




lowercase__							:	Union[str, Any]											=		"Alexander Joslin"

import operator as op

from .stack import Stack


def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





	snake_case_						       =	{"*": op.mul, "/": op.truediv, "+": op.add, "-": op.sub}

	snake_case_						       =	Stack()
	snake_case_						       =	Stack()

	for i in equation:
		if i.isdigit():
			# RULE 1
			operand_stack.push(int(_A				)				)
		elif i in operators:
			# RULE 2
			operator_stack.push(_A				)
		elif i == ")":
			# RULE 4
			snake_case_						       =	operator_stack.peek()
			operator_stack.pop()
			snake_case_						       =	operand_stack.peek()
			operand_stack.pop()
			snake_case_						       =	operand_stack.peek()
			operand_stack.pop()

			snake_case_						       =	operators[opr](_A	,      _A				)
			operand_stack.push(_A				)

    # RULE 5
	return operand_stack.peek()


if __name__ == "__main__":
		lowercase__							:	Tuple											=		"(5 + ((4 * 2) * (2 + 3)))"
		# answer = 45
		print(f'''{equation} = {dijkstras_two_stack_algorithm(equation)}''')






import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()


def 		lowerCamelCase__       (						_A	,      _A	,      _A				):







	'''simple docstring'''





	snake_case_						       =	MobileBertConfig.from_json_file(_A				)
	print(f"Building PyTorch model from configuration: {config}"				)
	snake_case_						       =	MobileBertForPreTraining(_A				)
	# Load weights from tf checkpoint
	snake_case_						       =	load_tf_weights_in_mobilebert(_A	,      _A	,      _A				)
	# Save pytorch-model
	print(f"Save PyTorch model to {pytorch_dump_path}"				)
	torch.save(model.state_dict()	,      _A				)


if __name__ == "__main__":
		lowercase__							:	Dict											=		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(
		    "--mobilebert_config_file",
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        "The config json file corresponding to the pre-trained MobileBERT 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."
		)
		lowercase__							:	Tuple											=		parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)
