'''simple docstring'''




from __future__ import annotations

import random

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




def 							lowerCamelCase	(      UpperCAmelCase__       :					str      ,      UpperCAmelCase__       :					str )			->      tuple[str, float]:
							lowercase_					:			List[str]										=       len([g for position, g in enumerate(UpperCAmelCase__ ) if g == main_target[position]] )
							return (item, float(UpperCAmelCase__ ))




def 							lowerCamelCase	(      UpperCAmelCase__       :					str      ,      UpperCAmelCase__       :					str )			->      tuple[str, str]:
							lowercase_					:			str										=       random.randint(0      ,      len(UpperCAmelCase__ ) - 1 )
							lowercase_					:			Any										=       parent_a[:random_slice] + parent_a[random_slice:]
							lowercase_					:			Tuple										=       parent_a[:random_slice] + parent_a[random_slice:]
							return (child_a, child_a)




def 							lowerCamelCase	(      UpperCAmelCase__       :					str      ,      UpperCAmelCase__       :					list[str] )			->      str:
							lowercase_					:			int										=       list(UpperCAmelCase__ )
							if random.uniform(0      ,      1 ) < MUTATION_PROBABILITY:
														lowercase_					:			Dict										=       random.choice(UpperCAmelCase__ )
							return "".join(UpperCAmelCase__ )




def 							lowerCamelCase	(      UpperCAmelCase__       :					tuple[str, float]      ,      UpperCAmelCase__       :					list[tuple[str, float]]      ,      UpperCAmelCase__       :					list[str]      ,      )			->      list[str]:
							lowercase_					:			Tuple										=       []
							# Generate more children proportionally to the fitness score.
							lowercase_					:			Optional[Any]										=       int(parent_a[1] * 100 ) + 1
							lowercase_					:			Optional[int]										=       10 if child_n >= 10 else child_n
							for _ in range(UpperCAmelCase__ ):
														lowercase_					:			List[Any]										=       population_score[random.randint(0      ,      UpperCAmelCase__ )][0]

														lowercase_						,						lowercase_					:			str										=       crossover(parent_a[0]      ,      UpperCAmelCase__ )
														# Append new string to the population list.
														pop.append(mutate(UpperCAmelCase__      ,      UpperCAmelCase__ ) )
														pop.append(mutate(UpperCAmelCase__      ,      UpperCAmelCase__ ) )
							return pop




def 							lowerCamelCase	(      UpperCAmelCase__       :					str      ,      UpperCAmelCase__       :					list[str]      ,      UpperCAmelCase__       :					bool = True )			->      tuple[int, int, str]:

							# Verify if N_POPULATION is bigger than N_SELECTED
							if N_POPULATION < N_SELECTED:
														lowercase_					:			Tuple										=       F'''{N_POPULATION} must be bigger than {N_SELECTED}'''
														raise ValueError(UpperCAmelCase__ )
							# Verify that the target contains no genes besides the ones inside genes variable.
							lowercase_					:			List[Any]										=       sorted({c for c in target if c not in genes} )
							if not_in_genes_list:
														lowercase_					:			Tuple										=       F'''{not_in_genes_list} is not in genes list, evolution cannot converge'''
														raise ValueError(UpperCAmelCase__ )

							# Generate random starting population.
							lowercase_					:			Optional[int]										=       []
							for _ in range(UpperCAmelCase__ ):
														population.append("""""".join([random.choice(UpperCAmelCase__ ) for i in range(len(UpperCAmelCase__ ) )] ) )

							# Just some logs to know what the algorithms is doing.
							lowercase_						,						lowercase_					:			str										=       0, 0

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

														# 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.
														lowercase_					:			Dict										=       [evaluate(UpperCAmelCase__      ,      UpperCAmelCase__ ) for item in population]

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

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


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






'''simple docstring'''




_lowercase						: str						=						tuple[float, float, float]
_lowercase						: List[Any]						=						tuple[float, float, float]




def 							lowerCamelCase	(      UpperCAmelCase__       :					Pointad      ,      UpperCAmelCase__       :					Pointad )			->      Vectorad:
							lowercase_					:			List[str]										=       end_pointa[0] - end_pointa[0]
							lowercase_					:			Union[str, Any]										=       end_pointa[1] - end_pointa[1]
							lowercase_					:			List[Any]										=       end_pointa[2] - end_pointa[2]
							return (x, y, z)




def 							lowerCamelCase	(      UpperCAmelCase__       :					Vectorad      ,      UpperCAmelCase__       :					Vectorad )			->      Vectorad:
							lowercase_					:			List[Any]										=       ab[1] * ac[2] - ab[2] * ac[1]  # *i
							lowercase_					:			Union[str, Any]										=       (ab[0] * ac[2] - ab[2] * ac[0]) * -1  # *j
							lowercase_					:			List[str]										=       ab[0] * ac[1] - ab[1] * ac[0]  # *k
							return (x, y, z)




def 							lowerCamelCase	(      UpperCAmelCase__       :					Vectorad      ,      UpperCAmelCase__       :					int )			->      bool:
							return tuple(round(UpperCAmelCase__      ,      UpperCAmelCase__ ) for x in vector ) == (0, 0, 0)




def 							lowerCamelCase	(      UpperCAmelCase__       :					Pointad      ,      UpperCAmelCase__       :					Pointad      ,      UpperCAmelCase__       :					Pointad      ,      UpperCAmelCase__       :					int = 10 )			->      bool:
							lowercase_					:			Dict										=       create_vector(UpperCAmelCase__      ,      UpperCAmelCase__ )
							lowercase_					:			Optional[int]										=       create_vector(UpperCAmelCase__      ,      UpperCAmelCase__ )
							return is_zero_vector(get_ad_vectors_cross(UpperCAmelCase__      ,      UpperCAmelCase__ )      ,      UpperCAmelCase__ )





