import json

from fam_edge.oracle_db import OracleDB


def _db(tmp_path):
    return OracleDB(str(tmp_path / "oracle.db"))


def _set_heartbeat_age(db, age_sec):
    """把心跳时间戳直接改写成"距现在 age_sec 秒前"，用于测试新鲜度阈值边界。"""
    from datetime import datetime, timedelta, timezone
    ts = (datetime.now(timezone(timedelta(hours=8))) - timedelta(seconds=age_sec))
    db.set_cursor('motion_heartbeat_at', ts.strftime('%Y-%m-%d %H:%M:%S'))


def test_upsert_person_same_gender_merges_into_one_row(tmp_path):
    db = _db(tmp_path)
    db.upsert_person("人物A", features={"gender": "男", "hair": "短发黑色"})
    db.upsert_person("人物A", features={"gender": "男", "clothing": "蓝色T恤"})
    rows = db._conn.execute("SELECT * FROM people").fetchall()
    assert len(rows) == 1
    assert rows[0]["appearances"] == 2
    feats = json.loads(rows[0]["features_json"])
    assert feats["hair"] == "短发黑色"
    assert feats["clothing"] == "蓝色T恤"


def test_upsert_person_gender_conflict_splits_into_new_label(tmp_path):
    """核心诉求: 大模型给的 人物A/B/C 这类 uid 只在单次视频分析内稳定，不同视频
    独立编号，同一字符串完全可能撞到不同真人（实测 "人物A" 混了男女两个人）。
    性别冲突时不能直接合并覆盖，要拆成新 label，避免两个人的特征越merge越乱。"""
    db = _db(tmp_path)
    db.upsert_person("人物A", features={"gender": "男", "clothing": "蓝色Polo衫"})
    db.upsert_person("人物A", features={"gender": "女", "clothing": "白色上衣"})
    rows = {r["label"]: r for r in db._conn.execute("SELECT * FROM people").fetchall()}
    assert set(rows.keys()) == {"人物A", "人物A#2"}
    assert rows["人物A"]["appearances"] == 1
    assert json.loads(rows["人物A"]["features_json"])["gender"] == "男"
    assert rows["人物A#2"]["appearances"] == 1
    assert json.loads(rows["人物A#2"]["features_json"])["gender"] == "女"
    # 派生行的 display_uid 仍然记录原始大模型 uid，方便追溯来源
    assert rows["人物A#2"]["display_uid"] == "人物A"


def test_upsert_person_gender_conflict_allocates_next_free_suffix(tmp_path):
    db = _db(tmp_path)
    db.upsert_person("人物A", features={"gender": "男"})
    db.upsert_person("人物A", features={"gender": "女"})  # -> 人物A#2
    db.upsert_person("人物A", features={"gender": "unknown"})  # unknown 不冲突，合并回 人物A
    db.upsert_person("人物A", features={"gender": "男", "hair": "光头"})  # 冲突，人物A 有 gender 男了不冲突；应仍合并
    labels = {r["label"] for r in db._conn.execute("SELECT label FROM people").fetchall()}
    assert labels == {"人物A", "人物A#2"}
    # 再来一次性别冲突（对着 人物A#2，性别女）应该分配 人物A#3，而不是复用 人物A#2
    db.upsert_person("人物A", features={"gender": "男"})
    db.upsert_person("人物A", features={"gender": "女"})
    # 这次新的女性冲突会先撞到 人物A（此时是男），分裂出下一个空闲后缀
    labels = {r["label"] for r in db._conn.execute("SELECT label FROM people").fetchall()}
    assert "人物A" in labels
    assert len(labels) >= 2


def test_upsert_person_unknown_gender_never_triggers_split(tmp_path):
    db = _db(tmp_path)
    db.upsert_person("人物A", features={"gender": "男"})
    db.upsert_person("人物A", features={"gender": "unknown"})
    db.upsert_person("人物A", features={"gender": "未知"})
    rows = db._conn.execute("SELECT * FROM people").fetchall()
    assert len(rows) == 1
    assert rows[0]["appearances"] == 3


def test_upsert_person_no_features_never_triggers_split(tmp_path):
    db = _db(tmp_path)
    db.upsert_person("人物A", features={"gender": "男"})
    db.upsert_person("人物A")  # 无 features（source 更新等场景）
    rows = db._conn.execute("SELECT * FROM people").fetchall()
    assert len(rows) == 1
    assert rows[0]["appearances"] == 2


# ----------------------------------------------------------------------
# 运动侦测事件（NAS 推送）
# ----------------------------------------------------------------------

def test_record_motion_events_upserts_by_event_id(tmp_path):
    db = _db(tmp_path)
    n = db.record_motion_events([
        {"event_id": 1, "camera_id": 2, "event_type": 10, "start_time": 1000, "duration": 5},
        {"event_id": 2, "camera_id": 2, "event_type": 10, "start_time": 2000, "duration": 3},
    ])
    assert n == 2
    rows = db._conn.execute("SELECT * FROM ss_motion_events ORDER BY event_id").fetchall()
    assert len(rows) == 2
    # 重复推送同一个 event_id（幂等）应该更新而不是新增一行
    db.record_motion_events(
        [{"event_id": 1, "camera_id": 2, "event_type": 10, "start_time": 1000, "duration": 99}])
    rows = db._conn.execute("SELECT * FROM ss_motion_events").fetchall()
    assert len(rows) == 2
    updated = db._conn.execute(
        "SELECT duration FROM ss_motion_events WHERE event_id=1").fetchone()
    assert updated['duration'] == 99


def test_record_motion_events_skips_missing_event_id(tmp_path):
    db = _db(tmp_path)
    n = db.record_motion_events([{"camera_id": 2, "start_time": 1000}])
    assert n == 0


def test_heartbeat_age_none_when_never_recorded(tmp_path):
    db = _db(tmp_path)
    assert db.get_motion_heartbeat_age_sec() is None


def test_heartbeat_age_near_zero_right_after_recording(tmp_path):
    db = _db(tmp_path)
    db.record_motion_heartbeat()
    age = db.get_motion_heartbeat_age_sec()
    assert age is not None and age < 5


def test_has_motion_in_range_local_fails_open_without_heartbeat(tmp_path):
    """核心诉求: 从未收到过心跳（冷启动，NAS 推送链路还没接上）必须 fail-open，
    不能因为本地表是空的就悄悄跳过分析。"""
    db = _db(tmp_path)
    assert db.has_motion_in_range_local(1000, 2000) is None


def test_has_motion_in_range_local_fails_open_when_heartbeat_stale(tmp_path):
    """核心诉求: 表里有大量历史运动事件（曾经推送链路是健康的），但心跳已经
    过期太久（NAS 服务挂了/网络断了/DSM Webhook 规则被误关）——这时候不能信任
    "查询结果是 0 条 = 确认无运动"，必须当作链路已死，fail-open。"""
    db = _db(tmp_path)
    db.record_motion_events(
        [{"event_id": 1, "camera_id": 2, "event_type": 10, "start_time": 500, "duration": 10}])
    _set_heartbeat_age(db, 1000)  # 超过默认阈值 900s
    assert db.has_motion_in_range_local(2000, 3000, max_heartbeat_age_sec=900) is None


def test_has_motion_in_range_local_trusts_result_when_heartbeat_fresh(tmp_path):
    db = _db(tmp_path)
    db.record_motion_heartbeat()
    assert db.has_motion_in_range_local(2000, 3000, max_heartbeat_age_sec=900) is False
    db.record_motion_events(
        [{"event_id": 1, "camera_id": 2, "event_type": 10, "start_time": 2500, "duration": 5}])
    assert db.has_motion_in_range_local(2000, 3000, max_heartbeat_age_sec=900) is True


def test_has_motion_in_range_local_respects_heartbeat_boundary(tmp_path):
    db = _db(tmp_path)
    _set_heartbeat_age(db, 899)
    assert db.has_motion_in_range_local(2000, 3000, max_heartbeat_age_sec=900) is not None
    _set_heartbeat_age(db, 901)
    assert db.has_motion_in_range_local(2000, 3000, max_heartbeat_age_sec=900) is None


def test_has_motion_in_range_local_overlap_semantics(tmp_path):
    """事件区间 [start_time, start_time+duration] 只要和查询窗口有重叠就算命中，
    不要求事件完全落在窗口内部（也不要求窗口完全覆盖事件）。"""
    db = _db(tmp_path)
    db.record_motion_heartbeat()
    # 事件在窗口开始之前就开始，但持续到窗口内 -> 应该命中
    db.record_motion_events(
        [{"event_id": 1, "camera_id": 2, "event_type": 10, "start_time": 1990, "duration": 20}])
    assert db.has_motion_in_range_local(2000, 3000) is True


def test_has_motion_in_range_local_ignores_non_motion_event_type(tmp_path):
    db = _db(tmp_path)
    db.record_motion_heartbeat()
    db.record_motion_events(
        [{"event_id": 1, "camera_id": 2, "event_type": 99, "start_time": 2500, "duration": 5}])
    assert db.has_motion_in_range_local(2000, 3000) is False


def test_has_motion_in_range_local_filters_by_camera_id(tmp_path):
    db = _db(tmp_path)
    db.record_motion_heartbeat()
    db.record_motion_events(
        [{"event_id": 1, "camera_id": 99, "event_type": 10, "start_time": 2500, "duration": 5}])
    assert db.has_motion_in_range_local(2000, 3000, camera_id=2) is False
    assert db.has_motion_in_range_local(2000, 3000, camera_id=99) is True