U Ö9%eöã@sBdZddlmZddlmZe e¡ZddiZGdd„deƒZ dS) z BioGPT model configurationé)ÚPretrainedConfig)Úloggingzmicrosoft/biogptz@https://huggingface.co/microsoft/biogpt/resolve/main/config.jsoncs&eZdZdZdZd‡fdd„ Z‡ZS)Ú BioGptConfiga This is the configuration class to store the configuration of a [`BioGptModel`]. It is used to instantiate an BioGPT 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 BioGPT [microsoft/biogpt](https://huggingface.co/microsoft/biogpt) 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 42384): Vocabulary size of the BioGPT model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`BioGptModel`]. hidden_size (`int`, *optional*, defaults to 1024): Dimension of the encoder layers and the pooler layer. num_hidden_layers (`int`, *optional*, defaults to 24): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer encoder. intermediate_size (`int`, *optional*, defaults to 4096): 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 probabilitiy 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 1024): 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). 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. scale_embedding (`bool`, *optional*, defaults to `True`): Scale embeddings by diving by sqrt(d_model). use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if `config.is_decoder=True`. layerdrop (`float`, *optional*, defaults to 0.0): Please refer to the paper about LayerDrop: https://arxiv.org/abs/1909.11556 for further details activation_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for activations inside the fully connected layer. pad_token_id (`int`, *optional*, defaults to 1) Padding token id. bos_token_id (`int`, *optional*, defaults to 0) Beginning of stream token id. eos_token_id (`int`, *optional*, defaults to 2) End of stream token id. Example: ```python >>> from transformers import BioGptModel, BioGptConfig >>> # Initializing a BioGPT microsoft/biogpt style configuration >>> configuration = BioGptConfig() >>> # Initializing a model from the microsoft/biogpt style configuration >>> model = BioGptModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```Zbiogpté¥ééééÚgeluçš™™™™™¹?ç{®Gáz”?çê-™—q=Tçéééc sx||_| |_||_||_||_||_||_||_||_| |_ | |_ | |_ | |_ ||_ ||_tƒjf|||dœ|—ŽdS)N)Ú pad_token_idÚ bos_token_idÚ eos_token_id)Ú vocab_sizeÚmax_position_embeddingsÚ hidden_sizeÚnum_hidden_layersÚnum_attention_headsÚintermediate_sizeÚ hidden_actÚhidden_dropout_probÚattention_probs_dropout_probÚinitializer_rangeÚlayer_norm_epsÚscale_embeddingÚ use_cacheÚ layerdropÚactivation_dropoutÚsuperÚ__init__)Úselfrrrrrrrrrrrr r!r"r#rrrÚkwargs©Ú __class__©ún/var/www/html/Darija-Ai-API/env/lib/python3.8/site-packages/transformers/models/biogpt/configuration_biogpt.pyr%as zBioGptConfig.__init__)rrrrr r r r rr r TTrrrrr)Ú__name__Ú __module__Ú __qualname__Ú__doc__Z model_typer%Ú __classcell__r*r*r(r+rs*AírN) r/Zconfiguration_utilsrÚutilsrZ get_loggerr,ÚloggerZ$BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAPrr*r*r*r+Ús   ÿ