"""
Contract tests for the recognition engine.

No YOLO weights and no network: a fake detector and a fake matcher stand in for
the model, so these tests defend the engine's own contracts - normalization,
locking, isolation between clients, empty-slot geometry, and the still-image
store.
"""

from __future__ import annotations

import os
import sys
import threading
import time
import unittest

current_dir = os.path.dirname(os.path.abspath(__file__))
if current_dir not in sys.path:
    sys.path.insert(0, current_dir)

from engine import (  # noqa: E402
    EngineSettings,
    ImageExpired,
    ImageStore,
    RecognitionEngine,
    UndecodableImage,
    UnknownMethod,
    identity_payload,
)


class FakeDetector:
    """Records concurrency and returns fixed boxes."""

    weights_path = "fake.pt"

    def __init__(self, boxes=None):
        self.boxes = boxes if boxes is not None else [
            {"id": 0, "box": [10, 10, 60, 90], "confidence": 0.9, "class_id": 0,
             "width": 50, "height": 80, "center": [35, 50]},
            {"id": 1, "box": [70, 10, 120, 90], "confidence": 0.8, "class_id": 0,
             "width": 50, "height": 80, "center": [95, 50]},
        ]
        self.concurrent = 0
        self.max_concurrent = 0
        self.calls = 0
        self.lock = threading.Lock()

    def detect(self, image, conf_threshold=0.25, iou_threshold=0.45):
        with self.lock:
            self.calls += 1
            self.concurrent += 1
            self.max_concurrent = max(self.max_concurrent, self.concurrent)
        time.sleep(0.01)
        with self.lock:
            self.concurrent -= 1
        return {"status": "success", "boxes": [dict(b) for b in self.boxes]}

    def detect_empty_slots(self, image_shape, boxes, min_gap_ratio=0.75):
        # One gap between the two boxes, expressed in the frame the engine hands
        # over. Scaling with that frame is what makes the normalization test
        # meaningful: the engine must translate the answer back to the client's
        # pixel space.
        height, width = int(image_shape[0]), int(image_shape[1])
        x1, x2 = int(width * 0.30), int(width * 0.35)
        return [{
            "id": "empty_1",
            "box": [x1, int(height * 0.10), x2, int(height * 0.90)],
            "width": x2 - x1,
            "height": int(height * 0.80),
            "missing_items_est": 1,
            "center": [(x1 + x2) // 2, height // 2],
            "row_index": 1,
        }]


class FakeMatcher:
    def __init__(self):
        self.calls = 0

    def score_crop(self, crop, method="hybrid"):
        self.calls += 1
        time.sleep(0.01)
        return {"sku_a": 40.0, "sku_b": 2.0}, {"sku_a": {"score": 40.0}}

    def score_crop_fast(self, crop, shortlist=5):
        return self.score_crop(crop)

    def classify_crop(self, crop, method="hybrid"):
        from identity import decide_identity

        scores, details = self.score_crop(crop, method=method)
        return decide_identity(scores, details=details, method=method)

    def generate_match_visualization(self, crop, sku, method="surf", max_matches=30):
        return crop


class FakeInventory:
    def __init__(self):
        self.products = {
            "sku_a": {"sku_id": "sku_a", "product_name": "Alpha", "price": 100.0,
                      "number_in": 10, "number_out": 2, "number_on": 8,
                      "receipt_date": "2026-01-01", "expiration_date": "2026-12-31",
                      "local_image_path": None},
            "sku_b": {"sku_id": "sku_b", "product_name": "Beta", "price": 200.0,
                      "number_in": 5, "number_out": 1, "number_on": 4,
                      "receipt_date": "2026-01-02", "expiration_date": "2026-12-30",
                      "local_image_path": None},
        }
        self.lock = threading.Lock()

    def list_all_skus(self):
        return list(self.products)

    def get_product(self, sku_id):
        return self.products.get(sku_id)

    def update_stock(self, sku_id, delta):
        with self.lock:
            product = self.products.get(sku_id)
            if product is None:
                return False
            product["number_on"] = max(0, product["number_on"] + delta)
            if delta < 0:
                product["number_out"] += abs(delta)
            else:
                product["number_in"] += delta
            return True


def make_engine(**overrides):
    settings = EngineSettings(device="cpu", classify_workers=4, **overrides)
    detector = FakeDetector()
    matcher = FakeMatcher()
    inventory = FakeInventory()
    engine = RecognitionEngine(detector, matcher, inventory, settings=settings)
    return engine, detector, matcher, inventory


def make_image(width=200, height=120):
    import numpy as np

    return np.zeros((height, width, 3), dtype="uint8")


class EngineBasicsTests(unittest.TestCase):
    def test_detect_normalizes_and_reindexes(self):
        engine, _, _, _ = make_engine()
        boxes = engine.detect(make_image())
        self.assertEqual(len(boxes), 2)
        self.assertEqual([b["id"] for b in boxes], [0, 1])
        self.assertEqual(boxes[0]["width"], 50)
        self.assertEqual(boxes[0]["center"], [35, 50])

    def test_detector_calls_never_overlap(self):
        engine, detector, _, _ = make_engine()
        threads = [threading.Thread(target=engine.detect, args=(make_image(),)) for _ in range(8)]
        for thread in threads:
            thread.start()
        for thread in threads:
            thread.join()
        self.assertEqual(detector.calls, 8)
        self.assertEqual(detector.max_concurrent, 1, "the model must be used by one caller at a time")

    def test_unknown_method_is_rejected(self):
        engine, _, _, _ = make_engine()
        with self.assertRaises(UnknownMethod):
            engine.score_crop(make_image(), method="not-a-method")

    def test_undecodable_payload_raises(self):
        engine, _, _, _ = make_engine()
        with self.assertRaises(UndecodableImage):
            engine.decode_image(b"this is not an image")

    def test_crop_is_a_copy(self):
        engine, _, _, _ = make_engine()
        image = make_image()
        crop = engine.crop(image, [0, 0, 10, 10])
        crop[0, 0, 0] = 255
        self.assertEqual(int(image[0, 0, 0]), 0)


class EngineClassificationTests(unittest.TestCase):
    def test_classify_boxes_accepts_detection_dicts_without_size_fields(self):
        """The API hands classify_boxes stored detections, which carry no width/height."""
        engine, _, _, _ = make_engine()
        detections = [
            {"id": 0, "box": [10, 10, 60, 90], "confidence": 0.9, "identity": identity_payload(None)},
            {"id": 1, "box": [70, 10, 120, 90], "confidence": 0.8, "identity": identity_payload(None)},
        ]
        rows = engine.classify_boxes(make_image(), detections, method="hybrid")
        self.assertEqual(len(rows), 2)
        self.assertEqual(rows[0]["identity"]["state"], "recognized")
        self.assertEqual(rows[0]["identity"]["sku_id"], "sku_a")

    def test_parallel_classification_matches_sequential(self):
        engine, _, matcher, _ = make_engine()
        boxes = [
            {"id": index, "box": [10 + index, 10, 60 + index, 90], "confidence": 0.9,
             "width": 50, "height": 80, "center": [35, 50]}
            for index in range(12)
        ]
        parallel = engine.classify_boxes(make_image(), boxes, method="hybrid")
        sequential = [engine.score_crop(engine.crop(make_image(), box["box"]), "hybrid") for box in boxes]
        self.assertEqual(len(parallel), 12)
        self.assertEqual(len(sequential), 12)
        for row in parallel:
            self.assertEqual(row["identity"]["sku_id"], "sku_a")

    def test_summary_counts_states(self):
        engine, _, _, _ = make_engine()
        detections = [
            {"identity": {"state": "recognized", "sku_id": "sku_a"}},
            {"identity": {"state": "recognized", "sku_id": "sku_a"}},
            {"identity": {"state": "needs_confirmation", "sku_id": None}},
            {"identity": {"state": "unrecognized", "sku_id": None}},
        ]
        summary = engine.summarize(detections, [])
        self.assertEqual(summary["recognized"], 2)
        self.assertEqual(summary["needs_confirmation"], 1)
        self.assertEqual(summary["unrecognized"], 1)
        self.assertEqual(summary["by_sku"], [{"sku_id": "sku_a", "facings": 2}])


class EmptySlotNormalizationTests(unittest.TestCase):
    def test_slots_scale_with_the_frame(self):
        """The detector's thresholds are absolute pixels, so a half-size frame must
        produce the same slots (scaled) as the reference-size frame."""
        engine, _, _, _ = make_engine()
        boxes_small = [
            {"box": [5, 5, 30, 45]},
            {"box": [35, 5, 60, 45]},
        ]
        slots_small = engine.empty_slots((60, 100), boxes_small)
        boxes_big = [
            {"box": [10, 10, 60, 90]},
            {"box": [70, 10, 120, 90]},
        ]
        slots_big = engine.empty_slots((120, 200), boxes_big)
        self.assertEqual(len(slots_small), 1)
        self.assertEqual(len(slots_big), 1)
        # the small frame is scaled up to the reference width, so the returned
        # slot comes back in the small frame's own pixels
        self.assertLess(slots_small[0]["box"][2], slots_big[0]["box"][2])


class ImageStoreTests(unittest.TestCase):
    def test_roundtrip_and_expiry(self):
        store = ImageStore(max_items=2, ttl_s=0.05, max_bytes=10_000_000)
        stored = store.put(make_image())
        self.assertEqual(store.get(stored.image_id).width, 200)
        time.sleep(0.08)
        with self.assertRaises(ImageExpired):
            store.get(stored.image_id)

    def test_bounded_by_count(self):
        store = ImageStore(max_items=2, ttl_s=60, max_bytes=10_000_000)
        ids = [store.put(make_image()).image_id for _ in range(4)]
        with self.assertRaises(ImageExpired):
            store.get(ids[0])
        self.assertEqual(store.get(ids[-1]).width, 200)


class CatalogTests(unittest.TestCase):
    def test_catalog_and_stock_update(self):
        engine, _, _, _ = make_engine()
        catalog = engine.catalog()
        self.assertEqual(len(catalog), 2)
        self.assertEqual(catalog[0]["image_url"], "/v1/catalog/sku_a/image")
        product = engine.update_stock("sku_a", -1)
        self.assertEqual(product["number_on"], 7)
        self.assertEqual(product["number_out"], 3)
        self.assertIsNone(engine.update_stock("nope", -1))

    def test_catalog_image_path_is_resolved_through_inventory(self):
        engine, _, _, _ = make_engine()
        self.assertIsNone(engine.catalog_image_path("../../etc/passwd"))
        self.assertIsNone(engine.catalog_image_path("sku_a"))


if __name__ == "__main__":
    unittest.main()