



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					( A_      ):





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





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__				):
						return (x_in - self.means[key]) / self.stds[key]





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__				):
						return x_in * self.stds[key] + self.means[key]





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





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__							,    __magic_name__				):
						for key, val in cond.items():
									lowerCamelCase__ :      Dict								=		val.clone()
						return x_in





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__				):
						lowerCamelCase__ :      Optional[Any]								=		x.shape[0]
						lowerCamelCase__ :      List[str]								=		None
						for i in tqdm.tqdm(self.scheduler.timesteps				):
									# create batch of timesteps to pass into model
									lowerCamelCase__ :      Optional[Any]								=		torch.full((batch_size,)							,    __magic_name__							,    device=self.unet.device							,    dtype=torch.long				)
									for _ in range(__magic_name__				):
												with torch.enable_grad():
															x.requires_grad_()

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

															lowerCamelCase__ :      int								=		self.scheduler._get_variance(__magic_name__				)
															lowerCamelCase__ :      Tuple								=		torch.exp(0.5 * posterior_variance				)
															lowerCamelCase__ :      int								=		model_std * grad

												lowerCamelCase__ :      Dict								=		0
												lowerCamelCase__ :      Any								=		x.detach()
												lowerCamelCase__ :      Any								=		x + scale * grad
												lowerCamelCase__ :      Any								=		self.reset_xa(__magic_name__							,    __magic_name__							,    self.action_dim				)

									lowerCamelCase__ :      int								=		self.unet(x.permute(0							,    2							,    1				)							,    __magic_name__				).sample.permute(0							,    2							,    1				)

									# TODO: verify deprecation of this kwarg
									lowerCamelCase__ :      Optional[Any]								=		self.scheduler.step(__magic_name__							,    __magic_name__							,    __magic_name__							,    predict_epsilon=__magic_name__				)["""prev_sample"""]

									# apply conditions to the trajectory (set the initial state)
									lowerCamelCase__ :      Dict								=		self.reset_xa(__magic_name__							,    __magic_name__							,    self.action_dim				)
									lowerCamelCase__ :      Union[str, Any]								=		self.to_torch(__magic_name__				)
						return x, y





			def __call__(self							,    __magic_name__							,    __magic_name__=64							,    __magic_name__=32							,    __magic_name__=2							,    __magic_name__=0.1				):
						# normalize the observations and create  batch dimension
						lowerCamelCase__ :      int								=		self.normalize(__magic_name__							,    """observations"""				)
						lowerCamelCase__ :      int								=		obs[None].repeat(__magic_name__							,    axis=0				)

						lowerCamelCase__ :      List[str]								=		{0: self.to_torch(__magic_name__				)}
						lowerCamelCase__ :      List[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)
						lowerCamelCase__ :      List[Any]								=		randn_tensor(__magic_name__							,    device=self.unet.device				)
						lowerCamelCase__ :      Any								=		self.reset_xa(__magic_name__							,    __magic_name__							,    self.action_dim				)
						lowerCamelCase__ :      Any								=		self.to_torch(__magic_name__				)

						# run the diffusion process
						lowerCamelCase__							,lowerCamelCase__ :      List[str]								=		self.run_diffusion(__magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__				)

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

						# select the action with the highest value
						if y is not None:
									lowerCamelCase__ :      List[Any]								=		0
						else:
									# if we didn't run value guiding, select a random action
									lowerCamelCase__ :      List[str]								=		np.random.randint(0							,    __magic_name__				)

						lowerCamelCase__ :      List[str]								=		denorm_actions[selected_index, 0]
						return denorm_actions








# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

_lowercase     							=						TypeVar('''T''')
class 				__A					( Generic[T]      ):





			def __init__(self							,    __magic_name__ = True				):
						lowerCamelCase__ :      dict[T, list[T]]								=		{}  # dictionary of lists
						lowerCamelCase__ :      List[str]								=		directed





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__				):

						if not self.directed:  # For undirected graphs
									# if both source vertex and destination vertex are both present in the
									# adjacency list, add destination vertex to source vertex list of adjacent
									# vertices and add source vertex to destination vertex list of adjacent
									# vertices.
									if source_vertex in self.adj_list and destination_vertex in self.adj_list:
												self.adj_list[source_vertex].append(__magic_name__				)
												self.adj_list[destination_vertex].append(__magic_name__				)
									# if only source vertex is present in adjacency list, add destination vertex
									# to source vertex list of adjacent vertices, then create a new vertex with
									# destination vertex as key and assign a list containing the source vertex
									# as it's first adjacent vertex.
									elif source_vertex in self.adj_list:
												self.adj_list[source_vertex].append(__magic_name__				)
												lowerCamelCase__ :      int								=		[source_vertex]
									# if only destination vertex is present in adjacency list, add source vertex
									# to destination vertex list of adjacent vertices, then create a new vertex
									# with source vertex as key and assign a list containing the source vertex
									# as it's first adjacent vertex.
									elif destination_vertex in self.adj_list:
												self.adj_list[destination_vertex].append(__magic_name__				)
												lowerCamelCase__ :      Dict								=		[destination_vertex]
									# if both source vertex and destination vertex are not present in adjacency
									# list, create a new vertex with source vertex as key and assign a list
									# containing the destination vertex as it's first adjacent vertex also
									# create a new vertex with destination vertex as key and assign a list
									# containing the source vertex as it's first adjacent vertex.
									else:
												lowerCamelCase__ :      Any								=		[destination_vertex]
												lowerCamelCase__ :      Optional[Any]								=		[source_vertex]
						else:  # For directed graphs
									# if both source vertex and destination vertex are present in adjacency
									# list, add destination vertex to source vertex list of adjacent vertices.
									if source_vertex in self.adj_list and destination_vertex in self.adj_list:
												self.adj_list[source_vertex].append(__magic_name__				)
									# if only source vertex is present in adjacency list, add destination
									# vertex to source vertex list of adjacent vertices and create a new vertex
									# with destination vertex as key, which has no adjacent vertex
									elif source_vertex in self.adj_list:
												self.adj_list[source_vertex].append(__magic_name__				)
												lowerCamelCase__ :      Optional[Any]								=		[]
									# if only destination vertex is present in adjacency list, create a new
									# vertex with source vertex as key and assign a list containing destination
									# vertex as first adjacent vertex
									elif destination_vertex in self.adj_list:
												lowerCamelCase__ :      Tuple								=		[destination_vertex]
									# if both source vertex and destination vertex are not present in adjacency
									# list, create a new vertex with source vertex as key and a list containing
									# destination vertex as it's first adjacent vertex. Then create a new vertex
									# with destination vertex as key, which has no adjacent vertex
									else:
												lowerCamelCase__ :      Optional[int]								=		[destination_vertex]
												lowerCamelCase__ :      List[Any]								=		[]

						return self





			def __repr__(self				):
						return pformat(self.adj_list				)



