import h5py
import torch
import numpy as np
import itertools
from torch.utils.data import Dataset
from torch.utils.data.sampler import Sampler


class LAHeart(Dataset):
    """ LA Dataset """

    def __init__(self, base_dir=None, split='train', num=None, transform=None):
        self._base_dir = base_dir
        self.transform = transform
        self.sample_list = []
        if split == 'train':
            with open(self._base_dir + '/../train.list', 'r') as f:
                self.image_list = f.readlines()
        elif split == 'test':
            with open(self._base_dir + '/../test.list', 'r') as f:
                self.image_list = f.readlines()
        self.image_list = [item.strip() for item in self.image_list]
        if num is not None:
            self.image_list = self.image_list[:num]
        print("total {} samples".format(len(self.image_list)))

    def __len__(self):
        return len(self.image_list)

    def __getitem__(self, idx):
        image_name = self.image_list[idx]
        # print(image_name)
        h5f = h5py.File(self._base_dir + "/" + image_name + "/mri_norm2.h5", 'r')
        image = h5f['image'][:]
        label = h5f['label'][:]
        sample = {'image': image, 'label': label}
        if self.transform:
            sample = self.transform(sample)
        return sample

class RandomCrop(object):
    """
    Crop randomly the image in a sample
    Args:
    output_size (int): Desired output size
    """

    def __init__(self, output_size):
        self.output_size = output_size

    def __call__(self, sample):
        image, label = sample['image'], sample['label']

        # pad the sample if necessary
        if label.shape[0] <= self.output_size[0] or label.shape[1] <= self.output_size[1] or label.shape[2] <= \
                self.output_size[2]:
            pw = max((self.output_size[0] - label.shape[0]) // 2 + 3, 0)
            ph = max((self.output_size[1] - label.shape[1]) // 2 + 3, 0)
            pd = max((self.output_size[2] - label.shape[2]) // 2 + 3, 0)
            image = np.pad(image, [(pw, pw), (ph, ph), (pd, pd)], mode='constant', constant_values=0)
            label = np.pad(label, [(pw, pw), (ph, ph), (pd, pd)], mode='constant', constant_values=0)

        (w, h, d) = image.shape
        w1 = np.random.randint(0, w - self.output_size[0])
        h1 = np.random.randint(0, h - self.output_size[1])
        d1 = np.random.randint(0, d - self.output_size[2])

        label = label[w1:w1 + self.output_size[0], h1:h1 + self.output_size[1], d1:d1 + self.output_size[2]]
        image = image[w1:w1 + self.output_size[0], h1:h1 + self.output_size[1], d1:d1 + self.output_size[2]]
        return {'image': image, 'label': label}

class CenterCrop(object):
    def __init__(self, output_size):
        self.output_size = output_size

    def __call__(self, sample):
        image, label = sample['image'], sample['label']

        # pad the sample if necessary
        if label.shape[0] <= self.output_size[0] or label.shape[1] <= self.output_size[1] or label.shape[2] <= \
                self.output_size[2]:
            pw = max((self.output_size[0] - label.shape[0]) // 2 + 3, 0)
            ph = max((self.output_size[1] - label.shape[1]) // 2 + 3, 0)
            pd = max((self.output_size[2] - label.shape[2]) // 2 + 3, 0)
            image = np.pad(image, [(pw, pw), (ph, ph), (pd, pd)], mode='constant', constant_values=0)
            label = np.pad(label, [(pw, pw), (ph, ph), (pd, pd)], mode='constant', constant_values=0)

        (w, h, d) = image.shape

        w1 = int(round((w - self.output_size[0]) / 2.))
        h1 = int(round((h - self.output_size[1]) / 2.))
        d1 = int(round((d - self.output_size[2]) / 2.))

        label = label[w1:w1 + self.output_size[0], h1:h1 + self.output_size[1], d1:d1 + self.output_size[2]]
        image = image[w1:w1 + self.output_size[0], h1:h1 + self.output_size[1], d1:d1 + self.output_size[2]]

        return {'image': image, 'label': label}


class RandomRotFlip(object):
    """
    Crop randomly flip the dataset in a sample
    Args:
    output_size (int): Desired output size
    """

    def __call__(self, sample):
        image, label = sample['image'], sample['label']
        k = np.random.randint(0, 4)
        image = np.rot90(image, k)
        label = np.rot90(label, k)
        axis = np.random.randint(0, 2)
        image = np.flip(image, axis=axis).copy()
        label = np.flip(label, axis=axis).copy()

        return {'image': image, 'label': label}


class ToTensor(object):
    """Convert ndarrays in sample to Tensors."""

    def __call__(self, sample):
        image = sample['image']
        image = image.reshape(1, image.shape[0], image.shape[1], image.shape[2]).astype(np.float32)
        return {'image': torch.from_numpy(image), 'label': torch.from_numpy(sample['label']).long()}


class TwoStreamBatchSampler(Sampler):
    """Iterate two sets of indices

    An 'epoch' is one iteration through the primary indices.
    During the epoch, the secondary indices are iterated through
    as many times as needed.
    """
    def __init__(self, primary_indices, secondary_indices, batch_size, secondary_batch_size):
        self.primary_indices = primary_indices
        self.secondary_indices = secondary_indices
        self.secondary_batch_size = secondary_batch_size
        self.primary_batch_size = batch_size - secondary_batch_size

        assert len(self.primary_indices) >= self.primary_batch_size > 0
        assert len(self.secondary_indices) >= self.secondary_batch_size > 0

    def __iter__(self):
        primary_iter = iterate_once(self.primary_indices)
        secondary_iter = iterate_eternally(self.secondary_indices)
        return (
            primary_batch + secondary_batch
            for (primary_batch, secondary_batch)
            in zip(grouper(primary_iter, self.primary_batch_size),
                    grouper(secondary_iter, self.secondary_batch_size))
        )

    def __len__(self):
        return len(self.primary_indices) // self.primary_batch_size

def iterate_once(iterable):
    return np.random.permutation(iterable)


def iterate_eternally(indices):
    def infinite_shuffles():
        while True:
            yield np.random.permutation(indices)
    return itertools.chain.from_iterable(infinite_shuffles())


def grouper(iterable, n):
    "Collect data into fixed-length chunks or blocks"
    # grouper('ABCDEFG', 3) --> ABC DEF"
    args = [iter(iterable)] * n
    return zip(*args)


if __name__ == '__main__':
    train_set = LAHeart('E:/data/LASet/data')
    print(len(train_set))
    # data = train_set[0]
    # image, label = data['image'], data['label']
    # print(image.shape, label.shape)
    labeled_idxs = list(range(25))
    unlabeled_idxs = list(range(25,123))
    batch_sampler = TwoStreamBatchSampler(labeled_idxs, unlabeled_idxs, 4, 2)
    i = 0
    for x in batch_sampler:
        i += 1
        print('%02d'%i,'\t',x)