






import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier




def 				A		(					lowercase__  :  dict  )		-> tuple:
	return (data["data"], data["target"])




def 				A		(					lowercase__  :  np.ndarray ,		lowercase__  :  np.ndarray  )		-> XGBClassifier:
	UpperCamelCase__ :Tuple						=      XGBClassifier()
	classifier.fit(lowercase__ ,		lowercase__  )
	return classifier




def 				A		(					)		-> None:
	UpperCamelCase__ :str						=      load_iris()
	UpperCamelCase__				,		UpperCamelCase__ :int						=      data_handling(lowercase__  )
	UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__ :int						=      train_test_split(
	    lowercase__ ,		lowercase__ ,		test_size=0.25  )

	UpperCamelCase__ :Optional[int]						=      iris["""target_names"""]

	# Create an XGBoost Classifier from the training data
	UpperCamelCase__ :Optional[Any]						=      xgboost(lowercase__ ,		lowercase__  )

	# Display the confusion matrix of the classifier with both training and test sets
	ConfusionMatrixDisplay.from_estimator(
	    lowercase__ ,		lowercase__ ,		lowercase__ ,		display_labels=lowercase__ ,		cmap="""Blues""" ,		normalize="""true""" ,		)
	plt.title("""Normalized Confusion Matrix - IRIS Dataset"""  )
	plt.show()


if __name__ == "__main__":
							import doctest

							doctest.testmod(verbose=True)
							main()


"""simple docstring"""


import qiskit



def   _A					(	_a	:						int     ,   _a	:						int				):



      """simple docstring"""






      A				  =		qiskit.Aer.get_backend("""aer_simulator"""				)

      # Create a Quantum Circuit acting on the q register
      A				  =		qiskit.QuantumCircuit(_a     ,   _a				)

      # Apply X (NOT) Gate to Qubits 0 & 1
      circuit.x(0				)
      circuit.x(1				)

      # Map the quantum measurement to the classical bits
      circuit.measure([0, 1]     ,   [0, 1]				)

      # Execute the circuit on the qasm simulator
      A				  =		qiskit.execute(_a     ,   _a     ,   shots=1_0_0_0				)

      # Return the histogram data of the results of the experiment.
      return job.result().get_counts(_a				)


if __name__ == "__main__":
  UpperCAmelCase													=single_qubit_measure(2, 2)
  print(f"""Total count for various states are: {counts}""")




