import os
import sys


inp_text = os.environ.get("inp_text")
inp_wav_dir = os.environ.get("inp_wav_dir")
exp_name = os.environ.get("exp_name")
i_part = os.environ.get("i_part")
all_parts = os.environ.get("all_parts")
if "_CUDA_VISIBLE_DEVICES" in os.environ:
    os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"]
from feature_extractor import cnhubert


opt_dir = os.environ.get("opt_dir")
cnhubert.cnhubert_base_path = os.environ.get("cnhubert_base_dir")
import torch


is_half = eval(os.environ.get("is_half", "True")) and torch.cuda.is_available()

import traceback

import librosa
import numpy as np
from scipy.io import wavfile


now_dir = os.getcwd()
sys.path.append(now_dir)
import shutil

# from config import cnhubert_base_path
# cnhubert.cnhubert_base_path=cnhubert_base_path
# inp_text=sys.argv[1]
# inp_wav_dir=sys.argv[2]
# exp_name=sys.argv[3]
# i_part=sys.argv[4]
# all_parts=sys.argv[5]
# os.environ["CUDA_VISIBLE_DEVICES"]=sys.argv[6]
# cnhubert.cnhubert_base_path=sys.argv[7]
# opt_dir="/data/docker/liujing04/gpt-vits/fine_tune_dataset/%s"%exp_name
from time import time as ttime

from gsv_tools.my_utils import clean_path, load_audio


def my_save(fea, path):  #####fix issue: torch.save doesn't support chinese path
    dir = os.path.dirname(path)
    name = os.path.basename(path)
    # tmp_path="%s/%s%s.pth"%(dir,ttime(),i_part)
    tmp_path = f"{ttime()}{i_part}.pth"
    torch.save(fea, tmp_path)
    shutil.move(tmp_path, f"{dir}/{name}")


hubert_dir = f"{opt_dir}/4-cnhubert"
wav32dir = f"{opt_dir}/5-wav32k"
os.makedirs(opt_dir, exist_ok=True)
os.makedirs(hubert_dir, exist_ok=True)
os.makedirs(wav32dir, exist_ok=True)

maxx = 0.95
alpha = 0.5
if torch.cuda.is_available():
    device = "cuda:0"
# elif torch.backends.mps.is_available():
#     device = "mps"
else:
    device = "cpu"
model = cnhubert.get_model()
# is_half=False
if is_half:
    model = model.half().to(device)
else:
    model = model.to(device)

nan_fails = []


def name2go(wav_name, wav_path):
    hubert_path = f"{hubert_dir}/{wav_name}.pt"
    if os.path.exists(hubert_path):
        return
    tmp_audio = load_audio(wav_path, 32000)
    tmp_max = np.abs(tmp_audio).max()
    if tmp_max > 2.2:
        print(f"{wav_name}-filtered,{tmp_max}")
        return
    tmp_audio32 = (tmp_audio / tmp_max * (maxx * alpha * 32768)) + ((1 - alpha) * 32768) * tmp_audio
    tmp_audio32b = (tmp_audio / tmp_max * (maxx * alpha * 1145.14)) + ((1 - alpha) * 1145.14) * tmp_audio
    tmp_audio = librosa.resample(tmp_audio32b, orig_sr=32000, target_sr=16000)  # 不是重采样问题
    tensor_wav16 = torch.from_numpy(tmp_audio)
    if is_half:
        tensor_wav16 = tensor_wav16.half().to(device)
    else:
        tensor_wav16 = tensor_wav16.to(device)
    ssl = model.model(tensor_wav16.unsqueeze(0))["last_hidden_state"].transpose(1, 2).cpu()  # torch.Size([1, 768, 215])
    if np.isnan(ssl.detach().numpy()).sum() != 0:
        nan_fails.append((wav_name, wav_path))
        print(f"nan filtered:{wav_name}")
        return
    wavfile.write(
        f"{wav32dir}/{wav_name}",
        32000,
        tmp_audio32.astype("int16"),
    )
    my_save(ssl, hubert_path)


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

for line in lines[int(i_part) :: int(all_parts)]:
    try:
        # wav_name,text=line.split("\t")
        wav_name, spk_name, language, text = line.split("|")
        wav_name = clean_path(wav_name)
        if inp_wav_dir != "" and inp_wav_dir is not None:
            wav_name = os.path.basename(wav_name)
            wav_path = f"{inp_wav_dir}/{wav_name}"

        else:
            wav_path = wav_name
            wav_name = os.path.basename(wav_name)
        name2go(wav_name, wav_path)
    except:
        print(line, traceback.format_exc())

if len(nan_fails) > 0 and is_half:
    is_half = False
    model = model.float()
    for wav in nan_fails:
        try:
            name2go(wav[0], wav[1])
        except:
            print(wav_name, traceback.format_exc())