


from __future__ import annotations

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


@dataclass
class 	A_ :




      '''simple docstring'''


      __snake_case 	=       42
      __snake_case 	=       None
      __snake_case 	=       None



def 				UpperCamelCase__					(  ):
 __lowerCamelCase		:							List[str]		 =						Node(1						)
 __lowerCamelCase		:							Any		 =						Node(2						)
 __lowerCamelCase		:							List[Any]		 =						Node(3						)
 __lowerCamelCase		:							Tuple		 =						Node(4						)
 __lowerCamelCase		:							Any		 =						Node(5						)
 return tree
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 return [root.data, *preorder(root.left						), *preorder(root.right						)] if root else []
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 return postorder(root.left						) + postorder(root.right						) + [root.data] if root else []
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 return [*inorder(root.left						), root.data, *inorder(root.right						)] if root else []
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 return (max(height(root.left						)      ,	height(root.right						)						) + 1) if root else 0
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							list[Any]		 =						[]

 if root is None:
  return output

 __lowerCamelCase		:							Optional[int]		 =						deque([root]						)

 while process_queue:
  __lowerCamelCase		:							Optional[int]		 =						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 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							list[Any]		 =						[]

 def populate_output(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						) -> 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(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						)
 return output
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							list[Any]		 =						[]

 def populate_output(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						) -> 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(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						)
 return output
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 if root is None:
  return []

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

 __lowerCamelCase		:							Union[str, Any]		 =						0
 __lowerCamelCase		:							Tuple		 =						height(SCREAMING_SNAKE_CASE__						)

 for h in range(1      ,	height_tree + 1						):
  if not flag:
   output.append(get_nodes_from_left_to_right(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						)						)
   __lowerCamelCase		:							List[str]		 =						1
  else:
   output.append(get_nodes_from_right_to_left(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						)						)
   __lowerCamelCase		:							Tuple		 =						0

 return output
def 				UpperCamelCase__					(  ):  # Main function for testing.
 __lowerCamelCase		:							List[Any]		 =						make_tree()
 print(f'In-order Traversal: {inorder(SCREAMING_SNAKE_CASE__						)}'						)
 print(f'Pre-order Traversal: {preorder(SCREAMING_SNAKE_CASE__						)}'						)
 print(f'Post-order Traversal: {postorder(SCREAMING_SNAKE_CASE__						)}'      ,	'\n'						)

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

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

 print('Level-wise order Traversal: '						)

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

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


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       main()





from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_torch_available():
       import torch

if is_torch_tpu_available(check_device=False):
       import torch_xla.core.xla_model as xm


lowercase_	   =     logging.get_logger(__name__)


@dataclass
class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      __snake_case 	=       [
          """no_inference""",
          """no_cuda""",
          """no_tpu""",
          """no_speed""",
          """no_memory""",
          """no_env_print""",
          """no_multi_process""",
      ]




      def __init__(    self:	Any						,				**a:	Optional[Any]    ):
       for deprecated_arg in self.deprecated_args:
        if deprecated_arg in kwargs:
         __lowerCamelCase		:							str		 =						deprecated_arg[3:]
         setattr(self						,				a						,				not kwargs.pop(a    )    )
         logger.warning(
             F'{deprecated_arg} is depreciated. Please use --no_{positive_arg} or'
             F' {positive_arg}={kwargs[positive_arg]}'    )

       __lowerCamelCase		:							str		 =						kwargs.pop('torchscript'						,				self.torchscript    )
       __lowerCamelCase		:							int		 =						kwargs.pop('torch_xla_tpu_print_metrics'						,				self.torch_xla_tpu_print_metrics    )
       __lowerCamelCase		:							Dict		 =						kwargs.pop('fp16_opt_level'						,				self.fpaa_opt_level    )
       super().__init__(**a    )

      __snake_case 	=       field(default=__UpperCamelCase ,				metadata={"""help""": """Trace the models using torchscript"""}   )
      __snake_case 	=       field(default=__UpperCamelCase ,				metadata={"""help""": """Print Xla/PyTorch tpu metrics"""}   )
      __snake_case 	=       field(
          default="""O1""" ,				metadata={
              """help""": (
                  """For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. """
                  """See details at https://nvidia.github.io/apex/amp.html"""
              )
          } ,				)




      @cached_property
      def 					_snake_case     (    self:	Dict    ):
       requires_backends(self						,				['torch']    )
       logger.info('PyTorch: setting up devices'    )
       if not self.cuda:
        __lowerCamelCase		:							Dict		 =						torch.device('cpu'    )
        __lowerCamelCase		:							str		 =						0
       elif is_torch_tpu_available():
        __lowerCamelCase		:							Optional[int]		 =						xm.xla_device()
        __lowerCamelCase		:							Dict		 =						0
       else:
        __lowerCamelCase		:							Any		 =						torch.device('cuda' if torch.cuda.is_available() else 'cpu'    )
        __lowerCamelCase		:							Union[str, Any]		 =						torch.cuda.device_count()
       return device, n_gpu




      @property
      def 					_snake_case     (    self:	Optional[Any]    ):
       return is_torch_tpu_available() and self.tpu




      @property
      def 					_snake_case     (    self:	Union[str, Any]    ):
       requires_backends(self						,				['torch']    )
       # TODO(PVP): currently only single GPU is supported
       return torch.cuda.current_device()




      @property
      def 					_snake_case     (    self:	int    ):
       requires_backends(self						,				['torch']    )
       return self._setup_devices[0]




      @property
      def 					_snake_case     (    self:	Union[str, Any]    ):
       requires_backends(self						,				['torch']    )
       return self._setup_devices[1]




      @property
      def 					_snake_case     (    self:	List[Any]    ):
       return self.n_gpu > 0

