# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
# 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


def parse_args():
    parser = argparse.ArgumentParser(description=__doc__)

    parser.add_argument(
        '--use_gpu',
        type=eval,
        default=False,
        help='Whether using gpu [True|False]')

    parser.add_argument(
        "--learning_rate",
        type=float,
        default=0.001,
        help="learning rate for optimizer")

    parser.add_argument(
        "--num_layers",
        type=int,
        default=1,
        help="layers number of encoder and decoder")

    parser.add_argument(
        "--hidden_size",
        type=int,
        default=100,
        help="hidden size of encoder and decoder")

    parser.add_argument(
        "--batch_size", type=int, default=128, help="Batch size of each step")

    parser.add_argument(
        "--max_epoch", type=int, default=50, help="max epoch for the training")

    parser.add_argument(
        "--max_len",
        type=int,
        default=50,
        help="max length for source and target sentence")

    parser.add_argument(
        "--max_grad_norm",
        type=float,
        default=5.0,
        help="max grad norm for global norm clip")

    parser.add_argument(
        "--log_freq",
        type=int,
        default=200,
        help="The frequency to print training logs")

    parser.add_argument(
        "--model_path",
        type=str,
        default='model',
        help="model path for model to save")

    parser.add_argument(
        "--init_from_ckpt",
        type=str,
        default=None,
        help="The path of checkpoint to be loaded.")

    parser.add_argument(
        "--infer_output_file",
        type=str,
        default='infer_output',
        help="file name for inference output")

    parser.add_argument(
        "--beam_size", type=int, default=10, help="file name for inference")

    args = parser.parse_args()
    return args