# -*- coding: utf-8 -*-
# File: expreplay.py
# Author: Yuxin Wu

import numpy as np
import copy
from collections import deque, namedtuple
import threading
from six.moves import queue, range

from tensorpack.dataflow import DataFlow
from tensorpack.utils import logger
from tensorpack.utils.utils import get_tqdm, get_rng
from tensorpack.utils.stats import StatCounter
from tensorpack.utils.concurrency import LoopThread, ShareSessionThread
from tensorpack.callbacks.base import Callback

__all__ = ['ExpReplay']

Experience = namedtuple('Experience',
                        ['state', 'action', 'reward', 'isOver'])


class ReplayMemory(object):
    def __init__(self, max_size, state_shape, history_len):
        self.max_size = int(max_size)
        self.state_shape = state_shape
        self.history_len = int(history_len)

        self.state = np.zeros((self.max_size,) + state_shape, dtype='uint8')
        self.action = np.zeros((self.max_size,), dtype='int32')
        self.reward = np.zeros((self.max_size,), dtype='float32')
        self.isOver = np.zeros((self.max_size,), dtype='bool')

        self._curr_size = 0
        self._curr_pos = 0
        self._hist = deque(maxlen=history_len - 1)

    def append(self, exp):
        """
        Args:
            exp (Experience):
        """
        if self._curr_size < self.max_size:
            self._assign(self._curr_pos, exp)
            self._curr_pos = (self._curr_pos + 1) % self.max_size
            self._curr_size += 1
        else:
            self._assign(self._curr_pos, exp)
            self._curr_pos = (self._curr_pos + 1) % self.max_size
        if exp.isOver:
            self._hist.clear()
        else:
            self._hist.append(exp)

    def recent_state(self):
        """ return a list of (hist_len-1,) + STATE_SIZE """
        lst = list(self._hist)
        states = [np.zeros(self.state_shape, dtype='uint8')] * (self._hist.maxlen - len(lst))
        states.extend([k.state for k in lst])
        return states

    def sample(self, idx):
        """ return a tuple of (s,r,a,o),
            where s is of shape STATE_SIZE + (hist_len+1,)"""
        idx = (self._curr_pos + idx) % self._curr_size
        k = self.history_len + 1
        if idx + k <= self._curr_size:
            state = self.state[idx: idx + k]
            reward = self.reward[idx: idx + k]
            action = self.action[idx: idx + k]
            isOver = self.isOver[idx: idx + k]
        else:
            end = idx + k - self._curr_size
            state = self._slice(self.state, idx, end)
            reward = self._slice(self.reward, idx, end)
            action = self._slice(self.action, idx, end)
            isOver = self._slice(self.isOver, idx, end)
        ret = self._pad_sample(state, reward, action, isOver)
        return ret

    # the next_state is a different episode if current_state.isOver==True
    def _pad_sample(self, state, reward, action, isOver):
        for k in range(self.history_len - 2, -1, -1):
            if isOver[k]:
                state = copy.deepcopy(state)
                state[:k + 1].fill(0)
                break
        state = state.transpose(1, 2, 0)
        return (state, reward[-2], action[-2], isOver[-2])

    def _slice(self, arr, start, end):
        s1 = arr[start:]
        s2 = arr[:end]
        return np.concatenate((s1, s2), axis=0)

    def __len__(self):
        return self._curr_size

    def _assign(self, pos, exp):
        self.state[pos] = exp.state
        self.reward[pos] = exp.reward
        self.action[pos] = exp.action
        self.isOver[pos] = exp.isOver


class ExpReplay(DataFlow, Callback):
    """
    Implement experience replay in the paper
    `Human-level control through deep reinforcement learning
    <http://www.nature.com/nature/journal/v518/n7540/full/nature14236.html>`_.

    This implementation provides the interface as a :class:`DataFlow`.
    This DataFlow is __not__ fork-safe (thus doesn't support multiprocess prefetching).

    This implementation assumes that state is
    batch-able, and the network takes batched inputs.
    """

    def __init__(self,
                 predictor_io_names,
                 player,
                 state_shape,
                 batch_size,
                 memory_size, init_memory_size,
                 init_exploration,
                 update_frequency, history_len):
        """
        Args:
            predictor_io_names (tuple of list of str): input/output names to
                predict Q value from state.
            player (RLEnvironment): the player.
            history_len (int): length of history frames to concat. Zero-filled
                initial frames.
            update_frequency (int): number of new transitions to add to memory
                after sampling a batch of transitions for training.
        """
        init_memory_size = int(init_memory_size)

        for k, v in locals().items():
            if k != 'self':
                setattr(self, k, v)
        self.exploration = init_exploration
        self.num_actions = player.action_space.n
        logger.info("Number of Legal actions: {}".format(self.num_actions))

        self.rng = get_rng(self)
        self._init_memory_flag = threading.Event()  # tell if memory has been initialized

        # a queue to receive notifications to populate memory
        self._populate_job_queue = queue.Queue(maxsize=5)

        self.mem = ReplayMemory(memory_size, state_shape, history_len)
        self._current_ob = self.player.reset()
        self._player_scores = StatCounter()
        self._current_game_score = StatCounter()

    def get_simulator_thread(self):
        # spawn a separate thread to run policy
        def populate_job_func():
            self._populate_job_queue.get()
            for _ in range(self.update_frequency):
                self._populate_exp()
        th = ShareSessionThread(LoopThread(populate_job_func, pausable=False))
        th.name = "SimulatorThread"
        return th

    def _init_memory(self):
        logger.info("Populating replay memory with epsilon={} ...".format(self.exploration))

        with get_tqdm(total=self.init_memory_size) as pbar:
            while len(self.mem) < self.init_memory_size:
                self._populate_exp()
                pbar.update()
        self._init_memory_flag.set()

    # quickly fill the memory for debug
    def _fake_init_memory(self):
        from copy import deepcopy
        with get_tqdm(total=self.init_memory_size) as pbar:
            while len(self.mem) < 5:
                self._populate_exp()
                pbar.update()
            while len(self.mem) < self.init_memory_size:
                self.mem.append(deepcopy(self.mem._hist[0]))
                pbar.update()
        self._init_memory_flag.set()

    def _populate_exp(self):
        """ populate a transition by epsilon-greedy"""
        old_s = self._current_ob
        if self.rng.rand() <= self.exploration or (len(self.mem) <= self.history_len):
            act = self.rng.choice(range(self.num_actions))
        else:
            # build a history state
            history = self.mem.recent_state()
            history.append(old_s)
            history = np.stack(history, axis=2)

            # assume batched network
            q_values = self.predictor(history[None, :, :, :])[0][0]  # this is the bottleneck
            act = np.argmax(q_values)
        self._current_ob, reward, isOver, info = self.player.step(act)
        self._current_game_score.feed(reward)
        if isOver:
            if info['ale.lives'] == 0:  # only record score when a whole game is over (not when an episode is over)
                self._player_scores.feed(self._current_game_score.sum)
                self._current_game_score.reset()
            self.player.reset()
        self.mem.append(Experience(old_s, act, reward, isOver))

    def _debug_sample(self, sample):
        import cv2

        def view_state(comb_state):
            state = comb_state[:, :, :-1]
            next_state = comb_state[:, :, 1:]
            r = np.concatenate([state[:, :, k] for k in range(self.history_len)], axis=1)
            r2 = np.concatenate([next_state[:, :, k] for k in range(self.history_len)], axis=1)
            r = np.concatenate([r, r2], axis=0)
            cv2.imshow("state", r)
            cv2.waitKey()
        print("Act: ", sample[2], " reward:", sample[1], " isOver: ", sample[3])
        if sample[1] or sample[3]:
            view_state(sample[0])

    def _process_batch(self, batch_exp):
        state = np.asarray([e[0] for e in batch_exp], dtype='uint8')
        reward = np.asarray([e[1] for e in batch_exp], dtype='float32')
        action = np.asarray([e[2] for e in batch_exp], dtype='int8')
        isOver = np.asarray([e[3] for e in batch_exp], dtype='bool')
        return [state, action, reward, isOver]

    # DataFlow method:
    def get_data(self):
        # wait for memory to be initialized
        self._init_memory_flag.wait()

        while True:
            idx = self.rng.randint(
                self._populate_job_queue.maxsize * self.update_frequency,
                len(self.mem) - self.history_len - 1,
                size=self.batch_size)
            batch_exp = [self.mem.sample(i) for i in idx]

            yield self._process_batch(batch_exp)
            self._populate_job_queue.put(1)

    # Callback methods:
    def _setup_graph(self):
        self.predictor = self.trainer.get_predictor(*self.predictor_io_names)

    def _before_train(self):
        self._init_memory()
        self._simulator_th = self.get_simulator_thread()
        self._simulator_th.start()

    def _trigger(self):
        v = self._player_scores
        try:
            mean, max = v.average, v.max
            self.trainer.monitors.put_scalar('expreplay/mean_score', mean)
            self.trainer.monitors.put_scalar('expreplay/max_score', max)
        except Exception:
            logger.exception("Cannot log training scores.")
        v.reset()


if __name__ == '__main__':
    from .atari import AtariPlayer
    import sys

    def predictor(x):
        np.array([1, 1, 1, 1])
    player = AtariPlayer(sys.argv[1], viz=0, frame_skip=10, height_range=(36, 204))
    E = ExpReplay(predictor,
                  player=player,
                  num_actions=player.get_action_space().num_actions(),
                  populate_size=1001,
                  history_len=4)
    E._init_memory()

    for k in E.get_data():
        import IPython as IP
        IP.embed(config=IP.terminal.ipapp.load_default_config())
        pass
