^j9jdZddlmZddlmZddlmZedeGdd eZd gZy ) zLayoutLMv3 model configuration)strict)PreTrainedConfig)auto_docstringzmicrosoft/layoutlmv3-base) checkpointc eZdZUdZdZdZeed<dZeed<dZ eed<dZ eed <d Z eed <d Z e ed <dZeezed<dZeezed<dZeed<dZeed<dZeed<dZeed<dZedzed<dZedzed<dZeeezdzed<dZeed <d!Zeed"<d!Zeed#<d$Zeed%<d&Zeed'<d!Zeed(<d)Z eed*<d+Z!eed,<d$Z"eed-<d$Z#eed.<d$Z$eed/<d0Z%eed1<d2Z&eed3<d4Z'eeeze(eefzed5<dZ)eezdzed6<y)7LayoutLMv3ConfigaC max_2d_position_embeddings (`int`, *optional*, defaults to 1024): The maximum value that the 2D position embedding might ever be used with. Typically set this to something large just in case (e.g., 1024). coordinate_size (`int`, *optional*, defaults to `128`): Dimension of the coordinate embeddings. shape_size (`int`, *optional*, defaults to `128`): Dimension of the width and height embeddings. has_relative_attention_bias (`bool`, *optional*, defaults to `True`): Whether or not to use a relative attention bias in the self-attention mechanism. rel_pos_bins (`int`, *optional*, defaults to 32): The number of relative position bins to be used in the self-attention mechanism. max_rel_pos (`int`, *optional*, defaults to 128): The maximum number of relative positions to be used in the self-attention mechanism. rel_2d_pos_bins (`int`, *optional*, defaults to 64): The number of 2D relative position bins in the self-attention mechanism. max_rel_2d_pos (`int`, *optional*, defaults to 256): The maximum number of relative 2D positions in the self-attention mechanism. has_spatial_attention_bias (`bool`, *optional*, defaults to `True`): Whether or not to use a spatial attention bias in the self-attention mechanism. text_embed (`bool`, *optional*, defaults to `True`): Whether or not to add text embeddings. visual_embed (`bool`, *optional*, defaults to `True`): Whether or not to add patch embeddings. input_size (`int`, *optional*, defaults to `224`): The size (resolution) of the images. Example: ```python >>> from transformers import LayoutLMv3Config, LayoutLMv3Model >>> # Initializing a LayoutLMv3 microsoft/layoutlmv3-base style configuration >>> configuration = LayoutLMv3Config() >>> # Initializing a model (with random weights) from the microsoft/layoutlmv3-base style configuration >>> model = LayoutLMv3Model(configuration) >>> # Accessing the model configuration >>> configuration = model.config ``` layoutlmv3iY vocab_sizei hidden_size num_hidden_layersnum_attention_headsi intermediate_sizegelu hidden_actg?hidden_dropout_probattention_probs_dropout_probimax_position_embeddingstype_vocab_sizeg{Gz?initializer_rangegh㈵>layer_norm_epsN pad_token_idr bos_token_id eos_token_idimax_2d_position_embeddingscoordinate_size shape_sizeThas_relative_attention_bias rel_pos_bins max_rel_pos@rel_2d_pos_binsmax_rel_2d_poshas_spatial_attention_bias text_embed visual_embed input_sizer num_channels patch_sizeclassifier_dropout)*__name__ __module__ __qualname____doc__ model_typer int__annotations__r rrrrstrrfloatrrrrrrrrlistrr r!r"boolr$r%r'r)r*r+r,r.r/r1tupler2/var/www/ramen.bs-engineer-server.com/venv/lib/python3.12/site-packages/transformers/models/layoutlmv3/configuration_layoutlmv3.pyr r s( TJJKs!!!s!J'**03 %#+3#&S&OS#u# NE  L#*  L#* +,L#S /D(,&**OSJ(,,L#KOSNC'++JL$JL#46Jd3i%S/16-1 d*1r@r N) r6huggingface_hub.dataclassesrconfiguration_utilsrutilsrr __all__r?r@rArFsK%.3#67J2'J28J2Z  r@