Added test for empty data

tests/nlu/classifiers/test_diet_classifier.py

@@ -1003,3 +1003,32 @@ async def test_dropping_of_last_partial_batch(
     data_generator, _ = train_utils.create_data_generators(model_data, batch_size, 1)
 
     assert len(data_generator) == expected_num_batches
+
+
+@pytest.mark.timeout(120, func_only=True)
+async def test_dropping_of_last_partial_batch_empty_data(
+    create_diet: Callable[..., DIETClassifier],
+    train_and_preprocess: Callable[..., Tuple[TrainingData, List[GraphComponent]]],
+):
+    """test that diets data processing produces the right amount of batches.
+
+    We introduced a change to only keep the last incomplete batch if
+    1. it has more than 50% of examples of batch size
+    2. or it is the only batch in the epoch
+    """
+
+    pipeline = [
+        {"component": WhitespaceTokenizer},
+        {"component": CountVectorsFeaturizer},
+    ]
+    diet = create_diet(
+        {ENTITY_RECOGNITION: False, RANDOM_SEED: 1, EPOCHS: 1, RUN_EAGERLY: True}
+    )
+    training_data, loaded_pipeline = train_and_preprocess(
+        pipeline, training_data=TrainingData()
+    )
+
+    model_data = diet.preprocess_train_data(training_data)
+    data_generator, _ = train_utils.create_data_generators(model_data, 64, 1)
+
+    assert len(data_generator) == 0