import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()




def       lowerCAmelCase_     (		lowercase:							Tuple  ,					lowercase:							List[Any]  ,					lowercase:							Union[str, Any]		)    ->				Dict:







 '''simple docstring'''
 _UpperCamelCase:     Optional[Any]							= BertConfig.from_json_file(lowercase		)
 print(F"""Building PyTorch model from configuration: {config}"""		)
 _UpperCamelCase:     List[str]							= BertForPreTraining(lowercase		)

 # Load weights from tf checkpoint
 load_tf_weights_in_bert(lowercase  ,					lowercase  ,					lowercase		)

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


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 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.'''
  )
  UpperCAmelCase_			    =							parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
def       lowerCAmelCase_     (		lowercase:							int = 600_851_475_143		)    ->				int:







 '''simple docstring'''

 try:
  _UpperCamelCase:     Optional[int]							= int(lowercase		)
 except (TypeError, ValueError):
  raise TypeError('''Parameter n must be int or castable to int.'''		)
 if n <= 0:
  raise ValueError('''Parameter n must be greater than or equal to one.'''		)
 _UpperCamelCase:     Dict							= 1
 _UpperCamelCase:     Tuple							= 2
 while i * i <= n:
  while n % i == 0:
   _UpperCamelCase:     List[Any]							= i
   n //= i
  i += 1
 if n > 1:
  _UpperCamelCase:     Any							= n
 return int(lowercase		)


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