"""simple docstring"""





from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


lowerCAmelCase_     =	'''\
@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_     =	'''\
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_     =	'''
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      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Optional[Any]:
							return float((preds == labels).mean()  )



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE="binary"  )->    int:
							_SCREAMING_SNAKE_CASE			  =    simple_accuracy(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )
							_SCREAMING_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      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Dict:
							_SCREAMING_SNAKE_CASE			  =    {}
							for id_pred, label in zip(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ):
														_SCREAMING_SNAKE_CASE			  =    F"""{id_pred["idx"]["paragraph"]}-{id_pred["idx"]["question"]}"""
														_SCREAMING_SNAKE_CASE			  =    id_pred["""prediction"""]
														if question_id in question_map:
																					question_map[question_id].append((pred, label)  )
														else:
																					_SCREAMING_SNAKE_CASE			  =    [(pred, label)]
							_SCREAMING_SNAKE_CASE			  =    [], []
							for question, preds_labels in question_map.items():
														_SCREAMING_SNAKE_CASE			  =    zip(*__SCREAMING_SNAKE_CASE  )
														_SCREAMING_SNAKE_CASE			  =    fa_score(y_true=__SCREAMING_SNAKE_CASE     ,	y_pred=__SCREAMING_SNAKE_CASE     ,	average="""macro"""  )
														fas.append(__SCREAMING_SNAKE_CASE  )
														_SCREAMING_SNAKE_CASE			  =    int(sum(pred == label for pred, label in preds_labels  ) == len(__SCREAMING_SNAKE_CASE  )  )
														ems.append(__SCREAMING_SNAKE_CASE  )
							_SCREAMING_SNAKE_CASE			  =    float(sum(__SCREAMING_SNAKE_CASE  ) / len(__SCREAMING_SNAKE_CASE  )  )
							_SCREAMING_SNAKE_CASE			  =    sum(__SCREAMING_SNAKE_CASE  ) / len(__SCREAMING_SNAKE_CASE  )
							_SCREAMING_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  _snake_case		(    datasets.Metric ):







				"""simple docstring"""






				def 		_lowerCAmelCase		(      self							:							str):



											"""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							:							Dict):



											"""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							:							int      ,    _A							:							Optional[Any]      ,    _A							:							List[str]):



											"""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":
																		_SCREAMING_SNAKE_CASE			  =    [
																		    {
																		        """qas""": [
																		            {"""id""": ref["""idx"""]["""query"""], """answers""": [{"""text""": ans} for ans in ref["""answers"""]]}
																		            for ref in references
																		        ]
																		    }
																		]
																		_SCREAMING_SNAKE_CASE			  =    {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"""





from __future__ import annotations



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	)->    tuple:
							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()


