import subprocess as sp
import os

# Download if not exists
os.makedirs("checkpoints", exist_ok=True)

if not os.path.exists("checkpoints/text2semantic-medium-v1-2k.pth"):
    print("Downloading text2semantic-medium-v1-2k.pth")
    sp.run(["wget", "-q", "-O", "checkpoints/text2semantic-medium-v1-2k.pth", os.environ["CKPT_SEMANTIC"]])

if not os.path.exists("checkpoints/vq-gan-group-fsq-2x1024.pth"):
    print("Downloading vq-gan-group-fsq-2x1024.pth")
    sp.run(["wget", "-q", "-O", "checkpoints/vq-gan-group-fsq-2x1024.pth", os.environ["CKPT_VQGAN"]])

print("All checkpoints downloaded")

import html
from argparse import ArgumentParser
from io import BytesIO
from pathlib import Path

import gradio as gr
import librosa
import spaces
import torch
from loguru import logger
from torchaudio import functional as AF
from transformers import AutoTokenizer

from tools.llama.generate import generate_long
from tools.llama.generate import load_model as load_llama_model
from tools.vqgan.inference import load_model as load_vqgan_model

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


HEADER_MD = """# Fish Speech

A text-to-speech model based on VQ-GAN and Llama developed by [Fish Audio](https://fish.audio).  
由 [Fish Audio](https://fish.audio) 研发的基于 VQ-GAN 和 Llama 的多语种语音合成. 

You can find the source code [here](https://github.com/fishaudio/fish-speech) and models [here](https://huggingface.co/fishaudio/fish-speech-1).  
你可以在 [这里](https://github.com/fishaudio/fish-speech) 找到源代码和 [这里](https://huggingface.co/fishaudio/fish-speech-1) 找到模型.  

Related code are released under BSD-3-Clause License, and weights are released under CC BY-NC-SA 4.0 License.  
相关代码使用 BSD-3-Clause 许可证发布，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.
"""

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


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


@spaces.GPU
def inference(
    text,
    enable_reference_audio,
    reference_audio,
    reference_text,
    max_new_tokens,
    chunk_length,
    top_k,
    top_p,
    repetition_penalty,
    temperature,
    speaker=None,
):
    if len(reference_text) > 100:
        return None, "Ref text is too long, please keep it under 100 characters."

    if args.max_gradio_length > 0 and len(text) > args.max_gradio_length:
        return None, "Text is too long, please keep it under 1000 characters."

    # Parse reference audio aka prompt
    prompt_tokens = None
    if enable_reference_audio and reference_audio is not None:
        # reference_audio_sr, reference_audio_content = reference_audio
        reference_audio_content, _ = librosa.load(
            reference_audio, sr=vqgan_model.sampling_rate, mono=True
        )
        audios = torch.from_numpy(reference_audio_content).to(vqgan_model.device)[
            None, None, :
        ]

        logger.info(
            f"Loaded audio with {audios.shape[2] / vqgan_model.sampling_rate:.2f} seconds"
        )

        # VQ Encoder
        audio_lengths = torch.tensor(
            [audios.shape[2]], device=vqgan_model.device, dtype=torch.long
        )
        prompt_tokens = vqgan_model.encode(audios, audio_lengths)[0][0]

    # LLAMA Inference
    result = generate_long(
        model=llama_model,
        tokenizer=llama_tokenizer,
        device=vqgan_model.device,
        decode_one_token=decode_one_token,
        max_new_tokens=max_new_tokens,
        text=text,
        top_k=int(top_k) if top_k > 0 else None,
        top_p=top_p,
        repetition_penalty=repetition_penalty,
        temperature=temperature,
        compile=args.compile,
        iterative_prompt=chunk_length > 0,
        chunk_length=chunk_length,
        max_length=args.max_length,
        speaker=speaker if speaker else None,
        prompt_tokens=prompt_tokens if enable_reference_audio else None,
        prompt_text=reference_text if enable_reference_audio else None,
    )

    codes = next(result)

    # VQGAN Inference
    feature_lengths = torch.tensor([codes.shape[1]], device=vqgan_model.device)
    fake_audios = vqgan_model.decode(
        indices=codes[None], feature_lengths=feature_lengths, return_audios=True
    )[0, 0]

    fake_audios = fake_audios.float().cpu().numpy()

    return (vqgan_model.sampling_rate, fake_audios), None


def build_app():
    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', 'light');window.location.search = params.toString();}}",
        )

        # Inference
        with gr.Row():
            with gr.Column(scale=3):
                text = gr.Textbox(
                    label="Input Text / 输入文本",
                    placeholder=TEXTBOX_PLACEHOLDER,
                    lines=15,
                )

                with gr.Row():
                    with gr.Tab(label="Advanced Config / 高级参数"):
                        chunk_length = gr.Slider(
                            label="Iterative Prompt Length, 0 means off / 迭代提示长度，0 表示关闭",
                            minimum=0,
                            maximum=100,
                            value=30,
                            step=8,
                        )

                        max_new_tokens = gr.Slider(
                            label="Maximum tokens per batch, 0 means no limit / 每批最大令牌数，0 表示无限制",
                            minimum=128,
                            maximum=512,
                            value=512,  # 0 means no limit
                            step=8,
                        )

                        top_k = gr.Slider(
                            label="Top-K", minimum=0, maximum=5, value=0, step=1
                        )

                        top_p = gr.Slider(
                            label="Top-P", minimum=0, maximum=1, value=0.7, step=0.01
                        )

                        repetition_penalty = gr.Slider(
                            label="Repetition Penalty",
                            minimum=0,
                            maximum=2,
                            value=1.5,
                            step=0.01,
                        )

                        temperature = gr.Slider(
                            label="Temperature",
                            minimum=0,
                            maximum=2,
                            value=0.7,
                            step=0.01,
                        )

                        # speaker = gr.Textbox(
                        #     label="Speaker / 说话人",
                        #     placeholder="Type name of the speaker / 输入说话人的名称",
                        #     lines=1,
                        # )

                    with gr.Tab(label="Reference Audio / 参考音频"):
                        gr.Markdown(
                            "5 to 10 seconds of reference audio, useful for specifying speaker. \n5 到 10 秒的参考音频，适用于指定音色。"
                        )

                        enable_reference_audio = gr.Checkbox(
                            label="Enable Reference Audio / 启用参考音频",
                        )
                        reference_audio = gr.Audio(
                            label="Reference Audio / 参考音频",
                            type="filepath",
                        )
                        reference_text = gr.Textbox(
                            label="Reference Text / 参考文本",
                            placeholder="参考文本",
                            lines=1,
                        )

            with gr.Column(scale=3):
                with gr.Row():
                    error = gr.HTML(label="Error Message / 错误信息")
                with gr.Row():
                    audio = gr.Audio(label="Generated Audio / 音频", type="numpy")

                with gr.Row():
                    with gr.Column(scale=3):
                        generate = gr.Button(
                            value="\U0001F3A7 Generate / 合成", variant="primary"
                        )

        # # Submit
        generate.click(
            inference,
            [
                text,
                enable_reference_audio,
                reference_audio,
                reference_text,
                max_new_tokens,
                chunk_length,
                top_k,
                top_p,
                repetition_penalty,
                temperature,
                # speaker,
            ],
            [audio, error],
        )

    return app


def parse_args():
    parser = ArgumentParser()
    parser.add_argument(
        "--llama-checkpoint-path",
        type=Path,
        default="checkpoints/text2semantic-medium-v1-2k.pth",
    )
    parser.add_argument(
        "--llama-config-name", type=str, default="dual_ar_2_codebook_medium"
    )
    parser.add_argument(
        "--vqgan-checkpoint-path",
        type=Path,
        default="checkpoints/vq-gan-group-fsq-2x1024.pth",
    )
    parser.add_argument("--vqgan-config-name", type=str, default="vqgan_pretrain")
    parser.add_argument("--tokenizer", type=str, default="fishaudio/fish-speech-1")
    parser.add_argument("--device", type=str, default="cuda")
    parser.add_argument("--half", action="store_true")
    parser.add_argument("--max-length", type=int, default=2048)
    parser.add_argument("--compile", action="store_true")
    parser.add_argument("--max-gradio-length", type=int, default=1024)

    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_model, decode_one_token = load_llama_model(
        config_name=args.llama_config_name,
        checkpoint_path=args.llama_checkpoint_path,
        device=args.device,
        precision=args.precision,
        max_length=args.max_length,
        compile=args.compile,
    )
    llama_tokenizer = AutoTokenizer.from_pretrained(args.tokenizer)
    logger.info("Llama model loaded, loading VQ-GAN model...")

    vqgan_model = load_vqgan_model(
        config_name=args.vqgan_config_name,
        checkpoint_path=args.vqgan_checkpoint_path,
        device=args.device,
    )

    logger.info("VQ-GAN model loaded, warming up...")

    # Dry run to check if the model is loaded correctly and avoid the first-time latency
    inference(
        text="Hello, world!",
        enable_reference_audio=False,
        reference_audio=None,
        reference_text="",
        max_new_tokens=0,
        chunk_length=0,
        top_k=0,  # 0 means no limit
        top_p=0.7,
        repetition_penalty=1.5,
        temperature=0.7,
        speaker=None,
    )

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

    app = build_app()
    app.launch(show_api=False)