from __future__ import annotations

import sys
import unittest
from pathlib import Path

sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "src"))

try:
    import torch
except ImportError:  # pragma: no cover - lightweight CI
    torch = None


@unittest.skipUnless(torch is not None, "PyTorch is not installed")
class TinyTCNTest(unittest.TestCase):
    def setUp(self) -> None:
        from turn_detection.models.tiny_tcn import TinyTCNConfig, TinyTurnDetector

        self.config = TinyTCNConfig(
            channels=32,
            num_blocks=3,
            kernel_size=3,
            dilation_cycle=(1, 2, 4),
            attention_channels=16,
            head_hidden=16,
            dropout=0.0,
        )
        torch.manual_seed(3)
        self.model = TinyTurnDetector(self.config).eval()

    def test_output_shapes_are_logits(self) -> None:
        features = torch.randn(3, 80, 51)
        mask = torch.ones(3, 51, dtype=torch.bool)
        output = self.model(features, mask)
        self.assertEqual(tuple(output.endpoint_logits.shape), (3,))
        self.assertEqual(tuple(output.midfiller_logits.shape), (3,))
        self.assertEqual(tuple(output.endfiller_logits.shape), (3,))
        self.assertTrue(bool(torch.isfinite(output.endpoint_logits).all()))

    def test_right_padding_does_not_change_valid_prediction(self) -> None:
        features = torch.randn(2, 80, 40)
        mask = torch.ones(2, 40, dtype=torch.bool)
        padded = torch.cat([features, torch.randn(2, 80, 17) * 100], dim=-1)
        padded_mask = torch.cat([mask, torch.zeros(2, 17, dtype=torch.bool)], dim=-1)
        with torch.inference_mode():
            original = self.model(features, mask).endpoint_logits
            after_padding = self.model(padded, padded_mask).endpoint_logits
        torch.testing.assert_close(original, after_padding, atol=1e-6, rtol=1e-6)

    def test_default_model_is_in_intended_tiny_parameter_range(self) -> None:
        from turn_detection.models.tiny_tcn import TinyTurnDetector

        count = sum(parameter.numel() for parameter in TinyTurnDetector().parameters())
        self.assertGreaterEqual(count, 300_000)
        self.assertLess(count, 1_000_000)

    def test_invalid_feature_shape_is_rejected(self) -> None:
        with self.assertRaises(ValueError):
            self.model(torch.randn(2, 79, 20))


@unittest.skipUnless(torch is not None, "PyTorch is not installed")
class FrontendTest(unittest.TestCase):
    def test_feature_and_mask_lengths(self) -> None:
        from turn_detection.models.features import LogMelFrontend

        frontend = LogMelFrontend()
        waveform = torch.randn(2, 16_000)
        features, mask = frontend(waveform, torch.tensor([16_000, 8_000]))
        self.assertEqual(tuple(features.shape[:2]), (2, 80))
        self.assertEqual(int(mask[0].sum()), 98)
        self.assertEqual(int(mask[1].sum()), 48)
        self.assertTrue(bool((features[1, :, ~mask[1]] == 0).all()))

    def test_deployment_frontend_parity(self) -> None:
        try:
            import numpy as np
        except ImportError:
            self.skipTest("numpy is not installed")
        from turn_detection.models.features import LogMelConfig, LogMelFrontend
        from turn_detection.runtime.features import FrontendConfig, log_mel_spectrogram

        audio = np.random.default_rng(17).standard_normal(12_345).astype(np.float32) * 0.1
        runtime_features, runtime_mask = log_mel_spectrogram(
            audio,
            16_000,
            FrontendConfig(max_seconds=1.0, normalization="whisper", pad_side="left"),
        )
        training_frontend = LogMelFrontend(
            LogMelConfig(
                normalize=False,
                mel_scale="htk",
                log_scale="whisper",
                center=True,
                drop_last_frame=True,
                pad_side="left",
            )
        )
        padded = torch.zeros(1, 16_000)
        padded[0, -len(audio) :] = torch.from_numpy(audio)
        training_features, training_mask = training_frontend(padded, torch.tensor([len(audio)]))
        torch.testing.assert_close(
            training_features[0],
            torch.from_numpy(runtime_features),
            atol=5e-6,
            rtol=1e-5,
        )
        self.assertEqual(training_mask[0].to(torch.float32).tolist(), runtime_mask.tolist())

    def test_export_metadata_loads_in_runtime(self) -> None:
        import json
        import tempfile

        from turn_detection.models.deployment import build_runtime_metadata
        from turn_detection.models.features import LogMelConfig
        from turn_detection.runtime.predictor import ModelMetadata

        config = LogMelConfig(
            normalize=False,
            log_scale="whisper",
            center=True,
            drop_last_frame=True,
            pad_side="left",
        )
        payload = build_runtime_metadata(
            config,
            max_seconds=4.0,
            threshold=0.61,
            model_name="preview",
            architecture="tiny_tcn",
            development_only=True,
            training_status="preview-only",
            data_scope="one shard",
            data_revision="abc123",
            parameter_count=151_812,
        )
        with tempfile.TemporaryDirectory() as directory:
            path = Path(directory) / "model_metadata.json"
            path.write_text(json.dumps(payload), encoding="utf-8")
            loaded = ModelMetadata.from_path(path)
        self.assertEqual(loaded.input_features_name, "log_mel")
        self.assertEqual(loaded.frame_mask_name, "frame_mask")
        self.assertEqual(loaded.output_type, "probability")
        self.assertEqual(loaded.frontend.target_frames, 400)
        self.assertTrue(loaded.development_only)
        self.assertEqual(loaded.training_status, "preview-only")
        self.assertEqual(loaded.data_scope, "one shard")
        self.assertEqual(loaded.data_revision, "abc123")
        self.assertEqual(loaded.parameter_count, 151_812)
        self.assertEqual(loaded.controller.endpoint_threshold, 0.61)
        self.assertAlmostEqual(loaded.controller.long_pause_threshold, 0.43)


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