o ZŽhã@s@dZddlmZddlmZe e¡ZGdd„deƒZdgZ dS)zPixtral model configurationé)ÚPretrainedConfig)Úloggingcs<eZdZdZdZ         d‡fd d „ Z‡ZS)ÚPixtralVisionConfigaT This is the configuration class to store the configuration of a [`PixtralVisionModel`]. It is used to instantiate an Pixtral vision encoder according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to the vision encoder used by Pixtral-12B. e.g. [pixtral-hf/pixtral-9b](https://huggingface.co/pixtral-hf/pixtral-9b) 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): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 4096): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 24): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads in the Transformer encoder. num_channels (`int`, *optional*, defaults to 3): Number of input channels in the input images. image_size (`int`, *optional*, defaults to 1024): Max dimension of the input images. patch_size (`int`, *optional*, defaults to 16): Size of the image patches. hidden_act (`str`, *optional*, defaults to `"gelu"`): Activation function used in the hidden layers. attention_dropout (`float`, *optional*, defaults to 0.0): Dropout probability for the attention layers. rope_theta (`float`, *optional*, defaults to 10000.0): The base period of the RoPE embeddings. 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 import PixtralVisionModel, PixtralVisionConfig >>> # Initializing a Pixtral-12B style configuration >>> config = PixtralVisionConfig() >>> # Initializing a model (with randomly initialized weights) from the configuration >>> model = PixtralVisionModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```ZpixtraléééérÚgeluççˆÃ@ç{®Gáz”?c  sbtƒjdi| ¤Ž||_||_||_||_||_||_||_| |_ ||_ | |_ |||_ | |_ dS)N©)ÚsuperÚ__init__Ú hidden_sizeÚintermediate_sizeÚnum_hidden_layersÚnum_attention_headsÚ num_channelsÚ patch_sizeÚ image_sizeÚattention_dropoutÚ hidden_actÚ rope_thetaZhead_dimÚinitializer_range) ÚselfrrrrrrrrrrrÚkwargs©Ú __class__r ú`/var/www/auris/lib/python3.10/site-packages/transformers/models/pixtral/configuration_pixtral.pyrKs  zPixtralVisionConfig.__init__) rrrrrrrr r r r )Ú__name__Ú __module__Ú __qualname__Ú__doc__Z model_typerÚ __classcell__r r rrrs1ôrN) r#Zconfiguration_utilsrÚutilsrZ get_loggerr ÚloggerrÚ__all__r r r rÚs     S