'''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    a__				(       UpperCAmelCase__			):






     def __init__(			self		:		List[str]  ,    a		:		UNetaDModel  ,    a		:		UNetaDModel  ,    a		:		DDPMScheduler  ,    a		:		str  ,    ):





         """simple docstring"""



         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        SCREAMING_SNAKE_CASE__       (			self		:		Optional[Any]  ,    a		:		Dict  ,    a		:		str   ):





         """simple docstring"""



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






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[int]  ,    a		:		int  ,    a		:		List[Any]   ):





         """simple docstring"""



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






     def        SCREAMING_SNAKE_CASE__       (			self		:		str  ,    a		:		List[str]   ):





         """simple docstring"""



         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        SCREAMING_SNAKE_CASE__       (			self		:		Tuple  ,    a		:		Optional[Any]  ,    a		:		str  ,    a		:		Dict   ):





         """simple docstring"""



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






     def        SCREAMING_SNAKE_CASE__       (			self		:		Dict  ,    a		:		List[Any]  ,    a		:		Union[str, Any]  ,    a		:		Optional[Any]  ,    a		:		Union[str, Any]   ):





         """simple docstring"""



         __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,)  ,    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
                     __lowerCamelCase          =  self.value_function(x.permute(0  ,    2  ,    1   )  ,    a   ).sample
                     __lowerCamelCase          =  torch.autograd.grad([y.sum()]  ,    [x]   )[0]

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

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

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

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

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






     def __call__(			self		:		Optional[int]  ,    a		:		Optional[int]  ,    a		:		str=64  ,    a		:		Any=32  ,    a		:		str=2  ,    a		:		Tuple=0.1   ):





         """simple docstring"""



         __lowerCamelCase          =  self.normalize(a  ,    '''observations'''   )
         __lowerCamelCase          =  obs[None].repeat(a  ,    axis=0   )

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

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

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

         __lowerCamelCase          =  denorm_actions[selected_index, 0]
         return denorm_actions

'''simple docstring'''




from __future__ import annotations

from typing import Any






def __lowerCAmelCase			(							UpperCamelCase__    )     ->   None:
    create_state_space_tree(UpperCamelCase__    ,  []    ,  0    )






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    )     ->   None:

    if index == len(UpperCamelCase__    ):
        print(UpperCamelCase__    )
        return

    create_state_space_tree(UpperCamelCase__    ,  UpperCamelCase__    ,  index + 1    )
    current_subsequence.append(sequence[index]    )
    create_state_space_tree(UpperCamelCase__    ,  UpperCamelCase__    ,  index + 1    )
    current_subsequence.pop()


if __name__ == "__main__":
  __UpperCAmelCase												=[3, 1, 2, 4]
  generate_all_subsequences(seq)

  seq.clear()
  seq.extend(["A", "B", "C"])
  generate_all_subsequences(seq)
