


'''simple docstring'''


import qiskit
from qiskit.providers import Backend


def 		_lowercase   (SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





     __A     ,__A  :					List[str]						     =       bin(__lowerCamelCase       )[2:], bin(__lowerCamelCase       )[2:]  # Remove leading '0b'

     # Ensure that both strings are of the same length
     if len(__lowerCamelCase       ) > len(__lowerCamelCase       ):
          __A  :					List[Any]						     =       y.zfill(len(__lowerCamelCase       )       )
     else:
          __A  :					List[str]						     =       x.zfill(len(__lowerCamelCase       )       )

     # We need (3 * number of bits in the larger number)+1 qBits
     # The second parameter is the number of classical registers, to measure the result
     __A  :					List[str]						     =       qiskit.QuantumCircuit((len(__lowerCamelCase       ) * 3) + 1					,				len(__lowerCamelCase       ) + 1       )

     # We are essentially "not-ing" the bits that are 1
     # Reversed because it's easier to perform ops on more significant bits
     for i in range(len(__lowerCamelCase       )       ):
          if x[::-1][i] == "1":
               circuit.x(__lowerCamelCase       )
     for j in range(len(__lowerCamelCase       )       ):
          if y[::-1][j] == "1":
               circuit.x(len(__lowerCamelCase       ) + j       )

     return circuit, x, y


def 		_lowercase   (SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE					,				):




     '''simple docstring'''





     circuit.ccx(__lowerCamelCase					,				__lowerCamelCase					,				__lowerCamelCase       )
     circuit.cx(__lowerCamelCase					,				__lowerCamelCase       )
     circuit.ccx(__lowerCamelCase					,				__lowerCamelCase					,				__lowerCamelCase       )
     circuit.cx(__lowerCamelCase					,				__lowerCamelCase       )
     circuit.cx(__lowerCamelCase					,				__lowerCamelCase       )


def 		_lowercase   (SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE = qiskit.Aer.get_backend("aer_simulator"       )					,				):




     '''simple docstring'''





     if vala < 0 or vala < 0:
          raise ValueError("Both Integers must be positive!"       )

     # Store the Integers
     __A     ,__A     ,__A  :					List[str]						     =       store_two_classics(__lowerCamelCase					,				__lowerCamelCase       )
     for i in range(len(__lowerCamelCase       )       ):
          full_adder(__lowerCamelCase					,				__lowerCamelCase					,				len(__lowerCamelCase       ) + i					,				len(__lowerCamelCase       ) + len(__lowerCamelCase       ) + i					,				len(__lowerCamelCase       ) + len(__lowerCamelCase       ) + i + 1       )
          circuit.barrier()  # Optional, just for aesthetics

     # Measure the resultant qBits
     for i in range(len(__lowerCamelCase       ) + 1       ):
          circuit.measure([(len(__lowerCamelCase       ) * 2) + i]					,				[i]       )

     __A  :					List[Any]						     =       qiskit.execute(__lowerCamelCase					,				__lowerCamelCase					,				shots=1       ).result()

     # The result is in binary. Convert it back to int
     return int(list(res.get_counts()       )[0]					,				2       )


if __name__ == "__main__":
      import doctest

      doctest.testmod()





'''simple docstring'''


from sklearn.metrics import accuracy_score

import datasets


_UpperCamelCase          =     """
Accuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:
Accuracy = (TP + TN) / (TP + TN + FP + FN)
 Where:
TP: True positive
TN: True negative
FP: False positive
FN: False negative
"""


_UpperCamelCase          =     """
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    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.

Examples:

    Example 1-A simple example
        >>> accuracy_metric = datasets.load_metric(\"accuracy\")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])
        >>> print(results)
        {'accuracy': 0.5}

    Example 2-The same as Example 1, except with `normalize` set to `False`.
        >>> accuracy_metric = datasets.load_metric(\"accuracy\")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)
        >>> print(results)
        {'accuracy': 3.0}

    Example 3-The same as Example 1, except with `sample_weight` set.
        >>> accuracy_metric = datasets.load_metric(\"accuracy\")
        >>> 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])
        >>> print(results)
        {'accuracy': 0.8778625954198473}
"""


_UpperCamelCase          =     """
@article{scikit-learn,
  title={Scikit-learn: Machine Learning in {P}ython},
  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
  journal={Journal of Machine Learning Research},
  volume={12},
  pages={2825--2830},
  year={2011}
}
"""







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

     """simple docstring"""






     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          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 lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase=True ,    lowerCamelCase=None		):


          '''simple docstring'''





          return {
              "accuracy": float(
                  accuracy_score(lowerCamelCase ,    lowerCamelCase ,    normalize=lowerCamelCase ,    sample_weight=lowerCamelCase		)		)
          }

