
    fTh\                     `    S r SSKJr  SSKJr  \R
                  " \5      r " S S\5      rS/r	g)zSegGpt model configuration   )PretrainedConfig)loggingc                   b   ^  \ rS rSrSrSrSSSSSSS	S
S/SSSSSSSSS/ SQS4U 4S jjrSrU =r$ )SegGptConfig   a  
This is the configuration class to store the configuration of a [`SegGptModel`]. It is used to instantiate a SegGPT
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 SegGPT
[BAAI/seggpt-vit-large](https://huggingface.co/BAAI/seggpt-vit-large) 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 encoder layers and the pooler layer.
    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 for each attention layer in the Transformer encoder.
    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.
    hidden_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 (`List[int]`, *optional*, defaults to `[896, 448]`):
        The size (resolution) of each 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.
    qkv_bias (`bool`, *optional*, defaults to `True`):
        Whether to add a bias to the queries, keys and values.
    mlp_dim (`int`, *optional*):
        The dimensionality of the MLP layer in the Transformer encoder. If unset, defaults to
        `hidden_size` * 4.
    drop_path_rate (`float`, *optional*, defaults to 0.1):
        The drop path rate for the dropout layers.
    pretrain_image_size (`int`, *optional*, defaults to 224):
        The pretrained size of the absolute position embeddings.
    decoder_hidden_size (`int`, *optional*, defaults to 64):
        Hidden size for decoder.
    use_relative_position_embeddings (`bool`, *optional*, defaults to `True`):
        Whether to use relative position embeddings in the attention layers.
    merge_index (`int`, *optional*, defaults to 2):
        The index of the encoder layer to merge the embeddings.
    intermediate_hidden_state_indices (`List[int]`, *optional*, defaults to `[5, 11, 17, 23]`):
        The indices of the encoder layers which we store as features for the decoder.
    beta (`float`, *optional*, defaults to 0.01):
        Regularization factor for SegGptLoss (smooth-l1 loss).

Example:

```python
>>> from transformers import SegGptConfig, SegGptModel

>>> # Initializing a SegGPT seggpt-vit-large style configuration
>>> configuration = SegGptConfig()

>>> # Initializing a model (with random weights) from the seggpt-vit-large style configuration
>>> model = SegGptModel(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config
```seggpti      gelug        g{Gz?gư>i  i  r   TNg?   @      )            g{Gz?c                   > [         TU ]  " S0 UD6  U[        U5      :  a  [        SU< SU< 35      eXl        X l        X0l        X@l        XPl        X`l	        Xpl
        Xl        Xl        Xl        Xl        Xl        Xl        Xl        UU l        UU l        UU l        UU l        Uc  [-        US-  5      U l        g UU l        g )NzTMerge index must be less than the minimum encoder output index, but got merge_index=z' and intermediate_hidden_state_indices=    )super__init__min
ValueErrorhidden_sizenum_hidden_layersnum_attention_heads
hidden_acthidden_dropout_probinitializer_rangelayer_norm_eps
image_size
patch_sizenum_channelsqkv_biasdrop_path_ratepretrain_image_sizedecoder_hidden_size use_relative_position_embeddingsmerge_index!intermediate_hidden_state_indicesbetaintmlp_dim)selfr   r   r   r   r   r   r   r    r!   r"   r#   r,   r$   r%   r&   r'   r(   r)   r*   kwargs	__class__s                        g/var/www/auris/envauris/lib/python3.13/site-packages/transformers/models/seggpt/configuration_seggpt.pyr   SegGptConfig.__init__]   s    . 	"6">??g[fZh  iQ  oP  nR  S  '!2#6 $#6 !2,$$( ,#6 #6 0P-&1R.	/6s;?+G    )r*   r&   r$   r   r   r   r    r   r)   r   r(   r,   r   r"   r   r!   r%   r#   r'   )	__name__
__module____qualname____firstlineno____doc__
model_typer   __static_attributes____classcell__)r/   s   @r0   r   r      s\    @D J :)-*9)/L /Lr2   r   N)
r7   configuration_utilsr   utilsr   
get_loggerr3   loggerr   __all__r   r2   r0   <module>r@      s>    ! 3  
		H	%tL# tLn 
r2   