


'''simple docstring'''






import math






def 	_A				(lowerCAmelCase__				,						lowerCAmelCase__		)   ->  int:




     '''simple docstring'''



     if 0 not in (x, y):
          # We use the relation x^y = y*log10(x), where 10 is the base.
          return y * math.logaa(lowerCAmelCase__		)
     else:
          if x == 0:  # 0 raised to any number is 0
               return 0
          elif y == 0:
               return 1  # any number raised to 0 is 1
     raise AssertionError('This should never happen'		)


if __name__ == "__main__":  # Main function
 # Read two numbers from input and typecast them to int using map function.
 # Here x is the base and y is the power.
 a_					:    Any          =    '''Enter the base and the power separated by a comma: '''
 a_					:    Dict          =    map(int, input(prompt).split(","))
 a_					:    Optional[int]          =    map(int, input(prompt).split(","))

 # We find the log of each number, using the function res(), which takes two
 # arguments.
 a_					:    Union[str, Any]          =    res(xa, ya)
 a_					:    int          =    res(xa, ya)

 # We check for the largest number
 if resa > resa:
  print("Largest number is", xa, "^", ya)
 elif resa > resa:
  print("Largest number is", xa, "^", ya)
 else:
  print("Both are equal")





'''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)

