# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
"""
PyTorch Hub models https://pytorch.org/hub/ultralytics_yolov5

Usage:
    import torch
    model = torch.hub.load('ultralytics/yolov5', 'yolov5s')  # official model
    model = torch.hub.load('ultralytics/yolov5:master', 'yolov5s')  # from branch
    model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s.pt')  # custom/local model
    model = torch.hub.load('.', 'custom', 'yolov5s.pt', source='local')  # local repo
"""

import torch


def _create(
    name,
    pretrained=True,
    channels=3,
    classes=80,
    autoshape=True,
    verbose=True,
    device=None,
):
    """Creates or loads a YOLOv5 model

    Arguments:
        name (str): model name 'yolov5s' or path 'path/to/best.pt'
        pretrained (bool): load pretrained weights into the model
        channels (int): number of input channels
        classes (int): number of model classes
        autoshape (bool): apply YOLOv5 .autoshape() wrapper to model
        verbose (bool): print all information to screen
        device (str, torch.device, None): device to use for model parameters

    Returns:
        YOLOv5 model
    """
    from pathlib import Path

    from models.common import AutoShape, DetectMultiBackend
    from models.experimental import attempt_load
    from models.yolo import ClassificationModel, DetectionModel, SegmentationModel
    from utils.downloads import attempt_download
    from utils.general import LOGGER, check_requirements, intersect_dicts, logging
    from utils.torch_utils import select_device

    if not verbose:
        LOGGER.setLevel(logging.WARNING)
    check_requirements(exclude=("opencv-python", "tensorboard", "thop"))
    name = Path(name)
    path = (
        name.with_suffix(".pt")
        if name.suffix == "" and not name.is_dir()
        else name
    )  # checkpoint path
    try:
        device = select_device(device)
        if pretrained and channels == 3 and classes == 80:
            try:
                model = DetectMultiBackend(
                    path, device=device, fuse=autoshape
                )  # detection model
                if autoshape:
                    if model.pt and isinstance(
                        model.model, ClassificationModel
                    ):
                        LOGGER.warning(
                            "WARNING ⚠️ YOLOv5 ClassificationModel is not yet AutoShape compatible. "
                            "You must pass torch tensors in BCHW to this model, i.e. shape(1,3,224,224)."
                        )
                    elif model.pt and isinstance(
                        model.model, SegmentationModel
                    ):
                        LOGGER.warning(
                            "WARNING ⚠️ YOLOv5 SegmentationModel is not yet AutoShape compatible. "
                            "You will not be able to run inference with this model."
                        )
                    else:
                        model = AutoShape(
                            model
                        )  # for file/URI/PIL/cv2/np inputs and NMS
            except Exception:
                model = attempt_load(
                    path, device=device, fuse=False
                )  # arbitrary model
        else:
            cfg = list(
                (Path(__file__).parent / "models").rglob(f"{path.stem}.yaml")
            )[
                0
            ]  # model.yaml path
            model = DetectionModel(cfg, channels, classes)  # create model
            if pretrained:
                ckpt = torch.load(
                    attempt_download(path), map_location=device
                )  # load
                csd = (
                    ckpt["model"].float().state_dict()
                )  # checkpoint state_dict as FP32
                csd = intersect_dicts(
                    csd, model.state_dict(), exclude=["anchors"]
                )  # intersect
                model.load_state_dict(csd, strict=False)  # load
                if len(ckpt["model"].names) == classes:
                    model.names = ckpt[
                        "model"
                    ].names  # set class names attribute
        if not verbose:
            LOGGER.setLevel(logging.INFO)  # reset to default
        return model.to(device)

    except Exception as e:
        help_url = "https://github.com/ultralytics/yolov5/issues/36"
        s = f"{e}. Cache may be out of date, try `force_reload=True` or see {help_url} for help."
        raise Exception(s) from e


def custom(
    path="path/to/model.pt", autoshape=True, _verbose=True, device=None
):
    # YOLOv5 custom or local model
    return _create(path, autoshape=autoshape, verbose=_verbose, device=device)


def yolov5n(
    pretrained=True,
    channels=3,
    classes=80,
    autoshape=True,
    _verbose=True,
    device=None,
):
    # YOLOv5-nano model https://github.com/ultralytics/yolov5
    return _create(
        "yolov5n", pretrained, channels, classes, autoshape, _verbose, device
    )


def yolov5s(
    pretrained=True,
    channels=3,
    classes=80,
    autoshape=True,
    _verbose=True,
    device=None,
):
    # YOLOv5-small model https://github.com/ultralytics/yolov5
    return _create(
        "yolov5s", pretrained, channels, classes, autoshape, _verbose, device
    )


def yolov5m(
    pretrained=True,
    channels=3,
    classes=80,
    autoshape=True,
    _verbose=True,
    device=None,
):
    # YOLOv5-medium model https://github.com/ultralytics/yolov5
    return _create(
        "yolov5m", pretrained, channels, classes, autoshape, _verbose, device
    )


def yolov5l(
    pretrained=True,
    channels=3,
    classes=80,
    autoshape=True,
    _verbose=True,
    device=None,
):
    # YOLOv5-large model https://github.com/ultralytics/yolov5
    return _create(
        "yolov5l", pretrained, channels, classes, autoshape, _verbose, device
    )


def yolov5x(
    pretrained=True,
    channels=3,
    classes=80,
    autoshape=True,
    _verbose=True,
    device=None,
):
    # YOLOv5-xlarge model https://github.com/ultralytics/yolov5
    return _create(
        "yolov5x", pretrained, channels, classes, autoshape, _verbose, device
    )


def yolov5n6(
    pretrained=True,
    channels=3,
    classes=80,
    autoshape=True,
    _verbose=True,
    device=None,
):
    # YOLOv5-nano-P6 model https://github.com/ultralytics/yolov5
    return _create(
        "yolov5n6", pretrained, channels, classes, autoshape, _verbose, device
    )


def yolov5s6(
    pretrained=True,
    channels=3,
    classes=80,
    autoshape=True,
    _verbose=True,
    device=None,
):
    # YOLOv5-small-P6 model https://github.com/ultralytics/yolov5
    return _create(
        "yolov5s6", pretrained, channels, classes, autoshape, _verbose, device
    )


def yolov5m6(
    pretrained=True,
    channels=3,
    classes=80,
    autoshape=True,
    _verbose=True,
    device=None,
):
    # YOLOv5-medium-P6 model https://github.com/ultralytics/yolov5
    return _create(
        "yolov5m6", pretrained, channels, classes, autoshape, _verbose, device
    )


def yolov5l6(
    pretrained=True,
    channels=3,
    classes=80,
    autoshape=True,
    _verbose=True,
    device=None,
):
    # YOLOv5-large-P6 model https://github.com/ultralytics/yolov5
    return _create(
        "yolov5l6", pretrained, channels, classes, autoshape, _verbose, device
    )


def yolov5x6(
    pretrained=True,
    channels=3,
    classes=80,
    autoshape=True,
    _verbose=True,
    device=None,
):
    # YOLOv5-xlarge-P6 model https://github.com/ultralytics/yolov5
    return _create(
        "yolov5x6", pretrained, channels, classes, autoshape, _verbose, device
    )


if __name__ == "__main__":
    import argparse
    from pathlib import Path

    import numpy as np
    from PIL import Image

    from utils.general import cv2, print_args

    # Argparser
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--model", type=str, default="yolov5s", help="model name"
    )
    opt = parser.parse_args()
    print_args(vars(opt))

    # Model
    model = _create(
        name=opt.model,
        pretrained=True,
        channels=3,
        classes=80,
        autoshape=True,
        verbose=True,
    )
    # model = custom(path='path/to/model.pt')  # custom

    # Images
    imgs = [
        "data/images/zidane.jpg",  # filename
        Path("data/images/zidane.jpg"),  # Path
        "https://ultralytics.com/images/zidane.jpg",  # URI
        cv2.imread("data/images/bus.jpg")[:, :, ::-1],  # OpenCV
        Image.open("data/images/bus.jpg"),  # PIL
        np.zeros((320, 640, 3)),
    ]  # numpy

    # Inference
    results = model(imgs, size=320)  # batched inference

    # Results
    results.print()
    results.save()