import argparse
import torch

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

    def initialize(self):
        self.parser.add_argument('--name', type=str, default='demo', help='name of the experiment. It decides where to store samples and models')
        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('--norm', type=str, default='instance', help='instance normalization or batch normalization')        
        self.parser.add_argument('--use_dropout', action='store_true', help='use dropout for the generator')
        self.parser.add_argument('--data_type', default=32, type=int, choices=[8, 16, 32], help="Supported data type i.e. 8, 16, 32 bit")
        self.parser.add_argument('--verbose', action='store_true', default=False, help='toggles verbose')

        self.parser.add_argument('--batchSize', type=int, default=1, help='input batch size')
        self.parser.add_argument('--loadSize', type=int, default=512, help='scale images to this size')
        self.parser.add_argument('--fineSize', type=int, default=512, 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('--dataroot', type=str,
                                 default='/home/sh0089/sen/fashion/') 
        self.parser.add_argument('--resize_or_crop', type=str, default='scale_width', help='scaling and cropping of images at load time [resize_and_crop|crop|scale_width|scale_width_and_crop]')
        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('--no_flip', action='store_true', help='if specified, do not flip the images for data argumentation') 
        self.parser.add_argument('--nThreads', default=1, type=int, help='# threads for loading data')                
        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('--display_winsize', type=int, default=512,  help='display window size')
        self.parser.add_argument('--tf_log', action='store_true', help='if specified, use tensorboard logging. Requires tensorflow installed')

        self.initialized = True

    def parse(self, save=True):
        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)

        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 ----------------')

        return self.opt