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#           This file was automatically generated from src/transformers/models/d_fine/modular_d_fine.py.
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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.
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import verify_backbone_config_arguments
from ..auto import CONFIG_MAPPING


logger = logging.get_logger(__name__)


# TODO: Attribute map assignment logic should be fixed in modular
# as well as super() call parsing because otherwise we cannot re-write args after initialization
class DFineConfig(PretrainedConfig):
    """
    This is the configuration class to store the configuration of a [`DFineModel`]. It is used to instantiate a D-FINE
    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
    defaults will yield a similar configuration to that of D-FINE-X-COCO "[ustc-community/dfine-xlarge-coco"](https://huggingface.co/ustc-community/dfine-xlarge-coco").
    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.

    Args:
        initializer_range (`float`, *optional*, defaults to 0.01):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        initializer_bias_prior_prob (`float`, *optional*):
            The prior probability used by the bias initializer to initialize biases for `enc_score_head` and `class_embed`.
            If `None`, `prior_prob` computed as `prior_prob = 1 / (num_labels + 1)` while initializing model weights.
        layer_norm_eps (`float`, *optional*, defaults to 1e-05):
            The epsilon used by the layer normalization layers.
        batch_norm_eps (`float`, *optional*, defaults to 1e-05):
            The epsilon used by the batch normalization layers.
        backbone_config (`Dict`, *optional*, defaults to `RTDetrResNetConfig()`):
            The configuration of the backbone model.
        backbone (`str`, *optional*):
            Name of backbone to use when `backbone_config` is `None`. If `use_pretrained_backbone` is `True`, this
            will load the corresponding pretrained weights from the timm or transformers library. If `use_pretrained_backbone`
            is `False`, this loads the backbone's config and uses that to initialize the backbone with random weights.
        use_pretrained_backbone (`bool`, *optional*, defaults to `False`):
            Whether to use pretrained weights for the backbone.
        use_timm_backbone (`bool`, *optional*, defaults to `False`):
            Whether to load `backbone` from the timm library. If `False`, the backbone is loaded from the transformers
            library.
        freeze_backbone_batch_norms (`bool`, *optional*, defaults to `True`):
            Whether to freeze the batch normalization layers in the backbone.
        backbone_kwargs (`dict`, *optional*):
            Keyword arguments to be passed to AutoBackbone when loading from a checkpoint
            e.g. `{'out_indices': (0, 1, 2, 3)}`. Cannot be specified if `backbone_config` is set.
        encoder_hidden_dim (`int`, *optional*, defaults to 256):
            Dimension of the layers in hybrid encoder.
        encoder_in_channels (`list`, *optional*, defaults to `[512, 1024, 2048]`):
            Multi level features input for encoder.
        feat_strides (`list[int]`, *optional*, defaults to `[8, 16, 32]`):
            Strides used in each feature map.
        encoder_layers (`int`, *optional*, defaults to 1):
            Total of layers to be used by the encoder.
        encoder_ffn_dim (`int`, *optional*, defaults to 1024):
            Dimension of the "intermediate" (often named feed-forward) layer in decoder.
        encoder_attention_heads (`int`, *optional*, defaults to 8):
            Number of attention heads for each attention layer in the Transformer encoder.
        dropout (`float`, *optional*, defaults to 0.0):
            The ratio for all dropout layers.
        activation_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for activations inside the fully connected layer.
        encode_proj_layers (`list[int]`, *optional*, defaults to `[2]`):
            Indexes of the projected layers to be used in the encoder.
        positional_encoding_temperature (`int`, *optional*, defaults to 10000):
            The temperature parameter used to create the positional encodings.
        encoder_activation_function (`str`, *optional*, defaults to `"gelu"`):
            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
            `"relu"`, `"silu"` and `"gelu_new"` are supported.
        activation_function (`str`, *optional*, defaults to `"silu"`):
            The non-linear activation function (function or string) in the general layer. If string, `"gelu"`,
            `"relu"`, `"silu"` and `"gelu_new"` are supported.
        eval_size (`tuple[int, int]`, *optional*):
            Height and width used to computes the effective height and width of the position embeddings after taking
            into account the stride.
        normalize_before (`bool`, *optional*, defaults to `False`):
            Determine whether to apply layer normalization in the transformer encoder layer before self-attention and
            feed-forward modules.
        hidden_expansion (`float`, *optional*, defaults to 1.0):
            Expansion ratio to enlarge the dimension size of RepVGGBlock and CSPRepLayer.
        d_model (`int`, *optional*, defaults to 256):
            Dimension of the layers exclude hybrid encoder.
        num_queries (`int`, *optional*, defaults to 300):
            Number of object queries.
        decoder_in_channels (`list`, *optional*, defaults to `[256, 256, 256]`):
            Multi level features dimension for decoder
        decoder_ffn_dim (`int`, *optional*, defaults to 1024):
            Dimension of the "intermediate" (often named feed-forward) layer in decoder.
        num_feature_levels (`int`, *optional*, defaults to 3):
            The number of input feature levels.
        decoder_n_points (`int`, *optional*, defaults to 4):
            The number of sampled keys in each feature level for each attention head in the decoder.
        decoder_layers (`int`, *optional*, defaults to 6):
            Number of decoder layers.
        decoder_attention_heads (`int`, *optional*, defaults to 8):
            Number of attention heads for each attention layer in the Transformer decoder.
        decoder_activation_function (`str`, *optional*, defaults to `"relu"`):
            The non-linear activation function (function or string) in the decoder. If string, `"gelu"`,
            `"relu"`, `"silu"` and `"gelu_new"` are supported.
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        num_denoising (`int`, *optional*, defaults to 100):
            The total number of denoising tasks or queries to be used for contrastive denoising.
        label_noise_ratio (`float`, *optional*, defaults to 0.5):
            The fraction of denoising labels to which random noise should be added.
        box_noise_scale (`float`, *optional*, defaults to 1.0):
            Scale or magnitude of noise to be added to the bounding boxes.
        learn_initial_query (`bool`, *optional*, defaults to `False`):
            Indicates whether the initial query embeddings for the decoder should be learned during training
        anchor_image_size (`tuple[int, int]`, *optional*):
            Height and width of the input image used during evaluation to generate the bounding box anchors. If None, automatic generate anchor is applied.
        with_box_refine (`bool`, *optional*, defaults to `True`):
            Whether to apply iterative bounding box refinement, where each decoder layer refines the bounding boxes
            based on the predictions from the previous layer.
        is_encoder_decoder (`bool`, *optional*, defaults to `True`):
            Whether the architecture has an encoder decoder structure.
        matcher_alpha (`float`, *optional*, defaults to 0.25):
            Parameter alpha used by the Hungarian Matcher.
        matcher_gamma (`float`, *optional*, defaults to 2.0):
            Parameter gamma used by the Hungarian Matcher.
        matcher_class_cost (`float`, *optional*, defaults to 2.0):
            The relative weight of the class loss used by the Hungarian Matcher.
        matcher_bbox_cost (`float`, *optional*, defaults to 5.0):
            The relative weight of the bounding box loss used by the Hungarian Matcher.
        matcher_giou_cost (`float`, *optional*, defaults to 2.0):
            The relative weight of the giou loss of used by the Hungarian Matcher.
        use_focal_loss (`bool`, *optional*, defaults to `True`):
            Parameter informing if focal focal should be used.
        auxiliary_loss (`bool`, *optional*, defaults to `True`):
            Whether auxiliary decoding losses (loss at each decoder layer) are to be used.
        focal_loss_alpha (`float`, *optional*, defaults to 0.75):
            Parameter alpha used to compute the focal loss.
        focal_loss_gamma (`float`, *optional*, defaults to 2.0):
            Parameter gamma used to compute the focal loss.
        weight_loss_vfl (`float`, *optional*, defaults to 1.0):
            Relative weight of the varifocal loss in the object detection loss.
        weight_loss_bbox (`float`, *optional*, defaults to 5.0):
            Relative weight of the L1 bounding box loss in the object detection loss.
        weight_loss_giou (`float`, *optional*, defaults to 2.0):
            Relative weight of the generalized IoU loss in the object detection loss.
        weight_loss_fgl (`float`, *optional*, defaults to 0.15):
            Relative weight of the fine-grained localization loss in the object detection loss.
        weight_loss_ddf (`float`, *optional*, defaults to 1.5):
            Relative weight of the decoupled distillation focal loss in the object detection loss.
        eos_coefficient (`float`, *optional*, defaults to 0.0001):
            Relative classification weight of the 'no-object' class in the object detection loss.
        eval_idx (`int`, *optional*, defaults to -1):
            Index of the decoder layer to use for evaluation. If negative, counts from the end
            (e.g., -1 means use the last layer). This allows for early prediction in the decoder
            stack while still training later layers.
        layer_scale (`float`, *optional*, defaults to `1.0`):
            Scaling factor for the hidden dimension in later decoder layers. Used to adjust the
            model capacity after the evaluation layer.
        max_num_bins (`int`, *optional*, defaults to 32):
            Maximum number of bins for the distribution-guided bounding box refinement.
            Higher values allow for more fine-grained localization but increase computation.
        reg_scale (`float`, *optional*, defaults to 4.0):
            Scale factor for the regression distribution. Controls the range and granularity
            of the bounding box refinement process.
        depth_mult (`float`, *optional*, defaults to 1.0):
            Multiplier for the number of blocks in RepNCSPELAN4 layers. Used to scale the model's
            depth while maintaining its architecture.
        top_prob_values (`int`, *optional*, defaults to 4):
            Number of top probability values to consider from each corner's distribution.
        lqe_hidden_dim (`int`, *optional*, defaults to 64):
            Hidden dimension size for the Location Quality Estimator (LQE) network.
        lqe_layers (`int`, *optional*, defaults to 2):
            Number of layers in the Location Quality Estimator MLP.
        decoder_offset_scale (`float`, *optional*, defaults to 0.5):
            Offset scale used in deformable attention.
        decoder_method (`str`, *optional*, defaults to `"default"`):
            The method to use for the decoder: `"default"` or `"discrete"`.
        up (`float`, *optional*, defaults to 0.5):
            Controls the upper bounds of the Weighting Function.
    """

    model_type = "d_fine"
    layer_types = ["basic", "bottleneck"]
    attribute_map = {
        "hidden_size": "d_model",
        "num_attention_heads": "encoder_attention_heads",
    }

    def __init__(
        self,
        initializer_range=0.01,
        initializer_bias_prior_prob=None,
        layer_norm_eps=1e-5,
        batch_norm_eps=1e-5,
        # backbone
        backbone_config=None,
        backbone=None,
        use_pretrained_backbone=False,
        use_timm_backbone=False,
        freeze_backbone_batch_norms=True,
        backbone_kwargs=None,
        # encoder HybridEncoder
        encoder_hidden_dim=256,
        encoder_in_channels=[512, 1024, 2048],
        feat_strides=[8, 16, 32],
        encoder_layers=1,
        encoder_ffn_dim=1024,
        encoder_attention_heads=8,
        dropout=0.0,
        activation_dropout=0.0,
        encode_proj_layers=[2],
        positional_encoding_temperature=10000,
        encoder_activation_function="gelu",
        activation_function="silu",
        eval_size=None,
        normalize_before=False,
        hidden_expansion=1.0,
        # decoder DFineTransformer
        d_model=256,
        num_queries=300,
        decoder_in_channels=[256, 256, 256],
        decoder_ffn_dim=1024,
        num_feature_levels=3,
        decoder_n_points=4,
        decoder_layers=6,
        decoder_attention_heads=8,
        decoder_activation_function="relu",
        attention_dropout=0.0,
        num_denoising=100,
        label_noise_ratio=0.5,
        box_noise_scale=1.0,
        learn_initial_query=False,
        anchor_image_size=None,
        with_box_refine=True,
        is_encoder_decoder=True,
        # Loss
        matcher_alpha=0.25,
        matcher_gamma=2.0,
        matcher_class_cost=2.0,
        matcher_bbox_cost=5.0,
        matcher_giou_cost=2.0,
        use_focal_loss=True,
        auxiliary_loss=True,
        focal_loss_alpha=0.75,
        focal_loss_gamma=2.0,
        weight_loss_vfl=1.0,
        weight_loss_bbox=5.0,
        weight_loss_giou=2.0,
        weight_loss_fgl=0.15,
        weight_loss_ddf=1.5,
        eos_coefficient=1e-4,
        eval_idx=-1,
        layer_scale=1,
        max_num_bins=32,
        reg_scale=4.0,
        depth_mult=1.0,
        top_prob_values=4,
        lqe_hidden_dim=64,
        lqe_layers=2,
        decoder_offset_scale=0.5,
        decoder_method="default",
        up=0.5,
        **kwargs,
    ):
        self.initializer_range = initializer_range
        self.initializer_bias_prior_prob = initializer_bias_prior_prob
        self.layer_norm_eps = layer_norm_eps
        self.batch_norm_eps = batch_norm_eps
        # backbone
        if backbone_config is None and backbone is None:
            logger.info(
                "`backbone_config` and `backbone` are `None`. Initializing the config with the default `HGNet-V2` backbone."
            )
            backbone_model_type = "hgnet_v2"
            config_class = CONFIG_MAPPING[backbone_model_type]
            # this will map it to RTDetrResNetConfig
            # note: we can instead create HGNetV2Config
            # and we would need to create HGNetV2Backbone
            backbone_config = config_class(
                num_channels=3,
                embedding_size=64,
                hidden_sizes=[256, 512, 1024, 2048],
                depths=[3, 4, 6, 3],
                layer_type="bottleneck",
                hidden_act="relu",
                downsample_in_first_stage=False,
                downsample_in_bottleneck=False,
                out_features=None,
                out_indices=[2, 3, 4],
            )
        elif isinstance(backbone_config, dict):
            backbone_model_type = backbone_config.pop("model_type")
            config_class = CONFIG_MAPPING[backbone_model_type]
            backbone_config = config_class.from_dict(backbone_config)

        verify_backbone_config_arguments(
            use_timm_backbone=use_timm_backbone,
            use_pretrained_backbone=use_pretrained_backbone,
            backbone=backbone,
            backbone_config=backbone_config,
            backbone_kwargs=backbone_kwargs,
        )

        self.backbone_config = backbone_config
        self.backbone = backbone
        self.use_pretrained_backbone = use_pretrained_backbone
        self.use_timm_backbone = use_timm_backbone
        self.freeze_backbone_batch_norms = freeze_backbone_batch_norms
        self.backbone_kwargs = backbone_kwargs
        # encoder
        self.encoder_hidden_dim = encoder_hidden_dim
        self.encoder_in_channels = encoder_in_channels
        self.feat_strides = feat_strides
        self.encoder_attention_heads = encoder_attention_heads
        self.encoder_ffn_dim = encoder_ffn_dim
        self.dropout = dropout
        self.activation_dropout = activation_dropout
        self.encode_proj_layers = encode_proj_layers
        self.encoder_layers = encoder_layers
        self.positional_encoding_temperature = positional_encoding_temperature
        self.eval_size = eval_size
        self.normalize_before = normalize_before
        self.encoder_activation_function = encoder_activation_function
        self.activation_function = activation_function
        self.hidden_expansion = hidden_expansion
        # decoder
        self.d_model = d_model
        self.num_queries = num_queries
        self.decoder_ffn_dim = decoder_ffn_dim
        self.decoder_in_channels = decoder_in_channels
        self.num_feature_levels = num_feature_levels
        self.decoder_n_points = decoder_n_points
        self.decoder_layers = decoder_layers
        self.decoder_attention_heads = decoder_attention_heads
        self.decoder_activation_function = decoder_activation_function
        self.attention_dropout = attention_dropout
        self.num_denoising = num_denoising
        self.label_noise_ratio = label_noise_ratio
        self.box_noise_scale = box_noise_scale
        self.learn_initial_query = learn_initial_query
        self.anchor_image_size = anchor_image_size
        self.auxiliary_loss = auxiliary_loss
        self.with_box_refine = with_box_refine
        # Loss
        self.matcher_alpha = matcher_alpha
        self.matcher_gamma = matcher_gamma
        self.matcher_class_cost = matcher_class_cost
        self.matcher_bbox_cost = matcher_bbox_cost
        self.matcher_giou_cost = matcher_giou_cost
        self.use_focal_loss = use_focal_loss
        self.focal_loss_alpha = focal_loss_alpha
        self.focal_loss_gamma = focal_loss_gamma
        self.weight_loss_vfl = weight_loss_vfl
        self.weight_loss_bbox = weight_loss_bbox
        self.weight_loss_giou = weight_loss_giou
        self.weight_loss_fgl = weight_loss_fgl
        self.weight_loss_ddf = weight_loss_ddf
        self.eos_coefficient = eos_coefficient
        # add the new attributes with the given values or defaults
        self.eval_idx = eval_idx
        self.layer_scale = layer_scale
        self.max_num_bins = max_num_bins
        self.reg_scale = reg_scale
        self.depth_mult = depth_mult
        self.decoder_offset_scale = decoder_offset_scale
        self.decoder_method = decoder_method
        self.top_prob_values = top_prob_values
        self.lqe_hidden_dim = lqe_hidden_dim
        self.lqe_layers = lqe_layers
        self.up = up

        if isinstance(self.decoder_n_points, list):
            if len(self.decoder_n_points) != self.num_feature_levels:
                raise ValueError(
                    f"Length of decoder_n_points list ({len(self.decoder_n_points)}) must match num_feature_levels ({self.num_feature_levels})."
                )

        head_dim = self.d_model // self.decoder_attention_heads
        if head_dim * self.decoder_attention_heads != self.d_model:
            raise ValueError(
                f"Embedded dimension {self.d_model} must be divisible by decoder_attention_heads {self.decoder_attention_heads}"
            )
        super().__init__(is_encoder_decoder=is_encoder_decoder, **kwargs)

    @property
    def num_attention_heads(self) -> int:
        return self.encoder_attention_heads

    @property
    def hidden_size(self) -> int:
        return self.d_model

    @property
    def sub_configs(self):
        return (
            {"backbone_config": type(self.backbone_config)}
            if getattr(self, "backbone_config", None) is not None
            else {}
        )

    @classmethod
    def from_backbone_configs(cls, backbone_config: PretrainedConfig, **kwargs):
        """Instantiate a [`DFineConfig`] (or a derived class) from a pre-trained backbone model configuration and DETR model
        configuration.

            Args:
                backbone_config ([`PretrainedConfig`]):
                    The backbone configuration.

            Returns:
                [`DFineConfig`]: An instance of a configuration object
        """
        return cls(
            backbone_config=backbone_config,
            **kwargs,
        )


__all__ = ["DFineConfig"]
