

'''simple docstring'''
from __future__ import annotations

from collections.abc import Iterator
from typing import Any


class        a__  :


   def __init__(			self      ,    _UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				Any      					=       data
      _lowercase      :				Node | None      					=       None




class        a__  :


   def __init__(			self  ):





      """simple docstring"""





      _lowercase      :				Optional[Any]      					=       None
      _lowercase      :				int      					=       None


   def __iter__(			self  ):





      """simple docstring"""





      _lowercase      :				int      					=       self.head
      while self.head:
         yield node.data
         _lowercase      :				Optional[Any]      					=       node.next
         if node == self.head:
            break


   def __len__(			self  ):





      """simple docstring"""





      return sum(1 for _ in self  )


   def __repr__(			self  ):





      """simple docstring"""





      return "->".join(str(_UpperCamelCase  ) for item in iter(self  )  )


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """simple docstring"""





      self.insert_nth(len(self  )      ,    _UpperCamelCase  )


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """simple docstring"""





      self.insert_nth(0      ,    _UpperCamelCase  )


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase  ):





      """simple docstring"""





      if index < 0 or index > len(self  ):
         raise IndexError("list index out of range."  )
      _lowercase      :				Dict      					=       Node(_UpperCamelCase  )
      if self.head is None:
         _lowercase      :				List[Any]      					=       new_node  # first node points itself
         _lowercase      :				Dict      					=       new_node
      elif index == 0:  # insert at head
         _lowercase      :				int      					=       self.head
         _lowercase      :				str      					=       new_node
      else:
         _lowercase      :				Tuple      					=       self.head
         for _ in range(index - 1  ):
            _lowercase      :				List[Any]      					=       temp.next
         _lowercase      :				Any      					=       temp.next
         _lowercase      :				Union[str, Any]      					=       new_node
         if index == len(self  ) - 1:  # insert at tail
            _lowercase      :				Tuple      					=       new_node


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      return self.delete_nth(0  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      return self.delete_nth(len(self  ) - 1  )


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase = 0  ):





      """simple docstring"""





      if not 0 <= index < len(self  ):
         raise IndexError("list index out of range."  )
      _lowercase      :				Union[str, Any]      					=       self.head
      if self.head == self.tail:  # just one node
         _lowercase      :				List[Any]      					=       None
      elif index == 0:  # delete head node
         _lowercase      :				str      					=       self.tail.next.next
         _lowercase      :				str      					=       self.head.next
      else:
         _lowercase      :				Any      					=       self.head
         for _ in range(index - 1  ):
            _lowercase      :				Optional[Any]      					=       temp.next
         _lowercase      :				List[str]      					=       temp.next
         _lowercase      :				Any      					=       temp.next.next
         if index == len(self  ) - 1:  # delete at tail
            _lowercase      :				Dict      					=       temp
      return delete_node.data






   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      return len(self  ) == 0







def  _A		(     )      ->     None:
   _lowercase      :				Optional[Any]      					=       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


_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'

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


_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        a__  (							datasets.Metric  ):


   def 						_lowerCamelCase       (			self  ):





      """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 						_lowerCamelCase       (			self  ):





      """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 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase=None      ,    _UpperCamelCase="uniform_average"      ,    _UpperCamelCase=True  ):





      """simple docstring"""





      _lowercase      :				str      					=       mean_squared_error(
          _UpperCamelCase      ,    _UpperCamelCase      ,    sample_weight=_UpperCamelCase      ,    multioutput=_UpperCamelCase      ,    squared=_UpperCamelCase  )

      return {"mse": mse}






