>> from transformers import SiglipVisionConfig, SiglipVisionModel >>> # Initializing a Aimv2VisionConfig with apple/aimv2-large-patch14-224 style configuration >>> configuration = Aimv2VisionConfig() >>> # Initializing a Aimv2VisionModel (with random weights) from the apple/aimv2-large-patch14-224 style configuration >>> model = Aimv2VisionModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```aimv2_vision_model vision_config hidden_sizeintermediate_sizenum_hidden_layersnum_attention_heads num_channels image_size patch_size rms_norm_epsattention_dropoutqkv_biasmlp_bias hidden_actinitializer_rangeuse_head is_nativec >[TU]"S0UD6 XlX lX0lX@lXPlXplX`lXl Xl Xl Xl Xl XlXlXlg)N)super__init__r r rrrrrrrrrrrrr)selfr r rrrrrrrrrrrrrkwargs __class__s e/var/www/html/shao/venv/lib/python3.13/site-packages/transformers/models/aimv2/configuration_aimv2.pyrAimv2VisionConfig.__init__[sh& "6"&!2!2#6 ($$!2$ !2  (")rrr rrr rrrrrrrrr)ii rh㈵>FFsilu{Gz?TF)__name__ __module__ __qualname____firstlineno____doc__ model_typebase_config_keyintfloatboolstrr__static_attributes__ __classcell__r!s@r"rrs6 p&J%O !%!##$"#& #'!$#$#$# $# ! $#  $#$#$#$#!$#$#$#$#!$#$# !$#$#r$rc^\rSrSrSrSrSrSS\S\S\S\S \S \S \S \ S \ S\ S\ \S\ \S\S\S\ 4U4Sjjjr Sr U=r$)Aimv2TextConfiga This is the configuration class to store the configuration of a [`Aimv2TextModel`]. It is used to instantiate a AIMv2 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 AIMv2 [apple/aimv2-large-patch14-224-lit](https://huggingface.co/apple/aimv2-large-patch14-224-lit) 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 49408): Vocabulary size of the AIMv2 text model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`Aimv2Model`]. hidden_size (`int`, *optional*, defaults to 768): Dimensionality of the encoder layers and the pooler layer. intermediate_size (`int`, *optional*, defaults to 2048): 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 6): Number of attention heads for each attention layer in the Transformer encoder. rms_norm_eps (`float`, *optional*, defaults to 1e-05): The epsilon used by the rms normalization layers. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. qkv_bias (`bool`, *optional*, defaults to `False`): Whether to add a bias to the queries, keys and values. mlp_bias (`bool`, *optional*, defaults to `False`): Whether to add a bias to the Linear layers or Not. hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): 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. 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. max_position_embeddings (`int`, *optional*, defaults to 77): 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). initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the for initializing all weight matrices. aimv2_text_model text_config vocab_sizer r rrrrrrr pad_token_id bos_token_id eos_token_idmax_position_embeddingsrc >[TU]"SXU S.UD6 XlX lX0lX@lXPlXlXlXpl Xl Xl Xl X`l g)N)rArBrCr)rrr@r r rrrDrrrrrr)rr@r r rrrrrrrrArBrCrDrr r!s r"rAimv2TextConfig.__init__sb& sl\hslrs$&!2!2#6 '>$$!2!2  (r$) rrr rr rDrrrrrr@)iii r)r*FFr+NNiMr,)r-r.r/r0r1r2r3r4r5r6r7rrr8r9r:s@r"r<r<s+Z$J#O !%!##$"#& &*&*!')"&!!)!)!) !)  !) ! !)!)!!)!)!)!)sm!)sm!)!)"%!)  !!)!)r$r<c@^\rSrSrSrSr\\S.rSU4Sjjr Sr U=r $) Aimv2Configa [`Aimv2Config`] is the configuration class to store the configuration of a [`Aimv2Model`]. It is used to instantiate a AIMv2 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 AIMv2 [apple/aimv2-large-patch14-224-lit](https://huggingface.co/apple/aimv2-large-patch14-224-lit) 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 [`Aimv2TextConfig`]. vision_config (`dict`, *optional*): Dictionary of configuration options used to initialize [`Aimv2VisionConfig`]. projection_dim (`int`, *optional*, defaults to 512): Dimensionality of text and vision projection layers. logit_scale_init_value (`float`, *optional*, defaults to 2.6592): The initial value of the *logit_scale* parameter. kwargs (*optional*): Dictionary of keyword arguments. Example: ```python >>> from transformers import Aimv2Config, Aimv2Model >>> # Initializing a Aimv2Config with apple/aimv2-large-patch14-224-lit style configuration >>> configuration = Aimv2Config() >>> # Initializing a Aimv2Model (with random weights) from the apple/aimv2-large-patch14-224-lit style configuration >>> model = Aimv2Model(configuration) >>> # Accessing the model configuration >>> configuration = model.config >>> # We can also initialize a Aimv2Config from a Aimv2TextConfig and a Aimv2VisionConfig >>> from transformers import Aimv2TextConfig, Aimv2VisionConfig >>> # Initializing a AIMv2Text and AIMv2Vision configuration >>> config_text = Aimv2TextConfig() >>> config_vision = Aimv2VisionConfig() >>> config = Aimv2Config(text_config=config_text, vision_config=config_vision) ```aimv2)r?r c >[TU]"S0UD6 Uc0n[RS5 Uc0n[RS5 [ S0UD6Ul[ S0UD6UlX0lX@l SUl g)NzP`text_config` is `None`. Initializing the `Aimv2TextConfig` with default values.zT`vision_config` is `None`. initializing the `Aimv2VisionConfig` with default values.gY@r) rrloggerinfor<r?rr projection_dimlogit_scale_init_valuemax_logit_scale)rr?r rQrRr r!s r"rAimv2Config.__init__sy "6"  K KKj k  M KKn o*9[9.??,&<#$r$)rRrSrQr?r )NNig/L F@) r-r.r/r0r1r2r<r sub_configsrr8r9r:s@r"rKrKs,+ ZJ"1DUVK`f%%r$rK)rKrr<N) typingrconfiguration_utilsrutilsr get_loggerr-rOrr<rK__all__rr$r"r[sZ,3   H %`#(`#FR)&R)jB%"B%J Br$