





'''simple docstring'''






from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


lowerCAmelCase									=   """\\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"""

lowerCAmelCase									=   """\\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"""

lowerCAmelCase									=   """\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_    :	Union[str, Any]   ,a_    :	Optional[int]						):
		return float((preds == labels).mean()						)






def   __A    (				a_    :	List[Any]   ,a_    :	Dict   ,a_    :	Union[str, Any]="binary"						):
		lowerCAmelCase  :     List[Any]       						= simple_accuracy(_a   ,_a						)
		lowerCAmelCase  :     Any       						= float(fa_score(y_true=_a   ,y_pred=_a   ,average=_a						)						)
		return {
		    "accuracy": acc,
		    "f1": fa,
		}






def   __A    (				a_    :	int   ,a_    :	str						):
		lowerCAmelCase  :     Tuple       						= {}
		for id_pred, label in zip(_a   ,_a						):
				lowerCAmelCase  :     List[str]       						= f'''{id_pred["idx"]["paragraph"]}-{id_pred["idx"]["question"]}'''
				lowerCAmelCase  :     Any       						= id_pred["prediction"]
				if question_id in question_map:
						question_map[question_id].append((pred, label)						)
				else:
						lowerCAmelCase  :     Tuple       						= [(pred, label)]
		lowerCAmelCase						,		lowerCAmelCase  :     int       						= [], []
		for question, preds_labels in question_map.items():
				lowerCAmelCase						,		lowerCAmelCase  :     int       						= zip(*_a						)
				lowerCAmelCase  :     Tuple       						= fa_score(y_true=_a   ,y_pred=_a   ,average="macro"						)
				fas.append(_a						)
				lowerCAmelCase  :     Union[str, Any]       						= int(sum(pred == label for pred, label in preds_labels						) == len(_a						)						)
				ems.append(_a						)
		lowerCAmelCase  :     Optional[Any]       						= float(sum(_a						) / len(_a						)						)
		lowerCAmelCase  :     List[str]       						= sum(_a						) / len(_a						)
		lowerCAmelCase  :     List[str]       						= 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 		lowerCamelCase	(   datasets.Metric							):



	def 	_lowerCamelCase     ( self						):
			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						):
			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_						):
			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":
					lowerCAmelCase  :     List[Any]       						= [
					    {
					        "qas": [
					            {"id": ref["idx"]["query"], "answers": [{"text": ans} for ans in ref["answers"]]}
					            for ref in references
					        ]
					    }
					]
					lowerCAmelCase  :     str       						= {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\",]"						)






"""simple docstring"""


from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate





@dataclass(frozen=SCREAMING_SNAKE_CASE    )
class lowerCamelCase__   (				SCREAMING_SNAKE_CASE    ):



    '''simple docstring'''




    _lowerCamelCase					=   field(default='''summarization'''     ,   metadata={'''include_in_asdict_even_if_is_default''': True}    )
    _lowerCamelCase					=   Features({'''text''': Value('''string'''    )}    )
    _lowerCamelCase					=   Features({'''summary''': Value('''string'''    )}    )
    _lowerCamelCase					=   "text"
    _lowerCamelCase					=   "summary"




    @property
    def UpperCamelCase__		( self    )    ->      Dict[str, str]:
          return {self.text_column: "text", self.summary_column: "summary"}




