





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:
     _snake_case  :    Optional[int]						    =  None

_snake_case  :    List[str]						    =  logging.get_logger(__name__)

_snake_case  :    Dict						    =  {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

_snake_case  :    str						    =  {
    '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'
        ),
    },
}

_snake_case  :    List[Any]						    =  {
    'moussaKam/mbarthez': 1024,
    'moussaKam/barthez': 1024,
    'moussaKam/barthez-orangesum-title': 1024,
}

_snake_case  :    List[Any]						    =  '▁'



class 				_UpperCAmelCase    (    _UpperCamelCase    ):

  """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_ :		List[Any]=None					,  lowerCAmelCase_ :		str=None					,  lowerCAmelCase_ :		Tuple="<s>"					,  lowerCAmelCase_ :		str="</s>"					,  lowerCAmelCase_ :		int="</s>"					,  lowerCAmelCase_ :		str="<s>"					,  lowerCAmelCase_ :		List[str]="<unk>"					,  lowerCAmelCase_ :		Optional[Any]="<pad>"					,  lowerCAmelCase_ :		str="<mask>"					,  **lowerCAmelCase_ :		Dict					,  )							->						Optional[int]:
     # Mask token behave like a normal word, i.e. include the space before it
     __lowerCAmelCase								=	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								=	vocab_file
     __lowerCAmelCase								=	False if not self.vocab_file else True






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

     if token_ids_a is None:
        return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
     __lowerCAmelCase								=	[self.cls_token_id]
     __lowerCAmelCase								=	[self.sep_token_id]
     return cls + token_ids_a + sep + sep + token_ids_a + sep






  def 				lowercase		(				self :		Optional[int]					,  lowerCAmelCase_ :		List[int]					,  lowerCAmelCase_ :		Optional[List[int]] = None     )							->						List[int]:
     __lowerCAmelCase								=	[self.sep_token_id]
     __lowerCAmelCase								=	[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 :		Optional[Any]					,  lowerCAmelCase_ :		str					,  lowerCAmelCase_ :		Optional[str] = None     )							->						Tuple[str]:
     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								=	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,)








# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_snake_case  :    Dict						    =  {'configuration_mra': ['MRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MraConfig']}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _snake_case  :    Optional[int]						    =  [
         'MRA_PRETRAINED_MODEL_ARCHIVE_LIST',
         'MraForMaskedLM',
         'MraForMultipleChoice',
         'MraForQuestionAnswering',
         'MraForSequenceClassification',
         'MraForTokenClassification',
         'MraLayer',
         'MraModel',
         'MraPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_mra import (
              MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
              MraForMaskedLM,
              MraForMultipleChoice,
              MraForQuestionAnswering,
              MraForSequenceClassification,
              MraForTokenClassification,
              MraLayer,
              MraModel,
              MraPreTrainedModel,
          )
else:
     import sys

     _snake_case  :    Tuple						    =  _LazyModule(__name__, globals()['__file__'], _import_structure)

