






'''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 				lowercase_					(     __A			:							Tuple       ,   __A			:							Union[str, Any]       ,   __A			:							Optional[int]      )							->   str:



 """simple docstring"""




 lowercase       : Union[str, Any]						=BertConfig.from_json_file(__A      )
 print(F'Building PyTorch model from configuration: {config}'      )
 lowercase       : Optional[int]						=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__":
      SCREAMING_SNAKE_CASE							=						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.'
      )
      SCREAMING_SNAKE_CASE							=						parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)











'''simple docstring'''



from __future__ import annotations


def 				lowercase_					(     __A			:							list[list[int]]      )							->   int:



 """simple docstring"""




 for i in range(1       ,   len(matrix[0]      )      ):
  matrix[0][i] += matrix[0][i - 1]

 # preprocessing the first column
 for i in range(1       ,   len(__A      )      ):
  matrix[i][0] += matrix[i - 1][0]

 # updating the path cost for current position
 for i in range(1       ,   len(__A      )      ):
  for j in range(1       ,   len(matrix[0]      )      ):
   matrix[i][j] += min(matrix[i - 1][j]       ,   matrix[i][j - 1]      )

 return matrix[-1][-1]


if __name__ == "__main__":
      import doctest

      doctest.testmod()



