content: import os
  import numpy as np
  import PIL
  from PIL import Image
  from torch.utils.data import Dataset
  from torchvision import transforms
  
  
  class LSUNBase(Dataset):
      def __init__(self,
                   txt_file,
                   data_root,
                   size=None,
                   interpolation="bicubic",
                   flip_p=0.5
                   ):
          self.data_paths = txt_file
          self.data_root = data_root
          with open(self.data_paths, "r") as f:
              self.image_paths = f.read().splitlines()
          self._length = len(self.image_paths)
          self.labels = {
              "relative_file_path_": [l for l in self.image_paths],
              "file_path_": [os.path.join(self.data_root, l)
                             for l in self.image_paths],
          }
  
          self.size = size
          self.interpolation = {"linear": PIL.Image.LINEAR,
                                "bilinear": PIL.Image.BILINEAR,
                                "bicubic": PIL.Image.BICUBIC,
                                "lanczos": PIL.Image.LANCZOS,
                                }[interpolation]
          self.flip = transforms.RandomHorizontalFlip(p=flip_p)
  
      def __len__(self):
          return self._length
  
      def __getitem__(self, i):
          example = dict((k, self.labels[k][i]) for k in self.labels)
          image = Image.open(example["file_path_"])
          if not image.mode == "RGB":
              image = image.convert("RGB")
  
          # default to score-sde preprocessing
          img = np.array(image).astype(np.uint8)
          crop = min(img.shape[0], img.shape[1])
          h, w, = img.shape[0], img.shape[1]
          img = img[(h - crop) // 2:(h + crop) // 2,
                (w - crop) // 2:(w + crop) // 2]
  
          image = Image.fromarray(img)
          if self.size is not None:
              image = image.resize((self.size, self.size), resample=self.interpolation)
  
          image = self.flip(image)
          image = np.array(image).astype(np.uint8)
          example["image"] = (image / 127.5 - 1.0).astype(np.float32)
          return example
  
  
  class LSUNChurchesTrain(LSUNBase):
      def __init__(self, **kwargs):
          super().__init__(txt_file="data/lsun/church_outdoor_train.txt", data_root="data/lsun/churches", **kwargs)
  
  
  class LSUNChurchesValidation(LSUNBase):
      def __init__(self, flip_p=0., **kwargs):
          super().__init__(txt_file="data/lsun/church_outdoor_val.txt", data_root="data/lsun/churches",
                           flip_p=flip_p, **kwargs)
  
  
  class LSUNBedroomsTrain(LSUNBase):
      def __init__(self, **kwargs):
          super().__init__(txt_file="data/lsun/bedrooms_train.txt", data_root="data/lsun/bedrooms", **kwargs)
  
  
  class LSUNBedroomsValidation(LSUNBase):
      def __init__(self, flip_p=0.0, **kwargs):
          super().__init__(txt_file="data/lsun/bedrooms_val.txt", data_root="data/lsun/bedrooms",
                           flip_p=flip_p, **kwargs)
  
  
  class LSUNCatsTrain(LSUNBase):
      def __init__(self, **kwargs):
          super().__init__(txt_file="data/lsun/cat_train.txt", data_root="data/lsun/cats", **kwargs)
  
  
  class LSUNCatsValidation(LSUNBase):
      def __init__(self, flip_p=0., **kwargs):
          super().__init__(txt_file="data/lsun/cat_val.txt", data_root="data/lsun/cats",
                           flip_p=flip_p, **kwargs)
  