




'''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 							__SCREAMING_SNAKE_CASE       (							__A     ):




      def __init__(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   )							->str:



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




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase      )							->Dict:



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




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase      )							->List[Any]:



         '''simple docstring'''
         return x_in * self.stds[key] + self.means[key]




      def  __UpperCamelCase							(  self ,   lowerCamelCase      )							->Union[str, Any]:



         '''simple docstring'''

         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      )							->str:



         '''simple docstring'''

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




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )							->Tuple:



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

                  __a				     =    self.scheduler._get_variance(lowerCamelCase      )
                  __a				     =    torch.exp(0.5 * posterior_variance      )
                  __a				     =    model_std * grad

               __a				     =    0
               __a				     =    x.detach()
               __a				     =    x + scale * grad
               __a				     =    self.reset_xa(lowerCamelCase ,   lowerCamelCase ,   self.action_dim      )

            __a				     =    self.unet(x.permute(0 ,   2 ,   1      ) ,   lowerCamelCase      ).sample.permute(0 ,   2 ,   1      )

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

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






      def __call__(  self ,   lowerCamelCase ,   lowerCamelCase=64 ,   lowerCamelCase=32 ,   lowerCamelCase=2 ,   lowerCamelCase=0.1      )							->Any:



         '''simple docstring'''
         __a				     =    self.normalize(lowerCamelCase ,   'observations'      )
         __a				     =    obs[None].repeat(lowerCamelCase ,   axis=0      )

         __a				     =    {0: self.to_torch(lowerCamelCase      )}
         __a				     =    (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)
         __a				     =    randn_tensor(lowerCamelCase ,   device=self.unet.device      )
         __a				     =    self.reset_xa(lowerCamelCase ,   lowerCamelCase ,   self.action_dim      )
         __a				     =    self.to_torch(lowerCamelCase      )

         # run the diffusion process
         __a				     =    self.run_diffusion(lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )

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

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

         __a				     =    denorm_actions[selected_index, 0]
         return denorm_actions







'''simple docstring'''



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	int    )  ->			bool:

   """simple docstring"""
   return number & 1 == 0


if __name__ == "__main__":
 import doctest

 doctest.testmod()