text: from collections import namedtuple
  import csv
  from functools import partial
  import torch
  import os
  import PIL
  from typing import Any, Callable, List, Optional, Union, Tuple
  from .vision import VisionDataset
  from .utils import download_file_from_google_drive, check_integrity, verify_str_arg
  
  CSV = namedtuple("CSV", ["header", "index", "data"])
  
  
  class CelebA(VisionDataset):
      """`Large-scale CelebFaces Attributes (CelebA) Dataset <http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html>`_ Dataset.
  
      Args:
          root (string): Root directory where images are downloaded to.
          split (string): One of {'train', 'valid', 'test', 'all'}.
              Accordingly dataset is selected.
          target_type (string or list, optional): Type of target to use, ``attr``, ``identity``, ``bbox``,
              or ``landmarks``. Can also be a list to output a tuple with all specified target types.
              The targets represent:
  
                  - ``attr`` (np.array shape=(40,) dtype=int): binary (0, 1) labels for attributes
                  - ``identity`` (int): label for each person (data points with the same identity are the same person)
                  - ``bbox`` (np.array shape=(4,) dtype=int): bounding box (x, y, width, height)
                  - ``landmarks`` (np.array shape=(10,) dtype=int): landmark points (lefteye_x, lefteye_y, righteye_x,
                    righteye_y, nose_x, nose_y, leftmouth_x, leftmouth_y, rightmouth_x, rightmouth_y)
  
              Defaults to ``attr``. If empty, ``None`` will be returned as target.
  
          transform (callable, optional): A function/transform that  takes in an PIL image
              and returns a transformed version. E.g, ``transforms.ToTensor``
          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.
      """
  
      base_folder = "celeba"
      # There currently does not appear to be a easy way to extract 7z in python (without introducing additional
      # dependencies). The "in-the-wild" (not aligned+cropped) images are only in 7z, so they are not available
      # right now.
      file_list = [
          # File ID                                      MD5 Hash                            Filename
          ("0B7EVK8r0v71pZjFTYXZWM3FlRnM", "00d2c5bc6d35e252742224ab0c1e8fcb", "img_align_celeba.zip"),
          # ("0B7EVK8r0v71pbWNEUjJKdDQ3dGc","b6cd7e93bc7a96c2dc33f819aa3ac651", "img_align_celeba_png.7z"),
          # ("0B7EVK8r0v71peklHb0pGdDl6R28", "b6cd7e93bc7a96c2dc33f819aa3ac651", "img_celeba.7z"),
          ("0B7EVK8r0v71pblRyaVFSWGxPY0U", "75e246fa4810816ffd6ee81facbd244c", "list_attr_celeba.txt"),
          ("1_ee_0u7vcNLOfNLegJRHmolfH5ICW-XS", "32bd1bd63d3c78cd57e08160ec5ed1e2", "identity_CelebA.txt"),
          ("0B7EVK8r0v71pbThiMVRxWXZ4dU0", "00566efa6fedff7a56946cd1c10f1c16", "list_bbox_celeba.txt"),
          ("0B7EVK8r0v71pd0FJY3Blby1HUTQ", "cc24ecafdb5b50baae59b03474781f8c", "list_landmarks_align_celeba.txt"),
          # ("0B7EVK8r0v71pTzJIdlJWdHczRlU", "063ee6ddb681f96bc9ca28c6febb9d1a", "list_landmarks_celeba.txt"),
          ("0B7EVK8r0v71pY0NSMzRuSXJEVkk", "d32c9cbf5e040fd4025c592c306e6668", "list_eval_partition.txt"),
      ]
  
      def __init__(
              self,
              root: str,
              split: str = "train",
              target_type: Union[List[str], str] = "attr",
              transform: Optional[Callable] = None,
              target_transform: Optional[Callable] = None,
              download: bool = False,
      ) -> None:
          super(CelebA, self).__init__(root, transform=transform,
                                       target_transform=target_transform)
          self.split = split
          if isinstance(target_type, list):
              self.target_type = target_type
          else:
              self.target_type = [target_type]
  
          if not self.target_type and self.target_transform is not None:
              raise RuntimeError('target_transform is specified but target_type is empty')
  
          if download:
              self.download()
  
          if not self._check_integrity():
              raise RuntimeError('Dataset not found or corrupted.' +
                                 ' You can use download=True to download it')
  
          split_map = {
              "train": 0,
              "valid": 1,
              "test": 2,
              "all": None,
          }
          split_ = split_map[verify_str_arg(split.lower(), "split",
                                            ("train", "valid", "test", "all"))]
          splits = self._load_csv("list_eval_partition.txt")
          identity = self._load_csv("identity_CelebA.txt")
          bbox = self._load_csv("list_bbox_celeba.txt", header=1)
          landmarks_align = self._load_csv("list_landmarks_align_celeba.txt", header=1)
          attr = self._load_csv("list_attr_celeba.txt", header=1)
  
          mask = slice(None) if split_ is None else (splits.data == split_).squeeze()
  
          self.filename = splits.index
          self.identity = identity.data[mask]
          self.bbox = bbox.data[mask]
          self.landmarks_align = landmarks_align.data[mask]
          self.attr = attr.data[mask]
          # map from {-1, 1} to {0, 1}
          self.attr = torch.div(self.attr + 1, 2, rounding_mode='floor')
          self.attr_names = attr.header
  
      def _load_csv(
          self,
          filename: str,
          header: Optional[int] = None,
      ) -> CSV:
          data, indices, headers = [], [], []
  
          fn = partial(os.path.join, self.root, self.base_folder)
          with open(fn(filename)) as csv_file:
              data = list(csv.reader(csv_file, delimiter=' ', skipinitialspace=True))
  
          if header is not None:
              headers = data[header]
              data = data[header + 1:]
  
          indices = [row[0] for row in data]
          data = [row[1:] for row in data]
          data_int = [list(map(int, i)) for i in data]
  
          return CSV(headers, indices, torch.tensor(data_int))
  
      def _check_integrity(self) -> bool:
          for (_, md5, filename) in self.file_list:
              fpath = os.path.join(self.root, self.base_folder, filename)
              _, ext = os.path.splitext(filename)
              # Allow original archive to be deleted (zip and 7z)
              # Only need the extracted images
              if ext not in [".zip", ".7z"] and not check_integrity(fpath, md5):
                  return False
  
          # Should check a hash of the images
          return os.path.isdir(os.path.join(self.root, self.base_folder, "img_align_celeba"))
  
      def download(self) -> None:
          import zipfile
  
          if self._check_integrity():
              print('Files already downloaded and verified')
              return
  
          for (file_id, md5, filename) in self.file_list:
              download_file_from_google_drive(file_id, os.path.join(self.root, self.base_folder), filename, md5)
  
          with zipfile.ZipFile(os.path.join(self.root, self.base_folder, "img_align_celeba.zip"), "r") as f:
              f.extractall(os.path.join(self.root, self.base_folder))
  
      def __getitem__(self, index: int) -> Tuple[Any, Any]:
          X = PIL.Image.open(os.path.join(self.root, self.base_folder, "img_align_celeba", self.filename[index]))
  
          target: Any = []
          for t in self.target_type:
              if t == "attr":
                  target.append(self.attr[index, :])
              elif t == "identity":
                  target.append(self.identity[index, 0])
              elif t == "bbox":
                  target.append(self.bbox[index, :])
              elif t == "landmarks":
                  target.append(self.landmarks_align[index, :])
              else:
                  # TODO: refactor with utils.verify_str_arg
                  raise ValueError("Target type \"{}\" is not recognized.".format(t))
  
          if self.transform is not None:
              X = self.transform(X)
  
          if target:
              target = tuple(target) if len(target) > 1 else target[0]
  
              if self.target_transform is not None:
                  target = self.target_transform(target)
          else:
              target = None
  
          return X, target
  
      def __len__(self) -> int:
          return len(self.attr)
  
      def extra_repr(self) -> str:
          lines = ["Target type: {target_type}", "Split: {split}"]
          return '\n'.join(lines).format(**self.__dict__)
  