





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__(						lowercase							:     str ,  lowercase							:     str					)			->     Tuple:
  __snake_case :					Union[str, Any]          =							len([g for position, g in enumerate(_lowerCamelCase					) if g == main_target[position]]					)
  return (item, float(_lowerCamelCase					))







def    lowerCAmelCase__(						lowercase							:     str ,  lowercase							:     str					)			->     int:
  __snake_case :					str          =							random.randint(0 ,  len(_lowerCamelCase					) - 1					)
  __snake_case :					str          =							parent_a[:random_slice] + parent_a[random_slice:]
  __snake_case :					Tuple          =							parent_a[:random_slice] + parent_a[random_slice:]
  return (child_a, child_a)







def    lowerCAmelCase__(						lowercase							:     str ,  lowercase							:     list[str]					)			->     List[Any]:
  __snake_case :					int          =							list(_lowerCamelCase					)
  if random.uniform(0 ,  1					) < MUTATION_PROBABILITY:
    __snake_case :					List[str]          =							random.choice(_lowerCamelCase					)
  return "".join(_lowerCamelCase					)







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

    __snake_case     , __snake_case :					str          =							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    lowerCAmelCase__(						lowercase							:     str ,  lowercase							:     list[str] ,  lowercase							:     bool = True					)			->     int:

  # Verify if N_POPULATION is bigger than N_SELECTED
  if N_POPULATION < N_SELECTED:
    __snake_case :					Union[str, Any]          =							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.
  __snake_case :					List[str]          =							sorted({c for c in target if c not in genes}					)
  if not_in_genes_list:
    __snake_case :					int          =							f"""{not_in_genes_list} is not in genes list, evolution cannot converge"""
    raise ValueError(_lowerCamelCase					)

  # Generate random starting population.
  __snake_case :					Union[str, Any]          =							[]
  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.
  __snake_case     , __snake_case :					Optional[Any]          =							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.
    __snake_case :					Dict          =							[evaluate(_lowerCamelCase ,  _lowerCamelCase					) for item in population]

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








'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__lowerCAmelCase				:      Optional[Any]						      =logging.get_logger(__name__)

__lowerCAmelCase				:      str						      ={
    "distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/config.json",
    "distilbert-base-uncased-distilled-squad": (
        "https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json"
    ),
    "distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/config.json",
    "distilbert-base-cased-distilled-squad": (
        "https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json"
    ),
    "distilbert-base-german-cased": "https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json",
    "distilbert-base-multilingual-cased": (
        "https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json"
    ),
    "distilbert-base-uncased-finetuned-sst-2-english": (
        "https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json"
    ),
}






class   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							"""distilbert"""
 __lowercase  						=							{
     """hidden_size""": """dim""",
     """num_attention_heads""": """n_heads""",
     """num_hidden_layers""": """n_layers""",
 }
 def __init__(							self :Tuple		,			lowercase_ :List[str]=3_05_22		,			lowercase_ :Tuple=5_12		,			lowercase_ :List[str]=False		,			lowercase_ :int=6		,			lowercase_ :Optional[int]=12		,			lowercase_ :Optional[int]=7_68		,			lowercase_ :str=4 * 7_68		,			lowercase_ :List[Any]=0.1		,			lowercase_ :str=0.1		,			lowercase_ :Optional[int]="gelu"		,			lowercase_ :Optional[Any]=0.0_2		,			lowercase_ :Optional[Any]=0.1		,			lowercase_ :List[Any]=0.2		,			lowercase_ :str=0		,			**lowercase_ :Union[str, Any]		,			)->				str:
     A__							=     vocab_size
     A__							=     max_position_embeddings
     A__							=     sinusoidal_pos_embds
     A__							=     n_layers
     A__							=     n_heads
     A__							=     dim
     A__							=     hidden_dim
     A__							=     dropout
     A__							=     attention_dropout
     A__							=     activation
     A__							=     initializer_range
     A__							=     qa_dropout
     A__							=     seq_classif_dropout
     super().__init__(**lowercase_		,			pad_token_id=lowercase_				)






class   UpperCAmelCase      (			UpperCamelCase__							):
 @property
 def 	UpperCAmelCase_				(							self :List[str]				)->				Mapping[str, Mapping[int, str]]:
     if self.task == "multiple-choice":
         A__							=     {0: "batch", 1: "choice", 2: "sequence"}
     else:
         A__							=     {0: "batch", 1: "sequence"}
     return OrderedDict(
         [
             ("input_ids", dynamic_axis),
             ("attention_mask", dynamic_axis),
         ]				)



