import os
import torch
from dotenv import load_dotenv
from wordle_env.state import update_from_mask
from .net import GreedyNet
from .utils import v_wrap


def get_play_model_path():
    load_dotenv()
    model_name = os.getenv('RS_WORDLE_MODEL_NAME')
    model_checkpoint_dir = os.path.join('checkpoints', 'best_models')
    return os.path.join(model_checkpoint_dir, model_name)


def get_net(env, pretrained_model_path):
    n_s = env.observation_space.shape[0]
    n_a = env.action_space.n
    words_list = env.words
    word_width = len(env.words[0])
    net = GreedyNet(n_s, n_a, words_list, word_width)
    net.load_state_dict(torch.load(pretrained_model_path))
    return net


def get_initial_state(env):
    state = env.reset()
    return state


def suggest(
        env,
        words,
        states,
        pretrained_model_path
) -> str:
    """
    Given a list of words and masks, return the next suggested word

    :param agent:
    :param env:
    :param sequence: History of moves and outcomes until now
    :return:
    """
    env = env.unwrapped
    net = get_net(env, pretrained_model_path)
    state = get_initial_state(env)
    for word, mask in zip(words, states):
        word = word.upper()
        mask = list(map(int, mask))
        state = update_from_mask(state, word, mask)
    return env.words[net.choose_action(v_wrap(state[None, :]))]


def play(env, pretrained_model_path, goal_word = None):
    env = env.unwrapped
    net = get_net(env, pretrained_model_path)
    state = get_initial_state(env)
    if goal_word:
        env.set_goal_word(goal_word)
    outcomes = []
    win = False
    for i in range(env.max_turns):
        action = net.choose_action(v_wrap(state[None, :]))
        state, reward, done, _ = env.step(action)
        outcomes.append(env.words[action])
        if done:
            if reward > 0:
                win = True
            break
    return win, outcomes