g&^dZddlmZddlmZejeZGddeZdS)zLiLT configuration)PretrainedConfig)loggingcJeZdZdZdZ dfd ZxZS) LiltConfiga This is the configuration class to store the configuration of a [`LiltModel`]. It is used to instantiate a LiLT 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 LiLT [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) 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 LiLT model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`LiltModel`]. hidden_size (`int`, *optional*, defaults to 768): Dimensionality of the encoder layers and the pooler layer. Should be a multiple of 24. 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): Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder. hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"silu"` 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 [`LiltModel`]. 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. position_embedding_type (`str`, *optional*, defaults to `"absolute"`): Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to [Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155). For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658). classifier_dropout (`float`, *optional*): The dropout ratio for the classification head. channel_shrink_ratio (`int`, *optional*, defaults to 4): The shrink ratio compared to the `hidden_size` for the channel dimension of the layout embeddings. 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). Examples: ```python >>> from transformers import LiltConfig, LiltModel >>> # Initializing a LiLT SCUT-DLVCLab/lilt-roberta-en-base style configuration >>> configuration = LiltConfig() >>> # Randomly initializing a model from the SCUT-DLVCLab/lilt-roberta-en-base style configuration >>> model = LiltModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```lilt:w gelu皙?{Gz?-q=absoluteNc tjdd| i|||_||_||_||_||_||_||_||_ | |_ | |_ | |_ | |_ ||_||_||_||_dS)N pad_token_id)super__init__ vocab_size hidden_sizenum_hidden_layersnum_attention_heads hidden_actintermediate_sizehidden_dropout_probattention_probs_dropout_probmax_position_embeddingstype_vocab_sizeinitializer_rangelayer_norm_epsposition_embedding_typeclassifier_dropoutchannel_shrink_ratiomax_2d_position_embeddings)selfrrrrr rr!r"r#r$r%r&rr'r(r)r*kwargs __class__s g/var/www/html/ai-engine/env/lib/python3.11/site-packages/transformers/models/lilt/configuration_lilt.pyrzLiltConfig.__init__Zs* ==l=f===$&!2#6 $!2#6 ,H)'>$.!2,'>$"4$8!*D''')rr r r r r r r rrrrrrNrr)__name__ __module__ __qualname____doc__ model_typer __classcell__)r-s@r.rrs= = ~J%( # *#'%&E&E&E&E&E&E&E&E&E&Er/rN) r3configuration_utilsrutilsr get_loggerr0loggerrrr/r.r:s333333  H % %hEhEhEhEhE!hEhEhEhEhEr/