

import argparse

import torch

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


logging.set_verbosity_info()




def      UpperCAmelCase__							(     _A :					List[Any]      , _A :					Optional[Any]      , _A :					int				):





  '''simple docstring'''







  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__":
       lowerCamelCase 	=   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 	=   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() = }""")
