"""交割单解析（券商导出的成交流水 → 结构化买卖记录）。

支持三类导出物（按内容嗅探，不信任扩展名）：
  - CSV/制表符文本（utf-8-sig / gbk / gb18030 自动探测）
  - Excel .xlsx（openpyxl；很多券商导出的 .xls 实为 xlsx 或 HTML，先按魔数分流）
  - HTML 表格（.xls 常见真身：<table><tr><td>）

列名模糊匹配兼容通达信/恒生/同花顺系的命名差异；业务名称含「买入/卖出」
才入库，银行转账、配号、利息、红利等非交易行跳过并计数。
"""
from __future__ import annotations

import csv
import io
import re
from dataclasses import dataclass, field
from datetime import date, datetime

from fastapi import HTTPException


@dataclass
class ParsedTrade:
    trade_date: date
    ts_code: str
    code: str
    name: str
    direction: str  # buy | sell
    price: float | None
    qty: float
    amount: float | None
    fee: float
    raw: dict = field(default_factory=dict)


@dataclass
class ParseResult:
    trades: list[ParsedTrade] = field(default_factory=list)
    skipped_other: int = 0  # 非证券买卖行（转账/配号/利息等）
    skipped_bad: list[str] = field(default_factory=list)  # 解析失败样例（截断到前 5 条）
    header_row_index: int = -1
    columns: dict[str, str] = field(default_factory=dict)  # 逻辑列 -> 实际列名


# ---------- 列名别名（归一化后做「包含」匹配，先命中的优先） ----------
COLUMN_ALIASES: dict[str, list[str]] = {
    "date": ["成交日期", "交割日期", "交收日期", "交易日期", "过户日期", "发生日期", "清算日期", "日期"],
    "op": ["业务名称", "业务摘要", "操作", "业务类型", "交易类型", "交易类别", "摘要", "方向", "买卖标志"],
    "code": ["证券代码", "股票代码", "产品代码", "代码"],
    "name": ["证券名称", "股票名称", "产品名称", "名称"],
    "qty": ["成交数量", "发生数量", "委托数量", "成交股数", "数量"],
    "price": ["成交价格", "成交均价", "成交价", "均价", "价格"],
    "amount": ["成交金额", "成交清算金额", "清算金额", "发生金额", "资金发生数", "金额"],
    "fee": ["手续费", "佣金", "印花税", "过户费", "其他费", "杂费", "规费"],
}
# 手续费类允许多列求和（手续费+印花税+过户费…），其余逻辑列取第一命中
_FEE_KEYS = ("手续费", "佣金", "印花税", "过户费", "其他费", "杂费", "规费")


def _norm_header(h: str) -> str:
    """列名归一化：去空白、去全角、去括号单位（如「成交数量(股)」）。"""
    h = str(h).strip().replace("　", "").replace(" ", "").replace("　", "")
    h = re.sub(r"[（(【\[].*?[)）】\]]", "", h)
    return h


def _match_columns(header: list[str]) -> dict[str, str]:
    """表头 -> 逻辑列映射。返回 {逻辑列: 实际列名}；费率类列全部收集到 fee(合并名)。"""
    out: dict[str, str] = {}
    fee_cols: list[str] = []
    for h in header:
        n = _norm_header(h)
        if not n:
            continue
        for key, aliases in COLUMN_ALIASES.items():
            if key == "fee":
                if any(a in n for a in _FEE_KEYS):
                    fee_cols.append(h)
                continue
            if key in out:
                continue
            if any(a in n for a in aliases):
                out[key] = h
                break
    # 「费用合计」列本身已含全部费用明细，取它即可，避免与手续费/印花税等列重复累加
    total_col = next((h for h in header if "费用合计" in _norm_header(h)), None)
    if total_col is not None:
        out["fee"] = total_col
    elif fee_cols:
        out["fee"] = "\x00".join(fee_cols)  # 多列合并存储，取值时拆开求和
    return out


def _looks_like_header(row: list[str]) -> bool:
    """前 10 行里找表头：≥3 个逻辑列可识别即认为是表头。"""
    return len(_match_columns(row)) >= 3


def _to_float(v) -> float | None:
    """'1,234.50' / '(123.45)' / '--' / '' → float；不可解析返回 None。"""
    if v is None:
        return None
    if isinstance(v, (int, float)):
        return float(v)
    s = str(v).strip().replace(",", "").replace("，", "")
    if not s or s in {"--", "-", "—"}:
        return None
    neg = s.startswith("(") and s.endswith(")")
    if neg:
        s = s[1:-1]
    try:
        f = float(s)
    except ValueError:
        return None
    return -f if neg else f


def _to_date(v) -> date | None:
    if isinstance(v, datetime):
        return v.date()
    if isinstance(v, date):
        return v
    if isinstance(v, (int, float)) and not isinstance(v, bool) and 30000 < v < 60000:
        # Excel 日期序列值（1982~2064），openpyxl 读无日期格式的单元格时会给出
        from datetime import timedelta
        return date(1899, 12, 30) + timedelta(days=int(v))
    s = str(v).strip()
    m = re.search(r"(\d{4})[-/.年](\d{1,2})[-/.月](\d{1,2})", s)
    if not m:
        m2 = re.fullmatch(r"(\d{4})(\d{2})(\d{2})", s)
        if not m2:
            return None
        m = m2
    y, mo, d = int(m.group(1)), int(m.group(2)), int(m.group(3))
    try:
        return date(y, mo, d)
    except ValueError:
        return None


def _to_code_suffix(code: str) -> str:
    """6 位代码 → 交易所后缀。委托 to_ts_code（含场内 ETF：51/56/58 沪、159 深）。"""
    from .data.symbols import to_ts_code

    ts_code = to_ts_code(code)
    return ts_code[ts_code.index("."):]


def _direction(op: str) -> str | None:
    s = str(op)
    if "买入" in s or "buy" in s.lower() or "证券买" in s:
        return "buy"
    if "卖出" in s or "sell" in s.lower() or "证券卖" in s:
        return "sell"
    return None


def _parse_rows(rows: list[list[object]]) -> ParseResult:
    """已抽成二维表的行集 → ParseResult。rows[0] 应是表头（调用方已定位）。"""
    res = ParseResult()
    if not rows:
        return res
    header = [str(h) for h in rows[0]]
    cols = _match_columns(header)
    res.columns = {k: v for k, v in cols.items()}
    res.header_row_index = 0
    need = ("date", "qty")
    if not all(k in cols for k in need) or not ("code" in cols or "name" in cols):
        raise HTTPException(
            status_code=422,
            detail="识别不到交割单表头（需要 成交日期/证券代码或证券名称/成交数量 等列），"
                   "请确认导出的是「交割单/历史成交」文件",
        )
    idx = {h: i for i, h in enumerate(header)}

    # 无「业务名称」列的导出（如部分招商证券格式）：靠发生金额正负判方向（买入为负）。
    # 仅当数据里确实存在负数金额才启用，避免「全正数」格式被误判。
    def _amount_of(row: list[object]) -> float | None:
        i = idx.get(cols["amount"])
        return _to_float(row[i]) if i is not None and i < len(row) else None

    sign_mode = "op" not in cols and "amount" in cols and any(
        (_amount_of(row) or 0) < 0 for row in rows[1:] if any(str(c).strip() for c in row)
    )

    def cell(row: list[object], col: str):
        i = idx.get(col)
        return row[i] if i is not None and i < len(row) else None

    for row in rows[1:]:
        d = _to_date(cell(row, cols["date"]))
        code = re.sub(r"\D", "", str(cell(row, cols["code"]) or "")) if "code" in cols else ""
        raw_amount = _amount_of(row) if sign_mode else None
        direction = (
            _direction(str(cell(row, cols["op"]) or "")) if "op" in cols
            else ("buy" if (raw_amount or 0) < 0 else "sell") if sign_mode
            else None
        )
        name = str(cell(row, cols["name"]) or "").strip() if "name" in cols else ""
        if d is None or (not code and not name) or direction is None:
            # 无日期/无代码且无名称/非买卖业务（银行转账、配号、利息、红利等）
            if any(str(c).strip() for c in row):
                res.skipped_other += 1
            continue
        if len(code) > 6:
            code = code[-6:]  # 个别导出带市场前缀（如 1:600000 / sh600000）
        qty = abs(_to_float(cell(row, cols["qty"])) or 0)
        if qty <= 0:
            res.skipped_bad.append(f"{d} {code or name} 数量无效：{cell(row, cols['qty'])!r}")
            continue
        price = _to_float(cell(row, cols["price"])) if "price" in cols else None
        amount = raw_amount if sign_mode else (_to_float(cell(row, cols["amount"])) if "amount" in cols else None)
        if amount is not None:
            amount = abs(amount)
        fee = 0.0
        if "fee" in cols:
            for fc in cols["fee"].split("\x00"):
                f = _to_float(cell(row, fc))
                if f:
                    fee += abs(f)
        # 无代码列（招商式导出）：ts_code 留空，由 API 层按 name 反查 stock_basic
        ts_code = code + _to_code_suffix(code) if code else ""
        res.trades.append(ParsedTrade(
            trade_date=d,
            code=code,
            ts_code=ts_code,
            name=name,
            direction=direction,
            price=price,
            qty=qty,
            amount=amount,
            fee=round(fee, 2),
            raw={h: row[i] if i < len(row) else None for i, h in enumerate(header)},
        ))
    res.skipped_bad = res.skipped_bad[:5]
    return res


def _find_header(rows: list[list[object]]) -> int:
    for i, row in enumerate(rows[:10]):
        if _looks_like_header([str(c) for c in row]):
            return i
    return -1


# ---------- 输入格式分流 ----------
def _rows_from_csv(data: bytes) -> list[list[object]]:
    """逗号/制表符分隔文本。sniff 分隔符；跳过全空行。"""
    text = None
    for enc in ("utf-8-sig", "gbk", "gb18030"):
        try:
            text = data.decode(enc)
            break
        except UnicodeDecodeError:
            continue
    if text is None:
        raise HTTPException(status_code=422, detail="文件编码无法识别（支持 UTF-8 / GBK）")
    sample = text[:4096]
    delim = "\t" if sample.count("\t") > sample.count(",") else ","
    lines = [ln for ln in text.splitlines() if ln.strip()]
    if not lines:
        raise HTTPException(status_code=422, detail="文件是空的")
    return [next(csv.reader([ln], delimiter=delim)) for ln in lines]


def _rows_from_xlsx(data: bytes) -> list[list[object]]:
    from openpyxl import load_workbook

    try:
        wb = load_workbook(io.BytesIO(data), read_only=True, data_only=True)
    except Exception as e:  # noqa: BLE001 - openpyxl 对损坏文件抛各种类型
        raise HTTPException(status_code=422, detail=f"Excel 文件无法读取：{e}") from e
    ws = wb.active
    rows = [[c for c in row] for row in ws.iter_rows(values_only=True)]
    wb.close()
    return rows


_TD_RE = re.compile(r"<t[dh][^>]*>(.*?)</t[dh]>", re.IGNORECASE | re.DOTALL)
_TR_RE = re.compile(r"<tr[^>]*>(.*?)</tr>", re.IGNORECASE | re.DOTALL)


def _rows_from_html(data: bytes) -> list[list[object]]:
    """券商导出的 .xls 常是 HTML 表格。去掉标签实体后按 <tr>/<td> 切。"""
    text = None
    for enc in ("utf-8", "gbk", "gb18030"):
        try:
            text = data.decode(enc)
            break
        except UnicodeDecodeError:
            continue
    if text is None:
        raise HTTPException(status_code=422, detail="文件编码无法识别（支持 UTF-8 / GBK）")
    import html as html_mod

    rows: list[list[object]] = []
    for tr in _TR_RE.findall(text):
        cells = [html_mod.unescape(re.sub(r"<[^>]+>", "", td)).strip() for td in _TD_RE.findall(tr)]
        rows.append(cells)
    if not rows:
        raise HTTPException(status_code=422, detail="HTML 里没有表格数据")
    return rows


def parse_statement(data: bytes, filename: str) -> ParseResult:
    """入口：按内容魔数/特征分流 → 定位表头 → 解析。"""
    if not data:
        raise HTTPException(status_code=422, detail="文件是空的")
    head = data[:512].lstrip()
    if head.startswith(b"PK"):
        rows = _rows_from_xlsx(data)
    elif head[:1] in (b"<",) or head.lower().startswith(b"\xef\xbb\xbf<"):
        rows = _rows_from_html(data)
    elif filename.lower().endswith((".xlsx", ".xls")) and not head.startswith((b"PK", b"<")):
        # 扩展名是 Excel 但内容既非 xlsx 也非 HTML → 试试当文本
        rows = _rows_from_csv(data)
    else:
        rows = _rows_from_csv(data)
    # 去尾部全空行，定位表头（导出物常有标题行/账户信息行在前）
    while rows and not any(str(c).strip() for c in rows[-1]):
        rows.pop()
    hi = _find_header(rows)
    if hi < 0:
        raise HTTPException(
            status_code=422,
            detail="找不到表头行（前 10 行内没有 成交日期/证券代码 等列名），请确认导出的是交割单",
        )
    return _parse_rows(rows[hi:])