fThSrSSKJr SSKJr SSKJr SSKJr SSK J r SSK J r \ R"\5r"S S \5r"S S \ 5rS S /rg )zMobileViT model configuration OrderedDict)Mapping)version)PretrainedConfig) OnnxConfig)loggingcj^\rSrSrSrSrSSS/SQ/SQS S S S SS SSSSSSS/SQSS4U4SjjrSrU=r$)MobileViTConfiga This is the configuration class to store the configuration of a [`MobileViTModel`]. It is used to instantiate a MobileViT 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 MobileViT [apple/mobilevit-small](https://huggingface.co/apple/mobilevit-small) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: num_channels (`int`, *optional*, defaults to 3): The number of input channels. image_size (`int`, *optional*, defaults to 256): The size (resolution) of each image. patch_size (`int`, *optional*, defaults to 2): The size (resolution) of each patch. hidden_sizes (`List[int]`, *optional*, defaults to `[144, 192, 240]`): Dimensionality (hidden size) of the Transformer encoders at each stage. neck_hidden_sizes (`List[int]`, *optional*, defaults to `[16, 32, 64, 96, 128, 160, 640]`): The number of channels for the feature maps of the backbone. num_attention_heads (`int`, *optional*, defaults to 4): Number of attention heads for each attention layer in the Transformer encoder. mlp_ratio (`float`, *optional*, defaults to 2.0): The ratio of the number of channels in the output of the MLP to the number of channels in the input. expand_ratio (`float`, *optional*, defaults to 4.0): Expansion factor for the MobileNetv2 layers. hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): The non-linear activation function (function or string) in the Transformer encoder and convolution layers. conv_kernel_size (`int`, *optional*, defaults to 3): The size of the convolutional kernel in the MobileViT layer. output_stride (`int`, *optional*, defaults to 32): The ratio of the spatial resolution of the output to the resolution of the input image. hidden_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the Transformer encoder. attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. classifier_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout ratio for attached classifiers. 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. qkv_bias (`bool`, *optional*, defaults to `True`): Whether to add a bias to the queries, keys and values. aspp_out_channels (`int`, *optional*, defaults to 256): Number of output channels used in the ASPP layer for semantic segmentation. atrous_rates (`List[int]`, *optional*, defaults to `[6, 12, 18]`): Dilation (atrous) factors used in the ASPP layer for semantic segmentation. aspp_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout ratio for the ASPP layer for semantic segmentation. semantic_loss_ignore_index (`int`, *optional*, defaults to 255): The index that is ignored by the loss function of the semantic segmentation model. Example: ```python >>> from transformers import MobileViTConfig, MobileViTModel >>> # Initializing a mobilevit-small style configuration >>> configuration = MobileViTConfig() >>> # Initializing a model from the mobilevit-small style configuration >>> model = MobileViTModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ``` mobilevitr)) @`ig@g@silurg?gg{Gz?gh㈵>T) c .>[TU]"S0UD6 XlX lX0lX@lXPlX`lXplXl Xl Xl Xl Xl XlXlXlUUlUUlUUlUUlUUlUUlg)N)super__init__ num_channels image_size patch_size hidden_sizesneck_hidden_sizesnum_attention_heads mlp_ratio expand_ratio hidden_actconv_kernel_size output_stridehidden_dropout_probattention_probs_dropout_probclassifier_dropout_probinitializer_rangelayer_norm_epsqkv_biasaspp_out_channels atrous_ratesaspp_dropout_probsemantic_loss_ignore_index)selfr$r%r&r'r(r)r*r+r,r-r.r/r0r1r2r3r4r5r6r7r8kwargs __class__s m/var/www/auris/envauris/lib/python3.13/site-packages/transformers/models/mobilevit/configuration_mobilevit.pyr#MobileViTConfig.__init__es2 "6"($$(!2#6 "($ 0*#6 ,H)'>$!2,  "3(!2*D')r7r5r6r0r1r-r+r,r/r'r%r2r3r*r(r)r$r.r&r4r8) __name__ __module__ __qualname____firstlineno____doc__ model_typer#__static_attributes__ __classcell__)r;s@r<r r s^B HJ$9%( # #&-1E1Er>r c\rSrSr\R "S5r\S\\ \\ \ 444Sj5r \S\\ \\ \ 444Sj5r \S\ 4Sj5rSrg) MobileViTOnnxConfigz1.11returnc([SSSSSS.4/5$)N pixel_valuesbatchr$heightwidth)rrrrr9s r<inputsMobileViTOnnxConfig.inputss ^^PX]d-efghhr>clURS:Xa[SSS04/5$[SSS04SSS04/5$)Nzimage-classificationlogitsrrMlast_hidden_state pooler_output)taskrrQs r<outputsMobileViTOnnxConfig.outputssL 99. .Aw< 89: :!4q'l CoXY[bWcEdef fr>cg)Ng-C6?r!rQs r<atol_for_validation'MobileViTOnnxConfig.atol_for_validationsr>r!N)r?r@rArBrparsetorch_onnx_minimum_versionpropertyrstrintrRrYfloatr\rEr!r>r<rHrHs!(v!6 iWS#X%6 67iiggc3h&7!78gg Ur>rHN)rC collectionsrtypingr packagingrconfiguration_utilsronnxr utilsr get_loggerr?loggerr rH__all__r!r>r<rmsY$#3   H %xE&xEv*& 3 4r>