from pathlib import Path

import pandas as pd
from commons.conn_mysql import MySQLPoolOnline

def get_conn():
    """
    数据库连接配置（你自己补）。
    """
    return MySQLPoolOnline()

def read_source_file(file_path: Path, sheet_name=0) -> pd.DataFrame:
    suffix = file_path.suffix.lower()
    if suffix in {".xlsx", ".xls"}:
        return pd.read_excel(file_path, sheet_name=sheet_name, dtype=object)
    if suffix == ".csv":
        return pd.read_csv(file_path, dtype=object, encoding="utf-8-sig")
    raise ValueError(f"不支持的文件类型: {suffix}")


def get_table_columns(conn, table_name: str):
    sql = """
    SELECT COLUMN_NAME
    FROM information_schema.COLUMNS
    WHERE TABLE_SCHEMA = DATABASE()
      AND TABLE_NAME = %s
    ORDER BY ORDINAL_POSITION
    """
    # 兼容 MySQLPoolOnline（select_data）和原生 pymysql 连接（cursor）
    if hasattr(conn, "select_data"):
        rows = conn.select_data(sql, (table_name,))
        return [r.get("COLUMN_NAME") for r in rows]
    with conn.cursor() as cur:
        cur.execute(sql, (table_name,))
        rows = cur.fetchall()
    return [r[0] for r in rows]


def clean_df(df: pd.DataFrame) -> pd.DataFrame:
    # 去掉空列名和列名前后空格
    df = df.rename(columns=lambda x: str(x).strip() if x is not None else "")
    df = df[[c for c in df.columns if c]]
    # 把 NaN 转为 None，便于写库
    return df.where(pd.notna(df), None)


def import_to_jd_provider_tmp(file_path: str, table_name="jd_provider_tmp", sheet_name=0, batch_size=500):
    file = Path(file_path)
    if not file.exists():
        raise FileNotFoundError(f"文件不存在: {file}")

    df = clean_df(read_source_file(file, sheet_name=sheet_name))
    if df.empty:
        print("源文件无数据，跳过导入。")
        return

    conn = get_conn()
    is_pool_conn = hasattr(conn, "select_data") and hasattr(conn, "execute_many")
    try:
        table_columns = get_table_columns(conn, table_name)
        if not table_columns:
            raise RuntimeError(f"未找到表或无字段: {table_name}")

        # 仅导入“表头和数据库字段同名”的列
        import_columns = [c for c in df.columns if c in table_columns]
        if not import_columns:
            raise RuntimeError("文件表头与数据库字段无交集，请检查表头是否与表字段同名。")

        missing_columns = [c for c in df.columns if c not in table_columns]
        if missing_columns:
            print(f"以下表头不在数据库表中，已自动忽略: {missing_columns}")

        insert_sql = (
            f"INSERT INTO `{table_name}` ({', '.join([f'`{c}`' for c in import_columns])}) "
            f"VALUES ({', '.join(['%s'] * len(import_columns))})"
        )

        values = [tuple(row[c] for c in import_columns) for _, row in df.iterrows()]
        total = len(values)
        inserted = 0

        if is_pool_conn:
            for i in range(0, total, batch_size):
                batch = values[i:i + batch_size]
                conn.execute_many(insert_sql, batch)
                inserted += len(batch)
                print(f"已导入: {inserted}/{total}")
        else:
            with conn.cursor() as cur:
                for i in range(0, total, batch_size):
                    batch = values[i:i + batch_size]
                    cur.executemany(insert_sql, batch)
                    inserted += len(batch)
                    print(f"已导入: {inserted}/{total}")
            conn.commit()
        print(f"导入完成，表 `{table_name}` 共导入 {inserted} 条。")
    except Exception:
        if not is_pool_conn:
            conn.rollback()
        raise
    finally:
        # 连接池对象无需 close；原生连接需要 close
        if hasattr(conn, "close") and not is_pool_conn:
            conn.close()


if __name__ == "__main__":
    fixed_file = Path.cwd() / "11111.xlsx"
    import_to_jd_provider_tmp(
        file_path=str(fixed_file),
        table_name="jd_provider_tmp",
        sheet_name=0,
        batch_size=500,
    )