

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       ):







  """simple docstring"""




  def __init__(			self    : Optional[Any]				,				UpperCAmelCase__    : UNetaDModel				,				UpperCAmelCase__    : UNetaDModel				,				UpperCAmelCase__    : DDPMScheduler				,				UpperCAmelCase__    : Any				,				)					->Dict:
        """simple docstring"""







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


  def  _lowercase      (			self    : Optional[Any]				,				UpperCAmelCase__    : Any				,				UpperCAmelCase__    : int		)					->Dict:
        """simple docstring"""







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


  def  _lowercase      (			self    : Optional[int]				,				UpperCAmelCase__    : str				,				UpperCAmelCase__    : List[str]		)					->str:
        """simple docstring"""







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


  def  _lowercase      (			self    : List[str]				,				UpperCAmelCase__    : Tuple		)					->Tuple:
        """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  _lowercase      (			self    : Optional[Any]				,				UpperCAmelCase__    : Optional[int]				,				UpperCAmelCase__    : Dict				,				UpperCAmelCase__    : Optional[Any]		)					->List[Any]:
        """simple docstring"""







        for key, val in cond.items():
              SCREAMING_SNAKE_CASE :      List[str]       =   val.clone()
        return x_in


  def  _lowercase      (			self    : Tuple				,				UpperCAmelCase__    : Optional[Any]				,				UpperCAmelCase__    : Any				,				UpperCAmelCase__    : Optional[Any]				,				UpperCAmelCase__    : Optional[int]		)					->List[Any]:
        """simple docstring"""







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

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

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

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

              # TODO: verify deprecation of this kwarg
              SCREAMING_SNAKE_CASE :      Union[str, Any]       =   self.scheduler.step(UpperCAmelCase__				,				UpperCAmelCase__				,				UpperCAmelCase__				,				predict_epsilon=UpperCAmelCase__		)["""prev_sample"""]

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




  def __call__(			self    : Any				,				UpperCAmelCase__    : Tuple				,				UpperCAmelCase__    : int=6_4				,				UpperCAmelCase__    : Tuple=3_2				,				UpperCAmelCase__    : List[Any]=2				,				UpperCAmelCase__    : List[str]=0.1		)					->List[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   self.normalize(UpperCAmelCase__				,				"""observations"""		)
        SCREAMING_SNAKE_CASE :      List[str]       =   obs[None].repeat(UpperCAmelCase__				,				axis=0		)

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

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

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

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

        SCREAMING_SNAKE_CASE :      List[str]       =   denorm_actions[selected_index, 0]
        return denorm_actions







from __future__ import annotations

import numpy as np






def 					__lowercase      (     _A			)     ->						tuple[np.ndarray, np.ndarray]:
      SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE :      Dict       =   np.shape(_A			)
      if rows != columns:
            SCREAMING_SNAKE_CASE :      int       =   (
                """'table' has to be of square shaped array but got a """
                F"{rows}x{columns} array:\n{table}"
            )
            raise ValueError(_A			)

      SCREAMING_SNAKE_CASE :      Union[str, Any]       =   np.zeros((rows, columns)			)
      SCREAMING_SNAKE_CASE :      Optional[Any]       =   np.zeros((rows, columns)			)
      for i in range(_A			):
            for j in range(_A			):
                  SCREAMING_SNAKE_CASE :      Tuple       =   sum(lower[i][k] * upper[k][j] for k in range(_A			)			)
                  if upper[j][j] == 0:
                        raise ArithmeticError("""No LU decomposition exists"""			)
                  SCREAMING_SNAKE_CASE :      Union[str, Any]       =   (table[i][j] - total) / upper[j][j]
            SCREAMING_SNAKE_CASE :      Optional[Any]       =   1
            for j in range(_A						,    _A			):
                  SCREAMING_SNAKE_CASE :      List[str]       =   sum(lower[i][k] * upper[k][j] for k in range(_A			)			)
                  SCREAMING_SNAKE_CASE :      Optional[Any]       =   table[i][j] - total
      return lower, upper


if __name__ == "__main__":
  import doctest

  doctest.testmod()




