



'''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_							: int						 =	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_							: int						 =	0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1_0_0_0))




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_		)->		tuple[str, float]:





       '''simple docstring'''


       _UpperCAmelCase  :						str			     = len([g for position, g in enumerate(SCREAMING_SNAKE_CASE_		) if g == main_target[position]]		)
       return (item, float(SCREAMING_SNAKE_CASE_		))




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_		)->		tuple[str, str]:





       '''simple docstring'''


       _UpperCAmelCase  :						int			     = random.randint(0       ,   len(SCREAMING_SNAKE_CASE_		) - 1		)
       _UpperCAmelCase  :						Any			     = parent_a[:random_slice] + parent_a[random_slice:]
       _UpperCAmelCase  :						List[str]			     = parent_a[:random_slice] + parent_a[random_slice:]
       return (child_a, child_a)




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_		)->		str:





       '''simple docstring'''


       _UpperCAmelCase  :						int			     = list(SCREAMING_SNAKE_CASE_		)
       if random.uniform(0       ,   1		) < MUTATION_PROBABILITY:
              _UpperCAmelCase  :						Dict			     = random.choice(SCREAMING_SNAKE_CASE_		)
       return "".join(SCREAMING_SNAKE_CASE_		)




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_       ,   )->		list[str]:





       '''simple docstring'''


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

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




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_ = True		)->		tuple[int, int, str]:





       '''simple docstring'''


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

       # Generate random starting population.
       _UpperCAmelCase  :						Optional[Any]			     = []
       for _ in range(SCREAMING_SNAKE_CASE_		):
              population.append("""""".join([random.choice(SCREAMING_SNAKE_CASE_		) for i in range(len(SCREAMING_SNAKE_CASE_		)		)]		)		)

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

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

              # 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.
              _UpperCAmelCase  :						Tuple			     = [evaluate(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_		) for item in population]

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

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


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





import os
from distutils.util import strtobool
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )				->		Optional[Any]:
			for e in env_keys:
						lowerCAmelCase__      :      Union[str, Any]         =		int(os.environ.get(SCREAMING_SNAKE_CASE_       ,							-1     )     )
						if val >= 0:
									return val
			return default
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_=False     )				->		List[str]:
			lowerCAmelCase__      :      Optional[int]         =		os.environ.get(SCREAMING_SNAKE_CASE_       ,							str(SCREAMING_SNAKE_CASE_     )     )
			return strtobool(SCREAMING_SNAKE_CASE_     ) == 1  # As its name indicates `strtobool` actually returns an int...







def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_="no"     )				->		List[str]:
			lowerCAmelCase__      :      Optional[int]         =		os.environ.get(SCREAMING_SNAKE_CASE_       ,							str(SCREAMING_SNAKE_CASE_     )     )
			return value