"""登录日志数据访问。"""

from sqlalchemy import Select, func, select
from sqlalchemy.orm import Session

from app.models import LoginLog
from app.repositories.query_utils import LogFilters, apply_log_filters

# user_agent 列宽，超长需截断否则插入报错
_USER_AGENT_MAX_LEN = 255


def add(
    session: Session,
    *,
    user_id: int | None,
    username: str,
    ip: str | None,
    user_agent: str | None,
    success: bool,
    fail_reason: str | None = None,
) -> None:
    """写入一条登录日志。只 flush 不 commit。

    **绝不记录密码** —— 明文与哈希都不写。日志是最容易泄漏凭据的地方。

    :param user_id: 账号不存在时传 None（列本身可空）
    :param username: 用户名原样记录，便于账号不存在时也能追溯
    """
    session.add(
        LoginLog(
            user_id=user_id,
            username=username,
            ip=ip,
            user_agent=user_agent[:_USER_AGENT_MAX_LEN] if user_agent else None,
            success=1 if success else 0,
            fail_reason=fail_reason,
        )
    )
    session.flush()


def list_logs(
    session: Session,
    *,
    offset: int,
    limit: int,
    filters: LogFilters,
    ip: str | None = None,
) -> list[LoginLog]:
    """分页查询登录日志。

    **按 id 倒序而非 create_time**：后者是秒级精度，同一秒内的多条记录排序
    不确定，翻页会重复或遗漏（与用户列表同一理由）。
    """
    stmt = _apply_filters(select(LoginLog), filters=filters, ip=ip)
    stmt = stmt.order_by(LoginLog.id.desc()).offset(offset).limit(limit)
    return list(session.execute(stmt).scalars())


def count_logs(
    session: Session,
    *,
    filters: LogFilters,
    ip: str | None = None,
) -> int:
    """统计符合条件的登录日志总数，供分页使用。"""
    stmt = _apply_filters(select(func.count(LoginLog.id)), filters=filters, ip=ip)
    return int(session.execute(stmt).scalar_one())


def _apply_filters(stmt: Select, *, filters: LogFilters, ip: str | None) -> Select:
    """应用筛选条件（公共部分复用 query_utils，与操作日志同口径）。"""
    stmt = apply_log_filters(stmt, LoginLog, filters)
    if ip:
        stmt = stmt.where(LoginLog.ip == ip)
    return stmt