Replace build() with build_in_name_scope() for some TF tests (#28046)

Replace build() with build_in_name_scope() for some tests

tests/models/bart/test_modeling_tf_bart.py

@@ -304,7 +304,7 @@ def test_save_load_after_resize_token_embeddings(self):
             old_total_size = config.vocab_size
             new_total_size = old_total_size + new_tokens_size
             model = model_class(config=copy.deepcopy(config))  # `resize_token_embeddings` mutates `config`
-            model.build()
+            model.build_in_name_scope()
             model.resize_token_embeddings(new_total_size)
 
             # fetch the output for an input exclusively made of new members of the vocabulary

tests/models/ctrl/test_modeling_tf_ctrl.py

@@ -225,7 +225,7 @@ def test_model_common_attributes(self):
 
         for model_class in self.all_model_classes:
             model = model_class(config)
-            model.build()  # may be needed for the get_bias() call below
+            model.build_in_name_scope()  # may be needed for the get_bias() call below
             assert isinstance(model.get_input_embeddings(), tf.keras.layers.Layer)
 
             if model_class in list_lm_models:

tests/test_modeling_tf_common.py

@@ -316,7 +316,7 @@ def test_onnx_compliancy(self):
 
             with tf.Graph().as_default() as g:
                 model = model_class(config)
-                model.build()
+                model.build_in_name_scope()
 
                 for op in g.get_operations():
                     model_op_names.add(op.node_def.op)
@@ -346,7 +346,7 @@ def test_onnx_runtime_optimize(self):
 
         for model_class in self.all_model_classes[:2]:
             model = model_class(config)
-            model.build()
+            model.build_in_name_scope()
 
             onnx_model_proto, _ = tf2onnx.convert.from_keras(model, opset=self.onnx_min_opset)
 
@@ -1088,7 +1088,7 @@ def test_resize_token_embeddings(self):
         def _get_word_embedding_weight(model, embedding_layer):
             if isinstance(embedding_layer, tf.keras.layers.Embedding):
                 # builds the embeddings layer
-                model.build()
+                model.build_in_name_scope()
                 return embedding_layer.embeddings
             else:
                 return model._get_word_embedding_weight(embedding_layer)
@@ -1151,7 +1151,7 @@ def test_save_load_after_resize_token_embeddings(self):
             old_total_size = config.vocab_size
             new_total_size = old_total_size + new_tokens_size
             model = model_class(config=copy.deepcopy(config))  # `resize_token_embeddings` mutates `config`
-            model.build()
+            model.build_in_name_scope()
             model.resize_token_embeddings(new_total_size)
 
             # fetch the output for an input exclusively made of new members of the vocabulary

tests/test_modeling_tf_utils.py

@@ -402,8 +402,8 @@ def test_checkpoint_sharding_local(self):
                 # Finally, check the model can be reloaded
                 new_model = TFBertModel.from_pretrained(tmp_dir)
 
-                model.build()
-                new_model.build()
+                model.build_in_name_scope()
+                new_model.build_in_name_scope()
 
                 for p1, p2 in zip(model.weights, new_model.weights):
                     self.assertTrue(np.allclose(p1.numpy(), p2.numpy()))
@@ -632,7 +632,7 @@ def test_push_to_hub(self):
         )
         model = TFBertModel(config)
         # Make sure model is properly initialized
-        model.build()
+        model.build_in_name_scope()
 
         logging.set_verbosity_info()
         logger = logging.get_logger("transformers.utils.hub")
@@ -701,7 +701,7 @@ def test_push_to_hub_in_organization(self):
         )
         model = TFBertModel(config)
         # Make sure model is properly initialized
-        model.build()
+        model.build_in_name_scope()
 
         model.push_to_hub("valid_org/test-model-tf-org", token=self._token)