import torch
from torch.utils.data import Dataset, DataLoader
from PIL import Image
import torchaudio

class OmniDataset(Dataset):
    def __init__(self, data_list, vision_tokenizer, audio_tokenizer):
        """
        data_list: List of dicts with {'text': str, 'img_path': str, 'audio_path': str}
        """
        self.data = data_list
        self.v_tok = vision_tokenizer
        self.a_tok = audio_tokenizer

    def __len__(self):
        return len(self.data)

    def __getitem__(self, idx):
        item = self.data[idx]
        
        # 1. Process Image to Visual Tokens
        image = Image.open(item['img_path']).convert("RGB").resize((224, 224))
        # Note: You'd use your vision_tokenizer.py logic here
        
        # 2. Process Audio to Acoustic Tokens
        waveform, sr = torchaudio.load(item['audio_path'])
        # Note: You'd use your audio_tokenizer.py logic here
        
        # 3. Text is handled by the main model's embedding layer
        text = item['text']

        return {
            "text": text,
            "image": image,
            "audio": waveform
        }

# Example: Initializing the loader for your Omni-training
# dataset = OmniDataset(my_data, v_tokenizer, a_tokenizer)
# loader = DataLoader(dataset, batch_size=4, shuffle=True)