from functools import partial
from typing import Any, Optional

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

from ....models.segmentation.fcn import FCN, _fcn_resnet
from .._api import Weights, WeightEntry
from .._meta import _VOC_CATEGORIES
from .._utils import _deprecated_param, _deprecated_positional, _ovewrite_value_param
from ..resnet import ResNet50Weights, ResNet101Weights, resnet50, resnet101


__all__ = ["FCN", "FCNResNet50Weights", "FCNResNet101Weights", "fcn_resnet50", "fcn_resnet101"]


_COMMON_META = {
    "categories": _VOC_CATEGORIES,
    "interpolation": InterpolationMode.BILINEAR,
}


class FCNResNet50Weights(Weights):
    CocoWithVocLabels_RefV1 = WeightEntry(
        url="https://download.pytorch.org/models/fcn_resnet50_coco-1167a1af.pth",
        transforms=partial(VocEval, resize_size=520),
        meta={
            **_COMMON_META,
            "recipe": "https://github.com/pytorch/vision/tree/main/references/segmentation#fcn_resnet50",
            "mIoU": 60.5,
            "acc": 91.4,
        },
        default=True,
    )


class FCNResNet101Weights(Weights):
    CocoWithVocLabels_RefV1 = WeightEntry(
        url="https://download.pytorch.org/models/fcn_resnet101_coco-7ecb50ca.pth",
        transforms=partial(VocEval, resize_size=520),
        meta={
            **_COMMON_META,
            "recipe": "https://github.com/pytorch/vision/tree/main/references/segmentation#deeplabv3_resnet101",
            "mIoU": 63.7,
            "acc": 91.9,
        },
        default=True,
    )


def fcn_resnet50(
    weights: Optional[FCNResNet50Weights] = None,
    progress: bool = True,
    num_classes: Optional[int] = None,
    aux_loss: Optional[bool] = None,
    weights_backbone: Optional[ResNet50Weights] = None,
    **kwargs: Any,
) -> FCN:
    if type(weights) == bool and weights:
        _deprecated_positional(kwargs, "pretrained", "weights", True)
    if "pretrained" in kwargs:
        weights = _deprecated_param(kwargs, "pretrained", "weights", FCNResNet50Weights.CocoWithVocLabels_RefV1)
    weights = FCNResNet50Weights.verify(weights)
    if type(weights_backbone) == bool and weights_backbone:
        _deprecated_positional(kwargs, "pretrained_backbone", "weights_backbone", True)
    if "pretrained_backbone" in kwargs:
        weights_backbone = _deprecated_param(
            kwargs, "pretrained_backbone", "weights_backbone", ResNet50Weights.ImageNet1K_RefV1
        )
    weights_backbone = ResNet50Weights.verify(weights_backbone)

    if weights is not None:
        weights_backbone = None
        num_classes = _ovewrite_value_param(num_classes, len(weights.meta["categories"]))
        aux_loss = _ovewrite_value_param(aux_loss, True)
    elif num_classes is None:
        num_classes = 21

    backbone = resnet50(weights=weights_backbone, replace_stride_with_dilation=[False, True, True])
    model = _fcn_resnet(backbone, num_classes, aux_loss)

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

    return model


def fcn_resnet101(
    weights: Optional[FCNResNet101Weights] = None,
    progress: bool = True,
    num_classes: Optional[int] = None,
    aux_loss: Optional[bool] = None,
    weights_backbone: Optional[ResNet101Weights] = None,
    **kwargs: Any,
) -> FCN:
    if type(weights) == bool and weights:
        _deprecated_positional(kwargs, "pretrained", "weights", True)
    if "pretrained" in kwargs:
        weights = _deprecated_param(kwargs, "pretrained", "weights", FCNResNet101Weights.CocoWithVocLabels_RefV1)
    weights = FCNResNet101Weights.verify(weights)
    if type(weights_backbone) == bool and weights_backbone:
        _deprecated_positional(kwargs, "pretrained_backbone", "weights_backbone", True)
    if "pretrained_backbone" in kwargs:
        weights_backbone = _deprecated_param(
            kwargs, "pretrained_backbone", "weights_backbone", ResNet101Weights.ImageNet1K_RefV1
        )
    weights_backbone = ResNet101Weights.verify(weights_backbone)

    if weights is not None:
        weights_backbone = None
        num_classes = _ovewrite_value_param(num_classes, len(weights.meta["categories"]))
        aux_loss = _ovewrite_value_param(aux_loss, True)
    elif num_classes is None:
        num_classes = 21

    backbone = resnet101(weights=weights_backbone, replace_stride_with_dilation=[False, True, True])
    model = _fcn_resnet(backbone, num_classes, aux_loss)

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

    return model
