content: import shutil
  
  from batchgenerators.utilities.file_and_folder_operations import *
  from nnunetv2.paths import nnUNet_raw
  from nnunetv2.dataset_conversion.generate_dataset_json import generate_dataset_json
  
  if __name__ == '__main__':
      downloaded_amos_dir = '/home/isensee/amos22/amos22' # downloaded and extracted from https://zenodo.org/record/7155725#.Y0OOCOxBztM
  
      target_dataset_id = 223
      target_dataset_name = f'Dataset{target_dataset_id:3.0f}_AMOS2022postChallenge'
  
      maybe_mkdir_p(join(nnUNet_raw, target_dataset_name))
      imagesTr = join(nnUNet_raw, target_dataset_name, 'imagesTr')
      imagesTs = join(nnUNet_raw, target_dataset_name, 'imagesTs')
      labelsTr = join(nnUNet_raw, target_dataset_name, 'labelsTr')
      maybe_mkdir_p(imagesTr)
      maybe_mkdir_p(imagesTs)
      maybe_mkdir_p(labelsTr)
  
      train_identifiers = []
      # copy images
      source = join(downloaded_amos_dir, 'imagesTr')
      source_files = nifti_files(source, join=False)
      train_identifiers += source_files
      for s in source_files:
          shutil.copy(join(source, s), join(imagesTr, s[:-7] + '_0000.nii.gz'))
  
      source = join(downloaded_amos_dir, 'imagesVa')
      source_files = nifti_files(source, join=False)
      train_identifiers += source_files
      for s in source_files:
          shutil.copy(join(source, s), join(imagesTr, s[:-7] + '_0000.nii.gz'))
  
      source = join(downloaded_amos_dir, 'imagesTs')
      source_files = nifti_files(source, join=False)
      for s in source_files:
          shutil.copy(join(source, s), join(imagesTs, s[:-7] + '_0000.nii.gz'))
  
      # copy labels
      source = join(downloaded_amos_dir, 'labelsTr')
      source_files = nifti_files(source, join=False)
      for s in source_files:
          shutil.copy(join(source, s), join(labelsTr, s))
  
      source = join(downloaded_amos_dir, 'labelsVa')
      source_files = nifti_files(source, join=False)
      for s in source_files:
          shutil.copy(join(source, s), join(labelsTr, s))
  
      old_dataset_json = load_json(join(downloaded_amos_dir, 'dataset.json'))
      new_labels = {v: k for k, v in old_dataset_json['labels'].items()}
  
      generate_dataset_json(join(nnUNet_raw, target_dataset_name), {0: 'nonCT'}, new_labels,
                            num_training_cases=len(train_identifiers), file_ending='.nii.gz', regions_class_order=None,
                            dataset_name=target_dataset_name, reference='https://zenodo.org/record/7155725#.Y0OOCOxBztM',
                            license=old_dataset_json['licence'],  # typo in OG dataset.json
                            description=old_dataset_json['description'],
                            release=old_dataset_json['release'])
  