import os
import sys
import gym
import matplotlib.pyplot as plt
import torch.multiprocessing as mp

from a3c.discrete_A3C import Net, Worker
from a3c.shared_adam import SharedAdam
from wordle_env.wordle import WordleEnvBase

os.environ["OMP_NUM_THREADS"] = "1"

if __name__ == "__main__":
    max_ep = int(sys.argv[1]) if len(sys.argv) > 1 else 100000
    env_id = sys.argv[2] if len(sys.argv) > 2 else 'WordleEnv100FullAction-v0'
    env = gym.make(env_id)
    n_s = env.observation_space.shape[0]
    n_a = env.action_space.n
    words_list = env.words
    word_width = len(env.words[0])
    gnet = Net(n_s, n_a, words_list, word_width)        # global network
    gnet.share_memory()         # share the global parameters in multiprocessing
    opt = SharedAdam(gnet.parameters(), lr=1e-4, betas=(0.92, 0.999))      # global optimizer
    global_ep, global_ep_r, res_queue, win_ep = mp.Value('i', 0), mp.Value('d', 0.), mp.Queue(), mp.Value('i', 0)

    # parallel training
    workers = [Worker(max_ep, gnet, opt, global_ep, global_ep_r, res_queue, i, env, n_s, n_a, words_list, word_width, win_ep) for i in range(mp.cpu_count())]
    [w.start() for w in workers]
    res = []                    # record episode reward to plot
    while True:
        r = res_queue.get()
        if r is not None:
            res.append(r)
        else:
            break
    [w.join() for w in workers]
    print("Jugadas:", global_ep.value)
    print("Ganadas:", win_ep.value)
    plt.plot(res)
    plt.ylabel('Moving average ep reward')
    plt.xlabel('Step')
    plt.show()