ó
    pyüi½  ã                   ór   • S r SSKJ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BEiT model configurationé    )Ústricté   )ÚBackboneConfigMixin)ÚPreTrainedConfig)Úauto_docstringz%microsoft/beit-base-patch16-224-pt22k)Ú
checkpointc                   ó\  ^ • \ 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'   Sr\\S'   Sr\\S'   Sr\\\   -  \\\4   -  \S'   Sr\\\   -  \\\4   -  \S'   Sr\\S'   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"\\S)'   S*r#\\S+'   S,r$\\S-'   S.r%\\S/'   Sr&\\S0'   S1r'\\S2'   S3r(\\   S3-  \S4'   S3r)\\   S3-  \S5'   Sr*\\S6'   S$r+\\S7'   U 4S8 jr,S9r-U =r.$ ):Ú
BeitConfigé   aC	  
use_mask_token (`bool`, *optional*, defaults to `False`):
    Whether to use a mask token for masked image modeling.
use_relative_position_bias (`bool`, *optional*, defaults to `False`):
    Whether to use T5-style relative position embeddings in the self-attention layers.
use_shared_relative_position_bias (`bool`, *optional*, defaults to `False`):
    Whether to use the same relative position embeddings across all self-attention layers of the Transformer.
use_mean_pooling (`bool`, *optional*, defaults to `True`):
    Whether to mean pool the final hidden states of the patches instead of using the final hidden state of the
    CLS token, before applying the classification head.
pool_scales (`tuple[int]`, *optional*, defaults to `[1, 2, 3, 6]`):
    Pooling scales used in Pooling Pyramid Module applied on the last feature map.
use_auxiliary_head (`bool`, *optional*, defaults to `True`):
    Whether to use an auxiliary head during training.
auxiliary_loss_weight (`float`, *optional*, defaults to 0.4):
    Weight of the cross-entropy loss of the auxiliary head.
auxiliary_channels (`int`, *optional*, defaults to 256):
    Number of channels to use in the auxiliary head.
auxiliary_num_convs (`int`, *optional*, defaults to 1):
    Number of convolutional layers to use in the auxiliary head.
auxiliary_concat_input (`bool`, *optional*, defaults to `False`):
    Whether to concatenate the output of the auxiliary head with the input before the classification layer.
add_fpn (`bool`, *optional*, defaults to `False`):
    Whether to add a FPN as part of the backbone. Only relevant for [`BeitBackbone`].
reshape_hidden_states (`bool`, *optional*, defaults to `True`):
    Whether to reshape the feature maps to 4D tensors of shape `(batch_size, hidden_size, height, width)` in
    case the model is used as backbone. If `False`, the feature maps will be 3D tensors of shape `(batch_size,
        seq_len, hidden_size)`. Only relevant for [`BeitBackbone`].

Example:

```python
>>> from transformers import BeitConfig, BeitModel

>>> # Initializing a BEiT beit-base-patch16-224-pt22k style configuration
>>> configuration = BeitConfig()

>>> # Initializing a model (with random weights) from the beit-base-patch16-224-pt22k style configuration
>>> model = BeitModel(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config
```Úbeiti    Ú
vocab_sizei   Úhidden_sizeé   Únum_hidden_layersÚnum_attention_headsi   Úintermediate_sizeÚgeluÚ
hidden_actg        Úhidden_dropout_probÚattention_probs_dropout_probg{®Gáz”?Úinitializer_rangegê-�™—q=Úlayer_norm_epséà   Ú
image_sizeé   Ú
patch_sizer   Únum_channelsFÚuse_mask_tokenÚ use_absolute_position_embeddingsÚuse_relative_position_biasÚ!use_shared_relative_position_biasgš™™™™™¹?Úlayer_scale_init_valueÚdrop_path_rateTÚuse_mean_pooling)é   é   r   é   .Úpool_scalesÚuse_auxiliary_headgš™™™™™Ù?Úauxiliary_loss_weighté   Úauxiliary_channelsr%   Úauxiliary_num_convsÚauxiliary_concat_inputéÿ   Úsemantic_loss_ignore_indexNÚ_out_featuresÚ_out_indicesÚadd_fpnÚreshape_hidden_statesc                 óT  >• SU;   a&  UR                  S5      c  UR                  S5      US'   S/[        SU R                  S-   5       Vs/ s H  nSU 3PM
     sn-   U l        U R                  UR                  SS 5      UR                  SS 5      S9  [        TU ]  " S0 UD6  g s  snf )	NÚsegmentation_indicesÚout_indicesÚstemr%   ÚstageÚout_features)r7   r:   © )ÚgetÚpopÚranger   Ústage_namesÚ"set_output_features_output_indicesÚsuperÚ__post_init__)ÚselfÚkwargsÚidxÚ	__class__s      €Úh/home/mande/repo/quber/.venv/lib/python3.13/site-packages/transformers/models/beit/configuration_beit.pyrB   ÚBeitConfig.__post_init__h   s°   ø€ Ø! VÓ+°·
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ô 	‰ÒÑ' Ó'ùò 'fs   ÁB%)r?   )/Ú__name__Ú
__module__Ú__qualname__Ú__firstlineno__Ú__doc__Ú
model_typer   ÚintÚ__annotations__r   r   r   r   r   Ústrr   Úfloatr   r   r   r   ÚlistÚtupler   r   r   Úboolr   r    r!   r"   r#   r$   r(   r)   r*   r,   r-   r.   r0   r1   r2   r3   r4   rB   Ú__static_attributes__Ú__classcell__)rF   s   @rG   r
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   Ú__all__r;   rX   rG   Ú<module>r^      sQ   ðñ å .å 1Ý 3Ý #ñ ÐBÑCØôY(Ð$Ð&6ó Y(ó ó DðY(ðx ˆ.�rX   