





'''simple docstring'''




from __future__ import annotations

import queue


class 					SCREAMING_SNAKE_CASE       :


     def __init__(		self		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					Optional[Any]						=			data
          __A		:					Tuple						=			None
          __A		:					str						=			None






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






def 			_lowerCAmelCase     (    __snake_case    :					TreeNode     )	->	None:
     if not isinstance(__snake_case					,				__snake_case     ) or not node:
          return
     print(node.data					,				end=','     )
     pre_order(node.left     )
     pre_order(node.right     )






def 			_lowerCAmelCase     (    __snake_case    :					TreeNode     )	->	None:
     if not isinstance(__snake_case					,				__snake_case     ) or not node:
          return
     in_order(node.left     )
     print(node.data					,				end=','     )
     in_order(node.right     )






def 			_lowerCAmelCase     (    __snake_case    :					TreeNode     )	->	None:
     if not isinstance(__snake_case					,				__snake_case     ) or not node:
          return
     post_order(node.left     )
     post_order(node.right     )
     print(node.data					,				end=','     )






def 			_lowerCAmelCase     (    __snake_case    :					TreeNode     )	->	None:
     if not isinstance(__snake_case					,				__snake_case     ) or not node:
          return
     __A		:					queue.Queue						=			queue.Queue()
     q.put(__snake_case     )
     while not q.empty():
          __A		:					Optional[Any]						=			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 			_lowerCAmelCase     (    __snake_case    :					TreeNode     )	->	None:
     if not isinstance(__snake_case					,				__snake_case     ) or not node:
          return
     __A		:					queue.Queue						=			queue.Queue()
     q.put(__snake_case     )
     while not q.empty():
          __A		:					str						=			[]
          while not q.empty():
               __A		:					List[Any]						=			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(__snake_case     )






def 			_lowerCAmelCase     (    __snake_case    :					TreeNode     )	->	None:
     if not isinstance(__snake_case					,				__snake_case     ) or not node:
          return
     __A		:					list[TreeNode]						=			[]
     __A		:					List[str]						=			node
     while n or stack:
          while n:  # start from root node, find its left child
               print(n.data					,				end=','     )
               stack.append(__snake_case     )
               __A		:					List[str]						=			n.left
          # end of while means current node doesn't have left child
          __A		:					Optional[int]						=			stack.pop()
          # start to traverse its right child
          __A		:					Optional[int]						=			n.right






def 			_lowerCAmelCase     (    __snake_case    :					TreeNode     )	->	None:
     if not isinstance(__snake_case					,				__snake_case     ) or not node:
          return
     __A		:					list[TreeNode]						=			[]
     __A		:					List[Any]						=			node
     while n or stack:
          while n:
               stack.append(__snake_case     )
               __A		:					Tuple						=			n.left
          __A		:					int						=			stack.pop()
          print(n.data					,				end=','     )
          __A		:					Optional[Any]						=			n.right






def 			_lowerCAmelCase     (    __snake_case    :					TreeNode     )	->	None:
     if not isinstance(__snake_case					,				__snake_case     ) or not node:
          return
     __A					,__A		:					int						=			[], []
     __A		:					Any						=			node
     stacka.append(__snake_case     )
     while stacka:  # to find the reversed order of post order, store it in stack2
          __A		:					Optional[int]						=			stacka.pop()
          if n.left:
               stacka.append(n.left     )
          if n.right:
               stacka.append(n.right     )
          stacka.append(__snake_case     )
     while stacka:  # pop up from stack2 will be the post order
          print(stacka.pop().data					,				end=','     )






def 			_lowerCAmelCase     (    __snake_case    :					str = ""					,				__snake_case    :					Dict=50					,				__snake_case    :					Optional[int]="*"     )	->	str:
     if not s:
          return "\n" + width * char
     __A					,__A		:					Optional[Any]						=			divmod(width - len(__snake_case     ) - 2					,				2     )
     return f'{left * char} {s} {(left + extra) * char}'


if __name__ == "__main__":
  import doctest

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

  lowercase__       : TreeNode         =       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())







from typing import Dict, Optional

import numpy as np

import datasets


UpperCAmelCase__      			=						'''
IoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union
between the predicted segmentation and the ground truth. For binary (two classes) or multi-class segmentation,
the mean IoU of the image is calculated by taking the IoU of each class and averaging them.
'''

UpperCAmelCase__      			=						'''
Args:
    predictions (`List[ndarray]`):
        List of predicted segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.
    references (`List[ndarray]`):
        List of ground truth segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.
    num_labels (`int`):
        Number of classes (categories).
    ignore_index (`int`):
        Index that will be ignored during evaluation.
    nan_to_num (`int`, *optional*):
        If specified, NaN values will be replaced by the number defined by the user.
    label_map (`dict`, *optional*):
        If specified, dictionary mapping old label indices to new label indices.
    reduce_labels (`bool`, *optional*, defaults to `False`):
        Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background,
        and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.

Returns:
    `Dict[str, float | ndarray]` comprising various elements:
    - *mean_iou* (`float`):
        Mean Intersection-over-Union (IoU averaged over all categories).
    - *mean_accuracy* (`float`):
        Mean accuracy (averaged over all categories).
    - *overall_accuracy* (`float`):
        Overall accuracy on all images.
    - *per_category_accuracy* (`ndarray` of shape `(num_labels,)`):
        Per category accuracy.
    - *per_category_iou* (`ndarray` of shape `(num_labels,)`):
        Per category IoU.

Examples:

    >>> import numpy as np

    >>> mean_iou = datasets.load_metric("mean_iou")

    >>> # suppose one has 3 different segmentation maps predicted
    >>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]])
    >>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]])

    >>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]])
    >>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]])

    >>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]])
    >>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]])

    >>> predicted = [predicted_1, predicted_2, predicted_3]
    >>> ground_truth = [actual_1, actual_2, actual_3]

    >>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False)
    >>> print(results) # doctest: +NORMALIZE_WHITESPACE
    {\'mean_iou\': 0.47750000000000004, \'mean_accuracy\': 0.5916666666666666, \'overall_accuracy\': 0.5263157894736842, \'per_category_iou\': array([0.   , 0.   , 0.375, 0.4  , 0.5  , 0.   , 0.5  , 1.   , 1.   , 1.   ]), \'per_category_accuracy\': array([0.        , 0.        , 0.75      , 0.66666667, 1.        , 0.        , 0.5       , 1.        , 1.        , 1.        ])}
'''

UpperCAmelCase__      			=						'''\
@software{MMSegmentation_Contributors_OpenMMLab_Semantic_Segmentation_2020,
author = {{MMSegmentation Contributors}},
license = {Apache-2.0},
month = {7},
title = {{OpenMMLab Semantic Segmentation Toolbox and Benchmark}},
url = {https://github.com/open-mmlab/mmsegmentation},
year = {2020}
}'''







def     a_   (__A				,       __A				,       __A				,       __A				,       __A = None				,       __A = False				,       )				->						Dict:



					"""simple docstring"""
					if label_map is not None:
										for old_id, new_id in label_map.items():
															__a					:						str      =					new_id

    # turn into Numpy arrays
					__a					:						Union[str, Any]      =					np.array(__A      )
					__a					:						Any      =					np.array(__A      )

					if reduce_labels:
										__a					:						Dict      =					255
										__a					:						Union[str, Any]      =					label - 1
										__a					:						int      =					255

					__a					:						Optional[Any]      =					label != ignore_index
					__a					:						int      =					np.not_equal(__A				,       __A      )
					__a					:						str      =					pred_label[mask]
					__a					:						List[str]      =					np.array(__A      )[mask]

					__a					:						Optional[int]      =					pred_label[pred_label == label]

					__a					:						Dict      =					np.histogram(__A				,       bins=__A				,       range=(0, num_labels - 1)      )[0]
					__a					:						Union[str, Any]      =					np.histogram(__A				,       bins=__A				,       range=(0, num_labels - 1)      )[0]
					__a					:						List[Any]      =					np.histogram(__A				,       bins=__A				,       range=(0, num_labels - 1)      )[0]

					__a					:						Optional[int]      =					area_pred_label + area_label - area_intersect

					return area_intersect, area_union, area_pred_label, area_label







def     a_   (__A				,       __A				,       __A				,       __A				,       __A = None				,       __A = False				,       )				->						Dict:



					"""simple docstring"""
					__a					:						Union[str, Any]      =					np.zeros((num_labels,)				,       dtype=np.floataa      )
					__a					:						List[str]      =					np.zeros((num_labels,)				,       dtype=np.floataa      )
					__a					:						Optional[Any]      =					np.zeros((num_labels,)				,       dtype=np.floataa      )
					__a					:						Optional[int]      =					np.zeros((num_labels,)				,       dtype=np.floataa      )
					for result, gt_seg_map in zip(__A				,       __A      ):
										__a      ,			__a      ,			__a      ,			__a					:						Dict      =					intersect_and_union(
										    __A				,       __A				,       __A				,       __A				,       __A				,       __A      )
										total_area_intersect += area_intersect
										total_area_union += area_union
										total_area_pred_label += area_pred_label
										total_area_label += area_label
					return total_area_intersect, total_area_union, total_area_pred_label, total_area_label







def     a_   (__A				,       __A				,       __A				,       __A				,       __A = None				,       __A = None				,       __A = False				,       )				->						Optional[int]:



					"""simple docstring"""
					__a      ,			__a      ,			__a      ,			__a					:						Optional[int]      =					total_intersect_and_union(
					    __A				,       __A				,       __A				,       __A				,       __A				,       __A      )

					# compute metrics
					__a					:						Any      =					{}

					__a					:						str      =					total_area_intersect.sum() / total_area_label.sum()
					__a					:						List[Any]      =					total_area_intersect / total_area_union
					__a					:						Union[str, Any]      =					total_area_intersect / total_area_label

					__a					:						Optional[int]      =					np.nanmean(__A      )
					__a					:						str      =					np.nanmean(__A      )
					__a					:						List[str]      =					all_acc
					__a					:						Dict      =					iou
					__a					:						Union[str, Any]      =					acc

					if nan_to_num is not None:
										__a					:						Tuple      =					{metric: np.nan_to_num(__A				,       nan=__A      ) for metric, metric_value in metrics.items()}

					return metrics




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,							_KWARGS_DESCRIPTION      )
class 							snake_case_       (    datasets.Metric      ):

						"""simple docstring"""



						def        UpperCAmelCase__					(self:					List[str]   )    ->     Tuple:




											'''simple docstring'''



											return datasets.MetricInfo(
											    description=_DESCRIPTION ,					citation=_CITATION ,					inputs_description=_KWARGS_DESCRIPTION ,					features=datasets.Features(
											        # 1st Seq - height dim, 2nd - width dim
											        {
											            "predictions": datasets.Sequence(datasets.Sequence(datasets.Value("uint16"   )   )   ),
											            "references": datasets.Sequence(datasets.Sequence(datasets.Value("uint16"   )   )   ),
											        }   ) ,					reference_urls=[
											        "https://github.com/open-mmlab/mmsegmentation/blob/71c201b1813267d78764f306a297ca717827c4bf/mmseg/core/evaluation/metrics.py"
											    ] ,					)



						def        UpperCAmelCase__					(self:					Optional[int] ,					__UpperCAmelCase:					int ,					__UpperCAmelCase:					Dict ,					__UpperCAmelCase:					int ,					__UpperCAmelCase:					bool ,					__UpperCAmelCase:					Optional[int] = None ,					__UpperCAmelCase:					Optional[Dict[int, int]] = None ,					__UpperCAmelCase:					bool = False ,					)    ->     List[str]:




											'''simple docstring'''



											__a					:						str      =					mean_iou(
											    results=__UpperCAmelCase ,					gt_seg_maps=__UpperCAmelCase ,					num_labels=__UpperCAmelCase ,					ignore_index=__UpperCAmelCase ,					nan_to_num=__UpperCAmelCase ,					label_map=__UpperCAmelCase ,					reduce_labels=__UpperCAmelCase ,					)
											return iou_result





