





'''simple docstring'''


from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


a__			:					Optional[int]       						=						'\\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'

a__			:					Tuple       						=						'\\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'

a__			:					Tuple       						=						'\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       __snake_case		( SCREAMING_SNAKE_CASE_ :			Tuple			,	SCREAMING_SNAKE_CASE_ :			Union[str, Any] )							->     List[Any]:

       """simple docstring"""


       return float((preds == labels).mean() )
def       __snake_case		( SCREAMING_SNAKE_CASE_ :			List[str]			,	SCREAMING_SNAKE_CASE_ :			int			,	SCREAMING_SNAKE_CASE_ :			Tuple="binary" )							->     List[str]:

       """simple docstring"""


       UpperCAmelCase  =						simple_accuracy(SCREAMING_SNAKE_CASE_			,	SCREAMING_SNAKE_CASE_ )
       UpperCAmelCase  =						float(fa_score(y_true=SCREAMING_SNAKE_CASE_			,	y_pred=SCREAMING_SNAKE_CASE_			,	average=SCREAMING_SNAKE_CASE_ ) )
       return {
           "accuracy": acc,
           "f1": fa,
       }
def       __snake_case		( SCREAMING_SNAKE_CASE_ :			Any			,	SCREAMING_SNAKE_CASE_ :			Optional[int] )							->     Tuple:

       """simple docstring"""


       UpperCAmelCase  =						{}
       for id_pred, label in zip(SCREAMING_SNAKE_CASE_			,	SCREAMING_SNAKE_CASE_ ):
              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(*SCREAMING_SNAKE_CASE_ )
              UpperCAmelCase  =						fa_score(y_true=SCREAMING_SNAKE_CASE_			,	y_pred=SCREAMING_SNAKE_CASE_			,	average='''macro''' )
              fas.append(SCREAMING_SNAKE_CASE_ )
              UpperCAmelCase  =						int(sum(pred == label for pred, label in preds_labels ) == len(SCREAMING_SNAKE_CASE_ ) )
              ems.append(SCREAMING_SNAKE_CASE_ )
       UpperCAmelCase  =						float(sum(SCREAMING_SNAKE_CASE_ ) / len(SCREAMING_SNAKE_CASE_ ) )
       UpperCAmelCase  =						sum(SCREAMING_SNAKE_CASE_ ) / len(SCREAMING_SNAKE_CASE_ )
       UpperCAmelCase  =						float(fa_score(y_true=SCREAMING_SNAKE_CASE_			,	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 			__snake_case      ( self					:				Tuple						):
              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					:				Union[str, Any]						):
              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					:				int       ,     a__					:				Any       ,     a__					:				str						):
              if self.config_name == "axb":
                     return {"matthews_correlation": matthews_corrcoef(a__       ,     a__						)}
              elif self.config_name == "cb":
                     return acc_and_fa(a__       ,     a__       ,     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(a__       ,     a__						)[0]
              elif self.config_name == "multirc":
                     return evaluate_multirc(a__       ,     a__						)
              elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
                     return {"accuracy": simple_accuracy(a__       ,     a__						)}
              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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available


snake_case   					=				{'''tokenization_herbert''': ['''HerbertTokenizer''']}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case   					=				['''HerbertTokenizerFast''']


if TYPE_CHECKING:
     from .tokenization_herbert import HerbertTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_herbert_fast import HerbertTokenizerFast

else:
     import sys

     snake_case   					=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





