import numpy as np
import torch
import torch.nn as nn


def guassian_kernel(source, target, kernel_mul=2.0, kernel_num=5, fix_sigma=None):
    n_samples = int(source.size()[0])+int(target.size()[0])
    total = torch.cat([source, target], dim=0)
    total0 = total.unsqueeze(0).expand(int(total.size(0)), int(total.size(0)), int(total.size(1)))
    total1 = total.unsqueeze(1).expand(int(total.size(0)), int(total.size(0)), int(total.size(1)))
    L2_distance = ((total0-total1)**2).sum(2)
    if fix_sigma:
        bandwidth = fix_sigma
    else:
        bandwidth = torch.sum(L2_distance.data) / (n_samples**2-n_samples)
    bandwidth /= kernel_mul ** (kernel_num // 2)
    bandwidth_list = [bandwidth * (kernel_mul**i) for i in range(kernel_num)]
    kernel_val = [torch.exp(-L2_distance / bandwidth_temp) for bandwidth_temp in bandwidth_list]
    return sum(kernel_val)#/len(kernel_val)


def DAN(source, target, kernel_mul=2.0, kernel_num=5, fix_sigma=None):
    batch_size = int(source.size()[0])
    kernels = guassian_kernel(source, target,
        kernel_mul=kernel_mul, kernel_num=kernel_num, fix_sigma=fix_sigma)

    loss1 = 0
    for s1 in range(batch_size):
        for s2 in range(s1+1, batch_size):
            t1, t2 = s1+batch_size, s2+batch_size
            loss1 += kernels[s1, s2] + kernels[t1, t2]
    loss1 = loss1 / float(batch_size * (batch_size - 1) / 2)

    loss2 = 0
    for s1 in range(batch_size):
        for s2 in range(batch_size):
            t1, t2 = s1+batch_size, s2+batch_size
            loss2 -= kernels[s1, t2] + kernels[s2, t1]
    loss2 = loss2 / float(batch_size * batch_size)
    return loss1 + loss2


def DAN_Linear(source, target, kernel_mul=2.0, kernel_num=5, fix_sigma=None):
    batch_size = int(source.size()[0])
    kernels = guassian_kernel(source, target,
        kernel_mul=kernel_mul, kernel_num=kernel_num, fix_sigma=fix_sigma)

    # Linear version
    loss = 0
    for i in range(batch_size):
        s1, s2 = i, (i+1)%batch_size
        t1, t2 = s1+batch_size, s2+batch_size
        loss += kernels[s1, s2] + kernels[t1, t2]
        loss -= kernels[s1, t2] + kernels[s2, t1]
    return loss / float(batch_size)