



import argparse

import torch

from transformers import ElectraConfig, ElectraForMaskedLM, ElectraForPreTraining, load_tf_weights_in_electra
from transformers.utils import logging


logging.set_verbosity_info()

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

				"""simple docstring"""
				# Initialise PyTorch model
				a__					   =		ElectraConfig.from_json_file(__a		)
				print(F'''Building PyTorch model from configuration: {config}'''		)

				if discriminator_or_generator == "discriminator":
								a__					   =		ElectraForPreTraining(__a		)
				elif discriminator_or_generator == "generator":
								a__					   =		ElectraForMaskedLM(__a		)
				else:
								raise ValueError("""The discriminator_or_generator argument should be either 'discriminator' or 'generator'"""		)

				# Load weights from tf checkpoint
				load_tf_weights_in_electra(
				    __a			,		__a			,		__a			,		discriminator_or_generator=__a		)

				# Save pytorch-model
				print(F'''Save PyTorch model to {pytorch_dump_path}'''		)
				torch.save(model.state_dict()			,		__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 path."""
	)
	parser.add_argument(
	    """--config_file""",
	    default=None,
	    type=str,
	    required=True,
	    help="""The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.""",
	)
	parser.add_argument(
	    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
	)
	parser.add_argument(
	    """--discriminator_or_generator""",
	    default=None,
	    type=str,
	    required=True,
	    help=(
	        """Whether to export the generator or the discriminator. Should be a string, either 'discriminator' or """
	        """'generator'."""
	    ),
	)
	UpperCAmelCase							=       parser.parse_args()
	convert_tf_checkpoint_to_pytorch(
	    args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.discriminator_or_generator
	)






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)

