

import math

import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute

def 		UpperCamelCase      (lowercase_:				int = 3      )  ->   qiskit.result.counts.Counts:
	if isinstance(lowercase_					,   lowercase_      ):
		raise TypeError("""number of qubits must be a integer."""      )
	if number_of_qubits <= 0:
		raise ValueError("""number of qubits must be > 0."""      )
	if math.floor(lowercase_      ) != number_of_qubits:
		raise ValueError("""number of qubits must be exact integer."""      )
	if number_of_qubits > 10:
		raise ValueError("""number of qubits too large to simulate(>10)."""      )

	A__							:					Any       =	QuantumRegister(lowercase_					,   """qr"""      )
	A__							:					Any       =	ClassicalRegister(lowercase_					,   """cr"""      )

	A__							:					Optional[Any]       =	QuantumCircuit(lowercase_					,   lowercase_      )

	A__							:					Optional[Any]       =	number_of_qubits

	for i in range(lowercase_      ):
		quantum_circuit.h(number_of_qubits - i - 1      )
		counter -= 1
		for j in range(lowercase_      ):
			quantum_circuit.cp(np.pi / 2 ** (counter - j)					,   lowercase_					,   lowercase_      )

	for k in range(number_of_qubits // 2      ):
		quantum_circuit.swap(lowercase_					,   number_of_qubits - k - 1      )

	# measure all the qubits
	quantum_circuit.measure(lowercase_					,   lowercase_      )
	# simulate with 10000 shots
	A__							:					int       =	Aer.get_backend("""qasm_simulator"""      )
	A__							:					List[Any]       =	execute(lowercase_					,   lowercase_					,   shots=10000      )

	return job.result().get_counts(lowercase_      )


if __name__ == "__main__":
	print(
	    f'''Total count for quantum fourier transform state is: \
    {quantum_fourier_transform(3)}'''
	)






from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt

def 		UpperCamelCase      (lowercase_:				np.ndarray					,   lowercase_:				np.ndarray					,   lowercase_:				np.ndarray					,   lowercase_:				int					,   lowercase_:				int      )  ->   np.ndarray:
	A__							:					Any       =	cva.getAffineTransform(lowercase_					,   lowercase_      )
	return cva.warpAffine(lowercase_					,   lowercase_					,   (rows, cols)      )


if __name__ == "__main__":
	# read original image
	A_ :				List[Any]						 =    cva.imread(
	    str(Path(__file__).resolve().parent.parent / 'image_data' / 'lena.jpg')
	)
	# turn image in gray scale value
	A_ :				List[Any]						 =    cva.cvtColor(image, cva.COLOR_BGR2GRAY)
	# get image shape
	A_ ,    A_ :				Optional[Any]						 =    gray_img.shape

	# set different points to rotate image
	A_ :				str						 =    np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
	A_ :				Dict						 =    np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
	A_ :				Optional[int]						 =    np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
	A_ :				Optional[int]						 =    np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

	# add all rotated images in a list
	A_ :				Dict						 =    [
	    gray_img,
	    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
	    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
	    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
	]

	# plot different image rotations
	A_ :				Union[str, Any]						 =    plt.figure(1)
	A_ :				Union[str, Any]						 =    ['Original', 'Rotation 1', 'Rotation 2', 'Rotation 3']
	for i, image in enumerate(images):
		plt.subplot(2, 2, i + 1), plt.imshow(image, 'gray')
		plt.title(titles[i])
		plt.axis('off')
		plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
	plt.show()



