


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		(		_lowercase					,  _lowercase					,  _lowercase )					-> Optional[Any]:


     """simple docstring"""


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

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

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


if __name__ == "__main__":
    __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.'''
    )
    __snake_case   							=						parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)




def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__		):
      if index == number_of_items:
            return 0
      UpperCamelCase__     : str											=					0
      UpperCamelCase__     : Optional[Any]											=					0
      UpperCamelCase__     : int											=					knapsack(UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		index + 1		)
      if weights[index] <= max_weight:
            UpperCamelCase__     : List[Any]											=					values[index] + knapsack(
                UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		max_weight - weights[index]			,		index + 1		)
      return max(UpperCamelCase__			,		UpperCamelCase__		)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


