'''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							(	lowerCamelCase					):




  '''simple docstring'''



  def __init__(      self			:       Tuple  ,							UpperCAmelCase__			:       UNetaDModel  ,							UpperCAmelCase__			:       UNetaDModel  ,							UpperCAmelCase__			:       DDPMScheduler  ,							UpperCAmelCase__			:       str  ,							)				->Optional[int]:
        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  lowerCAmelCase__ (      self			:       str  ,							UpperCAmelCase__			:       List[str]  ,							UpperCAmelCase__			:       Optional[int]  )				->List[Any]:
        return (x_in - self.means[key]) / self.stds[key]






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






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






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






  def  lowerCAmelCase__ (      self			:       int  ,							UpperCAmelCase__			:       Tuple  ,							UpperCAmelCase__			:       int  ,							UpperCAmelCase__			:       List[Any]  ,							UpperCAmelCase__			:       Any  )				->List[str]:
        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,)  ,							UpperCAmelCase__  ,							device=self.unet.device  ,							dtype=torch.long  )
              for _ in range(UpperCAmelCase__  ):
                    with torch.enable_grad():
                          x.requires_grad_()

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

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

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

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

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

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






  def __call__(      self			:       List[Any]  ,							UpperCAmelCase__			:       Optional[Any]  ,							UpperCAmelCase__			:       List[Any]=64  ,							UpperCAmelCase__			:       Union[str, Any]=32  ,							UpperCAmelCase__			:       List[Any]=2  ,							UpperCAmelCase__			:       Optional[int]=0.1  )				->Optional[Any]:
        # normalize the observations and create  batch dimension
        UpperCAmelCase_            =  self.normalize(UpperCAmelCase__  ,							'''observations'''  )
        UpperCAmelCase_            =  obs[None].repeat(UpperCAmelCase__  ,							axis=0  )

        UpperCAmelCase_            =  {0: self.to_torch(UpperCAmelCase__  )}
        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(UpperCAmelCase__  ,							device=self.unet.device  )
        UpperCAmelCase_            =  self.reset_xa(UpperCAmelCase__  ,							UpperCAmelCase__  ,							self.action_dim  )
        UpperCAmelCase_            =  self.to_torch(UpperCAmelCase__  )

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

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

        UpperCAmelCase_            =  denorm_actions[selected_index, 0]
        return denorm_actions






'''simple docstring'''
import re



def  __lowerCamelCase       (      _UpperCamelCase  :  str					):


      '''simple docstring'''


      return [char.split() for char in re.split(R'''[^ a-z A-Z 0-9 \s]'''							,    str_					)]



def  __lowerCamelCase       (      _UpperCamelCase  :  str					):


      '''simple docstring'''


      UpperCAmelCase_            =  split_input(str_					)
      return "".join(
          [''''''.join([char.capitalize() for char in sub_str]					) for sub_str in string_split]					)



def  __lowerCamelCase       (      _UpperCamelCase  :  str							,    _UpperCamelCase  :  bool							,    _UpperCamelCase  :  str					):


      '''simple docstring'''


      try:
            UpperCAmelCase_            =  split_input(_UpperCamelCase					)
            if upper:
                  UpperCAmelCase_            =  ''''''.join(
                      [
                          separator.join([char.upper() for char in sub_str]					)
                          for sub_str in string_split
                      ]					)
            else:
                  UpperCAmelCase_            =  ''''''.join(
                      [
                          separator.join([char.lower() for char in sub_str]					)
                          for sub_str in string_split
                      ]					)
            return res_str
      except IndexError:
            return "not valid string"



def  __lowerCamelCase       (      _UpperCamelCase  :  str					):


      '''simple docstring'''


      return to_simple_case(_UpperCamelCase					)



def  __lowerCamelCase       (      _UpperCamelCase  :  str					):


      '''simple docstring'''


      try:
            UpperCAmelCase_            =  to_simple_case(_UpperCamelCase					)
            return res_str[0].lower() + res_str[1:]
      except IndexError:
            return "not valid string"



def  __lowerCamelCase       (      _UpperCamelCase  :  str							,    _UpperCamelCase  :  bool					):


      '''simple docstring'''


      return to_complex_case(_UpperCamelCase							,    _UpperCamelCase							,    '''_'''					)



def  __lowerCamelCase       (      _UpperCamelCase  :  str							,    _UpperCamelCase  :  bool					):


      '''simple docstring'''


      return to_complex_case(_UpperCamelCase							,    _UpperCamelCase							,    '''-'''					)


if __name__ == "__main__":
      __import__("doctest").testmod()





