from __future__ import annotations

import random

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







def      _a					(   lowercase__	:      str  ,	lowercase__	:      str			):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__					:					Any      =		len([g for position, g in enumerate(lowercase__			) if g == main_target[position]]			)
     return (item, float(lowercase__			))







def      _a					(   lowercase__	:      str  ,	lowercase__	:      str			):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__					:					str      =		random.randint(0  ,	len(lowercase__			) - 1			)
     SCREAMING_SNAKE_CASE__					:					List[Any]      =		parent_a[:random_slice] + parent_a[random_slice:]
     SCREAMING_SNAKE_CASE__					:					List[str]      =		parent_a[:random_slice] + parent_a[random_slice:]
     return (child_a, child_a)







def      _a					(   lowercase__	:      str  ,	lowercase__	:      list[str]			):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__					:					int      =		list(lowercase__			)
     if random.uniform(0  ,	1			) < MUTATION_PROBABILITY:
          SCREAMING_SNAKE_CASE__					:					List[str]      =		random.choice(lowercase__			)
     return "".join(lowercase__			)







def      _a					(   lowercase__	:      tuple[str, float]  ,	lowercase__	:      list[tuple[str, float]]  ,	lowercase__	:      list[str]  ,	):







     '''simple docstring'''


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

          SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					Tuple      =		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      _a					(   lowercase__	:      str  ,	lowercase__	:      list[str]  ,	lowercase__	:      bool = True			):







     '''simple docstring'''


     if N_POPULATION < N_SELECTED:
          SCREAMING_SNAKE_CASE__					:					Union[str, 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.
     SCREAMING_SNAKE_CASE__					:					List[str]      =		sorted({c for c in target if c not in genes}			)
     if not_in_genes_list:
          SCREAMING_SNAKE_CASE__					:					int      =		f'''{not_in_genes_list} is not in genes list, evolution cannot converge'''
          raise ValueError(lowercase__			)

     # Generate random starting population.
     SCREAMING_SNAKE_CASE__					:					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.
     SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					Optional[int]      =		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.
          SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		[evaluate(lowercase__  ,	lowercase__			) for item in population]

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










'''simple docstring'''




import requests
from bsa import BeautifulSoup




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_							)   ->str:
	snake_case__	       =   BeautifulSoup(requests.get(UpperCAmelCase_					,				params=UpperCAmelCase_							).content					,				'html.parser'							)
	snake_case__	       =   soup.find('div'					,				attrs={'class': 'gs_ri'}							)
	snake_case__	       =   div.find('div'					,				attrs={'class': 'gs_fl'}							).find_all('a'							)
	return anchors[2].get_text()


if __name__ == "__main__":
			a__					:			int          =     {
			    '''title''': (
			        '''Precisely geometry controlled microsupercapacitors for ultrahigh areal '''
			        '''capacitance, volumetric capacitance, and energy density'''
			    ),
			    '''journal''': '''Chem. Mater.''',
			    '''volume''': 30,
			    '''pages''': '''3979-3990''',
			    '''year''': 2018,
			    '''hl''': '''en''',
			}
			print(get_citation('''https://scholar.google.com/scholar_lookup''', params=params))



