import torch
import torchvision
from PIL import Image
import numpy as np
from transformers import AutoTokenizer, VisualBertForQuestionAnswering
import requests
from io import BytesIO

def get_visual_embeddings_simple(image, device=None):
    
    model = torchvision.models.resnet50(pretrained=True)
    model = torch.nn.Sequential(*list(model.children())[:-1])
    model.to(device)
    model.eval()
    
    transform = torchvision.transforms.Compose([
        torchvision.transforms.Resize(256),
        torchvision.transforms.CenterCrop(224),
        torchvision.transforms.ToTensor(),
        torchvision.transforms.Normalize(
            mean=[0.485, 0.456, 0.406],
            std=[0.229, 0.224, 0.225]
        )
    ])
    
    if isinstance(image, str):
        image = Image.open(image).convert('RGB')
    elif isinstance(image, Image.Image):
        image = image.convert('RGB')
    else:
        raise ValueError("Image must be a PIL Image or path to image file")
    
    image_tensor = transform(image).unsqueeze(0).to(device)
    
    with torch.no_grad():
        features = model(image_tensor)
    
    batch_size = features.shape[0]
    feature_dim = features.shape[1]
    visual_seq_length = 10
    
    visual_embeds = features.squeeze(-1).squeeze(-1).unsqueeze(1).expand(batch_size, visual_seq_length, feature_dim)
    
    return visual_embeds

tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
model = VisualBertForQuestionAnswering.from_pretrained("uclanlp/visualbert-vqa-coco-pre")

response = requests.get("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg")
image = Image.open(BytesIO(response.content))
    
visual_embeds = get_visual_embeddings_simple(image)
    
inputs = tokenizer("What is shown in this image?", return_tensors="pt")
    
visual_token_type_ids = torch.ones(visual_embeds.shape[:-1], dtype=torch.long)
visual_attention_mask = torch.ones(visual_embeds.shape[:-1], dtype=torch.float)
    
inputs.update({
    "visual_embeds": visual_embeds,
    "visual_token_type_ids": visual_token_type_ids,
    "visual_attention_mask": visual_attention_mask,
})
    
with torch.no_grad():
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_answer_idx = logits.argmax(-1).item()

print(f"Predicted answer: {predicted_answer_idx}")