^j& jdZddlmZddlmZddlmZedeGdd eZd gZy ) zLeViT model configuration)strict)PreTrainedConfig)auto_docstringzfacebook/levit-128S) checkpointceZdZUdZdZdZeeezeeefze d<dZ ee d<dZ ee d<dZ ee d <d Z ee d <d Zeeezeeefze d <dZeeeedfze d<dZeeeedfze d<dZeeeedfze d<dZeeeedfze d<dZee d<dZeeeedfze d<dZeeeedfze d<dZee d<fdZxZS) LevitConfiga stride (`int`, *optional*, defaults to 2): The stride size for the initial convolution layers of patch embedding. padding (`int`, *optional*, defaults to 1): The padding size for the initial convolution layers of patch embedding. key_dim (`list[int]`, *optional*, defaults to `[16, 16, 16]`): The size of key in each of the encoder blocks. attention_ratio (`list[int]`, *optional*, defaults to `[2, 2, 2]`): Ratio of the size of the output dimension compared to input dimension of attention layers. Example: ```python >>> from transformers import LevitConfig, LevitModel >>> # Initializing a LeViT levit-128S style configuration >>> configuration = LevitConfig() >>> # Initializing a model (with random weights) from the levit-128S style configuration >>> model = LevitModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```levit image_sizer num_channels kernel_sizestridepadding patch_size)i. hidden_sizes) num_attention_heads)rrrdepths)rrrkey_dimrdrop_path_rate)rrr mlp_ratioattention_ratiog{Gz?initializer_rangec d|jd|jd|jdzdddgd|jd|jd|jdzdddgg|_t|di|y)N Subsamplerrrr)rrdown_opssuper __post_init__)selfkwargs __class__s x/var/www/ramen.bs-engineer-server.com/venv/lib/python3.12/site-packages/transformers/models/levit/configuration_levit.pyr'zLevitConfig.__post_init__Cs $,,q/4+<+r<sG .3#010("0(20(f /r,