
'''simple docstring'''




from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef

import datasets


lowercase			:				Dict    		=			'\\n@inproceedings{wang2019glue,\n  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n  note={In the Proceedings of ICLR.},\n  year={2019}\n}\n'

lowercase			:				Union[str, Any]    		=			'\\nGLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n'

lowercase			:				Optional[int]    		=			'\nCompute GLUE evaluation metric associated to each GLUE dataset.\nArgs:\n    predictions: list of predictions to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\nReturns: depending on the GLUE subset, one or several of:\n    "accuracy": Accuracy\n    "f1": F1 score\n    "pearson": Pearson Correlation\n    "spearmanr": Spearman Correlation\n    "matthews_correlation": Matthew Correlation\nExamples:\n\n    >>> glue_metric = datasets.load_metric(\'glue\', \'sst2\')  # \'sst2\' or any of ["mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'accuracy\': 1.0}\n\n    >>> glue_metric = datasets.load_metric(\'glue\', \'mrpc\')  # \'mrpc\' or \'qqp\'\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'accuracy\': 1.0, \'f1\': 1.0}\n\n    >>> glue_metric = datasets.load_metric(\'glue\', \'stsb\')\n    >>> references = [0., 1., 2., 3., 4., 5.]\n    >>> predictions = [0., 1., 2., 3., 4., 5.]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print({"pearson": round(results["pearson"], 2), "spearmanr": round(results["spearmanr"], 2)})\n    {\'pearson\': 1.0, \'spearmanr\': 1.0}\n\n    >>> glue_metric = datasets.load_metric(\'glue\', \'cola\')\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'matthews_correlation\': 1.0}\n'







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__						):




    '''simple docstring'''



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







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__						):




    '''simple docstring'''



    A       :   List[Any]					=						simple_accuracy(snake_case__		,				snake_case__						)
    A       :   List[Any]					=						float(fa_score(y_true=snake_case__		,				y_pred=snake_case__						)						)
    return {
        "accuracy": acc,
        "f1": fa,
    }







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__						):




    '''simple docstring'''



    A       :   Any					=						float(pearsonr(snake_case__		,				snake_case__						)[0]						)
    A       :   str					=						float(spearmanr(snake_case__		,				snake_case__						)[0]						)
    return {
        "pearson": pearson_corr,
        "spearmanr": spearman_corr,
    }







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,			_KWARGS_DESCRIPTION						)
class  A				(							datasets.Metric						):





     def 				__lowerCAmelCase							(							self	) ->				Optional[int]:






         """simple docstring"""






         if self.config_name not in [
             "sst2",
             "mnli",
             "mnli_mismatched",
             "mnli_matched",
             "cola",
             "stsb",
             "mrpc",
             "qqp",
             "qnli",
             "rte",
             "wnli",
             "hans",
         ]:
             raise KeyError(
                 '''You should supply a configuration name selected in '''
                 '''["sst2", "mnli", "mnli_mismatched", "mnli_matched", '''
                 '''"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]'''	)
         return datasets.MetricInfo(
             description=_DESCRIPTION						,			citation=_CITATION						,			inputs_description=_KWARGS_DESCRIPTION						,			features=datasets.Features(
                 {
                     '''predictions''': datasets.Value('''int64''' if self.config_name != '''stsb''' else '''float32'''	),
                     '''references''': datasets.Value('''int64''' if self.config_name != '''stsb''' else '''float32'''	),
                 }	)						,			codebase_urls=[]						,			reference_urls=[]						,			format='''numpy'''						,			)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) ->				Optional[Any]:






         """simple docstring"""






         if self.config_name == "cola":
             return {"matthews_correlation": matthews_corrcoef(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)}
         elif self.config_name == "stsb":
             return pearson_and_spearman(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)
         elif self.config_name in ["mrpc", "qqp"]:
             return acc_and_fa(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)
         elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
             return {"accuracy": simple_accuracy(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)}
         else:
             raise KeyError(
                 '''You should supply a configuration name selected in '''
                 '''["sst2", "mnli", "mnli_mismatched", "mnli_matched", '''
                 '''"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]'''	)











from __future__ import annotations

import math


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     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(_UpperCAmelCase ) + 1 )						,	6 ):
        if number % i == 0 or number % (i + 2) == 0:
            return False
    return True


_UpperCAmelCase				:       Any	   =				[num for num in range(3, 10_00_01, 2) if not is_prime(num)]


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

    lowerCamelCase__    :      int					 =		[]
    for num in range(len(_UpperCAmelCase ) ):
        lowerCamelCase__    :      Union[str, Any]					 =		0
        while 2 * i * i <= odd_composites[num]:
            lowerCamelCase__    :      Dict					 =		odd_composites[num] - 2 * i * i
            if is_prime(_UpperCAmelCase ):
                break
            i += 1
        else:
            list_nums.append(odd_composites[num] )
            if len(_UpperCAmelCase ) == n:
                return list_nums

    return []


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


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





