




'''simple docstring'''





from typing import Any

class   _a							:






     def __init__(					self   ,_SCREAMING_SNAKE_CASE       )				->     List[str]:
          _snake_case				= data
          _snake_case				= None







class   _a							:






     def __init__(					self       )				->     List[Any]:
          _snake_case				= None






     def 							_lowercase      (					self       )				->     str:
          _snake_case				= self.head
          while temp is not None:
               print(temp.data   ,end=" "       )
               _snake_case				= temp.next
          print()






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE       )				->     Dict:
          _snake_case				= Node(_SCREAMING_SNAKE_CASE       )
          _snake_case				= self.head
          _snake_case				= new_node






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )				->     Tuple:
          if node_data_a == node_data_a:
               return
          else:
               _snake_case				= self.head
               while node_a is not None and node_a.data != node_data_a:
                    _snake_case				= node_a.next

               _snake_case				= self.head
               while node_a is not None and node_a.data != node_data_a:
                    _snake_case				= node_a.next

               if node_a is None or node_a is None:
                    return

               _snake_case				,   _snake_case				= node_a.data, node_a.data


if __name__ == "__main__":
   UpperCamelCase_		:     str          =		LinkedList()
   for i in range(5, 0, -1):
      ll.push(i)

   ll.print_list()

   ll.swap_nodes(1, 4)
   print('''After swapping''')
   ll.print_list()








'''simple docstring'''





from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


UpperCamelCase_		:     Tuple          =		'''\
@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}
}
'''

UpperCamelCase_		:     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.
'''

UpperCamelCase_		:     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       __a      (  _UpperCamelCase:       Tuple   ,		_UpperCamelCase:       int       )		->					Optional[Any]:

     """simple docstring"""




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





def       __a      (  _UpperCamelCase:       Union[str, Any]   ,		_UpperCamelCase:       Optional[int]   ,		_UpperCamelCase:       int="binary"       )		->					Any:

     """simple docstring"""




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





def       __a      (  _UpperCamelCase:       str   ,		_UpperCamelCase:       Any       )		->					Tuple:

     """simple docstring"""




     _snake_case				= {}
     for id_pred, label in zip(_UpperCamelCase   ,		_UpperCamelCase       ):
          _snake_case				= F"""{id_pred['idx']['paragraph']}-{id_pred['idx']['question']}"""
          _snake_case				= id_pred["prediction"]
          if question_id in question_map:
               question_map[question_id].append((pred, label)       )
          else:
               _snake_case				= [(pred, label)]
     _snake_case				,   _snake_case				= [], []
     for question, preds_labels in question_map.items():
          _snake_case				,   _snake_case				= zip(*_UpperCamelCase       )
          _snake_case				= fa_score(y_true=_UpperCamelCase   ,		y_pred=_UpperCamelCase   ,		average="macro"       )
          fas.append(_UpperCamelCase       )
          _snake_case				= int(sum(pred == label for pred, label in preds_labels       ) == len(_UpperCamelCase       )       )
          ems.append(_UpperCamelCase       )
     _snake_case				= float(sum(_UpperCamelCase       ) / len(_UpperCamelCase       )       )
     _snake_case				= sum(_UpperCamelCase       ) / len(_UpperCamelCase       )
     _snake_case				= 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   _a							(		datasets.Metric				):






     def 							_lowercase      (					self       )				->     Union[str, 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 							_lowercase      (					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 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )				->     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":
               _snake_case				= [
                   {
                       "qas": [
                           {"id": ref["idx"]["query"], "answers": [{"text": ans} for ans in ref["answers"]]}
                           for ref in references
                       ]
                   }
               ]
               _snake_case				= {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\",]"       )


