import gc
import html
import io
import os
import queue
import wave
from argparse import ArgumentParser
from functools import partial
from pathlib import Path

import gradio as gr
import librosa
import numpy as np
import pyrootutils
import torch
from loguru import logger
from transformers import AutoTokenizer

pyrootutils.setup_root(__file__, indicator=".project-root", pythonpath=True)


from fish_speech.i18n import i18n
from fish_speech.text.chn_text_norm.text import Text as ChnNormedText
from fish_speech.utils import autocast_exclude_mps, set_seed
from tools.api import decode_vq_tokens, encode_reference
from tools.file import AUDIO_EXTENSIONS, audio_to_bytes, list_files, read_ref_text
from tools.llama.generate import (
    GenerateRequest,
    GenerateResponse,
    WrappedGenerateResponse,
    launch_thread_safe_queue,
)
from tools.schema import (
    GLOBAL_NUM_SAMPLES,
    ASRPackRequest,
    ServeASRRequest,
    ServeASRResponse,
    ServeASRSegment,
    ServeAudioPart,
    ServeForwardMessage,
    ServeMessage,
    ServeReferenceAudio,
    ServeRequest,
    ServeResponse,
    ServeStreamDelta,
    ServeStreamResponse,
    ServeTextPart,
    ServeTimedASRResponse,
    ServeTTSRequest,
    ServeVQGANDecodeRequest,
    ServeVQGANDecodeResponse,
    ServeVQGANEncodeRequest,
    ServeVQGANEncodeResponse,
    ServeVQPart,
)
from tools.vqgan.inference import load_model as load_decoder_model

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


HEADER_MD = f"""# Fish Speech

{i18n("A text-to-speech model based on VQ-GAN and Llama developed by [Fish Audio](https://fish.audio).")}  

{i18n("You can find the source code [here](https://github.com/fishaudio/fish-speech) and models [here](https://huggingface.co/fishaudio/fish-speech-1.5).")}  

{i18n("Related code and weights are released under CC BY-NC-SA 4.0 License.")}  

{i18n("We are not responsible for any misuse of the model, please consider your local laws and regulations before using it.")}  
"""

TEXTBOX_PLACEHOLDER = i18n("Put your text here.")
SPACE_IMPORTED = False


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


@torch.inference_mode()
def inference(req: ServeTTSRequest):

    idstr: str | None = req.reference_id
    prompt_tokens, prompt_texts = [], []
    if idstr is not None:
        ref_folder = Path("references") / idstr
        ref_folder.mkdir(parents=True, exist_ok=True)
        ref_audios = list_files(
            ref_folder, AUDIO_EXTENSIONS, recursive=True, sort=False
        )

        if req.use_memory_cache == "never" or (
            req.use_memory_cache == "on-demand" and len(prompt_tokens) == 0
        ):
            prompt_tokens = [
                encode_reference(
                    decoder_model=decoder_model,
                    reference_audio=audio_to_bytes(str(ref_audio)),
                    enable_reference_audio=True,
                )
                for ref_audio in ref_audios
            ]
            prompt_texts = [
                read_ref_text(str(ref_audio.with_suffix(".lab")))
                for ref_audio in ref_audios
            ]
        else:
            logger.info("Use same references")

    else:
        # Parse reference audio aka prompt
        refs = req.references

        if req.use_memory_cache == "never" or (
            req.use_memory_cache == "on-demand" and len(prompt_tokens) == 0
        ):
            prompt_tokens = [
                encode_reference(
                    decoder_model=decoder_model,
                    reference_audio=ref.audio,
                    enable_reference_audio=True,
                )
                for ref in refs
            ]
            prompt_texts = [ref.text for ref in refs]
        else:
            logger.info("Use same references")

    if req.seed is not None:
        set_seed(req.seed)
        logger.warning(f"set seed: {req.seed}")

    # LLAMA Inference
    request = dict(
        device=decoder_model.device,
        max_new_tokens=req.max_new_tokens,
        text=(
            req.text
            if not req.normalize
            else ChnNormedText(raw_text=req.text).normalize()
        ),
        top_p=req.top_p,
        repetition_penalty=req.repetition_penalty,
        temperature=req.temperature,
        compile=args.compile,
        iterative_prompt=req.chunk_length > 0,
        chunk_length=req.chunk_length,
        max_length=4096,
        prompt_tokens=prompt_tokens,
        prompt_text=prompt_texts,
    )

    response_queue = queue.Queue()
    llama_queue.put(
        GenerateRequest(
            request=request,
            response_queue=response_queue,
        )
    )

    segments = []

    while True:
        result: WrappedGenerateResponse = response_queue.get()
        if result.status == "error":
            yield None, None, build_html_error_message(result.response)
            break

        result: GenerateResponse = result.response
        if result.action == "next":
            break

        with autocast_exclude_mps(
            device_type=decoder_model.device.type, dtype=args.precision
        ):
            fake_audios = decode_vq_tokens(
                decoder_model=decoder_model,
                codes=result.codes,
            )

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

    if len(segments) == 0:
        return (
            None,
            None,
            build_html_error_message(
                i18n("No audio generated, please check the input text.")
            ),
        )

    # No matter streaming or not, we need to return the final audio
    audio = np.concatenate(segments, axis=0)
    yield None, (decoder_model.spec_transform.sample_rate, audio), None

    if torch.cuda.is_available():
        torch.cuda.empty_cache()
        gc.collect()


n_audios = 4

global_audio_list = []
global_error_list = []


def inference_wrapper(
    text,
    enable_reference_audio,
    reference_audio,
    reference_text,
    max_new_tokens,
    chunk_length,
    top_p,
    repetition_penalty,
    temperature,
    seed,
    batch_infer_num,
):
    audios = []
    errors = []

    for _ in range(batch_infer_num):
        result = inference(
            text,
            enable_reference_audio,
            reference_audio,
            reference_text,
            max_new_tokens,
            chunk_length,
            top_p,
            repetition_penalty,
            temperature,
            seed,
        )

        _, audio_data, error_message = next(result)

        audios.append(
            gr.Audio(value=audio_data if audio_data else None, visible=True),
        )
        errors.append(
            gr.HTML(value=error_message if error_message else None, visible=True),
        )

    for _ in range(batch_infer_num, n_audios):
        audios.append(
            gr.Audio(value=None, visible=False),
        )
        errors.append(
            gr.HTML(value=None, visible=False),
        )

    return None, *audios, *errors


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 normalize_text(user_input, use_normalization):
    if use_normalization:
        return ChnNormedText(raw_text=user_input).normalize()
    else:
        return user_input


def update_examples():
    examples_dir = Path("references")
    examples_dir.mkdir(parents=True, exist_ok=True)
    example_audios = list_files(examples_dir, AUDIO_EXTENSIONS, recursive=True)
    return gr.Dropdown(choices=example_audios + [""])


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', '%s');window.location.search = params.toString();}}"
            % args.theme,
        )

        # Inference
        with gr.Row():
            with gr.Column(scale=3):
                text = gr.Textbox(
                    label=i18n("Input Text"), placeholder=TEXTBOX_PLACEHOLDER, lines=10
                )
                refined_text = gr.Textbox(
                    label=i18n("Realtime Transform Text"),
                    placeholder=i18n(
                        "Normalization Result Preview (Currently Only Chinese)"
                    ),
                    lines=5,
                    interactive=False,
                )

                with gr.Row():
                    normalize = gr.Checkbox(
                        label=i18n("Text Normalization"),
                        value=False,
                    )

                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=300,
                                    value=200,
                                    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=0.6,
                                    maximum=0.9,
                                    value=0.7,
                                    step=0.01,
                                )

                                repetition_penalty = gr.Slider(
                                    label=i18n("Repetition Penalty"),
                                    minimum=1,
                                    maximum=1.5,
                                    value=1.2,
                                    step=0.01,
                                )

                            with gr.Row():
                                temperature = gr.Slider(
                                    label="Temperature",
                                    minimum=0.6,
                                    maximum=0.9,
                                    value=0.7,
                                    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=["never", "on-demand", "always"],
                                    value="on-demand",
                                )

                            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"
                        )

        text.input(fn=normalize_text, inputs=[text, normalize], outputs=[refined_text])

        def inference_wrapper(
            text,
            normalize,
            reference_id,
            reference_audio,
            reference_text,
            max_new_tokens,
            chunk_length,
            top_p,
            repetition_penalty,
            temperature,
            seed,
            use_memory_cache,
        ):
            references = []
            if reference_audio:
                # 将文件路径转换为字节
                with open(reference_audio, "rb") as audio_file:
                    audio_bytes = audio_file.read()
                references = [
                    ServeReferenceAudio(audio=audio_bytes, text=reference_text)
                ]

            req = ServeTTSRequest(
                text=text,
                normalize=normalize,
                reference_id=reference_id if reference_id else None,
                references=references,
                max_new_tokens=max_new_tokens,
                chunk_length=chunk_length,
                top_p=top_p,
                repetition_penalty=repetition_penalty,
                temperature=temperature,
                seed=int(seed) if seed else None,
                use_memory_cache=use_memory_cache,
            )

            for result in inference(req):
                if result[2]:  # Error message
                    return None, result[2]
                elif result[1]:  # Audio data
                    return result[1], None

            return None, i18n("No audio generated")

        # Submit
        generate.click(
            inference_wrapper,
            [
                refined_text,
                normalize,
                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/fish-speech-1.5",
    )
    parser.add_argument(
        "--decoder-checkpoint-path",
        type=Path,
        default="checkpoints/fish-speech-1.5/firefly-gan-vq-fsq-8x1024-21hz-generator.pth",
    )
    parser.add_argument("--decoder-config-name", type=str, default="firefly_gan_vq")
    parser.add_argument("--device", type=str, default="cuda")
    parser.add_argument("--half", action="store_true")
    parser.add_argument("--compile", action="store_true")
    parser.add_argument("--max-gradio-length", type=int, default=0)
    parser.add_argument("--theme", type=str, default="light")

    return parser.parse_args()


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

    # Check if CUDA is available
    if not torch.cuda.is_available():
        logger.info("CUDA is not available, running on CPU.")
        args.device = "cpu"

    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...")

    # Dry run to check if the model is loaded correctly and avoid the first-time latency
    list(
        inference(
            ServeTTSRequest(
                text="Hello world.",
                references=[],
                reference_id=None,
                max_new_tokens=0,
                chunk_length=200,
                top_p=0.7,
                repetition_penalty=1.5,
                temperature=0.7,
                emotion=None,
                format="wav",
            )
        )
    )

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

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