


'''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	(						lowercase_							):





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





							def 		a__   (  self		:Optional[int]  ,_UpperCamelCase		:Tuple  ,_UpperCamelCase		:Dict			):
									return (x_in - self.means[key]) / self.stds[key]





							def 		a__   (  self		:str  ,_UpperCamelCase		:Union[str, Any]  ,_UpperCamelCase		:Union[str, Any]			):
									return x_in * self.stds[key] + self.means[key]





							def 		a__   (  self		:Any  ,_UpperCamelCase		:int			):



									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 		a__   (  self		:Optional[int]  ,_UpperCamelCase		:Tuple  ,_UpperCamelCase		:Any  ,_UpperCamelCase		:Any			):



									for key, val in cond.items():
											snake_case_		:							List[Any]			       =						val.clone()
									return x_in





							def 		a__   (  self		:int  ,_UpperCamelCase		:int  ,_UpperCamelCase		:Optional[Any]  ,_UpperCamelCase		:List[str]  ,_UpperCamelCase		:List[str]			):
									snake_case_		:							Any			       =						x.shape[0]
									snake_case_		:							List[Any]			       =						None
									for i in tqdm.tqdm(self.scheduler.timesteps			):
											# create batch of timesteps to pass into model
											snake_case_		:							List[str]			       =						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
															snake_case_		:							Dict			       =						self.value_function(x.permute(0  ,2  ,1			)  ,lowerCamelCase_			).sample
															snake_case_		:							str			       =						torch.autograd.grad([y.sum()]  ,[x]			)[0]

															snake_case_		:							Optional[Any]			       =						self.scheduler._get_variance(lowerCamelCase_			)
															snake_case_		:							Tuple			       =						torch.exp(0.5 * posterior_variance			)
															snake_case_		:							Tuple			       =						model_std * grad

													snake_case_		:							List[Any]			       =						0
													snake_case_		:							int			       =						x.detach()
													snake_case_		:							Union[str, Any]			       =						x + scale * grad
													snake_case_		:							Tuple			       =						self.reset_xa(lowerCamelCase_  ,lowerCamelCase_  ,self.action_dim			)

											snake_case_		:							int			       =						self.unet(x.permute(0  ,2  ,1			)  ,lowerCamelCase_			).sample.permute(0  ,2  ,1			)

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

											# apply conditions to the trajectory (set the initial state)
											snake_case_		:							List[str]			       =						self.reset_xa(lowerCamelCase_  ,lowerCamelCase_  ,self.action_dim			)
											snake_case_		:							Tuple			       =						self.to_torch(lowerCamelCase_			)
									return x, y





							def __call__(  self		:Union[str, Any]  ,_UpperCamelCase		:Union[str, Any]  ,_UpperCamelCase		:Tuple=6_4  ,_UpperCamelCase		:List[str]=3_2  ,_UpperCamelCase		:Optional[Any]=2  ,_UpperCamelCase		:Any=0.1			):
									snake_case_		:							Optional[int]			       =						self.normalize(lowerCamelCase_  ,"""observations"""			)
									snake_case_		:							Optional[int]			       =						obs[None].repeat(lowerCamelCase_  ,axis=0			)

									snake_case_		:							Tuple			       =						{0: self.to_torch(lowerCamelCase_			)}
									snake_case_		:							Optional[Any]			       =						(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_		:							Tuple			       =						randn_tensor(lowerCamelCase_  ,device=self.unet.device			)
									snake_case_		:							List[str]			       =						self.reset_xa(lowerCamelCase_  ,lowerCamelCase_  ,self.action_dim			)
									snake_case_		:							str			       =						self.to_torch(lowerCamelCase_			)

									# run the diffusion process
									snake_case_		:							int			       =						self.run_diffusion(lowerCamelCase_  ,lowerCamelCase_  ,lowerCamelCase_  ,lowerCamelCase_			)

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

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

									snake_case_		:							Dict			       =						denorm_actions[selected_index, 0]
									return denorm_actions



'''simple docstring'''






def      UpperCAmelCase      (		lowerCamelCase_  :str ):







		'''simple docstring'''
		return " ".join(
		    """""".join(word[::-1] ) if len(lowerCamelCase_ ) > 4 else word for word in sentence.split() )


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							print(reverse_long_words('Hey wollef sroirraw'))