



import argparse

import torch

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


logging.set_verbosity_info()





def 	__snake_case      (     _UpperCAmelCase  ,					_UpperCAmelCase  ,					_UpperCAmelCase							):
    __a					=							RoFormerConfig.from_json_file(_A							)
    print(f'Building PyTorch model from configuration: {config}'							)
    __a					=							RoFormerForMaskedLM(_A							)

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

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


if __name__ == "__main__":
 __snake_case		:List[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.'''
 )
 __snake_case		:Optional[int] 						=	parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)








def      UpperCAmelCase__							(     _A :					int      , _A :					int				):





  '''simple docstring'''







  a__    =1.0
  for i in range(1      , r + 1				):
    ret *= (n + 1 - i) / i
  return round(_A				)




def      UpperCAmelCase__							(     _A :					int				):





  '''simple docstring'''







  return choose(8 + n      , _A				) + choose(9 + n      , _A				) - 10




def      UpperCAmelCase__							(     _A :					int				):





  '''simple docstring'''







  return sum(non_bouncy_exact(_A				) for i in range(1      , n + 1				)				)




def      UpperCAmelCase__							(     _A :					int = 1_00				):





  '''simple docstring'''







  return non_bouncy_upto(_A				)


if __name__ == "__main__":
       print(f"""{solution() = }""")
