# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.

# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

# This code was taken from 
# https://github.com/openai/baselines/blob/master/baselines/common/atari_wrappers.py
# and modified.

from collections import deque, defaultdict

import numpy as np

import gym
from gym import spaces
import cv2
cv2.ocl.setUseOpenCL(False)


class NoNegativeRewardEnv(gym.RewardWrapper):
    """Clip reward in negative direction."""
    def __init__(self, env=None, neg_clip=0.0):
        super(NoNegativeRewardEnv, self).__init__(env)
        self.neg_clip = neg_clip

    def _reward(self, reward):
        new_reward = self.neg_clip if reward < self.neg_clip else reward
        return new_reward


class NoopResetEnv(gym.Wrapper):

    def __init__(self, env, noop_max=30):
        """Sample initial states by taking random number of no-ops on reset.
        No-op is assumed to be action 0.
        """
        gym.Wrapper.__init__(self, env)
        self.noop_max = noop_max
        self.override_num_noops = None
        self.noop_action = 0

    def reset(self, **kwargs):
        """ Do no-op action for a number of steps in [1, noop_max]."""
        self.env.reset(**kwargs)
        if self.override_num_noops is not None:
            noops = self.override_num_noops
        else:
            noops = self.unwrapped.np_random.randint(1, self.noop_max + 1)  
        assert noops > 0
        obs = None
        for _ in range(noops):
            obs, _, done, _ = self.env.step(self.noop_action)
            if done:
                obs = self.env.reset(**kwargs)
        return obs

    def step(self, ac):
        return self.env.step(ac)


class FireResetEnv(gym.Wrapper):

    def __init__(self, env):
        """Take action on reset for environments that are fixed until firing."""
        gym.Wrapper.__init__(self, env)

    def reset(self, **kwargs):
        self.env.reset(**kwargs)
        obs, _, done, _ = self.env.step(1)
        if done:
            self.env.reset(**kwargs)
        obs, _, done, _ = self.env.step(2)
        if done:
            self.env.reset(**kwargs)
        return obs

    def step(self, ac):
        return self.env.step(ac)


class EpisodicLifeEnv(gym.Wrapper):

    def __init__(self, env):
        """Make end-of-life == end-of-episode, but only reset on true game over.
        Done by DeepMind for the DQN and co. since it helps value estimation.
        """
        gym.Wrapper.__init__(self, env)
        self.lives = 0
        self.was_real_done = True

    def step(self, action):
        obs, reward, done, info = self.env.step(action)
        self.was_real_done = done
        # check current lives, make loss of life terminal,
        # then update lives to handle bonus lives
        lives = self.env.unwrapped.ale.lives()
        if lives < self.lives and lives > 0:
            # for Qbert sometimes we stay in lives == 0 condition for a few frames
            # so it's important to keep lives > 0, so that we only reset once
            # the environment advertises done.
            done = True
        self.lives = lives
        return obs, reward, done, info

    def reset(self, **kwargs):
        """Reset only when lives are exhausted.
        This way all states are still reachable even though lives are episodic,
        and the learner need not know about any of this behind-the-scenes.
        """
        if self.was_real_done:
            obs = self.env.reset(**kwargs)
        else:
            # no-op step to advance from terminal/lost life state
            obs, _, _, _ = self.env.step(0)
        self.lives = self.env.unwrapped.ale.lives()
        return obs


class MaxAndSkipEnv(gym.Wrapper):

    def __init__(self, env, skip=4):
        """Return only every `skip`-th frame"""
        gym.Wrapper.__init__(self, env)
        # most recent raw observations (for max pooling across time steps)
        self._obs_buffer = np.zeros(
            (2,) + env.observation_space.shape, dtype=np.uint8)
        self._skip = skip

    def step(self, action):
        """Repeat action, sum reward, and max over last observations."""
        total_reward = 0.0
        done = None
        for i in range(self._skip):
            obs, reward, done, info = self.env.step(action)
            if i == self._skip - 2: self._obs_buffer[0] = obs
            if i == self._skip - 1: self._obs_buffer[1] = obs
            total_reward += reward
            if done:
                break
        # Note that the observation on the done=True frame
        # doesn't matter
        max_frame = self._obs_buffer.max(axis=0)

        return max_frame, total_reward, done, info

    def reset(self, **kwargs):
        return self.env.reset(**kwargs)


class ClipRewardEnv(gym.RewardWrapper):

    def __init__(self, env):
        gym.RewardWrapper.__init__(self, env)

    def reward(self, reward):
        """Bin reward to {+1, 0, -1} by its sign."""
        return np.sign(reward)


class WarpFrame(gym.ObservationWrapper):

    def __init__(self, env, width=84, height=84, grayscale=True):
        """Warp frames to 84x84 as done in the Nature paper and later work."""
        gym.ObservationWrapper.__init__(self, env)
        self.width = width
        self.height = height
        self.grayscale = grayscale
        if self.grayscale:
            self.observation_space = spaces.Box(
                low=0,
                high=255,
                shape=(self.height, self.width, 1),
                dtype=np.uint8)
        else:
            self.observation_space = spaces.Box(
                low=0,
                high=255,
                shape=(self.height, self.width, 3),
                dtype=np.uint8)

    def observation(self, frame):
        if self.grayscale:
            frame = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
        frame = cv2.resize(
            frame, (self.width, self.height), interpolation=cv2.INTER_AREA)
        if self.grayscale:
            frame = np.expand_dims(frame, -1)
        return frame


class FrameStack(gym.Wrapper):

    def __init__(self, env, k):
        """Stack k last frames.

        Returns lazy array, which is much more memory efficient.

        See Also
        --------
        baselines.common.atari_wrappers.LazyFrames
        """
        gym.Wrapper.__init__(self, env)
        self.k = k
        self.frames = deque([], maxlen=k)
        shp = env.observation_space.shape
        self.observation_space = spaces.Box(
            low=0,
            high=255,
            shape=(shp[:-1] + (shp[-1] * k,)),
            dtype=env.observation_space.dtype)

    def reset(self):
        ob = self.env.reset()
        for _ in range(self.k):
            self.frames.append(ob)
        return self._get_ob()

    def step(self, action):
        ob, reward, done, info = self.env.step(action)
        self.frames.append(ob)
        return self._get_ob(), reward, done, info

    def _get_ob(self):
        assert len(self.frames) == self.k
        return LazyFrames(list(self.frames))


class ScaledFloatFrame(gym.ObservationWrapper):

    def __init__(self, env):
        gym.ObservationWrapper.__init__(self, env)
        self.observation_space = gym.spaces.Box(
            low=0, high=1, shape=env.observation_space.shape, dtype=np.float32)

    def observation(self, observation):
        # careful! This undoes the memory optimization, use
        # with smaller replay buffers only.
        return np.array(observation).astype(np.float32) / 255.0


class LazyFrames(object):

    def __init__(self, frames):
        """This object ensures that common frames between the observations are only stored once.
        It exists purely to optimize memory usage which can be huge for DQN's 1M frames replay
        buffers.

        This object should only be converted to numpy array before being passed to the model.

        You'd not believe how complex the previous solution was."""
        self._frames = frames
        self._out = None

    def _force(self):
        if self._out is None:
            self._out = np.concatenate(self._frames, axis=-1)
            self._frames = None
        return self._out

    def __array__(self, dtype=None):
        out = self._force()
        if dtype is not None:
            out = out.astype(dtype)
        return out

    def __len__(self):
        return len(self._force())

    def __getitem__(self, i):
        return self._force()[i]


def make_atari(env_id, timelimit=True, noop=False):
    # XXX(john): remove timelimit argument after gym is upgraded to allow double wrapping
    env = gym.make(env_id)
    if not timelimit:
        env = env.env

    # NOTE: this is changed from original atari implementation
    # assert 'NoFrameskip' in env.spec.id
    if noop:
        env = NoopResetEnv(env, noop_max=30)
    env = MaxAndSkipEnv(env, skip=6)
    return env


# NOTE: this was changed so that episode_life is False by default
def wrap_deepmind(env,
                  episode_life=False,
                  clip_rewards=True,
                  frame_stack=False,
                  scale=True, 
                  fire=False):  # FYI scale=False in openai/baselines
    """Configure environment for DeepMind-style Atari.
    """
    if episode_life:
        env = EpisodicLifeEnv(env)
    if fire:
        if 'FIRE' in env.unwrapped.get_action_meanings():
            env = FireResetEnv(env)
    env = WarpFrame(env, width=42, height=42)
    if scale:
        env = ScaledFloatFrame(env)
    if clip_rewards:
        env = ClipRewardEnv(env)
    if frame_stack:
        env = FrameStack(env, 4)
    return env


# Taken from https://github.com/openai/baselines/blob/master/baselines/run.py
def get_env_type(env_id):
    # Re-parse the gym registry, since we could have new envs since last time.
    for env in gym.envs.registry.all():
        env_type = env._entry_point.split(':')[0].split('.')[-1]
        _game_envs[env_type].add(env.id)  # This is a set so add is idempotent

    if env_id in _game_envs.keys():
        env_type = env_id
        env_id = [g for g in _game_envs[env_type]][0]
    else:
        env_type = None
        for g, e in _game_envs.items():
            if env_id in e:
                env_type = g
                break
        assert env_type is not None, 'env_id {} is not recognized in env types'.format(
            env_id, _game_envs.keys())

    return env_type, env_id


class ImageToPyTorch(gym.ObservationWrapper):
    """
    Image shape to channels x weight x height
    """

    def __init__(self, env):
        super(ImageToPyTorch, self).__init__(env)
        old_shape = self.observation_space.shape
        self.observation_space = gym.spaces.Box(
            low=0.0,
            high=1.0,
            shape=(old_shape[-1], old_shape[0], old_shape[1]),
            dtype=np.uint8)

    def observation(self, observation):
        return np.swapaxes(observation, 2, 0)


def wrap_pytorch(env):
    return ImageToPyTorch(env)

