

"""simple docstring"""




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
      from .tokenization_albert import AlbertTokenizer
else:
      __UpperCAmelCase			=					None

__UpperCAmelCase			=					logging.get_logger(__name__)
__UpperCAmelCase			=					{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

__UpperCAmelCase			=					{
    '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',
    },
    'tokenizer_file': {
        'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json',
        'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json',
        'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json',
        'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json',
        'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json',
        'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json',
        'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json',
        'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json',
    },
}

__UpperCAmelCase			=					{
    '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,
}

__UpperCAmelCase			=					'▁'







class   __lowercase      (     __lowerCamelCase       ):
  snake_case_												=							VOCAB_FILES_NAMES
  snake_case_												=							PRETRAINED_VOCAB_FILES_MAP
  snake_case_												=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  snake_case_												=							AlbertTokenizer

  def __init__(	self		:      Tuple       ,A		:      Dict=None       ,A		:      List[Any]=None       ,A		:      Union[str, Any]=True       ,A		:      List[Any]=True       ,A		:      str=False       ,A		:      List[str]="[CLS]"       ,A		:      int="[SEP]"       ,A		:      List[Any]="<unk>"       ,A		:      Tuple="[SEP]"       ,A		:      Dict="<pad>"       ,A		:      int="[CLS]"       ,A		:      Any="[MASK]"       ,**A		:      Tuple       ,):






   '''simple docstring'''
   # Mask token behave like a normal word, i.e. include the space before it and
   # is included in the raw text, there should be a match in a non-normalized sentence.
   UpperCAmelCase__       :	Union[str, Any]        =       (
       AddedToken(A       ,lstrip=A       ,rstrip=A       ,normalized=A     )
       if isinstance(A       ,A     )
       else mask_token
   )

   super().__init__(
       A       ,tokenizer_file=A       ,do_lower_case=A       ,remove_space=A       ,keep_accents=A       ,bos_token=A       ,eos_token=A       ,unk_token=A       ,sep_token=A       ,pad_token=A       ,cls_token=A       ,mask_token=A       ,**A       ,)

   UpperCAmelCase__       :	Dict        =       do_lower_case
   UpperCAmelCase__       :	Dict        =       remove_space
   UpperCAmelCase__       :	Union[str, Any]        =       keep_accents
   UpperCAmelCase__       :	Union[str, Any]        =       vocab_file
   UpperCAmelCase__       :	Any        =       False if not self.vocab_file else True

  def  __lowercase							(	self		:      int       ,A		:      List[int]       ,A		:      Optional[List[int]] = None     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Tuple        =       [self.sep_token_id]
   UpperCAmelCase__       :	List[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  __lowercase							(	self		:      Optional[Any]       ,A		:      List[int]       ,A		:      Optional[List[int]] = None     ):






   '''simple docstring'''
   UpperCAmelCase__       :	str        =       [self.sep_token_id]
   UpperCAmelCase__       :	Union[str, 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  __lowercase							(	self		:      Dict       ,A		:      str       ,A		:      Optional[str] = None     ):






   '''simple docstring'''
   if not self.can_save_slow_tokenizer:
    raise ValueError(
        """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
        """tokenizer."""     )

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

   if os.path.abspath(self.vocab_file     ) != os.path.abspath(A     ):
    copyfile(self.vocab_file       ,A     )

   return (out_vocab_file,)









'''simple docstring'''


import functools


def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     __A	:       int      =							len(SCREAMING_SNAKE_CASE							)
     __A	:       int      =							len(SCREAMING_SNAKE_CASE							)

     @functools.cache
     def min_distance(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							) -> int:
          # if first word index is overflow - delete all from the second word
          if indexa >= len_worda:
               return len_worda - indexa
          # if second word index is overflow - delete all from the first word
          if indexa >= len_worda:
               return len_worda - indexa
          __A	:       Optional[int]      =							int(worda[indexa] != worda[indexa]							)  # current letters not identical
          return min(
              1 + min_distance(indexa + 1       ,					SCREAMING_SNAKE_CASE							)       ,					1 + min_distance(SCREAMING_SNAKE_CASE       ,					indexa + 1							)       ,					diff + min_distance(indexa + 1       ,					indexa + 1							)       ,					)

     return min_distance(0       ,					0							)


if __name__ == "__main__":
      import doctest

      doctest.testmod()

