





'''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_		(		__magic_name__		):




 def __init__(	self			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     )    ->							Tuple:
     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 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase		)    ->							Optional[Any]:
     return (x_in - self.means[key]) / self.stds[key]




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase		)    ->							Optional[Any]:
     return x_in * self.stds[key] + self.means[key]




 def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							Union[str, Any]:
     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			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase		)    ->							List[Any]:
     for key, val in cond.items():
         _lowerCAmelCase				=      val.clone()
     return x_in




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

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

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

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

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

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





 def __call__(	self			,     _lowerCAmelCase			,     _lowerCAmelCase=64			,     _lowerCAmelCase=32			,     _lowerCAmelCase=2			,     _lowerCAmelCase=0.1		)    ->							str:
     # normalize the observations and create  batch dimension
     _lowerCAmelCase				=      self.normalize(_lowerCAmelCase			,     "observations"		)
     _lowerCAmelCase				=      obs[None].repeat(_lowerCAmelCase			,     axis=0		)

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

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

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

     _lowerCAmelCase				=      denorm_actions[selected_index, 0]
     return denorm_actions









'''simple docstring'''


def 	__a(SCREAMING_SNAKE_CASE_  :					list[int]		,       SCREAMING_SNAKE_CASE_  :					list[int]		,       SCREAMING_SNAKE_CASE_  :					int    ):



    '''simple docstring'''





    return not any(
        neighbour == 1 and colored_vertices[i] == color
        for i, neighbour in enumerate(SCREAMING_SNAKE_CASE_    )    )


def 	__a(SCREAMING_SNAKE_CASE_  :					list[list[int]]		,       SCREAMING_SNAKE_CASE_  :					int		,       SCREAMING_SNAKE_CASE_  :					list[int]		,       SCREAMING_SNAKE_CASE_  :					int    ):



    '''simple docstring'''





    if index == len(SCREAMING_SNAKE_CASE_    ):
        return True

    # Recursive Step
    for i in range(SCREAMING_SNAKE_CASE_    ):
        if valid_coloring(graph[index]		,       SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_    ):
            # Color current vertex
            _lowerCAmelCase				=      i
            # Validate coloring
            if util_color(SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_		,       index + 1    ):
                return True
            # Backtrack
            _lowerCAmelCase				=      -1
    return False





def 	__a(SCREAMING_SNAKE_CASE_  :					list[list[int]]		,       SCREAMING_SNAKE_CASE_  :					int    ):



    '''simple docstring'''





    _lowerCAmelCase				=      [-1] * len(SCREAMING_SNAKE_CASE_    )

    if util_color(SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_		,       0    ):
        return colored_vertices

    return []


