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#!/usr/bin/env python3
"""
Mobile VLA ์‚ฌ์šฉ ์˜ˆ์ œ
"""

import torch
from transformers import AutoTokenizer, AutoProcessor
from PIL import Image
import numpy as np

def load_mobile_vla_model(model_name="minuum/mobile-vla"):
    """Mobile VLA ๋ชจ๋ธ ๋กœ๋“œ"""
    
    # ์—ฌ๊ธฐ์„œ ์‹ค์ œ ๋ชจ๋ธ ๋กœ๋”ฉ ๋กœ์ง ๊ตฌํ˜„
    print(f"Loading Mobile VLA model: {model_name}")
    
    # ์‹ค์ œ ๊ตฌํ˜„์—์„œ๋Š” MobileVLATrainer๋ฅผ ์‚ฌ์šฉ
    # from robovlms.train.mobile_vla_trainer import MobileVLATrainer
    # model = MobileVLATrainer.from_pretrained(model_name)
    
    return None  # ํ”Œ๋ ˆ์ด์Šคํ™€๋”

def predict_action(model, image_path, task_description):
    """์•ก์…˜ ์˜ˆ์ธก"""
    
    # ์ด๋ฏธ์ง€ ๋กœ๋“œ
    image = Image.open(image_path).convert("RGB")
    
    # ์ „์ฒ˜๋ฆฌ (์‹ค์ œ ๊ตฌํ˜„์—์„œ๋Š” mobile_vla_collate_fn ์‚ฌ์šฉ)
    # processed = preprocess_image(image)
    
    # ์˜ˆ์ธก (ํ”Œ๋ ˆ์ด์Šคํ™€๋”)
    dummy_action = [0.5, 0.2, 0.1]  # [linear_x, linear_y, angular_z]
    
    return dummy_action

def main():
    """๋ฉ”์ธ ์‹คํ–‰ ํ•จ์ˆ˜"""
    
    print("๐Ÿš€ Mobile VLA ์˜ˆ์ œ ์‹คํ–‰")
    
    # ๋ชจ๋ธ ๋กœ๋“œ
    model = load_mobile_vla_model()
    
    # ์˜ˆ์ œ ์˜ˆ์ธก
    task = "Navigate around obstacles to track the target cup"
    action = predict_action(model, "example_image.jpg", task)
    
    print(f"Task: {task}")
    print(f"Predicted Action: {action}")
    print(f"  - Linear X (forward/backward): {action[0]:.3f}")
    print(f"  - Linear Y (left/right): {action[1]:.3f}")
    print(f"  - Angular Z (rotation): {action[2]:.3f}")

if __name__ == "__main__":
    main()