


"""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     (snake_case__):





							'''simple docstring'''

							def __init__(				self							:      int		,       UpperCAmelCase_							:      UNetaDModel		,       UpperCAmelCase_							:      UNetaDModel		,       UpperCAmelCase_							:      DDPMScheduler		,       UpperCAmelCase_							:      Tuple		,       )					->Tuple:



														"""simple docstring"""



														super().__init__()
														snake_case_           =		value_function
														snake_case_           =		unet
														snake_case_           =		scheduler
														snake_case_           =		env
														snake_case_           =		env.get_dataset()
														snake_case_           =		{}
														for key in self.data.keys():
																					try:
																												snake_case_           =		self.data[key].mean()
																					except:  # noqa: E722
																												pass
														snake_case_           =		{}
														for key in self.data.keys():
																					try:
																												snake_case_           =		self.data[key].std()
																					except:  # noqa: E722
																												pass
														snake_case_           =		env.observation_space.shape[0]
														snake_case_           =		env.action_space.shape[0]



							def 		lowerCAmelCase       (				self							:      Tuple		,       UpperCAmelCase_							:      List[Any]		,       UpperCAmelCase_							:      List[str]       )					->Optional[Any]:



														"""simple docstring"""



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



							def 		lowerCAmelCase       (				self							:      List[Any]		,       UpperCAmelCase_							:      Dict		,       UpperCAmelCase_							:      Optional[Any]       )					->List[str]:



														"""simple docstring"""



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



							def 		lowerCAmelCase       (				self							:      Any		,       UpperCAmelCase_							:      Optional[Any]       )					->Union[str, Any]:



														"""simple docstring"""



														if type(UpperCAmelCase_       ) is dict:
																					return {k: self.to_torch(UpperCAmelCase_       ) for k, v in x_in.items()}
														elif torch.is_tensor(UpperCAmelCase_       ):
																					return x_in.to(self.unet.device       )
														return torch.tensor(UpperCAmelCase_		,       device=self.unet.device       )



							def 		lowerCAmelCase       (				self							:      Dict		,       UpperCAmelCase_							:      Optional[int]		,       UpperCAmelCase_							:      Optional[Any]		,       UpperCAmelCase_							:      Any       )					->int:



														"""simple docstring"""



														for key, val in cond.items():
																					snake_case_           =		val.clone()
														return x_in



							def 		lowerCAmelCase       (				self							:      List[Any]		,       UpperCAmelCase_							:      Optional[Any]		,       UpperCAmelCase_							:      Any		,       UpperCAmelCase_							:      str		,       UpperCAmelCase_							:      Any       )					->Union[str, Any]:



														"""simple docstring"""



														snake_case_           =		x.shape[0]
														snake_case_           =		None
														for i in tqdm.tqdm(self.scheduler.timesteps       ):
																					# create batch of timesteps to pass into model
																					snake_case_           =		torch.full((batch_size,)		,       UpperCAmelCase_		,       device=self.unet.device		,       dtype=torch.long       )
																					for _ in range(UpperCAmelCase_       ):
																												with torch.enable_grad():
																																			x.requires_grad_()

																																			# permute to match dimension for pre-trained models
																																			snake_case_           =		self.value_function(x.permute(0		,       2		,       1       )		,       UpperCAmelCase_       ).sample
																																			snake_case_           =		torch.autograd.grad([y.sum()]		,       [x]       )[0]

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

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

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

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

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



							def __call__(				self							:      Dict		,       UpperCAmelCase_							:      List[Any]		,       UpperCAmelCase_							:      str=64		,       UpperCAmelCase_							:      Optional[int]=32		,       UpperCAmelCase_							:      Any=2		,       UpperCAmelCase_							:      str=0.1       )					->str:



														"""simple docstring"""



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

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

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

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

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

														snake_case_           =		denorm_actions[selected_index, 0]
														return denorm_actions







"""simple docstring"""
from math import factorial







def 						_a				(      _SCREAMING_SNAKE_CASE = 20				)    ->				int:
							snake_case_           =		2 * n  # middle entry of odd rows starting at row 3 is the solution for n = 1,
							# 2, 3,...
							snake_case_           =		n // 2

							return int(factorial(_SCREAMING_SNAKE_CASE				) / (factorial(_SCREAMING_SNAKE_CASE				) * factorial(n - k				))				)


if __name__ == "__main__":
				import sys

				if len(sys.argv) == 1:
								print(solution(20))
				else:
								try:
												__SCREAMING_SNAKE_CASE							:    Optional[int]		=    int(sys.argv[1])
												print(solution(n))
								except ValueError:
												print('Invalid entry - please enter a number.')



