'''simple docstring'''

from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


lowercase_     		= '''\
@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}
}
'''

lowercase_     		= '''\
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.
'''

lowercase_     		= '''
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 							UpperCamelCase__			(			a__			,      a__      ):


						'''simple docstring'''






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



def 							UpperCamelCase__			(			a__			,      a__			,      a__="binary"      ):


						'''simple docstring'''






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



def 							UpperCamelCase__			(			a__			,      a__      ):


						'''simple docstring'''






						_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   SCREAMING_SNAKE_CASE			(						datasets.Metric):







		"""simple docstring"""






		def 		UpperCamelCase__       (   self				) ->     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 		UpperCamelCase__       (   self				) ->     Dict:
								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		,						__A		,						__A				) ->     Tuple:
								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":
														_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(__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'''

def 							UpperCamelCase__			(			a__      ):


						'''simple docstring'''






						_lowerCAmelCase										=set()
						# To detect a back edge, keep track of vertices currently in the recursion stack
						_lowerCAmelCase										=set()
						return any(
						    node not in visited and depth_first_search(a__			,      a__			,      a__			,      a__      )
						    for node in graph      )



def 							UpperCamelCase__			(			a__			,      a__			,      a__			,      a__      ):


						'''simple docstring'''






						visited.add(a__      )
						rec_stk.add(a__      )

						for node in graph[vertex]:
												if node not in visited:
																		if depth_first_search(a__			,      a__			,      a__			,      a__      ):
																								return True
												elif node in rec_stk:
																		return True

    # The node needs to be removed from recursion stack before function ends
						rec_stk.remove(a__      )
						return False


if __name__ == "__main__":
							from doctest import testmod

							testmod()





