




import argparse

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


logging.set_verbosity_info()

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	Optional[Any]     ,							lowercase     :	Any     ,							lowercase     :	Optional[Any]     ,							lowercase     :	Any					):


      '''simple docstring'''



      lowerCamelCase_							=   BigBirdConfig.from_json_file(lowercase					)
      print(f"""Building PyTorch model from configuration: {config}"""					)

      if is_trivia_qa:
            lowerCamelCase_							=   BigBirdForQuestionAnswering(lowercase					)
      else:
            lowerCamelCase_							=   BigBirdForPreTraining(lowercase					)

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

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


if __name__ == "__main__":
 lowerCamelCase    :		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."
 )
 lowerCamelCase    :		str			   =       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
 )









from ..utils import DummyObject, requires_backends







class   A(						metaclass=UpperCamelCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						['''transformers''', '''torch''', '''note_seq''']

 def __init__(    self :  Any						,							*A_ :  Any						,							**A_ :  Tuple  )       ->     List[Any]:

       """simple docstring"""






       requires_backends(self						,							['transformers', 'torch', 'note_seq']  )

 @classmethod
 def     a__			(    cls :  Tuple						,							*A_ :  Dict						,							**A_ :  Optional[Any]  )       ->     Union[str, Any]:

       """simple docstring"""






       requires_backends(cls						,							['transformers', 'torch', 'note_seq']  )





 @classmethod
 def     a__			(    cls :  int						,							*A_ :  Dict						,							**A_ :  List[str]  )       ->     Tuple:

       """simple docstring"""






       requires_backends(cls						,							['transformers', 'torch', 'note_seq']  )



