from collections import OrderedDict
from typing import Any, Dict

from torch import nn, Tensor
from torch.nn import functional as F

from ...utils import _log_api_usage_once
from .. import mobilenetv3
from ..feature_extraction import create_feature_extractor
from ._utils import _load_weights


__all__ = ["LRASPP", "lraspp_mobilenet_v3_large"]


model_urls = {
    "lraspp_mobilenet_v3_large_coco": "https://download.pytorch.org/models/lraspp_mobilenet_v3_large-d234d4ea.pth",
}


class LRASPP(nn.Module):
    """
    Implements a Lite R-ASPP Network for semantic segmentation from
    `"Searching for MobileNetV3"
    <https://arxiv.org/abs/1905.02244>`_.

    Args:
        backbone (nn.Module): the network used to compute the features for the model.
            The backbone should return an OrderedDict[Tensor], with the key being
            "high" for the high level feature map and "low" for the low level feature map.
        low_channels (int): the number of channels of the low level features.
        high_channels (int): the number of channels of the high level features.
        num_classes (int): number of output classes of the model (including the background).
        inter_channels (int, optional): the number of channels for intermediate computations.
    """

    def __init__(
        self, backbone: nn.Module, low_channels: int, high_channels: int, num_classes: int, inter_channels: int = 128
    ) -> None:
        super().__init__()
        _log_api_usage_once(self)
        self.backbone = backbone
        self.classifier = LRASPPHead(low_channels, high_channels, num_classes, inter_channels)

    def forward(self, input: Tensor) -> Dict[str, Tensor]:
        features = self.backbone(input)
        out = self.classifier(features)
        out = F.interpolate(out, size=input.shape[-2:], mode="bilinear", align_corners=False)

        result = OrderedDict()
        result["out"] = out

        return result


class LRASPPHead(nn.Module):
    def __init__(self, low_channels: int, high_channels: int, num_classes: int, inter_channels: int) -> None:
        super().__init__()
        self.cbr = nn.Sequential(
            nn.Conv2d(high_channels, inter_channels, 1, bias=False),
            nn.BatchNorm2d(inter_channels),
            nn.ReLU(inplace=True),
        )
        self.scale = nn.Sequential(
            nn.AdaptiveAvgPool2d(1),
            nn.Conv2d(high_channels, inter_channels, 1, bias=False),
            nn.Sigmoid(),
        )
        self.low_classifier = nn.Conv2d(low_channels, num_classes, 1)
        self.high_classifier = nn.Conv2d(inter_channels, num_classes, 1)

    def forward(self, input: Dict[str, Tensor]) -> Tensor:
        low = input["low"]
        high = input["high"]

        x = self.cbr(high)
        s = self.scale(high)
        x = x * s
        x = F.interpolate(x, size=low.shape[-2:], mode="bilinear", align_corners=False)

        return self.low_classifier(low) + self.high_classifier(x)


def _lraspp_mobilenetv3(backbone: mobilenetv3.MobileNetV3, num_classes: int) -> LRASPP:
    backbone = backbone.features
    # Gather the indices of blocks which are strided. These are the locations of C1, ..., Cn-1 blocks.
    # The first and last blocks are always included because they are the C0 (conv1) and Cn.
    stage_indices = [0] + [i for i, b in enumerate(backbone) if getattr(b, "_is_cn", False)] + [len(backbone) - 1]
    low_pos = stage_indices[-4]  # use C2 here which has output_stride = 8
    high_pos = stage_indices[-1]  # use C5 which has output_stride = 16
    low_channels = backbone[low_pos].out_channels
    high_channels = backbone[high_pos].out_channels
    backbone = create_feature_extractor(backbone, {str(low_pos): "low", str(high_pos): "high"})

    return LRASPP(backbone, low_channels, high_channels, num_classes)


def lraspp_mobilenet_v3_large(
    pretrained: bool = False,
    progress: bool = True,
    num_classes: int = 21,
    pretrained_backbone: bool = True,
    **kwargs: Any,
) -> LRASPP:
    """Constructs a Lite R-ASPP Network model with a MobileNetV3-Large backbone.

    Args:
        pretrained (bool): If True, returns a model pre-trained on COCO train2017 which
            contains the same classes as Pascal VOC
        progress (bool): If True, displays a progress bar of the download to stderr
        num_classes (int): number of output classes of the model (including the background)
        pretrained_backbone (bool): If True, the backbone will be pre-trained.
    """
    if kwargs.pop("aux_loss", False):
        raise NotImplementedError("This model does not use auxiliary loss")
    if pretrained:
        pretrained_backbone = False

    backbone = mobilenetv3.mobilenet_v3_large(pretrained=pretrained_backbone, dilated=True)
    model = _lraspp_mobilenetv3(backbone, num_classes)

    if pretrained:
        arch = "lraspp_mobilenet_v3_large_coco"
        _load_weights(arch, model, model_urls.get(arch, None), progress)
    return model
