ó
    Z– j$  ã                   ód   • S r SSKJr  SSKJr  SSKJr  \" SS9\ " S S	\5      5       5       rS	/rg
)zMobileViT model configurationé    )Ústricté   )ÚPreTrainedConfig)Úauto_docstringzgoogle/mobilenet_v2_1.0_224)Ú
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\   -  \\\4   -  \S'   Sr\\
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r\
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\   \\S4   -  \S'   Sr\\S'   Sr\\S'   Sr\\S'   Sr\\S'   Sr\\S'   Sr\\S'   Sr\\-  \S'   Sr\\-  \S'   Sr\\-  \S'   Sr\\S '   S!r\\S"'   S#r\\S$'   Sr\\S%'   S&r\
\   \\S4   -  \S''   Sr \\-  \S('   S)r!\\S*'   S+r"g,)-ÚMobileViTConfigé   a©  
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.
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.

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
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image_sizeé   Ú
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