






"""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, 1000))

def 				snake_case      (   A__	,A__						):
			UpperCAmelCase_     : Optional[Any]			     =					len([g for position, g in enumerate(A__						) if g == main_target[position]]						)
			return (item, float(A__						))

def 				snake_case      (   A__	,A__						):
			UpperCAmelCase_     : Tuple			     =					random.randint(0	,len(A__						) - 1						)
			UpperCAmelCase_     : Optional[int]			     =					parent_a[:random_slice] + parent_a[random_slice:]
			UpperCAmelCase_     : Optional[Any]			     =					parent_a[:random_slice] + parent_a[random_slice:]
			return (child_a, child_a)

def 				snake_case      (   A__	,A__						):
			UpperCAmelCase_     : List[str]			     =					list(A__						)
			if random.uniform(0	,1						) < MUTATION_PROBABILITY:
						UpperCAmelCase_     : Tuple			     =					random.choice(A__						)
			return "".join(A__						)

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

						UpperCAmelCase_     ,	UpperCAmelCase_     : List[str]			     =					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 				snake_case      (   A__	,A__	,A__ = True						):

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

			# Generate random starting population.
			UpperCAmelCase_     : Optional[int]			     =					[]
			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_     : List[str]			     =					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_     : Union[str, Any]			     =					[evaluate(A__	,A__						) for item in population]

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











"""simple docstring"""
from __future__ import annotations

import time
from collections.abc import Sequence
from random import randint

from matplotlib import pyplot as plt

def 				snake_case      (   A__	,A__	,A__						):
			if not arr:
						return None, None, 0
			if low == high:
						return low, high, arr[low]

			UpperCAmelCase_     : Dict			     =					(low + high) // 2
			UpperCAmelCase_     ,	UpperCAmelCase_     ,	UpperCAmelCase_     : Any			     =					max_subarray(A__	,A__	,A__						)
			UpperCAmelCase_     ,	UpperCAmelCase_     ,	UpperCAmelCase_     : Union[str, Any]			     =					max_subarray(A__	,mid + 1	,A__						)
			UpperCAmelCase_     ,	UpperCAmelCase_     ,	UpperCAmelCase_     : Optional[Any]			     =					max_cross_sum(A__	,A__	,A__	,A__						)
			if left_sum >= right_sum and left_sum >= cross_sum:
						return left_low, left_high, left_sum
			elif right_sum >= left_sum and right_sum >= cross_sum:
						return right_low, right_high, right_sum
			return cross_left, cross_right, cross_sum

def 				snake_case      (   A__	,A__	,A__	,A__						):
			UpperCAmelCase_     ,	UpperCAmelCase_     : str			     =					float("-inf"						), -1
			UpperCAmelCase_     ,	UpperCAmelCase_     : Optional[Any]			     =					float("-inf"						), -1

			UpperCAmelCase_     : int | float			     =					0
			for i in range(A__	,low - 1	,-1						):
						summ += arr[i]
						if summ > left_sum:
									UpperCAmelCase_     : str			     =					summ
									UpperCAmelCase_     : Any			     =					i

			UpperCAmelCase_     : Dict			     =					0
			for i in range(mid + 1	,high + 1						):
						summ += arr[i]
						if summ > right_sum:
									UpperCAmelCase_     : List[Any]			     =					summ
									UpperCAmelCase_     : Optional[Any]			     =					i

			return max_left, max_right, (left_sum + right_sum)

def 				snake_case      (   A__						):
			UpperCAmelCase_     : str			     =					[randint(1	,A__						) for _ in range(A__						)]
			UpperCAmelCase_     : str			     =					time.time()
			max_subarray(A__	,0	,input_size - 1						)
			UpperCAmelCase_     : int			     =					time.time()
			return end - start

def 				snake_case      (   ):
			UpperCAmelCase_     : int			     =					[10, 1_00, 10_00, 1_00_00, 5_00_00, 10_00_00, 20_00_00, 30_00_00, 40_00_00, 50_00_00]
			UpperCAmelCase_     : List[str]			     =					[time_max_subarray(A__						) for input_size in input_sizes]
			print("No of Inputs\t\tTime Taken"						)
			for input_size, runtime in zip(A__	,A__						):
						print(A__	,"\t\t"	,A__						)
			plt.plot(A__	,A__						)
			plt.xlabel("Number of Inputs"						)
			plt.ylabel("Time taken in seconds"						)
			plt.show()


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

			testmod()



