fTh/SrSSKJr SSKJr \R "\5r"SS\5r"SS\5r "SS \5r /S Qr g ) zSiglip model configuration)PretrainedConfig)loggingcR^\rSrSrSrSrSrSU4SjjrSrU=r $)SiglipTextConfiga This is the configuration class to store the configuration of a [`SiglipTextModel`]. It is used to instantiate a Siglip text 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 text encoder of the Siglip [google/siglip-base-patch16-224](https://huggingface.co/google/siglip-base-patch16-224) 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 32000): Vocabulary size of the Siglip text model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`SiglipModel`]. hidden_size (`int`, *optional*, defaults to 768): 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 12): Number of attention heads for each attention layer in the Transformer encoder. max_position_embeddings (`int`, *optional*, defaults to 64): 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). 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. pad_token_id (`int`, *optional*, defaults to 1): The id of the padding token in the vocabulary. bos_token_id (`int`, *optional*, defaults to 49406): The id of the beginning-of-sequence token in the vocabulary. eos_token_id (`int`, *optional*, defaults to 49407): The id of the end-of-sequence token in the vocabulary. projection_size (`int`, *optional*, defaults to `hidden_size`): The size of the projection head. Example: ```python >>> from transformers import SiglipTextConfig, SiglipTextModel >>> # Initializing a SiglipTextConfig with google/siglip-base-patch16-224 style configuration >>> configuration = SiglipTextConfig() >>> # Initializing a SiglipTextModel (with random weights) from the google/siglip-base-patch16-224 style configuration >>> model = SiglipTextModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```siglip_text_model text_configc >[TU]"SXU S.UD6 XlX lX0lX@lXPlX`lXlXpl Xl U bXl gUUl g)N) pad_token_id bos_token_id eos_token_id) super__init__ vocab_size hidden_sizeintermediate_sizenum_hidden_layersnum_attention_headsmax_position_embeddingslayer_norm_eps hidden_actattention_dropoutprojection_size)selfrrrrrrrrrr r r rkwargs __class__s g/var/www/auris/envauris/lib/python3.13/site-packages/transformers/models/siglip/configuration_siglip.pyrSiglipTextConfig.__init__Sse& sl\hslrs$&!2!2#6 '>$,$!22A2MS^) rrrrrrrrrr) i} r#@gelu_pytorch_tanhư>iiN __name__ __module__ __qualname____firstlineno____doc__ model_typebase_config_keyr__static_attributes__ __classcell__rs@rrrsL5 n%J#O "&!__r rcL^\rSrSrSrSrSrSU4SjjrSrU=r $)SiglipVisionConfigta This is the configuration class to store the configuration of a [`SiglipVisionModel`]. It is used to instantiate a Siglip 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 vision encoder of the Siglip [google/siglip-base-patch16-224](https://huggingface.co/google/siglip-base-patch16-224) architecture. 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 768): 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 12): 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 16): 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. Example: ```python >>> from transformers import SiglipVisionConfig, SiglipVisionModel >>> # Initializing a SiglipVisionConfig with google/siglip-base-patch16-224 style configuration >>> configuration = SiglipVisionConfig() >>> # Initializing a SiglipVisionModel (with random weights) from the google/siglip-base-patch16-224 style configuration >>> model = SiglipVisionModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```siglip_vision_model vision_configc  >[T U]"S0U D6 XlX lX0lX@lXPlXplX`lXl Xl Xl g)Nr) rrrrrr num_channels patch_size image_sizerrr) rrrrrr:r<r;rrrrrs rrSiglipVisionConfig.__init__sN "6"&!2!2#6 ($$!2,$r ) rrrr<rrrr:rr;) r!r"r#r#rr%r&r'r)r3s@rr5r5ts?- ^'J%O&%%r r5cZ^\rSrSrSrSr\\S.rS U4Sjjr \ S\S\4Sj5r S r U=r $) SiglipConfiga [`SiglipConfig`] is the configuration class to store the configuration of a [`SiglipModel`]. It is used to instantiate a Siglip model according to the specified arguments, defining the text model and vision model configs. Instantiating a configuration with the defaults will yield a similar configuration to that of the Siglip [google/siglip-base-patch16-224](https://huggingface.co/google/siglip-base-patch16-224) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: text_config (`dict`, *optional*): Dictionary of configuration options used to initialize [`SiglipTextConfig`]. vision_config (`dict`, *optional*): Dictionary of configuration options used to initialize [`SiglipVisionConfig`]. kwargs (*optional*): Dictionary of keyword arguments. Example: ```python >>> from transformers import SiglipConfig, SiglipModel >>> # Initializing a SiglipConfig with google/siglip-base-patch16-224 style configuration >>> configuration = SiglipConfig() >>> # Initializing a SiglipModel (with random weights) from the google/siglip-base-patch16-224 style configuration >>> model = SiglipModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config >>> # We can also initialize a SiglipConfig from a SiglipTextConfig and a SiglipVisionConfig >>> from transformers import SiglipTextConfig, SiglipVisionConfig >>> # Initializing a SiglipText and SiglipVision configuration >>> config_text = SiglipTextConfig() >>> config_vision = SiglipVisionConfig() >>> config = SiglipConfig.from_text_vision_configs(config_text, config_vision) ```siglipr r8c >[TU]"S0UD6 Uc0n[RS5 Uc0n[RS5 [ S0UD6Ul[ S0UD6UlSUlg)NzQ`text_config` is `None`. Initializing the `SiglipTextConfig` with default values.zU`vision_config` is `None`. initializing the `SiglipVisionConfig` with default values.g?r) rrloggerinforr r5r8initializer_factor)rr r8rrs rrSiglipConfig.__init__sk "6"  K KKk l  M KKo p+:k:/@-@"%r r r8c PU"SUR5UR5S.UD6$)z Instantiate a [`SiglipConfig`] (or a derived class) from siglip text model configuration and siglip vision model configuration. Returns: [`SiglipConfig`]: An instance of a configuration object rDr)to_dict)clsr r8rs rfrom_text_vision_configs%SiglipConfig.from_text_vision_configss,f{224MDYDYD[f_effr )rHr r8)NN)r*r+r,r-r.r/rr5 sub_configsr classmethodrMr1r2r3s@rrArAsH' RJ"2EWXK&  g3C gTf g gr rA)rArr5N) r.configuration_utilsrutilsr get_loggerr*rFrr5rA__all__rr rrUs^!3   H %Y_'Y_xL%)L%^Gg#GgT Er