def test_small_byt5_integration_test(self):
        """
        For comparision run:
        >>> import t5  # pip install t5==0.9.1

        >>> path_to_byt5_small_checkpoint = '<fill_in>'
        >>> t5_model = t5.models.MtfModel(model_dir=path_to_tf_checkpoint, batch_size=1, tpu=None)
        >>> vocab = t5.data.ByteVocabulary()
        >>> score = t5_model.score(inputs=["Hello there"], targets=["Hi I am"], vocabulary=vocab)
        """

        model = FlaxT5ForConditionalGeneration.from_pretrained("google/byt5-small")
        tokenizer = ByT5Tokenizer.from_pretrained("google/byt5-small")

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

        decoder_input_ids = shift_tokens_right(labels, model.config.pad_token_id, model.config.decoder_start_token_id)

        logits = model(input_ids, decoder_input_ids=decoder_input_ids).logits
        loss = optax.softmax_cross_entropy(logits, onehot(labels, logits.shape[-1])).mean()

        mtf_score = -(labels.shape[-1] * loss.item())

        EXPECTED_SCORE = -60.7397
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4)

        For comparision run:
        >>> import t5  # pip install t5==0.9.1

        >>> path_to_byt5_small_checkpoint = '<fill_in>'
        >>> t5_model = t5.models.MtfModel(model_dir=path_to_tf_checkpoint, batch_size=1, tpu=None)
        >>> vocab = t5.data.ByteVocabulary()
        >>> score = t5_model.score(inputs=["Hello there"], targets=["Hi I am"], vocabulary=vocab)
        
dpkd/transformers/tests/models/t5/test_modeling_flax_t5.py