from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
A__           =							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__           =							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__           =							0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1_000))





def  _lowerCamelCase					(   a_				: str						,			a_				: str):
			lowerCamelCase						:int						  =     len([g for position, g in enumerate(a_) if g == main_target[position]])
			return (item, float(a_))





def  _lowerCamelCase					(   a_				: str						,			a_				: str):
			lowerCamelCase						:str						  =     random.randint(0						,			len(a_) - 1)
			lowerCamelCase						:Union[str, Any]						  =     parent_a[:random_slice] + parent_a[random_slice:]
			lowerCamelCase						:List[str]						  =     parent_a[:random_slice] + parent_a[random_slice:]
			return (child_a, child_a)





def  _lowerCamelCase					(   a_				: str						,			a_				: list[str]):
			lowerCamelCase						:Tuple						  =     list(a_)
			if random.uniform(0						,			1) < MUTATION_PROBABILITY:
						lowerCamelCase						:Any						  =     random.choice(a_)
			return "".join(a_)





def  _lowerCamelCase					(   a_				: tuple[str, float]						,			a_				: list[tuple[str, float]]						,			a_				: list[str]						,			):
			lowerCamelCase						:Any						  =     []
			# Generate more children proportionally to the fitness score.
			lowerCamelCase						:List[Any]						  =     int(parent_a[1] * 1_00) + 1
			lowerCamelCase						:str						  =     10 if child_n >= 10 else child_n
			for _ in range(a_):
						lowerCamelCase						:str						  =     population_score[random.randint(0						,			a_)][0]

						lowerCamelCase  ,    lowerCamelCase						:Dict						  =     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_				: str						,			a_				: list[str]						,			a_				: bool = True):

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

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

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




def  _lowerCamelCase					(   a_				: list):

			if not grid or not grid[0]:
						raise TypeError('''The grid does not contain the appropriate information''')

			for cell_n in range(1						,			len(grid[0])):
						grid[0][cell_n] += grid[0][cell_n - 1]
			lowerCamelCase						:Any						  =     grid[0]

			for row_n in range(1						,			len(a_)):
						lowerCamelCase						:List[str]						  =     grid[row_n]
						lowerCamelCase						:Union[str, Any]						  =     fill_row(a_						,			a_)
						lowerCamelCase						:List[Any]						  =     grid[row_n]

			return grid[-1][-1]





def  _lowerCamelCase					(   a_				: list						,			a_				: list):
			current_row[0] += row_above[0]
			for cell_n in range(1						,			len(a_)):
						current_row[cell_n] += min(current_row[cell_n - 1]						,			row_above[cell_n])

			return current_row


if __name__ == "__main__":
			import doctest

			doctest.testmod()



