from __future__ import annotations

import random

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




def     UpperCamelCase						(	__lowercase						:			str						,__lowercase						:			str     ):
		'''simple docstring'''




		A_		:    Any         =				len([g for position, g in enumerate(__lowercase     ) if g == main_target[position]]     )
		return (item, float(__lowercase     ))




def     UpperCamelCase						(	__lowercase						:			str						,__lowercase						:			str     ):
		'''simple docstring'''




		A_		:    List[Any]         =				random.randint(0						,len(__lowercase     ) - 1     )
		A_		:    Optional[int]         =				parent_a[:random_slice] + parent_a[random_slice:]
		A_		:    int         =				parent_a[:random_slice] + parent_a[random_slice:]
		return (child_a, child_a)




def     UpperCamelCase						(	__lowercase						:			str						,__lowercase						:			list[str]     ):
		'''simple docstring'''




		A_		:    List[Any]         =				list(__lowercase     )
		if random.uniform(0						,1     ) < MUTATION_PROBABILITY:
				A_		:    Any         =				random.choice(__lowercase     )
		return "".join(__lowercase     )




def     UpperCamelCase						(	__lowercase						:			tuple[str, float]						,__lowercase						:			list[tuple[str, float]]						,__lowercase						:			list[str]						,):
		'''simple docstring'''




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

				A_       ,      A_		:    Tuple         =				crossover(parent_a[0]						,__lowercase     )
				# Append new string to the population list.
				pop.append(mutate(__lowercase						,__lowercase     )     )
				pop.append(mutate(__lowercase						,__lowercase     )     )
		return pop




def     UpperCamelCase						(	__lowercase						:			str						,__lowercase						:			list[str]						,__lowercase						:			bool = True     ):
		'''simple docstring'''




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

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

		# Just some logs to know what the algorithms is doing.
		A_       ,      A_		:    int         =				0, 0

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

				# 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_		:    Union[str, Any]         =				[evaluate(__lowercase						,__lowercase     ) for item in population]

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

				# This is selection
				for i in range(__lowercase     ):
						population.extend(select(population_score[int(__lowercase     )]						,__lowercase						,__lowercase     )     )
						# 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(__lowercase     ) > 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}"""
	)







import math




def     UpperCamelCase						(	__lowercase						:			int = 1_00     ):
		'''simple docstring'''




		A_		:    List[Any]         =				sum(i * i for i in range(1						,n + 1     )     )
		A_		:    int         =				int(math.pow(sum(range(1						,n + 1     )     )						,2     )     )
		return square_of_sum - sum_of_squares


if __name__ == "__main__":
	print(F"""{solution() = }""")






