





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       (	__lowercase						):



      def __init__(	self:						Optional[int]		,  _lowerCAmelCase:						UNetaDModel		,  _lowerCAmelCase:						UNetaDModel		,  _lowerCAmelCase:						DDPMScheduler		,  _lowerCAmelCase:						List[str]		,  )						->    int:
         '''simple docstring'''

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



      def 	lowerCAmelCase__    (	self:						Tuple		,  _lowerCAmelCase:						str		,  _lowerCAmelCase:						Optional[int]       )						->    Dict:
         '''simple docstring'''

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



      def 	lowerCAmelCase__    (	self:						Dict		,  _lowerCAmelCase:						Optional[Any]		,  _lowerCAmelCase:						Union[str, Any]       )						->    Union[str, Any]:
         '''simple docstring'''

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



      def 	lowerCAmelCase__    (	self:						Optional[Any]		,  _lowerCAmelCase:						List[Any]       )						->    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:						Optional[int]		,  _lowerCAmelCase:						Dict		,  _lowerCAmelCase:						str		,  _lowerCAmelCase:						Any       )						->    Any:
         '''simple docstring'''

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



      def 	lowerCAmelCase__    (	self:						Union[str, Any]		,  _lowerCAmelCase:						Tuple		,  _lowerCAmelCase:						Tuple		,  _lowerCAmelCase:						Tuple		,  _lowerCAmelCase:						int       )						->    Optional[int]:
         '''simple docstring'''

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

                  UpperCAmelCase_											=self.scheduler._get_variance(_lowerCAmelCase       )
                  UpperCAmelCase_											=torch.exp(0.5 * posterior_variance       )
                  UpperCAmelCase_											=model_std * grad

               UpperCAmelCase_											=0
               UpperCAmelCase_											=x.detach()
               UpperCAmelCase_											=x + scale * grad
               UpperCAmelCase_											=self.reset_xa(_lowerCAmelCase		,  _lowerCAmelCase		,  self.action_dim       )

            UpperCAmelCase_											=self.unet(x.permute(0		,  2		,  1       )		,  _lowerCAmelCase       ).sample.permute(0		,  2		,  1       )

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

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






      def __call__(	self:						int		,  _lowerCAmelCase:						List[Any]		,  _lowerCAmelCase:						Optional[Any]=64		,  _lowerCAmelCase:						int=32		,  _lowerCAmelCase:						Dict=2		,  _lowerCAmelCase:						Tuple=0.1       )						->    List[str]:
         '''simple docstring'''

         UpperCAmelCase_											=self.normalize(_lowerCAmelCase		,  "observations"       )
         UpperCAmelCase_											=obs[None].repeat(_lowerCAmelCase		,  axis=0       )

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

         # run the diffusion process
         UpperCAmelCase_						,       UpperCAmelCase_											=self.run_diffusion(_lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase       )

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

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

         UpperCAmelCase_											=denorm_actions[selected_index, 0]
         return denorm_actions











from __future__ import annotations




def     a__ (					lowercase__  ,    lowercase__  ):






   '''simple docstring'''






   UpperCAmelCase_						,       UpperCAmelCase_											=set(lowercase__  ), [start]

   while stack:
      UpperCAmelCase_											=stack.pop()
      explored.add(lowercase__  )
      # Differences from BFS:
      # 1) pop last element instead of first one
      # 2) add adjacent elements to stack without exploring them
      for adj in reversed(graph[v]  ):
         if adj not in explored:
            stack.append(lowercase__  )
   return explored


__lowercase  :   Dict						      ={
    """A""": ["""B""", """C""", """D"""],
    """B""": ["""A""", """D""", """E"""],
    """C""": ["""A""", """F"""],
    """D""": ["""B""", """D"""],
    """E""": ["""B""", """F"""],
    """F""": ["""C""", """E""", """G"""],
    """G""": ["""F"""],
}

if __name__ == "__main__":
     import doctest

     doctest.testmod()
     print(depth_first_search(G, """A"""))




