DataLoaderShard wrongly yields None instead of StopIteration when its dataloader returns StopIteration immediately

[Aleko2286]

System Info

- `Accelerate` version: 1.3.0
- Platform: Linux-5.14.0-508.el9.x86_64-x86_64-with-glibc2.39
- `accelerate` bash location: /usr/local/bin/accelerate
- Python version: 3.12.3
- Numpy version: 2.2.2
- PyTorch version (GPU?): 2.5.1+cu124 (True)
- PyTorch XPU available: False
- PyTorch NPU available: False
- PyTorch MLU available: False
- PyTorch MUSA available: False
- System RAM: 62.24 GB
- GPU type: NVIDIA GeForce RTX 2060 SUPER
- `Accelerate` default config:
        - compute_environment: LOCAL_MACHINE
        - distributed_type: NO
        - mixed_precision: no
        - use_cpu: False
        - debug: False
        - num_processes: 1
        - machine_rank: 0
        - num_machines: 1
        - gpu_ids: all
        - rdzv_backend: static
        - same_network: True
        - main_training_function: main
        - enable_cpu_affinity: False
        - downcast_bf16: no
        - tpu_use_cluster: False
        - tpu_use_sudo: False
        - tpu_env: []

Information

• The official example scripts
• My own modified scripts

Tasks

• One of the scripts in the examples/ folder of Accelerate or an officially supported no_trainer script in the examples folder of the transformers repo (such as run_no_trainer_glue.py)
• My own task or dataset (give details below)

Reproduction

The following code snippet will print "None" and then raise an UnboundLocalError:

from accelerate import Accelerator
import torch

accelerator = Accelerator()
dataset = range(10)
dataloader = torch.utils.data.DataLoader(dataset, batch_size=8, shuffle=True)
dataloader = accelerator.prepare(dataloader)

for x in accelerator.skip_first_batches(dataloader, len(dataloader)):
    print(x)

Expected behavior

The iterator should immediately return StopIteration, such that nothing is printed and the script exits normally.