分析：# Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
import torch
import torch.nn as nn
from functools import partial
from torch import Tensor
from typing import Optional

from timm.models.vision_transformer import VisionTransformer, _cfg
from timm.models.registry import register_model
from timm.models.layers import trunc_normal_, lecun_normal_

from timm.models.layers import DropPath, to_2tuple
from timm.models.vision_transformer import _load_weights

import math

from collections import namedtuple

from mamba_ssm.modules.mamba_simple import Mamba
from mamba_ssm.utils.generation import GenerationMixin
from mamba_ssm.utils.hf import load_config_hf, load_state_dict_hf

from rope import *
import random

from token_merge import bipartite_soft_matching
from token_merge import kth_bipartite_soft_matching

import time

import matplotlib
matplotlib.use("agg")
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.metrics.pairwise import cosine_similarity

from datetime import datetime
import numpy as np

try:
    from mamba_ssm.ops.triton.layernorm import RMSNorm, layer_norm_fn, rms_norm_fn
except ImportError:
    RMSNorm, layer_norm_fn, rms_norm_fn = None, None, None


__all__ = [
    'vim_tiny_patch16_224', 'vim_small_patch16_224', 'vim_base_patch16_224',
    'vim_tiny_patch16_384', 'vim_small_patch16_384', 'vim_base_patch16_384',
]

def visualize_cosine_similarity(hidden_states):
    print("Start to visualize cosine similarity")
    # 获取hidden_states的形状
    batch_size, seq_len, hidden_dim = hidden_states.shape

    # 将hidden_states重塑为二维数组
    hidden_states_reshaped = hidden_states.reshape(batch_size * seq_len, hidden_dim)

    if isinstance(hidden_states_reshaped, torch.Tensor):
       hidden_states_reshaped = hidden_states_reshaped.detach().cpu().numpy()

    print("hidden_states_reshaped.shape: ", hidden_states_reshaped.shape)
    # 计算余弦相似度矩阵
    cosine_sim_matrix = cosine_similarity(hidden_states_reshaped)
    
    print("cosine_sim_matrix success")
    # 使用 seaborn 绘制热图
    plt.figure(figsize=(10, 8))
    sns.heatmap(cosine_sim_matrix, cmap='viridis')
    plt.title("Cosine Similarity Matrix of Hidden States")
    plt.xlabel("Hidden State Index")
    plt.ylabel("Hidden State Index")
    plt.show()

class PatchEmbed(nn.Module):
    """ 2D Image to Patch Embedding
    """
    def __init__(self, img_size=224, patch_size=16, stride=16, in_chans=3, embed_dim=768, norm_layer=None, 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]) // stride + 1, (img_size[1] - patch_size[1]) // stride + 1)
        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=stride)
        self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()

    def forward(self, x):
        B, C, H, W = x.shape
        assert H == self.img_size[0] and W == self.img_size[1], \
            f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
        x = self.proj(x)
        if self.flatten:
            x = x.flatten(2).transpose(1, 2)  # BCHW -> BNC
        x = self.norm(x)
        return x
    

class Block(nn.Module):
    def __init__(
        self, dim, mixer_cls, token_merge=False, visualize=False, fusion_token=0, norm_cls=nn.LayerNorm, fused_add_norm=False, residual_in_fp32=False,drop_path=0.,
    ):
        """
        Simple block wrapping a mixer class with LayerNorm/RMSNorm and residual connection"

        This Block has a slightly different structure compared to a regular
        prenorm Transformer block.
        The standard block is: LN -> MHA/MLP -> Add.
        [Ref: https://arxiv.org/abs/2002.04745]
        Here we have: Add -> LN -> Mixer, returning both
        the hidden_states (output of the mixer) and the residual.
        This is purely for performance reasons, as we can fuse add and LayerNorm.
        The residual needs to be provided (except for the very first block).
        """
        super().__init__()
        self.token_merge = token_merge
        self.visualize = visualize
        self.fusion_token = fusion_token
        self.residual_in_fp32 = residual_in_fp32
        self.fused_add_norm = fused_add_norm
        self.mixer = mixer_cls(dim)
        self.norm = norm_cls(dim)
        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()

        self.layer_idx = 0
        self.hidden_states_np = None

        if self.fused_add_norm:
            assert RMSNorm is not None, "RMSNorm import fails"
            assert isinstance(
                self.norm, (nn.LayerNorm, RMSNorm)
            ), "Only LayerNorm and RMSNorm are supported for fused_add_norm"


    def forward(
        self, hidden_states: Tensor, residual: Optional[Tensor] = None, inference_params=None
    ):
        r"""Pass the input through the encoder layer.

        Args:
            hidden_states: the sequence to the encoder layer (required).
            residual: hidden_states = Mixer(LN(residual))
        """

        if not self.fused_add_norm:
            if residual is None:
                residual = hidden_states
            else:
                residual = residual + self.drop_path(hidden_states)
            
            hidden_states = self.norm(residual.to(dtype=self.norm.weight.dtype))
            if self.residual_in_fp32:
                residual = residual.to(torch.float32)
        else:
            fused_add_norm_fn = rms_norm_fn if isinstance(self.norm, RMSNorm) else layer_norm_fn
            if residual is None:
                hidden_states, residual = fused_add_norm_fn(
                    hidden_states,
                    self.norm.weight,
                    self.norm.bias,
                    residual=residual,
                    prenorm=True,
                    residual_in_fp32=self.residual_in_fp32,
                    eps=self.norm.eps,
                )
            else:
                hidden_states, residual = fused_add_norm_fn(
                    self.drop_path(hidden_states),
                    self.norm.weight,
                    self.norm.bias,
                    residual=residual,
                    prenorm=True,
                    residual_in_fp32=self.residual_in_fp32,
                    eps=self.norm.eps,
                )    
        
        if self.visualize:
            # visualize_cosine_similarity(hidden_states)
            self.hidden_states_np = hidden_states[0].detach().cpu().numpy()

        # if self.token_merge:
        #     t = hidden_states.shape[1]
        #     r = self.fusion_token
            
        #     if r > 0:
        #         # merge, unmerge = kth_bipartite_soft_matching(hidden_states, r)
        #         merge, unmerge = bipartite_soft_matching(hidden_states, r, class_token=True)
        #         hidden_states = merge(hidden_states)
        #         residual = merge(residual) if residual is not None else None

        hidden_states = self.mixer(hidden_states, inference_params=inference_params)



        return hidden_states, residual

    def allocate_inference_cache(self, batch_size, max_seqlen, dtype=None, **kwargs):
        return self.mixer.allocate_inference_cache(batch_size, max_seqlen, dtype=dtype, **kwargs)


def create_block(
    d_model,
    token_merge=False,
    visualize=False,
    fusion_token=0,
    ssm_cfg=None,
    norm_epsilon=1e-5,
    drop_path=0.,
    rms_norm=False,
    residual_in_fp32=False,
    fused_add_norm=False,
    layer_idx=None,
    device=None,
    dtype=None,
    if_bimamba=False,
    bimamba_type="none",
    if_devide_out=False,
    init_layer_scale=None,
):
    if if_bimamba:
        bimamba_type = "v1"
    if ssm_cfg is None:
        ssm_cfg = {}
    factory_kwargs = {"device": device, "dtype": dtype}
    mixer_cls = partial(Mamba, layer_idx=layer_idx, bimamba_type=bimamba_type, if_devide_out=if_devide_out, init_layer_scale=init_layer_scale, **ssm_cfg, **factory_kwargs)
    norm_cls = partial(
        nn.LayerNorm if not rms_norm else RMSNorm, eps=norm_epsilon, **factory_kwargs
    )
    block = Block(
        d_model,
        mixer_cls,
        token_merge=token_merge,
        visualize=visualize,
        fusion_token=fusion_token,
        norm_cls=norm_cls,
        drop_path=drop_path,
        fused_add_norm=fused_add_norm,
        residual_in_fp32=residual_in_fp32,
    )
    block.layer_idx = layer_idx
    return block


# https://github.com/huggingface/transformers/blob/c28d04e9e252a1a099944e325685f14d242ecdcd/src/transformers/models/gpt2/modeling_gpt2.py#L454
def _init_weights(
    module,
    n_layer,
    initializer_range=0.02,  # Now only used for embedding layer.
    rescale_prenorm_residual=True,
    n_residuals_per_layer=1,  # Change to 2 if we have MLP
):
    if isinstance(module, nn.Linear):
        if module.bias is not None:
            if not getattr(module.bias, "_no_reinit", False):
                nn.init.zeros_(module.bias)
    elif isinstance(module, nn.Embedding):
        nn.init.normal_(module.weight, std=initializer_range)

    if rescale_prenorm_residual:
        # Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme:
        #   > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale
        #   > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers.
        #   >   -- GPT-2 :: https://openai.com/blog/better-language-models/
        #
        # Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py
        for name, p in module.named_parameters():
            if name in ["out_proj.weight", "fc2.weight"]:
                # Special Scaled Initialization --> There are 2 Layer Norms per Transformer Block
                # Following Pytorch init, except scale by 1/sqrt(2 * n_layer)
                # We need to reinit p since this code could be called multiple times
                # Having just p *= scale would repeatedly scale it down
                nn.init.kaiming_uniform_(p, a=math.sqrt(5))
                with torch.no_grad():
                    p /= math.sqrt(n_residuals_per_layer * n_layer)


def segm_init_weights(m):
    if isinstance(m, nn.Linear):
        trunc_normal_(m.weight, std=0.02)
        if isinstance(m, nn.Linear) and m.bias is not None:
            nn.init.constant_(m.bias, 0)
    elif isinstance(m, nn.Conv2d):
        # NOTE conv was left to pytorch default in my original init
        lecun_normal_(m.weight)
        if m.bias is not None:
            nn.init.zeros_(m.bias)
    elif isinstance(m, (nn.LayerNorm, nn.GroupNorm, nn.BatchNorm2d)):
        nn.init.zeros_(m.bias)
        nn.init.ones_(m.weight)


class VisionMamba(nn.Module):
    def __init__(self, 
                 img_size=224, 
                 patch_size=16, 
                 stride=16,
                 depth=24, 
                 embed_dim=192, 
                 channels=3, 
                 num_classes=1000,
                 ssm_cfg=None, 
                 drop_rate=0.,
                 drop_path_rate=0.1,
                 norm_epsilon: float = 1e-5, 
                 rms_norm: bool = False, 
                 initializer_cfg=None,
                 fused_add_norm=False,
                 residual_in_fp32=False,
                 device=None,
                 dtype=None,
                 ft_seq_len=None,
                 pt_hw_seq_len=14,
                 if_bidirectional=False,
                 final_pool_type='none',
                 if_abs_pos_embed=False,
                 if_rope=False,
                 if_rope_residual=False,
                 flip_img_sequences_ratio=-1.,
                 if_bimamba=False,
                 bimamba_type="none",
                 if_cls_token=False,
                 if_devide_out=False,
                 init_layer_scale=None,
                 use_double_cls_token=False,
                 use_middle_cls_token=False,
                 fusion_strategy='no',
                 fusion_layer=0,
                 fusion_token=0,
                 visualize=False,
                 bi_merge=False,
                 **kwargs):
        factory_kwargs = {"device": device, "dtype": dtype}
        # add factory_kwargs into kwargs
        kwargs.update(factory_kwargs) 
        super().__init__()
        self.residual_in_fp32 = residual_in_fp32
        self.fused_add_norm = fused_add_norm
        self.if_bidirectional = if_bidirectional
        self.final_pool_type = final_pool_type
        self.if_abs_pos_embed = if_abs_pos_embed
        self.if_rope = if_rope
        self.if_rope_residual = if_rope_residual
        self.flip_img_sequences_ratio = flip_img_sequences_ratio
        self.if_cls_token = if_cls_token
        self.use_double_cls_token = use_double_cls_token
        self.use_middle_cls_token = use_middle_cls_token
        self.num_tokens = 1 if if_cls_token else 0
        self.fusion_strategy = fusion_strategy
        self.fusion_layer = fusion_layer
        self.fusion_token = fusion_token
        self.visualize = visualize
        self.bi_merge = bi_merge

        # pretrain parameters
        self.num_classes = num_classes
        self.d_model = self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models

        self.patch_embed = PatchEmbed(
            img_size=img_size, patch_size=patch_size, stride=stride, in_chans=channels, embed_dim=embed_dim)
        num_patches = self.patch_embed.num_patches

        if if_cls_token:
            if use_double_cls_token:
                self.cls_token_head = nn.Parameter(torch.zeros(1, 1, self.embed_dim))
                self.cls_token_tail = nn.Parameter(torch.zeros(1, 1, self.embed_dim))
                self.num_tokens = 2
            else:
                self.cls_token = nn.Parameter(torch.zeros(1, 1, self.embed_dim))
                # self.num_tokens = 1
            
        if if_abs_pos_embed:
            self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + self.num_tokens, self.embed_dim))
            self.pos_drop = nn.Dropout(p=drop_rate)

        if if_rope:
            half_head_dim = embed_dim // 2
            hw_seq_len = img_size // patch_size
            self.rope = VisionRotaryEmbeddingFast(
                dim=half_head_dim,
                pt_seq_len=pt_hw_seq_len,
                ft_seq_len=hw_seq_len
            )
        self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()


        # TODO: release this comment
        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
        # import ipdb;ipdb.set_trace()
        inter_dpr = [0.0] + dpr
        self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity()
                # transformer blocks

        blocklayer = []
        for i in range(depth):
            if ((self.fusion_strategy == 'lower' and i > self.fusion_layer) or
                (self.fusion_strategy == 'upper' and i < self.fusion_layer) or
                (self.fusion_strategy == 'alternate' and i % 2 == 1) or
                (self.fusion_strategy == 'all' and i >= 0)):
                created_block = create_block(
                    embed_dim,
                    token_merge=True,
                    visualize=self.visualize,
                    fusion_token=self.fusion_token,
                    ssm_cfg=ssm_cfg,
                    norm_epsilon=norm_epsilon,
                    rms_norm=rms_norm,
                    residual_in_fp32=residual_in_fp32,
                    fused_add_norm=fused_add_norm,
                    layer_idx=i,
                    if_bimamba=if_bimamba,
                    bimamba_type=bimamba_type,
                    drop_path=inter_dpr[i],
                    if_devide_out=if_devide_out,
                    init_layer_scale=init_layer_scale,
                    **factory_kwargs,
                )
            else:
                created_block = create_block(
                    embed_dim,
                    token_merge=False,
                    visualize=self.visualize,
                    fusion_token=self.fusion_token,
                    ssm_cfg=ssm_cfg,
                    norm_epsilon=norm_epsilon,
                    rms_norm=rms_norm,
                    residual_in_fp32=residual_in_fp32,
                    fused_add_norm=fused_add_norm,
                    layer_idx=i,
                    if_bimamba=if_bimamba,
                    bimamba_type=bimamba_type,
                    drop_path=dpr[i],
                    if_devide_out=if_devide_out,
                    init_layer_scale=init_layer_scale,
                    **factory_kwargs,
                )

            blocklayer.append(created_block)

        self.layers = nn.ModuleList(
            blocklayer
        )
        
        # output head
        self.norm_f = (nn.LayerNorm if not rms_norm else RMSNorm)(
            embed_dim, eps=norm_epsilon, **factory_kwargs
        )

        # self.pre_logits = nn.Identity()

        # original init
        self.patch_embed.apply(segm_init_weights)
        self.head.apply(segm_init_weights)
        if if_abs_pos_embed:
            trunc_normal_(self.pos_embed, std=.02)
        if if_cls_token:
            if use_double_cls_token:
                trunc_normal_(self.cls_token_head, std=.02)
                trunc_normal_(self.cls_token_tail, std=.02)
            else:
                trunc_normal_(self.cls_token, std=.02)

        # mamba init
        self.apply(
            partial(
                _init_weights,
                n_layer=depth,
                **(initializer_cfg if initializer_cfg is not None else {}),
            )
        )


    def allocate_inference_cache(self, batch_size, max_seqlen, dtype=None, **kwargs):
        return {
            i: layer.allocate_inference_cache(batch_size, max_seqlen, dtype=dtype, **kwargs)
            for i, layer in enumerate(self.layers)
        }

    @torch.jit.ignore
    def no_weight_decay(self):
        return {"pos_embed", "cls_token", "dist_token", "cls_token_head", "cls_token_tail"}

    @torch.jit.ignore()
    def load_pretrained(self, checkpoint_path, prefix=""):
        _load_weights(self, checkpoint_path, prefix)

    def forward_features(self, x, inference_params=None, if_random_cls_token_position=False, if_random_token_rank=False):
        # taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
        # with slight modifications to add the dist_token
        x = self.patch_embed(x)
        B, M, _ = x.shape
        if self.if_cls_token:
            if self.use_double_cls_token:
                cls_token_head = self.cls_token_head.expand(B, -1, -1)
                cls_token_tail = self.cls_token_tail.expand(B, -1, -1)
                token_position = [0, M + 1]
                x = torch.cat((cls_token_head, x, cls_token_tail), dim=1)
                M = x.shape[1]
            else:
                if self.use_middle_cls_token:
                    cls_token = self.cls_token.expand(B, -1, -1)
                    token_position = M // 2
                    # add cls token in the middle
                    x = torch.cat((x[:, :token_position, :], cls_token, x[:, token_position:, :]), dim=1)
                elif if_random_cls_token_position:
                    cls_token = self.cls_token.expand(B, -1, -1)
                    token_position = random.randint(0, M)
                    x = torch.cat((x[:, :token_position, :], cls_token, x[:, token_position:, :]), dim=1)
                    print("token_position: ", token_position)
                else:
                    cls_token = self.cls_token.expand(B, -1, -1)  # stole cls_tokens impl from Phil Wang, thanks
                    token_position = 0
                    x = torch.cat((cls_token, x), dim=1)
                M = x.shape[1]

        if self.if_abs_pos_embed:
            # if new_grid_size[0] == self.patch_embed.grid_size[0] and new_grid_size[1] == self.patch_embed.grid_size[1]:
            #     x = x + self.pos_embed
            # else:
            #     pos_embed = interpolate_pos_embed_online(
            #                 self.pos_embed, self.patch_embed.grid_size, new_grid_size,0
            #             )
            x = x + self.pos_embed
            x = self.pos_drop(x)

        if if_random_token_rank:

            # 生成随机 shuffle 索引
            shuffle_indices = torch.randperm(M)

            if isinstance(token_position, list):
                print("original value: ", x[0, token_position[0], 0], x[0, token_position[1], 0])
            else:
                print("original value: ", x[0, token_position, 0])
            print("original token_position: ", token_position)

            # 执行 shuffle
            x = x[:, shuffle_indices, :]

            if isinstance(token_position, list):
                # 找到 cls token 在 shuffle 之后的新位置
                new_token_position = [torch.where(shuffle_indices == token_position[i])[0].item() for i in range(len(token_position))]
                token_position = new_token_position
            else:
                # 找到 cls token 在 shuffle 之后的新位置
                token_position = torch.where(shuffle_indices == token_position)[0].item()

            if isinstance(token_position, list):
                print("new value: ", x[0, token_position[0], 0], x[0, token_position[1], 0])
            else:
                print("new value: ", x[0, token_position, 0])
            print("new token_position: ", token_position)




        if_flip_img_sequences = False
        if self.flip_img_sequences_ratio > 0 and (self.flip_img_sequences_ratio - random.random()) > 1e-5:
            x = x.flip([1])
            if_flip_img_sequences = True

        # mamba impl
        residual = None
        hidden_states = x
        
        if self.visualize:
            hidden_states_np_list = []

        if not self.if_bidirectional:
            for layer in self.layers:

                if if_flip_img_sequences and self.if_rope:
                    hidden_states = hidden_states.flip([1])
                    if residual is not None:
                        residual = residual.flip([1])

                # rope about
                if self.if_rope:
                    hidden_states = self.rope(hidden_states)
                    if residual is not None and self.if_rope_residual:
                        residual = self.rope(residual)

                if if_flip_img_sequences and self.if_rope:
                    hidden_states = hidden_states.flip([1])
                    if residual is not None:
                        residual = residual.flip([1])

                hidden_states, residual = layer(
                    hidden_states, residual, inference_params=inference_params
                )
                if self.visualize:
                    hidden_states_np_list.append(layer.hidden_states_np)
        else:
            # get two layers in a single for-loop
            for i in range(len(self.layers) // 2):
                if self.if_rope:
                    hidden_states = self.rope(hidden_states)
                    if residual is not None and self.if_rope_residual:
                        residual = self.rope(residual)

                hidden_states_f, residual_f = self.layers[i * 2](
                    hidden_states, residual, inference_params=inference_params
                )
                hidden_states_b, residual_b = self.layers[i * 2 + 1](
                    hidden_states.flip([1]), None if residual == None else residual.flip([1]), inference_params=inference_params
                )
                hidden_states = hidden_states_f + hidden_states_b.flip([1])
                residual = residual_f + residual_b.flip([1])

                if self.bi_merge:
                    if ((self.fusion_strategy == 'upper' and i > self.fusion_layer) or
                        (self.fusion_strategy == 'lower' and i < self.fusion_layer) or
                        (self.fusion_strategy == 'alternate' and i % 2 == 1) or
                        (self.fusion_strategy == 'all' and i >= 0)):
                        t = hidden_states.shape[1]
                        r = self.fusion_token
                        
                        if r > 0:
                            # merge, unmerge = kth_bipartite_soft_matching(hidden_states, r)
                            merge, unmerge = bipartite_soft_matching(hidden_states, r, class_token=True)
                            hidden_states = merge(hidden_states)
                            residual = merge(residual) if residual is not None else None

                if self.visualize:
                    hidden_states_np_list.append(self.layers[i * 2].hidden_states_np)
        
        if self.visualize: 
            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
            filename = f"./visualization/hidden_states_{timestamp}"
            np.savez(filename, **{f'layer_{i}': array for i, array in enumerate(hidden_states_np_list)})
            print(f"Saved hidden states to {filename}")


        if not self.fused_add_norm:
            if residual is None:
                residual = hidden_states
            else:
                residual = residual + self.drop_path(hidden_states)
            hidden_states = self.norm_f(residual.to(dtype=self.norm_f.weight.dtype))
        else:
            # Set prenorm=False here since we don't need the residual
            fused_add_norm_fn = rms_norm_fn if isinstance(self.norm_f, RMSNorm) else layer_norm_fn
            hidden_states = fused_add_norm_fn(
                self.drop_path(hidden_states),
                self.norm_f.weight,
                self.norm_f.bias,
                eps=self.norm_f.eps,
                residual=residual,
                prenorm=False,
                residual_in_fp32=self.residual_in_fp32,
            )

        # return only cls token if it exists
        if self.if_cls_token:
            if self.use_double_cls_token:
                return (hidden_states[:, token_position[0], :] + hidden_states[:, token_position[1], :]) / 2
            else:
                if self.use_middle_cls_token:
                    return hidden_states[:, 0, :]
                elif if_random_cls_token_position:
                    return hidden_states[:, token_position, :]
                else:
                    return hidden_states[:, token_position, :]



        if self.final_pool_type == 'none':
            return hidden_states[:, -1, :]
        elif self.final_pool_type == 'mean':
            return hidden_states.mean(dim=1)
        elif self.final_pool_type == 'max':
            return hidden_states
        elif self.final_pool_type == 'all':
            return hidden_states
        else:
            raise NotImplementedError

    def forward(self, x, return_features=False, inference_params=None, if_random_cls_token_position=False, if_random_token_rank=False):
        
        x = self.forward_features(x, inference_params, if_random_cls_token_position=if_random_cls_token_position, if_random_token_rank=if_random_token_rank)
        if return_features:
            return x
        x = self.head(x)
        if self.final_pool_type == 'max':
            x = x.max(dim=1)[0]

        return x


@register_model
def vim_tiny_patch16_224_bimambav2_final_pool_mean_abs_pos_embed_with_midclstok_div2(pretrained=False, **kwargs):
    model = VisionMamba(
        patch_size=16, embed_dim=192, depth=24, rms_norm=True, residual_in_fp32=True, fused_add_norm=True, final_pool_type='mean', if_abs_pos_embed=True, if_rope=False, if_rope_residual=False, bimamba_type="v2", if_cls_token=True, if_devide_out=True, use_middle_cls_token=False, if_bidirectional=True, **kwargs)
    model.default_cfg = _cfg()
    if pretrained:
        checkpoint = torch.hub.load_state_dict_from_url(
            url="to.do",
            map_location="cpu", check_hash=True
        )
        model.load_state_dict(checkpoint["model"])
    return model

@register_model
def vim_tiny_patch16_stride8_224_bimambav2_final_pool_mean_abs_pos_embed_with_midclstok_div2(pretrained=False, **kwargs):
    model = VisionMamba(
        patch_size=16, stride=8, embed_dim=192, depth=24, rms_norm=True, residual_in_fp32=True, fused_add_norm=True, final_pool_type='mean', if_abs_pos_embed=True, if_rope=False, if_rope_residual=False, bimamba_type="v2", if_cls_token=True, if_devide_out=True, use_middle_cls_token=True, **kwargs)
    model.default_cfg = _cfg()
    if pretrained:
        checkpoint = torch.hub.load_state_dict_from_url(
            url="to.do",
            map_location="cpu", check_hash=True
        )
        model.load_state_dict(checkpoint["model"])
    return model

@register_model
def vim_small_patch16_224_bimambav2_final_pool_mean_abs_pos_embed_with_midclstok_div2(pretrained=False, **kwargs):
    model = VisionMamba(
        patch_size=16, embed_dim=384, depth=24, rms_norm=True, residual_in_fp32=True, fused_add_norm=True, final_pool_type='mean', if_abs_pos_embed=True, if_rope=False, if_rope_residual=False, bimamba_type="v2", if_cls_token=True, if_devide_out=True, use_middle_cls_token=False, **kwargs)
    model.default_cfg = _cfg()
    if pretrained:
        checkpoint = torch.hub.load_state_dict_from_url(
            url="to.do",
            map_location="cpu", check_hash=True
        )
        model.load_state_dict(checkpoint["model"])
    return model

@register_model
def vim_small_patch16_stride8_224_bimambav2_final_pool_mean_abs_pos_embed_with_midclstok_div2(pretrained=False, **kwargs):
    model = VisionMamba(
        patch_size=16, stride=8, embed_dim=384, depth=24, rms_norm=True, residual_in_fp32=True, fused_add_norm=True, final_pool_type='mean', if_abs_pos_embed=True, if_rope=False, if_rope_residual=False, bimamba_type="v2", if_cls_token=True, if_devide_out=True, use_middle_cls_token=True, **kwargs)
    model.default_cfg = _cfg()
    if pretrained:
        checkpoint = torch.hub.load_state_dict_from_url(
            url="to.do",
            map_location="cpu", check_hash=True
        )
        model.load_state_dict(checkpoint["model"])
    return model，其中调用的函数：# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.

# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# --------------------------------------------------------

import math
from typing import Callable, Tuple

import torch


def do_nothing(x, mode=None):
    return x


def bipartite_soft_matching(
    metric: torch.Tensor,
    r: int,
    class_token: bool = False,
    distill_token: bool = False,
) -> Tuple[Callable, Callable]:
    """
    Applies ToMe with a balanced matching set (50%, 50%).

    Input size is [batch, tokens, channels].
    r indicates the number of tokens to remove (max 50% of tokens).

    Extra args:
     - class_token: Whether or not there's a class token.
     - distill_token: Whether or not there's also a distillation token.

    When enabled, the class token and distillation tokens won't get merged.
    """
    protected = 0
    if class_token:
        protected += 1
    if distill_token:
        protected += 1

    # We can only reduce by a maximum of 50% tokens
    t = metric.shape[1]
    r = min(r, (t - protected) // 2)

    if r <= 0:
        return do_nothing, do_nothing

    with torch.no_grad():
        metric = metric / metric.norm(dim=-1, keepdim=True)
        a, b = metric[..., ::2, :], metric[..., 1::2, :]
        scores = a @ b.transpose(-1, -2)

        if class_token:
            scores[..., 0, :] = -math.inf
        if distill_token:
            scores[..., :, 0] = -math.inf

        node_max, node_idx = scores.max(dim=-1)
        edge_idx = node_max.argsort(dim=-1, descending=True)[..., None]

        unm_idx = edge_idx[..., r:, :]  # Unmerged Tokens
        src_idx = edge_idx[..., :r, :]  # Merged Tokens
        dst_idx = node_idx[..., None].gather(dim=-2, index=src_idx)

        if class_token:
            # Sort to ensure the class token is at the start
            unm_idx = unm_idx.sort(dim=1)[0]

    def merge(x: torch.Tensor, mode="mean") -> torch.Tensor:
        src, dst = x[..., ::2, :], x[..., 1::2, :]
        n, t1, c = src.shape
        unm = src.gather(dim=-2, index=unm_idx.expand(n, t1 - r, c))
        src = src.gather(dim=-2, index=src_idx.expand(n, r, c))
        dst = dst.scatter_reduce(-2, dst_idx.expand(n, r, c), src, reduce=mode)

        if distill_token:
            return torch.cat([unm[:, :1], dst[:, :1], unm[:, 1:], dst[:, 1:]], dim=1)
        else:
            return torch.cat([unm, dst], dim=1)

    def unmerge(x: torch.Tensor) -> torch.Tensor:
        unm_len = unm_idx.shape[1]
        unm, dst = x[..., :unm_len, :], x[..., unm_len:, :]
        n, _, c = unm.shape

        src = dst.gather(dim=-2, index=dst_idx.expand(n, r, c))

        out = torch.zeros(n, metric.shape[1], c, device=x.device, dtype=x.dtype)

        out[..., 1::2, :] = dst
        out.scatter_(dim=-2, index=(2 * unm_idx).expand(n, unm_len, c), src=unm)
        out.scatter_(dim=-2, index=(2 * src_idx).expand(n, r, c), src=src)

        return out

    return merge, unmerge


def kth_bipartite_soft_matching(
    metric: torch.Tensor, k: int
) -> Tuple[Callable, Callable]:
    """
    Applies ToMe with the two sets as (every kth element, the rest).
    If n is the number of tokens, resulting number of tokens will be n // z.

    Input size is [batch, tokens, channels].
    z indicates the stride for the first set.
    z = 2 is equivalent to regular bipartite_soft_matching with r = 0.5 * N
    """
    if k <= 1:
        return do_nothing, do_nothing

    def split(x):
        t_rnd = (x.shape[1] // k) * k
        x = x[:, :t_rnd, :].view(x.shape[0], -1, k, x.shape[2])
        a, b = (
            x[:, :, : (k - 1), :].contiguous().view(x.shape[0], -1, x.shape[-1]),
            x[:, :, (k - 1), :],
        )
        return a, b

    with torch.no_grad():
        metric = metric / metric.norm(dim=-1, keepdim=True)
        a, b = split(metric)
        r = a.shape[1]
        scores = a @ b.transpose(-1, -2)

        _, dst_idx = scores.max(dim=-1)
        dst_idx = dst_idx[..., None]

    def merge(x: torch.Tensor, mode="mean") -> torch.Tensor:
        src, dst = split(x)
        n, _, c = src.shape
        dst = dst.scatter_reduce(-2, dst_idx.expand(n, r, c), src, reduce=mode)

        return dst

    def unmerge(x: torch.Tensor) -> torch.Tensor:
        n, _, c = x.shape
        dst = x

        src = dst.gather(dim=-2, index=dst_idx.expand(n, r, c)).to(x.dtype)

        src = src.view(n, -1, (k - 1), c)
        dst = dst.view(n, -1, 1, c)

        out = torch.cat([src, dst], dim=-2)
        out = out.contiguous().view(n, -1, c)

        return out

    return merge, unmerge


def random_bipartite_soft_matching(
    metric: torch.Tensor, r: int
) -> Tuple[Callable, Callable]:
    """
    Applies ToMe with the two sets as (r chosen randomly, the rest).
    Input size is [batch, tokens, channels].

    This will reduce the number of tokens by r.
    """
    if r <= 0:
        return do_nothing, do_nothing

    with torch.no_grad():
        B, N, _ = metric.shape
        rand_idx = torch.rand(B, N, 1, device=metric.device).argsort(dim=1)

        a_idx = rand_idx[:, :r, :]
        b_idx = rand_idx[:, r:, :]

        def split(x):
            C = x.shape[-1]
            a = x.gather(dim=1, index=a_idx.expand(B, r, C))
            b = x.gather(dim=1, index=b_idx.expand(B, N - r, C))
            return a, b

        metric = metric / metric.norm(dim=-1, keepdim=True)
        a, b = split(metric)
        scores = a @ b.transpose(-1, -2)

        _, dst_idx = scores.max(dim=-1)
        dst_idx = dst_idx[..., None]

    def merge(x: torch.Tensor, mode="mean") -> torch.Tensor:
        src, dst = split(x)
        C = src.shape[-1]
        dst = dst.scatter_reduce(-2, dst_idx.expand(B, r, C), src, reduce=mode)

        return dst

    def unmerge(x: torch.Tensor) -> torch.Tensor:
        C = x.shape[-1]
        dst = x
        src = dst.gather(dim=-2, index=dst_idx.expand(B, r, C))

        out = torch.zeros(B, N, C, device=x.device, dtype=x.dtype)

        out.scatter_(dim=-2, index=a_idx.expand(B, r, C), src=src)
        out.scatter_(dim=-2, index=b_idx.expand(B, N - r, C), src=dst)

        return out

    return merge, unmerge


def merge_wavg(
    merge: Callable, x: torch.Tensor, size: torch.Tensor = None
) -> Tuple[torch.Tensor, torch.Tensor]:
    """
    Applies the merge function by taking a weighted average based on token size.
    Returns the merged tensor and the new token sizes.
    """
    if size is None:
        size = torch.ones_like(x[..., 0, None])

    x = merge(x * size, mode="sum")
    size = merge(size, mode="sum")

    x = x / size
    return x, size


def merge_source(
    merge: Callable, x: torch.Tensor, source: torch.Tensor = None
) -> torch.Tensor:
    """
    For source tracking. Source is an adjacency matrix between the initial tokens and final merged groups.
    x is used to find out how many tokens there are in case the source is None.
    """
    if source is None:
        n, t, _ = x.shape
        source = torch.eye(t, device=x.device)[None, ...].expand(n, t, t)

    source = merge(source, mode="amax")
    return source