
"""simple docstring"""




from __future__ import annotations

from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any


@dataclass
class       __magic_name__							:
       UpperCamelCase_ =		42
       UpperCamelCase_ =		None
       UpperCamelCase_ =		None




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:      Union[str, Any]   			=  Node(1    )
  _lowercase:      Tuple   			=  Node(2    )
  _lowercase:      Tuple   			=  Node(3    )
  _lowercase:      int   			=  Node(4    )
  _lowercase:      Union[str, Any]   			=  Node(5    )
  return tree




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  return [root.data, *preorder(root.left    ), *preorder(root.right    )] if root else []




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  return postorder(root.left    ) + postorder(root.right    ) + [root.data] if root else []




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  return [*inorder(root.left    ), root.data, *inorder(root.right    )] if root else []




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  return (max(height(root.left    )			,					height(root.right    )    ) + 1) if root else 0




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      list[Any]   			=  []

  if root is None:
    return output

  _lowercase:      Any   			=  deque([root]    )

  while process_queue:
    _lowercase:      Any   			=  process_queue.popleft()
    output.append(node.data    )

    if node.left:
      process_queue.append(node.left    )
    if node.right:
      process_queue.append(node.right    )
  return output




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      list[Any]   			=  []

  def populate_output(_UpperCamelCase			,					_UpperCamelCase    ) -> None:
    if not root:
      return
    if level == 1:
      output.append(root.data    )
    elif level > 1:
      populate_output(root.left			,					level - 1    )
      populate_output(root.right			,					level - 1    )

  populate_output(_UpperCamelCase			,					_UpperCamelCase    )
  return output




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      list[Any]   			=  []

  def populate_output(_UpperCamelCase			,					_UpperCamelCase    ) -> None:
    if root is None:
      return
    if level == 1:
      output.append(root.data    )
    elif level > 1:
      populate_output(root.right			,					level - 1    )
      populate_output(root.left			,					level - 1    )

  populate_output(_UpperCamelCase			,					_UpperCamelCase    )
  return output




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  if root is None:
    return []

  _lowercase:      list[Sequence[Node | None]]   			=  []

  _lowercase:      str   			=  0
  _lowercase:      Dict   			=  height(_UpperCamelCase    )

  for h in range(1			,					height_tree + 1    ):
    if not flag:
      output.append(get_nodes_from_left_to_right(_UpperCamelCase			,					_UpperCamelCase    )    )
      _lowercase:      Tuple   			=  1
    else:
      output.append(get_nodes_from_right_to_left(_UpperCamelCase			,					_UpperCamelCase    )    )
      _lowercase:      Optional[Any]   			=  0

  return output




def 			_lowerCAmelCase		(		):  # Main function for testing.






  """simple docstring"""



  _lowercase:      Dict   			=  make_tree()
  print(f'''In-order Traversal: {inorder(_UpperCamelCase    )}'''    )
  print(f'''Pre-order Traversal: {preorder(_UpperCamelCase    )}'''    )
  print(f'''Post-order Traversal: {postorder(_UpperCamelCase    )}'''			,					'''\n'''    )

  print(f'''Height of Tree: {height(_UpperCamelCase    )}'''			,					'''\n'''    )

  print('''Complete Level Order Traversal: '''    )
  print(level_order(_UpperCamelCase    )			,					'''\n'''    )

  print('''Level-wise order Traversal: '''    )

  for level in range(1			,					height(_UpperCamelCase    ) + 1    ):
    print(f'''Level {level}:'''			,					get_nodes_from_left_to_right(_UpperCamelCase			,					level=_UpperCamelCase    )    )

  print('''\nZigZag order Traversal: '''    )
  print(zigzag(_UpperCamelCase    )    )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()


"""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		(		_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      int   			=  BigBirdConfig.from_json_file(_UpperCamelCase    )
  print(f'''Building PyTorch model from configuration: {config}'''    )

  if is_trivia_qa:
    _lowercase:      Union[str, Any]   			=  BigBirdForQuestionAnswering(_UpperCamelCase    )
  else:
    _lowercase:      Dict   			=  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__ :       List[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.'
  )
  A__ :       Optional[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
  )
