Immutability for data collators (#30603)

* immutability fix for seq2seq as well as immutability tests for the collators

* ensure we don't act on none labels and formatting

* remove tf/pt in respective tests as they are not required

* more type error fixes tf/np

* remove todo

* apply suggestions from code review

* formatting / style

src/transformers/data/data_collator.py

@@ -585,51 +585,84 @@ class DataCollatorForSeq2Seq:
     def __call__(self, features, return_tensors=None):
         if return_tensors is None:
             return_tensors = self.return_tensors
-        labels = [feature["labels"] for feature in features] if "labels" in features[0].keys() else None
-        # We have to pad the labels before calling `tokenizer.pad` as this method won't pad them and needs them of the
-        # same length to return tensors.
-        no_padding = self.padding is False or self.padding == PaddingStrategy.DO_NOT_PAD
-        if labels is not None and not no_padding:
-            max_padding = self.padding == PaddingStrategy.MAX_LENGTH and self.max_length is not None
-            max_label_length = max(len(l) for l in labels) if not max_padding else self.max_length
-            if self.pad_to_multiple_of is not None:
-                max_label_length = (
-                    (max_label_length + self.pad_to_multiple_of - 1)
-                    // self.pad_to_multiple_of
-                    * self.pad_to_multiple_of
-                )
-
-            padding_side = self.tokenizer.padding_side
-            for feature in features:
-                remainder = [self.label_pad_token_id] * (max_label_length - len(feature["labels"]))
-                if isinstance(feature["labels"], list):
-                    feature["labels"] = (
-                        feature["labels"] + remainder if padding_side == "right" else remainder + feature["labels"]
-                    )
-                elif padding_side == "right":
-                    feature["labels"] = np.concatenate([feature["labels"], remainder]).astype(np.int64)
-                else:
-                    feature["labels"] = np.concatenate([remainder, feature["labels"]]).astype(np.int64)
 
-        features = pad_without_fast_tokenizer_warning(
+        label_name = "label" if "label" in features[0].keys() else "labels"
+        labels = [feature[label_name] for feature in features] if label_name in features[0].keys() else None
+        # reconvert list[None] to None if necessary
+        # this might occur when we pass {..., "labels": None}
+        if labels is not None and all(label is None for label in labels):
+            labels = None
+        non_labels_features = [{k: v for k, v in feature.items() if k != label_name} for feature in features]
+
+        # run through tokenizer without labels to ensure no side effects
+        batch = pad_without_fast_tokenizer_warning(
             self.tokenizer,
-            features,
+            non_labels_features,
             padding=self.padding,
             max_length=self.max_length,
             pad_to_multiple_of=self.pad_to_multiple_of,
             return_tensors=return_tensors,
         )
 
+        # we have to pad the labels manually as we cannot rely on `tokenizer.pad` and we need them to be of the same length to return tensors
+        no_padding = self.padding is False or self.padding == PaddingStrategy.DO_NOT_PAD
+        if labels is not None:
+            if no_padding:
+                if isinstance(features[0][label_name], list):
+                    batch["labels"] = list(labels)
+                else:
+                    batch["labels"] = [np.concatenate([label, []]) for label in labels]
+            else:
+                max_padding = self.padding == PaddingStrategy.MAX_LENGTH and self.max_length is not None
+                max_label_length = max(len(l) for l in labels) if not max_padding else self.max_length
+                if self.pad_to_multiple_of is not None:
+                    max_label_length = (
+                        (max_label_length + self.pad_to_multiple_of - 1)
+                        // self.pad_to_multiple_of
+                        * self.pad_to_multiple_of
+                    )
+
+                padding_side = self.tokenizer.padding_side
+                if isinstance(features[0][label_name], list):
+                    batch["labels"] = [
+                        label + [self.label_pad_token_id] * (max_label_length - len(label))
+                        if padding_side == "right"
+                        else [self.label_pad_token_id] * (max_label_length - len(label)) + label
+                        for label in labels
+                    ]
+                else:
+                    batch["labels"] = [
+                        np.concatenate([label, [self.label_pad_token_id] * (max_label_length - len(label))])
+                        if padding_side == "right"
+                        else np.concatenate([[self.label_pad_token_id] * (max_label_length - len(label)), label])
+                        for label in labels
+                    ]
+
+        # reintroduce side effects via tokenizer that return respective datatypes for the `return_tensors` argument
+        if batch.get("labels", None) is not None:
+            if return_tensors == "pt":
+                import torch
+
+                batch["labels"] = torch.tensor(batch["labels"], dtype=torch.int64)
+            elif return_tensors == "tf":
+                import tensorflow as tf
+
+                batch["labels"] = tf.constant(batch["labels"], dtype=tf.int64)
+            else:
+                batch["labels"] = np.array(batch["labels"], dtype=np.int64)
+        else:
+            batch["labels"] = None
+
         # prepare decoder_input_ids
         if (
             labels is not None
             and self.model is not None
             and hasattr(self.model, "prepare_decoder_input_ids_from_labels")
         ):
-            decoder_input_ids = self.model.prepare_decoder_input_ids_from_labels(labels=features["labels"])
-            features["decoder_input_ids"] = decoder_input_ids
+            decoder_input_ids = self.model.prepare_decoder_input_ids_from_labels(labels=batch["labels"])
+            batch["decoder_input_ids"] = decoder_input_ids
 
-        return features
+        return batch
 
 
 @dataclass