💄 super

src/transformers/modeling_utils.py

@@ -47,7 +47,7 @@ except ImportError:
         """
 
         def __init__(self, *args, **kwargs):
-            super(Identity, self).__init__()
+            super().__init__()
 
         def forward(self, input):
             return input
@@ -97,7 +97,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
         return {"input_ids": torch.tensor(DUMMY_INPUTS)}
 
     def __init__(self, config, *inputs, **kwargs):
-        super(PreTrainedModel, self).__init__()
+        super().__init__()
         if not isinstance(config, PretrainedConfig):
             raise ValueError(
                 "Parameter config in `{}(config)` should be an instance of class `PretrainedConfig`. "
@@ -1102,7 +1102,7 @@ class Conv1D(nn.Module):
         """ Conv1D layer as defined by Radford et al. for OpenAI GPT (and also used in GPT-2)
             Basically works like a Linear layer but the weights are transposed
         """
-        super(Conv1D, self).__init__()
+        super().__init__()
         self.nf = nf
         w = torch.empty(nx, nf)
         nn.init.normal_(w, std=0.02)
@@ -1120,7 +1120,7 @@ class PoolerStartLogits(nn.Module):
     """ Compute SQuAD start_logits from sequence hidden states. """
 
     def __init__(self, config):
-        super(PoolerStartLogits, self).__init__()
+        super().__init__()
         self.dense = nn.Linear(config.hidden_size, 1)
 
     def forward(self, hidden_states, p_mask=None):
@@ -1145,7 +1145,7 @@ class PoolerEndLogits(nn.Module):
     """
 
     def __init__(self, config):
-        super(PoolerEndLogits, self).__init__()
+        super().__init__()
         self.dense_0 = nn.Linear(config.hidden_size * 2, config.hidden_size)
         self.activation = nn.Tanh()
         self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
@@ -1191,7 +1191,7 @@ class PoolerAnswerClass(nn.Module):
     """ Compute SQuAD 2.0 answer class from classification and start tokens hidden states. """
 
     def __init__(self, config):
-        super(PoolerAnswerClass, self).__init__()
+        super().__init__()
         self.dense_0 = nn.Linear(config.hidden_size * 2, config.hidden_size)
         self.activation = nn.Tanh()
         self.dense_1 = nn.Linear(config.hidden_size, 1, bias=False)
@@ -1276,7 +1276,7 @@ class SQuADHead(nn.Module):
     """
 
     def __init__(self, config):
-        super(SQuADHead, self).__init__()
+        super().__init__()
         self.start_n_top = config.start_n_top
         self.end_n_top = config.end_n_top
 
@@ -1368,7 +1368,7 @@ class SequenceSummary(nn.Module):
     """
 
     def __init__(self, config):
-        super(SequenceSummary, self).__init__()
+        super().__init__()
 
         self.summary_type = config.summary_type if hasattr(config, "summary_type") else "last"
         if self.summary_type == "attn":