在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
import random
import numpy as np
from .DCM import Counter_Guide_Enhanced

_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)
        self.counter_guide = Counter_Guide_Enhanced(768, 768)

    def masking_fea_combined(self, x_combined, event_x_combined, ratio=0.8):
        b, n, c = x_combined.shape
        b_event, n_event, c_event = event_x_combined.shape

        assert n == n_event  # 确保合并后的数据维度匹配

        len_out = int(n * ratio)

        mask = torch.rand(b, n).float()
        mask_event = torch.rand(b_event, n_event).float()
        mask = mask > 0.4
        mask_event = mask_event > 0.4

        #### 此处新增 不同模态互补性
        mask_event = ~mask + mask_event

        mask_idx = mask.float().sort(1, descending=True)[-1].to(device=x_combined.device)
        mask_event_idx = mask_event.float().sort(1, descending=True)[-1].to(device=event_x_combined.device)

        masked_x = torch.gather(x_combined, 1, mask_idx[:, :len_out, None].repeat(1, 1, c))
        masked_event_x = torch.gather(event_x_combined, 1, mask_event_idx[:, :len_out, None].repeat(1, 1, c_event))

        return masked_x, masked_event_x, {'x1': mask_idx[:, :len_out],
                                          'x0': mask_idx[:, len_out:],
                                          'ex1': mask_event_idx[:, :len_out],
                                          'ex0': mask_event_idx[:, len_out:]
                                         }
################################################################################################
    # masking之后的填充和重排序
    def pad_reorder(x, idx1, idx0):
        '''Reorder function using the indices provided by masking_fea.'''
        B,N,C = x.shape
        pad_x = torch.zeros([B, idx0.shape[1], C], device=x.device)
        x = torch.cat([x, pad_x], dim=1)
        combined_idx = torch.cat([idx1, idx0], dim=1)
        x_reordered = torch.zeros_like(x).scatter(1, combined_idx.unsqueeze(-1).expand(B, -1, C).to(torch.int64), x)
        return x_reordered



    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.patch_embed(x) 
        z = self.patch_embed(z) 
        z += self.pos_embed_z
        x += self.pos_embed_x   
        
        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
       
        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 = 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):
            ######################## 每隔一层应用遮罩处理 ###########################
            if i % 2 == 0:
                x, event_x, token_idx = self.masking_fea_combined(x,event_x,ratio=0.95)
                # 重排序逻辑
                # x = self.reorder(x, token_idx['x1'], token_idx['x0'])
                # event_x = self.reorder(event_x, token_idx['ex1'], token_idx['ex0'])  
            ########################################################################
            # 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)
        
            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 此处RGB模态特征作为主特征,event_x作为补充特征
                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，这里类似残差操作
                x = x + enhanced_x
                event_x = event_x + enhanced_event_x


        # 为了不影响masking_fea的随机掩膜操作，在此处增加 填充 & 重排序 逻辑
        x = self.pad_reorder(x, token_idx['x1'], token_idx['x0'])
        event_x = self.pad_reorder(event_x, token_idx['ex1'], token_idx['ex0']) 

        # 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 # cat仅仅是做 rgb 和 event voxel拼接，数据增强采用的随机遮蔽特征策略。


        aux_dict = {
            "attn": attn,
            "attn1": attn1,
            "removed_indexes_s": removed_indexes_s,  # used for visualization
            'removed_indexes_s1': removed_indexes_s1,
        }
        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，masking_fea会作用到第1层吗？