




from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


UpperCamelCase__         =      """\\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"""

UpperCamelCase__         =      """\\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"""

UpperCamelCase__         =      """\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_							:    Tuple				,				SCREAMING_SNAKE_CASE_							:    Dict					):
					return float((preds == labels).mean()					)







def      _a				(						SCREAMING_SNAKE_CASE_							:    Dict				,				SCREAMING_SNAKE_CASE_							:    Tuple				,				SCREAMING_SNAKE_CASE_							:    Union[str, Any]="binary"					):
					__lowerCAmelCase           =	simple_accuracy(A__				,				A__					)
					__lowerCAmelCase           =	float(fa_score(y_true=A__				,				y_pred=A__				,				average=A__					)					)
					return {
					    "accuracy": acc,
					    "f1": fa,
					}







def      _a				(						SCREAMING_SNAKE_CASE_							:    str				,				SCREAMING_SNAKE_CASE_							:    int					):
					__lowerCAmelCase           =	{}
					for id_pred, label in zip(A__				,				A__					):
										__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(*A__					)
										__lowerCAmelCase           =	fa_score(y_true=A__				,				y_pred=A__				,				average="macro"					)
										fas.append(A__					)
										__lowerCAmelCase           =	int(sum(pred == label for pred, label in preds_labels					) == len(A__					)					)
										ems.append(A__					)
					__lowerCAmelCase           =	float(sum(A__					) / len(A__					)					)
					__lowerCAmelCase           =	sum(A__					) / len(A__					)
					__lowerCAmelCase           =	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       a__   ( datasets.Metric				):






						def     __SCREAMING_SNAKE_CASE(       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     __SCREAMING_SNAKE_CASE(       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     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A      ):




											"""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":
																__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(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\",]"      )










from __future__ import annotations







def      _a				(						SCREAMING_SNAKE_CASE_							:    float				,				SCREAMING_SNAKE_CASE_							:    float				,				SCREAMING_SNAKE_CASE_							:    float				,				):
					if (electron_conc, hole_conc, intrinsic_conc).count(0					) != 1:
										raise ValueError("You cannot supply more or less than 2 values"					)
					elif electron_conc < 0:
										raise ValueError("Electron concentration cannot be negative in a semiconductor"					)
					elif hole_conc < 0:
										raise ValueError("Hole concentration cannot be negative in a semiconductor"					)
					elif intrinsic_conc < 0:
										raise ValueError(
										    "Intrinsic concentration cannot be negative in a semiconductor"					)
					elif electron_conc == 0:
										return (
										    "electron_conc",
										    intrinsic_conc**2 / hole_conc,
										)
					elif hole_conc == 0:
										return (
										    "hole_conc",
										    intrinsic_conc**2 / electron_conc,
										)
					elif intrinsic_conc == 0:
										return (
										    "intrinsic_conc",
										    (electron_conc * hole_conc) ** 0.5,
										)
					else:
										return (-1, -1)


if __name__ == "__main__":
					import doctest

					doctest.testmod()




