import os
import queue
from huggingface_hub import snapshot_download
import numpy as np
import wave
import io
import gc
from typing import Callable

# Download if not exists
os.makedirs("checkpoints", exist_ok=True)
snapshot_download(repo_id="fishaudio/openaudio-s1-mini", local_dir="./checkpoints/openaudio-s1-mini")

print("All checkpoints downloaded")

import html
import os
from argparse import ArgumentParser
from pathlib import Path

import gradio as gr
import torch
import torchaudio

torchaudio.set_audio_backend("soundfile")

from loguru import logger
from fish_speech.i18n import i18n
from fish_speech.inference_engine import TTSInferenceEngine
from fish_speech.models.dac.inference import load_model as load_decoder_model
from fish_speech.models.text2semantic.inference import launch_thread_safe_queue
from tools.webui.inference import get_inference_wrapper
from fish_speech.utils.schema import ServeTTSRequest

# Make einx happy
os.environ["EINX_FILTER_TRACEBACK"] = "false"


HEADER_MD = """# OpenAudio S1

## The demo in this space is OpenAudio S1, Please check [Fish Audio](https://fish.audio) for the best model.
## 该 Demo 为 OpenAudio S1 版本, 请在 [Fish Audio](https://fish.audio) 体验最新 DEMO.

A text-to-speech model based on DAC & Qwen3 developed by [Fish Audio](https://fish.audio).  
由 [Fish Audio](https://fish.audio) 研发的 DAC & Qwen3 多语种语音合成. 

You can find the source code [here](https://github.com/fishaudio/fish-speech) and models [here](https://huggingface.co/fishaudio/openaudio-s1-mini).  
你可以在 [这里](https://github.com/fishaudio/fish-speech) 找到源代码和 [这里](https://huggingface.co/fishaudio/openaudio-s1-mini) 找到模型.  

Related code and weights are released under CC BY-NC-SA 4.0 License.  
相关代码，权重使用 CC BY-NC-SA 4.0 许可证发布.

We are not responsible for any misuse of the model, please consider your local laws and regulations before using it.  
我们不对模型的任何滥用负责，请在使用之前考虑您当地的法律法规.

The model running in this WebUI is OpenAudio S1 Mini.
在此 WebUI 中运行的模型是 OpenAudio S1 Mini.
"""

TEXTBOX_PLACEHOLDER = """Put your text here. 在此处输入文本."""

try:
    import spaces

    GPU_DECORATOR = spaces.GPU
except ImportError:

    def GPU_DECORATOR(func):
        def wrapper(*args, **kwargs):
            return func(*args, **kwargs)

        return wrapper

def build_html_error_message(error):
    return f"""
    <div style="color: red; 
    font-weight: bold;">
        {html.escape(str(error))}
    </div>
    """

def wav_chunk_header(sample_rate=44100, bit_depth=16, channels=1):
    buffer = io.BytesIO()

    with wave.open(buffer, "wb") as wav_file:
        wav_file.setnchannels(channels)
        wav_file.setsampwidth(bit_depth // 8)
        wav_file.setframerate(sample_rate)

    wav_header_bytes = buffer.getvalue()
    buffer.close()
    return wav_header_bytes


def build_app(inference_fct: Callable, theme: str = "light") -> gr.Blocks:
    with gr.Blocks(theme=gr.themes.Base()) as app:
        gr.Markdown(HEADER_MD)

        # Use light theme by default
        app.load(
            None,
            None,
            js="() => {const params = new URLSearchParams(window.location.search);if (!params.has('__theme')) {params.set('__theme', '%s');window.location.search = params.toString();}}"
            % theme,
        )

        # Inference
        with gr.Row():
            with gr.Column(scale=3):
                text = gr.Textbox(
                    label=i18n("Input Text"), placeholder=TEXTBOX_PLACEHOLDER, lines=10
                )

                with gr.Row():
                    with gr.Column():
                        with gr.Tab(label=i18n("Advanced Config")):
                            with gr.Row():
                                chunk_length = gr.Slider(
                                    label=i18n("Iterative Prompt Length, 0 means off"),
                                    minimum=0,
                                    maximum=500,
                                    value=0,
                                    step=8,
                                )

                                max_new_tokens = gr.Slider(
                                    label=i18n(
                                        "Maximum tokens per batch, 0 means no limit"
                                    ),
                                    minimum=0,
                                    maximum=2048,
                                    value=0,
                                    step=8,
                                )

                            with gr.Row():
                                top_p = gr.Slider(
                                    label="Top-P",
                                    minimum=0.7,
                                    maximum=0.95,
                                    value=0.9,
                                    step=0.01,
                                )

                                repetition_penalty = gr.Slider(
                                    label=i18n("Repetition Penalty"),
                                    minimum=1,
                                    maximum=1.2,
                                    value=1.1,
                                    step=0.01,
                                )

                            with gr.Row():
                                temperature = gr.Slider(
                                    label="Temperature",
                                    minimum=0.7,
                                    maximum=1.0,
                                    value=0.9,
                                    step=0.01,
                                )
                                seed = gr.Number(
                                    label="Seed",
                                    info="0 means randomized inference, otherwise deterministic",
                                    value=0,
                                )

                        with gr.Tab(label=i18n("Reference Audio")):
                            with gr.Row():
                                gr.Markdown(
                                    i18n(
                                        "5 to 10 seconds of reference audio, useful for specifying speaker."
                                    )
                                )
                            with gr.Row():
                                reference_id = gr.Textbox(
                                    label=i18n("Reference ID"),
                                    placeholder="Leave empty to use uploaded references",
                                )

                            with gr.Row():
                                use_memory_cache = gr.Radio(
                                    label=i18n("Use Memory Cache"),
                                    choices=["on", "off"],
                                    value="on",
                                )

                            with gr.Row():
                                reference_audio = gr.Audio(
                                    label=i18n("Reference Audio"),
                                    type="filepath",
                                )
                            with gr.Row():
                                reference_text = gr.Textbox(
                                    label=i18n("Reference Text"),
                                    lines=1,
                                    placeholder="在一无所知中，梦里的一天结束了，一个新的「轮回」便会开始。",
                                    value="",
                                )

            with gr.Column(scale=3):
                with gr.Row():
                    error = gr.HTML(
                        label=i18n("Error Message"),
                        visible=True,
                    )
                with gr.Row():
                    audio = gr.Audio(
                        label=i18n("Generated Audio"),
                        type="numpy",
                        interactive=False,
                        visible=True,
                    )

                with gr.Row():
                    with gr.Column(scale=3):
                        generate = gr.Button(
                            value="\U0001f3a7 " + i18n("Generate"),
                            variant="primary",
                        )

        # Submit
        generate.click(
            inference_fct,
            [
                text,
                reference_id,
                reference_audio,
                reference_text,
                max_new_tokens,
                chunk_length,
                top_p,
                repetition_penalty,
                temperature,
                seed,
                use_memory_cache,
            ],
            [audio, error],
            concurrency_limit=1,
        )

    return app

def parse_args():
    parser = ArgumentParser()
    parser.add_argument(
        "--llama-checkpoint-path",
        type=Path,
        default="checkpoints/openaudio-s1-mini",
    )
    parser.add_argument(
        "--decoder-checkpoint-path",
        type=Path,
        default="checkpoints/openaudio-s1-mini/codec.pth",
    )
    parser.add_argument("--decoder-config-name", type=str, default="modded_dac_vq")
    parser.add_argument("--device", type=str, default="cuda")
    parser.add_argument("--half", action="store_true")
    parser.add_argument("--compile", action="store_true",default=True)
    parser.add_argument("--max-gradio-length", type=int, default=0)
    parser.add_argument("--theme", type=str, default="dark")

    return parser.parse_args()


if __name__ == "__main__":
    args = parse_args()
    args.precision = torch.half if args.half else torch.bfloat16

    logger.info("Loading Llama model...")
    llama_queue = launch_thread_safe_queue(
        checkpoint_path=args.llama_checkpoint_path,
        device=args.device,
        precision=args.precision,
        compile=args.compile,
    )
    logger.info("Llama model loaded, loading VQ-GAN model...")

    decoder_model = load_decoder_model(
        config_name=args.decoder_config_name,
        checkpoint_path=args.decoder_checkpoint_path,
        device=args.device,
    )

    logger.info("Decoder model loaded, warming up...")

    # Create the inference engine
    inference_engine = TTSInferenceEngine(
        llama_queue=llama_queue,
        decoder_model=decoder_model,
        compile=args.compile,
        precision=args.precision,
    )

    # Dry run to check if the model is loaded correctly and avoid the first-time latency
    list(
        inference_engine.inference(
            ServeTTSRequest(
                text="Hello world.",
                references=[],
                reference_id=None,
                max_new_tokens=1024,
                chunk_length=200,
                top_p=0.7,
                repetition_penalty=1.5,
                temperature=0.7,
                format="wav",
            )
        )
    )

    logger.info("Warming up done, launching the web UI...")

    inference_fct = get_inference_wrapper(inference_engine)

    app = build_app(inference_fct, args.theme)
    app.queue(api_open=True).launch(show_error=True, show_api=True)