'''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_		(    __lowercase       ):

  def __init__(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	)						->   Union[str, Any]:



        """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 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )						->   str:



        """simple docstring"""






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

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )						->   List[Any]:



        """simple docstring"""






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

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE   )						->   List[Any]:



        """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 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )						->   Optional[int]:



        """simple docstring"""






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

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )						->   Tuple:



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

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

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

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

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

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


  def __call__(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE=64    ,	_SCREAMING_SNAKE_CASE=32    ,	_SCREAMING_SNAKE_CASE=2    ,	_SCREAMING_SNAKE_CASE=0.1   )						->   str:



        """simple docstring"""






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

        UpperCamelCase       						=      {0: self.to_torch(__A   )}
        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(__A    ,	device=self.unet.device   )
        UpperCamelCase       						=      self.reset_xa(__A    ,	__A    ,	self.action_dim   )
        UpperCamelCase       						=      self.to_torch(__A   )

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

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

        UpperCamelCase       						=      denorm_actions[selected_index, 0]
        return denorm_actions








'''simple docstring'''

from math import factorial







def 					lowercase__				(   __UpperCamelCase = 20					)->				int:
      UpperCamelCase       						=      2 * n  # middle entry of odd rows starting at row 3 is the solution for n = 1,
      # 2, 3,...
      UpperCamelCase       						=      n // 2

      return int(factorial(__UpperCamelCase					) / (factorial(__UpperCamelCase					) * factorial(n - k					))					)


if __name__ == "__main__":
     import sys

     if len(sys.argv) == 1:
          print(solution(2_0))
     else:
          try:
               SCREAMING_SNAKE_CASE__											=     int(sys.argv[1])
               print(solution(n))
          except ValueError:
               print('Invalid entry - please enter a number.')
