import os
import torch
import torchvision.datasets as datasets


import pathlib
from typing import Callable, Optional, Any, Tuple

from PIL import Image

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

from datasets import load_dataset
from datasets import Dataset


class ArrowImageDataset(Dataset):
    def __init__(self, hf_dataset, transform=None):
        self.dataset = hf_dataset
        self.transform = transform
    
    def __len__(self):
        return len(self.dataset)
    
    def __getitem__(self, idx):
        item = self.dataset[idx]
        # print(item) # {'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x7F918328F100>, 'label': 0}
        
        image = item['image']  
        
        if self.transform:
            image = self.transform(image)
        
        label = item['label']
        return image, label
    
        

class PytorchStanfordCars(VisionDataset):
    """`Stanford Cars <https://ai.stanford.edu/~jkrause/cars/car_dataset.html>`_ Dataset

    The Cars dataset contains 16,185 images of 196 classes of cars. The data is
    split into 8,144 training images and 8,041 testing images, where each class
    has been split roughly in a 50-50 split

    .. note::

        This class needs `scipy <https://docs.scipy.org/doc/>`_ to load target files from `.mat` format.

    Args:
        root (string): Root directory of dataset
        split (string, optional): The dataset split, supports ``"train"`` (default) or ``"test"``.
        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."""

    def __init__(
        self,
        root: str,
        split: str = "train",
        transform: Optional[Callable] = None,
        target_transform: Optional[Callable] = None,
        download: bool = False,
    ) -> None:

        try:
            import scipy.io as sio
        except ImportError:
            raise RuntimeError("Scipy is not found. This dataset needs to have scipy installed: pip install scipy")

        super().__init__(root, transform=transform, target_transform=target_transform)

        self._split = verify_str_arg(split, "split", ("train", "test"))
        self._base_folder = pathlib.Path(root) / "stanford_cars"
        devkit = self._base_folder / "devkit"

        if self._split == "train":
            self._annotations_mat_path = devkit / "cars_train_annos.mat"
            self._images_base_path = self._base_folder / "cars_train"
        else:
            self._annotations_mat_path = devkit / "cars_test_annos_withlabels.mat"
            self._images_base_path = self._base_folder / "cars_test"

        if download:
            self.download()

        if not self._check_exists():
            raise RuntimeError("Dataset not found. You can use download=True to download it")

        self._samples = [
            (
                str(self._images_base_path / annotation["fname"]),
                annotation["class"] - 1,  # Original target mapping  starts from 1, hence -1
            )
            for annotation in sio.loadmat(self._annotations_mat_path, squeeze_me=True)["annotations"]
        ]

        self.classes = sio.loadmat(str(devkit / "cars_meta.mat"), squeeze_me=True)["class_names"].tolist()
        self.class_to_idx = {cls: i for i, cls in enumerate(self.classes)}

    def __len__(self) -> int:
        return len(self._samples)

    def __getitem__(self, idx: int) -> Tuple[Any, Any]:
        """Returns pil_image and class_id for given index"""
        image_path, target = self._samples[idx]
        pil_image = Image.open(image_path).convert("RGB")

        if self.transform is not None:
            pil_image = self.transform(pil_image)
        if self.target_transform is not None:
            target = self.target_transform(target)
        return pil_image, target


    def download(self) -> None:
        if self._check_exists():
            return

        download_and_extract_archive(
            url="https://ai.stanford.edu/~jkrause/cars/car_devkit.tgz",
            download_root=str(self._base_folder),
            md5="c3b158d763b6e2245038c8ad08e45376",
        )
        if self._split == "train":
            download_and_extract_archive(
                url="https://ai.stanford.edu/~jkrause/car196/cars_train.tgz",
                download_root=str(self._base_folder),
                md5="065e5b463ae28d29e77c1b4b166cfe61",
            )
        else:
            download_and_extract_archive(
                url="https://ai.stanford.edu/~jkrause/car196/cars_test.tgz",
                download_root=str(self._base_folder),
                md5="4ce7ebf6a94d07f1952d94dd34c4d501",
            )
            download_url(
                url="https://ai.stanford.edu/~jkrause/car196/cars_test_annos_withlabels.mat",
                root=str(self._base_folder),
                md5="b0a2b23655a3edd16d84508592a98d10",
            )

    def _check_exists(self) -> bool:
        if not (self._base_folder / "devkit").is_dir():
            return False

        return self._annotations_mat_path.exists() and self._images_base_path.is_dir()


class Cars:
    def __init__(self,
                 preprocess,
                 location=os.path.expanduser('~/data'),
                 batch_size=32,
                 num_workers=0):
        # Data loading code

        # self.train_dataset = PytorchStanfordCars(location, 'train', preprocess, download=False)
        
            
        data_path = os.path.join(location, 'stanford_cars')

        data = load_dataset(data_path)

        self.train_dataset = data['train']
        self.classnames = self.train_dataset.features['label'].names
        self.train_dataset = ArrowImageDataset(self.train_dataset, transform=preprocess)

        self.train_loader = torch.utils.data.DataLoader(
            self.train_dataset,
            shuffle=True,
            batch_size=batch_size,
            num_workers=num_workers,
        )

        # self.test_dataset = PytorchStanfordCars(location, 'test', preprocess, download=False)
        self.test_dataset = data['test']
        self.test_dataset = ArrowImageDataset(self.test_dataset, transform=preprocess)
            
        self.test_loader = torch.utils.data.DataLoader(
            self.test_dataset,
            batch_size=batch_size,
            num_workers=num_workers
        )
        self.test_loader_shuffle = torch.utils.data.DataLoader(
            self.test_dataset,
            shuffle=True,
            batch_size=batch_size,
            num_workers=num_workers
        )
        # idx_to_class = dict((v, k) for k, v in self.train_dataset.class_to_idx.items())
        # self.classnames = [idx_to_class[i].replace(
        #     '_', ' ') for i in range(len(idx_to_class))]
