"""simple docstring"""







import argparse

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


logging.set_verbosity_info()


def 					a__ (						UpperCAmelCase__    :     Dict						,     UpperCAmelCase__    :     Tuple						,     UpperCAmelCase__    :     Union[str, Any]     )->		str:
						UpperCAmelCase__   		=						ConvBertConfig.from_json_file(UpperCAmelCase__     )
						UpperCAmelCase__   		=						ConvBertModel(UpperCAmelCase__     )

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

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


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


"""simple docstring"""







class      _lowercase						:

				def __init__(       self    :      Tuple    ,      _lowercase    :      int       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						data
										UpperCAmelCase__   		=						None
										UpperCAmelCase__   		=						None


def 					a__ (						UpperCAmelCase__    :     BinaryTreeNode | None						,     UpperCAmelCase__    :     int     )->		BinaryTreeNode | None:
						if node is None:
												UpperCAmelCase__   		=						BinaryTreeNode(UpperCAmelCase__     )
												return node

						# binary search tree is not empty,
						# so we will insert it into the tree
						# if new_value is less than value of data in node,
						#  add it to left subtree and proceed recursively
						if new_value < node.data:
												UpperCAmelCase__   		=						insert(node.left_child						,     UpperCAmelCase__     )
						else:
												# if new_value is greater than value of data in node,
												#  add it to right subtree and proceed recursively
												UpperCAmelCase__   		=						insert(node.right_child						,     UpperCAmelCase__     )
						return node


def 					a__ (						UpperCAmelCase__    :     None | BinaryTreeNode     )->		list[int]:  # if node is None,return
						if node:
												UpperCAmelCase__   		=						inorder(node.left_child     )
												UpperCAmelCase__   		=						[*inorder_array, node.data]
												UpperCAmelCase__   		=						inorder_array + inorder(node.right_child     )
						else:
												UpperCAmelCase__   		=						[]
						return inorder_array


def 					a__ (						)->		BinaryTreeNode | None:
						UpperCAmelCase__   		=						insert(UpperCAmelCase__						,     1_5     )
						insert(UpperCAmelCase__						,     1_0     )
						insert(UpperCAmelCase__						,     2_5     )
						insert(UpperCAmelCase__						,     6     )
						insert(UpperCAmelCase__						,     1_4     )
						insert(UpperCAmelCase__						,     2_0     )
						insert(UpperCAmelCase__						,     6_0     )
						return root


def 					a__ (						)->		None:
						# main function
						UpperCAmelCase__   		=						make_tree()
						print("""Printing values of binary search tree in Inorder Traversal."""     )
						inorder(UpperCAmelCase__     )


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						main()

