from __future__ import annotations

import os

import pytest

from yolo_webui.config import AugmentationConfig, DatasetSplitConfig, MlflowConfig, TrainingConfig
from yolo_webui.trainer import TrainingRunner, mlflow_environment


def test_train_kwargs_omit_optional_empty_values() -> None:
    config = TrainingConfig(
        dataset=" dataset.yaml ",
        model=" model.pt ",
        augmentation=AugmentationConfig(enabled=False),
    )

    assert config.train_kwargs() == {
        "data": "dataset.yaml",
        "epochs": 100,
        "imgsz": 640,
        "batch": 16,
        "workers": 8,
        "patience": 100,
        "project": "runs/train",
        "verbose": True,
    }


def test_augmentation_kwargs_are_passed_to_ultralytics() -> None:
    config = TrainingConfig(
        dataset="dataset.yaml",
        model="model.pt",
        augmentation=AugmentationConfig(mosaic=0.5, mixup=0.2, close_mosaic=5),
    )

    kwargs = config.train_kwargs()

    assert kwargs["mosaic"] == 0.5
    assert kwargs["mixup"] == 0.2
    assert kwargs["close_mosaic"] == 5
    assert kwargs["auto_augment"] == "randaugment"


@pytest.mark.parametrize("field", ["mosaic", "fliplr", "erasing", "perspective"])
def test_augmentation_probabilities_are_validated(field: str) -> None:
    augmentation = AugmentationConfig(**{field: 1.1})

    with pytest.raises(ValueError, match="от 0 до 1"):
        augmentation.validate()


@pytest.mark.parametrize("batch", [0, -2])
def test_invalid_batch_is_rejected(batch: int) -> None:
    config = TrainingConfig(dataset="dataset.yaml", model="model.pt", batch_size=batch)

    with pytest.raises(ValueError, match="Batch"):
        config.validate()


def test_mlflow_environment_is_restored(monkeypatch: pytest.MonkeyPatch) -> None:
    monkeypatch.setenv("MLFLOW_TRACKING_URI", "previous")
    config = MlflowConfig(
        enabled=True,
        tracking_uri="http://127.0.0.1:5000",
        experiment_name="test-experiment",
        run_name="test-run",
    )

    with mlflow_environment(config):
        assert os.environ["MLFLOW_TRACKING_URI"] == config.tracking_uri
        assert os.environ["MLFLOW_EXPERIMENT_NAME"] == config.experiment_name
        assert os.environ["MLFLOW_RUN"] == config.run_name

    assert os.environ["MLFLOW_TRACKING_URI"] == "previous"
    assert "MLFLOW_EXPERIMENT_NAME" not in os.environ
    assert "MLFLOW_RUN" not in os.environ


def test_metrics_summary_skips_non_numeric_values() -> None:
    summary = TrainingRunner._metrics_summary(
        {
            "metrics/mAP50": 0.81234,
            "label": "invalid",
            "val/loss": 0.12345,
            "metrics/precision": 0.95678,
            "another": 12.34,
        }
    )

    assert summary == "mAP50=0.8123 · loss=0.1235 · precision=0.9568"


@pytest.mark.parametrize("ratio", [0.05, 0.98])
def test_dataset_split_ratio_is_validated(ratio: float) -> None:
    config = DatasetSplitConfig(enabled=True, train_ratio=ratio)
    with pytest.raises(ValueError, match="Доля обучающей выборки"):
        config.validate()


def test_classification_rejects_detection_style_auto_split() -> None:
    config = TrainingConfig(
        dataset="classification-dataset",
        model="model.pt",
        task="classify",
        split=DatasetSplitConfig(enabled=True),
    )

    with pytest.raises(ValueError, match="classify"):
        config.validate()