# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license

import sys
from unittest import mock

import torch

from tests import MODEL, SOURCE
from ultralytics import YOLO
from ultralytics.cfg import get_cfg
from ultralytics.engine.exporter import Exporter
from ultralytics.models.yolo import classify, detect, segment
from ultralytics.utils import ASSETS, DEFAULT_CFG, WEIGHTS_DIR


def test_func(*args, **kwargs):
    """Test function callback for evaluating YOLO model performance metrics."""
    print("callback test passed")


def test_export():
    """Test model exporting functionality by adding a callback and verifying its execution."""
    exporter = Exporter()
    exporter.add_callback("on_export_start", test_func)
    assert test_func in exporter.callbacks["on_export_start"], "callback test failed"
    f = exporter(model=YOLO("yolo26n.yaml").model)
    YOLO(f)(SOURCE)  # exported model inference


def test_detect():
    """Test YOLO object detection training, validation, and prediction functionality."""
    overrides = {"data": "coco8.yaml", "model": "yolo26n.yaml", "imgsz": 32, "epochs": 1, "save": False}
    cfg = get_cfg(DEFAULT_CFG)
    cfg.data = "coco8.yaml"
    cfg.imgsz = 32

    # Trainer
    trainer = detect.DetectionTrainer(overrides=overrides)
    trainer.add_callback("on_train_start", test_func)
    assert test_func in trainer.callbacks["on_train_start"], "callback test failed"
    trainer.train()

    # Validator
    val = detect.DetectionValidator(args=cfg)
    val.add_callback("on_val_start", test_func)
    assert test_func in val.callbacks["on_val_start"], "callback test failed"
    val(model=trainer.best)  # validate best.pt

    # Predictor
    pred = detect.DetectionPredictor(overrides={"imgsz": [64, 64]})
    pred.add_callback("on_predict_start", test_func)
    assert test_func in pred.callbacks["on_predict_start"], "callback test failed"
    # Confirm there is no issue with sys.argv being empty
    with mock.patch.object(sys, "argv", []):
        result = pred(source=ASSETS, model=MODEL)
        assert len(result), "predictor test failed"

    # Test resume functionality
    overrides["resume"] = trainer.last
    trainer = detect.DetectionTrainer(overrides=overrides)
    try:
        trainer.train()
    except Exception as e:
        print(f"Expected exception caught: {e}")
        return

    raise Exception("Resume test failed!")


def test_segment():
    """Test image segmentation training, validation, and prediction pipelines using YOLO models."""
    overrides = {
        "data": "coco8-seg.yaml",
        "model": "yolo26n-seg.yaml",
        "imgsz": 32,
        "epochs": 1,
        "save": False,
        "mask_ratio": 1,
        "overlap_mask": False,
    }
    cfg = get_cfg(DEFAULT_CFG)
    cfg.data = "coco8-seg.yaml"
    cfg.imgsz = 32

    # Trainer
    trainer = segment.SegmentationTrainer(overrides=overrides)
    trainer.add_callback("on_train_start", test_func)
    assert test_func in trainer.callbacks["on_train_start"], "callback test failed"
    trainer.train()

    # Validator
    val = segment.SegmentationValidator(args=cfg)
    val.add_callback("on_val_start", test_func)
    assert test_func in val.callbacks["on_val_start"], "callback test failed"
    val(model=trainer.best)  # validate best.pt

    # Predictor
    pred = segment.SegmentationPredictor(overrides={"imgsz": [64, 64]})
    pred.add_callback("on_predict_start", test_func)
    assert test_func in pred.callbacks["on_predict_start"], "callback test failed"
    result = pred(source=ASSETS, model=WEIGHTS_DIR / "yolo26n-seg.pt")
    assert len(result), "predictor test failed"

    # Test resume functionality
    overrides["resume"] = trainer.last
    trainer = segment.SegmentationTrainer(overrides=overrides)
    try:
        trainer.train()
    except Exception as e:
        print(f"Expected exception caught: {e}")
        return

    raise Exception("Resume test failed!")


def test_classify():
    """Test image classification including training, validation, and prediction phases."""
    overrides = {"data": "imagenet10", "model": "yolo26n-cls.yaml", "imgsz": 32, "epochs": 1, "save": False}
    cfg = get_cfg(DEFAULT_CFG)
    cfg.data = "imagenet10"
    cfg.imgsz = 32

    # Trainer
    trainer = classify.ClassificationTrainer(overrides=overrides)
    trainer.add_callback("on_train_start", test_func)
    assert test_func in trainer.callbacks["on_train_start"], "callback test failed"
    trainer.train()

    # Validator
    val = classify.ClassificationValidator(args=cfg)
    val.add_callback("on_val_start", test_func)
    assert test_func in val.callbacks["on_val_start"], "callback test failed"
    val(model=trainer.best)

    # Predictor
    pred = classify.ClassificationPredictor(overrides={"imgsz": [64, 64]})
    pred.add_callback("on_predict_start", test_func)
    assert test_func in pred.callbacks["on_predict_start"], "callback test failed"
    result = pred(source=ASSETS, model=trainer.best)
    assert len(result), "predictor test failed"


def test_nan_recovery():
    """Test NaN loss detection and recovery during training."""
    nan_injected = [False]

    def inject_nan(trainer):
        """Inject NaN into loss during batch processing to test recovery mechanism."""
        if trainer.epoch == 1 and trainer.tloss is not None and not nan_injected[0]:
            trainer.tloss *= torch.tensor(float("nan"))
            nan_injected[0] = True

    overrides = {"data": "coco8.yaml", "model": "yolo26n.yaml", "imgsz": 32, "epochs": 3}
    trainer = detect.DetectionTrainer(overrides=overrides)
    trainer.add_callback("on_train_batch_end", inject_nan)
    trainer.train()
    assert nan_injected[0], "NaN injection failed"
