
    <h                     ,    S SK Jr   " S S\5      rS/rg)   )PretrainedConfigc                      ^  \ rS rSrSrSr                          SS\S\S\S\S\S	\S
\4U 4S jjjrSr	U =r
$ )
EomtConfig   a  
This is the configuration class to store the configuration of a [`EomtForUniversalSegmentation`]. It is used to instantiate an EoMT 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 EoMT
[tue-mps/coco_panoptic_eomt_large_640](https://huggingface.co/tue-mps/coco_panoptic_eomt_large_640)
architecture.

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

Args:
    hidden_size (`int`, *optional*, defaults to 1024):
        Dimensionality of the hidden representations.
    num_hidden_layers (`int`, *optional*, defaults to 24):
        Number of hidden layers in the Transformer encoder.
    num_attention_heads (`int`, *optional*, defaults to 16):
        Number of attention heads in each attention layer.
    mlp_ratio (`int`, *optional*, defaults to 4):
        Ratio of the MLP hidden dimensionality to the hidden size.
    hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
        The non-linear activation function (function or string) in the encoder.
    hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
        The dropout probability for all fully connected layers in the embeddings and encoder.
    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 640):
        The size (resolution) of each input image.
    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.
    layerscale_value (`float`, *optional*, defaults to 1.0):
        Initial value for the LayerScale parameter.
    drop_path_rate (`float`, *optional*, defaults to 0.0):
        The stochastic depth rate (drop path) used during training.
    num_upscale_blocks (`int`, *optional*, defaults to 2):
        Number of upsampling blocks used in the decoder or segmentation head.
    attention_dropout (`float`, *optional*, defaults to 0.0):
        Dropout probability applied after attention projection.
    use_swiglu_ffn (`bool`, *optional*, defaults to `False`):
        Whether to use the SwiGLU feedforward neural network.
    num_blocks (`int`, *optional*, defaults to 4):
        Number of feature blocks or stages in the architecture.
    no_object_weight (`float`, *optional*, defaults to 0.1):
        Loss weight for the 'no object' class in panoptic/instance segmentation.
    class_weight (`float`, *optional*, defaults to 2.0):
        Loss weight for classification targets.
    mask_weight (`float`, *optional*, defaults to 5.0):
        Loss weight for mask prediction.
    dice_weight (`float`, *optional*, defaults to 5.0):
        Loss weight for the dice loss component.
    train_num_points (`int`, *optional*, defaults to 12544):
        Number of points to sample for mask loss computation during training.
    oversample_ratio (`float`, *optional*, defaults to 3.0):
        Oversampling ratio used in point sampling for mask training.
    importance_sample_ratio (`float`, *optional*, defaults to 0.75):
        Ratio of points to sample based on importance during training.
    num_queries (`int`, *optional*, defaults to 200):
        Number of object queries in the Transformer.
    num_register_tokens (`int`, *optional*, defaults to 4):
        Number of learnable register tokens added to the transformer input.

Example:

```python
>>> from transformers import EomtConfig, EomtForUniversalSegmentation

>>> # Initialize configuration
>>> config = EomtConfig()

>>> # Initialize model
>>> model = EomtForUniversalSegmentation(config)

>>> # Access config
>>> config = model.config
```eomtno_object_weightclass_weightmask_weightdice_weighttrain_num_pointsoversample_ratioimportance_sample_ratioc                 t  > [         TU ]  " S0 UD6  Xl        X l        X0l        XPl        X`l        Xpl        Xl        Xl	        Xl
        Xl        X@l        Xl        Xl        Xl        Xl        UU l        UU l        UU l        UU l        UU l        UU l        UU l        UU l        UU l        UU l        UU l        g )N )super__init__hidden_sizenum_hidden_layersnum_attention_heads
hidden_acthidden_dropout_probinitializer_rangelayer_norm_eps
image_size
patch_sizenum_channels	mlp_ratioattention_dropoutlayerscale_valuedrop_path_ratenum_upscale_blocksuse_swiglu_ffn
num_blocksr   r	   r
   r   r   r   r   num_queriesnum_register_tokens)selfr   r   r   r   r   r   r   r   r   r   r   r   r    r!   r   r"   r#   r   r	   r
   r   r   r   r   r$   r%   kwargs	__class__s                               c/var/www/html/shao/venv/lib/python3.13/site-packages/transformers/models/eomt/configuration_eomt.pyr   EomtConfig.__init__k   s    < 	"6"&!2#6 $#6 !2,$$("!2 0,"4,$ 0(&& 0 0'>$&#6     )r   r	   r   r    r   r   r   r   r   r   r   r   r
   r   r   r   r#   r   r   r$   r%   r!   r   r   r   r"   )i            gelu        g{Gz?gư>i  r-   r   g      ?r0      r0   Fr.   g?g       @      @r2   i 1  g      @g      ?   r.   )__name__
__module____qualname____firstlineno____doc__
model_typefloatintr   __static_attributes____classcell__)r(   s   @r)   r   r      s    M^ J "%!   %"%)-7:7&  ':7( ):7* +:7, -:7. /:70  1:72 "'3:7 :7r+   r   N)configuration_utilsr   r   __all__r   r+   r)   <module>r@      s#   , 4L7! L7^ .r+   