


'''simple docstring'''






from math import factorial






def 	_A				(lowerCAmelCase__  :int				,						lowerCAmelCase__  :int		)   ->  int:




     '''simple docstring'''



     if n < k or k < 0:
          raise ValueError('Please enter positive integers for n and k where n >= k'		)
     return factorial(lowerCAmelCase__		) // (factorial(lowerCAmelCase__		) * factorial(n - k		))


if __name__ == "__main__":
 print(
     "The number of five-card hands possible from a standard",
     f'''fifty-two card deck is: {combinations(5_2, 5)}\n''',
 )

 print(
     "If a class of 40 students must be arranged into groups of",
     f'''4 for group projects, there are {combinations(4_0, 4)} ways''',
     "to arrange them.\n",
 )

 print(
     "If 10 teams are competing in a Formula One race, there",
     f'''are {combinations(1_0, 3)} ways that first, second and''',
     "third place can be awarded.",
 )





'''simple docstring'''






import argparse

import torch

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


logging.set_verbosity_info()






def 	_A				(lowerCAmelCase__  :str				,						lowerCAmelCase__  :int				,						lowerCAmelCase__  :Any		)   ->  str:




     '''simple docstring'''



     _a			      =							BertConfig.from_json_file(lowerCAmelCase__		)
     print(f'Building PyTorch model from configuration: {config}'		)
     _a			      =							BertForPreTraining(lowerCAmelCase__		)

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

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


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

