
from __future__ import annotations

from collections.abc import Iterator
from typing import Any
class 					lowercase__:



	"""simple docstring"""




	def __init__(			self    :     List[str]			,				SCREAMING_SNAKE_CASE_    :     Any			)			->	str:
					lowercase_	    =   data
					lowercase_	    =   None

class 					lowercase__:



	"""simple docstring"""




	def __init__(			self    :     Any			)			->	str:
					lowercase_	    =   None
					lowercase_	    =   None
	def __iter__(			self    :     str			)			->	Iterator[Any]:
					lowercase_	    =   self.head
					while self.head:
									yield node.data
									lowercase_	    =   node.next
									if node == self.head:
													break
	def __len__(			self    :     Tuple			)			->	int:
					return sum(1 for _ in self			)
	def __repr__(			self    :     Tuple			)			->	List[str]:
					return "->".join(str(SCREAMING_SNAKE_CASE_			) for item in iter(self			)			)
	def 		_lowercase    (			self    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     Any			)			->	None:
					self.insert_nth(len(self			)			,				SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     str			,				SCREAMING_SNAKE_CASE_    :     Any			)			->	None:
					self.insert_nth(0			,				SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     Union[str, Any]			,				SCREAMING_SNAKE_CASE_    :     int			,				SCREAMING_SNAKE_CASE_    :     Any			)			->	None:
					if index < 0 or index > len(self			):
									raise IndexError('''list index out of range.'''			)
					lowercase_	    =   Node(SCREAMING_SNAKE_CASE_			)
					if self.head is None:
									lowercase_	    =   new_node  # first node points itself
									lowercase_	    =   lowercase_	    =   new_node
					elif index == 0:  # insert at head
									lowercase_	    =   self.head
									lowercase_	    =   lowercase_	    =   new_node
					else:
									lowercase_	    =   self.head
									for _ in range(index - 1			):
													lowercase_	    =   temp.next
									lowercase_	    =   temp.next
									lowercase_	    =   new_node
									if index == len(self			) - 1:  # insert at tail
													lowercase_	    =   new_node
	def 		_lowercase    (			self    :     List[Any]			)			->	Dict:
					return self.delete_nth(0			)
	def 		_lowercase    (			self    :     Any			)			->	Any:
					return self.delete_nth(len(self			) - 1			)
	def 		_lowercase    (			self    :     List[str]			,				SCREAMING_SNAKE_CASE_    :     int = 0			)			->	Any:
					if not 0 <= index < len(self			):
									raise IndexError('''list index out of range.'''			)
					lowercase_	    =   self.head
					if self.head == self.tail:  # just one node
									lowercase_	    =   lowercase_	    =   None
					elif index == 0:  # delete head node
									lowercase_	    =   self.tail.next.next
									lowercase_	    =   self.head.next
					else:
									lowercase_	    =   self.head
									for _ in range(index - 1			):
													lowercase_	    =   temp.next
									lowercase_	    =   temp.next
									lowercase_	    =   temp.next.next
									if index == len(self			) - 1:  # delete at tail
													lowercase_	    =   temp
					return delete_node.data




	def 		_lowercase    (			self    :     List[Any]			)			->	bool:
					return len(self			) == 0




def 							a		(				):


				'''simple docstring'''
				lowercase_	    =   CircularLinkedList()
				assert len(snake_case__   ) == 0
				assert circular_linked_list.is_empty() is True
				assert str(snake_case__   ) == ""

				try:
								circular_linked_list.delete_front()
								raise AssertionError  # This should not happen
				except IndexError:
								assert True  # This should happen

				try:
								circular_linked_list.delete_tail()
								raise AssertionError  # This should not happen
				except IndexError:
								assert True  # This should happen

				try:
								circular_linked_list.delete_nth(-1   )
								raise AssertionError
				except IndexError:
								assert True

				try:
								circular_linked_list.delete_nth(0   )
								raise AssertionError
				except IndexError:
								assert True

				assert circular_linked_list.is_empty() is True
				for i in range(5   ):
								assert len(snake_case__   ) == i
								circular_linked_list.insert_nth(snake_case__		,  i + 1   )
				assert str(snake_case__   ) == "->".join(str(snake_case__   ) for i in range(1		,  6   )   )

				circular_linked_list.insert_tail(6   )
				assert str(snake_case__   ) == "->".join(str(snake_case__   ) for i in range(1		,  7   )   )
				circular_linked_list.insert_head(0   )
				assert str(snake_case__   ) == "->".join(str(snake_case__   ) for i in range(0		,  7   )   )

				assert circular_linked_list.delete_front() == 0
				assert circular_linked_list.delete_tail() == 6
				assert str(snake_case__   ) == "->".join(str(snake_case__   ) for i in range(1		,  6   )   )
				assert circular_linked_list.delete_nth(2   ) == 3

				circular_linked_list.insert_nth(2		,  3   )
				assert str(snake_case__   ) == "->".join(str(snake_case__   ) for i in range(1		,  6   )   )

				assert circular_linked_list.is_empty() is False


if __name__ == "__main__":
						import doctest

						doctest.testmod()











'''simple docstring'''


from sklearn.metrics import mean_squared_error

import datasets


_SCREAMING_SNAKE_CASE									=				"\\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"

_SCREAMING_SNAKE_CASE									=				"\\nMean Squared Error(MSE) is the average of the square of difference between the predicted\nand actual values.\n"


_SCREAMING_SNAKE_CASE									=				"\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  lowerCAmelCase_		(		datasets.Metric		):




 def 				_snake_case (						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 				_snake_case (						self		)     ->				Tuple:
     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 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase=None      ,  _lowerCAmelCase="uniform_average"      ,  _lowerCAmelCase=True		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   mean_squared_error(
         _lowerCAmelCase      ,  _lowerCAmelCase      ,  sample_weight=_lowerCAmelCase      ,  multioutput=_lowerCAmelCase      ,  squared=_lowerCAmelCase		)

     return {"mse": mse}


