





"""simple docstring"""




from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
a		:				List[Any]  			=    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		:				Any  			=    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		:				str  			=    0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Dict					, _lowercase		:					str      )				->int:






	'''simple docstring'''






	a   :	Optional[Any]			=       len([g for position, g in enumerate(_lowerCamelCase      ) if g == main_target[position]]      )
	return (item, float(_lowerCamelCase      ))






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Union[str, Any]					, _lowercase		:					str      )				->Any:






	'''simple docstring'''






	a   :	Tuple			=       random.randint(0					, len(_lowerCamelCase      ) - 1      )
	a   :	List[str]			=       parent_a[:random_slice] + parent_a[random_slice:]
	a   :	Optional[Any]			=       parent_a[:random_slice] + parent_a[random_slice:]
	return (child_a, child_a)






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Optional[int]					, _lowercase		:					Any      )				->Optional[int]:






	'''simple docstring'''






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






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					int					, _lowercase		:					List[str]					, _lowercase		:					int					, )				->Optional[Any]:






	'''simple docstring'''






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

		a   :	int			=       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	(			_lowercase		:					str					, _lowercase		:					List[Any]					, _lowercase		:					Dict = True      )				->int:






	'''simple docstring'''






	if N_POPULATION < N_SELECTED:
		a   :	Tuple			=       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.
	a   :	Optional[int]			=       sorted({c for c in target if c not in genes}      )
	if not_in_genes_list:
		a   :	Any			=       F"""{not_in_genes_list} is not in genes list, evolution cannot converge"""
		raise ValueError(_lowerCamelCase      )

	# Generate random starting population.
	a   :	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.
	a   :	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.
		a   :	Any			=       [evaluate(_lowerCamelCase					, _lowerCamelCase      ) for item in population]

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









"""simple docstring"""




from random import randint
from tempfile import TemporaryFile

import numpy as np






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Optional[int]					, _lowercase		:					Optional[Any]					, _lowercase		:					Union[str, Any]      )				->Dict:






	'''simple docstring'''






	a   :	List[str]			=       0
	if start < end:
		a   :	Tuple			=       randint(_lowercase					, _lowercase      )
		a   :	List[str]			=       a[end]
		a   :	str			=       a[pivot]
		a   :	Optional[int]			=       temp

		a, a   :	Dict			=       _in_place_partition(_lowercase					, _lowercase					, _lowercase      )
		count += _in_place_quick_sort(_lowercase					, _lowercase					, p - 1      )
		count += _in_place_quick_sort(_lowercase					, p + 1					, _lowercase      )
	return count






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Optional[Any]					, _lowercase		:					str					, _lowercase		:					List[Any]      )				->str:






	'''simple docstring'''






	a   :	Union[str, Any]			=       0
	a   :	List[Any]			=       randint(_lowercase					, _lowercase      )
	a   :	int			=       a[end]
	a   :	List[str]			=       a[pivot]
	a   :	Tuple			=       temp
	a   :	Union[str, Any]			=       start - 1
	for index in range(_lowercase					, _lowercase      ):
		count += 1
		if a[index] < a[end]:  # check if current val is less than pivot value
			a   :	List[str]			=       new_pivot_index + 1
			a   :	Optional[int]			=       a[new_pivot_index]
			a   :	Union[str, Any]			=       a[index]
			a   :	List[Any]			=       temp

	a   :	Tuple			=       a[new_pivot_index + 1]
	a   :	str			=       a[end]
	a   :	Dict			=       temp
	return new_pivot_index + 1, count


a		:				int  			=    TemporaryFile()
a		:				Tuple  			=    100  # 1000 elements are to be sorted


a			,				a		:				int  			=    0, 1  # mean and standard deviation
a		:				List[Any]  			=    np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('''The array is''')
print(X)


outfile.seek(0)  # using the same array
a		:				int  			=    np.load(outfile)
a		:				Tuple  			=    len(M) - 1
a		:				Union[str, Any]  			=    _in_place_quick_sort(M, 0, r)

print(
    '''No of Comparisons for 100 elements selected from a standard normal distribution'''
    '''is :'''
)







print(z)


