



from sklearn.metrics import mean_squared_error

import datasets


A__  :  List[str]								=					'''\\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'''

A__  :  Any								=					'''\\nMean Squared Error(MSE) is the average of the square of difference between the predicted\nand actual values.\n'''


A__  :  List[Any]								=					'''\nArgs:\n    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Estimated target values.\n    references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Ground truth (correct) target values.\n    sample_weight: array-like of shape (n_samples,), default=None\n        Sample weights.\n    multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"\n        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n        "raw_values" : Returns a full set of errors in case of multioutput input.\n\n        "uniform_average" : Errors of all outputs are averaged with uniform weight.\n\n    squared : bool, default=True\n        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.\n\nReturns:\n    mse : mean squared error.\nExamples:\n\n    >>> mse_metric = datasets.load_metric("mse")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mse\': 0.375}\n    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)\n    >>> print(rmse_result)\n    {\'mse\': 0.6123724356957945}\n\n    If you\'re using multi-dimensional lists, then set the config as follows :\n\n    >>> mse_metric = datasets.load_metric("mse", "multilist")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mse\': 0.7083333333333334}\n    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {\'mse\': array([0.41666667, 1. ])}\n'''



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




							def    UpperCAmelCase__			(       self     :       Dict):
									return datasets.MetricInfo(
									    description=_DESCRIPTION				,				citation=_CITATION				,				inputs_description=_KWARGS_DESCRIPTION				,				features=datasets.Features(self._get_feature_types())				,				reference_urls=[
									        '''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html'''
									    ]				,				)




							def    UpperCAmelCase__			(       self     :       List[str]):







									if self.config_name == "multilist":
											return {
											    "predictions": datasets.Sequence(datasets.Value('''float''')),
											    "references": datasets.Sequence(datasets.Value('''float''')),
											}
									else:
											return {
											    "predictions": datasets.Value('''float'''),
											    "references": datasets.Value('''float'''),
											}







							def    UpperCAmelCase__			(       self     :       Optional[int]				,				A_     :       Optional[Any]				,				A_     :       str				,				A_     :       Dict=None				,				A_     :       Tuple="uniform_average"				,				A_     :       Any=True):
									lowerCAmelCase_			:     Any									= mean_squared_error(
									    lowerCamelCase__				,				lowerCamelCase__				,				sample_weight=lowerCamelCase__				,				multioutput=lowerCamelCase__				,				squared=lowerCamelCase__)

									return {"mse": mse}









'''simple docstring'''


def        __lowerCAmelCase       ():
		return [list(range(1_000 - i					,       -1_000 - i					,       -1      )      ) for i in range(1_000      )]


lowerCamelCase__					=   generate_large_matrix()
lowerCamelCase__					=   (
    [[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
    [[3, 2], [1, 0]],
    [[7, 7, 6]],
    [[7, 7, 6], [-1, -2, -3]],
    grid,
)






def        __lowerCAmelCase       (__lowerCAmelCase      ):
		assert all(row == sorted(__lowerCAmelCase					,       reverse=__lowerCAmelCase      ) for row in grid      )
		assert all(list(__lowerCAmelCase      ) == sorted(__lowerCAmelCase					,       reverse=__lowerCAmelCase      ) for col in zip(*__lowerCAmelCase      )      )






def        __lowerCAmelCase       (__lowerCAmelCase      ):
		_UpperCAmelCase						:				Any	    =					0
		_UpperCAmelCase						:				str	    =					len(__lowerCAmelCase      ) - 1

		# Edge cases such as no values or all numbers are negative.
		if not array or array[0] < 0:
				return 0

		while right + 1 > left:
				_UpperCAmelCase						:				Union[str, Any]	    =					(left + right) // 2
				_UpperCAmelCase						:				List[str]	    =					array[mid]

				# Num must be negative and the index must be greater than or equal to 0.
				if num < 0 and array[mid - 1] >= 0:
						return mid

				if num >= 0:
						_UpperCAmelCase						:				Tuple	    =					mid + 1
				else:
						_UpperCAmelCase						:				Optional[Any]	    =					mid - 1
    # No negative numbers so return the last index of the array + 1 which is the length.
		return len(__lowerCAmelCase      )






def        __lowerCAmelCase       (__lowerCAmelCase      ):
		_UpperCAmelCase						:				str	    =					0
		_UpperCAmelCase						:				int	    =					len(grid[0]      )

		for i in range(len(__lowerCAmelCase      )      ):
				_UpperCAmelCase						:				Dict	    =					find_negative_index(grid[i][:bound]      )
				total += bound
		return (len(__lowerCAmelCase      ) * len(grid[0]      )) - total






def        __lowerCAmelCase       (__lowerCAmelCase      ):
		return len([number for row in grid for number in row if number < 0]      )






def        __lowerCAmelCase       (__lowerCAmelCase      ):
		_UpperCAmelCase						:				Tuple	    =					0
		for row in grid:
				for i, number in enumerate(__lowerCAmelCase      ):
						if number < 0:
								total += len(__lowerCAmelCase      ) - i
								break
		return total






def        __lowerCAmelCase       ():
		from timeit import timeit

		print("Running benchmarks"      )
		_UpperCAmelCase						:				Tuple	    =					(
		    "from __main__ import count_negatives_binary_search, "
		    "count_negatives_brute_force, count_negatives_brute_force_with_break, grid"
		)
		for func in (
		    "count_negatives_binary_search",  # took 0.7727 seconds
		    "count_negatives_brute_force_with_break",  # took 4.6505 seconds
		    "count_negatives_brute_force",  # took 12.8160 seconds
		):
				_UpperCAmelCase						:				str	    =					timeit(F"""{func}(grid=grid)"""					,       setup=__lowerCAmelCase					,       number=500      )
				print(F"""{func}() took {time:0.4f} seconds"""      )


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						benchmark()



