





from __future__ import annotations

import random

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




def  __UpperCAmelCase(   lowercase_			,     lowercase_   ):
       _lowerCamelCase				:  List[str]						=		len([g for position, g in enumerate(lowercase_   ) if g == main_target[position]]   )
       return (item, float(lowercase_   ))




def  __UpperCAmelCase(   lowercase_			,     lowercase_   ):
       _lowerCamelCase				:  Optional[int]						=		random.randint(0			,     len(lowercase_   ) - 1   )
       _lowerCamelCase				:  Optional[int]						=		parent_a[:random_slice] + parent_a[random_slice:]
       _lowerCamelCase				:  Any						=		parent_a[:random_slice] + parent_a[random_slice:]
       return (child_a, child_a)




def  __UpperCAmelCase(   lowercase_			,     lowercase_   ):
       _lowerCamelCase				:  Tuple						=		list(lowercase_   )
       if random.uniform(0			,     1   ) < MUTATION_PROBABILITY:
              _lowerCamelCase				:  int						=		random.choice(lowercase_   )
       return "".join(lowercase_   )




def  __UpperCAmelCase(   lowercase_			,     lowercase_			,     lowercase_			,     ):
       _lowerCamelCase				:  Optional[int]						=		[]
       # Generate more children proportionally to the fitness score.
       _lowerCamelCase				:  Union[str, Any]						=		int(parent_a[1] * 1_00   ) + 1
       _lowerCamelCase				:  Any						=		10 if child_n >= 10 else child_n
       for _ in range(lowercase_   ):
              _lowerCamelCase				:  Any						=		population_score[random.randint(0			,     lowercase_   )][0]

              _lowerCamelCase,	_lowerCamelCase				:  int						=		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  __UpperCAmelCase(   lowercase_			,     lowercase_			,     lowercase_ = True   ):

       # Verify if N_POPULATION is bigger than N_SELECTED
       if N_POPULATION < N_SELECTED:
              _lowerCamelCase				:  Optional[Any]						=		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				:  Optional[Any]						=		sorted({c for c in target if c not in genes}   )
       if not_in_genes_list:
              _lowerCamelCase				:  str						=		F"""{not_in_genes_list} is not in genes list, evolution cannot converge"""
              raise ValueError(lowercase_   )

       # Generate random starting population.
       _lowerCamelCase				:  List[Any]						=		[]
       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,	_lowerCamelCase				:  Dict						=		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				:  List[str]						=		[evaluate(lowercase_			,     lowercase_   ) for item in population]

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








import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

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


_lowerCamelCase      							=   logging.get_logger(__name__)

_lowerCamelCase      							=   '▁'

_lowerCamelCase      							=   {
    'vocab_file': 'vocab.json',
    'spm_file': 'sentencepiece.bpe.model',
}

_lowerCamelCase      							=   {
    'vocab_file': {
        'facebook/s2t-small-librispeech-asr': (
            'https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json'
        ),
    },
    'spm_file': {
        'facebook/s2t-small-librispeech-asr': (
            'https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model'
        )
    },
}

_lowerCamelCase      							=   {
    'facebook/s2t-small-librispeech-asr': 1024,
}

_lowerCamelCase      							=   ['pt', 'fr', 'ru', 'nl', 'ro', 'it', 'es', 'de']

_lowerCamelCase      							=   {'mustc': MUSTC_LANGS}







class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      UpperCAmelCase__							=   VOCAB_FILES_NAMES
      UpperCAmelCase__							=   PRETRAINED_VOCAB_FILES_MAP
      UpperCAmelCase__							=   MAX_MODEL_INPUT_SIZES
      UpperCAmelCase__							=   ["""input_ids""", """attention_mask"""]

      UpperCAmelCase__							=   []


      def __init__(		self   ,				a__   ,				a__   ,				a__="<s>"   ,				a__="</s>"   ,				a__="<pad>"   ,				a__="<unk>"   ,				a__=False   ,				a__=False   ,				a__=None   ,				a__=None   ,				a__ = None   ,				**a__   ,				):



             """simple docstring"""





             _lowerCamelCase				:  str						=		{} if sp_model_kwargs is None else sp_model_kwargs

             super().__init__(
                 bos_token=a__   ,				eos_token=a__   ,				unk_token=a__   ,				pad_token=a__   ,				do_upper_case=a__   ,				do_lower_case=a__   ,				tgt_lang=a__   ,				lang_codes=a__   ,				sp_model_kwargs=self.sp_model_kwargs   ,				**a__   ,				)
             _lowerCamelCase				:  Optional[int]						=		do_upper_case
             _lowerCamelCase				:  Optional[Any]						=		do_lower_case

             _lowerCamelCase				:  Tuple						=		load_json(a__)
             _lowerCamelCase				:  Union[str, Any]						=		{v: k for k, v in self.encoder.items()}
             _lowerCamelCase				:  Tuple						=		spm_file
             _lowerCamelCase				:  Any						=		load_spm(a__   ,				self.sp_model_kwargs)

             if lang_codes is not None:
                    _lowerCamelCase				:  List[Any]						=		lang_codes
                    _lowerCamelCase				:  List[str]						=		LANGUAGES[lang_codes]
                    _lowerCamelCase				:  Any						=		[F"""<lang:{lang}>""" for lang in self.langs]
                    _lowerCamelCase				:  Optional[Any]						=		{lang: self.sp_model.PieceToId(F"""<lang:{lang}>""") for lang in self.langs}

                    _lowerCamelCase				:  List[str]						=		self.lang_tokens
                    _lowerCamelCase				:  str						=		tgt_lang if tgt_lang is not None else self.langs[0]

                    self.set_tgt_lang_special_tokens(self._tgt_lang)
             else:
                    _lowerCamelCase				:  Any						=		{}


      @property
      def 					__snake_case      (		self):



             """simple docstring"""





             return len(self.encoder)


      @property
      def 					__snake_case      (		self):



             """simple docstring"""





             return self._tgt_lang


      @tgt_lang.setter
      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             _lowerCamelCase				:  Any						=		new_tgt_lang
             self.set_tgt_lang_special_tokens(a__)


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             _lowerCamelCase				:  Optional[Any]						=		self.lang_code_to_id[tgt_lang]
             _lowerCamelCase				:  Any						=		[lang_code_id]


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             return self.sp_model.encode(a__   ,				out_type=a__)


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             return self.encoder.get(a__   ,				self.encoder[self.unk_token])


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             return self.decoder.get(a__   ,				self.unk_token)


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             _lowerCamelCase				:  Union[str, Any]						=		[]
             _lowerCamelCase				:  List[str]						=		''''''
             for token in tokens:
                    # make sure that special tokens are not decoded using sentencepiece model
                    if token in self.all_special_tokens:
                           _lowerCamelCase				:  List[Any]						=		self.sp_model.decode(a__)
                           out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
                           _lowerCamelCase				:  Optional[int]						=		[]
                    else:
                           current_sub_tokens.append(a__)
             _lowerCamelCase				:  Tuple						=		self.sp_model.decode(a__)
             out_string += decoded.upper() if self.do_upper_case else decoded
             return out_string.strip()


      def 					__snake_case      (		self   ,				a__   ,				a__=None):



             """simple docstring"""





             if token_ids_a is None:
                    return self.prefix_tokens + token_ids_a + [self.eos_token_id]
             # We don't expect to process pairs, but leave the pair logic for API consistency
             return self.prefix_tokens + token_ids_a + token_ids_a + [self.eos_token_id]


      def 					__snake_case      (		self   ,				a__   ,				a__ = None   ,				a__ = False):



             """simple docstring"""





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

             _lowerCamelCase				:  Tuple						=		[1] * len(self.prefix_tokens)
             _lowerCamelCase				:  Tuple						=		[1]
             if token_ids_a is None:
                    return prefix_ones + ([0] * len(a__)) + suffix_ones
             return prefix_ones + ([0] * len(a__)) + ([0] * len(a__)) + suffix_ones


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Union[str, Any]						=		self.encoder.copy()
             vocab.update(self.added_tokens_encoder)
             return vocab


      def __getstate__(		self):



             """simple docstring"""





             _lowerCamelCase				:  Union[str, Any]						=		self.__dict__.copy()
             _lowerCamelCase				:  str						=		None
             return state


      def __setstate__(		self   ,				a__):



             """simple docstring"""





             _lowerCamelCase				:  Optional[int]						=		d

             # for backward compatibility
             if not hasattr(self   ,				'''sp_model_kwargs'''):
                    _lowerCamelCase				:  List[Any]						=		{}

             _lowerCamelCase				:  Dict						=		load_spm(self.spm_file   ,				self.sp_model_kwargs)



      def 					__snake_case      (		self   ,				a__   ,				a__ = None):



             """simple docstring"""





             _lowerCamelCase				:  str						=		Path(a__)
             assert save_dir.is_dir(), F"""{save_directory} should be a directory"""
             _lowerCamelCase				:  Any						=		save_dir / (
                 (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''vocab_file''']
             )
             _lowerCamelCase				:  Optional[Any]						=		save_dir / (
                 (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''spm_file''']
             )

             save_json(self.encoder   ,				a__)

             if os.path.abspath(self.spm_file) != os.path.abspath(a__) and os.path.isfile(self.spm_file):
                    copyfile(self.spm_file   ,				a__)
             elif not os.path.isfile(self.spm_file):
                    with open(a__   ,				'''wb''') as fi:
                           _lowerCamelCase				:  List[str]						=		self.sp_model.serialized_model_proto()
                           fi.write(a__)

             return (str(a__), str(a__))




def  __UpperCAmelCase(   lowercase_			,     lowercase_   ):
       _lowerCamelCase				:  Optional[Any]						=		sentencepiece.SentencePieceProcessor(**lowercase_   )
       spm.Load(str(lowercase_   )   )
       return spm




def  __UpperCAmelCase(   lowercase_   ):
       with open(lowercase_			,     '''r'''   ) as f:
              return json.load(lowercase_   )




def  __UpperCAmelCase(   lowercase_			,     lowercase_   ):
       with open(lowercase_			,     '''w'''   ) as f:
              json.dump(lowercase_			,     lowercase_			,     indent=2   )

