
"""simple docstring"""




from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
UpperCAmelCase__   :					Optional[int]												=				2_0_0
# 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.
UpperCAmelCase__   :					Optional[Any]												=				5_0
# 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.
UpperCAmelCase__   :					Optional[int]												=				0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1_0_0_0))

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

def lowercase_						(     _snake_case     ,_snake_case		):
							SCREAMING_SNAKE_CASE__		: Tuple     =     random.randint(0     ,len(_snake_case		) - 1		)
							SCREAMING_SNAKE_CASE__		: str     =     parent_a[:random_slice] + parent_a[random_slice:]
							SCREAMING_SNAKE_CASE__		: Optional[int]     =     parent_a[:random_slice] + parent_a[random_slice:]
							return (child_a, child_a)

def lowercase_						(     _snake_case     ,_snake_case		):
							SCREAMING_SNAKE_CASE__		: List[str]     =     list(_snake_case		)
							if random.uniform(0     ,1		) < MUTATION_PROBABILITY:
														SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     random.choice(_snake_case		)
							return "".join(_snake_case		)

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

														SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__		: int     =     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 lowercase_						(     _snake_case     ,_snake_case     ,_snake_case = True		):

							# Verify if N_POPULATION is bigger than N_SELECTED
							if N_POPULATION < N_SELECTED:
														SCREAMING_SNAKE_CASE__		: Any     =     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.
							SCREAMING_SNAKE_CASE__		: int     =     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(_snake_case		)

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

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






"""simple docstring"""




from typing import TYPE_CHECKING

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


UpperCAmelCase__   :					Tuple												=				{
    'configuration_mask2former': [
        'MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Mask2FormerConfig',
    ],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__   :					str												=				['Mask2FormerImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__   :					Any												=				[
					    'MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'Mask2FormerForUniversalSegmentation',
					    'Mask2FormerModel',
					    'Mask2FormerPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_maskaformer import MaskaFormerImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_maskaformer import (
										    MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MaskaFormerForUniversalSegmentation,
										    MaskaFormerModel,
										    MaskaFormerPreTrainedModel,
										)


else:
					import sys

					UpperCAmelCase__   :					Union[str, Any]												=				_LazyModule(__name__, globals()['__file__'], _import_structure)




