import numpy as np
import torch
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
#from spectral import SpectralNorm
import numpy as np
from torch.nn import Module, Sequential, Conv2d, ReLU,AdaptiveMaxPool2d, AdaptiveAvgPool2d, \
    NLLLoss, BCELoss, CrossEntropyLoss, AvgPool2d, MaxPool2d, Parameter, Linear, Sigmoid, Softmax, Dropout, Embedding
from torch.nn import functional as F
from torch.autograd import Variable
torch_ver = torch.__version__[:3]

__all__ = ['PAM_Module', 'CAM_Module','SA_Module']


class PAM_Module(Module):
    """ Position attention module"""
    #Ref from SAGAN
    def __init__(self, in_dim):
        super(PAM_Module, self).__init__()
        self.chanel_in = in_dim

        self.query_conv = Conv2d(in_channels=in_dim, out_channels=in_dim, kernel_size=1)
        self.key_conv = Conv2d(in_channels=in_dim, out_channels=in_dim, kernel_size=1)
        self.value_conv = Conv2d(in_channels=in_dim, out_channels=in_dim, kernel_size=1)
        self.gamma = Parameter(torch.zeros(1))

        self.softmax = Softmax(dim=-1)
    def forward(self, x):
        """
            inputs :
                x : input feature maps( B X C X H X W)
            returns :
                out : attention value + input feature
                attention: B X (HxW) X (HxW)
        """
        m_batchsize, C, height, width = 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)
        energy = torch.bmm(proj_query, proj_key)
        attention = self.softmax(energy)
        proj_value = self.value_conv(x).view(m_batchsize, -1, width*height)

        out = torch.bmm(proj_value, attention.permute(0, 2, 1))
        out = out.view(m_batchsize, C, height, width)

        out = self.gamma*out + x
        return out


class CAM_Module(Module):
    """ Channel attention module"""
    def __init__(self, in_dim):
        super(CAM_Module, self).__init__()
        self.chanel_in = in_dim


        self.gamma = Parameter(torch.zeros(1))
        self.softmax  = Softmax(dim=-1)
    def forward(self,x):
        """
            inputs :
                x : input feature maps( B X C X H X W)
            returns :
                out : attention value + input feature
                attention: B X C X C
        """
        m_batchsize, C, height, width = x.size()
        proj_query = x.view(m_batchsize, C, -1)
        proj_key = x.view(m_batchsize, C, -1).permute(0, 2, 1)
        energy = torch.bmm(proj_query, proj_key)
        energy_new = torch.max(energy, -1, keepdim=True)[0].expand_as(energy)-energy
        attention = self.softmax(energy_new)
        proj_value = x.view(m_batchsize, C, -1)

        out = torch.bmm(attention, proj_value)
        out = out.view(m_batchsize, C, height, width)

        out = self.gamma*out + x
        return out

class SA_Module(nn.Module):
    """ Self attention Layer"""
    def __init__(self,in_dim,activation):
        super(SA_Module,self).__init__()
        self.chanel_in = in_dim
        self.activation = activation
        
        self.query_conv = nn.Conv2d(in_channels = in_dim , out_channels = in_dim//2, kernel_size= 1)
        self.key_conv = nn.Conv2d(in_channels = in_dim , out_channels = in_dim//2 , 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):
        """
            inputs :
                x : input feature maps( B X C X W X H)
            returns :
                out : self attention value + input feature 
                attention: B X N X N (N is Width*Height)
        """
        m_batchsize,C,width ,height = x.size()
        proj_query  = self.query_conv(x).view(m_batchsize,-1,width*height).permute(0,2,1) # B X CX(N)
        proj_key =  self.key_conv(x).view(m_batchsize,-1,width*height) # B X C x (*W*H)
        energy =  torch.bmm(proj_query,proj_key) # transpose check
        attention = self.softmax(energy) # BX (N) X (N) 
        proj_value = self.value_conv(x).view(m_batchsize,-1,width*height) # B X C X N

        out = torch.bmm(proj_value,attention.permute(0,2,1) )
        out = out.view(m_batchsize,C,width,height)
        
        out = self.gamma*out + x
        return out       