

from __future__ import annotations

import queue






class 	UpperCAmelCase__		:






  """simple docstring"""


  def __init__(							self						:		List[Any]     ,       __lowerCamelCase						:		str )		->							Dict:
       SCREAMING_SNAKE_CASE__   =   data
       SCREAMING_SNAKE_CASE__   =   None
       SCREAMING_SNAKE_CASE__   =   None



def     UpperCAmelCase_       (						):





     '''simple docstring'''




     print('''\n********Press N to stop entering at any point of time********\n'''    )
     SCREAMING_SNAKE_CASE__   =   input('''Enter the value of the root node: '''    ).strip().lower()
     SCREAMING_SNAKE_CASE__   =   queue.Queue()
     SCREAMING_SNAKE_CASE__   =   TreeNode(int(_A    )    )
     q.put(_A    )
     while not q.empty():
          SCREAMING_SNAKE_CASE__   =   q.get()
          SCREAMING_SNAKE_CASE__   =   F'''Enter the left node of {node_found.data}: '''
          SCREAMING_SNAKE_CASE__   =   input(_A    ).strip().lower() or '''n'''
          if check == "n":
               return tree_node
          SCREAMING_SNAKE_CASE__   =   TreeNode(int(_A    )    )
          SCREAMING_SNAKE_CASE__   =   left_node
          q.put(_A    )
          SCREAMING_SNAKE_CASE__   =   F'''Enter the right node of {node_found.data}: '''
          SCREAMING_SNAKE_CASE__   =   input(_A    ).strip().lower() or '''n'''
          if check == "n":
               return tree_node
          SCREAMING_SNAKE_CASE__   =   TreeNode(int(_A    )    )
          SCREAMING_SNAKE_CASE__   =   right_node
          q.put(_A    )
     raise
def     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




     if not isinstance(_A				,		_A    ) or not node:
          return
     print(node.data				,		end=''','''    )
     pre_order(node.left    )
     pre_order(node.right    )
def     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




     if not isinstance(_A				,		_A    ) or not node:
          return
     in_order(node.left    )
     print(node.data				,		end=''','''    )
     in_order(node.right    )
def     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




     if not isinstance(_A				,		_A    ) or not node:
          return
     post_order(node.left    )
     post_order(node.right    )
     print(node.data				,		end=''','''    )
def     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




     if not isinstance(_A				,		_A    ) or not node:
          return
     SCREAMING_SNAKE_CASE__   =   queue.Queue()
     q.put(_A    )
     while not q.empty():
          SCREAMING_SNAKE_CASE__   =   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_       (						_A    ):





     '''simple docstring'''




     if not isinstance(_A				,		_A    ) or not node:
          return
     SCREAMING_SNAKE_CASE__   =   queue.Queue()
     q.put(_A    )
     while not q.empty():
          SCREAMING_SNAKE_CASE__   =   []
          while not q.empty():
               SCREAMING_SNAKE_CASE__   =   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(_A    )
def     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




     if not isinstance(_A				,		_A    ) or not node:
          return
     SCREAMING_SNAKE_CASE__   =   []
     SCREAMING_SNAKE_CASE__   =   node
     while n or stack:
          while n:  # start from root node, find its left child
               print(n.data				,		end=''','''    )
               stack.append(_A    )
               SCREAMING_SNAKE_CASE__   =   n.left
          # end of while means current node doesn't have left child
          SCREAMING_SNAKE_CASE__   =   stack.pop()
          # start to traverse its right child
          SCREAMING_SNAKE_CASE__   =   n.right
def     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




     if not isinstance(_A				,		_A    ) or not node:
          return
     SCREAMING_SNAKE_CASE__   =   []
     SCREAMING_SNAKE_CASE__   =   node
     while n or stack:
          while n:
               stack.append(_A    )
               SCREAMING_SNAKE_CASE__   =   n.left
          SCREAMING_SNAKE_CASE__   =   stack.pop()
          print(n.data				,		end=''','''    )
          SCREAMING_SNAKE_CASE__   =   n.right
def     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




     if not isinstance(_A				,		_A    ) or not node:
          return
     SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   [], []
     SCREAMING_SNAKE_CASE__   =   node
     stacka.append(_A    )
     while stacka:  # to find the reversed order of post order, store it in stack2
          SCREAMING_SNAKE_CASE__   =   stacka.pop()
          if n.left:
               stacka.append(n.left    )
          if n.right:
               stacka.append(n.right    )
          stacka.append(_A    )
     while stacka:  # pop up from stack2 will be the post order
          print(stacka.pop().data				,		end=''','''    )
def     UpperCAmelCase_       (						_A = ""				,		_A=50				,		_A="*"    ):





     '''simple docstring'''




     if not s:
          return "\n" + width * char
     SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   divmod(width - len(_A    ) - 2				,		2    )
     return F'''{left * char} {s} {(left + extra) * char}'''


if __name__ == "__main__":
       import doctest

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

       _SCREAMING_SNAKE_CASE					:  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())





import datasets

from .evaluate import evaluate


_SCREAMING_SNAKE_CASE					:  Union[str, Any]             =      '''\
@inproceedings{Rajpurkar2016SQuAD10,
  title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
  author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
  booktitle={EMNLP},
  year={2016}
}
'''

_SCREAMING_SNAKE_CASE					:  Dict             =      '''
This metric wrap the official scoring script for version 1 of the Stanford Question Answering Dataset (SQuAD).

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by
crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,
from the corresponding reading passage, or the question might be unanswerable.
'''

_SCREAMING_SNAKE_CASE					:  str             =      '''
Computes SQuAD scores (F1 and EM).
Args:
    predictions: List of question-answers dictionaries with the following key-values:
        - \'id\': id of the question-answer pair as given in the references (see below)
        - \'prediction_text\': the text of the answer
    references: List of question-answers dictionaries with the following key-values:
        - \'id\': id of the question-answer pair (see above),
        - \'answers\': a Dict in the SQuAD dataset format
            {
                \'text\': list of possible texts for the answer, as a list of strings
                \'answer_start\': list of start positions for the answer, as a list of ints
            }
            Note that answer_start values are not taken into account to compute the metric.
Returns:
    \'exact_match\': Exact match (the normalized answer exactly match the gold answer)
    \'f1\': The F-score of predicted tokens versus the gold answer
Examples:

    >>> predictions = [{\'prediction_text\': \'1976\', \'id\': \'56e10a3be3433e1400422b22\'}]
    >>> references = [{\'answers\': {\'answer_start\': [97], \'text\': [\'1976\']}, \'id\': \'56e10a3be3433e1400422b22\'}]
    >>> squad_metric = datasets.load_metric("squad")
    >>> results = squad_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'exact_match\': 100.0, \'f1\': 100.0}
'''






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






  """simple docstring"""


  def 				lowercase_		(							self						:		List[Any] )		->							Optional[int]:
       return datasets.MetricInfo(
           description=_DESCRIPTION     ,       citation=_CITATION     ,       inputs_description=_KWARGS_DESCRIPTION     ,       features=datasets.Features(
               {
                   '''predictions''': {'''id''': datasets.Value('''string''' ), '''prediction_text''': datasets.Value('''string''' )},
                   '''references''': {
                       '''id''': datasets.Value('''string''' ),
                       '''answers''': datasets.features.Sequence(
                           {
                               '''text''': datasets.Value('''string''' ),
                               '''answer_start''': datasets.Value('''int32''' ),
                           } ),
                   },
               } )     ,       codebase_urls=['''https://rajpurkar.github.io/SQuAD-explorer/''']     ,       reference_urls=['''https://rajpurkar.github.io/SQuAD-explorer/''']     ,       )







  def 				lowercase_		(							self						:		Optional[Any]     ,       __lowerCamelCase						:		Dict     ,       __lowerCamelCase						:		List[Any] )		->							Optional[int]:
       SCREAMING_SNAKE_CASE__   =   {prediction['''id''']: prediction['''prediction_text'''] for prediction in predictions}
       SCREAMING_SNAKE_CASE__   =   [
           {
               '''paragraphs''': [
                   {
                       '''qas''': [
                           {
                               '''answers''': [{'''text''': answer_text} for answer_text in ref['''answers''']['''text''']],
                               '''id''': ref['''id'''],
                           }
                           for ref in references
                       ]
                   }
               ]
           }
       ]
       SCREAMING_SNAKE_CASE__   =   evaluate(dataset=__lowerCamelCase     ,       predictions=__lowerCamelCase )
       return score


