U ,È-eèã@sHdZddlmZddlmZe e¡ZdddddœZGd d „d eƒZ d S) z Splinter model configurationé)ÚPretrainedConfig)ÚloggingzAhttps://huggingface.co/tau/splinter-base/resolve/main/config.jsonzFhttps://huggingface.co/tau/splinter-base-qass/resolve/main/config.jsonzBhttps://huggingface.co/tau/splinter-large/resolve/main/config.jsonzGhttps://huggingface.co/tau/splinter-large-qass/resolve/main/config.json)ztau/splinter-baseztau/splinter-base-qassztau/splinter-largeztau/splinter-large-qasscs&eZdZdZdZd‡fdd„ Z‡ZS)ÚSplinterConfigai This is the configuration class to store the configuration of a [`SplinterModel`]. It is used to instantiate an Splinter 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 Splinter [tau/splinter-base](https://huggingface.co/tau/splinter-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 Splinter model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`SplinterModel`]. 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 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 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 [`SplinterModel`]. 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. 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`. question_token_id (`int`, *optional*, defaults to 104): The id of the `[QUESTION]` token. Example: ```python >>> from transformers import SplinterModel, SplinterConfig >>> # Initializing a Splinter tau/splinter-base style configuration >>> configuration = SplinterConfig() >>> # Initializing a model from the tau/splinter-base style configuration >>> model = SplinterModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```Zsplinteré:wéé é Úgeluçš™™™™™¹?ééç{®Gáz”?çê-™—q=Tééhc sntƒjfd|i|—Ž||_| |_||_||_||_||_||_||_ ||_ | |_ | |_ | |_ | |_||_dS)NÚ pad_token_id)ÚsuperÚ__init__Ú vocab_sizeÚmax_position_embeddingsÚ hidden_sizeÚnum_hidden_layersÚnum_attention_headsÚintermediate_sizeÚ hidden_actÚhidden_dropout_probÚattention_probs_dropout_probÚinitializer_rangeÚtype_vocab_sizeÚlayer_norm_epsÚ use_cacheÚquestion_token_id)Úselfrrrrrrrrrrrrr rr!Úkwargs©Ú __class__©út/var/www/html/Darija-Ai-Train/env/lib/python3.8/site-packages/transformers/models/splinter/configuration_splinter.pyr]szSplinterConfig.__init__)rrrrrr r r r r r rTrr)Ú__name__Ú __module__Ú __qualname__Ú__doc__Z model_typerÚ __classcell__r&r&r$r'r s$:ðrN) r+Zconfiguration_utilsrÚutilsrZ get_loggerr(ÚloggerZ&SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAPrr&r&r&r'Ús   ü