from functools import partial
from typing import Any, Optional

from torchvision.prototype.transforms import ImageNetEval
from torchvision.transforms.functional import InterpolationMode

from ...models.mobilenetv2 import MobileNetV2
from ._api import WeightsEnum, Weights
from ._meta import _IMAGENET_CATEGORIES
from ._utils import handle_legacy_interface, _ovewrite_named_param


__all__ = ["MobileNetV2", "MobileNet_V2_Weights", "mobilenet_v2"]


class MobileNet_V2_Weights(WeightsEnum):
    ImageNet1K_V1 = Weights(
        url="https://download.pytorch.org/models/mobilenet_v2-b0353104.pth",
        transforms=partial(ImageNetEval, crop_size=224),
        meta={
            "task": "image_classification",
            "architecture": "MobileNetV2",
            "publication_year": 2018,
            "num_params": 3504872,
            "size": (224, 224),
            "min_size": (1, 1),
            "categories": _IMAGENET_CATEGORIES,
            "interpolation": InterpolationMode.BILINEAR,
            "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#mobilenetv2",
            "acc@1": 71.878,
            "acc@5": 90.286,
        },
    )
    default = ImageNet1K_V1


@handle_legacy_interface(weights=("pretrained", MobileNet_V2_Weights.ImageNet1K_V1))
def mobilenet_v2(
    *, weights: Optional[MobileNet_V2_Weights] = None, progress: bool = True, **kwargs: Any
) -> MobileNetV2:
    weights = MobileNet_V2_Weights.verify(weights)

    if weights is not None:
        _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"]))

    model = MobileNetV2(**kwargs)

    if weights is not None:
        model.load_state_dict(weights.get_state_dict(progress=progress))

    return model
