"""simple docstring"""




import datasets

from .evaluate import (
    apply_no_ans_threshold,
    find_all_best_thresh,
    get_raw_scores,
    make_eval_dict,
    make_qid_to_has_ans,
    merge_eval,
)


UpperCAmelCase       :    Optional[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       :    Tuple									=			"\nThis metric wrap the official scoring script for version 2 of the Stanford Question\nAnswering 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\nSQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions\nwritten adversarially by crowdworkers to look similar to answerable ones.\nTo do well on SQuAD2.0, systems must not only answer questions when possible, but also\ndetermine when no answer is supported by the paragraph and abstain from answering.\n"

UpperCAmelCase       :    Union[str, Any]									=			"\nComputes SQuAD v2 scores (F1 and EM).\nArgs:\n    predictions: List of triple for question-answers to score with the following elements:\n        - the question-answer \'id\' field as given in the references (see below)\n        - the text of the answer\n        - the probability that the question has no 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 list of Dict {\'text\': text of the answer as a string}\n    no_answer_threshold: float\n        Probability threshold to decide that a question has no answer.\nReturns:\n    \'exact\': Exact match (the normalized answer exactly match the gold answer)\n    \'f1\': The F-score of predicted tokens versus the gold answer\n    \'total\': Number of score considered\n    \'HasAns_exact\': Exact match (the normalized answer exactly match the gold answer)\n    \'HasAns_f1\': The F-score of predicted tokens versus the gold answer\n    \'HasAns_total\': Number of score considered\n    \'NoAns_exact\': Exact match (the normalized answer exactly match the gold answer)\n    \'NoAns_f1\': The F-score of predicted tokens versus the gold answer\n    \'NoAns_total\': Number of score considered\n    \'best_exact\': Best exact match (with varying threshold)\n    \'best_exact_thresh\': No-answer probability threshold associated to the best exact match\n    \'best_f1\': Best F1 (with varying threshold)\n    \'best_f1_thresh\': No-answer probability threshold associated to the best F1\nExamples:\n\n    >>> predictions = [{\'prediction_text\': \'1976\', \'id\': \'56e10a3be3433e1400422b22\', \'no_answer_probability\': 0.}]\n    >>> references = [{\'answers\': {\'answer_start\': [97], \'text\': [\'1976\']}, \'id\': \'56e10a3be3433e1400422b22\'}]\n    >>> squad_v2_metric = datasets.load_metric(\"squad_v2\")\n    >>> results = squad_v2_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'exact\': 100.0, \'f1\': 100.0, \'total\': 1, \'HasAns_exact\': 100.0, \'HasAns_f1\': 100.0, \'HasAns_total\': 1, \'best_exact\': 100.0, \'best_exact_thresh\': 0.0, \'best_f1\': 100.0, \'best_f1_thresh\': 0.0}\n"

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







				def   lowercase_		(    self    :str  )      ->					List[str]:




									"""simple docstring"""



									return datasets.MetricInfo(
									    description=_DESCRIPTION      ,citation=_CITATION      ,inputs_description=_KWARGS_DESCRIPTION      ,features=datasets.Features(
									        {
									            '''predictions''': {
									                '''id''': datasets.Value('''string'''  ),
									                '''prediction_text''': datasets.Value('''string'''  ),
									                '''no_answer_probability''': datasets.Value('''float32'''  ),
									            },
									            '''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    :Dict      ,__UpperCAmelCase    :List[Any]      ,__UpperCAmelCase    :Tuple      ,__UpperCAmelCase    :Optional[int]=1.0  )      ->					str:




									"""simple docstring"""



									lowerCamelCase__		:   Dict  				=					{p['''id''']: p['''no_answer_probability'''] for p in predictions}
									lowerCamelCase__		:   str  				=					[{'''paragraphs''': [{'''qas''': references}]}]
									lowerCamelCase__		:   Dict  				=					{p['''id''']: p['''prediction_text'''] for p in predictions}

									lowerCamelCase__		:   Optional[int]  				=					make_qid_to_has_ans(UpperCAmelCase__  )  # maps qid to True/False
									lowerCamelCase__		:   List[Any]  				=					[k for k, v in qid_to_has_ans.items() if v]
									lowerCamelCase__		:   int  				=					[k for k, v in qid_to_has_ans.items() if not v]

									lowerCamelCase__				,						lowerCamelCase__		:   Any  				=					get_raw_scores(UpperCAmelCase__      ,UpperCAmelCase__  )
									lowerCamelCase__		:   Optional[Any]  				=					apply_no_ans_threshold(UpperCAmelCase__      ,UpperCAmelCase__      ,UpperCAmelCase__      ,UpperCAmelCase__  )
									lowerCamelCase__		:   List[str]  				=					apply_no_ans_threshold(UpperCAmelCase__      ,UpperCAmelCase__      ,UpperCAmelCase__      ,UpperCAmelCase__  )
									lowerCamelCase__		:   Tuple  				=					make_eval_dict(UpperCAmelCase__      ,UpperCAmelCase__  )

									if has_ans_qids:
														lowerCamelCase__		:   Any  				=					make_eval_dict(UpperCAmelCase__      ,UpperCAmelCase__      ,qid_list=UpperCAmelCase__  )
														merge_eval(UpperCAmelCase__      ,UpperCAmelCase__      ,'''HasAns'''  )
									if no_ans_qids:
														lowerCamelCase__		:   Union[str, Any]  				=					make_eval_dict(UpperCAmelCase__      ,UpperCAmelCase__      ,qid_list=UpperCAmelCase__  )
														merge_eval(UpperCAmelCase__      ,UpperCAmelCase__      ,'''NoAns'''  )
									find_all_best_thresh(UpperCAmelCase__      ,UpperCAmelCase__      ,UpperCAmelCase__      ,UpperCAmelCase__      ,UpperCAmelCase__      ,UpperCAmelCase__  )
									return dict(UpperCAmelCase__  )










"""simple docstring"""






class       lowerCamelCase_		:





				"""simple docstring"""







				class       lowerCamelCase_		:





								"""simple docstring"""







								_lowerCAmelCase   :   Dict   				=       "_prev", "_data", "_next"



								def __init__(    self							,		UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__  ):
												SCREAMING_SNAKE_CASE__  				=					link_p
												SCREAMING_SNAKE_CASE__  				=					element
												SCREAMING_SNAKE_CASE__  				=					link_n






								def 	lowerCAmelCase__		(    self  ):
												return (
												    f''' Prev -> {self._prev is not None}, Next -> {self._next is not None}'''
												)



				def __init__(    self  ):
								SCREAMING_SNAKE_CASE__  				=					self._Node(UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__  )
								SCREAMING_SNAKE_CASE__  				=					self._Node(UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__  )
								SCREAMING_SNAKE_CASE__  				=					self._trailer
								SCREAMING_SNAKE_CASE__  				=					self._header
								SCREAMING_SNAKE_CASE__  				=					0



				def __len__(    self  ):
								return self._size



				def 	lowerCAmelCase__		(    self  ):
								return self.__len__() == 0



				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__  ):
								# Create new_node by setting it's prev.link -> header
								# setting it's next.link -> trailer
								SCREAMING_SNAKE_CASE__  				=					self._Node(UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__  )
								SCREAMING_SNAKE_CASE__  				=					new_node
								SCREAMING_SNAKE_CASE__  				=					new_node
								self._size += 1
								return self






				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__  ):
								SCREAMING_SNAKE_CASE__  				=					node._prev
								SCREAMING_SNAKE_CASE__  				=					node._next

								SCREAMING_SNAKE_CASE__  				=					successor
								SCREAMING_SNAKE_CASE__  				=					predecessor
								self._size -= 1
								SCREAMING_SNAKE_CASE__  				=					node._data
								SCREAMING_SNAKE_CASE__  				=					SCREAMING_SNAKE_CASE__  				=					SCREAMING_SNAKE_CASE__  				=					None
								del node
								return temp


class       lowerCamelCase_		(      _DoublyLinkedBase ):





				"""simple docstring"""







				def 	lowerCAmelCase__		(    self  ):
								if self.is_empty():
												raise Exception("List is empty"  )
								return self._header._next._data



				def 	lowerCAmelCase__		(    self  ):
								if self.is_empty():
												raise Exception("List is empty"  )
								return self._trailer._prev._data



				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__  ):
								return self._insert(self._header							,		UpperCAmelCase__							,		self._header._next  )



				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__  ):
								return self._insert(self._trailer._prev							,		UpperCAmelCase__							,		self._trailer  )



				def 	lowerCAmelCase__		(    self  ):
								if self.is_empty():
												raise IndexError("remove_first from empty list"  )
								return self._delete(self._header._next  )






				def 	lowerCAmelCase__		(    self  ):
								if self.is_empty():
												raise IndexError("remove_first from empty list"  )
								return self._delete(self._trailer._prev  )
