"""Offline inference demo for Qwen3 TTS via vLLM Omni.

Provides single and batch sample inputs for CustomVoice, VoiceDesign, and Base
tasks, then runs Omni generation and saves output wav files.
"""

import asyncio
import logging
import os
import time
from typing import Any, NamedTuple

import soundfile as sf
import torch

os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"

from vllm_omni import AsyncOmni, Omni
from vllm_omni.utils.tracking_parser import TrackingArgumentParser

logger = logging.getLogger(__name__)


class QueryResult(NamedTuple):
    """Container for a prepared Omni request."""

    inputs: dict
    model_name: str


def _estimate_prompt_len(
    additional_information: dict[str, Any],
    model_name: str,
    _cache: dict[str, Any] = {},
) -> int:
    """Estimate prompt_token_ids placeholder length for the Talker stage.

    The AR Talker replaces all input embeddings via ``preprocess``, so the
    placeholder values are irrelevant but the **length** must match the
    embeddings that ``preprocess`` will produce.
    """
    try:
        from vllm_omni.model_executor.models.qwen3_tts.configuration_qwen3_tts import Qwen3TTSConfig
        from vllm_omni.model_executor.models.qwen3_tts.prompt_embeds_builder import (
            Qwen3TTSPromptEmbedsBuilder,
        )

        if model_name not in _cache:
            from transformers import AutoTokenizer

            tok = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True, padding_side="left")
            cfg = Qwen3TTSConfig.from_pretrained(model_name, trust_remote_code=True)

            # Load speech tokenizer (codec encoder) for exact ref_code_len.
            speech_tok = None
            try:
                import os

                from transformers.utils import cached_file

                from vllm_omni.model_executor.models.qwen3_tts.qwen3_tts_tokenizer import Qwen3TTSTokenizer

                st_cfg_path = cached_file(model_name, "speech_tokenizer/config.json")
                if st_cfg_path:
                    speech_tok = Qwen3TTSTokenizer.from_pretrained(
                        os.path.dirname(st_cfg_path), torch_dtype=torch.bfloat16
                    )
                    logger.info("Loaded speech tokenizer for exact ref_code_len estimation")
            except Exception as e:
                logger.debug("Could not load speech tokenizer: %s", e)

            _cache[model_name] = (tok, getattr(cfg, "talker_config", None), speech_tok)

        tok, tcfg, speech_tok = _cache[model_name]
        task_type = (additional_information.get("task_type") or ["CustomVoice"])[0]

        def _estimate_ref_code_len(ref_audio: object) -> int | None:
            """Encode ref_audio with the actual codec to get exact frame count."""
            if not isinstance(ref_audio, (str, list)):
                return None
            audio_path = ref_audio[0] if isinstance(ref_audio, list) else ref_audio
            if not isinstance(audio_path, str) or not audio_path.strip():
                return None
            try:
                from urllib.parse import urlparse

                import numpy as np

                def _is_url(path: str) -> bool:
                    try:
                        parsed = urlparse(path)
                        if parsed.scheme in ("http", "https"):
                            return bool(parsed.netloc)
                        return parsed.scheme in ("file", "data")
                    except Exception:
                        return False

                if _is_url(audio_path):
                    from vllm.multimodal.media import MediaConnector

                    connector = MediaConnector(allowed_local_media_path="/")
                    audio, sr = connector.fetch_audio(audio_path)
                else:
                    from vllm.multimodal.media.audio import load_audio

                    audio, sr = load_audio(audio_path, sr=None, mono=True)

                wav_np = np.asarray(audio, dtype=np.float32)

                if speech_tok is not None:
                    enc = speech_tok.encode(wav_np, sr=int(sr), return_dict=True)
                    ref_code = getattr(enc, "audio_codes", None)
                    if isinstance(ref_code, list):
                        ref_code = ref_code[0] if ref_code else None
                    if ref_code is not None and hasattr(ref_code, "shape"):
                        shape = ref_code.shape
                        return int(shape[0]) if len(shape) == 2 else int(shape[1]) if len(shape) == 3 else None

                # Fallback: estimate from duration
                codec_hz = getattr(tcfg, "codec_frame_rate", None) or 12
                return int(len(audio) / sr * codec_hz)
            except Exception:
                return None

        return Qwen3TTSPromptEmbedsBuilder.estimate_prompt_len_from_additional_information(
            additional_information=additional_information,
            task_type=task_type,
            tokenize_prompt=lambda t: tok(t, padding=False)["input_ids"],
            codec_language_id=getattr(tcfg, "codec_language_id", None),
            spk_is_dialect=getattr(tcfg, "spk_is_dialect", None),
            estimate_ref_code_len=_estimate_ref_code_len,
        )
    except Exception as exc:
        logger.warning("Failed to estimate prompt length, using fallback 2048: %s", exc)
        return 2048


def get_custom_voice_query(use_batch_sample: bool = False) -> QueryResult:
    """Build CustomVoice sample inputs.

    Args:
        use_batch_sample: When True, return a batch of prompts; otherwise a single prompt.

    Returns:
        QueryResult with Omni inputs and the CustomVoice model path.
    """
    task_type = "CustomVoice"
    model_name = "Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice"
    if use_batch_sample:
        texts = [
            "其实我真的有发现，我是一个特别善于观察别人情绪的人。",
            "She said she would be here by noon.",
            "I like you very much.",
            "Really, you do?",
            "Yes, absolutely.",
        ]
        instructs = ["", "Very happy.", "Very happy.", "Very happy.", "Very happy."]
        languages = ["Chinese", "English", "English", "English", "English"]
        speakers = ["Vivian", "Ryan", "Ryan", "Ryan", "Ryan"]
        inputs = []
        for text, instruct, language, speaker in zip(texts, instructs, languages, speakers):
            additional_information = {
                "task_type": [task_type],
                "text": [text],
                "instruct": [instruct],
                "language": [language],
                "speaker": [speaker],
                "max_new_tokens": [2048],
            }
            inputs.append(
                {
                    "prompt_token_ids": [0] * _estimate_prompt_len(additional_information, model_name),
                    "additional_information": additional_information,
                }
            )
    else:
        text = "其实我真的有发现，我是一个特别善于观察别人情绪的人。"
        language = "Chinese"
        speaker = "Vivian"
        instruct = "用特别愤怒的语气说"
        additional_information = {
            "task_type": [task_type],
            "text": [text],
            "language": [language],
            "speaker": [speaker],
            "instruct": [instruct],
            "max_new_tokens": [2048],
        }
        inputs = {
            "prompt_token_ids": [0] * _estimate_prompt_len(additional_information, model_name),
            "additional_information": additional_information,
        }
    return QueryResult(
        inputs=inputs,
        model_name=model_name,
    )


def get_voice_design_query(use_batch_sample: bool = False) -> QueryResult:
    """Build VoiceDesign sample inputs.

    Args:
        use_batch_sample: When True, return a batch of prompts; otherwise a single prompt.

    Returns:
        QueryResult with Omni inputs and the VoiceDesign model path.
    """
    task_type = "VoiceDesign"
    model_name = "Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign"
    if use_batch_sample:
        texts = [
            "哥哥，你回来啦，人家等了你好久好久了，要抱抱！",
            "It's in the top drawer... wait, it's empty? No way, that's impossible! I'm sure I put it there!",
        ]
        instructs = [
            "体现撒娇稚嫩的萝莉女声，音调偏高且起伏明显，营造出黏人、做作又刻意卖萌的听觉效果。",
            "Speak in an incredulous tone, but with a hint of panic beginning to creep into your voice.",
        ]
        languages = ["Chinese", "English"]
        inputs = []
        for text, instruct, language in zip(texts, instructs, languages):
            additional_information = {
                "task_type": [task_type],
                "text": [text],
                "language": [language],
                "instruct": [instruct],
                "max_new_tokens": [2048],
                "non_streaming_mode": [True],
            }
            inputs.append(
                {
                    "prompt_token_ids": [0] * _estimate_prompt_len(additional_information, model_name),
                    "additional_information": additional_information,
                }
            )
    else:
        text = "哥哥，你回来啦，人家等了你好久好久了，要抱抱！"
        instruct = "体现撒娇稚嫩的萝莉女声，音调偏高且起伏明显，营造出黏人、做作又刻意卖萌的听觉效果。"
        language = "Chinese"
        additional_information = {
            "task_type": [task_type],
            "text": [text],
            "language": [language],
            "instruct": [instruct],
            "max_new_tokens": [2048],
            "non_streaming_mode": [True],
        }
        inputs = {
            "prompt_token_ids": [0] * _estimate_prompt_len(additional_information, model_name),
            "additional_information": additional_information,
        }
    return QueryResult(
        inputs=inputs,
        model_name=model_name,
    )


def get_base_query(use_batch_sample: bool = False, mode_tag: str = "icl") -> QueryResult:
    """Build Base (voice clone) sample inputs.

    Args:
        use_batch_sample: When True, return a batch of prompts (Case 2).
        mode_tag: "icl" or "xvec_only" to control x_vector_only_mode behavior.

    Returns:
        QueryResult with Omni inputs and the Base model path.
    """
    task_type = "Base"
    model_name = "Qwen/Qwen3-TTS-12Hz-1.7B-Base"
    ref_audio_path_1 = "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen3-TTS-Repo/clone_2.wav"
    ref_audio_single = ref_audio_path_1
    ref_text_single = (
        "Okay. Yeah. I resent you. I love you. I respect you. But you know what? You blew it! And thanks to you."
    )
    syn_text_single = "Good one. Okay, fine, I'm just gonna leave this sock monkey here. Goodbye."
    syn_lang_single = "Auto"
    x_vector_only_mode = mode_tag == "xvec_only"
    if use_batch_sample:
        syn_text_batch = [
            "Good one. Okay, fine, I'm just gonna leave this sock monkey here. Goodbye.",
            "其实我真的有发现，我是一个特别善于观察别人情绪的人。",
        ]
        syn_lang_batch = ["Chinese", "English"]
        inputs = []
        for text, language in zip(syn_text_batch, syn_lang_batch):
            additional_information = {
                "task_type": [task_type],
                "ref_audio": [ref_audio_single],
                "ref_text": [ref_text_single],
                "text": [text],
                "language": [language],
                "x_vector_only_mode": [x_vector_only_mode],
                "max_new_tokens": [2048],
            }
            inputs.append(
                {
                    "prompt_token_ids": [0] * _estimate_prompt_len(additional_information, model_name),
                    "additional_information": additional_information,
                }
            )
    else:
        additional_information = {
            "task_type": [task_type],
            "ref_audio": [ref_audio_single],
            "ref_text": [ref_text_single],
            "text": [syn_text_single],
            "language": [syn_lang_single],
            "x_vector_only_mode": [x_vector_only_mode],
            "max_new_tokens": [2048],
        }
        inputs = {
            "prompt_token_ids": [0] * _estimate_prompt_len(additional_information, model_name),
            "additional_information": additional_information,
        }
    return QueryResult(
        inputs=inputs,
        model_name=model_name,
    )


query_map = {
    "CustomVoice": get_custom_voice_query,
    "VoiceDesign": get_voice_design_query,
    "Base": get_base_query,
}


def _build_inputs(args) -> tuple[str, list]:
    """Resolve model name and inputs list from CLI args."""
    if args.batch_size < 1 or (args.batch_size & (args.batch_size - 1)) != 0:
        raise ValueError(
            f"--batch-size must be a power of two (got {args.batch_size}); "
            "non-power-of-two values do not align with CUDA graph capture sizes "
            "of Code2Wav."
        )

    query_func = query_map[args.query_type]
    if args.query_type in {"CustomVoice", "VoiceDesign"}:
        query_result = query_func(use_batch_sample=args.use_batch_sample)
    elif args.query_type == "Base":
        query_result = query_func(use_batch_sample=args.use_batch_sample, mode_tag=args.mode_tag)
    else:
        query_result = query_func()

    model_name = query_result.model_name

    if args.txt_prompts:
        with open(args.txt_prompts) as f:
            lines = [line.strip() for line in f if line.strip()]
        if not lines:
            raise ValueError(f"No valid prompts found in {args.txt_prompts}")
        template = query_result.inputs if not isinstance(query_result.inputs, list) else query_result.inputs[0]
        template_info = template["additional_information"]
        inputs = [
            {
                "prompt_token_ids": [0] * _estimate_prompt_len({**template_info, "text": [t]}, model_name),
                "additional_information": {**template_info, "text": [t]},
            }
            for t in lines
        ]
    else:
        inputs = query_result.inputs if isinstance(query_result.inputs, list) else [query_result.inputs]

    return model_name, inputs


def _save_wav(output_dir: str, request_id: str, mm: dict) -> None:
    """Concatenate audio chunks and write to a wav file."""
    audio_data = mm["audio"]
    sr_raw = mm["sr"]
    sr_val = sr_raw[-1] if isinstance(sr_raw, list) and sr_raw else sr_raw
    sr = sr_val.item() if hasattr(sr_val, "item") else int(sr_val)
    audio_tensor = torch.cat(audio_data, dim=-1) if isinstance(audio_data, list) else audio_data
    out_wav = os.path.join(output_dir, f"output_{request_id}.wav")
    sf.write(out_wav, audio_tensor.float().cpu().numpy().flatten(), samplerate=sr, format="WAV")
    logger.info(f"Request ID: {request_id}, Saved audio to {out_wav}")


def main(args):
    """Run offline inference with Omni."""
    model_name, inputs = _build_inputs(args)
    output_dir = args.output_dir
    os.makedirs(output_dir, exist_ok=True)

    omni_kwargs = vars(args).copy()
    # Override CLI --model with the derived model_name.
    omni_kwargs["model"] = model_name
    omni = Omni(**omni_kwargs)

    batch_size = args.batch_size
    for batch_start in range(0, len(inputs), batch_size):
        batch = inputs[batch_start : batch_start + batch_size]
        for stage_outputs in omni.generate(batch):
            output = stage_outputs.request_output
            _save_wav(output_dir, output.request_id, output.outputs[0].multimodal_output)


async def main_streaming(args):
    """Run offline inference with AsyncOmni, logging each audio chunk as it arrives."""
    model_name, inputs = _build_inputs(args)
    output_dir = args.output_dir
    os.makedirs(output_dir, exist_ok=True)

    omni_kwargs = vars(args).copy()
    # Override CLI --model with the derived model_name.
    omni_kwargs["model"] = model_name
    omni = AsyncOmni(**omni_kwargs)

    for i, prompt in enumerate(inputs):
        request_id = str(i)
        t_start = time.perf_counter()
        t_prev = t_start
        chunk_idx = 0
        async for stage_output in omni.generate(prompt, request_id=request_id):
            mm = stage_output.request_output.outputs[0].multimodal_output
            if not stage_output.finished:
                t_now = time.perf_counter()
                audio = mm.get("audio")
                n = len(audio) if isinstance(audio, list) else (0 if audio is None else 1)
                dt_ms = (t_now - t_prev) * 1000
                ttfa_ms = (t_now - t_start) * 1000
                if chunk_idx == 0:
                    logger.info(f"Request {request_id}: chunk {chunk_idx} samples={n} TTFA={ttfa_ms:.1f}ms")
                else:
                    logger.info(f"Request {request_id}: chunk {chunk_idx} samples={n} inter_chunk={dt_ms:.1f}ms")
                t_prev = t_now
                chunk_idx += 1
            else:
                t_end = time.perf_counter()
                total_ms = (t_end - t_start) * 1000
                logger.info(f"Request {request_id}: done total={total_ms:.1f}ms chunks={chunk_idx}")
                _save_wav(output_dir, request_id, mm)


def parse_args():
    parser = TrackingArgumentParser(description="Demo on using vLLM for offline inference with audio language models")
    parser.add_argument(
        "--query-type",
        "-q",
        type=str,
        default="CustomVoice",
        choices=query_map.keys(),
        help="Query type.",
    )
    parser.add_argument(
        "--log-stats",
        action="store_true",
        default=False,
        help="Enable writing detailed statistics (default: disabled)",
    )
    parser.add_argument(
        "--stage-init-timeout",
        type=int,
        default=300,
        help="Timeout for initializing a single stage in seconds (default: 300)",
    )
    parser.add_argument(
        "--batch-timeout",
        type=int,
        default=5,
        help="Timeout for batching in seconds (default: 5)",
    )
    parser.add_argument(
        "--init-timeout",
        type=int,
        default=300,
        help="Timeout for initializing stages in seconds (default: 300)",
    )
    parser.add_argument(
        "--shm-threshold-bytes",
        type=int,
        default=65536,
        help="Threshold for using shared memory in bytes (default: 65536)",
    )
    parser.add_argument(
        "--output-dir",
        default="output_audio",
        help="Output directory for generated wav files (default: output_audio).",
    )
    parser.add_argument(
        "--num-prompts",
        type=int,
        default=1,
        help="Number of prompts to generate.",
    )
    parser.add_argument(
        "--txt-prompts",
        type=str,
        default=None,
        help="Path to a .txt file with one prompt per line (preferred).",
    )
    parser.add_argument(
        "--stage-configs-path",
        type=str,
        default=None,
        help="Path to a stage configs file.",
    )
    parser.add_argument(
        "--audio-path",
        "-a",
        type=str,
        default=None,
        help="Path to local audio file. If not provided, uses default audio asset.",
    )
    parser.add_argument(
        "--sampling-rate",
        type=int,
        default=16000,
        help="Sampling rate for audio loading (default: 16000).",
    )
    parser.add_argument(
        "--log-dir",
        type=str,
        default="logs",
        help="Log directory (default: logs).",
    )
    parser.add_argument(
        "--py-generator",
        action="store_true",
        default=False,
        help="Use py_generator mode. The returned type of Omni.generate() is a Python Generator object.",
    )
    parser.add_argument(
        "--use-batch-sample",
        action="store_true",
        default=False,
        help="Use batch input sample for CustomVoice/VoiceDesign/Base query.",
    )
    parser.add_argument(
        "--mode-tag",
        type=str,
        default="icl",
        choices=["icl", "xvec_only"],
        help="Mode tag for Base query x_vector_only_mode (default: icl).",
    )
    parser.add_argument(
        "--streaming",
        action="store_true",
        default=False,
        help="Stream audio chunks as they arrive via AsyncOmni (async_chunk mode only).",
    )
    parser.add_argument(
        "--batch-size",
        type=int,
        default=1,
        help="Number of prompts per batch (default: 1, sequential).",
    )

    return parser.parse_args()


if __name__ == "__main__":
    args = parse_args()
    if args.streaming:
        asyncio.run(main_streaming(args))
    else:
        main(args)