# Copyright 2021 Huawei Technologies Co., Ltd

#

# 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.



# !/usr/bin/python

# encoding=utf-8



import os

import sys

import copy

import time

import yaml

import shutil

import argparse

import numpy as np

import random

import torch

import torch.nn as nn

import torch.backends.cudnn as cudnn

from torch.utils.tensorboard import SummaryWriter



sys.path.append('./')

from models.model_ctc import *

from utils.data_loader import Vocab, SpeechDataset, SpeechDataLoader



supported_rnn = {'nn.LSTM': nn.LSTM, 'nn.GRU': nn.GRU, 'nn.RNN': nn.RNN}

supported_activate = {'relu': nn.ReLU, 'tanh': nn.Tanh, 'sigmoid': nn.Sigmoid}



parser = argparse.ArgumentParser()

parser.add_argument('--conf', help='conf file for training')

parser.add_argument('--batchsize', help='batchsize for preprocessing')



class Config(object):

    batch_size = 4

    dropout = 0.1



def main():

    args = parser.parse_args()

    try:

        conf = yaml.safe_load(open(args.conf,'r'))

    except:

        print("Config file not exist!")

        sys.exit(1)



    opts = Config()

    for k,v in conf.items():

        setattr(opts, k, v)

        print('{:50}:{}'.format(k, v))



    # Data Loader

    batchsize = int(args.batchsize)

    vocab = Vocab(opts.vocab_file)

    dev_dataset = SpeechDataset(vocab, opts.test_scp_path, opts.test_lab_path, opts)

    dev_loader = SpeechDataLoader(dev_dataset, batch_size=batchsize, shuffle=False, num_workers=opts.num_workers,

                                  drop_last=True, pin_memory=True)



    bin_path = "./lstm_bin_bs" + args.batchsize

    if os.path.exists(bin_path):

        shutil.rmtree(bin_path)

    os.makedirs(bin_path)

    i = -1

    for data in dev_loader:

        i = i + 1

        print("[info] file", "===", i)

        inputs, input_sizes, targets, target_sizes, utt_list = data

        inputs_np = inputs.numpy()

        inputs_np.tofile(os.path.join(bin_path, "inputs_" + str(i) + '.bin'))



if __name__ == '__main__':

    main()