# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import torch
import utils
import numpy as np
import torch.nn as nn


class generator(nn.Module):
    # Network Architecture is exactly same as in infoGAN (https://arxiv.org/abs/1606.03657)
    # Architecture : FC1024_BR-FC7x7x128_BR-(64)4dc2s_BR-(1)4dc2s_S
    def __init__(self, input_dim=100, output_dim=1, input_size=32, class_num=10):
        super(generator, self).__init__()
        self.input_dim = input_dim
        self.output_dim = output_dim
        self.input_size = input_size
        self.class_num = class_num

        self.fc = nn.Sequential(
            nn.Linear(self.input_dim + self.class_num, 1024),
            nn.BatchNorm1d(1024),
            nn.ReLU(),
            nn.Linear(1024, 128 * (self.input_size // 4) * (self.input_size // 4)),
            nn.BatchNorm1d(128 * (self.input_size // 4) * (self.input_size // 4)),
            nn.ReLU(),
        )
        self.deconv = nn.Sequential(
            nn.ConvTranspose2d(128, 64, 4, 2, 1),
            nn.BatchNorm2d(64),
            nn.ReLU(),
            nn.ConvTranspose2d(64, self.output_dim, 4, 2, 1),
            nn.Tanh(),
        )
        utils.initialize_weights(self)

    def forward(self, input):
        #x = torch.cat([input, label], 1)
        #x = self.fc(x)
        x = self.fc(input)
        x = x.view(-1, 128, (self.input_size // 4), (self.input_size // 4))
        x = self.deconv(x)

        return x

class discriminator(nn.Module):
    # Network Architecture is exactly same as in infoGAN (https://arxiv.org/abs/1606.03657)
    # Architecture : (64)4c2s-(128)4c2s_BL-FC1024_BL-FC1_S
    def __init__(self, input_dim=1, output_dim=1, input_size=32, class_num=10):
        super(discriminator, self).__init__()
        self.input_dim = input_dim
        self.output_dim = output_dim
        self.input_size = input_size
        self.class_num = class_num

        self.conv = nn.Sequential(
            nn.Conv2d(self.input_dim + self.class_num, 64, 4, 2, 1),
            nn.LeakyReLU(0.2),
            nn.Conv2d(64, 128, 4, 2, 1),
            nn.BatchNorm2d(128),
            nn.LeakyReLU(0.2),
        )
        self.fc = nn.Sequential(
            nn.Linear(128 * (self.input_size // 4) * (self.input_size // 4), 1024),
            nn.BatchNorm1d(1024),
            nn.LeakyReLU(0.2),
            nn.Linear(1024, self.output_dim),
            nn.Sigmoid(),
        )
        utils.initialize_weights(self)

    def forward(self, input, label):
        x = torch.cat([input, label], 1)
        x = self.conv(x)
        x = x.view(-1, 128 * (self.input_size // 4) * (self.input_size // 4))
        x = self.fc(x)

        return x