U ,-e @sdZddlmZddlmZddlmZddlmZddl m Z ddl m Z e eZd d d d d ddZGdddeZGddde ZdS)z MobileViT model configuration OrderedDict)Mapping)version)PretrainedConfig) OnnxConfig)loggingzEhttps://huggingface.co/apple/mobilevit-small/resolve/main/config.jsonzGhttps://huggingface.co/apple/mobilevit-x-small/resolve/main/config.jsonzHhttps://huggingface.co/apple/mobilevit-xx-small/resolve/main/config.jsonzOhttps://huggingface.co/apple/deeplabv3-mobilevit-small/resolve/main/config.jsonzQhttps://huggingface.co/apple/deeplabv3-mobilevit-x-small/resolve/main/config.jsonzRhttps://huggingface.co/apple/deeplabv3-mobilevit-xx-small/resolve/main/config.json)zapple/mobilevit-smallzapple/mobilevit-x-smallzapple/mobilevit-xx-smallzapple/deeplabv3-mobilevit-smallz!apple/deeplabv3-mobilevit-x-smallz"apple/deeplabv3-mobilevit-xx-smallcsjeZdZdZdZddddddgd d d d d ddgdddddd ddddddddddgddffdd ZZS)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 probabilitiy 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-5): 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 ```Z mobilevitr @`ig@g@Zsilug?gg{Gz?gh㈵>T c stjf|||_||_||_||_||_||_||_||_ | |_ | |_ | |_ | |_ | |_||_||_||_||_||_||_||_||_dS)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)selfrrrr r!r"r#r$r%r&r'r(r)r*r+r,r-r.r/r0r1kwargs __class__v/var/www/html/Darija-Ai-Train/env/lib/python3.8/site-packages/transformers/models/mobilevit/configuration_mobilevit.pyrts,zMobileViTConfig.__init__)__name__ __module__ __qualname____doc__Z model_typer __classcell__r6r6r4r7r .s0Cr c@sleZdZedZeeeee effdddZ eeeee effdddZ ee dddZ d S) MobileViTOnnxConfigz1.11)returncCstddddddfgS)NZ pixel_valuesbatchrheightwidth)rr rrr2r6r6r7inputsszMobileViTOnnxConfig.inputscCs<|jdkrtdddifgStdddifdddifgSdS)Nzimage-classificationZlogitsrr?Zlast_hidden_stateZ pooler_output)taskrrCr6r6r7outputss zMobileViTOnnxConfig.outputscCsdS)Ng-C6?r6rCr6r6r7atol_for_validationsz'MobileViTOnnxConfig.atol_for_validationN)r8r9r:rparseZtorch_onnx_minimum_versionpropertyrstrintrDrFfloatrGr6r6r6r7r=s   r=N)r; collectionsrtypingr packagingrZconfiguration_utilsrZonnxrutilsr Z get_loggerr8loggerZ'MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAPr r=r6r6r6r7s        z