models_mage_codec.py

from functools import partial

import torch
import torch.nn as nn

from timm.models.vision_transformer import PatchEmbed, DropPath, Mlp

from util.pos_embed import get_2d_sincos_pos_embed

from taming.models.vqgan import VQModel
from omegaconf import OmegaConf
import numpy as np
import scipy.stats as stats
from compressai.entropy_models import EntropyBottleneck
from compressai.layers import conv3x3, subpel_conv3x3
import math
from torch import Tensor
from einops import rearrange, repeat
import torch.nn.functional as F
import torchac
from typing import Any, Callable, List, Optional, Tuple, Union

SCALES_MIN = 0.11
SCALES_MAX = 256
SCALES_LEVELS = 64
def get_scale_table(min=SCALES_MIN, max=SCALES_MAX, levels=SCALES_LEVELS):
    return torch.exp(torch.linspace(math.log(min), math.log(max), levels))

def ste_round(x: Tensor) -> Tensor:
    return torch.round(x) - x.detach() + x

def conv(in_channels, out_channels, kernel_size=5, stride=2):
    return nn.Conv2d(
        in_channels,
        out_channels,
        kernel_size=kernel_size,
        stride=stride,
        padding=kernel_size // 2,
    )

def mask_by_random_topk(mask_len, probs, temperature=1.0):
    mask_len = mask_len.squeeze()
    # 使用Gumbel分布进行采样，增加随机性
    confidence = torch.log(probs) + torch.Tensor(temperature * np.random.gumbel(size=probs.shape)).cuda()
    sorted_confidence, _ = torch.sort(confidence, axis=-1)
    # Obtains cut off threshold given the mask lengths.
    cut_off = sorted_confidence[:, mask_len.long()-1:mask_len.long()]
    # Masks tokens with lower confidence.
    masking = (confidence <= cut_off)
    return masking

def adjust_mask_and_drop_embeddings(token_keep_mask):
    """
    Adjusts the token_keep_mask to the nearest square number of True values by randomly setting
    some of them to False, and then applies this adjusted mask to input_embeddings.

    Parameters:
    - input_embeddings: Tensor, The embeddings tensor.
    - token_keep_mask: BoolTensor, The mask tensor indicating which tokens to keep.

    Returns:
    - Tensor, Adjusted input embeddings after applying the modified token_keep_mask.
    """
    # 获取非零（即值为True）元素的索引
    non_zero_indices = token_keep_mask.nonzero(as_tuple=True)
    # 计算非零元素的数量
    non_zero_count = non_zero_indices[0].size(0)
    # 计算最近的整数平方倍
    next_square = math.floor(math.sqrt(non_zero_count))**2
    # 计算需要移除的元素数量
    remove_count = non_zero_count - next_square
    if remove_count > 0:
        # 如果需要移除元素以达到整数平方倍
        permuted_indices = torch.randperm(non_zero_count)[:remove_count]
        for idx in permuted_indices:
            token_keep_mask[non_zero_indices[0][idx], non_zero_indices[1][idx]] = False
    # 使用更新后的token_keep_mask
    # input_embeddings_after_drop = input_embeddings[token_keep_mask]

    return token_keep_mask


class FactorizedEntropyModel(EntropyBottleneck):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

    def forward(self, x: Tensor, training: Optional[bool] = None) -> Tuple[Tensor, Tensor]:
        if training is None:
            training = self.training

        # 输入形状已经是 [b, c, seq_len]，无需转置
        shape = x.size()

        # Add noise or quantize
        means = self._get_medians()
        # outputs = self.quantize(
        #     x, "noise" if training else "dequantize", means.long()
        # )
        outputs = self.quantize(
            x, "dequantize", means.long()
        )

        if not torch.jit.is_scripting():
            likelihood = self._likelihood(outputs)
            if self.use_likelihood_bound:
                likelihood = self.likelihood_lower_bound(likelihood)
        else:
            raise NotImplementedError("TorchScript is not yet supported")

        return outputs, likelihood
    
    def compress(self, x):
        # 构建索引，适用于单通道序列数据
        indexes = self._build_indexes(x.size())
        # 获取中位数，已经适配为单通道
        medians = self._get_medians().detach()
        # 调整 medians 的形状以匹配 x 的形状
        medians = medians.expand_as(x)
        # 调用基类的 compress 方法进行压缩
        return super().compress(x, indexes, medians)

    def decompress(self, strings, size):
        # 预期的输出大小应包括单个通道
        output_size = (len(strings), 1, *size)  # 这里 size 应该是 seq_len
        # 构建索引
        indexes = self._build_indexes(output_size).to(self._quantized_cdf.device)
        # 获取中位数并调整其形状以匹配预期输出的形状
        medians = self._extend_ndims(self._get_medians().detach(), len(size))
        medians = medians.expand(len(strings), 1, *([-1] * len(size)))
        # 调用基类的 decompress 方法进行解压缩
        return super().decompress(strings, indexes, medians.dtype, medians)
    
    def _preprocess(self, x):
        x = x.permute(0, 2, 3, 1).contiguous()
        return x


class Attention(nn.Module):
    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
        super().__init__()
        self.num_heads = num_heads
        head_dim = dim // num_heads
        # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
        self.scale = qk_scale or head_dim ** -0.5

        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)

    def forward(self, x):
        B, N, C = x.shape
        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)  # （3,B,num_heads,N,head_dim）
        q, k, v = qkv[0], qkv[1], qkv[2]   # make torchscript happy (cannot use tensor as tuple)

        with torch.cuda.amp.autocast(enabled=False):
            attn = (q.float() @ k.float().transpose(-2, -1)) * self.scale

        attn = attn - torch.max(attn, dim=-1, keepdim=True)[0]
        attn = attn.softmax(dim=-1)
        attn = self.attn_drop(attn)

        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
        x = self.proj(x)
        x = self.proj_drop(x)
        # x是经过self-attn后的feature，attn是注意力权重矩阵，描述输入序列中各个元素之间的相关性
        return x, attn


class Block(nn.Module):

    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
        super().__init__()
        self.norm1 = norm_layer(dim)
        self.attn = Attention(
            dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()   # drop_path=0
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)   # mlp_ratio=4
        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) # drop=0.1

    def forward(self, x, return_attention=False):
        if return_attention:
            _, attn = self.attn(self.norm1(x))
            return attn
        else:
            y, _ = self.attn(self.norm1(x))
            x = x + self.drop_path(y)
            x = x + self.drop_path(self.mlp(self.norm2(x)))
        return x


class LabelSmoothingCrossEntropy(nn.Module):
    """ NLL loss with label smoothing.
    """
    def __init__(self, smoothing=0.1):
        super(LabelSmoothingCrossEntropy, self).__init__()
        assert smoothing < 1.0
        self.smoothing = smoothing
        self.confidence = 1. - smoothing

    def forward(self, x: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
        logprobs = torch.nn.functional.log_softmax(x, dim=-1)
        nll_loss = -logprobs.gather(dim=-1, index=target.unsqueeze(1))
        nll_loss = nll_loss.squeeze(1)
        smooth_loss = -logprobs.mean(dim=-1)
        loss = self.confidence * nll_loss + self.smoothing * smooth_loss
        return loss


class BertEmbeddings(nn.Module):
    """Construct the embeddings from word, position and token_type embeddings."""

    def __init__(self, vocab_size, hidden_size, max_position_embeddings, dropout=0.1):
        super().__init__()
        self.word_embeddings = nn.Embedding(vocab_size, hidden_size)
        self.position_embeddings = nn.Embedding(max_position_embeddings, hidden_size)

        # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
        # any TensorFlow checkpoint file
        self.LayerNorm = nn.LayerNorm(hidden_size, eps=1e-6)
        self.dropout = nn.Dropout(dropout)
        # position_ids (1, len position emb) is contiguous in memory and exported when serialized
        self.register_buffer("position_ids", torch.arange(max_position_embeddings).expand((1, -1))) # (1, 257)
        # 创建一个形状为(1, max_position_embeddings)的缓冲张量position_ids，其包含了从0到max_position_embeddings-1的整数。
        # 这个缓冲张量将被用于获取position_embeddings的位置信息，以便在前向传播过程中使用

        torch.nn.init.normal_(self.word_embeddings.weight, std=.02)
        torch.nn.init.normal_(self.position_embeddings.weight, std=.02)

    def forward(
        self, input_ids
    ):
        input_shape = input_ids.size()  # input_ids: (B, N)（32,257)

        seq_length = input_shape[1]

        position_ids = self.position_ids[:, :seq_length]

        inputs_embeds = self.word_embeddings(input_ids) # (B, seq_len, embed_dim)

        position_embeddings = self.position_embeddings(position_ids)    # (1, seq_len, embed_dim)
        embeddings = inputs_embeds + position_embeddings

        embeddings = self.LayerNorm(embeddings)
        embeddings = self.dropout(embeddings)
        return embeddings


class MlmLayer(nn.Module):

    def __init__(self, feat_emb_dim, word_emb_dim, vocab_size):
        super().__init__()
        self.fc = nn.Linear(feat_emb_dim, word_emb_dim)
        self.gelu = nn.GELU()
        self.ln = nn.LayerNorm(word_emb_dim)
        self.bias = nn.Parameter(torch.zeros(1, 1, vocab_size))

    def forward(self, x, word_embeddings):  # x: (b, seq_len, embed_dim)
        mlm_hidden = self.fc(x)
        mlm_hidden = self.gelu(mlm_hidden)
        mlm_hidden = self.ln(mlm_hidden)
        word_embeddings = word_embeddings.transpose(0, 1)
        logits = torch.matmul(mlm_hidden, word_embeddings)
        logits = logits + self.bias
        return logits   # (b, seq_len, vocab_size) 表示对于输入序列中的每个位置，模型预测它对应词汇表中每个单词的原始单词的未归一化概率


class MaskedGenerativeEncoderViT(nn.Module):
    """ Masked Autoencoder with VisionTransformer backbone
    """
    def __init__(self, img_size=256, patch_size=16, in_chans=3,     # need to change the default value of img_size
                 embed_dim=1024, depth=24, num_heads=16,
                 decoder_embed_dim=512, decoder_depth=8, decoder_num_heads=16,
                 mlp_ratio=4., norm_layer=nn.LayerNorm, norm_pix_loss=False,
                 mask_ratio_min=0.5, mask_ratio_max=0.8, mask_ratio_mu=0.55, mask_ratio_std=0.25,
                 vqgan_ckpt_path='vqgan_jax_strongaug.ckpt'):
        super().__init__()

        # --------------------------------------------------------------------------
        # VQGAN specifics
        config = OmegaConf.load('config/vqgan.yaml').model
        self.vqgan = VQModel(ddconfig=config.params.ddconfig,
                             n_embed=config.params.n_embed, # 1024
                             embed_dim=config.params.embed_dim, # 256
                             ckpt_path=vqgan_ckpt_path)
        for param in self.vqgan.parameters():
            param.requires_grad = False

        self.codebook_size = config.params.n_embed  # 1024
        vocab_size = self.codebook_size + 1000 + 1  # 1024 codebook size, 1000 classes, 1 for mask token.
        self.fake_class_label = self.codebook_size + 1100 - 1024    # 1100
        self.mask_token_label = vocab_size - 1  # 2024
        self.token_emb = BertEmbeddings(vocab_size=vocab_size,  # 向量空间大小，1024个embedding + 1000 class + 1 mask token
                                        hidden_size=embed_dim,
                                        max_position_embeddings=img_size +1,
                                        # max_position_embeddings=256+1,  # 256个patch + 1 class token
                                        dropout=0.1)

        # MAGE variant masking ratio
        self.mask_ratio_min = mask_ratio_min
        self.mask_ratio_max = mask_ratio_max
        # self.mask_ratio_generator = stats.truncnorm((mask_ratio_min - mask_ratio_mu) / mask_ratio_std,
        #                                             (mask_ratio_max - mask_ratio_mu) / mask_ratio_std,
        #                                             loc=mask_ratio_mu, scale=mask_ratio_std)

        # --------------------------------------------------------------------------
        # MAGE encoder specifics
        dropout_rate = 0.1
        self.patch_embed = PatchEmbed(img_size, patch_size, in_chans, embed_dim)    # 256, 16, 3, 1024, (B,N,C) n: 256/16*256/16=256, c=1024
        num_patches = self.patch_embed.num_patches

        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim), requires_grad=False)  # fixed sin-cos embedding

        self.blocks = nn.ModuleList([   # encoder
            Block(embed_dim, num_heads, mlp_ratio, qkv_bias=True, qk_scale=None, norm_layer=norm_layer,
                  drop=dropout_rate, attn_drop=dropout_rate)
            for i in range(depth)]) # depth=12 for mage-vitb, embed_dim=768
        self.norm = norm_layer(embed_dim)   # layer norm
        # --------------------------------------------------------------------------

        # --------------------------------------------------------------------------
        # MAGE decoder specifics
        self.decoder_embed = nn.Linear(embed_dim, decoder_embed_dim, bias=True)

        self.mask_token = nn.Parameter(torch.zeros(1, 1, decoder_embed_dim))    # decoder_embed_dim=512
        self.pad_with_cls_token = True

        self.decoder_pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, decoder_embed_dim), requires_grad=False)  # fixed sin-cos embedding
        self.decoder_pos_embed_learned = nn.Parameter(torch.zeros(1, num_patches + 1, decoder_embed_dim))  # learnable pos embedding

        self.decoder_blocks = nn.ModuleList([
            Block(decoder_embed_dim, decoder_num_heads, mlp_ratio, qkv_bias=True, qk_scale=None, norm_layer=norm_layer,
                  drop=dropout_rate, attn_drop=dropout_rate)
            for i in range(decoder_depth)])  # decoder_depth=8 for mage-vitb

        self.decoder_norm = norm_layer(decoder_embed_dim)
        self.decoder_pred = nn.Linear(decoder_embed_dim, patch_size**2 * in_chans, bias=True) # decoder to patch
        # --------------------------------------------------------------------------

        # --------------------------------------------------------------------------
        # MlmLayer
        self.mlm_layer = MlmLayer(feat_emb_dim=decoder_embed_dim, word_emb_dim=embed_dim, vocab_size=vocab_size)

        self.norm_pix_loss = norm_pix_loss

        self.criterion = LabelSmoothingCrossEntropy(smoothing=0.1)
        # --------------------------------------------------------------------------
        self.entropy_bottleneck = FactorizedEntropyModel(1)

        self.initialize_weights()

    def initialize_weights(self):
        # initialization
        # initialize (and freeze) pos_embed by sin-cos embedding
        pos_embed = get_2d_sincos_pos_embed(self.pos_embed.shape[-1], int(self.patch_embed.num_patches**.5), cls_token=True)
        self.pos_embed.data.copy_(torch.from_numpy(pos_embed).float().unsqueeze(0))

        decoder_pos_embed = get_2d_sincos_pos_embed(self.decoder_pos_embed.shape[-1], int(self.patch_embed.num_patches**.5), cls_token=True)
        self.decoder_pos_embed.data.copy_(torch.from_numpy(decoder_pos_embed).float().unsqueeze(0))

        # initialize patch_embed like nn.Linear (instead of nn.Conv2d)
        w = self.patch_embed.proj.weight.data
        torch.nn.init.xavier_uniform_(w.view([w.shape[0], -1]))

        # timm's trunc_normal_(std=.02) is effectively normal_(std=0.02) as cutoff is too big (2.)
        torch.nn.init.normal_(self.cls_token, std=.02)
        torch.nn.init.normal_(self.mask_token, std=.02)
        torch.nn.init.normal_(self.decoder_pos_embed_learned, std=.02)

        # initialize nn.Linear and nn.LayerNorm
        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            # we use xavier_uniform following official JAX ViT:
            torch.nn.init.xavier_uniform_(m.weight)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)

    def random_sample_mask_rate(self):
        # 生成一个 （0, 1] 范围内的随机数
        random_sample = 1 - torch.rand(1)
        # 映射到 mask_ratio_min 到 mask_ratio_max 的范围
        mask_rate = self.mask_ratio_min + random_sample * (self.mask_ratio_max - self.mask_ratio_min)
        return mask_rate.item()  # 转换为Python的标量值

    def get_cdf_token_mask(self, token_all_mask):
        bsz, seq_len = token_all_mask.size()
        # --- use Normal distribution.
        dist_normal = torch.distributions.Normal(0, 2)
        cdf_mask_token = dist_normal.cdf(torch.arange(1, seq_len + 1))
        cdf_mask_token = (cdf_mask_token - .5) * 2 
        cdf_mask_token = repeat(cdf_mask_token, 'Lp -> b s Lp', 
                                    b=bsz, s=seq_len)
                                    
        cdf_mask_token = F.pad(cdf_mask_token, (1, 0))
        return cdf_mask_token
    
    # def get_cdf_token_mask(self, token_all_mask):
    #     bsz, seq_len = token_all_mask.size()
    #     # 直接生成一个0到1之间的线性空间
    #     linear_space = torch.linspace(0, 1, steps=seq_len+1)
    #     # 无需映射到-1到1
    #     cdf_mask_token = linear_space
    #     # 调整形状以匹配token_all_mask，并扩展到每个batch
    #     cdf_mask_token = repeat(cdf_mask_token, 'Lp -> b s Lp', 
    #                                 b=bsz, s=seq_len)
    #     # cdf_mask_token = cdf_mask_token.unsqueeze(0).unsqueeze(-1).repeat(bsz, 1, seq_len)
    #     # 添加填充以匹配原始代码的操作
    #     cdf_mask_token = F.pad(cdf_mask_token, (1, 0))
    #     return cdf_mask_token
    
    def pre_encoding(self, x, is_training=False, manual_mask_rate=None):
        """
        input: x: (B, 3, H, W)
        """
        # ============ 1. tokenization ============ #
        with torch.no_grad():
            z_q, _, token_tuple = self.vqgan.encode(x)  # z_q: (B, 256, 16, 16), token_tuple: (B, 256, 16, 16)
        
        _, _, token_indices = token_tuple   # token_indices: (B*H*W,)(8192)
        token_indices = token_indices.reshape(z_q.size(0), -1)  # token_indices: (B, H*W)
        gt_indices = token_indices.clone().detach().long()

        # ============ 2. masking process ============ # 
        bsz, seq_len = token_indices.size() # seq_len=h*w
        mask_ratio_min = self.mask_ratio_min    # 0.5

        if is_training:
            # mask_rate = self.mask_ratio_generator.rvs(1)[0]
            mask_rate = self.random_sample_mask_rate()
            num_dropped_tokens = int(np.ceil(seq_len * mask_ratio_min))
        else:
            num_dropped_tokens = 0
            if manual_mask_rate is not None:
                mask_rate = manual_mask_rate
            else:
                raise ValueError("mask_rate should be provided for inference!")
        
        num_masked_tokens = int(np.ceil(seq_len * mask_rate))
        mask_ratio = num_masked_tokens / seq_len    # for calculate vbr lambda 
        # it is possible that two elements of the noise is the same, so do a while loop to avoid it
        while True:
            noise = torch.rand(bsz, seq_len, device=x.device)  # noise in [0, 1]
            sorted_noise, _ = torch.sort(noise, dim=1)  # ascend: small is remove, large is keep
            if num_dropped_tokens > 0:
                cutoff_drop = sorted_noise[:, num_dropped_tokens-1:num_dropped_tokens]
            else:
                cutoff_drop = torch.zeros((bsz, 1), device=x.device)
            cutoff_mask = sorted_noise[:, num_masked_tokens-1:num_masked_tokens]
            token_drop_mask = (noise <= cutoff_drop).float() # 逻辑上标记那些token是被drop掉的
            token_all_mask = (noise <= cutoff_mask).float()  # 逻辑上标记那些token是被mask掉的
            if token_drop_mask.sum() == bsz*num_dropped_tokens and token_all_mask.sum() == bsz*num_masked_tokens:
                break
            else:
                print("Rerandom the noise!")

        # 获取unmasked token及其位置
        unmasked_pos = token_all_mask == 0  # 未被mask的位置
        unmaksed_token_indices = token_indices[unmasked_pos].reshape(bsz, -1)  # 未被mask的token

        return gt_indices, token_indices, unmaksed_token_indices, token_all_mask, token_drop_mask, mask_ratio

    def pre_decoding(self, gt_indices, unmaksed_token_indices, token_all_mask, token_drop_mask):
        bsz, seq_len = gt_indices.size()  
        padded_token_indices = torch.full_like(gt_indices, fill_value=self.mask_token_label)
        # 将未被mask的token填充回去
        # 我们需要一个计数器来追踪每个batch中已经填充了多少个unmaksed_token_indices
        unmasked_token_counter = [0 for _ in range(bsz)]
    
        for b in range(bsz):
            for idx in range(seq_len):
                # 如果当前位置未被mask，则从unmaksed_token_indices填充；否则，保留mask_token_label
                if (token_all_mask[b, idx] == 0):  # 检查是否未被mask
                    # 替换相应的unmaksed token
                    padded_token_indices[b, idx] = unmaksed_token_indices[b, unmasked_token_counter[b]]
                    # 更新计数器
                    unmasked_token_counter[b] += 1
        
        token_indices = padded_token_indices
        # ============ 3. Adding class token ============ #
        # concate class token, add [CLS] token to aggregate sequence-level representations
        token_indices = torch.cat([torch.zeros(token_indices.size(0), 1).cuda(device=token_indices.device), token_indices], dim=1)
        token_indices[:, 0] = self.fake_class_label # [B, 257]
        # Masks (token_drop_mask and token_all_mask) are updated to account for the added class token, 
        # ensuring the first position is always kept by setting it to 0 (indicating "do not mask/drop")
        # 添加0向量，和token_indices，表示[CLS] token不会被mask/drop
        token_drop_mask = torch.cat([torch.zeros(token_indices.size(0), 1).cuda(), token_drop_mask], dim=1)
        token_all_mask = torch.cat([torch.zeros(token_indices.size(0), 1).cuda(), token_all_mask], dim=1)
        token_indices = token_indices.long()

        # ============ 4. Embedding and Dropout ============ #
        # bert embedding
        input_embeddings = self.token_emb(token_indices)    # get embeddings [B, 257, 768]
        # print("Input embedding shape:", input_embeddings.shape)
        bsz, seq_len, emb_dim = input_embeddings.shape

        # dropping
        token_keep_mask = 1 - token_drop_mask
        input_embeddings_after_drop = input_embeddings[token_keep_mask.nonzero(as_tuple=True)].reshape(bsz, -1, emb_dim)
        # print("Input embedding after drop shape:", input_embeddings_after_drop.shape)
        
        # ============ 5. Transformer encoding ============ #
        x = input_embeddings_after_drop # (B, seq_len_after_drop, embed_dim)    # 32, 129, 768
        for blk in self.blocks:
            x = blk(x)  # each block has a multi-head self-attention and a mlp
        x = self.norm(x)
        # print("Encoder representation shape:", x.shape)

        return x, token_indices, token_all_mask, token_drop_mask

    def forward_decoding(self, x, token_drop_mask, token_all_mask):
        """
            x: output x of forward_encoder()
            token_drop_mask: positions for dropped tokens
            token_all_mask: positions for masked tokens
        """
        # ============ 1. Prepare Embedding and padding tokens ============ #
        # embed tokens
        x = self.decoder_embed(x)   # input_embedding_after_padding

        # append mask tokens to sequence
        # replicates the [CLS] token embedding across the sequence length where masking is to be applied
        if self.pad_with_cls_token: # True
            mask_tokens = x[:, 0:1].repeat(1, token_all_mask.shape[1], 1)
        else:
            mask_tokens = self.mask_token.repeat(token_all_mask.shape[0], token_all_mask.shape[1], 1)

        # ============ 2. Prepare positional embedding ============ #
        # put undropped tokens into original sequence
        x_after_pad = mask_tokens.clone() # 未被drop的tokens被填充回去
        x_after_pad[(1 - token_drop_mask).nonzero(as_tuple=True)] = x.reshape(x.shape[0] * x.shape[1], x.shape[2])
        # set undropped but masked positions with mask
        x_after_pad = torch.where(token_all_mask.unsqueeze(-1).bool(), mask_tokens, x_after_pad)    # 被drop的也padding

        # add pos embed
        x = x_after_pad + self.decoder_pos_embed_learned    # add learnable pos embedding

        # apply Transformer blocks
        for blk in self.decoder_blocks:
            x = blk(x)

        x = self.decoder_norm(x)

        word_embeddings = self.token_emb.word_embeddings.weight.data.detach()
        logits = self.mlm_layer(x, word_embeddings)  #  produce predictions for masked tokens
        print("Logits shape:", logits.shape)

        return logits

    def forward_loss(self, gt_indices, logits, mask):
        bsz, seq_len = gt_indices.size()
        # logits and mask are with seq_len+1 but gt_indices is with seq_len
        loss = self.criterion(logits[:, 1:, :self.codebook_size].reshape(bsz*seq_len, -1), gt_indices.reshape(bsz*seq_len))
        loss = loss.reshape(bsz, seq_len)
        loss = (loss * mask[:, 1:]).sum() / mask[:, 1:].sum()  # mean loss on removed patches
        return loss

    def cal_lmbda(self, mask_ratio, A=5e-1, B=8):
        lmbda = A * torch.exp(B * (1 - mask_ratio))
        return lmbda
    
    def cal_loss(self, logits, gt_indices, mask, mask_ratio):
        mask_ratio = torch.tensor(mask_ratio)
        ## cal cross entropy loss
        task_loss = self.forward_loss(gt_indices, logits, mask)
        lmbda = self.cal_lmbda(mask_ratio)
        ## cal total loss for codec optimization
        return task_loss, lmbda

    def forward(self, imgs, is_training=False, manual_mask_rate=None):
        ## ---------- encoding process ---------- ##
        gt_indices, token_indices, latent, token_all_mask, token_drop_mask, mask_ratio = self.pre_encoding(imgs, is_training, manual_mask_rate)
        latent = latent.unsqueeze(1)

        latent_hat, latent_likelihoods = self.entropy_bottleneck(latent)
        # 判断latent_hat和latent是否相等
        # print((latent_hat == latent).all())
        cdf_mask_token = self.get_cdf_token_mask(token_all_mask).cpu()
        sym = (token_all_mask.short() + 1).cpu()
        bs_mask_token = torchac.encode_float_cdf(cdf_mask_token, sym, check_input_bounds=True)
        mask_vis = rearrange(token_all_mask, 'b (h w) -> b h w', h=16, w=16).unsqueeze(1)

        ## ---------- decoding process ---------- ##
        decoded_sym = torchac.decode_float_cdf(cdf_mask_token, bs_mask_token)
        decoded_mask = (decoded_sym - 1).to(device=imgs.device)
        latent_hat = latent_hat.squeeze(1)
        x, token_indices, token_all_mask, token_drop_mask = self.pre_decoding(gt_indices, latent_hat, decoded_mask, token_drop_mask)
        logits = self.forward_decoding(x, token_drop_mask, token_all_mask)
        ## calculate loss
        task_loss, lmbda = self.cal_loss(logits, gt_indices, token_all_mask, mask_ratio)
        return_dict = {
            'logits': logits,
            'likelihoods': latent_likelihoods,
            'task_loss': task_loss,
            'token_indices': token_indices,
            'token_all_mask': token_all_mask,
            'bs_mask_token': bs_mask_token,
            'mask_ratio': mask_ratio,
            'lambda': lmbda,
            'mask_vis': 1 - mask_vis,
        }
        return return_dict

    # def update(self, scale_table=None, force=False):
    #     if scale_table is None:
    #         scale_table = get_scale_table()
    #     updated = self.gaussian_conditional.update_scale_table(scale_table, force=force)
    #     updated |= super().update(force=force)
    #     return updated
    
    def gen_img(self, logits, token_all_mask, token_indices, num_iter=12, choice_temperature=4.5):
        """
        generated image at inference
            seed: random seed
            logits: predicted logits by model decoder
            token_all_mask: mask token indices
            token_indices: token indices of the input image after the vq tokenizer
            num_iter: number of iterations for sampling
            choice_temperature: temperature for sampling
        """
        # torch.manual_seed(seed)
        # np.random.seed(seed)
        bsz = logits.size(0)
        codebook_emb_dim = 256
        codebook_size = 1024
        mask_token_id = self.mask_token_label
        _CONFIDENCE_OF_KNOWN_TOKENS = +np.inf
        unknown_number_in_the_beginning = torch.sum(token_all_mask, dim=-1, keepdims=True).float()
        for step in range(num_iter):
            if step == 0:
                cur_ids = token_indices.clone().long()  # token_indices represent the current state of the sequence(unmasked tokens)
                cur_ids = cur_ids[:, 1:]  # 从第二列开始到最后一列
                logits = logits[:, 1:, :codebook_size]
                # the author said a little tricky here, "For iter=1, they use argmax and temp=0.0. 
                # For iter=6, we use categorical sampling and temp=4.5." 
                sample_dist = torch.distributions.categorical.Categorical(logits=logits)
                sampled_ids = sample_dist.sample()  # sampled_ids = torch.argmax(logits, dim=-1)
                # get ids for next step
                # unknown_map: type bool，shape is same as cur_ids and sampled_ids, indicate where the value will be replace
                # 根据unknown_map的值，在相应位置上选择sampled_ids或cur_ids中的值，并将其存储到sampled_ids张量中。
                # 换句话说，它将模型预测的类别（在未知位置）与之前已知的类别（在已知位置）合并到一个张量中。
                unknown_map = (cur_ids == mask_token_id)    
                sampled_ids = torch.where(unknown_map, sampled_ids, cur_ids)
                # Defines the mask ratio for the next round. The number to mask out is
                # determined by mask_ratio * unknown_number_in_the_beginning.
                ratio = 1. * (step + 1) / num_iter
                mask_ratio = np.cos(math.pi / 2. * ratio)   # ratio = cosine(Π/2 * i/num_iter)

                # sample ids according to prediction confidence
                probs = torch.nn.functional.softmax(logits, dim=-1)
                selected_probs = torch.squeeze(
                    torch.gather(probs, dim=-1, index=torch.unsqueeze(sampled_ids, -1)), -1)

                selected_probs = torch.where(unknown_map, selected_probs.double(), _CONFIDENCE_OF_KNOWN_TOKENS).float()
                unknown_number_in_the_beginning = unknown_number_in_the_beginning.clone().detach().cuda()
                mask_ratio = torch.tensor(mask_ratio).cuda()
                # mask_len = torch.tensor([np.floor(unknown_number_in_the_beginning.numpy() * mask_ratio.numpy())]).cuda()
                mask_len = torch.floor(unknown_number_in_the_beginning * mask_ratio).long() # 每个iter剩余的mask token数
                # Keeps at least one of prediction in this round and also masks out at least
                # one and for the next iteration
                mask_len = torch.maximum(torch.Tensor([1]).cuda(),
                                         torch.minimum(torch.sum(unknown_map, dim=-1, keepdims=True) - 1, mask_len))

                # Sample masking tokens for next iteration
                masking = mask_by_random_topk(mask_len[0], selected_probs, choice_temperature * (1 - ratio))
                # Masks tokens with lower confidence.
                token_indices = torch.where(masking, mask_token_id, sampled_ids)
            else:
                cur_ids = token_indices.clone().long()  # .long(): to int64
                token_indices = torch.cat(
                    [torch.zeros(token_indices.size(0), 1).cuda(device=token_indices.device), token_indices], dim=1)
                token_indices[:, 0] = self.fake_class_label
                token_indices = token_indices.long()
                token_all_mask = token_indices == mask_token_id

                token_drop_mask = torch.zeros_like(token_indices)

                # token embedding
                input_embeddings = self.token_emb(token_indices)   # get input embeddings

                # encoder
                x = input_embeddings
                for blk in self.blocks:
                    x = blk(x)
                x = self.norm(x)

                # decoder
                logits = self.forward_decoding(x, token_drop_mask, token_all_mask)
                logits = logits[:, 1:, :codebook_size]  # remove the cls token and dims > codebook_size

                # get token prediction
                # the author said a little tricky here, "For iter=1, they use argmax and temp=0.0. 
                # For iter=6, we use categorical sampling and temp=4.5." 
                sample_dist = torch.distributions.categorical.Categorical(logits=logits)
                sampled_ids = sample_dist.sample()  # sampled_ids = torch.argmax(logits, dim=-1)

                # get ids for next step
                # unknown_map: type bool，shape is same as cur_ids and sampled_ids, indicate where the value will be replace
                # 根据unknown_map的值，在相应位置上选择sampled_ids或cur_ids中的值，并将其存储到sampled_ids张量中。
                # 换句话说，它将模型预测的类别（在未知位置）与之前已知的类别（在已知位置）合并到一个张量中。
                unknown_map = (cur_ids == mask_token_id)    
                sampled_ids = torch.where(unknown_map, sampled_ids, cur_ids)
                # Defines the mask ratio for the next round. The number to mask out is
                # determined by mask_ratio * unknown_number_in_the_beginning.
                ratio = 1. * (step + 1) / num_iter

                mask_ratio = np.cos(math.pi / 2. * ratio)   # ratio = cosine(Π/2 * i/num_iter)

                # sample ids according to prediction confidence
                probs = torch.nn.functional.softmax(logits, dim=-1)
                selected_probs = torch.squeeze(
                    torch.gather(probs, dim=-1, index=torch.unsqueeze(sampled_ids, -1)), -1)

                selected_probs = torch.where(unknown_map, selected_probs.double(), _CONFIDENCE_OF_KNOWN_TOKENS).float()
                unknown_number_in_the_beginning = unknown_number_in_the_beginning.clone().detach().cuda()
                mask_ratio = torch.tensor(mask_ratio).cuda()
                mask_len = torch.floor(unknown_number_in_the_beginning * mask_ratio).long() # 每个iter剩余的mask token数
                # Keeps at least one of prediction in this round and also masks out at least
                # one and for the next iteration
                mask_len = torch.maximum(torch.Tensor([1]).cuda(),
                                         torch.minimum(torch.sum(unknown_map, dim=-1, keepdims=True) - 1, mask_len))

                # Sample masking tokens for next iteration
                masking = mask_by_random_topk(mask_len[0], selected_probs, choice_temperature * (1 - ratio))
                # Masks tokens with lower confidence.
                token_indices = torch.where(masking, mask_token_id, sampled_ids)
            
        # vqgan visualization
        z_q = self.vqgan.quantize.get_codebook_entry(sampled_ids, shape=(bsz, 16, 16, codebook_emb_dim))
        gen_images = self.vqgan.decode(z_q)
        return gen_images


def mage_vit_base_patch16(**kwargs):
    model = MaskedGenerativeEncoderViT(
        patch_size=16, embed_dim=768, depth=12, num_heads=12,
        decoder_embed_dim=768, decoder_depth=8, decoder_num_heads=16,
        mlp_ratio=4, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
    return model


def mage_vit_large_patch16(**kwargs):
    model = MaskedGenerativeEncoderViT(
        patch_size=16, embed_dim=1024, depth=24, num_heads=16,
        decoder_embed_dim=1024, decoder_depth=8, decoder_num_heads=16,
        mlp_ratio=4, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
    return model