"""A synthetic collector volume: the collector's `items` table (same DDL as apps/collector
src/db.ts) + JPEG crops. Classes are told apart by COLOUR so even a random-init backbone's
features separate them — the tests check the plumbing (split, floor, export, sidecar),
not accuracy on real cars."""

from __future__ import annotations

import sqlite3
from datetime import datetime, timedelta, timezone
from pathlib import Path

import numpy as np
import pytest

DDL = """
CREATE TABLE IF NOT EXISTS items (
  id TEXT PRIMARY KEY, booth TEXT NOT NULL, kind TEXT NOT NULL DEFAULT 'wash',
  order_ref TEXT NOT NULL, at TEXT NOT NULL, operator_ref TEXT NOT NULL DEFAULT '',
  operator_category_id TEXT NOT NULL DEFAULT '', operator_category_name TEXT NOT NULL DEFAULT '',
  operator_classes TEXT NOT NULL DEFAULT '[]', service TEXT NOT NULL, vision_class TEXT NOT NULL,
  vision_confidence REAL NOT NULL, vision_category_id TEXT, downgraded INTEGER NOT NULL DEFAULT 0,
  image_width INTEGER NOT NULL, image_height INTEGER NOT NULL, plate_blurred INTEGER NOT NULL,
  image_path TEXT NOT NULL, received_at TEXT NOT NULL, review_label TEXT, reviewed_at TEXT, reviewer TEXT
);
"""

COLOURS = {"sedan": (200, 40, 40), "suv": (40, 200, 40), "van": (40, 40, 200), "truck": (200, 200, 40)}


def write_jpeg(path: Path, colour: tuple[int, int, int], rng: np.random.Generator) -> None:
    import cv2

    path.parent.mkdir(parents=True, exist_ok=True)
    h, w = int(rng.integers(120, 200)), int(rng.integers(160, 260))
    img = np.empty((h, w, 3), np.uint8)
    img[:] = colour[::-1]  # BGR
    noise = rng.integers(-20, 20, size=img.shape, dtype=np.int16)
    img = np.clip(img.astype(np.int16) + noise, 0, 255).astype(np.uint8)
    cv2.imwrite(str(path), img, [cv2.IMWRITE_JPEG_QUALITY, 85])


@pytest.fixture
def collector_dir(tmp_path: Path) -> Path:
    """40 labelled crops per class for sedan/suv/van, 5 for truck (below the minimum), a few
    unusable, a few pending, one labelled row whose file is missing."""
    rng = np.random.default_rng(1)
    con = sqlite3.connect(tmp_path / "collector.sqlite")
    con.executescript(DDL)
    t0 = datetime(2026, 9, 1, tzinfo=timezone.utc)
    n = 0

    def add(label: str | None, reviewed: bool, kind: str = "wash", missing: bool = False) -> None:
        nonlocal n
        n += 1
        item = f"item-{n:04d}"
        rel = f"crops/booth-2/{item}.jpg"
        colour = COLOURS.get(label or "sedan", (128, 128, 128))
        if not missing:
            write_jpeg(tmp_path / rel, colour, rng)
        at = (t0 + timedelta(minutes=10 * n)).isoformat().replace("+00:00", "Z")
        reviewed_at = (
            (t0 + timedelta(days=1, minutes=n)).isoformat().replace("+00:00", "Z") if reviewed else None
        )
        con.execute(
            "INSERT INTO items (id, booth, kind, order_ref, at, service, vision_class, vision_confidence, "
            "image_width, image_height, plate_blurred, image_path, received_at, review_label, "
            "reviewed_at, reviewer) "
            "VALUES (?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?)",
            (
                item,
                "booth-2",
                kind,
                "o",
                at,
                "wash",
                "car" if label != "truck" else "truck",
                0.9,
                200,
                150,
                1,
                rel,
                at,
                label if reviewed else None,
                reviewed_at,
                "reviewer" if reviewed else None,
            ),
        )

    # Interleaved in time so every class exists on both sides of the time split.
    for i in range(40):
        for label in ("sedan", "suv", "van"):
            add(label, True)
        if i % 8 == 0:
            add("truck", True)
    add("unusable", True)
    add("unusable", True)
    add("sedan", True, missing=True)
    for _ in range(6):
        add(None, False, kind="entry")
    con.commit()
    con.close()
    return tmp_path