import sys
import math

import torch
import torch.nn as nn
import torchvision

class ResNet_FeatureExtractor(nn.Module):
    """ FeatureExtractor of FAN (http://openaccess.thecvf.com/content_ICCV_2017/papers/Cheng_Focusing_Attention_Towards_ICCV_2017_paper.pdf) """

    def __init__(self, input_channel, output_channel=512):
        super(ResNet_FeatureExtractor, self).__init__()
        self.ConvNet = ResNet(input_channel, output_channel, BasicBlock, [1, 2, 5, 3])

    def forward(self, input):
        return self.ConvNet(input)
    
class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(BasicBlock, self).__init__()
        self.conv1 = self._conv3x3(inplanes, planes)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = self._conv3x3(planes, planes)
        self.bn2 = nn.BatchNorm2d(planes)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def _conv3x3(self, in_planes, out_planes, stride=1):
        "3x3 convolution with padding"
        return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                         padding=1, bias=False)

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            residual = self.downsample(x)
        out += residual
        out = self.relu(out)

        return out

class ResNet(nn.Module):

    def __init__(self, input_channel, output_channel, block, layers):
        super(ResNet, self).__init__()

        self.output_channel_block = [int(output_channel / 4), int(output_channel / 2), output_channel, output_channel]

        self.inplanes = int(output_channel / 8)
        self.conv0_1 = nn.Conv2d(input_channel, int(output_channel / 16),
                                 kernel_size=3, stride=1, padding=1, bias=False)
        self.bn0_1 = nn.BatchNorm2d(int(output_channel / 16))
        self.conv0_2 = nn.Conv2d(int(output_channel / 16), self.inplanes,
                                 kernel_size=3, stride=1, padding=1, bias=False)
        self.bn0_2 = nn.BatchNorm2d(self.inplanes)
        self.relu = nn.ReLU(inplace=True)

        self.maxpool1 = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
        self.layer1 = self._make_layer(block, self.output_channel_block[0], layers[0])
        self.conv1 = nn.Conv2d(self.output_channel_block[0], self.output_channel_block[
                               0], kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(self.output_channel_block[0])

        self.maxpool2 = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
        self.layer2 = self._make_layer(block, self.output_channel_block[1], layers[1], stride=1)
        self.conv2 = nn.Conv2d(self.output_channel_block[1], self.output_channel_block[
                               1], kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(self.output_channel_block[1])

        self.maxpool3 = nn.MaxPool2d(kernel_size=2, stride=(2, 1), padding=(0, 1))
        self.layer3 = self._make_layer(block, self.output_channel_block[2], layers[2], stride=1)
        self.conv3 = nn.Conv2d(self.output_channel_block[2], self.output_channel_block[
                               2], kernel_size=3, stride=1, padding=1, bias=False)
        self.bn3 = nn.BatchNorm2d(self.output_channel_block[2])

        self.layer4 = self._make_layer(block, self.output_channel_block[3], layers[3], stride=1)
        self.conv4_1 = nn.Conv2d(self.output_channel_block[3], self.output_channel_block[
                                 3], kernel_size=2, stride=(2, 1), padding=(0, 1), bias=False)
        self.bn4_1 = nn.BatchNorm2d(self.output_channel_block[3])
        self.conv4_2 = nn.Conv2d(self.output_channel_block[3], self.output_channel_block[
                                 3], kernel_size=2, stride=1, padding=0, bias=False)
        self.bn4_2 = nn.BatchNorm2d(self.output_channel_block[3])

    def _make_layer(self, block, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample))
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes))

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv0_1(x)
        x = self.bn0_1(x)
        x = self.relu(x)
        x = self.conv0_2(x)
        x = self.bn0_2(x)
        x = self.relu(x)

        x = self.maxpool1(x)
        x = self.layer1(x)
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)

        x = self.maxpool2(x)
        x = self.layer2(x)
        x = self.conv2(x)
        x = self.bn2(x)
        x = self.relu(x)

        x = self.maxpool3(x)
        x = self.layer3(x)
        x = self.conv3(x)
        x = self.bn3(x)
        x = self.relu(x)

        x = self.layer4(x)
        x = self.conv4_1(x)
        x = self.bn4_1(x)
        x = self.relu(x)
        x = self.conv4_2(x)
        x = self.bn4_2(x)
        x = self.relu(x)

        return x
    
    
    
def conv3x3(in_planes, out_planes, stride=1):
    """3x3 convolution with padding"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,padding=1, bias=False)

def conv1x1(in_planes, out_planes, stride=1):
    """1x1 convolution"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)


def get_sinusoid_encoding(n_position, feat_dim, wave_length=10000):
    # [n_position]
    positions = torch.arange(0, n_position)#.cuda()
    # [feat_dim]
    dim_range = torch.arange(0, feat_dim)#.cuda()
    dim_range = torch.pow(wave_length, 2 * (dim_range // 2) / feat_dim)
    # [n_position, feat_dim]
    angles = positions.unsqueeze(1) / dim_range.unsqueeze(0)
    angles = angles.float()
    angles[:, 0::2] = torch.sin(angles[:, 0::2])
    angles[:, 1::2] = torch.cos(angles[:, 1::2])
    return angles

class AsterBlock(nn.Module):
    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(AsterBlock, self).__init__()
        self.conv1 = conv1x1(inplanes, planes, stride)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = nn.BatchNorm2d(planes)
        self.downsample = downsample
        self.stride = stride
        
    def forward(self, x):
        residual = x
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)
        out = self.conv2(out)
        out = self.bn2(out)
        
        if self.downsample is not None:
            residual = self.downsample(x)
        out += residual
        out = self.relu(out)
        return out


class ResNet_ASTER(nn.Module):
    """For aster or crnn
    borrowed from: https://github.com/ayumiymk/aster.pytorch
    """
    def __init__(self, in_channels=1, out_channel=512, n_group=1):
        super(ResNet_ASTER, self).__init__()
        self.n_group = n_group

        in_channels = in_channels
        self.layer0 = nn.Sequential(
            nn.Conv2d(in_channels, 32, kernel_size=(3, 3), stride=1, padding=1, bias=False),
            nn.BatchNorm2d(32),
            nn.ReLU(inplace=True))

        self.inplanes = 32
        self.layer1 = self._make_layer(32,  3, [2, 2]) # [16, 50]
        self.layer2 = self._make_layer(64,  4, [2, 2]) # [8, 25]
        self.layer3 = self._make_layer(128, 6, [2, 2]) # [4, 25]
        self.layer4 = self._make_layer(256, 6, [1, ]) # [2, 25]
        self.layer5 = self._make_layer(out_channel, 3, [1, 1]) # [1, 25]

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
                
    def _make_layer(self, planes, blocks, stride):
        downsample = None
        if stride != [1, 1] or self.inplanes != planes:
            downsample = nn.Sequential(
                conv1x1(self.inplanes, planes, stride),
                nn.BatchNorm2d(planes))

        layers = []
        layers.append(AsterBlock(self.inplanes, planes, stride, downsample))
        self.inplanes = planes
        for _ in range(1, blocks):
            layers.append(AsterBlock(self.inplanes, planes))
        return nn.Sequential(*layers)
    
    def forward(self, x):
        x0 = self.layer0(x)
        x1 = self.layer1(x0)
        x2 = self.layer2(x1)
        x3 = self.layer3(x2)
        x4 = self.layer4(x3)
        x5 = self.layer5(x4)

        return x5


def numel(model):
    return sum(p.numel() for p in model.parameters())