Add camembert integration tests (#3375)

* add integration tests for camembert

* use jplu/tf-camembert fro the moment

* make style

tests/test_modeling_tf_camembert.py

@@ -0,0 +1,50 @@
+# 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 .utils import require_tf, slow
+
+
+if is_tf_available():
+    import tensorflow as tf
+    import numpy as np
+    from transformers import TFCamembertModel
+
+
+@require_tf
+class TFCamembertModelIntegrationTest(unittest.TestCase):
+    @slow
+    def test_output_embeds_base_model(self):
+        model = TFCamembertModel.from_pretrained("jplu/tf-camembert-base")
+
+        input_ids = tf.convert_to_tensor(
+            [[5, 121, 11, 660, 16, 730, 25543, 110, 83, 6]], dtype=tf.int32,
+        )  # J'aime le camembert !"
+
+        output = model(input_ids)[0]
+        expected_shape = tf.TensorShape((1, 10, 768))
+        self.assertEqual(output.shape, expected_shape)
+        # compare the actual values for a slice.
+        expected_slice = tf.convert_to_tensor(
+            [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]], dtype=tf.float32,
+        )
+        # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
+        # camembert.eval()
+        # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()
+
+        self.assertTrue(np.allclose(output[:, :3, :3].numpy(), expected_slice.numpy(), atol=1e-4))