import os
import shutil
import traceback
import faiss
import gradio as gr
import numpy as np
from sklearn.cluster import MiniBatchKMeans
from random import shuffle
from glob import glob
from infer.modules.train.train import train
from zero import zero


def write_filelist(exp_dir: str) -> None:
    if_f0_3 = True
    spk_id5 = 0
    gt_wavs_dir = "%s/0_gt_wavs" % (exp_dir)
    feature_dir = "%s/3_feature768" % (exp_dir)

    if if_f0_3:
        f0_dir = "%s/2a_f0" % (exp_dir)
        f0nsf_dir = "%s/2b-f0nsf" % (exp_dir)
        names = (
            set([name.split(".")[0] for name in os.listdir(gt_wavs_dir)])
            & set([name.split(".")[0] for name in os.listdir(feature_dir)])
            & set([name.split(".")[0] for name in os.listdir(f0_dir)])
            & set([name.split(".")[0] for name in os.listdir(f0nsf_dir)])
        )
    else:
        names = set([name.split(".")[0] for name in os.listdir(gt_wavs_dir)]) & set(
            [name.split(".")[0] for name in os.listdir(feature_dir)]
        )
    opt = []
    for name in names:
        if if_f0_3:
            opt.append(
                "%s/%s.wav|%s/%s.npy|%s/%s.wav.npy|%s/%s.wav.npy|%s"
                % (
                    gt_wavs_dir.replace("\\", "\\\\"),
                    name,
                    feature_dir.replace("\\", "\\\\"),
                    name,
                    f0_dir.replace("\\", "\\\\"),
                    name,
                    f0nsf_dir.replace("\\", "\\\\"),
                    name,
                    spk_id5,
                )
            )
        else:
            opt.append(
                "%s/%s.wav|%s/%s.npy|%s"
                % (
                    gt_wavs_dir.replace("\\", "\\\\"),
                    name,
                    feature_dir.replace("\\", "\\\\"),
                    name,
                    spk_id5,
                )
            )
    fea_dim = 768

    now_dir = os.getcwd()
    sr2 = "40k"
    if if_f0_3:
        for _ in range(2):
            opt.append(
                "%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s/logs/mute/2a_f0/mute.wav.npy|%s/logs/mute/2b-f0nsf/mute.wav.npy|%s"
                % (now_dir, sr2, now_dir, fea_dim, now_dir, now_dir, spk_id5)
            )
    else:
        for _ in range(2):
            opt.append(
                "%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s"
                % (now_dir, sr2, now_dir, fea_dim, spk_id5)
            )
    shuffle(opt)
    with open("%s/filelist.txt" % exp_dir, "w") as f:
        f.write("\n".join(opt))


@zero(duration=240)
def train_model(exp_dir: str) -> str:
    shutil.copy("config.json", exp_dir)
    write_filelist(exp_dir)
    train(exp_dir)

    models = glob(f"{exp_dir}/G_*.pth")
    print(models)
    if not models:
        raise gr.Error("No model found")

    latest_model = max(models, key=os.path.getctime)
    return latest_model


def train_index(exp_dir: str) -> str:
    feature_dir = "%s/3_feature768" % (exp_dir)
    if not os.path.exists(feature_dir):
        raise gr.Error("Please extract features first.")
    listdir_res = list(os.listdir(feature_dir))
    if len(listdir_res) == 0:
        raise gr.Error("Please extract features first.")
    npys = []
    for name in sorted(listdir_res):
        phone = np.load("%s/%s" % (feature_dir, name))
        npys.append(phone)
    big_npy = np.concatenate(npys, 0)
    big_npy_idx = np.arange(big_npy.shape[0])
    np.random.shuffle(big_npy_idx)
    big_npy = big_npy[big_npy_idx]
    if big_npy.shape[0] > 2e5:
        print("Trying doing kmeans %s shape to 10k centers." % big_npy.shape[0])
        try:
            big_npy = (
                MiniBatchKMeans(
                    n_clusters=10000,
                    verbose=True,
                    batch_size=256 * 8,
                    compute_labels=False,
                    init="random",
                )
                .fit(big_npy)
                .cluster_centers_
            )
        except:
            info = traceback.format_exc()
            print(info)
            raise gr.Error(info)

    np.save("%s/total_fea.npy" % exp_dir, big_npy)
    n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39)
    print("%s,%s" % (big_npy.shape, n_ivf))
    index = faiss.index_factory(768, "IVF%s,Flat" % n_ivf)
    # index = faiss.index_factory(256if version19=="v1"else 768, "IVF%s,PQ128x4fs,RFlat"%n_ivf)
    print("training")
    index_ivf = faiss.extract_index_ivf(index)  #
    index_ivf.nprobe = 1
    index.train(big_npy)
    faiss.write_index(
        index,
        "%s/trained_IVF%s_Flat_nprobe_%s.index" % (exp_dir, n_ivf, index_ivf.nprobe),
    )
    print("adding")
    batch_size_add = 8192
    for i in range(0, big_npy.shape[0], batch_size_add):
        index.add(big_npy[i : i + batch_size_add])
    faiss.write_index(
        index,
        "%s/added_IVF%s_Flat_nprobe_%s.index" % (exp_dir, n_ivf, index_ivf.nprobe),
    )
    print("built added_IVF%s_Flat_nprobe_%s.index" % (n_ivf, index_ivf.nprobe))

    return "%s/added_IVF%s_Flat_nprobe_%s.index" % (exp_dir, n_ivf, index_ivf.nprobe)


class TrainTab:
    def __init__(self):
        pass

    def ui(self):
        gr.Markdown("# Training")
        gr.Markdown(
            "You can start training the model by clicking the button below. "
            "Each time you click the button, the model will train for 10 epochs, which takes about 3 minutes on ZeroGPU (A100). "
            "Tha latest *training checkpoint* will be avaible below."
        )

        with gr.Row():
            self.train_btn = gr.Button(value="Train", variant="primary")
            self.latest_checkpoint = gr.File(label="Latest checkpoint")
        with gr.Row():
            self.train_index_btn = gr.Button(value="Train index", variant="primary")
            self.trained_index = gr.File(label="Trained index")

    def build(self, exp_dir: gr.Textbox):
        self.train_btn.click(
            fn=train_model,
            inputs=[exp_dir],
            outputs=[self.latest_checkpoint],
        )

        self.train_index_btn.click(
            fn=train_index,
            inputs=[exp_dir],
            outputs=[self.trained_index],
        )