# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license

from __future__ import annotations

import ast
import json
import platform
import zipfile
from collections import OrderedDict, namedtuple
from pathlib import Path
from typing import Any

import cv2
import numpy as np
import torch
import torch.nn as nn
from PIL import Image

from ultralytics.utils import ARM64, IS_JETSON, LINUX, LOGGER, PYTHON_VERSION, ROOT, YAML, is_jetson
from ultralytics.utils.checks import check_requirements, check_suffix, check_version, check_yaml, is_rockchip
from ultralytics.utils.downloads import attempt_download_asset, is_url
from ultralytics.utils.nms import non_max_suppression


def check_class_names(names: list | dict) -> dict[int, str]:
    """
    Check class names and convert to dict format if needed.

    Args:
        names (list | dict): Class names as list or dict format.

    Returns:
        (dict): Class names in dict format with integer keys and string values.

    Raises:
        KeyError: If class indices are invalid for the dataset size.
    """
    if isinstance(names, list):  # names is a list
        names = dict(enumerate(names))  # convert to dict
    if isinstance(names, dict):
        # Convert 1) string keys to int, i.e. '0' to 0, and non-string values to strings, i.e. True to 'True'
        names = {int(k): str(v) for k, v in names.items()}
        n = len(names)
        if max(names.keys()) >= n:
            raise KeyError(
                f"{n}-class dataset requires class indices 0-{n - 1}, but you have invalid class indices "
                f"{min(names.keys())}-{max(names.keys())} defined in your dataset YAML."
            )
        if isinstance(names[0], str) and names[0].startswith("n0"):  # imagenet class codes, i.e. 'n01440764'
            names_map = YAML.load(ROOT / "cfg/datasets/ImageNet.yaml")["map"]  # human-readable names
            names = {k: names_map[v] for k, v in names.items()}
    return names


def default_class_names(data: str | Path | None = None) -> dict[int, str]:
    """
    Apply default class names to an input YAML file or return numerical class names.

    Args:
        data (str | Path, optional): Path to YAML file containing class names.

    Returns:
        (dict): Dictionary mapping class indices to class names.
    """
    if data:
        try:
            return YAML.load(check_yaml(data))["names"]
        except Exception:
            pass
    return {i: f"class{i}" for i in range(999)}  # return default if above errors


class AutoBackend(nn.Module):
    """
    Handle dynamic backend selection for running inference using Ultralytics YOLO models.

    The AutoBackend class is designed to provide an abstraction layer for various inference engines. It supports a wide
    range of formats, each with specific naming conventions as outlined below:

        Supported Formats and Naming Conventions:
            | Format                | File Suffix       |
            | --------------------- | ----------------- |
            | PyTorch               | *.pt              |
            | TorchScript           | *.torchscript     |
            | ONNX Runtime          | *.onnx            |
            | ONNX OpenCV DNN       | *.onnx (dnn=True) |
            | OpenVINO              | *openvino_model/  |
            | CoreML                | *.mlpackage       |
            | TensorRT              | *.engine          |
            | TensorFlow SavedModel | *_saved_model/    |
            | TensorFlow GraphDef   | *.pb              |
            | TensorFlow Lite       | *.tflite          |
            | TensorFlow Edge TPU   | *_edgetpu.tflite  |
            | PaddlePaddle          | *_paddle_model/   |
            | MNN                   | *.mnn             |
            | NCNN                  | *_ncnn_model/     |
            | IMX                   | *_imx_model/      |
            | RKNN                  | *_rknn_model/     |
            | Triton Inference      | triton://model    |
            | ExecuTorch            | *.pte             |

    Attributes:
        model (torch.nn.Module): The loaded YOLO model.
        device (torch.device): The device (CPU or GPU) on which the model is loaded.
        task (str): The type of task the model performs (detect, segment, classify, pose).
        names (dict): A dictionary of class names that the model can detect.
        stride (int): The model stride, typically 32 for YOLO models.
        fp16 (bool): Whether the model uses half-precision (FP16) inference.
        nhwc (bool): Whether the model expects NHWC input format instead of NCHW.
        pt (bool): Whether the model is a PyTorch model.
        jit (bool): Whether the model is a TorchScript model.
        onnx (bool): Whether the model is an ONNX model.
        xml (bool): Whether the model is an OpenVINO model.
        engine (bool): Whether the model is a TensorRT engine.
        coreml (bool): Whether the model is a CoreML model.
        saved_model (bool): Whether the model is a TensorFlow SavedModel.
        pb (bool): Whether the model is a TensorFlow GraphDef.
        tflite (bool): Whether the model is a TensorFlow Lite model.
        edgetpu (bool): Whether the model is a TensorFlow Edge TPU model.
        tfjs (bool): Whether the model is a TensorFlow.js model.
        paddle (bool): Whether the model is a PaddlePaddle model.
        mnn (bool): Whether the model is an MNN model.
        ncnn (bool): Whether the model is an NCNN model.
        imx (bool): Whether the model is an IMX model.
        rknn (bool): Whether the model is an RKNN model.
        triton (bool): Whether the model is a Triton Inference Server model.
        pte (bool): Whether the model is a PyTorch ExecuTorch model.

    Methods:
        forward: Run inference on an input image.
        from_numpy: Convert numpy array to tensor.
        warmup: Warm up the model with a dummy input.
        _model_type: Determine the model type from file path.

    Examples:
        >>> model = AutoBackend(model="yolo11n.pt", device="cuda")
        >>> results = model(img)
    """

    @torch.no_grad()
    def __init__(
        self,
        model: str | torch.nn.Module = "yolo11n.pt",
        device: torch.device = torch.device("cpu"),
        dnn: bool = False,
        data: str | Path | None = None,
        fp16: bool = False,
        fuse: bool = True,
        verbose: bool = True,
    ):
        """
        Initialize the AutoBackend for inference.

        Args:
            model (str | torch.nn.Module): Path to the model weights file or a module instance.
            device (torch.device): Device to run the model on.
            dnn (bool): Use OpenCV DNN module for ONNX inference.
            data (str | Path, optional): Path to the additional data.yaml file containing class names.
            fp16 (bool): Enable half-precision inference. Supported only on specific backends.
            fuse (bool): Fuse Conv2D + BatchNorm layers for optimization.
            verbose (bool): Enable verbose logging.
        """
        super().__init__()
        nn_module = isinstance(model, torch.nn.Module)
        (
            pt,
            jit,
            onnx,
            xml,
            engine,
            coreml,
            saved_model,
            pb,
            tflite,
            edgetpu,
            tfjs,
            paddle,
            mnn,
            ncnn,
            imx,
            rknn,
            pte,
            triton,
        ) = self._model_type("" if nn_module else model)
        fp16 &= pt or jit or onnx or xml or engine or nn_module or triton  # FP16
        nhwc = coreml or saved_model or pb or tflite or edgetpu or rknn  # BHWC formats (vs torch BCWH)
        stride, ch = 32, 3  # default stride and channels
        end2end, dynamic = False, False
        metadata, task = None, None

        # Set device
        cuda = isinstance(device, torch.device) and torch.cuda.is_available() and device.type != "cpu"  # use CUDA
        if cuda and not any([nn_module, pt, jit, engine, onnx, paddle]):  # GPU dataloader formats
            device = torch.device("cpu")
            cuda = False

        # Download if not local
        w = attempt_download_asset(model) if pt else model  # weights path

        # PyTorch (in-memory or file)
        if nn_module or pt:
            if nn_module:
                pt = True
                if fuse:
                    if IS_JETSON and is_jetson(jetpack=5):
                        # Jetson Jetpack5 requires device before fuse https://github.com/ultralytics/ultralytics/pull/21028
                        model = model.to(device)
                    model = model.fuse(verbose=verbose)
                model = model.to(device)
            else:  # pt file
                from ultralytics.nn.tasks import load_checkpoint

                model, _ = load_checkpoint(model, device=device, fuse=fuse)  # load model, ckpt

            # Common PyTorch model processing
            if hasattr(model, "kpt_shape"):
                kpt_shape = model.kpt_shape  # pose-only
            stride = max(int(model.stride.max()), 32)  # model stride
            names = model.module.names if hasattr(model, "module") else model.names  # get class names
            model.half() if fp16 else model.float()
            ch = model.yaml.get("channels", 3)
            for p in model.parameters():
                p.requires_grad = False
            self.model = model  # explicitly assign for to(), cpu(), cuda(), half()

        # TorchScript
        elif jit:
            import torchvision  # noqa - https://github.com/ultralytics/ultralytics/pull/19747

            LOGGER.info(f"Loading {w} for TorchScript inference...")
            extra_files = {"config.txt": ""}  # model metadata
            model = torch.jit.load(w, _extra_files=extra_files, map_location=device)
            model.half() if fp16 else model.float()
            if extra_files["config.txt"]:  # load metadata dict
                metadata = json.loads(extra_files["config.txt"], object_hook=lambda x: dict(x.items()))

        # ONNX OpenCV DNN
        elif dnn:
            LOGGER.info(f"Loading {w} for ONNX OpenCV DNN inference...")
            check_requirements("opencv-python>=4.5.4")
            net = cv2.dnn.readNetFromONNX(w)

        # ONNX Runtime and IMX
        elif onnx or imx:
            LOGGER.info(f"Loading {w} for ONNX Runtime inference...")
            check_requirements(("onnx", "onnxruntime-gpu" if cuda else "onnxruntime"))
            import onnxruntime

            providers = ["CPUExecutionProvider"]
            if cuda:
                if "CUDAExecutionProvider" in onnxruntime.get_available_providers():
                    providers.insert(0, "CUDAExecutionProvider")
                else:  # Only log warning if CUDA was requested but unavailable
                    LOGGER.warning("Failed to start ONNX Runtime with CUDA. Using CPU...")
                    device = torch.device("cpu")
                    cuda = False
            LOGGER.info(f"Using ONNX Runtime {onnxruntime.__version__} {providers[0]}")
            if onnx:
                session = onnxruntime.InferenceSession(w, providers=providers)
            else:
                check_requirements(
                    ("model-compression-toolkit>=2.4.1", "sony-custom-layers[torch]>=0.3.0", "onnxruntime-extensions")
                )
                w = next(Path(w).glob("*.onnx"))
                LOGGER.info(f"Loading {w} for ONNX IMX inference...")
                import mct_quantizers as mctq
                from sony_custom_layers.pytorch.nms import nms_ort  # noqa

                session_options = mctq.get_ort_session_options()
                session_options.enable_mem_reuse = False  # fix the shape mismatch from onnxruntime
                session = onnxruntime.InferenceSession(w, session_options, providers=["CPUExecutionProvider"])

            output_names = [x.name for x in session.get_outputs()]
            metadata = session.get_modelmeta().custom_metadata_map
            dynamic = isinstance(session.get_outputs()[0].shape[0], str)
            fp16 = "float16" in session.get_inputs()[0].type
            if not dynamic:
                io = session.io_binding()
                bindings = []
                for output in session.get_outputs():
                    out_fp16 = "float16" in output.type
                    y_tensor = torch.empty(output.shape, dtype=torch.float16 if out_fp16 else torch.float32).to(device)
                    io.bind_output(
                        name=output.name,
                        device_type=device.type,
                        device_id=device.index if cuda else 0,
                        element_type=np.float16 if out_fp16 else np.float32,
                        shape=tuple(y_tensor.shape),
                        buffer_ptr=y_tensor.data_ptr(),
                    )
                    bindings.append(y_tensor)

        # OpenVINO
        elif xml:
            LOGGER.info(f"Loading {w} for OpenVINO inference...")
            check_requirements("openvino>=2024.0.0")
            import openvino as ov

            core = ov.Core()
            device_name = "AUTO"
            if isinstance(device, str) and device.startswith("intel"):
                device_name = device.split(":")[1].upper()  # Intel OpenVINO device
                device = torch.device("cpu")
                if device_name not in core.available_devices:
                    LOGGER.warning(f"OpenVINO device '{device_name}' not available. Using 'AUTO' instead.")
                    device_name = "AUTO"
            w = Path(w)
            if not w.is_file():  # if not *.xml
                w = next(w.glob("*.xml"))  # get *.xml file from *_openvino_model dir
            ov_model = core.read_model(model=str(w), weights=w.with_suffix(".bin"))
            if ov_model.get_parameters()[0].get_layout().empty:
                ov_model.get_parameters()[0].set_layout(ov.Layout("NCHW"))

            metadata = w.parent / "metadata.yaml"
            if metadata.exists():
                metadata = YAML.load(metadata)
                batch = metadata["batch"]
                dynamic = metadata.get("args", {}).get("dynamic", dynamic)
            # OpenVINO inference modes are 'LATENCY', 'THROUGHPUT' (not recommended), or 'CUMULATIVE_THROUGHPUT'
            inference_mode = "CUMULATIVE_THROUGHPUT" if batch > 1 and dynamic else "LATENCY"
            ov_compiled_model = core.compile_model(
                ov_model,
                device_name=device_name,
                config={"PERFORMANCE_HINT": inference_mode},
            )
            LOGGER.info(
                f"Using OpenVINO {inference_mode} mode for batch={batch} inference on {', '.join(ov_compiled_model.get_property('EXECUTION_DEVICES'))}..."
            )
            input_name = ov_compiled_model.input().get_any_name()

        # TensorRT
        elif engine:
            LOGGER.info(f"Loading {w} for TensorRT inference...")

            if IS_JETSON and check_version(PYTHON_VERSION, "<=3.8.10"):
                # fix error: `np.bool` was a deprecated alias for the builtin `bool` for JetPack 4 and JetPack 5 with Python <= 3.8.10
                check_requirements("numpy==1.23.5")

            try:  # https://developer.nvidia.com/nvidia-tensorrt-download
                import tensorrt as trt
            except ImportError:
                if LINUX:
                    check_requirements("tensorrt>7.0.0,!=10.1.0")
                import tensorrt as trt
            check_version(trt.__version__, ">=7.0.0", hard=True)
            check_version(trt.__version__, "!=10.1.0", msg="https://github.com/ultralytics/ultralytics/pull/14239")
            if device.type == "cpu":
                device = torch.device("cuda:0")
            Binding = namedtuple("Binding", ("name", "dtype", "shape", "data", "ptr"))
            logger = trt.Logger(trt.Logger.INFO)
            # Read file
            with open(w, "rb") as f, trt.Runtime(logger) as runtime:
                try:
                    meta_len = int.from_bytes(f.read(4), byteorder="little")  # read metadata length
                    metadata = json.loads(f.read(meta_len).decode("utf-8"))  # read metadata
                    dla = metadata.get("dla", None)
                    if dla is not None:
                        runtime.DLA_core = int(dla)
                except UnicodeDecodeError:
                    f.seek(0)  # engine file may lack embedded Ultralytics metadata
                model = runtime.deserialize_cuda_engine(f.read())  # read engine

            # Model context
            try:
                context = model.create_execution_context()
            except Exception as e:  # model is None
                LOGGER.error(f"TensorRT model exported with a different version than {trt.__version__}\n")
                raise e

            bindings = OrderedDict()
            output_names = []
            fp16 = False  # default updated below
            dynamic = False
            is_trt10 = not hasattr(model, "num_bindings")
            num = range(model.num_io_tensors) if is_trt10 else range(model.num_bindings)
            for i in num:
                if is_trt10:
                    name = model.get_tensor_name(i)
                    dtype = trt.nptype(model.get_tensor_dtype(name))
                    is_input = model.get_tensor_mode(name) == trt.TensorIOMode.INPUT
                    if is_input:
                        if -1 in tuple(model.get_tensor_shape(name)):
                            dynamic = True
                            context.set_input_shape(name, tuple(model.get_tensor_profile_shape(name, 0)[1]))
                        if dtype == np.float16:
                            fp16 = True
                    else:
                        output_names.append(name)
                    shape = tuple(context.get_tensor_shape(name))
                else:  # TensorRT < 10.0
                    name = model.get_binding_name(i)
                    dtype = trt.nptype(model.get_binding_dtype(i))
                    is_input = model.binding_is_input(i)
                    if model.binding_is_input(i):
                        if -1 in tuple(model.get_binding_shape(i)):  # dynamic
                            dynamic = True
                            context.set_binding_shape(i, tuple(model.get_profile_shape(0, i)[1]))
                        if dtype == np.float16:
                            fp16 = True
                    else:
                        output_names.append(name)
                    shape = tuple(context.get_binding_shape(i))
                im = torch.from_numpy(np.empty(shape, dtype=dtype)).to(device)
                bindings[name] = Binding(name, dtype, shape, im, int(im.data_ptr()))
            binding_addrs = OrderedDict((n, d.ptr) for n, d in bindings.items())

        # CoreML
        elif coreml:
            check_requirements("coremltools>=8.0")
            LOGGER.info(f"Loading {w} for CoreML inference...")
            import coremltools as ct

            model = ct.models.MLModel(w)
            dynamic = model.get_spec().description.input[0].type.HasField("multiArrayType")
            metadata = dict(model.user_defined_metadata)

        # TF SavedModel
        elif saved_model:
            LOGGER.info(f"Loading {w} for TensorFlow SavedModel inference...")
            import tensorflow as tf

            keras = False  # assume TF1 saved_model
            model = tf.keras.models.load_model(w) if keras else tf.saved_model.load(w)
            metadata = Path(w) / "metadata.yaml"

        # TF GraphDef
        elif pb:  # https://www.tensorflow.org/guide/migrate#a_graphpb_or_graphpbtxt
            LOGGER.info(f"Loading {w} for TensorFlow GraphDef inference...")
            import tensorflow as tf

            from ultralytics.utils.export.tensorflow import gd_outputs

            def wrap_frozen_graph(gd, inputs, outputs):
                """Wrap frozen graphs for deployment."""
                x = tf.compat.v1.wrap_function(lambda: tf.compat.v1.import_graph_def(gd, name=""), [])  # wrapped
                ge = x.graph.as_graph_element
                return x.prune(tf.nest.map_structure(ge, inputs), tf.nest.map_structure(ge, outputs))

            gd = tf.Graph().as_graph_def()  # TF GraphDef
            with open(w, "rb") as f:
                gd.ParseFromString(f.read())
            frozen_func = wrap_frozen_graph(gd, inputs="x:0", outputs=gd_outputs(gd))
            try:  # find metadata in SavedModel alongside GraphDef
                metadata = next(Path(w).resolve().parent.rglob(f"{Path(w).stem}_saved_model*/metadata.yaml"))
            except StopIteration:
                pass

        # TFLite or TFLite Edge TPU
        elif tflite or edgetpu:  # https://ai.google.dev/edge/litert/microcontrollers/python
            try:  # https://coral.ai/docs/edgetpu/tflite-python/#update-existing-tf-lite-code-for-the-edge-tpu
                from tflite_runtime.interpreter import Interpreter, load_delegate
            except ImportError:
                import tensorflow as tf

                Interpreter, load_delegate = tf.lite.Interpreter, tf.lite.experimental.load_delegate
            if edgetpu:  # TF Edge TPU https://coral.ai/software/#edgetpu-runtime
                device = device[3:] if str(device).startswith("tpu") else ":0"
                LOGGER.info(f"Loading {w} on device {device[1:]} for TensorFlow Lite Edge TPU inference...")
                delegate = {"Linux": "libedgetpu.so.1", "Darwin": "libedgetpu.1.dylib", "Windows": "edgetpu.dll"}[
                    platform.system()
                ]
                interpreter = Interpreter(
                    model_path=w,
                    experimental_delegates=[load_delegate(delegate, options={"device": device})],
                )
                device = "cpu"  # Required, otherwise PyTorch will try to use the wrong device
            else:  # TFLite
                LOGGER.info(f"Loading {w} for TensorFlow Lite inference...")
                interpreter = Interpreter(model_path=w)  # load TFLite model
            interpreter.allocate_tensors()  # allocate
            input_details = interpreter.get_input_details()  # inputs
            output_details = interpreter.get_output_details()  # outputs
            # Load metadata
            try:
                with zipfile.ZipFile(w, "r") as zf:
                    name = zf.namelist()[0]
                    contents = zf.read(name).decode("utf-8")
                    if name == "metadata.json":  # Custom Ultralytics metadata dict for Python>=3.12
                        metadata = json.loads(contents)
                    else:
                        metadata = ast.literal_eval(contents)  # Default tflite-support metadata for Python<=3.11
            except (zipfile.BadZipFile, SyntaxError, ValueError, json.JSONDecodeError):
                pass

        # TF.js
        elif tfjs:
            raise NotImplementedError("Ultralytics TF.js inference is not currently supported.")

        # PaddlePaddle
        elif paddle:
            LOGGER.info(f"Loading {w} for PaddlePaddle inference...")
            check_requirements(
                "paddlepaddle-gpu"
                if torch.cuda.is_available()
                else "paddlepaddle==3.0.0"  # pin 3.0.0 for ARM64
                if ARM64
                else "paddlepaddle>=3.0.0"
            )
            import paddle.inference as pdi

            w = Path(w)
            model_file, params_file = None, None
            if w.is_dir():
                model_file = next(w.rglob("*.json"), None)
                params_file = next(w.rglob("*.pdiparams"), None)
            elif w.suffix == ".pdiparams":
                model_file = w.with_name("model.json")
                params_file = w

            if not (model_file and params_file and model_file.is_file() and params_file.is_file()):
                raise FileNotFoundError(f"Paddle model not found in {w}. Both .json and .pdiparams files are required.")

            config = pdi.Config(str(model_file), str(params_file))
            if cuda:
                config.enable_use_gpu(memory_pool_init_size_mb=2048, device_id=0)
            predictor = pdi.create_predictor(config)
            input_handle = predictor.get_input_handle(predictor.get_input_names()[0])
            output_names = predictor.get_output_names()
            metadata = w / "metadata.yaml"

        # MNN
        elif mnn:
            LOGGER.info(f"Loading {w} for MNN inference...")
            check_requirements("MNN")  # requires MNN
            import os

            import MNN

            config = {"precision": "low", "backend": "CPU", "numThread": (os.cpu_count() + 1) // 2}
            rt = MNN.nn.create_runtime_manager((config,))
            net = MNN.nn.load_module_from_file(w, [], [], runtime_manager=rt, rearrange=True)

            def torch_to_mnn(x):
                return MNN.expr.const(x.data_ptr(), x.shape)

            metadata = json.loads(net.get_info()["bizCode"])

        # NCNN
        elif ncnn:
            LOGGER.info(f"Loading {w} for NCNN inference...")
            check_requirements("git+https://github.com/Tencent/ncnn.git" if ARM64 else "ncnn", cmds="--no-deps")
            import ncnn as pyncnn

            net = pyncnn.Net()
            net.opt.use_vulkan_compute = cuda
            w = Path(w)
            if not w.is_file():  # if not *.param
                w = next(w.glob("*.param"))  # get *.param file from *_ncnn_model dir
            net.load_param(str(w))
            net.load_model(str(w.with_suffix(".bin")))
            metadata = w.parent / "metadata.yaml"

        # NVIDIA Triton Inference Server
        elif triton:
            check_requirements("tritonclient[all]")
            from ultralytics.utils.triton import TritonRemoteModel

            model = TritonRemoteModel(w)
            metadata = model.metadata

        # RKNN
        elif rknn:
            if not is_rockchip():
                raise OSError("RKNN inference is only supported on Rockchip devices.")
            LOGGER.info(f"Loading {w} for RKNN inference...")
            check_requirements("rknn-toolkit-lite2")
            from rknnlite.api import RKNNLite

            w = Path(w)
            if not w.is_file():  # if not *.rknn
                w = next(w.rglob("*.rknn"))  # get *.rknn file from *_rknn_model dir
            rknn_model = RKNNLite()
            rknn_model.load_rknn(str(w))
            rknn_model.init_runtime()
            metadata = w.parent / "metadata.yaml"

        # ExecuTorch
        elif pte:
            LOGGER.info(f"Loading {w} for ExecuTorch inference...")
            # TorchAO release compatibility table bug https://github.com/pytorch/ao/issues/2919
            check_requirements("setuptools<71.0.0")  # Setuptools bug: https://github.com/pypa/setuptools/issues/4483
            check_requirements(("executorch==1.0.0", "flatbuffers"))
            from executorch.runtime import Runtime

            w = Path(w)
            if w.is_dir():
                model_file = next(w.rglob("*.pte"))
                metadata = w / "metadata.yaml"
            else:
                model_file = w
                metadata = w.parent / "metadata.yaml"

            program = Runtime.get().load_program(str(model_file))
            model = program.load_method("forward")

        # Any other format (unsupported)
        else:
            from ultralytics.engine.exporter import export_formats

            raise TypeError(
                f"model='{w}' is not a supported model format. Ultralytics supports: {export_formats()['Format']}\n"
                f"See https://docs.ultralytics.com/modes/predict for help."
            )

        # Load external metadata YAML
        if isinstance(metadata, (str, Path)) and Path(metadata).exists():
            metadata = YAML.load(metadata)
        if metadata and isinstance(metadata, dict):
            for k, v in metadata.items():
                if k in {"stride", "batch", "channels"}:
                    metadata[k] = int(v)
                elif k in {"imgsz", "names", "kpt_shape", "kpt_names", "args"} and isinstance(v, str):
                    metadata[k] = eval(v)
            stride = metadata["stride"]
            task = metadata["task"]
            batch = metadata["batch"]
            imgsz = metadata["imgsz"]
            names = metadata["names"]
            kpt_shape = metadata.get("kpt_shape")
            kpt_names = metadata.get("kpt_names")
            end2end = metadata.get("args", {}).get("nms", False)
            dynamic = metadata.get("args", {}).get("dynamic", dynamic)
            ch = metadata.get("channels", 3)
        elif not (pt or triton or nn_module):
            LOGGER.warning(f"Metadata not found for 'model={w}'")

        # Check names
        if "names" not in locals():  # names missing
            names = default_class_names(data)
        names = check_class_names(names)

        self.__dict__.update(locals())  # assign all variables to self

    def forward(
        self,
        im: torch.Tensor,
        augment: bool = False,
        visualize: bool = False,
        embed: list | None = None,
        **kwargs: Any,
    ) -> torch.Tensor | list[torch.Tensor]:
        """
        Run inference on an AutoBackend model.

        Args:
            im (torch.Tensor): The image tensor to perform inference on.
            augment (bool): Whether to perform data augmentation during inference.
            visualize (bool): Whether to visualize the output predictions.
            embed (list, optional): A list of feature vectors/embeddings to return.
            **kwargs (Any): Additional keyword arguments for model configuration.

        Returns:
            (torch.Tensor | list[torch.Tensor]): The raw output tensor(s) from the model.
        """
        _b, _ch, h, w = im.shape  # batch, channel, height, width
        if self.fp16 and im.dtype != torch.float16:
            im = im.half()  # to FP16
        if self.nhwc:
            im = im.permute(0, 2, 3, 1)  # torch BCHW to numpy BHWC shape(1,320,192,3)

        # PyTorch
        if self.pt or self.nn_module:
            y = self.model(im, augment=augment, visualize=visualize, embed=embed, **kwargs)

        # TorchScript
        elif self.jit:
            y = self.model(im)

        # ONNX OpenCV DNN
        elif self.dnn:
            im = im.cpu().numpy()  # torch to numpy
            self.net.setInput(im)
            y = self.net.forward()

        # ONNX Runtime
        elif self.onnx or self.imx:
            if self.dynamic:
                im = im.cpu().numpy()  # torch to numpy
                y = self.session.run(self.output_names, {self.session.get_inputs()[0].name: im})
            else:
                if not self.cuda:
                    im = im.cpu()
                self.io.bind_input(
                    name="images",
                    device_type=im.device.type,
                    device_id=im.device.index if im.device.type == "cuda" else 0,
                    element_type=np.float16 if self.fp16 else np.float32,
                    shape=tuple(im.shape),
                    buffer_ptr=im.data_ptr(),
                )
                self.session.run_with_iobinding(self.io)
                y = self.bindings
            if self.imx:
                if self.task == "detect":
                    # boxes, conf, cls
                    y = np.concatenate([y[0], y[1][:, :, None], y[2][:, :, None]], axis=-1)
                elif self.task == "pose":
                    # boxes, conf, kpts
                    y = np.concatenate([y[0], y[1][:, :, None], y[2][:, :, None], y[3]], axis=-1)

        # OpenVINO
        elif self.xml:
            im = im.cpu().numpy()  # FP32

            if self.inference_mode in {"THROUGHPUT", "CUMULATIVE_THROUGHPUT"}:  # optimized for larger batch-sizes
                n = im.shape[0]  # number of images in batch
                results = [None] * n  # preallocate list with None to match the number of images

                def callback(request, userdata):
                    """Place result in preallocated list using userdata index."""
                    results[userdata] = request.results

                # Create AsyncInferQueue, set the callback and start asynchronous inference for each input image
                async_queue = self.ov.AsyncInferQueue(self.ov_compiled_model)
                async_queue.set_callback(callback)
                for i in range(n):
                    # Start async inference with userdata=i to specify the position in results list
                    async_queue.start_async(inputs={self.input_name: im[i : i + 1]}, userdata=i)  # keep image as BCHW
                async_queue.wait_all()  # wait for all inference requests to complete
                y = [list(r.values()) for r in results]
                y = [np.concatenate(x) for x in zip(*y)]
            else:  # inference_mode = "LATENCY", optimized for fastest first result at batch-size 1
                y = list(self.ov_compiled_model(im).values())

        # TensorRT
        elif self.engine:
            if self.dynamic and im.shape != self.bindings["images"].shape:
                if self.is_trt10:
                    self.context.set_input_shape("images", im.shape)
                    self.bindings["images"] = self.bindings["images"]._replace(shape=im.shape)
                    for name in self.output_names:
                        self.bindings[name].data.resize_(tuple(self.context.get_tensor_shape(name)))
                else:
                    i = self.model.get_binding_index("images")
                    self.context.set_binding_shape(i, im.shape)
                    self.bindings["images"] = self.bindings["images"]._replace(shape=im.shape)
                    for name in self.output_names:
                        i = self.model.get_binding_index(name)
                        self.bindings[name].data.resize_(tuple(self.context.get_binding_shape(i)))

            s = self.bindings["images"].shape
            assert im.shape == s, f"input size {im.shape} {'>' if self.dynamic else 'not equal to'} max model size {s}"
            self.binding_addrs["images"] = int(im.data_ptr())
            self.context.execute_v2(list(self.binding_addrs.values()))
            y = [self.bindings[x].data for x in sorted(self.output_names)]

        # CoreML
        elif self.coreml:
            im = im.cpu().numpy()
            if self.dynamic:
                im = im.transpose(0, 3, 1, 2)
            else:
                im = Image.fromarray((im[0] * 255).astype("uint8"))
            # im = im.resize((192, 320), Image.BILINEAR)
            y = self.model.predict({"image": im})  # coordinates are xywh normalized
            if "confidence" in y:  # NMS included
                from ultralytics.utils.ops import xywh2xyxy

                box = xywh2xyxy(y["coordinates"] * [[w, h, w, h]])  # xyxy pixels
                cls = y["confidence"].argmax(1, keepdims=True)
                y = np.concatenate((box, np.take_along_axis(y["confidence"], cls, axis=1), cls), 1)[None]
            else:
                y = list(y.values())
            if len(y) == 2 and len(y[1].shape) != 4:  # segmentation model
                y = list(reversed(y))  # reversed for segmentation models (pred, proto)

        # PaddlePaddle
        elif self.paddle:
            im = im.cpu().numpy().astype(np.float32)
            self.input_handle.copy_from_cpu(im)
            self.predictor.run()
            y = [self.predictor.get_output_handle(x).copy_to_cpu() for x in self.output_names]

        # MNN
        elif self.mnn:
            input_var = self.torch_to_mnn(im)
            output_var = self.net.onForward([input_var])
            y = [x.read() for x in output_var]

        # NCNN
        elif self.ncnn:
            mat_in = self.pyncnn.Mat(im[0].cpu().numpy())
            with self.net.create_extractor() as ex:
                ex.input(self.net.input_names()[0], mat_in)
                # WARNING: 'output_names' sorted as a temporary fix for https://github.com/pnnx/pnnx/issues/130
                y = [np.array(ex.extract(x)[1])[None] for x in sorted(self.net.output_names())]

        # NVIDIA Triton Inference Server
        elif self.triton:
            im = im.cpu().numpy()  # torch to numpy
            y = self.model(im)

        # RKNN
        elif self.rknn:
            im = (im.cpu().numpy() * 255).astype("uint8")
            im = im if isinstance(im, (list, tuple)) else [im]
            y = self.rknn_model.inference(inputs=im)

        # ExecuTorch
        elif self.pte:
            y = self.model.execute([im])

        # TensorFlow (SavedModel, GraphDef, Lite, Edge TPU)
        else:
            im = im.cpu().numpy()
            if self.saved_model:  # SavedModel
                y = self.model(im, training=False) if self.keras else self.model.serving_default(im)
                if not isinstance(y, list):
                    y = [y]
            elif self.pb:  # GraphDef
                y = self.frozen_func(x=self.tf.constant(im))
            else:  # Lite or Edge TPU
                details = self.input_details[0]
                is_int = details["dtype"] in {np.int8, np.int16}  # is TFLite quantized int8 or int16 model
                if is_int:
                    scale, zero_point = details["quantization"]
                    im = (im / scale + zero_point).astype(details["dtype"])  # de-scale
                self.interpreter.set_tensor(details["index"], im)
                self.interpreter.invoke()
                y = []
                for output in self.output_details:
                    x = self.interpreter.get_tensor(output["index"])
                    if is_int:
                        scale, zero_point = output["quantization"]
                        x = (x.astype(np.float32) - zero_point) * scale  # re-scale
                    if x.ndim == 3:  # if task is not classification, excluding masks (ndim=4) as well
                        # Denormalize xywh by image size. See https://github.com/ultralytics/ultralytics/pull/1695
                        # xywh are normalized in TFLite/EdgeTPU to mitigate quantization error of integer models
                        if x.shape[-1] == 6 or self.end2end:  # end-to-end model
                            x[:, :, [0, 2]] *= w
                            x[:, :, [1, 3]] *= h
                            if self.task == "pose":
                                x[:, :, 6::3] *= w
                                x[:, :, 7::3] *= h
                        else:
                            x[:, [0, 2]] *= w
                            x[:, [1, 3]] *= h
                            if self.task == "pose":
                                x[:, 5::3] *= w
                                x[:, 6::3] *= h
                    y.append(x)
            # TF segment fixes: export is reversed vs ONNX export and protos are transposed
            if len(y) == 2:  # segment with (det, proto) output order reversed
                if len(y[1].shape) != 4:
                    y = list(reversed(y))  # should be y = (1, 116, 8400), (1, 160, 160, 32)
                if y[1].shape[-1] == 6:  # end-to-end model
                    y = [y[1]]
                else:
                    y[1] = np.transpose(y[1], (0, 3, 1, 2))  # should be y = (1, 116, 8400), (1, 32, 160, 160)
            y = [x if isinstance(x, np.ndarray) else x.numpy() for x in y]

        # for x in y:
        #     print(type(x), len(x)) if isinstance(x, (list, tuple)) else print(type(x), x.shape)  # debug shapes
        if isinstance(y, (list, tuple)):
            if len(self.names) == 999 and (self.task == "segment" or len(y) == 2):  # segments and names not defined
                nc = y[0].shape[1] - y[1].shape[1] - 4  # y = (1, 32, 160, 160), (1, 116, 8400)
                self.names = {i: f"class{i}" for i in range(nc)}
            return self.from_numpy(y[0]) if len(y) == 1 else [self.from_numpy(x) for x in y]
        else:
            return self.from_numpy(y)

    def from_numpy(self, x: np.ndarray) -> torch.Tensor:
        """
        Convert a numpy array to a tensor.

        Args:
            x (np.ndarray): The array to be converted.

        Returns:
            (torch.Tensor): The converted tensor
        """
        return torch.tensor(x).to(self.device) if isinstance(x, np.ndarray) else x

    def warmup(self, imgsz: tuple[int, int, int, int] = (1, 3, 640, 640)) -> None:
        """
        Warm up the model by running one forward pass with a dummy input.

        Args:
            imgsz (tuple): The shape of the dummy input tensor in the format (batch_size, channels, height, width)
        """
        warmup_types = self.pt, self.jit, self.onnx, self.engine, self.saved_model, self.pb, self.triton, self.nn_module
        if any(warmup_types) and (self.device.type != "cpu" or self.triton):
            im = torch.empty(*imgsz, dtype=torch.half if self.fp16 else torch.float, device=self.device)  # input
            for _ in range(2 if self.jit else 1):
                self.forward(im)  # warmup model
                warmup_boxes = torch.rand(1, 84, 16, device=self.device)  # 16 boxes works best empirically
                warmup_boxes[:, :4] *= imgsz[-1]
                non_max_suppression(warmup_boxes)  # warmup NMS

    @staticmethod
    def _model_type(p: str = "path/to/model.pt") -> list[bool]:
        """
        Take a path to a model file and return the model type.

        Args:
            p (str): Path to the model file.

        Returns:
            (list[bool]): List of booleans indicating the model type.

        Examples:
            >>> model = AutoBackend(model="path/to/model.onnx")
            >>> model_type = model._model_type()  # returns "onnx"
        """
        from ultralytics.engine.exporter import export_formats

        sf = export_formats()["Suffix"]  # export suffixes
        if not is_url(p) and not isinstance(p, str):
            check_suffix(p, sf)  # checks
        name = Path(p).name
        types = [s in name for s in sf]
        types[5] |= name.endswith(".mlmodel")  # retain support for older Apple CoreML *.mlmodel formats
        types[8] &= not types[9]  # tflite &= not edgetpu
        if any(types):
            triton = False
        else:
            from urllib.parse import urlsplit

            url = urlsplit(p)
            triton = bool(url.netloc) and bool(url.path) and url.scheme in {"http", "grpc"}

        return [*types, triton]
