"""Тесты cross-camera re-id логики.

Используем синтетические 512-мерные эмбеддинги (без InsightFace).
Проверяем, что find_topk_in_window:
  1. Возвращает соседей в правильном порядке по cos-дистанции.
  2. Фильтрует по camera_id (исключает ту же камеру).
  3. Фильтрует по временному окну.
"""
from datetime import datetime, timedelta

import numpy as np
import pytest

from database import (
    save_embedding_with_meta,
    find_topk_in_window,
    find_nearest_patient,
    attach_track_to_patient,
    delete_patient_embeddings,
)


def normed(vec: np.ndarray) -> np.ndarray:
    return (vec / np.linalg.norm(vec)).astype(np.float32)


def make_embedding(seed: int) -> np.ndarray:
    rng = np.random.default_rng(seed)
    return normed(rng.standard_normal(512))


def test_topk_in_window_basic(seed_camera_and_track):
    """Из 3 эмбеддингов на 3 разных камерах находим 2 ближайших к query (исключая саму камеру query)."""
    cam_a, track_a = seed_camera_and_track("A")
    cam_b, track_b = seed_camera_and_track("B")
    cam_c, track_c = seed_camera_and_track("C")

    base = make_embedding(seed=42)

    # Соседи: tweak base слегка для cam_b, сильнее для cam_c.
    near = normed(base + 0.05 * make_embedding(seed=43))
    far = normed(base + 0.5 * make_embedding(seed=44))

    save_embedding_with_meta(base, track_a, cam_a, quality=0.9, captured_at=datetime.utcnow())
    save_embedding_with_meta(near, track_b, cam_b, quality=0.9, captured_at=datetime.utcnow())
    save_embedding_with_meta(far, track_c, cam_c, quality=0.9, captured_at=datetime.utcnow())

    # Запрос с cam_a — должен вернуть cam_b раньше cam_c, cam_a исключаем.
    results = find_topk_in_window(
        embedding=base.tolist(),
        camera_id=cam_a,
        window_minutes=5,
        k=5,
        exclude_same_camera=True,
    )

    cam_ids_in_results = [r["camera_id"] for r in results]
    assert cam_a not in cam_ids_in_results
    assert cam_b in cam_ids_in_results
    assert cam_c in cam_ids_in_results
    # Порядок: ближе → дальше
    assert results[0]["camera_id"] == cam_b
    assert results[0]["distance"] < results[-1]["distance"]


def test_topk_filters_by_window(seed_camera_and_track):
    """Старый эмбеддинг (вне окна) не должен попадать в результат."""
    cam_a, track_a = seed_camera_and_track("A")
    cam_b, track_b = seed_camera_and_track("B")

    base = make_embedding(seed=7)

    save_embedding_with_meta(
        base, track_b, cam_b, quality=0.9,
        captured_at=datetime.utcnow() - timedelta(hours=1),  # вне окна 5 мин
    )

    results = find_topk_in_window(
        embedding=base.tolist(),
        camera_id=cam_a,
        window_minutes=5,
        k=5,
    )

    cam_ids = [r["camera_id"] for r in results]
    assert cam_b not in cam_ids


def test_find_nearest_patient_only_consented(db_conn, seed_camera_and_track):
    """find_nearest_patient ищет только среди эмбеддингов с patient_id IS NOT NULL."""
    cam_a, track_a = seed_camera_and_track("A")
    base = make_embedding(seed=100)

    # Сохраняем эмбеддинг без patient_id.
    save_embedding_with_meta(base, track_a, cam_a, quality=0.9)

    # Ищем — никого не должно найти.
    assert find_nearest_patient(base.tolist(), threshold=0.5) is None

    # Создаём пациента и привязываем трек.
    import uuid
    patient_id = str(uuid.uuid4())
    with db_conn.cursor() as cur:
        cur.execute(
            "INSERT INTO patients (id, full_name, updated_at) VALUES (%s, %s, NOW())",
            (patient_id, "Тестовый Пациент"),
        )
    db_conn.commit()

    affected = attach_track_to_patient(track_a, patient_id)
    assert affected == 1

    # Теперь должны найти.
    result = find_nearest_patient(base.tolist(), threshold=0.5)
    assert result is not None
    assert result["patient_id"] == patient_id
    assert result["distance"] < 0.01  # тот же эмбеддинг

    # Очистка.
    deleted = delete_patient_embeddings(patient_id)
    assert deleted == 1
    with db_conn.cursor() as cur:
        cur.execute("DELETE FROM patients WHERE id = %s", (patient_id,))
    db_conn.commit()