

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

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

											__lowerCamelCase				:				Optional[Any]					       = self.scheduler._get_variance(SCREAMING_SNAKE_CASE__)
											__lowerCamelCase				:				Optional[int]					       = torch.exp(0.5 * posterior_variance)
											__lowerCamelCase				:				List[str]					       = model_std * grad

										__lowerCamelCase				:				str					       = 0
										__lowerCamelCase				:				Optional[Any]					       = x.detach()
										__lowerCamelCase				:				Optional[Any]					       = x + scale * grad
										__lowerCamelCase				:				Optional[int]					       = self.reset_xa(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,self.action_dim)

									__lowerCamelCase				:				Optional[int]					       = self.unet(x.permute(0   ,2   ,1)   ,SCREAMING_SNAKE_CASE__).sample.permute(0   ,2   ,1)

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

									# apply conditions to the trajectory (set the initial state)
									__lowerCamelCase				:				Optional[Any]					       = self.reset_xa(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,self.action_dim)
									__lowerCamelCase				:				List[Any]					       = self.to_torch(SCREAMING_SNAKE_CASE__)
								return x, y


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

								__lowerCamelCase				:				Optional[int]					       = {0: self.to_torch(SCREAMING_SNAKE_CASE__)}
								__lowerCamelCase				:				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)
								__lowerCamelCase				:				Any					       = randn_tensor(SCREAMING_SNAKE_CASE__   ,device=self.unet.device)
								__lowerCamelCase				:				Tuple					       = self.reset_xa(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,self.action_dim)
								__lowerCamelCase				:				List[str]					       = self.to_torch(SCREAMING_SNAKE_CASE__)

								# run the diffusion process
								__lowerCamelCase       , __lowerCamelCase				:				Optional[Any]					       = self.run_diffusion(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__)

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

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

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










def 	_a      (						SCREAMING_SNAKE_CASE_							:				int = 1_00_00_00     ):
					__lowerCAmelCase				   =       [i - 1 for i in range(limit + 1     )]

					for i in range(2      ,      limit + 1     ):
										if phi[i] == i - 1:
															for j in range(2 * i      ,      limit + 1      ,      SCREAMING_SNAKE_CASE_     ):
																				phi[j] -= phi[j] // i

					return sum(phi[2 : limit + 1]     )


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




