from .base_options import BaseOptions


class TrainOptions(BaseOptions):
    def initialize(self, parser):
        BaseOptions.initialize(self, parser)
        parser.add_argument('--display_freq', type=int, default=100,
                            help='frequency of showing training results on screen')
        parser.add_argument('--print_freq', type=int, default=100,
                            help='frequency of showing training results on console')
        parser.add_argument('--save_latest_freq', type=int, default=5000, help='frequency of saving the latest results')
        parser.add_argument('--save_epoch_freq', type=int, default=10,
                            help='frequency of saving checkpoints at the end of epochs')
        parser.add_argument('--no_html', action='store_true',
                            help='do not save intermediate training results to [opt.checkpoints_dir]/[opt.name]/web/')
        parser.add_argument('--debug', action='store_true', help='only do one epoch and displays at each iteration')
        parser.add_argument('--tf_log', action='store_true',
                            help='if specified, use tensorboard logging. Requires tensorflow installed')

        parser.add_argument('--continue_train', action='store_true', help='continue training: load the latest model')
        parser.add_argument('--which_epoch', type=str, default='latest',
                            help='which epoch to load? set to latest to use latest cached model')
        parser.add_argument('--niter', type=int, default=50,
                            help='# of iter at starting learning rate. This is NOT the total #epochs. Totla #epochs is niter + niter_decay')
        parser.add_argument('--niter_decay', type=int, default=0,
                            help='# of iter to linearly decay learning rate to zero')
        parser.add_argument('--optimizer', type=str, default='adam')
        parser.add_argument('--beta1', type=float, default=0.5, help='momentum term of adam')
        parser.add_argument('--beta2', type=float, default=0.999, help='momentum term of adam')
        parser.add_argument('--lr', type=float, default=0.0002, help='initial learning rate for adam')
        parser.add_argument('--D_steps_per_G', type=int, default=1,
                            help='number of discriminator iterations per generator iterations.')

        parser.add_argument('--ndf', type=int, default=64, help='# of discrim filters in first conv layer')
        parser.add_argument('--lambda_feat', type=float, default=10.0, help='weight for feature matching loss')
        parser.add_argument('--lambda_vgg', type=float, default=10.0, help='weight for vgg loss')
        parser.add_argument('--no_ganFeat_loss', action='store_true',
                            help='if specified, do *not* use discriminator feature matching loss')
        parser.add_argument('--no_vgg_loss', action='store_true',
                            help='if specified, do *not* use VGG feature matching loss')
        parser.add_argument('--gan_mode', type=str, default='hinge', help='(ls|original|hinge)')
        parser.add_argument('--netD', type=str,
                            help='(NLayerDiscriminator|MultiscaleDiscriminator|swin_transformer_conditional_discriminator)')
        parser.add_argument('--no_TTUR', action='store_true', help='Use TTUR training scheme')
        parser.add_argument('--lambda_kld', type=float, default=0.05)

        parser.add_argument('--lambda_z_reg', type=float, default=0.0, help='weight for latent z-vector regularization')
        parser.add_argument('--lambda_night_content', type=float, default=0.0,
                            help='weight for VGG content loss specifically at night')

        self.isTrain = True
        return parser