code: 
  
  
  import argparse
  
  import torch
  
  from transformers import ElectraConfig, ElectraForMaskedLM, ElectraForPreTraining, load_tf_weights_in_electra
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase     )    ->Any:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   Tuple					   =		ElectraConfig.from_json_file(_UpperCamelCase     )
        print(f"""Building PyTorch model from configuration: {config}"""     )
  
        if discriminator_or_generator == "discriminator":
              lowercase			:   List[Any]					   =		ElectraForPreTraining(_UpperCamelCase     )
        elif discriminator_or_generator == "generator":
              lowercase			:   Any					   =		ElectraForMaskedLM(_UpperCamelCase     )
        else:
              raise ValueError('''The discriminator_or_generator argument should be either \'discriminator\' or \'generator\''''     )
  
        # Load weights from tf checkpoint
        load_tf_weights_in_electra(
            _UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase,    discriminator_or_generator=_UpperCamelCase     )
  
        # Save pytorch-model
        print(f"""Save PyTorch model to {pytorch_dump_path}"""     )
        torch.save(model.state_dict(),    _UpperCamelCase     )
  
  
  if __name__ == "__main__":
    __a   				=      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(
        '''--config_file''',
        default=None,
        type=str,
        required=True,
        help='''The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.''',
    )
    parser.add_argument(
        '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
    )
    parser.add_argument(
        '''--discriminator_or_generator''',
        default=None,
        type=str,
        required=True,
        help=(
            '''Whether to export the generator or the discriminator. Should be a string, either \'discriminator\' or '''
            '''\'generator\'.'''
        ),
    )
    __a   				=      parser.parse_args()
    convert_tf_checkpoint_to_pytorch(
        args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.discriminator_or_generator
    )
  
  
style_context: 
  
  
  __a   				=      [
      [0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
      [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0],
      [0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
      [0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0],
      [0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0],
      [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],
      [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0],
      [0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0],
  ]
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase     )    ->bool:
  
  
  
  
  
  
  
        """simple docstring"""
  
        return 0 <= row < rows and 0 <= col < cols
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase     )    ->int:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   Optional[int]					   =		len(_UpperCamelCase     )
        lowercase			:   str					   =		len(mat[0]     )
        if is_safe(_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase     ) and (row, col) not in seen and mat[row][col] == 1:
              seen.add((row, col)     )
              return (
                  1
                  + depth_first_search(row + 1,    _UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase     )
                  + depth_first_search(row - 1,    _UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase     )
                  + depth_first_search(_UpperCamelCase,    col + 1,    _UpperCamelCase,    _UpperCamelCase     )
                  + depth_first_search(_UpperCamelCase,    col - 1,    _UpperCamelCase,    _UpperCamelCase     )
              )
        else:
              return 0
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase     )    ->int:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   set					   =		set()
  
        lowercase			:   Tuple					   =		0
        for row, line in enumerate(_UpperCamelCase     ):
              for col, item in enumerate(_UpperCamelCase     ):
                    if item == 1 and (row, col) not in seen:
                          # Maximizing the area
                          lowercase			:   Tuple					   =		max(_UpperCamelCase,    depth_first_search(_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase     )     )
        return max_area
  
  
  if __name__ == "__main__":
    import doctest
  
    print(find_max_area(matrix))  # Output -> 6
    doctest.testmod()
  
  