






from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


lowerCamelCase : List[str]						   =       '''\
@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}
}
'''

lowerCamelCase : 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.
'''

lowerCamelCase : str						   =       '''
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_		(     lowerCAmelCase_       :						List[str]       ,       lowerCAmelCase_       :						Any      ):
       return float((preds == labels).mean()      )
def       snake_case_		(     lowerCAmelCase_       :						Any       ,       lowerCAmelCase_       :						Optional[int]       ,       lowerCAmelCase_       :						str="binary"      ):
       __lowercase      :							Optional[Any]				=     simple_accuracy(__UpperCamelCase       ,       __UpperCamelCase      )
       __lowercase      :							Dict				=     float(fa_score(y_true=__UpperCamelCase       ,       y_pred=__UpperCamelCase       ,       average=__UpperCamelCase      )      )
       return {
           "accuracy": acc,
           "f1": fa,
       }
def       snake_case_		(     lowerCAmelCase_       :						str       ,       lowerCAmelCase_       :						int      ):
       __lowercase      :							List[Any]				=     {}
       for id_pred, label in zip(__UpperCamelCase       ,       __UpperCamelCase      ):
              __lowercase      :							Optional[int]				=     F"{id_pred['idx']['paragraph']}-{id_pred['idx']['question']}"
              __lowercase      :							Union[str, Any]				=     id_pred["""prediction"""]
              if question_id in question_map:
                     question_map[question_id].append((pred, label)      )
              else:
                     __lowercase      :							str				=     [(pred, label)]
       __lowercase      :							List[str]				=     [], []
       for question, preds_labels in question_map.items():
              __lowercase      :							Optional[Any]				=     zip(*__UpperCamelCase      )
              __lowercase      :							int				=     fa_score(y_true=__UpperCamelCase       ,       y_pred=__UpperCamelCase       ,       average="""macro"""      )
              fas.append(__UpperCamelCase      )
              __lowercase      :							Dict				=     int(sum(pred == label for pred, label in preds_labels      ) == len(__UpperCamelCase      )      )
              ems.append(__UpperCamelCase      )
       __lowercase      :							Optional[int]				=     float(sum(__UpperCamelCase      ) / len(__UpperCamelCase      )      )
       __lowercase      :							Any				=     sum(__UpperCamelCase      ) / len(__UpperCamelCase      )
       __lowercase      :							int				=     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 		lowerCAmelCase     ( datasets.Metric			):

     '''simple docstring'''

     def       lowerCAmelCase				(  self				:	Any							)			->  Optional[int]:


            """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				:	Tuple							)			->  Optional[int]:


            """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]		,     __a				:	Optional[int]		,     __a				:	Tuple							)			->  List[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":
                   __lowercase      :							Optional[Any]				=     [
                       {
                           """qas""": [
                               {"""id""": ref["""idx"""]["""query"""], """answers""": [{"""text""": ans} for ans in ref["""answers"""]]}
                               for ref in references
                           ]
                       }
                   ]
                   __lowercase      :							Dict				=     {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\",]"""							)







import fire

from utils import calculate_rouge, save_json
def       snake_case_		(     lowerCAmelCase_       :						Any       ,       lowerCAmelCase_       :						str       ,       lowerCAmelCase_       :						str=None       ,       **lowerCAmelCase_       :						str      ):
							__lowercase      :							Tuple				=     [x.strip() for x in open(lowerCAmelCase_      ).readlines()]
							__lowercase      :							Dict				=     [x.strip() for x in open(lowerCAmelCase_      ).readlines()][: len(lowerCAmelCase_      )]
							__lowercase      :							Tuple				=     calculate_rouge(lowerCAmelCase_       ,       lowerCAmelCase_       ,       **lowerCAmelCase_      )
							if save_path is not None:
														save_json(lowerCAmelCase_       ,       lowerCAmelCase_       ,       indent=lowerCAmelCase_      )
							return metrics  # these print nicely


if __name__ == "__main__":
	fire.Fire(calculate_rouge_path)