ZjdSrSSKJr SSKJr SSKJr \"SS9\"SS \555rS /rg ) zX-MOD configuration)strict)PreTrainedConfig)auto_docstringzfacebook/xmod-base) checkpointc\rSrSr%SrSrSr\\S'Sr \\S'Sr \\S 'Sr \\S 'S r \\S 'S r \\S'Sr\\-\S'Sr\\-\S'Sr\\S'Sr\\S'Sr\\S'Sr\\S'Sr\S-\S'Sr\S-\S'Sr\\\-S-\S'S r\\S!'Sr\\-S-\S"'S#r\\S$'Sr\\S%'S#r\\S&'S r \\S''S r!\\S('S)r"\\\#\S*4-\S+'Sr$\S-\S,'S#r%\\S-'S#r&\\S.'S r'\\S/'S0r(g)1 XmodConfiga pre_norm (`bool`, *optional*, defaults to `False`): Whether to apply layer normalization before each block. adapter_reduction_factor (`int` or `float`, *optional*, defaults to 2): The factor by which the dimensionality of the adapter is reduced relative to `hidden_size`. adapter_layer_norm (`bool`, *optional*, defaults to `False`): Whether to apply a new layer normalization before the adapter modules (shared across all adapters). adapter_reuse_layer_norm (`bool`, *optional*, defaults to `True`): Whether to reuse the second layer normalization and apply it before the adapter modules as well. ln_before_adapter (`bool`, *optional*, defaults to `True`): Whether to apply the layer normalization before the residual connection around the adapter module. languages (`Iterable[str]`, *optional*, defaults to `["en_XX"]`): An iterable of language codes for which adapter modules should be initialized. default_language (`str`, *optional*): Language code of a default language. It will be assumed that the input is in this language if no language codes are explicitly passed to the forward method. Examples: ```python >>> from transformers import XmodConfig, XmodModel >>> # Initializing an X-MOD facebook/xmod-base style configuration >>> configuration = XmodConfig() >>> # Initializing a model (with random weights) from the facebook/xmod-base style configuration >>> model = XmodModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```xmodi:w vocab_sizei hidden_size num_hidden_layersnum_attention_headsi intermediate_sizegelu hidden_actg?hidden_dropout_probattention_probs_dropout_probimax_position_embeddingstype_vocab_sizeg{Gz?initializer_rangeg-q=layer_norm_epsN pad_token_idr bos_token_id eos_token_idT use_cacheclassifier_dropoutFpre_normadapter_reduction_factoradapter_layer_normadapter_reuse_layer_normln_before_adapter)en_XX. languagesdefault_language is_decoderadd_cross_attentiontie_word_embeddings))__name__ __module__ __qualname____firstlineno____doc__ model_typer int__annotations__r rrrrstrrfloatrrrrrrrrlistrboolr r!r"r#r$r%r'tupler(r)r*r+__static_attributes__r,|/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/transformers/models/xmod/configuration_xmod.pyr r sg @JJKs!!!s!J'**03 %#+3#&S&OS#u#!NE! L#*  L#* +,L#S /D(,It-1 d*1Hd$%c%$$%)d)"t"-7ItCy5c?*7#'cDj'J %% $$r;r N) r1huggingface_hub.dataclassesrconfiguration_utilsrutilsrr __all__r,r;r<rAsG.3#/0=%!=%1=%@ .r;