
"""simple docstring"""




from sklearn.metrics import accuracy_score

import datasets


UpperCAmelCase__				:   Any   							=			'\nAccuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:\nAccuracy = (TP + TN) / (TP + TN + FP + FN)\n Where:\nTP: True positive\nTN: True negative\nFP: False positive\nFN: False negative\n'


UpperCAmelCase__				:   List[str]   							=			'\nArgs:\n    predictions (`list` of `int`): Predicted labels.\n    references (`list` of `int`): Ground truth labels.\n    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.\n    sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n    accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.\n\nExamples:\n\n    Example 1-A simple example\n        >>> accuracy_metric = datasets.load_metric("accuracy")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])\n        >>> print(results)\n        {\'accuracy\': 0.5}\n\n    Example 2-The same as Example 1, except with `normalize` set to `False`.\n        >>> accuracy_metric = datasets.load_metric("accuracy")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)\n        >>> print(results)\n        {\'accuracy\': 3.0}\n\n    Example 3-The same as Example 1, except with `sample_weight` set.\n        >>> accuracy_metric = datasets.load_metric("accuracy")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])\n        >>> print(results)\n        {\'accuracy\': 0.8778625954198473}\n'


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



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


				"""simple docstring"""



				def      __magic_name__   (self   ) ->		Dict:



											"""simple docstring"""




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



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__=True       ,	SCREAMING_SNAKE_CASE__=None   ) ->		int:



											"""simple docstring"""




											return {
											    "accuracy": float(
											        accuracy_score(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	normalize=SCREAMING_SNAKE_CASE__       ,	sample_weight=SCREAMING_SNAKE_CASE__   )   )
											}






"""simple docstring"""




def lowercase_  (       _snake_case       ,_snake_case						):
							SCREAMING_SNAKE_CASE__						:	Optional[int]									=       []
							SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       len(_snake_case						)
							for i in range(_snake_case						):
														SCREAMING_SNAKE_CASE__						:	Dict									=       []
														for j in range(_snake_case						):
																					SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       0
																					for k in range(_snake_case						):
																												SCREAMING_SNAKE_CASE__						:	Optional[int]									=       val + matrix_a[i][k] * matrix_b[k][j]
																					list_a.append(_snake_case						)
														matrix_c.append(_snake_case						)
							return matrix_c

def lowercase_  (       _snake_case						):
							return [[int(row == column						) for column in range(_snake_case						)] for row in range(_snake_case						)]

def lowercase_  (       _snake_case						):
							if n <= 1:
														return n
							SCREAMING_SNAKE_CASE__						:	Tuple									=       identity(2						)
							SCREAMING_SNAKE_CASE__						:	str									=       [[1, 1], [1, 0]]
							SCREAMING_SNAKE_CASE__						:	str									=       n - 1
							while n > 0:
														if n % 2 == 1:
																					SCREAMING_SNAKE_CASE__						:	List[str]									=       multiply(_snake_case       ,_snake_case						)
														SCREAMING_SNAKE_CASE__						:	int									=       multiply(_snake_case       ,_snake_case						)
														SCREAMING_SNAKE_CASE__						:	List[str]									=       int(n / 2						)
							return res_matrix[0][0]

def lowercase_  (       _snake_case						):
							if n <= 1:
														return n
							SCREAMING_SNAKE_CASE__						:	int									=       0
							SCREAMING_SNAKE_CASE__						:	str									=       1
							for _ in range(2       ,n + 1						):
														SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__						:	Optional[int]									=       fiba, fiba + fiba
							return fiba

def lowercase_  (       ):
							for ordinal in "0th 1st 2nd 3rd 10th 100th 1000th".split():
														SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       int("""""".join(c for c in ordinal if c in """0123456789"""						)						)  # 1000th --> 1000
														print(
														    f'''{ordinal} fibonacci number using matrix exponentiation is '''
														    f'''{nth_fibonacci_matrix(_snake_case						)} and using bruteforce is '''
														    f'''{nth_fibonacci_bruteforce(_snake_case						)}\n'''						)
							# from timeit import timeit
							# print(timeit("nth_fibonacci_matrix(1000000)",
							#              "from main import nth_fibonacci_matrix", number=5))
							# print(timeit("nth_fibonacci_bruteforce(1000000)",
							#              "from main import nth_fibonacci_bruteforce", number=5))
							# 2.3342058970001744
							# 57.256506615000035


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					main()




