




'''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    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				Optional[Any]   ,						UpperCamelCase__						:				Optional[Any]   ,						UpperCamelCase__						:				Optional[int]							):




     """simple docstring"""


     a_    :       Optional[int]    	=     RoFormerConfig.from_json_file(UpperCamelCase__							)
     print(F"Building PyTorch model from configuration: {config}"							)
     a_    :       Optional[int]    	=     RoFormerForMaskedLM(UpperCamelCase__							)

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

     # Save pytorch-model
     print(F"Save PyTorch model to {pytorch_dump_path}"							)
     torch.save(model.state_dict()   ,						UpperCamelCase__   ,						_use_new_zipfile_serialization=UpperCamelCase__							)


if __name__ == "__main__":
 lowerCAmelCase_			:     Union[str, Any]							    =     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.'
 )
 lowerCAmelCase_			:     Optional[int]							    =     parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






'''simple docstring'''


from .stack import Stack



def    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				str							):




     """simple docstring"""


     a_    :       Stack[str]    	=     Stack()
     a_    :       Tuple    	=     {"""(""": """)""", """[""": """]""", """{""": """}"""}
     for bracket in parentheses:
          if bracket in bracket_pairs:
               stack.push(UpperCamelCase__							)
          elif bracket in (")", "]", "}"):
               if stack.is_empty() or bracket_pairs[stack.pop()] != bracket:
                    return False
     return stack.is_empty()


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

 testmod()

 lowerCAmelCase_			:     Any							    =     ['((()))', '((())', '(()))']
 print('Balanced parentheses demonstration:\n')
 for example in examples:
  lowerCAmelCase_			:     Optional[Any]							    =     '' if balanced_parentheses(example) else 'not '
  print(f"{example} is {not_str}balanced")
