text: # coding=utf-8
  # Copyright 2018 The Google AI Language Team Authors.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  import unittest
  
  from transformers import is_tf_available
  from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
  
  
  if is_tf_available():
      import tensorflow as tf
  
      from transformers import AutoTokenizer, TFAutoModelForSeq2SeqLM
  
  
  @require_tf
  @require_sentencepiece
  @require_tokenizers
  class TFMT5ModelIntegrationTest(unittest.TestCase):
      @slow
      def test_small_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_mt5_checkpoint = '<fill_in>'
          >>> path_to_mtf_small_mt5_spm_model_path = '<fill_in>'
          >>> t5_model = t5.models.MtfModel(model_dir=path_to_mtf_small_mt5_checkpoint, batch_size=1, tpu=None)
          >>> vocab = SentencePieceVocabulary(path_to_mtf_small_mt5_spm_model_path, extra_ids=100)
          >>> score = t5_model.score(inputs=["Hello there"], targets=["Hi I am"], vocabulary=vocab)
          """
  
          model = TFAutoModelForSeq2SeqLM.from_pretrained("google/mt5-small")
          tokenizer = AutoTokenizer.from_pretrained("google/mt5-small")
  
          input_ids = tokenizer("Hello there", return_tensors="tf").input_ids
          labels = tokenizer("Hi I am", return_tensors="tf").input_ids
  
          loss = model(input_ids, labels=labels).loss
          mtf_score = -tf.math.reduce_sum(loss).numpy()
  
          EXPECTED_SCORE = -84.9127
          self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4)
  