"""simple docstring"""






from __future__ import annotations

import random

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



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



def   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				UpperCAmelCase__             =							random.randint(0					,			len(lowerCamelCase       ) - 1       )
				UpperCAmelCase__             =							parent_a[:random_slice] + parent_a[random_slice:]
				UpperCAmelCase__             =							parent_a[:random_slice] + parent_a[random_slice:]
				return (child_a, child_a)



def   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				UpperCAmelCase__             =							list(lowerCamelCase       )
				if random.uniform(0					,			1       ) < MUTATION_PROBABILITY:
								UpperCAmelCase__             =							random.choice(lowerCamelCase       )
				return "".join(lowerCamelCase       )



def   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase					,			):
				UpperCAmelCase__             =							[]
				# Generate more children proportionally to the fitness score.
				UpperCAmelCase__             =							int(parent_a[1] * 1_0_0       ) + 1
				UpperCAmelCase__             =							1_0 if child_n >= 1_0 else child_n
				for _ in range(lowerCamelCase       ):
								UpperCAmelCase__             =							population_score[random.randint(0					,			lowerCamelCase       )][0]

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



def   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase = True       ):

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

				# Generate random starting population.
				UpperCAmelCase__             =							[]
				for _ in range(lowerCamelCase       ):
								population.append(''.join([random.choice(lowerCamelCase       ) for i in range(len(lowerCamelCase       )       )]       )       )

				# Just some logs to know what the algorithms is doing.
				UpperCAmelCase__      ,	UpperCAmelCase__             =							0, 0

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

								# 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__             =							[evaluate(lowerCamelCase					,			lowerCamelCase       ) for item in population]

								# Check if there is a matching evolution.
								UpperCAmelCase__             =							sorted(lowerCamelCase					,			key=lambda lowerCamelCase       : x[1]					,			reverse=lowerCamelCase       )
								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 % 1_0 == 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__             =							population[: int(N_POPULATION / 3       )]
								population.clear()
								population.extend(lowerCamelCase       )
								# Normalize population score to be between 0 and 1.
								UpperCAmelCase__             =							[
								    (item, score / len(lowerCamelCase       )) for item, score in population_score
								]

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


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




"""simple docstring"""






import requests
from bsa import BeautifulSoup



def   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				UpperCAmelCase__             =							BeautifulSoup(requests.get(lowerCamelCase					,			params=lowerCamelCase       ).content					,			'html.parser'       )
				UpperCAmelCase__             =							soup.find('div'					,			attrs={'class': 'gs_ri'}       )
				UpperCAmelCase__             =							div.find('div'					,			attrs={'class': 'gs_fl'}       ).find_all('a'       )
				return anchors[2].get_text()


if __name__ == "__main__":
						lowerCAmelCase__  :  Optional[int]						     =	{
						    'title': (
						        'Precisely geometry controlled microsupercapacitors for ultrahigh areal '
						        'capacitance, volumetric capacitance, and energy density'
						    ),
						    'journal': 'Chem. Mater.',
						    'volume': 30,
						    'pages': '3979-3990',
						    'year': 2_018,
						    'hl': 'en',
						}
						print(get_citation('https://scholar.google.com/scholar_lookup', params=params))



