





"""simple docstring"""
from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
lowerCamelCase_       =		2_0_0
# 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_       =		5_0
# 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, 1_0_0_0))



def 					__lowerCamelCase       (							a_					:		str			,       a_					:		str							)      ->				tuple[str, float]:
      __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  len([g for position, g in enumerate(a_							) if g == main_target[position]]							)
      return (item, float(a_							))



def 					__lowerCamelCase       (							a_					:		str			,       a_					:		str							)      ->				tuple[str, str]:
      __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  random.randint(0			,       len(a_							) - 1							)
      __SCREAMING_SNAKE_CASE       :Tuple		    =  parent_a[:random_slice] + parent_a[random_slice:]
      __SCREAMING_SNAKE_CASE       :int		    =  parent_a[:random_slice] + parent_a[random_slice:]
      return (child_a, child_a)



def 					__lowerCamelCase       (							a_					:		str			,       a_					:		list[str]							)      ->				str:
      __SCREAMING_SNAKE_CASE       :List[Any]		    =  list(a_							)
      if random.uniform(0			,       1							) < MUTATION_PROBABILITY:
            __SCREAMING_SNAKE_CASE       :Optional[int]		    =  random.choice(a_							)
      return "".join(a_							)



def 					__lowerCamelCase       (							a_					:		tuple[str, float]			,       a_					:		list[tuple[str, float]]			,       a_					:		list[str]			,       )      ->				list[str]:
      __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  []
      # Generate more children proportionally to the fitness score.
      __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  int(parent_a[1] * 1_00							) + 1
      __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  10 if child_n >= 10 else child_n
      for _ in range(a_							):
            __SCREAMING_SNAKE_CASE       :int		    =  population_score[random.randint(0			,       a_							)][0]

            __SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE       :str		    =  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       (							a_					:		str			,       a_					:		list[str]			,       a_					:		bool = True							)      ->				tuple[int, int, str]:

      # Verify if N_POPULATION is bigger than N_SELECTED
      if N_POPULATION < N_SELECTED:
            __SCREAMING_SNAKE_CASE       :Union[str, 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.
      __SCREAMING_SNAKE_CASE       :List[Any]		    =  sorted({c for c in target if c not in genes}							)
      if not_in_genes_list:
            __SCREAMING_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.
      __SCREAMING_SNAKE_CASE       :List[str]		    =  []
      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.
      __SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE       :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.
            __SCREAMING_SNAKE_CASE       :Dict		    =  [evaluate(a_			,       a_							) for item in population]

            # Check if there is a matching evolution.
            __SCREAMING_SNAKE_CASE       :Optional[int]		    =  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.
            __SCREAMING_SNAKE_CASE       :List[str]		    =  population[: int(N_POPULATION / 3							)]
            population.clear()
            population.extend(a_							)
            # Normalize population score to be between 0 and 1.
            __SCREAMING_SNAKE_CASE       :Optional[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_      ,					lowerCamelCase_      ,					lowerCamelCase_       =		basic(target_str, genes_list)
  print(
      f'\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}'
  )






"""simple docstring"""
def 					__lowerCamelCase       (							a_					:		int = 10			,       a_					:		int = 22							)      ->				int:
      __SCREAMING_SNAKE_CASE       :Optional[int]		    =  range(1			,       a_							)
      __SCREAMING_SNAKE_CASE       :List[Any]		    =  range(1			,       a_							)
      return sum(
          1 for power in powers for base in bases if len(str(base**power							)							) == power							)


if __name__ == "__main__":
  print(f'{solution(1_0, 2_2) = }')