

'''simple docstring'''
from __future__ import annotations

import random

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







def  _UpperCamelCase (     _a	:	Any				,     _a	:	Tuple			):




    """simple docstring"""



    __UpperCamelCase	:	int		    =  len([g for position, g in enumerate(_lowercase			) if g == main_target[position]]			)
    return (item, float(_lowercase			))







def  _UpperCamelCase (     _a	:	Any				,     _a	:	int			):




    """simple docstring"""



    __UpperCamelCase	:	Optional[Any]		    =  random.randint(0				,     len(_lowercase			) - 1			)
    __UpperCamelCase	:	List[str]		    =  parent_a[:random_slice] + parent_a[random_slice:]
    __UpperCamelCase	:	Optional[int]		    =  parent_a[:random_slice] + parent_a[random_slice:]
    return (child_a, child_a)







def  _UpperCamelCase (     _a	:	str				,     _a	:	List[Any]			):




    """simple docstring"""



    __UpperCamelCase	:	Any		    =  list(_lowercase			)
    if random.uniform(0				,     1			) < MUTATION_PROBABILITY:
        __UpperCamelCase	:	List[Any]		    =  random.choice(_lowercase			)
    return "".join(_lowercase			)







def  _UpperCamelCase (     _a	:	str				,     _a	:	Tuple				,     _a	:	List[str]				,     ):




    """simple docstring"""



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

        __UpperCamelCase	:	Optional[Any]		    =  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 (     _a	:	Union[str, Any]				,     _a	:	Dict				,     _a	:	int = True			):




    """simple docstring"""



    if N_POPULATION < N_SELECTED:
        __UpperCamelCase	:	List[Any]		    =  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	:	str		    =  sorted({c for c in target if c not in genes}			)
    if not_in_genes_list:
        __UpperCamelCase	:	List[Any]		    =  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	:	Dict		    =  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	:	List[Any]		    =  [evaluate(_lowercase				,     _lowercase			) for item in population]

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




'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


a=  {}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a=  ['''NllbTokenizer''']

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a=  ['''NllbTokenizerFast''']


if TYPE_CHECKING:
  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_nllb import NllbTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_nllb_fast import NllbTokenizerFast

else:
  import sys

  a=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



