





'''simple docstring'''


from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


lowerCAmelCase   :	List[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}
}
'''

lowerCAmelCase   :	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.
'''

lowerCAmelCase   :	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 				UpperCAmelCase_    (		__lowerCamelCase   :   Optional[Any]      ,__lowerCamelCase   :   Dict					):
   return float((preds == labels).mean()					)
def 				UpperCAmelCase_    (		__lowerCamelCase   :   Any      ,__lowerCamelCase   :   str      ,__lowerCamelCase   :   Union[str, Any]="binary"					):
   lowercase_     :Tuple            =					simple_accuracy(__lowerCamelCase      ,__lowerCamelCase					)
   lowercase_     :str            =					float(fa_score(y_true=__lowerCamelCase      ,y_pred=__lowerCamelCase      ,average=__lowerCamelCase					)					)
   return {
       "accuracy": acc,
       "f1": fa,
   }
def 				UpperCAmelCase_    (		__lowerCamelCase   :   Union[str, Any]      ,__lowerCamelCase   :   Optional[int]					):
   lowercase_     :Any            =					{}
   for id_pred, label in zip(__lowerCamelCase      ,__lowerCamelCase					):
      lowercase_     :str            =					F'{id_pred["idx"]["paragraph"]}-{id_pred["idx"]["question"]}'
      lowercase_     :Tuple            =					id_pred["prediction"]
      if question_id in question_map:
         question_map[question_id].append((pred, label)					)
      else:
         lowercase_     :List[str]            =					[(pred, label)]
   lowercase_     ,				lowercase_     :int            =					[], []
   for question, preds_labels in question_map.items():
      lowercase_     ,				lowercase_     :List[Any]            =					zip(*__lowerCamelCase					)
      lowercase_     :Optional[int]            =					fa_score(y_true=__lowerCamelCase      ,y_pred=__lowerCamelCase      ,average="macro"					)
      fas.append(__lowerCamelCase					)
      lowercase_     :Optional[Any]            =					int(sum(pred == label for pred, label in preds_labels					) == len(__lowerCamelCase					)					)
      ems.append(__lowerCamelCase					)
   lowercase_     :List[Any]            =					float(sum(__lowerCamelCase					) / len(__lowerCamelCase					)					)
   lowercase_     :Tuple            =					sum(__lowerCamelCase					) / len(__lowerCamelCase					)
   lowercase_     :Dict            =					float(fa_score(y_true=__lowerCamelCase      ,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]		):





        """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						:   List[Any]		):





        """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						:   str      , lowercase						:   List[Any]      , lowercase						:   Union[str, Any]		):





        """simple docstring"""




        if self.config_name == "axb":
           return {"matthews_correlation": matthews_corrcoef(snake_case__      , snake_case__		)}
        elif self.config_name == "cb":
           return acc_and_fa(snake_case__      , snake_case__      , fa_avg="macro"		)
        elif self.config_name == "record":
           lowercase_     :Union[str, Any]            =					[
               {
                   "qas": [
                       {"id": ref["idx"]["query"], "answers": [{"text": ans} for ans in ref["answers"]]}
                       for ref in references
                   ]
               }
           ]
           lowercase_     :int            =					{pred["idx"]["query"]: pred["prediction_text"] for pred in predictions}
           return evaluate_record(snake_case__      , snake_case__		)[0]
        elif self.config_name == "multirc":
           return evaluate_multirc(snake_case__      , snake_case__		)
        elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
           return {"accuracy": simple_accuracy(snake_case__      , 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\",]"		)








from __future__ import annotations

import math


def 					_a							(					lowerCAmelCase )->	bool:

		if 1 < number < 4:
				# 2 and 3 are primes
				return True
		elif number < 2 or number % 2 == 0 or number % 3 == 0:
				# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
				return False

		# All primes number are in format of 6k +/- 1
		for i in range(5    ,						int(math.sqrt(lowerCAmelCase ) + 1 )    ,						6 ):
				if number % i == 0 or number % (i + 2) == 0:
						return False
		return True


SCREAMING_SNAKE_CASE:     List[str]									= [num for num in range(3, 1_0_0_0_0_1, 2) if not is_prime(num)]


def 					_a							(					lowerCAmelCase )->	list[int]:
		if not isinstance(lowerCAmelCase    ,						lowerCAmelCase ):
				raise ValueError('n must be an integer' )
		if n <= 0:
				raise ValueError('n must be >= 0' )

		SCREAMING_SNAKE_CASE_					      =							[]
		for num in range(len(lowerCAmelCase ) ):
				SCREAMING_SNAKE_CASE_					      =							0
				while 2 * i * i <= odd_composites[num]:
						SCREAMING_SNAKE_CASE_					      =							odd_composites[num] - 2 * i * i
						if is_prime(lowerCAmelCase ):
								break
						i += 1
				else:
						list_nums.append(odd_composites[num] )
						if len(lowerCAmelCase ) == n:
								return list_nums

		return []


def 					_a							(					)->	int:
		return compute_nums(1 )[0]


if __name__ == "__main__":
						print(f"""{solution() = }""")