




from __future__ import annotations



def 						lowerCAmelCase		(		_lowerCAmelCase							:   str						,							_lowerCAmelCase							:   Union[str, Any]						,							_lowerCAmelCase							:   Optional[int]	):




	"""simple docstring"""







	UpperCAmelCase__							 =						list(range(len(__A	)	)	)
	UpperCAmelCase__							 =						[v / w for v, w in zip(__A						,							__A	)]
	index.sort(key=lambda _lowerCAmelCase	: ratio[i]						,							reverse=__A	)

	UpperCAmelCase__							 =						0
	UpperCAmelCase__							 =						[0] * len(__A	)
	for i in index:
		if weight[i] <= capacity:
			UpperCAmelCase__							 =						1
			max_value += value[i]
			capacity -= weight[i]
		else:
			UpperCAmelCase__							 =						capacity / weight[i]
			max_value += value[i] * capacity / weight[i]
			break

	return max_value, fractions


if __name__ == "__main__":
		import doctest

		doctest.testmod()











import argparse

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


logging.set_verbosity_info()



def 						lowerCAmelCase		(		_lowerCAmelCase							:   Any						,							_lowerCAmelCase							:   List[str]						,							_lowerCAmelCase							:   Optional[int]						,							_lowerCAmelCase							:   Union[str, Any]	):




 """simple docstring"""







 UpperCAmelCase__							 =						BigBirdConfig.from_json_file(_lowerCAmelCase	)
 print(F'''Building PyTorch model from configuration: {config}'''	)

 if is_trivia_qa:
  UpperCAmelCase__							 =						BigBirdForQuestionAnswering(_lowerCAmelCase	)
 else:
  UpperCAmelCase__							 =						BigBirdForPreTraining(_lowerCAmelCase	)

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

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


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(
      "--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."
  )
  _lowerCAmelCase						:   Dict		=   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
  )





