




'''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:
							a    :							Dict										=    None

a    :							Optional[int]										=    logging.get_logger(__name__)
a    :							Any										=    {'vocab_file': 'sentencepiece.model', 'tokenizer_file': 'tokenizer.json'}

a    :							Any										=    {
    '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',
    },
}

a    :							str										=    {
    'google/rembert': 256,
}

a    :							Any										=    '▁'






class 		a							(  _lowerCamelCase	):
				snake_case_           =						VOCAB_FILES_NAMES
				snake_case_           =						PRETRAINED_VOCAB_FILES_MAP
				snake_case_           =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				snake_case_           =						RemBertTokenizer


				def __init__(						self       :   List[str]			,							lowercase_       :   List[str]=None			,							lowercase_       :   Optional[int]=None			,							lowercase_       :   Optional[Any]=True			,							lowercase_       :   List[str]=True			,							lowercase_       :   Optional[int]=False			,							lowercase_       :   Tuple="[CLS]"			,							lowercase_       :   Union[str, Any]="[SEP]"			,							lowercase_       :   Tuple="<unk>"			,							lowercase_       :   Optional[Any]="[SEP]"			,							lowercase_       :   Optional[Any]="<pad>"			,							lowercase_       :   Optional[int]="[CLS]"			,							lowercase_       :   Any="[MASK]"			,							**lowercase_       :   List[str]			,							):
									# Mask token behave like a normal word, i.e. include the space before it
									snake_case_		     =							AddedToken(lowercase_			,							lstrip=lowercase_			,							rstrip=lowercase_   ) if isinstance(lowercase_			,							lowercase_   ) else mask_token

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

									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       :   Optional[Any]			,							lowercase_       :   List[int]			,							lowercase_       :   Optional[List[int]] = None   ):
									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       :   List[Any]			,							lowercase_       :   List[int]			,							lowercase_       :   Optional[List[int]] = None			,							lowercase_       :   bool = False   ):

									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(lowercase_   )) + [1] + ([0] * len(lowercase_   )) + [1]
									return [1] + ([0] * len(lowercase_   )) + [1]


				def     A_					(						self       :   int			,							lowercase_       :   List[int]			,							lowercase_       :   Optional[List[int]] = None   ):
									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       :   Optional[Any]			,							lowercase_       :   str			,							lowercase_       :   Optional[str] = None   ):
									if not os.path.isdir(lowercase_   ):
														logger.error('''Vocabulary path ({}) should be a directory'''.format(lowercase_   )   )
														return
									snake_case_		     =							os.path.join(
									    lowercase_			,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']   )

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

									return (out_vocab_file,)







'''simple docstring'''

from typing import TYPE_CHECKING

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


a    :							Tuple										=    {
    'configuration_encodec': [
        'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'EncodecConfig',
    ],
    'feature_extraction_encodec': ['EncodecFeatureExtractor'],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a    :							str										=    [
							    'ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'EncodecModel',
							    'EncodecPreTrainedModel',
							]

if TYPE_CHECKING:
							from .configuration_encodec import (
							    ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    EncodecConfig,
							)
							from .feature_extraction_encodec import EncodecFeatureExtractor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_encodec import (
														    ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
														    EncodecModel,
														    EncodecPreTrainedModel,
														)

else:
							import sys

							a    :							List[Any]										=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

