






"""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 						A_							(      snake_case_  ):







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







				def     _snake_case					(							self							:						List[str]						,    __lowerCamelCase							:						Dict						,    __lowerCamelCase							:						int	)				->					Dict:
										return (x_in - self.means[key]) / self.stds[key]







				def     _snake_case					(							self							:						Any						,    __lowerCamelCase							:						Tuple						,    __lowerCamelCase							:						Optional[int]	)				->					Union[str, Any]:
										return x_in * self.stds[key] + self.means[key]







				def     _snake_case					(							self							:						Optional[int]						,    __lowerCamelCase							:						Optional[int]	)				->					Any:
										if type(__lowerCamelCase	) is dict:
																return {k: self.to_torch(__lowerCamelCase	) for k, v in x_in.items()}
										elif torch.is_tensor(__lowerCamelCase	):
																return x_in.to(self.unet.device	)
										return torch.tensor(__lowerCamelCase						,    device=self.unet.device	)







				def     _snake_case					(							self							:						str						,    __lowerCamelCase							:						List[Any]						,    __lowerCamelCase							:						List[Any]						,    __lowerCamelCase							:						List[str]	)				->					Any:
										for key, val in cond.items():
																__magic_name__            =     val.clone()
										return x_in







				def     _snake_case					(							self							:						str						,    __lowerCamelCase							:						str						,    __lowerCamelCase							:						Optional[Any]						,    __lowerCamelCase							:						List[str]						,    __lowerCamelCase							:						int	)				->					Dict:
										__magic_name__            =     x.shape[0]
										__magic_name__            =     None
										for i in tqdm.tqdm(self.scheduler.timesteps	):
																# create batch of timesteps to pass into model
																__magic_name__            =     torch.full((batch_size,)						,    __lowerCamelCase						,    device=self.unet.device						,    dtype=torch.long	)
																for _ in range(__lowerCamelCase	):
																						with torch.enable_grad():
																												x.requires_grad_()

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

																												__magic_name__            =     self.scheduler._get_variance(__lowerCamelCase	)
																												__magic_name__            =     torch.exp(0.5 * posterior_variance	)
																												__magic_name__            =     model_std * grad

																						__magic_name__            =     0
																						__magic_name__            =     x.detach()
																						__magic_name__            =     x + scale * grad
																						__magic_name__            =     self.reset_xa(__lowerCamelCase						,    __lowerCamelCase						,    self.action_dim	)

																__magic_name__            =     self.unet(x.permute(0						,    2						,    1	)						,    __lowerCamelCase	).sample.permute(0						,    2						,    1	)

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

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







				def __call__(							self							:						Dict						,    __lowerCamelCase							:						List[Any]						,    __lowerCamelCase							:						Union[str, Any]=6_4						,    __lowerCamelCase							:						Union[str, Any]=3_2						,    __lowerCamelCase							:						Any=2						,    __lowerCamelCase							:						Union[str, Any]=0.1	)				->					Tuple:
										# normalize the observations and create  batch dimension
										__magic_name__            =     self.normalize(__lowerCamelCase						,    "observations"	)
										__magic_name__            =     obs[None].repeat(__lowerCamelCase						,    axis=0	)

										__magic_name__            =     {0: self.to_torch(__lowerCamelCase	)}
										__magic_name__            =     (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)
										__magic_name__            =     randn_tensor(__lowerCamelCase						,    device=self.unet.device	)
										__magic_name__            =     self.reset_xa(__lowerCamelCase						,    __lowerCamelCase						,    self.action_dim	)
										__magic_name__            =     self.to_torch(__lowerCamelCase	)

										# run the diffusion process
										__magic_name__      ,    __magic_name__            =     self.run_diffusion(__lowerCamelCase						,    __lowerCamelCase						,    __lowerCamelCase						,    __lowerCamelCase	)

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

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

										__magic_name__            =     denorm_actions[selected_index, 0]
										return denorm_actions











"""simple docstring"""

def _lowerCAmelCase	(  __lowerCamelCase:str   ):

						'''simple docstring'''






						return "".join(chr(ord(__lowerCamelCase   ) - 3_2   ) if "a" <= char <= "z" else char for char in word   )


if __name__ == "__main__":
			from doctest import testmod

			testmod()



