

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, 1000))






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__      )  ->					tuple[str, float]:
	__lowerCamelCase    :    Any 	=			len([g for position, g in enumerate(lowerCamelCase__      ) if g == main_target[position]]      )
	return (item, float(lowerCamelCase__      ))






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__      )  ->					tuple[str, str]:
	__lowerCamelCase    :    Union[str, Any] 	=			random.randint(0     ,			len(lowerCamelCase__      ) - 1      )
	__lowerCamelCase    :    List[str] 	=			parent_a[:random_slice] + parent_a[random_slice:]
	__lowerCamelCase    :    Any 	=			parent_a[:random_slice] + parent_a[random_slice:]
	return (child_a, child_a)






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__      )  ->					str:
	__lowerCamelCase    :    List[Any] 	=			list(lowerCamelCase__      )
	if random.uniform(0     ,			1      ) < MUTATION_PROBABILITY:
		__lowerCamelCase    :    List[str] 	=			random.choice(lowerCamelCase__      )
	return "".join(lowerCamelCase__      )






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			)  ->					list[str]:
	__lowerCamelCase    :    List[Any] 	=			[]
	# Generate more children proportionally to the fitness score.
	__lowerCamelCase    :    Union[str, Any] 	=			int(parent_a[1] * 1_0_0      ) + 1
	__lowerCamelCase    :    Union[str, Any] 	=			1_0 if child_n >= 1_0 else child_n
	for _ in range(lowerCamelCase__      ):
		__lowerCamelCase    :    Tuple 	=			population_score[random.randint(0     ,			lowerCamelCase__      )][0]

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






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__ = True      )  ->					tuple[int, int, str]:

	# Verify if N_POPULATION is bigger than N_SELECTED
	if N_POPULATION < N_SELECTED:
		__lowerCamelCase    :    Optional[Any] 	=			F"{N_POPULATION} must be bigger than {N_SELECTED}"
		raise ValueError(lowerCamelCase__      )
	# Verify that the target contains no genes besides the ones inside genes variable.
	__lowerCamelCase    :    Any 	=			sorted({c for c in target if c not in genes}      )
	if not_in_genes_list:
		__lowerCamelCase    :    int 	=			F"{not_in_genes_list} is not in genes list, evolution cannot converge"
		raise ValueError(lowerCamelCase__      )

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

	# Just some logs to know what the algorithms is doing.
	__lowerCamelCase	,			__lowerCamelCase    :    List[str] 	=			0, 0

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

		# 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    :    Union[str, Any] 	=			[evaluate(lowerCamelCase__     ,			lowerCamelCase__      ) for item in population]

		# Check if there is a matching evolution.
		__lowerCamelCase    :    List[str] 	=			sorted(lowerCamelCase__     ,			key=lambda lowerCamelCase__      : x[1]     ,			reverse=lowerCamelCase__      )
		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 % 1_0 == 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    :    Union[str, Any] 	=			population[: int(N_POPULATION / 3      )]
		population.clear()
		population.extend(lowerCamelCase__      )
		# Normalize population score to be between 0 and 1.
		__lowerCamelCase    :    Optional[Any] 	=			[
		    (item, score / len(lowerCamelCase__      )) for item, score in population_score
		]

		# This is selection
		for i in range(lowerCamelCase__      ):
			population.extend(select(population_score[int(lowerCamelCase__      )]     ,			lowerCamelCase__     ,			lowerCamelCase__      )      )
			# 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(lowerCamelCase__      ) > 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_torch_available, is_vision_available


a		={
    """configuration_mask2former""": [
        """MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Mask2FormerConfig""",
    ],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=["""Mask2FormerImageProcessor"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=[
					    """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

					a		=_LazyModule(__name__, globals()["""__file__"""], _import_structure)




