


'''simple docstring'''






from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
a_   :      int				    =      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.
a_   :      Optional[Any]				    =      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.
a_   :      Optional[int]				    =      0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1_0_0_0))






def 		_A			(lowerCAmelCase__					:str			,  lowerCAmelCase__					:str						)				->					tuple[str, float]:




     '''simple docstring'''



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






def 		_A			(lowerCAmelCase__					:str			,  lowerCAmelCase__					:str						)				->					tuple[str, str]:




     '''simple docstring'''



     _a      =							random.randint(0			,  len(lowerCAmelCase__						) - 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			(lowerCAmelCase__					:str			,  lowerCAmelCase__					:list[str]						)				->					str:




     '''simple docstring'''



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






def 		_A			(lowerCAmelCase__					:tuple[str, float]			,  lowerCAmelCase__					:list[tuple[str, float]]			,  lowerCAmelCase__					:list[str]			,  )				->					list[str]:




     '''simple docstring'''



     _a      =							[]
     # Generate more children proportionally to the fitness score.
     _a      =							int(parent_a[1] * 1_00						) + 1
     _a      =							10 if child_n >= 10 else child_n
     for _ in range(lowerCAmelCase__						):
          _a      =							population_score[random.randint(0			,  lowerCAmelCase__						)][0]

          _a      =							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__					:str			,  lowerCAmelCase__					:list[str]			,  lowerCAmelCase__					:bool = True						)				->					tuple[int, int, str]:




     '''simple docstring'''



     if N_POPULATION < N_SELECTED:
          _a      =							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.
     _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(lowerCAmelCase__						)

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

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





'''simple docstring'''






def 		_A			(lowerCAmelCase__					:list						)				->					float:




     '''simple docstring'''



     _a      =							0
     while len(lowerCAmelCase__						) > 1:
          _a      =							0
          # Consider two files with minimum cost to be merged
          for _ in range(2						):
               _a      =							files.index(min(lowerCAmelCase__						)						)
               temp += files[min_index]
               files.pop(lowerCAmelCase__						)
          files.append(lowerCAmelCase__						)
          optimal_merge_cost += temp
     return optimal_merge_cost


if __name__ == "__main__":
 import doctest

 doctest.testmod()

