


"""simple docstring"""
from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


__SCREAMING_SNAKE_CASE							:    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'

__SCREAMING_SNAKE_CASE							:    Optional[int]		=    '\\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'

__SCREAMING_SNAKE_CASE							:    Any		=    '\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 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				Any:
							return float((preds == labels).mean()				)







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE="binary"				)    ->				str:
							snake_case_           =		simple_accuracy(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)
							snake_case_           =		float(fa_score(y_true=_SCREAMING_SNAKE_CASE				, y_pred=_SCREAMING_SNAKE_CASE				, average=_SCREAMING_SNAKE_CASE				)				)
							return {
							    "accuracy": acc,
							    "f1": fa,
							}







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				Optional[Any]:
							snake_case_           =		{}
							for id_pred, label in zip(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				):
														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(*_SCREAMING_SNAKE_CASE				)
														snake_case_           =		fa_score(y_true=_SCREAMING_SNAKE_CASE				, y_pred=_SCREAMING_SNAKE_CASE				, average="""macro"""				)
														fas.append(_SCREAMING_SNAKE_CASE				)
														snake_case_           =		int(sum(pred == label for pred, label in preds_labels				) == len(_SCREAMING_SNAKE_CASE				)				)
														ems.append(_SCREAMING_SNAKE_CASE				)
							snake_case_           =		float(sum(_SCREAMING_SNAKE_CASE				) / len(_SCREAMING_SNAKE_CASE				)				)
							snake_case_           =		sum(_SCREAMING_SNAKE_CASE				) / len(_SCREAMING_SNAKE_CASE				)
							snake_case_           =		float(fa_score(y_true=_SCREAMING_SNAKE_CASE				, 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      __A     (datasets.Metric):





							'''simple docstring'''

							def 		lowerCAmelCase       (				self							:      Optional[int]       )					->Union[str, Any]:



														"""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 		lowerCAmelCase       (				self							:      List[Any]       )					->Any:



														"""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 		lowerCAmelCase       (				self							:      List[str]		,       UpperCAmelCase_							:      str		,       UpperCAmelCase_							:      Any       )					->Dict:



														"""simple docstring"""



														if self.config_name == "axb":
																					return {"matthews_correlation": matthews_corrcoef(UpperCAmelCase_		,       UpperCAmelCase_       )}
														elif self.config_name == "cb":
																					return acc_and_fa(UpperCAmelCase_		,       UpperCAmelCase_		,       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(UpperCAmelCase_		,       UpperCAmelCase_       )[0]
														elif self.config_name == "multirc":
																					return evaluate_multirc(UpperCAmelCase_		,       UpperCAmelCase_       )
														elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
																					return {"accuracy": simple_accuracy(UpperCAmelCase_		,       UpperCAmelCase_       )}
														else:
																					raise KeyError(
																					    """You should supply a configuration name selected in """
																					    """[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]"""       )







"""simple docstring"""
from math import factorial







def 						_a				(      _SCREAMING_SNAKE_CASE = 100				)    ->				int:
							return sum(int(_SCREAMING_SNAKE_CASE				) for x in str(factorial(_SCREAMING_SNAKE_CASE				)				)				)


if __name__ == "__main__":
				print(solution(int(input('Enter the Number: ').strip())))



