





from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
UpperCAmelCase_           =	2_00
# 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, 10_00))
def      A__       (    SCREAMING_SNAKE_CASE_   :      str				,	SCREAMING_SNAKE_CASE_   :      str						)  ->  tuple[str, float]:






   """simple docstring"""







   _UpperCAmelCase           =   len([g for position, g in enumerate(SCREAMING_SNAKE_CASE_						) if g == main_target[position]]						)
   return (item, float(SCREAMING_SNAKE_CASE_						))
def      A__       (    SCREAMING_SNAKE_CASE_   :      str				,	SCREAMING_SNAKE_CASE_   :      str						)  ->  tuple[str, str]:






   """simple docstring"""







   _UpperCAmelCase           =   random.randint(0				,	len(SCREAMING_SNAKE_CASE_						) - 1						)
   _UpperCAmelCase           =   parent_a[:random_slice] + parent_a[random_slice:]
   _UpperCAmelCase           =   parent_a[:random_slice] + parent_a[random_slice:]
   return (child_a, child_a)
def      A__       (    SCREAMING_SNAKE_CASE_   :      str				,	SCREAMING_SNAKE_CASE_   :      list[str]						)  ->  str:






   """simple docstring"""







   _UpperCAmelCase           =   list(SCREAMING_SNAKE_CASE_						)
   if random.uniform(0				,	1						) < MUTATION_PROBABILITY:
      _UpperCAmelCase           =   random.choice(SCREAMING_SNAKE_CASE_						)
   return "".join(SCREAMING_SNAKE_CASE_						)
def      A__       (    SCREAMING_SNAKE_CASE_   :      tuple[str, float]				,	SCREAMING_SNAKE_CASE_   :      list[tuple[str, float]]				,	SCREAMING_SNAKE_CASE_   :      list[str]				,	)  ->  list[str]:






   """simple docstring"""







   _UpperCAmelCase           =   []
   # Generate more children proportionally to the fitness score.
   _UpperCAmelCase           =   int(parent_a[1] * 1_00						) + 1
   _UpperCAmelCase           =   10 if child_n >= 10 else child_n
   for _ in range(SCREAMING_SNAKE_CASE_						):
      _UpperCAmelCase           =   population_score[random.randint(0				,	SCREAMING_SNAKE_CASE_						)][0]

      _UpperCAmelCase       ,      _UpperCAmelCase           =   crossover(parent_a[0]				,	SCREAMING_SNAKE_CASE_						)
      # Append new string to the population list.
      pop.append(mutate(SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_						)						)
      pop.append(mutate(SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_						)						)
   return pop
def      A__       (    SCREAMING_SNAKE_CASE_   :      str				,	SCREAMING_SNAKE_CASE_   :      list[str]				,	SCREAMING_SNAKE_CASE_   :      bool = True						)  ->  tuple[int, int, str]:






   """simple docstring"""







   if N_POPULATION < N_SELECTED:
      _UpperCAmelCase           =   F'''{N_POPULATION} must be bigger than {N_SELECTED}'''
      raise ValueError(SCREAMING_SNAKE_CASE_						)
   # Verify that the target contains no genes besides the ones inside genes variable.
   _UpperCAmelCase           =   sorted({c for c in target if c not in genes}						)
   if not_in_genes_list:
      _UpperCAmelCase           =   F'''{not_in_genes_list} is not in genes list, evolution cannot converge'''
      raise ValueError(SCREAMING_SNAKE_CASE_						)

   # Generate random starting population.
   _UpperCAmelCase           =   []
   for _ in range(SCREAMING_SNAKE_CASE_						):
      population.append(''''''.join([random.choice(SCREAMING_SNAKE_CASE_						) for i in range(len(SCREAMING_SNAKE_CASE_						)						)]						)						)

   # Just some logs to know what the algorithms is doing.
   _UpperCAmelCase       ,      _UpperCAmelCase           =   0, 0

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

      # 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           =   [evaluate(SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_						) for item in population]

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

      # This is selection
      for i in range(SCREAMING_SNAKE_CASE_						):
         population.extend(select(population_score[int(SCREAMING_SNAKE_CASE_						)]				,	SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_						)						)
         # 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(SCREAMING_SNAKE_CASE_						) > 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_       ,     UpperCAmelCase_       ,     UpperCAmelCase_           =	basic(target_str, genes_list)
   print(
       f'''\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}'''
   )






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


UpperCAmelCase_           =	logging.get_logger(__name__)

UpperCAmelCase_           =	{
    "microsoft/biogpt": "https://huggingface.co/microsoft/biogpt/resolve/main/config.json",
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}




class 			__UpperCamelCase    (							A__    ):
     __A	:  Any							  =   """biogpt"""





     def __init__(     self				,							_UpperCamelCase=42384				,							_UpperCamelCase=1024				,							_UpperCamelCase=24				,							_UpperCamelCase=16				,							_UpperCamelCase=4096				,							_UpperCamelCase="gelu"				,							_UpperCamelCase=0.1				,							_UpperCamelCase=0.1				,							_UpperCamelCase=1024				,							_UpperCamelCase=0.02				,							_UpperCamelCase=1e-12				,							_UpperCamelCase=True				,							_UpperCamelCase=True				,							_UpperCamelCase=0.0				,							_UpperCamelCase=0.0				,							_UpperCamelCase=1				,							_UpperCamelCase=0				,							_UpperCamelCase=2				,							**_UpperCamelCase				,							):
        _UpperCAmelCase           =   vocab_size
        _UpperCAmelCase           =   max_position_embeddings
        _UpperCAmelCase           =   hidden_size
        _UpperCAmelCase           =   num_hidden_layers
        _UpperCAmelCase           =   num_attention_heads
        _UpperCAmelCase           =   intermediate_size
        _UpperCAmelCase           =   hidden_act
        _UpperCAmelCase           =   hidden_dropout_prob
        _UpperCAmelCase           =   attention_probs_dropout_prob
        _UpperCAmelCase           =   initializer_range
        _UpperCAmelCase           =   layer_norm_eps
        _UpperCAmelCase           =   scale_embedding
        _UpperCAmelCase           =   use_cache
        _UpperCAmelCase           =   layerdrop
        _UpperCAmelCase           =   activation_dropout
        super().__init__(pad_token_id=_UpperCamelCase				,							bos_token_id=_UpperCamelCase				,							eos_token_id=_UpperCamelCase				,							**_UpperCamelCase					)