在# 将 4输入分开，构建新的相同模态结合的2输入，2分支
import math
import logging
from functools import partial
from collections import OrderedDict
from copy import deepcopy

import torch
import torch.nn as nn
import torch.nn.functional as F

from timm.models.layers import to_2tuple

from lib.models.layers.patch_embed import PatchEmbed, PatchEmbed_event, xcorr_depthwise
from .utils import combine_tokens, recover_tokens
from .vit import VisionTransformer
from ..layers.attn_blocks import CEBlock
from .ad_counter_guide import Counter_Guide_Enhanced
from .Att import *
_logger = logging.getLogger(__name__)
                            

class VisionTransformerCE(VisionTransformer):
    """ Vision Transformer with candidate elimination (CE) module

    A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale`
        - https://arxiv.org/abs/2010.11929

    Includes distillation token & head support for `DeiT: Data-efficient Image Transformers`
        - https://arxiv.org/abs/2012.12877
    """
    def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12,
                 num_heads=12, mlp_ratio=4., qkv_bias=True, representation_size=None, distilled=False,
                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0., embed_layer=PatchEmbed, norm_layer=None,
                 act_layer=None, weight_init='',
                 ce_loc=None, ce_keep_ratio=None):
        super().__init__()
        if isinstance(img_size, tuple):
            self.img_size = img_size
        else:
            self.img_size = to_2tuple(img_size)
        self.patch_size = patch_size
        self.in_chans = in_chans

        self.num_classes = num_classes
        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
        self.num_tokens = 2 if distilled else 1
        norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
        act_layer = act_layer or nn.GELU

        self.patch_embed = embed_layer(
            img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
        num_patches = self.patch_embed.num_patches

        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
        self.dist_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) if distilled else None
        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + self.num_tokens, embed_dim))
        self.pos_drop = nn.Dropout(p=drop_rate)
        self.pos_embed_event = PatchEmbed_event(in_chans=32, embed_dim=768, kernel_size=4, stride=4)
        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
        blocks = []
        ce_index = 0
        self.ce_loc = ce_loc
        for i in range(depth):
            ce_keep_ratio_i = 1.0
            if ce_loc is not None and i in ce_loc:
                ce_keep_ratio_i = ce_keep_ratio[ce_index]
                ce_index += 1

            blocks.append(
                CEBlock(
                    dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, drop=drop_rate,
                    attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, act_layer=act_layer,
                    keep_ratio_search=ce_keep_ratio_i)
            )

        self.blocks = nn.Sequential(*blocks)
        self.norm = norm_layer(embed_dim)

        self.init_weights(weight_init)
    
        # 添加交互模块counter_guide
        self.counter_guide = Counter_Guide_Enhanced(768, 768)

        # 添加注意力模块
        self.sab = SAB(n_feat = in_chans, kernel_size = 3, reduction = 16, bias = True, act = nn.ReLU(inplace=True))
        self.pab = PAB(n_feat = in_chans, kernel_size = 3, reduction = 16, bias = True, act = nn.ReLU(inplace=True))
        self.cab = CAB(n_feat = in_chans, kernel_size = 3, reduction = 16, bias = True, act = nn.ReLU(inplace=True))
        
    def forward_features(self, z, x, event_z, event_x,
                         mask_z=None, mask_x=None,
                         ce_template_mask=None, ce_keep_rate=None,
                         return_last_attn=False
                         ):
        # 分支1 处理流程
        B, H, W = x.shape[0], x.shape[2], x.shape[3]
        x = self.sab(x)
        z = self.sab(z)
        x = self.patch_embed(x)
        z = self.patch_embed(z)  
        
        z += self.pos_embed_z
        x += self.pos_embed_x        
        
        if mask_z is not None and mask_x is not None:
            mask_z = F.interpolate(mask_z[None].float(), scale_factor=1. / self.patch_size).to(torch.bool)[0]
            mask_z = mask_z.flatten(1).unsqueeze(-1)

            mask_x = F.interpolate(mask_x[None].float(), scale_factor=1. / self.patch_size).to(torch.bool)[0]
            mask_x = mask_x.flatten(1).unsqueeze(-1)

            mask_x = combine_tokens(mask_z, mask_x, mode=self.cat_mode)
            mask_x = mask_x.squeeze(-1)

        if self.add_cls_token:
            cls_tokens = self.cls_token.expand(B, -1, -1)
            cls_tokens = cls_tokens + self.cls_pos_embed
        if self.add_sep_seg:
            x += self.search_segment_pos_embed
            z += self.template_segment_pos_embed
        x = combine_tokens(z, x, mode=self.cat_mode)
        if self.add_cls_token:
            x = torch.cat([cls_tokens, x], dim=1)
        x = self.pos_drop(x)

        lens_z = self.pos_embed_z.shape[1]
        lens_x = self.pos_embed_x.shape[1]
        global_index_t = torch.linspace(0, lens_z - 1, lens_z).to(x.device)
        global_index_t = global_index_t.repeat(B, 1)
        global_index_s = torch.linspace(0, lens_x - 1, lens_x).to(x.device)
        global_index_s = global_index_s.repeat(B, 1)
        removed_indexes_s = []
        # # 分支2 处理流程
        event_x = self.pos_embed_event(event_x)
        event_z = self.pos_embed_event(event_z)
        event_x += self.pos_embed_x
        event_z += self.pos_embed_z

        event_x = combine_tokens(event_z, event_x, mode=self.cat_mode)

        if self.add_cls_token:
            event_x = torch.cat([cls_tokens, event_x], dim=1)

        lens_z = self.pos_embed_z.shape[1]
        lens_x = self.pos_embed_x.shape[1]

        global_index_t1 = torch.linspace(0, lens_z - 1, lens_z).to(event_x.device)
        global_index_t1 = global_index_t1.repeat(B, 1)
        global_index_s1 = torch.linspace(0, lens_x - 1, lens_x).to(event_x.device)
        global_index_s1 = global_index_s1.repeat(B, 1)

        removed_indexes_s1 = []

        for i, blk in enumerate(self.blocks):
            # #  引入空间注意力机制，在空间维度上提高模型对局部噪声的鲁棒性
            # x = self.sab(x)
            # event_x = self.sab(event_x)
            # 第一个分支处理
            x, global_index_t, global_index_s, removed_index_s, attn = \
                blk(x, global_index_t, global_index_s, mask_x, ce_template_mask, ce_keep_rate)
            # 第二个分支处理
            event_x, global_index_t1, global_index_s1, removed_index_s1, attn1 = \
                blk(event_x, global_index_t1, global_index_s1, mask_x, ce_template_mask, ce_keep_rate)


            if self.ce_loc is not None and i in self.ce_loc:
                removed_indexes_s.append(removed_index_s)
                removed_indexes_s1.append(removed_index_s1)

            # 在第1层增加counter_guide模块,验证早期融合效果
            if i == 0 :
                # 新增原始特征（在counter_guide之前的特征，便于后续的loss计算）
                A_original_x = x.clone() # 保存原始x特征 （RGB）
                A_original_event_x = event_x.clone() # 保存原始event特征 （event）

                # 引入counter_guide进行特征增强
                enhanced_x, enhanced_event_x = self.counter_guide(x, event_x)

                # 保存经过counter_guide增强的特征
                A_enhanced_x = enhanced_x  # 保存增强后的特征
                A_enhanced_event_x = enhanced_event_x  # 保存增强后的特征
                # 将增强后的特征与原特征相加
                x = x + enhanced_x
                event_x = event_x + enhanced_event_x
        
        # 恢复特征tokens ###############
        if removed_indexes_s and removed_indexes_s[0] is not None:
            removed_indexes_cat = torch.cat(removed_indexes_s, dim=1)
            pruned_lens_x = lens_x - global_index_s.shape[1]
            pad_x = torch.zeros([B, pruned_lens_x, x.shape[2]], device=x.device)
            x = torch.cat([x, pad_x], dim=1)
            index_all = torch.cat([global_index_s, removed_indexes_cat], dim=1)
            C = x.shape[-1]
            x = torch.zeros_like(x).scatter(dim=1, index=index_all.unsqueeze(-1).expand(B, -1, C).to(torch.int64), src=x)
        
        x = recover_tokens(x, global_index_t.shape[1], lens_x, mode=self.cat_mode)
        
        
        if removed_indexes_s1 and removed_indexes_s1[0] is not None:
            removed_indexes_cat1 = torch.cat(removed_indexes_s1, dim=1)
            pruned_lens_x1 = lens_x - global_index_s1.shape[1]
            pad_x1 = torch.zeros([B, pruned_lens_x1, event_x.shape[2]], device=event_x.device)
            event_x = torch.cat([event_x, pad_x1], dim=1)
            index_all1 = torch.cat([global_index_s1, removed_indexes_cat1], dim=1)
            C = event_x.shape[-1]
            event_x = torch.zeros_like(event_x).scatter_(dim=1, index=index_all1.unsqueeze(-1).expand(B, -1, C).to(torch.int64), src=event_x)
        
        event_x = recover_tokens(event_x, global_index_t1.shape[1], lens_x, mode=self.cat_mode)
        
        
        #################################
        
        
        # 应用LayerNorm归一化处理
        x = self.norm(x) 
        event_x = self.norm(event_x)
        x_cat = torch.cat([event_x,x], dim=1)
        x = x_cat
        aux_dict = {
            "attn": attn,
            'attn1': attn1,
            "removed_indexes_s": removed_indexes_s,  # used for visualization
            'removed_indexes_s1': removed_indexes_s1,
            # 在aux_dict中返回原始特征和增强特征
            'A_original_x': A_original_x,
            'A_original_event_x': A_original_event_x,
            'A_enhanced_x': A_enhanced_x,
            'A_enhanced_event_x': A_enhanced_event_x,
        }
        
        return x, aux_dict 

    def forward(self, z, x, event_z, event_x,
                ce_template_mask=None, ce_keep_rate=None,
                tnc_keep_rate=None,
                return_last_attn=False):

        x, aux_dict = self.forward_features(z, x, event_z, event_x, ce_template_mask=ce_template_mask, ce_keep_rate=ce_keep_rate,)

        return x, aux_dict


def _create_vision_transformer(pretrained=False, **kwargs):
    model = VisionTransformerCE(**kwargs)
    
    if pretrained:
        if 'npz' in pretrained:
            model.load_pretrained(pretrained, prefix='')
        else:
            checkpoint = torch.load(pretrained, map_location="cpu")
            missing_keys, unexpected_keys = model.load_state_dict(checkpoint["model"], strict=False)
            print('Load pretrained model from: ' + pretrained)

    return model


def vit_base_patch16_224_ce(pretrained=False, **kwargs):
    """ ViT-Base model (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929).
    """
    model_kwargs = dict(
        patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs)
    model = _create_vision_transformer(pretrained=pretrained, **model_kwargs)
    return model


def vit_large_patch16_224_ce(pretrained=False, **kwargs):
    """ ViT-Large model (ViT-L/16) from original paper (https://arxiv.org/abs/2010.11929).
    """
    model_kwargs = dict(
        patch_size=16, embed_dim=1024, depth=24, num_heads=16, **kwargs)
    model = _create_vision_transformer(pretrained=pretrained, **model_kwargs)
    return model中，SAB模块期望维度是B,C,H,W，其中SAB设计为：class SAB(nn.Module):
    def __init__(self, n_feat, kernel_size, reduction, bias, act):
        super(SAB, self).__init__()
        modules_body = [conv(n_feat, n_feat, kernel_size, bias=bias), act, conv(n_feat, n_feat, kernel_size, bias=bias)]
        self.body = nn.Sequential(*modules_body)
        self.SA = SALayer(kernel_size=7)

    def forward(self, x):
        print('sa_block:', x.size()) # torch.Size([2, 256, 768])
        res = self.body(x)
        res = self.SA(res)
        res += x
        return res，那么在patch_embed：class PatchEmbed(nn.Module):
    """ 2D Image to Patch Embedding
    """

    def __init__(self, img_size=256, patch_size=16, in_chans=3, embed_dim=768, norm_layer=False, flatten=True):
        super().__init__()
        img_size = to_2tuple(img_size)
        patch_size = to_2tuple(patch_size)
        self.img_size = img_size
        self.patch_size = patch_size
        self.grid_size = (img_size[0] // patch_size[0], img_size[1] // patch_size[1]) # 224 / 16 = 14
        self.num_patches = self.grid_size[0] * self.grid_size[1]
        self.flatten = flatten
        
        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
        self.norm = nn.LayerNorm(embed_dim) if norm_layer else nn.Identity()

    def forward(self, x):
        x = self.proj(x)
        if self.flatten:
            x = x.flatten(2).transpose(1, 2)  # BCHW -> BNC
        x = self.norm(x)
        return x


class PatchEmbed_event(nn.Module):
    def __init__(self, in_chans=320, embed_dim=768, kernel_size=5, stride=1, flatten=True, norm_layer=False):
        super().__init__()
        self.pos_embedding = nn.Conv1d(in_channels=3, out_channels=16, kernel_size=1, stride=1)
        self.in_chans = in_chans
        self.flatten = flatten
        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=kernel_size, stride=stride)
        # self.proj2 = nn.Conv2d(embed_dim, embed_dim, kernel_size=1, stride=1)
        self.norm = nn.LayerNorm(embed_dim) if norm_layer else nn.Identity()
        # self.attn = SelfAttn(768, 4, 3072, 0.1, 'relu')

    def forward(self, x):
        # allow different input size
        x = x.type(torch.cuda.FloatTensor)
        xyz = self.pos_embedding(x.squeeze(dim=1)[:, :3, :])
        xyz = F.relu(xyz)
        x = torch.cat([xyz, x.squeeze(dim=1)[:, 3:, :]], dim=1)
        B, N, C = x.shape        # 1 1 19 10000
        H = W = int(np.sqrt(N*C//self.in_chans))
        x = x.reshape(B, self.in_chans, H, W)       #  B 19 100 100
        x = self.proj(x)        # B 768 16 16
        # x = self.proj2(x)        # B 768 16 16

        if self.flatten:
            x = x.flatten(2).transpose(1, 2)  # BCHW -> BNC  768*256
        # x = self.attn(src1=x, pos_src1=pos_embed)
        x = self.norm(x)
        return x处理之后用的话，会出现维度不匹配的问题。那么在patchEmbed之前进行sab增强，维度就正常，但是在patch_embed之前用sab是否能发挥sab的作用？