[PretrainedTokenizer] Factor out tensor conversion method (#3777)

src/transformers/tokenization_utils.py

@@ -517,7 +517,7 @@ class PreTrainedTokenizer(SpecialTokensMixin):
 
         self.max_len = max_len if max_len is not None else int(1e12)
 
-        # Padding side is right by default and over-riden in subclasses. If specified in the kwargs, it is changed.
+        # Padding side is right by default and overridden in subclasses. If specified in the kwargs, it is changed.
         self.padding_side = kwargs.pop("padding_side", self.padding_side)
         self.model_input_names = kwargs.pop("model_input_names", self.model_input_names)
 
@@ -1447,35 +1447,38 @@ class PreTrainedTokenizer(SpecialTokensMixin):
 
         if return_tensors is not None:
 
-            # Do the tensor conversion in batch
-            for key, value in batch_outputs.items():
-                if return_tensors == "tf" and is_tf_available():
-                    try:
-                        batch_outputs[key] = tf.constant(value)
-                    except ValueError:
-                        if None in [item for sequence in value for item in sequence]:
-                            raise ValueError(self.NO_PAD_TOKEN_FOR_BATCH_MSG)
-                        else:
-                            raise ValueError(self.UNEVEN_SEQUENCES_FOR_BATCH_MSG)
-                elif return_tensors == "pt" and is_torch_available():
-                    try:
-                        batch_outputs[key] = torch.tensor(value)
-                    except ValueError:
-                        raise ValueError(self.UNEVEN_SEQUENCES_FOR_BATCH_MSG)
-                    except RuntimeError:
-                        if None in [item for sequence in value for item in sequence]:
-                            raise ValueError(self.NO_PAD_TOKEN_FOR_BATCH_MSG)
-                        else:
-                            raise
-                elif return_tensors is not None:
-                    logger.warning(
-                        "Unable to convert output to tensors format {}, PyTorch or TensorFlow is not available.".format(
-                            return_tensors
-                        )
-                    )
-
+            self.convert_to_tensors_(batch_outputs, return_tensors)
         return BatchEncoding(batch_outputs)
 
+    def convert_to_tensors_(self, batch_outputs: dict, return_tensors: str) -> None:
+        # Do the tensor conversion in batch
+        for key, value in batch_outputs.items():
+            if return_tensors == "tf" and is_tf_available():
+                try:
+                    batch_outputs[key] = tf.constant(value)
+                except ValueError:
+                    if None in [item for sequence in value for item in sequence]:
+                        raise ValueError(self.NO_PAD_TOKEN_FOR_BATCH_MSG)
+                    else:
+                        raise ValueError(self.UNEVEN_SEQUENCES_FOR_BATCH_MSG)
+            elif return_tensors == "pt" and is_torch_available():
+                try:
+                    batch_outputs[key] = torch.tensor(value)
+                except ValueError:
+                    raise ValueError(self.UNEVEN_SEQUENCES_FOR_BATCH_MSG)
+                except RuntimeError:
+                    if None in [item for sequence in value for item in sequence]:
+                        raise ValueError(self.NO_PAD_TOKEN_FOR_BATCH_MSG)
+                    else:
+                        raise
+
+            elif return_tensors is not None:
+                logger.warning(
+                    "Unable to convert output to tensors format {}, PyTorch or TensorFlow is not available.".format(
+                        return_tensors
+                    )
+                )
+
     def prepare_for_model(
         self,
         ids: List[int],