# ------------------------------------------------------------------------------
# code modified form https://github.com/HRNet/HRNet-Image-Classification
# ------------------------------------------------------------------------------
from easydict import EasyDict
import os
import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
from dpcv.modeling.module.weight_init_helper import initialize_weights
from .build import NETWORK_REGISTRY


hr_net_cfg = EasyDict(
    {'MODEL': {'EXTRA': {'STAGE1': {'BLOCK': 'BOTTLENECK',
                                    'FUSE_METHOD': 'SUM',
                                    'NUM_BLOCKS': [4],
                                    'NUM_CHANNELS': [64],
                                    'NUM_MODULES': 1,
                                    'NUM_RANCHES': 1},
                         'STAGE2': {'BLOCK': 'BASIC',
                                    'FUSE_METHOD': 'SUM',
                                    'NUM_BLOCKS': [4, 4],
                                    'NUM_BRANCHES': 2,
                                    'NUM_CHANNELS': [18, 36],
                                    'NUM_MODULES': 1},
                         'STAGE3': {'BLOCK': 'BASIC',
                                    'FUSE_METHOD': 'SUM',
                                    'NUM_BLOCKS': [4, 4, 4],
                                    'NUM_BRANCHES': 3,
                                    'NUM_CHANNELS': [18, 36, 72],
                                    'NUM_MODULES': 4},
                         'STAGE4': {'BLOCK': 'BASIC',
                                    'FUSE_METHOD': 'SUM',
                                    'NUM_BLOCKS': [4, 4, 4, 4],
                                    'NUM_BRANCHES': 4,
                                    'NUM_CHANNELS': [18, 36, 72, 144],
                                    'NUM_MODULES': 3}},
               'HEATMAP_SIZE': [64, 64],
               'IMAGE_SIZE': [224, 224],
               'INIT_WEIGHTS': True,
               'NAME': 'cls_hrnet',
               'NUM_CLASSES': 5,
               'NUM_JOINTS': 17,
               'PRETRAINED': '',
               'SIGMA': 2,
               'TAG_PER_JOINT': True,
               'TARGET_TYPE': 'gaussian'}
     }
)

BN_MOMENTUM = 0.1
logger = logging.getLogger(__name__)


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)


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(BasicBlock, self).__init__()
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM)
        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 Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM)
        self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * self.expansion, momentum=BN_MOMENTUM)
        self.relu = nn.ReLU(inplace=True)
        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)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

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

        out += residual
        out = self.relu(out)

        return out


class HighResolutionModule(nn.Module):
    def __init__(self, num_branches, blocks, num_blocks, num_inchannels,
                 num_channels, fuse_method, multi_scale_output=True):
        super(HighResolutionModule, self).__init__()
        self._check_branches(
            num_branches, blocks, num_blocks, num_inchannels, num_channels
        )

        self.num_inchannels = num_inchannels
        self.fuse_method = fuse_method
        self.num_branches = num_branches

        self.multi_scale_output = multi_scale_output

        self.branches = self._make_branches(
            num_branches, blocks, num_blocks, num_channels
        )
        self.fuse_layers = self._make_fuse_layers()
        self.relu = nn.ReLU(False)

    def _check_branches(self, num_branches, blocks, num_blocks, num_inchannels, num_channels):
        if num_branches != len(num_blocks):
            error_msg = 'NUM_BRANCHES({}) <> NUM_BLOCKS({})'.format(num_branches, len(num_blocks))
            logger.error(error_msg)
            raise ValueError(error_msg)

        if num_branches != len(num_channels):
            error_msg = 'NUM_BRANCHES({}) <> NUM_CHANNELS({})'.format(num_branches, len(num_channels))
            logger.error(error_msg)
            raise ValueError(error_msg)

        if num_branches != len(num_inchannels):
            error_msg = 'NUM_BRANCHES({}) <> NUM_INCHANNELS({})'.format(num_branches, len(num_inchannels))
            logger.error(error_msg)
            raise ValueError(error_msg)

    def _make_one_branch(self, branch_index, block, num_blocks, num_channels, stride=1):
        downsample = None
        if stride != 1 or self.num_inchannels[branch_index] != num_channels[branch_index] * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(
                    self.num_inchannels[branch_index],
                    num_channels[branch_index] * block.expansion,
                    kernel_size=1, stride=stride, bias=False
                ),
                nn.BatchNorm2d(num_channels[branch_index] * block.expansion, momentum=BN_MOMENTUM),
            )

        layers = []
        layers.append(
            block(
                self.num_inchannels[branch_index], num_channels[branch_index],
                stride,
                downsample
            )
        )
        self.num_inchannels[branch_index] = num_channels[branch_index] * block.expansion
        for i in range(1, num_blocks[branch_index]):
            layers.append(block(self.num_inchannels[branch_index], num_channels[branch_index]))

        return nn.Sequential(*layers)

    def _make_branches(self, num_branches, block, num_blocks, num_channels):
        branches = []

        for i in range(num_branches):
            branches.append(
                self._make_one_branch(i, block, num_blocks, num_channels))

        return nn.ModuleList(branches)

    def _make_fuse_layers(self):
        if self.num_branches == 1:
            return None

        num_branches = self.num_branches
        num_inchannels = self.num_inchannels
        fuse_layers = []
        for i in range(num_branches if self.multi_scale_output else 1):
            fuse_layer = []
            for j in range(num_branches):
                if j > i:
                    fuse_layer.append(
                        nn.Sequential(
                            nn.Conv2d(num_inchannels[j], num_inchannels[i], 1, 1, 0, bias=False),
                            nn.BatchNorm2d(num_inchannels[i], momentum=BN_MOMENTUM),
                            nn.Upsample(scale_factor=2 ** (j - i), mode='nearest')
                        )
                    )
                elif j == i:
                    fuse_layer.append(None)
                else:
                    conv3x3s = []
                    for k in range(i - j):
                        if k == i - j - 1:
                            num_outchannels_conv3x3 = num_inchannels[i]
                            conv3x3s.append(
                                nn.Sequential(
                                    nn.Conv2d(num_inchannels[j], num_outchannels_conv3x3, 3, 2, 1, bias=False),
                                    nn.BatchNorm2d(num_outchannels_conv3x3, momentum=BN_MOMENTUM)
                                )
                            )
                        else:
                            num_outchannels_conv3x3 = num_inchannels[j]
                            conv3x3s.append(
                                nn.Sequential(
                                    nn.Conv2d(num_inchannels[j], num_outchannels_conv3x3, 3, 2, 1, bias=False),
                                    nn.BatchNorm2d(num_outchannels_conv3x3, momentum=BN_MOMENTUM),
                                    nn.ReLU(False)
                                )
                            )
                    fuse_layer.append(nn.Sequential(*conv3x3s))
            fuse_layers.append(nn.ModuleList(fuse_layer))

        return nn.ModuleList(fuse_layers)

    def get_num_inchannels(self):
        return self.num_inchannels

    def forward(self, x):
        if self.num_branches == 1:
            return [self.branches[0](x[0])]

        for i in range(self.num_branches):
            x[i] = self.branches[i](x[i])

        x_fuse = []
        for i in range(len(self.fuse_layers)):
            y = x[0] if i == 0 else self.fuse_layers[i][0](x[0])
            for j in range(1, self.num_branches):
                if i == j:
                    y = y + x[j]
                else:
                    y = y + self.fuse_layers[i][j](x[j])
            x_fuse.append(self.relu(y))

        return x_fuse


blocks_dict = {
    'BASIC': BasicBlock,
    'BOTTLENECK': Bottleneck
}


class HighResolutionNet(nn.Module):

    def __init__(self, cfg, init_weights=True, normalize_output=True, return_feature=False, **kwargs):
        super(HighResolutionNet, self).__init__()
        self.normalize_output = normalize_output
        self.return_feature = return_feature
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(64, momentum=BN_MOMENTUM)
        self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(64, momentum=BN_MOMENTUM)
        self.relu = nn.ReLU(inplace=True)

        self.stage1_cfg = cfg['MODEL']['EXTRA']['STAGE1']
        num_channels = self.stage1_cfg['NUM_CHANNELS'][0]
        block = blocks_dict[self.stage1_cfg['BLOCK']]
        num_blocks = self.stage1_cfg['NUM_BLOCKS'][0]
        self.layer1 = self._make_layer(block, 64, num_channels, num_blocks)
        stage1_out_channel = block.expansion * num_channels

        self.stage2_cfg = cfg['MODEL']['EXTRA']['STAGE2']
        num_channels = self.stage2_cfg['NUM_CHANNELS']
        block = blocks_dict[self.stage2_cfg['BLOCK']]
        num_channels = [
            num_channels[i] * block.expansion for i in range(len(num_channels))
        ]
        self.transition1 = self._make_transition_layer(
            [stage1_out_channel], num_channels
        )
        self.stage2, pre_stage_channels = self._make_stage(
            self.stage2_cfg, num_channels
        )
        self.stage3_cfg = cfg['MODEL']['EXTRA']['STAGE3']
        num_channels = self.stage3_cfg['NUM_CHANNELS']
        block = blocks_dict[self.stage3_cfg['BLOCK']]
        num_channels = [
            num_channels[i] * block.expansion for i in range(len(num_channels))
        ]
        self.transition2 = self._make_transition_layer(
            pre_stage_channels, num_channels
        )
        self.stage3, pre_stage_channels = self._make_stage(
            self.stage3_cfg, num_channels
        )
        self.stage4_cfg = cfg['MODEL']['EXTRA']['STAGE4']
        num_channels = self.stage4_cfg['NUM_CHANNELS']
        block = blocks_dict[self.stage4_cfg['BLOCK']]
        num_channels = [
            num_channels[i] * block.expansion for i in range(len(num_channels))
        ]
        self.transition3 = self._make_transition_layer(
            pre_stage_channels, num_channels
        )
        self.stage4, pre_stage_channels = self._make_stage(
            self.stage4_cfg, num_channels, multi_scale_output=True
        )

        # Classification Head
        self.incre_modules, self.downsamp_modules, \
        self.final_layer = self._make_head(pre_stage_channels)

        self.classifier = nn.Linear(2048, cfg["MODEL"]["NUM_CLASSES"])

        if init_weights:
            initialize_weights(self)

    def _make_head(self, pre_stage_channels):
        head_block = Bottleneck
        head_channels = [32, 64, 128, 256]

        # Increasing the #channels on each resolution
        # from C, 2C, 4C, 8C to 128, 256, 512, 1024
        incre_modules = []
        for i, channels in enumerate(pre_stage_channels):
            incre_module = self._make_layer(
                head_block,
                channels,
                head_channels[i],
                1,
                stride=1
            )
            incre_modules.append(incre_module)
        incre_modules = nn.ModuleList(incre_modules)

        # downsampling modules
        downsamp_modules = []
        for i in range(len(pre_stage_channels) - 1):
            in_channels = head_channels[i] * head_block.expansion
            out_channels = head_channels[i + 1] * head_block.expansion

            downsamp_module = nn.Sequential(
                nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=3, stride=2, padding=1),
                nn.BatchNorm2d(out_channels, momentum=BN_MOMENTUM),
                nn.ReLU(inplace=True)
            )

            downsamp_modules.append(downsamp_module)
        downsamp_modules = nn.ModuleList(downsamp_modules)

        final_layer = nn.Sequential(
            nn.Conv2d(
                in_channels=head_channels[3] * head_block.expansion,
                out_channels=2048, kernel_size=1, stride=1, padding=0
            ),
            nn.BatchNorm2d(2048, momentum=BN_MOMENTUM),
            nn.ReLU(inplace=True)
        )

        return incre_modules, downsamp_modules, final_layer

    def _make_transition_layer(self, num_channels_pre_layer, num_channels_cur_layer):
        num_branches_cur = len(num_channels_cur_layer)
        num_branches_pre = len(num_channels_pre_layer)

        transition_layers = []
        for i in range(num_branches_cur):
            if i < num_branches_pre:
                if num_channels_cur_layer[i] != num_channels_pre_layer[i]:
                    transition_layers.append(
                        nn.Sequential(
                            nn.Conv2d(num_channels_pre_layer[i], num_channels_cur_layer[i], 3, 1, 1, bias=False),
                            nn.BatchNorm2d(num_channels_cur_layer[i], momentum=BN_MOMENTUM),
                            nn.ReLU(inplace=True),
                        )
                    )
                else:
                    transition_layers.append(None)
            else:
                conv3x3s = []
                for j in range(i + 1 - num_branches_pre):
                    inchannels = num_channels_pre_layer[-1]
                    outchannels = num_channels_cur_layer[i] if j == i - num_branches_pre else inchannels
                    conv3x3s.append(
                        nn.Sequential(
                            nn.Conv2d(inchannels, outchannels, 3, 2, 1, bias=False),
                            nn.BatchNorm2d(outchannels, momentum=BN_MOMENTUM),
                            nn.ReLU(inplace=True)
                        )
                    )
                transition_layers.append(nn.Sequential(*conv3x3s))

        return nn.ModuleList(transition_layers)

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

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

        return nn.Sequential(*layers)

    def _make_stage(self, layer_config, num_inchannels, multi_scale_output=True):
        num_modules = layer_config['NUM_MODULES']
        num_branches = layer_config['NUM_BRANCHES']
        num_blocks = layer_config['NUM_BLOCKS']
        num_channels = layer_config['NUM_CHANNELS']
        block = blocks_dict[layer_config['BLOCK']]
        fuse_method = layer_config['FUSE_METHOD']

        modules = []
        for i in range(num_modules):
            # multi_scale_output is only used last module
            if not multi_scale_output and i == num_modules - 1:
                reset_multi_scale_output = False
            else:
                reset_multi_scale_output = True

            modules.append(
                HighResolutionModule(
                    num_branches,
                    block,
                    num_blocks,
                    num_inchannels,
                    num_channels,
                    fuse_method,
                    reset_multi_scale_output
                )
            )
            num_inchannels = modules[-1].get_num_inchannels()

        return nn.Sequential(*modules), num_inchannels

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.conv2(x)
        x = self.bn2(x)
        x = self.relu(x)
        x = self.layer1(x)

        x_list = []
        for i in range(self.stage2_cfg['NUM_BRANCHES']):
            if self.transition1[i] is not None:
                x_list.append(self.transition1[i](x))
            else:
                x_list.append(x)
        y_list = self.stage2(x_list)

        x_list = []
        for i in range(self.stage3_cfg['NUM_BRANCHES']):
            if self.transition2[i] is not None:
                x_list.append(self.transition2[i](y_list[-1]))
            else:
                x_list.append(y_list[i])
        y_list = self.stage3(x_list)

        x_list = []
        for i in range(self.stage4_cfg['NUM_BRANCHES']):
            if self.transition3[i] is not None:
                x_list.append(self.transition3[i](y_list[-1]))
            else:
                x_list.append(y_list[i])
        y_list = self.stage4(x_list)

        # Classification Head
        y = self.incre_modules[0](y_list[0])
        for i in range(len(self.downsamp_modules)):
            y = self.incre_modules[i + 1](y_list[i + 1]) + self.downsamp_modules[i](y)

        y = self.final_layer(y)

        if torch._C._get_tracing_state():
            y = y.flatten(start_dim=2).mean(dim=2)
        else:
            y = F.avg_pool2d(y, kernel_size=y.size()[2:]).view(y.size(0), -1)
        feat = y
        y = self.classifier(y)
        if self.normalize_output:
            y = torch.sigmoid(y)
        if self.return_feature:
            return y, feat
        return y

    def init_weights(self, pretrained='', ):
        logger.info('=> init weights from normal distribution')
        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)
        if os.path.isfile(pretrained):
            pretrained_dict = torch.load(pretrained)
            logger.info('=> loading pretrained model {}'.format(pretrained))
            model_dict = self.state_dict()
            pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict.keys()}
            for k, _ in pretrained_dict.items():
                logger.info('=> loading {} pretrained model {}'.format(k, pretrained))
            model_dict.update(pretrained_dict)
            self.load_state_dict(model_dict)


def get_hr_net_model(cfg=None, **kwargs):
    config = hr_net_cfg
    model = HighResolutionNet(config, **kwargs)
    model.init_weights()
    model.to(device=torch.device("cuda" if torch.cuda.is_available() else "cpu"))
    return model


@NETWORK_REGISTRY.register()
def hr_net_model(cfg=None, **kwargs):
    config = hr_net_cfg
    model = HighResolutionNet(config, return_feature=cfg.MODEL.RETURN_FEATURE, **kwargs)
    model.to(device=torch.device("cuda" if torch.cuda.is_available() else "cpu"))
    return model


@NETWORK_REGISTRY.register()
def hr_net_true_personality(cfg=None, **kwargs):
    config = hr_net_cfg
    model = HighResolutionNet(config, normalize_output=False, **kwargs)
    model.to(device=torch.device("cuda" if torch.cuda.is_available() else "cpu"))
    return model


if __name__ == "__main__":
    xin = torch.randn((1, 3, 224, 224)).cuda()
    hr_net = get_hr_net_model()
    y = hr_net(xin)
    print(y.shape)
