What does this PR do?

PoC to post-process box coordinates returned by the model. The following should work:

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = FuyuForCausalLM.from_pretrained(model_id, device_map=device, torch_dtype=dtype)
processor = FuyuProcessor(image_processor=FuyuImageProcessor(), tokenizer=tokenizer)

# Prompt appropriate for bounding box detection
text = "statistics"
prompt = f"When presented with a box, perform OCR to extract text contained within it. If provided with text, generate the corresponding bounding box.\n{text}"
image = Image.open("screen2words_ui_example.png")

model_inputs = processor(text=prompt, images=[image]).to(device)    
generation_output = model.generate(**model_inputs, max_new_tokens=40)

results = processor.post_process_box_coordinates(generation_output, target_sizes=torch.Tensor([image.size[::-1]]))

# TODO: maybe unbox the <box> here as well??
decoded = processor.decode(results[0], skip_special_tokens=True)
print(decoded)
# <box>60, 124, 100, 268</box>

I'd like to validate whether this approach is appropriate, what do you think @amyeroberts? If it is, then we can:

• Support point coordinates too.
• Perform the reverse transformations on input prompts. There's already code in the processor for that purpose, I think we could maybe simplify it a bit.
• Maybe provide an optional resizing + padding pre-processing step for images, only for the bounding box detection task. According to our conversations with the original authors (and our tests), this task only works properly when the input image size is close to (1080, 1920). The correct approach is to downscale larger images, and then pad to match that size.

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@amyeroberts, @molbap