Remove subclass for sortish sampler (#9907)

* Remove subclass for sortish sampler

* Use old Seq2SeqTrainer in script

* Styling

examples/seq2seq/finetune_trainer.py

@@ -20,6 +20,8 @@ from dataclasses import dataclass, field
 from typing import Optional
 
 import transformers
+from seq2seq_trainer import Seq2SeqTrainer
+from seq2seq_training_args import Seq2SeqTrainingArguments
 from transformers import (
     AutoConfig,
     AutoModelForSeq2SeqLM,
@@ -27,8 +29,6 @@ from transformers import (
     HfArgumentParser,
     MBartTokenizer,
     MBartTokenizerFast,
-    Seq2SeqTrainer,
-    Seq2SeqTrainingArguments,
     set_seed,
 )
 from transformers.trainer_utils import EvaluationStrategy, is_main_process
@@ -286,6 +286,7 @@ def main():
     trainer = Seq2SeqTrainer(
         model=model,
         args=training_args,
+        data_args=data_args,
         train_dataset=train_dataset,
         eval_dataset=eval_dataset,
         data_collator=Seq2SeqDataCollator(
@@ -323,9 +324,7 @@ def main():
     if training_args.do_eval:
         logger.info("*** Evaluate ***")
 
-        metrics = trainer.evaluate(
-            metric_key_prefix="val", max_length=data_args.val_max_target_length, num_beams=data_args.eval_beams
-        )
+        metrics = trainer.evaluate(metric_key_prefix="val")
         metrics["val_n_objs"] = data_args.n_val
         metrics["val_loss"] = round(metrics["val_loss"], 4)
 
@@ -337,12 +336,7 @@ def main():
     if training_args.do_predict:
         logger.info("*** Predict ***")
 
-        test_output = trainer.predict(
-            test_dataset=test_dataset,
-            metric_key_prefix="test",
-            max_length=data_args.val_max_target_length,
-            num_beams=data_args.eval_beams,
-        )
+        test_output = trainer.predict(test_dataset=test_dataset, metric_key_prefix="test")
         metrics = test_output.metrics
         metrics["test_n_objs"] = data_args.n_test