from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os
import logging

import torch
import torch.nn as nn
import numpy as np
import torch.nn.functional as F

from .config import cfg, update_config


BN_MOMENTUM = 0.01
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(True)

    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]),
                            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),
                                )
                            )
                        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),
                                    nn.ReLU(True),
                                )
                            )
                    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 PoseHighResolutionNet(nn.Module):
    def __init__(self, cfg, heads):
        self.inplanes = 64
        extra = cfg.MODEL.EXTRA
        super(PoseHighResolutionNet, self).__init__()

        # stem net
        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.layer1 = self._make_layer(Bottleneck, 64, 4)

        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([256], 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
        )

        logger.info("=> init weights from normal distribution")
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.normal_(m.weight, std=0.001)
                for name, _ in m.named_parameters():
                    if name in ["bias"]:
                        nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.ConvTranspose2d):
                nn.init.normal_(m.weight, std=0.001)
                for name, _ in m.named_parameters():
                    if name in ["bias"]:
                        nn.init.constant_(m.bias, 0)

        self.heads = heads

        last_inp_channels = np.int(np.sum(pre_stage_channels))

        head_conv = 256
        for head in self.heads:
            classes = self.heads[head]
            fc = nn.Sequential(
                nn.Conv2d(
                    last_inp_channels, head_conv, kernel_size=3, padding=1, bias=True
                ),
                nn.ReLU(inplace=True),
                nn.Conv2d(
                    head_conv,
                    classes,
                    kernel_size=extra.FINAL_CONV_KERNEL,
                    stride=1,
                    padding=extra.FINAL_CONV_KERNEL // 2,
                    bias=True,
                ),
            )
            if "hm" in head:
                fc[-1].bias.data.fill_(-2.19)
            else:
                fill_fc_weights(fc)
            self.__setattr__(head, fc)

        self.pretrained_layers = cfg["MODEL"]["EXTRA"]["PRETRAINED_LAYERS"]

    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]),
                            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),
                            nn.ReLU(inplace=True),
                        )
                    )
                transition_layers.append(nn.Sequential(*conv3x3s))

        return nn.ModuleList(transition_layers)

    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, momentum=BN_MOMENTUM),
            )

        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 _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:
                if i < self.stage2_cfg["NUM_BRANCHES"]:
                    x_list.append(self.transition2[i](y_list[i]))
                else:
                    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:
                if i < self.stage3_cfg["NUM_BRANCHES"]:
                    x_list.append(self.transition3[i](y_list[i]))
                else:
                    x_list.append(self.transition3[i](y_list[-1]))
            else:
                x_list.append(y_list[i])
        x = self.stage4(x_list)

        # Upsampling
        x0_h, x0_w = x[0].size(2), x[0].size(3)
        x1 = F.upsample(x[1], size=(x0_h, x0_w), mode="bilinear")
        x2 = F.upsample(x[2], size=(x0_h, x0_w), mode="bilinear")
        x3 = F.upsample(x[3], size=(x0_h, x0_w), mode="bilinear")

        x = torch.cat([x[0], x1, x2, x3], 1)

        z = {}
        for head in self.heads:
            z[head] = self.__getattr__(head)(x)
        return [z]

    def init_weights(self, pretrained=""):
        if os.path.isfile(pretrained):
            pretrained_state_dict = torch.load(pretrained)
            logger.info("=> loading pretrained model {}".format(pretrained))
            print("=> loading pretrained model {}".format(pretrained))

            need_init_state_dict = {}
            for name, m in pretrained_state_dict.items():
                if (
                    name.split(".")[0] in self.pretrained_layers
                    or self.pretrained_layers[0] is "*"
                ):
                    need_init_state_dict[name] = m
            self.load_state_dict(need_init_state_dict, strict=False)
        elif pretrained:
            logger.error("=> please download pre-trained models first!")
            raise ValueError("{} is not exist!".format(pretrained))


def fill_fc_weights(layers):
    for m in layers.modules():
        if isinstance(m, nn.Conv2d):
            if m.bias is not None:
                nn.init.constant_(m.bias, 0)


def get_pose_net(pretrained, num_layers, heads, head_conv):
    if num_layers == 32:
        cfg_dir = "../src/lib/models/networks/config/hrnet_w32.yaml"
    elif num_layers == 18:
        cfg_dir = "../src/lib/models/networks/config/hrnet_w18.yaml"
    else:
        cfg_dir = "../src/lib/models/networks/config/hrnet_w18.yaml"
    update_config(cfg, cfg_dir)
    model = PoseHighResolutionNet(cfg, heads)
    model.init_weights(cfg.MODEL.PRETRAINED if pretrained else "")

    return model
