
"""simple docstring"""







from math import atan, cos, radians, sin, tan

from .haversine_distance import haversine_distance

_SCREAMING_SNAKE_CASE	       =      6_378_137.0
_SCREAMING_SNAKE_CASE	       =      6_356_752.314_245
_SCREAMING_SNAKE_CASE	       =      6_378_137
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     float:







							"""simple docstring"""


							__snake_case     			=       (AXIS_A - AXIS_B) / AXIS_A
							# Parametric latitudes
							# https://en.wikipedia.org/wiki/Latitude#Parametric_(or_reduced)_latitude
							__snake_case     			=       atan((1 - flattening) * tan(radians(SCREAMING_SNAKE_CASE ) ) )
							__snake_case     			=       atan((1 - flattening) * tan(radians(SCREAMING_SNAKE_CASE ) ) )

							# Compute central angle between two points
							# using haversine theta. sigma =  haversine_distance / equatorial radius
							__snake_case     			=       haversine_distance(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE ) / EQUATORIAL_RADIUS

							# Intermediate P and Q values
							__snake_case     			=       (b_lata + b_lata) / 2
							__snake_case     			=       (b_lata - b_lata) / 2

							# Intermediate X value
							# X = (sigma - sin(sigma)) * sin^2Pcos^2Q / cos^2(sigma/2)
							__snake_case     			=       (sin(SCREAMING_SNAKE_CASE ) ** 2) * (cos(SCREAMING_SNAKE_CASE ) ** 2)
							__snake_case     			=       cos(sigma / 2 ) ** 2
							__snake_case     			=       (sigma - sin(SCREAMING_SNAKE_CASE )) * (x_numerator / x_demonimator)

							# Intermediate Y value
							# Y = (sigma + sin(sigma)) * cos^2Psin^2Q / sin^2(sigma/2)
							__snake_case     			=       (cos(SCREAMING_SNAKE_CASE ) ** 2) * (sin(SCREAMING_SNAKE_CASE ) ** 2)
							__snake_case     			=       sin(sigma / 2 ) ** 2
							__snake_case     			=       (sigma + sin(SCREAMING_SNAKE_CASE )) * (y_numerator / y_denominator)

							return EQUATORIAL_RADIUS * (sigma - ((flattening / 2) * (x_value + y_value)))


if __name__ == "__main__":
		import doctest

		doctest.testmod()


"""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
