





'''simple docstring'''






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 					_a						(       _lowerCamelCase    ,	_lowerCamelCase ) ->				tuple[str, float]:



			"""simple docstring"""


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







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase ) ->				tuple[str, str]:



			"""simple docstring"""


			__snake_case					:      Optional[Any]							      =     random.randint(0    ,	len(_lowerCamelCase ) - 1 )
			__snake_case					:      str							      =     parent_a[:random_slice] + parent_a[random_slice:]
			__snake_case					:      List[str]							      =     parent_a[:random_slice] + parent_a[random_slice:]
			return (child_a, child_a)







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase ) ->				str:



			"""simple docstring"""


			__snake_case					:      str							      =     list(_lowerCamelCase )
			if random.uniform(0    ,	1 ) < MUTATION_PROBABILITY:
						__snake_case					:      List[str]							      =     random.choice(_lowerCamelCase )
			return "".join(_lowerCamelCase )







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	) ->				list[str]:



			"""simple docstring"""


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

						__snake_case      ,     __snake_case					:      Optional[Any]							      =     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    ,	_lowerCamelCase    ,	_lowerCamelCase = True ) ->				tuple[int, int, str]:



			"""simple docstring"""


			if N_POPULATION < N_SELECTED:
						__snake_case					:      Dict							      =     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.
			__snake_case					:      List[str]							      =     sorted({c for c in target if c not in genes} )
			if not_in_genes_list:
						__snake_case					:      int							      =     F'''{not_in_genes_list} is not in genes list, evolution cannot converge'''
						raise ValueError(_lowerCamelCase )

			# Generate random starting population.
			__snake_case					:      Union[str, Any]							      =     []
			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.
			__snake_case      ,     __snake_case					:      Any							      =     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.
						__snake_case					:      Any							      =     [evaluate(_lowerCamelCase    ,	_lowerCamelCase ) for item in population]

						# Check if there is a matching evolution.
						__snake_case					:      List[str]							      =     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.
						__snake_case					:      Tuple							      =     population[: int(N_POPULATION / 3 )]
						population.clear()
						population.extend(_lowerCamelCase )
						# Normalize population score to be between 0 and 1.
						__snake_case					:      int							      =     [
						    (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__":
	__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'''






from __future__ import annotations







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase = None    ,	_lowerCamelCase = None ) ->				None:



			"""simple docstring"""


			if start is None:
						__snake_case					:      Optional[Any]							      =     0

			if end is None:
						__snake_case					:      Optional[Any]							      =     len(_lowerCamelCase ) - 1

			if start >= end:
						return

			__snake_case					:      Tuple							      =     (start + end) // 2

			slowsort(_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase )
			slowsort(_lowerCamelCase    ,	mid + 1    ,	_lowerCamelCase )

			if sequence[end] < sequence[mid]:
						__snake_case      ,     __snake_case					:      str							      =     sequence[mid], sequence[end]

			slowsort(_lowerCamelCase    ,	_lowerCamelCase    ,	end - 1 )


if __name__ == "__main__":
	from doctest import testmod

	testmod()


