

'''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 						UpperCamelCase	(			a		,						a			)				->     tuple[str, float]:






     '''simple docstring'''
     __magic_name__						       =    len([g for position, g in enumerate(a			) if g == main_target[position]]			)
     return (item, float(a			))




def 						UpperCamelCase	(			a		,						a			)				->     tuple[str, str]:






     '''simple docstring'''
     __magic_name__						       =    random.randint(0		,						len(a			) - 1			)
     __magic_name__						       =    parent_a[:random_slice] + parent_a[random_slice:]
     __magic_name__						       =    parent_a[:random_slice] + parent_a[random_slice:]
     return (child_a, child_a)




def 						UpperCamelCase	(			a		,						a			)				->     str:






     '''simple docstring'''
     __magic_name__						       =    list(a			)
     if random.uniform(0		,						1			) < MUTATION_PROBABILITY:
          __magic_name__						       =    random.choice(a			)
     return "".join(a			)




def 						UpperCamelCase	(			a		,						a		,						a		,						)				->     list[str]:






     '''simple docstring'''
     __magic_name__						       =    []
     # Generate more children proportionally to the fitness score.
     __magic_name__						       =    int(parent_a[1] * 100			) + 1
     __magic_name__						       =    10 if child_n >= 10 else child_n
     for _ in range(a			):
          __magic_name__						       =    population_score[random.randint(0		,						a			)][0]

          __magic_name__       ,  __magic_name__						       =    crossover(parent_a[0]		,						a			)
          # Append new string to the population list.
          pop.append(mutate(a		,						a			)			)
          pop.append(mutate(a		,						a			)			)
     return pop




def 						UpperCamelCase	(			a		,						a		,						a = True			)				->     tuple[int, int, str]:






     '''simple docstring'''

     # Verify if N_POPULATION is bigger than N_SELECTED
     if N_POPULATION < N_SELECTED:
          __magic_name__						       =    F'''{N_POPULATION} must be bigger than {N_SELECTED}'''
          raise ValueError(a			)
     # Verify that the target contains no genes besides the ones inside genes variable.
     __magic_name__						       =    sorted({c for c in target if c not in genes}			)
     if not_in_genes_list:
          __magic_name__						       =    F'''{not_in_genes_list} is not in genes list, evolution cannot converge'''
          raise ValueError(a			)

     # Generate random starting population.
     __magic_name__						       =    []
     for _ in range(a			):
          population.append(''''''.join([random.choice(a			) for i in range(len(a			)			)]			)			)

     # Just some logs to know what the algorithms is doing.
     __magic_name__       ,  __magic_name__						       =    0, 0

     # This loop will end when we find a perfect match for our target.
     while True:
          generation += 1
          total_population += len(a			)

          # 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.
          __magic_name__						       =    [evaluate(a		,						a			) for item in population]

          # Check if there is a matching evolution.
          __magic_name__						       =    sorted(a		,						key=lambda a			: x[1]		,						reverse=a			)
          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.
          __magic_name__						       =    population[: int(N_POPULATION / 3			)]
          population.clear()
          population.extend(a			)
          # Normalize population score to be between 0 and 1.
          __magic_name__						       =    [
              (item, score / len(a			)) for item, score in population_score
          ]

          # This is selection
          for i in range(a			):
               population.extend(select(population_score[int(a			)]		,						a		,						a			)			)
               # 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(a			) > 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}'''
      )









'''simple docstring'''




import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer




def 						UpperCamelCase	(			a		,						a		,						a		,						a=1024			)				->     Union[str, Any]:






     '''simple docstring'''
     __magic_name__       ,  __magic_name__						       =    [], []

     __magic_name__						       =    list(zip(a		,						a			)			)
     __magic_name__       ,  __magic_name__						       =    sorted_examples[0]

     def is_too_big(a			):
          return tok(a		,						return_tensors='''pt'''			).input_ids.shape[1] > max_tokens

     for src, tgt in tqdm(sorted_examples[1:]			):
          __magic_name__						       =    new_src + ''' ''' + src
          __magic_name__						       =    new_tgt + ''' ''' + tgt
          if is_too_big(a			) or is_too_big(a			):  # cant fit, finalize example
               finished_src.append(a			)
               finished_tgt.append(a			)
               __magic_name__       ,  __magic_name__						       =    src, tgt
          else:  # can fit, keep adding
               __magic_name__       ,  __magic_name__						       =    cand_src, cand_tgt

    # cleanup
     if new_src:
          assert new_tgt
          finished_src.append(a			)
          finished_tgt.append(a			)
     return finished_src, finished_tgt




def 						UpperCamelCase	(			a		,						a		,						a		,						a			)				->     Any:






     '''simple docstring'''
     __magic_name__						       =    Path(a			)
     save_path.mkdir(exist_ok=a			)
     for split in ["train"]:
          __magic_name__       ,  __magic_name__						       =    data_dir / F'''{split}.source''', data_dir / F'''{split}.target'''
          __magic_name__						       =    [x.rstrip() for x in Path(a			).open().readlines()]
          __magic_name__						       =    [x.rstrip() for x in Path(a			).open().readlines()]
          __magic_name__       ,  __magic_name__						       =    pack_examples(a		,						a		,						a		,						a			)
          print(F'''packed {split} split from {len(a			)} examples -> {len(a			)}.'''			)
          Path(save_path / F'''{split}.source'''			).open('''w'''			).write('''\n'''.join(a			)			)
          Path(save_path / F'''{split}.target'''			).open('''w'''			).write('''\n'''.join(a			)			)
     for split in ["val", "test"]:
          __magic_name__       ,  __magic_name__						       =    data_dir / F'''{split}.source''', data_dir / F'''{split}.target'''
          shutil.copyfile(a		,						save_path / F'''{split}.source'''			)
          shutil.copyfile(a		,						save_path / F'''{split}.target'''			)




def 						UpperCamelCase	(			)				->     List[str]:






     '''simple docstring'''
     __magic_name__						       =    argparse.ArgumentParser()
     parser.add_argument('''--tok_name'''		,						type=a		,						help='''like facebook/bart-large-cnn,t5-base, etc.'''			)
     parser.add_argument('''--max_seq_len'''		,						type=a		,						default=128			)
     parser.add_argument('''--data_dir'''		,						type=a			)
     parser.add_argument('''--save_path'''		,						type=a			)
     __magic_name__						       =    parser.parse_args()
     __magic_name__						       =    AutoTokenizer.from_pretrained(args.tok_name			)
     return pack_data_dir(a		,						Path(args.data_dir			)		,						args.max_seq_len		,						args.save_path			)


if __name__ == "__main__":
      packer_cli()






