#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
FinEval 数据准备：CSV -> JSON

来源：HuggingFace SUFE-AIFLM-Lab/FinEval（cc-by-nc-sa-4.0）
      对应 NAACL 2025 论文中的「金融学术知识」部分：4,661 道选择题 / 34 学科。
      另外三部分（金融行业知识 1434 / 金融安全知识 1640 / 金融代理 616）官方未开源。

口径（与 C-Eval 基线对齐，保证两套基准可比）：
  few-shot 取自 dev（每科恰好 5 题），评测集为 val；test 集官方不公开答案，不使用。

输出：data/fineval/json/<subject>/{dev,val}.json
      字段与 C-Eval 导出保持一致：id / question / A / B / C / D / answer
"""
import csv
import glob
import json
import os

ROOT = r"D:\ceval-baseline"
SRC = os.path.join(ROOT, "data", "fineval", "extracted")
OUT = os.path.join(ROOT, "data", "fineval", "json")

# 34 学科中文名（用于 prompt 头部「以下是中国关于<学科>的单项选择题」）
CN = {
    "accounting": "会计",
    "advanced_financial_accounting": "高级财务会计",
    "auditing": "审计",
    "banking_practitioner_qualification_certificate": "银行从业资格",
    "central_banking": "中央银行学",
    "certified_management_accountant": "管理会计师",
    "certified_practising_accountant": "注册会计师",
    "china_actuary": "中国精算师",
    "commercial_bank_finance": "商业银行金融",
    "corporate_finance": "公司金融",
    "corporate_strategy_and_risk_management": "公司战略与风险管理",
    "cost_accounting": "成本会计",
    "econometrics": "计量经济学",
    "economic_law": "经济法",
    "finance": "金融学",
    "financial_engineering": "金融工程",
    "financial_management": "财务管理",
    "financial_markets": "金融市场",
    "fund_qualification_certificate": "基金从业资格",
    "futures_practitioner_qualification_certificate": "期货从业资格",
    "insurance": "保险",
    "intermediate_financial_accounting": "中级财务会计",
    "international_economics": "国际经济学",
    "international_finance": "国际金融",
    "investments": "投资学",
    "macroeconomics": "宏观经济学",
    "management_accounting": "管理会计",
    "microeconomics": "微观经济学",
    "monetary_finance": "货币金融",
    "political_economy": "政治经济学",
    "public_finance": "财政学",
    "securities_practitioner_qualification_certificate": "证券从业资格",
    "statistics": "统计学",
    "tax_law": "税法",
}

# FinEval 把 34 科分为金融/经济/会计/金融类考证 4 大类
GROUP = {
    "finance": ["finance", "corporate_finance", "financial_engineering", "financial_management",
                "financial_markets", "investments", "international_finance", "monetary_finance",
                "commercial_bank_finance", "central_banking", "insurance"],
    "economy": ["microeconomics", "macroeconomics", "econometrics", "international_economics",
                "political_economy", "public_finance", "statistics", "economic_law"],
    "accounting": ["accounting", "advanced_financial_accounting", "intermediate_financial_accounting",
                   "auditing", "cost_accounting", "management_accounting"],
    "certificate": ["certified_practising_accountant", "certified_management_accountant",
                    "china_actuary", "banking_practitioner_qualification_certificate",
                    "securities_practitioner_qualification_certificate",
                    "fund_qualification_certificate",
                    "futures_practitioner_qualification_certificate",
                    "corporate_strategy_and_risk_management", "tax_law"],
}
SUBJ_GROUP = {s: g for g, ss in GROUP.items() for s in ss}


def load_split(split):
    out = {}
    for f in sorted(glob.glob(os.path.join(SRC, split, "*.csv"))):
        subj = os.path.basename(f)[: -len(f"_{split}.csv")]
        rows = []
        with open(f, encoding="utf-8-sig") as fh:
            for row in csv.DictReader(fh):
                if row.get("answer") not in list("ABCD"):
                    continue
                # 选项文本里可能残留 LaTeX 转义（如 3$\%$），统一清洗为普通字符
                clean = lambda s: (s or "").replace("$\\%$", "%").replace("\\%", "%").strip()
                rows.append({
                    "id": f"{subj}_{row['id']}",
                    "question": (row.get("question") or "").strip(),
                    "A": clean(row.get("A")),
                    "B": clean(row.get("B")),
                    "C": clean(row.get("C")),
                    "D": clean(row.get("D")),
                    "answer": row["answer"],
                })
        out[subj] = rows
    return out


def main():
    dev = load_split("dev")
    val = load_split("val")
    os.makedirs(OUT, exist_ok=True)

    tot_dev = tot_val = 0
    summary = []
    for subj in sorted(dev):
        if subj not in val:
            print("!! val 缺少学科:", subj)
            continue
        d, v = dev[subj], val[subj]
        os.makedirs(os.path.join(OUT, subj), exist_ok=True)
        with open(os.path.join(OUT, subj, "dev.json"), "w", encoding="utf-8") as f:
            json.dump(d, f, ensure_ascii=False, indent=1)
        with open(os.path.join(OUT, subj, "val.json"), "w", encoding="utf-8") as f:
            json.dump(v, f, ensure_ascii=False, indent=1)
        tot_dev += len(d)
        tot_val += len(v)
        summary.append({
            "subject": subj, "subject_cn": CN.get(subj, subj),
            "group": SUBJ_GROUP.get(subj, "?"),
            "n_dev": len(d), "n_val": len(v),
        })

    with open(os.path.join(ROOT, "data", "fineval", "inventory.json"), "w", encoding="utf-8") as f:
        json.dump({"n_subjects": len(summary), "n_dev": tot_dev, "n_val": tot_val,
                   "subjects": summary}, f, ensure_ascii=False, indent=2)

    print(f"学科数 {len(summary)} | dev {tot_dev} 题 | val {tot_val} 题")
    # 缺中文名的学科
    miss = [s["subject"] for s in summary if s["subject"] not in CN]
    if miss:
        print("!! 缺中文名:", miss)
    # 分组统计
    import collections
    c = collections.Counter(s["group"] for s in summary)
    print("按大类:", dict(c))
    print("每科 dev 题数分布:", sorted({s["n_dev"] for s in summary}))
    print("输出 ->", OUT)


if __name__ == "__main__":
    main()