import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 						a__							(   __snake_case		):
			A__				:   Optional[int]								=			(LMSDiscreteScheduler,)
			A__				:   List[str]								=			10



			def __SCREAMING_SNAKE_CASE							(						self      ,						**UpperCAmelCase    ) ->    List[str]:
								__a							=   {
								    'num_train_timesteps': 1_1_0_0,
								    'beta_start': 0.0_001,
								    'beta_end': 0.02,
								    'beta_schedule': 'linear',
								}

								config.update(**UpperCAmelCase    )
								return config



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Union[str, Any]:
								for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
													self.check_over_configs(num_train_timesteps=UpperCAmelCase    )



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Dict:
								for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]      ,						[0.0_002, 0.002, 0.02]    ):
													self.check_over_configs(beta_start=UpperCAmelCase      ,						beta_end=UpperCAmelCase    )



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    List[str]:
								for schedule in ["linear", "scaled_linear"]:
													self.check_over_configs(beta_schedule=UpperCAmelCase    )



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    List[Any]:
								for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(prediction_type=UpperCAmelCase    )



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Optional[int]:
								for t in [0, 5_0_0, 8_0_0]:
													self.check_over_forward(time_step=UpperCAmelCase    )



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    List[str]:
								__a							=   self.scheduler_classes[0]
								__a							=   self.get_scheduler_config()
								__a							=   scheduler_class(**UpperCAmelCase    )

								scheduler.set_timesteps(self.num_inference_steps    )

								__a							=   self.dummy_model()
								__a							=   self.dummy_sample_deter * scheduler.init_noise_sigma

								for i, t in enumerate(scheduler.timesteps    ):
													__a							=   scheduler.scale_model_input(UpperCAmelCase      ,						UpperCAmelCase    )

													__a							=   model(UpperCAmelCase      ,						UpperCAmelCase    )

													__a							=   scheduler.step(UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase    )
													__a							=   output.prev_sample

								__a							=   torch.sum(torch.abs(UpperCAmelCase    )    )
								__a							=   torch.mean(torch.abs(UpperCAmelCase    )    )

								assert abs(result_sum.item() - 1_006.388    ) < 1e-2
								assert abs(result_mean.item() - 1.31    ) < 1e-3



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    str:
								__a							=   self.scheduler_classes[0]
								__a							=   self.get_scheduler_config(prediction_type='v_prediction'    )
								__a							=   scheduler_class(**UpperCAmelCase    )

								scheduler.set_timesteps(self.num_inference_steps    )

								__a							=   self.dummy_model()
								__a							=   self.dummy_sample_deter * scheduler.init_noise_sigma

								for i, t in enumerate(scheduler.timesteps    ):
													__a							=   scheduler.scale_model_input(UpperCAmelCase      ,						UpperCAmelCase    )

													__a							=   model(UpperCAmelCase      ,						UpperCAmelCase    )

													__a							=   scheduler.step(UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase    )
													__a							=   output.prev_sample

								__a							=   torch.sum(torch.abs(UpperCAmelCase    )    )
								__a							=   torch.mean(torch.abs(UpperCAmelCase    )    )

								assert abs(result_sum.item() - 0.0_017    ) < 1e-2
								assert abs(result_mean.item() - 2.2676e-06    ) < 1e-3



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    str:
								__a							=   self.scheduler_classes[0]
								__a							=   self.get_scheduler_config()
								__a							=   scheduler_class(**UpperCAmelCase    )

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

								__a							=   self.dummy_model()
								__a							=   self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
								__a							=   sample.to(UpperCAmelCase    )

								for i, t in enumerate(scheduler.timesteps    ):
													__a							=   scheduler.scale_model_input(UpperCAmelCase      ,						UpperCAmelCase    )

													__a							=   model(UpperCAmelCase      ,						UpperCAmelCase    )

													__a							=   scheduler.step(UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase    )
													__a							=   output.prev_sample

								__a							=   torch.sum(torch.abs(UpperCAmelCase    )    )
								__a							=   torch.mean(torch.abs(UpperCAmelCase    )    )

								assert abs(result_sum.item() - 1_006.388    ) < 1e-2
								assert abs(result_mean.item() - 1.31    ) < 1e-3







			def __SCREAMING_SNAKE_CASE							(						self    ) ->    List[str]:
								__a							=   self.scheduler_classes[0]
								__a							=   self.get_scheduler_config()
								__a							=   scheduler_class(**UpperCAmelCase      ,						use_karras_sigmas=UpperCAmelCase    )

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

								__a							=   self.dummy_model()
								__a							=   self.dummy_sample_deter.to(UpperCAmelCase    ) * scheduler.init_noise_sigma
								__a							=   sample.to(UpperCAmelCase    )

								for t in scheduler.timesteps:
													__a							=   scheduler.scale_model_input(UpperCAmelCase      ,						UpperCAmelCase    )

													__a							=   model(UpperCAmelCase      ,						UpperCAmelCase    )

													__a							=   scheduler.step(UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase    )
													__a							=   output.prev_sample

								__a							=   torch.sum(torch.abs(UpperCAmelCase    )    )
								__a							=   torch.mean(torch.abs(UpperCAmelCase    )    )

								assert abs(result_sum.item() - 3_812.9_927    ) < 2e-2
								assert abs(result_mean.item() - 4.9_648    ) < 1e-3





from typing import Any

import numpy as np
def 							lowerCAmelCase(	__lowerCamelCase						):
					assert np.shape(__lowerCamelCase						)[0] == np.shape(__lowerCamelCase						)[1]

					# If matrix not symmetric, exit right away.
					if np.allclose(__lowerCamelCase      ,				matrix.T						) is False:
										return False

					# Get eigenvalues and eignevectors for a symmetric matrix.
					__a					,      __a							=   np.linalg.eigh(__lowerCamelCase						)

					# Check sign of all eigenvalues.
					# np.all returns a value of type np.bool_
					return bool(np.all(eigen_values > 0						)						)
def 							lowerCAmelCase(	__lowerCamelCase						):
					__a							=   np.random.randn(__lowerCamelCase      ,				__lowerCamelCase						)
					__a							=   np.dot(__lowerCamelCase      ,				random_matrix.T						)
					assert _is_matrix_spd(__lowerCamelCase						)
					return spd_matrix
def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase = 1000      ,				__lowerCamelCase = 1e-8      ,				):
					assert np.shape(__lowerCamelCase						)[0] == np.shape(__lowerCamelCase						)[1]
					assert np.shape(__lowerCamelCase						)[0] == np.shape(__lowerCamelCase						)[0]
					assert _is_matrix_spd(__lowerCamelCase						)

					# Initialize solution guess, residual, search direction.
					__a							=   np.zeros((np.shape(__lowerCamelCase						)[0], 1)						)
					__a							=   np.copy(__lowerCamelCase						)
					__a							=   np.copy(__lowerCamelCase						)

					# Set initial errors in solution guess and residual.
					__a							=   1e9
					__a							=   1e9
					__a							=   1e9

					# Set iteration counter to threshold number of iterations.
					__a							=   0

					while error > tol:
										# Save this value so we only calculate the matrix-vector product once.
										__a							=   np.dot(__lowerCamelCase      ,				__lowerCamelCase						)

										# The main algorithm.

										# Update search direction magnitude.
										__a							=   np.dot(ra.T      ,				__lowerCamelCase						) / np.dot(pa.T      ,				__lowerCamelCase						)
										# Update solution guess.
										__a							=   xa + alpha * pa
										# Calculate new residual.
										__a							=   ra - alpha * w
										# Calculate new Krylov subspace scale.
										__a							=   np.dot(r.T      ,				__lowerCamelCase						) / np.dot(ra.T      ,				__lowerCamelCase						)
										# Calculate new A conjuage search direction.
										__a							=   r + beta * pa

										# Calculate errors.
										__a							=   np.linalg.norm(r - ra						)
										__a							=   np.linalg.norm(x - xa						)
										__a							=   np.maximum(__lowerCamelCase      ,				__lowerCamelCase						)

										# Update variables.
										__a							=   np.copy(__lowerCamelCase						)
										__a							=   np.copy(__lowerCamelCase						)
										__a							=   np.copy(__lowerCamelCase						)

										# Update number of iterations.
										iterations += 1
										if iterations > max_iterations:
															break

					return x
def 							lowerCAmelCase(	):
					__a							=   3
					__a							=   _create_spd_matrix(__lowerCamelCase						)
					__a							=   np.random.randn(__lowerCamelCase      ,				1						)
					__a							=   np.dot(__lowerCamelCase      ,				__lowerCamelCase						)

					# Numpy solution.
					__a							=   np.linalg.solve(__lowerCamelCase      ,				__lowerCamelCase						)

					# Our implementation.
					__a							=   conjugate_gradient(__lowerCamelCase      ,				__lowerCamelCase						)

					# Ensure both solutions are close to x_true (and therefore one another).
					assert np.linalg.norm(x_numpy - x_true						) <= 1e-6
					assert np.linalg.norm(x_conjugate_gradient - x_true						) <= 1e-6


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	test_conjugate_gradient()




