import torch
import lmdb
import os
import pickle
from io import BytesIO
from data.dataset import CARS, ImprovedImageFolder
import torchvision
from tqdm import tqdm


def dataset_to_lmdb(dataset, lmdb_path):
    env = lmdb.open(lmdb_path, map_size=1099511627776 * 2, subdir=os.path.isdir(lmdb_path))
    with env.begin(write=True) as txn:
        for i in tqdm(range(len(dataset))):
            buffer = BytesIO()
            torch.save(dataset[i], buffer)
            txn.put("{}".format(i).encode(), buffer.getvalue())
        txn.put(b"classes_list", pickle.dumps(dataset.classes_list))
        txn.put(b"__len__", pickle.dumps(len(dataset)))


def main():
    data_transform = torchvision.transforms.Compose([
        torchvision.transforms.Resize([int(224 * 1.15), int(224 * 1.15)]),
        torchvision.transforms.CenterCrop(224),
        torchvision.transforms.ToTensor(),
        torchvision.transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ])
    origin_dataset = ImprovedImageFolder("/data/few-shot/CUB_200_2011/CUB_200_2011/images", transform=data_transform)
    dataset_to_lmdb(origin_dataset, "/data/few-shot/lmdb/CUB_200_2011/data.lmdb")


if __name__ == '__main__':
    main()