





from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


__lowercase  :   Dict						      ="""\\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"""

__lowercase  :   str						      ="""\\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"""

__lowercase  :   Union[str, Any]						      ="""\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__ (					lowercase__  ,    lowercase__  ):






   '''simple docstring'''






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




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






   '''simple docstring'''






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




def     a__ (					lowercase__  ,    lowercase__  ):






   '''simple docstring'''






   UpperCAmelCase_											={}
   for id_pred, label in zip(_lowerCamelCase  ,    _lowerCamelCase  ):
      UpperCAmelCase_											=F'{id_pred["idx"]["paragraph"]}-{id_pred["idx"]["question"]}'
      UpperCAmelCase_											=id_pred["""prediction"""]
      if question_id in question_map:
         question_map[question_id].append((pred, label)  )
      else:
         UpperCAmelCase_											=[(pred, label)]
   UpperCAmelCase_											=[], []
   for question, preds_labels in question_map.items():
      UpperCAmelCase_											=zip(*_lowerCamelCase  )
      UpperCAmelCase_											=fa_score(y_true=_lowerCamelCase  ,    y_pred=_lowerCamelCase  ,    average="macro"  )
      fas.append(_lowerCamelCase  )
      UpperCAmelCase_											=int(sum(pred == label for pred, label in preds_labels  ) == len(_lowerCamelCase  )  )
      ems.append(_lowerCamelCase  )
   UpperCAmelCase_											=float(sum(_lowerCamelCase  ) / len(_lowerCamelCase  )  )
   UpperCAmelCase_											=sum(_lowerCamelCase  ) / len(_lowerCamelCase  )
   UpperCAmelCase_											=float(fa_score(y_true=_lowerCamelCase  ,    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      A       (	datasets.Metric						):



      def 	lowerCAmelCase__    (	self:						Optional[int]       )						->    Optional[Any]:
         '''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 	lowerCAmelCase__    (	self:						List[str]       )						->    Any:
         '''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 	lowerCAmelCase__    (	self:						List[str]		,  _lowerCAmelCase:						Dict		,  _lowerCAmelCase:						Tuple       )						->    str:
         '''simple docstring'''

         if self.config_name == "axb":
            return {"matthews_correlation": matthews_corrcoef(_lowercase		,  _lowercase       )}
         elif self.config_name == "cb":
            return acc_and_fa(_lowercase		,  _lowercase		,  fa_avg="macro"       )
         elif self.config_name == "record":
            UpperCAmelCase_											=[
                {
                    """qas""": [
                        {"""id""": ref["""idx"""]["""query"""], """answers""": [{"""text""": ans} for ans in ref["""answers"""]]}
                        for ref in references
                    ]
                }
            ]
            UpperCAmelCase_											={pred["""idx"""]["""query"""]: pred["""prediction_text"""] for pred in predictions}
            return evaluate_record(_lowercase		,  _lowercase       )[0]
         elif self.config_name == "multirc":
            return evaluate_multirc(_lowercase		,  _lowercase       )
         elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
            return {"accuracy": simple_accuracy(_lowercase		,  _lowercase       )}
         else:
            raise KeyError(
                "You should supply a configuration name selected in "
                "[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]"       )











__lowercase  :   Optional[Any]						      =[
    (1000, """M"""),
    (900, """CM"""),
    (500, """D"""),
    (400, """CD"""),
    (100, """C"""),
    (90, """XC"""),
    (50, """L"""),
    (40, """XL"""),
    (10, """X"""),
    (9, """IX"""),
    (5, """V"""),
    (4, """IV"""),
    (1, """I"""),
]




def     a__ (					lowercase__  ):






   '''simple docstring'''






   UpperCAmelCase_											={"I": 1, "V": 5, "X": 1_0, "L": 5_0, "C": 1_0_0, "D": 5_0_0, "M": 1_0_0_0}
   UpperCAmelCase_											=0
   UpperCAmelCase_											=0
   while place < len(lowercase__  ):
      if (place + 1 < len(lowercase__  )) and (vals[roman[place]] < vals[roman[place + 1]]):
         total += vals[roman[place + 1]] - vals[roman[place]]
         place += 2
      else:
         total += vals[roman[place]]
         place += 1
   return total




def     a__ (					lowercase__  ):






   '''simple docstring'''






   UpperCAmelCase_											=[]
   for arabic, roman in ROMAN:
      ((UpperCAmelCase_)						,       (UpperCAmelCase_))											=divmod(lowercase__  ,    lowercase__  )
      result.append(roman * factor  )
      if number == 0:
         break
   return "".join(lowercase__  )


if __name__ == "__main__":
     import doctest

     doctest.testmod()




