# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""
This example shows how to use vLLM for running offline inference
with the correct prompt format on Qwen3-Omni (thinker only).
"""

import os
import time
from typing import NamedTuple

import numpy as np
import soundfile as sf
import vllm
from PIL import Image
from vllm import SamplingParams
from vllm.assets.audio import AudioAsset
from vllm.assets.image import ImageAsset
from vllm.assets.video import VideoAsset, video_to_ndarrays
from vllm.multimodal.image import convert_image_mode
from vllm.multimodal.media.audio import load_audio

from vllm_omni.entrypoints.omni import Omni
from vllm_omni.utils.tracking_parser import TrackingArgumentParser

SEED = 42


class QueryResult(NamedTuple):
    inputs: dict
    limit_mm_per_prompt: dict[str, int]


# NOTE: The default `max_num_seqs` and `max_model_len` may result in OOM on
# lower-end GPUs.
# Unless specified, these settings have been tested to work on a single L4.

default_system = (
    "You are Qwen, a virtual human developed by the Qwen Team, Alibaba "
    "Group, capable of perceiving auditory and visual inputs, as well as "
    "generating text and speech."
)


def get_text_query(question: str = None) -> QueryResult:
    if question is None:
        question = "Explain the system architecture for a scalable audio generation pipeline. Answer in 15 words."
    prompt = (
        f"<|im_start|>system\n{default_system}<|im_end|>\n"
        "<|im_start|>user\n"
        f"{question}<|im_end|>\n"
        f"<|im_start|>assistant\n"
    )
    return QueryResult(
        inputs={
            "prompt": prompt,
        },
        limit_mm_per_prompt={},
    )


def get_video_query(question: str = None, video_path: str | None = None, num_frames: int = 16) -> QueryResult:
    if question is None:
        question = "Why is this video funny?"
    prompt = (
        f"<|im_start|>system\n{default_system}<|im_end|>\n"
        "<|im_start|>user\n<|vision_start|><|video_pad|><|vision_end|>"
        f"{question}<|im_end|>\n"
        f"<|im_start|>assistant\n"
    )

    if video_path:
        if not os.path.exists(video_path):
            raise FileNotFoundError(f"Video file not found: {video_path}")
        video_frames = video_to_ndarrays(video_path, num_frames=num_frames)
    else:
        video_frames = VideoAsset(name="baby_reading", num_frames=num_frames).np_ndarrays

    return QueryResult(
        inputs={
            "prompt": prompt,
            "multi_modal_data": {
                "video": video_frames,
            },
        },
        limit_mm_per_prompt={"video": 1},
    )


def get_image_query(question: str = None, image_path: str | None = None) -> QueryResult:
    if question is None:
        question = "What is the content of this image?"
    prompt = (
        f"<|im_start|>system\n{default_system}<|im_end|>\n"
        "<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>"
        f"{question}<|im_end|>\n"
        f"<|im_start|>assistant\n"
    )

    if image_path:
        if not os.path.exists(image_path):
            raise FileNotFoundError(f"Image file not found: {image_path}")
        pil_image = Image.open(image_path)
        image_data = convert_image_mode(pil_image, "RGB")
    else:
        image_data = convert_image_mode(ImageAsset("cherry_blossom").pil_image, "RGB")

    return QueryResult(
        inputs={
            "prompt": prompt,
            "multi_modal_data": {
                "image": image_data,
            },
        },
        limit_mm_per_prompt={"image": 1},
    )


def get_audio_query(question: str = None, audio_path: str | None = None, sampling_rate: int = 16000) -> QueryResult:
    if question is None:
        question = "What is the content of this audio?"
    prompt = (
        f"<|im_start|>system\n{default_system}<|im_end|>\n"
        "<|im_start|>user\n<|audio_start|><|audio_pad|><|audio_end|>"
        f"{question}<|im_end|>\n"
        f"<|im_start|>assistant\n"
    )

    if audio_path:
        if not os.path.exists(audio_path):
            raise FileNotFoundError(f"Audio file not found: {audio_path}")
        audio_signal, sr = load_audio(audio_path, sr=sampling_rate)
        audio_data = (audio_signal.astype(np.float32), sr)
    else:
        audio_data = AudioAsset("mary_had_lamb").audio_and_sample_rate

    return QueryResult(
        inputs={
            "prompt": prompt,
            "multi_modal_data": {
                "audio": audio_data,
            },
        },
        limit_mm_per_prompt={"audio": 1},
    )


def get_mixed_modalities_query(
    video_path: str | None = None,
    image_path: str | None = None,
    audio_path: str | None = None,
    num_frames: int = 16,
    sampling_rate: int = 16000,
) -> QueryResult:
    question = "What is recited in the audio? What is the content of this image? Why is this video funny?"
    prompt = (
        f"<|im_start|>system\n{default_system}<|im_end|>\n"
        "<|im_start|>user\n<|audio_start|><|audio_pad|><|audio_end|>"
        "<|vision_start|><|image_pad|><|vision_end|>"
        "<|vision_start|><|video_pad|><|vision_end|>"
        f"{question}<|im_end|>\n"
        f"<|im_start|>assistant\n"
    )

    # Load video
    if video_path:
        if not os.path.exists(video_path):
            raise FileNotFoundError(f"Video file not found: {video_path}")
        video_frames = video_to_ndarrays(video_path, num_frames=num_frames)
    else:
        video_frames = VideoAsset(name="baby_reading", num_frames=num_frames).np_ndarrays

    # Load image
    if image_path:
        if not os.path.exists(image_path):
            raise FileNotFoundError(f"Image file not found: {image_path}")
        pil_image = Image.open(image_path)
        image_data = convert_image_mode(pil_image, "RGB")
    else:
        image_data = convert_image_mode(ImageAsset("cherry_blossom").pil_image, "RGB")

    # Load audio
    if audio_path:
        if not os.path.exists(audio_path):
            raise FileNotFoundError(f"Audio file not found: {audio_path}")
        audio_signal, sr = load_audio(audio_path, sr=sampling_rate)
        audio_data = (audio_signal.astype(np.float32), sr)
    else:
        audio_data = AudioAsset("mary_had_lamb").audio_and_sample_rate

    return QueryResult(
        inputs={
            "prompt": prompt,
            "multi_modal_data": {
                "audio": audio_data,
                "image": image_data,
                "video": video_frames,
            },
        },
        limit_mm_per_prompt={"audio": 1, "image": 1, "video": 1},
    )


def get_multi_audios_query() -> QueryResult:
    question = "Are these two audio clips the same?"
    prompt = (
        f"<|im_start|>system\n{default_system}<|im_end|>\n"
        "<|im_start|>user\n<|audio_start|><|audio_pad|><|audio_end|>"
        "<|audio_start|><|audio_pad|><|audio_end|>"
        f"{question}<|im_end|>\n"
        f"<|im_start|>assistant\n"
    )
    return QueryResult(
        inputs={
            "prompt": prompt,
            "multi_modal_data": {
                "audio": [
                    AudioAsset("winning_call").audio_and_sample_rate,
                    AudioAsset("mary_had_lamb").audio_and_sample_rate,
                ],
            },
        },
        limit_mm_per_prompt={
            "audio": 2,
        },
    )


def get_use_audio_in_video_query() -> QueryResult:
    question = "Describe the content of the video in details, then convert what the baby say into text."
    prompt = (
        f"<|im_start|>system\n{default_system}<|im_end|>\n"
        "<|im_start|>user\n<|vision_start|><|video_pad|><|vision_end|>"
        f"{question}<|im_end|>\n"
        f"<|im_start|>assistant\n"
    )
    asset = VideoAsset(name="baby_reading", num_frames=16)
    audio = asset.get_audio(sampling_rate=16000)
    return QueryResult(
        inputs={
            "prompt": prompt,
            "multi_modal_data": {
                "video": asset.np_ndarrays,
                "audio": audio,
            },
            "mm_processor_kwargs": {
                "use_audio_in_video": True,
            },
        },
        limit_mm_per_prompt={"audio": 1, "video": 1},
    )


query_map = {
    "text": get_text_query,
    "use_audio": get_audio_query,
    "use_image": get_image_query,
    "use_video": get_video_query,
    "use_multi_audios": get_multi_audios_query,
    "use_mixed_modalities": get_mixed_modalities_query,
    "use_audio_in_video": get_use_audio_in_video_query,
}


def main(args):
    model_name = args.model
    print("=" * 20, "\n", f"vllm version: {vllm.__version__}", "\n", "=" * 20)

    # Get paths from args
    video_path = getattr(args, "video_path", None)
    image_path = getattr(args, "image_path", None)
    audio_path = getattr(args, "audio_path", None)

    # Get the query function and call it with appropriate parameters
    query_func = query_map[args.query_type]
    if args.query_type == "use_video":
        query_result = query_func(video_path=video_path, num_frames=getattr(args, "num_frames", 16))
    elif args.query_type == "use_image":
        query_result = query_func(image_path=image_path)
    elif args.query_type == "use_audio":
        query_result = query_func(audio_path=audio_path, sampling_rate=getattr(args, "sampling_rate", 16000))
    elif args.query_type == "mixed_modalities":
        query_result = query_func(
            video_path=video_path,
            image_path=image_path,
            audio_path=audio_path,
            num_frames=getattr(args, "num_frames", 16),
            sampling_rate=getattr(args, "sampling_rate", 16000),
        )
    elif args.query_type == "multi_audios":
        query_result = query_func()
    elif args.query_type == "use_audio_in_video":
        query_result = query_func()
    else:
        query_result = query_func()

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

    thinker_sampling_params = SamplingParams(
        temperature=0.9,
        top_p=0.9,
        top_k=-1,
        max_tokens=1200,
        repetition_penalty=1.05,
        logit_bias={},
        seed=SEED,
    )

    talker_sampling_params = SamplingParams(
        temperature=0.9,
        top_k=50,
        max_tokens=4096,
        seed=SEED,
        detokenize=False,
        repetition_penalty=1.05,
        stop_token_ids=[2150],  # TALKER_CODEC_EOS_TOKEN_ID
    )

    # Sampling parameters for Code2Wav stage (audio generation)
    code2wav_sampling_params = SamplingParams(
        temperature=0.0,
        top_p=1.0,
        top_k=-1,
        max_tokens=4096 * 16,
        seed=SEED,
        detokenize=True,
        repetition_penalty=1.1,
    )

    all_sampling_params = [
        thinker_sampling_params,
        talker_sampling_params,  # code predictor is integrated into talker for Qwen3 Omni
        code2wav_sampling_params,
    ]
    # Match sampling params to the number of configured stages
    num_stages = omni.num_stages
    sampling_params_list = all_sampling_params[:num_stages]

    if args.txt_prompts is None:
        prompts = [query_result.inputs for _ in range(args.num_prompts)]
    else:
        assert args.query_type == "text", "txt-prompts is only supported for text query type"
        with open(args.txt_prompts, encoding="utf-8") as f:
            lines = [ln.strip() for ln in f.readlines()]
            prompts = [get_text_query(ln).inputs for ln in lines if ln != ""]
            print(f"[Info] Loaded {len(prompts)} prompts from {args.txt_prompts}")

    if args.modalities is not None:
        output_modalities = args.modalities.split(",")
        for i, prompt in enumerate(prompts):
            prompt["modalities"] = output_modalities

    profiler_enabled = args.enable_profiler
    if profiler_enabled:
        omni.start_profile(stages=args.profiler_stages)
    omni_generator = omni.generate(prompts, sampling_params_list, py_generator=args.py_generator)
    # Determine output directory: prefer --output-dir; fallback to --output-wav
    output_dir = args.output_dir if getattr(args, "output_dir", None) else args.output_wav
    os.makedirs(output_dir, exist_ok=True)

    total_requests = len(prompts)
    processed_count = 0

    print(f"query type: {args.query_type}")

    for stage_outputs in omni_generator:
        output = stage_outputs
        if stage_outputs.final_output_type == "text":
            request_id = output.request_id
            text_output = output.outputs[0].text
            # Save aligned text file per request
            prompt_text = output.prompt
            out_txt = os.path.join(output_dir, f"{request_id}.txt")
            lines = []
            lines.append("Prompt:\n")
            lines.append(str(prompt_text) + "\n")
            lines.append("vllm_text_output:\n")
            lines.append(str(text_output).strip() + "\n")
            try:
                with open(out_txt, "w", encoding="utf-8") as f:
                    f.writelines(lines)
            except Exception as e:
                print(f"[Warn] Failed writing text file {out_txt}: {e}")
            print(f"Request ID: {request_id}, Text saved to {out_txt}")
        elif stage_outputs.final_output_type == "audio":
            request_id = output.request_id
            audio_tensor = output.outputs[0].multimodal_output["audio"]
            output_wav = os.path.join(output_dir, f"output_{request_id}.wav")

            # Convert to numpy array and ensure correct format
            # In async_chunk mode, audio may arrive as a list of chunks
            if isinstance(audio_tensor, list):
                import torch

                audio_tensor = torch.cat(
                    [(t if isinstance(t, torch.Tensor) else torch.tensor(t)).flatten() for t in audio_tensor]
                )
            audio_numpy = audio_tensor.float().detach().cpu().numpy()

            # Ensure audio is 1D (flatten if needed)
            if audio_numpy.ndim > 1:
                audio_numpy = audio_numpy.flatten()

            # Save audio file with explicit WAV format
            sf.write(output_wav, audio_numpy, samplerate=24000, format="WAV")
            print(f"Request ID: {request_id}, Saved audio to {output_wav}")

        processed_count += 1
        if profiler_enabled and processed_count >= total_requests:
            print(f"[Info] Processed {processed_count}/{total_requests}. Stopping profiler inside active loop...")
            # Stop the profiler while workers are still alive
            omni.stop_profile(stages=args.profiler_stages)

            print("[Info] Waiting 30s for workers to write trace files to disk...")
            time.sleep(30)
            print("[Info] Trace export wait time finished.")
    omni.close()


def parse_args():
    parser = TrackingArgumentParser(description="Demo on using vLLM for offline inference with audio language models")
    parser.add_argument(
        "--model",
        type=str,
        default="Qwen/Qwen3-Omni-30B-A3B-Instruct",
        help="Model name or path.",
    )
    parser.add_argument(
        "--query-type",
        "-q",
        type=str,
        default="use_mixed_modalities",
        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-wav",
        default="output_audio",
        help="[Deprecated] Output wav directory (use --output-dir).",
    )
    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(
        "--deploy-config",
        type=str,
        default=None,
        help="Path to a deploy config YAML.",
    )
    parser.add_argument(
        "--video-path",
        "-v",
        type=str,
        default=None,
        help="Path to local video file. If not provided, uses default video asset.",
    )
    parser.add_argument(
        "--image-path",
        "-i",
        type=str,
        default=None,
        help="Path to local image file. If not provided, uses default image asset.",
    )
    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(
        "--num-frames",
        type=int,
        default=16,
        help="Number of frames to extract from video (default: 16).",
    )
    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(
        "--modalities",
        type=str,
        default=None,
        help="Output modalities to use for the prompts.",
    )
    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(
        "--enable-profiler",
        action="store_true",
        default=False,
        help="Enables profiling when set.",
    )
    parser.add_argument(
        "--profiler-stages",
        type=int,
        nargs="*",
        default=None,
        help="List of stage IDs to profile. If not set, profiles all stages.",
    )
    parser.add_argument(
        "--dtype",
        type=str,
        default="auto",
        help="Model dtype (auto, half, float16, bfloat16, float, float32).",
    )

    return parser.parse_args()


if __name__ == "__main__":
    args = parse_args()
    main(args)