

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase_    =					logging.get_logger(__name__)

lowerCamelCase_    =					{
    '''dandelin/vilt-b32-mlm''': '''https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json'''
}







class  __lowerCamelCase				(	__snake_case	):
      lowerCamelCase_	:      int          =					'vilt'







      def __init__(     self							,  lowerCamelCase=30522							,  lowerCamelCase=2							,  lowerCamelCase=2							,  lowerCamelCase=40							,  lowerCamelCase=768							,  lowerCamelCase=12							,  lowerCamelCase=12							,  lowerCamelCase=3072							,  lowerCamelCase="gelu"							,  lowerCamelCase=0.0							,  lowerCamelCase=0.0							,  lowerCamelCase=0.02							,  lowerCamelCase=1e-12							,  lowerCamelCase=384							,  lowerCamelCase=32							,  lowerCamelCase=3							,  lowerCamelCase=True							,  lowerCamelCase=-1							,  lowerCamelCase=False							,  lowerCamelCase=-1							,  **lowerCamelCase							,  )						->       int:
        super().__init__(tie_word_embeddings=lowerCamelCase							,  **lowerCamelCase   )

        snake_case_       						=      vocab_size
        snake_case_       						=      type_vocab_size
        snake_case_       						=      modality_type_vocab_size
        snake_case_       						=      max_position_embeddings

        snake_case_       						=      hidden_size
        snake_case_       						=      num_hidden_layers
        snake_case_       						=      num_attention_heads
        snake_case_       						=      intermediate_size
        snake_case_       						=      hidden_act
        snake_case_       						=      hidden_dropout_prob
        snake_case_       						=      attention_probs_dropout_prob
        snake_case_       						=      initializer_range
        snake_case_       						=      layer_norm_eps

        snake_case_       						=      image_size
        snake_case_       						=      patch_size
        snake_case_       						=      num_channels
        snake_case_       						=      qkv_bias
        snake_case_       						=      max_image_length
        snake_case_       						=      num_images



'''simple docstring'''






import warnings

from ...utils import logging
from .image_processing_mobilenet_va import MobileNetVaImageProcessor


__UpperCAmelCase    				=							logging.get_logger(__name__)




class 		a__				(							a__  ):





							'''simple docstring'''






							def __init__(		self					,    *lowerCamelCase_					,    **lowerCamelCase_   )       ->							None:
														warnings.warn(
														    '''The class MobileNetV2FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
														    ''' Please use MobileNetV2ImageProcessor instead.'''					,    lowerCamelCase_					,    )
														super().__init__(*lowerCamelCase_					,    **lowerCamelCase_   )