import os
import os.path as osp
import logging
import yaml
from utils.util import OrderedYaml
Loader, Dumper = OrderedYaml()

def parse(opt_path, is_train=True):
    with open(opt_path, mode='r') as f:
        opt = yaml.load(f, Loader=Loader)

    opt['is_train'] = is_train

    # datasets
    if 'datasets' in opt.keys():
        for phase, dataset in opt['datasets'].items():
            phase = phase.split('_')[0]
            dataset['phase'] = phase
            is_lmdb = False
            ''' LMDB is not supported at this point with the mods I've been making.
            if dataset.get('dataroot_GT', None) is not None:
                dataset['dataroot_GT'] = osp.expanduser(dataset['dataroot_GT'])
                if dataset['dataroot_GT'].endswith('lmdb'):
                    is_lmdb = True
            if dataset.get('dataroot_LQ', None) is not None:
                dataset['dataroot_LQ'] = osp.expanduser(dataset['dataroot_LQ'])
                if dataset['dataroot_LQ'].endswith('lmdb'):
                    is_lmdb = True
            '''
            dataset['data_type'] = 'lmdb' if is_lmdb else 'img'
            if dataset['mode'].endswith('mc'):  # for memcached
                dataset['data_type'] = 'mc'
                dataset['mode'] = dataset['mode'].replace('_mc', '')

    # path
    if 'path' in opt.keys():
        for key, path in opt['path'].items():
            if path and key in opt['path'] and (key not in ['strict_load', 'optimizer_reset']):
                opt['path'][key] = osp.expanduser(path)
    else:
        opt['path'] = {}
    opt['path']['root'] = osp.abspath(osp.join(__file__, osp.pardir, osp.pardir, osp.pardir))
    if is_train:
        experiments_root = osp.join(opt['path']['root'], 'experiments', opt['name'])
        opt['path']['experiments_root'] = experiments_root
        opt['path']['models'] = osp.join(experiments_root, 'models')
        opt['path']['training_state'] = osp.join(experiments_root, 'training_state')
        opt['path']['log'] = experiments_root
        opt['path']['val_images'] = osp.join(experiments_root, 'val_images')

        # change some options for debug mode
        if 'debug' in opt['name']:
            opt['train']['val_freq'] = 8
            opt['logger']['print_freq'] = 1
            opt['logger']['save_checkpoint_freq'] = 8
    else:  # test
        results_root = osp.join(opt['path']['root'], 'results', opt['name'])
        opt['path']['results_root'] = results_root
        opt['path']['log'] = results_root

    return opt


def dict2str(opt, indent_l=1):
    '''dict to string for logger'''
    msg = ''
    for k, v in opt.items():
        if isinstance(v, dict):
            msg += ' ' * (indent_l * 2) + k + ':[\n'
            msg += dict2str(v, indent_l + 1)
            msg += ' ' * (indent_l * 2) + ']\n'
        else:
            msg += ' ' * (indent_l * 2) + k + ': ' + str(v) + '\n'
    return msg


class NoneDict(dict):
    def __missing__(self, key):
        return None


# convert to NoneDict, which return None for missing key.
def dict_to_nonedict(opt):
    if isinstance(opt, dict):
        new_opt = dict()
        for key, sub_opt in opt.items():
            new_opt[key] = dict_to_nonedict(sub_opt)
        return NoneDict(**new_opt)
    elif isinstance(opt, list):
        return [dict_to_nonedict(sub_opt) for sub_opt in opt]
    else:
        return opt


def check_resume(opt, resume_iter):
    '''Check resume states and pretrain_model paths'''
    logger = logging.getLogger('base')
    if opt['path']['resume_state']:
        if opt['path'].get('pretrain_model_G', None) is not None or opt['path'].get(
                'pretrain_model_D', None) is not None:
            logger.warning('pretrain_model path will be ignored when resuming training.')

        # Automatically fill in the network paths for a given resume iteration.
        for k in opt['networks'].keys():
            pt_key = 'pretrain_model_%s' % (k,)
            if pt_key in opt['path'].keys():
                # This is a dicey, error prone situation that has bitten me in both ways it can be handled. Opt for
                # a big, verbose error message.
                print("!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
                print("!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
                print("!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
                print("!!!!!!!!!!!!!!WARNING!! YOU SPECIFIED A PRETRAINED MODEL PATH!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
                print("!!!!!!!!!!!!!!!!!!!!!!!!AND A RESUME STATE PATH. THERE IS NO!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
                print("!!!!!!!!!!!!!!!!!!!!!!!!GOOD WAY TO HANDLE THIS SO WE JUST IGNORE!!!!!!!!!!!!!!!!!!!!!!!!!!!")
                print("!!!!!!!!!!!!!!!!!!!!!!!!THE MODEL PATH!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
                print("!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
                print("!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
                print("!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
            opt['path'][pt_key] = osp.join(opt['path']['models'],
                                      '{}_{}.pth'.format(resume_iter, k))
            logger.info('Set model [%s] to %s' % (k, opt['path'][pt_key]))