from __future__ import annotations

import random

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



def   __A		(			_A							,				_A					):



 """simple docstring"""


 __a       =  len([g for position, g in enumerate(_A					) if g == main_target[position]]					)
 return (item, float(_A					))



def   __A		(			_A							,				_A					):



 """simple docstring"""


 __a       =  random.randint(0							,				len(_A					) - 1					)
 __a       =  parent_a[:random_slice] + parent_a[random_slice:]
 __a       =  parent_a[:random_slice] + parent_a[random_slice:]
 return (child_a, child_a)



def   __A		(			_A							,				_A					):



 """simple docstring"""


 __a       =  list(_A					)
 if random.uniform(0							,				1					) < MUTATION_PROBABILITY:
  __a       =  random.choice(_A					)
 return "".join(_A					)



def   __A		(			_A							,				_A							,				_A							,				):



 """simple docstring"""


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

  __a       ,     __a       =  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   __A		(			_A							,				_A							,				_A = True					):



 """simple docstring"""


 if N_POPULATION < N_SELECTED:
  __a       =  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.
 __a       =  sorted({c for c in target if c not in genes}					)
 if not_in_genes_list:
  __a       =  f"""{not_in_genes_list} is not in genes list, evolution cannot converge"""
  raise ValueError(_A					)

 # Generate random starting population.
 __a       =  []
 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.
 __a       ,     __a       =  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.
  __a       =  [evaluate(_A							,				_A					) for item in population]

  # Check if there is a matching evolution.
  __a       =  sorted(_A							,				key=lambda _A					: 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.
  __a       =  population[: int(N_POPULATION / 3					)]
  population.clear()
  population.extend(_A					)
  # Normalize population score to be between 0 and 1.
  __a       =  [
      (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__":
  SCREAMING_SNAKE_CASE :	List[Any]							     =		(
      """This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!"""
  )
  SCREAMING_SNAKE_CASE :	Optional[Any]							     =		list(
      """ ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm"""
      """nopqrstuvwxyz.,;!?+-*#@^'èéòà€ù=)(&%$£/\\"""
  )
  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE :	Tuple							     =		basic(target_str, genes_list)
  print(
      f'''\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}'''
  )



def   __A		(			_A = 100_0000					):



 """simple docstring"""


 __a       =  [i - 1 for i in range(limit + 1					)]

 for i in range(2							,				limit + 1					):
  if phi[i] == i - 1:
   for j in range(2 * i							,				limit + 1							,				_A					):
    phi[j] -= phi[j] // i

 return sum(phi[2 : limit + 1]					)


if __name__ == "__main__":
  print(solution())


