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

from __future__ import annotations

import contextlib
import pickle
import re
import threading
from copy import deepcopy
from pathlib import Path

import torch
from torch import nn

from ultralytics.nn.autobackend import check_class_names
from ultralytics.nn.modules import (
    AIFI,
    C1,
    C2,
    C2PSA,
    C3,
    C3TR,
    ELAN1,
    OBB,
    OBB26,
    PSA,
    SPP,
    SPPELAN,
    SPPF,
    A2C2f,
    AConv,
    ADown,
    Bottleneck,
    BottleneckCSP,
    C2f,
    C2fAttn,
    C2fCIB,
    C2fPSA,
    C3Ghost,
    C3k2,
    C3x,
    CBFuse,
    CBLinear,
    Classify,
    Concat,
    Conv,
    Conv2,
    ConvTranspose,
    Depth,
    Detect,
    DWConv,
    DWConvTranspose2d,
    Focus,
    GhostBottleneck,
    GhostConv,
    HGBlock,
    HGStem,
    ImagePoolingAttn,
    Index,
    LRPCHead,
    Pose,
    Pose26,
    RepC3,
    RepConv,
    RepNCSPELAN4,
    RepVGGDW,
    ResNetLayer,
    RTDETRDecoder,
    SCDown,
    Segment,
    Segment26,
    SemanticSegment,
    TorchVision,
    WorldDetect,
    YOLOEDetect,
    YOLOESegment,
    YOLOESegment26,
    v10Detect,
)
from ultralytics.utils import (
    DEFAULT_CFG_DICT,
    LOGGER,
    SAFE_LOAD,
    SETTINGS,
    WINDOWS,
    YAML,
    IterableSimpleNamespace,
    colorstr,
    emojis,
)
from ultralytics.utils.checks import REMOTE_FILE_PREFIXES, check_file, check_requirements, check_suffix, check_yaml
from ultralytics.utils.loss import (
    DepthLoss26,
    E2ELoss,
    PoseLoss26,
    SemanticSegmentationLoss,
    v8ClassificationLoss,
    v8DetectionLoss,
    v8OBBLoss,
    v8PoseLoss,
    v8SegmentationLoss,
)
from ultralytics.utils.ops import make_divisible
from ultralytics.utils.patches import torch_load
from ultralytics.utils.torch_utils import (
    fuse_conv_and_bn,
    fuse_deconv_and_bn,
    initialize_weights,
    intersect_dicts,
    model_info,
    scale_img,
    smart_inference_mode,
    time_sync,
)


class BaseModel(torch.nn.Module):
    """Base class for all YOLO models in the Ultralytics family.

    This class provides common functionality for YOLO models including forward pass handling, model fusion, information
    display, and weight loading capabilities.

    Attributes:
        model (torch.nn.Sequential): The neural network model.
        save (list): List of layer indices to save outputs from.
        stride (torch.Tensor): Model stride values.

    Methods:
        forward: Perform forward pass for training or inference.
        predict: Perform inference on input tensor.
        fuse: Fuse Conv/BatchNorm layers and reparameterize for optimization.
        info: Print model information.
        load: Load weights into the model.
        loss: Compute loss for training.

    Examples:
        Create a BaseModel instance
        >>> model = BaseModel()
        >>> model.info()  # Display model information
    """

    def forward(self, x, *args, **kwargs):
        """Perform forward pass of the model for either training or inference.

        If x is a dict, calculates and returns the loss for training. Otherwise, returns predictions for inference.

        Args:
            x (torch.Tensor | dict): Input tensor for inference, or dict with image tensor and labels for training.
            *args (Any): Variable length argument list.
            **kwargs (Any): Arbitrary keyword arguments.

        Returns:
            (torch.Tensor): Loss if x is a dict (training), or network predictions (inference).
        """
        if isinstance(x, dict):  # for cases of training and validating while training.
            return self.loss(x, *args, **kwargs)
        return self.predict(x, *args, **kwargs)

    def predict(self, x, profile=False, augment=False, embed=None):
        """Perform a forward pass through the network.

        Args:
            x (torch.Tensor): The input tensor to the model.
            profile (bool): Print the computation time of each layer if True.
            augment (bool): Augment image during prediction.
            embed (list, optional): A list of layer indices to return embeddings from.

        Returns:
            (torch.Tensor): The last output of the model.
        """
        if augment:
            return self._predict_augment(x)
        return self._predict_once(x, profile, embed)

    def _predict_once(self, x, profile=False, embed=None):
        """Perform a forward pass through the network.

        Args:
            x (torch.Tensor): The input tensor to the model.
            profile (bool): Print the computation time of each layer if True.
            embed (list, optional): A list of layer indices to return embeddings from.

        Returns:
            (torch.Tensor): The last output of the model.
        """
        y, dt, embeddings = [], [], []  # outputs
        embed = frozenset(embed) if embed else {-1}
        max_idx = max(embed)
        for m in self.model:
            if m.f != -1:  # if not from previous layer
                x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f]  # from earlier layers
            if profile:
                self._profile_one_layer(m, x, dt)
            x = m(x)  # run
            y.append(x if m.i in self.save else None)  # save output
            if m.i in embed:
                embeddings.append(torch.nn.functional.adaptive_avg_pool2d(x, (1, 1)).squeeze(-1).squeeze(-1))  # flatten
                if m.i == max_idx:
                    return torch.unbind(torch.cat(embeddings, 1), dim=0)
        return x

    def _predict_augment(self, x):
        """Perform augmentations on input image x and return augmented inference."""
        LOGGER.warning(
            f"{self.__class__.__name__} does not support 'augment=True' prediction. "
            f"Reverting to single-scale prediction."
        )
        return self._predict_once(x)

    def _profile_one_layer(self, m, x, dt):
        """Profile the computation time and FLOPs of a single layer of the model on a given input.

        Args:
            m (torch.nn.Module): The layer to be profiled.
            x (torch.Tensor): The input data to the layer.
            dt (list): A list to store the computation time of the layer.
        """
        try:
            import thop
        except ImportError:
            thop = None  # conda support without 'ultralytics-thop' installed

        c = m == self.model[-1] and isinstance(x, list)  # is final layer list, copy input as inplace fix
        flops = thop.profile(m, inputs=[x.copy() if c else x], verbose=False)[0] / 1e9 * 2 if thop else 0
        device = next(self.parameters()).device
        t = time_sync(device)
        for _ in range(10):
            m(x.copy() if c else x)
        dt.append((time_sync(device) - t) * 100)
        if m == self.model[0]:
            LOGGER.info(f"{'time (ms)':>10s} {'GFLOPs':>10s} {'params':>10s}  module")
        LOGGER.info(f"{dt[-1]:10.2f} {flops:10.2f} {m.np:10.0f}  {m.type}")
        if c:
            LOGGER.info(f"{sum(dt):10.2f} {'-':>10s} {'-':>10s}  Total")

    def fuse(self, verbose=True, imgsz=640):
        """Fuse Conv/ConvTranspose and BatchNorm layers, and reparameterize RepConv/RepVGGDW for improved efficiency.

        Args:
            verbose (bool): Whether to print model information after fusion.
            imgsz (int | list): Input image size used for FLOPs calculation.

        Returns:
            (torch.nn.Module): The fused model is returned.
        """
        if not self.is_fused():
            for m in self.model.modules():
                if isinstance(m, (Conv, Conv2, DWConv)) and hasattr(m, "bn"):
                    if isinstance(m, Conv2):
                        m.fuse_convs()
                    m.conv = fuse_conv_and_bn(m.conv, m.bn)  # update conv
                    delattr(m, "bn")  # remove batchnorm
                    m.forward = m.forward_fuse  # update forward
                if isinstance(m, ConvTranspose) and hasattr(m, "bn"):
                    m.conv_transpose = fuse_deconv_and_bn(m.conv_transpose, m.bn)
                    delattr(m, "bn")  # remove batchnorm
                    m.forward = m.forward_fuse  # update forward
                if isinstance(m, RepConv):
                    m.fuse_convs()
                    m.forward = m.forward_fuse  # update forward
                if isinstance(m, RepVGGDW):
                    m.fuse()
                    m.forward = m.forward_fuse
                if isinstance(m, Detect) and getattr(m, "end2end", False):
                    m.fuse()  # remove one2many head
            self.info(verbose=verbose, imgsz=imgsz)

        return self

    def is_fused(self, thresh=10):
        """Check if the model has less than a certain threshold of normalization layers.

        Args:
            thresh (int, optional): The threshold number of normalization layers.

        Returns:
            (bool): True if the number of normalization layers in the model is less than the threshold, False otherwise.
        """
        bn = tuple(v for k, v in torch.nn.__dict__.items() if "Norm" in k)  # normalization layers, i.e. BatchNorm2d()
        return sum(isinstance(v, bn) for v in self.modules()) < thresh  # True if < 'thresh' BatchNorm layers in model

    def info(self, detailed=False, verbose=True, imgsz=640):
        """Print model information.

        Args:
            detailed (bool): If True, prints out detailed information about the model.
            verbose (bool): If True, prints out the model information.
            imgsz (int): The size of the image used for computing model information.
        """
        return model_info(self, detailed=detailed, verbose=verbose, imgsz=imgsz)

    def _apply(self, fn):
        """Apply a function to all tensors in the model, including Detect head attributes like stride and anchors.

        Args:
            fn (function): The function to apply to the model.

        Returns:
            (BaseModel): An updated BaseModel object.
        """
        super()._apply(fn)
        m = self.model[-1]  # Detect()
        if isinstance(
            m, Detect
        ):  # includes all Detect subclasses like Segment, Pose, OBB, WorldDetect, YOLOEDetect, YOLOESegment
            m.stride = fn(m.stride)
            m.anchors = fn(m.anchors)
            m.strides = fn(m.strides)
        return self

    def load(self, weights, verbose=True):
        """Load weights into the model.

        Args:
            weights (dict | torch.nn.Module): The pre-trained weights to be loaded.
            verbose (bool, optional): Whether to log the transfer progress.
        """
        model = weights["model"] if isinstance(weights, dict) else weights  # torchvision models are not dicts
        csd = model.float().state_dict()  # checkpoint state_dict as FP32

        # Remap classification head rows by class-name when nc differs (e.g. Obj365 -> COCO fine-tune)
        cls_remapped = self._remap_cls_by_names(csd, model, verbose=verbose)

        updated_csd = intersect_dicts(csd, self.state_dict())  # intersect
        self.load_state_dict(updated_csd, strict=False)  # load
        len_updated_csd = len(updated_csd) + cls_remapped
        first_conv = "model.0.conv.weight"  # hard-coded to yolo models for now
        # mostly used to boost multi-channel training
        state_dict = self.state_dict()
        if first_conv not in updated_csd and first_conv in state_dict:
            c1, c2, h, w = state_dict[first_conv].shape
            cc1, cc2, ch, cw = csd[first_conv].shape
            if ch == h and cw == w:
                c1, c2 = min(c1, cc1), min(c2, cc2)
                state_dict[first_conv][:c1, :c2] = csd[first_conv][:c1, :c2]
                len_updated_csd += 1
        if verbose:
            LOGGER.info(f"Transferred {len_updated_csd}/{len(self.model.state_dict())} items from pretrained weights")

    def _remap_cls_by_names(self, csd: dict[str, torch.Tensor], src_model: torch.nn.Module, verbose: bool = True):
        """Remap pretrained classification head rows to current class order by name.

        Copies rows from pretrained cls layers into the current model's state_dict where the destination class name
        matches a source class name (case-insensitive, whitespace-stripped). Useful when fine-tuning across datasets
        with overlapping classes, whether the class counts differ (e.g. Objects365 -> COCO) or match but the class
        order differs. Mutates the destination tensors in-place via state_dict references; matched cls tensors are
        removed from `csd` so the subsequent intersect_dicts skips them.

        Args:
            csd (dict): Pretrained checkpoint state_dict (will be mutated).
            src_model (torch.nn.Module): Pretrained module, used to read `.names` and `.nc`.
            verbose (bool): Log mapping summary.

        Returns:
            (int): Number of cls tensors remapped (counted toward "Transferred" log line).
        """
        src_names = getattr(src_model, "names", None)
        tgt_names = getattr(self, "names", None)
        if not (isinstance(src_names, dict) and isinstance(tgt_names, dict)):
            return 0
        src_nc, tgt_nc = len(src_names), len(tgt_names)

        def _norm(s):
            return str(s).strip().lower()

        # Skip default placeholder names {0:"0", 1:"1", ...} (also catches empty dicts) — nothing to match on
        if any(all(str(k) == str(v) for k, v in n.items()) for n in (src_names, tgt_names)):
            return 0

        src_lookup = {_norm(v): k for k, v in src_names.items()}
        idx = torch.tensor([src_lookup.get(_norm(tgt_names.get(k)), -1) for k in range(tgt_nc)], dtype=torch.long)
        n_match = int((idx >= 0).sum())
        # Skip if nothing matches, or class names already share order and count (intersect_dicts copies directly)
        if n_match == 0 or (src_nc == tgt_nc and torch.equal(idx, torch.arange(tgt_nc))):
            return 0

        valid = idx >= 0
        state_dict = self.state_dict()
        # Exact class-logit conv weight/bias keys from the detection head(s) — restricting to these avoids
        # class-ordering tensors that merely share the nc dimension (backbone blocks, box/mask/pose branches).
        cls_keys = {
            f"{name}.{attr}.{i}.{len(seq) - 1}.{p}"
            for name, m in self.named_modules()
            if isinstance(m, Detect)
            for attr in ("cv3", "one2one_cv3")
            for i, seq in enumerate(getattr(m, attr, ()))
            if getattr(seq[-1], "out_channels", None) == tgt_nc
            for p in ("weight", "bias")
        }
        remapped = 0
        for k in cls_keys & csd.keys():
            v_src, v_tgt = csd[k], state_dict[k]
            if v_src.shape[1:] != v_tgt.shape[1:]:  # cls-conv weight input width (c3) differs across nc; copy bias only
                continue
            v_tgt[valid] = v_src[idx[valid]].to(v_tgt.dtype)
            csd.pop(k)  # prevent intersect_dicts from copying these rows in the wrong (source) order
            remapped += 1
        if verbose and remapped:
            LOGGER.info(f"Remapped {n_match}/{tgt_nc} cls head rows from pretrained weights by class name")
        return remapped

    def loss(self, batch, preds=None):
        """Compute loss.

        Args:
            batch (dict): Batch to compute loss on.
            preds (torch.Tensor | list[torch.Tensor], optional): Predictions.
        """
        if getattr(self, "criterion", None) is None:
            self.criterion = self.init_criterion()

        if preds is None:
            preds = self.forward(batch["img"])
        return self.criterion(preds, batch)

    def init_criterion(self):
        """Initialize the loss criterion for the BaseModel."""
        raise NotImplementedError("compute_loss() needs to be implemented by task heads")


def _initialize_yolo_model(model, cfg, ch, nc, verbose):
    """Initialize common YOLO model attributes from a YAML config."""
    model.yaml = cfg if isinstance(cfg, dict) else yaml_model_load(cfg)  # cfg dict
    if model.yaml["backbone"][0][2] == "Silence":
        LOGGER.warning(
            "YOLOv9 `Silence` module is deprecated in favor of torch.nn.Identity. "
            "Please delete local *.pt file and re-download the latest model checkpoint."
        )
        model.yaml["backbone"][0][2] = "nn.Identity"

    model.yaml["channels"] = ch  # save channels
    if nc and nc != model.yaml["nc"]:
        LOGGER.info(f"Overriding model.yaml nc={model.yaml['nc']} with nc={nc}")
        model.yaml["nc"] = nc  # override YAML value
    model.model, model.save = parse_model(deepcopy(model.yaml), ch=ch, verbose=verbose)  # model, savelist
    model.names = {i: f"{i}" for i in range(model.yaml["nc"])}  # default names dict
    model.inplace = model.yaml.get("inplace", True)


class DetectionModel(BaseModel):
    """YOLO detection model.

    This class implements the YOLO detection architecture, handling model initialization, forward pass, augmented
    inference, and loss computation for object detection tasks.

    Attributes:
        yaml (dict): Model configuration dictionary.
        model (torch.nn.Sequential): The neural network model.
        save (list): List of layer indices to save outputs from.
        names (dict): Class names dictionary.
        inplace (bool): Whether to use inplace operations.
        end2end (bool): Whether the model uses end-to-end detection.
        stride (torch.Tensor): Model stride values.

    Methods:
        __init__: Initialize the YOLO detection model.
        _predict_augment: Perform augmented inference.
        _descale_pred: De-scale predictions following augmented inference.
        _clip_augmented: Clip YOLO augmented inference tails.
        init_criterion: Initialize the loss criterion.

    Examples:
        Initialize a detection model
        >>> model = DetectionModel("yolo26n.yaml", ch=3, nc=80)
        >>> results = model.predict(image_tensor)
    """

    def __init__(self, cfg="yolo26n.yaml", ch=3, nc=None, verbose=True):
        """Initialize the YOLO detection model with the given config and parameters.

        Args:
            cfg (str | dict): Model configuration file path or dictionary.
            ch (int): Number of input channels.
            nc (int, optional): Number of classes.
            verbose (bool): Whether to display model information.
        """
        super().__init__()
        _initialize_yolo_model(self, cfg, ch, nc, verbose)

        # Build strides
        m = self.model[-1]  # Detect()
        if isinstance(m, Detect):  # includes all Detect subclasses like Segment, Pose, OBB, YOLOEDetect, YOLOESegment
            s = 256  # 2x min stride
            m.inplace = self.inplace

            def _forward(x):
                """Perform a forward pass through the model, handling different Detect subclass types accordingly."""
                output = self.forward(x)
                if self.end2end:
                    output = output["one2many"]
                return output["feats"]

            self.model.eval()  # Avoid changing batch statistics until training begins
            m.training = True  # Setting it to True to properly return strides
            m.stride = torch.tensor([s / x.shape[-2] for x in _forward(torch.zeros(1, ch, s, s))])  # forward
            self.stride = m.stride
            self.model.train()  # Set model back to training(default) mode
            m.bias_init()  # only run once
        else:
            self.stride = torch.Tensor([32])  # default stride, e.g., RTDETR

        # Init weights, biases
        initialize_weights(self)
        if verbose:
            self.info()
            LOGGER.info("")

    @property
    def end2end(self):
        """Return whether the model uses end-to-end NMS-free detection."""
        return getattr(self.model[-1], "end2end", False)

    @end2end.setter
    def end2end(self, value):
        """Override the end-to-end detection mode."""
        self.set_head_attr(end2end=value)

    def set_head_attr(self, **kwargs):
        """Set attributes of the model head (last layer).

        Args:
            **kwargs (Any): Arbitrary keyword arguments representing attributes to set.
        """
        head = self.model[-1]
        for k, v in kwargs.items():
            if not hasattr(head, k):
                LOGGER.warning(f"Head has no attribute '{k}'.")
                continue
            setattr(head, k, v)

    def _predict_augment(self, x):
        """Perform augmentations on input image x and return augmented inference and train outputs.

        Args:
            x (torch.Tensor): Input image tensor.

        Returns:
            (tuple[torch.Tensor, None]): Augmented inference output and None for train output.
        """
        if getattr(self, "end2end", False) or type(self.model[-1]) is not Detect:
            LOGGER.warning("Model does not support 'augment=True', reverting to single-scale prediction.")
            return self._predict_once(x)
        img_size = x.shape[-2:]  # height, width
        s = [1, 0.83, 0.67]  # scales
        f = [None, 3, None]  # flips (2-ud, 3-lr)
        y = []  # outputs
        for si, fi in zip(s, f):
            xi = scale_img(x.flip(fi) if fi else x, si, gs=int(self.stride.max()))
            yi = super().predict(xi)[0]  # forward
            yi = self._descale_pred(yi, fi, si, img_size)
            y.append(yi)
        y = self._clip_augmented(y)  # clip augmented tails
        return torch.cat(y, -1), None  # augmented inference, train

    @staticmethod
    def _descale_pred(p, flips, scale, img_size, dim=1):
        """De-scale predictions following augmented inference (inverse operation).

        Args:
            p (torch.Tensor): Predictions tensor.
            flips (int | None): Flip type (None=none, 2=ud, 3=lr).
            scale (float): Scale factor.
            img_size (tuple): Original image size (height, width).
            dim (int): Dimension to split at.

        Returns:
            (torch.Tensor): De-scaled predictions.
        """
        p[:, :4] /= scale  # de-scale
        x, y, wh, cls = p.split((1, 1, 2, p.shape[dim] - 4), dim)
        if flips == 2:
            y = img_size[0] - y  # de-flip ud
        elif flips == 3:
            x = img_size[1] - x  # de-flip lr
        return torch.cat((x, y, wh, cls), dim)

    def _clip_augmented(self, y):
        """Clip YOLO augmented inference tails.

        Args:
            y (list[torch.Tensor]): List of detection tensors.

        Returns:
            (list[torch.Tensor]): Clipped detection tensors.
        """
        nl = self.model[-1].nl  # number of detection layers (P3-P5)
        g = sum(4**x for x in range(nl))  # grid points
        e = 1  # exclude layer count
        i = (y[0].shape[-1] // g) * sum(4**x for x in range(e))  # indices
        y[0] = y[0][..., :-i]  # large
        i = (y[-1].shape[-1] // g) * sum(4 ** (nl - 1 - x) for x in range(e))  # indices
        y[-1] = y[-1][..., i:]  # small
        return y

    def init_criterion(self):
        """Initialize the loss criterion for the DetectionModel."""
        return E2ELoss(self) if getattr(self, "end2end", False) else v8DetectionLoss(self)


class OBBModel(DetectionModel):
    """YOLO Oriented Bounding Box (OBB) model.

    This class extends DetectionModel to handle oriented bounding box detection tasks, providing specialized loss
    computation for rotated object detection.

    Methods:
        __init__: Initialize YOLO OBB model.
        init_criterion: Initialize the loss criterion for OBB detection.

    Examples:
        Initialize an OBB model
        >>> model = OBBModel("yolo26n-obb.yaml", ch=3, nc=80)
        >>> results = model.predict(image_tensor)
    """

    def __init__(self, cfg="yolo26n-obb.yaml", ch=3, nc=None, verbose=True):
        """Initialize YOLO OBB model with given config and parameters.

        Args:
            cfg (str | dict): Model configuration file path or dictionary.
            ch (int): Number of input channels.
            nc (int, optional): Number of classes.
            verbose (bool): Whether to display model information.
        """
        super().__init__(cfg=cfg, ch=ch, nc=nc, verbose=verbose)

    def init_criterion(self):
        """Initialize the loss criterion for the model."""
        return E2ELoss(self, v8OBBLoss) if getattr(self, "end2end", False) else v8OBBLoss(self)


class SegmentationModel(DetectionModel):
    """YOLO segmentation model.

    This class extends DetectionModel to handle instance segmentation tasks, providing specialized loss computation for
    pixel-level object detection and segmentation.

    Methods:
        __init__: Initialize YOLO segmentation model.
        init_criterion: Initialize the loss criterion for segmentation.

    Examples:
        Initialize a segmentation model
        >>> model = SegmentationModel("yolo26n-seg.yaml", ch=3, nc=80)
        >>> results = model.predict(image_tensor)
    """

    def __init__(self, cfg="yolo26n-seg.yaml", ch=3, nc=None, verbose=True):
        """Initialize Ultralytics YOLO segmentation model with given config and parameters.

        Args:
            cfg (str | dict): Model configuration file path or dictionary.
            ch (int): Number of input channels.
            nc (int, optional): Number of classes.
            verbose (bool): Whether to display model information.
        """
        super().__init__(cfg=cfg, ch=ch, nc=nc, verbose=verbose)

    def init_criterion(self):
        """Initialize the loss criterion for the SegmentationModel."""
        return E2ELoss(self, v8SegmentationLoss) if getattr(self, "end2end", False) else v8SegmentationLoss(self)


class SemanticSegmentationModel(BaseModel):
    """YOLO semantic segmentation model.

    This class implements a semantic segmentation model that produces per-pixel class predictions. Unlike
    SegmentationModel (instance segmentation), this does not produce bounding boxes.

    Methods:
        __init__: Initialize the semantic segmentation model.
        init_criterion: Initialize the loss criterion for semantic segmentation.

    Examples:
        Initialize a semantic segmentation model
        >>> model = SemanticSegmentationModel("yolo26n-sem.yaml", ch=3, nc=19)
    """

    def __init__(self, cfg="yolo26n-sem.yaml", ch=3, nc=None, verbose=True):
        """Initialize the YOLO semantic segmentation model.

        Args:
            cfg (str | dict): Model configuration file path or dictionary.
            ch (int): Number of input channels.
            nc (int, optional): Number of classes.
            verbose (bool): Whether to display model information.
        """
        super().__init__()
        _initialize_yolo_model(self, cfg, ch, nc, verbose)

        # Build strides: track smallest spatial size across all layers to find the deepest
        # backbone stride (e.g. P5/32). Head input alone is insufficient: the FPN upsamples
        # P5 away before the head, but the encoder still requires inputs aligned to that
        # deepest stride or FPN concats fail on rounding mismatches.
        m = self.model[-1]
        if isinstance(m, SemanticSegment):
            s = 256
            self.model.eval()
            m.training = True  # get training output (stride-4)
            min_h = [s]

            def _record(_m, _inp, out, _h=min_h):
                if isinstance(out, torch.Tensor) and out.ndim == 4:
                    _h[0] = min(_h[0], out.shape[-2])

            hooks = [layer.register_forward_hook(_record) for layer in self.model]
            try:
                self.forward(torch.zeros(1, ch, s, s))
            finally:
                for h in hooks:
                    h.remove()
            m.stride = torch.tensor([s / min_h[0]], dtype=torch.float32)  # e.g., 256/8 = 32
            self.stride = m.stride
            self.model.train()
        else:
            self.stride = torch.Tensor([32])

        initialize_weights(self)
        if verbose:
            self.info()
            LOGGER.info("")

    def init_criterion(self):
        """Initialize the loss criterion for semantic segmentation."""
        return SemanticSegmentationLoss(self)

    def _apply(self, fn):
        """Apply a function to all tensors in the model."""
        super()._apply(fn)
        m = self.model[-1]
        if isinstance(m, SemanticSegment):
            m.stride = fn(m.stride)
        return self


class PoseModel(DetectionModel):
    """YOLO pose model.

    This class extends DetectionModel to handle human pose estimation tasks, providing specialized loss computation for
    keypoint detection and pose estimation.

    Attributes:
        kpt_shape (tuple): Shape of keypoints data (num_keypoints, num_dimensions).

    Methods:
        __init__: Initialize YOLO pose model.
        init_criterion: Initialize the loss criterion for pose estimation.

    Examples:
        Initialize a pose model
        >>> model = PoseModel("yolo26n-pose.yaml", ch=3, nc=1, data_kpt_shape=(17, 3))
        >>> results = model.predict(image_tensor)
    """

    def __init__(self, cfg="yolo26n-pose.yaml", ch=3, nc=None, data_kpt_shape=(None, None), verbose=True):
        """Initialize Ultralytics YOLO Pose model.

        Args:
            cfg (str | dict): Model configuration file path or dictionary.
            ch (int): Number of input channels.
            nc (int, optional): Number of classes.
            data_kpt_shape (tuple): Shape of keypoints data.
            verbose (bool): Whether to display model information.
        """
        if not isinstance(cfg, dict):
            cfg = yaml_model_load(cfg)  # load model YAML
        if any(data_kpt_shape) and list(data_kpt_shape) != list(cfg["kpt_shape"]):
            LOGGER.info(f"Overriding model.yaml kpt_shape={cfg['kpt_shape']} with kpt_shape={data_kpt_shape}")
            cfg["kpt_shape"] = data_kpt_shape
        super().__init__(cfg=cfg, ch=ch, nc=nc, verbose=verbose)

    def init_criterion(self):
        """Initialize the loss criterion for the PoseModel."""
        loss = PoseLoss26 if isinstance(self.model[-1], Pose26) else v8PoseLoss
        return E2ELoss(self, loss) if self.end2end else loss(self)


class DepthModel(DetectionModel):
    """YOLO depth estimation model.

    This class extends DetectionModel for monocular depth estimation, using YOLO backbone + FPN with a DPT-style dense
    depth decoder head. Follows the Depth Anything approach adapted to YOLO architecture.

    Examples:
        >>> model = DepthModel("yolo26n-depth.yaml", ch=3)
        >>> results = model(image_tensor)
    """

    def __init__(self, cfg="yolo26n-depth.yaml", ch=3, nc=None, verbose=True):
        """Initialize YOLO Depth model."""
        super().__init__(cfg=cfg, ch=ch, nc=nc, verbose=verbose)

    def init_criterion(self):
        """Initialize the depth loss criterion."""
        return DepthLoss26(self)


class ClassificationModel(BaseModel):
    """YOLO classification model.

    This class implements the YOLO classification architecture for image classification tasks, providing model
    initialization, configuration, and output reshaping capabilities.

    Attributes:
        yaml (dict): Model configuration dictionary.
        model (torch.nn.Sequential): The neural network model.
        stride (torch.Tensor): Model stride values.
        names (dict): Class names dictionary.

    Methods:
        __init__: Initialize ClassificationModel.
        _from_yaml: Set model configurations and define architecture.
        reshape_outputs: Update model to specified class count.
        init_criterion: Initialize the loss criterion.

    Examples:
        Initialize a classification model
        >>> model = ClassificationModel("yolo26n-cls.yaml", ch=3, nc=1000)
        >>> results = model.predict(image_tensor)
    """

    def __init__(self, cfg="yolo26n-cls.yaml", ch=3, nc=None, verbose=True):
        """Initialize ClassificationModel with YAML, channels, number of classes, verbose flag.

        Args:
            cfg (str | dict): Model configuration file path or dictionary.
            ch (int): Number of input channels.
            nc (int, optional): Number of classes.
            verbose (bool): Whether to display model information.
        """
        super().__init__()
        self._from_yaml(cfg, ch, nc, verbose)

    def _from_yaml(self, cfg, ch, nc, verbose):
        """Set Ultralytics YOLO model configurations and define the model architecture.

        Args:
            cfg (str | dict): Model configuration file path or dictionary.
            ch (int): Number of input channels.
            nc (int, optional): Number of classes.
            verbose (bool): Whether to display model information.
        """
        self.yaml = cfg if isinstance(cfg, dict) else yaml_model_load(cfg)  # cfg dict

        # Define model
        ch = self.yaml["channels"] = self.yaml.get("channels", ch)  # input channels
        if nc and nc != self.yaml["nc"]:
            LOGGER.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}")
            self.yaml["nc"] = nc  # override YAML value
        elif not nc and not self.yaml.get("nc", None):
            raise ValueError("nc not specified. Must specify nc in model.yaml or function arguments.")
        self.model, self.save = parse_model(deepcopy(self.yaml), ch=ch, verbose=verbose)  # model, savelist
        self.stride = torch.Tensor([1])  # no stride constraints
        self.names = {i: f"{i}" for i in range(self.yaml["nc"])}  # default names dict
        self.info()

    @staticmethod
    def reshape_outputs(model, nc):
        """Update a TorchVision classification model to class count 'nc' if required.

        Args:
            model (torch.nn.Module): Model to update.
            nc (int): New number of classes.
        """
        name, m = list((model.model if hasattr(model, "model") else model).named_children())[-1]  # last module
        if isinstance(m, Classify):  # YOLO Classify() head
            if m.linear.out_features != nc:
                m.linear = torch.nn.Linear(m.linear.in_features, nc)
        elif isinstance(m, torch.nn.Linear):  # ResNet, EfficientNet
            if m.out_features != nc:
                setattr(model, name, torch.nn.Linear(m.in_features, nc))
        elif isinstance(m, torch.nn.Sequential):
            types = [type(x) for x in m]
            if torch.nn.Linear in types:
                i = len(types) - 1 - types[::-1].index(torch.nn.Linear)  # last torch.nn.Linear index
                if m[i].out_features != nc:
                    m[i] = torch.nn.Linear(m[i].in_features, nc)
            elif torch.nn.Conv2d in types:
                i = len(types) - 1 - types[::-1].index(torch.nn.Conv2d)  # last torch.nn.Conv2d index
                if m[i].out_channels != nc:
                    m[i] = torch.nn.Conv2d(
                        m[i].in_channels, nc, m[i].kernel_size, m[i].stride, bias=m[i].bias is not None
                    )

    def init_criterion(self):
        """Initialize the loss criterion for the ClassificationModel."""
        return v8ClassificationLoss()


class RTDETRDetectionModel(DetectionModel):
    """RTDETR (Real-time DEtection and Tracking using Transformers) Detection Model class.

    This class is responsible for constructing the RTDETR architecture, defining loss functions, and facilitating both
    the training and inference processes. RTDETR is an object detection and tracking model that extends from the
    DetectionModel base class.

    Attributes:
        nc (int): Number of classes for detection.
        criterion (RTDETRDetectionLoss): Loss function for training.

    Methods:
        __init__: Initialize the RTDETRDetectionModel.
        init_criterion: Initialize the loss criterion.
        loss: Compute loss for training.
        predict: Perform forward pass through the model.

    Examples:
        Initialize an RTDETR model
        >>> model = RTDETRDetectionModel("rtdetr-l.yaml", ch=3, nc=80)
        >>> results = model.predict(image_tensor)
    """

    def __init__(self, cfg="rtdetr-l.yaml", ch=3, nc=None, verbose=True):
        """Initialize the RTDETRDetectionModel.

        Args:
            cfg (str | dict): Configuration file name or path.
            ch (int): Number of input channels.
            nc (int, optional): Number of classes.
            verbose (bool): Print additional information during initialization.
        """
        super().__init__(cfg=cfg, ch=ch, nc=nc, verbose=verbose)

    def _remap_cls_by_names(self, csd: dict[str, torch.Tensor], src_model: torch.nn.Module, verbose: bool = True):
        """Remap RT-DETR decoder cls-head rows by class name.

        Overrides BaseModel's YOLO-specific implementation: RT-DETR's classification tensors live under
        `score_head` and `class_embed` inside `RTDETRDecoder` rather than `Detect.cv3`. All of them are
        row-per-class, including the training-only `denoising_class_embed` embedding, so matched class rows
        transfer even when source and target `nc` differ; any residual shape mismatch is dropped by
        `intersect_dicts`.

        Args:
            csd (dict): Pretrained checkpoint state_dict (will be mutated).
            src_model (torch.nn.Module): Pretrained module, used to read `.names`.
            verbose (bool): Log mapping summary.

        Returns:
            (int): Number of cls tensors remapped (counted toward "Transferred" log line).
        """
        src_names = getattr(src_model, "names", None)
        tgt_names = getattr(self, "names", None)
        if not (isinstance(src_names, dict) and isinstance(tgt_names, dict)):
            return 0
        # Skip default placeholder names {0:"0", 1:"1", ...} (also catches empty dicts)
        if any(all(str(k) == str(v) for k, v in n.items()) for n in (src_names, tgt_names)):
            return 0

        src_lookup = {str(v).strip().lower(): k for k, v in src_names.items()}
        tgt_nc = len(tgt_names)
        idx = torch.tensor(
            [src_lookup.get(str(tgt_names[k]).strip().lower(), -1) for k in range(tgt_nc)], dtype=torch.long
        )
        n_match = int((idx >= 0).sum())
        # Skip if nothing matches, or class names already share order and count (intersect_dicts handles it directly)
        if n_match == 0 or (len(src_names) == tgt_nc and torch.equal(idx, torch.arange(tgt_nc))):
            return 0

        valid = idx >= 0
        state_dict = self.state_dict()
        cls_keys = {k for k in csd if ("score_head" in k or "class_embed" in k) and k in state_dict}
        remapped = 0
        for k in cls_keys:
            v_src, v_tgt = csd[k], state_dict[k]
            if v_src.ndim != v_tgt.ndim or v_src.shape[1:] != v_tgt.shape[1:]:
                continue
            v_tgt[valid] = v_src[idx[valid]].to(v_tgt.dtype)
            csd.pop(k)  # prevent intersect_dicts from copying these rows in the wrong (source) order
            remapped += 1
        if verbose and remapped:
            LOGGER.info(f"Remapped {n_match}/{tgt_nc} decoder cls head rows from pretrained weights by class name")
        return remapped

    def _apply(self, fn):
        """Apply a function to all tensors in the model, including decoder anchors and valid mask.

        Args:
            fn (function): The function to apply to the model.

        Returns:
            (RTDETRDetectionModel): An updated RTDETRDetectionModel object.
        """
        super()._apply(fn)
        m = self.model[-1]
        m.anchors = fn(m.anchors)
        m.valid_mask = fn(m.valid_mask)
        return self

    def init_criterion(self):
        """Initialize the loss criterion for the RTDETRDetectionModel."""
        from ultralytics.models.utils.loss import RTDETRDetectionLoss

        return RTDETRDetectionLoss(nc=self.nc, use_vfl=True)

    def loss(self, batch, preds=None):
        """Compute the loss for the given batch of data.

        Args:
            batch (dict): Dictionary containing image and label data.
            preds (tuple, optional): Precomputed model predictions.

        Returns:
            (torch.Tensor): Total loss value.
            (dict): Main three losses in a dict.
        """
        if not hasattr(self, "criterion"):
            self.criterion = self.init_criterion()

        img = batch["img"]
        # NOTE: preprocess gt_bbox and gt_labels to list.
        bs = img.shape[0]
        batch_idx = batch["batch_idx"]
        gt_groups = [(batch_idx == i).sum().item() for i in range(bs)]
        targets = {
            "cls": batch["cls"].to(img.device, dtype=torch.long).view(-1),
            "bboxes": batch["bboxes"].to(device=img.device),
            "batch_idx": batch_idx.to(img.device, dtype=torch.long).view(-1),
            "gt_groups": gt_groups,
        }

        if preds is None:
            preds = self.predict(img, batch=targets)
        dec_bboxes, dec_scores, enc_bboxes, enc_scores, dn_meta = preds if self.training else preds[1]
        if dn_meta is None:
            dn_bboxes, dn_scores = None, None
        else:
            dn_bboxes, dec_bboxes = torch.split(dec_bboxes, dn_meta["dn_num_split"], dim=2)
            dn_scores, dec_scores = torch.split(dec_scores, dn_meta["dn_num_split"], dim=2)

        dec_bboxes = torch.cat([enc_bboxes.unsqueeze(0), dec_bboxes])  # (7, bs, 300, 4)
        dec_scores = torch.cat([enc_scores.unsqueeze(0), dec_scores])

        loss = self.criterion(
            (dec_bboxes, dec_scores), targets, dn_bboxes=dn_bboxes, dn_scores=dn_scores, dn_meta=dn_meta
        )
        # NOTE: There are like 12 losses in RTDETR, backward with all losses but only show the main three losses.
        return sum(loss.values()), {
            "giou_loss": loss["loss_giou"].detach(),
            "cls_loss": loss["loss_class"].detach(),
            "l1_loss": loss["loss_bbox"].detach(),
        }

    def predict(self, x, profile=False, batch=None, augment=False, embed=None):
        """Perform a forward pass through the model.

        Args:
            x (torch.Tensor): The input tensor.
            profile (bool): If True, profile the computation time for each layer.
            batch (dict, optional): Ground truth data for evaluation.
            augment (bool): If True, perform data augmentation during inference.
            embed (list, optional): A list of layer indices to return embeddings from.

        Returns:
            (torch.Tensor): Model's output tensor.
        """
        y, dt, embeddings = [], [], []  # outputs
        embed = frozenset(embed) if embed else {-1}
        max_idx = max(embed)
        for m in self.model[:-1]:  # except the head part
            if m.f != -1:  # if not from previous layer
                x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f]  # from earlier layers
            if profile:
                self._profile_one_layer(m, x, dt)
            x = m(x)  # run
            y.append(x if m.i in self.save else None)  # save output
            if m.i in embed:
                embeddings.append(torch.nn.functional.adaptive_avg_pool2d(x, (1, 1)).squeeze(-1).squeeze(-1))  # flatten
                if m.i == max_idx:
                    return torch.unbind(torch.cat(embeddings, 1), dim=0)
        head = self.model[-1]
        x = head([y[j] for j in head.f], batch)  # head inference
        return x


class WorldModel(DetectionModel):
    """YOLOv8 World Model.

    This class implements the YOLOv8 World model for open-vocabulary object detection, supporting text-based class
    specification and CLIP model integration for zero-shot detection capabilities.

    Attributes:
        txt_feats (torch.Tensor): Text feature embeddings for classes.
        clip_model (torch.nn.Module): CLIP model for text encoding.

    Methods:
        __init__: Initialize YOLOv8 world model.
        set_classes: Set classes for offline inference.
        get_text_pe: Get text positional embeddings.
        predict: Perform forward pass with text features.
        loss: Compute loss with text features.

    Examples:
        Initialize a world model
        >>> model = WorldModel("yolov8s-world.yaml", ch=3, nc=80)
        >>> model.set_classes(["person", "car", "bicycle"])
        >>> results = model.predict(image_tensor)
    """

    def __init__(self, cfg="yolov8s-world.yaml", ch=3, nc=None, verbose=True):
        """Initialize YOLOv8 world model with given config and parameters.

        Args:
            cfg (str | dict): Model configuration file path or dictionary.
            ch (int): Number of input channels.
            nc (int, optional): Number of classes.
            verbose (bool): Whether to display model information.
        """
        self.txt_feats = torch.randn(1, nc or 80, 512)  # features placeholder
        self.clip_model = None  # CLIP model placeholder
        super().__init__(cfg=cfg, ch=ch, nc=nc, verbose=verbose)

    def set_classes(self, text, batch=80, cache_clip_model=True):
        """Set classes in advance so that model could do offline-inference without clip model.

        Args:
            text (list[str]): List of class names.
            batch (int): Batch size for processing text tokens.
            cache_clip_model (bool): Whether to cache the CLIP model.
        """
        self.txt_feats = self.get_text_pe(text, batch=batch, cache_clip_model=cache_clip_model)
        self.model[-1].nc = len(text)

    def get_text_pe(self, text, batch=80, cache_clip_model=True):
        """Get text positional embeddings using the CLIP model.

        Args:
            text (list[str]): List of class names.
            batch (int): Batch size for processing text tokens.
            cache_clip_model (bool): Whether to cache the CLIP model.

        Returns:
            (torch.Tensor): Text positional embeddings.
        """
        from ultralytics.nn.text_model import build_text_model

        device = next(self.model.parameters()).device
        if not getattr(self, "clip_model", None) and cache_clip_model:
            # For backwards compatibility of models lacking clip_model attribute
            self.clip_model = build_text_model("clip:ViT-B/32", device=device)
        model = self.clip_model if cache_clip_model else build_text_model("clip:ViT-B/32", device=device)
        text_token = model.tokenize(text)
        txt_feats = [model.encode_text(token).detach() for token in text_token.split(batch)]
        txt_feats = txt_feats[0] if len(txt_feats) == 1 else torch.cat(txt_feats, dim=0)
        return txt_feats.reshape(-1, len(text), txt_feats.shape[-1])

    def predict(self, x, profile=False, txt_feats=None, augment=False, embed=None):
        """Perform a forward pass through the model.

        Args:
            x (torch.Tensor): The input tensor.
            profile (bool): If True, profile the computation time for each layer.
            txt_feats (torch.Tensor, optional): The text features, use it if it's given.
            augment (bool): If True, perform data augmentation during inference.
            embed (list, optional): A list of layer indices to return embeddings from.

        Returns:
            (torch.Tensor): Model's output tensor.
        """
        txt_feats = (self.txt_feats if txt_feats is None else txt_feats).to(device=x.device, dtype=x.dtype)
        if txt_feats.shape[0] != x.shape[0] or self.model[-1].export:
            txt_feats = txt_feats.expand(x.shape[0], -1, -1)
        ori_txt_feats = txt_feats.clone()
        y, dt, embeddings = [], [], []  # outputs
        embed = frozenset(embed) if embed else {-1}
        max_idx = max(embed)
        for m in self.model:  # except the head part
            if m.f != -1:  # if not from previous layer
                x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f]  # from earlier layers
            if profile:
                self._profile_one_layer(m, x, dt)
            if isinstance(m, C2fAttn):
                x = m(x, txt_feats)
            elif isinstance(m, WorldDetect):
                x = m(x, ori_txt_feats)
            elif isinstance(m, ImagePoolingAttn):
                txt_feats = m(x, txt_feats)
            else:
                x = m(x)  # run

            y.append(x if m.i in self.save else None)  # save output
            if m.i in embed:
                embeddings.append(torch.nn.functional.adaptive_avg_pool2d(x, (1, 1)).squeeze(-1).squeeze(-1))  # flatten
                if m.i == max_idx:
                    return torch.unbind(torch.cat(embeddings, 1), dim=0)
        return x

    def loss(self, batch, preds=None):
        """Compute loss.

        Args:
            batch (dict): Batch to compute loss on.
            preds (torch.Tensor | list[torch.Tensor], optional): Predictions.
        """
        if not hasattr(self, "criterion"):
            self.criterion = self.init_criterion()

        if preds is None:
            preds = self.forward(batch["img"], txt_feats=batch["txt_feats"])
        return self.criterion(preds, batch)


class YOLOEModel(DetectionModel):
    """YOLOE detection model.

    This class implements the YOLOE architecture for efficient object detection with text and visual prompts, supporting
    both prompt-based and prompt-free inference modes.

    Attributes:
        pe (torch.Tensor): Prompt embeddings for classes.
        clip_model (torch.nn.Module): CLIP model for text encoding.

    Methods:
        __init__: Initialize YOLOE model.
        get_text_pe: Get text positional embeddings.
        get_visual_pe: Get visual embeddings.
        set_vocab: Set vocabulary for prompt-free model.
        get_vocab: Get fused vocabulary layer.
        set_classes: Set classes for offline inference.
        get_cls_pe: Get class positional embeddings.
        predict: Perform forward pass with prompts.
        loss: Compute loss with prompts.

    Examples:
        Initialize a YOLOE model
        >>> model = YOLOEModel("yoloe-v8s.yaml", ch=3, nc=80)
        >>> results = model.predict(image_tensor, tpe=text_embeddings)
    """

    def __init__(self, cfg="yoloe-v8s.yaml", ch=3, nc=None, verbose=True):
        """Initialize YOLOE model with given config and parameters.

        Args:
            cfg (str | dict): Model configuration file path or dictionary.
            ch (int): Number of input channels.
            nc (int, optional): Number of classes.
            verbose (bool): Whether to display model information.
        """
        super().__init__(cfg=cfg, ch=ch, nc=nc, verbose=verbose)
        self.text_model = self.yaml.get("text_model", "mobileclip:blt")

    @smart_inference_mode()
    def get_text_pe(self, text, batch=80, cache_clip_model=False, without_reprta=False):
        """Get text positional embeddings using the CLIP model.

        Args:
            text (list[str]): List of class names.
            batch (int): Batch size for processing text tokens.
            cache_clip_model (bool): Whether to cache the CLIP model.
            without_reprta (bool): Whether to return text embeddings without reprta module processing.

        Returns:
            (torch.Tensor): Text positional embeddings.
        """
        from ultralytics.nn.text_model import build_text_model

        device = next(self.model.parameters()).device
        if not getattr(self, "clip_model", None) and cache_clip_model:
            # For backwards compatibility of models lacking clip_model attribute
            self.clip_model = build_text_model(getattr(self, "text_model", "mobileclip:blt"), device=device)

        model = (
            self.clip_model
            if cache_clip_model
            else build_text_model(getattr(self, "text_model", "mobileclip:blt"), device=device)
        )
        text_token = model.tokenize(text)
        txt_feats = [model.encode_text(token).detach() for token in text_token.split(batch)]
        txt_feats = txt_feats[0] if len(txt_feats) == 1 else torch.cat(txt_feats, dim=0)
        txt_feats = txt_feats.reshape(-1, len(text), txt_feats.shape[-1])
        if without_reprta:
            return txt_feats

        head = self.model[-1]
        assert isinstance(head, YOLOEDetect)
        return head.get_tpe(txt_feats)  # run auxiliary text head

    @smart_inference_mode()
    def get_visual_pe(self, img, visual):
        """Get visual positional embeddings.

        Args:
            img (torch.Tensor): Input image tensor.
            visual (torch.Tensor): Visual features.

        Returns:
            (torch.Tensor): Visual positional embeddings.
        """
        return self(img, vpe=visual, return_vpe=True)

    def set_vocab(self, vocab, names):
        """Set vocabulary for the prompt-free model.

        Args:
            vocab (nn.ModuleList): List of vocabulary items.
            names (list[str]): List of class names.
        """
        assert not self.training
        head = self.model[-1]
        assert isinstance(head, YOLOEDetect)

        # Cache anchors for head
        device = next(self.parameters()).device
        self(torch.empty(1, 3, self.args["imgsz"], self.args["imgsz"]).to(device))  # warmup

        cv3 = getattr(head, "one2one_cv3", head.cv3)
        cv2 = getattr(head, "one2one_cv2", head.cv2)

        # re-parameterization for prompt-free model
        self.model[-1].lrpc = nn.ModuleList(
            LRPCHead(cls, pf[-1], loc[-1], enabled=i != 2) for i, (cls, pf, loc) in enumerate(zip(vocab, cv3, cv2))
        )
        for loc_head, cls_head in zip(head.cv2, head.cv3):
            assert isinstance(loc_head, nn.Sequential)
            assert isinstance(cls_head, nn.Sequential)
            del loc_head[-1]
            del cls_head[-1]
        self.model[-1].nc = len(names)
        self.names = check_class_names(names)

    def get_vocab(self, names):
        """Get fused vocabulary layer from the model.

        Args:
            names (list[str]): List of class names.

        Returns:
            (nn.ModuleList): List of vocabulary modules.
        """
        assert not self.training
        head = self.model[-1]
        assert isinstance(head, YOLOEDetect)
        assert not head.is_fused

        tpe = self.get_text_pe(names)
        self.set_classes(names, tpe)
        device = next(self.model.parameters()).device
        head.fuse(self.pe.to(device))  # fuse prompt embeddings to classify head

        cv3 = getattr(head, "one2one_cv3", head.cv3)
        vocab = nn.ModuleList()
        for cls_head in cv3:
            assert isinstance(cls_head, nn.Sequential)
            vocab.append(cls_head[-1])
        return vocab

    def set_classes(self, names, embeddings):
        """Set classes in advance so that model could do offline-inference without clip model.

        Args:
            names (list[str]): List of class names.
            embeddings (torch.Tensor): Embeddings tensor.
        """
        assert not hasattr(self.model[-1], "lrpc"), (
            "Prompt-free model does not support setting classes. Please try with Text/Visual prompt models."
        )
        assert embeddings.ndim == 3
        self.pe = embeddings
        self.model[-1].nc = len(names)
        self.names = check_class_names(names)

    def get_cls_pe(self, tpe, vpe):
        """Get class positional embeddings.

        Args:
            tpe (torch.Tensor | None): Text positional embeddings.
            vpe (torch.Tensor | None): Visual positional embeddings.

        Returns:
            (torch.Tensor): Class positional embeddings.
        """
        all_pe = []
        if tpe is not None:
            assert tpe.ndim == 3
            all_pe.append(tpe)
        if vpe is not None:
            assert vpe.ndim == 3
            all_pe.append(vpe)
        if not all_pe:
            all_pe.append(getattr(self, "pe", torch.zeros(1, 80, 512)))
        return torch.cat(all_pe, dim=1)

    def predict(self, x, profile=False, tpe=None, augment=False, embed=None, vpe=None, return_vpe=False):
        """Perform a forward pass through the model.

        Args:
            x (torch.Tensor): The input tensor.
            profile (bool): If True, profile the computation time for each layer.
            tpe (torch.Tensor, optional): Text positional embeddings.
            augment (bool): If True, perform data augmentation during inference.
            embed (list, optional): A list of layer indices to return embeddings from.
            vpe (torch.Tensor, optional): Visual positional embeddings.
            return_vpe (bool): If True, return visual positional embeddings.

        Returns:
            (torch.Tensor): Model's output tensor.
        """
        y, dt, embeddings = [], [], []  # outputs
        b = x.shape[0]
        embed = frozenset(embed) if embed else {-1}
        max_idx = max(embed)
        for m in self.model:  # except the head part
            if m.f != -1:  # if not from previous layer
                x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f]  # from earlier layers
            if profile:
                self._profile_one_layer(m, x, dt)
            if isinstance(m, YOLOEDetect):
                vpe = m.get_vpe(x, vpe) if vpe is not None else None
                if return_vpe:
                    assert vpe is not None
                    assert not self.training
                    return vpe
                cls_pe = self.get_cls_pe(m.get_tpe(tpe), vpe).to(device=x[0].device, dtype=x[0].dtype)
                if cls_pe.shape[0] != b or m.export:
                    cls_pe = cls_pe.expand(b, -1, -1)
                x.append(cls_pe)  # adding cls embedding
            x = m(x)  # run

            y.append(x if m.i in self.save else None)  # save output
            if m.i in embed:
                embeddings.append(torch.nn.functional.adaptive_avg_pool2d(x, (1, 1)).squeeze(-1).squeeze(-1))  # flatten
                if m.i == max_idx:
                    return torch.unbind(torch.cat(embeddings, 1), dim=0)
        return x

    def loss(self, batch, preds=None):
        """Compute loss.

        Args:
            batch (dict): Batch to compute loss on.
            preds (torch.Tensor | list[torch.Tensor], optional): Predictions.
        """
        if not hasattr(self, "criterion"):
            from ultralytics.utils.loss import TVPDetectLoss

            visual_prompt = batch.get("visuals", None) is not None  # TODO
            self.criterion = (
                (E2ELoss(self, TVPDetectLoss) if getattr(self, "end2end", False) else TVPDetectLoss(self))
                if visual_prompt
                else self.init_criterion()
            )
        if preds is None:
            preds = self.forward(
                batch["img"],
                tpe=None if "visuals" in batch else batch.get("txt_feats", None),
                vpe=batch.get("visuals", None),
            )
        return self.criterion(preds, batch)


class YOLOESegModel(YOLOEModel, SegmentationModel):
    """YOLOE segmentation model.

    This class extends YOLOEModel to handle instance segmentation tasks with text and visual prompts, providing
    specialized loss computation for pixel-level object detection and segmentation.

    Methods:
        __init__: Initialize YOLOE segmentation model.
        loss: Compute loss with prompts for segmentation.

    Examples:
        Initialize a YOLOE segmentation model
        >>> model = YOLOESegModel("yoloe-v8s-seg.yaml", ch=3, nc=80)
        >>> results = model.predict(image_tensor, tpe=text_embeddings)
    """

    def __init__(self, cfg="yoloe-v8s-seg.yaml", ch=3, nc=None, verbose=True):
        """Initialize YOLOE segmentation model with given config and parameters.

        Args:
            cfg (str | dict): Model configuration file path or dictionary.
            ch (int): Number of input channels.
            nc (int, optional): Number of classes.
            verbose (bool): Whether to display model information.
        """
        super().__init__(cfg=cfg, ch=ch, nc=nc, verbose=verbose)

    def loss(self, batch, preds=None):
        """Compute loss.

        Args:
            batch (dict): Batch to compute loss on.
            preds (torch.Tensor | list[torch.Tensor], optional): Predictions.
        """
        if not hasattr(self, "criterion"):
            from ultralytics.utils.loss import TVPSegmentLoss

            visual_prompt = batch.get("visuals", None) is not None  # TODO
            self.criterion = (
                (E2ELoss(self, TVPSegmentLoss) if getattr(self, "end2end", False) else TVPSegmentLoss(self))
                if visual_prompt
                else self.init_criterion()
            )

        return super().loss(batch, preds)


class Ensemble(torch.nn.ModuleList):
    """Ensemble of models.

    This class allows combining multiple YOLO models into an ensemble for improved performance through model averaging
    or other ensemble techniques.

    Methods:
        __init__: Initialize an ensemble of models.
        forward: Generate predictions from all models in the ensemble.

    Examples:
        Create an ensemble of models
        >>> ensemble = Ensemble()
        >>> ensemble.append(model1)
        >>> ensemble.append(model2)
        >>> results = ensemble(image_tensor)
    """

    def __init__(self):
        """Initialize an ensemble of models."""
        super().__init__()

    def forward(self, x, augment=False, profile=False):
        """Run ensemble forward pass and concatenate predictions from all models.

        Args:
            x (torch.Tensor): Input tensor.
            augment (bool): Whether to augment the input.
            profile (bool): Whether to profile the model.

        Returns:
            (torch.Tensor): Concatenated predictions from all models.
            (None): Always None for ensemble inference.
        """
        y = [module(x, augment, profile)[0] for module in self]
        # y = torch.stack(y).max(0)[0]  # max ensemble
        # y = torch.stack(y).mean(0)  # mean ensemble
        y = torch.cat(y, 2)  # nms ensemble, y shape(B, HW, C*num_models)
        return y, None  # inference, train output


# Functions ------------------------------------------------------------------------------------------------------------


@contextlib.contextmanager
def temporary_modules(modules=None, attributes=None):
    """Context manager for temporarily adding or modifying modules in Python's module cache (`sys.modules`).

    This function can be used to change the module paths during runtime. It's useful when refactoring code, where you've
    moved a module from one location to another, but you still want to support the old import paths for backwards
    compatibility.

    Args:
        modules (dict, optional): A dictionary mapping old module paths to new module paths.
        attributes (dict, optional): A dictionary mapping old module attributes to new module attributes.

    Examples:
        >>> with temporary_modules({"old.module": "new.module"}, {"old.module.attribute": "new.module.attribute"}):
        >>> import old.module  # this will now import new.module
        >>> from old.module import attribute  # this will now import new.module.attribute

    Notes:
        The changes are only in effect inside the context manager and are undone once the context manager exits.
        Be aware that directly manipulating `sys.modules` can lead to unpredictable results, especially in larger
        applications or libraries. Use this function with caution.
    """
    if modules is None:
        modules = {}
    if attributes is None:
        attributes = {}
    import sys
    from importlib import import_module

    try:
        # Set attributes in sys.modules under their old name
        for old, new in attributes.items():
            old_module, old_attr = old.rsplit(".", 1)
            new_module, new_attr = new.rsplit(".", 1)
            setattr(import_module(old_module), old_attr, getattr(import_module(new_module), new_attr))

        # Set modules in sys.modules under their old name
        for old, new in modules.items():
            sys.modules[old] = import_module(new)

        yield
    finally:
        # Remove the temporary module paths
        for old in modules:
            if old in sys.modules:
                del sys.modules[old]


class _SafeLoad:
    """Opt-in restricted checkpoint loading: reconstruct only known model classes (`weights_only=True` plus an
    allow-list) and build models without `eval()`.

    Enabled per-process by the `ULTRALYTICS_SAFE_LOAD` env flag, or per-call by `torch_safe_load(..., safe_only=True)`.
    Default loading (flag off) is unchanged.
    """

    # Restricted loading reconstructs allow-listed classes via the torch.serialization.safe_globals context manager,
    # added in torch 2.5. On older torch it is unavailable, so restricted loading degrades to a standard load there.
    SUPPORTED = hasattr(torch.serialization, "safe_globals")
    _globals = None  # cached allow-list, built once
    _local = threading.local()  # per-thread flag set while a weights_only load is in progress

    @classmethod
    def restricted(cls):
        """Whether model construction should use the no-eval, known-layer path (env flag or an in-progress load)."""
        return cls.SUPPORTED and (SAFE_LOAD or getattr(cls._local, "active", False))

    @classmethod
    @contextlib.contextmanager
    def loading(cls):
        """Load with `weights_only=True`: scope the allow-list to this load and mark the thread restricted, so a
        checkpoint that reaches model construction (parse_model) also uses the no-eval, known-layer path.
        """
        if cls._globals is None:
            cls._globals = cls._build()
        cls._local.active = True
        try:
            with torch.serialization.safe_globals(cls._globals):
                yield
        finally:
            cls._local.active = False

    @staticmethod
    def activation(act):
        """Resolve a model-yaml `activation` spec to a `torch.nn` module instance without `eval()`.

        Accepts only the documented `[torch.]nn.<Class>(literal args)` shape (e.g. `nn.SiLU()`,
        `torch.nn.LeakyReLU(0.1)`) with literal arguments, and rejects anything else.
        """
        import ast

        try:
            call = ast.parse(act.strip(), mode="eval").body
            assert isinstance(call, ast.Call)
            attrs = []
            node = call.func
            while isinstance(node, ast.Attribute):  # unwind e.g. torch.nn.SiLU -> ["SiLU","nn","torch"]
                attrs.append(node.attr)
                node = node.value
            assert isinstance(node, ast.Name)
            attrs.append(node.id)  # e.g. ["SiLU", "nn"] or ["SiLU", "nn", "torch"]
            assert attrs[1:] in (["nn"], ["nn", "torch"]), "activation must be a torch.nn class"
            klass = getattr(nn, attrs[0])
            assert isinstance(klass, type) and issubclass(klass, nn.Module)
            args = [ast.literal_eval(a) for a in call.args]
            kwargs = {kw.arg: ast.literal_eval(kw.value) for kw in call.keywords}
            return klass(*args, **kwargs)
        except Exception as e:
            raise TypeError(
                emojis(f"ERROR ❌️ unsupported activation '{act}' blocked during restricted model load.")
            ) from e

    @classmethod
    def _build(cls):
        """Auto-discover `nn.Module` subclasses across `torch.nn` and the ultralytics model families, registered under
        every namespace path they are reachable from (covering re-exports such as `block.RealNVP` as
        `head.RealNVP`), plus torchvision transforms and legacy aliases.

        Returns:
            (list): Items for `torch.serialization.safe_globals` — classes and `(obj, "module.Name")` aliases.
        """
        import enum
        import importlib
        import inspect
        import pathlib
        import pkgutil

        import torch.nn.modules as torch_nn

        import ultralytics.nn.modules as ul_nn
        from ultralytics.nn import tasks as ul_tasks  # noqa: PLW0406

        allow = []

        def _scan(pkg):
            mods = [pkg]
            if hasattr(pkg, "__path__"):  # package: include all submodules
                for info in pkgutil.iter_modules(pkg.__path__, f"{pkg.__name__}."):
                    try:
                        mods.append(importlib.import_module(info.name))
                    except Exception:  # noqa: S112  # optional/oddball submodule — skip
                        continue
            for mod in mods:
                for name, klass in inspect.getmembers(mod, inspect.isclass):
                    if issubclass(klass, nn.Module):
                        # Register under the path the class is reachable from — matches how a checkpoint pickled it.
                        allow.append((klass, f"{mod.__name__}.{name}"))

        _scan(torch_nn)  # PyTorch nn modules
        _scan(ul_nn)  # ultralytics block/conv/head/transformer
        _scan(ul_tasks)  # ultralytics task models

        # Non-nn.Module data globals in official checkpoints, incl. the pre-8.0.44 `ultralytics.yolo.utils` path.
        allow.append(IterableSimpleNamespace)
        allow.append((IterableSimpleNamespace, "ultralytics.yolo.utils.IterableSimpleNamespace"))

        # Classification preprocessing transforms.
        try:
            import torchvision.transforms.transforms as tvt
            from torchvision.transforms.functional import InterpolationMode

            allow += [tvt.Compose, tvt.Normalize, tvt.Resize, tvt.CenterCrop, tvt.ToTensor, InterpolationMode]
        except ImportError:
            pass

        # Legacy/cross-platform aliases (pickled paths with no current class namespace), mirroring temporary_modules().
        from ultralytics.utils.loss import E2EDetectLoss

        def _getattr(obj, name):  # ckpts pickle `Detect.forward` and `InterpolationMode.BILINEAR` via getattr
            if isinstance(obj, type) and not name.startswith("__") and issubclass(obj, (nn.Module, enum.Enum)):
                return getattr(obj, name)
            raise pickle.UnpicklingError(f"unsafe getattr({obj!r}, {name!r}) blocked during restricted model load")

        allow += [
            (nn.Identity, "ultralytics.nn.modules.block.Silence"),  # YOLOv9e
            (DetectionModel, "ultralytics.nn.tasks.YOLOv10DetectionModel"),  # YOLOv10
            (E2EDetectLoss, "ultralytics.utils.loss.v10DetectLoss"),  # YOLOv10
            (_getattr, "builtins.getattr"),  # non-det YOLOv8, YOLO11 ckpts (restrict to nn.Module attrs)
        ]
        if WINDOWS:
            allow += [
                pathlib.WindowsPath,
                (pathlib.WindowsPath, "pathlib.WindowsPath"),
                (pathlib.WindowsPath, "pathlib.PosixPath"),
                (pathlib.WindowsPath, f"{pathlib.PosixPath.__module__}.{pathlib.PosixPath.__qualname__}"),
            ]
        else:
            allow += [
                pathlib.PosixPath,
                (pathlib.PosixPath, "pathlib.PosixPath"),
                (pathlib.PosixPath, "pathlib.WindowsPath"),
                (pathlib.PosixPath, f"{pathlib.WindowsPath.__module__}.{pathlib.WindowsPath.__qualname__}"),
            ]
        return allow


def torch_safe_load(weight, safe_only=None):
    """Attempt to load a PyTorch model with the torch.load() function. If a ModuleNotFoundError is raised, it catches
    the error, logs a warning message, and attempts to install the missing module via the check_requirements()
    function. After installation, the function again attempts to load the model using torch.load().

    Args:
        weight (str | Path): The file path of the PyTorch model.
        safe_only (bool, optional): Load with `torch.load(weights_only=True)`, reconstructing only the known
            Ultralytics/torch model classes on the allow-list. Defaults to the `ULTRALYTICS_SAFE_LOAD` environment
            variable (off), so standard usage is unchanged; set the env to opt in.

    Returns:
        (dict): The loaded model checkpoint.
        (str): The loaded filename.

    Examples:
        >>> from ultralytics.nn.tasks import torch_safe_load
        >>> ckpt, file = torch_safe_load("path/to/best.pt", safe_only=True)
    """
    from ultralytics.utils.downloads import GITHUB_ASSETS_NAMES, attempt_download_asset

    if safe_only is None:
        safe_only = SAFE_LOAD
    if safe_only and not _SafeLoad.SUPPORTED:
        safe_only = False
    check_suffix(file=weight, suffix=".pt")
    file = attempt_download_asset(weight)  # search online if missing locally

    def _load():
        with temporary_modules(
            modules={
                "ultralytics.yolo.utils": "ultralytics.utils",
                "ultralytics.yolo.v8": "ultralytics.models.yolo",
                "ultralytics.yolo.data": "ultralytics.data",
            },
            attributes={
                "ultralytics.nn.modules.block.Silence": "torch.nn.Identity",  # YOLOv9e
                "ultralytics.nn.tasks.YOLOv10DetectionModel": "ultralytics.nn.tasks.DetectionModel",  # YOLOv10
                "ultralytics.utils.loss.v10DetectLoss": "ultralytics.utils.loss.E2EDetectLoss",  # YOLOv10
                # resolve cross-platform pathlib pickle incompatibility
                **(
                    {"pathlib.PosixPath": "pathlib.WindowsPath"}
                    if WINDOWS
                    else {"pathlib.WindowsPath": "pathlib.PosixPath"}
                ),
            },
        ):
            if safe_only:
                with _SafeLoad.loading():  # weights_only load scoped to the known-class allow-list
                    return torch_load(file, map_location="cpu", weights_only=True)
            return torch_load(file, map_location="cpu")

    # weights_only=True raises on a TorchScript archive; the default path returns a ScriptModule instead.
    torchscript_error = emojis(
        f"ERROR ❌️ {weight} is a TorchScript archive, not an Ultralytics PyTorch checkpoint.\n"
        f"Load the original .pt weights, or export again with format='torchscript' and load that file directly."
    )

    try:
        ckpt = _load()

    except (RuntimeError, EOFError, pickle.UnpicklingError) as e:
        # An unreadable file reaches the loader as one of three internal errors depending on how it is damaged:
        # RuntimeError for a truncated zip, EOFError for an empty one, UnpicklingError for bytes that are not a
        # pickle at all (an image or archive renamed .pt). They are one user-facing condition, so they share one
        # handler and one message.
        if isinstance(e, RuntimeError) and "TorchScript archive" in str(e):
            raise TypeError(torchscript_error) from e
        if isinstance(e, RuntimeError) and "PytorchStreamReader" not in str(e):
            raise  # an unrelated RuntimeError is a real failure, not a damaged file
        if safe_only and isinstance(e, pickle.UnpicklingError):
            # weights_only=True refused a global outside the allow-list: a format problem, not a damaged file
            raise TypeError(
                emojis(
                    f"ERROR ❌️ {weight} references types outside the supported Ultralytics checkpoint format. "
                    f"Use an official Ultralytics model, i.e. 'yolo predict model=yolo26n.pt'"
                )
            ) from e
        # Recover only a corrupt cached official asset requested by bare name; never touch user-supplied paths.
        name = Path(str(weight)).name
        if str(weight) != name or name not in GITHUB_ASSETS_NAMES:
            raise TypeError(
                emojis(
                    f"ERROR ❌️ {weight} is not a loadable checkpoint — the file is empty, truncated or corrupted "
                    f"({type(e).__name__}: {e}).\nRecommend fixes are to re-download or re-export the file, or to "
                    f"run a command with an official Ultralytics model, i.e. 'yolo predict model=yolo26n.pt'"
                )
            ) from e
        LOGGER.warning(f"Corrupt cache {file}, re-downloading {weight}...")
        Path(file).unlink(missing_ok=True)
        file = attempt_download_asset(weight)
        ckpt = _load()

    except ModuleNotFoundError as e:  # e.name is missing module name
        if e.name in {"models", "models.yolo", "models.common", "models.experimental"}:
            raise TypeError(
                emojis(
                    f"ERROR ❌️ {weight} appears to be an Ultralytics YOLOv5 model originally trained "
                    f"with https://github.com/ultralytics/yolov5. This model is NOT forwards compatible with "
                    f"YOLOv8 at https://github.com/ultralytics/ultralytics."
                    f"\nRecommend fixes are to train a new model using the latest 'ultralytics' package or to "
                    f"run a command with an official Ultralytics model, i.e. 'yolo predict model=yolo26n.pt'"
                )
            ) from e
        elif e.name == "numpy._core":
            raise ModuleNotFoundError(
                emojis(
                    f"ERROR ❌️ {weight} requires numpy>=1.26.1, however numpy=={__import__('numpy').__version__} is installed."
                )
            ) from e
        elif e.name and e.name.startswith("ultralytics."):
            raise ModuleNotFoundError(
                emojis(
                    f"ERROR ❌️ {weight} requires missing Ultralytics module '{e.name}'. "
                    "Train a new model using the latest 'ultralytics' package or run a command with an official "
                    "Ultralytics model, i.e. 'yolo predict model=yolo26n.pt'"
                )
            ) from e
        if safe_only:
            # Under weights_only loading, do not auto-install a module named by the checkpoint or fall back to a
            # weights_only=False reload.
            raise
        LOGGER.warning(
            f"{weight} appears to require '{e.name}', which is not in Ultralytics requirements."
            f"\nAutoInstall will run now for '{e.name}' but this feature will be removed in the future."
            f"\nRecommend fixes are to train a new model using the latest 'ultralytics' package or to "
            f"run a command with an official Ultralytics model, i.e. 'yolo predict model=yolo26n.pt'"
        )
        check_requirements(e.name)  # install missing module
        ckpt = torch_load(file, map_location="cpu")

    if isinstance(ckpt, torch.jit.ScriptModule):
        raise TypeError(torchscript_error)  # default path: torch.load dispatched to torch.jit.load and succeeded

    if not isinstance(ckpt, dict):
        # File is likely a YOLO instance saved with i.e. torch.save(model, "saved_model.pt")
        LOGGER.warning(
            f"The file '{weight}' appears to be improperly saved or formatted. "
            f"For optimal results, use model.save('filename.pt') to correctly save YOLO models."
        )
        ckpt = {"model": ckpt.model}

    return ckpt, file


def load_checkpoint(weight, device=None, inplace=True, fuse=False):
    """Load single model weights.

    Args:
        weight (str | Path): Model weight path.
        device (torch.device, optional): Device to load model to.
        inplace (bool): Whether to do inplace operations.
        fuse (bool): Whether to fuse model.

    Returns:
        (torch.nn.Module): Loaded model.
        (dict): Model checkpoint dictionary.
    """
    if str(weight).lower().startswith(REMOTE_FILE_PREFIXES):
        weight = check_file(weight, download_dir=SETTINGS["weights_dir"])
    ckpt, weight = torch_safe_load(weight)  # load ckpt
    args = {**DEFAULT_CFG_DICT, **(ckpt.get("train_args", {}))}  # combine model and default args, preferring model args
    candidate = ckpt.get("ema") or ckpt.get("model")
    if not isinstance(candidate, torch.nn.Module):
        raise TypeError(
            emojis(
                f"ERROR ❌️ {weight} references types outside the supported Ultralytics checkpoint format. "
                f"Use an official Ultralytics model, i.e. 'yolo predict model=yolo26n.pt'"
            )
        )
    model = candidate.float()  # FP32 model

    # Model compatibility updates
    model.args = args  # attach args to model
    model.pt_path = str(weight)  # attach *.pt file path to model as string (avoids WindowsPath pickle issues)
    model.task = getattr(model, "task", guess_model_task(model))
    if not hasattr(model, "stride"):
        model.stride = torch.tensor([32.0])

    model = (model.fuse() if fuse and hasattr(model, "fuse") else model).eval().to(device)  # model in eval mode

    # Module updates
    for m in model.modules():
        if hasattr(m, "inplace"):
            m.inplace = inplace
        elif isinstance(m, torch.nn.Upsample) and not hasattr(m, "recompute_scale_factor"):
            m.recompute_scale_factor = None  # torch 1.11.0 compatibility

    # Return model and ckpt
    return model, ckpt


def parse_model(d, ch, verbose=True):
    """Parse a YOLO model.yaml dictionary into a PyTorch model.

    Args:
        d (dict): Model dictionary.
        ch (int): Input channels.
        verbose (bool): Whether to print model details.

    Returns:
        (torch.nn.Sequential): PyTorch model.
        (list): Sorted list of layer indices whose outputs need to be saved.
    """
    import ast

    # Args
    legacy = True  # backward compatibility for v3/v5/v8/v9 models
    max_channels = float("inf")
    nc, act, scales, end2end = (d.get(x) for x in ("nc", "activation", "scales", "end2end"))
    reg_max = d.get("reg_max", 16)
    depth, width, kpt_shape = (d.get(x, 1.0) for x in ("depth_multiple", "width_multiple", "kpt_shape"))
    scale = d.get("scale")
    if scales:
        if not scale:
            scale = next(iter(scales.keys()))
            LOGGER.warning(f"no model scale passed. Assuming scale='{scale}'.")
        depth, width, max_channels = scales[scale]

    restricted = _SafeLoad.restricted()
    if act:
        # redefine default activation, i.e. Conv.default_act = torch.nn.SiLU(). Under restricted loading, resolve the
        # spec without eval() (see _SafeLoad.activation).
        Conv.default_act = _SafeLoad.activation(act) if restricted else eval(act)
        if verbose:
            LOGGER.info(f"{colorstr('activation:')} {act}")  # print

    if verbose:
        LOGGER.info(f"\n{'':>3}{'from':>20}{'n':>3}{'params':>10}  {'module':<45}{'arguments':<30}")
    ch = [ch]
    layers, save, c2 = [], [], ch[-1]  # layers, savelist, ch out
    base_modules = frozenset(
        {
            Classify,
            Conv,
            ConvTranspose,
            GhostConv,
            Bottleneck,
            GhostBottleneck,
            SPP,
            SPPF,
            C2fPSA,
            C2PSA,
            DWConv,
            Focus,
            BottleneckCSP,
            C1,
            C2,
            C2f,
            C3k2,
            RepNCSPELAN4,
            ELAN1,
            ADown,
            AConv,
            SPPELAN,
            C2fAttn,
            C3,
            C3TR,
            C3Ghost,
            torch.nn.ConvTranspose2d,
            DWConvTranspose2d,
            C3x,
            RepC3,
            PSA,
            SCDown,
            C2fCIB,
            A2C2f,
        }
    )
    repeat_modules = frozenset(  # modules with 'repeat' arguments
        {
            BottleneckCSP,
            C1,
            C2,
            C2f,
            C3k2,
            C2fAttn,
            C3,
            C3TR,
            C3Ghost,
            C3x,
            RepC3,
            C2fPSA,
            C2fCIB,
            C2PSA,
            A2C2f,
        }
    )
    for i, (f, n, m, args) in enumerate(d["backbone"] + d["head"]):  # from, number, module, args
        m = (
            getattr(torch.nn, m[3:])
            if m.startswith("nn.")
            else getattr(__import__("torchvision").ops, m[16:])
            if m.startswith("torchvision.ops.")
            else globals()[m]
        )  # get module
        if restricted and not (isinstance(m, type) and issubclass(m, torch.nn.Module)):
            # Under restricted loading, only known model layers may be named here.
            raise TypeError(emojis(f"ERROR ❌️ module '{m}' is not a permitted model layer under restricted loading."))
        for j, a in enumerate(args):
            if isinstance(a, str):
                with contextlib.suppress(ValueError):
                    args[j] = locals()[a] if a in locals() else ast.literal_eval(a)
        n = n_ = max(round(n * depth), 1) if n > 1 else n  # depth gain
        if m in base_modules:
            c1, c2 = ch[f], args[0]
            if m is not Classify:  # Classify() output must stay at nc; every other layer scales by width
                c2 = make_divisible(min(c2, max_channels) * width, 8)
            if m is C2fAttn:  # set 1) embed channels and 2) num heads
                args[1] = make_divisible(min(args[1], max_channels // 2) * width, 8)
                args[2] = int(max(round(min(args[2], max_channels // 2 // 32)) * width, 1) if args[2] > 1 else args[2])

            args = [c1, c2, *args[1:]]
            if m in repeat_modules:
                args.insert(2, n)  # number of repeats
                n = 1
            if m is C3k2:  # for M/L/X sizes
                legacy = False
                if scale in "mlx":
                    args[3] = True
            if m is A2C2f:
                legacy = False
                if scale in "lx":  # for L/X sizes
                    args.extend((True, 1.2))
            if m is C2fCIB:
                legacy = False
        elif m is AIFI:
            args = [ch[f], *args]
        elif m in frozenset({HGStem, HGBlock}):
            c1, cm, c2 = ch[f], args[0], args[1]
            args = [c1, cm, c2, *args[2:]]
            if m is HGBlock:
                args.insert(4, n)  # number of repeats
                n = 1
        elif m is ResNetLayer:
            c2 = args[1] if args[3] else args[1] * 4
        elif m is torch.nn.BatchNorm2d:
            args = [ch[f]]
        elif m is Concat:
            c2 = sum(ch[x] for x in f)
        elif m in frozenset(
            {
                Detect,
                WorldDetect,
                YOLOEDetect,
                Segment,
                Segment26,
                YOLOESegment,
                YOLOESegment26,
                Pose,
                Pose26,
                OBB,
                OBB26,
            }
        ):
            args.extend([reg_max, end2end, [ch[x] for x in f]])
            if m is Segment or m is YOLOESegment or m is Segment26 or m is YOLOESegment26:
                args[2] = make_divisible(min(args[2], max_channels) * width, 8)
            if m in {Detect, YOLOEDetect, Segment, Segment26, YOLOESegment, YOLOESegment26, Pose, Pose26, OBB, OBB26}:
                m.legacy = legacy
        elif m is Depth:
            args = [*args[:1], [ch[x] for x in f]]  # c_mid, ch tuple; drops the legacy mode arg old checkpoints store
        elif m is SemanticSegment:
            args.append([ch[x] for x in f])  # nc, ch tuple
        elif m is v10Detect:
            args.append([ch[x] for x in f])
        elif m is ImagePoolingAttn:
            args.insert(1, [ch[x] for x in f])  # channels as second arg
        elif m is RTDETRDecoder:  # special case, channels arg must be passed in index 1
            args.insert(1, [ch[x] for x in f])
        elif m is CBLinear:
            c2 = args[0]
            c1 = ch[f]
            args = [c1, c2, *args[1:]]
        elif m is CBFuse:
            c2 = ch[f[-1]]
        elif m in frozenset({TorchVision, Index}):
            c2 = args[0]
            c1 = ch[f]
            args = [*args[1:]]
        else:
            c2 = ch[f]

        m_ = torch.nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args)  # module
        t = str(m)[8:-2].replace("__main__.", "")  # module type
        m_.np = sum(x.numel() for x in m_.parameters())  # number params
        m_.i, m_.f, m_.type = i, f, t  # attach index, 'from' index, type
        if verbose:
            LOGGER.info(f"{i:>3}{f!s:>20}{n_:>3}{m_.np:10.0f}  {t:<45}{args!s:<30}")  # print
        save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1)  # append to savelist
        layers.append(m_)
        if i == 0:
            ch = []
        ch.append(c2)
    return torch.nn.Sequential(*layers), sorted(save)


def yaml_model_load(path):
    """Load a YOLO model from a YAML file.

    Args:
        path (str | Path): Path to the YAML file.

    Returns:
        (dict): Model dictionary.
    """
    path = Path(path)
    if path.stem in (f"yolov{d}{x}6" for x in "nsmlx" for d in (5, 8)):
        new_stem = re.sub(r"(\d+)([nslmx])6(.+)?$", r"\1\2-p6\3", path.stem)
        LOGGER.warning(f"Ultralytics YOLO P6 models now use -p6 suffix. Renaming {path.stem} to {new_stem}.")
        path = path.with_name(new_stem + path.suffix)

    unified_path = re.sub(r"(\d+)([nslmx])(.+)?$", r"\1\3", str(path))  # i.e. yolov8x.yaml -> yolov8.yaml
    yaml_file = check_yaml(unified_path, hard=False) or check_yaml(path)
    d = YAML.load(yaml_file)  # model dict
    d["scale"] = guess_model_scale(path)
    d["yaml_file"] = str(path)
    return d


def guess_model_scale(model_path):
    """Extract the size character n, s, m, l, or x of the model's scale from the model path.

    Args:
        model_path (str | Path): The path to the YOLO model's YAML file.

    Returns:
        (str): The size character of the model's scale (n, s, m, l, or x), or empty string if not found.
    """
    try:
        return re.search(r"yolo(e-)?[v]?\d+([nslmx])", Path(model_path).stem).group(2)
    except AttributeError:
        return ""


def guess_model_task(model):
    """Guess the task of a PyTorch model from its architecture or configuration.

    Args:
        model (torch.nn.Module | dict | str | Path): PyTorch model, model configuration dict, or model file path.

    Returns:
        (str): Task of the model ('detect', 'segment', 'classify', 'pose', 'obb', 'semantic', 'depth').
    """

    def cfg2task(cfg):
        """Guess from YAML dictionary."""
        m = cfg["head"][-1][-2].lower()  # output module name
        if m in {"classify", "classifier", "cls", "fc"}:
            return "classify"
        if "detect" in m:
            return "detect"
        if "semanticsegment" in m:
            return "semantic"
        if "segment" in m:
            return "segment"
        if "pose" in m:
            return "pose"
        if "obb" in m:
            return "obb"
        if "depth" in m:
            return "depth"

    # Guess from model cfg
    if isinstance(model, dict):
        with contextlib.suppress(Exception):
            return cfg2task(model)
    # Guess from PyTorch model
    if isinstance(model, torch.nn.Module):  # PyTorch model
        for x in "model.args", "model.model.args", "model.model.model.args":
            with contextlib.suppress(Exception):
                return eval(x)["task"]  # nosec B307: safe eval of known attribute paths
        for x in "model.yaml", "model.model.yaml", "model.model.model.yaml":
            with contextlib.suppress(Exception):
                return cfg2task(eval(x))  # nosec B307: safe eval of known attribute paths
        for m in model.modules():
            if isinstance(m, SemanticSegment):
                return "semantic"
            elif isinstance(m, (Segment, YOLOESegment)):
                return "segment"
            elif isinstance(m, Classify):
                return "classify"
            elif isinstance(m, Pose):
                return "pose"
            elif isinstance(m, OBB):
                return "obb"
            elif isinstance(m, Depth):
                return "depth"
            elif isinstance(m, (Detect, WorldDetect, YOLOEDetect, v10Detect)):
                return "detect"

    # Guess from model filename
    if isinstance(model, (str, Path)):
        model = Path(model)
        if "-sem" in model.stem or "semantic" in model.parts:
            return "semantic"
        elif "-seg" in model.stem or "segment" in model.parts:
            return "segment"
        elif "-cls" in model.stem or "classify" in model.parts:
            return "classify"
        elif "-pose" in model.stem or "pose" in model.parts:
            return "pose"
        elif "-obb" in model.stem or "obb" in model.parts:
            return "obb"
        elif "-depth" in model.stem or "depth" in model.parts:
            return "depth"
        elif "detect" in model.parts:
            return "detect"

    # Unable to determine task from model
    LOGGER.warning(
        "Unable to automatically guess model task, assuming 'task=detect'. "
        "Explicitly define task for your model, i.e. 'task=detect', 'segment', 'classify', 'pose', 'obb' or 'semantic'."
    )
    return "detect"  # assume detect
