



'''simple docstring'''





from .balanced_parentheses import balanced_parentheses
from .stack import Stack

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

def 	a_					(							_lowerCAmelCase	)				->				str:
  if not balanced_parentheses(_lowerCAmelCase	):
    raise ValueError('Mismatched parentheses'	)
  __lowerCamelCase	:	Stack[str]   	=			Stack()
  __lowerCamelCase	:	Dict   	=			[]
  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

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()

def 	a_					(							_lowerCAmelCase	,_lowerCAmelCase	)				->				List[Any]:
  # Initialize PyTorch model
  __lowerCamelCase	:	Union[str, Any]   	=			CanineConfig()
  __lowerCamelCase	:	Optional[Any]   	=			CanineModel(_lowerCAmelCase	)
  model.eval()

  print(F'Building PyTorch model from configuration: {config}'	)

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

  # Save pytorch-model (weights and configuration)
  print(F'Save PyTorch model to {pytorch_dump_path}'	)
  model.save_pretrained(_lowerCAmelCase	)

  # Save tokenizer files
  __lowerCamelCase	:	str   	=			CanineTokenizer()
  print(F'Save tokenizer files to {pytorch_dump_path}'	)
  tokenizer.save_pretrained(_lowerCAmelCase	)


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. Should end with model.ckpt',
   )
   parser.add_argument(
       '--pytorch_dump_path',
       default=None,
       type=str,
       required=True,
       help='Path to a folder where the PyTorch model will be placed.',
   )
   _UpperCamelCase							    =						parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)






