

from __future__ import annotations

from random import choice







def 						lowerCamelCase__      ( __A    :Dict		):


       """simple docstring"""




       return choice(UpperCamelCase__		)







def 						lowerCamelCase__      ( __A    :list[int]	,__A    :int		):


       """simple docstring"""




       __snake_case       	=							random_pivot(UpperCamelCase__		)

       # partition based on pivot
       # linear time
       __snake_case       	=							[e for e in lst if e < pivot]
       __snake_case       	=							[e for e in lst if e > pivot]

       # if we get lucky, pivot might be the element we want.
       # we can easily see this:
       # small (elements smaller than k)
       # + pivot (kth element)
       # + big (elements larger than k)
       if len(UpperCamelCase__		) == k - 1:
              return pivot
       # pivot is in elements bigger than k
       elif len(UpperCamelCase__		) < k - 1:
              return kth_number(UpperCamelCase__	,k - len(UpperCamelCase__		) - 1		)
       # pivot is in elements smaller than k
       else:
              return kth_number(UpperCamelCase__	,UpperCamelCase__		)


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       UpperCamelCase__(			UpperCamelCase__     :			Any					,	UpperCamelCase__     :			Optional[Any]					,	UpperCamelCase__     :			Dict					,	UpperCamelCase__     :			int    )->Union[str, Any]:
       # 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__":
       a__: Optional[int]							   =						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.'
       )
       a__: Tuple							   =						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
       )





