from collections import OrderedDict

import torch.nn as nn
import torch.nn.functional as F
from torchvision.models.densenet import _DenseBlock, _Transition

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


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, dropout_rate=0, num_classes=1000,
                 small_inputs=True):

        super(DenseNet, self).__init__()

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

        # 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=dropout_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 _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:
        print('Loading pre-trained models not supported.')
    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
    """
    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
    """
    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
    """
    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
    """
    return _densenet('densenet201', 32, (6, 12, 48, 32), 64, pretrained, progress,
                     **kwargs)
