import os
import pickle

from dassl.data.datasets import DATASET_REGISTRY, Datum, DatasetBase
from dassl.utils import mkdir_if_missing

from .oxford_pets import OxfordPets
from .dtd import DescribableTextures as DTD

NEW_CNAMES = {
    "AnnualCrop": "Annual Crop Land",
    "Forest": "Forest",
    "HerbaceousVegetation": "Herbaceous Vegetation Land",
    "Highway": "Highway or Road",
    "Industrial": "Industrial Buildings",
    "Pasture": "Pasture Land",
    "PermanentCrop": "Permanent Crop Land",
    "Residential": "Residential Buildings",
    "River": "River",
    "SeaLake": "Sea or Lake",
}


@DATASET_REGISTRY.register()
class EuroSAT(DatasetBase):

    dataset_dir = "eurosat"

    def __init__(self, cfg):
        root = os.path.abspath(os.path.expanduser(cfg.DATASET.ROOT))
        self.dataset_dir = os.path.join(root, self.dataset_dir)
        self.image_dir = os.path.join(self.dataset_dir, "2750")
        self.split_path = os.path.join(self.dataset_dir,
                                       "split_zhou_EuroSAT.json")
        self.split_fewshot_dir = os.path.join(self.dataset_dir,
                                              "split_fewshot")
        mkdir_if_missing(self.split_fewshot_dir)

        if os.path.exists(self.split_path):
            train, val, test = OxfordPets.read_split(self.split_path,
                                                     self.image_dir)
        else:
            train, val, test = DTD.read_and_split_data(self.image_dir,
                                                       new_cnames=NEW_CNAMES)
            OxfordPets.save_split(train, val, test, self.split_path,
                                  self.image_dir)

        num_shots = cfg.DATASET.NUM_SHOTS
        if num_shots >= 1:
            seed = cfg.SEED
            preprocessed = os.path.join(self.split_fewshot_dir,
                                        f"shot_{num_shots}-seed_{seed}.pkl")

            if os.path.exists(preprocessed):
                print(
                    f"Loading preprocessed few-shot data from {preprocessed}")
                with open(preprocessed, "rb") as file:
                    data = pickle.load(file)
                    train, val = data["train"], data["val"]
            else:
                train = self.generate_fewshot_dataset(train,
                                                      num_shots=num_shots)
                val = self.generate_fewshot_dataset(val,
                                                    num_shots=min(
                                                        num_shots, 4))
                data = {"train": train, "val": val}
                print(f"Saving preprocessed few-shot data to {preprocessed}")
                with open(preprocessed, "wb") as file:
                    pickle.dump(data, file, protocol=pickle.HIGHEST_PROTOCOL)

        subsample = cfg.DATASET.SUBSAMPLE_CLASSES
        train, val, test = OxfordPets.subsample_classes(train,
                                                        val,
                                                        test,
                                                        subsample=subsample)

        super().__init__(train_x=train, val=val, test=test)

    def update_classname(self, dataset_old):
        dataset_new = []
        for item_old in dataset_old:
            cname_old = item_old.classname
            cname_new = NEW_CLASSNAMES[cname_old]
            item_new = Datum(impath=item_old.impath,
                             label=item_old.label,
                             classname=cname_new)
            dataset_new.append(item_new)
        return dataset_new
