import json
from datetime import datetime
import copy


def evaluate_policy(env, agent, turns=3):
    """
    评估策略
    Args:
        env: 环境对象
        agent: 智能体对象
        turns: 评估轮数
    Returns:
        int: 平均得分
    """
    total_scores = 0

    # for j in range(turns):
    s = env.reset()
    done = False
    eval_info = {'action_series': [],
                 #  'state_series': [],
                 'reward_series': []}
    info_lt = []

    while not done:
        a = agent.select_action(s, deterministic=True)
        s_next, r, dw, tr, info = env.step(a)
        done = (dw or tr)

        eval_info['action_series'].append(a)
        eval_info['reward_series'].append(r)
        info_lt.append(copy.deepcopy(info))

        total_scores += r
        s = s_next

    print(eval_info)
    save_best_solution(info_lt)

    return int(total_scores/turns)


def save_best_solution(info_lt):
    # 找出这一轮中最优的解
    best_info = min(info_lt, key=lambda x: x['best_time'])

    # 读取已有的最优解
    try:
        with open('solutions/dqn_params_100_100_6.json', 'r') as f:
            saved_solution = json.load(f)
            saved_time = saved_solution['best_time']
    except FileNotFoundError:
        saved_time = float('inf')

    # 如果新的解更好，则更新json文件
    if best_info['best_time'] < saved_time:
        best_solution = {
            'best_time': best_info['best_time'],
            'row_cuts': best_info['row_cuts'],
            'col_cuts': best_info['col_cuts'],
            'best_path': best_info['best_path'],
            'timestamp': datetime.now().strftime('%Y-%m-%d %H:%M:%S')
        }

        with open('solutions/dqn_params_100_100_6.json', 'w') as f:
            json.dump(best_solution, f, indent=4)

        print(f"发现新的最优解！时间: {best_info['best_time']}")


def compare_lists(list1, list2):
    return len(list1) == len(list2) and all(a == b for a, b in zip(list1, list2))

# You can just ignore this funciton. Is not related to the RL.


def str2bool(v):
    '''transfer str to bool for argparse'''
    if isinstance(v, bool):
        return v
    if v.lower() in ('yes', 'True', 'true', 'TRUE', 't', 'y', '1'):
        return True
    elif v.lower() in ('no', 'False', 'false', 'FALSE', 'f', 'n', '0'):
        return False
    else:
        print('Wrong Input.')
        raise