import torch.nn as nn
from torch.nn import functional as F
import math
import torch.utils.model_zoo as model_zoo
import torch
import numpy as np
from torch.autograd import Variable
import functools
affine_par = True

import sys, os
from libs import InPlaceABN, InPlaceABNSync
from cc_attention import CrissCrossAttention

BatchNorm2d = functools.partial(InPlaceABNSync, activation='none')

def outS(i):
    i = int(i)
    i = (i+1)/2
    i = int(np.ceil((i+1)/2.0))
    i = (i+1)/2
    return i

def conv3x3(in_planes, out_planes, stride=1):
    "3x3 convolution with padding"
    return nn.Conv2d(in_planes, out_planes, kernel_size=3,
                     stride=stride, padding=1, bias=False)

class Bottleneck(nn.Module):
    expansion = 4
    def __init__(self, inplanes, planes, stride=1, dilation=1, downsample=None, fist_dilation=1, multi_grid=1):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
        self.bn1 = BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
                               padding=dilation*multi_grid, dilation=dilation*multi_grid, bias=False)
        self.bn2 = BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
        self.bn3 = BatchNorm2d(planes * 4)
        self.relu = nn.ReLU(inplace=False)
        self.relu_inplace = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.dilation = dilation
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out = out + residual
        out = self.relu_inplace(out)

        return out

class RCCAModule(nn.Module):
    def __init__(self, in_channels, out_channels, num_classes):
        super(RCCAModule, self).__init__()
        inter_channels = in_channels // 4
        self.conva = nn.Sequential(nn.Conv2d(in_channels, inter_channels, 3, padding=1, bias=False),
                                   InPlaceABNSync(inter_channels))
        self.cca = CrissCrossAttention(inter_channels)
        self.convb = nn.Sequential(nn.Conv2d(inter_channels, inter_channels, 3, padding=1, bias=False),
                                   InPlaceABNSync(inter_channels))

        self.bottleneck = nn.Sequential(
            nn.Conv2d(in_channels+inter_channels, out_channels, kernel_size=3, padding=1, dilation=1, bias=False),
            InPlaceABNSync(out_channels),
            nn.Dropout2d(0.1),
            nn.Conv2d(512, num_classes, kernel_size=1, stride=1, padding=0, bias=True)
        )

    def forward(self, x, recurrence=1):
        output = self.conva(x)
        for i in range(recurrence):
            output = self.cca(output)
        output = self.convb(output)

        output = self.bottleneck(torch.cat([x, output], 1))
        return output

class ResNet(nn.Module):
    def __init__(self, block, layers, num_classes):
        self.inplanes = 128
        super(ResNet, self).__init__()
        self.conv1 = conv3x3(3, 64, stride=2)
        self.bn1 = BatchNorm2d(64)
        self.relu1 = nn.ReLU(inplace=False)
        self.conv2 = conv3x3(64, 64)
        self.bn2 = BatchNorm2d(64)
        self.relu2 = nn.ReLU(inplace=False)
        self.conv3 = conv3x3(64, 128)
        self.bn3 = BatchNorm2d(128)
        self.relu3 = nn.ReLU(inplace=False)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

        self.relu = nn.ReLU(inplace=False)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1, ceil_mode=True) # change
        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=1, dilation=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=1, dilation=4, multi_grid=(1, 1, 1))
        self.head = RCCAModule(2048, 512, num_classes)

        self.dsn = nn.Sequential(
            nn.Conv2d(1024, 512, kernel_size=3, stride=1, padding=1),
            InPlaceABNSync(512),
            nn.Dropout2d(0.1),
            nn.Conv2d(512, num_classes, kernel_size=1, stride=1, padding=0, bias=True)
        )

    def get_learnable_parameters(self, freeze_layers=[True, True, True, True, False, False, False]):
        lr_parameters = []

        if not freeze_layers[0]:
            for i in [self.conv1, self.bn1, self.conv2, self.bn2, self.conv3, self.bn3]:
                params = i.named_parameters()
                for name, p in params:
                    print(name)
                    lr_parameters.append(p)

        layers = [self.layer1, self.layer2, self.layer3, self.layer4, self.layer5, self.layer6]
        for freeze, layer in zip(freeze_layers[1:], layers):
            if not freeze:
                params = layer.named_parameters()
                for name, p in params:
                    print(name)
                    lr_parameters.append(p)

        return lr_parameters

    def _make_layer(self, block, planes, blocks, stride=1, dilation=1, multi_grid=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
                BatchNorm2d(planes * block.expansion, affine=affine_par)
            )

        layers = []
        generate_multi_grid = lambda index, grids: grids[index % len(grids)] if isinstance(grids, tuple) else 1
        layers.append(block(self.inplanes, planes, stride, dilation=dilation, downsample=downsample, multi_grid=generate_multi_grid(0, multi_grid)))
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes, dilation=dilation, multi_grid=generate_multi_grid(i, multi_grid)))

        return nn.Sequential(*layers)

    def forward(self, x, recurrence=1):
        x = self.relu1(self.bn1(self.conv1(x)))
        x = self.relu2(self.bn2(self.conv2(x)))
        x = self.relu3(self.bn3(self.conv3(x)))
        x = self.maxpool(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x_dsn = self.dsn(x)
        x = self.layer4(x)
        x = self.head(x, recurrence)
        return [x, x_dsn]

def Res_Deeplab(num_classes=14):
    print('Res_Deeplab')
    model = ResNet(Bottleneck, [3, 4, 23, 3], num_classes)
    print('load model')
    return model