# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
import torch.nn as nn

class Conv_BN_Relu_first(nn.Module):
    def __init__(self,in_channels,out_channels,kernel_size,padding,groups,bias):
        super(Conv_BN_Relu_first,self).__init__()
        kernel_size = 3
        padding = 1
        features = 64
        groups =1 
        self.conv = nn.Conv2d(in_channels=channels, out_channels=features, kernel_size=kernel_size, padding=padding,groups=groups, bias=False)
        self.bn = nn.BatchNorm2d(features)
        self.relu = nn.ReLU(inplace=True)
    def forward(self,x):
        return self.relu(self.bn(self.conv(x)))

class Conv_BN_Relu_other(nn.Module):
    def __init__(self,in_channels,out_channels,kernel_size,padding,groups,bias):
        super(Conv_BN_Relu_other,self).__init__()
        kernel_size = 3
        padding = 1
        features = out_channels
        groups =1 
        self.conv = nn.Conv2d(in_channels=in_channels, out_channels=features, kernel_size=kernel_size, padding=padding,groups=groups, bias=False)
        self.bn = nn.BatchNorm2d(features)
        self.relu = nn.ReLU(inplace=True)
    def forward(self,x):
        return self.relu(self.bn(self.conv(x)))


class Conv(nn.Module):
    def __init__(self,in_channels,out_channels,kernel_size,padding,groups,bais):
        super(Conv,self).__init__()
        kernel_size = 3
        padding = 1
        features = 1
        groups =1 
        self.conv = nn.Conv2d(in_channels=channels, out_channels=features, kernel_size=kernel_size, padding=padding,groups=groups, bias=False)
    def forward(self,x):
        return self.conv(x)

class Self_Attn(nn.Module):
    def __init__(self,in_dim):
        super(Self_Attn,self).__init__()
        self.chanel_in = in_dim
        self.query_conv = nn.Conv2d(in_channels=in_dim,out_channels=in_dim//8,kernel_size=1)
        self.key_conv = nn.Conv2d(in_channels=in_dim,out_channels=in_dim//8,kernel_size=1)
        self.value_conv = nn.Conv2d(in_channels=in_dim,out_channels=in_dim,kernel_size=1)
        self.gamma=nn.Parameter(torch.zeros(1))
        self.softmax=nn.Softmax(dim=-1)
    def forward(self,x):
        m_batchsize, C, width,height = x.size()
        proj_query = self.query_conv(x).view(m_batchsize,-1,width*height).permute(0,2,1)
        proj_key = self.key_conv(x).view(m_batchsize,-1,width*height)
        print (proj_query.size())
        print (proj_key.size())
        print ('5')
        energy = torch.bmm(proj_query,proj_key)
        print ('6')
        #print energy.size()
        attention = self.softmax(energy)
        proj_value = self.value_conv(x).view(m_batchsize,-1,width*height) 
        print ('1')
        out = torch.bmm(proj_value,attention.permute(0,2,1))
        print ('2')
        out = out.view(m_batchsize,C,width,height)
        out = self.gamma*out + x
        return out, attention

class ADNet(nn.Module):
    def __init__(self, channels, num_of_layers=15):
        super(ADNet, self).__init__()
        kernel_size = 3
        padding = 1
        features = 64
        groups =1 
        layers = []
        kernel_size1 = 1
        '''
        #self.gamma = nn.Parameter(torch.zeros(1))
        '''
        self.conv1_1 = nn.Sequential(nn.Conv2d(in_channels=channels,out_channels=features,kernel_size=kernel_size,padding=padding,groups=groups,bias=False),nn.BatchNorm2d(features),nn.ReLU(inplace=True))
        self.conv1_2 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=kernel_size,padding=2,groups=groups,bias=False,dilation=2),nn.BatchNorm2d(features),nn.ReLU(inplace=True))
        self.conv1_3 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=kernel_size,padding=1,groups=groups,bias=False),nn.BatchNorm2d(features),nn.ReLU(inplace=True))
        self.conv1_4 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=kernel_size,padding=1,groups=groups,bias=False),nn.BatchNorm2d(features),nn.ReLU(inplace=True))
        self.conv1_5 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=kernel_size,padding=2,groups=groups,bias=False,dilation=2),nn.BatchNorm2d(features),nn.ReLU(inplace=True))
        self.conv1_6 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=kernel_size,padding=1,groups=groups,bias=False),nn.BatchNorm2d(features),nn.ReLU(inplace=True))
        self.conv1_7 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=kernel_size,padding=padding,groups=groups,bias=False),nn.BatchNorm2d(features),nn.ReLU(inplace=True))
        self.conv1_8 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=kernel_size,padding=1,groups=groups,bias=False),nn.BatchNorm2d(features),nn.ReLU(inplace=True))
        self.conv1_9 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=kernel_size,padding=2,groups=groups,bias=False,dilation=2),nn.BatchNorm2d(features),nn.ReLU(inplace=True))
        self.conv1_10 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=kernel_size,padding=1,groups=groups,bias=False),nn.BatchNorm2d(features),nn.ReLU(inplace=True))
        self.conv1_11 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=kernel_size,padding=1,groups=groups,bias=False),nn.BatchNorm2d(features),nn.ReLU(inplace=True))
        self.conv1_12 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=kernel_size,padding=2,groups=groups,bias=False,dilation=2),nn.BatchNorm2d(features),nn.ReLU(inplace=True))
        self.conv1_13 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=kernel_size,padding=padding,groups=groups,bias=False),nn.BatchNorm2d(features),nn.ReLU(inplace=True))
        self.conv1_14 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=kernel_size,padding=padding,groups=groups,bias=False),nn.BatchNorm2d(features),nn.ReLU(inplace=True))
        self.conv1_15 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=kernel_size,padding=1,groups=groups,bias=False),nn.BatchNorm2d(features),nn.ReLU(inplace=True))
        self.conv1_16 = nn.Conv2d(in_channels=features,out_channels=1,kernel_size=kernel_size,padding=1,groups=groups,bias=False)
        self.conv3 = nn.Conv2d(in_channels=2,out_channels=1,kernel_size=1,stride=1,padding=0,groups=1,bias=True)
        self.ReLU = nn.ReLU(inplace=True)
        self.Tanh= nn.Tanh()
        self.sigmoid = nn.Sigmoid()
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                # n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, (2 / (9.0 * 64)) ** 0.5)
            if isinstance(m, nn.BatchNorm2d):
                m.weight.data.normal_(0, (2 / (9.0 * 64)) ** 0.5)
                clip_b = 0.025
                w = m.weight.data.shape[0]
                for j in range(w):
                    if m.weight.data[j] >= 0 and m.weight.data[j] < clip_b:
                        m.weight.data[j] = clip_b
                    elif m.weight.data[j] > -clip_b and m.weight.data[j] < 0:
                        m.weight.data[j] = -clip_b
                m.running_var.fill_(0.01)
    def _make_layers(self, block,features, kernel_size, num_of_layers, padding=1, groups=1, bias=False):
        layers = []
        for _ in range(num_of_layers):
            layers.append(block(in_channels=features, out_channels=features, kernel_size=kernel_size, padding=padding, groups=groups, bias=bias))
        return nn.Sequential(*layers)
    def forward(self, x):
        input = x 
        x1 = self.conv1_1(x)
        x1 = self.conv1_2(x1)
        x1 = self.conv1_3(x1)
        x1 = self.conv1_4(x1)
        x1 = self.conv1_5(x1)
        x1 = self.conv1_6(x1)
        x1 = self.conv1_7(x1)   
        x1t = self.conv1_8(x1)
        x1 = self.conv1_9(x1t)
        x1 = self.conv1_10(x1)
        x1 = self.conv1_11(x1)
        x1 = self.conv1_12(x1)
        x1 = self.conv1_13(x1)
        x1 = self.conv1_14(x1)
        x1 = self.conv1_15(x1)
        x1 = self.conv1_16(x1)
        out = torch.cat([x,x1],1)
        out= self.Tanh(out)
        out = self.conv3(out)
        out = out*x1
        out2 = x - out
        return out2