Remove skipping logic now that set_epoch exists (#30501)

* Remove skipping logic now that set_epoch exists

* Working version, clean

src/transformers/trainer.py

@@ -96,7 +96,6 @@
     distributed_broadcast_scalars,
     distributed_concat,
     find_batch_size,
-    get_dataloader_sampler,
     get_model_param_count,
     get_module_class_from_name,
     get_parameter_names,
@@ -2137,24 +2136,6 @@ def _inner_training_loop(
 
         self.control = self.callback_handler.on_train_begin(args, self.state, self.control)
 
-        # Skip the first epochs_trained epochs to get the random state of the dataloader at the right point.
-        if not args.ignore_data_skip:
-            for epoch in range(epochs_trained):
-                sampler = get_dataloader_sampler(train_dataloader)
-                sampler_kinds = [RandomSampler]
-                if version.parse(accelerate_version) > version.parse("0.23.0"):
-                    sampler_kinds.append(SeedableRandomSampler)
-                is_random_sampler = isinstance(sampler, tuple(sampler_kinds))
-                if not is_random_sampler:
-                    # We just need to begin an iteration to create the randomization of the sampler.
-                    for _ in train_dataloader:
-                        break
-                else:
-                    # Otherwise we need to call the whooooole sampler cause there is some random operation added
-                    # AT THE VERY END!
-                    sampler = sampler if sampler is not None else []
-                    _ = list(sampler)
-
         total_batched_samples = 0
         for epoch in range(epochs_trained, num_train_epochs):
             epoch_iterator = train_dataloader