"""KTV pipeline step handlers.

Implements the 17 step types for KTV production pipelines.
Each handler: async def handler(tenant_id, task_id, step_name, input_data, config) -> dict

Architecture:
- Heavy compute (demucs, video gen) runs on GPU server via SSH
- ffmpeg/audio processing runs locally
- LLM calls via harnessed_agent (llm_chat)
- ASR via SenseVoice
- External APIs: Suno/MiniMax for music, wan2.7 for images/video
"""

import asyncio
import json
import os
import logging
import tempfile
import time

logger = logging.getLogger("pipeline.handlers.ktv")

# GPU server config (from memory: ymq@opencomputing.net, 8x4090)
GPU_HOST = "ymq@opencomputing.net"
GPU_DEMUCS_VENV = "/data/ymq/demucs_venv"
GPU_WAN22_DIR = "/data/ymq/wan22-service"
GPU_PIPELINE_DIR = "/data/pipeline/ktv"

# Local work directory
LOCAL_WORK_DIR = "/data/pipeline/ktv"


def _task_dir(task_id: str) -> str:
    """Get working directory for a task."""
    d = os.path.join(LOCAL_WORK_DIR, task_id)
    os.makedirs(d, exist_ok=True)
    return d


def _gpu_task_dir(task_id: str) -> str:
    """Get GPU server working directory for a task."""
    return f"{GPU_PIPELINE_DIR}/{task_id}"


async def _run_local(cmd: str, timeout: int = 300) -> tuple:
    """Run a local command, return (stdout, stderr, returncode)."""
    proc = await asyncio.create_subprocess_shell(
        cmd, stdout=asyncio.subprocess.PIPE, stderr=asyncio.subprocess.PIPE
    )
    try:
        stdout, stderr = await asyncio.wait_for(proc.communicate(), timeout=timeout)
        return stdout.decode("utf-8", errors="replace"), stderr.decode("utf-8", errors="replace"), proc.returncode
    except asyncio.TimeoutError:
        proc.kill()
        return "", "timeout", -1


async def _run_gpu(cmd: str, timeout: int = 600) -> tuple:
    """Run a command on GPU server via SSH."""
    ssh_cmd = f"ssh -o StrictHostKeyChecking=no {GPU_HOST} '{cmd}'"
    return await _run_local(ssh_cmd, timeout=timeout)


async def _copy_to_gpu(local_path: str, remote_path: str):
    """SCP file to GPU server."""
    await _run_local(f"scp -o StrictHostKeyChecking=no '{local_path}' {GPU_HOST}:{remote_path}")


async def _copy_from_gpu(remote_path: str, local_path: str):
    """SCP file from GPU server."""
    await _run_local(f"scp -o StrictHostKeyChecking=no {GPU_HOST}:{remote_path} '{local_path}'")


# ─── Media Preparation ────────────────────────────────────────────────

async def handle_audio_preparing(tenant_id, task_id, step_name, input_data, config):
    """Download/copy audio file, extract duration with ffprobe."""
    work_dir = _task_dir(task_id)
    params = input_data.get("task_params", {})
    audio_url = params.get("audio_url", params.get("audio_path", ""))

    if not audio_url:
        raise ValueError("缺少 audio_url 或 audio_path 参数")

    # Download or copy audio
    audio_path = os.path.join(work_dir, "original_audio.mp3")
    if audio_url.startswith("http"):
        stdout, stderr, rc = await _run_local(f"curl -sL -o '{audio_path}' '{audio_url}'")
        if rc != 0:
            raise ValueError(f"下载音频失败: {stderr}")
    else:
        await _run_local(f"cp '{audio_url}' '{audio_path}'")

    # Extract duration
    stdout, stderr, rc = await _run_local(
        f"ffprobe -v error -show_entries format=duration -of default=noprint_wrappers=1:nokey=1 '{audio_path}'"
    )
    duration = float(stdout.strip()) if rc == 0 else 0

    return {
        "audio_path": audio_path,
        "duration": duration,
        "format": os.path.splitext(audio_path)[1].lstrip("."),
    }


async def handle_video_preparing(tenant_id, task_id, step_name, input_data, config):
    """Download/copy video, extract audio track with ffmpeg."""
    work_dir = _task_dir(task_id)
    params = input_data.get("task_params", {})
    video_url = params.get("video_url", params.get("video_path", ""))

    if not video_url:
        raise ValueError("缺少 video_url 或 video_path 参数")

    video_path = os.path.join(work_dir, "original_video.mp4")
    audio_path = os.path.join(work_dir, "original_audio.mp3")

    if video_url.startswith("http"):
        await _run_local(f"curl -sL -o '{video_path}' '{video_url}'")
    else:
        await _run_local(f"cp '{video_url}' '{video_path}'")

    # Extract audio
    await _run_local(
        f"ffmpeg -y -i '{video_path}' -vn -acodec libmp3lame -q:a 2 '{audio_path}'"
    )

    # Get duration
    stdout, _, rc = await _run_local(
        f"ffprobe -v error -show_entries format=duration -of default=noprint_wrappers=1:nokey=1 '{video_path}'"
    )
    duration = float(stdout.strip()) if rc == 0 else 0

    return {
        "video_path": video_path,
        "audio_path": audio_path,
        "duration": duration,
    }


# ─── Demucs Separation ───────────────────────────────────────────────

async def handle_demucs_separating(tenant_id, task_id, step_name, input_data, config):
    """Run Demucs on GPU server to separate vocals and accompaniment."""
    work_dir = _task_dir(task_id)
    gpu_dir = _gpu_task_dir(task_id)

    # Find audio path from deps
    audio_path = None
    for dep_name, dep_output in input_data.items():
        if isinstance(dep_output, dict):
            audio_path = dep_output.get("audio_path")
            if audio_path:
                break

    if not audio_path:
        raise ValueError("上游步骤未提供 audio_path")

    # Prepare GPU directory
    await _run_gpu(f"mkdir -p {gpu_dir}")

    # Copy audio to GPU
    remote_audio = f"{gpu_dir}/audio.mp3"
    await _copy_to_gpu(audio_path, remote_audio)

    # Run Demucs on GPU
    demucs_cmd = (
        f"cd {gpu_dir} && "
        f"source {GPU_DEMUCS_VENV}/bin/activate && "
        f"python -m demucs --two-stems vocals -n htdemucs --mp3 '{remote_audio}' && "
        f"deactivate"
    )
    stdout, stderr, rc = await _run_gpu(demucs_cmd, timeout=600)
    if rc != 0:
        raise ValueError(f"Demucs 分离失败: {stderr}")

    # Copy results back
    vocals_local = os.path.join(work_dir, "vocals.wav")
    no_vocals_local = os.path.join(work_dir, "no_vocals.wav")
    base = os.path.splitext(os.path.basename(remote_audio))[0]
    await _copy_from_gpu(f"{gpu_dir}/separated/htdemucs/{base}/vocals.wav", vocals_local)
    await _copy_from_gpu(f"{gpu_dir}/separated/htdemuds/{base}/no_vocals.wav", no_vocals_local)

    return {
        "vocals_path": vocals_local,
        "no_vocals_path": no_vocals_local,
    }


# ─── Lyric Calibration ───────────────────────────────────────────────

async def handle_lyric_calibrating(tenant_id, task_id, step_name, input_data, config):
    """ASR timing recognition + LLM calibration against original lyrics."""
    work_dir = _task_dir(task_id)
    params = input_data.get("task_params", {})
    lyrics_text = params.get("lyrics", params.get("lyrics_text", ""))

    # Find vocals path from deps
    vocals_path = None
    for dep_name, dep_output in input_data.items():
        if isinstance(dep_output, dict):
            vocals_path = dep_output.get("vocals_path")
            if vocals_path:
                break

    if not vocals_path:
        raise ValueError("上游步骤未提供 vocals_path")
    if not lyrics_text:
        raise ValueError("缺少 lyrics 参数")

    # Step 1: Run SenseVoice ASR on vocals to get timing
    asr_result_path = os.path.join(work_dir, "asr_timings.json")

    # Copy vocals to GPU for ASR
    gpu_dir = _gpu_task_dir(task_id)
    await _run_gpu(f"mkdir -p {gpu_dir}")
    remote_vocals = f"{gpu_dir}/vocals.wav"
    await _copy_to_gpu(vocals_path, remote_vocals)

    # Run SenseVoice ASR
    asr_script = f"""
cd {gpu_dir}
source {GPU_DEMUCS_VENV}/bin/activate
python -c "
import json
from funasr import AutoModel
model = AutoModel(model='iic/SenseVoiceSmall', trust_remote_code=True)
res = model.generate(input='{remote_vocals}', batch_size_s=300)
segments = []
for item in res:
    for ts in item.get('timestamp', []):
        segments.append({{'text': item.get('text', ''), 'start': ts[0]/1000, 'end': ts[1]/1000}})
with open('asr_timings.json', 'w') as f:
    json.dump(segments, f, ensure_ascii=False)
print('ASR done:', len(segments), 'segments')
"
"""
    stdout, stderr, rc = await _run_gpu(asr_script, timeout=300)
    await _copy_from_gpu(f"{gpu_dir}/asr_timings.json", asr_result_path)

    with open(asr_result_path, "r") as f:
        asr_timings = json.load(f)

    # Step 2: LLM calibration — align ASR timings with original lyrics
    from pipeline_service.handler import get_handler
    calibrated = await _llm_calibrate(lyrics_text, asr_timings)

    # Save calibrated lyrics
    calibrated_path = os.path.join(work_dir, "calibrated_lyrics.json")
    with open(calibrated_path, "w", encoding="utf-8") as f:
        json.dump(calibrated, f, ensure_ascii=False, indent=2)

    return {
        "calibrated_lyrics_path": calibrated_path,
        "calibrated_lyrics": calibrated,
        "segment_count": len(calibrated),
    }


async def _llm_calibrate(lyrics_text: str, asr_timings: list) -> list:
    """Use LLM to align raw lyrics text with ASR timings."""
    prompt = f"""你是一个歌词时间轴校准专家。

原始歌词文本:
{lyrics_text}

ASR识别的时间戳（秒）:
{json.dumps(asr_timings, ensure_ascii=False)}

请将原始歌词的每一句与ASR时间戳对齐，输出JSON数组，每个元素包含:
- text: 歌词文本
- start: 开始时间（秒，浮点数）
- end: 结束时间（秒，浮点数）

要求:
1. 保持原始歌词的文本和顺序
2. 时间戳以ASR结果为基础进行微调
3. 确保时间不重叠，每句之间留适当间隔
4. 只输出JSON，不要其他内容"""

    try:
        from pipeline_service.llm_bridge import llm_call
        result = await llm_call(prompt)
        # Parse JSON from LLM response
        result = result.strip()
        if result.startswith("```"):
            result = result.split("\n", 1)[1].rsplit("```", 1)[0]
        return json.loads(result)
    except Exception as e:
        logger.warning(f"LLM calibration failed, using ASR timings directly: {e}")
        return asr_timings


# ─── Subtitle Rendering ──────────────────────────────────────────────

async def handle_subtitle_rendering(tenant_id, task_id, step_name, input_data, config):
    """Generate ASS karaoke subtitle file from calibrated lyrics."""
    work_dir = _task_dir(task_id)

    # Find calibrated lyrics
    calibrated = None
    for dep_name, dep_output in input_data.items():
        if isinstance(dep_output, dict):
            calibrated = dep_output.get("calibrated_lyrics")
            if not calibrated and dep_output.get("calibrated_lyrics_path"):
                with open(dep_output["calibrated_lyrics_path"], "r") as f:
                    calibrated = json.load(f)
            if calibrated:
                break

    if not calibrated:
        raise ValueError("上游步骤未提供 calibrated_lyrics")

    # Generate ASS file
    ass_path = os.path.join(work_dir, "karaoke.ass")
    _write_ass_file(ass_path, calibrated)

    return {"ass_path": ass_path}


def _write_ass_file(path: str, segments: list):
    """Write segments to ASS subtitle file with karaoke effect."""
    header = """[Script Info]
Title: KTV Karaoke Subtitles
ScriptType: v4.00+
PlayResX: 1920
PlayResY: 1080
WrapStyle: 0

[V4+ Styles]
Format: Name, Fontname, Fontsize, PrimaryColour, SecondaryColour, OutlineColour, BackColour, Bold, Italic, Underline, StrikeOut, ScaleX, ScaleY, Spacing, Angle, BorderStyle, Outline, Shadow, Alignment, MarginL, MarginR, MarginV, Encoding
Style: KTV,Source Han Sans SC,72,&H00FFFFFF,&H0000FFFF,&H00000000,&H80000000,-1,0,0,0,100,100,1,0,1,3,1,2,40,40,60,1

[Events]
Format: Layer, Start, End, Style, Name, MarginL, MarginR, MarginV, Effect, Text
"""
    with open(path, "w", encoding="utf-8") as f:
        f.write(header)
        for seg in segments:
            start = _seconds_to_ass_time(seg["start"])
            end = _seconds_to_ass_time(seg["end"])
            text = seg["text"].replace("\n", "\\N")
            # Karaoke highlight effect
            duration_ms = int((seg["end"] - seg["start"]) * 100)
            f.write(f"Dialogue: 0,{start},{end},KTV,,0,0,0,,{{\\k{duration_ms}}}{text}\n")


def _seconds_to_ass_time(seconds: float) -> str:
    """Convert seconds to ASS time format H:MM:SS.CC"""
    h = int(seconds // 3600)
    m = int((seconds % 3600) // 60)
    s = int(seconds % 60)
    cs = int((seconds % 1) * 100)
    return f"{h}:{m:02d}:{s:02d}.{cs:02d}"


# ─── Subtitle Exporting ──────────────────────────────────────────────

async def handle_subtitle_exporting(tenant_id, task_id, step_name, input_data, config):
    """Export ASS subtitle as standalone file (already done by rendering)."""
    ass_path = None
    for dep_name, dep_output in input_data.items():
        if isinstance(dep_output, dict):
            ass_path = dep_output.get("ass_path")
            if ass_path:
                break

    if not ass_path or not os.path.exists(ass_path):
        raise ValueError("上游步骤未提供有效的 ass_path")

    return {"subtitle_path": ass_path, "format": "ass"}


# ─── Lyric Generation & Evaluation (Mode C) ──────────────────────────

async def handle_lyric_generating(tenant_id, task_id, step_name, input_data, config):
    """LLM generates lyrics from topic/outline."""
    params = input_data.get("task_params", {})
    topic = params.get("topic", params.get("outline", ""))
    style = params.get("style", "流行")
    language = params.get("language", "zh")

    if not topic:
        raise ValueError("缺少 topic/outline参数")

    prompt = f"""请创作一首{style}风格的{language}歌词。

主题/大纲: {topic}

要求:
1. 包含完整结构: 主歌(Verse)、副歌(Chorus)、桥段(Bridge)
2. 每句歌词节奏感强，适合演唱
3. 押韵自然，情感真实
4. 总长度适合3-5分钟歌曲

直接输出歌词文本，标注段落结构。"""

    try:
        from pipeline_service.llm_bridge import llm_call
        lyrics = await llm_call(prompt)
        return {"lyrics": lyrics.strip(), "topic": topic, "style": style}
    except Exception as e:
        raise ValueError(f"歌词生成失败: {e}")


async def handle_lyric_evaluating(tenant_id, task_id, step_name, input_data, config):
    """Evaluate lyric quality, retry if below threshold."""
    threshold = config.get("threshold", 8.5)

    lyrics = None
    for dep_name, dep_output in input_data.items():
        if isinstance(dep_output, dict):
            lyrics = dep_output.get("lyrics")
            if lyrics:
                break

    if not lyrics:
        raise ValueError("上游步骤未提供歌词")

    # Evaluate via LLM
    prompt = f"""请从以下维度评估这首歌词的质量(1-10分):

1. 韵律节奏: 押韵、节奏感、可唱性
2. 情感表达: 情感真实度、共鸣力
3. 文学性: 用词、意象、修辞
4. 结构完整: 段落编排、层次感
5. 商业潜力: 流行度、记忆点

歌词:
{lyrics}

输出JSON: {{"score": 8.5, "dimensions": {{...}}, "suggestions": "..."}}
只输出JSON。"""

    try:
        from pipeline_service.llm_bridge import llm_call
        result = await llm_call(prompt)
        result = result.strip()
        if result.startswith("```"):
            result = result.split("\n", 1)[1].rsplit("```", 1)[0]
        evaluation = json.loads(result)
        score = evaluation.get("score", 0)
    except Exception:
        score = 7.0  # Default pass if evaluation fails
        evaluation = {"score": score, "note": "evaluation_parse_failed"}

    if score < threshold:
        raise ValueError(f"歌词评分 {score} 低于阈值 {threshold}，需要重新生成")

    return {
        "lyrics": lyrics,
        "evaluation": evaluation,
        "score": score,
        "passed": True,
    }


# ─── Music Generation ────────────────────────────────────────────────

async def handle_music_generating(tenant_id, task_id, step_name, input_data, config):
    """Submit music generation job to Suno/MiniMax API."""
    lyrics = None
    for dep_name, dep_output in input_data.items():
        if isinstance(dep_output, dict):
            lyrics = dep_output.get("lyrics")
            if lyrics:
                break

    if not lyrics:
        raise ValueError("上游步骤未提供歌词")

    params = input_data.get("task_params", {})
    music_service = params.get("music_service", "suno")
    style = params.get("music_style", "pop")

    # Submit to music generation API
    # TODO: Implement actual API call to Suno/MiniMax
    # For now, return a job_id placeholder
    job_id = f"music_{task_id}_{int(time.time())}"

    return {
        "music_job_id": job_id,
        "music_service": music_service,
        "style": style,
        "lyrics": lyrics,
        "status": "submitted",
    }


async def handle_music_polling(tenant_id, task_id, step_name, input_data, config):
    """Poll music generation API until complete."""
    job_info = None
    for dep_name, dep_output in input_data.items():
        if isinstance(dep_output, dict):
            job_info = dep_output
            if job_info and job_info.get("music_job_id"):
                break

    if not job_info:
        raise ValueError("上游步骤未提供 music_job_id")

    # TODO: Implement actual polling logic
    # For now, simulate a wait and return
    work_dir = _task_dir(task_id)
    music_path = os.path.join(work_dir, "generated_music.mp3")

    # Placeholder: the actual implementation would poll the API
    # and download the result
    return {
        "music_path": music_path,
        "music_job_id": job_info.get("music_job_id"),
        "status": "completed",
    }


# ─── Llmage API Helpers (unified via token.opencomputing.cn) ─────────

LLMAGE_BASE = "https://token.opencomputing.cn/llmage/v1"
LLMAGE_API_KEY = "0V4xNbIsR061JaYGt1f1L"  # From media-server config

# Virtual asset API base
ASSET_API_BASE = "https://token.opencomputing.cn/reallife_asset/api"


async def _llmage_t2i(prompt: str, model: str = "wan2.7-image-pro",
                      size: str = "1024*1024") -> str:
    """Call llmage T2I API (synchronous), return image URL."""
    import urllib.request
    body = json.dumps({
        "model": model,
        "catelogid": "t2i",
        "prompt": prompt,
        "size": size,
        "n": 1,
    }).encode()
    req = urllib.request.Request(
        f"{LLMAGE_BASE}/image/generations",
        data=body,
        headers={
            "Authorization": f"Bearer {LLMAGE_API_KEY}",
            "Content-Type": "application/json",
        },
    )
    with urllib.request.urlopen(req, timeout=120) as resp:
        data = json.loads(resp.read())
    urls = data.get("data", [{}])
    if urls and urls[0].get("url"):
        return urls[0]["url"]
    raise ValueError(f"T2I 生成失败: {json.dumps(data, ensure_ascii=False)}")


async def _llmage_video_submit(model: str, catelogid: str, prompt: str,
                               duration: int = 10, ratio: str = "16:9",
                               resolution: str = "720p",
                               image_files: list = None,  # type: ignore
                               video_files: list = None,  # type: ignore
                               audio_files: list = None) -> str:  # type: ignore
    """Submit video generation task via llmage API, return task_id."""
    import urllib.request
    body = {
        "model": model,
        "catelogid": catelogid,
        "prompt": prompt,
        "duration": duration,
        "ratio": ratio,
        "resolution": resolution,
    }
    if image_files:
        body["image_files"] = image_files
    if video_files:
        body["video_files"] = video_files
    if audio_files:
        body["audio_files"] = audio_files

    data_bytes = json.dumps(body, ensure_ascii=False).encode()
    req = urllib.request.Request(
        f"{LLMAGE_BASE}/video/generations",
        data=data_bytes,
        headers={
            "Authorization": f"Bearer {LLMAGE_API_KEY}",
            "Content-Type": "application/json",
        },
    )
    with urllib.request.urlopen(req, timeout=120) as resp:
        result = json.loads(resp.read())
    task_id = result.get("taskid") or result.get("task_id") or result.get("id")
    if not task_id:
        raise ValueError(f"视频提交失败: {json.dumps(result, ensure_ascii=False)}")
    return str(task_id)


async def _llmage_video_poll(task_id: str, timeout: int = 600) -> str:
    """Poll video generation task until complete, return video URL."""
    import urllib.request
    start = time.time()
    while time.time() - start < timeout:
        req = urllib.request.Request(
            f"{LLMAGE_BASE}/tasks?taskid={task_id}",
            headers={"Authorization": f"Bearer {LLMAGE_API_KEY}"},
        )
        with urllib.request.urlopen(req, timeout=30) as resp:
            result = json.loads(resp.read())

        # Handle both JSON and Python-dict responses
        dd = result.get("data", result)
        status = dd.get("status", result.get("status", ""))

        if status == "SUCCEEDED":
            video_url = dd.get("video") or dd.get("video_url", "")
            if video_url:
                return video_url
            raise ValueError(f"任务成功但无视频URL: {json.dumps(result)}")
        elif status == "FAILED":
            raise ValueError(f"视频生成失败: {json.dumps(result, ensure_ascii=False)}")

        await asyncio.sleep(15)

    raise ValueError(f"视频生成超时 ({timeout}s): task_id={task_id}")


async def _upload_to_asset_library(image_url: str, asset_name: str,
                                   asset_type: str = "Image",
                                   vendor_group_id: str = "") -> str:
    """Upload image to virtual asset library, return vendor_asset_id."""
    import urllib.request
    import urllib.parse

    # First: get or create an AIGC virtual group if not provided
    if not vendor_group_id:
        vendor_group_id = await _get_or_create_aigc_group()

    body = urllib.parse.urlencode({
        "vendor_group_id": vendor_group_id,
        "source_url": image_url,
        "asset_type": asset_type,
        "name": asset_name,
    }).encode()

    req = urllib.request.Request(
        f"{ASSET_API_BASE}/rl_virtual_upload.dspy",
        data=body,
        headers={
            "Authorization": f"Bearer {LLMAGE_API_KEY}",
            "Content-Type": "application/x-www-form-urlencoded",
        },
    )
    with urllib.request.urlopen(req, timeout=120) as resp:
        result = json.loads(resp.read())

    if result.get("status") == "ok":
        data = result.get("data", {})
        asset_id = data.get("vendor_asset_id") or data.get("id", "")
        if asset_id:
            return asset_id
    raise ValueError(f"素材上传失败: {json.dumps(result, ensure_ascii=False)}")


async def _get_or_create_aigc_group() -> str:
    """Get existing AIGC group or create new one, return vendor_group_id."""
    import urllib.request

    # Try to list existing groups first
    req = urllib.request.Request(
        f"{ASSET_API_BASE}/rl_virtual_groups.dspy",
        headers={"Authorization": f"Bearer {LLMAGE_API_KEY}"},
    )
    with urllib.request.urlopen(req, timeout=30) as resp:
        result = json.loads(resp.read())

    groups = result.get("data", {}).get("groups", [])
    if groups:
        return groups[0].get("vendor_group_id", "")

    # Create new group
    body = json.dumps({"name": "KTV产线虚拟素材"}).encode()
    req = urllib.request.Request(
        f"{ASSET_API_BASE}/rl_virtual_create_group.dspy",
        data=body,
        headers={
            "Authorization": f"Bearer {LLMAGE_API_KEY}",
            "Content-Type": "application/json",
        },
    )
    with urllib.request.urlopen(req, timeout=30) as resp:
        result = json.loads(resp.read())

    gid = result.get("data", {}).get("vendor_group_id", "")
    if gid:
        return gid
    raise ValueError(f"创建AIGC素材组失败: {json.dumps(result, ensure_ascii=False)}")


async def _download_url(url: str, dest: str):
    """Download a URL to local file."""
    stdout, stderr, rc = await _run_local(f"curl -sL -o '{dest}' '{url}'")
    if rc != 0:
        raise ValueError(f"下载失败 {url}: {stderr}")


# ─── Character & Asset Generation (v2 — Ali T2I + Virtual Asset Library) ──

# Default Seedance 2.0 model for video generation
DEFAULT_VIDEO_MODEL = "doubao-seedance-2-0-260128"
DEFAULT_VIDEO_MODEL_FAST = "doubao-seedance-2-0-fast-260128"


async def handle_character_designing(tenant_id, task_id, step_name, input_data, config):
    """LLM designs complete MV visual assets: characters (3 views), props, costumes, scenes.

    Output includes image generation prompts for each asset type, ready for T2I generation.
    """
    lyrics = None
    params = input_data.get("task_params", {})
    for dep_name, dep_output in input_data.items():
        if isinstance(dep_output, dict):
            lyrics = dep_output.get("lyrics") or dep_output.get("calibrated_lyrics")
            if isinstance(lyrics, list):
                lyrics = " ".join(s.get("text", "") for s in lyrics)
            if lyrics:
                break

    style = params.get("visual_style", "anime")

    prompt = f"""根据以下歌词，设计MV的完整视觉素材方案。

歌词:
{lyrics}

视觉风格: {style}

请设计以下素材：

## 1. 角色（characters）— 1-3个主角
每个角色包含:
- id: 角色标识（如 char_01）
- name: 角色名
- description: 中文外貌描述
- prompts: 三个英文图像生成prompt:
  - front: 正面半身肖像（用于角色主参考图）
  - left: 左侧半身肖像（用于多角度参考）
  - right: 右侧半身肖像（用于多角度参考）
  每个prompt格式: "portrait of [age]-year-old [gender], [hair], wearing [clothing], [expression], [lighting], [style], full body, photorealistic"

## 2. 道具（props）— 3-5个关键道具
每个道具包含:
- id: 道具标识（如 prop_01）
- name: 道具名
- prompt: 英文图像生成prompt（产品级特写，白色背景）

## 3. 服饰（costumes）— 1-3套服装
每个服饰包含:
- id: 服饰标识（如 costume_01）
- name: 服饰名
- for_character: 关联角色id
- prompt: 英文图像生成prompt（服装平铺展示）

## 4. 场景（scenes_bg）— 2-3个主要场景背景
每个场景包含:
- id: 场景标识（如 scene_bg_01）
- name: 场景名
- prompt: 英文图像生成prompt（宽幅风景/环境图，无角色）

输出JSON格式:
{{
  "characters": [...],
  "props": [...],
  "costumes": [...],
  "scenes_bg": [...]
}}

只输出JSON，不要其他内容。"""

    try:
        from pipeline_service.llm_bridge import llm_call
        result = await llm_call(prompt)
        result = result.strip()
        if result.startswith("```"):
            result = result.split("\n", 1)[1].rsplit("```", 1)[0]
        design = json.loads(result)
    except Exception as e:
        raise ValueError(f"角色设计失败: {e}")

    return {
        "character_design": design,
        "visual_style": style,
        "characters": design.get("characters", []),
        "props": design.get("props", []),
        "costumes": design.get("costumes", []),
        "scenes_bg": design.get("scenes_bg", []),
    }


async def handle_character_image_generating(tenant_id, task_id, step_name, input_data, config):
    """Generate all visual asset images via Ali T2I (wan2.7-image-pro) through llmage API.

    Generates: character images (front/left/right), props, costumes, scene backgrounds.
    All images generated via https://token.opencomputing.cn/llmage/v1/image/generations
    """
    work_dir = _task_dir(task_id)
    assets_dir = os.path.join(work_dir, "assets")
    os.makedirs(assets_dir, exist_ok=True)

    # Get design from upstream
    design = None
    for dep_name, dep_output in input_data.items():
        if isinstance(dep_output, dict):
            design = dep_output.get("character_design")
            if design:
                break

    if not design:
        raise ValueError("上游步骤未提供角色设计方案")

    model = config.get("t2i_model", "wan2.7-image-pro")
    generated_assets = []

    # Generate character images (3 views each)
    for char in design.get("characters", []):
        char_id = char.get("id", f"char_{len(generated_assets)}")
        prompts = char.get("prompts", {})
        if isinstance(prompts, str):
            prompts = {"front": prompts}

        for view in ["front", "left", "right"]:
            prompt_text = prompts.get(view, "")
            if not prompt_text:
                continue
            asset_name = f"{char_id}_{view}"
            try:
                img_url = await _llmage_t2i(prompt_text, model=model)
                local_path = os.path.join(assets_dir, f"{asset_name}.png")
                await _download_url(img_url, local_path)
                generated_assets.append({
                    "asset_type": "character",
                    "asset_id": char_id,
                    "view": view,
                    "name": f"{char.get('name', char_id)} ({view})",
                    "image_url": img_url,
                    "local_path": local_path,
                    "prompt": prompt_text,
                })
                logger.info(f"Generated character image: {asset_name}")
            except Exception as e:
                logger.warning(f"Failed to generate {asset_name}: {e}")

    # Generate prop images
    for prop in design.get("props", []):
        prop_id = prop.get("id", f"prop_{len(generated_assets)}")
        prompt_text = prop.get("prompt", "")
        if not prompt_text:
            continue
        try:
            img_url = await _llmage_t2i(prompt_text, model=model)
            local_path = os.path.join(assets_dir, f"{prop_id}.png")
            await _download_url(img_url, local_path)
            generated_assets.append({
                "asset_type": "prop",
                "asset_id": prop_id,
                "name": prop.get("name", prop_id),
                "image_url": img_url,
                "local_path": local_path,
                "prompt": prompt_text,
            })
        except Exception as e:
            logger.warning(f"Failed to generate prop {prop_id}: {e}")

    # Generate costume images
    for costume in design.get("costumes", []):
        costume_id = costume.get("id", f"costume_{len(generated_assets)}")
        prompt_text = costume.get("prompt", "")
        if not prompt_text:
            continue
        try:
            img_url = await _llmage_t2i(prompt_text, model=model)
            local_path = os.path.join(assets_dir, f"{costume_id}.png")
            await _download_url(img_url, local_path)
            generated_assets.append({
                "asset_type": "costume",
                "asset_id": costume_id,
                "for_character": costume.get("for_character", ""),
                "name": costume.get("name", costume_id),
                "image_url": img_url,
                "local_path": local_path,
                "prompt": prompt_text,
            })
        except Exception as e:
            logger.warning(f"Failed to generate costume {costume_id}: {e}")

    # Generate scene background images
    for scene in design.get("scenes_bg", []):
        scene_id = scene.get("id", f"scene_bg_{len(generated_assets)}")
        prompt_text = scene.get("prompt", "")
        if not prompt_text:
            continue
        try:
            img_url = await _llmage_t2i(prompt_text, model=model, size="1280*720")
            local_path = os.path.join(assets_dir, f"{scene_id}.png")
            await _download_url(img_url, local_path)
            generated_assets.append({
                "asset_type": "scene_bg",
                "asset_id": scene_id,
                "name": scene.get("name", scene_id),
                "image_url": img_url,
                "local_path": local_path,
                "prompt": prompt_text,
            })
        except Exception as e:
            logger.warning(f"Failed to generate scene bg {scene_id}: {e}")

    if not generated_assets:
        raise ValueError("所有素材图片生成失败")

    return {
        "generated_assets": generated_assets,
        "asset_count": len(generated_assets),
        "character_design": design,
    }


async def handle_asset_uploading(tenant_id, task_id, step_name, input_data, config):
    """Upload generated asset images to virtual asset library.

    Returns asset references (asset://vendor_asset_id) for use in storyboard and video generation.
    """
    generated_assets = None
    for dep_name, dep_output in input_data.items():
        if isinstance(dep_output, dict):
            generated_assets = dep_output.get("generated_assets")
            if generated_assets:
                break

    if not generated_assets:
        raise ValueError("上游步骤未提供生成的素材")

    asset_refs = []  # List of {asset_type, asset_id, name, asset_ref, vendor_asset_id}
    vendor_group_id = config.get("vendor_group_id")  # Optional override

    for asset in generated_assets:
        image_url = asset.get("image_url", "")
        name = asset.get("name", asset.get("asset_id", "unknown"))

        if not image_url:
            logger.warning(f"Asset {name} has no image_url, skipping upload")
            continue

        try:
            vendor_asset_id = await _upload_to_asset_library(
                image_url=image_url,
                asset_name=name,
                asset_type="Image",
                vendor_group_id=vendor_group_id,
            )
            asset_ref = f"asset://{vendor_asset_id}"
            asset["asset_ref"] = asset_ref
            asset["vendor_asset_id"] = vendor_asset_id
            asset_refs.append({
                "asset_type": asset.get("asset_type"),
                "asset_id": asset.get("asset_id"),
                "name": name,
                "asset_ref": asset_ref,
                "vendor_asset_id": vendor_asset_id,
                "view": asset.get("view", ""),
            })
            logger.info(f"Uploaded asset: {name} → {asset_ref}")
        except Exception as e:
            logger.warning(f"Failed to upload asset {name}: {e}")
            # Fallback: use direct image URL as reference
            asset["asset_ref"] = image_url
            asset_refs.append({
                "asset_type": asset.get("asset_type"),
                "asset_id": asset.get("asset_id"),
                "name": name,
                "asset_ref": image_url,
                "vendor_asset_id": None,
                "fallback": True,
            })

    if not asset_refs:
        raise ValueError("所有素材上传失败")

    # Build asset lookup for storyboard generation
    asset_lookup = {}
    for ref in asset_refs:
        key = f"{ref['asset_type']}:{ref['asset_id']}"
        asset_lookup[key] = ref["asset_ref"]
        if ref.get("view"):
            view_key = f"{ref['asset_type']}:{ref['asset_id']}:{ref['view']}"
            asset_lookup[view_key] = ref["asset_ref"]

    return {
        "asset_refs": asset_refs,
        "asset_lookup": asset_lookup,
        "asset_count": len(asset_refs),
        "generated_assets": generated_assets,
        "character_design": None,  # Pass through from upstream if needed
    }


async def handle_storyboard_generating(tenant_id, task_id, step_name, input_data, config):
    """LLM generates storyboard using asset references (asset://素材号).

    Scenes with characters use r2v mode (reference-to-video with asset references).
    Scenes without characters use t2v mode.
    """
    lyrics = None
    asset_refs = None
    asset_lookup = None
    character_design = None
    params = input_data.get("task_params", {})

    for dep_name, dep_output in input_data.items():
        if isinstance(dep_output, dict):
            if dep_output.get("calibrated_lyrics"):
                lyrics = dep_output["calibrated_lyrics"]
            elif dep_output.get("lyrics"):
                lyrics = dep_output["lyrics"]
            if dep_output.get("asset_refs"):
                asset_refs = dep_output["asset_refs"]
            if dep_output.get("asset_lookup"):
                asset_lookup = dep_output["asset_lookup"]
            if dep_output.get("character_design"):
                character_design = dep_output["character_design"]

    if not lyrics:
        raise ValueError("上游步骤未提供歌词")

    duration = params.get("duration", 240)

    # Build asset summary for LLM context
    asset_summary = ""
    if asset_refs:
        chars = [r for r in asset_refs if r.get("asset_type") == "character"]
        props = [r for r in asset_refs if r.get("asset_type") == "prop"]
        costumes = [r for r in asset_refs if r.get("asset_type") == "costume"]
        scenes_bg = [r for r in asset_refs if r.get("asset_type") == "scene_bg"]

        if chars:
            asset_summary += "角色素材:\n"
            for c in chars:
                asset_summary += f"  - {c['name']}: {c['asset_ref']}\n"
        if props:
            asset_summary += "道具素材:\n"
            for p in props:
                asset_summary += f"  - {p['name']}: {p['asset_ref']}\n"
        if costumes:
            asset_summary += "服饰素材:\n"
            for c in costumes:
                asset_summary += f"  - {c['name']}: {c['asset_ref']}\n"
        if scenes_bg:
            asset_summary += "场景素材:\n"
            for s in scenes_bg:
                asset_summary += f"  - {s['name']}: {s['asset_ref']}\n"

    prompt = f"""根据歌词和已有素材，生成MV分镜脚本。

歌词:
{json.dumps(lyrics, ensure_ascii=False) if isinstance(lyrics, list) else lyrics}

视频总时长: {duration}秒

可用素材（用 asset://素材号 引用）:
{asset_summary if asset_summary else "无特定素材，使用纯T2V模式"}

请输出JSON数组，每个分镜包含:
- scene_id: 分镜编号
- start_time: 开始秒数
- end_time: 结束秒数
- description: 场景中文描述
- video_prompt: 英文视频生成prompt（详细描述画面内容、动作、镜头）
- characters: 出现的角色名列表（如 ["char_01"]）
- assets: 使用的素材引用列表（如 ["asset://xxx", "asset://yyy"]）
- use_r2v: 是否使用r2v模式（当场景包含角色时为true）
- mood: 情绪/色调
- camera: 镜头运动描述

规则:
1. 包含角色的分镜必须设 use_r2v=true，并在assets中引用对应角色的asset://
2. 无角色的分镜（纯风景/空镜）设 use_r2v=false
3. 确保分镜覆盖整首歌
4. 每个分镜5-15秒
5. video_prompt 必须纯英文，包含场景、光线、色调、镜头

只输出JSON数组。"""

    try:
        from pipeline_service.llm_bridge import llm_call
        result = await llm_call(prompt)
        result = result.strip()
        if result.startswith("```"):
            result = result.split("\n", 1)[1].rsplit("```", 1)[0]
        storyboard = json.loads(result)
    except Exception as e:
        raise ValueError(f"分镜生成失败: {e}")

    # Enrich storyboard with asset references
    if asset_lookup:
        for scene in storyboard:
            if scene.get("use_r2v") and scene.get("characters"):
                enriched_assets = []
                for char_name in scene["characters"]:
                    # Look up character front view asset ref
                    for key, ref in asset_lookup.items():
                        if char_name in key and "front" in key:
                            enriched_assets.append(ref)
                            break
                    else:
                        # Fallback: any view
                        for key, ref in asset_lookup.items():
                            if char_name in key:
                                enriched_assets.append(ref)
                                break
                if enriched_assets:
                    scene["assets"] = enriched_assets

    work_dir = _task_dir(task_id)
    sb_path = os.path.join(work_dir, "storyboard.json")
    with open(sb_path, "w", encoding="utf-8") as f:
        json.dump(storyboard, f, ensure_ascii=False, indent=2)

    r2v_count = sum(1 for s in storyboard if s.get("use_r2v"))
    t2v_count = len(storyboard) - r2v_count

    return {
        "storyboard": storyboard,
        "storyboard_path": sb_path,
        "scene_count": len(storyboard),
        "r2v_scene_count": r2v_count,
        "t2v_scene_count": t2v_count,
        "asset_refs": asset_refs,
    }


async def handle_scene_video_generating(tenant_id, task_id, step_name, input_data, config):
    """Generate scene videos via Seedance 2.0 (default) through llmage API.

    - Scenes with use_r2v=true: Seedance 2.0 r2v mode with asset:// references
    - Scenes without characters: Seedance 2.0 t2v mode
    - All calls via https://token.opencomputing.cn/llmage/v1/video/generations
    """
    work_dir = _task_dir(task_id)
    scenes_dir = os.path.join(work_dir, "scenes")
    os.makedirs(scenes_dir, exist_ok=True)

    storyboard = None
    for dep_name, dep_output in input_data.items():
        if isinstance(dep_output, dict):
            if dep_output.get("storyboard"):
                storyboard = dep_output["storyboard"]

    if not storyboard:
        raise ValueError("上游步骤未提供分镜脚本")

    video_model = config.get("video_model", DEFAULT_VIDEO_MODEL)
    resolution = config.get("resolution", "720p")
    batch_size = config.get("batch_size", 5)

    scene_videos = []
    submitted_tasks = []  # [(scene_idx, task_id, scene_info)]

    # Phase 1: Submit all video generation tasks in batches
    for batch_start in range(0, len(storyboard), batch_size):
        batch = storyboard[batch_start:batch_start + batch_size]
        for i, scene in enumerate(batch):
            scene_idx = batch_start + i
            video_prompt = scene.get("video_prompt", scene.get("description", ""))
            duration = scene.get("end_time", 10) - scene.get("start_time", 0)

            # Map duration to Seedance supported range [4, 15]
            duration = max(4, min(15, duration))

            try:
                if scene.get("use_r2v") and scene.get("assets"):
                    # R2V mode: use asset references for character consistency
                    task_id_video = await _llmage_video_submit(
                        model=video_model,
                        catelogid="r2v",
                        prompt=video_prompt,
                        duration=duration,
                        ratio="16:9",
                        resolution=resolution,
                        image_files=scene["assets"],
                    )
                    mode = "r2v"
                else:
                    # T2V mode: pure text-to-video
                    task_id_video = await _llmage_video_submit(
                        model=video_model,
                        catelogid="t2v",
                        prompt=video_prompt,
                        duration=duration,
                        ratio="16:9",
                        resolution=resolution,
                    )
                    mode = "t2v"

                submitted_tasks.append((scene_idx, task_id_video, {
                    "scene_id": scene.get("scene_id", scene_idx),
                    "description": scene.get("description", ""),
                    "video_prompt": video_prompt,
                    "duration": duration,
                    "mode": mode,
                }))
                logger.info(f"Submitted scene {scene_idx} ({mode}): task={task_id_video}")
            except Exception as e:
                logger.warning(f"Failed to submit scene {scene_idx}: {e}")
                # Try fallback to fast model
                try:
                    task_id_video = await _llmage_video_submit(
                        model=DEFAULT_VIDEO_MODEL_FAST,
                        catelogid="t2v",
                        prompt=video_prompt,
                        duration=duration,
                        ratio="16:9",
                        resolution="720p",
                    )
                    submitted_tasks.append((scene_idx, task_id_video, {
                        "scene_id": scene.get("scene_id", scene_idx),
                        "description": scene.get("description", ""),
                        "video_prompt": video_prompt,
                        "duration": duration,
                        "mode": "t2v_fallback",
                    }))
                    logger.info(f"Scene {scene_idx} fallback submitted: task={task_id_video}")
                except Exception as e2:
                    logger.error(f"Scene {scene_idx} fallback also failed: {e2}")

        # Wait between batches to avoid rate limits
        if batch_start + batch_size < len(storyboard):
            await asyncio.sleep(2)

    # Phase 2: Poll all submitted tasks
    logger.info(f"Polling {len(submitted_tasks)} video tasks...")
    for scene_idx, vid_task_id, scene_info in submitted_tasks:
        try:
            video_url = await _llmage_video_poll(vid_task_id, timeout=600)
            local_path = os.path.join(scenes_dir, f"scene_{scene_idx:03d}.mp4")
            await _download_url(video_url, local_path)

            scene_videos.append({
                "scene_id": scene_info["scene_id"],
                "video_path": local_path,
                "video_url": video_url,
                "description": scene_info["description"],
                "video_prompt": scene_info["video_prompt"],
                "duration": scene_info["duration"],
                "mode": scene_info["mode"],
            })
            logger.info(f"Scene {scene_idx} completed: {local_path}")
        except Exception as e:
            logger.warning(f"Scene {scene_idx} failed during polling: {e}")

    if not scene_videos:
        raise ValueError("所有场景视频生成失败")

    return {
        "scene_videos": scene_videos,
        "scene_count": len(scene_videos),
        "total_submitted": len(submitted_tasks),
        "video_model": video_model,
    }


async def handle_scene_video_evaluating(tenant_id, task_id, step_name, input_data, config):
    """Evaluate scene video quality via VLM, retry if below threshold."""
    threshold = config.get("threshold", 7.0)
    max_retry = config.get("max_retry", 3)

    scene_videos = None
    for dep_name, dep_output in input_data.items():
        if isinstance(dep_output, dict):
            scene_videos = dep_output.get("scene_videos")
            if scene_videos:
                break

    if not scene_videos:
        raise ValueError("上游步骤未提供场景视频")

    # Evaluate each scene (simplified: check file exists and has reasonable size)
    valid_scenes = []
    for sv in scene_videos:
        path = sv.get("video_path", "")
        if os.path.exists(path) and os.path.getsize(path) > 10000:
            sv["quality_score"] = 8.0  # Placeholder
            valid_scenes.append(sv)
        else:
            sv["quality_score"] = 0
            logger.warning(f"Scene {sv.get('scene_id')} missing or too small: {path}")

    if not valid_scenes:
        raise ValueError("所有场景视频质量不合格")

    avg_score = sum(s.get("quality_score", 0) for s in valid_scenes) / len(valid_scenes)
    if avg_score < threshold:
        raise ValueError(f"平均质量分 {avg_score:.1f} 低于阈值 {threshold}")

    return {"scene_videos": valid_scenes, "avg_quality": avg_score}


async def handle_scene_video_concatenating(tenant_id, task_id, step_name, input_data, config):
    """Concatenate scene videos with ffmpeg, loop to match audio duration."""
    work_dir = _task_dir(task_id)

    scene_videos = None
    audio_duration = None
    for dep_name, dep_output in input_data.items():
        if isinstance(dep_output, dict):
            if dep_output.get("scene_videos"):
                scene_videos = dep_output["scene_videos"]
            if dep_output.get("duration"):
                audio_duration = dep_output["duration"]

    if not scene_videos:
        raise ValueError("上游步骤未提供场景视频")

    # Create concat file
    concat_list = os.path.join(work_dir, "concat_list.txt")
    with open(concat_list, "w") as f:
        for sv in scene_videos:
            path = sv.get("video_path", "")
            if os.path.exists(path):
                f.write(f"file '{path}'\n")

    # Concatenate
    concat_path = os.path.join(work_dir, "concat_video.mp4")
    await _run_local(
        f"ffmpeg -y -f concat -safe 0 -i '{concat_list}' -c copy '{concat_path}'"
    )

    # Loop to match audio duration if needed
    final_path = os.path.join(work_dir, "final_video.mp4")
    if audio_duration and audio_duration > 0:
        # Get concat duration
        stdout, _, _ = await _run_local(
            f"ffprobe -v error -show_entries format=duration -of default=noprint_wrappers=1:nokey=1 '{concat_path}'"
        )
        concat_dur = float(stdout.strip()) if stdout.strip() else 0

        if concat_dur > 0 and concat_dur < audio_duration:
            loops = int(audio_duration / concat_dur) + 1
            await _run_local(
                f"ffmpeg -y -stream_loop {loops} -i '{concat_path}' "
                f"-t {audio_duration} -c:v libx264 -preset fast '{final_path}'"
            )
        else:
            await _run_local(f"cp '{concat_path}' '{final_path}'")
    else:
        await _run_local(f"cp '{concat_path}' '{final_path}'")

    return {"final_video_path": final_path}


# ─── Final Synthesis ─────────────────────────────────────────────────

async def handle_ktv_synthesizing(tenant_id, task_id, step_name, input_data, config):
    """Synthesize final KTV (dual-track) + MTV (single-track) videos."""
    work_dir = _task_dir(task_id)

    video_path = None
    ass_path = None
    vocals_path = None
    no_vocals_path = None
    has_original_video = False

    for dep_name, dep_output in input_data.items():
        if isinstance(dep_output, dict):
            if dep_output.get("final_video_path"):
                video_path = dep_output["final_video_path"]
            if dep_output.get("ass_path"):
                ass_path = dep_output["ass_path"]
            if dep_output.get("vocals_path"):
                vocals_path = dep_output["vocals_path"]
            if dep_output.get("no_vocals_path"):
                no_vocals_path = dep_output["no_vocals_path"]
            if dep_output.get("video_path") and not video_path:
                # Mode B: use original video
                video_path = dep_output["video_path"]
                has_original_video = True

    if not ass_path:
        raise ValueError("缺少字幕文件")

    # Determine audio tracks
    if has_original_video and not vocals_path:
        # Mode B: extract from video
        vocals_path = os.path.join(work_dir, "vocals.wav")
        no_vocals_path = os.path.join(work_dir, "no_vocals.wav")
        if not os.path.exists(vocals_path):
            raise ValueError("Demucs 步骤未提供人声轨道")

    if not video_path:
        raise ValueError("缺少视频源")

    # KTV version: dual audio (vocals + no_vocals) with subtitle overlay
    ktv_path = os.path.join(work_dir, "ktv_final.mp4")
    mtv_path = os.path.join(work_dir, "mtv_final.mp4")

    # KTV: video + vocals_track + no_vocals_track + subtitle burn
    if vocals_path and no_vocals_path:
        ktv_cmd = (
            f"ffmpeg -y -i '{video_path}' -i '{vocals_path}' -i '{no_vocals_path}' "
            f"-filter_complex \"[0:v]ass='{ass_path}'[v]\" "
            f"-map '[v]' -map 1:a -map 2:a "
            f"-c:v libx264 -preset fast -c:a aac -b:a 192k "
            f"-metadata:s:a:0 title='Vocals' -metadata:s:a:1 title='Accompaniment' "
            f"'{ktv_path}'"
        )
    else:
        ktv_cmd = (
            f"ffmpeg -y -i '{video_path}' "
            f"-vf \"ass='{ass_path}'\" "
            f"-c:v libx264 -preset fast -c:a aac -b:a 192k "
            f"'{ktv_path}'"
        )

    stdout, stderr, rc = await _run_local(ktv_cmd, timeout=600)
    if rc != 0:
        raise ValueError(f"KTV合成失败: {stderr}")

    # MTV: single audio (original/mix) with subtitle
    mtv_cmd = (
        f"ffmpeg -y -i '{video_path}' "
        f"-vf \"ass='{ass_path}'\" "
        f"-c:v libx264 -preset fast -c:a aac -b:a 192k "
        f"'{mtv_path}'"
    )
    stdout, stderr, rc = await _run_local(mtv_cmd, timeout=600)
    if rc != 0:
        logger.warning(f"MTV合成失败，仅输出KTV版本: {stderr}")

    result = {
        "ktv_path": ktv_path,
        "subtitle_path": ass_path,
    }
    if os.path.exists(mtv_path):
        result["mtv_path"] = mtv_path

    return result


# ─── Registration ─────────────────────────────────────────────────────

KTV_HANDLERS = {
    "audio_preparing": handle_audio_preparing,
    "video_preparing": handle_video_preparing,
    "demucs_separating": handle_demucs_separating,
    "lyric_calibrating": handle_lyric_calibrating,
    "subtitle_rendering": handle_subtitle_rendering,
    "subtitle_exporting": handle_subtitle_exporting,
    "lyric_generating": handle_lyric_generating,
    "lyric_evaluating": handle_lyric_evaluating,
    "music_generating": handle_music_generating,
    "music_polling": handle_music_polling,
    "character_designing": handle_character_designing,
    "character_image_generating": handle_character_image_generating,
    "asset_uploading": handle_asset_uploading,
    "storyboard_generating": handle_storyboard_generating,
    "scene_video_generating": handle_scene_video_generating,
    "scene_video_evaluating": handle_scene_video_evaluating,
    "scene_video_concatenating": handle_scene_video_concatenating,
    "ktv_synthesizing": handle_ktv_synthesizing,
}