






from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


__UpperCamelCase					:		Optional[int]	  =						"""\
@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}
}
"""

__UpperCamelCase					:		Optional[Any]	  =						"""\
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.
"""

__UpperCamelCase					:		int	  =						"""
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_ (      _A      ,   _A  )			->			str:







				"""simple docstring"""
				return float((preds == labels).mean()  )
def      a_ (      _A      ,   _A      ,   _A="binary"  )			->			Union[str, Any]:







				"""simple docstring"""
				snake_case__									=     simple_accuracy(_A      ,   _A  )
				snake_case__									=     float(fa_score(y_true=_A      ,   y_pred=_A      ,   average=_A  )  )
				return {
				    "accuracy": acc,
				    "f1": fa,
				}
def      a_ (      _A      ,   _A  )			->			List[Any]:







				"""simple docstring"""
				snake_case__									=     {}
				for id_pred, label in zip(_A      ,   _A  ):
								snake_case__									=     f'''{id_pred['idx']['paragraph']}-{id_pred['idx']['question']}'''
								snake_case__									=     id_pred['prediction']
								if question_id in question_map:
												question_map[question_id].append((pred, label)  )
								else:
												snake_case__									=     [(pred, label)]
				snake_case__							,						snake_case__									=     [], []
				for question, preds_labels in question_map.items():
								snake_case__							,						snake_case__									=     zip(*_A  )
								snake_case__									=     fa_score(y_true=_A      ,   y_pred=_A      ,   average='macro'  )
								fas.append(_A  )
								snake_case__									=     int(sum(pred == label for pred, label in preds_labels  ) == len(_A  )  )
								ems.append(_A  )
				snake_case__									=     float(sum(_A  ) / len(_A  )  )
				snake_case__									=     sum(_A  ) / len(_A  )
				snake_case__									=     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   __SCREAMING_SNAKE_CASE(	datasets.Metric	):
							def lowerCAmelCase_ (     self:      Optional[int]						)		->		Tuple:
											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:      Optional[int]						)		->		str:
											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:      List[str]       ,	UpperCamelCase:      Tuple       ,	UpperCamelCase:      Any						)		->		Optional[Any]:
											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":
															snake_case__									=     [
															    {
															        'qas': [
															            {'id': ref['idx']['query'], 'answers': [{'text': ans} for ans in ref['answers']]}
															            for ref in references
															        ]
															    }
															]
															snake_case__									=     {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 argparse import ArgumentParser

from .env import EnvironmentCommand
def      a_ (      )			->			Optional[Any]:







				"""simple docstring"""
				snake_case__									=     ArgumentParser('Diffusers CLI tool'      ,   usage='diffusers-cli <command> [<args>]'  )
				snake_case__									=     parser.add_subparsers(help='diffusers-cli command helpers'  )

				# Register commands
				EnvironmentCommand.register_subcommand(_A  )

				# Let's go
				snake_case__									=     parser.parse_args()

				if not hasattr(_A      ,   'func'  ):
								parser.print_help()
								exit(1  )

				# Run
				snake_case__									=     args.func(_A  )
				service.run()


if __name__ == "__main__":
							main()




