# coding: utf-8

# In[2]:


# import nbimporter


# In[3]:


import numpy as np
from torch import nn
from torch import  autograd
import torch
import os
import pdb


# In[ ]:


class Concat_embed(nn.Module):

    def __init__(self, embed_dim, projected_embed_dim):
        super(Concat_embed, self).__init__()
        self.projection = nn.Sequential(
            nn.Linear(in_features=embed_dim, out_features=projected_embed_dim),
            nn.BatchNorm1d(num_features=projected_embed_dim),
            nn.LeakyReLU(negative_slope=0.2, inplace=True)
            )

    def forward(self, inp, embed):
        projected_embed = self.projection(embed)
        replicated_embed = projected_embed.repeat(4, 4, 1, 1).permute(2,  3, 0, 1)
        hidden_concat = torch.cat([inp, replicated_embed], 1)

        return hidden_concat

class Utils(object):

    @staticmethod
    def smooth_label(tensor, offset):
        return tensor + offset

    @staticmethod
    def save_checkpoint(netD, netG, dir_path, subdir_path, epoch):
        path =  os.path.join(dir_path, subdir_path)
        if not os.path.exists(path):
            os.makedirs(path)

        torch.save(netD.state_dict(), '{0}/disc_{1}.pth'.format(path, epoch))
        torch.save(netG.state_dict(), '{0}/gen_{1}.pth'.format(path, epoch))

    @staticmethod
    def weights_init(m):
        classname = m.__class__.__name__
        if classname.find('Conv') != -1:
            m.weight.data.normal_(0.0, 0.02)
        elif classname.find('BatchNorm') != -1:
            m.weight.data.normal_(1.0, 0.02)
            m.bias.data.fill_(0)


class Logger(object):

    def log_iteration_gan(self, epoch, iteration, d_loss, g_loss, real_score, fake_score):
        print("Epoch: %d, Iter: %d, d_loss= %f, g_loss= %f, D(X)= %f, D(G(X))= %f" % (
            epoch, iteration, d_loss.data.cpu().mean(), g_loss.data.cpu().mean(), real_score.data.cpu().mean(),
            fake_score.data.cpu().mean()))