from rllab.envs.base import Step
from rllab.misc.overrides import overrides
from .mujoco_env import MujocoEnv
import numpy as np
from rllab.core.serializable import Serializable
from rllab.misc import logger
from rllab.misc import autoargs


class SwimmerRandGoalEnv(MujocoEnv, Serializable):

    FILE = 'swimmer.xml'

    @autoargs.arg('ctrl_cost_coeff', type=float,
                  help='cost coefficient for controls')
    def __init__(
            self,
            ctrl_cost_coeff=1e-2,
            *args, **kwargs):
        self.ctrl_cost_coeff = ctrl_cost_coeff
        self._goal_vel = None
        super(SwimmerRandGoalEnv, self).__init__(*args, **kwargs)
        Serializable.quick_init(self, locals())

    def get_current_obs(self):
        return np.concatenate([
            self.model.data.qpos.flat,
            self.model.data.qvel.flat,
            self.get_body_com("torso").flat,
        ]).reshape(-1)

    @overrides
    def reset(self, init_state=None, reset_args=None, **kwargs):
        goal_vel = reset_args
        if goal_vel is not None:
            self._goal_vel = goal_vel
        elif self._goal is None:
            self._goal_vel = np.random.uniform(0.1, 0.2)
        self.reset_mujoco(init_state)
        self.model.forward()
        self.current_com = self.model.data.com_subtree[0]
        self.dcom = np.zeros_like(self.current_com)
        return self.get_current_obs()

    def step(self, action):
        self.forward_dynamics(action)
        next_obs = self.get_current_obs()
        lb, ub = self.action_bounds
        scaling = (ub - lb) * 0.5
        ctrl_cost = 0.5 * self.ctrl_cost_coeff * np.sum(
            np.square(action / scaling))
        forward_reward = -1.5*np.abs(self.get_body_comvel("torso")[0] - self._goal_vel)
        reward = forward_reward - ctrl_cost
        done = False
        return Step(next_obs, reward, done)

    @overrides
    def log_diagnostics(self, paths, prefix=''):
        progs = [
            path["observations"][-1][-3] - path["observations"][0][-3]
            for path in paths
        ]
        logger.record_tabular(prefix+'AverageForwardProgress', np.mean(progs))
        logger.record_tabular(prefix+'MaxForwardProgress', np.max(progs))
        logger.record_tabular(prefix+'MinForwardProgress', np.min(progs))
        logger.record_tabular(prefix+'StdForwardProgress', np.std(progs))
