import random
import torch.utils.data as data


class SearchDataset(data.Dataset):

    def __init__(self, name, data, train_split, valid_split, check=True):
        self.datasetname = name
        if isinstance(data, (list, tuple)): # new type of SearchDataset
            assert len(data) == 2, 'invalid length: {:}'.format( len(data) )
            self.train_data  = data[0]
            self.valid_data  = data[1]
            self.train_split = train_split.copy()
            self.valid_split = valid_split.copy()
            self.mode_str    = 'V2' # new mode
        else:
            self.mode_str    = 'V1' # old mode
            self.data        = data
            self.train_split = train_split.copy()
            self.valid_split = valid_split.copy()
            if check:
                intersection = set(train_split).intersection(set(valid_split))
                assert len(intersection) == 0, 'the splitted train and validation sets should have no intersection'
        self.length      = len(self.train_split)

    def __repr__(self):
        return ('{name}(name={datasetname}, train={tr_L}, valid={val_L}, version={ver})'.format(name=self.__class__.__name__, datasetname=self.datasetname, tr_L=len(self.train_split), val_L=len(self.valid_split), ver=self.mode_str))

    def __len__(self):
        return self.length

    def __getitem__(self, index):
        assert index >= 0 and index < self.length, 'invalid index = {:}'.format(index)
        train_index = self.train_split[index]
        valid_index = random.choice( self.valid_split )
        if self.mode_str == 'V1':
            train_image, train_label = self.data[train_index]
            valid_image, valid_label = self.data[valid_index]
        elif self.mode_str == 'V2':
            train_image, train_label = self.train_data[train_index]
            valid_image, valid_label = self.valid_data[valid_index]
        else: raise ValueError('invalid mode : {:}'.format(self.mode_str))
        return train_image, train_label, valid_image, valid_label
