fTh*SrSSKJr SSKJrJr \R "\5r\"5(aSSK r "SS\5r S/r g)zLayoutLMv2 model configuration)PretrainedConfig)is_detectron2_availableloggingNc^\rSrSrSrSrSSSSSSS S S S S S SSSSSSSS/SQSSSSSS4U4Sjjr\S5rSr Sr U=r $)LayoutLMv2Configa6 This is the configuration class to store the configuration of a [`LayoutLMv2Model`]. It is used to instantiate an LayoutLMv2 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 LayoutLMv2 [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlmv2-base-uncased) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 30522): Vocabulary size of the LayoutLMv2 model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`LayoutLMv2Model`] or [`TFLayoutLMv2Model`]. hidden_size (`int`, *optional*, defaults to 768): Dimension of the encoder layers and the pooler layer. num_hidden_layers (`int`, *optional*, defaults to 12): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 12): Number of attention heads for each attention layer in the Transformer encoder. intermediate_size (`int`, *optional*, defaults to 3072): Dimension of the "intermediate" (i.e., feed-forward) 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 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout ratio for the attention probabilities. max_position_embeddings (`int`, *optional*, defaults to 512): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). type_vocab_size (`int`, *optional*, defaults to 2): The vocabulary size of the `token_type_ids` passed when calling [`LayoutLMv2Model`] or [`TFLayoutLMv2Model`]. 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-12): The epsilon used by the layer normalization layers. 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). max_rel_pos (`int`, *optional*, defaults to 128): The maximum number of relative positions to be used 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. fast_qkv (`bool`, *optional*, defaults to `True`): Whether or not to use a single matrix for the queries, keys, values in the self-attention layers. max_rel_2d_pos (`int`, *optional*, defaults to 256): The maximum number of relative 2D positions 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. image_feature_pool_shape (`List[int]`, *optional*, defaults to [7, 7, 256]): The shape of the average-pooled feature map. 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. has_spatial_attention_bias (`bool`, *optional*, defaults to `True`): Whether or not to use a spatial attention bias in the self-attention mechanism. has_visual_segment_embedding (`bool`, *optional*, defaults to `False`): Whether or not to add visual segment embeddings. detectron2_config_args (`dict`, *optional*): Dictionary containing the configuration arguments of the Detectron2 visual backbone. Refer to [this file](https://github.com/microsoft/unilm/blob/master/layoutlmft/layoutlmft/models/layoutlmv2/detectron2_config.py) for details regarding default values. Example: ```python >>> from transformers import LayoutLMv2Config, LayoutLMv2Model >>> # Initializing a LayoutLMv2 microsoft/layoutlmv2-base-uncased style configuration >>> configuration = LayoutLMv2Config() >>> # Initializing a model (with random weights) from the microsoft/layoutlmv2-base-uncased style configuration >>> model = LayoutLMv2Model(configuration) >>> # Accessing the model configuration >>> configuration = model.config ``` layoutlmv2i:wi i gelug?g{Gz?g-q=ri T@)rrFNc 4>[TU]"SUUUUUUUUU U U U U S. UD6 XlXlUUlUUlUUlUUlUUlUUl UUl UUl UUl UUl UUlUbUUlgUR5Ulg)N) vocab_size hidden_sizenum_hidden_layersnum_attention_headsintermediate_size hidden_acthidden_dropout_probattention_probs_dropout_probmax_position_embeddingstype_vocab_sizeinitializer_rangelayer_norm_eps pad_token_id)super__init__max_2d_position_embeddings max_rel_pos rel_pos_binsfast_qkvmax_rel_2d_posrel_2d_pos_binsconvert_sync_batchnormimage_feature_pool_shapecoordinate_size shape_sizehas_relative_attention_biashas_spatial_attention_biashas_visual_segment_embeddingget_default_detectron2_configdetectron2_config_args)selfrrrrrrrrrrrr r!r%r&r'r(r)r*r+r,r-r.r/r0r1r3kwargs __class__s o/var/www/auris/envauris/lib/python3.13/site-packages/transformers/models/layoutlmv2/configuration_layoutlmv2.pyr$LayoutLMv2Config.__init__ss>  !#/ 3/! 3)E$;+/)%  +E'&(  ,.&<#(@%.$+F(*D',H)&<&H " #NRNpNpNr #c 0SS_S/SQ_SS_S/SQ_S S /S /S /S /S//_S/SQ_SS_SS_SS_SS_SS_SS_S/SQ_SS_SS _S!S"_S#S$_S%S&S'S /S /S /S /S///S(Q//SQS S)S*S+. 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