T5 & mT5 (#8552)

* add mt5 and t5v1_1 model

* fix tests

* correct some imports

* add tf model

* finish tf t5

* improve examples

* fix copies

* clean doc

tests/test_modeling_tf_mt5.py

@@ -0,0 +1,56 @@
+# 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)