import os
import numpy as np
import gradio as gr
import pyopenjtalk
from pypinyin import lazy_pinyin
from util import preprocess_input, get_tokenizer, load_pitch_dict

from espnet_model_zoo.downloader import ModelDownloader
from espnet2.fileio.read_text import read_label
from espnet2.bin.svs_inference import SingingGenerate


singer_embeddings = {
    "singer1 (female)": "resource/singer/singer_embedding_opencpop.npy",
}

langs = {
    "zh": 2,
    "jp": 1,
}

def gen_song(lang, tempo, texts, durs, pitchs, spk):
    fs = 44100
    # PRETRAIN_MODEL = "espnet/aceopencpop_svs_visinger2_40singer_pretrain"
    # pretrain_downloaded = {
    #     "train_config": "/data7/tyx/espnet/egs2/mixed/svs1/exp/svs_train_visinger2_spk_embed_lang_raw_phn_None_mix_all/config.yaml",
    #     "model_file": "/data7/tyx/espnet/egs2/mixed/svs1/exp/svs_train_visinger2_spk_embed_lang_raw_phn_None_mix_all/500epoch.pth",
    # }
    if texts is None:
        return (fs, np.array([0.0])), "Error: No Text provided!"
    if durs is None:
        return (fs, np.array([0.0])), "Error: No Dur provided!"
    if pitchs is None:
        return (fs, np.array([0.0])), "Error: No Pitch provided!"

    # preprocess
    if lang == "zh":
        texts = preprocess_input(texts, "")
        text_list = lazy_pinyin(texts)
    elif lang == "jp":
        texts = preprocess_input(texts, " ")
        text_list = texts.strip().split()
    durs = preprocess_input(durs, " ")
    dur_list = durs.strip().split()
    pitchs = preprocess_input(pitchs, " ")
    pitch_list = pitchs.strip().split()

    if len(text_list) != len(dur_list):
        return (fs, np.array([0.0])), f"Error: len in text({len(text_list)}) mismatch with duration({len(dur_list)})!"
    if len(text_list) != len(pitch_list):
        return (fs, np.array([0.0])), f"Error: len in text({len(text_list)}) mismatch with pitch({len(pitch_list)})!"

    ## text to phoneme
    tokenizer = get_tokenizer(lang)
    sybs = []
    for text in text_list:
        if text == "AP" or text == "SP":
            rev = [text]
        else:
            rev = tokenizer(text)
            rev = [phn + f"@{lang}" for phn in rev]
        if rev == False:
            return (fs, np.array([0.0])), f"Error: text `{text}` is invalid!"
        phns = "_".join(rev)
        sybs.append(phns)

    pitch_dict = load_pitch_dict()

    labels = []
    notes = []
    st = 0
    for phns, dur, pitch in zip(sybs, dur_list, pitch_list):
        if pitch not in pitch_dict:
            return (fs, np.array([0.0])), f"Error: pitch `{pitch}` is invalid!"
        pitch = pitch_dict[pitch]
        dur = float(dur)
        phn_list = phns.split("_")
        lyric = "".join(phn_list)
        note = [st, st + dur, lyric, pitch, phns]
        st += dur
        notes.append(note)
        for phn in phn_list:
            labels.append(phn)

    phns_str = " ".join(labels)
    batch = {
        "score": (
            int(tempo),
            notes,
        ),
        "text": phns_str,
    }
    print(batch)
    return (fs, np.array([0.0])), "success!"

    # Infer
    device = "cpu"
    # device = "cuda" if torch.cuda.is_available() else "cpu"
    # d = ModelDownloader()
    # pretrain_downloaded = d.download_and_unpack(PRETRAIN_MODEL)
    svs = SingingGenerate(
        train_config = pretrain_downloaded["train_config"],
        model_file = pretrain_downloaded["model_file"],
        device = device
    )
    # sid = spks[spk]
    lid = langs[lang]
    spk_embed = np.load(singer_embeddings[spk])
    # output_dict = svs(batch, sids=np.array([sid]))
    output_dict = svs(batch, lids=np.array([lid]), spembs=spk_embed)
    wav_info = output_dict["wav"].cpu().numpy()
    return (fs, wav_info), "success!"


title = "Demo of Singing Voice Synthesis in Muskits-ESPnet"

description = """
<div style="font-size: 20px;">
  <p>This is the demo page of our toolkit <b>Muskits-ESPnet: A Comprehensive Toolkit for Singing Voice Synthesis in New Paradigm</b>.</p>
  <p>Singing Voice Synthesis (SVS) takes a music score as input and generates singing vocal with the voice of a specific singer.
  Music score contains information about tempo (singing speed), lyrics, as well as duration and pitch of each word in lyrics.</p>

  <img src="resource/Muskits_ESPnet_logo.png" alt="Muskits-ESPnet logo" style="width: 300px; height: auto;" />

  <p>How to use:</p>
  <ol>
    <li> <b>Choose language ID</b>: "zh" indicates lyrics input in Chinese, and "jp" indicates lyrics input in Japanese. </li>
    <li> <b>Input tempo</b>: tempo is a positive integer, typically ranging between 60 and 180, representing the number of beats per minute. </li>
    <li> <b>Input lyrics, duration, pitch in equal length</b>: lyrics should match the language setting. Each word of input should be split with " " (blankspace) or "\\n" (newline) without quotes. Examples are provided.</li>
    <li> <b>Choose one singer</b> </li>
    <li> <b>Click submit button</b> </li>
  </ol>

  <b>Notice</b>: Values outside this range may result in suboptimal generation quality!
</div>
"""

article = """
<div style='margin:20px auto;'>

<p>References: <a href="https://arxiv.org/abs/2409.07226">Muskits-ESPnet paper</a> |
<a href="https://github.com/espnet/espnet">espnet GitHub</a> |
<a href="https://huggingface.co/espnet/aceopencpop_svs_visinger2_40singer_pretrain">pretrained model</a></p>

<pre>
@inproceedings{wu2024muskits,
  title = {{Muskits-ESPnet}: A Comprehensive Toolkit for Singing Voice Synthesis in New Paradigm},
  author = {Yuning Wu and Jiatong Shi and Yifeng Yu and Yuxun Tang and Tao Qian and Yueqian Lin and Jionghao Han and Xinyi Bai and Shinji Watanabe and Qin Jin},
  booktitle={Proceedings of the 32st ACM International Conference on Multimedia},
  year={2024},
}
</pre>

</div>
"""


# SP: silence, AP: aspirate.
examples = [
    ["zh", 89, "雨 淋 湿 了 SP 天 空 AP\n毁 的 SP 很 讲 究 AP", "0.23 0.16 0.36 0.16 0.07 0.28 0.5 0.21 0.3 0.12 0.12 0.25 0.5 0.48 0.34", "60 62 62 62 0 62 58 0 58 58 0 58 58 63 0", "singer1 (female)"],
    # ["zh", 89, "雨 淋 湿 了 SP 天 空 AP", "0.23 0.16 0.36 0.16 0.07 0.28 0.50 0.21", "C4 D4 D4 D4 rest D4 A#3 rest", "singer2 (male)"],
    # ["zh", 89, "雨 淋 湿 了 SP 天 空 AP", "0.23 0.16 0.36 0.16 0.07 0.28 0.50 0.21", "C4 D4 D4 D4 rest D4 Bb3 rest", "singer3 (female)"],
    ["jp", 152, "きっ と と べ ば そ ら ま で と ど く AP", "0.39 2.76 0.2 0.2 0.39 0.39 0.2 0.2 0.39 0.2 0.2 0.59 1.08", "64 71 68 69 71 71 69 68 66 68 69 68 0", "singer1 (female)"],
]

app = gr.Interface(
    fn=gen_song,
    inputs=[
        gr.Radio(label="language", choices=["zh", "jp"], value="zh"),
        gr.Textbox(label="Tempo"),
        gr.Textbox(label="Text"),
        gr.Textbox(label="Duration"),
        gr.Textbox(label="Pitch"),
        gr.Radio(
            label="Singer",
            choices=[
                "singer1 (female)",
            ],
            value="singer1 (female)"
        ),
    ],
    outputs=[
        gr.Audio(label="Generated Song", type="numpy"),
        gr.Textbox(label="Running Status"),
    ],
    title=title,
    description=description,
    article=article,
    examples=examples,
)

app.launch()