


"""simple docstring"""
import datasets

from .evaluate import evaluate


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

SCREAMING_SNAKE_CASE :    Union[str, Any]									=				"""
This metric wrap the official scoring script for version 1 of the Stanford Question Answering Dataset (SQuAD).

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by
crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,
from the corresponding reading passage, or the question might be unanswerable.
"""

SCREAMING_SNAKE_CASE :    Union[str, Any]									=				"""
Computes SQuAD scores (F1 and EM).
Args:
    predictions: List of question-answers dictionaries with the following key-values:
        - 'id': id of the question-answer pair as given in the references (see below)
        - 'prediction_text': the text of the answer
    references: List of question-answers dictionaries with the following key-values:
        - 'id': id of the question-answer pair (see above),
        - 'answers': a Dict in the SQuAD dataset format
            {
                'text': list of possible texts for the answer, as a list of strings
                'answer_start': list of start positions for the answer, as a list of ints
            }
            Note that answer_start values are not taken into account to compute the metric.
Returns:
    'exact_match': Exact match (the normalized answer exactly match the gold answer)
    'f1': The F-score of predicted tokens versus the gold answer
Examples:

    >>> predictions = [{'prediction_text': '1976', 'id': '56e10a3be3433e1400422b22'}]
    >>> references = [{'answers': {'answer_start': [97], 'text': ['1976']}, 'id': '56e10a3be3433e1400422b22'}]
    >>> squad_metric = datasets.load_metric(\"squad\")
    >>> results = squad_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'exact_match': 100.0, 'f1': 100.0}
"""



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






		'''simple docstring'''

		def 				SCREAMING_SNAKE_CASE  (self					):





			'''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'''					)},
			            '''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 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_					):





			'''simple docstring'''

			__snake_case	:						List[str]						=				{prediction['''id''']: prediction['''prediction_text'''] for prediction in predictions}
			__snake_case	:						List[str]						=				[
			    {
			        '''paragraphs''': [
			            {
			                '''qas''': [
			                    {
			                        '''answers''': [{'''text''': answer_text} for answer_text in ref['''answers''']['''text''']],
			                        '''id''': ref['''id'''],
			                    }
			                    for ref in references
			                ]
			            }
			        ]
			    }
			]
			__snake_case	:						List[str]						=				evaluate(dataset=a_				,       predictions=a_					)
			return score









"""simple docstring"""
from math import factorial, radians






def 		lowercase							(     _snake_case						:      float     ,   _snake_case						:      int = 18     ,   _snake_case						:      int = 10     )     ->float:
	"""simple docstring"""





	__snake_case	:						Any						=				angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)

	# Converting from degrees to radians
	__snake_case	:						int						=				radians(_snake_case     )

	__snake_case	:						str						=				angle_in_radians
	__snake_case	:						Optional[int]						=				3
	__snake_case	:						List[Any]						=				-1

	for _ in range(_snake_case     ):
		result += (b * (angle_in_radians**a)) / factorial(_snake_case     )

		__snake_case	:						int						=				-b  # One positive term and the next will be negative and so on...
		a += 2  # Increased by 2 for every term.

	return round(_snake_case     ,   _snake_case     )


if __name__ == "__main__":
			__import__("""doctest""").testmod()





