

import numpy as np
import qiskit




def       __lowerCAmelCase      (						A_   : int = 8						,	A_   : int | None = None					)    ->  str:
  __UpperCAmelCase						    =       np.random.default_rng(seed=A_					)

  # Roughly 25% of the qubits will contribute to the key.
  # So we take more than we need.
  __UpperCAmelCase						    =       6 * key_len
  # Measurement basis for Alice's qubits.
  __UpperCAmelCase						    =       rng.integers(2						,	size=A_					)
  # The set of states Alice will prepare.
  __UpperCAmelCase						    =       rng.integers(2						,	size=A_					)
  # Measurement basis for Bob's qubits.
  __UpperCAmelCase						    =       rng.integers(2						,	size=A_					)

  # Quantum Circuit to simulate BB84
  __UpperCAmelCase						    =       qiskit.QuantumCircuit(A_						,	name="BB84"					)

  # Alice prepares her qubits according to rules above.
  for index, _ in enumerate(A_					):
    if alice_state[index] == 1:
      bbaa_circ.x(A_					)
    if alice_basis[index] == 1:
      bbaa_circ.h(A_					)
  bbaa_circ.barrier()

  # Bob measures the received qubits according to rules above.
  for index, _ in enumerate(A_					):
    if bob_basis[index] == 1:
      bbaa_circ.h(A_					)

  bbaa_circ.barrier()
  bbaa_circ.measure_all()

  # Simulate the quantum circuit.
  __UpperCAmelCase						    =       qiskit.Aer.get_backend("aer_simulator"					)
  # We only need to run one shot because the key is unique.
  # Multiple shots will produce the same key.
  __UpperCAmelCase						    =       qiskit.execute(A_						,	A_						,	shots=1						,	seed_simulator=A_					)
  # Returns the result of measurement.
  __UpperCAmelCase						    =       job.result().get_counts(A_					).most_frequent()

  # Extracting the generated key from the simulation results.
  # Only keep measurement results where Alice and Bob chose the same basis.
  __UpperCAmelCase						    =       "".join(
      [
          result_bit
          for alice_basis_bit, bob_basis_bit, result_bit in zip(
              A_						,	A_						,	A_					)
          if alice_basis_bit == bob_basis_bit
      ]					)

  # Get final key. Pad with 0 if too short, otherwise truncate.
  __UpperCAmelCase						    =       gen_key[:key_len] if len(A_					) >= key_len else gen_key.ljust(A_						,	"0"					)
  return key


if __name__ == "__main__":
       print(F"The generated key is : {bbaa(8, seed=0)}")
       from doctest import testmod

       testmod()




from sklearn.metrics import fa_score

import datasets


a_							=      """
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
"""


a_							=      """
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    labels (`list` of `int`): The set of labels to include when `average` is not set to `'binary'`, and the order of the labels if `average` is `None`. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. Labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in `predictions` and `references` are used in sorted order. Defaults to None.
    pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.
    average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.

        - 'binary': Only report results for the class specified by `pos_label`. This is applicable only if the classes found in `predictions` and `references` are binary.
        - 'micro': Calculate metrics globally by counting the total true positives, false negatives and false positives.
        - 'macro': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
        - 'weighted': Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `'macro'` to account for label imbalance. This option can result in an F-score that is not between precision and recall.
        - 'samples': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    f1 (`float` or `array` of `float`): F1 score or list of f1 scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher f1 scores are better.

Examples:

    Example 1-A simple binary example
        >>> f1_metric = datasets.load_metric(\"f1\")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])
        >>> print(results)
        {'f1': 0.5}

    Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.
        >>> f1_metric = datasets.load_metric(\"f1\")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)
        >>> print(round(results['f1'], 2))
        0.67

    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.
        >>> f1_metric = datasets.load_metric(\"f1\")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])
        >>> print(round(results['f1'], 2))
        0.35

    Example 4-A multiclass example, with different values for the `average` input.
        >>> predictions = [0, 2, 1, 0, 0, 1]
        >>> references = [0, 1, 2, 0, 1, 2]
        >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"macro\")
        >>> print(round(results['f1'], 2))
        0.27
        >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"micro\")
        >>> print(round(results['f1'], 2))
        0.33
        >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"weighted\")
        >>> print(round(results['f1'], 2))
        0.27
        >>> results = f1_metric.compute(predictions=predictions, references=references, average=None)
        >>> print(results)
        {'f1': array([0.8, 0. , 0. ])}
"""


a_							=      """
@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 			UpperCAmelCase__    (		datasets.Metric					):



  """simple docstring"""






  def 	_UpperCAmelCase				(				self:			int   ) -> Dict:




    '''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.f1_score.html"]				,				)






  def 	_UpperCAmelCase				(				self:			Any				,				__lowerCAmelCase:			Tuple				,				__lowerCAmelCase:			Any				,				__lowerCAmelCase:			List[Any]=None				,				__lowerCAmelCase:			Optional[Any]=1				,				__lowerCAmelCase:			List[Any]="binary"				,				__lowerCAmelCase:			List[Any]=None   ) -> Optional[int]:




    '''simple docstring'''




    __UpperCAmelCase						    =       fa_score(
        __lowerCAmelCase				,				__lowerCAmelCase				,				labels=__lowerCAmelCase				,				pos_label=__lowerCAmelCase				,				average=__lowerCAmelCase				,				sample_weight=__lowerCAmelCase   )
    return {"f1": float(__lowerCAmelCase   ) if score.size == 1 else score}



