adapt style to predefined style layout

src/transformers/modeling_ctrl.py

@@ -492,7 +492,7 @@ class CTRLLMHeadModel(CTRLPreTrainedModel):
 
     def prepare_inputs_for_generation(self, input_ids, **kwargs):
         # only last token for inputs_ids if past is defined in kwargs
-        if 'past' in kwargs and kwargs['past']:
+        if "past" in kwargs and kwargs["past"]:
             input_ids = input_ids[:, -1].unsqueeze(-1)
 
         inputs = {"input_ids": input_ids}

src/transformers/modeling_gpt2.py

@@ -561,7 +561,7 @@ class GPT2LMHeadModel(GPT2PreTrainedModel):
 
     def prepare_inputs_for_generation(self, input_ids, **kwargs):
         # only last token for inputs_ids if past is defined in kwargs
-        if 'past' in kwargs and kwargs['past']:
+        if "past" in kwargs and kwargs["past"]:
             input_ids = input_ids[:, -1].unsqueeze(-1)
 
         inputs = {"input_ids": input_ids}

src/transformers/modeling_transfo_xl.py

@@ -935,7 +935,7 @@ class TransfoXLLMHeadModel(TransfoXLPreTrainedModel):
         inputs = {"input_ids": input_ids}
 
         # if past is defined in model kwargs then use it for faster decoding
-        if 'past' in model_kwargs and model_kwargs['past']:
-            inputs['mems'] = model_kwargs['past']
+        if "past" in model_kwargs and model_kwargs["past"]:
+            inputs["mems"] = model_kwargs["past"]
 
         return inputs

src/transformers/modeling_utils.py

@@ -540,8 +540,8 @@ class PreTrainedModel(nn.Module):
         return {"input_ids": input_ids}
 
     def _do_output_past(self, outputs):
-        has_output_past = hasattr(self.config, 'output_past') and self.config.output_past
-        has_mem_len = hasattr(self.config, 'mem_len') and self.config.mem_len
+        has_output_past = hasattr(self.config, "output_past") and self.config.output_past
+        has_mem_len = hasattr(self.config, "mem_len") and self.config.mem_len
 
         if has_output_past and not has_mem_len and len(outputs) > 1:
             return True

src/transformers/modeling_xlnet.py

@@ -1031,8 +1031,8 @@ class XLNetLMHeadModel(XLNetPreTrainedModel):
         inputs = {"input_ids": input_ids, "perm_mask": perm_mask, "target_mapping": target_mapping}
 
         # if past is defined in model kwargs then use it for faster decoding
-        if 'past' in model_kwargs and model_kwargs['past']:
-            inputs['mems'] = model_kwargs['past']
+        if "past" in model_kwargs and model_kwargs["past"]:
+            inputs["mems"] = model_kwargs["past"]
 
         return inputs