text: import os
  
  from rasa.core.test import _generate_trackers, collect_story_predictions, test
  
  # we need this import to ignore the warning...
  # noinspection PyUnresolvedReferences
  from rasa.nlu.test import run_evaluation
  from tests.core.conftest import (
      DEFAULT_STORIES_FILE,
      E2E_STORY_FILE_UNKNOWN_ENTITY,
      END_TO_END_STORY_FILE,
  )
  
  
  async def test_evaluation_image_creation(tmpdir, default_agent):
      stories_path = os.path.join(tmpdir.strpath, "failed_stories.md")
      img_path = os.path.join(tmpdir.strpath, "story_confmat.pdf")
  
      await test(
          stories=DEFAULT_STORIES_FILE,
          agent=default_agent,
          out_directory=tmpdir.strpath,
          max_stories=None,
          e2e=False,
      )
  
      assert os.path.isfile(img_path)
      assert os.path.isfile(stories_path)
  
  
  async def test_action_evaluation_script(tmpdir, default_agent):
      completed_trackers = await _generate_trackers(
          DEFAULT_STORIES_FILE, default_agent, use_e2e=False
      )
      story_evaluation, num_stories = collect_story_predictions(
          completed_trackers, default_agent, use_e2e=False
      )
  
      assert not story_evaluation.evaluation_store.has_prediction_target_mismatch()
      assert len(story_evaluation.failed_stories) == 0
      assert num_stories == 3
  
  
  async def test_end_to_end_evaluation_script(tmpdir, default_agent):
      completed_trackers = await _generate_trackers(
          END_TO_END_STORY_FILE, default_agent, use_e2e=True
      )
  
      story_evaluation, num_stories = collect_story_predictions(
          completed_trackers, default_agent, use_e2e=True
      )
  
      assert not story_evaluation.evaluation_store.has_prediction_target_mismatch()
      assert len(story_evaluation.failed_stories) == 0
      assert num_stories == 2
  
  
  async def test_end_to_end_evaluation_script_unknown_entity(tmpdir, default_agent):
      completed_trackers = await _generate_trackers(
          E2E_STORY_FILE_UNKNOWN_ENTITY, default_agent, use_e2e=True
      )
  
      story_evaluation, num_stories = collect_story_predictions(
          completed_trackers, default_agent, use_e2e=True
      )
  
      assert story_evaluation.evaluation_store.has_prediction_target_mismatch()
      assert len(story_evaluation.failed_stories) == 1
      assert num_stories == 1
  