# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0

import torch.nn as nn
import torch.nn.functional as F
import torch
from detectron2.utils.registry import Registry

LOCAL_FEATURE_EXTRACTOR_REGISTRY = Registry("LOCAL_FEATURE_EXTRACTOR")


def build_hybrid_feature_extractor(cfg, input_shape):
    """
    Build a mask head defined by `cfg.MODEL.ROI_MASK_HEAD.NAME`.
    """
    name = cfg.MODEL.LOCAL_FEATURE_EXTRACTOR.NAME
    out_channels = cfg.MODEL.LOCAL_FEATURE_EXTRACTOR.NUM_FEATURES
    return LOCAL_FEATURE_EXTRACTOR_REGISTRY.get(name)(3, out_channels)


@LOCAL_FEATURE_EXTRACTOR_REGISTRY.register()
class ResNetFeatureExtractor(nn.Module):

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

    def forward(self, input):
        return self.ConvNet(input)


@LOCAL_FEATURE_EXTRACTOR_REGISTRY.register()
class ResNetFeatureExtractorV2(nn.Module):

    def __init__(self, input_channel, output_channel=512):
        super(ResNet_FeatureExtractorV2, self).__init__()
        self.ConvNet = ResNetV2(input_channel, output_channel, BasicBlock, [1, 2, 5, 3])

    def forward(self, input):
        return self.ConvNet(input)


# For Gated RCNN
class GRCL(nn.Module):

    def __init__(self, input_channel, output_channel, num_iteration, kernel_size, pad):
        super(GRCL, self).__init__()
        self.wgf_u = nn.Conv2d(input_channel, output_channel, 1, 1, 0, bias=False)
        self.wgr_x = nn.Conv2d(output_channel, output_channel, 1, 1, 0, bias=False)
        self.wf_u = nn.Conv2d(input_channel, output_channel, kernel_size, 1, pad, bias=False)
        self.wr_x = nn.Conv2d(output_channel, output_channel, kernel_size, 1, pad, bias=False)

        self.BN_x_init = nn.BatchNorm2d(output_channel)

        self.num_iteration = num_iteration
        self.GRCL = [GRCL_unit(output_channel) for _ in range(num_iteration)]
        self.GRCL = nn.Sequential(*self.GRCL)

    def forward(self, input):
        """ The input of GRCL is consistant over time t, which is denoted by u(0)
        thus wgf_u / wf_u is also consistant over time t.
        """
        wgf_u = self.wgf_u(input)
        wf_u = self.wf_u(input)
        x = F.relu(self.BN_x_init(wf_u))

        for i in range(self.num_iteration):
            x = self.GRCL[i](wgf_u, self.wgr_x(x), wf_u, self.wr_x(x))

        return x


class GRCL_unit(nn.Module):

    def __init__(self, output_channel):
        super(GRCL_unit, self).__init__()
        self.BN_gfu = nn.BatchNorm2d(output_channel)
        self.BN_grx = nn.BatchNorm2d(output_channel)
        self.BN_fu = nn.BatchNorm2d(output_channel)
        self.BN_rx = nn.BatchNorm2d(output_channel)
        self.BN_Gx = nn.BatchNorm2d(output_channel)

    def forward(self, wgf_u, wgr_x, wf_u, wr_x):
        G_first_term = self.BN_gfu(wgf_u)
        G_second_term = self.BN_grx(wgr_x)
        G = F.sigmoid(G_first_term + G_second_term)

        x_first_term = self.BN_fu(wf_u)
        x_second_term = self.BN_Gx(self.BN_rx(wr_x) * G)
        x = F.relu(x_first_term + x_second_term)

        return x


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, 0), 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)

        if x.shape[0] > 0:
            x = self.maxpool1(x)
        x = self.layer1(x)
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)

        if x.shape[0] > 0:
            x = self.maxpool2(x)
        x = self.layer2(x)
        x = self.conv2(x)
        x = self.bn2(x)
        x = self.relu(x)

        if x.shape[0] > 0:
            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)

        return x


class ResNetV2(nn.Module):

    def __init__(self, input_channel, output_channel, block, layers):
        super().__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=3, stride=1, padding=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)

        if x.shape[0] > 0:
            x = self.maxpool1(x)
        x = self.layer1(x)
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)

        if x.shape[0] > 0:
            x = self.maxpool2(x)
        x = self.layer2(x)
        x = self.conv2(x)
        x = self.bn2(x)
        x = self.relu(x)

        # if x.shape[0]>0:
        #     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


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 FeatureAttention(nn.Module):
    def __init__(self, input_channel, output_channel):
        super(FeatureAttention, self).__init__()
        self.ConvNet = nn.Sequential(
            nn.Conv2d(input_channel, output_channel, (3, 3), stride=1, padding=1), nn.Sigmoid())

    def forward(self, input):
        return self.ConvNet(input)


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, dtype=torch.float32):
    # [n_position]
    positions = torch.arange(0, n_position, dtype=dtype)
    # [feat_dim]
    dim_range = torch.arange(0, feat_dim, dtype=dtype)
    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[:, 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"""

    def __init__(self, in_channels, with_lstm=False, n_group=1):
        super(ResNet_ASTER, self).__init__()
        self.with_lstm = with_lstm
        self.n_group = n_group

        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, 1])  # [4, 25]
        self.layer4 = self._make_layer(256, 6, [2, 1])  # [2, 25]
        self.layer5 = self._make_layer(512, 3, [2, 1])  # [1, 25]

        if with_lstm:
            self.rnn = nn.LSTM(512, 256, bidirectional=True, num_layers=2, batch_first=True)
            self.out_planes = 2 * 256
        else:
            self.out_planes = 512

        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)

        cnn_feat = x5.squeeze(2)  # [N, c, w]
        cnn_feat = cnn_feat.transpose(2, 1)
        if self.with_lstm:
            rnn_feat, _ = self.rnn(cnn_feat)
            return rnn_feat
        else:
            return cnn_feat
