
'''simple docstring'''

from __future__ import annotations

import queue




class    lowerCAmelCase       :


 def __init__(    self  : Dict			,							a__  : List[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Tuple     		=	data
  lowerCAmelCase__     :   Any     		=	None
  lowerCAmelCase__     :   Any     		=	None



def        UpperCAmelCase_ (  ):







 """simple docstring"""


 print("\n********Press N to stop entering at any point of time********\n"			)
 lowerCAmelCase__     :   Optional[Any]     		=	input("Enter the value of the root node: "			).strip().lower()
 lowerCAmelCase__     :   queue.Queue     		=	queue.Queue()
 lowerCAmelCase__     :   Union[str, Any]     		=	TreeNode(int(lowerCamelCase_			)			)
 q.put(lowerCamelCase_			)
 while not q.empty():
  lowerCAmelCase__     :   Union[str, Any]     		=	q.get()
  lowerCAmelCase__     :   Tuple     		=	f'''Enter the left node of {node_found.data}: '''
  lowerCAmelCase__     :   Any     		=	input(lowerCamelCase_			).strip().lower() or "n"
  if check == "n":
   return tree_node
  lowerCAmelCase__     :   Union[str, Any]     		=	TreeNode(int(lowerCamelCase_			)			)
  lowerCAmelCase__     :   Any     		=	left_node
  q.put(lowerCamelCase_			)
  lowerCAmelCase__     :   Union[str, Any]     		=	f'''Enter the right node of {node_found.data}: '''
  lowerCAmelCase__     :   Optional[int]     		=	input(lowerCamelCase_			).strip().lower() or "n"
  if check == "n":
   return tree_node
  lowerCAmelCase__     :   str     		=	TreeNode(int(lowerCamelCase_			)			)
  lowerCAmelCase__     :   Optional[int]     		=	right_node
  q.put(lowerCamelCase_			)
 raise


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 if not isinstance(lowerCamelCase_      ,			lowerCamelCase_			) or not node:
  return
 print(node.data      ,			end=","			)
 pre_order(node.left			)
 pre_order(node.right			)


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 if not isinstance(lowerCamelCase_      ,			lowerCamelCase_			) or not node:
  return
 in_order(node.left			)
 print(node.data      ,			end=","			)
 in_order(node.right			)


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 if not isinstance(lowerCamelCase_      ,			lowerCamelCase_			) or not node:
  return
 post_order(node.left			)
 post_order(node.right			)
 print(node.data      ,			end=","			)


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 if not isinstance(lowerCamelCase_      ,			lowerCamelCase_			) or not node:
  return
 lowerCAmelCase__     :   queue.Queue     		=	queue.Queue()
 q.put(lowerCamelCase_			)
 while not q.empty():
  lowerCAmelCase__     :   Tuple     		=	q.get()
  print(node_dequeued.data      ,			end=","			)
  if node_dequeued.left:
   q.put(node_dequeued.left			)
  if node_dequeued.right:
   q.put(node_dequeued.right			)


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 if not isinstance(lowerCamelCase_      ,			lowerCamelCase_			) or not node:
  return
 lowerCAmelCase__     :   queue.Queue     		=	queue.Queue()
 q.put(lowerCamelCase_			)
 while not q.empty():
  lowerCAmelCase__     :   Tuple     		=	[]
  while not q.empty():
   lowerCAmelCase__     :   Optional[int]     		=	q.get()
   print(node_dequeued.data      ,			end=","			)
   if node_dequeued.left:
    list_.append(node_dequeued.left			)
   if node_dequeued.right:
    list_.append(node_dequeued.right			)
  print()
  for node in list_:
   q.put(lowerCamelCase_			)


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 if not isinstance(lowerCamelCase_      ,			lowerCamelCase_			) or not node:
  return
 lowerCAmelCase__     :   list[TreeNode]     		=	[]
 lowerCAmelCase__     :   Tuple     		=	node
 while n or stack:
  while n:  # start from root node, find its left child
   print(n.data      ,			end=","			)
   stack.append(lowerCamelCase_			)
   lowerCAmelCase__     :   Union[str, Any]     		=	n.left
  # end of while means current node doesn't have left child
  lowerCAmelCase__     :   Tuple     		=	stack.pop()
  # start to traverse its right child
  lowerCAmelCase__     :   int     		=	n.right


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 if not isinstance(lowerCamelCase_      ,			lowerCamelCase_			) or not node:
  return
 lowerCAmelCase__     :   list[TreeNode]     		=	[]
 lowerCAmelCase__     :   str     		=	node
 while n or stack:
  while n:
   stack.append(lowerCamelCase_			)
   lowerCAmelCase__     :   Union[str, Any]     		=	n.left
  lowerCAmelCase__     :   List[str]     		=	stack.pop()
  print(n.data      ,			end=","			)
  lowerCAmelCase__     :   Optional[int]     		=	n.right


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 if not isinstance(lowerCamelCase_      ,			lowerCamelCase_			) or not node:
  return
 lowerCAmelCase__		,     lowerCAmelCase__     :   Tuple     		=	[], []
 lowerCAmelCase__     :   Union[str, Any]     		=	node
 stacka.append(lowerCamelCase_			)
 while stacka:  # to find the reversed order of post order, store it in stack2
  lowerCAmelCase__     :   List[Any]     		=	stacka.pop()
  if n.left:
   stacka.append(n.left			)
  if n.right:
   stacka.append(n.right			)
  stacka.append(lowerCamelCase_			)
 while stacka:  # pop up from stack2 will be the post order
  print(stacka.pop().data      ,			end=","			)


def        UpperCAmelCase_ (  lowerCamelCase_ = ""      ,			lowerCamelCase_=5_0      ,			lowerCamelCase_="*"			):







 """simple docstring"""


 if not s:
  return "\n" + width * char
 lowerCAmelCase__		,     lowerCAmelCase__     :   List[Any]     		=	divmod(width - len(lowerCamelCase_			) - 2      ,			2			)
 return f'''{left * char} {s} {(left + extra) * char}'''


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     print(prompt("""Binary Tree Traversals"""))

     snake_case									=    build_tree()
     print(prompt("""Pre Order Traversal"""))
     pre_order(node)
     print(prompt() + """\n""")

     print(prompt("""In Order Traversal"""))
     in_order(node)
     print(prompt() + """\n""")

     print(prompt("""Post Order Traversal"""))
     post_order(node)
     print(prompt() + """\n""")

     print(prompt("""Level Order Traversal"""))
     level_order(node)
     print(prompt() + """\n""")

     print(prompt("""Actual Level Order Traversal"""))
     level_order_actual(node)
     print("""*""" * 50 + """\n""")

     print(prompt("""Pre Order Traversal - Iteration Version"""))
     pre_order_iter(node)
     print(prompt() + """\n""")

     print(prompt("""In Order Traversal - Iteration Version"""))
     in_order_iter(node)
     print(prompt() + """\n""")

     print(prompt("""Post Order Traversal - Iteration Version"""))
     post_order_iter(node)
     print(prompt())







'''simple docstring'''

import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image
else:




     class    lowerCAmelCase       :


      @staticmethod
      def    _A	(    *a__  : str			,							**a__  : List[str]    ):
       '''simple docstring'''

       pass


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   List[str]     		=	hashlib.mda(image.tobytes()			)
 return m.hexdigest()







@is_pipeline_test
@require_vision
@require_timm
@require_torch
class    lowerCAmelCase       (     unittest.TestCase  ):
 A_				:				List[Any]	=			MODEL_FOR_DEPTH_ESTIMATION_MAPPING


 def    _A	(    self  : List[Any]			,							a__  : int			,							a__  : Dict			,							a__  : Optional[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   List[str]     		=	DepthEstimationPipeline(model=a__			,							image_processor=a__    )
  return depth_estimator, [
      "./tests/fixtures/tests_samples/COCO/000000039769.png",
      "./tests/fixtures/tests_samples/COCO/000000039769.png",
  ]


 def    _A	(    self  : int			,							a__  : Optional[int]			,							a__  : List[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Any     		=	depth_estimator("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
  self.assertEqual({"predicted_depth": ANY(torch.Tensor    ), "depth": ANY(Image.Image    )}			,							a__    )
  import datasets

  lowerCAmelCase__     :   List[str]     		=	datasets.load_dataset("hf-internal-testing/fixtures_image_utils"			,							"image"			,							split="test"    )
  lowerCAmelCase__     :   List[str]     		=	depth_estimator(
      [
          Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    ),
          "http://images.cocodataset.org/val2017/000000039769.jpg",
          # RGBA
          dataset[0]["file"],
          # LA
          dataset[1]["file"],
          # L
          dataset[2]["file"],
      ]    )
  self.assertEqual(
      [
          {"predicted_depth": ANY(torch.Tensor    ), "depth": ANY(Image.Image    )},
          {"predicted_depth": ANY(torch.Tensor    ), "depth": ANY(Image.Image    )},
          {"predicted_depth": ANY(torch.Tensor    ), "depth": ANY(Image.Image    )},
          {"predicted_depth": ANY(torch.Tensor    ), "depth": ANY(Image.Image    )},
          {"predicted_depth": ANY(torch.Tensor    ), "depth": ANY(Image.Image    )},
      ]			,							a__			,							)


 @require_tf
 @unittest.skip("Depth estimation is not implemented in TF"    )
 def    _A	(    self  : Optional[Any]    ):
  '''simple docstring'''

  pass


 @slow
 @require_torch
 def    _A	(    self  : Any    ):
  '''simple docstring'''

  lowerCAmelCase__     :   str     		=	"Intel/dpt-large"
  lowerCAmelCase__     :   Dict     		=	pipeline("depth-estimation"			,							model=a__    )
  lowerCAmelCase__     :   Any     		=	depth_estimator("http://images.cocodataset.org/val2017/000000039769.jpg"    )
  lowerCAmelCase__     :   Any     		=	hashimage(outputs["depth"]    )

  # This seems flaky.
  # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
  self.assertEqual(nested_simplify(outputs["predicted_depth"].max().item()    )			,							29.304    )
  self.assertEqual(nested_simplify(outputs["predicted_depth"].min().item()    )			,							2.662    )


 @require_torch
 def    _A	(    self  : Any    ):
  '''simple docstring'''

  self.skipTest("There is not hf-internal-testing tiny model for either GLPN nor DPT"    )





