# Import necessary libraries
from transformers import AutoModelForSequenceClassification, AutoTokenizer, AlignVisionConfig
from PIL import Image
import numpy as np

# Load the pre-trained model and tokenizer for image captioning
model = AutoModelForSequenceClassification.from_pretrained("facebook/dino-vit-base") # Load the pre-trained model for image captioning
tokenizer = AutoTokenizer.from_pretrained("facebook/dino-captioner") # Load the tokenizer for the model
config = AlignVisionConfig(image_size=224) # Set the image size to 224x224 pixels for the model

# Load an example image and convert it into a numpy array
img = Image.open('example.jpg') # Open the example image
np_arr = np.array(img, dtype='uint8') # Convert the image into a numpy array with unsigned integer data type

# Preprocess the input data for the model (resize, normalize, etc.)
inputs = tokenizer(["A beautiful sunset over the ocean."], return_tensors="pt", padding=True, truncation=True, max_length=128) # Tokenize the input text and set the maximum length of captions to 128 words
image_input = np.expand_dims(np.transpose(np.divide(np.subtract(np.multiply(np.array([255], dtype='float32') / 255, np_arr), [0.485, 0.456, 0.406]), [1., 1., 1.]), [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), axis=0) # Normalize the input image to values between 0 and 1 and reshape it into a format that can be fed into the model's input shape (batch size x channels x height x width)

# Combine the text and image inputs for the model to process simultaneously
inputs = {**inputs, "image": torch.tensor(np_arr), "pixel_values": torch.tensor(image_input)} # Add the image and pixel values as additional input features to the input dictionary

# Run inference on the preprocessed data using the loaded model
outputs = model(**inputs) # The output is a dictionary containing various information about the prediction, including logits (probabilities), loss, etc.