💄 super

src/transformers/modeling_encoder_decoder.py

@@ -37,7 +37,7 @@ class PreTrainedEncoderDecoder(nn.Module):
     """
 
     def __init__(self, encoder, decoder):
-        super(PreTrainedEncoderDecoder, self).__init__()
+        super().__init__()
         self.encoder = encoder
         self.decoder = decoder
 
@@ -290,7 +290,7 @@ class Model2Model(PreTrainedEncoderDecoder):
     """
 
     def __init__(self, *args, **kwargs):
-        super(Model2Model, self).__init__(*args, **kwargs)
+        super().__init__(*args, **kwargs)
         self.tie_weights()
 
     def tie_weights(self):
@@ -321,7 +321,7 @@ class Model2Model(PreTrainedEncoderDecoder):
         ):
             raise ValueError("Only the Bert model is currently supported.")
 
-        model = super(Model2Model, cls).from_pretrained(
+        model = super().from_pretrained(
             encoder_pretrained_model_name_or_path=pretrained_model_name_or_path,
             decoder_pretrained_model_name_or_path=pretrained_model_name_or_path,
             *args,
@@ -345,5 +345,5 @@ class Model2LSTM(PreTrainedEncoderDecoder):
                     "      E.g. `decoder_config={'input_size': 768, 'hidden_size': 768, 'num_layers': 2}`"
                 )
             kwargs["decoder_model"] = torch.nn.LSTM(kwargs.pop("decoder_config"))
-        model = super(Model2LSTM, cls).from_pretrained(*args, **kwargs)
+        model = super().from_pretrained(*args, **kwargs)
         return model