code: def test_data_preprocessor_with_data_augmentation():
      data_augmenter_spec = DataAugmenterSpec(
          perturbation_specs=[
              PerturbationSpec(
                  class_name="helm.benchmark.augmentations.extra_space_perturbation.ExtraSpacePerturbation",
                  args={"num_spaces": 5},
              )
          ],
          should_augment_train_instances=False,
          should_include_original_train=False,
          should_augment_eval_instances=True,
          should_include_original_eval=True,
      )
      data_preprocessor = DataPreprocessor(data_augmenter_spec)
      scenario: Scenario = create_scenario(get_scenario_spec1())
      instances = with_instance_ids(scenario.get_instances())
      instances: List[Instance] = data_preprocessor.preprocess(
          instances
      )
      assert (
          len(instances) == 10 + 10 + 10
      )  # original train + original eval + perturbed eval
  
      # After the data preprocessing, check that the data augmentation has been applied
      # by verifying that the instances with the perturbation tag are perturbed
      for instance in instances:
          if (
              instance.perturbation
              and instance.perturbation.name == "extra_space"
          ):
              assert " " * 5 in instance.input.text
          else:
              assert " " * 5 not in instance.input.text
  
file_path: H2O/h2o_hf/helm/src/helm/benchmark/test_data_preprocessor.py
imports: from typing import List
  from .augmentations.data_augmenter import DataAugmenterSpec
  from .augmentations.perturbation import PerturbationSpec
  from .data_preprocessor import DataPreprocessor
  from .run_specs import get_scenario_spec1
  from .scenarios.scenario import create_scenario, Instance, Scenario, with_instance_ids
label_desc: Use image node_id 2 for calling a global function with example usage: test_data_preprocessor_with_data_augmentation() without return types