




from sklearn.metrics import mean_absolute_error

import datasets


UpperCAmelCase__    :							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'

UpperCAmelCase__    :							int       		=     '\\nMean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual\nvalues.\n'


UpperCAmelCase__    :							Tuple       		=     '\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\nReturns:\n    mae : mean absolute error.\n        If multioutput is "raw_values", then mean absolute error is returned for each output separately. If multioutput is "uniform_average" or an ndarray of weights, then the weighted average of all output errors is returned.\n        MAE output is non-negative floating point. The best value is 0.0.\nExamples:\n\n    >>> mae_metric = datasets.load_metric("mae")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mae_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mae\': 0.5}\n\n    If you\'re using multi-dimensional lists, then set the config as follows :\n\n    >>> mae_metric = datasets.load_metric("mae", "multilist")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mae_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mae\': 0.75}\n    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')\n    >>> print(results)\n    {\'mae\': array([0.5, 1. ])}\n'

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






   def 			_a    (						self :							Tuple  )->      Any:
     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_absolute_error.html'
         ]    ,       )






   def 			_a    (						self :							Optional[Any]  )->      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 			_a    (						self :							Optional[Any]    ,       UpperCamelCase_ :							List[str]    ,       UpperCamelCase_ :							str    ,       UpperCamelCase_ :							Dict=None    ,       UpperCamelCase_ :							List[str]="uniform_average"  )->      List[str]:
     _lowercase:			str      =     mean_absolute_error(UpperCamelCase_    ,       UpperCamelCase_    ,       sample_weight=UpperCamelCase_    ,       multioutput=UpperCamelCase_  )

     return {"mae": mae_score}






from __future__ import annotations

class   UpperCAmelCase    :






   def __init__(						self :							List[Any]    ,       UpperCamelCase_ :							int  )->      None:
     _lowercase:			Optional[Any]      =     data
     _lowercase:			Optional[int]      =     None

class   UpperCAmelCase    :






   def __init__(						self :							Optional[Any]  )->      Union[str, Any]:
     _lowercase:			List[str]      =     None






   def 			_a    (						self :							List[Any]    ,       UpperCamelCase_ :							int  )->      int:
     _lowercase:			Any      =     Node(UpperCamelCase_  )
     _lowercase:			List[Any]      =     self.head
     _lowercase:			List[str]      =     new_node
     return self.head.data






   def 			_a    (						self :							Tuple  )->      int | None:
     _lowercase:			List[str]      =     self.head
     _lowercase:			str      =     self.head
     if self.head:
       while fast_pointer and fast_pointer.next:
         _lowercase:			Optional[int]      =     fast_pointer.next.next
         _lowercase:			Tuple      =     slow_pointer.next
       return slow_pointer.data
     else:
       print('No element found.'  )
       return None


if __name__ == "__main__":
   UpperCAmelCase__    :							Union[str, Any]       		=     LinkedList()
   for _ in range(int(input().strip())):
      UpperCAmelCase__    :							List[Any]       		=     int(input().strip())
      link.push(data)
   print(link.middle_element())
