from PIL import Image
import os
import os.path
import numpy as np
from typing import Any, Callable, Optional, Tuple

from torchvision.datasets.vision import VisionDataset
from torchvision.datasets.utils import check_integrity, download_and_extract_archive, verify_str_arg


class STL10Albumentations(VisionDataset):
    """`STL10 <https://cs.stanford.edu/~acoates/stl10/>`_ Dataset.
    DatasetModified for Albumentations augmentations library.

    Args:
        root (string): Root directory of dataset where directory
            ``stl10_binary`` exists.
        split (string): One of {'train', 'test', 'unlabeled', 'train+unlabeled'}.
            Accordingly dataset is selected.
        folds (int, optional): One of {0-9} or None.
            For training, loads one of the 10 pre-defined folds of 1k samples for the
            standard evaluation procedure. If no value is passed, loads the 5k samples.
        transform (callable, optional): A function/transform that  takes in an PIL image
            and returns a transformed version. E.g, ``transforms.RandomCrop``
        target_transform (callable, optional): A function/transform that takes in the
            target and transforms it.
        download (bool, optional): If true, downloads the dataset from the internet and
            puts it in root directory. If dataset is already downloaded, it is not
            downloaded again.
    """
    base_folder = 'stl10_binary'
    url = "http://ai.stanford.edu/~acoates/stl10/stl10_binary.tar.gz"
    filename = "stl10_binary.tar.gz"
    tgz_md5 = '91f7769df0f17e558f3565bffb0c7dfb'
    class_names_file = 'class_names.txt'
    folds_list_file = 'fold_indices.txt'
    train_list = [
        ['train_X.bin', '918c2871b30a85fa023e0c44e0bee87f'],
        ['train_y.bin', '5a34089d4802c674881badbb80307741'],
        ['unlabeled_X.bin', '5242ba1fed5e4be9e1e742405eb56ca4']
    ]

    test_list = [
        ['test_X.bin', '7f263ba9f9e0b06b93213547f721ac82'],
        ['test_y.bin', '36f9794fa4beb8a2c72628de14fa638e']
    ]
    splits = ('train', 'train+unlabeled', 'unlabeled', 'test')

    def __init__(
            self,
            root: str,
            split: str = "train",
            folds: Optional[int] = None,
            transform: Optional[Callable] = None,
            target_transform: Optional[Callable] = None,
            download: bool = False,
    ) -> None:
        super(STL10Albumentations, self).__init__(root, transform=transform,
                                                  target_transform=target_transform)
        self.split = verify_str_arg(split, "split", self.splits)
        self.folds = self._verify_folds(folds)

        if download:
            self.download()
        elif not self._check_integrity():
            raise RuntimeError(
                'Dataset not found or corrupted. '
                'You can use download=True to download it')

        # now load the picked numpy arrays
        self.labels: Optional[np.ndarray]
        if self.split == 'train':
            self.data, self.labels = self.__loadfile(
                self.train_list[0][0], self.train_list[1][0])
            self.__load_folds(folds)

        elif self.split == 'train+unlabeled':
            self.data, self.labels = self.__loadfile(
                self.train_list[0][0], self.train_list[1][0])
            self.__load_folds(folds)
            unlabeled_data, _ = self.__loadfile(self.train_list[2][0])
            self.data = np.concatenate((self.data, unlabeled_data))
            self.labels = np.concatenate(
                (self.labels, np.asarray([-1] * unlabeled_data.shape[0])))

        elif self.split == 'unlabeled':
            self.data, _ = self.__loadfile(self.train_list[2][0])
            self.labels = np.asarray([-1] * self.data.shape[0])
        else:  # self.split == 'test':
            self.data, self.labels = self.__loadfile(
                self.test_list[0][0], self.test_list[1][0])

        class_file = os.path.join(
            self.root, self.base_folder, self.class_names_file)
        if os.path.isfile(class_file):
            with open(class_file) as f:
                self.classes = f.read().splitlines()

    def _verify_folds(self, folds: Optional[int]) -> Optional[int]:
        if folds is None:
            return folds
        elif isinstance(folds, int):
            if folds in range(10):
                return folds
            msg = ("Value for argument folds should be in the range [0, 10), "
                   "but got {}.")
            raise ValueError(msg.format(folds))
        else:
            msg = "Expected type None or int for argument folds, but got type {}."
            raise ValueError(msg.format(type(folds)))

    def __getitem__(self, index: int) -> Tuple[Any, Any]:
        """
        Args:
            index (int): Index

        Returns:
            tuple: (image, target) where target is index of the target class.
        """
        target: Optional[int]
        if self.labels is not None:
            image, target = self.data[index], int(self.labels[index])
        else:
            image, target = self.data[index], None

        # doing this so that it is consistent with all other datasets
        # to return a PIL Image
        image = Image.fromarray(np.transpose(image, (1, 2, 0)))

        # for Albumentations augs
        image = np.array(image)

        if self.transform is not None:
            image = self.transform(image=image)['image']

        if self.target_transform is not None:
            target = self.target_transform(target)

        return image, target

    def __len__(self) -> int:
        return self.data.shape[0]

    def __loadfile(self, data_file: str, labels_file: Optional[str] = None) -> Tuple[np.ndarray, Optional[np.ndarray]]:
        labels = None
        if labels_file:
            path_to_labels = os.path.join(
                self.root, self.base_folder, labels_file)
            with open(path_to_labels, 'rb') as f:
                labels = np.fromfile(f, dtype=np.uint8) - 1  # 0-based

        path_to_data = os.path.join(self.root, self.base_folder, data_file)
        with open(path_to_data, 'rb') as f:
            # read whole file in uint8 chunks
            everything = np.fromfile(f, dtype=np.uint8)
            images = np.reshape(everything, (-1, 3, 96, 96))
            images = np.transpose(images, (0, 1, 3, 2))

        return images, labels

    def _check_integrity(self) -> bool:
        root = self.root
        for fentry in (self.train_list + self.test_list):
            filename, md5 = fentry[0], fentry[1]
            fpath = os.path.join(root, self.base_folder, filename)
            if not check_integrity(fpath, md5):
                return False
        return True

    def download(self) -> None:
        if self._check_integrity():
            print('Files already downloaded and verified')
            return
        download_and_extract_archive(self.url, self.root, filename=self.filename, md5=self.tgz_md5)
        self._check_integrity()

    def extra_repr(self) -> str:
        return "Split: {split}".format(**self.__dict__)

    def __load_folds(self, folds: Optional[int]) -> None:
        # loads one of the folds if specified
        if folds is None:
            return
        path_to_folds = os.path.join(
            self.root, self.base_folder, self.folds_list_file)
        with open(path_to_folds, 'r') as f:
            str_idx = f.read().splitlines()[folds]
            list_idx = np.fromstring(str_idx, dtype=np.int64, sep=' ')
            self.data = self.data[list_idx, :, :, :]
            if self.labels is not None:
                self.labels = self.labels[list_idx]
