


from __future__ import annotations

from collections.abc import Iterable, Iterator
from dataclasses import dataclass

A							:					Any     						=     (3, 9, -11, 0, 7, 5, 1, -1)
A							:					Any     						=     (4, 6, 2, 0, 8, 10, 3, -2)






@dataclass
class     UpperCamelCase:
					snake_case_		:				int
					snake_case_		:				Node | None






class     UpperCamelCase:


					def __init__(						self	:		List[str]     ,    SCREAMING_SNAKE_CASE	:		Iterable[int]						)			->	None:




											'''simple docstring'''

											__snake_case				=		None
											for i in sorted(SCREAMING_SNAKE_CASE     ,    reverse=SCREAMING_SNAKE_CASE						):
																	__snake_case				=		Node(SCREAMING_SNAKE_CASE     ,    self.head						)


					def __iter__(						self	:		Optional[int]						)			->	Iterator[int]:




											'''simple docstring'''

											__snake_case				=		self.head
											while node:
																	yield node.data
																	__snake_case				=		node.next_node


					def __len__(						self	:		int						)			->	int:




											'''simple docstring'''

											return sum(1 for _ in self						)






					def __str__(						self	:		Tuple						)			->	str:




											'''simple docstring'''

											return " -> ".join([str(SCREAMING_SNAKE_CASE						) for node in self]						)





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase )						->	SortedLinkedList:


						'''simple docstring'''






						return SortedLinkedList(list(_lowerCAmelCase ) + list(_lowerCAmelCase ) )


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	A							:					Tuple     						=     SortedLinkedList
	print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))









from sklearn.metrics import mean_squared_error

import datasets


A							:					List[Any]     						=     '\\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							:					Optional[int]     						=     '\\nMean Squared Error(MSE) is the average of the square of difference between the predicted\nand actual values.\n'


A							:					str     						=     '\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     UpperCamelCase(  datasets.Metric   ):


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict						)			->	Union[str, Any]:




											'''simple docstring'''

											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 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]						)			->	Dict:




											'''simple docstring'''

											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 							SCREAMING_SNAKE_CASE_  (						self	:		Dict     ,    SCREAMING_SNAKE_CASE	:		int     ,    SCREAMING_SNAKE_CASE	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		str=None     ,    SCREAMING_SNAKE_CASE	:		int="uniform_average"     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=True						)			->	Tuple:




											'''simple docstring'''

											__snake_case				=		mean_squared_error(
											    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    sample_weight=SCREAMING_SNAKE_CASE     ,    multioutput=SCREAMING_SNAKE_CASE     ,    squared=SCREAMING_SNAKE_CASE						)

											return {"mse": mse}





