import os

class DeviceManager:
    @staticmethod
    def get_optimal_device() -> str:
        """
        Detects the best available runtime (CUDA -> DirectML -> CPU).
        Returns the execution provider for ONNX Runtime.
        """
        # For simplicity, we assume if we are asked to use onnxruntime-gpu, we check for CUDA.
        # onnxruntime provides get_available_providers()
        try:
            import onnxruntime as ort
            providers = ort.get_available_providers()
            if 'CUDAExecutionProvider' in providers:
                return 'CUDAExecutionProvider'
            if 'DmlExecutionProvider' in providers:
                return 'DmlExecutionProvider'
            return 'CPUExecutionProvider'
        except ImportError:
            return 'CPUExecutionProvider'

    @staticmethod
    def get_optimal_batch_size() -> int:
        """
        Determines batch size based on available VRAM or CPU RAM.
        """
        device = DeviceManager.get_optimal_device()
        if device == 'CUDAExecutionProvider':
            try:
                import torch
                if torch.cuda.is_available():
                    vram_gb = torch.cuda.get_device_properties(0).total_memory / (1024 ** 3)
                    if vram_gb >= 16:
                        return 1024
                    elif vram_gb >= 8:
                        return 512
                    else:
                        return 256
            except ImportError:
                return 256
        # CPU or DML fallback
        return 128