"""simple docstring"""




from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
_a   :	Dict 					=     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.
_a   :	str 					=     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.
_a   :	Optional[Any] 					=     0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1_000))





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: str    ,_lowerCamelCase		: str			) ->   tuple[str, float]:
  _lowerCAmelCase		: Dict						    =  len([g for position, g in enumerate(_lowerCamelCase			) if g == main_target[position]]			)
  return (item, float(_lowerCamelCase			))





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: str    ,_lowerCamelCase		: str			) ->   tuple[str, str]:
  _lowerCAmelCase		: Optional[Any]						    =  random.randint(0    ,len(_lowerCamelCase			) - 1			)
  _lowerCAmelCase		: str						    =  parent_a[:random_slice] + parent_a[random_slice:]
  _lowerCAmelCase		: List[str]						    =  parent_a[:random_slice] + parent_a[random_slice:]
  return (child_a, child_a)





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: str    ,_lowerCamelCase		: list[str]			) ->   str:
  _lowerCAmelCase		: str						    =  list(_lowerCamelCase			)
  if random.uniform(0    ,1			) < MUTATION_PROBABILITY:
    _lowerCAmelCase		: Dict						    =  random.choice(_lowerCamelCase			)
  return "".join(_lowerCamelCase			)





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: tuple[str, float]    ,_lowerCamelCase		: list[tuple[str, float]]    ,_lowerCamelCase		: list[str]    ,) ->   list[str]:
  _lowerCAmelCase		: Optional[int]						    =  []
  # Generate more children proportionally to the fitness score.
  _lowerCAmelCase		: List[str]						    =  int(parent_a[1] * 100			) + 1
  _lowerCAmelCase		: str						    =  10 if child_n >= 10 else child_n
  for _ in range(_lowerCamelCase			):
    _lowerCAmelCase		: Optional[int]						    =  population_score[random.randint(0    ,_lowerCamelCase			)][0]

    _lowerCAmelCase    ,			_lowerCAmelCase		: Optional[int]						    =  crossover(parent_a[0]    ,_lowerCamelCase			)
    # Append new string to the population list.
    pop.append(mutate(_lowerCamelCase    ,_lowerCamelCase			)			)
    pop.append(mutate(_lowerCamelCase    ,_lowerCamelCase			)			)
  return pop





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: str    ,_lowerCamelCase		: list[str]    ,_lowerCamelCase		: bool = True			) ->   tuple[int, int, str]:

  # Verify if N_POPULATION is bigger than N_SELECTED
  if N_POPULATION < N_SELECTED:
    _lowerCAmelCase		: Optional[int]						    =  f"{N_POPULATION} must be bigger than {N_SELECTED}"
    raise ValueError(_lowerCamelCase			)
  # Verify that the target contains no genes besides the ones inside genes variable.
  _lowerCAmelCase		: Tuple						    =  sorted({c for c in target if c not in genes}			)
  if not_in_genes_list:
    _lowerCAmelCase		: str						    =  f"{not_in_genes_list} is not in genes list, evolution cannot converge"
    raise ValueError(_lowerCamelCase			)

  # Generate random starting population.
  _lowerCAmelCase		: str						    =  []
  for _ in range(_lowerCamelCase			):
    population.append("""""".join([random.choice(_lowerCamelCase			) for i in range(len(_lowerCamelCase			)			)]			)			)

  # Just some logs to know what the algorithms is doing.
  _lowerCAmelCase    ,			_lowerCAmelCase		: List[str]						    =  0, 0

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

    # 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.
    _lowerCAmelCase		: int						    =  [evaluate(_lowerCamelCase    ,_lowerCamelCase			) for item in population]

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

    # This is selection
    for i in range(_lowerCamelCase			):
      population.extend(select(population_score[int(_lowerCamelCase			)]    ,_lowerCamelCase    ,_lowerCamelCase			)			)
      # 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(_lowerCamelCase			) > N_POPULATION:
        break


if __name__ == "__main__":
     _a   :	Optional[int] 					=     (
         'This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!'
     )
     _a   :	Dict 					=     list(
         ' ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm'
         'nopqrstuvwxyz.,;!?+-*#@^\'èéòà€ù=)(&%$£/\\'
     )
     _a					,     _a					,     _a   :	List[str] 					=     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_tokenizers_available, is_torch_available


_a   :	int 					=     {
    'configuration_altclip': [
        'ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'AltCLIPConfig',
        'AltCLIPTextConfig',
        'AltCLIPVisionConfig',
    ],
    'processing_altclip': ['AltCLIPProcessor'],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _a   :	Dict 					=     [
         'ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
         'AltCLIPPreTrainedModel',
         'AltCLIPModel',
         'AltCLIPTextModel',
         'AltCLIPVisionModel',
     ]


if TYPE_CHECKING:
     from .configuration_altclip import (
         ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
         AltCLIPConfig,
         AltCLIPTextConfig,
         AltCLIPVisionConfig,
     )
     from .processing_altclip import AltCLIPProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_altclip import (
              ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
              AltCLIPModel,
              AltCLIPPreTrainedModel,
              AltCLIPTextModel,
              AltCLIPVisionModel,
          )


else:
     import sys

     _a   :	List[Any] 					=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




