import argparse
import os
from util import util
import torch

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

    def initialize(self):
        self.parser.add_argument('--dataroot', required=True, 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('--imH', type=int, default=128, help='imH')
        self.parser.add_argument('--imW', type=int, default=416, help='imW')
        self.parser.add_argument('--max_lk_iter_num', type=int, default=10, help='maximum iteration for LK update')

        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('--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('--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('--display_single_pane_ncols', type=int, default=0, help='if positive, display all images in a single visdom web panel with certain number of images per row.')



    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