implementing tflxmertmodel integration test (#12497)

* implementing tflxmertmodel integration test

* move import

* revert and fix

tests/test_modeling_tf_lxmert.py

@@ -17,6 +17,8 @@ import os
 import tempfile
 import unittest
 
+import numpy as np
+
 from transformers import LxmertConfig, is_tf_available
 from transformers.testing_utils import require_tf, slow
 
@@ -555,8 +557,6 @@ class TFLxmertModelTest(TFModelTesterMixin, unittest.TestCase):
             tf_hidden_states = tfo[0].numpy()
             pt_hidden_states = pto[0].numpy()
 
-            import numpy as np
-
             tf_nans = np.copy(np.isnan(tf_hidden_states))
             pt_nans = np.copy(np.isnan(pt_hidden_states))
 
@@ -768,3 +768,30 @@ class TFLxmertModelTest(TFModelTesterMixin, unittest.TestCase):
 
                 for attention, attention_shape in zip(attentions, attention_shapes):
                     self.assertListEqual(list(attention[0].shape[-3:]), attention_shape)
+
+
+@require_tf
+class TFLxmertModelIntegrationTest(unittest.TestCase):
+    @slow
+    def test_inference_masked_lm(self):
+        model = TFLxmertModel.from_pretrained("unc-nlp/lxmert-base-uncased")
+        input_ids = tf.constant([[101, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 102]])
+
+        num_visual_features = 10
+        _, visual_feats = np.random.seed(0), np.random.rand(1, num_visual_features, model.config.visual_feat_dim)
+        _, visual_pos = np.random.seed(0), np.random.rand(1, num_visual_features, 4)
+        visual_feats = tf.convert_to_tensor(visual_feats, dtype=tf.float32)
+        visual_pos = tf.convert_to_tensor(visual_pos, dtype=tf.float32)
+        output = model(input_ids, visual_feats=visual_feats, visual_pos=visual_pos)[0]
+        expected_shape = [1, 11, 768]
+        self.assertEqual(expected_shape, output.shape)
+        expected_slice = tf.constant(
+            [
+                [
+                    [0.24170142, -0.98075, 0.14797261],
+                    [1.2540525, -0.83198136, 0.5112344],
+                    [1.4070463, -1.1051831, 0.6990401],
+                ]
+            ]
+        )
+        tf.debugging.assert_near(output[:, :3, :3], expected_slice, atol=1e-4)