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

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint


class _AttrDict(dict):
    """dict with attribute access; replaces addict/easydict for inference."""

    def __getattr__(self, name):
        try:
            return self[name]
        except KeyError as exc:
            raise AttributeError(name) from exc

    __setattr__ = dict.__setitem__


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


# ===================clip============================================================


class LayerNormfp32(torch.nn.LayerNorm):
    """Subclass torch's LayerNorm to handle fp16."""

    def forward(self, x: torch.Tensor):
        orig_type = x.dtype
        ret = super().forward(x.type(torch.float32))
        return ret.type(orig_type)


def get_abs_pos(abs_pos, tgt_size):
    # abs_pos: L, C
    # tgt_size: M
    # return: M, C

    # print(tgt_size)
    # print(abs_pos.shape)
    # exit()
    dim = abs_pos.size(-1)
    # print(dim)
    abs_pos_new = abs_pos.squeeze(0)
    cls_token, old_pos_embed = abs_pos_new[:1], abs_pos_new[1:]

    src_size = int(math.sqrt(abs_pos_new.shape[0] - 1))
    tgt_size = int(math.sqrt(tgt_size))
    dtype = abs_pos.dtype

    if src_size != tgt_size:
        old_pos_embed = old_pos_embed.view(1, src_size, src_size, dim).permute(0, 3, 1, 2).contiguous()
        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)
        new_pos_embed = new_pos_embed.view(tgt_size * tgt_size, dim)
        vision_pos_embed = torch.cat([cls_token, new_pos_embed], dim=0)
        vision_pos_embed = vision_pos_embed.view(1, tgt_size * tgt_size + 1, dim)
        return vision_pos_embed
    else:
        return abs_pos


# Not @torch.jit.script: TorchScript fuses this into an NVRTC kernel that needs
# libnvrtc-builtins.so matching the torch CUDA build (e.g. .13.0). That library is
# often missing from the runtime LD path on training boxes, and HF generate (non-vLLM
# GRPO rollout) hits this on every vision forward. Plain aten ops are enough here.
def quick_gelu(x):
    return x * torch.sigmoid(1.702 * x)


class CLIPVisionEmbeddings(nn.Module):
    def __init__(self, hidden_size=1024, image_size=224, patch_size=14, num_channels=3):
        super().__init__()
        self.embed_dim = hidden_size
        self.image_size = image_size
        self.patch_size = patch_size

        self.class_embedding = torch.nn.Parameter(torch.randn(self.embed_dim))

        self.patch_embedding = torch.nn.Conv2d(
            in_channels=num_channels,
            out_channels=self.embed_dim,
            kernel_size=self.patch_size,
            stride=self.patch_size,
            bias=False,
        )

        self.num_patches = (self.image_size // self.patch_size) ** 2
        self.num_positions = self.num_patches + 1
        self.position_embedding = torch.nn.Embedding(self.num_positions, self.embed_dim)

    def forward(self, pixel_values, patch_embeds):
        batch_size = pixel_values.shape[0]
        if patch_embeds is None:
            patch_embeds = self.patch_embedding(pixel_values)
        patch_embeds = patch_embeds.flatten(2).transpose(1, 2)

        class_embeds = self.class_embedding.expand(batch_size, 1, -1)
        embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
        # Rebuild ids every forward: non-persistent buffers are uninitialized
        # after transformers meta-device load (same class of bug as RoPE inv_freq).
        position_ids = torch.arange(self.num_positions, device=embeddings.device).unsqueeze(0)
        embeddings = embeddings + get_abs_pos(self.position_embedding(position_ids), embeddings.size(1))
        return embeddings


class NoTPFeedForward(nn.Module):
    def __init__(
        self,
        cfg,
        dim: int,
        hidden_dim: int,
    ):
        super().__init__()

        self.fc1 = torch.nn.Linear(dim, hidden_dim, bias=True)
        self.fc2 = torch.nn.Linear(hidden_dim, dim, bias=True)

    def forward(self, x):
        output = self.fc2(quick_gelu(self.fc1(x)))
        return output


def _math_sdpa(q, k, v):
    try:
        from torch.nn.attention import SDPBackend, sdpa_kernel

        ctx = sdpa_kernel(SDPBackend.MATH)
    except (ImportError, AttributeError):
        ctx = torch.backends.cuda.sdp_kernel(enable_flash=False, enable_mem_efficient=False, enable_math=True)
    with ctx:
        return F.scaled_dot_product_attention(q, k, v)


class NoTPAttention(torch.nn.Module):
    def __init__(self, cfg):
        super().__init__()
        self.num_heads = cfg.num_attention_heads
        self.n_local_heads = cfg.num_attention_heads
        self.head_dim = cfg.hidden_size // cfg.num_attention_heads
        self.max_seq_len = cfg.seq_length
        self.use_flash_attention = cfg.use_flash_attn

        self.qkv_proj = torch.nn.Linear(cfg.hidden_size, cfg.hidden_size * 3, bias=True)
        self.out_proj = torch.nn.Linear(cfg.hidden_size, cfg.hidden_size, bias=True)

        # self.core_attention = CoreAttention(cfg, AttnType.self_attn)

        self.attn_drop = cfg.attention_dropout

    def forward(
        self,
        x: torch.Tensor,
    ):
        bsz, seqlen, _ = x.shape
        xqkv = self.qkv_proj(x).view(bsz, seqlen, 3, self.num_heads, self.head_dim)
        xq, xk, xv = (t.squeeze(2).permute(0, 2, 1, 3).contiguous() for t in torch.split(xqkv, 1, dim=2))
        # Default SDPA still picks Flash/cuDNN; those kernels device-assert on
        # CLIP seq 101 (640-tile) and 257 (1024 global) in bf16.
        if self.use_flash_attention:
            output = F.scaled_dot_product_attention(xq, xk, xv)
        else:
            output = _math_sdpa(xq, xk, xv)
        output = output.permute(0, 2, 1, 3).reshape(bsz, seqlen, -1)
        output = self.out_proj(output)
        return output


class NoTPTransformerBlock(nn.Module):
    def __init__(self, cfg, layer_id: int, multiple_of=256):
        super().__init__()

        self.n_heads = cfg.num_attention_heads
        self.dim = cfg.hidden_size
        self.head_dim = cfg.hidden_size // cfg.num_attention_heads
        self.self_attn = NoTPAttention(cfg)
        self.mlp = NoTPFeedForward(cfg, dim=cfg.hidden_size, hidden_dim=cfg.ffn_hidden_size)
        self.layer_id = layer_id
        self.layer_norm1 = torch.nn.LayerNorm(cfg.hidden_size, eps=cfg.layernorm_epsilon)
        self.layer_norm2 = torch.nn.LayerNorm(cfg.hidden_size, eps=cfg.layernorm_epsilon)

    def forward(self, x: torch.Tensor):
        residual = self.self_attn.forward(self.layer_norm1(x))
        h = x + residual
        out = h + self.mlp.forward(self.layer_norm2(h))
        return out


class NoTPTransformer(nn.Module):
    def __init__(self, cfg):
        super().__init__()

        self.cfg = cfg
        # self.recompute_list = self.cfg.get("recompute_list", [])
        self.num_layers = cfg.num_layers  # _get_num_layers(cfg)

        self.layers = torch.nn.ModuleList()
        for layer_id in range(self.num_layers):
            self.layers.append(
                NoTPTransformerBlock(
                    cfg,
                    layer_id + 1,
                )
            )

    def forward(
        self,
        hidden_states,
    ):

        for lid, layer in enumerate(self.layers):
            # if lid in self.recompute_list:
            #     def custom(layer_id):
            #         def custom_forward(*args, **kwargs):
            #             x_ = self.layers[layer_id](*args, **kwargs)
            #             return x_

            #         return custom_forward

            #     assert hidden_states.requires_grad == True, logger.warning(
            #         "When using recalculation, the input must have grad fn"
            #     )
            #     hidden_states = tensor_parallel.checkpoint(
            #         custom(lid),
            #         False,
            #         hidden_states.contiguous()
            #     )
            # else:
            hidden_states = layer(hidden_states)

        return hidden_states


# from megatron.core.tensor_parallel.layers import non_tensor_paralleled, local_dp_reduce, local_dp_scatter


class VitModel(nn.Module):
    def __init__(self, cfg, freeze_embed=False, freeze_pre_norm=False) -> None:
        super().__init__()

        self.embeddings = CLIPVisionEmbeddings(
            hidden_size=cfg.hidden_size, image_size=cfg.image_size, patch_size=cfg.patch_size
        )

        if freeze_embed:
            for name, param in self.embeddings.named_parameters():
                param.requires_grad = False

        self.transformer = NoTPTransformer(cfg=cfg)

        if cfg.get("fp32norm", False):
            self.pre_layrnorm = LayerNormfp32(
                cfg.hidden_size,
                eps=cfg.get("pre_layernorm_epsilon", 1e-5),
            )
        else:
            self.pre_layrnorm = torch.nn.LayerNorm(
                cfg.hidden_size,
                eps=cfg.get("pre_layernorm_epsilon", 1e-5),
            )

        # self.pre_layrnorm = RMSNorm(
        #     cfg.hidden_size,
        #     eps=cfg.get("pre_layernorm_epsilon", 1e-5),
        #     sequence_parallel=False,
        #     use_fp32=True,
        #     use_optimus=True,
        # )

        if freeze_pre_norm:
            for name, param in self.pre_layrnorm.named_parameters():
                param.requires_grad = False

        for p in self.parameters():
            p.micro_dp = True

    def set_input_tensor(self, input_tensor):
        if not isinstance(input_tensor, list):
            input_tensor = [input_tensor]
        self.transformer.set_input_tensor(input_tensor[0])

    def __str__(self) -> str:
        return "open_clip"

    def forward(self, x, patch_embeds):
        x = self.embeddings(x, patch_embeds)
        hidden_states = self.pre_layrnorm(x)

        # hidden_states, dis = local_dp_scatter(hidden_states)
        output = self.transformer(hidden_states)

        # output = local_dp_reduce(output, dis)

        return output


vit_model_cfg = _AttrDict(
    num_layers=24,
    hidden_size=1024,
    num_heads=16,
    num_attention_heads=16,
    ffn_hidden_size=4096,
    seq_length=256,
    max_position_embeddings=256,
    use_flash_attn=False,
    understand_projector_stride=2,
    hidden_dropout=0.0,
    attention_dropout=0.0,
    no_persist_layer_norm=False,
    layernorm_epsilon=1e-5,
    pre_layernorm_epsilon=1e-5,
    image_size=224,
    patch_size=14,
    recompute_list=[],
)


def build_clip_l():
    return VitModel(
        cfg=vit_model_cfg,
        freeze_embed=False,
        freeze_pre_norm=False,
    )


# =========================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.global_attn_indexes = tuple(global_attn_indexes)

        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, 1024, kernel_size=3, stride=2, padding=1, bias=False)
        # SAM GC is independent of HF LLM GC (full SAM recompute is slow).
        # Modes: "off" | "global" (only global-attn blocks) | "full".
        self.gradient_checkpointing = False
        self.gradient_checkpointing_mode = "off"

    def set_gradient_checkpointing(self, mode: str = "off") -> None:
        """Configure SAM activation checkpointing.

        Args:
            mode: ``"off"`` (default, fastest), ``"global"`` (checkpoint blocks in
                ``global_attn_indexes`` only — main VRAM hotspot, ~4/12 blocks),
                or ``"full"`` (every block, slowest / least memory).
        """
        mode = (mode or "off").lower()
        if mode not in ("off", "global", "full"):
            raise ValueError(f"Invalid SAM gradient_checkpointing mode: {mode!r}")
        self.gradient_checkpointing_mode = mode
        self.gradient_checkpointing = mode != "off"

    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))

        mode = self.gradient_checkpointing_mode if self.training else "off"
        if mode == "full":
            for blk in self.blocks:
                x = torch.utils.checkpoint.checkpoint(blk, x, use_reentrant=False)
        elif mode == "global":
            global_ids = set(self.global_attn_indexes)
            for i, blk in enumerate(self.blocks):
                if i in global_ids:
                    x = torch.utils.checkpoint.checkpoint(blk, x, use_reentrant=False)
                else:
                    x = blk(x)
        else:
            for blk in self.blocks:
                x = blk(x)

        x = self.neck(x.permute(0, 3, 1, 2))
        x2 = self.net_2(x)
        # No clone: net_3 does not inplace-modify x2; clone only inflated activation memory.
        x3 = self.net_3(x2)
        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
        S = H * W
        # qkv → (3, B, nHead, S, C) then (B, nHead, S, C) each
        qkv = self.qkv(x).reshape(B, S, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
        q, k, v = qkv.unbind(0)

        if self.use_rel_pos:
            # Dense rel-pos bias. Do not torch.compile flex-attn here: a shared
            # compiled score_mod specialises on window=14 and then gathers OOB
            # on global layers (40 for 640-px tiles, 64 for 1024-px).
            q_flat = q.reshape(B * self.num_heads, S, -1)
            rel_h, rel_w = add_decomposed_rel_pos(q_flat, self.rel_pos_h, self.rel_pos_w, (H, W), (H, W))
            rel_h = rel_h.view(B, self.num_heads, S, H, 1)
            rel_w = rel_w.view(B, self.num_heads, S, 1, W)
            attn_bias = (rel_h + rel_w).view(B, self.num_heads, S, S)
            x = F.scaled_dot_product_attention(q, k, v, attn_mask=attn_bias)
        else:
            x = F.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)
    # Float rounding can produce 2N-1 when the table only has 2N-1 rows (valid 0..2N-2).
    idx = relative_coords.long().clamp(0, rel_pos_resized.shape[0] - 1)
    return rel_pos_resized[idx]


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_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
        )
    return image_encoder