




"""simple docstring"""




from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


lowerCAmelCase							:			Optional[Any]     							=							"""\
@article{wang2019superglue,
  title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
  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},
  journal={arXiv preprint arXiv:1905.00537},
  year={2019}
}
"""

lowerCAmelCase							:			Optional[int]     							=							"""\
SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after
GLUE with a new set of more difficult language understanding tasks, improved
resources, and a new public leaderboard.
"""

lowerCAmelCase							:			List[Any]     							=							"""
Compute SuperGLUE evaluation metric associated to each SuperGLUE dataset.
Args:
    predictions: list of predictions to score. Depending on the SuperGlUE subset:
        - for 'record': list of question-answer dictionaries with the following keys:
            - 'idx': index of the question as specified by the dataset
            - 'prediction_text': the predicted answer text
        - for 'multirc': list of question-answer dictionaries with the following keys:
            - 'idx': index of the question-answer pair as specified by the dataset
            - 'prediction': the predicted answer label
        - otherwise: list of predicted labels
    references: list of reference labels. Depending on the SuperGLUE subset:
        - for 'record': list of question-answers dictionaries with the following keys:
            - 'idx': index of the question as specified by the dataset
            - 'answers': list of possible answers
        - otherwise: list of reference labels
Returns: depending on the SuperGLUE subset:
    - for 'record':
        - 'exact_match': Exact match between answer and gold answer
        - 'f1': F1 score
    - for 'multirc':
        - 'exact_match': Exact match between answer and gold answer
        - 'f1_m': Per-question macro-F1 score
        - 'f1_a': Average F1 score over all answers
    - for 'axb':
        'matthews_correlation': Matthew Correlation
    - for 'cb':
        - 'accuracy': Accuracy
        - 'f1': F1 score
    - for all others:
        - 'accuracy': Accuracy
Examples:

    >>> super_glue_metric = datasets.load_metric('super_glue', 'copa')  # any of [\"copa\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"boolq\", \"axg\"]
    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0}

    >>> super_glue_metric = datasets.load_metric('super_glue', 'cb')
    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0, 'f1': 1.0}

    >>> super_glue_metric = datasets.load_metric('super_glue', 'record')
    >>> predictions = [{'idx': {'passage': 0, 'query': 0}, 'prediction_text': 'answer'}]
    >>> references = [{'idx': {'passage': 0, 'query': 0}, 'answers': ['answer', 'another_answer']}]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'exact_match': 1.0, 'f1': 1.0}

    >>> super_glue_metric = datasets.load_metric('super_glue', 'multirc')
    >>> predictions = [{'idx': {'answer': 0, 'paragraph': 0, 'question': 0}, 'prediction': 0}, {'idx': {'answer': 1, 'paragraph': 2, 'question': 3}, 'prediction': 1}]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'exact_match': 1.0, 'f1_m': 1.0, 'f1_a': 1.0}

    >>> super_glue_metric = datasets.load_metric('super_glue', 'axb')
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'matthews_correlation': 1.0}
"""



def     a__ (				snake_case__       , snake_case__				)						->					str:
			return float((preds == labels).mean()				)



def     a__ (				snake_case__       , snake_case__       , snake_case__="binary"				)						->					Optional[Any]:
			lowerCamelCase			      =					simple_accuracy(snake_case__       , snake_case__				)
			lowerCamelCase			      =					float(fa_score(y_true=snake_case__       , y_pred=snake_case__       , average=snake_case__				)				)
			return {
			    "accuracy": acc,
			    "f1": fa,
			}



def     a__ (				snake_case__       , snake_case__				)						->					int:
			lowerCamelCase			      =					{}
			for id_pred, label in zip(snake_case__       , snake_case__				):
						lowerCamelCase			      =					F'{id_pred["idx"]["paragraph"]}-{id_pred["idx"]["question"]}'
						lowerCamelCase			      =					id_pred["""prediction"""]
						if question_id in question_map:
									question_map[question_id].append((pred, label)				)
						else:
									lowerCamelCase			      =					[(pred, label)]
			lowerCamelCase      ,   lowerCamelCase			      =					[], []
			for question, preds_labels in question_map.items():
						lowerCamelCase      ,   lowerCamelCase			      =					zip(*snake_case__				)
						lowerCamelCase			      =					fa_score(y_true=snake_case__       , y_pred=snake_case__       , average="""macro"""				)
						fas.append(snake_case__				)
						lowerCamelCase			      =					int(sum(pred == label for pred, label in preds_labels				) == len(snake_case__				)				)
						ems.append(snake_case__				)
			lowerCamelCase			      =					float(sum(snake_case__				) / len(snake_case__				)				)
			lowerCamelCase			      =					sum(snake_case__				) / len(snake_case__				)
			lowerCamelCase			      =					float(fa_score(y_true=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     __magic_name__							(       datasets.Metric				):






				'''simple docstring'''





				def    _lowerCAmelCase				(       self ):





							"""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 ):





							"""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		, _a		, _a ):





							"""simple docstring"""
							if self.config_name == "axb":
										return {"matthews_correlation": matthews_corrcoef(_a		, _a )}
							elif self.config_name == "cb":
										return acc_and_fa(_a		, _a		, fa_avg="""macro""" )
							elif self.config_name == "record":
										lowerCamelCase			      =					[
										    {
										        """qas""": [
										            {"""id""": ref["""idx"""]["""query"""], """answers""": [{"""text""": ans} for ans in ref["""answers"""]]}
										            for ref in references
										        ]
										    }
										]
										lowerCamelCase			      =					{pred["""idx"""]["""query"""]: pred["""prediction_text"""] for pred in predictions}
										return evaluate_record(_a		, _a )[0]
							elif self.config_name == "multirc":
										return evaluate_multirc(_a		, _a )
							elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
										return {"accuracy": simple_accuracy(_a		, _a )}
							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"""




def     a__ (				snake_case__				)						->					list:

			if len(snake_case__				) < 2:
						return collection

			def circle_sort_util(snake_case__       , snake_case__       , snake_case__				) -> bool:
						lowerCamelCase			      =					False

						if low == high:
									return swapped

						lowerCamelCase			      =					low
						lowerCamelCase			      =					high

						while left < right:
									if collection[left] > collection[right]:
												lowerCamelCase      ,   lowerCamelCase			      =					(
												    collection[right],
												    collection[left],
												)
												lowerCamelCase			      =					True

									left += 1
									right -= 1

						if left == right and collection[left] > collection[right + 1]:
									lowerCamelCase      ,   lowerCamelCase			      =					(
									    collection[right + 1],
									    collection[left],
									)

									lowerCamelCase			      =					True

						lowerCamelCase			      =					low + int((high - low) / 2				)
						lowerCamelCase			      =					circle_sort_util(snake_case__       , snake_case__       , snake_case__				)
						lowerCamelCase			      =					circle_sort_util(snake_case__       , mid + 1       , snake_case__				)

						return swapped or left_swap or right_swap

			lowerCamelCase			      =					True

			while is_not_sorted is True:
						lowerCamelCase			      =					circle_sort_util(snake_case__       , 0       , len(snake_case__				) - 1				)

			return collection


if __name__ == "__main__":
					lowerCAmelCase							:			Tuple     							=							input("""Enter numbers separated by a comma:\n""").strip()
					lowerCAmelCase							:			List[Any]     							=							[int(item) for item in user_input.split(""",""")]
					print(circle_sort(unsorted))



