Allow OOV Image Token for LLaVa Next Variants
Author: alex-jw-brooks
Pull request: yes

# What does this PR do?

Fixes https://github.com/huggingface/transformers/issues/35683

Allows image tokens for LLaVa Next models that aren't defined in the LLM embedding; if the image token is out of range, the input IDs are copied, and set to idx 0 when embedded with the LLM (value does not matter since the image features will be scattered into the indices of the image token mask).

We could alternatively allocate the embeddings up front with `torch.empty` and mask the text features in, but I thought this is probably cleaner for the way the current model is written.

Can be verified with:
```python
from transformers import LlavaNextProcessor, LlavaNextForConditionalGeneration, AutoConfig
MODEL_NAME = "llava-hf/llava-v1.6-mistral-7b-hf"

device = "cuda"
cfg = AutoConfig.from_pretrained(MODEL_NAME)
# Define a new image token out of the llm vocab size
OLD_IMAGE_TOKEN = cfg.image_token_index
OOV_IMAGE_TOKEN = cfg.vocab_size + 1

# Load a processor, and process inputs normal
url = "https://github.com/haotian-liu/LLaVA/blob/1a91fc274d7c35a9b50b3cb29c4247ae5837ce39/images/llava_v1_5_radar.jpg?raw=true"
conversation = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": url},
            {"type": "text", "text": "What is shown in this image?"},
        ],
    },
]

processor = LlavaNextProcessor.from_pretrained(MODEL_NAME)
inputs = processor.apply_chat_template(
    conversation,
    add_generation_prompt=True,
    tokenize=True,
    return_dict=True,
    return_tensors="pt"
).to(device)
# Replace the actual image token with the new out of range one
inputs["input_ids"][inputs["input_ids"] == OLD_IMAGE_TOKEN] = OOV_IMAGE_TOKEN

# Load the model and update the image token to match the out of range value
model = LlavaNextForConditionalGeneration.from_pretrained(MODEL_NAME).to(device)
model.config.image_token_index = OOV_IMAGE_TOKEN

# See if it explodes!
output = model.generate(**inputs, max_new_tokens=1)
```

Will blow up with `RuntimeError: CUDA error: device-side assert triggered` (on CUDA devices) `or IndexError: index out of range in self` (on CPU devices) on `main`, but will work on this branch.



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@zucchini-nlp @ArthurZucker - sorry for the delay in opening the PR here 🙂 

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