






from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


snake_case__   :						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}
}
"""

snake_case__   :						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.
"""

snake_case__   :						Dict													=   """
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     (__lowercase     ,   __lowercase):
   return float((preds == labels).mean())






def      _snake_case     (__lowercase     ,   __lowercase     ,   __lowercase="binary"):
   UpperCamelCase_						=  simple_accuracy(__lowercase     ,   __lowercase)
   UpperCamelCase_						=  float(fa_score(y_true=__lowercase     ,   y_pred=__lowercase     ,   average=__lowercase))
   return {
       "accuracy": acc,
       "f1": fa,
   }






def      _snake_case     (__lowercase     ,   __lowercase):
   UpperCamelCase_						=  {}
   for id_pred, label in zip(__lowercase     ,   __lowercase):
      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_		,   UpperCamelCase_						=  [], []
   for question, preds_labels in question_map.items():
      UpperCamelCase_		,   UpperCamelCase_						=  zip(*__lowercase)
      UpperCamelCase_						=  fa_score(y_true=__lowercase     ,   y_pred=__lowercase     ,   average='macro')
      fas.append(__lowercase)
      UpperCamelCase_						=  int(sum(pred == label for pred, label in preds_labels) == len(__lowercase))
      ems.append(__lowercase)
   UpperCamelCase_						=  float(sum(__lowercase) / len(__lowercase))
   UpperCamelCase_						=  sum(__lowercase) / len(__lowercase)
   UpperCamelCase_						=  float(fa_score(y_true=__lowercase     ,   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     ):






  """simple docstring"""





  def  _UpperCAmelCase  (  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  _UpperCAmelCase  (  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  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			Tuple:
     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":
        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(_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",]'			)













import datasets

from .evaluate import evaluate


snake_case__   :						int													=   """\
@article{hendrycks2021cuad,
      title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
      author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},
      journal={arXiv preprint arXiv:2103.06268},
      year={2021}
}
"""

snake_case__   :						Union[str, Any]													=   """
This metric wrap the official scoring script for version 1 of the Contract
Understanding Atticus Dataset (CUAD).
Contract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510
commercial legal contracts that have been manually labeled to identify 41 categories of important
clauses that lawyers look for when reviewing contracts in connection with corporate transactions.
"""

snake_case__   :						Any													=   """
Computes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).
Args:
    predictions: List of question-answers dictionaries with the following key-values:
        - 'id': id of the question-answer pair as given in the references (see below)
        - 'prediction_text': list of possible texts for the answer, as a list of strings
        depending on a threshold on the confidence probability of each prediction.
    references: List of question-answers dictionaries with the following key-values:
        - 'id': id of the question-answer pair (see above),
        - 'answers': a Dict in the CUAD dataset format
            {
                'text': list of possible texts for the answer, as a list of strings
                'answer_start': list of start positions for the answer, as a list of ints
            }
            Note that answer_start values are not taken into account to compute the metric.
Returns:
    'exact_match': Exact match (the normalized answer exactly match the gold answer)
    'f1': The F-score of predicted tokens versus the gold answer
    'aupr': Area Under the Precision-Recall curve
    'prec_at_80_recall': Precision at 80% recall
    'prec_at_90_recall': Precision at 90% recall
Examples:
    >>> predictions = [{'prediction_text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.'], 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]
    >>> references = [{'answers': {'answer_start': [143, 49], 'text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.']}, 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]
    >>> cuad_metric = datasets.load_metric(\"cuad\")
    >>> results = cuad_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'exact_match': 100.0, 'f1': 100.0, 'aupr': 0.0, 'prec_at_80_recall': 1.0, 'prec_at_90_recall': 1.0}
"""


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,							_KWARGS_DESCRIPTION     )
class     _a							(	datasets.Metric     ):






  """simple docstring"""





  def  _UpperCAmelCase  (  self			)      ->			Optional[int]:
     return datasets.MetricInfo(
         description=_DESCRIPTION		,		citation=_CITATION		,		inputs_description=_KWARGS_DESCRIPTION		,		features=datasets.Features(
             {
                 'predictions': {
                     'id': datasets.Value('string'			),
                     'prediction_text': datasets.features.Sequence(datasets.Value('string'			)			),
                 },
                 'references': {
                     'id': datasets.Value('string'			),
                     'answers': datasets.features.Sequence(
                         {
                             'text': datasets.Value('string'			),
                             'answer_start': datasets.Value('int32'			),
                         }			),
                 },
             }			)		,		codebase_urls=['https://www.atticusprojectai.org/cuad']		,		reference_urls=['https://www.atticusprojectai.org/cuad']		,		)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			Dict:
     UpperCamelCase_						=  {prediction['id']: prediction['prediction_text'] for prediction in predictions}
     UpperCamelCase_						=  [
         {
             'paragraphs': [
                 {
                     'qas': [
                         {
                             'answers': [{'text': answer_text} for answer_text in ref['answers']['text']],
                             'id': ref['id'],
                         }
                         for ref in references
                     ]
                 }
             ]
         }
     ]
     UpperCamelCase_						=  evaluate(dataset=_UpperCAmelCase		,		predictions=_UpperCAmelCase			)
     return score





