"""题库匹配 + 大模型兜底，与平台无关。

插件负责两头：把平台的题目转成统一格式、把答案填回平台；中间「这题选什么」由这里决定：
  1. 题库命中（题干归一化后：精确 -> 互相包含 -> 相似度）就直接用；
  2. 没命中的题交给设置里配置的大模型；没配大模型就留空，由插件决定怎么处理。

题目格式：{"no": 题号, "type": 题型名（单选/多选/判断），"title": 题干, "options": [{"key": "A", "text": "..."}]}
题库格式（插件目录里的 *.json，可放多个）：[{"q": 题干, "a": "B"}]，多选写 "ABC"。
返回：{题号: {"answer": ["A", "C"], "source": "bank" | "llm"}}，没答出的题不在结果里。
"""
import difflib
import json
import re
from pathlib import Path

from study_tool import runtime
from study_tool.config.settings import load_settings
from study_tool.llm import llm_chat

BATCH = 5  # 大模型每批问几题，单批失败只影响这一批
FUZZY = 0.9  # 相似度命中的下限
MIN_CONTAIN = 8  # 互相包含匹配时，两边都至少要有这么多字，免得短题干乱命中
SYSTEM = (
    "你是考试答题助手。根据题干和选项判断正确答案。\n"
    "单选题返回一个选项字母；多选题返回全部正确的选项字母连写（如 AC）；判断题返回 A（正确）或 B（错误）。\n"
    '只输出 JSON：{"answers":{"题号":"答案"}}，不要输出解释或多余文字。'
)


def norm(s):
    """去掉空白和中英文标点、统一大小写，题干才能跨页面格式比较。"""
    return re.sub(r"[\W_]+", "", str(s or "")).lower()


def letters(raw, options=None):
    """把 "A#C" / "AC" / ["A", "C"] 收敛成选项字母列表；有选项时只留合法字母，不是字母答案返回空。"""
    keys = [o.get("key") for o in options or [] if o.get("key")]
    if isinstance(raw, (list, tuple)):
        raw = "".join(map(str, raw))
    s = re.sub(r"[#,，、/\s]+", "", str(raw or "")).upper()
    if not re.fullmatch(r"[A-F]+", s):
        return []
    return [c for c in dict.fromkeys(s) if not keys or c in keys]


class Bank:
    def __init__(self, items):
        self.items = [(norm(i["q"]), i["a"]) for i in items if i.get("q") and i.get("a")]
        self.exact = dict(self.items)

    def find(self, title):
        """返回题库里的答案原文，没命中返回空串。"""
        t = norm(title)
        if not t:
            return ""
        if t in self.exact:
            return self.exact[t]
        for q, a in self.items:
            if min(len(q), len(t)) >= MIN_CONTAIN and (q in t or t in q):
                return a
        q, a = max(self.items, key=lambda x: difflib.SequenceMatcher(None, t, x[0]).ratio(), default=("", ""))
        return a if q and difflib.SequenceMatcher(None, t, q).ratio() >= FUZZY else ""


def load(folder):
    """读目录下全部 *.json 合并成一个题库；目录不存在就是空题库。"""
    items = []
    for f in sorted(Path(folder).glob("*.json")):
        items += json.loads(f.read_text(encoding="utf-8"))
    return Bank(items)


def ask(part, cfg):
    """问大模型一批题，返回 {题号: 原始答案}。"""
    lines = []
    for q in part:
        opts = "；".join("%s. %s" % (o.get("key"), o.get("text")) for o in q.get("options") or [])
        lines.append("第%s题（%s）%s\n选项：%s" % (q.get("no"), q.get("type") or "单选", q.get("title") or "", opts or "无"))
    content = llm_chat(
        [{"role": "system", "content": cfg.get("prompt") or SYSTEM}, {"role": "user", "content": "\n\n".join(lines)}],
        cfg,
        max_tokens=1600,
    )
    m = re.search(r"\{.*\}", content.replace("```json", "").replace("```", ""), re.S)
    data = json.loads(m.group(0)) if m else {}
    data = data.get("answers") if isinstance(data.get("answers"), dict) else data
    return {str(k): v for k, v in data.items()}


def llm_fill(key, miss, out):
    cfg = load_settings()["llm"]
    if not (cfg.get("api_key") and cfg.get("base_url")):
        runtime.log(key, "题库没命中 %d 题，且还没配置大模型（右上角「设置」），这些题留空" % len(miss), level="warn")
        return
    before = len(out)
    for i in range(0, len(miss), BATCH):
        part = miss[i : i + BATCH]
        try:
            got = ask(part, cfg)
        except Exception as e:
            runtime.log(key, "AI 答题第 %d 批失败：%s" % (i // BATCH + 1, str(e)[:160]), level="warn")
            continue
        for q in part:
            a = letters(got.get(str(q.get("no"))), q.get("options"))
            if a:
                out[str(q.get("no"))] = {"answer": a, "source": "llm"}
    runtime.log(key, "AI 补答 %d/%d 题（答案仅供参考，未核对）" % (len(out) - before, len(miss)))


def answer(key, questions, bank=None, use_llm=True):
    """题库优先，没命中的走大模型。日志统一走 runtime.log(key, ...)。"""
    out, miss = {}, []
    for q in questions:
        a = letters(bank.find(q.get("title")), q.get("options")) if bank else []
        if a:
            out[str(q.get("no"))] = {"answer": a, "source": "bank"}
        else:
            miss.append(q)
    runtime.log(key, "题库命中 %d/%d 题" % (len(out), len(questions)))
    if miss and use_llm:
        llm_fill(key, miss, out)
    return out