import gym
import numpy as np
from mp_lib import det_promp

from alr_envs.utils.mps.mp_environments import MPEnv
from alr_envs.utils.mps.mp_wrapper import MPWrapper


class DetPMPWrapper(MPWrapper):
    def __init__(self, env: MPEnv, num_dof: int, num_basis: int, width: int, duration: int = 1, dt: float = 0.01,
                 post_traj_time: float = 0., policy_type: str = None, weights_scale: float = 1.,
                 zero_start: bool = False, zero_goal: bool = False, **mp_kwargs):
        self.duration = duration  # seconds

        super().__init__(env, num_dof, dt, duration, post_traj_time, policy_type, weights_scale, num_basis=num_basis,
                         width=width, zero_start=zero_start, zero_goal=zero_goal, **mp_kwargs)

        self.dt = dt

        action_bounds = np.inf * np.ones((self.mp.n_basis * self.mp.n_dof))
        self.action_space = gym.spaces.Box(low=-action_bounds, high=action_bounds, dtype=np.float32)


    def initialize_mp(self, num_dof: int, duration: int, dt: float, num_basis: int = 5, width: float = None,
                      zero_start: bool = False, zero_goal: bool = False):
        pmp = det_promp.DeterministicProMP(n_basis=num_basis, n_dof=num_dof, width=width, off=0.01,
                                           zero_start=zero_start, zero_goal=zero_goal)

        weights = np.zeros(shape=(num_basis, num_dof))
        pmp.set_weights(duration, weights)

        return pmp

    def mp_rollout(self, action):
        params = np.reshape(action, (self.mp.n_basis, self.mp.n_dof)) * self.weights_scale
        self.mp.set_weights(self.duration, params)
        _, des_pos, des_vel, _ = self.mp.compute_trajectory(1 / self.dt, 1.)
        if self.mp.zero_start:
            des_pos += self.start_pos[None, :]

        return des_pos, des_vel