Timm wrapper label names
Author: qubvel
Pull request: yes
Labels: name: run-slow

# What does this PR do?

At the moment, `TimmWrapper` does not load ImageNet label names/descriptions because they are not present in the config. This PR adds support for label names resolved with `timm` and stored in the configuration as `label_names` to be compatible with both Transformers and Timm.

To reproduce:

```python
from PIL import Image
from urllib.request import urlopen
from transformers import pipeline

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
pipe = pipeline("image-classification", model="timm/convnext_nano.r384_ad_in12k", function_to_apply="softmax")
print(pipe(image))
```

Output on main:
```
[{'label': 'LABEL_8512', 'score': 0.9789659976959229}, {'label': 'LABEL_8345', 'score': 0.0032310006208717823}, {'label': 'LABEL_7597', 'score': 0.002803963143378496}, {'label': 'LABEL_7516', 'score': 0.001975387567654252}, {'label': 'LABEL_8527', 'score': 0.001948392833583057}]
```

Output on branch (correct labels):
```
[{'label': 'cafe au lait', 'score': 0.9789659976959229}, {'label': 'wish-wash', 'score': 0.0032310006208717823}, {'label': 'powdered sugar', 'score': 0.002803963143378496}, {'label': 'continental breakfast, petit dejeuner', 'score': 0.001975387567654252}, {'label': 'cocoa, chocolate, hot chocolate, drinking chocolate', 'score': 0.001948392833583057}]
```

Fixed examples:

1. Loading default timm imagenet pretrained model, `label2id` is not created due to collisions in label names - indexes
```python
>>> model = TimmWrapperForImageClassification.from_pretrained("timm/resnet18.a1_in1k")
>>> print("label2id:", model.config.label2id)
>>> print("id2label:", list(model.config.id2label.items())[:2])
label2id: None
id2label: [(0, 'tench, Tinca tinca'), (1, 'goldfish, Carassius auratus')]
```

2. Custom model with provided `num_labels`
```python
>>> model = TimmWrapperForImageClassification.from_pretrained("timm/resnet18.a1_in1k", num_labels=5, ignore_mismatched_sizes=True)
>>> print(model.config.label2id)
>>> print(model.config.id2label)
{'LABEL_0': 0, 'LABEL_1': 1, 'LABEL_2': 2, 'LABEL_3': 3, 'LABEL_4': 4}
{0: 'LABEL_0', 1: 'LABEL_1', 2: 'LABEL_2', 3: 'LABEL_3', 4: 'LABEL_4'}
```

3. Custom model with provided mappings
```python
>>> model = TimmWrapperForImageClassification.from_pretrained(
...     "timm/resnet18.a1_in1k", num_labels=5, ignore_mismatched_sizes=True,
...     id2label={0: "label_0", 1: "label_1", 2: "label_2", 3: "label_3", 4: "label_4"},
...     label2id={"label_0": 0, "label_1": 1, "label_2": 2, "label_3": 3, "label_4": 4}
>>> )
>>> print(model.config.label2id)
>>> print(model.config.id2label)
{'label_0': 0, 'label_1': 1, 'label_2': 2, 'label_3': 3, 'label_4': 4}
{0: 'label_0', 1: 'label_1', 2: 'label_2', 3: 'label_3', 4: 'label_4'}
```


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