


from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


A							:					List[Any]     						=     '\\n@article{wang2019superglue,\n  title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},\n  author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},\n  journal={arXiv preprint arXiv:1905.00537},\n  year={2019}\n}\n'

A							:					Tuple     						=     '\\nSuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after\nGLUE with a new set of more difficult language understanding tasks, improved\nresources, and a new public leaderboard.\n'

A							:					Tuple     						=     '\nCompute SuperGLUE evaluation metric associated to each SuperGLUE dataset.\nArgs:\n    predictions: list of predictions to score. Depending on the SuperGlUE subset:\n        - for \'record\': list of question-answer dictionaries with the following keys:\n            - \'idx\': index of the question as specified by the dataset\n            - \'prediction_text\': the predicted answer text\n        - for \'multirc\': list of question-answer dictionaries with the following keys:\n            - \'idx\': index of the question-answer pair as specified by the dataset\n            - \'prediction\': the predicted answer label\n        - otherwise: list of predicted labels\n    references: list of reference labels. Depending on the SuperGLUE subset:\n        - for \'record\': list of question-answers dictionaries with the following keys:\n            - \'idx\': index of the question as specified by the dataset\n            - \'answers\': list of possible answers\n        - otherwise: list of reference labels\nReturns: depending on the SuperGLUE subset:\n    - for \'record\':\n        - \'exact_match\': Exact match between answer and gold answer\n        - \'f1\': F1 score\n    - for \'multirc\':\n        - \'exact_match\': Exact match between answer and gold answer\n        - \'f1_m\': Per-question macro-F1 score\n        - \'f1_a\': Average F1 score over all answers\n    - for \'axb\':\n        \'matthews_correlation\': Matthew Correlation\n    - for \'cb\':\n        - \'accuracy\': Accuracy\n        - \'f1\': F1 score\n    - for all others:\n        - \'accuracy\': Accuracy\nExamples:\n\n    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'copa\')  # any of ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]\n    >>> predictions = [0, 1]\n    >>> references = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'accuracy\': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'cb\')\n    >>> predictions = [0, 1]\n    >>> references = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'accuracy\': 1.0, \'f1\': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'record\')\n    >>> predictions = [{\'idx\': {\'passage\': 0, \'query\': 0}, \'prediction_text\': \'answer\'}]\n    >>> references = [{\'idx\': {\'passage\': 0, \'query\': 0}, \'answers\': [\'answer\', \'another_answer\']}]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'exact_match\': 1.0, \'f1\': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'multirc\')\n    >>> predictions = [{\'idx\': {\'answer\': 0, \'paragraph\': 0, \'question\': 0}, \'prediction\': 0}, {\'idx\': {\'answer\': 1, \'paragraph\': 2, \'question\': 3}, \'prediction\': 1}]\n    >>> references = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'exact_match\': 1.0, \'f1_m\': 1.0, \'f1_a\': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'axb\')\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'matthews_correlation\': 1.0}\n'





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase )						->	List[str]:


						'''simple docstring'''






						return float((preds == labels).mean() )





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase="binary" )						->	str:


						'''simple docstring'''






						__snake_case				=		simple_accuracy(_lowerCAmelCase      ,	_lowerCAmelCase )
						__snake_case				=		float(fa_score(y_true=_lowerCAmelCase      ,	y_pred=_lowerCAmelCase      ,	average=_lowerCAmelCase ) )
						return {
						    "accuracy": acc,
						    "f1": fa,
						}





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase )						->	Any:


						'''simple docstring'''






						__snake_case				=		{}
						for id_pred, label in zip(_lowerCAmelCase      ,	_lowerCAmelCase ):
												__snake_case				=		F'''{id_pred['idx']['paragraph']}-{id_pred['idx']['question']}'''
												__snake_case				=		id_pred["prediction"]
												if question_id in question_map:
																		question_map[question_id].append((pred, label) )
												else:
																		__snake_case				=		[(pred, label)]
						__snake_case		,					__snake_case				=		[], []
						for question, preds_labels in question_map.items():
												__snake_case		,					__snake_case				=		zip(*_lowerCAmelCase )
												__snake_case				=		fa_score(y_true=_lowerCAmelCase      ,	y_pred=_lowerCAmelCase      ,	average="macro" )
												fas.append(_lowerCAmelCase )
												__snake_case				=		int(sum(pred == label for pred, label in preds_labels ) == len(_lowerCAmelCase ) )
												ems.append(_lowerCAmelCase )
						__snake_case				=		float(sum(_lowerCAmelCase ) / len(_lowerCAmelCase ) )
						__snake_case				=		sum(_lowerCAmelCase ) / len(_lowerCAmelCase )
						__snake_case				=		float(fa_score(y_true=_lowerCAmelCase      ,	y_pred=[id_pred["prediction"] for id_pred in ids_preds] ) )
						return {"exact_match": em, "f1_m": fa_m, "f1_a": fa_a}






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


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any						)			->	Optional[int]:




											'''simple docstring'''

											if self.config_name not in [
											    "boolq",
											    "cb",
											    "copa",
											    "multirc",
											    "record",
											    "rte",
											    "wic",
											    "wsc",
											    "wsc.fixed",
											    "axb",
											    "axg",
											]:
																	raise KeyError(
																	    "You should supply a configuration name selected in "
																	    "[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]"						)
											return datasets.MetricInfo(
											    description=_DESCRIPTION     ,    citation=_CITATION     ,    inputs_description=_KWARGS_DESCRIPTION     ,    features=datasets.Features(self._get_feature_types()						)     ,    codebase_urls=[]     ,    reference_urls=[]     ,    format="numpy" if not self.config_name == "record" and not self.config_name == "multirc" else None     ,    )


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]						)			->	Tuple:




											'''simple docstring'''

											if self.config_name == "record":
																	return {
																	    "predictions": {
																	        "idx": {
																	            "passage": datasets.Value("int64"						),
																	            "query": datasets.Value("int64"						),
																	        },
																	        "prediction_text": datasets.Value("string"						),
																	    },
																	    "references": {
																	        "idx": {
																	            "passage": datasets.Value("int64"						),
																	            "query": datasets.Value("int64"						),
																	        },
																	        "answers": datasets.Sequence(datasets.Value("string"						)						),
																	    },
																	}
											elif self.config_name == "multirc":
																	return {
																	    "predictions": {
																	        "idx": {
																	            "answer": datasets.Value("int64"						),
																	            "paragraph": datasets.Value("int64"						),
																	            "question": datasets.Value("int64"						),
																	        },
																	        "prediction": datasets.Value("int64"						),
																	    },
																	    "references": datasets.Value("int64"						),
																	}
											else:
																	return {
																	    "predictions": datasets.Value("int64"						),
																	    "references": datasets.Value("int64"						),
																	}






					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		Dict     ,    SCREAMING_SNAKE_CASE	:		Tuple						)			->	Union[str, Any]:




											'''simple docstring'''

											if self.config_name == "axb":
																	return {"matthews_correlation": matthews_corrcoef(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)}
											elif self.config_name == "cb":
																	return acc_and_fa(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    fa_avg="macro"						)
											elif self.config_name == "record":
																	__snake_case				=		[
																	    {
																	        "qas": [
																	            {"id": ref["idx"]["query"], "answers": [{"text": ans} for ans in ref["answers"]]}
																	            for ref in references
																	        ]
																	    }
																	]
																	__snake_case				=		{pred["idx"]["query"]: pred["prediction_text"] for pred in predictions}
																	return evaluate_record(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)[0]
											elif self.config_name == "multirc":
																	return evaluate_multirc(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)
											elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
																	return {"accuracy": simple_accuracy(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)}
											else:
																	raise KeyError(
																	    "You should supply a configuration name selected in "
																	    "[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]"						)









def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	int:


						'''simple docstring'''






						__snake_case				=		abs(_lowerCAmelCase )
						__snake_case				=		0
						while n > 0:
												res += n % 10
												n //= 10
						return res





def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	int:


						'''simple docstring'''






						__snake_case				=		abs(_lowerCAmelCase )
						return n if n < 10 else n % 10 + sum_of_digits(n // 10 )





def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	int:


						'''simple docstring'''






						return sum(int(_lowerCAmelCase ) for c in str(abs(_lowerCAmelCase ) ) )





def 	_lowerCAmelCase       (    )						->	None:


						'''simple docstring'''






						from collections.abc import Callable
						from timeit import timeit

						def benchmark_a_function(_lowerCAmelCase      ,	_lowerCAmelCase ) -> None:
												__snake_case				=		F'''{func.__name__}({value})'''
												__snake_case				=		timeit(F'''__main__.{call}'''      ,	setup="import __main__" )
												print(F'''{call:56} = {func(_lowerCAmelCase )} -- {timing:.4f} seconds''' )

						for value in (26_2144, 1125_8999_0684_2624, 126_7650_6002_2822_9401_4967_0320_5376):
												for func in (sum_of_digits, sum_of_digits_recursion, sum_of_digits_compact):
																		benchmark_a_function(_lowerCAmelCase      ,	_lowerCAmelCase )
												print()


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	benchmark()





