#!/usr/bin/env python3
import pathlib
import toml
import time
import heurams.services.timer as timer

class Electron:
    """电子: 记忆分析元数据及算法"""
    algorithm = "SM-2"  # 暂时使用 SM-2 算法进行记忆拟合, 考虑 SM-15 替代

    def __init__(self, content: str, metadata: dict):
        self.content = content
        self.metadata = metadata
        if metadata == {}:
            # print("NULL")
            self._default_init()

    def _default_init(self):
        defaults = {
            'efactor': 2.5,  # 易度系数, 越大越简单, 最大为5
            'real_rept': 0,  # (实际)重复次数
            'rept': 0,  # (有效)重复次数
            'interval': 0,  # 最佳间隔
            'last_date': 0,  # 上一次复习的时间戳
            'next_date': 0,  # 将要复习的时间戳
            'is_activated': 0,  # 激活状态
            # *NOTE: 此处"时间戳"是以天为单位的整数, 即 UNIX 时间戳除以一天的秒数取整
            'last_modify': time.time()  # 最后修改时间戳(此处是UNIX时间戳)
        }
        self.metadata = defaults

    def activate(self):
        self.metadata['is_activated'] = 1
        self.metadata['last_modify'] = time.time()

    def modify(self, var: str, value):
        if var in self.metadata:
            self.metadata[var] = value
            self.metadata['last_modify'] = time.time()
        else:
            print(f"警告: '{var}' 非已知元数据字段")

    def revisor(self, quality: int = 5, is_new_activation: bool = False):
        """SM-2 算法迭代决策机制实现
        根据 quality(0 ~ 5) 进行参数迭代最佳间隔
        quality 由主程序评估

        Args:
            quality (int): 记忆保留率量化参数
        """
        print(f"REVISOR: {quality}, {is_new_activation}")
        if quality == -1:
            return -1

        self.metadata['efactor'] = self.metadata['efactor'] + (
            0.1 - (5 - quality) * (0.08 + (5 - quality) * 0.02)
        )
        self.metadata['efactor'] = max(1.3, self.metadata['efactor'])

        if quality < 3:
            # 若保留率低于 3，重置重复次数
            self.metadata['rept'] = 0
            self.metadata['interval'] = 0  # 设为0，以便下面重新计算 I(1)
        else:
            self.metadata['rept'] += 1

        self.metadata['real_rept'] += 1

        if is_new_activation:  # 初次激活
            self.metadata['rept'] = 0
            self.metadata['efactor'] = 2.5

        if self.metadata['rept'] == 0:  # 刚被重置或初次激活后复习
            self.metadata['interval'] = 1  # I(1)
        elif self.metadata['rept'] == 1:
            self.metadata['interval'] = 6  # I(2) 经验公式
        else:
            self.metadata['interval'] = round(
                self.metadata['interval'] * self.metadata['efactor']
            )

        self.metadata['last_date'] = timer.get_daystamp()
        self.metadata['next_date'] = timer.get_daystamp() + self.metadata['interval']
        self.metadata['last_modify'] = time.time()

    def __str__(self):
        return (
            f"记忆单元预览 \n"
            f"内容: '{self.content}' \n"
            f"易度系数: {self.metadata['efactor']:.2f} \n"
            f"已经重复的次数: {self.metadata['rept']} \n"
            f"下次间隔: {self.metadata['interval']} 天 \n"
            f"下次复习日期时间戳: {self.metadata['next_date']}"
        )

    def __eq__(self, other):
        if self.content == other.content:
            return True
        return False

    def __hash__(self):
        return hash(self.content)

    def __getitem__(self, key):
        if key == "content":
            return self.content
        if key in self.metadata:
            return self.metadata[key]
        else:
            raise KeyError(f"Key '{key}' not found in metadata.")

    def __setitem__(self, key, value):
        if key == "content":
            raise AttributeError("content 应为只读")
        self.metadata[key] = value
        self.metadata['last_modify'] = time.time()

    def __iter__(self):
        yield from self.metadata.keys()

    def __len__(self):
        return len(self.metadata)

    @staticmethod
    def placeholder():
        return Electron("电子对象样例内容", {})