class AutoBackend(nn.Module):

    def __init__(self,
                 weights='yolov8n.pt',
                 device=torch.device('cpu'),
                 dnn=False,
                 data=None,
                 fp16=False,
                 fuse=True,
                 verbose=True):
        """
        MultiBackend class for python inference on various platforms using Ultralytics YOLO.

        Args:
            weights (str): The path to the weights file. Default: 'yolov8n.pt'
            device (torch.device): The device to run the model on.
            dnn (bool): Use OpenCV's DNN module for inference if True, defaults to False.
            data (str), (Path): Additional data.yaml file for class names, optional
            fp16 (bool): If True, use half precision. Default: False
            fuse (bool): Whether to fuse the model or not. Default: True
            verbose (bool): Whether to run in verbose mode or not. Default: True

        Supported formats and their naming conventions:
            | Format                | Suffix           |
            |-----------------------|------------------|
            | PyTorch               | *.pt             |
            | TorchScript           | *.torchscript    |
            | ONNX Runtime          | *.onnx           |
            | ONNX OpenCV DNN       | *.onnx dnn=True  |
            | OpenVINO              | *.xml            |
            | CoreML                | *.mlmodel        |
            | TensorRT              | *.engine         |
            | TensorFlow SavedModel | *_saved_model    |
            | TensorFlow GraphDef   | *.pb             |
            | TensorFlow Lite       | *.tflite         |
            | TensorFlow Edge TPU   | *_edgetpu.tflite |
            | PaddlePaddle          | *_paddle_model   |
        """
        super().__init__()
        w = str(weights[0] if isinstance(weights, list) else weights)
        nn_module = isinstance(weights, torch.nn.Module)
        pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle, triton = self._model_type(w)
        fp16 &= pt or jit or onnx or engine or nn_module or triton  # FP16
        nhwc = coreml or saved_model or pb or tflite or edgetpu  # BHWC formats (vs torch BCWH)
        stride = 32  # default stride
        model, metadata = None, None
        cuda = torch.cuda.is_available() and device.type != 'cpu'  # use CUDA
        if not (pt or triton or nn_module):
            w = attempt_download_asset(w)  # download if not local

        # NOTE: special case: in-memory pytorch model
        if nn_module:
            model = weights.to(device)
            model = model.fuse(verbose=verbose) if fuse else model
            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()
            self.model = model  # explicitly assign for to(), cpu(), cuda(), half()
            pt = True
        elif pt:  # PyTorch
            from ultralytics.nn.tasks import attempt_load_weights
            model = attempt_load_weights(weights if isinstance(weights, list) else w,
                                         device=device,
                                         inplace=True,
                                         fuse=fuse)
            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()
            self.model = model  # explicitly assign for to(), cpu(), cuda(), half()
        elif jit:  # TorchScript
            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()))
        elif dnn:  # ONNX OpenCV DNN
            LOGGER.info(f'Loading {w} for ONNX OpenCV DNN inference...')
            check_requirements('opencv-python>=4.5.4')
            net = cv2.dnn.readNetFromONNX(w)
        elif onnx:  # ONNX Runtime
            LOGGER.info(f'Loading {w} for ONNX Runtime inference...')
            check_requirements(('onnx', 'onnxruntime-gpu' if cuda else 'onnxruntime'))
            import onnxruntime
            providers = ['CUDAExecutionProvider', 'CPUExecutionProvider'] if cuda else ['CPUExecutionProvider']
            session = onnxruntime.InferenceSession(w, providers=providers)
            output_names = [x.name for x in session.get_outputs()]
            metadata = session.get_modelmeta().custom_metadata_map  # metadata
        elif xml:  # OpenVINO
            LOGGER.info(f'Loading {w} for OpenVINO inference...')
            check_requirements('openvino')  # requires openvino-dev: https://pypi.org/project/openvino-dev/
            from openvino.runtime import Core, Layout, get_batch  # noqa
            ie = Core()
            w = Path(w)
            if not w.is_file():  # if not *.xml
                w = next(w.glob('*.xml'))  # get *.xml file from *_openvino_model dir
            network = ie.read_model(model=str(w), weights=w.with_suffix('.bin'))
            if network.get_parameters()[0].get_layout().empty:
                network.get_parameters()[0].set_layout(Layout('NCHW'))
            batch_dim = get_batch(network)
            if batch_dim.is_static:
                batch_size = batch_dim.get_length()
            executable_network = ie.compile_model(network, device_name='CPU')  # device_name="MYRIAD" for NCS2
            metadata = w.parent / 'metadata.yaml'
        elif engine:  # TensorRT
            LOGGER.info(f'Loading {w} for TensorRT inference...')
            try:
                import tensorrt as trt  # noqa https://developer.nvidia.com/nvidia-tensorrt-download
            except ImportError:
                if LINUX:
                    check_requirements('nvidia-tensorrt', cmds='-U --index-url https://pypi.ngc.nvidia.com')
                import tensorrt as trt  # noqa
            check_version(trt.__version__, '7.0.0', hard=True)  # require tensorrt>=7.0.0
            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:
                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
                model = runtime.deserialize_cuda_engine(f.read())  # read engine
            context = model.create_execution_context()
            bindings = OrderedDict()
            output_names = []
            fp16 = False  # default updated below
            dynamic = False
            for i in range(model.num_bindings):
                name = model.get_binding_name(i)
                dtype = trt.nptype(model.get_binding_dtype(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)[2]))
                    if dtype == np.float16:
                        fp16 = True
                else:  # output
                    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())
            batch_size = bindings['images'].shape[0]  # if dynamic, this is instead max batch size
        elif coreml:  # CoreML
            LOGGER.info(f'Loading {w} for CoreML inference...')
            import coremltools as ct
            model = ct.models.MLModel(w)
            metadata = dict(model.user_defined_metadata)
        elif saved_model:  # TF SavedModel
            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'
        elif pb:  # GraphDef 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.yolo.engine.exporter 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))
        elif tflite or edgetpu:  # https://www.tensorflow.org/lite/guide/python#install_tensorflow_lite_for_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
                LOGGER.info(f'Loading {w} 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)])
            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
            with contextlib.suppress(zipfile.BadZipFile):
                with zipfile.ZipFile(w, 'r') as model:
                    meta_file = model.namelist()[0]
                    metadata = ast.literal_eval(model.read(meta_file).decode('utf-8'))
        elif tfjs:  # TF.js
            raise NotImplementedError('YOLOv8 TF.js inference is not supported')
        elif paddle:  # PaddlePaddle
            LOGGER.info(f'Loading {w} for PaddlePaddle inference...')
            check_requirements('paddlepaddle-gpu' if cuda else 'paddlepaddle')
            import paddle.inference as pdi  # noqa
            w = Path(w)
            if not w.is_file():  # if not *.pdmodel
                w = next(w.rglob('*.pdmodel'))  # get *.pdmodel file from *_paddle_model dir
            config = pdi.Config(str(w), str(w.with_suffix('.pdiparams')))
            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.parents[1] / 'metadata.yaml'
        elif triton:  # NVIDIA Triton Inference Server
            LOGGER.info('Triton Inference Server not supported...')
            '''
            TODO:
            check_requirements('tritonclient[all]')
            from utils.triton import TritonRemoteModel
            model = TritonRemoteModel(url=w)
            nhwc = model.runtime.startswith("tensorflow")
            '''
        else:
            from ultralytics.yolo.engine.exporter import export_formats
            raise TypeError(f"model='{w}' is not a supported model format. "
                            'See https://docs.ultralytics.com/modes/predict for help.'
                            f'\n\n{export_formats()}')

        # Load external metadata YAML
        if isinstance(metadata, (str, Path)) and Path(metadata).exists():
            metadata = yaml_load(metadata)
        if metadata:
            for k, v in metadata.items():
                if k in ('stride', 'batch'):
                    metadata[k] = int(v)
                elif k in ('imgsz', 'names', 'kpt_shape') 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')
        elif not (pt or triton or nn_module):
            LOGGER.warning(f"WARNING ⚠️ Metadata not found for 'model={weights}'")

        # Check names
        if 'names' not in locals():  # names missing
            names = self._apply_default_class_names(data)
        names = check_class_names(names)

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

    def forward(self, im, augment=False, visualize=False):
        """
        Runs inference on the YOLOv8 MultiBackend model.

        Args:
            im (torch.Tensor): The image tensor to perform inference on.
            augment (bool): whether to perform data augmentation during inference, defaults to False
            visualize (bool): whether to visualize the output predictions, defaults to False

        Returns:
            (tuple): Tuple containing the raw output tensor, and processed output for visualization (if visualize=True)
        """
        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)

        if self.pt or self.nn_module:  # PyTorch
            y = self.model(im, augment=augment, visualize=visualize) if augment or visualize else self.model(im)
        elif self.jit:  # TorchScript
            y = self.model(im)
        elif self.dnn:  # ONNX OpenCV DNN
            im = im.cpu().numpy()  # torch to numpy
            self.net.setInput(im)
            y = self.net.forward()
        elif self.onnx:  # ONNX Runtime
            im = im.cpu().numpy()  # torch to numpy
            y = self.session.run(self.output_names, {self.session.get_inputs()[0].name: im})
        elif self.xml:  # OpenVINO
            im = im.cpu().numpy()  # FP32
            y = list(self.executable_network([im]).values())
        elif self.engine:  # TensorRT
            if self.dynamic and im.shape != self.bindings['images'].shape:
                i = self.model.get_binding_index('images')
                self.context.set_binding_shape(i, im.shape)  # reshape if dynamic
                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)]
        elif self.coreml:  # CoreML
            im = im[0].cpu().numpy()
            im_pil = Image.fromarray((im * 255).astype('uint8'))
            # im = im.resize((192, 320), Image.ANTIALIAS)
            y = self.model.predict({'image': im_pil})  # coordinates are xywh normalized
            if 'confidence' in y:
                box = xywh2xyxy(y['coordinates'] * [[w, h, w, h]])  # xyxy pixels
                conf, cls = y['confidence'].max(1), y['confidence'].argmax(1).astype(np.float)
                y = np.concatenate((box, conf.reshape(-1, 1), cls.reshape(-1, 1)), 1)
            elif len(y) == 1:  # classification model
                y = list(y.values())
            elif len(y) == 2:  # segmentation model
                y = list(reversed(y.values()))  # reversed for segmentation models (pred, proto)
        elif self.paddle:  # PaddlePaddle
            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]
        elif self.triton:  # NVIDIA Triton Inference Server
            y = self.model(im)
        else:  # TensorFlow (SavedModel, GraphDef, Lite, Edge TPU)
            im = im.cpu().numpy()
            if self.saved_model:  # SavedModel
                y = self.model(im, training=False) if self.keras else self.model(im)
                if not isinstance(y, list):
                    y = [y]
            elif self.pb:  # GraphDef
                y = self.frozen_func(x=self.tf.constant(im))
                if len(y) == 2 and len(self.names) == 999:  # segments and names not defined
                    ip, ib = (0, 1) if len(y[0].shape) == 4 else (1, 0)  # index of protos, boxes
                    nc = y[ib].shape[1] - y[ip].shape[3] - 4  # y = (1, 160, 160, 32), (1, 116, 8400)
                    self.names = {i: f'class{i}' for i in range(nc)}
            else:  # Lite or Edge TPU
                input = self.input_details[0]
                int8 = input['dtype'] == np.int8  # is TFLite quantized int8 model
                if int8:
                    scale, zero_point = input['quantization']
                    im = (im / scale + zero_point).astype(np.int8)  # de-scale
                self.interpreter.set_tensor(input['index'], im)
                self.interpreter.invoke()
                y = []
                for output in self.output_details:
                    x = self.interpreter.get_tensor(output['index'])
                    if int8:
                        scale, zero_point = output['quantization']
                        x = (x.astype(np.float32) - zero_point) * scale  # re-scale
                    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)
                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]
            # y[0][..., :4] *= [w, h, w, h]  # xywh normalized to pixels

        # 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)):
            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):
        """
         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=(1, 3, 640, 640)):
        """
        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)

        Returns:
            (None): This method runs the forward pass and don't return any value
        """
        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

    @staticmethod
    def _apply_default_class_names(data):
        """Applies default class names to an input YAML file or returns numerical class names."""
        with contextlib.suppress(Exception):
            return yaml_load(check_yaml(data))['names']
        return {i: f'class{i}' for i in range(999)}  # return default if above errors

    @staticmethod
    def _model_type(p='path/to/model.pt'):
        """
        This function takes a path to a model file and returns the model type

        Args:
            p: path to the model file. Defaults to path/to/model.pt
        """
        # Return model type from model path, i.e. path='path/to/model.onnx' -> type=onnx
        # types = [pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle]
        from ultralytics.yolo.engine.exporter import export_formats
        sf = list(export_formats().Suffix)  # export suffixes
        if not is_url(p, check=False) and not isinstance(p, str):
            check_suffix(p, sf)  # checks
        url = urlparse(p)  # if url may be Triton inference server
        types = [s in Path(p).name for s in sf]
        types[8] &= not types[9]  # tflite &= not edgetpu
        triton = not any(types) and all([any(s in url.scheme for s in ['http', 'grpc']), url.netloc])
        return types + [triton]