



def       A_   (						__a  :					dict			,		__a  :					int		):

				"""simple docstring"""
				if not root or root not in binary_tree_mirror_dictionary:
								return
				a__   ,      a__					   =		binary_tree_mirror_dictionary[root][:2]
				a__					   =		[right_child, left_child]
				binary_tree_mirror_dict(__a			,		__a		)
				binary_tree_mirror_dict(__a			,		__a		)

def       A_   (						__a  :					dict			,		__a  :					int = 1		):

				"""simple docstring"""
				if not binary_tree:
								raise ValueError("""binary tree cannot be empty"""		)
				if root not in binary_tree:
								a__					   =		F'''root {root} is not present in the binary_tree'''
								raise ValueError(__a		)
				a__					   =		dict(__a		)
				binary_tree_mirror_dict(__a			,		__a		)
				return binary_tree_mirror_dictionary


if __name__ == "__main__":
	UpperCAmelCase							=       {1: [2, 3], 2: [4, 5], 3: [6, 7], 7: [8, 9]}
	print(f"""Binary tree: {binary_tree}""")
	UpperCAmelCase							=       binary_tree_mirror(binary_tree, 5)
	print(f"""Binary tree mirror: {binary_tree_mirror_dictionary}""")


"""simple docstring"""




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      UpperCamelCase__(      __A			):
						lowerCAmelCase__	:    str    				=		'vilt'





						def __init__(							self		,__UpperCAmelCase=3_05_22		,__UpperCAmelCase=2		,__UpperCAmelCase=2		,__UpperCAmelCase=40		,__UpperCAmelCase=7_68		,__UpperCAmelCase=12		,__UpperCAmelCase=12		,__UpperCAmelCase=30_72		,__UpperCAmelCase="gelu"		,__UpperCAmelCase=0.0		,__UpperCAmelCase=0.0		,__UpperCAmelCase=0.0_2		,__UpperCAmelCase=1e-12		,__UpperCAmelCase=3_84		,__UpperCAmelCase=32		,__UpperCAmelCase=3		,__UpperCAmelCase=True		,__UpperCAmelCase=-1		,__UpperCAmelCase=False		,__UpperCAmelCase=-1		,**__UpperCAmelCase		,)       ->					Tuple:
													super().__init__(tie_word_embeddings=__UpperCAmelCase		,**__UpperCAmelCase  )

													A__						  =				vocab_size
													A__						  =				type_vocab_size
													A__						  =				modality_type_vocab_size
													A__						  =				max_position_embeddings

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

													A__						  =				image_size
													A__						  =				patch_size
													A__						  =				num_channels
													A__						  =				qkv_bias
													A__						  =				max_image_length
													A__						  =				num_images





