



'''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]     						=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.
_A				:    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.
_A				:    Any     						=0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1_0_0_0))







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







def 						__UpperCamelCase				(							_lowercase,				_lowercase					) ->			tuple[str, str]:
	_lowercase					:       List[str]       =		random.randint(0,				len(_lowercase					) - 1					)
	_lowercase					:       Optional[int]       =		parent_a[:random_slice] + parent_a[random_slice:]
	_lowercase					:       str       =		parent_a[:random_slice] + parent_a[random_slice:]
	return (child_a, child_a)







def 						__UpperCamelCase				(							_lowercase,				_lowercase					) ->			str:
	_lowercase					:       int       =		list(_lowercase					)
	if random.uniform(0,				1					) < MUTATION_PROBABILITY:
		_lowercase					:       List[str]       =		random.choice(_lowercase					)
	return "".join(_lowercase					)







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

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







def 						__UpperCamelCase				(							_lowercase,				_lowercase,				_lowercase = True					) ->			tuple[int, int, str]:

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

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

	# Just some logs to know what the algorithms is doing.
	_lowercase					:       str       =		0, 0

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

		# 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.
		_lowercase					:       List[Any]       =		[evaluate(_lowercase,				_lowercase					) for item in population]

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

		# This is selection
		for i in range(_lowercase					):
			population.extend(select(population_score[int(_lowercase					)],				_lowercase,				_lowercase					)					)
			# 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(_lowercase					) > N_POPULATION:
				break


if __name__ == "__main__":
				_A				:    List[Any]     						=(
				    '''This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!'''
				)
				_A				:    Optional[Any]     						=list(
				    ''' ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm'''
				    '''nopqrstuvwxyz.,;!?+-*#@^\'èéòà€ù=)(&%$£/\\'''
				)
				_A				:    List[Any]     						=basic(target_str, genes_list)
				print(
				    F'''\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}'''
				)





'''simple docstring'''

import os
from collections.abc import Iterator







def 						__UpperCamelCase				(							_lowercase = "."					) ->			Iterator[str]:
	for dir_path, dir_names, filenames in os.walk(_lowercase					):
		_lowercase					:       Optional[int]       =		[d for d in dir_names if d != 'scripts' and d[0] not in '._']
		for filename in filenames:
			if filename == "__init__.py":
				continue
			if os.path.splitext(_lowercase					)[1] in (".py", ".ipynb"):
				yield os.path.join(_lowercase,				_lowercase					).lstrip('./'					)







def 						__UpperCamelCase				(							_lowercase					) ->			List[str]:
	return f'''{i * "  "}*''' if i else "\n##"







def 						__UpperCamelCase				(							_lowercase,				_lowercase					) ->			str:
	_lowercase					:       Optional[Any]       =		old_path.split(os.sep					)
	for i, new_part in enumerate(new_path.split(os.sep					)					):
		if (i + 1 > len(_lowercase					) or old_parts[i] != new_part) and new_part:
			print(f'''{md_prefix(_lowercase					)} {new_part.replace("_",				" "					).title()}'''					)
	return new_path







def 						__UpperCamelCase				(							_lowercase = "."					) ->			None:
	_lowercase					:       Dict       =		''
	for filepath in sorted(good_file_paths(_lowercase					)					):
		_lowercase  ,    _lowercase					:       Optional[Any]       =		os.path.split(_lowercase					)
		if filepath != old_path:
			_lowercase					:       Dict       =		print_path(_lowercase,				_lowercase					)
		_lowercase					:       Optional[int]       =		(filepath.count(os.sep					) + 1) if filepath else 0
		_lowercase					:       Dict       =		f'''{filepath}/{filename}'''.replace(' ',				'%20'					)
		_lowercase					:       Optional[int]       =		os.path.splitext(filename.replace('_',				' '					).title()					)[0]
		print(f'''{md_prefix(_lowercase					)} [{filename}]({url})'''					)


if __name__ == "__main__":
		print_directory_md('''.''')
