




from __future__ import annotations

import random

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






def     _snake_case    (__lowercase    ,      __lowercase):
   UpperCamelCase_									=     len([g for position, g in enumerate(__lowercase) if g == main_target[position]])
   return (item, float(__lowercase))






def     _snake_case    (__lowercase    ,      __lowercase):
   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     _snake_case    (__lowercase    ,      __lowercase):
   UpperCamelCase_									=     list(__lowercase)
   if random.uniform(0    ,      1) < MUTATION_PROBABILITY:
      UpperCamelCase_									=     random.choice(__lowercase)
   return "".join(__lowercase)






def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase    ,      ):
   UpperCamelCase_									=     []
   # Generate more children proportionally to the fitness score.
   UpperCamelCase_									=     int(parent_a[1] * 100) + 1
   UpperCamelCase_									=     10 if child_n >= 10 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     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase = True):

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













import warnings

from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor


snake_case__      :	List[str]       	=      logging.get_logger(__name__)


class 						_a       (  UpperCAmelCase__    ):






  """simple docstring"""





  def __init__(     self		,		*_UpperCAmelCase		,		**_UpperCAmelCase )			-> None:
     warnings.warn(
         'The class VideoMAEFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
         ' Please use VideoMAEImageProcessor instead.'		,		_UpperCAmelCase		,		)
     super().__init__(*_UpperCAmelCase		,		**_UpperCAmelCase )





