
from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
lowerCamelCase     :							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.
lowerCamelCase     :							Any    =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.
lowerCamelCase     :							Optional[int]    =0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))







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







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase      )			->     tuple[str, str]:
     UpperCamelCase__      :       Tuple	    =  random.randint(0 ,   len(__lowerCAmelCase      ) - 1      )
     UpperCamelCase__      :       Tuple	    =  parent_a[:random_slice] + parent_a[random_slice:]
     UpperCamelCase__      :       Dict	    =  parent_a[:random_slice] + parent_a[random_slice:]
     return (child_a, child_a)







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase      )			->     str:
     UpperCamelCase__      :       Any	    =  list(__lowerCAmelCase      )
     if random.uniform(0 ,   1      ) < MUTATION_PROBABILITY:
          UpperCamelCase__      :       Dict	    =  random.choice(__lowerCAmelCase      )
     return "".join(__lowerCAmelCase      )







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

          UpperCamelCase__    ,					UpperCamelCase__      :       List[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 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase = True      )			->     tuple[int, int, str]:

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

     # Generate random starting population.
     UpperCamelCase__      :       int	    =  []
     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.
     UpperCamelCase__    ,					UpperCamelCase__      :       Union[str, 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.
          UpperCamelCase__      :       List[str]	    =  [evaluate(__lowerCAmelCase ,   __lowerCAmelCase      ) for item in population]

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







from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowerCamelCase     :							List[Any]    =logging.get_logger(__name__)

lowerCamelCase     :							Optional[Any]    ={
    '''google/bit-50''': '''https://huggingface.co/google/bit-50/resolve/main/config.json''',
}





class       __a  (   A__	,       A__	):
 _lowerCAmelCase	:	Union[str, Any]				=			'''bit'''
 _lowerCAmelCase	:	List[str]				=			['''preactivation''', '''bottleneck''']
 _lowerCAmelCase	:	Any				=			['''SAME''', '''VALID''']







 def __init__(		self :       str							,       SCREAMING_SNAKE_CASE :       Tuple=3							,       SCREAMING_SNAKE_CASE :       str=64							,       SCREAMING_SNAKE_CASE :       List[Any]=[2_56, 5_12, 10_24, 20_48]							,       SCREAMING_SNAKE_CASE :       Union[str, Any]=[3, 4, 6, 3]							,       SCREAMING_SNAKE_CASE :       str="preactivation"							,       SCREAMING_SNAKE_CASE :       Any="relu"							,       SCREAMING_SNAKE_CASE :       Dict=None							,       SCREAMING_SNAKE_CASE :       List[Any]=32							,       SCREAMING_SNAKE_CASE :       Tuple=0.0							,       SCREAMING_SNAKE_CASE :       Dict=False							,       SCREAMING_SNAKE_CASE :       int=32							,       SCREAMING_SNAKE_CASE :       str=1							,       SCREAMING_SNAKE_CASE :       List[str]=None							,       SCREAMING_SNAKE_CASE :       Optional[int]=None							,       **SCREAMING_SNAKE_CASE :       int							,       ):


      '''simple docstring'''






      super().__init__(**SCREAMING_SNAKE_CASE						)
      if layer_type not in self.layer_types:
           raise ValueError(F'layer_type={layer_type} is not one of {",".join(self.layer_types						)}'						)
      if global_padding is not None:
           if global_padding.upper() in self.supported_padding:
                UpperCamelCase__      :       Any	    =  global_padding.upper()
           else:
                raise ValueError(F'Padding strategy {global_padding} not supported'						)
      UpperCamelCase__      :       Dict	    =  num_channels
      UpperCamelCase__      :       Dict	    =  embedding_size
      UpperCamelCase__      :       Tuple	    =  hidden_sizes
      UpperCamelCase__      :       Any	    =  depths
      UpperCamelCase__      :       Optional[int]	    =  layer_type
      UpperCamelCase__      :       int	    =  hidden_act
      UpperCamelCase__      :       str	    =  global_padding
      UpperCamelCase__      :       Any	    =  num_groups
      UpperCamelCase__      :       str	    =  drop_path_rate
      UpperCamelCase__      :       Optional[Any]	    =  embedding_dynamic_padding
      UpperCamelCase__      :       Tuple	    =  output_stride
      UpperCamelCase__      :       List[str]	    =  width_factor

      UpperCamelCase__      :       Any	    =  ["stem"] + [F'stage{idx}' for idx in range(1							,       len(SCREAMING_SNAKE_CASE						) + 1						)]
      UpperCamelCase__    ,					UpperCamelCase__      :       Union[str, Any]	    =  get_aligned_output_features_output_indices(
          out_features=SCREAMING_SNAKE_CASE							,       out_indices=SCREAMING_SNAKE_CASE							,       stage_names=self.stage_names						)