from __future__ import annotations

import importlib.util
import json
import tempfile
import unittest
from pathlib import Path

from turn_detection.runtime.features import FrontendConfig
from turn_detection.runtime.predictor import ModelMetadata


class FrontendConfigTest(unittest.TestCase):
    def test_frame_contract(self) -> None:
        config = FrontendConfig()
        self.assertEqual(config.max_samples, 128_000)
        self.assertEqual(config.target_frames, 800)

    def test_invalid_bounds_are_rejected(self) -> None:
        with self.assertRaises(ValueError):
            FrontendConfig(f_max=20_000)

    def test_metadata_round_trip(self) -> None:
        metadata = ModelMetadata(model_name="smoke", architecture="tinytcn")
        with tempfile.TemporaryDirectory() as directory:
            path = Path(directory) / "model_metadata.json"
            path.write_text(json.dumps(metadata.to_dict()), encoding="utf-8")
            loaded = ModelMetadata.from_path(path)
        self.assertEqual(loaded, metadata)

    @unittest.skipUnless(
        importlib.util.find_spec("numpy"), "numpy is optional in this test environment"
    )
    def test_features_have_expected_shape_and_finite_values(self) -> None:
        import numpy as np

        from turn_detection.runtime.features import log_mel_spectrogram

        audio = np.zeros(16_000, dtype=np.float32)
        features, mask = log_mel_spectrogram(audio, 16_000)
        self.assertEqual(features.shape, (80, 800))
        self.assertEqual(mask.shape, (800,))
        self.assertTrue(np.isfinite(features).all())
        self.assertEqual(int(mask.sum()), 100)


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