remove the staticmethod used to load the config

transformers/modeling_bert.py

@@ -715,7 +715,7 @@ class BertDecoderModel(BertPreTrainedModel):
 
     """
     def __init__(self, config):
-        super(BertModel, self).__init__(config)
+        super(BertDecoderModel, self).__init__(config)
 
         self.embeddings = BertEmbeddings(config)
         self.decoder = BertDecoder(config)
@@ -1357,28 +1357,27 @@ class Bert2Rnd(BertPreTrainedModel):
         pretrained weights we need to override the `from_pretrained` method of the base `PreTrainedModel`
         class.
         """
-        pretrained_encoder = BertModel.from_pretrained(pretrained_model_or_path, *model_args, **model_kwargs)
-
-        config = cls._load_config(pretrained_model_or_path, *model_args, **model_kwargs)
-        model = cls(config)
-        model.encoder = pretrained_encoder
-
-        return model
 
-    def _load_config(self, pretrained_model_name_or_path, *args, **kwargs):
-        config = kwargs.pop('config', None)
+        # Load the configuration
+        config = model_kwargs.pop('config', None)
         if config is None:
-            cache_dir = kwargs.pop('cache_dir', None)
-            force_download = kwargs.pop('force_download', False)
-            config, _ = self.config_class.from_pretrained(
-                pretrained_model_name_or_path,
-                *args,
+            cache_dir = model_kwargs.pop('cache_dir', None)
+            force_download = model_kwargs.pop('force_download', False)
+            config, _ = cls.config_class.from_pretrained(
+                pretrained_model_or_path,
+                *model_args,
                 cache_dir=cache_dir,
                 return_unused_kwargs=True,
                 force_download=force_download,
-                **kwargs
+                **model_kwargs
             )
-        return config
+        model = cls(config)
+
+        # The encoder is loaded with pretrained weights
+        pretrained_encoder = BertModel.from_pretrained(pretrained_model_or_path, *model_args, **model_kwargs)
+        model.encoder = pretrained_encoder
+
+        return model
 
     def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None):
         encoder_outputs = self.encoder(input_ids,