



from __future__ import annotations

from random import random






class      UpperCamelCase    :



	def __init__(       self			,	UpperCAmelCase__ = None		):
							A__							=       value
							A__							=       random()
							A__							=       None
							A__							=       None



	def __repr__(       self		):
							from pprint import pformat

							if self.left is None and self.right is None:
													return F"""'{self.value}: {self.prior:.5}'"""
							else:
													return pformat(
													    {F"""{self.value}: {self.prior:.5}""": (self.left, self.right)}			,	indent=1		)



	def __str__(       self		):
							A__							=       str(self.value		) + " "
							A__							=       str(self.left or ""		)
							A__							=       str(self.right or ""		)
							return value + left + right





def  UpperCamelCase						(       _A			:				Node | None			,							_A			:				int    )->       tuple[Node | None, Node | None]:





						"""simple docstring"""





						if root is None:  # None tree is split into 2 Nones
												return None, None
						elif root.value is None:
												return None, None
						else:
												if value < root.value:
																		A__						,       A__							=       split(root.left			,							_A    )
																		return left, root
												else:
																		A__						,       A__							=       split(root.right			,							_A    )
																		return root, right
def  UpperCamelCase						(       _A			:				Node | None			,							_A			:				Node | None    )->       Node | None:





						"""simple docstring"""





						if (not left) or (not right):  # If one node is None, return the other
												return left or right
						elif left.prior < right.prior:
												A__							=       merge(left.right			,							_A    )
												return left
						else:
												A__							=       merge(_A			,							right.left    )
												return right
def  UpperCamelCase						(       _A			:				Node | None			,							_A			:				int    )->       Node | None:





						"""simple docstring"""





						A__							=       Node(_A    )
						A__						,       A__							=       split(_A			,							_A    )
						return merge(merge(_A			,							_A    )			,							_A    )
def  UpperCamelCase						(       _A			:				Node | None			,							_A			:				int    )->       Node | None:





						"""simple docstring"""





						A__						,       A__							=       split(_A			,							value - 1    )
						A__						,       A__							=       split(_A			,							_A    )
						return merge(_A			,							_A    )
def  UpperCamelCase						(       _A			:				Node | None    )->       None:





						"""simple docstring"""





						if not root:  # None
												return
						else:
												inorder(root.left    )
												print(root.value			,							end=","    )
												inorder(root.right    )
def  UpperCamelCase						(       _A			:				Node | None			,							_A			:				str    )->       Node | None:





						"""simple docstring"""





						for arg in args.split():
												if arg[0] == "+":
																		A__							=       insert(_A			,							int(arg[1:]    )    )

												elif arg[0] == "-":
																		A__							=       erase(_A			,							int(arg[1:]    )    )

												else:
																		print("Unknown command"    )

						return root
def  UpperCamelCase						(       )->       None:





						"""simple docstring"""





						A__							=       None
						print(
						    "enter numbers to create a tree, + value to add value into treap, "
						    "- value to erase all nodes with value. 'q' to quit. "    )

						A__							=       input()
						while args != "q":
												A__							=       interact_treap(_A			,							_A    )
												print(_A    )
												A__							=       input()

						print("good by!"    )


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						main()










import datasets

from .evaluate import evaluate


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

UpperCAmelCase_							:      Any      =  "\nThis metric wrap the official scoring script for version 1 of the Stanford Question Answering Dataset (SQuAD).\n\nStanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by\ncrowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,\nfrom the corresponding reading passage, or the question might be unanswerable.\n"

UpperCAmelCase_							:      Tuple      =  "\nComputes SQuAD scores (F1 and EM).\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': the text of the answer\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 SQuAD 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\nExamples:\n\n    >>> predictions = [{'prediction_text': '1976', 'id': '56e10a3be3433e1400422b22'}]\n    >>> references = [{'answers': {'answer_start': [97], 'text': ['1976']}, 'id': '56e10a3be3433e1400422b22'}]\n    >>> squad_metric = datasets.load_metric(\"squad\")\n    >>> results = squad_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'exact_match': 100.0, 'f1': 100.0}\n"






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



	def 			__A							(       self		):
							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 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__		):
							A__							=       {prediction["id"]: prediction["prediction_text"] for prediction in predictions}
							A__							=       [
							    {
							        "paragraphs": [
							            {
							                "qas": [
							                    {
							                        "answers": [{"text": answer_text} for answer_text in ref["answers"]["text"]],
							                        "id": ref["id"],
							                    }
							                    for ref in references
							                ]
							            }
							        ]
							    }
							]
							A__							=       evaluate(dataset=UpperCAmelCase__			,	predictions=UpperCAmelCase__		)
							return score





