






from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
lowerCAmelCase_						   =    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.
lowerCAmelCase_						   =    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.
lowerCAmelCase_						   =    0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))


def     lowerCamelCase_	(      lowerCAmelCase:	Optional[Any]    ,							lowerCAmelCase:	List[Any]				)->		str:
  _snake_case   :				int														=	len([g for position, g in enumerate(A_				) if g == main_target[position]]				)
  return (item, float(A_				))


def     lowerCamelCase_	(      lowerCAmelCase:	int    ,							lowerCAmelCase:	List[Any]				)->		Dict:
  _snake_case   :				Tuple														=	random.randint(0    ,							len(A_				) - 1				)
  _snake_case   :				Dict														=	parent_a[:random_slice] + parent_a[random_slice:]
  _snake_case   :				str														=	parent_a[:random_slice] + parent_a[random_slice:]
  return (child_a, child_a)


def     lowerCamelCase_	(      lowerCAmelCase:	Dict    ,							lowerCAmelCase:	Any				)->		List[Any]:
  _snake_case   :				Union[str, Any]														=	list(A_				)
  if random.uniform(0    ,							1				) < MUTATION_PROBABILITY:
    _snake_case   :				Tuple														=	random.choice(A_				)
  return "".join(A_				)


def     lowerCamelCase_	(      lowerCAmelCase:	str    ,							lowerCAmelCase:	List[str]    ,							lowerCAmelCase:	Optional[int]    ,							)->		int:
  _snake_case   :				int														=	[]
  # Generate more children proportionally to the fitness score.
  _snake_case   :				List[str]														=	int(parent_a[1] * 1_00				) + 1
  _snake_case   :				Dict														=	10 if child_n >= 10 else child_n
  for _ in range(A_				):
    _snake_case   :				Union[str, Any]														=	population_score[random.randint(0    ,							A_				)][0]

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


def     lowerCamelCase_	(      lowerCAmelCase:	int    ,							lowerCAmelCase:	Union[str, Any]    ,							lowerCAmelCase:	Union[str, Any] = True				)->		int:





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

  # Generate random starting population.
  _snake_case   :				Optional[int]														=	[]
  for _ in range(A_				):
    population.append(''.join([random.choice(A_				) for i in range(len(A_				)				)]				)				)

  # Just some logs to know what the algorithms is doing.
  _snake_case							,					_snake_case   :				Optional[int]														=	0, 0

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

    # 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.
    _snake_case   :				str														=	[evaluate(A_    ,							A_				) for item in population]

    # Check if there is a matching evolution.
    _snake_case   :				List[Any]														=	sorted(A_    ,							key=lambda lowerCAmelCase				: x[1]    ,							reverse=A_				)
    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.
    _snake_case   :				List[str]														=	population[: int(N_POPULATION / 3				)]
    population.clear()
    population.extend(A_				)
    # Normalize population score to be between 0 and 1.
    _snake_case   :				List[Any]														=	[
        (item, score / len(A_				)) for item, score in population_score
    ]

    # This is selection
    for i in range(A_				):
      population.extend(select(population_score[int(A_				)]    ,							A_    ,							A_				)				)
      # 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(A_				) > N_POPULATION:
        break


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











def     lowerCamelCase_	(      lowerCAmelCase:	int				)->		list:
  _snake_case   :				List[Any]														=	int(lowerCAmelCase				)
  if n_element < 1:
    _snake_case   :				int														=	ValueError('a should be a positive number'				)
    raise my_error

  _snake_case   :				Union[str, Any]														=	[1]
  _snake_case							,					_snake_case							,					_snake_case   :				Any														=	(0, 0, 0)
  _snake_case   :				str														=	1
  while index < n_element:
    while hamming_list[i] * 2 <= hamming_list[-1]:
      i += 1
    while hamming_list[j] * 3 <= hamming_list[-1]:
      j += 1
    while hamming_list[k] * 5 <= hamming_list[-1]:
      k += 1
    hamming_list.append(
        min(hamming_list[i] * 2    ,							hamming_list[j] * 3    ,							hamming_list[k] * 5				)				)
    index += 1
  return hamming_list


if __name__ == "__main__":
      lowerCAmelCase_						   =    input("""Enter the last number (nth term) of the Hamming Number Series: """)
      print("""Formula of Hamming Number Series => 2^i * 3^j * 5^k""")
      lowerCAmelCase_						   =    hamming(int(n))
      print("""-----------------------------------------------------""")
      print(F"""The list with nth numbers is: {hamming_numbers}""")
      print("""-----------------------------------------------------""")




