




'''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 			_snake_case		(     lowercase_							):




			def __init__( self    ,							a__    ,							a__    ,							a__    ,							a__    ,							)    ->    str:

										'''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    ,							a__    ,							a__							)    ->    Union[str, Any]:

										'''simple docstring'''




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




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__							)    ->    str:

										'''simple docstring'''




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




			def        lowerCAmelCase__    ( self    ,							a__							)    ->    Any:

										'''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        lowerCAmelCase__    ( self    ,							a__    ,							a__    ,							a__							)    ->    Dict:

										'''simple docstring'''




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




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__    ,							a__    ,							a__							)    ->    str:

										'''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,)    ,							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
																															snake_case_      =				self.value_function(x.permute(0    ,							2    ,							1							)    ,							a__							).sample
																															snake_case_      =				torch.autograd.grad([y.sum()]    ,							[x]							)[0]

																															snake_case_      =				self.scheduler._get_variance(a__							)
																															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(a__    ,							a__    ,							self.action_dim							)

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

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

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




			def __call__( self    ,							a__    ,							a__=64    ,							a__=32    ,							a__=2    ,							a__=0.1							)    ->    Optional[Any]:

										'''simple docstring'''




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

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

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

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

										snake_case_      =				denorm_actions[selected_index, 0]
										return denorm_actions










'''simple docstring'''





from __future__ import annotations

from scipy.special import comb  # type: ignore




class 			_snake_case		:




			def __init__( self    ,							a__							)    ->    Union[str, Any]:

										'''simple docstring'''




										snake_case_      =				list_of_points
										# Degree determines the flexibility of the curve.
										# Degree = 1 will produce a straight line.
										snake_case_      =				len(a__							) - 1




			def        lowerCAmelCase__    ( self    ,							a__							)    ->    list[float]:

										'''simple docstring'''




										assert 0 <= t <= 1, "Time t must be between 0 and 1."
										snake_case_      =				[]
										for i in range(len(self.list_of_points							)							):
																	# basis function for each i
																	output_values.append(
																	    comb(self.degree    ,							a__							) * ((1 - t) ** (self.degree - i)) * (t**i)							)
										# the basis must sum up to 1 for it to produce a valid Bezier curve.
										assert round(sum(a__							)    ,							5							) == 1
										return output_values




			def        lowerCAmelCase__    ( self    ,							a__							)    ->    tuple[float, float]:

										'''simple docstring'''




										assert 0 <= t <= 1, "Time t must be between 0 and 1."

										snake_case_      =				self.basis_function(a__							)
										snake_case_      =				0.0
										snake_case_      =				0.0
										for i in range(len(self.list_of_points							)							):
																	# For all points, sum up the product of i-th basis function and i-th point.
																	x += basis_function[i] * self.list_of_points[i][0]
																	y += basis_function[i] * self.list_of_points[i][1]
										return (x, y)




			def        lowerCAmelCase__    ( self    ,							a__ = 0.0_1							)    ->    Any:

										'''simple docstring'''




										from matplotlib import pyplot as plt  # type: ignore

										snake_case_      =				[]  # x coordinates of points to plot
										snake_case_      =				[]  # y coordinates of points to plot

										snake_case_      =				0.0
										while t <= 1:
																	snake_case_      =				self.bezier_curve_function(a__							)
																	to_plot_x.append(value[0]							)
																	to_plot_y.append(value[1]							)
																	t += step_size

										snake_case_      =				[i[0] for i in self.list_of_points]
										snake_case_      =				[i[1] for i in self.list_of_points]

										plt.plot(
										    a__    ,							a__    ,							color="blue"    ,							label="Curve of Degree " + str(self.degree							)    ,							)
										plt.scatter(a__    ,							a__    ,							color="red"    ,							label="Control Points"							)
										plt.legend()
										plt.show()


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		BezierCurve([(1, 2), (3, 5)]).plot_curve()  # degree 1
		BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve()  # degree 2
		BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve()  # degree 3




