"""simple docstring"""



from __future__ import annotations

import random

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



def 					_lowerCamelCase(					a ,  a							):
       __a     				=  len([g for position, g in enumerate(lowerCAmelCase__							) if g == main_target[position]]							)
       return (item, float(lowerCAmelCase__							))



def 					_lowerCamelCase(					a ,  a							):
       __a     				=  random.randint(0 ,  len(lowerCAmelCase__							) - 1							)
       __a     				=  parent_a[:random_slice] + parent_a[random_slice:]
       __a     				=  parent_a[:random_slice] + parent_a[random_slice:]
       return (child_a, child_a)



def 					_lowerCamelCase(					a ,  a							):
       __a     				=  list(lowerCAmelCase__							)
       if random.uniform(0 ,  1							) < MUTATION_PROBABILITY:
              __a     				=  random.choice(lowerCAmelCase__							)
       return "".join(lowerCAmelCase__							)



def 					_lowerCamelCase(					a ,  a ,  a ,  ):
       __a     				=  []
       # Generate more children proportionally to the fitness score.
       __a     				=  int(parent_a[1] * 1_0_0							) + 1
       __a     				=  1_0 if child_n >= 1_0 else child_n
       for _ in range(lowerCAmelCase__							):
              __a     				=  population_score[random.randint(0 ,  lowerCAmelCase__							)][0]

              __a     				=  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 					_lowerCamelCase(					a ,  a ,  a = True							):

       # Verify if N_POPULATION is bigger than N_SELECTED
       if N_POPULATION < N_SELECTED:
              __a     				=  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.
       __a     				=  sorted({c for c in target if c not in genes}							)
       if not_in_genes_list:
              __a     				=  F"{not_in_genes_list} is not in genes list, evolution cannot converge"
              raise ValueError(lowerCAmelCase__							)

       # Generate random starting population.
       __a     				=  []
       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.
       __a     				=  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.
              __a     				=  [evaluate(lowerCAmelCase__ ,  lowerCAmelCase__							) for item in population]

              # Check if there is a matching evolution.
              __a     				=  sorted(lowerCAmelCase__ ,  key=lambda a							: 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 % 1_0 == 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.
              __a     				=  population[: int(N_POPULATION / 3							)]
              population.clear()
              population.extend(lowerCAmelCase__							)
              # Normalize population score to be between 0 and 1.
              __a     				=  [
                  (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__":
   SCREAMING_SNAKE_CASE__:Optional[int]			=	(
       """This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!"""
   )
   SCREAMING_SNAKE_CASE__:Dict			=	list(
       """ ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm"""
       """nopqrstuvwxyz.,;!?+-*#@^\'èéòà€ù=)(&%$£/\\"""
   )
   SCREAMING_SNAKE_CASE__	,      SCREAMING_SNAKE_CASE__	,      SCREAMING_SNAKE_CASE__:List[Any]			=	basic(target_str, genes_list)
   print(
       F'''\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}'''
   )

"""simple docstring"""



import json
import os
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE__:int			=	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__:List[Any]			=	{"""vocab_file""": """vocab.json"""}

SCREAMING_SNAKE_CASE__:Optional[int]			=	{
    """vocab_file""": {
        """mgp-str""": """https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json""",
    }
}

SCREAMING_SNAKE_CASE__:Optional[Any]			=	{"""mgp-str""": 27}






class 				snake_case__     (   snake_case_   ):
 _snake_case   :			Tuple           =		VOCAB_FILES_NAMES
 _snake_case   :			Tuple           =		PRETRAINED_VOCAB_FILES_MAP
 _snake_case   :			Optional[Any]           =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES


 def __init__(				self  ,						lowerCamelCase  ,						lowerCamelCase="[GO]"  ,						lowerCamelCase="[GO]"  ,						lowerCamelCase="[s]"  ,						lowerCamelCase="[GO]"  ,						**lowerCamelCase		):
        super().__init__(
            unk_token=lowerCamelCase  ,						bos_token=lowerCamelCase  ,						eos_token=lowerCamelCase  ,						pad_token=lowerCamelCase  ,						**lowerCamelCase  ,						)

        with open(lowerCamelCase  ,						encoding="utf-8"		) as vocab_handle:
               __a     				=  json.load(lowerCamelCase		)
        __a     				=  {v: k for k, v in self.vocab.items()}


 @property
 def 		a__ (				self		):
        return len(self.vocab		)


 def 		a__ (				self		):
        return dict(self.vocab  ,						**self.added_tokens_encoder		)


 def 		a__ (				self  ,						lowerCamelCase		):
        __a     				=  []
        for s in text:
               char_tokens.extend(lowerCamelCase		)
        return char_tokens


 def 		a__ (				self  ,						lowerCamelCase		):
        return self.vocab.get(lowerCamelCase  ,						self.vocab.get(self.unk_token		)		)


 def 		a__ (				self  ,						lowerCamelCase		):
        return self.decoder.get(lowerCamelCase		)






 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None		):
        if not os.path.isdir(lowerCamelCase		):
               logger.error("Vocabulary path ({}) should be a directory".format(lowerCamelCase		)		)
               return
        __a     				=  os.path.join(
            lowerCamelCase  ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

        with open(lowerCamelCase  ,						"w"  ,						encoding="utf-8"		) as f:
               f.write(json.dumps(self.vocab  ,						indent=2  ,						sort_keys=lowerCamelCase  ,						ensure_ascii=lowerCamelCase		) + "\n"		)

        return (vocab_file,)
