

'''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 						lowerCAmelCase__   (      a					):



 """simple docstring"""



 def __init__(		self					:       List[Any]					,							__SCREAMING_SNAKE_CASE					:       UNetaDModel					,							__SCREAMING_SNAKE_CASE					:       UNetaDModel					,							__SCREAMING_SNAKE_CASE					:       DDPMScheduler					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]					,							)  -> Optional[Any]:


      """simple docstring"""




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


      """simple docstring"""




      return (x_in - self.means[key]) / self.stds[key]
 def       UpperCAmelCase__		(		self					:       Optional[Any]					,							__SCREAMING_SNAKE_CASE					:       str					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]			)  -> str:


      """simple docstring"""




      return x_in * self.stds[key] + self.means[key]
 def       UpperCAmelCase__		(		self					:       Optional[int]					,							__SCREAMING_SNAKE_CASE					:       List[Any]			)  -> int:


      """simple docstring"""




      if type(__SCREAMING_SNAKE_CASE			) is dict:
           return {k: self.to_torch(__SCREAMING_SNAKE_CASE			) for k, v in x_in.items()}
      elif torch.is_tensor(__SCREAMING_SNAKE_CASE			):
           return x_in.to(self.unet.device			)
      return torch.tensor(__SCREAMING_SNAKE_CASE					,							device=self.unet.device			)
 def       UpperCAmelCase__		(		self					:       Optional[Any]					,							__SCREAMING_SNAKE_CASE					:       Tuple					,							__SCREAMING_SNAKE_CASE					:       Dict					,							__SCREAMING_SNAKE_CASE					:       Union[str, Any]			)  -> str:


      """simple docstring"""




      for key, val in cond.items():
           __SCREAMING_SNAKE_CASE     =      val.clone()
      return x_in
 def       UpperCAmelCase__		(		self					:       int					,							__SCREAMING_SNAKE_CASE					:       List[Any]					,							__SCREAMING_SNAKE_CASE					:       Any					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]					,							__SCREAMING_SNAKE_CASE					:       List[str]			)  -> List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      x.shape[0]
      __SCREAMING_SNAKE_CASE     =      None
      for i in tqdm.tqdm(self.scheduler.timesteps			):
           # create batch of timesteps to pass into model
           __SCREAMING_SNAKE_CASE     =      torch.full((batch_size,)					,							__SCREAMING_SNAKE_CASE					,							device=self.unet.device					,							dtype=torch.long			)
           for _ in range(__SCREAMING_SNAKE_CASE			):
                with torch.enable_grad():
                     x.requires_grad_()

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

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

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

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

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

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


 def __call__(		self					:       Optional[int]					,							__SCREAMING_SNAKE_CASE					:       Any					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]=64					,							__SCREAMING_SNAKE_CASE					:       Optional[int]=32					,							__SCREAMING_SNAKE_CASE					:       Any=2					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]=0.1			)  -> Union[str, Any]:


      """simple docstring"""




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

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

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

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

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

      __SCREAMING_SNAKE_CASE     =      denorm_actions[selected_index, 0]
      return denorm_actions









'''simple docstring'''



class 						lowerCAmelCase__   :



 """simple docstring"""



 def __init__(		self					:       List[Any]					,							__SCREAMING_SNAKE_CASE					:       int			)  -> List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      n
      __SCREAMING_SNAKE_CASE     =      [None] * self.n
      __SCREAMING_SNAKE_CASE     =      0  # index of the first element
      __SCREAMING_SNAKE_CASE     =      0
      __SCREAMING_SNAKE_CASE     =      0
 def __len__(		self					:       Tuple			)  -> int:


      """simple docstring"""




      return self.size
 def       UpperCAmelCase__		(		self					:       Optional[Any]			)  -> bool:


      """simple docstring"""




      return self.size == 0
 def       UpperCAmelCase__		(		self					:       Any			)  -> int:


      """simple docstring"""




      return False if self.is_empty() else self.array[self.front]
 def       UpperCAmelCase__		(		self					:       Dict					,							__SCREAMING_SNAKE_CASE					:       Union[str, Any]			)  -> Dict:


      """simple docstring"""




      if self.size >= self.n:
           raise Exception("""QUEUE IS FULL"""			)

      __SCREAMING_SNAKE_CASE     =      data
      __SCREAMING_SNAKE_CASE     =      (self.rear + 1) % self.n
      self.size += 1
      return self


 def       UpperCAmelCase__		(		self					:       List[str]			)  -> Optional[Any]:


      """simple docstring"""




      if self.size == 0:
           raise Exception("""UNDERFLOW"""			)

      __SCREAMING_SNAKE_CASE     =      self.array[self.front]
      __SCREAMING_SNAKE_CASE     =      None
      __SCREAMING_SNAKE_CASE     =      (self.front + 1) % self.n
      self.size -= 1
      return temp






