import re
import warnings
from functools import partial
from typing import Any, Optional, Tuple

import torch.nn as nn
from torchvision.transforms.functional import InterpolationMode

from ...models.densenet import DenseNet
from ..transforms.presets import ImageNetEval
from ._api import Weights, WeightEntry
from ._meta import _IMAGENET_CATEGORIES


__all__ = [
    "DenseNet",
    "DenseNet121Weights",
    "DenseNet161Weights",
    "DenseNet169Weights",
    "DenseNet201Weights",
    "densenet121",
    "densenet161",
    "densenet169",
    "densenet201",
]


def _load_state_dict(model: nn.Module, weights: Weights, progress: bool) -> None:
    # '.'s are no longer allowed in module names, but previous _DenseLayer
    # has keys 'norm.1', 'relu.1', 'conv.1', 'norm.2', 'relu.2', 'conv.2'.
    # They are also in the checkpoints in model_urls. This pattern is used
    # to find such keys.
    pattern = re.compile(
        r"^(.*denselayer\d+\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$"
    )

    state_dict = weights.state_dict(progress=progress)
    for key in list(state_dict.keys()):
        res = pattern.match(key)
        if res:
            new_key = res.group(1) + res.group(2)
            state_dict[new_key] = state_dict[key]
            del state_dict[key]
    model.load_state_dict(state_dict)


def _densenet(
    growth_rate: int,
    block_config: Tuple[int, int, int, int],
    num_init_features: int,
    weights: Optional[Weights],
    progress: bool,
    **kwargs: Any,
) -> DenseNet:
    if weights is not None:
        kwargs["num_classes"] = len(weights.meta["categories"])

    model = DenseNet(growth_rate, block_config, num_init_features, **kwargs)

    if weights is not None:
        _load_state_dict(model=model, weights=weights, progress=progress)

    return model


_common_meta = {
    "size": (224, 224),
    "categories": _IMAGENET_CATEGORIES,
    "interpolation": InterpolationMode.BILINEAR,
    "recipe": None,  # weights ported from LuaTorch
}


class DenseNet121Weights(Weights):
    ImageNet1K_Community = WeightEntry(
        url="https://download.pytorch.org/models/densenet121-a639ec97.pth",
        transforms=partial(ImageNetEval, crop_size=224),
        meta={
            **_common_meta,
            "acc@1": 74.434,
            "acc@5": 91.972,
        },
    )


class DenseNet161Weights(Weights):
    ImageNet1K_Community = WeightEntry(
        url="https://download.pytorch.org/models/densenet161-8d451a50.pth",
        transforms=partial(ImageNetEval, crop_size=224),
        meta={
            **_common_meta,
            "acc@1": 77.138,
            "acc@5": 93.560,
        },
    )


class DenseNet169Weights(Weights):
    ImageNet1K_Community = WeightEntry(
        url="https://download.pytorch.org/models/densenet169-b2777c0a.pth",
        transforms=partial(ImageNetEval, crop_size=224),
        meta={
            **_common_meta,
            "acc@1": 75.600,
            "acc@5": 92.806,
        },
    )


class DenseNet201Weights(Weights):
    ImageNet1K_Community = WeightEntry(
        url="https://download.pytorch.org/models/densenet201-c1103571.pth",
        transforms=partial(ImageNetEval, crop_size=224),
        meta={
            **_common_meta,
            "acc@1": 76.896,
            "acc@5": 93.370,
        },
    )


def densenet121(weights: Optional[DenseNet121Weights] = None, progress: bool = True, **kwargs: Any) -> DenseNet:
    if "pretrained" in kwargs:
        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
        weights = DenseNet121Weights.ImageNet1K_Community if kwargs.pop("pretrained") else None
    weights = DenseNet121Weights.verify(weights)

    return _densenet(32, (6, 12, 24, 16), 64, weights, progress, **kwargs)


def densenet161(weights: Optional[DenseNet161Weights] = None, progress: bool = True, **kwargs: Any) -> DenseNet:
    if "pretrained" in kwargs:
        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
        weights = DenseNet161Weights.ImageNet1K_Community if kwargs.pop("pretrained") else None
    weights = DenseNet161Weights.verify(weights)

    return _densenet(48, (6, 12, 36, 24), 96, weights, progress, **kwargs)


def densenet169(weights: Optional[DenseNet169Weights] = None, progress: bool = True, **kwargs: Any) -> DenseNet:
    if "pretrained" in kwargs:
        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
        weights = DenseNet169Weights.ImageNet1K_Community if kwargs.pop("pretrained") else None
    weights = DenseNet169Weights.verify(weights)

    return _densenet(32, (6, 12, 32, 32), 64, weights, progress, **kwargs)


def densenet201(weights: Optional[DenseNet201Weights] = None, progress: bool = True, **kwargs: Any) -> DenseNet:
    if "pretrained" in kwargs:
        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
        weights = DenseNet201Weights.ImageNet1K_Community if kwargs.pop("pretrained") else None
    weights = DenseNet201Weights.verify(weights)

    return _densenet(32, (6, 12, 48, 32), 64, weights, progress, **kwargs)
