





'''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 			UpperCAmelCase					(      UpperCAmelCase__       ):




      def __init__(      self      :			Optional[int]      ,      __snake_case      :			List[str]      ,      __snake_case      :			Dict      ,      __snake_case      :			Optional[int]      ,      __snake_case      :			int      ,      )    ->				List[Any]:
            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     lowercase__   (      self      :			Optional[int]      ,      __snake_case      :			Optional[int]      ,      __snake_case      :			Optional[Any]  )    ->				Tuple:
            return (x_in - self.means[key]) / self.stds[key]




      def     lowercase__   (      self      :			Optional[Any]      ,      __snake_case      :			List[str]      ,      __snake_case      :			List[Any]  )    ->				Union[str, Any]:
            return x_in * self.stds[key] + self.means[key]




      def     lowercase__   (      self      :			List[Any]      ,      __snake_case      :			Union[str, Any]  )    ->				Optional[Any]:




            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     lowercase__   (      self      :			List[str]      ,      __snake_case      :			Union[str, Any]      ,      __snake_case      :			List[Any]      ,      __snake_case      :			List[Any]  )    ->				str:




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




      def     lowercase__   (      self      :			List[str]      ,      __snake_case      :			int      ,      __snake_case      :			List[Any]      ,      __snake_case      :			List[str]      ,      __snake_case      :			int  )    ->				int:
            _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,)      ,      _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
                              _lowerCAmelCase												=							self.value_function(x.permute(0      ,      2      ,      1  )      ,      _SCREAMING_SNAKE_CASE  ).sample
                              _lowerCAmelCase												=							torch.autograd.grad([y.sum()]      ,      [x]  )[0]

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

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

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

                  # TODO: verify deprecation of this kwarg
                  _lowerCAmelCase												=							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)
                  _lowerCAmelCase												=							self.reset_xa(_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      ,      self.action_dim  )
                  _lowerCAmelCase												=							self.to_torch(_SCREAMING_SNAKE_CASE  )
            return x, y




      def __call__(      self      :			Tuple      ,      __snake_case      :			List[Any]      ,      __snake_case      :			Tuple=64      ,      __snake_case      :			Union[str, Any]=32      ,      __snake_case      :			Union[str, Any]=2      ,      __snake_case      :			Any=0.1  )    ->				Tuple:
            _lowerCAmelCase												=							self.normalize(_SCREAMING_SNAKE_CASE      ,      """observations"""  )
            _lowerCAmelCase												=							obs[None].repeat(_SCREAMING_SNAKE_CASE      ,      axis=0  )

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

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

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

            _lowerCAmelCase												=							denorm_actions[selected_index, 0]
            return denorm_actions









'''simple docstring'''






def        UpperCamelCase__				(		lowerCAmelCase = 4_00_00_00  ):





      """simple docstring"""


      _lowerCAmelCase												=							[]
      _lowerCAmelCase					,							_lowerCAmelCase												=							0, 1
      while b <= n:
            if b % 2 == 0:
                  even_fibs.append(lowerCAmelCase  )
            _lowerCAmelCase					,							_lowerCAmelCase												=							b, a + b
      return sum(lowerCAmelCase  )


if __name__ == "__main__":
      print(F"""{solution() = }""")






