enumerate(scheduler.timesteps ):
														__magic_name__	:	Tuple								=scheduler.scale_model_input(_A    , _A )

														__magic_name__	:	int								=model(_A    , _A )

														__magic_name__	:	List[str]								=scheduler.step(_A    , _A    , _A )
														__magic_name__	:	Any								=output.prev_sample

										__magic_name__	:	Optional[int]								=torch.sum(torch.abs(_A ) )
										__magic_name__	:	Dict								=torch.mean(torch.abs(_A ) )

										if torch_device in ["mps"]:
														assert abs(result_sum.item() - 167.47821044921875 ) < 1E-2
														assert abs(result_mean.item() - 0.2178705964565277 ) < 1E-3
										elif torch_device in ["cuda"]:
														assert abs(result_sum.item() - 171.59352111816406 ) < 1E-2
														assert abs(result_mean.item() - 0.22342906892299652 ) < 1E-3
										else:
														assert abs(result_sum.item() - 162.52383422851562 ) < 1E-2
														assert abs(result_mean.item() - 0.211619570851326 ) < 1E-3

						def     A__						(		self   :Tuple ):
										'''simple docstring'''

										__magic_name__	:	Optional[int]								=self.scheduler_classes[0]
										__magic_name__	:	Optional[int]								=self.get_scheduler_config(prediction_type="""v_prediction""" )
										__magic_name__	:	Any								=scheduler_class(**_A )

										scheduler.set_timesteps(self.num_inference_steps )

										__magic_name__	:	Any								=self.dummy_model()
										__magic_name__	:	int								=self.dummy_sample_deter * scheduler.init_noise_sigma
										__magic_name__	:	Any								=sample.to(_A )

										for i, t in enumerate(scheduler.timesteps ):
														__magic_name__	:	str								=scheduler.scale_model_input(_A    , _A )

														__magic_name__	:	List[str]								=model(_A    , _A )

														__magic_name__	:	List[str]								=scheduler.step(_A    , _A    , _A )
														__magic_name__	:	Optional[int]								=output.prev_sample

										__magic_name__	:	List[Any]								=torch.sum(torch.abs(_A ) )
										__magic_name__	:	str								=torch.mean(torch.abs(_A ) )

										if torch_device in ["mps"]:
														assert abs(result_sum.item() - 124.77149200439453 ) < 1E-2
														assert abs(result_mean.item() - 0.16226289014816284 ) < 1E-3
										elif torch_device in ["cuda"]:
														assert abs(result_sum.item() - 128.1663360595703 ) < 1E-2
														assert abs(result_mean.item() - 0.16688326001167297 ) < 1E-3
										else:
														assert abs(result_sum.item() - 119.8487548828125 ) < 1E-2
														assert abs(result_mean.item() - 0.1560530662536621 ) < 1E-3

						def     A__						(		self   :int ):
										'''simple docstring'''

										__magic_name__	:	Dict								=self.scheduler_classes[0]
										__magic_name__	:	Union[str, Any]								=self.get_scheduler_config()
										__magic_name__	:	List[str]								=scheduler_class(**_A )

										scheduler.set_timesteps(self.num_inference_steps    , device=_A )

										__magic_name__	:	Dict								=self.dummy_model()
										__magic_name__	:	List[Any]								=self.dummy_sample_deter.to(_A ) * scheduler.init_noise_sigma

										for t in scheduler.timesteps:
														__magic_name__	:	Dict								=scheduler.scale_model_input(_A    , _A )

														__magic_name__	:	Optional[Any]								=model(_A    , _A )

														__magic_name__	:	Optional[Any]								=scheduler.step(_A    , _A    , _A )
														__magic_name__	:	List[Any]								=output.prev_sample

										__magic_name__	:	Dict								=torch.sum(torch.abs(_A ) )
										__magic_name__	:	Tuple								=torch.mean(torch.abs(_A ) )

										if torch_device in ["mps"]:
														assert abs(result_sum.item() - 167.46957397460938 ) < 1E-2
														assert abs(result_mean.item() - 0.21805934607982635 ) < 1E-3
										elif torch_device in ["cuda"]:
														assert abs(result_sum.item() - 171.59353637695312 ) < 1E-2
														assert abs(result_mean.item() - 0.22342908382415771 ) < 1E-3
										else:
														assert abs(result_sum.item() - 162.52383422851562 ) < 1E-2
														assert abs(result_mean.item() - 0.211619570851326 ) < 1E-3


						def     A__						(		self   :Dict ):
										'''simple docstring'''

										__magic_name__	:	Optional[Any]								=self.scheduler_classes[0]
										__magic_name__	:	List[str]								=self.get_scheduler_config()
										__magic_name__	:	Optional[Any]								=scheduler_class(**_A    , use_karras_sigmas=_A )

										scheduler.set_timesteps(self.num_inference_steps    , device=_A )

										__magic_name__	:	Tuple								=self.dummy_model()
										__magic_name__	:	List[str]								=self.dummy_sample_deter.to(_A ) * scheduler.init_noise_sigma
										__magic_name__	:	Optional[Any]								=sample.to(_A )

										for t in scheduler.timesteps:
														__magic_name__	:	Dict								=scheduler.scale_model_input(_A    , _A )

														__magic_name__	:	List[str]								=model(_A    , _A )

														__magic_name__	:	List[str]								=scheduler.step(_A    , _A    , _A )
														__magic_name__	:	List[str]								=output.prev_sample

										__magic_name__	:	Union[str, Any]								=torch.sum(torch.abs(_A ) )
										__magic_name__	:	Dict								=torch.mean(torch.abs(_A ) )

										if torch_device in ["mps"]:
														assert abs(result_sum.item() - 176.66974135742188 ) < 1E-2
														assert abs(result_mean.item() - 0.23003872730981811 ) < 1E-2
										elif torch_device in ["cuda"]:
														assert abs(result_sum.item() - 177.63653564453125 ) < 1E-2
														assert abs(result_mean.item() - 0.23003872730981811 ) < 1E-2
										else:
														assert abs(result_sum.item() - 170.3135223388672 ) < 1E-2
														assert abs(result_mean.item() - 0.23003872730981811 ) < 1E-2










import numpy as np




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase = 1E-12	,     lowerCamelCase = 100	,     ):
				assert np.shape(lowerCamelCase   )[0] == np.shape(lowerCamelCase   )[1]
				# Ensure proper dimensionality.
				assert np.shape(lowerCamelCase   )[0] == np.shape(lowerCamelCase   )[0]
				# Ensure inputs are either both complex or both real
				assert np.iscomplexobj(lowerCamelCase   ) == np.iscomplexobj(lowerCamelCase   )
				__magic_name__	:	List[Any]								=np.iscomplexobj(lowerCamelCase   )
				if is_complex:
								# Ensure complex input_matrix is Hermitian
								assert np.array_equal(lowerCamelCase	,     input_matrix.conj().T   )

				# Set convergence to False. Will define convergence when we exceed max_iterations
				# or when we have small changes from one iteration to next.

				__magic_name__	:	List[Any]								=False
				__magic_name__	:	List[Any]								=0
				__magic_name__	:	List[str]								=0
				__magic_name__	:	str								=1E12

				while not convergence:
								# Multiple matrix by the vector.
								__magic_name__	:	Dict								=np.dot(lowerCamelCase	,     lowerCamelCase   )
								# Normalize the resulting output vector.
								__magic_name__	:	List[str]								=w / np.linalg.norm(lowerCamelCase   )
								# Find rayleigh quotient
								# (faster than usual b/c we know vector is normalized already)
								__magic_name__	:	Any								=vector.conj().T if is_complex else vector.T
								__magic_name__	:	List[str]								=np.dot(lowerCamelCase	,     np.dot(lowerCamelCase	,     lowerCamelCase   )   )

								# Check convergence.
								__magic_name__	:	List[str]								=np.abs(lambda_ - lambda_previous   ) / lambda_
								iterations += 1

								if error <= error_tol or iterations >= max_iterations:
												__magic_name__	:	int								=True

								__magic_name__	:	Optional[Any]								=lambda_

				if is_complex:
								__magic_name__	:	Union[str, Any]								=np.real(lambda_   )

				return lambda_, vector




def 		lowerCAmelCase_   (	):
				__magic_name__	:	Optional[int]								=np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]   )
				__magic_name__	:	Any								=np.array([41, 4, 20]   )
				__magic_name__	:	Optional[Any]								=real_input_matrix.astype(np.complexaaa   )
				__magic_name__	:	str								=np.triu(1J * complex_input_matrix	,     1   )
				complex_input_matrix += imag_matrix
				complex_input_matrix += -1 * imag_matrix.T
				__magic_name__	:	List[Any]								=np.array([41, 4, 20]   ).astype(np.complexaaa   )

				for problem_type in ["real", "complex"]:
								if problem_type == "real":
												__magic_name__	:	Union[str, Any]								=real_input_matrix
												__magic_name__	:	List[Any]								=real_vector
								elif problem_type == "complex":
												__magic_name__	:	Optional[Any]								=complex_input_matrix
												__magic_name__	:	Tuple								=complex_vector

								# Our implementation.
								__magic_name__				, __magic_name__	:	Any								=power_iteration(lowerCamelCase	,     lowerCamelCase   )

								# Numpy implementation.

								# Get eigenvalues and eigenvectors using built-in numpy
								# eigh (eigh used for symmetric or hermetian matrices).
								__magic_name__				, __magic_name__	:	int								=np.linalg.eigh(lowerCamelCase   )
								# Last eigenvalue is the maximum one.
								__magic_name__	:	List[str]								=eigen_values[-1]
								# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
								__magic_name__	:	Dict								=eigen_vectors[:, -1]

								# Check our implementation and numpy gives close answers.
								assert np.abs(eigen_value - eigen_value_max   ) <= 1E-6
								# Take absolute values element wise of each eigenvector.
								# as they are only unique to a minus sign.
								assert np.linalg.norm(np.abs(lowerCamelCase   ) - np.abs(lowerCamelCase   )   ) <= 1E-6


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							test_power_iteration()




