import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


A__     =				logging.get_logger(__name__)
A__     =				{"""vocab_file""": """spiece.model"""}

A__     =				{
    """vocab_file""": {
        """albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/spiece.model""",
        """albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/spiece.model""",
        """albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model""",
        """albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model""",
        """albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/spiece.model""",
        """albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/spiece.model""",
        """albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model""",
        """albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model""",
    }
}

A__     =				{
    """albert-base-v1""": 512,
    """albert-large-v1""": 512,
    """albert-xlarge-v1""": 512,
    """albert-xxlarge-v1""": 512,
    """albert-base-v2""": 512,
    """albert-large-v2""": 512,
    """albert-xlarge-v2""": 512,
    """albert-xxlarge-v2""": 512,
}

A__     =				"""▁"""
class 	_lowerCAmelCase		(  snake_case__					):
							_UpperCAmelCase         =  VOCAB_FILES_NAMES
							_UpperCAmelCase         =  PRETRAINED_VOCAB_FILES_MAP
							_UpperCAmelCase         =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES







							def __init__(					self  :     str						,       __snake_case  :     List[str]						,       __snake_case  :     Dict=True						,       __snake_case  :     Tuple=True						,       __snake_case  :     Union[str, Any]=False						,       __snake_case  :     Union[str, Any]="[CLS]"						,       __snake_case  :     List[Any]="[SEP]"						,       __snake_case  :     Optional[Any]="<unk>"						,       __snake_case  :     Optional[Any]="[SEP]"						,       __snake_case  :     str="<pad>"						,       __snake_case  :     Optional[int]="[CLS]"						,       __snake_case  :     int="[MASK]"						,       __snake_case  :     Optional[Any] = None						,       **__snake_case  :     Union[str, Any]						,       ):
										lowerCamelCase    :str    	=  (
										    AddedToken(_UpperCamelCase						,       lstrip=_UpperCamelCase						,       rstrip=_UpperCamelCase						,       normalized=_UpperCamelCase		)
										    if isinstance(_UpperCamelCase						,       _UpperCamelCase		)
										    else mask_token
										)

										lowerCamelCase    :Optional[Any]    	=  {} if sp_model_kwargs is None else sp_model_kwargs

										super().__init__(
										    do_lower_case=_UpperCamelCase						,       remove_space=_UpperCamelCase						,       keep_accents=_UpperCamelCase						,       bos_token=_UpperCamelCase						,       eos_token=_UpperCamelCase						,       unk_token=_UpperCamelCase						,       sep_token=_UpperCamelCase						,       pad_token=_UpperCamelCase						,       cls_token=_UpperCamelCase						,       mask_token=_UpperCamelCase						,       sp_model_kwargs=self.sp_model_kwargs						,       **_UpperCamelCase						,       )

										lowerCamelCase    :Optional[Any]    	=  do_lower_case
										lowerCamelCase    :List[str]    	=  remove_space
										lowerCamelCase    :Dict    	=  keep_accents
										lowerCamelCase    :str    	=  vocab_file

										lowerCamelCase    :Dict    	=  spm.SentencePieceProcessor(**self.sp_model_kwargs		)
										self.sp_model.Load(_UpperCamelCase		)







							@property
							def 					snake_case	(					self  :     Optional[int]		):
										return len(self.sp_model		)







							def 					snake_case	(					self  :     Union[str, Any]		):
										lowerCamelCase    :Tuple    	=  {self.convert_ids_to_tokens(_UpperCamelCase		): i for i in range(self.vocab_size		)}
										vocab.update(self.added_tokens_encoder		)
										return vocab







							def __getstate__(					self  :     List[Any]		):
										lowerCamelCase    :List[Any]    	=  self.__dict__.copy()
										lowerCamelCase    :Union[str, Any]    	=  None
										return state







							def __setstate__(					self  :     Any						,       __snake_case  :     int		):
										lowerCamelCase    :Tuple    	=  d

										# for backward compatibility
										if not hasattr(self						,       '''sp_model_kwargs'''		):
													lowerCamelCase    :Dict    	=  {}

										lowerCamelCase    :int    	=  spm.SentencePieceProcessor(**self.sp_model_kwargs		)
										self.sp_model.Load(self.vocab_file		)







							def 					snake_case	(					self  :     Dict						,       __snake_case  :     Optional[Any]		):


										if self.remove_space:
													lowerCamelCase    :str    	=  ''' '''.join(inputs.strip().split()		)
										else:
													lowerCamelCase    :List[Any]    	=  inputs
										lowerCamelCase    :Dict    	=  outputs.replace('''``'''						,       '''\"'''		).replace('''\'\''''						,       '''\"'''		)

										if not self.keep_accents:
													lowerCamelCase    :List[str]    	=  unicodedata.normalize('''NFKD'''						,       _UpperCamelCase		)
													lowerCamelCase    :Optional[Any]    	=  ''''''.join([c for c in outputs if not unicodedata.combining(_UpperCamelCase		)]		)
										if self.do_lower_case:
													lowerCamelCase    :Optional[Any]    	=  outputs.lower()

										return outputs







							def 					snake_case	(					self  :     Dict						,       __snake_case  :     List[Any]		):
										lowerCamelCase    :Any    	=  self.preprocess_text(_UpperCamelCase		)
										lowerCamelCase    :List[Any]    	=  self.sp_model.encode(_UpperCamelCase						,       out_type=_UpperCamelCase		)
										lowerCamelCase    :int    	=  []
										for piece in pieces:
													if len(_UpperCamelCase		) > 1 and piece[-1] == str(''','''		) and piece[-2].isdigit():
																lowerCamelCase    :Optional[int]    	=  self.sp_model.EncodeAsPieces(piece[:-1].replace(_UpperCamelCase						,       ''''''		)		)
																if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																			if len(cur_pieces[0]		) == 1:
																						lowerCamelCase    :Tuple    	=  cur_pieces[1:]
																			else:
																						lowerCamelCase    :Any    	=  cur_pieces[0][1:]
																cur_pieces.append(piece[-1]		)
																new_pieces.extend(_UpperCamelCase		)
													else:
																new_pieces.append(_UpperCamelCase		)

										return new_pieces







							def 					snake_case	(					self  :     Dict						,       __snake_case  :     Tuple		):
										return self.sp_model.PieceToId(_UpperCamelCase		)







							def 					snake_case	(					self  :     int						,       __snake_case  :     List[Any]		):
										return self.sp_model.IdToPiece(_UpperCamelCase		)







							def 					snake_case	(					self  :     List[str]						,       __snake_case  :     List[str]		):
										lowerCamelCase    :str    	=  []
										lowerCamelCase    :str    	=  ''''''
										lowerCamelCase    :Union[str, Any]    	=  False
										for token in tokens:
													# make sure that special tokens are not decoded using sentencepiece model
													if token in self.all_special_tokens:
																if not prev_is_special:
																			out_string += " "
																out_string += self.sp_model.decode(_UpperCamelCase		) + token
																lowerCamelCase    :Optional[Any]    	=  True
																lowerCamelCase    :Optional[Any]    	=  []
													else:
																current_sub_tokens.append(_UpperCamelCase		)
																lowerCamelCase    :List[Any]    	=  False
										out_string += self.sp_model.decode(_UpperCamelCase		)
										return out_string.strip()







							def 					snake_case	(					self  :     List[Any]						,       __snake_case  :     List[str]						,       __snake_case  :     str = None		):
										lowerCamelCase    :Optional[int]    	=  [self.sep_token_id]
										lowerCamelCase    :str    	=  [self.cls_token_id]
										if token_ids_a is None:
													return cls + token_ids_a + sep
										return cls + token_ids_a + sep + token_ids_a + sep







							def 					snake_case	(					self  :     Optional[int]						,       __snake_case  :     Dict						,       __snake_case  :     Optional[int] = None						,       __snake_case  :     Union[str, Any] = False		):


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

										if token_ids_a is not None:
													return [1] + ([0] * len(_UpperCamelCase		)) + [1] + ([0] * len(_UpperCamelCase		)) + [1]
										return [1] + ([0] * len(_UpperCamelCase		)) + [1]







							def 					snake_case	(					self  :     Union[str, Any]						,       __snake_case  :     Optional[Any]						,       __snake_case  :     Any = None		):
										lowerCamelCase    :int    	=  [self.sep_token_id]
										lowerCamelCase    :List[Any]    	=  [self.cls_token_id]

										if token_ids_a is None:
													return len(cls + token_ids_a + sep		) * [0]
										return len(cls + token_ids_a + sep		) * [0] + len(token_ids_a + sep		) * [1]







							def 					snake_case	(					self  :     int						,       __snake_case  :     Optional[Any]						,       __snake_case  :     List[Any] = None		):


										if not os.path.isdir(_UpperCamelCase		):
													logger.error(F"Vocabulary path ({save_directory}) should be a directory"		)
													return
										lowerCamelCase    :Optional[Any]    	=  os.path.join(
										    _UpperCamelCase						,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)

										if os.path.abspath(self.vocab_file		) != os.path.abspath(_UpperCamelCase		) and os.path.isfile(self.vocab_file		):
													copyfile(self.vocab_file						,       _UpperCamelCase		)
										elif not os.path.isfile(self.vocab_file		):
													with open(_UpperCamelCase						,       '''wb'''		) as fi:
																lowerCamelCase    :int    	=  self.sp_model.serialized_model_proto()
																fi.write(_UpperCamelCase		)

										return (out_vocab_file,)






from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
UpperCamelCase__		 =     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.
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, 1000))







def 						lowerCamelCase__      ( __A    :str	,__A    :str		):


       """simple docstring"""




       __snake_case       	=							len([g for position, g in enumerate(__A		) if g == main_target[position]]		)
       return (item, float(__A		))







def 						lowerCamelCase__      ( __A    :str	,__A    :str		):


       """simple docstring"""




       __snake_case       	=							random.randint(0	,len(__A		) - 1		)
       __snake_case       	=							parent_a[:random_slice] + parent_a[random_slice:]
       __snake_case       	=							parent_a[:random_slice] + parent_a[random_slice:]
       return (child_a, child_a)







def 						lowerCamelCase__      ( __A    :str	,__A    :list[str]		):


       """simple docstring"""




       __snake_case       	=							list(__A		)
       if random.uniform(0	,1		) < MUTATION_PROBABILITY:
              __snake_case       	=							random.choice(__A		)
       return "".join(__A		)







def 						lowerCamelCase__      ( __A    :tuple[str, float]	,__A    :list[tuple[str, float]]	,__A    :list[str]	,):


       """simple docstring"""




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

              __snake_case	,    __snake_case       	=							crossover(parent_a[0]	,__A		)
              # Append new string to the population list.
              pop.append(mutate(__A	,__A		)		)
              pop.append(mutate(__A	,__A		)		)
       return pop







def 						lowerCamelCase__      ( __A    :str	,__A    :list[str]	,__A    :bool = True		):


       """simple docstring"""




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

       # Generate random starting population.
       __snake_case       	=							[]
       for _ in range(__A		):
              population.append("""""".join([random.choice(__A		) for i in range(len(__A		)		)]		)		)

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

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

              # 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.
              __snake_case       	=							[evaluate(__A	,__A		) for item in population]

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

              # This is selection
              for i in range(__A		):
                     population.extend(select(population_score[int(__A		)]	,__A	,__A		)		)
                     # 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(__A		) > 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}'
    )

