



from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
A_		:     Dict        =     2_00
# 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_		:     str        =     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_		:     List[str]        =     0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 10_00))


def 	UpperCAmelCase__      (      UpperCAmelCase__ :str ,					UpperCAmelCase__ :str					):






 '''simple docstring'''

 a						=		len([g for position, g in enumerate(UpperCAmelCase__					) if g == main_target[position]]					)
 return (item, float(UpperCAmelCase__					))


def 	UpperCAmelCase__      (      UpperCAmelCase__ :str ,					UpperCAmelCase__ :str					):






 '''simple docstring'''

 a						=		random.randint(0 ,					len(UpperCAmelCase__					) - 1					)
 a						=		parent_a[:random_slice] + parent_a[random_slice:]
 a						=		parent_a[:random_slice] + parent_a[random_slice:]
 return (child_a, child_a)


def 	UpperCAmelCase__      (      UpperCAmelCase__ :str ,					UpperCAmelCase__ :list[str]					):






 '''simple docstring'''

 a						=		list(UpperCAmelCase__					)
 if random.uniform(0 ,					1					) < MUTATION_PROBABILITY:
  a						=		random.choice(UpperCAmelCase__					)
 return "".join(UpperCAmelCase__					)


def 	UpperCAmelCase__      (      UpperCAmelCase__ :tuple[str, float] ,					UpperCAmelCase__ :list[tuple[str, float]] ,					UpperCAmelCase__ :list[str] ,					):






 '''simple docstring'''

 a						=		[]
 # Generate more children proportionally to the fitness score.
 a						=		int(parent_a[1] * 1_00					) + 1
 a						=		10 if child_n >= 10 else child_n
 for _ in range(UpperCAmelCase__					):
  a						=		population_score[random.randint(0 ,					UpperCAmelCase__					)][0]

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


def 	UpperCAmelCase__      (      UpperCAmelCase__ :str ,					UpperCAmelCase__ :list[str] ,					UpperCAmelCase__ :bool = True					):






 '''simple docstring'''

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

 # Generate random starting population.
 a						=		[]
 for _ in range(UpperCAmelCase__					):
  population.append("".join([random.choice(UpperCAmelCase__					) for i in range(len(UpperCAmelCase__					)					)]					)					)

 # Just some logs to know what the algorithms is doing.
 a		,  a						=		0, 0

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

  # 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						=		[evaluate(UpperCAmelCase__ ,					UpperCAmelCase__					) for item in population]

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

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


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






import re
from pathlib import Path
from unittest import TestCase

import pytest



@pytest.mark.integration
class 		_lowercase		(       UpperCAmelCase__	):

   def 		A							(      self			:       Optional[int]							,   __lowerCAmelCase			:       str							)       ->				Union[str, Any]:


    """simple docstring"""






    with open(__lowerCAmelCase							,   encoding="utf-8"							) as input_file:
     a						=		re.compile(R"(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)"							)
     a						=		input_file.read()
     a						=		regexp.search(__lowerCAmelCase							)

    return match

   def 		A							(      self			:       List[Any]							,   __lowerCAmelCase			:       str							)       ->				Dict:


    """simple docstring"""






    with open(__lowerCAmelCase							,   encoding="utf-8"							) as input_file:
     a						=		re.compile(R"#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()"							,   re.DOTALL							)
     a						=		input_file.read()
     # use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
     a						=		regexp.finditer(__lowerCAmelCase							)

    a						=		[match for match in matches if match is not None and match.group(1							) is not None]
    return matches[0] if matches else None

   def 		A							(      self			:       List[str]							)       ->				List[Any]:


    """simple docstring"""






    a						=		Path("./datasets"							)
    a						=		list(dataset_paths.absolute().glob("**/*.py"							)							)

    for dataset in dataset_files:
     if self._no_encoding_on_file_open(str(__lowerCAmelCase							)							):
      raise AssertionError(f"""open(...) must use utf-8 encoding in {dataset}"""							)




   def 		A							(      self			:       Tuple							)       ->				Union[str, Any]:


    """simple docstring"""






    a						=		Path("./datasets"							)
    a						=		list(dataset_paths.absolute().glob("**/*.py"							)							)

    for dataset in dataset_files:
     if self._no_print_statements(str(__lowerCAmelCase							)							):
      raise AssertionError(f"""print statement found in {dataset}. Use datasets.logger/logging instead."""							)

