import os

import argparse

import tensorflow

class BaseOptions():

    def __init__(self):

        self.initialized = False

    def initialize(self, parser):

        """This class defines options used during both training and test time.

        """

        parser.add_argument('--dataroot', type=str, default='./datasets/sample_nifti_4D', help='path to data')

        parser.add_argument('--dataformat', type=str,  default='NIFTI', help='data format (e.g., NIFTI, DICOM)')

        parser.add_argument('--max_dataset_size', type=float, default=float("inf"), help=' ')

        parser.add_argument('--gpu_ids', type=str, default='0', help='gpu ids: e.g. 0  0,1,2, 0,2, -1 for CPU mode')

        # data parameters

        parser.add_argument('--preprocess_carson', type=str, default='reshape_to_carson', help=' ')

        parser.add_argument('--preprocess_carmen', type=str, default='reshape_to_carmen', help=' ')

        parser.add_argument('--preprocess', type=str, default='crop_zscore', help=' ')

        parser.add_argument('--image_shape', type=tuple, default=(128,128,1), help=' ')

        parser.add_argument('--volume_shape', type=tuple, default=(128,128,16,1), help=' ')

        parser.add_argument('--nlabels', type=int, default=4, help='number of tissue classes')

        # data parameters

        parser.add_argument('--order', type=int, default=1, help='resampling order')

        parser.add_argument('--mode', type=str, default='nearest', help='resampling mode')

        parser.add_argument('--in_plane_resolution_mm', type=float, default=1.25, help='resample to in_plane_resolution_mm')

        parser.add_argument('--number_of_slices', type=int, default=None, help='resample to number_of_slices')

        parser.add_argument('--slice_thickness_mm', type=float, default=None, help='resample to slice_thickness_mm')

        # special tasks

        self.initialized = True

        return parser

    def gather_options(self):

        """Initialize our parser with basic options(only once).

        Add additional model-specific and dataset-specific options.

        These options are defined in the <modify_commandline_options> function

        in model and dataset classes.

        """

        if not self.initialized:  # check if it has been initialized

            parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)

            parser = self.initialize(parser)

        # get the basic options

        opt, _ = parser.parse_known_args()

        # save and return the parser

        self.parser = parser

        return parser.parse_args()

    def print_options(self, opt):

        """Print and save options

        It will print both current options and default values(if different).

        It will save options into a text file / [checkpoints_dir] / opt.txt

        """

        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):

        """Parse our options, create checkpoints directory suffix, and set up gpu device."""

        opt = self.gather_options()

        self.opt = opt

        return self.opt