#-*- coding: utf-8 -*-

import argparse

parser = argparse.ArgumentParser()

def add_argument_group(name):

    arg = parser.add_argument_group(name)

    return arg

misc_arg = add_argument_group('misc')

misc_arg.add_argument('--split', type=bool, default = True)

misc_arg.add_argument('--input_size', type=int, default = 256, 

                      help='multiplies of 256 by the structure of the model') 

misc_arg.add_argument('--use_network', type=bool, default = False)

data_arg = add_argument_group('data')

data_arg.add_argument('--downloading', type=bool, default = False)

graph_arg = add_argument_group('graph')

graph_arg.add_argument('--filter_length', type=int, default = 32)

graph_arg.add_argument('--kernel_size', type=int, default = 16)

graph_arg.add_argument('--drop_rate', type=float, default = 0.2)

train_arg = add_argument_group('train')

train_arg.add_argument('--feature', type=str, default = "MLII",

                       help='one of MLII, V1, V2, V4, V5. Favorably MLII or V1')

train_arg.add_argument('--epochs', type=int, default = 80)

train_arg.add_argument('--batch', type=int, default = 256)

train_arg.add_argument('--patience', type=int, default = 10)

train_arg.add_argument('--min_lr', type=float, default = 0.00005)

train_arg.add_argument('--checkpoint_path', type=str, default = None)

train_arg.add_argument('--resume_epoch', type=int)

train_arg.add_argument('--ensemble', type=bool, default = False)

train_arg.add_argument('--trained_model', type=str, default = None, 

                       help='dir and filename of the trained model for usage.')

predict_arg = add_argument_group('predict')

predict_arg.add_argument('--num', type=int, default = None)

predict_arg.add_argument('--upload', type=bool, default = False)

predict_arg.add_argument('--sample_rate', type=int, default = None)

predict_arg.add_argument('--cinc_download', type=bool, default = False)

def get_config():

    config, unparsed = parser.parse_known_args()

    return config