import os
import pickle
from collections import OrderedDict

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

from .oxford_pets import OxfordPets


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

    dataset_dir = "imagenet"  # 21k-split-1k/imagenet-extra-01
    dataset_name = 'imagenet'

    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, "images")
        self.preprocessed = os.path.join(self.dataset_dir, "preprocessed.pkl")
        self.split_fewshot_dir = os.path.join(self.dataset_dir, "split_fewshot")
        mkdir_if_missing(self.split_fewshot_dir)

        if os.path.exists(self.preprocessed) and True:
            with open(self.preprocessed, "rb") as f:
                preprocessed = pickle.load(f)
                train = preprocessed["train"]
                test = preprocessed["test"]
        else:
            text_file = os.path.join(self.dataset_dir, "classnames.txt")
            classnames = self.read_classnames(text_file)
            train = self.read_data(classnames, "train")
            # Follow standard practice to perform evaluation on the val set
            # Also used as the val set (so evaluate the last-step model)
            test = self.read_data(classnames, "val")

            preprocessed = {"train": train, "test": test}
            with open(self.preprocessed, "wb") as f:
                pickle.dump(preprocessed, f, protocol=pickle.HIGHEST_PROTOCOL)

        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 = data["train"]
            else:
                train = self.generate_fewshot_dataset(train, num_shots=num_shots)
                data = {"train": train}
                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, test = OxfordPets.subsample_classes(train, test, subsample=subsample)

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

    @staticmethod
    def read_classnames(text_file):
        """Return a dictionary containing
        key-value pairs of <folder name>: <class name>.
        """
        classnames = OrderedDict()
        with open(text_file, "r") as f:
            lines = f.readlines()
            for line in lines:
                line = line.strip().split(" ")
                folder = line[0]
                classname = " ".join(line[1:])
                classnames[folder] = classname
        return classnames

    def read_data(self, classnames, split_dir):
        split_dir = os.path.join(self.image_dir, split_dir)
        folders = sorted(f.name for f in os.scandir(split_dir) if f.is_dir())
        items = []

        for label, folder in enumerate(folders):
            imnames = listdir_nohidden(os.path.join(split_dir, folder))
            classname = classnames[folder]
            for imname in imnames:
                impath = os.path.join(split_dir, folder, imname)
                item = Datum(impath=impath, label=label, classname=classname)
                items.append(item)

        return items


@DATASET_REGISTRY.register()
class ImageNet100_MCM(ImageNet):

    dataset_dir = "imagenet100-MCM"
    dataset_name = 'imagenet100'

    def __init__(self, cfg):
        super().__init__(cfg)


@DATASET_REGISTRY.register()
class ImageNet100_NEW(ImageNet):

    dataset_dir = "imagenet100-NEW"
    dataset_name = 'imagenet100'

    def __init__(self, cfg):
        super().__init__(cfg)


@DATASET_REGISTRY.register()
class ImageNet200_UNION(ImageNet):

    dataset_dir = "imagenet200-UNION"
    dataset_name = 'imagenet100'

    def __init__(self, cfg):
        super().__init__(cfg)


@DATASET_REGISTRY.register()
class ImageNetR(DatasetBase):
    """ImageNet-R(endition).

    This dataset is used for testing only.
    """

    dataset_dir = "imagenet-r"
    dataset_name = 'imagenet-r'

    def __init__(self, cfg):
        self.full_dir = f'data/{self.dataset_dir[:-2]}/images/val'
        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)

        text_file = os.path.join(self.dataset_dir, "classnames.txt")
        classnames = ImageNet.read_classnames(text_file)
        train = self.read_data(classnames, 'images')
        # Follow standard practice to perform evaluation on the val set
        # Also used as the val set (so evaluate the last-step model)
        test = self.read_data(classnames, 'images')


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

        super().__init__(train_x=train, val=test, test=test)
        self._num_classes = len(self.valid_classes)
        folders = sorted(os.listdir(self.full_dir))
        self._classnames = [classnames[f] for f in folders]

    def read_data(self, classnames, split_dir):
        import torch
        split_dir = os.path.join(self.image_dir, split_dir)
        folders = sorted(f.name for f in os.scandir(self.full_dir) if f.is_dir())
        items = []
        self.valid_classes = torch.full((len(folders),), False)

        for label, folder in enumerate(folders):
            data_dir = os.path.join(split_dir, folder)
            if not os.path.exists(data_dir):
                continue
            self.valid_classes[label] = True
            imnames = listdir_nohidden(data_dir)
            classname = classnames[folder]
            for imname in imnames:
                impath = os.path.join(split_dir, folder, imname)
                item = Datum(impath=impath, label=label, classname=classname)
                items.append(item)

        return items