

'''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:
    A_       						=							None

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

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",
    },
    "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",
    },
}

A_       						=							{
    "albert-base-v1": 5_12,
    "albert-large-v1": 5_12,
    "albert-xlarge-v1": 5_12,
    "albert-xxlarge-v1": 5_12,
    "albert-base-v2": 5_12,
    "albert-large-v2": 5_12,
    "albert-xlarge-v2": 5_12,
    "albert-xxlarge-v2": 5_12,
}

A_       						=							"▁"





class 							_snake_case (       _a						):
   _A       :     Optional[Any]       =					VOCAB_FILES_NAMES
   _A       :     Any       =					PRETRAINED_VOCAB_FILES_MAP
   _A       :     Any       =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _A       :     Union[str, Any]       =					AlbertTokenizer
   def __init__( self			:      Union[str, Any]     ,SCREAMING_SNAKE_CASE__			:      Dict=None     ,SCREAMING_SNAKE_CASE__			:      Any=None     ,SCREAMING_SNAKE_CASE__			:      Tuple=True     ,SCREAMING_SNAKE_CASE__			:      List[Any]=True     ,SCREAMING_SNAKE_CASE__			:      List[Any]=False     ,SCREAMING_SNAKE_CASE__			:      Any="[CLS]"     ,SCREAMING_SNAKE_CASE__			:      Dict="[SEP]"     ,SCREAMING_SNAKE_CASE__			:      Optional[int]="<unk>"     ,SCREAMING_SNAKE_CASE__			:      int="[SEP]"     ,SCREAMING_SNAKE_CASE__			:      List[Any]="<pad>"     ,SCREAMING_SNAKE_CASE__			:      List[Any]="[CLS]"     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]="[MASK]"     ,**SCREAMING_SNAKE_CASE__			:      List[Any]     ,):
     # 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.
     SCREAMING_SNAKE_CASE:Optional[int]						      =					(
         AddedToken(SCREAMING_SNAKE_CASE__     ,lstrip=SCREAMING_SNAKE_CASE__     ,rstrip=SCREAMING_SNAKE_CASE__     ,normalized=SCREAMING_SNAKE_CASE__							)
         if isinstance(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)
         else mask_token
     )

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

     SCREAMING_SNAKE_CASE:Dict						      =					do_lower_case
     SCREAMING_SNAKE_CASE:str						      =					remove_space
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					keep_accents
     SCREAMING_SNAKE_CASE:Dict						      =					vocab_file
     SCREAMING_SNAKE_CASE:str						      =					False if not self.vocab_file else True
   def     __UpperCamelCase						( self			:      int     ,SCREAMING_SNAKE_CASE__			:      List[int]     ,SCREAMING_SNAKE_CASE__			:      Optional[List[int]] = None							):
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					[self.sep_token_id]
     SCREAMING_SNAKE_CASE:List[Any]						      =					[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     __UpperCamelCase						( self			:      Union[str, Any]     ,SCREAMING_SNAKE_CASE__			:      List[int]     ,SCREAMING_SNAKE_CASE__			:      Optional[List[int]] = None							):
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					[self.sep_token_id]
     SCREAMING_SNAKE_CASE: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     __UpperCamelCase						( self			:      Optional[Any]     ,SCREAMING_SNAKE_CASE__			:      str     ,SCREAMING_SNAKE_CASE__			:      Optional[str] = None							):
     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(SCREAMING_SNAKE_CASE__							):
       logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''							)
       return
     SCREAMING_SNAKE_CASE:Optional[int]						      =					os.path.join(
         SCREAMING_SNAKE_CASE__     ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]							)

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

     return (out_vocab_file,)









'''simple docstring'''

import warnings
from functools import wraps
from typing import Callable





def    A_  ( snake_case ):

  @wraps(snake_case )
  def _inner_fn(*snake_case      ,       **snake_case ):
    warnings.warn(
        (F'''\'{fn.__name__}\' is experimental and might be subject to breaking changes in the future.''')      ,       snake_case      ,       )
    return fn(*snake_case      ,       **snake_case )

  return _inner_fn






