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

import operator as op

from .stack import Stack







def        A__					(						lowerCamelCase						)  ->	int:
				UpperCamelCase_:							List[str]	=						{"""*""": op.mul, """/""": op.truediv, """+""": op.add, """-""": op.sub}

				UpperCamelCase_:							Stack[int]	=						Stack()
				UpperCamelCase_:							Stack[str]	=						Stack()

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

												UpperCamelCase_:							Optional[int]	=						operators[opr](lowerCamelCase     ,    lowerCamelCase						)
												operand_stack.push(lowerCamelCase						)

    # RULE 5
				return operand_stack.peek()


if __name__ == "__main__":
							lowerCamelCase_						: str         =  """(5 + ((4 * 2) * (2 + 3)))"""
							# answer = 45
							print(F"""{equation} = {dijkstras_two_stack_algorithm(equation)}""")








import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()







def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)  ->	Union[str, Any]:
				# Initialise PyTorch model
				UpperCamelCase_:							List[Any]	=						TaConfig.from_json_file(lowerCamelCase						)
				print(F'''Building PyTorch model from configuration: {config}'''						)
				UpperCamelCase_:							Any	=						TaForConditionalGeneration(lowerCamelCase						)

				# Load weights from tf checkpoint
				load_tf_weights_in_ta(lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)

				# Save pytorch-model
				print(F'''Save PyTorch model to {pytorch_dump_path}'''						)
				model.save_pretrained(lowerCamelCase						)


if __name__ == "__main__":
							lowerCamelCase_						: Tuple         =  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 T5 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."""
							)
							lowerCamelCase_						: Union[str, Any]         =  parser.parse_args()
							convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)






