diff --git a/src/transformers/models/flava/modeling_flava.py b/src/transformers/models/flava/modeling_flava.py
index 5acbad05c3..dbc4e51703 100644
--- a/src/transformers/models/flava/modeling_flava.py
+++ b/src/transformers/models/flava/modeling_flava.py
@@ -472,8 +472,6 @@ class FlavaSelfAttention(nn.Module):
 
         # Normalize the attention scores to probabilities.
         attention_probs = nn.functional.softmax(attention_scores, dim=-1)
-        # Normalize the attention scores to probabilities.
-        attention_probs = nn.functional.softmax(attention_scores, dim=-1)
 
         # This is actually dropping out entire tokens to attend to, which might
         # seem a bit unusual, but is taken from the original Transformer paper.
diff --git a/tests/models/flava/test_modeling_flava.py b/tests/models/flava/test_modeling_flava.py
index 7e067115e7..388e2f041f 100644
--- a/tests/models/flava/test_modeling_flava.py
+++ b/tests/models/flava/test_modeling_flava.py
@@ -1285,7 +1285,7 @@ class FlavaModelIntegrationTest(unittest.TestCase):
         # verify the embeddings
         self.assertAlmostEqual(outputs.image_embeddings.sum().item(), -1352.53540, places=4)
         self.assertAlmostEqual(outputs.text_embeddings.sum().item(), -198.98225, places=4)
-        self.assertAlmostEqual(outputs.multimodal_embeddings.sum().item(), -4030.4602050, places=4)
+        self.assertAlmostEqual(outputs.multimodal_embeddings.sum().item(), -4030.4604492, places=4)
 
 
 @require_vision