#!/usr/bin/env python3
# -*- coding:utf-8 -*-
#############################################################
# File: Generator.py
# Created Date: Sunday January 16th 2022
# Author: Chen Xuanhong
# Email: chenxuanhongzju@outlook.com
# Last Modified:  Saturday, 19th February 2022 6:25:38 pm
# Modified By: Chen Xuanhong
# Copyright (c) 2022 Shanghai Jiao Tong University
#############################################################

import torch
from torch import nn

# from components.DeConv_Invo import DeConv

class Demodule(nn.Module):
    def __init__(self, epsilon=1e-8):
        """
            @notice: avoid in-place ops.
            https://discuss.pytorch.org/t/encounter-the-runtimeerror-one-of-the-variables-needed-for-gradient-computation-has-been-modified-by-an-inplace-operation/836/3
        """
        super(Demodule, self).__init__()
        self.epsilon = epsilon

    def forward(self, x):
        tmp = torch.mul(x, x) # or x ** 2
        tmp = torch.rsqrt(torch.mean(tmp, (2, 3), True) + self.epsilon)
        return x * tmp


class Modulation(nn.Module):
    def __init__(self, latent_size, channels):
        super(Modulation, self).__init__()
        self.linear = nn.Linear(latent_size, channels)

    def forward(self, x, latent):
        style = self.linear(latent)  # style => [batch_size, n_channels*2]
        shape = [-1, x.size(1), 1, 1]
        style = style.view(shape)    # [batch_size, 2, n_channels, ...]
        #x = x * (style[:, 0] + 1.) + style[:, 1]
        x = x * style
        return x

class ResnetBlock_Modulation(nn.Module):
    def __init__(self, dim, latent_size, padding_type, activation=nn.ReLU(True),res_mode="depthwise"):
        super(ResnetBlock_Modulation, self).__init__()

        p = 0
        conv1 = []
        if padding_type == 'reflect':
            conv1 += [nn.ReflectionPad2d(1)]
        elif padding_type == 'replicate':
            conv1 += [nn.ReplicationPad2d(1)]
        elif padding_type == 'zero':
            p = 1
        else:
            raise NotImplementedError('padding [%s] is not implemented' % padding_type)
        if res_mode.lower() == "conv":
            conv1 += [nn.Conv2d(dim, dim, kernel_size=3, padding = p), Demodule()]
        elif res_mode.lower() == "depthwise":
            conv1 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p,groups=dim, bias=False),
                    nn.Conv2d(dim, dim, kernel_size=1),
                    Demodule()]
        elif res_mode.lower() == "depthwise_eca":
            from components.ECA_Depthwise_Conv import ECADW
            conv1 += [ECADW(dim, kernel_size=3, padding=p, stride=2),
                nn.Conv2d(dim, dim, kernel_size=1),
                Demodule()]
        self.conv1 = nn.Sequential(*conv1)
        self.style1 = Modulation(latent_size, dim)
        self.act1 = activation

        p = 0
        conv2 = []
        if padding_type == 'reflect':
            conv2 += [nn.ReflectionPad2d(1)]
        elif padding_type == 'replicate':
            conv2 += [nn.ReplicationPad2d(1)]
        elif padding_type == 'zero':
            p = 1
        else:
            raise NotImplementedError('padding [%s] is not implemented' % padding_type)
        if res_mode.lower() == "conv":
            conv2 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p), Demodule()]
        elif res_mode.lower() == "depthwise":
            conv2 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p,groups=dim, bias=False),
                nn.Conv2d(dim, dim, kernel_size=1),
                Demodule()]
        elif res_mode.lower() == "depthwise_eca":
            from components.ECA_Depthwise_Conv import ECADW
            conv2 += [ECADW(dim, kernel_size=3, padding=p, stride=2),
                nn.Conv2d(dim, dim, kernel_size=1),
                Demodule()]
        
        self.conv2 = nn.Sequential(*conv2)
        self.style2 = Modulation(latent_size, dim)


    def forward(self, x, dlatents_in_slice):
        y = self.style1(x, dlatents_in_slice)
        y = self.conv1(y)
        
        y = self.act1(y)
        y = self.style2(y, dlatents_in_slice)
        y = self.conv2(y)
        
        out = x + y
        return out

class Generator(nn.Module):
    def __init__(
                self,
                **kwargs
                ):
        super().__init__()

        id_dim      = kwargs["id_dim"]
        k_size      = kwargs["g_kernel_size"]
        res_num     = kwargs["res_num"]
        in_channel  = kwargs["in_channel"]
        up_mode     = kwargs["up_mode"]
        res_mode    = kwargs["res_mode"]
        conv_mode   = kwargs["conv_mode"]
        
        padding_size= int((k_size -1)/2)
        padding_type= 'reflect'
        
        activation = nn.ReLU(True)

        from components.ECA_Depthwise_Conv import ECADW

        # self.first_layer = nn.Sequential(nn.ReflectionPad2d(3), nn.Conv2d(3, 64, kernel_size=7, padding=0, bias=False),
                                # nn.BatchNorm2d(64), activation)
        self.first_layer = nn.Sequential(nn.ReflectionPad2d(1), 
                                nn.Conv2d(3, in_channel, kernel_size=3, padding=0, bias=False),
                                nn.BatchNorm2d(in_channel), activation)
        # self.first_layer = nn.Sequential(nn.Conv2d(3, 64, kernel_size=3, padding=1, bias=False),
        #                         nn.BatchNorm2d(64), activation)
        ### downsample
        self.down1 = nn.Sequential(ECADW(in_channel,kernel_size=3, padding=1, stride=2),
                                nn.Conv2d(in_channel, in_channel*2, kernel_size=1, bias=False),
                                nn.BatchNorm2d(in_channel*2), activation)
                                
        self.down2 = nn.Sequential(ECADW(in_channel*2, kernel_size=3, padding=1, stride=2),
                                nn.Conv2d(in_channel*2, in_channel*4, kernel_size=1, bias=False),
                                nn.BatchNorm2d(in_channel*4), activation)

        self.down3 = nn.Sequential(ECADW(in_channel*4, kernel_size=3, padding=1, stride=2),
                                nn.Conv2d(in_channel*4, in_channel*8, kernel_size=1, bias=False),
                                nn.BatchNorm2d(in_channel*8), activation)

        self.down4 = nn.Sequential(ECADW(in_channel*8, kernel_size=3, padding=1, stride=2),
                                nn.Conv2d(in_channel*8, in_channel*8, kernel_size=1, bias=False),
                                nn.BatchNorm2d(in_channel*8), activation)

        ### resnet blocks
        BN = []
        for i in range(res_num):
            BN += [
                ResnetBlock_Modulation(in_channel*8, latent_size=id_dim, padding_type=padding_type, activation=activation)]
        self.BottleNeck = nn.Sequential(*BN)

        if conv_mode.lower() == "conv":
            from components.DeConv import DeConv
            Deconv = DeConv
        elif conv_mode.lower() == "depthwise":
            from components.DeConv_Depthwise import DeConv
            Deconv = DeConv
        elif conv_mode.lower() == "depthwise_eca":
            from components.DeConv_Depthwise_ECA import DeConv
            Deconv = DeConv

        self.up4 = nn.Sequential(
            DeConv(in_channel*8,in_channel*8,3),
            nn.BatchNorm2d(in_channel*8), activation
        )
        
        self.up3 = nn.Sequential(
            DeConv(in_channel*8,in_channel*4,3),
            nn.BatchNorm2d(in_channel*4), activation
        )
        
        self.up2 = nn.Sequential(
            DeConv(in_channel*4,in_channel*2,3),
            nn.BatchNorm2d(in_channel*2), activation
        )

        self.up1 = nn.Sequential(
            DeConv(in_channel*2,in_channel,3),
            nn.BatchNorm2d(in_channel), activation
        )
        # self.last_layer = nn.Sequential(nn.Conv2d(64, 3, kernel_size=3, padding=1))
        self.last_layer = nn.Sequential(nn.ReflectionPad2d(1),
                    nn.Conv2d(in_channel, 3, kernel_size=3, padding=0))
        # self.last_layer = nn.Sequential(nn.ReflectionPad2d(3),
        #             nn.Conv2d(64, 3, kernel_size=7, padding=0))


    #     self.__weights_init__()

    # def __weights_init__(self):
    #     for layer in self.encoder:
    #         if isinstance(layer,nn.Conv2d):
    #             nn.init.xavier_uniform_(layer.weight)

    #     for layer in self.encoder2:
    #         if isinstance(layer,nn.Conv2d):
    #             nn.init.xavier_uniform_(layer.weight)

    def forward(self, img, id):
        res = self.first_layer(img)
        res = self.down1(res)
        res = self.down2(res)
        res = self.down3(res)
        res = self.down4(res)

        for i in range(len(self.BottleNeck)):
            res = self.BottleNeck[i](res, id)

        res = self.up4(res)
        res = self.up3(res)
        res = self.up2(res)
        res = self.up1(res)
        res = self.last_layer(res)

        return res