

from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


__lowercase				      =						"""\
@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}
}
"""

__lowercase				      =						"""\
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.
"""

__lowercase				      =						"""
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 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   ):
      '''simple docstring'''







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







def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE="binary"   ):
      '''simple docstring'''







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







def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   ):
      '''simple docstring'''







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

   def     UpperCamelCase					(				self	:    Tuple       ) ->			str:
         """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     UpperCamelCase					(				self	:    Optional[int]       ) ->			str:
         """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     UpperCamelCase					(				self	:    Union[str, Any]			,						lowerCamelCase__	:    int			,						lowerCamelCase__	:    List[Any]       ) ->			Union[str, Any]:
         """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":
               A_							       =		[
                   {
                       '''qas''': [
                           {'''id''': ref['''idx''']['''query'''], '''answers''': [{'''text''': ans} for ans in ref['''answers''']]}
                           for ref in references
                       ]
                   }
               ]
               A_							       =		{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",]'''       )








import random

class    _lowercase				:

   @staticmethod
   def     UpperCamelCase					(				lowerCamelCase__	:    str       ) ->			tuple[list[int], list[int]]:
         """simple docstring"""





         A_							       =		[ord(lowerCamelCase__       ) for i in text]
         A_							       =		[]
         A_							       =		[]
         for i in plain:
               A_							       =		random.randint(1			,						3_0_0       )
               A_							       =		(i + k) * k
               cipher.append(lowerCamelCase__       )
               key.append(lowerCamelCase__       )
         return cipher, key





   @staticmethod
   def     UpperCamelCase					(				lowerCamelCase__	:    list[int]			,						lowerCamelCase__	:    list[int]       ) ->			str:
         """simple docstring"""





         A_							       =		[]
         for i in range(len(lowerCamelCase__       )       ):
               A_							       =		int((cipher[i] - (key[i]) ** 2) / key[i]       )
               plain.append(chr(lowerCamelCase__       )       )
         return "".join(lowerCamelCase__       )


if __name__ == "__main__":
      __lowercase      ,				__lowercase				      =						Onepad().encrypt("""Hello""")
      print(c, k)
      print(Onepad().decrypt(c, k))





