






from __future__ import annotations

from collections import deque
from enum import Enum
from math import atana, degrees
from sys import maxsize
class 							A_      (  __lowercase     ):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:     Optional[Any]	=   1
 _SCREAMING_SNAKE_CASE							:     Dict	=   2
 _SCREAMING_SNAKE_CASE							:     Tuple	=   3


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

     """simple docstring"""



     return f'''{self.__class__.__name__}.{self.name}'''



def        _lowerCamelCase		(  __A      :							tuple[int, int]						,						__A      :							int						,						__A      :							int	) ->   float:
    _UpperCAmelCase			: Tuple     					=			point
    return degrees(atana(y - miny						,						x - minx	)	)

def        _lowerCamelCase		(  __A      :							tuple[int, int]						,						__A      :							tuple[int, int]						,						__A      :							tuple[int, int]	) ->   Direction:
    _UpperCAmelCase			: Tuple     					=			starting
    _UpperCAmelCase			: List[Any]     					=			via
    _UpperCAmelCase			: List[str]     					=			target
    _UpperCAmelCase			: Dict     					=			degrees(atana(ya - ya						,						xa - xa	)	)
    via_angle %= 360
    _UpperCAmelCase			: Dict     					=			degrees(atana(ya - ya						,						xa - xa	)	)
    target_angle %= 360
    # t-
    #  \ \
    #   \ v
    #    \|
    #     s
    # via_angle is always lower than target_angle, if direction is left.
    # If they are same, it means they are on a same line of convex hull.
    if target_angle > via_angle:
        return Direction.left
    elif target_angle == via_angle:
        return Direction.straight
    else:
        return Direction.right


def        _lowerCamelCase		(  __A      :							list[tuple[int, int]]	) ->   list[tuple[int, int]]:

    if len(__A	) <= 2:
        # There is no convex hull
        raise ValueError('''graham_scan: argument must contain more than 3 points.'''	)
    if len(__A	) == 3:
        return points
    # find the lowest and the most left point
    _UpperCAmelCase			: Dict     					=			0
    _UpperCAmelCase			: List[Any]     					=			maxsize, maxsize
    for i, point in enumerate(__A	):
        _UpperCAmelCase			: Optional[int]     					=			point[0]
        _UpperCAmelCase			: str     					=			point[1]
        if y < miny:
            _UpperCAmelCase			: Optional[Any]     					=			y
            _UpperCAmelCase			: List[Any]     					=			x
            _UpperCAmelCase			: str     					=			i
        if y == miny and x < minx:
            _UpperCAmelCase			: Dict     					=			x
            _UpperCAmelCase			: List[Any]     					=			i

    # remove the lowest and the most left point from points for preparing for sort
    points.pop(__A	)

    _UpperCAmelCase			: Optional[int]     					=			sorted(__A						,						key=lambda __A	: angle_comparer(__A						,						__A						,						__A	)	)
    # This insert actually costs complexity,
    # and you should instead add (minx, miny) into stack later.
    # I'm using insert just for easy understanding.
    sorted_points.insert(0						,						(minx, miny)	)

    _UpperCAmelCase			: deque[tuple[int, int]]     					=			deque()
    stack.append(sorted_points[0]	)
    stack.append(sorted_points[1]	)
    stack.append(sorted_points[2]	)
    # In any ways, the first 3 points line are towards left.
    # Because we sort them the angle from minx, miny.
    _UpperCAmelCase			: Dict     					=			Direction.left

    for i in range(3						,						len(__A	)	):
        while True:
            _UpperCAmelCase			: Any     					=			stack[-2]
            _UpperCAmelCase			: str     					=			stack[-1]
            _UpperCAmelCase			: List[Any]     					=			sorted_points[i]
            _UpperCAmelCase			: Tuple     					=			check_direction(__A						,						__A						,						__A	)

            if next_direction == Direction.left:
                _UpperCAmelCase			: List[Any]     					=			Direction.left
                break
            if next_direction == Direction.straight:
                if current_direction == Direction.left:
                    # We keep current_direction as left.
                    # Because if the straight line keeps as straight,
                    # we want to know if this straight line is towards left.
                    break
                elif current_direction == Direction.right:
                    # If the straight line is towards right,
                    # every previous points on those straigh line is not convex hull.
                    stack.pop()
            if next_direction == Direction.right:
                stack.pop()
        stack.append(sorted_points[i]	)
    return list(__A	)












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,
)


SCREAMING_SNAKE_CASE								=  '\\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'

SCREAMING_SNAKE_CASE								=  '\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'

SCREAMING_SNAKE_CASE								=  '\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     ):






 '''simple docstring'''




 def 	snake_case__			(		self)	->						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 	snake_case__			(		self							,					_A							,					_A							,					_A=1.0)	->						List[Any]:

     """simple docstring"""



     _UpperCAmelCase			: str     					=			{p['''id''']: p['''no_answer_probability'''] for p in predictions}
     _UpperCAmelCase			: List[str]     					=			[{'''paragraphs''': [{'''qas''': references}]}]
     _UpperCAmelCase			: List[Any]     					=			{p['''id''']: p['''prediction_text'''] for p in predictions}

     _UpperCAmelCase			: List[str]     					=			make_qid_to_has_ans(_A)  # maps qid to True/False
     _UpperCAmelCase			: str     					=			[k for k, v in qid_to_has_ans.items() if v]
     _UpperCAmelCase			: List[Any]     					=			[k for k, v in qid_to_has_ans.items() if not v]

     _UpperCAmelCase					,	_UpperCAmelCase			: Union[str, Any]     					=			get_raw_scores(_A							,					_A)
     _UpperCAmelCase			: Optional[Any]     					=			apply_no_ans_threshold(_A							,					_A							,					_A							,					_A)
     _UpperCAmelCase			: List[Any]     					=			apply_no_ans_threshold(_A							,					_A							,					_A							,					_A)
     _UpperCAmelCase			: Optional[Any]     					=			make_eval_dict(_A							,					_A)

     if has_ans_qids:
         _UpperCAmelCase			: Tuple     					=			make_eval_dict(_A							,					_A							,					qid_list=_A)
         merge_eval(_A							,					_A							,					'''HasAns''')
     if no_ans_qids:
         _UpperCAmelCase			: Any     					=			make_eval_dict(_A							,					_A							,					qid_list=_A)
         merge_eval(_A							,					_A							,					'''NoAns''')
     find_all_best_thresh(_A							,					_A							,					_A							,					_A							,					_A							,					_A)
     return dict(_A)




