




from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
__A        =    2_00
# 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, 10_00))







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





				"""simple docstring"""


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







def 			lowerCamelCase_		(       UpperCamelCase__				:      str ,     UpperCamelCase__				:      str      )     ->       tuple[str, str]:





				"""simple docstring"""


				__lowerCamelCase		       =    random.randint(0 ,     len(UpperCamelCase__      ) - 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 			lowerCamelCase_		(       UpperCamelCase__				:      str ,     UpperCamelCase__				:      list[str]      )     ->       str:





				"""simple docstring"""


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







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





				"""simple docstring"""


				__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(UpperCamelCase__      ):
								__lowerCamelCase		       =    population_score[random.randint(0 ,     UpperCamelCase__      )][0]

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







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





				"""simple docstring"""


				if N_POPULATION < N_SELECTED:
								__lowerCamelCase		       =    F"""{N_POPULATION} must be bigger than {N_SELECTED}"""
								raise ValueError(UpperCamelCase__      )
				# 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(UpperCamelCase__      )

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

				# 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(UpperCamelCase__      )

								# 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(UpperCamelCase__ ,     UpperCamelCase__      ) for item in population]

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

								# This is selection
								for i in range(UpperCamelCase__      ):
												population.extend(select(population_score[int(UpperCamelCase__      )] ,     UpperCamelCase__ ,     UpperCamelCase__      )      )
												# 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(UpperCamelCase__      ) > 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}'''
					)








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available


__A        =    {
    "configuration_audio_spectrogram_transformer": [
        "AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "ASTConfig",
    ]
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__A        =    [
					    "AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "ASTForAudioClassification",
					    "ASTModel",
					    "ASTPreTrainedModel",
					]

try:
					if not is_speech_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__A        =    ["ASTFeatureExtractor"]

if TYPE_CHECKING:
					from .configuration_audio_spectrogram_transformer import (
					    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    ASTConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_audio_spectrogram_transformer import (
										    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ASTForAudioClassification,
										    ASTModel,
										    ASTPreTrainedModel,
										)

					try:
										if not is_speech_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor


else:
					import sys

					__A        =    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


