import numpy as np
from gymnasium.envs.classic_control.pendulum import PendulumEnv
from gymnasium.utils import EzPickle

from ray.rllib.env.apis.task_settable_env import TaskSettableEnv


class PendulumMassEnv(PendulumEnv, EzPickle, TaskSettableEnv):
    """PendulumMassEnv varies the weight of the pendulum

    Tasks are defined to be weight uniformly sampled between [0.5,2]
    """

    def sample_tasks(self, n_tasks):
        # Sample new pendulum masses (random floats between 0.5 and 2).
        return np.random.uniform(low=0.5, high=2.0, size=(n_tasks,))

    def set_task(self, task):
        """
        Args:
            task: Task of the meta-learning environment (here: mass of
                the pendulum).
        """
        # self.m is the mass property of the pendulum.
        self.m = task

    def get_task(self):
        """
        Returns:
            float: The current mass of the pendulum (self.m in the PendulumEnv
                object).
        """
        return self.m
