






from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
A__	:    Dict			 = 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__	:    List[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, 1_000))

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

def 			_snake_case    (					lowerCamelCase__     :   str    ,						lowerCamelCase__     :   str  )    ->     tuple[str, str]:
							lowerCamelCase_	:							int									=random.randint(0    ,						len(lowerCamelCase__  ) - 1  )
							lowerCamelCase_	:							Dict									=parent_a[:random_slice] + parent_a[random_slice:]
							lowerCamelCase_	:							List[Any]									=parent_a[:random_slice] + parent_a[random_slice:]
							return (child_a, child_a)

def 			_snake_case    (					lowerCamelCase__     :   str    ,						lowerCamelCase__     :   list[str]  )    ->     str:
							lowerCamelCase_	:							int									=list(lowerCamelCase__  )
							if random.uniform(0    ,						1  ) < MUTATION_PROBABILITY:
														lowerCamelCase_	:							Union[str, Any]									=random.choice(lowerCamelCase__  )
							return "".join(lowerCamelCase__  )

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

														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 			_snake_case    (					lowerCamelCase__     :   str    ,						lowerCamelCase__     :   list[str]    ,						lowerCamelCase__     :   bool = True  )    ->     tuple[int, int, str]:

							# Verify if N_POPULATION is bigger than N_SELECTED
							if N_POPULATION < N_SELECTED:
														lowerCamelCase_	:							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.
							lowerCamelCase_	:							Union[str, Any]									=sorted({c for c in target if c not in genes}  )
							if not_in_genes_list:
														lowerCamelCase_	:							str									=F"""{not_in_genes_list} is not in genes list, evolution cannot converge"""
														raise ValueError(lowerCamelCase__  )

							# Generate random starting population.
							lowerCamelCase_	:							str									=[]
							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_	:							Tuple									=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_	:							List[Any]									=[evaluate(lowerCamelCase__    ,						lowerCamelCase__  ) for item in population]

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











"""simple docstring"""



import pytest

from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested

from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows

def 			_snake_case    (					lowerCamelCase__     :   Any  )    ->     Union[str, Any]:  # picklable for multiprocessing
							return i + 1

@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def 			_snake_case    (					)    ->     List[Any]:
							with parallel_backend("spark"  ):
														assert ParallelBackendConfig.backend_name == "spark"

							lowerCamelCase_	:							Optional[Any]									=[1, 2, 3]
							with pytest.raises(lowerCamelCase__  ):
														with parallel_backend("unsupported backend"  ):
																					map_nested(lowerCamelCase__    ,						lowerCamelCase__    ,						num_proc=2  )

							with pytest.raises(lowerCamelCase__  ):
														with parallel_backend("unsupported backend"  ):
																					map_nested(lowerCamelCase__    ,						lowerCamelCase__    ,						num_proc=-1  )

@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize("num_proc"    ,						[2, -1]  )
def 			_snake_case    (					lowerCamelCase__     :   Tuple  )    ->     Optional[Any]:
							lowerCamelCase_	:							str									=[1, 2]
							lowerCamelCase_	:							List[str]									={"a": 1, "b": 2}
							lowerCamelCase_	:							List[str]									={"a": [1, 2], "b": [3, 4]}
							lowerCamelCase_	:							Optional[int]									={"a": {"1": 1}, "b": 2}
							lowerCamelCase_	:							int									={"a": 1, "b": 2, "c": 3, "d": 4}
							lowerCamelCase_	:							Optional[int]									=[2, 3]
							lowerCamelCase_	:							List[Any]									={"a": 2, "b": 3}
							lowerCamelCase_	:							int									={"a": [2, 3], "b": [4, 5]}
							lowerCamelCase_	:							str									={"a": {"1": 2}, "b": 3}
							lowerCamelCase_	:							Dict									={"a": 2, "b": 3, "c": 4, "d": 5}

							with parallel_backend("spark"  ):
														assert map_nested(lowerCamelCase__    ,						lowerCamelCase__    ,						num_proc=lowerCamelCase__  ) == expected_map_nested_sa
														assert map_nested(lowerCamelCase__    ,						lowerCamelCase__    ,						num_proc=lowerCamelCase__  ) == expected_map_nested_sa
														assert map_nested(lowerCamelCase__    ,						lowerCamelCase__    ,						num_proc=lowerCamelCase__  ) == expected_map_nested_sa
														assert map_nested(lowerCamelCase__    ,						lowerCamelCase__    ,						num_proc=lowerCamelCase__  ) == expected_map_nested_sa
														assert map_nested(lowerCamelCase__    ,						lowerCamelCase__    ,						num_proc=lowerCamelCase__  ) == expected_map_nested_sa



