




from __future__ import annotations




class       __lowerCAmelCase				:






       def __init__(      self:    Union[str, Any]				,    _lowerCAmelCase:    int ):
         lowercase			:Optional[int]							=  data
         lowercase			:Node | None							=  None
         lowercase			:Node | None							=  None







def 					UpperCAmelCase__     (   lowerCamelCase      ):  # In Order traversal of the tree
  if tree:
    display(tree.left      )
    print(tree.data      )
    display(tree.right      )



def 					UpperCAmelCase__     (   lowerCamelCase      ):
  return 1 + max(depth_of_tree(tree.left      ),	depth_of_tree(tree.right      )      ) if tree else 0



def 					UpperCAmelCase__     (   lowerCamelCase      ):
  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 					UpperCAmelCase__     (   ):  # Main function for testing.
  lowercase			:Optional[Any]							=  Node(1      )
  lowercase			:Dict							=  Node(2      )
  lowercase			:Optional[int]							=  Node(3      )
  lowercase			:Dict							=  Node(4      )
  lowercase			:int							=  Node(5      )
  lowercase			:Union[str, Any]							=  Node(6      )
  lowercase			:Optional[int]							=  Node(7      )
  lowercase			:Union[str, Any]							=  Node(8      )
  lowercase			:List[Any]							=  Node(9      )

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


if __name__ == "__main__":
     main()









from typing import Union

import fire
import torch
from tqdm import tqdm



def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase = "cpu",	lowerCamelCase = None      ):
  lowercase			:Optional[Any]							=  torch.load(lowerCamelCase,	map_location=lowerCamelCase      )
  for k, v in tqdm(state_dict.items()      ):
    if not isinstance(lowerCamelCase,	torch.Tensor      ):
      raise TypeError("FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin"      )
    lowercase			:List[Any]							=  v.half()
  if save_path is None:  # overwrite src_path
    lowercase			:Optional[Any]							=  src_path
  torch.save(lowerCamelCase,	lowerCamelCase      )


if __name__ == "__main__":
     fire.Fire(convert)



