#!/usr/bin/env python3
# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import logging
import torch
from tqdm import tqdm
import numpy as np
import json

import torch.distributed as distr
import pathlib
import shutil
from distributed import init_distributed_context

import logging
logger = logging.getLogger(__name__)
import os
import sys
import re
import glob


def single_job(encoder, wav_fp, save_fp, device,sample_rate=16000):
    item_name = os.path.basename(wav_fp).split('.')[0]
    data, sr = torchaudio.load(wav_fp)
    data = resample_wav(data,sr,target_sr=sample_rate)
    encoded = encoder(data.to(device))
    units = encoded["units"].detach().cpu().numpy()
    np.save(save_fp,units)


def extract_speech_token(args, rank, world_size):
    all_data = []
    test_fp = '/apdcephfs_nj7/share_303172353/ggyzhang/taiji_demo/entropy_vc/data/mls_en/mls_en_witext/test_data.json'
    train_fp = '/apdcephfs_nj7/share_303172353/ggyzhang/taiji_demo/entropy_vc/data/mls_en/mls_en_witext/train_data.json'
    valid_fp = '/apdcephfs_nj7/share_303172353/ggyzhang/taiji_demo/entropy_vc/data/mls_en/mls_en_witext/valid_data.json'
    with open(train_fp,'r') as fp:
        cur_data = json.load(fp)
        all_data.extend(cur_data)
    with open(valid_fp,'r') as fp:
        cur_data = json.load(fp)
        all_data.extend(cur_data)
    with open(test_fp,'r') as fp:
        cur_data = json.load(fp)
        all_data.extend(cur_data)

    # wavs = glob.glob(f'{args.wav_dir}/**/*.wav',recursive=True)

    print(len(all_data))
    for i in tqdm(range(rank, len(all_data), world_size)):
        item = all_data[i]
        wav_fn = item['code_fp'].replace('/apdcephfs_nj7/share_303172353/ggyzhang/projects/data/mls_en_hubert_code','/apdcephfs_qy4/share_1213607/shared_data/raw_data/open_source/asr_data/mls_en')
        wav_fn = wav_fn.replace('.npy','.wav')
        new_fp = wav_fn.replace('/apdcephfs_qy4/share_1213607/shared_data/raw_data/open_source/asr_data/mls_en','/apdcephfs_nj7/share_303172353/ggyzhang/projects/data/mls_en')
        if os.path.exists(new_fp):
            continue
        new_dir = os.path.dirname(new_fp)
        os.makedirs(new_dir,exist_ok=True)
        shutil.copy2(wav_fn, new_fp)



def main(args):
    context = init_distributed_context(args.distributed_port)
    logger.info(f"Distributed context {context}")

    n_gpus = torch.cuda.device_count()
    with torch.cuda.device(context.local_rank % n_gpus):
        extract_speech_token(args, context.rank, context.world_size)

    if context.world_size > 1:
        distr.barrier()

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--distributed_port", type=int, default=58564)
    args = parser.parse_args()

    main(args)