






'''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      __lowercase	(_lowercase,    _lowercase,    _lowercase,    _lowercase  ) ->  Dict:


     """simple docstring"""
     # Initialise PyTorch model
     __lowerCamelCase    :							List[str]					 =					BigBirdConfig.from_json_file(_lowerCAmelCase  )
     print(f"Building PyTorch model from configuration: {config}"  )

     if is_trivia_qa:
          __lowerCamelCase    :							Dict					 =					BigBirdForQuestionAnswering(_lowerCAmelCase  )
     else:
          __lowerCamelCase    :							Any					 =					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__":
   UpperCAmelCase__    :Optional[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."""
   )
   UpperCAmelCase__    :List[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
   )








'''simple docstring'''





# Algorithm for the pigeonhole sorting



def 			a_	(				_lowerCAmelCase			) -> List[str]:
  __lowerCamelCase			:			int       			=					min(_lowerCAmelCase			)  # min() finds the minimum value
  __lowerCamelCase			:			List[Any]       			=					max(_lowerCAmelCase			)  # max() finds the maximum value

  __lowerCamelCase			:			List[Any]       			=					max_val - min_val + 1  # size is difference of max and min values plus one

  # list of pigeonholes of size equal to the variable size
  __lowerCamelCase			:			List[Any]       			=					[0] * size

  # Populate the pigeonholes.
  for x in a:
    assert isinstance(_lowerCAmelCase			,_lowerCAmelCase			), "integers only please"
    holes[x - min_val] += 1

  # Putting the elements back into the array in an order.
  __lowerCamelCase			:			Tuple       			=					0
  for count in range(_lowerCAmelCase			):
    while holes[count] > 0:
      holes[count] -= 1
      __lowerCamelCase			:			List[Any]       			=					count + min_val
      i += 1

def 			a_	(				) -> str:
  __lowerCamelCase			:			Optional[Any]       			=					[8, 3, 2, 7, 4, 6, 8]
  pigeonhole_sort(_lowerCAmelCase			)
  print('Sorted order is:'			,' '.join(_lowerCAmelCase			)			)


if __name__ == "__main__":
   main()






