from abc import ABC
from pathlib import Path

from torch.utils.data import Dataset
from ml_lib.point_toolset.sampling import FarthestpointSampling


class _Point_Dataset(ABC, Dataset):

    @property
    def setting(self) -> str:
        raise NotImplementedError

    headers = ['x', 'y', 'z', 'nx', 'ny', 'nz', 'label', 'cl_idx']

    def __init__(self, root=Path('data'), sampling_k=2048, transforms=None, load_preprocessed=True, *args, **kwargs):
        super(_Point_Dataset, self).__init__()

        self.load_preprocessed = load_preprocessed
        self.transforms = transforms if transforms else lambda x: x
        self.sampling_k = sampling_k
        self.sampling = FarthestpointSampling(K=self.sampling_k)
        self.root = Path(root)
        self.raw = root / 'raw'
        self.processed_ext = '.pik'
        self.raw_ext = '.xyz'
        self.processed = root / self.setting

        self._files = list(self.raw.glob(f'*{self.setting}*'))


    def __len__(self):
        raise NotImplementedError

    def __getitem__(self, item):
        raise NotImplementedError