"""Borrowed from https://github.com/pytorch/vision.
"""
import re
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as cp
from collections import OrderedDict
from torch.utils.model_zoo import load_url as load_state_dict_from_url
from torch import Tensor
from torch.jit.annotations import List


__all__ = ["DenseNet", "densenet121", "densenet169", "densenet201", "densenet161"]

model_urls = {
    "densenet121": "https://download.pytorch.org/models/densenet121-a639ec97.pth",
    "densenet169": "https://download.pytorch.org/models/densenet169-b2777c0a.pth",
    "densenet201": "https://download.pytorch.org/models/densenet201-c1103571.pth",
    "densenet161": "https://download.pytorch.org/models/densenet161-8d451a50.pth",
}


class _DenseLayer(nn.Module):
    def __init__(
        self,
        num_input_features,
        growth_rate,
        bn_size,
        drop_rate,
        memory_efficient=False,
    ):
        super(_DenseLayer, self).__init__()
        self.add_module("norm1", nn.BatchNorm2d(num_input_features)),
        self.add_module("relu1", nn.ReLU(inplace=True)),
        self.add_module(
            "conv1",
            nn.Conv2d(
                num_input_features,
                bn_size * growth_rate,
                kernel_size=1,
                stride=1,
                bias=False,
            ),
        ),
        self.add_module("norm2", nn.BatchNorm2d(bn_size * growth_rate)),
        self.add_module("relu2", nn.ReLU(inplace=True)),
        self.add_module(
            "conv2",
            nn.Conv2d(
                bn_size * growth_rate,
                growth_rate,
                kernel_size=3,
                stride=1,
                padding=1,
                bias=False,
            ),
        ),
        self.drop_rate = float(drop_rate)
        self.memory_efficient = memory_efficient

    def bn_function(self, inputs):
        # type: (List[Tensor]) -> Tensor
        concated_features = torch.cat(inputs, 1)
        bottleneck_output = self.conv1(
            self.relu1(self.norm1(concated_features))
        )  # noqa: T484
        return bottleneck_output

    # todo: rewrite when torchscript supports any
    def any_requires_grad(self, input):
        # type: (List[Tensor]) -> bool
        for tensor in input:
            if tensor.requires_grad:
                return True
        return False

    @torch.jit.unused  # noqa: T484
    def call_checkpoint_bottleneck(self, input):
        # type: (List[Tensor]) -> Tensor
        def closure(*inputs):
            return self.bn_function(inputs)

        return cp.checkpoint(closure, *input)

    @torch.jit._overload_method  # noqa: F811
    def forward(self, input):
        # type: (List[Tensor]) -> (Tensor)
        pass

    @torch.jit._overload_method  # noqa: F811
    def forward(self, input):
        # type: (Tensor) -> (Tensor)
        pass

    # torchscript does not yet support *args, so we overload method
    # allowing it to take either a List[Tensor] or single Tensor
    def forward(self, input):  # noqa: F811
        if isinstance(input, Tensor):
            prev_features = [input]
        else:
            prev_features = input

        if self.memory_efficient and self.any_requires_grad(prev_features):
            if torch.jit.is_scripting():
                raise Exception("Memory Efficient not supported in JIT")

            bottleneck_output = self.call_checkpoint_bottleneck(prev_features)
        else:
            bottleneck_output = self.bn_function(prev_features)

        new_features = self.conv2(self.relu2(self.norm2(bottleneck_output)))
        if self.drop_rate > 0:
            new_features = F.dropout(
                new_features, p=self.drop_rate, training=self.training
            )
        return new_features


class _DenseBlock(nn.ModuleDict):
    _version = 2

    def __init__(
        self,
        num_layers,
        num_input_features,
        bn_size,
        growth_rate,
        drop_rate,
        memory_efficient=False,
    ):
        super(_DenseBlock, self).__init__()
        for i in range(num_layers):
            layer = _DenseLayer(
                num_input_features + i * growth_rate,
                growth_rate=growth_rate,
                bn_size=bn_size,
                drop_rate=drop_rate,
                memory_efficient=memory_efficient,
            )
            self.add_module("denselayer%d" % (i + 1), layer)

    def forward(self, init_features):
        features = [init_features]
        for name, layer in self.items():
            new_features = layer(features)
            features.append(new_features)
        return torch.cat(features, 1)


class _Transition(nn.Sequential):
    def __init__(self, num_input_features, num_output_features):
        super(_Transition, self).__init__()
        self.add_module("norm", nn.BatchNorm2d(num_input_features))
        self.add_module("relu", nn.ReLU(inplace=True))
        self.add_module(
            "conv",
            nn.Conv2d(
                num_input_features,
                num_output_features,
                kernel_size=1,
                stride=1,
                bias=False,
            ),
        )
        self.add_module("pool", nn.AvgPool2d(kernel_size=2, stride=2))


class DenseNet(nn.Module):
    r"""Densenet-BC model class, based on
    `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_

    Args:
        growth_rate (int) - how many filters to add each layer (`k` in paper)
        block_config (list of 4 ints) - how many layers in each pooling block
        num_init_features (int) - the number of filters to learn in the first convolution layer
        bn_size (int) - multiplicative factor for number of bottle neck layers
          (i.e. bn_size * k features in the bottleneck layer)
        drop_rate (float) - dropout rate after each dense layer
        num_classes (int) - number of classification classes
        memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,
          but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_
    """

    def __init__(
        self,
        growth_rate=32,
        block_config=(6, 12, 24, 16),
        num_init_features=64,
        bn_size=4,
        drop_rate=0,
        num_classes=1000,
        memory_efficient=False,
    ):

        super(DenseNet, self).__init__()

        # First convolution
        self.features = nn.Sequential(
            OrderedDict(
                [
                    (
                        "conv0",
                        nn.Conv2d(
                            3,
                            num_init_features,
                            kernel_size=7,
                            stride=2,
                            padding=3,
                            bias=False,
                        ),
                    ),
                    ("norm0", nn.BatchNorm2d(num_init_features)),
                    ("relu0", nn.ReLU(inplace=True)),
                    ("pool0", nn.MaxPool2d(kernel_size=3, stride=2, padding=1)),
                ]
            )
        )

        # Each denseblock
        num_features = num_init_features
        for i, num_layers in enumerate(block_config):
            block = _DenseBlock(
                num_layers=num_layers,
                num_input_features=num_features,
                bn_size=bn_size,
                growth_rate=growth_rate,
                drop_rate=drop_rate,
                memory_efficient=memory_efficient,
            )
            self.features.add_module("denseblock%d" % (i + 1), block)
            num_features = num_features + num_layers * growth_rate
            if i != len(block_config) - 1:
                trans = _Transition(
                    num_input_features=num_features,
                    num_output_features=num_features // 2,
                )
                self.features.add_module("transition%d" % (i + 1), trans)
                num_features = num_features // 2

        # Final batch norm
        self.features.add_module("norm5", nn.BatchNorm2d(num_features))

        # Linear layer
        # self.classifier = nn.Linear(num_features, num_classes)
        self.num_features = num_features

        # Official init from torch repo.
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight)
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.Linear):
                nn.init.constant_(m.bias, 0)

    def forward(self, x):
        features = self.features(x)
        out = F.relu(features, inplace=True)
        out = F.adaptive_avg_pool2d(out, (1, 1))
        out = torch.flatten(out, 1)
        # out = self.classifier(out)
        return out


def _load_state_dict(model, model_url, progress):
    # '.'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 = load_state_dict_from_url(model_url, 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(
    arch, growth_rate, block_config, num_init_features, pretrained, progress, **kwargs
):
    model = DenseNet(growth_rate, block_config, num_init_features, **kwargs)
    if pretrained:
        _load_state_dict(model, model_urls[arch], progress)
    return model


def densenet121(pretrained=False, progress=True, **kwargs):
    r"""Densenet-121 model from
    `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
        memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,
          but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_
    """
    backbone = _densenet(
        "densenet121", 32, (6, 12, 24, 16), 64, pretrained, progress, **kwargs
    )
    backbone.feature_dim = backbone.num_features

    return backbone
    # return _densenet(
    #     "densenet121", 32, (6, 12, 24, 16), 64, pretrained, progress, **kwargs
    # )


def densenet161(pretrained=False, progress=True, **kwargs):
    r"""Densenet-161 model from
    `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
        memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,
          but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_
    """
    return _densenet(
        "densenet161", 48, (6, 12, 36, 24), 96, pretrained, progress, **kwargs
    )


def densenet169(pretrained=False, progress=True, **kwargs):
    r"""Densenet-169 model from
    `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
        memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,
          but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_
    """
    return _densenet(
        "densenet169", 32, (6, 12, 32, 32), 64, pretrained, progress, **kwargs
    )


def densenet201(pretrained=False, progress=True, **kwargs):
    r"""Densenet-201 model from
    `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
        memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,
          but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_
    """
    return _densenet(
        "densenet201", 32, (6, 12, 48, 32), 64, pretrained, progress, **kwargs
    )

