






'''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_				: int    	=							logging.get_logger(__name__)
lowerCamelCase_				: str    	=							{'''vocab_file''': '''sentencepiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCamelCase_				: Dict    	=							{
    '''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_				: Dict    	=							{
    '''google/rembert''': 256,
}

lowerCamelCase_				: List[Any]    	=							'''▁'''
class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





			__a							:							Any					     =					VOCAB_FILES_NAMES
			__a							:							Dict					     =					PRETRAINED_VOCAB_FILES_MAP
			__a							:							List[str]					     =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__a							:							Union[str, Any]					     =					RemBertTokenizer






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

									'''simple docstring'''







									UpperCamelCase__         = 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 ,      )

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






			def 				A      (   self							:						Optional[int] ,      lowercase							:						List[int] ,      lowercase							:						Optional[List[int]] = None			)							->    List[int]:

									'''simple docstring'''







									UpperCamelCase__         = [self.sep_token_id]
									UpperCamelCase__         = [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							:						Tuple ,      lowercase							:						List[int] ,      lowercase							:						Optional[List[int]] = None ,      lowercase							:						bool = False			)							->    List[int]:

									'''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(lowercase			)) + [1] + ([0] * len(lowercase			)) + [1]
									return [1] + ([0] * len(lowercase			)) + [1]






			def 				A      (   self							:						int ,      lowercase							:						List[int] ,      lowercase							:						Optional[List[int]] = None			)							->    List[int]:

									'''simple docstring'''







									UpperCamelCase__         = [self.sep_token_id]
									UpperCamelCase__         = [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] ,      lowercase							:						str ,      lowercase							:						Optional[str] = None			)							->    Tuple[str]:

									'''simple docstring'''







									if not os.path.isdir(lowercase			):
															logger.error("""Vocabulary path ({}) should be a directory""".format(lowercase			)			)
															return
									UpperCamelCase__         = 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 _LazyModule


lowerCamelCase_				: List[Any]    	=							{
    '''config''': [
        '''EXTERNAL_DATA_FORMAT_SIZE_LIMIT''',
        '''OnnxConfig''',
        '''OnnxConfigWithPast''',
        '''OnnxSeq2SeqConfigWithPast''',
        '''PatchingSpec''',
    ],
    '''convert''': ['''export''', '''validate_model_outputs'''],
    '''features''': ['''FeaturesManager'''],
    '''utils''': ['''ParameterFormat''', '''compute_serialized_parameters_size'''],
}


if TYPE_CHECKING:
					from .config import (
					    EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
					    OnnxConfig,
					    OnnxConfigWithPast,
					    OnnxSeqaSeqConfigWithPast,
					    PatchingSpec,
					)
					from .convert import export, validate_model_outputs
					from .features import FeaturesManager
					from .utils import ParameterFormat, compute_serialized_parameters_size

else:
					import sys

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