






'''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 UpperCAmelCase_       :




      """simple docstring"""




      UpperCamelCase_             =      42
      UpperCamelCase_             =      None
      UpperCamelCase_             =      None





def 				lowercase_					(     )							->   Node | None:



 """simple docstring"""




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


def 				lowercase_					(     __A			:							Node | None      )							->   list[int]:



 """simple docstring"""




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


def 				lowercase_					(     __A			:							Node | None      )							->   list[int]:



 """simple docstring"""




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


def 				lowercase_					(     __A			:							Node | None      )							->   list[int]:



 """simple docstring"""




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


def 				lowercase_					(     __A			:							Node | None      )							->   int:



 """simple docstring"""




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


def 				lowercase_					(     __A			:							Node | None      )							->   Sequence[Node | None]:



 """simple docstring"""




 lowercase       : list[Any]						=[]

 if root is None:
  return output

 lowercase       : Dict						=deque([root]      )

 while process_queue:
  lowercase       : 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 				lowercase_					(     __A			:							Node | None       ,   __A			:							int      )							->   Sequence[Node | None]:



 """simple docstring"""




 lowercase       : list[Any]						=[]

 def populate_output(__A			:							Node | None       ,   __A			:							int      ) -> 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(__A       ,   __A      )
 return output


def 				lowercase_					(     __A			:							Node | None       ,   __A			:							int      )							->   Sequence[Node | None]:



 """simple docstring"""




 lowercase       : list[Any]						=[]

 def populate_output(__A			:							Node | None       ,   __A			:							int      ) -> 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(__A       ,   __A      )
 return output


def 				lowercase_					(     __A			:							Node | None      )							->   Sequence[Node | None] | list[Any]:



 """simple docstring"""




 if root is None:
  return []

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

 lowercase       : str						=0
 lowercase       : int						=height(__A      )

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

 return output


def 				lowercase_					(     )							->   None:  # Main function for testing.



 """simple docstring"""




 lowercase       : int						=make_tree()
 print(F'In-order Traversal: {inorder(__A      )}'      )
 print(F'Pre-order Traversal: {preorder(__A      )}'      )
 print(F'Post-order Traversal: {postorder(__A      )}'       ,   '''\n'''      )

 print(F'Height of Tree: {height(__A      )}'       ,   '''\n'''      )

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

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

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

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


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      main()







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


a			:			Optional[Any]												=					{
    '''configuration_llama''': ['''LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LlamaConfig'''],
}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a			:			Optional[int]												=					['''LlamaTokenizer''']

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a			:			Optional[int]												=					['''LlamaTokenizerFast''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a			:			Any												=					[
	    '''LlamaForCausalLM''',
	    '''LlamaModel''',
	    '''LlamaPreTrainedModel''',
	    '''LlamaForSequenceClassification''',
	]


if TYPE_CHECKING:
	from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_llama import LlamaTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_llama_fast import LlamaTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
	import sys

	a			:			Dict												=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


