gY$dZddlZddlmZddlmZddlmZddlm Z ej e Z Gd d eZ Gd d eZdS) zIdefics3 model configurationN)Union)PretrainedConfig)logging)CONFIG_MAPPINGcxeZdZdZdZ dfd Zedeee j fddfdZ xZ S)Idefics3VisionConfiga This is the configuration class to store the configuration of a [`Idefics3VisionModel`]. It is used to instantiate a Idefics3 vision encoder according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the SigLIP checkpoint [google/siglip-base-patch16-224](https://huggingface.co/google/siglip-base-patch16-224) used in the Idefics3 model [HuggingFaceM4/Idefics3-8B-Llama3](https://huggingface.co/HuggingFaceM4/Idefics3-8B-Llama3). Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: hidden_size (`int`, *optional*, defaults to 1152): Dimensionality of the encoder layers and the pooler layer. intermediate_size (`int`, *optional*, defaults to 3072): Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. num_hidden_layers (`int`, *optional*, defaults to 12): 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. num_channels (`int`, *optional*, defaults to 3): Number of channels in the input images. image_size (`int`, *optional*, defaults to 224): The size (resolution) of each image. patch_size (`int`, *optional*, defaults to 32): The size (resolution) of each patch. hidden_act (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` `"quick_gelu"` are supported. layer_norm_eps (`float`, *optional*, defaults to 1e-06): The epsilon used by the layer normalization layers. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. intializer_range (`float`, *optional*, defaults to 0.02): The standard deviation for initializing all weight matrices in the model. Example: ```python >>> from transformers.models.idefics3.modeling_idefics3 import Idefics3VisionTransformer >>> from transformers.models.idefics3.configuration_idefics3 import Idefics3VisionConfig >>> # Initializing a Idefics3VisionConfig with google/siglip-base-patch16-224 style configuration >>> configuration = Idefics3VisionConfig() >>> # Initializing a Idefics3VisionTransformer (with random weights) from the google/siglip-base-patch16-224 style configuration >>> model = Idefics3VisionTransformer(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```idefics3 r gelu_pytorch_tanhư>{Gz?c  tjdi| ||_||_||_||_||_||_||_| |_ | |_ ||_ | |_ dS)N) super__init__ hidden_sizeintermediate_sizenum_hidden_layersnum_attention_heads num_channels patch_size image_sizeattention_dropoutlayer_norm_eps hidden_actinitializer_range)selfrrrrrr rr#r"r!r$kwargs __class__s o/var/www/html/ai-engine/env/lib/python3.11/site-packages/transformers/models/idefics3/configuration_idefics3.pyrzIdefics3VisionConfig.__init__Qs} ""6"""&!2!2#6 ($$!2,$!2pretrained_model_name_or_pathreturnrc N|||j|fi|\}}|ddkr|d}d|vrMt|dr=|d|jkr,t d|dd|jd|j|fi|S)N model_typer vision_configzYou are using a model of type z to instantiate a model of type zN. This is not supported for all configurations of models and can yield errors.)_set_token_in_kwargsget_config_dictgethasattrr-loggerwarning from_dict)clsr*r& config_dicts r(from_pretrainedz$Idefics3VisionConfig.from_pretrainedns   (((1c12OZZSYZZ V ??< ( (J 6 6%o6K ; & &73 +E+E &+VbJcgjguJuJu NNr\1Jrr>rrr    s}[33F333r)) r r rrrrrrrrr) __name__ __module__ __qualname____doc__r-r classmethodrstrosPathLiker8 __classcell__r's@r(r r s1 1 fJ&333333:4E#r{BR>> from transformers import Idefics3Model, Idefics3Config >>> # Initializing configuration >>> configuration = Idefics3Config() >>> # Initializing a model from the configuration >>> model = Idefics3Model(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```r TFNrc T||_||_||_|.t|_t dnCt|trtd i||_nt|tr||_t|tr*d|vr|dnd|d<t|dd i|}n4|2t dtdd|d}||_ ||_ tj d i|d|idS) Nz2vision_config is None, using default vision configr-llamaz.text_config is None, using default text configgh㈵>F) rms_norm_eps pad_token_idtie_word_embeddingsrKr)image_token_id use_cacherKr r.r3info isinstancedictr text_config scale_factorrr) r%rMrLrKr.rQrRrJr&r's r(rzIdefics3Config.__init__sY-"#6  !5!7!7D  KKL M M M M  t , , /!5!F!F !F!FD    '; < < /!.D  k4 ( ( EQU`E`E` L(A(AfmK %(\)BCRRkRRKK  KKH I I I(1!)$)K '(KK6KK7JKKKKKKr))TrEFNNrrF)r9r:r;r<r-is_compositionrrArBs@r(rDrDsu# # JJN!%L%L%L%L%L%L%L%L%L%Lr)rD)r<r?typingrconfiguration_utilsrutilsrautor get_loggerr9r3r rDrr)r(rYs#" 333333!!!!!!  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