
"""simple docstring"""

class        _lowercase   :




	def __init__( self					)		->							None:
			# numbermap keys are the number and its values are lists of indexes sorted
			# in ascending order
			lowerCamelCase     :			dict[int, list[int]]		=    {}
			# indexmap keys are an index and it's values are the number at that index
			lowerCamelCase     :			dict[int, int]		=    {}




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_		, UpperCAmelCase_					)		->							list[int]:
			if isinstance(UpperCAmelCase_		, (range, str)					):
					lowerCamelCase     :			Optional[Any]		=    list(UpperCAmelCase_					)
			elif not isinstance(UpperCAmelCase_		, UpperCAmelCase_					):
					raise TypeError(
					    'binary_search_delete() only accepts either a list, range or str'					)

			lowerCamelCase     :			Optional[int]		=    0
			lowerCamelCase     :			List[str]		=    len(UpperCAmelCase_					) - 1

			while low <= high:
					lowerCamelCase     :			Union[str, Any]		=    (low + high) // 2
					if array[mid] == item:
							array.pop(UpperCAmelCase_					)
							return array
					elif array[mid] < item:
							lowerCamelCase     :			Tuple		=    mid + 1
					else:
							lowerCamelCase     :			Tuple		=    mid - 1
			raise ValueError(
			    'Either the item is not in the array or the array was unsorted'					)




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_		, UpperCAmelCase_					)		->							list[int]:
			if isinstance(UpperCAmelCase_		, (range, str)					):
					lowerCamelCase     :			Optional[int]		=    list(UpperCAmelCase_					)
			elif not isinstance(UpperCAmelCase_		, UpperCAmelCase_					):
					raise TypeError(
					    'binary_search_insert() only accepts either a list, range or str'					)

			lowerCamelCase     :			List[Any]		=    0
			lowerCamelCase     :			Optional[Any]		=    len(UpperCAmelCase_					) - 1

			while low <= high:
					lowerCamelCase     :			Optional[int]		=    (low + high) // 2
					if array[mid] == index:
							# If the item already exists in the array,
							# insert it after the existing item
							array.insert(mid + 1		, UpperCAmelCase_					)
							return array
					elif array[mid] < index:
							lowerCamelCase     :			Optional[Any]		=    mid + 1
					else:
							lowerCamelCase     :			List[str]		=    mid - 1

        # If the item doesn't exist in the array, insert it at the appropriate position
			array.insert(UpperCAmelCase_		, UpperCAmelCase_					)
			return array




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_		, UpperCAmelCase_					)		->							None:
			# Remove previous index
			if index in self.indexmap:
					lowerCamelCase     :			Any		=    self.indexmap[index]
					if len(self.numbermap[n]					) == 1:
							del self.numbermap[n]
					else:
							lowerCamelCase     :			List[Any]		=    self.binary_search_delete(self.numbermap[n]		, UpperCAmelCase_					)

        # Set new index
			lowerCamelCase     :			Any		=    number

			# Number not seen before or empty so insert number value
			if number not in self.numbermap:
					lowerCamelCase     :			Union[str, Any]		=    [index]

			# Here we need to perform a binary search insertion in order to insert
			# The item in the correct place
			else:
					lowerCamelCase     :			Union[str, Any]		=    self.binary_search_insert(
					    self.numbermap[number]		, UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_					)		->							int:
			return self.numbermap.get(UpperCAmelCase_		, [-1]					)[0]


if __name__ == "__main__":
					import doctest

					doctest.testmod()











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_	       =   "\\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_	       =   "\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_	       =   "\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 							a_  (		datasets.Metric				):

						def 	__a       (    self	:List[Any])		->					List[str]:
												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 	__a       (    self	:int   ,						_lowercase	:Tuple   ,						_lowercase	:List[str]   ,						_lowercase	:Optional[Any]=1.0)		->					str:
												UpperCAmelCase_     							=     {p['''id''']: p['''no_answer_probability'''] for p in predictions}
												UpperCAmelCase_     							=     [{'''paragraphs''': [{'''qas''': references}]}]
												UpperCAmelCase_     							=     {p['''id''']: p['''prediction_text'''] for p in predictions}

												UpperCAmelCase_     							=     make_qid_to_has_ans(_lowercase)  # maps qid to True/False
												UpperCAmelCase_     							=     [k for k, v in qid_to_has_ans.items() if v]
												UpperCAmelCase_     							=     [k for k, v in qid_to_has_ans.items() if not v]

												UpperCAmelCase_							,      UpperCAmelCase_     							=     get_raw_scores(_lowercase   ,						_lowercase)
												UpperCAmelCase_     							=     apply_no_ans_threshold(_lowercase   ,						_lowercase   ,						_lowercase   ,						_lowercase)
												UpperCAmelCase_     							=     apply_no_ans_threshold(_lowercase   ,						_lowercase   ,						_lowercase   ,						_lowercase)
												UpperCAmelCase_     							=     make_eval_dict(_lowercase   ,						_lowercase)

												if has_ans_qids:
																		UpperCAmelCase_     							=     make_eval_dict(_lowercase   ,						_lowercase   ,						qid_list=_lowercase)
																		merge_eval(_lowercase   ,						_lowercase   ,						'''HasAns''')
												if no_ans_qids:
																		UpperCAmelCase_     							=     make_eval_dict(_lowercase   ,						_lowercase   ,						qid_list=_lowercase)
																		merge_eval(_lowercase   ,						_lowercase   ,						'''NoAns''')
												find_all_best_thresh(_lowercase   ,						_lowercase   ,						_lowercase   ,						_lowercase   ,						_lowercase   ,						_lowercase)
												return dict(_lowercase)





