





'''simple docstring'''






def       lowercase_      (		lowercase__				,  lowercase__						)   ->str:
						_snake_case:   list[list[str]]  					=		[[] for _ in range(lowercase__						)]
						_snake_case:   Dict  					=		key - 1

						if key <= 0:
												raise ValueError('Height of grid can\'t be 0 or negative'						)
						if key == 1 or len(lowercase__						) <= key:
												return input_string

						for position, character in enumerate(lowercase__						):
												_snake_case:   int  					=		position % (lowest * 2)  # puts it in bounds
												_snake_case:   Union[str, Any]  					=		min(lowercase__				,  lowest * 2 - num						)  # creates zigzag pattern
												temp_grid[num].append(lowercase__						)
						_snake_case:   int  					=		[''.join(lowercase__						) for row in temp_grid]
						_snake_case:   List[Any]  					=		''.join(lowercase__						)

						return output_string





def       lowercase_      (		lowercase__				,  lowercase__						)   ->str:
						_snake_case:   Union[str, Any]  					=		[]
						_snake_case:   Tuple  					=		key - 1

						if key <= 0:
												raise ValueError('Height of grid can\'t be 0 or negative'						)
						if key == 1:
												return input_string

						_snake_case:   list[list[str]]  					=		[[] for _ in range(lowercase__						)]  # generates template
						for position in range(len(lowercase__						)						):
												_snake_case:   str  					=		position % (lowest * 2)  # puts it in bounds
												_snake_case:   Optional[Any]  					=		min(lowercase__				,  lowest * 2 - num						)  # creates zigzag pattern
												temp_grid[num].append('*'						)

						_snake_case:   Any  					=		0
						for row in temp_grid:  # fills in the characters
												_snake_case:   Dict  					=		input_string[counter : counter + len(lowercase__						)]
												grid.append(list(lowercase__						)						)
												counter += len(lowercase__						)

						_snake_case:   Optional[Any]  					=		''  # reads as zigzag
						for position in range(len(lowercase__						)						):
												_snake_case:   Any  					=		position % (lowest * 2)  # puts it in bounds
												_snake_case:   str  					=		min(lowercase__				,  lowest * 2 - num						)  # creates zigzag pattern
												output_string += grid[num][0]
												grid[num].pop(0						)
						return output_string





def       lowercase_      (		lowercase__						)   ->dict[int, str]:
						_snake_case:   Dict  					=		{}
						for key_guess in range(1				,  len(lowercase__						)						):  # tries every key
												_snake_case:   Optional[Any]  					=		decrypt(lowercase__				,  lowercase__						)
						return results


if __name__ == "__main__":
		import doctest

		doctest.testmod()












'''simple docstring'''






from sklearn.metrics import matthews_corrcoef

import datasets


A   : Dict             =			'\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It takes\ninto account true and false positives and negatives and is generally\nregarded as a balanced measure which can be used even if the classes are of\nvery different sizes. The MCC is in essence a correlation coefficient value\nbetween -1 and +1. A coefficient of +1 represents a perfect prediction, 0\nan average random prediction and -1 an inverse prediction.  The statistic\nis also known as the phi coefficient. [source: Wikipedia]\n'

A   : int             =			'\nArgs:\n    predictions (list of int): Predicted labels, as returned by a model.\n    references (list of int): Ground truth labels.\n    sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.\nReturns:\n    matthews_correlation (dict containing float): Matthews correlation.\nExamples:\n    Example 1, a basic example with only predictions and references as inputs:\n        >>> matthews_metric = datasets.load_metric("matthews_correlation")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3])\n        >>> print(round(results[\'matthews_correlation\'], 2))\n        0.54\n\n    Example 2, the same example as above, but also including sample weights:\n        >>> matthews_metric = datasets.load_metric("matthews_correlation")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3],\n        ...                                     sample_weight=[0.5, 3, 1, 1, 1, 2])\n        >>> print(round(results[\'matthews_correlation\'], 2))\n        0.1\n\n    Example 3, the same example as above, but with sample weights that cause a negative correlation:\n        >>> matthews_metric = datasets.load_metric("matthews_correlation")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3],\n        ...                                     sample_weight=[0.5, 1, 0, 0, 0, 1])\n        >>> print(round(results[\'matthews_correlation\'], 2))\n        -0.25\n'

A   : 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  ):


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[Any]       ):





											'''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    SCREAMING_SNAKE_CASE_		(					self	:  Optional[Any]       ,     __snake_case	:  int       ,     __snake_case	:  List[Any]       ,     __snake_case	:  Union[str, Any]=None       ):





											'''simple docstring'''





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





