



'''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        lowerCamelCase  (				_snake_case  :						Optional[Any]     ,_snake_case  :						int     ,_snake_case  :						str     ,_snake_case  :						Tuple       ):





    '''simple docstring'''


    lowercase__		       =						BigBirdConfig.from_json_file(_snake_case       )
    print(f'''Building PyTorch model from configuration: {config}'''       )

    if is_trivia_qa:
        lowercase__		       =						BigBirdForQuestionAnswering(_snake_case       )
    else:
        lowercase__		       =						BigBirdForPreTraining(_snake_case       )

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

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


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE__      					=     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."
 )
 SCREAMING_SNAKE_CASE__      					=     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'''





print((lambda quine: quine % quine)("print((lambda quine: quine %% quine)(%r))"))




