





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 (    _a  ):
			lowerCamelCase_  :  List[str]     =   '''vilt'''






			def __init__(self      ,							__magic_name__=3_0522      ,							__magic_name__=2      ,							__magic_name__=2      ,							__magic_name__=40      ,							__magic_name__=768      ,							__magic_name__=12      ,							__magic_name__=12      ,							__magic_name__=3072      ,							__magic_name__="gelu"      ,							__magic_name__=0.0      ,							__magic_name__=0.0      ,							__magic_name__=0.02      ,							__magic_name__=1e-12      ,							__magic_name__=384      ,							__magic_name__=32      ,							__magic_name__=3      ,							__magic_name__=True      ,							__magic_name__=-1      ,							__magic_name__=False      ,							__magic_name__=-1      ,							**__magic_name__      ,							)  ->     List[Any]:


							'''simple docstring'''



							super().__init__(tie_word_embeddings=__magic_name__      ,							**__magic_name__					)

							snake_case_		:					Tuple			    =						vocab_size
							snake_case_		:					Dict			    =						type_vocab_size
							snake_case_		:					Any			    =						modality_type_vocab_size
							snake_case_		:					List[Any]			    =						max_position_embeddings

							snake_case_		:					Union[str, Any]			    =						hidden_size
							snake_case_		:					Tuple			    =						num_hidden_layers
							snake_case_		:					Union[str, Any]			    =						num_attention_heads
							snake_case_		:					Any			    =						intermediate_size
							snake_case_		:					Dict			    =						hidden_act
							snake_case_		:					Optional[int]			    =						hidden_dropout_prob
							snake_case_		:					Optional[int]			    =						attention_probs_dropout_prob
							snake_case_		:					str			    =						initializer_range
							snake_case_		:					Any			    =						layer_norm_eps

							snake_case_		:					List[str]			    =						image_size
							snake_case_		:					List[Any]			    =						patch_size
							snake_case_		:					Tuple			    =						num_channels
							snake_case_		:					Optional[int]			    =						qkv_bias
							snake_case_		:					Optional[int]			    =						max_image_length
							snake_case_		:					Optional[int]			    =						num_images







import warnings

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


lowerCAmelCase_												=       logging.get_logger(__name__)



class       __lowerCAmelCase (    _a  ):






			def __init__(self      ,							*__magic_name__      ,							**__magic_name__					)  ->     None:


							'''simple docstring'''



							warnings.warn(
							    '''The class MobileNetV2FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
							    ''' Please use MobileNetV2ImageProcessor instead.'''      ,							__magic_name__      ,							)
							super().__init__(*__magic_name__      ,							**__magic_name__					)
