
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 						lowercase__  (						_UpperCAmelCase  ):
	def __init__(			self							:   Tuple       ,   UpperCAmelCase_							:   UNetaDModel       ,   UpperCAmelCase_							:   UNetaDModel       ,   UpperCAmelCase_							:   DDPMScheduler       ,   UpperCAmelCase_							:   Tuple       ,   ):
						super().__init__()
						SCREAMING_SNAKE_CASE__													= value_function
						SCREAMING_SNAKE_CASE__													= unet
						SCREAMING_SNAKE_CASE__													= scheduler
						SCREAMING_SNAKE_CASE__													= env
						SCREAMING_SNAKE_CASE__													= env.get_dataset()
						SCREAMING_SNAKE_CASE__													= {}
						for key in self.data.keys():
											try:
																SCREAMING_SNAKE_CASE__													= self.data[key].mean()
											except:  # noqa: E722
																pass
						SCREAMING_SNAKE_CASE__													= {}
						for key in self.data.keys():
											try:
																SCREAMING_SNAKE_CASE__													= self.data[key].std()
											except:  # noqa: E722
																pass
						SCREAMING_SNAKE_CASE__													= env.observation_space.shape[0]
						SCREAMING_SNAKE_CASE__													= env.action_space.shape[0]
	def      A_				(			self							:   Dict       ,   UpperCAmelCase_							:   Any       ,   UpperCAmelCase_							:   Any							):
						return (x_in - self.means[key]) / self.stds[key]
	def      A_				(			self							:   int       ,   UpperCAmelCase_							:   List[str]       ,   UpperCAmelCase_							:   List[Any]							):
						return x_in * self.stds[key] + self.means[key]
	def      A_				(			self							:   List[Any]       ,   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      A_				(			self							:   List[Any]       ,   UpperCAmelCase_							:   List[str]       ,   UpperCAmelCase_							:   Tuple       ,   UpperCAmelCase_							:   Dict							):
						for key, val in cond.items():
											SCREAMING_SNAKE_CASE__													= val.clone()
						return x_in
	def      A_				(			self							:   List[str]       ,   UpperCAmelCase_							:   Dict       ,   UpperCAmelCase_							:   Dict       ,   UpperCAmelCase_							:   Dict       ,   UpperCAmelCase_							:   Tuple							):
						SCREAMING_SNAKE_CASE__													= x.shape[0]
						SCREAMING_SNAKE_CASE__													= None
						for i in tqdm.tqdm(self.scheduler.timesteps							):
											# create batch of timesteps to pass into model
											SCREAMING_SNAKE_CASE__													= 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
																					SCREAMING_SNAKE_CASE__													= self.value_function(x.permute(0       ,   2       ,   1							)       ,   UpperCAmelCase_							).sample
																					SCREAMING_SNAKE_CASE__													= torch.autograd.grad([y.sum()]       ,   [x]							)[0]

																					SCREAMING_SNAKE_CASE__													= self.scheduler._get_variance(UpperCAmelCase_							)
																					SCREAMING_SNAKE_CASE__													= torch.exp(0.5 * posterior_variance							)
																					SCREAMING_SNAKE_CASE__													= model_std * grad

																SCREAMING_SNAKE_CASE__													= 0
																SCREAMING_SNAKE_CASE__													= x.detach()
																SCREAMING_SNAKE_CASE__													= x + scale * grad
																SCREAMING_SNAKE_CASE__													= self.reset_xa(UpperCAmelCase_       ,   UpperCAmelCase_       ,   self.action_dim							)

											SCREAMING_SNAKE_CASE__													= self.unet(x.permute(0       ,   2       ,   1							)       ,   UpperCAmelCase_							).sample.permute(0       ,   2       ,   1							)

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

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





	def __call__(			self							:   Any       ,   UpperCAmelCase_							:   List[str]       ,   UpperCAmelCase_							:   str=64       ,   UpperCAmelCase_							:   Optional[int]=32       ,   UpperCAmelCase_							:   str=2       ,   UpperCAmelCase_							:   Optional[Any]=0.1							):
						# normalize the observations and create  batch dimension
						SCREAMING_SNAKE_CASE__													= self.normalize(UpperCAmelCase_       ,   'observations'							)
						SCREAMING_SNAKE_CASE__													= obs[None].repeat(UpperCAmelCase_       ,   axis=0							)

						SCREAMING_SNAKE_CASE__													= {0: self.to_torch(UpperCAmelCase_							)}
						SCREAMING_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)
						SCREAMING_SNAKE_CASE__													= randn_tensor(UpperCAmelCase_       ,   device=self.unet.device							)
						SCREAMING_SNAKE_CASE__													= self.reset_xa(UpperCAmelCase_       ,   UpperCAmelCase_       ,   self.action_dim							)
						SCREAMING_SNAKE_CASE__													= self.to_torch(UpperCAmelCase_							)

						# run the diffusion process
						SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= self.run_diffusion(UpperCAmelCase_       ,   UpperCAmelCase_       ,   UpperCAmelCase_       ,   UpperCAmelCase_							)

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

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

						SCREAMING_SNAKE_CASE__													= denorm_actions[selected_index, 0]
						return denorm_actions







def 							_lowercase							(     )		->		Optional[int]:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= []
					SCREAMING_SNAKE_CASE__													= 1

					while len(UpperCamelCase_		) < 1e6:
										constant.append(str(UpperCamelCase_		)		)
										i += 1

					SCREAMING_SNAKE_CASE__													= ''.join(UpperCamelCase_		)

					return (
					    int(constant[0]		)
					    * int(constant[9]		)
					    * int(constant[99]		)
					    * int(constant[999]		)
					    * int(constant[9999]		)
					    * int(constant[99999]		)
					    * int(constant[999999]		)
					)


if __name__ == "__main__":
				print(solution())





