






def 						_UpperCamelCase (     UpperCamelCase_							:       dict     ,							UpperCamelCase_							:       int		)	-> List[str]:

						"""simple docstring"""



						if not root or root not in binary_tree_mirror_dictionary:
												return
						lowerCAmelCase__    ,							lowerCAmelCase__         =    binary_tree_mirror_dictionary[root][:2]
						lowerCAmelCase__         =    [right_child, left_child]
						binary_tree_mirror_dict(__snake_case     ,							__snake_case		)
						binary_tree_mirror_dict(__snake_case     ,							__snake_case		)







def 						_UpperCamelCase (     UpperCamelCase_							:       dict     ,							UpperCamelCase_							:       int = 1		)	-> dict:

						"""simple docstring"""



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


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







import argparse

from transformers import ConvBertConfig, ConvBertModel, TFConvBertModel, load_tf_weights_in_convbert
from transformers.utils import logging


logging.set_verbosity_info()
def        __lowercase(							__snake_case				:		Dict    ,__snake_case				:		List[str]    ,__snake_case				:		List[Any]					)       ->   str:
       __snake_case						       =   ConvBertConfig.from_json_file(__snake_case					)
       __snake_case						       =   ConvBertModel(__snake_case					)

       __snake_case						       =   load_tf_weights_in_convbert(__snake_case    ,__snake_case    ,__snake_case					)
       model.save_pretrained(__snake_case					)

       __snake_case						       =   TFConvBertModel.from_pretrained(__snake_case    ,from_pt=__snake_case					)
       tf_model.save_pretrained(__snake_case					)


if __name__ == "__main__":
 lowerCamelCase_				:							List[Any]       =    argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
 )
 parser.add_argument(
     "--convbert_config_file",
     default=None,
     type=str,
     required=True,
     help=(
         "The config json file corresponding to the pre-trained ConvBERT model. \n"
         "This specifies the model architecture."
     ),
 )
 parser.add_argument(
     "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
 )
 lowerCamelCase_				:							Union[str, Any]       =    parser.parse_args()
 convert_orig_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.convbert_config_file, args.pytorch_dump_path)
