"""
Qwen 3.5 Omni Engine — End-to-end speech-to-speech translation.
Takes English audio in, returns translated audio + transcript out.
No separate ASR/MT/TTS needed — Qwen handles everything in one call.
"""

import os
import base64
import struct
import subprocess
import tempfile
import time
import shutil
import logging

logger = logging.getLogger(__name__)

QWEN_MODEL = "qwen3.5-omni-plus"
QWEN_BASE_URL = "https://dashscope-intl.aliyuncs.com/compatible-mode/v1"


def _get_client():
    """Create OpenAI-compatible client for Qwen Dashscope API."""
    from openai import OpenAI
    api_key = os.environ.get("DASHSCOPE_API_KEY", "")
    if not api_key:
        raise RuntimeError(
            "DASHSCOPE_API_KEY not set. Add it as a Space secret."
        )
    return OpenAI(api_key=api_key, base_url=QWEN_BASE_URL)


def _wav_to_base64(wav_path):
    """Read WAV file and return base64 string."""
    with open(wav_path, "rb") as f:
        return base64.b64encode(f.read()).decode("utf-8")


def _base64_to_wav(b64_data, output_path):
    """Convert raw PCM base64 audio to WAV file (24kHz, mono, 16-bit)."""
    audio_bytes = base64.b64decode(b64_data)
    sample_rate = 24000
    num_channels = 1
    bits_per_sample = 16
    byte_rate = sample_rate * num_channels * bits_per_sample // 8
    block_align = num_channels * bits_per_sample // 8
    data_size = len(audio_bytes)
    with open(output_path, "wb") as f:
        f.write(b"RIFF")
        f.write(struct.pack("<I", 36 + data_size))
        f.write(b"WAVE")
        f.write(b"fmt ")
        f.write(struct.pack("<I", 16))
        f.write(struct.pack("<H", 1))
        f.write(struct.pack("<H", num_channels))
        f.write(struct.pack("<I", sample_rate))
        f.write(struct.pack("<I", byte_rate))
        f.write(struct.pack("<H", block_align))
        f.write(struct.pack("<H", bits_per_sample))
        f.write(b"data")
        f.write(struct.pack("<I", data_size))
        f.write(audio_bytes)


def _extract_audio_chunk(video_path, output_wav, start_sec, duration_sec):
    """Extract a chunk of audio from video as 16kHz mono WAV."""
    subprocess.run(
        ["ffmpeg", "-y", "-ss", str(start_sec), "-t", str(duration_sec),
         "-i", video_path, "-vn", "-acodec", "pcm_s16le",
         "-ar", "16000", "-ac", "1", output_wav],
        capture_output=True, check=True,
    )


def _get_duration(filepath):
    """Get media file duration in seconds."""
    result = subprocess.run(
        ["ffprobe", "-v", "quiet", "-show_entries", "format=duration",
         "-of", "default=noprint_wrappers=1:nokey=1", filepath],
        capture_output=True, text=True,
    )
    return float(result.stdout.strip())


def _concatenate_wavs(wav_files, output_path):
    """Concatenate WAV files using ffmpeg."""
    if len(wav_files) == 1:
        shutil.copy2(wav_files[0], output_path)
        return
    list_file = output_path + ".txt"
    with open(list_file, "w") as f:
        for wav in wav_files:
            f.write(f"file '{wav}'\n")
    subprocess.run(
        ["ffmpeg", "-y", "-f", "concat", "-safe", "0",
         "-i", list_file, "-c", "copy", output_path],
        capture_output=True, check=True,
    )
    os.remove(list_file)


def _build_system_prompt(language_name):
    """Build Qwen system prompt for a target language."""
    return (
        f"You are a professional video dubbing translator. You will receive audio in English.\n"
        f"Your task:\n"
        f"1. Listen carefully to the English speech.\n"
        f"2. Translate it into natural, fluent {language_name}.\n"
        f"3. Respond ONLY with the {language_name} translation spoken aloud — no English, no commentary,\n"
        f"   no meta-text, no transliteration. Speak entirely in {language_name}.\n"
        f"4. Match the tone, emotion, and pacing of the original speaker as closely as possible.\n"
        f"5. If there are pauses or silence in the original audio, maintain similar pacing.\n"
        f"6. Translate idioms and cultural references into their {language_name} equivalents.\n"
        f"7. Use clear, professional pronunciation suitable for a broad audience."
    )


def translate_chunk_qwen(wav_path, voice, language_name, chunk_index=0):
    """
    Translate a single audio chunk using Qwen Omni.
    
    Args:
        wav_path: Path to input WAV file (English audio)
        voice: Qwen voice name (e.g. "Ethan", "Cherry")
        language_name: Full language name for the system prompt
        chunk_index: For logging
    
    Returns:
        (output_wav_path, transcript) or (None, transcript) if no audio
    """
    client = _get_client()
    audio_b64 = _wav_to_base64(wav_path)
    output_wav = wav_path.replace(".wav", f"_qwen_{chunk_index}.wav")

    system_prompt = _build_system_prompt(language_name)
    user_prompt = f"Translate this English speech into {language_name}. Respond only with the spoken {language_name} translation."

    t0 = time.time()
    completion = client.chat.completions.create(
        model=QWEN_MODEL,
        messages=[
            {"role": "system", "content": system_prompt},
            {
                "role": "user",
                "content": [
                    {
                        "type": "input_audio",
                        "input_audio": {
                            "data": f"data:audio/wav;base64,{audio_b64}",
                            "format": "wav",
                        },
                    },
                    {"type": "text", "text": user_prompt},
                ],
            },
        ],
        modalities=["text", "audio"],
        audio={"voice": voice, "format": "wav"},
        stream=True,
        stream_options={"include_usage": True},
    )

    audio_chunks = []
    transcript_parts = []

    for event in completion:
        if not event.choices:
            continue
        delta = event.choices[0].delta
        if hasattr(delta, "content") and delta.content:
            transcript_parts.append(delta.content)
        if hasattr(delta, "audio") and delta.audio:
            if isinstance(delta.audio, dict):
                if "data" in delta.audio:
                    audio_chunks.append(delta.audio["data"])
            elif hasattr(delta.audio, "data") and delta.audio.data:
                audio_chunks.append(delta.audio.data)

    transcript = "".join(transcript_parts)
    elapsed = time.time() - t0
    logger.info(f"Qwen chunk {chunk_index}: {elapsed:.1f}s, transcript={transcript[:60]}")

    if audio_chunks:
        full_audio_b64 = "".join(audio_chunks)
        _base64_to_wav(full_audio_b64, output_wav)
        return output_wav, transcript

    return None, transcript


def dub_video_qwen(video_path, language_name, voice="Ethan", chunk_seconds=120, progress_fn=None):
    """
    Full video dubbing pipeline using Qwen Omni.
    Splits video into chunks, translates each chunk via Qwen API,
    concatenates results, and muxes back onto video.
    
    Args:
        video_path: Path to input video
        language_name: Full language name (e.g. "French", "Arabic")
        voice: Qwen voice name
        chunk_seconds: Audio chunk duration for API calls
        progress_fn: Optional gradio progress callback
    
    Returns:
        (output_video_path, log_text)
    """
    tmp_dir = tempfile.mkdtemp(prefix=f"qwen_dub_")
    log = []

    try:
        # Duration
        if progress_fn:
            progress_fn(0.05, desc="Analyzing video...")
        total_duration = _get_duration(video_path)
        log.append(f"**Video:** {total_duration:.1f}s")
        log.append(f"**Engine:** Qwen 3.5 Omni")
        log.append(f"**Voice:** {voice}")
        log.append(f"**Language:** {language_name}")

        if total_duration > 3600:
            return None, "Video longer than 1 hour — please use a shorter clip."

        # Split into chunks
        if progress_fn:
            progress_fn(0.1, desc="Extracting audio chunks...")
        num_chunks = max(1, int(total_duration // chunk_seconds) + (1 if total_duration % chunk_seconds > 0 else 0))
        log.append(f"**Chunks:** {num_chunks} ({chunk_seconds}s each)")

        input_chunks = []
        for i in range(num_chunks):
            start = i * chunk_seconds
            duration = min(chunk_seconds, total_duration - start)
            chunk_path = os.path.join(tmp_dir, f"chunk_{i:03d}.wav")
            _extract_audio_chunk(video_path, chunk_path, start, duration)
            input_chunks.append(chunk_path)

        # Translate each chunk
        output_chunks = []
        all_transcripts = []

        for i, chunk_path in enumerate(input_chunks):
            if progress_fn:
                frac = 0.15 + 0.7 * (i / num_chunks)
                progress_fn(frac, desc=f"Translating chunk {i+1}/{num_chunks}...")

            result_path, transcript = translate_chunk_qwen(
                chunk_path, voice, language_name, i
            )
            if transcript:
                all_transcripts.append(f"**[{i+1}]** {transcript}")

            if result_path:
                output_chunks.append(result_path)
            else:
                # Silence fallback
                duration = _get_duration(chunk_path)
                silence_path = os.path.join(tmp_dir, f"silence_{i:03d}.wav")
                subprocess.run(
                    ["ffmpeg", "-y", "-f", "lavfi",
                     "-i", "anullsrc=r=24000:cl=mono",
                     "-t", str(duration), "-acodec", "pcm_s16le", silence_path],
                    capture_output=True, check=True,
                )
                output_chunks.append(silence_path)

        # Concatenate
        if progress_fn:
            progress_fn(0.88, desc="Assembling audio...")
        full_audio = os.path.join(tmp_dir, "full_dubbed.wav")
        _concatenate_wavs(output_chunks, full_audio)

        # Mux onto video
        if progress_fn:
            progress_fn(0.93, desc="Combining audio and video...")
        output_video = os.path.join(tmp_dir, "dubbed_output.mp4")
        subprocess.run(
            ["ffmpeg", "-y", "-i", video_path, "-i", full_audio,
             "-c:v", "copy", "-map", "0:v:0", "-map", "1:a:0",
             "-shortest", output_video],
            capture_output=True, check=True,
        )

        if progress_fn:
            progress_fn(1.0, desc="Done!")

        log.append(f"\n**Transcript:**")
        log.extend(all_transcripts)

        return output_video, "\n".join(log)

    except Exception as e:
        logger.exception("Qwen dubbing failed")
        shutil.rmtree(tmp_dir, ignore_errors=True)
        return None, f"Error: {str(e)}"