



'''simple docstring'''




from __future__ import annotations

import random

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



def      a      (			lowerCamelCase__						,				lowerCamelCase__   ):
			'''simple docstring'''





			A_     :						List[str]     =					len([g for position, g in enumerate(lowerCamelCase__   ) if g == main_target[position]]   )
			return (item, float(lowerCamelCase__   ))



def      a      (			lowerCamelCase__						,				lowerCamelCase__   ):
			'''simple docstring'''





			A_     :						Optional[Any]     =					random.randint(0						,				len(lowerCamelCase__   ) - 1   )
			A_     :						Union[str, Any]     =					parent_a[:random_slice] + parent_a[random_slice:]
			A_     :						Any     =					parent_a[:random_slice] + parent_a[random_slice:]
			return (child_a, child_a)



def      a      (			lowerCamelCase__						,				lowerCamelCase__   ):
			'''simple docstring'''





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



def      a      (			lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__						,				):
			'''simple docstring'''





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

						A_,   A_     :						List[str]     =					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   ):
			'''simple docstring'''





			if N_POPULATION < N_SELECTED:
						A_     :						List[Any]     =					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_     :						str     =					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(lowerCamelCase__   )

			# Generate random starting population.
			A_     :						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.
			A_,   A_     :						str     =					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_     :						List[str]     =					[evaluate(lowerCamelCase__						,				lowerCamelCase__   ) for item in population]

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




'''simple docstring'''




def      a      (			lowerCamelCase__						,				lowerCamelCase__   ):
			'''simple docstring'''





			if density <= 0:
						raise ValueError("""Impossible fluid density"""   )
			if bulk_modulus <= 0:
						raise ValueError("""Impossible bulk modulus"""   )

			return (bulk_modulus / density) ** 0.5


if __name__ == "__main__":
						import doctest

						doctest.testmod()