
import random

def  __snake_case							(	__magic_name__		,    __magic_name__						):







				'''simple docstring'''


				lowercase     ,	lowercase     ,	lowercase         =      [], [], []
				for element in data:
								if element < pivot:
												less.append(__magic_name__						)
								elif element > pivot:
												greater.append(__magic_name__						)
								else:
												equal.append(__magic_name__						)
				return less, equal, greater




def  __snake_case							(	__magic_name__		,    __magic_name__						):







				'''simple docstring'''


				if index >= len(__magic_name__						) or index < 0:
								return None

				lowercase         =      items[random.randint(0		,    len(__magic_name__						) - 1						)]
				lowercase         =      0
				lowercase     ,	lowercase     ,	lowercase         =      _partition(__magic_name__		,    __magic_name__						)
				lowercase         =      len(__magic_name__						)
				lowercase         =      len(__magic_name__						)

				# index is the pivot
				if m <= index < m + count:
								return pivot
				# must be in smaller
				elif m > index:
								return quick_select(__magic_name__		,    __magic_name__						)
				# must be in larger
				else:
								return quick_select(__magic_name__		,    index - (m + count)						)


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   UpperCamelCase_      (      __a		):







						'''simple docstring'''



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





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





						def        SCREAMING_SNAKE_CASE(			self							:List[Any]		,    lowerCAmelCase__							:Optional[Any]     )  ->Tuple:
										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        SCREAMING_SNAKE_CASE(			self							:Optional[int]		,    lowerCAmelCase__							:Union[str, Any]		,    lowerCAmelCase__							:Optional[Any]		,    lowerCAmelCase__							:List[str]     )  ->Any:
										for key, val in cond.items():
														lowercase         =      val.clone()
										return x_in





						def        SCREAMING_SNAKE_CASE(			self							:str		,    lowerCAmelCase__							:Union[str, Any]		,    lowerCAmelCase__							:str		,    lowerCAmelCase__							:Optional[int]		,    lowerCAmelCase__							:Dict     )  ->Tuple:
										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,)		,    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
																						lowercase         =      self.value_function(x.permute(0		,    2		,    1     )		,    lowerCAmelCase__     ).sample
																						lowercase         =      torch.autograd.grad([y.sum()]		,    [x]     )[0]

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

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

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

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

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





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

										lowercase         =      {0: self.to_torch(lowerCAmelCase__     )}
										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(lowerCAmelCase__		,    device=self.unet.device     )
										lowercase         =      self.reset_xa(lowerCAmelCase__		,    lowerCAmelCase__		,    self.action_dim     )
										lowercase         =      self.to_torch(lowerCAmelCase__     )

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

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

										lowercase         =      denorm_actions[selected_index, 0]
										return denorm_actions
