





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	(   UpperCamelCase__):







      def __init__(   self		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				)       ->						Optional[int]:



        '''simple docstring'''




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







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						List[str]:



        '''simple docstring'''




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







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						List[Any]:



        '''simple docstring'''




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







      def        _lowercase  (   self		,				lowerCAmelCase__							)       ->						Union[str, Any]:



        '''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        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						Dict:



        '''simple docstring'''




        for key, val in cond.items():
          a__     : List[str]  =val.clone()
        return x_in







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						List[str]:



        '''simple docstring'''




        a__     : Optional[int]  =x.shape[0]
        a__     : Dict  =None
        for i in tqdm.tqdm(self.scheduler.timesteps							):
          # create batch of timesteps to pass into model
          a__     : Union[str, Any]  =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
              a__     : Optional[Any]  =self.value_function(x.permute(0		,				2		,				1							)		,				lowerCAmelCase__							).sample
              a__     : List[str]  =torch.autograd.grad([y.sum()]		,				[x]							)[0]

              a__     : str  =self.scheduler._get_variance(lowerCAmelCase__							)
              a__     : Any  =torch.exp(0.5 * posterior_variance							)
              a__     : Optional[int]  =model_std * grad

            a__     : List[str]  =0
            a__     : str  =x.detach()
            a__     : Optional[Any]  =x + scale * grad
            a__     : Dict  =self.reset_xa(lowerCAmelCase__		,				lowerCAmelCase__		,				self.action_dim							)

          a__     : int  =self.unet(x.permute(0		,				2		,				1							)		,				lowerCAmelCase__							).sample.permute(0		,				2		,				1							)

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

          # apply conditions to the trajectory (set the initial state)
          a__     : List[str]  =self.reset_xa(lowerCAmelCase__		,				lowerCAmelCase__		,				self.action_dim							)
          a__     : Optional[int]  =self.to_torch(lowerCAmelCase__							)
        return x, y







      def __call__(   self		,				lowerCAmelCase__		,				lowerCAmelCase__=6_4		,				lowerCAmelCase__=3_2		,				lowerCAmelCase__=2		,				lowerCAmelCase__=0.1							)       ->						Union[str, Any]:



        '''simple docstring'''




        a__     : Any  =self.normalize(lowerCAmelCase__		,				"observations"							)
        a__     : Dict  =obs[None].repeat(lowerCAmelCase__		,				axis=0							)

        a__     : int  ={0: self.to_torch(lowerCAmelCase__							)}
        a__     : str  =(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)
        a__     : Optional[Any]  =randn_tensor(lowerCAmelCase__		,				device=self.unet.device							)
        a__     : Optional[Any]  =self.reset_xa(lowerCAmelCase__		,				lowerCAmelCase__		,				self.action_dim							)
        a__     : Dict  =self.to_torch(lowerCAmelCase__							)

        # run the diffusion process
        a__ ,							a__     : str  =self.run_diffusion(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__							)

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

        # select the action with the highest value
        if y is not None:
          a__     : Any  =0
        else:
          # if we didn't run value guiding, select a random action
          a__     : Optional[int]  =np.random.randint(0		,				lowerCAmelCase__							)

        a__     : Dict  =denorm_actions[selected_index, 0]
        return denorm_actions













def 					_A	(		SCREAMING_SNAKE_CASE					:       int = 1	,	SCREAMING_SNAKE_CASE					:       int = 1_000						):





  """simple docstring"""



  a__     : Any  =1
  a__     : Any  =0

  for divide_by_number in range(SCREAMING_SNAKE_CASE	,	digit + 1						):
    a__     : list[int]  =[]
    a__     : int  =numerator
    for _ in range(1	,	digit + 1						):
      if now_divide in has_been_divided:
        if longest_list_length < len(SCREAMING_SNAKE_CASE						):
          a__     : Optional[Any]  =len(SCREAMING_SNAKE_CASE						)
          a__     : List[str]  =divide_by_number
      else:
        has_been_divided.append(SCREAMING_SNAKE_CASE						)
        a__     : List[Any]  =now_divide * 10 % divide_by_number

  return the_digit


# Tests
if __name__ == "__main__":
     import doctest

     doctest.testmod()






