    def __init__(
        self,
        *,
        transformer_dim: int,
        transformer: nn.Module,
        num_multimask_outputs: int = 3,
        activation: Type[nn.Module] = nn.GELU,
        iou_head_depth: int = 3,
        iou_head_hidden_dim: int = 256,
        use_high_res_features: bool = False,
        iou_prediction_use_sigmoid=False,
        dynamic_multimask_via_stability=False,
        dynamic_multimask_stability_delta=0.05,
        dynamic_multimask_stability_thresh=0.98,
        pred_obj_scores: bool = False,
        pred_obj_scores_mlp: bool = False,
        use_multimask_token_for_obj_ptr: bool = False,
    ) -> None:
        """
        Predicts masks given an image and prompt embeddings, using a
        transformer architecture.

        Arguments:
          transformer_dim (int): the channel dimension of the transformer
          transformer (nn.Module): the transformer used to predict masks
          num_multimask_outputs (int): the number of masks to predict
            when disambiguating masks
          activation (nn.Module): the type of activation to use when
            upscaling masks
          iou_head_depth (int): the depth of the MLP used to predict
            mask quality
          iou_head_hidden_dim (int): the hidden dimension of the MLP
            used to predict mask quality
        """
        super().__init__()
        self.transformer_dim = transformer_dim
        self.transformer = transformer

        self.num_multimask_outputs = num_multimask_outputs

        self.iou_token = nn.Embedding(1, transformer_dim)
        self.num_mask_tokens = num_multimask_outputs + 1
        self.mask_tokens = nn.Embedding(self.num_mask_tokens, transformer_dim)

        self.pred_obj_scores = pred_obj_scores
        if self.pred_obj_scores:
            self.obj_score_token = nn.Embedding(1, transformer_dim)
        self.use_multimask_token_for_obj_ptr = use_multimask_token_for_obj_ptr

        self.output_upscaling = nn.Sequential(
            nn.ConvTranspose2d(
                transformer_dim, transformer_dim // 4, kernel_size=2, stride=2
            ),
            LayerNorm2d(transformer_dim // 4),
            activation(),
            nn.ConvTranspose2d(
                transformer_dim // 4, transformer_dim // 8, kernel_size=2, stride=2
            ),
            activation(),
        )
        self.use_high_res_features = use_high_res_features
        if use_high_res_features:
            self.conv_s0 = nn.Conv2d(
                transformer_dim, transformer_dim // 8, kernel_size=1, stride=1
            )
            self.conv_s1 = nn.Conv2d(
                transformer_dim, transformer_dim // 4, kernel_size=1, stride=1
            )

        self.output_hypernetworks_mlps = nn.ModuleList(
            [
                MLP(transformer_dim, transformer_dim, transformer_dim // 8, 3)
                for i in range(self.num_mask_tokens)
            ]
        )

        self.iou_prediction_head = MLP(
            transformer_dim,
            iou_head_hidden_dim,
            self.num_mask_tokens,
            iou_head_depth,
            sigmoid_output=iou_prediction_use_sigmoid,
        )
        if self.pred_obj_scores:
            self.pred_obj_score_head = nn.Linear(transformer_dim, 1)
            if pred_obj_scores_mlp:
                self.pred_obj_score_head = MLP(transformer_dim, transformer_dim, 1, 3)

        # When outputting a single mask, optionally we can dynamically fall back to the best
        # multimask output token if the single mask output token gives low stability scores.
        self.dynamic_multimask_via_stability = dynamic_multimask_via_stability
        self.dynamic_multimask_stability_delta = dynamic_multimask_stability_delta
        self.dynamic_multimask_stability_thresh = dynamic_multimask_stability_thresh
Predicts masks given an image and prompt embeddings, using a
transformer architecture.

Arguments:
transformer_dim (int): the channel dimension of the transformer
transformer (nn.Module): the transformer used to predict masks
num_multimask_outputs (int): the number of masks to predict
when disambiguating masks
activation (nn.Module): the type of activation to use when
upscaling masks
iou_head_depth (int): the depth of the MLP used to predict
mask quality
iou_head_hidden_dim (int): the hidden dimension of the MLP
used to predict mask quality
projects/llava_sam2/hf/models/sam2.py