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

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

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

__all__ = ['ExpReplay']

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


class ReplayMemory(object):
    def __init__(self, max_size, state_shape, history_len, dtype='uint8'):
        """
        Args:
            state_shape (tuple[int]): shape (without history) of state
            dtype: numpy dtype for the state
        """
        self.max_size = int(max_size)
        self.state_shape = state_shape
        assert len(state_shape) in [1, 2, 3], state_shape
        self._output_shape = self.state_shape + (history_len + 1, )
        self.history_len = int(history_len)
        self.dtype = dtype

        all_state_shape = (self.max_size,) + state_shape
        logger.info("Creating experience replay buffer of {:.1f} GB ... "
                    "use a smaller buffer if you don't have enough CPU memory.".format(
                        np.prod(all_state_shape) / 1024.0**3))
        self.state = np.zeros(all_state_shape, dtype=self.dtype)
        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`` elements, each of shape ``self.state_shape`` """
        lst = list(self._hist)
        states = [np.zeros(self.state_shape, dtype=self.dtype)] * (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 self._output_shape, which is
            [H, W, (hist_len+1) * channel] if input is (H, W, channel)"""
        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):
        # state: Hist+1,H,W,C
        for k in range(self.history_len - 2, -1, -1):
            if isOver[k]:
                state = copy.deepcopy(state)
                state[:k + 1].fill(0)
                break
        # move the first dim (history) to the last
        state = np.moveaxis(state, 0, -1)
        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,
                 state_dtype='uint8'):
        """
        Args:
            predictor_io_names (tuple of list of str): input/output names to
                predict Q value from state.
            player (gym.Env): the player.
            state_shape (tuple):
            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.
        """
        assert len(state_shape) in [1, 2, 3], state_shape
        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=-1)  # state_shape + (Hist,)
            history = np.expand_dims(history, axis=0)

            # assume batched network
            q_values = self.predictor(history)[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 'ale.lives' in info:  # if running Atari, do something special for logging:
                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()
            else:
                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):
            # this function assumes comb_state is 3D
            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=self.state_dtype)
        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 __iter__(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 _ in E.get_data():
        import IPython as IP
        IP.embed(config=IP.terminal.ipapp.load_default_config())
