


"""simple docstring"""




import fire

from utils import calculate_rouge, save_json

def        lowercase							(		lowerCAmelCase__ :     Tuple    ,					lowerCAmelCase__ :     str    ,					lowerCAmelCase__ :     List[str]=None    ,					**lowerCAmelCase__ :     Dict			) ->      int:
		__a           =			[x.strip() for x in open(lowerCAmelCase__			).readlines()]
		__a           =			[x.strip() for x in open(lowerCAmelCase__			).readlines()][: len(lowerCAmelCase__			)]
		__a           =			calculate_rouge(lowerCAmelCase__    ,					lowerCAmelCase__    ,					**lowerCAmelCase__			)
		if save_path is not None:
				save_json(lowerCAmelCase__    ,					lowerCAmelCase__    ,					indent=lowerCAmelCase__			)
		return metrics  # these print nicely


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











import argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase				):
    # Initialise PyTorch model
    __a						       =				BigBirdConfig.from_json_file(_UpperCAmelCase				)
    print(f'Building PyTorch model from configuration: {config}'				)

    if is_trivia_qa:
        __a						       =				BigBirdForQuestionAnswering(_UpperCAmelCase				)
    else:
        __a						       =				BigBirdForPreTraining(_UpperCAmelCase				)

    # Load weights from tf checkpoint
    load_tf_weights_in_big_bird(_UpperCAmelCase   , _UpperCAmelCase   , is_trivia_qa=_UpperCAmelCase				)

    # Save pytorch-model
    print(f'Save PyTorch model to {pytorch_dump_path}'				)
    model.save_pretrained(_UpperCAmelCase				)


if __name__ == "__main__":
 __snake_case						:Tuple      		=     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(
     '''--big_bird_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.'''
 )
 parser.add_argument(
     '''--is_trivia_qa''', action='''store_true''', help='''Whether to convert a model with a trivia_qa head.'''
 )
 __snake_case						:Any      		=     parser.parse_args()
 convert_tf_checkpoint_to_pytorch(
     args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
 )





