import argparse
import os
from util import util
import torch
import models
import data


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

    def initialize(self, parser):
        
        parser.add_argument('--batchSize', type=int, default=2, help='input batch size')
        parser.add_argument('--ngf', type=int, default=64, help='# of gen filters in first conv layer')
        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('--name', type=str, default='facefh_dictionary', help='name of the experiment. It decides where to store samples and models')
        parser.add_argument('--model', type=str, default='faceDict', help='chooses which model to use. cycle_gan, pix2pix, test')
        parser.add_argument('--which_direction', type=str, default='BtoA', help='AtoB or BtoA')
        parser.add_argument('--nThreads', default=8, type=int, help='# threads for loading data')
        parser.add_argument('--checkpoints_dir', type=str, default='./checkpoints', help='models are saved here')
        parser.add_argument('--norm', type=str, default='instance', help='instance normalization or batch normalization')
        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('--resize_or_crop', type=str, default='degradation', help='scaling and cropping of images at load time [resize_and_crop|crop|scale_width|scale_width_and_crop]')
        parser.add_argument('--init_type', type=str, default='kaiming', help='network initialization [normal|xavier|kaiming|orthogonal]')
        parser.add_argument('--init_gain', type=float, default=0.02, help='scaling factor for normal, xavier and orthogonal.')
        parser.add_argument('--verbose', action='store_true', help='if specified, print more debugging information')
        parser.add_argument('--suffix', default='', type=str, help='customized suffix: opt.name = opt.name + suffix: e.g., {model}_{which_model_netG}_size{loadSize}')
        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, _ = 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)

        opt, _ = parser.parse_known_args()  # parse again with the new defaults

        # modify dataset-related parser options
        dataset_name = opt.dataset_mode

        dataset_option_setter = data.get_option_setter(dataset_name)
        parser = dataset_option_setter(parser, self.isTrain)

        self.parser = parser

        return parser.parse_args()

    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)

        # save to the disk
        expr_dir = os.path.join(opt.checkpoints_dir, 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(message)
            opt_file.write('\n')

    def parse(self):

        opt = self.gather_options()
        opt.isTrain = self.isTrain   # train or test

        # process opt.suffix
        if opt.suffix:
            suffix = ('_' + opt.suffix.format(**vars(opt))) if opt.suffix != '' else ''
            opt.name = opt.name + suffix

        # self.print_options(opt)

        # 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])

        self.opt = opt
        return self.opt