def test_small_v1_1_integration_test(self):
        """
        For comparision run:
        >>> import t5  # pip install t5==0.7.1
        >>> from t5.data.sentencepiece_vocabulary import SentencePieceVocabulary

        >>> path_to_mtf_small_t5_v1_1_checkpoint = '<fill_in>'
        >>> path_to_mtf_small_spm_model_path = '<fill_in>'
        >>> t5_model = t5.models.MtfModel(model_dir=path_to_mtf_small_t5_v1_1_checkpoint, batch_size=1, tpu=None)
        >>> vocab = SentencePieceVocabulary(path_to_mtf_small_spm_model_path, extra_ids=100)
        >>> score = t5_model.score(inputs=["Hello there"], targets=["Hi I am"], vocabulary=vocab)
        """

        model = T5ForConditionalGeneration.from_pretrained("google/t5-v1_1-small").to(torch_device)
        tokenizer = T5Tokenizer.from_pretrained("google/t5-v1_1-small")

        input_ids = tokenizer("Hello there", return_tensors="pt").input_ids
        labels = tokenizer("Hi I am", return_tensors="pt").input_ids

        loss = model(input_ids.to(torch_device), labels=labels.to(torch_device)).loss
        mtf_score = -(labels.shape[-1] * loss.item())

        EXPECTED_SCORE = -59.0293
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4)

        For comparision run:
        >>> import t5  # pip install t5==0.7.1
        >>> from t5.data.sentencepiece_vocabulary import SentencePieceVocabulary

        >>> path_to_mtf_small_t5_v1_1_checkpoint = '<fill_in>'
        >>> path_to_mtf_small_spm_model_path = '<fill_in>'
        >>> t5_model = t5.models.MtfModel(model_dir=path_to_mtf_small_t5_v1_1_checkpoint, batch_size=1, tpu=None)
        >>> vocab = SentencePieceVocabulary(path_to_mtf_small_spm_model_path, extra_ids=100)
        >>> score = t5_model.score(inputs=["Hello there"], targets=["Hi I am"], vocabulary=vocab)
        
dpkd/transformers/tests/models/t5/test_modeling_t5.py