

from sklearn.metrics import mean_absolute_error

import datasets


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


__A    	=			'\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 						lowercase_					(						datasets.Metric  ):



      def 		UpperCamelCase_	(    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 		UpperCamelCase_	(    self   :       Tuple	)					->  Any:
        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   :       Dict    ,      A__   :       List[str]    ,      A__   :       Union[str, Any]    ,      A__   :       List[str]=None    ,      A__   :       str="uniform_average"	)					->  Optional[int]:
        _snake_case		=							mean_absolute_error(A__    ,      A__    ,      sample_weight=A__    ,      multioutput=A__	)

        return {"mae": mae_score}



from __future__ import annotations

from typing import Generic, TypeVar

__A    	=			TypeVar('''T''')



class 						lowercase_					(						Generic[T]  ):



      def __init__(    self   :       str    ,      A__   :       T	)					->  Union[str, Any]:
        _snake_case		=							data  # Assign data
        _snake_case		=							None  # Initialize next as null
        _snake_case		=							None  # Initialize prev as null



class 						lowercase_					(						Generic[T]  ):



      def __init__(    self   :       List[str]	)					->  None:
        _snake_case		=							None



      def 		UpperCamelCase_	(    self   :       List[str]    ,      A__   :       T	)					->  None:
        if self.head is None:
          _snake_case		=							Node(A__	)
        else:
          _snake_case		=							Node(A__	)
          _snake_case		=							new_node
          _snake_case		=							self.head
          _snake_case		=							None
          _snake_case		=							new_node



      def 		UpperCamelCase_	(    self   :       Tuple	)					->  T | None:
        if self.head is None:
          return None
        else:
          assert self.head is not None
          _snake_case		=							self.head.data
          _snake_case		=							self.head.next
          if self.head is not None:
            _snake_case		=							None
          return temp



      def 		UpperCamelCase_	(    self   :       Union[str, Any]	)					->  T | None:
        return self.head.data if self.head is not None else None



      def __len__(    self   :       Optional[int]	)					->  int:
        _snake_case		=							self.head
        _snake_case		=							0
        while temp is not None:
          count += 1
          _snake_case		=							temp.next
        return count



      def 		UpperCamelCase_	(    self   :       Union[str, Any]	)					->  bool:
        return self.head is None







      def 		UpperCamelCase_	(    self   :       Dict	)					->  None:
        print('''stack elements are:'''	)
        _snake_case		=							self.head
        while temp is not None:
          print(temp.data    ,      end='''->'''	)
          _snake_case		=							temp.next


# Code execution starts here
if __name__ == "__main__":
 # Start with the empty stack
 __A    	=			Stack()

 # Insert 4 at the beginning. So stack becomes 4->None
 print('''Stack operations using Doubly LinkedList''')
 stack.push(4)

 # Insert 5 at the beginning. So stack becomes 4->5->None
 stack.push(5)

 # Insert 6 at the beginning. So stack becomes 4->5->6->None
 stack.push(6)

 # Insert 7 at the beginning. So stack becomes 4->5->6->7->None
 stack.push(7)

 # Print the stack
 stack.print_stack()

 # Print the top element
 print('''\nTop element is ''', stack.top())

 # Print the stack size
 print('''Size of the stack is ''', len(stack))

 # pop the top element
 stack.pop()

 # pop the top element
 stack.pop()

 # two elements have now been popped off
 stack.print_stack()

 # Print True if the stack is empty else False
 print('''\nstack is empty:''', stack.is_empty())

