import random
import time
from typing import List, Tuple

# Assuming a predefined Tree class
class Tree:
    """
    Mock Tree Class representing a RRT (Rapidly-Exploring Random Tree)
    """
    def __init__(self, start_point: Tuple[float, float]):
        pass

    def find_nearest_node(self, point: Tuple[float, float]) -> Tuple[float, float]:
        pass

    def add_node(self, new_node: Tuple[float, float], parent: Tuple[float, float]) -> None:
        pass

    def find_path_to_goal(self, goal: Tuple[float, float]) -> List[Tuple[float, float]]:
        pass


class PKERRTAgent:
    """
    Multi-goal Path Finding Agent using Probabilistic Kinematic Extend Rapidly 
    Exploring Random Tree (PKE-RRT) Algorithm to assess efficiency.
    """
    def __init__(self):
        self.tree = None  # RRT Tree

    def initialize_tree(self, start_point: Tuple[float, float]) -> None:
        """
        Initialize the RRT tree with the starting point.
        :param start_point: Start point coordinates in (x, y) format.
        """
        self.tree = Tree(start_point)

    def extend_tree(self, random_point: Tuple[float, float]) -> None:
        """
        Extend the RRT tree towards a randomly generated point.
        :param random_point: Random point coordinates in (x, y) format.
        """
        nearest_node = self.tree.find_nearest_node(random_point)
        self.tree.add_node(random_point, parent=nearest_node)

    def find_multi_goal_path(self, goal_points: List[Tuple[float, float]]) -> List[Tuple[float, float]]:
        """
        Find a path in the tree that reaches all goal points.
        :param goal_points: List of goal points coordinates in (x, y) format.
        :return: List of points constituting the path.
        """
        path = []
        for goal in goal_points:
            goal_path = self.tree.find_path_to_goal(goal)
            if goal_path:
                path.extend(goal_path)
        return path

    def assess_pkerrt_efficiency(self, start_point: Tuple[float, float], 
                                 goal_points: List[Tuple[float, float]]) -> float:
        """
        Assess the efficiency of PKE-RRT algorithm by running it and measuring time taken.
        :param start_point: Start point coordinates in (x, y) format.
        :param goal_points: List of goal points coordinates in (x, y) format.
        :return: Simulation run time.
        """
        start_time = time.time()

        self.initialize_tree(start_point)

        iteration = 0
        while goal_points and iteration < 10000:
            iteration += 1

            random_point = (random.uniform(-10, 10), random.uniform(-10, 10))
            self.extend_tree(random_point)

            for goal in goal_points[:]:
                if self.tree.find_path_to_goal(goal):
                    goal_points.remove(goal)

        end_time = time.time()
        return end_time - start_time


if __name__ == "__main__":
    pkerrt_agent = PKERRTAgent()
    start_point = (0, 0)  
    goal_points = [(5, 5), (10, 10), (-5, -5), (-10, -10)]
    efficiency = pkerrt_agent.assess_pkerrt_efficiency(start_point, goal_points)
    print(f"PKE-RRT Efficiency (in seconds): {efficiency}")
