FIX: Minimal fix for loading PEFT weights
Author: BenjaminBossan
Pull request: yes

# What does this PR do?

After the weight conversion PR #41580, some adjustments were still required for loading PEFT weights. This PR presents a minimal fix to make it work again.

Besides renaming keys, this PR does not address possible conversions that might be required to be applied to the PEFT weights themselves (most wouldn't work anyway, but e.g. chunking should be possible to implement).

As for test, the existing test_peft_from_pretrained in test_peft_integration.py actually fails on main right now, this PR fixes it. As the tests are slow tests, normal CI won't pick this up though.

## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
      Pull Request section?
- [x] Was this discussed/approved on slack.
- [ ] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?


Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_42387). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

One failing test in CI, `tests/models/mixtral/test_modeling_mixtral.py::MixtralModelTest::test_load_balancing_loss`, should be unrelated. LMK if/how slow tests should be triggered. I ran them locally, there are 11 failing tests without this PR, they all pass with this PR:

```
FAILED tests/peft_integration/test_peft_integration.py::PeftIntegrationTester::test_peft_add_adapter_from_pretrained - AttributeError: 'list' object has no attribute 'items'
FAILED tests/peft_integration/test_peft_integration.py::PeftIntegrationTester::test_peft_from_pretrained - AttributeError: 'list' object has no attribute 'items'
FAILED tests/peft_integration/test_peft_integration.py::PeftIntegrationTester::test_peft_from_pretrained_hub_kwargs - AttributeError: 'list' object has no attribute 'items'
FAILED tests/peft_integration/test_peft_integration.py::PeftIntegrationTester::test_peft_from_pretrained_kwargs - AttributeError: 'list' object has no attribute 'items'
FAILED tests/peft_integration/test_peft_integration.py::PeftIntegrationTester::test_peft_load_adapter_training_inference_mode_false - AttributeError: 'list' object has no attribute 'items'
FAILED tests/peft_integration/test_peft_integration.py::PeftIntegrationTester::test_peft_load_adapter_training_inference_mode_true - AttributeError: 'list' object has no attribute 'items'
FAILED tests/peft_integration/test_peft_integration.py::PeftIntegrationTester::test_peft_pipeline - AttributeError: 'list' object has no attribute 'items'
FAILED tests/peft_integration/test_peft_integration.py::PeftIntegrationTester::test_peft_save_pretrained - AttributeError: 'list' object has no attribute 'items'
FAILED tests/peft_integration/test_peft_integration.py::PeftIntegrationTester::test_peft_save_quantized - AttributeError: 'list' object has no attribute 'items'
FAILED tests/peft_integration/test_peft_integration.py::PeftIntegrationTester::test_peft_save_quantized_regression - AttributeError: 'list' object has no attribute 'items'
FAILED tests/peft_integration/test_peft_integration.py::PeftIntegrationTester::test_peft_state_dict - AttributeError: 'list' object has no attribute 'items'
```

@ArthurZucker Thanks for the feedback, this looks much more succinct, I changed the code accordingly. The failing test (`tests/models/regnet/test_modeling_regnet.py::RegNetModelTest::test_can_load_ignoring_mismatched_shapes`) looks like it's unrelated.

Merging, we still have todo for weight conversion