"""
Edge device / CPU inference for OpenVLA-Micro.

This script is optimized for resource-constrained environments.
Two modes:

  1. Standard CPU  – float32, ~3-5 sec/step on modern x86, ~6GB RAM
  2. Low-RAM (4-bit) – uses bitsandbytes 4-bit quantization, ~2.5GB RAM,
                       slightly slower but usable on 4GB devices like RPi 5
                       with sufficient swap.

Usage:
    python inference_cpu.py --image demo.jpg "pick up the red block"
    python inference_cpu.py --low-ram --image demo.jpg "pick up the red block"
    python inference_cpu.py --checkpoint ./openvla-micro-distill.pt --image demo.jpg "pick up the red block"
"""
import argparse
from PIL import Image
from modeling_openvla_micro import OpenVLAMicro


def main():
    parser = argparse.ArgumentParser(description="OpenVLA-Micro CPU/edge inference")
    parser.add_argument("--checkpoint", type=str, default="theguy21/openvla-micro",
                        help="HF repo ID or local .pt path")
    parser.add_argument("--image", type=str, required=True, help="Input image path")
    parser.add_argument("--low-ram", action="store_true",
                        help="4-bit quantized LLM (~2.5GB peak, requires bitsandbytes)")
    parser.add_argument("instruction", type=str, nargs="?", default="pick up the red block",
                        help="Task instruction (positional, optional)")
    args = parser.parse_args()

    device = "cpu"
    llm_kwargs = {}

    if args.low_ram:
        print("Low-RAM mode: 4-bit quantization (requires bitsandbytes)")
        llm_kwargs = {
            "load_in_4bit": True,
            "bnb_4bit_compute_dtype": "float32",
            "bnb_4bit_use_double_quant": True,
        }
    else:
        print("Standard CPU mode: float32 (~6GB RAM)")
        llm_kwargs["torch_dtype"] = "float32"

    print(f"Loading OpenVLA-Micro from {args.checkpoint} on CPU...")
    model = OpenVLAMicro.from_pretrained(args.checkpoint, device=device, llm_kwargs=llm_kwargs)
    model.eval()
    n_params = sum(p.numel() for p in model.parameters()) / 1e6
    print(f"Model loaded ({n_params:.0f}M params)")

    image = Image.open(args.image).convert("RGB")
    print(f"Image: {image.size}")
    print(f"Instruction: {args.instruction}")
    action = model.predict_action(image, args.instruction)
    print(f"Action (7-DoF): {action}")


if __name__ == "__main__":
    main()