U ,È-eÞã@sBdZddlmZddlmZe e¡ZddiZGdd„deƒZ dS) z LiLT configurationé)ÚPretrainedConfig)Úloggingz!SCUT-DLVCLab/lilt-roberta-en-basezQhttps://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.jsoncs&eZdZdZdZd‡fdd„ Z‡ZS)Ú LiltConfigaì 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 ```Zlilté:wéé é Úgeluçš™™™™™¹?ééç{®Gáz”?çê-™—q=éÚabsoluteNééc sztƒjfd| i|—Ž||_||_||_||_||_||_||_||_ | |_ | |_ | |_ | |_ ||_||_||_||_dS)NÚ pad_token_id)ÚsuperÚ__init__Ú vocab_sizeÚ hidden_sizeÚnum_hidden_layersÚnum_attention_headsÚ hidden_actÚintermediate_sizeÚhidden_dropout_probÚattention_probs_dropout_probÚmax_position_embeddingsÚtype_vocab_sizeÚinitializer_rangeÚlayer_norm_epsÚposition_embedding_typeÚclassifier_dropoutÚchannel_shrink_ratioÚmax_2d_position_embeddings)Úselfrrrrrrrrrrr r!rr"r#r$r%Úkwargs©Ú __class__©úl/var/www/html/Darija-Ai-Train/env/lib/python3.8/site-packages/transformers/models/lilt/configuration_lilt.pyr_s"zLiltConfig.__init__)rrrrrr r r r r r rrrNrr)Ú__name__Ú __module__Ú __qualname__Ú__doc__Z model_typerÚ __classcell__r*r*r(r+rs(>îrN) r/Zconfiguration_utilsrÚutilsrZ get_loggerr,ÚloggerZ"LILT_PRETRAINED_CONFIG_ARCHIVE_MAPrr*r*r*r+Ús   þ