"""Auto-generated stub for module: classification_runtime_adapters."""
from typing import Any

from .eval_model_loader import load_model_with_runtime

# Functions
def allocate_buffers(engine: Any) -> tuple[Any, Any, Any, Any, Any]:
    """
    Create host/device buffers for TensorRT engine I/O.
    """
    ...
def do_inference(_engine: Any, context: Any, h_input: Any, d_input: Any, h_output: Any, d_output: Any, stream: Any) -> Any:
    """
    Run one TensorRT inference step and return output buffer.
    """
    ...
def get_onnx_inference_results(loader: Any, model: Any, action_tracker: Any) -> tuple[Any.Any, Any.Any, Any.Any]:
    """
    Run classification inference on ONNX Runtime.
    """
    ...
def get_openvino_inference_results(loader: Any, model: tuple[Any, Any], action_tracker: Any) -> tuple[Any.Any, Any.Any, Any.Any]:
    """
    Run classification inference on OpenVINO runtime.
    """
    ...
def get_pytorch_inference_results(loader: Any, model: Any, action_tracker: Any) -> tuple[Any.Any, Any.Any, Any.Any]:
    """
    Run classification inference on PyTorch/TorchScript runtime.
    """
    ...
def get_tensorrt_inference_results(loader: Any, model: tuple[Any, Any], action_tracker: Any) -> tuple[Any.Any, Any.Any, Any.Any]:
    """
    Run classification inference on TensorRT runtime.
    """
    ...
def load_classification_model(action_tracker: Any, runtime_framework: str = 'pytorch') -> Any:
    """
    Load classification model artifact for requested runtime.
    """
    ...
def load_engine(trt_filename: str) -> tuple[Any, Any]:
    """
    Deserialize TensorRT engine and build execution context.
    """
    ...
def setup_tensorrt() -> tuple[Any, Any]:
    """
    Import TensorRT + PyCUDA lazily to keep base runtime lightweight.
    """
    ...
def validate_device(loaded_model: Any.Any.Any) -> tuple[Any.Any.Any, Any.Any]:
    """
    Move model to best available device; fallback to CPU safely.
    """
    ...
