



'''simple docstring'''


from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
lowercase							       =			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.
lowercase							       =			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.
lowercase							       =			0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1_000))






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ):


     '''simple docstring'''







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






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     a_       		=random.randint(0       ,	len(lowercase__       ) - 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_       (		lowercase__       ,	lowercase__       ):


     '''simple docstring'''







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






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	lowercase__       ,	):


     '''simple docstring'''







     a_       		=[]
     # Generate more children proportionally to the fitness score.
     a_       		=int(parent_a[1] * 1_0_0       ) + 1
     a_       		=1_0 if child_n >= 1_0 else child_n
     for _ in range(lowercase__       ):
          a_       		=population_score[random.randint(0       ,	lowercase__       )][0]

          a_       		=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  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	lowercase__ = True       ):


     '''simple docstring'''







     if N_POPULATION < N_SELECTED:
          a_       		=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.
     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(lowercase__       )

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

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











'''simple docstring'''


import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	lowercase__       ,	lowercase__=5       ):


     '''simple docstring'''







     assert masked_input.count("<mask>"       ) == 1
     a_       		=torch.tensor(tokenizer.encode(lowercase__       ,	add_special_tokens=lowercase__       )       ).unsqueeze(0       )  # Batch size 1
     a_       		=model(lowercase__       )[0]  # The last hidden-state is the first element of the output tuple
     a_       		=(input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
     a_       		=logits[0, masked_index, :]
     a_       		=logits.softmax(dim=0       )
     a_		,						a_       		=prob.topk(k=lowercase__       ,	dim=0       )
     a_       		=" ".join(
         [tokenizer.convert_ids_to_tokens(indices[i].item()       ) for i in range(len(lowercase__       )       )]       )
     a_       		=tokenizer.mask_token
     a_       		=[]
     for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(" "       )       ):
          a_       		=predicted_token_bpe.replace("\u2581"       ,	" "       )
          if " {0}".format(lowercase__       ) in masked_input:
               topk_filled_outputs.append(
                   (
                       masked_input.replace(" {0}".format(lowercase__       )       ,	lowercase__       ),
                       values[index].item(),
                       predicted_token,
                   )       )
          else:
               topk_filled_outputs.append(
                   (
                       masked_input.replace(lowercase__       ,	lowercase__       ),
                       values[index].item(),
                       predicted_token,
                   )       )
     return topk_filled_outputs


lowercase							       =			CamembertTokenizer.from_pretrained('''camembert-base''')
lowercase							       =			CamembertForMaskedLM.from_pretrained('''camembert-base''')
model.eval()

lowercase							       =			'''Le camembert est <mask> :)'''

print(fill_mask(masked_input, model, tokenizer, topk=3))


