


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


__lowerCAmelCase     :			int      =logging.get_logger(__name__)

__lowerCAmelCase     :			List[str]      ={
    'dandelin/vilt-b32-mlm': 'https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json'
}
class 	_lowercase				(      A__	):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__    :      int      =		'''vilt'''


				def __init__(	self      :Tuple      ,      lowerCAmelCase__      :Union[str, Any]=30_522      ,      lowerCAmelCase__      :List[str]=2      ,      lowerCAmelCase__      :Dict=2      ,      lowerCAmelCase__      :Any=40      ,      lowerCAmelCase__      :Dict=768      ,      lowerCAmelCase__      :Union[str, Any]=12      ,      lowerCAmelCase__      :Any=12      ,      lowerCAmelCase__      :Union[str, Any]=3_072      ,      lowerCAmelCase__      :int="gelu"      ,      lowerCAmelCase__      :Optional[int]=0.0      ,      lowerCAmelCase__      :Dict=0.0      ,      lowerCAmelCase__      :List[Any]=0.02      ,      lowerCAmelCase__      :str=1E-1_2      ,      lowerCAmelCase__      :Optional[Any]=384      ,      lowerCAmelCase__      :Optional[int]=32      ,      lowerCAmelCase__      :str=3      ,      lowerCAmelCase__      :Optional[int]=True      ,      lowerCAmelCase__      :Any=-1      ,      lowerCAmelCase__      :int=False      ,      lowerCAmelCase__      :Optional[int]=-1      ,      **lowerCAmelCase__      :str      ,      )		->      List[Any]:
										super().__init__(tie_word_embeddings=lowerCAmelCase__      ,      **lowerCAmelCase__     )

										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		vocab_size
										__SCREAMING_SNAKE_CASE						:					Dict		      =		type_vocab_size
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		modality_type_vocab_size
										__SCREAMING_SNAKE_CASE						:					Dict		      =		max_position_embeddings

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

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




def 	_UpperCamelCase							(				lowercase__   ,	lowercase__				):
						__SCREAMING_SNAKE_CASE						:					int		      =		len(lowercase__				)
						__SCREAMING_SNAKE_CASE						:					int		      =		len(lowercase__				)
						__SCREAMING_SNAKE_CASE						:					int		      =		(
						    first_input_list_length
						    if first_input_list_length > second_input_list_length
						    else second_input_list_length
						)
						__SCREAMING_SNAKE_CASE						:					list		      =		[]
						for char_count in range(lowercase__				):
												if char_count < first_input_list_length:
																		output_result_list.append(first_input_list[char_count]				)
												if char_count < second_input_list_length:
																		output_result_list.append(second_input_list[char_count]				)

						return output_result_list


if __name__ == "__main__":
						print(alternative_list_arrange(['A', 'B', 'C'], [1, 2, 3, 4, 5]), end=' ')
