
'''simple docstring'''

from .balanced_parentheses import balanced_parentheses
from .stack import Stack







def   A     (      UpperCamelCase_					:     str      )					->       int:







			'''simple docstring'''



			return {"+": 1, "-": 1, "*": 2, "/": 2, "^": 3}.get(UpperCamelCase_						,	-1      )







def   A     (      UpperCamelCase_					:     str      )					->       str:







			'''simple docstring'''



			if not balanced_parentheses(UpperCamelCase_      ):
						raise ValueError("Mismatched parentheses"      )
			lowerCAmelCase__								=							Stack()
			lowerCAmelCase__								=							[]
			for char in expression_str:
						if char.isalpha() or char.isdigit():
									postfix.append(UpperCamelCase_      )
						elif char == "(":
									stack.push(UpperCamelCase_      )
						elif char == ")":
									while not stack.is_empty() and stack.peek() != "(":
												postfix.append(stack.pop()      )
									stack.pop()
						else:
									while not stack.is_empty() and precedence(UpperCamelCase_      ) <= precedence(stack.peek()      ):
												postfix.append(stack.pop()      )
									stack.push(UpperCamelCase_      )
			while not stack.is_empty():
						postfix.append(stack.pop()      )
			return " ".join(UpperCamelCase_      )


if __name__ == "__main__":
		from doctest import testmod

		testmod()
		UpperCAmelCase__   : Dict					=		"a+b*(c^d-e)^(f+g*h)-i"

		print("Infix to Postfix Notation demonstration:\n")
		print("Infix notation: " + expression)
		print("Postfix notation: " + infix_to_postfix(expression))







'''simple docstring'''

import argparse

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


logging.set_verbosity_info()







def   A     (      UpperCamelCase_					:     List[str]						,	UpperCamelCase_					:     Union[str, Any]						,	UpperCamelCase_					:     Optional[Any]      )					->       List[str]:







			'''simple docstring'''



			lowerCAmelCase__								=							TaConfig.from_json_file(UpperCamelCase_      )
			print(F"""Building PyTorch model from configuration: {config}"""      )
			lowerCAmelCase__								=							TaForConditionalGeneration(UpperCamelCase_      )

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

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


if __name__ == "__main__":
		UpperCAmelCase__   : List[str]					=		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."
		)
		UpperCAmelCase__   : Tuple					=		parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)





