

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     __snake_case    (				snake_case__  ):
       """simple docstring"""







       __SCREAMING_SNAKE_CASE				    =							VOCAB_FILES_NAMES
       __SCREAMING_SNAKE_CASE				    =							PRETRAINED_VOCAB_FILES_MAP
       __SCREAMING_SNAKE_CASE				    =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __SCREAMING_SNAKE_CASE				    =							AlbertTokenizer


       def __init__(		self      , _UpperCamelCase=None      , _UpperCamelCase=None      , _UpperCamelCase=True      , _UpperCamelCase=True      , _UpperCamelCase=False      , _UpperCamelCase="[CLS]"      , _UpperCamelCase="[SEP]"      , _UpperCamelCase="<unk>"      , _UpperCamelCase="[SEP]"      , _UpperCamelCase="<pad>"      , _UpperCamelCase="[CLS]"      , _UpperCamelCase="[MASK]"      , **_UpperCamelCase      , )      ->							int:






              """simple docstring"""


              __snake_case       	=							(
                  AddedToken(_UpperCamelCase      , lstrip=_UpperCamelCase      , rstrip=_UpperCamelCase      , normalized=_UpperCamelCase   )
                  if isinstance(_UpperCamelCase      , _UpperCamelCase   )
                  else mask_token
              )

              super().__init__(
                  _UpperCamelCase      , tokenizer_file=_UpperCamelCase      , 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      , **_UpperCamelCase      , )

              __snake_case       	=							do_lower_case
              __snake_case       	=							remove_space
              __snake_case       	=							keep_accents
              __snake_case       	=							vocab_file
              __snake_case       	=							False if not self.vocab_file else True


       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase = None   )      ->							List[int]:






              """simple docstring"""


              __snake_case       	=							[self.sep_token_id]
              __snake_case       	=							[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 			a		(		self      , _UpperCamelCase      , _UpperCamelCase = None   )      ->							List[int]:






              """simple docstring"""


              __snake_case       	=							[self.sep_token_id]
              __snake_case       	=							[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 			a		(		self      , _UpperCamelCase      , _UpperCamelCase = None   )      ->							Tuple[str]:






              """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(_UpperCamelCase   ):
                     logger.error(F'Vocabulary path ({save_directory}) should be a directory'   )
                     return
              __snake_case       	=							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   ):
                     copyfile(self.vocab_file      , _UpperCamelCase   )

              return (out_vocab_file,)




from argparse import ArgumentParser

from . import BaseTransformersCLICommand







def 						lowerCamelCase__      ( __A    :Optional[int]		):


       """simple docstring"""




       return DownloadCommand(args.model	,args.cache_dir	,args.force	,args.trust_remote_code		)




class     __snake_case    (				snake_case__  ):
       """simple docstring"""







       @staticmethod
       def 			a		(		_UpperCamelCase   )      ->							Any:






              """simple docstring"""


              __snake_case       	=							parser.add_parser("""download"""   )
              download_parser.add_argument(
                  """--cache-dir"""      , type=_UpperCamelCase      , default=_UpperCamelCase      , help="""Path to location to store the models"""   )
              download_parser.add_argument(
                  """--force"""      , action="""store_true"""      , help="""Force the model to be download even if already in cache-dir"""   )
              download_parser.add_argument(
                  """--trust-remote-code"""      , action="""store_true"""      , help="""Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you've reviewed the code as it will execute on your local machine"""      , )
              download_parser.add_argument("""model"""      , type=_UpperCamelCase      , help="""Name of the model to download"""   )
              download_parser.set_defaults(func=_UpperCamelCase   )


       def __init__(		self      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase   )      ->							Any:






              """simple docstring"""


              __snake_case       	=							model
              __snake_case       	=							cache
              __snake_case       	=							force
              __snake_case       	=							trust_remote_code






       def 			a		(		self   )      ->							List[Any]:






              """simple docstring"""


              from ..models.auto import AutoModel, AutoTokenizer

              AutoModel.from_pretrained(
                  self._model      , cache_dir=self._cache      , force_download=self._force      , trust_remote_code=self._trust_remote_code   )
              AutoTokenizer.from_pretrained(
                  self._model      , cache_dir=self._cache      , force_download=self._force      , trust_remote_code=self._trust_remote_code   )

