


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				(     UpperCamelCase__     ):

							def __init__(				self:      Tuple       ,		UpperCamelCase_:      UNetaDModel       ,		UpperCamelCase_:      UNetaDModel       ,		UpperCamelCase_:      DDPMScheduler       ,		UpperCamelCase_:      str       ,		)     ->  List[Any]:





									"""simple docstring"""







									super().__init__()
									lowercase__				= value_function
									lowercase__				= unet
									lowercase__				= scheduler
									lowercase__				= env
									lowercase__				= env.get_dataset()
									lowercase__				= {}
									for key in self.data.keys():
											try:
													lowercase__				= self.data[key].mean()
											except:  # noqa: E722
													pass
									lowercase__				= {}
									for key in self.data.keys():
											try:
													lowercase__				= self.data[key].std()
											except:  # noqa: E722
													pass
									lowercase__				= env.observation_space.shape[0]
									lowercase__				= env.action_space.shape[0]

							def  lowerCamelCase_					(				self:      Dict       ,		UpperCamelCase_:      List[str]       ,		UpperCamelCase_:      Tuple				)     ->  List[Any]:





									"""simple docstring"""







									return (x_in - self.means[key]) / self.stds[key]

							def  lowerCamelCase_					(				self:      List[Any]       ,		UpperCamelCase_:      Optional[Any]       ,		UpperCamelCase_:      List[str]				)     ->  Dict:





									"""simple docstring"""







									return x_in * self.stds[key] + self.means[key]

							def  lowerCamelCase_					(				self:      int       ,		UpperCamelCase_:      Union[str, Any]				)     ->  Any:





									"""simple docstring"""







									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:      Optional[int]       ,		UpperCamelCase_:      Optional[int]       ,		UpperCamelCase_:      Optional[Any]       ,		UpperCamelCase_:      Union[str, Any]				)     ->  Optional[int]:





									"""simple docstring"""







									for key, val in cond.items():
											lowercase__				= val.clone()
									return x_in

							def  lowerCamelCase_					(				self:      Tuple       ,		UpperCamelCase_:      Tuple       ,		UpperCamelCase_:      Union[str, Any]       ,		UpperCamelCase_:      Tuple       ,		UpperCamelCase_:      str				)     ->  str:





									"""simple docstring"""







									lowercase__				= x.shape[0]
									lowercase__				= None
									for i in tqdm.tqdm(self.scheduler.timesteps				):
											# create batch of timesteps to pass into model
											lowercase__				= 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__				= self.value_function(x.permute(0       ,		2       ,		1				)       ,		UpperCamelCase_				).sample
															lowercase__				= torch.autograd.grad([y.sum()]       ,		[x]				)[0]

															lowercase__				= self.scheduler._get_variance(UpperCamelCase_				)
															lowercase__				= torch.exp(0.5 * posterior_variance				)
															lowercase__				= model_std * grad

													lowercase__				= 0
													lowercase__				= x.detach()
													lowercase__				= x + scale * grad
													lowercase__				= self.reset_xa(UpperCamelCase_       ,		UpperCamelCase_       ,		self.action_dim				)

											lowercase__				= self.unet(x.permute(0       ,		2       ,		1				)       ,		UpperCamelCase_				).sample.permute(0       ,		2       ,		1				)

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

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


							def __call__(				self:      Any       ,		UpperCamelCase_:      List[str]       ,		UpperCamelCase_:      Optional[int]=64       ,		UpperCamelCase_:      Any=32       ,		UpperCamelCase_:      Any=2       ,		UpperCamelCase_:      Any=0.1				)     ->  Optional[Any]:





									"""simple docstring"""







									lowercase__				= self.normalize(UpperCamelCase_       ,		'''observations'''				)
									lowercase__				= obs[None].repeat(UpperCamelCase_       ,		axis=0				)

									lowercase__				= {0: self.to_torch(UpperCamelCase_				)}
									lowercase__				= (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__				= randn_tensor(UpperCamelCase_       ,		device=self.unet.device				)
									lowercase__				= self.reset_xa(UpperCamelCase_       ,		UpperCamelCase_       ,		self.action_dim				)
									lowercase__				= self.to_torch(UpperCamelCase_				)

									# run the diffusion process
									lowercase__	,						lowercase__				= self.run_diffusion(UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_				)

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

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

									lowercase__				= denorm_actions[selected_index, 0]
									return denorm_actions




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	int = 2_00_00_00       )  ->     int:
						_lowercase					       =					[0 for i in range(n + 1       )]
						_lowercase					       =					1
						_lowercase					       =					1

						for i in range(2		,   int(n**0.5       ) + 1       ):
												if primality_list[i] == 0:
																		for j in range(i * i		,   n + 1		,   SCREAMING_SNAKE_CASE_       ):
																								_lowercase					       =					1
						_lowercase					       =					0
						for i in range(SCREAMING_SNAKE_CASE_       ):
												if primality_list[i] == 0:
																		sum_of_primes += i
						return sum_of_primes


if __name__ == "__main__":
		print(F'{solution() = }')