
import math

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







def     A__   (  lowerCamelCase = 3  )					->		qiskit.result.counts.Counts:
				if isinstance(lowerCamelCase       ,      lowerCamelCase  ):
								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(lowerCamelCase  ) != 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)."""  )

				UpperCamelCase_:							str		=  QuantumRegister(lowerCamelCase       ,      """qr"""  )
				UpperCamelCase_:							Dict		=  ClassicalRegister(lowerCamelCase       ,      """cr"""  )

				UpperCamelCase_:							Any		=  QuantumCircuit(lowerCamelCase       ,      lowerCamelCase  )

				UpperCamelCase_:							int		=  number_of_qubits

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

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

				# measure all the qubits
				quantum_circuit.measure(lowerCamelCase       ,      lowerCamelCase  )
				# simulate with 10000 shots
				UpperCamelCase_:							List[Any]		=  Aer.get_backend("""qasm_simulator"""  )
				UpperCamelCase_:							Optional[Any]		=  execute(lowerCamelCase       ,      lowerCamelCase       ,      shots=1_00_00  )

				return job.result().get_counts(lowerCamelCase  )


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








import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


lowerCamelCase_  :	Any 				=					5_00_00
lowerCamelCase_  :	Any 				=					50_00

lowerCamelCase_				,    lowerCamelCase_  :	Tuple 				=					os.path.split(__file__)
lowerCamelCase_  :	int 				=					os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))







@get_duration
def     A__   (  lowerCamelCase       ,      lowerCamelCase  )					->		Tuple:
				for i in range(lowerCamelCase  ):
								UpperCamelCase_:							Dict		=  dataset[i]







@get_duration
def     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )					->		Tuple:
				for i in range(0       ,      len(lowerCamelCase  )       ,      lowerCamelCase  ):
								UpperCamelCase_:							List[Any]		=  dataset[i : i + batch_size]







@get_duration
def     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )					->		Any:
				with dataset.formatted_as(type=lowerCamelCase  ):
								for i in range(lowerCamelCase  ):
												UpperCamelCase_:							List[str]		=  dataset[i]







@get_duration
def     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )					->		Optional[int]:
				with dataset.formatted_as(type=lowerCamelCase  ):
								for i in range(0       ,      lowerCamelCase       ,      lowerCamelCase  ):
												UpperCamelCase_:							Union[str, Any]		=  dataset[i : i + batch_size]







def     A__   (  )					->		Tuple:
				UpperCamelCase_:							int		=  {"""num examples""": SPEED_TEST_N_EXAMPLES}
				UpperCamelCase_:							Union[str, Any]		=  [
				    (read, {"""length""": SMALL_TEST}),
				    (read, {"""length""": SPEED_TEST_N_EXAMPLES}),
				    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 10}),
				    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 1_00}),
				    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 10_00}),
				    (read_formatted, {"""type""": """numpy""", """length""": SMALL_TEST}),
				    (read_formatted, {"""type""": """pandas""", """length""": SMALL_TEST}),
				    (read_formatted, {"""type""": """torch""", """length""": SMALL_TEST}),
				    (read_formatted, {"""type""": """tensorflow""", """length""": SMALL_TEST}),
				    (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 10}),
				    (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 10_00}),
				]

				UpperCamelCase_:							Tuple		=  [
				    (read, {"""length""": SMALL_TEST}),
				    (read, {"""length""": SPEED_TEST_N_EXAMPLES}),
				    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 10}),
				    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 1_00}),
				    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 10_00}),
				    (read_formatted, {"""type""": """numpy""", """length""": SMALL_TEST}),
				    (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 10}),
				    (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 10_00}),
				]
				with tempfile.TemporaryDirectory() as tmp_dir:
								print("""generating dataset"""  )
								UpperCamelCase_:							int		=  datasets.Features(
								    {"""list""": datasets.Sequence(datasets.Value("""float32"""  )  ), """numbers""": datasets.Value("""float32"""  )}  )
								UpperCamelCase_:							Optional[int]		=  generate_example_dataset(
								    os.path.join(lowerCamelCase       ,      """dataset.arrow"""  )       ,      lowerCamelCase       ,      num_examples=lowerCamelCase       ,      seq_shapes={"""list""": (1_00,)}       ,      )
								print("""first set of iterations"""  )
								for func, kwargs in functions:
												print(func.__name__       ,      str(lowerCamelCase  )  )
												UpperCamelCase_:							List[Any]		=  func(lowerCamelCase       ,      **lowerCamelCase  )

								print("""shuffling dataset"""  )
								UpperCamelCase_:							Dict		=  dataset.shuffle()
								print("""Second set of iterations (after shuffling"""  )
								for func, kwargs in functions_shuffled:
												print("""shuffled """       ,      func.__name__       ,      str(lowerCamelCase  )  )
												UpperCamelCase_:							List[Any]		=  func(
												    lowerCamelCase       ,      **lowerCamelCase  )

				with open(lowerCamelCase       ,      """wb"""  ) as f:
								f.write(json.dumps(lowerCamelCase  ).encode("""utf-8"""  )  )


if __name__ == "__main__":  # useful to run the profiler
							benchmark_iterating()






