deepencoderv2.py

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


from typing import Optional, Tuple

# from megatron.model import LayerNorm

import transformers


from typing import Optional, Tuple, Type
from functools import partial



class MlpProjector(nn.Module):

    def __init__(self, cfg):

        super().__init__()

        self.cfg = cfg

        if cfg.projector_type == "identity":
            modules = nn.Identity()

        elif cfg.projector_type == "linear":
            modules = nn.Linear(cfg.input_dim, cfg.n_embed)

        elif cfg.projector_type == "mlp_gelu":
            mlp_depth = cfg.get("depth", 1)
            modules = [nn.Linear(cfg.input_dim, cfg.n_embed)]
            for _ in range(1, mlp_depth):
                modules.append(nn.GELU())
                modules.append(nn.Linear(cfg.n_embed, cfg.n_embed))
            modules = nn.Sequential(*modules)
        
        elif cfg.projector_type == "normlayer_downsample_mlp_gelu":
            mlp_depth = cfg.get("depth", 1)
            mlp_ratio = cfg.get("mlp_ratio", 1)
            modules = [
                nn.LayerNorm(cfg.input_dim * cfg.downsample_ratio * cfg.downsample_ratio),
                nn.Linear(cfg.input_dim * cfg.downsample_ratio * cfg.downsample_ratio, cfg.n_embed * mlp_ratio)
            ]
            for _ in range(1, mlp_depth - 1):
                modules.append(nn.GELU())
                modules.append(nn.Linear(cfg.n_embed * mlp_ratio, cfg.n_embed * mlp_ratio))
            modules.append(nn.GELU())
            modules.append(nn.Linear(cfg.n_embed * mlp_ratio, cfg.n_embed))
            modules = nn.Sequential(*modules)
        
        elif cfg.projector_type == "downsample_mlp_gelu":
            mlp_depth = cfg.get("depth", 1)
            mlp_ratio = cfg.get("mlp_ratio", 1)
            modules = [nn.Linear(cfg.input_dim * cfg.downsample_ratio * cfg.downsample_ratio, cfg.n_embed * mlp_ratio)]
            for _ in range(1, mlp_depth - 1):
                modules.append(nn.GELU())
                modules.append(nn.Linear(cfg.n_embed * mlp_ratio, cfg.n_embed * mlp_ratio))
            modules.append(nn.GELU())
            modules.append(nn.Linear(cfg.n_embed * mlp_ratio, cfg.n_embed))
            modules = nn.Sequential(*modules)

        elif cfg.projector_type == "low_high_hybrid_split_mlp_gelu":
            mlp_depth = cfg.get("depth", 1)
            self.high_up_proj = nn.Linear(cfg.input_dim, cfg.n_embed // 2)
            self.low_up_proj = nn.Linear(cfg.input_dim, cfg.n_embed // 2)

            modules = []
            for _ in range(1, mlp_depth):
                modules.append(nn.GELU())
                modules.append(nn.Linear(cfg.n_embed, cfg.n_embed))
            modules = nn.Sequential(*modules)

        elif cfg.projector_type == "hybrid_split_feature_mlp_gelu":
            mlp_depth = cfg.get("depth", 1)
            channel_div = cfg.get("channel_div", 0.5)
            self.high_up_proj = nn.Linear(cfg.input_dim[0], int(cfg.n_embed * channel_div))
            self.low_up_proj = nn.Linear(cfg.input_dim[1], cfg.n_embed - int(cfg.n_embed * channel_div))

            modules = []
            for _ in range(1, mlp_depth):
                modules.append(nn.GELU())
                modules.append(nn.Linear(cfg.n_embed, cfg.n_embed))
            modules = nn.Sequential(*modules)

        elif cfg.projector_type == "low_high_split_mlp_gelu":
            mlp_depth = cfg.get("depth", 1)
            modules = []
            for _ in range(1, mlp_depth):
                modules.append(nn.GELU())
                modules.append(nn.Linear(cfg.n_embed // 2, cfg.n_embed // 2))
            modules = nn.Sequential(*modules)
            self.high_layers = nn.Sequential(*modules)
            self.low_layers = copy.deepcopy(modules)

        else:
            raise ValueError(f"Unknown projector type: {cfg.projector_type}")

        if cfg.get("token_pooling", False):
            self.token_pooling_layer = nn.Linear(cfg.input_dim * 4, cfg.input_dim)

        if cfg.get("conv_fusion_high_low_features", False):
            self.fusion_layer = nn.Linear(cfg.input_dim, cfg.input_dim)
        self.layers = modules

    def forward(self, x):
        if self.cfg.get("token_pooling", False):
            batch_size, wxh, channels = x.shape
            w = h = int(wxh**0.5)
            x = x.view(batch_size, w, h, channels)
            x = x.permute(0, 3, 1, 2)
            # import ipdb; ipdb.set_trace()
            patches = x.unfold(2, 2, 2).unfold(3, 2, 2)
            batch_size, channels, h_patches, w_patches, _, _ = patches.size()
            # 在通道维度上拼接
            patches = patches.contiguous().view(batch_size, channels, h_patches * w_patches, -1)

            # 通过线性层
            patches = patches.permute(0, 2, 1, 3).contiguous()
            patches = patches.view(batch_size, h_patches * w_patches, channels * 4)

            x = self.token_pooling_layer(patches)
        
        if self.cfg.get("conv_fusion_high_low_features", False):
            x = self.fusion_layer(x[:, 0]) + x[:, 1]

        if self.cfg.projector_type == 'low_high_hybrid_split_mlp_gelu':
            high_x, low_x = x[0], x[1]
            high_x = self.high_up_proj(high_x)
            low_x = self.low_up_proj(low_x)
            x = torch.concat([high_x, low_x], dim=-1)
        
        if self.cfg.projector_type == 'hybrid_split_feature_mlp_gelu':
            high_x = x[...,:self.cfg.input_dim[0]]
            low_x = x[...,self.cfg.input_dim[0]:]
            high_x = self.high_up_proj(high_x)
            low_x = self.low_up_proj(low_x)
            x = torch.concat([high_x, low_x], dim=-1)
        
        if self.cfg.projector_type == 'low_high_split_mlp_gelu':
            high_x, low_x = x[0], x[1]
            high_x = self.high_layers(high_x)
            low_x = self.low_layers(low_x)
            x = torch.concat([high_x, low_x], dim=-1)
            return x
        
        if self.cfg.projector_type == 'downsample_mlp_gelu' or self.cfg.projector_type == 'normlayer_downsample_mlp_gelu':
            bs, hw, input_dim = x.shape
            h = w = int((hw) ** 0.5)

            """compute padding"""
            if h % self.cfg.downsample_ratio:
                pad = self.cfg.downsample_ratio - h % self.cfg.downsample_ratio
            else:
                pad = 0
            x = x.reshape(bs, h, w, input_dim)
            if pad > 0:
                x = F.pad(x, (0, 0, 0, pad, 0, pad), "constant", 0)

            """4 to 1 concat"""
            x = x.permute(0, 3, 1, 2)  # B, C, H, W
            x = F.unfold(x, kernel_size=self.cfg.downsample_ratio, stride=self.cfg.downsample_ratio, padding=0) # B, C*4, HW // 4
            x = x.permute(0, 2, 1)
            
        return self.layers(x)

    @staticmethod
    def get_flops_per_sample(cfg):
        if cfg.projector_type == "linear":
            fwd = 2 * cfg.input_dim * cfg.n_embed

        elif "mlp_gelu" in cfg.projector_type :
            mlp_depth = cfg.get("depth", 1)
            downsample_ratio = cfg.get("downsample_ratio", 1)
            input_dim = sum(cfg.input_dim) if isinstance(cfg.input_dim, list) else cfg.input_dim
            input_dim = input_dim * downsample_ratio * downsample_ratio
            fwd = 2 * input_dim * cfg.n_embed + (mlp_depth - 1) * 2 * cfg.n_embed * cfg.n_embed
        else:
            fwd = 0

        return fwd * 3
    

#===================qwen2================================

class CustomQwen2Decoder(nn.Module):
    """
    Qwen2 visual encoder
    non-causal attention + causal attention
    token_type_ids ：0=non-causal, 1=causal
    """
    
    def __init__(
        self,
        decoder_layer: int = 24,
        max_position_embeddings: int = 131072,
        hidden_dimension: int = 896,
        num_attention_heads: int = 14,
        num_key_value_heads: int = 2,
        intermediate_size: int = 4864,
        vocab_size: int = 151936,
        attn_implementation: str = "sdpa",  # ⭐ 
        rms_norm_eps: float = 1e-06,
        rope_theta: float = 1000000.0,
        attention_dropout: float = 0.0,
        hidden_act: str = "silu",
        initializer_range: float = 0.02,
    ):
        super().__init__()
        
        # attn_implementation check
        if attn_implementation == "flash_attention_2":
            raise ValueError(
                "CustomQwen2Decoder do not support flash_attention_2，"
                "new attention mask needs 'sdpa' or 'eager'"
            )
        
        # load
        Qwen2Model = getattr(transformers.models.qwen2.modeling_qwen2, 'Qwen2Model')
        Qwen2Config = getattr(transformers, 'Qwen2Config')
        
        # config
        config = Qwen2Config(
            hidden_size=hidden_dimension,
            num_hidden_layers=decoder_layer,
            num_attention_heads=num_attention_heads,
            num_key_value_heads=num_key_value_heads,
            intermediate_size=intermediate_size,
            max_position_embeddings=max_position_embeddings,
            vocab_size=vocab_size,
            rms_norm_eps=rms_norm_eps,
            rope_theta=rope_theta,
            attention_dropout=attention_dropout,
            hidden_act=hidden_act,
            initializer_range=initializer_range,
            _attn_implementation=attn_implementation,  # ⭐ 
        )
        
        # 
        self.model = self._create_custom_model(Qwen2Model, config)

        del self.model.embed_tokens 
    
    def _create_custom_model(self, Qwen2Model, config):
        """ Qwen2Model """
        
        class CustomQwen2ModelInner(Qwen2Model):

            
            def forward(
                self,
                input_ids=None,
                attention_mask=None,
                position_ids=None,
                past_key_values=None,
                inputs_embeds=None,
                token_type_ids=None,  # ⭐
                use_cache=None,
                output_attentions=None,
                output_hidden_states=None,
                return_dict=None,
                cache_position=None,
            ):
                # token_type_ids
                self._current_token_type_ids = token_type_ids
                
                outputs = super().forward(
                    input_ids=input_ids,
                    attention_mask=attention_mask,
                    position_ids=position_ids,
                    past_key_values=past_key_values,
                    inputs_embeds=inputs_embeds,
                    use_cache=use_cache,
                    output_attentions=output_attentions,
                    output_hidden_states=output_hidden_states,
                    return_dict=return_dict,
                    cache_position=cache_position,
                )
                
                return outputs
            
            def _update_causal_mask(
                self,
                attention_mask,
                input_tensor,
                cache_position,
                past_key_values,
                output_attentions,
            ):
                dtype, device = input_tensor.dtype, input_tensor.device
                min_dtype = torch.finfo(dtype).min
                batch_size, sequence_length = input_tensor.shape[0], input_tensor.shape[1]
                
                token_type_ids = self._current_token_type_ids
                
                # attention mask
                causal_mask = self._create_custom_4d_mask(
                    sequence_length=sequence_length,
                    dtype=dtype,
                    device=device,
                    batch_size=batch_size,
                    token_type_ids=token_type_ids,
                )
                
                #  padding mask
                if attention_mask is not None and attention_mask.dim() == 2:
                    padding_mask = attention_mask[:, None, None, :].to(dtype=dtype)
                    padding_mask = (1.0 - padding_mask) * min_dtype
                    causal_mask = causal_mask + padding_mask
                
                return causal_mask
            
            def _create_custom_4d_mask(
                self,
                sequence_length,
                dtype,
                device,
                batch_size,
                token_type_ids,
            ):
                min_dtype = torch.finfo(dtype).min
                
                masks = []
                for b in range(batch_size):
                    mask = torch.full(
                        (sequence_length, sequence_length),
                        fill_value=min_dtype,
                        dtype=dtype,
                        device=device
                    )
                    
                    type_ids = token_type_ids[b]
                    
                    image_positions = (type_ids == 0).nonzero(as_tuple=True)[0]
                    text_positions = (type_ids == 1).nonzero(as_tuple=True)[0]
                    
                    # non-casual
                    if len(image_positions) > 0:
                        mask[image_positions[:, None], image_positions] = 0.0
                    
                    # causal
                    for i, text_pos in enumerate(text_positions):
                        if len(image_positions) > 0:
                            mask[text_pos, image_positions] = 0.0
                        mask[text_pos, text_positions[:i+1]] = 0.0
                    
                    masks.append(mask)
                
                mask = torch.stack(masks, dim=0).unsqueeze(1)
                return mask
        
        return CustomQwen2ModelInner(config)
    
    def forward(
        self,
        inputs_embeds,
        token_type_ids,
        attention_mask=None,
        **kwargs
    ):
        """
        Args:
            inputs_embeds: [batch_size, seq_len, hidden_dim]
            token_type_ids: [batch_size, seq_len], 0=non-causal, 1=causal
            attention_mask: [batch_size, seq_len], optional
        """
        return self.model(
            inputs_embeds=inputs_embeds,
            token_type_ids=token_type_ids,
            attention_mask=attention_mask,
            **kwargs
        )





# batch_size = 2
# inputs_embeds = torch.randn(batch_size, 512, 896).cuda()

# inputs_embeds = torch.randn(batch_size, 512, 896).cuda()
# token_type_ids = torch.cat([
#     torch.zeros(batch_size, 256, dtype=torch.long),
#     torch.ones(batch_size, 256, dtype=torch.long),
# ], dim=1).cuda()

# # start = time.time()
# with torch.no_grad():
#     outputs_sdpa = decoder_sdpa(inputs_embeds, token_type_ids)
#     print(outputs_sdpa[0].shape)
# print(f"SDPA time: {time.time() - start:.4f}s")



class Qwen2Decoder2Encoder(nn.Module):
    """
    Decoder based on Multilingual BART
    Set the initial weights and configuration with a pretrained multilingual BART model,
    and modify the detailed configurations as a Nougat decoder
    """

    def __init__(
        self,
        decoder_layer: int,
        hidden_dimension: int,
        num_attention_heads: int,
        num_key_value_heads: int,
        intermediate_size: int,
        max_query: int,
    ):
        super().__init__()

        self.model = CustomQwen2Decoder(
            decoder_layer=decoder_layer,
            hidden_dimension=hidden_dimension,
            num_attention_heads=num_attention_heads,
            num_key_value_heads=num_key_value_heads,
            intermediate_size=intermediate_size,
            attn_implementation="sdpa", 
        )




        self.query_768 = nn.Embedding(144, hidden_dimension)
        self.query_1024 = nn.Embedding(256, hidden_dimension)


        # self.query_refixation = nn.Embedding(int(math.sqrt(max_query)), hidden_dimension)


    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x  = x.flatten(2).transpose(1, 2)

        bs, n_query, _ = x.shape

        if n_query == 144:
            param_img = self.query_768.weight
        elif n_query == 256:
            param_img = self.query_1024.weight

        batch_query_imgs = param_img.unsqueeze(0).expand(
            bs, -1, -1
        )  # (batch_size, num_queries, hidden_size)



        x_combined = torch.cat([x, batch_query_imgs], dim=1)

        token_type_ids = torch.cat([
            torch.zeros(bs, n_query, dtype=torch.long),
            torch.ones(bs, n_query, dtype=torch.long),
        ], dim=1)


        y = self.model(x_combined, token_type_ids)[0]


        y = y[:, n_query:, :] # causal flow query


        return y


def build_qwen2_decoder_as_encoder(
    decoder_layer=24,
    hidden_dimension=896,
    num_attention_heads=14,
    num_key_value_heads=2,
    intermediate_size=4864,
    max_query = 400,
    checkpoint=None,
):

    decoder_as_encoder = Qwen2Decoder2Encoder(
            decoder_layer=decoder_layer,
            hidden_dimension = hidden_dimension,
            num_attention_heads = num_attention_heads,
            num_key_value_heads = num_key_value_heads,
            intermediate_size = intermediate_size,
            max_query = max_query
        )



    
    if checkpoint is not None:
        # with open(checkpoint, "rb") as f:
        state_dict = torch.load(checkpoint)

        decoder_as_encoder.load_state_dict(state_dict, strict=True)
        # tob
        print(checkpoint)
    return decoder_as_encoder




#=========================Sam-Vary=================================


def get_abs_pos_sam(abs_pos, tgt_size):

    dtype = abs_pos.dtype

    src_size = abs_pos.size(1)

    if src_size != tgt_size:
        old_pos_embed = abs_pos.permute(0, 3, 1, 2)
        old_pos_embed = old_pos_embed.to(torch.float32)
        new_pos_embed = F.interpolate(
            old_pos_embed,
            size=(tgt_size, tgt_size),
            mode='bicubic',
            antialias=True,
            align_corners=False,
        ).to(dtype)
        new_pos_embed = new_pos_embed.permute(0, 2, 3, 1)
        return new_pos_embed
    else:
        return abs_pos




class MLPBlock(nn.Module):
    def __init__(
        self,
        embedding_dim: int,
        mlp_dim: int,
        act: Type[nn.Module] = nn.GELU,
    ) -> None:
        super().__init__()
        self.lin1 = nn.Linear(embedding_dim, mlp_dim)
        self.lin2 = nn.Linear(mlp_dim, embedding_dim)
        self.act = act()

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return self.lin2(self.act(self.lin1(x)))


# From https://github.com/facebookresearch/detectron2/blob/main/detectron2/layers/batch_norm.py # noqa
# Itself from https://github.com/facebookresearch/ConvNeXt/blob/d1fa8f6fef0a165b27399986cc2bdacc92777e40/models/convnext.py#L119  # noqa
class LayerNorm2d(nn.Module):
    def __init__(self, num_channels: int, eps: float = 1e-6) -> None:
        super().__init__()
        self.weight = nn.Parameter(torch.ones(num_channels))
        self.bias = nn.Parameter(torch.zeros(num_channels))
        self.eps = eps

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        u = x.mean(1, keepdim=True)
        s = (x - u).pow(2).mean(1, keepdim=True)
        x = (x - u) / torch.sqrt(s + self.eps)
        x = self.weight[:, None, None] * x + self.bias[:, None, None]
        return x


# This class and its supporting functions below lightly adapted from the ViTDet backbone available at: https://github.com/facebookresearch/detectron2/blob/main/detectron2/modeling/backbone/vit.py # noqa
class ImageEncoderViT(nn.Module):
    def __init__(
        self,
        img_size: int = 1024,
        patch_size: int = 16,
        in_chans: int = 3,
        embed_dim: int = 768,
        depth: int = 12,
        num_heads: int = 12,
        mlp_ratio: float = 4.0,
        out_chans: int = 256,
        qkv_bias: bool = True,
        norm_layer: Type[nn.Module] = nn.LayerNorm,
        act_layer: Type[nn.Module] = nn.GELU,
        use_abs_pos: bool = True,
        use_rel_pos: bool = False,
        rel_pos_zero_init: bool = True,
        window_size: int = 0,
        global_attn_indexes: Tuple[int, ...] = (),
    ) -> None:
        """
        Args:
            img_size (int): Input image size.
            patch_size (int): Patch size.
            in_chans (int): Number of input image channels.
            embed_dim (int): Patch embedding dimension.
            depth (int): Depth of ViT.
            num_heads (int): Number of attention heads in each ViT block.
            mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
            qkv_bias (bool): If True, add a learnable bias to query, key, value.
            norm_layer (nn.Module): Normalization layer.
            act_layer (nn.Module): Activation layer.
            use_abs_pos (bool): If True, use absolute positional embeddings.
            use_rel_pos (bool): If True, add relative positional embeddings to the attention map.
            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
            window_size (int): Window size for window attention blocks.
            global_attn_indexes (list): Indexes for blocks using global attention.
        """
        super().__init__()
        self.img_size = img_size

        self.patch_embed = PatchEmbed(
            kernel_size=(patch_size, patch_size),
            stride=(patch_size, patch_size),
            in_chans=in_chans,
            embed_dim=embed_dim,
        )

        self.pos_embed: Optional[nn.Parameter] = None
        if use_abs_pos:
            # Initialize absolute positional embedding with pretrain image size.
            self.pos_embed = nn.Parameter(
                torch.zeros(1, img_size // patch_size, img_size // patch_size, embed_dim)
            )

        self.blocks = nn.ModuleList()
        for i in range(depth):
            block = Block(
                dim=embed_dim,
                num_heads=num_heads,
                mlp_ratio=mlp_ratio,
                qkv_bias=qkv_bias,
                norm_layer=norm_layer,
                act_layer=act_layer,
                use_rel_pos=use_rel_pos,
                rel_pos_zero_init=rel_pos_zero_init,
                window_size=window_size if i not in global_attn_indexes else 0,
                input_size=(img_size // patch_size, img_size // patch_size),
            )
            self.blocks.append(block)

        self.neck = nn.Sequential(
            nn.Conv2d(
                embed_dim,
                out_chans,
                kernel_size=1,
                bias=False,
            ),
            LayerNorm2d(out_chans),
            nn.Conv2d(
                out_chans,
                out_chans,
                kernel_size=3,
                padding=1,
                bias=False,
            ),
            LayerNorm2d(out_chans),
        )

        self.net_2 = nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1, bias=False)
        self.net_3 = nn.Conv2d(512, 896, kernel_size=3, stride=2, padding=1, bias=False)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = self.patch_embed(x)
        if self.pos_embed is not None:
            # x = x + self.pos_embed
            x = x + get_abs_pos_sam(self.pos_embed, x.size(1))

        for blk in self.blocks:
            x = blk(x)

        x = self.neck(x.permute(0, 3, 1, 2))
        x2 = self.net_2(x)
        x3 = self.net_3(x2.clone())

        return x3


class Block(nn.Module):
    """Transformer blocks with support of window attention and residual propagation blocks"""

    def __init__(
        self,
        dim: int,
        num_heads: int,
        mlp_ratio: float = 4.0,
        qkv_bias: bool = True,
        norm_layer: Type[nn.Module] = nn.LayerNorm,
        act_layer: Type[nn.Module] = nn.GELU,
        use_rel_pos: bool = False,
        rel_pos_zero_init: bool = True,
        window_size: int = 0,
        input_size: Optional[Tuple[int, int]] = None,
    ) -> None:
        """
        Args:
            dim (int): Number of input channels.
            num_heads (int): Number of attention heads in each ViT block.
            mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
            qkv_bias (bool): If True, add a learnable bias to query, key, value.
            norm_layer (nn.Module): Normalization layer.
            act_layer (nn.Module): Activation layer.
            use_rel_pos (bool): If True, add relative positional embeddings to the attention map.
            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
            window_size (int): Window size for window attention blocks. If it equals 0, then
                use global attention.
            input_size (tuple(int, int) or None): Input resolution for calculating the relative
                positional parameter size.
        """
        super().__init__()
        self.norm1 = norm_layer(dim)
        self.attn = Attention(
            dim,
            num_heads=num_heads,
            qkv_bias=qkv_bias,
            use_rel_pos=use_rel_pos,
            rel_pos_zero_init=rel_pos_zero_init,
            input_size=input_size if window_size == 0 else (window_size, window_size),
        )

        self.norm2 = norm_layer(dim)
        self.mlp = MLPBlock(embedding_dim=dim, mlp_dim=int(dim * mlp_ratio), act=act_layer)

        self.window_size = window_size

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        shortcut = x
        x = self.norm1(x)
        # Window partition
        if self.window_size > 0:
            H, W = x.shape[1], x.shape[2]
            x, pad_hw = window_partition(x, self.window_size)

        x = self.attn(x)
        # Reverse window partition
        if self.window_size > 0:
            x = window_unpartition(x, self.window_size, pad_hw, (H, W))

        x = shortcut + x
        x = x + self.mlp(self.norm2(x))

        return x


class Attention(nn.Module):
    """Multi-head Attention block with relative position embeddings."""

    def __init__(
        self,
        dim: int,
        num_heads: int = 8,
        qkv_bias: bool = True,
        use_rel_pos: bool = False,
        rel_pos_zero_init: bool = True,
        input_size: Optional[Tuple[int, int]] = None,
    ) -> None:
        """
        Args:
            dim (int): Number of input channels.
            num_heads (int): Number of attention heads.
            qkv_bias (bool):  If True, add a learnable bias to query, key, value.
            rel_pos (bool): If True, add relative positional embeddings to the attention map.
            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
            input_size (tuple(int, int) or None): Input resolution for calculating the relative
                positional parameter size.
        """
        super().__init__()
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = head_dim**-0.5

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

        self.use_rel_pos = use_rel_pos
        if self.use_rel_pos:
            assert (
                input_size is not None
            ), "Input size must be provided if using relative positional encoding."
            # initialize relative positional embeddings
            self.rel_pos_h = nn.Parameter(torch.zeros(2 * input_size[0] - 1, head_dim))
            self.rel_pos_w = nn.Parameter(torch.zeros(2 * input_size[1] - 1, head_dim))

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        B, H, W, _ = x.shape
        # qkv with shape (3, B, nHead, H * W, C)
        qkv = self.qkv(x).reshape(B, H * W, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
        # q, k, v with shape (B * nHead, H * W, C)
        q, k, v = qkv.reshape(3, B * self.num_heads, H * W, -1).unbind(0)

        rel_h, rel_w = None, None
        if self.use_rel_pos:
            rel_h, rel_w = add_decomposed_rel_pos(q, self.rel_pos_h, self.rel_pos_w, (H, W), (H, W))

        q = q.view(B, self.num_heads, H * W, -1)
        k = k.view(B, self.num_heads, H * W, -1)
        v = v.view(B, self.num_heads, H * W, -1)

        if self.use_rel_pos:
            rel_h = rel_h.view(B, self.num_heads, rel_h.size(1), rel_h.size(2), rel_h.size(3))
            rel_w = rel_w.view(B, self.num_heads, rel_w.size(1), rel_w.size(2), rel_w.size(3))
            attn_bias = (rel_h + rel_w).view(B, self.num_heads, rel_h.size(2), rel_h.size(3) * rel_w.size(4))
            x = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=attn_bias)
            # x = _attention_rel_h_rel_w(q, k, v, rel_h, rel_w)
        else:
            x = torch.nn.functional.scaled_dot_product_attention(q, k, v)

        x = x.view(B, self.num_heads, H, W, -1).permute(0, 2, 3, 1, 4).reshape(B, H, W, -1)

        x = self.proj(x)

        return x


def window_partition(x: torch.Tensor, window_size: int) -> Tuple[torch.Tensor, Tuple[int, int]]:
    """
    Partition into non-overlapping windows with padding if needed.
    Args:
        x (tensor): input tokens with [B, H, W, C].
        window_size (int): window size.

    Returns:
        windows: windows after partition with [B * num_windows, window_size, window_size, C].
        (Hp, Wp): padded height and width before partition
    """
    B, H, W, C = x.shape

    pad_h = (window_size - H % window_size) % window_size
    pad_w = (window_size - W % window_size) % window_size
    if pad_h > 0 or pad_w > 0:
        x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h))
    Hp, Wp = H + pad_h, W + pad_w

    x = x.view(B, Hp // window_size, window_size, Wp // window_size, window_size, C)
    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
    return windows, (Hp, Wp)


def window_unpartition(
    windows: torch.Tensor, window_size: int, pad_hw: Tuple[int, int], hw: Tuple[int, int]
) -> torch.Tensor:
    """
    Window unpartition into original sequences and removing padding.
    Args:
        windows (tensor): input tokens with [B * num_windows, window_size, window_size, C].
        window_size (int): window size.
        pad_hw (Tuple): padded height and width (Hp, Wp).
        hw (Tuple): original height and width (H, W) before padding.

    Returns:
        x: unpartitioned sequences with [B, H, W, C].
    """
    Hp, Wp = pad_hw
    H, W = hw
    B = windows.shape[0] // (Hp * Wp // window_size // window_size)
    x = windows.view(B, Hp // window_size, Wp // window_size, window_size, window_size, -1)
    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, Hp, Wp, -1)

    if Hp > H or Wp > W:
        x = x[:, :H, :W, :].contiguous()
    return x


def get_rel_pos(q_size: int, k_size: int, rel_pos: torch.Tensor) -> torch.Tensor:
    """
    Get relative positional embeddings according to the relative positions of
        query and key sizes.
    Args:
        q_size (int): size of query q.
        k_size (int): size of key k.
        rel_pos (Tensor): relative position embeddings (L, C).

    Returns:
        Extracted positional embeddings according to relative positions.
    """
    max_rel_dist = int(2 * max(q_size, k_size) - 1)
    # Interpolate rel pos if needed.
    if rel_pos.shape[0] != max_rel_dist:
        # Interpolate rel pos.
        dtype = rel_pos.dtype
        rel_pos = rel_pos.to(torch.float32)
        rel_pos_resized = F.interpolate(
            rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1),
            size=max_rel_dist,
            mode="linear",
        ).to(dtype)
        rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0)
    else:
        rel_pos_resized = rel_pos

    # Scale the coords with short length if shapes for q and k are different.
    q_coords = torch.arange(q_size, device=rel_pos.device)[:, None] * max(k_size / q_size, 1.0)
    k_coords = torch.arange(k_size, device=rel_pos.device)[None, :] * max(q_size / k_size, 1.0)
    relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0)

    return rel_pos_resized[relative_coords.long()]


def add_decomposed_rel_pos(
    q: torch.Tensor,
    rel_pos_h: torch.Tensor,
    rel_pos_w: torch.Tensor,
    q_size: Tuple[int, int],
    k_size: Tuple[int, int],
) -> torch.Tensor:
    """
    Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`.
    https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py   # noqa B950
    Args:
        q (Tensor): query q in the attention layer with shape (B, q_h * q_w, C).
        rel_pos_h (Tensor): relative position embeddings (Lh, C) for height axis.
        rel_pos_w (Tensor): relative position embeddings (Lw, C) for width axis.
        q_size (Tuple): spatial sequence size of query q with (q_h, q_w).
        k_size (Tuple): spatial sequence size of key k with (k_h, k_w).

    Returns:
        attn (Tensor): attention map with added relative positional embeddings.
    """
    q_h, q_w = q_size
    k_h, k_w = k_size
    Rh = get_rel_pos(q_h, k_h, rel_pos_h)
    Rw = get_rel_pos(q_w, k_w, rel_pos_w)

    B, _, dim = q.shape
    r_q = q.reshape(B, q_h, q_w, dim)
    rel_h = torch.einsum("bhwc,hkc->bhwk", r_q, Rh)
    rel_w = torch.einsum("bhwc,wkc->bhwk", r_q, Rw)
    rel_h = rel_h.unsqueeze(-1)
    rel_w = rel_w.unsqueeze(-2)
    rel_h = rel_h.reshape(B, q_h * q_w, k_h, 1)
    rel_w = rel_w.reshape(B, q_h * q_w, 1, k_w)

    return rel_h, rel_w


class PatchEmbed(nn.Module):
    """
    Image to Patch Embedding.
    """

    def __init__(
        self,
        kernel_size: Tuple[int, int] = (16, 16),
        stride: Tuple[int, int] = (16, 16),
        padding: Tuple[int, int] = (0, 0),
        in_chans: int = 3,
        embed_dim: int = 768,
    ) -> None:
        """
        Args:
            kernel_size (Tuple): kernel size of the projection layer.
            stride (Tuple): stride of the projection layer.
            padding (Tuple): padding size of the projection layer.
            in_chans (int): Number of input image channels.
            embed_dim (int): Patch embedding dimension.
        """
        super().__init__()

        self.proj = nn.Conv2d(
            in_chans, embed_dim, kernel_size=kernel_size, stride=stride, padding=padding
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = self.proj(x)
        # B C H W -> B H W C
        x = x.permute(0, 2, 3, 1)
        return x


def build_sam_vit_b(checkpoint=None):
    return _build_sam(
        encoder_embed_dim=768,
        encoder_depth=12,
        encoder_num_heads=12,
        encoder_global_attn_indexes=[2, 5, 8, 11],
        checkpoint=checkpoint,
    )

def build_sam_fast_vit_b(checkpoint=None, compile_mode='max-autotune', dtype=torch.bfloat16):
    image_encoder = build_sam_vit_b(checkpoint).eval().to(dtype)
    # sam = _apply_eval_dtype_sam(sam, dtype)
    image_encoder = torch.compile(image_encoder, mode=compile_mode)
    return image_encoder


def _build_sam(
    encoder_embed_dim,
    encoder_depth,
    encoder_num_heads,
    encoder_global_attn_indexes,
    checkpoint=None,
):
    prompt_embed_dim = 256
    image_size = 1024
    vit_patch_size = 16
    image_embedding_size = image_size // vit_patch_size
    image_encoder=ImageEncoderViT(
            depth=encoder_depth,
            embed_dim=encoder_embed_dim,
            img_size=image_size,
            mlp_ratio=4,
            norm_layer=partial(torch.nn.LayerNorm, eps=1e-6),
            num_heads=encoder_num_heads,
            patch_size=vit_patch_size,
            qkv_bias=True,
            use_rel_pos=True,
            global_attn_indexes=encoder_global_attn_indexes,
            window_size=14,
            out_chans=prompt_embed_dim,
        )
    image_encoder.eval()
    if checkpoint is not None:
        # with open(checkpoint, "rb") as f:
        state_dict = torch.load(checkpoint)
        # print(state_dict.keys())
        # for key in state_dict:
        # image_encoder.load_state_dict({k[14:]: v for k, v in state_dict.items() if 'image_encoder' in k}, strict=False)
        # ocr-anyting
        # image_encoder.load_state_dict(state_dict, strict=True)
        # tob
        image_encoder.load_state_dict({k[30:]: v for k, v in state_dict.items() if 'vision_tower_high' in k}, strict=True)
        print(checkpoint)
    return image_encoder