






from __future__ import annotations

import math






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


UpperCamelCase								=		[num for num in range(3, 100_001, 2) if not is_prime(num)]






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

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

							return []






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


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











"""simple docstring"""






from sklearn.metrics import matthews_corrcoef

import datasets


__UpperCAmelCase						=			'''
Compute the Matthews correlation coefficient (MCC)

The Matthews correlation coefficient is used in machine learning as a
measure of the quality of binary and multiclass classifications. It takes
into account true and false positives and negatives and is generally
regarded as a balanced measure which can be used even if the classes are of
very different sizes. The MCC is in essence a correlation coefficient value
between -1 and +1. A coefficient of +1 represents a perfect prediction, 0
an average random prediction and -1 an inverse prediction.  The statistic
is also known as the phi coefficient. [source: Wikipedia]
'''

__UpperCAmelCase						=			'''
Args:
    predictions (list of int): Predicted labels, as returned by a model.
    references (list of int): Ground truth labels.
    sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.
Returns:
    matthews_correlation (dict containing float): Matthews correlation.
Examples:
    Example 1, a basic example with only predictions and references as inputs:
        >>> matthews_metric = datasets.load_metric("matthews_correlation")
        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
        ...                                     predictions=[1, 2, 2, 0, 3, 3])
        >>> print(round(results[\'matthews_correlation\'], 2))
        0.54

    Example 2, the same example as above, but also including sample weights:
        >>> matthews_metric = datasets.load_metric("matthews_correlation")
        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
        ...                                     predictions=[1, 2, 2, 0, 3, 3],
        ...                                     sample_weight=[0.5, 3, 1, 1, 1, 2])
        >>> print(round(results[\'matthews_correlation\'], 2))
        0.1

    Example 3, the same example as above, but with sample weights that cause a negative correlation:
        >>> matthews_metric = datasets.load_metric("matthews_correlation")
        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
        ...                                     predictions=[1, 2, 2, 0, 3, 3],
        ...                                     sample_weight=[0.5, 1, 0, 0, 0, 1])
        >>> print(round(results[\'matthews_correlation\'], 2))
        -0.25
'''

__UpperCAmelCase						=			'''\
@article{scikit-learn,
  title={Scikit-learn: Machine Learning in {P}ython},
  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
  journal={Journal of Machine Learning Research},
  volume={12},
  pages={2825--2830},
  year={2011}
}
'''



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,  _KWARGS_DESCRIPTION  )
class 					__UpperCAmelCase							(						datasets.Metric  ):
				def       UpperCAmelCase      (		self    :			Optional[int]    )	->	str:



							'''simple docstring'''



							return datasets.MetricInfo(
							    description=_DESCRIPTION	, citation=_CITATION	, inputs_description=_KWARGS_DESCRIPTION	, features=datasets.Features(
							        {
							            "predictions": datasets.Value("int32"    ),
							            "references": datasets.Value("int32"    ),
							        }    )	, reference_urls=[
							        "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html"
							    ]	, )






				def       UpperCAmelCase      (		self    :			Tuple	, a_    :			Optional[Any]	, a_    :			Optional[Any]	, a_    :			Any=None    )	->	Union[str, Any]:



							'''simple docstring'''



							return {
							    "matthews_correlation": float(matthews_corrcoef(a_	, a_	, sample_weight=a_    )    ),
							}