import tempfile
import unittest
from pathlib import Path

from assistant_bot.speech import SpeechRecognitionError, SpeechRecognizer


class FakeSegment:
    def __init__(self, text: str) -> None:
        self.text = text


class SpeechRecognizerTests(unittest.TestCase):
    def test_transcribe_joins_non_empty_segments(self) -> None:
        factory_calls = []
        transcribe_calls = []

        class FakeModel:
            def transcribe(self, audio_path, **kwargs):
                transcribe_calls.append((audio_path, kwargs))
                return iter(
                    [FakeSegment(" Привет "), FakeSegment(""), FakeSegment("мир")]
                ), None

        def model_factory(*args, **kwargs):
            factory_calls.append((args, kwargs))
            return FakeModel()

        recognizer = SpeechRecognizer(
            model_name="small",
            device="cpu",
            compute_type="int8",
            language="ru",
            model_factory=model_factory,
        )

        with tempfile.TemporaryDirectory() as directory:
            audio_path = Path(directory) / "voice.ogg"
            result = recognizer.transcribe(audio_path)

        self.assertEqual(result, "Привет мир")
        self.assertEqual(
            factory_calls[0],
            (("small",), {"device": "cpu", "compute_type": "int8"}),
        )
        self.assertEqual(
            transcribe_calls[0][1],
            {"language": "ru", "beam_size": 5, "vad_filter": True},
        )

    def test_transcribe_wraps_model_errors(self) -> None:
        def failing_factory(*_args, **_kwargs):
            raise RuntimeError("model is unavailable")

        recognizer = SpeechRecognizer(
            model_name="small",
            device="cpu",
            compute_type="int8",
            language=None,
            model_factory=failing_factory,
        )

        with self.assertRaises(SpeechRecognitionError):
            recognizer.transcribe("voice.ogg")


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