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+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from src/transformers/models/hgnet_v2/modular_hgnet_v2.py.
+#               Do NOT edit this file manually as any edits will be overwritten by the generation of
+#             the file from the modular. If any change should be done, please apply the change to the
+#                          modular_hgnet_v2.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# 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.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
+
+
+# TODO: Modular conversion for resnet must be fixed as
+# it provides incorrect import for configuration like resnet_resnet
+class HGNetV2Config(BackboneConfigMixin, PretrainedConfig):
+    """
+    This is the configuration class to store the configuration of a [`HGNetV2Backbone`]. It is used to instantiate a HGNet-V2
+    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 B4 "[ustc-community/dfine_x_coco"](https://huggingface.co/ustc-community/dfine_x_coco").
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Args:
+        num_channels (`int`, *optional*, defaults to 3):
+            The number of input channels.
+        embedding_size (`int`, *optional*, defaults to 64):
+            Dimensionality (hidden size) for the embedding layer.
+        depths (`list[int]`, *optional*, defaults to `[3, 4, 6, 3]`):
+            Depth (number of layers) for each stage.
+        hidden_sizes (`list[int]`, *optional*, defaults to `[256, 512, 1024, 2048]`):
+            Dimensionality (hidden size) at each stage.
+        hidden_act (`str`, *optional*, defaults to `"relu"`):
+            The non-linear activation function in each block. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"`
+            are supported.
+        out_features (`list[str]`, *optional*):
+            If used as backbone, list of features to output. Can be any of `"stem"`, `"stage1"`, `"stage2"`, etc.
+            (depending on how many stages the model has). If unset and `out_indices` is set, will default to the
+            corresponding stages. If unset and `out_indices` is unset, will default to the last stage. Must be in the
+            same order as defined in the `stage_names` attribute.
+        out_indices (`list[int]`, *optional*):
+            If used as backbone, list of indices of features to output. Can be any of 0, 1, 2, etc. (depending on how
+            many stages the model has). If unset and `out_features` is set, will default to the corresponding stages.
+            If unset and `out_features` is unset, will default to the last stage. Must be in the
+            same order as defined in the `stage_names` attribute.
+        stem_channels (`list[int]`, *optional*, defaults to `[3, 32, 48]`):
+            Channel dimensions for the stem layers:
+            - First number (3) is input image channels
+            - Second number (32) is intermediate stem channels
+            - Third number (48) is output stem channels
+        stage_in_channels (`list[int]`, *optional*, defaults to `[48, 128, 512, 1024]`):
+            Input channel dimensions for each stage of the backbone.
+            This defines how many channels the input to each stage will have.
+        stage_mid_channels (`list[int]`, *optional*, defaults to `[48, 96, 192, 384]`):
+            Mid-channel dimensions for each stage of the backbone.
+            This defines the number of channels used in the intermediate layers of each stage.
+        stage_out_channels (`list[int]`, *optional*, defaults to `[128, 512, 1024, 2048]`):
+            Output channel dimensions for each stage of the backbone.
+            This defines how many channels the output of each stage will have.
+        stage_num_blocks (`list[int]`, *optional*, defaults to `[1, 1, 3, 1]`):
+            Number of blocks to be used in each stage of the backbone.
+            This controls the depth of each stage by specifying how many convolutional blocks to stack.
+        stage_downsample (`list[bool]`, *optional*, defaults to `[False, True, True, True]`):
+            Indicates whether to downsample the feature maps at each stage.
+            If `True`, the spatial dimensions of the feature maps will be reduced.
+        stage_light_block (`list[bool]`, *optional*, defaults to `[False, False, True, True]`):
+            Indicates whether to use light blocks in each stage.
+            Light blocks are a variant of convolutional blocks that may have fewer parameters.
+        stage_kernel_size (`list[int]`, *optional*, defaults to `[3, 3, 5, 5]`):
+            Kernel sizes for the convolutional layers in each stage.
+        stage_numb_of_layers (`list[int]`, *optional*, defaults to `[6, 6, 6, 6]`):
+            Number of layers to be used in each block of the stage.
+        use_learnable_affine_block (`bool`, *optional*, defaults to `False`):
+            Whether to use Learnable Affine Blocks (LAB) in the network.
+            LAB adds learnable scale and bias parameters after certain operations.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+    """
+
+    model_type = "hgnet_v2"
+
+    def __init__(
+        self,
+        num_channels=3,
+        embedding_size=64,
+        depths=[3, 4, 6, 3],
+        hidden_sizes=[256, 512, 1024, 2048],
+        hidden_act="relu",
+        out_features=None,
+        out_indices=None,
+        stem_channels=[3, 32, 48],
+        stage_in_channels=[48, 128, 512, 1024],
+        stage_mid_channels=[48, 96, 192, 384],
+        stage_out_channels=[128, 512, 1024, 2048],
+        stage_num_blocks=[1, 1, 3, 1],
+        stage_downsample=[False, True, True, True],
+        stage_light_block=[False, False, True, True],
+        stage_kernel_size=[3, 3, 5, 5],
+        stage_numb_of_layers=[6, 6, 6, 6],
+        use_learnable_affine_block=False,
+        initializer_range=0.02,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.num_channels = num_channels
+        self.embedding_size = embedding_size
+        self.depths = depths
+        self.hidden_sizes = hidden_sizes
+        self.hidden_act = hidden_act
+        self.stage_names = ["stem"] + [f"stage{idx}" for idx in range(1, len(depths) + 1)]
+        self._out_features, self._out_indices = get_aligned_output_features_output_indices(
+            out_features=out_features, out_indices=out_indices, stage_names=self.stage_names
+        )
+        self.stem_channels = stem_channels
+        self.stage_in_channels = stage_in_channels
+        self.stage_mid_channels = stage_mid_channels
+        self.stage_out_channels = stage_out_channels
+        self.stage_num_blocks = stage_num_blocks
+        self.stage_downsample = stage_downsample
+        self.stage_light_block = stage_light_block
+        self.stage_kernel_size = stage_kernel_size
+        self.stage_numb_of_layers = stage_numb_of_layers
+        self.use_learnable_affine_block = use_learnable_affine_block
+        self.initializer_range = initializer_range
+
+        if not (
+            len(stage_in_channels)
+            == len(stage_mid_channels)
+            == len(stage_out_channels)
+            == len(stage_num_blocks)
+            == len(stage_downsample)
+            == len(stage_light_block)
+            == len(stage_kernel_size)
+            == len(stage_numb_of_layers)
+        ):
+            raise ValueError("All stage configuration lists must have the same length.")
+
+
+__all__ = ["HGNetV2Config"]