


"""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 _lowerCAmelCase							( SCREAMING_SNAKE_CASE__      ):







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

       '''simple docstring'''




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







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__		)      ->						Optional[int]:

       '''simple docstring'''




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







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

       '''simple docstring'''




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







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		)      ->						Dict:

       '''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 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		)      ->						List[str]:

       '''simple docstring'''




       for key, val in cond.items():
           snake_case       : Optional[Any]    =       val.clone()
       return x_in







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		)      ->						Optional[int]:

       '''simple docstring'''




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

                   snake_case       : str    =       self.scheduler._get_variance(__lowerCAmelCase		)
                   snake_case       : str    =       torch.exp(0.5 * posterior_variance		)
                   snake_case       : Any    =       model_std * grad

               snake_case       : Tuple    =       0
               snake_case       : Optional[int]    =       x.detach()
               snake_case       : int    =       x + scale * grad
               snake_case       : Dict    =       self.reset_xa(__lowerCAmelCase		,      __lowerCAmelCase		,      self.action_dim		)

           snake_case       : Union[str, Any]    =       self.unet(x.permute(0		,      2		,      1		)		,      __lowerCAmelCase		).sample.permute(0		,      2		,      1		)

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

           # apply conditions to the trajectory (set the initial state)
           snake_case       : Tuple    =       self.reset_xa(__lowerCAmelCase		,      __lowerCAmelCase		,      self.action_dim		)
           snake_case       : Dict    =       self.to_torch(__lowerCAmelCase		)
       return x, y







   def __call__(      self		,      UpperCamelCase__		,      UpperCamelCase__=64		,      UpperCamelCase__=32		,      UpperCamelCase__=2		,      UpperCamelCase__=0.1		)      ->						int:

       '''simple docstring'''




       snake_case       : int    =       self.normalize(__lowerCAmelCase		,      "observations"		)
       snake_case       : List[str]    =       obs[None].repeat(__lowerCAmelCase		,      axis=0		)

       snake_case       : Tuple    =       {0: self.to_torch(__lowerCAmelCase		)}
       snake_case       : Dict    =       (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)
       snake_case       : Tuple    =       randn_tensor(__lowerCAmelCase		,      device=self.unet.device		)
       snake_case       : List[Any]    =       self.reset_xa(__lowerCAmelCase		,      __lowerCAmelCase		,      self.action_dim		)
       snake_case       : Optional[Any]    =       self.to_torch(__lowerCAmelCase		)

       # run the diffusion process
       snake_case       : List[Any]    =       self.run_diffusion(__lowerCAmelCase		,      __lowerCAmelCase		,      __lowerCAmelCase		,      __lowerCAmelCase		)

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

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

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













'''simple docstring'''

from __future__ import annotations

from collections import namedtuple



def 				snake_case       (  a_				:			float     , a_				:			float     , a_				:			float						)							->					tuple:





      """simple docstring"""

      UpperCamelCase_					: Tuple            =				namedtuple("""result"""     , """name value"""						)
      if (voltage, current, power).count(0						) != 1:
            raise ValueError("""Only one argument must be 0"""						)
      elif power < 0:
            raise ValueError(
                """Power cannot be negative in any electrical/electronics system"""						)
      elif voltage == 0:
            return result("""voltage"""     , power / current						)
      elif current == 0:
            return result("""current"""     , power / voltage						)
      elif power == 0:
            return result("""power"""     , float(round(abs(voltage * current						)     , 2						)						)						)
      else:
            raise ValueError("""Exactly one argument must be 0"""						)


if __name__ == "__main__":
      import doctest

      doctest.testmod()






