



from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
_snake_case		=  200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
_snake_case		=  50
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
_snake_case		=  0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))






def 				A				(					_lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Any    =    len([g for position, g in enumerate(_lowerCamelCase  ) if g == main_target[position]]  )
							return (item, float(_lowerCamelCase  ))






def 				A				(					_lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Optional[Any]    =    random.randint(0				, len(_lowerCamelCase  ) - 1  )
							_lowerCAmelCase     :							Tuple    =    parent_a[:random_slice] + parent_a[random_slice:]
							_lowerCAmelCase     :							Union[str, Any]    =    parent_a[:random_slice] + parent_a[random_slice:]
							return (child_a, child_a)






def 				A				(					_lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							int    =    list(_lowerCamelCase  )
							if random.uniform(0				, 1  ) < MUTATION_PROBABILITY:
														_lowerCAmelCase     :							str    =    random.choice(_lowerCamelCase  )
							return "".join(_lowerCamelCase  )






def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, ):





							'''simple docstring'''




							_lowerCAmelCase     :							int    =    []
							# Generate more children proportionally to the fitness score.
							_lowerCAmelCase     :							int    =    int(parent_a[1] * 100  ) + 1
							_lowerCAmelCase     :							List[str]    =    10 if child_n >= 10 else child_n
							for _ in range(_lowerCamelCase  ):
														_lowerCAmelCase     :							Optional[Any]    =    population_score[random.randint(0				, _lowerCamelCase  )][0]

														_lowerCAmelCase     :							Any    =    crossover(parent_a[0]				, _lowerCamelCase  )
														# Append new string to the population list.
														pop.append(mutate(_lowerCamelCase				, _lowerCamelCase  )  )
														pop.append(mutate(_lowerCamelCase				, _lowerCamelCase  )  )
							return pop






def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase = True  ):





							'''simple docstring'''




							if N_POPULATION < N_SELECTED:
														_lowerCAmelCase     :							List[Any]    =    F"{N_POPULATION} must be bigger than {N_SELECTED}"
														raise ValueError(_lowerCamelCase  )
							# Verify that the target contains no genes besides the ones inside genes variable.
							_lowerCAmelCase     :							List[str]    =    sorted({c for c in target if c not in genes}  )
							if not_in_genes_list:
														_lowerCAmelCase     :							Optional[int]    =    F"{not_in_genes_list} is not in genes list, evolution cannot converge"
														raise ValueError(_lowerCamelCase  )

							# Generate random starting population.
							_lowerCAmelCase     :							int    =    []
							for _ in range(_lowerCamelCase  ):
														population.append("".join([random.choice(_lowerCamelCase  ) for i in range(len(_lowerCamelCase  )  )]  )  )

							# Just some logs to know what the algorithms is doing.
							_lowerCAmelCase     :							List[Any]    =    0, 0

							# This loop will end when we find a perfect match for our target.
							while True:
														generation += 1
														total_population += len(_lowerCamelCase  )

														# Random population created. Now it's time to evaluate.

														# Adding a bit of concurrency can make everything faster,
														#
														# import concurrent.futures
														# population_score: list[tuple[str, float]] = []
														# with concurrent.futures.ThreadPoolExecutor(
														#                                   max_workers=NUM_WORKERS) as executor:
														#     futures = {executor.submit(evaluate, item) for item in population}
														#     concurrent.futures.wait(futures)
														#     population_score = [item.result() for item in futures]
														#
														# but with a simple algorithm like this, it will probably be slower.
														# We just need to call evaluate for every item inside the population.
														_lowerCAmelCase     :							Tuple    =    [evaluate(_lowerCamelCase				, _lowerCamelCase  ) for item in population]

														# Check if there is a matching evolution.
														_lowerCAmelCase     :							Tuple    =    sorted(_lowerCamelCase				, key=lambda _lowerCamelCase  : x[1]				, reverse=_lowerCamelCase  )
														if population_score[0][0] == target:
																					return (generation, total_population, population_score[0][0])

														# Print the best result every 10 generation.
														# Just to know that the algorithm is working.
														if debug and generation % 10 == 0:
																					print(
																					    F"\nGeneration: {generation}"
																					    F"\nTotal Population:{total_population}"
																					    F"\nBest score: {population_score[0][1]}"
																					    F"\nBest string: {population_score[0][0]}"  )

														# Flush the old population, keeping some of the best evolutions.
														# Keeping this avoid regression of evolution.
														_lowerCAmelCase     :							str    =    population[: int(N_POPULATION / 3  )]
														population.clear()
														population.extend(_lowerCamelCase  )
														# Normalize population score to be between 0 and 1.
														_lowerCAmelCase     :							Union[str, Any]    =    [
														    (item, score / len(_lowerCamelCase  )) for item, score in population_score
														]

														# This is selection
														for i in range(_lowerCamelCase  ):
																					population.extend(select(population_score[int(_lowerCamelCase  )]				, _lowerCamelCase				, _lowerCamelCase  )  )
																					# Check if the population has already reached the maximum value and if so,
																					# break the cycle.  If this check is disabled, the algorithm will take
																					# forever to compute large strings, but will also calculate small strings in
																					# a far fewer generations.
																					if len(_lowerCamelCase  ) > N_POPULATION:
																												break


if __name__ == "__main__":
						_snake_case		=  (
						    "This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!"
						)
						_snake_case		=  list(
						    " ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm"
						    "nopqrstuvwxyz.,;!?+-*#@^\'èéòà€ù=)(&%$£/\\"
						)
						_snake_case,						_snake_case,						_snake_case		=  basic(target_str, genes_list)
						print(
						    f'''\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}'''
						)

import math

import qiskit



def lowerCamelCase  (   SCREAMING_SNAKE_CASE = 1  ,			SCREAMING_SNAKE_CASE = 1  ,			SCREAMING_SNAKE_CASE = 1	):
    '''simple docstring'''







    if (
        isinstance(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)
        or isinstance(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)
        or isinstance(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)
    ):
        raise TypeError('''inputs must be integers.'''	)

    if (input_a < 0) or (input_a < 0) or (carry_in < 0):
        raise ValueError('''inputs must be positive.'''	)

    if (
        (math.floor(SCREAMING_SNAKE_CASE	) != input_a)
        or (math.floor(SCREAMING_SNAKE_CASE	) != input_a)
        or (math.floor(SCREAMING_SNAKE_CASE	) != carry_in)
    ):
        raise ValueError('''inputs must be exact integers.'''	)

    if (input_a > 2) or (input_a > 2) or (carry_in > 2):
        raise ValueError('''inputs must be less or equal to 2.'''	)

    # build registers
    __UpperCamelCase     :List[str]			  =		qiskit.QuantumRegister(4  ,			'''qr'''	)
    __UpperCamelCase     :str			  =		qiskit.ClassicalRegister(2  ,			'''cr'''	)
    # list the entries
    __UpperCamelCase     :Tuple			  =		[input_a, input_a, carry_in]

    __UpperCamelCase     :Optional[int]			  =		qiskit.QuantumCircuit(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)

    for i in range(0  ,			3	):
        if entry[i] == 2:
            quantum_circuit.h(SCREAMING_SNAKE_CASE	)  # for hadamard entries
        elif entry[i] == 1:
            quantum_circuit.x(SCREAMING_SNAKE_CASE	)  # for 1 entries
        elif entry[i] == 0:
            quantum_circuit.i(SCREAMING_SNAKE_CASE	)  # for 0 entries

    # build the circuit
    quantum_circuit.ccx(0  ,			1  ,			3	)  # ccx = toffoli gate
    quantum_circuit.cx(0  ,			1	)
    quantum_circuit.ccx(1  ,			2  ,			3	)
    quantum_circuit.cx(1  ,			2	)
    quantum_circuit.cx(0  ,			1	)

    quantum_circuit.measure([2, 3]  ,			SCREAMING_SNAKE_CASE	)  # measure the last two qbits

    __UpperCamelCase     :Optional[Any]			  =		qiskit.Aer.get_backend('''aer_simulator'''	)
    __UpperCamelCase     :Tuple			  =		qiskit.execute(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			shots=1_000	)

    return job.result().get_counts(SCREAMING_SNAKE_CASE	)


if __name__ == "__main__":
    print(F'Total sum count for state is: {quantum_full_adder(1, 1, 1)}')





