class_docstr: Basic tests to count the number of examples/episodes and to check a few utterances.
  
  # Counting num episodes (from the original internal copy of the data)
  cat /checkpoint/parlai/tasks/empathetic_dialogues/train.csv | grep -E '^hit:[0-9]+_conv:[0-9]+,2' | wc  # 19531
  cat /checkpoint/parlai/tasks/empathetic_dialogues/train.csv | grep -E '^hit:[0-9]+_conv:[0-9]+,(2|3)' | wc  # 39057
  cat /checkpoint/parlai/tasks/empathetic_dialogues/valid_random_cands.csv | grep -E '^hit:[0-9]+_conv:[0-9]+,2' | wc  # 2769
  cat /checkpoint/parlai/tasks/empathetic_dialogues/test_random_cands.csv | grep -E '^hit:[0-9]+_conv:[0-9]+,2' | wc  # 2547
  # We count the number of lines with turn_idx=2 because this means that we have at
  # least one full utterance in the conversation. For train_experiencer_only==False,
  # we also include turn_idx=3 to count the Listener-based conversations in the same
  # manner.
  
  # Count num examples (from the original internal copy of the data)
  grep -E 'hit:[0-9]+_conv:[0-9]+,(2|4|6|8|10|12),' /checkpoint/parlai/tasks/empathetic_dialogues/train.csv | wc  # 40254
  grep -E 'hit:[0-9]+_conv:[0-9]+,(2|3|4|5|6|7|8|9|10|11|12),' /checkpoint/parlai/tasks/empathetic_dialogues/train.csv | wc  # 64636 (--train-experiencer-only False)
  grep -E 'hit:[0-9]+_conv:[0-9]+,(2|4|6|8|10|12),' /checkpoint/parlai/tasks/empathetic_dialogues/valid_random_cands.csv | wc  # 5738
  grep -E 'hit:[0-9]+_conv:[0-9]+,(2|4|6|8|10|12),' /checkpoint/parlai/tasks/empathetic_dialogues/test_random_cands.csv | wc  # 5259
code: def test_counts(self):
      with testing_utils.tempdir() as tmpdir:
          data_path = tmpdir
  
          # Check EmpatheticDialoguesTeacher, with multiple examples per episode
          opts_episodes_and_examples = [
              (
                  {"datatype": "train"},
                  EPISODE_COUNTS["train_both_sides"],
                  EXAMPLE_COUNTS["train_both_sides"],
              ),  # Test the default mode
              (
                  {"datatype": "train", "train_experiencer_only": True},
                  EPISODE_COUNTS["train_experiencer_only"],
                  EXAMPLE_COUNTS["train_experiencer_only"],
              ),
              (
                  {
                      "datatype": "train",
                      "train_experiencer_only": False,
                  },
                  EPISODE_COUNTS["train_both_sides"],
                  EXAMPLE_COUNTS["train_both_sides"],
              ),
              (
                  {"datatype": "valid"},
                  EPISODE_COUNTS["valid"],
                  EXAMPLE_COUNTS["valid"],
              ),
              (
                  {"datatype": "test"},
                  EPISODE_COUNTS["test"],
                  EXAMPLE_COUNTS["test"],
              ),
          ]
          for teacher_class in [EmpatheticDialoguesTeacher]:
              for (
                  opt,
                  num_episodes,
                  num_examples,
              ) in opts_episodes_and_examples:
                  full_opt = Opt({**opt, "datapath": data_path})
                  teacher = teacher_class(full_opt)
                  self.assertEqual(teacher.num_episodes(), num_episodes)
                  self.assertEqual(teacher.num_examples(), num_examples)
  
file_path: ParlAI/tests/tasks/test_empathetic_dialogues.py
imports: import unittest
  from parlai.core.opt import Opt
  from parlai.tasks.empathetic_dialogues.agents import EmpatheticDialoguesTeacher
  from parlai.utils import testing
label_desc: Use image node_id 1 for calling the TestEDTeacher obj's underlying member method code with example usage: obj.test_counts() without return types