

'''simple docstring'''

from __future__ import annotations

from typing import Generic, TypeVar

lowercase__            =     TypeVar("T")




class 			A_					(							Generic[T]				):






     '''simple docstring'''



     def __init__(						self					:	List[str]	,     lowercase_					:	T			)		->      List[str]:
      UpperCAmelCase			:      Any		   =					data  # Assign data
      UpperCAmelCase			:      Node[T] | None		   =					None  # Initialize next as null
      UpperCAmelCase			:      Node[T] | None		   =					None  # Initialize prev as null




class 			A_					(							Generic[T]				):






     '''simple docstring'''



     def __init__(						self					:	Tuple			)		->      None:
      UpperCAmelCase			:      Node[T] | None		   =					None


     def   UpperCAmelCase_			(						self					:	Dict	,     lowercase_					:	T			)		->      None:
      if self.head is None:
       UpperCAmelCase			:      List[str]		   =					Node(lowercase_			)
      else:
       UpperCAmelCase			:      Tuple		   =					Node(lowercase_			)
       UpperCAmelCase			:      Dict		   =					new_node
       UpperCAmelCase			:      List[Any]		   =					self.head
       UpperCAmelCase			:      Union[str, Any]		   =					None
       UpperCAmelCase			:      Tuple		   =					new_node


     def   UpperCAmelCase_			(						self					:	Tuple			)		->      T | None:
      if self.head is None:
       return None
      else:
       assert self.head is not None
       UpperCAmelCase			:      Union[str, Any]		   =					self.head.data
       UpperCAmelCase			:      Tuple		   =					self.head.next
       if self.head is not None:
        UpperCAmelCase			:      Dict		   =					None
       return temp


     def   UpperCAmelCase_			(						self					:	List[Any]			)		->      T | None:
      return self.head.data if self.head is not None else None


     def __len__(						self					:	Union[str, Any]			)		->      int:
      UpperCAmelCase			:      Union[str, Any]		   =					self.head
      UpperCAmelCase			:      List[str]		   =					0
      while temp is not None:
       count += 1
       UpperCAmelCase			:      Optional[Any]		   =					temp.next
      return count


     def   UpperCAmelCase_			(						self					:	Optional[int]			)		->      bool:
      return self.head is None




     def   UpperCAmelCase_			(						self					:	Union[str, Any]			)		->      None:
      print('stack elements are:'			)
      UpperCAmelCase			:      Dict		   =					self.head
      while temp is not None:
       print(temp.data	,     end='->'			)
       UpperCAmelCase			:      Optional[int]		   =					temp.next


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







'''simple docstring'''

from sklearn.metrics import accuracy_score

import datasets


lowercase__            =     "\nAccuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:\nAccuracy = (TP + TN) / (TP + TN + FP + FN)\n Where:\nTP: True positive\nTN: True negative\nFP: False positive\nFN: False negative\n"


lowercase__            =     "\nArgs:\n    predictions (`list` of `int`): Predicted labels.\n    references (`list` of `int`): Ground truth labels.\n    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.\n    sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n    accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.\n\nExamples:\n\n    Example 1-A simple example\n        >>> accuracy_metric = datasets.load_metric(\"accuracy\")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])\n        >>> print(results)\n        {'accuracy': 0.5}\n\n    Example 2-The same as Example 1, except with `normalize` set to `False`.\n        >>> accuracy_metric = datasets.load_metric(\"accuracy\")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)\n        >>> print(results)\n        {'accuracy': 3.0}\n\n    Example 3-The same as Example 1, except with `sample_weight` set.\n        >>> accuracy_metric = datasets.load_metric(\"accuracy\")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])\n        >>> print(results)\n        {'accuracy': 0.8778625954198473}\n"


lowercase__            =     "\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"




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






     '''simple docstring'''



     def   UpperCAmelCase_			(						self					:	int			)		->      str:
      return datasets.MetricInfo(
          description=_DESCRIPTION	,     citation=_CITATION	,     inputs_description=_KWARGS_DESCRIPTION	,     features=datasets.Features(
              {
                  'predictions': datasets.Sequence(datasets.Value('int32'			)			),
                  'references': datasets.Sequence(datasets.Value('int32'			)			),
              }
              if self.config_name == 'multilabel'
              else {
                  'predictions': datasets.Value('int32'			),
                  'references': datasets.Value('int32'			),
              }			)	,     reference_urls=['https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html']	,     )




     def   UpperCAmelCase_			(						self					:	str	,     lowercase_					:	Union[str, Any]	,     lowercase_					:	Any	,     lowercase_					:	Tuple=True	,     lowercase_					:	Union[str, Any]=None			)		->      Dict:
      return {
          "accuracy": float(
              accuracy_score(lowercase_	,     lowercase_	,     normalize=lowercase_	,     sample_weight=lowercase_			)			)
      }




