"""Wrapper for creating the swimmer environment."""

import math
import os

import mujoco_py
import numpy as np
from gym import utils
from gym.envs.mujoco import mujoco_env

from d4rl_alt import offline_env
from d4rl_alt.locomotion import goal_reaching_env, maze_env, mujoco_goal_env

GYM_ASSETS_DIR = os.path.join(os.path.dirname(mujoco_env.__file__), "assets")


class SwimmerEnv(mujoco_env.MujocoEnv, utils.EzPickle):
    """Basic swimmer locomotion environment."""

    FILE = os.path.join(GYM_ASSETS_DIR, "swimmer.xml")

    def __init__(self, file_path=None, expose_all_qpos=False, non_zero_reset=False):
        if file_path is None:
            file_path = self.FILE

        self._expose_all_qpos = expose_all_qpos

        mujoco_env.MujocoEnv.__init__(self, file_path, 5)
        utils.EzPickle.__init__(self)

    @property
    def physics(self):
        # Check mujoco version is greater than version 1.50 to call correct physics
        # model containing PyMjData object for getting and setting position/velocity.
        # Check https://github.com/openai/mujoco-py/issues/80 for updates to api.
        if mujoco_py.get_version() >= "1.50":
            return self.sim
        else:
            return self.model

    def _step(self, a):
        return self.step(a)

    def step(self, a):
        ctrl_cost_coeff = 0.0001
        xposbefore = self.sim.data.qpos[0]
        self.do_simulation(a, self.frame_skip)
        xposafter = self.sim.data.qpos[0]
        reward_fwd = (xposafter - xposbefore) / self.dt
        reward_ctrl = -ctrl_cost_coeff * np.square(a).sum()
        reward = reward_fwd + reward_ctrl
        ob = self._get_obs()
        return ob, reward, False, dict(reward_fwd=reward_fwd, reward_ctrl=reward_ctrl)

    def _get_obs(self):
        if self._expose_all_qpos:
            obs = np.concatenate(
                [
                    self.physics.data.qpos.flat[:5],  # Ensures only swimmer obs.
                    self.physics.data.qvel.flat[:5],
                ]
            )
        else:
            obs = np.concatenate(
                [
                    self.physics.data.qpos.flat[2:5],
                    self.physics.data.qvel.flat[:5],
                ]
            )

        return obs

    def reset_model(self):
        qpos = self.init_qpos + self.np_random.uniform(
            size=self.model.nq, low=-0.1, high=0.1
        )
        qvel = self.init_qvel + self.np_random.randn(self.model.nv) * 0.1

        # Set everything other than swimmer to original position and 0 velocity.
        qpos[5:] = self.init_qpos[5:]
        qvel[5:] = 0.0
        self.set_state(qpos, qvel)
        return self._get_obs()

    def get_xy(self):
        return self.physics.data.qpos[:2]

    def set_xy(self, xy):
        qpos = np.copy(self.physics.data.qpos)
        qpos[0] = xy[0]
        qpos[1] = xy[1]
        qvel = self.physics.data.qvel
        self.set_state(qpos, qvel)


class GoalReachingSwimmerEnv(goal_reaching_env.GoalReachingEnv, SwimmerEnv):
    """Swimmer locomotion rewarded for goal-reaching."""

    BASE_ENV = SwimmerEnv

    def __init__(
        self,
        goal_sampler=goal_reaching_env.disk_goal_sampler,
        file_path=None,
        expose_all_qpos=False,
        non_zero_reset=False,
        eval=False,
        reward_type="dense",
        **kwargs
    ):
        goal_reaching_env.GoalReachingEnv.__init__(
            self, goal_sampler, eval=eval, reward_type=reward_type
        )
        SwimmerEnv.__init__(
            self,
            file_path=file_path,
            expose_all_qpos=expose_all_qpos,
            non_zero_reset=non_zero_reset,
        )


class SwimmerMazeEnv(maze_env.MazeEnv, GoalReachingSwimmerEnv, offline_env.OfflineEnv):
    """Swimmer navigating a maze."""

    LOCOMOTION_ENV = GoalReachingSwimmerEnv

    def __init__(
        self,
        goal_sampler=None,
        expose_all_qpos=True,
        reward_type="dense",
        *args,
        **kwargs
    ):
        if goal_sampler is None:
            goal_sampler = lambda np_rand: maze_env.MazeEnv.goal_sampler(self, np_rand)
        maze_env.MazeEnv.__init__(
            self,
            *args,
            manual_collision=False,
            goal_sampler=goal_sampler,
            expose_all_qpos=expose_all_qpos,
            reward_type=reward_type,
            **kwargs
        )
        offline_env.OfflineEnv.__init__(self, **kwargs)

    def set_target(self, target_location=None):
        return self.set_target_goal(target_location)
