from PIL import Image
from torch.utils.data import Dataset, DataLoader

from augmentations import ha_augment_sample, resize_sample, spatial_augment_sample
from utils import to_tensor_sample


def image_transforms(shape, jittering):
    def train_transforms(sample):
        sample = resize_sample(sample, image_shape=shape)
        sample = spatial_augment_sample(sample)
        sample = to_tensor_sample(sample)
        sample = ha_augment_sample(sample, jitter_paramters=jittering)
        return sample

    return {"train": train_transforms}


class GetData(Dataset):
    def __init__(self, config, transforms=None):
        """
        Get the list containing all images and labels.
        """
        datafile = open(config.train_txt, "r")
        lines = datafile.readlines()

        dataset = []
        for line in lines:
            line = line.rstrip()
            data = line.split()
            dataset.append(data[0])

        self.config = config
        self.dataset = dataset
        self.root = config.train_root

        self.transforms = transforms

    def __getitem__(self, index):
        """
        Return image'data and its label.
        """
        img_path = self.dataset[index]
        img_file = self.root + img_path
        img = Image.open(img_file)

        # image.mode == 'L' means the image is in gray scale
        if img.mode == "L":
            img_new = Image.new("RGB", img.size)
            img_new.paste(img)
            sample = {"image": img_new, "idx": index}
        else:
            sample = {"image": img, "idx": index}

        if self.transforms:
            sample = self.transforms(sample)

        return sample

    def __len__(self):
        """
        Return the number of all data.
        """
        return len(self.dataset)


def get_data_loader(
    config,
    transforms=None,
    sampler=None,
    drop_last=True,
):
    """
    Return batch data for training.
    """
    transforms = image_transforms(shape=config.image_shape, jittering=config.jittering)
    dataset = GetData(config, transforms=transforms["train"])

    train_loader = DataLoader(
        dataset,
        batch_size=config.batch_size,
        shuffle=config.shuffle,
        sampler=sampler,
        num_workers=config.num_workers,
        pin_memory=config.pin_memory,
        drop_last=drop_last,
    )

    return train_loader