




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





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





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





def __UpperCamelCase			(       _A ,  _A ,  _A ,  ):
						lowerCAmelCase_      			=  []
						# Generate more children proportionally to the fitness score.
						lowerCAmelCase_      			=  int(parent_a[1] * 100  ) + 1
						lowerCAmelCase_      			=  10 if child_n >= 10 else child_n
						for _ in range(_A  ):
												lowerCAmelCase_      			=  population_score[random.randint(0 ,  _A  )][0]

												lowerCAmelCase_			,					lowerCAmelCase_      			=  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 __UpperCamelCase			(       _A ,  _A ,  _A = True  ):

						# Verify if N_POPULATION is bigger than N_SELECTED
						if N_POPULATION < N_SELECTED:
												lowerCAmelCase_      			=  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.
						lowerCAmelCase_      			=  sorted({c for c in target if c not in genes}  )
						if not_in_genes_list:
												lowerCAmelCase_      			=  f"{not_in_genes_list} is not in genes list, evolution cannot converge"
												raise ValueError(_A  )

						# Generate random starting population.
						lowerCAmelCase_      			=  []
						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.
						lowerCAmelCase_			,					lowerCAmelCase_      			=  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.
												lowerCAmelCase_      			=  [evaluate(_A ,  _A  ) for item in population]

												# Check if there is a matching evolution.
												lowerCAmelCase_      			=  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.
												lowerCAmelCase_      			=  population[: int(N_POPULATION / 3  )]
												population.clear()
												population.extend(_A  )
												# Normalize population score to be between 0 and 1.
												lowerCAmelCase_      			=  [
												    (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__":
						_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}"
						)










import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


_A     =			logging.get_logger(__name__)


class  A		(  __UpperCAmelCase     ):



		def __init__(     self,	*UpperCamelCase__,	**UpperCamelCase__   ):







								"""simple docstring"""



								warnings.warn(
								    '''The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
								    ''' use BeitImageProcessor instead.''',	UpperCamelCase__,	)
								super().__init__(*UpperCamelCase__,	**UpperCamelCase__   )




