import numpy as np
from collections import deque
import gym
from gym import spaces
from skimage import color, transform

class NoopResetEnv(gym.Wrapper):
    def __init__(self, env=None, noop_max=30):
        """Sample initial states by taking random number of no-ops on reset.
        No-op is assumed to be action 0.
        """
        super(NoopResetEnv, self).__init__(env)
        self.noop_max = noop_max
        assert env.unwrapped.get_action_meanings()[0] == 'NOOP'

    def reset(self):
        """ Do no-op action for a number of steps in [1, noop_max]."""
        self.env.reset()
        noops = np.random.randint(1, self.noop_max + 1)
        for _ in range(noops):
            obs, _, _, _ = self.env.step(0)
        return obs

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

class FireResetEnv(gym.Wrapper):
    def __init__(self, env=None):
        """Take action on reset for environments that are fixed until firing."""
        super(FireResetEnv, self).__init__(env)
        assert env.unwrapped.get_action_meanings()[1] == 'FIRE'
        assert len(env.unwrapped.get_action_meanings()) >= 3

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

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

class EpisodicLifeEnv(gym.Wrapper):
    def __init__(self, env=None):
        """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.
        """
        super(EpisodicLifeEnv, self).__init__(env)
        self.lives = 0
        self.was_real_done  = True
        self.was_realreset = False

    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 somtimes we stay in lives == 0 condtion for a few frames
            # so its 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):
        """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()
            self.was_realreset = True
        else:
            # no-op step to advance from terminal/lost life state
            obs, _, _, _ = self.env.step(0)
            self.was_realreset = False
        self.lives = self.env.unwrapped.ale.lives()
        return obs

class MaxAndSkipEnv(gym.Wrapper):
    def __init__(self, env=None, skip=4):
        """Return only every `skip`-th frame"""
        super(MaxAndSkipEnv, self).__init__(env)
        # most recent raw observations (for max pooling across time steps)
        self._obs_buffer = deque(maxlen=2)
        self._skip       = skip

    def step(self, action):
        total_reward = 0.0
        done = None
        for _ in range(self._skip):
            obs, reward, done, info = self.env.step(action)
            self._obs_buffer.append(obs)
            total_reward += reward
            if done:
                break

        max_frame = np.max(np.stack(self._obs_buffer), axis=0)

        return max_frame, total_reward, done, info

    def reset(self):
        """Clear past frame buffer and init. to first obs. from inner env."""
        self._obs_buffer.clear()
        obs = self.env.reset()
        self._obs_buffer.append(obs)
        return obs

class SkipEnv(gym.Wrapper):
    def __init__(self, env=None, skip=4):
        """Return only every `skip`-th frame"""
        super(SkipEnv, self).__init__(env)
        self._skip       = skip

    def step(self, action):
        total_reward = 0.0
        done = None
        for _ in range(self._skip):
            obs, reward, done, info = self.env.step(action)
            total_reward += reward
            if done:
                break

        return obs, total_reward, done, info

    def reset(self):
        obs = self.env.reset()
        return obs

class DatasetEnv(gym.Wrapper):
    def __init__(self, env=None):
        super(DatasetEnv, self).__init__(env)
        self.saved_obs = []
        self.saved_actions = []

    def get_saved(self):
        return self.saved_obs, self.saved_actions

    def clear_saved(self):
        self.saved_obs = []
        self.saved_actions = []

    def step(self, action):
        obs, reward, done, info = self.env.step(action)
        self.saved_actions.append(action)
        self.saved_obs.append(obs)
        return obs, reward, done, info

    def reset(self):
        obs = self.env.reset()
        self.saved_obs.append(obs)
        return obs

class ProcessFrame(gym.Wrapper):
    def __init__(self, env=None, frame_size=84):
        super(ProcessFrame, self).__init__(env)
        self.frame_size = frame_size
        self.observation_space = spaces.Box(low=0, high=255, shape=(1, frame_size, frame_size), dtype=np.uint8)

    def process(self, obs):
        obs = color.rgb2gray(obs)
        obs = transform.resize(obs, (self.frame_size, self.frame_size), mode='constant')
        obs = (255 * obs).astype(np.uint8).reshape((1, ) + obs.shape)
        return obs

    def step(self, action):
        obs, reward, done, info = self.env.step(action)
        return self.process(obs), reward, done, info

    def reset(self):
        return self.process(self.env.reset())

class NormalizeFrame(gym.Wrapper):
    def __init__(self, env=None):
        super(NormalizeFrame, self).__init__(env)

    def _normalize(self, obs):
        return np.asarray(obs, dtype=np.float32) / 255.0

    def step(self, action):
        obs, reward, done, info = self.env.step(action)
        return self._normalize(obs), reward, done, info

    def reset(self):
        return self._normalize(self.env.reset())

class StackFrame(gym.Wrapper):
    def __init__(self, env=None, history_length=1):
        super(StackFrame, self).__init__(env)
        self.history_length = history_length
        self.buffer = None

    def reset(self):
        state = self.env.reset()
        self.buffer = [state] * self.history_length
        return np.asarray(np.vstack(self.buffer))

    def step(self, action):
        state, reward, done, info = self.env.step(action)
        self.buffer.pop(0)
        self.buffer.append(state)
        return np.asarray(np.vstack(self.buffer)), reward, done, info