





'''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     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase	):
     # Initialise PyTorch model
     _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							BertConfig.from_json_file(lowerCamelCase_	)
     print(f"""Building PyTorch model from configuration: {config}"""	)
     _SCREAMING_SNAKE_CASE       :	str						    =							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__":
       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__			(__lowerCamelCase = 10**9	):
     _SCREAMING_SNAKE_CASE       :	List[str]						    =							1
     _SCREAMING_SNAKE_CASE       :	Any						    =							2

     _SCREAMING_SNAKE_CASE       :	List[Any]						    =							0
     _SCREAMING_SNAKE_CASE       :	Dict						    =							0
     _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							0
     while perimeter <= max_perimeter:
          perimeters_sum += perimeter

          prev_value += 2 * value
          value += prev_value

          _SCREAMING_SNAKE_CASE       :	Tuple						    =							2 * value + 2 if i % 2 == 0 else 2 * value - 2
          i += 1

     return perimeters_sum


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