U ,È-e³ã@sBdZddlmZddlmZe e¡ZddiZGdd„deƒZ dS) z' Swinv2 Transformer model configurationé)ÚPretrainedConfig)Úloggingz(microsoft/swinv2-tiny-patch4-window8-256zXhttps://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.jsoncsbeZdZdZdZdddœZdddd d d d d gdd d d gdddddddddddf‡fdd„ Z‡ZS)Ú Swinv2Configaœ This is the configuration class to store the configuration of a [`Swinv2Model`]. It is used to instantiate a Swin Transformer v2 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 Swin Transformer v2 [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) 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-12): The epsilon used by the layer normalization layers. encoder_stride (`int`, `optional`, defaults to 32): Factor to increase the spatial resolution by in the decoder head for masked image modeling. Example: ```python >>> from transformers import Swinv2Config, Swinv2Model >>> # Initializing a Swinv2 microsoft/swinv2-tiny-patch4-window8-256 style configuration >>> configuration = Swinv2Config() >>> # Initializing a model (with random weights) from the microsoft/swinv2-tiny-patch4-window8-256 style configuration >>> model = Swinv2Model(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```Zswinv2Ú num_headsÚ num_layers)Znum_attention_headsZnum_hidden_layerséàéré`ééé éég@Tggš™™™™™¹?ZgeluFg{®Gáz”?gñh㈵øä>é c s¢tƒjf|Ž||_||_||_||_||_t|ƒ|_||_ ||_ ||_ | |_ | |_ | |_| |_| |_||_||_||_||_t|dt|ƒdƒ|_d|_dS)Nr é)érrr)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_rangeÚencoder_strideÚintZ hidden_sizeZpretrained_window_sizes)Úselfrrrrrrrrrrrrr r!r#r"r$Úkwargs©Ú __class__©úp/var/www/html/Darija-Ai-Train/env/lib/python3.8/site-packages/transformers/models/swinv2/configuration_swinv2.pyrcs* zSwinv2Config.__init__)Ú__name__Ú __module__Ú __qualname__Ú__doc__Z model_typeZ attribute_maprÚ __classcell__r*r*r(r+rs.=þ  îrN) r/Zconfiguration_utilsrÚutilsrZ get_loggerr,ÚloggerZ$SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAPrr*r*r*r+Ús   þ