sgldZddlmZddlmZddlmZmZeje Z GddeeZ y)z>Dilated Neighborhood Attention Transformer model configuration)PretrainedConfig)logging)BackboneConfigMixin*get_aligned_output_features_output_indicescveZdZdZdZdddZdddgd gd d gd gd gdgdgdddddddddddffd ZxZS) DinatConfiga This is the configuration class to store the configuration of a [`DinatModel`]. It is used to instantiate a Dinat 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 Dinat [shi-labs/dinat-mini-in1k-224](https://huggingface.co/shi-labs/dinat-mini-in1k-224) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: patch_size (`int`, *optional*, defaults to 4): The size (resolution) of each patch. NOTE: Only patch size of 4 is supported at the moment. num_channels (`int`, *optional*, defaults to 3): The number of input channels. embed_dim (`int`, *optional*, defaults to 64): Dimensionality of patch embedding. depths (`List[int]`, *optional*, defaults to `[3, 4, 6, 5]`): Number of layers in each level of the encoder. num_heads (`List[int]`, *optional*, defaults to `[2, 4, 8, 16]`): Number of attention heads in each layer of the Transformer encoder. kernel_size (`int`, *optional*, defaults to 7): Neighborhood Attention kernel size. dilations (`List[List[int]]`, *optional*, defaults to `[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]]`): Dilation value of each NA layer in the Transformer encoder. mlp_ratio (`float`, *optional*, defaults to 3.0): 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. 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. layer_scale_init_value (`float`, *optional*, defaults to 0.0): The initial value for the layer scale. Disabled if <=0. out_features (`List[str]`, *optional*): If used as backbone, list of features to output. Can be any of `"stem"`, `"stage1"`, `"stage2"`, etc. (depending on how many stages the model has). If unset and `out_indices` is set, will default to the corresponding stages. If unset and `out_indices` is unset, will default to the last stage. Must be in the same order as defined in the `stage_names` attribute. out_indices (`List[int]`, *optional*): If used as backbone, list of indices of features to output. Can be any of 0, 1, 2, etc. (depending on how many stages the model has). If unset and `out_features` is set, will default to the corresponding stages. If unset and `out_features` is unset, will default to the last stage. Must be in the same order as defined in the `stage_names` attribute. Example: ```python >>> from transformers import DinatConfig, DinatModel >>> # Initializing a Dinat shi-labs/dinat-mini-in1k-224 style configuration >>> configuration = DinatConfig() >>> # Initializing a model (with random weights) from the shi-labs/dinat-mini-in1k-224 style configuration >>> model = DinatModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```dinat num_heads num_layers)num_attention_headsnum_hidden_layersr@)rr)r)rr)rrrr)rrrrrr)rrrrrg@Tgg?gelug{Gz?gh㈵>Nc t|di|||_||_||_||_t ||_||_||_ ||_ ||_ | |_ | |_ | |_| |_| |_||_||_t'|dt |dz zz|_||_dgt-dt |dzDcgc]}d| c}z|_t1|||j.\|_|_ycc}w)Nrrstemstage) out_features out_indices stage_names)super__init__ patch_size num_channels embed_dimdepthslenr r kernel_size dilations mlp_ratioqkv_biashidden_dropout_probattention_probs_dropout_probdrop_path_rate hidden_actlayer_norm_epsinitializer_rangeint hidden_sizelayer_scale_init_valuerangerr _out_features _out_indices)selfr!r"r#r$r r&r'r(r)r*r+r,r-r/r.r2rrkwargsidx __class__s `/var/www/html/venv/lib/python3.12/site-packages/transformers/models/dinat/configuration_dinat.pyr zDinatConfig.__init__fs, "6"$(" f+"&""  #6 ,H),$,!2y1Vq+AAB&<#"8aVWX@Y&Zse}&ZZ0Z%;DL\L\1 -D-'[s D )__name__ __module__ __qualname____doc__ model_type attribute_mapr __classcell__)r9s@r:rrslC JJ +)Ml,>P%("'/ / rN) r>configuration_utilsrutilsrutils.backbone_utilsrr get_loggerr;loggerrrrBr:rHs:E3c   H %| %'7| rB