



from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


_lowercase: Union[str, 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"

_lowercase: Optional[Any]      			=		"\\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"

_lowercase: Dict      			=		"\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(      A       :    Tuple ,     A       :    Any       )		->       Optional[int]:
					"""simple docstring"""





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






def     a(      A       :    Tuple ,     A       :    Optional[int] ,     A       :    str="binary"       )		->       int:
					"""simple docstring"""





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






def     a(      A       :    int ,     A       :    List[str]       )		->       Any:
					"""simple docstring"""





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





	"""simple docstring"""




	def      UpperCamelCase_       (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      UpperCamelCase_       (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      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

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





import random






def     a(      A       :    Optional[Any] ,     A       :    Optional[Any] ,     A       :    str       )		->       List[Any]:
					"""simple docstring"""





					a					     =   a[left_index]
					a					     =   left_index + 1
					for j in range(left_index + 1 ,     A       ):
										if a[j] < pivot:
															a     ,				a					     =   a[i], a[j]
															i += 1
					a     ,				a					     =   a[i - 1], a[left_index]
					return i - 1






def     a(      A       :    List[Any] ,     A       :    List[Any] ,     A       :    Union[str, Any]       )		->       List[Any]:
					"""simple docstring"""





					if left < right:
										a					     =   random.randint(A ,     right - 1       )
										a     ,				a					     =   (
										    a[left],
										    a[pivot],
										)  # switches the pivot with the left most bound
										a					     =   partition(A ,     A ,     A       )
										quick_sort_random(
										    A ,     A ,     A       )  # recursive quicksort to the left of the pivot point
										quick_sort_random(
										    A ,     pivot_index + 1 ,     A       )  # recursive quicksort to the right of the pivot point






def     a(      )		->       Any:
					"""simple docstring"""





					a					     =   input("Enter numbers separated by a comma:\n"       ).strip()
					a					     =   [int(A       ) for item in user_input.split(","       )]

					quick_sort_random(A ,     0 ,     len(A       )       )

					print(A       )


if __name__ == "__main__":
				main()
