# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved

"""
From https://github.com/meliketoy/wide-resnet.pytorch
"""

import sys

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
from torch.autograd import Variable


def conv3x3(in_planes, out_planes, stride=1):
    return nn.Conv2d(
        in_planes,
        out_planes,
        kernel_size=3,
        stride=stride,
        padding=1,
        bias=True)


def conv_init(m):
    classname = m.__class__.__name__
    if classname.find('Conv') != -1:
        init.xavier_uniform_(m.weight, gain=np.sqrt(2))
        init.constant_(m.bias, 0)
    elif classname.find('BatchNorm') != -1:
        init.constant_(m.weight, 1)
        init.constant_(m.bias, 0)


class wide_basic(nn.Module):
    def __init__(self, in_planes, planes, dropout_rate, stride=1):
        super(wide_basic, self).__init__()
        self.bn1 = nn.BatchNorm2d(in_planes)
        self.conv1 = nn.Conv2d(
            in_planes, planes, kernel_size=3, padding=1, bias=True)
        self.dropout = nn.Dropout(p=dropout_rate)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(
            planes, planes, kernel_size=3, stride=stride, padding=1, bias=True)

        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != planes:
            self.shortcut = nn.Sequential(
                nn.Conv2d(
                    in_planes, planes, kernel_size=1, stride=stride,
                    bias=True), )

    def forward(self, x):
        out = self.dropout(self.conv1(F.relu(self.bn1(x))))
        out = self.conv2(F.relu(self.bn2(out)))
        out += self.shortcut(x)

        return out


class Wide_ResNet(nn.Module):
    """Wide Resnet with the softmax layer chopped off"""
    def __init__(self, input_shape, depth, widen_factor, dropout_rate):
        super(Wide_ResNet, self).__init__()
        self.in_planes = 16

        assert ((depth - 4) % 6 == 0), 'Wide-resnet depth should be 6n+4'
        n = (depth - 4) / 6
        k = widen_factor

        # print('| Wide-Resnet %dx%d' % (depth, k))
        nStages = [16, 16 * k, 32 * k, 64 * k]

        self.conv1 = conv3x3(input_shape[0], nStages[0])
        self.layer1 = self._wide_layer(
            wide_basic, nStages[1], n, dropout_rate, stride=1)
        self.layer2 = self._wide_layer(
            wide_basic, nStages[2], n, dropout_rate, stride=2)
        self.layer3 = self._wide_layer(
            wide_basic, nStages[3], n, dropout_rate, stride=2)
        self.bn1 = nn.BatchNorm2d(nStages[3])
        self.layer4 = self._wide_layer(wide_basic, nStages[3], n, dropout_rate, stride=2)
        self.bn2 = nn.BatchNorm2d(nStages[3])
        self.n_outputs = nStages[3]
        if input_shape[1:3] == (128, 128):
            self.n_outputs = 512
    def _wide_layer(self, block, planes, num_blocks, dropout_rate, stride):
        strides = [stride] + [1] * (int(num_blocks) - 1)
        layers = []

        for stride in strides:
            layers.append(block(self.in_planes, planes, dropout_rate, stride))
            self.in_planes = planes

        return nn.Sequential(*layers)

    def forward(self, x):
        out = self.conv1(x)
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = F.relu(self.bn1(out))
        out = F.avg_pool2d(out, 16)
        return out.view(len(x), -1)


