# coding=utf-8
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from abc import ABCMeta, abstractmethod
from third_party.baselines import logger

class AlreadySteppingError(Exception):
    """
    Raised when an asynchronous step is running while
    step_async() is called again.
    """
    def __init__(self):
        msg = 'already running an async step'
        Exception.__init__(self, msg)

class NotSteppingError(Exception):
    """
    Raised when an asynchronous step is not running but
    step_wait() is called.
    """
    def __init__(self):
        msg = 'not running an async step'
        Exception.__init__(self, msg)

class VecEnv(object):
    """
    An abstract asynchronous, vectorized environment.
    """
    __metaclass__ = ABCMeta

    def __init__(self, num_envs, observation_space, action_space):
        self.num_envs = num_envs
        self.observation_space = observation_space
        self.action_space = action_space

    @abstractmethod
    def reset(self):
        """
        Reset all the environments and return an array of
        observations, or a tuple of observation arrays.

        If step_async is still doing work, that work will
        be cancelled and step_wait() should not be called
        until step_async() is invoked again.
        """
        pass

    @abstractmethod
    def step_async(self, actions):
        """
        Tell all the environments to start taking a step
        with the given actions.
        Call step_wait() to get the results of the step.

        You should not call this if a step_async run is
        already pending.
        """
        pass

    @abstractmethod
    def step_wait(self):
        """
        Wait for the step taken with step_async().

        Returns (obs, rews, dones, infos):
         - obs: an array of observations, or a tuple of
                arrays of observations.
         - rews: an array of rewards
         - dones: an array of "episode done" booleans
         - infos: a sequence of info objects
        """
        pass

    @abstractmethod
    def close(self):
        """
        Clean up the environments' resources.
        """
        pass

    def step(self, actions):
        self.step_async(actions)
        return self.step_wait()

    def render(self, mode='human'):
        logger.warn('Render not defined for %s'%self)

    @property
    def unwrapped(self):
        if isinstance(self, VecEnvWrapper):
            return self.venv.unwrapped
        else:
            return self

class VecEnvWrapper(VecEnv):
    def __init__(self, venv, observation_space=None, action_space=None):
        self.venv = venv
        VecEnv.__init__(self,
            num_envs=venv.num_envs,
            observation_space=observation_space or venv.observation_space,
            action_space=action_space or venv.action_space)

    def step_async(self, actions):
        self.venv.step_async(actions)

    @abstractmethod
    def reset(self):
        pass

    @abstractmethod
    def step_wait(self):
        pass

    def close(self):
        return self.venv.close()

    def render(self):
        self.venv.render()

class CloudpickleWrapper(object):
    """
    Uses cloudpickle to serialize contents (otherwise multiprocessing tries to use pickle)
    """
    def __init__(self, x):
        self.x = x
    def __getstate__(self):
        import cloudpickle
        return cloudpickle.dumps(self.x)
    def __setstate__(self, ob):
        import pickle
        self.x = pickle.loads(ob)
