




from .balanced_parentheses import balanced_parentheses
from .stack import Stack



def    UpperCAmelCase							(  _snake_case				):
							return {"+": 1, "-": 1, "*": 2, "/": 2, "^": 3}.get(_lowerCAmelCase    ,			-1				)



def    UpperCAmelCase							(  _snake_case				):

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


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

		testmod()
		UpperCAmelCase_    ="""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

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()
def    A_ ( _lowerCAmelCase  :       Dict,  _lowerCAmelCase  :       Tuple,  _lowerCAmelCase  :       Optional[Any]							):






     """simple docstring"""

     _a     						=					RoFormerConfig.from_json_file(_lowerCAmelCase							)
     print(f'Building PyTorch model from configuration: {config}'							)
     _a     						=					RoFormerForMaskedLM(_lowerCAmelCase							)

     # Load weights from tf checkpoint
     load_tf_weights_in_roformer(_lowerCAmelCase,  _lowerCAmelCase,  _lowerCAmelCase							)

     # Save pytorch-model
     print(f'Save PyTorch model to {pytorch_dump_path}'							)
     torch.save(model.state_dict(),  _lowerCAmelCase,  _use_new_zipfile_serialization=_lowerCAmelCase							)


if __name__ == "__main__":
   __snake_case												=							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(
       '''--bert_config_file''',
       default=None,
       type=str,
       required=True,
       help=(
           '''The config json file corresponding to the pre-trained BERT 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.'''
   )
   __snake_case												=							parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)