Use name transformers_config in Keras serialization

Be explicit that this is config for the transformers package (as these
layers may coexist with other custom stuff in a Keras model, plus the
Keras container itself is called config, and config["config"] is not
great)

Add explicit error handling for initializer calls that have neither
the `config` nor the `transformers_config` argument, or have both.

src/transformers/modeling_tf_utils.py

@@ -54,10 +54,20 @@ def keras_serializable(cls):
         raise AttributeError("Must set `config_class` to use @keras_serializable")
 
     @functools.wraps(initializer)
-    def wrapped_init(self, config, *args, **kwargs):
-        if isinstance(config, dict):
-            config = config_class.from_dict(config)
-        initializer(self, config, *args, **kwargs)
+    def wrapped_init(self, *args, **kwargs):
+        transformers_config = kwargs.pop("transformers_config", None)
+        config = args[0] if args and isinstance(args[0], PretrainedConfig) else kwargs.get("config", None)
+        if config is not None and transformers_config is not None:
+            raise ValueError("Must pass either `config` or `transformers_config`, not both")
+        elif config is not None:
+            # normal layer construction, call with unchanged args (config is already in there)
+            initializer(self, *args, **kwargs)
+        elif transformers_config is not None:
+            # Keras deserialization, convert dict to config
+            config = config_class.from_dict(transformers_config)
+            initializer(self, config, *args, **kwargs)
+        else:
+            raise ValueError("Must pass either `config` (PretrainedConfig) or `transformers_config` (dict)")
         self._transformers_config = config
 
     cls.__init__ = wrapped_init
@@ -68,7 +78,7 @@ def keras_serializable(cls):
 
         def get_config(self):
             cfg = super(cls, self).get_config()
-            cfg["config"] = self._transformers_config.to_dict()
+            cfg["transformers_config"] = self._transformers_config.to_dict()
             return cfg
 
         cls.get_config = get_config