qiLddlmZmZddlmZddlmZmZGddeZdgZ y) )AnyLiteral)PretrainedConfig)CONFIG_MAPPING AutoConfigceZdZUdZdZeedZeee fe d< dde dzde dzde dzd e dzd e d d e dzd edzffd ZxZS)ModernVBertConfiga This is the configuration class to store the configuration of a [`ModernVBert`] model. It is used to instantiate a ModernVBert model according to the specified arguments and defines the model architecture. e.g. [ModernVBERT/modernvbert](https://huggingface.co/ModernVBERT/modernvbert). Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. See the documentation for [`PretrainedConfig`] for more details. Args: text_config (`AutoConfig`, *optional*): Configuration for the text encoder. vision_config (`ModernVBertVisionConfig`, *optional*): Configuration for the vision encoder. image_token_id (`int | None`, *optional*, defaults to 50407): The token id reserved for image tokens inserted into the text stream. pixel_shuffle_factor (`int | None`, *optional*, defaults to 4): Scale factor used by any pixel-shuffle / upsampling operations in the vision head. initializer_range (`float | None`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. initializer_cutoff_factor (`float | None`, *optional*, defaults to 2.0): The cutoff factor for the truncated_normal_initializer for initializing all weight matrices. classifier_pooling (`Literal["cls", "mean"]`, *optional*, defaults to `"cls"`): The pooling strategy to use for classification tasks. classifier_dropout (`float | None`, *optional*, defaults to 0.0): The dropout probability for the classification head. classifier_bias (`bool | None`, *optional*, defaults to `False`): Whether to add a bias term to the classification head. Example: ```python >>> from transformers import ModernVBertConfig >>> # Initializing configuration >>> configuration = ModernVBertConfig() >>> # Initializing a model from the configuration (model class is implemented in >>> # `modernvbert.modeling_modernvbert`) >>> from transformers import ModernVBertModel >>> model = ModernVBertModel(configuration) >>> # Accessing the model configuration >>> cfg = model.config ``` modernvbert) text_config vision_config sub_configsNimage_token_idpixel_shuffle_factorinitializer_rangeinitializer_cutoff_factorclassifier_poolingclsmeanclassifier_dropoutclassifier_biasc  v|dvrtd|d|td}nt|trtddi|}||_|td}nt|trtddi|}||_||_||_||_||_ ||_ | |_ t |4dd|i| y)NrzQInvalid value for `classifier_pooling`, should be either "cls" or "mean", but is . modernbertsiglip_vision_modelr) ValueErrorr isinstancedictr rrrrrrrsuper__init__) selfr rrrrrrrrkwargs __class__s k/opt/pipecat/venv/lib/python3.12/site-packages/transformers/models/modernvbert/configuration_modernvbert.pyr#zModernVBertConfig.__init__Cs _ 4cdvcwwxy   (68K  T *(6EEK&  *+@ACM  t ,*+@ARMRM*$8!!2)B&"4"4. AA&A) NNig{Gz?g@rgF)__name__ __module__ __qualname____doc__ model_typer rr!strr__annotations__intfloatrboolr# __classcell__)r&s@r'r r s" HJ2r9s,* 3-MB(MB`  r(