text: """
  Copyright (C) 2019 NVIDIA Corporation.  All rights reserved.
  Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
  """
  
  import sys
  import argparse
  import os
  from util import util
  import torch
  import models
  import data
  import pickle
  
  
  class BaseOptions():
      def __init__(self):
          self.initialized = False
  
      def initialize(self, parser):
          # experiment specifics
          parser.add_argument('--name', type=str, default='label2coco', help='name of the experiment. It decides where to store samples and models')
  
          parser.add_argument('--gpu_ids', type=str, default='0', help='gpu ids: e.g. 0  0,1,2, 0,2. use -1 for CPU')
          parser.add_argument('--checkpoints_dir', type=str, default='./checkpoints', help='models are saved here')
          parser.add_argument('--model', type=str, default='pix2pix', help='which model to use')
          parser.add_argument('--norm_G', type=str, default='spectralinstance', help='instance normalization or batch normalization')
          parser.add_argument('--norm_D', type=str, default='spectralinstance', help='instance normalization or batch normalization')
          parser.add_argument('--norm_E', type=str, default='spectralinstance', help='instance normalization or batch normalization')
          parser.add_argument('--phase', type=str, default='train', help='train, val, test, etc')
  
          # input/output sizes
          parser.add_argument('--batchSize', type=int, default=1, help='input batch size')
          parser.add_argument('--preprocess_mode', type=str, default='scale_width_and_crop', help='scaling and cropping of images at load time.', choices=("resize_and_crop", "crop", "scale_width", "scale_width_and_crop", "scale_shortside", "scale_shortside_and_crop", "fixed", "none"))
          parser.add_argument('--load_size', type=int, default=1024, help='Scale images to this size. The final image will be cropped to --crop_size.')
          parser.add_argument('--crop_size', type=int, default=512, help='Crop to the width of crop_size (after initially scaling the images to load_size.)')
          parser.add_argument('--aspect_ratio', type=float, default=1.0, help='The ratio width/height. The final height of the load image will be crop_size/aspect_ratio')
          parser.add_argument('--label_nc', type=int, default=182, help='# of input label classes without unknown class. If you have unknown class as class label, specify --contain_dopntcare_label.')
          parser.add_argument('--contain_dontcare_label', action='store_true', help='if the label map contains dontcare label (dontcare=255)')
          parser.add_argument('--output_nc', type=int, default=3, help='# of output image channels')
          parser.add_argument('--not_sort', action='store_true', help='if specified, do *not* sort the input paths')
  
          # for setting inputs
          parser.add_argument('--dataroot', type=str, default='./datasets/cityscapes/')
          parser.add_argument('--dataset_mode', type=str, default='coco')
          parser.add_argument('--serial_batches', action='store_true', help='if true, takes images in order to make batches, otherwise takes them randomly')
          parser.add_argument('--no_flip', action='store_true', help='if specified, do not flip the images for data argumentation')
          parser.add_argument('--nThreads', default=0, type=int, help='# threads for loading data')
          parser.add_argument('--max_dataset_size', type=int, default=sys.maxsize, help='Maximum number of samples allowed per dataset. If the dataset directory contains more than max_dataset_size, only a subset is loaded.')
          parser.add_argument('--load_from_opt_file', action='store_true', help='load the options from checkpoints and use that as default')
          parser.add_argument('--cache_filelist_write', action='store_true', help='saves the current filelist into a text file, so that it loads faster')
          parser.add_argument('--cache_filelist_read', action='store_true', help='reads from the file list cache')
          parser.add_argument('--rgb', action='store_true', help='input labels are rgb rather than one hot')
  
          # for displays
          parser.add_argument('--display_winsize', type=int, default=400, help='display window size')
  
          # for generator
          parser.add_argument('--netG', type=str, default='spade', help='selects model to use for netG (pix2pixhd | spade)')
          parser.add_argument('--ngf', type=int, default=64, help='# of gen filters in first conv layer')
          parser.add_argument('--init_type', type=str, default='xavier', help='network initialization [normal|xavier|kaiming|orthogonal]')
          parser.add_argument('--init_variance', type=float, default=0.02, help='variance of the initialization distribution')
          parser.add_argument('--z_dim', type=int, default=256,
                              help="dimension of the latent z vector")
  
          # for instance-wise features
          parser.add_argument('--no_instance', action='store_true', help='if specified, do *not* add instance map as input')
          parser.add_argument('--nef', type=int, default=16, help='# of encoder filters in the first conv layer')
          parser.add_argument('--use_vae', default = False,action='store_true', help='enable training with an image encoder.')
  
          self.initialized = True
          return parser
  
      def gather_options(self):
          # initialize parser with basic options
          if not self.initialized:
              parser = argparse.ArgumentParser(
                  formatter_class=argparse.ArgumentDefaultsHelpFormatter)
              parser = self.initialize(parser)
  
          # get the basic options
          opt, unknown = parser.parse_known_args()
  
          # modify model-related parser options
          model_name = opt.model
          model_option_setter = models.get_option_setter(model_name)
          parser = model_option_setter(parser, self.isTrain)
  
          # modify dataset-related parser options
          dataset_mode = opt.dataset_mode
          dataset_option_setter = data.get_option_setter(dataset_mode)
          parser = dataset_option_setter(parser, self.isTrain)
  
          opt, unknown = parser.parse_known_args()
  
          # if there is opt_file, load it.
          # The previous default options will be overwritten
          if opt.load_from_opt_file:
              parser = self.update_options_from_file(parser, opt)
  
          opt = parser.parse_args()
          self.parser = parser
          return opt
  
      def print_options(self, opt):
          message = ''
          message += '----------------- Options ---------------\n'
          for k, v in sorted(vars(opt).items()):
              comment = ''
              default = self.parser.get_default(k)
              if v != default:
                  comment = '\t[default: %s]' % str(default)
              message += '{:>25}: {:<30}{}\n'.format(str(k), str(v), comment)
          message += '----------------- End -------------------'
          print(message)
  
      def option_file_path(self, opt, makedir=False):
          expr_dir = os.path.join(opt.checkpoints_dir, opt.name)
          if makedir:
              util.mkdirs(expr_dir)
          file_name = os.path.join(expr_dir, 'opt')
          return file_name
  
      def save_options(self, opt):
          file_name = self.option_file_path(opt, makedir=True)
          with open(file_name + '.txt', 'wt') as opt_file:
              for k, v in sorted(vars(opt).items()):
                  comment = ''
                  default = self.parser.get_default(k)
                  if v != default:
                      comment = '\t[default: %s]' % str(default)
                  opt_file.write('{:>25}: {:<30}{}\n'.format(str(k), str(v), comment))
  
          with open(file_name + '.pkl', 'wb') as opt_file:
              pickle.dump(opt, opt_file)
  
      def update_options_from_file(self, parser, opt):
          new_opt = self.load_options(opt)
          for k, v in sorted(vars(opt).items()):
              if hasattr(new_opt, k) and v != getattr(new_opt, k):
                  new_val = getattr(new_opt, k)
                  parser.set_defaults(**{k: new_val})
          return parser
  
      def load_options(self, opt):
          file_name = self.option_file_path(opt, makedir=False)
          new_opt = pickle.load(open(file_name + '.pkl', 'rb'))
          return new_opt
  
      def parse(self, save=False):
  
          opt = self.gather_options()
          opt.isTrain = self.isTrain   # train or test
  
          self.print_options(opt)
          if opt.isTrain:
              self.save_options(opt)
  
          # Set semantic_nc based on the option.
          # This will be convenient in many places
          opt.semantic_nc = opt.label_nc + \
              (1 if opt.contain_dontcare_label else 0) + \
              (0 if opt.no_instance else 1)
          # set gpu ids
          str_ids = opt.gpu_ids.split(',')
          opt.gpu_ids = []
          for str_id in str_ids:
              id = int(str_id)
              if id >= 0:
                  opt.gpu_ids.append(id)
          if len(opt.gpu_ids) > 0:
              torch.cuda.set_device(opt.gpu_ids[0])
  
          assert len(opt.gpu_ids) == 0 or opt.batchSize % len(opt.gpu_ids) == 0, \
              "Batch size %d is wrong. It must be a multiple of # GPUs %d." \
              % (opt.batchSize, len(opt.gpu_ids))
  
          self.opt = opt
          return self.opt
  