"""
 Copyright 2020 Tianshu AI Platform. All Rights Reserved.

 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
 You may obtain a copy of the License at

     http://www.apache.org/licenses/LICENSE-2.0

 Unless required by applicable law or agreed to in writing, software
 distributed under the License is distributed on an "AS IS" BASIS,
 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License.
 =============================================================
"""


from torchvision.datasets import VisionDataset
from PIL import Image
import torch

class LabelConcatDataset(VisionDataset):
    """Dataset as a concatenation of dataset's lables.

    This class is useful to assemble the same dataset's labels.

    Arguments:
        datasets (sequence): List of datasets to be concatenated
        tasks (list) : List of teacher tasks  
        transform (callable, optional): A function/transform that takes in an PIL image and returns a transformed version.
        target_transform (callable, optional): A function/transform that takes in the target and transforms it.
        transforms (callable, optional): A function/transform that takes input sample and its target as entry and returns a transformed version.
    """

    def __init__(self, datasets, transforms=None, transform=None, target_transform=None):
        super(LabelConcatDataset, self).__init__(
            root=None, transforms=transforms, transform=transform, target_transform=target_transform)
        assert len(datasets) > 0, 'datasets should not be an empty iterable'
        self.datasets = list(datasets)
        for d in self.datasets:
            for t in ('transform', 'transforms', 'target_transform'):
                if hasattr( d, t ):
                    setattr( d, t, None )

    def __getitem__(self, idx):
        targets_list = []
        for dst in self.datasets:
            image, target = dst[idx]
            targets_list.append(target)
        if self.transforms is not None:
            image, *targets_list = self.transforms( image, *targets_list ) 
        data = [ image, *targets_list ]
        return data

    def __len__(self):
        return len(self.datasets[0].images)