#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
#           This file was automatically generated from src/transformers/models/lw_detr/modular_lw_detr.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_lw_detr.py file directly. One of our CI enforces this.
#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math

from ...configuration_utils import PreTrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
from ..auto import CONFIG_MAPPING, AutoConfig


logger = logging.get_logger(__name__)


class LwDetrViTConfig(BackboneConfigMixin, PreTrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`LwDetrViTModel`]. It is used to instantiate an
    LW-DETR ViT model according to the specified arguments, defining the model architecture. Instantiating a configuration
    with the defaults will yield a similar configuration to that of the LW-DETR ViT
    [stevenbucaille/lwdetr_small_60e_coco](https://huggingface.co/stevenbucaille/lwdetr_small_60e_coco) architecture.

    LW-DETR ViT is the Vision Transformer backbone used in the LW-DETR model for real-time object detection. It features
    interleaved window and global attention mechanisms to reduce computational complexity while maintaining high performance.
    The model uses a window-major feature map organization for efficient attention computation.

    Configuration objects inherit from [`VitDetConfig`] and can be used to control the model outputs. Read the
    documentation from [`VitDetConfig`] for more information.

    Args:
        hidden_size (`int`, *optional*, defaults to 768):
            Dimensionality of the encoder layers and the pooler layer.
        num_hidden_layers (`int`, *optional*, defaults to 12):
            Number of hidden layers in the Transformer encoder.
        num_attention_heads (`int`, *optional*, defaults to 12):
            Number of attention heads for each attention layer in the Transformer encoder.
        mlp_ratio (`int`, *optional*, defaults to 4):
            Ratio of mlp hidden dim to embedding dim.
        hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
            `"relu"`, `"selu"` and `"gelu_new"` are supported.
        dropout_prob (`float`, *optional*, defaults to 0.0):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        layer_norm_eps (`float`, *optional*, defaults to 1e-06):
            The epsilon used by the layer normalization layers.
        image_size (`int`, *optional*, defaults to 256):
            The size (resolution) of each image.
        pretrain_image_size (`int`, *optional*, defaults to 224):
            The size (resolution) of each image during pretraining.
        patch_size (`int`, *optional*, defaults to 16):
            The size (resolution) of each patch.
        num_channels (`int`, *optional*, defaults to 3):
            The number of input channels.
        qkv_bias (`bool`, *optional*, defaults to `True`):
            Whether to add a bias to the queries, keys and values.
        window_block_indices (`list[int]`, *optional*, defaults to `[]`):
            List of indices of blocks that should have window attention instead of regular global self-attention.
        use_absolute_position_embeddings (`bool`, *optional*, defaults to `True`):
            Whether to add absolute position embeddings to the patch embeddings.
        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.
        cae_init_values (`float`, *optional*, defaults to 0.1):
            Initialization value for CAE parameters when `use_cae` is enabled.
        num_windows (`int`, *optional*, defaults to 16):
            Number of windows for window-based attention. Must be a perfect square and the image size must be
            divisible by the square root of this value. This enables efficient window-major feature map organization.

    Example:

    ```python
    >>> from transformers import LwDetrViTConfig, LwDetrViTModel

    >>> # Initializing a LW-DETR ViT configuration
    >>> configuration = LwDetrViTConfig()

    >>> # Initializing a model (with random weights) from the configuration
    >>> model = LwDetrViTModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""

    model_type = "lw_detr_vit"

    def __init__(
        self,
        hidden_size=768,
        num_hidden_layers=12,
        num_attention_heads=12,
        mlp_ratio=4,
        hidden_act="gelu",
        dropout_prob=0.0,
        initializer_range=0.02,
        layer_norm_eps=1e-6,
        image_size=256,
        pretrain_image_size=224,
        patch_size=16,
        num_channels=3,
        qkv_bias=True,
        window_block_indices=[],
        use_absolute_position_embeddings=True,
        out_features=None,
        out_indices=None,
        cae_init_values: float = 0.1,
        num_windows=16,
        **kwargs,
    ):
        super().__init__(**kwargs)

        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.mlp_ratio = mlp_ratio
        self.hidden_act = hidden_act
        self.dropout_prob = dropout_prob
        self.initializer_range = initializer_range
        self.layer_norm_eps = layer_norm_eps
        self.image_size = image_size
        self.pretrain_image_size = pretrain_image_size
        self.patch_size = patch_size
        self.num_channels = num_channels
        self.qkv_bias = qkv_bias
        self.window_block_indices = window_block_indices
        self.use_absolute_position_embeddings = use_absolute_position_embeddings

        self.stage_names = ["stem"] + [f"stage{idx}" for idx in range(1, self.num_hidden_layers + 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.cae_init_values = cae_init_values
        if num_windows % math.sqrt(num_windows) != 0:
            raise ValueError(
                f"`num_windows` has to be a perfect square, where num_windows % math.sqrt(num_windows) != 0, but got {num_windows}."
            )
        if image_size / num_windows % math.sqrt(num_windows) != 0:
            raise ValueError(
                f"`image_size` has to be divisible by `num_windows`, where image_size / num_windows % math.sqrt(num_windows) != 0,but got {image_size} and {num_windows}."
            )
        self.num_windows = num_windows
        self.num_windows_side = int(math.sqrt(num_windows))


class LwDetrConfig(PreTrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`LwDetrModel`]. It is used to instantiate
    a LW-DETR model according to the specified arguments, defining the model architecture. Instantiating a
    configuration with the defaults will yield a similar configuration to that of the LW-DETR
    [stevenbucaille/lwdetr_small_60e_coco](https://huggingface.co/stevenbucaille/lwdetr_small_60e_coco) architecture.

    LW-DETR (Lightweight Detection Transformer) is a transformer-based object detection model designed for real-time
    detection tasks. It replaces traditional CNN-based detectors like YOLO with a more efficient transformer architecture
    that achieves competitive performance while being computationally lightweight.

    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.

    Args:
        backbone_config (`PretrainedConfig` or `dict`, *optional*):
            The configuration of the backbone model. If not provided, will default to `LwDetrViTConfig` with
            a small ViT architecture optimized for detection tasks.
        projector_scale_factors (`list[float]`, *optional*, defaults to `[]`):
            Scale factors for the feature pyramid network. Each scale factor determines the resolution of features
            at different levels. Supported values are 0.5, 1.0, and 2.0.
        hidden_expansion (`float`, *optional*, defaults to 0.5):
            Expansion factor for hidden dimensions in the projector layers.
        c2f_num_blocks (`int`, *optional*, defaults to 3):
            Number of blocks in the C2F layer.
        activation_function (`str`, *optional*, defaults to `"silu"`):
            The non-linear activation function in the projector. Supported values are `"silu"`, `"relu"`, `"gelu"`.
        batch_norm_eps (`float`, *optional*, defaults to 1e-05):
            The epsilon value for batch normalization layers.
        d_model (`int`, *optional*, defaults to 256):
            Dimension of the model layers and the number of expected features in the decoder inputs.
        dropout (`float`, *optional*, defaults to 0.1):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        decoder_ffn_dim (`int`, *optional*, defaults to 2048):
            Dimension of the "intermediate" (often named feed-forward) layer in decoder.
        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 3):
            Number of decoder layers in the transformer.
        decoder_self_attention_heads (`int`, *optional*, defaults to 8):
            Number of attention heads for each attention layer in the decoder self-attention.
        decoder_cross_attention_heads (`int`, *optional*, defaults to 16):
            Number of attention heads for each attention layer in the decoder cross-attention.
        decoder_activation_function (`str`, *optional*, defaults to `"relu"`):
            The non-linear activation function in the decoder. Supported values are `"relu"`, `"silu"`, `"gelu"`.
        num_queries (`int`, *optional*, defaults to 300):
            Number of object queries, i.e. detection slots. This is the maximal number of objects
            [`LwDetrModel`] can detect in a single image.
        attention_bias (`bool`, *optional*, defaults to `True`):
            Whether to add bias to the attention layers.
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        activation_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for activations inside the fully connected layer.
        group_detr (`int`, *optional*, defaults to 13):
            Number of groups for Group DETR attention mechanism, which helps reduce computational complexity.
        init_std (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        disable_custom_kernels (`bool`, *optional*, defaults to `True`):
            Disable the use of custom CUDA and CPU kernels. This option is necessary for the ONNX export, as custom
            kernels are not supported by PyTorch ONNX export.
        class_cost (`float`, *optional*, defaults to 2):
            Relative weight of the classification error in the Hungarian matching cost.
        bbox_cost (`float`, *optional*, defaults to 5):
            Relative weight of the L1 error of the bounding box coordinates in the Hungarian matching cost.
        giou_cost (`float`, *optional*, defaults to 2):
            Relative weight of the generalized IoU loss of the bounding box in the Hungarian matching cost.
        mask_loss_coefficient (`float`, *optional*, defaults to 1):
            Relative weight of the Focal loss in the panoptic segmentation loss.
        dice_loss_coefficient (`float`, *optional*, defaults to 1):
            Relative weight of the DICE/F-1 loss in the panoptic segmentation loss.
        bbox_loss_coefficient (`float`, *optional*, defaults to 5):
            Relative weight of the L1 bounding box loss in the object detection loss.
        giou_loss_coefficient (`float`, *optional*, defaults to 2):
            Relative weight of the generalized IoU loss in the object detection loss.
        eos_coefficient (`float`, *optional*, defaults to 0.1):
            Relative classification weight of the 'no-object' class in the object detection loss.
        focal_alpha (`float`, *optional*, defaults to 0.25):
            Alpha parameter in the focal loss.
        auxiliary_loss (`bool`, *optional*, defaults to `True`):
            Whether auxiliary decoding losses (loss at each decoder layer) are to be used.

    Examples:

    ```python
    >>> from transformers import LwDetrConfig, LwDetrModel

    >>> # Initializing a LW-DETR stevenbucaille/lwdetr_small_60e_coco style configuration
    >>> configuration = LwDetrConfig()

    >>> # Initializing a model (with random weights) from the stevenbucaille/lwdetr_small_60e_coco style configuration
    >>> model = LwDetrModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""

    model_type = "lw_detr"
    sub_configs = {"backbone_config": AutoConfig}

    def __init__(
        self,
        # backbone
        backbone_config=None,
        # projector
        projector_scale_factors: list[float] = [],
        hidden_expansion=0.5,
        c2f_num_blocks=3,
        activation_function="silu",
        batch_norm_eps=1e-5,
        # decoder
        d_model=256,
        dropout=0.1,
        decoder_ffn_dim=2048,
        decoder_n_points=4,
        decoder_layers: int = 3,
        decoder_self_attention_heads: int = 8,
        decoder_cross_attention_heads: int = 16,
        decoder_activation_function="relu",
        # model
        num_queries=300,
        attention_bias=True,
        attention_dropout=0.0,
        activation_dropout=0.0,
        group_detr: int = 13,
        init_std=0.02,
        disable_custom_kernels=True,
        # loss
        class_cost=2,
        bbox_cost=5,
        giou_cost=2,
        mask_loss_coefficient=1,
        dice_loss_coefficient=1,
        bbox_loss_coefficient=5,
        giou_loss_coefficient=2,
        eos_coefficient=0.1,
        focal_alpha=0.25,
        auxiliary_loss=True,
        **kwargs,
    ):
        self.batch_norm_eps = batch_norm_eps

        # backbone
        if backbone_config is None:
            logger.info(
                "`backbone_config` and `backbone` are `None`. Initializing the config with the default `LwDetrViT` backbone."
            )
            backbone_config = LwDetrViTConfig(
                image_size=1024,
                hidden_size=192,
                num_hidden_layers=10,
                num_attention_heads=12,
                window_block_indices=[0, 1, 3, 6, 7, 9],
                out_indices=[2, 4, 5, 9],
                **kwargs,
            )
        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)

        self.backbone_config = backbone_config
        # projector
        self.projector_scale_factors = projector_scale_factors
        for scale in projector_scale_factors:
            if scale not in [0.5, 1.0, 2.0]:
                raise ValueError(f"Unsupported scale factor: {scale}")
        self.projector_in_channels = [d_model] * len(projector_scale_factors)
        self.projector_out_channels = d_model
        self.activation_function = activation_function
        self.hidden_expansion = hidden_expansion
        self.c2f_num_blocks = c2f_num_blocks
        # decoder
        self.d_model = d_model
        self.dropout = dropout
        self.num_queries = num_queries
        self.decoder_ffn_dim = decoder_ffn_dim
        self.num_feature_levels = len(self.projector_scale_factors)
        self.decoder_n_points = decoder_n_points
        self.decoder_layers = decoder_layers
        self.decoder_activation_function = decoder_activation_function
        self.decoder_self_attention_heads = decoder_self_attention_heads
        self.decoder_cross_attention_heads = decoder_cross_attention_heads
        self.attention_bias = attention_bias
        self.attention_dropout = attention_dropout
        self.activation_dropout = activation_dropout
        # model
        self.init_std = init_std
        self.group_detr = group_detr
        # Loss
        self.auxiliary_loss = auxiliary_loss
        # Hungarian matcher
        self.class_cost = class_cost
        self.bbox_cost = bbox_cost
        self.giou_cost = giou_cost
        # Loss coefficients
        self.dice_loss_coefficient = dice_loss_coefficient
        self.bbox_loss_coefficient = bbox_loss_coefficient
        self.giou_loss_coefficient = giou_loss_coefficient
        self.eos_coefficient = eos_coefficient
        self.focal_alpha = focal_alpha
        self.disable_custom_kernels = disable_custom_kernels
        super().__init__(**kwargs)


__all__ = ["LwDetrConfig", "LwDetrViTConfig"]
