#coding=utf-8
import math

import torch
import torch.nn as nn
from torch.nn import functional as F
from torchvision import models

import numpy as np

from bpgm.model.utils import init_weights, construct_basegrid
from bpgm.model.blocks import FeatureL2Norm, Down, ConvBlock


class FeatureExtraction(nn.Module):
    def __init__(self, input_nc, ngf=64, n_layers=3, norm_layer=nn.BatchNorm2d, use_dropout=False):
        super(FeatureExtraction, self).__init__()
        downconv = nn.Conv2d(input_nc, ngf, kernel_size=4, stride=2, padding=1)
        model = [downconv, nn.ReLU(True), norm_layer(ngf)]
        for i in range(n_layers):
            in_ngf = 2**i * ngf if 2**i * ngf < 512 else 512
            out_ngf = 2**(i+1) * ngf if 2**i * ngf < 512 else 512
            downconv = nn.Conv2d(in_ngf, out_ngf, kernel_size=4, stride=2, padding=1)
            model += [downconv, nn.ReLU(True)]
            model += [norm_layer(out_ngf)]
        model += [nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1), nn.ReLU(True)]
        model += [norm_layer(512)]
        model += [nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1), nn.ReLU(True)]
        
        self.model = nn.Sequential(*model)
        init_weights(self.model, init_type='normal')

    def forward(self, x):
        return self.model(x)

    
class FeatureCorrelation(nn.Module):
    def __init__(self):
        super(FeatureCorrelation, self).__init__()
    
    def forward(self, feature_A, feature_B):
        b,c,h,w = feature_A.size()
        # reshape features for matrix multiplication
        feature_A = feature_A.transpose(2,3).contiguous().view(b,c,h*w)
        feature_B = feature_B.view(b,c,h*w).transpose(1,2)
        # perform matrix mult.
        feature_mul = torch.bmm(feature_B,feature_A)
        correlation_tensor = feature_mul.view(b,h,w,h*w).transpose(2,3).transpose(1,2)
        return correlation_tensor
    

class FeatureRegression(nn.Module):
    def __init__(self, input_nc=512, output_dim=6, linear_dim=64*4*3, use_cuda=True):
        super(FeatureRegression, self).__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(input_nc, 512, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 256, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 128, kernel_size=3, padding=1),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True),
            nn.Conv2d(128, 64, kernel_size=3, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
        )
        
        self.linear = nn.Linear(linear_dim, output_dim)
        self.tanh = nn.Tanh()
        if use_cuda:
            self.conv.cuda()
            self.linear.cuda()
            self.tanh.cuda()

    def forward(self, x):
        x = self.conv(x)
        x = x.contiguous().view(x.size(0), -1)
        x = self.linear(x)
        x = self.tanh(x)
        return x

        
class TpsGridGen(nn.Module):
    def __init__(self, out_h=256, out_w=192, use_regular_grid=True, grid_size=3, reg_factor=0, use_cuda=True):
        super(TpsGridGen, self).__init__()
        self.out_h, self.out_w = out_h, out_w
        self.reg_factor = reg_factor
        self.use_cuda = use_cuda

        # create grid in numpy
        self.grid = np.zeros( [self.out_h, self.out_w, 3], dtype=np.float32)
        # sampling grid with dim-0 coords (Y)
        self.grid_X,self.grid_Y = np.meshgrid(np.linspace(-1,1,out_w), np.linspace(-1,1,out_h))
        # grid_X,grid_Y: size [1,H,W,1,1]
        self.grid_X = torch.FloatTensor(self.grid_X).unsqueeze(0).unsqueeze(3)
        self.grid_Y = torch.FloatTensor(self.grid_Y).unsqueeze(0).unsqueeze(3)
        if use_cuda:
            self.grid_X = self.grid_X.cuda()
            self.grid_Y = self.grid_Y.cuda()

        # initialize regular grid for control points P_i
        if use_regular_grid:
            axis_coords = np.linspace(-1,1,grid_size)
            self.N = grid_size*grid_size
            P_Y,P_X = np.meshgrid(axis_coords,axis_coords)
            P_X = np.reshape(P_X,(-1,1)) # size (N,1)
            P_Y = np.reshape(P_Y,(-1,1)) # size (N,1)
            P_X = torch.FloatTensor(P_X)
            P_Y = torch.FloatTensor(P_Y)
            self.P_X_base = P_X.clone()
            self.P_Y_base = P_Y.clone()
            self.Li = self.compute_L_inverse(P_X,P_Y).unsqueeze(0)
            self.P_X = P_X.unsqueeze(2).unsqueeze(3).unsqueeze(4).transpose(0,4)
            self.P_Y = P_Y.unsqueeze(2).unsqueeze(3).unsqueeze(4).transpose(0,4)
            if use_cuda:
                self.P_X = self.P_X.cuda()
                self.P_Y = self.P_Y.cuda()
                self.P_X_base = self.P_X_base.cuda()
                self.P_Y_base = self.P_Y_base.cuda()

    def forward(self, theta):
        warped_grid = self.apply_transformation(theta,torch.cat((self.grid_X,self.grid_Y),3))
        
        return warped_grid
    
    def compute_L_inverse(self,X,Y):
        N = X.size()[0] # num of points (along dim 0)
        # construct matrix K
        Xmat = X.expand(N,N)
        Ymat = Y.expand(N,N)
        P_dist_squared = torch.pow(Xmat-Xmat.transpose(0,1),2)+torch.pow(Ymat-Ymat.transpose(0,1),2)
        P_dist_squared[P_dist_squared==0]=1 # make diagonal 1 to avoid NaN in log computation
        K = torch.mul(P_dist_squared,torch.log(P_dist_squared))
        # construct matrix L
        O = torch.FloatTensor(N,1).fill_(1)
        Z = torch.FloatTensor(3,3).fill_(0)       
        P = torch.cat((O,X,Y),1)
        L = torch.cat((torch.cat((K,P),1),torch.cat((P.transpose(0,1),Z),1)),0)
        Li = torch.inverse(L)
        if self.use_cuda:
            Li = Li.cuda()
        return Li
        
    def apply_transformation(self,theta,points):
        orig_device = theta.device
        if self.use_cuda:
            theta = theta.to("cuda:0")
        if theta.dim()==2:
            theta = theta.unsqueeze(2).unsqueeze(3)
        # points should be in the [B,H,W,2] format,
        # where points[:,:,:,0] are the X coords  
        # and points[:,:,:,1] are the Y coords  
        
        # input are the corresponding control points P_i
        batch_size = theta.size()[0]
        # split theta into point coordinates
        Q_X = theta[:,:self.N,:,:].squeeze(3)
        Q_Y = theta[:,self.N:,:,:].squeeze(3)
        Q_X = Q_X + self.P_X_base.expand_as(Q_X)
        Q_Y = Q_Y + self.P_Y_base.expand_as(Q_Y)
        
        # get spatial dimensions of points
        points_b = points.size()[0]
        points_h = points.size()[1]
        points_w = points.size()[2]
        
        # repeat pre-defined control points along spatial dimensions of points to be transformed
        P_X = self.P_X.expand((1,points_h,points_w,1,self.N))
        P_Y = self.P_Y.expand((1,points_h,points_w,1,self.N))
        
        # compute weigths for non-linear part
        W_X = torch.bmm(self.Li[:,:self.N,:self.N].expand((batch_size,self.N,self.N)),Q_X)
        W_Y = torch.bmm(self.Li[:,:self.N,:self.N].expand((batch_size,self.N,self.N)),Q_Y)
        # reshape
        # W_X,W,Y: size [B,H,W,1,N]
        W_X = W_X.unsqueeze(3).unsqueeze(4).transpose(1,4).repeat(1,points_h,points_w,1,1)
        W_Y = W_Y.unsqueeze(3).unsqueeze(4).transpose(1,4).repeat(1,points_h,points_w,1,1)
        # compute weights for affine part
        A_X = torch.bmm(self.Li[:,self.N:,:self.N].expand((batch_size,3,self.N)),Q_X)
        A_Y = torch.bmm(self.Li[:,self.N:,:self.N].expand((batch_size,3,self.N)),Q_Y)
        # reshape
        # A_X,A,Y: size [B,H,W,1,3]
        A_X = A_X.unsqueeze(3).unsqueeze(4).transpose(1,4).repeat(1,points_h,points_w,1,1)
        A_Y = A_Y.unsqueeze(3).unsqueeze(4).transpose(1,4).repeat(1,points_h,points_w,1,1)
        
        # compute distance P_i - (grid_X,grid_Y)
        # grid is expanded in point dim 4, but not in batch dim 0, as points P_X,P_Y are fixed for all batch
        points_X_for_summation = points[:,:,:,0].unsqueeze(3).unsqueeze(4).expand(points[:,:,:,0].size()+(1,self.N))
        points_Y_for_summation = points[:,:,:,1].unsqueeze(3).unsqueeze(4).expand(points[:,:,:,1].size()+(1,self.N))
        
        if points_b==1:
            delta_X = points_X_for_summation-P_X
            delta_Y = points_Y_for_summation-P_Y
        else:
            # use expanded P_X,P_Y in batch dimension
            delta_X = points_X_for_summation-P_X.expand_as(points_X_for_summation)
            delta_Y = points_Y_for_summation-P_Y.expand_as(points_Y_for_summation)
            
        dist_squared = torch.pow(delta_X,2)+torch.pow(delta_Y,2)
        # U: size [1,H,W,1,N]
        dist_squared[dist_squared==0]=1 # avoid NaN in log computation
        U = torch.mul(dist_squared,torch.log(dist_squared)) 
        
        # expand grid in batch dimension if necessary
        points_X_batch = points[:,:,:,0].unsqueeze(3)
        points_Y_batch = points[:,:,:,1].unsqueeze(3)
        if points_b==1:
            points_X_batch = points_X_batch.expand((batch_size,)+points_X_batch.size()[1:])
            points_Y_batch = points_Y_batch.expand((batch_size,)+points_Y_batch.size()[1:])
        
        points_X_prime = A_X[:,:,:,:,0]+ \
                       torch.mul(A_X[:,:,:,:,1],points_X_batch) + \
                       torch.mul(A_X[:,:,:,:,2],points_Y_batch) + \
                       torch.sum(torch.mul(W_X,U.expand_as(W_X)),4)
                    
        points_Y_prime = A_Y[:,:,:,:,0]+ \
                       torch.mul(A_Y[:,:,:,:,1],points_X_batch) + \
                       torch.mul(A_Y[:,:,:,:,2],points_Y_batch) + \
                       torch.sum(torch.mul(W_Y,U.expand_as(W_Y)),4)
        
        points_Y_prime = points_Y_prime.to(orig_device)
        points_X_prime = points_X_prime.to(orig_device)
        
        return torch.cat((points_X_prime,points_Y_prime),3)


class Vgg19(nn.Module):
    def __init__(self, requires_grad=False):
        super(Vgg19, self).__init__()
        vgg_pretrained_features = models.vgg19(pretrained=True).features
        self.slice1 = torch.nn.Sequential()
        self.slice2 = torch.nn.Sequential()
        self.slice3 = torch.nn.Sequential()
        self.slice4 = torch.nn.Sequential()
        self.slice5 = torch.nn.Sequential()
        for x in range(2):
            self.slice1.add_module(str(x), vgg_pretrained_features[x])
        for x in range(2, 7):
            self.slice2.add_module(str(x), vgg_pretrained_features[x])
        for x in range(7, 12):
            self.slice3.add_module(str(x), vgg_pretrained_features[x])
        for x in range(12, 21):
            self.slice4.add_module(str(x), vgg_pretrained_features[x])
        for x in range(21, 30):
            self.slice5.add_module(str(x), vgg_pretrained_features[x])
        if not requires_grad:
            for param in self.parameters():
                param.requires_grad = False

    def forward(self, X):
        h_relu1 = self.slice1(X)
        h_relu2 = self.slice2(h_relu1)
        h_relu3 = self.slice3(h_relu2)
        h_relu4 = self.slice4(h_relu3)
        h_relu5 = self.slice5(h_relu4)
        out = [h_relu1, h_relu2, h_relu3, h_relu4, h_relu5]
        return out

        
class BPGM(nn.Module):
    """ Geometric Matching Module
    """
    def __init__(self, opt):
        super(BPGM, self).__init__()
        # self.extractionA = FeatureExtraction(3+16, ngf=64, n_layers=3, norm_layer=nn.BatchNorm2d) 
        if "bpgm_id" in opt:
            n_layers = int(math.log(int(opt.bpgm_id.split("_")[0]), 2)) - 5
            self.resolution = int(opt.bpgm_id.split("_")[0])
            self.resolution = (self.resolution, int(self.resolution * (opt.img_size[1] / opt.img_size[0])))
            
            self.in_channels = int(opt.bpgm_id.split("_")[1])
            opt.grid_size = int(opt.bpgm_id.split("_")[2])
        else:
            n_layers = int(math.log(opt.img_size[0], 2)) - 5
            self.resolution = opt.img_size
            
            if opt.old:
                self.in_channels = 29
            else:
                self.in_channels = 26
            
        # linear_dim = 64 * 4 * (2 + (opt.dataset == "viton"))
        linear_dim = 64 * 4 * 3
            
        assert n_layers > 0, "img_size must be >= 64"
        
        ngf = 2 ** (3 - n_layers) * 64
        
        self.extractionA = FeatureExtraction(self.in_channels, ngf=ngf, n_layers=n_layers, norm_layer=nn.BatchNorm2d)
            
        self.extractionB = FeatureExtraction(3, ngf=ngf, n_layers=n_layers, norm_layer=nn.BatchNorm2d)
        self.l2norm = FeatureL2Norm()
        self.correlation = FeatureCorrelation()
        self.regression = FeatureRegression(input_nc=self.resolution[1], output_dim=2 * opt.grid_size**2, linear_dim=linear_dim, use_cuda=True)
        self.gridGen = TpsGridGen(*self.resolution, use_cuda=True, grid_size=opt.grid_size)
        
    def forward(self, inputA, inputB):
        featureA = self.extractionA(inputA)
        featureB = self.extractionB(inputB)
        
        featureA = self.l2norm(featureA)
        featureB = self.l2norm(featureB)
        
        correlation = self.correlation(featureA, featureB)
        
        theta = self.regression(correlation)
        grid = self.gridGen(theta)
        return grid


