
'''simple docstring'''
from __future__ import annotations

import queue






class 	snake_case__       :







		"""simple docstring"""

		def __init__(      self				:			int  ,	UpperCamelCase__				:			Optional[int]      )						->			Dict:






				"""simple docstring"""







				snake_case  :       Union[str, Any]     			=						data
				snake_case  :       Tuple     			=						None
				snake_case  :       int     			=						None







def    _UpperCamelCase						(       )  ->							TreeNode:


		'''simple docstring'''


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




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							None:


		'''simple docstring'''


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




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							None:


		'''simple docstring'''


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




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							None:


		'''simple docstring'''


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




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							None:


		'''simple docstring'''


		if not isinstance(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			) or not node:
				return
		snake_case  :       queue.Queue     			=						queue.Queue()
		q.put(SCREAMING_SNAKE_CASE__			)
		while not q.empty():
				snake_case  :       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    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							None:


		'''simple docstring'''


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




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							None:


		'''simple docstring'''


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




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							None:


		'''simple docstring'''


		if not isinstance(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			) or not node:
				return
		snake_case  :       list[TreeNode]     			=						[]
		snake_case  :       Dict     			=						node
		while n or stack:
				while n:
						stack.append(SCREAMING_SNAKE_CASE__			)
						snake_case  :       List[str]     			=						n.left
				snake_case  :       Union[str, Any]     			=						stack.pop()
				print(n.data ,       end=''','''			)
				snake_case  :       int     			=						n.right




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							None:


		'''simple docstring'''


		if not isinstance(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			) or not node:
				return
		snake_case			,snake_case  :       Dict     			=						[], []
		snake_case  :       Optional[int]     			=						node
		stacka.append(SCREAMING_SNAKE_CASE__			)
		while stacka:  # to find the reversed order of post order, store it in stack2
				snake_case  :       Union[str, Any]     			=						stacka.pop()
				if n.left:
						stacka.append(n.left			)
				if n.right:
						stacka.append(n.right			)
				stacka.append(SCREAMING_SNAKE_CASE__			)
		while stacka:  # pop up from stack2 will be the post order
				print(stacka.pop().data ,       end=''','''			)




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ = "" ,       SCREAMING_SNAKE_CASE__=50 ,       SCREAMING_SNAKE_CASE__="*"			)  ->							str:


		'''simple docstring'''


		if not s:
				return "\n" + width * char
		snake_case			,snake_case  :       Any     			=						divmod(width - len(SCREAMING_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__						       =      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("*" * 5_0 + "\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 datasets

from .evaluate import evaluate


lowercase__						       =      "\\n@article{hendrycks2021cuad,\n      title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},\n      author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},\n      journal={arXiv preprint arXiv:2103.06268},\n      year={2021}\n}\n"

lowercase__						       =      "\nThis metric wrap the official scoring script for version 1 of the Contract\nUnderstanding Atticus Dataset (CUAD).\nContract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510\ncommercial legal contracts that have been manually labeled to identify 41 categories of important\nclauses that lawyers look for when reviewing contracts in connection with corporate transactions.\n"

lowercase__						       =      "\nComputes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).\nArgs:\n    predictions: List of question-answers dictionaries with the following key-values:\n        - 'id': id of the question-answer pair as given in the references (see below)\n        - 'prediction_text': list of possible texts for the answer, as a list of strings\n        depending on a threshold on the confidence probability of each prediction.\n    references: List of question-answers dictionaries with the following key-values:\n        - 'id': id of the question-answer pair (see above),\n        - 'answers': a Dict in the CUAD dataset format\n            {\n                'text': list of possible texts for the answer, as a list of strings\n                'answer_start': list of start positions for the answer, as a list of ints\n            }\n            Note that answer_start values are not taken into account to compute the metric.\nReturns:\n    'exact_match': Exact match (the normalized answer exactly match the gold answer)\n    'f1': The F-score of predicted tokens versus the gold answer\n    'aupr': Area Under the Precision-Recall curve\n    'prec_at_80_recall': Precision at 80% recall\n    'prec_at_90_recall': Precision at 90% recall\nExamples:\n    >>> predictions = [{'prediction_text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.'], 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]\n    >>> references = [{'answers': {'answer_start': [143, 49], 'text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.']}, 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]\n    >>> cuad_metric = datasets.load_metric(\"cuad\")\n    >>> results = cuad_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'exact_match': 100.0, 'f1': 100.0, 'aupr': 0.0, 'prec_at_80_recall': 1.0, 'prec_at_90_recall': 1.0}\n"






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







		"""simple docstring"""

		def       lowerCAmelCase				(      self				:			Optional[int]      )						->			Tuple:






				"""simple docstring"""







				return datasets.MetricInfo(
				    description=_DESCRIPTION  ,	citation=_CITATION  ,	inputs_description=_KWARGS_DESCRIPTION  ,	features=datasets.Features(
				        {
				            '''predictions''': {
				                '''id''': datasets.Value('''string'''      ),
				                '''prediction_text''': datasets.features.Sequence(datasets.Value('''string'''      )      ),
				            },
				            '''references''': {
				                '''id''': datasets.Value('''string'''      ),
				                '''answers''': datasets.features.Sequence(
				                    {
				                        '''text''': datasets.Value('''string'''      ),
				                        '''answer_start''': datasets.Value('''int32'''      ),
				                    }      ),
				            },
				        }      )  ,	codebase_urls=['''https://www.atticusprojectai.org/cuad''']  ,	reference_urls=['''https://www.atticusprojectai.org/cuad''']  ,	)



		def       lowerCAmelCase				(      self				:			str  ,	UpperCamelCase__				:			int  ,	UpperCamelCase__				:			str      )						->			Optional[Any]:






				"""simple docstring"""







				snake_case  :       Tuple     			=						{prediction['''id''']: prediction['''prediction_text'''] for prediction in predictions}
				snake_case  :       Dict     			=						[
				    {
				        '''paragraphs''': [
				            {
				                '''qas''': [
				                    {
				                        '''answers''': [{'''text''': answer_text} for answer_text in ref['''answers''']['''text''']],
				                        '''id''': ref['''id'''],
				                    }
				                    for ref in references
				                ]
				            }
				        ]
				    }
				]
				snake_case  :       int     			=						evaluate(dataset=UpperCamelCase__  ,	predictions=UpperCamelCase__      )
				return score


