'''simple docstring'''




from __future__ import annotations

import random

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






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






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    )     ->   tuple[str, str]:
				__lowerCamelCase          =  random.randint(0    ,  len(UpperCamelCase__    ) - 1    )
				__lowerCamelCase          =  parent_a[:random_slice] + parent_a[random_slice:]
				__lowerCamelCase          =  parent_a[:random_slice] + parent_a[random_slice:]
				return (child_a, child_a)






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    )     ->   str:
				__lowerCamelCase          =  list(UpperCamelCase__    )
				if random.uniform(0    ,  1    ) < MUTATION_PROBABILITY:
								__lowerCamelCase          =  random.choice(UpperCamelCase__    )
				return "".join(UpperCamelCase__    )






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

								__lowerCamelCase     ,  __lowerCamelCase          =  crossover(parent_a[0]    ,  UpperCamelCase__    )
								# Append new string to the population list.
								pop.append(mutate(UpperCamelCase__    ,  UpperCamelCase__    )    )
								pop.append(mutate(UpperCamelCase__    ,  UpperCamelCase__    )    )
				return pop






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__ = True    )     ->   tuple[int, int, str]:

				# Verify if N_POPULATION is bigger than N_SELECTED
				if N_POPULATION < N_SELECTED:
								__lowerCamelCase          =  f"""{N_POPULATION} must be bigger than {N_SELECTED}"""
								raise ValueError(UpperCamelCase__    )
				# Verify that the target contains no genes besides the ones inside genes variable.
				__lowerCamelCase          =  sorted({c for c in target if c not in genes}    )
				if not_in_genes_list:
								__lowerCamelCase          =  f"""{not_in_genes_list} is not in genes list, evolution cannot converge"""
								raise ValueError(UpperCamelCase__    )

				# Generate random starting population.
				__lowerCamelCase          =  []
				for _ in range(UpperCamelCase__    ):
								population.append(''''''.join([random.choice(UpperCamelCase__    ) for i in range(len(UpperCamelCase__    )    )]    )    )

				# Just some logs to know what the algorithms is doing.
				__lowerCamelCase     ,  __lowerCamelCase          =  0, 0

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

								# 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.
								__lowerCamelCase          =  [evaluate(UpperCamelCase__    ,  UpperCamelCase__    ) for item in population]

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

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


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

'''simple docstring'''




def __lowerCAmelCase			(							UpperCamelCase__    )     ->   str:
    __lowerCamelCase          =  []
    __lowerCamelCase          =  set({'''(''', '''[''', '''{'''}    )
    __lowerCamelCase          =  set({''')''', ''']''', '''}'''}    )
    __lowerCamelCase          =  {'''{''': '''}''', '''[''': ''']''', '''(''': ''')'''}

    for i in range(len(UpperCamelCase__    )    ):
        if s[i] in open_brackets:
            stack.append(s[i]    )

        elif s[i] in closed_brackets and (
            len(UpperCamelCase__    ) == 0 or (len(UpperCamelCase__    ) > 0 and open_to_closed[stack.pop()] != s[i])
        ):
            return False

    return len(UpperCamelCase__    ) == 0






def __lowerCAmelCase			(							)     ->   Union[str, Any]:
    __lowerCamelCase          =  input('''Enter sequence of brackets: '''    )
    if is_balanced(UpperCamelCase__    ):
        print(UpperCamelCase__    ,  '''is balanced'''    )
    else:
        print(UpperCamelCase__    ,  '''is not balanced'''    )


if __name__ == "__main__":
  main()
