




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 						__lowercase		(UpperCamelCase__							):




      """simple docstring"""





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

      def      UpperCAmelCase				(				self    ,				A    ,				A     )		->    int:
         return (x_in - self.means[key]) / self.stds[key]

      def      UpperCAmelCase				(				self    ,				A    ,				A     )		->    Union[str, Any]:
         return x_in * self.stds[key] + self.means[key]

      def      UpperCAmelCase				(				self    ,				A     )		->    int:
         if type(A     ) is dict:
            return {k: self.to_torch(A     ) for k, v in x_in.items()}
         elif torch.is_tensor(A     ):
            return x_in.to(self.unet.device     )
         return torch.tensor(A    ,				device=self.unet.device     )

      def      UpperCAmelCase				(				self    ,				A    ,				A    ,				A     )		->    List[str]:
         for key, val in cond.items():
            snake_case				:    List[str]           =	val.clone()
         return x_in

      def      UpperCAmelCase				(				self    ,				A    ,				A    ,				A    ,				A     )		->    Optional[int]:
         snake_case				:    Tuple           =	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				:    Optional[int]           =	torch.full((batch_size,)    ,				A    ,				device=self.unet.device    ,				dtype=torch.long     )
            for _ in range(A     ):
               with torch.enable_grad():
                  x.requires_grad_()

                  # permute to match dimension for pre-trained models
                  snake_case				:    Union[str, Any]           =	self.value_function(x.permute(0    ,				2    ,				1     )    ,				A     ).sample
                  snake_case				:    Any           =	torch.autograd.grad([y.sum()]    ,				[x]     )[0]

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

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

            snake_case				:    Optional[int]           =	self.unet(x.permute(0    ,				2    ,				1     )    ,				A     ).sample.permute(0    ,				2    ,				1     )

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

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

      def __call__(				self    ,				A    ,				A=6_4    ,				A=3_2    ,				A=2    ,				A=0.1     )		->    Optional[int]:
         # normalize the observations and create  batch dimension
         snake_case				:    List[str]           =	self.normalize(A    ,				"""observations"""     )
         snake_case				:    List[str]           =	obs[None].repeat(A    ,				axis=0     )

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

         # run the diffusion process
         snake_case     , snake_case				:    int           =	self.run_diffusion(A    ,				A    ,				A    ,				A     )

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

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

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











import warnings
from functools import wraps
from typing import Callable




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       Callable:

   @wraps(lowercase					)
   def _inner_fn(*lowercase						,**lowercase					):
      warnings.warn(
          (f"""'{fn.__name__}' is experimental and might be subject to breaking changes in the future.""")						,lowercase						,)
      return fn(*lowercase						,**lowercase					)

   return _inner_fn





