

'''simple docstring'''


from bisect import bisect
from itertools import accumulate




def 	_a       (				__lowerCAmelCase		:      Optional[int]   ,							__lowerCAmelCase		:      Tuple   ,							__lowerCAmelCase		:      List[Any]   ,							__lowerCAmelCase		:      Optional[Any]     ):

		"""simple docstring"""

		snake_case__						:       Any       						=  sorted(zip(__lowerCAmelCase   ,							__lowerCAmelCase     )   ,							key=lambda __lowerCAmelCase     : x[0] / x[1]   ,							reverse=__lowerCAmelCase     )
		snake_case__						:       Union[str, Any]       						=  [i[0] for i in r], [i[1] for i in r]
		snake_case__						:       Dict       						=  list(accumulate(__lowerCAmelCase     )     )
		snake_case__						:       Optional[int]       						=  bisect(__lowerCAmelCase   ,							__lowerCAmelCase     )
		return (
		    0
		    if k == 0
		    else sum(vl[:k]     ) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
		    if k != n
		    else sum(vl[:k]     )
		)


if __name__ == "__main__":
					import doctest

					doctest.testmod()






'''simple docstring'''


import argparse

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


logging.set_verbosity_info()




def 	_a       (				__lowerCAmelCase		:      Union[str, Any]   ,							__lowerCAmelCase		:      Any   ,							__lowerCAmelCase		:      Optional[int]   ,							__lowerCAmelCase		:      List[Any]     ):

		"""simple docstring"""

		snake_case__						:       Any       						=  BigBirdConfig.from_json_file(__lowerCAmelCase     )
		print(F"""Building PyTorch model from configuration: {config}"""     )

		if is_trivia_qa:
				snake_case__						:       str       						=  BigBirdForQuestionAnswering(__lowerCAmelCase     )
		else:
				snake_case__						:       Tuple       						=  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__						:       Union[str, 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__						:       Optional[int]										=						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
					)



