from __future__ import annotations






class 		a__						:

					def __init__(   self				:	Union[str, Any],_A				:	int   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   value
							SCREAMING_SNAKE_CASE_  :		Node | None    				=   None
							SCREAMING_SNAKE_CASE_  :		Node | None    				=   None







class 		a__						:
					A     	=							42

					def __init__(   self				:	Optional[Any]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[str]    				=   0

					def     __UpperCamelCase       (   self				:	List[Any],_A				:	Node | None,_A				:	int   ):







							"""simple docstring"""






							if node is None:
									return

							if path_sum == self.target:
									self.paths += 1

							if node.left:
									self.depth_first_search(node.left,path_sum + node.left.value   )
							if node.right:
									self.depth_first_search(node.right,path_sum + node.right.value   )






					def     __UpperCamelCase       (   self				:	List[str],_A				:	Node | None,_A				:	int | None = None   ):







							"""simple docstring"""






							if node is None:
									return 0
							if target is not None:
									SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   target

							self.depth_first_search(_A,node.value   )
							self.path_sum(node.left   )
							self.path_sum(node.right   )

							return self.paths


if __name__ == "__main__":
		import doctest

		doctest.testmod()

import argparse

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


logging.set_verbosity_info()




def 				_snake_case     (       lowerCAmelCase							:       Dict       ,			lowerCAmelCase							:       int       ,			lowerCAmelCase							:       Union[str, Any]     ):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		List[Any]    				=   ConvBertConfig.from_json_file(lowerCAmelCase     )
		SCREAMING_SNAKE_CASE_  :		Tuple    				=   ConvBertModel(lowerCAmelCase     )

		SCREAMING_SNAKE_CASE_  :		Dict    				=   load_tf_weights_in_convbert(lowerCAmelCase       ,			lowerCAmelCase       ,			lowerCAmelCase     )
		model.save_pretrained(lowerCAmelCase     )

		SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   TFConvBertModel.from_pretrained(lowerCAmelCase       ,			from_pt=lowerCAmelCase     )
		tf_model.save_pretrained(lowerCAmelCase     )


if __name__ == "__main__":
		__lowerCamelCase :     Optional[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 :     Optional[int]          =  parser.parse_args()
		convert_orig_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.convbert_config_file, args.pytorch_dump_path)
