






from __future__ import annotations







class  _lowercase   :
      def __init__(     self				,			a   ):
       snake_case__  :    Optional[int]							       =data
       snake_case__  :    Dict							       =None
       snake_case__  :    Optional[int]							       =None







def 			A__							(	_a  :					int	):  # In Order traversal of the tree





 '''simple docstring'''







 if tree:
  display(tree.left	)
  print(tree.data	)
  display(tree.right	)







def 			A__							(	_a  :					List[str]	):





 '''simple docstring'''







 return 1 + max(depth_of_tree(tree.left	)		,  depth_of_tree(tree.right	)	) if tree else 0







def 			A__							(	_a  :					Optional[int]	):





 '''simple docstring'''







 if not tree:
  return True
 if tree.left and tree.right:
  return is_full_binary_tree(tree.left	) and is_full_binary_tree(tree.right	)
 else:
  return not tree.left and not tree.right







def 			A__							(	):  # Main function for testing.





 '''simple docstring'''







 snake_case__  :    Dict							       =Node(1	)
 snake_case__  :    List[str]							       =Node(2	)
 snake_case__  :    Tuple							       =Node(3	)
 snake_case__  :    Any							       =Node(4	)
 snake_case__  :    List[Any]							       =Node(5	)
 snake_case__  :    str							       =Node(6	)
 snake_case__  :    int							       =Node(7	)
 snake_case__  :    Dict							       =Node(8	)
 snake_case__  :    Any							       =Node(9	)

 print(is_full_binary_tree(_lowercase	)	)
 print(depth_of_tree(_lowercase	)	)
 print("""Tree is: """	)
 display(_lowercase	)


if __name__ == "__main__":
       main()













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__							(      _a  :					int		,  _a  :					Any		,  _a  :					Union[str, Any]		,  _a  :					Tuple	):





 '''simple docstring'''







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

 if is_trivia_qa:
  snake_case__  :    str							       =BigBirdForQuestionAnswering(_a	)
 else:
  snake_case__  :    Optional[int]							       =BigBirdForPreTraining(_a	)

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

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


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	:		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
       )





