





'''simple docstring'''




from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


a__					:			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}
}
'''

a__					:			List[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.
'''

a__					:			Optional[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 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_							)   ->Dict:
	return float((preds == labels).mean()							)




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_="binary"							)   ->str:
	snake_case__	       =   simple_accuracy(UpperCAmelCase_					,				UpperCAmelCase_							)
	snake_case__	       =   float(fa_score(y_true=UpperCAmelCase_					,				y_pred=UpperCAmelCase_					,				average=UpperCAmelCase_							)							)
	return {
	    "accuracy": acc,
	    "f1": fa,
	}




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_							)   ->Union[str, Any]:
	snake_case__	       =   {}
	for id_pred, label in zip(UpperCAmelCase_					,				UpperCAmelCase_							):
		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(*UpperCAmelCase_							)
		snake_case__	       =   fa_score(y_true=UpperCAmelCase_					,				y_pred=UpperCAmelCase_					,				average='macro'							)
		fas.append(UpperCAmelCase_							)
		snake_case__	       =   int(sum(pred == label for pred, label in preds_labels							) == len(UpperCAmelCase_							)							)
		ems.append(UpperCAmelCase_							)
	snake_case__	       =   float(sum(UpperCAmelCase_							) / len(UpperCAmelCase_							)							)
	snake_case__	       =   sum(UpperCAmelCase_							) / len(UpperCAmelCase_							)
	snake_case__	       =   float(fa_score(y_true=UpperCAmelCase_					,				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							):





	def   _snake_case   (		self		)      ->		Optional[Any]:
		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   _snake_case   (		self		)      ->		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   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		Optional[int]:
		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",]'		)










'''simple docstring'''




a__					:			Optional[Any]          =     '''Alexander Joslin'''

import operator as op

from .stack import Stack




def 						__lowerCamelCase			(	UpperCAmelCase_							)   ->int:
	snake_case__	       =   {'*': op.mul, '/': op.truediv, '+': op.add, '-': op.sub}

	snake_case__	       =   Stack()
	snake_case__	       =   Stack()

	for i in equation:
		if i.isdigit():
			# RULE 1
			operand_stack.push(int(UpperCAmelCase_							)							)
		elif i in operators:
			# RULE 2
			operator_stack.push(UpperCAmelCase_							)
		elif i == ")":
			# RULE 4
			snake_case__	       =   operator_stack.peek()
			operator_stack.pop()
			snake_case__	       =   operand_stack.peek()
			operand_stack.pop()
			snake_case__	       =   operand_stack.peek()
			operand_stack.pop()

			snake_case__	       =   operators[opr](UpperCAmelCase_					,				UpperCAmelCase_							)
			operand_stack.push(UpperCAmelCase_							)

    # RULE 5
	return operand_stack.peek()


if __name__ == "__main__":
			a__					:			Any          =     '''(5 + ((4 * 2) * (2 + 3)))'''
			# answer = 45
			print(f"""{equation} = {dijkstras_two_stack_algorithm(equation)}""")



