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# coding=utf-8
# Copyright 2025 Baidu Inc and 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 functools import partial
from typing import Optional, Union

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

from ...activations import ACT2CLS, ACT2FN
from ...image_transforms import center_to_corners_format, corners_to_center_format
from ...modeling_outputs import BaseModelOutput
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import compile_compatible_method_lru_cache
from ...utils import ModelOutput, auto_docstring, is_torchdynamo_compiling, torch_int
from ...utils.backbone_utils import load_backbone
from .configuration_rt_detr_v2 import RTDetrV2Config


def multi_scale_deformable_attention_v2(
    value: Tensor,
    value_spatial_shapes: Tensor,
    sampling_locations: Tensor,
    attention_weights: Tensor,
    num_points_list: list[int],
    method="default",
) -> Tensor:
    batch_size, _, num_heads, hidden_dim = value.shape
    _, num_queries, num_heads, num_levels, num_points = sampling_locations.shape
    value_list = (
        value.permute(0, 2, 3, 1)
        .flatten(0, 1)
        .split([height * width for height, width in value_spatial_shapes], dim=-1)
    )
    # sampling_offsets [8, 480, 8, 12, 2]
    if method == "default":
        sampling_grids = 2 * sampling_locations - 1
    elif method == "discrete":
        sampling_grids = sampling_locations
    sampling_grids = sampling_grids.permute(0, 2, 1, 3, 4).flatten(0, 1)
    sampling_grids = sampling_grids.split(num_points_list, dim=-2)
    sampling_value_list = []
    for level_id, (height, width) in enumerate(value_spatial_shapes):
        # 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].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]
        # batch_size*num_heads, hidden_dim, num_queries, num_points
        if method == "default":
            sampling_value_l_ = nn.functional.grid_sample(
                value_l_, sampling_grid_l_, mode="bilinear", padding_mode="zeros", align_corners=False
            )
        elif method == "discrete":
            sampling_coord = (sampling_grid_l_ * torch.tensor([[width, height]], device=value.device) + 0.5).to(
                torch.int64
            )

            # Separate clamping for x and y coordinates
            sampling_coord_x = sampling_coord[..., 0].clamp(0, width - 1)
            sampling_coord_y = sampling_coord[..., 1].clamp(0, height - 1)

            # Combine the clamped coordinates
            sampling_coord = torch.stack([sampling_coord_x, sampling_coord_y], dim=-1)
            sampling_coord = sampling_coord.reshape(batch_size * num_heads, num_queries * num_points_list[level_id], 2)
            sampling_idx = (
                torch.arange(sampling_coord.shape[0], device=value.device)
                .unsqueeze(-1)
                .repeat(1, sampling_coord.shape[1])
            )
            sampling_value_l_ = value_l_[sampling_idx, :, sampling_coord[..., 1], sampling_coord[..., 0]]
            sampling_value_l_ = sampling_value_l_.permute(0, 2, 1).reshape(
                batch_size * num_heads, hidden_dim, num_queries, num_points_list[level_id]
            )
        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.permute(0, 2, 1, 3).reshape(
        batch_size * num_heads, 1, num_queries, sum(num_points_list)
    )
    output = (
        (torch.concat(sampling_value_list, dim=-1) * attention_weights)
        .sum(-1)
        .view(batch_size, num_heads * hidden_dim, num_queries)
    )
    return output.transpose(1, 2).contiguous()


# the main change
class RTDetrV2MultiscaleDeformableAttention(nn.Module):
    """
    RTDetrV2 version of multiscale deformable attention, extending the base implementation
    with improved offset handling and initialization.
    """

    def __init__(self, config: RTDetrV2Config):
        super().__init__()
        num_heads = config.decoder_attention_heads
        n_points = config.decoder_n_points

        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 RTDetrV2MultiscaleDeformableAttention 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

        # V2-specific attributes
        self.n_levels = config.decoder_n_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.offset_scale = config.decoder_offset_scale
        self.method = config.decoder_method

        # Initialize n_points list and scale
        n_points_list = [self.n_points for _ in range(self.n_levels)]
        self.n_points_list = n_points_list
        n_points_scale = [1 / n for n in n_points_list for _ in range(n)]
        self.register_buffer("n_points_scale", torch.tensor(n_points_scale, dtype=torch.float32))

    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,
    ):
        # Process inputs up to sampling locations calculation using parent class logic
        if position_embeddings is not None:
            hidden_states = hidden_states + position_embeddings

        batch_size, num_queries, _ = hidden_states.shape
        batch_size, sequence_length, _ = encoder_hidden_states.shape
        if not is_torchdynamo_compiling() and (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:
            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)

        # V2-specific sampling offsets shape
        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)

        # V2-specific sampling locations calculation
        if reference_points.shape[-1] == 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 reference_points.shape[-1] == 4:
            n_points_scale = self.n_points_scale.to(dtype=hidden_states.dtype).unsqueeze(-1)
            offset = sampling_offsets * n_points_scale * reference_points[:, :, None, :, 2:] * self.offset_scale
            sampling_locations = reference_points[:, :, None, :, :2] + offset
        else:
            raise ValueError(f"Last dim of reference_points must be 2 or 4, but got {reference_points.shape[-1]}")

        # V2-specific attention implementation choice
        output = multi_scale_deformable_attention_v2(
            value, spatial_shapes_list, sampling_locations, attention_weights, self.n_points_list, self.method
        )

        output = self.output_proj(output)
        return output, attention_weights


class RTDetrV2MultiheadAttention(nn.Module):
    """
    Multi-headed attention from 'Attention Is All You Need' paper.

    Here, we add position embeddings to the queries and keys (as explained in the Deformable DETR paper).
    """

    def __init__(
        self,
        embed_dim: int,
        num_heads: int,
        dropout: float = 0.0,
        bias: bool = True,
    ):
        super().__init__()
        self.embed_dim = embed_dim
        self.num_heads = num_heads
        self.dropout = dropout
        self.head_dim = embed_dim // num_heads
        if self.head_dim * 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`:"
                f" {num_heads})."
            )
        self.scaling = self.head_dim**-0.5

        self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
        self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
        self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
        self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)

    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 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,
        position_embeddings: Optional[torch.Tensor] = None,
        output_attentions: bool = False,
    ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
        """Input shape: Batch x Time x Channel"""

        batch_size, target_len, embed_dim = hidden_states.size()
        # add position embeddings to the hidden states before projecting to queries and keys
        if position_embeddings is not None:
            hidden_states_original = hidden_states
            hidden_states = self.with_pos_embed(hidden_states, position_embeddings)

        # get queries, keys and values
        query_states = self.q_proj(hidden_states) * self.scaling
        key_states = self._reshape(self.k_proj(hidden_states), -1, batch_size)
        value_states = self._reshape(self.v_proj(hidden_states_original), -1, batch_size)

        proj_shape = (batch_size * self.num_heads, -1, self.head_dim)
        query_states = self._reshape(query_states, target_len, batch_size).view(*proj_shape)
        key_states = key_states.view(*proj_shape)
        value_states = value_states.view(*proj_shape)

        source_len = key_states.size(1)

        attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))

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

        # expand attention_mask
        if attention_mask is not None:
            # [seq_len, seq_len] -> [batch_size, 1, target_seq_len, source_seq_len]
            attention_mask = attention_mask.expand(batch_size, 1, *attention_mask.size())

        if attention_mask is not None:
            if attention_mask.size() != (batch_size, 1, target_len, source_len):
                raise ValueError(
                    f"Attention mask should be of size {(batch_size, 1, target_len, source_len)}, but is"
                    f" {attention_mask.size()}"
                )
            if attention_mask.dtype == torch.bool:
                attention_mask = torch.zeros_like(attention_mask, dtype=attn_weights.dtype).masked_fill_(
                    attention_mask, -torch.inf
                )
            attn_weights = attn_weights.view(batch_size, self.num_heads, target_len, source_len) + attention_mask
            attn_weights = attn_weights.view(batch_size * self.num_heads, target_len, source_len)

        attn_weights = nn.functional.softmax(attn_weights, dim=-1)

        if output_attentions:
            # this operation is a bit awkward, but it's required to
            # make sure that attn_weights keeps its gradient.
            # In order to do so, attn_weights have to reshaped
            # twice and have to be reused in the following
            attn_weights_reshaped = attn_weights.view(batch_size, self.num_heads, target_len, source_len)
            attn_weights = attn_weights_reshaped.view(batch_size * self.num_heads, target_len, source_len)
        else:
            attn_weights_reshaped = None

        attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)

        attn_output = torch.bmm(attn_probs, value_states)

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

        attn_output = attn_output.view(batch_size, self.num_heads, target_len, self.head_dim)
        attn_output = attn_output.transpose(1, 2)
        attn_output = attn_output.reshape(batch_size, target_len, embed_dim)

        attn_output = self.out_proj(attn_output)

        return attn_output, attn_weights_reshaped


class RTDetrV2DecoderLayer(nn.Module):
    def __init__(self, config: RTDetrV2Config):
        super().__init__()
        # self-attention
        self.self_attn = RTDetrV2MultiheadAttention(
            embed_dim=config.d_model,
            num_heads=config.decoder_attention_heads,
            dropout=config.attention_dropout,
        )
        self.dropout = config.dropout
        self.activation_fn = ACT2FN[config.decoder_activation_function]
        self.activation_dropout = config.activation_dropout

        self.self_attn_layer_norm = nn.LayerNorm(config.d_model, eps=config.layer_norm_eps)
        # override only the encoder attention module with v2 version
        self.encoder_attn = RTDetrV2MultiscaleDeformableAttention(config)
        self.encoder_attn_layer_norm = nn.LayerNorm(config.d_model, eps=config.layer_norm_eps)
        # feedforward neural networks
        self.fc1 = nn.Linear(config.d_model, config.decoder_ffn_dim)
        self.fc2 = nn.Linear(config.decoder_ffn_dim, config.d_model)
        self.final_layer_norm = nn.LayerNorm(config.d_model, eps=config.layer_norm_eps)

    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,
        encoder_hidden_states: Optional[torch.Tensor] = None,
        encoder_attention_mask: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = False,
    ):
        """
        Args:
            hidden_states (`torch.FloatTensor`):
                Input to the layer of shape `(seq_len, batch, embed_dim)`.
            position_embeddings (`torch.FloatTensor`, *optional*):
                Position embeddings that are added to the queries and keys in the self-attention layer.
            reference_points (`torch.FloatTensor`, *optional*):
                Reference points.
            spatial_shapes (`torch.LongTensor`, *optional*):
                Spatial shapes.
            level_start_index (`torch.LongTensor`, *optional*):
                Level start index.
            encoder_hidden_states (`torch.FloatTensor`):
                cross attention input to the layer of shape `(seq_len, batch, embed_dim)`
            encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size
                `(batch, 1, target_len, source_len)` where padding elements are indicated by very large negative
                values.
            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

        # Self Attention
        hidden_states, self_attn_weights = self.self_attn(
            hidden_states=hidden_states,
            attention_mask=encoder_attention_mask,
            position_embeddings=position_embeddings,
            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)

        second_residual = hidden_states

        # Cross-Attention
        cross_attn_weights = None
        hidden_states, cross_attn_weights = self.encoder_attn(
            hidden_states=hidden_states,
            encoder_hidden_states=encoder_hidden_states,
            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 = second_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, cross_attn_weights)

        return outputs


@auto_docstring
class RTDetrV2PreTrainedModel(PreTrainedModel):
    config: RTDetrV2Config
    base_model_prefix = "rt_detr_v2"
    main_input_name = "pixel_values"
    _no_split_modules = [r"RTDetrV2HybridEncoder", r"RTDetrV2DecoderLayer"]

    def _init_weights(self, module):
        """Initialize the weights"""
        if isinstance(module, (RTDetrV2ForObjectDetection, RTDetrV2Decoder)):
            if module.class_embed is not None:
                for layer in module.class_embed:
                    prior_prob = self.config.initializer_bias_prior_prob or 1 / (self.config.num_labels + 1)
                    bias = float(-math.log((1 - prior_prob) / prior_prob))
                    nn.init.xavier_uniform_(layer.weight)
                    nn.init.constant_(layer.bias, bias)

            if module.bbox_embed is not None:
                for layer in module.bbox_embed:
                    nn.init.constant_(layer.layers[-1].weight, 0)
                    nn.init.constant_(layer.layers[-1].bias, 0)

        elif isinstance(module, RTDetrV2MultiscaleDeformableAttention):
            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, RTDetrV2Model):
            prior_prob = self.config.initializer_bias_prior_prob or 1 / (self.config.num_labels + 1)
            bias = float(-math.log((1 - prior_prob) / prior_prob))
            nn.init.xavier_uniform_(module.enc_score_head.weight)
            nn.init.constant_(module.enc_score_head.bias, bias)

        elif isinstance(module, (nn.Linear, nn.Conv2d, nn.BatchNorm2d)):
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.bias is not None:
                module.bias.data.zero_()

        elif isinstance(module, nn.LayerNorm):
            module.weight.data.fill_(1.0)
            module.bias.data.zero_()

        if hasattr(module, "weight_embedding") and self.config.learn_initial_query:
            nn.init.xavier_uniform_(module.weight_embedding.weight)
        if hasattr(module, "denoising_class_embed") and self.config.num_denoising > 0:
            nn.init.xavier_uniform_(module.denoising_class_embed.weight)


@dataclass
@auto_docstring(
    custom_intro="""
    Base class for outputs of the RTDetrV2Decoder. 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 RTDetrV2DecoderOutput(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_logits (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, sequence_length, config.num_labels)`):
        Stacked intermediate logits (logits 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).
    intermediate_predicted_corners (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`):
        Stacked intermediate predicted corners (predicted corners of each layer of the decoder).
    initial_reference_points (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`):
        Stacked initial reference points (initial reference points of each layer of the decoder).
    cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` and `config.add_cross_attention=True` is passed or when `config.output_attentions=True`):
        Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
        sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax,
        used to compute the weighted average in the cross-attention heads.
    """

    last_hidden_state: Optional[torch.FloatTensor] = None
    intermediate_hidden_states: Optional[torch.FloatTensor] = None
    intermediate_logits: Optional[torch.FloatTensor] = None
    intermediate_reference_points: Optional[torch.FloatTensor] = None
    intermediate_predicted_corners: Optional[torch.FloatTensor] = None
    initial_reference_points: Optional[torch.FloatTensor] = None
    hidden_states: Optional[tuple[torch.FloatTensor]] = None
    attentions: Optional[tuple[torch.FloatTensor]] = None
    cross_attentions: Optional[tuple[torch.FloatTensor]] = None


def inverse_sigmoid(x, eps=1e-5):
    x = x.clamp(min=0, max=1)
    x1 = x.clamp(min=eps)
    x2 = (1 - x).clamp(min=eps)
    return torch.log(x1 / x2)


class RTDetrV2Decoder(RTDetrV2PreTrainedModel):
    def __init__(self, config: RTDetrV2Config):
        super().__init__(config)

        self.dropout = config.dropout
        self.layers = nn.ModuleList([RTDetrV2DecoderLayer(config) for _ in range(config.decoder_layers)])
        self.query_pos_head = RTDetrV2MLPPredictionHead(config, 4, 2 * config.d_model, config.d_model, num_layers=2)

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

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

    def forward(
        self,
        inputs_embeds=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        position_embeddings=None,
        reference_points=None,
        spatial_shapes=None,
        spatial_shapes_list=None,
        level_start_index=None,
        valid_ratios=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.
            encoder_hidden_states (`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. Used in the cross-attention
                of the decoder.
            encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
                Mask to avoid performing cross-attention on padding pixel_values of the encoder. Mask values selected
                in `[0, 1]`:
                - 1 for pixels that are real (i.e. **not masked**),
                - 0 for pixels that are padding (i.e. **masked**).
            position_embeddings (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`, *optional*):
                Position embeddings that are added to the queries and keys in each self-attention layer.
            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.
            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.

            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_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None
        intermediate = ()
        intermediate_reference_points = ()
        intermediate_logits = ()

        reference_points = F.sigmoid(reference_points)

        # https://github.com/lyuwenyu/RT-DETR/blob/94f5e16708329d2f2716426868ec89aa774af016/RTDetrV2_pytorch/src/zoo/RTDetrV2/RTDetrV2_decoder.py#L252
        for idx, decoder_layer in enumerate(self.layers):
            reference_points_input = reference_points.unsqueeze(2)
            position_embeddings = self.query_pos_head(reference_points)

            if output_hidden_states:
                all_hidden_states += (hidden_states,)

            layer_outputs = decoder_layer(
                hidden_states,
                position_embeddings=position_embeddings,
                encoder_hidden_states=encoder_hidden_states,
                reference_points=reference_points_input,
                spatial_shapes=spatial_shapes,
                spatial_shapes_list=spatial_shapes_list,
                level_start_index=level_start_index,
                encoder_attention_mask=encoder_attention_mask,
                output_attentions=output_attentions,
            )

            hidden_states = layer_outputs[0]

            # hack implementation for iterative bounding box refinement
            if self.bbox_embed is not None:
                predicted_corners = self.bbox_embed[idx](hidden_states)
                new_reference_points = F.sigmoid(predicted_corners + inverse_sigmoid(reference_points))
                reference_points = new_reference_points.detach()

            intermediate += (hidden_states,)
            intermediate_reference_points += (
                (new_reference_points,) if self.bbox_embed is not None else (reference_points,)
            )

            if self.class_embed is not None:
                logits = self.class_embed[idx](hidden_states)
                intermediate_logits += (logits,)

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

                if encoder_hidden_states is not None:
                    all_cross_attentions += (layer_outputs[2],)

        # Keep batch_size as first dimension
        intermediate = torch.stack(intermediate, dim=1)
        intermediate_reference_points = torch.stack(intermediate_reference_points, dim=1)
        if self.class_embed is not None:
            intermediate_logits = torch.stack(intermediate_logits, dim=1)

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

        if not return_dict:
            return tuple(
                v
                for v in [
                    hidden_states,
                    intermediate,
                    intermediate_logits,
                    intermediate_reference_points,
                    all_hidden_states,
                    all_self_attns,
                    all_cross_attentions,
                ]
                if v is not None
            )
        return RTDetrV2DecoderOutput(
            last_hidden_state=hidden_states,
            intermediate_hidden_states=intermediate,
            intermediate_logits=intermediate_logits,
            intermediate_reference_points=intermediate_reference_points,
            hidden_states=all_hidden_states,
            attentions=all_self_attns,
            cross_attentions=all_cross_attentions,
        )


@dataclass
@auto_docstring(
    custom_intro="""
    Base class for outputs of the RT-DETR encoder-decoder model.
    """
)
class RTDetrV2ModelOutput(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.
    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_logits (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, sequence_length, config.num_labels)`):
        Stacked intermediate logits (logits 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).
    intermediate_predicted_corners (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`):
        Stacked intermediate predicted corners (predicted corners of each layer of the decoder).
    initial_reference_points (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`):
        Initial reference points used for the first decoder layer.
    init_reference_points (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`):
        Initial reference points sent through the Transformer decoder.
    enc_topk_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`):
        Predicted bounding boxes scores where the top `config.two_stage_num_proposals` scoring bounding boxes are
        picked as region proposals in the encoder stage. Output of bounding box binary classification (i.e.
        foreground and background).
    enc_topk_bboxes (`torch.FloatTensor` of shape `(batch_size, sequence_length, 4)`):
        Logits of predicted bounding boxes coordinates in the encoder stage.
    enc_outputs_class (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`, *optional*, returned when `config.with_box_refine=True` and `config.two_stage=True`):
        Predicted bounding boxes scores where the top `config.two_stage_num_proposals` 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.with_box_refine=True` and `config.two_stage=True`):
        Logits of predicted bounding boxes coordinates in the first stage.
    denoising_meta_values (`dict`):
        Extra dictionary for the denoising related values.
    """

    last_hidden_state: Optional[torch.FloatTensor] = None
    intermediate_hidden_states: Optional[torch.FloatTensor] = None
    intermediate_logits: Optional[torch.FloatTensor] = None
    intermediate_reference_points: Optional[torch.FloatTensor] = None
    intermediate_predicted_corners: Optional[torch.FloatTensor] = None
    initial_reference_points: Optional[torch.FloatTensor] = None
    decoder_hidden_states: Optional[tuple[torch.FloatTensor]] = None
    decoder_attentions: Optional[tuple[torch.FloatTensor]] = None
    cross_attentions: Optional[tuple[torch.FloatTensor]] = None
    encoder_last_hidden_state: Optional[torch.FloatTensor] = None
    encoder_hidden_states: Optional[tuple[torch.FloatTensor]] = None
    encoder_attentions: Optional[tuple[torch.FloatTensor]] = None
    init_reference_points: Optional[torch.FloatTensor] = None
    enc_topk_logits: Optional[torch.FloatTensor] = None
    enc_topk_bboxes: Optional[torch.FloatTensor] = None
    enc_outputs_class: Optional[torch.FloatTensor] = None
    enc_outputs_coord_logits: Optional[torch.FloatTensor] = None
    denoising_meta_values: Optional[dict] = None


class RTDetrV2FrozenBatchNorm2d(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 `RTDetrV2FrozenBatchNorm2d`.

    Args:
        model (torch.nn.Module):
            input model
    """
    for name, module in model.named_children():
        if isinstance(module, nn.BatchNorm2d):
            new_module = RTDetrV2FrozenBatchNorm2d(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 RTDetrV2ConvEncoder(nn.Module):
    """
    Convolutional backbone using the modeling_rt_detr_v2_resnet.py.

    nn.BatchNorm2d layers are replaced by RTDetrV2FrozenBatchNorm2d as defined above.
    https://github.com/lyuwenyu/RT-DETR/blob/main/RTDetrV2_pytorch/src/nn/backbone/presnet.py#L142
    """

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

        backbone = load_backbone(config)

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

    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).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 RTDetrV2ConvNormLayer(nn.Module):
    def __init__(self, config, in_channels, out_channels, kernel_size, stride, padding=None, activation=None):
        super().__init__()
        self.conv = nn.Conv2d(
            in_channels,
            out_channels,
            kernel_size,
            stride,
            padding=(kernel_size - 1) // 2 if padding is None else padding,
            bias=False,
        )
        self.norm = nn.BatchNorm2d(out_channels, config.batch_norm_eps)
        self.activation = nn.Identity() if activation is None else ACT2CLS[activation]()

    def forward(self, hidden_state):
        hidden_state = self.conv(hidden_state)
        hidden_state = self.norm(hidden_state)
        hidden_state = self.activation(hidden_state)
        return hidden_state


class RTDetrV2EncoderLayer(nn.Module):
    def __init__(self, config: RTDetrV2Config):
        super().__init__()
        self.normalize_before = config.normalize_before

        # self-attention
        self.self_attn = RTDetrV2MultiheadAttention(
            embed_dim=config.encoder_hidden_dim,
            num_heads=config.num_attention_heads,
            dropout=config.dropout,
        )
        self.self_attn_layer_norm = nn.LayerNorm(config.encoder_hidden_dim, eps=config.layer_norm_eps)
        self.dropout = config.dropout
        self.activation_fn = ACT2FN[config.encoder_activation_function]
        self.activation_dropout = config.activation_dropout
        self.fc1 = nn.Linear(config.encoder_hidden_dim, config.encoder_ffn_dim)
        self.fc2 = nn.Linear(config.encoder_ffn_dim, config.encoder_hidden_dim)
        self.final_layer_norm = nn.LayerNorm(config.encoder_hidden_dim, eps=config.layer_norm_eps)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: torch.Tensor,
        position_embeddings: Optional[torch.Tensor] = None,
        output_attentions: bool = False,
        **kwargs,
    ):
        """
        Args:
            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
            attention_mask (`torch.FloatTensor`): attention mask of size
                `(batch, 1, target_len, source_len)` where padding elements are indicated by very large negative
                values.
            position_embeddings (`torch.FloatTensor`, *optional*):
                Object queries (also called content embeddings), to be added to the hidden states.
            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
        if self.normalize_before:
            hidden_states = self.self_attn_layer_norm(hidden_states)

        hidden_states, attn_weights = self.self_attn(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            position_embeddings=position_embeddings,
            output_attentions=output_attentions,
        )

        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
        hidden_states = residual + hidden_states
        if not self.normalize_before:
            hidden_states = self.self_attn_layer_norm(hidden_states)

        if self.normalize_before:
            hidden_states = self.final_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
        if not self.normalize_before:
            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)

        outputs = (hidden_states,)

        if output_attentions:
            outputs += (attn_weights,)

        return outputs


class RTDetrV2RepVggBlock(nn.Module):
    """
    RepVGG architecture block introduced by the work "RepVGG: Making VGG-style ConvNets Great Again".
    """

    def __init__(self, config: RTDetrV2Config):
        super().__init__()

        activation = config.activation_function
        hidden_channels = int(config.encoder_hidden_dim * config.hidden_expansion)
        self.conv1 = RTDetrV2ConvNormLayer(config, hidden_channels, hidden_channels, 3, 1, padding=1)
        self.conv2 = RTDetrV2ConvNormLayer(config, hidden_channels, hidden_channels, 1, 1, padding=0)
        self.activation = nn.Identity() if activation is None else ACT2CLS[activation]()

    def forward(self, x):
        y = self.conv1(x) + self.conv2(x)
        return self.activation(y)


class RTDetrV2CSPRepLayer(nn.Module):
    """
    Cross Stage Partial (CSP) network layer with RepVGG blocks.
    """

    def __init__(self, config: RTDetrV2Config):
        super().__init__()

        in_channels = config.encoder_hidden_dim * 2
        out_channels = config.encoder_hidden_dim
        num_blocks = 3
        activation = config.activation_function

        hidden_channels = int(out_channels * config.hidden_expansion)
        self.conv1 = RTDetrV2ConvNormLayer(config, in_channels, hidden_channels, 1, 1, activation=activation)
        self.conv2 = RTDetrV2ConvNormLayer(config, in_channels, hidden_channels, 1, 1, activation=activation)
        self.bottlenecks = nn.Sequential(*[RTDetrV2RepVggBlock(config) for _ in range(num_blocks)])
        if hidden_channels != out_channels:
            self.conv3 = RTDetrV2ConvNormLayer(config, hidden_channels, out_channels, 1, 1, activation=activation)
        else:
            self.conv3 = nn.Identity()

    def forward(self, hidden_state):
        hidden_state_1 = self.conv1(hidden_state)
        hidden_state_1 = self.bottlenecks(hidden_state_1)
        hidden_state_2 = self.conv2(hidden_state)
        return self.conv3(hidden_state_1 + hidden_state_2)


class RTDetrV2Encoder(nn.Module):
    def __init__(self, config: RTDetrV2Config):
        super().__init__()

        self.layers = nn.ModuleList([RTDetrV2EncoderLayer(config) for _ in range(config.encoder_layers)])

    def forward(self, src, src_mask=None, pos_embed=None, output_attentions: bool = False) -> torch.Tensor:
        hidden_states = src
        for layer in self.layers:
            hidden_states = layer(
                hidden_states,
                attention_mask=src_mask,
                position_embeddings=pos_embed,
                output_attentions=output_attentions,
            )
        return hidden_states


class RTDetrV2HybridEncoder(nn.Module):
    """
    Decoder consisting of a projection layer, a set of `RTDetrV2Encoder`, a top-down Feature Pyramid Network
    (FPN) and a bottom-up Path Aggregation Network (PAN). More details on the paper: https://huggingface.co/papers/2304.08069

    Args:
        config: RTDetrV2Config
    """

    def __init__(self, config: RTDetrV2Config):
        super().__init__()
        self.config = config
        self.in_channels = config.encoder_in_channels
        self.feat_strides = config.feat_strides
        self.encoder_hidden_dim = config.encoder_hidden_dim
        self.encode_proj_layers = config.encode_proj_layers
        self.positional_encoding_temperature = config.positional_encoding_temperature
        self.eval_size = config.eval_size
        self.out_channels = [self.encoder_hidden_dim for _ in self.in_channels]
        self.out_strides = self.feat_strides
        self.num_fpn_stages = len(self.in_channels) - 1
        self.num_pan_stages = len(self.in_channels) - 1
        activation = config.activation_function

        # encoder transformer
        self.encoder = nn.ModuleList([RTDetrV2Encoder(config) for _ in range(len(self.encode_proj_layers))])

        # top-down FPN
        self.lateral_convs = nn.ModuleList()
        self.fpn_blocks = nn.ModuleList()
        for _ in range(self.num_fpn_stages):
            lateral_conv = RTDetrV2ConvNormLayer(
                config,
                in_channels=self.encoder_hidden_dim,
                out_channels=self.encoder_hidden_dim,
                kernel_size=1,
                stride=1,
                activation=activation,
            )
            fpn_block = RTDetrV2CSPRepLayer(config)
            self.lateral_convs.append(lateral_conv)
            self.fpn_blocks.append(fpn_block)

        # bottom-up PAN
        self.downsample_convs = nn.ModuleList()
        self.pan_blocks = nn.ModuleList()
        for _ in range(self.num_pan_stages):
            downsample_conv = RTDetrV2ConvNormLayer(
                config,
                in_channels=self.encoder_hidden_dim,
                out_channels=self.encoder_hidden_dim,
                kernel_size=3,
                stride=2,
                activation=activation,
            )
            pan_block = RTDetrV2CSPRepLayer(config)
            self.downsample_convs.append(downsample_conv)
            self.pan_blocks.append(pan_block)

    @staticmethod
    def build_2d_sincos_position_embedding(
        width, height, embed_dim=256, temperature=10000.0, device="cpu", dtype=torch.float32
    ):
        grid_w = torch.arange(torch_int(width), device=device).to(dtype)
        grid_h = torch.arange(torch_int(height), device=device).to(dtype)
        grid_w, grid_h = torch.meshgrid(grid_w, grid_h, indexing="ij")
        if embed_dim % 4 != 0:
            raise ValueError("Embed dimension must be divisible by 4 for 2D sin-cos position embedding")
        pos_dim = embed_dim // 4
        omega = torch.arange(pos_dim, device=device).to(dtype) / pos_dim
        omega = 1.0 / (temperature**omega)

        out_w = grid_w.flatten()[..., None] @ omega[None]
        out_h = grid_h.flatten()[..., None] @ omega[None]

        return torch.concat([out_w.sin(), out_w.cos(), out_h.sin(), out_h.cos()], dim=1)[None, :, :]

    def forward(
        self,
        inputs_embeds=None,
        attention_mask=None,
        position_embeddings=None,
        spatial_shapes=None,
        level_start_index=None,
        valid_ratios=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
    ):
        r"""
        Args:
            inputs_embeds (`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.
            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**).
                [What are attention masks?](../glossary#attention-mask)
            position_embeddings (`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.
            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.
            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

        hidden_states = inputs_embeds

        encoder_states = () if output_hidden_states else None
        all_attentions = () if output_attentions else None

        # encoder
        if self.config.encoder_layers > 0:
            for i, enc_ind in enumerate(self.encode_proj_layers):
                if output_hidden_states:
                    encoder_states = encoder_states + (hidden_states[enc_ind],)
                height, width = hidden_states[enc_ind].shape[2:]
                # flatten [batch, channel, height, width] to [batch, height*width, channel]
                src_flatten = hidden_states[enc_ind].flatten(2).permute(0, 2, 1)
                if self.training or self.eval_size is None:
                    pos_embed = self.build_2d_sincos_position_embedding(
                        width,
                        height,
                        self.encoder_hidden_dim,
                        self.positional_encoding_temperature,
                        device=src_flatten.device,
                        dtype=src_flatten.dtype,
                    )
                else:
                    pos_embed = None

                layer_outputs = self.encoder[i](
                    src_flatten,
                    pos_embed=pos_embed,
                    output_attentions=output_attentions,
                )
                hidden_states[enc_ind] = (
                    layer_outputs[0].permute(0, 2, 1).reshape(-1, self.encoder_hidden_dim, height, width).contiguous()
                )

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

            if output_hidden_states:
                encoder_states = encoder_states + (hidden_states[enc_ind],)

        # top-down FPN
        fpn_feature_maps = [hidden_states[-1]]
        for idx, (lateral_conv, fpn_block) in enumerate(zip(self.lateral_convs, self.fpn_blocks)):
            backbone_feature_map = hidden_states[self.num_fpn_stages - idx - 1]
            top_fpn_feature_map = fpn_feature_maps[-1]
            # apply lateral block
            top_fpn_feature_map = lateral_conv(top_fpn_feature_map)
            fpn_feature_maps[-1] = top_fpn_feature_map
            # apply fpn block
            top_fpn_feature_map = F.interpolate(top_fpn_feature_map, scale_factor=2.0, mode="nearest")
            fused_feature_map = torch.concat([top_fpn_feature_map, backbone_feature_map], dim=1)
            new_fpn_feature_map = fpn_block(fused_feature_map)
            fpn_feature_maps.append(new_fpn_feature_map)

        fpn_feature_maps = fpn_feature_maps[::-1]

        # bottom-up PAN
        pan_feature_maps = [fpn_feature_maps[0]]
        for idx, (downsample_conv, pan_block) in enumerate(zip(self.downsample_convs, self.pan_blocks)):
            top_pan_feature_map = pan_feature_maps[-1]
            fpn_feature_map = fpn_feature_maps[idx + 1]
            downsampled_feature_map = downsample_conv(top_pan_feature_map)
            fused_feature_map = torch.concat([downsampled_feature_map, fpn_feature_map], dim=1)
            new_pan_feature_map = pan_block(fused_feature_map)
            pan_feature_maps.append(new_pan_feature_map)

        if not return_dict:
            return tuple(v for v in [pan_feature_maps, encoder_states, all_attentions] if v is not None)
        return BaseModelOutput(
            last_hidden_state=pan_feature_maps, hidden_states=encoder_states, attentions=all_attentions
        )


def get_contrastive_denoising_training_group(
    targets,
    num_classes,
    num_queries,
    class_embed,
    num_denoising_queries=100,
    label_noise_ratio=0.5,
    box_noise_scale=1.0,
):
    """
    Creates a contrastive denoising training group using ground-truth samples. It adds noise to labels and boxes.

    Args:
        targets (`list[dict]`):
            The target objects, each containing 'class_labels' and 'boxes' for objects in an image.
        num_classes (`int`):
            Total number of classes in the dataset.
        num_queries (`int`):
            Number of query slots in the transformer.
        class_embed (`callable`):
            A function or a model layer to embed class labels.
        num_denoising_queries (`int`, *optional*, defaults to 100):
            Number of denoising queries.
        label_noise_ratio (`float`, *optional*, defaults to 0.5):
            Ratio of noise applied to labels.
        box_noise_scale (`float`, *optional*, defaults to 1.0):
            Scale of noise applied to bounding boxes.
    Returns:
        `tuple` comprising various elements:
        - **input_query_class** (`torch.FloatTensor`) --
          Class queries with applied label noise.
        - **input_query_bbox** (`torch.FloatTensor`) --
          Bounding box queries with applied box noise.
        - **attn_mask** (`torch.FloatTensor`) --
           Attention mask for separating denoising and reconstruction queries.
        - **denoising_meta_values** (`dict`) --
          Metadata including denoising positive indices, number of groups, and split sizes.
    """

    if num_denoising_queries <= 0:
        return None, None, None, None

    num_ground_truths = [len(t["class_labels"]) for t in targets]
    device = targets[0]["class_labels"].device

    max_gt_num = max(num_ground_truths)
    if max_gt_num == 0:
        return None, None, None, None

    num_groups_denoising_queries = num_denoising_queries // max_gt_num
    num_groups_denoising_queries = 1 if num_groups_denoising_queries == 0 else num_groups_denoising_queries
    # pad gt to max_num of a batch
    batch_size = len(num_ground_truths)

    input_query_class = torch.full([batch_size, max_gt_num], num_classes, dtype=torch.int32, device=device)
    input_query_bbox = torch.zeros([batch_size, max_gt_num, 4], device=device)
    pad_gt_mask = torch.zeros([batch_size, max_gt_num], dtype=torch.bool, device=device)

    for i in range(batch_size):
        num_gt = num_ground_truths[i]
        if num_gt > 0:
            input_query_class[i, :num_gt] = targets[i]["class_labels"]
            input_query_bbox[i, :num_gt] = targets[i]["boxes"]
            pad_gt_mask[i, :num_gt] = 1
    # each group has positive and negative queries.
    input_query_class = input_query_class.tile([1, 2 * num_groups_denoising_queries])
    input_query_bbox = input_query_bbox.tile([1, 2 * num_groups_denoising_queries, 1])
    pad_gt_mask = pad_gt_mask.tile([1, 2 * num_groups_denoising_queries])
    # positive and negative mask
    negative_gt_mask = torch.zeros([batch_size, max_gt_num * 2, 1], device=device)
    negative_gt_mask[:, max_gt_num:] = 1
    negative_gt_mask = negative_gt_mask.tile([1, num_groups_denoising_queries, 1])
    positive_gt_mask = 1 - negative_gt_mask
    # contrastive denoising training positive index
    positive_gt_mask = positive_gt_mask.squeeze(-1) * pad_gt_mask
    denoise_positive_idx = torch.nonzero(positive_gt_mask)[:, 1]
    denoise_positive_idx = torch.split(
        denoise_positive_idx, [n * num_groups_denoising_queries for n in num_ground_truths]
    )
    # total denoising queries
    num_denoising_queries = torch_int(max_gt_num * 2 * num_groups_denoising_queries)

    if label_noise_ratio > 0:
        mask = torch.rand_like(input_query_class, dtype=torch.float) < (label_noise_ratio * 0.5)
        # randomly put a new one here
        new_label = torch.randint_like(mask, 0, num_classes, dtype=input_query_class.dtype)
        input_query_class = torch.where(mask & pad_gt_mask, new_label, input_query_class)

    if box_noise_scale > 0:
        known_bbox = center_to_corners_format(input_query_bbox)
        diff = torch.tile(input_query_bbox[..., 2:] * 0.5, [1, 1, 2]) * box_noise_scale
        rand_sign = torch.randint_like(input_query_bbox, 0, 2) * 2.0 - 1.0
        rand_part = torch.rand_like(input_query_bbox)
        rand_part = (rand_part + 1.0) * negative_gt_mask + rand_part * (1 - negative_gt_mask)
        rand_part *= rand_sign
        known_bbox += rand_part * diff
        known_bbox.clip_(min=0.0, max=1.0)
        input_query_bbox = corners_to_center_format(known_bbox)
        input_query_bbox = inverse_sigmoid(input_query_bbox)

    input_query_class = class_embed(input_query_class)

    target_size = num_denoising_queries + num_queries
    attn_mask = torch.full([target_size, target_size], 0, dtype=torch.float, device=device)
    # match query cannot see the reconstruction
    attn_mask[num_denoising_queries:, :num_denoising_queries] = -torch.inf

    # reconstructions cannot see each other
    for i in range(num_groups_denoising_queries):
        idx_block_start = max_gt_num * 2 * i
        idx_block_end = max_gt_num * 2 * (i + 1)
        attn_mask[idx_block_start:idx_block_end, :idx_block_start] = -torch.inf
        attn_mask[idx_block_start:idx_block_end, idx_block_end:num_denoising_queries] = -torch.inf

    denoising_meta_values = {
        "dn_positive_idx": denoise_positive_idx,
        "dn_num_group": num_groups_denoising_queries,
        "dn_num_split": [num_denoising_queries, num_queries],
    }

    return input_query_class, input_query_bbox, attn_mask, denoising_meta_values


@auto_docstring(
    custom_intro="""
    RT-DETR Model (consisting of a backbone and encoder-decoder) outputting raw hidden states without any head on top.
    """
)
class RTDetrV2Model(RTDetrV2PreTrainedModel):
    def __init__(self, config: RTDetrV2Config):
        super().__init__(config)

        # Create backbone
        self.backbone = RTDetrV2ConvEncoder(config)
        intermediate_channel_sizes = self.backbone.intermediate_channel_sizes

        # Create encoder input projection layers
        # https://github.com/lyuwenyu/RT-DETR/blob/94f5e16708329d2f2716426868ec89aa774af016/RTDetrV2_pytorch/src/zoo/RTDetrV2/hybrid_encoder.py#L212
        num_backbone_outs = len(intermediate_channel_sizes)
        encoder_input_proj_list = []
        for _ in range(num_backbone_outs):
            in_channels = intermediate_channel_sizes[_]
            encoder_input_proj_list.append(
                nn.Sequential(
                    nn.Conv2d(in_channels, config.encoder_hidden_dim, kernel_size=1, bias=False),
                    nn.BatchNorm2d(config.encoder_hidden_dim),
                )
            )
        self.encoder_input_proj = nn.ModuleList(encoder_input_proj_list)

        # Create encoder
        self.encoder = RTDetrV2HybridEncoder(config)

        # denoising part
        if config.num_denoising > 0:
            self.denoising_class_embed = nn.Embedding(
                config.num_labels + 1, config.d_model, padding_idx=config.num_labels
            )

        # decoder embedding
        if config.learn_initial_query:
            self.weight_embedding = nn.Embedding(config.num_queries, config.d_model)

        # encoder head
        self.enc_output = nn.Sequential(
            nn.Linear(config.d_model, config.d_model),
            nn.LayerNorm(config.d_model, eps=config.layer_norm_eps),
        )
        self.enc_score_head = nn.Linear(config.d_model, config.num_labels)
        self.enc_bbox_head = RTDetrV2MLPPredictionHead(config, config.d_model, config.d_model, 4, num_layers=3)

        # init encoder output anchors and valid_mask
        if config.anchor_image_size:
            self.anchors, self.valid_mask = self.generate_anchors(dtype=self.dtype)

        # Create decoder input projection layers
        # https://github.com/lyuwenyu/RT-DETR/blob/94f5e16708329d2f2716426868ec89aa774af016/RTDetrV2_pytorch/src/zoo/RTDetrV2/RTDetrV2_decoder.py#L412
        num_backbone_outs = len(config.decoder_in_channels)
        decoder_input_proj_list = []
        for _ in range(num_backbone_outs):
            in_channels = config.decoder_in_channels[_]
            decoder_input_proj_list.append(
                nn.Sequential(
                    nn.Conv2d(in_channels, config.d_model, kernel_size=1, bias=False),
                    nn.BatchNorm2d(config.d_model, config.batch_norm_eps),
                )
            )
        for _ in range(config.num_feature_levels - num_backbone_outs):
            decoder_input_proj_list.append(
                nn.Sequential(
                    nn.Conv2d(in_channels, config.d_model, kernel_size=3, stride=2, padding=1, bias=False),
                    nn.BatchNorm2d(config.d_model, config.batch_norm_eps),
                )
            )
            in_channels = config.d_model
        self.decoder_input_proj = nn.ModuleList(decoder_input_proj_list)
        # decoder
        self.decoder = RTDetrV2Decoder(config)

        self.post_init()

    def get_encoder(self):
        return self.encoder

    def freeze_backbone(self):
        for param in self.backbone.parameters():
            param.requires_grad_(False)

    def unfreeze_backbone(self):
        for param in self.backbone.parameters():
            param.requires_grad_(True)

    @compile_compatible_method_lru_cache(maxsize=32)
    def generate_anchors(self, spatial_shapes=None, grid_size=0.05, device="cpu", dtype=torch.float32):
        if spatial_shapes is None:
            spatial_shapes = [
                [int(self.config.anchor_image_size[0] / s), int(self.config.anchor_image_size[1] / s)]
                for s in self.config.feat_strides
            ]
        anchors = []
        for level, (height, width) in enumerate(spatial_shapes):
            grid_y, grid_x = torch.meshgrid(
                torch.arange(end=height, device=device).to(dtype),
                torch.arange(end=width, device=device).to(dtype),
                indexing="ij",
            )
            grid_xy = torch.stack([grid_x, grid_y], -1)
            grid_xy = grid_xy.unsqueeze(0) + 0.5
            grid_xy[..., 0] /= width
            grid_xy[..., 1] /= height
            wh = torch.ones_like(grid_xy) * grid_size * (2.0**level)
            anchors.append(torch.concat([grid_xy, wh], -1).reshape(-1, height * width, 4))
        # define the valid range for anchor coordinates
        eps = 1e-2
        anchors = torch.concat(anchors, 1)
        valid_mask = ((anchors > eps) * (anchors < 1 - eps)).all(-1, keepdim=True)
        anchors = torch.log(anchors / (1 - anchors))
        anchors = torch.where(valid_mask, anchors, torch.tensor(torch.finfo(dtype).max, dtype=dtype, device=device))

        return anchors, valid_mask

    @auto_docstring
    def forward(
        self,
        pixel_values: torch.FloatTensor,
        pixel_mask: Optional[torch.LongTensor] = None,
        encoder_outputs: Optional[torch.FloatTensor] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[list[dict]] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[tuple[torch.FloatTensor], RTDetrV2ModelOutput]:
        r"""
        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
            Optionally, instead of passing the flattened feature map (output of the backbone + projection layer), you
            can choose to directly pass a flattened representation of an image.
        decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`, *optional*):
            Optionally, instead of initializing the queries with a tensor of zeros, you can choose to directly pass an
            embedded representation.
        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
        >>> from transformers import AutoImageProcessor, RTDetrV2Model
        >>> from PIL import Image
        >>> import requests

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

        >>> image_processor = AutoImageProcessor.from_pretrained("PekingU/RTDetrV2_r50vd")
        >>> model = RTDetrV2Model.from_pretrained("PekingU/RTDetrV2_r50vd")

        >>> inputs = image_processor(images=image, return_tensors="pt")

        >>> outputs = model(**inputs)

        >>> last_hidden_states = outputs.last_hidden_state
        >>> list(last_hidden_states.shape)
        [1, 300, 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

        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)), device=device)

        features = self.backbone(pixel_values, pixel_mask)

        proj_feats = [self.encoder_input_proj[level](source) for level, (source, mask) in enumerate(features)]

        if encoder_outputs is None:
            encoder_outputs = self.encoder(
                proj_feats,
                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 BaseModelOutput when return_dict=True
        elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
            encoder_outputs = BaseModelOutput(
                last_hidden_state=encoder_outputs[0],
                hidden_states=encoder_outputs[1] if output_hidden_states else None,
                attentions=encoder_outputs[2]
                if len(encoder_outputs) > 2
                else encoder_outputs[1]
                if output_attentions
                else None,
            )

        # Equivalent to def _get_encoder_input
        # https://github.com/lyuwenyu/RT-DETR/blob/94f5e16708329d2f2716426868ec89aa774af016/RTDetrV2_pytorch/src/zoo/RTDetrV2/RTDetrV2_decoder.py#L412
        sources = []
        for level, source in enumerate(encoder_outputs[0]):
            sources.append(self.decoder_input_proj[level](source))

        # Lowest resolution feature maps are obtained via 3x3 stride 2 convolutions on the final stage
        if self.config.num_feature_levels > len(sources):
            _len_sources = len(sources)
            sources.append(self.decoder_input_proj[_len_sources](encoder_outputs[0])[-1])
            for i in range(_len_sources + 1, self.config.num_feature_levels):
                sources.append(self.decoder_input_proj[i](encoder_outputs[0][-1]))

        # Prepare encoder inputs (by flattening)
        source_flatten = []
        spatial_shapes_list = []
        spatial_shapes = torch.empty((len(sources), 2), device=device, dtype=torch.long)
        for level, source in enumerate(sources):
            height, width = source.shape[-2:]
            spatial_shapes[level, 0] = height
            spatial_shapes[level, 1] = width
            spatial_shapes_list.append((height, width))
            source = source.flatten(2).transpose(1, 2)
            source_flatten.append(source)
        source_flatten = torch.cat(source_flatten, 1)
        level_start_index = torch.cat((spatial_shapes.new_zeros((1,)), spatial_shapes.prod(1).cumsum(0)[:-1]))

        # prepare denoising training
        if self.training and self.config.num_denoising > 0 and labels is not None:
            (
                denoising_class,
                denoising_bbox_unact,
                attention_mask,
                denoising_meta_values,
            ) = get_contrastive_denoising_training_group(
                targets=labels,
                num_classes=self.config.num_labels,
                num_queries=self.config.num_queries,
                class_embed=self.denoising_class_embed,
                num_denoising_queries=self.config.num_denoising,
                label_noise_ratio=self.config.label_noise_ratio,
                box_noise_scale=self.config.box_noise_scale,
            )
        else:
            denoising_class, denoising_bbox_unact, attention_mask, denoising_meta_values = None, None, None, None

        batch_size = len(source_flatten)
        device = source_flatten.device
        dtype = source_flatten.dtype

        # prepare input for decoder
        if self.training or self.config.anchor_image_size is None:
            # Pass spatial_shapes as tuple to make it hashable and make sure
            # lru_cache is working for generate_anchors()
            spatial_shapes_tuple = tuple(spatial_shapes_list)
            anchors, valid_mask = self.generate_anchors(spatial_shapes_tuple, device=device, dtype=dtype)
        else:
            anchors, valid_mask = self.anchors, self.valid_mask
            anchors, valid_mask = anchors.to(device, dtype), valid_mask.to(device, dtype)

        # use the valid_mask to selectively retain values in the feature map where the mask is `True`
        memory = valid_mask.to(source_flatten.dtype) * source_flatten

        output_memory = self.enc_output(memory)

        enc_outputs_class = self.enc_score_head(output_memory)
        enc_outputs_coord_logits = self.enc_bbox_head(output_memory) + anchors

        _, topk_ind = torch.topk(enc_outputs_class.max(-1).values, self.config.num_queries, dim=1)

        reference_points_unact = enc_outputs_coord_logits.gather(
            dim=1, index=topk_ind.unsqueeze(-1).repeat(1, 1, enc_outputs_coord_logits.shape[-1])
        )

        enc_topk_bboxes = F.sigmoid(reference_points_unact)
        if denoising_bbox_unact is not None:
            reference_points_unact = torch.concat([denoising_bbox_unact, reference_points_unact], 1)

        enc_topk_logits = enc_outputs_class.gather(
            dim=1, index=topk_ind.unsqueeze(-1).repeat(1, 1, enc_outputs_class.shape[-1])
        )

        # extract region features
        if self.config.learn_initial_query:
            target = self.weight_embedding.tile([batch_size, 1, 1])
        else:
            target = output_memory.gather(dim=1, index=topk_ind.unsqueeze(-1).repeat(1, 1, output_memory.shape[-1]))
            target = target.detach()

        if denoising_class is not None:
            target = torch.concat([denoising_class, target], 1)

        init_reference_points = reference_points_unact.detach()

        # decoder
        decoder_outputs = self.decoder(
            inputs_embeds=target,
            encoder_hidden_states=source_flatten,
            encoder_attention_mask=attention_mask,
            reference_points=init_reference_points,
            spatial_shapes=spatial_shapes,
            spatial_shapes_list=spatial_shapes_list,
            level_start_index=level_start_index,
            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_topk_logits, enc_topk_bboxes, enc_outputs_class, enc_outputs_coord_logits]
                if value is not None
            )
            dn_outputs = tuple(value if value is not None else None for value in [denoising_meta_values])
            tuple_outputs = decoder_outputs + encoder_outputs + (init_reference_points,) + enc_outputs + dn_outputs

            return tuple_outputs

        return RTDetrV2ModelOutput(
            last_hidden_state=decoder_outputs.last_hidden_state,
            intermediate_hidden_states=decoder_outputs.intermediate_hidden_states,
            intermediate_logits=decoder_outputs.intermediate_logits,
            intermediate_reference_points=decoder_outputs.intermediate_reference_points,
            intermediate_predicted_corners=decoder_outputs.intermediate_predicted_corners,
            initial_reference_points=decoder_outputs.initial_reference_points,
            decoder_hidden_states=decoder_outputs.hidden_states,
            decoder_attentions=decoder_outputs.attentions,
            cross_attentions=decoder_outputs.cross_attentions,
            encoder_last_hidden_state=encoder_outputs.last_hidden_state,
            encoder_hidden_states=encoder_outputs.hidden_states,
            encoder_attentions=encoder_outputs.attentions,
            init_reference_points=init_reference_points,
            enc_topk_logits=enc_topk_logits,
            enc_topk_bboxes=enc_topk_bboxes,
            enc_outputs_class=enc_outputs_class,
            enc_outputs_coord_logits=enc_outputs_coord_logits,
            denoising_meta_values=denoising_meta_values,
        )


# taken from https://github.com/facebookresearch/detr/blob/master/models/detr.py
class RTDetrV2MLPPredictionHead(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
    Origin from https://github.com/lyuwenyu/RT-DETR/blob/94f5e16708329d2f2716426868ec89aa774af016/RTDetrV2_paddle/ppdet/modeling/transformers/utils.py#L453

    """

    def __init__(self, config, input_dim, d_model, output_dim, num_layers):
        super().__init__()
        self.num_layers = num_layers
        h = [d_model] * (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 [`RTDetrV2ForObjectDetection`].
    """
)
class RTDetrV2ObjectDetectionOutput(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 [`~RTDetrV2ImageProcessor.post_process_object_detection`] to retrieve the
        unnormalized (absolute) 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)`):
        Sequence of hidden-states at the output of the last layer of the decoder of the model.
    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_logits (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, config.num_labels)`):
        Stacked intermediate logits (logits 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).
    intermediate_predicted_corners (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`):
        Stacked intermediate predicted corners (predicted corners of each layer of the decoder).
    initial_reference_points (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`):
        Stacked initial reference points (initial reference points of each layer of the decoder).
    init_reference_points (`torch.FloatTensor` of shape  `(batch_size, num_queries, 4)`):
        Initial reference points sent through the Transformer decoder.
    enc_topk_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`, *optional*, returned when `config.with_box_refine=True` and `config.two_stage=True`):
        Logits of predicted bounding boxes coordinates in the encoder.
    enc_topk_bboxes (`torch.FloatTensor` of shape `(batch_size, sequence_length, 4)`, *optional*, returned when `config.with_box_refine=True` and `config.two_stage=True`):
        Logits of predicted bounding boxes coordinates in the encoder.
    enc_outputs_class (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`, *optional*, returned when `config.with_box_refine=True` and `config.two_stage=True`):
        Predicted bounding boxes scores where the top `config.two_stage_num_proposals` 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.with_box_refine=True` and `config.two_stage=True`):
        Logits of predicted bounding boxes coordinates in the first stage.
    denoising_meta_values (`dict`):
        Extra dictionary for the denoising related values
    """

    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
    intermediate_hidden_states: Optional[torch.FloatTensor] = None
    intermediate_logits: Optional[torch.FloatTensor] = None
    intermediate_reference_points: Optional[torch.FloatTensor] = None
    intermediate_predicted_corners: Optional[torch.FloatTensor] = None
    initial_reference_points: Optional[torch.FloatTensor] = None
    decoder_hidden_states: Optional[tuple[torch.FloatTensor]] = None
    decoder_attentions: Optional[tuple[torch.FloatTensor]] = None
    cross_attentions: Optional[tuple[torch.FloatTensor]] = None
    encoder_last_hidden_state: Optional[torch.FloatTensor] = None
    encoder_hidden_states: Optional[tuple[torch.FloatTensor]] = None
    encoder_attentions: Optional[tuple[torch.FloatTensor]] = None
    init_reference_points: Optional[tuple[torch.FloatTensor]] = None
    enc_topk_logits: Optional[torch.FloatTensor] = None
    enc_topk_bboxes: Optional[torch.FloatTensor] = None
    enc_outputs_class: Optional[torch.FloatTensor] = None
    enc_outputs_coord_logits: Optional[torch.FloatTensor] = None
    denoising_meta_values: Optional[dict] = None


@auto_docstring(
    custom_intro="""
    RT-DETR Model (consisting of a backbone and encoder-decoder) outputting bounding boxes and logits to be further
    decoded into scores and classes.
    """
)
class RTDetrV2ForObjectDetection(RTDetrV2PreTrainedModel):
    # When using clones, all layers > 0 will be clones, but layer 0 *is* required
    _tied_weights_keys = ["bbox_embed", "class_embed"]
    # We can't initialize the model on meta device as some weights are modified during the initialization
    _no_split_modules = None

    def __init__(self, config: RTDetrV2Config):
        super().__init__(config)
        # RTDETR encoder-decoder model
        self.model = RTDetrV2Model(config)

        # Detection heads on top
        class_embed = partial(nn.Linear, config.d_model, config.num_labels)
        bbox_embed = partial(RTDetrV2MLPPredictionHead, config, config.d_model, config.d_model, 4, num_layers=3)

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

        self.model.decoder.class_embed = self.class_embed
        self.model.decoder.bbox_embed = self.bbox_embed

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

    @torch.jit.unused
    def _set_aux_loss(self, outputs_class, outputs_coord):
        # this is a workaround to make torchscript happy, as torchscript
        # doesn't support dictionary with non-homogeneous values, such
        # as a dict having both a Tensor and a list.
        return [{"logits": a, "pred_boxes": b} for a, b in zip(outputs_class, outputs_coord)]

    @auto_docstring
    def forward(
        self,
        pixel_values: torch.FloatTensor,
        pixel_mask: Optional[torch.LongTensor] = None,
        encoder_outputs: Optional[torch.FloatTensor] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[list[dict]] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        **kwargs,
    ) -> Union[tuple[torch.FloatTensor], RTDetrV2ObjectDetectionOutput]:
        r"""
        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
            Optionally, instead of passing the flattened feature map (output of the backbone + projection layer), you
            can choose to directly pass a flattened representation of an image.
        decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`, *optional*):
            Optionally, instead of initializing the queries with a tensor of zeros, you can choose to directly pass an
            embedded representation.
        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
        >>> from transformers import RTDetrV2ImageProcessor, RTDetrV2ForObjectDetection
        >>> from PIL import Image
        >>> import requests
        >>> import torch

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

        >>> image_processor = RTDetrV2ImageProcessor.from_pretrained("PekingU/RTDetrV2_r50vd")
        >>> model = RTDetrV2ForObjectDetection.from_pretrained("PekingU/RTDetrV2_r50vd")

        >>> # prepare image for the model
        >>> inputs = image_processor(images=image, return_tensors="pt")

        >>> # forward pass
        >>> outputs = model(**inputs)

        >>> logits = outputs.logits
        >>> list(logits.shape)
        [1, 300, 80]

        >>> boxes = outputs.pred_boxes
        >>> list(boxes.shape)
        [1, 300, 4]

        >>> # convert outputs (bounding boxes and class logits) to Pascal VOC format (xmin, ymin, xmax, ymax)
        >>> target_sizes = torch.tensor([image.size[::-1]])
        >>> results = image_processor.post_process_object_detection(outputs, threshold=0.9, target_sizes=target_sizes)[
        ...     0
        ... ]

        >>> for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
        ...     box = [round(i, 2) for i in box.tolist()]
        ...     print(
        ...         f"Detected {model.config.id2label[label.item()]} with confidence "
        ...         f"{round(score.item(), 3)} at location {box}"
        ...     )
        Detected sofa with confidence 0.97 at location [0.14, 0.38, 640.13, 476.21]
        Detected cat with confidence 0.96 at location [343.38, 24.28, 640.14, 371.5]
        Detected cat with confidence 0.958 at location [13.23, 54.18, 318.98, 472.22]
        Detected remote with confidence 0.951 at location [40.11, 73.44, 175.96, 118.48]
        Detected remote with confidence 0.924 at location [333.73, 76.58, 369.97, 186.99]
        ```"""
        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

        outputs = self.model(
            pixel_values,
            pixel_mask=pixel_mask,
            encoder_outputs=encoder_outputs,
            inputs_embeds=inputs_embeds,
            decoder_inputs_embeds=decoder_inputs_embeds,
            labels=labels,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        denoising_meta_values = (
            outputs.denoising_meta_values if return_dict else outputs[-1] if self.training else None
        )

        outputs_class = outputs.intermediate_logits if return_dict else outputs[2]
        outputs_coord = outputs.intermediate_reference_points if return_dict else outputs[3]
        predicted_corners = outputs.intermediate_predicted_corners if return_dict else outputs[4]
        initial_reference_points = outputs.initial_reference_points if return_dict else outputs[5]

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

        loss, loss_dict, auxiliary_outputs, enc_topk_logits, enc_topk_bboxes = None, None, None, None, None
        if labels is not None:
            enc_topk_logits = outputs.enc_topk_logits if return_dict else outputs[-5]
            enc_topk_bboxes = outputs.enc_topk_bboxes if return_dict else outputs[-4]
            loss, loss_dict, auxiliary_outputs = self.loss_function(
                logits,
                labels,
                self.device,
                pred_boxes,
                self.config,
                outputs_class,
                outputs_coord,
                enc_topk_logits=enc_topk_logits,
                enc_topk_bboxes=enc_topk_bboxes,
                denoising_meta_values=denoising_meta_values,
                predicted_corners=predicted_corners,
                initial_reference_points=initial_reference_points,
                **kwargs,
            )

        if not return_dict:
            if auxiliary_outputs is not None:
                output = (logits, pred_boxes) + (auxiliary_outputs,) + outputs
            else:
                output = (logits, pred_boxes) + outputs
            return ((loss, loss_dict) + output) if loss is not None else output

        return RTDetrV2ObjectDetectionOutput(
            loss=loss,
            loss_dict=loss_dict,
            logits=logits,
            pred_boxes=pred_boxes,
            auxiliary_outputs=auxiliary_outputs,
            last_hidden_state=outputs.last_hidden_state,
            intermediate_hidden_states=outputs.intermediate_hidden_states,
            intermediate_logits=outputs.intermediate_logits,
            intermediate_reference_points=outputs.intermediate_reference_points,
            intermediate_predicted_corners=outputs.intermediate_predicted_corners,
            initial_reference_points=outputs.initial_reference_points,
            decoder_hidden_states=outputs.decoder_hidden_states,
            decoder_attentions=outputs.decoder_attentions,
            cross_attentions=outputs.cross_attentions,
            encoder_last_hidden_state=outputs.encoder_last_hidden_state,
            encoder_hidden_states=outputs.encoder_hidden_states,
            encoder_attentions=outputs.encoder_attentions,
            init_reference_points=outputs.init_reference_points,
            enc_topk_logits=outputs.enc_topk_logits,
            enc_topk_bboxes=outputs.enc_topk_bboxes,
            enc_outputs_class=outputs.enc_outputs_class,
            enc_outputs_coord_logits=outputs.enc_outputs_coord_logits,
            denoising_meta_values=outputs.denoising_meta_values,
        )


__all__ = ["RTDetrV2Model", "RTDetrV2PreTrainedModel", "RTDetrV2ForObjectDetection"]
