import os
from collections import OrderedDict

import torch
import torch.nn as nn
import torch.nn.functional as F

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


class _DenseLayer(nn.Sequential):
    def __init__(self, num_input_features, growth_rate, bn_size, drop_rate):
        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 = drop_rate

    def forward(self, x):
        new_features = super(_DenseLayer, self).forward(x)
        if self.drop_rate > 0:
            new_features = F.dropout(
                new_features, p=self.drop_rate, training=self.training
            )
        return torch.cat([x, new_features], 1)


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


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
    """

    def __init__(
        self,
        growth_rate=32,
        block_config=(6, 12, 24, 16),
        num_init_features=64,
        bn_size=4,
        drop_rate=0,
        num_classes=10,
    ):

        super(DenseNet, self).__init__()

        # First convolution

        # CIFAR-10: kernel_size 7 ->3, stride 2->1, padding 3->1
        self.features = nn.Sequential(
            OrderedDict(
                [
                    (
                        "conv0",
                        nn.Conv2d(
                            3,
                            num_init_features,
                            kernel_size=3,
                            stride=1,
                            padding=1,
                            bias=False,
                        ),
                    ),
                    ("norm0", nn.BatchNorm2d(num_init_features)),
                    ("relu0", nn.ReLU(inplace=True)),
                    ("pool0", nn.MaxPool2d(kernel_size=3, stride=2, padding=1)),
                ]
            )
        )
        # END

        # 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,
            )
            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)

        # 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)).view(features.size(0), -1)
        out = self.classifier(out)
        return out


def _densenet(
    arch,
    growth_rate,
    block_config,
    num_init_features,
    pretrained,
    progress,
    device,
    **kwargs
):
    model = DenseNet(growth_rate, block_config, num_init_features, **kwargs)
    if pretrained:
        script_dir = os.path.dirname(__file__)
        state_dict = torch.load(
            script_dir + "/state_dicts/" + arch + ".pt", map_location=device
        )
        model.load_state_dict(state_dict)
    return model


def densenet121(pretrained=False, progress=True, device="cpu", **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
    """
    return _densenet(
        "densenet121", 32, (6, 12, 24, 16), 64, pretrained, progress, device, **kwargs
    )


def densenet161(pretrained=False, progress=True, device="cpu", **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
    """
    return _densenet(
        "densenet161", 48, (6, 12, 36, 24), 96, pretrained, progress, device, **kwargs
    )


def densenet169(pretrained=False, progress=True, device="cpu", **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
    """
    return _densenet(
        "densenet169", 32, (6, 12, 32, 32), 64, pretrained, progress, device, **kwargs
    )
