




"""simple docstring"""

from __future__ import annotations

import random

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






def 		_snake_case	(   _snake_case				:   str							,    _snake_case				:   str				)       ->   tuple[str, float]:






      '''simple docstring'''


      _A     =  len([g for position, g in enumerate(_snake_case				) if g == main_target[position]]				)
      return (item, float(_snake_case				))






def 		_snake_case	(   _snake_case				:   str							,    _snake_case				:   str				)       ->   tuple[str, str]:






      '''simple docstring'''


      _A     =  random.randint(0							,    len(_snake_case				) - 1				)
      _A     =  parent_a[:random_slice] + parent_a[random_slice:]
      _A     =  parent_a[:random_slice] + parent_a[random_slice:]
      return (child_a, child_a)






def 		_snake_case	(   _snake_case				:   str							,    _snake_case				:   list[str]				)       ->   str:






      '''simple docstring'''


      _A     =  list(_snake_case				)
      if random.uniform(0							,    1				) < MUTATION_PROBABILITY:
            _A     =  random.choice(_snake_case				)
      return "".join(_snake_case				)






def 		_snake_case	(   _snake_case				:   tuple[str, float]							,    _snake_case				:   list[tuple[str, float]]							,    _snake_case				:   list[str]							,    )       ->   list[str]:






      '''simple docstring'''


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

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






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






      '''simple docstring'''


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

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

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

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

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

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

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


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








'''simple docstring'''




import warnings

from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor


a__					:			int          =     logging.get_logger(__name__)






class 				__snake_case     (					__magic_name__							):





	def __init__(		self      ,       *UpperCamelCase_      ,       **UpperCamelCase_		)      ->		None:
		warnings.warn(
		    'The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
		    ' Please use PerceiverImageProcessor instead.'      ,       UpperCamelCase_      ,       )
		super().__init__(*UpperCamelCase_      ,       **UpperCamelCase_		)



