

from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

A_       : str         =			TypeVar('T')

def 						UpperCamelCase					(lowercase_: int					)     ->      int:
	return (position - 1) // 2

def 						UpperCamelCase					(lowercase_: int					)     ->      int:
	return (2 * position) + 1

def 						UpperCamelCase					(lowercase_: int					)     ->      int:
	return (2 * position) + 2



class     _a						(Generic[T]				):


					'''simple docstring'''






					def __init__( self			):
						A__							:    list[tuple[T, int]]        =		[]
						A__							:    dict[T, int]        =		{}
						A__							:    int        =		0






					def __len__( self			):
						return self.elements






					def __repr__( self			):
						return str(self.heap			)






					def   __A			( self			):
						# Check if the priority queue is empty
						return self.elements == 0






					def   __A			( self    ,       A__    ,       A__			):
						# Add an element with given priority to the queue
						self.heap.append((elem, weight)			)
						A__							:    Union[str, Any]        =		self.elements
						self.elements += 1
						self._bubble_up(A__			)






					def   __A			( self			):
						# Remove and return the element with lowest weight (highest priority)
						if self.elements > 1:
							self._swap_nodes(0    ,       self.elements - 1			)
						A__   ,   A__							:    Any        =		self.heap.pop()
						del self.position_map[elem]
						self.elements -= 1
						if self.elements > 0:
							A__   ,   A__							:    Tuple        =		self.heap[0]
							self._bubble_down(A__			)
						return elem






					def   __A			( self    ,       A__    ,       A__			):
						# Update the weight of the given key
						A__							:    Any        =		self.position_map[elem]
						A__							:    int        =		(elem, weight)
						if position > 0:
							A__							:    Union[str, Any]        =		get_parent_position(A__			)
							A__   ,   A__							:    Optional[Any]        =		self.heap[parent_position]
							if parent_weight > weight:
								self._bubble_up(A__			)
							else:
								self._bubble_down(A__			)
						else:
							self._bubble_down(A__			)






					def   __A			( self    ,       A__			):
						# Place a node at the proper position (upward movement) [to be used internally
						# only]
						A__							:    Tuple        =		self.position_map[elem]
						if curr_pos == 0:
							return None
						A__							:    Union[str, Any]        =		get_parent_position(A__			)
						A__   ,   A__							:    List[Any]        =		self.heap[curr_pos]
						A__   ,   A__							:    Optional[int]        =		self.heap[parent_position]
						if parent_weight > weight:
							self._swap_nodes(A__    ,       A__			)
							return self._bubble_up(A__			)
						return None






					def   __A			( self    ,       A__			):
						# Place a node at the proper position (downward movement) [to be used
						# internally only]
						A__							:    Optional[int]        =		self.position_map[elem]
						A__   ,   A__							:    int        =		self.heap[curr_pos]
						A__							:    List[str]        =		get_child_left_position(A__			)
						A__							:    Tuple        =		get_child_right_position(A__			)
						if child_left_position < self.elements and child_right_position < self.elements:
							A__   ,   A__							:    Any        =		self.heap[child_left_position]
							A__   ,   A__							:    str        =		self.heap[child_right_position]
							if child_right_weight < child_left_weight and child_right_weight < weight:
								self._swap_nodes(A__    ,       A__			)
								return self._bubble_down(A__			)
						if child_left_position < self.elements:
							A__   ,   A__							:    Optional[int]        =		self.heap[child_left_position]
							if child_left_weight < weight:
								self._swap_nodes(A__    ,       A__			)
								return self._bubble_down(A__			)
						else:
							return None
						if child_right_position < self.elements:
							A__   ,   A__							:    List[str]        =		self.heap[child_right_position]
							if child_right_weight < weight:
								self._swap_nodes(A__    ,       A__			)
								return self._bubble_down(A__			)
						return None






					def   __A			( self    ,       A__    ,       A__			):
						# Swap the nodes at the given positions
						A__							:    Optional[Any]        =		self.heap[nodea_pos][0]
						A__							:    List[Any]        =		self.heap[nodea_pos][0]
						A__   ,   A__							:    int        =		(
						    self.heap[nodea_pos],
						    self.heap[nodea_pos],
						)
						A__							:    Union[str, Any]        =		nodea_pos
						A__							:    Any        =		nodea_pos



class     _a						(Generic[T]				):


					'''simple docstring'''






					def __init__( self			):
						A__							:    dict[T, dict[T, int]]        =		{}
						A__							:    int        =		0






					def __repr__( self			):
						return str(self.connections			)






					def __len__( self			):
						return self.nodes






					def   __A			( self    ,       A__			):
						# Add a node in the graph if it is not in the graph
						if node not in self.connections:
							A__							:    Optional[Any]        =		{}
							self.nodes += 1






					def   __A			( self    ,       A__    ,       A__    ,       A__			):
						# Add an edge between 2 nodes in the graph
						self.add_node(A__			)
						self.add_node(A__			)
						A__							:    Tuple        =		weight
						A__							:    Dict        =		weight



def 						UpperCamelCase					(lowercase_: GraphUndirectedWeighted[T]    ,      )     ->      tuple[dict[T, int], dict[T, T | None]]:
	A__							:    dict[T, int]        =		{node: maxsize for node in graph.connections}
	A__							:    dict[T, T | None]        =		{node: None for node in graph.connections}

	A__							:    MinPriorityQueue[T]        =		MinPriorityQueue()
	for node, weight in dist.items():
		priority_queue.push(lowercase_    ,      lowercase_					)

	if priority_queue.is_empty():
		return dist, parent

	# initialization
	A__							:    List[str]        =		priority_queue.extract_min()
	A__							:    Union[str, Any]        =		0
	for neighbour in graph.connections[node]:
		if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
			A__							:    List[str]        =		dist[node] + graph.connections[node][neighbour]
			priority_queue.update_key(lowercase_    ,      dist[neighbour]					)
			A__							:    Any        =		node

    # running prim's algorithm
	while not priority_queue.is_empty():
		A__							:    Tuple        =		priority_queue.extract_min()
		for neighbour in graph.connections[node]:
			if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
				A__							:    Tuple        =		dist[node] + graph.connections[node][neighbour]
				priority_queue.update_key(lowercase_    ,      dist[neighbour]					)
				A__							:    int        =		node
	return dist, parent






import darl  # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline


A_       : List[str]         =			{
    'n_samples': 64,
    'horizon': 32,
    'num_inference_steps': 20,
    'n_guide_steps': 2,  # can set to 0 for faster sampling, does not use value network
    'scale_grad_by_std': True,
    'scale': 0.1,
    'eta': 0.0,
    't_grad_cutoff': 2,
    'device': 'cpu',
}


if __name__ == "__main__":
	A_       : Optional[int]         =			'hopper-medium-v2'
	A_       : List[Any]         =			gym.make(env_name)

	A_       : str         =			ValueGuidedRLPipeline.from_pretrained(
	    'bglick13/hopper-medium-v2-value-function-hor32',
	    env=env,
	)

	env.seed(0)
	A_       : List[Any]         =			env.reset()
	A_       : Optional[int]         =			0
	A_       : str         =			0
	A_       : Optional[Any]         =			1000
	A_       : Union[str, Any]         =			[obs.copy()]
	try:
		for t in tqdm.tqdm(range(T)):
			# call the policy
			A_       : Tuple         =			pipeline(obs, planning_horizon=32)

			# execute action in environment
			A_       ,			A_       ,			A_       ,			A_       : Dict         =			env.step(denorm_actions)
			A_       : List[str]         =			env.get_normalized_score(total_reward)

			# update return
			total_reward += reward
			total_score += score
			print(
			    f'''Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:'''
			    f''' {total_score}'''
			)

			# save observations for rendering
			rollout.append(next_observation.copy())

			A_       : int         =			next_observation
	except KeyboardInterrupt:
		pass

	print(f'''Total reward: {total_reward}''')



