



import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()

def       A_   (						__a  :					List[str]			,		__a  :					Optional[Any]		):

				"""simple docstring"""
				# Initialize PyTorch model
				a__					   =		CanineConfig()
				a__					   =		CanineModel(__a		)
				model.eval()

				print(F'''Building PyTorch model from configuration: {config}'''		)

				# Load weights from tf checkpoint
				load_tf_weights_in_canine(__a			,		__a			,		__a		)

				# Save pytorch-model (weights and configuration)
				print(F'''Save PyTorch model to {pytorch_dump_path}'''		)
				model.save_pretrained(__a		)

				# Save tokenizer files
				a__					   =		CanineTokenizer()
				print(F'''Save tokenizer files to {pytorch_dump_path}'''		)
				tokenizer.save_pretrained(__a		)


if __name__ == "__main__":
	UpperCAmelCase							=       argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--tf_checkpoint_path""",
	    default=None,
	    type=str,
	    required=True,
	    help="""Path to the TensorFlow checkpoint. Should end with model.ckpt""",
	)
	parser.add_argument(
	    """--pytorch_dump_path""",
	    default=None,
	    type=str,
	    required=True,
	    help="""Path to a folder where the PyTorch model will be placed.""",
	)
	UpperCAmelCase							=       parser.parse_args()
	convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)






from __future__ import annotations

class 			__snake_case   :







							'''simple docstring'''






							def __init__(				self	,	a_ = 0		):
											a__					   =		value
											a__					   =		None
											a__					   =		None




def       A_   (						__a  :					Node | None			,		__a  :					Node | None		):

				"""simple docstring"""
				if treea is None:
								return treea
				if treea is None:
								return treea

				a__					   =		treea.value + treea.value
				a__					   =		merge_two_binary_trees(treea.left			,		treea.left		)
				a__					   =		merge_two_binary_trees(treea.right			,		treea.right		)
				return treea

def       A_   (						__a  :					Node | None		):

				"""simple docstring"""
				if root:
								print(root.value		)
								print_preorder(root.left		)
								print_preorder(root.right		)


if __name__ == "__main__":
	UpperCAmelCase							=       Node(1)
	UpperCAmelCase							=       Node(2)
	UpperCAmelCase							=       Node(3)
	UpperCAmelCase							=       Node(4)

	UpperCAmelCase							=       Node(2)
	UpperCAmelCase							=       Node(4)
	UpperCAmelCase							=       Node(6)
	UpperCAmelCase							=       Node(9)
	UpperCAmelCase							=       Node(5)

	print("""Tree1 is: """)
	print_preorder(treea)
	print("""Tree2 is: """)
	print_preorder(treea)
	UpperCAmelCase							=       merge_two_binary_trees(treea, treea)
	print("""Merged Tree is: """)
	print_preorder(merged_tree)

