^jOxdZddlmZddlmZddlmZddlmZedeGd d eeZ d gZ y ) zBEiT model configuration)strict)BackboneConfigMixin)PreTrainedConfig)auto_docstringz%microsoft/beit-base-patch16-224-pt22k) checkpointcReZdZUdZdZdZeed<dZeed<dZ eed<dZ eed <d Z eed <d Z e ed <dZeezed<dZeezed<dZeed<dZeed<dZeeezeeefzed<dZeeezeeefzed<dZeed<dZeed<dZeed<dZeed<dZeed<d Zeed!<d Zeezed"<d#Zeed$<d%Z eeeed&fzed'<d#Z!eed(<d)Z"eed*<d+Z#eed,<d-Z$eed.<dZ%eed/<d0Z&eed1<d2Z'ee d2zed3<d2Z(eed2zed4<dZ)eed5<d#Z*eed6<fd7Z+xZ,S)8 BeitConfiga 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 ```beiti vocab_sizei hidden_size num_hidden_layersnum_attention_headsi intermediate_sizegelu hidden_actghidden_dropout_probattention_probs_dropout_probg{Gz?initializer_rangeg-q=layer_norm_eps image_size patch_sizer num_channelsFuse_mask_token use_absolute_position_embeddingsuse_relative_position_bias!use_shared_relative_position_biasg?layer_scale_init_valuedrop_path_rateTuse_mean_pooling)r. pool_scalesuse_auxiliary_headg?auxiliary_loss_weightauxiliary_channelsr$auxiliary_num_convsauxiliary_concat_inputsemantic_loss_ignore_indexN _out_features _out_indicesadd_fpnreshape_hidden_statesc d|vr%|jd|jd|d<dgtd|jdzDcgc]}d| c}z|_|j |jdd|jdd|j r/|jt|jdk7r td t|,d i|ycc}w) Nsegmentation_indices out_indicesstemr$stage out_features)r6r9zBeitConfig requires `out_indices` to be a list of exactly 4 integers when `add_fpn=True`, specifying which features to use from the backbone. One can use `out_indices=[3, 5, 7, 11]` for a base-sized architecture.) getpopranger stage_names"set_output_features_output_indicesr2r1len ValueErrorsuper __post_init__)selfkwargsidx __class__s v/var/www/ramen.bs-engineer-server.com/venv/lib/python3.12/site-packages/transformers/models/beit/configuration_beit.pyrDzBeitConfig.__post_init__hs !V + =0I0Q$*JJ/E$FF= !#8aI_I_bcIc@d&ese}&ee // =$7fjjQ_aeFf 0  <O>O:PTU:U1  '''fs C)-__name__ __module__ __qualname____doc__ model_typer int__annotations__r rrrrstrrfloatrrrrlisttuplerrrboolrrr r!r"r#r'r(r)r+r,r-r/r0r1r2r3rD __classcell__)rHs@rIr r s* XJJKs!!!s!J'**03 %#+3#u#!NE!47Jd3i%S/1746Jd3i%S/16L# ND -2$d2',,.3%t3$'E'"%NECK%!d!/;KcU38_,;###&5&!!  #(D(&))&*M49t#*%)L$s)d")GT"&4&((r N) rMhuggingface_hub.dataclassesrbackbone_utilsrconfiguration_utilsrutilsrr __all__r;rWrIr]sQ.13#BC`($&6`(D`(F .rW