from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
__UpperCamelCase	: str    		=  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	: Tuple    		=  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	: Optional[int]    		=  0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))
def    A			(					_lowercase	,						_lowercase		):
						SCREAMING_SNAKE_CASE							:					int           =							len([g for position, g in enumerate(_lowercase		) if g == main_target[position]]		)
						return (item, float(_lowercase		))
def    A			(					_lowercase	,						_lowercase		):
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							random.randint(0	,						len(_lowercase		) - 1		)
						SCREAMING_SNAKE_CASE							:					List[str]           =							parent_a[:random_slice] + parent_a[random_slice:]
						SCREAMING_SNAKE_CASE							:					Optional[int]           =							parent_a[:random_slice] + parent_a[random_slice:]
						return (child_a, child_a)
def    A			(					_lowercase	,						_lowercase		):
						SCREAMING_SNAKE_CASE							:					Any           =							list(_lowercase		)
						if random.uniform(0	,						1		) < MUTATION_PROBABILITY:
												SCREAMING_SNAKE_CASE							:					List[Any]           =							random.choice(_lowercase		)
						return "".join(_lowercase		)
def    A			(					_lowercase	,						_lowercase	,						_lowercase	,						):
						SCREAMING_SNAKE_CASE							:					List[str]           =							[]
						# Generate more children proportionally to the fitness score.
						SCREAMING_SNAKE_CASE							:					Optional[int]           =							int(parent_a[1] * 100		) + 1
						SCREAMING_SNAKE_CASE							:					Tuple           =							10 if child_n >= 10 else child_n
						for _ in range(_lowercase		):
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							population_score[random.randint(0	,						_lowercase		)][0]

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							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    A			(					_lowercase	,						_lowercase	,						_lowercase = True		):

						# Verify if N_POPULATION is bigger than N_SELECTED
						if N_POPULATION < N_SELECTED:
												SCREAMING_SNAKE_CASE							:					List[Any]           =							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.
						SCREAMING_SNAKE_CASE							:					str           =							sorted({c for c in target if c not in genes}		)
						if not_in_genes_list:
												SCREAMING_SNAKE_CASE							:					List[Any]           =							f"""{not_in_genes_list} is not in genes list, evolution cannot converge"""
												raise ValueError(_lowercase		)

						# Generate random starting population.
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							[]
						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.
						SCREAMING_SNAKE_CASE							:					Dict           =							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.
												SCREAMING_SNAKE_CASE							:					List[Any]           =							[evaluate(_lowercase	,						_lowercase		) for item in population]

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




import random
def    A			(					_lowercase	,						_lowercase		):
						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					Tuple           =							[], [], []
						for element in data:
												if element < pivot:
																		less.append(_lowercase		)
												elif element > pivot:
																		greater.append(_lowercase		)
												else:
																		equal.append(_lowercase		)
						return less, equal, greater



def    A			(					_lowercase	,						_lowercase		):
						# index = len(items) // 2 when trying to find the median
						#   (value of index when items is sorted)

						# invalid input
						if index >= len(_lowercase		) or index < 0:
												return None

						SCREAMING_SNAKE_CASE							:					Dict           =							items[random.randint(0	,						len(_lowercase		) - 1		)]
						SCREAMING_SNAKE_CASE							:					int           =							0
						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					List[str]           =							_partition(_lowercase	,						_lowercase		)
						SCREAMING_SNAKE_CASE							:					List[Any]           =							len(_lowercase		)
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							len(_lowercase		)

						# index is the pivot
						if m <= index < m + count:
												return pivot
						# must be in smaller
						elif m > index:
												return quick_select(_lowercase	,						_lowercase		)
						# must be in larger
						else:
												return quick_select(_lowercase	,						index - (m + count)		)





