"""
Tests for Whisper speech recognition support.
"""

import os
import sys
import types
from unittest.mock import MagicMock, patch  # noqa: F401

# Mock GTK and other dependencies before importing voculinux
sys.modules["gi"] = MagicMock()
sys.modules["gi.repository"] = MagicMock()
sys.modules["vosk"] = MagicMock()
sys.modules["pyaudio"] = MagicMock()
sys.modules["numpy"] = MagicMock()
sys.modules["pynput"] = MagicMock()
sys.modules["pynput.keyboard"] = MagicMock()


class TestWhisperSupport:
    """Test cases for Whisper speech recognition support."""

    def test_whisper_transcription_method_exists(self):
        """Test that the Whisper transcription method exists."""
        # Mock whisper and torch before importing
        whisper_mock = MagicMock()
        torch_mock = MagicMock()
        torch_mock.cuda.is_available.return_value = False

        with patch.dict(sys.modules, {"whisper": whisper_mock, "torch": torch_mock}):
            from vocalinux.speech_recognition.recognition_manager import SpeechRecognitionManager

            # Test that the method exists without actually calling it
            assert hasattr(SpeechRecognitionManager, "_transcribe_with_whisper")

            # Test method signature
            import inspect

            sig = inspect.signature(SpeechRecognitionManager._transcribe_with_whisper)
            params = list(sig.parameters.keys())
            assert "self" in params
            assert "audio_buffer" in params

    def test_whisper_model_validation(self):
        """Test that invalid model sizes are handled correctly."""
        # Valid Whisper model sizes
        valid_models = ["tiny", "base", "small", "medium", "large"]

        # Test that all valid models are accepted
        for model in valid_models:
            assert model in valid_models

        # Test that invalid model would fallback to 'base'
        invalid_model = "unknown"
        assert invalid_model not in valid_models
        # The code defaults to "base" for invalid models

    def test_whisper_init_and_transcribe_methods_exist(self):
        """Test that Whisper-related methods are properly defined."""
        # Mock whisper and torch before importing
        whisper_mock = MagicMock()
        torch_mock = MagicMock()
        torch_mock.cuda.is_available.return_value = False

        with patch.dict(sys.modules, {"whisper": whisper_mock, "torch": torch_mock}):
            from vocalinux.speech_recognition.recognition_manager import SpeechRecognitionManager

            manager_class = SpeechRecognitionManager

            # Check that _transcribe_with_whisper method exists
            assert hasattr(manager_class, "_transcribe_with_whisper")

            # Check that _init_whisper method exists
            assert hasattr(manager_class, "_init_whisper")

            # Test that the method has proper type hints
            import inspect

            sig = inspect.signature(manager_class._transcribe_with_whisper)
            assert "audio_buffer" in sig.parameters
            assert sig.return_annotation == str

    def test_whisper_engine_initialization_with_mocks(self):
        """Test Whisper engine initialization with mocked dependencies."""
        # Setup mocks
        whisper_mock = MagicMock()
        torch_mock = MagicMock()
        torch_mock.cuda.is_available.return_value = False
        model_mock = MagicMock()
        whisper_mock.load_model.return_value = model_mock

        # os.path.exists is patched so the checkpoint looks present; load_model is
        # mocked, so nothing touches the network or the filesystem.
        with (
            patch.dict(sys.modules, {"whisper": whisper_mock, "torch": torch_mock}),
            patch("os.path.exists", return_value=True),
        ):
            from vocalinux.speech_recognition.recognition_manager import SpeechRecognitionManager

            # Create manager with Whisper engine and defer_download=False to trigger model loading
            manager = SpeechRecognitionManager(
                engine="whisper", model_size="base", defer_download=False
            )

            # Verify Whisper was initialized correctly
            assert manager.engine == "whisper"
            assert manager.model_size == "base"
            whisper_mock.load_model.assert_called_once()

    def test_whispercpp_falls_back_to_cpu_on_16bit_storage_error(self):
        model_ctor = MagicMock(
            side_effect=[
                RuntimeError("device does not support 16-bit storage"),
                MagicMock(),
            ]
        )

        pywhispercpp_pkg = types.ModuleType("pywhispercpp")
        pywhispercpp_model = types.ModuleType("pywhispercpp.model")
        setattr(pywhispercpp_model, "Model", model_ctor)
        setattr(pywhispercpp_pkg, "model", pywhispercpp_model)

        psutil_mock = MagicMock()
        psutil_mock.virtual_memory.return_value = MagicMock(total=8 * (1024**3))

        with patch.dict(
            sys.modules,
            {
                "pywhispercpp": pywhispercpp_pkg,
                "pywhispercpp.model": pywhispercpp_model,
                "psutil": psutil_mock,
            },
        ):
            from vocalinux.speech_recognition import recognition_manager as rm

            with (
                patch("os.makedirs"),
                patch("os.path.exists", return_value=True),
                patch("os.path.getsize", return_value=40 * 1024 * 1024),
                patch("multiprocessing.cpu_count", return_value=4),
                patch(
                    "vocalinux.utils.whispercpp_model_info.detect_compute_backend",
                    return_value=("vulkan", "Intel GPU"),
                ),
                patch(
                    "vocalinux.utils.whispercpp_model_info.get_backend_display_name",
                    return_value="Vulkan",
                ),
                patch(
                    "vocalinux.speech_recognition.recognition_manager.get_model_path",
                    return_value="/tmp/mock-ggml-tiny.bin",
                ),
                patch(
                    "vocalinux.speech_recognition.recognition_manager._show_notification"
                ) as notify_mock,
                patch.object(
                    rm.SpeechRecognitionManager,
                    "_whispercpp_model_is_verified",
                    return_value=True,
                ),
                patch.dict(
                    os.environ,
                    {"GGML_VULKAN": "1", "GGML_CUDA": "1"},
                    clear=False,
                ),
            ):
                manager = rm.SpeechRecognitionManager(
                    engine="whisper_cpp", model_size="tiny", defer_download=False
                )

                assert manager.model is not None
                assert model_ctor.call_count == 2
                assert os.environ["GGML_VULKAN"] == "0"
                assert os.environ["GGML_CUDA"] == "0"
                notify_mock.assert_called_once()