# ------------------------------------------------------------------------------
# Copyright (c) Microsoft
# Licensed under the MIT License.
# Written by Bin Xiao (Bin.Xiao@microsoft.com)
# Modified by Sen Yang (yangsenius@seu.edu.cn)
# Modified to Conditional Top Down by Mu Zhou, Lucas Stoffl et al. (ICCV 2023)
# ------------------------------------------------------------------------------

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

import os
import logging
import math

import torch
import torch.nn.functional as F
from torch import nn, Tensor
import torchvision.transforms.functional as TF

from collections import OrderedDict

import copy
from typing import Optional, List

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 TransformerEncoder(nn.Module):
    def __init__(self, encoder_layer, num_layers,
                 norm=None, pe_only_at_begin=False, return_atten_map=False):
        super().__init__()
        self.layers = _get_clones(encoder_layer, num_layers)
        self.num_layers = num_layers
        self.norm = norm
        self.pe_only_at_begin = pe_only_at_begin
        self.return_atten_map = return_atten_map
        self._reset_parameters()

    def _reset_parameters(self):
        for p in self.parameters():
            if p.dim() > 1:
                nn.init.xavier_uniform_(p)

    def forward(self, src,
                mask: Optional[Tensor] = None,
                src_key_padding_mask: Optional[Tensor] = None,
                pos: Optional[Tensor] = None):
        output = src
        atten_maps_list = []
        for layer in self.layers:
            if self.return_atten_map:
                output, att_map = layer(output, src_mask=mask, pos=pos,
                                        src_key_padding_mask=src_key_padding_mask)
                atten_maps_list.append(att_map)
            else:
                output = layer(output, src_mask=mask,  pos=pos,
                               src_key_padding_mask=src_key_padding_mask)

            # only add position embedding to the first atttention layer
            pos = None if self.pe_only_at_begin else pos

        if self.norm is not None:
            output = self.norm(output)

        if self.return_atten_map:
            return output, torch.stack(atten_maps_list)
        else:
            return output


def _get_clones(module, N):
    return nn.ModuleList([copy.deepcopy(module) for i in range(N)])


def _get_activation_fn(activation):
    """Return an activation function given a string"""
    if activation == "relu":
        return F.relu
    if activation == "gelu":
        return F.gelu
    if activation == "glu":
        return F.glu
    raise RuntimeError(F"activation should be relu/gelu, not {activation}.")


class TransformerEncoderLayer(nn.Module):
    """ Modified from https://github.com/facebookresearch/detr/blob/master/models/transformer.py"""

    def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1,
                 activation="relu", normalize_before=False, return_atten_map=False):
        super().__init__()
        self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)

        self.linear1 = nn.Linear(d_model, dim_feedforward)
        self.dropout = nn.Dropout(dropout)
        self.linear2 = nn.Linear(dim_feedforward, d_model)

        self.norm1 = nn.LayerNorm(d_model)
        self.norm2 = nn.LayerNorm(d_model)
        self.dropout1 = nn.Dropout(dropout)
        self.dropout2 = nn.Dropout(dropout)

        self.activation = _get_activation_fn(activation)
        self.normalize_before = normalize_before
        self.return_atten_map = return_atten_map

    def with_pos_embed(self, tensor, pos: Optional[Tensor]):
        return tensor if pos is None else tensor + pos

    def forward_post(self,
                     src,
                     src_mask: Optional[Tensor] = None,
                     src_key_padding_mask: Optional[Tensor] = None,
                     pos: Optional[Tensor] = None):
        q = k = self.with_pos_embed(src, pos)
        if self.return_atten_map:
            src2, att_map = self.self_attn(q, k, value=src,
                                           attn_mask=src_mask,
                                           key_padding_mask=src_key_padding_mask)
        else:
            src2 = self.self_attn(q, k, value=src, attn_mask=src_mask,
                                  key_padding_mask=src_key_padding_mask)[0]
        src = src + self.dropout1(src2)
        src = self.norm1(src)
        src2 = self.linear2(self.dropout(self.activation(self.linear1(src))))
        src = src + self.dropout2(src2)
        src = self.norm2(src)
        if self.return_atten_map:
            return src, att_map
        else:
            return src

    def forward_pre(self, src,
                    src_mask: Optional[Tensor] = None,
                    src_key_padding_mask: Optional[Tensor] = None,
                    pos: Optional[Tensor] = None):
        src2 = self.norm1(src)
        q = k = self.with_pos_embed(src2, pos)
        if self.return_atten_map:
            src2, att_map = self.self_attn(q, k, value=src,
                                           attn_mask=src_mask,
                                           key_padding_mask=src_key_padding_mask)
        else:
            src2 = self.self_attn(q, k, value=src, attn_mask=src_mask,
                                  key_padding_mask=src_key_padding_mask)[0]
        src = src + self.dropout1(src2)
        src2 = self.norm2(src)
        src2 = self.linear2(self.dropout(self.activation(self.linear1(src2))))
        src = src + self.dropout2(src2)
        if self.return_atten_map:
            return src, att_map
        else:
            return src

    def forward(self, src,
                src_mask: Optional[Tensor] = None,
                src_key_padding_mask: Optional[Tensor] = None,
                pos: Optional[Tensor] = None):
        if self.normalize_before:
            return self.forward_pre(src, src_mask, src_key_padding_mask, pos)
        return self.forward_post(src, src_mask, src_key_padding_mask, pos)


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 TransPoseH(nn.Module):

    def __init__(self, cfg, **kwargs):
        self.inplanes = 64

        self.cfg = cfg

        extra = cfg['MODEL']['EXTRA']
        super(TransPoseH, 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 = 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 = 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, multi_scale_output=False)

        d_model = cfg.MODEL.DIM_MODEL
        dim_feedforward = cfg.MODEL.DIM_FEEDFORWARD
        encoder_layers_num = cfg.MODEL.ENCODER_LAYERS
        n_head = cfg.MODEL.N_HEAD
        pos_embedding_type = cfg.MODEL.POS_EMBEDDING
        w, h = cfg.MODEL.IMAGE_SIZE

        # image features from CNN are transformed to d*H*W by 1x1 convolution
        self.reduce = nn.Conv2d(pre_stage_channels[0], d_model, 1, bias=False)
        
        # conditional input is transformed to 16*H*W
        if self.cfg.MODEL.EXTRA.USE_ATTENTION:
            self.trans_cond = nn.Conv2d(3, 16, 1, bias=False)
            d_model += 16

        self._make_position_embedding(w, h, d_model, pos_embedding_type)

        encoder_layer = TransformerEncoderLayer(
            d_model=d_model, nhead=n_head, dim_feedforward=dim_feedforward,
            activation='relu')
        self.global_encoder = TransformerEncoder(
            encoder_layer, encoder_layers_num)

        self.final_layer = nn.Conv2d(
            in_channels=d_model,
            out_channels=cfg['MODEL']['NUM_JOINTS'],
            kernel_size=extra['FINAL_CONV_KERNEL'],
            stride=1,
            padding=1 if extra['FINAL_CONV_KERNEL'] == 3 else 0
        )

        self.pretrained_layers = extra['PRETRAINED_LAYERS']

    def _make_position_embedding(self, w, h, d_model, pe_type='sine'):
        assert pe_type in ['none', 'learnable', 'sine']
        if pe_type == 'none':
            self.pos_embedding = None
            logger.info("==> Without any PositionEmbedding~")
        else:
            with torch.no_grad():
                self.pe_h = h // 4
                self.pe_w = w // 4
                length = self.pe_h * self.pe_w
            if pe_type == 'learnable':
                self.pos_embedding = nn.Parameter(
                    torch.randn(length, 1, d_model))
                logger.info("==> Add Learnable PositionEmbedding~")
            else:
                self.pos_embedding = nn.Parameter(
                    self._make_sine_position_embedding(d_model),
                    requires_grad=False)
                logger.info("==> Add Sine PositionEmbedding~")

    def _make_sine_position_embedding(self, d_model, temperature=10000,
                                      scale=2 * math.pi):
        h, w = self.pe_h, self.pe_w
        area = torch.ones(1, h, w)  # [b, h, w]
        y_embed = area.cumsum(1, dtype=torch.float32)
        x_embed = area.cumsum(2, dtype=torch.float32)

        one_direction_feats = d_model // 2

        eps = 1e-6
        y_embed = y_embed / (y_embed[:, -1:, :] + eps) * scale
        x_embed = x_embed / (x_embed[:, :, -1:] + eps) * scale

        dim_t = torch.arange(one_direction_feats, dtype=torch.float32)
        dim_t = temperature ** (2 * (dim_t // 2) / one_direction_feats)

        pos_x = x_embed[:, :, :, None] / dim_t
        pos_y = y_embed[:, :, :, None] / dim_t
        pos_x = torch.stack(
            (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3)
        pos_y = torch.stack(
            (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3)
        pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
        pos = pos.flatten(2).permute(2, 0, 1)
        return pos  # [h*w, 1, d_model]

    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 = x.cuda()
        if self.cfg.MODEL.EXTRA.USE_ATTENTION:
            x_ = x[:,:3]
            cond_hm = x[:,3:]
            if cond_hm.shape[1] == 0:
                raise Exception("condition is empty, please check your dataloader")
        else:
            x_ = 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 = self.reduce(y_list[0])
        bs, c, h, w = x.shape

        if self.cfg.MODEL.EXTRA.USE_ATTENTION:
            # resize conditional input and bring it to token format
            cond_hm = TF.resize(cond_hm, (h,w))
            x_cond = self.trans_cond(cond_hm)
            _, c_cond, _, _ = x_cond.shape
            x = torch.cat((x,x_cond), dim=1)
            c += c_cond

        x = x.flatten(2).permute(2, 0, 1)
        x = self.global_encoder(x, pos=self.pos_embedding)
        x = x.permute(1, 2, 0).contiguous().view(bs, c, h, w)
        x = self.final_layer(x)

        return x

    def init_weights(self, pretrained='', print_load_info=False):
        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)

        if os.path.isfile(pretrained):
            pretrained_state_dict = torch.load(pretrained)
            logger.info('=> loading pretrained model {}'.format(pretrained))

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


def get_pose_net(cfg, is_train, **kwargs):
    model = TransPoseH(cfg, **kwargs)

    if is_train and cfg['MODEL']['INIT_WEIGHTS']:
        model.init_weights(cfg['MODEL']['PRETRAINED'])

    return model