


"""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_barthez import BarthezTokenizer
else:
  _lowerCAmelCase							:    Optional[Any]					   =	None

_lowerCAmelCase							:    Optional[int]					   =	logging.get_logger(__name__)

_lowerCAmelCase							:    Union[str, Any]					   =	{'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

_lowerCAmelCase							:    List[Any]					   =	{
    '''vocab_file''': {
        '''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model''',
        '''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model''',
        '''moussaKam/barthez-orangesum-title''': (
            '''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json''',
        '''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json''',
        '''moussaKam/barthez-orangesum-title''': (
            '''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json'''
        ),
    },
}

_lowerCAmelCase							:    Dict					   =	{
    '''moussaKam/mbarthez''': 1024,
    '''moussaKam/barthez''': 1024,
    '''moussaKam/barthez-orangesum-title''': 1024,
}

_lowerCAmelCase							:    Union[str, Any]					   =	'''▁'''




class 							A_      (       _a ):
  lowerCAmelCase__      			=	VOCAB_FILES_NAMES
  lowerCAmelCase__      			=	PRETRAINED_VOCAB_FILES_MAP
  lowerCAmelCase__      			=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  lowerCAmelCase__      			=	['input_ids', 'attention_mask']
  lowerCAmelCase__      			=	BarthezTokenizer


  def __init__(			self:						List[Any]							,__lowerCAmelCase:						Any=None							,__lowerCAmelCase:						Tuple=None							,__lowerCAmelCase:						Any="<s>"							,__lowerCAmelCase:						List[str]="</s>"							,__lowerCAmelCase:						int="</s>"							,__lowerCAmelCase:						List[Any]="<s>"							,__lowerCAmelCase:						List[Any]="<unk>"							,__lowerCAmelCase:						List[Any]="<pad>"							,__lowerCAmelCase:						Tuple="<mask>"							,**__lowerCAmelCase:						Tuple							,):







     '''simple docstring'''







     _lowerCamelCase   :				int							=     AddedToken(__lowerCAmelCase							,lstrip=__lowerCAmelCase							,rstrip=__lowerCAmelCase    ) if isinstance(__lowerCAmelCase							,__lowerCAmelCase    ) else mask_token

     super().__init__(
         __lowerCAmelCase							,tokenizer_file=__lowerCAmelCase							,bos_token=__lowerCAmelCase							,eos_token=__lowerCAmelCase							,unk_token=__lowerCAmelCase							,sep_token=__lowerCAmelCase							,cls_token=__lowerCAmelCase							,pad_token=__lowerCAmelCase							,mask_token=__lowerCAmelCase							,**__lowerCAmelCase							,)

     _lowerCamelCase   :				Optional[int]							=     vocab_file
     _lowerCamelCase   :				Union[str, Any]							=     False if not self.vocab_file else True


  def 							_lowercase					(			self:						Dict							,__lowerCAmelCase:						List[int]							,__lowerCAmelCase:						Optional[List[int]] = None    ):







     '''simple docstring'''







     if token_ids_a is None:
        return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
     _lowerCamelCase   :				str							=     [self.cls_token_id]
     _lowerCamelCase   :				Optional[Any]							=     [self.sep_token_id]
     return cls + token_ids_a + sep + sep + token_ids_a + sep


  def 							_lowercase					(			self:						Any							,__lowerCAmelCase:						List[int]							,__lowerCAmelCase:						Optional[List[int]] = None    ):







     '''simple docstring'''







     _lowerCamelCase   :				str							=     [self.sep_token_id]
     _lowerCamelCase   :				List[Any]							=     [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 + sep + token_ids_a + sep    ) * [0]






  def 							_lowercase					(			self:						Union[str, Any]							,__lowerCAmelCase:						str							,__lowerCAmelCase:						Optional[str] = None    ):







     '''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(__lowerCAmelCase    ):
        logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""    )
        return
     _lowerCamelCase   :				List[str]							=     os.path.join(
         __lowerCAmelCase							,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]    )

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

     return (out_vocab_file,)






'''simple docstring'''




from typing import TYPE_CHECKING

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


a__					:			Dict          =     {
    '''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineConfig'''],
    '''tokenization_canine''': ['''CanineTokenizer'''],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a__					:			Union[str, Any]          =     [
			    '''CANINE_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''CanineForMultipleChoice''',
			    '''CanineForQuestionAnswering''',
			    '''CanineForSequenceClassification''',
			    '''CanineForTokenClassification''',
			    '''CanineLayer''',
			    '''CanineModel''',
			    '''CaninePreTrainedModel''',
			    '''load_tf_weights_in_canine''',
			]


if TYPE_CHECKING:
			from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
			from .tokenization_canine import CanineTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_canine import (
						    CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
						    CanineForMultipleChoice,
						    CanineForQuestionAnswering,
						    CanineForSequenceClassification,
						    CanineForTokenClassification,
						    CanineLayer,
						    CanineModel,
						    CaninePreTrainedModel,
						    load_tf_weights_in_canine,
						)


else:
			import sys

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



