import os
import glob
import json
import argparse
import traceback
import logging
import gradio as gr
import numpy as np
import librosa
import torch
import asyncio
import edge_tts
import yt_dlp
import ffmpeg
import subprocess
import sys
import io
import wave
from datetime import datetime
from fairseq import checkpoint_utils
from lib.infer_pack.models import (
    SynthesizerTrnMs256NSFsid,
    SynthesizerTrnMs256NSFsid_nono,
    SynthesizerTrnMs768NSFsid,
    SynthesizerTrnMs768NSFsid_nono,
)
from vc_infer_pipeline import VC
from config import Config
config = Config()
logging.getLogger("numba").setLevel(logging.WARNING)
limitation = os.getenv("SYSTEM") == "spaces"  # limit audio length in huggingface spaces

audio_mode = []
f0method_mode = []
f0method_info = ""

if limitation is True:
    audio_mode = ["Upload audio", "TTS Audio"]
    f0method_mode = ["pm", "harvest"]
    f0method_info = "PM is fast, Harvest is good but extremely slow, Rvmpe is alternative to harvest (might be better). (Default: PM)"
else:
    audio_mode = ["Input path", "Upload audio", "Youtube", "TTS Audio"]
    f0method_mode = ["pm", "harvest", "crepe"]
    f0method_info = "PM is fast, Harvest is good but extremely slow, Rvmpe is alternative to harvest (might be better), and Crepe effect is good but requires GPU (Default: PM)"

if os.path.isfile("rmvpe.pt"):
    f0method_mode.insert(2, "rmvpe")

def create_vc_fn(model_name, tgt_sr, net_g, vc, if_f0, version, file_index):
    def vc_fn(
        vc_audio_mode,
        vc_input, 
        vc_upload,
        tts_text,
        tts_voice,
        f0_up_key,
        f0_method,
        index_rate,
        filter_radius,
        resample_sr,
        rms_mix_rate,
        protect
    ):
        try:
            print(f"Converting using {model_name}...")
            if vc_audio_mode == "Input path" or "Youtube" and vc_input != "":
                audio, sr = librosa.load(vc_input, sr=16000, mono=True)
            elif vc_audio_mode == "Upload audio":
                if vc_upload is None:
                    return "You need to upload an audio", None
                sampling_rate, audio = vc_upload
                duration = audio.shape[0] / sampling_rate
                if duration > 20 and limitation:
                    return "Please upload an audio file that is less than 20 seconds. If you need to generate a longer audio file, please use Colab.", None
                audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
                if len(audio.shape) > 1:
                    audio = librosa.to_mono(audio.transpose(1, 0))
                if sampling_rate != 16000:
                    audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
            elif vc_audio_mode == "TTS Audio":
                if len(tts_text) > 100 and limitation:
                    return "Text is too long", None
                if tts_text is None or tts_voice is None:
                    return "You need to enter text and select a voice", None
                asyncio.run(edge_tts.Communicate(tts_text, "-".join(tts_voice.split('-')[:-1])).save("tts.mp3"))
                audio, sr = librosa.load("tts.mp3", sr=16000, mono=True)
                vc_input = "tts.mp3"
            times = [0, 0, 0]
            f0_up_key = int(f0_up_key)
            audio_opt = vc.pipeline(
                hubert_model,
                net_g,
                0,
                audio,
                vc_input,
                times,
                f0_up_key,
                f0_method,
                file_index,
                # file_big_npy,
                index_rate,
                if_f0,
                filter_radius,
                tgt_sr,
                resample_sr,
                rms_mix_rate,
                version,
                protect,
                f0_file=None,
            )
            info = f"[{datetime.now().strftime('%Y-%m-%d %H:%M')}]: npy: {times[0]}, f0: {times[1]}s, infer: {times[2]}s"
            print(info)
            return info, (tgt_sr, audio_opt)
        except:
            info = traceback.format_exc()
            print(f"{model_name} | {info}")
            return info, (tgt_sr, audio_opt)
    return vc_fn

def load_model():
    models = []
    with open(f"weights/model_info.json", "r", encoding="utf-8") as f:
        models_info = json.load(f)
    for character_name, info in models_info.items():
        if not info['enable']:
            continue
        model_title = info['title']
        model_name = info['model_path']
        model_author = info.get("author", None)
        model_cover = f"weights/{character_name}/{info['cover']}"
        model_index = f"weights/{character_name}/{info['feature_retrieval_library']}"
        cpt = torch.load(f"weights/{character_name}/{model_name}", map_location="cpu")
        tgt_sr = cpt["config"][-1]
        cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]  # n_spk
        if_f0 = cpt.get("f0", 1)
        version = cpt.get("version", "v1")
        if version == "v1":
            if if_f0 == 1:
                net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half)
            else:
                net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
            model_version = "V1"
        elif version == "v2":
            if if_f0 == 1:
                net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half)
            else:
                net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
            model_version = "V2"
        del net_g.enc_q
        print(net_g.load_state_dict(cpt["weight"], strict=False))
        net_g.eval().to(config.device)
        if config.is_half:
            net_g = net_g.half()
        else:
            net_g = net_g.float()
        vc = VC(tgt_sr, config)
        print(f"Model loaded: {character_name} / {info['feature_retrieval_library']} | ({model_version})")
        models.append((character_name, model_title, model_author, model_cover, model_version, create_vc_fn(model_title, tgt_sr, net_g, vc, if_f0, version, model_index)))
    return models

def cut_vocal_and_inst(url, audio_provider, split_model):
    if url != "":
        if not os.path.exists("dl_audio"):
            os.mkdir("dl_audio")
        if audio_provider == "Youtube":
            ydl_opts = {
            'noplaylist': True,
            'format': 'bestaudio/best',
            'postprocessors': [{
                'key': 'FFmpegExtractAudio',
                'preferredcodec': 'wav',
            }],
            "outtmpl": 'dl_audio/youtube_audio',
            }
            with yt_dlp.YoutubeDL(ydl_opts) as ydl:
                ydl.download([url])
            audio_path = "dl_audio/youtube_audio.wav"
        if split_model == "htdemucs":
            command = f"demucs --two-stems=vocals {audio_path} -o output"
            result = subprocess.run(command.split(), stdout=subprocess.PIPE)
            print(result.stdout.decode())
            return "output/htdemucs/youtube_audio/vocals.wav", "output/htdemucs/youtube_audio/no_vocals.wav", audio_path, "output/htdemucs/youtube_audio/vocals.wav"
        else:
            command = f"demucs --two-stems=vocals -n mdx_extra_q {audio_path} -o output"
            result = subprocess.run(command.split(), stdout=subprocess.PIPE)
            print(result.stdout.decode())
            return "output/mdx_extra_q/youtube_audio/vocals.wav", "output/mdx_extra_q/youtube_audio/no_vocals.wav", audio_path, "output/mdx_extra_q/youtube_audio/vocals.wav"
    else:
        raise gr.Error("URL Required!")
        return None, None, None, None

def combine_vocal_and_inst(audio_data, audio_volume, split_model):
    if not os.path.exists("output/result"):
        os.mkdir("output/result")
    vocal_path = "output/result/output.wav"
    output_path = "output/result/combine.mp3"
    if split_model == "htdemucs":
        inst_path = "output/htdemucs/youtube_audio/no_vocals.wav"
    else:
        inst_path = "output/mdx_extra_q/youtube_audio/no_vocals.wav"
    with wave.open(vocal_path, "w") as wave_file:
        wave_file.setnchannels(1) 
        wave_file.setsampwidth(2)
        wave_file.setframerate(audio_data[0])
        wave_file.writeframes(audio_data[1].tobytes())
    command =  f'ffmpeg -y -i {inst_path} -i {vocal_path} -filter_complex [1:a]volume={audio_volume}dB[v];[0:a][v]amix=inputs=2:duration=longest -b:a 320k -c:a libmp3lame {output_path}'
    result = subprocess.run(command.split(), stdout=subprocess.PIPE)
    print(result.stdout.decode())
    return output_path

def load_hubert():
    global hubert_model
    models, _, _ = checkpoint_utils.load_model_ensemble_and_task(
        ["hubert_base.pt"],
        suffix="",
    )
    hubert_model = models[0]
    hubert_model = hubert_model.to(config.device)
    if config.is_half:
        hubert_model = hubert_model.half()
    else:
        hubert_model = hubert_model.float()
    hubert_model.eval()

def change_audio_mode(vc_audio_mode):
    if vc_audio_mode == "Input path":
        return (
            # Input & Upload
            gr.Textbox.update(visible=True),
            gr.Checkbox.update(visible=False),
            gr.Audio.update(visible=False),
            # Youtube
            gr.Dropdown.update(visible=False),
            gr.Textbox.update(visible=False),
            gr.Dropdown.update(visible=False),
            gr.Button.update(visible=False),
            gr.Audio.update(visible=False),
            gr.Audio.update(visible=False),
            gr.Audio.update(visible=False),
            gr.Slider.update(visible=False),
            gr.Audio.update(visible=False),
            gr.Button.update(visible=False),
            # TTS
            gr.Textbox.update(visible=False),
            gr.Dropdown.update(visible=False)
        )
    elif vc_audio_mode == "Upload audio":
        return (
            # Input & Upload
            gr.Textbox.update(visible=False),
            gr.Checkbox.update(visible=True),
            gr.Audio.update(visible=True),
            # Youtube
            gr.Dropdown.update(visible=False),
            gr.Textbox.update(visible=False),
            gr.Dropdown.update(visible=False),
            gr.Button.update(visible=False),
            gr.Audio.update(visible=False),
            gr.Audio.update(visible=False),
            gr.Audio.update(visible=False),
            gr.Slider.update(visible=False),
            gr.Audio.update(visible=False),
            gr.Button.update(visible=False),
            # TTS
            gr.Textbox.update(visible=False),
            gr.Dropdown.update(visible=False)
        )
    elif vc_audio_mode == "Youtube":
        return (
            # Input & Upload
            gr.Textbox.update(visible=False),
            gr.Checkbox.update(visible=False),
            gr.Audio.update(visible=False),
            # Youtube
            gr.Dropdown.update(visible=True),
            gr.Textbox.update(visible=True),
            gr.Dropdown.update(visible=True),
            gr.Button.update(visible=True),
            gr.Audio.update(visible=True),
            gr.Audio.update(visible=True),
            gr.Audio.update(visible=True),
            gr.Slider.update(visible=True),
            gr.Audio.update(visible=True),
            gr.Button.update(visible=True),
            # TTS
            gr.Textbox.update(visible=False),
            gr.Dropdown.update(visible=False)
        )
    elif vc_audio_mode == "TTS Audio":
        return (
            # Input & Upload
            gr.Textbox.update(visible=False),
            gr.Checkbox.update(visible=False),
            gr.Audio.update(visible=False),
            # Youtube
            gr.Dropdown.update(visible=False),
            gr.Textbox.update(visible=False),
            gr.Dropdown.update(visible=False),
            gr.Button.update(visible=False),
            gr.Audio.update(visible=False),
            gr.Audio.update(visible=False),
            gr.Audio.update(visible=False),
            gr.Slider.update(visible=False),
            gr.Audio.update(visible=False),
            gr.Button.update(visible=False),
            # TTS
            gr.Textbox.update(visible=True),
            gr.Dropdown.update(visible=True)
        )
    else:
        return (
            # Input & Upload
            gr.Textbox.update(visible=False),
            gr.Checkbox.update(visible=True),
            gr.Audio.update(visible=True),
            # Youtube
            gr.Dropdown.update(visible=False),
            gr.Textbox.update(visible=False),
            gr.Dropdown.update(visible=False),
            gr.Button.update(visible=False),
            gr.Audio.update(visible=False),
            gr.Audio.update(visible=False),
            gr.Audio.update(visible=False),
            gr.Slider.update(visible=False),
            gr.Audio.update(visible=False),
            gr.Button.update(visible=False),
            # TTS
            gr.Textbox.update(visible=False),
            gr.Dropdown.update(visible=False)
        )

def use_microphone(microphone):
    if microphone == True:
        return gr.Audio.update(source="microphone")
    else:
        return gr.Audio.update(source="upload")

if __name__ == '__main__':
    load_hubert()
    models = load_model()
    tts_voice_list = asyncio.new_event_loop().run_until_complete(edge_tts.list_voices())
    voices = [f"{v['ShortName']}-{v['Gender']}" for v in tts_voice_list]
    with gr.Blocks() as app:
        gr.Markdown(
            "# <center> Combined Genshin Impact RVC Models\n"
            "## <center> The input audio should be clean and pure voice without background music.\n"
            "### <center> It is recommended to use google colab for more features. \n"
            "[![image](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1Tgr6q9kKiB5P37rUitrB3CsNl8JP9iQZ?usp=sharing)\n\n"
            "[![Original Repo](https://badgen.net/badge/icon/github?icon=github&label=Original%20Repo)](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI)"
        )
        with gr.Tabs():
            for (name, title, author, cover, model_version, vc_fn) in models:
                with gr.TabItem(name):
                    with gr.Row():
                        gr.Markdown(
                            '<div align="center">'
                            f'<div>{title}</div>\n'+
                            f'<div>RVC {model_version} Model</div>\n'+
                            (f'<div>Model author: {author}</div>' if author else "")+
                            (f'<img style="width:auto;height:300px;" src="file/{cover}">' if cover else "")+
                            '</div>'
                        )
                    with gr.Row():
                        with gr.Column():
                            vc_audio_mode = gr.Dropdown(label="Input voice", choices=audio_mode, allow_custom_value=False, value="Upload audio")
                            # Input
                            vc_input = gr.Textbox(label="Input audio path", visible=False)
                            # Upload
                            vc_microphone_mode = gr.Checkbox(label="Use Microphone", value=False, visible=True, interactive=True)
                            vc_upload = gr.Audio(label="Upload audio file", source="upload", visible=True, interactive=True)
                            # Youtube
                            vc_download_audio = gr.Dropdown(label="Provider", choices=["Youtube"], allow_custom_value=False, visible=False, value="Youtube", info="Select provider (Default: Youtube)")
                            vc_link = gr.Textbox(label="Youtube URL", visible=False, info="Example: https://www.youtube.com/watch?v=Nc0sB1Bmf-A", placeholder="https://www.youtube.com/watch?v=...")
                            vc_split_model = gr.Dropdown(label="Splitter Model", choices=["htdemucs", "mdx_extra_q"], allow_custom_value=False, visible=False, value="htdemucs", info="Select the splitter model (Default: htdemucs)")
                            vc_split = gr.Button("Split Audio", variant="primary", visible=False)
                            vc_vocal_preview = gr.Audio(label="Vocal Preview", visible=False)
                            vc_inst_preview = gr.Audio(label="Instrumental Preview", visible=False)
                            vc_audio_preview = gr.Audio(label="Audio Preview", visible=False)
                            # TTS
                            tts_text = gr.Textbox(visible=False, label="TTS text", info="Text to speech input")
                            tts_voice = gr.Dropdown(label="Edge-tts speaker", choices=voices, visible=False, allow_custom_value=False, value="en-US-AnaNeural-Female")
                        with gr.Column():
                            vc_transform0 = gr.Number(label="Transpose", value=0, info='Type "12" to change from male to female voice. Type "-12" to change female to male voice')
                            f0method0 = gr.Radio(
                                label="Pitch extraction algorithm",
                                info=f0method_info,
                                choices=f0method_mode,
                                value="pm",
                                interactive=True
                            )
                            index_rate1 = gr.Slider(
                                minimum=0,
                                maximum=1,
                                label="Retrieval feature ratio",
                                info="(Default: 0.7)",
                                value=0.7,
                                interactive=True,
                            )
                            filter_radius0 = gr.Slider(
                                minimum=0,
                                maximum=7,
                                label="Apply Median Filtering",
                                info="The value represents the filter radius and can reduce breathiness.",
                                value=3,
                                step=1,
                                interactive=True,
                            )
                            resample_sr0 = gr.Slider(
                                minimum=0,
                                maximum=48000,
                                label="Resample the output audio",
                                info="Resample the output audio in post-processing to the final sample rate. Set to 0 for no resampling",
                                value=0,
                                step=1,
                                interactive=True,
                            )
                            rms_mix_rate0 = gr.Slider(
                                minimum=0,
                                maximum=1,
                                label="Volume Envelope",
                                info="Use the volume envelope of the input to replace or mix with the volume envelope of the output. The closer the ratio is to 1, the more the output envelope is used",
                                value=1,
                                interactive=True,
                            )
                            protect0 = gr.Slider(
                                minimum=0,
                                maximum=0.5,
                                label="Voice Protection",
                                info="Protect voiceless consonants and breath sounds to prevent artifacts such as tearing in electronic music. Set to 0.5 to disable. Decrease the value to increase protection, but it may reduce indexing accuracy",
                                value=0.5,
                                step=0.01,
                                interactive=True,
                            )
                        with gr.Column():
                            vc_log = gr.Textbox(label="Output Information", interactive=False)
                            vc_output = gr.Audio(label="Output Audio", interactive=False)
                            vc_convert = gr.Button("Convert", variant="primary")
                            vc_volume = gr.Slider(
                                minimum=0,
                                maximum=10,
                                label="Vocal volume",
                                value=4,
                                interactive=True,
                                step=1,
                                info="Adjust vocal volume (Default: 4}",
                                visible=False
                            )
                            vc_combined_output = gr.Audio(label="Output Combined Audio", visible=False)
                            vc_combine =  gr.Button("Combine",variant="primary", visible=False)
                vc_convert.click(
                    fn=vc_fn, 
                    inputs=[
                        vc_audio_mode,
                        vc_input,
                        vc_upload,
                        tts_text,
                        tts_voice,
                        vc_transform0,
                        f0method0,
                        index_rate1,
                        filter_radius0,
                        resample_sr0,
                        rms_mix_rate0,
                        protect0,
                    ], 
                    outputs=[vc_log ,vc_output]
                )
                vc_split.click(
                    fn=cut_vocal_and_inst, 
                    inputs=[vc_link, vc_download_audio, vc_split_model], 
                    outputs=[vc_vocal_preview, vc_inst_preview, vc_audio_preview, vc_input]
                )
                vc_combine.click(
                    fn=combine_vocal_and_inst,
                    inputs=[vc_output, vc_volume, vc_split_model],
                    outputs=[vc_combined_output]
                )
                vc_microphone_mode.change(
                    fn=use_microphone,
                    inputs=vc_microphone_mode,
                    outputs=vc_upload
                )
                vc_audio_mode.change(
                    fn=change_audio_mode,
                    inputs=[vc_audio_mode],
                    outputs=[
                        vc_input,
                        vc_microphone_mode, 
                        vc_upload,
                        vc_download_audio,
                        vc_link,
                        vc_split_model,
                        vc_split,
                        vc_vocal_preview,
                        vc_inst_preview,
                        vc_audio_preview,
                        vc_volume,
                        vc_combined_output,
                        vc_combine,
                        tts_text,
                        tts_voice
                    ]
                )
        app.queue(concurrency_count=1, max_size=20, api_open=config.api).launch(share=config.colab)