




"""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   UpperCAmelCase_    (			snake_case		):
		def __init__(						self				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						)						->       Any:
					super().__init__()
					__lowercase			:  Optional[Any]								=       value_function
					__lowercase			:  Tuple								=       unet
					__lowercase			:  Any								=       scheduler
					__lowercase			:  List[str]								=       env
					__lowercase			:  Dict								=       env.get_dataset()
					__lowercase			:  Dict								=       {}
					for key in self.data.keys():
								try:
											__lowercase			:  List[str]								=       self.data[key].mean()
								except:  # noqa: E722
											pass
					__lowercase			:  List[str]								=       {}
					for key in self.data.keys():
								try:
											__lowercase			:  str								=       self.data[key].std()
								except:  # noqa: E722
											pass
					__lowercase			:  Any								=       env.observation_space.shape[0]
					__lowercase			:  List[Any]								=       env.action_space.shape[0]
		def 	_lowerCamelCase							(						self				,						UpperCamelCase_				,						UpperCamelCase_       )						->       Any:
					return (x_in - self.means[key]) / self.stds[key]
		def 	_lowerCamelCase							(						self				,						UpperCamelCase_				,						UpperCamelCase_       )						->       Tuple:
					return x_in * self.stds[key] + self.means[key]
		def 	_lowerCamelCase							(						self				,						UpperCamelCase_       )						->       List[Any]:
					if type(UpperCamelCase_       ) is dict:
								return {k: self.to_torch(UpperCamelCase_       ) for k, v in x_in.items()}
					elif torch.is_tensor(UpperCamelCase_       ):
								return x_in.to(self.unet.device       )
					return torch.tensor(UpperCamelCase_				,						device=self.unet.device       )
		def 	_lowerCamelCase							(						self				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_       )						->       Union[str, Any]:
					for key, val in cond.items():
								__lowercase			:  Optional[int]								=       val.clone()
					return x_in
		def 	_lowerCamelCase							(						self				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_       )						->       str:
					__lowercase			:  List[str]								=       x.shape[0]
					__lowercase			:  int								=       None
					for i in tqdm.tqdm(self.scheduler.timesteps       ):
								# create batch of timesteps to pass into model
								__lowercase			:  Tuple								=       torch.full((batch_size,)				,						UpperCamelCase_				,						device=self.unet.device				,						dtype=torch.long       )
								for _ in range(UpperCamelCase_       ):
											with torch.enable_grad():
														x.requires_grad_()

														# permute to match dimension for pre-trained models
														__lowercase			:  Optional[int]								=       self.value_function(x.permute(0				,						2				,						1       )				,						UpperCamelCase_       ).sample
														__lowercase			:  List[Any]								=       torch.autograd.grad([y.sum()]				,						[x]       )[0]

														__lowercase			:  Dict								=       self.scheduler._get_variance(UpperCamelCase_       )
														__lowercase			:  str								=       torch.exp(0.5 * posterior_variance       )
														__lowercase			:  Tuple								=       model_std * grad

											__lowercase			:  Dict								=       0
											__lowercase			:  Optional[int]								=       x.detach()
											__lowercase			:  Optional[Any]								=       x + scale * grad
											__lowercase			:  List[str]								=       self.reset_xa(UpperCamelCase_				,						UpperCamelCase_				,						self.action_dim       )

								__lowercase			:  List[Any]								=       self.unet(x.permute(0				,						2				,						1       )				,						UpperCamelCase_       ).sample.permute(0				,						2				,						1       )

								# TODO: verify deprecation of this kwarg
								__lowercase			:  Tuple								=       self.scheduler.step(UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						predict_epsilon=UpperCamelCase_       )['''prev_sample''']

								# apply conditions to the trajectory (set the initial state)
								__lowercase			:  Any								=       self.reset_xa(UpperCamelCase_				,						UpperCamelCase_				,						self.action_dim       )
								__lowercase			:  Dict								=       self.to_torch(UpperCamelCase_       )
					return x, y
		def __call__(						self				,						UpperCamelCase_				,						UpperCamelCase_=64				,						UpperCamelCase_=32				,						UpperCamelCase_=2				,						UpperCamelCase_=0.1       )						->       List[Any]:
					# normalize the observations and create  batch dimension
					__lowercase			:  Optional[Any]								=       self.normalize(UpperCamelCase_				,						'''observations'''       )
					__lowercase			:  Any								=       obs[None].repeat(UpperCamelCase_				,						axis=0       )

					__lowercase			:  Optional[int]								=       {0: self.to_torch(UpperCamelCase_       )}
					__lowercase			:  Optional[int]								=       (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)
					__lowercase			:  int								=       randn_tensor(UpperCamelCase_				,						device=self.unet.device       )
					__lowercase			:  int								=       self.reset_xa(UpperCamelCase_				,						UpperCamelCase_				,						self.action_dim       )
					__lowercase			:  List[Any]								=       self.to_torch(UpperCamelCase_       )

					# run the diffusion process
					__lowercase			,__lowercase			:  str								=       self.run_diffusion(UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_       )

					# sort output trajectories by value
					__lowercase			:  Optional[Any]								=       y.argsort(0				,						descending=UpperCamelCase_       ).squeeze()
					__lowercase			:  List[Any]								=       x[sorted_idx]
					__lowercase			:  Union[str, Any]								=       sorted_values[:, :, : self.action_dim]
					__lowercase			:  List[str]								=       actions.detach().cpu().numpy()
					__lowercase			:  Optional[Any]								=       self.de_normalize(UpperCamelCase_				,						key='''actions'''       )

					# select the action with the highest value
					if y is not None:
								__lowercase			:  str								=       0
					else:
								# if we didn't run value guiding, select a random action
								__lowercase			:  Optional[int]								=       np.random.randint(0				,						UpperCamelCase_       )

					__lowercase			:  Optional[Any]								=       denorm_actions[selected_index, 0]
					return denorm_actions










import numpy as np
def      A__       (    SCREAMING_SNAKE_CASE_   :      np.ndarray				,	SCREAMING_SNAKE_CASE_   :      float						)  ->  np.ndarray:






   """simple docstring"""







   return np.where(vector > 0				,	SCREAMING_SNAKE_CASE_				,	(alpha * (np.exp(SCREAMING_SNAKE_CASE_						) - 1))						)


if __name__ == "__main__":
   import doctest

   doctest.testmod()