import shutil
from os import path
from pathlib import Path

import pytest
from ultralytics import YOLO

import constants as c
import train as train_mod
import exports as exports_mod

_PROJECT_ROOT = Path(__file__).resolve().parent.parent
_DATASET_IMAGES = _PROJECT_ROOT / "_docs/00_problem/input_data/dataset/images"
_DATASET_LABELS = _PROJECT_ROOT / "_docs/00_problem/input_data/dataset/labels"
_CONFIG_TEST = _PROJECT_ROOT / "config.test.yaml"


@pytest.fixture(scope="module")
def e2e_result(tmp_path_factory):
    base = tmp_path_factory.mktemp("e2e")

    old_config = c.config
    c.config = c.Config.from_yaml(str(_CONFIG_TEST), root=str(base / "azaion"))

    data_img = Path(c.config.data_images_dir)
    data_lbl = Path(c.config.data_labels_dir)
    data_img.mkdir(parents=True)
    data_lbl.mkdir(parents=True)
    Path(c.config.models_dir).mkdir(parents=True)

    for img in sorted(_DATASET_IMAGES.glob("*.jpg")):
        shutil.copy2(img, data_img / img.name)
        lbl = _DATASET_LABELS / f"{img.stem}.txt"
        if lbl.exists():
            shutil.copy2(lbl, data_lbl / lbl.name)

    from augmentation import Augmentator
    Augmentator().augment_annotations()

    train_mod.train_dataset()

    exports_mod.export_onnx(c.config.current_pt_model)
    exports_mod.export_coreml(c.config.current_pt_model)

    today_ds = path.join(c.config.datasets_dir, train_mod.today_folder)

    yield {
        "today_dataset": today_ds,
    }

    c.config = old_config


@pytest.mark.e2e
class TestTrainingPipeline:
    def test_augmentation_produced_output(self, e2e_result):
        proc = Path(c.config.processed_images_dir)
        assert len(list(proc.glob("*.jpg"))) == 800

    def test_dataset_formed(self, e2e_result):
        base = Path(e2e_result["today_dataset"])
        for split in ("train", "valid", "test"):
            assert (base / split / "images").is_dir()
            assert (base / split / "labels").is_dir()
        total = sum(
            len(list((base / s / "images").glob("*.jpg")))
            for s in ("train", "valid", "test")
        )
        assert total == 800

    def test_data_yaml_created(self, e2e_result):
        yaml_path = Path(e2e_result["today_dataset"]) / "data.yaml"
        assert yaml_path.exists()
        content = yaml_path.read_text()
        assert "nc: 80" in content
        assert "train:" in content
        assert "val:" in content

    def test_training_produces_pt(self, e2e_result):
        pt = Path(c.config.current_pt_model)
        assert pt.exists()
        assert pt.stat().st_size > 0

    def test_export_onnx(self, e2e_result):
        p = Path(c.config.current_onnx_model)
        assert p.exists()
        assert p.suffix == ".onnx"
        assert p.stat().st_size > 0

    def test_export_coreml(self, e2e_result):
        pkgs = list(Path(c.config.models_dir).glob("*.mlpackage"))
        assert len(pkgs) >= 1

    def test_onnx_inference(self, e2e_result):
        onnx_model = YOLO(c.config.current_onnx_model)
        img = sorted(_DATASET_IMAGES.glob("*.jpg"))[0]
        results = onnx_model.predict(source=str(img), imgsz=c.config.export.onnx_imgsz, verbose=False)
        assert len(results) == 1
        assert results[0].boxes is not None