o ZŽh™ã@s@dZddlmZddlmZe e¡ZGdd„deƒZdgZ dS)z*Donut Swin Transformer model configurationé)ÚPretrainedConfig)ÚloggingcsXeZdZdZdZdddœZdddd gd ¢gd ¢d d ddddddddf‡fdd„ Z‡ZS)ÚDonutSwinConfiga This is the configuration class to store the configuration of a [`DonutSwinModel`]. It is used to instantiate a Donut model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the Donut [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: image_size (`int`, *optional*, defaults to 224): The size (resolution) of each image. patch_size (`int`, *optional*, defaults to 4): The size (resolution) of each patch. num_channels (`int`, *optional*, defaults to 3): The number of input channels. embed_dim (`int`, *optional*, defaults to 96): Dimensionality of patch embedding. depths (`list(int)`, *optional*, defaults to `[2, 2, 6, 2]`): Depth of each layer in the Transformer encoder. num_heads (`list(int)`, *optional*, defaults to `[3, 6, 12, 24]`): Number of attention heads in each layer of the Transformer encoder. window_size (`int`, *optional*, defaults to 7): Size of windows. mlp_ratio (`float`, *optional*, defaults to 4.0): Ratio of MLP hidden dimensionality to embedding dimensionality. qkv_bias (`bool`, *optional*, defaults to `True`): Whether or not a learnable bias should be added to the queries, keys and values. hidden_dropout_prob (`float`, *optional*, defaults to 0.0): The dropout probability for all fully connected layers in the embeddings and encoder. attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. drop_path_rate (`float`, *optional*, defaults to 0.1): Stochastic depth rate. hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported. use_absolute_embeddings (`bool`, *optional*, defaults to `False`): Whether or not to add absolute position embeddings to the patch embeddings. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. layer_norm_eps (`float`, *optional*, defaults to 1e-05): The epsilon used by the layer normalization layers. Example: ```python >>> from transformers import DonutSwinConfig, DonutSwinModel >>> # Initializing a Donut naver-clova-ix/donut-base style configuration >>> configuration = DonutSwinConfig() >>> # Randomly initializing a model from the naver-clova-ix/donut-base style configuration >>> model = DonutSwinModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```z donut-swinÚ num_headsÚ num_layers)Znum_attention_headsZnum_hidden_layerséàéré`)ér ér )rr é éég@Tggš™™™™™¹?ZgeluFg{®Gáz”?gñh㈵øä>c sštƒjdi|¤Ž||_||_||_||_||_t|ƒ|_||_ ||_ ||_ | |_ | |_ | |_| |_| |_||_||_||_t|dt|ƒdƒ|_dS)Nr é©)ÚsuperÚ__init__Ú image_sizeÚ patch_sizeÚ num_channelsÚ embed_dimÚdepthsÚlenrrÚ window_sizeÚ mlp_ratioÚqkv_biasÚhidden_dropout_probÚattention_probs_dropout_probÚdrop_path_rateÚ hidden_actÚuse_absolute_embeddingsÚlayer_norm_epsÚinitializer_rangeÚintZ hidden_size)Úselfrrrrrrrrrrrrrr r"r!Úkwargs©Ú __class__rúa/var/www/auris/lib/python3.10/site-packages/transformers/models/donut/configuration_donut_swin.pyr[s& zDonutSwinConfig.__init__)Ú__name__Ú __module__Ú __qualname__Ú__doc__Z model_typeZ attribute_maprÚ __classcell__rrr&r(rs.;þïrN) r,Zconfiguration_utilsrÚutilsrZ get_loggerr)ÚloggerrÚ__all__rrrr(Ús     o