Use class decorator instead of superclass

When supplied by Keras deserialization, the config parameter to initializers
will be a dict. So intercept it and convert to PretrainedConfig object (and
store in instance attribute for get_config to get at it) before passing to the
actual initializer. To accomplish this, and repeat as little code as possible,
use a class decorator on TF*MainLayer classes.

tests/test_modeling_tf_common.py

@@ -22,7 +22,6 @@ import unittest
 from importlib import import_module
 
 from transformers import is_tf_available, is_torch_available
-from transformers.modeling_tf_utils import TFMainLayer
 
 from .utils import _tf_gpu_memory_limit, require_tf
 
@@ -90,6 +89,7 @@ class TFModelTesterMixin:
                 model.save_pretrained(tmpdirname)
                 model = model_class.from_pretrained(tmpdirname)
                 after_outputs = model(inputs_dict)
+
                 self.assert_outputs_same(after_outputs, outputs)
 
     def test_keras_save_load(self):
@@ -100,10 +100,14 @@ class TFModelTesterMixin:
             for model_class in self.all_model_classes
             for module in (import_module(model_class.__module__),)
             for module_member_name in dir(module)
+            if module_member_name.endswith("MainLayer")
             for module_member in (getattr(module, module_member_name),)
-            if isinstance(module_member, type) and TFMainLayer in module_member.__bases__
+            if isinstance(module_member, type) and tf.keras.layers.Layer in module_member.__bases__
         )
         for main_layer_class in tf_main_layer_classes:
+            if main_layer_class.__name__ == "TFT5MainLayer":
+                # Not really a “main layer” as in the other models, as this one doesn't receive the test inputs directly
+                continue
             main_layer = main_layer_class(config)
             symbolic_inputs = {
                 name: tf.keras.Input(tensor.shape[1:], dtype=tensor.dtype) for name, tensor in inputs_dict.items()
@@ -125,6 +129,7 @@ class TFModelTesterMixin:
         # Make sure we don't have nans
         out_1 = after_outputs[0].numpy()
         out_2 = outputs[0].numpy()
+        self.assertEqual(out_1.shape, out_2.shape)
         out_1 = out_1[~np.isnan(out_1)]
         out_2 = out_2[~np.isnan(out_2)]
         max_diff = np.amax(np.abs(out_1 - out_2))