




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__(					__lowerCamelCase):
      def __init__(				self: Dict    ,       UpperCamelCase_: UNetaDModel    ,       UpperCamelCase_: UNetaDModel    ,       UpperCamelCase_: DDPMScheduler    ,       UpperCamelCase_: Optional[int]    ,       ):
             super().__init__()
             __lowerCamelCase   =						value_function
             __lowerCamelCase   =						unet
             __lowerCamelCase   =						scheduler
             __lowerCamelCase   =						env
             __lowerCamelCase   =						env.get_dataset()
             __lowerCamelCase   =						{}
             for key in self.data.keys():
                    try:
                           __lowerCamelCase   =						self.data[key].mean()
                    except:  # noqa: E722
                           pass
             __lowerCamelCase   =						{}
             for key in self.data.keys():
                    try:
                           __lowerCamelCase   =						self.data[key].std()
                    except:  # noqa: E722
                           pass
             __lowerCamelCase   =						env.observation_space.shape[0]
             __lowerCamelCase   =						env.action_space.shape[0]
      def 			lowerCAmelCase__						(				self: Union[str, Any]    ,       UpperCamelCase_: List[Any]    ,       UpperCamelCase_: List[str]   ):
             return (x_in - self.means[key]) / self.stds[key]
      def 			lowerCAmelCase__						(				self: List[Any]    ,       UpperCamelCase_: Union[str, Any]    ,       UpperCamelCase_: Optional[int]   ):
             return x_in * self.stds[key] + self.means[key]
      def 			lowerCAmelCase__						(				self: int    ,       UpperCamelCase_: int   ):
             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_: Union[str, Any]    ,       UpperCamelCase_: Tuple    ,       UpperCamelCase_: Union[str, Any]   ):
             for key, val in cond.items():
                    __lowerCamelCase   =						val.clone()
             return x_in
      def 			lowerCAmelCase__						(				self: int    ,       UpperCamelCase_: List[Any]    ,       UpperCamelCase_: Optional[Any]    ,       UpperCamelCase_: Tuple    ,       UpperCamelCase_: int   ):
             __lowerCamelCase   =						x.shape[0]
             __lowerCamelCase   =						None
             for i in tqdm.tqdm(self.scheduler.timesteps   ):
                    # create batch of timesteps to pass into model
                    __lowerCamelCase   =						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
                                  __lowerCamelCase   =						self.value_function(x.permute(0    ,       2    ,       1   )    ,       UpperCamelCase_   ).sample
                                  __lowerCamelCase   =						torch.autograd.grad([y.sum()]    ,       [x]   )[0]

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

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

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

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

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




      def __call__(				self: List[str]    ,       UpperCamelCase_: int    ,       UpperCamelCase_: List[Any]=64    ,       UpperCamelCase_: Optional[Any]=32    ,       UpperCamelCase_: Tuple=2    ,       UpperCamelCase_: List[str]=0.1   ):
             # normalize the observations and create  batch dimension
             __lowerCamelCase   =						self.normalize(UpperCamelCase_    ,       """observations"""   )
             __lowerCamelCase   =						obs[None].repeat(UpperCamelCase_    ,       axis=0   )

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

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

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

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

             __lowerCamelCase   =						denorm_actions[selected_index, 0]
             return denorm_actions







from __future__ import annotations




def 					lowerCamelCase__       (						A__       :					list[int | float]  ,			A__       :					int  ,			A__       :					int	):





       '''simple docstring'''



       if len(A__	) == 0:
              raise ValueError("""find_max() arg is an empty sequence"""	)
       if (
           left >= len(A__	)
           or left < -len(A__	)
           or right >= len(A__	)
           or right < -len(A__	)
       ):
              raise IndexError("""list index out of range"""	)
       if left == right:
              return nums[left]
       __lowerCamelCase   =						(left + right) >> 1  # the middle
       __lowerCamelCase   =						find_max(A__  ,			A__  ,			A__	)  # find max in range[left, mid]
       __lowerCamelCase   =						find_max(A__  ,			mid + 1  ,			A__	)  # find max in range[mid + 1, right]

       return left_max if left_max >= right_max else right_max


if __name__ == "__main__":
       import doctest

       doctest.testmod(verbose=True)

