[`bnb`] fix bnb failing test
# What does this PR do?

Fixes https://github.com/huggingface/transformers/actions/runs/4538560416/jobs/7997676956

The PR https://github.com/huggingface/transformers/pull/22377 introduced the correct way to compute the device_map for int8 models to avoid issues in some corner cases. 

The `max_memory` argument was causing some issues and led to failing test due to the presence of a non-empty `special_dtypes`. Script to reproduce:

```python
import torch
from accelerate import init_empty_weights, infer_auto_device_map
from transformers import BloomForCausalLM, AutoConfig

config = AutoConfig.from_pretrained("bigscience/bloom-560m")

max_memory = {0: 1000000000, 1: 1000000000}

with init_empty_weights():
    model = BloomForCausalLM(config)

torch_dtype = torch.float16
modules_not_to_convert = ['transformer.word_embeddings', 'lm_head']
special_dtypes = {
    name: torch_dtype
    for name, _ in model.named_parameters()
    if any(m in name for m in modules_not_to_convert)
}

device_map = infer_auto_device_map(model, max_memory=max_memory, no_split_module_classes=['BloomBlock'], dtype=torch.int8, special_dtypes=special_dtypes)

print(set(device_map.values()))
```
I assume that the previous test was too much a corner case (with hardcoded `max_memory`), the correct fix should be just to use `balanced` in the `device_map` argument.

cc @sgugger 
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