text: import bisect
  import math
  from collections import defaultdict
  
  import numpy as np
  from mmcv.utils import print_log
  from torch.utils.data.dataset import ConcatDataset as _ConcatDataset
  
  from .builder import DATASETS
  from .coco import CocoDataset
  
  
  @DATASETS.register_module()
  class ConcatDataset(_ConcatDataset):
      """A wrapper of concatenated dataset.
  
      Same as :obj:`torch.utils.data.dataset.ConcatDataset`, but
      concat the group flag for image aspect ratio.
  
      Args:
          datasets (list[:obj:`Dataset`]): A list of datasets.
          separate_eval (bool): Whether to evaluate the results
              separately if it is used as validation dataset.
              Defaults to True.
      """
  
      def __init__(self, datasets, separate_eval=True):
          super(ConcatDataset, self).__init__(datasets)
          self.CLASSES = datasets[0].CLASSES
          self.separate_eval = separate_eval
          if not separate_eval:
              if any([isinstance(ds, CocoDataset) for ds in datasets]):
                  raise NotImplementedError(
                      'Evaluating concatenated CocoDataset as a whole is not'
                      ' supported! Please set "separate_eval=True"')
              elif len(set([type(ds) for ds in datasets])) != 1:
                  raise NotImplementedError(
                      'All the datasets should have same types')
  
          if hasattr(datasets[0], 'flag'):
              flags = []
              for i in range(0, len(datasets)):
                  flags.append(datasets[i].flag)
              self.flag = np.concatenate(flags)
  
      def get_cat_ids(self, idx):
          """Get category ids of concatenated dataset by index.
  
          Args:
              idx (int): Index of data.
  
          Returns:
              list[int]: All categories in the image of specified index.
          """
  
          if idx < 0:
              if -idx > len(self):
                  raise ValueError(
                      'absolute value of index should not exceed dataset length')
              idx = len(self) + idx
          dataset_idx = bisect.bisect_right(self.cumulative_sizes, idx)
          if dataset_idx == 0:
              sample_idx = idx
          else:
              sample_idx = idx - self.cumulative_sizes[dataset_idx - 1]
          return self.datasets[dataset_idx].get_cat_ids(sample_idx)
  
      def evaluate(self, results, logger=None, **kwargs):
          """Evaluate the results.
  
          Args:
              results (list[list | tuple]): Testing results of the dataset.
              logger (logging.Logger | str | None): Logger used for printing
                  related information during evaluation. Default: None.
  
          Returns:
              dict[str: float]: AP results of the total dataset or each separate
              dataset if `self.separate_eval=True`.
          """
          assert len(results) == self.cumulative_sizes[-1], \
              ('Dataset and results have different sizes: '
               f'{self.cumulative_sizes[-1]} v.s. {len(results)}')
  
          # Check whether all the datasets support evaluation
          for dataset in self.datasets:
              assert hasattr(dataset, 'evaluate'), \
                      f'{type(dataset)} does not implement evaluate function'
  
          if self.separate_eval:
              dataset_idx = -1
              total_eval_results = dict()
              for size, dataset in zip(self.cumulative_sizes, self.datasets):
                  start_idx = 0 if dataset_idx == -1 else \
                      self.cumulative_sizes[dataset_idx]
                  end_idx = self.cumulative_sizes[dataset_idx + 1]
  
                  results_per_dataset = results[start_idx:end_idx]
                  print_log(
                      f'\nEvaluateing {dataset.ann_file} with '
                      f'{len(results_per_dataset)} images now',
                      logger=logger)
  
                  eval_results_per_dataset = dataset.evaluate(
                      results_per_dataset, logger=logger, **kwargs)
                  dataset_idx += 1
                  for k, v in eval_results_per_dataset.items():
                      total_eval_results.update({f'{dataset_idx}_{k}': v})
  
              return total_eval_results
          elif any([isinstance(ds, CocoDataset) for ds in self.datasets]):
              raise NotImplementedError(
                  'Evaluating concatenated CocoDataset as a whole is not'
                  ' supported! Please set "separate_eval=True"')
          elif len(set([type(ds) for ds in self.datasets])) != 1:
              raise NotImplementedError(
                  'All the datasets should have same types')
          else:
              original_data_infos = self.datasets[0].data_infos
              self.datasets[0].data_infos = sum(
                  [dataset.data_infos for dataset in self.datasets], [])
              eval_results = self.datasets[0].evaluate(
                  results, logger=logger, **kwargs)
              self.datasets[0].data_infos = original_data_infos
              return eval_results
  
  
  @DATASETS.register_module()
  class RepeatDataset(object):
      """A wrapper of repeated dataset.
  
      The length of repeated dataset will be `times` larger than the original
      dataset. This is useful when the data loading time is long but the dataset
      is small. Using RepeatDataset can reduce the data loading time between
      epochs.
  
      Args:
          dataset (:obj:`Dataset`): The dataset to be repeated.
          times (int): Repeat times.
      """
  
      def __init__(self, dataset, times):
          self.dataset = dataset
          self.times = times
          self.CLASSES = dataset.CLASSES
          if hasattr(self.dataset, 'flag'):
              self.flag = np.tile(self.dataset.flag, times)
  
          self._ori_len = len(self.dataset)
  
      def __getitem__(self, idx):
          return self.dataset[idx % self._ori_len]
  
      def get_cat_ids(self, idx):
          """Get category ids of repeat dataset by index.
  
          Args:
              idx (int): Index of data.
  
          Returns:
              list[int]: All categories in the image of specified index.
          """
  
          return self.dataset.get_cat_ids(idx % self._ori_len)
  
      def __len__(self):
          """Length after repetition."""
          return self.times * self._ori_len
  
  
  # Modified from https://github.com/facebookresearch/detectron2/blob/41d475b75a230221e21d9cac5d69655e3415e3a4/detectron2/data/samplers/distributed_sampler.py#L57 # noqa
  @DATASETS.register_module()
  class ClassBalancedDataset(object):
      """A wrapper of repeated dataset with repeat factor.
  
      Suitable for training on class imbalanced datasets like LVIS. Following
      the sampling strategy in the `paper <https://arxiv.org/abs/1908.03195>`_,
      in each epoch, an image may appear multiple times based on its
      "repeat factor".
      The repeat factor for an image is a function of the frequency the rarest
      category labeled in that image. The "frequency of category c" in [0, 1]
      is defined by the fraction of images in the training set (without repeats)
      in which category c appears.
      The dataset needs to instantiate :func:`self.get_cat_ids` to support
      ClassBalancedDataset.
  
      The repeat factor is computed as followed.
  
      1. For each category c, compute the fraction # of images
         that contain it: :math:`f(c)`
      2. For each category c, compute the category-level repeat factor:
         :math:`r(c) = max(1, sqrt(t/f(c)))`
      3. For each image I, compute the image-level repeat factor:
         :math:`r(I) = max_{c in I} r(c)`
  
      Args:
          dataset (:obj:`CustomDataset`): The dataset to be repeated.
          oversample_thr (float): frequency threshold below which data is
              repeated. For categories with ``f_c >= oversample_thr``, there is
              no oversampling. For categories with ``f_c < oversample_thr``, the
              degree of oversampling following the square-root inverse frequency
              heuristic above.
          filter_empty_gt (bool, optional): If set true, images without bounding
              boxes will not be oversampled. Otherwise, they will be categorized
              as the pure background class and involved into the oversampling.
              Default: True.
      """
  
      def __init__(self, dataset, oversample_thr, filter_empty_gt=True):
          self.dataset = dataset
          self.oversample_thr = oversample_thr
          self.filter_empty_gt = filter_empty_gt
          self.CLASSES = dataset.CLASSES
  
          repeat_factors = self._get_repeat_factors(dataset, oversample_thr)
          repeat_indices = []
          for dataset_idx, repeat_factor in enumerate(repeat_factors):
              repeat_indices.extend([dataset_idx] * math.ceil(repeat_factor))
          self.repeat_indices = repeat_indices
  
          flags = []
          if hasattr(self.dataset, 'flag'):
              for flag, repeat_factor in zip(self.dataset.flag, repeat_factors):
                  flags.extend([flag] * int(math.ceil(repeat_factor)))
              assert len(flags) == len(repeat_indices)
          self.flag = np.asarray(flags, dtype=np.uint8)
  
      def _get_repeat_factors(self, dataset, repeat_thr):
          """Get repeat factor for each images in the dataset.
  
          Args:
              dataset (:obj:`CustomDataset`): The dataset
              repeat_thr (float): The threshold of frequency. If an image
                  contains the categories whose frequency below the threshold,
                  it would be repeated.
  
          Returns:
              list[float]: The repeat factors for each images in the dataset.
          """
  
          # 1. For each category c, compute the fraction # of images
          #   that contain it: f(c)
          category_freq = defaultdict(int)
          num_images = len(dataset)
          for idx in range(num_images):
              cat_ids = set(self.dataset.get_cat_ids(idx))
              if len(cat_ids) == 0 and not self.filter_empty_gt:
                  cat_ids = set([len(self.CLASSES)])
              for cat_id in cat_ids:
                  category_freq[cat_id] += 1
          for k, v in category_freq.items():
              category_freq[k] = v / num_images
  
          # 2. For each category c, compute the category-level repeat factor:
          #    r(c) = max(1, sqrt(t/f(c)))
          category_repeat = {
              cat_id: max(1.0, math.sqrt(repeat_thr / cat_freq))
              for cat_id, cat_freq in category_freq.items()
          }
  
          # 3. For each image I, compute the image-level repeat factor:
          #    r(I) = max_{c in I} r(c)
          repeat_factors = []
          for idx in range(num_images):
              cat_ids = set(self.dataset.get_cat_ids(idx))
              if len(cat_ids) == 0 and not self.filter_empty_gt:
                  cat_ids = set([len(self.CLASSES)])
              repeat_factor = 1
              if len(cat_ids) > 0:
                  repeat_factor = max(
                      {category_repeat[cat_id]
                       for cat_id in cat_ids})
              repeat_factors.append(repeat_factor)
  
          return repeat_factors
  
      def __getitem__(self, idx):
          ori_index = self.repeat_indices[idx]
          return self.dataset[ori_index]
  
      def __len__(self):
          """Length after repetition."""
          return len(self.repeat_indices)
  