[housekeeping] Upgrade `# type` Python 2 syntax

cc @sshleifer

src/transformers/configuration_utils.py

@@ -52,8 +52,8 @@ class PretrainedConfig(object):
             torchscript (:obj:`bool`, `optional`, defaults to :obj:`False`):
                 Is the model used with Torchscript (for PyTorch models).
     """
-    pretrained_config_archive_map = {}  # type: Dict[str, str]
-    model_type = ""  # type: str
+    pretrained_config_archive_map: Dict[str, str] = {}
+    model_type: str = ""
 
     def __init__(self, **kwargs):
         # Attributes with defaults

src/transformers/modeling_bart.py

@@ -570,7 +570,7 @@ class SelfAttention(nn.Module):
         need_weights=False,
     ) -> Tuple[Tensor, Optional[Tensor]]:
         """Input shape: Time(SeqLen) x Batch x Channel"""
-        static_kv = self.encoder_decoder_attention  # type: bool
+        static_kv: bool = self.encoder_decoder_attention
         tgt_len, bsz, embed_dim = query.size()
         assert embed_dim == self.embed_dim
         assert list(query.size()) == [tgt_len, bsz, embed_dim]
@@ -666,7 +666,7 @@ class SelfAttention(nn.Module):
                 assert v is not None
                 v = torch.cat([prev_value, v], dim=1)
         assert k is not None and v is not None
-        prev_key_padding_mask = saved_state.get("prev_key_padding_mask", None)  # type: Optional[Tensor]
+        prev_key_padding_mask: Optional[Tensor] = saved_state.get("prev_key_padding_mask", None)
         key_padding_mask = self._cat_prev_key_padding_mask(
             key_padding_mask, prev_key_padding_mask, bsz, k.size(1), static_kv
         )
@@ -798,7 +798,7 @@ class BartModel(PretrainedBartModel):
         input_ids,
         attention_mask=None,
         decoder_input_ids=None,
-        encoder_outputs=None,  # type: Tuple
+        encoder_outputs: Optional[Tuple] = None,
         decoder_attention_mask=None,
         decoder_cached_states=None,
         use_cache=False,
@@ -831,9 +831,9 @@ class BartModel(PretrainedBartModel):
             use_cache=use_cache,
         )
         # Attention and hidden_states will be [] or None if they aren't needed
-        decoder_outputs = _filter_out_falsey_values(decoder_outputs)  # type: tuple
+        decoder_outputs: Tuple = _filter_out_falsey_values(decoder_outputs)
         assert isinstance(decoder_outputs[0], torch.Tensor)
-        encoder_outputs = _filter_out_falsey_values(encoder_outputs)  # type: tuple
+        encoder_outputs: Tuple = _filter_out_falsey_values(encoder_outputs)
         return decoder_outputs + encoder_outputs
 
     def get_input_embeddings(self):