





"""simple docstring"""


from .data_collator import (
    DataCollatorForLanguageModeling,
    DataCollatorForPermutationLanguageModeling,
    DataCollatorForSeqaSeq,
    DataCollatorForSOP,
    DataCollatorForTokenClassification,
    DataCollatorForWholeWordMask,
    DataCollatorWithPadding,
    DefaultDataCollator,
    default_data_collator,
)
from .metrics import glue_compute_metrics, xnli_compute_metrics
from .processors import (
    DataProcessor,
    InputExample,
    InputFeatures,
    SingleSentenceClassificationProcessor,
    SquadExample,
    SquadFeatures,
    SquadVaProcessor,
    SquadVaProcessor,
    glue_convert_examples_to_features,
    glue_output_modes,
    glue_processors,
    glue_tasks_num_labels,
    squad_convert_examples_to_features,
    xnli_output_modes,
    xnli_processors,
    xnli_tasks_num_labels,
)







"""simple docstring"""


from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
lowerCamelCase__				  =   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.
lowerCamelCase__				  =   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.
lowerCamelCase__				  =   0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))




def 					lowercase__    (					lowercase_		,lowercase_			)					->		tuple[str, float]:




      """simple docstring"""

      _UpperCamelCase      :	str						=       len([g for position, g in enumerate(lowercase_			) if g == main_target[position]]			)
      return (item, float(lowercase_			))




def 					lowercase__    (					lowercase_		,lowercase_			)					->		tuple[str, str]:




      """simple docstring"""

      _UpperCamelCase      :	Tuple						=       random.randint(0		,len(lowercase_			) - 1			)
      _UpperCamelCase      :	Dict						=       parent_a[:random_slice] + parent_a[random_slice:]
      _UpperCamelCase      :	Tuple						=       parent_a[:random_slice] + parent_a[random_slice:]
      return (child_a, child_a)




def 					lowercase__    (					lowercase_		,lowercase_			)					->		str:




      """simple docstring"""

      _UpperCamelCase      :	int						=       list(lowercase_			)
      if random.uniform(0		,1			) < MUTATION_PROBABILITY:
            _UpperCamelCase      :	int						=       random.choice(lowercase_			)
      return "".join(lowercase_			)




def 					lowercase__    (					lowercase_		,lowercase_		,lowercase_		,)					->		list[str]:




      """simple docstring"""

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

            _UpperCamelCase,  _UpperCamelCase      :	Dict						=       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 					lowercase__    (					lowercase_		,lowercase_		,lowercase_ = True			)					->		tuple[int, int, str]:




      """simple docstring"""

      if N_POPULATION < N_SELECTED:
            _UpperCamelCase      :	List[str]						=       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      :	int						=       sorted({c for c in target if c not in genes}			)
      if not_in_genes_list:
            _UpperCamelCase      :	int						=       F'''{not_in_genes_list} is not in genes list, evolution cannot converge'''
            raise ValueError(lowercase_			)

      # Generate random starting population.
      _UpperCamelCase      :	Union[str, Any]						=       []
      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,  _UpperCamelCase      :	str						=       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      :	int						=       [evaluate(lowercase_		,lowercase_			) for item in population]

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