

"""simple docstring"""




from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
__UpperCAmelCase =				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.
__UpperCAmelCase =				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.
__UpperCAmelCase =				0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))
def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase      ):
 '''simple docstring'''





 UpperCAmelCase__					:  Tuple					   =					len([g for position, g in enumerate(__UpperCamelCase      ) if g == main_target[position]]      )
 return (item, float(__UpperCamelCase      ))
def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase      ):
 '''simple docstring'''





 UpperCAmelCase__					:  Optional[Any]					   =					random.randint(0							,    len(__UpperCamelCase      ) - 1      )
 UpperCAmelCase__					:  List[str]					   =					parent_a[:random_slice] + parent_a[random_slice:]
 UpperCAmelCase__					:  Optional[Any]					   =					parent_a[:random_slice] + parent_a[random_slice:]
 return (child_a, child_a)
def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase      ):
 '''simple docstring'''





 UpperCAmelCase__					:  int					   =					list(__UpperCamelCase      )
 if random.uniform(0							,    1      ) < MUTATION_PROBABILITY:
  UpperCAmelCase__					:  Optional[Any]					   =					random.choice(__UpperCamelCase      )
 return "".join(__UpperCamelCase      )
def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase							,    ):
 '''simple docstring'''





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

  UpperCAmelCase__   ,      UpperCAmelCase__					:  Optional[int]					   =					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   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase = True      ):
 '''simple docstring'''





 if N_POPULATION < N_SELECTED:
  UpperCAmelCase__					:  int					   =					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.
 UpperCAmelCase__					:  Tuple					   =					sorted({c for c in target if c not in genes}      )
 if not_in_genes_list:
  UpperCAmelCase__					:  Dict					   =					F"{not_in_genes_list} is not in genes list, evolution cannot converge"
  raise ValueError(__UpperCamelCase      )

 # Generate random starting population.
 UpperCAmelCase__					:  int					   =					[]
 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.
 UpperCAmelCase__   ,      UpperCAmelCase__					:  List[str]					   =					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.
  UpperCAmelCase__					:  List[str]					   =					[evaluate(__UpperCamelCase							,    __UpperCamelCase      ) for item in population]

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








"""simple docstring"""




from datetime import datetime as dt
import os

from github import Github


__UpperCAmelCase =				[
    'good first issue',
    'good second issue',
    'good difficult issue',
    'feature request',
    'new model',
    'wip',
]
def   lowerCAmelCase						(					):
 '''simple docstring'''





 UpperCAmelCase__					:  str					   =					Github(os.environ["""GITHUB_TOKEN"""]      )
 UpperCAmelCase__					:  int					   =					g.get_repo("""huggingface/transformers"""      )
 UpperCAmelCase__					:  Optional[int]					   =					repo.get_issues(state="""open"""      )

 for issue in open_issues:
  UpperCAmelCase__					:  Any					   =					sorted([comment for comment in issue.get_comments()]							,    key=lambda __UpperCamelCase      : i.created_at							,    reverse=__UpperCamelCase      )
  UpperCAmelCase__					:  Union[str, Any]					   =					comments[0] if len(__UpperCamelCase      ) > 0 else None
  if (
      last_comment is not None
      and last_comment.user.login == "github-actions[bot]"
      and (dt.utcnow() - issue.updated_at).days > 7
      and (dt.utcnow() - issue.created_at).days >= 30
      and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()      )
  ):
   # print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
   issue.edit(state="""closed"""      )
  elif (
      (dt.utcnow() - issue.updated_at).days > 23
      and (dt.utcnow() - issue.created_at).days >= 30
      and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()      )
  ):
   # print(f"Would add stale comment to {issue.number}")
   issue.create_comment(
       """This issue has been automatically marked as stale because it has not had """
       """recent activity. If you think this still needs to be addressed """
       """please comment on this thread.\n\nPlease note that issues that do not follow the """
       """[contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) """
       """are likely to be ignored."""      )


if __name__ == "__main__":
      main()





