Remove subclass for sortish sampler (#9907)

* Remove subclass for sortish sampler

* Use old Seq2SeqTrainer in script

* Styling

src/transformers/trainer_seq2seq.py

@@ -17,14 +17,10 @@ from typing import Any, Dict, List, Optional, Tuple, Union
 import torch
 from packaging import version
 from torch import nn
-from torch.utils.data import DistributedSampler, RandomSampler
 from torch.utils.data.dataset import Dataset
 
-from .file_utils import is_torch_tpu_available
 from .trainer import Trainer
-from .trainer_pt_utils import get_tpu_sampler
 from .trainer_utils import PredictionOutput
-from .training_args import ParallelMode
 from .utils import logging
 
 
@@ -36,24 +32,6 @@ logger = logging.get_logger(__name__)
 
 
 class Seq2SeqTrainer(Trainer):
-    def _get_train_sampler(self) -> Optional[torch.utils.data.sampler.Sampler]:
-        if isinstance(self.train_dataset, torch.utils.data.IterableDataset):
-            return None
-        elif is_torch_tpu_available():
-            return get_tpu_sampler(self.train_dataset)
-        else:
-            if self.args.sortish_sampler:
-                self.train_dataset.make_sortish_sampler(
-                    self.args.per_device_train_batch_size,
-                    distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED),
-                )
-
-            return (
-                RandomSampler(self.train_dataset)
-                if self.args.local_rank == -1
-                else DistributedSampler(self.train_dataset)
-            )
-
     def evaluate(
         self,
         eval_dataset: Optional[Dataset] = None,