o Zh!@sbdZddlmZddlmZddlmZmZee Z GdddeZ Gdd d eZ d dgZ d S) zIdefics3 model configuration)PretrainedConfig)logging)CONFIG_MAPPING AutoConfigcs@eZdZdZdZdZ      dfdd ZZS)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. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. 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 ```Zidefics3_vision vision_config r gelu_pytorch_tanhư>{Gz?c  sXtjdi| ||_||_||_||_||_||_||_| |_ | |_ ||_ | |_ dS)N) super__init__ hidden_sizeintermediate_sizenum_hidden_layersnum_attention_heads num_channels patch_size image_sizeattention_dropoutlayer_norm_eps hidden_actinitializer_range) selfrrrrrrrrrrr kwargs __class__rb/var/www/auris/lib/python3.10/site-packages/transformers/models/idefics3/configuration_idefics3.pyrOs zIdefics3VisionConfig.__init__) r r r r rr rrrrr)__name__ __module__ __qualname____doc__ model_typeZbase_config_keyr __classcell__rrr#r%rs 3rcs>eZdZdZdZeedZ       d fd d ZZ S) Idefics3Configa This is the configuration class to store the configuration of a [`Idefics3Model`]. It is used to instantiate a Idefics3 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 model of the Idefics3 [HuggingFaceM4/Idefics3-8B-Llama3](https://huggingface.co/HuggingFaceM4/Idefics3-8B-Llama3) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should cache the key/value pairs of the attention mechanism. Only relevant if `config.is_decoder=True`. image_token_id (`int`, *optional*, defaults to 128257): The id of the "image" token. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether or not to tie the word embeddings with the token embeddings. vision_config (`IdeficsVisionConfig` or `dict`, *optional*, defaults to `IdeficsVisionConfig`): Custom vision config or dict for the vision tower text_config (`PretrainedConfig` or `dict`, *optional*, defaults to `LlamaConfig`): Custom text config or dict for the text model scale_factor (`int`, *optional*, defaults to 2): The scale factor for the image encoder. pad_token_id (`int`, *optional*, defaults to 128002): The id of the padding token. Example: ```python >>> 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 ```Zidefics3) text_configrTFNrc  s||_||_||_|durt|_tdnt|tr%td i||_nt|tr-||_t|trJd|vr:|dnd|d<t |dd i|}n|dur\tdt dd|dd}||_ ||_ t j d i|||ddS) Nz2vision_config is None, using default vision configr*llamaz.text_config is None, using default text configgh㈵>F)Z rms_norm_eps pad_token_idtie_word_embeddings)r1r2r)image_token_id use_cacher2rrloggerinfo isinstancedictrr- scale_factorrr) r!r4r3r2rr-r9r1r"r#rr%rs.       zIdefics3Config.__init__)Tr.FNNrr/) r&r'r(r)r*rrZ sub_configsrr+rrr#r%r,ms% r,N)r)Zconfiguration_utilsrutilsrautorrZ get_loggerr&r5rr,__all__rrrr%s   U Q