






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_  (		__lowerCamelCase		):




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




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase		):
          return (x_in - self.means[key]) / self.stds[key]




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase		):
          return x_in * self.stds[key] + self.means[key]




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase		):
          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   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase		):
          for key, val in cond.items():
               UpperCAmelCase__      :  List[Any]				      =			val.clone()
          return x_in




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

                         UpperCAmelCase__      :  Union[str, Any]				      =			self.scheduler._get_variance(_lowerCAmelCase		)
                         UpperCAmelCase__      :  Optional[int]				      =			torch.exp(0.5 * posterior_variance		)
                         UpperCAmelCase__      :  str				      =			model_std * grad

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

               UpperCAmelCase__      :  Tuple				      =			self.unet(x.permute(0	,						2	,						1		)	,						_lowerCAmelCase		).sample.permute(0	,						2	,						1		)

               # TODO: verify deprecation of this kwarg
               UpperCAmelCase__      :  Tuple				      =			self.scheduler.step(_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						predict_epsilon=_lowerCAmelCase		)["""prev_sample"""]

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




     def __call__(				self	,						_lowerCAmelCase	,						_lowerCAmelCase=64	,						_lowerCAmelCase=32	,						_lowerCAmelCase=2	,						_lowerCAmelCase=0.1		):
          # normalize the observations and create  batch dimension
          UpperCAmelCase__      :  str				      =			self.normalize(_lowerCAmelCase	,						"""observations"""		)
          UpperCAmelCase__      :  Union[str, Any]				      =			obs[None].repeat(_lowerCAmelCase	,						axis=0		)

          UpperCAmelCase__      :  List[Any]				      =			{0: self.to_torch(_lowerCAmelCase		)}
          UpperCAmelCase__      :  Union[str, 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)
          UpperCAmelCase__      :  Dict				      =			randn_tensor(_lowerCAmelCase	,						device=self.unet.device		)
          UpperCAmelCase__      :  int				      =			self.reset_xa(_lowerCAmelCase	,						_lowerCAmelCase	,						self.action_dim		)
          UpperCAmelCase__      :  Union[str, Any]				      =			self.to_torch(_lowerCAmelCase		)

          # run the diffusion process
          UpperCAmelCase__      ,		UpperCAmelCase__      :  int				      =			self.run_diffusion(_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase		)

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

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

          UpperCAmelCase__      :  Any				      =			denorm_actions[selected_index, 0]
          return denorm_actions











'''simple docstring'''

from __future__ import annotations







def a_     (    __UpperCAmelCase					)				->					list[int]:





			"""simple docstring"""







			snake_case:    Tuple      							=[True] * limit
			snake_case:    Optional[int]      							=False
			snake_case:    Union[str, Any]      							=False
			snake_case:    List[Any]      							=True

			for i in range(3				,							int(limit**0.5 + 1					)				,							2					):
						snake_case:    str      							=i * 2
						while index < limit:
									snake_case:    List[Any]      							=False
									snake_case:    List[str]      							=index + i

			snake_case:    Union[str, Any]      							=[2]

			for i in range(3				,							__UpperCAmelCase				,							2					):
						if is_prime[i]:
									primes.append(__UpperCAmelCase					)

			return primes







def a_     (    __UpperCAmelCase = 1_00_00_00					)				->					int:





			"""simple docstring"""







			snake_case:    str      							=prime_sieve(__UpperCAmelCase					)
			snake_case:    str      							=0
			snake_case:    str      							=0

			for i in range(len(__UpperCAmelCase					)					):
						for j in range(i + length				,							len(__UpperCAmelCase					)					):
									snake_case:    Tuple      							=sum(primes[i:j]					)
									if sol >= ceiling:
												break

									if sol in primes:
												snake_case:    List[str]      							=j - i
												snake_case:    Optional[int]      							=sol

			return largest


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




