


'''simple docstring'''

from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


a_  :				Any						= """\
@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_  :				Dict						= """\
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_  :				Tuple						= """
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 __snake_case					(      UpperCAmelCase_						:						Tuple					,				UpperCAmelCase_						:						Tuple		):
 return float((preds == labels).mean()		)





def __snake_case					(      UpperCAmelCase_						:						Any					,				UpperCAmelCase_						:						Union[str, Any]					,				UpperCAmelCase_						:						int="binary"		):
 lowerCamelCase_      =  simple_accuracy(UpperCAmelCase_					,				UpperCAmelCase_		)
 lowerCamelCase_      =  float(fa_score(y_true=UpperCAmelCase_					,				y_pred=UpperCAmelCase_					,				average=UpperCAmelCase_		)		)
 return {
     "accuracy": acc,
     "f1": fa,
 }





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





  """simple docstring"""




  def   snake_case		(    self     ):







   """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   snake_case		(    self     ):







   """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   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   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":
    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(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'''

from __future__ import annotations

from fractions import Fraction





def __snake_case					(      UpperCAmelCase_						:						int					,				UpperCAmelCase_						:						int		):
 return (
     num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
 )





def __snake_case					(      UpperCAmelCase_						:						int		):
 lowerCamelCase_      =  []
 lowerCamelCase_      =  11
 lowerCamelCase_      =  int("1" + "0" * digit_len		)
 for num in range(UpperCAmelCase_					,				UpperCAmelCase_		):
  while den <= 99:
   if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
    if is_digit_cancelling(UpperCAmelCase_					,				UpperCAmelCase_		):
     solutions.append(F'''{num}/{den}'''		)
   den += 1
  num += 1
  lowerCamelCase_      =  10
 return solutions





def __snake_case					(      UpperCAmelCase_						:						int = 2		):
 lowerCamelCase_      =  1.0
 for fraction in fraction_list(UpperCAmelCase_		):
  lowerCamelCase_      =  Fraction(UpperCAmelCase_		)
  result *= frac.denominator / frac.numerator
 return int(UpperCAmelCase_		)


if __name__ == "__main__":
     print(solution())



