import sys
from unittest.mock import MagicMock

# Comprehensive workaround for missing _lzma in some Python builds
try:
    import lzma
except ImportError:
    mock_lzma = MagicMock()
    mock_lzma.FORMAT_XZ = 1
    mock_lzma.FORMAT_ALONE = 2
    mock_lzma.FORMAT_RAW = 3
    mock_lzma.CHECK_NONE = 0
    mock_lzma.CHECK_CRC32 = 1
    mock_lzma.CHECK_CRC64 = 4
    mock_lzma.CHECK_SHA256 = 10
    sys.modules["_lzma"] = MagicMock()
    sys.modules["lzma"] = mock_lzma

import sys
import time
import numpy as np
import sounddevice as sd
import torch
import mlx_whisper
from silero_vad import load_silero_vad, get_speech_timestamps
import queue
import argparse

# Audio Constants
SAMPLERATE = 16000
BLOCK_SIZE = 512
VAD_THRESHOLD = 0.5

def transcribe_with_controls(audio, transcribe_kwargs):
    """Call mlx_whisper.transcribe with graceful fallback for unsupported args."""
    try:
        return mlx_whisper.transcribe(audio, **transcribe_kwargs)
    except Exception as exc:
        # Fallback logic for beam_size or other specific MLX implementation gaps
        if "beam_size" in transcribe_kwargs:
            transcribe_kwargs.pop("beam_size")
            return mlx_whisper.transcribe(audio, **transcribe_kwargs)
        raise exc

def audio_callback(indata, frames, time, status, audio_queue):
    if status:
        print(status, file=sys.stderr)
    audio_queue.put(indata.copy())

def run_transcription(out_queue, args):
    device = "mps" if torch.backends.mps.is_available() else "cpu"
    print(f"[Transcribe] Loading Whisper model '{args.model}' on {device}...")
    
    vad_model = load_silero_vad()
    audio_queue = queue.Queue()
    
    # Selection of device
    device_index = args.device

    def callback_wrapper(indata, frames, time, status):
        audio_callback(indata, frames, time, status, audio_queue)

    audio_buffer = []
    speech_started = False
    buffer_limit = SAMPLERATE * args.max_buffer
    
    print(f"[Transcribe] Starting audio stream on device {device_index}...")
    
    try:
        with sd.InputStream(samplerate=SAMPLERATE, channels=args.channels, callback=callback_wrapper, blocksize=BLOCK_SIZE, device=device_index):
            while True:
                while not audio_queue.empty():
                    data = audio_queue.get()
                    if args.channels > 1:
                        data = np.mean(data, axis=1) # Mix to mono
                    audio_buffer.append(data.flatten())

                if audio_buffer:
                    current_audio = np.concatenate(audio_buffer)
                    audio_tensor = torch.from_numpy(current_audio)
                    buffer_duration = len(current_audio) / SAMPLERATE
                    
                    speech_timestamps = get_speech_timestamps(
                        audio_tensor, 
                        vad_model, 
                        sampling_rate=SAMPLERATE,
                        threshold=VAD_THRESHOLD,
                        min_silence_duration_ms=args.silence
                    )

                    if speech_timestamps:
                        speech_started = True
                        last_end = speech_timestamps[-1]['end']
                        buffer_len_samples = len(current_audio)
                        
                        if (buffer_len_samples - last_end) > (SAMPLERATE * args.silence / 1000) or buffer_len_samples > buffer_limit:
                            # 1. Transcribe
                            transcribe_kwargs = {
                                "path_or_hf_repo": args.model,
                                "temperature": (0.0, 0.2, 0.4, 0.6, 0.8, 1.0),
                            }
                            if args.lang:
                                transcribe_kwargs["language"] = args.lang

                            result = transcribe_with_controls(current_audio, transcribe_kwargs)
                            text = result['text'].strip()
                            detected_lang = result.get('language', 'en')

                            if text:
                                # Send to Translation Process
                                out_queue.put({
                                    "original": text,
                                    "detected_lang": detected_lang,
                                    "ts": time.time()
                                })
                                print(f"[Transcribe] {detected_lang.upper()}: {text}")

                            audio_buffer = []
                            speech_started = False
                    
                    elif not speech_started and len(current_audio) > SAMPLERATE * 2:
                        audio_buffer = []

    except KeyboardInterrupt:
        print("[Transcribe] Stopped.")
    except Exception as e:
        print(f"[Transcribe] Error: {e}")

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--model", type=str, default="mlx-community/whisper-base-mlx")
    parser.add_argument("--device", type=int, default=None)
    parser.add_argument("--lang", type=str, default=None)
    parser.add_argument("--silence", type=int, default=1000)
    parser.add_argument("--max-buffer", type=int, default=20)
    parser.add_argument("--channels", type=int, default=1)
    args = parser.parse_args()
    
    # This part would normally be called by the main coordinator
    # For testing, we can use a dummy queue
    import multiprocessing
    q = multiprocessing.Queue()
    run_transcription(q, args)