Black preview (#17217)

* Black preview

* Fixup too!

* Fix check copies

* Use the same version as the CI

* Bump black

examples/legacy/seq2seq/seq2seq_trainer.py

@@ -57,9 +57,10 @@ class Seq2SeqTrainer(Trainer):
         super().__init__(*args, **kwargs)
 
         if config is None:
-            assert isinstance(
-                self.model, PreTrainedModel
-            ), f"If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is {self.model.__class__}"
+            assert isinstance(self.model, PreTrainedModel), (
+                "If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
+                f" {self.model.__class__}"
+            )
             self.config = self.model.config
         else:
             self.config = config
@@ -68,13 +69,15 @@ class Seq2SeqTrainer(Trainer):
         self.vocab_size = self.config.tgt_vocab_size if isinstance(self.config, FSMTConfig) else self.config.vocab_size
 
         if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
-            assert (
-                self.config.pad_token_id is not None
-            ), "Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss calculation or doing label smoothing."
+            assert self.config.pad_token_id is not None, (
+                "Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
+                " calculation or doing label smoothing."
+            )
 
         if self.config.pad_token_id is None and self.config.eos_token_id is not None:
             logger.warning(
-                f"The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for padding.."
+                f"The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for"
+                " padding.."
             )
 
         if self.args.label_smoothing == 0:
@@ -248,7 +251,8 @@ class Seq2SeqTrainer(Trainer):
 
         if pad_token_id is None:
             raise ValueError(
-                f"Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be padded to `max_length`={max_length}"
+                "Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be"
+                f" padded to `max_length`={max_length}"
             )
 
         padded_tensor = pad_token_id * torch.ones(