
"""simple docstring"""







import numpy as np
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE = 1e-12							,    SCREAMING_SNAKE_CASE = 1_00							,    )  ->     tuple[float, np.ndarray]:







							"""simple docstring"""


							assert np.shape(SCREAMING_SNAKE_CASE )[0] == np.shape(SCREAMING_SNAKE_CASE )[1]
							# Ensure proper dimensionality.
							assert np.shape(SCREAMING_SNAKE_CASE )[0] == np.shape(SCREAMING_SNAKE_CASE )[0]
							# Ensure inputs are either both complex or both real
							assert np.iscomplexobj(SCREAMING_SNAKE_CASE ) == np.iscomplexobj(SCREAMING_SNAKE_CASE )
							__snake_case     			=       np.iscomplexobj(SCREAMING_SNAKE_CASE )
							if is_complex:
														# Ensure complex input_matrix is Hermitian
														assert np.array_equal(SCREAMING_SNAKE_CASE							,    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.

							__snake_case     			=       False
							__snake_case     			=       0
							__snake_case     			=       0
							__snake_case     			=       1e12

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

														# Check convergence.
														__snake_case     			=       np.abs(lambda_ - lambda_previous ) / lambda_
														iterations += 1

														if error <= error_tol or iterations >= max_iterations:
																					__snake_case     			=       True

														__snake_case     			=       lambda_

							if is_complex:
														__snake_case     			=       np.real(lambda_ )

							return lambda_, vector
def   __UpperCamelCase   (					)  ->     None:







							"""simple docstring"""


							__snake_case     			=       np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]] )
							__snake_case     			=       np.array([41, 4, 20] )
							__snake_case     			=       real_input_matrix.astype(np.complexaaa )
							__snake_case     			=       np.triu(1j * complex_input_matrix							,    1 )
							complex_input_matrix += imag_matrix
							complex_input_matrix += -1 * imag_matrix.T
							__snake_case     			=       np.array([41, 4, 20] ).astype(np.complexaaa )

							for problem_type in ["real", "complex"]:
														if problem_type == "real":
																					__snake_case     			=       real_input_matrix
																					__snake_case     			=       real_vector
														elif problem_type == "complex":
																					__snake_case     			=       complex_input_matrix
																					__snake_case     			=       complex_vector

														# Our implementation.
														__snake_case			,   __snake_case     			=       power_iteration(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )

														# Numpy implementation.

														# Get eigenvalues and eigenvectors using built-in numpy
														# eigh (eigh used for symmetric or hermetian matrices).
														__snake_case			,   __snake_case     			=       np.linalg.eigh(SCREAMING_SNAKE_CASE )
														# Last eigenvalue is the maximum one.
														__snake_case     			=       eigen_values[-1]
														# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
														__snake_case     			=       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(SCREAMING_SNAKE_CASE ) - np.abs(SCREAMING_SNAKE_CASE ) ) <= 1e-6


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		test_power_iteration()


"""simple docstring"""







import numpy as np
import torch
import tqdm

from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler







class 							__magic_name__     (		lowercase__   ):





					def __init__(				self   :							Any							,						snake_case_   :							UNetaDModel							,						snake_case_   :							UNetaDModel							,						snake_case_   :							DDPMScheduler							,						snake_case_   :							Any							,						):
												super().__init__()
												__snake_case     			=       value_function
												__snake_case     			=       unet
												__snake_case     			=       scheduler
												__snake_case     			=       env
												__snake_case     			=       env.get_dataset()
												__snake_case     			=       {}
												for key in self.data.keys():
																			try:
																										__snake_case     			=       self.data[key].mean()
																			except:  # noqa: E722
																										pass
												__snake_case     			=       {}
												for key in self.data.keys():
																			try:
																										__snake_case     			=       self.data[key].std()
																			except:  # noqa: E722
																										pass
												__snake_case     			=       env.observation_space.shape[0]
												__snake_case     			=       env.action_space.shape[0]





					def 							lowerCAmelCase      (				self   :							Any							,						snake_case_   :							str							,						snake_case_   :							Any    ):
												return (x_in - self.means[key]) / self.stds[key]





					def 							lowerCAmelCase      (				self   :							Any							,						snake_case_   :							Optional[Any]							,						snake_case_   :							Optional[Any]    ):
												return x_in * self.stds[key] + self.means[key]





					def 							lowerCAmelCase      (				self   :							Tuple							,						snake_case_   :							Dict    ):
												if type(snake_case_    ) is dict:
																			return {k: self.to_torch(snake_case_    ) for k, v in x_in.items()}
												elif torch.is_tensor(snake_case_    ):
																			return x_in.to(self.unet.device    )
												return torch.tensor(snake_case_							,						device=self.unet.device    )





					def 							lowerCAmelCase      (				self   :							List[str]							,						snake_case_   :							Tuple							,						snake_case_   :							Union[str, Any]							,						snake_case_   :							Union[str, Any]    ):
												for key, val in cond.items():
																			__snake_case     			=       val.clone()
												return x_in





					def 							lowerCAmelCase      (				self   :							Any							,						snake_case_   :							Tuple							,						snake_case_   :							Optional[Any]							,						snake_case_   :							Any							,						snake_case_   :							Optional[Any]    ):
												__snake_case     			=       x.shape[0]
												__snake_case     			=       None
												for i in tqdm.tqdm(self.scheduler.timesteps    ):
																			# create batch of timesteps to pass into model
																			__snake_case     			=       torch.full((batch_size,)							,						snake_case_							,						device=self.unet.device							,						dtype=torch.long    )
																			for _ in range(snake_case_    ):
																										with torch.enable_grad():
																																	x.requires_grad_()

																																	# permute to match dimension for pre-trained models
																																	__snake_case     			=       self.value_function(x.permute(0							,						2							,						1    )							,						snake_case_    ).sample
																																	__snake_case     			=       torch.autograd.grad([y.sum()]							,						[x]    )[0]

																																	__snake_case     			=       self.scheduler._get_variance(snake_case_    )
																																	__snake_case     			=       torch.exp(0.5 * posterior_variance    )
																																	__snake_case     			=       model_std * grad

																										__snake_case     			=       0
																										__snake_case     			=       x.detach()
																										__snake_case     			=       x + scale * grad
																										__snake_case     			=       self.reset_xa(snake_case_							,						snake_case_							,						self.action_dim    )

																			__snake_case     			=       self.unet(x.permute(0							,						2							,						1    )							,						snake_case_    ).sample.permute(0							,						2							,						1    )

																			# TODO: verify deprecation of this kwarg
																			__snake_case     			=       self.scheduler.step(snake_case_							,						snake_case_							,						snake_case_							,						predict_epsilon=snake_case_    )["prev_sample"]

																			# apply conditions to the trajectory (set the initial state)
																			__snake_case     			=       self.reset_xa(snake_case_							,						snake_case_							,						self.action_dim    )
																			__snake_case     			=       self.to_torch(snake_case_    )
												return x, y





					def __call__(				self   :							Union[str, Any]							,						snake_case_   :							List[Any]							,						snake_case_   :							Any=64							,						snake_case_   :							str=32							,						snake_case_   :							List[str]=2							,						snake_case_   :							Tuple=0.1    ):
												# normalize the observations and create  batch dimension
												__snake_case     			=       self.normalize(snake_case_							,						"observations"    )
												__snake_case     			=       obs[None].repeat(snake_case_							,						axis=0    )

												__snake_case     			=       {0: self.to_torch(snake_case_    )}
												__snake_case     			=       (batch_size, planning_horizon, self.state_dim + self.action_dim)

												# generate initial noise and apply our conditions (to make the trajectories start at current state)
												__snake_case     			=       randn_tensor(snake_case_							,						device=self.unet.device    )
												__snake_case     			=       self.reset_xa(snake_case_							,						snake_case_							,						self.action_dim    )
												__snake_case     			=       self.to_torch(snake_case_    )

												# run the diffusion process
												__snake_case			,   __snake_case     			=       self.run_diffusion(snake_case_							,						snake_case_							,						snake_case_							,						snake_case_    )

												# sort output trajectories by value
												__snake_case     			=       y.argsort(0							,						descending=snake_case_    ).squeeze()
												__snake_case     			=       x[sorted_idx]
												__snake_case     			=       sorted_values[:, :, : self.action_dim]
												__snake_case     			=       actions.detach().cpu().numpy()
												__snake_case     			=       self.de_normalize(snake_case_							,						key="actions"    )

												# select the action with the highest value
												if y is not None:
																			__snake_case     			=       0
												else:
																			# if we didn't run value guiding, select a random action
																			__snake_case     			=       np.random.randint(0							,						snake_case_    )

												__snake_case     			=       denorm_actions[selected_index, 0]
												return denorm_actions
