import os
inp_text=                           os.environ.get("inp_text")
exp_name=                           os.environ.get("exp_name")
i_part=                             os.environ.get("i_part")
all_parts=                          os.environ.get("all_parts")
os.environ["CUDA_VISIBLE_DEVICES"]= os.environ.get("_CUDA_VISIBLE_DEVICES")
opt_dir=                            os.environ.get("opt_dir")
pretrained_s2G=                     os.environ.get("pretrained_s2G")
s2config_path=                      os.environ.get("s2config_path")
is_half=eval(os.environ.get("is_half","True"))
import math,traceback
import multiprocessing
import sys,pdb
now_dir = os.getcwd()
sys.path.append(now_dir)
from random import shuffle
import torch.multiprocessing as mp
from glob import glob
from tqdm import tqdm
import logging,librosa,utils,torch
from module.models import SynthesizerTrn
logging.getLogger("numba").setLevel(logging.WARNING)
# from config import pretrained_s2G

# inp_text=sys.argv[1]
# exp_name=sys.argv[2]
# i_part=sys.argv[3]
# all_parts=sys.argv[4]
# os.environ["CUDA_VISIBLE_DEVICES"]=sys.argv[5]
# opt_dir="/data/docker/liujing04/gpt-vits/fine_tune_dataset/%s"%exp_name


hubert_dir="%s/4-cnhubert"%(opt_dir)
semantic_path="%s/6-name2semantic-%s.tsv"%(opt_dir,i_part)
if(os.path.exists(semantic_path)==False):
    os.makedirs(opt_dir,exist_ok=True)

    device="cuda:0"
    hps = utils.get_hparams_from_file(s2config_path)
    vq_model = SynthesizerTrn(
        hps.data.filter_length // 2 + 1,
        hps.train.segment_size // hps.data.hop_length,
        n_speakers=hps.data.n_speakers,
        **hps.model)
    if(is_half==True):
        vq_model=vq_model.half().to(device)
    else:
        vq_model = vq_model.to(device)
    vq_model.eval()
    # utils.load_checkpoint(utils.latest_checkpoint_path(hps.s2_ckpt_dir, "G_*.pth"), vq_model, None, True)
    # utils.load_checkpoint(pretrained_s2G, vq_model, None, True)
    print(vq_model.load_state_dict(torch.load(pretrained_s2G,map_location="cpu")["weight"], strict=False))

    def name2go(wav_name,lines):
        hubert_path = "%s/%s.pt" % (hubert_dir, wav_name)
        if(os.path.exists(hubert_path)==False):return
        ssl_content = torch.load(hubert_path, map_location="cpu")
        if(is_half==True):
            ssl_content=ssl_content.half().to(device)
        else:
            ssl_content = ssl_content.to(device)
        codes = vq_model.extract_latent(ssl_content)
        semantic = " ".join([str(i) for i in codes[0, 0, :].tolist()])
        lines.append("%s\t%s"%(wav_name,semantic))

    with open(inp_text,"r",encoding="utf8")as f:
        lines=f.read().strip("\n").split("\n")

    lines1=[]
    for line in lines[int(i_part)::int(all_parts)]:
        # print(line)
        try:
            # wav_name,text=line.split("\t")
            wav_name, spk_name, language, text = line.split("|")
            wav_name=os.path.basename(wav_name)
            # name2go(name,lines1)
            name2go(wav_name,lines1)
        except:
            print(line,traceback.format_exc())
    with open(semantic_path,"w",encoding="utf8")as f:f.write("\n".join(lines1))