'''simple docstring'''

from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


_A			:      Union[str, Any]					      =				'''\
@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			:      Tuple					      =				'''\
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      UpperCamelCase_						(					snake_case_       :       Optional[Any]						,		snake_case_       :       Any      )					->     List[Any]:






 '''simple docstring'''

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







def      UpperCamelCase_						(					snake_case_       :       List[Any]						,		snake_case_       :       Tuple						,		snake_case_       :       Tuple="binary"      )					->     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      UpperCamelCase_						(					snake_case_       :       Tuple						,		snake_case_       :       Union[str, Any]      )					->     Tuple:






 '''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 				_lowercase     (				datasets.Metric			):


 '''simple docstring'''






 def a					(      self   :	Optional[Any]							)  ->    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 a					(      self   :	Dict							)  ->    List[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 a					(      self   :	Dict      ,       SCREAMING_SNAKE_CASE__   :	Dict      ,       SCREAMING_SNAKE_CASE__   :	Dict							)  ->    Optional[Any]:
  if self.config_name == "axb":
   return {"matthews_correlation": matthews_corrcoef(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)}
  elif self.config_name == "cb":
   return acc_and_fa(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       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(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)[0]
  elif self.config_name == "multirc":
   return evaluate_multirc(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)
  elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
   return {"accuracy": simple_accuracy(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)}
  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 warnings

from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor


_A			:      Optional[int]					      =				logging.get_logger(__name__)







class 				_lowercase     (				UpperCAmelCase__			):


 '''simple docstring'''






 def __init__(      self   :	Dict      ,       *SCREAMING_SNAKE_CASE__   :	Optional[int]      ,       **SCREAMING_SNAKE_CASE__   :	List[str]							)  ->    None:
  warnings.warn(
      """The class SegformerFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
      """ Please use SegformerImageProcessor instead."""      ,       SCREAMING_SNAKE_CASE__      ,       )
  super().__init__(*SCREAMING_SNAKE_CASE__      ,       **SCREAMING_SNAKE_CASE__							)






