


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:
    A      :    Union[str, Any]		=					None

A      :    Tuple		=					logging.get_logger(__name__)

A      :    Dict		=					{"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

A      :    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"
        ),
    },
}

A      :    Union[str, Any]		=					{
    "moussaKam/mbarthez": 1024,
    "moussaKam/barthez": 1024,
    "moussaKam/barthez-orangesum-title": 1024,
}

A      :    Union[str, Any]		=					"▁"







class 			_lowercase (							lowercase__):


       """simple docstring"""




       A__											=			VOCAB_FILES_NAMES
       A__											=			PRETRAINED_VOCAB_FILES_MAP
       A__											=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       A__											=			["input_ids", "attention_mask"]
       A__											=			BarthezTokenizer





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


             '''simple docstring'''



             lowerCamelCase__		:				Dict    =  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__		:				List[str]    =  vocab_file
             lowerCamelCase__		:				Union[str, Any]    =  False if not self.vocab_file else True





       def 						lowerCAmelCase     (   self							:			Optional[Any]					,      __lowerCamelCase							:			List[str]					,      __lowerCamelCase							:			int = None       ):


             '''simple docstring'''



             if token_ids_a is None:
                   return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
             lowerCamelCase__		:				Tuple    =  [self.cls_token_id]
             lowerCamelCase__		:				Union[str, Any]    =  [self.sep_token_id]
             return cls + token_ids_a + sep + sep + token_ids_a + sep





       def 						lowerCAmelCase     (   self							:			Any					,      __lowerCamelCase							:			List[str]					,      __lowerCamelCase							:			Any = None       ):


             '''simple docstring'''



             lowerCamelCase__		:				Optional[Any]    =  [self.sep_token_id]
             lowerCamelCase__		:				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 						lowerCAmelCase     (   self							:			int					,      __lowerCamelCase							:			Union[str, Any]					,      __lowerCamelCase							:			Union[str, Any] = 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__		:				Union[str, Any]    =  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,)






from typing import TYPE_CHECKING

from ..utils import _LazyModule


UpperCAmelCase__   	=							{
    '''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

  UpperCAmelCase__   	=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

