# 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 .UEP import UEPModule
_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)
        self.uep_module1 = UEPModule(256, 
                                    embed_dim, 
                                    embed_dim // 4, 
                                    self.img_size)
        self.uep_module2 = UEPModule(64, 
                                    embed_dim, 
                                    embed_dim // 4, 
                                    self.img_size)
    def forward_features(self, z, x, event_z, event_x, event_z2, event_x2,
                         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.patch_embed(x)
        z = self.patch_embed(z)
        x = self.uep_module1(x)
        z = self.uep_module2(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 = []
        
        ## 分支3 处理流程
        event_x2 = self.patch_embed(event_x2)
        event_z2 = self.patch_embed(event_z2)
        event_x2 = self.uep_module1(event_x2)
        event_z2 = self.uep_module2(event_z2)
        event_x2 += self.pos_embed_x
        event_z2 += self.pos_embed_z
        event_x2 = combine_tokens(event_z2, event_x2, mode=self.cat_mode)

        if self.add_cls_token:
            event_x2 = torch.cat([cls_tokens, event_x2], dim=1)

        lens_z2 = self.pos_embed_z.shape[1]
        lens_x2 = self.pos_embed_x.shape[1]

        global_index_t2 = torch.linspace(0, lens_z2 - 1, lens_z2).to(event_x2.device)
        global_index_t2 = global_index_t1.repeat(B, 1)
        global_index_s2 = torch.linspace(0, lens_x2 - 1, lens_x2).to(event_x2.device)
        global_index_s2 = global_index_s2.repeat(B, 1)

        removed_indexes_s2 = []

        for i, blk in enumerate(self.blocks):
            # 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)

            event_x2, global_index_t2, global_index_s2, removed_index_s2, attn2 = \
                blk(event_x2, global_index_t2, global_index_s2, 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)
                removed_indexes_s2.append(removed_index_s2)
        ###############
        # 在第1层增加 counter_guide 模块,验证早期融合效果
        if i == 0:
            # 对 x 和 event_x 进行特征交互与增强
            enhanced_x_1, enhanced_event_x = self.counter_guide(x, event_x)
            # 对 x 和 event_x2 进行特征交互与增强
            enhanced_x_2, enhanced_event_x2 = self.counter_guide(x, event_x2)
            # 将增强后的特征与原特征相加
            x = x + enhanced_x_1 + enhanced_x_2
            event_x = event_x + enhanced_event_x
            event_x2 = event_x2 + enhanced_event_x2
        ###############

        # 恢复特征tokens ###############
        # 应用LayerNorm归一化处理
        x = self.norm(x) 
        lens_x_new = global_index_s.shape[1]
        lens_z_new = global_index_t.shape[1]
        
        z = x[:, :lens_z_new]
        x = x[:, lens_z_new:]
        
        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 - lens_x_new
            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)
        x = x[:,:lens_x]
        ##############################
        event_x = self.norm(event_x)
        lens_x_new1 = global_index_s1.shape[1]
        lens_z_new1 = global_index_t1.shape[1]
        event_z = event_x[:, :lens_z_new1]
        event_x = event_x[:, lens_z_new1:]
        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 - lens_x_new1
            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)
        event_x = event_x[:,:lens_x]
        
        ########################
        event_x2 = self.norm(event_x2)
        lens_x_new2 = global_index_s2.shape[1]
        lens_z_new2 = global_index_t2.shape[1]
        event_z2 = event_x2[:, :lens_z_new2]
        event_x2 = event_x2[:, lens_z_new2:]
        if removed_indexes_s2 and removed_indexes_s2[0] is not None:
            removed_indexes_cat2 = torch.cat(removed_indexes_s2, dim=1)
            pruned_lens_x2 = lens_x2 - lens_x_new2
            pad_x2 = torch.zeros([B, pruned_lens_x2, event_x2.shape[2]], device=event_x2.device)
            event_x2 = torch.cat([event_x2, pad_x2], dim=1)
            index_all2 = torch.cat([global_index_s2, removed_indexes_cat2], dim=1)
            C = event_x2.shape[-1]
            event_x2 = torch.zeros_like(event_x2).scatter_(dim=1, index=index_all2.unsqueeze(-1).expand(B, -1, C).to(torch.int64), src=event_x2)
        
        event_x2 = recover_tokens(event_x2, global_index_t2.shape[1], lens_x2, mode=self.cat_mode)
        event_x2 = event_x2[:,:lens_x]
        #################################
        x_cat = torch.cat([event_x, event_x2, x], dim=1)
        x = x_cat

        aux_dict = {
            "attn": attn,
            'attn1': attn1,
            'attn2': attn2,
            "removed_indexes_s": removed_indexes_s,  # used for visualization
            'removed_indexes_s1': removed_indexes_s1,
            'removed_indexes_s2': removed_indexes_s2,
        }
        
        return x, aux_dict 

    def forward(self, z, x, event_z, event_x, event_z2, event_x2,
                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, event_z2, event_x2,ce_template_mask=ce_template_mask, ce_keep_rate=ce_keep_rate,)
        # print('x.shape:',x.shape) # x.shape: torch.Size([6, 960, 768]) # 320*3 = 960 (batch_size, num_tokens, embed_dim)
        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中的counter_guide设置是：class Cross_Attention(nn.Module):
    def __init__(self, input_channels, output_channels):
        super(Cross_Attention, self).__init__()
        self.query = nn.Linear(input_channels, output_channels)
        self.key = nn.Linear(input_channels, output_channels)
        self.value = nn.Linear(input_channels, output_channels)
        self.scale = output_channels ** -0.5  # 缩放因子，防止softmax的梯度消失/爆炸
    
    def forward(self, assistant, present):
        # 生成查询、键和值
        q = self.query(assistant)
        k = self.key(present)
        v = self.value(present)
        
        # 计算注意力分数
        attention_scores = torch.bmm(q, k.transpose(1, 2)) * self.scale
        attention = F.softmax(attention_scores, dim=-1)
        
        # 应用注意力机制
        attended_features = torch.bmm(attention, v)
        return attended_features

class Multi_Context(nn.Module):
    def __init__(self, input_channels, output_channels):  # 修正了def init为def init
        super(Multi_Context, self).__init__()
        self.linear1 = nn.Linear(input_channels, output_channels)
        self.linear2 = nn.Linear(input_channels, output_channels)
        self.linear3 = nn.Linear(input_channels, output_channels)
        self.linear_final = nn.Linear(output_channels * 3, output_channels)

    def forward(self, x):
        x1 = F.relu(self.linear1(x))
        x2 = F.relu(self.linear2(x))
        x3 = F.relu(self.linear3(x))
        x = torch.cat([x1, x2, x3], dim=-1)  # Concatenate along the feature dimension
        x = self.linear_final(x)
        return x

class EnhancedMultiContext(nn.Module):
    def __init__(self, input_channels, output_channels):
        super(EnhancedMultiContext, self).__init__()
        self.linear1 = nn.Linear(input_channels, output_channels)
        self.linear2 = nn.Linear(input_channels, output_channels)
        self.linear3 = nn.Linear(input_channels, output_channels)
        self.relu1 = nn.ReLU()
        self.leaky_relu2 = nn.LeakyReLU()
        self.swish3 = nn.SiLU()  # Alternatively, you can use nn.SiLU() for Swish activation
        self.linear_final = nn.Linear(output_channels * 3, output_channels)

    def forward(self, x):
        x1 = self.relu1(self.linear1(x))
        x2 = self.leaky_relu2(self.linear2(x))
        x3 = self.swish3(self.linear3(x))
        x = torch.cat([x1, x2, x3], dim=-1)  
        x = self.linear_final(x)  
        return x

class Adaptive_Weight(nn.Module):
    def __init__(self, input_channels):  # 修正了def init为def init
        super(Adaptive_Weight, self).__init__()
        self.fc1 = nn.Linear(input_channels, input_channels // 2)
        self.fc2 = nn.Linear(input_channels // 2, input_channels)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        x_avg = torch.mean(x, dim=1)  # Global Average Pooling along the ‘sequence length’ dimension
        weight = F.relu(self.fc1(x_avg))
        weight = self.fc2(weight)
        weight = self.sigmoid(weight).unsqueeze(1)
        out = x * weight
        return out
    
class Multi_Adaptive_Weight(nn.Module):
    def __init__(self, input_channels):  
        super(Multi_Adaptive_Weight, self).__init__()
        self.fc1 = nn.Linear(input_channels, input_channels // 4)
        self.fc2 = nn.Linear(input_channels // 4, input_channels)
        self.sigmoid = nn.Sigmoid()
        self.global_avg_pool = nn.AdaptiveAvgPool1d(1)
        self.global_max_pool = nn.AdaptiveMaxPool1d(1)

    def forward(self, x):
        x_avg = self.global_avg_pool(x.transpose(1, 2)).squeeze(-1)  
        x_max = self.global_max_pool(x.transpose(1, 2)).squeeze(-1)  
        weight = F.relu(self.fc1(x_avg + x_max))
        weight = self.fc2(weight)
        weight = self.sigmoid(weight).unsqueeze(1)
        out = x * weight
        return out
class Counter_Guide_Enhanced(nn.Module):
    def __init__(self, input_channels, output_channels):
        super(Counter_Guide_Enhanced, self).__init__()
        self.mc = Multi_Context(input_channels, output_channels)
        self.cross_attention = Cross_Attention(output_channels, output_channels)
        self.adaptive_weight = Adaptive_Weight(output_channels)
    
    def forward(self, x, event_x):
        enhanced_event_x = self.mc(event_x)
        enhanced_x = self.cross_attention(x, event_x)
        enhanced_event_x_via_x = self.cross_attention(event_x, enhanced_x)
        adaptive_enhanced_x = self.adaptive_weight(enhanced_x)
        adaptive_enhanced_event_x = self.adaptive_weight(enhanced_event_x_via_x)
        return adaptive_enhanced_x, adaptive_enhanced_event_x，其中自适应权重调整模块的原本设置是：self.fc1 = nn.Linear(input_channels, input_channels // 4)
        self.fc2 = nn.Linear(input_channels // 4, input_channels)，我们改为了 //2，当前train loss和val loss曲线几乎重合，但是两个loss曲线并未降低到很低。