






'''simple docstring'''


def 	lowerCAmelCase__      (       SCREAMING_SNAKE_CASE__		):
 assert column_title.isupper()
 __a     :						List[str]          =    0
 __a     :						Dict          =    len(__A		) - 1
 __a     :						int          =    0

 while index >= 0:
  __a     :						Optional[int]          =    (ord(column_title[index]		) - 64) * pow(26							,		__A		)
  answer += value
  power += 1
  index -= 1

 return answer


if __name__ == "__main__":
  from doctest import testmod

  testmod()









"""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  __snake_case							(	__A      :						Union[str, Any]							,		__A      :						Any							,		__A      :						Dict     )     ->       Tuple:






				'''simple docstring'''
				# Initialise PyTorch model
				SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     BertConfig.from_json_file(__A     )
				print(F"""Building PyTorch model from configuration: {config}"""     )
				SCREAMING_SNAKE_CASE						:							List[Any]							=     BertForPreTraining(__A     )

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

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


if __name__ == "__main__":
							A_						:    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_						:    str				=      parser.parse_args()
							convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)





