from .base_options import BaseOptions


class TrainOptions(BaseOptions):
    def initialize(self):
        BaseOptions.initialize(self)
        # for displays
        self.parser.add_argument('--display_freq', type=int, default=100,
                                 help='frequency of showing training results on screen')
        self.parser.add_argument('--print_freq', type=int, default=100,
                                 help='frequency of showing training results on console')
        self.parser.add_argument('--save_latest_freq', type=int,
                                 default=1000, help='frequency of saving the latest results')
        self.parser.add_argument('--save_epoch_freq', type=int, default=10,
                                 help='frequency of saving checkpoints at the end of epochs')
        self.parser.add_argument('--no_html', action='store_true',
                                 help='do not save intermediate training results to [opt.checkpoints_dir]/[opt.name]/web/')
        self.parser.add_argument('--debug', action='store_true',
                                 help='only do one epoch and displays at each iteration')

        # for training
        self.parser.add_argument('--load_pretrain', type=str, default='./checkpoints/label2city',
                                 help='load the pretrained model from the specified location')
        self.parser.add_argument('--which_epoch', type=str, default='latest',
                                 help='which epoch to load? set to latest to use latest cached model')
        self.parser.add_argument(
            '--phase', type=str, default='test', help='train, val, test, etc')
        self.parser.add_argument('--serial_batches', action='store_true',
                                 help='if true, takes images in order to make batches, otherwise takes them randomly')
        self.parser.add_argument(
            '--niter', type=int, default=100, help='# of iter at starting learning rate')
        self.parser.add_argument('--niter_decay', type=int, default=100,
                                 help='# of iter to linearly decay learning rate to zero')
        self.parser.add_argument(
            '--beta1', type=float, default=0.5, help='momentum term of adam')
        self.parser.add_argument(
            '--lr', type=float, default=0.0002, help='initial learning rate for adam')

        # for discriminators
        self.parser.add_argument(
            '--num_D', type=int, default=2, help='number of discriminators to use')
        self.parser.add_argument(
            '--n_layers_D', type=int, default=3, help='only used if which_model_netD==n_layers')
        self.parser.add_argument(
            '--ndf', type=int, default=64, help='# of discrim filters in first conv layer')
        self.parser.add_argument(
            '--lambda_feat', type=float, default=10.0, help='weight for feature matching loss')
        self.parser.add_argument('--no_ganFeat_loss', action='store_true',
                                 help='if specified, do *not* use discriminator feature matching loss')
        self.parser.add_argument('--no_vgg_loss', action='store_true',
                                 help='if specified, do *not* use VGG feature matching loss')
        self.parser.add_argument('--no_lsgan', action='store_true',
                                 help='do *not* use least square GAN, if false, use vanilla GAN')
        self.parser.add_argument('--pool_size', type=int, default=0,
                                 help='the size of image buffer that stores previously generated images')

        self.isTrain = True