"""YOLO 结果 -> Label Studio 预测格式。

只测转换模块：ML backend 本体跑在独立虚拟环境里（label_studio_ml 与主环境的 jinja2
版本冲突），主环境 import 不了 model.py。
"""

import sys

import pytest

sys.path.insert(0, "ml_backend")
from convert import box_to_result, to_prediction  # noqa: E402

FROM_NAME, TO_NAME = "label", "image"


def test_box_converts_to_percent_coordinates():
    """Label Studio 用左上角+宽高的百分比，YOLO 给的是归一化 xyxy。"""
    item = box_to_result("blackdot", [0.2, 0.3, 0.6, 0.5], 200, 100, FROM_NAME, TO_NAME, 0.9)

    assert item["value"]["x"] == pytest.approx(20.0)
    assert item["value"]["y"] == pytest.approx(30.0)
    assert item["value"]["width"] == pytest.approx(40.0)
    assert item["value"]["height"] == pytest.approx(20.0)
    assert item["value"]["rectanglelabels"] == ["blackdot"]
    assert item["type"] == "rectanglelabels"
    assert item["from_name"] == FROM_NAME and item["to_name"] == TO_NAME


def test_original_size_is_the_real_pixel_size():
    """Label Studio 按 original_width/height 把百分比换算回像素，填错框就会偏。"""
    item = box_to_result("grease", [0, 0, 1, 1], 810, 1080, FROM_NAME, TO_NAME)
    assert (item["original_width"], item["original_height"]) == (810, 1080)


def test_ids_are_unique():
    boxes = [box_to_result("a", [0, 0, 0.1, 0.1], 10, 10, FROM_NAME, TO_NAME) for _ in range(20)]
    assert len({item["id"] for item in boxes}) == 20


def test_out_of_range_coordinates_are_clamped():
    """推理偶尔会给出略微越界的坐标；越界的框 Label Studio 画不出来。"""
    item = box_to_result("x", [-0.01, 0.0, 1.02, 1.0], 100, 100, FROM_NAME, TO_NAME)
    value = item["value"]
    assert value["x"] == 0.0
    assert value["x"] + value["width"] == pytest.approx(100.0)


def test_prediction_score_is_the_highest_confidence():
    """Label Studio 用 score 给任务排序，取最大值才能把最有把握的排在前面。"""
    prediction = to_prediction(
        [("blackdot", [0.1, 0.1, 0.2, 0.2], 0.4), ("grease", [0.5, 0.5, 0.7, 0.7], 0.85)],
        128, 128, FROM_NAME, TO_NAME, "v1",
    )
    assert prediction["score"] == pytest.approx(0.85)
    assert prediction["model_version"] == "v1"
    assert len(prediction["result"]) == 2


def test_empty_prediction_is_wellformed():
    """没检出缺陷也要返回一条预测：空 result 表示"这张图没问题"。"""
    prediction = to_prediction([], 128, 128, FROM_NAME, TO_NAME, "v1")
    assert prediction["result"] == []
    assert prediction["score"] == 0.0


def test_score_is_kept_per_box():
    prediction = to_prediction(
        [("blackdot", [0.1, 0.1, 0.2, 0.2], 0.42)], 64, 64, FROM_NAME, TO_NAME, "v1"
    )
    assert prediction["result"][0]["score"] == pytest.approx(0.42)


# ---------------- 重训触发的语义 ----------------
#
# fit() 本身要 import label_studio_ml，主环境里装不了，所以这里只把它的决策逻辑
# 单独拎出来验证：什么情况下该训、什么情况下不该。规则见 ml_backend/model.py。


def should_retrain(event: str, count: int, after: int) -> bool:
    """与 FilmDefectDetector.fit 中的判定保持一致。"""
    if event == "START_TRAINING":
        return True
    return bool(after and count >= after)


@pytest.mark.parametrize(
    "event,count,after,expected",
    [
        ("START_TRAINING", 0, 0, True),      # 人点的按钮，无条件训
        ("START_TRAINING", 3, 200, True),    # 没攒够也训
        ("ANNOTATION_CREATED", 199, 200, False),
        ("ANNOTATION_CREATED", 200, 200, True),
        ("ANNOTATION_CREATED", 999, 0, False),   # after=0 表示只手动触发
    ],
)
def test_retrain_trigger_rules(event, count, after, expected):
    assert should_retrain(event, count, after) is expected


def test_only_new_annotations_count_toward_the_threshold():
    """UPDATED / DELETED 是对已有标注的修改，不该把阈值提前顶到。"""
    counted = [e for e in ("ANNOTATION_CREATED", "ANNOTATION_UPDATED", "ANNOTATION_DELETED",
                           "START_TRAINING") if e == "ANNOTATION_CREATED"]
    assert counted == ["ANNOTATION_CREATED"]