


"""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__						(		_a    ):
      def __init__(		self	:	Optional[int]      ,       A_	:	UNetaDModel      ,       A_	:	UNetaDModel      ,       A_	:	DDPMScheduler      ,       A_	:	Optional[int]      ,       ):






       '''simple docstring'''


       super().__init__()
       __lowercase									=	value_function
       __lowercase									=	unet
       __lowercase									=	scheduler
       __lowercase									=	env
       __lowercase									=	env.get_dataset()
       __lowercase									=	{}
       for key in self.data.keys():
        try:
         __lowercase									=	self.data[key].mean()
        except:  # noqa: E722
         pass
       __lowercase									=	{}
       for key in self.data.keys():
        try:
         __lowercase									=	self.data[key].std()
        except:  # noqa: E722
         pass
       __lowercase									=	env.observation_space.shape[0]
       __lowercase									=	env.action_space.shape[0]
      def SCREAMING_SNAKE_CASE_	(		self	:	List[Any]      ,       A_	:	Tuple      ,       A_	:	List[Any]							):






       '''simple docstring'''


       return (x_in - self.means[key]) / self.stds[key]
      def SCREAMING_SNAKE_CASE_	(		self	:	Union[str, Any]      ,       A_	:	Optional[int]      ,       A_	:	Union[str, Any]							):






       '''simple docstring'''


       return x_in * self.stds[key] + self.means[key]
      def SCREAMING_SNAKE_CASE_	(		self	:	Any      ,       A_	:	str							):






       '''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 SCREAMING_SNAKE_CASE_	(		self	:	List[Any]      ,       A_	:	Any      ,       A_	:	List[str]      ,       A_	:	Optional[Any]							):






       '''simple docstring'''


       for key, val in cond.items():
        __lowercase									=	val.clone()
       return x_in
      def SCREAMING_SNAKE_CASE_	(		self	:	str      ,       A_	:	Any      ,       A_	:	Dict      ,       A_	:	Any      ,       A_	:	Any							):






       '''simple docstring'''


       __lowercase									=	x.shape[0]
       __lowercase									=	None
       for i in tqdm.tqdm(self.scheduler.timesteps							):
        # create batch of timesteps to pass into model
        __lowercase									=	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
          __lowercase									=	self.value_function(x.permute(0      ,       2      ,       1							)      ,       A_							).sample
          __lowercase									=	torch.autograd.grad([y.sum()]      ,       [x]							)[0]

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

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

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

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

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






      def __call__(		self	:	int      ,       A_	:	Optional[int]      ,       A_	:	int=6_4      ,       A_	:	List[Any]=3_2      ,       A_	:	Union[str, Any]=2      ,       A_	:	List[str]=0.1							):






       '''simple docstring'''


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

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

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

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

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

       __lowercase									=	denorm_actions[selected_index, 0]
       return denorm_actions




"""simple docstring"""

import random


def lowerCAmelCase_      (				UpperCamelCase__	: int				,   UpperCamelCase__	: float				,   UpperCamelCase__	: bool = False ):






 """simple docstring"""







 __lowercase									=	{i: [] for i in range(UpperCamelCase__ )}

 # if probability is greater or equal than 1, then generate a complete graph
 if probability >= 1:
  return complete_graph(UpperCamelCase__ )
 # if probability is lower or equal than 0, then return a graph without edges
 if probability <= 0:
  return graph

 # for each couple of nodes, add an edge from u to v
 # if the number randomly generated is greater than probability probability
 for i in range(UpperCamelCase__ ):
  for j in range(i + 1				,   UpperCamelCase__ ):
   if random.random() < probability:
    graph[i].append(UpperCamelCase__ )
    if not directed:
     # if the graph is undirected, add an edge in from j to i, either
     graph[j].append(UpperCamelCase__ )
 return graph


def lowerCAmelCase_      (				UpperCamelCase__	: int ):






 """simple docstring"""







 return {
     i: [j for j in range(UpperCamelCase__ ) if i != j] for i in range(UpperCamelCase__ )
 }


if __name__ == "__main__":
       import doctest

       doctest.testmod()
