regenerate modeling

src/transformers/models/plm/modeling_plm.py

@@ -289,7 +289,12 @@ class PLMAttention(nn.Module):
     ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
         batch_size, seq_length = hidden_states.shape[:-1]
         query_shape = (batch_size, seq_length, -1, self.qk_head_dim)
-        key_shape = (batch_size, seq_length, -1, self.qk_nope_head_dim + self.v_head_dim)
+        key_shape = (
+            batch_size,
+            seq_length,
+            -1,
+            self.qk_nope_head_dim + self.v_head_dim,
+        )
         if self.q_lora_rank is not None:
             q_states = (
                 self.q_b_proj(self.q_a_layernorm(self.q_a_proj(hidden_states))).view(query_shape).transpose(1, 2)