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# Copyright 2025 The Meta AI Authors and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import math
from collections.abc import Callable
from dataclasses import dataclass

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import Tensor

from ... import initialization as init
from ...activations import ACT2FN
from ...modeling_layers import GradientCheckpointingLayer
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
from ...processing_utils import Unpack
from ...pytorch_utils import compile_compatible_method_lru_cache
from ...utils import ModelOutput, auto_docstring, can_return_tuple, logging
from ...utils.generic import TransformersKwargs, is_flash_attention_requested, merge_with_config_defaults
from ...utils.output_capturing import OutputRecorder, capture_outputs
from ..auto import AutoModel
from .configuration_edgetam import (
    EdgeTamConfig,
    EdgeTamMaskDecoderConfig,
    EdgeTamPromptEncoderConfig,
    EdgeTamVisionConfig,
)


logger = logging.get_logger(__name__)


class EdgeTamLayerNorm(nn.LayerNorm):
    r"""LayerNorm that supports two data formats: channels_last (default) or channels_first.
    The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch_size, height,
    width, channels) while channels_first corresponds to inputs with shape (batch_size, channels, height, width).
    """

    def __init__(self, normalized_shape, *, eps=1e-6, data_format="channels_last", **kwargs):
        super().__init__(normalized_shape, eps=eps, **kwargs)
        if data_format not in ["channels_last", "channels_first"]:
            raise NotImplementedError(f"Unsupported data format: {data_format}")
        self.data_format = data_format

    def forward(self, features: torch.Tensor) -> torch.Tensor:
        """
        Args:
            features: Tensor of shape (batch_size, channels, height, width) OR (batch_size, height, width, channels)
        """
        if self.data_format == "channels_first":
            features = features.permute(0, 2, 3, 1)
            features = super().forward(features)
            features = features.permute(0, 3, 1, 2)
        else:
            features = super().forward(features)
        return features


@dataclass
@auto_docstring(custom_intro="Base class for the vision encoder's outputs.")
class EdgeTamVisionEncoderOutput(BaseModelOutputWithPooling):
    r"""
    last_hidden_state (`torch.FloatTensor` of shape `(batch_size, height, width, hidden_size)`):
        Sequence of hidden-states at the output of the last layer of the model.
    hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
        Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
        one for the output of each stage) of shape `(batch_size, height, width, hidden_size)`. Hidden-states of the
        model at the output of each stage.
    attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
        Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
        sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
        the self-attention heads.
    fpn_hidden_states (`tuple(torch.FloatTensor)`):
        Tuple of `torch.FloatTensor` (one for each feature level, from high to low resolution) of shape
        `(batch_size, hidden_size, height, width)`. Feature maps from the Feature Pyramid Network neck.
    fpn_position_encoding (`tuple(torch.FloatTensor)`):
        Tuple of `torch.FloatTensor` (one for each feature level, from high to low resolution) of shape
        `(batch_size, hidden_size, height, width)`. Positional encodings corresponding to the `fpn_hidden_states`.
    """

    fpn_hidden_states: torch.FloatTensor | None = None
    fpn_position_encoding: torch.FloatTensor | None = None


def eager_attention_forward(
    module: nn.Module,
    query: torch.Tensor,
    key: torch.Tensor,
    value: torch.Tensor,
    attention_mask: torch.Tensor | None,
    scaling: float,
    dropout: float = 0.0,
    **kwargs,
):
    attn_weights = torch.matmul(query, key.transpose(2, 3)) * scaling
    if attention_mask is not None:
        attn_weights = attn_weights + attention_mask

    attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
    attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
    attn_output = torch.matmul(attn_weights, value)
    attn_output = attn_output.transpose(1, 2).contiguous()

    return attn_output, attn_weights


class EdgeTamAttention(nn.Module):
    """
    EDGETAM's attention layer that allows for downscaling the size of the embedding after projection to queries, keys, and
    values.
    """

    def __init__(self, config, downsample_rate=None):
        super().__init__()
        downsample_rate = config.attention_downsample_rate if downsample_rate is None else downsample_rate
        self.config = config
        self.hidden_size = config.hidden_size
        self.internal_dim = config.hidden_size // downsample_rate
        self.num_attention_heads = config.num_attention_heads
        self.head_dim = self.internal_dim // config.num_attention_heads
        self.scaling = self.head_dim**-0.5
        self.is_causal = False

        self.q_proj = nn.Linear(self.hidden_size, self.internal_dim)
        self.k_proj = nn.Linear(self.hidden_size, self.internal_dim)
        self.v_proj = nn.Linear(self.hidden_size, self.internal_dim)
        self.o_proj = nn.Linear(self.internal_dim, self.hidden_size)

    def forward(
        self,
        query: torch.Tensor,
        key: torch.Tensor,
        value: torch.Tensor,
        attention_similarity: torch.Tensor | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> tuple[torch.Tensor, torch.Tensor]:
        # Input projections
        batch_size, point_batch_size = query.shape[:2]
        new_shape = (batch_size * point_batch_size, -1, self.num_attention_heads, self.head_dim)

        query = self.q_proj(query).view(*new_shape).transpose(1, 2)
        key = self.k_proj(key).view(*new_shape).transpose(1, 2)
        value = self.v_proj(value).view(*new_shape).transpose(1, 2)

        attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface(
            self.config._attn_implementation, eager_attention_forward
        )

        if is_flash_attention_requested(self.config) and attention_similarity is not None:
            # Target guided masks are represented as float masks and are incompatible with Flash Attention
            # Fallback to SDPA for this call only so the rest of the model can still benefit from FA
            attention_interface = ALL_ATTENTION_FUNCTIONS["sdpa"]
            logger.warning_once(
                "Falling back to SDPA for target-guided attention because "
                "Flash Attention does not support additive bias masks."
            )

        attn_output, attn_weights = attention_interface(
            self,
            query,
            key,
            value,
            attention_mask=attention_similarity,
            dropout=0.0,
            scaling=self.scaling,
            is_causal=self.is_causal,
            **kwargs,
        )

        attn_output = attn_output.reshape(
            batch_size, point_batch_size, -1, self.num_attention_heads * self.head_dim
        ).contiguous()
        attn_output = self.o_proj(attn_output)

        return attn_output, attn_weights


class EdgeTamTwoWayAttentionBlock(GradientCheckpointingLayer):
    def __init__(self, config: EdgeTamMaskDecoderConfig, skip_first_layer_pe: bool = False):
        """
        A transformer block with four layers:
            (1) self-attention of sparse inputs (2) cross attention of sparse inputs -> dense inputs (3) mlp block on
            sparse inputs (4) cross attention of dense inputs -> sparse inputs

        Arguments:
            config (`EdgeTamMaskDecoderConfig`):
                The configuration file used to instantiate the block
            attention_downsample_rate (*optionalk*, int, defaults to 2):
                The downsample ratio of the block used to reduce the inner dim of the attention.
            skip_first_layer_pe (*optional*, bool, defaults to `False`):
                Whether or not to skip the addition of the query_point_embedding on the first layer.
        """
        super().__init__()
        self.self_attn = EdgeTamAttention(config, downsample_rate=1)
        self.layer_norm1 = nn.LayerNorm(config.hidden_size)

        self.cross_attn_token_to_image = EdgeTamAttention(config)
        self.layer_norm2 = nn.LayerNorm(config.hidden_size)

        self.mlp = EdgeTamFeedForward(
            config.hidden_size, config.mlp_dim, config.hidden_size, num_layers=config.num_hidden_layers
        )
        self.layer_norm3 = nn.LayerNorm(config.hidden_size)

        self.layer_norm4 = nn.LayerNorm(config.hidden_size)
        self.cross_attn_image_to_token = EdgeTamAttention(config)

        self.skip_first_layer_pe = skip_first_layer_pe

    def forward(
        self,
        queries: Tensor,
        keys: Tensor,
        query_point_embedding: Tensor,
        key_point_embedding: Tensor,
        attention_similarity: Tensor,
        **kwargs: Unpack[TransformersKwargs],
    ):
        # Self attention block
        if self.skip_first_layer_pe:
            queries, _ = self.self_attn(query=queries, key=queries, value=queries)
        else:
            query = queries + query_point_embedding
            attn_out, _ = self.self_attn(query=query, key=query, value=queries)
            queries = queries + attn_out
        queries = self.layer_norm1(queries)

        # Cross attention block, tokens attending to image embedding
        query = queries + query_point_embedding
        key = keys + key_point_embedding

        attn_out, _ = self.cross_attn_token_to_image(
            query=query, key=key, value=keys, attention_similarity=attention_similarity
        )
        queries = queries + attn_out

        queries = self.layer_norm2(queries)

        # MLP block
        mlp_out = self.mlp(queries)
        queries = queries + mlp_out
        queries = self.layer_norm3(queries)

        # Cross attention block, image embedding attending to tokens
        query = queries + query_point_embedding
        key = keys + key_point_embedding

        attn_out, _ = self.cross_attn_image_to_token(query=key, key=query, value=queries)
        keys = keys + attn_out

        keys = self.layer_norm4(keys)
        return queries, keys, attn_out


class EdgeTamFeedForward(nn.Module):
    def __init__(
        self,
        input_dim: int,
        hidden_dim: int,
        output_dim: int,
        num_layers: int,
        activation: str = "relu",
        sigmoid_output: bool = False,
    ):
        super().__init__()
        self.num_layers = num_layers
        self.activation = ACT2FN[activation]
        self.proj_in = nn.Linear(input_dim, hidden_dim)
        self.proj_out = nn.Linear(hidden_dim, output_dim)
        self.layers = nn.ModuleList([nn.Linear(hidden_dim, hidden_dim) for _ in range(num_layers - 2)])
        self.sigmoid_output = sigmoid_output

    def forward(self, hidden_states):
        hidden_states = self.proj_in(hidden_states)
        hidden_states = self.activation(hidden_states)
        for layer in self.layers:
            hidden_states = self.activation(layer(hidden_states))

        hidden_states = self.proj_out(hidden_states)
        if self.sigmoid_output:
            hidden_states = F.sigmoid(hidden_states)
        return hidden_states


@auto_docstring
class EdgeTamPreTrainedModel(PreTrainedModel):
    config_class = EdgeTamConfig
    base_model_prefix = "edgetam"
    main_input_name = "pixel_values"
    input_modalities = ("image",)
    _supports_sdpa = True
    _supports_flash_attn = True
    _supports_attention_backend = True
    _keys_to_ignore_on_load_unexpected = None

    @torch.no_grad()
    def _init_weights(self, module):
        super()._init_weights(module)
        if isinstance(module, EdgeTamModel):
            if module.no_memory_embedding is not None:
                init.zeros_(module.no_memory_embedding)
        elif hasattr(module, "positional_embedding"):
            init.normal_(module.positional_embedding, std=module.scale)


# copied and adapted from original implementation, also practically equal to DetrSinePositionEmbedding
class EdgeTamSinePositionEmbedding(nn.Module):
    """
    This is a more standard version of the position embedding, very similar to the one used by the Attention is all you
    need paper, generalized to work on images.
    """

    def __init__(
        self, num_pos_feats: int = 64, temperature: int = 10000, normalize: bool = False, scale: float | None = None
    ):
        super().__init__()
        if scale is not None and normalize is False:
            raise ValueError("normalize should be True if scale is passed")
        self.num_pos_feats = num_pos_feats
        self.temperature = temperature
        self.normalize = normalize
        self.scale = 2 * math.pi if scale is None else scale

    @compile_compatible_method_lru_cache(maxsize=1)
    def forward(
        self,
        shape: torch.Size,
        device: torch.device | str,
        dtype: torch.dtype,
        mask: Tensor | None = None,
    ) -> Tensor:
        if mask is None:
            mask = torch.zeros((shape[0], shape[2], shape[3]), device=device, dtype=torch.bool)
        not_mask = (~mask).to(dtype)
        y_embed = not_mask.cumsum(1)
        x_embed = not_mask.cumsum(2)
        if self.normalize:
            eps = 1e-6
            y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
            x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale

        dim_t = torch.arange(self.num_pos_feats, dtype=torch.int64, device=device).to(dtype)
        dim_t = self.temperature ** (2 * torch.div(dim_t, 2, rounding_mode="floor") / self.num_pos_feats)

        pos_x = x_embed[:, :, :, None] / dim_t
        pos_y = y_embed[:, :, :, None] / dim_t
        pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3)
        pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3)
        pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
        return pos


class EdgeTamVisionNeck(nn.Module):
    def __init__(self, config: EdgeTamVisionConfig):
        super().__init__()
        self.config = config

        self.position_encoding = EdgeTamSinePositionEmbedding(
            num_pos_feats=config.fpn_hidden_size // 2, normalize=True
        )
        self.convs = nn.ModuleList()
        for in_channels in config.backbone_channel_list:
            self.convs.append(
                nn.Conv2d(
                    in_channels=in_channels,
                    out_channels=config.fpn_hidden_size,
                    kernel_size=config.fpn_kernel_size,
                    stride=config.fpn_stride,
                    padding=config.fpn_padding,
                ),
            )
        self.fpn_top_down_levels = config.fpn_top_down_levels

    def forward(self, hidden_states: torch.Tensor) -> tuple[tuple[torch.Tensor, ...], tuple[torch.Tensor, ...]]:
        fpn_hidden_states = ()
        fpn_position_encoding = ()

        # forward in top-down order (from low to high resolution)
        n = len(self.convs) - 1
        for i in range(n, -1, -1):
            lateral_features = hidden_states[i].permute(0, 3, 1, 2)
            lateral_features = self.convs[n - i](lateral_features.to(self.convs[i].weight.dtype))
            if i not in self.fpn_top_down_levels or i == n:
                prev_features = lateral_features
            else:
                top_down_features = F.interpolate(
                    prev_features.to(dtype=torch.float32),
                    scale_factor=2.0,
                    mode="nearest",
                    align_corners=None,
                    antialias=False,
                ).to(lateral_features.dtype)
                prev_features = lateral_features + top_down_features

            prev_position_encoding = self.position_encoding(
                prev_features.shape, prev_features.device, prev_features.dtype
            ).to(prev_features.dtype)

            fpn_hidden_states += (prev_features,)
            fpn_position_encoding += (prev_position_encoding,)

        return fpn_hidden_states, fpn_position_encoding


@auto_docstring(
    custom_intro="""
    The vision model from EdgeTAM without any head or projection on top.
    """
)
class EdgeTamVisionModel(EdgeTamPreTrainedModel):
    config_class = EdgeTamVisionConfig
    main_input_name = "pixel_values"
    # TODO: TimmWrapper models aren't compatible with _can_record_outputs yet. We specifically set this to
    # an empty dict to avoid the _can_record_outputs from Sam2VisionModel being inherited here.
    _can_record_outputs = {}

    def __init__(self, config: EdgeTamVisionConfig):
        super().__init__(config)
        self.config = config

        self.backbone = AutoModel.from_config(config.backbone_config)

        self.neck = EdgeTamVisionNeck(config)
        self.num_feature_levels = config.num_feature_levels

        self.post_init()

    @merge_with_config_defaults
    @capture_outputs
    def forward(
        self,
        pixel_values: torch.FloatTensor | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> tuple | EdgeTamVisionEncoderOutput:
        if pixel_values is None:
            raise ValueError("You have to specify pixel_values")

        # Forward through backbone
        backbone_output = self.backbone(pixel_values, **kwargs)
        intermediate_hidden_states = backbone_output.last_hidden_state
        intermediate_hidden_states = [hidden_state.permute(0, 2, 3, 1) for hidden_state in intermediate_hidden_states]

        fpn_hidden_states, fpn_position_encoding = self.neck(intermediate_hidden_states)
        # Select last `num_feature_levels` feature levels from FPN and reverse order to get features from high to low resolution
        fpn_hidden_states = fpn_hidden_states[-self.num_feature_levels :][::-1]
        fpn_position_encoding = fpn_position_encoding[-self.num_feature_levels :][::-1]

        return EdgeTamVisionEncoderOutput(
            last_hidden_state=intermediate_hidden_states[-1],
            fpn_hidden_states=fpn_hidden_states,
            fpn_position_encoding=fpn_position_encoding,
            hidden_states=backbone_output.hidden_states,
        )


@dataclass
@auto_docstring(custom_intro="Base class for the EdgeTam model's output.")
class EdgeTamImageSegmentationOutput(ModelOutput):
    r"""
    iou_scores (`torch.FloatTensor` of shape `(batch_size, point_batch_size, num_masks)`):
        The Intersection over Union (IoU) scores of the predicted masks.
    pred_masks (`torch.FloatTensor` of shape `(batch_size, point_batch_size, num_masks, height, width)`):
        The predicted low-resolution masks. This is an alias for `low_res_masks`. These masks need to be post-processed
        by the processor to be brought to the original image size.
    object_score_logits (`torch.FloatTensor` of shape `(batch_size, point_batch_size, 1)`):
        Logits for the object score, indicating if an object is present.
    image_embeddings (`tuple(torch.FloatTensor)`):
        The features from the FPN, which are used by the mask decoder. This is a tuple of `torch.FloatTensor` where each
        tensor has shape `(batch_size, channels, height, width)`.
    vision_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True`):
        Tuple of `torch.FloatTensor` (one for the output of each stage) of shape `(batch_size, height, width, hidden_size)`.
        Hidden-states of the vision model at the output of each stage.
    vision_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True`):
        Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.
        Attentions weights of the vision model.
    mask_decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True`):
        Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.
        Attentions weights of the mask decoder.
    """

    iou_scores: torch.FloatTensor | None = None
    pred_masks: torch.FloatTensor | None = None
    object_score_logits: torch.FloatTensor | None = None
    image_embeddings: tuple[torch.FloatTensor, ...] = None
    vision_hidden_states: tuple[torch.FloatTensor, ...] | None = None
    vision_attentions: tuple[torch.FloatTensor, ...] | None = None
    mask_decoder_attentions: tuple[torch.FloatTensor, ...] | None = None


class EdgeTamPositionalEmbedding(nn.Module):
    def __init__(self, config: EdgeTamPromptEncoderConfig):
        super().__init__()
        self.scale = config.scale
        positional_embedding = self.scale * torch.randn((2, config.hidden_size // 2))
        self.register_buffer("positional_embedding", positional_embedding)

    def forward(self, input_coords, input_shape=None):
        """Positionally encode points that are normalized to [0,1]."""
        coordinates = input_coords.clone()

        if input_shape is not None:
            coordinates[:, :, :, 0] = coordinates[:, :, :, 0] / input_shape[1]
            coordinates[:, :, :, 1] = coordinates[:, :, :, 1] / input_shape[0]
        coordinates.to(torch.float32)

        # assuming coords are in [0, 1]^2 square and have d_1 x ... x d_n x 2 shape
        coordinates = 2 * coordinates - 1
        coordinates = coordinates.to(self.positional_embedding.dtype)
        coordinates = coordinates @ self.positional_embedding
        coordinates = 2 * np.pi * coordinates
        # outputs d_1 x ... x d_n x channel shape
        return torch.cat([torch.sin(coordinates), torch.cos(coordinates)], dim=-1)


class EdgeTamMaskEmbedding(nn.Module):
    def __init__(self, config: EdgeTamPromptEncoderConfig):
        super().__init__()
        self.mask_input_channels = config.mask_input_channels // 4
        self.activation = ACT2FN[config.hidden_act]
        self.conv1 = nn.Conv2d(1, self.mask_input_channels, kernel_size=2, stride=2)
        self.conv2 = nn.Conv2d(self.mask_input_channels, config.mask_input_channels, kernel_size=2, stride=2)
        self.conv3 = nn.Conv2d(config.mask_input_channels, config.hidden_size, kernel_size=1)
        self.layer_norm1 = EdgeTamLayerNorm(
            self.mask_input_channels, eps=config.layer_norm_eps, data_format="channels_first"
        )
        self.layer_norm2 = EdgeTamLayerNorm(
            self.mask_input_channels * 4, eps=config.layer_norm_eps, data_format="channels_first"
        )

    def forward(self, masks):
        hidden_states = self.conv1(masks)
        hidden_states = self.layer_norm1(hidden_states)
        hidden_states = self.activation(hidden_states)

        hidden_states = self.conv2(hidden_states)
        hidden_states = self.layer_norm2(hidden_states)
        hidden_states = self.activation(hidden_states)
        dense_embeddings = self.conv3(hidden_states)
        return dense_embeddings


class EdgeTamPromptEncoder(nn.Module):
    def __init__(self, config: EdgeTamPromptEncoderConfig):
        super().__init__()
        self.shared_embedding = EdgeTamPositionalEmbedding(config)
        self.mask_embed = EdgeTamMaskEmbedding(config)
        self.no_mask_embed = nn.Embedding(1, config.hidden_size)

        self.image_embedding_size = (config.image_size // config.patch_size, config.image_size // config.patch_size)
        self.mask_input_size = (4 * config.image_size // config.patch_size, 4 * config.image_size // config.patch_size)
        self.input_image_size = config.image_size

        self.point_embed = nn.Embedding(config.num_point_embeddings, config.hidden_size)
        self.hidden_size = config.hidden_size
        self.not_a_point_embed = nn.Embedding(1, config.hidden_size)

    def _embed_points(self, points: torch.Tensor, labels: torch.Tensor, pad: bool) -> torch.Tensor:
        """Embeds point prompts."""
        points = points + 0.5  # Shift to center of pixel
        if pad:
            points = torch.nn.functional.pad(points, (0, 0, 0, 1), mode="constant", value=0)
            labels = torch.nn.functional.pad(labels, (0, 1), mode="constant", value=-1)
        input_shape = (self.input_image_size, self.input_image_size)
        point_embedding = self.shared_embedding(points, input_shape)

        # torch.where and expanding the labels tensor is required by the ONNX export
        point_embedding = torch.where(labels[..., None] == -1, self.not_a_point_embed.weight, point_embedding)

        # This is required for the ONNX export. The dtype, device need to be explicitly
        # specified as otherwise torch.onnx.export interprets as double
        point_embedding = torch.where(
            labels[..., None] != -10,
            point_embedding,
            torch.zeros_like(point_embedding),
        )

        # Add point embeddings for labels >= 0
        point_embedding = point_embedding + self.point_embed(labels.clamp(min=0)) * (labels >= 0).unsqueeze(-1)

        return point_embedding

    def _embed_boxes(self, boxes: torch.Tensor) -> torch.Tensor:
        """Embeds box prompts."""
        boxes = boxes + 0.5  # Shift to center of pixel
        coords = boxes.view(*boxes.shape[:2], 2, 2)
        # add padding point for consistency with the original implementation
        coords = torch.nn.functional.pad(coords, (0, 0, 0, 1), mode="constant", value=0)
        corner_embedding = self.shared_embedding(coords, (self.input_image_size, self.input_image_size))
        corner_embedding[:, :, 0, :] += self.point_embed.weight[2]
        corner_embedding[:, :, 1, :] += self.point_embed.weight[3]
        corner_embedding[:, :, 2, :] = self.not_a_point_embed.weight.expand_as(corner_embedding[:, :, 2, :])
        return corner_embedding

    def forward(
        self,
        input_points: tuple[torch.Tensor, torch.Tensor] | None,
        input_labels: torch.Tensor | None,
        input_boxes: torch.Tensor | None,
        input_masks: torch.Tensor | None,
    ) -> tuple[torch.Tensor, torch.Tensor]:
        """
        Embeds different types of prompts, returning both sparse and dense embeddings.

        Args:
            points (`torch.Tensor`, *optional*):
                point coordinates and labels to embed.
            boxes (`torch.Tensor`, *optional*):
                boxes to embed
            masks (`torch.Tensor`, *optional*):
                masks to embed
        """
        sparse_embeddings = None
        batch_size = 1
        if input_points is not None:
            batch_size = input_points.shape[0]
            if input_labels is None:
                raise ValueError("If points are provided, labels must also be provided.")
            point_embeddings = self._embed_points(input_points, input_labels, pad=(input_boxes is None))
            sparse_embeddings = point_embeddings
        if input_boxes is not None:
            batch_size = input_boxes.shape[0]
            box_embeddings = self._embed_boxes(input_boxes)
            if sparse_embeddings is None:
                sparse_embeddings = box_embeddings
            else:
                sparse_embeddings = torch.cat([sparse_embeddings, box_embeddings], dim=2)
        if input_masks is not None:
            dense_embeddings = self.mask_embed(input_masks)
        else:
            dense_embeddings = self.no_mask_embed.weight.reshape(1, -1, 1, 1).expand(
                batch_size, -1, self.image_embedding_size[0], self.image_embedding_size[1]
            )

        return sparse_embeddings, dense_embeddings


class EdgeTamTwoWayTransformer(nn.Module):
    def __init__(self, config: EdgeTamMaskDecoderConfig):
        super().__init__()
        self.config = config

        self.num_hidden_layers = config.num_hidden_layers
        self.layers = nn.ModuleList()

        for i in range(self.num_hidden_layers):
            self.layers.append(EdgeTamTwoWayAttentionBlock(config, skip_first_layer_pe=(i == 0)))

        self.final_attn_token_to_image = EdgeTamAttention(config)
        self.layer_norm_final_attn = nn.LayerNorm(config.hidden_size)

    def forward(
        self,
        point_embeddings: Tensor,
        image_embeddings: Tensor,
        image_positional_embeddings: Tensor,
        attention_similarity: Tensor,
        target_embedding=None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> tuple | BaseModelOutput:
        if image_embeddings is None:
            raise ValueError("You have to specify an image_embedding")

        image_embeddings = image_embeddings.flatten(2).permute(0, 2, 1).unsqueeze(1)
        image_positional_embeddings = image_positional_embeddings.flatten(2).permute(0, 2, 1).unsqueeze(1)

        # Prepare queries
        queries = point_embeddings
        keys = image_embeddings

        # Apply transformer blocks and final layernorm
        for layer in self.layers:
            if target_embedding is not None:
                queries += target_embedding

            queries, keys, _ = layer(
                queries=queries,
                keys=keys,
                query_point_embedding=point_embeddings,
                key_point_embedding=image_positional_embeddings,
                attention_similarity=attention_similarity,
                **kwargs,
            )
        # Apply the final attention layer from the points to the image
        query = queries + point_embeddings
        key = keys + image_positional_embeddings

        attn_out, _ = self.final_attn_token_to_image(query=query, key=key, value=keys)

        queries = queries + attn_out
        queries = self.layer_norm_final_attn(queries)
        return queries, keys


class EdgeTamMaskDecoder(nn.Module):
    def __init__(self, config: EdgeTamMaskDecoderConfig):
        super().__init__()
        self.config = config
        self.hidden_size = config.hidden_size

        self.num_multimask_outputs = config.num_multimask_outputs
        self.num_mask_tokens = config.num_multimask_outputs + 1

        self.iou_token = nn.Embedding(1, self.hidden_size)
        self.mask_tokens = nn.Embedding(self.num_mask_tokens, self.hidden_size)

        self.transformer = EdgeTamTwoWayTransformer(config)

        # should we create a new class for this?
        self.upscale_conv1 = nn.ConvTranspose2d(self.hidden_size, self.hidden_size // 4, kernel_size=2, stride=2)
        self.upscale_conv2 = nn.ConvTranspose2d(self.hidden_size // 4, self.hidden_size // 8, kernel_size=2, stride=2)
        self.upscale_layer_norm = EdgeTamLayerNorm(self.hidden_size // 4, data_format="channels_first")
        self.activation = nn.GELU()

        mlps_list = []
        for _ in range(self.num_mask_tokens):
            mlps_list += [EdgeTamFeedForward(self.hidden_size, self.hidden_size, self.hidden_size // 8, 3)]
        self.output_hypernetworks_mlps = nn.ModuleList(mlps_list)
        self.iou_prediction_head = EdgeTamFeedForward(
            self.hidden_size,
            config.iou_head_hidden_dim,
            self.num_mask_tokens,
            config.iou_head_depth,
            sigmoid_output=True,
        )

        self.conv_s0 = nn.Conv2d(config.hidden_size, config.hidden_size // 8, kernel_size=1, stride=1)
        self.conv_s1 = nn.Conv2d(config.hidden_size, config.hidden_size // 4, kernel_size=1, stride=1)

        self.obj_score_token = nn.Embedding(1, self.hidden_size)
        self.pred_obj_score_head = EdgeTamFeedForward(self.hidden_size, self.hidden_size, 1, 3)

        self.dynamic_multimask_via_stability = config.dynamic_multimask_via_stability
        self.dynamic_multimask_stability_delta = config.dynamic_multimask_stability_delta
        self.dynamic_multimask_stability_thresh = config.dynamic_multimask_stability_thresh

    def forward(
        self,
        image_embeddings: torch.Tensor,
        image_positional_embeddings: torch.Tensor,
        sparse_prompt_embeddings: torch.Tensor,
        dense_prompt_embeddings: torch.Tensor,
        multimask_output: bool,
        high_resolution_features: list[torch.Tensor],
        attention_similarity: torch.Tensor | None = None,
        target_embedding: torch.Tensor | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
        """
        Predict masks given image and prompt embeddings.

        Args:
            image_embeddings (`torch.Tensor`):
                The embeddings from the image encoder.
            image_positional_embeddings (`torch.Tensor`):
                Positional encoding with the shape of image_embeddings.
            sparse_prompt_embeddings (`torch.Tensor`):
                The embeddings of the points and boxes.
            dense_prompt_embeddings (`torch.Tensor`):
                The embeddings of the mask inputs.
            multimask_output (`bool`):
                Whether to return multiple masks or a single mask.
            high_resolution_features (`list[torch.Tensor]`, *optional*):
                The high-resolution features from the vision encoder.
            attention_similarity (`torch.Tensor`, *optional*):
                The attention similarity tensor.
            target_embedding (`torch.Tensor`, *optional*):
                The target embedding.
        """
        batch_size, num_channels, height, width = image_embeddings.shape
        point_batch_size = sparse_prompt_embeddings.shape[1]
        # Concatenate output tokens
        output_tokens = torch.cat(
            [
                self.obj_score_token.weight,
                self.iou_token.weight,
                self.mask_tokens.weight,
            ],
            dim=0,
        )
        output_tokens = output_tokens.repeat(batch_size, point_batch_size, 1, 1)

        if sparse_prompt_embeddings.shape[0] != 0:
            tokens = torch.cat((output_tokens, sparse_prompt_embeddings), dim=2)
        else:
            tokens = output_tokens
        point_embeddings = tokens.to(self.iou_token.weight.dtype)

        # Expand per-image data in batch direction to be per-mask
        image_embeddings = image_embeddings + dense_prompt_embeddings
        image_embeddings = image_embeddings.repeat_interleave(point_batch_size, dim=0)
        image_positional_embeddings = image_positional_embeddings.repeat_interleave(point_batch_size, 0)
        # Run the transformer
        point_embeddings, image_embeddings = self.transformer(
            point_embeddings=point_embeddings,
            image_embeddings=image_embeddings,
            image_positional_embeddings=image_positional_embeddings,
            attention_similarity=attention_similarity,
            target_embedding=target_embedding,
            **kwargs,
        )
        iou_token_out = point_embeddings[:, :, 1, :]
        mask_tokens_out = point_embeddings[:, :, 2 : (2 + self.num_mask_tokens), :]

        # Upscale mask embeddings and predict masks using the mask tokens
        image_embeddings = image_embeddings.transpose(2, 3).view(
            batch_size * point_batch_size, num_channels, height, width
        )

        feat_s0, feat_s1 = high_resolution_features
        feat_s0 = feat_s0.repeat_interleave(point_batch_size, dim=0)
        feat_s1 = feat_s1.repeat_interleave(point_batch_size, dim=0)
        upscaled_embedding = self.upscale_conv1(image_embeddings) + feat_s1
        upscaled_embedding = self.activation(self.upscale_layer_norm(upscaled_embedding))
        upscaled_embedding = self.activation(self.upscale_conv2(upscaled_embedding) + feat_s0)

        hyper_in_list: list[torch.Tensor] = []
        for i in range(self.num_mask_tokens):
            current_mlp = self.output_hypernetworks_mlps[i]
            hyper_in_list += [current_mlp(mask_tokens_out[:, :, i, :])]
        hyper_in = torch.stack(hyper_in_list, dim=2)

        _, num_channels, height, width = upscaled_embedding.shape
        upscaled_embedding = upscaled_embedding.view(batch_size, point_batch_size, num_channels, height * width)
        masks = (hyper_in @ upscaled_embedding).view(batch_size, point_batch_size, -1, height, width)

        # Generate mask quality predictions
        iou_pred = self.iou_prediction_head(iou_token_out)
        object_score_logits = self.pred_obj_score_head(point_embeddings[:, :, 0, :])

        # Select the correct mask or masks for output
        if multimask_output:
            mask_slice = slice(1, None)
            masks = masks[:, :, mask_slice, :, :]
            iou_pred = iou_pred[:, :, mask_slice]
        elif self.dynamic_multimask_via_stability and not self.training:
            mask_slice = slice(0, 1)
            masks, iou_pred = self._dynamic_multimask_via_stability(masks, iou_pred)
        else:
            mask_slice = slice(0, 1)
            masks = masks[:, :, mask_slice, :, :]
            iou_pred = iou_pred[:, :, mask_slice]

        sam_tokens_out = mask_tokens_out[:, :, mask_slice]  # [b, 3, c] shape

        return masks, iou_pred, sam_tokens_out, object_score_logits

    def _get_stability_scores(self, mask_logits):
        """
        Compute stability scores of the mask logits based on the IoU between upper and
        lower thresholds.
        """
        mask_logits = mask_logits.flatten(-2)
        stability_delta = self.dynamic_multimask_stability_delta
        area_i = torch.sum(mask_logits > stability_delta, dim=-1).float()
        area_u = torch.sum(mask_logits > -stability_delta, dim=-1).float()
        stability_scores = torch.where(area_u > 0, area_i / area_u, 1.0)
        return stability_scores

    def _dynamic_multimask_via_stability(self, all_mask_logits, all_iou_scores):
        """
        When outputting a single mask, if the stability score from the current single-mask
        output (based on output token 0) falls below a threshold, we instead select from
        multi-mask outputs (based on output token 1~3) the mask with the highest predicted
        IoU score. This is intended to ensure a valid mask for both clicking and tracking.
        """
        # The best mask from multimask output tokens (1~3)
        multimask_logits = all_mask_logits[:, :, 1:, :, :]
        multimask_iou_scores = all_iou_scores[:, :, 1:]
        best_scores_inds = torch.argmax(multimask_iou_scores, dim=-1)  # [B, P]
        best_scores_inds_expanded = best_scores_inds.unsqueeze(-1).unsqueeze(-1).unsqueeze(-1)
        best_scores_inds_expanded = best_scores_inds_expanded.expand(
            -1, -1, 1, multimask_logits.size(-2), multimask_logits.size(-1)
        )
        best_multimask_logits = torch.gather(multimask_logits, 2, best_scores_inds_expanded)  # [B, P, 1, H, W]
        best_multimask_iou_scores = torch.gather(multimask_iou_scores, 2, best_scores_inds.unsqueeze(-1))  # [B, P, 1]

        # The mask from singlemask output token 0 and its stability score
        singlemask_logits = all_mask_logits[:, :, 0:1, :, :]
        singlemask_iou_scores = all_iou_scores[:, :, 0:1]
        stability_scores = self._get_stability_scores(singlemask_logits)
        is_stable = stability_scores >= self.dynamic_multimask_stability_thresh

        # Dynamically fall back to best multimask output upon low stability scores.
        mask_logits_out = torch.where(
            is_stable[..., None, None].expand_as(singlemask_logits),
            singlemask_logits,
            best_multimask_logits,
        )
        iou_scores_out = torch.where(
            is_stable.expand_as(singlemask_iou_scores),
            singlemask_iou_scores,
            best_multimask_iou_scores,
        )
        return mask_logits_out, iou_scores_out


@auto_docstring(
    custom_intro="""
    Segment Anything Model 2 (SAM 2) for generating segmentation masks, given an input image and
    input points and labels, boxes, or masks.
    """
)
class EdgeTamModel(EdgeTamPreTrainedModel):
    input_modalities = ("image", "text")
    _can_record_outputs = {"mask_decoder_attentions": OutputRecorder(EdgeTamTwoWayAttentionBlock, index=2)}
    _tied_weights_keys = {}
    _keys_to_ignore_on_load_unexpected = [
        r"^memory_.*",
        r"^mask_downsample.*",
        r"spatial_perceiver.*",
        r"^object_pointer_proj.*",
        r"^temporal_positional_encoding_projection_layer.*",
        "no_memory_positional_encoding",
        "no_object_pointer",
        "occlusion_spatial_embedding_parameter",
    ]

    def __init__(self, config: EdgeTamConfig):
        super().__init__(config)
        self.shared_image_embedding = EdgeTamPositionalEmbedding(config.prompt_encoder_config)
        self.vision_encoder = AutoModel.from_config(config.vision_config)
        self.prompt_encoder = EdgeTamPromptEncoder(config.prompt_encoder_config)
        # The module using it is not a PreTrainedModel subclass so we need this
        config.mask_decoder_config._attn_implementation = config._attn_implementation
        self.mask_decoder = EdgeTamMaskDecoder(config.mask_decoder_config)

        self.num_feature_levels = config.vision_config.num_feature_levels
        self.backbone_feature_sizes = config.vision_config.backbone_feature_sizes
        # a single token to indicate no memory embedding from previous frames
        self.hidden_dim = config.vision_config.fpn_hidden_size
        self.no_memory_embedding = torch.nn.Parameter(torch.zeros(1, 1, self.hidden_dim))

        self.post_init()

    def get_image_wide_positional_embeddings(self) -> torch.Tensor:
        size = self.prompt_encoder.image_embedding_size
        target_device = self.shared_image_embedding.positional_embedding.device
        target_dtype = self.shared_image_embedding.positional_embedding.dtype
        grid = torch.ones(size, device=target_device, dtype=target_dtype)
        y_embed = grid.cumsum(dim=0) - 0.5
        x_embed = grid.cumsum(dim=1) - 0.5
        y_embed = y_embed / size[0]
        x_embed = x_embed / size[1]

        positional_embedding = self.shared_image_embedding(torch.stack([x_embed, y_embed], dim=-1))
        return positional_embedding.permute(2, 0, 1).unsqueeze(0)  # channel x height x width

    @torch.no_grad()
    def get_image_embeddings(
        self,
        pixel_values: torch.FloatTensor,
        **kwargs: Unpack[TransformersKwargs],
    ) -> list[torch.Tensor]:
        r"""
        Returns the image embeddings by passing the pixel values through the vision encoder.

        Args:
            pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
                Input pixel values
        """
        batch_size = pixel_values.shape[0]
        image_outputs = self.get_image_features(pixel_values, return_dict=True, **kwargs)
        feature_maps = image_outputs.fpn_hidden_states

        # add no memory embedding to the last feature map
        feature_maps[-1] = feature_maps[-1] + self.no_memory_embedding

        # reshape feature maps to the same shape as the backbone feature sizes
        image_embeddings = [
            feat.permute(1, 2, 0).view(batch_size, -1, *feat_size)
            for feat, feat_size in zip(feature_maps, self.backbone_feature_sizes)
        ]

        return image_embeddings

    @torch.no_grad()
    def get_prompt_embeddings(
        self,
        input_points: torch.FloatTensor | None = None,
        input_labels: torch.LongTensor | None = None,
        input_boxes: torch.FloatTensor | None = None,
        input_masks: torch.LongTensor | None = None,
    ):
        r"""
        Returns the prompt embeddings by passing the input points, labels, boxes and masks through the prompt encoder.

        Args:
            input_points (`torch.FloatTensor` of shape `(batch_size, point_batch_size, num_points_per_image, 2)`):
                Optional input points for the prompt encoder. The padding of the point is automatically done by the
                processor. `point_batch_size` refers to the number of masks that we want the model to predict per
                point. The model will output `point_batch_size` times 3 masks in total.
            input_labels (`torch.LongTensor` of shape `(batch_size, point_batch_size, num_points_per_image)`):
                Optional input labels for the prompt encoder. The padding of the labels is automatically done by the
                processor, or can be fed by the user.
            input_boxes (`torch.FloatTensor` of shape `(batch_size, num_boxes_per_image, 4)`):
                Optional input boxes for the prompt encoder. The padding of the boxes is automatically done by the
                processor. users can also pass manually the input boxes.
            input_masks (`torch.LongTensor` of shape `(batch_size, image_size, image_size)`):
                Optional input masks for the prompt encoder.
        """
        prompt_output = self.prompt_encoder(
            input_points=input_points,
            input_labels=input_labels,
            input_boxes=input_boxes,
            input_masks=input_masks,
        )
        return prompt_output

    @merge_with_config_defaults
    @capture_outputs
    @auto_docstring
    def forward(
        self,
        pixel_values: torch.FloatTensor | None = None,
        input_points: torch.FloatTensor | None = None,
        input_labels: torch.LongTensor | None = None,
        input_boxes: torch.FloatTensor | None = None,
        input_masks: torch.LongTensor | None = None,
        image_embeddings: torch.FloatTensor | None = None,
        multimask_output: bool = True,
        attention_similarity: torch.FloatTensor | None = None,
        target_embedding: torch.FloatTensor | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> EdgeTamImageSegmentationOutput:
        r"""
        input_points (`torch.FloatTensor` of shape `(batch_size, num_points, 2)`):
            Input 2D spatial points, this is used by the prompt encoder to encode the prompt. Generally yields to much
            better results. The points can be obtained by passing a list of list of list to the processor that will
            create corresponding `torch` tensors of dimension 4. The first dimension is the image batch size, the
            second dimension is the point batch size (i.e. how many segmentation masks do we want the model to predict
            per input point), the third dimension is the number of points per segmentation mask (it is possible to pass
            multiple points for a single mask), and the last dimension is the x (vertical) and y (horizontal)
            coordinates of the point. If a different number of points is passed either for each image, or for each
            mask, the processor will create "PAD" points that will correspond to the (0, 0) coordinate, and the
            computation of the embedding will be skipped for these points using the labels.
        input_labels (`torch.LongTensor` of shape `(batch_size, point_batch_size, num_points)`):
            Input labels for the points, this is used by the prompt encoder to encode the prompt. According to the
            official implementation, there are 3 types of labels

            - `1`: the point is a point that contains the object of interest
            - `0`: the point is a point that does not contain the object of interest
            - `-1`: the point corresponds to the background

            We added the label:

            - `-10`: the point is a padding point, thus should be ignored by the prompt encoder

            The padding labels should be automatically done by the processor.
        input_boxes (`torch.FloatTensor` of shape `(batch_size, num_boxes, 4)`):
            Input boxes for the points, this is used by the prompt encoder to encode the prompt. Generally yields to
            much better generated masks. The boxes can be obtained by passing a list of list of list to the processor,
            that will generate a `torch` tensor, with each dimension corresponding respectively to the image batch
            size, the number of boxes per image and the coordinates of the top left and bottom right point of the box.
            In the order (`x1`, `y1`, `x2`, `y2`):

            - `x1`: the x coordinate of the top left point of the input box
            - `y1`: the y coordinate of the top left point of the input box
            - `x2`: the x coordinate of the bottom right point of the input box
            - `y2`: the y coordinate of the bottom right point of the input box
        input_masks (`torch.FloatTensor` of shape `(batch_size, image_size, image_size)`):
            SAM model also accepts segmentation masks as input. The mask will be embedded by the prompt encoder to
            generate a corresponding embedding, that will be fed later on to the mask decoder. These masks needs to be
            manually fed by the user, and they need to be of shape (`batch_size`, `image_size`, `image_size`).
        image_embeddings (`torch.FloatTensor` of shape `(batch_size, output_channels, window_size, window_size)`):
            Image embeddings, this is used by the mask decoder to generate masks and iou scores. For more memory
            efficient computation, users can first retrieve the image embeddings using the `get_image_embeddings`
            method, and then feed them to the `forward` method instead of feeding the `pixel_values`.
        multimask_output (`bool`, *optional*):
            In the original implementation and paper, the model always outputs 3 masks per image (or per point / per
            bounding box if relevant). However, it is possible to just output a single mask, that corresponds to the
            "best" mask, by specifying `multimask_output=False`.
        attention_similarity (`torch.FloatTensor`, *optional*):
            Attention similarity tensor, to be provided to the mask decoder for target-guided attention in case the
            model is used for personalization as introduced in [PerSAM](https://huggingface.co/papers/2305.03048).
        target_embedding (`torch.FloatTensor`, *optional*):
            Embedding of the target concept, to be provided to the mask decoder for target-semantic prompting in case
            the model is used for personalization as introduced in [PerSAM](https://huggingface.co/papers/2305.03048).

        Example:

        ```python
        >>> from PIL import Image
        >>> import httpx
        >>> from io import BytesIO
        >>> from transformers import AutoModel, AutoProcessor

        >>> model = AutoModel.from_pretrained("danelcsb/edgetam.1_hiera_tiny")
        >>> processor = AutoProcessor.from_pretrained("danelcsb/edgetam.1_hiera_tiny")

        >>> url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/sam-car.png"
        >>> with httpx.stream("GET", url) as response:
        ...     raw_image = Image.open(BytesIO(response.read())).convert("RGB")
        >>> input_points = [[[400, 650]]]  # 2D location of a window on the car
        >>> inputs = processor(images=raw_image, input_points=input_points, return_tensors="pt")

        >>> # Get segmentation mask
        >>> outputs = model(**inputs)

        >>> # Postprocess masks
        >>> masks = processor.post_process_masks(
        ...     outputs.pred_masks, inputs["original_sizes"]
        ... )
        ```
        """
        if not ((pixel_values is None) ^ (image_embeddings is None)):
            raise ValueError("Exactly one of pixel_values or image_embeddings must be provided.")
        if input_points is not None and input_boxes is not None:
            if input_points.shape[1] != input_boxes.shape[1]:
                raise ValueError(
                    f"You should provide as many bounding boxes as input points per box. Got {input_points.shape[1]} and {input_boxes.shape[1]}."
                )

        image_positional_embeddings = self.get_image_wide_positional_embeddings()
        # repeat with batch size
        batch_size = pixel_values.shape[0] if pixel_values is not None else image_embeddings[-1].shape[0]
        image_positional_embeddings = image_positional_embeddings.repeat(batch_size, 1, 1, 1)

        vision_attentions = None
        vision_hidden_states = None

        if pixel_values is not None:
            image_outputs: EdgeTamVisionEncoderOutput = self.get_image_features(
                pixel_values, return_dict=True, **kwargs
            )
            feature_maps = image_outputs.fpn_hidden_states
            vision_hidden_states = image_outputs.hidden_states
            vision_attentions = image_outputs.attentions

            # add no memory embedding to the last feature map
            feature_maps[-1] = feature_maps[-1] + self.no_memory_embedding

            # reshape feature maps to the same shape as the backbone feature sizes
            image_embeddings = [
                feat.permute(1, 2, 0).view(batch_size, -1, *feat_size)
                for feat, feat_size in zip(feature_maps, self.backbone_feature_sizes)
            ]

        if input_points is not None and input_labels is None:
            input_labels = torch.ones_like(input_points[:, :, :, 0], dtype=torch.int, device=input_points.device)

        if input_points is None and input_boxes is None:
            # If no points are provide, pad with an empty point (with label -1)
            input_points = torch.zeros(
                batch_size, 1, 1, 2, dtype=image_embeddings[-1].dtype, device=image_embeddings[-1].device
            )
            input_labels = -torch.ones(batch_size, 1, 1, dtype=torch.int32, device=image_embeddings[-1].device)

        if input_masks is not None:
            # If mask_inputs is provided, downsize it into low-res mask input if needed
            # and feed it as a dense mask prompt into the SAM mask encoder
            if input_masks.shape[-2:] != self.prompt_encoder.mask_input_size:
                input_masks = F.interpolate(
                    input_masks.float(),
                    size=self.prompt_encoder.mask_input_size,
                    align_corners=False,
                    mode="bilinear",
                    antialias=True,  # use antialias for downsampling
                ).to(input_masks.dtype)

        sparse_embeddings, dense_embeddings = self.prompt_encoder(
            input_points=input_points,
            input_labels=input_labels,
            input_boxes=input_boxes,
            input_masks=input_masks,
        )
        low_res_multimasks, iou_scores, _, object_score_logits = self.mask_decoder(
            image_embeddings=image_embeddings[-1],
            image_positional_embeddings=image_positional_embeddings,
            sparse_prompt_embeddings=sparse_embeddings,
            dense_prompt_embeddings=dense_embeddings,
            multimask_output=multimask_output,
            high_resolution_features=image_embeddings[:-1],
            attention_similarity=attention_similarity,
            target_embedding=target_embedding,
            **kwargs,
        )

        return EdgeTamImageSegmentationOutput(
            iou_scores=iou_scores,
            pred_masks=low_res_multimasks,
            object_score_logits=object_score_logits,
            image_embeddings=image_embeddings,
            vision_hidden_states=vision_hidden_states,
            vision_attentions=vision_attentions,
        )

    @can_return_tuple
    @auto_docstring
    def get_image_features(
        self,
        pixel_values: torch.FloatTensor,
        **kwargs: Unpack[TransformersKwargs],
    ) -> tuple | EdgeTamVisionEncoderOutput:
        r"""
        pixel_values (`torch.FloatTensor`):
            Input pixel values of shape `(batch_size, num_channels, height, width)`.
        """
        vision_outputs: EdgeTamVisionEncoderOutput = self.vision_encoder(pixel_values, return_dict=True, **kwargs)

        feature_maps = vision_outputs.fpn_hidden_states
        feature_maps_position_embeddings = vision_outputs.fpn_position_encoding

        # precompute projected level 0 and level 1 features in SAM decoder
        # to avoid running it again on every SAM click
        feature_maps = list(feature_maps)
        feature_maps[0] = self.mask_decoder.conv_s0(feature_maps[0])
        feature_maps[1] = self.mask_decoder.conv_s1(feature_maps[1])

        # flatten NxCxHxW to HWxNxC
        feature_maps = [feature_map.flatten(2).permute(2, 0, 1) for feature_map in feature_maps]
        feature_maps_position_embeddings = [
            feature_map_position_embedding.flatten(2).permute(2, 0, 1)
            for feature_map_position_embedding in feature_maps_position_embeddings
        ]
        vision_outputs.fpn_hidden_states = feature_maps
        vision_outputs.fpn_position_encoding = feature_maps_position_embeddings

        return vision_outputs


__all__ = ["EdgeTamModel", "EdgeTamVisionModel", "EdgeTamPreTrainedModel"]
