#!/usr/bin/env python

import tensorflow as tf
import gym

# our lib imports here!
import sys
sys.path.append('..')
import tianshou.core.losses as losses
from tianshou.data.replay_buffer.utils import get_replay_buffer
import tianshou.core.policy.dqn as policy


# THIS EXAMPLE IS NOT FINISHED YET!!!


def policy_net(observation, action_dim):
    """
    Constructs the policy network. NOT NEEDED IN THE LIBRARY! this is pure tf

    :param observation: Placeholder for the observation. A tensor of shape (bs, x, y, channels)
    :param action_dim: int. The number of actions.
    :param scope: str. Specifying the scope of the variables.
    """
    net = tf.layers.conv2d(observation, 16, 8, 4, 'valid', activation=tf.nn.relu)
    net = tf.layers.conv2d(net, 32, 4, 2, 'valid', activation=tf.nn.relu)
    net = tf.layers.flatten(net)
    net = tf.layers.dense(net, 256, activation=tf.nn.relu)

    q_values = tf.layers.dense(net, action_dim)

    return q_values


if __name__ == '__main__':
    env = gym.make('PongNoFrameskip-v4')
    observation_dim = env.observation_space.shape
    action_dim = env.action_space.n

    # 1. build network with pure tf
    # TODO:
    # pass the observation variable to the replay buffer or find a more reasonable way to help replay buffer
    # access this observation variable.
    observation = tf.placeholder(tf.float32, shape=(None,) + observation_dim, name="dqn_observation") # network input
    action = tf.placeholder(dtype=tf.int32, shape=(None,)) # batch of integer actions


    with tf.variable_scope('q_net'):
        q_values = policy_net(observation, action_dim)
    with tf.variable_scope('target_net'):
        q_values_target = policy_net(observation, action_dim)

    # 2. build losses, optimizers
    q_net = policy.DQNRefactor(q_values, observation_placeholder=observation, action_placeholder=action) # YongRen: policy.DQN
    target_net = policy.DQNRefactor(q_values_target, observation_placeholder=observation, action_placeholder=action)

    target = tf.placeholder(dtype=tf.float32, shape=[None]) # target value for DQN

    dqn_loss = losses.dqn_loss(action, target, q_net) # TongzhengRen
    global_step = tf.Variable(0, name='global_step', trainable=False)
    train_var_list = tf.get_collection(
        tf.GraphKeys.TRAINABLE_VARIABLES)  # TODO: better management of TRAINABLE_VARIABLES
    total_loss = dqn_loss
    optimizer = tf.train.AdamOptimizer(1e-3)
    train_op = optimizer.minimize(total_loss, var_list=train_var_list, global_step=tf.train.get_global_step())
    # 3. define data collection
    # configuration should be given as parameters, different replay buffer has different parameters.
    replay_memory = get_replay_buffer('rank_based', env, q_values, q_net, target_net,
                                      {'size': 1000, 'batch_size': 64, 'learn_start': 20})
                                                             # ShihongSong: Replay(env, q_net, advantage_estimation.qlearning_target(target_network)), use your ReplayMemory, interact as follows. Simplify your advantage_estimation.dqn to run before YongRen's DQN
    # maybe a dict to manage the elements to be collected

    # 4. start training
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())

        minibatch_count = 0
        collection_count = 0
        # need to first collect some then sample, collect_freq must be larger than batch_size
        collect_freq = 100
        while True: # until some stopping criterion met...
            # collect data
            for i in range(0, collect_freq):
                replay_memory.collect() # ShihongSong
                collection_count += 1
                print('Collected {} times.'.format(collection_count))

            # update network
            data = replay_memory.next_batch(10) # YouQiaoben, ShihongSong
            # TODO: auto managing of the placeholders? or add this to params of data.Batch
            sess.run(train_op, feed_dict={observation: data['observations'], action: data['actions'], target: data['target']})
            minibatch_count += 1
            print('Trained {} minibatches.'.format(minibatch_count))

            # TODO: assigning pi to pi_old is not implemented yet