


'''simple docstring'''

from __future__ import annotations

import random

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





def __snake_case      (   UpperCAmelCase_		:		Dict      ,       UpperCAmelCase_		:		Optional[Any]	):
 lowerCamelCase_  				=						len([g for position, g in enumerate(_lowercase	) if g == main_target[position]]	)
 return (item, float(_lowercase	))





def __snake_case      (   UpperCAmelCase_		:		Tuple      ,       UpperCAmelCase_		:		Any	):
 lowerCamelCase_  				=						random.randint(0      ,       len(_lowercase	) - 1	)
 lowerCamelCase_  				=						parent_a[:random_slice] + parent_a[random_slice:]
 lowerCamelCase_  				=						parent_a[:random_slice] + parent_a[random_slice:]
 return (child_a, child_a)





def __snake_case      (   UpperCAmelCase_		:		int      ,       UpperCAmelCase_		:		Optional[Any]	):
 lowerCamelCase_  				=						list(_lowercase	)
 if random.uniform(0      ,       1	) < MUTATION_PROBABILITY:
  lowerCamelCase_  				=						random.choice(_lowercase	)
 return "".join(_lowercase	)





def __snake_case      (   UpperCAmelCase_		:		Union[str, Any]      ,       UpperCAmelCase_		:		Union[str, Any]      ,       UpperCAmelCase_		:		Dict      ,       ):
 lowerCamelCase_  				=						[]
 # Generate more children proportionally to the fitness score.
 lowerCamelCase_  				=						int(parent_a[1] * 100	) + 1
 lowerCamelCase_  				=						10 if child_n >= 10 else child_n
 for _ in range(_lowercase	):
  lowerCamelCase_  				=						population_score[random.randint(0      ,       _lowercase	)][0]

  lowerCamelCase_  				=						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 __snake_case      (   UpperCAmelCase_		:		Union[str, Any]      ,       UpperCAmelCase_		:		str      ,       UpperCAmelCase_		:		Optional[Any] = True	):

 # Verify if N_POPULATION is bigger than N_SELECTED
 if N_POPULATION < N_SELECTED:
  lowerCamelCase_  				=						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_  				=						sorted({c for c in target if c not in genes}	)
 if not_in_genes_list:
  lowerCamelCase_  				=						F'''{not_in_genes_list} is not in genes list, evolution cannot converge'''
  raise ValueError(_lowercase	)

 # Generate random starting population.
 lowerCamelCase_  				=						[]
 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_  				=						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_  				=						[evaluate(_lowercase      ,       _lowercase	) for item in population]

  # Check if there is a matching evolution.
  lowerCamelCase_  				=						sorted(_lowercase      ,       key=lambda UpperCAmelCase_	: 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_  				=						population[: int(N_POPULATION / 3	)]
  population.clear()
  population.extend(_lowercase	)
  # Normalize population score to be between 0 and 1.
  lowerCamelCase_  				=						[
      (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_						:       Optional[Any]      			=				basic(target_str, genes_list)
       print(
           f'''\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}'''
       )







'''simple docstring'''

from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput





@dataclass
class   snake_case					(					lowercase    ):





  """simple docstring"""




  _lowerCamelCase						=						None
  _lowerCamelCase						=						None
  _lowerCamelCase						=						None
  _lowerCamelCase						=						None





class   snake_case					(					lowercase    ):





  """simple docstring"""




  def __init__(					self    ,						UpperCamelCase=1    ,						UpperCamelCase=0    ,						UpperCamelCase=2    ,						UpperCamelCase=512    ,						UpperCamelCase="cls"    ,						UpperCamelCase=False    ,						UpperCamelCase=True    ,						**UpperCamelCase    ,						):







   """simple docstring"""
   super().__init__(pad_token_id=UpperCamelCase    ,						bos_token_id=UpperCamelCase    ,						eos_token_id=UpperCamelCase    ,						**UpperCamelCase  )
   lowerCamelCase_  				=						project_dim
   lowerCamelCase_  				=						pooler_fn
   lowerCamelCase_  				=						learn_encoder
   lowerCamelCase_  				=						use_attention_mask





class   snake_case					(					lowercase    ):





  """simple docstring"""




  _lowerCamelCase						=						[r"pooler", r"logit_scale"]
  _lowerCamelCase						=						[r"position_ids", r"predictions.decoder.bias"]
  _lowerCamelCase						=						"roberta"
  _lowerCamelCase						=						RobertaSeriesConfig






  def __init__(					self    ,						UpperCamelCase  ):







   """simple docstring"""
   super().__init__(UpperCamelCase  )
   lowerCamelCase_  				=						XLMRobertaModel(UpperCamelCase  )
   lowerCamelCase_  				=						nn.Linear(config.hidden_size    ,						config.project_dim  )
   lowerCamelCase_  				=						getattr(UpperCamelCase    ,						"has_pre_transformation"    ,						UpperCamelCase  )
   if self.has_pre_transformation:
    lowerCamelCase_  				=						nn.Linear(config.hidden_size    ,						config.project_dim  )
    lowerCamelCase_  				=						nn.LayerNorm(config.hidden_size    ,						eps=config.layer_norm_eps  )
   self.post_init()







  def 						snake_case	(					self    ,						UpperCamelCase = None    ,						UpperCamelCase = None    ,						UpperCamelCase = None    ,						UpperCamelCase = None    ,						UpperCamelCase = None    ,						UpperCamelCase = None    ,						UpperCamelCase = None    ,						UpperCamelCase = None    ,						UpperCamelCase = None    ,						UpperCamelCase = None    ,						UpperCamelCase = None    ,						):







   """simple docstring"""
   lowerCamelCase_  				=						return_dict if return_dict is not None else self.config.use_return_dict

   lowerCamelCase_  				=						self.base_model(
       input_ids=UpperCamelCase    ,						attention_mask=UpperCamelCase    ,						token_type_ids=UpperCamelCase    ,						position_ids=UpperCamelCase    ,						head_mask=UpperCamelCase    ,						inputs_embeds=UpperCamelCase    ,						encoder_hidden_states=UpperCamelCase    ,						encoder_attention_mask=UpperCamelCase    ,						output_attentions=UpperCamelCase    ,						output_hidden_states=True if self.has_pre_transformation else output_hidden_states    ,						return_dict=UpperCamelCase    ,						)

   if self.has_pre_transformation:
    lowerCamelCase_  				=						outputs["hidden_states"][-2]
    lowerCamelCase_  				=						self.pre_LN(UpperCamelCase  )
    lowerCamelCase_  				=						self.transformation_pre(UpperCamelCase  )

    return TransformationModelOutput(
        projection_state=UpperCamelCase    ,						last_hidden_state=outputs.last_hidden_state    ,						hidden_states=outputs.hidden_states    ,						attentions=outputs.attentions    ,						)
   else:
    lowerCamelCase_  				=						self.transformation(outputs.last_hidden_state  )
    return TransformationModelOutput(
        projection_state=UpperCamelCase    ,						last_hidden_state=outputs.last_hidden_state    ,						hidden_states=outputs.hidden_states    ,						attentions=outputs.attentions    ,						)



