o ¾e¦ieã@sLdZddlmZddlmZddlmZe e¡Z Gdd„deƒZ dgZ dS)zPixtral model configurationé)ÚPreTrainedConfig)ÚRopeParameters)Úloggingcs¢eZdZdZdZ         dd ed Bd ed Bded Bded Bded Bded Bded Bded Bded Bdee eefBd Bded Bf‡fdd„ Z ‡Z S)ÚPixtralVisionConfiga8 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_parameters (`RopeParameters`, *optional*): The RopeParameters 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 ```ÚpixtraléééérÚgeluçNç{®Gáz”?Ú hidden_sizeÚintermediate_sizeÚnum_hidden_layersÚnum_attention_headsÚ num_channelsÚ image_sizeÚ patch_sizeÚ hidden_actÚattention_dropoutÚrope_parametersÚinitializer_rangec  sb||_||_||_||_||_||_||_| |_||_|||_ | |_ | |_ t ƒj di| ¤ŽdS)N©)rrrrrrrrrÚhead_dimrrÚsuperÚ__init__) ÚselfrrrrrrrrrrrÚkwargs©Ú __class__rúo/home/ubuntu/transcripts/venv/lib/python3.10/site-packages/transformers/models/pixtral/configuration_pixtral.pyrKs zPixtralVisionConfig.__init__) rrr r rrr r r Nr ) Ú__name__Ú __module__Ú __qualname__Ú__doc__Ú model_typeÚintÚstrÚfloatrÚdictrÚ __classcell__rrrr!rsJ1ôþýüûúùø ÷ ö õ ôrN) r%Úconfiguration_utilsrÚmodeling_rope_utilsrÚutilsrÚ get_loggerr"ÚloggerrÚ__all__rrrr!Ús     S