



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[str]			,						__lowerCAmelCase					:					UNetaDModel			,						__lowerCAmelCase					:					UNetaDModel			,						__lowerCAmelCase					:					DDPMScheduler			,						__lowerCAmelCase					:					Any			,						)   ->	Any:
    super().__init__()
    _A      =       value_function
    _A      =       unet
    _A      =       scheduler
    _A      =       env
    _A      =       env.get_dataset()
    _A      =       {}
    for key in self.data.keys():
       try:
          _A      =       self.data[key].mean()
       except:  # noqa: E722
          pass
    _A      =       {}
    for key in self.data.keys():
       try:
          _A      =       self.data[key].std()
       except:  # noqa: E722
          pass
    _A      =       env.observation_space.shape[0]
    _A      =       env.action_space.shape[0]

 def     snake_case_      (    self					:					Union[str, Any]			,						__lowerCAmelCase					:					Any			,						__lowerCAmelCase					:					int      )   ->	Any:
    return (x_in - self.means[key]) / self.stds[key]

 def     snake_case_      (    self					:					Union[str, Any]			,						__lowerCAmelCase					:					Union[str, Any]			,						__lowerCAmelCase					:					Any      )   ->	Any:
    return x_in * self.stds[key] + self.means[key]

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

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

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

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

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

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

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

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

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

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

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

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

    _A      =       denorm_actions[selected_index, 0]
    return denorm_actions








UpperCAmelCase_		 =					0  # The first color of the flag.
UpperCAmelCase_		 =					1  # The second color of the flag.
UpperCAmelCase_		 =					2  # The third color of the flag.
UpperCAmelCase_		 =					(red, white, blue)

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:list				)  ->     list:
   if not sequence:
      return []
   if len(_snake_case				) == 1:
      return list(_snake_case				)
   _A      =       0
   _A      =       len(_snake_case				) - 1
   _A      =       0
   while mid <= high:
      if sequence[mid] == colors[0]:
         _A  ,    _A      =       sequence[mid], sequence[low]
         low += 1
         mid += 1
      elif sequence[mid] == colors[1]:
         mid += 1
      elif sequence[mid] == colors[2]:
         _A  ,    _A      =       sequence[high], sequence[mid]
         high -= 1
      else:
         _A      =       F'''The elements inside the sequence must contains only {colors} values'''
         raise ValueError(_snake_case				)
   return sequence


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   UpperCAmelCase_		 =					input("""Enter numbers separated by commas:\n""").strip()
   UpperCAmelCase_		 =					[int(item.strip()) for item in user_input.split(""",""")]
   print(f'{dutch_national_flag_sort(unsorted)}')



