Rm duplicate pad_across_processes (#24780)

Rm duplicate

src/transformers/trainer.py

@@ -3257,41 +3257,6 @@ class Trainer:
             tensors = distributed_concat(tensors)
         return tensors
 
-    # Copied from Accelerate.
-    def _pad_across_processes(self, tensor, pad_index=-100):
-        """
-        Recursively pad the tensors in a nested list/tuple/dictionary of tensors from all devices to the same size so
-        they can safely be gathered.
-        """
-        if isinstance(tensor, (list, tuple)):
-            return type(tensor)(self._pad_across_processes(t, pad_index=pad_index) for t in tensor)
-        elif isinstance(tensor, dict):
-            return type(tensor)({k: self._pad_across_processes(v, pad_index=pad_index) for k, v in tensor.items()})
-        elif not isinstance(tensor, torch.Tensor):
-            raise TypeError(
-                f"Can't pad the values of type {type(tensor)}, only of nested list/tuple/dicts of tensors."
-            )
-
-        if len(tensor.shape) < 2:
-            return tensor
-        # Gather all sizes
-        size = torch.tensor(tensor.shape, device=tensor.device)[None]
-        sizes = self._nested_gather(size).cpu()
-
-        max_size = max(s[1] for s in sizes)
-        # When extracting XLA graphs for compilation, max_size is 0,
-        # so use inequality to avoid errors.
-        if tensor.shape[1] >= max_size:
-            return tensor
-
-        # Then pad to the maximum size
-        old_size = tensor.shape
-        new_size = list(old_size)
-        new_size[1] = max_size
-        new_tensor = tensor.new_zeros(tuple(new_size)) + pad_index
-        new_tensor[:, : old_size[1]] = tensor
-        return new_tensor
-
     def prediction_step(
         self,
         model: nn.Module,