import argparse
import os

class Options():
    """This class defines options used during both training and test time."""

    def __init__(self):
        """Reset the class; indicates the class hasn't been initailized"""
        self.initialized = False

    def initialize(self, parser):
        # model parameters
        parser.add_argument('--netG', type=str, default='Unet', help='[resnet | Unet]')
        parser.add_argument('--netD', type=str, default='PatchGAN', help='[PatchGAN | PixelGAN]')
        parser.add_argument('--ndf', default=128, type=int, help='number of filters in discriminator')
        parser.add_argument('--ngf', default=64, type=int, help='number of filters in generator')
        parser.add_argument('--generatorLR', type=float, default=0.0002, help='learning rate for generator')
        parser.add_argument('--discriminatorLR', type=float, default=0.0002, help='learning rate for discriminator')
        parser.add_argument('--workers', default=8, type=int, help='number of data loading workers')
        parser.add_argument('--lamb', type=float, default=100, help='weight on L1 term in objective')
        parser.add_argument('--checkpoints_dir', type=str, default='./checkpoints', help='models are saved here')
        parser.add_argument('--generatorWeights', type=str, default='./checkpoints/g.pth', help="path to generator weights (to continue training)")
        parser.add_argument('--discriminatorWeights', type=str, default='./checkpoints/d.pth', help="path to discriminator weights (to continue training)")

        # basic parameters
        parser.add_argument('--direction', type=str, default='image_to_label', help='image_to_label or label_to_image')
        parser.add_argument('--data_path', type=str, default='./Data_folder/train')
        parser.add_argument('--val_path', type=str, default='./Data_folder/test/')
        parser.add_argument('--increase_factor_data',  default=5, type=int, help='Increase the data number passed each epoch')
        parser.add_argument('--output', type=str, default='./checkpoints/')
        parser.add_argument('--gpu_ids', type=str, default='1,2', help='gpu ids: e.g. 0  0,1,2, 0,2. use -1 for CPU')
        parser.add_argument('--save_fre', type=int, default=25, help='checkpoint save frequency')

        # dataset parameters
        parser.add_argument('--resample', default=False, help='Decide or not to rescale the images to a new resolution')
        parser.add_argument('--new_resolution', default=(0.6, 0.6, 2.5), help='New resolution')
        parser.add_argument('--min_pixel', default=1, help='Percentage of minimum non-zero pixels in the cropped label')
        parser.add_argument('--drop_ratio', default=0, help='Probability to drop a cropped area if the label is empty. All empty patches will be dropped for 0 and accept all cropped patches if set to 1')
        parser.add_argument('--batch_size', type=int, default=4, help='batch size')
        parser.add_argument('--patch_size', default=[128, 128, 64], help='Size of the patches extracted from the image')
        parser.add_argument('--img_channel', default=1, type=int, help='Channels of the image')
        parser.add_argument("--stride_inplane", type=int, nargs=1, default=32, help="Stride size in 2D plane")
        parser.add_argument("--stride_layer", type=int, nargs=1, default=16, help="Stride size in z direction")

        # training parameters
        parser.add_argument('--epoch_count', type=int, default=1, help='the starting epoch count')
        parser.add_argument('--niter', type=int, default=100, help='# of iter at starting learning rate')
        parser.add_argument('--niter_decay', type=int, default=100, help='# of iter to linearly decay learning rate to zero')
        parser.add_argument('--lr_policy', type=str, default='lambda', help='learning rate policy: lambda|step|plateau|cosine')
        parser.add_argument('--lr_decay_iters', type=int, default=50, help='multiply by a gamma every lr_decay_iters iterations')
        parser.add_argument('--resume', default=0, type=int, help='resume training or not default:0/not')

        # Inference
        # This is just a trick to make the predict script working
        parser.add_argument('--image', default=None, help='Keep this empty and go to predict_single_image script')
        parser.add_argument('--result', default=None, help='Keep this empty and go to predict_single_image script')
        parser.add_argument('--weights', default=None, help='Keep this empty and go to predict_single_image script')
        parser.add_argument('--multi_gpu', default=None, help='Keep this empty and go to predict_single_image script')

        self.initialized = True
        return parser

    def parse(self):
        if not self.initialized:
            parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
            parser = self.initialize(parser)
        opt = parser.parse_args()
        # set gpu ids
        if opt.gpu_ids != '-1':
            os.environ["CUDA_VISIBLE_DEVICES"] = opt.gpu_ids
        return opt





