


'''simple docstring'''







import argparse

import torch

from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging


logging.set_verbosity_info()






def 			lowercase				(_A   ,						_A   ,						_A					):







   """simple docstring"""







   _lowerCAmelCase    :							Optional[Any]   			=       LxmertConfig.from_json_file(lowercase__					)
   print(f'Building PyTorch model from configuration: {config}'					)
   _lowerCAmelCase    :							Union[str, Any]   			=       LxmertForPreTraining(lowercase__					)

   # Load weights from tf checkpoint
   load_tf_weights_in_lxmert(lowercase__   ,						lowercase__   ,						lowercase__					)

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


if __name__ == "__main__":
       lowerCAmelCase    :     List[Any]					=						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."""
       )
       lowerCAmelCase    :     Any					=						parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)





'''simple docstring'''







from typing import Any






def 			lowercase				(_A					):







       """simple docstring"""







       if not input_list:
              return []
       _lowerCAmelCase    :							Optional[int]   			=       [input_list.count(_A					) for value in input_list]
       _lowerCAmelCase    :							int   			=       max(_A					)  # Gets the maximum count in the input list.
       # Gets values of modes
       return sorted({input_list[i] for i, value in enumerate(_A					) if value == y}					)


if __name__ == "__main__":
       import doctest

       doctest.testmod()

