






'''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__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase					)->				Union[str, Any]:
      # Initialise PyTorch model
      UpperCamelCase       						=      LxmertConfig.from_json_file(__UpperCamelCase					)
      print(F"Building PyTorch model from configuration: {config}"					)
      UpperCamelCase       						=      LxmertForPreTraining(__UpperCamelCase					)

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

      # Save pytorch-model
      print(F"Save PyTorch model to {pytorch_dump_path}"					)
      torch.save(model.state_dict()	,		__UpperCamelCase					)


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(
         '--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.'
     )
     SCREAMING_SNAKE_CASE__											=     parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)








'''simple docstring'''

import fire

from utils import calculate_rouge, save_json







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase=None	,		**__UpperCamelCase					)->				int:
      UpperCamelCase       						=      [x.strip() for x in open(__UpperCamelCase					).readlines()]
      UpperCamelCase       						=      [x.strip() for x in open(__UpperCamelCase					).readlines()][: len(__UpperCamelCase					)]
      UpperCamelCase       						=      calculate_rouge(__UpperCamelCase	,		__UpperCamelCase	,		**__UpperCamelCase					)
      if save_path is not None:
            save_json(__UpperCamelCase	,		__UpperCamelCase	,		indent=__UpperCamelCase					)
      return metrics  # these print nicely


if __name__ == "__main__":
     fire.Fire(calculate_rouge_path)
