



"""simple docstring"""
from typing import Dict, List, Optional

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


A:     Any										=	logging.get_logger(__name__)


A:     List[str]										=	{
    "nielsr/canine-s": 2_0_4_8,
}

# Unicode defines 1,114,112 total “codepoints”
A:     int										=	1_1_1_4_1_1_2

# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
A:     List[str]										=	0

A:     Optional[Any]										=	0Xe_000
A:     Optional[Any]										=	0Xe_001
A:     Optional[int]										=	0Xe_002
A:     List[Any]										=	0Xe_003
A:     int										=	0Xe_004

# Maps special codepoints to human-readable names.
A:     Dict[int, str]										=	{
    # Special symbols are represented using codepoints values that are valid,
    # but designated as "Private Use", meaning that they will never be assigned
    # characters by the Unicode Consortium, and are thus safe for use here.
    #
    # NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
    # excluded and should fail with a hard error.
    CLS: "[CLS]",
    SEP: "[SEP]",
    BOS: "[BOS]",
    MASK: "[MASK]",
    PAD: "[PAD]",
    RESERVED: "[RESERVED]",
}

# Maps special codepoint human-readable names to their codepoint values.
A:     Dict[str, int]										=	{name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}



class    SCREAMING_SNAKE_CASE__						(      UpperCAmelCase__				):
   __lowerCAmelCase						:     List[str]		=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE=chr(_SCREAMING_SNAKE_CASE		)   ,  _SCREAMING_SNAKE_CASE=chr(_SCREAMING_SNAKE_CASE		)   ,  _SCREAMING_SNAKE_CASE=chr(_SCREAMING_SNAKE_CASE		)   ,  _SCREAMING_SNAKE_CASE=chr(_SCREAMING_SNAKE_CASE		)   ,  _SCREAMING_SNAKE_CASE=chr(_SCREAMING_SNAKE_CASE		)   ,  _SCREAMING_SNAKE_CASE=chr(_SCREAMING_SNAKE_CASE		)   ,  _SCREAMING_SNAKE_CASE=False   ,  _SCREAMING_SNAKE_CASE=2048   ,  **_SCREAMING_SNAKE_CASE   ,  )						->   int:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[int]			=			AddedToken(_SCREAMING_SNAKE_CASE   ,  lstrip=_SCREAMING_SNAKE_CASE   ,  rstrip=_SCREAMING_SNAKE_CASE		) if isinstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		) else bos_token
    UpperCAmelCase			:      int			=			AddedToken(_SCREAMING_SNAKE_CASE   ,  lstrip=_SCREAMING_SNAKE_CASE   ,  rstrip=_SCREAMING_SNAKE_CASE		) if isinstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		) else eos_token
    UpperCAmelCase			:      Union[str, Any]			=			AddedToken(_SCREAMING_SNAKE_CASE   ,  lstrip=_SCREAMING_SNAKE_CASE   ,  rstrip=_SCREAMING_SNAKE_CASE		) if isinstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		) else sep_token
    UpperCAmelCase			:      Dict			=			AddedToken(_SCREAMING_SNAKE_CASE   ,  lstrip=_SCREAMING_SNAKE_CASE   ,  rstrip=_SCREAMING_SNAKE_CASE		) if isinstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		) else cls_token
    UpperCAmelCase			:      Optional[Any]			=			AddedToken(_SCREAMING_SNAKE_CASE   ,  lstrip=_SCREAMING_SNAKE_CASE   ,  rstrip=_SCREAMING_SNAKE_CASE		) if isinstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		) else pad_token

    # Mask token behave like a normal word, i.e. include the space before it
    UpperCAmelCase			:      Optional[Any]			=			AddedToken(_SCREAMING_SNAKE_CASE   ,  lstrip=_SCREAMING_SNAKE_CASE   ,  rstrip=_SCREAMING_SNAKE_CASE		) if isinstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		) else mask_token

    super().__init__(
        bos_token=_SCREAMING_SNAKE_CASE   ,  eos_token=_SCREAMING_SNAKE_CASE   ,  sep_token=_SCREAMING_SNAKE_CASE   ,  cls_token=_SCREAMING_SNAKE_CASE   ,  pad_token=_SCREAMING_SNAKE_CASE   ,  mask_token=_SCREAMING_SNAKE_CASE   ,  add_prefix_space=_SCREAMING_SNAKE_CASE   ,  model_max_length=_SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE   ,  )

    # Creates a mapping for looking up the IDs of special symbols.
    UpperCAmelCase			:      Dict[str, int]			=			{}
    for codepoint, name in SPECIAL_CODEPOINTS.items():
     UpperCAmelCase			:      Dict			=			codepoint

    # Creates a mapping for looking up the string forms of special symbol IDs.
    UpperCAmelCase			:      Dict[int, str]			=			{
        codepoint: name for name, codepoint in self._special_codepoints.items()
    }

    UpperCAmelCase			:      List[str]			=			UNICODE_VOCAB_SIZE
    UpperCAmelCase			:      Optional[int]			=			len(self._special_codepoints		)

   @property
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   int:





    '''simple docstring'''




    return self._unicode_vocab_size

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE		)						->   List[str]:





    '''simple docstring'''




    return list(_SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE		)						->   int:





    '''simple docstring'''




    try:
     return ord(_SCREAMING_SNAKE_CASE		)
    except TypeError:
     raise ValueError(F"invalid token: '{token}'"		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE		)						->   str:





    '''simple docstring'''




    try:
     if index in SPECIAL_CODEPOINTS:
      return SPECIAL_CODEPOINTS[index]
     return chr(_SCREAMING_SNAKE_CASE		)
    except TypeError:
     raise ValueError(F"invalid id: {index}"		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE		)						->   Dict:





    '''simple docstring'''




    return "".join(_SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE = None		)						->   List[int]:





    '''simple docstring'''




    UpperCAmelCase			:      Any			=			[self.sep_token_id]
    UpperCAmelCase			:      Union[str, Any]			=			[self.cls_token_id]

    UpperCAmelCase			:      Optional[Any]			=			cls + token_ids_a + sep
    if token_ids_a is not None:
     result += token_ids_a + sep
    return result

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE = None   ,  _SCREAMING_SNAKE_CASE = False		)						->   List[int]:





    '''simple docstring'''




    if already_has_special_tokens:
     return super().get_special_tokens_mask(
         token_ids_a=_SCREAMING_SNAKE_CASE   ,  token_ids_a=_SCREAMING_SNAKE_CASE   ,  already_has_special_tokens=_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      Optional[Any]			=			[1] + ([0] * len(_SCREAMING_SNAKE_CASE		)) + [1]
    if token_ids_a is not None:
     result += ([0] * len(_SCREAMING_SNAKE_CASE		)) + [1]
    return result

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE = None		)						->   List[int]:





    '''simple docstring'''




    UpperCAmelCase			:      List[str]			=			[self.sep_token_id]
    UpperCAmelCase			:      Optional[int]			=			[self.cls_token_id]

    UpperCAmelCase			:      Union[str, Any]			=			len(cls + token_ids_a + sep		) * [0]
    if token_ids_a is not None:
     result += len(token_ids_a + sep		) * [1]
    return result




   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE = None		)						->   str:





    '''simple docstring'''




    return ()





"""simple docstring"""
from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
A:     Union[str, Any]										=	2_0_0
# 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:     int										=	5_0
# 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:     Tuple										=	0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1_0_0_0))

def    _snake_case		(		UpperCamelCase		:	str    ,	UpperCamelCase		:	str		):
 UpperCAmelCase			:      Tuple			=			len([g for position, g in enumerate(UpperCamelCase		) if g == main_target[position]]		)
 return (item, float(UpperCamelCase		))

def    _snake_case		(		UpperCamelCase		:	str    ,	UpperCamelCase		:	str		):
 UpperCAmelCase			:      List[str]			=			random.randint(0    ,	len(UpperCamelCase		) - 1		)
 UpperCAmelCase			:      List[str]			=			parent_a[:random_slice] + parent_a[random_slice:]
 UpperCAmelCase			:      List[str]			=			parent_a[:random_slice] + parent_a[random_slice:]
 return (child_a, child_a)

def    _snake_case		(		UpperCamelCase		:	str    ,	UpperCamelCase		:	list[str]		):
 UpperCAmelCase			:      str			=			list(UpperCamelCase		)
 if random.uniform(0    ,	1		) < MUTATION_PROBABILITY:
  UpperCAmelCase			:      int			=			random.choice(UpperCamelCase		)
 return "".join(UpperCamelCase		)

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

  UpperCAmelCase			,	UpperCAmelCase			:      Any			=			crossover(parent_a[0]    ,	UpperCamelCase		)
  # Append new string to the population list.
  pop.append(mutate(UpperCamelCase    ,	UpperCamelCase		)		)
  pop.append(mutate(UpperCamelCase    ,	UpperCamelCase		)		)
 return pop

def    _snake_case		(		UpperCamelCase		:	str    ,	UpperCamelCase		:	list[str]    ,	UpperCamelCase		:	bool = True		):

 # Verify if N_POPULATION is bigger than N_SELECTED
 if N_POPULATION < N_SELECTED:
  UpperCAmelCase			:      Dict			=			F"{N_POPULATION} must be bigger than {N_SELECTED}"
  raise ValueError(UpperCamelCase		)
 # Verify that the target contains no genes besides the ones inside genes variable.
 UpperCAmelCase			:      str			=			sorted({c for c in target if c not in genes}		)
 if not_in_genes_list:
  UpperCAmelCase			:      Optional[Any]			=			F"{not_in_genes_list} is not in genes list, evolution cannot converge"
  raise ValueError(UpperCamelCase		)

 # Generate random starting population.
 UpperCAmelCase			:      Optional[int]			=			[]
 for _ in range(UpperCamelCase		):
  population.append("""""".join([random.choice(UpperCamelCase		) for i in range(len(UpperCamelCase		)		)]		)		)

 # Just some logs to know what the algorithms is doing.
 UpperCAmelCase			,	UpperCAmelCase			:      Any			=			0, 0

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

  # 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			:      str			=			[evaluate(UpperCamelCase    ,	UpperCamelCase		) for item in population]

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

  # This is selection
  for i in range(UpperCamelCase		):
   population.extend(select(population_score[int(UpperCamelCase		)]    ,	UpperCamelCase    ,	UpperCamelCase		)		)
   # 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(UpperCamelCase		) > N_POPULATION:
    break


if __name__ == "__main__":
      A:     Union[str, Any]										=	(
          "This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!"
      )
      A:     Dict										=	list(
          " ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm"
          "nopqrstuvwxyz.,;!?+-*#@^'èéòà€ù=)(&%$£/\\"
      )
      A							,   A							,   A:     List[Any]										=	basic(target_str, genes_list)
      print(
          f"""\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}"""
      )
