


'''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_rembert import RemBertTokenizer
else:
       lowerCAmelCase    :     str					=						None

lowerCAmelCase    :     str					=						logging.get_logger(__name__)
lowerCAmelCase    :     List[str]					=						{"""vocab_file""": """sentencepiece.model""", """tokenizer_file""": """tokenizer.json"""}

lowerCAmelCase    :     int					=						{
    """vocab_file""": {
        """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/sentencepiece.model""",
    },
    """tokenizer_file""": {
        """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/tokenizer.json""",
    },
}

lowerCAmelCase    :     Optional[int]					=						{
    """google/rembert""": 2_56,
}

lowerCAmelCase    :     Any					=						"""▁"""
class 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_	):



     """simple docstring"""






     __magic_name__    			=    VOCAB_FILES_NAMES
     __magic_name__    			=    PRETRAINED_VOCAB_FILES_MAP
     __magic_name__    			=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __magic_name__    			=    RemBertTokenizer







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

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[int]   			=       AddedToken(snake_case__      ,	lstrip=snake_case__      ,	rstrip=snake_case__   ) if isinstance(snake_case__      ,	snake_case__   ) else mask_token

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

            _lowerCAmelCase    :							Optional[int]   			=       do_lower_case
            _lowerCAmelCase    :							Any   			=       remove_space
            _lowerCAmelCase    :							Tuple   			=       keep_accents
            _lowerCAmelCase    :							List[Any]   			=       vocab_file
            _lowerCAmelCase    :							Dict   			=       False if not self.vocab_file else True







     def a	( self      ,	snake_case__      ,	snake_case__ = None   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[str]   			=       [self.sep_token_id]
            _lowerCAmelCase    :							Tuple   			=       [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      ,	snake_case__      ,	snake_case__ = None      ,	snake_case__ = False   ):

            '''simple docstring'''

            if already_has_special_tokens:
                   if token_ids_a is not None:
                          raise ValueError(
                              'You should not supply a second sequence if the provided sequence of '
                              'ids is already formatted with special tokens for the model.'   )
                   return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

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







     def a	( self      ,	snake_case__      ,	snake_case__ = None   ):

            '''simple docstring'''

            _lowerCAmelCase    :							str   			=       [self.sep_token_id]
            _lowerCAmelCase    :							Optional[int]   			=       [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      ,	snake_case__      ,	snake_case__ = None   ):

            '''simple docstring'''

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

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

            return (out_vocab_file,)





'''simple docstring'''







from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase    :     Optional[int]					=						{
    """configuration_mctct""": ["""MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MCTCTConfig"""],
    """feature_extraction_mctct""": ["""MCTCTFeatureExtractor"""],
    """processing_mctct""": ["""MCTCTProcessor"""],
}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase    :     List[str]					=						[
           """MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """MCTCTForCTC""",
           """MCTCTModel""",
           """MCTCTPreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
       from .feature_extraction_mctct import MCTCTFeatureExtractor
       from .processing_mctct import MCTCTProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
       import sys

       lowerCAmelCase    :     Tuple					=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

