

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__      ( __A    :str	,__A    :str		):


       """simple docstring"""




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







def 						lowerCamelCase__      ( __A    :str	,__A    :str		):


       """simple docstring"""




       __snake_case       	=							random.randint(0	,len(__A		) - 1		)
       __snake_case       	=							parent_a[:random_slice] + parent_a[random_slice:]
       __snake_case       	=							parent_a[:random_slice] + parent_a[random_slice:]
       return (child_a, child_a)







def 						lowerCamelCase__      ( __A    :str	,__A    :list[str]		):


       """simple docstring"""




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







def 						lowerCamelCase__      ( __A    :tuple[str, float]	,__A    :list[tuple[str, float]]	,__A    :list[str]	,):


       """simple docstring"""




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

              __snake_case	,    __snake_case       	=							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 						lowerCamelCase__      ( __A    :str	,__A    :list[str]	,__A    :bool = True		):


       """simple docstring"""




       if N_POPULATION < N_SELECTED:
              __snake_case       	=							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.
       __snake_case       	=							sorted({c for c in target if c not in genes}		)
       if not_in_genes_list:
              __snake_case       	=							F'{not_in_genes_list} is not in genes list, evolution cannot converge'
              raise ValueError(__A		)

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

              # Check if there is a matching evolution.
              __snake_case       	=							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 % 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.
              __snake_case       	=							population[: int(N_POPULATION / 3		)]
              population.clear()
              population.extend(__A		)
              # Normalize population score to be between 0 and 1.
              __snake_case       	=							[
                  (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__":
    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}'
    )




from argparse import ArgumentParser

from . import BaseTransformersCLICommand







def 						lowerCamelCase__      ( __A    :Optional[int]		):


       """simple docstring"""




       return DownloadCommand(args.model	,args.cache_dir	,args.force	,args.trust_remote_code		)




class     __snake_case    (				snake_case__  ):
       """simple docstring"""







       @staticmethod
       def 			a		(		_UpperCamelCase   )      ->							Any:






              """simple docstring"""


              __snake_case       	=							parser.add_parser("""download"""   )
              download_parser.add_argument(
                  """--cache-dir"""      , type=_UpperCamelCase      , default=_UpperCamelCase      , help="""Path to location to store the models"""   )
              download_parser.add_argument(
                  """--force"""      , action="""store_true"""      , help="""Force the model to be download even if already in cache-dir"""   )
              download_parser.add_argument(
                  """--trust-remote-code"""      , action="""store_true"""      , help="""Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you've reviewed the code as it will execute on your local machine"""      , )
              download_parser.add_argument("""model"""      , type=_UpperCamelCase      , help="""Name of the model to download"""   )
              download_parser.set_defaults(func=_UpperCamelCase   )


       def __init__(		self      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase   )      ->							Any:






              """simple docstring"""


              __snake_case       	=							model
              __snake_case       	=							cache
              __snake_case       	=							force
              __snake_case       	=							trust_remote_code






       def 			a		(		self   )      ->							List[Any]:






              """simple docstring"""


              from ..models.auto import AutoModel, AutoTokenizer

              AutoModel.from_pretrained(
                  self._model      , cache_dir=self._cache      , force_download=self._force      , trust_remote_code=self._trust_remote_code   )
              AutoTokenizer.from_pretrained(
                  self._model      , cache_dir=self._cache      , force_download=self._force      , trust_remote_code=self._trust_remote_code   )

