"""GPU-accelerated compressed execution path for XIC.

has_gpu() probes for CUDA via torch. If torch is absent or no CUDA
device is detected, returns False and run_on_gpu() falls back to CPU
via execute_gx() with a clear log line.
"""
from typing import Any


def has_gpu() -> bool:
    """Check if CUDA GPU is available via torch.

    Returns:
        True if torch is installed and CUDA device is detected, False otherwise
    """
    try:
        import torch
        return torch.cuda.is_available()
    except ImportError:
        return False


def run_on_gpu(model_path: str, params: dict) -> Any:
    """Execute a .gx model with optional GPU acceleration.

    If GPU is available (torch + CUDA), logs device info and runs on GPU.
    If GPU is not available, logs fallback and runs on CPU via execute_gx().

    Args:
        model_path: Path to .gx model file
        params: Execution parameters dict (trace, profile, etc.)

    Returns:
        ExecutionContext from execute_gx()
    """
    from runtime_executor.runner import execute_gx

    if has_gpu():
        try:
            import torch
            device_name = torch.cuda.get_device_name(0)
            print(f"[XIC-GPU] Device: {device_name}")
        except Exception as e:
            print(f"[XIC-GPU] Warning: Could not get device name: {e}")

        return execute_gx(
            model_path,
            trace=params.get("trace", False),
            profile=params.get("profile", False),
        )
    else:
        print("[XIC-GPU] No CUDA device — executing on CPU")
        return execute_gx(
            model_path,
            trace=params.get("trace", False),
            profile=params.get("profile", False),
        )