o i5@sjddlmZddlmZddlmZeeZGdddeZ GdddeZ Gd d d eZ gd Z d S) )Optional)PretrainedConfig)loggingcseZdZdZdZdZ         d"dededededededededededede dededeffd d! Z Z S)#Aimv2VisionConfiga This is the configuration class to store the configuration of a [`Aimv2VisionModel`]. It is used to instantiate a AIMv2 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 AIMv2 [apple/aimv2-large-patch14-224](https://huggingface.co/apple/aimv2-large-patch14-224) 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 1024): Dimensionality of the encoder layers and the pooler layer. intermediate_size (`int`, *optional*, defaults to 2816): Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. num_hidden_layers (`int`, *optional*, defaults to 24): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 8): 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 14): The size (resolution) of each patch. 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. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the for initializing all weight matrices. use_head (`str`, *optional*, defaults to `True`): Whether to use Attention Pooling Head or Not. is_native (`str`, *optional*, defaults to `False`): Whether to use ckpt trained for image native resolution or not. Example: ```python >>> 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 rh㈵>Fsilu{Gz?T 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 sptjdi|||_||_||_||_||_||_||_| |_ | |_ ||_ | |_ | |_ | |_||_||_dS)N)super__init__rrrrrrrrrr rrrrr!)selfrrrrrrrrrrrrrr r!kwargs __class__r"j/home/ubuntu/veenaModal/venv/lib/python3.10/site-packages/transformers/models/aimv2/configuration_aimv2.pyr$[s  zAimv2VisionConfig.__init__)r r r r rr rrrFFrrTF) __name__ __module__ __qualname____doc__ model_typebase_config_keyintfloatboolstrr$ __classcell__r"r"r'r)rsd8     rcseZdZdZdZdZ        d"dededededededededede de ede edededeffd d! Z Z S)#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 rrFrNMr vocab_sizerrrrrrrrr pad_token_id bos_token_id eos_token_idmax_position_embeddingsrc sftjd| | | d|||_||_||_||_||_||_| |_||_ ||_ | |_ ||_ ||_ dS)N)r@rArBr")r#r$r?rrrrrCrrrrrr)r%r?rrrrrrrrrr@rArBrCrr&r'r"r)r$s zAimv2TextConfig.__init__)r8r9r:r;r<rrFFrNNr=r>r) r*r+r,r-r.r/r0r1r2r3rr$r4r"r"r'r)r5sd-     r5cs2eZdZdZdZeedZ d fdd ZZ S) 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)r7rN/L F@c sttjdi||duri}td|duri}tdtdi||_tdi||_||_||_ d|_ dS)NzP`text_config` is `None`. Initializing the `Aimv2TextConfig` with default values.zT`vision_config` is `None`. initializing the `Aimv2VisionConfig` with default values.gY@r") r#r$loggerinfor5r7rrprojection_dimlogit_scale_init_valuemax_logit_scale)r%r7rrJrKr&r'r"r)r$s   zAimv2Config.__init__)NNrFrG) r*r+r,r-r.r5r sub_configsr$r4r"r"r'r)rDs - rD)rDrr5N) typingrconfiguration_utilsrutilsr get_loggerr*rHrr5rD__all__r"r"r"r)s    cU E