

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  :Optional[int]										=       None

__a  :Tuple										=       logging.get_logger(__name__)
__a  :Union[str, Any]										=       {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

__a  :Union[str, Any]										=       {
    '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  :Optional[int]										=       {
    '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  :int										=       '▁'

class    _a (       __SCREAMING_SNAKE_CASE							):




      """simple docstring"""

      _lowerCamelCase    :       List[str]							=    VOCAB_FILES_NAMES
      _lowerCamelCase    :       List[Any]							=    PRETRAINED_VOCAB_FILES_MAP
      _lowerCamelCase    :       str							=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _lowerCamelCase    :       int							=    AlbertTokenizer





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

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





      def 						__A     (					self				:							Any		,       UpperCAmelCase				:							List[Any]		,       UpperCAmelCase				:							List[str] = None     ):
            A_		   =							[self.sep_token_id]
            A_		   =							[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				:							Optional[Any]		,       UpperCAmelCase				:							List[Any]		,       UpperCAmelCase				:							str = None     ):
            A_		   =							[self.sep_token_id]
            A_		   =							[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				:							List[str]		,       UpperCAmelCase				:							List[str]		,       UpperCAmelCase				:							Dict = 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(_a     ):
                  logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''     )
                  return
            A_		   =							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 warnings

from ..trainer import Trainer
from ..utils import logging


lowercase_			     =  logging.get_logger(__name__)
class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		def __init__(  self							, _a=None							, **_a      ):
				warnings.warn(
				    '''`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` '''
				    '''instead.'''							, _a							, )
				super().__init__(args=_a							, **_a      )






