Switch test files to the standard test_*.py scheme.

tests/test_modeling_tf_auto.py

@@ -0,0 +1,105 @@
+# 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.
+from __future__ import absolute_import, division, print_function
+
+import logging
+import unittest
+
+from transformers import is_tf_available
+
+from .utils import SMALL_MODEL_IDENTIFIER, require_tf, slow
+
+
+if is_tf_available():
+    from transformers import (
+        AutoConfig,
+        BertConfig,
+        TFAutoModel,
+        TFBertModel,
+        TFAutoModelWithLMHead,
+        TFBertForMaskedLM,
+        TFAutoModelForSequenceClassification,
+        TFBertForSequenceClassification,
+        TFAutoModelForQuestionAnswering,
+        TFBertForQuestionAnswering,
+    )
+
+
+@require_tf
+class TFAutoModelTest(unittest.TestCase):
+    @slow
+    def test_model_from_pretrained(self):
+        import h5py
+
+        self.assertTrue(h5py.version.hdf5_version.startswith("1.10"))
+
+        logging.basicConfig(level=logging.INFO)
+        # for model_name in list(TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
+        for model_name in ["bert-base-uncased"]:
+            config = AutoConfig.from_pretrained(model_name)
+            self.assertIsNotNone(config)
+            self.assertIsInstance(config, BertConfig)
+
+            model = TFAutoModel.from_pretrained(model_name)
+            self.assertIsNotNone(model)
+            self.assertIsInstance(model, TFBertModel)
+
+    @slow
+    def test_lmhead_model_from_pretrained(self):
+        logging.basicConfig(level=logging.INFO)
+        # for model_name in list(TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
+        for model_name in ["bert-base-uncased"]:
+            config = AutoConfig.from_pretrained(model_name)
+            self.assertIsNotNone(config)
+            self.assertIsInstance(config, BertConfig)
+
+            model = TFAutoModelWithLMHead.from_pretrained(model_name)
+            self.assertIsNotNone(model)
+            self.assertIsInstance(model, TFBertForMaskedLM)
+
+    @slow
+    def test_sequence_classification_model_from_pretrained(self):
+        logging.basicConfig(level=logging.INFO)
+        # for model_name in list(TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
+        for model_name in ["bert-base-uncased"]:
+            config = AutoConfig.from_pretrained(model_name)
+            self.assertIsNotNone(config)
+            self.assertIsInstance(config, BertConfig)
+
+            model = TFAutoModelForSequenceClassification.from_pretrained(model_name)
+            self.assertIsNotNone(model)
+            self.assertIsInstance(model, TFBertForSequenceClassification)
+
+    @slow
+    def test_question_answering_model_from_pretrained(self):
+        logging.basicConfig(level=logging.INFO)
+        # for model_name in list(TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
+        for model_name in ["bert-base-uncased"]:
+            config = AutoConfig.from_pretrained(model_name)
+            self.assertIsNotNone(config)
+            self.assertIsInstance(config, BertConfig)
+
+            model = TFAutoModelForQuestionAnswering.from_pretrained(model_name)
+            self.assertIsNotNone(model)
+            self.assertIsInstance(model, TFBertForQuestionAnswering)
+
+    def test_from_pretrained_identifier(self):
+        logging.basicConfig(level=logging.INFO)
+        model = TFAutoModelWithLMHead.from_pretrained(SMALL_MODEL_IDENTIFIER)
+        self.assertIsInstance(model, TFBertForMaskedLM)
+
+
+if __name__ == "__main__":
+    unittest.main()