"""simple docstring"""




from __future__ import annotations

import random

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





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





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





def 				SCREAMING_SNAKE_CASE	(     _lowerCamelCase		:			List[Any]     ,_lowerCamelCase		:			str      )			-> Optional[Any]:
  _lowerCAmelCase		:			Tuple         =	list(lowercase_      )
  if random.uniform(0     ,1      ) < MUTATION_PROBABILITY:
    _lowerCAmelCase		:			int         =	random.choice(lowercase_      )
  return "".join(lowercase_      )





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

    _lowerCAmelCase		:			int         =	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 				SCREAMING_SNAKE_CASE	(     _lowerCamelCase		:			List[str]     ,_lowerCamelCase		:			Optional[int]     ,_lowerCamelCase		:			str = True      )			-> Optional[Any]:

  # Verify if N_POPULATION is bigger than N_SELECTED
  if N_POPULATION < N_SELECTED:
    _lowerCAmelCase		:			Optional[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.
  _lowerCAmelCase		:			Optional[Any]         =	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(lowercase_      )

  # Generate random starting population.
  _lowerCAmelCase		:			List[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.
  _lowerCAmelCase		:			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.
    _lowerCAmelCase		:			List[str]         =	[evaluate(lowercase_     ,lowercase_      ) for item in population]

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











from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCamelCase      							=   logging.get_logger(__name__)

_lowerCamelCase      							=   {
    'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json',
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}







class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      UpperCAmelCase__							=   """vit_msn"""


      def __init__(		self   ,				a__=768   ,				a__=12   ,				a__=12   ,				a__=3072   ,				a__="gelu"   ,				a__=0.0   ,				a__=0.0   ,				a__=0.02   ,				a__=1e-06   ,				a__=224   ,				a__=16   ,				a__=3   ,				a__=True   ,				**a__   ,				):



             """simple docstring"""





             super().__init__(**a__)

             _lowerCamelCase				:  Tuple						=		hidden_size
             _lowerCamelCase				:  str						=		num_hidden_layers
             _lowerCamelCase				:  int						=		num_attention_heads
             _lowerCamelCase				:  Optional[int]						=		intermediate_size
             _lowerCamelCase				:  Union[str, Any]						=		hidden_act
             _lowerCamelCase				:  str						=		hidden_dropout_prob
             _lowerCamelCase				:  List[Any]						=		attention_probs_dropout_prob
             _lowerCamelCase				:  Optional[int]						=		initializer_range
             _lowerCamelCase				:  Optional[Any]						=		layer_norm_eps
             _lowerCamelCase				:  Dict						=		image_size
             _lowerCamelCase				:  Tuple						=		patch_size
             _lowerCamelCase				:  List[str]						=		num_channels
             _lowerCamelCase				:  Optional[int]						=		qkv_bias

