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# coding=utf-8
# Copyright 2025 The HuggingFace Inc. team.
#
# 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
import warnings
from dataclasses import dataclass
from typing import Optional, Union

import torch
import torch.nn.functional as F
from torch import Tensor, nn

from ...activations import ACT2FN
from ...file_utils import ModelOutput, is_timm_available, requires_backends
from ...integrations import use_kernel_forward_from_hub
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import meshgrid
from ...utils import auto_docstring
from ...utils.backbone_utils import load_backbone
from ..auto.modeling_auto import AutoModel
from .configuration_mm_grounding_dino import MMGroundingDinoConfig


if is_timm_available():
    from timm import create_model


class MMGroundingDinoContrastiveEmbedding(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.max_text_len = config.max_text_len
        self.bias = nn.Parameter(torch.tensor(0.0))

    def forward(
        self,
        vision_hidden_state: torch.FloatTensor,
        text_hidden_state: torch.FloatTensor,
        text_token_mask: torch.BoolTensor,
    ) -> torch.FloatTensor:
        res = vision_hidden_state @ text_hidden_state.transpose(-1, -2)
        res = res / math.sqrt(vision_hidden_state.shape[-1])
        res = res + self.bias
        res.masked_fill_(~text_token_mask[:, None, :], float("-inf"))

        # padding to max_text_len
        new_res = torch.full((*res.shape[:-1], self.max_text_len), float("-inf"), device=res.device)
        new_res[..., : res.shape[-1]] = res

        return new_res


@use_kernel_forward_from_hub("MultiScaleDeformableAttention")
class MultiScaleDeformableAttention(nn.Module):
    def forward(
        self,
        value: Tensor,
        value_spatial_shapes: Tensor,
        value_spatial_shapes_list: list[tuple],
        level_start_index: Tensor,
        sampling_locations: Tensor,
        attention_weights: Tensor,
        im2col_step: int,
    ):
        batch_size, _, num_heads, hidden_dim = value.shape
        _, num_queries, num_heads, num_levels, num_points, _ = sampling_locations.shape
        value_list = value.split([height * width for height, width in value_spatial_shapes_list], dim=1)
        sampling_grids = 2 * sampling_locations - 1
        sampling_value_list = []
        for level_id, (height, width) in enumerate(value_spatial_shapes_list):
            # batch_size, height*width, num_heads, hidden_dim
            # -> batch_size, height*width, num_heads*hidden_dim
            # -> batch_size, num_heads*hidden_dim, height*width
            # -> batch_size*num_heads, hidden_dim, height, width
            value_l_ = (
                value_list[level_id]
                .flatten(2)
                .transpose(1, 2)
                .reshape(batch_size * num_heads, hidden_dim, height, width)
            )
            # batch_size, num_queries, num_heads, num_points, 2
            # -> batch_size, num_heads, num_queries, num_points, 2
            # -> batch_size*num_heads, num_queries, num_points, 2
            sampling_grid_l_ = sampling_grids[:, :, :, level_id].transpose(1, 2).flatten(0, 1)
            # batch_size*num_heads, hidden_dim, num_queries, num_points
            sampling_value_l_ = nn.functional.grid_sample(
                value_l_,
                sampling_grid_l_,
                mode="bilinear",
                padding_mode="zeros",
                align_corners=False,
            )
            sampling_value_list.append(sampling_value_l_)
        # (batch_size, num_queries, num_heads, num_levels, num_points)
        # -> (batch_size, num_heads, num_queries, num_levels, num_points)
        # -> (batch_size, num_heads, 1, num_queries, num_levels*num_points)
        attention_weights = attention_weights.transpose(1, 2).reshape(
            batch_size * num_heads, 1, num_queries, num_levels * num_points
        )
        output = (
            (torch.stack(sampling_value_list, dim=-2).flatten(-2) * attention_weights)
            .sum(-1)
            .view(batch_size, num_heads * hidden_dim, num_queries)
        )
        return output.transpose(1, 2).contiguous()


class MMGroundingDinoLearnedPositionEmbedding(nn.Module):
    """
    This module learns positional embeddings up to a fixed maximum size.
    """

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

        embedding_dim = config.d_model // 2
        self.row_embeddings = nn.Embedding(50, embedding_dim)
        self.column_embeddings = nn.Embedding(50, embedding_dim)

    def forward(self, pixel_values, pixel_mask=None):
        height, width = pixel_values.shape[-2:]
        width_values = torch.arange(width, device=pixel_values.device)
        height_values = torch.arange(height, device=pixel_values.device)
        x_emb = self.column_embeddings(width_values)
        y_emb = self.row_embeddings(height_values)
        pos = torch.cat([x_emb.unsqueeze(0).repeat(height, 1, 1), y_emb.unsqueeze(1).repeat(1, width, 1)], dim=-1)
        pos = pos.permute(2, 0, 1)
        pos = pos.unsqueeze(0)
        pos = pos.repeat(pixel_values.shape[0], 1, 1, 1)
        return pos


class MMGroundingDinoMultiscaleDeformableAttention(nn.Module):
    """
    Multiscale deformable attention as proposed in Deformable DETR.
    """

    def __init__(self, config: MMGroundingDinoConfig, num_heads: int, n_points: int):
        super().__init__()

        self.attn = MultiScaleDeformableAttention()

        if config.d_model % num_heads != 0:
            raise ValueError(
                f"embed_dim (d_model) must be divisible by num_heads, but got {config.d_model} and {num_heads}"
            )
        dim_per_head = config.d_model // num_heads
        # check if dim_per_head is power of 2
        if not ((dim_per_head & (dim_per_head - 1) == 0) and dim_per_head != 0):
            warnings.warn(
                "You'd better set embed_dim (d_model) in MMGroundingDinoMultiscaleDeformableAttention to make the"
                " dimension of each attention head a power of 2 which is more efficient in the authors' CUDA"
                " implementation."
            )

        self.im2col_step = 64

        self.d_model = config.d_model
        self.n_levels = config.num_feature_levels
        self.n_heads = num_heads
        self.n_points = n_points

        self.sampling_offsets = nn.Linear(config.d_model, num_heads * self.n_levels * n_points * 2)
        self.attention_weights = nn.Linear(config.d_model, num_heads * self.n_levels * n_points)
        self.value_proj = nn.Linear(config.d_model, config.d_model)
        self.output_proj = nn.Linear(config.d_model, config.d_model)

        self.disable_custom_kernels = config.disable_custom_kernels

    def with_pos_embed(self, tensor: torch.Tensor, position_embeddings: Optional[Tensor]):
        return tensor if position_embeddings is None else tensor + position_embeddings

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        position_embeddings: Optional[torch.Tensor] = None,
        reference_points=None,
        spatial_shapes=None,
        spatial_shapes_list=None,
        level_start_index=None,
        output_attentions: bool = False,
    ):
        # add position embeddings to the hidden states before projecting to queries and keys
        if position_embeddings is not None:
            hidden_states = self.with_pos_embed(hidden_states, position_embeddings)

        batch_size, num_queries, _ = hidden_states.shape
        batch_size, sequence_length, _ = encoder_hidden_states.shape
        # Ignore copy
        if (spatial_shapes[:, 0] * spatial_shapes[:, 1]).sum() != sequence_length:
            raise ValueError(
                "Make sure to align the spatial shapes with the sequence length of the encoder hidden states"
            )

        value = self.value_proj(encoder_hidden_states)
        if attention_mask is not None:
            # we invert the attention_mask
            value = value.masked_fill(~attention_mask[..., None], float(0))
        value = value.view(batch_size, sequence_length, self.n_heads, self.d_model // self.n_heads)
        sampling_offsets = self.sampling_offsets(hidden_states).view(
            batch_size, num_queries, self.n_heads, self.n_levels, self.n_points, 2
        )
        attention_weights = self.attention_weights(hidden_states).view(
            batch_size, num_queries, self.n_heads, self.n_levels * self.n_points
        )
        attention_weights = F.softmax(attention_weights, -1).view(
            batch_size, num_queries, self.n_heads, self.n_levels, self.n_points
        )
        # batch_size, num_queries, n_heads, n_levels, n_points, 2
        num_coordinates = reference_points.shape[-1]
        if num_coordinates == 2:
            offset_normalizer = torch.stack([spatial_shapes[..., 1], spatial_shapes[..., 0]], -1)
            sampling_locations = (
                reference_points[:, :, None, :, None, :]
                + sampling_offsets / offset_normalizer[None, None, None, :, None, :]
            )
        elif num_coordinates == 4:
            sampling_locations = (
                reference_points[:, :, None, :, None, :2]
                + sampling_offsets / self.n_points * reference_points[:, :, None, :, None, 2:] * 0.5
            )
        else:
            raise ValueError(f"Last dim of reference_points must be 2 or 4, but got {reference_points.shape[-1]}")

        output = self.attn(
            value,
            spatial_shapes,
            spatial_shapes_list,
            level_start_index,
            sampling_locations,
            attention_weights,
            self.im2col_step,
        )

        output = self.output_proj(output)

        return output, attention_weights


class MMGroundingDinoBiMultiHeadAttention(nn.Module):
    def __init__(self, config):
        super().__init__()

        vision_dim = text_dim = config.d_model
        embed_dim = config.encoder_ffn_dim // 2
        num_heads = config.encoder_attention_heads // 2
        dropout = config.fusion_dropout

        self.embed_dim = embed_dim
        self.num_heads = num_heads
        self.head_dim = embed_dim // num_heads
        self.vision_dim = vision_dim
        self.text_dim = text_dim

        if self.head_dim * self.num_heads != self.embed_dim:
            raise ValueError(
                f"`embed_dim` must be divisible by `num_heads` (got `embed_dim`: {self.embed_dim} and `num_heads`: {self.num_heads})."
            )
        self.scale = self.head_dim ** (-0.5)
        self.dropout = dropout

        self.vision_proj = nn.Linear(self.vision_dim, self.embed_dim)
        self.text_proj = nn.Linear(self.text_dim, self.embed_dim)
        self.values_vision_proj = nn.Linear(self.vision_dim, self.embed_dim)
        self.values_text_proj = nn.Linear(self.text_dim, self.embed_dim)

        self.out_vision_proj = nn.Linear(self.embed_dim, self.vision_dim)
        self.out_text_proj = nn.Linear(self.embed_dim, self.text_dim)

    def _reshape(self, tensor: torch.Tensor, seq_len: int, batch_size: int):
        return tensor.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()

    def forward(
        self,
        vision_features: torch.FloatTensor,
        text_features: torch.FloatTensor,
        vision_attention_mask: Optional[torch.BoolTensor] = None,
        text_attention_mask: Optional[torch.BoolTensor] = None,
    ) -> tuple[tuple[torch.FloatTensor, torch.FloatTensor], tuple[torch.FloatTensor, torch.FloatTensor]]:
        """Image-to-text and text-to-image cross-attention

        Args:
            vision_features (`torch.FloatTensor` of shape `(batch_size, vision_sequence_length, hidden_dim)`):
                Projected flattened image features generated by the vision backbone.
            text_features (`torch.FloatTensor` of shape `(batch_size, text_sequence_length, hidden_dim)`):
                Projected text features generated by the text encoder.
            vision_attention_mask (`torch.BoolTensor`, **optional**):
                Attention mask for image-to-text cross-attention. False for real tokens and True for padding tokens.
            text_attention_mask (`torch.BoolTensor`, **optional**):
                Attention mask for text-to-image cross-attention. False for real tokens and True for padding tokens.

        Returns:
            `tuple(tuple(torch.FloatTensor), tuple(torch.FloatTensor))` where each inner tuple comprises an attention
            output and weights:
            - **vision_attn_output** (`torch.FloatTensor` of shape `(batch_size, vision_sequence_length, hidden_din)`)
              --
                Output of the image-to-text cross-attention layer.
            - **vision_attn_weights** (`torch.FloatTensor` of shape `(batch_size, num_heads, vision_sequence_length,
              vision_sequence_length)`) --
                Attention weights of the image-to-text cross-attention layer.
            - **text_attn_output** (`torch.FloatTensor` of shape `(batch_size, text_sequence_length, hidden_dim)`) --
                Output of the text-to-image cross-attention layer.
            - **text_attn_weights** (`torch.FloatTensor` of shape `(batch_size, num_heads, text_sequence_length,
              text_sequence_length)`) --
                Attention weights of the text-to-image cross-attention layer.
        """
        batch_size, tgt_len, _ = vision_features.size()

        vision_query_states = self.vision_proj(vision_features) * self.scale
        vision_query_states = self._reshape(vision_query_states, tgt_len, batch_size)

        text_key_states = self.text_proj(text_features)
        text_key_states = self._reshape(text_key_states, -1, batch_size)

        vision_value_states = self.values_vision_proj(vision_features)
        vision_value_states = self._reshape(vision_value_states, -1, batch_size)

        text_value_states = self.values_text_proj(text_features)
        text_value_states = self._reshape(text_value_states, -1, batch_size)

        proj_shape = (batch_size * self.num_heads, -1, self.head_dim)

        vision_query_states = vision_query_states.view(*proj_shape)
        text_key_states = text_key_states.view(*proj_shape)
        vision_value_states = vision_value_states.view(*proj_shape)
        text_value_states = text_value_states.view(*proj_shape)

        src_len = text_key_states.size(1)
        attn_weights = torch.bmm(vision_query_states, text_key_states.transpose(1, 2))  # bs*nhead, nimg, ntxt

        if attn_weights.size() != (batch_size * self.num_heads, tgt_len, src_len):
            raise ValueError(
                f"Attention weights should be of size {(batch_size * self.num_heads, tgt_len, src_len)}, but is {attn_weights.size()}"
            )

        attn_weights = attn_weights - attn_weights.max()
        # Do not increase -50000/50000, data type half has quite limited range
        attn_weights = torch.clamp(attn_weights, min=-50000, max=50000)

        attn_weights_transposed = attn_weights.transpose(1, 2)
        text_attn_weights = attn_weights_transposed - torch.max(attn_weights_transposed, dim=-1, keepdim=True)[0]

        # Do not increase -50000/50000, data type half has quite limited range
        text_attn_weights = torch.clamp(text_attn_weights, min=-50000, max=50000)

        # mask vision for language
        if vision_attention_mask is not None:
            vision_attention_mask = (
                vision_attention_mask[:, None, None, :].repeat(1, self.num_heads, 1, 1).flatten(0, 1)
            )
            text_attn_weights.masked_fill_(vision_attention_mask, float("-inf"))

        text_attn_weights = text_attn_weights.softmax(dim=-1)

        # mask language for vision
        if text_attention_mask is not None:
            text_attention_mask = text_attention_mask[:, None, None, :].repeat(1, self.num_heads, 1, 1).flatten(0, 1)
            attn_weights.masked_fill_(text_attention_mask, float("-inf"))
        vision_attn_weights = attn_weights.softmax(dim=-1)

        vision_attn_probs = F.dropout(vision_attn_weights, p=self.dropout, training=self.training)
        text_attn_probs = F.dropout(text_attn_weights, p=self.dropout, training=self.training)

        vision_attn_output = torch.bmm(vision_attn_probs, text_value_states)
        text_attn_output = torch.bmm(text_attn_probs, vision_value_states)

        if vision_attn_output.size() != (batch_size * self.num_heads, tgt_len, self.head_dim):
            raise ValueError(
                f"`vision_attn_output` should be of size {(batch_size, self.num_heads, tgt_len, self.head_dim)}, but is {vision_attn_output.size()}"
            )

        if text_attn_output.size() != (batch_size * self.num_heads, src_len, self.head_dim):
            raise ValueError(
                f"`text_attn_output` should be of size {(batch_size, self.num_heads, src_len, self.head_dim)}, but is {text_attn_output.size()}"
            )

        vision_attn_output = vision_attn_output.view(batch_size, self.num_heads, tgt_len, self.head_dim)
        vision_attn_output = vision_attn_output.transpose(1, 2)
        vision_attn_output = vision_attn_output.reshape(batch_size, tgt_len, self.embed_dim)

        text_attn_output = text_attn_output.view(batch_size, self.num_heads, src_len, self.head_dim)
        text_attn_output = text_attn_output.transpose(1, 2)
        text_attn_output = text_attn_output.reshape(batch_size, src_len, self.embed_dim)

        vision_attn_output = self.out_vision_proj(vision_attn_output)
        text_attn_output = self.out_text_proj(text_attn_output)

        return (vision_attn_output, vision_attn_weights), (text_attn_output, text_attn_weights)


def drop_path(input: torch.Tensor, drop_prob: float = 0.0, training: bool = False) -> torch.Tensor:
    """
    Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).

    """
    if drop_prob == 0.0 or not training:
        return input
    keep_prob = 1 - drop_prob
    shape = (input.shape[0],) + (1,) * (input.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
    random_tensor = keep_prob + torch.rand(shape, dtype=input.dtype, device=input.device)
    random_tensor.floor_()  # binarize
    output = input.div(keep_prob) * random_tensor
    return output


class MMGroundingDinoDropPath(nn.Module):
    """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""

    def __init__(self, drop_prob: Optional[float] = None) -> None:
        super().__init__()
        self.drop_prob = drop_prob

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        return drop_path(hidden_states, self.drop_prob, self.training)

    def extra_repr(self) -> str:
        return f"p={self.drop_prob}"


class MMGroundingDinoFusionLayer(nn.Module):
    def __init__(self, config):
        super().__init__()
        drop_path = config.fusion_droppath

        # pre layer norm
        self.layer_norm_vision = nn.LayerNorm(config.d_model, config.layer_norm_eps)
        self.layer_norm_text = nn.LayerNorm(config.d_model, config.layer_norm_eps)
        self.attn = MMGroundingDinoBiMultiHeadAttention(config)

        # add layer scale for training stability
        self.drop_path = MMGroundingDinoDropPath(drop_path) if drop_path > 0.0 else nn.Identity()
        init_values = 1e-4
        self.vision_param = nn.Parameter(init_values * torch.ones(config.d_model), requires_grad=True)
        self.text_param = nn.Parameter(init_values * torch.ones(config.d_model), requires_grad=True)

    def forward(
        self,
        vision_features: torch.FloatTensor,
        text_features: torch.FloatTensor,
        attention_mask_vision: Optional[torch.BoolTensor] = None,
        attention_mask_text: Optional[torch.BoolTensor] = None,
    ) -> tuple[tuple[torch.FloatTensor, torch.FloatTensor], tuple[torch.FloatTensor, torch.FloatTensor]]:
        """Image and text features fusion

        Args:
            vision_features (`torch.FloatTensor` of shape `(batch_size, vision_sequence_length, hidden_dim)`):
                Projected flattened image features generated by the vision backbone.
            text_features (`torch.FloatTensor` of shape `(batch_size, text_sequence_length, hidden_dim)`):
                Projected text features generated by the text encoder.
            attention_mask_vision (`torch.BoolTensor`, **optional**):
                Attention mask for image-to-text cross-attention. False for real tokens and True for padding tokens.
            attention_mask_text (`torch.BoolTensor`, **optional**):
                Attention mask for text-to-image cross-attention. False for real tokens and True for padding tokens.

        Returns:
            `tuple(tuple(torch.FloatTensor), tuple(torch.FloatTensor))` where each inner tuple comprises an enhanced
            feature and attention output and weights:
            - **vision_features** (`torch.FloatTensor` of shape `(batch_size, vision_sequence_length, vision_dim)`) --
                Updated vision features with attention output from image-to-text cross-attention layer.
            - **vision_attn_weights** (`torch.FloatTensor` of shape `(batch_size, num_heads, vision_sequence_length,
              vision_sequence_length)`) --
                Attention weights of the image-to-text cross-attention layer.
            - **text_features** (`torch.FloatTensor` of shape `(batch_size, text_sequence_length, text_dim)`) --
                Updated text features with attention output from text-to-image cross-attention layer.
            - **text_attn_weights** (`torch.FloatTensor` of shape `(batch_size, num_heads, text_sequence_length,
              text_sequence_length)`) --
                Attention weights of the text-to-image cross-attention layer.
        """
        vision_features = self.layer_norm_vision(vision_features)
        text_features = self.layer_norm_text(text_features)
        (delta_v, vision_attn), (delta_t, text_attn) = self.attn(
            vision_features,
            text_features,
            vision_attention_mask=attention_mask_vision,
            text_attention_mask=attention_mask_text,
        )
        vision_features = vision_features + self.drop_path(self.vision_param * delta_v)
        text_features = text_features + self.drop_path(self.text_param * delta_t)

        return (vision_features, vision_attn), (text_features, text_attn)


@auto_docstring
class MMGroundingDinoPreTrainedModel(PreTrainedModel):
    config: MMGroundingDinoConfig
    base_model_prefix = "model"
    main_input_name = "pixel_values"
    input_modalities = ["image", "text"]

    def _init_weights(self, module):
        std = self.config.init_std

        if isinstance(module, MMGroundingDinoLearnedPositionEmbedding):
            nn.init.uniform_(module.row_embeddings.weight)
            nn.init.uniform_(module.column_embeddings.weight)
        elif isinstance(module, MMGroundingDinoMultiscaleDeformableAttention):
            nn.init.constant_(module.sampling_offsets.weight.data, 0.0)
            default_dtype = torch.get_default_dtype()
            thetas = torch.arange(module.n_heads, dtype=torch.int64).to(default_dtype) * (
                2.0 * math.pi / module.n_heads
            )
            grid_init = torch.stack([thetas.cos(), thetas.sin()], -1)
            grid_init = (
                (grid_init / grid_init.abs().max(-1, keepdim=True)[0])
                .view(module.n_heads, 1, 1, 2)
                .repeat(1, module.n_levels, module.n_points, 1)
            )
            for i in range(module.n_points):
                grid_init[:, :, i, :] *= i + 1
            with torch.no_grad():
                module.sampling_offsets.bias = nn.Parameter(grid_init.view(-1))
            nn.init.constant_(module.attention_weights.weight.data, 0.0)
            nn.init.constant_(module.attention_weights.bias.data, 0.0)
            nn.init.xavier_uniform_(module.value_proj.weight.data)
            nn.init.constant_(module.value_proj.bias.data, 0.0)
            nn.init.xavier_uniform_(module.output_proj.weight.data)
            nn.init.constant_(module.output_proj.bias.data, 0.0)
        elif isinstance(module, MMGroundingDinoBiMultiHeadAttention):
            nn.init.xavier_uniform_(module.vision_proj.weight)
            module.vision_proj.bias.data.fill_(0)
            nn.init.xavier_uniform_(module.text_proj.weight)
            module.text_proj.bias.data.fill_(0)
            nn.init.xavier_uniform_(module.values_vision_proj.weight)
            module.values_vision_proj.bias.data.fill_(0)
            nn.init.xavier_uniform_(module.values_text_proj.weight)
            module.values_text_proj.bias.data.fill_(0)
            nn.init.xavier_uniform_(module.out_vision_proj.weight)
            module.out_vision_proj.bias.data.fill_(0)
            nn.init.xavier_uniform_(module.out_text_proj.weight)
            module.out_text_proj.bias.data.fill_(0)
        elif isinstance(module, MMGroundingDinoFusionLayer):
            module.vision_param.data.fill_(1e-4)
            module.text_param.data.fill_(1e-4)
        elif isinstance(module, (nn.Linear, nn.Conv2d, nn.BatchNorm2d)):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, (nn.LayerNorm, nn.GroupNorm)):
            module.weight.data.fill_(1.0)
            module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()
        elif isinstance(module, MMGroundingDinoMLPPredictionHead):
            nn.init.constant_(module.layers[-1].weight.data, 0)
            nn.init.constant_(module.layers[-1].bias.data, 0)

        if hasattr(module, "reference_points") and not self.config.two_stage:
            nn.init.xavier_uniform_(module.reference_points.weight.data, gain=1.0)
            nn.init.constant_(module.reference_points.bias.data, 0.0)
        if hasattr(module, "level_embed"):
            nn.init.normal_(module.level_embed)
        if isinstance(module, MMGroundingDinoContrastiveEmbedding):
            nn.init.constant_(module.bias, -math.log((1 - 0.01) / 0.01))

    def _set_gradient_checkpointing(self, module, value=False):
        if isinstance(module, MMGroundingDinoDecoder):
            module.gradient_checkpointing = value


class MMGroundingDinoFrozenBatchNorm2d(nn.Module):
    """
    BatchNorm2d where the batch statistics and the affine parameters are fixed.

    Copy-paste from torchvision.misc.ops with added eps before rqsrt, without which any other models than
    torchvision.models.resnet[18,34,50,101] produce nans.
    """

    def __init__(self, n):
        super().__init__()
        self.register_buffer("weight", torch.ones(n))
        self.register_buffer("bias", torch.zeros(n))
        self.register_buffer("running_mean", torch.zeros(n))
        self.register_buffer("running_var", torch.ones(n))

    def _load_from_state_dict(
        self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
    ):
        num_batches_tracked_key = prefix + "num_batches_tracked"
        if num_batches_tracked_key in state_dict:
            del state_dict[num_batches_tracked_key]

        super()._load_from_state_dict(
            state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
        )

    def forward(self, x):
        # move reshapes to the beginning
        # to make it user-friendly
        weight = self.weight.reshape(1, -1, 1, 1)
        bias = self.bias.reshape(1, -1, 1, 1)
        running_var = self.running_var.reshape(1, -1, 1, 1)
        running_mean = self.running_mean.reshape(1, -1, 1, 1)
        epsilon = 1e-5
        scale = weight * (running_var + epsilon).rsqrt()
        bias = bias - running_mean * scale
        return x * scale + bias


def replace_batch_norm(model):
    r"""
    Recursively replace all `torch.nn.BatchNorm2d` with `MMGroundingDinoFrozenBatchNorm2d`.

    Args:
        model (torch.nn.Module):
            input model
    """
    for name, module in model.named_children():
        if isinstance(module, nn.BatchNorm2d):
            new_module = MMGroundingDinoFrozenBatchNorm2d(module.num_features)

            if module.weight.device != torch.device("meta"):
                new_module.weight.data.copy_(module.weight)
                new_module.bias.data.copy_(module.bias)
                new_module.running_mean.data.copy_(module.running_mean)
                new_module.running_var.data.copy_(module.running_var)

            model._modules[name] = new_module

        if len(list(module.children())) > 0:
            replace_batch_norm(module)


class MMGroundingDinoConvEncoder(nn.Module):
    """
    Convolutional backbone, using either the AutoBackbone API or one from the timm library.

    nn.BatchNorm2d layers are replaced by MMGroundingDinoFrozenBatchNorm2d as defined above.

    """

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

        self.config = config

        if config.use_timm_backbone:
            requires_backends(self, ["timm"])
            backbone = create_model(
                config.backbone,
                pretrained=config.use_pretrained_backbone,
                features_only=True,
                **config.backbone_kwargs,
            )
        else:
            backbone = load_backbone(config)

        # replace batch norm by frozen batch norm
        with torch.no_grad():
            replace_batch_norm(backbone)
        self.model = backbone
        self.intermediate_channel_sizes = (
            self.model.feature_info.channels() if config.use_timm_backbone else self.model.channels
        )

        backbone_model_type = None
        if config.backbone is not None:
            backbone_model_type = config.backbone
        elif config.backbone_config is not None:
            backbone_model_type = config.backbone_config.model_type
        else:
            raise ValueError("Either `backbone` or `backbone_config` should be provided in the config")

        if "resnet" in backbone_model_type:
            for name, parameter in self.model.named_parameters():
                if config.use_timm_backbone:
                    if "layer2" not in name and "layer3" not in name and "layer4" not in name:
                        parameter.requires_grad_(False)
                else:
                    if "stage.1" not in name and "stage.2" not in name and "stage.3" not in name:
                        parameter.requires_grad_(False)

    def forward(self, pixel_values: torch.Tensor, pixel_mask: torch.Tensor):
        # send pixel_values through the model to get list of feature maps
        features = self.model(pixel_values) if self.config.use_timm_backbone else self.model(pixel_values).feature_maps

        out = []
        for feature_map in features:
            # downsample pixel_mask to match shape of corresponding feature_map
            mask = nn.functional.interpolate(pixel_mask[None].float(), size=feature_map.shape[-2:]).to(torch.bool)[0]
            out.append((feature_map, mask))
        return out


class MMGroundingDinoConvModel(nn.Module):
    """
    This module adds 2D position embeddings to all intermediate feature maps of the convolutional encoder.
    """

    def __init__(self, conv_encoder, position_embedding):
        super().__init__()
        self.conv_encoder = conv_encoder
        self.position_embedding = position_embedding

    def forward(self, pixel_values, pixel_mask):
        # send pixel_values and pixel_mask through backbone to get list of (feature_map, pixel_mask) tuples
        out = self.conv_encoder(pixel_values, pixel_mask)
        pos = []
        for feature_map, mask in out:
            # position encoding
            pos.append(self.position_embedding(feature_map, mask).to(feature_map.dtype))

        return out, pos


@dataclass
@auto_docstring(
    custom_intro="""
    Base class for outputs of the MMGroundingDinoEncoder. This class extends BaseModelOutput, due to:
    - vision and text last hidden states
    - vision and text intermediate hidden states
    """
)
class MMGroundingDinoEncoderOutput(ModelOutput):
    r"""
    last_hidden_state_vision (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
        Sequence of hidden-states at the output of the last layer of the vision encoder.
    last_hidden_state_text (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
        Sequence of hidden-states at the output of the last layer of the text encoder.
    vision_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 vision embeddings + one for the output of each
        layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the vision encoder at the
        output of each layer plus the initial embedding outputs.
    text_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 text embeddings + one for the output of each layer)
        of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the text encoder at the output of
        each layer plus the initial embedding outputs.
    """

    last_hidden_state_vision: Optional[torch.FloatTensor] = None
    last_hidden_state_text: Optional[torch.FloatTensor] = None
    vision_hidden_states: Optional[tuple[torch.FloatTensor]] = None
    text_hidden_states: Optional[tuple[torch.FloatTensor]] = None
    attentions: Optional[tuple[tuple[torch.FloatTensor]]] = None


class MMGroundingDinoMultiheadAttention(nn.Module):
    """Equivalent implementation of nn.MultiheadAttention with `batch_first=True`."""

    def __init__(self, config, num_attention_heads=None):
        super().__init__()
        if config.hidden_size % num_attention_heads != 0 and not hasattr(config, "embedding_size"):
            raise ValueError(
                f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
                f"heads ({num_attention_heads})"
            )

        self.num_attention_heads = num_attention_heads
        self.attention_head_size = int(config.hidden_size / num_attention_heads)
        self.all_head_size = self.num_attention_heads * self.attention_head_size

        self.query = nn.Linear(config.hidden_size, self.all_head_size)
        self.key = nn.Linear(config.hidden_size, self.all_head_size)
        self.value = nn.Linear(config.hidden_size, self.all_head_size)

        self.out_proj = nn.Linear(config.hidden_size, config.hidden_size)

        self.dropout = nn.Dropout(config.attention_dropout)

    def forward(
        self,
        queries: torch.Tensor,
        keys: torch.Tensor,
        values: torch.Tensor,
        attention_mask: Optional[torch.FloatTensor] = None,
        output_attentions: Optional[bool] = False,
    ) -> tuple[torch.Tensor]:
        batch_size, seq_length, _ = queries.shape
        query_layer = (
            self.query(queries)
            .view(batch_size, -1, self.num_attention_heads, self.attention_head_size)
            .transpose(1, 2)
        )
        key_layer = (
            self.key(keys).view(batch_size, -1, self.num_attention_heads, self.attention_head_size).transpose(1, 2)
        )
        value_layer = (
            self.value(values).view(batch_size, -1, self.num_attention_heads, self.attention_head_size).transpose(1, 2)
        )

        # Take the dot product between "query" and "key" to get the raw attention scores.
        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))

        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
        if attention_mask is not None:
            # Apply the attention mask is (precomputed for all layers in MMGroundingDinoModel forward() function)
            attention_scores = attention_scores + attention_mask

        # Normalize the attention scores to probabilities.
        attention_probs = nn.functional.softmax(attention_scores, dim=-1)

        # This is actually dropping out entire tokens to attend to, which might
        # seem a bit unusual, but is taken from the original Transformer paper.
        attention_probs = self.dropout(attention_probs)

        context_layer = torch.matmul(attention_probs, value_layer)

        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
        context_layer = context_layer.view(new_context_layer_shape)

        context_layer = self.out_proj(context_layer)

        outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)

        return outputs


class MMGroundingDinoTextEnhancerLayer(nn.Module):
    """Vanilla Transformer with text embeddings as input"""

    def __init__(self, config):
        super().__init__()
        self.self_attn = MMGroundingDinoMultiheadAttention(
            config, num_attention_heads=config.encoder_attention_heads // 2
        )

        # Implementation of Feedforward model
        self.fc1 = nn.Linear(config.d_model, config.encoder_ffn_dim // 2)
        self.fc2 = nn.Linear(config.encoder_ffn_dim // 2, config.d_model)

        self.layer_norm_before = nn.LayerNorm(config.d_model, config.layer_norm_eps)
        self.layer_norm_after = nn.LayerNorm(config.d_model, config.layer_norm_eps)

        self.activation = ACT2FN[config.activation_function]
        self.num_heads = config.encoder_attention_heads // 2
        self.dropout = config.text_enhancer_dropout

    def with_pos_embed(self, hidden_state: Tensor, position_embeddings: Optional[Tensor]):
        return hidden_state if position_embeddings is None else hidden_state + position_embeddings

    def forward(
        self,
        hidden_states: torch.FloatTensor,
        attention_masks: Optional[torch.BoolTensor] = None,
        position_embeddings: Optional[torch.FloatTensor] = None,
    ) -> tuple[torch.FloatTensor, torch.FloatTensor]:
        """Text self-attention to enhance projection of text features generated by
        the text encoder (AutoModel based on text_config) within MMGroundingDinoEncoderLayer

        Args:
            hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_dim)`):
                Text features generated by the text encoder.
            attention_masks (`torch.BoolTensor`, *optional*):
                Attention mask for text self-attention. False for real tokens and True for padding tokens.
            position_embeddings (`torch.FloatTensor`, *optional*):
                Position embeddings to be added to the hidden states.

        Returns:
            `tuple(torch.FloatTensor)` comprising two elements:
            - **hidden_states** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`) --
                Output of the text self-attention layer.
            - **attention_weights** (`torch.FloatTensor` of shape `(batch_size, num_heads, sequence_length,
              sequence_length)`) --
                Attention weights of the text self-attention layer.
        """

        # repeat attn mask
        if attention_masks.dim() == 3 and attention_masks.shape[0] == hidden_states.shape[0]:
            # batch_size, num_queries, num_keys
            attention_masks = attention_masks[:, None, :, :]
            attention_masks = attention_masks.repeat(1, self.num_heads, 1, 1)

            dtype = hidden_states.dtype
            attention_masks = attention_masks.to(dtype=dtype)  # fp16 compatibility
            attention_masks = (1.0 - attention_masks) * torch.finfo(dtype).min

        queries = keys = self.with_pos_embed(hidden_states, position_embeddings)
        attention_output, attention_weights = self.self_attn(
            queries=queries,
            keys=keys,
            values=hidden_states,
            attention_mask=attention_masks,
            output_attentions=True,
        )
        attention_output = nn.functional.dropout(attention_output, p=self.dropout, training=self.training)
        hidden_states = hidden_states + attention_output
        hidden_states = self.layer_norm_before(hidden_states)

        residual = hidden_states
        hidden_states = self.activation(self.fc1(hidden_states))
        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
        hidden_states = self.fc2(hidden_states)
        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
        hidden_states = hidden_states + residual
        hidden_states = self.layer_norm_after(hidden_states)

        return hidden_states, attention_weights


class MMGroundingDinoDeformableLayer(nn.Module):
    def __init__(self, config: MMGroundingDinoConfig):
        super().__init__()
        self.embed_dim = config.d_model
        self.self_attn = MMGroundingDinoMultiscaleDeformableAttention(
            config, num_heads=config.encoder_attention_heads, n_points=config.encoder_n_points
        )
        self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim, config.layer_norm_eps)
        self.dropout = config.dropout
        self.activation_fn = ACT2FN[config.activation_function]
        self.activation_dropout = config.activation_dropout
        self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim)
        self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim)
        self.final_layer_norm = nn.LayerNorm(self.embed_dim, config.layer_norm_eps)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: torch.Tensor,
        position_embeddings: Optional[torch.Tensor] = None,
        reference_points=None,
        spatial_shapes=None,
        spatial_shapes_list=None,
        level_start_index=None,
        output_attentions: bool = False,
    ):
        """
        Args:
            hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
                Input to the layer.
            attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
                Attention mask.
            position_embeddings (`torch.FloatTensor`, *optional*):
                Position embeddings, to be added to `hidden_states`.
            reference_points (`torch.FloatTensor`, *optional*):
                Reference points.
            spatial_shapes (`torch.LongTensor`, *optional*):
                Spatial shapes of the backbone feature maps.
            spatial_shapes_list (`list[tuple[int, int]]`, *optional*):
                Spatial shapes of the backbone feature maps (but as list for export compatibility).
            level_start_index (`torch.LongTensor`, *optional*):
                Level start index.
            output_attentions (`bool`, *optional*):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more detail.
        """
        residual = hidden_states

        # Apply Multi-scale Deformable Attention Module on the multi-scale feature maps.
        hidden_states, attn_weights = self.self_attn(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            encoder_hidden_states=hidden_states,
            encoder_attention_mask=attention_mask,
            position_embeddings=position_embeddings,
            reference_points=reference_points,
            spatial_shapes=spatial_shapes,
            spatial_shapes_list=spatial_shapes_list,
            level_start_index=level_start_index,
            output_attentions=output_attentions,
        )

        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
        hidden_states = residual + hidden_states
        hidden_states = self.self_attn_layer_norm(hidden_states)

        residual = hidden_states
        hidden_states = self.activation_fn(self.fc1(hidden_states))
        hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)

        hidden_states = self.fc2(hidden_states)
        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)

        hidden_states = residual + hidden_states
        hidden_states = self.final_layer_norm(hidden_states)

        if self.training:
            if torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any():
                clamp_value = torch.finfo(hidden_states.dtype).max - 1000
                hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)

        return hidden_states, attn_weights


# Based on https://github.com/IDEA-Research/MMGroundingDino/blob/2b62f419c292ca9c518daae55512fabc3fead4a4/MMGroundingDino/models/MMGroundingDino/utils.py#L24
def get_sine_pos_embed(
    pos_tensor: torch.Tensor, num_pos_feats: int = 128, temperature: int = 10000, exchange_xy: bool = True
) -> Tensor:
    """
    Generate sine position embeddings from a position tensor.

    Args:
        pos_tensor (torch.Tensor):
            Tensor containing positions. Shape: [..., n].
        num_pos_feats (`int`, *optional*, defaults to 128):
            Projected shape for each float in the tensor.
        temperature (`int`, *optional*, defaults to 10000):
            Temperature in the sine/cosine function.
        exchange_xy (`bool`, *optional*, defaults to `True`):
            Exchange pos x and pos y. For example, input tensor is [x,y], the results will be [pos(y), pos(x)].

    Returns:
        position_embeddings (torch.Tensor): shape: [..., n * hidden_size].
    """
    scale = 2 * math.pi
    dim_t = torch.arange(num_pos_feats, dtype=torch.float32, device=pos_tensor.device)
    dim_t = temperature ** (2 * torch.div(dim_t, 2, rounding_mode="floor") / num_pos_feats)

    def sine_func(x: torch.Tensor):
        sin_x = x * scale / dim_t
        sin_x = torch.stack((sin_x[..., 0::2].sin(), sin_x[..., 1::2].cos()), dim=3).flatten(2)
        return sin_x

    pos_tensor = pos_tensor.split([1] * pos_tensor.shape[-1], dim=-1)
    position_embeddings = [sine_func(x) for x in pos_tensor]
    if exchange_xy:
        position_embeddings[0], position_embeddings[1] = position_embeddings[1], position_embeddings[0]
    position_embeddings = torch.cat(position_embeddings, dim=-1)
    return position_embeddings


class MMGroundingDinoEncoderLayer(nn.Module):
    def __init__(self, config) -> None:
        super().__init__()

        self.d_model = config.d_model

        self.text_enhancer_layer = MMGroundingDinoTextEnhancerLayer(config)
        self.fusion_layer = MMGroundingDinoFusionLayer(config)
        self.deformable_layer = MMGroundingDinoDeformableLayer(config)

    def get_text_position_embeddings(
        self,
        text_features: Tensor,
        text_position_embedding: Optional[torch.Tensor],
        text_position_ids: Optional[torch.Tensor],
    ) -> Tensor:
        batch_size, seq_length, _ = text_features.shape
        if text_position_embedding is None and text_position_ids is None:
            text_position_embedding = torch.arange(seq_length, device=text_features.device)
            text_position_embedding = text_position_embedding.float()
            text_position_embedding = text_position_embedding.unsqueeze(0).unsqueeze(-1)
            text_position_embedding = text_position_embedding.repeat(batch_size, 1, 1)
            text_position_embedding = get_sine_pos_embed(
                text_position_embedding, num_pos_feats=self.d_model, exchange_xy=False
            )
        if text_position_ids is not None:
            text_position_embedding = get_sine_pos_embed(
                text_position_ids[..., None], num_pos_feats=self.d_model, exchange_xy=False
            )

        return text_position_embedding

    def forward(
        self,
        vision_features: Tensor,
        vision_position_embedding: Tensor,
        spatial_shapes: Tensor,
        spatial_shapes_list: list[tuple[int, int]],
        level_start_index: Tensor,
        key_padding_mask: Tensor,
        reference_points: Tensor,
        text_features: Optional[Tensor] = None,
        text_attention_mask: Optional[Tensor] = None,
        text_position_embedding: Optional[Tensor] = None,
        text_self_attention_masks: Optional[Tensor] = None,
        text_position_ids: Optional[Tensor] = None,
    ):
        text_position_embedding = self.get_text_position_embeddings(
            text_features, text_position_embedding, text_position_ids
        )

        (vision_features, vision_fused_attn), (text_features, text_fused_attn) = self.fusion_layer(
            vision_features=vision_features,
            text_features=text_features,
            attention_mask_vision=key_padding_mask,
            attention_mask_text=text_attention_mask,
        )

        (text_features, text_enhanced_attn) = self.text_enhancer_layer(
            hidden_states=text_features,
            attention_masks=~text_self_attention_masks,  # note we use ~ for mask here
            position_embeddings=(text_position_embedding if text_position_embedding is not None else None),
        )

        (vision_features, vision_deformable_attn) = self.deformable_layer(
            hidden_states=vision_features,
            attention_mask=~key_padding_mask,
            position_embeddings=vision_position_embedding,
            reference_points=reference_points,
            spatial_shapes=spatial_shapes,
            spatial_shapes_list=spatial_shapes_list,
            level_start_index=level_start_index,
        )

        return (
            (vision_features, text_features),
            (vision_fused_attn, text_fused_attn, text_enhanced_attn, vision_deformable_attn),
        )


class MMGroundingDinoEncoder(MMGroundingDinoPreTrainedModel):
    """
    Transformer encoder consisting of *config.encoder_layers* deformable attention layers. Each layer is a
    [`MMGroundingDinoEncoderLayer`].

    The encoder updates the flattened multi-scale feature maps through multiple deformable attention layers.

    Args:
        config: MMGroundingDinoConfig
    """

    def __init__(self, config: MMGroundingDinoConfig):
        super().__init__(config)

        self.dropout = config.dropout
        self.layers = nn.ModuleList([MMGroundingDinoEncoderLayer(config) for _ in range(config.encoder_layers)])

        # Initialize weights and apply final processing
        self.post_init()

    @staticmethod
    def get_reference_points(spatial_shapes, valid_ratios, device):
        """
        Get reference points for each feature map.

        Args:
            spatial_shapes (`torch.LongTensor` of shape `(num_feature_levels, 2)`):
                Spatial shapes of each feature map.
            valid_ratios (`torch.FloatTensor` of shape `(batch_size, num_feature_levels, 2)`):
                Valid ratios of each feature map.
            device (`torch.device`):
                Device on which to create the tensors.
        Returns:
            `torch.FloatTensor` of shape `(batch_size, num_queries, num_feature_levels, 2)`
        """
        reference_points_list = []
        for level, (height, width) in enumerate(spatial_shapes):
            ref_y, ref_x = meshgrid(
                torch.linspace(0.5, height - 0.5, height, dtype=torch.float32, device=device),
                torch.linspace(0.5, width - 0.5, width, dtype=torch.float32, device=device),
                indexing="ij",
            )
            # TODO: valid_ratios could be useless here. check https://github.com/fundamentalvision/Deformable-DETR/issues/36
            ref_y = ref_y.reshape(-1)[None] / (valid_ratios[:, None, level, 1] * height)
            ref_x = ref_x.reshape(-1)[None] / (valid_ratios[:, None, level, 0] * width)
            ref = torch.stack((ref_x, ref_y), -1)
            reference_points_list.append(ref)
        reference_points = torch.cat(reference_points_list, 1)
        reference_points = reference_points[:, :, None] * valid_ratios[:, None]
        return reference_points

    def forward(
        self,
        vision_features: Tensor,
        vision_attention_mask: Tensor,
        vision_position_embedding: Tensor,
        spatial_shapes: Tensor,
        spatial_shapes_list: list[tuple[int, int]],
        level_start_index: Tensor,
        valid_ratios=None,
        text_features: Optional[Tensor] = None,
        text_attention_mask: Optional[Tensor] = None,
        text_position_embedding: Optional[Tensor] = None,
        text_self_attention_masks: Optional[Tensor] = None,
        text_position_ids: Optional[Tensor] = None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
    ):
        r"""
        Args:
            vision_features (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
                Flattened feature map (output of the backbone + projection layer) that is passed to the encoder.
            vision_attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
                Mask to avoid performing attention on padding pixel features. Mask values selected in `[0, 1]`:
                - 0 for pixel features that are real (i.e. **not masked**),
                - 1 for pixel features that are padding (i.e. **masked**).
                [What are attention masks?](../glossary#attention-mask)
            vision_position_embedding (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
                Position embeddings that are added to the queries and keys in each self-attention layer.
            spatial_shapes (`torch.LongTensor` of shape `(num_feature_levels, 2)`):
                Spatial shapes of each feature map.
            spatial_shapes_list (`list[tuple[int, int]]`):
                Spatial shapes of each feature map (but as list for export compatibility).
            level_start_index (`torch.LongTensor` of shape `(num_feature_levels)`):
                Starting index of each feature map.
            valid_ratios (`torch.FloatTensor` of shape `(batch_size, num_feature_levels, 2)`):
                Ratio of valid area in each feature level.
            text_features (`torch.FloatTensor` of shape `(batch_size, text_seq_len, hidden_size)`):
                Flattened text features that are passed to the encoder.
            text_attention_mask (`torch.Tensor` of shape `(batch_size, text_seq_len)`, *optional*):
                Mask to avoid performing attention on padding text features. Mask values selected in `[0, 1]`:
                - 0 for text features that are real (i.e. **not masked**),
                - 1 for text features that are padding (i.e. **masked**).
                [What are attention masks?](../glossary#attention-mask)
            text_position_embedding (`torch.FloatTensor` of shape `(batch_size, text_seq_len)`):
                Position embeddings that are added to the queries and keys in each self-attention layer.
            text_self_attention_masks (`torch.BoolTensor` of shape `(batch_size, text_seq_len, text_seq_len)`):
                Masks to avoid performing attention between padding text features. Mask values selected in `[0, 1]`:
                - 1 for text features that are real (i.e. **not masked**),
                - 0 for text features that are padding (i.e. **masked**).
            text_position_ids (`torch.LongTensor` of shape `(batch_size, num_queries)`):
                Position ids for text features.
            output_attentions (`bool`, *optional*):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more detail.
            output_hidden_states (`bool`, *optional*):
                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
                for more detail.
            return_dict (`bool`, *optional*):
                Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
        """
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        reference_points = self.get_reference_points(spatial_shapes, valid_ratios, device=vision_features.device)

        encoder_vision_states = () if output_hidden_states else None
        encoder_text_states = () if output_hidden_states else None
        all_attns = () if output_attentions else None
        all_attn_fused_text = () if output_attentions else None
        all_attn_fused_vision = () if output_attentions else None
        all_attn_enhanced_text = () if output_attentions else None
        all_attn_deformable = () if output_attentions else None
        for i, encoder_layer in enumerate(self.layers):
            if output_hidden_states:
                encoder_vision_states += (vision_features,)
                encoder_text_states += (text_features,)

            (vision_features, text_features), attentions = encoder_layer(
                vision_features=vision_features,
                vision_position_embedding=vision_position_embedding,
                spatial_shapes=spatial_shapes,
                spatial_shapes_list=spatial_shapes_list,
                level_start_index=level_start_index,
                key_padding_mask=vision_attention_mask,
                reference_points=reference_points,
                text_features=text_features,
                text_attention_mask=text_attention_mask,
                text_position_embedding=text_position_embedding,
                text_self_attention_masks=text_self_attention_masks,
                text_position_ids=text_position_ids,
            )

            if output_attentions:
                all_attn_fused_vision += (attentions[0],)
                all_attn_fused_text += (attentions[1],)
                all_attn_enhanced_text += (attentions[2],)
                all_attn_deformable += (attentions[3],)

        if output_hidden_states:
            encoder_vision_states += (vision_features,)
            encoder_text_states += (text_features,)

        if output_attentions:
            all_attns = (all_attn_fused_vision, all_attn_fused_text, all_attn_enhanced_text, all_attn_deformable)

        if not return_dict:
            enc_outputs = [vision_features, text_features, encoder_vision_states, encoder_text_states, all_attns]
            return tuple(v for v in enc_outputs if v is not None)
        return MMGroundingDinoEncoderOutput(
            last_hidden_state_vision=vision_features,
            last_hidden_state_text=text_features,
            vision_hidden_states=encoder_vision_states,
            text_hidden_states=encoder_text_states,
            attentions=all_attns,
        )


@dataclass
@auto_docstring(
    custom_intro="""
    Base class for outputs of the MMGroundingDinoDecoder. This class adds two attributes to
    BaseModelOutputWithCrossAttentions, namely:
    - a stacked tensor of intermediate decoder hidden states (i.e. the output of each decoder layer)
    - a stacked tensor of intermediate reference points.
    """
)
class MMGroundingDinoDecoderOutput(ModelOutput):
    r"""
    intermediate_hidden_states (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, hidden_size)`):
        Stacked intermediate hidden states (output of each layer of the decoder).
    intermediate_reference_points (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, sequence_length, hidden_size)`):
        Stacked intermediate reference points (reference points of each layer of the decoder).
    """

    last_hidden_state: Optional[torch.FloatTensor] = None
    intermediate_hidden_states: Optional[torch.FloatTensor] = None
    intermediate_reference_points: Optional[torch.FloatTensor] = None
    hidden_states: Optional[tuple[torch.FloatTensor]] = None
    attentions: Optional[tuple[tuple[torch.FloatTensor]]] = None


class MMGroundingDinoDecoderLayer(nn.Module):
    def __init__(self, config: MMGroundingDinoConfig):
        super().__init__()
        self.embed_dim = config.d_model

        # self-attention
        self.self_attn = MMGroundingDinoMultiheadAttention(config, num_attention_heads=config.decoder_attention_heads)

        self.dropout = config.dropout
        self.activation_fn = ACT2FN[config.activation_function]
        self.activation_dropout = config.activation_dropout

        self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim, config.layer_norm_eps)
        # cross-attention text
        self.encoder_attn_text = MMGroundingDinoMultiheadAttention(
            config, num_attention_heads=config.decoder_attention_heads
        )
        self.encoder_attn_text_layer_norm = nn.LayerNorm(self.embed_dim, config.layer_norm_eps)
        # cross-attention
        self.encoder_attn = MMGroundingDinoMultiscaleDeformableAttention(
            config,
            num_heads=config.decoder_attention_heads,
            n_points=config.decoder_n_points,
        )
        self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim, config.layer_norm_eps)
        # feedforward neural networks
        self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim)
        self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim)
        self.final_layer_norm = nn.LayerNorm(self.embed_dim, config.layer_norm_eps)

    def with_pos_embed(self, tensor: torch.Tensor, position_embeddings: Optional[Tensor]):
        return tensor if position_embeddings is None else tensor + position_embeddings

    def forward(
        self,
        hidden_states: torch.Tensor,
        position_embeddings: Optional[torch.Tensor] = None,
        reference_points=None,
        spatial_shapes=None,
        spatial_shapes_list=None,
        level_start_index=None,
        vision_encoder_hidden_states: Optional[torch.Tensor] = None,
        vision_encoder_attention_mask: Optional[torch.Tensor] = None,
        text_encoder_hidden_states: Optional[torch.Tensor] = None,
        text_encoder_attention_mask: Optional[torch.Tensor] = None,
        self_attn_mask: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = False,
    ):
        residual = hidden_states

        # Self Attention
        queries = keys = self.with_pos_embed(hidden_states, position_embeddings)
        hidden_states, self_attn_weights = self.self_attn(
            queries=queries,
            keys=keys,
            values=hidden_states,
            attention_mask=self_attn_mask,
            output_attentions=True,
        )

        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
        hidden_states = residual + hidden_states
        hidden_states = self.self_attn_layer_norm(hidden_states)

        second_residual = hidden_states

        # Cross-Attention Text
        queries = self.with_pos_embed(hidden_states, position_embeddings)
        hidden_states, text_cross_attn_weights = self.encoder_attn_text(
            queries=queries,
            keys=text_encoder_hidden_states,
            values=text_encoder_hidden_states,
            attention_mask=text_encoder_attention_mask,
            output_attentions=True,
        )

        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
        hidden_states = second_residual + hidden_states
        hidden_states = self.encoder_attn_text_layer_norm(hidden_states)

        third_residual = hidden_states

        # Cross-Attention
        cross_attn_weights = None
        hidden_states, cross_attn_weights = self.encoder_attn(
            hidden_states=hidden_states,
            attention_mask=vision_encoder_attention_mask,
            encoder_hidden_states=vision_encoder_hidden_states,
            encoder_attention_mask=vision_encoder_attention_mask,
            position_embeddings=position_embeddings,
            reference_points=reference_points,
            spatial_shapes=spatial_shapes,
            spatial_shapes_list=spatial_shapes_list,
            level_start_index=level_start_index,
            output_attentions=output_attentions,
        )

        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
        hidden_states = third_residual + hidden_states
        hidden_states = self.encoder_attn_layer_norm(hidden_states)

        # Fully Connected
        residual = hidden_states
        hidden_states = self.activation_fn(self.fc1(hidden_states))
        hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
        hidden_states = self.fc2(hidden_states)
        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
        hidden_states = residual + hidden_states
        hidden_states = self.final_layer_norm(hidden_states)

        outputs = (hidden_states,)

        if output_attentions:
            outputs += (self_attn_weights, text_cross_attn_weights, cross_attn_weights)

        return outputs


class MMGroundingDinoDecoder(MMGroundingDinoPreTrainedModel):
    """
    Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`MMGroundingDinoDecoderLayer`].

    The decoder updates the query embeddings through multiple self-attention and cross-attention layers.

    Some tweaks for Grounding DINO:

    - `position_embeddings`, `reference_points`, `spatial_shapes` and `valid_ratios` are added to the forward pass.
    - it also returns a stack of intermediate outputs and reference points from all decoding layers.

    Args:
        config: MMGroundingDinoConfig
    """

    def __init__(self, config: MMGroundingDinoConfig):
        super().__init__(config)

        self.dropout = config.dropout
        self.layer_norm = nn.LayerNorm(config.d_model, config.layer_norm_eps)
        self.layers = nn.ModuleList([MMGroundingDinoDecoderLayer(config) for _ in range(config.decoder_layers)])
        self.reference_points_head = MMGroundingDinoMLPPredictionHead(
            config.query_dim // 2 * config.d_model, config.d_model, config.d_model, 2
        )
        self.gradient_checkpointing = False

        # hack implementation for iterative bounding box refinement as in two-stage Deformable DETR
        self.bbox_embed = None
        self.class_embed = None
        self.query_scale = None

        # Initialize weights and apply final processing
        self.post_init()

    def forward(
        self,
        inputs_embeds,
        vision_encoder_hidden_states,
        vision_encoder_attention_mask=None,
        text_encoder_hidden_states=None,
        text_encoder_attention_mask=None,
        reference_points=None,
        spatial_shapes=None,
        spatial_shapes_list=None,
        level_start_index=None,
        valid_ratios=None,
        self_attn_mask=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
    ):
        r"""
        Args:
            inputs_embeds (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`):
                The query embeddings that are passed into the decoder.
            vision_encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
                Last hidden state from encoder related to vision feature map.
            vision_encoder_attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
                Mask to avoid performing attention on padding pixel features. Mask values selected in `[0, 1]`:
                - 1 for pixel features that are real (i.e. **not masked**),
                - 0 for pixel features that are padding (i.e. **masked**).
            text_encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, text_seq_len, hidden_size)`):
                Last hidden state from encoder related to text features.
            text_encoder_attention_mask (`torch.Tensor` of shape `(batch_size, text_seq_len)`, *optional*):
                Mask to avoid performing attention on padding text features. Mask values selected in `[0, 1]`:
                - 0 for text features that are real (i.e. **not masked**),
                - 1 for text features that are padding (i.e. **masked**).
            reference_points (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)` is `as_two_stage` else `(batch_size, num_queries, 2)` or , *optional*):
                Reference point in range `[0, 1]`, top-left (0,0), bottom-right (1, 1), including padding area.
            spatial_shapes (`torch.FloatTensor` of shape `(num_feature_levels, 2)`):
                Spatial shapes of the feature maps.
            spatial_shapes_list (`list[tuple[int, int]]`):
                Spatial shapes of the feature maps (but as list for export compatibility).
            level_start_index (`torch.LongTensor` of shape `(num_feature_levels)`, *optional*):
                Indexes for the start of each feature level. In range `[0, sequence_length]`.
            valid_ratios (`torch.FloatTensor` of shape `(batch_size, num_feature_levels, 2)`, *optional*):
                Ratio of valid area in each feature level.
            self_attn_mask (`torch.BoolTensor` of shape `(batch_size, text_seq_len)`):
                Masks to avoid performing self-attention between vision hidden state. Mask values selected in `[0, 1]`:
                - 1 for queries that are real (i.e. **not masked**),
                - 0 for queries that are padding (i.e. **masked**).
            output_attentions (`bool`, *optional*):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more detail.
            output_hidden_states (`bool`, *optional*):
                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
                for more detail.
            return_dict (`bool`, *optional*):
                Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
        """
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        if inputs_embeds is not None:
            hidden_states = inputs_embeds

        # decoder layers
        all_hidden_states = () if output_hidden_states else None
        all_self_attns = () if output_attentions else None
        all_attns = () if output_attentions else None
        all_cross_attns_vision = () if (output_attentions and vision_encoder_hidden_states is not None) else None
        all_cross_attns_text = () if (output_attentions and text_encoder_hidden_states is not None) else None
        intermediate = ()
        intermediate_reference_points = ()

        if text_encoder_attention_mask is not None:
            dtype = text_encoder_hidden_states.dtype

            text_encoder_attention_mask = text_encoder_attention_mask[:, None, None, :]
            text_encoder_attention_mask = text_encoder_attention_mask.repeat(
                1, self.config.decoder_attention_heads, self.config.num_queries, 1
            )
            text_encoder_attention_mask = text_encoder_attention_mask.to(dtype=dtype)
            text_encoder_attention_mask = text_encoder_attention_mask * torch.finfo(dtype).min

        for idx, decoder_layer in enumerate(self.layers):
            num_coordinates = reference_points.shape[-1]
            if num_coordinates == 4:
                reference_points_input = (
                    reference_points[:, :, None] * torch.cat([valid_ratios, valid_ratios], -1)[:, None]
                )
            elif num_coordinates == 2:
                reference_points_input = reference_points[:, :, None] * valid_ratios[:, None]
            else:
                raise ValueError("Last dim of reference_points must be 2 or 4, but got {reference_points.shape[-1]}")
            query_pos = get_sine_pos_embed(reference_points_input[:, :, 0, :], num_pos_feats=self.config.d_model // 2)
            query_pos = self.reference_points_head(query_pos)

            # In original implementation they apply layer norm before outputting intermediate hidden states
            # Though that's not through between layers so the layers use as input the output of the previous layer
            # without layer norm
            if output_hidden_states:
                all_hidden_states += (self.layer_norm(hidden_states),)

            if self.gradient_checkpointing and self.training:

                def create_custom_forward(module):
                    def custom_forward(*inputs):
                        return module(*inputs, output_attentions)

                    return custom_forward

                layer_outputs = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(decoder_layer),
                    hidden_states,
                    query_pos,
                    reference_points_input,
                    spatial_shapes,
                    level_start_index,
                    vision_encoder_hidden_states,
                    vision_encoder_attention_mask,
                    text_encoder_hidden_states,
                    text_encoder_attention_mask,
                    self_attn_mask,
                    None,
                )
            else:
                layer_outputs = decoder_layer(
                    hidden_states=hidden_states,
                    position_embeddings=query_pos,
                    reference_points=reference_points_input,
                    spatial_shapes=spatial_shapes,
                    spatial_shapes_list=spatial_shapes_list,
                    level_start_index=level_start_index,
                    vision_encoder_hidden_states=vision_encoder_hidden_states,
                    vision_encoder_attention_mask=vision_encoder_attention_mask,
                    text_encoder_hidden_states=text_encoder_hidden_states,
                    text_encoder_attention_mask=text_encoder_attention_mask,
                    self_attn_mask=self_attn_mask,
                    output_attentions=output_attentions,
                )

            hidden_states = layer_outputs[0]

            # hack implementation for iterative bounding box refinement
            if self.bbox_embed is not None:
                tmp = self.bbox_embed[idx](hidden_states)
                num_coordinates = reference_points.shape[-1]
                if num_coordinates == 4:
                    new_reference_points = tmp + torch.special.logit(reference_points, eps=1e-5)
                    new_reference_points = new_reference_points.sigmoid()
                elif num_coordinates == 2:
                    new_reference_points = tmp
                    new_reference_points[..., :2] = tmp[..., :2] + torch.special.logit(reference_points, eps=1e-5)
                    new_reference_points = new_reference_points.sigmoid()
                else:
                    raise ValueError(
                        f"Last dim of reference_points must be 2 or 4, but got {reference_points.shape[-1]}"
                    )
                reference_points = new_reference_points.detach()

            intermediate += (self.layer_norm(hidden_states),)
            intermediate_reference_points += (reference_points,)

            if output_attentions:
                all_self_attns += (layer_outputs[1],)

                if text_encoder_hidden_states is not None:
                    all_cross_attns_text += (layer_outputs[2],)

                if vision_encoder_hidden_states is not None:
                    all_cross_attns_vision += (layer_outputs[3],)

        # Keep batch_size as first dimension
        intermediate = torch.stack(intermediate, dim=1)
        intermediate_reference_points = torch.stack(intermediate_reference_points, dim=1)
        hidden_states = self.layer_norm(hidden_states)

        # add hidden states from the last decoder layer
        if output_hidden_states:
            all_hidden_states += (hidden_states,)

        if output_attentions:
            all_attns += (all_self_attns, all_cross_attns_text, all_cross_attns_vision)

        if not return_dict:
            return tuple(
                v
                for v in [
                    hidden_states,
                    intermediate,
                    intermediate_reference_points,
                    all_hidden_states,
                    all_attns,
                ]
                if v is not None
            )
        return MMGroundingDinoDecoderOutput(
            last_hidden_state=hidden_states,
            intermediate_hidden_states=intermediate,
            intermediate_reference_points=intermediate_reference_points,
            hidden_states=all_hidden_states,
            attentions=all_attns,
        )


@dataclass
@auto_docstring(
    custom_intro="""
    Base class for outputs of the Grounding DINO encoder-decoder model.
    """
)
class MMGroundingDinoModelOutput(ModelOutput):
    r"""
    last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`):
        Sequence of hidden-states at the output of the last layer of the decoder of the model.
    init_reference_points (`torch.FloatTensor` of shape  `(batch_size, num_queries, 4)`):
        Initial reference points sent through the Transformer decoder.
    intermediate_hidden_states (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, hidden_size)`):
        Stacked intermediate hidden states (output of each layer of the decoder).
    intermediate_reference_points (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`):
        Stacked intermediate reference points (reference points of each layer of the decoder).
    encoder_last_hidden_state_vision (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
        Sequence of hidden-states at the output of the last layer of the encoder of the model.
    encoder_last_hidden_state_text (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
        Sequence of hidden-states at the output of the last layer of the encoder of the model.
    encoder_vision_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 vision embeddings + one for the output of each
        layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the vision encoder at the
        output of each layer plus the initial embedding outputs.
    encoder_text_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 text embeddings + one for the output of each layer)
        of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the text encoder at the output of
        each layer plus the initial embedding outputs.
    encoder_attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
        Tuple of tuples of `torch.FloatTensor` (one for attention 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 text-vision attention, vision-text attention, text-enhancer (self-attention) and
        multi-scale deformable attention heads. attention softmax, used to compute the weighted average in the
        bi-attention heads.
    enc_outputs_class (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`, *optional*, returned when `config.two_stage=True`):
        Predicted bounding boxes scores where the top `config.num_queries` scoring bounding boxes are picked as
        region proposals in the first stage. Output of bounding box binary classification (i.e. foreground and
        background).
    enc_outputs_coord_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, 4)`, *optional*, returned when `config.two_stage=True`):
        Logits of predicted bounding boxes coordinates in the first stage.
    encoder_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`, *optional*, returned when `config.two_stage=True`):
        Logits of top `config.num_queries` scoring bounding boxes in the first stage.
    encoder_pred_boxes (`torch.FloatTensor` of shape `(batch_size, sequence_length, 4)`, *optional*, returned when `config.two_stage=True`):
        Coordinates of top `config.num_queries` scoring bounding boxes in the first stage.
    """

    last_hidden_state: Optional[torch.FloatTensor] = None
    init_reference_points: Optional[torch.FloatTensor] = None
    intermediate_hidden_states: Optional[torch.FloatTensor] = None
    intermediate_reference_points: Optional[torch.FloatTensor] = None
    decoder_hidden_states: Optional[tuple[torch.FloatTensor]] = None
    decoder_attentions: Optional[tuple[tuple[torch.FloatTensor]]] = None
    encoder_last_hidden_state_vision: Optional[torch.FloatTensor] = None
    encoder_last_hidden_state_text: Optional[torch.FloatTensor] = None
    encoder_vision_hidden_states: Optional[tuple[torch.FloatTensor]] = None
    encoder_text_hidden_states: Optional[tuple[torch.FloatTensor]] = None
    encoder_attentions: Optional[tuple[tuple[torch.FloatTensor]]] = None
    enc_outputs_class: Optional[torch.FloatTensor] = None
    enc_outputs_coord_logits: Optional[torch.FloatTensor] = None
    encoder_logits: Optional[torch.FloatTensor] = None
    encoder_pred_boxes: Optional[torch.FloatTensor] = None


class MMGroundingDinoSinePositionEmbedding(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, config):
        super().__init__()
        self.embedding_dim = config.d_model // 2
        self.temperature = config.positional_embedding_temperature
        self.scale = 2 * math.pi

    def forward(self, pixel_values, pixel_mask):
        y_embed = pixel_mask.cumsum(1, dtype=torch.float32)
        x_embed = pixel_mask.cumsum(2, dtype=torch.float32)
        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.embedding_dim, dtype=torch.float32, device=pixel_values.device)
        dim_t = self.temperature ** (2 * torch.div(dim_t, 2, rounding_mode="floor") / self.embedding_dim)

        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


def build_position_encoding(config):
    if config.position_embedding_type == "sine":
        position_embedding = MMGroundingDinoSinePositionEmbedding(config)
    elif config.position_embedding_type == "learned":
        position_embedding = MMGroundingDinoLearnedPositionEmbedding(config)
    else:
        raise ValueError(f"Not supported {config.position_embedding_type}")

    return position_embedding


# these correspond to [CLS], [SEP], . and ?
SPECIAL_TOKENS = [101, 102, 1012, 1029]


def generate_masks_with_special_tokens_and_transfer_map(input_ids: torch.LongTensor) -> tuple[Tensor, Tensor]:
    """Generate attention mask between each pair of special tokens and positional ids.
    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.
    Returns:
        `tuple(torch.Tensor)` comprising attention mask between each special tokens and position_ids:
        - **attention_mask** (`torch.BoolTensor` of shape `(batch_size, sequence_length, sequence_length)`)
        - **position_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`)
    """
    batch_size, num_token = input_ids.shape
    # special_tokens_mask: batch_size, num_token. 1 for special tokens. 0 for normal tokens
    special_tokens_mask = torch.zeros((batch_size, num_token), device=input_ids.device).bool()
    for special_token in SPECIAL_TOKENS:
        special_tokens_mask = torch.logical_or(special_tokens_mask, input_ids == special_token)

    # idxs: each row is a list of indices of special tokens
    idxs = torch.nonzero(special_tokens_mask)

    # generate attention mask and positional ids
    attention_mask = torch.eye(num_token, device=input_ids.device).bool().unsqueeze(0).repeat(batch_size, 1, 1)
    position_ids = torch.zeros((batch_size, num_token), device=input_ids.device)
    previous_col = 0
    for i in range(idxs.shape[0]):
        row, col = idxs[i]
        if (col == 0) or (col == num_token - 1):
            attention_mask[row, col, col] = True
            position_ids[row, col] = 0
        else:
            attention_mask[row, previous_col + 1 : col + 1, previous_col + 1 : col + 1] = True
            position_ids[row, previous_col + 1 : col + 1] = torch.arange(
                0, col - previous_col, device=input_ids.device
            )

        previous_col = col

    return attention_mask, position_ids.to(torch.long)


@auto_docstring(
    custom_intro="""
    The bare Grounding DINO Model (consisting of a backbone and encoder-decoder Transformer) outputting raw
    hidden-states without any specific head on top.
    """
)
class MMGroundingDinoModel(MMGroundingDinoPreTrainedModel):
    def __init__(self, config: MMGroundingDinoConfig):
        super().__init__(config)

        # Create backbone + positional encoding
        backbone = MMGroundingDinoConvEncoder(config)
        position_embeddings = build_position_encoding(config)
        self.backbone = MMGroundingDinoConvModel(backbone, position_embeddings)

        # Create input projection layers
        num_backbone_outs = len(backbone.intermediate_channel_sizes)
        input_proj_list = []
        for i in range(num_backbone_outs):
            in_channels = backbone.intermediate_channel_sizes[i]
            input_proj_list.append(
                nn.Sequential(
                    nn.Conv2d(in_channels, config.d_model, kernel_size=1),
                    nn.GroupNorm(32, config.d_model),
                )
            )
        for _ in range(config.num_feature_levels - num_backbone_outs):
            input_proj_list.append(
                nn.Sequential(
                    nn.Conv2d(in_channels, config.d_model, kernel_size=3, stride=2, padding=1),
                    nn.GroupNorm(32, config.d_model),
                )
            )
            in_channels = config.d_model
        self.input_proj_vision = nn.ModuleList(input_proj_list)

        # Create text backbone
        self.text_backbone = AutoModel.from_config(config.text_config, add_pooling_layer=False)
        self.text_projection = nn.Linear(config.text_config.hidden_size, config.d_model)

        if config.embedding_init_target or not config.two_stage:
            self.query_position_embeddings = nn.Embedding(config.num_queries, config.d_model)

        self.encoder = MMGroundingDinoEncoder(config)
        self.decoder = MMGroundingDinoDecoder(config)

        self.level_embed = nn.Parameter(torch.Tensor(config.num_feature_levels, config.d_model))

        self.enc_output = nn.Linear(config.d_model, config.d_model)
        self.enc_output_norm = nn.LayerNorm(config.d_model, config.layer_norm_eps)
        self.encoder_output_bbox_embed = MMGroundingDinoMLPPredictionHead(
            input_dim=config.d_model, hidden_dim=config.d_model, output_dim=4, num_layers=3
        )
        self.encoder_output_class_embed = MMGroundingDinoContrastiveEmbedding(config)

        self.post_init()

    def freeze_backbone(self):
        for name, param in self.backbone.conv_encoder.model.named_parameters():
            param.requires_grad_(False)

    def unfreeze_backbone(self):
        for name, param in self.backbone.conv_encoder.model.named_parameters():
            param.requires_grad_(True)

    def get_valid_ratio(self, mask):
        """Get the valid ratio of all feature maps."""

        _, height, width = mask.shape
        valid_height = torch.sum(mask[:, :, 0], 1)
        valid_width = torch.sum(mask[:, 0, :], 1)
        valid_ratio_height = valid_height.float() / height
        valid_ratio_width = valid_width.float() / width
        valid_ratio = torch.stack([valid_ratio_width, valid_ratio_height], -1)
        return valid_ratio

    def generate_encoder_output_proposals(self, enc_output, padding_mask, spatial_shapes):
        """Generate the encoder output proposals from encoded enc_output.

        Args:
            enc_output (`torch.Tensor[batch_size, sequence_length, hidden_size]`): Output of the encoder.
            padding_mask (`torch.Tensor[batch_size, sequence_length]`): Padding mask for `enc_output`.
            spatial_shapes (`torch.Tensor[num_feature_levels, 2]`): Spatial shapes of the feature maps.

        Returns:
            `tuple(torch.FloatTensor)`: A tuple of feature map and bbox prediction.
                - object_query (Tensor[batch_size, sequence_length, hidden_size]): Object query features. Later used to
                  directly predict a bounding box. (without the need of a decoder)
                - output_proposals (Tensor[batch_size, sequence_length, 4]): Normalized proposals, after an inverse
                  sigmoid.
        """
        batch_size = enc_output.shape[0]
        proposals = []
        current_position = 0
        for level, (height, width) in enumerate(spatial_shapes):
            mask_flatten_ = padding_mask[:, current_position : (current_position + height * width)]
            mask_flatten_ = mask_flatten_.view(batch_size, height, width, 1)
            valid_height = torch.sum(~mask_flatten_[:, :, 0, 0], 1)
            valid_width = torch.sum(~mask_flatten_[:, 0, :, 0], 1)

            grid_y, grid_x = meshgrid(
                torch.linspace(0, height - 1, height, dtype=torch.float32, device=enc_output.device),
                torch.linspace(0, width - 1, width, dtype=torch.float32, device=enc_output.device),
                indexing="ij",
            )
            grid = torch.cat([grid_x.unsqueeze(-1), grid_y.unsqueeze(-1)], -1)

            scale = torch.cat([valid_width.unsqueeze(-1), valid_height.unsqueeze(-1)], 1).view(batch_size, 1, 1, 2)
            grid = (grid.unsqueeze(0).expand(batch_size, -1, -1, -1) + 0.5) / scale
            width_height = torch.ones_like(grid) * 0.05 * (2.0**level)
            proposal = torch.cat((grid, width_height), -1).view(batch_size, -1, 4)
            proposals.append(proposal)
            current_position += height * width

        output_proposals = torch.cat(proposals, 1)
        output_proposals_valid = ((output_proposals > 0.01) & (output_proposals < 0.99)).all(-1, keepdim=True)
        output_proposals = torch.log(output_proposals / (1 - output_proposals))  # inverse sigmoid
        output_proposals = output_proposals.masked_fill(padding_mask.unsqueeze(-1), float("inf"))
        output_proposals = output_proposals.masked_fill(~output_proposals_valid, float("inf"))

        # assign each pixel as an object query
        object_query = enc_output
        object_query = object_query.masked_fill(padding_mask.unsqueeze(-1), float(0))
        object_query = object_query.masked_fill(~output_proposals_valid, float(0))
        object_query = self.enc_output_norm(self.enc_output(object_query))
        return object_query, output_proposals

    @auto_docstring
    def forward(
        self,
        pixel_values: Tensor,
        input_ids: Tensor,
        token_type_ids: Optional[Tensor] = None,
        attention_mask: Optional[Tensor] = None,
        pixel_mask: Optional[Tensor] = None,
        encoder_outputs=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
    ):
        r"""
        input_ids (`torch.LongTensor` of shape `(batch_size, text_sequence_length)`):
            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
            it.

            Indices can be obtained using [`AutoTokenizer`]. See [`BertTokenizer.__call__`] for details.
        token_type_ids (`torch.LongTensor` of shape `(batch_size, text_sequence_length)`, *optional*):
            Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
            1]`: 0 corresponds to a `sentence A` token, 1 corresponds to a `sentence B` token

            [What are token type IDs?](../glossary#token-type-ids)

        Examples:

        ```python
        >>> from transformers import AutoProcessor, AutoModel
        >>> from PIL import Image
        >>> import requests

        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
        >>> image = Image.open(requests.get(url, stream=True).raw)
        >>> text = "a cat."

        >>> processor = AutoProcessor.from_pretrained("IDEA-Research/grounding-dino-tiny")
        >>> model = AutoModel.from_pretrained("IDEA-Research/grounding-dino-tiny")

        >>> inputs = processor(images=image, text=text, return_tensors="pt")
        >>> outputs = model(**inputs)

        >>> last_hidden_states = outputs.last_hidden_state
        >>> list(last_hidden_states.shape)
        [1, 900, 256]
        ```"""
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        text_self_attention_masks, position_ids = generate_masks_with_special_tokens_and_transfer_map(input_ids)

        if attention_mask is None:
            attention_mask = torch.ones_like(input_ids)

        if token_type_ids is None:
            token_type_ids = torch.zeros_like(input_ids)

        text_token_mask = attention_mask.bool()  # just to avoid renaming everywhere

        max_text_len = self.config.max_text_len
        if text_self_attention_masks.shape[1] > max_text_len:
            text_self_attention_masks = text_self_attention_masks[:, :max_text_len, :max_text_len]
            position_ids = position_ids[:, :max_text_len]
            input_ids = input_ids[:, :max_text_len]
            token_type_ids = token_type_ids[:, :max_text_len]
            text_token_mask = text_token_mask[:, :max_text_len]

        # 3D -> 4D correction (add head dim)
        # NOTE: we squeeze this later again as there is custom 3D logic in this model
        if text_self_attention_masks.ndim == 3:
            text_self_attention_masks = text_self_attention_masks[:, None, :, :]

        # Extract text features from text backbone
        text_outputs = self.text_backbone(
            input_ids, text_self_attention_masks, token_type_ids, position_ids, return_dict=return_dict
        )
        text_features = text_outputs.last_hidden_state if return_dict else text_outputs[0]
        text_features = self.text_projection(text_features)

        batch_size, num_channels, height, width = pixel_values.shape
        device = pixel_values.device

        if pixel_mask is None:
            pixel_mask = torch.ones(((batch_size, height, width)), dtype=torch.long, device=device)

        # Extract multi-scale feature maps of same resolution `config.d_model` (cf Figure 4 in paper)
        # First, sent pixel_values + pixel_mask through Backbone to obtain the features
        # which is a list of tuples
        vision_features, position_embeddings_list = self.backbone(pixel_values, pixel_mask)

        # Then, apply 1x1 convolution to reduce the channel dimension to d_model (256 by default)
        feature_maps = []
        masks = []
        for level, (source, mask) in enumerate(vision_features):
            feature_maps.append(self.input_proj_vision[level](source))
            masks.append(mask)

        # Lowest resolution feature maps are obtained via 3x3 stride 2 convolutions on the final stage
        if self.config.num_feature_levels > len(feature_maps):
            _len_sources = len(feature_maps)
            for level in range(_len_sources, self.config.num_feature_levels):
                if level == _len_sources:
                    source = self.input_proj_vision[level](vision_features[-1][0])
                else:
                    source = self.input_proj_vision[level](feature_maps[-1])
                mask = nn.functional.interpolate(pixel_mask[None].float(), size=source.shape[-2:]).to(torch.bool)[0]
                pos_l = self.backbone.position_embedding(source, mask).to(source.dtype)
                feature_maps.append(source)
                masks.append(mask)
                position_embeddings_list.append(pos_l)

        # Create queries
        query_embeds = None
        if self.config.embedding_init_target or self.config.two_stage:
            query_embeds = self.query_position_embeddings.weight

        # Prepare encoder inputs (by flattening)
        source_flatten = []
        mask_flatten = []
        lvl_pos_embed_flatten = []
        spatial_shapes_list = []
        for level, (source, mask, pos_embed) in enumerate(zip(feature_maps, masks, position_embeddings_list)):
            batch_size, num_channels, height, width = source.shape
            spatial_shape = (height, width)
            spatial_shapes_list.append(spatial_shape)
            source = source.flatten(2).transpose(1, 2)
            mask = mask.flatten(1)
            pos_embed = pos_embed.flatten(2).transpose(1, 2)
            lvl_pos_embed = pos_embed + self.level_embed[level].view(1, 1, -1)
            lvl_pos_embed_flatten.append(lvl_pos_embed)
            source_flatten.append(source)
            mask_flatten.append(mask)
        source_flatten = torch.cat(source_flatten, 1)
        mask_flatten = torch.cat(mask_flatten, 1)
        lvl_pos_embed_flatten = torch.cat(lvl_pos_embed_flatten, 1)
        spatial_shapes = torch.as_tensor(spatial_shapes_list, dtype=torch.long, device=source_flatten.device)
        level_start_index = torch.cat((spatial_shapes.new_zeros((1,)), spatial_shapes.prod(1).cumsum(0)[:-1]))
        valid_ratios = torch.stack([self.get_valid_ratio(m) for m in masks], 1)
        valid_ratios = valid_ratios.float()

        # Fourth, sent source_flatten + mask_flatten + lvl_pos_embed_flatten (backbone + proj layer output) through encoder
        # Also provide spatial_shapes, level_start_index and valid_ratios
        if encoder_outputs is None:
            encoder_outputs = self.encoder(
                vision_features=source_flatten,
                vision_attention_mask=~mask_flatten,
                vision_position_embedding=lvl_pos_embed_flatten,
                spatial_shapes=spatial_shapes,
                spatial_shapes_list=spatial_shapes_list,
                level_start_index=level_start_index,
                valid_ratios=valid_ratios,
                text_features=text_features,
                text_attention_mask=~text_token_mask,
                text_position_embedding=None,
                text_self_attention_masks=~text_self_attention_masks.squeeze(1),
                text_position_ids=position_ids,
                output_attentions=output_attentions,
                output_hidden_states=output_hidden_states,
                return_dict=return_dict,
            )
        # If the user passed a tuple for encoder_outputs, we wrap it in a MMGroundingDinoEncoderOutput when return_dict=True
        elif return_dict and not isinstance(encoder_outputs, MMGroundingDinoEncoderOutput):
            encoder_outputs = MMGroundingDinoEncoderOutput(
                last_hidden_state_vision=encoder_outputs[0],
                last_hidden_state_text=encoder_outputs[1],
                vision_hidden_states=encoder_outputs[2] if output_hidden_states else None,
                text_hidden_states=encoder_outputs[3] if output_hidden_states else None,
                attentions=encoder_outputs[-1] if output_attentions else None,
            )

        # Fifth, prepare decoder inputs
        topk_proposals = None
        enc_outputs_class = None
        enc_outputs_coord_logits = None
        encoder_logits = None
        encoder_pred_boxes = None
        if self.config.two_stage:
            object_query_embedding, output_proposals = self.generate_encoder_output_proposals(
                encoder_outputs[0], ~mask_flatten, spatial_shapes
            )

            # hack implementation as in two-stage Deformable DETR
            # apply a detection head to each pixel (A.4 in paper)
            # linear projection for bounding box binary classification (i.e. foreground and background)
            enc_outputs_class = self.encoder_output_class_embed(
                object_query_embedding, encoder_outputs[1], text_token_mask
            )
            # 3-layer FFN to predict bounding boxes coordinates (bbox regression branch)
            delta_bbox = self.encoder_output_bbox_embed(object_query_embedding)
            enc_outputs_coord_logits = delta_bbox + output_proposals

            # only keep top scoring `config.num_queries` proposals
            topk = self.config.num_queries
            topk_logits = enc_outputs_class.max(-1)[0]
            topk_proposals = torch.topk(topk_logits, topk, dim=1)[1]
            topk_coords_logits = torch.gather(
                enc_outputs_coord_logits, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, 4)
            )

            topk_coords_logits = topk_coords_logits.detach()
            reference_points = topk_coords_logits.sigmoid()
            init_reference_points = reference_points
            if query_embeds is not None:
                target = query_embeds.unsqueeze(0).repeat(batch_size, 1, 1)
            else:
                target = torch.gather(
                    object_query_embedding, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, self.d_model)
                ).detach()

            # Set intermediate topk proposals (coords and class) for loss computation
            encoder_pred_boxes = reference_points
            encoder_logits = self.encoder_output_class_embed(target, text_features, text_token_mask)
        else:
            target = query_embeds.unsqueeze(0).repeat(batch_size, 1, 1)
            reference_points = self.reference_points.weight.unsqueeze(0).repeat(batch_size, 1, 1).sigmoid()
            init_reference_points = reference_points

        decoder_outputs = self.decoder(
            inputs_embeds=target,
            vision_encoder_hidden_states=encoder_outputs[0],
            vision_encoder_attention_mask=mask_flatten,
            text_encoder_hidden_states=encoder_outputs[1],
            text_encoder_attention_mask=~text_token_mask,
            reference_points=reference_points,
            spatial_shapes=spatial_shapes,
            spatial_shapes_list=spatial_shapes_list,
            level_start_index=level_start_index,
            valid_ratios=valid_ratios,
            self_attn_mask=None,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        if not return_dict:
            enc_outputs = tuple(
                value
                for value in [
                    enc_outputs_class,
                    enc_outputs_coord_logits,
                    encoder_logits,
                    encoder_pred_boxes,
                ]
                if value is not None
            )
            tuple_outputs = (
                (decoder_outputs[0], init_reference_points) + decoder_outputs[1:] + encoder_outputs + enc_outputs
            )

            return tuple_outputs

        return MMGroundingDinoModelOutput(
            last_hidden_state=decoder_outputs.last_hidden_state,
            init_reference_points=init_reference_points,
            intermediate_hidden_states=decoder_outputs.intermediate_hidden_states,
            intermediate_reference_points=decoder_outputs.intermediate_reference_points,
            decoder_hidden_states=decoder_outputs.hidden_states,
            decoder_attentions=decoder_outputs.attentions,
            encoder_last_hidden_state_vision=encoder_outputs.last_hidden_state_vision,
            encoder_last_hidden_state_text=encoder_outputs.last_hidden_state_text,
            encoder_vision_hidden_states=encoder_outputs.vision_hidden_states,
            encoder_text_hidden_states=encoder_outputs.text_hidden_states,
            encoder_attentions=encoder_outputs.attentions,
            enc_outputs_class=enc_outputs_class,
            enc_outputs_coord_logits=enc_outputs_coord_logits,
            encoder_logits=encoder_logits,
            encoder_pred_boxes=encoder_pred_boxes,
        )


class MMGroundingDinoMLPPredictionHead(nn.Module):
    """
    Very simple multi-layer perceptron (MLP, also called FFN), used to predict the normalized center coordinates,
    height and width of a bounding box w.r.t. an image.

    Copied from https://github.com/facebookresearch/detr/blob/master/models/detr.py

    """

    def __init__(self, input_dim, hidden_dim, output_dim, num_layers):
        super().__init__()
        self.num_layers = num_layers
        h = [hidden_dim] * (num_layers - 1)
        self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim]))

    def forward(self, x):
        for i, layer in enumerate(self.layers):
            x = nn.functional.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
        return x


@dataclass
@auto_docstring(
    custom_intro="""
    Output type of [`MMGroundingDinoForObjectDetection`].
    """
)
class MMGroundingDinoObjectDetectionOutput(ModelOutput):
    r"""
    loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` are provided)):
        Total loss as a linear combination of a negative log-likehood (cross-entropy) for class prediction and a
        bounding box loss. The latter is defined as a linear combination of the L1 loss and the generalized
        scale-invariant IoU loss.
    loss_dict (`Dict`, *optional*):
        A dictionary containing the individual losses. Useful for logging.
    logits (`torch.FloatTensor` of shape `(batch_size, num_queries, num_classes + 1)`):
        Classification logits (including no-object) for all queries.
    pred_boxes (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`):
        Normalized boxes coordinates for all queries, represented as (center_x, center_y, width, height). These
        values are normalized in [0, 1], relative to the size of each individual image in the batch (disregarding
        possible padding). You can use [`~MMGroundingDinoProcessor.post_process_grounded_object_detection`] to retrieve the
        unnormalized bounding boxes.
    auxiliary_outputs (`list[Dict]`, *optional*):
        Optional, only returned when auxiliary losses are activated (i.e. `config.auxiliary_loss` is set to `True`)
        and labels are provided. It is a list of dictionaries containing the two above keys (`logits` and
        `pred_boxes`) for each decoder layer.
    last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`, *optional*):
        Sequence of hidden-states at the output of the last layer of the decoder of the model.
    init_reference_points (`torch.FloatTensor` of shape  `(batch_size, num_queries, 4)`):
        Initial reference points sent through the Transformer decoder.
    intermediate_hidden_states (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, hidden_size)`):
        Stacked intermediate hidden states (output of each layer of the decoder).
    intermediate_reference_points (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`):
        Stacked intermediate reference points (reference points of each layer of the decoder).
    encoder_last_hidden_state_vision (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
        Sequence of hidden-states at the output of the last layer of the encoder of the model.
    encoder_last_hidden_state_text (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
        Sequence of hidden-states at the output of the last layer of the encoder of the model.
    encoder_vision_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 vision embeddings + one for the output of each
        layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the vision encoder at the
        output of each layer plus the initial embedding outputs.
    encoder_text_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 text embeddings + one for the output of each layer)
        of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the text encoder at the output of
        each layer plus the initial embedding outputs.
    enc_outputs_class (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`, *optional*, returned when `config.two_stage=True`):
        Predicted bounding boxes scores where the top `config.num_queries` scoring bounding boxes are picked as
        region proposals in the first stage. Output of bounding box binary classification (i.e. foreground and
        background).
    enc_outputs_coord_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, 4)`, *optional*, returned when `config.two_stage=True`):
        Logits of predicted bounding boxes coordinates in the first stage.
    encoder_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`, *optional*, returned when `config.two_stage=True`):
        Logits of top `config.num_queries` scoring bounding boxes in the first stage.
    encoder_pred_boxes (`torch.FloatTensor` of shape `(batch_size, sequence_length, 4)`, *optional*, returned when `config.two_stage=True`):
        Coordinates of top `config.num_queries` scoring bounding boxes in the first stage.
    input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
        Encoded candidate labels sequence. Used in processor to post process object detection result.
    """

    loss: Optional[torch.FloatTensor] = None
    loss_dict: Optional[dict] = None
    logits: Optional[torch.FloatTensor] = None
    pred_boxes: Optional[torch.FloatTensor] = None
    auxiliary_outputs: Optional[list[dict]] = None
    last_hidden_state: Optional[torch.FloatTensor] = None
    init_reference_points: Optional[torch.FloatTensor] = None
    intermediate_hidden_states: Optional[torch.FloatTensor] = None
    intermediate_reference_points: Optional[torch.FloatTensor] = None
    decoder_hidden_states: Optional[tuple[torch.FloatTensor]] = None
    decoder_attentions: Optional[tuple[tuple[torch.FloatTensor]]] = None
    encoder_last_hidden_state_vision: Optional[torch.FloatTensor] = None
    encoder_last_hidden_state_text: Optional[torch.FloatTensor] = None
    encoder_vision_hidden_states: Optional[tuple[torch.FloatTensor]] = None
    encoder_text_hidden_states: Optional[tuple[torch.FloatTensor]] = None
    encoder_attentions: Optional[tuple[tuple[torch.FloatTensor]]] = None
    enc_outputs_class: Optional[torch.FloatTensor] = None
    enc_outputs_coord_logits: Optional[torch.FloatTensor] = None
    encoder_logits: Optional[torch.FloatTensor] = None
    encoder_pred_boxes: Optional[torch.FloatTensor] = None
    input_ids: Optional[torch.LongTensor] = None


def build_label_maps(logits: torch.FloatTensor, input_ids: torch.LongTensor) -> tuple[torch.FloatTensor]:
    """
    Computes a mapping between tokens and their corresponding labels, where `num_labels` is determined by the number of classes in the input prompt.
    The function identifies segments of tokens between specific delimiter tokens and generates label maps for those segments.
    Args:
        logits (`torch.Tensor` of shape `(batch_size, seq_length, hidden_size)`):
            The output logits from the model, where `hidden_size` corresponds to the dimension of the model's output features.

        input_ids (`torch.Tensor` of shape `(batch_size, seq_length)`):
            The input token IDs corresponding to the input prompt. For example, given the prompt "fish. shark.",
            `input_ids` might look like `[101, 3869, 1012, 11420, 1012, 102]` where each number corresponds to a token including special tokens.
    Returns:
        tuple: A tuple containing label maps for each instance in the batch.
        - label_maps (tuple of `torch.Tensor`):
            A tuple of tensors, where each tensor in the tuple corresponds to an instance in the batch. Each tensor
            has shape `(num_labels, hidden_size)` and contains binary values (0 or 1), where `1` indicates the tokens
            that are associated with a specific label (class) between delimiter tokens, and `0` elsewhere.
    Example:
        Given an input prompt "fish. shark." and corresponding `input_ids` as `[101, 3869, 1012, 11420, 1012, 102]`:
        - The function identifies the tokens for "fish" (IDs `[3869]`) and "shark" (IDs `[11420]`).
        - The function then constructs label maps for these tokens, where each label map indicates which tokens
          correspond to which label between the delimiter tokens (e.g., between the period `.`).
        - The output is a tuple of label maps, one for each instance in the batch.
    Note:
        - `SPECIAL_TOKENS` should be a predefined list of tokens that are considered special (e.g., `[CLS]`, `[SEP]`, etc.).
    """
    max_seq_len = logits.shape[-1]
    # Add [PAD] token to the list of special tokens
    delimiter_tokens = torch.tensor(SPECIAL_TOKENS + [0], device=input_ids.device)

    delimiter_token_masks = torch.isin(input_ids, delimiter_tokens)
    label_groups = torch.cumsum(delimiter_token_masks, dim=1) * (~delimiter_token_masks).to(torch.int32)

    label_maps = ()

    # Iterate over batch dimension as we can have different number of labels
    for label_group in label_groups:
        # `label_group` is a tensor of shape `(seq_len,)` with zeros for non-label tokens and integers for label tokens
        # label tokens with same integer value are part of the same label group

        # Get unique labels and exclude 0 (i.e. non-label tokens)
        unique_labels = torch.unique(label_group)[1:, None]
        num_labels = unique_labels.shape[0]

        # Create one-hot encoding for each label group
        label_map = label_group.unsqueeze(0).repeat(num_labels, 1)
        label_map = torch.where(label_map == unique_labels, 1, 0)

        # Pad label_map to match `max_seq_len`
        label_map = F.pad(label_map, (0, max_seq_len - label_map.shape[1]), value=0)

        label_maps += (label_map,)

    return label_maps


def build_text_mask(logits, attention_mask):
    """
    Create text_mask based on the matching indices
    """
    seq_len = attention_mask.shape[1]
    text_mask = torch.zeros_like(logits, device=logits.device, dtype=attention_mask.dtype)
    text_mask[:, :, :seq_len] = attention_mask[:, None, :]

    return text_mask.bool()


@auto_docstring(
    custom_intro="""
    Grounding DINO Model (consisting of a backbone and encoder-decoder Transformer) with object detection heads on top,
    for tasks such as COCO detection.
    """
)
class MMGroundingDinoForObjectDetection(MMGroundingDinoPreTrainedModel):
    _tied_weights_keys = [
        r"bbox_embed\.[1-9]\d*",
        r"model\.decoder\.bbox_embed\.[0-9]\d*",
        r"class_embed\.[1-9]\d*",
        r"model\.decoder\.class_embed\.[0-9]\d*",
    ]

    def __init__(self, config: MMGroundingDinoConfig):
        super().__init__(config)

        self.model = MMGroundingDinoModel(config)

        self.class_embed = nn.ModuleList(
            [MMGroundingDinoContrastiveEmbedding(config) for _ in range(config.decoder_layers)]
        )

        self.bbox_embed = nn.ModuleList(
            [
                MMGroundingDinoMLPPredictionHead(
                    input_dim=config.d_model, hidden_dim=config.d_model, output_dim=4, num_layers=3
                )
                for _ in range(config.decoder_layers)
            ]
        )

        # hack for box-refinement
        self.model.decoder.bbox_embed = self.bbox_embed
        # hack implementation for two-stage
        self.model.decoder.class_embed = self.class_embed

        # Initialize weights and apply final processing
        self.post_init()

    @auto_docstring
    def forward(
        self,
        pixel_values: torch.FloatTensor,
        input_ids: torch.LongTensor,
        token_type_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.LongTensor] = None,
        pixel_mask: Optional[torch.BoolTensor] = None,
        encoder_outputs: Optional[Union[MMGroundingDinoEncoderOutput, tuple]] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        labels: Optional[list[dict[str, Union[torch.LongTensor, torch.FloatTensor]]]] = None,
    ):
        r"""
        input_ids (`torch.LongTensor` of shape `(batch_size, text_sequence_length)`):
            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
            it.

            Indices can be obtained using [`AutoTokenizer`]. See [`BertTokenizer.__call__`] for details.
        token_type_ids (`torch.LongTensor` of shape `(batch_size, text_sequence_length)`, *optional*):
            Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
            1]`: 0 corresponds to a `sentence A` token, 1 corresponds to a `sentence B` token

            [What are token type IDs?](../glossary#token-type-ids)
        labels (`list[Dict]` of len `(batch_size,)`, *optional*):
            Labels for computing the bipartite matching loss. List of dicts, each dictionary containing at least the
            following 2 keys: 'class_labels' and 'boxes' (the class labels and bounding boxes of an image in the batch
            respectively). The class labels themselves should be a `torch.LongTensor` of len `(number of bounding boxes
            in the image,)` and the boxes a `torch.FloatTensor` of shape `(number of bounding boxes in the image, 4)`.

        Examples:

        ```python
        >>> import requests

        >>> import torch
        >>> from PIL import Image
        >>> from transformers import AutoProcessor, AutoModelForZeroShotObjectDetection

        >>> model_id = "IDEA-Research/grounding-dino-tiny"
        >>> device = "cuda"

        >>> processor = AutoProcessor.from_pretrained(model_id)
        >>> model = AutoModelForZeroShotObjectDetection.from_pretrained(model_id).to(device)

        >>> image_url = "http://images.cocodataset.org/val2017/000000039769.jpg"
        >>> image = Image.open(requests.get(image_url, stream=True).raw)
        >>> # Check for cats and remote controls
        >>> text_labels = [["a cat", "a remote control"]]

        >>> inputs = processor(images=image, text=text_labels, return_tensors="pt").to(device)
        >>> with torch.no_grad():
        ...     outputs = model(**inputs)

        >>> results = processor.post_process_grounded_object_detection(
        ...     outputs,
        ...     threshold=0.4,
        ...     text_threshold=0.3,
        ...     target_sizes=[(image.height, image.width)]
        ... )
        >>> # Retrieve the first image result
        >>> result = results[0]
        >>> for box, score, text_label in zip(result["boxes"], result["scores"], result["text_labels"]):
        ...     box = [round(x, 2) for x in box.tolist()]
        ...     print(f"Detected {text_label} with confidence {round(score.item(), 3)} at location {box}")
        Detected a cat with confidence 0.479 at location [344.7, 23.11, 637.18, 374.28]
        Detected a cat with confidence 0.438 at location [12.27, 51.91, 316.86, 472.44]
        Detected a remote control with confidence 0.478 at location [38.57, 70.0, 176.78, 118.18]
        ```"""
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        if attention_mask is None:
            attention_mask = torch.ones_like(input_ids)

        # First, sent images through Grounding DINO base model to obtain encoder + decoder outputs
        outputs = self.model(
            pixel_values=pixel_values,
            input_ids=input_ids,
            token_type_ids=token_type_ids,
            attention_mask=attention_mask,
            pixel_mask=pixel_mask,
            encoder_outputs=encoder_outputs,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        idx = 5 + (1 if output_attentions else 0) + (1 if output_hidden_states else 0)
        enc_text_hidden_state = outputs.encoder_last_hidden_state_text if return_dict else outputs[idx]
        hidden_states = outputs.intermediate_hidden_states if return_dict else outputs[2]
        init_reference_points = outputs.init_reference_points if return_dict else outputs[1]
        inter_references_points = outputs.intermediate_reference_points if return_dict else outputs[3]

        # class logits + predicted bounding boxes
        outputs_classes = []
        outputs_coords = []

        # hidden_states are of shape (batch_size, num_stages, height, width)
        # predict class and bounding box deltas for each stage
        num_levels = hidden_states.shape[1]
        for level in range(num_levels):
            if level == 0:
                reference = init_reference_points
            else:
                reference = inter_references_points[:, level - 1]
            reference = torch.special.logit(reference, eps=1e-5)
            outputs_class = self.class_embed[level](
                vision_hidden_state=hidden_states[:, level],
                text_hidden_state=enc_text_hidden_state,
                text_token_mask=attention_mask.bool(),
            )
            delta_bbox = self.bbox_embed[level](hidden_states[:, level])

            reference_coordinates = reference.shape[-1]
            if reference_coordinates == 4:
                outputs_coord_logits = delta_bbox + reference
            elif reference_coordinates == 2:
                delta_bbox[..., :2] += reference
                outputs_coord_logits = delta_bbox
            else:
                raise ValueError(f"reference.shape[-1] should be 4 or 2, but got {reference.shape[-1]}")
            outputs_coord = outputs_coord_logits.sigmoid()
            outputs_classes.append(outputs_class)
            outputs_coords.append(outputs_coord)
        outputs_class = torch.stack(outputs_classes)
        outputs_coord = torch.stack(outputs_coords)

        logits = outputs_class[-1]
        pred_boxes = outputs_coord[-1]

        loss, loss_dict, auxiliary_outputs = None, None, None
        if labels is not None:
            label_maps = build_label_maps(logits, input_ids)
            text_mask = build_text_mask(logits, attention_mask)
            loss, loss_dict, auxiliary_outputs = self.loss_function(
                logits,
                labels,
                self.device,
                pred_boxes,
                self.config,
                label_maps,
                text_mask,
                outputs_class=outputs_class,
                outputs_coord=outputs_coord,
                encoder_logits=outputs[-2],
                encoder_pred_boxes=outputs[-1],
            )

        if not return_dict:
            auxiliary_outputs = auxiliary_outputs if auxiliary_outputs is not None else []
            output = [loss, loss_dict, logits, pred_boxes, *auxiliary_outputs, *outputs, input_ids]
            output = tuple(out for out in output if out is not None)
            return output

        dict_outputs = MMGroundingDinoObjectDetectionOutput(
            loss=loss,
            loss_dict=loss_dict,
            logits=logits,
            pred_boxes=pred_boxes,
            last_hidden_state=outputs.last_hidden_state,
            auxiliary_outputs=auxiliary_outputs,
            decoder_hidden_states=outputs.decoder_hidden_states,
            decoder_attentions=outputs.decoder_attentions,
            encoder_last_hidden_state_vision=outputs.encoder_last_hidden_state_vision,
            encoder_last_hidden_state_text=outputs.encoder_last_hidden_state_text,
            encoder_vision_hidden_states=outputs.encoder_vision_hidden_states,
            encoder_text_hidden_states=outputs.encoder_text_hidden_states,
            encoder_attentions=outputs.encoder_attentions,
            intermediate_hidden_states=outputs.intermediate_hidden_states,
            intermediate_reference_points=outputs.intermediate_reference_points,
            init_reference_points=outputs.init_reference_points,
            enc_outputs_class=outputs.enc_outputs_class,
            enc_outputs_coord_logits=outputs.enc_outputs_coord_logits,
            encoder_logits=outputs.encoder_logits,
            encoder_pred_boxes=outputs.encoder_pred_boxes,
            input_ids=input_ids,
        )

        return dict_outputs


__all__ = ["MMGroundingDinoForObjectDetection", "MMGroundingDinoModel", "MMGroundingDinoPreTrainedModel"]
