

from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
_a:    Union[str, Any] 							=			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.
_a:    List[Any] 							=			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.
_a:    str 							=			0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))



def       __lowerCAmelCase      (						A       ,     A    ):
	UpperCAmelCase_       				=			len([g for position, g in enumerate(A    ) if g == main_target[position]]    )
	return (item, float(A    ))



def       __lowerCAmelCase      (						A       ,     A    ):
	UpperCAmelCase_       				=			random.randint(0       ,     len(A    ) - 1    )
	UpperCAmelCase_       				=			parent_a[:random_slice] + parent_a[random_slice:]
	UpperCAmelCase_       				=			parent_a[:random_slice] + parent_a[random_slice:]
	return (child_a, child_a)



def       __lowerCAmelCase      (						A       ,     A    ):
	UpperCAmelCase_       				=			list(A    )
	if random.uniform(0       ,     1    ) < MUTATION_PROBABILITY:
		UpperCAmelCase_       				=			random.choice(A    )
	return "".join(A    )



def       __lowerCAmelCase      (						A       ,     A       ,     A       ,     ):
	UpperCAmelCase_       				=			[]
	# Generate more children proportionally to the fitness score.
	UpperCAmelCase_       				=			int(parent_a[1] * 100    ) + 1
	UpperCAmelCase_       				=			10 if child_n >= 10 else child_n
	for _ in range(A    ):
		UpperCAmelCase_       				=			population_score[random.randint(0       ,     A    )][0]

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



def       __lowerCAmelCase      (						A       ,     A       ,     A = True    ):

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

	# Generate random starting population.
	UpperCAmelCase_       				=			[]
	for _ in range(A    ):
		population.append("".join([random.choice(A    ) for i in range(len(A    )    )]    )    )

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

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

		# 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_       				=			[evaluate(A       ,     A    ) for item in population]

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

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


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


from collections.abc import Sequence



def       __lowerCAmelCase      (						A       ,     A = False    ):
	if not arr:
		return 0

	UpperCAmelCase_       				=			0 if allow_empty_subarrays else float("-inf"    )
	UpperCAmelCase_       				=			0.0
	for num in arr:
		UpperCAmelCase_       				=			max(0 if allow_empty_subarrays else num       ,     curr_sum + num    )
		UpperCAmelCase_       				=			max(A       ,     A    )

	return max_sum


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

				testmod()

				_a:    Any 							=			[-2, 1, -3, 4, -1, 2, 1, -5, 4]
				print(F'{max_subarray_sum(nums) = }')