text: from pathlib import Path
  import torch
  import torch.utils.data
  from .torchvision_datasets import CocoDetection
  
  from .dataset import build
  from .dataset_fewshot import build as build_fewshot
  
  
  # Meta-settings for few-shot object detection: base / novel category split
  coco_base_class_ids = [
      8, 10, 11, 13, 14, 15, 22, 23, 24, 25, 27, 28, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 46, 47,
      48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 65, 70, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84,
      85, 86, 87, 88, 89, 90
  ]
  
  coco_novel_class_ids = [
      1, 2, 3, 4, 5, 6, 7, 9, 16, 17, 18, 19, 20, 21, 44, 62, 63, 64, 67, 72
  ]
  
  voc_base1_class_ids = [
      1, 2, 4, 5, 7, 8, 9, 11, 12, 13, 15, 16, 17, 19, 20
  ]
  
  voc_novel1_class_ids = [
      3, 6, 10, 14, 18
  ]
  
  voc_base2_class_ids = [
      2, 3, 4, 6, 7, 8, 9, 11, 12, 14, 15, 16, 17, 19, 20
  ]
  
  voc_novel2_class_ids = [
      1, 5, 10, 13, 18
  ]
  
  voc_base3_class_ids = [
      1, 2, 3, 5, 6, 7, 9, 10, 11, 12, 13, 15, 16, 19, 20
  ]
  
  voc_novel3_class_ids = [
      4, 8, 14, 17, 18
  ]
  
  
  def get_class_ids(dataset, type):
      if dataset == 'coco_base':
          if type == 'all':
              ids = (coco_base_class_ids + coco_novel_class_ids)
              ids.sort()
              return ids
          elif type == 'base':
              return coco_base_class_ids
          elif type == 'novel':
              return coco_novel_class_ids
          else:
              raise ValueError
      if dataset == 'coco':
          if type == 'all':
              ids = (coco_base_class_ids + coco_novel_class_ids)
              ids.sort()
              return ids
          else:
              raise ValueError
      if dataset == 'voc_base1':
          if type == 'all':
              ids = (voc_base1_class_ids + voc_novel1_class_ids)
              ids.sort()
              return ids
          elif type == 'base':
              return voc_base1_class_ids
          elif type == 'novel':
              return voc_novel1_class_ids
          else:
              raise ValueError
      if dataset == 'voc_base2':
          if type == 'all':
              ids = (voc_base2_class_ids + voc_novel2_class_ids)
              ids.sort()
              return ids
          elif type == 'base':
              return voc_base2_class_ids
          elif type == 'novel':
              return voc_novel2_class_ids
          else:
              raise ValueError
      if dataset == 'voc_base3':
          if type == 'all':
              ids = (voc_base3_class_ids + voc_novel3_class_ids)
              ids.sort()
              return ids
          elif type == 'base':
              return voc_base3_class_ids
          elif type == 'novel':
              return voc_novel3_class_ids
          else:
              raise ValueError
      if dataset == 'voc':
          if type == 'all':
              ids = (voc_base1_class_ids + voc_novel1_class_ids)
              ids.sort()
              return ids
          else:
              raise ValueError
      raise ValueError
  
  
  def get_coco_api_from_dataset(dataset):
      for _ in range(10):
          if isinstance(dataset, torch.utils.data.Subset):
              dataset = dataset.dataset
      if isinstance(dataset, CocoDetection):
          return dataset.coco
  
  
  def build_dataset(image_set, args):
      assert image_set in ['train', 'val', 'fewshot'], "image_set must be 'train', 'val' or 'fewshot'."
      # For training set, need to perform base/novel category filtering.
      # For training set, we use dataset with support to construct meta-tasks
      if image_set == 'train':
          if args.dataset_file == 'coco':
              root = Path('data/coco')
              img_folder = root / "train2017"
              ann_file = root / "annotations" / 'instances_train2017.json'
              class_ids = coco_base_class_ids + coco_novel_class_ids
              class_ids.sort()
              return build(args, img_folder, ann_file, image_set, activated_class_ids=class_ids, with_support=True)
          if args.dataset_file == 'coco_base':
              root = Path('data/coco')
              img_folder = root / "train2017"
              ann_file = root / "annotations" / 'instances_train2017.json'
              return build(args, img_folder, ann_file, image_set, activated_class_ids=coco_base_class_ids, with_support=True)
          if args.dataset_file == 'voc':
              root = Path('data/voc')
              img_folder = root / "images"
              ann_file = root / "annotations" / 'pascal_trainval0712.json'
              return build(args, img_folder, ann_file, image_set, activated_class_ids=list(range(1, 20+1)), with_support=True)
          if args.dataset_file == 'voc_base1':
              root = Path('data/voc')
              img_folder = root / "images"
              ann_file = root / "annotations" / 'pascal_trainval0712.json'
              return build(args, img_folder, ann_file, image_set, activated_class_ids=voc_base1_class_ids, with_support=True)
          if args.dataset_file == 'voc_base2':
              root = Path('data/voc')
              img_folder = root / "images"
              ann_file = root / "annotations" / 'pascal_trainval0712.json'
              return build(args, img_folder, ann_file, image_set, activated_class_ids=voc_base2_class_ids, with_support=True)
          if args.dataset_file == 'voc_base3':
              root = Path('data/voc')
              img_folder = root / "images"
              ann_file = root / "annotations" / 'pascal_trainval0712.json'
              return build(args, img_folder, ann_file, image_set, activated_class_ids=voc_base3_class_ids, with_support=True)
  
      # For valid set, no need to perform base/novel category filtering.
      # This is because that evaluation should be performed on all images.
      # For valid set, we do not need support dataset.
      if image_set == 'val':
          if args.dataset_file in ['coco', 'coco_base']:
              root = Path('data/coco')
              img_folder = root / "val2017"
              ann_file = root / "annotations" / 'instances_val2017.json'
              class_ids = coco_base_class_ids + coco_novel_class_ids
              class_ids.sort()
              return build(args, img_folder, ann_file, image_set, activated_class_ids=class_ids, with_support=False)
          if args.dataset_file in ['voc', 'voc_base1', 'voc_base2', 'voc_base3']:
              root = Path('data/voc')
              img_folder = root / "images"
              ann_file = root / "annotations" / 'pascal_test2007.json'
              return build(args, img_folder, ann_file, image_set, activated_class_ids=list(range(1, 20+1)), with_support=False)
  
      if image_set == 'fewshot':
          if args.dataset_file in ['coco', 'coco_base']:
              class_ids = coco_base_class_ids + coco_novel_class_ids
              class_ids.sort()
              return build_fewshot(args, image_set, activated_class_ids=class_ids, with_support=True)
          if args.dataset_file in ['voc', 'voc_base1', 'voc_base2', 'voc_base3']:
              return build_fewshot(args, image_set, activated_class_ids=list(range(1, 20+1)), with_support=True)
  
      raise ValueError(f'{image_set} of dataset {args.dataset_file}  not supported.')
  