import re
import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict
from torchvision.models.densenet import load_state_dict_from_url, model_urls
from .layer import BasicReshape, FunctionWrapper, BasicCatDim1, BasicIdentity
from functools import partial
import networkx as nx
'''
densenet in pytorch 0.4
'''

def densenet121(pretrained=False, **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
    """
    model = DenseNet(num_init_features=64, growth_rate=32, block_config=(6, 12, 24, 16),
                     **kwargs)
    if pretrained:
        # '.'s are no longer allowed in module names, but pervious _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_urls['densenet121'])
        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)
    return model


def densenet169(pretrained=False, **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
    """
    model = DenseNet(num_init_features=64, growth_rate=32, block_config=(6, 12, 32, 32),
                     **kwargs)
    if pretrained:
        # '.'s are no longer allowed in module names, but pervious _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_urls['densenet169'])
        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)
    return model


def densenet201(pretrained=False, **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
    """
    model = DenseNet(num_init_features=64, growth_rate=32, block_config=(6, 12, 48, 32),
                     **kwargs)
    if pretrained:
        # '.'s are no longer allowed in module names, but pervious _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_urls['densenet201'])
        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)
    return model


def densenet161(pretrained=False, **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
    """
    model = DenseNet(num_init_features=96, growth_rate=48, block_config=(6, 12, 36, 24),
                     **kwargs)
    if pretrained:
        # '.'s are no longer allowed in module names, but pervious _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_urls['densenet161'])
        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)
    return model


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)

    def parse_graph(self, G, x, vertex_id):
        concat = BasicCatDim1()
        identity = BasicIdentity()
        input_id = vertex_id
        input_tensor = x

        op = nn.Sequential(self.norm1, self.relu1)
        x = op(x)
        G.add_node(vertex_id + 1, cost=x.numel())
        G.add_edge(vertex_id, vertex_id + 1, cost=0, module=op)
        vertex_id += 1

        op = self.conv1
        x = op(x)
        G.add_node(vertex_id + 1, cost=x.numel())
        G.add_edge(vertex_id, vertex_id + 1, cost=0, module=op)
        vertex_id += 1

        op = nn.Sequential(self.norm2, self.relu2)
        x = op(x)
        G.add_node(vertex_id + 1, cost=x.numel())
        G.add_edge(vertex_id, vertex_id + 1, cost=0, module=op)
        vertex_id += 1

        op = self.conv2
        x = op(x)
        G.add_node(vertex_id + 1, cost=x.numel())
        G.add_edge(vertex_id, vertex_id + 1, cost=0, module=op)
        vertex_id += 1

        if self.drop_rate > 0:
            op = FunctionWrapper(partial(F.dropout, p=self.drop_rate, training=self.training))
            x = op(x)
            G.add_node(vertex_id + 1, cost=x.numel())
            G.add_edge(vertex_id, vertex_id + 1, cost=0, module=op)
            vertex_id += 1

        op = concat
        x = op([input_tensor, x])
        G.add_node(vertex_id + 1, cost=x.numel(), transition=op)
        G.nodes[vertex_id + 1]['transition_input_order'] = []
        for id in [input_id, vertex_id]:
            edge_id = G.add_edge(id, vertex_id + 1, cost=0, module=identity)
            G.nodes[vertex_id + 1]['transition_input_order'].append((id, edge_id))
        vertex_id += 1
        return G, x, vertex_id


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

    def parse_graph(self, G, x, vertex_id):
        for i in range(self.num_layers):
            layer = self.__getattr__('denselayer%d' % (i + 1))
            G, x, vertex_id = layer.parse_graph(G, x, vertex_id)

        return G, x, vertex_id

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))

    def parse_graph(self, G, x, vertex_id):
        op = nn.Sequential(self.norm, self.relu)
        x = op(x)
        G.add_node(vertex_id + 1, cost=x.numel())
        G.add_edge(vertex_id, vertex_id + 1, cost=0, module=op)
        vertex_id += 1

        op = self.conv
        x = op(x)
        G.add_node(vertex_id + 1, cost=x.numel())
        G.add_edge(vertex_id, vertex_id + 1, cost=0, module=op)
        vertex_id += 1

        op = self.pool
        x = op(x)
        G.add_node(vertex_id + 1, cost=x.numel())
        G.add_edge(vertex_id, vertex_id + 1, cost=0, module=op)
        vertex_id += 1

        return G, x, vertex_id


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=1000):

        super(DenseNet, self).__init__()

        self.block_config = block_config
        # 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)
            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.data)
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()
            elif isinstance(m, nn.Linear):
                m.bias.data.zero_()

    def forward(self, x):
        features = self.features(x)
        out = F.relu(features, inplace=True)
        out = F.avg_pool2d(out, kernel_size=7, stride=1).view(features.size(0), -1)
        out = self.classifier(out)
        return out

    def parse_graph(self, x):
        G = nx.MultiDiGraph()
        source = 0
        vertex_id = 0
        G.add_node(vertex_id, cost=x.numel())


        op = self.features.conv0
        x = op(x)
        G.add_node(vertex_id + 1, cost=x.numel())
        G.add_edge(vertex_id, vertex_id + 1, cost=0, module=op)
        vertex_id += 1

        op = nn.Sequential(self.features.norm0, self.features.relu0)
        x = op(x)
        G.add_node(vertex_id + 1, cost=x.numel())
        G.add_edge(vertex_id, vertex_id + 1, cost=0, module=op)
        vertex_id += 1

        op = self.features.pool0
        x = op(x)
        G.add_node(vertex_id + 1, cost=x.numel())
        G.add_edge(vertex_id, vertex_id + 1, cost=0, module=op)
        vertex_id += 1

        for i, num_layers in enumerate(self.block_config):
            block = getattr(self.features, 'denseblock%d' % (i + 1))
            G, x, vertex_id = block.parse_graph(G, x, vertex_id)
            if i != len(self.block_config) - 1:
                block = getattr(self.features, 'transition%d' % (i + 1))
                G, x, vertex_id = block.parse_graph(G, x, vertex_id)

        op = nn.Sequential(self.features.norm5, FunctionWrapper(partial(F.relu, inplace=True)))
        x = op(x)
        G.add_node(vertex_id + 1, cost=x.numel())
        G.add_edge(vertex_id, vertex_id + 1, cost=0, module=op)
        vertex_id += 1

        op = FunctionWrapper(partial(F.adaptive_avg_pool2d, output_size=(1, 1)))
        x = op(x)
        G.add_node(vertex_id + 1, cost=x.numel())
        G.add_edge(vertex_id, vertex_id + 1, cost=0, module=op)
        vertex_id += 1

        reshape = BasicReshape()
        op = nn.Sequential(reshape, self.classifier)
        x = op(x)
        G.add_node(vertex_id + 1, cost=x.numel())
        G.add_edge(vertex_id, vertex_id + 1, cost=0, module=op)
        vertex_id += 1

        target = vertex_id

        return G, source, target
