
"""simple docstring"""

from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
UpperCamelCase			:       str									=				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.
UpperCamelCase			:       Tuple									=				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.
UpperCamelCase			:       Optional[int]									=				0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1_0_0_0))






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






def 							A		(       snake_case   :Dict       ,      snake_case   :Union[str, Any]	)   ->			Dict:
 __UpperCamelCase       						= random.randint(0       ,      len(_lowercase	) - 1	)
 __UpperCamelCase       						= parent_a[:random_slice] + parent_a[random_slice:]
 __UpperCamelCase       						= parent_a[:random_slice] + parent_a[random_slice:]
 return (child_a, child_a)






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






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

  __UpperCamelCase       						= 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 							A		(       snake_case   :Tuple       ,      snake_case   :Tuple       ,      snake_case   :List[Any] = True	)   ->			Any:

 # Verify if N_POPULATION is bigger than N_SELECTED
 if N_POPULATION < N_SELECTED:
  __UpperCamelCase       						= 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.
 __UpperCamelCase       						= sorted({c for c in target if c not in genes}	)
 if not_in_genes_list:
  __UpperCamelCase       						= f'{not_in_genes_list} is not in genes list, evolution cannot converge'
  raise ValueError(_lowercase	)

 # Generate random starting population.
 __UpperCamelCase       						= []
 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.
 __UpperCamelCase       						= 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.
  __UpperCamelCase       						= [evaluate(_lowercase       ,      _lowercase	) for item in population]

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





"""simple docstring"""

import argparse
from collections import defaultdict






def 							A		(       snake_case   :Tuple       ,      snake_case   :List[Any]       ,      snake_case   :List[Any]       ,      snake_case   :Union[str, Any]       ,      snake_case   :Optional[Any]	)   ->			Union[str, Any]:
 __UpperCamelCase       						= f'{file}_{class_name}_{test_name}'
 done_test[_id] += 1

 with open(snake_case       ,      'r'	) as f:
  __UpperCamelCase       						= f.readlines()

 __UpperCamelCase       						= f'class {class_name}('
 __UpperCamelCase       						= f'{4 * " "}def {test_name}('
 __UpperCamelCase       						= f'{8 * " "}{correct_line.split()[0]}'
 __UpperCamelCase       						= f'{1_6 * " "}{correct_line.split()[0]}'
 __UpperCamelCase       						= False
 __UpperCamelCase       						= False
 __UpperCamelCase       						= False
 __UpperCamelCase       						= False
 __UpperCamelCase       						= 0
 __UpperCamelCase       						= 0

 __UpperCamelCase       						= []
 for line in lines:
  if line.startswith(snake_case	):
   __UpperCamelCase       						= True
  elif in_class and line.startswith(snake_case	):
   __UpperCamelCase       						= True
  elif in_class and in_func and (line.startswith(snake_case	) or line.startswith(snake_case	)):
   __UpperCamelCase       						= len(line.split(correct_line.split()[0]	)[0]	)
   count += 1

   if count == done_test[_id]:
    __UpperCamelCase       						= True

  if in_class and in_func and in_line:
   if ")" not in line:
    continue
   else:
    __UpperCamelCase       						= True

  if in_class and in_func and in_line and insert_line:
   new_lines.append(f'{spaces * " "}{correct_line}'	)
   __UpperCamelCase       						= __UpperCamelCase       						= __UpperCamelCase       						= __UpperCamelCase       						= False
  else:
   new_lines.append(snake_case	)

 with open(snake_case       ,      'w'	) as f:
  for line in new_lines:
   f.write(snake_case	)






def 							A		(       snake_case   :List[str]       ,      snake_case   :str=None	)   ->			Tuple:
 if fail is not None:
  with open(snake_case       ,      'r'	) as f:
   __UpperCamelCase       						= {l.strip() for l in f.readlines()}
 else:
  __UpperCamelCase       						= None

 with open(snake_case       ,      'r'	) as f:
  __UpperCamelCase       						= f.readlines()

 __UpperCamelCase       						= defaultdict(snake_case	)
 for line in correct_lines:
  __UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase       						= line.split(';'	)
  if test_failures is None or "::".join([file, class_name, test_name]	) in test_failures:
   overwrite_file(snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case	)


if __name__ == "__main__":
      UpperCamelCase			:       str									=				argparse.ArgumentParser()
      parser.add_argument("--correct_filename", help="filename of tests with expected result")
      parser.add_argument("--fail_filename", help="filename of test failures", type=str, default=None)
      UpperCamelCase			:       List[Any]									=				parser.parse_args()

      main(args.correct_filename, args.fail_filename)



