import argparse
import os
from util import util
import torch


class BaseOptions():
    def __init__(self):
        self.parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
        self.initialized = False

    def initialize(self):
        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('--lsgan', action='store_true', help='do *not* use least square GAN, if false, use vanilla GAN')
        self.parser.add_argument('--sketchy_dataset', action='store_true', help='sketchy database used')
        self.parser.add_argument('--autocomplete_dataset_outline', action='store_true', help='autocomplete database used')
        self.parser.add_argument('--autocomplete_dataset_edges', action='store_true', help='autocomplete database used')
        self.parser.add_argument('--edges_outlines_dataset', action='store_true', help='autocomplete database used')
        self.parser.add_argument('--lambda_info_con', type=float, default=1.0, help='weight for cycle loss (A -> B -> A)')
        self.parser.add_argument('--lambda_info_dis', type=float, default=1.0, help='weight for cycle loss (A -> B -> A)')
        self.parser.add_argument('--dataroot', help='path to images (should have subfolders trainA, trainB, valA, valB, etc)')
        self.parser.add_argument('--batchSize', type=int, default=1, help='input batch size')
        self.parser.add_argument('--loadSize', type=int, default=286, help='scale images to this size')
        self.parser.add_argument('--fineSize', type=int, default=256, help='then crop to this size')
        self.parser.add_argument('--sparseSize', type=int, default=4, help='then crop to this size')
        self.parser.add_argument('--input_nc', type=int, default=3, help='# of input image channels')
        self.parser.add_argument('--output_nc', type=int, default=3, help='# of output image channels')
        self.parser.add_argument('--nz', type=int, default=8, help='# of gen filters in first conv layer')
        self.parser.add_argument('--ngf', type=int, default=32, help='# of gen filters in first conv layer')
        self.parser.add_argument('--ndf_gate', type=int, default=16, help='# of neurons in the gate block')
        self.parser.add_argument('--ngf_gate', type=int, default=16, help='# of neurons in the gate block')
        self.parser.add_argument('--ngres_gate', type=int, default=16, help='# of neurons in the gate block')
        self.parser.add_argument('--ngres', type=int, default=24, help='# of residual blocks')
        self.parser.add_argument('--ngres_up_down', type=int, default=3, help='# of residual blocks')
        self.parser.add_argument('--ndres_down', type=int, default=4, help='# of residual blocks')
        self.parser.add_argument('--ndres_gate', type=int, default=16, help='# of neurons in the gate block')
        self.parser.add_argument('--ndres', type=int, default=24, help='# of residual blocks')
        self.parser.add_argument('--ndf', type=int, default=32, help='# of discrim filters in first conv layer')
        self.parser.add_argument('--ndisc_out_filters', type=int, default=1, help='# of discrim filters in first conv layer')
        self.parser.add_argument('--which_model_netD', type=str, default='basic', help='selects model to use for netD')
        self.parser.add_argument('--which_model_netG', type=str, default='resnet_9blocks', help='selects model to use for netG')
        self.parser.add_argument('--which_model_gated_netG', type=str, default='simple', help='selects model to use for netG')
        self.parser.add_argument('--n_layers_D', type=int, default=3, help='only used if which_model_netD==n_layers')
        self.parser.add_argument('--num_groups', type=int, default=8, help='Num Groups in Group Normalization')
        self.parser.add_argument('--gpu_ids', type=str, default='0', help='gpu ids: e.g. 0  0,1,2, 0,2. use -1 for CPU')
        self.parser.add_argument('--name', type=str, default='experiment_name', help='name of the experiment. It decides where to store samples and models')
        self.parser.add_argument('--dataset_mode', type=str, default='labeled', help='chooses how datasets are loaded. [unaligned | aligned | single | labeled]')
        self.parser.add_argument('--model', type=str, default='cycle_gan',
                                 help='chooses which model to use. cycle_gan, pix2pix, test')
        self.parser.add_argument('--which_direction', type=str, default='AtoB', help='AtoB or BtoA')
        self.parser.add_argument('--nThreads', default=2, type=int, help='# threads for loading data')
        self.parser.add_argument('--checkpoints_dir', type=str, default='./checkpoints', help='models are saved here')
        self.parser.add_argument('--norm', type=str, default='instance', help='instance normalization or batch normalization')
        self.parser.add_argument('--norm_G', type=str, default='instance', help='instance normalization or batch normalization')
        self.parser.add_argument('--norm_D', type=str, default='instance', help='instance normalization or batch normalization')
        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('--display_winsize', type=int, default=256,  help='display window size')
        self.parser.add_argument('--display_id', type=int, default=1, help='window id of the web display')
        self.parser.add_argument('--display_port', type=int, default=8097, help='visdom port of the web display')
        self.parser.add_argument('--no_dropout', action='store_true', help='no dropout for the generator')
        self.parser.add_argument('--dropout' , type = float , default=0.0 , help = 'Dropout rate' )
        self.parser.add_argument('--dropout_G' , type = float , default=0.0 , help = 'Dropout rate' )
        self.parser.add_argument('--dropout_D' , type = float , default=0.0 , help = 'Dropout rate' )
        self.parser.add_argument('--dropout_gate' , type = float , default=0.0 , help = 'Dropout rate' )
        self.parser.add_argument('--max_dataset_size', type=int, default=float("inf"), help='Maximum number of samples allowed per dataset. If the dataset directory contains more than max_dataset_size, only a subset is loaded.')
        self.parser.add_argument('--resize_or_crop', type=str, default='resize_and_crop', help='scaling and cropping of images at load time [resize_and_crop|crop|scale_width|scale_width_and_crop]')
        self.parser.add_argument('--which_baseline', type=str, default='input', help='which baseline to use')
        self.parser.add_argument('--no_flip', action='store_true', help='if specified, do not flip the images for data augmentation')
        self.parser.add_argument('--init_type', type=str, default='xavier', help='network initialization [normal|xavier|kaiming|orthogonal]')
        self.parser.add_argument('--init_variance', type=float, default=0.02, help='variance of the initialization distribution')
        self.parser.add_argument('--res_op', type=str, default='add', help='which residual operation [add|max]')

        self.parser.add_argument('--n_classes', type=int, default=10, help='the number of classes for the label gated pix2pix variant')
        self.parser.add_argument('--nc', type=int, default=3, help='Number of channels in the images')
        self.parser.add_argument('--label_nc', type=int, default=3, help='Number of channels in the images')
        self.parser.add_argument('--wgan_gp_lambda', type=float, default=10.0, help='Number of channels in the images')
        self.parser.add_argument('--wgan_gp_center', type=float, default=0.0, help='Number of channels in the images')

        self.parser.add_argument('--img_conditional_D',dest='img_conditional_D',action='store_true' , help='whether to use spectral normalization in the discriminator')
        self.parser.add_argument('--use_vae',dest='use_vae',action='store_true' , help='whether to use VAE for pix2pixhd')
        self.parser.add_argument('--spectral_D', dest='spectral_D',action='store_true' ,help='whether to use spectral normalization in the discriminator')
        self.parser.add_argument('--spectral_G', dest='spectral_G',action='store_true' ,help='whether to use spectral normalization in the generator')
        self.parser.add_argument('--gate_affine',dest='gate_affine',action='store_true')
        self.parser.add_argument('--no_sparse_add',dest='no_sparse_add',action='store_true')
        self.parser.add_argument('--num_interpolate', type=int, default=20, help='Number of channels in the images')
        self.initialized = True

    def parse(self):
        if not self.initialized:
            self.initialize()
        self.opt = self.parser.parse_args()
        self.opt.isTrain = self.isTrain   # train or test

        str_ids = self.opt.gpu_ids.split(',')
        self.opt.gpu_ids = []
        for str_id in str_ids:
            id = int(str_id)
            if id >= 0:
                self.opt.gpu_ids.append(id)

        # set gpu ids
        if len(self.opt.gpu_ids) > 0:
            torch.cuda.set_device(self.opt.gpu_ids[0])

        args = vars(self.opt)

        print('------------ Options -------------')
        for k, v in sorted(args.items()):
            print('%s: %s' % (str(k), str(v)))
        print('-------------- End ----------------')

        # save to the disk
        expr_dir = os.path.join(self.opt.checkpoints_dir, self.opt.name)
        util.mkdirs(expr_dir)
        file_name = os.path.join(expr_dir, 'opt.txt')
        with open(file_name, 'wt') as opt_file:
            opt_file.write('------------ Options -------------\n')
            for k, v in sorted(args.items()):
                opt_file.write('%s: %s\n' % (str(k), str(v)))
            opt_file.write('-------------- End ----------------\n')
        return self.opt
