





"""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						(				_lowerCAmelCase    ):






				def __init__(			self		:   Dict,			lowerCAmelCase__		:   UNetaDModel,			lowerCAmelCase__		:   UNetaDModel,			lowerCAmelCase__		:   DDPMScheduler,			lowerCAmelCase__		:   int,			)     ->				List[Any]:




										'''simple docstring'''



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






				def     snake_case     (			self		:   Union[str, Any],			lowerCAmelCase__		:   Union[str, Any],			lowerCAmelCase__		:   int							)     ->				List[str]:




										'''simple docstring'''



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






				def     snake_case     (			self		:   List[str],			lowerCAmelCase__		:   Tuple,			lowerCAmelCase__		:   str							)     ->				Any:




										'''simple docstring'''



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






				def     snake_case     (			self		:   Dict,			lowerCAmelCase__		:   List[str]							)     ->				int:




										'''simple docstring'''



										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		:   Optional[int],			lowerCAmelCase__		:   List[Any],			lowerCAmelCase__		:   str,			lowerCAmelCase__		:   Any							)     ->				Dict:




										'''simple docstring'''



										for key, val in cond.items():
																_UpperCamelCase      : List[Any]         =   val.clone()
										return x_in






				def     snake_case     (			self		:   Any,			lowerCAmelCase__		:   int,			lowerCAmelCase__		:   int,			lowerCAmelCase__		:   Any,			lowerCAmelCase__		:   Tuple							)     ->				Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase      : int         =   x.shape[0]
										_UpperCamelCase      : int         =   None
										for i in tqdm.tqdm(self.scheduler.timesteps							):
																# create batch of timesteps to pass into model
																_UpperCamelCase      : List[str]         =   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
																												_UpperCamelCase      : Union[str, Any]         =   self.value_function(x.permute(0,			2,			1							),			lowerCAmelCase__							).sample
																												_UpperCamelCase      : Dict         =   torch.autograd.grad([y.sum()],			[x]							)[0]

																												_UpperCamelCase      : Optional[int]         =   self.scheduler._get_variance(lowerCAmelCase__							)
																												_UpperCamelCase      : str         =   torch.exp(0.5 * posterior_variance							)
																												_UpperCamelCase      : Optional[Any]         =   model_std * grad

																						_UpperCamelCase      : str         =   0
																						_UpperCamelCase      : Any         =   x.detach()
																						_UpperCamelCase      : Dict         =   x + scale * grad
																						_UpperCamelCase      : Optional[Any]         =   self.reset_xa(lowerCAmelCase__,			lowerCAmelCase__,			self.action_dim							)

																_UpperCamelCase      : int         =   self.unet(x.permute(0,			2,			1							),			lowerCAmelCase__							).sample.permute(0,			2,			1							)

																# TODO: verify deprecation of this kwarg
																_UpperCamelCase      : Union[str, Any]         =   self.scheduler.step(lowerCAmelCase__,			lowerCAmelCase__,			lowerCAmelCase__,			predict_epsilon=lowerCAmelCase__							)['''prev_sample''']

																# apply conditions to the trajectory (set the initial state)
																_UpperCamelCase      : int         =   self.reset_xa(lowerCAmelCase__,			lowerCAmelCase__,			self.action_dim							)
																_UpperCamelCase      : Dict         =   self.to_torch(lowerCAmelCase__							)
										return x, y






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




										'''simple docstring'''



										_UpperCamelCase      : Optional[int]         =   self.normalize(lowerCAmelCase__,			'''observations'''							)
										_UpperCamelCase      : List[str]         =   obs[None].repeat(lowerCAmelCase__,			axis=0							)

										_UpperCamelCase      : List[str]         =   {0: self.to_torch(lowerCAmelCase__							)}
										_UpperCamelCase      : str         =   (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      : Tuple         =   randn_tensor(lowerCAmelCase__,			device=self.unet.device							)
										_UpperCamelCase      : Any         =   self.reset_xa(lowerCAmelCase__,			lowerCAmelCase__,			self.action_dim							)
										_UpperCamelCase      : Optional[Any]         =   self.to_torch(lowerCAmelCase__							)

										# run the diffusion process
										_UpperCamelCase       ,							_UpperCamelCase      : List[Any]         =   self.run_diffusion(lowerCAmelCase__,			lowerCAmelCase__,			lowerCAmelCase__,			lowerCAmelCase__							)

										# sort output trajectories by value
										_UpperCamelCase      : int         =   y.argsort(0,			descending=lowerCAmelCase__							).squeeze()
										_UpperCamelCase      : List[str]         =   x[sorted_idx]
										_UpperCamelCase      : Tuple         =   sorted_values[:, :, : self.action_dim]
										_UpperCamelCase      : Any         =   actions.detach().cpu().numpy()
										_UpperCamelCase      : Optional[Any]         =   self.de_normalize(lowerCAmelCase__,			key='''actions'''							)

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

										_UpperCamelCase      : Optional[int]         =   denorm_actions[selected_index, 0]
										return denorm_actions










"""simple docstring"""




from __future__ import annotations

from random import choice





def 	a_    (							_lowercase       ):
						return choice(_lowercase       )





def 	a_    (							_lowercase	,						_lowercase       ):
						_UpperCamelCase      : Optional[int]         =   random_pivot(_lowercase       )

						# partition based on pivot
						# linear time
						_UpperCamelCase      : Union[str, Any]         =   [e for e in lst if e < pivot]
						_UpperCamelCase      : int         =   [e for e in lst if e > pivot]

						# if we get lucky, pivot might be the element we want.
						# we can easily see this:
						# small (elements smaller than k)
						# + pivot (kth element)
						# + big (elements larger than k)
						if len(_lowercase       ) == k - 1:
												return pivot
						# pivot is in elements bigger than k
						elif len(_lowercase       ) < k - 1:
												return kth_number(_lowercase	,						k - len(_lowercase       ) - 1       )
						# pivot is in elements smaller than k
						else:
												return kth_number(_lowercase	,						_lowercase       )


if __name__ == "__main__":
						import doctest

						doctest.testmod()



