






'''simple docstring'''







from __future__ import annotations

import random

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





def 		_UpperCAmelCase   (							__A							:   List[Any]						,		__A							:   Dict      ):
				a_		:  int											=      len([g for position, g in enumerate(_lowercase      ) if g == main_target[position]]      )
				return (item, float(_lowercase      ))





def 		_UpperCAmelCase   (							__A							:   str						,		__A							:   List[Any]      ):
				a_		:  Optional[Any]											=      random.randint(0						,		len(_lowercase      ) - 1      )
				a_		:  List[str]											=      parent_a[:random_slice] + parent_a[random_slice:]
				a_		:  Optional[int]											=      parent_a[:random_slice] + parent_a[random_slice:]
				return (child_a, child_a)





def 		_UpperCAmelCase   (							__A							:   Optional[Any]						,		__A							:   Any      ):
				a_		:  Any											=      list(_lowercase      )
				if random.uniform(0						,		1      ) < MUTATION_PROBABILITY:
								a_		:  List[Any]											=      random.choice(_lowercase      )
				return "".join(_lowercase      )





def 		_UpperCAmelCase   (							__A							:   Tuple						,		__A							:   Dict						,		__A							:   Optional[Any]						,		):
				a_		:  List[str]											=      []
				# Generate more children proportionally to the fitness score.
				a_		:  Optional[int]											=      int(parent_a[1] * 1_00      ) + 1
				a_		:  Tuple											=      10 if child_n >= 10 else child_n
				for _ in range(_lowercase      ):
								a_		:  Optional[int]											=      population_score[random.randint(0						,		_lowercase      )][0]

								a_		:  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 		_UpperCAmelCase   (							__A							:   Optional[int]						,		__A							:   Optional[Any]						,		__A							:   List[str] = True      ):

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

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

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












'''simple docstring'''







import warnings


warnings.warn(
    'memory_utils has been reorganized to utils.memory. Import `find_executable_batchsize` from the main `__init__`: '
    '`from accelerate import find_executable_batch_size` to avoid this warning.',
    FutureWarning,
)




