Full rework of the TF input/output embeddings and bias resizing (#9193)

* Start rework resizing

* Rework bias/decoder resizing

* Full resizing rework

* Full resizing rework

* Start to update the models with the new approach

* Finish to update the models

* Update all the tests

* Update the template

* Fix tests

* Fix tests

* Test a new approach

* Refactoring

* Refactoring

* Refactoring

* New rework

* Rework BART

* Rework bert+blenderbot

* Rework CTRL

* Rework Distilbert

* Rework DPR

* Rework Electra

* Rework Flaubert

* Rework Funnel

* Rework GPT2

* Rework Longformer

* Rework Lxmert

* Rework marian+mbart

* Rework mobilebert

* Rework mpnet

* Rework openai

* Rework pegasus

* Rework Roberta

* Rework T5

* Rework xlm+xlnet

* Rework template

* Fix TFT5EncoderOnly + DPRs

* Restore previous methods

* Fix Funnel

* Fix CTRL and TransforXL

* Apply style

* Apply Sylvain's comments

* Restore a test in DPR

* Address the comments

* Fix bug

* Apply style

* remove unused import

* Fix test

* Forgot a method

* missing test

* Trigger CI

* naming update

* Rebase

* Trigger CI

tests/test_modeling_tf_albert.py

@@ -274,14 +274,24 @@ class TFAlbertModelTest(TFModelTesterMixin, unittest.TestCase):
 
     def test_model_common_attributes(self):
         config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        list_lm_models = [TFAlbertForPreTraining, TFAlbertForMaskedLM]
 
         for model_class in self.all_model_classes:
             model = model_class(config)
             assert isinstance(model.get_input_embeddings(), tf.keras.layers.Layer)
-            x = model.get_output_layer_with_bias()
-            assert x is None
-            name = model.get_prefix_bias_name()
-            assert name is None
+
+            if model_class in list_lm_models:
+                x = model.get_output_embeddings()
+                assert isinstance(x, tf.keras.layers.Layer)
+                name = model.get_bias()
+                assert isinstance(name, dict)
+                for k, v in name.items():
+                    assert isinstance(v, tf.Variable)
+            else:
+                x = model.get_output_embeddings()
+                assert x is None
+                name = model.get_bias()
+                assert name is None
 
     @slow
     def test_model_from_pretrained(self):