




'''simple docstring'''




from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


a_      :		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}
}
"""

a_      :		int							      =       """\
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      a_							(      __snake_case							:			Dict   ,       __snake_case							:			Any   )							->						int:



       """simple docstring"""
       return float((preds == labels).mean()   )





def      a_							(      __snake_case							:			List[Any]   ,       __snake_case							:			Tuple   ,       __snake_case							:			List[Any]="binary"   )							->						List[str]:



       """simple docstring"""
       lowerCamelCase_							=simple_accuracy(__snake_case   ,       __snake_case   )
       lowerCamelCase_							=float(fa_score(y_true=__snake_case   ,       y_pred=__snake_case   ,       average=__snake_case   )   )
       return {
           "accuracy": acc,
           "f1": fa,
       }





def      a_							(      __snake_case							:			Union[str, Any]   ,       __snake_case							:			List[str]   )							->						List[Any]:



       """simple docstring"""
       lowerCamelCase_							={}
       for id_pred, label in zip(__snake_case   ,       __snake_case   ):
              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(*__snake_case   )
              lowerCamelCase_							=fa_score(y_true=__snake_case   ,       y_pred=__snake_case   ,       average='''macro'''   )
              fas.append(__snake_case   )
              lowerCamelCase_							=int(sum(pred == label for pred, label in preds_labels   ) == len(__snake_case   )   )
              ems.append(__snake_case   )
       lowerCamelCase_							=float(sum(__snake_case   ) / len(__snake_case   )   )
       lowerCamelCase_							=sum(__snake_case   ) / len(__snake_case   )
       lowerCamelCase_							=float(fa_score(y_true=__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   __UpperCamelCase       ( datasets.Metric				):






       def      lowercase__     (							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      lowercase__     (							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      lowercase__     (							self,		lowerCAmelCase,		lowerCAmelCase  ):



              """simple docstring"""







              if self.config_name == "axb":
                     return {"matthews_correlation": matthews_corrcoef(lowerCAmelCase,		lowerCAmelCase  )}
              elif self.config_name == "cb":
                     return acc_and_fa(lowerCAmelCase,		lowerCAmelCase,		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(lowerCAmelCase,		lowerCAmelCase  )[0]
              elif self.config_name == "multirc":
                     return evaluate_multirc(lowerCAmelCase,		lowerCAmelCase  )
              elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
                     return {"accuracy": simple_accuracy(lowerCAmelCase,		lowerCAmelCase  )}
              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"""







import unittest

import torch
from torch import nn

from diffusers.models.activations import get_activation


class       SCREAMING_SNAKE_CASE__				(		unittest.TestCase				):
       """simple docstring"""


       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     int								=							get_activation("swish"						)

         self.assertIsInstance(snake_case__		,     nn.SiLU						)

         self.assertEqual(act(torch.tensor(-100		,     dtype=torch.floataa						)						).item()		,     0						)
         self.assertNotEqual(act(torch.tensor(-1		,     dtype=torch.floataa						)						).item()		,     0						)
         self.assertEqual(act(torch.tensor(0		,     dtype=torch.floataa						)						).item()		,     0						)
         self.assertEqual(act(torch.tensor(20		,     dtype=torch.floataa						)						).item()		,     20						)

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Union[str, Any]								=							get_activation("silu"						)

         self.assertIsInstance(snake_case__		,     nn.SiLU						)

         self.assertEqual(act(torch.tensor(-100		,     dtype=torch.floataa						)						).item()		,     0						)
         self.assertNotEqual(act(torch.tensor(-1		,     dtype=torch.floataa						)						).item()		,     0						)
         self.assertEqual(act(torch.tensor(0		,     dtype=torch.floataa						)						).item()		,     0						)
         self.assertEqual(act(torch.tensor(20		,     dtype=torch.floataa						)						).item()		,     20						)

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Dict								=							get_activation("mish"						)

         self.assertIsInstance(snake_case__		,     nn.Mish						)

         self.assertEqual(act(torch.tensor(-200		,     dtype=torch.floataa						)						).item()		,     0						)
         self.assertNotEqual(act(torch.tensor(-1		,     dtype=torch.floataa						)						).item()		,     0						)
         self.assertEqual(act(torch.tensor(0		,     dtype=torch.floataa						)						).item()		,     0						)
         self.assertEqual(act(torch.tensor(20		,     dtype=torch.floataa						)						).item()		,     20						)







       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Union[str, Any]								=							get_activation("gelu"						)

         self.assertIsInstance(snake_case__		,     nn.GELU						)

         self.assertEqual(act(torch.tensor(-100		,     dtype=torch.floataa						)						).item()		,     0						)
         self.assertNotEqual(act(torch.tensor(-1		,     dtype=torch.floataa						)						).item()		,     0						)
         self.assertEqual(act(torch.tensor(0		,     dtype=torch.floataa						)						).item()		,     0						)
         self.assertEqual(act(torch.tensor(20		,     dtype=torch.floataa						)						).item()		,     20						)





