Fix typos

src/transformers/modeling_utils.py

@@ -302,7 +302,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
             self._tie_or_clone_weights(output_embeddings, self.get_input_embeddings())
 
     def _tie_or_clone_weights(self, output_embeddings, input_embeddings):
-        """ Tie or clone module weights depending of weither we are using TorchScript or not
+        """ Tie or clone module weights depending of whether we are using TorchScript or not
         """
         if self.config.torchscript:
             output_embeddings.weight = nn.Parameter(input_embeddings.weight.clone())
@@ -1524,7 +1524,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
         return decoded
 
     # force one of token_ids to be generated by setting prob of all other tokens to 0.
-    def _force_token_ids_generation(self, scores, token_ids):
+    def _force_token_ids_generation(self, scores, token_ids) -> None:
         if isinstance(token_ids, int):
             token_ids = [token_ids]
         all_but_token_ids_mask = torch.tensor(
@@ -2025,8 +2025,8 @@ def create_position_ids_from_input_ids(input_ids, padding_idx):
     """
     # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
     mask = input_ids.ne(padding_idx).int()
-    incremental_indicies = torch.cumsum(mask, dim=1).type_as(mask) * mask
-    return incremental_indicies.long() + padding_idx
+    incremental_indices = torch.cumsum(mask, dim=1).type_as(mask) * mask
+    return incremental_indices.long() + padding_idx
 
 
 def prune_linear_layer(layer, index, dim=0):