
    Z j                     d    S r SSKJr  SSKJr  SSKJr  \" SS9\ " S S	\5      5       5       rS	/rg
)zSegGpt model configuration    )strict   )PreTrainedConfig)auto_docstringzBAAI/seggpt-vit-large)
checkpointc                     ^  \ rS rSr% SrSrSr\\S'   Sr	\\S'   Sr
\\S	'   S
r\\S'   Sr\\-  \S'   Sr\\S'   Sr\\S'   Sr\\\   -  \\S4   -  \S'   Sr\\\   -  \\\4   -  \S'   Sr\\S'   Sr\\S'   Sr\S-  \S'   Sr\\-  \S'   Sr\\\   -  \\\4   -  \S'   S r\\S!'   Sr\\S"'   S#r\\S$'   S%r\\   \\S4   -  \S&'   S'r\\S('   U 4S) jr S* r!S+r"U =r#$ ),SegGptConfig   a  
mlp_dim (`int`, *optional*):
    The dimensionality of the MLP layer in the Transformer encoder. If unset, defaults to
    `hidden_size` * 4.
pretrain_image_size (`int`, *optional*, defaults to 224):
    The pretrained size of the absolute position embeddings.
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   hidden_size   num_hidden_layers   num_attention_headsgelu
hidden_actg        hidden_dropout_probg{Gz?initializer_rangegư>layer_norm_eps)i  i  .
image_size
patch_sizer   num_channelsTqkv_biasNmlp_dimg?drop_path_rate   pretrain_image_size@   decoder_hidden_size use_relative_position_embeddings   merge_index)            !intermediate_hidden_state_indicesg{Gz?betac                    > U R                   c  [        U R                  S-  5      OU R                   U l         [        TU ]  " S0 UD6  g )N    )r   intr   super__post_init__)selfkwargs	__class__s     ڀ/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/transformers/models/seggpt/configuration_seggpt.pyr.   SegGptConfig.__post_init__M   s9    48LL4Hs4++a/0dll''    c                     U R                   [        U R                  5      :  a'  [        SU R                   < SU R                  < 35      eg)zOPart of `@strict`-powered validation. Validates the architecture of the config.zYMerge index must be less than the minimum encoder output index, but got self.merge_index=z, and self.intermediate_hidden_state_indices=N)r"   minr'   
ValueError)r/   s    r2   validate_architecture"SegGptConfig.validate_architectureQ   sb    c$"H"HIIl[_[k[kZm  n[sw  tZ  tZ  s\  ]  Jr4   )r   )$__name__
__module____qualname____firstlineno____doc__
model_typer   r,   __annotations__r   r   r   strr   floatr   r   r   listtupler   r   r   boolr   r   r   r   r    r"   r'   r(   r.   r8   __static_attributes____classcell__)r1   s   @r2   r	   r	      s@   < JKs!!J'**#u# NE 4>Jd3i%S/1>46Jd3i%S/16L#HdGS4Z"%NECK%=@tCy5c?:@!!-1$d1KET%tCy5c?'BTD%( r4   r	   N)	r>   huggingface_hub.dataclassesr   configuration_utilsr   utilsr   r	   __all__r+   r4   r2   <module>rL      sH    ! . 3 # 23># >  4>B 
r4   